AI News Archive: July 22, 2026 — Part 18
Sourced from 500+ daily AI sources, scored by relevance.
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- FastEBM: Fast, Scalable, and Uncertainty-Aware Event-Based Disease Progression Modeling
Event-based models (EBMs) are used to infer ordering of biomarker alteration patterns with respect to disease progression. However, EBM approaches rely on computationally expensive permutation-based inference, assumptions of feature independence, and likelihood optimization that can limit scalability and stability in high-dimensional settings. Here, we introduce Fast Event-Based Model (FastEBM), a scalable, uncertainty aware, Markov-chain-based framework that reformulates disease progression inference as a subject-ordering problem on a data-driven diffusion manifold. The progression uncertainty, used to derive positional variance diagrams, is quantified using first-passage-time variability derived directly from the inferred Markov process. Using synthetic experiments varying feature dimensionality, cohort size, noise level, and feature-correlation structure, we compared FastEBM with established methods, including Gaussian mixture model EBM (GMM-EBM), kernel density estimation EBM (KDE-EBM), and discriminative EBM (DEBM). FastEBM achieved the best accuracy and runtime. In low-subject/high-dimensional stress tests, FastEBM retained event-order recovery. FastEBM remained robust in simulations containing correlated and redundant features after decorrelation and feature-group handling. We applied FastEBM to real-world data to characterize biomarker progression in Alzheimer's disease. First, we evaluated a low-dimensional multimodal dataset from The Alzheimer's Disease Prediction Of Longitudinal Evolution (TADPOLE) challenge. Second, to demonstrate high-dimensional disease progression mapping, we applied FastEBM to regional cortical tau-PET data from the Alzheimer's Disease Neuroimaging Initiative (ADNI). In both cases, FastEBM recovered progression patterns broadly consistent with the literature, also revealing lateralized progression trends. These results show that diffusion-based Markov geometry provides a scalable and robust alternative to conventional event-based modeling. FastEBM is available at: https://github.com/sjusc07/FastEBM.
- Biologically Plausible Dopamine-Modulated STDP Model of Pavlovian Learning in Spiking Neural Networks
Spike-timing-dependent plasticity (STDP) and dopamine (DA) are fundamental to reward-based learning and memory formation. A widely used DA-modulated STDP model explains how neural networks associate stimuli with delayed dopaminergic rewards through an eligibility trace. However, we show that this model supports learning even at unrealistically high DA concentrations because DA simply scales the magnitude of STDP without changing its temporal profile. In contrast, experiments demonstrate that DA nonlinearly reshapes the STDP window, converting long-term depression (LTD) into long-term potentiation (LTP) at high DA levels. We therefore propose a DA-modulated STDP rule in which increasing DA progressively biases plasticity toward potentiation while receptor saturation limits further DA effects beyond a critical concentration. Simulations of recurrent networks of Izhikevich neurons show that the proposed rule supports robust conditioning only within a biologically realistic DA range (0.04~0.70 microM). Successful learning produces a hybrid network architecture consisting of a strong feedforward backbone embedded within recurrent circuitry and generates enhanced burst responses selectively to reward-associated stimuli. At the upper limit of the biologically plausible DA range, the network passes through a narrow bistable regime, converging to one of two distinct stable configurations. At higher DA concentrations, conditioning fails altogether. These results provide a biologically grounded model of DA-dependent plasticity and offer new insight into how abnormal dopamine signaling can impair learning in neurological disorders.
- GEM-GPT Enables Personalized Cell Type-Resolved Therapeutic Design for Systems Pharmacology
Generative artificial intelligence (AI) has emerged as a powerful framework for drug discovery, yet most current approaches follow one-drug-one-gene target-based paradigms that struggle to capture the complexity and heterogeneity of chronic and systemic diseases. Omics-driven systems pharmacology provides a promising strategy to overcome these limitations, but generative AI tools specifically designed for systems pharmacology-oriented drug design remain scarce. To address this gap, we introduce GEM-GPT, a transcriptomics-based molecule generation framework that designs personalized therapeutic compounds capable of reverting cell type-specific disease states back to a healthy phenotype. GEM-GPT employs a biology-inspired deep fusion architecture that couples a single-cell RNA-sequencing (scRNA-seq) foundation model with a molecular GPT model, enabling the modeling of cell type-specific chemical-gene interactions during molecule generation. This integration allows GEM-GPT to outperform state-of-the-art baselines, generate distinct molecules for different cell types, and generalize robustly to previously unseen cell types. We further demonstrate the utility of GEM-GPT through a case study in personalized drug discovery for opioid use disorder (OUD). In this application, GEM-GPT successfully identifies both therapeutic compounds possessing distinct chemotypes and existing FDA-approved drugs predicted to modulate cell type-specific OUD disease phenotypes in individual patients. Together, these results establish GEM-GPT as an advance in AI-driven systems pharmacology by bridging single-cell omics and molecular generation to support personalized, systems-aware therapeutic design.
- Muon Reduces the Training Cost of Regulatory DNA Transformers
Gene-therapy design depends on identifying regulatory sequences that drive the right level, timing, and cell-type specificity of expression. Regulatory DNA models offer a way to prioritize such sequences computationally before committing candidates to biological testing. Biological validation involves DNA synthesis, cloning, cell culture, sequencing, and functional screening, so training compute is part of the same constrained discovery pipeline rather than an isolated modeling expense. Reducing the compute required to reach a target pretraining quality could shift time and budget toward larger candidate screens, additional assays, more cell contexts, and broader follow-up validation. Given that Adam-style optimizers are widely used for training genomic sequence models, we study whether Muon can provide a more compute-efficient alternative for regulatory DNA pretraining. We provide an in-depth analysis by training Transformer models (26M-420M parameters) on ENCODE cis-regulatory sequences with Adam and Muon while holding architecture, data, and non-optimizer hyperparameters fixed and varying optimizer family, norm-control scheme, learning rate, and model width. In the largest-scale matched-target comparison, Muon reaches Adam-matched perplexity targets with a median FLOP reduction of 35.4% and a median wall-clock time reduction of 38.5%. The analysis further shows that optimizer rankings depend on norm control: independent weight decay pairs more favorably with Muon than Hyperball in this setting. These findings indicate that optimizer update structure and norm-control choices are practical levers for reducing the training resources required to reach matched perplexity targets in regulatory DNA pretraining.
- A Brain Circuit for Status Epilepticus
Status epilepticus (SE) is a life-threatening persistent epileptic seizure that can arise from various brain structures, leaving its brain circuit unknown. In this study, we utilize brain imaging changes during SE to reveal the brain architecture and circuit of persistent seizures. Multimodal lesion mapping identified that brain imaging changes during SE localize to a specific predisposed brain architecture characterized by increased metabolic rate, high synaptic and mitochondrial density, glutamate (mGLUR5 and NMDA) and GABA receptors. Gene expression patterns within lesion locations revealed a transcriptomic profile enriched for epilepsy pathologies (including SE), neuronal and synaptic processes, and glutamate signaling. Lesion network mapping demonstrated these same lesions map to a common brain circuit, unifying a traditionally heterogeneous patient population. Findings were validated in an independent cohort and the identified SE circuit distinguished brain imaging changes during SE from other lesion etiologies with excellent accuracy (91%), significantly outperforming all other tested maps. With this SE circuit, we identify therapeutic targets for precision therapy that could modulate this circuit. This study demonstrates brain imaging changes in SE converge on a unified brain circuit that could help diagnostic workup of patients in critical care and guide clinical trials of precision therapy for persistent seizures.
- Deep Learning Based Cross-Modality Histological Brain Section Registration in Multiple Species Using Synthetic Images
Large-scale brain mapping increasingly relies on integrating histological imaging datasets within standardized anatomical reference frameworks. To accomplish this, registration techniques are used to align imaging datasets between different modalities or to reference atlases. Deep learning approaches have emerged a fast and scalable framework for approaching this challenge, but such methods typically require large annotated datasets that are unavailable in this setting. To address this, we developed a framework for training a convolutional neural network for this task using entirely simulated data. We show that the same approach can be used for different species (mouse and marmoset), and we provide accuracy validation in terms of Dice and Hausdorff distance between anatomical regions in comparison to an alternative method. This approach has the potential to accelerate large or high throughput studies of brain anatomy.
- Learning Minimal Gene Programs for Disease-Aligned Representations
Identifying small, interpretable gene sets that robustly capture disease-associated variation in single-cell transcriptomic data remains a central challenge for biological interpretation and experimental follow-up. In practice, commonly used differential expression and sparsity-based approaches often produce large, unstable gene lists that fail to generalize across patients due to strong donor-specific confounding. We study sparse gene selection for reconstructing donor-robust, disease-aligned cellular trajectories in real single-cell RNA-seq datasets. We introduce Sparse Linear Manifold Control (SLMC), a practical workflow that defines a disease-aligned score after removing donor-associated variation and selects minimal gene programs whose expression reconstructs this score. We focus on diagnosing the structure of the resulting reconstruction objective and evaluating selection strategies under realistic health data conditions. Across five human single-cell datasets spanning oncology and neurodegeneration, we find that the reconstruction objective exhibits strong diminishing returns, explaining why simple greedy selection methods perform well in practice. Under strict donor-held-out evaluation, greedy methods consistently outperform LASSO at small gene budgets and achieve accurate reconstruction with as few as 25 genes. Together, these results highlight how careful objective design and empirical evaluation enable robust and interpretable gene selection for disease-aligned representation learning in single-cell health data.
- The "dark magic mushroom" co-produces amatoxins and psilocybin
The most famous chemicals produced by mushrooms are the psychedelic compound psilocybin from "magic mushrooms" and amatoxins from deadly poisonous mushrooms. These compounds are known to occur in multiple phylogenetically disjunct fungal lineages but have never been shown to co-occur within a single species. Here we show that the "dark magic mushroom" Galerina indica produces both psilocybin and amatoxins. Mass spectrometry revealed psilocybin and amatoxins in mushroom tissues, and genomic analyses identified corresponding biosynthetic genes. Phylogenetic analyses suggest that G. indica acquired psilocybin biosynthesis via horizontal gene transfer after amatoxin biosynthesis was already established, and that psilocybin biosynthesis was acquired twice independently within Galerina. Intriguingly, acquisition of psilocybin biosynthesis in G. indica may have coincided with reduced amatoxin potency. These findings reveal how horizontal gene transfer can combine powerful bioactive systems in a single species, potentially altering the ecological roles of both compound classes and the evolutionary fitness of the species.
- scLEMBAS: Context-Aware Modeling of Signaling Pathway Activity at Single-Cell Resolution
Cells sense and integrate extracellular cues through intracellular signaling networks that reshape transcription factor activity to dictate cellular responses. Signaling activity is difficult to decipher: it is non-linear, and it contains extensive feedback and crosstalk. Furthermore, the same perturbation can elicit markedly different responses depending on context (e.g., cell type, disease state, and tissue microenvironment) such that identical stimuli produce diverse responses in multicellular populations. Consequently, there is a vast combinatorial space of complex interactions and context-dependent responses that necessitate computational models. Computational models of single-cell perturbation responses are demonstrated to predict cellular responses, but are often limited in mechanistic insight. Prior knowledge networks offer a route to bridge predictive capability and interpretability. Here we present scLEMBAS, a context-aware, gray-box neural network that models signaling pathway activity at single-cell resolution while preserving mechanistic grounding. scLEMBAS encodes a prior-knowledge network of protein-protein interactions as a recurrent neural network whose learnable edge weights correspond to signaling interaction strengths. It also captures context and individual cell variance through compositional bias terms. An adversarial approach allows the model to answer a single-cell counterfactual - what a given cell's TF activity would be under a different perturbation or context - while involving mechanistic rather than simply relational information. Across two scRNA-seq datasets spanning single- and multi-perturbation settings, scLEMBAS accurately predicts out-of-distribution combinations of perturbation and context. Capturing population variance across individual cells enables the model to predict cell subtype specific perturbation responses, despite being agnostic to such labels. Beyond prediction, scLEMBAS learned parameters are biologically interpretable: learned edge weights carry information beyond network topology and "self-prune" spurious interactions, while the categorical bias nominates proteins associated with cell-type-specific perturbation states. Overall, scLEMBAS enables quantitative dissection of how signaling pathway activity is reshaped by perturbation within specific cellular contexts.
- Structural basis of nick translation in human DNA replication
Nick translation during Okazaki fragment maturation requires iterative coordination of DNA polymerase delta (Pol {delta}), which displaces the downstream primer, and flap endonuclease FEN1, which cleaves the resulting flap, on the sliding clamp PCNA. The structural basis of this coordination is unknown. We present cryo-EM structures of human Pol {delta}-PCNA, Pol {delta}-PCNA-FEN1 and FEN1-PCNA on flap DNA, capturing four states of the nick translation cycle. Pol {delta} strand displacement emerges from structural elements intrinsic to the B-family fold rather than dedicated separation machinery, with a conserved palm loop acting as separation wedge and PCNA engagement required for melting of the downstream duplex. In the Pol {delta}-PCNA-FEN1 toolbelt, FEN1 is pre-positioned opposite Pol {delta} on PCNA to receive the substrate. Nucleotide removal triggers DNA handoff while both enzymes remain clamp-bound, followed by Pol {delta} dissociation. A post-handoff structure reveals stable DNA retention by FEN1-PCNA after flap cleavage, explaining the slow nick translation kinetics and the obligate role of Ligase 1 in sealing.
- Bacterial RNA polymerase exemplifies a general physical mechanism for accelerating protein-DNA association
Bacterial RNA polymerases (RNAPs) have two flexibly tethered subunit C-terminal domains (-CTDs) that bind DNA. Interaction between -CTDs and some promoter DNA motifs is known to accelerate transcription initiation, but the physical mechanism by which it does so is unclear. We used single-molecule multiwavelength fluorescence microscopy to test how the diffusion-limited binding kinetics of core RNAP to non-promoter DNA differ from those of mutant RNAPs that lack one or both -CTDs. We find that even though -CTDs and their tethers are small compared to the complete RNAP molecule, the presence of two -CTDs accelerates DNA binding by ~10-fold and ~55-fold respectively relative to RNAP constructs in which one or both -CTDs are deleted. In contrast, the presence of -CTDs did not have a detectable effect on RNAP-DNA complex lifetimes in the absence of RNA synthesis. We explain how -CTDs achieve the dramatic acceleration of RNAP binding to DNA using a quantitative three-state kinetic model that includes a transient binding intermediate where only the -CTD(s) are bound to DNA, tethering the rest of the RNAP in the vicinity of DNA. The model and assumed parameters are validated using Brownian dynamics simulations of the DNA association reactions for two-, one-, or zero-CTD RNAP constructs. The combination of single-molecule experiments, mathematical theory, and simulations suggests that adding a flexible DNA-binding tether is a general physical mechanism which can accelerate the diffusion-limited binding of a large protein like RNAP to DNA and quantitatively defines the conditions under which this acceleration can occur.
- 19F Ultrafast MAS NMR Reveals the Dynamic Basis of pH-Dependent Regulation in Proteorhodopsin
19F NMR spectroscopy is a powerful approach for studying complex biomolecular systems because of its high sensitivity, exceptional responsiveness to local structural changes, and simplified spectra. Here, we demonstrate the application of 19F ultrafast MAS NMR to 5-fluorotryptophan-labelled proteorhodopsin reconstituted in lipid bilayers. By assigning 9 of the 10 tryptophan resonances, pH-dependent analyses of chemical shifts, line shapes, and conformational exchange reveal the dynamics of two functionally important residues: W34 in the interprotomer His-Asp-Trp triad and W98 within the retinal-binding pocket. The results identify W34 as a dynamic regulator of proton transport and support a model in which slow ring flipping on the seconds timescale transiently modulates the W34-H75 interaction, thereby acting as a pH-dependent molecular throttle. The spectral characteristics of W98 further suggest that it functions as a dynamic regulator of the photocycle within the retinal-binding pocket. Beyond these mechanistic insights, we show that a MAS rate of 100 kHz markedly enhances the resolution of this 19F-labelled membrane protein. Combined with a simple chemical-shift scoring metric and advanced, linear-scaling AF-QM/MM-based 19F chemical shift calculations of all sites within this protein, this workflow provides a robust and broadly applicable framework for characterizing membrane protein structure and dynamics in native-like lipid environments.
- Lessons learned from real-time nowcasting: The 2024 dengue outbreak in Puerto Rico
Real-time nowcasting enhances situational awareness by mitigating reporting delays that obscure transmission dynamics. We applied Nowcasting by Bayesian Smoothing (NobBS) to the 2024 dengue outbreak in Puerto Rico (PR), using case surveillance data from the PR Department of Health. The method accurately captured the epidemic trajectory and consistently outperformed a baseline model, although reporting anomalies occasionally reduced performance. We also conducted analyses by dengue virus serotype and health region, as well as previous years. For analyses with few dengue cases, a model in which parameters are jointly estimated across groups generally achieved better performance than the independent one. Historical analyses revealed that years with higher variability in reporting delays generally exhibited higher uncertainty. The findings here underscore key lessons for real-time dengue nowcasting: alternative models may be needed in complex circumstances, but with stable reporting patterns and continuous evaluation, nowcasts can be a reliable and valuable public health tool.
- Reconstructing time-resolved inter-residue distance distributions in a protein ensemble during functional dynamics in solution
Reconstructing time-resolved inter-residue distance distributions during protein functional dynamics in the solution state is known to be a difficult and important problem. This article presents a technique for extracting spin-spin (as a proxy for residue-residue) distance distributions on doubly-spin-labeled proteins from rapid-scan time-resolved Gd-Gd electron paramagnetic resonance (rs-TiGGER) spectra recorded near room temperature in solution at 240 GHz. We use a best-fit technique that convolves a dipolar kernel matrix with an intrinsic, non-dipolar-broadened (single-labeled) spectrum. The kernel incorporates the effect of solution-state tumbling on the dipolar broadening using a correlation function that bridges the static and rapidly tumbling regimes. We apply the technique to AsLOV2, a protein domain with a dark-state crystal structure that is well-known from X-ray crystallography, but a less well-characterized and disordered tertiary structure that manifests after photoactivation at 450 nm. Informed by principal component analysis, we assume that the underlying distance distribution may be approximated by a sum of two Gaussian distributions. The fits returned time-resolved, light-activated populations with mean distances of 2.596 nm (+0.049/-0.045) (dark) and 4.04 nm (+0.92/-0.45) (lit) in the wild type, and 2.55 nm (+0.13/-0.11) (dark) and 4.4 nm (+1.6/-0.9) (lit) in an N414Q mutant, with nearly complete unfolding (within fit uncertainty) of the active, light-sensitive fraction. The extracted distance distributions and their accompanying uncertainties are consistent within uncertainty with molecular dynamics simulations of the equilibrated protein structure.
- Bidirectional hybridization between Ulva prolifera and U. linza (Ulvophyceae, Chlorophyta): Evidence for compatibility and paternal chloroplast inheritance
Ulva prolifera and U. linza are closely related species, with abundant adult thalli and reproductive cells co-occurring extensively in time and space during the Yellow Sea green tides. Elucidating their hybridization compatibility is crucial for species delimitation, assessing interspecific gene flow, and evaluating the ecological impacts of green tides. Previous studies suggested asymmetric gamete compatibility (only U. prolifera mt x U. linza mt-), but lacked sex-linked markers to reliably identify hybrid diploids and their reproductive modes, and did not examine chloroplast inheritance. Here, we performed bidirectional crosses using sexual strains of different geographic origins from both parents, with sex-linked markers to quantify progeny genotypes, examine fertility and reproduction pathway of F1 hybrids, and trace chloroplast inheritance using the species-specific petB marker. Our results showed that: (1) F1 hybrids were obtained in both cross directions, with significantly higher frequency in the direct cross (U. linza mt x U. prolifera mt-), indicating no complete reproductive isolation in either direction; the biased compatibility likely reflected genetic background differences among the limited strain combinations in a single study. (2) A considerable number of germinated progeny arose from parthenogenesis of parental gametes. (3) F1 hybrids from both crosses could undergo meiosis to form gametes and develop into gametophytes; additionally, F1 from the reciprocal cross produced diploid spores for asexual reproduction, suggesting meiotic disturbance. (4) Chloroplasts were maternally inherited in selfing of U. prolifera parent, but in all F1 hybrids they were paternally inherited, indicating a potential reversal of the inheritance pattern due to interspecific hybridization. These findings provided new insights into the potential for genetic exchange between U. prolifera and U. linza.
- Long-term realized genetic gain and population dynamics under genomic selection in Brazilian cassava germplasm
Genomic selection has become an important strategy in cassava breeding, enabling faster selection cycles and sustained genetic progress. Despite its widespread adoption, long-term evaluations integrating predictive performance, realized genetic gain, and genetic diversity remain scarce, particularly in clonally propagated crops. We present a comprehensive assessment of genomic selection outcomes in the Brazilian cassava breeding program across four recurrent selection cycles (C0 to C3) implemented between 2011 and 2024, using historical phenotypic and genomic data from 210 multi-environment trials. Predictive ability of genomic best linear unbiased prediction models ranged from low to moderate, depending on the trait's genetic architecture and heritability. Prediction accuracies were highest in early cycles (C0 and C1) and showed modest declines in later cycles (C2 and C3). Root yield, shoot yield, plant height, starch content, and dry matter content exhibited stable predictive performance across cycles, with a gradual reduction in RMSE, indicating improved model calibration as training populations expanded. Regression analyses of genomic estimated breeding values revealed significant realized genetic gains for most yield-related traits. In contrast, dry matter content and starch content exhibited small, non-significant negative trends, consistent with known unfavorable genetic correlations with yield. Targeted reductions in plant architecture scores reflected deliberate selection for ideotypes suited to mechanized production systems. At the same time, analyses of genetic diversity revealed a slight decrease in observed heterozygosity, with higher values in the most advanced selection cycle. These results provide an integrated framework for monitoring predictive performance, realized genetic gain, and population genetic dynamics under long-term genomic selection. Collectively, they offer valuable insights into balancing short-term genetic improvement with long-term sustainability and support the development of strategies to optimize selection decisions, breeding planning, and population management in Brazilian cassava breeding programs.
- The Environmental Games
For over 4 billion years, the environment has guided evolutionary selection by presenting biological entities with opportunities and challenges and by mediating their interactions. In evolutionary game theory (EGT), the replicator equation defines the game according to direct interactions between biological entities. Eco-evolutionary models have described environmental feedback by modeling environmental states dynamically. Yet, these models channel environmental pressures through the direct interaction coefficients instead of creating a distinct game. Here, I invent Pure Environmental Games (PEGs) and formally introduce the environment as a strategy in EGT. The PEGs describe the neutral selection game and environmental games operating in parallel. Through the net specific accumulation rates of microbes parametrized by the environmental state, Environmental Biotechnology --- an engineering discipline designing wastewater treatment plants worldwide --- offers a collection of instances of PEGs. The environmental strategy resists invasion from biological strategies when it is a strict Nash equilibrium. The PEGs provide a template for identifying regimes of environmental selection within an environmental game (e.g., the r/K selection game). This mapping identifies persistence as a new dynamic regime under extinction. In a closed system, life persists by partnering with the environmental strategy consistent with Schrodinger's negative entropy.
- Haplotype and diversity signatures of ultra-soft selective sweeps in HIV-1
Many urgent medical and agricultural challenges are driven by resistance evolution via soft selective sweeps of multiple simultaneous mutations. Standard approaches to detect these mutations involve genome scans for regions with reduced diversity and increased haplotype lengths. However, it is unknown the extent to which those signatures persist as the number of mutations driving resistance grows. Here, we analyzed longitudinal linkage-resolved data from 10 intra-host HIV populations treated with broadly neutralizing antibody 10-1074. We found that HIV escapes 10-1074 with minimal perturbations to diversity and haplotype homozygosity in the region surrounding the sweep in the majority (8/10) of treated individuals. We matched these in vivo escape trajectories to forward simulations and found that adaptive mutations conferring escape must have been present on 20 or more genetic backgrounds to generate these signatures. These "ultra-soft" sweep signatures more closely resemble genetic patterns in a treatment non-responder without an adaptive response to 10-1074 than those of two other trial participants where adaptation occurred via harder selective sweeps. Our results demonstrate that HIV can adapt to a broadly neutralizing antibody treatment while retaining nearly all of its standing genetic diversity and that selection scans dependent on regional diversity and haplotype homozygosity signatures fail in this "ultra-soft" regime.
- Reconstructing Prehistoric Waterscapes and Human Mobility during MIS 5 in North Africa
The spatial organization of early Homo sapiens populations in North Africa during Marine Isotope Stage 5 (MIS 5) remains a key question for understanding past demographic and cultural dynamics. Climatic fluctuations during this period produced alternating humid and arid phases, reshaping suitable habitats and influencing potential movement corridors. Previous studies have emphasized the importance of hydrographic networks for human dispersal, yet the extent to which climate-driven hydrological changes structured regional connectivity across MIS 5 substages remains poorly understood. This study integrates downscaled paleoclimate simulations, hydrological modelling, GIS-based optimal-path analyses, and archaeological site distributions to reconstruct patterns of human mobility during MIS 5. We model paleohydrographic networks for each MIS 5 substage and evaluate how precipitation variability influenced freshwater availability and landscape connectivity. Our results reveal spatio-temporal shifts in hydrographic networks and identify river corridors that likely facilitated movement between coastal North Africa and the Central Sahara. These findings provide new insights into the role of waterscapes in structuring human mobility and contribute to broader discussions of Late Pleistocene human dispersal in Africa.
- Multi-Omic Dissection of Autism Reveals Dominant Effects of Family, Sex, and Host-Microbe Metabolic Interactions
Autism spectrum disorder (ASD) has been associated with gut microbiome and metabolic alterations, but reported biomarkers are inconsistent and often inadequately account for family and shared environment. We analysed 620 children, including 334 with ASD and 286 neurotypical siblings, with a median age of 6.0 years (IQR 4.0-8.0). Faecal and urine samples were collected monthly up to eight times. After quality control, 869 microbiome, 754 faecal metabolome and 787 urine metabolome samples were analysed using 16S rRNA sequencing, NMR and LC-MS metabolomics. Mixed models accounted for family, repeated sampling and biological sex. Family membership explained 46.1% of variation across microbiome and metabolome profiles, compared with 8.0% attributable to ASD. After family adjustment, ASD explained only 0.1% to 0.3% of microbiome beta diversity. No stable taxonomic biomarkers were identified, and microbial classification was poor (AUROC <0.75). Urinary metabolomics identified elevated 5-hydroxy-L-tryptophan and altered phenylalanine metabolism. Structural equation modelling found no direct associations between selected taxa and metabolites. These findings argue against a universal ASD microbiome and indicate that family context contributes more strongly than diagnosis to microbial and metabolic variation.
- In vitro evolution of uropathogenic Escherichia coli to fosfomycin resistance in a 3D cultured human bladder microtissue model
In vitro studies of antimicrobial resistance (AMR) using laboratory growth media produce important, fundamental information. However, their inability to more closely replicate the in vivo environment limits the translational potential of this work. Here, we used a 3D cultured microtissue model which reflects the human bladder microenvironment to select for resistance to fosfomycin in two uropathogenic strains of Escherichia coli, UTI-34 and UTI-59. To assess the clinical relevance of the mutations produced, we screened the observed mutations in the fosfomycin-selected variants against a curated dataset of 14,163 E. coli genomes isolated from urine. The four independent fosfomycin-selected variants of UTI-34 contained diverse mutations, while the mutations in the five independent fosfomycin-selected variants of UTI-59 were more constrained. All variants contained mutations in glpT, uhpT, uhpA and uhpC, which are commonly linked to fosfomycin-resistance in clinical isolates of E. coli. Screening of the mutations against the 14,163 E. coli genomes from urine confirmed that four of these mutations were found as exact matches in the dataset, while other mutation types were confirmed at a regional and gene level. These mutations did not result in any collateral susceptibility or resistance to other antibiotics recommended for the treatment of urinary tract infections. The use of a human 3D microtissue model, which closely replicates the urothelial microenvironment to study AMR during urinary tract infection treatment, could improve the clinical relevance of in vitro AMR studies. This has the potential to provide a better understanding of how AMR is acquired and expressed, and inform new strategies to combat AMR.
- Motor Learning and Transfer Are Symmetric Across Hands
Functional asymmetry between the cerebral hemispheres is a defining feature of the sensorimotor system, with the dominant hemisphere playing a central role in motor control. Whether motor learning is similarly lateralized, however, remains unresolved. To tackle this question, we combined a comprehensive meta-analysis (114 datasets) with a series of well-powered, preregistered experiments (N = 526) to test two core behavioral predictions of hemispheric lateralization in sensorimotor adaptation, a canonical form of motor learning: (1) adaptation is preferentially expressed in the dominant hand and (2) transfers asymmetrically between limbs. Across both approaches, we found that adaptation and interlimb transfer were strikingly symmetric. Together, these findings support a fundamental dissociation in the neural organization of skilled behavior: whereas motor control is lateralized to the dominant hemisphere, motor learning is supported by a neural architecture that functions symmetrically.
- A novel, complex-spike burst-dependent form of BCM-like metaplasticity regulates the induction of behavioral timescale synaptic plasticity
The induction of Hebbian LTP can produce a persistent, heterosynaptic suppression of LTP induction at other synapses. This form of metaplasticity, originally formalized in the Bienenstock, Cooper, and Munro (BCM) plasticity rule, generates competitive interactions between synapses and is thought to support sparse information encoding during memory formation. Importantly, although a non-Hebbian, burst-dependent form of synaptic plasticity known as behavioral timescale plasticity (BTSP) is essential for hippocampal memory encoding, little is known about the role of metaplasticity in BTSP. Thus, I examined whether the induction of BTSP at one set of synapses in the CA1 region of mouse hippocampal slices alters plasticity at other synapses. I find that the induction of BTSP by EPSP-evoked complex-spike (CS) bursts triggers a robust, but transient, heterosynaptic depression of excitatory synaptic transmission. This depression is induced by postsynaptic CS bursts, requires activation of L-type Ca2+ channels, and is mediated by activation of A1-type adenosine receptors. Notably, the heterosynaptic depression triggered by the induction of BTSP generates a CS burst-dependent form of BCM-like metaplasticity that transiently suppresses the induction of BTSP at other synapses. Together, these results provide experimental support for computational predictions that burst-dependent forms of plasticity are constrained by a distinct, burst-dependent form of BCM metaplasticity, and identify a mechanism that may contribute to sparse memory encoding during BTSP induction.
- Social and neuroendocrine phenotypes reprogrammed by endocrine-disrupting chemicals can be mitigated by Limosilactobacillus reuteri modulation of the gut microbiome-thyroid-oxytocin axis
Introduction: Environmental factors are increasingly implicated in the etiology of autism spectrum disorder (ASD). Polybrominated diphenyl ethers (PBDEs) are anthropogenic toxicants added as flame retardants to consumer products that have become ubiquitous environmental contaminants and disrupt thyroid hormone (TH) and neuroendocrine systems. We have previously shown that developmental PBDE exposure produces ASD-like traits with involvement of oxytocin (OXT)-thyroid hormone signaling. Limosilactobacillus reuteri (LR), a widely used probiotic bacterium, has been shown to improve social functioning and increase TH and OXT levels in murine models. Therefore, we tested the hypothesis that LR supplementation (LR) prevents PBDE-induced deficits in socioemotional behavior with concomitant modulation of TH signaling genes on hypothalamic OXT neurons. Methods: C57BL/6N mouse offspring were exposed to a commercial penta-mixture of PBDE congeners, DE-71, at an environmentally realistic concentration, 0.1 mg/kg/d (DE-71), or to corn oil vehicle (VEH/CON) via their mothers during gestation and lactation. Offspring received supplementation with LR ATCC PTA 6475 (107-108 CFU/mL, po) indirectly via the dam or continuation directly through adulthood. Unsupplemented controls were given saline. Results: Fecal microbiome analysis in offspring confirmed colonization of LR at postnatal day (P) 40 and depletion by P104. LR treatment increased plasma total thyroxine in DE-71 and plasma OXT in VEH/CON dams. In DE-71 offspring of both sexes, LR normalized deficient scores on social novelty preference and emotional recognition in adult females and males and deficient long-term social recognition memory (SRM) in adult DE-71 females; DE-71 males were normal. Reduced olfactory dishabituation between two social odors may partly explain the compromised socioemotional behavior produced by DE-71 in an LR-dependent manner. Multiplex RNA in situ hybridization performed on immunoreactive OXT-ergic neurons in the paraventricular hypothalamic nucleus (PVH) revealed significant upregulation of TH transporter monocarboxylate transporter 8 (Mct8) and downregulation of iodothyronine deiodinase 3 (Dio3) in DE-71 relative to VEH/CON females. This toxicant-induced reprogramming was prevented by probiotic treatment. DE-71 males expressed reduction in Mct8 and Dio3 transcripts on OXT-ergic neurons with minimal LR protection. In the female supraoptic nucleus (SON), Mct8 and Dio3 were downregulated by DE-71 and normalized in DE-71+LR; there were no group effects on transcript levels in male SON. Results of fecal 16S rRNA sequencing indicated reduced -diversity and altered {beta}-diversity in the gut bacterial community of female but not male DE-71 exposed offspring; most changes were correctable by LR. Alterations in taxa-level abundance caused by DE-71 and reversed by LR were observed in both sexes. These involved Bifidobacterium, Coprococcus, Desulfovibrio, Oscillospira, and Peptococcaceae in females and Desulfovibrionaceae, Rikenella, and Turicibacter in males. Exposed dams showed no detriment in - and {beta}-diversity while showing reduced abundance of several Firmicutes and Proteobacteria taxa that could be rescued by LR. The relative abundance of Lactobacillus was upregulated in DE-71 males and DE-71+LR males and dams. Conclusions: These results indicate that developmental probiotic supplementation effectively mitigated organohalogen-induced ASD-like deficits in socioemotional behavior and partially corrected dysbiosis of gut bacterial communities in exposed offspring of both sexes. Concomitantly, PBDEs altered the expression of TH regulatory genes Mct8 and Dio3 in PVH OXT neurons in a sex-dependent manner, suggesting that TH regulation of OXT neuroendocrine cells may modulate the emergence of toxicant-induced ASD-relevant behavior. While LR reinstated normal behavioral outcomes in PBDE-exposed offspring of both sexes, coincident normalization of hypothalamic TH signaling transcripts occurred more broadly in females, indicating the existence of unique parallel processes influencing the preventive effects of LR on ASD-relevant behavioral deficits in both sexes.
- Endocannabinoid ligands (CBD, Δ9THC, and Terpenes) inhibit excitability of mouse dorsal root ganglion neurons and exhibit synergistic inhibitory effects
The need for improved treatments for chronic pain has driven increased interest in cannabis-based therapeutics. Peripheral dorsal root ganglion (DRG) neurons, including nociceptors, express cannabinoid receptors (CB1 and CB2), suggesting that modulation of DRG excitability may provide an effective strategy for peripheral analgesia. Here, we investigated the effects of cannabidiol (CBD), delta-9-tetrahydrocannabinol (THC), terpene mixtures as well as cannabis plant extracts on neuronal excitability in small-diameter mouse DRG neurons using whole-cell current-clamp electrophysiology and assessed potential synergistic interactions. Both CBD and THC produced a concentration- and time-dependent inhibition of rheobase-evoked action potential firing, which were reversible in the presence of bovine serum albumin (BSA), both with similar estimated IC50 values of 5 micromolar (Terpene mixtures, as well as individual terpenes (linalool, beta-pinene, and myrcene), similarly reduced neuronal firing. Co-application of CBD with THC or terpenes enhanced inhibition, consistent with synergistic interactions and the known entourage effect. Application of WIN55,212-2 (WIN), a non-selective cannabinoid receptor agonist, in the presence of CBD also accelerated the time-dependent inhibition of neuronal firing. The inhibition of firing by the CB2-selective inverse agonist JTE-907 indicated the presence of CB2 receptors on DRG neurons. Plant extracts from the Cannabis sativa leaves also reversibly inhibited neuronal firing. CBD and a terpenes mixture produced modest effects on hERG channels, whereas plants extracts had negligible effects. Collectively, these findings demonstrate that phytocannabinoids and terpenes suppress peripheral sensory neuron excitability via receptor-dependent and indirect mechanisms, supporting their potential as non-opioid analgesics. Their synergistic interactions suggest that multi-component formulations may enhance analgesic effects.
- Kinetic proofreading decouples signal strength and range in paracrine gradient formation
Spatial gradients of signaling molecules pattern multicellular tissues with high precision. The canonical synthesis-diffusion-degradation (SDD) framework imposes a tradeoff on these gradients: ligand-receptor interactions that generate downstream signaling activity are also responsible for consuming the ligand. Correspondingly, at a fixed ligand synthesis rate, raising ligand-receptor affinity increases local signal strength at the expense of spatial range, and lowering it extends range at the expense of strength. Recent live-imaging measurements appear to violate this seemingly fundamental tradeoff, with low-affinity ligands of the epidermal growth factor receptor (EGFR) diffusing farther {it and} driving spatially broader signaling activity compared to high-affinity ligands. Here we explain these observations with a model of multi-step ligand processing at the receptor, and show that the activity--range tradeoff is a consequence of receptor architecture rather than a physical necessity. When receptors process ligand through a multi-step phosphorylation cascade with kinetic-proofreading-like resetting, the states that generate activity decouple from those that consume ligand, and signaling activity and range increase together over a finite window of ligand residence time. This lets cells tune how far a signal travels independently of how strongly it acts through tuning signaling parameters. Realistic EGFR parameters place the low-affinity ligands in this window. Because multi-site phosphorylation and preferential degradation of the active receptor recur across multiple receptor families, kinetic proofreading may be a general strategy for controlling signaling range.
- Integrating single-cell and bulk transcriptomic perturbation resources reveals complementary therapeutic spaces for drug repurposing
Transcriptome-based drug repurposing can accelerate therapeutic discovery, but is limited by fragmented resources, inconsistent quality control, and reliance on single perturbation databases. We developed CDRPipe (Computational Drug Repurposing Pipeline), a unified framework that interrogates disease signatures against drug perturbation signatures generated by distinct experimental technologies. Specifically, CDRPipe harmonizes microarray perturbation profiles from the Connectivity Map (CMap; 1,968 quality-filtered experiments) with pseudo-bulk profiles derived from large-scale single-cell RNA sequencing experiments in the Tahoe-100M database (56,827 experiments). CDRPipe standardizes preprocessing, computes rank-based connectivity scores, and evaluates significance using empirical null models. We applied CDRPipe to 233 curated disease signatures from GEO and CREEDS and evaluated performance using known drug-disease associations from Open Targets. Single-cell-derived pseudo-bulk profiles recovered more annotated therapeutics than microarray profiles (median recall 50.0% vs. 6.2%; Wilcoxon p < 10 ^ -11), though these differences partly reflect differences in drug library composition and clinical annotation coverage. Importantly, the two resources were highly complementary, with only 3.5% overlap in recovered drugs, indicating that integrating predictions across independent perturbation resources expands therapeutic coverage and enables identification of high-confidence consensus candidates. Case studies in autoimmune disease and endometriosis further demonstrate that CDRPipe recovers clinically relevant therapies while revealing technology-dependent patterns of discovery. These results show that integrating heterogeneous transcriptomic perturbation resources improves the robustness and interpretability of transcriptional drug repurposing.
- Engineered Amphiregulin Promotes Tissue Healing via Dual Regenerative and Immunomodulatory Functions in Tissue-Resident Non-Immune Cells
Amphiregulin (AREG), a growth factor prominently expressed by immune cells, has emerged as an important mediator of tissue healing. However, its therapeutic potential and immunomodulatory effects remain elusive. Here, we engineered an optimized AREG (eAREG) with enhanced signaling and show that it promotes robust skin repair and muscle regeneration in murine models. Beyond its growth factor function, eAREG acts on tissue-resident non-immune cells to suppress inflammation-induced chemokine programs, thereby limiting the recruitment of pro-inflammatory immune cells. We further demonstrate that eAREG constrains chromatin accessibility at regulatory regions of key chemokine genes, revealing an epigenetic mechanism of immune regulation operating within non-immune tissue compartments. Importantly, the regenerative and immunomodulatory effects of eAREG are preserved in diabetic mice with elevated inflammation and impaired healing. Together, these findings identify eAREG as a dual-function regenerative biologic and establish a design principle for regenerative therapies that integrate morphogenic signaling with immunomodulation mediated by tissue-resident cells.
- Does transection severity determine the way of spinal cord repair in the spiny mouse?
The mechanisms of the spinal cord regeneration after complete spinal cord transection were investigated in spiny mice. In some animals, the appearance of quadrupedal overground stepping together with rewiring of the direct propriospinal projections between the cervical and lumbar enlargements was revealed. In others, no stepping recovery was detected, whereas numerous cells labeled by the neuronal proteins NeuN and {beta}III-tubulin were observed within the injury region. We suggest that depending on trauma severity, different repair mechanisms are elicited: only connectome restoration or both connectome restoration and the activation of neurogenesis. To confirm the high neurogenic potential of spiny mice, a primary culture of bone marrow was established. Unlike in other mammals, bone marrow pluripotent cells in the culture differentiated into neuronal cells without any chemical stimulation. These findings provide strong evidence for the high differentiation potential of spiny mouse stem cells toward neural lineages.
- Multimodal EEG World Model: Self-Supervised Latent Transition Learning for Wearable EEG Seizure Detection
Existing supervised and self-supervised EEG models mainly learn discriminative or reconstructive representations within individual segments, while the transition information between adjacent EEG segments remains underexplored. In this study, we propose a Multimodal self-supervised EEG World Model for wearable seizure detection. Inspired by Le World Model, the proposed method encodes consecutive EEG segments into a shared latent space and predicts the next-segment latent representation from the current-segment representation conditioned on synchronized physiological information from ECG, EMG, and movement (MOV) signals. A learnable query-based fusion module aggregates the auxiliary multimodal representations into a compact physiological condition, while Sketched Isotropic Gaussian Regularization (SIGReg) is applied to stabilize the latent space and prevent representation collapse. After pretraining, only the pretrained EEG encoder is retained and frozen for linear binary probing, enabling EEG-only downstream seizure detection. We evaluated the proposed model on the SeizeIT2 wearable focal epilepsy dataset using a strict patient-wise training, validation, and test split. The proposed Multimodal EEG World Model achieved an AUPRC of 0.3748 , ROC-AUC of 0.8025 , and balanced accuracy of 0.7308 , ranking first on these three metrics among the ablation studies. It also achieved the highest AUPRC, ROC-AUC, balanced accuracy, and F1-score among the evaluated external baselines. These findings demonstrate that synchronized multimodal physiological information can provide useful contextual information for latent EEG transition learning and improve wearable EEG representation learning.
- How does CBT work? Causal discovery modelling locates the mechanisms of change in cognitive behavioural treatment for eating disorders
Background: Cognitive behavioural therapy (CBT) is the most frequently used and recommended therapy program for mental health conditions, including for eating disorders. Despite decades of use, little is known about how change is produced in CBT across mental illnesses, and there has been no empirical validation of the causal structure of change for CBT treatment of eating disorders. Progress in optimisation of CBT and the development of alternatives particularly for non-responders, has been constrained by a lack of understanding of the mechanisms through which the therapy works. This study aimed to investigate, for the first time, mechanism by mechanism the causal direction of change across a therapeutic CBT package. Methods: Directed acyclical graphs (DAGs) were estimated using causal discovery machine learning methods applied to data from a randomised controlled trial of a digital CBT program in 114 participants with bulimia nervosa. Eating disorder symptoms and psychological distress were assessed at each treatment session across the 10-session intervention in both conditions. Treatment effects were then estimated using the inferred causal structure. Results: The inferred causal structure estimated direct effects of treatment on eating restraint (small to medium), alongside indirect effects upon preoccupation with eating, fear of weight gain, desire for weight loss, binge days, loss of control, over-exercise and fasting (small to medium). Eating restraint emerged as the first and only node that treatment directly affected, suggesting that it is the primary mechanism through which treatment produces subsequent change in other behavioural and cognitive symptoms. Conclusion: These findings provide the first empirical evidence, beyond cross-sectional or correlational analyses, for the long theorised mechanisms underlying CBT for eating disorders. Contrary to the conceptual model which posits CBT acts directly on dietary restraint and shape and weight concerns, our findings suggest treatment acts primarily through reductions in eating restraint, with downstream effects on shape- and weight-related concerns and other symptoms. To build our understanding of the processes by which effective treatments for eating disorders operate, progress personalised medicine, and optimise therapeutic outcomes, the methodology developed here can be applied broadly to examination of causal change structures in both direction and magnitude across different interventions.
- Learning shared forecast-error structure to improve ensemble forecasts of seasonal respiratory outbreaks
Real-time forecasts of seasonal respiratory outbreaks are critical for public health preparedness and healthcare planning. Multi-model ensembles, which combine predictions from individual models, have become a leading approach for operational outbreak forecasting. Their success, however, depends in part on the assumption that component models make sufficiently independent errors. Here, we examined this assumption using archived real-time forecasts for influenza hospitalizations and influenza-like illness (ILI) in the United States. We found that component models with diverse structures and calibration methods shared systematic forecast errors during epidemic growth and around epidemic peaks, reflecting the common challenge of tracking rapid changes in epidemic dynamics from real-time surveillance data. Because such shared errors cannot be fully corrected by ensembling alone, we developed a deep learning framework that learns structured residual errors from historical forecasts and uses them to correct ensemble predictions. This framework improved influenza hospitalization forecasts across horizons and geographic scales, reducing the Weighted Interval Score by up to 20% at the national level and 12% across states relative to official ensemble forecasts, with the largest improvements at the near-term horizon and during epidemic growth and peak periods. We further showed that learned residual structures transferred across ensembles formed from different component models, making the approach robust to changes in model participation across seasons. The framework also improved ensemble forecasts for ILI, although gains were more modest. These findings reveal a fundamental challenge in ensemble forecasting and provide a generalizable approach for improving real-time epidemic forecasts.
- Learning Diagnostic Proficiency in Robotic-Assisted Bronchoscopy with Integrated Cone-Beam CT: A 680-Lesion Learning Curve Analysis
Background. Robotic-assisted bronchoscopy combined with integrated cone-beam computed tomography (RAB+CBCT) enables accurate sampling of peripheral pulmonary lesions (PPLs), but the acquisition of diagnostic proficiency and program-level efficiency remains incompletely characterized. Methods. We conducted a single-center cohort study of consecutive RAB+CBCT (Ion endoluminal system, Cios Spin) procedures performed by two experienced interventional pulmonologists. Strict lesion-level diagnostic yield was the primary outcome. Learning curve cumulative sum (LC-CUSUM) analysis determined operator-specific proficiency, followed by conventional CUSUM monitoring of post-proficiency performance. Secondary outcomes included procedure and intubation times, temporal changes in case complexity, and adverse events. Results. Overall, 427 procedures comprising 680 PPLs were analyzed. Median lesion long-axis diameter was 11 mm, 14.4% had a bronchus sign, and 36.5% of procedures involved multiple lesions. Strict lesion-level diagnostic yield was 85.4% (581/680). LC-CUSUM demonstrated proficiency after 48 and 86 PPLs, respectively; thereafter, both operators maintained acceptable performance without crossing the predefined CUSUM decision limit. Median procedure time decreased from 65 minutes during the first 10 procedures to 38 minutes during the last 10. The median intubation time was 70 minutes and declined significantly with increasing experience. Most indicators of lesion complexity remained stable, while short-axis diameter and bronchus-sign prevalence decreased modestly. Adverse-event frequency declined significantly over time. Conclusion. RAB+CBCT achieved high strict diagnostic yield, with heterogeneous operator-specific learning trajectories within a maturing multidisciplinary program. Diagnostic performance, procedural efficiency, and safety improved despite stable or modestly increasing case complexity. These findings support individualized, outcome-based proficiency assessment and longitudinal monitoring, rather than comparative operator ranking or reliance on fixed procedural-volume thresholds.
- Pathways from inequality to health: Social determinants of health shape the gut microbiome in low-resource U.S. communities
The gut microbiome influences cardiovascular and gastrointestinal disease risk, yet the indirect pathways through which structural social determinants of health (SDoH) shape microbial composition remain understudied. We investigated causal pathways linking SDoH - systemic conditions that shape health opportunities - to the gut microbiome in two low-resource, flood-prone U.S. communities. We collected questionnaires, dried blood spots, and fecal samples (n=131; ages 3-79) in Mississippi and Illinois (2022-2023) as part of a longitudinal project on infrastructure and health, identifying bacterial composition via 16S rRNA sequencing. Controlling for covariates, structural equation modeling showed education positively predicted microbiome richness and evenness, partially mediated through income and diet. This suggests that structural inequities in education, food access, and income significantly influence the microbiome. Notably, contrary to established literature, greater consumption of processed and fried foods was associated with increased alpha diversity. Therefore, in this context, diet may covary with environmental vulnerability, such as opportunistic pathogen exposure from frequent flooding. Analysis of beta diversity showed direct effects from education, diet, and CRP; income and homeownership had indirect effects mediated through diet. The taxa driving these relationships likely include Tannerellaceae for diet and a subgroup of Firmicutes for income, based on our differential abundance analysis. These findings indicate that pathways linking SDoH to the microbiome may vary by local infrastructure and environmental context. This research highlights how structural inequality may be biologically embedded, suggesting that public health interventions should target infrastructural inequities linked with environmental racism, rather than individual behavioral change.
- Determinants of virological non-suppression among children receiving antiretroviral therapy in Chokwe, Mozambique: a retrospective cohort study
Introduction: Despite major progress in antiretroviral therapy (ART) scale-up, viral suppression (VS) among children living with Human Immunodeficiency Virus (HIV) remains below global targets in Mozambique. Evidence on determinants of virological non-suppression (VNS) in decentralized rural settings is still limited. Objective: This study aimed to analyse factors associated with VNS among children receiving ART in Chokwe District, Gaza Province, Mozambique. Methods: We conducted a retrospective cohort study including children younger than 15 years who initiated ART between 2022 and 2024 in 27 health facilities in Chokwe District. Sociodemographic, clinical, laboratory, and health care data were extracted from paper medical records and electronic databases. VNS was defined as viral load (VL) [≥]1,000 copies/mL of blood after at least 6 months of ART initiation. Kaplan-Meier methods and Cox proportional hazards regression models, adjusted for key sociodemographic, clinical, and health care characteristics, were used to identify factors associated with time to VNS. Adjusted hazard ratios (aHR) and 95% confidence intervals (95% CI) were estimated.We conducted statistical analyses using the STATA 15 and R 4.5.1 software packages. Results: A total of 285 children were included. At 6 months after ART initiation, 28.2% of children presented VNS, while 23.3% presented VNS at 18 months. In the adjusted analysis for the first 6 months of follow-up each additional year of age at ART initiation was associated with lower risk of VNS, whereas not initiating treatment on the same day of diagnosis increased the risk of VNS. At 18 months of follow-up, age at ART initiation showed evidence of a non-linear association with VNS. Tuberculosis co-infection and undernutrition at 6 months were also associated with a higher risk of VNS. Conclusion: VNS remains frequent among children receiving ART in Chokwe District. Younger age during early follow-up, delayed ART initiation, tuberculosis co-infection, and undernutrition were important determinants of poor virological outcomes. Strengthening early HIV diagnosis, timely ART initiation, integrated management of TB and undernutrition, and age-responsive follow-up strategies may improve long-term virological outcomes among children living with HIV in resource-limited settings.
- Associations between occupations and the occurrence of sarcomas: results of the French population-based case-control study ETIOSARC
Objective Sarcomas are rare tumors of connective tissue that can develop in soft-tissue, viscera organs, or bones. Previous occupational studies have mainly focused on men and soft-tissue sarcomas. This study describes associations between occupations and sarcomas, in men and women, for soft-tissue sarcomas (STS), including visceral sarcomas, and bone sarcomas (BS). Methods The ETIOSARC study was a multicenter case-control study conducted across six French geographical areas between 2019 and 2023. Occupational histories were collected by interview and coded according to the International Standard Classification of Occupations (2008). Conditional logistic regression models were applied to estimate odds ratios with 90% confidence intervals for each occupation included in the study. Results A total of 374 male cases (336 STS and 38 BS), 371 female cases (335 STS and 36 BS), and 1,387 controls were included. Positive associations were observed among male STS for painters (OR=5.37, 90% CI=1.83-15.71), waiters (OR=4.54, 90% CI=1.88-10.97), and among male BS for electricians (OR=3.67, 90% CI=0.91-14.86). Among women, increased STS risk was found among real estate agents (OR=3.48, 90% CI=1.03-11.79), food preparation assistants (OR=3.47, 90% CI=1.09-11.04), and administrative and executive secretaries (OR=2.37, 90% CI=1.45-3.86). For BS, a higher risk was observed among sales workers (OR=5.61, 90% CI=1.65-18.99). Conclusions Our study highlights hitherto unreported occupational associations with sarcomas, among both men and women, as well as the importance of sex-stratified analyses. Further research should aim to confirm these results and disentangle the respective roles of occupational exposures, environmental factors, and lifestyle characteristics.
- Trajectories of Ankle-Brachial Index Values and Their Relation to Cardiovascular Health Measured by Life's Essential 8 in the Atherosclerosis Risk in Communities Study
Background: Peripheral artery disease (PAD) is an occlusive arterial disease primarily affecting the lower extremities. It impacts over 230 million people worldwide and is associated with significant morbidity and mortality. The ankle brachial index (ABI) test is a non-invasive method to detect PAD that compares the blood pressure in the ankle and arm to evaluate lower extremity blood flow. An estimated 20-50% of individuals with detectable PAD are asymptomatic and remain undiagnosed; however, ABI screening in high-risk, asymptomatic populations is not currently guideline-recommended. Few studies have evaluated change in ABI over time in asymptomatic populations. Therefore, we aimed to identify distinct trajectories of ABI values from mid- to late-life. Methods: We utilized data from the Atherosclerosis Risk in Communities (ARIC) study; a longitudinal cohort study initiated in 1987 that enrolled 15,792 participants aged 45-64. ABI measurements were collected at five visits over a 30-year period. We used group-based trajectory modeling to identify trajectories of ABI from mid- to late-life. Final model selection was based on visual fit, statistical criteria, group sizes, and substantive knowledge. Lastly, we compared baseline demographics, social determinants of health, and overall cardiovascular (CV) health, assessed using the American Heart Association's Life's Essential 8 (LE8) framework, across trajectory groups. Results: We identified 4,121 participants with ?3 ABI measurements over the study period in at least one limb. At baseline, participants had an average age of 51.4 {+/-} 4.9 years, were 57.3% female, 22.2% Black, and had an average overall LE8 score of 68.0 {+/-} 13.9 points. Our final model identified three linear trajectories: low-normal, high-normal, and declining. Overall LE8 scores varied significantly across trajectory groups: 67.3 {+/-} 10.7 (high-normal), 61.9 {+/-} 13.3 (low-normal), and 50.1 {+/-} 15.8 points (declining). Women had lower average ABI values, were more likely to experience a declining ABI trajectory, and had a delayed onset of decline compared to men. A greater proportion of Black participants experienced declining ABIs, with earlier, faster, and more severe declines than White participants. Conclusions: Poor overall CV health and common CV risk factors are associated with ABI decline. Targeted ABI screening in middle age may help detect PAD in its beginning stages and support early intervention.
- The Prognostic Value of Normal Troponin on Admission in STEMI Patients: An 8-Year Multicenter Cohort
Background: ST-elevation myocardial infarction (STEMI) remains a major cause of global mortality. While troponin is the gold-standard biomarker for myocardial injury, a subset of patients presents with troponin levels below the clinical "rule-in" threshold upon hospital admission. The long-term prognostic significance of these initial "low-troponin" presentations in a large-scale population remains insufficiently characterized. Methods: We conducted a retrospective multicenter cohort study using the "KINERET" database, analyzing 8,394 patients diagnosed with STEMI who underwent percutaneous coronary intervention (PCI) at four academic medical centers in Israel between 2016 and 2023. Patients were stratified into two groups based on ESC rule-in criteria for high-sensitivity cardiac troponin (hs-cTn) at admission: a High trop group (above rule-in cutoff) and a Low trop group (below rule-in cutoff). The primary outcome was all-cause mortality at 5 years. Results: Of the 8,394 patients (mean age 68.3{+/-}13.3 years; 76% male), 36.5% (n=3,064) presented with troponin levels below the rule-in cutoff. Patients in the High trop group were older and had a higher prevalence of comorbidities, including heart failure (46.7% vs. 28.7%) and chronic kidney disease (13.9% vs. 9%). The Low trop group demonstrated significantly higher survival rates at both 1 year (92.9% vs. 84.0%, p<0.001) and 5 years (85.7% vs. 75.0%, p<0.001). After adjusting for age, sex, and comorbidities in a multivariate Cox regression model, initially elevated troponin remained a robust independent predictor of 5-year mortality (HR 1.15, 95% CI 1.14-1.16, p<0.001), alongside age >75, female sex, and chronic kidney disease. Conclusions: STEMI patients presenting with initial troponin levels below the diagnostic rule-in threshold have a significantly better short- and long-term prognosis compared to those with early troponin elevation. Moreover, admission troponin levels serve as a powerful predictor of 5-year mortality and may be used as an independent prognostic factor following STEMI.
- Optimizing Wastewater Surveillance Sites for COVID-19 Hospitalization Forecasting Across U.S. States
In this study, we analyze viral load data from wastewater treatment plants (WWTPs) across multiple U.S. states and address the challenge of selecting an optimal subset of sites to improve COVID-19 hospitalization forecasts. Using forward (greedy) regression with a baseline ARIMA model, we identify the most informative WWTPs that enhance forecast accuracy while reducing the number of sampling locations. Our analysis, based on NWSS data, shows that the optimal number of sites typically ranges from 2-8, though some states, including NY, IL, and WI, benefit from a larger set (10-20). We also leverage a Virginia-level digital twin model specifically designed for wastewater data modeling and analysis and our results show that for different parameter settings, that forecast accuracy can be achieved with strategically chosen small number of sites, providing