Balancing experimental control with ecological validity remains a central challenge for studying brain function. Here, we developed the Tower Foraging Park (TFP), a self-paced behavioral paradigm that emulates patch foraging and in which mice collect rewards by performing directional quarter-turns around square towers (exploit) and switching between them as they become depleted (explore). Mice rapidly learned the rewarded turning direction, increased movement speed, reduced trajectory variability, and typically abandoned towers before depletion. Reversal of the rewarded direction triggered rapid adaptation, accompanied by a dissociation between movement variability and speed. Repeated reversals progressively improved flexibility. Increasing the difficulty of locating rewarding towers prolonged exploitation, revealing adaptive regulation of patch-leaving decisions. Finally, mice trained under flexible contingencies ultimately outperformed those trained under stable contingencies in a challenging context. Altogether, the TFP reveals mechanisms underlying flexible foraging and provides a versatile, ecologically grounded platform for investigating the neural bases of adaptive behavior.
An erratum was issued for: Fluorescence-Guided Laparoscopic Regional Anatomical Subsegmental Liver Resection Combined with Cholecystectomy through the Laennec Approach. The Affiliation section was updated from: Zhiheng Zhang1 Tong Mu2 Baobing Hao1 Yongxiang Yi3 Decai Yu1 Wei Hu1 1Department of General Surgery, Nanjing University 2Department of Hepatobiliary Surgery, Nanjing University of Chinese Medicine 3Department of Hepatobiliary Surgery, Nanjing University of Chinese Medicine to: Zhiheng Zhang1 Tong Mu2 Baobing Hao1 Yongxiang Yi2 Decai Yu1 Wei Hu1 1Division of Hepatobiliary and Transplantation Surgery, Department of General Surgery, Nanjing Drum Tower Hospital, Affiliated Hospital of Medical School, Nanjing University 2Department of Hepatobiliary Surgery, Nanjing Drum Tower Hospital, Nanjing University of Chinese Medicine.
Rising global temperatures have emerged as a critical concern in recent decades and are recognized as one of the biggest threats to human health. In Africa, low-income communities face disproportionate exposure and vulnerability to extreme temperatures due to poorly planned housing structures and limited adaptive capacity. As passive heat adaptation interventions gain traction across the continent, this review evaluates their technical effectiveness and community feasibility within low-income African communities. Using the Joanna Briggs Institute feasibility, appropriateness, meaningfulness, and effectiveness framework, the review examines how building modification interventions have been developed, tested, and implemented, their capacity to improve indoor thermal comfort, and the socio-technical factors influencing their uptake and scalability. The findings indicate that greening systems, house insulation, screened windows, reflective surfaces and window opening have the most potential for widespread and sustainable implementation in low-income communities. In contrast, interventions such as solar chimneys, metal roofs, wind towers, nozzle air funnels, closed eaves, open eaves, thatched roofs, bottle houses, earthbag houses and passive solar houses demonstrate higher context-specific and structural limitations despite their technical effectiveness. The review identifies critical gaps in long-term performance, scalability and community acceptability of technically effective passive heat adaptation interventions, especially in low-income communities. Overall, the study provides an analytical foundation for guiding the selection of effective, contextually appropriate passive heat adaptation interventions and underscores the need for participatory and inclusive implementation approaches to enhance heat resilience in vulnerable African communities.
Type 2 diabetes mellitus (T2DM) progresses through prediabetes (PD), which is characterized by insulin resistance and β-cell dysfunction. More than 50% of T2DM cases remain undiagnosed, underscoring the need for specific molecular markers beyond standard diagnostic tests. This study aimed to compare sociodemographic, biochemical, and molecular profiles among groups to improve risk stratification. This cross-sectional study recruited 90 participants, including healthy controls (HC), individuals with PD, and patients with T2DM (n = 30 each), at Smart Health Tower, Sulaymaniyah, Iraq, from January to June 2025. Approximately 6.0 mL of blood was collected from each participant and analyzed for glycated hemoglobin (HbA1C), random blood glucose (RBG), C-peptide, lipid profile, urea, and creatinine, as well as molecular markers, including micro-ribonucleic acids (miRNAs) such as miRNA-126 and miRNA-132. Variables were subsequently compared among the groups. Glycemic markers differed markedly among groups, with HbA1C and RBG highest in T2DM (P < 0.050). In PD, C-peptide (3.04 ± 1.33 ng/mL, P < 0.010) and high-density lipoprotein (HDL) (48.2 ± 12.3 mg/dL, P = 0.028) were highest. Renal markers showed the lowest creatinine level in T2DM (0.72 ± 0.18 mg/dL, P < 0.010), whereas urea levels were comparable among groups (P > 0.05). In patients with T2DM, correlations were observed between RBG and HbA1C (r = 0.792, P < 0.001), C-peptide and triglycerides (r = 0.598, P < 0.001), and HbA1C and creatinine (r = -0.452, P = 0.012). In the PD group, RBG correlated with C-peptide (r = 0.387, P = 0.035). In contrast, miRNA-132 expression was lowest in HC (0.90 ± 0.63; 95% CI, 0.67 - 1.14), significantly increased in PD (2.50 ± 1.86; 95% CI, 1.74 - 3.26), and highest in T2DM (3.56 ± 2.04; 95% CI, 2.80 - 4.32), with highly significant differences between HC and PD and between HC and T2DM (P < 0.001), as well as between PD and T2DM (P = 0.045). Additionally, miRNA-126 expression was lowest in HC (1.22 ± 0.52; 95% CI, 1.03 - 1.42), moderately elevated in T2DM (1.29 ± 0.85; 95% CI, 0.97 - 1.61), and highest in PD (1.70 ± 0.74; 95% CI, 1.41 - 1.98), with significant differences between PD and HC (P = 0.021) and between PD and T2DM (P = 0.029), while no significant difference was observed between HC and T2DM (P = 0.252). The identified interconnections among glycemic, lipid, and renal indicators underscore the importance of early identification, comprehensive biochemical evaluation, and timely care during the PD phase to prevent progression to overt DM and its associated complications.
To identify independent risk factors for postcontrast acute kidney injury (PC-AKI) in patients with postoperative AKI (PO-AKI) following acute Stanford type A aortic dissection (ATAAD), and to develop a clinically applicable prediction model. This retrospective cohort study enrolled 604 PO-AKI patients (2014-2024, Nanjing Drum Tower Hospital) who underwent ≥1 postoperative contrast-enhanced CTA. PC-AKI was diagnosed per 2018 ESUR guidelines (sCr elevation ≥26.5 μmol/L or ≥1.5 times baseline within 48-72 h, with baseline defined as the most recent pre-CTA sCr). Three variable-selection strategies (backward stepwise AIC, LASSO and XGBoost-SHAP) were used. A multivariable logistic regression model was constructed, internally validated by bootstrap resampling (1,000 repetitions), and evaluated via AUC, calibration curves, Brier score, decision curve analysis, and clinical impact curve. PC-AKI incidence was 9.8% (59/604), with striking recovery-dependent stratification: 3.5% in fully recovered PO-AKI vs. 52.5% in unrecovered PO-AKI. Independent predictors included PO-AKI stage 3 (OR = 3.144, 95% CI: 1.41-7.06) and unrecovered PO-AKI before first CTA (OR = 25.212, 95% CI: 12.57-53.49). The model exhibited good discrimination (AUC=0.848, 95% CI: 0.78-0.91) and calibration (Brier=0.057). PC-AKI was independently associated with prolonged ICU stay (RR = 1.521, 95% CI: 1.19-1.97) and incomplete renal recovery at discharge (OR = 2.554, 95% CI: 1.30-4.86), but not with 30-day mortality (P = 0.606). Dynamic PO-AKI recovery and advanced AKI stage are strongly associated with PC-AKI risk in post-ATAAD patients. The internally validated model may aid individualized risk stratification before contrast procedures in this high-risk subgroup. External validation is needed before clinical deployment.
Spatial transcriptomics (ST) data analysis and visualization face several challenges due to low sampling, diversity of tissue morphology and high drop-out inherent to the technique. New analysis methods are needed to overcome these challenges and promote continued biological discoveries. To overcome these constraints, we herein describe SpatialFlux, an R package developed to perform reference-based distance gradient analysis. SpatialFlux allows the identification and comprehensive visualization, in either an unbiased or biased manner, of differentially expressed genes and pathways across multiple ST tissues sections and along various axes, thus overcoming many inherent ST limitations and supporting continued biological discovery. SpatialFlux package source code and vignette are freely available on Github (https://github.com/towerlab/SpatialFlux) and Zenodo (https://zenodo.org/records/21039284). Supplementary data are available at Bioinformatics online.
Despite advances in molecular diagnostics, sputum culture remains fundamental for the microbiological diagnosis of lower respiratory tract infections. Current guidelines recommend rejecting unacceptable sputum specimens, yet their actual diagnostic yield, pathogen distribution, and impact on antimicrobial resistance assessment have not been systematically evaluated using large-scale data. A retrospective analysis was performed on 143,101 sputum culture and quality assessment records collected at Nanjing Drum Tower Hospital between 2015 and 2025. Specimens were classified as acceptable or unacceptable according to the Murray-Washington criteria, with the same period bronchoalveolar lavage fluid cultures included as comparator group. The overall culture-positivity rate was significantly higher in acceptable than in unacceptable specimens (57.28% vs. 50.87%; Odds Ratio = 1.29, 95% CI: 1.27-1.32, P < 0.001). Dominant organisms, including Klebsiella pneumoniae and Acinetobacter baumannii, were detected at higher rates in acceptable specimens, whereas yeast-like fungi were significantly enriched in unacceptable sputum, indicating oropharyngeal contamination. Filamentous fungi (e.g., Aspergillus spp.) showed a stepwise decreasing trend in BALF > acceptable sputum > unacceptable sputum. Antimicrobial resistance rates of K. pneumoniae, Pseudomonas aeruginosa, and Staphylococcus aureus were significantly higher in sputum isolates than in BALF isolates, suggesting that sputum cultures may overestimate resistance risk. The large-scale findings support cautious, contextual interpretation of unacceptable sputum results, particularly when better specimens cannot be obtained. This approach helps preserve critical diagnostic information and avoid unnecessary antimicrobial escalation.
Pulmonary alveolar proteinosis (PAP) is a rare pulmonary syndrome characterized by impaired surfactant clearance, driven by dysfunctional cholesterol efflux in alveolar macrophages (AMs). However, the molecular determinants governing AM cholesterol homeostasis remain incompletely defined. Here, through a genome-wide CRISPR screen in foamy macrophages and bulk RNA sequencing of AMs from PAP patients, we identify DTX4 as a pivotal regulator of cholesterol efflux in AMs. In mice, AAV-mediated silencing of DTX4 led to excessive AM lipid accumulation, exacerbated proteinosis, increased lung opacities, and deteriorated pulmonary function. Similarly, DTX4 depletion in primary AMs impaired cholesterol efflux and promoted intracellular lipid deposition. Conversely, AM-specific overexpression of DTX4 in the Csf2ra-/- PAP model markedly alleviated lipid accumulation, mitigated alveolar proteinosis, restored lung densities, and rescued pulmonary function. Mechanistically, DTX4 stabilizes the GM-CSF receptor via an E3-independent interaction to sustain JAK2/STAT5 signaling, which reciprocally maintains DTX4 transcription. This positive-feedback loop drives PPARγ expression, and its disruption in PAP impairs cholesterol efflux, a defect partially reversible by ectopic PPARγ expression. Collectively, our findings identify DTX4 as a central orchestrator of AM cholesterol efflux and surfactant homeostasis, positioning it as a promising therapeutic target for PAP.
Atopic dermatitis (AD) is a chronic inflammatory skin disorder with complex pathogenesis, and current therapies face limitations in efficacy and safety. Huangqin (Scutellaria baicalensis) exhibits anti-inflammatory properties, yet its multi-target mechanisms against AD remain unclear. A systems pharmacology approach integrating multi-omics profiling was utilized to decode Huangqin anti-AD mechanisms. First, bioactive compounds and their potential targets were systematically identified, followed by constructing compound-target networks and enriching key pathways. Then, machine learning algorithms (Support Vector Machine/Recursive Feature/Least Absolute Shrinkage and Selection Operator) were applied to prioritize hub targets from network-derived candidates. Finally, molecular docking was conducted to validate ligand-receptor binding affinity. Twenty-nine bioactive compounds were identified, interacting with 55 AD-related targets. AKT1 emerged as the most central hub in the protein-protein interaction network. Gene Ontology and Kyoto Encyclopedia of Genes and Genomes enrichment analysis revealed Huangqin potential roles in modulating bacterial infection responses and regulating pathways such as IL-17, TNF, HIF-1α, and PI3K-AKT signaling. Machine learning algorithms were applied to prioritize key genes, which highlighted AKT1, solute carrier family 6 member 4, and chemokine ligand 2 as core targets, with molecular docking confirming strong binding between wogonin, baicalein, beta-sitosterol, and these targets. These findings suggest that Huangqin exerts multi-target effects on AD, centered on AKT1-mediated signaling crosstalk, to regulate inflammatory and immune pathways. This mechanistic insight establishes a foundation for clinical translation and AKT1-focused drug development.
Mallory-Weiss tear (MWT) is a mucosal laceration at the esophagogastric junction. This case report aims to present a rare case of MWT associated with small bowel obstruction (SBO). A 74-year-old female presented with hematemesis and abdominal pain. Esophagogastroduodenoscopy (EGD) showed blood in the esophagus and stomach with a tear in the cardia. Computed tomography scan of the abdomen demonstrated multiple dilated small bowel loops with a distal ileal transition point in the pelvis. The patient underwent a midline laparotomy. Seven cases of MWT and SBO were identified. The most common presentation was hematemesis, seen in six patients (85.71%). Abdominal pain was reported in one case (14.28%). Two patients were treated with medication (28.57%), and five underwent surgery (71.42%). This study highlights the importance of recognizing MWT not merely as an isolated cause of upper gastrointestinal bleeding, but potentially as a secondary manifestation of increased intra-abdominal pressure.
Cell- or bacteria-derived membrane vesicles (MVs) serve as platforms for delivering antitumor small-molecule drugs, characterized by targeted delivery, immunostimulatory activity, and ease of engineering. Despite extensive innovative research, clinical translation remains limited. Here, we report a clinically data- and AI- supported engineered fusion vesicle delivery system, ECMVRGD. The design was supported and guided by a systematic meta-analysis identifying clinically safe and potentially effective RGD peptides and AI molecular dynamics simulations to determine optimal insertion sites and copy numbers (one RGD [RGD4C] each at Loop 2 and Loop 3 of OmpA) on a previously validated safe and effective engineered membrane vesicle (EMV) platform, which was further fused with cancer cell-derived membrane vesicles (CMVs). This engineering strategy not only places translational potential at the forefront from the outset but also when loaded with doxorubicin (DOX), demonstrates comprehensive improvements over CMV in both tumor-targeted delivery and antitumor immune activation in vitro and in vivo. Mechanistic studies further revealed that ECMVRGD@DOX achieved complete recurrence-free antitumor immunochemotherapeutic efficacy (0/6 recurrence) by synergistically promoting dendritic cell (DC) activation and CD8+ T cell infiltration. This engineered fusion vesicle platform provides a novel strategy to advance the design of MVs-based delivery systems with improved translational potential.
Synergy therapy of Chinese herbs is an effective strategy for wound repair and its further development focuses on the chronological release of specific herbs with healing proceeding. Herein, we report a novel asiatic acid/baicalein nanocarrier integrated microcapsule with spatiotemporal release feature from microfluidic electrospray for wound healing. Benefiting from advantages of both nanocarrier formulation and integration capacity of microfluidic electrospray, synthesized baicalein-tannic acid nanoparticles are integrated into the shell of microcapsule while fabricated asiatic acid liposomes are located at the core region. This design enables chronological release: baicalein is first released to kill bacteria, followed by releasing asiatic acid liposomes to enhance cell migration and granulation tissue formation. In vitro tests confirm the excellent biocompatibility, antibacterial and pro-migration property of microcapsules. The results from in vivo wound healing studies showed the outcomes of reduced inflammation and accelerated wound closure in microcapsules-treated wounds. Therefore, it is believed that this herbal microcapsule with spatiotemporal and hierarchical release of baicalein and asiatic acid is an effective therapeutic platform for clinical wound treatment.
Influenza A-associated invasive pulmonary aspergillosis (IAPA) is a severe fungal complication with high mortality, while early identification remains difficult because of nonspecific clinical manifestations. This study aimed to develop and validate a machine learning (ML) model for early screening of IAPA in hospitalized influenza A patients. This retrospective single-center study enrolled 234 hospitalized influenza A patients from January 2023 to December 2025, including 59 patients with IPA. Eligible patients were randomly divided into a training cohort (70%) for model development and a validation cohort (30%) for internal validation. Univariate and multivariate logistic regression analyses were performed to identify independent predictors of IAPA. Five machine learning algorithms, including Logistic Regression (LR), Random Forest (RF), Support Vector Machine (SVM), XGBoost, and LightGBM, were constructed and compared to identify the most clinically applicable model for early screening. Multivariate logistic regression identified seven independent predictors of IAPA, including smoking history, autoimmune disease, fibrinogen level, lymphocyte count, hemoglobin level, cumulative systemic corticosteroid dose, and corticosteroid treatment course of 8-28 days. Among the evaluated algorithms, LightGBM demonstrated the highest sensitivity (0.76) in the validation cohort and was considered the most suitable model for early screening. The LightGBM model achieved an AUC of 0.800 (95% CI, 0.714-0.886), with a specificity of 0.69 and an accuracy of 0.68. LightGBM serves as a robust early-warning tool for identifying influenza A patients at high risk of IAPA. Utilizing routinely available clinical data, this model facilitates bedside risk stratification and early diagnostic intervention.
Milk is an essential nutritional source for human health, yet developing efficient and sustainable indoor dairy production systems to produce milk-associated components remains a major challenge. Here, we propose a biomimetic mammary gland-on-a-chip system that recapitulates both the structural features and lactation mechanism of the mammary gland for producing milk-associated bioactive components. The chip is designed with a medium channel providing circulation, an intermediate porous hydrogel layer for substance diffusion, and highly specialized microchambers for culturing mammary epithelial cells. The mammary gland-on-a-chip enables tight-junction formation and active secretion of milk bioactive components such as lactotransferrin and triglycerides. More importantly, by expanding the microchambers and parallelizing the chips, increased collection of partial milklike secretions containing selected bioactive components was achieved. Our work represents the successful development of a robust microphysiological system as a potential in vitro platform for mammary gland research and is promising in addressing future food and environmental crises.
Background: Shift work disorder and insufficient sleep are prevalent among nurses, leading to fatigue, reduced well-being, and potential safety concerns. Increasing use of wearable sleep-tracking devices presents an opportunity to evaluate nurses' sleep quality objectively. Objective: The primary objective was to evaluate the feasibility of wearable-based sleep monitoring and to obtain preliminary evidence of agreement with validated actigraphy among nurses. The secondary objective was to describe nurses' sleep characteristics and examine exploratory associations between sociodemographic characteristics, shift patterns, sleep hygiene, and sleep parameters. Methods: A two-phase feasibility observational cohort study was conducted in a tertiary hospital in Singapore. In Phase 1, five nurses concurrently wore a consumer-grade wrist-worn wearable Apple Watch Series 10 and a validated actigraph (GENEActiv®) for two weeks. Preliminary agreement between Apple Watch and GENEActiv® was examined using intraclass correlation coefficients (ICC). Feasibility was determined via wear compliance and completeness of data. In Phase 2, 50 nurses working rotating or single shifts completed demographic and work-related questionnaires and Sleep Hygiene Index. Multiple linear regression analyses were performed to examine exploratory associations between selected covariates and sleep parameters, with adjustment for age, sex, Body Mass Index (BMI), parental status, workplace, total length of service, and sleep hygiene. Results: Apple Watch showed preliminary evidence of agreement with GENEActiv® for total sleep time, in-bed wake time, and sleep efficiency (ICC= 0.95, 0.72, and 0.69, respectively), with high wear compliance and minimal missing data, supporting feasibility for sleep monitoring. Mean total sleep time was 381 ± 55 min, and mean sleep efficiency was 94.8%. Shift nurses reported poorer sleep hygiene than non-shift nurses; however, shift work status was not independently associated with sleep outcomes after adjustment. Higher BMI was associated with shorter total sleep time (B = -3.76 minutes per kg/m², p = 0.01), reduced rapid eye movement sleep (B = -1.18 minutes, p = 0.03), shorter core sleep (B = -2.72 minutes, p = 0.02) and reduced time in bed (B = -4.05 minutes, p < 0.01). Age was negatively associated with deep sleep duration, with older age associated with less deep sleep (B = -1.00 minutes per year, p < 0.01). Conclusion: Apple Watch-based monitoring was feasible and showed preliminary agreement with actigraphy. Nurses obtained less sleep than recommended, and BMI and age were associated with sleep outcomes in exploratory analyses. These findings support larger studies and workplace strategies to improve sleep opportunity and healthy sleep behaviours. Not applicable.
Neonatal pain assessment primarily relies on behavioral rating scales. Although these tools provide objective measures, their scores are susceptible to inter-rater variability, potentially introducing bias. Moreover, intermittent assessments cannot capture pain progression over time. Multimodal large language models (MLLMs) offer a novel approach to addressing these limitations; however, their effectiveness in neonatal pain recognition remains largely unexplored. This study aimed to evaluate the performance of several MLLMs in neonatal pain video classification and investigate their applicability, limitations, and potential for clinical implementation. A previously established, annotation-validated multimodal dataset of acute neonatal pain was used. The dataset comprised 426 video recordings of neonatal heel lance procedures. Three MLLMs with dynamic video analysis capabilities (Qwen3-VL-Plus, Gemini-3-Pro, and ERNIE-4.5-Turbo) were evaluated. Carefully designed prompts guided the models to simulate nurses' pain assessments. Model performance was comprehensively evaluated using accuracy, weighted Cohen's kappa coefficient, precision, recall, and F1 score. Gemini-3-Pro achieved the best classification performance, with an accuracy of 86.9% and a weighted kappa coefficient of 0.695, followed by Qwen3-VL-Plus (83.6%, kappa = 0.624) and ERNIE-4.5-Turbo (78.4%, kappa = 0.563). Gemini-3-Pro demonstrated an excellent recall of 0.974 and an F1 score of 0.905 for the pain category. However, all models showed relatively lower recall for the no-pain category (0.664-0.868), suggesting a trade-off between pain and no-pain classification and indicating challenges in achieving both high sensitivity and specificity. MLLMs demonstrate considerable potential for neonatal pain assessment, with particularly strong performance in pain screening. Their structured reasoning and interpretable outputs may support clinical decision-making. However, the observed performance imbalance between pain and no-pain classification suggests that these models are not yet capable of independently replacing clinical judgment and are better suited as assistive tools to optimize clinical workflows.
Chronic diarrhea is a common clinical sign in cats, caused by various underlying conditions. Chronic diarrhea can be classified as either small or large bowel diarrhea based on clinical signs. The mechanisms involved are complex, including osmotic and secretory diarrhea, impaired intestinal barrier function, and dysregulated intestinal motility. Given the complexity of the causes of chronic diarrhea, accurate diagnosis of the underlying mechanisms is crucial for effective treatment. Additionally, nutritional management is indispensable for the long-term treatment of chronic diarrhea. This article will review the diseases that may cause chronic diarrhea, introduce their pathophysiology and diagnostic methods, and provide related nutritional management strategies.
Colorectal cancer (CRC) is a prevalent malignancy associated with alterations in the gut microbiota and host metabolic profiles. This cross-sectional study aimed to characterize gut microbiota and serum metabolite differences among healthy controls (HC), patients with non-metastatic colorectal cancer (CRC-nm), and patients with metastatic colorectal cancer (CRC-m). Stool metagenomic sequencing and untargeted serum metabolomics were performed in 107 participants, followed by exploratory differential analyses and internally cross-validated modeling to identify candidate microbial and metabolic features and evaluate their discriminatory performance. Differential analyses identified two CRC-m-enriched species-level features (Enterocloster clostridioformis and Lactobacillus crispatus) and two CRC-m-depleted features (Megamonas rupellensis and Phocaeicola plebeius) across comparisons with both CRC-nm and HC groups. Metabolomic analysis identified eight pathway-mapped metabolites, mainly involved in amino acid-related metabolic pathways. In modeling analyses, metabolite-only models provided the primary discriminatory signal, whereas adding bacterial features did not improve predictive performance. Integrated microbiota-metabolite models showed lower internal performance than metabolite-only models in some comparisons, including CRC-m versus CRC-nm. Overall, these findings suggest that observed discriminatory performance was primarily driven by serum metabolite features rather than additional bacterial features, and highlight candidate microbial and metabolic markers for future validation. Because all CRC-m cases were stage IV and all CRC-nm cases were stages I-III, these results should be interpreted as exploratory cross-sectional group differences that may reflect disease stage, tumor burden, or broader progression-related changes rather than metastasis-specific biology.
Regulatory T (Treg) cells in visceral adipose tissue (VAT) play essential roles in systemic metabolic homeostasis under distinct physiological and pathological conditions. However, the metabolic cues that drive Treg cell subset specialization in the obese VAT niche remain elusive. Here, we demonstrated that palmitic acid instigated chronic VAT inflammation and systemic metabolic disturbance by compromising the immunosuppressive function of the ICOShi Treg subset. Palmitic acid, but not oleic acid, activated Crebzf expression in VAT Treg cells from HFHS diet-induced obese and ob/ob mice. Crebzf deficiency significantly attenuated diet-induced obesity and inflammation by upregulating the suppressive function of VAT ICOShi Treg cells. Moreover, adoptive transfer of Crebzf-deficient ICOShi Treg cells into Rag1-/- mice alleviated HFHS diet-induced inflammation and metabolic disorders more effectively than transfer of Crebzf-sufficient ICOShi Treg cells. Mechanistically, CREBZF interacted with c-JUN to inhibit Foxp3 activity, thereby impairing the stability and inhibitory cytokine production of ICOShi Treg cells. In human subjects, CREBZF levels in VAT Treg cells were elevated and negatively correlated with FOXP3 activity. Collectively, these findings uncover a specific ICOShi Treg subset that responds to palmitic acid, thereby coupling obesogenic signals to VAT remodeling and systemic metabolic homeostasis.
Metabolic dysfunction-associated steatotic liver disease (MASLD) is increasingly linked to cognitive decline, yet the hepatic factors associated with imaging-derived neurovascular coupling (NVC) remain unclear. This study aimed to investigate whether liver stiffness or liver fat content was more closely associated with resting-state CBF-ReHo surrogate measures. A total of 130 participants including 98 MASLD patients and 32 age- and education-matched healthy controls (HCs) underwent clinical assessment, neuropsychological testing, and multi-modal MRI. Liver stiffness and fat content were quantified using MR elastography (MRE) and MRI-proton density fat fraction (PDFF). Imaging-derived NVC was assessed using global cerebral blood flow (CBF)-regional homogeneity (ReHo) coupling and voxel-wise CBF/ReHo ratios, interpreted as resting-state surrogates rather than direct stimulus-evoked NVC. Multivariable regression and exploratory mediation analyses were employed. Compared to HCs and patients with lower liver stiffness (MASLD_low), those with higher liver stiffness (MASLD_high) showed reduced global CBF-ReHo coupling and altered CBF/ReHo ratios, primarily localized to the bilateral superior temporal pole/superior temporal gyrus (TPOsup). In multivariable regression, liver stiffness remained independently associated with TPOsup CBF/ReHo ratios (p < 0.001). Exploratory mediation analysis showed a statistically significant indirect association between hepatocellular injury markers and TPOsup CBF/ReHo ratios involving MRE-derived liver stiffness (95% CI: 0.0795-0.2790). Within this cohort and the observed PDFF range, MRE-derived liver stiffness was more closely associated with imaging-derived NVC surrogate measures than liver fat content in MASLD. These findings are hypothesis-generating and require validation in longitudinal studies.