Training performance modeling

OBJECTIVE: The goal of this study was to assess the safety of mapping spinal cord locomotor networks using penetrating stimulation microelectrodes in Yucatan minipigs (YMPs) as a clinically translational animal model. METHODS: Eleven YMPs were trained to walk up and down a straight line. Motion capture was performed, and electromyographic (EMG) activity of hindlimb muscles was recorded during overground walking. The YMPs underwent a laminectomy and durotomy to expose the lumbar spinal cord...

BACKGROUND: Artificial intelligence (AI), more specifically large language models (LLMs), holds significant potential in revolutionizing emergency care delivery by optimizing clinical workflows and enhancing the quality of decision-making. Although enthusiasm for integrating LLMs into emergency medicine (EM) is growing, the existing literature is characterized by a disparate collection of individual studies, conceptual analyses, and preliminary implementations. Given these complexities and gaps in understanding, a cohesive framework is needed to comprehend the existing body of knowledge on the application of LLMs in EM...

BACKGROUND: Acute myocardial infarction (AMI) is one of the most severe cardiovascular diseases and is associated with a high risk of in-hospital mortality. However, the current deep learning models for in-hospital mortality prediction lack interpretability. OBJECTIVE: This study aims to establish an explainable deep learning model to provide individualized in-hospital mortality prediction and risk factor assessment for patients with AMI. METHODS: In this retrospective multicenter study, we used data for consecutive patients hospitalized with AMI from the Chongqing University Central Hospital between July 2016 and December 2022 and the Electronic Intensive Care Unit Collaborative Research Database...

RNA editing, as an epigenetic mechanism, exhibits a strong correlation with the occurrence and development of cancers. Nevertheless, few studies have been conducted to investigate the impact of RNA editing on cervical squamous cell carcinoma and endocervical adenocarcinoma (CESC). In order to study the connection between RNA editing and CESC patients' prognoses, we obtained CESC-related information from The Cancer Genome Atlas (TCGA) database and randomly allocated the patients into the training group or testing group...

The heterogeneity of Hepatocellular Carcinoma (HCC) poses a barrier to effective treatment. Stratifying highly heterogeneous HCC into molecular subtypes with similar features is crucial for personalized anti-tumor therapies. Although driver genes play pivotal roles in cancer progression, their potential in HCC subtyping has been largely overlooked. This study aims to utilize driver genes to construct HCC subtype models and unravel their molecular mechanisms. Utilizing a novel computational framework, we expanded the initially identified 96 driver genes to 1192 based on mutational aspects and an additional 233 considering driver dysregulation...

The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) caused the COVID-19 disease, which represents a new life-threatening disaster. Regarding viral infection, many therapeutics have been investigated to alleviate the epidemiology such as vaccines and receptor decoys. However, the continuous mutating coronavirus, especially the variants of Delta and Omicron, are tended to invalidate the therapeutic biological product. Thus, it is necessary to develop molecular entities as broad-spectrum antiviral drugs...

BACKGROUND: Low back pain (LBP) is a major global disability contributor with profound health and socio-economic implications. The predominant form is non-specific LBP (NSLBP), lacking treatable pathology. Active physical interventions tailored to individual needs and capabilities are crucial for its management. However, the intricate nature of NSLBP and complexity of clinical classification systems necessitating extensive clinical training, hinder customised treatment access. Recent advancements in machine learning and computer vision demonstrate promise in characterising NSLBP altered movement patters through wearable sensors and optical motion capture...

Radiation therapy treatment planning requires balancing the delivery of the target dose while sparing normal tissues, making it a complex process. To streamline the planning process and enhance its quality, there is a growing demand for knowledge-based planning (KBP). Ensemble learning has shown impressive power in various deep learning tasks, and it has great potential to improve the performance of KBP. However, the effectiveness of ensemble learning heavily depends on the diversity and individual accuracy of the base learners...

BACKGROUND: Accurately predicting kidney outcomes in IgA nephropathy is crucial for clinical decision making. Insufficient use of longitudinal data in previous studies has limited the accuracy and interpretability of prediction models for failing to reflect the chronic nature of IgA nephropathy. This study aimed at establishing a multivariable dynamic deep learning model using comprehensive longitudinal data for the prediction of kidney outcomes in IgA nephropathy. METHODS: In this retrospective cohort study of 2,056 IgA nephropathy patients at 18 kidney centers, a total of 28,317 data points were collected by the sliding window method...

BACKGROUND: The Feeling Good App is an automated stand-alone digital mobile mental health tool currently undergoing beta testing with the goal of providing evidence-informed self-help lessons and exercises to help individuals reduce depressive symptoms without guidance from a mental health provider. Users work through intensive basic training (IBT) and ongoing training models that provide education regarding cognitive behavioral therapy principles from a smartphone. OBJECTIVE: The key objective of this study was to perform a nonsponsored third-party academic assessment of an industry-generated data set; this data set focused on the safety, feasibility, and accessibility of a commercial automated digital mobile mental health app that was developed to reduce feelings associated with depression...

IMPORTANCE: Black physicians are substantially underrepresented in the US health care workforce, with detrimental effects on the health and health care experiences of Black individuals. These contemporary gaps can be traced to the early days of the medical profession using the first edition of the American Medical Directory (AMD). OBJECTIVE: To identify state- and county-level patterns related to the training and availability of Black physicians relative to their White counterparts in the 1906 AMD...

PURPOSE: To build a stroke territory classifier model in DWI by designing the problem as a multiclass segmentation task by defining each stroke territory as distinct segmentation targets and leveraging the guidance of voxel wise dense predictions. MATERIALS AND METHODS: Retrospective analysis of DWI images of 218 consecutive acute anterior or posterior ischemic stroke patients examined between January 2017 to April 2020 in a single center was carried out. Each stroke area was defined as distinct segmentation target with different class labels...

BACKGROUND: Maximal strength may contribute to soccer players' performance. Several resistance training modalities offer the potential to improve maximal strength. During recent years, a large number of plyometric jump training (PJT) studies showed evidence for maximal strength improvements in soccer players. However, a comprehensive summary of the available data is lacking. OBJECTIVE: To examine the effects of PJT compared with active, passive or intervention controls on the maximal strength of soccer players, irrespective of age, sex or competitive level...

It is unknown whether the July Effect (a theory that medical errors and organizational inefficiencies increase during the influx of new surgical residents) exists in urologic robotic-assisted surgery. The aim of this study was to investigate the impact of urology resident training on robotic operative times at the beginning of the academic year. A retrospective chart review was conducted for urologic robotic surgeries performed at a single institution between 2008 and 2019. Univariate and multivariate mix model analyses were performed to determine the association between operative time and patient age, estimated blood loss, case complexity, robotic surgical system (Si or Xi), and time of the academic year...

Medical image classification plays a pivotal role within the field of medicine. Existing models predominantly rely on supervised learning methods, which necessitate large volumes of labeled data for effective training. However, acquiring and annotating medical image data is both an expensive and time-consuming endeavor. In contrast, semi-supervised learning methods offer a promising approach by harnessing limited labeled data alongside abundant unlabeled data to enhance the performance of medical image classification...

MXenes have attracted substantial attention for their various applications in energy storage, sensors, and catalysts. Experimental exploration of MXenes with hybrid terminal surfaces offers a unique means of property tailoring that is crucial for expanding the performance space of MXenes, wherein the formation energy of an MXene with mixed surface terminals plays a key role in determining its relative stability and practical applications. However, the challenge of identifying energetically stable MXenes with multifunctional surfaces persists, primarily due to the absence of precise surface modification during experiments and the vast structural space for DFT calculations...

INTRODUCTION: Acute lymphoblastic leukemia (ALL) presents a formidable challenge in hematological malignancies, necessitating swift and precise diagnostic techniques for effective intervention. The conventional manual microscopy of blood smears, although widely practiced, suffers from significant limitations including labor-intensity and susceptibility to human error, particularly in distinguishing the subtle differences between normal and leukemic cells. METHODS: To overcome these limitations, our research introduces the ALLDet classifier, an innovative tool employing deep transfer learning for the automated analysis and categorization of ALL from White Blood Cell (WBC) nuclei images...

OBJECTIVE: Develop and validate a mortality risk calculator that could be utilized at the time of transfer, leveraging routinely collected variables that could be obtained by trained non-clinical transfer personnel. SUMMARY BACKGROUND DATA: There are no objective tools to predict mortality at the time of inter-hospital transfer for Emergency General Surgery (EGS) patients that are "unseen" by the accepting system. METHODS: Patients transferred to general or colorectal surgery services from January 2016 through August 2022 were retrospectively identified and randomly divided into training and validation cohorts (3:1 ratio)...

Large language models (LLMs) such as ChatGPT have potential applications in healthcare, including dentistry. Priming, the practice of providing LLMs with initial, relevant information, is an approach to improve their output quality. This study aimed to evaluate the performance of ChatGPT 3 and ChatGPT 4 on self-assessment questions for dentistry, through the Swiss Federal Licensing Examination in Dental Medicine (SFLEDM), and allergy and clinical immunology, through the European Examination in Allergy and Clinical Immunology (EEAACI)...

Background: Simulation models enable learners to have repeated practise at their own time, to master the psycho-motor and sensory acuity aspects of surgery and build their confidence in the procedure. The study aims to develop and evaluate the feasibility of a low-cost drilling model to train surgeons in the drilling task. The model targets three aspects of drilling - (1) Reduce plunge depth, (2) Ability to differentiate between bone and medullary canal and (3) Increase accuracy drilling in various angles. Methods: This cross-sectional study was conducted after obtaining ethics approval...