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OBJECTIVE: To understand factors associated with missed academic time after concussion to improve support for patients. Our goal was to assess patient-specific predictors of total school time lost after pediatric/adolescent concussion. STUDY DESIGN: We performed a prospective cohort study of children and adolescents (8-18 years of age) seen within 14 days of concussion from seven pediatric medical centers across the United States. We collected outcomes via the Concussion Learning Assessment & School Survey (CLASS) and constructed a multivariable predictive model evaluating patient factors associated with school time loss...

Advances in machine learning have enabled sufficiently accurate predictions of protein structure to be used in macromolecular structure determination with crystallography and cryo-electron microscopy data. The Phenix software suite has AlphaFold predictions integrated into an automated pipeline that can start with an amino acid sequence and data, and automatically perform model-building and refinement to return a protein model fitted into the data. Due to the steep technical requirements of running AlphaFold efficiently, we have implemented a Phenix-AlphaFold webservice that enables all Phenix users to run AlphaFold predictions remotely from the Phenix GUI starting with the official 1...

BACKGROUND: The use of simulation-based methods for teaching and learning in the education of health professions is increasing, but its prevalence in Saudi Arabia among respiratory therapy programs has yet to be investigated. The purpose of this study is to identify the use of simulation-based learning (SBL) in respiratory therapy programs in Saudi Arabia. METHODS: A cross-sectional study was performed by sending Google forms survey via Email to directors of respiratory therapy programs in Saudi Arabia (N=16) to evaluate how each one used simulations as an educational tool...

Using spatial mapping processes to learn about threat and safety in an environment is crucial for survival. Research using conditioning paradigms has explored the effects of state (transient arousal) and trait anxiety (anxiety as an aspect of personality) on threat learning and acquisition. However, results are mixed, and little is known regarding why some individuals do not learn to discriminate between threat and safety during contextual conditioning. We used a virtual reality (VR) contextual threat conditioning paradigm to elucidate the effects of state and trait anxiety on contextual threat learning...

BACKGROUND: Texture analysis extracts many quantitative image features, offering a valuable, cost-effective, and non-invasive approach for individual medicine. Furthermore, multimodal machine learning could have a large impact for precision medicine, as texture biomarkers can underlie tissue microstructure. This study aims to investigate imaging-based biomarkers of radio-induced neurotoxicity in pediatric patients with metastatic medulloblastoma, using radiomic and dosiomic analysis. METHODS: This single-center study retrospectively enrolled children diagnosed with metastatic medulloblastoma (MB) and treated with hyperfractionated craniospinal irradiation (CSI)...

BACKGROUND: Due to the considerably heightened risk of overdose immediately following jail or prison release, expansion of harm reduction interventions for citizens returning to the community after an incarceration episode should be of utmost concern. However, there are no studies examining the adoption and use of harm reduction among this population. This short report examines the use of individual-level harm reduction strategies (HRS) among people who use drugs, comparing those who have lifetime histories of incarceration with those who do not...

INTRODUCTION: General surgery is a highly litigious specialty. Lawsuits can be a source of emotional distress and burnout for surgeons. Major hepatic and pancreatic surgeries are technically challenging general surgical oncology procedures associated with an increased risk of complications and mortality. It is unclear whether these operations are associated with an increased risk of lawsuits. The objective of the present study was to summarize the medical malpractice claims surrounding pancreatic and hepatic surgeries from publicly available court records...

This study employs seven advanced machine learning approaches to conduct numerical predictions of the next-day returns of VIX constant-maturity futures (VIX CMFs) using the term structure information derived from VIX CMFs. Based on precise numerical predictions, this study proposes a new Constrained-Mean-Variance Portfolio Optimization (C-MVO) trading strategy and tests it against a benchmark long-short trading strategy to evaluate the profitability of the machine learning numerical predictions. This study applies three unique feature sets, each incrementally incorporating the VIX CMFs' term structure features, to individually examine the predictive ability of the seven machine learning models and their backtesting performance...

Background : Social restrictions during the COVID-19 pandemic had a major impact on the mental health of children. Yet, analyses on the mental health of younger children in the later course of the pandemic are scarce. The present study assessed 8- to 11-year-olds' internalising disorder symptoms during the last three weeks, from the third week of February through to the first week of March, of the 2021 national lockdown. Method : One hundred and forty-five pupils, including a subset of keyworkers' children who had face-toface schooling, completed the validated Revised Child Anxiety and Depression scales, items on COVID-related stress at home, and evaluations of home-learning and school's measures for reopening...

BACKGROUND: Artificial intelligence (AI) technology has been increasingly developed in oral and maxillofacial imaging. The aim of this systematic review was to assess the applications and performance of the developed algorithms in different dentomaxillofacial imaging modalities. STUDY DESIGN: A systematic search of PubMed and Scopus databases was performed. The search strategy was set as a combination of the following keywords: "Artificial Intelligence," "Machine Learning," "Deep Learning," "Neural Networks," "Head and Neck Imaging," and "Maxillofacial Imaging...

Digitally enabled higher education involves the in-depth use of new-generation digital technology, which has subverted and innovated the traditional teaching mode, driven the development of high-quality teaching and learning, and improved teachers' teaching experience, and increased efficiency. Based on ecosystem theory, this paper constructs a higher education ecosystem with the government, enterprises, and universities as the core participating subjects. It considers the participating subjects' effort level and the ecosystem's overall benefits under the three scenarios of noncooperative research and development (R&D), cost sharing, and cooperative R&D...

BACKGROUND: The Inspiration4 (I4) mission, the first all-civilian orbital flight mission, investigated the physiological effects of short-duration spaceflight through a multi-omic approach. Despite advances, there remains much to learn about human adaptation to spaceflight's unique challenges, including microgravity, immune system perturbations, and radiation exposure. METHODS: To provide a detailed genetics analysis of the mission, we collected dried blood spots pre-, during, and post-flight for DNA extraction...

OBJECTIVES: We aimed to assess the impact of in-person and distance school learning models on children's visits to the emergency department (ED) for psychological or social ("psychosocial") complaints. METHODS: We analyzed presentations to one emergency department in a mid-sized Midwestern city. We used the public school system schedule to determine in-person and distance learning periods by the grade level. We calculated the incidence of visits to the emergency department during academic years 2018-2019, 2019-2020, and 2020-2021, with particular attention to the time after March 13, 2020, which was the pandemic period...

A pathology-supported genetic testing (PSGT) framework was established in South Africa to improve access to precision medicine for patients with breast carcinomas. Nevertheless, the frequent identification of variants of uncertain significance (VUSs) with the use of genome-scale next-generation sequencing has created a bottleneck in the return of results to patients. This review highlights the importance of incorporating functional genomics into the PSGT framework as a proposed initiative. Here, we explore various model systems and experimental methods available for conducting functional studies in South Africa to enhance both variant classification and clinical interpretation...

While artificial intelligence drives remarkable progress in natural sciences, its broader societal implications are mostly disregarded. In this study, we evaluate environmental impacts of deep learning in materials science through extensive benchmarking. In particular, a set of diverse neural networks is trained for a given supervised learning task to assess greenhouse gas (GHG) emissions during training and inference phases. A chronological perspective showed diminishing returns, manifesting themselves as a 28% decrease in mean absolute error and nearly a 15,000% increase in the carbon footprint of model training in 2016-2022...

Systematically predicting the effects of mutations on protein fitness is essential for the understanding of genetic diseases. Indeed, predictions complement experimental efforts in analyzing how variants lead to dysfunctional proteins that in turn can cause diseases. Here we present our new fitness predictor, FiTMuSiC, which leverages structural, evolutionary and coevolutionary information. We show that FiTMuSiC predicts fitness with high accuracy despite the simplicity of its underlying model: it was among the top predictors on the hydroxymethylbilane synthase (HMBS) target of the sixth round of the Critical Assessment of Genome Interpretation challenge (CAGI6) and performs as well as much more complex deep learning models such as AlphaMissense...

Exposure-based therapy is effective in treating anxiety, but a return of fear in the form of relapse is common. Exposure is based on the extinction of Pavlovian fear conditioning. Both animal and human studies point to increased arousal during immediate compared to delayed extinction (>+24 h), which presumably impairs extinction learning and increases the subsequent return of fear. Impaired extinction learning under arousal might interfere with psychotherapeutic interventions. The aim of the present study was to investigate whether arousal before extinction differs between extinction groups and whether arousal before extinction predicts the return of fear in a later (retention) test...

In this work we seek to enhance the frameworks practitioners in asset management and wealth management may adopt to asses how different screening rules may influence the diversification benefits of portfolios. The problem arises naturally in the area of Environmental, Social, and Governance (ESG) based investing practices as practitioners often need to select subsets of the total available assets based on some ESG screening rule. Once a screening rule is identified, one constructs a dynamic portfolio which is usually compared with another dynamic portfolio to check if it satisfies or outperforms the risk and return profile set by the company...

BACKGROUND AND PURPOSE: With the growing interest for physical therapists to incorporate musculoskeletal (MSK) ultrasound comes a need to understand how to organize training to promote the transfer of training to clinical practice. A common training strategy blends asynchronous learning through online modules and virtual simulations with synchronous practice on live simulated participants. However, few physical therapists who attend MSK ultrasound continuing education courses integrate ultrasound into clinical practice...

Introduction Artificial intelligence (AI) models using large language models (LLMs) and non-specific domains have gained attention for their innovative information processing. As AI advances, it's essential to regularly evaluate these tools' competency to maintain high standards, prevent errors or biases, and avoid flawed reasoning or misinformation that could harm patients or spread inaccuracies. Our study aimed to determine the performance of Chat Generative Pre-trained Transformer (ChatGPT) by OpenAI and Google BARD (BARD) in orthopedic surgery, assess performance based on question types, contrast performance between different AIs and compare AI performance to orthopedic residents...