Effect of Autologous Dendritic Cell Administration on Changes in Renal Hemodynamics and Inflammatory Biomarkers in Diabetic Kidney Disease Patients

Effect of Autologous Dendritic Cell Administration on Changes in Renal Hemodynamics and Inflammatory Biomarkers in Diabetic Kidney Disease Patients | 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 Effect of Autologous Dendritic Cell Administration on Changes in Renal Hemodynamics and Inflammatory Biomarkers in Diabetic Kidney Disease Patients Endang Drajat, Jonny Jonny, Aditya Pratama Lokeswara, Elvita Rahmi Daulay, and 3 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5667385/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 Purpose Chronic hyperglycemia in DKD increases proinflammatory cytokines that can cause fibrosis and affect renal hemodynamics. This study aims to evaluate the effect of autologous dendritic cell administration in DKD patients, assessed by Doppler ultrasound examination (PSV and EDV), and measurement of TGF-β and MMP-9 biomarkers. Methods This study was a one group pretest posttest with 29 DKD patients. Measurement of PSV and EDV blood flow using doppler ultrasound, as well as blood collection for TGF β and MMP 9 biomarkers were performed before and after administration of autologous dendritic cells. Results The results showed that before administration, the median PSV value was 47.1 ± 23.87 cm/s, which decreased to 27.85 ± 20.53 cm/s with a p-value of 0.044, and EDV increased from 13 ± 5.32 cm/s to 15.7 ± 12.55 cm/s with a p-value of 0.039. The female group showed a significant decrease in PSV with a p-value of 0.03 and a significant increase in EDV with a p-value of 0.044. The microalbuminuria group showed a significant decrease in PSV with a p-value of 0.011. Analysis of TGF β and MMP 9 showed before administration of autologous dendritic cells, each increase of one unit of MMP 9 increased TGF β by 13.112, and after administration, it became 7.622. Conclusion This study shows that the administration of dendritic cells can improve renal hemodynamics and, in the long term, is expected to reduce fibrosis in the kidney. Diabetic Kidney Disease Autologous dendritic cells Doppler ultrasound Transforming Growth Factor Beta Matrix Metalloproteinase 9 Figures Figure 1 Introduction Diabetic kidney disease (DKD) is a major risk factor for End-Stage Renal Disease[ 1 ]. In the world, every year, there are 2.6 million new cases of DKD, and the number is expected to increase[ 2 ]. The mortality of patients with DKD in the world is 31.1%[ 3 ]. In DKD patients, there is an increase in hyperglycemia, which causes inflammation and produces increased cytokines. [ 4 ]. Inflammation plays a role in renal hemodynamics, which causes changes in blood flow in the kidneys. This increase in blood flow will initially cause glomerular hyperfiltration. PSV and EDV measurements on Doppler ultrasound can be used to estimate intraglomerular pressure, which is important in glomerular hyperfiltration [ 5 ]. Chronic inflammation can cause fibrosis and damage renal tissue, leading to decreased renal function. [ 6 ]. Fibrosis in DKD has an important role in reducing kidney function. Some studies suggest that activation of MMP 9 can activate latent TGF β into its active form, which can cause fibrosis [ 7 ]. Research studies have shown no effective therapy to improve kidney function decline. Drugs such as SGLT2, ACEI, ARB, or combination therapy have not been effectively used [ 8 ]. DC through tolerogenic DC has anti-inflammatory properties [ 9 , 10 ]. The signal that stimulates immature dendritic cells (iDC) to become tolerogenic mature dendritic cells (mDC) is a tolerogenic stimulus, namely TGF β [ 9 , 10 ]. This study aims to determine the effect of dendritic cell administration on changes in renal hemodynamics and a decrease in fibrosis biomarkers. Materials and methods Research design This research is a Quasi experimental design , namely one group pretest posttest design. The research site was the Army Central Hospital. The sampling technique was nonprobability sampling. This research has been approved by the ethics committee of the Army hospital based on ethical feasibility no 109/VIII/KEPK/2024, dated 23 Agustus 2024. All subjects agreed to written informed consent. Research subject figure 1 shows the flow of research subjects. In this study, there were a total of 10,930 subjects from polyclinics in the hospital. Of this total, 1,280 subjects were from the endocrine clinic and 312 subjects were from the renal clinic. From this group, 80 subjects agreed to participate in the study, and after the exclusion process, 73 subjects were left eligible. Of the remaining 73 subjects, 36 subjects were willing to undergo ultrasound examination. However, after further exclusion, seven subjects were excluded, resulting in a final sample size of 29 who participated in the study and underwent ultrasound examination. Inclusion criteria include subjects who meet the criteria for diagnosis of Diabetes Mellitus based on PERKENI 2021 guidelines, age more than 18 years, understand and agree to comply with research procedures and provide written consent, eGFR ≥ 30 mL/min/1.73 m², Urinary Albumin Creatinine Ratio (UACR) ≥ 30 mg/g. Exclusion criteria included subjects who had received immunosuppressive therapy in the past four weeks, subjects with a history of other kidney diseases, subjects with other DM diagnoses, subjects with a positive pregnancy test, subjects with a previous history of thromboembolism or a genetic predisposition to thromboembolism, or subjects on anti-thromboembolic therapy other than low-dose aspirin, have a physical or mental disability that prevents daily activities, in the judgment of the investigator have an illness or medical condition that may interfere with the study, including acute, subacute, intermittent, or chronic conditions that put the subject at risk of injury or cannot realistically follow the study protocol. Subjects who are excessively obese (BMI > 40) or uncontrolled hypertension (systole > 180 mmHg, diastole > 100 mmHg) and subjects who are unwilling to provide written informed consent. Research procedure The research procedure was subject preparation, blood collection for baseline (MMP-9, TGF-β) and for the manufacture of autologous dendritic cells, urine collection, and ultrasound (PSV and EDV). After one week, autologous dendritic cells were injected subcutaneously into the patient's arm. After four weeks of injection, cytokine levels (MMP-9, TGF-β) and renal blood flow velocity (PSV, EDV) were assessed by Doppler ultrasound. Autologous dendritic cell generation Subjects had 40 cc of blood drawn at baseline. The blood was isolated and incubated with GM-CSF (Granulocyte Macrophage Colony Stimulating Factor) media and IL 4 for five days, then dendritic cells were formed. The antigen was incubated for two days to stimulate dendritic cell maturation. Laboratory examination and ultrasound TGF β and MMP 9 were examined using sandwich ELISA kits (Reed Biotech Ltd). Ultrasound examination was performed by two specialist radiologists. The ultrasound device used was a Siemens acuson sequoia ultrasound. Doppler ultrasound examination was performed on the interlobar artery of the right and left kidney, and then the results of both kidneys were averaged. Statistics A data normality test was conducted on each variable. Shapiro-Wilk normality test was used for samples below 50, while Kolmogorov-Smirnov test was used for samples above 50. PSV and EDV variables were analysed using paired t-test for normally distributed data, while non-normally distributed data were analysed using Wilcoxon signed ranks test. MMP 9 and TGF β variables used linear regression analysis tests to see the effect before and after administration of autologous dendritic cells. Results Subject characteristics Table 1 shows the characteristics of the research subjects. This study involved 29 research samples. The age group is mostly over 60 years old. Based on gender, the male group (13 people) has more than women (16 people). Based on a history of hypertension, as much as 96.6% is the most common disease in the study. Based on the history of consumption of antidiabetic drugs, Insulin (69%) was the most common drug in the study. Based on the history of antihypertensive drug consumption, ARB (72.4%) was the most common drug in the study. Table 1 Characteristics of research subjects Count Table N % Gender Women 16 55.2% Men 13 44.8% Age < 60 9 31.0% ≥ 60 20 69.0% BMI Underweight 2 6.9% Normal weight 6 20.7% Overweight 0 0.0% Obesity I 13 44.8% Obesity II 8 27.6% Hypertension No 1 3.4% Yes 28 96.6% Stroke No 24 82.8% Infarction 5 17.2% Hemorrhagic 0 0.0% Heart disease No 19 65.5% Yes 10 34.5% Retinopathy No 25 86.2% Yes 4 13.8% Neuropathy No 13 44.8% Yes 16 55.2% Biguanid No 20 69.0% Yes 9 31.0% Thiazolidinedione No 29 100.0% Yes 0 0.0% Glinid No 29 100.0% Yes 0 0.0% α glucosidase Inhibitor No 26 89.7% Yes 3 10.3% Insulin No 9 31.0% Yes 20 69.0% Gliptin No 23 79.3% Yes 6 20.7% SGLT2 No 26 89.7% Yes 3 10.3% Sulphonylurea No 17 58.6% Yes 12 41.4% Central alpha agonist No 29 100.0% Yes 0 0.0% Diuretics No 27 93.1% HCT 2 6.9% Spironolacton 0 0.0% Alpha-blockers No 28 96.6% Yes 1 3.4% CCB No 9 31.0% Dihydropyridine 15 51.7% Non- Dihydropyridine 5 17.2% DHP AND NON DHP 0 0.0% β Blockers No 21 72.4% Yes 8 27.6% ARB No 8 27.6% Yes 21 72.4% Abbreviations : HCT = Hydrochlorothiazide, CCB = Calcium Channel Blocker, ARB = Angiotensin II Receptor Blocker. PSV and EDV Analysis Table 2 shows the changes in PSV and EDV. Autologous dendritic cell administration showed significant changes in PSV and EDV parameters. Before dendritic cell administration, the median PSV value was 47.1 ± 23.87 cm/s. After dendritic cell administration, the median PSV value decreased to 27.85 ± 20.53cm/s. This decrease was statistically significant, with a p-value of 0.044. The median EDV value before administration was 13 ± 5.32 cm/s. After dendritic cell administration, the median EDV value decreased to 15.7 ± 12.55 cm/s. This decrease was statistically significant, with a p-value of 0.039. Table 2 Results of PSV and EDV analysis before and after autologous dendritic cell administration Before autologous dendritic cells (cm/s) After autologous dendritic cells (cm/s) P value PSV (Median ± IQR) 47.1 ± 23.87 27.85 ± 20.53 0.044 EDV (Median ± IQR) 13 ± 5.32 15.7 ± 12.55 0.039 Abbreviations : PSV = Peak Systolic Velocity, EDV = End Diastolic Velocity, IQR = Interquartile Range Table 3 shows the PSV values based on gender, age and UACR. In the male group, the median PSV value before dendritic cell administration was 47.77 ± 14.96 cm/s, and after dendritic cell administration the median value decreased to 27.05 ± 42.38 cm/s, although not statistically significant (p = 0.422). In the female group, there was a significant decrease from 51.65 ± 24.8 cm/s to 31.72 ± 18.31 cm/s with p value = 0.03. Table 3 PSV by sex, age, and UACR before and after autologous dendritic cell administration PSV Before autologous dendritic cell (cm/s) PSV After autologous dendritic cell (cm/s) P Value Gender Men (Median ±IQR) 47.1 ± 23.3 27.05 ± 42.38 0.422 Women (Median ±IQR) 51.65 ± 24.8 31.72 ± 18.31 0.03 Age < 60 (Mean ±SD) 52.56 ± 18.41 42.32 ± 24.80 0.225 ≥ 60 (Median ±IQR) 47.02 ± 24.97 29 ± 20.43 0,121 UACR Microalbuminuria (Median ±IQR) 54.6 ± 23.46 27.65 ± 16.74 0.011 Macroalbuminuria (Median ±IQR) 47.05 ± 32.3 35.7 ± 32.28 0.834 Abbreviations : PSV = Peak Systolic Velocity, IQR = Interquartile Range, SD = Standard Deviation, UACR = Urinary Albumin-to-Creatinine Ratio. In the age group below 60 years, the mean value of PSV before dendritic cell administration was 52.56 ± 18.41 cm/s, and after dendritic cell administration the mean value decreased to 42.32 ± 24.80 cm/s, although not statistically significant (p = 0.225). In the age group above 59 years, there was a decrease in the median value from 47.02 ± 24.97 cm/s to 29 ± 20.43 cm/s with p value = 0.121. Changes in PSV were also found in the UACR group before and after the administration of autologous dendritic cells. In the microalbuminuria group, PSV before administration of autologous dendritic cells had a median value of 54.6 ± 23.46 cm/s. PSV after autologous dendritic cell administration had a median value of 27.65 ± 16.74 cm/s. Hypothesis testing with a p-value of 0.011 states that there is a significant difference. In the macroalbuminuria group, the median value of PSV before the administration of autologous dendritic cells was 47.05 ± 32.3 cm/s. PSV, after administration of autologous dendritic cells, had a median value of 35.7 ± 32.28cm/s. Hypothesis testing showed that this change was not significant, with a p-value of 0.834. Table 4 shows the EDV analysis based on gender, age, and UACR group. EDV analysis based on gender, the results showed that in the male group, the median EDV value before dendritic cell administration was 12.55 ± 6.97 cm/s. After dendritic cell administration, there was an increase to 15.7 ± 21.9 cm/s. This increase was not statistically significant, with a p-value of 0.249. In the female group, the median EDV value before dendritic cell administration was 13.27 ± 6.8 cm/s. After dendritic cell administration, there was a significant increase to 15.04 ± 11.08 cm/s, with a p-value of 0.044. Table 4 EDV by sex, age, and UACR before and after autologous dendritic cell administration EDV Before autologous dendritic cell (cm/s) EDV After autologous dendritic cell (cm/s) P value Gender Men (Median ±IQR) 12.55 ± 6.97 15.7 ± 21.9 0.249 Women (Median ±IQR) 13.27 ± 6.8 15.04 ± 11.08 0.044 Age < 60 (Mean ±SD) 15.53 ± 6.10 23.03 ± 14.93 0.137 ≥ 60 (Median ±IQR) 4.11 ± 6.08 12.64 ± 11.08 0.126 UACR Microalbuminuria (Median ± IQR) 13.8 ± 5.36 14.19 ± 11.18 0.234 Macroalbuminuria (Median ±IQR) 11.15 ± 6.28 16.4 ± 17.75 0.087 Abbreviations: EDV = End Diastolic Velocity, IQR = Interquartile Range, SD = Standard Deviation, UACR = Urinary Albumin-to-Creatinine Ratio. In the age group below 60 years, the mean value of EDV before administration of autologous dendritic cells was 15.53 ± 6.10 cm/s. After administration of autologous dendritic cells, there was an increase in the mean value of EDV by 23.03 ± 14.93 cm/s. This increase was not statistically significant, with a p-value of 0.137. In the age group above 60 years, the median value before the administration of autologous dendritic cells was 4.11 ± 6.08 cm/s. After administration of autologous dendritic cells, there was an increase in the median value of 12.64 ± 11.08 cm/s. This increase was not statistically significant, with a p-value of 0.126. EDV in the microalbuminuria group with a median value of 13.8 ± 5.36 cm/s before autologous dendritic cell administration, after autologous dendritic cell administration the median value increased to 14.19 ± 11.18 cm/s. This increase was not statistically significant, with a p-value of 0.234. In the macroalbuminuria group, the median value before the administration of autologous dendritic cells was 11.15 ± 6.28 cm/s. After administration of dendritic cells increased to 16.4 ± 17.75 cm/s, this increase was not statistically significant, with a p-value of 0.234. Analysis of TGF β and MMP 9 Table 5 shows the linear regression test of TGF β and MMP 9 before and after administration of autologous dendritic cells. The linear regression test before the action showed that every increase of one unit of MMP 9 would increase TGF β by 13.112, but this result was close to significant with p-value = 0.058. The linear regression test after the treatment showed that every one unit increase of MMP 9 will increase TGF β by 7.622 with a near significant p-value (p-value = 0.066). However, when comparing the value of MMP 9 to TGF β before and after autologous dendritic cell administration, there was a decrease in the value of MMP 9 to TGF β after autologous dendritic cell administration. Table 5 Linear regression test of TGF β and MMP 9 Variables Coefficient (β) P value Pre MMP 9 13.112 0.058 Dependent variable Pre TGF β Post MMP 9 7.622 0.066 Dependent variable Post TGF β Abbreviations: MMP-9 = Matrix Metalloproteinase-9, TGF-β = Transforming Growth Factor Beta. Table 6 shows the analysis of the relationship between the study variables. The variables before and after the administration of autologous dendritic cells were combined, and then the correlation test between variables was performed. Table 6 Analysis of the relationship between variables Variables MMP 9 (r,p) PSV (r,p) EDV(r,p) TGF β 0.413, 0.001** -0.101, 0.452 -0.071, 0.598 MMP 9 - -0.015, 0.909 -0.048, 0.721 PSV -0.015, 0.909 - 0.675, 0.000** EDV -0.048, 0.721 0.675, 0.000** - Abbreviations: MMP-9 = Matrix Metalloproteinase-9, TGF-β = Transforming Growth Factor Beta, PSV = Peak Systolic Velocity, EDV = End Diastolic Velocity. Note: **p < 0.01 Relationship test between research variables using Spearman showed there was a significant relationship between TGF β and MMP 9 with a p value of 0.001. There is a significant relationship between PSV and EDV with a p value of 0.000. Discussion In the study on chronic kidney disease, there were some important findings related to the history of hypertension and the use of antihypertensive and antidiabetic drugs. Most of the subjects in this study had a history of hypertension, reaching 93.1%. This is in line with the literature showing that hypertension is a major risk factor for the development of DKD, and good blood pressure management can slow the progression of kidney disease in diabetic patients [11]. Hypertension through the vasoactive hormone pathway will affect the development of DKD, which will increase kidney damage through vasoconstriction. Hypertension will exacerbate kidney damage by increasing pressure in the glomerulus and stimulating inflammation and fibrosis [12]. History of angiotensin receptor blocker drug use was the most common drug used in this study. He et al. showed that ACEi or ARB can reduce UACR and improve renal function [ 13 ]. ARBs, especially combined with mineralocorticoid receptor antagonists, can increase the risk of hyperkalemia [ 14 ]. This study showed that there was no significant difference between the UACR groups on PSV, EDV TGF β and MMP 9 before the administration of autologous dendritic cells. In addition, there was no significant difference based on the history of the disease or the use of diabetic drugs and the history of antihypertensive drug use. Overall, there were significant changes in PSV and EDV after administration of autologous dendritic cells. There are studies that mention resistance index (RI), which is associated with PSV and EDV, is associated with increased C reactive protein (CRP), indicating a pro-inflammatory state in hypertensive patients, especially in patients with DM [ 15 ]. This decrease in PSV is related to UACR and eGFR. There are studies that show an increase in UACR is associated with a decrease in kidney function, this decrease in kidney function is measured through eGFR. This reflects kidney damage and changes in renal blood flow [ 16 ]. Some studies explain that microalbuminuria is associated with high PSV [ 17 ]. Significant increase in EDV after autologous dendritic cell administration. This increase in EDV is associated with improved renal perfusion, which in turn affects the increase in GFR. Research shows that EDV is positively correlated with CKD and GFR [ 18 ]. In kidney disease, this decrease in EDV will increase resistance in blood vessels and cause impaired perfusion and decreased GFR [ 19 ]. Decreased EDV will also lead to increased albuminuria through decreased GFR [ 20 ]. Some studies also explain the existence of an immune response, namely inflammation, in patients with microalbuminuria and macroalbuminuria [ 21 ]. Based on gender, there were significant changes in PSV and EDV values in the female group, while in men, there were changes that were not significant. This may be due to differences in immune responses in men and women. This is in line with research conducted by Korte et al., which found that women tend to have a stronger immune response than men, largely due to the influence of estrogen, which can increase antibody production [ 22 ]. The differences in renal physiology and immune response based on age are the basis for grouping in this study. This is in line with research conducted by Costagliola et al., which showed differences in immune responses based on age [ 23 ]. Research conducted by Weinstein et al. shows that there are changes in renal blood flow with age [ 24 ]. However, in this study, in both the age groups under 60 and over 60 years, there were no significant changes in PSV and EDV. There are different pathophysiological mechanisms in microalbuminuria and macroalbuminuria. Microalbuminuria is an early marker of kidney damage [ 25 ]. Whereas in macroalbuminuria there is further damage with significant structural changes in the glomerulus [ 26 ]. In the microalbuminuria group, autologous dendritic cells can significantly reduce the median PSV value after administration of autologous dendritic cells, while in EDV, there is an increase but not significant. In the macroalbuminuria group, PSV and EDV values did not have significant changes. The macroalbuminuria group requires the administration of autologous dendritic cells more than once with longer follow-up because, in the macroalbuminuria group, there is significant structural damage to the glomerulus [ 26 ]. Linear regression analysis showed that after the administration of autologous dendritic cells, the effect of MMP 9 on TGFβ decreased, although this was almost statistically significant. This is different from the Spearman test between TGF β and MMP 9, which showed an association between TGF β and MMP 9. This difference may be due to differences in sample size. Several studies have shown that there is a relationship between TGF β and MMP 9. Research conducted by Kundu et al. showed that an increase in TGF β was associated with an increase in MMP 9 in diabetic animal models [ 27 ]. Research conducted by Gu et al. showed that inhibition of TGF β can reduce MMP 9 [ 28 ]. Muscella et al. stated that TGFβ activates cell migration through MMP 2 and MMP 9 [ 29 ]. In this study, there was a decrease in the influence of MMP 9 on TGF β after the administration of autologous dendritic cells. The decrease in TGF β is expected to reduce fibrosis in the kidney. TGF β activates Smad2 and Smad 3 and then interacts with transcription factors involved in fibrogenesis [ 30 , 31 ]. Conclusion This study shows that the administration of autologous dendritic cells can affect changes in renal blood flow in DKD patients. After the administration of autologous dendritic cells, there was a significant decrease in PSV and a significant increase in EDV, which would improve blood flow in the kidney. There is a relationship between TGF β and MMP 9. Linear regression analysis showed a decrease in the influence of MMP 9 on TGF β, a decrease in TGF β is expected to reduce fibrosis in DKD patients in the long term. Declarations Ethics Committee Information This research was approved by the Ethics Committee of the Gatot Soebroto Army Hospital, Jakarta, under ethical feasibility decision number 109/VIII/KEPK/2024, dated 23 August 2024. All subjects provided written informed consent prior to participation in the study. Acknowledgements Not applicable. Research Funding Partially funded by PT JES. Author Contribution E.D designed the study, supervised data collection, performed data analysis, and wrote the manuscript. J. supervised data collection and reviewed the manuscript. A.P.L collected data and managed administrative work. E.R.D provided senior supervision and reviewed the manuscript. A.G.I provided senior supervision and reviewed the manuscript. F. provided senior supervision and reviewed the manuscript. T.A.P provided senior supervision and reviewed the manuscript. Corresponding author Aziza Ghanie Icksan Ethics declarations Ethical statement The research was conducted according to the declaration of Helsinki, and approved by the Institutional Review Commission (or Ethics Committee) of Gatot Soebroto Army Hospital (no 109/VIII/KEPK/2024). Conflict of Interest Endang drajat declares no conflict of interest. Jonny declares no conflict of interest. Aditya Pratama Lokeswara declares no conflict of interest. Elvita Rahmi Daulay declares no conflict of interest. Aziza Ghanie Icksan declares no conflict of interest. Farhat declares no conflict of interest. Terawan Agus Putranto declares no conflict of interest. Informed consent Informed consent was obtained from all study subjects. Data Availability All data is available upon request. References Selby NM, Taal MW (2020) An updated overview of diabetic nephropathy: Diagnosis, prognosis, treatment goals and latest guidelines. 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Journal of the American Society of Nephrology 33:1569–1580. https://doi.org/10.1681/ASN.2022020207/-/DCSUPPLEMENTAL Spatola L, Andrulli S (2016) Doppler ultrasound in kidney diseases: a key parameter in clinical long-term follow-up. J Ultrasound 19:243. https://doi.org/10.1007/S40477-016-0201-X Yang J, Yang S, Xu Y, et al (2021) Evaluation of Renal Oxygenation and Hemodynamics in Patients with Chronic Kidney Disease by Blood Oxygenation Level-dependent Magnetic Resonance Imaging and Intrarenal Doppler Ultrasonography. Nephron 145:653–663. https://doi.org/10.1159/000516637 Gao J, Perlman A, Kalache S, et al (2017) Multiparametric Quantitative Ultrasound Imaging in Assessment of Chronic Kidney Disease. 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Immun Inflamm Dis 9:331. https://doi.org/10.1002/IID3.404 Weinstein JR, Anderson S (2010) THE AGING KIDNEY: PHYSIOLOGICAL CHANGES. Adv Chronic Kidney Dis 17:302. https://doi.org/10.1053/J.ACKD.2010.05.002 Rani PK, Raman R, Gupta A, et al (2011) Albuminuria and diabetic retinopathy in type 2 diabetes mellitus sankara nethralaya diabetic retinopathy epidemiology and molecular genetic study (SN-DREAMS, report 12). Diabetol Metab Syndr 3:1–8. https://doi.org/10.1186/1758-5996-3-9/TABLES/4 Suzuki A, Moriya T, Hayashi A, et al (2024) Arteriolar Hyalinosis Predicts the Onset of Both Macroalbuminuria and Impaired Renal Function in Patients with Type 2 Diabetes. Nephron 148:390–398. https://doi.org/10.1159/000535875 Kundu S, Pushpakumar SB, Tyagi A, et al (2013) Hydrogen sulfide deficiency and diabetic renal remodeling: role of matrix metalloproteinase-9. Am J Physiol Endocrinol Metab 304:E1365–E1378. https://doi.org/10.1152/AJPENDO.00604.2012 Gu D, Shi Y, Ding Y, et al (2013) Dramatic early event in chronic allograft nephropathy: Increased but not decreased expression of MMP-9 gene. Diagn Pathol 8:1–11. https://doi.org/10.1186/1746-1596-8-13/FIGURES/7 Muscella A, Vetrugno C, Cossa LG, Marsigliante S (2020) TGF-β1 activates RSC96 Schwann cells migration and invasion through MMP-2 and MMP-9 activities. J Neurochem 153:525–538. https://doi.org/10.1111/JNC.14913 Zhang K, Fan C, Cai D, et al (2020) Contribution of TGF-Beta-Mediated NLRP3-HMGB1 Activation to Tubulointerstitial Fibrosis in Rat With Angiotensin II-Induced Chronic Kidney Disease. Front Cell Dev Biol 8:496359. https://doi.org/10.3389/FCELL.2020.00001/BIBTEX Feng J, Xie L, Kong R, et al (2017) RACK1 silencing attenuates renal fibrosis by inhibiting TGF-β signaling. Int J Mol Med 40:1965–1970. https://doi.org/10.3892/IJMM.2017.3154/HTML Additional Declarations No competing interests reported. Supplementary Files data.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. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. 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-5667385","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":392026370,"identity":"a1d94f29-f41e-4330-af80-f557036d79e0","order_by":0,"name":"Endang Drajat","email":"","orcid":"","institution":"Gatot Soebroto Army Hospital","correspondingAuthor":false,"prefix":"","firstName":"Endang","middleName":"","lastName":"Drajat","suffix":""},{"id":392026372,"identity":"419c074d-7d42-4795-917f-9cd70b46b0c3","order_by":1,"name":"Jonny Jonny","email":"","orcid":"","institution":"Indonesia Army Cellcure Center, RSPAD Gatot Soebroto Jakarta","correspondingAuthor":false,"prefix":"","firstName":"Jonny","middleName":"","lastName":"Jonny","suffix":""},{"id":392026373,"identity":"65f1b395-aa1a-45e4-b12c-36db3daa3606","order_by":2,"name":"Aditya Pratama Lokeswara","email":"","orcid":"","institution":"Gatot Soebroto Army Hospital","correspondingAuthor":false,"prefix":"","firstName":"Aditya","middleName":"Pratama","lastName":"Lokeswara","suffix":""},{"id":392026374,"identity":"043ae349-1eb0-4bba-ae7a-bb4a4e25088d","order_by":3,"name":"Elvita Rahmi Daulay","email":"","orcid":"","institution":"Universitas Prima Indonesia","correspondingAuthor":false,"prefix":"","firstName":"Elvita","middleName":"Rahmi","lastName":"Daulay","suffix":""},{"id":392026375,"identity":"22fc66b4-1f49-40c7-8496-37d12f9a2b70","order_by":4,"name":"Aziza Ghanie Icksan","email":"data:image/png;base64,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","orcid":"","institution":"Universitas Prima Indonesia","correspondingAuthor":true,"prefix":"","firstName":"Aziza","middleName":"Ghanie","lastName":"Icksan","suffix":""},{"id":392026376,"identity":"eab1ff6c-825a-433d-a9a5-4cca77de5cf7","order_by":5,"name":"Farhat Farhat","email":"","orcid":"","institution":"Universitas Prima Indonesia","correspondingAuthor":false,"prefix":"","firstName":"Farhat","middleName":"","lastName":"Farhat","suffix":""},{"id":392026380,"identity":"f2179c83-dadd-4c3c-858e-837b446bcc88","order_by":6,"name":"Terawan Agus Putranto","email":"","orcid":"","institution":"Gatot Soebroto Army Hospital","correspondingAuthor":false,"prefix":"","firstName":"Terawan","middleName":"Agus","lastName":"Putranto","suffix":""}],"badges":[],"createdAt":"2024-12-18 08:08:34","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5667385/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5667385/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":72289967,"identity":"99dfe43e-cce0-4443-98b1-1d12a34e6417","added_by":"auto","created_at":"2024-12-24 17:22:52","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":17239,"visible":true,"origin":"","legend":"\u003cp\u003eThe flow of research subjects\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-5667385/v1/91091b44830a82b42867d48b.png"},{"id":73793033,"identity":"3767d718-160e-4bde-87dc-67595df1de64","added_by":"auto","created_at":"2025-01-14 17:23:41","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":979017,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5667385/v1/d2ce298a-c5d4-4aa1-9937-77f6cb5a9dce.pdf"},{"id":72289970,"identity":"81ac029b-f624-4c3e-817e-9134a4823261","added_by":"auto","created_at":"2024-12-24 17:22:53","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":54567,"visible":true,"origin":"","legend":"","description":"","filename":"data.docx","url":"https://assets-eu.researchsquare.com/files/rs-5667385/v1/a44e40ec8393d5297e654977.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Effect of Autologous Dendritic Cell Administration on Changes in Renal Hemodynamics and Inflammatory Biomarkers in Diabetic Kidney Disease Patients","fulltext":[{"header":"Introduction","content":"\u003cp\u003eDiabetic kidney disease (DKD) is a major risk factor for End-Stage Renal Disease[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. In the world, every year, there are 2.6\u0026nbsp;million new cases of DKD, and the number is expected to increase[\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. The mortality of patients with DKD in the world is 31.1%[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eIn DKD patients, there is an increase in hyperglycemia, which causes inflammation and produces increased cytokines. [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. Inflammation plays a role in renal hemodynamics, which causes changes in blood flow in the kidneys. This increase in blood flow will initially cause glomerular hyperfiltration. PSV and EDV measurements on Doppler ultrasound can be used to estimate intraglomerular pressure, which is important in glomerular hyperfiltration [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. Chronic inflammation can cause fibrosis and damage renal tissue, leading to decreased renal function. [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. Fibrosis in DKD has an important role in reducing kidney function. Some studies suggest that activation of MMP 9 can activate latent TGF β into its active form, which can cause fibrosis [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eResearch studies have shown no effective therapy to improve kidney function decline. Drugs such as SGLT2, ACEI, ARB, or combination therapy have not been effectively used [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. DC through tolerogenic DC has anti-inflammatory properties [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. The signal that stimulates immature dendritic cells (iDC) to become tolerogenic mature dendritic cells (mDC) is a tolerogenic stimulus, namely TGF β [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. This study aims to determine the effect of dendritic cell administration on changes in renal hemodynamics and a decrease in fibrosis biomarkers.\u003c/p\u003e"},{"header":"Materials and methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eResearch design\u003c/h2\u003e \u003cp\u003eThis research is a \u003cem\u003eQuasi experimental design\u003c/em\u003e, namely \u003cem\u003eone group pretest posttest design.\u003c/em\u003e The research site was the Army Central Hospital. The sampling technique was nonprobability sampling. This research has been approved by the ethics committee of the Army hospital based on ethical feasibility no 109/VIII/KEPK/2024, dated 23 Agustus 2024. All subjects agreed to written informed consent.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eResearch subject\u003c/h3\u003e\n\u003cp\u003efigure \u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e shows the flow of research subjects. In this study, there were a total of 10,930 subjects from polyclinics in the hospital. Of this total, 1,280 subjects were from the endocrine clinic and 312 subjects were from the renal clinic. From this group, 80 subjects agreed to participate in the study, and after the exclusion process, 73 subjects were left eligible. Of the remaining 73 subjects, 36 subjects were willing to undergo ultrasound examination. However, after further exclusion, seven subjects were excluded, resulting in a final sample size of 29 who participated in the study and underwent ultrasound examination.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e Inclusion criteria include subjects who meet the criteria for diagnosis of Diabetes Mellitus based on PERKENI 2021 guidelines, age more than 18 years, understand and agree to comply with research procedures and provide written consent, eGFR\u0026thinsp;\u0026ge;\u0026thinsp;30 mL/min/1.73 m\u0026sup2;, Urinary Albumin Creatinine Ratio (UACR)\u0026thinsp;\u0026ge;\u0026thinsp;30 mg/g. Exclusion criteria included subjects who had received immunosuppressive therapy in the past four weeks, subjects with a history of other kidney diseases, subjects with other DM diagnoses, subjects with a positive pregnancy test, subjects with a previous history of thromboembolism or a genetic predisposition to thromboembolism, or subjects on anti-thromboembolic therapy other than low-dose aspirin, have a physical or mental disability that prevents daily activities, in the judgment of the investigator have an illness or medical condition that may interfere with the study, including acute, subacute, intermittent, or chronic conditions that put the subject at risk of injury or cannot realistically follow the study protocol. Subjects who are excessively obese (BMI\u0026thinsp;\u0026gt;\u0026thinsp;40) or uncontrolled hypertension (systole\u0026thinsp;\u0026gt;\u0026thinsp;180 mmHg, diastole\u0026thinsp;\u0026gt;\u0026thinsp;100 mmHg) and subjects who are unwilling to provide written informed consent.\u003c/p\u003e\n\u003ch3\u003eResearch procedure\u003c/h3\u003e\n\u003cp\u003eThe research procedure was subject preparation, blood collection for baseline (MMP-9, TGF-β) and for the manufacture of autologous dendritic cells, urine collection, and ultrasound (PSV and EDV). After one week, autologous dendritic cells were injected subcutaneously into the patient's arm. After four weeks of injection, cytokine levels (MMP-9, TGF-β) and renal blood flow velocity (PSV, EDV) were assessed by Doppler ultrasound.\u003c/p\u003e\n\u003ch3\u003eAutologous dendritic cell generation\u003c/h3\u003e\n\u003cp\u003eSubjects had 40 cc of blood drawn at baseline. The blood was isolated and incubated with GM-CSF (Granulocyte Macrophage Colony Stimulating Factor) media and IL 4 for five days, then dendritic cells were formed. The antigen was incubated for two days to stimulate dendritic cell maturation.\u003c/p\u003e\n\u003ch3\u003eLaboratory examination and ultrasound\u003c/h3\u003e\n\u003cp\u003eTGF β and MMP 9 were examined using sandwich ELISA kits (Reed Biotech Ltd). Ultrasound examination was performed by two specialist radiologists. The ultrasound device used was a Siemens acuson sequoia ultrasound. Doppler ultrasound examination was performed on the interlobar artery of the right and left kidney, and then the results of both kidneys were averaged.\u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eStatistics\u003c/h2\u003e \u003cp\u003eA data normality test was conducted on each variable. Shapiro-Wilk normality test was used for samples below 50, while Kolmogorov-Smirnov test was used for samples above 50. PSV and EDV variables were analysed using paired t-test for normally distributed data, while non-normally distributed data were analysed using Wilcoxon signed ranks test. MMP 9 and TGF β variables used linear regression analysis tests to see the effect before and after administration of autologous dendritic cells.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec10\"\u003e\n \u003ch2\u003eSubject characteristics\u003c/h2\u003e\n \u003cp\u003eTable\u0026nbsp;\u003cspan\u003e1\u003c/span\u003e shows the characteristics of the research subjects. This study involved 29 research samples. The age group is mostly over 60 years old. Based on gender, the male group (13 people) has more than women (16 people). Based on a history of hypertension, as much as 96.6% is the most common disease in the study. Based on the history of consumption of antidiabetic drugs, Insulin (69%) was the most common drug in the study. Based on the history of antihypertensive drug consumption, ARB (72.4%) was the most common drug in the study.\u003c/p\u003e\n \u003cdiv\u003e\n \u003ctable id=\"Tab1\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv\u003eTable 1\u003c/div\u003e\n \u003cdiv\u003e\n \u003cp\u003eCharacteristics of research subjects\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003ccolgroup cols=\"4\"\u003e\u003c/colgroup\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" colspan=\"2\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eCount\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eTable N %\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eGender\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eWomen\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e55.2%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMen\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e44.8%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eAge\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e31.0%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026ge;\u0026thinsp;60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e69.0%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"5\"\u003e\n \u003cp\u003eBMI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eUnderweight\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e6.9%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNormal weight\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e20.7%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eOverweight\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.0%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eObesity I\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e44.8%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eObesity II\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e27.6%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eHypertension\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3.4%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e96.6%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"3\"\u003e\n \u003cp\u003eStroke\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e82.8%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eInfarction\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e17.2%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHemorrhagic\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.0%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eHeart disease\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e65.5%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e34.5%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eRetinopathy\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e86.2%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e13.8%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eNeuropathy\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e44.8%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e55.2%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eBiguanid\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e69.0%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e31.0%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eThiazolidinedione\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e100.0%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.0%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eGlinid\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e100.0%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.0%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003e\u0026alpha; glucosidase Inhibitor\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e89.7%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e10.3%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eInsulin\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e31.0%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e69.0%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eGliptin\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e79.3%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e20.7%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eSGLT2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e89.7%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e10.3%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eSulphonylurea\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e58.6%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e41.4%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eCentral alpha agonist\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e100.0%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.0%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"3\"\u003e\n \u003cp\u003eDiuretics\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e93.1%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHCT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e6.9%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSpironolacton\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.0%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eAlpha-blockers\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e96.6%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3.4%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"4\"\u003e\n \u003cp\u003eCCB\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e31.0%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDihydropyridine\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e51.7%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNon- Dihydropyridine\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e17.2%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDHP AND NON DHP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.0%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003e\u0026beta; Blockers\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e72.4%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e27.6%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eARB\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e27.6%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e72.4%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cp\u003e\u003cstrong\u003eAbbreviations\u003c/strong\u003e: HCT = Hydrochlorothiazide, CCB = Calcium Channel Blocker, ARB = Angiotensin II Receptor Blocker.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec11\"\u003e\n \u003ch2\u003ePSV and EDV Analysis\u003c/h2\u003e\n \u003cp\u003eTable\u0026nbsp;\u003cspan\u003e2\u003c/span\u003e shows the changes in PSV and EDV. Autologous dendritic cell administration showed significant changes in PSV and EDV parameters. Before dendritic cell administration, the median PSV value was 47.1\u0026thinsp;\u0026plusmn;\u0026thinsp;23.87 cm/s. After dendritic cell administration, the median PSV value decreased to 27.85\u0026thinsp;\u0026plusmn;\u0026thinsp;20.53cm/s. This decrease was statistically significant, with a p-value of 0.044. The median EDV value before administration was 13\u0026thinsp;\u0026plusmn;\u0026thinsp;5.32 cm/s. After dendritic cell administration, the median EDV value decreased to 15.7\u0026thinsp;\u0026plusmn;\u0026thinsp;12.55 cm/s. This decrease was statistically significant, with a p-value of 0.039.\u003c/p\u003e\n \u003cdiv\u003e\n \u003ctable id=\"Tab2\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv\u003eTable 2\u003c/div\u003e\n \u003cdiv\u003e\n \u003cp\u003eResults of PSV and EDV analysis before and after autologous dendritic cell administration\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003ccolgroup cols=\"4\"\u003e\u003c/colgroup\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eBefore autologous dendritic cells (cm/s)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eAfter autologous dendritic cells (cm/s)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eP value\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePSV (Median\u0026thinsp;\u0026plusmn;\u0026thinsp;IQR)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e47.1\u0026thinsp;\u0026plusmn;\u0026thinsp;23.87\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e27.85\u0026thinsp;\u0026plusmn;\u0026thinsp;20.53\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.044\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eEDV (Median\u0026thinsp;\u0026plusmn;\u0026thinsp;IQR)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e13\u0026thinsp;\u0026plusmn;\u0026thinsp;5.32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e15.7\u0026thinsp;\u0026plusmn;\u0026thinsp;12.55\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.039\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cp\u003e\u003cstrong\u003eAbbreviations\u003c/strong\u003e: PSV = Peak Systolic Velocity, EDV = End Diastolic Velocity, IQR = Interquartile Range\u003c/p\u003e\n \u003cp\u003eTable\u0026nbsp;\u003cspan\u003e3\u003c/span\u003e shows the PSV values based on gender, age and UACR. In the male group, the median PSV value before dendritic cell administration was 47.77\u0026thinsp;\u0026plusmn;\u0026thinsp;14.96 cm/s, and after dendritic cell administration the median value decreased to 27.05\u0026thinsp;\u0026plusmn;\u0026thinsp;42.38 cm/s, although not statistically significant (p\u0026thinsp;=\u0026thinsp;0.422). In the female group, there was a significant decrease from 51.65\u0026thinsp;\u0026plusmn;\u0026thinsp;24.8 cm/s to 31.72\u0026thinsp;\u0026plusmn;\u0026thinsp;18.31 cm/s with p value\u0026thinsp;=\u0026thinsp;0.03.\u003c/p\u003e\n \u003cdiv\u003e\n \u003ctable id=\"Tab3\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv\u003eTable 3\u003c/div\u003e\n \u003cdiv\u003e\n \u003cp\u003ePSV by sex, age, and UACR before and after autologous dendritic cell administration\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003ccolgroup cols=\"5\"\u003e\u003c/colgroup\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" colspan=\"2\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ePSV Before autologous dendritic cell (cm/s)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ePSV After autologous dendritic cell (cm/s)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eP Value\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eGender\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMen (Median\u003c/p\u003e\n \u003cp\u003e\u0026plusmn;IQR)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e47.1\u003c/p\u003e\n \u003cp\u003e\u0026plusmn;\u0026thinsp;23.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e27.05\u003c/p\u003e\n \u003cp\u003e\u0026plusmn;\u0026thinsp;42.38\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.422\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eWomen (Median\u003c/p\u003e\n \u003cp\u003e\u0026plusmn;IQR)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e51.65\u003c/p\u003e\n \u003cp\u003e\u0026plusmn;\u0026thinsp;24.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e31.72\u003c/p\u003e\n \u003cp\u003e\u0026plusmn;\u0026thinsp;18.31\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.03\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eAge\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;60 (Mean\u003c/p\u003e\n \u003cp\u003e\u0026plusmn;SD)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e52.56\u003c/p\u003e\n \u003cp\u003e\u0026plusmn;\u0026thinsp;18.41\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e42.32\u003c/p\u003e\n \u003cp\u003e\u0026plusmn;\u0026thinsp;24.80\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.225\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026ge;\u0026thinsp;60 (Median\u003c/p\u003e\n \u003cp\u003e\u0026plusmn;IQR)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e47.02\u003c/p\u003e\n \u003cp\u003e\u0026plusmn;\u0026thinsp;24.97\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e29\u003c/p\u003e\n \u003cp\u003e\u0026plusmn;\u0026thinsp;20.43\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0,121\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eUACR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMicroalbuminuria (Median\u003c/p\u003e\n \u003cp\u003e\u0026plusmn;IQR)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e54.6\u003c/p\u003e\n \u003cp\u003e\u0026plusmn;\u0026thinsp;23.46\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e27.65\u003c/p\u003e\n \u003cp\u003e\u0026plusmn;\u0026thinsp;16.74\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.011\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMacroalbuminuria (Median\u003c/p\u003e\n \u003cp\u003e\u0026plusmn;IQR)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e47.05\u003c/p\u003e\n \u003cp\u003e\u0026plusmn;\u0026thinsp;32.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e35.7\u003c/p\u003e\n \u003cp\u003e\u0026plusmn;\u0026thinsp;32.28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.834\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003ctfoot\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"5\"\u003e\u003cstrong\u003eAbbreviations\u003c/strong\u003e: PSV\u0026thinsp;=\u0026thinsp;Peak Systolic Velocity, IQR\u0026thinsp;=\u0026thinsp;Interquartile Range, SD\u0026thinsp;=\u0026thinsp;Standard Deviation, UACR\u0026thinsp;=\u0026thinsp;Urinary Albumin-to-Creatinine Ratio.\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tfoot\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cp\u003eIn the age group below 60 years, the mean value of PSV before dendritic cell administration was 52.56\u0026thinsp;\u0026plusmn;\u0026thinsp;18.41 cm/s, and after dendritic cell administration the mean value decreased to 42.32\u0026thinsp;\u0026plusmn;\u0026thinsp;24.80 cm/s, although not statistically significant (p\u0026thinsp;=\u0026thinsp;0.225). In the age group above 59 years, there was a decrease in the median value from 47.02\u0026thinsp;\u0026plusmn;\u0026thinsp;24.97 cm/s to 29\u0026thinsp;\u0026plusmn;\u0026thinsp;20.43 cm/s with p value\u0026thinsp;=\u0026thinsp;0.121.\u003c/p\u003e\n \u003cp\u003eChanges in PSV were also found in the UACR group before and after the administration of autologous dendritic cells. In the microalbuminuria group, PSV before administration of autologous dendritic cells had a median value of 54.6\u0026thinsp;\u0026plusmn;\u0026thinsp;23.46 cm/s. PSV after autologous dendritic cell administration had a median value of 27.65\u0026thinsp;\u0026plusmn;\u0026thinsp;16.74 cm/s. Hypothesis testing with a p-value of 0.011 states that there is a significant difference. In the macroalbuminuria group, the median value of PSV before the administration of autologous dendritic cells was 47.05\u0026thinsp;\u0026plusmn;\u0026thinsp;32.3 cm/s. PSV, after administration of autologous dendritic cells, had a median value of 35.7\u0026thinsp;\u0026plusmn;\u0026thinsp;32.28cm/s. Hypothesis testing showed that this change was not significant, with a p-value of 0.834.\u003c/p\u003e\n \u003cp\u003eTable\u0026nbsp;\u003cspan\u003e4\u003c/span\u003e shows the EDV analysis based on gender, age, and UACR group. EDV analysis based on gender, the results showed that in the male group, the median EDV value before dendritic cell administration was 12.55\u0026thinsp;\u0026plusmn;\u0026thinsp;6.97 cm/s. After dendritic cell administration, there was an increase to 15.7\u0026thinsp;\u0026plusmn;\u0026thinsp;21.9 cm/s. This increase was not statistically significant, with a p-value of 0.249. In the female group, the median EDV value before dendritic cell administration was 13.27\u0026thinsp;\u0026plusmn;\u0026thinsp;6.8 cm/s. After dendritic cell administration, there was a significant increase to 15.04\u0026thinsp;\u0026plusmn;\u0026thinsp;11.08 cm/s, with a p-value of 0.044.\u003c/p\u003e\n \u003cdiv\u003e\n \u003ctable id=\"Tab4\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv\u003eTable 4\u003c/div\u003e\n \u003cdiv\u003e\n \u003cp\u003eEDV by sex, age, and UACR before and after autologous dendritic cell administration\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003ccolgroup cols=\"5\"\u003e\u003c/colgroup\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eEDV Before autologous dendritic cell (cm/s)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eEDV After autologous dendritic cell (cm/s)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eP value\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eGender\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMen (Median\u003c/p\u003e\n \u003cp\u003e\u0026plusmn;IQR)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e12.55\u003c/p\u003e\n \u003cp\u003e\u0026plusmn;\u0026thinsp;6.97\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e15.7\u003c/p\u003e\n \u003cp\u003e\u0026plusmn;\u0026thinsp;21.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.249\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eWomen (Median\u003c/p\u003e\n \u003cp\u003e\u0026plusmn;IQR)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e13.27\u003c/p\u003e\n \u003cp\u003e\u0026plusmn;\u0026thinsp;6.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e15.04\u003c/p\u003e\n \u003cp\u003e\u0026plusmn;\u0026thinsp;11.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.044\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eAge\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;60 (Mean\u003c/p\u003e\n \u003cp\u003e\u0026plusmn;SD)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e15.53\u003c/p\u003e\n \u003cp\u003e\u0026plusmn;\u0026thinsp;6.10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e23.03\u003c/p\u003e\n \u003cp\u003e\u0026plusmn;\u0026thinsp;14.93\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.137\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026ge;\u0026thinsp;60 (Median\u003c/p\u003e\n \u003cp\u003e\u0026plusmn;IQR)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.11\u003c/p\u003e\n \u003cp\u003e\u0026plusmn;\u0026thinsp;6.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e12.64\u003c/p\u003e\n \u003cp\u003e\u0026plusmn;\u0026thinsp;11.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.126\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eUACR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMicroalbuminuria (Median\u0026thinsp;\u0026plusmn;\u0026thinsp;IQR)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e13.8\u003c/p\u003e\n \u003cp\u003e\u0026plusmn;\u0026thinsp;5.36\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e14.19\u003c/p\u003e\n \u003cp\u003e\u0026plusmn;\u0026thinsp;11.18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.234\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMacroalbuminuria (Median\u003c/p\u003e\n \u003cp\u003e\u0026plusmn;IQR)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e11.15\u003c/p\u003e\n \u003cp\u003e\u0026plusmn;\u0026thinsp;6.28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e16.4\u003c/p\u003e\n \u003cp\u003e\u0026plusmn;\u0026thinsp;17.75\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.087\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cp\u003e\u003cstrong\u003eAbbreviations:\u003c/strong\u003e EDV = End Diastolic Velocity, IQR = Interquartile Range, SD = Standard Deviation, UACR = Urinary Albumin-to-Creatinine Ratio.\u003c/p\u003e\n \u003cp\u003eIn the age group below 60 years, the mean value of EDV before administration of autologous dendritic cells was 15.53\u0026thinsp;\u0026plusmn;\u0026thinsp;6.10 cm/s. After administration of autologous dendritic cells, there was an increase in the mean value of EDV by 23.03\u0026thinsp;\u0026plusmn;\u0026thinsp;14.93 cm/s. This increase was not statistically significant, with a p-value of 0.137. In the age group above 60 years, the median value before the administration of autologous dendritic cells was 4.11\u0026thinsp;\u0026plusmn;\u0026thinsp;6.08 cm/s. After administration of autologous dendritic cells, there was an increase in the median value of 12.64\u0026thinsp;\u0026plusmn;\u0026thinsp;11.08 cm/s. This increase was not statistically significant, with a p-value of 0.126.\u003c/p\u003e\n \u003cp\u003eEDV in the microalbuminuria group with a median value of 13.8\u0026thinsp;\u0026plusmn;\u0026thinsp;5.36 cm/s before autologous dendritic cell administration, after autologous dendritic cell administration the median value increased to 14.19\u0026thinsp;\u0026plusmn;\u0026thinsp;11.18 cm/s. This increase was not statistically significant, with a p-value of 0.234. In the macroalbuminuria group, the median value before the administration of autologous dendritic cells was 11.15\u0026thinsp;\u0026plusmn;\u0026thinsp;6.28 cm/s. After administration of dendritic cells increased to 16.4\u0026thinsp;\u0026plusmn;\u0026thinsp;17.75 cm/s, this increase was not statistically significant, with a p-value of 0.234.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec12\"\u003e\n \u003ch2\u003eAnalysis of TGF \u0026beta; and MMP 9\u003c/h2\u003e\n \u003cp\u003eTable\u0026nbsp;\u003cspan\u003e5\u003c/span\u003e shows the linear regression test of TGF \u0026beta; and MMP 9 before and after administration of autologous dendritic cells. The linear regression test before the action showed that every increase of one unit of MMP 9 would increase TGF \u0026beta; by 13.112, but this result was close to significant with p-value\u0026thinsp;=\u0026thinsp;0.058. The linear regression test after the treatment showed that every one unit increase of MMP 9 will increase TGF \u0026beta; by 7.622 with a near significant p-value (p-value\u0026thinsp;=\u0026thinsp;0.066). However, when comparing the value of MMP 9 to TGF \u0026beta; before and after autologous dendritic cell administration, there was a decrease in the value of MMP 9 to TGF \u0026beta; after autologous dendritic cell administration.\u003c/p\u003e\n \u003cdiv\u003e\n \u003ctable id=\"Tab5\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv\u003eTable 5\u003c/div\u003e\n \u003cdiv\u003e\n \u003cp\u003eLinear regression test of TGF \u0026beta; and MMP 9\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003ccolgroup cols=\"3\"\u003e\u003c/colgroup\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eVariables\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eCoefficient (\u0026beta;)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eP value\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePre MMP 9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e13.112\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.058\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDependent variable Pre TGF \u0026beta;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePost MMP 9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e7.622\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.066\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDependent variable Post TGF \u0026beta;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cp\u003e\u003cstrong\u003eAbbreviations:\u003c/strong\u003e MMP-9 = Matrix Metalloproteinase-9, TGF-\u0026beta; = Transforming Growth Factor Beta.\u003c/p\u003e\n \u003cp\u003eTable\u0026nbsp;\u003cspan\u003e6\u003c/span\u003e shows the analysis of the relationship between the study variables. The variables before and after the administration of autologous dendritic cells were combined, and then the correlation test between variables was performed.\u003c/p\u003e\n \u003cdiv\u003e\n \u003ctable id=\"Tab6\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv\u003eTable 6\u003c/div\u003e\n \u003cdiv\u003e\n \u003cp\u003eAnalysis of the relationship between variables\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003ccolgroup cols=\"4\"\u003e\u003c/colgroup\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eVariables\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eMMP 9 (r,p)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ePSV (r,p)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eEDV(r,p)\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eTGF \u0026beta;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.413, 0.001**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.101, 0.452\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.071, 0.598\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eMMP 9\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.015, 0.909\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.048, 0.721\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003ePSV\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.015, 0.909\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.675, 0.000**\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eEDV\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.048, 0.721\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.675, 0.000**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cp\u003e\u003cstrong\u003eAbbreviations:\u003c/strong\u003e MMP-9 = Matrix Metalloproteinase-9, TGF-\u0026beta; = Transforming Growth Factor Beta, PSV = Peak Systolic Velocity, EDV = End Diastolic Velocity.\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eNote:\u003c/strong\u003e **p \u0026lt; 0.01\u003c/p\u003e\n \u003cp\u003eRelationship test between research variables using Spearman showed there was a significant relationship between TGF \u0026beta; and MMP 9 with a p value of 0.001. There is a significant relationship between PSV and EDV with a p value of 0.000.\u003c/p\u003e\n\u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003e In the study on chronic kidney disease, there were some important findings related to the history of hypertension and the use of antihypertensive and antidiabetic drugs. Most of the subjects in this study had a history of hypertension, reaching 93.1%. This is in line with the literature showing that hypertension is a major risk factor for the development of DKD, and good blood pressure management can slow the progression of kidney disease in diabetic patients [11]. Hypertension through the vasoactive hormone pathway will affect the development of DKD, which will increase kidney damage through vasoconstriction. Hypertension will exacerbate kidney damage by increasing pressure in the glomerulus and stimulating inflammation and fibrosis [12].\u003c/p\u003e \u003cp\u003eHistory of angiotensin receptor blocker drug use was the most common drug used in this study. He et al. showed that ACEi or ARB can reduce UACR and improve renal function [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. ARBs, especially combined with mineralocorticoid receptor antagonists, can increase the risk of hyperkalemia [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThis study showed that there was no significant difference between the UACR groups on PSV, EDV TGF β and MMP 9 before the administration of autologous dendritic cells. In addition, there was no significant difference based on the history of the disease or the use of diabetic drugs and the history of antihypertensive drug use.\u003c/p\u003e \u003cp\u003eOverall, there were significant changes in PSV and EDV after administration of autologous dendritic cells. There are studies that mention resistance index (RI), which is associated with PSV and EDV, is associated with increased C reactive protein (CRP), indicating a pro-inflammatory state in hypertensive patients, especially in patients with DM [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThis decrease in PSV is related to UACR and eGFR. There are studies that show an increase in UACR is associated with a decrease in kidney function, this decrease in kidney function is measured through eGFR. This reflects kidney damage and changes in renal blood flow [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. Some studies explain that microalbuminuria is associated with high PSV [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eSignificant increase in EDV after autologous dendritic cell administration. This increase in EDV is associated with improved renal perfusion, which in turn affects the increase in GFR. Research shows that EDV is positively correlated with CKD and \u003cem\u003eGFR\u003c/em\u003e [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. In kidney disease, this decrease in EDV will increase resistance in blood vessels and cause impaired perfusion and decreased \u003cem\u003eGFR\u003c/em\u003e [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. Decreased EDV will also lead to increased albuminuria through decreased \u003cem\u003eGFR\u003c/em\u003e [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. Some studies also explain the existence of an immune response, namely inflammation, in patients with microalbuminuria and macroalbuminuria [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eBased on gender, there were significant changes in PSV and EDV values in the female group, while in men, there were changes that were not significant. This may be due to differences in immune responses in men and women. This is in line with research conducted by Korte et al., which found that women tend to have a stronger immune response than men, largely due to the influence of estrogen, which can increase antibody production [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe differences in renal physiology and immune response based on age are the basis for grouping in this study. This is in line with research conducted by Costagliola et al., which showed differences in immune responses based on age [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. Research conducted by Weinstein et al. shows that there are changes in renal blood flow with age [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. However, in this study, in both the age groups under 60 and over 60 years, there were no significant changes in PSV and EDV.\u003c/p\u003e \u003cp\u003eThere are different pathophysiological mechanisms in microalbuminuria and macroalbuminuria. Microalbuminuria is an early marker of kidney damage [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. Whereas in macroalbuminuria there is further damage with significant structural changes in the glomerulus [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eIn the microalbuminuria group, autologous dendritic cells can significantly reduce the median PSV value after administration of autologous dendritic cells, while in EDV, there is an increase but not significant. In the macroalbuminuria group, PSV and EDV values did not have significant changes. The macroalbuminuria group requires the administration of autologous dendritic cells more than once with longer follow-up because, in the macroalbuminuria group, there is significant structural damage to the glomerulus [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eLinear regression analysis showed that after the administration of autologous dendritic cells, the effect of MMP 9 on TGFβ decreased, although this was almost statistically significant. This is different from the Spearman test between TGF β and MMP 9, which showed an association between TGF β and MMP 9. This difference may be due to differences in sample size. Several studies have shown that there is a relationship between TGF β and MMP 9. Research conducted by Kundu et al. showed that an increase in TGF β was associated with an increase in MMP 9 in diabetic animal models [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. Research conducted by Gu et al. showed that inhibition of TGF β can reduce MMP 9 [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. Muscella et al. stated that TGFβ activates cell migration through MMP 2 and MMP 9 [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. In this study, there was a decrease in the influence of MMP 9 on TGF β after the administration of autologous dendritic cells. The decrease in TGF β is expected to reduce fibrosis in the kidney. TGF β activates Smad2 and Smad 3 and then interacts with transcription factors involved in fibrogenesis [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e].\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThis study shows that the administration of autologous dendritic cells can affect changes in renal blood flow in DKD patients. After the administration of autologous dendritic cells, there was a significant decrease in PSV and a significant increase in EDV, which would improve blood flow in the kidney. There is a relationship between TGF β and MMP 9. Linear regression analysis showed a decrease in the influence of MMP 9 on TGF β, a decrease in TGF β is expected to reduce fibrosis in DKD patients in the long term.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics Committee Information\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis research was approved by the Ethics Committee of the Gatot Soebroto Army Hospital, Jakarta, under ethical feasibility decision number 109/VIII/KEPK/2024, dated 23 August 2024. All subjects provided written informed consent prior to participation in the study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResearch Funding\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003ePartially funded by PT JES.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor Contribution\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eE.D designed the study, supervised data collection, performed data analysis, and wrote the manuscript. J. supervised data collection and reviewed the manuscript. A.P.L collected data and managed administrative work. E.R.D provided senior supervision and reviewed the manuscript. A.G.I provided senior supervision and reviewed the manuscript. F. provided senior supervision and reviewed the manuscript. T.A.P provided senior supervision and reviewed the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCorresponding author\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAziza Ghanie Icksan\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics declarations\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthical statement\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe research was conducted according to the declaration of Helsinki, and approved by the Institutional Review Commission (or Ethics Committee) of Gatot Soebroto Army Hospital (no 109/VIII/KEPK/2024).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflict of Interest\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eEndang drajat declares no conflict of interest.\u003c/p\u003e\n\u003cp\u003eJonny declares no conflict of interest.\u003c/p\u003e\n\u003cp\u003eAditya Pratama Lokeswara declares no conflict of interest.\u003c/p\u003e\n\u003cp\u003eElvita Rahmi Daulay declares no conflict of interest.\u003c/p\u003e\n\u003cp\u003eAziza Ghanie Icksan declares no conflict of interest.\u003c/p\u003e\n\u003cp\u003eFarhat declares no conflict of interest.\u003c/p\u003e\n\u003cp\u003eTerawan Agus Putranto declares no conflict of interest.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eInformed consent\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eInformed consent was obtained from all study subjects.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData Availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll data is available upon request.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eSelby NM, Taal MW (2020) An updated overview of diabetic nephropathy: Diagnosis, prognosis, treatment goals and latest guidelines. Diabetes Obes Metab 22:3\u0026ndash;15. https://doi.org/10.1111/dom.14007\u003c/li\u003e\n\u003cli\u003eDeng Y, Li N, Wu Y, et al (2021) Global, Regional, and National Burden of Diabetes-Related Chronic Kidney Disease From 1990 to 2019. Front Endocrinol (Lausanne) 12:. https://doi.org/10.3389/fendo.2021.672350\u003c/li\u003e\n\u003cli\u003eAfkarian M, Sachs MC, Kestenbaum B, et al (2013) Kidney disease and increased mortality risk in type 2 diabetes. Journal of the American Society of Nephrology 24:302\u0026ndash;308. https://doi.org/10.1681/ASN.2012070718/-/DCSUPPLEMENTAL\u003c/li\u003e\n\u003cli\u003eDonate-Correa J, Ferri CM, S\u0026aacute;nchez-Quintana F, et al (2021) Inflammatory Cytokines in Diabetic Kidney Disease: Pathophysiologic and Therapeutic Implications. Front Med (Lausanne) 7:628289. https://doi.org/10.3389/FMED.2020.628289/BIBTEX\u003c/li\u003e\n\u003cli\u003eCollard D, van Brussel PM, van de Velde L, et al (2020) Estimation of intraglomerular pressure using invasive renal arterial pressure and flow velocity measurements in humans. Journal of the American Society of Nephrology 31:1905\u0026ndash;1914. https://doi.org/10.1681/ASN.2019121272/-/DCSUPPLEMENTAL\u003c/li\u003e\n\u003cli\u003eWang B, Ding C, Ding X, et al (2022) WNT1-inducible signaling pathway protein 1 regulates kidney inflammation through the NF-\u0026kappa;B pathway. Clin Sci (Lond) 136:29\u0026ndash;44. https://doi.org/10.1042/CS20210663\u003c/li\u003e\n\u003cli\u003eKobayashi T, Kim HJ, Liu X, et al (2014) Matrix metalloproteinase-9 activates TGF-\u0026beta; and stimulates fibroblast contraction of collagen gels. 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Front Immunol 8:315167. https://doi.org/10.3389/FIMMU.2017.01764/BIBTEX\u003c/li\u003e\n\u003cli\u003eZoppini G, Targher G, Chonchol M, et al (2012) Predictors of estimated GFR decline in patients with type 2 diabetes and preserved kidney function. Clin J Am Soc Nephrol 7:401\u0026ndash;408. https://doi.org/10.2215/CJN.07650711\u003c/li\u003e\n\u003cli\u003ePatel DM, Bose M, Cooper ME (2020) Glucose and Blood Pressure-Dependent Pathways\u0026ndash;The Progression of Diabetic Kidney Disease. International Journal of Molecular Sciences 2020, Vol 21, Page 2218 21:2218. https://doi.org/10.3390/IJMS21062218\u003c/li\u003e\n\u003cli\u003eHe D, Zhang Y, Zhang W, et al (2020) Effects of ACE Inhibitors and Angiotensin Receptor Blockers in Normotensive Patients with Diabetic Kidney Disease. Horm Metab Res 52:289\u0026ndash;297. https://doi.org/10.1055/A-1138-0959\u003c/li\u003e\n\u003cli\u003eLuo X, Xu J, Zhou S, et al (2023) Influence of SGLT2i and RAASi and Their Combination on Risk of Hyperkalemia in DKD: A Network Meta-Analysis. Clin J Am Soc Nephrol 18:1019\u0026ndash;1030. https://doi.org/10.2215/CJN.0000000000000205\u003c/li\u003e\n\u003cli\u003eAdrian G, Mehedintu A (2024) LINKING ULTRASOUND ASSESSMENT OF RENAL ARTERIES TO THE BIOLOGICAL PROFILE OF INFLAMMATION AND COAGULATION AT HYPERTENSIVE PATIENTS WITH OR WITHOUT DIABETES MELLITUS. J Hypertens 42:e145\u0026ndash;e146. https://doi.org/10.1097/01.HJH.0001020816.05500.07\u003c/li\u003e\n\u003cli\u003eProvenzano M, Puchades MJ, Garofalo C, et al (2022) Albuminuria-Lowering Effect of Dapagliflozin, Eplerenone, and their Combination in Patients with Chronic Kidney Disease: A Randomized Cross-over Clinical Trial. Journal of the American Society of Nephrology 33:1569\u0026ndash;1580. https://doi.org/10.1681/ASN.2022020207/-/DCSUPPLEMENTAL\u003c/li\u003e\n\u003cli\u003eSpatola L, Andrulli S (2016) Doppler ultrasound in kidney diseases: a key parameter in clinical long-term follow-up. J Ultrasound 19:243. https://doi.org/10.1007/S40477-016-0201-X\u003c/li\u003e\n\u003cli\u003eYang J, Yang S, Xu Y, et al (2021) Evaluation of Renal Oxygenation and Hemodynamics in Patients with Chronic Kidney Disease by Blood Oxygenation Level-dependent Magnetic Resonance Imaging and Intrarenal Doppler Ultrasonography. Nephron 145:653\u0026ndash;663. https://doi.org/10.1159/000516637\u003c/li\u003e\n\u003cli\u003eGao J, Perlman A, Kalache S, et al (2017) Multiparametric Quantitative Ultrasound Imaging in Assessment of Chronic Kidney Disease. J Ultrasound Med 36:2245. https://doi.org/10.1002/JUM.14209\u003c/li\u003e\n\u003cli\u003eNorris KC, Smoyer KE, Rolland C, et al (2018) Albuminuria, serum creatinine, and estimated glomerular filtration rate as predictors of cardio-renal outcomes in patients with type 2 diabetes mellitus and kidney disease: A systematic literature review. BMC Nephrol 19:1\u0026ndash;13. https://doi.org/10.1186/S12882-018-0821-9/FIGURES/3\u003c/li\u003e\n\u003cli\u003eGupta J, Mitra N, Kanetsky PA, et al (2012) Association between albuminuria, kidney function, and inflammatory biomarker profile in CKD in CRIC. Clin J Am Soc Nephrol 7:1938\u0026ndash;1946. https://doi.org/10.2215/CJN.03500412/-/DCSUPPLEMENTAL\u003c/li\u003e\n\u003cli\u003eKorte W, Buljan M, R\u0026ouml;sslein M, et al (2021) SARS-CoV-2 IgG and IgA antibody response is gender dependent; and IgG antibodies rapidly decline early on. J Infect 82:e11\u0026ndash;e14. https://doi.org/10.1016/J.JINF.2020.08.032\u003c/li\u003e\n\u003cli\u003eCostagliola G, Spada E, Consolini R (2021) Age‐related differences in the immune response could contribute to determine the spectrum of severity of COVID‐19. Immun Inflamm Dis 9:331. https://doi.org/10.1002/IID3.404\u003c/li\u003e\n\u003cli\u003eWeinstein JR, Anderson S (2010) THE AGING KIDNEY: PHYSIOLOGICAL CHANGES. Adv Chronic Kidney Dis 17:302. https://doi.org/10.1053/J.ACKD.2010.05.002\u003c/li\u003e\n\u003cli\u003eRani PK, Raman R, Gupta A, et al (2011) Albuminuria and diabetic retinopathy in type 2 diabetes mellitus sankara nethralaya diabetic retinopathy epidemiology and molecular genetic study (SN-DREAMS, report 12). Diabetol Metab Syndr 3:1\u0026ndash;8. https://doi.org/10.1186/1758-5996-3-9/TABLES/4\u003c/li\u003e\n\u003cli\u003eSuzuki A, Moriya T, Hayashi A, et al (2024) Arteriolar Hyalinosis Predicts the Onset of Both Macroalbuminuria and Impaired Renal Function in Patients with Type 2 Diabetes. Nephron 148:390\u0026ndash;398. https://doi.org/10.1159/000535875\u003c/li\u003e\n\u003cli\u003eKundu S, Pushpakumar SB, Tyagi A, et al (2013) Hydrogen sulfide deficiency and diabetic renal remodeling: role of matrix metalloproteinase-9. Am J Physiol Endocrinol Metab 304:E1365\u0026ndash;E1378. https://doi.org/10.1152/AJPENDO.00604.2012\u003c/li\u003e\n\u003cli\u003eGu D, Shi Y, Ding Y, et al (2013) Dramatic early event in chronic allograft nephropathy: Increased but not decreased expression of MMP-9 gene. Diagn Pathol 8:1\u0026ndash;11. https://doi.org/10.1186/1746-1596-8-13/FIGURES/7\u003c/li\u003e\n\u003cli\u003eMuscella A, Vetrugno C, Cossa LG, Marsigliante S (2020) TGF-\u0026beta;1 activates RSC96 Schwann cells migration and invasion through MMP-2 and MMP-9 activities. J Neurochem 153:525\u0026ndash;538. https://doi.org/10.1111/JNC.14913\u003c/li\u003e\n\u003cli\u003eZhang K, Fan C, Cai D, et al (2020) Contribution of TGF-Beta-Mediated NLRP3-HMGB1 Activation to Tubulointerstitial Fibrosis in Rat With Angiotensin II-Induced Chronic Kidney Disease. Front Cell Dev Biol 8:496359. https://doi.org/10.3389/FCELL.2020.00001/BIBTEX\u003c/li\u003e\n\u003cli\u003eFeng J, Xie L, Kong R, et al (2017) RACK1 silencing attenuates renal fibrosis by inhibiting TGF-\u0026beta; signaling. Int J Mol Med 40:1965\u0026ndash;1970. https://doi.org/10.3892/IJMM.2017.3154/HTML\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":"Diabetic Kidney Disease, Autologous dendritic cells, Doppler ultrasound, Transforming Growth Factor Beta, Matrix Metalloproteinase 9","lastPublishedDoi":"10.21203/rs.3.rs-5667385/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5667385/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e \u003cb\u003ePurpose\u003c/b\u003e \u003c/p\u003e \u003cp\u003eChronic hyperglycemia in DKD increases proinflammatory cytokines that can cause fibrosis and affect renal hemodynamics. This study aims to evaluate the effect of autologous dendritic cell administration in DKD patients, assessed by Doppler ultrasound examination (PSV and EDV), and measurement of TGF-β and MMP-9 biomarkers.\u003c/p\u003e \u003cp\u003e \u003cb\u003eMethods\u003c/b\u003e \u003c/p\u003e \u003cp\u003eThis study was a one group pretest posttest with 29 DKD patients. Measurement of PSV and EDV blood flow using doppler ultrasound, as well as blood collection for TGF β and MMP 9 biomarkers were performed before and after administration of autologous dendritic cells.\u003c/p\u003e \u003cp\u003e \u003cb\u003eResults\u003c/b\u003e \u003c/p\u003e \u003cp\u003eThe results showed that before administration, the median PSV value was 47.1\u0026thinsp;\u0026plusmn;\u0026thinsp;23.87 cm/s, which decreased to 27.85\u0026thinsp;\u0026plusmn;\u0026thinsp;20.53 cm/s with a p-value of 0.044, and EDV increased from 13\u0026thinsp;\u0026plusmn;\u0026thinsp;5.32 cm/s to 15.7\u0026thinsp;\u0026plusmn;\u0026thinsp;12.55 cm/s with a p-value of 0.039. The female group showed a significant decrease in PSV with a p-value of 0.03 and a significant increase in EDV with a p-value of 0.044. The microalbuminuria group showed a significant decrease in PSV with a p-value of 0.011. Analysis of TGF β and MMP 9 showed before administration of autologous dendritic cells, each increase of one unit of MMP 9 increased TGF β by 13.112, and after administration, it became 7.622.\u003c/p\u003e \u003cp\u003e \u003cb\u003eConclusion\u003c/b\u003e \u003c/p\u003e \u003cp\u003eThis study shows that the administration of dendritic cells can improve renal hemodynamics and, in the long term, is expected to reduce fibrosis in the kidney.\u003c/p\u003e","manuscriptTitle":"Effect of Autologous Dendritic Cell Administration on Changes in Renal Hemodynamics and Inflammatory Biomarkers in Diabetic Kidney Disease Patients","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-12-24 17:22:47","doi":"10.21203/rs.3.rs-5667385/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":"db84d7f8-2d7f-4be4-8833-404404f6d732","owner":[],"postedDate":"December 24th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-01-14T17:23:29+00:00","versionOfRecord":[],"versionCreatedAt":"2024-12-24 17:22:47","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-5667385","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5667385","identity":"rs-5667385","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}