1,25-dihydroxyvitamin D mediated hypercalcemia in seminoma.

Hypercalcemia has rarely been associated with seminomas. Due to the limited data available, the pathophysiology of hypercalcemia in seminoma has not been established in literature. We present a case of a 59-year-old male who presented with weakness, abdominal fullness, fatigue, constipation, and a 14 lb unintentional weight loss.

Deep learning for detecting and characterizing oil and gas well pads in satellite imagery.

Methane emissions from the oil and gas sector are a large contributor to climate change. Robust emission quantification and source attribution are needed for mitigating methane emissions, requiring a transparent, comprehensive, and accurate geospatial database of oil and gas infrastructure. Realizing such a database is hindered by data gaps nationally and globally.

Probabilistic Prediction of Laboratory Test Information Yield.

Low-yield repetitive laboratory diagnostics burden patients and inflate cost of care. In this study, we assess whether stability in repeated laboratory diagnostic measurements is predictable with uncertainty estimates using electronic health record data available before the diagnostic is ordered. We use probabilistic regression to predict a distribution of plausible values, allowing use-time customization for various definitions of "stability" given dynamic ranges and clinical scenarios.

VetLLM: Large Language Model for Predicting Diagnosis from Veterinary Notes.

Lack of diagnosis coding is a barrier to leveraging veterinary notes for medical and public health research. Previous work is limited to develop specialized rule-based or customized supervised learning models to predict diagnosis coding, which is tedious and not easily transferable. In this work, we show that open-source large language models (LLMs) pretrained on general corpus can achieve reasonable performance in a zero-shot setting.

Evaluation in a porcine wound model and long-term clinical assessment of an autologous heterogeneous skin construct used to close full-thickness wounds.

Acute and chronic wounds involving deeper layers of the skin are often not adequately healed by dressings alone and require therapies such as skin grafting, skin substitutes, or growth factors. Here we report the development of an autologous heterogeneous skin construct (AHSC) that aids wound closure. AHSC is manufactured from a piece of healthy full-thickness skin.

Metal reactivity is present in dogs with tibial plateau leveling osteotomy and total hip replacement implants.

Objective: Determine whether dogs with well-functioning orthopedic metal implants can develop metal reactivity.

Sample: Client-owned dogs that had tibial plateau leveling osteotomy (TPLO) or total hip replacement (THR) implants for 12 months or more and control dogs with no implants.

Procedures: Lymphocyte transformation testing was performed by exposing peripheral blood lymphocytes to nickel (Ni), chromium (Cr), cobalt (Co), or a combination of these metals.

Electrospun Conducting Polymers: Approaches and Applications.

Inherently conductive polymers (CPs) can generally be switched between two or more stable oxidation states, giving rise to changes in properties including conductivity, color, and volume. The ability to prepare CP nanofibers could lead to applications including water purification, sensors, separations, nerve regeneration, wound healing, wearable electronic devices, and flexible energy storage. Electrospinning is a relatively inexpensive, simple process that is used to produce polymer nanofibers from solution.

Electroactive Polymer-Based Spray Ionization for Direct Mass Spectrometric Analysis.

Electrochemically deposited electroactive polymer (EAP) films were investigated for their potential to enhance the performance of ambient ionization mass spectrometry (MS). Several EAPs of varying hydrophobicity were evaluated, including the superhydrophobic polymer poly[3,4-(2-dodecylethylenedioxy)thiophene] (PEDOT-C). The EAPs were electropolymerized onto indium tin oxide-coated glass, placed in front of the inlet of a mass spectrometer, and charged to 3.

Predictors of engagement and retention in care at a low-threshold substance use disorder bridge clinic.

Introduction: People with substance user disorder (SUD) have frequent intersections with the health care system; however, engagement and retention in SUD care remain low, particularly for marginalized populations. Low-threshold treatment models that aim to eliminate barriers to care are one proposed intervention to increase access and equity in SUD treatment.

Methods: This is a retrospective, cohort study of patients treated at a low-threshold bridge clinic from 2016 to 2021.

A Pragmatic, Stepped-Wedge, Cluster-controlled Clinical Trial of Real-Time Pneumonia Clinical Decision Support.

Care of emergency department (ED) patients with pneumonia can be challenging. Clinical decision support may decrease unnecessary variation and improve care. To report patient outcomes and processes of care after deployment of electronic pneumonia clinical decision support (ePNa): a comprehensive, open loop, real-time clinical decision support embedded within the electronic health record.

Enabling Conducting Polymer Applications: Methods for Achieving High Molecular Weight in Chemical Oxidative Polymerization in Alkyl- and Ether-Substituted Thiophenes.

Polythiophenes (PTs) constitute a diverse array of promising materials for conducting polymer applications. However, many of the synthetic methods to produce PTs have been optimized only for the prototypical alkyl-substituted example poly(3-hexylthiophene) (P3HT). Improvement of these methods beyond P3HT is key to enabling the widespread application of PTs.

CheXED: Comparison of a Deep Learning Model to a Clinical Decision Support System for Pneumonia in the Emergency Department.

Purpose: Patients with pneumonia often present to the emergency department (ED) and require prompt diagnosis and treatment. Clinical decision support systems for the diagnosis and management of pneumonia are commonly utilized in EDs to improve patient care. The purpose of this study is to investigate whether a deep learning model for detecting radiographic pneumonia and pleural effusions can improve functionality of a clinical decision support system (CDSS) for pneumonia management (ePNa) operating in 20 EDs.

Adapting an evidence-supported optimization program for mental health and sport performance in collegiate athletes to fit youth from ethnic/racial minority and low-income neighborhoods: A National Institutes of Health stage model feasibility study.

The current study addresses the need to empirically develop effective mental health interventions for youth from ethnic/racial minority and low-income neighborhoods. Using Stage Model evaluation methods supported by the National Institutes of Health in the US to address underutilization of mental healthcare among racial/ethnic minority youth, this feasibility study demonstrates empirical adaptation of an innovative sport-specific psychological intervention for use in youth from ethnic/racial minority and low-income neighborhoods. An international group of professionals familiar with sport performance and mental health intervention serving the target population experientially examined the adapted intervention protocols in workshops and provided feedback.

Physical characterization of swine and human skin: Correlations between Raman spectroscopy, Tensile testing, Atomic force microscopy (AFM), Scanning electron microscopy (SEM), and Multiphoton microscopy (MPM).

Background: Swine dorsum is commonly utilized as a model for studying skin wounds and assessment of dermatological and cosmetic medicaments. The human abdomen is a common location for dermatological intervention.

Objective: This study provides a correlation between spectral, mechanical, and structural characterization techniques, utilized for evaluating human abdominal skin and swine dorsum.

Erratum: Author Correction: PENet-a scalable deep-learning model for automated diagnosis of pulmonary embolism using volumetric CT imaging.

[This corrects the article DOI: 10.1038/s41746-020-0266-y.].

Sex chromosome complement influences vulnerability to cocaine in mice.

Women acquire cocaine habits faster and are more motivated to obtain drug than men. In general, female rodents acquire intravenous cocaine self-administration (SA) faster and show greater locomotor responses to cocaine than males. Sex differences are attributed to differences in circulating estradiol.

Evaluation of a Machine Learning Model Based on Pretreatment Symptoms and Electroencephalographic Features to Predict Outcomes of Antidepressant Treatment in Adults With Depression: A Prespecified Secondary Analysis of a Randomized Clinical Trial.

Importance: Despite the high prevalence and potential outcomes of major depressive disorder, whether and how patients will respond to antidepressant medications is not easily predicted.

Objective: To identify the extent to which a machine learning approach, using gradient-boosted decision trees, can predict acute improvement for individual depressive symptoms with antidepressants based on pretreatment symptom scores and electroencephalographic (EEG) measures.

Design, Setting, And Participants: This prognostic study analyzed data collected as part of the International Study to Predict Optimized Treatment in Depression, a randomized, prospective open-label trial to identify clinically useful predictors and moderators of response to commonly used first-line antidepressant medications.

Incorporating machine learning and social determinants of health indicators into prospective risk adjustment for health plan payments.

Background: Risk adjustment models are employed to prevent adverse selection, anticipate budgetary reserve needs, and offer care management services to high-risk individuals. We aimed to address two unknowns about risk adjustment: whether machine learning (ML) and inclusion of social determinants of health (SDH) indicators improve prospective risk adjustment for health plan payments.

Methods: We employed a 2-by-2 factorial design comparing: (i) linear regression versus ML (gradient boosting) and (ii) demographics and diagnostic codes alone, versus additional ZIP code-level SDH indicators.

PENet-a scalable deep-learning model for automated diagnosis of pulmonary embolism using volumetric CT imaging.

Pulmonary embolism (PE) is a life-threatening clinical problem and computed tomography pulmonary angiography (CTPA) is the gold standard for diagnosis. Prompt diagnosis and immediate treatment are critical to avoid high morbidity and mortality rates, yet PE remains among the diagnoses most frequently missed or delayed. In this study, we developed a deep learning model-PENet, to automatically detect PE on volumetric CTPA scans as an end-to-end solution for this purpose.

AppendiXNet: Deep Learning for Diagnosis of Appendicitis from A Small Dataset of CT Exams Using Video Pretraining.

The development of deep learning algorithms for complex tasks in digital medicine has relied on the availability of large labeled training datasets, usually containing hundreds of thousands of examples. The purpose of this study was to develop a 3D deep learning model, AppendiXNet, to detect appendicitis, one of the most common life-threatening abdominal emergencies, using a small training dataset of less than 500 training CT exams. We explored whether pretraining the model on a large collection of natural videos would improve the performance of the model over training the model from scratch.

Human-machine partnership with artificial intelligence for chest radiograph diagnosis.

Human-in-the-loop (HITL) AI may enable an ideal symbiosis of human experts and AI models, harnessing the advantages of both while at the same time overcoming their respective limitations. The purpose of this study was to investigate a novel collective intelligence technology designed to amplify the diagnostic accuracy of networked human groups by forming real-time systems modeled on biological swarms. Using small groups of radiologists, the swarm-based technology was applied to the diagnosis of pneumonia on chest radiographs and compared against human experts alone, as well as two state-of-the-art deep learning AI models.

Biomedical Application of Electroactive Polymers in Electrochemical Sensors: A Review.

Conducting polymers are of interest due to their unique behavior on exposure to electric fields, which has led to their use in flexible electronics, sensors, and biomaterials. The unique electroactive properties of conducting polymers allow them to be used to prepare biosensors that enable real time, point of care (POC) testing. Potential advantages of these devices include their low cost and low detection limit, ultimately resulting in increased access to treatment.

Transgenerational Bisphenol A Causes Deficits in Social Recognition and Alters Postsynaptic Density Genes in Mice.

Bisphenol A (BPA) is a ubiquitous endocrine-disrupting chemical. Developmental exposure produces changes in behavior and gene expression in the brain. Here, we examined social recognition behaviors in mice from the third familial generation (F3) after exposure to gestational BPA.