Publications by Year: 2026

Intrinsic enteric neurons regulate gut motility, secretion, and absorption, whereas extrinsic dorsal root ganglia sensory neurons are key in colon interoception and visceral pain. A new study raises the question of whether some initial interoceptive signals could originate from enteric neurons.

Direct sputum whole genome sequencing (dsWGS) can revolutionize Mycobacterium tuberculosis (Mtb) diagnosis by enabling rapid detection clinically relevant resistance mutations and strain diversity without the biohazard of culture. We searched PubMed, Web of Science, and Google Scholar, identifying 8 studies meeting inclusion criteria for testing protocols for dsWGS. Utilising meta-regression, we identified factors positively associated with dsWGS success, including higher Mtb bacillary load, mechanical disruption, enzymatic/chemical lysis and sequencing volume. Decontamination with sodium hydroxide (NaOH) was negatively associated with dsWGS success (OR = 0.00032, 95 % CI: 1.33 × 10ˆ-6-0.077; p = 0.004), likely due to its harsh effects on Mtb cells. Mechanical lysis (OR = 6120, 95 % CI: 7.23-5.18 × 10ˆ6; p = 0.011) and enzymatic/chemical lysis (OR = 131, 95 % CI: 1.68-1.03 × 10ˆ4; p = 0.028) were positively associated with sequencing success, as was heat inactivation (OR = 4.66, 95 % CI: 1.24-17.5; p = 0.023). Total sequencing volume was also strongly associated with dsWGS success (OR = 10.35, 95 % CI: 4.43-24.2; p = 6.53 × 10ˆ-8). In addition to these effects, we also observed high variability in pre-processing approaches, highlighting the need for standardized practices and identified pre-processing steps including decontamination and DNA extraction as priorities for further optimization. Considering the strong association between Mtb load and successful dsWGS, protocols for optimal sputum sample collection, handling, and storage could also further enhance the success rate of dsWGS.

Gastrointestinal adenocarcinomas, including colorectal cancer (CRC) and gastroesophageal junction (GEJ) carcinoma, represent a significant global health burden. Recent advances in large-scale multi-omics profiling, particularly through The Cancer Genome Atlas (TCGA), have revealed the genetic heterogeneity and underlying biology of these tumors. Integrating molecular biomarkers with histopathology into routine practice guides classification, prognosis, and targeted interventions. In CRC, hypermutated subtypes-defined by microsatellite instability (MSI) or polymerase epsilon (POLE) mutations-demonstrate high tumor mutational burden (TMB) and robust response to immune checkpoint blockade. Alternatively, non-hypermutated tumors, driven by chromosomal instability, harbor recurrent alterations in RAS, BRAF, HER2, and NTRK, enabling biomarker-based stratification for targeted therapies. Exploratory markers such as PIK3CA mutations and TMB are being investigated, although their predictive value in microsatellite-stable CRC remains limited. Similarly, GEJ carcinomas can be classified into four molecular subgroups: Epstein-Barr virus (EBV)-associated, MSI, chromosomal instability, and genomically stable. Each subtype is defined by characteristic biology and carries distinct therapeutic implications, with actionable targets including HER2 amplification, PD-L1 expression, and claudin 18.2 (CLDN18.2). Established clinical biomarkers such as MSI, PD-L1, and HER2 are standard in precision oncology, while emerging markers like CLDN18.2, TMB, and KRAS G12C, extend the therapeutic landscape. Combining biomarker-driven immunotherapy and targeted approaches such as PD-1 blockade in MSI-H or EBV-positive tumors, HER2-directed therapy, and CLDN18.2 inhibition, has demonstrated a paradigm shift in the clinical management. This review highlights a pathologist-centered perspective on molecularly defined subgroups, actionable biomarkers, and evolving therapeutic paradigms in CRC and GEJ carcinoma, advancing precision oncology.

OBJECTIVE: CHWs are a key workforce to address health disparities and offer expertise in community engagement, health promotion, and system navigation. Academic Medical Institutions (AMIs) play a critical role in supporting CHW workforce development and training, yet a systematic review of how AMIs engage with CHWs has not been conducted.

METHODS: Literature was systematically searched in November 2022 and February 2024 from the following databases: PubMed, Web of Science, CINAHL, SocINDEX, and PsychInfo. Forward and backward citation searches in February 2025 identified an additional 64 articles. We reviewed 347 full-text articles, and 136 were included in the final sample.

RESULTS: CHW/AMI engagement was delineated by three, non-mutually exclusive categories: 1) intervention implementation/evaluation (n = 104); 2) workforce development (n = 32), and 3) community-based participatory research (CBPR) (n = 23). Intervention implementation and evaluation studies measured the effectiveness of CHWs in a variety of healthcare settings. Among intervention studies that assessed efficacy, 52 (79%) found that CHWs significantly improved at least one health outcome. In workforce development, AMIs developed specialized training for CHWs or incorporated CHWs into training for medical students and residents. In CBPR studies, CHWs contributed to recruitment, community engagement, needs assessment, data collection, and community expertise. However, CHWs were rarely included in the interpretation or dissemination of findings, or as authors.

CONCLUSIONS: CHWs contribute to AMI’s tripartite mission and preventive medicine efforts including addressing health disparities, improving patient outcomes and educating future doctors. Developing sustainable CHW career paths with equitable payment structures is essential to move from engagement to partnership.

SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12939-026-02770-w.

BACKGROUND: Precise, quantitative assessment of disease features on histological images from preclinical models is essential for therapeutic development in diseases such as pulmonary fibrosis. However, current histological fibrosis scoring methods, such as Ashcroft Scoring have several limitations, including high time and labor requirements by expert pathologist readers, subjective semi-quantitative assessment and interobserver variability. The goal of this study was to assess the feasibility of a supervised AI/ML-based framework for automated, rapid, objective quantification of fibrosis content and spatial distribution in histology slides obtained from a variety of preclinical fibrosis models.

METHODS: Lung histology slides stained with Masson’s trichrome were obtained from 194 individual mice from two independent cohorts of preclinical mouse models of pulmonary fibrosis. A supervised AI/ML algorithm was trained, validated and independently tested to automatically detect, segment and quantify fibrosis compared against independent Ashcroft scoring by an expert pathologist reader. Spatial distribution of AI/ML-segmented fibrosis patterns were compared across histology images.

RESULTS: AI/ML-based fibrosis quantification demonstrated strong correlation with Ashcroft score, both in the validation cohort (Spearman ρ = 0.85, CI: 0.72–0.92), and in the independent, de novo test cohort (Spearman ρ = 0.89, CI: 0.84–0.93) with rapid assessment time (  1.5 times faster). Additionally, Ripley’s K analysis revealed differences in spatial distribution of AI-segmented fibrosis patterns among samples with similar Ashcroft scores and overall fibrosis content.

CONCLUSIONS: The AI/ML framework developed and independently validated in this study provides a robust, computationally-efficient method for precise, user-friendly, objective measurement of fibrosis content and spatial distribution, which would have major utility in preclinical therapeutic trials and investigations of disease pathogenesis.

Inhaled nitric oxide (iNO), long used as a selective pulmonary vasodilator, has demonstrated potential antimicrobial and antiviral properties when administered at high concentrations (> 20 parts per million, ppm). While definitive evidence is still lacking, this narrative review synthesizes the emerging clinical and mechanistic properties supporting high-dose iNO as a potential therapeutic strategy for lower respiratory tract infections, including drug-resistant bacterial pneumonias, COVID-19, nontuberculous mycobacteria, and bronchiolitis. We summarize safety data from laboratory studies, Phase I trials, clinical findings from 27 predominantly early-phase studies, and highlight its as both hospital-based and home-based therapy. High-dose iNO acts through multiple pathways, including direct microbial killing, biofilm disruption, immune modulation, and mucociliary enhancement, and holds promise in addressing unmet needs in respiratory infection management. We also propose a roadmap for future research to optimize dosing, delivery, and efficacy endpoints in well-defined patient populations.

OBJECTIVE: Obstructive hydrocephalus is a critical radiographic finding requiring emergent treatment. Its identification on head CT by an AI model could facilitate sooner life-saving interventions, although there are common co-occurring findings, including intracranial hemorrhage, that can confound this interpretation. This external validation assessed the accuracy of an AI model at identifying obstructive hydrocephalus, including in the presence or absence of other findings.

MATERIALS AND METHODS: This retrospective cohort included 177 thin (≤ 1.5 mm) series and 194 thick (> 1.5 and ≤ 5 mm) series from 200 non-contrast head CT cases. These cases were obtained from patients aged ≥ 18 years at 5 hospitals in the United States. Each case was interpreted independently by up to three neuroradiologists. Each series was then interpreted by the AI model.

RESULTS: The AI model performed with an area under the curve of 0.988 (95% confidence interval (CI): 0.971-0.998) on thin series and 0.986 (95% CI: 0.969-0.997) on thick series. These results were broadly maintained in subgroups for the presence or absence of intracranial hemorrhage, parenchymal abnormality, and ventricular drain, and across demographic and scanner manufacturer subgroups.

CONCLUSIONS: The AI model accurately identified obstructive hydrocephalus in this dataset. Its performance in subgroup analyses reflected its robustness.

KEY POINTS: Question Can an artificial intelligence model accurately identify obstructive hydrocephalus on head computed tomography, including in the presence or absence of common co-occurring imaging findings? Findings This model accurately identified obstructive hydrocephalus on thin and thick series, including in the presence or absence of intracranial hemorrhage, parenchymal abnormality, and ventricular drain. Clinical relevance This model could assist with triaging abnormal cases, enabling earlier identification and management of obstructive hydrocephalus. Its maintained performance with or without co-occurring findings suggests it specifically identifies obstructive hydrocephalus rather than these findings.

BACKGROUND: Interhospital transfer (IHT), the movement of patients between acute care hospitals, has traditionally been based on the need to provide patients with care not available at the hospital to which they initially present. However, additional factors influencing medicine transfer decision-making and their interplay have not been described within a comprehensive framework.

OBJECTIVE: The objective of the study is to characterize and integrate factors shaping IHT decision-making for medicine patients into a comprehensive conceptual model.

DESIGN: This is a qualitative study using focus groups guided by clinical cases and semistructured discussion guides as part of the POINT Study ("Identification and Prevention of Potentially Inappropriate Inter-Hospital Transfer," AHRQ R01 HS028621). Data were analyzed using thematic analysis with deductive and inductive approaches.

PARTICIPANTS/SETTING: We used purposive convenience sampling to recruit patients/families, clinicians, and hospital leadership from 18 academic medical centers in the POINT Study and their affiliates. Patient/family representatives were recruited from a volunteer patient family advisory council.

MAIN OUTCOMES AND MEASURES: IHT decision-making themes were identified through thematic analysis.

KEY RESULTS: Seven 1-h focus groups included 39 participants from 13 tertiary hospitals and their affiliates. We grouped factors that shape IHT decision-making into themes and subthemes and describe their interactions. Specifically, we identified that medical necessity and contextual factors (e.g., hospital capacity) influence transfer decisions, with contextual factors playing a larger role than previously recognized. Participants also considered patient-, clinician-, and organization-level outcomes, as well as constraints imposed by IHT processes and the broader healthcare ecosystem. Based on these findings, we developed a conceptual model that captures the interrelationships among factors, potential IHT outcomes, and system constraints that influence IHT decision-making.

CONCLUSIONS: IHT decision-making reflects a complex interplay of medical necessity, contextual factors, anticipated outcomes, and system constraints. Our conceptual model provides a nuanced understanding of these dynamics and offers targets for improving transfer processes and supporting informed IHT decision-making.

Social interaction supports brain health and recovery after neurological injury. Yet no validated tool exists for real-time measurement in individuals with and without neurological deficits. We developed SocialBit, a lightweight, privacy-preserving machine learning algorithm that detects social interactions using ambient audio features on a commercial smartwatch. In a prospective validation study, we evaluated SocialBit against livestream minute-by-minute human-coded ground truth in 153 hospitalized stroke patients who wore the device for up to 8 days, generating 88,918 min of observation. In these patients, the stroke severity and cognition spanned broad clinical ranges (NIH Stroke Scale 0-25; Montreal Cognitive Assessment 8-30), and 24 patients had aphasia across diverse subtypes, including severe presentations. SocialBit achieved high overall performance (sensitivity 0.87, specificity 0.88, area under the curve 0.94) and maintained accuracy in patients with language deficits (AUC 0.93). Despite lower temporal sampling, SocialBit produced interaction frequency distributions closely matching minute-by-minute human coding. Performance was robust across environments and interaction types. Of clinical relevance, SocialBit showed that patients with more severe strokes engaged in less social interaction, paralleling human-coded results. SocialBit is an accurate digital biomarker of social interaction with potential applications in remote monitoring and clinical trials.