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Brain white matter undergoes structural and functional alterations linked to late-life cognitive decline, but the cellular and molecular basis of its selective vulnerability remains incompletely defined. Here, in naturally aged mice, we demonstrate that senescent and disease-associated microglia (DAM) phenotypes converge in hippocampal-adjacent white matter, particularly in the fimbria. Using regional gene expression profiling, immunolabeling, GeoMx digital spatial profiling and CosMx spatial molecular imaging, we identify an aged brain-exclusive microglial population concentrated in white matter that expresses DAM genes together with a 'SenBrain' senescence gene signature, including galectin-3 (GAL3/Lgals3). Single-cell spatial trajectory analyses suggest that multiple cell fate transitions may give rise to this aged, proinflammatory, senescent- and DAM-linked state. Pharmacogenetic or pharmacological senotherapeutic interventions reduced white matter GAL3+ DAM abundance and restored a more youthful microglial organization in aged fimbria. These findings identify a senescence- and DAM-enriched microglial state as a prominent and partially reversible feature of aged brain white matter.

Autism spectrum disorder (ASD) is a pervasive neurodevelopmental disorder that can develop in infancy and is caused by a combination of genetic predisposition and environmental factors. Fine particulate matter (PM2.5) is one of the most dangerous air pollutants, capable of carrying various toxic substances deep into body tissues and causing complex health effects. Exposure to PM2.5 during pregnancy may increase the risk of neurodevelopmental disorders in offspring. This study aimed to investigate the association between maternal exposure to PM2.5 and its major components during pregnancy and childhood ASD, and to identify critical gestational windows of vulnerability. All participants were recruited exclusively from the Xinyang Central Hospital, and covariates were ascertained through structured clinical interviews. PM2.5 and its components, including sulfate ( SO 4 2 - ), nitrate ( NO 3 - ), ammonium ( NH 4 + ), organic matter (OM), and black carbon (BC), were derived from Tracking Air Pollution in China (TAP). Statistical analysis was performed using SPSS, employing the chi-square test, rank-sum test, and binary logistic regression analysis. Positive associations were observed between PM2.5 and ASD (OR = 5.197, 95% CI: 3.294-8.200), and between SO 4 2 - , NO 3 - , NH 4 + , BC, and OM and ASD. Monthly risk estimates for all pollutants exhibited a right-skewed distribution, with the strongest associations consistently observed during the gestational months 4-7. Maternal exposure to PM2.5, SO 4 2 - , NO 3 - , NH 4 + , BC, and OM was associated with ASD in children, particularly SO 4 2 - and BC. The 4th-7th gestational months represent a critical window of vulnerability. These findings support targeted public health interventions to reduce prenatal air pollution exposure.

This scoping review examines relevant evidence to understand the associations between operating room (OpR) PM and SSI. Surgical site infections (SSIs), a substantial cause of postoperative morbidity, lead to prolonged hospitalization and increased healthcare cost. Studies have demonstrated airborne particulate matter (PM) to be a vector of exogenous bacterial contamination of the sterile field. A scoping review across PubMed®, Embase, Scopus, and CINAHL databases for eligible studies including OR particulate matter, microbial load, ventilation, and SSI was performed. Non-English and non-human studies were excluded. Among 1,681 records, 21 met inclusion criteria - reporting 51 independent comparisons. Quantitative and qualitative analyses were performed. Three (14.3%) studies employed direct PM measurement, whereas 18 studies (85.7%) used indirect proxy indicators such as ventilation system characteristics (n=17, 81.0%) and microbial air load (n=1, 4.8%). Substantial heterogeneity was demonstrated across four exposure domains: (1) particle size resolution, (2) temporal resolution, (3) spatial characteristics, (4) viable vs total PM. PM is a biologically plausible, modifiable contributor to SSI development. Current approaches optimize room-level air metrics that correlate poorly with biologically relevant pathogen exposure at the surgical field. Dynamic, size-discriminatory measurement of PM remains a crucial gap essential to establish objective markers of SSI risk, thereby enabling the development and implementation of targeted, actionable environmental interventions for SSI prevention.

Fine particulate matter (PM2.5; aerodynamic diameter ≤2.5 µm) is a leading contributor to the global burden of disease and is associated with substantial morbidity and mortality. However, its health effects are often evaluated within single-exposure frameworks, limiting causal inference and reducing the translational relevance of evidence for policy development. In this study, we reconceptualise PM2.5 within the exposome framework by integrating external exposures, internal biological responses, and structural determinants of health across the life course. PM2.5 comprises a complex and dynamic mixture of pollutants from ambient and household sources, including biomass combustion, traffic emissions, and industrial activities. After inhalation, PM2.5 particles can penetrate the respiratory tract, and finer fractions may enter the systemic circulation, where they induce oxidative stress, inflammation, epigenetic dysregulation, and endocrine disruption. These mechanisms contribute to a broad spectrum of adverse health outcomes, including cardiovascular, respiratory, metabolic, and neurodegenerative diseases as well as cancers. The multi-level and temporally accumulated nature of these effects highlights the limitations of reductionist exposure assessment and underscores the need for integrative, systems-based approaches. Integrating cumulative impact assessment with exposome-wide approaches that leverage satellite, clinical, land-use, urban planning, and socioeconomic data can improve the characterisation of context-specific cumulative health effects. Positioning PM2.5 as a sentinel indicator of cumulative environmental risk provides a unifying framework for exposome-informed science and supports the development of more precise, equitable, and policy-relevant interventions.

Microplastics (MPs) are emerging contaminants ubiquitously present in the environment, yet their rapid quantification in complex matrices such as soils and biosolids remains challenging because of spectral interference from organic matter (OM). This study investigates, for the first time, how high OM contents influence the spectral detectability and machine-learning-based quantification of high-density polyethylene (HDPE) and polystyrene (PS) polymers (at 0-8% v/v) in soils and biosolids. Pre-processed NIR spectra were analysed using partial least squares-discriminant analysis (PLS-DA) to identify MP- and OM-related wavelength regions via variable importance in projection (VIP), which were subsequently used as inputs for four regression algorithms (PLS, support vector machines, random forest and custom neural network). In soils with low OM (0.78-1.23%), HDPE and PS produced polymer-specific absorption features and were predicted with excellent accuracy (up to R2 = 0.96 and residual prediction deviation, RPD = 5.19), using a relatively small set of VIP-selected wavelengths. In biosolids with very high OM (64-72%), polymer bands were partially masked, and VIP scores shifted towards OM-dominated regions. However, prediction performance remained robust, especially for HDPE with neural network model (R2 = 0.98, RPD = 6.41). Overall, although the OM strongly influenced the spectral regions driving model predictions and partially reduced discrimination among MP concentration levels, it does not prevent accurate quantification of MPs when appropriate variable selection and machine-learning strategies were applied. These findings demonstrate that NIR spectroscopy coupled with targeted variable selection and advanced regression can provide robust, non-destructive estimation of MP contamination in OM-poor soils and OM-rich biosolids, highlighting key spectral features to prioritise in future studies targeting natural organic-rich samples with low-MP concentration.

Grape seed proanthocyanidins extract (GSPE) has demonstrated significant neuroprotective efficacy in various neurodevelopmental disorders, nevertheless its potential beneficial role in preterm white matter injury (PWMI) remains unclear. This study aims to evaluate the therapeutic potential of GSPE against PWMI and the underlying mechanisms. GSPE (20 mg/Kg) was taken orally by the mouse after PWMI modeling. The survival rate, incidence of macroscopic lesions, body weight change were calculated. The myelin damage was evaluated. Mitochondrial homeostasis‌ was detected in PWMI model mice and cultured oligodendrocyte precursor cells (OPCs). Brain tissues from mice groups underwent RNA-seq. A dual-luciferase reporter assay was employed to validate the direct binding interaction between miR-153 and IMMP2L mRNA. The results showed that treatment of GSPE ameliorated cerebral ischemic injury in PWMI mice and improved behaviour ability and cognition deficits. GSPE restored mitochondria homeostasis in both PWMI mice and OPCs. Additionally, IMMP2L was found to be increased, while ROS was diminished by GSPE intervention. KEGG analysis showed that Wnt signaling pathway, the downstream of IMMP2L, changed significantly in PWMI group, while GSPE reversed it. The dual-luciferase reporter assay demonstrated that miR-153-3p directly suppressed IMMP2L expression through these binding sites. In summary, our findings revealed that GSPE treatment alleviated PWMI and restored mitochondrial homeostasis in mice.These beneficial effects are likely be attributed to the improvement of the activity of IMMP2L-related signaling pathways by GSPE.

The detrimental effects of tire particles are closely related to additive leaching, yet how the exposure sequence of sunlight and natural organic matter (NOM) regulates this process remains unclear. We investigated the release dynamics of 11 additives, dissolved organic carbon, and nontarget molecular features in tire tread particle (TTP) leachates over 21 d under two exposure scenarios: (i) dry-photoaging followed by incubation in different NOM, and (ii) wet-photoaging occurring directly in humic acid (HA). For dry-photoaged TTPs, NOM suppressed the release of most organic additives with increasing photoaging duration and NOM concentration through surface passivation and depletion of leachable reservoirs, whereas Zn and diphenylguanidine release were enhanced by up to 44.5% and 47.2%, respectively. By comparison, HA facilitated organic additive release but suppressed Zn during wet-photoaging, with additive levels remaining below those from dry-photoaged TTPs. Compared to dry-photoaging, wet-photoaging in HA also resulted in lower contributions of quantified additives to measured DOC (2-5%), greater reduction in environmentally persistent free radicals (22%), and enhanced formation of 3HA* and 1O2, indicating the photosensitizing role of HA. Nontarget screening further revealed greater diversity of molecular features after prolonged HA incubation than in NOM-free or early stage HA treatments. These findings underscore the exposure-order-dependent divergence in reshaping chemical complexity and the fate of tire-derived mixtures.

Phytoplankton cells exude a wide array of chemicals in the water column, generating a localized microenvironment known as the phycosphere. Although it is now well accepted that the phycosphere mediates interactions between phytoplankton and bacteria, the chemical gradients around individual phytoplankton cells have never been explicitly measured, and their shape has been classically assumed to be set by ideal diffusion. Here we used Raman microspectroscopy to obtain micrometer-scale measurements of the concentration profile of a phytoplankton metabolite (fucoxanthin) around individual phytoplankton cells of different species, having radii between [Formula: see text] and 60 [Formula: see text]m. We found that fucoxanthin concentration decreases more rapidly with distance from the cell than predicted by ideal diffusion, showing that the phycosphere includes compounds whose diffusion is characterized by nonideal effects. We explain this observation using a space-dependent diffusivity model where nonideality arises from viscosity and solubility gradients in the extracellular environment. Our results suggest an onion-structured model of the phycosphere, in which small hydrophilic solutes that obey ideal diffusion generate broad but weak gradients, whereas insoluble compounds are retained within [Formula: see text] to [Formula: see text] from the phytoplankton cell surface and yield steep gradients of organic matter. These observations, supported by evidence that fucoxanthin can act as an effective chemoattractant for marine bacteria, show the existence of strong and highly localized chemical cues with potentially far-reaching impacts on microbial interactions in aquatic environments. These findings highlight the importance of directly measuring the microscale chemical landscape experienced by marine microbes.

Previous studies, including ours, have reported that olfactory test scores (OTSs) are associated with gray matter volumes (GMVs) in brain regions including the hippocampus and amygdala, even after adjustment for the Japanese version of the Montreal Cognitive Assessment (MoCA-J) score. However, it has not been comprehensively investigated whether decreases in GMV fully explain the association between olfactory and cognitive function test scores. We analyzed the association between OTSs obtained using multiple odor intensities and the MoCA-J score in 1,444 adults (36.1% male) aged 31-91 years using multivariable regression models adjusted separately for (1) whole-brain GMV, (2) total GMV across olfactory limbic (memory-related) regions, and (3) total GMV across brain regions significantly associated with the MoCA-J score. Among participants aged ≥ 65 years, OTSs showed a positive association with the MoCA-J score across all three GMV-adjusted models (standardized β = 0.318, p = 0.001; β = 0.302, p = 0.002; and β = 0.308, p = 0.001, respectively), although effect sizes were small. These findings support the possibility that decreases in GMV may not fully explain the relationship between olfactory and cognitive function in older adults. Longitudinal studies are needed to clarify the causal relationship between olfactory function and cognitive decline.

Acute respiratory infections (ARIs) remain a major global health concern. Although long-term air pollution exposure has been linked to ARIs, prospective evidence from community-based populations remains limited. This study aimed to quantify the burden of ARIs in the community and examine the associations between long-term exposure to particulate matter 2.5 (PM2.5) and ozone (O3) and the risks of ARIs, with additional analyses using influenza-like illness (ILI) as a more specific outcome. We conducted a prospective cohort study including 3617 residents in Shanghai, China, who were followed weekly for 1 year. Individual-level exposure to PM2.5 and O3 concentrations was estimated using high-resolution datasets. Cox proportional hazards models with shared frailty were applied to assess associations with ARIs. Exposure windows of 3, 6, 9, and 12 months were evaluated, and the optimal window was selected based on the Akaike information criterion. Effect estimates were reported per IQR increase. Dose-response relationships, subgroup analyses, and multiple sensitivity analyses were performed. During 3217 person-years of follow-up, 885 ARI events were documented (0.27 per person-year). In the fully adjusted model using the 12-month exposure window, each IQR increase in PM2.5 was associated with higher risks of ARIs (hazard ratio [HR] 1.594, 95% CI 1.340-1.897), with stronger associations observed for ILI (HR 1.948, 95% CI 1.484-2.557). For O3, the corresponding HRs were 1.510 (95% CI 1.135-2.007) for ARIs, with stronger associations for ILI (HR 2.229, 95% CI 1.385-3.588). PM2.5 showed a nonlinear association with ARIs, whereas linear relationships were observed for PM2.5 with ILI and O3 with both outcomes. Evidence of effect modification was observed by age, residence, and season for PM2.5 and by season for O3. Results were robust across multiple sensitivity analyses. Long-term exposure to PM2.5 and O3 is associated with increased risk of ARIs, with similar associations observed for ILI. These findings highlight the importance of long-term air pollution control and targeted interventions for susceptible populations, particularly during cold seasons.

To determine whether human blastocysts requiring an additional day of in vitro culture to achieve expansion exhibit differences in trophectoderm transcriptomic profiles associated with developmental and implantation-related processes. Prospective in vitro experimental cohort study including RNA sequencing of mural trophectoderm biopsies from day 5 (D5) and day 6 (D6) (euploid) blastocysts. Twenty-one blastocysts donated by 16 couples undergoing intracytoplasmic sperm injection (ICSI) and preimplantation genetic testing for aneuploidy (PGT-A). Transcriptomic comparison focused on 21 blastocysts, classified as D5 (n=13) versus D6 (n=8) embryos, and euploid D5 (n=6) versus euploid D6 (n=4) embryos. Blastocyst developmental timing (D5 vs D6) and embryo ploidy status. Identification of differentially expressed genes (DEGs) and deregulated molecular pathways between D5 and D6 (euploid) embryos, including functional enrichment analysis to explore their potential roles in embryo development and implantation. Global transcriptomic analysis revealed partial overlap between D5 and D6 blastocysts, indicating a largely shared gene expression landscape with underlying biological variability. Differential expression analysis identified 111 differentially expressed genes (DEGs) between D5 and D6 embryos, with functional enrichment analyses highlighting pathways related to translation, metabolism, and cellular organisation.An exploratory euploid-only subanalysis yielded a larger number of genes meeting the selected differential expression thresholds, potentially reflecting reduced biological variability within the subset, together with enrichment of pathways associated with oxidative phosphorylation, ribosomal function, and energy metabolism, although these findings should be interpreted cautiously given the limited sample size. Human D5 blastocysts exhibit transcriptomic profiles enriched in pathways related to translation, metabolism, and cell adhesion compared to D6 blastocysts. However, these differences are subtle and occur within a largely shared global transcriptomic landscape, suggesting that they reflect variations in developmental timing rather than clearly distinct functional states. While these findings are consistent with biological processes relevant to implantation, they should be interpreted within the broader context of embryo development, where additional molecular and cellular mechanisms also contribute to implantation competence.

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Differential gene expression (DGE) analysis is foundational to transcriptomic research, yet tool selection can substantially influence results. This study compares two widely used DGE tools, edgeR and DESeq2, using real and semi-simulated bulk RNA-Seq data sets, mostly from human patients, spanning viral infection, bacterial infection, and fibrotic conditions. We evaluated tool performance across four dimensions: (1) sensitivity to sample size and robustness to outliers; (2) classification performance of uniquely identified gene sets within the discovery dataset; (3) pathway-level concordance of significant DEG sets; and (4) generalizability of tool-specific gene sets across independent studies. First, using Bonferroni-adjusted p-value&#x2009;<&#x2009;0.05 and absolute log2 fold change greater than 1 (i.e., |log2FC|>1) as significance criteria, repeated subsampling showed that DESeq2 generally identified more Differentially Expressed Genes (DEGs) than edgeR at smaller sample sizes, while the tools became more concordant as sample size increased. Both tools showed similar responses to simulated outliers, with Jaccard similarity decreasing as more swapped samples were introduced. Second, classification models trained on tool-specific genes showed that edgeR achieved higher F1 scores in 9 of 13 contrasts and more frequently reached perfect or near-perfect precision. Third, Hallmark and KEGG pathway enrichment analyses showed that many contrasts retained substantial pathway-level agreement between tools, although selected contrasts still showed tool-specific enriched pathways. Finally, in cross-study validation using four independent SARS-CoV-2 datasets, edgeR-specific genes yielded higher AUC, precision, and recall in held-out datasets, with some test cases achieving perfect separation. Overall, our findings show that DESeq2 may identify more DEGs under stringent thresholds, whereas edgeR often yields more conservative, predictive, and generalizable gene sets. These findings emphasize that DGE tool choice should be guided not only by DEG yield, but also by the downstream reproducibility, predictive value, and biological interpretability of the resulting gene sets.

Deep learning (DL) chest radiograph (CXR) models are often trained on downsampled images to reduce computational overhead, despite clinical workflows operating at high resolution. Previous studies have investigated the impact of input resolution on CXR classification accuracy, yet two fundamental pillars of safe and trustworthy AI, explainability and generalizability, remain underexplored. In this retrospective study, we evaluated how training image resolution affects CXR classification performance and explanation quality in internal versus external testing. We trained Convolutional Neural Networks (CNN) for disease classification on the SIIM-ACR Pneumothorax and RSNA Pneumonia datasets at six resolutions (ranging from 64&#xd7;64 to 1024&#xd7;1024) using five-fold cross-validation and evaluated models on internal and external test sets. Internal performance was high across resolutions (AUROC >0.85), but external testing showed substantially worse generalizability at lower training resolutions, with internal-to-external drops >20% versus 4.2%-10.7% at higher resolutions (512&#xd7;512 to 1024&#xd7;1024). Higher resolutions also produced more concise explanations, with the tightest saliency-map coverage at 1024&#xd7;1024 (<4%) across models and datasets, and improved explanation quality on external data (peak precision plateauing at 768&#xd7;768 for pneumothorax). Overall, training at higher CXR resolutions improved both generalizability and explainability, providing practical guidance for radiology AI design beyond internal test performance.