ObjectivePancreatic ductal adenocarcinoma (PDAC) exhibits a limited response to current treatments due to its dense fibrotic stroma and highly immunosuppressive tumor microenvironment. In recent years, advancements in cellular immunotherapy, particularly chimeric antigen receptor macrophage (CAR-M) therapy, have offered new hope for pancreatic cancer treatment. Although CAR-M therapy demonstrates dual potential in directly killing tumor cells and remodeling the immune microenvironment, it still faces challenges such as complex in vitro preparation processes and low in vivo targeting and delivery efficiency. Therefore, developing strategies for efficient and targeted in vivo delivery of CAR genes has become crucial for overcoming current therapeutic limitations. This study aims to develop an orally administrable nano-gene delivery system for the targeted delivery of CAR genes to pancreatic tumor sites. MethodsCore nano-gene particles (PNP/pCAR) were constructed by loading plasmid DNA encoding CAR (pCAR) with cationic polypeptides (PNP). Subsequently, PNP/pCAR was surface-modified with β-glucan to prepare the targeted nanoparticles (βGlus-PNP/pCAR). The loading efficiency of PNP for pCAR was quantitatively assessed by gel retardation assay. The particle size, Zeta potential, morphology, and storage stability of PNP/pCAR were characterized using a Malvern particle size analyzer and transmission electron microscopy. At the cellular level, RAW 264.7 macrophages were selected. The cytotoxicity of PNP/pCAR was evaluated using the CCK-8 assay. The cellular uptake efficiency and lysosomal escape ability of the nanoparticles were assessed via flow cytometry and confocal microscopy. Transfection efficiency was quantitatively evaluated by detecting the expression of the reporter gene GFP using flow cytometry. At the in vivo level, an orthotopic pancreatic cancer mouse model was established. Cy7-labeled βGlus-PNP/pCAR nanoparticles were administered orally, and the fluorescence distribution in mice was dynamically monitored at 1, 2, 4, 8, and 16 h post-administration using a small animal in vivo imaging system. Forty-eight hours after oral gavage, the mice were euthanized, and pancreatic tumor tissues were collected for further analysis of intratumoral fluorescence signals using the imaging system. Additionally, βGlus-PNP/pCAR-GFP nanoparticles loaded with the reporter gene (GFP) were administered orally. Forty-eight hours post-administration, pancreatic tumor tissues were harvested to prepare frozen sections, and GFP expression was observed and analyzed under a fluorescence microscope. ResultsThe PNP carrier exhibited a high loading capacity for pCAR. The successfully prepared PNP/pCAR nanoparticles were regular spheres with a hydrodynamic diameter of approximately (120±10) nm and a Zeta potential of about +(6±1) mV. They maintained good structural stability after incubation in PBS buffer for 7 d. Cell experiments demonstrated that PNP/pCAR exhibited no significant cytotoxicity in RAW 264.7 cells while being efficiently internalized and effectively escaping lysosomal degradation. The transfection positive rate of PNP/pCAR-GFP in RAW 264.7 cells reached (25±3)%, surpassing that of Lipofectamine 2000-loaded pCAR-GFP (Lipo/pCAR-GFP), which was (20±1)%.In vivo experiments revealed that, compared to unmodified PNP/pCAR, βGlus-PNP/pCAR exhibited strongerin situ pancreatic tumor targeting ability after oral administration. Furthermore, oral administration of βGlus-PNP/pCAR-GFP resulted in significant GFP protein expression detectable within pancreatic tumor tissues. ConclusionThis study successfully constructed and validated an orally administrable, pancreatic cancer-targeting polypeptide-based nano-gene delivery system. It provides an important technological foundation in delivery systems and experimental basis for the subsequent development of in situ CAR-M-based therapeutic strategies for pancreatic cancer.

ObjectivePancreatic ductal adenocarcinoma (PDAC) exhibits a limited response to current treatments due to its dense fibrotic stroma and highly immunosuppressive tumor microenvironment. In recent years, advancements in cellular immunotherapy, particularly chimeric antigen receptor macrophage (CAR-M) therapy, have offered new hope for pancreatic cancer treatment. Although CAR-M therapy demonstrates dual potential in directly killing tumor cells and remodeling the immune microenvironment, it still faces challenges such as complex in vitro preparation processes and low in vivo targeting and delivery efficiency. Therefore, developing strategies for efficient and targeted in vivo delivery of CAR genes has become crucial for overcoming current therapeutic limitations. This study aims to develop an orally administrable nano-gene delivery system for the targeted delivery of CAR genes to pancreatic tumor sites. MethodsCore nano-gene particles (PNP/pCAR) were constructed by loading plasmid DNA encoding CAR (pCAR) with cationic polypeptides (PNP). Subsequently, PNP/pCAR was surface-modified with β-glucan to prepare the targeted nanoparticles (βGlus-PNP/pCAR). The loading efficiency of PNP for pCAR was quantitatively assessed by gel retardation assay. The particle size, Zeta potential, morphology, and storage stability of PNP/pCAR were characterized using a Malvern particle size analyzer and transmission electron microscopy. At the cellular level, RAW 264.7 macrophages were selected. The cytotoxicity of PNP/pCAR was evaluated using the CCK-8 assay. The cellular uptake efficiency and lysosomal escape ability of the nanoparticles were assessed via flow cytometry and confocal microscopy. Transfection efficiency was quantitatively evaluated by detecting the expression of the reporter gene GFP using flow cytometry. At the in vivo level, an orthotopic pancreatic cancer mouse model was established. Cy7-labeled βGlus-PNP/pCAR nanoparticles were administered orally, and the fluorescence distribution in mice was dynamically monitored at 1, 2, 4, 8, and 16 h post-administration using a small animal in vivo imaging system. Forty-eight hours after oral gavage, the mice were euthanized, and pancreatic tumor tissues were collected for further analysis of intratumoral fluorescence signals using the imaging system. Additionally, βGlus-PNP/pCAR-GFP nanoparticles loaded with the reporter gene (GFP) were administered orally. Forty-eight hours post-administration, pancreatic tumor tissues were harvested to prepare frozen sections, and GFP expression was observed and analyzed under a fluorescence microscope. ResultsThe PNP carrier exhibited a high loading capacity for pCAR. The successfully prepared PNP/pCAR nanoparticles were regular spheres with a hydrodynamic diameter of approximately (120±10) nm and a Zeta potential of about +(6±1) mV. They maintained good structural stability after incubation in PBS buffer for 7 d. Cell experiments demonstrated that PNP/pCAR exhibited no significant cytotoxicity in RAW 264.7 cells while being efficiently internalized and effectively escaping lysosomal degradation. The transfection positive rate of PNP/pCAR-GFP in RAW 264.7 cells reached (25±3)%, surpassing that of Lipofectamine 2000-loaded pCAR-GFP (Lipo/pCAR-GFP), which was (20±1)%.In vivo experiments revealed that, compared to unmodified PNP/pCAR, βGlus-PNP/pCAR exhibited strongerin situ pancreatic tumor targeting ability after oral administration. Furthermore, oral administration of βGlus-PNP/pCAR-GFP resulted in significant GFP protein expression detectable within pancreatic tumor tissues. ConclusionThis study successfully constructed and validated an orally administrable, pancreatic cancer-targeting polypeptide-based nano-gene delivery system. It provides an important technological foundation in delivery systems and experimental basis for the subsequent development of in situ CAR-M-based therapeutic strategies for pancreatic cancer.

Microwave thermochemotherapy (MTC) has been applied to treat lip squamous cell carcinoma (LSCC), but a deeper understanding of its therapeutic mechanisms and molecular biology is needed. To address this, we used single-cell transcriptomics (scRNA-seq) and spatial transcriptomics (ST) to highlight the pivotal role of tumor-associated neutrophils (TANs) among tumor-infiltrating immune cells and their therapeutic response to MTC. MNDA⁺ TANs with anti-tumor activity (N1-phenotype) are found to be abundantly infiltrated by MTC with benefit of increased blood perfusion, and these TANs are characterized by enhanced cytotoxicity, ameliorated hypoxia, and upregulated IL1B, activating T&NK cells and fibroblasts via IL1B-IL1R. In this highly anti-tumor immunogenic and hypoxia-reversed microenvironment under MTC, fibroblasts accumulated in the tumor front (TF) can recruit N1-TANs via CXCL2-CXCR2 and clear N2-TANs (pro-tumor phenotype) via CXCL12-CXCR4, which results in the aggregation of N1-TANs and extracellular matrix (ECM) deposition. In addition, we construct an N1-TANs marker, MX2, which positively correlates with better prognosis in LSCC patients, and employ deep learning techniques to predict expression of MX2 from hematoxylin-eosin (H&E)-stained images so as to conveniently guide decision making in clinical practice. Collectively, our findings demonstrate that the N1-TANs/fibroblasts defense wall formed in response to MTC effectively combat LSCC.
Humans ; Neutrophils/metabolism* ; Single-Cell Analysis ; Lip Neoplasms/genetics* ; Hyperthermia, Induced/methods* ; Microwaves/therapeutic use* ; Transcriptome ; Carcinoma, Squamous Cell/immunology* ; Tumor Microenvironment

This paper uses literature and network data to systematically sort out the theoretical and practical foundations of global public health cooperation,combines expert interviews to conduct empirical analyses,and further explores China's strategies for participating in global public health cooperation through quantitative statistics and text mining of interview data,and proposes a plan for China's participation in global public health cooperation under the current international situation.Under the countercurrents to globalization,China should take its own public health capacity building as the foundation,put global security and health equity at the core,with a philosophy of open cooperation and sustainable development,actively promote bilateral and multilateral cooperation,focus on cultivating global health talents,and enhance the effectiveness of disease prevention and control by making use of existing platforms,international mechanisms and digital health technologies,so as to help build a Global Community of Health for All.

Accurate analysis of metal nanoparticles(MNPs)in sediments is a prerequisite for assessing the ecological risks of MNPs in aquatic environmental sediments.In this study,an analytical method for quantitative detection of concentration and particle size distribution of silver-containing nanoparticles(Ag-NPs),zinc-containing nanoparticles(Zn-NPs),cerium-containing nanoparticles(Ce-NPs),and titanium-containing nanoparticles(Ti-NPs)in sediments was established based on ultrasonic extraction-single particle inductively coupled plasma mass spectrometry(SP-ICP-MS).The effects of sample preparation conditions such as extraction solvent type,solid-liquid ratio,ultrasonic time,and settling time on the recovery of MNPs were investigated.The results showed that the extraction of MNPs from sediment by distilled water could effectively eliminate the high background signal interference introduced by the extractant under the conditions of solid-liquid ratio of 1∶400(g∶mL),ultrasonic extraction time of 1 h and settling time of 3 h.The detection limits for particle size of Ag-NPs,Zn-NPs,Ce-NPs and Ti-NPs in sediments were 31,35,26 and 85 nm,respectively,while the detection limits of particle concentrations were 1.21×104,1.90×104,5.26×107 and 1.48×107 particles/g,respectively.The spiking recoveries of Ag-NPs,Zn-NPs,Ce-NPs and Ti-NPs in sediments were 62.1％-108.7％,with relative standard deviations below 10％.This method could rapidly,accurately and simultaneously determine the concentration and particle size distribution of various MNPs in sediments,and was successfully applied to analysis of Ag-NPs,Zn-NPs,Ce-NPs,and Ti-NPs in authentic marine sediments.

OBJECTIVE To explore and evaluate the machine learning model for prediction of bacterial bloodstream infections established based on routine test data.METHODS By means of retrospective survey,a total of 5 421 pa-tients who were hospitalized in 3 medical institutions from Jan.2015 to Dec.2022 were recruited as the research subjects,1 914 of whom were assigned as the bloodstream infection group,and 3 507 were assigned as the non-bloodstream infection group.The baseline data including gender and age and the results of routine laboratory tests were collected from the enrolled patients.The 3 types of machine learning algorithms,logistic regression,support vector machine and random forest,were respectively used for the screening of the optimal prediction model;the contribution of feature variables to the predictive capability of the model was interpreted through SHAP.The fea-ture variables of the model were optimized by using recursive feature elimination method,and the predictive effi-ciency of the model was evaluated by the area under the curve(AUC)of receiver operating characteristic(ROC)curves.RESULTS Totally 26 variables involving age,gender and blood routine test indexes were included.The random forest was chosen as the optimal machine learning algorithm for the establishment of prediction model for bloodstream infections,and the accuracy of the model was 0.709,with the AUC 0.706.The result of SHAP ex-planation indicated that the age,hematokrit and erythrocyte volume distribution width-CV had remarkable effect on the model's making right decisions.17 variables of the prediction model showed more remarkable effect than 26 variable on distinguishing from the gram-positive bacteria bloodstream infections from the gram-negative bacteria bloodstream infections,with the AUC 0.715,the sensitivity 0.701,the specificity 0.632.CONCLUSIONS The prediction model that is established based on the blood routine test indexes by machine learning algorithm can pre-dict the bacterial bloodstream infection.Meanwhile,the feature selection strategy can further improve the predic-tive efficiency of the model on basis of lowering the dimensionality.

OBJECTIVE To explore and evaluate the machine learning model for prediction of bacterial bloodstream infections established based on routine test data.METHODS By means of retrospective survey,a total of 5 421 pa-tients who were hospitalized in 3 medical institutions from Jan.2015 to Dec.2022 were recruited as the research subjects,1 914 of whom were assigned as the bloodstream infection group,and 3 507 were assigned as the non-bloodstream infection group.The baseline data including gender and age and the results of routine laboratory tests were collected from the enrolled patients.The 3 types of machine learning algorithms,logistic regression,support vector machine and random forest,were respectively used for the screening of the optimal prediction model;the contribution of feature variables to the predictive capability of the model was interpreted through SHAP.The fea-ture variables of the model were optimized by using recursive feature elimination method,and the predictive effi-ciency of the model was evaluated by the area under the curve(AUC)of receiver operating characteristic(ROC)curves.RESULTS Totally 26 variables involving age,gender and blood routine test indexes were included.The random forest was chosen as the optimal machine learning algorithm for the establishment of prediction model for bloodstream infections,and the accuracy of the model was 0.709,with the AUC 0.706.The result of SHAP ex-planation indicated that the age,hematokrit and erythrocyte volume distribution width-CV had remarkable effect on the model's making right decisions.17 variables of the prediction model showed more remarkable effect than 26 variable on distinguishing from the gram-positive bacteria bloodstream infections from the gram-negative bacteria bloodstream infections,with the AUC 0.715,the sensitivity 0.701,the specificity 0.632.CONCLUSIONS The prediction model that is established based on the blood routine test indexes by machine learning algorithm can pre-dict the bacterial bloodstream infection.Meanwhile,the feature selection strategy can further improve the predic-tive efficiency of the model on basis of lowering the dimensionality.

Objective To identify high-risk factors for healthcare-associated infection(HAI)in patients in inten-sive care units(ICUs),and develop a quick response(QR)code-based APP prediction tool.Methods Information of inpatients in general ICUs of three hospitals in Guizhou Province from January to December 2024 were collected.Risk factors were analyzed with a logistic regression model.QR code-based APP was constructed and validated.Results A total of 1 782 patients in general ICUs of three hospitals in Guizhou Province in 2024 were included in the analysis,out of which 410 were HAI cases,and the incidence of HAI was 23.01％.Multivariate logistic regre-ssion analysis results of HAI in ICU inpatients showed that regional gross domestic product(GDP)≥58 685 Yuan,performing pathogen culture during this hospitalization,history of diabetes mellitus,history of cancer,length of hospital stay ≥7 days before infection,and duration of persistent fever＞5 days before infection were independent risk factors for HAI in ICU patients(all P＜0.05).The discrimination of the model(area under the receiver operating characteristic curve[AUC]of 0.841),calibration(Brier score of 0.129),and clinical effectiveness(net benefit of 11.4％when the risk threshold was 5％-74％)all performed well.Conclusion The QR code-based APP prediction tool is of great significance for scientific research transformation and precise HAI control.

A single-center, randomized, double-blind, dose-controlled trial of modified Sini Powder in treating mild to moderate generalized anxiety disorder(GAD) in the patients with syndrome of liver depression transforming into fire was conducted at Xiyuan Hospital, China Academy of Chinese Medical Sciences. A total of 80 patients with mild to moderate GAD and the syndrome of liver depression transforming into fire were included. Patients were assigned by the central randomization system at a ratio of 3∶1 into an observation group(n=60, receiving a conventional-dose of granules of modified Sini Powder) and a control group(n=20, receiving low-dose granules with the active ingredients being 50% of that in observation group). Assessments were conducted before treatment(baseline), after 2 weeks of introduction, after 2/4/8 weeks of treatment, and after 4 weeks of follow-up. The results were summarized as follows. In terms of primary outcome indicators, the observation group(62.2%) showed higher total response rate than the control group(26.6%)(P<0.05), and greater Hamilton anxiety scale(HAMA) score reduction after 8 weeks of treatment(P<0.05). In terms of secondary outcome indicators, the HAMA score(somatic anxiety score), traditional Chinese medicine(TCM) syndrome scores, Pittsburgh sleep quality index(PSQI) scale, and clinical global impression(CGI) scale score in the observation group showed a significant compared to the control group at each visit points(P<0.05). Adverse events occurred in 10 cases, including 9(16.9%) cases in the observation group and 1(6.6%) case in the control group. No adverse reaction was observed. In conclusion, conventional-dose modified Sini Powder demonstrated superior efficacy and favorable safety for mild and moderate GAD in the patients with the syndrome of liver depression transforming into fire over low-dose treatment.
Humans ; Male ; Female ; Adult ; Middle Aged ; Double-Blind Method ; Drugs, Chinese Herbal/administration & dosage* ; Anxiety Disorders/drug therapy* ; Treatment Outcome ; Young Adult ; Powders ; Aged ; Liver/drug effects* ; Generalized Anxiety Disorder

Extensive studies have identified potential adverse effects on semen quality of obesity, based on body mass index, but the association between body fat distribution, a more relevant indicator for obesity, and semen quality remains less clear. We conducted a longitudinal study of 4304 sperm donors from the Guangdong Provincial Human Sperm Bank (Guangzhou, China) during 2017-2021. A body composition analyzer was used to measure total and local body fat percentage for each participant. Generalized estimating equations were employed to assess the association between body fat percentage and sperm count, motility, and morphology. We estimated that each 10% increase in total body fat percentage (estimated change [95% confidence interval, 95% CI]) was significantly associated with a 0.18 × 10 6 (0.09 × 10 6 -0.27 × 10 6 ) ml and 12.21 × 10 6 (4.52 × 10 6 -19.91 × 10 6 ) reduction in semen volume and total sperm count, respectively. Categorical analyses and exposure-response curves showed that the association of body fat distribution with semen volume and total sperm count was stronger at higher body fat percentages. In addition, the association still held among normal weight and overweight participants. We observed similar associations for upper limb, trunk, and lower limb body fact distributions. In conclusion, we found that a higher body fat distribution was significantly associated with lower semen quality (especially semen volume) even in men with a normal weight. These findings provide useful clues in exploring body fat as a risk factor for semen quality decline and add to evidence for improving semen quality for those who are expected to conceive.
Humans ; Male ; Adult ; Semen Analysis ; China ; Body Fat Distribution ; Longitudinal Studies ; Sperm Count ; Sperm Motility ; Body Mass Index ; Tissue Donors ; Obesity/complications* ; Spermatozoa ; Young Adult ; Middle Aged ; East Asian People