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PURPOSE: The aim is to assess GPT-4Vs (OpenAI) diagnostic accuracy and its capability to identify glaucoma-related features compared to expert evaluations. DESIGN: Evaluation of multimodal large language models for reviewing fundus images in glaucoma. SUBJECTS: A total of 300 fundus images from 3 public datasets (ACRIMA, ORIGA, and RIM-One v3) that included 139 glaucomatous and 161 nonglaucomatous cases were analyzed. METHODS: Preprocessing ensured each image was centered on the optic disc. GPT-4s vision-preview model (GPT-4V) assessed each image for various glaucoma-related criteria: image quality, image gradability, cup-to-disc ratio, peripapillary atrophy, disc hemorrhages, rim thinning (by quadrant and clock hour), glaucoma status, and estimated probability of glaucoma. Each image was analyzed twice by GPT-4V to evaluate consistency in its predictions. Two expert graders independently evaluated the same images using identical criteria. Comparisons between GPT-4Vs assessments, expert evaluations, and dataset labels were made to determine accuracy, sensitivity, specificity, and Cohen kappa. MAIN OUTCOME MEASURES: The main parameters measured were the accuracy, sensitivity, specificity, and Cohen kappa of GPT-4V in detecting glaucoma compared with expert evaluations. RESULTS: GPT-4V successfully provided glaucoma assessments for all 300 fundus images across the datasets, although approximately 35% required multiple prompt submissions. GPT-4Vs overall accuracy in glaucoma detection was slightly lower (0.68, 0.70, and 0.81, respectively) than that of expert graders (0.78, 0.80, and 0.88, for expert grader 1 and 0.72, 0.78, and 0.87, for expert grader 2, respectively), across the ACRIMA, ORIGA, and RIM-ONE datasets. In Glaucoma detection, GPT-4V showed variable agreement by dataset and expert graders, with Cohen kappa values ranging from 0.08 to 0.72. In terms of feature detection, GPT-4V demonstrated high consistency (repeatability) in image gradability, with an agreement accuracy of ≥89% and substantial agreement in rim thinning and cup-to-disc ratio assessments, although kappas were generally lower than expert-to-expert agreement. CONCLUSIONS: GPT-4V shows promise as a tool in glaucoma screening and detection through fundus image analysis, demonstrating generally high agreement with expert evaluations of key diagnostic features, although agreement did vary substantially across datasets. FINANCIAL DISCLOSURES: Proprietary or commercial disclosure may be found in the Footnotes and Disclosures at the end of this article.

The present study examined the relationship between motivation, sport anxiety, big five personality traits and athlete burnout, making use of a cross section research design. A total of 200 university athletes between the ages of 18-26 years participated in the study, completing a battery of scales measuring motivation, sport anxiety, and personality traits. Pearson product moment correlation, multiple hierarchical regression analyses and mediations analyses were conducted to evaluate the results. The findings show that sport motivation (intrinsic regulation, introjected regulation and amotivation), personality traits (neuroticism, conscientiousness), and sport anxiety are correlated with and predicted various dimensions of athlete burnout. Sport anxiety mediated the relationship between introjected regulation, intrinsic regulation and amotivation. The study identified several antecedents of burnout in university athletes than can help address symptoms of burnout and prevent the negative consequences that arise as a result of burnout.

Artificial intelligence (AI) has undergone considerable advancement in the contemporary period and represents an emerging technology in higher education. Cultural contexts significantly shape individuals' perceptions, attitudes, and behaviors, particularly in the realm of technology acceptance. By adopting a cross-cultural lens, this research explores the potential variations across Chinese and international students from diverse countries in terms of attitudes and their behavioral intentions toward AI use. With a technology acceptance model (TAM) framework, the research used a survey approach, employing questionnaires as the primary means of data collection. The data were then analyzed through structural equation modeling and descriptive statistics. A substantial discrepancy was found in the prevalence, attitudes, and behavioral intentions toward AI use between Chinese and international students. Findings further revealed a stronger effect of perceived ease of use on both attitudes and behavioral intentions among international students compared with their Chinese counterparts. Findings suggest that cultural backgrounds and prior technological exposure play intricate roles in shaping perceptions of AI technology. The study emphasizes the need for tailored educational strategies to regulate diverse cultural perspectives, provide language-specific support, and ensure user-friendly interfaces. These insights contribute to the evolving discourse on technology acceptance in higher education and offer practical implications for educators and institutions toward optimizing AI integration in pedagogical practices.

In order to understand how faculty members, feel about using Moodle as a learning management system (LMS) tool for teaching and learning in Jordanian universities, a mixed-methods study was conducted. Surveys and interviews were used to gather the data. 270 professors from three Jordanian universities took part in the study. The results show that while the participating faculty members thought using Moodle in instruction was a useful teaching tool, they weren't happy with it. The Internet crisis, interaction, the need for education for electronic platforms, online tests, and self-regulation were among the difficulties mentioned by these participants. The potential use of Moodle in education is encouraged through pedagogical implication. In conclusion, the implementation of the Moodle system for distance learning has been very remarkable in recent years. The effectiveness of its use depends on the active involvement of business managers and the willingness of teachers to adopt e-learning. By addressing the challenges ahead and providing incentives for faculty, universities can ensure success and use of the system. As the use of the Moodle LMS continues to grow, it is important to focus on the development of the tool and provide ongoing support to improve its effectiveness and impact on distance education.

Educational data mining is widely deployed to extract valuable information and patterns from academic data. This research explores new features that can help predict the future performance of undergraduate students and identify at-risk students early on. It answers some crucial and intuitive questions that are not addressed by previous studies. Most of the existing research is conducted on data from 2-3 years in an absolute grading scheme. We examined the effects of historical academic data of 15 years on predictive modelling. Additionally, we explore the performance of undergraduate students in a relative grading scheme and examine the effects of grades in core courses and initial semesters on future performances. As a pilot study, we analyzed the academic performance of Computer Science university students. Many exciting discoveries were made; the duration and size of the historical data play a significant role in predicting future performance, mainly due to changes in curriculum, faculty, society, and evolving trends. Furthermore, predicting grades in advanced courses based on initial pre-requisite courses is challenging in a relative grading scheme, as students' performance depends not only on their efforts but also on their peers. In short, educational data mining can come to the rescue by uncovering valuable insights from academic data to predict future performances and identify the critical areas that need significant improvement.

Effective communication and academic writing in English hinge on a solid grasp of grammar, with pronouns representing a pivotal aspect. However, non-native, particularly English as a second language (ESL) students, often find difficulties in employing pronouns effectively, especially in reference to their antecedents within sentences. The current study investigates errors in pronoun-antecedent agreement among ESL students in their academic writing by employing content analysis. By using purposive sampling technique, 40 students from four universities in Karachi city were selected and assigned to write a paragraph on any personality they are familiar with. Subsequently, all of the writing samples were examined thoroughly utilizing a systematic approach. The analysis revealed that errors predominantly occurred with personal pronouns, followed by possessive pronouns, relative pronouns, and demonstrative pronouns. While this study offers valuable insights into the difficulties ESL students face in mastering pronoun-antecedent agreement, it is essential to note limitations. The sample size may not fully represent the diverse range of ESL students, and the focus on academic writing may not capture all contexts in which pronoun-antecedent agreement challenges arise. Despite these limitations, the findings provide important considerations for ESL teachers. The implications of these findings extend to the design of effective language instruction, aimed at enhancing students' writing skills and mitigating common pronoun-antecedent agreement errors.

BACKGROUND: Understanding environmental correlates of sedentary behaviour (SB) among young people is important as such data can identify approaches to limit sedentary time. This paper estimates associations of parent-reported neighbourhood and adolescent-reported home environments with SB among adolescents aged 11-19 years from 14 countries. METHODS: In the International Physical activity and the Environment Network (IPEN) Adolescent Study (an observational, cross-sectional multi-country study), adolescents wore a triaxial accelerometer for seven days that assessed sedentary time (ST). Adolescents completed survey measures of sedentary behaviour (SB) related to recreational screen time and sitting time in motor vehicles. Parents and adolescents completed surveys assessing neighbourhood and home environments. Accelerometer based ST was available in 3,982 adolescents while survey data were available for 6,302 dyads. We estimated the total and direct effects of each environmental attribute on ST and SB. Sex of the adolescent and city/country were examined as moderators. RESULTS: The average ST in adolescents from 14 countries ranged from 7.8 to 10.5 h/day. Personal social media was the only significant correlate of total ST across both sexes. With respect to self-reported SB, adolescents accumulated an average of 3.8 h of non-school screen time per day and nearly 40 min of transport-related sitting time. Screen time was associated with all home environment variables, including social media account, as well as land use mix-diversity, traffic safety, and crime safety. Transport-related sitting time was related to land use mix-diversity, recreation facilities, walking facilities, and pedestrian infrastructure, but no home environment variables. City/country and sex were significant moderators of several associations. CONCLUSIONS: Both home and neighbourhood environment features were related to ST and SB. Having social media accounts emerged as a major contributor towards sedentarism in adolescents.

BACKGROUND: Cancer is the leading cause of disease-related mortality in children. Causes of leukemia, the most common form, are largely unknown. Growing evidence points to an origin in-utero, when global redistribution of DNA methylation occurs driving tissue differentiation. METHODS: Epigenome-wide DNA methylation was profiled in surrogate (blood) and target (bone marrow) tissues at birth, diagnosis, remission and relapse of pediatric pre-B acute lymphoblastic leukemia (pre-B ALL) patients. Double-blinded analyses was performed between prospective cohorts extending from birth to diagnosis and retrospective studies backtracking from clinical disease to birth. Validation was carried out using independent technologies and populations. RESULTS: The imprinted and immuno-modulating VTRNA2-1 was hypermethylated (FDR

Genome analysis of individuals affected by retinitis pigmentosa (RP) identified two rare nucleotide substitutions at the same genomic location on chromosome 11 (g.61392563 [GRCh38]), 69 base pairs upstream of the start codon of the ciliopathy gene TMEM216 (c.-69G>A, c.-69G>T [GenBank: NM_001173991.3]), in individuals of South Asian and African ancestry, respectively. Genotypes included 71 homozygotes and 3 mixed heterozygotes in trans with a predicted loss-of-function allele. Haplotype analysis showed single-nucleotide variants (SNVs) common across families, suggesting ancestral alleles within the two distinct ethnic populations. Clinical phenotype analysis of 62 available individuals from 49 families indicated a similar clinical presentation with night blindness in the first decade and progressive peripheral field loss thereafter. No evident systemic ciliopathy features were noted. Functional characterization of these variants by luciferase reporter gene assay showed reduced promotor activity. Nanopore sequencing confirmed the lower transcription of the TMEM216 c.-69G>T allele in blood-derived RNA from a heterozygous carrier, and reduced expression was further recapitulated by qPCR, using both leukocytes-derived RNA of c.-69G>T homozygotes and total RNA from genome-edited hTERT-RPE1 cells carrying homozygous TMEM216 c.-69G>A. In conclusion, these variants explain a significant proportion of unsolved cases, specifically in individuals of African ancestry, suggesting that reduced TMEM216 expression might lead to abnormal ciliogenesis and photoreceptor degeneration.