AI News Archive: July 10, 2026 — Part 17
Sourced from 500+ daily AI sources, scored by relevance.
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- SentryAlert for Tesla
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- Aequitas AI
The AI that abstains instead of bluffing
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- Evaluating the cross-species transferability and scaling of sequence-to-function predictions in AlphaGenome
Deep learning models that predict molecular phenotypes directly from DNA sequence offer a powerful framework for interpreting genomic variation. Recently, AlphaGenome was introduced as a deep sequence-to-function architecture capable of predicting observations that historically required experiments. While the model has shown high accuracy, it was primarily evaluated on human variants scored against a reference genome. Here, we test performance on mouse data, the other species AlphaGenome was trained on although with fivefold fewer features than human (1,128 versus 5,930). We demonstrate that AlphaGenome's predictive performance varies considerably depending on the functional task. Specifically, predicted quantitative expression effects are directionally weak and compressed roughly 100-fold relative to empirical benchmarks across both reconstructed-haplotype and single-variant regimes. In contrast, canonical splice-site disruptions are recognized with near-identical accuracy in mouse and human (AUC 0.96 versus 0.98), displaying no cross-species divergence in predicted effect magnitude. We developed a scoring-approach for AI-agents to autonomously assess AlphaGenome prediction confidence and accurately differentiate between AlphaGenome's robust sequence-level recognition across species and its current limitations when interpreting un-fine-mapped regulatory variants. This demonstrates how GenAI innovations that are still under development can safely be harnessed by wrapping a responsible AI layer around the call to intercept flawed results, thereby adhering to international standards, such as the Australian Voluntary AI Safety Standard (VAISS).
- MAERM: Predicting Enzyme-Reaction Matching Relationships with a Mixed-Attention Model
Harnessing enzyme specificity requires a thorough understanding of enzyme promiscuity, which determines enzymes' catalytic scope; however, measuring this scope still relies heavily on labor-intensive analytical approaches. While data-driven approaches have emerged to predict the catalytic scope of enzymes, these methods continue to face challenges such as restricted datasets and insufficient integration of enzyme structural information and reaction transformations. Here, we introduce MAERM, an innovative mixed-attention model designed to predict enzyme-reaction matching relationships. Built on our MAERM-DB, a dataset with broad coverage of validated and chemoenzymatic catalysis data, MAERM utilizes a local-global attention module to integrate multimodal enzyme information with fine-grained reaction representations, thereby predicting enzyme-reaction matching probabilities. Results show that MAERM consistently outperforms all baselines, with an average F1-score of 0.984. Notably, on challenging test samples with less than 40% sequence identity to the training set, MAERM outperforms the second-ranked model by 5.9% in F1-score. In addition, MAERM achieves the highest top-10 success rate of 51.7% on Enzyme-405 and the highest balanced accuracy of 0.697 on BioCat-547, further supporting its generalizability in enzyme screening and chemoenzymatic catalysis. Finally, MAERM can serve as an efficient scoring module. When integrated with ProteinMPNN, MAERM has successfully guided novel enzyme design for two carbonyl reduction reactions, resulting in enhanced catalytic potential for the native substrate and demonstrating broad compatibility. Overall, MAERM has the potential to reduce the experimental cost of measuring enzymes' catalytic scope, facilitate enzyme design, and ultimately accelerate the design-build-test-learn cycle in enzyme engineering.
- Interaction-finder: automated literature-based discovery of biological entity associations with quote-level provenance
Identifying interactions between biological entities is a cornerstone of molecular research, but assembling such lists from the literature is slow and tedious. For many research questions, no curated database exists, leaving researchers to survey the relevant literature themselves. We present interaction-finder, a tool that automates this process: given a topic string and user-defined entity types, it discovers relevant literature through LLM-guided iterative search, extracts candidate associations from full-text articles, and produces a ranked list where every association is backed by quoted passages verified against the source text. A self-contained interactive HTML report enables rapid triage of the results. Evaluated across 60 topics in three domains (celltype-cellmarker, disease-gene, and ligand-receptor), interaction-finder recalls 1.2-4.3x as many known associations as single-shot prompting and an off-the-shelf deep-research framework, with all extracted quotes verified against source text. To assess candidates unrecognised from the gold-standard databases, we scored each candidate using an independent LLM judge blind to the tool's reasoning. Across the three domains, unverified candidates score similarly to gold-standard associations. We find the gold-standard associations are enriched at the top of our ranked candidates, with an overall recall@20 of 0.61. Interaction-finder is freely available at https://github.com/tecosaur/interaction_finder under an MIT licence.
- Safeguarding open-weight genomic foundation models through weight locking
Background. Genomic foundation models can dramatically accelerate biological research by learning general-purpose representations of genomic data that transfer across tasks, enabling researchers to predict variant effects, regulatory elements, molecular function, and more. To safeguard against potential biosecurity threats and malicious misuse of open-weight models, a common strategy involves excluding human-infecting viral genomes from the model's training corpora. This strategy, however, can be easily circumvented by fine-tuning models on abundantly available viral data. Weight-locking with spectral deformation has been proposed as a potential method to prevent fine-tuning of neural networks, but has not been systematically evaluated in biological AI models. Methods. We applied spectral deformation locking to the Evo-1-8k-base genomic foundation model and evaluated a panel of attack configurations spanning naive fine-tuning, low-rank adaptation (LoRA), a simple inserted-layer bypass baseline, and a white-box singular value decomposition (SVD) chain factorisation at chain lengths k in {2, 3, 5}. Recovered virological capability was quantified on three Human Virome Understanding Evaluation (HVUE) tasks. Results. The lock defended against the naive attacker by either standard pipeline. Naive full fine-tuning under the strong lock drove downstream virological capability significantly below the pretrained baseline on pathogenicity and host tropism, converting the attack into a capability loss rather than a gain, while naive low-rank adaptation neither moved held-out perplexity (PPL) nor recovered downstream capability above pretrained. Thus, we conclude that by neither route does the naive attacker reach the gain achieved by fine-tuning an unlocked model. Consistent with previous results in non-biological models, an informed attacker who implements the SVD-chain construction does recover capability on pathogenicity prediction, at the cost of increased computational requirements for the fine-tuning process.
- InsectDCT: A generalized pipeline for detection, taxonomic classification, and tracking of insects in camera-trap recordings
Automated monitoring of insect pollinators in natural environments with insect camera traps and trained deep learning algorithms provides novel data for insect ecological studies. However, efficient and accurate image recognition analysis of the recorded images or videos is challenging, particularly for images containing small insects against complex backgrounds with diverse vegetation communities. Even when insects can be detected in images, identifying their taxonomy remains difficult, particularly in footage with low image resolution, light conditions, and distances from the plants, and in cases where insects appear blurry or only partially visible. In this work, we present InsectDCT, an AI-based pipeline for automated detection, hierarchical classification, and tracking of insects in footage of natural vegetation tested in different environments. The InsectDCT pipeline consists of three levels: insect Detection and localization, hierarchical taxonomic Classification, and spatio-temporal Tracking. In the first stage, insects are detected in time-lapse images or video recordings using the You Only Look Once (YOLO11) object detection architecture. Detection performance is improved using motion-enhanced images, which improve robustness in cluttered and 3 dimensional environments. The detector is trained on an extensive dataset that contains more than 60,000 images collected using camera traps deployed across a wide range of plant families and floral habitats. In the second stage, detected insects are classified using a hierarchical taxonomy-aware classification framework that covers 80 taxonomic groups. Classification is performed at multiple taxonomic levels, including order, family, and genus/species, allowing coarse and fine-grained ecological analyzes while accounting for varying levels of visual ambiguity. In the third stage, a multi-object tracking module is applied to high temporal-resolution image sequences and video data to associate detections of the same individual across time. InsectDCT code and all datasets are made publicly available.
- Benchmarking AI-Driven PTIm-mAb Across Eleven FDA-Approved Bispecific Antibodies: A Cross-Tool Validation Study
Background. Late-stage attrition in therapeutic antibody discovery is dominated by developability liabilities: aggregation, polyspecificity, charge-driven non-specific binding, and chain-mispairing artefacts. Bispecific antibodies amplify these risks because each additional binding arm adds a new biophysical envelope that must be jointly satisfied. The existing in-silico ecosystem addresses individual axes of this problem (humanization, structure prediction, single-metric developability scoring) but few platforms integrate them end-to-end. PTIm-mAb (SANSHI Bio Solutions Pvt Ltd) is a multi-objective, AI/ML-driven antibody design platform that jointly optimizes sequence liabilities, surface aggregation, charge balance, humanness, and predicted binding affinity, and recommends a bispecific architecture in a single workflow. Methods. We applied PTIm-mAb to the published sequences of eleven FDA-approved bispecific antibodies using the platform's default-parameter Pareto-acceptance optimization loop, run to convergence or to the internal iteration ceiling, with no human curation between the platform run and the external profiler. Both wild-type and platform-optimized sequences were profiled independently with three publicly available developability tools: Aggrescan, CamSol, and the Therapeutic Antibody Profiler (TAP). Paired-sample tests (Wilcoxon signed-rank, exact binomial sign test, McNemar exact test) evaluated the direction and significance of changes. Results. Across the 17 evaluable paired arms profiled by TAP, PTIm-mAb cleared four wild-type CDR-vicinity Positive Charge Patch (PPC) flags Blinatumomab-Arm1 (1.9952 to 0.6885), Mosunetuzumab-Arm1 (1.3391 to 0.0568), Linvoseltamab-Arm2 (0.8060 to 0.0), and the headline Elranatamab-Arm1 case (1.7981 to 0.5799) achieved without trading off any other in-range metric and corroborated by Aggrescan and CamSol on the same arm. Total CDR length was significantly shortened across the cohort (Wilcoxon two-sided p = 0.0075, one-sided p = 0.0037, effect size r = 0.65): significant improvement on the metric most directly under the optimizer's control. The directional shift on Aggrescan integrated aggregation propensity was also significant by sign test (24 of 36 chains improved, 2 unchanged, 10 worsened; p = 0.021). On the already-clean Zenocutuzumab profile the optimizer identified residual headroom (PPC 0.1191 tp 0.0; SFvCSP 12.5 to 6.0), demonstrating that the platform's value extends to candidates that pass all flags. Three results: Teclistamab Arm-1, Emicizumab, and Talquetamab Arm-2 did not clear all flags and are presented as candidates for iterative re-invocation of the platform pipeline on the optimized output (planned follow-up; Section 5). The remaining TAP metrics (PSH, PPC magnitude, PNC, |SFvCSP|) trended in the improvement direction without reaching significance in this cohort, a pattern consistent with the expected statistical signature of a multi-objective optimizer applied to molecules already within the clinical-stage envelope. The platform reported a mean of 12.8 months and USD 723,889 of computational front-loading per project across the nine-project cohort (range 9.0 to16.0 months; USD 510,000 to 960,000); the underlying cost assumptions are tabulated in Supplementary Table S3. Conclusion. PTIm-mAb produces externally verifiable, literature-aligned improvements on the metrics most directly under its control, clears CDR-vicinity charge-patch flags on a meaningful fraction of flagged candidates, and front-loads substantial design-iteration work. The cohort-level pattern is consistent with a calibrated multi-objective optimizer operating at the edge of detectable headroom on a deliberately hard benchmark. We position the platform as an early-stage triage and lead-optimization layer in bispecific antibody discovery. For molecules whose first-pass result does not clear all flags, iterative re-invocation of the pipeline on the optimized output is a natural follow-up direction.
- MARiO: predicting cancer variant pathogenicity by integrating in silico evaluation and patient-level mutational contexts
Comprehensive genomic profiling (CGP) supports precision medicine in cancer care, but accurate assessment of missense variant pathogenicity, especially for variants without established consensus, remains challenging. Various computational tools have been developed for variant functional prediction, but most current tools rely solely on variant-level features and do not capture the clinical context of individual patients. To address this limitation, we developed MARiO (Missense Alteration Risk for Oncogenicity), a machine-learning model that integrates variant-level features and patient-level clinical and genomic contexts to effectively predict the pathogenicity of missense variants in cancer. We collected a total of 10,642 missense variants from 1271 patients, and evaluated candidate features for their association with variant pathogenicity, identifying informative features including in silico functional predictions, population allele frequency, variant allele frequency, and tumor mutational burden. Using these selected features, MARiO was developed with extreme gradient boosting. The model integrates multiple in silico prediction tools and patient-specific genomic contexts while accommodating missing values frequently observed in real-world CGP datasets. MARiO outperformed existing tools, achieving an area under the receiver operating characteristic curve of 0.942. The model demonstrated strong generalizability across multiple external datasets and showed consistency with real-world molecular treatment proposals. MARiO offers a robust and clinically relevant approach for missense variant pathogenicity assessment by integrating variant- and patient-level features and serves as a valuable tool to support clinical decision-making.
- Assessing AI and Neurologist Diagnostic Reasoning Against Neuropathological Ground Truth
BACKGROUND Accurate differential diagnosis of complex neurological disorders remains challenging due to overlapping clinical features and heterogeneous disease presentations. Although large language models (LLMs) show promise in clinical reasoning, prior studies benchmark performance against clinician consensus rather than biological ground truth. A neuropathologically confirmed benchmark dataset for evaluating diagnostic AI in neurology is currently lacking. METHODS We introduce NeuroBench, a curated benchmark of complex neurological cases with neuropathologically confirmed gold-standard diagnoses, and DIAGNO, a confidence-aware LLM-based system for neurological diagnosis. NeuroBench comprises 203 retrospective case summaries from the Massachusetts General Hospital Brain Cutting Conference with corresponding autopsy-confirmed diagnoses. DIAGNO generated top-3 differential diagnoses, employing retrieval-augmented generation (RAG) for lower-confidence cases. Performance was assessed by three independent blinded adjudicators who evaluated both DIAGNO and neurologists against neuropathological ground truth. RESULTS NeuroBench encompassed 79 unique neuropathological diagnoses, spanning conditions including cerebrovascular disease, brain tumors, neurological infections, and various neurodegenerative and inflammatory disorders. DIAGNO matched or outperformed neurologists in top-3 accuracy (0.67 versus 0.63) and taxonomy-level accuracy (0.74 versus 0.66). In cases of disagreement, DIAGNO was more often correct than neurologists (29 versus 19 cases). Diagnostic concordance between DIAGNO and neurologists was high (90% agreement in top-3 predictions), even when both were incorrect, suggesting strong alignment in diagnostic reasoning. On NeuroBench, DIAGNO also outperformed GPT-4o baseline and DeepSeek R1 across all top-k accuracy metrics. In a real-world evaluation on eight complex cases with differentials from Mass General Brigham, neurologists rated DIAGNO's reasoning favorably (mean 4.03/5) across multiple dimensions of clinical utility and safety. CONCLUSIONS NeuroBench establishes neuropathological confirmation as the appropriate standard for evaluating diagnostic AI in neurology, moving beyond clinician-referenced benchmarking to define the ceiling of diagnostic accuracy. Evaluated against this standard, DIAGNO achieved expert-level diagnostic performance and received favorable clinician ratings in real-world applications, supporting its potential as a clinical decision-support tool in neurology.
- RadGuide AI: Development and Technical Evaluation of a General Nuclear Medicine Agent for Traceable Radiopharmaceutical Decision Support
Background: Nuclear medicine and radiopharmaceutical development require coordinated radiochemistry, dosimetry, molecular imaging, radiation-safety and clinical decision processes. Current workflows remain fragmented, difficult to audit and poorly standardised for evaluating domain-specific AI support. Methods: We developed RadGuide AI, a nuclear medicine agent built around a traceable data-model-tool loop. Patent, literature and clinical-trial records were converted into 15,596 initial QA items; relevance screening, completeness checks, semantic deduplication and cross-validation retained 5,474 core QA items. MedGemma-27B-Instruct served as the foundation model and was adapted with LoRA. The system incorporated 55 MCP-wrapped tools covering radiopharmaceutical R&D, clinical decision support, imaging analysis and radiation-safety/dosimetry. Evaluation used a locked N=200 benchmark with predefined denominators, leakage control, expert scoring, statistical procedures, factuality audits and tool-execution metrics. Results: RadGuide-LLM achieved 88.5% answer accuracy (177/200; 95% CI, 83.3-92.2%) and a Macro-Average score of 21.5/25 (bootstrap 95% CI, 20.9-22.0), exceeding GPT-4o, DeepSeek-V3.2 and the base MedGemma model in this technical evaluation. Supplementary audits reported guideline compliance, terminology recall, knowledge coverage, tool-routing success and preclinical/phantom dosimetry agreement with explicit denominators and confidence intervals. Interpretation: RadGuide AI converts nuclear medicine queries into auditable retrieval, tool selection, calculation, verification and reporting workflows. The findings support technical feasibility, not definitive patient-level clinical validation; prospective multicentre studies and external benchmark release remain required before clinical deployment.
- Automated Interpretation of EEG Reports Using a Large Language Model with Structured Confidence Outputs
Background: Free-text EEG reports typically lack structure, hindering scalable analysis. We evaluate a large language model (LLM) pipeline to extract structured diagnostic labels and confidence levels from these reports. Methods: We developed a hierarchical annotation schema to classify EEG reports for four specific abnormality types using a four-point confidence scale. To establish ground truth, two certified EEG technicians annotated a diverse dataset of reports authored by neurologists with distinct writing styles. We then implemented a grammar-constrained Mistral-7B pipeline, iteratively prompt-tuned on a development set to mirror these expert annotations. The pipeline's effectiveness was evaluated against the human expert benchmark using core agreement (diagnostic accuracy) and certainty-adjusted agreement (confidence alignment), with classical NLP models serving as a secondary baseline. Results: Mistral-7B significantly outperformed baselines, achieving 96% accuracy for overall abnormality detection, approaching the human benchmark of 98%. Crucially, the model successfully identified rare epileptiform abnormalities where traditional models failed and generalized robustly across distinct reporting styles. While diagnostic accuracy was high, a performance gap persisted in certainty-adjusted agreement, indicating that accurately modeling nuanced clinical confidence remains a challenge. Conclusion: LLMs can effectively automate the extraction of structured diagnostic information from EEG reports with near-human accuracy and strong generalization. While confidence calibration requires further refinement, the combination of accurate classification and explainability makes this pipeline a promising tool for standardizing clinical data at scale. Keywords: Routine Clinical Electroencephalography; Large Language Models; Clinical NLP; Confidence Assessment; Explainable AI; Neurophysiological Evaluation
- Automated Disease Activity Assessment in Systemic Lupus Erythematosus Using Privacy-Preserving Large Language Models
The Systemic Lupus Erythematosus Disease Activity Index 2000 (SLEDAI-2K) is a crucial but labor-intensive tool for managing SLE. We developed a privacy-preserving, model-agnostic large language model (LLM) framework to automate SLEDAI-2K assessment from real-world electronic health records. The framework was developed on a specialist-verified ground truth of 658 clinical notes and externally validated on 56 MIMIC-IV discharge summaries. Seven open-source LLMs were evaluated using advanced prompting and ensemble strategies. The top-performing model, a two-layered GPT-OSS-120B + verifier, achieved a micro-F1 of 94.2% for descriptor classification and an 86% exact match for SLEDAI-2K scores on the internal set, with corresponding external validation performance of 87.7% and 64%, respectively. To demonstrate clinical utility, the LLMs were deployed on 2,576 serial notes from 108 SLE patients. Patients identified by the LLMs as achieving sustained low disease activity had a significantly lower incidence of stage 3 chronic kidney disease (log-rank p = 0.0053), the need for kidney replacement therapy (p = 0.044), and hospitalization (p = 0.021) over 18.3 years of follow-up. These findings demonstrate that privacy-preserving LLMs, when guided by a well-designed framework, can assist in specialist-level reasoning in autoimmune diseases, offering a scalable solution for clinical decision support and patient management.
- Assessment of Zero-Shot Large Language Model (LLM) Assisted Clinical Trial Matching Processes: A Metastatic Cancer Use Case
Introduction: For oncology patients with limited treatment options, clinical trials may be a critical lifesaving pathway. Identifying relevant trials, however, is a time-consuming and difficult task. Several patient-trial matching processes incorporating large language models (LLMs) have been proposed to alleviate the burden on patients and oncologists. We aim to explore the benefits and practical challenges of zero-shot LLM-assisted trial matching processes by analyzing the results for a single pancreatic cancer patient. Materials and Methods: The results of a simple zero-shot LLM-assisted clinical trial matching process for our patient were compared to those of a "human benchmark," which was developed manually by two of the authors interfacing directly with ClinicalTrials.gov. Performance metrics -- sensitivity, specificity, precision, and accuracy -- were calculated. In addition, a qualitative content analysis (QCA) of LLM reasoning text was done to identify patterns in "errors," which we define as a human-LLM discrepancy in final patient eligibility. Implications and severity of errors are discussed. Results: The zero-shot LLM-assisted process returned potential trials with a sensitivity, specificity, and precision of 81.1%, 89.3%, and 86.5% respectively compared to the human benchmark. Qualitative error analyses revealed that about 73% of errors could potentially be alleviated with improved prompting and information access. Overall performance seemed comparable to that of human reviewers. Conclusion: The results from this preliminary real-world case study provide additional evidence to the literature in support of the integration of LLMs in clinical trial matching to provide benefit to patients with metastatic cancer with limited options.
- Neural Correlates of Cognitive Alterations and Minor and Structured Hallucinations in Parkinson's Disease
Background: Hallucinations, ranging from minor (MH) to structured, are a common non-motor symptom in Parkinson's disease (PD). Structured hallucinations have been associated with altered functional connectivity (FC) between dorsal/ventral attention (DAN, VAN) and default mode (DMN) networks. As structured hallucinations are linked to rapid cognitive decline and MH are often viewed as their precursor, it is imperative to understand the neural basis of MH, and its relationship with cognitive alterations. Objectives: We aimed to identify a whole-brain FC pattern associated with MH and alterations in attention-executive functioning in PD, leveraging a robotic procedure inducing presence hallucinations (riPH) experimentally, to which patients with hallucinations previously showed increased sensitivity. Methods: Non-demented PD patients (N = 53) were categorized into three subgroups based on their hallucination symptoms: no hallucinations (nH; n = 19), MH (n = 18), and structured hallucinations, with or without MH (SMH; n = 16). We combined results from the riPH procedure and neuropsychological tests and applied multivariate methods capturing their shared variance in resting-state fMRI data across the three subgroups. Results: We identified a distributed FC pattern more strongly expressed in patients with hallucinations (MH, SMH), and equally so across both groups, significantly associated with alterations in attention-executive functions and differences in riPH sensitivity. The pattern was primarily driven by FC between subcortical areas and visual network, DAN and DMN, and within-cerebellar and within-subcortical FC. Conclusions: Our results highlight the role of subcortical-cortical connectivity in PD hallucinations, associated with cognitive alterations and already present in less advanced MH patients.
- Thalamic tFUS for Post-Stroke Motor Recovery: A Pilot Multimodal Neurobehavioral Study
Effective modulation of cortical-subcortical motor circuits is essential for post-stroke recovery, yet progress has been constrained by the absence of non-invasive tools capable of precisely targeting deep brain structures. In this pilot proof of concept study, we explored the feasibility and preliminary neuromodulatory effects of a 12-minute transcranial focused ultrasound (tFUS) protocol targeting the ipsilesional ventral lateral posterior (VLp) thalamus in ischemic stroke patients. Six individuals with upper-limb hemiparesis received individualized, neuronavigation-guided tFUS. Sensorimotor tracking performance improved signiffcantly after a single session. Concurrent EEG revealed reversible beta-power suppression over the ipsilesional motor cortex and enhanced theta-phase synchronization in frontoparietal networks, both of which were associated with behavioral gains. Resting-state fMRI indicated rebalancing of inter-hemispheric motor networks. These preliminary ffndings suggest that thalamic tFUS can modulate both local and networklevel neural activity and is associated with immediate functional improvement, highlighting its potential as a feasible neuromodulation approach for deep motor circuit engagement in post-stroke rehabilitation.
- Performance of automated anterior segment OCT-based quantitative imaging in adult anterior chamber inflammation
Objective: To investigate the performance of anterior segment (AS) OCT quantitative imaging of anterior chamber inflammation in uveitis patients with diverse demographics. Design: Prospective cross-sectional study. Participants: 144 adult patients managed at a tertiary care service in the UK Methods: Repeated swept-source ASOCT imaging was performed pre- and post-pupil dilation (i.e. 4 scan sets). Inflammation was quantified using a validated human in the loop automated image analysis pipeline, Minuscule Cell Detection (MCD), which identified and counted putative inflammatory cells on AS-OCT. Main Outcome Measures: Test-retest variability of ASOCT and diagnostic accuracy of various ASOCT derived measurands (minimum, maximum, median counts per cross sectional image, and total counts across volume image sets per eye or MINCC, MAXCC, MEDCC and TOTCC) versus Standardization of Uveitis Nomenclature (SUN) grading system as assessed by a uveitis specialist. Results: A total of 281 eyes were included in the analysis. Median age was 48 years (IQR 36 to 64). Strong test-retest measurand reliability was demonstrated, with a 95% tolerance interval ratio 0.3 to 3.0. The best diagnostic performances for SUN activity were observed with the MINCC threshold of 3 particles (negative predictive value for clinical activity of 89.8%, 95% CI 83.0 to 94.1). Associations between ASOCT measurands and patient age (adjusted coefficient 7.5 additional particles, 95% CI 0.5 to 14.6, p<0.04 for age over 60 years versus under 44), and pigment load (52.8, 11.8 to 92.9, p<0.01 in eyes with AC pigment versus without) were noted. Conclusions: ASOCT assessment of anterior chamber inflammation in uveitis meets current recommendations for quantitative imaging biomarkers, demonstrating strong repeatability, linearity with clinical assessment scores and stability with pupil dilation and patient characteristics of ethnicity and lens status. The absence of variability in diagnostic indices across derived measurands suggests similar performance across different acquisition protocols. Further longitudinal cross-platform studies are needed to determine limitations of use.
- Comparison of Relapse Rate and Disease Severity among patients with Type 2 Lepra Reaction receiving Tofacitinib and Thalidomide separately as an adjuvant to systemic steroids: A Longitudinal Analytical Study
Background Erythema nodosum leprosum (ENL) is a severe immune-mediated complication of multibacillary leprosy requiring prolonged immunosuppression. Steroid-sparing agents are essential to reduce relapse and treatment-related morbidity. Methods This longitudinal analytical observational study compared outcomes in patients with ENL treated with prednisolone plus thalidomide (Group A; n=30) and prednisolone plus tofacitinib (Group B; n=31). Patients were followed for 6 months. Primary outcomes included relapse rate and ENLIST ENL Severity Score (EESS). Secondary outcomes were neutrophil-lymphocyte ratio (NLR), Dermatology Life Quality Index (DLQI), steroid dependency, and adverse events. Inter-group comparisons and longitudinal analyses were performed using non-parametric tests. Correlations between NLR, EESS, and DLQI were assessed using Spearmans rank correlation. Results Relapse occurred in 36.7% of patients in Group A and 71.0% in Group B (p=0.007). The mean number of relapses was significantly lower in Group A (0.70{+/-}1.06 vs 1.84{+/-}1.51, p=0.002). At 3 and 6 months, Group A demonstrated significantly lower NLR values (p=0.017 and p<0.001, respectively). DLQI and EESS scores improved in both groups; however, sustained improvement was more consistent in Group A. Steroid-free status at 6 months was achieved in 93.3% of Group A compared with 58.1% of Group B (p<0.001). NLR showed a positive correlation with EESS ({rho}=0.269, p=0.018) and DLQI ({rho}=0.604, p<0.001) at 6 months. On multivariable logistic regression analysis adjusting for baseline confounders, patients receiving tofacitinib had significantly higher odds of relapse compared with those receiving thalidomide (adjusted OR 9.87, 95% CI 1.73-27.12; p = 0.006).Adverse events were predominantly mild to moderate, with differing safety profiles between groups. Conclusion Thalidomide demonstrated superior relapse prevention and steroid-sparing efficacy compared with tofacitinib in ENL. NLR correlated with disease severity and quality of life, supporting its role as a useful biomarker for monitoring disease activity during follow-up.
- ANTI-HUMAN T-LYMPHOTROPIC VIRUS TYPE 1 (HTLV-1) SEROPOSITIVITY IN HAEMATOLOGICAL MALIGNANCIES AT A MAJOR CLINICAL SETTING IN GHANA
Background Human T-cell lymphotropic virus - 1 (HTLV-1) is the causative agent of Adult T-cell Leukaemia/Lymphoma (ATLL), a malignancy of CD4+ cells, and HTLV-1-associated Myelopathy/Tropical Spastic Paraparesis (HAM/TSP), a demyelinating disease. Globally, 10-20 million people are infected, though most remain asymptomatic and about 5% progress to severe disease. Transmission occurs mainly through breastfeeding, sexual contact, contaminated needles, and blood transfusion. In Ghana, evidence on the role of HTLV-1 in haematological malignancies remains scarce. Methods This was a cross-sectional study involving 200 patients with haematological malignancies (Acute Lymphoblastic Leukaemia - 4, Acute Myeloid Leukaemia - 6, Chronic Lymphocytic Leukaemia - 27, Chronic Myeloid Leukaemia - 63, Hodgkin Lymphoma - 21, Multiple Myeloma - 31, Myelodysplasia - 6, Myeloproliferative Neoplasm - 11) at the Haematology Day Care of the Korle-Bu Teaching Hospital. After informed consent was obtained, sera from study participants were tested for anti-HTLV-1 using MP Diagnostics GmbH ELISA immunoassay. Data were analysed using R software version 4.0.2 and SPSS version 31.0.0. Results The study population had a mean age of 49.1{+/-}17.7 years, with majority being females (n=109, 54.5%). Of the 200 samples, 16 (8.0%) were seropositive for HTLV-1, and these were detected in 4 males and 12 females. No statistically significant association was found between HTLV-1 infection and haematological malignancy (exact p = 0.061), sex (p=0.061), and history of blood transfusion (exact p= 1.000). Conclusion The findings show the seroprevalence of HTLV-1 of 8.0% among patients with haematological malignancies. Although there was no probable association between HTLV-1 and haematological malignancies, screening for HTLV-1 in patients with haematological malignancies may help to unravel the exact contribution in these conditions.
- Predicting radiological severity of pulmonary tuberculosis in children: an assessment of the WHO-criteria and novel prediction scores on an individual participant dataset
Background The World Health Organization (WHO) recommends 4-month treatment for children with non-severe pulmonary tuberculosis, outlining eligibility criteria for settings with and without chest X-ray (CXR). We evaluated the diagnostic accuracy of the WHO eligibility criteria in settings without CXR (WHO-criteria) and developed clinical scores to support disease classification. Methods Using data from an individual participant dataset (IPD; Decide TB) of children with confirmed/unconfirmed tuberculosis from four diagnostic studies (RaPaed-TB, Umoya, TB-Speed HIV, TB-Speed Decentralisation), we assessed the diagnostic accuracy of the WHO-criteria (with/without bacteriological testing) using expert CXR interpretation as a reference. We developed two multivariable logistic regression models with (Score 1) and without (Score 2) bacteriological testing, converted coefficients into integer scores with a threshold of >10 corresponding to a sensitivity [≥]70%. Results Of 2,383 children in the Decide TB-IPD, 633 (26.6%) met the eligibility criteria for a 4-month regimen, of whom 116 (18.3%) had radiologically severe disease. With and without bacteriological testing, the WHO-criteria had sensitivities of 30.1% (95%CI: 20.3%-40.2%) and 21.7% (95%CI: 10.4%-34.5%), and specificities of 83.4% (95%CI: 80.2%-86.4%) and 81.9% (95%CI: 78.8%-84.9%), respectively. Score 1 and Score 2 had sensitivities of 41.1% (95%CI: 32.4%-49.5%) and 30.9% (95%CI: 22.6%-40.4%), and specificities of 77.3% (95%CI: 73.6%-80.8%) and 83.0% (95%CI: 79.5%-86.3%) respectively. Using WHO-criteria, 91/116 (78.4%) and 105/116 (90.5%) of children were at risk of undertreatment, compared to 68/116 (58.6%) and 80/116 (68.9%) when using developed scores. Conclusions Developed scores demonstrated better sensitivity than WHO-criteria, however, performance remains suboptimal. Implementing shorter antituberculosis regimens without CXR remains challenging in children.
- Benchmarking long-read variant sensitivity across ONT and PacBio platforms using known clinically reported variants in a cohort of critically ill newborns
Long-read whole genome sequencing (lrWGS) shows promise as an all-in-one test to detect clinically relevant variants and variants difficult to detect by current short-read whole genome sequencing (srWGS) pipelines. Comparisons between lrWGS and srWGS (or exome sequencing) pipelines will become commonplace as lrWGS is more widely adopted for clinical testing, particularly for individuals not diagnosed by srWGS. However, the sensitivity of lrWGS for detecting variants previously identified and prioritized by clinical srWGS has yet to be assessed. As part of the SeqFirst-neo study, a subset of critically ill newborns and their parents who underwent clinical srWGS also underwent lrWGS on the Oxford Nanopore Technologies (ONT) and Pacific Biosciences (PacBio) platforms. In total, 134 families were sequenced across multiple technologies including 128 families with clinical srWGS who were sequenced on both lrWGS platforms. We compared the variants reported by clinical testing with the variants identified by lrWGS. Among the 128 families sequenced on all three platforms, 89 SNV/indels and 14 SV/CNVs clinically reported by the srWGS testing pipeline were evaluated. All variants assessed in probands were ultimately detected by both lrWGS platforms, although three events were not detected prior to application of an updated variant caller, highlighting the rapid evolution of lrWGS variant calling. Additionally, breakpoint coordinates and event sizes often differed substantially between calls from srWGS and events called in lrWGS data. Our work demonstrates that while most clinically reported variants from srWGS can be detected by lrWGS pipelines, challenges remain when attempting direct comparisons, particularly for SV/CNVs.
- Low-cost rare variant detection for population scale genetic screening
Genetic screening for rare pathogenic variants facilitates early detection and prevention of disease manifestations in medically actionable disorders, but sequencing costs limit widespread use. We introduce DoBSeq, a low-cost, high-throughput screening framework for detecting rare, single-nucleotide variants and indels. The framework includes: extraction of DNA from dried blood spots used in neonatal screening, automation of two-dimensional DNA pooling and library preparation, high-depth targeted sequencing using a 582-gene custom panel, and a probabilistic model to assign rare pathogenic variants to individuals. Benchmarked against whole-genome sequencing across 582 genes in a batch of 576 individuals, the framework detected 95% of all variants and recovered all clinically relevant pathogenic single-nucleotide variants in American College of Medical Genetics and Genomics (ACMG) actionable genes. Applied to 2304 anonymised blood donors, it yielded variant frequencies consistent with existing population estimates. At a sample cost of 29 USD, including 11 USD running costs, this framework provides a cost-efficient approach to population-level genetic screening.
- Chronic adaptations following eccentric cycling training at different cadences
Pedalling cadence during an acute eccentric cycling exercise altered physiological and perceptual responses. We examined the influence of cycling cadence on neuromuscular adaptation induced by a 6-week eccentric cycling training period. Eighteen participants performed training (eighteen sessions) at a cadence of 30 or 60 rpm over six weeks. Power output was the same between the two groups. Perceived effort and heart rate were recorded at each training session. Muscle pain and fatigue were reported the day after each session. Maximal voluntary contractions torque, as well as concentric and eccentric cycling efficiency, were assessed before and after training. Additionally, the loss of maximal voluntary isometric torque was assessed after the first and last training sessions. Heart rate and perceived effort increased in the second week of training and then plateaued, with no difference between groups. Muscle pain and fatigue remained low throughout the training, with no difference between groups. Isometric (+28%) and eccentric (+13%) maximal voluntary torque of knee extensor muscles increased regardless of training cadence. Concentric maximal voluntary torque increased for the group pedalling at 60 rpm only (+21%). Cycling efficiency was improved in eccentric mode only (+43%), with no difference between the two training groups. Finally, the voluntary isometric torque loss induced by the first and last sessions were similar. While six weeks of eccentric cycling training improved neuromuscular and functional capacities, cadence had no observable effect. This finding suggest that patients could choose their preferred cadence to obtain better adherence to the rehabilitation program without altering the adaptations.
- Muscle stability deficits are strongly associated with musculoskeletal complaints in football (soccer) players: the AF-Ratio outperforms conventional strength parameters--a cross-sectional study with preliminary follow-up
Objectives: Given the high injury burden in football and the documented limitations of strength-based screening, novel approaches are warranted. Adaptive Force (AF)being closer to injury-prone movements than pushing/pulling strength--offers an alternative. This study examined the association between AF-based muscle stability and musculoskeletal complaints in football players and compared AF-derived and conventional strength parameters in their discriminative capacity, complemented by a preliminary prospective follow-up. Methods: AF and maximal voluntary isometric contraction (MVIC) were measured in 23 male football players across five bilateral muscle groups (knee extensors/flexors; hip flexors/adductors/abductors). AF parameters (maximal isometric AF, maximal AF, AF-Ratio), MVIC and hamstrings-to-quadriceps (H:Q) ratio were compared between players with and without complaints assessed via questionnaire at baseline and six-month follow-up (n=13). Results: Stability deficits were strongly associated with complaints (OR=54.0, 82% side concordance). AF-Ratio discriminated clearly between players with and without complaints (d=-1.47), with hip abductors showing the strongest effect (d=-1.64). Players with subsequent complaints showed lower baseline AF-Ratio (d=-1.45) and more stability deficits (d=1.67). MVIC and H:Q ratio did not discriminate (p>0.430). Conclusion: The findings suggest that muscle stability assessment outperforms conventional strength parameters in discriminating players with and without complaints, with preliminary follow-up data providing tentative support for predictive value. The concept of functional instability syndrome (FIS) provides a mechanistic framework for non-contact injuries and musculoskeletal complaints. AF assessment offers potential for screening, including return-to-sport decisions. Further studies are needed to verify the results, investigate predictive value, and evaluate whether personalised stability-based interventions can reduce injury incidence.
- WHO SHOULD BEAR THE COST OF COVID-19 NON-VACCINATION? A LUCK EGALITARIAN ASSESSMENT IN A SOUTH AFRICAN INSURED POPULATION
Background Personal choice in health behaviours raises difficult questions: when individuals freely decline effective preventive interventions, who should bear the resulting costs? This tension is acute in insurance systems where resources are pooled, yet all health systems pursuing Universal Health Coverage must navigate the boundary between collective solidarity and individual accountability. During the COVID-19 pandemic, vaccines were freely available to members of South African private medical schemes, creating conditions in which non-vaccination could plausibly be examined as a matter of personal choice rather than constrained access. This study applied a luck egalitarian framework to assess whether non-vaccination reflected personal choice or constrained circumstance, and to quantify resulting excess costs. Methods A contextual review assessed barriers to vaccination. Using de-identified claims data for approximately 550,000 individuals (March 2020 to December 2022), logistic regression estimated each person's predicted probability of vaccination based on demographic and clinical factors, with observed and predicted rates compared across strata to infer choice versus circumstance. A zero-inflated negative binomial model estimated predicted expenditure among vaccinated members, applied to the full population to simulate universal vaccination. Excess costs were calculated across predicted probability strata. Results Predicted and observed vaccination rates were closely aligned, suggesting that residual non-vaccination in higher-probability groups reflected personal choice rather than constrained circumstance. Observed costs exceeded predicted costs by 22% under universal vaccination, concentrated among older adults and those with comorbidities. Among those with a 60 to 70% predicted probability of vaccination, observed costs exceeded predicted costs by 127.6%. In contrast, among younger, low-risk members, predicted costs slightly exceeded observed expenditure, as vaccination costs were not offset by reduced hospitalisation. Conclusion Risk pooling depends on solidarity, yet non-vaccination due to personal choice shifts costs in ways that challenge fairness in community-rated insurance. These findings highlight the need for transparent deliberation about when personal responsibility should inform equitable health financing design.
- Comparative Spatiotemporal Analysis of Global HIV-1 Subtype C Hotspots: Applying Bayesian Hierarchical Modeling, SaTScan, and Getis-Ord Gi* Statistics
Background: Despite the global importance of HIV-1 subtype C, a global-scale GIS characterization of its geographic clustering and the temporal persistence of hotspots is lacking, and systematic cross-method comparisons are scarce. Methods: We assembled 2,220 country-year observations from 111 countries between 2005 and 2024, comprising 161,025 subtype C sequences, and generated internally standardized expected counts. We compared hotspot detection using Getis-Ord Gi-star statistics in ArcGIS, SaTScan space-time scan statistics, and Bayesian hierarchical models with spatiotemporal smoothing, and quantified temporal persistence and cross-model concordance. Results: Documented subtype C sequences showed increasing geographic concentration over time, shifting from relatively widespread detection toward progressively localized clustering, with the strongest and intensifying concentration in Southern Africa. SaTScan and Bayesian models identified fewer hotspots but showed greater temporal stability, whereas Gi-star detected more localized and short-term spatial fluctuations. High-stability hotspots with sustained multi-year detection were predominantly located in Southern Africa. Zimbabwe was the only country classified as a high-stability hotspot across all three frameworks; Eswatini, Botswana, Malawi, and South Africa showed high stability in at least two models, indicating robust, model-consistent persistence. Conclusions: Integrating complementary hotspot methods reveals both convergent and method-specific patterns and provides a quantitative basis to prioritize long-term persistence for targeted surveillance, resource allocation, and precision prevention.
- Genomic basis of developmental defects of enamel and sex-specific effects
We conducted a multi-ancestry genome-wide association study (GWAS) of developmental defects of enamel (DDE) in the primary dentition among 6,061 U.S. preschool-aged children (3--5 years). We investigated four DDE phenotypes (demarcated opacities, diffuse opacities, hypoplastic defects, and a combined DDE trait) leveraging main-effect models, joint gene-sex interaction testing (2df), and sex-stratified analyses. SNP-based heritability for the combined DDE trait was estimated at 20%, with concordance analyses robustly supporting a genetic etiology. We identified 39 unique genome-wide significant loci (P<5 x 10-8;), with five surpassing a study-wide Bonferroni-corrected statistical significance criterion (P<1.25 x 10-9), including Y RNA and ALDH1A1. The main-effect GWAS identified 20 loci, including HBS1L and MYB, genes regulating hematopoiesis with plausible roles in amelogenesis. Joint test and sex-stratified analyses revealed 19 additional loci, including ALDH1A1, TENM2, and DLGAP2, demonstrating sex-specific heterogeneity. Nineteen loci exhibited sex-specific differences after Bonferroni correction (P<2 x 10-3), including genes involved in retinoic acid signaling (ALDH1A1), odontogenesis (TENM2), and neurodevelopment (DLGAP2, CDH10). Pathway enrichment highlighted ectodermal and synapse organization networks, suggesting shared etiological mechanisms between DDE and systemic conditions like neurofibromatosis and autism spectrum disorder. Notably, no locus generalized in an external GWAS of permanent dentition DDE, underscoring fundamental biological differences in the genetic architectures governing primary versus permanent enamel formation. Crucially, a comprehensive cross-trait pleiotropy lookup against early childhood caries (ECC) revealed no shared genetic architecture, supporting the notion that the established clinical and epidemiological association between DDE and ECC is likely driven by structural defects increasing caries lesion susceptibility rather than genetic pleiotropy. By integrating gene-sex interaction testing, this study offers novel insights into the complex, sexually dimorphic genetic etiology of DDE and augments the biological evidence base that can support the development of precision pediatric dentistry.
- Organism spectrum and no-growth fraction of deep specimens in code-defined orthopedic infection: a reproducible, cross-sectional MIMIC-IV benchmark
Abstract Introduction. Culture data guide orthopedic-infection management, yet the organism spectrum, resistance, and no-growth fraction are reported inconsistently and mostly within proprietary registries. We characterized these in a public, reproducible dataset. Methods. Retrospective cross-sectional study using MIMIC-IV version 3.1, a de-identified single-center US database. Episodes with an International Classification of Diseases diagnosis of prosthetic joint infection (PJI) or native osteomyelitis were identified; organism-spectrum and no-growth analyses were restricted to the 46% with at least one deep musculoskeletal culture (tissue or bone, synovial or joint fluid, implant sonication), so the benchmark describes culture-sampled, not all, coded episodes. Proportions carry exact 95% CIs; variation was tested by logistic regression with Benjamini-Hochberg control, and an out-of-fold logistic model quantified how well no-growth was anticipated by structured data. Results. Of 7697 episodes (median age, 60 years; 35.5% female), 1089 were PJI, 5715 native osteomyelitis, and 893 other device infection. Among 7700 deep specimens (3560 episodes; 2603 patients), 35.7% showed no growth (patient-clustered 95% CI, 34.0%-37.3%). The fraction was higher in PJI than osteomyelitis (48.6% vs 26.6%) but rose with sampling intensity (24.5% to 50.7%), indicating differential ascertainment. S. aureus led (32.5%; 43.3% methicillin-resistant), and PJI was less often polymicrobial than osteomyelitis (adjusted OR, 0.44). No-growth was weakly anticipated by structured data (out-of-fold AUROC, 0.63). Conclusions. About one-third of deep specimens from code-defined orthopedic infection showed no growth. This specimen-level fraction differs from a criterion-confirmed culture-negative-infection rate and depends on sampling intensity; it is released as a re-runnable benchmark on identical open data, not a transferable rate.
- Empirical contact networks reveal heterogeneous outbreak risks in a UK long-term care facility: a modelling study
Long-term care facilities (LTCs) worldwide experienced disproportionately high infection and mortality rates during the COVID-19 pandemic, where essential care limits opportunities for contact segregation. However, empirical contact data remain scarce, limiting our understanding of how individual contact behaviours shape transmission in these settings. In this study, we developed a stochastic network-based transmission model parameterised using real-world self-reported contact data collected from a median-sized UK LTC unit. By incorporating high-resolution observational data that reflect routine care delivery patterns, we quantified how heterogeneity in contact networks influences outbreak dynamics. We found substantial variation in contact behaviour between individuals, resulting in highly heterogeneous transmission outcomes. Outbreak occurrence, timing, final size, and the likelihood of super-spreading events all varied markedly depending on the structure of the underlying contact network and the characteristics of the index case. Individuals with high contact activity were considerably more likely to initiate large outbreaks than those with fewer contacts. For a per-contact transmission probability of 10%, introduction of infection through the most highly connected individuals resulted in a greater than 75% probability of a large outbreak. Our findings indicate that preventing infection introduction through both residents and staff is critical for outbreak control in LTCs. Individuals with high contact activity were consistently associated with a greater probability of initiating large outbreaks, highlighting the importance of accounting for contact heterogeneity when designing surveillance and infection-control measures. More broadly, this study demonstrates the importance of accounting for contact-network heterogeneity when designing infection prevention and control measures in LTC settings, and highlights the value of integrating empirical contact data with transmission modelling to inform evidence-based outbreak preparedness, targeted surveillance, and infection-control strategies in long-term care facilities.
- Effectiveness and cost-effectiveness of the Keep-on-Keep-up (KOKU) digital falls prevention programme in community-dwelling older adults: Results of a randomised controlled trial.
Background Falls are a leading cause of injury-related hospital admissions among older adults with substantial burden on health and social care systems. Digital exercise programmes may improve physical function at scale and complement traditional services. Keep-On-Keep-Up (KOKU) is an NHS-approved digital programme offering progressive, evidence-based exercises and education on fall prevention. We aimed to evaluate the effectiveness and cost-effectiveness of KOKU for improving balance, physical function and reducing fall risk among community-dwelling older adults. Methods A two-arm, parallel group randomised controlled trial was conducted with community-dwelling older adults (>=60 years). Participants were randomised (1:1) to receive KOKU alongside standard care (strength and balance exercise advice and a falls prevention leaflet) or standard care alone. The primary outcome was balance function at 12 weeks (Berg Balance Score). Secondary outcomes included lower limb strength, concerns about falling, falls, mood, pain, fatigue, healthcare utilisation, health-related quality of life and usability. A modified intention-to-treat approach was used to analyse effectiveness and cost effectiveness. Results A total of 202 older adults (mean age 76.8 years, 72.8% female) were enrolled (102 intervention; 100 control). Retention at 12-weeks was 89.1% (91 intervention; 89 control). Compared with standard care, KOKU significantly improved balance function at 12 weeks after adjusting for baseline scores (mean difference: 6.35, 95% CI: 4.48, 8.22). KOKU was associated with lower mean falls related costs (incremental cost (GBP): -62.98, 95% CI -218.54 to 40.22) and a QALY gain of 0.020 (95% CI 0.003 to 0.035). Conclusion The KOKU programme improves balance with preliminary evidence of cost-effectiveness among community-dwelling older adults.
- Structural Variant Imputation in Samoans Using a Population-Specific Reference Panel
Structural variants (SVs) are often excluded from genetic research because they are difficult to call, but they can have substantial effects on phenotypic traits. SVs have not previously been characterized in Samoans, an understudied population with a high burden of complex diseases. Using short-read whole genome sequencing data, we called SVs in 1,276 Samoans and created a Samoan-specific imputation panel inclusive of both SVs and single nucleotide variants (SNVs), called the Soifua Manuia-SV panel. Using this panel, we imputed SVs and SNVs in 3,611 Samoans with array data, enabling analysis of SV-phenotype associations in a sample of 4,887 Samoan participants. We evaluated imputation performance in Samoans against two other reference panels: (i) an SNV-only Samoan-specific reference panel, to assess whether SV inclusion impacts SNV imputation, and (ii) an SV and SNV, multi-ancestry reference panel composed of 1000 Genomes participants, which did not include Polynesians, to assess the importance of including the target population in the reference panel. The Soifua Manuia-SV panel substantially outperformed the multi-ancestry SV and SNV panel, yielding 5.5 million more high-quality (r2[≥]0.8) variants, including over 8,000 more high-quality SVs. SNV imputation based on the two Samoan-specific panels performed similarly overall, suggesting that SV inclusion does not strongly impact SNV imputation quality. This work highlights the importance of population representation for accurate imputation.
- Wearable Electrical Impedance Myography for Continuous, Non-Invasive Detection of Acute Compartment Syndrome: A Preclinical Feasibility Study
Introduction: Acute compartment syndrome (ACS) is a limb-threatening complication of extremity trauma that requires timely diagnosis to prevent irreversible muscle and nerve injury. Current diagnostic methods are invasive, intermittent, and operator-dependent. We evaluated the feasibility of a novel, Bluetooth-enabled electrical impedance myography (EIM) device (mAlert, Myolex, Inc., Brookline, MA, USA) for continuous, noninvasive detection of ACS-related tissue changes. Methods: Ten Yorkshire swine underwent anterior tibial compartment monitoring using three ACS models: albumin infusion (ALB, n=3), femoral artery and vein ligation (LIG, n=3), and combined albumin infusion plus ligation (ALB+LIG, n=4). Resistance (R), reactance (X), and phase (P) were measured every minute across 1 to 199 kHz alongside continuous intra-compartmental pressure (ICP) monitoring. Group differences in normalized impedance trends were evaluated using the Kruskal Wallis test with Dunn post hoc correction. As a proof-of-concept human study, nine healthy volunteers wore the device for up to five days to assess electrode durability and signal stability. Tissue ischemia was validated using pimonidazole immunohistochemistry. Results: ALB infusion produced progressive, frequency-dependent decreases in R, X, and P, whereas LIG produced consistent increases in R and X across frequencies. The ALB+LIG model generated mixed responses, reflecting the competing effects of edema and ischemia. Normalized phase slopes differed significantly among groups (H=6.14, p=0.046), with post hoc testing showing significant divergence between the ALB and LIG models (p=0.041). Control limbs remained stable throughout monitoring. Pimonidazole staining confirmed hypoxic injury in the intervention limb. In the human pilot study, three participants completed five days of monitoring, demonstrating sustained signal acquisition, while electrode degradation limited data collection in the remaining participants. Conclusions: This preliminary feasibility study demonstrates that wearable EIM can continuously detect model-specific physiological changes associated with ACS in a large-animal model. These findings support further development and clinical evaluation of wearable EIM as a non-invasive monitoring technology for early ACS detection in trauma patients.
- Gene-Temperature Interactions and Risk of Childhood Acute Lymphoblastic Leukemia
Background: High ambient temperature in early pregnancy has been linked to an increased risk of childhood acute lymphoblastic leukemia (ALL). To better understand biological mechanisms, the current study evaluated potential interaction between temperature and genetic characteristics. Methods: We used data from California birth records (1982-2008) and California Cancer Registry (1988-2011) to identify ALL cases (n=3,353) diagnosed <=14 years of age and non-cancer controls (n=3,530) matched 1:1 on sex, race, ethnicity, and birth year and month. Weekly ambient temperatures throughout pregnancy were assessed on a 1-km grid around the birth address, while genetic data were available from a genome-wide association study using neonatal blood spots. We evaluated the association between ambient temperature and ALL risk by quartiles of established genetic risk score for ALL. Next, we formally tested gene-temperature interactions in the association with ALL, correcting for multiple testing, for genes previously identified with epigenetic changes due to both temperature and ALL. All analyses were adjusted for potential confounders. Results: The elevated risk of ALL per 5 degrees C increase of weekly mean ambient temperature, confined to early pregnancy, was more pronounced among children with the lowest genetic susceptibility to ALL, especially among Latino children (first quartile: odds ratio [OR] = 1.50, 95% confidence interval [CI]: 1.14-1.97); fourth quartile: OR=1.03, 95% CI: 0.83-1.28). There were significant interactions (p<0.002) between ambient temperature and polymorphisms in BNC1 among non-Latino White children, and suggestive interactions (p<0.05) with TBPL2 and NRXN1 in the full population. Conclusions: Our findings suggest that there may be interactions between ambient temperature in early pregnancy and offspring genotype in the risk of childhood ALL. Impact: If replicated, these findings could help elucidate the biological mechanisms linking high ambient temperature in early pregnancy and the risk of childhood ALL.
- The effect of dietary fiber based on fermentability and viscosity on the gut microbial metabolites in chronic kidney disease: a systematic review and meta-analysis of experimental and clinical trials
Background: Chronic kidney disease (CKD) is associated with alterations in the gut microbiome that promote the accumulation of gut-derived uremic solutes and contribute to systemic inflammation, vascular dysfunction, and disease progression. Dietary fiber has emerged as a promising modulator of gut microbial metabolism, yet the influence of fiber physicochemical properties, particularly fermentability and viscosity, on uremic metabolite production in CKD remains poorly understood. Objective: To systematically evaluate the effects of isolated dietary fiber interventions, classified by fermentability and viscosity, on gut microbial metabolites in CKD across experimental rodent models and randomized clinical trials, and to determine whether these fiber properties modify microbial metabolites. Methods: A systematic search of PubMed, Embase, CINAHL, and Cochrane Library (through June 2026) identified randomized controlled trials and controlled rodent studies assessing isolated dietary fiber in CKD. Eligible studies reported at least one gut-derived metabolite (i.e., indoxyl sulfate (IS), p-cresyl sulfate (PCS), trimethylamine-N-oxide (TMAO), tryptophan-derived indoles, or short-chain fatty acids (SCFAs)). Random-effects models were used for pooled estimates using weighted mean differences (WMD) for human studies and standardized mean differences (SMD) for animal studies. Subgroup analyses evaluated fiber fermentability, viscosity, intervention dose, duration, and CKD stage. Risk of bias was assessed with ROB-2 and SYRCLE, and evidence certainty with GRADE. Results: Twenty-eight studies (13 human, 15 animal) met eligibility criteria, comprising 511 participants and 312 animals with CKD. Isolated fiber supplementation, primarily fermentable and non-viscous fibers, reduced IS (human: -0.13 mg/dL; 95% CI: -0.25, -0.01; p = 0.03; animal: -1.99; 95% CI: -3.06, -0.92; p < 0.0001) and pCS (human: -0.23 mg/dL; 95% CI: -0.46, 0.001; p = 0.051; animal: -1.56; 95% CI: -2.08, -1.03; p < 0.0001). SCFAs increased in animal studies, including cecal acetate (2.00, 95% CI: 0.78 to 3.22; p = 0.001) and circulating propionate (1.51, 95% CI: 0.054 to 2.96; p=0.04). There were no dose-dependent effects, but longer interventions (>8 weeks) tended to lower pCS (-0.26 mg/dL, 95% CI: -0.55 to 0.02; p=0.06). Some heterogeneity and low-to-moderate certainty were observed. Conclusion: Isolated dietary fiber reduces major gut-derived uremic solutes in CKD, with fermentability influencing metabolic responsiveness, but with minimal studies on viscous fibers. Larger, longer-duration trials with standardized reporting of total fiber intake and clinical endpoints are needed to guide evidence-based dietary recommendations in CKD.
- Biomarker-informed CSF proteomics reveals ENPP2-LPA lipid signaling associated with Alzheimer's disease
Background: Alzheimer's disease (AD) involves complex molecular alterations in the cerebrospinal fluid (CSF) proteome, yet the links between these protein changes and hallmark AD pathology remain incompletely defined. We investigated the relationship between the CSF proteome with CSF biomarkers of Alzheimer's disease (AD). Methods: CSF was collected in 500 individuals of non-Hispanic white, African Americans, and Caribbean Hispanic individuals. CSF biomarkers of AD were measured including P-tau181, A{beta}40, A{beta}42, total-tau, neurofilament light chain (NfL) and glial fibrillary acidic protein (GFAP). CSF was depleted of abundant proteins followed by precipitation, cysteine reduction/alkylation, and proteolytic cleavage by trypsin. Peptides were measured using a Q-Exactive HF mass spectrometer (Thermo Scientific). Association of individual and co-abundant modules of proteins were tested using elevated CSF P-tau181 and reduced A{beta}42/A{beta}40 to confirm the diagnosis of AD. We validated results in CSF from 397 participants in the Accelerated Medicine Partnership-Alzheimer's Disease cohort. Associated proteins were functionally validated in postmortem human brains and zebrafish. Results: We detected 1030 proteins, yielding an overall data completeness value of 97%. CSF levels of 75 (7.3%) proteins were significantly associated with CSF P-tau181 levels after multiple testing correction. Notably phospholipase D3 (PLD3, p=2.41E-09), apoE (p=4.25e-08) and osteopontin (OPN p=1.4E-16) were increased and autotaxin (ATX/ENPP2, p= 8.39E-09) and ceruloplasmin (CP) (p=2.72E-07) were lower among individuals with high P-tau181 levels. These proteins were also associated with CSF A{beta}42/A{beta}40 ratio and total tau levels but not with NfL. OPN was also associated with CSF levels of GFAP (p=1.32e-05). Among proteins associated with P-tau181 levels, pathways related to axon development (p=2.4E-12), axonogenesis (p=1.45E-11) and regulation of axonogenesis (p=5.1E-09) were enriched. Immunostaining on postmortem human and zebrafish brain found that ENPP2 expression, the gene encoding ATX, was significantly reduced in AD brain and in the amyloidosis model in zebrafish. Reduced ENPP2 expression was consistent with reduced lysophosphatidic acid (LPA) levels in the CSF of individuals with AD. LPA administration into zebrafish CSF reduced the pathological changes in synapses and vasculature due to A{beta}42. Conclusion: Unbiased profiling of circulating CSF proteins among individuals with antemortem diagnosis of AD, identified key proteins PLD3, apoE, OPN, ATX, and ceruloplasmin. Validation in postmortem human brains and zebrafish models support potential roles in endosomal sorting and APP processing, inflammation, angiogenesis, lipid transport, and oxidative stress.
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