Trend analysis and cross nations inequality analysis of infectious skin diseases from 1990 to 2021.

The skin serves as a crucial barrier against microbial invasion, but breaches in this barrier can lead to infectious skin diseases (ISD). ISD, including bacterial (BSD), fungal (FSD) and viral Skin Disease (VSD), pose significant global health challenges. Age-standardized and age specific incidence, prevalence, and lived with disability (YLDs) data for ISD were sourced from the Global Burden of Disease Study 2021 (GBD 2021) and demographic data from the United Nations.

Strong Heteroatomic Bond-Induced Confined Restructuring on Ir-Mn Intermetallics Enable Robust PEM Water Electrolyzers.

Low-iridium acid-stabilized electrocatalysts for efficient oxygen evolution reaction (OER) are crucial for the market deployment of proton exchange membrane (PEM) water electrolysis. Manipulating the in situ reconstruction of Ir-based catalysts with favorable kinetics is highly desirable but remains elusive. Herein, we propose an atomic ordering strategy to modulate the dynamic surface restructuring of catalysts to break the activity/stability trade-off.

Structural Regulating of Cu-Based Metallic Electrocatalysts for CO to C Products Conversion.

Electrochemical carbon dioxide reduction reaction (CORR) to highly value-added multi-carbon (C) fuels or chemicals is a promising pathway to address environment issues and energy crisis. In the periodic table, Cu as only the candidate can convert CO to C products such as CH and CHOH due to the suitable absorption energy to reaction intermediate. However, application of Cu is limited for its low activity and poor selectivity.

Recurrent Enteritis and Intestinal Obstruction in a Patient with Chronic Mucocutaneous Candidiasis due to STAT1 Gain-of-Function Mutation.

We presented a case of chronic mucocutaneous candidiasis (CMC) due to STAT1 GOF mutation with recurrent enteritis and intestinal obstruction. A 33-year-old woman complained of recurrent oral erosion and finger (toe) nails damage for over 30 years. Candida albicans were cultured from the oral mucosa and nails.

Orthogonal hologram memory extending from visible to UV mediated with TaO/TiO:Ag heterojunctions.

The photon-energy conversion covering the full spectral wave band is crucial for detecting and storing information. Schottky junctions in nanoscale such as TiO:Ag enable multicolor photochromism and information storage in the visible region. However, the photoelectrons from the UV-excited semiconductor cause the loss of information.

Convergence of Tandem Catalysis and Nanoconfinement Promotes Electroreduction of CO to C Products.

An efficient electrocatalytic conversion of CO into valuable multicarbon (C) products requires enhanced C-C coupling of C1 intermediates. Herein, we combine a tandem effect with a confinement strategy to construct a hollow CuO@Ag nanoshell electrocatalyst with a well-defined porous structure to improve the *CO intermediate coverage on the catalyst surface. In CO electroreduction, in situ Raman spectroscopy shows that the introduction of Ag can not only promote the CO intermediate production but also improve the stability of Cu to capture the *CO intermediate due to a CO-tandem effect, and the fine-tuned hollowness degree and pore size of CuO@Ag create a spatially confined microenvironment for trapping CO as well as the enrichment of CO, which greatly facilitate subsequent C-C coupling for C product.

Predicting Unplanned Return to Operating Room Following Primary Total Shoulder Arthroplasty: Insights from Fair and Explainable Ensemble Machine Learning.

Reoperation is the most significant complication following any surgical procedure. Developing machine learning methods that predict the need for reoperation will allow for improved shared surgical decision making and patient-specific and preoperative optimisation. Yet, no precise machine learning models have been published to perform well in predicting the need for reoperation within 30 days following primary total shoulder arthroplasty (TSA).

A Case Demonstration of the Open Health Natural Language Processing Toolkit From the National COVID-19 Cohort Collaborative and the Researching COVID to Enhance Recovery Programs for a Natural Language Processing System for COVID-19 or Postacute Sequelae of SARS CoV-2 Infection: Algorithm Development and Validation.

Background: A wealth of clinically relevant information is only obtainable within unstructured clinical narratives, leading to great interest in clinical natural language processing (NLP). While a multitude of approaches to NLP exist, current algorithm development approaches have limitations that can slow the development process. These limitations are exacerbated when the task is emergent, as is the case currently for NLP extraction of signs and symptoms of COVID-19 and postacute sequelae of SARS-CoV-2 infection (PASC).

Adoption of the OMOP CDM for Cancer Research using Real-world Data: Current Status and Opportunities.

Background: The Observational Medical Outcomes Partnership (OMOP) common data model (CDM) that is developed and maintained by the Observational Health Data Sciences and Informatics (OHDSI) community supports large scale cancer research by enabling distributed network analysis. As the number of studies using the OMOP CDM for cancer research increases, there is a growing need for an overview of the scope of cancer research that relies on the OMOP CDM ecosystem.

Objectives: In this study, we present a comprehensive review of the adoption of the OMOP CDM for cancer research and offer some insights on opportunities in leveraging the OMOP CDM ecosystem for advancing cancer research.

Efficacy and safety of low-dose interleukin 2 in the treatment of moderate-to-severe bullous pemphigoid: A single center perspective-controlled trial.

Background: Regulatory T cells (Tregs) are reduced in the peripheral blood and skin lesions of patients with bullous pemphigoid (BP). Low-dose interleukin 2 (IL-2) therapy can stimulate Tregs specifically, suggesting potential for the treatment of BP.

Objective: To evaluate the response to low-dose IL-2 therapy in the treatment of moderate-to-severe BP.

Enhancing Clinical Relevance of Pretrained Language Models Through Integration of External Knowledge: Case Study on Cardiovascular Diagnosis From Electronic Health Records.

Background: Despite their growing use in health care, pretrained language models (PLMs) often lack clinical relevance due to insufficient domain expertise and poor interpretability. A key strategy to overcome these challenges is integrating external knowledge into PLMs, enhancing their adaptability and clinical usefulness. Current biomedical knowledge graphs like UMLS (Unified Medical Language System), SNOMED CT (Systematized Medical Nomenclature for Medicine-Clinical Terminology), and HPO (Human Phenotype Ontology), while comprehensive, fail to effectively connect general biomedical knowledge with physician insights.

Sex differences in sociodemographic, clinical, and laboratory variables in childhood asthma: A birth cohort study.

Background: There are marked sex differences in the prevalence and severity of asthma, both during childhood and adulthood. There is a relative lack of comprehensive studies exploring sexdifferences in pediatric asthma cohorts.

Objective: To identify the most relevant sex differences in sociodemographic, clinical, and laboratory variables in a well-characterized large pediatric asthma cohort.

Identification of immune-associated biomarkers of diabetes nephropathy tubulointerstitial injury based on machine learning: a bioinformatics multi-chip integrated analysis.

Background: Diabetic nephropathy (DN) is a major microvascular complication of diabetes and has become the leading cause of end-stage renal disease worldwide. A considerable number of DN patients have experienced irreversible end-stage renal disease progression due to the inability to diagnose the disease early. Therefore, reliable biomarkers that are helpful for early diagnosis and treatment are identified.

Considerations for Quality Control Monitoring of Machine Learning Models in Clinical Practice.

Integrating machine learning (ML) models into clinical practice presents a challenge of maintaining their efficacy over time. While existing literature offers valuable strategies for detecting declining model performance, there is a need to document the broader challenges and solutions associated with the real-world development and integration of model monitoring solutions. This work details the development and use of a platform for monitoring the performance of a production-level ML model operating in Mayo Clinic.