AI News Archive: July 6, 2026 — Part 11
Sourced from 500+ daily AI sources, scored by relevance.
- Do All Visual Tokens Matter Equally? Object-Evidence Preserving Token Merging for Vision-Language Retrieval
Multi-vector vision-language retrieval preserves fine-grained visual evidence through maximum-similarity late interaction, but dense image-side tokens make storage and scoring expensive. Existing token compression methods reduce this cost, yet they can remove or collapse object- and region-level evi...
- SpliSync: Genomic language model-driven splice site correction of long RNA sequencing reads
Long RNA sequencing reads are rapidly replacing short reads in transcriptomic analyses, enabling full-length transcript sequencing and better identification of isoforms, alternative splicing events, and other transcript variants. However, their higher sequencing error rates can cause misalignments, especially at splice junctions, reducing the accuracy of transcript reconstruction and analysis. We developed SpliSync, a genomic language model-driven method for splice site correction that integrates a pre-trained genomic sequence model (HyenaDNA), alignment data, and a U-net architecture to predict splice sites at nucleotide resolution. SpliSync substantially improved the precision of RNA long-read alignments by 27%-194% across diverse datasets and consistently outperformed competing tools. As a preprocessing step, it increased alternative splicing detection accuracy by 26%-330%. In contrast, its benefit for transcript reconstruction was limited, likely due to the tools' built-in correction mechanisms. The code, developed in Python using the PyTorch package, is freely available at https://github.com/splicebox/SpliSync.
- Accurate ΔTm Prediction Without Protein Structure Inputs for Biomolecular Stability
Predicting protein stability, like changes in melting temperature ({Delta}Tm) caused by mutations, is a critical task in therapeutic protein engineering and drug discovery. This is reflected by a growing solution space, including both AI-based sequence and structure based methods. This paper demonstrates that accurate {Delta}Tm prediction does not require structural input features, but can achieve state-of-the-art results with a careful training design for large sequence-based protein language models. We combine an autoresearch-inspired setup search with controlled ablation studies and show that a well-tuned sequence-only ESM2-650M model outperforms structure-informed methods in our benchmark, achieving the lowest error (MAE/RMSE) and competitive Pearson correlation without pH or structural inputs. We further show that choices such as loss function, pooling strategy, auxiliary supervision, and fine-tuning regime materially affect performance.
- Benchmarking AlphaFold and related deep learning approaches for modeling antibody and TCR antigen recognition
Determining the structural basis of antigen recognition by antibodies and T cell receptors (TCRs) provides critical insights into effective immune targeting and can inform design of biotherapeutics and vaccines. Accurate computational modeling of antibodies and TCRs in complex with their targets poses a major challenge for predictive methods, including AlphaFold, which is generally accurate for modeling protein complexes but has shown limited success for immune recognition. In this study we assessed the performance of AlphaFold2, AlphaFold3, increased sampling protocols, and related deep learning methods for modeling antibody-protein, antibody-peptide, and TCR-peptide-major histocompatibility complex (pMHC) recognition. We show that increased sampling and AlphaFold3 generally improve performance relative to default sampling and AlphaFold2, however predictive accuracy and improvement levels varied considerably among interface classes, with antibody-peptide complexes representing a challenge despite their small antigen size. Comparing per-case success across methods showed some complementarity, indicating opportunities for increased success through model pooling approaches, for instance increasing antibody-peptide near-native success from 41% to 59%. Analysis of AlphaFold confidence scores and modeling of a noncanonical complex provided further insights into predictive performance. These results highlight considerations for predictive antibody and TCR complex modeling efforts, while revealing key distinctions among protocols, scoring, and immune complex classes.
- Multimodal temporal mapping of macrophage transcriptome remodeling during Salmonella infection
Macrophages are equipped to eliminate invading pathogens, yet several intracellular bacteria exploit them as replicative niches. Salmonella enterica serovar Typhimurium subverts host immunity by injecting effector proteins that remodel macrophage functions. While macrophages typically induce a pro-inflammatory program upon bacterial invasion, Salmonella can redirect them toward an anti-inflammatory and replication-permissive state via manipulation of the NFkB and STAT3 host transcription factors. How the integration of the effects on these two transcription factors and potentially others underpins this reprogramming remains poorly charted. Here, we use a multipronged approach combining a bacterial reporter, temporal single-cell RNA-seq with RNA metabolic labeling, transcription factor (TF) footprinting, and single-cell CRISPR perturbations to dissect macrophage polarization dynamics during early infection. We catch the bifurcation during infection, where a subset of macrophages transition toward the anti-inflammatory phenotype. This shift involves the activation of Salmonella pathogenicity island 2 (SPI2) leading to both the dampening of the initial NFkB-driven inflammatory program and the induction of specific transcriptional modules beyond NFkB and STAT3, with possible contributions from AP-1 and Maf family members. Together, our study uncovers host decision points in macrophage polarization circuitry and reveals a vulnerability exploited by Salmonella to modulate host immunity.
- Actors' Facial Movement Magnitude and Cardiac Dynamics Predict Observers' Emotion Believability Ratings
In non-verbal communication, observers infer emotions from visible facial movements, yet emotional experiences are described in internal bodily terms (e.g., "my heart skipped a beat"). This contrast highlights a tension between external sensory cues and internal signals. In this context, we examined an overlooked gap in affective science: what makes an emotional portrayal believable, and do believability judgments reflect only what observers can see or also the portraying person's internal cardiac dynamics? To test this, we created 311 scenario-driven acting clips designed to avoid prototypical posed displays. For each clip, we quantified facial movement magnitude from the video, recorded ECG during preparation and enactment, and collected actors' self-reports. Online participants (N = 371) viewed these clips and provided emotion recognition responses and continuous ratings of believability, valence, or arousal. The results show that believability decreased as movement magnitude increased, with a non-linear relationship indicating a stronger penalty as motion increased. Valence further shaped this pattern, with increasing movement reducing believability more strongly for portrayals with negative valence. This effect persisted after accounting for intended emotion, perceived arousal, and emotion recognizability. Cardiac dynamics varied during performance, and actors' higher heart rate variability was associated with higher believability for positively valenced portrayals. Together, these findings show that believability is driven by visible movement cues interpreted in relation to valence, with actors' cardiac dynamics showing selective alignment with believability. These results identify core components of believable emotional expressions and provide a basis for studying such judgments in everyday social interaction.
- Solvation Shapes the Conformational Landscape of a Therapeutically Relevant SMN2 Splice-Site Defect
The SMN2 exon 7 5' splice-site/U1 snRNA duplex contains an A$_{-1}$ bulge that weakens splice-site recognition and represents a therapeutically relevant RNA connectivity defect, yet its conformational landscape and coupling to solvation remain poorly understood. Here, we performed enhanced-sampling Hamiltonian replica-exchange molecular dynamics simulations of the SMN2 splice-site duplex using four explicit-solvent models (OPC, TIP4P-Ew, TIP3P, and SPC/E) and characterized the sampled ensemble using linear and machine-learned latent representations. Across representations, the A$_{-1}$ defect consistently populated three metastable conformational states distinguished by local duplex geometry, base stacking, hydrogen-bonding patterns, and solvent exposure. The relative populations of these states, together with first-shell hydration and Na$^+$ distributions around the defect, varied substantially across water models, demonstrating that hydration and ion organization actively shape the equilibrium between locally accommodated and solvent-exposed conformations of the SMN2 splice-site bulge. Our results shed light on the conformational components of this therapeutic RNA target and highlight the impact of solvation model as an important consideration for molecular simulations of RNA splice-site recognition and small-molecule repair.
- There is no convincing evidence that Methylobacterium extorquens AM1 can produce N-deoxyschizokinen A
It was recently reported that Methylobacterium extorquens AM1 produces the citrate-hydroxamate siderophore N-deoxyschizokinen A, identified by LC-HRMS. Multiple properties were inconsistent with the assignment: the feature eluted far later than the other schizokinen derivatives (17 min versus 6-8 min), a reversed-phase shift larger than a single-hydroxyl difference in a molecule can explain, further its accurate mass deviated from the calculated one by 28 ppm, well outside the error on the co-analyzed standards and its diagnostic m/z 105 and 77 fragments suggest a molecule with an aromatic moiety. A replicate comparison of identical samples in plastic versus glass autosampler vials was decisive: the m/z 387 feature was reproducibly present with plastic vials and absent with glass. We therefore conclude that the reported detection of N-deoxyschizokinen A in M. extorquens AM1 is an artifact, and recommend glass-vial and solvent-blank controls, an explicit accurate-mass threshold, and narrow MS/MS isolation when assigning trace siderophore-like features from complex extracts.
- Solvent-buffer effects in molecular dynamics simulations of nucleic acids
Molecular dynamics simulations of nucleic acids are performed using a solvent-buffer distance of 10 [A] between the solute surface and the simulation box boundary. Although this cell size has been extensively explored in protein simulations, its implications for nucleic acid dynamics are not well understood. Nucleic acids are elongated, highly charged, and flexible structures with hydration and dynamical properties distinct from those of proteins and therefore, they may require different solvent-layer considerations in simulations. In this study, we investigated the effect of simulation cell size on nucleic acid dynamics by simulating a 30-base-pair double-helical nucleic acid structure and its two single-stranded forms using solvent-buffer distances of 3, 5, 10, 15, and 20 [A]. Smaller cells may impose restricted hydration, molecular crowding, and periodic image interactions. However, larger cells provide solvent space for conformational relaxation. A total of 45 s of molecular dynamics simulations were performed (3 structures x 5 cell sizes x 3 replicates x 1 s). Our results show that while the commonly used 10 [A] buffer may be sufficient to maintain the stability of the double-stranded nucleic acid, larger cells are required to capture the conformational dynamics of single-stranded structures. In both, increasing the cell size to 15 or 20 [A] enables broader conformational sampling. The first hydration shell exhibits reduced crowding in the 20 [A] cell, consistent with more relaxed conformations. At larger cell sizes, single-stranded nucleic acids adopt compact, self-associated conformations for stability. Together, this study presents physical insight into how simulation cell size and solvent environment influence nucleic acid dynamics.
- Integrated analysis of ribosomal DNA copy number and methylation using nanopore long-read sequencing
Ribosomal RNA (rRNA) provides the structural and catalytic core of ribosomes and is encoded by ribosomal RNA genes (rDNA) arranged in tandem repeat arrays. rDNA copy number (CN) is highly dynamic, representing a clinically relevant form of structural variation, but its accurate quantification has been challenging due to its highly repetitive and GC-rich nature. Here, we present RICO (Ribosomal DNA Integrated Copy Number and Methylation Analysis), a novel computational pipeline for integrated estimation of rDNA CN and methylation using nanopore long-read sequencing. RICO leverages long sequencing reads that span entire rDNA repeats, mapped to an rDNA-augmented reference genome, and normalizes coverage using an array of single-copy genes. We show that RICO provides accurate rDNA CN estimates in simulated datasets and reproducible measurements across human samples, with strong agreement to short-read sequencing and PCR-based methods. As biological validation, RICO detects a ~40% reduction in rDNA CN in Atrx-knockout mouse cells, consistent with established effects of ATRX loss on rDNA CN, and captures detected increased total and active rDNA CN in malignant cells from a MYC-driven B-cell lymphoma mouse model, in line with prior psoralen-based chromatin studies. Applying RICO to independent human cohorts, we uncover that individuals with higher total rDNA CN consistently exhibited higher fractions of high-methylated rDNA copies, suggesting a dosage compensation mechanism that potentially maintains a similar number of active rDNA copies across individuals. Together, RICO enables integrated analysis of rDNA CN and methylation state, providing a scalable framework for investigating rDNA regulation across population and disease studies.
- FORGE reveals an information spectrum encoded in RNA tertiary-structure geometry
Coarse RNA coordinate representations are increasingly used for inverse folding and structural annotation, but the biological information encoded in such representations is not well quantified. We introduce FORGE (Feature-engineered RNA Geometry Evaluation), a feature-engineering framework that extracts 935 interpretable descriptors from six backbone atoms and one glycosidic-anchor atom per residue. In a temporal test on 4,135 post-2025 PDB RNA chains, FORGE recovered 64.6% of native nucleotides; a six-atom control without the glycosidic nitrogen retained 58.5%, and abstaining from the least-confident half of calibration positions retained 94.4% accuracy. The same representation predicted base-pair state (79.2% accuracy), a RibonanzaNet-inferred DMS reactivity proxy ($R^2=0.329$) and protein-proximal context (AUC approximately 0.67). Native-decoy, OpenKnot and solved AI-designed pseudoknot tests further show that nucleotide identifiability, foldability and design score are distinct objectives. FORGE provides a reproducible audit layer for RNA structural interpretation.
- Scop3P in 2026: an expanded proteomics-informed resource contextualizing phosphorylation sites through sequence, structure, mutation, and experimental provenance
Protein phosphorylation is a central regulatory mechanism controlling protein activity, interactions, and cellular signalling, and its dysregulation is implicated in numerous diseases. Advances in mass spectrometry--based phosphoproteomics have led to a rapid expansion in the number of reported phosphorylation sites; however, interpretation of these data remains challenging due to fragmented evidence, limited structural context, and the lack of uniform experimental provenance across resources. Interpretation is further complicated by the fact that the biological meaning of reported phosphosites can vary substantially across tissues, cell lines, perturbations, and disease settings. Here, we present a major update of Scop3P, a proteomics-informed knowledgebase that contextualizes human phosphorylation sites within integrated sequence, structural, biophysical, evolutionary, and mutational frameworks. The current release incorporates uniformly reprocessed human phosphoproteomics data from 116 PRIDE datasets alongside curated UniProt annotations, retaining peptide-spectrum matches, site localization confidence, and direct links to primary mass spectrometry evidence via Universal Spectrum Identifiers. This integration yields 152,350 unique serine, threonine, and tyrosine phosphorylation sites across 16,533 human proteins, supported by full experimental provenance. Beyond site identification, Scop3P provides residue-level contextual annotations derived from experimentally determined protein structures and proteome-wide AlphaFold models, enabling near-complete structural coverage of phosphorylation sites. Structural context is further complemented by residue-level biophysical, evolutionary, and mutational annotations, supporting integrated assessment of phosphorylation in functional and disease-related settings. The current release also introduces residue interaction network representations derived from AlphaFold-predicted structures, capturing spatial connectivity and local interaction environments of phosphorylation and mutation sites. A redesigned web interface enables interactive exploration through coordinated 1D, 2D, 2.5D, and 3D visualizations, peptide-level coverage views, and direct access to original spectra via PRIDE. By bridging experimental phosphoproteomics with structural, functional, and disease-related context, Scop3P provides a scalable and provenance-aware resource for phosphosite interpretation, hypothesis generation, and data-driven modelling of phosphorylation-dependent regulation.
- EDTA v2: enabling scalable TE annotation in animal genomes
The Extensive de-novo TE Annotator (EDTA) automates transposable element annotation in plant genomes but lacks direct LINE/SINE detection, limiting its applicability to animal genomes. We present EDTA v2, which integrates LINE and SINE detection, completely rewrites TIR-Learner for deployability and scalability, and accelerates structural detectors by up to two orders of magnitude. Tested in 30 animal genomes from the Vertebrate Genomes Project Phase I, EDTA v2 bridges the non-LTR detection gap that has prevented automated TE annotation in animals.
- Paradoxical Th1 activation and CTLA-4 regulation is beneficial during latent cryptococcosis
Cryptococcus neoformans is the predominant causative agent of cryptococcal meningitis in immunocompromised individuals. Conversely in immunocompetent individuals, C. neoformans establishes a latent pulmonary infection characterized by a paucity of clinical symptoms. Using a mouse inhalation model of latent C. neoformans infection, we previously showed that CD4 T-cells are necessary for preventing fungal proliferation in the lungs. In the current study, we performed single cell RNA sequencing (scRNAseq) and found that the CD4 T-cell response was both highly heterogenous and dichotomous during pulmonary C. neoformans infection, with concomitant expression of genes related to Th1 polarization (Tbx21, Ifng) and immune regulation (Ctla4). First, we demonstrated that cells with Th1-like phenotypes are necessary and sufficient to control latent infection via adoptive transfer of T-bet positive cells into infection-matched CD4-depleted recipient mice. Second, scRNAseq analysis revealed the subpopulation of effector CD4 T-cells that co-expressed Ctla4 and Gata3 was significantly higher than a subpopulation that co-expressed Ctla4 and Tbx21. Furthermore, our data suggested that CTLA-4 upregulation is beneficial against C. neoformans infection, as CTLA-4 blockade promoted fungal proliferation. Thus, we propose a model wherein Th1 control of latent C. neoformans infection is supported by CTLA-4 suppression of detrimental Th2 activation.
- Fitness flux in SARS-CoV-2 and influenza H3N2
The tempo of viral adaptation is usually read indirectly from the composition of mutations, through measures such as dN/dS. Here we measure it directly from the dynamics of variant frequencies, where we use multinomial logistic regression to estimate a fitness for each co-circulating variant. We aggregate these estimates to derive the rate of change of mean population fitness, referred to as fitness flux. Tracing SARS-CoV-2 from its emergence, we find that it initially adapted rapidly, doubling in fitness every 6 months from Jan 2021 to Jun 2022, but slowing to every 2.4 years from Jul 2022 to Dec 2025. Seasonal influenza H3N2 sustained a slower, steadier pace doubling in fitness every 10.0 years. In both, the rate of fitness gain closely tracks the variance in fitness, matching the 1:1 expectation of Fisher's fundamental theorem. Phylogenetic contrasts between parent and child lineages localize most fitness gain to spike, and within spike to the receptor-binding domain, where a simple count of spike S1 substitutions predicts lineage fitness about as well as deep-learning escape and protein-language-model scores. Measuring fitness directly thus offers a transparent, frequency-based alternative to mutational proxies for tracking and anticipating viral adaptation. The website https://blab.github.io/fitness-flux/ is the intended reading experience of this paper, providing responsive layout and interactive figures.
- Virus-driven tRNA competition universally represses host genes with similar codon usage
Viral infection induces tRNA competition between viral and host genes, often repressing host translation. However, how endogenous genes are affected by this competition remains unclear. Three possible hypotheses are considered: abundant-tRNA shortage, rare-tRNA shortage, and viral similarity repression. Pan-virus Ribo-seq data show that endogenous genes with codon usage bias (CUB) matching host tRNA supply or viral CUB are strongly repressed, due to a positive correlation between endogenous CUB-tRNA mismatch and endogenous-viral CUB difference, supporting the abundant-tRNA shortage and viral similarity repression hypotheses. In E. coli experiments with synonymous gentamicin resistance proteins, this positive correlation supports abundant-tRNA shortage, while a non-positive correlation supports rare-tRNA shortage, and both positive and non-positive correlation types support viral similarity repression. Finally, analysis of human virus genomes reveals this positive correlation for most viruses, but a non-positive correlation in a few, reflecting diverse virus-host interaction strategies. These findings establish viral similarity repression as a universal principle, uncovering previously unrecognized complexity in virus-host coevolution.
- Combinatorial community coalescence in early tomato assembly reveals a rhizosphere attractor in composition and abundance architecture
The rhizosphere microbiome plays fundamental roles in plant health and productivity, yet the ecological rules governing microbiome assembly remain poorly understood. Here, we investigated early rhizosphere community assembly in tomato using a replicated combinatorial community coalescence framework, in which seven distinct natural bacterial communities were inoculated individually and in all possible pairwise and triplet combinations. Single inoculum communities clustered according to inoculum identity, indicating a strong effect of source community composition on assembly trajectories. However, when all communities were analyzed jointly, samples formed a continuous compositional landscape with no clear evidence of discrete community states. Despite major differences in source community composition, rhizosphere communities consistently converged toward the same uneven rank abundance structure, with two ASVs accounting for 50% and a median of nineteen ASVs for 90% of total abundance. While assembly was dominated by a very small number of Pseudomonas ASVs, limited evidence of alternative dominant states was observed. Increasing inoculum complexity did not increase stochasticity but instead promoted stronger convergence toward a global rhizosphere compositional centroid. Moreover, dominance hierarchies emerging from community coalescence closely mirrored the distance of source communities to this centroid. Communities derived from orchard soils consistently showed the highest dominance, suggesting that historical contingency and prior adaptation to horticultural crop rhizospheres may influence competitive success. Together, these results support the existence of a canonical rhizosphere attractor in both community composition and abundance architecture, with patterns consistent with assembly occurring under a limited number of dominant ecological niches imposed by the tomato rhizosphere.
- Adaptation of Pseudomonas aeruginosa to the lung allograft environment in cystic fibrosis lung transplant recipients
Lung transplantation (LT) is the ultimate treatment option for patients suffering from end stage cystic fibrosis (CF). Most LT-patients, colonized pre-LT by Pseudomonas aeruginosa witness colonization of their non-CF allograft within a few days or weeks post-LT, thereby compromising graft and life expectancy. How P. aeruginosa isolates adapted for years to the specific CF lung environment efficiently colonize and survive in the non-CF allograft environment remains unclear. To address this question, we collected sequential isolates from CF LT-recipients and non-CF LT-recipients and performed phenotypic and genetic analyses of pairs of early and late isolates from LT-patients. We found evidence for mutations compatible with a switch from biofilm to planktonic lifestyle as well as loss of mucoid phenotypes. Hypermutators, characteristic of chronic CF-adapted isolates, were also found in four LT-patients. Their persistence in the non-CF allograft environment suggests a continuous seeding from the sinuses. Our results suggest that in CF LT-recipients efficient colonisation by P. aeruginosa of the allograft implies both adaptation and continuous seeding from the sinuses to the lower respiratory tract.
- First community challenge for automated virus taxonomy
The rapid rate of virus discovery renders manual curation by taxonomy experts increasingly impractical, creating a need for reliable software that can reproducibly assign viral contigs to taxa at all fifteen ranks of the virus taxonomy. We led an open community challenge for the computational taxonomic classification of viruses and assembled a dataset of virus sequences combining expert-curated and metagenomic sequences. Seventeen teams contributed a total of thirty-four automated, fully reproducible classification pipelines. Most tools correctly assigned viruses belonging to established species, genera, or families, but viruses that are unclassified at those lower ranks remain challenging. This study provides datasets, open-source software, novel approaches, and recommendations to benchmark computational taxonomic classification of viruses, and support organizing the many viruses discovered in big omics data.
- Cross-Resistance Limits the Ability of Antimicrobial Peptide Combinations to Delay Resistance Evolution
Aims: Antimicrobial peptide (AMP) combinations have been proposed to delay resistance evolution, but it remains unclear what properties of a peptide pair determine whether a combination reduces resistance evolution relative to its component AMPs used alone. One suggested factor is mode of action, yet this has rarely been tested experimentally. In the current study we have asked whether mode of action or physicochemical similarity between peptides better predicts which combinations delay resistance. Methods: We evolved Staphylococcus aureus with six AMPs with reported membrane-targeting and intracellular-targeting activity, individually and in all 15 pairwise combinations. We quantified resistance evolution, cross-resistance and fitness costs across the full AMP panel, and performed whole-genome sequencing on 126 evolved lineages. Results: Resistance varied across AMPs and correlated with peptide chain length, not mode of action. Cross-resistance was associated with physicochemical similarity, and similar peptides selected for overlapping mutations. Most combinations reduced resistance relative to single-AMP treatment, but those whose components shared cross-resistance were less effective, channeling evolution into convergent trajectories that resolve both selective pressures at once. Notably, mode of action did not predict combination outcome. Conclusions: Cross-resistance, not mode of action, is a key factor in determining AMP combination efficacy. Physicochemical distance between peptides may serve as a practical predictor for cross-resistance, enabling selection of AMP combinations that are more likely to constrain resistance evolution.
- Culture-Free Rapid Phenotypic Antimicrobial Susceptibility Testing for Helicobacter pylori Based on Fluorescence Rapid On-Site Evaluation Technology: A Preliminary Study
Background: Phenotypic antibiotic susceptibility testing (AST) for Helicobacter pylori (H. pylori) has relied on bacterial culture for three decades, requiring 5-7 days to yield results. Genotypic rapid tests can only detect known resistance mutations and fail to reliably identify amoxicillin resistance. To our knowledge, no culture-free rapid phenotypic AST method for H. pylori has been previously reported. Methods: We developed a phenotypic AST method based on fluorescence rapid on-site evaluation (ROSE) technology that completely bypasses bacterial culture. Gastric mucosal biopsy specimens from 40 H. pylori-positive patients were homogenized and co-incubated with an acridine orange/ethidium bromide (AO/EB)-based viability staining reagent and three first-line antibiotics (amoxicillin, clarithromycin, and levofloxacin) at concentrations corresponding to the European Committee on Antimicrobial Susceptibility Testing (EUCAST) breakpoints for H. pylori, at 37C for 1 hour. Fluorescence intensity was measured using a microplate reader. A reduction in fluorescence relative to an antibiotic-free control indicated susceptibility, whereas no significant reduction indicated resistance. Conventional culture-based AST (E-test) served as the reference method. The overall concordance rate, sensitivity, specificity, and Cohen's kappa coefficient were calculated. Results: Fourteen of the 40 samples had unsuccessful culture and were excluded, leaving 26 samples for statistical analysis of each antibiotic. The overall concordance rates between the ROSE method and culture-based AST were 84.6% (22/26) for amoxicillin, 76.9% (20/26) for levofloxacin, and 69.2% (18/26) for clarithromycin. Cohen's kappa coefficients indicated moderate agreement for all three antibiotics ({kappa} = 0.523, 0.539, and 0.412, respectively). Unlike genotypic methods, the ROSE method successfully assessed amoxicillin susceptibility in all 40 patients, a critical first-line antibiotic for which no reliable genetic resistance marker currently exists. The turnaround time was approximately 1 hour (55-65 minutes), compared with 5-7 days for culture-based methods; preliminary estimates indicated a cost reduction of approximately 3,000-5,000 Chinese yuan (CNY) per patient, mainly attributable to the elimination of culture media, prolonged incubation, and repeat clinic visits. Conclusions: This study reports, for the first time, a culture-free 1-hour phenotypic AST for H. pylori. The method enables same-day, susceptibility-guided treatment decisions, addressing an unmet clinical need spanning three decades. Algorithm optimization and a prospective randomized controlled trial are currently underway to further improve diagnostic accuracy and validate clinical utility.
- Robust taxonomic classification in gut and vaginal microbiomes demonstrated through benchmarking with age-specific synthetic communities
Accurate taxonomic profiling of human microbiomes is essential for advancing research and understanding the complex role microbial communities play in human health. When using shotgun metagenomics, the sequencing data is analyzed through metagenomic pipelines, which incorporate various open-source tools and classify microbes based on matched paired-end DNA reads. However, differences in sequencing and computational approaches can produce substantially different microbiome profiles from the same sample, making validation critical. One approach for validation is benchmarking with realistic mock communities, but this remains relatively rare. Additionally, existing benchmarks often overlook microbiome variability across life stages and body sites, limiting their clinical and research utility. Here, we developed age- and body site-stratified synthetic metagenomes, enabling context-aware benchmarking of microbiome pipelines. Using novelty-based sampling to prioritize microbial diversity and minimize redundancy among selected samples, we selected 300 representative, real biological samples spanning six categories: adult, child, toddler, and infant (>6 months and <6 months) gut samples, as well as adult vaginal samples. We validated three pipelines, Tiny Health's proprietary Metagenomic Classifier v2 (THMCv2), MetaPhlAn4, and Kraken2+Bracken, using precision, recall, F1 score, and area under the precision-recall curve (AUPR) across age groups and sample types. THMCv2 demonstrated higher recall and F1 scores, detecting more taxa across sample types and ages, while MetaPhlAn4 achieved the highest precision. THMCv2 also achieved the highest area under the precision-recall curve, reflecting peak performance across both abundant and rare species. When analyses were weighted by abundance, THMCv2 and MetaPhlAn4 each characterized the mock community nearly perfectly. Errors for THMCv2 were largely restricted to very low-abundance taxa (<0.001%), whereas MetaPhlAn4 occasionally produced false positives for higher-abundance taxa. Species-level analyses of clinically relevant microbes confirmed these patterns, with THMCv2 demonstrating higher sensitivity, MetaPhlAn4 higher specificity, and Kraken2 lower overall performance. These results demonstrate clear precision-recall trade-offs in metagenomic profiling. This benchmarking framework provides a reproducible approach for evaluating pipeline performance across diverse microbiome contexts and life stages.
- Accelerated Measurement of Chemical Exchange Saturation Transfer by Accordion NMR Spectroscopy
Chemical exchange saturation transfer (CEST) has become an indispensable NMR method to characterize slow exchange affecting biomacromolecules, especially for cases involving exchange between a major state and a minor state, the latter of which is often invisible in the spectrum. The CEST method is based on successive irradiation of selective regions of the NMR spectrum using a weak radiofrequency field, B1, while observing the effect on the visible major state when the B1 field saturates the invisible minor state. The need for selective saturation of narrow spectral regions has to date required acquisition of many tens of two-dimensional CEST spectra to sample the entire spectrum with sufficient resolution. Here we present the ACCEST method which measures an entire CEST profile from a single two-dimensional accordion-CEST spectrum plus a reference spectrum. ACCEST is based on the concept of accordion spectroscopy, where in the present implementation the carrier frequency of the weak saturating B1 field is stepped in synchrony with the dwell-time incrementation in the indirect dimension of the two-dimensional spectrum. We benchmarked ACCEST against conventional CEST, resulting in excellent agreement for both backbone 15N and methyl 13C CEST profiles. ACCEST offers substantial time savings that scale linearly with the number of spectra required in the corresponding conventional CEST experiment. Thus, ACCEST can dramatically speed up lengthy serial experiments, such as ligand titrations or temperature-dependent studies, and enable studies of non-equilibrium systems or samples with limited lifetimes.
- Enhancing predictive accuracy of yield traits in cassava through multi-trait genomic prediction
Multi-trait genomic prediction offers a practical route to improve selection for costly, complex traits in clonally propagated crops such as cassava. In a Brazilian breeding panel of 1,078 cassava clones genotyped with 25,923 SNPs and phenotyped for six agronomic traits, we compared single-trait (ST) and multi-trait (MT) GBLUP models. Stage-wise mixed models produced BLUEs that fed into ST and MT-GBLUP. We tested five cross-validation schemes that mimic breeder realities: ST baseline (CV1); naive all-traits MT prediction for unphenotyped candidates (CV2); MT prediction using auxiliary trait phenotypes in the test set (CV3); and two sparse-phenotyping regimes with missingness by trait (CV4) or by clone (CV5) at 25%, 50%, and 75% levels. The main results were that, under the ST baseline (CV1), predictive ability ranged from 0.50 for DMC and 0.45 for FRY down to 0.13 for Le.Dis. A naive full MT model (CV2) performed approximately on par with ST-GBLUP. In contrast, MT designs (CV3) that included informative auxiliary traits, such as shoot yield and combinations with plant vigor and leaf disease severity, yielded small gains for DMC with predictive ability of approximately 0.51 (+2%), while FRY predictive ability increased to approximately 0.65 (+44%), accompanied by RMSE reductions for FRY up to approximately 13.5% (e.g. RMSE approximately 6.2). Sparse-phenotyping simulations (CV4/CV5) demonstrated that MT models sustain or even improve predictive ability under realistic missing-data regimes (PA {approx} 0.62 - 0.65). Selection concordance between MT and ST top-10% sets was generally high (>0.80), and MT configurations produced measurable improvements in expected selection response and genetic gain per cycle for several target traits. These results indicate that strategically implemented MT-GBLUP, using a small set of biologically and operationally informative auxiliary traits and optimized sparse phenotyping, can materially increase predictive accuracy and selection efciency for economically critical cassava traits while reducing phenotyping burden.
- Model of naturally occurring refractive error (NORE) in mice
Purpose: Animal models of myopia typically induce monocular refractive shifts via form deprivation (FD) or lens-induced myopia (LIM), modeling susceptibility to myopia, but with potentially limited applicability to childhood myopia. Here we describe a novel, genetically diverse mouse model of naturally occurring refractive error (NORE) with three distinct refractive phenotypes: hyperopic, myopic, and intermediate. Methods: C57BL/6J mice were mated to 129S2/SvPasCrl mice to create F1 or F2 offspring. Refractive errors in male and female F1 (N=21) and F2 (N=101) mice were assessed on postnatal days (P) 28 and 42 using photorefractometry. In a subset of mice (N=30 - 40), corneal radius of curvature, axial ocular dimensions, retinal and visual function were assessed. Results: F2 mice were classified as NORE with either hyperopic (RE [≥] 0 diopters (D) at P28 and P42), myopic (RE<0D at P28 and P42) or intermediate (RE<0D at P28 and RE [≥] 0D at P42) refractions based on individual trajectories. All ocular parameters changed with age, with significantly slower growth in axial length and vitreous chamber depth in the intermediate versus myopic mice (p<0.05). Lens thickness was smaller in the myopic group at P28. Differences in refraction were not attributed to variances in retinal function or dopamine signaling. Conclusions: NORE mice represent a novel, genetically diverse wild-type mouse model that, unlike traditional models, does not require interventions such as FD or LIM to induce myopia. NORE mice provide a valuable tool for future investigations of genetic and environmental mechanisms and targeted therapeutic strategies for refractive errors.
- Detecting domain-level organization in genome-wide association study summary statistics using frequency-impact-reliability profiles
Genome-wide association studies (GWAS) identify trait-associated variants but are typically interpreted through single-variant significance and locus-level peaks, treating summary statistics as collections of independent signals. Here we show that GWAS summary statistics exhibit previously unrecognized local genetic-statistical organization along genomic coordinates. We develop FIR-GWAS, a framework that integrates allele frequency, effect magnitude and statistical reliability to define frequency-impact-reliability (FIR) profiles and quantify their spatial continuity. Across EUR height GWAS, ancestry-specific datasets and eight additional human complex traits, we find consistent enrichment of same-profile adjacency and coordinate-contiguous FIR domains beyond chromosome-preserving null expectations. These patterns persist after removal of genome-wide significant variants and are reproducible across SNP- and window-based analyses. We further show that FIR-domain architecture separates genome-wide significant structured regions from isolated association peaks and identifies subthreshold domains with coherent statistical organization. FIR-domain structure is consistently associated with regulatory annotations and trait-related gene sets, and highlights biologically plausible subthreshold candidate regions. Across Arabidopsis and chicken GWAS, we observe that FIR-domain architecture is not restricted to human traits but recurs across independent association-summary landscapes. Decomposition analyses suggest that this spatial regularity arises from coordinated local continuity in allele frequency, effect size and statistical reliability. Together, these results reveal GWAS summary statistics as structured genetic-statistical landscapes rather than collections of independent signals, defining a domain-level layer of organization that complements conventional single-variant and locus-based interpretation.
- Validation of aEEG-CSA Neonatal Seizure Detection Algorithm on Hypothermia Treated Infants with HIE
Abstract Objective To validate a neonatal seizure detection algorithm that is based on extracted clinical features of the aEEG and CSA on a cohort of cooled neonatal patients with HIE. Methods A seizure detection algorithm was designed using aEEG margin features, CSA features, trained on a public dataset of 79 neonatal EEGs with three supervised machine learning classifiers. It was subsequently tested on an inhouse cohort of 23 neonates with asphyxia whose EEGs were collected during hypothermia therapy. Results The trained Random Forest Classifier, Support Vector Machines and Artificial Neural Network classifiers had an AUC of 0.76, 0.77, and 0.77 and an average accuracy of 0.85, 0.86, and 0.85 respectively. Finally, the average AUC across the 10 seizure patients included was 0.85. Conclusion A neonatal seizure detection algorithm that uses a combination of aEEG and CSA clinical features can capture seizures in HIE patients. Performance across seizure patients is not correlated with seizure duration.
- OptiLITT: A Computer-Assisted Planning System for Dual-Fiber Laser Interstitial Thermal Therapy using Cylindrical Ablation Optimization
Laser Interstitial Thermal Therapy (LITT) is a minimally invasive neurosurgical technique in which a stereotactically-implanted fiber delivers thermal energy to ablate intracranial lesions. Existing computer-assisted planning systems optimize trajectories against a one-dimensional line abstraction, then approximate the ablation zone as a fixed-radius cylinder post-hoc to estimate coverage. Trajectories selected as optimal under this model are not guaranteed to remain optimal once the cylindrical extent is applied, which introduces a mismatch between predicted and true ablation coverage. This may also underestimate spillover into surrounding healthy tissue. We present OptiLITT, a treatment planning system that represents the laser probe as a cylindrical ablation volume from the onset of optimization, jointly solving dual-fiber placement, lesion coverage, and healthy-tissue spillover as a single coupled problem. All planning parameters are exposed through a user-configurable graphical user interface supporting intraoperative refinement between planning stages.
- Working with the future for the future: a peer- led educational intervention on antimicrobial resistance; a quasi-experimental study
Antimicrobial resistance (AMR) is a global threat to public health and development. Failure to address it could return society to a pre-antibiotic era with increased morbidity and mortality. Because human behaviour is crucial to AMR management, interventions modifying knowledge, attitudes, and practices are therefore essential. Modifying health-related behaviours presents a significant challenge, yet it is crucial for public health. Engaging populations during periods of shifting perceptions can address this challenge and ensure the sustainability of interventions. Adolescents and young people attending school represent a key demographic. The primary objective of this study is to evaluate student's awareness, perceptions, and behaviours concerning antimicrobial resistance (AMR) and hygiene, while enhancing and empowering children as agents of change within the community. In this study, students from Allied Health Sciences (AHS) across various disciplines were recruited to serve as peer educators for an evidence-informed educational workshop. A pilot delivery of these activities was conducted among a few students in a school before the final delivery was executed in three schools. Schools following a comparable educational board and curriculum were selected for inclusion in the study. A structured questionnaire was employed to assess the effects before and after the intervention. Statistically significant improvements were observed in participant's knowledge, attitudes, and practices (p < 0.001). Additionally, feedback was collected from participants, teachers, and the school nurse attending the session. By triangulating these findings, a notable immediate improvement was observed in students' knowledge, attitudes, and practices. This study provides evidence that employing multimodal teaching led by peer education is a valid and effective method for delivering health messages. It further underscores the mutual benefits for stakeholders (peer educators and peer learners) by offering a two-way learning opportunity. The benefits extend beyond academic and core scientific learning to include increased confidence as effective health educators and future-ready healthcare professionals.
- NEXIM: A Nash Equilibrium-Based Framework for Stable Explainable AI in Medical Applications
Reliable explanations are important for trustworthy medical applications of artificial intelligence (AI), but attribution-based explanations can vary across model randomization and small analytic changes. We present NEXIM (Nash Equilibrium-based Explainability and Interpretability Model), implemented here as an accuracy-constrained, equilibrium-inspired model-selection framework that jointly evaluates held-out prediction error, explanation stability, and cross-model connectivity. The implementation evaluated ten GradientBoostingRegressor models per prediction horizon, differing only by random seed (0-9), using a fixed 75/25 patient split. Kernel SHAP attribution vectors were compared using Spearman rank correlation, and graph connectivity summarized whether each model belonged to a dense explanation-similarity region. Candidate models within 0.02 Montreal Cognitive Assessment points of the best root mean squared error (RMSE) were ranked using a multiplicative Explanation Equilibrium Score. In longitudinal Parkinson's Progression Markers Initiative data, NEXIM selected the RMSE-optimal model at the one- and three-year horizons. At the two-year horizon, it selected Model 4 rather than the RMSE-only Model 8, increasing scaled stability from 0.8757 to 0.8847 and normalized graph connectivity from 0.889 to 1.000 while increasing RMSE by only 0.0014. The two models retained the same top-20 feature set but differed modestly in feature order, illustrating that NEXIM primarily acted as a reproducibility screen rather than identifying clinically contradictory explanations. Stability and consensus are treated as reproducibility criteria, not evidence of causal faithfulness, clinical usefulness, or improved patient outcomes. NEXIM may therefore serve as a governance checkpoint for model refresh and documentation, but external validation, stronger model-family baselines, and prospective clinical evaluation remain necessary.
- MIRA-Net: A Cross-Cohort Representation Learning Framework for Parkinson's Disease Classification Using Acoustic and Beta-Band MEG Biomarkers
Accurate diagnosis of Parkinson's disease (PD) remains challenging due to substantial inter-subject variability and the absence of widely accessible, objective multimodal biomarkers. Although speech and magnetoencephalography (MEG) biomarkers have individually demonstrated strong discriminative potential, their joint utilization is constrained by the absence of subject-level paired datasets - a fundamental gap that has prevented cross-modal validation at the individual level. We argue that this makes cross-cohort representation learning not merely a pragmatic workaround, but the most realistic and clinically transferable framework for multimodal PD assessment. In real-world deployment, acoustic screening and neuroimaging biomarkers are acquired through separate clinical pathways and must be integrated across heterogeneous patient populations. To address this, we propose MIRA-Net (Modality-Invariant Residual Adversarial Network). This cross-cohort representation learning framework integrates acoustic speech features from four established UCI datasets (n = 193) with beta-band MEG biomarkers from the NatMEG-PD dataset (n = 127) for PD classification. MIRA-Net employs RF-SHAP feature selection, gradient-reversal-based domain adaptation, and supervised contrastive alignment to learn participant-independent, modality-invariant embeddings. The framework is evaluated under Rest, Go, and Passive task conditions against Early Fusion, Vanilla DANN, and Supervised Contrastive Learning baselines. MIRA-Net achieves a peak accuracy of 86.23% (Go condition, Stacking classifier) with AUC values exceeding 0.88 under repeated cross-validation, alongside a sensitivity of 89.4% and specificity of 83.1%. Friedman tests confirm statistically significant performance differences among fusion strategies (p < 0.003 across all conditions). These results demonstrate that cross-cohort representation learning can extract robust disease-discriminative signatures without synchronized multimodal recordings, offering a practical pathway toward AI-assisted PD assessment in resource-constrained clinical settings.
- Tacrolimus variability and creatinine predict readmission after liver transplantation
Unplanned readmissions after liver transplantation occur in over 30% of recipients, yet no validated prediction models exist, and prior observational studies suffer from immortal time bias. The optimal readmission window for outcome prediction and the feasibility of early risk stratification remain undefined. This study is a retrospective analysis of 922 adult liver transplant recipients (August 2018-August 2025) at a single center. Time-varying Cox regression evaluated 14-, 30-, and 90-day readmission windows as predictors of 1-year mortality, correcting for immortal time bias. Gradient-boosted machine learning models leveraging 528,400 laboratory measurements (28 analytes) predicted 90-day readmission using either complete hospitalization data or data restricted to postoperative day 7. Feature importance was quantified by gain, and clinical utility was assessed through risk stratification. Among 902 hospital survivors, 342 (37.9%) experienced an unplanned readmission within 90 days of initial discharge. Only the 90-day readmission window predicted 1-year mortality in time-varying analysis (HR 1.73, 95% CI 1.17-2.57, p=0.006). The model for readmission using complete data achieved AUC 0.614 (95% CI 0.576-0.652); the postoperative day 7 restricted model achieved AUC 0.615 (95% CI 0.577-0.652), with no meaningful performance difference. The tacrolimus coefficient of variation x peak creatinine interaction was the dominant predictor in both the complete model (17.3% importance, rank 1) and the day 7 restricted model (20.4% importance, rank 2). This interaction stratified patients into high-risk (tacrolimus CV >0.3 and creatinine >2.0 mg/dL; 49.8% readmission) versus low-risk (24.8% readmission) groups (risk ratio 2.01, p<0.001). These results identify a modifiable biological determinant of readmission and establish a framework for targeted interventions to reduce unplanned readmission and improve post-transplant outcomes.
- Emergency Department Presenting Concerns Among Admissions With Hypercapnia: A Retrospective NLP Study of MIMIC-IV
Background Hypercapnia may indicate a primary ventilatory syndrome, a complication of another illness, or an epiphenomenon of severe disease. The presenting context of hypercapnia is poorly quantified, limiting clinical interpretation and synthesis of epidemiologic studies. Methods We performed a retrospective cross-sectional study of Medical Information Mart for Intensive Care IV (MIMIC-IV) hospital admissions linked to an emergency department (ED) presentation from 2011 through 2019. Admissions were included if the triage chief complaint was not missing and at least one prespecified criterion for hypercapnia was met: an International Classification of Diseases (ICD) code for hypercapnic respiratory failure or obesity hypoventilation syndrome, arterial blood gas (ABG) PCO2 45 mmHg, venous blood gas (VBG) PCO2 50 mmHg, or indeterminate-source blood gas PCO2 50 mmHg. Triage chief-complaint text was classified by natural language processing (NLP) into 17 National Hospital Ambulatory Medical Care Survey reason-for-visit (RFV) categories using a multi-label framework. Primary analyses estimated admission-level RFV category prevalences; secondary analyses compared distributions by overlapping ascertainment indicator, age, and acidemia. Results The total cohort included 11,941 admissions: 1,542 (12.9%) met both blood-gas and ICD-code criteria, 9,958 (83.4%) met blood-gas criteria only, and 441 (3.7%) met ICD-code criteria only. Median age at admission was 68 years (IQR 56-78), and 6,423 admissions (53.8%) were for male patients. Respiratory RFV categories were most prevalent (30.2%), followed by administrative reasons (17.5%), digestive symptoms (14.0%), injuries and adverse effects (14.0%), and nervous-system symptoms (13.8%); categories were not mutually exclusive. Respiratory categories were more common in ICD-positive admissions (50.2%) than in VBG-defined (36.3%) or ABG-defined admissions (27.3%). Injuries and adverse effects were most prevalent among admissions for patients aged 18-39 years (34.4%), whereas respiratory categories increased from 13.7% among admissions for patients aged 18-39 years to 36.5% among admissions for patients aged 80 years. NLP-derived classifications showed mean set-F1 of 0.84 against adjudicated clinician labels in the full annotated benchmark sample. Conclusions Among ED-linked admissions with hypercapnia by diagnosis code, blood gas, or both, respiratory complaints were the most common chief-complaint category but represented fewer than one-third of admissions. Presentation context should be incorporated when defining, comparing, and interpreting hypercapnia cohorts, particularly those ascertained by blood-gas criteria.
- CerebAI: Explainable Three-Class Stroke CT Classification via ConvNeXt and Integrated Gradients
Stroke is a leading cause of death and long-term disability worldwide, affecting approximately 15 million individuals annually. Prompt and accurate subtype differentiation between ischemic and hemorrhagic stroke is clinically critical, as the two conditions demand diametrically opposite interventions - thrombolytic therapy versus surgical decompression. Yet the majority of existing deep learning approaches reduce this problem to binary detection, and virtually none address the opacity of their decision-making in a clinically actionable manner. We present CerebAI, an explainable, deployment-oriented three-class CT stroke classification system built on a fine-tuned ConvNeXt-Base backbone with Integrated Gradients (IG) attribution. Trained on 6,774 non-contrast CT scans stratified across No Stroke, Ischemic Stroke, and Hemorrhagic Stroke, CerebAI achieves a weighted F1-score of 0.9746 (95% CI: [0.9625, 0.9851]), accuracy of 97.47%, macro-averaged AUC of 0.9921, mean Intersection-over-Union (mIoU) of 0.9276, Expected Calibration Error (ECE) of 0.0115, mean Brier Score of 0.0150, and Cohen's {kappa} of 0.9483 - surpassing ResNet-50, EfficientNet-B4, and Vision Transformer (ViT-B/16) baselines across all reported metrics. Integrated Gradients produce pixel-precise saliency maps that localize pathological regions with greater anatomical fidelity than Gradient-weighted Class Activation Mapping (Grad-CAM), a finding we support with side-by-side qualitative comparison. CerebAI additionally incorporates a native DICOM processing pipeline to facilitate future clinical translation. Code and model weights are publicly available to support reproducibility and further research.
- Expert Discrimination of AI-Generated versus Authentic Radiologic Images: A Multimodal, Pre-Registered Visual Turing Test
Background: Frontier text-to-image models can synthesise radiologic images of high realism, raising the question of whether expert radiologists can serve as a provenance safeguard for the medical image record. Methods: We conducted a prospective, pre-registered visual Turing test in which 60 invited Korean board-certified radiology faculty and trainees judged authentic (teaching-repository) and AI-generated radiologic images from a locked pool of 241 displayable cells (82 entities; nine subspecialties; six modalities; 60 readers x 60 trials = 3,600 reader-image observations) produced by two contemporary commercial generators. The primary endpoint was the confidence-weighted, reader-averaged multi-reader multi-case area under the curve for AI versus authentic images, conditional on the locked image pool; the key secondary endpoint was the Faculty-minus-Junior difference under a two one-sided tests equivalence framework. The pre-specified statistical analysis plan was registered on the Open Science Framework before data lock. Findings: All 60 readers completed the test. The pooled confidence-weighted area under the curve was 0.71 (95% CI, 0.69 to 0.74), above the null value of 0.5 but within the pre-specified modest tier (0.60 to 0.75). The Faculty-minus-Junior contrast was 0.04 (95% CI, -0.02 to 0.10), including zero, and the two one-sided tests established equivalence within the +/-0.10 margin. No reader stratum and no pre-specified sensitivity analysis reached the deployable-classifier threshold (area under the curve >= 0.75). Interpretation: In this single-country cohort, expert radiologists distinguished frontier-generated from authentic radiologic images only modestly, without a meaningful expertise gradient (equivalence within +/-0.10) and with no reader stratum reaching a standalone provenance safeguard. These findings support radiology AI-literacy training and pipeline-level provenance safeguards rather than reliance on reader judgment, and warrant retesting in an independent reader cohort. Funding: This research was supported by a grant of the Korea Health Technology R&D Project through the Korea Health Industry Development Institute (KHIDI), funded by the Ministry of Health & Welfare, Republic of Korea (grant number: RS-2025-02213531).
- Care Home Residence and Carbapenemase-Producing Enterobacterales Positivity: A Matched Case-Control Study
Background: Carbapenemase producing Enterobacterales (CPE) remain a major infection prevention and control challenge. Although care home residence is frequently perceived as a risk factor for CPE carriage, its independent association with CPE positivity remains uncertain. Objective: To investigate the relationship between care home residence on admission and CPE positivity among patients undergoing CPE screening. Methods: A retrospective matched case control study was conducted at a single NHS acute hospital in England. Adult patients with laboratory confirmed CPE positivity from screening samples between 1 November 2022 and 1 November 2025 were matched to CPE negative controls at a ratio of up to 1:4 based on ward, specimen year and age no more than 5 years older or younger. Conditional logistic regression was used to assess the association between care home residence and CPE positivity. An adjusted model included previous hospital admission within 12 months. Results: A total of 108 CPE positive cases were successfully matched to 412 controls. Care home residence was identified in 14 (13.0%) cases and 49 (11.9%) controls. In the matched conditional logistic regression model, care home residence was not associated with CPE positivity (OR 1.15, 95% CI 0.58 to 2.28; p=0.690) and remained non-significant after adjustment (aOR 1.32, 95% CI 0.66 to 2.64; p=0.439). Discussion: Care home residence was not independently associated with CPE positivity in this low-prevalence setting. Significance and impact: The findings do not support the use of care home residence alone to guide CPE screening. Further multicentre studies are required to clarify the contribution of care home residence to CPE epidemiology.
- Age-structured modelling of Ebola convalescence and re-emergence risk: a Bayesian approach
Background. Increasing evidence indicates that Ebola virus disease (EVD) survivors can remain a source of infection long after clinical recovery. Confirmed survivor-associated transmission events and genomic evidence linking the 2021 Guinea outbreak to viral lineages from the 2013-2016 West African epidemic have demonstrated that persistent infection in survivors can contribute to post-epidemic re-emergence. However, the population-level conditions under which survivor reservoirs may sustain recrudescence remain poorly understood. Methods. We developed an age-structured Bayesian transmission model to quantify survivor-driven recrudescence risk using historical Ebola outbreak data (1976-2022) and empirical viral persistence data from male survivors. Age-specific viral clearance probabilities were estimated for three age groups (less or equal to 25, 26-35, and >35 years). The recrudescence reproduction number (Rc) was derived using the next-generation matrix approach. Sensitivity analyses examined alternative assumptions regarding viral clearance and the potential contribution of female survivors. Results. The posterior mean recrudescence reproduction number remained below the persistence threshold (Rc=1) across all viral-clearance scenarios under the assumption of no female survivor contribution. Only by assuming the slowest rate of viral clearance and maximal female survivor contribution did the posterior mean for Rc exceed one (1.052; 95% CrI: 0.428-2.229), suggesting that survivor-driven transmission alone is unlikely to sustain Ebola re-emergence given our current understanding of recrudescence dynamics. Simulations showed that survivor-driven outbreak pressure (rate of survivor-initiated outbreaks) was driven primarily by outbreak size and clustering. Outbreaks involving less or equal to 5,000 EVD cases generally produced outbreak pressure below the estimated natural spillover rate, whereas outbreaks comparable in size to the 2013-2016 West African epidemic generated transient survivor-driven outbreak rates up to 7.8-fold higher than the natural spillover rate before declining to comparable levels within 3-7 years. Moreover, across all viral-clearance scenarios, older (>35 years) male survivors consistently exhibited the longest effective persistence durations and made the largest contribution to the recrudescence reproduction number. Conclusions. The human survivor reservoir represents a plausible complementary pathway for Ebola re-emergence, particularly following large epidemics and should be considered alongside zoonotic spillover as an important source of future outbreaks. Age-dependent viral clearance strongly shapes recrudescence dynamics, with older survivors contributing disproportionately to transmission potential. These findings support age-stratified survivor monitoring, extended persistence surveillance, and improved characterization of viral persistence in both male and female survivors to strengthen post-epidemic preparedness.
- Body Mass Index trajectories from early pregnancy to one year postpartum and the rising burden of overweight and obesity over the last two decades in Bhaktapur, Nepal.
Background and aims: Maternal overweight and obesity are increasing worldwide, including Nepal. This study assessed BMI trajectories from early pregnancy to one year postpartum and trends in overweight and obesity over the past two decades in Bhaktapur, Nepal. Methods: In the most recent study, BMI was measured in 800 Nepalese women at three time points: at early pregnancy, 6 and 12 months postpartum (2017-2021). The prevalence of undernutrition, overweight, and obesity was estimated using the World Health Organization and the Asian specific cut-offs. Long-term trends were assessed by comparing these findings with three population-based studies conducted in Bhaktapur between 2001 and 2021 among 2400 women at similar life stages. Results: Mean (SD) BMI increased from 23.7 (3.0) kg/m^2 in early pregnancy to 26.1 (3.3) kg/meter squre at 6 and 25.2 (3.3) kg/m^2 and 12 months. The prevalence of overweight increased from 32.9% in early pregnancy to 48% at 6 months. Using the Asia-specific cut-offs, the prevalences were higher. Results from the three previous population-based studies demonstrated an upward trend where postpartum overweight increased from 11.4% in 2001- 2002 to 44.6% in 2017- 2021. The obesity prevalence rose from 1.8% to 10.9% during this period. Conclusion: Overweight and obesity among Nepalese women have risen dramatically over the past two decades, with postpartum overweight increasing nearly fourfold and obesity more than sixfold. These findings highlight the need for interventions to prevent excessive weight retention and reduce adverse health outcomes.
- Sex differences in frailty trajectories among older adults in Mexico: a 17-year longitudinal cohort study
Introduction Frailty is an ageing-related state associated with disability and mortality. Women often experience higher frailty but lower mortality than men, a pattern described as the male-female health-survival paradox. Evidence from low- and middle-income settings is limited. We examined sex differences in frailty trajectories and terminal decline in Mexico. Methods We analysed five waves (2001-2018) of the nationally representative Mexican Health and Aging Study (MHAS) including 12,440 adults ([≥]50 years at baseline). Frailty was measured using a 31-deficit frailty index (FI score; 0-1). We used survey-weighted linear mixed-effects models with time interactions, adjusted for sociodemographic, behavioural and health covariates to model sex differences in frailty trajectories. Terminal decline in FI was modelled among those who died using mixed-effects models on the time-to-death scale. Results A total of 12,440 adults aged 50 to 105 years were included, with a mean age of 62.1 years (SD 9.6); 5,698 men (45.8%) and 6,742 women (54.2%). Mean baseline FI was 0.17 (SD 0.12), higher in women than men (0.19 vs 0.16; P<0.001). After adjusting, women had a 0.014 higher mean FI than men at baseline (adjusted mean difference; 95%CI 0.008, 0.020), with difference widening over follow-up, increasing from 0.016 at 2 years to 0.029 at 17 years. Analysis of terminal decline found that accumulation of frailty accelerated in the years preceding death; with results suggesting that women reached death with higher frailty than men (difference 0.029; 95%CI 0.009, 0.048). Conclusion Women experienced higher and more rapidly increasing frailty compared to men and carried a greater frailty burden in the years preceding death. These findings underscore the importance of considering sex differences in frailty trajectories when developing healthy ageing strategies that address the life-course vulnerabilities disproportionately driving frailty accumulation in women in low- and middle-income countries.
- Transcranial Photobiomodulation on Language and Cognitive Performance in Down Syndrome: A Pilot Randomized Sham-Controlled Trial
Background: Down syndrome (DS) is associated with persistent language and cognitive impairments and with abnormalities in cortical oscillatory activity, including in the gamma range. Transcranial photobiomodulation (tPBM) is a noninvasive neuromodulatory intervention with potential benefits for cortical physiology, language, and cognition. Methods: We conducted a pilot randomized, double-blind, sham-controlled trial of repeated 40-Hz near-infrared tPBM in adolescents and young adults with DS. Fourteen participants were randomized 1:1 to active tPBM or sham and received 18 sessions over 6 weeks, followed by short-term and long-term follow-up. Outcomes included resting-state EEG gamma power, connected-speech measures, language and cognitive indices, and selected computerized tasks. Results: Active tPBM did not significantly increase global resting-state EEG gamma power relative to sham at either follow-up. Pre-registered analyses did not show broad treatment benefit across outcomes, although they did identify a significant short-term advantage for active tPBM on grammatical morpheme accuracy in connected speech; in contrast, picture naming favored sham at long-term follow-up. Exploratory mechanistic analyses did not show a robust biological treatment signal. Both active and sham procedures were well tolerated, with no serious adverse events. Conclusions: In this underpowered pilot sample, 6 weeks of 40-Hz near-infrared tPBM--delivered unilaterally, at low power, on target areas--did not demonstrate a meaningful effect size in DS. A dose-finding study for tPBM in DS, also accounting for age of participants, is recommended.
- Effects of Morning Bright Light Therapy on Sleep, Alertness, Mood, and Cognition in Healthy University Students: A Randomized Crossover Trial
Objectives. To test whether one week of self-administered morning bright light therapy (BLT) improves sleep, daytime sleepiness and alertness, mood, and objective cognition in healthy university students. Methods. Thirty-three healthy students completed a two-week randomized within-subject crossover trial comparing one week of morning BLT (30 min of 10,000 lx; melanopic equivalent daylight illuminance of approximately 8,989 lx) with one week of usual-light control in counterbalanced order, with no washout. Sleep was assessed with wrist-worn Fitbit sleep tracking and daily diaries; daytime sleepiness (Karolinska and Stanford Sleepiness Scales), positive and negative affect (PANAS), mood (POMS), and a cognitive battery (Stroop, Flanker, Corsi, verbal span) were also assessed, alongside post-trial semi-structured interviews. Outcomes were analyzed with linear mixed-effects models, with Holm correction across five primary outcomes. Results. BLT reduced daytime sleepiness in a time-of-day-specific manner (condition x time-of-day interaction; largest reduction at 12:00, dz = -0.58, with a smaller but still significant reduction at 15:00), reduced night-to-night variability in sleep duration (dz = -0.52), increased Fitbit sleep efficiency (dz = 0.81), and increased PANAS positive affect (dz = 0.41). Objective cognition was unchanged across all measures. Interviews indicated that participants experienced BLT primarily as a sleep and alertness intervention, with minor tolerability issues. Conclusions. Brief morning BLT improved alertness, sleep regularity and efficiency, and positive affect, but not objective cognition, in healthy students, supporting morning light as a low-burden strategy for daytime functioning while cautioning against overstating cognitive benefits.
- A low-cost, time-efficient, sensitive quantitative thin layer chromatography reveals unaltered exogenous sphingosine utilisation from erythrocytes of MAFLD patients.
BACKGROUND: Erythrophagocytosis constitutes a major pathogenic mechanism of metabolic dysfunction associated fatty liver disease (MAFLD). Our previous research established a quantitative thin-layer chromatography (TLC) technique for sphingomyelin, revealing reduced levels in the red blood cells (erythrocytes) of patients with metabolic dysfunction associated fatty liver disease (MAFLD). This reduction was accompanied by erythrocyte sphingosine accumulation, a driver of pro-inflammatory erythrophagocytosis, though sphingosine 1-phosphate release remained stable. To better understand erythrocyte sphingosine metabolism, we adapted our quantitative TLC method to analyze sphingosine within the erythrocyte-conditioned media (ECM) of MAFLD patients. Methodology Separation was performed on 10X10cm Silica gel 60 F254 plates using a mobile phase of chloroform, methanol, acetic acid, and water (60:50:1:4 v/v/v/v). The dynamic range, linearity, and range of linearity were assessed by analysing sphingosine levels from 0.1 to 10microg/spot. We validated the system precision and sensitivity by performing triplicate analyses of sphingosine standards (1.25, 2.5, and microg). The limits of detection and quantification were derived from the calibration curve slope and standard deviation (3.3 XSD/slope for LOD; 10 XSD/slope for LOQ). Accuracy was assessed via recovery tests at 100%, 200%, and 300% of a 2.5microg load. We confirmed specificity by evaluating the retention factors against other lipid species. This protocol was applied to Folch-extracted lipids from the ECM (5 X 107 cells/ml) of four MAFLD patients and four healthy controls, spiked with 5microg of sphingosine. Findings The calibration model, based on combined Green and Blue color intensities, followed the linear equation y = -11.171x + 353.25(R2 = 0.94). Interday precision values were 0.21%, 1.65%, and 0.44%, while recovery rates (accuracy) ranged from 94.5% to 98.7%. The measured LOD and LOQ were 0.75microg and 1.21microg, respectively. The sensitivity was calculated at 90ng. Statistical analysis showed no significant variance in sphingosine concentrations in erythrocyte-conditioned media between the MAFLD group and the control group. Summary The described thin layer chromatography is accurate, precise, sensitive, with good limits of detection and quantification, and most importantly is low-cost and time-efficient. Using this method, we show that while erythrocytes of MAFLD patients exhibit sphingosine accumulation, the utilisation of exogenous sphingosine from their erythrocytes is not affected. This suggests that the metabolic shift may be driven by increased sphingosine supply from the plasma.
- GLP Medications and Severe Post-COVID-19 Outcomes Among Individuals with Type 2 Diabetes Mellitus
Background: Glucagon-like peptide-1 receptor agonist-based therapies (GLP) have recently emerged as promising treatments across a wide range of health conditions. These medications may have protective effects against severe long-term consequences of COVID-19 by promoting weight loss, exerting antihyperglycemic and anti-inflammatory effects, and providing cardiovascular and endothelial protection. Methods: We evaluated electronic health record data from a retrospective cohort of individuals in the National Clinical Cohort Collaborative. We included individuals with type 2 diabetes mellitus and comorbid COVID-19 who were prescribed either GLP (treatment) or a sodium-glucose co-transporter 2 inhibitor (SGLT2i) and subsequently developed acute COVID-19 between October 1, 2021, and April 1, 2023. We compared the 12-month cumulative incidence of mortality and Long COVID (Long COVID diagnosis and probable Long COVID via computational phenotype) between groups. We applied targeted maximum likelihood estimation to compare outcome risks by exposure status, controlling for covariates of interest. Results: We analyzed data from 14,215 individuals with COVID-19 and comorbid type 2 diabetes (mean age, 60 years; mean BMI, 37). Compared to SGLT2i, a prescription for GLP medication was associated with a lower risk of mortality (adjusted risk ratio [aRR] 0.71; 95% CI 0.53, 0.95), but not Long COVID diagnosis (aRR 1.01; 95% CI 0.80, 1.27) or probable Long COVID (aRR 0.94; 95% CI 0.88, 1.01). Conclusions: We found that among individuals with type 2 diabetes and comorbid COVID-19, a prescription for GLP vs. SGLT2i medications was associated with a lower risk of mortality, but not Long COVID.
- Cardiovascular events in individuals with small/medium LDL particle discordance
Aims: Despite similar LDL-C levels, size and composition of LDL particles (LDL-P) varies widely. Among the metabolically perturbed, or those with altered function of lipid regulatory proteins, LDL-C levels mask elevated atherogenic small-medium LDL-P (S/M LDL-P). We assessed the contribution of such discordance in S/M LDL-P on major adverse cardiovascular event risk (MACE). Methods and results: UK Biobank participants with Nightingale NMR metabolomics (487,521 participants), were classified as high or low cardiometabolic burden. S/M LDL-P discordance was defined as the difference between LDL-C predicted S/M LDL-P and observed S/M LDL-P. Genetic variants encoding cholesterol ester transfer protein (CETP), which regulates cholesterol-triglyceride exchange and the production of small LDL particles, were identified via whole genome sequencing. Adjusted Cox proportional hazard regression was used to estimate MACE associations. S/M LDL-P discordance showed an LDL-C and Apo-B independent association with MACE (47,935 cases), which differed by cardiometabolic burden group: hazard ratio (HR) per standard deviation 1.09 (95%CI 1.05; 1.13) and HR 1.24 (95%CI 1.21; 1.27) for low/high burden, respectively. Loss of function (LoF) CETP variants were strongly associated with lower levels of both S/M LDL-P and S/M LDL-P discordance. For example, the S/M LDL-P discordance effect of CETP LoF carriership for low/high metabolic burden, respectively, was -4.62 nmol/L (95%CI -8.40; -0.83) compared to -11.10 nmol/L (95%CI -15.57; -6.63). Conclusion: S/M LDL-P discordance (overabundance) is strongly associated with MACE risk, especially in people with high cardiometabolic burden. S/M LDL-P discordance is modified by CETP genetic variation, suggesting a role for CETP-mediated lipid remodelling beyond LDL-C changes.
- Association of Neutrophil-to-Lymphocyte Ratio and Systemic Immune-Inflammation Index With Mortality in Patients With Pericarditis: A Retrospective Dual-Cohort Study Using Two Independent Databases
Background: Risk stratification in pericarditis relies mainly on clinical presentation, suspected etiology, imaging findings, and conventional inflammatory biomarkers. Whether complete blood count-derived inflammatory indices are associated with mortality in pericarditis and whether these associations are directionally consistent across independent real-world datasets remain unclear. Methods: We conducted a retrospective dual-cohort study of hospitalized adults with pericarditis using a Hong Kong cohort from the Clinical Data Analysis and Reporting System (CDARS) as the primary analysis cohort and the Medical Information Mart for Intensive Care IV (MIMIC-IV) cohort as an independent reproducibility cohort. Baseline neutrophil-to-lymphocyte ratio (NLR) and systemic immune-inflammation index (SII) were analyzed as continuous variables and cohort-specific tertiles. The primary outcome was long-term all-cause mortality in the Hong Kong cohort. Secondary and reproducibility outcomes included 90-day mortality in the Hong Kong cohort and 30-day, 90-day, and observable follow-up mortality in MIMIC-IV. Cox models were adjusted for age, sex, renal disease, diabetes mellitus, hypertension, ischemic heart disease, and malignancy. Results: Among 504 patients in the Hong Kong cohort and 464 patients in MIMIC-IV, all-cause mortality occurred in 241 and 113 patients during cohort-specific follow-up, respectively. In the Hong Kong cohort, higher NLR was associated with long-term all-cause mortality after full adjustment. Compared with NLR tertile 1, the adjusted hazard ratio was 1.60 for tertile 3. Higher SII was also associated with long-term mortality, with an adjusted hazard ratio of 1.55 for tertile 3 versus tertile 1. NLR and SII showed directionally consistent associations with 90-day mortality in the Hong Kong cohort and with 30-day, 90-day, and observable follow-up mortality in MIMIC-IV. Sensitivity analyses yielded broadly consistent findings. Conclusions: In two independent real-world cohorts of hospitalized patients with pericarditis, higher baseline NLR and SII were associated with increased all-cause mortality, with NLR showing the more consistent prognostic signal. These complete blood count-derived indices may provide simple adjunctive information for mortality risk stratification, although prospective validation is needed before incorporation into formal management algorithms.
- Trends in the Assessment, Treatment and Outcomes of Patients with Suspected Acute Coronary Syndrome
Background: Suspected acute coronary syndrome is a frequent Emergency Department (ED) presentation, requiring safe and efficient assessment. We interrogated long-term trends in whole population care for these patients using a new multi-centre regional registry. Methods: The DataLoch Heart Disease Registry links relevant data from primary and secondary healthcare records, with national administrative data for patients registered within the Lothian Health Board region of Scotland (~1M population). We included all adult patients presenting to secondary- or tertiary-care EDs in the region between 2014 and 2024, in whom high-sensitivity cardiac troponin was measured within 24 hours of presentation. Annual diagnostic rates for myocardial infarction, pharmacological and interventional management, and outcomes up to 1 year after ED presentation were studied. Logistic regression models were used to report change in annual trends for myocardial infarction, cardiac death, cardiovascular death and all-cause mortality, adjusted for age, sex, ethnicity, socioeconomic deprivation and comorbidity. Results: Over 10 years, 117,142 consecutive patients (mean age 58 +/- 18 years, 48% female, 6.6% with confirmed myocardial infarction) were included. Cardiac troponin testing increased year on year, from 61 per 1000 ED attendances in 2014 to 103 per 1000 in 2024 (P<0.001), but the proportion of patients admitted to hospital fell (59% in 2014 to 38% in 2024, P<0.001). Associated with these trends, the tested population had fewer cardiovascular risk factors and myocardial infarction incidence fell from 73 per 1000 tested patients in 2014 to 47 per 1000 in 2024 (adjusted odds ratio 0.62, 95% confidence intervals 0.56 to 0.69, P<0.001). In patients diagnosed with myocardial infarction, prescriptions of preventative therapies and numbers of revascularisation procedures were unchanged. After adjustment, no change over time was observed in one-year cardiac or cardiovascular mortality in those with a diagnosis of myocardial infarction. Conclusions: ED testing using cardiac troponin has extended to a broader population at lower risk of myocardial infarction. Despite this trend, early rule-out pathways have reduced hospital admissions, without observable changes in outcomes for those with myocardial infarction.
- Diagnostic accuracy and acceptability of self- and health worker-collected tongue swabs for Mycobacterium tuberculosis complex detection in adults in South Africa
Tongue swabs (TSs) are a non invasive specimen type for the detection of Mycobacterium tuberculosis complex (MTBC) and can expand access to testing for individuals unable to produce sputum. This study evaluated the diagnostic performance and user acceptability of self collected and health worker (HW) collected tongue swabs using the Xpert MTB/RIF Ultra (Ultra) assay and assessed participant perspectives on self collection. In this prospective, cross sectional study, symptomatic and asymptomatic adults under investigation for TB were enrolled from a high HIV prevalence setting. Each participant provided both a self collected and a HW collected TS, which were tested using Ultra. Ultra TS results were compared to liquid culture as the reference standard and sputum Ultra as a comparator. Participant perspectives on self collection were captured via questionnaires. Sensitivity on Ultra for both self and HW collected TSs was 68% (95% CI:51.9 to 81.9), compared to liquid culture. This sensitivity was significantly higher than that of sputum smear microscopy (46%, 95% CI: 30.7 to 62.6; McNemar's p = 0.003). Tongue swab sensitivity was lower than sputum Ultra (80.5%; p<0.001) and decreased with low bacillary loads. Importantly, TSs enabled MTBC detection in six participants unable to produce sputum. Most participants (>90%) found self collection instructions easy to follow, reporting high confidence and comfort, and trust in results from self collected TSs. This study demonstrates that self collected TSs perform comparably to those collected by health workers for TB detection using Ultra and are both feasible and acceptable in a high TB/HIV burden setting. To maximize impact, clear training instructions and robust linkage to care remain critical priorities.
- Optimal Duration of Antibiotic Treatment for Group A Streptococcal Pharyngitis in Children: A Systematic Review and Dose-Response Meta-Analysis
Background: Group A streptococcal (GAS) pharyngitis drives substantial antibiotic prescribing in children. The 10-day standard burdens adherence and prolongs exposure, increasing selective pressure for resistance. Yet, whether shorter courses achieve comparable outcomes remains unresolved. Purpose: To address how the duration of oral antibiotics affects clinical outcomes in children and adolescents with suspected or confirmed GAS pharyngitis. Data Sources: MEDLINE, Embase, CENTRAL, Web of Science, and CINAHL from inception to July 2025. Reviewers also searched reference lists of eligible trials and relevant systematic reviews. Study Selection: Randomized trials enrolling children and adolescents [≤]18 years with suspected or confirmed GAS pharyngitis comparing different durations of oral antibiotics, or oral antibiotics against placebo or no treatment. Data Extraction: Paired reviewers independently screened records, extracted data, and assessed risk of bias. Data Synthesis: We performed random-effects dose-response meta-analyses with restricted cubic splines and rated the certainty of evidence using GRADE. Forty-five trials enrolling 22,636 participants met eligibility criteria. Across outcomes, low to moderate certainty evidence suggests that 3, 5, and 10 days of antibiotic treatment may produce little to no difference. Moderate certainty evidence supports similar effects of 5 and 10 days on clinical cure, relapse, and adverse events. Evidence comparing 3 and 10 days carries lower certainty. Serious adverse events were rare: no deaths, 4 cases of acute rheumatic fever, and 4 cases of post-streptococcal glomerulonephritis among 776, 8,818, and 9,096 participants, respectively, making clinically important differences across treatment durations unlikely. Limitations: Evidence on 3-day courses came almost exclusively from trials of azithromycin, limiting inference about shorter penicillin regimens. Findings apply most directly to high-income settings. Conclusion: These findings challenge the long-standing 10-day standard for pediatric GAS pharyngitis and show that 5 days of oral antibiotics are likely as effective and safe as 10 days.
- Decision support for preventing elective surgery cancellations: cost-sensitive risk ranking with cross-site validation in the NHS
Elective surgery late cancellations and ``did not attend'' (LCDNA) events waste theatre capacity, lengthen waiting lists, and impose avoidable costs on NHS Trusts. We present a decision-support approach that ranks upcoming elective procedures by expected cancellation cost and supports capacity-constrained outreach by selecting the highest-risk Top-K cases for intervention. Using cost-sensitive learning and a clinically grounded cost model, the policy reduces expected cost from approximately 103 GBP per case under business-as-usual to 77.08 GBP per case in a hospital-holdout (cross-site) evaluation designed to mimic deployment to a new hospital. In a complementary time-forward evaluation, representing prospective use within the same service environment, expected cost falls further to 70.97 GBP per case. The 6.11 GBP per-case difference between the two regimes highlights the added uncertainty introduced by cross-site operational shift and supports a conservative roll-out with local calibration and monitoring. Explainability analyses suggest that booking-to-procedure lead time, specialty or service line, calendar effects, and prior cancellation history are the strongest drivers of prediction, helping to inform tiered intervention workflows that prioritise near-term bookings and use model--pathway mismatches as an audit signal. Overall, the framework turns predictive performance into practical, capacity-aware policy guidance for reducing avoidable cancellations while supporting safe and equitable implementation.
- criTRia: A Classification System and Evidence Criteria for Tandem Repeat Locus-Disease Relationships
Introduction Tandem repeats (TRs), including short tandem repeats (1-6 bp motifs) and variable number tandem repeats (7+ bp motifs), have been linked to more than 50 Mendelian diseases. However, current frameworks for evaluating gene-disease relationships do not adequately address TR-specific complexities. As a result, proposed TR locus-disease relationships are often incorrectly classified, under-evaluated, or excluded entirely, limiting discovery and leading to underdiagnosis of TR disorders. Methods We developed criTRia, a scoring framework designed to accurately evaluate TR locus-disease relationships at the locus level rather than the gene level. Building on ClinGen best practices, criTRia introduces TR-specific evidence categories and reweighted scoring. We applied criTRia to curate 65 loci from STRchive, a database of disease-associated TRs. Results We compared criTRia curations with gene-level curations from nine Gene Curation Coalition (GenCC) groups. Of 65 newly scored loci, 7 had not been previously evaluated by GenCC and 17 showed significant disagreement across groups. These differences have direct implications for whether a disease is recommended for inclusion in a diagnostic gene panel. The criTRia framework also enabled curation of previously unassessed associations, bringing the total to 77 curated TR locus-disease associations and identifying four contradictory associations. Discussion By incorporating TR-specific evidence, criTRia provides a reproducible methodology for assessing TR locus-disease relationships, improving classification consistency and establishing a foundation for better integrating tandem repeats into clinical genetic medicine and providing more accurate diagnoses.