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Long-read sequencing reveals widespread novel splicing and neojunction-derived neoantigens in nasopharyngeal carcinoma.

The widespread transcriptomic diversity driven by alternative splicing (AS) contributes to all hallmarks of cancer and represents a critical source of neoantigens for personalized immunotherapy. However, unlike other major malignancies, the full repertoire of AS in nasopharyngeal carcinoma (NPC) remains underexplored. Here, we employ long-read sequencing (LR-seq) to generate a high-resolution, isoform-level transcriptomic atlas from a cohort of 14 NPC tumor samples and four immortalized nasopharyngeal epithelial cell lines. We identify a substantial number of full-length novel transcripts (22,687; ∼44.38%), which reveal diverse splicing patterns and previously unannotated splicing events. By integrating short-read RNA-seq data to quantify isoform expression, we discover a subset of novel transcripts that are differentially expressed between tumor samples and immortalized nasopharyngeal epithelial cell lines. Furthermore, LR-seq enables precise identification of chimeric readthrough fusion transcripts, such as CLDN15-FIS1 and FOXRED2-TXN2 Finally, we develop a computational framework, tumor-specific splicing neoantigen detection (TS-SNAD), to predict neoantigens originating from novel exon-exon junctions (neojunctions) in tumor-specific novel transcripts. Using this framework, we identify neojunction-derived neoantigens and experimentally validate the immunogenicity of selected HLA-B*40:01-restricted neoantigens. These neojunction-derived peptides constitute a new class of noncanonical neoantigens with significant potential for developing personalized cancer vaccines for NPC.

Humans

Cross-species variant-to-function analyses implicate MEIS1 in conferring sleep abnormalities and impaired cerebellar development.

Genome-wide association studies (GWAS) have identified numerous loci for insomnia, yet functional validation of effector genes remains limited because most risk variants lie in noncoding regions, and the true causal gene is not known. Here, we use prior human cell-based variant-to-gene mapping to nominate six insomnia effector genes and test them in zebrafish, a tractable diurnal vertebrate model well suited for sleep phenotyping. Our CRISPR-based behavioral screening identifies the MEIS1 ortholog, meis1b, as a regulator of sleep maintenance, with crispants displaying impaired nighttime-specific sleep maintenance and increased sleep latency. Comparative chromatin analyses reveal conserved regulatory architecture spanning the human insomnia-associated locus and selectively implicate meis1b, whereas the duplicated ohnolog meis1a was dispensable. Developmental profiling further shows that meis1b is expressed in cerebellar granule progenitors, paralleling human MEIS1 expression, and that its disruption impairs cerebellar development. Together, these findings establish zebrafish as an efficient vertebrate platform for functional interrogation of GWAS candidates and support an evolutionarily conserved cerebellar role for MEIS1 in sleep maintenance.

Animals

Knowledge-driven interpretable neural networks provide mechanistic insight.

Analyzing omics data in the context of pathway knowledge is critical for understanding the molecular mechanisms underlying pathological changes. However, current pathway analysis methods do not model the detailed mechanistic nature of biological interactions, limiting the understanding of pathway behavior to a relatively shallow level. To address this issue, we present a knowledge-driven machine learning framework that embeds features into pathway graphs and models reactions analytically, producing interpretable feature hierarchies and subnetworks in which functional associations are estimated to model biological interactions. The approach is agnostic to feature selection, enabling the use of full omics data sets without discarding weak signals. Applications to breast cancer microRNA-gene regulation data and COVID-19 metabolomic data highlight immune and metabolic pathways relevant to disease progression. This framework bridges predictive modeling with mechanistic interpretation and offers a foundation for integrative pathway analysis.

Humans

A pangenome framework uncovers the role of deletions in repeated evolution of cave-derived traits.

Structural variants (SVs) are increasingly recognized as key contributors to adaptive evolution, yet they remain underexplored compared with single-nucleotide variation. To understand how large-scale genomic changes shape repeated evolution, we leveraged multiple levels of sequence data across the powerful evolutionary model system of the Mexican tetra fish (Astyanax mexicanus). We constructed one of the first pangenome graphs from a naturally evolving vertebrate, enabling comprehensive discovery of SVs among 120 fish from 11 populations. We discover substantial amounts of structural variation and explore the roles of genomic biases and selection in shaping the distribution of these variants. More than 2400 high-confidence cave-specific deletions are enriched in biological pathways involved in vision, metabolism, and behavior and cluster nonrandomly in quantitative trait loci linked to cavefish traits. Additionally, 67 genes harbor unique deletions between independent cavefish lineages. These reused genes show evidence of population-specific selection (99% contain selective sweeps compared with 8%-15% in genes lacking SVs), indicating that deletions likely rose in frequency through repeated positive selection rather than drift. Together, these results reveal that recurrent deletion events have repeatedly contributed to the evolution of cave-adapted phenotypes and highlight deletions as underexplored contributors of adaptive evolution in extreme environments.

Animals

Identification of genomic features that uniquely impact estrogen receptor alpha binding and its effects on gene expression in endometrial cancer.

Estrogen receptor 1 (ESR1, also known as estrogen receptor alpha or ER) is an established oncogenic transcription factor in breast and endometrial cancer; however, more is known about the mechanisms controlling ER behavior in breast cancer, and therapies targeting ER have been much more successful in breast cancer. To address this disparity, we characterize the genomic features that control ER in endometrial cancer and determine to what extent these factors differ from those in breast cancer. We focus on the locations of estrogen response elements (EREs), ER's preferred DNA-binding motif, throughout the human genome. To identify factors that predict ER genomic binding and effects on target gene expression, we apply machine learning to genomic data for each ERE in Ishikawa cells (ER-positive endometrial cancer) and T-47D cells (ER-positive breast cancer). Many of these factors, such as chromatin accessibility and histone modifications, are predictive of ER activity in both cell lines. However, the transcription factors that predict ER activity are cell type specific, including FOXA1 and GATA3 in T-47D cells and ETV4 and SOX17 in Ishikawa cells. In addition, the features that predict ER binding and effects on gene expression differ, with transcription at EREs in the absence of estrogen being predictive of ER regulatory activity. A CRISPR knockout screen in Ishikawa cells, as well as follow-up experiments, confirms the discovery that SOX17 controls ER activity in endometrial cancer cells. These results identify important genomic features of ER binding and regulatory activity and how these features differ between endometrial cancer and breast cancer cells.

Humans

A sequence-based classifier distinguishes phenotype-associated genes from other gene models in plants.

Only a small fraction of annotated plant genes possess experimentally validated associations with specific phenotypes. Phenotype-associated genes have distinct structural, molecular, and evolutionary characteristics compared with nonvalidated gene models. Here, we develop a simple classifier that uses sequence and evolutionary features, which can be generated for any species with an annotated reference genome assembly, to accurately distinguish phenotype-associated genes from both the overall population of annotated gene models and a specific set of genes identified as being tolerant of premature stop mutations. A model trained solely on genes from maize (Zea mays) identifies and prioritizes rice (Oryza sativa) and Arabidopsis (Arabidopsis thaliana) genes that are highly enriched in genes with experimentally validated links to phenotypes in both of these evolutionarily distant species. Gene models predicted to have a higher probability of being linked to phenotypes display patterns consistent with known biological properties of phenotype-associated genes. Notably, the sets of genes predicted to have a high probability of being linked to phenotype variation do not consist exclusively of well-characterized gene families but included many uncharacterized gene families carrying domains of unknown function. The quantitative scores generated by this model offer a valuable resource for prioritizing and exploring the vast number of uncharacterized gene models in plants, reducing the risk of failure in future reverse genetic efforts and potentially accelerating gene discovery and functional annotation in crops.

Phenotype

Beyond Photometric Consistency: Addressing Loss Insensitivity to Depth Noise in Endoscopic Estimation via Error Calibration.

Self-supervised monocular depth estimation in endoscopy is fundamentally constrained by the ill-posed nature of photometric supervision. In this work, we identify a critical yet overlooked cause of this ambiguity: the inherent insensitivity of photometric loss to depth noise. To overcome this intrinsic limitation, we propose Depth Error Calibration Learning (DECL), a two-stage framework that suppresses prediction variance and mitigates residual errors in self-supervised depth estimation. In Stage I (Variance Reduction), a cyclic depth generation strategy produces multiple depth hypotheses for the input image. The per-pixel empirical variance is quantified and integrated into a dedicated variance loss term, which penalizes inconsistent predictions and encourages the network to generate more stable and reliable depth estimates. In Stage II (Bias Calibration), an image-conditioned diffusion model refines the Stage-I depth prior and mitigates structured residuals through iterative denoising, thereby improving geometric accuracy and global consistency. Extensive experiments on three public endoscopic datasets demonstrate that DECL achieves consistent improvements over representative self-supervised monocular depth estimation methods under the evaluated protocols. Moreover, ablation studies on two representative backbones indicate that DECL is not restricted to a single network implementation, while broader validation on additional backbone families remains necessary. The source code is publicly available at https://github.com/DavidLuBit/EndoDenoising.

Journal Article

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

Temporal Trends and Spatial Variation in Preterm Prelabour Rupture of Membranes: A Population-Based Study.

OBJECTIVE: To describe the temporal trends in Preterm prelabour rupture of membranes (PPROM) in metropolitan France and the geographical distribution at the administrative division level. DESIGN: Exploratory population-based study using administrative data of the French National Health Data System. SETTING: Metropolitan France, 2015 to 2023. POPULATION: Pregnancy with a diagnosis of PROM before 37 SA. METHODS: Annual crude incidence of PPROM was calculated by dividing the number of pregnancies with PPROM diagnosis by the number of live births recorded during the same period. Annual trend was estimated by a binomial negative mixed model. Smoothed standardised incidence ratios were estimated based on a BYM2 model, which accounts for spatial variability between departments. MAIN OUTCOME: PPROM cases, defined as pregnancies with first hospitalizations with a diagnosis of PROM before 37 weeks. RESULTS: Over the study period, we included 150 615 PPROM cases representing 16 735 (±596) per year. Incidence of PPROM cases showed an ascending trend over time (incidence rate ratio 1.023 per year; 95% CI: 1.017-1.030) with an annual crude incidence ranging from 2.2% in 2015 to 2.7% in 2023. A decrease in the incidence was observed in 2020 relative to other years (incidence rate ratio 0.903, 95% CI: 0.887-0.920). A map of smoothed SIRs of PPROM cases at the French administrative division level revealed geographical inequalities. CONCLUSIONS: This first population-based study describing PPROM cases in metropolitan France paves the way for further studies to explore environmental hypotheses. Identifying temporal and geographical disparities in PPROM incidence is relevant to public health policy and practice as such disparities argue for the development of targeted prevention strategies in high-risk areas.

French national health data system

Simulating the effects of an alcohol minimum unit price policy on distilled spirits sales in 28 states of the USA.

BACKGROUND AND AIMS: Excessive alcohol use is a leading preventable chronic disease risk factor. Alcohol minimum unit pricing (MUP) policies are not used in the United States despite evidence of associations with reduced drinking and alcohol-related harms. To inform potential population-level chronic disease prevention strategies, we estimated effects of various hypothetical MUPs on alcohol sales. METHOD: Simulation based on observational time-series data. We used weekly off-premises product-specific alcohol retail sales and prices in 28 states of the United States for November 2022-November 2023 from NielsenIQ to estimate the own-price elasticity of spirits and cross-price elasticities of wine, beer and ready-to-drinks with respect to spirits. Using estimated elasticities, we simulated changes in total alcohol sales associated with hypothetical spirits MUPs ranging from $0.10 to $1.10 per standard drink (0.6 fluid ounces of alcohol). RESULTS: A hypothetical MUP of $0.80 per standard drink on spirits yielded the largest estimated decrease in alcohol sales (-1.7%) and would affect 5374 of 26 249 spirits products. To reach the $0.80 MUP, the sales-weighted average price increase among affected products was $0.24 per drink. CONCLUSIONS: Minimum unit pricing policies on distilled spirits in the United States could shift purchasing behavior and help reduce alcohol-related harms.

alcohol policy

The Association Between NT-Pro BNP, Nephropathy and Endothelial Dysfunction in Patients With Type 2 Diabetes Mellitus.

BACKGROUND: N-terminal pro-B-type natriuretic peptide (NT-Pro BNP) is an established biomarker of heart failure and has been recommended for cardiovascular risk stratification in type 2 diabetes mellitus (T2DM). However, its relationship with diabetic nephropathy and endothelial dysfunction across varying stages of kidney impairment remains unclear. This study examined the associations of NT-Pro BNP, renal impairment, albuminuria and endothelial dysfunction in patients with T2DM without overt heart failure. METHODS: A comparative cross-sectional study was conducted among 192 adults with T2DM. Participants were stratified by KDIGO eGFR groups (&#x2265;&#x2009;90, 60-89, 30-59&#x2009;mL/min/1.73m2). NT-Pro BNP was considered abnormal at a cut-off of &#x2265;&#x2009;125&#x2009;pg/mL. Albuminuria was categorized using the urinary albumin-to-creatinine ratio (uACR). Endothelial function was assessed by brachial artery flow-mediated dilatation (FMD). Logistic regression analysis was performed to identify independent factors of elevated NT-Pro BNP, with p-values <&#x2009;0.05 considered statistically significant. RESULTS: NT-Pro BNP levels were significantly higher in the lower eGFR groups compared with normal eGFR (204.3 vs. 96.8 vs. 62.2&#x2009;pg/mL, p&#x2009;<&#x2009;0001). A weak but significant positive correlation was observed between NT-Pro BNP and uACR (r&#x2009;=&#x2009;0.31, p&#x2009;<&#x2009;0.001). However, no significant association was found between NT-Pro BNP and FMD (p&#x2009;=&#x2009;0.388). Following multivariable adjustments, older age (adjusted OR 1.14, 95% CI: 1.06-1.23, p&#x2009;<&#x2009;0.001), higher systolic blood pressure (adjusted OR 1.05, 95% CI: 1.02-1.08, p&#x2009;=&#x2009;0.010), lower eGFR (adjusted OR 0.97, 95% CI: 0.95-0.99, p&#x2009;=&#x2009;0.003) and beta-blocker use (adjusted OR 4.65, 95% CI: 1.61-13.45, p <&#x2009;0.001) were independently associated with elevated NT-Pro BNP. CONCLUSION: In patients with T2DM without overt heart failure, elevated NT-Pro BNP showed a statistically significant association with lower eGFR and higher albuminuria. Moderate to severe albuminuria becomes an independent factor for elevated NT-Pro BNP after adjustment excluding eGFR. The lack of association with endothelial dysfunction suggests that NT-Pro BNP may reflect different pathophysiological pathways. NT-Pro BNP may serve as a useful biomarker for early cardiovascular risk stratification and identification of individuals at risk of pre-heart failure in diabetic kidney disease.

NT&#x2010;Pro BNP

Real-World Efficacy and Safety of Standard-of-Care Chimeric Antigen Receptor T-Cell (CART) and Bispecific T-Cell Engager (TCE) Therapies in Relapsed/Refractory Multiple Myeloma (RRMM).

We aimed to evaluate the real-world (RW) efficacy and safety of standard-of-care CART versus TCE therapies in relapsed/refractory myeloma (RRMM), to assess utilization, outcomes, and tolerability of these therapies in a RW oncology in the US. Data were derived from the US-based, electronic health record-derived deidentified Flatiron Health Research Database, 2021-2024. A total of 419 patients (CART n&#x2009;=&#x2009;220; TCE n&#x2009;=&#x2009;199) with a confirmed diagnosis of myeloma who received CART or TCE as a standard-of-care treatment after at least 2 prior lines of therapy were included. Patients in the CART cohort were younger, had better ECOG PS, and a higher receipt of a prior autologous stem cell transplant versus bispecific TCE cohort. In CART versus TCE cohort, the overall response rates (ORR) were 83.3% versus 66.3%, median duration of response 7.9&#x2009;months versus 4.3&#x2009;months, progression free survival (PFS) 13.6&#x2009;months versus 10.5&#x2009;months, and overall survival (OS) of 29.8&#x2009;months versus 21.9&#x2009;months, respectively. A higher percentage of hematologic toxicity, infections, and cytokine release syndrome (CRS) were noted in the CART versus TCE cohort. This study provides insights on the RW effectiveness of CART versus TCE in the treatment of RRMM; highlights the differences in patient selection, clinical responses, treatment duration, and toxicity profiles.

CART

Quantifying the Evolutionary Potential for Delta Smelt Persistence in a Warming Habitat.

Long-term persistence of managed species will depend, in part, on whether the species harbors the physiological or genetic potential to adjust to warming temperatures, and whether relevant genetic variation is modified by management practices. The critically endangered Delta Smelt (Hypomesus transpacificus) is intensively managed, but little is known about the presence of genetic variation for resistance to elevated temperature. Using a pedigree and whole genome sequencing data, we characterized the genetic basis of CTMax (as a metric of upper thermal tolerance) across control and elevated rearing temperatures, alongside covarying traits (body size and degree of hatchery ancestry). Warmer rearing temperatures increased CTMax through acclimation but also resulted in reduced additive genetic variation for the trait. We observed modest heritability for CTMax at rearing temperatures of 15&#xb0;C and 18&#xb0;C (0.26 and 0.16, respectively), but only a limited number of loci were identified that had consistent effects on CTMax across rearing temperatures. Instead, the genomic basis of thermal tolerance was highly dependent on rearing temperature (i.e., many loci detected with a GxE effect). This temperature-dependent genomic architecture is consistent with our finding that additive genetic variation for CTMax was reduced under warmer rearing conditions, indicating a potential constraint on adaptive evolutionary change. The influence of domestication selection was indicated by changes in allele frequency, and divergence in upper thermal tolerance and plasticity, between low and high hatchery ancestry groups. Minimal overlap between loci associated with domestication and CTMax suggests that these traits possess separate genetic underpinnings. Knowledge of genetic variation supporting ecologically relevant physiological variation may be useful for captive management and may inform supplementation of fish to the wild in an ever-warming environment.

conservation physiology

MET expression by immunohistochemistry as a biomarker in pancreatic neuroendocrine tumours.

INTRODUCTION: MET (c-MET) is a receptor tyrosine kinase implicated in numerous cancers, including pancreatic neuroendocrine tumours (pNETs), by promoting cell proliferation, survival, invasion and angiogenesis. Recognizing its oncogenic potential, there is significant interest in MET-targeted therapies for malignancies like pNETs, which often develop treatment resistance. Immunohistochemistry (IHC) has become a practical method for detecting MET overexpression in cancers. This study evaluates MET expression in pNETs by IHC and assesses its correlation with prognostic variables and survival outcomes. METHODS AND RESULTS: Tissue microarrays containing well-differentiated neuroendocrine tumours from the gastrointestinal tract were analysed. The study included 125 pNET cores from 112 patients after application of inclusion criteria. MET expression was determined using the H-score system. Different variables were assessed for H-score distribution and cross-tables. Survival analyses were conducted based on progression-free survival and overall survival. Positive MET expression was found in 83.5% of cases. Higher MET H-scores were seen in patients with lymphovascular invasion (LVI), distant metastases and higher tumour grade (P&#x2009;<&#x2009;0.05). When assessing different variables for higher MET H-scores, a significant association emerged at the 150-cut-off-point for LVI, perineural invasion, radiological evidence of progression and overall survival. For survival analysis, at a MET H-score threshold of 200, high MET expression was significantly associated with shorter progression-free survival (mean 8.7 versus 13.4&#x2009;years, P&#x2009;<&#x2009;0.05) and overall survival (mean 3.6 versus 7.7&#x2009;years, P&#x2009;<&#x2009;0.05). CONCLUSION: Elevated MET expression is linked to adverse histopathological features and worse clinical outcomes in pNET. Standardizing MET IHC evaluation is critical as anti-MET therapies develop, and identifying patients likely to benefit from these treatments remains essential.

MET protein

First Report of Fibromyxoma in a Greater Amberjack (Seriola dumerili) From Aquarium of Genoa.

In teleosts, mesenchymal tumours like fibromas are frequently observed, whereas their malignant counterparts, fibrosarcomas, occur only sporadically. In our study, an adult female greater amberjack (Seriola dumerili) reared at the Aquarium of Genoa developed a slow-growing mass protruding from the ventral right side of the head. The tissue was firm and white greyish with localized cranial haemorrhaging on the external surface. Cut-section examination showed a homogeneous, dry, whitish and richly vascularized tissue layout. Tissue samples from the mass were stained with Masson's Trichrome, Alcian Blue-PAS (pH&#x2009;2.5), and Toluidine blue for differential diagnosis. Microscopically, it was characterized by alternating collagenous spindle-cell septa and hypocellular metachromatic myxoid areas positive for acidic mucopolysaccharides and glycosaminoglycans. The absence of cellular atypia, mitotic activity and necrosis confirms the diagnosis of fibromyxoma. Documenting rare tumours in aquarium fish expands comparative pathology literature and highlights public aquaria as valuable research platforms for long-term health monitoring under controlled husbandry conditions.

fibromyxoma

Behind the Curtain of Care. Nurses' Experiences Providing Care to Consumers With Alcohol and Other Drug Issues: A Qualitative Scoping Review.

AIM: To scope and synthesise qualitative literature relating to nurses' experiences of providing care to consumers with alcohol and other drug issues and explore how meaning is constructed in practice. DESIGN: Scoping review. METHODS: A scoping review was conducted following Arksey and O'Malley's framework. Findings were analysed using thematic analysis. DATA SOURCES: Systematic searches were conducted between September and November 2025 across Medline, Emcare, CINAHL and Google Scholar, using controlled vocabulary and keywords relevant to nurses' experiences of providing care to consumers with alcohol and other drug issues. RESULTS: Twenty-four studies from 12 countries were included. Seven themes were identified: emotional aspects of care, education, training and skills in practice, the spectrum of stigma, ethical issues in professional practice, navigating pain management, limited support, and how meaning is constructed in practice. CONCLUSION: Nurses' experiences of providing care to consumers with alcohol and other drug issues are shaped by multiple intersecting factors influencing care delivery and professional practice. Further research is needed to examine how workplace culture, language and interpersonal interactions influence healthcare experiences, and inform education, service development and support needs. REPORTING METHOD: Reported in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews (PRISMA-ScR) checklist. PATIENT OR PUBLIC CONTRIBUTION: No patient or public contribution.

alcohol and other drugs