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Aim: Regular screening of large number of people with diabetes for diabetic retinopathy (DR) with the support of available human resources alone is a global challenge. Digital health innovation is a boon in screening for DR. Multiple artificial intelligence (AI)-based deep learning (DL) algorithms have shown promise for accurate diagnosis of referable DR (RDR). The aim of this review is to evaluate the use of AI for DR screening and the various currently available automated DR detection algorithms., Methods: We reviewed articles published up to May 15th 2024, on the use of AI for DR by searching PubMed, Medline, Embase, Scopus, and Google Scholar using keywords like diabetic retinopathy, retinal imaging, teleophthalmology, automated detection, artificial intelligence, deep learning and fundus photography., Results: This narrative review, traces the advent of AI and its use in digital health, the key concepts in AI and DL algorithm development for diagnosis of DR, some crucial AI algorithms that have been validated for detection of DR and the benefits and challenges of use of AI in detection and management of DR. While there are many approved AI algorithms that are in use globally for DR detection, IDx-DR, EyeArt, and AEYE Diagnostic Screening (AEYE-DS) are the algorithms that have been approved so far by USFDA for automated DR screening., Conclusion: AI has revolutionized screening of DR by enabling early automated detection. Continuous advances in AI technology, combined with high-quality retinal imaging, can lead to early diagnosis of sight-threatening DR, appropriate referrals, and better outcomes.

Objective: To evaluate the appropriateness of responses generated by an online chat-based artificial intelligence (AI) model for diabetic retinopathy (DR) related questions., Design: Cross-sectional study., Methods: A set of 20 questions framed from the patient's perspective addressing DR-related queries, such as the definition of disease, symptoms, prevention methods, treatment options, diagnostic methods, visual impact, and complications, were formulated for input into ChatGPT-4. Peer-reviewed, literature-based answers were collected from popular search engines for the selected questions and three retinal experts reviewed the responses. An inter-human agreement was analyzed for consensus expert responses and also between experts. The answers generated by the AI model were compared with those provided by the experts. The experts rated the response generated by ChatGPT-4 on a scale of 0-5 for appropriateness and completeness., Results: The answers provided by ChatGPT-4 were appropriate and complete for most of the DR-related questions. The response to questions on the adverse effects of laser photocoagulation therapy and compliance to treatment was not perfectly complete. The average rating given by the three retina expert evaluators was 4.84 for appropriateness and 4.38 for completeness of answers provided by the AI model. This corresponds to an overall 96.8% agreement among the experts for appropriateness and 87.6% for completeness regarding AI-generated answers., Conclusion: ChatGPT-4 exhibits a high level of accuracy in generating appropriate responses for a range of questions in DR. However, there is a need to improvise the model to generate complete answers for certain DR-related topics., (Copyright © 2024 Copyright: © 2024 Indian Journal of Ophthalmology.)

Aim: To assess the performance of smartphone based wide-field retinal imaging (WFI) versus ultra-wide-field imaging (UWFI) for assessment of sight-threatening diabetic retinopathy (STDR) as well as locating predominantly peripheral lesions (PPL) of DR., Methods: Individuals with type 2 diabetes with varying grades of DR underwent nonmydriatic UWFI with Daytona Plus camera followed by mydriatic WFI with smartphone-based Vistaro camera at a tertiary care diabetes centre in South India in 2021-22. Grading of DR as well as identification of PPL (DR lesions beyond the posterior pole) in the retinal images of both cameras was performed by senior retina specialists. STDR was defined by the presence of severe non-proliferative DR, proliferative DR or diabetic macular oedema (DME). The sensitivity and specificity of smartphone based WFI for detection of PPL and STDR was assessed. Agreement between the graders for both cameras was compared., Results: Retinal imaging was carried out in 318 eyes of 160 individuals (mean age 54.7 ± 9 years; mean duration of diabetes 16.6 ± 7.9 years). The sensitivity and specificity for detection of STDR by Vistaro camera was 92.7% (95% CI 80.1-98.5) and 96.6% (95% CI 91.5-99.1) respectively and 95.1% (95% CI 83.5-99.4) and 95.7% (95% CI 90.3-98.6) by Daytona Plus respectively. PPL were detected in 89 (27.9%) eyes by WFI by Vistaro camera and in 160 (50.3%) eyes by UWFI. However, this did not translate to any significant difference in the grading of STDR between the two imaging systems. In both devices, PPL were most common in supero-temporal quadrant (34%). The prevalence of PPL increased with increasing severity of DR with both cameras (p < 0.001). The kappa comparison between the 2 graders for varying grades of severity of DR was 0.802 (p < 0.001) for Vistaro and 0.753 (p < 0.001) for Daytona Plus camera., Conclusion: Mydriatic smartphone-based widefield imaging has high sensitivity and specificity for detecting STDR and can be used to screen for peripheral retinal lesions beyond the posterior pole in individuals with diabetes., (© 2024. The Author(s).)

Background: National estimates of the prevalence of vision impairment and blindness in people with diabetes are required to inform resource allocation. People with diabetes are more susceptible to conditions such as diabetic retinopathy that can impair vision; however, these are often missed in national studies. This study aims to determine the prevalence and risk factors of vision impairment and blindness in people with diabetes in India., Methods: Data from the SMART-India study, a cross-sectional survey with national coverage of 42 147 Indian adults aged 40 years and older, collected using a complex sampling design, were used to obtain nationally representative estimates for the prevalence of vision impairment and blindness in people with diabetes in India. Vulnerable adults (primarily those who did not have capacity to provide consent); pregnant and breastfeeding women; anyone deemed too ill to be screened; those who did not provide consent; and people with type 1 diabetes, gestational diabetes, or secondary diabetes were excluded from the study. Vision impairment was defined as presenting visual acuity of 0·4 logMAR or higher and blindness as presenting a visual acuity of 1·0 logMAR or higher in the better-seeing eye. Demographic, anthropometric, and laboratory data along with geographic distribution were analysed in all participants with available data. Non-mydriatic retinal images were used to grade diabetic retinopathy, and risk factors were also assessed., Findings: A total of 7910 people with diabetes were included in the analysis, of whom 5689 had known diabetes and 2221 were undiagnosed. 4387 (55·5%) of 7909 participants with available sex data were female and 3522 (44·5%) participants were male. The estimated national prevalence of vision impairment was 21·1% (95% CI 15·7-27·7) and blindness 2·4% (1·7-3·4). A higher prevalence of any vision impairment (29·2% vs 19·6%; p=0·016) and blindness (6·7% vs 1·6%; p<0·0001) was observed in those with ungradable images. In known diabetes, diabetic retinopathy (adjusted odds ratio [aOR] 3·06 [95% CI 1·25-7·51]), vision-threatening diabetic retinopathy (aOR 7·21 [3·52-14·75]), and diabetic macular oedema (aOR 5·41 [2·20-13·33]) were associated with blindness in adjusted analysis. Common sociodemographic risk factors for vision impairment and blindness include older age, lower educational attainment, and unemployment., Interpretation: Based on the estimated 101 million people with diabetes in 2021 and the interpretation of the data from this study, approximately 21 million people with diabetes have vision impairment in India, of whom 2·4 million are blind. Higher prevalence is observed in those from lower socio-economic strata and policy makers should focus on these groups to reduce inequalities in health care., Funding: Global Challenge Research Fund of United Kingdom Research and Innovation through the Medical Research Council., Competing Interests: Declaration of interests No competing interests are declared for the submitted work. SS reports consultancy fees from AbbVie, Amgen, Apellis, Bayer, Biogen, Boehringer Ingelheim, Novartis, Eyebiotech, Eyepoint Phamaceuticals, Janssen Pharmaceuticals, Novo Nordisk, Optos, Ocular Therapeutix, Kriya Therapeutics, OcuTerra, Roche, Stealth Biotherapeutics, and Sanofi; travel fees from Bayer and Roche; lecture fees from Bayer and Boehringer Ingelheim; stock options from Eyebiotech; and is a member of the data monitoring committee and advisory committee of Bayer and Novo Nordisk, outside of the submitted work. ATP reports payment for a trial design lecture from Bayer and for membership of a trial data monitoring committee from Roche, outside of the submitted work. All other authors declare no competing interests., (Copyright © 2024 The Author(s). Published by Elsevier Ltd. This is an Open Access article under the CC BY 4.0 license. Published by Elsevier Ltd.. All rights reserved.)

Rice is considered the king of cereals. It is the only cereal that is being consumed by half of the population in the world. Rice and rice products have potential health benefits. One such rice aided product is rice washed water which is discussed in this article. Rice-washed water, which is commonly regarded as waste water and discarded, is a rich source of minerals and nutrients. The processing of rice washed water, nutritional analysis; edible fungi productions are detailed in this review. The article goes into detail about rice-washed water, which has been used for various purposes since our ancestors' time. The article provides a comprehensive report on the uses of rice-washed water in plant growth, Ayurveda, food, Cosmetics and a variety of other applications. Rice washed water is being utilized for various ailments since primitive times. The detailed report on the treatment utilizing rice washed water is also provided in this article. There are scarce researches with rice washed water, this article address the conventional uses of rice washed water, which can be taken up by the research community which needs more scientific validation. This review article also includes details about the composition and a variety of other important information about rice-washed water., Supplementary Information: The online version contains supplementary material available at 10.1007/s13197-023-05722-2., Competing Interests: Conflict of interestThe authors declare that they have no conflict of interest., (© Association of Food Scientists & Technologists (India) 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.)

Competing Interests: No conflicting relationship exists for any author in this communication.

The coronavirus disease-19 (COVID-19) pandemic, declared in early 2020, has left an indelible mark on global health, with over 7.0 million deaths and persistent challenges. While the pharmaceutical industry raced to develop vaccines, the emergence of mutant severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) strains continues to pose a significant threat. Beyond the immediate concerns, the long-term health repercussions of COVID-19 survivors are garnering attention, particularly due to documented cases of cardiovascular issues, liver dysfunction, pulmonary complications, kidney impairments, and notable neurocognitive deficits. Recent studies have delved into the pathophysiological changes in various organs following post-acute infection with murine hepatitis virus-1 (MHV-1), a coronavirus, in mice. One aspect that stands out is the impact on the skin, a previously underexplored facet of long-term COVID-19 effects. The research reveals significant cutaneous findings during both the acute and long-term phases post-MHV-1 infection, mirroring certain alterations observed in humans post-SARS-CoV-2 infection. In the acute stages, mice exhibited destruction of the epidermal layer, increased hair follicles, extensive collagen deposition in the dermal layer, and hyperplasticity of sebaceous glands. Moreover, the thinning of the panniculus carnosus and adventitial layer was noted, consistent with human studies. A long-term investigation revealed the absence of hair follicles, destruction of adipose tissues, and further damage to the epidermal layer. Remarkably, treatment with a synthetic peptide, SPIKENET (SPK), designed to prevent Spike glycoprotein-1 binding with host receptors and elicit a potent anti-inflammatory response, showed protection against MHV-1 infection. Precisely, SPK treatment restored hair follicle loss in MHV-1 infection, re-architected the epidermal and dermal layers, and successfully overhauled fatty tissue destruction. These promising findings underscore the potential of SPK as a therapeutic intervention to prevent long-term skin alterations initiated by SARS-CoV-2, providing a glimmer of hope in the battle against the lingering effects of the pandemic.

The Internet of Things (IoT), which provides seamless connectivity between people and things, improves our quality of life. In the medical field, predictive analytics can help transform a reactive healthcare (HC) strategy into a proactive one. The HC industry embraces cutting-edge artificial intelligence and machine learning (ML) technologies. ML's area of deep learning has the revolutionary potential to reliably analyze massive volumes of data quickly, produce insightful revelations and solve challenging issues. This article proposes an energy-aware heart disease prediction (HDP) system based on enhanced spider monkey optimization (ESMO) and a weight-optimized neural network for an IoT-based HC environment. The proposed work consists of two essential phases: energy-efficient data transmission and HDP. In energy-efficient transmission, the cluster leaders are optimally selected using ESMO and the cluster formation is done based on Euclidean distance. In HDP, the patient data are collected from the dataset, and essential features are extracted. After that, the dimensionality reduction is carried out using the modified linear discriminant analysis approach to reduce over-fitting issues. Finally, the HDP uses the enhanced Archimedes weight-optimized deep neural network (EAWO-DNN). The simulation findings demonstrate that the proposed optimal clustering mechanism enhances the network's lifespan by consuming minimal energy compared to the existing techniques. Also, the proposed EAWO-DNN classifier achieves higher prediction accuracy, precision, recall and f-measure than the conventional methods for predicting heart disease in IoT.Communicated by Ramaswamy H. Sarma.

Purpose: To evaluate the ability of an artificial intelligence (AI) model, ChatGPT, in predicting the diabetic retinopathy (DR) risk., Methods: This retrospective observational study utilized an anonymized dataset of 111 patients with diabetes who underwent a comprehensive eye examination along with clinical and biochemical assessments. Clinical and biochemical data along with and without central subfield thickness (CST) values of the macula from OCT were uploaded to ChatGPT-4, and the response from the ChatGPT was compared to the clinical DR diagnosis made by an ophthalmologist., Results: The study assessed the consistency of responses provided by ChatGPT, yielding an Intraclass Correlation Coefficient (ICC) value of 0.936 (95% CI, 0.913-0.954, p < 0.001) (with CST) and 0.915 (95% CI, 0.706-0.846, p < 0.001) (without CST), both situations indicated excellent reliability. The sensitivity and specificity of ChatGPT in predicting the DR cases were evaluated. The results revealed a sensitivity of 67% with CST and 73% without CST. The specificity was 68% with CST and 54% without CST. However, Cohen's kappa revealed only a fair agreement between ChatGPT predictions and clinical DR status in both situations, with CST (kappa = 0.263, p = 0.005) and without CST (kappa = 0.351, p < 0.001)., Conclusion: This study suggests that ChatGPT has the potential of a preliminary DR screening tool with further optimization needed for clinical use., Competing Interests: No conflicting relationship exists for any author., (© 2023 Raghu et al.)

Purpose: Risk factors (RFs), like 'body mass index (BMI),' 'age,' and 'gender' correlate with Diabetic Retinopathy (DR) diagnosis and have been widely studied. This study examines how these three secondary RFs independently affect the predictive capacity of primary RFs., Methods: The dataset consisted of four population-based studies on the prevalence of DR and associated RFs in India between 2001 and 2010. An Autoencoder was employed to categorize RFs as primary or secondary. This study evaluated six primary RFs coupled independently with each secondary RF on five machine-learning models., Results: The secondary RF 'gender' gave a maximum increase in Area under the curve (AUC) score to predict DR when combined separately with 'insulin treatment,' 'fasting plasma glucose,' 'hypertension history,' and 'glycosylated hemoglobin' with a maximum increase in AUC for the Naive Bayes model from 0.573 to 0.646, for the Support Vector Machines (SVM) model from 0.644 to 0.691, for the SVM model from 0.487 to 0.607, and for the Decision Tree model from 0.8 to 0.848, respectively. The secondary RFs 'age' and 'BMI' gave a maximum increase in AUC score to predict DR when combined separately with 'diabetes mellitus duration' and 'systolic blood pressure,' with a maximum increase in AUC for the SVM model from 0.389 to 0.621, and for the Decision Tree model from 0.617 to 0.713, respectively., Conclusion: The risk factor 'gender' was the best secondary RF in predicting DR compared to 'age' and 'BMI,' increasing the predictive power of four primary RFs.

Objective: To assess if optical coherence tomography (OCT) and OCT angiography (OCTA) measures are associated with the development and worsening of diabetic retinopathy (DR) over four years., Methods: 280 participants with type 2 diabetes underwent ultra-wide field fundus photography, OCT and OCTA. OCT-derived macular thickness measures, retinal nerve fibre layer and ganglion cell-inner plexiform layer thickness and OCTA-derived foveal avascular zone area, perimeter, circularity, vessel density (VD) and macular perfusion (MP) were examined in relation to the development and worsening of DR over four years., Results: After four years, 206 eyes of 219 participants were eligible for analysis. 27 of the 161 eyes (16.7%) with no DR at baseline developed new DR, which was associated with a higher baseline HbA 1c and longer diabetes duration. Of the 45 eyes with non-proliferative DR (NPDR) at baseline, 17 (37.7%) showed DR progression. Baseline VD (12.90 vs. 14.90 mm/mm 2 , p = 0.032) and MP (31.79% vs. 36.96%, p = 0.043) were significantly lower in progressors compared to non-progressors. Progression of DR was inversely related to VD ((hazard ratio [HR] = 0.825) and to MP (HR = 0.936). The area under the receiver operating characteristic curves for VD was AUC = 0.643, with 77.4% sensitivity and 41.8% specificity for a cut-off of 15.85 mm/mm 2 and for MP it was AUC = 0.635, with 77.4% sensitivity and 25.5% specificity for a cut-off of 40.8%., Conclusions: OCTA metrics have utility in predicting progression rather than the development of DR in individuals with type 2 diabetes., (© 2023. The Author(s), under exclusive licence to The Royal College of Ophthalmologists.)

Aims: To estimate the prevalence of undiagnosed diabetes and suboptimally controlled diabetes and the associated risk factors by community screening in India., Methods: In this multi-centre, cross-sectional study, house-to-house screening was conducted in people aged ≥40 years in urban and rural areas across 10 states and one union territory in India between November 2018 and March 2020. Participants underwent anthropometry, clinical and biochemical assessments. Capillary random blood glucose and point-of-care glycated haemoglobin (HbA 1c ) were used to diagnose diabetes. The prevalence of undiagnosed diabetes and suboptimal control (HbA 1c ≥53 mmol/mol [≥7%]) among those with known diabetes was assessed., Results: Among the 42,146 participants screened (22,150 urban, 19,996 rural), 5689 had known diabetes. The age-standardised prevalence of known diabetes was 13.1% (95% CI 12.8-13.4); 17.2% in urban areas and 9.4% in rural areas. The age-standardised prevalence of undiagnosed diabetes was 6.0% (95% CI 5.7-6.2); similar in both urban and rural areas with the highest proportions seen in the East (8.0%) and South (7.8%) regions. When we consider all people with diabetes in the population, 22.8% of individuals in urban areas and 36.7% in rural areas had undiagnosed diabetes. Almost 75% of the individuals with known diabetes had suboptimal glycaemic control., Conclusions: High prevalence of undiagnosed diabetes and suboptimally controlled diabetes emphasises the urgent need to identify and optimally treat people with diabetes to reduce the burden of diabetes., (© 2023 The Authors. Diabetic Medicine published by John Wiley & Sons Ltd on behalf of Diabetes UK.)

Aim: To study the association between cystatin C and sight-threatening diabetic retinopathy (STDR) in Asian Indians with type 2 diabetes (T2DM)., Methods: In a cross-sectional study carried out at two tertiary centres in India in 2022, individuals with T2DM underwent clinical and ophthalmic assessments and estimation of serum cystatin C. Grading of DR was done by retina specialists. STDR was defined by the presence of severe non-proliferative DR (NPDR), proliferative DR (PDR) and/or diabetic macular edema. Receiver operating characteristic (ROC) curves were used to identify cystatin C cut-off value for detecting STDR., Results: Among 420 individuals with T2DM (mean age 56 ± 9 years; mean duration of diabetes 14.5 ± 7.9 years), 121 (24.1 %) had No-DR, 119 (28.3 %) had No-STDR and 200 (49.6 %) had STDR. Mean cystatin C level was significantly higher in individuals with STDR compared to those with no-STDR and No-DR (1.34 vs 1.06 vs 0.93 mg/L, p < 0.001). Cystatin C cut-off value ≥1.11 mg/L had a C statistic of 0.944 (95 % CI: 0.909-0.968, p < 0.001), 96.8 % sensitivity and 78.2 % specificity for detection of STDR., Conclusion: Elevated serum cystatin C was strongly associated with STDR and could possibly be used as a biomarker for screening for sight-threatening diabetic retinopathy., Competing Interests: Declaration of competing interest None., (Copyright © 2023 Elsevier Inc. All rights reserved.)

This paper discusses the importance of investigating DR using machine learning and a computational method to rank DR risk factors by importance using different machine learning models. The dataset was collected from four large population-based studies conducted in India between 2001 and 2010 on the prevalence of DR and its risk factors. We deployed different machine learning models on the dataset to rank the importance of the variables (risk factors). The study uses a t -test and Shapely additive explanations (SHAP) to rank the risk factors. Then, it uses five machine learning models (K-Nearest Neighbor, Decision Tree, Support Vector Machines, Logistic Regression, and Naive Bayes) to identify the unimportant risk factors based on the area under the curve criterion to predict DR. To determine the overall significance of risk variables, a weighted average of each classifier's importance is used. The ranking of risk variables is provided to machine learning models. To construct a model for DR prediction, the combination of risk factors with the highest AUC is chosen. The results show that the risk factors glycosylated hemoglobin and systolic blood pressure were present in the top three risk factors for DR in all five machine learning models when the t -test was used for ranking. Furthermore, the risk factors, namely, systolic blood pressure and history of hypertension, were present in the top five risk factors for DR in all the machine learning models when SHAP was used for ranking. Finally, when an ensemble of the five machine learning models was employed, independently with both the t -test and SHAP, systolic blood pressure and diabetes mellitus duration were present in the top four risk factors for diabetic retinopathy. Decision Tree and K-Nearest Neighbor resulted in the highest AUCs of 0.79 ( t -test) and 0.77 (SHAP). Moreover, K-Nearest Neighbor predicted DR with 82.6% ( t -test) and 78.3% (SHAP) accuracy.

Underwater visible light communication (UVLC) has recently come to light as a viable wireless carrier for signal transmission in risky, uncharted, and delicate aquatic environments like seas. Despite the potential of UVLC as a green, clean, and safe alternative to conventional communication methods, it is challenged by significant signal attenuation and turbulent channel conditions compared to long-distance terrestrial communication. To address linear and nonlinear impairments in UVLC systems, this paper presents an adaptive fuzzy logic deep-learning equalizer (AFL-DLE) for 64 Quadrature Amplitude Modulation-Component minimal Amplitude Phase shift (QAM-CAP)-modulated UVLC systems. The proposed AFL-DLE is dependent on complex-valued neural networks and constellation partitioning schemes and utilizes the Enhanced Chaotic Sparrow Search Optimization Algorithm (ECSSOA) to improve overall system performance. Experimental outcomes demonstrate that the suggested equalizer achieves significant reductions in bit error rate (55%), distortion rate (45%), computational complexity (48%), and computation cost (75%) while maintaining a high transmission rate (99%). This approach enables the development of high-speed UVLC systems capable of processing data online, thereby advancing state-of-the-art underwater communication.

Purpose: The study was conducted to compare the compliance to intravitreal injection treatment and follow-up in patients with center-involving diabetic macular edema (CI-DME) and treatment outcomes between a tertiary eye care facility and a tertiary diabetes care center., Methods: A retrospective review was conducted on treatment naïve DME patients who had received intravitreal anti-vascular endothelial growth factor (anti-VEGF) injections in 2019. Participants were people with type 2 diabetes who were under regular care at the eye care center or the diabetes care center in Chennai. The outcome measures were noted at months 1, 2, 3, 6, and 12., Results: A review of 136 patients treated for CI-DME (72 from the eye care center and 64 from a diabetes care center) was carried out. The severity of diabetic retinopathy (DR) was similar in both centers. There was no statistically significant (P > 0.05) difference in the choice of initial intravitreal drug in the two centers. At 12-month follow-up, only 29.16% came for a follow-up in the eye center vs. 76.56% in a diabetes care center (P = 0.000). The multivariate logistic regression showed increasing age was associated with non-compliance in both the groups (eye care center: odds ratio [OR] 0.91; 95% confidence interval [CI] 0.82-1.21; P = 0.044) and diabetes care center (OR 1.15; 95% CI 1.02-1.29; P = 0.020)., Conclusion: The follow-up rate between eye care and diabetic care center with DME showed a significant disparity. By providing comprehensive diabetes care for all complications under one roof, compliance with follow-up can be improved in people with DME., Competing Interests: None

Diabetic macular edema (DME) is an important cause of visual impairment in the working-age group. Deep learning methods have been developed to detect DME from two-dimensional retinal images and also from optical coherence tomography (OCT) images. The performances of these algorithms vary and often create doubt regarding their clinical utility. In resource-constrained health-care systems, these algorithms may play an important role in determining referral and treatment. The survey provides a diversified overview of macular edema detection methods, including cutting-edge research, with the objective of providing pertinent information to research groups, health-care professionals, and diabetic patients about the applications of deep learning in retinal image detection and classification process. Electronic databases such as PubMed, IEEE Explore, BioMed, and Google Scholar were searched from inception to March 31, 2022, and the reference lists of published papers were also searched. The study followed the preferred reporting items for systematic review and meta-analysis (PRISMA) reporting guidelines. Examination of various deep learning models and their exhibition regarding precision, epochs, their capacity to detect anomalies for less training data, concepts, and challenges that go deep into the applications were analyzed. A total of 53 studies were included that evaluated the performance of deep learning models in a total of 1,414,169°CT volumes, B-scans, patients, and 472,328 fundus images. The overall area under the receiver operating characteristic curve (AUROC) was 0.9727. The overall sensitivity for detecting DME using OCT images was 96% (95% confidence interval [CI]: 0.94-0.98). The overall sensitivity for detecting DME using fundus images was 94% (95% CI: 0.90-0.96)., Competing Interests: None

Objective: To examine the inter-observer agreement between two retina specialists in grading diabetic retinopathy (DR) severity in ultra-wide-field fundus photographs., Methods: Two hundred and seventy patients with diabetes, who visited the vitreoretinal specialty at a tertiary eye care hospital, with or without DR underwent comprehensive ophthalmic examination, dilated retinal exam and Optos ultra-wide-field (UWF) retinal photography. Optos images were graded for DR severity based on the International Clinical Diabetic Retinopathy Disease Severity Scale by two retina specialists with same number of years of experience, masked to the clinical details of the participants., Results: The two graders showed agreement in 229/270 images (84.8%) and disagreement in 41/270 images (15.2%). The unweighted kappa for agreement between graders was k = 0.715, SE = 0.037 and the weighted kappa was k = 0.838, SE = 0.022. No DR was identified in 170/270 (62.9%) patients, mild NPDR in 15/270 (5.6%) patients, moderate NPDR in 35/270 (12.9%) patients, severe NPDR in 4/270 (1.48%) patient and PDR in 5/270 (1.85%) patients by both graders. Disagreement was neither related to the learning curve of graders nor with the patient's age (p = 0.574), gender (p = 0.169), duration of diabetes (0.660) or the lens being phakic or pseudophakic (p = 0.171) on logistic regression., Conclusions: The impact of disagreement noted between observers in grading DR on UWF fundus photographs should be considered when utilizing UWF system in clinical studies., (© 2022. The Author(s), under exclusive licence to The Royal College of Ophthalmologists.)

Diabetic retinopathy (DR) at risk of vision loss (referable DR) needs to be identified by retinal screening and referred to an ophthalmologist. Existing automated algorithms have mostly been developed from images acquired with high cost mydriatic retinal cameras and cannot be applied in the settings used in most low- and middle-income countries. In this prospective multicentre study, we developed a deep learning system (DLS) that detects referable DR from retinal images acquired using handheld non-mydriatic fundus camera by non-technical field workers in 20 sites across India. Macula-centred and optic-disc-centred images from 16,247 eyes (9778 participants) were used to train and cross-validate the DLS and risk factor based logistic regression models. The DLS achieved an AUROC of 0.99 (1000 times bootstrapped 95% CI 0.98-0.99) using two-field retinal images, with 93.86 (91.34-96.08) sensitivity and 96.00 (94.68-98.09) specificity at the Youden's index operational point. With single field inputs, the DLS reached AUROC of 0.98 (0.98-0.98) for the macula field and 0.96 (0.95-0.98) for the optic-disc field. Intergrader performance was 90.01 (88.95-91.01) sensitivity and 96.09 (95.72-96.42) specificity. The image based DLS outperformed all risk factor-based models. This DLS demonstrated a clinically acceptable performance for the identification of referable DR despite challenging image capture conditions., (© 2023. The Author(s).)

Colorectal Cancer is one of the most common cancers found in human beings, and polyps are the predecessor of this cancer. Accurate Computer-Aided polyp detection and segmentation system can help endoscopists to detect abnormal tissues and polyps during colonoscopy examination, thereby reducing the chance of polyps growing into cancer. Many of the existing techniques fail to delineate the polyps accurately and produce a noisy/broken output map if the shape and size of the polyp are irregular or small. We propose an end-to-end pixel-wise polyp segmentation model named Guided Attention Residual Network (GAR-Net) by combining the power of both residual blocks and attention mechanisms to obtain a refined continuous segmentation map. An enhanced Residual Block is proposed that suppresses the noise and captures low-level feature maps, thereby facilitating information flow for a more accurate semantic segmentation. We propose a special learning technique with a novel attention mechanism called Guided Attention Learning that can capture the refined attention maps both in earlier and deeper layers regardless of the size and shape of the polyp. To study the effectiveness of the proposed GAR-Net, various experiments were carried out on two benchmark collections viz., CVC-ClinicDB (CVC-612) and Kvasir-SEG dataset. From the experimental evaluations, it is shown that GAR-Net outperforms other previously proposed models such as FCN8, SegNet, U-Net, U-Net with Gated Attention, ResUNet, and DeepLabv3. Our proposed model achieves 91% Dice co-efficient and 83.12% mean Intersection over Union (mIoU) on the benchmark CVC-ClinicDB (CVC-612) dataset and 89.15% dice co-efficient and 81.58% mean Intersection over Union (mIoU) on the Kvasir-SEG dataset. The proposed GAR-Net model provides a robust solution for polyp segmentation from colonoscopy video frames.

Background: National and subnational estimates of the prevalence of diabetic retinopathy and vision-threatening diabetic retinopathy (VTDR) are needed to inform the stepwise implementation of systematic retinal screening for people with diabetes in India to decrease the rate of blindness. We aimed to assess these national and subnational estimates and to stratify the prevalence of diabetic retinopathy and VTDR on the basis of people with known versus undiagnosed diabetes, urban versus rural residence, and epidemiological transition level (ETL) and Socio-demographic Index (SDI) categories of states., Methods: We did a multicentre cross-sectional screening study for diabetic retinopathy using a complex cluster sampling design in people aged 40 years or older in ten Indian states and one union territory between Dec 20, 2018, and March 20, 2020. We did non-mydriatic retinal screening and assessed risk factor burden for people with diabetes. We estimated nationally weighted prevalence of diabetic retinopathy and VTDR for individuals with known and undiagnosed diabetes by urban versus rural residence, and by state categorisation by ETL and SDI. We also assessed adjusted risk factors., Findings: From 42 146 participants screened, 7910 (18·8%) were identified to have diabetes. Of these, 6133 (77·5%; 4350 with known diabetes and 1783 with undiagnosed diabetes) had gradable retinal images. 3411 (56%) participants were women and 2722 (44%) were men, and the median age was 56 years (IQR 49-65). The estimated national prevalence was 12·5% (95% CI 11·0-14·2) for diabetic retinopathy and 4·0% (3·4-4·8) for VTDR, with no significant differences between urban and rural residence for diabetic retinopathy. Compared with individuals with undiagnosed diabetes, we observed a higher prevalence of diabetic retinopathy (15·5% [13·4-17·8] vs 8·0% [6·3-10·1]) and VTDR (5·3% [4·5-6·3] vs 2·4% [1·6-3·6]) in individuals with known diabetes. The prevalence was significantly lower in low ETL-SDI states compared with high and middle ETL-SDI states for diabetic retinopathy (by 7·0%, 1·9-12·2, p=0·024) and VTDR (by 4·8%, 3·0-6·6, p<0·0001). Hyperglycaemia was the strongest modifiable risk factor., Interpretation: We estimate that, in absolute numbers, approximately 3 million people aged 40 years or older have VTDR in India, with a higher prevalence in those with known diabetes residing in high and middle ETI-SDI states., Funding: UKRI Global Challenge Research Fund., Competing Interests: Declaration of interests SS reports consultancy and payments for lectures from Bayer, Boehringer Ingelheim, Novartis, Oxurion, Roche, Allergan, and Apellis, outside the submitted work. ATP reports payment for a trial design lecture from Bayer and for membership of a trial data monitoring committee from Roche, outside the submitted work. All other authors declare no competing interests., (Copyright © 2022 The Author(s). Published by Elsevier Ltd. This is an Open Access article under the CC BY 4.0 license. Published by Elsevier Ltd.. All rights reserved.)

Purpose: To correlate optical coherence tomography (OCT)-based morphological patterns of diabetic macular edema (DME), biomarkers and grade of diabetic retinopathy (DR) in patients with various stages of chronic kidney disease (CKD) secondary to diabetes., Design: Multicentric retrospective cross-sectional study was conducted at seven centers across India., Methods: Data from medical records of patients with DME and CKD were entered in a common excel sheet across all seven centers. Staging of CKD was based on estimated glomerular filtration rate (eGFR)., Results: The most common morphological pattern of DME was cystoid pattern (42%) followed by the mixed pattern (31%). The proportion of different morphological patterns did not significantly vary across various CKD stages (p = 0.836). The presence of external limiting membrane-ellipsoid zone (ELM-EZ) defects (p < 0.001) and foveal sub-field thickness (p = 0.024) showed a direct correlation with the stage of CKD which was statistically significant. The presence of hyperreflective dots (HRD) and disorganization of inner retinal layers (DRIL) showed no significant correlation with the stage of CKD. Sight threatening DR was found to increase from 70% in CKD stage 3 to 82% in stages 4 and 5 of CKD, and this was statistically significant (p = 0.03)., Conclusion: Cystoid morphological pattern followed by mixed type was the most common pattern of DME on OCT found in patients suffering from stage 3 to 5 of CKD. However, the morphological patterns of DME did not significantly vary across various CKD stages. ELM-EZ defects may be considered as an important OCT biomarker for advanced stage of CKD., (© 2022. The Author(s), under exclusive licence to Springer Nature B.V.)

Hyperglycemia contributes to the development of cognition impairment and related disorders, induces oxidative stress in neuronal cells; thereby, impairs normal signaling mechanisms involved in cognition processes. Studies have shown a significant decrease in the vitamin D in individuals with hyperglycemia and cognition impairment. But whether supplementing vitamin D has any beneficiary impact on mitigating hyperglycemia-induced cognition impairment is unknown. We have first tested the impact of hyperglycemia on the induction of cognition deficiency in a zebrafish model. Next, the molecular mechanisms related to oxidative stress, which are deregulated in hyperglycemic zebrafish brains, have been explored. Subsequently, the impact of supplementing the water with vitamin D and a known activator of nuclear factor erythroid-2 related factor 2 (Nrf2) i.e., sulforaphane (SFN) on learning and memory functions were assessed. We showed a significant increase in the oxidative stress in the brain tissue of zebrafish residing in hyperglycemic water (111 mM glucose). Addition of vitamin D and SFN increased Nrf2, but differentially modulated its target genes (NQO1, SOD, GPx etc) activity in zebrafish and neuronal cell lines thereby improved the hyperglycemia-induced decline of cognition impairment. Mechanistically, vitamin D binds to the Keap1 protein; thereby, interfering with its binding to Nrf2, which leads to the activation of antioxidant mechanisms in the cells. In summary, reducing the oxidative stress through vitamin D treatment is a possible option for controlling the cognition impairment in diabetic population, but studies testing this possibility in clinical trials are currently needed.

Objective: To examine the relationship of visual function as assessed by visual acuity, contrast sensitivity, and multifocal electroretinography (mfERG) to macular structural and microvascular measures on optical coherence tomography (OCT) and angiography (OCTA) in individuals with diabetes., Methods: This is a prospective observational study conducted at a tertiary eye care centre in India. Right eyes of 121 adults with type 2 diabetes with no diabetic retinopathy (DR), mild or moderate nonproliferative DR (NPDR) were examined. Severe NPDR, proliferative DR and diabetic macular oedema were excluded. Participants underwent assessment of glycated haemoglobin (HbA 1C ), blood pressure, best corrected visual acuity (LogMAR), contrast sensitivity (CS), mfERG, ultrawide field fundus photography, OCT and OCTA. Correlations were assessed by Spearman's rank correlation (rho)., Results: Of the total of 121 eyes, 89 had No DR, 32 had mild to moderate NPDR. In the No DR group, the LogMAR acuity was significantly and negatively correlated to central subfoveal thickness (CST) (rho = -0.420), macular vessel density (rho = -0.270) and perfusion (rho = -0.270). (ii) Contrast sensitivity correlated to foveal avascular zone circularity (rho = 0.297); (iii) mfERG P1 response densities were better with higher macular perfusion index (rho = 0.240). In the NPDR group, the LogMAR acuity also showed a significant negative correlation to CST (rho = -0.379). Other correlations were not significant., Conclusion: Retinal and visual functional changes are evident in diabetic patients with No DR and are correlated to subclinical retinal structural changes detectable using multimodal imaging., (© 2021. The Author(s), under exclusive licence to The Royal College of Ophthalmologists.)

The Wireless Sensor Network is a network formed in areas human beings cannot access. The data need to be sensed by the sensor and transferred to the sink node. Many routing protocols are designed to route data from a single node to the sink node. One of the routing protocols is the hierarchical routing protocol, which passes on the sensed data hierarchically. The Low Energy Adaptive Clustering Hierarchy (LEACH) is one of the hierarchical methods in which communication happens in two steps: the setup phase and the steady-state phase. The efficiency of the LEACH has to be optimized to improve the network lifetime. Therefore, the k -means clustering algorithm, which comes under the unsupervised machine learning method, is incorporated with the LEACH algorithm and has shown better results. But the selection of cluster head needs to improvise because it will transfer the summed-up data to the sink node, so it is to be efficient enough. So, this paper proposes the modified k -means algorithm with LEACH protocol for optimizing the Wireless Sensor Network. In the modified k -means algorithm, the weight of the cluster head is tested and elected, and the clusters are formed using the Euclidean distance formula. The proposed work yields 48.85% efficiency compared to the existing protocol. It is also proven that the proposed work showed more successful data transfer to the sink node. The cluster head selection process elects the more efficient node as the head with less failure rate. The proposed work optimistically balanced the whole network in terms of energy and successful data transfer., Competing Interests: The authors declare that there are no conflicts of interest regarding the publication of this paper., (Copyright © 2022 S. Ramesh et al.)

Purpose: To determine the associations of predominant peripheral lesions (PPLs) with systemic comorbidities in individuals with diabetic retinopathy., Methods: This is a multicenter cross-sectional observational study conducted across three tertiary eye care centers in south India between January 2019 and July 2021. Ultra-widefield fundus images of consecutive patients with varying severity of diabetic retinopathy with data on systemic comorbidities were classified based on the presence or absence of PPL. Systemic comorbidities (hypertension, diabetic kidney disease, coronary artery disease, dyslipidemia, and anemia) were compared between the two groups., Results: A total of 879 participants (70.1% males) were included in the study, of which 443 (50.4%) patients had PPL. The mean age of the study participants was 56 ± 10 years, mean age of onset of diabetes was 41.24 ± 11.6 years, and mean duration of diabetes was 15.39 ± 7.6 years. The number of PPL increased with increasing severity of DR. Of all the systemic comorbidities analyzed, we found that coronary artery disease (CAD) had a significant association with PPL (Odds ratio [OR]-1.69; 95% confidence interval [CI], 1.12-2.55; P = 0.013) after adjusting for diabetic retinopathy severity, duration of diabetes, and age of onset of diabetes., Conclusion: The presence of PPL is a marker for coronary artery disease and early referral to cardiology is warranted., Competing Interests: None

Aim: To evaluate the effectiveness of tele-ophthalmology (TO) versus face-to-face screening for diabetic retinopathy (DR) in diabetes care centers (DCC) across India. Methods: This is an observational, multicenter, retrospective, cross-sectional study of DR screening in individuals with diabetes performed across 35 branches of a chain of DCC in 20 cities in India over 1 year. In 30 DCC, DR screening was performed by TO, where retinal images obtained using Fundus on Phone camera were uploaded through the telemedicine network for centralized DR grading by eight retina specialists. In five DCC, DR screening was performed by fundus examination (FE) by the same retina specialists. The rate of detection of sight-threatening DR (STDR) (defined as the presence of proliferative DR and/or diabetic macular edema) through the two modes was compared. Results: A total of 58,612 individuals were screened for DR from January 1, 2018 to December 31, 2018: 25,316 by TO and 33,296 by FE. The mean age and mean duration of diabetes of the individuals with diabetes screened by TO was 55.8 ± 11.2 years and 9.5 ± 7.3 years; and in individuals screened by FE, it was 57.5 ± 11.6 years and 11.5 ± 8.0 years respectively. The mean glycated hemoglobin was 8.8% ± 2.1% and 8.5% ± 1.9% in the two groups, respectively. Any DR was detected in 31.7% (95% confidence interval [CI]: 31.0-32.3) by tele-screening and in 38.5% (95% CI: 37.9-39.0) by FE, whereas STDR was detected in 7.3% (95% CI: 7.0-7.7) by TO and in 10.5% (95% CI: 10.2-10.9) by FE. Overall, 11.4% individuals with diabetes in the TO group, including 4.1% with ungradable images, were advised referral to retina specialists for further management. Conclusion: Screening for DR at DCC using TO is feasible and effective for STDR detection in India and may be adopted throughout India.

Background: Delayed diagnosis and treatment of sight threatening diabetic retinopathy (STDR) is a common cause of visual impairment in people with Type 2 diabetes. Therefore, systematic regular retinal screening is recommended, but global coverage of such services is challenging. We aimed to develop and validate predictive models for STDR to identify 'at-risk' population for retinal screening., Methods: Models were developed using datasets obtained from general practices in inner London, United Kingdom (UK) on adults with type 2 Diabetes during the period 2007-2017. Three models were developed using Cox regression and model performance was assessed using C statistic, calibration slope and observed to expected ratio measures. Models were externally validated in cohorts from Wales, UK and India., Findings: A total of 40,334 people were included in the model development phase of which 1427 (3·54%) people developed STDR. Age, gender, diabetes duration, antidiabetic medication history, glycated haemoglobin (HbA1c), and history of retinopathy were included as predictors in the Model 1, Model 2 excluded retinopathy status, and Model 3 further excluded HbA1c. All three models attained strong discrimination performance in the model development dataset with C statistics ranging from 0·778 to 0·832, and in the external validation datasets (C statistic 0·685 - 0·823) with calibration slopes closer to 1 following re-calibration of the baseline survival., Interpretation: We have developed new risk prediction equations to identify those at risk of STDR in people with type 2 diabetes in any resource-setting so that they can be screened and treated early. Future testing, and piloting is required before implementation., Funding: This study was funded by the GCRF UKRI (MR/P207881/1) and supported by the NIHR Biomedical Research Centre at Moorfields Eye Hospital NHS Foundation Trust and UCL Institute of Ophthalmology., Competing Interests: SS reports personal fees from Novartis, personal fees from Bayer, grants from Boehringer Ingleheim, grants and personal fees from Allergan, personal fees from Oxurion, personal fees from Apellis, personal fees from Roche, outside the submitted work; ATP reports grants from UKRI to employer King's College London, during the conduct of the study; personal fees from Roche and a grant from Wellcome Trust, outside the submitted work. RM reports personal fees from AMGEN, outside the submitted work. None of the other authors declare that they have any competing interests related to the submitted work., (© 2022 The Author(s).)

To examine the retinal structure and function in relation to diabetes duration and glycemia in patients without diabetic retinopathy (DR). 85 adults with type 2 diabetes without DR or macular edema underwent dilated indirect ophthalmoscopy, optical coherence tomography (OCT), ultra-wide field fundus photography, multifocal electroretinography (mfERG) and HbA 1C assessment. Patients were stratified as those with diabetes duration < 10 years and ≥ 10 years. Right eyes of all participants were analyzed. mfERG was analysed as ring 12, 34, 56. No significant differences were noted in OCT-derived retinal thickness measures between groups. mfERG P1 latencies were delayed, and amplitudes (nV/deg 2 ) were reduced in all three rings in those with diabetes duration ≥ 10 years vs. < 10 years, with significant correlations to diabetes duration in all rings. Logistic regression showed that duration of diabetes ≥ 10 years was associated with greater age (odds ratio (OR) 1.081, 95% CI 1.022, 1.143) and lower P1 amplitudes in the middle ring (OR 0.924, 95% CI 0.854, 0.999). No significant correlations were observed between HbA 1c and retinal measures. In the absence of DR, early retinal functional alterations are detectable on mfERG in patients with longer diabetes duration, but with no difference in OCT-derived retinal thickness., (© 2022. The Author(s).)

Specific stem cell-based therapies for treating Alzheimer's disease, Parkinson's disease, and schizophrenia are gaining importance in recent years. Accumulating data is providing further support by demonstrating the efficacy of neural stem cells in enhancing the neurogenesis in the aging brain. In addition to stem cells, recent studies have shown the efficacy of supplementing vitamin D in promoting neurogenesis and neuronal survival. Studies have also demonstrated the presence of mutational variants and single-nucleotide polymorphisms of the vitamin D receptor (VDR) in neurological disorders; however, implications of these mutations in the pathophysiology and response to drug treatment are yet to be explored. Hence, in this article, we have reviewed recent reports pertaining to the role of neural stem cells and VDR-mediated cellular signaling cascades that are involved in enhancing the neurogenesis through Wnt/β-catenin and Sonic Hedgehog pathways. This review benefits neurobiologists and pharmaceutical industry experts to develop stem cell-based and vitamin D-based therapies to better treat the patients suffering from neurological diseases., (© 2022. The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.)

Background: Disorders of mental health are known to affect cognitive functions, hence called as cognitive disorders. Impaired glucose metabolism, insulin resistance, vitamin-D deficiency and oxidative stress are some of the key early events reported to be involved in the pathogenesis of most common cognitive disorders, which include Alzheimer's disease. Type-2 diabetes mellitus (T2DM) is one of the known contributing factors of cognitive impairment and dementia., Methods: A cross sectional study was carried out in 145 subjects, who were assessed for cognitive function by modified mini mental status examination (3MS). In addition, measurement of fasting blood sugar (FBS), fasting insulin, HbA1c, lipid profile, vitamin D and oxidative markers was performed. Participants were divided into different groups based on (a) vitamin D insufficiency and sufficiency; (b) diabetic and non-diabetic with and without cognitive impairment., Results: The study included a total of 145 subjects; 51 males and 94 females and the mean age was 68.24±9.70 years. Among diabetics with vitamin D insufficiency, 35 subjects (71.43%) had cognitive impairment, but, among non-diabetics with vitamin D insufficiency, 27 subjects (62.79%) had cognitive impairment. Chi square test showed no significant association between diabetes, vitamin D insufficiency and cognitive impairment. Glutathione peroxidase (GPx) and superoxide dismutase (SOD) levels were non-significantly lower in cognition-impaired subjects, when compared to cognition normal subjects in diabetics with vitamin D insufficiency., Conclusion: Our study showed that cognitive impairment is more predominant in individuals with diabetes. However, our study did not find any significant relationship between T2DM, vitamin D deficiency, cognitive impairment, and oxidative stress. A significant association was found only with GPx and 3MSE score in vitamin D insufficient non-diabetics., Competing Interests: The authors have declared that no competing interests exist.

Purpose: To examine (1) the retinal structure by optical coherence tomography (OCT) and function by means of multifocal electroretinography (mfERG) in eyes with and without nonproliferative diabetic retinopathy (NPDR) (2) for correspondence between local retinal function and OCT zones with retinal lesions., Methods: One hundred and thirty-two eligible participants (30 with nonproliferative DR (NPDR) and 102 with diabetes with no DR) underwent comprehensive ophthalmic examination, optical coherence tomography for retinal thickness measures, mfERG, and ultra-wide field fundus photography. OCT Early Treatment Diabetic Retinopathy Study (ETDRS) grid was overlaid on to mfERG plots., Results: Those with NPDR had significantly thicker full retinal measures in the nine (ETDRS) zones compared to no DR. mfERG P1 latencies in rings 1-6 were significantly delayed, while the response densities in rings 4-6 were lower in the NPDR group. Significant negative correlation was noted between OCT thickness and mfERG P1 response densities in many ETDRS zones. Significant positive correlation was noted between P1 latencies and OCT thickness in a few zones. The combination of cystic spaces, microaneurysms, and hard exudates were present in all zones and were associated with a decrease in P1 response densities compared to no lesions. Reduced P1 response densities were associated with a sporadic delay in the mfERG latencies and vice versa. The number of lesions did not show correspondence to the mfERG measures., Conclusions: In eyes with NPDR, retinal function is differentially correlated with the DR lesions on OCT and can be assessed using multimodal imaging modalities., (© 2021. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.)

Aim: To assess the prevalence of diabetic retinopathy (DR) and associated risk factors in Asian Indians with prediabetes., Methods: In a cross-sectional study conducted at two tertiary care diabetes centres in Chennai, India, clinical and biochemical assessment and nonmydriatic ultra-wide field fundus photography was performed in individuals with prediabetes (impaired fasting glucose [IFG] and/or impaired glucose tolerance [IGT]) based on oral glucose tolerance test (OGTT) and/or glycated hemoglobin (HbA1c) between 5.7% and 6.4% in 2019. The retinal photographs were graded by certified ophthalmologists. Systemic risk factors associated with DR in prediabetes were assessed., Results: The mean age of the 192 individuals with prediabetes was 48 ± 13 years (55.2% were males). DR was present in 12 (6.3%) individuals of which nine (4.7%) had mild non-proliferative DR (NPDR) and three (1.6%) had moderate NPDR. None had severe sight-threatening DR. The Poisson multiple regression analysis showed that after adjusting for other systemic covariates, HbA1c values ≥ 6% (6-6.4%) was associated with 2 times higher relative risk of DR (Risk ratio 1.95 (95% CI 1.07-3.545, p = 0.028) in comparison to HbA1c < 6%)., Conclusion: DR was present in about 6% of the Asian Indians with prediabetes. Higher HbA1c values among individuals with prediabetes was associated with twice the relative risk for DR. Robust control of HbA1c should be encouraged even before the diagnosis of diabetes is established., (Copyright © 2022 The Authors. Published by Elsevier Inc. All rights reserved.)

Objective: To formulate guidelines for screening of diabetic retinopathy (DR) for the World Health Organization (WHO) South-East Asia Region (SEAR) aligned with the current infrastructure and human resources for health (HRH)., Design: A consultative group discussion of technical experts of the International Agency for the Prevention of Blindness (IAPB) from SEAR., Participants: IAPB country chairs and DR technical experts from SEAR countries., Methods: Data related to DR in SEAR was collected from published literature on available DM and DR guidelines and the participating experts. The 10 SEAR countries (the Democratic Republic of Korea was not included for lack of sufficient data) were divided into 3 resource levels (low, medium, and high) based on gross national income/per capita, cataract service indicators (cataract surgical rate and cataract surgical service), current infrastructure and available HRH. Two countries each were assigned to low (Myanmar, Timor-Leste) and high resource (India, Thailand) levels, and the remaining 6 countries (Bangladesh, Bhutan, Indonesia, Maldives, Nepal, Sri Lanka) were assigned the medium resource level. The DR care system was divided into 3 levels of care (essential, recommended, and desirable) and 3 levels of service delivery (primary, secondary, and tertiary)., Main Outcome Measures: Primary, secondary, and tertiary level guidelines for screening of DR., Results: Nine WHO SEAR countries participated in the formulation of the new country-specific DR screening guidelines. The DR screening recommendations were: advocacy at the community level, visual acuity measurement, and non-mydriatic fundus photography at the primary level, comprehensive eye examination and retinal laser at the secondary level, and intravitreal therapy and vitrectomy at the tertiary level. The systemic care of DM and hypertension are recommended at all levels commiserating with their care capabilities., Conclusions: The DR guidelines for the SEAR region are the first region-specific and resource-aligned recommendations for comprehensive DR care in each country of the region. In the future, the new technological advances in retinal camera technology, teleophthalmology, and artificial intelligence should be included within the structure of the public DR care system.

Introduction: To evaluate the effect of metabolic surgery on microvascular changes associated with diabetic retinopathy (DR) and diabetic kidney disease (DKD) in obese Asian Indians with type 2 diabetes (T2DM), one year after metabolic surgery., Methods: This is a follow up study in 21 obese Asian Indians with T2DM who underwent metabolic surgery (MS). Diabetic microvascular complications were assessed before and one-year post surgery using urinary albumin, protein creatinine ratio, eGFR, retinal colour photography and Optical coherence tomography (OCT)., Results: Microalbuminuria (54±26 vs 28±16 vs 21±6 µg/mg, p&lt;0.001) and protein creatinine ratio (0.4±0.1 vs 0.2±0.03 vs 0.1±0.02, p&lt;0.05) reduced significantly 6 months and one year after Metabolic surgery (MS) respectively compared to baseline values. Estimated Glomerular Filtration (eGFR) rate and creatinine was stable and there was no decline in renal function one year after MS. DR was present in eight individuals at baseline. After metabolic surgery, 12 % of individuals achieved regression of DR and 12% individuals showed a one step regression from severe to moderate non proliferative DR while 12 % individuals progressed from moderate to severe non proliferative DR. Of the 14 (53.8%) individuals who had micro or macroalbuminuria at baseline, 43% individuals reverted back to normoalbuminuria. There was also a reduction in the usage of anti- hypertensive medications after MS., Conclusion: In obese Asian Indians with T2DM, metabolic surgery reduced urinary microalbuminuria and protein creatinine ratios at one-year post MS. MS resulted in stable D. Retionpathy status one-year post surgery. MS may help to improve in stabilisation of the microvascular complications in obese patients with T2DM., (© Journal of the Association of Physicians of India 2011.)

Purpose: To study the zonal variations in diabetic retinopathy (DR) and associated factors in people with known type 2 diabetes mellitus (T2DM) attending large eye care facilities in different regions of India., Methods: In this cross-sectional eye-care facility-based study, India was divided into five zones; large eye care facilities with a good referral base and offering an entire range of care for patients with DR were invited. First-time T2DM attendees aged ≥18 years were recruited. All subjects received a comprehensive systemic and ophthalmic examination. DR and systemic diseases were classified as per the international/national standards. Findings were compared between the zones and with the national average., Results: Fourteen eye-care facilities (15% public) from five zones participated. In the cohort of 11,173 people, there were more males (59%); the average age was above 45 years, and in 57%, DM had been diagnosed more than 5 years earlier. Compared with the overall study population, the proportion of people with any DR, sight-threatening DR, and blind were higher in the east zone (42.5%, 95% confidence interval [CI]: 40.2-44.8; 24.3%, 95% CI 22.3-26.3, and 11.5%, respectively); diabetic macular edema was more frequent in the south zone (12.2%, 95% CI 11.2-13.2); people with moderate-to-severe visual impairment were more in the west zone (32.1%) and higher proportion of people in the south-central zone had systemic hypertension (56.8%, 95% CI 54.8-58.9)., Conclusion: The zonal variation in DR and related vision loss could be related to variable health-seeking behavior, availability, and confidence in the available services., Competing Interests: None

Purpose: To examine (i) the retinal structure and function using optical coherence tomography angiography (OCTA) and multifocal electroretinography (mfERG), respectively, in eyes with and without nonproliferative diabetic retinopathy (NPDR), (ii) and their interrelationship between retinal structure (OCTA) and function (mfERG) in the two groups independently., Methods: This was a prospective observational study. One hundred twenty-one eligible participants with type 2 diabetes with No DR (n = 89), or with mild or moderate NPDR (n = 32) underwent ophthalmic examination, ultrawide field-view fundus photography, OCTA, and mfERG. Group differences were assessed using a Mann-Whitney U test. Correlations were assessed using Spearman's rho., Results: There were no significant differences in OCTA measures between the two groups. The mfERG P1 implicit times (rings 1-6) were significantly delayed and P1 response densities in rings 5 and 6 were significantly lower in participants with NPDR compared to those with No DR. In those with No DR, P1 implicit times in almost all rings were delayed in relation to lower vessel density and perfusion (maximum variance noted was 13%). In individuals with NPDR, the P1 response density in rings 2 and 3 showed a positive nonsignificant correlation with macular perfusion., Conclusion: In those with diabetes with No DR, retinal neuronal function is influenced by lower macular vessel density and perfusion. The retinal neuronal function is abnormal in individuals with NPDR compared to those with No DR and is not correlated with OCT angiometric measures, suggesting the likelihood of a different retinal structural correlate., Competing Interests: None

The ORNATE India project is an interdisciplinary, multifaceted United Kingdom (UK)-India collaborative study aimed to build research capacity and capability in India and the UK to tackle the burden of diabetes-related visual impairment. For 51 months (October 2017-December 2021), this project built collaboration between six institutions in the UK and seven in India, including the Government of Kerala. Diabetic retinopathy (DR) screening models were evaluated in the public system in Kerala. An epidemiological study of diabetes and its complications was conducted through 20 centers across India covering 10 states and one union territory. The statistical analysis is not yet complete. In the UK, risk models for diabetes and its complications and artificial intelligence-aided tools are being developed. These were complemented by joint studies on various aspects of diabetes between collaborators in the UK and India. This interdisciplinary team enabled increased capability in several workstreams, resulting in an increased number of publications, development of cost-effective risk models, algorithms for risk-based screening, and policy for state-wide implementation of sustainable DR screening and treatment programs in primary care in Kerala. The increase in research capacity included multiple disciplines from field workers, administrators, project managers, project leads, screeners, graders, optometrists, nurses, general practitioners, and research associates in various disciplines. Cross-fertilization of these disciplines enabled the development of several collaborations external to this project. This collaborative project has made a significant impact on research capacity development in both India and the UK., Competing Interests: None

The increased burden of diabetes in India has resulted in an increase in the complications of diabetes including sight-threatening diabetic retinopathy (DR). Visual impairment and blindness due to DR can be prevented by early detection and management of sight-threatening DR. Life-long evaluation by repetitive retinal screening of people with diabetes is an essential strategy as DR has an asymptomatic presentation. Fundus examination by trained ophthalmologists and fundus photography are established modes of screening. Various modes of opportunistic screening have been followed in India. Hospital-based screening (diabetes care/eye care) and community-based screening are the common modes. Tele-ophthalmology programs based on retinal imaging, remote interpretation, and grading of DR by trained graders/ophthalmologists have facilitated greater coverage of DR screening and enabled timely referral of those with sight-threatening DR. DR screening programs use nonmydriatic or mydriatic fundus cameras for retinal photography. Hand-held/smartphone-based fundus cameras that are portable, less expensive, and easy to use in remote places are gaining popularity. Good retinal image quality and accurate diagnosis play an important role in reducing unnecessary referrals. Recent advances like nonmydriatic ultrawide field fundus photography can be used for DR screening, though likely to be more expensive. The advent of artificial intelligence and deep learning has raised the possibility of automated detection of DR. Efforts to increase the awareness regarding DR is essential to ensure compliance to regular follow-up. Cost-effective sustainable models will ensure systematic nation-wide DR screening in the country., Competing Interests: None

Diabetes mellitus (DM) is a significant common metabolic disorder seen all over the world. In 2020, according to the International Diabetes Federation (IDF), out of 463 million people who have diabetes all over the world, 77 million belong to India. As per the statistical prediction, the affected numbers are probably expected to rise to 642 million by 2040. The commercially available anti-diabetic drugs in the market include metformin, sulphonyl urea, meglitinides, miglitol, acarbose, biguanides, and thiazolidinediones cause side effects like hypoglycaemia, dizziness, liver cell injury, digestive discomfort, neurological defects, etc. Hence, bioactive organosulphur based functional ligands are chosen in this study to arrive at a newer drug for DM. In this work, in silico analysis of organosulphur molecular descriptors like physicochemical properties, solubility, drug score, and toxicity predictions are evaluated using OSIRIS and Toxtree freeware. The essential parameters for discovering drugs for biopharmaceutical formulations viz the solubility of drugs and toxicity have been calculated. The protein target Dipeptidyl peptidase DPP4 (PID: 2RIP) was docked against energy minimised sulphur compounds using Hex 6.3. The results indicate that the drug likeliness of the molecule 4, that is, N-[(3,3-dimethyl piperidin-2-yl) methyl]-4-ethyl sulphonyl aniline is active with decreasing binding energy score (-212.24 Kcal mol -1 ) with no toxicity and also few sulphur compounds are active against diabetes compared to standard drug metformin (-158.33 Kcal mol -1 ). The best drug-like ligand N-[(3,3-dimethyl piperidin-2-yl) methyl]-4-ethyl sulphonyl aniline, was docked using commercial Maestro Schrodinger software to predict the results., (© 2021 John Wiley & Sons Ltd.)

Purpose: To present clinical profile and risk factors of sight-threatening diabetic retinopathy (STDR) among people with age of onset of diabetes (AOD) <25 versus ≥25 years., Methods: A retrospective chart analysis of consecutive patients with diabetic retinopathy (DR) n = 654) treated at 14 eye care centers across India between 2018 and 2019 was performed. Patients were divided into two groups, Group 1: AOD <25 years and Group 2: AOD ≥25 years. DR and diabetic macular edema (DME) were classified using the International Clinical Classification of DR severity scale. STDR included severe nonproliferative DR (NPDR), proliferative DR (PDR), and moderate to severe DME. A multilevel mixed-effects model was used for comparison between two groups: 1) Patients with DR and AOD <25 years and 2) Patients with DR and AOD ≥25 years. Bivariate and multivariate regression analyses were used to evaluate risk factors between the two groups., Results: A total of 654 patients were included, 161 (307 eyes) in AOD <25 and 493 (927 eyes) in AOD >25 group. There was a higher prevalence of PDR with high-risk characteristics in AOD <25 group (24% vs. 12%) at baseline and 12-month follow-up (25% vs. 6%); P < 0.001. Systolic hypertension and poor glycemic control were risk factors in both groups, with no difference in these modifiable risk factors between groups., Conclusion: People with youth-onset DM are likely to present with severer form of STDR despite similar modifiable risk factors. Therefore, strict control of systolic blood pressure, glycemic status, and regular screening for DR are recommended to reduce the risk of STDR irrespective of the age of onset of diabetes., Competing Interests: None

Purpose: There is an exponential rise in the prevalence of diabetes mellitus (DM) in India. Ideally all people with DM should be periodically screening for diabetic retinopathy (DR) but is not practical with current infrastructure. An alternate strategy is to identify high-risk individuals with vision-threatening diabetic retinopathy (VTDR) for priority screening and treatment., Methods: We reanalyzed four population-based studies, conducted in South India between 2001 and 2010, and reclassified individuals above 40 years into known and newly diagnosed diabetes. Multiple regression analysis was done to identify risk factors in people with known and new DM., Results: The prevalence of DR in 44,599 subjects aged ≥40 years was 14.8% (18.4 and 4.7% in known and new DM, respectively), and the prevalence of VTDR was 5.1%. Higher risk factors of VTDR were older age >50 years, diabetes duration >5 years, and systolic blood pressure >140 mm Hg. Targeted screening of people with diabetes using high-risk criteria obtained from this study was able to detect 93.5% of all individuals with VTDR., Conclusion: In a limited resource country like India, a high-risk group-based targeted screening of individuals with DM could be prioritized while continuing the current opportunistic screening till India adopts universal screening of all people with DM., Competing Interests: None

Gold nanoparticles with their excellent biocompatibility are extensively used in pharma and biological applications. Terminalia bellerica (TB) dry fruit parts mediated gold nanoparticles were synthesized using the aqueous extracts. The secondary metabolites screening of the aqueous extracts was done using phytochemical analysis. The green synthesized gold nanoparticles show vibrant colours. They were characterized using UV-Visible spectroscopy, FT-IR spectroscopy, XRD analysis and FE-SEM. The analytical characterization methods ensured the formation of nanoparticles and could predict the nanometric size of the nanoparticles. The study also lay to determine the antibacterial potential of the TB fruit parts and TB fruit parts mediated gold nanoparticles. The pathogens chosen for the study were pathogens from clinical species such as Acinetobacter pneumonia , Bacillus subtilis , and Enterococcus faecalis which cause common infections. The TB fruit part extracts, as well as TB fruit parts mediated gold nanoparticles were capable enough to destroy clinical pathogens., Supplementary Information: The online version contains supplementary material available at 10.1007/s12088-021-00937-3., Competing Interests: Conflict of interestAuthors declare there is no conflict of interest., (© Association of Microbiologists of India 2021.)

Prediction models for population-based screening need, for global usage, to be resource-driven, involving predictors that are affordably resourced. Here, we report the development and validation of three resource-driven risk models to identify people with type 2 diabetes (T2DM) at risk of stage 3 CKD defined by a decline in estimated glomerular filtration rate (eGFR) to below 60 mL/min/1.73m 2 . The observational study cohort used for model development consisted of data from a primary care dataset of 20,510 multi-ethnic individuals with T2DM from London, UK (2007-2018). Discrimination and calibration of the resulting prediction models developed using cox regression were assessed using the c-statistic and calibration slope, respectively. Models were internally validated using tenfold cross-validation and externally validated on 13,346 primary care individuals from Wales, UK. The simplest model was simplified into a risk score to enable implementation in community-based medicine. The derived full model included demographic, laboratory parameters, medication-use, cardiovascular disease history (CVD) and sight threatening retinopathy status (STDR). Two less resource-intense models were developed by excluding CVD and STDR in the second model and HbA1c and HDL in the third model. All three 5-year risk models had good internal discrimination and calibration (optimism adjusted C-statistics were each 0.85 and calibration slopes 0.999-1.002). In Wales, models achieved excellent discrimination(c-statistics ranged 0.82-0.83). Calibration slopes at 5-years suggested models over-predicted risks, however were successfully updated to accommodate reduced incidence of stage 3 CKD in Wales, which improved their alignment with the observed rates in Wales (E/O ratios near to 1). The risk score demonstrated similar model performance compared to direct evaluation of the cox model. These resource-driven risk prediction models may enable universal screening for Stage 3 CKD to enable targeted early optimisation of risk factors for CKD.

Screening effectively identifies patients at risk of sight-threatening diabetic retinopathy (STDR) when retinal images are captured through dilated pupils. Pharmacological mydriasis is not logistically feasible in non-clinical, community DR screening, where acquiring gradable retinal images using handheld devices exhibits high technical failure rates, reducing STDR detection. Deep learning (DL) based gradability predictions at acquisition could prompt device operators to recapture insufficient quality images, increasing gradable image proportions and consequently STDR detection. Non-mydriatic retinal images were captured as part of SMART India, a cross-sectional, multi-site, community-based, house-to-house DR screening study between August 2018 and December 2019 using the Zeiss Visuscout 100 handheld camera. From 18,277 patient eyes (40,126 images), 16,170 patient eyes (35,319 images) were eligible and 3261 retinal images (1490 patient eyes) were sampled then labelled by two ophthalmologists. Compact DL model area under the receiver operator characteristic curve was 0.93 (0.01) following five-fold cross-validation. Compact DL model agreement (Kappa) were 0.58, 0.69 and 0.69 for high specificity, balanced sensitivity/specificity and high sensitivity operating points compared to an inter-grader agreement of 0.59. Compact DL gradability model performance was favourable compared to ophthalmologists. Compact DL models can effectively classify non-mydriatic, handheld retinal image gradability with potential applications within community-based DR screening.

The morphology of a nanoparticle strongly controls the path of electronic interaction, which directly correlates with the physicochemical properties and also the electrochemical comportment. Combining it with a two-dimensional (2D) material for a layer-by-layer approach will increase its possibilities in applications such as energy conversion and storage. Here, two different morphologies of MnFe 2 O 4 , nanoparticles and nanoneedles, are developed by a facile hydrothermal approach and sandwiched with reduced graphene oxide for constructing a 2D/3D sandwiched architecture. The rGO planar structure with abundant hierarchical short pores facilitates the thorough utilization of the utmost surface area to permeate the electrolyte within the structure to minimize the accumulation of rGO nanosheets laterally. The ferrite composited with rGO manifests high specific capacitance as the EDLC behaviour surpasses the faradaic pseudocapacitance boosting electrical conductivity compared to the as-synthesized MnFe 2 O 4 structures. Benefiting from a 3D structure and the synergetic contribution of the MnFe 2 O 4 nanoneedles and electrically conductive rGO layer, the MnFe 2 O 4 nanoneedles@rGO electrode exhibits a high areal capacitance of 890 mF cm -2 and a remarkable specific capacitance of 1327 F g -1 at a current density of 5 mA cm -2 . 93.36% of the initial capacitance was retained after 5000 cycles in 1 mol L -1 Na 2 SO 4 indicating its high cycling stability. The synthesis route proves to be beneficial for a comprehensive yield of MnFe 2 O 4 @rGO nanosheets of different morphologies for use in the sophisticated design of energy-storing devices. This research strongly suggests that nanoparticle geometry, in addition to two-dimensional carbon-based materials, is a critical factor in a supercapacitor design., Competing Interests: There are no conflicts to declare in the present manuscript., (This journal is © The Royal Society of Chemistry.)

Diabetic retinopathy (DR) is an emerging preventable cause of blindness in India. All India Ophthalmology Society (AIOS) and Vitreo-Retinal Society of India (VRSI) have initiated several measures to improve of DR screening in India. This article is a consensus statement of the AIOS DR task force and VRSI on practical guidelines of DR screening in India. Although there are regional variations in the prevalence of diabetes in India at present, all the States in India should screen their population for diabetes and its complications. The purpose of DR screening is to identify people with sight-threatening DR (STDR) so that they are treated promptly to prevent blindness. This statement provides strategies for the identification of people with diabetes for DR screening, recommends screening intervals in people with diabetes with and without DR, and describes screening models that are feasible in India. The logistics of DR screening emphasizes the need for dynamic referral pathways with feedback mechanisms. It provides the clinical standards required for DR screening and treatment of STDR and addresses the governance and quality assurance (QA) standards for DR screening in Indian settings. Other aspects incorporate education and training, recommendations on Information technology (IT) infrastructure, potential use of artificial intelligence for grading, data capture, and requirements for maintenance of a DR registry. Finally, the recommendations include public awareness and the need to work with diabetologists to control the risk factors so as to have a long-term impact on prevention of diabetes blindness in India., Competing Interests: None

Aim: To study the clinical characteristics and incidence of microvascular complications among childhood and adolescent onset type 1 (T1DM) and type 2 diabetes (T2DM) seen at a tertiary care diabetes center in India., Methods: From our electronic medical records, we retrieved clinical and biochemical details of 4555 individuals with childhood and adolescent onset diabetes (diagnosed below the age of 20 years) seen between 1992 and 2017. T1DM was diagnosed if there was history of ketoacidosis or fasting C-peptide <0.3 PMol/mL and stimulated C-peptide <0.6 PMol/mL or if insulin treatment was required from the time of diagnosis. T2DM was diagnosed based on absence of ketosis, or fasting C-peptide ≥0.6 PMol/mL and stimulated >1.0 PMoL/mL, or response to oral hypoglycemic agents for more than 2 years. We calculated the incidence rates of retinopathy (presence of at least one definite microaneurysm by retinal photography), nephropathy (urinary albumin excretion ≥30 μg/mg of creatinine) and neuropathy (vibration perception threshold ≥20 V) per 1000 person-years of follow up., Results: Among the 4555 individuals with childhood and adolescent-onset diabetes, 71.4% had T1DM, 19.5% T2DM and 9.1% other forms of diabetes. Age at first visit and duration of diabetes were significantly higher in T2DM when compared to T1DM. The age adjusted incidence of retinopathy was 52.9/1000 person years (Confidence Intervals [CI]: 42.9-62.8) in T1DM and 49.8/1000 person years (CI 30.8-68.8) in T2DM; nephropathy, 6.2 (CI 3.3-9.0) and 13.8 (CI 5.6-22.0); and neuropathy, 8.8(CI 3.6-14.0) and 24.0 (CI 9.8-38.2) in T1DM and T2DM, respectively., Conclusion: The incidence of microvascular complications is high among childhood and adolescent-onset T1DM and T2DM and these calls for more aggressive control of diabetes., (© 2020 John Wiley & Sons A/S. Published by John Wiley & Sons Ltd.)

Introduction: The COVID-19 pandemic resulted in a national lockdown in India from midnight on 25 March 2020, with conditional relaxation by phases and zones from 20 April. We evaluated the impact of the lockdown in terms of healthcare provisions, physical health, mental health and social well-being within a multicentre cross-sectional study in India., Methods: The SMART India study is an ongoing house-to-house survey conducted across 20 regions including 11 states and 1 union territory in India to study diabetes and its complications in the community. During the lockdown, we developed an online questionnaire and delivered it in English and seven popular Indian languages (Hindi, Tamil, Marathi, Telegu, Kannada, Bengali, Malayalam) to random samples of SMART-India participants in two rounds from 5 May 2020 to 24 May 2020. We used multivariable logistic regression to evaluate the overall impact on health and healthcare provision in phases 3 and 4 of lockdown in red and non-red zones and their interactions., Results: A total of 2003 participants completed this multicentre survey. The bivariate relationships between the outcomes and lockdown showed significant negative associations. In the multivariable analyses, the interactions between the red zones and lockdown showed that all five dimensions of healthcare provision were negatively affected (non-affordability: OR 1.917 (95% CI 1.126 to 3.264), non-accessibility: OR 2.458 (95% CI 1.549 to 3.902), inadequacy: OR 3.015 (95% CI 1.616 to 5.625), inappropriateness: OR 2.225 (95% CI 1.200 to 4.126) and discontinuity of care: OR 6.756 (95% CI 3.79 to 12.042)) and associated depression and social loneliness., Conclusion: The impact of COVID-19 pandemic and lockdown on health and healthcare was negative. The exaggeration of income inequality during lockdown can be expected to extend the negative impacts beyond the lockdown., Competing Interests: Competing interests: None declared., (© Author(s) (or their employer(s)) 2021. Re-use permitted under CC BY-NC. No commercial re-use. See rights and permissions. Published by BMJ.)