Diabetes Screening Research Articles

Existing and emerging GLP-1 receptor agonist therapy: Ramifications for diabetic retinopathy screening.

GLP-1 receptor agonist treatment revolutionised the management of type 2 diabetes mellitus with significant enhancement of cardiovascular risk reduction. They have been instrumental in effectively managing the glycaemic control of this at-risk patient group. This class of drugs are associated with rapid improvement in glucose levels and consequently, transient early worsening of pre-existing diabetic retinopathy (DR) which is well-recognised, but this paradox is less commonly perceived in routine clinical practice. The recent shortage of supply has resulted in an enforced hiatus to prescribing all existing GLP-1 receptor agonists, which is expected to last all through 2024. This becomes even more pertinent as their DR could have progressed due to worsening HbA1c as a result of the unforeseen interruption to GLP-1 receptor agonist treatment. Therefore, when these medications are recommenced in a few months' time, all prescribers need to be aware of these patients' most up-to-date DR status and liaise with their affiliated screening service.

Determinants of Type II Diabetes Mellitus among Tuberculosis Patients in Yangon Region, Myanmar

Background: Although having Diabetes Mellitus might adversely affect tuberculosis treatment outcomes and reduce the likelihood of a favorable outcome, studies are still limited to identify many problems related to diabetes mellitus. Objectives: To identify factors influencing Type 2 diabetes mellitus among tuberculosis patients in Yangon region, Myanmar. Methods: A case control study conducted in the Yangon Region of Myanmar. Structured interview questionnaires were used for data collection after obtaining the informed consent. This study included 73 cases (TB patients with DM) and 146 controls (TB patients without DM) who were under tuberculosis treatment in 2023. Unconditional multivariable logistic regressions, crude odds ratios, and Adjusted Odds Ratios (AOR) with 95% confidence intervals and P-values < 0.05 were applied for significant association. Results: Of the 219 TB patients, 112 (51.14%) were male and 107 (48.86%) were female. The study observed that the risk factors of type 2 diabetes mellitus comorbidity among tuberculosis patients were: female TB patients (AOR= 2.84, 95% CI:1.36-5.94), aged 45 years and older (AOR=3.04, 95% CI: 1.45-6.37), who were married (AOR=2.28,95% CI:1.03-5.04), diagnosed by GeneXpert (AOR=2.66, 95% CI:1.22-5.79), those who had a family history of DM (AOR=4.08, 95% CI: 1.94-8.58), and those who had BMI ≥25 (AOR=9.00, 95% CI: 2.71-29.95). Conclusion: Based on findings, women TB patients, TB patients aged 45 years and older, married TB patients, TB patients detected by GeneXpert results, and TB patients with a family history of DM and a higher BMI were strong determinants of having TBDM. Therefore, encouraging health promotion on physical exercise, TB awareness campaign, pre-diabetes and diabetes screening and management among TB patients is crucial for the management of lifestyle and effective measures for the TB and comorbid disease control program.

Estimating the Rates of Undiagnosed Prediabetes and Diabetes Among People Experiencing Homelessness

ObjectivesThe rising prevalence of type 2 diabetes in Canada poses a significant health challenge. Despite the convenience of screening for diabetes with glycated hemoglobin (A1C) concentration, people experiencing homelessness (PEH) often face barriers to accessing diabetes screening, potentially leading to underdiagnosis. In this study, we aim to assess the prevalence of undiagnosed diabetes among PEH in Calgary, Alberta, and contribute insights for planning health-care services and public health initiatives. MethodsFour screening clinics were held, and participants were recruited through posters and word of mouth. Participants underwent point-of-care A1C testing using the Siemens DCA Vantage point-of-care analyzer. Descriptive statistics were used to identify the proportions of prediabetes and diabetes, whereas CANRISK survey scores were used to identify the pre-test probability of diabetes. ResultsThe mean age of participants (n=102) was 47.6 years, and the self-reported causes of homelessness among the participants were diverse, including housing and financial issues (n=53), interpersonal and family issues (n=35), and health- or corrections-related factors (n=27). The average A1C was 5.60% (standard deviation 0.57%), with 5 values in the diabetes range and 12 in the prediabetes range, for a total of 17 participants found to have previously undiagnosed dysglycemia. ConclusionsThe high rate of undiagnosed prediabetes and diabetes among PEH reflects at least what is already seen in the general population in Canada. More resources are required to reduce the barriers to screening for diabetes among this population.

IJCM_197A: A cross-sectional study to assess the risk of diabetes among undergraduate medical students in a tertiary care hospital

Background: Diabetes mellitus is one of the major public health problems that affects all age groups. Usually, medical students have sedentary lifestyle owing to academic requirements and there are only limited studies on diabetes screening have been conducted among them. Thus, this study aimed to assess risk of Type 2 Diabetes Mellitus and to study association of diabetes risk with other factors among undergraduate medical students. Objectives: 1. To assess the risk of Type 2 Diabetes Mellitus among undergraduate medical students in a tertiary care hospital. 2. To study the association of risk of Type 2 Diabetes Mellitus with other factors among undergraduate medical students. Methodology: A cross-sectional study conducted among 272 undergraduate medical students in a tertiary care hospital during July 2023 to October 2023. Data collection was done using sociodemographic questionnaire and risk for Type 2 Diabetes Mellitus was assessed using Indian Diabetes Risk Score (IDRS). Results: Among 272 study participants, about 21% had moderate risk score and 1.5% had severe risk score. About 64% medical students belonged to nuclear family, 44.6% had family history of diabetes mellitus and 38% were overweight/ obese. Statistically significant association of moderate-high diabetes risk with nuclear family, positive family history of diabetes, no/mild physical activity and body mass index (BMI) was seen. Conclusion: A significant proportion of undergraduate student had moderate to high risk of Type 2 Diabetes Mellitus. Risk of developing type 2 diabetes mellitus found to associated with nuclear family, family history of diabetes, no or mild physical activity and overweight.

Nonproliferative diabetic retinopathy dataset(NDRD): A database for diabetic retinopathy screening research and deep learning evaluation.

In this article, we provide a database of nonproliferative diabetes retinopathy, which focuses on early diabetes retinopathy with hard exudation, and further explore its clinical application in disease recognition. We collect the photos of nonproliferative diabetes retinopathy taken by Optos Panoramic 200 laser scanning ophthalmoscope, filter out the pictures with poor quality, and label the hard exudative lesions in the images under the guidance of professional medical personnel. To validate the effectiveness of the datasets, five deep learning models are used to perform learning predictions on the datasets. Furthermore, we evaluate the performance of the model using evaluation metrics. Nonproliferative diabetes retinopathy is smaller than proliferative retinopathy and more difficult to identify. The existing segmentation models have poor lesion segmentation performance, while the intersection over union (IOU) value for deep lesion segmentation of models targeting small lesions can reach 66.12%, which is higher than ordinary lesion segmentation models, but there is still a lot of room for improvement. The segmentation of small hard exudative lesions is more challenging than that of large hard exudative lesions. More targeted datasets are needed for model training. Compared with the previous diabetes retina datasets, the NDRD dataset pays more attention to micro lesions.

Compound Scaling Encoder-Decoder (CoSED) Network for Diabetic Retinopathy Related Bio-marker Detection.

Biomedical image segmentation plays an important role in Diabetic Retinopathy (DR)-related biomarker detection. DR is an ocular disease that affects the retina in people with diabetes and could lead to visual impairment if management measures are not taken in a timely manner. In DR screening programs, the presence and severity of DR are identified and classified based on various microvascular lesions detected by qualified ophthalmic screeners. Such a detection process is time-consuming and error-prone, given the small size of the microvascular lesions and the volume of images, especially with the increasing prevalence of diabetes. Automated image processing using deep learning methods is recognized as a promising approach to support diabetic retinopathy screening. In this paper, we propose a novel compound scaling encoder-decoder network architecture to improve the accuracy and running efficiency of microvascular lesion segmentation. In the encoder phase, we develop a lightweight encoder to speed up the training process, where the encoder network is scaled up in depth, width, and resolution dimensions. In the decoder phase, an attention mechanism is introduced to yield higher accuracy. Specifically, we employ Concurrent Spatial and Channel Squeeze and Channel Excitation (scSE) blocks to fully utilise both spatial and channel-wise information. Additionally, a compound loss function is incorporated with transfer learning to handle the problem of imbalanced data and further improve performance. To assess performance, our method is evaluated on two large-scale lesion segmentation datasets: DDR and FGADR datasets. Experimental results demonstrate the superiority of our method compared to other competent methods. Our codes are available at https://github.com/DeweiYi/CoSED-Net.

Confluence of newborn's sex and their mother's characteristics resulting in fetal macrosomia among Indian neonates

BackgroundA newborn's birth weight ≥4000 g is defined as fetal macrosomia, which is recognized as a reproductive and serious child health concern. ObjectivesOur study aims to reveal existence of any connection between maternal factors and newborn sex in giving birth to newborn ≥4000 g in an Indian context. MethodsData were drawn from the fifth round of National Family Health Survey (NFHS-5). A cross-sectional observational study was carried out with a total of 152,827 children born to women in reproductive age group (15–49) who had most recent live birth in the five years preceding the survey. Descriptive analyses, cross-tabulation, test of association and multivariate logistic regression analyses were performed. ResultsIn India, the prevalence of macrosomia was found in 3.8% of the total study participants. Considering newborn characteristics, fetal macrosomia was more prevalent among male neonates than female (AOR: 0.730; 95% CI: 0.687–0.775). Regarding maternal characteristics, overweight (AOR: 1.468; 95% CI: 2.042–2.559) and obese (AOR: 2.764; 95% CI: 2.394–3.192) motherswith gestational diabetes (AOR: 1.731, 95% CI: 1.385–2.164) and hypertension (AOR: 1.288, 95% CI: 1.116–1.488) were more likely to giving birth of macrosomic babies. Multiparous mothers (AOR: 1.207, 95% CI: 1.128–1.293) and women who did not undergo proper antenatal care (ANC) follow up had also greater risk of developing fetal macrosomia. Muslim women (AOR: 1.223, 95% CI: 1.119–1.338), and women belonging to a tribe (AOR: 1.476, 95% CI: 0.922–2.361) were significantly associated with the risk of having newborn ≥4000 g. ConclusionEmphasis should be given on counseling for mothers for desired weight management before and during pregnancy, gestational diabetes and hypertension screening, physical activity during pregnancy, adequate ANC follow up and balanced dietary intake among pregnant women.

330 Investigating Adherence to Diabetic Nephropathy Screening and Management in a Value-Based Primary Care Setting: A Call to Action

330 Investigating Adherence to Diabetic Nephropathy Screening and Management in a Value-Based Primary Care Setting: A Call to Action

IJCM_172A: Risk Scoring of Type 2 Diabetes Mellitus and Coverage of Screening Services through Primary Health Care System among Adults in a Block of Murshidabad District, West Bengal

Background: Type 2 diabetes is a major public health challenge. Indian Diabetes Risk Score (IDRS), a simple and cost-effective tool consisting of two modifiable (waist circumference, physical activity) and two non-modifiable risk factors (age, parental history) for diabetes, can be easily applied in mass screening programmes. Opportunistic screening for diabetes among individuals aged = 30 years is being done under the programme for Prevention and Control of Non-communicable Diseases. Objective: To assess the risk of type 2 diabetes mellitus using IDRS, coverage of screening services for diabetes through the primary health care delivery system and to identify their correlates. Methodology: : A descriptive, cross-sectional study was conducted in a block of Murshidabad district, West Bengal during September to November 2023 among permanent residents aged = 30 years on a calculated sample of 300 selected through multi-stage sampling. Required data were collected by interviewing the study subjects using pre-designed, pre-tested schedule including IDRS, reviewing medical records and measuring waist circumference. Analysis was done using SPSS-20. Results: Among 300 participants, 40.7% had high risk; 54% and 5.3% had moderate and low risk of diabetes respectively according to IDRS. In multivariable analysis, age =35 years (AOR 18.10, 95% CI 5.07-64.66), female gender (AOR 2.36, 95% CI 1.40-4.00) and being in joint family (AOR 3.53, 95% CI 1.77-7.04) were significant correlates for high-risk category according to IDRS. Only 67% were screened for diabetes within last six months, of which 89% were through primary health care delivery system. Screening status varied significantly by age group (OR 3.81, 95% CI 1.98-7.35) and IDRS risk categories (OR 1.72, 95% CI 1.06-2.80). Conclusion: Alarmingly substantial proportion of people in the area are at high/moderate risk of diabetes coupled with low opportunistic screening coverage, which highlights the need for intensive behaviour change communication and stronger service delivery.

Increased risk of cardiovascular and renal disease, and diabetes for all women diagnosed with gestational diabetes mellitus in New Zealand-A national retrospective cohort study.

Gestational diabetes mellitus increases the risk of developing type 2 diabetes. The aim of this study is to compare cardiometabolic and renal outcomes for all women in New Zealand with gestational diabetes (2001-2010) with women without diabetes, 10-20 years following delivery. A retrospective cohort study, utilizing a national dataset providing information for all women who gave birth between 1 January 2001 and 31 December 2010 (n = 604 398). Adolescent girls <15 years, women ≥50 years and women with prepregnancy diabetes were excluded. In total 11 459 women were diagnosed with gestational diabetes and 11 447 were matched (for age and year of delivery) with 57 235 unexposed (control) women. A national hospital dataset was used to compare primary outcomes until 31 May 2021. After controlling for ethnicity, women with gestational diabetes were significantly more likely than control women to develop diabetes-adjusted hazard ratio (HR) 20.06 and 95% confidence interval (CI) 18.46-21.79; a first cardiovascular event 2.19 (1.86-2.58); renal disease 6.34 (5.35-7.51) and all-cause mortality 1.55 (1.31-1.83), all p values <.0001. The HR and 95% CI remained similar after controlling for significant covariates: diabetes 18.89 (17.36-20.56), cardiovascular events 1.79 (1.52-2.12), renal disease 5.42 (4.55-6.45), and all-cause mortality 1.44 (1.21-1.70). When time-dependent diabetes was added to the model, significance remained for cardiovascular events 1.33 (1.10-1.61), p = .003 and renal disease 2.33 (1.88-2.88), p < .0001 but not all-cause mortality. Women diagnosed with gestational diabetes have an increased risk of adverse cardiometabolic and renal outcomes. Findings highlight the importance of follow-up screening for diabetes, cardiovascular risk factors, and renal disease.

Infrared spectroscopy for fast screening of diabetes and periodontitis

SignificanceFT-IR is an important and emerging tool, providing information related to the biochemical composition of biofluids. It is important to demonstrate that there is an efficacy in separating healthy and diseased groups, helping to establish FT-IR uses as fast screening tool. AimVia saliva diagnosis evaluate the accuracy of FT-IR associate with machine learning model for classification among healthy (control group), diabetic (D) and periodontitis (P) patients and the association of both diseases (DP). ApproachEighty patients diagnosed with diabetes and periodontitis through conventional methods were recruited and allocated in one of the four groups. Saliva samples were collected from participants of each group (n = 20) and were processed using Bruker Alpha II spectrometer in a FT-IR spectral fingerprint region between 600 and–1800 cm−1, followed by data preprocessing and analysis using machine learning tools. ResultsVarious FTI-R peaks were detectable and attributed to specific vibrational modes, which were classified based on confusion matrices showed in paired groups. The highest true positive rates (TPR) appeared between groups C vs D (93.5 % ± 2.7 %), groups C vs. DP (89.2 % ± 4.1 %), and groups D and P (90.4 % ± 3.2 %). However, P vs DP presented higher TPR for DP (84.1 % ±3.1 %) while D vs. DP the highest rate for DP was 81.7 % ± 4.3 %. Analyzing all groups together, the TPR decreased. ConclusionThe system used is portable and robust and can be widely used in clinical environments and hospitals as a new diagnostic technique. Studies in our groups are being conducted to solidify and expand data analysis methods with friendly language for healthcare professionals. It was possible to classify healthy patients in a range of 78–93 % of accuracy. Range over 80 % of accuracy between periodontitis and diabetes were observed. A general classification model with lower TPR instead of a pairwise classification would only have advantages in scenarios where no prior patient information is available regarding diabetes and periodontitis status.

Diagnosis and Risk Factors of Prediabetes and Diabetes in People Living With Human Immunodeficiency Virus: Evaluation of Clinical and Microbiome Parameters.

Diabetes mellitus (DM) is more common among people living with human immunodeficiency virus (PLWH) compared with healthy individuals. In a prospective multicenter study (N = 248), we identified normoglycemic (48.7%), prediabetic (44.4%), and diabetic (6.9%) PLWH. Glycosylated hemoglobin (HbA1c) and fasting blood glucose (FBG) sensitivity in defining dysglycemia was 96.8%, while addition of oral glucose tolerance test led to reclassification of only 4 patients. Inclusion of 93 additional PLWH with known DM enabled identification of multiple independent predictors of dysglycemia or diabetes: older age, higher body mass index, Ethiopian origin, HIV duration, lower integrase inhibitor exposure, and advanced disease at diagnosis. Shotgun metagenomic microbiome analysis revealed 4 species that were significantly expanded with hyperglycemia/hyperinsulinemia, and 2 species that were differentially more prevalent in prediabetic/diabetic PLWH. Collectively, we uncover multiple potential host and microbiome predictors of altered glycemic status in PLWH, while demonstrating that FBG and HbA1c likely suffice for diabetes screening. These potential diabetic predictors merit future prospective validation.

Improving diabetes screening in acute coronary syndrome patients: A quality improvement initiative

Improving diabetes screening in acute coronary syndrome patients: A quality improvement initiative

Target Product Profile for a Machine Learning-Automated Retinal Imaging Analysis Software for Use in English Diabetic Eye Screening: Protocol for a Mixed Methods Study.

Diabetic eye screening (DES) represents a significant opportunity for the application of machine learning (ML) technologies, which may improve clinical and service outcomes. However, successful integration of ML into DES requires careful product development, evaluation, and implementation. Target product profiles (TPPs) summarize the requirements necessary for successful implementation so these can guide product development and evaluation. This study aims to produce a TPP for an ML-automated retinal imaging analysis software (ML-ARIAS) system for use in DES in England. This work will consist of 3 phases. Phase 1 will establish the characteristics to be addressed in the TPP. A list of candidate characteristics will be generated from the following sources: an overview of systematic reviews of diagnostic test TPPs; a systematic review of digital health TPPs; and the National Institute for Health and Care Excellence's Evidence Standards Framework for Digital Health Technologies. The list of characteristics will be refined and validated by a study advisory group (SAG) made up of representatives from key stakeholders in DES. This includes people with diabetes; health care professionals; health care managers and leaders; and regulators and policy makers. In phase 2, specifications for these characteristics will be drafted following a series of semistructured interviews with participants from these stakeholder groups. Data collected from these interviews will be analyzed using the shortlist of characteristics as a framework, after which specifications will be drafted to create a draft TPP. Following approval by the SAG, in phase 3, the draft will enter an internet-based Delphi consensus study with participants sought from the groups previously identified, as well as ML-ARIAS developers, to ensure feasibility. Participants will be invited to score characteristic and specification pairs on a scale from "definitely exclude" to "definitely include," and suggest edits. The document will be iterated between rounds based on participants' feedback. Feedback on the draft document will be sought from a group of ML-ARIAS developers before its final contents are agreed upon in an in-person consensus meeting. At this meeting, representatives from the stakeholder groups previously identified (minus ML-ARIAS developers, to avoid bias) will be presented with the Delphi results and feedback of the user group and asked to agree on the final contents by vote. Phase 1 was completed in November 2023. Phase 2 is underway and expected to finish in March 2024. Phase 3 is expected to be complete in July 2024. The multistakeholder development of a TPP for an ML-ARIAS for use in DES in England will help developers produce tools that serve the needs of patients, health care providers, and their staff. The TPP development process will also provide methods and a template to produce similar documents in other disease areas. DERR1-10.2196/50568.

Novel Technologies in Artificial Intelligence and Telemedicine for Glaucoma Screening.

To provide an overview of novel technologies in telemedicine and artificial intelligence (AI) approaches for cost-effective glaucoma screening. A narrative review was performed by summarizing research results, recent developments in glaucoma detection and care, and considerations related to telemedicine and AI in glaucoma screening. Telemedicine and AI approaches provide the opportunity for novel glaucoma screening programs in primary care, optometry, portable, and home-based settings. These approaches offer several advantages for glaucoma screening, including increasing access to care, lowering costs, identifying patients in need of urgent treatment, and enabling timely diagnosis and early intervention. However, challenges remain in implementing these systems, including integration into existing clinical workflows, ensuring equity for patients, and meeting ethical and regulatory requirements. Leveraging recent work towards standardized data acquisition as well as tools and techniques developed for automated diabetic retinopathy screening programs may provide a model for a cost-effective approach to glaucoma screening. Leveraging novel technologies and advances in telemedicine and AI-based approaches to glaucoma detection show promise for improving our ability to detect moderate and advanced glaucoma in primary care settings and target higher individuals at high risk for having the disease.

AI APPLICATIONS IN SCREENING AND DIAGNOSIS OF DIABETIC RETINOPATHY IN RURAL SETTINGS

Diabetic retinopathy (DR) remains a significant cause of vision impairment and blindness, particularly in rural settings where access to specialized healthcare services is limited. The integration of artificial intelligence (AI) holds promise in revolutionizing the screening and diagnosis of DR, offering a scalable solution to bridge the gap in healthcare disparities. This systematic review synthesizes existing literature on AI applications tailored for screening and diagnosing diabetic retinopathy in rural areas. Through a comprehensive search across various databases, including PubMed, IEEE Xplore, and Google Scholar, a total of 88 studies meeting the inclusion criteria were identified. These studies encompassed a range of AI techniques, including deep learning algorithms, machine learning models, and image processing methods, deployed in diverse rural healthcare settings globally. The findings reveal that AI-based systems demonstrate high accuracy, sensitivity, and specificity in detecting diabetic retinopathy from fundus images, thereby enabling early identification and timely intervention. Moreover, the scalability and cost-effectiveness of these AI solutions make them particularly suitable for resource-constrained rural environments. However, several challenges persist, including the need for robust validation studies, integration with existing healthcare infrastructure, and addressing ethical and regulatory concerns. Additionally, considerations regarding data privacy, patient acceptance, and healthcare provider training are crucial for the successful implementation of AI-driven DR screening programs in rural settings. This systematic review underscores the transformative potential of AI technologies in improving access to diabetic retinopathy screening and diagnosis in rural areas. Future research should focus on addressing the identified challenges and optimizing AI systems to enhance their efficacy and accessibility in underserved communities.&#x0D; Keywords: AI, Rural, Diagnosis, Diabetic, Retinopathy, Rural, Review.

Diabetes Screening Rates and Practices in Patients With Sickle Cell Disease.

Diabetes Screening Rates and Practices in Patients With Sickle Cell Disease.

Extending the diabetic retinopathy screening intervals in Singapore: methodology and preliminary findings of a cohort study

BackgroundThe Diabetic Retinopathy Extended Screening Study (DRESS) aims to develop and validate a new DR/diabetic macular edema (DME) risk stratification model in patients with Type 2 diabetes (DM) to identify low-risk groups who can be safely assigned to biennial or triennial screening intervals. We describe the study methodology, participants’ baseline characteristics, and preliminary DR progression rates at the first annual follow-up.MethodsDRESS is a 3-year ongoing longitudinal study of patients with T2DM and no or mild non-proliferative DR (NPDR, non-referable) who underwent teleophthalmic screening under the Singapore integrated Diabetic Retinopathy Programme (SiDRP) at four SingHealth Polyclinics. Patients with referable DR/DME (> mild NPDR) or ungradable fundus images were excluded. Sociodemographic, lifestyle, medical and clinical information was obtained from medical records and interviewer-administered questionnaires at baseline. These data are extracted from medical records at 12, 24 and 36 months post-enrollment. Baseline descriptive characteristics stratified by DR severity at baseline and rates of progression to referable DR at 12-month follow-up were calculated.ResultsOf 5,840 eligible patients, 78.3% (n = 4,570, median [interquartile range [IQR] age 61.0 [55–67] years; 54.7% male; 68.0% Chinese) completed the baseline assessment. At baseline, 97.4% and 2.6% had none and mild NPDR (worse eye), respectively. Most participants had hypertension (79.2%) and dyslipidemia (92.8%); and almost half were obese (43.4%, BMI ≥ 27.5 kg/m2). Participants without DR (vs mild DR) reported shorter DM duration, and had lower haemoglobin A1c, triglycerides and urine albumin/creatinine ratio (all p < 0.05). To date, we have extracted 41.8% (n = 1909) of the 12-month follow-up data. Of these, 99.7% (n = 1,904) did not progress to referable DR. Those who progressed to referable DR status (0.3%) had no DR at baseline.ConclusionsIn our prospective study of patients with T2DM and non-referable DR attending polyclinics, we found extremely low annual DR progression rates. These preliminary results suggest that extending screening intervals beyond 12 months may be viable and safe for most participants, although our 3-year follow up data are needed to substantiate this claim and develop the risk stratification model to identify low-risk patients with T2DM who can be assigned biennial or triennial screening intervals.

Observation of the Effects of Different Enzyme Digestion Time and Enzyme Digestion Methods on the Extraction of Primary Renal Tubular Epithelial Cells from Sprague-Dawley Suckling Mice.

Renal tubular epithelial cells are one of the essential functional cells in the kidney. Optimizing the isolation and culture method of primary renal tubular epithelial cells from SD mammary rats provides better experimental materials for renal tubule-related studies, which is essential for studying the pathogenesis of renal diseases, especially diabetic nephropathy and drug screening. SD rat renal tubular epithelial cells were isolated and purified by 2.5-mg/ml collagenase II or 2mg/ml trypsin + 2.5mg/ml collagenase II enzymatic digestion. The isolation and purification were observed at different time points (15min, 30min, 45min, and 60min) to determine the optimal extraction time for the enzymatic digestion method. After comparing the two enzymatic methods, it was determined that the trypsin + collagenase II enzymatic method was more effective. The primary renal tubular epithelial cells extracted by the trypsin + collagenase II digestion method were identified by the marker Cytokeratin 18 of renal tubular epithelial cells at 45min of digestion with high purity. We established a simple, efficient, and reproducible method for isolation and culture of renal tubular epithelial cells in SD mammary gland rats.

Head to head comparison of diagnostic performance of three non-mydriatic cameras for diabetic retinopathy screening with artificial intelligence

BackgroundDiabetic Retinopathy (DR) is a leading cause of blindness worldwide, affecting people with diabetes. The timely diagnosis and treatment of DR are essential in preventing vision loss. Non-mydriatic fundus cameras and artificial intelligence (AI) software have been shown to improve DR screening efficiency. However, few studies have compared the diagnostic performance of different non-mydriatic cameras and AI software.MethodsThis clinical study was conducted at the endocrinology clinic of Akdeniz University with 900 volunteer patients that were previously diagnosed with diabetes but not with diabetic retinopathy. Fundus images of each patient were taken using three non-mydriatic fundus cameras and EyeCheckup AI software was used to diagnose more than mild diabetic retinopathy, vision-threatening diabetic retinopathy, and clinically significant diabetic macular oedema using images from all three cameras. Then patients underwent dilation and 4 wide-field fundus photography. Three retina specialists graded the 4 wide-field fundus images according to the diabetic retinopathy treatment preferred practice patterns of the American Academy of Ophthalmology. The study was pre-registered on clinicaltrials.gov with the ClinicalTrials.gov Identifier: NCT04805541.ResultsThe Canon CR2 AF AF camera had a sensitivity and specificity of 95.65% / 95.92% for diagnosing more than mild DR, the Topcon TRC-NW400 had 95.19% / 96.46%, and the Optomed Aurora had 90.48% / 97.21%. For vision threatening diabetic retinopathy, the Canon CR2 AF had a sensitivity and specificity of 96.00% / 96.34%, the Topcon TRC-NW400 had 98.52% / 95.93%, and the Optomed Aurora had 95.12% / 98.82%. For clinically significant diabetic macular oedema, the Canon CR2 AF had a sensitivity and specificity of 95.83% / 96.83%, the Topcon TRC-NW400 had 98.50% / 96.52%, and the Optomed Aurora had 94.93% / 98.95%.ConclusionThe study demonstrates the potential of using non-mydriatic fundus cameras combined with artificial intelligence software in detecting diabetic retinopathy. Several cameras were tested and, notably, each camera exhibited varying but adequate levels of sensitivity and specificity. The Canon CR2 AF emerged with the highest accuracy in identifying both more than mild diabetic retinopathy and vision-threatening cases, while the Topcon TRC-NW400 excelled in detecting clinically significant diabetic macular oedema. The findings from this study emphasize the importance of considering a non mydriatic camera and artificial intelligence software for diabetic retinopathy screening. However, further research is imperative to explore additional factors influencing the efficiency of diabetic retinopathy screening using AI and non mydriatic cameras such as costs involved and effects of screening using and on an ethnically diverse population.