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Diabetic retinopathy (DR), a prevalent microvascular complication of diabetes, is the fifth leading cause of blindness worldwide. Given the critical nature of the disease, it is paramount that individuals with diabetes undergo annual screening for early and timely detection of DR, facilitating prompt ophthalmic assessment and intervention. However, screening for DR, which involves assessing visual acuity and retinal examination through ophthalmoscopy or retinal photography, presents a significant global challenge due to the massive volume of individuals requiring annual reviews. To counter this challenge, there has been an increasing interest in the potential of artificial intelligence (AI) tools for automated diagnosis of DR. The AI tools primarily utilize deep learning (DL) techniques and are tailored to analyse extensive medical image data and provide diagnostic outputs, essentially streamline the DR screening process. However, the development of such AI tools requires access to a comprehensive retinal image database with a plethora of high-resolution fundus images from various cameras, covering all DR lesions. Additionally, the accurate training of these AI algorithms necessitates skilled professionals, such as optometrists or ophthalmologists, to provide reliable ground truths that ensure the precision of the diagnostic outputs. To address these prerequisites, we have initiated a study involving multiple institutions to establish a large-scale online 'Retinal Image Database’ in India, aiming to contribute significantly to DR research. This paper delineates the methodology employed for this significant undertaking, detailing the steps taken to create the large retinal image database, as well as the framework for developing a cost-effective, robust AI-based DR diagnostic tool. Our work is expected to mark a significant stride in DR detection and management, promising a more efficient and scalable solution for tackling this global health challenge.

The Geropathology Grading Platform (GGP) that is being developed by the Geropathology Research Network provides a grading system that allows investigators to assess biological aging in mice by measuring the pathological status of a wide range of tissues in a standardized scoring system. The GGP is a grading system that generates a numerical score for the total lesions in each tissue, which when averaged over the mice in the cohort provides a composite lesion score (CLS) for each tissue and mouse. In this study, we tested ability of the GGP to predict accelerated aging in mice null for Cu/Zn-superoxide dismutase (Sod1KO mice), which have been shown to have reduced lifespan and healthspan. Using the GGP, we evaluated the pathological status of 11 tissues from male and female wild-type (WT) and Sod1KO mice at 9 to 10 months of age. The whole animal CLS was 2- to 3.5-fold higher for both male and female Sod1KO mice compared to WT mice. The tissues most affected in the Sod1KO mice were the liver, lung, and kidney. These data demonstrate that the GGP is able to predict the accelerated aging phenotype observed in the Sod1KO mice and correlates with the changes in healthspan that have been reported for Sod1KO mice. Thus, the GGP is a new paradigm for evaluating the effect of an intervention on the pathological status of an animal as well as the healthspan of the mice.

The outbreak of COVID-19 has caused significant disruption in all sectors and industries around the world. To tackle the spread of the novel coronavirus, the learning process and the modes of delivery had to be altered. Most courses are delivered traditionally with face-to-face or a blended approach through online learning platforms. In addition, researchers and educational specialists around the globe always had a keen interest in predicting a student’s performance based on the student’s information such as previous exam results obtained and experiences. With the upsurge in using online learning platforms, predicting the student’s performance by including their interactions such as discussion forums could be integrated to create a predictive model. The aims of the research are to provide a predictive model to forecast students’ performance (grade/engagement) and to analyse the effect of online learning platform’s features. The model created in this study made use of machine learning techniques to predict the final grade and engagement level of a learner. The quantitative approach for student’s data analysis and processing proved that the Random Forest classifier outperformed the others. An accuracy of 85% and 83% were recorded for grade and engagement prediction respectively with attributes related to student profile and interaction on a learning platform.

The methylation status of the O 6 -methylguanine-DNA methyltransferase ( MGMT ) gene is an important predictive biomarker for benefit from alkylating agent therapy in glioblastoma. Recent studies in anaplastic glioma suggest a prognostic value for MGMT methylation. Investigation of pathogenetic and epigenetic features of this intriguingly distinct behavior requires accurate MGMT classification to assess high throughput molecular databases. Promoter methylation-mediated gene silencing is strongly dependent on the location of the methylated CpGs, complicating classification. Using the HumanMethylation450 (HM - 450K) BeadChip interrogating 176 CpGs annotated for the MGMT gene, with 14 located in the promoter, two distinct regions in the CpG island of the promoter were identified with high importance for gene silencing and outcome prediction. A logistic regression model (MGMT-STP27) comprising probes cg1243587 and cg12981137 provided good classification properties and prognostic value (kappa = 0.85; log-rank p  < 0.001) using a training-set of 63 glioblastomas from homogenously treated patients, for whom MGMT methylation was previously shown to be predictive for outcome based on classification by methylation-specific PCR. MGMT-STP27 was successfully validated in an independent cohort of chemo-radiotherapy-treated glioblastoma patients ( n  = 50; kappa = 0.88; outcome, log-rank p  < 0.001). Lower prevalence of MGMT methylation among CpG island methylator phenotype (CIMP) positive tumors was found in glioblastomas from The Cancer Genome Atlas than in low grade and anaplastic glioma cohorts, while in CIMP-negative gliomas MGMT was classified as methylated in approximately 50 % regardless of tumor grade. The proposed MGMT-STP27 prediction model allows mining of datasets derived on the HM - 450K or HM-27K BeadChip to explore effects of distinct epigenetic context of MGMT methylation suspected to modulate treatment resistance in different tumor types.

As of 2021, 89% of the Australian population are active internet users. Although the internet is widely used, there are concerns about the quality, accuracy, and credibility of health-related websites. A 2015 systematic assessment of infant feeding websites and apps available in Australia found that 61% of websites were of poor quality and readability, with minimal coverage of infant feeding topics and lack of author credibility. We aimed to systematically assess the quality, interactivity, readability, and comprehensibility of information targeting infant health behaviors on websites globally and provide an update of the 2015 systematic assessment. Keywords related to infant milk feeding behaviors, solid feeding behaviors, active play, screen time, and sleep were used to identify websites targeting infant health behaviors on the Google search engine on Safari. The websites were assessed by a subset of the authors using predetermined criteria between July 2021 and February 2022 and assessed for information content based on the Australian Infant Feeding Guidelines and National Physical Activity Recommendations. The Suitability Assessment of Materials, Quality Component Scoring System, the Health-Related Website Evaluation Form, and the adherence to the Health on the Net code were used to evaluate the suitability and quality of information. Readability was assessed using 3 web-based readability tools. Of the 450 websites screened, 66 were included based on the selection criteria and evaluated. Overall, the quality of websites was mostly adequate. Media-related sources, nongovernmental organizations, hospitals, and privately owned websites had the highest median quality scores, whereas university websites received the lowest median score (35%). The information covered within the websites was predominantly poor: 91% (60/66) of the websites received an overall score of ≤74% (mean 53%, SD 18%). The suitability of health information was mostly rated adequate for literacy demand, layout, and learning and motivation of readers. The median readability score for the websites was grade 8.5, which is higher than the government recommendations (

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