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Multi-consistency testing during flexible endoscopic evaluation of swallowing (FEES) is clinically necessary but introduces selection bias: worst scores inflate severity because the number of consistencies tested covaries with disease severity. In this retrospective observational study of hospitalized neurological patients, we derived and validated the FEES Dysphagia Index (FDI) in two temporally independent cohorts (Cohort 1: 2013-2018, N=1,257; Cohort 2: 2021-2025, N=1,686) from a single center. FDI-S averages Penetration-Aspiration Scale (PAS) scores across tested consistencies (0-100 scale); FDI-E uses Yale Pharyngeal Residue scores; FDI-C combines both. Selection bias was quantified using sequential branching-tree inverse probability weighting (IPW). Worst PAS overestimated severity by 24%; FDI deviated by <2%. FDI-C was significantly superior to Worst PAS for hospital-acquired pneumonia (HAP; AUC 0.70 vs. 0.60, p<0.001), mortality (0.71 vs. 0.62, p=0.040), and restricted oral intake (0.90 vs. 0.74, p<0.001), and statistically equivalent to clinician-rated severity. FDI-C mapped linearly onto ordinal Functional Oral Intake Scale values (FOIS; proportional odds RCS p=0.99). With functional status and diagnosis, FDI-C reconstructed the clinicians oral intake recommendation with AUC up to 0.93. The FDI-C-mortality relationship was sigmoidal with a clinically relevant transition zone between [~]50 and [~]85. FDI-C is a bias-resilient, bedside-calculable score with interval-scale properties that captures expert clinical judgment, suitable as both a clinical decision support tool and a continuous research endpoint.

Most clinical large language model (LLM) benchmarks rely on clean, concise vignettes that do not reflect the noisy, long-form documentation typical of real clinical records. How LLM performance degrades under realistic chart conditions remains poorly characterised. Here we test whether structured retrieval workflows protect National Institutes of Health Stroke Scale (NIHSS) scoring accuracy under systematic context stress. Using 100 de-identified acute stroke cases and a fully crossed 4 x 4 x 3 x 3 condition matrix (144 conditions per case), we vary context acquisition method, document length, distractor load and critical-information position across four Gemini models (57,047 retained runs). Structured retrieval reduces mean absolute error (MAE) from 4.58 to 2.96 points relative to non-agentic baselines (mean gain 1.62 MAE points; 95% CI 1.57 to 1.67; 35% relative reduction), with consistent gains across all 36 stress combinations. Lower-cost models show disproportionately larger gains (2.76 versus 0.45 MAE points). Tool-retrieved pipelines outperform retrieval-augmented generation in 33 of 36 combinations. These findings indicate that retrieval architecture, rather than model scale alone, is a tractable lever for robust, equitable clinical LLM deployment.

Molecular syndromic panels such as the BioFire FilmArray Gastrointestinal Panel (BF-GIP) have been widely adopted for gastrointestinal illness diagnosis due to their fast turnaround times and broad pathogen coverage. Recently, the BF-GIP demonstrated increased rates of norovirus false-positive detections, prompting a Class II recall of more than two million tests in February 2024. We examined the prevalence of BF-GIP norovirus false positives across four hospitals from December 2024 to June 2025. Among 185 BF-GIP norovirus-positive results confirmed with the BD MAX Enteric Viral Panel, the false discovery rate ranged from 31 to 74% across sites, with the highest rate seen at a specialized cancer care hospital. Deep sequencing of BF-GIP pouches (n=42) confirmed the Noro-1 assay as the primary source of off-target amplification, identifying 78 off-target species, predominantly commensal stool bacteria, compared to only two species for the Noro-2 assay. Off-target species amplified by the Noro-1 assay were recovered from both false-positive and true-negative pouches, suggesting no single species accounted for the false-positive results. Partial primer complementarity at off-target loci and amplicon Tm values within the acceptable range support mispriming of gut microbiota as the underlying cause. False-positive pouches exhibited significantly higher Cp values than true positives for both assays (Noro-1: 26.6 vs. 11.1, p=0.013; Noro-2: 30.0 vs. 13.1, p<0.001), consistent with low-level off-target amplification. These findings highlight the high false discovery rate of the Noro-1 assay, identify bacterial species involved in mispriming, and demonstrate the need to redesign this assay to ensure reliable testing and improved patient care.

Cancer treatment has shifted toward personalized therapy based on molecular profiling, particularly in advanced disease. Existing circulating tumor DNA panels are often broad, generating many non-actionable variants and incurring costs that limit routine use in molecular tumor boards. We developed and validated a manufacturer-independent, 109-gene liquid biopsy-centered pan-cancer open next generation sequencing panel (LION panel), combined with an in-house bioinformatic pipeline to support clinical decision-making. A total of 87 samples were analyzed, including 17 reference samples, 21 healthy blood donor controls, and 49 patient samples including nine tumor entities. The LION panel achieved 92% sensitivity and 99% specificity in reference samples, with high concordance to digital droplet PCR (r = 0.99). It detected variant allele frequencies as low as 0.05% (tumor-informed) and 0.5% (tumor-uninformed). Clinical concordance reached 82% with blood-based digital droplet PCR and 75% with whole exome tissue sequencing. In representative cases, variant dynamics correlated with disease progression and revealed additional targetable variants. Overall, the LION panel supports clinical decision-making by enabling identification of targetable variants, disease monitoring, and detection of treatment resistance, particularly when tumor tissue is unavailable.

The high heterogeneity of pediatric cancers presents significant diagnostic challenges, underscoring the need for accurate classification. Although molecular profiling supports first-line diagnostics and guides treatment, it can delay final diagnosis. While Nanopore-based methylation analysis has enabled rapid CNS tumor diagnosis, its application to pediatric solid tumors and lymphomas has remained largely unexplored. We developed Tucan, a deep-learning classifier trained on 3,818 methylation array profiles representing 84 subtypes, designed to classify tumors from sparse Nanopore methylation data. In retrospective validation (n=514), Tucan generated confident predictions (CFT[&ge;] 0.7) within 30 minutes of sequencing in 385 cases, achieving 372 correct diagnoses (F1-score: 0.98). In prospective testing (n=74; 63 classifiable), 52 samples reached the confidence threshold with 96% accuracy, confirming the original diagnosis in 47 cases and correctly refining or revising it in three. Together, Tucan enables rapid, high-confidence molecular classification of pediatric solid tumors and lymphomas.

In July 2021, the California Code of Regulations Title 17 required all laboratories performing SARS-CoV-2 whole genome sequencing (WGS) to report their sequencing results to the California Department of Public Health (CDPH). These viral genomic data and patient metadata were compiled into the Integrated Genomic Epidemiology Database (IGED). Linking anonymized viral sequences with patient-level information enabled monitoring of infectiousness, pathogenicity, transmission dynamics, evolution, and vaccine evasion among emerging SARS-CoV-2 lineages. Laboratories performing SARS-CoV-2 WGS transmitted sequencing results to CDPH through Electronic Laboratory Reporting (ELR) and non-ELR pathways. CDPH applied uniform reporting requirements but allowed flexibility in specific data formats to accommodate diverse data systems. To preserve data quality and interoperability across heterogeneous sources, CDPH implemented standardization, validation, and deduplication protocols. Snowflake, a cloud-based data storage and analytics platform, and Posit Connect, a cloud deployment and automation platform, supported the management, processing, and integration of data within the IGED. The IGED established links between SARS-CoV-2 WGS data and epidemiologic metadata for 801,418 sequences, representing 81.7% of all sequences reported in California. Lineages reported to the IGED showed strong concordance with lineage proportions in GISAID. Sequences reported to the IGED had average turnaround times longer than one month, and the majority of sequencing was performed in Southern California and Los Angeles. The IGED enhanced genomic surveillance through predictive modeling and monitoring concerning evolutionary trends such as recombination and saltations in persistent infections. Development of the IGED highlighted the need for standardized data requirements, sustained funding for sequencing, incentives for data submission, and interdisciplinary collaboration to build an effective genomic surveillance system. This framework for linking genomic and epidemiologic data has not only generated critical insights for SARS-CoV-2 but also provided the foundation for CDPH and other public health organizations to develop similar IGED-like systems for other priority pathogens as genomic surveillance expands.

Gut microbiome studies in Parkinsons disease (PD) are challenged by high dimensionality, sparsity, compositionality, and substantial between-cohort heterogeneity, all of which complicate robust community typing and disease-status classification. Here, we developed a variational autoencoder (VAE)-based methodology for deep enterotyping and PD diagnosis prediction (i.e., predicting diseased vs. control status) using a harmonized multi-cohort gut microbiome compendium comprising 1,957 16S rRNA samples from six PD case-control cohorts and an independent shotgun metagenomic validation cohort of 725 samples. Compared with conventional enterotyping approaches such as partitioning around medoids (PAM) and Dirichlet multinomial mixture (DMM) modelling, the VAE-derived latent space supported a clearer and more reproducible three-cluster solution. These three enterotype-like community states were biologically interpretable and were annotated as Enterococcus-type, Bacteroides-type, and Ruminococcus-type configurations. The same broad three-enterotype structure was independently recapitulated in the metagenomic dataset, supporting cross-platform robustness. Across the three inferred types, the proportion of PD samples was similar, and both the primary generalized linear mixed-effects model and sensitivity model showed that enterotype assignment was not a significant differentiating factor for PD status and that the lack of association was not dependent on a single modelling strategy. In the supervised branch, VAE-derived representations supported PD case-control classification while also providing a shared latent representation for clustering, enterotype transfer, and downstream interpretation. Collectively, these findings show that deep representation learning can improve the resolution, reproducibility, and interpretability of enterotype inference in heterogeneous microbiome datasets, and provide a practical methodology for organizing broad community structure in PD. In this setting, the main advantage of the VAE method lies in its ability to link unsupervised community typing with supervised prediction through a shared latent representation, even when broad community types do not function as stand-alone disease biomarkers.

Background Intracranial aneurysm rupture is the leading cause of spontaneous subarachnoid hemorrhage and is associated with substantial mortality and long term neurological disability. Emerging evidence suggests that the gut microbiome may influence vascular inflammation and endothelial integrity through immune and metabolic pathways, yet human evidence linking gut microbial alterations to intracranial aneurysm remains fragmented and inconsistent. Objective This systematic review and meta analysis aimed to synthesize available human evidence on the association between gut microbiome alterations and intracranial aneurysm formation or rupture, with a primary focus on microbial dysbiosis and differences in gut microbial alpha diversity. Methods This study was conducted according to PRISMA 2020 guidelines and the protocol was prospectively registered in PROSPERO (CRD420261360785). A comprehensive search of PubMed, Scopus, Web of Science, Embase, and Cochrane CENTRAL was performed from database inception until April 1, 2026, with additional screening of grey literature sources. Observational human studies evaluating gut microbiome characteristics in patients with intracranial aneurysm were included. Mendelian randomization (MR) studies investigating genetically predicted microbial taxa and aneurysm outcomes were also reviewed. Random effects meta analysis using standardized mean differences (SMD) was performed for alpha diversity outcomes. MR taxa reported in at least two independent studies were quantitatively synthesized using inverse variance weighting of log odds ratios. Results The systematic search identified 396 records. After removal of duplicates and eligibility screening, 20 studies met inclusion criteria, including 12 observational clinical studies and 8 Mendelian randomization analyses. Meta analysis of three microbiome sequencing studies demonstrated significantly reduced gut microbial alpha diversity in patients with ruptured intracranial aneurysms compared with controls. Sensitivity analyses confirmed the robustness of pooled estimates. In addition, MR evidence identified several microbial taxa, including Ruminococcus1, Bilophila, Fusicatenibacter, and Porphyromonadaceae, as potentially protective factors against aneurysm related outcomes. Across observational studies, gut dysbiosis was frequently associated with inflammatory pathways and alterations in microbial metabolites implicated in vascular dysfunction. Conclusion Current human evidence suggests a potential association between gut microbiome dysbiosis and intracranial aneurysm pathophysiology, particularly in relation to aneurysm rupture. Reduced microbial diversity and specific microbial taxa may influence vascular inflammation and aneurysm wall stability. However, existing evidence remains limited and heterogeneous. Large prospective cohorts and mechanistic studies are required to clarify causal relationships and evaluate whether microbiome targeted interventions could contribute to aneurysm risk stratification or prevention strategies.

Massively multiplexed qPCR is primarily constrained by increasing primer dimer formation as the number of distinct primers in a single reaction increases. Previous multiplex primer design algorithms either fail to sufficiently suppress primer dimers at 100+ plex, or take exceedingly high amounts of computational resources to complete. Here, we present DIMPLE, a linear-runtime primer design algorithm that effectively generates 10,000+ primers to amplify thousands of potential amplicons in a single qPCR reaction. As one clinical demonstration of this algorithm, we designed an assay to detect 2,302 distinct KMT2A gene fusion subtypes using 204 primers in a single tube. In contrast to FISH and convention NGS approaches with 2% variant allele frequency (VAF) limit of detection, our DIMPLE qPCR assay was able to analytically detect gene fusions down to 0.05% VAF. We also constructed proof-of-concept multiplex qPCR panels for additional oncology gene fusions, multiplex pathogen detection, and DNA methylation markers. The scalability and low computational cost DIMPLE are complementary to new instrument platforms for massively multiplex qPCR readout for enabling rapid, point-of-care nucleic acid testing.

Circulating tumor DNA (ctDNA) from blood plasma has emerged as a promising biomarker for noninvasive profiling of tumor mutational landscapes and disease monitoring across cancers. In this study, we developed a targeted next-generation sequencing approach to explore the role of ctDNA for comprehensive tumor genotyping, early response prediction, and characterization of clonal heterogeneity in patients with advanced and rare cancers treated within molecular tumor boards. We applied our technology to 157 plasma specimens from 57 patients at distinct disease milestones and detected tumor variants in 96% of baseline samples, with 65% of them harboring actionable aberrations. Longitudinal monitoring of baseline mutations in on-treatment plasma revealed that ctDNA dynamics were significantly associated with clinical outcomes and enabled early prediction of disease progression. Finally, we observed substantial clonal heterogeneity over time, identifying emerging mutations in all analyzed plasma samples obtained at progression, including potentially targetable variants for subsequent personalized therapies.

Withdrawal StatementThis article has been withdrawn by medRxiv because it was submitted with false information.

Objective. Biomedical knowledge graphs (KGs) such as PrimeKG, Hetionet, UMLS, and PharmGKB are increasingly used as the substrate for downstream machine-learning, retrieval-augmented generation, drug-repurposing, and electronic health record (EHR) augmentation pipelines. The dominant assumption in published work is that integrating two or more such KGs is a tractable engineering step solved by identifier (ID) matching. This paper interrogates that assumption empirically. We quantify how much concept overlap survives realistic alignment, and we characterize the new failure modes introduced by the methods that practitioners reach for when ID matching is insufficient. Materials and Methods. We compared four widely used biomedical KGs (PrimeKG, Hetionet v1.0, the full UMLS Metathesaurus, and PharmGKB) across eleven node types using a tiered alignment pipeline: (1) direct ID matching for nodes sharing a primary vocabulary; (2) cross-ontology bridging using standard mappings (e.g., MONDO-DOID, HPO-UMLS, HPO-UMLS-MeSH for side effects, NCBI Gene-HGNC-UMLS, UBERON-FMA/SNOMEDCT_US/NCI/MeSH for anatomy); (3) ClinicalBERT cosine-similarity grouping at threshold >= 0.98 for over-segmented disease nodes, with a deterministic suffix-stripping canonicalizer; (4) exact name matching for ontology-poor types (anatomy, REACTOME pathways); and (5) embedding-based fuzzy matching with UMLS lookup (SapBERT and ClinicalBERT) for free-text microbiome concepts. We applied the pipeline to a 698-concept gut-microbiome benchmark spanning taxa, pathways, and disease labels, validated grouping decisions against the curated SSSOM mappings released by the MONDO project, and audited the ClinicalBERT consolidation against five clinical-genetics case studies drawn from the literature. Results. Per-type pairwise coverage was strikingly asymmetric. Genes/proteins and the three Gene Ontology categories aligned cleanly across PrimeKG and Hetionet (mutual coverage 94-99%), but disease overlap was sparse: only 0.7% of PrimeKG individual disease nodes mapped to Hetionet, rising to 2.0% after MONDO grouping (versus 78.7% and 18.4% from the Hetionet side). PrimeKG-to-UMLS coverage spanned 100% (effect/phenotype via HPO) down to 20.8% (REACTOME pathways), with drugs at 73.7% and anatomy at 58.8%. PrimeKG-to-PharmGKB drug coverage required up to two bridging hops (DrugBank -> UMLS -> RxNorm/ATC/MeSH). Bigger was not uniformly more complete: on a 698-concept microbiome drug benchmark, Hetionet missed 0 concepts while PrimeKG missed 16. ClinicalBERT-based grouping consolidated 22,205 raw MONDO disease nodes into 17,080 groups but introduced three reproducible failure modes documented in case studies: (i) peer over-merging: for example, all 22 osteogenesis imperfecta subtypes collapsed into a single node despite distinct severity classes; (ii) parent-child collapse: e.g. acute myeloid leukemia merged with myeloid leukemia, erasing the acute/chronic distinction that drives clinical management; and (iii) lexical false positives: neurofibromatosis and schwannomatosis grouped together despite cellular-pathology differences. Discussion. Identifier matching alone is a weak baseline for biomedical KG integration. Cross-ontology bridges and embedding-based consolidation expand coverage but do so at the cost of clinically meaningful resolution, and the resulting failures are systematic rather than random. Reporting only aggregate coverage statistics obscures these losses, which propagate silently into downstream tasks. Conclusion. We provide reusable per-type coverage tables, a taxonomy of three integration failure modes, and concrete recommendations for downstream studies that depend on a unified biomedical KG. We argue that future KG integration work should report per-type coverage and per-cluster confidence rather than aggregate match rates.

Macrolinguistic discourse analysis offers valuable insight into how patients with neurogenic communication disorders organize and produce informative speech, yet it remains a largely manual and labor-intensive process. We report an automated pipeline for macrolinguistic discourse analysis for individuals with aphasia and dementia that integrates automatic speech recognition (ASR), utterance segmentation, sentence-level embeddings, centroid-based main-concept matching, and rule-based coherence error classification. These algorithms were applied to Cinderella story retellings from 309 participants (113 controls, 102 post-stroke aphasia (PWA), and 94 dementia). The algorithm reliably identified main concepts (83% accuracy against human labels) and derived interpretable features such as semantic distance to a main concept centroid, main concept coverage, and coherence error rates. Crucially, diagnostic classification results showed that logistic-regression classifiers trained on 10 macrolinguistic features distinguished aphasia from controls with high accuracy (AUC {approx} 0.94) but showed weaker separation for dementia (controls vs dementia AUC {approx} 0.66; aphasia vs dementia AUC {approx} 0.58). Semantic distance to the centroid emerged as a robust, informative predictor for diagnostic classification, demonstrating that the ability to produce narrative-aligned speech is clinically important. The automated pipeline enables scalable macrolinguistic discourse analysis that could support screening and longitudinal monitoring of discourse impairments across neurogenic populations.

Although GLP1/GIP receptor agonists demonstrate unprecedented weight loss efficacy, their rapid clinical adoption has revealed significant real-world tolerability challenges. To evaluate their dynamic safety profiles, we developed a macro to micro pharmacovigilance framework by combining global FAERS reports with local UT Physician EHR. Macroscopically, we distilled 17 shared adverse events across the drug class from FAERS with disproportionality analysis. Microscopically, local EHR data (289,655 longitudinal treatment sessions across 71,316 patients) revealed 51.6% of GLP1 sessions terminated within 90 days. Furthermore, temporal stratified logistic regression demonstrated that initial exposure (0 to 30 days) correlated strongly with nausea and vomiting, which attenuated in extended sessions, whereas extended exposure (>2 years) uncovered late onset risks, notably incident hepatic steatosis. Ultimately, this time aware framework reveals that GLP1 safety profiles are profoundly duration dependent, providing critical insights into both acute intolerances and long-term medication safety.

Normal pressure hydrocephalus (NPH) is a potentially reversible neurological disorder characterized by urinary incontinence, gait impairment, and cognitive decline. However, postoperative improvement after shunt placement is variable, and reliable preoperative predictors are lacking, leaving patients exposed to uncertain surgical benefit and procedural risk. We therefore asked whether preoperative cerebrospinal fluid (CSF) metabolic profiles capture biological states associated with recovery potential. We analyzed ventricular CSF from patients undergoing shunt placement and identified metabolic patterns that differed between patients who improved postoperatively and those who did not. These signatures were detectable prior to intervention and were consistent across analytical approaches and patient cohorts. Multivariate models based on metabolite features were associated with postoperative improvement, with strongest performance observed for cognitive outcomes. Pathway-level analyses indicated coordinated alterations in processes related to redox balance, immune-metabolic signaling, and energy substrate utilization. These findings indicate that preoperative CSF metabolite profiles reflect biological states associated with recovery potential in NPH. The results further suggest that metabolic and immune-metabolic processes contribute to variability in surgical responsiveness and support the development of predictive biomarkers for patient stratification.

Blood-based biomarkers are increasingly used to investigate brain health, but collecting venous blood is difficult in remote and field settings. Capillary microsampling offers a practical alternative, although the ability to delay processing and its agreement with gold-standard venous blood require validation. We evaluated Tasso+, a minimally invasive upper-arm capillary blood collection system, for measuring neurological and host-response biomarkers in plasma and serum during an exercise-based protocol. Sampling occurred before, immediately after, and approximately 24-to-36 hours after exercise; Tasso+ samples were processed with or without a 72-hour room-temperature delay. Tasso+ samples were compared with matched venous blood, and Capitainer SEP10 dried plasma spots were also evaluated, using Quanterix Simoa and Alamar Biosciences NULISAseq CNS panel. Tasso+ enabled reliable measurement of several key biomarkers, including GFAP and NfL, even after delayed processing. These findings support capillary microsampling for neurological biomarker studies where venepuncture is challenging, including field-based research and participant-led remote sampling.

Background: Unstructured data represent about 80% of total electronic health records (EHR) data. Structuring this free text is essential for advancing clinical research, including cohort selection for trials, retrospective studies, and the development of disease registries. While manual chart review (MCR) remains the gold standard for extracting this clinical data, the process is inherently slow, resource-intensive, and susceptible to errors from human fatigue. We evaluated the extraction accuracy, safety, and efficiency of the HeLIX (Hepatology Logic-Integrated Extraction) framework, a Large Language Model (LLM) protocol using Google Gemini 3 Pro, compared to a gold-standard Manual Chart Review (MCR). Methods: A prospective validation study was conducted using 50 high-complexity, simulated hepatology discharge summaries designed to replicate the real-world heterogeneity of EHRs. The HeLIX framework employed a Zero-Shot, Structured Chain-of-Thought (CoT) prompting strategy enforced by a three-layer architecture: Clinical Reasoning Trace, Schema Enforcement, and Evidence Verification. The model extracted 45 distinct clinical variables. Performance was benchmarked against a consensus MCR. Results: Across 2,250 evaluated data points, the model achieved an overall Extraction Accuracy of 99.24% (95% CI: 98.8%-99.5%), with perfect concordance in 35/45 (77.8%) variables. For binary diagnostic variables, the model demonstrated an overall F1-score of 0.98, Recall of 0.99 and substantial inter-rater reliability (Cohens {kappa} = 0.97). Hallucinations were exceptionally rare (2/2250; 0.08%). Critical errors affecting clinical management occurred in only 2 instances (<0.1% of total data), both involving etiological misattribution in complex multifactorial diagnoses. The AI workflow was 13.4-fold faster and 95.1% more cost-effective than manual extraction. Conclusion: The HeLIX framework demonstrates physician-level accuracy and reliability in extracting complex hepatology data. It offers a scalable, efficient, and economical alternative to manual chart review. Such frameworks could accelerate clinical research, enabling healthcare systems globally to build comprehensive patient registries for a fraction of the traditional cost.

Carbapenem-resistant Gram-negative bacteria (CRGNB) infections remain difficult to manage because treatment decisions must balance heterogeneous patient risk, limited antibiotic options, potential toxicity and emerging resistance. Clinical care in this setting requires not only single-endpoint risk prediction, but also decision-support frameworks that can jointly enable prognosis assessment, result interpretation, and individualized treatment comparison. Here we present Dr.BUG, an interactive clinical AI agent for personalized decision support in CRGNB infection. Dr.BUG integrates stable feature-set selection, multi-task prognostic modelling, interpretability analysis and model-based simulation of antibiotic regimen recommendation into a unified workflow. Using a development cohort, a temporally independent validation cohort, and external cohorts from the MIMIC-IV dataset, we developed and validated models for four clinically relevant tasks: clinical efficacy, survival outcome, polymyxin resistance and treatment duration. Model inputs were derived primarily from routinely available and relatively low-cost clinical variables, supporting translational feasibility. Across the major tasks, selected-feature models matched or exceeded the performance of their full-feature counterparts while using fewer variables, as reflected in 82.0% of optimized-metric comparisons in the development cohort, and remained robust in both temporal and external validation. Dr.BUG further provided both population-level and patient-level interpretability and generated individualized rankings of candidate antibiotic regimens. In the retrospective analysis of non-survivors, clinician review suggested that regimens recommended by Dr.BUG might be associated with higher predicted survival probabilities. These findings support a broader role for clinical AI in complex drug-resistant infections, extending its utility from offline risk prediction to interpretable, deployable, and personalized decision support.

Accurate HLA typing is critical for transplantation, pharmacogenomics, and disease risk prediction, yet short-read approaches cannot resolve the HLA region's extreme polymorphism. Long-read sequencing improves resolution, but its adoption has been limited by higher cost, reduced base accuracy, limited throughput, and reliance on long-range PCR. To overcome these limitations, we present a multiplexed long-read hybrid capture workflow for PacBio and Oxford Nanopore sequencing that enriches all classical HLA loci and the complete HLA Class III region. A single-step enzymatic fragmentation and barcoding strategy enables automated library prep. We also introduce HLA-Resolve, an HLA typing program optimized for HiFi reads, and validate workflow performance against the Genome in a Bottle, Human Pangenome Reference Consortium, and International Histocompatibility Working Group benchmarks using 32 geographically diverse samples. These advances offer a cost-effective approach for high-resolution HLA typing with clinical applicability and enable investigation of the role of HLA Class III variation in disease.

The human small intestine (SI) plays a central role in nutrient processing, host-microbe interactions, and immune regulation, yet remains poorly characterized due to the lack of minimally disruptive sampling methods. Here, we present a protocol for deploying, recovering, and analyzing samples collected using an ingestible device that enables multi-region, lumen-targeted SI sampling during normal digestion. The device incorporates a ~30-cm collapsible tube wound into pH- or time-responsive layers that sequentially unfurl in situ, typically capturing three spatially ordered samples with high yield and reliable retrieval. This protocol outlines study design, participant handling, device recovery, contamination control, and standardized workflows for analyses, including cell quantification, culturomics, sequencing, and metabolomics. We further describe benchmarking approaches for evaluating spatial resolution and strategies for assay prioritization when sample volume is limiting. By reducing participant burden and facilitating integration with stool, saliva, and clinical metadata, this approach enables longitudinal and large-cohort studies linking SI microbial ecology and host physiology to human health.