Effect of obesity on the acute response to SARS-CoV-2 infection and development of post-acute sequelae of COVID-19 (PASC) in nonhuman primates.

Long-term adverse consequences of SARS-CoV-2 infection, termed "long COVID" or post-acute sequelae of COVID (PASC), are a major component of overall COVID-19 disease burden. Prior obesity and metabolic disease increase the severity of acute disease, but SARS-CoV-2 infection also contributes to the development of new-onset metabolic disease. Since the COVID pandemic occurred in the context of the global obesity epidemic, an important question is the extent to which pre-existing obesity modifies long-term responses to SARS-CoV-2 infection.

Utilizing physical educators to monitor muscular strength and neuromuscular control among children with varied recess time.

Introduction: Inactivity levels among children are climbing at alarming rates, leading to a lack of physical activities that produce muscular strength (MusS) development, which in turn creates effective neuromuscular control (NC) development. Developing appropriate MusS during childhood decreases the chances of physical injuries and many chronic diseases such as type II diabetes and cancer, which leads to healthier, active future adults. The purpose of this study was to utilize the physical education setting to examine MusS and NC factors in the Fall and Spring (Time 1 to Time 2) of one school year in a predominately Hispanic sample of second-grade children who received 60 min or 20 min of daily recess.

EHR-ML: A data-driven framework for designing machine learning applications with electronic health records.

Objective: The healthcare landscape is experiencing a transformation with the integration of Artificial Intelligence (AI) into traditional analytic workflows. However, its integration faces challenges resulting in a crisis of generalisability. Key obstacles include; 1) Insufficient consideration of local contextual factors, such as institution-specific data formats, practices, and protocols, which can lead to variability in clinical practices across different institutions.

Reinventing the evaluation wheel: COGwheel's co-designed digital innovation using the Qualtrics heat map.

The involvement of service-users, clinicians, and other health service end-users is recognised as an essential part of health and medical research. This collaborative approach can significantly contribute to methodological advancements including the development of research instruments and measures that ensure their suitability for research participants. The current paper details the co-design, development and implementation of the novel, digitised COGwheel (Co-designed Outcomes for Guests Evaluation Wheel).

A Radiograph Dataset for the Classification, Localization, and Segmentation of Primary Bone Tumors.

Primary malignant bone tumors are the third highest cause of cancer-related mortality among patients under the age of 20. X-ray scan is the primary tool for detecting bone tumors. However, due to the varying morphologies of bone tumors, it is challenging for radiologists to make a definitive diagnosis based on radiographs.

SCREEN: A Graph-based Contrastive Learning Tool to Infer Catalytic Residues and Assess Enzyme Mutations.

Unlabelled: The accurate identification of catalytic residues contributes to our understanding of enzyme functions in biological processes and pathways. The increasing number of protein sequences necessitates computational tools for the automated prediction of catalytic residues in enzymes. Here, we introduce SCREEN, a graph neural network for the high-throughput prediction of catalytic residues via the integration of enzyme functional and structural information.

Liver transplantation for critically ill patients with acute on chronic liver failure: a prospective national programme of waitlist prioritisation.

Background: Acute on Chronic Liver Failure (ACLF) complicates chronic liver disease (CLD) combining rapidly progressive hepatic with extra-hepatic multiple organ failure and high short-term mortality. Effective therapeutic options are very limited, and liver transplantation (LT) seldom utilised through concerns of high recipient mortality and resource use. Retrospective reports suggest recent outcomes may have improved, but use of LT for ACLF has not been prospectively assessed.

Dispositional Traits, Characteristic Adaptations, and Narrative Identity Reconstructions in Individuals With Depersonalization and Derealization.

Introduction: Depersonalization and derealization disorder (DPDR) is a debilitating condition. To date, little was known about the role of personality structure and of perceived social support and loneliness in DPDR.

Methods: Three studies investigated, respectively: (i) broadband personality traits (five-factor model), maladaptive trait domains (PID-5), and perceived support and loneliness in individuals with self-reported DPDR (N = 160) versus a general population sample (N = 303), using network modeling; (ii) structure and interconnectivity of personality, perceived support and loneliness, and DPDR traits (frequency/duration) in individuals with self-reported DPDR (N = 160); (iii) characteristic adaptations and narrative identities in individuals with self-reported DPDR (N = 19), using thematic analysis.

FcRn-enhancing mutations lead to increased and prolonged levels of the HIV CCR5-blocking monoclonal antibody leronlimab in the fetuses and newborns of pregnant rhesus macaques.

Prenatal administration of monoclonal antibodies (mAbs) is a strategy that could be exploited to prevent viral infections during pregnancy and early life. To reach protective levels in fetuses, mAbs must be transported across the placenta, a selective barrier that actively and specifically promotes the transfer of antibodies (Abs) into the fetus through the neonatal Fc receptor (FcRn). Because FcRn also regulates Ab half-life, Fc mutations like the M428L/N434S, commonly known as LS mutations, and others have been developed to enhance binding affinity to FcRn and improve drug pharmacokinetics.

Advancing microRNA target site prediction with transformer and base-pairing patterns.

MicroRNAs (miRNAs) are short non-coding RNAs involved in various cellular processes, playing a crucial role in gene regulation. Identifying miRNA targets remains a central challenge and is pivotal for elucidating the complex gene regulatory networks. Traditional computational approaches have predominantly focused on identifying miRNA targets through perfect Watson-Crick base pairings within the seed region, referred to as canonical sites.

Utilising accessible and reproducible neurological assessments in clinical studies: Insights from use of the Neurological Impairment Scale in the multi-centre COVID-CNS study.

Reproducible and standardised neurological assessment scales are important in quantifying research outcomes. These scales are often performed by non-neurologists and/or non-clinicians and must be robust, quantifiable, reproducible and comparable to a neurologist's assessment. COVID-CNS is a multi-centre study which utilised the Neurological Impairment Scale (NIS) as a core assessment tool in studying neurological outcomes following COVID-19 infection.

Predicting drug resistance using artificial intelligence and clinical MALDI-TOF mass spectra.

Matrix-assisted laser desorption/ionization-time of flight mass spectrometry (MALDI-TOF MS) is widely used in clinical microbiology laboratories for bacterial identification but its use for detection of antimicrobial resistance (AMR) remains limited. Here, we used MALDI-TOF MS with artificial intelligence (AI) approaches to successfully predict AMR in , a priority pathogen with complex AMR mechanisms. The highest performance was achieved for modern β-lactam/β-lactamase inhibitor drugs, namely, ceftazidime/avibactam and ceftolozane/tazobactam.

A Survey on Graph Neural Networks for Time Series: Forecasting, Classification, Imputation, and Anomaly Detection.

Time series are the primary data type used to record dynamic system measurements and generated in great volume by both physical sensors and online processes (virtual sensors). Time series analytics is therefore crucial to unlocking the wealth of information implicit in available data. With the recent advancements in graph neural networks (GNNs), there has been a surge in GNN-based approaches for time series analysis.

"PrEP a double-edged sword": Integrating implementation science methodology with Photovoice to guide culturally-tailored pre-exposure prophylaxis (PrEP) programs for Latino/a and non-Latino/a men who have sex with men in South Florida.

Background: DiversiPrEP is a culturally-tailored PrEP program for LMSM offered in South Florida. DiversiPrEP navigates LMSM through their PrEP journey, including education, deciding if PrEP is relevant for them, payment, and accessing/maintaining PrEP use. DiversiPrEP includes five ERIC strategies (Increase Demand, Promote Adaptability, Alter Client Fees, Intervene with Clients to Enhance Uptake and Adherence, and Tailor Strategies).

Effect of metabolic status on response to SIV infection and antiretroviral therapy in nonhuman primates.

Current antiretroviral therapy (ART) regimens efficiently limit HIV replication, thereby improving the life expectancy of people living with HIV; however, they also cause metabolic side effects. The ongoing obesity epidemic has resulted in more people with metabolic comorbidities at the time of HIV infection, yet the effect of preexisting metabolic dysregulation on infection sequelae and response to ART is unclear. Here, to investigate the impact of preexisting obesity and insulin resistance on acute infection and subsequent long-term ART, we infected a cohort of lean and obese adult male macaques with SIV and administered ART.

Barriers and facilitators for strengthening physiotherapy services in Nepal: perspectives from physiotherapists and health providers.

Background: Physiotherapy provides non-invasive and non-pharmaceutical intervention for curative, rehabilitation and preventative purposes. Physiotherapy is also a central provider of health promotion. As the global burden of non-communicable diseases and chronic health conditions is rising, the importance of physiotherapy services increases.

Immune restoration by TIGIT blockade is insufficient to control chronic SIV infection.

TIGIT is a negative immune checkpoint receptor associated with T cell exhaustion in cancer and HIV. TIGIT upregulation in virus-specific CD8 T cells and NK cells during HIV/SIV infection results in dysfunctional effector capabilities. studies targeting TIGIT on CD8 T cells suggest TIGIT blockade as a viable strategy to restore SIV-specific T cell responses.

An augmented reality-guided biopsy system using a high-speed motion tracking and real-time registration platform.

Biopsies play a crucial role in diagnosis of various diseases including cancers. In this study, we developed an augmented reality (AR) system to improve biopsy procedures and increase targeting accuracy. Our AR-guided biopsy system uses a high-speed motion tracking technology and an AR headset to display a holographic representation of the organ, lesions, and other structures of interest superimposed on real physical objects.

Stakeholder perspectives following implementation of Vietnam's first speech-language pathology degrees: recommendations for future curriculum development.

Purpose: To investigate perspectives of multiple stakeholders involved in development and delivery of Vietnam's first speech-language pathology degrees and derive recommendations for future degrees in Vietnam and other Majority World countries.

Methods: An exploratory-descriptive qualitative research design using focus groups and individual semi-structured interviews in the preferred language (English or Vietnamese) was used, with 70 participants from five stakeholder groups: project managers, students, academic educators, placement supervisors and interpreters. Transcriptions were analysed using thematic network analysis.

GraphormerDTI: A graph transformer-based approach for drug-target interaction prediction.

The application of Artificial Intelligence (AI) to screen drug molecules with potential therapeutic effects has revolutionized the drug discovery process, with significantly lower economic cost and time consumption than the traditional drug discovery pipeline. With the great power of AI, it is possible to rapidly search the vast chemical space for potential drug-target interactions (DTIs) between candidate drug molecules and disease protein targets. However, only a small proportion of molecules have labelled DTIs, consequently limiting the performance of AI-based drug screening.

Hepatic encephalopathy: a neurologist's perspective.

Liver disease is increasingly common, estimated to affect over 25% of the world's population. Failure of the liver to maintain a normal metabolic milieu leads to impaired brain function (hepatic encephalopathy), and conditions that cause liver disease can themselves predispose to neurological disease. As neurologists' involvement with the acute take increases, it is important that we are familiar with the neurological complications of liver disease, their investigation and management, and to know which other neurological diseases occur in this patient population.