Effects of Green tea supplementation on the CRP, ESR, and CBC in the patients with COVID-19, a double-blind placebo-controlled clinical trial

Effects of Green tea supplementation on the CRP, ESR, and CBC in the patients with COVID-19, a double-blind placebo-controlled clinical trial | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Effects of Green tea supplementation on the CRP, ESR, and CBC in the patients with COVID-19, a double-blind placebo-controlled clinical trial Mojtaba Yousefi, Zahra Hosseinzade, Sara Mahmoodi, Ali Mahmoodabadi, and 6 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4976013/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background This study investigated the effects of green tea extract on biomarkers and signs of COVID-19 patients who were hospitalized. Methods This study was a double-blind clinical trial that involved 74 patients who were under hospital care. These individuals were randomly divided into two groups. One group received a 900mg/d dosage of green tea supplement along with standard patient care, while the other group received a placebo alongside standard patient care. This administration lasted for 14 days. Blood factors and anthropometric factors were measured before and after the intervention. Additionally, dietary intake was assessed during the study. Results After the intervention, there was a significant decrease in C-reactive protein (CRP) [Mean Differences (MD)18.34 and 95%CI (8.05 to 28.62)] and Erythrocyte Sedimentation Rate (ESR) [MD 16.68 and 95%CI (7.41 to 25.94)] levels in the green tea group compared to the placebo group. There were also significant changes in neutrophils, lymphocytes, red blood cells (RBC), and blood oxygen saturation in the green tea group(p < 0.05). However, there were no significant differences in other blood indices between the two groups. Conclusions The results suggest that green tea extract supplementation may positively affect inflammation and blood markers in COVID-19 patients and potentially improve blood oxygen saturation levels. Trial registration IRCT20150711023153N3 (https//irct.behdasht.gov.ir/trial/55948), Registration date 20210604 COVID-19 green tea extract Complete Blood Count C-reactive protein Erythrocyte Sedimentation Rate Figures Figure 1 Background Coronaviruses are part of a large family of viruses known as coronaviridae [ 1 ]. They have various generations and can cause significant damage to the respiratory system and other vital organs [ 2 , 3 ]. The COVID-19 pandemic has affected millions of people worldwide and has claimed over 6 million lives. The virus can lead to hypertension, fatigue, lung fibrosis, arterial and cardiac thrombosis, inflammation, and stroke. Symptoms of COVID-19 include fatigue, shortness of breath, muscle and joint pain, headache, cough, chest pain, altered sense of smell and taste, and diarrhea [ 4 – 6 ]. Studies have shown that severe impacts from the virus can lead to a cytokine storm, which is an overly active immune system that can cause sudden and lethal hyper-cytokine-mia and multiple organ failure syndrome [ 7 , 8 ]. The virus attaches to angiotensin II receptors, which leads to increased T lymphocyte activity and the generation of inflammatory agents [ 9 ]. The host's nutritional status and immune system greatly affect the severity of COVID-19 [ 10 ]. Green tea is considered to be one of the nutritional factors that can affect both inflammation and the immune system [ 11 ]. Green tea is produced from the leaves of the Camellia sinensis plant and holds a range of antioxidant components, like catechins [ 12 ]. The catechins found in green tea include Epigallocatechin gallate (EGCG), epicatechin gallate (ECG), epicatechin (EC), and galloatechin gallate (GCG). The concentration of polyphenol catechins tends to be higher in older plants [ 13 ]. Among these catechins, EGCG is present in the highest concentration and is known for its potential anti-inflammatory, antimicrobial, and antioxidant properties. It has also shown beneficial effects in cardiovascular health and oral care [ 14 , 15 ]. Studies on green tea catechins, particularly EGCG, have demonstrated their antiviral effects against various viruses [ 16 ]. Green tea can impact influenza virus infection by interacting with viral hemagglutinin (HA) and interfering with viral RNA synthesis within cells. Catechins also inhibit the activity of viral RNA polymerase endonuclease. Molecular studies have indicated that EGCG exhibits a strong binding affinity with main protease (MPRO), which are involved in the formation of viral molecules. This suggests that EGCG may possess significant phytochemical properties for inhibiting the spike glycoprotein (S) and main protease of the Covid-19 virus. Based on previous studies, phenolic compounds have shown promising inhibitory effects on MPRO protease and S virus protein when compared to chemical drugs [ 14 – 16 ]. Furthermore, additional pathways, such as ACE inhibitory effects, have been suggested to contribute to the effectiveness of green tea products against inflammatory disorders [ 17 ]. Besides, COVID-19 is an inflammatory disease characterized by elevated levels of inflammatory markers such as C-reactive protein (CRP) and erythrocyte sedimentation rate (ESR)[ 18 ]. Some studies have been conducted on the benefits of green tea on the COVID-19 virus [ 19 – 21 ]. However, these studies were in vitro or a weak methodology, rendering the results unreliable. Therefore, a new study with a larger sample size and stronger methodology was necessary to verify the effects of green tea on infected patients. Therefore, the current study was carried out to investigate the effect of green tea supplementation on inflammatory biomarkers in individuals with mild to moderate symptoms. Material and methods Study design This randomized, double-blind, placebo-controlled trial was conducted at Shahid Jalil Hospital in Yasuj, southwest Iran. The purpose of the study was to investigate whether the consumption of green tea extract could help reduce inflammatory biomarkers in COVID-19 patients. The trial included 74 patients who were receiving hospital care. The participants were randomly divided into two groups, with one group receiving a daily dosage of 900mg of green tea supplement and the other group receiving a placebo. The study followed a 1:1 allocation ratio with a 2-armed parallel design. All participants provided informed consent and were screened and educated remotely via phone interviews. The study adhered to the guidelines outlined in the Helsinki Declaration. The study gathered information by collecting whole blood samples and conducting interviews. The blood samples were taken after a 12-hour overnight fast to assess the complete blood count (CBC) and measure inflammatory biomarkers such as CRP and ESR at the beginning of the study and again after 14 days of intervention. Patients who were discharged earlier than expected were monitored through follow-up phone interviews to ensure their compliance with the study protocols. Expert nutritionists collected 24-hour dietary recalls on three occasions (days 1, 7, and 14) to assess the detailed intake of nutrients and food patterns. The dietary intakes were analyzed using the Nutritionist IV software (First Databank Inc.), and compliance with the study guidelines was assessed through 24-hour dietary recalls and interviews. Both patient groups were instructed to avoid consuming green tea during this period and were assessed at the end. For each patient, the energy and macronutrient content were obtained from the 24-hour dietary recall. The patient’s intake was compared between the intervention and control groups to ensure that there were no significant differences in their diets. Admission and discharge dates, disease complications, prescribed medications, and dosages were documented. The CBC was assessed using a cell counter (kh-21n, Sysmex, Japan), while CRP levels were measured using a serologic kit (PAADCO, Spain). The ESR was determined utilizing an automatic ESR reader (DA-717, NOVIN GOSTAR, Iran) from whole blood samples. Demographic information, including age, sex, and occupation was collected through a questionnaire. Additionally, the patient's medical history, medications, and supplement usage were documented. Height, weight, and body mass index (BMI) were measured, and blood oxygen saturation levels were recorded using a pulse oximeter (PO 80 Pulse Oximeter, Beurer, Germany). Blood pressure readings were obtained using an Omron M7 sphygmomanometer (Omron, Japan), and the respiratory rate was determined by counting the breaths per minute. Sample size determination The sample size for this study was determined by considering a type 1 error rate of 5% and a power of 80%. Considering the mean and standard deviation of CRP levels from a previous article [ 22 ] (mean ± SD: 0.10 ± 0.06 in the placebo group and 0.24 ± 0.21 in the intervention group) and the minimum clinically significant difference (MCID) for CRP (MCID > 2.6 mg/L) from another study [ 19 ], a correlation coefficient of 0.4 was taken into account. Using the method of covariance analysis, a sample size of 31 individuals was calculated for each group. Considering a potential attrition rate of 20%, a total sample size of 37 individuals per group was determined. Thus, a total of 74 participants were calculated to be required for this study. The sample size calculations were performed using STATA software. Participant We included adult patients who were hospitalized and diagnosed with mild to moderate COVID-19 using RT-PCR in our study. Patients with critical infection, pregnant or breastfeeding women, those with disseminated intravascular coagulation or other coagulopathies, individuals with chronic kidney disease or coronary heart disease, and participants in a clinical trial within the past 30 days were not included in the study. The study was conducted when the Omicron strain was the dominant variant of COVID-19. Randomization Eligible patients were randomly assigned to either the intervention or placebo group, with an equal 1:1 ratio. Randomization will be carried out using permuted blocks of size 10, resulting in 8 blocks. The randomization process will take into account the sex and age (≥ 50 years and < 50 years) categories. The researchers utilized randomization.com to create a list of randomized allocations. An independent third party was responsible for placing individuals on the list. Subsequently, the list was secured within a sealed envelope within the academic institution until the study's conclusion. The trial participants and personnel engaged in the study remain unaware of the contents of the packages containing the study substances. These packages were fashioned by the study staff to possess an indistinguishable semblance. The sole distinction among the packages is a tag affixed by an external group, denoting the associated randomization list. Blinding A neutral third party was enlisted to allocate labels and adhesive stickers, classifying them as either GTE or placebo. This third individual, who underwent a validation process, affixed labels onto sealed packages containing either genuine GTE or placebo, as produced by the appropriate manufacturer. After preparation, the package was returned to the research team, ensuring the sole discrepancy lay in the labels themselves. The table outlining the sequential number labels for randomization was exclusively retained by the third party until the end of the study. To evaluate blinding, personnel, patients, and assessors were prompted to speculate on the kind of treatment administered to the patients. This assessment covered 80% of the patients and 75% of the assessors involved. Upon the study's culmination, over 85% of the patients believed they had been administered GTE supplements. Intervention The main pool of participants was primarily gathered from Shahid Jalil Hospital in Yasuj, Iran. This was achieved by conducting interviews with individuals aged 18 and above who had been diagnosed positive in PCR tests. Following the initial assessment and categorization for the intervention, the patients were randomly assigned to one of two groups: those who would receive the GTE supplement and those assigned to the placebo group. In addition to the standard care provided to the patients at the hospital, individuals in the GTE group were given two GTE tablets per day for 14 days. These tablets contained 450mg of green tea extract and were taken after lunch and dinner. Meanwhile, participants in the control group were given similar placebo Tablets, containing 450 mg of cellulose. In addition, a pharmaceutical company was instructed to package the supplement and placebo in identical containers. To determine the rate of adherence, the remaining count of either GTE or placebo tabs was assessed in every package at the end of the intervention. Compliance percent was then computed using the following formula: Compliance (%) = (Number of tablets consumed * 100) / 28. A lower percentage obtained through this calculation indicates higher compliance with the regimen. Statistical analysis We conducted a comprehensive analysis of all the gathered data using STATA version 14. Our approach was based on the central limit theorem, which assumes that the means follow a normal distribution. This justified the use of parametric tests. We presented the data in terms of means and standard deviation [SD]. Additionally, we calculated the mean difference (MD) and established a 95% confidence interval (CI) based on the mean [SD] and the sample size, using STATA for these computations. To compare the disparities in chosen variables between the two groups, we employed the independent-samples t-test separately for each group. The Paired t-test was employed to evaluate the differences between the two groups before and after the intervention. Furthermore, we applied ANOVA-ANCOVA to evaluate the primary outcomes (CRP, ESR). Statistical significance was considered to be present at a threshold of P < 0.05. Result Out of the 492 patients who were admitted to the hospital, a total of 310 patients were found to meet the eligibility criteria. From this pool of eligible patients, 74 individuals were selected for participation and then randomly assigned to two groups: the GTE group and the placebo group. Out of these, 70 patients completed the study, with 35 in the GTE group and 35 in the placebo group. The patients displayed a compliance rate of over 96%, as illustrated in Fig. 1. The collected data underwent analysis using both pre-protocol and intention-to-treat (ITT) methodologies. No significant differences were observed between these two approaches, and ultimately, the outcomes were reported based on the ITT approach. Adverse events were reported by three patients (9%) in the placebo group and one patient (3%) in the GTE group. The most frequently reported adverse events included weight loss and vomiting, which were experienced by a total of four subjects (10%). Figure 1: Flow of participants through the study At the start of the study (Table 1 ), there were no significant variations in age, weight, medical background, medication usage, vital signs, and BMI between the groups. The mean and standard deviation age of patients was 48.63[13.33] years in the GTE group and 50.02[15.03] years in the placebo group. Similarly, no significant distinctions were observed in terms of energy and nutrient consumption between the two groups, as shown by the dietary records collected over the course of the intervention (Table 2 ). Table 1 Initial characteristics of the groups receiving green tea and the placebo. Green tea (n = 37) Placebo (n = 37) P -value Age (y) 48.63[13.33] 50.02[15.03] 0.68 Weight (kg) 80.45[13.03] 77.88[14.22] 0.43 BMI (kg/m 2 ) 27.80[4.43] 27.87[5.08] 0.94 Sex (male: female) (%) 0.81 Hospitalized duration (day) 5.85[2.32] 5.42[1.83] 0.39 Medical history (%) Diabetes 15 13 Hypertension 13 14 Dyslipidemia 2 3 Others 5 5 Infection Severity (%) Mild 18 18 ND Moderate 17 17 ND Drug history (%) Supplements 1 35 35 ND Antibiotic 35 35 ND Antiviral 35 35 ND Anti-inflammatory 35 35 ND Vital sign, BP Respiratory Rate 21.71[2.51] 21.28[2.56] 0.48 Puls Rate 90.20[16.67] 89.71[14.92] 0.89 SBP 124.28[15.56] 121.71[12.69] 0.45 DBP 79.22[9.37] 74.77[10.61] 0.067 The data is exhibited as number (percentage) for categorical factors and as mean [standard deviation]. The recorded parameters include BMI; Body mass Index, BP; Blood Pressure, SBP; Systolic Blood Pressure, DBP; Diastolic Blood Pressure, Y; year, ND; no difference. Table 2 The consumption of specific nutrients throughout the intervention period in both groups receiving the GTE and the placebo. Nutrients Green tea Placebo P -value Carbohydrates (g) 199.74[20.45] 209.64[39.38] ND Proteins (g) 71.94[11.26] 74.33[12.22] ND Total fat (g) 62.70[15.52] 64.09[11.88] ND Energy (kcal) 1638.18[166.55] 1750.78[263.09] ND PUFA n-3 (g) 0.13 [25] 0.09 [21] ND Magnesium (mg) 294.95[113.82] 287.54[98.04] ND Calcium (mg) 691.42[234.19] 720.99[207.05] ND Phosphorus (mg) 694.87[265.08] 645.48[231.08] ND Potassium (mg) 1804.91[236.07] 1795.45[460.11] ND Cu (mcg) 914[415.98] 908.45[381.43] ND Fe (mg) 9.97[3.36] 10.87[3.09] ND Zn (mg) 9.75[3.47] 10.04[3.89] ND Total Fiber (g) 18.53[8.87] 15.41[6.74] ND Vitamin C (mg) 80.91 [25.81] 80.73[24.82] ND 1 Vitamin C (mg) sup 290.94 [2.87] 285.99 [5.31] ND 1 Vitamin D (IU) sup 3571.42 3571.42 ND Vitamin E (mg) 12.27[6.49] 12.13[3.95] ND Data are displayed in the form of the mean [standard deviation]. PUFA refers to polyunsaturated fatty acids, including omega-3 (PUFA n-3). The units are g (gram), mg (milligram), and mcg (microgram). Elements include Cu (copper), Fe (iron), and Zn (zinc). 1 Supplements included vitamins C and D, while ND indicates no difference. Table 1 . Initial characteristics of the groups receiving green tea and the placebo. Table 2 : The consumption of specific nutrients throughout the intervention period in both groups receiving the GTE and the placebo. The laboratory data analysis findings are presented in Table 3 , displaying the pre- and post-intervention comparisons between the groups. By the end of the trial (day 14) compared to before the intervention, the GTE group exhibited significant reductions in CRP and ESR as primary outcomes, along with decreased Neutrophil levels as a secondary outcome, and increased RBC, lymphocytes (Lym), and blood oxygen saturation (BOS) levels (Table 3 ). Conversely, in the placebo group, MCHC, Lym, and BOS showed significant increases. At the same time, platelet lymphocyte ratio (PLR), neutrophil-lymphocyte ratio (NLR), and Neutrophils, as secondary outcomes, showed substantial decreases by the end of the trial (day 14) (Table 3 ). By the trial's conclusion, significant differences between the two groups were observed in MCH, NLR, ESR, BOS, CRP, and PLR (P < 0.05). In addition, primary outcomes (CRP, ESR) baseline values were adjusted. No other indices showed a statistical significance difference between the two groups after the 14 days. Table 3 Values of inflammatory indicators and blood parameters before and at the end of intervention* Variables GTE Placebo MD (95%CI) MD (95% CI) (Cohen’s d) P -value a CRP (mg/L) Baseline 40.67[24.26] 36.83[23.15] Day-14 22.33[19.89] † 33.16[20.53] † -10.83(-20.19to -1.46) -0.53 (-0.99 to -0.07) 0.02 MD (95%CI) 18.34 (8.05 to 28.62) 3.67(-6.47 to 13.81) P- value b 0.000 0.35 ESR (mm/h) Baseline 41.19[22.52] 39.46[17.15] Day-14 24.51[17.08] † 32.76 [15.14] † -8.25(-15.73 to -0.76) -0.51(-0.97 to -0.04) 0.03 MD (95%CI) 16.68 (7.41 to 25.94) 6.7(-.79 to 14.19) P- value b 0.000 0.070 RBC (x1000/µL) Baseline 4.53[0.58] 4.80[0.75] Day-14 4.78[0.54] 4.86[0.80] -0.08 (-0.39 to 0.23) -0.11 (-0.57 to 0.33) 0.61 MD (95%CI) -0.25 (-0.50 to 0.01) -0.06 (-0.41 to 0.29) P- value b 0.01 0.56 HB (g/dL) Baseline 13.13[1.99] 13.74[2.11] Day-14 13.30[1.37] 13.75[1.78] -0.45 (-1.18 to 0.28) -0.28 (-0.74 to 0.17) 0.61 MD (95%CI) -0.17 (-.96 to 0.62) -0.01 (-0.91 to 0.89) P- value b 0.49 0.94 HCT (%) Baseline 40.70[5.05] 40.73[4.88] Day-14 40.98[4.14] 40.73[5.53] 0.25 (-2.01 to 2.51) 0.05 (-0.40 to 0.50) 0.82 MD (95%CI) − .28(-2.42 to 1.86) 0 (-2.41 to 2.41) P- value b 0.76 0.99 MCV (fl) Baseline 90.46[7.61] 88.17[8.79] Day-14 88.69[6.41] 87.31[8.64] 1.38 (-2.14 to 4.90) 0.18(-0.27 to 0.63) 0.43 MD (95%CI) 1.77(-1.49 to 5.03) 0.86 (-3.17 to 4.89) P- value b 0.10 0.55 MCH (pg) Baseline 29.05[3.08] 28.79[3.38] Day-14 28.47[3.51] 30.03[3.10] -1.56 (-3.09 to -0.02) − .47 (-0.93 to − .007) 0.05 MD (95%CI) 0.58 (-0.95 to 2.11) -1.24 (-2.74 to 0.26) P- value b 0.15 0.05 MCHC (g/dL) Baseline 32.05[1.43] 32.22[1.45] Day-14 32.55[2.23] 33.24[1.74] -0.69 (-1.61 to 0.23) -0.34 (-0.80 to 0.11) 0.15 MD (95%CI) -0.5 (-1.36 to 0.36) -1.02 (-1.76 to -0.27) P- value b 0.11 0.01 WBC (x1000/µL) Baseline 10.48[4.12] 10.34[2.79] Day-14 9.96[3.54] 10.02[2.95] -0.06 (-1.57 to 1.45) -0.01 (-0.47 to 0.43) 0.94 MD (95%CI) 0.52 (-1.26 to 2.30) 0.32 (-1.01 to 1.65) P- value b 0.45 0.53 Lym (%) Baseline 12.46[8.01] 14.03[8.23] Day-14 20.23[14.25] 19.91[9.20] 0.32 (-5.23 to 5.87) 0.02 (-0.42 to 0.48) 0.91 MD (95%CI) -7.77 (-13.12 to -2.41) -5.88 (-9.92 to -1.83) P- value b 0.005 0.004 Mono (%) Baseline 530.89[245.55] 523.16[244.34] Day-14 551.43[637.46] 534.92[227.44] 16.51 (-205.29 to 238.31) .03(-0.42 to 0.49) 0.88 MD (95%CI) -20.54 (-244.41 to 203.33) -11.76 (-121.15 to 97.63) P- value b 0.84 0.77 Neutrophils (%) Baseline 81.73[9.40] 79.14[9.93] Day-14 70.01[17.42] 69.33[17.24] 0.68 (-7.35 to 8.71) 0.03(-0.41 to 0.49) 0.68 MD (95%CI) 11.72 (5.23 to 18.20) 9.81 (3.28 to 16.33) P- value b 0.002 0.004 Plts (x1000/µL) Baseline 226.19[86.50] 260.41[60.76] Day-14 272.11[80.25] 260.84[55.21] 11.27 (-20.65 to 43.19) 0.16 (-0.29 to 0.61) 0.38 MD (95%CI) -45.92 (-84.58 to -7.25) -0.43 (-27.33 to 26.47) P- value b 0.30 0.95 PLR Baseline 31.69[23.06] 26.89[17.03] Day-14 27.15[27.66] 17.21[11.22] 9.94 (0.15 to 19.72) 0.47 (0.007 to 0.93) 0.047 MD (95%CI) 4.54 (-7.26 to 16.34) 9.68 (2.99 to 16.36) P- value b 0.36 0.005 NLR Baseline 10.04[7.00] 8.62[6.14] Day-14 7.93[7.78] 4.67[4.06] 3.26 (0.38 to 6.13) 0.52 (0.05 to 0.98) 0.03 MD (95%CI) 2.11 (-1.31 to 5.53) 3.95 (1.53 to 6.36) P- value b 0.17 0.001 BOS (%) Baseline 91.97[3.57] 90.29[4.47] Day-14 93.48[2.96] 91.78[2.83] 1.7 (0.35 to 3.04) 0.58 (0.11 to 1.05) 0.01 MD (95%CI) -1.51 (-3.02 to 0.009) -1.49 (-3.22 to 0.24) P- value b 0.006 0.017 *Baseline, n = 74 in both groups. On day 14th, the green tea group, n = 35, and placebo group, n = 35, Data are presented as Mean [standard deviation] and Mean Difference (95%Confidence Interval) or standardized Mean Difference (Cohen’s d (95%Confidence Interval)) and †adjusted Mean [standard deviation]. P-value a : calculated by independent t-test, P-value b : calculated by pair t-test, The recorded parameters include RBC (red blood cell), HB (hemoglobin), WBC (white blood cell), HCT (hematocrit), MCV (mean corpuscular volume), MCHC (mean corpuscular hemoglobin concentration), MCH (mean corpuscular hemoglobin), Lym (lymphocytes), Eos (eosinophils), Plt (platelets), ESR (erythrocyte sedimentation rate), PLR (platelets-to-lymphocytes ratio), NLR (neutrophils-to-lymphocytes ratio), BOS (blood oxygen saturation), and CRP (C-reactive protein). Table 3 : Values of inflammatory indicators and blood parameters before and at the end of intervention* Discussion The outcomes of this study, reveal that the provision of 900 mg of green tea supplementation over 14 days to adults infected with COVID-19 can lead to significant reductions in both inflammatory cytokines and SPO2 levels. In addition, the study demonstrates significant changes in CRP, ESR, PLR, NLR, and MCH levels. Based on our research, we found that green tea extract (GTE) can effectively reduce inflammation as indicated by the CRP (P-value = 0.02). It can be suggested that the supplementation of GTE results in a considerable reduction of CRP levels, which is a positive outcome that can be recommended for use in hospitals. These results were in line with the findings of other researchers and were contradicted by others [ 23 – 29 ], while studies such as Cialdella-Kam et al.[ 30 ] reported different results, the treatment was hybrid, also the study population was healthy people, Nimen et al, treatment was hybrid, and the sample size was small. In these studies, due to the small sample size, low dose of supplement or use of green tea itself, and short duration of the study different results have been reported or maybe the effect observed in this study can be attributed to the aided supplement dose [ 31 – 33 , 30 , 34 ]. Our findings showed changes in ESR levels (P-value = 0.03), indicating that green tea extract reduces inflammation. Other studies support this result [ 35 ]. Increased levels of CRP and ESR are prognostic factors of poor outcomes in COVID-19 patients [ 36 , 37 ]. The inhibitory effects of green tea extract on inflammatory parameters could probably be attributed to high levels of polyphenols such as epigallocatechin. For this effect, previous molecular studies suggested various mechanisms such as inhibiting MPRO or ACE [ 26 , 38 ]. Also, green tea polyphenols can affect protease activity through binding with His41 and Cys 145 residues in MPRO [ 26 ]. In this RCT, the investigated CBCs showed improvement at the end of the intervention. Red blood cells in the GTE group showed a significant increase in the intervention group. However, hemoglobin and hematocrit levels increased slightly at the end of the intervention in both groups. Although MCV and MCH decreased, MCHC increased slightly in the intervention group, which was not significant compared to the placebo group. WBC showed a significant decrease at the end of the intervention period, although the decrease did not differ between the two groups. Lymphopenia is one of the characteristics of the disease. In the present study, lymphocytes showed a significant increase after the intervention, but this difference was not significant between the two groups. Neutrophilia accurses in Covid disease, and our results showed that at the end of the intervention, Neutrophils showed a significant decrease, but compared to the placebo group, this difference was not significant. Monocytes and neutrophils decreased at the end of the intervention, and this decrease in neutrophils was significant, but there was no significant difference in the intervention group compared to the placebo group in the two groups. At the end of the intervention, there was an increase in platelets in both groups, and this increase was not different compared to before the intervention and in the comparison between the two groups. Some studies have suggested that virus-induced apoptosis may explain the changes in blood cells. These findings are consistent with data from other studies [ 39 – 44 ]. Our findings in CBC could be the result of an inflammatory state caused by cytokine storm [ 44 ]. PLR and NLR decreased at the end of the intervention in both groups. The decrease was significant in the placebo group compared to before the intervention and the intervention group. The decrease in PLR and NLR is considered a prominent indicator of improvement in COVID-19. These contrasting findings could be attributed to imbalanced baseline values and unintended therapeutic effects of the placebo treatments. Blood oxygen saturation increased significantly at the end of the intervention in both groups compared to before the intervention, and it was significant in the intervention group compared to the placebo. Limitation The randomized clinical trial had a strong methodology, but it also had some limitations. It didn't measure blood vitamin D levels, and there was no independent test to assess the purity and strength of the green tea extract. Consequently, the findings cannot be generalized to all brands of green tea extract supplements. Additionally, investigating possible links between green tea metabolites and their effects on urine and blood could have improved understanding of these effects, but this was not addressed in the study. Conclusions Our findings indicate that green tea extract could potentially reduce inflammatory indices in individuals with COVID-19. Additionally, this extract enhances blood oxygen saturation. These results suggest that green tea extract may be a helpful complementary approach for COVID-19 patients. While further research is necessary, our study suggests that incorporating green tea could be a promising additional treatment option for COVID-19, potentially improving recovery rates and overall patient management. Abbreviations BMI Body mass Index, BOS blood oxygen saturation, BP Blood Pressure, CRP C-reactive protein, Cu copper, DBP Diastolic Blood Pressure, Eos eosinophils, ESR erythrocyte sedimentation rate, Fe iron, g gram, GTE green tea extract, HB hemoglobin, HCT hematocrit, Lym lymphocytes, MCV mean corpuscular volume, MCHC mean corpuscular hemoglobin concentration, mcg microgram, MCH mean corpuscular hemoglobin, mg milligram, ND no difference, NLR neutrophils-to-lymphocytes ratio, Plt platelets, PLR platelets-to-lymphocytes ratio, PUFA n-3 polyunsaturated fatty acids omega-3 RBC red blood cell, SBP Systolic Blood Pressure, WBC white blood cell, Y year, Zn zinc, Declarations Ethics approval and consent to participate Yasuj University of Medical Sciences approved the study and was registered with the Iranian Registry of Clinical Trials (IRCT: 20150711023153N3). The study protocol was published in the Trial Journal in August 2021 (doi.org/10.1186/s13063-021-05462-8). Consent for publication We have a separate patient consent form for participating in the study and publishing the results. This paper does not contain any identifying images or personal/clinical details of participants, and this information will not be disclosed anywhere else. Access to participant materials and the informed consent form is restricted to the Ethics Committee of Yasuj University of Medical Sciences. Availability of data and materials Study data can be obtained by contacting the corresponding author for result confirmation or further research. Competing interests The authors state that there are no conflicts of interest. Funding Financial backing and assistance for this research were provided by Yasuj University of Medical Sciences (Grant number: 990245), located in Yasuj, Kohgilouyeh and Boyer-Ahmad Province, Iran. The funding entities were not involved in shaping the study's design, implementing the intervention, gathering or analyzing data, interpreting results, or composing the manuscript. Authors' contributions SBP and MY are the Principal Investigators; they formulated the research idea. SBP, JMM, and MY took the lead in crafting the project proposal and its advancement. MY, SM, AM, ZH, AP, MN, MH, and ZS participated in gathering data, refining the study design, and advancing the research. SBP, JMM, and MY were involved in refining statistical analyses. SBP, JMM, and MY are reviewing the manuscript. All authors have reviewed and endorsed the final version of the manuscript. Acknowledgments The writers thank the Vice-Chancellor of Research at Yasuj University of Medical Sciences for supporting their COVID-19 research and appreciate all individuals who contributed to the study. They also appreciate the Dine company for preparing and packaging the supplements and placebo. Authors' information (optional) 1 Department of Nutrition, School of Health and Nutrition, Yasuj University of Medical Sciences, Yasuj, Iran. 2 MSc student of Nutrition, School of Nutritional Sciences and Dietetics, Tehran University of Medical Sciences, Tehran, Iran. 3 Assistant Professor of Nutrition, Department of Nutrition, School of Health and Nutrition, Yasuj University of Medical Sciences, Yasuj, Iran. 4 Assistant Professor of Infectious Disease, Department of Internal Medicine, School of Medicine, Yasuj University of Medical Sciences, Yasuj, Iran. 5 MSc of Nutrition, Department of Nutrition, School of Health and Nutrition, Yasuj University of Medical Sciences, Yasuj, Iran. 6 Instructor of Biology, Department of Nutrition, School of Health and Nutrition, Yasuj University of Medical Sciences, Yasuj, Iran. 7 Assistant Professor of Nutrition , Social Determinants of Health Research Center, Yasuj University of Medical Sciences, Yasuj, Iran References He F, Deng Y, Li W. 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Effects of green tea extract supplementation and endurance training on irisin, pro-inflammatory cytokines, and adiponectin concentrations in overweight middle-aged men. European journal of applied physiology. 2020 Apr;120(4):915-23. Bagheri R, Rashidlamir A, Ashtary‐Larky D, Wong A, Alipour M, Motevalli MS, et al. Does green tea extract enhance the anti‐inflammatory effects of exercise on fat loss? British journal of clinical pharmacology. 2020;86(4):753-62. Diniz LRL, Elshabrawy HA, Souza MTS, Duarte ABS, Datta S, de Sousa DP. Catechins: Therapeutic Perspectives in COVID-19-Associated Acute Kidney Injury. Molecules (Basel, Switzerland). 2021 Sep 30;26(19). Ghosh R, Chakraborty A, Biswas A, Chowdhuri S. Evaluation of green tea polyphenols as novel corona virus (SARS CoV-2) main protease (Mpro) inhibitors - an in silico docking and molecular dynamics simulation study. Journal of biomolecular structure & dynamics. 2021 Aug;39(12):4362-74. Henss L, Auste A, Schürmann C, Schmidt C, von Rhein C, Mühlebach MD, et al. The green tea catechin epigallocatechin gallate inhibits SARS-CoV-2 infection. The Journal of general virology. 2021 Apr;102(4). Ohgitani E, Shin-Ya M, Ichitani M, Kobayashi M, Takihara T, Kawamoto M, et al. Rapid Inactivation In Vitro of SARS-CoV-2 in Saliva by Black Tea and Green Tea. Pathogens (Basel, Switzerland). 2021 Jun 8;10(6). Ohgitani E, Shin-Ya M, Ichitani M, Kobayashi M, Takihara T, Kawamoto M, et al. Significant Inactivation of SARS-CoV-2 In Vitro by a Green Tea Catechin, a Catechin-Derivative, and Black Tea Galloylated Theaflavins. Molecules (Basel, Switzerland). 2021 Jun 11;26(12). Cialdella-Kam L, Nieman DC, Knab AM, Shanely RA, Meaney MP, Jin F, et al. A Mixed Flavonoid-Fish Oil Supplement Induces Immune-Enhancing and Anti-Inflammatory Transcriptomic Changes in Adult Obese and Overweight Women-A Randomized Controlled Trial. Nutrients. 2016 May 11;8(5). de Maat MP, Pijl H, Kluft C, Princen HM. Consumption of black and green tea had no effect on inflammation, haemostasis and endothelial markers in smoking healthy individuals. European journal of clinical nutrition. 2000 Oct;54(10):757-63. Nieman DC, Gillitt ND, Knab AM, Shanely RA, Pappan KL, Jin F, et al. Influence of a polyphenol-enriched protein powder on exercise-induced inflammation and oxidative stress in athletes: a randomized trial using a metabolomics approach. PloS one. 2013;8(8):e72215. Koutelidakis AE, Rallidis L, Koniari K, Panagiotakos D, Komaitis M, Zampelas A, et al. Effect of green tea on postprandial antioxidant capacity, serum lipids, C-reactive protein and glucose levels in patients with coronary artery disease. European journal of nutrition. 2014;53(2):479-86. Nieman DC, Ramamoorthy S, Kay CD, Goodman CL, Capps CR, Shue ZL, et al. Influence of Ingesting a Flavonoid-Rich Supplement on the Metabolome and Concentration of Urine Phenolics in Overweight/Obese Women. Journal of proteome research. 2017 Aug 4;16(8):2924-35. Ramachandran B, Jayavelu S, Murhekar K, Rajkumar T. Repeated dose studies with pure Epigallocatechin-3-gallate demonstrated dose and route dependant hepatotoxicity with associated dyslipidemia. Toxicology reports. 2016;3:336-45. Cao B, Wang Y, Wen D, Liu W, Wang J, Fan G, et al. A Trial of Lopinavir-Ritonavir in Adults Hospitalized with Severe Covid-19. The New England journal of medicine. 2020 May 7;382(19):1787-99. Cao YC, Deng QX, Dai SX. Remdesivir for severe acute respiratory syndrome coronavirus 2 causing COVID-19: An evaluation of the evidence. Travel medicine and infectious disease. 2020 May-Jun;35:101647. Liu J, Bodnar BH, Meng F, Khan AI, Wang X, Saribas S, et al. Epigallocatechin gallate from green tea effectively blocks infection of SARS-CoV-2 and new variants by inhibiting spike binding to ACE2 receptor. Cell & bioscience. 2021 Aug 30;11(1):168. Manthou E, Georgakouli K, Deli CK, Sotiropoulos A, Fatouros IG, Kouretas D, et al. Effect of pomegranate juice consumption on biochemical parameters and complete blood count. Experimental and therapeutic medicine. 2017 Aug;14(2):1756-62. Velavan TP, Meyer CG. Mild versus severe COVID-19: Laboratory markers. International journal of infectious diseases : IJID : official publication of the International Society for Infectious Diseases. 2020 Jun;95:304-07. Kaur S, Bansal R, Kollimuttathuillam S, Gowda AM, Singh B, Mehta D, et al. The looming storm: Blood and cytokines in COVID-19. Blood reviews. 2021 Mar;46:100743. Kumar Gothwal S, Singh VB, Shrivastava M, Banseria R, Goyal K, Singh S, et al. Complete Blood-count-based Inflammatory Score (CBCS) of COVID-19 Patients at Tertiary Care Center. Alternative therapies in health and medicine. 2021 Jun;27(S1):18-24. Palladino M. Complete blood count alterations in COVID-19 patients: A narrative review. Biochemia medica. 2021 Oct 15;31(3):030501. Pozdnyakova O, Connell NT, Battinelli EM, Connors JM, Fell G, Kim AS. Clinical Significance of CBC and WBC Morphology in the Diagnosis and Clinical Course of COVID-19 Infection. American journal of clinical pathology. 2021 Feb 11;155(3):364-75. Additional Declarations No competing interests reported. Supplementary Files GraphicalAbstract.jpg Highlights.docx Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-4976013","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":352187867,"identity":"b39aad29-daa1-4ee9-b0f2-a2465daa4a39","order_by":0,"name":"Mojtaba Yousefi","email":"","orcid":"","institution":"Yasuj University of Medical Sciences","correspondingAuthor":false,"prefix":"","firstName":"Mojtaba","middleName":"","lastName":"Yousefi","suffix":""},{"id":352187868,"identity":"d1caeec7-3e3a-493b-85da-a9eb7ec2b16f","order_by":1,"name":"Zahra Hosseinzade","email":"","orcid":"","institution":"Yasuj University of Medical 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They have various generations and can cause significant damage to the respiratory system and other vital organs [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. The COVID-19 pandemic has affected millions of people worldwide and has claimed over 6\u0026nbsp;million lives. The virus can lead to hypertension, fatigue, lung fibrosis, arterial and cardiac thrombosis, inflammation, and stroke. Symptoms of COVID-19 include fatigue, shortness of breath, muscle and joint pain, headache, cough, chest pain, altered sense of smell and taste, and diarrhea [\u003cspan additionalcitationids=\"CR5\" citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. Studies have shown that severe impacts from the virus can lead to a cytokine storm, which is an overly active immune system that can cause sudden and lethal hyper-cytokine-mia and multiple organ failure syndrome [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. The virus attaches to angiotensin II receptors, which leads to increased T lymphocyte activity and the generation of inflammatory agents [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. The host's nutritional status and immune system greatly affect the severity of COVID-19 [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. Green tea is considered to be one of the nutritional factors that can affect both inflammation and the immune system [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eGreen tea is produced from the leaves of the Camellia sinensis plant and holds a range of antioxidant components, like catechins [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. The catechins found in green tea include Epigallocatechin gallate (EGCG), epicatechin gallate (ECG), epicatechin (EC), and galloatechin gallate (GCG). The concentration of polyphenol catechins tends to be higher in older plants [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. Among these catechins, EGCG is present in the highest concentration and is known for its potential anti-inflammatory, antimicrobial, and antioxidant properties. It has also shown beneficial effects in cardiovascular health and oral care [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. Studies on green tea catechins, particularly EGCG, have demonstrated their antiviral effects against various viruses [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. Green tea can impact influenza virus infection by interacting with viral hemagglutinin (HA) and interfering with viral RNA synthesis within cells. Catechins also inhibit the activity of viral RNA polymerase endonuclease. Molecular studies have indicated that EGCG exhibits a strong binding affinity with main protease (MPRO), which are involved in the formation of viral molecules. This suggests that EGCG may possess significant phytochemical properties for inhibiting the spike glycoprotein (S) and main protease of the Covid-19 virus. Based on previous studies, phenolic compounds have shown promising inhibitory effects on MPRO protease and S virus protein when compared to chemical drugs [\u003cspan additionalcitationids=\"CR15\" citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. Furthermore, additional pathways, such as ACE inhibitory effects, have been suggested to contribute to the effectiveness of green tea products against inflammatory disorders [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. Besides, COVID-19 is an inflammatory disease characterized by elevated levels of inflammatory markers such as C-reactive protein (CRP) and erythrocyte sedimentation rate (ESR)[\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eSome studies have been conducted on the benefits of green tea on the COVID-19 virus [\u003cspan additionalcitationids=\"CR20\" citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. However, these studies were in vitro or a weak methodology, rendering the results unreliable. Therefore, a new study with a larger sample size and stronger methodology was necessary to verify the effects of green tea on infected patients. Therefore, the current study was carried out to investigate the effect of green tea supplementation on inflammatory biomarkers in individuals with mild to moderate symptoms.\u003c/p\u003e"},{"header":"Material and methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStudy design\u003c/h2\u003e \u003cp\u003eThis randomized, double-blind, placebo-controlled trial was conducted at Shahid Jalil Hospital in Yasuj, southwest Iran. The purpose of the study was to investigate whether the consumption of green tea extract could help reduce inflammatory biomarkers in COVID-19 patients. The trial included 74 patients who were receiving hospital care. The participants were randomly divided into two groups, with one group receiving a daily dosage of 900mg of green tea supplement and the other group receiving a placebo. The study followed a 1:1 allocation ratio with a 2-armed parallel design. All participants provided informed consent and were screened and educated remotely via phone interviews. The study adhered to the guidelines outlined in the Helsinki Declaration.\u003c/p\u003e \u003cp\u003eThe study gathered information by collecting whole blood samples and conducting interviews. The blood samples were taken after a 12-hour overnight fast to assess the complete blood count (CBC) and measure inflammatory biomarkers such as CRP and ESR at the beginning of the study and again after 14 days of intervention. Patients who were discharged earlier than expected were monitored through follow-up phone interviews to ensure their compliance with the study protocols.\u003c/p\u003e \u003cp\u003eExpert nutritionists collected 24-hour dietary recalls on three occasions (days 1, 7, and 14) to assess the detailed intake of nutrients and food patterns. The dietary intakes were analyzed using the Nutritionist IV software (First Databank Inc.), and compliance with the study guidelines was assessed through 24-hour dietary recalls and interviews.\u003c/p\u003e \u003cp\u003eBoth patient groups were instructed to avoid consuming green tea during this period and were assessed at the end. For each patient, the energy and macronutrient content were obtained from the 24-hour dietary recall. The patient\u0026rsquo;s intake was compared between the intervention and control groups to ensure that there were no significant differences in their diets. Admission and discharge dates, disease complications, prescribed medications, and dosages were documented.\u003c/p\u003e \u003cp\u003eThe CBC was assessed using a cell counter (kh-21n, Sysmex, Japan), while CRP levels were measured using a serologic kit (PAADCO, Spain). The ESR was determined utilizing an automatic ESR reader (DA-717, NOVIN GOSTAR, Iran) from whole blood samples. Demographic information, including age, sex, and occupation was collected through a questionnaire. Additionally, the patient's medical history, medications, and supplement usage were documented. Height, weight, and body mass index (BMI) were measured, and blood oxygen saturation levels were recorded using a pulse oximeter (PO 80 Pulse Oximeter, Beurer, Germany). Blood pressure readings were obtained using an Omron M7 sphygmomanometer (Omron, Japan), and the respiratory rate was determined by counting the breaths per minute.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eSample size determination\u003c/h2\u003e \u003cp\u003eThe sample size for this study was determined by considering a type 1 error rate of 5% and a power of 80%. Considering the mean and standard deviation of CRP levels from a previous article [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e] (mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD: 0.10\u0026thinsp;\u0026plusmn;\u0026thinsp;0.06 in the placebo group and 0.24\u0026thinsp;\u0026plusmn;\u0026thinsp;0.21 in the intervention group) and the minimum clinically significant difference (MCID) for CRP (MCID\u0026thinsp;\u0026gt;\u0026thinsp;2.6 mg/L) from another study [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e], a correlation coefficient of 0.4 was taken into account. Using the method of covariance analysis, a sample size of 31 individuals was calculated for each group. Considering a potential attrition rate of 20%, a total sample size of 37 individuals per group was determined. Thus, a total of 74 participants were calculated to be required for this study. The sample size calculations were performed using STATA software.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eParticipant\u003c/h2\u003e \u003cp\u003eWe included adult patients who were hospitalized and diagnosed with mild to moderate COVID-19 using RT-PCR in our study. Patients with critical infection, pregnant or breastfeeding women, those with disseminated intravascular coagulation or other coagulopathies, individuals with chronic kidney disease or coronary heart disease, and participants in a clinical trial within the past 30 days were not included in the study. The study was conducted when the Omicron strain was the dominant variant of COVID-19.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eRandomization\u003c/h2\u003e \u003cp\u003eEligible patients were randomly assigned to either the intervention or placebo group, with an equal 1:1 ratio. Randomization will be carried out using permuted blocks of size 10, resulting in 8 blocks. The randomization process will take into account the sex and age (\u0026ge;\u0026thinsp;50 years and \u0026lt;\u0026thinsp;50 years) categories. The researchers utilized randomization.com to create a list of randomized allocations. An independent third party was responsible for placing individuals on the list. Subsequently, the list was secured within a sealed envelope within the academic institution until the study's conclusion. The trial participants and personnel engaged in the study remain unaware of the contents of the packages containing the study substances. These packages were fashioned by the study staff to possess an indistinguishable semblance. The sole distinction among the packages is a tag affixed by an external group, denoting the associated randomization list.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eBlinding\u003c/h2\u003e \u003cp\u003eA neutral third party was enlisted to allocate labels and adhesive stickers, classifying them as either GTE or placebo. This third individual, who underwent a validation process, affixed labels onto sealed packages containing either genuine GTE or placebo, as produced by the appropriate manufacturer. After preparation, the package was returned to the research team, ensuring the sole discrepancy lay in the labels themselves. The table outlining the sequential number labels for randomization was exclusively retained by the third party until the end of the study. To evaluate blinding, personnel, patients, and assessors were prompted to speculate on the kind of treatment administered to the patients. This assessment covered 80% of the patients and 75% of the assessors involved. Upon the study's culmination, over 85% of the patients believed they had been administered GTE supplements.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eIntervention\u003c/h2\u003e \u003cp\u003eThe main pool of participants was primarily gathered from Shahid Jalil Hospital in Yasuj, Iran. This was achieved by conducting interviews with individuals aged 18 and above who had been diagnosed positive in PCR tests. Following the initial assessment and categorization for the intervention, the patients were randomly assigned to one of two groups: those who would receive the GTE supplement and those assigned to the placebo group. In addition to the standard care provided to the patients at the hospital, individuals in the GTE group were given two GTE tablets per day for 14 days. These tablets contained 450mg of green tea extract and were taken after lunch and dinner. Meanwhile, participants in the control group were given similar placebo Tablets, containing 450 mg of cellulose. In addition, a pharmaceutical company was instructed to package the supplement and placebo in identical containers.\u003c/p\u003e \u003cp\u003eTo determine the rate of adherence, the remaining count of either GTE or placebo tabs was assessed in every package at the end of the intervention. Compliance percent was then computed using the following formula:\u003c/p\u003e \u003cp\u003eCompliance (%) = (Number of tablets consumed * 100) / 28.\u003c/p\u003e \u003cp\u003eA lower percentage obtained through this calculation indicates higher compliance with the regimen.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis\u003c/h2\u003e \u003cp\u003eWe conducted a comprehensive analysis of all the gathered data using STATA version 14. Our approach was based on the central limit theorem, which assumes that the means follow a normal distribution. This justified the use of parametric tests. We presented the data in terms of means and standard deviation [SD]. Additionally, we calculated the mean difference (MD) and established a 95% confidence interval (CI) based on the mean [SD] and the sample size, using STATA for these computations.\u003c/p\u003e \u003cp\u003eTo compare the disparities in chosen variables between the two groups, we employed the independent-samples t-test separately for each group. The Paired t-test was employed to evaluate the differences between the two groups before and after the intervention. Furthermore, we applied ANOVA-ANCOVA to evaluate the primary outcomes (CRP, ESR). Statistical significance was considered to be present at a threshold of P\u0026thinsp;\u0026lt;\u0026thinsp;0.05.\u003c/p\u003e \u003c/div\u003e"},{"header":"Result","content":"\u003cp\u003eOut of the 492 patients who were admitted to the hospital, a total of 310 patients were found to meet the eligibility criteria. From this pool of eligible patients, 74 individuals were selected for participation and then randomly assigned to two groups: the GTE group and the placebo group. Out of these, 70 patients completed the study, with 35 in the GTE group and 35 in the placebo group. The patients displayed a compliance rate of over 96%, as illustrated in Fig.\u0026nbsp;1.\u003c/p\u003e \u003cp\u003eThe collected data underwent analysis using both pre-protocol and intention-to-treat (ITT) methodologies. No significant differences were observed between these two approaches, and ultimately, the outcomes were reported based on the ITT approach. Adverse events were reported by three patients (9%) in the placebo group and one patient (3%) in the GTE group. The most frequently reported adverse events included weight loss and vomiting, which were experienced by a total of four subjects (10%).\u003c/p\u003e \u003cp\u003e \u003cb\u003eFigure 1: Flow of participants through the study\u003c/b\u003e \u003c/p\u003e \u003cp\u003eAt the start of the study (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e), there were no significant variations in age, weight, medical background, medication usage, vital signs, and BMI between the groups. The mean and standard deviation age of patients was 48.63[13.33] years in the GTE group and 50.02[15.03] years in the placebo group. Similarly, no significant distinctions were observed in terms of energy and nutrient consumption between the two groups, as shown by the dietary records collected over the course of the intervention (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eInitial characteristics of the groups receiving green tea and the placebo.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eGreen tea (n\u0026thinsp;=\u0026thinsp;37)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003ePlacebo (n\u0026thinsp;=\u0026thinsp;37)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e-value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eAge (y)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e48.63[13.33]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e50.02[15.03]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.68\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eWeight (kg)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e80.45[13.03]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e77.88[14.22]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.43\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eBMI (kg/m\u003csup\u003e2\u003c/sup\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e27.80[4.43]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e27.87[5.08]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.94\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eSex (male: female) (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.81\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eHospitalized duration (day)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5.85[2.32]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5.42[1.83]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.39\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e \u003cp\u003eMedical history (%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDiabetes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHypertension\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDyslipidemia\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOthers\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e \u003cp\u003eInfection Severity (%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMild\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eND\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eModerate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eND\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e \u003cp\u003eDrug history (%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSupplements\u003csup\u003e1\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eND\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAntibiotic\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eND\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAntiviral\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eND\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAnti-inflammatory\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eND\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e \u003cp\u003eVital sign, BP\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRespiratory Rate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e21.71[2.51]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e21.28[2.56]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.48\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePuls Rate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e90.20[16.67]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e89.71[14.92]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.89\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSBP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e124.28[15.56]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e121.71[12.69]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.45\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDBP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e79.22[9.37]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e74.77[10.61]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.067\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003eThe data is exhibited as number (percentage) for categorical factors and as mean [standard deviation]. The recorded parameters include BMI; Body mass Index, BP; Blood Pressure, SBP; Systolic Blood Pressure, DBP; Diastolic Blood Pressure, Y; year, ND; no difference.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eThe consumption of specific nutrients throughout the intervention period in both groups receiving the GTE and the placebo.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNutrients\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGreen tea\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePlacebo\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e-value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCarbohydrates (g)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e199.74[20.45]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e209.64[39.38]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eND\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eProteins (g)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e71.94[11.26]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e74.33[12.22]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eND\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal fat (g)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e62.70[15.52]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e64.09[11.88]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eND\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEnergy (kcal)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1638.18[166.55]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1750.78[263.09]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eND\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePUFA n-3 (g)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.13 [25]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.09 [21]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eND\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMagnesium (mg)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e294.95[113.82]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e287.54[98.04]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eND\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCalcium (mg)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e691.42[234.19]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e720.99[207.05]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eND\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePhosphorus (mg)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e694.87[265.08]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e645.48[231.08]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eND\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePotassium (mg)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1804.91[236.07]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1795.45[460.11]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eND\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCu (mcg)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e914[415.98]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e908.45[381.43]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eND\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFe (mg)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e9.97[3.36]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e10.87[3.09]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eND\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eZn (mg)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e9.75[3.47]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e10.04[3.89]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eND\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal Fiber (g)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e18.53[8.87]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e15.41[6.74]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eND\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVitamin C (mg)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e80.91 [25.81]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e80.73[24.82]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eND\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003csup\u003e1\u003c/sup\u003eVitamin C (mg) sup\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e290.94 [2.87]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e285.99 [5.31]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eND\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003csup\u003e1\u003c/sup\u003eVitamin D (IU) sup\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3571.42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3571.42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eND\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVitamin E (mg)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e12.27[6.49]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e12.13[3.95]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eND\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"4\"\u003eData are displayed in the form of the mean [standard deviation]. PUFA refers to polyunsaturated fatty acids, including omega-3 (PUFA n-3). The units are g (gram), mg (milligram), and mcg (microgram). Elements include Cu (copper), Fe (iron), and Zn (zinc). \u003csup\u003e1\u003c/sup\u003eSupplements included vitamins C and D, while ND indicates no difference.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. Initial characteristics of the groups receiving green tea and the placebo.\u003c/p\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e: The consumption of specific nutrients throughout the intervention period in both groups receiving the GTE and the placebo.\u003c/p\u003e \u003cp\u003eThe laboratory data analysis findings are presented in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e, displaying the pre- and post-intervention comparisons between the groups. By the end of the trial (day 14) compared to before the intervention, the GTE group exhibited significant reductions in CRP and ESR as primary outcomes, along with decreased Neutrophil levels as a secondary outcome, and increased RBC, lymphocytes (Lym), and blood oxygen saturation (BOS) levels (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). Conversely, in the placebo group, MCHC, Lym, and BOS showed significant increases. At the same time, platelet lymphocyte ratio (PLR), neutrophil-lymphocyte ratio (NLR), and Neutrophils, as secondary outcomes, showed substantial decreases by the end of the trial (day 14) (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). By the trial's conclusion, significant differences between the two groups were observed in MCH, NLR, ESR, BOS, CRP, and PLR (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05). In addition, primary outcomes (CRP, ESR) baseline values were adjusted. No other indices showed a statistical significance difference between the two groups after the 14 days.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eValues of inflammatory indicators and blood parameters before and at the end of intervention*\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eVariables\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eGTE\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003ePlacebo\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eMD (95%CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eMD (95% CI) (Cohen\u0026rsquo;s d)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e-value\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e \u003cp\u003eCRP (mg/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBaseline\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e40.67[24.26]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e36.83[23.15]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDay-14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e22.33[19.89] \u003csup\u003e\u0026dagger;\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e33.16[20.53] \u003csup\u003e\u0026dagger;\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-10.83(-20.19to -1.46)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.53 (-0.99 to -0.07)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.02\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMD (95%CI)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e18.34 (8.05 to 28.62)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.67(-6.47 to 13.81)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eP-\u003c/em\u003evalue \u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e \u003cp\u003eESR (mm/h)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBaseline\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e41.19[22.52]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e39.46[17.15]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDay-14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e24.51[17.08]\u003csup\u003e\u0026dagger;\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e32.76 [15.14] \u003csup\u003e\u0026dagger;\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-8.25(-15.73 to -0.76)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.51(-0.97 to -0.04)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.03\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMD (95%CI)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e16.68 (7.41 to 25.94)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6.7(-.79 to 14.19)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eP-\u003c/em\u003evalue\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.070\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e \u003cp\u003eRBC (x1000/\u0026micro;L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBaseline\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4.53[0.58]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4.80[0.75]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDay-14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4.78[0.54]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4.86[0.80]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.08 (-0.39 to 0.23)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.11 (-0.57 to 0.33)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.61\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMD (95%CI)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.25 (-0.50 to 0.01)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.06 (-0.41 to 0.29)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eP-\u003c/em\u003evalue\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.56\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e \u003cp\u003eHB (g/dL)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBaseline\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e13.13[1.99]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e13.74[2.11]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDay-14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e13.30[1.37]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e13.75[1.78]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.45 (-1.18 to 0.28)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.28 (-0.74 to 0.17)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.61\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMD (95%CI)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.17 (-.96 to 0.62)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.01 (-0.91 to 0.89)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eP-\u003c/em\u003evalue \u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.94\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e \u003cp\u003eHCT (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBaseline\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e40.70[5.05]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e40.73[4.88]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDay-14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e40.98[4.14]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e40.73[5.53]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.25 (-2.01 to 2.51)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.05 (-0.40 to 0.50)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.82\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMD (95%CI)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.28(-2.42 to 1.86)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0 (-2.41 to 2.41)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eP-\u003c/em\u003evalue \u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.76\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.99\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e \u003cp\u003eMCV (fl)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBaseline\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e90.46[7.61]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e88.17[8.79]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDay-14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e88.69[6.41]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e87.31[8.64]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.38 (-2.14 to 4.90)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.18(-0.27 to 0.63)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.43\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMD (95%CI)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.77(-1.49 to 5.03)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.86 (-3.17 to 4.89)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eP-\u003c/em\u003evalue \u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e \u003cp\u003eMCH (pg)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBaseline\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e29.05[3.08]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e28.79[3.38]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDay-14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e28.47[3.51]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e30.03[3.10]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-1.56 (-3.09 to -0.02)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.47 (-0.93 to \u0026minus;\u0026thinsp;.007)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.05\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMD (95%CI)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.58 (-0.95 to 2.11)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-1.24 (-2.74 to 0.26)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eP-\u003c/em\u003evalue\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e \u003cp\u003eMCHC (g/dL)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBaseline\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e32.05[1.43]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e32.22[1.45]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDay-14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e32.55[2.23]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e33.24[1.74]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.69 (-1.61 to 0.23)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.34 (-0.80 to 0.11)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.15\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMD (95%CI)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.5 (-1.36 to 0.36)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-1.02 (-1.76 to -0.27)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eP-\u003c/em\u003evalue \u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e \u003cp\u003eWBC (x1000/\u0026micro;L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBaseline\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e10.48[4.12]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e10.34[2.79]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDay-14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e9.96[3.54]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e10.02[2.95]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.06 (-1.57 to 1.45)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.01 (-0.47 to 0.43)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.94\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMD (95%CI)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.52 (-1.26 to 2.30)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.32 (-1.01 to 1.65)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eP-\u003c/em\u003evalue \u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.53\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e \u003cp\u003eLym (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBaseline\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e12.46[8.01]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e14.03[8.23]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDay-14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e20.23[14.25]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e19.91[9.20]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.32 (-5.23 to 5.87)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.02 (-0.42 to 0.48)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.91\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMD (95%CI)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-7.77 (-13.12 to -2.41)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-5.88 (-9.92 to -1.83)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eP-\u003c/em\u003evalue \u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.005\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.004\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e \u003cp\u003eMono (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBaseline\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e530.89[245.55]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e523.16[244.34]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDay-14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e551.43[637.46]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e534.92[227.44]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e16.51 (-205.29 to 238.31)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e.03(-0.42 to 0.49)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.88\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMD (95%CI)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-20.54 (-244.41 to 203.33)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-11.76 (-121.15 to 97.63)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eP-\u003c/em\u003evalue \u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.84\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.77\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e \u003cp\u003eNeutrophils (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBaseline\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e81.73[9.40]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e79.14[9.93]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDay-14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e70.01[17.42]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e69.33[17.24]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.68 (-7.35 to 8.71)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.03(-0.41 to 0.49)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.68\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMD (95%CI)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e11.72 (5.23 to 18.20)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e9.81 (3.28 to 16.33)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eP-\u003c/em\u003evalue\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.004\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e \u003cp\u003ePlts (x1000/\u0026micro;L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBaseline\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e226.19[86.50]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e260.41[60.76]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDay-14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e272.11[80.25]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e260.84[55.21]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e11.27 (-20.65 to 43.19)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.16 (-0.29 to 0.61)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.38\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMD (95%CI)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-45.92 (-84.58 to -7.25)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.43 (-27.33 to 26.47)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eP-\u003c/em\u003evalue\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.95\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e \u003cp\u003ePLR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBaseline\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e31.69[23.06]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e26.89[17.03]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDay-14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e27.15[27.66]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e17.21[11.22]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e9.94 (0.15 to 19.72)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.47 (0.007 to 0.93)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.047\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMD (95%CI)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4.54 (-7.26 to 16.34)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e9.68 (2.99 to 16.36)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eP-\u003c/em\u003evalue\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.005\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e \u003cp\u003eNLR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBaseline\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e10.04[7.00]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e8.62[6.14]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDay-14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7.93[7.78]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4.67[4.06]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3.26 (0.38 to 6.13)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.52 (0.05 to 0.98)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.03\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMD (95%CI)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.11 (-1.31 to 5.53)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.95 (1.53 to 6.36)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eP-\u003c/em\u003evalue \u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e \u003cp\u003eBOS (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBaseline\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e91.97[3.57]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e90.29[4.47]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDay-14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e93.48[2.96]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e91.78[2.83]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.7 (0.35 to 3.04)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.58 (0.11 to 1.05)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMD (95%CI)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-1.51 (-3.02 to 0.009)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-1.49 (-3.22 to 0.24)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eP-\u003c/em\u003evalue \u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.006\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.017\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"7\"\u003e*Baseline, n\u0026thinsp;=\u0026thinsp;74 in both groups. On day 14th, the green tea group, n\u0026thinsp;=\u0026thinsp;35, and placebo group, n\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"7\"\u003e=\u0026thinsp;35, Data are presented as Mean [standard deviation] and Mean Difference (95%Confidence Interval) or standardized Mean Difference (Cohen\u0026rsquo;s d (95%Confidence Interval)) and \u0026dagger;adjusted Mean [standard deviation]. P-value\u003csup\u003ea\u003c/sup\u003e: calculated by independent t-test, P-value\u003csup\u003eb\u003c/sup\u003e: calculated by pair t-test, The recorded parameters include RBC (red blood cell), HB (hemoglobin), WBC (white blood cell), HCT (hematocrit), MCV (mean corpuscular volume), MCHC (mean corpuscular hemoglobin concentration), MCH (mean corpuscular hemoglobin), Lym (lymphocytes), Eos (eosinophils), Plt (platelets), ESR (erythrocyte sedimentation rate), PLR (platelets-to-lymphocytes ratio), NLR (neutrophils-to-lymphocytes ratio), BOS (blood oxygen saturation), and CRP (C-reactive protein).\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e: Values of inflammatory indicators and blood parameters before and at the end of intervention*\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThe outcomes of this study, reveal that the provision of 900 mg of green tea supplementation over 14 days to adults infected with COVID-19 can lead to significant reductions in both inflammatory cytokines and SPO2 levels. In addition, the study demonstrates significant changes in CRP, ESR, PLR, NLR, and MCH levels.\u003c/p\u003e \u003cp\u003eBased on our research, we found that green tea extract (GTE) can effectively reduce inflammation as indicated by the CRP (P-value\u0026thinsp;=\u0026thinsp;0.02). It can be suggested that the supplementation of GTE results in a considerable reduction of CRP levels, which is a positive outcome that can be recommended for use in hospitals.\u003c/p\u003e \u003cp\u003eThese results were in line with the findings of other researchers and were contradicted by others [\u003cspan additionalcitationids=\"CR24 CR25 CR26 CR27 CR28\" citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e], while studies such as Cialdella-Kam et al.[\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e] reported different results, the treatment was hybrid, also the study population was healthy people, Nimen et al, treatment was hybrid, and the sample size was small. In these studies, due to the small sample size, low dose of supplement or use of green tea itself, and short duration of the study different results have been reported or maybe the effect observed in this study can be attributed to the aided supplement dose [\u003cspan additionalcitationids=\"CR32\" citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eOur findings showed changes in ESR levels (P-value\u0026thinsp;=\u0026thinsp;0.03), indicating that green tea extract reduces inflammation. Other studies support this result [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eIncreased levels of CRP and ESR are prognostic factors of poor outcomes in COVID-19 patients [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e, \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]. The inhibitory effects of green tea extract on inflammatory parameters could probably be attributed to high levels of polyphenols such as epigallocatechin. For this effect, previous molecular studies suggested various mechanisms such as inhibiting MPRO or ACE [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e, \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e]. Also, green tea polyphenols can affect protease activity through binding with His41 and Cys 145 residues in MPRO [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eIn this RCT, the investigated CBCs showed improvement at the end of the intervention. Red blood cells in the GTE group showed a significant increase in the intervention group. However, hemoglobin and hematocrit levels increased slightly at the end of the intervention in both groups. Although MCV and MCH decreased, MCHC increased slightly in the intervention group, which was not significant compared to the placebo group. WBC showed a significant decrease at the end of the intervention period, although the decrease did not differ between the two groups. Lymphopenia is one of the characteristics of the disease. In the present study, lymphocytes showed a significant increase after the intervention, but this difference was not significant between the two groups. Neutrophilia accurses in Covid disease, and our results showed that at the end of the intervention, Neutrophils showed a significant decrease, but compared to the placebo group, this difference was not significant. Monocytes and neutrophils decreased at the end of the intervention, and this decrease in neutrophils was significant, but there was no significant difference in the intervention group compared to the placebo group in the two groups. At the end of the intervention, there was an increase in platelets in both groups, and this increase was not different compared to before the intervention and in the comparison between the two groups. Some studies have suggested that virus-induced apoptosis may explain the changes in blood cells. These findings are consistent with data from other studies [\u003cspan additionalcitationids=\"CR40 CR41 CR42 CR43\" citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e]. Our findings in CBC could be the result of an inflammatory state caused by cytokine storm [\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e]. PLR and NLR decreased at the end of the intervention in both groups. The decrease was significant in the placebo group compared to before the intervention and the intervention group. The decrease in PLR and NLR is considered a prominent indicator of improvement in COVID-19. These contrasting findings could be attributed to imbalanced baseline values and unintended therapeutic effects of the placebo treatments. Blood oxygen saturation increased significantly at the end of the intervention in both groups compared to before the intervention, and it was significant in the intervention group compared to the placebo.\u003c/p\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eLimitation\u003c/h2\u003e \u003cp\u003eThe randomized clinical trial had a strong methodology, but it also had some limitations. It didn't measure blood vitamin D levels, and there was no independent test to assess the purity and strength of the green tea extract. Consequently, the findings cannot be generalized to all brands of green tea extract supplements. Additionally, investigating possible links between green tea metabolites and their effects on urine and blood could have improved understanding of these effects, but this was not addressed in the study.\u003c/p\u003e \u003c/div\u003e"},{"header":"Conclusions","content":"\u003cp\u003eOur findings indicate that green tea extract could potentially reduce inflammatory indices in individuals with COVID-19. Additionally, this extract enhances blood oxygen saturation. These results suggest that green tea extract may be a helpful complementary approach for COVID-19 patients. While further research is necessary, our study suggests that incorporating green tea could be a promising additional treatment option for COVID-19, potentially improving recovery rates and overall patient management.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eBMI\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Body mass Index,\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eBOS \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;blood oxygen saturation,\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eBP\u0026nbsp;\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;Blood Pressure,\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eCRP \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;C-reactive protein,\u003c/p\u003e\n\u003cp\u003eCu \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;copper,\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eDBP\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;Diastolic Blood Pressure,\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eEos \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;eosinophils,\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eESR\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;erythrocyte sedimentation rate,\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eFe \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;iron,\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eg \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;gram,\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eGTE \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;green tea extract,\u003c/p\u003e\n\u003cp\u003eHB \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;hemoglobin,\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eHCT \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;hematocrit,\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eLym \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;lymphocytes,\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eMCV \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;mean corpuscular volume,\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eMCHC \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;mean corpuscular hemoglobin concentration,\u0026nbsp;\u003c/p\u003e\n\u003cp\u003emcg \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;microgram,\u003c/p\u003e\n\u003cp\u003eMCH \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;mean corpuscular hemoglobin,\u0026nbsp;\u003c/p\u003e\n\u003cp\u003emg \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;milligram,\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eND \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;no difference,\u003c/p\u003e\n\u003cp\u003eNLR \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;neutrophils-to-lymphocytes ratio,\u0026nbsp;\u003c/p\u003e\n\u003cp\u003ePlt \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;platelets,\u0026nbsp;\u003c/p\u003e\n\u003cp\u003ePLR \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;platelets-to-lymphocytes ratio,\u0026nbsp;\u003c/p\u003e\n\u003cp\u003ePUFA n-3 \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;polyunsaturated fatty acids omega-3\u003c/p\u003e\n\u003cp\u003eRBC \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;red blood cell,\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eSBP \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;Systolic Blood Pressure,\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eWBC \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;white blood cell,\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eY \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;year,\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eZn \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;zinc,\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eYasuj University of Medical Sciences approved the study and was registered with the Iranian Registry of Clinical Trials (IRCT: 20150711023153N3). The study protocol was published in the Trial Journal in August 2021 (doi.org/10.1186/s13063-021-05462-8).\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe have a separate patient consent form for participating in the study and publishing the results. This paper does not contain any identifying images or personal/clinical details of participants, and this information will not be disclosed anywhere else. Access to participant materials and the informed consent form is restricted to the Ethics Committee of Yasuj University of Medical Sciences.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eStudy data can be obtained by contacting the corresponding author for result confirmation or further research.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors state that there are no conflicts of interest.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFinancial backing and assistance for this research were provided by Yasuj University of Medical Sciences (Grant number: 990245), located in Yasuj, Kohgilouyeh and Boyer-Ahmad Province, Iran. The funding entities were not involved in shaping the study\u0026apos;s design, implementing the intervention, gathering or analyzing data, interpreting results, or composing the manuscript.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026apos; contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSBP and MY are the Principal Investigators; they formulated the research idea. SBP, JMM, and MY took the lead in crafting the project proposal and its advancement. MY, SM, AM, ZH, AP, MN, MH, and ZS participated in gathering data, refining the study design, and advancing the research. SBP, JMM, and MY were involved in refining statistical analyses. SBP, JMM, and MY are reviewing the manuscript. All authors have reviewed and endorsed the final version of the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgments\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe writers thank the Vice-Chancellor of Research at Yasuj University of Medical Sciences for supporting their COVID-19 research and appreciate all individuals who contributed to the study. They also appreciate the Dine company for preparing and packaging the supplements and placebo.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026apos; information (optional)\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003csup\u003e1\u003c/sup\u003e\u003c/strong\u003eDepartment of Nutrition, School of Health and Nutrition, Yasuj University of Medical Sciences, Yasuj, Iran.\u003csup\u003e2\u003c/sup\u003eMSc student\u0026nbsp;of Nutrition,\u0026nbsp;School of Nutritional Sciences and Dietetics, Tehran University of Medical Sciences, Tehran, Iran.\u003csup\u003e3\u003c/sup\u003eAssistant Professor of Nutrition, Department of Nutrition, School of Health and Nutrition, Yasuj University of Medical Sciences, Yasuj, Iran. \u003csup\u003e4\u003c/sup\u003eAssistant Professor of Infectious Disease, Department of Internal Medicine, School of Medicine, Yasuj University of Medical Sciences, Yasuj, Iran. \u003csup\u003e5\u003c/sup\u003eMSc of Nutrition, Department of Nutrition, School of Health and Nutrition, Yasuj University of Medical Sciences, Yasuj, Iran. \u003csup\u003e6\u003c/sup\u003eInstructor of Biology, Department of Nutrition, School of Health and Nutrition, Yasuj University of Medical Sciences, Yasuj, Iran. \u003csup\u003e7\u003c/sup\u003eAssistant Professor of Nutrition\u003cem\u003e, \u003cem\u003eSocial Determinants of Health Research Center, Yasuj University of Medical Sciences, Yasuj, Iran\u003c/em\u003e\u003c/em\u003e\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eHe F, Deng Y, Li W. 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Blood reviews. 2021 Mar;46:100743.\u003c/li\u003e\n\u003cli\u003eKumar Gothwal S, Singh VB, Shrivastava M, Banseria R, Goyal K, Singh S, et al. Complete Blood-count-based Inflammatory Score (CBCS) of COVID-19 Patients at Tertiary Care Center. Alternative therapies in health and medicine. 2021 Jun;27(S1):18-24.\u003c/li\u003e\n\u003cli\u003ePalladino M. Complete blood count alterations in COVID-19 patients: A narrative review. Biochemia medica. 2021 Oct 15;31(3):030501.\u003c/li\u003e\n\u003cli\u003ePozdnyakova O, Connell NT, Battinelli EM, Connors JM, Fell G, Kim AS. Clinical Significance of CBC and WBC Morphology in the Diagnosis and Clinical Course of COVID-19 Infection. American journal of clinical pathology. 2021 Feb 11;155(3):364-75.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"COVID-19, green tea extract, Complete Blood Count, C-reactive protein, Erythrocyte Sedimentation Rate","lastPublishedDoi":"10.21203/rs.3.rs-4976013/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4976013/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eThis study investigated the effects of green tea extract on biomarkers and signs of COVID-19 patients who were hospitalized.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eThis study was a double-blind clinical trial that involved 74 patients who were under hospital care. These individuals were randomly divided into two groups. One group received a 900mg/d dosage of green tea supplement along with standard patient care, while the other group received a placebo alongside standard patient care. This administration lasted for 14 days. Blood factors and anthropometric factors were measured before and after the intervention. Additionally, dietary intake was assessed during the study.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eAfter the intervention, there was a significant decrease in C-reactive protein (CRP) [Mean Differences (MD)18.34 and 95%CI (8.05 to 28.62)] and Erythrocyte Sedimentation Rate (ESR) [MD 16.68 and 95%CI (7.41 to 25.94)] levels in the green tea group compared to the placebo group. There were also significant changes in neutrophils, lymphocytes, red blood cells (RBC), and blood oxygen saturation in the green tea group(p\u0026thinsp;\u0026lt;\u0026thinsp;0.05). However, there were no significant differences in other blood indices between the two groups.\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e \u003cp\u003eThe results suggest that green tea extract supplementation may positively affect inflammation and blood markers in COVID-19 patients and potentially improve blood oxygen saturation levels.\u003c/p\u003e\u003ch2\u003eTrial registration\u003c/h2\u003e \u003cp\u003eIRCT20150711023153N3 (https//irct.behdasht.gov.ir/trial/55948), Registration date 20210604\u003c/p\u003e","manuscriptTitle":"Effects of Green tea supplementation on the CRP, ESR, and CBC in the patients with COVID-19, a double-blind placebo-controlled clinical trial","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-10-18 08:37:01","doi":"10.21203/rs.3.rs-4976013/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"05ff71a0-52c0-4e99-a646-898bafd06372","owner":[],"postedDate":"October 18th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-06-06T18:59:16+00:00","versionOfRecord":[],"versionCreatedAt":"2024-10-18 08:37:01","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4976013","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4976013","identity":"rs-4976013","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}