Correlation of salivary hs-CRP With conventional cardiovascular risk markers and hba1c in prediabetic subjects

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Prediabetes is also associated with obesity and dyslipidemia. Chronic low-grade inflammation is a crucial element in the clinical course of metabolic syndrome, and cardiovascular and cerebrovascular diseases. C-Reactive Protein (CRP) is a biomarker of inflammation that tends to increase in patients with diabetes and prediabetes. Most serological markers of diabetes are detected using invasive techniques that cause anxiety and pain. Therefore, the use of noninvasive techniques for frequent biomarker monitoring is necessary. Salivary diagnostics is an upcoming field for sensitive biomarker detection, such as hs-CRP. HbA1C is a valuable glycemic risk marker and has proven to be a reliable test of time. Both HbA1C and CRP play pivotal roles in diabetes and prediabetes. hs-CRP is a measure of CRP level with greater accuracy. The lower limit of its measurement is 0.01 mg/L and the measurement is more than100 times as sensitive than the CRP measurement (lower limit 5 mg/L). Therefore, our study aimed to evaluate and establish a relationship between these factors in prediabetics. Since metabolic syndrome and prediabetes are the cause and effect of each other, correlating anthropometric measurements and lipid profile with HbA1C and hs-CRP will be our focus." } { "@context": "http://schema.org", "@type": "BreadcrumbList", "itemListElement": [ { "@type": "ListItem", "position": "1", "item": { "@id": "https://f1000research.com/", "name": "Home" } }, { "@type": "ListItem", "position": "2", "item": { "@id": "https://f1000research.com/browse/articles", "name": "Browse" } }, { "@type": "ListItem", "position": "3", "item": { "@id": "https://f1000research.com/articles/13-457", "name": "Correlation of salivary hs-CRP With conventional cardiovascular risk..." } } ] } Home Browse Correlation of salivary hs-CRP With conventional cardiovascular risk... ALL Metrics - Views Downloads Get PDF Get XML Cite How to cite this article Bedi DG and Acharya S. Correlation of salivary hs-CRP With conventional cardiovascular risk markers and hba1c in prediabetic subjects [version 1; peer review: awaiting peer review] . F1000Research 2024, 13 :457 ( https://doi.org/10.12688/f1000research.144824.1 ) NOTE: If applicable, it is important to ensure the information in square brackets after the title is included in all citations of this article. Close Copy Citation Details Export Export Citation Sciwheel EndNote Ref. Manager Bibtex ProCite Sente EXPORT Select a format first Track Share ▬ ✚ Study Protocol Correlation of salivary hs-CRP With conventional cardiovascular risk markers and hba1c in prediabetic subjects [version 1; peer review: awaiting peer review] Dr. Gautam Bedi https://orcid.org/0009-0009-9669-8864 1 , Sourya Acharya 1 Dr. Gautam Bedi https://orcid.org/0009-0009-9669-8864 1 , Sourya Acharya 1 PUBLISHED 07 May 2024 Author details Author details 1 Department of Medicine, Jawaharlal Nehru Medical College, Datta Meghe Institute of Higher Education & Research, Wardha, 442001, India Dr. Gautam Bedi Roles: Methodology, Project Administration, Writing – Original Draft Preparation, Writing – Review & Editing Sourya Acharya Roles: Methodology, Project Administration, Writing – Original Draft Preparation, Writing – Review & Editing OPEN PEER REVIEW REVIEWER STATUS AWAITING PEER REVIEW This article is included in the Datta Meghe Institute of Higher Education and Research collection. Abstract Prediabetes has attracted considerable attention because it increases the risk of developing diabetes and cardiovascular disease. Prediabetes is also associated with obesity and dyslipidemia. Chronic low-grade inflammation is a crucial element in the clinical course of metabolic syndrome, and cardiovascular and cerebrovascular diseases. C-Reactive Protein (CRP) is a biomarker of inflammation that tends to increase in patients with diabetes and prediabetes. Most serological markers of diabetes are detected using invasive techniques that cause anxiety and pain. Therefore, the use of noninvasive techniques for frequent biomarker monitoring is necessary. Salivary diagnostics is an upcoming field for sensitive biomarker detection, such as hs-CRP. HbA1C is a valuable glycemic risk marker and has proven to be a reliable test of time. Both HbA1C and CRP play pivotal roles in diabetes and prediabetes. hs-CRP is a measure of CRP level with greater accuracy. The lower limit of its measurement is 0.01 mg/L and the measurement is more than100 times as sensitive than the CRP measurement (lower limit 5 mg/L). Therefore, our study aimed to evaluate and establish a relationship between these factors in prediabetics. Since metabolic syndrome and prediabetes are the cause and effect of each other, correlating anthropometric measurements and lipid profile with HbA1C and hs-CRP will be our focus. READ ALL READ LESS Keywords Prediabetes, Salivary hs-CRP (high sensitivity C- Reactive Protein), HbA1C, Dyslipdemia. Corresponding Author(s) Sourya Acharya ( [email protected] ) Close Corresponding author: Sourya Acharya Competing interests: No competing interests were disclosed. Grant information: The author(s) declared that no grants were involved in supporting this work. Copyright: © 2024 Bedi DG and Acharya S. This is an open access article distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. How to cite: Bedi DG and Acharya S. Correlation of salivary hs-CRP With conventional cardiovascular risk markers and hba1c in prediabetic subjects [version 1; peer review: awaiting peer review] . F1000Research 2024, 13 :457 ( https://doi.org/10.12688/f1000research.144824.1 ) First published: 07 May 2024, 13 :457 ( https://doi.org/10.12688/f1000research.144824.1 ) Latest published: 07 May 2024, 13 :457 ( https://doi.org/10.12688/f1000research.144824.1 ) Introduction Background “Prediabetes” is used for individuals whose glucose levels do not meet the criteria for diabetes but are too high to be considered normal. 1 The prevalence of prediabetes is growing worldwide, and it is expected that more than 470 million people will develop prediabetes by 2030, and 5–10% of prediabetics will progress to diabetes. 2 According to World Health Organization (WHO), prediabetes is a state of intermediate hyperglycemia characterised by, impaired fasting glucose (IFG) /fasting plasma glucose (FPG) of 6.1-6.9 mmol/L (110 to 125 mg/dL) and impaired glucose tolerance (IGT) /2 hour plasma glucose of 7.8-11.0 mmol/L (140-200 mg/dL) after ingestion of 75 g of oral glucose load. 3 On the other hand, according to the American Diabetes Association (ADA), IGT (140-199 mg/dL) has the same cut-off value, but IFG (100-125 mg/dL) has a lower cut-off value and has an additional hemoglobin A1c (HbA1c)-based criteria of a level of 5.7%–6.4% to define prediabetes. 1 , 3 Prediabetes is of great concern, as it is associated with an increased risk of diabetes and cardiovascular diseases. Prediabetes is linked to obesity (mainly abdominal or visceral obesity), hypertension, and dyslipidemia (high triglycerides and/or low HDL cholesterol). 1 HbA1C has an upper hand over FPG and OGTT due to greater convenience (fasting not required) and preanalytical stability. They are also less affected by day-to-day perturbations during stress and illness. 1 Studies in the past 10 years have proved that HbA1C still continues to be the most valuable glycemic risk marker. 4 C-reactive protein (CRP) in blood is commonly used as a biomarker for inflammation and infection. However, recent studies have targeted the use of salivary CRP as a noninvasive alternative. Most serological markers for diabetes are detected using invasive techniques. Thus, the development of noninvasive techniques for monitoring biomarkers is needed in the current scenario. 5 , 6 One of the advantages of using salivary CRP as a biomarker is its noninvasive nature. Saliva can be easily collected and does not require any special preparation, making it a convenient option for both patients and health care providers. Salivary CRP has been found to be as efficacious as blood CRP. Human saliva is a rich reservoir of analytes (3,000 proteins and 12,000 peptides) and shares 30% proteins and 10% peptides with the serum proteome and peptidome. 5 , 7 Inflammation induces hepatic synthesis of acute-phase proteins, such as serum ferritin and high-sensitivity C-reactive protein (hs-CRP), which play a significant role in insulin resistance and atherosclerosis. hs-CRP is a sensitive marker of the inflammatory activity in the arterial wall. hs-CRP is a measure of CRP level with greater accuracy. The lower limit of its measurement is 0.01 mg/L and the measurement is more than100 times as sensitive than CRP measurement (lower limit, 5 mg/L). 8 The American Heart Association (AHA) defines Metabolic Syndrome based on five factors: fasting blood pressure, blood glucose, triglycerides, HDL-C, and waist circumference. High-sensitivity C-reactive sensitivity CRP (hs-CRP) level is a measure of systemic inflammatory conditions and is considered an important biomarker of diabetes. 9 Chronic low-grade inflammation has been proposed as a key factor for both metabolic syndrome (MetS) and subsequent clinical outcomes, such as cardiovascular diseases (CVD) and cerebrovascular disease. Chronic low-grade inflammation causes disruption of the vascular endothelial glycocalyx by C-reactive protein (CRP), which leads to its dysfunction and increases susceptibility to proatherogenic factors. Inflammation-induced vascular changes cannot be evaluated using cardiac imaging. Therefore, biomarker detection is of great significance. 10 Studies have shown the relationship between salivary hsCRP and type 2 diabetes and the relationship between plasma hs-CRP and prediabetes; however, our study aimed to determine the relationship between prediabetes and salivary hs-CRP so that timely intervention can help to reverse it. Thus, we can save many prediabetics from converting to overt diabetes and decrease the predisposition to cardiac diseases. This will also help reduce the immense economic burden on society. Aim To establish a correlation between salivary hs-CRP levels and conventional cardiovascular risk markers and HbA1C in prediabetic subjects. Objectives • To measure anthropometric parameters like (body mass index, waist circumference, waist-to-hip ratio, and neck circumference) in prediabetic subjects. • To estimate the fasting lipid profile (Total Cholesterol, HDL, LDL, VLDL, Triglycerides) in prediabetic subjects. • To estimate HbA1C levels in prediabetic subjects. • To correlate anthropometric and fasting lipid profile variables with HbA1C in prediabetic subjects. Method Study type: Cross sectional, observational study. Sample size: 95 patients. Allocation: This cross-sectional, observational study will be conducted in patients attending medicine OPD or admitted to the medical ward at Acharya Vinobha Bhave Rural Hospital, DMIHER Wardha, fitting into the exclusion and inclusion criteria of the study. Study model PURPOSE: To establish a correlation between salivary high-sensitivity C-reactive protein (hs-CRP) levels and conventional cardiovascular risk markers and HbA1C in prediabetic subjects. Thus, we can save many prediabetics from converting to overt diabetes and decrease the predisposition to cardiac diseases. This will also help reduce the immense economic burden on society. Inclusion criteria All consenting patients aged 45 years and above with risk factors, attending medicine OPD, or being admitted to the medicine ward of the DMIHER Wardha. Exclusion criteria 1. Patients refusing informed consent. 2. Patients who have previous diagnosis of diabetes mellitus. 3. Patients suffering from iron deficiency and sickle cell anemia. 4. Patients receiving HIV treatment. 5. Pregnancy. 6. Postpartum patients. 7. Patients on hemodialysis and chronic kidney disease. 8. Patients with chronic liver disease. 9. Patients who have received recent blood transfusion or are on erythropoietin therapy. 10. Patients who have undergone salivary gland surgery. 11. Patients who are smokers and alcoholics. Study design: Cross sectional, observational study Study: Department of Medicine, Acharya Vinoba Bhave Rural Hospital (AVBRH), JNMC, DMIHER (Deemed University), Wardha. Study population: This is a cross-sectional, observational study that will be conducted in patients attending medicine OPD or admitted to the medical ward at Acharya Vinobha Bhave Rural Hospital, DMIHER Wardha, fitting into the exclusion and inclusion criteria of the study. Duration of study: 2 years Tools: 1. The following instruments will be used in this study for vitals and anthropometric measurements: • Sphygmomanometer (Lifeline) • Stethoscope (Littmann) • Weight machine (Venus) • Non stretchable measuring tape. 2. The following instruments will be used in this study for assessing lipid profile: • Tubes for blood collection (plain andEDTA tubes) • Miscellaneous things: spirit, cotton swab, syringes, needles, tourniquet, vein Detector. Fully automated analyzer-Virtos ® integrated system 5600-Orthoclinical diagnostics 3. Blood for HbA1C 4. Salivary hs-CRP Outcome measures 1. Anthropometric measurments 2. Lipid profile 3. Blood HbA1C 4. Salivary hs-CRP Sample size: 95 patients Formula: n = ( Zα + Z 1 − β ) 2 P ( 1 − P ) E 2 Zα is 95% confidence level = 1.96 Z1-β is 80% power of study = 0.8413 P is prevalence of prediabetes = 14% 11 E is the absolute error = 10% Study reference: Jose J et al. 11 Statistical analysis The data were entered in MS EXCEL and analyzed using R Studio software version 4.3.1. Quantitative data was analyzed using Student’s T-test and ANOVA analysis. Methods After explaining the study in detail to the subjects included in the study, informed consent written and oral was obtained. In the medicine OPD/ward, the subjects will undergo an assessment of vitals and measurement of anthropometric indices. The values of the same will be recorded in designated proforma. As per Performa, relevant history will be obtained from the subjects after fulfilling the inclusion and exclusion criteria. Further, the subjects will be instructed regarding the need for overnight fasting for 8 hours after dinner and to report to the medicine OPD the next morning for collection of blood samples for lipid profile estimation. Salivary samples will also be collected. Anthropometric measurements The following parameters will be included • Body weight— Weight, in kilograms, will be measured with high accuracy. The participant will stand still on a weight scale without shoes and wear only light clothing. • Height— Height will be measured in centimeters (to the closest 0.5 cm) with the patient standing in an upright stance without shoes, with the head held so that the apex of the external auditory meatus is aligned with the bony orbital margin (Frankfurt Plane) • BMI calculation = Weight in kgs ( Height in meters ) 2 Obesity is defined as a BMI of 25 or higher. WHO criteria for BMI for Asia-pacific region were used in this study: • Underweight is BMI less than 18.5 kg/m 2 • Lean or Normal BMI is between 18.5-22.9 kg/m 2 • Overweight is BMI between 23.0-25.0 kg/m 2 • Obese patients are BMI more than 25 kg/m 2 Waist circumference • According to WHO’s step-by-step technique, measurements should be made at the midpoint between the tip of the iliac crest and the border of the last palpable rib. • Abnormal waist circumference (Asian criteria) Male: ≥ 90 cm Female: ≥ 80 cm Lipid profile Fasting Lipid Profile— The fasting lipid profiles of all patients will be examined, including Triglycerides, Total cholesterol HDL and LDL. Standard laboratory procedures were used to measure all the biochemical parameters. After a 10- to 12-hour overnight fast, blood samples were collected and centrifuged for five minutes at 3000 rpm to determine the serum lipid levels. This will be performed with a Virtos integrated 5600 ortho clinical diagnostics analyzer. Estimation of serum triglyceride A LIQUID STABLE GPO-PAP technique will be used by a machine called a Virtos Integrated System - Ortho diagnostics chemical analyzer to quantify serum triglycerides. PRINCIPLE - The lipoprotein lipase enzyme breaks down proteins into free fatty acids and glycerol . In the presence of glycerol kinase , glycerol is converted to glycerol-3-phosphate , which is then oxidized to dihydroxy acetone phosphate , which then interacts with p-chlorophenol and 4-amino-antipyrine to produce a red quinoneimine dye complex that is red in colour. The number of triglycerides in the sample is directly correlated with the intensity of the color produced. Estimation of serum HDL By Using the Direct Enzymatic Method, the Virtos Integrated System - Ortho Diagnostics Chemicals Analyzer, serum HDL was evaluated. PRINCIPLE - Serum HDL cholesterol levels were directly determined without sample pre-treatment or centrifugation. This technique hinges on a detergent’s ability to dissolve just HDL cholesterol , which is then released to interact with chromogens, cholesterol esterase, and cholesterol oxidase to produce color. LDL, VLDL, and chylomicrons, which are non-HDL lipoproteins, are prevented from interacting with enzymes because detergents are absorbed onto their surfaces. The amount of HDL cholesterol present in the sample correlates directly with the resulting color intensity. Estimation of serum cholestrol Using the Enzymatic Method, the Virtos Integrated System - Ortho Diagnostics Chemicals Analyzer serum cholesterol will be assessed. PRINCIPLE- Absorbance is often measured at a wavelength of around 500 nm in the visible region of the spectrum. To break down cholesteryl esters, reagents commonly use a bacterial enzyme called cholesteryl ester hydrolase: Assays are typically linear up to 600–700 mg/dL (15.54–18.13 mmo/L). • Estimation of VLDL based on the Friedwald equation. VLDL = Triglyceride/5 • Estimation of LDL using The Friedwald equation. LDL cholesterol = Total cholesterol – HDL Cholesterol – triglyceride/5 Showing machine for FLP: Fully automated analyzer–Virtos interated system-5600- Ortho clinical diagnostics Samples will be processed in the central clinical lab (CCL) of AVB Rural Hospital, Sawangi (Meghe), Wardha. Total Cholesterol Normal—Less than equal to 200 mg/dl Borderline—Between 200-239 mg/dl High—240 mg/dl or more LDL Cholesterol Normal—Less than equal to 100 mg/dl Borderline—Between 130-159 mg/dl High—160 mg/dl or more HDL Cholesterol Normal—In Males 40 mg/dl or more In Females 50 mg/dl or more Abnormal—Less than 40mg/dl in Males Less than 50 mg/dl in Females Triglyceride levels Normal—Less than equal 149 mg/dl Borderline—Between 150-199 mg/dl High—200 mg/dl or more Fasting Blood Glucose, 2 Hour Postmeal Glucose, HbA1C ADA Criteria 2019— Prediabetes — FBG-100-125 mg/dl 2h post glucose—140-199 mg/dl HbA1C—5.7-6.4% Fasting plasma glucose It is estimated by the glucose oxidase-peroxidase (GOD-POD) method using a Robonic Semi Automatic Chemical Analyzer. PRINCIPLE: Glucose oxidase oxidizes (GOD) glucose to gluconic acid and hydrogen peroxide. Hydrogen peroxide reacts with phenol and 4-amino-antipyrin (4AAP) in the presence of peroxidase (POD) to form quinone-imine dyes. The intensity of the color was directly proportional to the amount of glucose in the sample and was measured at 505 nm (500 – 520 nm or with a green filter). Glucose + O 2 + H 2 O → GOD Gluconate + H 2 O 2 H 2 O 2 + Chromogen + 4 AAP → POD Coloured complex + H 2 O Specimen collection After 8 h of overnight fasting, the subject’s venous sample was taken from the anterior cubital vein and collected in a sugar bulb containing sodium fluoride (NaF) as an anticoagulant. This will be done to prevent glycolysis and improve separation of serum or plasma as soon as possible. Reagent storage and stability The liquid glucose reagent (Anamol Laboratories Pvt. catalogue number of the reagent: 2939149) is stable until the expiry date when stored at 2-8°C. Procedure 1000 μL The reagent will be added to 10 μL of plasma (dissolved in an anticoagulant). Incubation was performed for 15 min at room temperature and the results were read using a Robonic Semi Automatic Chemical Analyzer. Calculation Glucose (mg/dl) = Optical Density of Test × 100/Optical Density of Standard HbA1C Using the Enzymatic Method, the Virtos Integrated System 5600 - Ortho Diagnostics Chemicals Analyzer serum HbA1C was assessed. Salivary sample collection for hs–CRP Two milliliters of unstimulated saliva were collected by draining in a sterile container (Eppendorf tube). All samples were maintained at a stable room temperature. Patients will be instructed not to consume food or any other substance for two hours before saliva collection. All samples were taken in the morning between 9am-11am. Laboratory method Saliva was centrifuged at 3000 rpm for 10 min and CRP levels were estimated. 12 Dissemination Articles arising from this study will be published in the index journals. Study status Not yet started, is expected to begin from October 2023. Discussion Salivary hs-CRP values were calculated using appropriate kits. Serum HbA1C levels, lipid profiles, and FBS will be collected. Anthropometric measurements were then performed. After entry into the master chart and calculation of the p value, an attempt will be made to evaluate the relationship between different parameters. The advantage of using salivary hs-CRP as a biomarker is its non-invasive nature, which causes minimal hassle to the patient and health care provider. Thus, we adopted this method in our study, making it more innovative. hs-CRP is a sensitive biomarker of inflammation and its levels tend to increase in patients with diabetes and prediabetes. HbA1C is a valuable glycemic risk marker and has proven to be a reliable test of time. Since metabolic syndrome and prediabetes are the cause and effect of each other, correlating anthropometric measurements and lipid profile with HbA1C and hs-CRP will be our focus. Studies have shown the relationship between salivary hsCRP and type 2 diabetes and the relationship of plasma hs-CRP with prediabetes and metabolic syndrome; however, our study aimed to determine the relationship between prediabetes and salivary hs-CRP, so that timely intervention can help reverse it. Thus, we can save many prediabetics from converting to overt diabetes and decrease the predisposition to cardiac diseases. This will also help reduce the immense economic burden on society. Ethical considerations The Institutional Ethics Committee of Datta Meghe Institute of Higher Education and Research (DU) has granted approval to the study protocol (Reference number: REF/2023/07/070519, Date: 17/07/2023). After explaining the study in detail to the subjects included in the study, informed consent written and oral was obtained. Data availability No data associated with this article. References 1. Cefalu WT, Berg EG, Saraco M, et al. : Classification and diagnosis of diabetes: standards of medical care in diabetes-2019. Diabetes Care. 2019 Jan 1; 42 : S13–S28. Publisher Full Text 2. Tabák AG, Herder C, Rathmann W, et al. : Prediabetes: a high-risk state for diabetes development. Lancet. 2012 Jun 16; 379 (9833): 2279–2290. PubMed Abstract | Publisher Full Text | Free Full Text 3. Bansal N: Prediabetes diagnosis and treatment: A review. World J. Diabetes. 2015; 6 (2): 296–303. PubMed Abstract | Publisher Full Text | Free Full Text 4. Kilpatrick ES: Glycated haemoglobin in the year 2000. J. Clin. Pathol. 2000 May 1; 53 (5): 335–339. PubMed Abstract | Publisher Full Text | Free Full Text 5. Srinivasan M, Blackburn C, Mohamed M, et al. : Literature–Based Discovery of Salivary Biomarkers for Type 2 Diabetes Mellitus. Biomark. Insights 2015; 10 : BMI.S22177. Publisher Full Text 6. Agho ET, Owotade FJ, Kolawole BA, et al. : Salivary inflammatory biomarkers and glycated haemoglobin among patients with type 2 diabetic mellitus. BMC Oral Health. 2021 Dec; 21 (1): 1–8. Publisher Full Text 7. Loo JA, Yan W, Ramachandran P, et al. : Comparative human salivary and plasma proteomes. J. Dent. Res. 2010 Oct; 89 (10): 1016–1023. PubMed Abstract | Publisher Full Text | Free Full Text 8. Gupta R, Pamecha H: To Study Relationship of Serum hsCRP with Type 2 Diabetes Mellitus, its Vascular Complications and Non-Diabetics-Case Control Study. J. Assoc. Physicians India 2020 Aug 1; 68 (8): 25–29. PubMed Abstract 9. Haddad NS: High sensitivity C-reactive protein (hs-CRP) and metabolic syndrome: Correlation with number and type of metabolic syndrome components in Iraqi patients. The Medical Journal of Basrah University. 2012 Jun 28; 30 (1): 49–54. Publisher Full Text 10. Koziarska-Rościszewska M, Gluba-Brzózka A, Franczyk B, et al. : High-Sensitivity C-Reactive Protein Relationship with Metabolic Disorders and Cardiovascular Diseases Risk Factors. Life. 2021 Jul 26; 11 (8): 742. PubMed Abstract | Publisher Full Text | Free Full Text 11. Jose J, Thomas N: How should one tackle prediabetes in India? Indian J. Med. Res. 2018 Dec; 148 (6): 675–676. PubMed Abstract | Publisher Full Text | Free Full Text 12. Patil SA, Aditya A, Jangam DK: Evaluation of Salivary C-Reactive Protein and Psychological Factors in Patients with Oral Lichen Planus: A Cross-sectional Study. J. Clin. Diagn. Res. 2021 May 1; 15 (5). Publisher Full Text Comments on this article Comments (0) Version 1 VERSION 1 PUBLISHED 07 May 2024 ADD YOUR COMMENT Comment Author details Author details 1 Department of Medicine, Jawaharlal Nehru Medical College, Datta Meghe Institute of Higher Education & Research, Wardha, 442001, India Dr. Gautam Bedi Roles: Methodology, Project Administration, Writing – Original Draft Preparation, Writing – Review & Editing Sourya Acharya Roles: Methodology, Project Administration, Writing – Original Draft Preparation, Writing – Review & Editing Competing interests No competing interests were disclosed. Grant information The author(s) declared that no grants were involved in supporting this work. Article Versions (1) version 1 Published: 07 May 2024, 13:457 https://doi.org/10.12688/f1000research.144824.1 Copyright © 2024 Bedi DG and Acharya S. 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Register $(document).ready(function () { signIn.createSignInAsRow($("#sign-in-form-gfb-popup")); $(".target-field").each(function () { var uris = $(this).val().split("/"); if (uris.pop() === "login") { $(this).val(uris.toString().replace(",","/")); } }); });