The water-sediment interface is an important zone for the migration and transformation of chemical substances,but traditional sampling methods make in situ sampling challenging,thus it is difficult to capture information on environmental processes at the micro-scale.In this study,diffusive gradients in thin films(DGT)probe technique was used as in situ tool to detect 18 kinds of typical per-and polyfluoroalkyl substances(PFAS)in the water-sediment system,elucidating their vertical distribution characteristics and patterns.The results showed that different PFAS exhibited distinct variation patterns with depth in water and sediment.Short-chain PFAS exhibited greater mobility,while the migration of long-chain PFAS was restricted in sediment and pore water due to their strong hydrophobicity and adsorption capacity,resulting in relatively small concentration changes in the sediment column.The PFAS concentrations measured by DGT ranged from 11.2 μg/L to 1305 μg/L.For long-chain PFAS,the concentrations measured by DGT were higher than those obtained from direct measurement of water and pore water,while the opposite results were obtained for short-chain PFAS.The DGT results indicated that although long-chain PFAS exhibited strong adsorption to sediment particles,the adsorbed PFAS were still bioavailable.DGT probe technique,with its advantages of in situ sampling and high spatial resolution,provides deep insights into the complex environmental processes occurring at the microscale in the water-sediment systems.DGT offers important technical support and scientific evidence for assessing the adsorption-desorption behavior,bioavailability,and ecological risks of these emerging pollutants in aquatic environment.

Aim To investigate the effect of chrysoeriol on pulmonary vascular remodeling in pulmonary hyper-tension by animal experiments combined with cell ex-periments,and to explore its potential therapeutic tar-gets by network pharmacology.Methods The target of chrysoeriol was collected in Targetnet,SEA and SwissTargetPrediction database.Pulmonary arterial hy-pertension(PAH)targets were collected in the Dis-GeNET and GeneCards databases,and PPI network map was drawn in the STRING database,and key tar-gets were screened.The GO and KEGG pathway en-richment analysis was carried out through DAVID data-base and Weishengxing platform.AutoDock software was used for molecular docking of key core targets.The PAH model of rats was constructed,and the pulmo-nary hemodynamics and vascular remodeling were de-tected by echocardiography,HE and Masson staining.Primary pulmonary smooth muscle cells were extracted,and the effects of drugs on pathway proteins were de-tected in vitro.Results The results of network phar-macology showed that chrysoeriol exerted therapeutic effects on pulmonary hypertension by affecting key tar-gets such as AKT1,SRC,EGFR,MMP9 and gsk3 β,and signaling pathways such as EGFR and PI3K-AKT.Molecular docking showed that chrysoeriol had good binding ability with 5 key target genes.Animal experi-ments showed that the pulmonary hemodynamic func-tion of PAH rats was significantly improved after ad-ministration of chrysoeriol.The remodeling of small pulmonary arteries was significantly reduced.Cell ex-periments showed that chrysoeriol could inhibit the ex-pression of proliferation,migration and phenotypic transformation genes.Conclusion Chrysoeriol may play a role in the treatment of pulmonary hypertension through multiple targets.

Aim To investigate the effect of chrysoeriol on pulmonary vascular remodeling in pulmonary hyper-tension by animal experiments combined with cell ex-periments,and to explore its potential therapeutic tar-gets by network pharmacology.Methods The target of chrysoeriol was collected in Targetnet,SEA and SwissTargetPrediction database.Pulmonary arterial hy-pertension(PAH)targets were collected in the Dis-GeNET and GeneCards databases,and PPI network map was drawn in the STRING database,and key tar-gets were screened.The GO and KEGG pathway en-richment analysis was carried out through DAVID data-base and Weishengxing platform.AutoDock software was used for molecular docking of key core targets.The PAH model of rats was constructed,and the pulmo-nary hemodynamics and vascular remodeling were de-tected by echocardiography,HE and Masson staining.Primary pulmonary smooth muscle cells were extracted,and the effects of drugs on pathway proteins were de-tected in vitro.Results The results of network phar-macology showed that chrysoeriol exerted therapeutic effects on pulmonary hypertension by affecting key tar-gets such as AKT1,SRC,EGFR,MMP9 and gsk3 β,and signaling pathways such as EGFR and PI3K-AKT.Molecular docking showed that chrysoeriol had good binding ability with 5 key target genes.Animal experi-ments showed that the pulmonary hemodynamic func-tion of PAH rats was significantly improved after ad-ministration of chrysoeriol.The remodeling of small pulmonary arteries was significantly reduced.Cell ex-periments showed that chrysoeriol could inhibit the ex-pression of proliferation,migration and phenotypic transformation genes.Conclusion Chrysoeriol may play a role in the treatment of pulmonary hypertension through multiple targets.

Objective:This study was aimed to explore the preference and heterogeneity in community health management service model selection among older patients with multiple chronic diseases,and to provide scientific evidence for optimizing the model.Methods:A multi-stage stratified and convenience sampling approach was adopted.A discrete choice experiment was conducted with 360 elderly patients with multiple chronic diseases from six regions in Jiangsu Province.The Mixed Logit Model was used to analyze service selection preferences and willingness to pay,while the Latent Class Logit Model was applied to explore heterogeneity among patient groups.Results:Patients showed a stronger preference for a service model featuring"twice-monthly visits,medication guidance+lifestyle counseling,and face-to-face consultations,"with willingness to pay values of 170.18 CNY,162.90 CNY,and 112.70 CNY,respectively.Willingness to pay decreased as out-of-pocket costs increased.Heterogeneity analysis identified three distinct preference groups,with statistically significant differences in urban-rural distribution,income levels,and health insurance types.Conclusions and suggestions:Medication and lifestyle guidance are the most valued components of community health management services among older patients with multiple chronic diseases.Patients'demographic and socioeconomic characteristics have a structural influence on their service preferences,highlighting the need to tailor service provision to different population groups.It is recommended to enhance medication guidance capacity at the primary care level,especially in rural areas;promote physical medicine integration to improve the accuracy and effectiveness of lifestyle guidance provided by primary healthcare personnel;strengthen digital infrastructure and streamline service processes to moderately increase the frequency of face-to-face consultations;and improve the integration of medical insurance and public health funding mechanisms to enhance service accessibility and equity.

Objective:To investigate the application value of risk prediction model for acute kidney injury (AKI) after donation of cardiac death (DCD) liver transplantation based on machine learning algorithm.Methods:The retrospective cohort study was conducted. The clinicopathological data of 1 001 pairs of DCD liver transplant donors and recipients at five hospitals, including The First Affiliated Hospital of Zhejiang University School of Medicine et al, in the Chinese Liver Transplan-tation Registry from January 2015 to December 2023 were collected. Of the donors, there were 825 males and 176 females. Of the recipients, there were 806 males and 195 females, aged 52 (range, 18-75)years. There were 281 recipients included using oversampling technique, and all 1 282 recipients were divided to the training set of 897 recipients and the validation set of 385 recipients by a ratio of 7∶3 using computer-generated random numbers. Seven prediction models, including Random Forest (RF), Extreme Gradient Boosting (XGBoost), Support Vector Machine (SVM), Logistic Regression (LR), Decision Tree (DT), K-Nearest Neighbors (KNN), and Categorical Boosting (CatBoost), were constructed for AKI after liver transplantation based on machine learning algorithm. Observation indicators: (1) comparison of clinicopathological characteristics between recipients with and without AKI and donors; (2) follow-up and survival of recipients with and without AKI; (3) construction and validation of nomogram prediction model of AKI after liver transplantation; (4) construction and validation of machine learning prediction model of AKI after liver transplantation. Comparison of measurement data with normal distribution between groups was conducted using the independent sample t test. Comparison of measurement data with skewed distribution between groups was conducted using the Mann-Whitney U test, and comparison among groups was conducted using the Kruskal-Wallis H test. Comparison of count data between groups was conducted using the chi-square test or corrected chi-square test. Kaplan-Meier method was used to calculate survival rates and plot survival curves. Logistic regression model was performed for univariate and multivariate analyses. The receiver operating characteristic (ROC) curve was plotted to calculate area under curve (AUC) and 95% confidence interval ( CI). The performance of prediction model was evaluated using DeLong test, accuracy, sensitivity, specificity. The calibration curve was plotted to evaluate the performance of predicted probability and actual probability. The interpretability analysis of machine learning algorithm and SHapley Additive exPlanations was used to explain the model decision separately. Results:(1) Comparison of clinicopathological characteristics between recipients with and without AKI and donors. Of 1 001 recipients, there were 360 cases with AKI and 641 cases without AKI after liver transplantation. There were significant differences in body mass index (BMI), hepatic encepha-lopathy, hepatitis B surfact antigen (HBsAg), hepatorenal syndrome (HRS) and donor diabetes, donor blood urea nitrogen, donor alanine aminotransferase, donor aspartate aminotransferase, mass of graft, volume of blood loss during liver transplantation, warm ischema time of donor liver, and operation time between recipients with and without AKI ( Z=-4.337, χ2=9.751, 9.088, H=11.142, χ2=5.286, Z=-3.360, -2.539, -3.084, -1.730, -3.497, -1.996, -2.644, P<0.05). (2) Follow-up and survival of recipients with and without AKI. All the 1 001 recipients received follow-up. The recipients with AKI after liver transplantation were followed up for 18.6(range, 0-102.3)months, and recipients without AKI after liver transplantation were followed up for 31.9(range, 0.1-105.5)months. The 1-, 3-, and 5-year overall survival rates were 72.1%, 63.5%, and 59.3% of recipients with AKI, versus 86.7%, 76.7%, and 72.5% of recipients without AKI, respectively, showing a significant difference in overall survival between them ( χ2=26.028, P<0.05). (3) Construction and validation of nomogram predic-tion model of AKI after liver transplantation. Results of multivariate analysis showed that recipient BMI, recipient creatinine, recipient HBsAg, recipient HRS, donor blood urea nitrogen, donor crea-tinine, anhepatic phase and volume of blood loss during liver transplantation were independent risk factors for AKI of recipients after liver transplantation ( odds ratio=1.113, 0.998, 0.605, 1.580, 1.047, 0.998, 1.006, 1.157, 95% CI as 1.070-1.157, 0.996-1.000, 0.450-0.812, 1.021-2.070, 1.021-1.074, 0.996-0.999, 1.000-1.012, 1.045-1.281, P<0.05). The nomogram prediction model of AKI after liver transplantation was constructed based on the results of multivariate analysis. Results of ROC curve showed that the AUC of 0.666 (95% CI as 0.637-0.696). (4) Construction and validation of machine learning prediction model of AKI after liver transplantation. Based on the Lasso regression analysis, seven machine learning algorithm prediction models, including RF, XGBoost, SVM, LR, DT, KNN, and CatBoost, were constructed, with ROC curves of the validation set plotted. The AUC of above models were 0.863, 0.841, 0.721, 0.637, 0.620, 0.708, 0.731, accuracies were 0.764, 0.782, 0.701, 0.592, 0.605, 0.605, 0.681, sensitivities were 0.764, 0.789, 0.719, 0.588, 0.694, 0.694, 0.704, specificities were 0.763, 0.774, 0.683, 0.597, 0.511, 0.511, 0.656, respectively. Delong test showed that the RF model with the highest AUC of 0.863(95% CI as 0.828-0.899). Calibration curve analysis showed the predicted probability closest to the actual probability of RF model, indicating the model with a good validation value. Further sorting of SHAP of different clinical factors based on RF model showed that recipient BMI, donor blood urea nitrogen, volume of blood loss during liver transplantation, donor age had large effects on the output outcomes. Conclusion:The nomogram prediction model and seven machine learning algorithm prediction models for AKI after DCD liver transplantation are constructed, and the RF model based on machine learning has a better predictive performance.

Objective:To evaluate the safety of neoadjuvant therapy with pembrolizumab combined with 5-fluorouracil (5-FU) and cisplatin in patients with advanced temporal bone squamous cell carcinoma (TBSCC), and its impact on tumor response rate and disease-free survival (DFS).Methods:This prospective, single-arm, open-label clinical study enrolled patients with advanced (Stage Ⅲ/Ⅳ) TBSCC from Sun Yat-sen Memorial Hospital. Patients received 2-3 cycles of neoadjuvant therapy with pembrolizumab, 5-FU, and cisplatin, followed by definitive surgery. Postoperatively, patients received 6 cycles of pembrolizumab combined with radiotherapy. The primary endpoint was the 2-year disease-free survival (DFS) rate. Secondary endpoints included objective response rate (ORR) and safety indicators. Survival analysis was performed using the Kaplan-Meier method. Adverse events (AE) were assessed using the National Cancer Institute′s Common Terminology Criteria for Adverse Events (CTCAE), version 5.0. Statistical analyses were conducted using SPSS software, version 22.0.Results:From August 2021 to April 2024, 16 patients with advanced TBSCC were enrolled (13 males and 3 females), with a median age of 54 years and a median follow-up time of 2.32 years. Following neoadjuvant therapy, the objective response rate (ORR) was 64.3% (9/14), and the disease control rate (DCR) was 92.9% (13/14). The 2-year DFS rate was 86.6%. Common treatment-related adverse events (TRAE) included leukopenia (56.3%, 9/16), nausea and vomiting (50.0%, 8/16), diarrhea, oral mucositis, and elevated liver function tests (25.0%, 4/16). One patient (6.25%) experienced a grade 3 adverse event.Conclusion:Neoadjuvant pembrolizumab-chemotherapy significantly enhances objective response rate and disease-free survival in advanced TBSCC.

Objective: To investigate the effect of integrin α5 on the expression of NOD-like receptor thermal protein domain associated protein 3 (NLRP3) in periodontal ligament fibroblasts (PDLFs) within an inflammatory microenvironment. Methods: This study was approved by the Ethics Committee of Laboratory animals. After rat PDLFs were treated with LPS (0.5, 5, and 50 µg/mL) for 24 h, the primary medium was discarded and replaced with serum-free culture medium. After 24 h, the supernatant was collected and mixed with DMEM medium containing 10% exosome-free serum at a volume ratio of 1:1 to obtain conditioned medium (CM). The groups were labeled as the 0.5-CM, 5-CM, and 50-CM groups. In addition, PDLFs cultured in DMEM medium containing 10% exosome-free serum were considered the 0-CM group. PDLFs were cultured with the above CM. In the inhibitor group, PDLFs were cultured in 0-CM containing different concentrations of integrin α5 inhibitor ATN-161 (0, 0.025, 0.25, 2.5, 25, and 250 μg/mL). The effect of CM and integrin α5 inhibitor ATN-161 on cell viability was assessed using the CCK-8 assay. According to the CCK-8 results, in further inhibitor intervention experiments, PDLFs were cultured in 0-CM, 5-CM (without/with 25 μg/mL ATN-161), and 0-CM containing 25 μg/mL ATN-161, which were labeled as the 0-CM, 5-CM, ATN-161+5-CM, and ATN-161 groups, respectively. The expression changes of integrin α5 and NLRP3 were detected using Western blot and qRT-PCR techniques. For in vivo experiments, 24 rats were randomly divided into four groups (n=6). The control group contained healthy rats that received no treatment. The rats in the other three groups were injected with 40 µL of 0-CM containing 25 μg/mL ATN-161 or 5-CM (without or with 25 μg/mL ATN-161) on the palatal side of the left maxillary first molar every three days; these groups were classified as the ATN-161, 5-CM, and ATN-161+5-CM groups, respectively. On the 30th day, the left maxillary tissue of rats was used for Micro-CT, HE staining, and immunohistochemical detection. Results : The CCK-8 assay showed that CM, 25 μg/mL ATN-161, and ATN-161 concentrations below 25 μg/mL had no significant effect on cell viability at 12 h and 24 h (P > 0.05). 50-CM and 25 μg/mL ATN-161 significantly inhibited cell viability at 48 h (P < 0.05). For in vitro experiments, compared to the 0-CM group, both the protein and mRNA levels of integrin α5 and NLRP3 were significantly increased in rat PDLFs in the 5-CM group (P < 0.05). Intervention with 25 μg/mL ATN-161 significantly attenuated the enhancement of 5-CM on the expression of integrin α5 and NLRP3 (P < 0.05). For in vivo experiments, compared to the control group, alveolar bone resorption and periodontal inflammatory cell infiltration were significantly increased in the 5-CM and ATN-161+5-CM groups, and the expression of integrin α5 and NLRP3 was significantly increased (P < 0.01). However, compared to the 5-CM group, the ATN-161+5-CM group had less alveolar bone resorption and fewer periodontal inflammatory cells. Further, the expression of integrin α5 and NLRP3 was significantly reduced (P < 0.01). Conclusion In vitro and in vivo experiments showed that integrin α5 mediated NLRP3 expression in PDLFs under an inflammatory microenvironment. ATN-161 inhibited the expression of integrin α5, thus significantly downregulating the expression of NLRP3, which plays a role in inhibiting inflammation.

Objective:To evaluate the safety of neoadjuvant therapy with pembrolizumab combined with 5-fluorouracil (5-FU) and cisplatin in patients with advanced temporal bone squamous cell carcinoma (TBSCC), and its impact on tumor response rate and disease-free survival (DFS).Methods:This prospective, single-arm, open-label clinical study enrolled patients with advanced (Stage Ⅲ/Ⅳ) TBSCC from Sun Yat-sen Memorial Hospital. Patients received 2-3 cycles of neoadjuvant therapy with pembrolizumab, 5-FU, and cisplatin, followed by definitive surgery. Postoperatively, patients received 6 cycles of pembrolizumab combined with radiotherapy. The primary endpoint was the 2-year disease-free survival (DFS) rate. Secondary endpoints included objective response rate (ORR) and safety indicators. Survival analysis was performed using the Kaplan-Meier method. Adverse events (AE) were assessed using the National Cancer Institute′s Common Terminology Criteria for Adverse Events (CTCAE), version 5.0. Statistical analyses were conducted using SPSS software, version 22.0.Results:From August 2021 to April 2024, 16 patients with advanced TBSCC were enrolled (13 males and 3 females), with a median age of 54 years and a median follow-up time of 2.32 years. Following neoadjuvant therapy, the objective response rate (ORR) was 64.3% (9/14), and the disease control rate (DCR) was 92.9% (13/14). The 2-year DFS rate was 86.6%. Common treatment-related adverse events (TRAE) included leukopenia (56.3%, 9/16), nausea and vomiting (50.0%, 8/16), diarrhea, oral mucositis, and elevated liver function tests (25.0%, 4/16). One patient (6.25%) experienced a grade 3 adverse event.Conclusion:Neoadjuvant pembrolizumab-chemotherapy significantly enhances objective response rate and disease-free survival in advanced TBSCC.

Sleep deprivation (SD) has emerged as a significant modifiable risk factor for Alzheimer’s disease (AD), with mounting evidence demonstrating its multifaceted role in accelerating AD pathogenesis through diverse molecular, cellular, and systemic mechanisms. SD is refined within the broader spectrum of sleep-wake and circadian disruption, emphasizing that both acute total sleep loss and chronic sleep restriction destabilize the homeostatic and circadian processes governing glymphatic clearance of neurotoxic proteins. During normal sleep, concentrations of interstitial Aβ and tau fall as cerebrospinal fluid oscillations flush extracellular waste; SD abolishes this rhythm, causing overnight rises in soluble Aβ and tau species in rodent hippocampus and human CSF. Orexinergic neurons sustain arousal, and become hyperactive under SD, further delaying sleep onset and amplifying Aβ production. At the molecular level, SD disrupts Aβ homeostasis through multiple converging pathways, including enhanced production via beta-site APP cleaving enzyme 1 (BACE1) upregulation, coupled with impaired clearance mechanisms involving the glymphatic system dysfunction and reduced Aβ-degrading enzymes (neprilysin and insulin-degrading enzyme). Cellular and histological analyses revealed that these proteinopathies are significantly exacerbated by SD-induced neuroinflammatory cascades characterized by microglial overactivation, astrocyte reactivity, and sustained elevation of pro-inflammatory cytokines (IL-1β, TNF-α, IL-6) through NF‑κB signaling and NLRP3 inflammasome activation, creating a self-perpetuating cycle of neurotoxicity. The synaptic and neuronal consequences of chronic SD are particularly profound and potentially irreversible, featuring reduced expression of critical synaptic markers (PSD95, synaptophysin), impaired long-term potentiation (LTP), dendritic spine loss, and diminished neurotrophic support, especially brain-derived neurotrophic factor (BDNF) depletion, which collectively contribute to progressive cognitive decline and memory deficits. Mechanistic investigations identify three core pathways through which SD exerts its neurodegenerative effects: circadian rhythm disruption via BMAL1 suppression, orexin system hyperactivity leading to sustained wakefulness and metabolic stress, and oxidative stress accumulation through mitochondrial dysfunction and reactive oxygen species overproduction. The review critically evaluates promising therapeutic interventions including pharmacological approaches (melatonin, dual orexin receptor antagonists), metabolic strategies (ketogenic diets, and Mediterranean diets rich in omega-3 fatty acids), lifestyle modifications (targeted exercise regimens, cognitive behavioral therapy for insomnia), and emerging technologies (non-invasive photobiomodulation, transcranial magnetic stimulation). Current research limitations include insufficient understanding of dose-response relationships between SD duration/intensity and AD pathology progression, lack of long-term longitudinal clinical data in genetically vulnerable populations (particularly APOE ε4 carriers and those with familial AD mutations), the absence of standardized SD protocols across experimental models that accurately mimic human chronic sleep restriction patterns, and limited investigation of sex differences in SD-induced AD risk. The accumulated evidence underscores the importance of addressing sleep disturbances as part of multimodal AD prevention strategies and highlights the urgent need for clinical trials evaluating sleep-focused interventions in at-risk populations. The review proposes future directions focused on translating mechanistic insights into precision medicine approaches, emphasizing the need for biomarkers to identify SD-vulnerable individuals, chronotherapeutic strategies aligned with circadian biology, and multi-omics integration across sleep, proteostasis and immune profiles may delineate precision-medicine strategies for at-risk populations. By systematically examining these critical connections, this analysis positions sleep quality optimization as a viable strategy for AD prevention and early intervention while providing a comprehensive roadmap for future mechanistic and interventional research in this rapidly evolving field.