Anticipated facilitators and barriers for long-acting injectable antiretrovirals as HIV treatment and prevention in Vietnam: a qualitative study among healthcare workers.

Background: Long-acting injectable antiretrovirals (LAI-ARVs) for HIV prevention and treatment have been demonstrated in clinical trials to be non-inferior to daily oral medications, providing an additional option to help users overcome the challenges of daily adherence. Approval and implementation of these regimens in low- and middle-income settings have been limited.

Method: This study describes the anticipated barriers and facilitators to implementing LAI-ARVs in Vietnam to inform future roll-out.

Deep Learning-Based Body Composition Analysis for Cancer Patients Using Computed Tomographic Imaging.

Malnutrition is a commonly observed side effect in cancer patients, with a 30-85% worldwide prevalence in this population. Existing malnutrition screening tools miss ~ 20% of at-risk patients at initial screening and do not capture the abnormal body composition phenotype. Meanwhile, the gold-standard clinical criteria to diagnose malnutrition use changes in body composition as key parameters, particularly body fat and skeletal muscle mass loss.

: An Improved Python Package for Loading Datasets from the UCI Machine Learning Repository.

The University of California-Irvine (UCI) Machine Learning (ML) Repository (UCIMLR) is consistently cited as one of the most popular dataset repositories, hosting hundreds of high-impact datasets. However, a significant portion, including 28.4% of the top 250, cannot be imported via the package that is provided and recommended by the UCIMLR website.

Automated Extraction of Stroke Severity From Unstructured Electronic Health Records Using Natural Language Processing.

Background: Multicenter electronic health records can support quality improvement and comparative effectiveness research in stroke. However, limitations of electronic health record-based research include challenges in abstracting key clinical variables, including stroke severity, along with missing data. We developed a natural language processing model that reads electronic health record notes to directly extract the National Institutes of Health Stroke Scale score when documented and predict the score from clinical documentation when missing.

Extracting seizure control metrics from clinic notes of patients with epilepsy: A natural language processing approach.

Objectives: Monitoring seizure control metrics is key to clinical care of patients with epilepsy. Manually abstracting these metrics from unstructured text in electronic health records (EHR) is laborious. We aimed to abstract the date of last seizure and seizure frequency from clinical notes of patients with epilepsy using natural language processing (NLP).

Detection and Localization of Spine Disorders from Plain Radiography.

Spine disorders can cause severe functional limitations, including back pain, decreased pulmonary function, and increased mortality risk. Plain radiography is the first-line imaging modality to diagnose suspected spine disorders. Nevertheless, radiographical appearance is not always sufficient due to highly variable patient and imaging parameters, which can lead to misdiagnosis or delayed diagnosis.

Innate Immune Sensing of Adeno-Associated Virus Vectors.

Adeno-associated virus (AAV) based viral vectors are widely used in human gene therapy and form the basis of approved treatments for several genetic diseases. Immune responses to vector and transgene products, however, substantially complicate these applications in clinical practice. The role of innate immune recognition of AAV vectors was initially unclear, given that inflammatory responses early after vector administration were typically mild in animal models.

Proximal femur fracture detection on plain radiography via feature pyramid networks.

Hip fractures exceed 250,000 cases annually in the United States, with the worldwide incidence projected to increase by 240-310% by 2050. Hip fractures are predominantly diagnosed by radiologist review of radiographs. In this study, we developed a deep learning model by extending the VarifocalNet Feature Pyramid Network (FPN) for detection and localization of proximal femur fractures from plain radiography with clinically relevant metrics.

An Automated Vertebrae Localization, Segmentation, and Osteoporotic Compression Fracture Detection Pipeline for Computed Tomographic Imaging.

Osteoporosis is the most common chronic metabolic bone disease worldwide. Vertebral compression fracture (VCF) is the most common type of osteoporotic fracture. Approximately 700,000 osteoporotic VCFs are diagnosed annually in the USA alone, resulting in an annual economic burden of ~$13.

Rapid patient-specific FEM meshes from 3D smart-phone based scans.

The objective of this study was to describe and evaluate a smart-phone based method to rapidly generate subject-specific finite element method (FEM) meshes. More accurate FEM meshes should lead to more accurate thoracic electrical impedance tomography (EIT) images.The method was evaluated on an iPhonethat utilized an app called Heges, to obtain 3D scans (colored, surface triangulations), a custom belt, and custom open-source software developed to produce the subject-specific meshes.

Spontaneous Middle Meningeal Artery Aneurysms: A Case Report and Review of the Literature.

Most reported aneurysms concerning the middle meningeal artery (MMA) are pseudoaneurysms; however, there have been rare reports of non-traumatic MMA aneurysms in the literature. In this paper, we present the case of a 70-year-old female with a true 5-mm aneurysm in the anterior division of the left MMA that was causing erosion through the left temporal bone and was successfully treated with coil embolization. We also present a comprehensive literature review of non-traumatic MMA aneurysms reported since 1930.

Guidelines for best practices in monitoring established coeliac disease in adult patients.

Coeliac disease (CeD) is an immunological disease triggered by the consumption of gluten contained in food in individuals with a genetic predisposition. Diagnosis is based on the presence of small bowel mucosal atrophy and circulating autoantibodies (anti-type 2 transglutaminase antibodies). After diagnosis, patients follow a strict, life-long gluten-free diet.

Gamified versus non-gamified online educational modules for teaching clinical laboratory medicine to first-year medical students at a large allopathic medical school in the United States.

Background: Medical educators seek innovative ways to engage learners efficiently and effectively. Gamification has been explored as one way to accomplish this feat; however, questions remain about which contexts gamification would be most useful. Time constraints and student interest present major barriers for teaching laboratory medicine to students.

TLR9-independent CD8 T cell responses in hepatic AAV gene transfer through IL-1R1-MyD88 signaling.

Upon viral infection of the liver, CD8 T cell responses may be triggered despite the immune suppressive properties that manifest in this organ. We sought to identify pathways that activate responses to a neoantigen expressed in hepatocytes, using adeno-associated viral (AAV) gene transfer. It was previously established that cooperation between plasmacytoid dendritic cells (pDCs), which sense AAV genomes by Toll-like receptor 9 (TLR9), and conventional DCs promotes cross-priming of capsid-specific CD8 T cells.

Distinct functions and transcriptional signatures in orally induced regulatory T cell populations.

Oral administration of antigen induces regulatory T cells (Treg) that can not only control local immune responses in the small intestine, but also traffic to the central immune system to deliver systemic suppression. Employing murine models of the inherited bleeding disorder hemophilia, we find that oral antigen administration induces three CD4+ Treg subsets, namely FoxP3+LAP-, FoxP3+LAP+, and FoxP3-LAP+. These T cells act in concert to suppress systemic antibody production induced by therapeutic protein administration.

An Institutional Curriculum for Opioid Prescribing Education: Outcomes From 2017 to 2022.

Introduction: Prescription opioids, including those prescribed after surgery, have greatly contributed to the US opioid epidemic. Educating opioid prescribers is a crucial component of ensuring the safe use of opioids among surgical patients.

Methods: An annual opioid prescribing education curriculum was implemented among new surgical prescribers at our institution between 2017 and 2022.

Fibroblast growth factors induce hepatic tumorigenesis post radiofrequency ablation.

Image-guided radiofrequency ablation (RFA) is used to treat focal tumors in the liver and other organs. Despite potential advantages over surgery, hepatic RFA can promote local and distant tumor growth by activating pro-tumorigenic growth factor and cytokines. Thus, strategies to identify and suppress pro-oncogenic effects of RFA are urgently required to further improve the therapeutic effect.

Application of Multiple Omics to Understand Postoperative Delirium Pathophysiology in Humans.

Delirium, an acute change in cognition, is common, morbid, and costly, particularly among hospitalized older adults. Despite growing knowledge of its epidemiology, far less is known about delirium pathophysiology. Initial work understanding delirium pathogenesis has focused on assaying single or a limited subset of molecules or genetic loci.

Circulating Citrate Is Associated with Liver Fibrosis in Nonalcoholic Fatty Liver Disease and Nonalcoholic Steatohepatitis.

Nonalcoholic fatty liver disease (NAFLD) is associated with mitochondrial damage. Circulating mitochondrial metabolites may be elevated in NAFLD but their associations with liver damage is not known. This study aimed to assess the association of key mitochondrial metabolites with the degree of liver fibrosis in the context of NAFLD and nonalcoholic steatohepatitis (NASH).

A protocol for an international, multicenter, prospective, non-interventional observational registry for patients with hypertriglyceridemia.

Background: In the study on triglyceride-induced pancreatitis (TG-IAP), a core clinical dataset using the Jandhyala method was developed to collect the minimum amount of information for each patient presenting with TG-IAP globally. This approach offered a unified framework for observing multiple populations of TG-IAP patients using the same set of indicators, resulting in a considerably larger and uniform real-world population. It was understood that when this core dataset is implemented in a patient registry it could address the issue of missing data in observational studies and produce higher-quality research.

How surgeons use risk calculators and non-clinical factors for informed consent and shared decision making: A qualitative study.

Background: The discussion of risks, benefits, and alternatives to surgery with patients is a defining component of informed consent. As shared-decision making has become central to surgeon-patient communication, risk calculators have emerged as a tool to aid communication and decision-making. To optimize informed consent, it is necessary to understand how surgeons assess and communicate risk, and the role of risk calculators in this process.

Decoding information about cognitive health from the brainwaves of sleep.

Sleep electroencephalogram (EEG) signals likely encode brain health information that may identify individuals at high risk for age-related brain diseases. Here, we evaluate the correlation of a previously proposed brain age biomarker, the "brain age index" (BAI), with cognitive test scores and use machine learning to develop and validate a series of new sleep EEG-based indices, termed "sleep cognitive indices" (SCIs), that are directly optimized to correlate with specific cognitive scores. Three overarching cognitive processes were examined: total, fluid (a measure of cognitive processes involved in reasoning-based problem solving and susceptible to aging and neuropathology), and crystallized cognition (a measure of cognitive processes involved in applying acquired knowledge toward problem-solving).