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PubMed2026

A reanalysis of the Hitachi cohort study evaluating the effectiveness of low-dose CT screening for lung cancer.

The effectiveness of low-dose thoracic computed tomography (CT) screening for lung cancer for non-smokers or light smokers has been unclear. The results of the Hitachi cohort study performed by the conventional multivariable analysis suggested the reduction of lung cancer mortality by thoracic CT screening, but also revealed the lower all-cause mortality in the CT group, which indicated the existence of self-selection bias. Because the background of the subjects in the CT screening group and that in the X-ray screening group were very different, it is critical to adjust appropriately the confounding factors. In this brief report, we describe a re-evaluation of the results of the Hitachi Cohort Study performed by using more flexible methods, propensity score matching and inverse probability weighting.

epidemiology/public health↗
PubMed2026

Ribosomal protein S3: a critical regulator of human disease mechanisms.

Ribosomal protein S3 (RPS3) is an essential structural component of the 40S ribosomal subunit, yet growing evidence highlights crucial extraribosomal roles in genome maintenance, cell-cycle control, and immune signaling. Dysregulation of RPS3 contributes to diverse human disorders, including cancer, inflammatory diseases, neurodegeneration, and resistance to antimicrobial and anticancer therapies. As a cofactor of NF-κB and a participant in DNA damage responses, RPS3 occupies a node that integrates stress signaling with transcriptional reprogramming, enabling both protective and pathological outcomes. The present review critically evaluates mechanistic insights into RPS3 biology, emphasizing recent findings that delineate its context-dependent effects, discrepancies across models, and remaining gaps that restrict translational applications. Understanding these complexities is essential to assess RPS3's potential as a biomarker and therapeutic target.

Humans↗
PubMed2026

S-layer-phage interaction in Clostridioides difficile.

Successful infection by a bacteriophage requires the injection of the phage genome into the cytoplasm of the host bacterium. To achieve this, an infecting phage must traverse the layers of the host cell envelope, including the membrane(s) and the cell wall. This process is further complicated in bacterial species that produce a proteinaceous S-layer on the outermost surface of the cell. Surprisingly little is known about the mechanistic basis of these early stages in the phage lifecycle, and even less is known about infection of S-layer producing bacteria. Recent advances in structural biology, particularly in cryoEM, have dramatically improved our understanding of the structures of both bacterial S-layers and phage virions separately, but we still lack a molecular view combining both phage and S-layer in the process of infection. Here, we review our current understanding of phage-S-layer interactions, using the human pathogen Clostridioides difficile as an example host.

Clostridioides difficile↗
PubMed2026

Hierarchical modeling of tumor subtypes in cell lines using large-scale genomic datasets.

Cancer cell lines (CLs) are widely used to study tumor biology and drug response, yet their translational relevance is often limited by inaccurate subtype annotations. Existing CL-tumor matching approaches are frequently constrained by flat classification schemes, weak subtype definitions, and the exclusion of normal tissue references, leading to potential confounding of tumor-specific and tissue-of-origin signals. To address these limitations, a hierarchical classification (HC) framework is presented in which CLs are aligned with patient tumors across biological resolutions, from organ to molecular subtype. Gene expression profiles from 802 CLs, 5,612 tumors from The Cancer Genome Atlas (TCGA) , and 8,939 non-cancerous tissues were integrated to separate oncogenic signals from tissue-specific signals. Node-specific features were selected using maximum relevance minimum redundancy, and balanced accuracies of 89% in cross-validation and 75%, and 80% on external datasets were achieved. Through the framework, 43 CLs were reassigned, and clinically relevant underrepresented subtypes were identified.

cancer cell lines↗
PubMed2026

The landscape of pruning for large language models: A systematic review and unified taxonomy.

Confronting the inherent tension between the exceptional capabilities and the immense computational costs of Large Language Models (LLMs), pruning has become a crucial technique for achieving efficient deployment. However, a systematic analytical framework dedicated specifically to LLM pruning remains absent. In this paper, we aim to bridge this gap. We first elucidate the theoretical foundations that underpin the effectiveness of pruning, namely overparameterization and redundancy, and then propose a multidimensional taxonomy that organizes existing approaches along the axes of granularity, timing, and criteria. Building upon this unified perspective, we further analyze performance recovery mechanisms and the broader evaluation ecosystem, while also exploring forward-looking challenges such as interpretability, automation, and hardware-algorithm co-design. Through this comprehensive synthesis, we seek to provide an integrated and coherent analytical lens for advancing both research and practice in LLM pruning.

Large Language Models↗
PubMed2026

Spatial transcriptomics of Ciona adult brains reveals functional zonalization and insights into neural gland function.

The ascidian Ciona is a pivotal chordate model for illuminating the evolutionary origins of the vertebrate brain. Here, spatial transcriptomics of the adult Ciona neural complex, combined with image-based computational super-resolution mapping, resolved distinct tissue domains including the cerebral ganglion, neural gland, ciliated funnel, neural gland duct/dorsal strand, and body wall muscle. Within the cerebral ganglion, high-resolution mapping revealed clear molecular zonalization separating the cortex and medulla, alongside regional specialization within the cortex itself. The neural gland exhibited localized enrichment of genes associated with extracellular matrix and cell-cell interactions. These spatial features suggest that the neural gland functions as a homeostatic and signaling interface, reminiscent of primitive vertebrate meninges or choroid plexus. Overall, this spatially defined gene expression map provides a foundational framework for understanding functional regionalization in the tunicate brain and its evolutionary relationship to vertebrate nervous systems.

Ciona↗
PubMed2026

Loss, persistence and reversal of phenotypic traits.

The irreversibility of complex trait loss has long been a tenet of evolutionary biology. However, this idea is increasingly at odds with the numerous documented exceptions across the Tree of Life. We synthesise this growing body of evidence across a diverse array of taxa and traits, exploring the evolutionary conditions that enable evolutionary reversal. By integrating macroevolutionary, genetic, and developmental information, we argue that trait reversal is commonly fostered by some form of persistence in the generative developmental pathway of the lost trait. We identify three overarching modes of trait reversal and support them with multiple case studies: by pleiotropy (the involvement of the same generative components in other traits and/or functions), by plasticity (environment-dependent expression of the trait) and by hemiplasy (persistence in another lineage, followed by reticulate evolution). We also examine important affinities between trait reversal and evolutionary novelties, undermining a neat distinction between what is old and what is new in evolution. This survey may provide a useful framework for future explorations of the developmental mechanisms underlying these still overlooked macroevolutionary dynamics.

Phenotype↗
PubMed2026

Mechanisms of Hematopoietic Stem Cell Aging and Emerging Rejuvenation Strategies.

Hematopoietic stem cell (HSCs) aging is a complex biological process driven by both cell-intrinsic alterations and extrinsic cues from the bone marrow niche. Understanding these mechanisms is critical for developing therapies against aging-related hematopoietic disorders. This review synthesizes recent advances in the molecular mechanisms underlying HSCs aging, including microenvironmental aging, genomic instability, epigenetic dysregulation, mitochondrial dysfunction, and aberrant nuclear mechanotransduction. We summarize that the functional decline of HSCs during aging drives a compensatory expansion of the phenotypically defined stem cell pool, leading to an aberrant increase in cell number. We also highlight aging-associated HSCs heterogeneity, including CD150high and P-selectin-positive subsets that enrich for myeloid-biased or functionally compromised HSCs states while emphasizing that surface phenotype alone may not fully indicate functional rejuvenation. Finally, we discuss emerging rejuvenation strategies-including targeting myeloid-biased HSCs, modulating inflammatory pathways, and implementing epigenetic or metabolic interventions-supported by cutting-edge technologies such as single-cell multi-omics, gene editing, and computational modeling. These approaches hold promise for counteracting age-related hematopoietic decline and restoring immune competence.

Humans↗
PubMed2026

SGLF-Net:Staged Global-to-Local Cross-Scale Fusion Network for Colonoscopic Polyp Segmentation.

Polyp segmentation in colonoscopy images plays a pivotal role in computer-aided medical diagnosis and the early prevention of colorectal cancer. However, existing methods often suffer from performance degradation when confronted with extreme polyp scale variation and polyp boundary ambiguity. To address these challenges, we propose the Staged Global-to-Local Cross-Scale Fusion Network (SGLF-Net), which adopts a novel staged global-to-local learning paradigm to progressively refine segmentation from coarse global semantics to fine-grained local details. Specifically, the Global Semantic Perception Stage integrates a Swin Transformer Encoder and a Dynamic Attentive Decoder (DAD) to construct comprehensive multi-scale contextual representations. The Local Detail Refinement Stage employs an Edge-aware Dynamic Attentive Decoder (E-DAD) to enhance structural fidelity and boundary precision through explicit edge-guided supervision. Furthermore, we introduce the Cross Spatial-Scale Feature Aggregation and Reconstitution (CSSAR) module, equipped with hybrid attention mechanisms, to facilitate efficient semantic structural interaction between the two cascaded stages. Extensive experiments on five public benchmark datasets demonstrate that SGLF-Net consistently outperforms state-of-the-art methods in both segmentation accuracy and boundary preservation.

Journal Article↗
PubMed2026

Metabolic engineering of Candida yeasts for biotechnological applications.

Candida yeasts represent a versatile yet underexploited platform for industrial biotechnology. These yeasts utilize a remarkably broad range of carbon sources, particularly for hydrophobic carbon sources, coupled with robust growth and diverse biosynthetic capacities, making them promising hosts for sustainable production of chemicals, fuels, and proteins. Despite these advantages, industrial deployment of Candida species has been hindered by concerns regarding opportunistic pathogenicity and the historical lack of efficient genetic manipulation tools, leading to a substantial gap between metabolic potential and practical utilization. Recent advances in functional genomics, genome editing, and systems metabolic engineering are rapidly overcoming these barriers, enabling more precise and efficient strain development. In this review, we systematically summarize recent progress in the metabolic engineering of Candida species as microbial cell factories, with particular emphasis on expanding genetic toolkits, utilizting renewable and non-conventional carbon sources, and biosynthesizing high-value compounds. In addition, we propose a biosafety-oriented classification framework to support their safe industrial deployment. Finally, we discuss current challenges and emerging opportunities, emphasizing that the synergy of synthetic biology and artificial intelligence-driven design holds the key to unlocking the biotechnological potential of Candida yeasts.

Candida↗
PubMed2026

Predictive evolutionary genomics: principles, validation, and practice.

Climate change and habitat loss are driving rapid evolutionary responses in populations world-wide, which creates an urgent need for evolutionary forecasting in conservation and agriculture. Such forecasting can be categorized into three time scales: trait-based models that use multivariate quantitative genetic equations to project correlated phenotypic responses up to c. 20 generations, allele-based analyses that model allele frequency dynamics up to 100 generations, and composite adaptation scores that aggregate many small effects to yield predictions across longer horizons. However, these approaches have remained largely disconnected. Here, we present a Bayesian framework that integrates these three complementary approaches for evolutionary prediction. Our framework combines genomic, phenotypic, and environmental data to yield probabilistic predictions with explicit uncertainty. We show how predictive evolutionary forecasts can be validated with experimental evolution, field experimentation, historical specimens, and reciprocal transplants. These validated forecasts can help advance conservation and agricultural programmes by helping predict which populations are at risk of future extinction, optimizing breeding programmes for future climates, and planning ecosystem management under environmental change. By supporting a shift towards more predictive approaches in evolutionary biology, this framework may help improve our ability to manage biodiversity and food security in a changing world.

Genomics↗
PubMed2026

Ancient DNA and Human Physiology.

Ancient DNA (aDNA) enables the reconstruction of chronologically sampled genomes from ancient humans, animals, plants, pathogens, and microorganisms, as well as environmental DNA, providing a record of biological changes through time. Improvements in short and degraded DNA extraction methods and low-cost sequencing now enable the generation of broad, cross-regional datasets that expand evolutionary analyses from past population demography to biological mechanisms. By tracking temporal shifts of allele frequencies, integrating functional genomics resources (e.g., gene expression, chromatin structure variation), modeling population demography to separate selection from genetic drift, and aligning genetic changes with archaeological, cultural, and climatic data, aDNA has the potential to link sequence variation to physiological function within their temporal and environmental contexts. In this review, we summarize illustrative case studies from aDNA research spanning complex traits, dietary adaptations, and responses to pathogens and other environmental changes, showing how human biology has evolved under multiple selective pressures through time. These dated signals help triage experimental work and expose mechanisms that are rare or absent in living cohorts. Although some challenges remain, such as geographic and temporal sampling disparities, limitations in data resolution and variant detection, and genotype-phenotype uncertainties, rapid methodological progress and stronger ethical frameworks are expanding what can be inferred, making aDNA a promising tool for refining physiological pathways, their timing, and their drivers.

Humans↗
PubMed2026

Review: The African turquoise killifish as a model for the integrative physiology of vertebrate aging.

With increasing emphasis on extending healthy lifespan, aging research requires vertebrate models that permit efficient mechanistic investigation and intervention testing within practical time and cost constraints. The African turquoise killifish (Nothobranchius furzeri) has attracted growing attention because it combines an exceptionally short life cycle with an intact vertebrate physiological context and an expanding genetic toolkit, enabling relatively rapid evaluation of candidate aging interventions and mechanistic analysis across molecular, tissue, and organismal levels. This review assesses N. furzeri from an integrative-physiology perspective, focusing on germline-soma interactions, gut microbiota-host crosstalk, nutrient sensing and metabolic remodeling, temperature responsiveness, and AMPK-mTOR-linked programs. It also examines expanding genome-engineering and reporter approaches that support mechanistic and tissue-resolved investigation of these physiological processes. Building on recent reviews of killifish biology, disease modeling, regeneration, and the hallmarks of aging, we synthesize evidence across major intervention domains, distinguish established phenotypic effects from incompletely resolved mechanisms, and highlight functional endpoints, methodological standardization, and the appropriate interpretation of the model's translational relevance. Together, these features position N. furzeri as a strategically useful vertebrate platform for rapid mechanistic testing, intervention evaluation, and prioritization of aging-related pathways. Future progress will require improved methodological standardization, tissue-resolved causal studies, and question-driven cross-species validation where appropriate.

Animals↗
PubMed2026

Proteomics in environmental pollution research: Advances, challenges, and future directions.

Environmental proteomics has emerged as a powerful approach for elucidating the molecular mechanisms underlying pollutant-induced biological effects. Although this field has developed rapidly, the systematic review of recent proteomics applications in environmental pollution research remains limited. This review explored the emerging roles of toxicoproteomics in biomarker discovery and mechanistic elucidation, as well as ecotoxicoproteomics in ecological risk assessment and bioremediation strategies. Here, we review the field, highlighting recent trends such as the integration of proteomics with genomics, transcriptomics, and metabolomics to provide a comprehensive view of biological responses to environmental stressors. We further discuss the growing application of artificial intelligence in improving proteomics data interpretation and accelerating biomarker discovery. In addition, recent technological advances in environmental proteomics are highlighted, including next-generation tissue microarray proteomics, nanoscale proteomics, single-cell proteomics, and spatial proteomics. Despite its potential, proteomics faces challenges, such as high operational costs, computational complexity in analysis, and technical limitations in low-abundance protein detection. We propose that the convergence of proteomics with artificial intelligence and multi-omics approaches offers promising solutions to these challenges, enhancing the practical application of proteomics in environmental monitoring and risk assessment.

Proteomics↗
PubMed2026

The impact of sex, age, and genetic ancestry on DNA methylation across tissues.

Understanding the consequences of individual DNA methylation variation is crucial for advancing our knowledge of human biology and disease, yet the collective impact of individual traits on DNA methylation and their downstream effects on gene expression across human tissues remains poorly understood. Here, we quantify the contributions of sex, age, genetic ancestry, and BMI on autosomal DNA methylation variation across nine human tissues and 424 individuals from the Genotype-Tissue Expression project. We show that genetic ancestry and age have a greater impact on DNA methylation compared with sex, with aging effects being more widespread but less pronounced. On average, <10% of the gene expression variation in sex, age, and ancestry is mediated by DNA methylation differences, with ancestry showing the largest proportion of mediation. We further show that ancestry-associated DNA methylation differences accumulate at CpG sites with extreme methylation states and are largely under genetic control. The female autosomal genome exhibits consistent hypermethylation across tissues at Polycomb-repressed regions. Ultimately, we show that age-related Polycomb target hypermethylation is observed across multiple tissues but not in the gonads. Our multi-individual, multitissue approach defines the key drivers of human DNA methylation variation in healthy conditions, establishing a baseline for the interpretation of DNA methylation changes in disease contexts.

Humans↗
PubMed2026

Biological characterization of Candida parapsilosis haploids induced by voriconazole.

OBJECTIVES: Candida parapsilosis is an important opportunistic fungal pathogen causing serious human infections in nosocomial settings. It has long been thought that C. parapsilosis has a diploid genome with a high homozygosity between chromosome homologs. METHODS: In this study, we report the discovery of C. parapsilosis haploids induced by voriconazole, a triazole with broad antifungal activity against fungal pathogens, in an experimental evolutionary assay. RESULTS: The haploid strains were able to undergo auto-diploidization under in vitro culture conditions or during systemic infection at a low frequency. Compared to the progenitor diploid strain, C. parapsilosis haploid and auto-diploid strains exhibited a reduced ability of invasive growth and biofilm formation. Global transcriptional expression analysis indicated that haploid and auto-diploid strains had a similar transcriptomic profile, which showed a remarkable difference from the progenitor diploid strain perhaps due to the loss of chromosome heterozygosity. Moreover, the haploid and diploid strains had distinct fungal burdens in different animal tissues, suggesting the haploid state could have a colonization advantage over the diploids in certain tissues such as the brain and spleen. CONCLUSIONS: The discovery of C. parapsilosis haploids not only sheds lights on the biology of this important fungal pathogen, but also provides a tool for genetic modifications for the field.

Voriconazole↗
PubMed2026

Sarcomas: Research on Ultrarare Subtypes Gains Ground.

Rare cancers account for almost one fourth of all cancers. Sarcomas belong to the group of cancerous diseases with an incidence of less than 6 cases per 100,000 inhabitants. Over the past decade, activities were launched worldwide to elucidate the peculiarities of many of the more than 100 sarcoma subtypes described in the World Health Organization handbook. The major contributor to exact diagnosis is molecular pathology. The subgroup of ultrarare sarcomas (URS) poses a significant problem as each URS type has its own morphology, biology, natural history, and prognosis. In 2020, 35 international sarcoma centers agreed to standards of evaluating URS. The threshold was set to an incidence of less than 1 case per 1,000,000 inhabitants, and 77 URS subtypes were defined. Also quality criteria for centers to be selected for retrieving data to registries were consented. This issue of Cancer Epidemiology, Biomarkers & Prevention contains the first article to validate these principles of URS using the data from a nationwide cancer database. The authors from Taiwan also pointed out limitations of the approach. Combination with the national death database allowed to calculate overall survival (OS) and identified age as a significant factor for OS per URS type. These new data might foster future research on diagnosis and treatment of URS. See related article by Lee et al., p. 1654.

Humans↗
PubMed2026

Beyond species trees: pervasive gene flow limits phylogenomic resolution in the diversification of Juniperus from the Qinghai-Tibet Plateau.

Understanding how lineages diversify despite persistent ancestral polymorphism and recurrent gene flow remains a central challenge in evolutionary biology. Juniperus distributed across the Qinghai-Tibet Plateau provide an ideal system for addressing this question because repeated geological uplift and climatic oscillations have likely promoted cycles of lineage divergence, range shifts, and secondary contact. Here, we combined approximately 1.08 million genome-wide SNPs from 164 individuals representing thirteen Juniperus lineages with phylogenomic datasets comprising 3,381 nuclear single-copy genes and nearly complete plastomes. We detected extensive phylogenomic discordance and cytonuclear incongruence across genomic datasets. Topology weighting, coalescent simulations, quartet-based tests, and analyses of gene flow and reticulation collectively support the interpretation that these patterns were shaped by the combined effects of prolonged incomplete lineage sorting and gene flow during lineage diversification. Ecological niche analyses further provide a spatial and climatic context in which environmentally similar lineages may have had greater opportunities for secondary contact during historical range shifts. Collectively, our results reveal that the evolutionary history of Qinghai-Tibet Plateau Juniperus is characterized by reticulate diversification rather than strictly bifurcating evolution, and demonstrate how genome-wide discordance can provide biological insights into the evolutionary processes underlying lineage diversification.

Gene Flow↗