Citations

Microplastics may pose health risks, particularly for chronic lung diseases. Clarifying the link between circulating microplastics and pulmonary disease is vital for shaping research and interventions. The objective of this study was to evaluate whether microplastics in peripheral blood are associated with COPD or IPF vs. no lung disease. In this pilot prospective case-control study, participants were grouped as control (n = 10), COPD (n = 9), or IPF (n = 10). Relevant comorbidities and exposures were obtained from records and questionnaires. All underwent standardized blood collection (PlasticTox©). Samples were analyzed for total microplastic concentration, stratified by size (<10 µm, 10–30 µm, 30–70 µm). The primary outcome was to show a difference in total microplastic burden between lung disease and controls. Secondary measures were to determine size-specific concentrations and associations with demographic variables and smoking history. Among 29 participants (median age 70 (IQR 64–80); 14 women (48.3%)), COPD/IPF groups had significantly higher total microplastic concentrations vs. controls (median 26.0 vs. 3.5 particles/100 µL; p < 0.01). Particle burdens <10 µm and 10–30 µm were particularly elevated (both p < 0.01). After adjusting for smoking, only the <10 µm fraction remained independently associated with lung disease (adjusted odds ratio 1.94 (95% CI, 1.23–7.04)). In this pilot exploratory study, individuals with COPD or IPF showed greater circulating microplastic levels than controls. These findings should be interpreted as hypothesis-generating, and larger analytically validated studies are needed to clarify directionality, causal mechanisms, contamination control, and the clinical relevance of circulating microplastic burden.

Background: Tablet splitting is widely used to individualize dosing of metoprolol tartrate when low-dose formulations are unavailable. However, splitting may lead to variability in dose accuracy, which can be clinically relevant at low dose strengths. This study compared the accuracy and precision of manual tablet splitting versus automated 3D printing for producing low-dose metoprolol tartrate tablets in a hospital setting. Methods: Commercial 25 mg metoprolol tartrate tablets from two manufacturers were manually split at Mayo Clinic into 12.5 mg halves and 6.25 mg quarters following established procedures. CurifyLabs’ Compounding System, including the Pharma Printer and CuraBlend®-based formulations, was used at two hospital pharmacy sites and a compounding pharmacy to produce 6.25 mg and 12.5 mg tablets via 3D printing. Mass variation and content uniformity were evaluated using USP <905> criteria and validated HPLC methods. Results: Manually split tablets showed high variability, with 6.25 mg quarters ranging from 24.9% to 142.8% API content and 12.5 mg halves from one manufacturer averaging only 66.6%, indicating frequent underdosing. In contrast, 3D-printed tablets achieved mean API contents within 90–103%, standard deviations below 5%, and acceptance values under 15 across all sites and formulations. Conclusion: Manual tablet splitting may not reliably meet pharmacopeial expectations for dose accuracy and content uniformity at lower dose strengths. Automated 3D printing produced consistent low-dose tablets and may offer a validated alternative where available.

The diagnosis of coccidioidomycosis is often achieved serologically by the detection of antibodies against fungal antigens. While several serologic tests are available for coccidioidomycosis, all of them are performed in a laboratory setting causing delays in diagnosis and therapeutic intervention. Point-of-care testing offers the ability to provide a shorter time to result by avoiding specimen send-out, minimizing processing steps, and employing expeditious immunochemical techniques. A preliminary trial of a rapid anti-coccidioidal antibody lateral flow assay (LFA) using fingerstick blood was performed on 22 patients with coccidioidomycosis at the point of care during outpatient clinic visits. Patients were tested longitudinally over the course of one year. An LFA reader was implemented to provide an objective result by quantifying the intensity of the test line band. There was close qualitative concordance observed between positive LFA results with send-out immunodiffusion (89.5%) and complement fixation (78.4%) standard of care clinical laboratory assays. Additionally, the relationship between LFA test line density values and traditional complement fixation antibody titers was assessed.

Hip arthroscopy is an increasingly common procedure to treat intra‐articular conditions such as labral tears and femoroacetabular impingement. With the increase in frequency of such procedures, so too are revision arthroscopic hip procedures. Capsular repair in the setting of revision hip arthroscopy can be challenging due to the presence of adhesions and scar tissue formation that obscure tissue planes, suboptimal quality of residual capsular tissue, and the presence of capsular tissue defects. This technical note aims to describe the key surgical principles and considerations of revision hip capsular closure.

Background: Agentic artificial intelligence (AI) systems, characterized by autonomous goal-directed behavior, multi-step reasoning, task decomposition, and tool use, are increasingly proposed for healthcare applications. However, their autonomy raises concerns regarding transparency, accountability, and human oversight. While explainable AI (XAI) has been widely studied in traditional predictive models, less is known about how explainability is implemented within agentic architectures. Objective: To map the emerging literature on explainable agentic AI (XAAI) in healthcare and characterize the types, scope, and forms of explainability used in these systems. Methods: A scoping review was conducted following PRISMA-ScR guidelines. PubMed, Embase, IEEE Xplore, and ACM Digital Library were searched through November 2025. Eligible studies described healthcare-related agentic AI systems incorporating explicit explainability mechanisms. Data were extracted on system architecture, explainability type (intrinsic, post hoc, hybrid), explanation scope (local, global), explanation form, and reported clinical outcomes. Results: Nine studies met the inclusion criteria. All systems demonstrated core agentic features, including autonomy, task decomposition, and tool integration, often within multi-agent frameworks. Explainability was predominantly intrinsic and workflow-native, typically delivered through textual reasoning traces and example-based grounding in retrieved clinical evidence. Feature-based and global explanations were comparatively rare and largely confined to hybrid architectures. Across domains including radiology, neurology, psychiatry, and biomedical research, XAAI systems were reported to improve performance and interpretability relative to baseline models in the included studies. However, these findings were derived from heterogeneous, predominantly experimental or retrospective studies, and structured human-in-the-loop oversight was infrequently described. Conclusions: Current XAAI systems appear to emphasize process transparency and evidence grounding rather than mechanistic model-level attribution. The available evidence remains limited and heterogeneous, and findings should be interpreted as early trends rather than established characteristics. Further progress will require standardized evaluation frameworks, clearer reporting of oversight mechanisms, and validation in real-world clinical settings to support safe and trustworthy integration of agentic AI into healthcare practice.

ABSTRACT Our study aimed to develop a novel, reproducible, minimally invasive turkey knee osteoarthritis (OA) model and evaluate whether intra‐articular hyaluronan (HA) alone or combined with purified exosome product (PEP) attenuates OA‐related functional and structural deterioration. OA was induced arthroscopically by transection of the anterior and posterior cruciate ligaments with resection of the anterior horn of the medial meniscus. Animals received intra‐articular injections of saline, HA, or PEP + HA at postoperative 2–5 weeks. Gait was assessed at 4, 8, and 12 weeks, and cartilage degeneration, subchondral bone sclerosis, osteophyte formation, synovial fibrosis, heterotopic ossification, and meniscal integrity were evaluated by quantitative CT and histology at week 12. Compared with saline, HA improved lameness, preserved cartilage glycosaminoglycan content, and reduced meniscal rupture, subchondral bone sclerosis, and periarticular heterotopic ossification. PEP + HA provided additional benefits, including prolonged stance phase, reduced synovial fibrosis, and greater chondroprotection than HA alone, while plantar pressure variables, subchondral bone remodeling, and patellar osteophytes did not differ between HA and PEP + HA. This turkey model reliably produces human‐like OA changes and enables longitudinal functional and structural assessment. Intra‐articular HA mitigated OA progression, and the addition of PEP conferred incremental joint protection.

Study objectives To evaluate and compare the performance of two proprietary frontier large language models (LLMs), ChatGPT-5 and Grok-4, on diagnostic reasoning and foundational knowledge tasks within the specialty domain of sleep medicine. Methods The models were evaluated on two tasks: case-based reasoning using 79 clinical vignettes from the AASM Case Book of Sleep Medicine and knowledge assessment using 897 multiple-choice questions (MCQs) from board review materials. For vignettes, final diagnosis was scored by concept-level exact match, and differential diagnosis (DDx) was scored on a fixed top-5 output using concept-level matching with synonym normalization to compute precision, recall, and F 1-score. MCQ performance was the proportion correct. Inter-model performance was compared using the Mann–Whitney U test. Results Both models achieved high accuracy for final diagnosis (92.4% for both; 95% CI 86.4, 98.4) and MCQs (ChatGPT-5: 93.0%; Grok-4: 92.8%). However, performance on generating a comprehensive differential diagnosis was suboptimal, with modest F 1-scores for both ChatGPT-5 (0.55 ± 0.20) and Grok-4 (0.59 ± 0.20). There were no statistically significant differences in performance between the two models across any metric ( p &gt; 0.05). Conclusions Frontier LLMs demonstrated high accuracy in sleep medicine tasks requiring knowledge recall and direct pattern recognition but showed more limited performance in complex clinical reasoning tasks such as generating a comprehensive differential diagnosis. These findings suggest that current general-purpose models may be more reliable for focused knowledge support than for broad hypothesis generation. Future studies should evaluate whether domain-adapted models or clinician-in-the-loop workflows can improve real-world diagnostic usefulness and safety.

Background Respiratory allergic diseases are experiencing changes influenced by genetic factors, environmental shifts, and social and demographic elements. These evolving patterns, together with rapid advances in diagnosis and treatment, require a thorough review of emerging trends. Objective To examine future directions in managing respiratory allergic diseases, focusing on changing disease patterns, environmental factors, diagnostic innovations, and therapeutic advances toward precision medicine. A literature search was conducted in PubMed and Scopus databases covering 2015–2025. Studies addressing human populations with allergic rhinitis or asthma were included, emphasizing emerging patterns, environmental factors, diagnostic technologies, and therapeutic innovations, while reviews, conference proceedings, case reports, and studies without clinical relevance were excluded. Initial screening identified 52 studies, and 21 additional studies were identified through complementary searches, resulting in 73 studies in the final analysis. Summary of findings Climate change is a key factor affecting disease patterns, with pollen seasons starting 10–40 days earlier and yearly emissions increasing by up to 200%. Regarding pathogenesis, early-life rhinovirus C infections with IgE sensitization significantly increase asthma risk (HR = 4.06), while severe asthma shows 40–84% eosinophilic patterns, depending on the assessment approach. On the diagnostic front, advances include multiplex platforms, proteomic biomarkers, and microRNAs. Therapeutically, innovations encompass biologics combined with allergen immunotherapy, nanobody-based therapeutics, and microbiota interventions. Conclusions These developments point toward personalized management of respiratory allergic diseases. However, challenges remain in research with underrepresented populations and accessibility. Moving forward, the key priority is integrating this diverse knowledge into practical strategies that advance precision medicine in respiratory allergic diseases.

Retrieval-augmented generation (RAG) is rapidly emerging as a transformative paradigm for large language models (LLMs), especially in high-stakes domains like oncology that demand precision, factual grounding, and up-to-date knowledge. By pairing LLMs with external knowledge repositories, RAG systems explicitly ground model outputs in relevant retrieved documents, helping to reduce hallucinations and ensure responses reflect current evidence. In oncology, where clinical knowledge evolves continually with new research and drug approvals, RAG offers a way to integrate the latest data (e.g., trial results, guidelines, genomic databases) into decision-making. This review synthesizes the technical foundations of RAG, including its architecture and key components, and examines current applications in oncology such as clinical decision support, patient education, radiology reporting, pathology analysis, and genomics-driven precision medicine. We highlight recent studies that demonstrate RAG’s potential—for instance, improving treatment recommendations by incorporating genetic profiles and literature, and enhancing diagnostic accuracy by integrating guidelines. We also discuss emerging developments like multimodal RAG (combining text with imaging or other data), ensemble model approaches, and new explainability tools that trace model outputs to sources. Finally, we critically analyze the limitations and challenges of deploying RAG in healthcare, including computational costs, retrieval errors, noise or conflicts in retrieved information, and ethical and regulatory considerations. While RAG-based systems show promise in augmenting oncologists’ expertise with timely knowledge, careful implementation, high-quality curation of knowledge bases, and human oversight will be crucial for safe and effective adoption in clinical practice.

Abstract Background The IEC exposure index (EI), deviation index (DI), and target exposure index (EI T ), represent critical standardized metrics for the evaluation of exposure and quality in radiographic imaging. Purpose This work develops and validates a systematic procedure to estimate the EI T for eight of the most common radiography imaging protocols utilizing automatic exposure control (AEC) from measurements acquired under reference conditions. Methods A model was developed to define the relationship between a systems AEC logic, and an estimation of the EI under flat‐field conditions (EIFFC). Separately, clinical data and acquisition protocol information for resultant EI during patient studies were also collected for the eight protocols studied: Chest posteroanterior (PA), Chest lateral, Abdomen anteroposterior (AP), Pelvis AP, L‐Spine AP, C‐Spine AP, T‐Spine AP, and Ribs AP. Data were collected from 41 x‐ray units spanning seven institutions. For each protocol on each unit the EIFFC was computed based on the acquisition protocol, as well as median EI from clinical exams to produce a scaling factor (SF). Kruskal–Wallis statistical tests were used to compare SF's between vendors and AEC cell configurations. Results SFs for eight radiographic imaging protocols have been produced per vendor and per AEC cell selection. A workflow has been established for end‐users to follow to apply these SFs to flat‐field measurements taken at their own locations to establish local EI T . Conclusions The study results show that in seven of the eight imaging protocols, the SFs for most units included in the study report SFs within 1 DI (± 25%) of their respective final vendor reported SF (218/228). The ribs protocol is the exception to this finding (n = 26). SFs have high utility for establishing EI T values on individual x‐ray units and normalizing EI value distributions for quality assurance purposes.