Citations

ABSTRACT Herpes simplex virus type 1 (HSV-1), herpes simplex virus type 2 (HSV-2), and varicella-zoster virus (VZV) are associated with a range of clinical manifestations depending on the anatomical site of infection. HSV and VZV lesions often exhibit similar phenotypical characteristics, making clinical differentiation challenging. Detection and differentiation of HSV-1, HSV-2, and VZV are essential for diagnosis and treatment management. An automated multiplex real-time PCR assay, Alinity m HSV 1 & 2/VZV assay, was developed to detect and differentiate HSV-1, HSV-2, and VZV. The assay was validated for use with cutaneous and mucocutaneous lesion swab specimens collected in commercial viral transport media from symptomatic patients. The assay demonstrated a limit of detection of 5.90 TCID 50 /mL for HSV-1, 2.07 TCID 50 /mL for HSV-2, and 0.055 TCID 50 /mL for VZV. Testing with clinical swab specimens demonstrated greater than 95% clinical sensitivity and specificity for all three analytes. The Alinity m HSV 1 & 2/VZV assay provides a reliable and accurate method for detection and differentiation of HSV-1, HSV-2, and VZV in lesion swab specimens for improved diagnostic accuracy and patient management. IMPORTANCE Herpes simplex virus type 1 (HSV-1), herpes simplex virus type 2 (HSV-2), and varicella-zoster virus (VZV) can cause skin and mucocutaneous lesions that often appear clinically similar, making accurate diagnosis difficult without laboratory testing. Rapid and reliable identification of the causative virus is important because patient counseling, treatment decisions, and infection management differ among these infections. In this multicenter study, a fully automated molecular assay was evaluated that simultaneously detects and differentiates HSV-1, HSV-2, and VZV from a single-lesion swab specimen. Testing of more than 1,200 clinical specimens demonstrated high sensitivity and specificity for all three viruses. The assay also offers a streamlined workflow with minimal hands-on time and rapid turnaround. These findings support the use of this multiplex assay as a practical diagnostic tool that can improve laboratory efficiency and provide clinicians with timely, accurate results to guide patient care.

Background: Staphylococcus aureus causes bloodstream and skin infections in humans. The prevalence of multidrug-resistant S. aureus strains means new antibiotics are needed. A novel antimicrobial drug named SK-03-92, a synthetic aromatic organic stilbenoid compound, kills S. aureus cells within 30 min, but an increase in both biofilm formation and persister cells occurs. SK-03-92 treatment downregulates transcription of the biofilm regulating protein regulator (brpR) gene and biofilm regulating protein sensor (brpS) gene in S. aureus. BrpR/BrpS system may be a LytTR regulatory system tied to biofilm formation, creation of persister cells, and late-stage competence in S. aureus. The aim of this study was to determine what biofilm, late-stage competence, and persister-associated genes were regulated in a brpR mutant compared to wild-type strains. Methods: In this study, involvement of BrpR in regulating other genes was assessed by comparing transcriptional changes in a brpR mutant strain to the S. aureus parent strain via RNA sequencing (RNA-Seq). Bioinformatic analysis was then performed on the RNA-Seq data to assess what biochemical pathways might be involved. Results: From these analyses, 440 genes were identified that had significant differences in transcript abundance when comparing the brpR mutant to wild-type strains. Quantitative reverse transcription polymerase chain reaction analysis confirmed bacA, icd, metE, and pdhA transcript levels were lower, whereas alr and mraY were higher in the brpR mutant versus wild-type strain. Furthermore, an enzymatic assay targeting NADH production from the pyruvate dehydrogenase complex showed lower levels in the mutant compared to wild-type strain. Conclusions: Overall, the study demonstrated several biosynthetic pathways tied to biofilm formation and late-stage competency may be regulated by BrpR and some potential leads for the mechanism of action of the SK-03-92 drug were uncovered.

Despite advances in pharmacogenomics that have led to innovative therapeutic strategies, substantial interindividual variability in drug efficacy and toxicity persists. Both endogenous and exogenous factors contribute to this unexplained variation, resulting in suboptimal treatment effectiveness, unnecessary healthcare costs, preventable mortality, and significant human suffering. The emerging field of exposomics is aimed at addressing the complex exposures that impact human health and offers an opportunity to interrogate how these exposures influence drug actions and outcomes. Here, we introduce pharmacoexposomics as a complementary framework that systematically quantifies the body's metabolic response to drug and environmental exposures using high-resolution mass spectrometry. We then present IndiPHARM (Individualized Pharmacology), a multi-institution, Advanced Research Projects Agency for Health–supported platform designed to integrate population-scale pharmacoexposomics with pharmacogenomics and clinical data to elucidate interindividual variability in drug response, identify adverse reactions, and inform tailored, effective treatment strategies that advance precision medicine.

Aims Extensor mechanism disruption is a major complication following total knee arthroplasty, with treatment often consisting of extensor mechanism reconstruction utilizing monofilament polypropylene mesh (Marlex mesh, C. R. Bard, Inc). Infection following this procedure is devastating, often resulting in revision surgery and mesh removal, with permanent limb dysfunction. This study evaluated the in vitro ability of vancomycin-loaded polypropylene mesh to inhibit growth of isolates of three Staphylococcus species from patients with periprosthetic joint infection. Methods The mesh was prepared in two configurations – single-layer and eight-layer – and soaked for ten minutes in either sterile water (control) or vancomycin solution (50 mg/ml), after which the mesh was placed on tryptic soy agar plates inoculated with staphylococci and incubated at 37°C for 18 to 24 hours. Zones of inhibition were measured daily over seven days, with mesh transferred to freshly inoculated plates daily. Testing was performed in triplicate, with experiments repeated three times for each bacterial isolate. A total of 30 clinical isolates were tested (five methicillin-susceptible Staphylococcus epidermidis , five methicillin-resistant S. epidermidis , ten methicillin-resistant Staphylococcus aureus , and ten Staphylococcus lugdunensis ). Results Across all species, the eight-layer vancomycin-loaded mesh produced statistically significantly larger median zones of inhibition than the single-layer mesh. The antibacterial activity of the eight-layer mesh persisted through to day 7, while the single-layer mesh showed reduced activity by days 4 to 5. Conclusion Compared with single-layer vancomycin-loaded mesh, multilayer vancomycin-loaded mesh, which mimics clinical application, provides enhanced and sustained antibacterial activity in vitro. Cite this article: Bone Joint Res 2026;15(8):998–1004.

Summary Background Body composition is recognized as a major determinant of health outcomes, but its multidimensional nature makes clinical adoption challenging. We sought to develop and validate a body composition index (BCI) for all-cause mortality risk assessment, integrating variables of six body composition tissues. Methods We analyzed 28509 consecutive patients undergoing myocardial perfusion imaging with routine low-dose chest CT attenuation correction (CTAC) scans acquired during myocardial perfusion imaging (MPI) at 12 centers across four countries. An artificial intelligence-based BCI was developed in a cohort of 15037 patients’ CTACs by integrating the CT-derived metrics of bone, skeletal muscle, and four adipose tissue compartments, coronary artery calcium score, and basic demographic variables (age, sex, BMI). The performance of BCI for mortality prediction was validated in an internal cohort of 6444 patients and an external cohort of 7028 patients by prognosis, calibration, net benefit, and explainability. Model-based simulation of tissue metrics modification was performed to evaluate estimated mortality risk reduction. Findings During a median of 3.5 (IQR [1.9, 5.1]) years, 4697 (16%) patients died. In the external testing cohort, the BCI demonstrated excellent discrimination for mortality (area under receiver operating characteristic curve 0.78 (95% CI [0.76, 0.79]) and Harrell’s concordance index 0.75 [0.73, 0.76]), calibration, and net benefit overall and across pre-specified subgroups stratified by patient characteristics and imaging protocols. Visceral adipose tissue attenuation was the most influential body composition measure, followed by skeletal muscle volume. Simulated improvement in body composition was associated with significant mortality risk reduction. Interpretation An index combining six body composition measures obtained opportunistically from routine chest CT provides robust mortality risk stratification. By converting complex body composition information into a single interpretable score, the BCI can facilitate clinical implementation of opportunistic CT biomarkers and guide individualized preventive strategies. Funding National Institute of Health Research in context Evidence before this study We searched PubMed and Google Scholar on July 7, 2026 for English language studies using the terms (“mortality prediction” OR “death prediction”) AND (“integrated body composition analysis” OR “body composition metrics integration”) AND (“predictive model” OR “model development”), and identified 41 publications describing predictive models incorporating body composition metrics. Recent advances in artificial intelligence (AI) have enabled increasingly granular quantification of body composition from CT, with multiple tissues and tissue-specific metrics showing independent prognostic value for all-cause mortality (ACM) prediction. However, reported associations of body composition metrics with mortality risk were highly inconsistent or even contradictory across studies. This heterogeneity likely reflected the differences in study design or the inherent complexity of body composition analysis, such as the interaction among variables derived from multiple tissues. Thus, it is difficult to interpret body composition results reliably in clinical practice. We hypothesized that integrating multiple body composition metrics could achieve comprehensive and reliable prediction of mortality across patient populations. Added value of this study In this large international, multicenter longitudinal cohort study of 28509 consecutive patients with suspected or known coronary artery disease (CAD) undergoing myocardial perfusion imaging across 12 sites in four countries, we developed and externally validated an artificial intelligence-based body composition index (BCI) for all-cause mortality prediction. The BCI integrated chest CT-derived metrics of six body composition tissue compartments – bone, skeletal muscle (SM), subcutaneous adipose tissue (SAT), intramuscular adipose tissue (IMAT), visceral adipose tissue (VAT), and epicardial adipose tissue (EAT), together with demographics (age, sex, BMI) and CT-derived coronary artery calcium (CAC) score, all derived automatically from routine low-dose attenuation correction CT scans without additional imaging or laboratory tests. In an external validation cohort of 7028 patients, the BCI achieved robust discrimination for mortality prediction, substantially outperforming a baseline model using demographics and calcium score alone, and exceeded the discriminative performance of prior body composition models. Performance was consistent across subgroups defined by sex, age, body mass index, cardiometabolic risk factors, imaging modality, and acquisition protocol, supporting generalizability across clinical settings. The BCI demonstrated excellent calibration and net clinical benefit. Explainability analysis identified visceral adipose tissue attenuation and skeletal muscle volume as the dominant contributors to mortality risk, offering tissue-specific therapeutic targets that complement generic weight-based metrics. Model-based simulations suggested that improvements in these specific compartments could reduce estimated mortality risk more effectively than equivalent changes in BMI alone, pointing toward precision therapeutic targets beyond weight reduction. Our study improved over previous literatures on body composition-based predictive models in one or more of the following ways: (1) we considered six types of body composition tissues while existing studies considered only three or less tissues, thus significantly higher granularity and improved ability to capture refined tissue interactions; (2) our model was rigorously validated in external sites, showing cross-site generalizability while most previous work lacked external validation; (3) our model achieved significant prediction performance improvement over previous body composition predictive models, offering better clinical outcomes prediction and facilitating the suitability for widespread clinical adoption; (4) our study was conducted with a large multi-center cohort across multiple countries, achieving statistical power and demonstrating reliability towards patients characteristics and imaging acquisition protocols while previous studies involved small single-center cohorts; (5) our model required only CT scans and basic demographics which were widely available in clinical routine while the existing models required dedicated laboratory tests or specialized imaging modalities, enhancing the applicability in routine clinical practice. Implications of all the available evidence BCI offers an explainable and clinically deployable tool for improved prediction of mortality risk from existing chest CT scans. Its use could inform updates to existing risk models, guide precision preventive strategies, and support development of therapeutics targeting specific body composition phenotypes.

Objective Our objective was to identify and describe the clinical characteristics and outcomes in patients with pre‐existing idiopathic inflammatory myopathies (IIMs) in the Veterans Health Administration (VHA) treated with immune checkpoint inhibitors (ICIs). Methods All veterans receiving ICI infusions were identified. Patients with at least two International Classification of Disease codes for IIM before first ICI infusion underwent chart review to identify patients with confirmed IIM by American College of Rheumatology/EULAR criteria. The demographics, cancer diagnoses, laboratory findings, clinical course, and mortality rates were reported. IIM cases were also reviewed to determine if patients developed IIM flare following ICI treatment. Results We identified 29,539 veterans who received at least one ICI infusion in the VHA between June 6, 2011, and February 14, 2023. The eight patients with confirmed IIM before ICI treatment were mostly White men with an average age of 71.7 years at first ICI infusion. The IIM diagnoses were dermatomyositis in three patients, polymyositis in two, antisynthetase syndrome in two, and one rheumatoid arthritis (RA)/myositis overlap. Cancer diagnoses were lung (two), melanoma (two), head and neck (two), kidney/other urinary (one), and mesothelioma (one). Diagnosis of IIM was made on average 3.1 years before ICI treatment. One patient had an IIM flare after ICI. No IIM flares were identified in other patients. Conclusion Our findings show that an IIM flare after ICI therapy can occur, but most patients did not develop a flare. These observations suggest ICIs can be considered as an option in patients with cancer with IIM with shared decision‐making and close monitoring.

Abstract Introduction Patients with metastatic spine disease (MSD) present with varying degrees of systemic illness and frailty. Given the palliative intent of surgery and radiation treatment for MSD, treatment risks must be judiciously weighed against potential benefits. We sought to examine the impact of peroperative nutritional status as measured by the PG-SGA on survival in patients undergoing surgery and radiation for MSD. Methods This study involved an international, multicenter prospective, observational registry of patients with MSD: AO Knowledge Forum Tumor’s Metastatic Tumor Research and Outcomes Network (MTRON). Variables including baseline demographics, oncologic data, adverse events (medical adverse events and surgical complications), length of stay, and nutritional status were prospectively collected. The Patient-Generated Subjective Global Assessment (PG-SGA) was used to evaluate nutritional status. Results A total of 589 patients were included in this study; 469 (80%) of whom underwent surgery and radiation and 120 (20%) of whom underwent radiation alone. The majority of patients (362, 61%) were classified as well nourished, however, 68 patients (12%) were classified as severely malnourished. Among patients that underwent surgery and radiation, the median overall survival of well nourished patients was 491 days compared to 117 days in the severely malnourished patients (p < 0.001). A multivariate analysis demonstrated severe malnourishment and ECOG performance status of 3 or 4 were associated with worse overall survival. Conclusion Malnutrition as measured by the PG-SGA demonstrated to be significantly and independently associated with perioperative survival in patients with MSD. Further studies that investigate optimal pre-treatment optimization for patients with MSD in order to mitigate morbidity and mortality risk are essential.

Importance Elevated low-density lipoprotein cholesterol (LDL-C) is a modifiable risk factor for cardiovascular disease, the leading cause of premature death worldwide. Assessing the LDL-C–related burden is critical for guiding prevention and treatment strategies. Objectives To estimate the global, regional, and national burden of ischemic heart disease and ischemic stroke attributable to elevated LDL-C (relative to 35-54 mg/dL) from 1990 to 2023 and to quantify the contributions of population growth, aging, risk-deleted burden, and exposure changes to burden trends. Design, Setting, and Population This comparative risk assessment, part of the Global Burden of Disease Study 2023, estimated population-level LDL-C exposure and associated health loss in 204 countries and territories. Mean LDL-C levels were estimated using spatiotemporal gaussian process regression based on 806 studies across 161 countries. Relative risks were derived from meta-analyses of 38 randomized clinical trials. Population-attributable fractions for deaths and disability-adjusted life-years (DALYs) were estimated by age and sex for adults aged 25 years or older from 1990 to 2023, with 95% uncertainty intervals. Exposure Population-level LDL-C concentrations. Main Outcomes and Measures Population-attributable fractions, counts, and rates (all ages and age standardized per 100 000) of LDL-C–attributable deaths and DALYs from ischemic heart disease and ischemic stroke, with uncertainty intervals. Results In 2023, elevated LDL-C accounted for 3.6 million deaths (95% uncertainty interval, 2.2-5.4 million; 6.0% of global mortality) and 90.7 million DALYs (95% uncertainty interval, 58.9-123.3 million; 3.2% of DALYs). Although global all-ages rates remained stable, age-standardized death and DALY rates decreased by 45.6% and 39.5%, respectively, since 1990. In 2023, age-standardized LDL-C–attributable DALY rates were highest in Eastern Europe and lowest in high-income Asia-Pacific. One-third of the global LDL-C burden occurred in India and China. Population growth and aging drove the increasing burden, with notable regional disparities in LDL-C exposure and risk-deleted DALY rates shifting toward middle-sociodemographic settings. Conclusions and Relevance Despite declining age-standardized rates, the absolute LDL-C burden has increased since 1990 due to demographic changes and has shifted toward middle-sociodemographic countries. Measurement and surveillance gaps persist. Strengthened prevention, diagnosis, and treatment access strategies are essential to mitigate the health burden of LDL-C.