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Objective: Diabetic retinopathy is a serious complication of diabetes mellitus that may lead to vision loss. Retinal problems are more likely to occur as the illness advances. Micro- and macro-vascular angiopathy is both linked to diabetes mellitus. Examining the impact of diabetes on blood vessels is one approach to understanding the disease's outward symptoms. The purpose of this research is to evaluate the morphology and breadth of conjunctival vessels in people with type 2 diabetes mellitus and to establish a correlation between these alterations and clinical retinal changes., Patients and Methods: A case-control study was conducted in the Department of Ophthalmology, KIMS, Koppal, Karnataka. The study included diabetic patients who voluntarily participated and were assessed at the Ophthalmology Outpatient Department (OPD). The general and ophthalmic history was taken for both cases and control. The best corrected visual acuity was estimated. Each subject, including cases and controls, had a general and ophthalmic history recorded. The anterior segment was evaluated with a slit lamp. After completing the clinical examination, the subjects underwent a conjunctival vessel imaging study using the slit lamp. The imaging included the study of conjunctival vessel morphology using ImageNet., Results: Both the test and control groups are comparable in terms of age and gender. The severity of diabetic retinopathy was shown to correlate with the conjunctival width range. Mild diabetic retinopathy is characterized by a conjunctival width of 30-34 microns (mean: 34.9), moderate diabetic retinopathy by 35-39 microns (mean: 37.3), severe diabetic retinopathy by 40-44 microns (mean: 42.4), and proliferative diabetic retinopathy by 50-54 microns (mean: 45.6)., Conclusions: Diabetes mellitus patients have larger conjunctival arteries as the disease becomes more severe. Dilated and tortuous conjunctival arteries are visible indicators that correlate with worsening diabetic retinopathy. This could be used for screening to ensure timely referral.

In the fireworks industry (FI), many accidents and explosions frequently happen due to human error (HE). Human factors (HFs) always play a dynamic role in the incidence of accidents in workplace environments. Preventing HE is a main challenge for safety and precautions in the FI. Clarifying the relationship between HFs can help in identifying the correlation between unsafe behaviors and influential factors in hazardous chemical warehouse accidents. This paper aims to investigate the impact of HFs that contribute to HE, which has caused FI disasters, explosions, and incidents in the past. This paper investigates why and how HEs contribute to the most severe accidents that occur while storing and using hazardous chemicals. The impact of fireworks and match industry disasters has motivated the planning of mitigation in this proposal. This analysis used machine learning (ML) and recommends an expert system (ES). There were many significant correlations between individual behaviors and the chance of HE to occur. This paper proposes an ML-based prediction model for fireworks and match work industries in Sivakasi, Tamil Nadu. For this study analysis, the questionnaire responses are reviewed for accuracy and coded from 500 participants from the fireworks and match industries in Tamil Nadu who were chosen to fill out a questionnaire. The Chief Inspectorate of Factories in Chennai and the Training Centre for Industrial Safety and Health in Sivakasi, Tamil Nadu, India, significantly contributed to the collection of accident datasets for the FI in Tamil Nadu, India. The data are analyzed and presented in the following categories based on this study's objectives: the effect of physical, psychological, and organizational factors. The output implemented by comparing ML models, support vector machine (SVM), random forest (RF), and Naïve Bayes (NB) accuracy is 86.45%, 91.6%, and 92.1%, respectively. Extreme Gradient Boosting (XGBoost) has the optimal classification accuracy of 94.41% of ML models. This research aims to create a new ES to mitigate HE risks in the fireworks and match work industries. The proposed ES reduces HE risk and improves workplace safety in unsafe, uncertain workplaces. Proper safety management systems (SMS) can prevent deaths and injuries such as fires and explosions.

To explore global trends in manual wheelchair service provision knowledge across geographic, professional, and socioeconomic domains. A secondary analysis of a dataset from the International Society of Wheelchair Professionals' Wheelchair Service Provision Basic Knowledge Test was conducted. The dataset included test takers from around the world and was extracted from Test.com and International Society of Wheelchair Professionals' Wheelchair International Network. Participants 2,467 unique test takers from 86 countries. Interventions Not applicable. International Society of Wheelchair Professionals' Wheelchair Service Provision Basic Knowledge Test. We identified significant inverse associations between pass rate and the following variables: education (high school and some college), test taker motivation (required by academic program or employer), and country income setting (low and middle). There were significant positive associations between pass rate and the following variables: training received (offered by Mobility India or 'other NGO'), and age group served (early childhood). Global wheelchair knowledge trends related to key variables such as training, occupation, and income setting have been preliminarily explored. Future work includes further validation of the primary outcome measure and recruitment of a larger sample size to further explore significant associations between additional test taker variables.