Leveraging LLMs and wearables to provide personalized recommendations for enhancing student well-being and academic performance through a proof of concept.

Traditional one-size-fits-all recommendations for student well-being and academic success may not be optimal. Personalized recommendations based on individual data hold promise. This study explores the potential of Large Language Models (LLMs) to generate personalized recommendations for 12 high school students to enhance their well-being and academic performance.

Harnessing Artificial Intelligence to Predict Ovarian Stimulation Outcomes in In Vitro Fertilization: Scoping Review.

Background: In the realm of in vitro fertilization (IVF), artificial intelligence (AI) models serve as invaluable tools for clinicians, offering predictive insights into ovarian stimulation outcomes. Predicting and understanding a patient's response to ovarian stimulation can help in personalizing doses of drugs, preventing adverse outcomes (eg, hyperstimulation), and improving the likelihood of successful fertilization and pregnancy. Given the pivotal role of accurate predictions in IVF procedures, it becomes important to investigate the landscape of AI models that are being used to predict the outcomes of ovarian stimulation.

Machine Learning-Based Approach for Identifying Research Gaps: COVID-19 as a Case Study.

Background: Research gaps refer to unanswered questions in the existing body of knowledge, either due to a lack of studies or inconclusive results. Research gaps are essential starting points and motivation in scientific research. Traditional methods for identifying research gaps, such as literature reviews and expert opinions, can be time consuming, labor intensive, and prone to bias.

The Performance of Wearable AI in Detecting Stress Among Students: Systematic Review and Meta-Analysis.

Background: Students usually encounter stress throughout their academic path. Ongoing stressors may lead to chronic stress, adversely affecting their physical and mental well-being. Thus, early detection and monitoring of stress among students are crucial.

Formulation of novel bioactive gelatin inspired by cinnamaldehyde for combating multi-drug resistant bacteria: Characterization, molecular docking, pharmacokinetic analyses, and in vitro assessments.

This study set out to formulate antibacterial and antioxidant gelatin boosted by cinnamaldehyde for combating multi-drug resistant bacteria previously obtained from chronic wounds. Towards this end, gelatin amine groups were conjugated with carbonyl groups of cinnamaldehyde, producing cinnamyl-gelatin Schiff bases. The physicochemical attributes of cinnamyl-gelatin Schiff bases were probed concerning alterations in chemical structures and microstructures compared to native gelatin.

TLE1 Expression in NUT Carcinoma: A Case Report Highlighting a Potential Diagnostic Pitfall for the Pathologist.

NUT carcinoma is a rare, aggressive malignancy defined as a carcinoma with a chromosomal rearrangement affecting the nuclear protein in testis () gene. This small round blue cell tumor classically exhibits focal abrupt keratinization and immunohistochemical positivity for keratin and squamous markers. However, keratinization is not always present and reports of positivity for other markers that may obscure the diagnosis are increasing.

The secrets of environmental Pseudomonas aeruginosa in slaughterhouses: Antibiogram profile, virulence, and antibiotic resistance genes.

Environmental pollution is a serious problem that can cause sicknesses, fatality, and biological contaminants such as bacteria, which can trigger allergic reactions and infectious illnesses. There is also evidence that environmental pollutants can have an impact on the gut microbiome and contribute to the development of various mental health and metabolic disorders. This study aimed to study the antibiotic resistance and virulence potential of environmental Pseudomonas aeruginosa (P.

Wearable Artificial Intelligence for Detecting Anxiety: Systematic Review and Meta-Analysis.

Background: Anxiety disorders rank among the most prevalent mental disorders worldwide. Anxiety symptoms are typically evaluated using self-assessment surveys or interview-based assessment methods conducted by clinicians, which can be subjective, time-consuming, and challenging to repeat. Therefore, there is an increasing demand for using technologies capable of providing objective and early detection of anxiety.

The performance of serious games for enhancing attention in cognitively impaired older adults.

Attention, which is the process of noticing the surrounding environment and processing information, is one of the cognitive functions that deteriorate gradually as people grow older. Games that are used for other than entertainment, such as improving attention, are often referred to as serious games. This study examined the effectiveness of serious games on attention among elderly individuals suffering from cognitive impairment.

Performance of Artificial Intelligence in Predicting Future Depression Levels.

Depression is a prevalent mental condition that is challenging to diagnose using conventional techniques. Using machine learning and deep learning models with motor activity data, wearable AI technology has shown promise in reliably and effectively identifying or predicting depression. In this work, we aim to examine the performance of simple linear and non-linear models in the prediction of depression levels.

Wearable AI Reveals the Impact of Intermittent Fasting on Stress Levels in School Children During Ramadan.

Intermittent fasting has been practiced for centuries across many cultures globally. Recently many studies have reported intermittent fasting for its lifestyle benefits, the major shift in eating habits and patterns is associated with several changes in hormones and circadian rhythms. Whether there are accompanying changes in stress levels is not widely reported especially in school children.

Anti-capsular activity of CuO nanoparticles against Acinetobacter baumannii produce efflux pump.

Copper oxide nanoparticles are modern kinds of antimicrobials, which may get a lot of interest in the clinical application. This study aimed to detect the anti-capsular activity of CuO nanoparticles against Acinetobacter baumannii produce efflux pump. Thirty-four different clinical A.

Binary CuO\CoO nanoparticles inhibit biofilm formation and reduce the expression of papC and fimH genes in multidrug-resistant Klebsiella oxytoca.

Background And Aim: Binary copper-cobalt oxide nanoparticles (CuO\CoO NPs) are modern kinds of antimicrobials, which may get a lot of interest in clinical application. This study aimed to detect the effect of the binary CuO\CoO NPs on the expression of papC and fimH genes in multidrug-resistant (MDR) isolates of Klebsiella oxytoca to reduce medication time and improve outcomes.

Methods: Ten isolates of K.

Large Language Models in Medical Education: Opportunities, Challenges, and Future Directions.

The integration of large language models (LLMs), such as those in the Generative Pre-trained Transformers (GPT) series, into medical education has the potential to transform learning experiences for students and elevate their knowledge, skills, and competence. Drawing on a wealth of professional and academic experience, we propose that LLMs hold promise for revolutionizing medical curriculum development, teaching methodologies, personalized study plans and learning materials, student assessments, and more. However, we also critically examine the challenges that such integration might pose by addressing issues of algorithmic bias, overreliance, plagiarism, misinformation, inequity, privacy, and copyright concerns in medical education.

Systematic review and meta-analysis of performance of wearable artificial intelligence in detecting and predicting depression.

Given the limitations of traditional approaches, wearable artificial intelligence (AI) is one of the technologies that have been exploited to detect or predict depression. The current review aimed at examining the performance of wearable AI in detecting and predicting depression. The search sources in this systematic review were 8 electronic databases.

Serious Games for Learning Among Older Adults With Cognitive Impairment: Systematic Review and Meta-analysis.

Background: Learning disabilities are among the major cognitive impairments caused by aging. Among the interventions used to improve learning among older adults are serious games, which are participative electronic games designed for purposes other than entertainment. Although some systematic reviews have examined the effectiveness of serious games on learning, they are undermined by some limitations, such as focusing on older adults without cognitive impairments, focusing on particular types of serious games, and not considering the comparator type in the analysis.

The Effectiveness of Wearable Devices Using Artificial Intelligence for Blood Glucose Level Forecasting or Prediction: Systematic Review.

Background: In 2021 alone, diabetes mellitus, a metabolic disorder primarily characterized by abnormally high blood glucose (BG) levels, affected 537 million people globally, and over 6 million deaths were reported. The use of noninvasive technologies, such as wearable devices (WDs), to regulate and monitor BG in people with diabetes is a relatively new concept and yet in its infancy. Noninvasive WDs coupled with machine learning (ML) techniques have the potential to understand and conclude meaningful information from the gathered data and provide clinically meaningful advanced analytics for the purpose of forecasting or prediction.

Correction: Wearable Artificial Intelligence for Anxiety and Depression: Scoping Review.

[This corrects the article DOI: 10.2196/42672.].

Wearable devices for anxiety & depression: A scoping review.

Background: The rates of mental health disorders such as anxiety and depression are at an all-time high especially since the onset of COVID-19, and the need for readily available digital health care solutions has never been greater. Wearable devices have increasingly incorporated sensors that were previously reserved for hospital settings. The availability of wearable device features that address anxiety and depression is still in its infancy, but consumers will soon have the potential to self-monitor moods and behaviors using everyday commercially-available devices.

Chatbot features for anxiety and depression: A scoping review.

Chatbots can provide valuable support to patients in assessing and guiding management of various health problems particularly when human resources are scarce. Chatbots can be affordable and efficient on-demand virtual assistants for mental health conditions, including anxiety and depression. We review features of chatbots available for anxiety or depression.

Wearable Artificial Intelligence for Anxiety and Depression: Scoping Review.

Background: Anxiety and depression are the most common mental disorders worldwide. Owing to the lack of psychiatrists around the world, the incorporation of artificial intelligence (AI) into wearable devices (wearable AI) has been exploited to provide mental health services.

Objective: This review aimed to explore the features of wearable AI used for anxiety and depression to identify application areas and open research issues.