Ningbo College of Health Sciences

BACKGROUNDPancreatic cancer (PC), a highly malignant gastrointestinal cancer, is generally diagnosed at an advanced stage. However, traditional therapies for advanced PC are limited and often unsuitable for older patients.AIMTo identify clinical predictors in older patients with advanced PC to facilitate individualized treatment.METHODSThis was a retrospective clinical analysis involving 99 patients aged ≥ 65 years with advanced PC who received programmed death-1 (PD-1) inhibitors at Ningbo Medical Center Lihuili Hospital from January 2019 to January 2025. Univariate and multivariate analyses were conducted to identify clinical predictors for survival outcomes, utilizing blood levels and other clinical information.RESULTSThe median progression-free survival (PFS) was 4.6 months [95% confidence interval (CI): 3.700-5.800], and the median overall survival (OS) was 6.5 months (95%CI: 5.700-8.200). Multivariate analysis helped identify meaningful clinical differences in PFS and OS across subgroups, including factors such as Eastern Cooperative Oncology Group performance status, prognostic nutritional index, and triglyceride levels. Univariate analysis showed that factors such as the location of primary PC, carbohydrate antigen 199 levels, systemic immune-inflammation, neutrophil-to-lymphocyte ratio, and the combination therapy comprising PD-1 inhibitors and radiotherapy are of significant clinical relevance to both PFS and OS.CONCLUSIONThe treatment of advanced PC with PD-1 inhibitors presented several potential independent clinical predictive indicators of survival outcomes in older patients. This study highlighted the importance of pre-treatment clinical characteristics and hematological variables for predicting treatment outcomes in older patients with PC.

Background:Short telomere length (TL) has been associated with chronic diseases and reduced lifespan. Vitamin D may help preserve telomeres through its anti-inflammatory effects; however, the relationship between serum 25-hydroxyvitamin D (25(OH)D) levels and TL remains inconclusive. This meta-analysis was conducted to evaluate the association between circulating 25(OH)D and leukocyte TL (LTL).Methods:A comprehensive literature search was performed across PubMed, Scopus, Google Scholar, ClinicalTrials.gov, and Cochrane Library to identify relevant studies published up to February 2025. Standardized β coefficients with 95% confidence intervals were applied as the effect size metric to evaluate the associations using a random effect model.Results:A total of 21 studies comprising 185,191 participants were analyzed. The overall results demonstrated a positive association between serum 25(OH)D levels and LTL (β = 0.04, 95% CI = 0.02–0.06), with remarkable heterogeneity across studies (I²= 89.1%, P ≤.001). This association was supported in adults (β = 0.04, 95% CI = 0.03–0.06), women (β = 0.05, 95% CI = 0.01–0.08), individuals with vitamin D deficiency (β = 0.22, 95% CI = 0.01–0.43), and studies that adjusted for covariates (β = 0.05, 95% CI = 0.01–0.08). No significant associations were found in men, participants with serum 25(OH)D levels ≥ 30 ng/mL, children, or studies without covariate adjustments. The relationships were not influenced by the method of TL assessment, body mass index, smoking status, and sample size.Conclusion:Serum 25(OH)D levels showed a positive correlation with LTL in women, adults, and individuals with vitamin D deficiency.

Dry age-related macular degeneration (dAMD) is the leading cause of blindness among elderly people in developed countries. The main objective of this study is to investigate the causal relationship between gut microbiota (GM), blood metabolites, and dAMD among European participants. Based on the genome-wide association analysis database, double sample Mendelian randomization (MR) analysis was performed on GM, blood metabolites, and dAMD. The inverse-variance weighted method is used to estimate the causal relationship between GM, blood metabolites, and dAMD, while multiple methods are employed to eliminate pleiotropy and heterogeneity. A 2-step MR analysis quantitatively assessed the effect of metabolite-mediated GM on dAMD. In MR analysis, 15 GM were found to be associated with increased or decreased risk of dAMD, and 18 blood metabolites were found to be associated with increased or decreased risk of dAMD. Our research also found that the potential association between GM and dAMD may be mediated by blood metabolite levels, specifically, ADpSGEGDFXAEGGGVR levels accounted for 38.9% of the causal pathway from genus Parasutterella to dAMD. Our research findings indicate that certain GM and blood metabolites can affect the onset of dAMD, and increasing the abundance of genus Parasottella can increase the risk of dAMD through the mediation of ADpSGEGDFXAEGGGVR levels.

With the rapid development and increasing sophistication of multimedia technologies, extensive visual-verbal pre-training has yielded favorable outcomes in subsequent tasks. Current methodologies mostly depend on the assumption that image-text pairings sourced from the Internet represent flawless one-to-one correspondences. The emergence of powerful pre-training models in recent years has facilitated a series of researches on migrating pre-training models to downstream tasks. Inspired by related technologies, this paper takes the CLIP (Contrastive Language-Image Pre-Training) model as the backbone model, and proposes a new method named CORCLIP (Context Optimization and Region of Interest Contrastive Language-Image Pre-Training), which addresses the issue of requiring extensive time for word refinement via context optimization, and introduces an attention mechanism to guide ROI (Region of Interest) for image segmentation task to divide the image into different regions or objects. By selecting regions of interest, processing can be focused on key regions to improve the efficiency of segmenting images. Using comprehensive qualitative experimentation and analysis on Flickr30K and MSCOCO datasets, we show that the suggested strategy is better.

Frailty is common in older adults and significantly increases the risk of multiple medical conditions, yet the temporal progression and interaction of these diseases remain poorly understood. Analyzing disease trajectories alongside comorbidity networks provides a strategy for exploring the temporal patterns and system evolutionary features of diseases. In the current study, we evaluated baseline frailty and systematically identified main disease clusters and death trajectories in a frail population. Data from the UK Biobank, including 18,999 individuals classified as frail and 94,995 matched controls selected after propensity score matching, were evaluated. Diagnoses were reclassified into 385 medical conditions and 14 categories of death causes using PhecodeX. Disease trajectory and comorbidity network analyses were conducted to describe the patterns of disease development in the frail population. Over a median follow-up of 14.9 years, a phenome-wide association study using Cox regression analysis revealed that individuals with frailty had significantly higher risks for 147 medical conditions and 6 causes of death. Modeling disease pairs and trajectories in combination with comorbidity networks revealed three main disease clusters: musculoskeletal/psychiatric disorders, respiratory/multi-system diseases, and cardiovascular/metabolic diseases, with cardiovascular diseases central in mortality outcomes. Individuals with frailty have a greater risk of experiencing various medical conditions and death. These risks often involve multiple interconnected trajectories, highlighting potential targets to prevent further deterioration in health.

This study explores how tourists’ knowledge acquisition, sharing, and application behaviours influence tourism satisfaction in smart tourism environments. Drawing on knowledge management theory and the information system success model, it introduces a dual mediation model involving perceived personalisation and tourist participation. Based on data from 315 valid questionnaires and structural equation modelling (SEM), results showed that knowledge behaviour positively impacted satisfaction both directly and indirectly through enhanced personalisation and participation. This research extends knowledge management theory to digital tourism, highlighting the importance of guiding user knowledge behaviours. It suggests that platforms should leverage intelligent recommendation and co-creation mechanisms to improve personalisation and engagement, offering theoretical insights and practical implications for designing knowledge-driven and value co-creation services in smart tourism.

Hepatocellular carcinoma (HCC) remains a significant global health concern, often diagnosed at advanced stages, limiting the efficacy of surgical interventions. Early and accurate diagnosis is critical for improving surgical outcomes and reducing mortality. Traditional biomarkers, such as alpha-fetoprotein (AFP), des-gamma-carboxyprothrombin (DCP), and the lectin-bound fraction of AFP (AFP-L3), show limited sensitivity and specificity. Advanced diagnostic models, including GALAD, TAGALAD, and GAP_TALAD, offer a promising multi-biomarker approach but lack extensive evaluation in surgical contexts. This retrospective study included a cohort of 267 untreated hepatocellular carcinoma patients and 231 control patients (with hepatitis or cirrhosis). We applied the predefined formulas for the TAGALAD, GAP_TALAD, and other models to the cohort data. The diagnostic performance of each model and individual biomarker for detecting HCC was assessed using receiver operating characteristic (ROC) curve analysis to determine the area under the curve (AUC), sensitivity, and specificity at optimal cut-offs. Additionally, key clinical subgroups, including pathologically confirmed HCC, clinically diagnosed HCC, early-stage HCC (TNM I+II), patients with complete data (no imputation), and hepatitis B virus (HBV)-related disease, were also analyzed. TAGALAD and GAP_TALAD demonstrated superior performance compared to the GALAD model and traditional biomarkers across all patient subgroups. Notably, TAGALAD achieved the highest diagnostic accuracy, with an AUC of 0.880, sensitivity of 0.760, and specificity of 0.861, followed closely by GAP_TALAD (AUC = 0.874). Both models demonstrated excellent performance in early-stage HCC detection (TAGALAD AUC = 0.860, GAP_TALAD AUC = 0.867), highlighting their potential in identifying candidates for surgical resection or transplant at an early curative stage. In HBV-related HCC, TAGALAD (AUC = 0.874) and GAP_TALAD (AUC = 0.857) showed superior diagnostic accuracy compared to GALAD (AUC = 0.731) and single biomarkers (AUC = 0.598-0.799). The TAGALAD and GAP_TALAD models offer a robust and reliable framework that supports early diagnosis of HCC. Their superior accuracy indicates a more reliable foundation for identifying candidates for curative surgical interventions, suggesting the potential to refine patient selection. Future research should focus on multi-center validation and the integration of novel biomarkers to further optimize these models for surgical decision-making and personalized treatment strategies.

This paper addresses the problem of rapid, precise, and efficient identification and diagnosis of grape leaf diseases by proposing the Deep-Broad Learning Network Model (ABLSS), which combines a Broad Learning network model with deep learning techniques. The model is optimized using the Adam algorithm based on BLS, and incorporates the LTM mechanism, which significantly enhances learning efficiency, stability, and recognition accuracy. Additionally, by integrating deep learning network optimization techniques, a SENet attention mechanism is added between the mapping and enhancement layers of BLS. Furthermore, based on the U-Net segmentation model, the method integrates dilated spatial pyramid pooling and feature pyramid networks. Dilated convolutions with varying dilation rates are used to capture multi-scale contextual information, which providing rich semantic information and high-resolution details during the decoding process. This improves the ABLSS model’s ability to identify small disease spots. Experimental results show that the ABLSS model achieves the highest recognition accuracy for three types of diseases with similar features on grape leaves, with an average accuracy improvement of 7.69% over BLS and 4.48% over deep learning networks. The MIOU of the segmentation model reaches 86.61%, which is a 6.48% improvement over the original U-Net model, and the MPA is 90.23%, a 8.09% improvement over the original U-Net. These results demonstrate that the proposed method significantly improves the algorithm’s recognition accuracy for small and irregular complex images. The ABLSS model recognizes images 0.375 seconds faster than the deep learning network, achieving a 72.12% speed improvement, thereby significantly enhancing the recognition efficiency of fine features. The ABLSS model combines the high recognition accuracy of deep learning with the fast processing speed of Broad Learning, while overcoming the limitations of BLS in recognizing complex images. This study provides valuable support for the development of smart orchard technologies and the optimization of learning network models.