Betel quid (BQ) chewing, a prevalent practice affecting over 600 million people globally, is associated with systemic toxicity and neurological alterations. While dysbiosis of the gut microbiota is implicated in neuropsychiatric disorders via the gut-brain axis (GBA), its role in BQ chewers remains unexplored. This exploratory study aimed to investigate whether chronic BQ chewing is associated with gut dysbiosis and alterations in spontaneous brain activity. Fecal samples (n = 30 BQ chewers, n = 19 healthy controls) were subjected to whole metagenome shotgun sequencing (WMGS) to assess microbial composition and function. Amplitude of low-frequency fluctuations (ALFF) values, a resting-state functional magnetic resonance imaging metric reflecting regional spontaneous neural activity, were assessed in a subset of 29 BQ chewers and 21 healthy controls. Group differences in microbiota and ALFF were analyzed using the Wilcoxon rank-sum test and two-sample t-test (adjusted for age, sex, education, smoking and alchohol). Partial Spearman's correlation analysis was performed to link microbial taxa with ALFF alterations. Motivated by the presence of complex polysaccharides and polyphenols in BQ, carbohydrate-active enzyme (CAZyme) profiles were also assessed. Chronic BQ chewers exhibited significant gut microbiome alterations, characterized by reduced microbial diversity, enrichment of pro-inflammatory genera, and depletion of beneficial taxa. Analysis of carbohydrate-active enzymes further revealed altered metabolic potential in BQ chewers. Furthermore, reduced ALFF was observed in the limbic lobe of BQ chewers. At a nominal significance level, Streptococcus abundance correlated positively with limbic ALFF (partial ρ = 0.35, 95% CI [0.07, 0.58], raw p = 0.04), whereas Dorea formicigenerans exhibited a negative correlation (partial ρ = -0.36, 95% CI [- 0.55, - 0.08], raw p = 0.04). Chronic BQ chewing is associated with gut microbial dysbiosis and functional metabolic shifts. Exploratory analyses suggest that these microbial features may correlate with spontaneous neural activity in the limbic lobe, providing preliminary evidence for a potential involvement of the GBA in BQ‑associated neurological sequelae. These findings highlight the need for further investigation into microbiota‑targeted strategies in BQ chewers.

BackgroundHigh body mass index (BMI) is a modifiable risk factor for Alzheimer's disease and other dementias (ADODs), but a global assessment of BMI-attributable ADOD burden and its future trends is limited.ObjectiveTo quantify the spatiotemporal patterns, inequalities, and projections of BMI-attributable ADOD burden across 204 countries and territories from 1990 to 2021, with forecasts to 2040.MethodsUsing Global Burden of Disease (GBD) 2021 risk estimates, we analyzed high BMI-attributable ADOD deaths, disability-adjusted life years (DALYs), age-standardized mortality rate (ASMR), and age-standardized DALY rate (ASDR) by age, sex, region, and Socio-demographic Index (SDI). Trends were assessed with estimated annual percentage change (EAPC). Drivers were examined using decomposition analyses, and future trends were projected with autoregressive integrated moving average (ARIMA) models. Population attributable fractions (PAFs) were calculated for selected countries.ResultsHigh BMI-attributable ADOD deaths increased from 31,577 in 1990 to 139,439 in 2021, with ASMR rising from 1.22 to 1.79 per 100,000 people (EAPC=1.17%). DALYs grew from 644,750 to 2,665,746, with ASDR increasing from 21.39 to 32.86 per 100,000 (EAPC=1.32%). Females faced a higher absolute burden, while males showed faster increases in standardized rates. Growth was concentrated in low- and middle-SDI regions, particularly East/Southeast Asia and parts of sub-Saharan Africa.ConclusionsBMI-attributable ADOD burden has risen significantly since 1990, with notable socioeconomic disparities, and is expected to increase through 2040. Urgent action is needed for obesity prevention and integrated risk management.

The MAPRE3 gene is aberrantly expressed in several cancers. We profiled DNA methylation in tumor tissues from early‐stage non‐small cell lung cancer (NSCLC) patients and assessed associations with overall survival (OS). Significant CpG probes were validated in The Cancer Genome Atlas (TCGA). The methylation level of cg12821679 MAPRE3 showed significant associations with OS in lung squamous cell carcinoma (LUSC) (HR = 0.32, P = 6.55 × 10 −7 ), but it was not observed in lung adenocarcinoma (LUAD). In LUSC, MAPRE3 expression was significantly correlated with cg12821679 MAPRE3 ( r = 0.17, P = 2.96 × 10 −3 ) and potential trans ‐regulated genes were enriched in the Nicotine addiction pathway. Additionally, MAPRE3 expression showed significant associations with OS in both LUAD and LUSC (LUAD: HR low vs high = 2.28, P = 2.40 × 10 −3 ; LUSC: HR low vs high = 1.61, P = 0.0244). The association between smoking cessation and overall survival was significantly modified by MAPRE3 expression (HR interaction = 0.69, P = 0.0282). Smoking cessation improved OS only in patients with high MAPRE3 expression (HR = 0.56, P = 2.82 × 10 −3 ). We conclude MAPRE3 may predict NSCLC prognosis and influence the prognostic benefit of smoking cessation.

Early and accurate diagnosis of mild cognitive impairment (MCI), a prodromal stage of Alzheimer's disease (AD), is critical for timely intervention and management. Nevertheless, effectively integrating heterogeneous multi-modal data for AD diagnosis remains worthy of further investigation. Therefore, we propose a supervised contrastive learning framework that integrates single nucleotide polymorphisms (SNPs), plasma proteomics, and T1-weighted structural magnetic resonance imaging (sMRI) from a biologically informed perspective, with SNPs influencing protein structure or gene expression levels, ultimately altering brain structure. Through a supervised contrastive learning mechanism, we construct a cross-modal feature space and introduce a similarity-based symmetrical attention mechanism to capture intermodal interactions and mitigate modality heterogeneity. We validate the proposed method on the Alzheimer's Disease Neuroimaging Initiative dataset, and experimental results demonstrate accuracy of 96.1%, 86.2%, and 86.1% for the AD-NC task, MCI-NC task, and AD-MCI task. In addition, the application of explainable methods to our model identified multi-modal biomarkers related to AD diagnosis. The experimental results validate the effectiveness of our model in the diagnosis of AD and MCI.

[This corrects the article DOI: 10.3389/fmed.2026.1727004.].

Host–microbiome interactions play essential roles in the development of Alzheimer’s disease (AD), yet the host genetic impacts on gut microbial alterations in AD remain poorly understood. Here, we simultaneously profiled host genotype and gut microbiome in 252 Chinese individuals with varying degrees of cognitive disability. Using the latent Dirichlet allocation topic model, we identified the Anaerostipes-enriched enterosignature (ES-Ana) at the microbial subgroup level as significantly negatively associated with cognitive disability, which could be recapitulated in external cohorts. With the whole-genome sequencing data, we performed microbiome genome-wide association studies for the ES-Ana relative abundance. We prioritized 41 lead genetic variants and confirmed that the high ES-Ana relative abundance showed a negative correlation with the polygenic risk score of AD, indicating its protective effect against AD. Furthermore, we identified 174 ES-Ana-associated genes, which are enriched in AD-related biological functions and phenotypes, and exhibite pervasive underexpression in glial cells during brain aging. In summary, our study reveals the complex genetic effects on the gut microbiota in AD, and provides novel evidence for the roles of the gut–brain axis in AD.

Genes impacting the bioaccumulation of perfluoroalkyl and polyfluoroalkyl substances (PFASs)and their neurotoxic effects on the brain and behavior remain unclear. Here,we examined genome-wide associations with serum accumulation of 13 PFASs in 6,823 Chinese adults. We revealed that perfluoroheptanoic acid (PFHpA) accumulation was associated with genetic variants at two loci (3q29: P = 5.20 ×10-19; 6p22.2: P = 3.69 ×10-23), mapping to 56 genes.Blood expression of 27 of these genes was associated with PFHpA accumulation in 573 subsamples. Eight genes showed potential causal effects on PFHpA accumulation,functionally linked to innate immunity (TRIM38, ZDHHC19, MUC20)and organic solute transport (SLC51A and SLC17A3). We assessed the impact of PFASs on cortical thickness and surface area, white matter fractional anisotropy and mean diffusivity,along with 25 behavioral phenotypes. We identified that seven PFASs were correlated with reduced cortical morphology, primarily in the prefrontal cortex. We also found a statistical causal effect of PFHpA accumulation on the surface area in the right frontomarginal cortex, which mediated the effect of PFHpA on anxiety. These findings indicate that serum PFHpA accumulation may be regulated by genes related to innate immunity and solute transport, heightening anxiety by impairing the prefrontal cortex.

Background and objective Graph Convolutional Networks (GCNs) have demonstrated accurate classification in Alzheimer’s disease (AD), but they suffer from over-smoothing.To address this limitation, we proposed and applied a multimodal Transformer-GCN (TransGCN) framework to magnetic resonance imaging (MRI)-based quantitative maps for the accurate diagnosis of AD. Methods A total of 157 participants were enrolled for MRI examination and neuropsychological tests. T1 mapsand quantitative susceptibility mapping (QSM) were derived from the dual-TR multiple-echo gradient-echo sequence. Covariance networks for both T1 maps and QSM were constructed. The TransGCN model was proposed, based on few-shot learning and incorporates local attention mechanism.Subsequently, network features and clinical features were combined to construct inter-subject correlations for the TransGCN, and five-fold cross-validation was used to evaluate the performance of conventional GCN and TransGCN in AD diagnosis. Results The results showed that the GCN model for AD diagnosis reached an average accuracy (ACC) of 0.703 and an area under the curve (AUC) of 0.832 in five-fold cross-validation. In contrast, our proposed TransGCN model for AD diagnosis achieved anaverageACC of 0.851 and anaverageAUC of 0.924, indicating that local attention Transformer blocks above the GCN layer can greatly improve the classification ability of AD. Conclusions The results indicate that combining TransGCN with MRI-based quantitative maps can enhance the performance of classification. Additionally, the proposed TransGCN provides an efficient approach for multimodal data fusion and shows promise as a decision-support tool to complement clinical assessments and to aid in the early identification of at-risk individuals.

Abstract Background Older adults at high-risk conditions are particular vulnerable to cognitive impairment; however, population-based assessments targeting this group remain limited. To exam cognitive status and associated risk factors in high-risk older adults in China. Methods This population-based cross-sectional study was conducted across Hainan Province comprising adults aged ≥ 60 years with at least one high-risk condition: physical activity limitations, disability, stroke history, mental health symptoms, or subjective memory concerns, through stratified regional sampling. Pre-mild cognitive impairment (Pre-MCI) and MCI were assessed using the validated BABRI-brain health system. Demographic and health-related data collected from registries and self-reports. The prevalence rates of Pre-MCI and MCI were calculated, and multivariable logistic regression was applied to identify risk factors. Results Among 228,087 participants (mean age 73.6 ± 8.6 years; 57.6% female; 67.1% with primary education or less), the prevalence of Pre-MCI and MCI was 51.4% and 32.0%, respectively. Significant risk factors for cognitive outcomes included older age (OR range for Pre-MCI: 1.27 [1.24–1.30] to 3.09 [2.77–3.45]; for MCI: 1.04 [1.01–1.07] to 4.28 [3.84–4.78]), unmarried status (OR 1.07 [1.03–1.12] for Pre-MCI; 1.45 [1.39–1.52] for MCI), lower education (OR range for Pre-MCI: 1.37 [1.32–1.41] to 3.74 [3.59–3.89]; for MCI: 1.61 [1.54–1.68] to 6.92 [6.60–7.25]), occupation as farmer/housemaker (OR 1.37 [1.31–1.43] for Pre-MCI; 1.59 [1.51–1.67] for MCI), and residence in medium GDP regions (OR 1.74 [1.66–1.82] for Pre-MCI; 1.38 [1.32–1.45] for MCI). Additional risk factors included hearing impairment, cerebral hemorrhage, family dementia history, and hypertension. Sex-specific differences were observed. Conclusion The high prevalence of cognitive impairment in high-risk older adults highlights the need for tailored public health strategies. Particular attention should be given to those who are unmarried, with lower education, hearing impairment, and hypertension.

BackgroundThe loss of an only child represents a profound psychological trauma that is a significant risk factor for adverse mental health outcomes, including post-traumatic stress disorder (PTSD) and executive dysfunction. Research indicates that cerebral small vessel disease (CSVD) shares partial pathophysiological mechanisms with PTSD and may directly contribute to cognitive impairment through multiple pathways. Therefore, CSVD could serve as a pivotal entry point for understanding the neural mechanisms underlying executive dysfunction in parents who have lost their only child.MethodsWe utilized resting-state fMRI in a cross-sectional design, comparing 39 individuals with executive dysfunction with 115 matched trauma-exposed controls without executive dysfunction. We quantified spontaneous neural activity via fractional amplitude of low-frequency fluctuations (fALFF), regional homogeneity (ReHo), and ALFF, while CSVD burden was assessed. Moderation analysis was used to identify the moderating role of CSVD on executive dysfunction-related neural alterations in adults who lost their only child.ResultsIndividuals with executive dysfunction exhibited decreased fALFF in the left superior frontal gyrus (SFG) and reduced ReHo in the right medial SFG, alongside elevated ALFF and fALFF in the superior temporal gyrus (STG). fALFF in the left SFG demonstrated higher diagnostic accuracy for detecting executive dysfunction. Crucially, moderation analysis revealed that higher CSVD burden was associated with a greater reduction in fALFF in the left SFG, among individuals in the executive dysfunction group.ConclusionExecutive dysfunction subjects demonstrated abnormal spontaneous activity in SFG and STG. The moderation analysis suggested that CSVD burden may be associated with a greater reduction in executive dysfunction-related frontal hypoactivity, supporting the construction of a "vascular-neuro-cognitive" triad model.

Background: Alzheimer’s disease (AD) involves progressive cognitive decline and hippocampal dysfunction, while its macroscale hippocampal topographic alterations and underlying mechanisms remain unclear. Methods: This study used examine hippocampal topography in AD through hippocampal correlation (HippoCORR) derived from resting-state functional magnetic resonance imaging (rs-fMRI) in two independent cohorts. Partial least square (PLS) regression was used to identify the association between gene transcription and aberrant HippoCORR in AD. spatial correlation was applied to investigate the links between hippoCORR and neuropathophysiological mapping. Findings: AD showed specific hippocampal topographic alterations strongly associated with cognitive impairment. Gene enrichment highlighted synaptic function, ion channel activity, and calcium ion binding. Altered HippoCORR was related to neurotransmitter systems. Interpretation: These findings reveal that AD-specific hippocampal topographic alterations are linked to cognitive impairment, genetic pathways, and neurotransmitter dysfunction, providing a novel perspective for understanding AD pathogenesis and identifying potential therapeutic targets.

BACKGROUND:Post-traumatic stress disorder (PTSD) is the most common mental disorder following traumatic experiences. Environmental disasters such as super typhoons can severely disrupt daily life and may trigger PTSD in exposed individuals. White matter alterations have been observed in patients with PTSD. Fixel-based analysis (FBA), a recently developed diffusion MRI technique, allows detailed assessment of white matter microstructure. This study aimed to evaluate the potential of FBA as an imaging biomarker in typhoon survivors, reducing the subjective bias associated with clinical symptom scales. METHODS:Whole-brain diffusion MRI data from the PTSD group (n = 27), trauma-exposed controls (TEC, n = 33), and healthy controls (HC, n = 30) were analyzed to identify white matter fiber tracts showing abnormalities in FBA metrics, including fiber density (FD), fiber cross-section (FC), and fiber density-cross section (FDC). The study then examined whether these FBA-derived features, when combined with machine learning, could improve the identification of potential PTSD biomarkers. RESULTS:Compared with the HC group, patients with PTSD showed increased fiber density (FD) in the right frontopontine tract and right middle longitudinal fascicle, as well as higher fiber density-cross section (FDC) values in the bilateral frontopontine tract and left thalamo-premotor tract (Bonferroni correction, p < 0.05/18 = 0.003). To differentiate PTSD from TEC, binary and multiclass machine learning models with five-fold cross-validation were developed. The binary model (PTSD vs. TEC) achieved high performance (accuracy = 0.89, sensitivity = 0.97, specificity = 0.71, precision = 0.87, AUC = 0.95), whereas the multiclass model (PTSD vs. TEC vs. HC) demonstrated excellent results (macro-averaged precision = 0.99, recall = 0.99, F1-score = 0.99). The top 20 contributing features of the optimal model were analyzed using Shapley additive explanation (SHAP) values to illustrate model interpretability. CONCLUSION:Most typhoon-exposed individuals with PTSD may exhibit structural alterations in brain white matter. By combining fixel-based analysis (FBA) with machine learning, this study identified diffusion markers within specific white matter tracts and demonstrated their potential diagnostic value for distinguishing PTSD from trauma-exposed controls. These findings enhance our understanding of microstructural white matter changes and their spatial distribution in PTSD and also suggest potential imaging biomarkers for its diagnosis.

Objective To evaluate whether high-strength deep learning image reconstruction (DLIR-H) combined with second-generation whole-heart motion correction (SnapShot Freeze 2, SSF2) improves coronary CT angiography (CCTA) image quality in patients with atrial fibrillation, compared with adaptive statistical iterative reconstruction-V 50% (ASiR-V 50%) combined with SSF2 or first-generation motion correction (SSF1). Materials and Methods Thirty patients with atrial fibrillation who underwent CCTA between October 2024 and April 2025 were retrospectively included. Images were reconstructed at diastole (75% R–R interval) and systole (45% R–R interval) with three protocols: Group A (DLIR-H with SSF2), Group B (ASiR-V 50% with SSF2), and Group C (ASiR-V 50% with SSF1), yielding six datasets (A1–C2; A1–C1 diastole, A2–C2 systole). CT attenuation and standard deviation (SD) were measured at the aortic root and coronary segments; signal-to-noise ratio (SNR), contrast-to-noise ratio (CNR), and motion artifact index (AI) were calculated. Two blinded radiologists rated image quality on a 5-point scale, and interobserver agreement was evaluated with weighted kappa. Results CT attenuation was generally comparable among groups, with a few segment- and phase-specific differences without a consistent directional pattern. Group A demonstrated lower SD and higher SNR/CNR overall than Groups B and C (all P < 0.001). At diastolic proximal right coronary artery (RCA), SD was 11.16 ± 4.91 HU in A1 versus 17.34 ± 8.02 HU in B1 and 25.27 ± 10.89 HU in C1. AI was lower in Group A in both phases (P < 0.001); at systolic mid RCA, AI was 16.30 ± 10.26 in A2 versus 18.27 ± 11.56 in B2 and 23.15 ± 15.46 in C2. Subjective scores were higher in Group A (P < 0.001), with medians of 3 (2.75–4) for A1 and 4 (4–4) for A2; agreement was highest for A1 (weighted kappa = 0.915). Conclusion In atrial fibrillation CCTA, DLIR-H with SSF2 may reduce noise, increase SNR/CNR, and mitigate motion artifacts in both diastole and systole compared with ASiR-V50% with SSF2 or SSF1, potentially improving overall image quality.

BACKGROUND:Glymphatic system dysfunction has been increasingly implicated in Alzheimer's disease (AD), yet its relationships with cerebral small vessel disease (CSVD), plasma biomarkers, and cognitive impairment across the AD remain incompletely understood. METHODS:We prospectively recruited 216 participants from Hainan General Hospital, including healthy controls (HC), individuals with subjective cognitive decline (SCD), mild cognitive impairment (MCI), and AD dementia. All participants underwent brain magnetic resonance imaging, plasma biomarker testing, and neuropsychological assessments. White matter hyperintensity (WMH) volume from T2-weighted fluid-attenuated inversion recovery images served as a marker of CSVD. The diffusion tensor image analysis along the perivascular space (DTI-ALPS) index assessed glymphatic function. Plasma amyloid β-protein (Aβ) concentrations measured peripheral Aβ levels as a surrogate indicator of amyloid pathology. RESULTS:The ALPS index was significantly lower in AD patients compared with HC, SCD, and MCI groups (all P < 0.01) and tended to be lower in the MCI group relative to SCD. After controlling for demographics and APOE4 status, ALPS positively correlated with the plasma Aβ42/Aβ40 ratio (r = 0.16, P = 0.038). ALPS index showed significant negative correlations with log-transformed juxtaventricular and juxtacortical WMH volumes (r = -0.32, P < 0.001; r = -0.19, P = 0.010), with marginal correlation for periventricular WMH (r = -0.13, P = 0.052). CONCLUSION:Plasma Aβ levels and regional WMH burden are associated with glymphatic dysfunction as indicated by reduced ALPS. Impaired glymphatic clearance also correlates with cognitive impairment, providing theoretical support for novel pathophysiological hypotheses and potential therapeutic targets in AD pathogenesis.

OBJECTIVES:To evaluate whether empirical calibration of P values using negative controls can effectively control type I and type II errors under unmeasured confounding bias in both simulated and real-world observational settings. STUDY DESIGN AND SETTING:A simulation study was conducted under five settings reflecting different degrees of adherence to the U-comparability assumption-that is, the extent to which negative controls share the same unmeasured confounding structure as the exposure of interest. These included three primary scenarios (ideal, realistic, and violation of U-comparability) and two mixed scenarios reflecting partial violations. We varied sample size, the direction and strength of unmeasured confounding bias, and the number of negative controls. Based on UK Biobank data, the method was also applied to evaluate the association between hypertension and peripheral artery disease (PAD) in individuals with type 2 diabetes mellitus. RESULTS:Standard logistic regression showed inflated type I error rates across almost all settings, peaking at 44.2% under realistic U-comparability with a sample size of 20,000. In contrast, empirical calibration generally controlled type I error close to the nominal 5% level and reduced bias by 80%-100% under both ideal and realistic U-comparability. Type I error control improved with more negative controls, while type II error control was influenced by whether the unmeasured confounding bias acted in the same or opposite direction as the true exposure-outcome effect. In the UK Biobank case study, 4 of 15 negative controls showed P < .05 after adjustment for measured confounders, indicating residual unmeasured confounding. After empirical calibration with 5, 10, or 15 negative controls, the association between hypertension and PAD remained statistically significant (calibrated P ≈ .004-.006). CONCLUSION:Empirical calibration of P values can mitigate residual unmeasured confounding and reduce type I error inflation in observational studies. Its performance depends on the validity and number of negative controls. PLAIN LANGUAGE SUMMARY:When researchers use large health databases to study whether a treatment or risk factor causes a disease, results can sometimes be misleading. This can happen because of unmeasured confounding-hidden factors that influence both the exposure and the outcome-leading to "false alarms," or false positive findings. We evaluated a statistical method called empirical calibration of P values, designed to correct this bias. The method uses negative controls (exposures known not to have a causal effect on the outcome) to estimate the amount of bias in the data and then adjust the P value for the exposure of interest. Our simulation study showed that this approach effectively reduced the false alarm rate to the expected 5% level, but only under certain conditions. Its success depended on selecting appropriate negative controls that shared the same bias structure as the exposure of interest and on using a sufficient number of controls to ensure stable results. The method failed when the negative controls were poorly chosen or unrelated. When applied to real-world data from the UK Biobank, it successfully corrected for unmeasured bias while still confirming the true, significant link between high blood pressure and PAD. These findings suggest that empirical calibration of P values can make observational research more reliable, provided that enough well-chosen negative controls are available.

Background SMARCA4-deficient non-small cell lung cancer (SMARCA4-dNSCLC) is a rare primary malignant epithelial tumour of the lungs. This study aimed to characterize the CT imaging findings and clinical features of SMARCA4-dNSCLC in a descriptive case series Methods The CT findings and clinical data from 32 patients with histologically confirmed SMARCA4-dNSCLC treated at Hainan General Hospital from September 2022 to August 2024 were retrospectively analysed. CT findings included location, size, density, presence of the lobulated and speculated sign, internal features, surrounding conditions, and pleural and/or pericardial effusion. Results Among the 32 patients, the age ranged from 33 to 84 years (median 63 years, average 63.6 ± 11 years); 28 were male (87.5%), and 4 were females (12.5%); Thirty-one patients presented with pulmonary nodules and masses (96.9%), among whom 25(78.1%) had peripherally. The tumour size ranged from 9 to 123 mm (median 34 mm, average 38.8 ± 23.4 mm). The tumour composition was solid in 28 patients (90.3%). Twenty-nine patients had the lobulated sign (93.5%), 18 had the spicule sign (58.1%), 15 had necrosis (48.4%) and 24 had the pleural contact sign (77.4%). Sixteen patients had mediastinal and hilar lymph node metastases (50%), and 21 had distant metastases (65.6%). Conclusion SMARCA4-dNSCLC was predominantly observed in elderly men with a heavy smoking history. On CT, the tumors commonly presented as large solid peripheral masses, usually found in the upper lobes of both lungs, with internal necrosis, lobulation, spiculation, and pleural contact. The tumours exhibit rapid growth (mean volume doubling time 55.8 days), with metastasis being common at presentation.

Understanding how trauma reshapes the brain's large-scale functional architecture requires a framework that integrates both spatial and temporal dimensions. In this study, we applied a multimodal analytic framework that combines functional connectivity gradient mapping with energy landscape analysis. This approach allowed us to characterize cortical reconfiguration in trauma-exposed controls (TEC) and individuals with posttraumatic stress disorder (PTSD). Gradient-based analyses revealed widespread spatial displacements along the first two functional dimensions - particularly within the visual, salience and default mode networks - for both TEC and PTSD participants. PTSD participants further displayed amplified disruptions along a higher-order cognitive gradient, indicating a more pronounced breakdown of large-scale functional organization. We also introduced a novel gradient-based distance metric to more precisely quantify topological alterations within the embedding space. State-based modeling of these network dynamics revealed that trauma exposure reshapes the brain's coactivation landscape. While both trauma-exposed controls and PTSD participants deviated from normative dynamic profiles, individuals with PTSD exhibited less frequent but more stable anomalous states. This stability may be indicative of a shift away from flexible adaptation towards pathological consolidation. Our findings delineate how trauma reorganizes brain function across complementary facets of spatial embedding and dynamic coordination, highlighting the utility of integrative frameworks for advancing system-level models of trauma-related disorders. Thus, this work offers a conceptual foothold for future investigations into the functional architecture of PTSD.

BACKGROUND:As an extension of diffusion tensor imaging (DTI), diffusion kurtosis imaging (DKI) quantifies non-Gaussian water diffusion and has been applied to explore brain disorders. However, the genetic architecture of brain DKI phenotypes remains unknown. METHODS:Here, we estimated heritability and conducted genome-wide association studies (GWASs) for 804 DKI phenotypes across 188 brain structures in 4183 participants. To determine whether DKI-GWASs provides genetic insights beyond DTI-GWASs, we compared results from 804 DKI-GWASs and 752 DTI-GWASs in the same cohort. To clarify the biological significance of DKI phenotypes, we examined associations between DKI phenotypes and brain health-related outcomes within the CHIMGEN, and explored associations between polygenic risk scores (PRSs) of DKI phenotypes and mental disorders in the UK Biobank. FINDINGS:Of 804 DKI phenotypes, 275 showed significant heritability (P < 0.05; h2 range: 0.143-0.602). We detected 280 significant associations (P < 5 × 10-8), with 38 surviving Bonferroni correction (P < 1.54 × 10-10). These associations were unevenly distributed across chromosomes, DKI phenotype subgroups, and brain structures. Among 229 independent variant-structure associations for DKI, 175 (76.4%) were DKI-specific. We observed 930 associations between DKI phenotypes and brain health-related outcomes (P < 0.05; ten Bonferroni-significant with P < 1.02 × 10-5), and 200 between PRSs and mental disorders (P < 0.05; one Bonferroni-significant with P < 9.61 × 10-5). INTERPRETATION:This study delineates the genetic architecture of brain DKI phenotypes, identifies complementary genetic insights into brain microstructure, and provides biologically relevant endophenotypes for investigating neural mechanisms underlying brain disorders. FUNDING:National Natural Science Foundation of China, National Key Research and Development Program of China, Tianjin Key Medical Discipline Construction Project, and Tianjin Natural Science Foundation.

Predictive models in healthcare are widely published, yet few achieve routine clinical use due to gaps in methodological rigor, workflow integration, and governance. Existing guidelines primarily focus on clinical settings, with few addressing broader healthcare delivery contexts. We propose a practical framework for translating code to continuous care: Development, Implementation, And MONitoring for Dependable AI prediction model (DIAMOND). Models must be built for explicit clinical use cases, supported by interoperable data, standardized predictors, and rigorous validation with prospective designs. Translation into practice requires workflow integration, proportionate regulatory oversight of intended use, transparency, uncertainty, bias, and accountability, and continued post-deployment evaluation—testing transportability across settings, monitoring and updating for data shift and performance degradation, and assessing health-economic impact to inform iterative refinement. By systematically linking these stages, the DIAMOND framework provides a structured pathway for advancing AI predictive models from promising algorithms to dependable clinical tools.