Objective To investigate the epidemiology of elderly respiratory tract infection (RTI) cases in a hospital in Xuzhou region from 2020 to 2022. Methods The cases of RTI patients in a hospital were screened from May 2020 to December 2022, and 548 cases that met the criteria were included in the study. Patient case data were analyzed for symptoms, pathogen distribution, and differences in patient distribution under different screening conditions (age, disease, and season). Results More than 90.00% of the included RTI patients presented with symptoms of cough, sputum, wet rales and pleural effusion was less common. The top three comorbidities were cardiovascular disease (153 patients, 27.92%), cerebrovascular disease (133 patients, 24.27%), and gastrointestinal disease (105 patients, 19.16%).All 548 elderly patients tested positive for respiratory pathogens (100.00%). There were 540 cases of single pathogen infection (98.54%) and 8 cases of mixed infection (1.46%). The top five single pathogen infections were Pseudomonas aeruginosa (92 cases, 16.76%), Escherichia coli (78 cases, 14.21%), drug-resistant Staphylococcus aureus (69 cases, 12.57%), Klebsiella pneumoniae (65 cases, 11.84%), and Mycoplasma pneumoniae (46 cases, 8.38%). The highest detection rate of respiratory pathogens was found in patients >90 years old, whose main pathogens were Pseudomonas aeruginosa, Klebsiella pneumoniae and drug-resistant Staphylococcus aureus. The next highest rates of pathogen detection were found in patients aged 86-91 and 81-85 years, unlike patients >90 years, who had a higher rate of Escherichia coli detection. Unlike other age groups, patients <75 years old had a higher percentage of influenza B virus detection. The highest incidence of pneumonia was found in 45.62% (250 cases). Escherichia coli had the highest detection rate in acute bronchitis/episodes and pneumonia, respiratory syncytial virus had the highest detection rate in wheezing bronchitis, Klebsiella pneumoniae had the highest detection rate in bronchopneumonia, and Pseudomonas aeruginosa had the highest detection rate in fever. The highest detection rate of pathogens was found in fall (36.50%), followed by spring (27.01%). The distribution of pathogen infections in all seasons was matched with the results of pathogenicity testing. Streptococcus oxysporus had the highest number of infections in the fall (χ²=20.33, P<0.001). Conclusion Elderly respiratory tract infections in this region are most common in patients over 90 years old, with the highest incidence of pneumonia and high incidence in fall, and the pathogens are mainly Pseudomonas aeruginosa, Escherichia coli and drug-resistant Staphylococcus aureus. Attention to distinguish the above characteristics can provide some support for early diagnosis and treatment of respiratory infections in the elderly in this region.

Objective To investigate the effects of cucurbitacin B (CucB) on alleviating skin lesions and inflammation in psoriasis mice via the cGAS-STING signaling pathway. Methods The expression of genes associated with the cGAS-STING signaling pathway in psoriatic lesions and non-lesional skin was analyzed, and hallmark gene set enrichment analysis was performed. The cytotoxicity of CucB on BMDMs was evaluated using the CCK-8 assay. The expression levels of genes and proteins related to the cGAS-STING signaling pathway, along with the secretion of inflammatory cytokines, were measured at different concentrations of CucB using quantitative PCR, Western blotting, and ELISA. Imiquimod-induced psoriasis BALB/c mice were divided into four groups: normal group, model group, low-dose CucB group [0.1 mg/ (kg.d)], and high-dose CucB group [0.4 mg/ (kg.d)], with five mice per group. PASI scoring was performed to assess the severity of psoriasis after 6 days of treatment, and HE staining was conducted to observe pathological damage. Meanwhile, the mRNA levels of inflammatory cytokines and their secretion were detected by qPCR and ELISA. Results Most cGAS-STING signaling-related genes were upregulated in lesional skin of psoriasis patients, and the hallmark gene set enrichment analysis revealed that the most significantly upregulated genes were primarily associated with immune response signaling pathways. CucB inhibited dsDNA-induced phosphorylation of interferon regulatory factor 3 (IRF3) and STING proteins in both bone-marrow derived macrophages(BMDMs) and THP-1 cells. CucB also suppressed dsDNA-induced mRNA expression of IFNB1, TNF, IFIT1, CXCL10, ISG15, and reduced the secretion of cytokines such as IFN-β, IL-1β, and TNF-α in THP-1 cells. In the imiquimod-induced psoriasis mouse model, CucB treatment reduced psoriatic symptoms, alleviated skin lesions, and attenuated inflammation. ELISA and qPCR results showed that CucB significantly reduced serum secretion levels of IL-6, TNF-α, and IL-1β, as well as the mRNA levels of IL23A, IL1B, IL6, TNF, and IFNB1. Conclusion CucB inhibits cytoplasmic DNA-induced activationc of the GAS-STING pathway. CucB significantly attenuates skin lesions and inflammation in IMQ-induced psoriatic mice, and the potential molecular mechanism may be related to the down-regulation of the cGAS-STING pathway.
Animals ; Psoriasis/pathology* ; Signal Transduction/drug effects* ; Membrane Proteins/genetics* ; Mice ; Nucleotidyltransferases/genetics* ; Disease Models, Animal ; Mice, Inbred BALB C ; Skin/metabolism* ; Triterpenes/therapeutic use* ; Humans ; Cytokines/metabolism* ; Inflammation/drug therapy* ; Male

Druggability evaluation is one of the core processes in new drug development, yet the inaccuracy and high cost of existing in vitro liver models have been a major technical bottleneck, leading to an increasing demand from the pharmaceutical industry for reliable in vitro liver models to enhance the efficiency of new drug research and development. Traditional animal models and in vitro 2D culture models have their limitations in simulating in vivo physiological and pathological conditions, making it challenging to accurately predict drug efficacy and safety. With the advancement of microfluidic technology, in vitro cell culture, and biosensor technology, liver-on-a-chip (LOC) has garnered increasing attention in the field of new drug development in recent years, and is expected to become a powerful tool for addressing the challenges in druggability evaluation. While introducing the construction technology of LOC, this article mainly summarizes the research and application of existing LOC from the perspectives of disease model construction, drug metabolism research, and drug safety evaluation. Furthermore, it analyzes the role of LOC in druggability evaluation and discusses the current challenges and prospects in this field.

Objective:To screen the Alzheimer's disease(AD)-related genes and construct its diagnostic model using bioinformatics technology and machine learning(ML)algorithms,to discuss the immunological characteristics of AD patients,and to provide novel biomarkers for AD diagnosis.Methods:The AD-related gene expression dataset GSE125583 was downloaded from the Gene Expression Omnibus(GEO)database.Differentially expressed genes(DEGs)were identified through differential analysis.Gene Ontology(GO)functional enrichment and Kyoto Encyclopedia of Genes and Genomes(KEGG)signaling pathway enrichment analyses were performed to explore the biological functions and signaling pathways of DEGs.A protein-protein interaction(PPI)network was constructed,and hub genes were screened using Cytoscape software combined with three ML algorithms:Least Absolute Shrinkage and Selection Operator(LASSO),eXtreme Gradient Boosting(XGBoost),and Random Forest(RF).The screened hub genes were utilized to build an AD diagnostic model via RF,followed by feature importance ranking.The model's efficacy and key genes were evaluated using a test set.Single-sample gene set enrichment analysis(ssGSEA)was used for immune cell infiltration analysis between AD group and control group.Results:Differential analysis identified 1 287 DEGs.The GO functional enrichment analysis results revealed that DEGs were primarily involved in biological functions related to neural signaling,synapses,and vesicles.KEGG signaling pathway enrichment analysis indicated significant enrichment of DEGs in ion transport,neurotransmitter,and ligand-gated channel pathways.Nine overlapping hub genes were screened by the three ML algorithms.In the AD diagnostic model,the top four key genes with highest diagnostic performance were adenylate cyclase-activating polypeptide 1(ADCYAP1),brain-derived neurotrophic factor(BDNF),platelet-derived growth factor receptor β(PDGFRB),and C-X-C motif chemokine receptor 4(CXCR4),with corresponding area under the curve(AUC)values of 0.852,0.795,0.820,and 0.756,respectively.The model achieved an AUC of 0.828,accuracy of 81.25%,sensitivity of 84.40%,and specificity of 71.43%.The immune cell infiltration analysis results demonstrated higher infiltration of macrophages,monocytes,natural killer(NK)cells,and lymphocytes in AD tissue.Among these,NK/natural killer T(NKT)cells and plasmacytoid dendritic cells showed significant correlations with the four key genes(P<0.05).Conclusion:The feature genes screened based on bioinformatics and ML exhibit diagnostic potential for AD.Genes such as ADCYAP1 may serve as potential biomarkers for AD diagnosis,offering significant implications for early prevention and treatment.

Objective To independently design a protective restraint band used for the interventional catheter bed and to evaluate its application effect.Methods The self-designed restraint belt consisted of a restraint belt body,first movable band and second movable band.A total of 200 patients,who received interventional operation at the Affiliated Run Run Shaw Hospital,School of Medicine of Zhejiang University of China from October 2022 to March 2023,were collected for this study.The patients were divided into the study group(n=100,using the self-designed restraint belt)and the control group(n=100,not using the self-designed restraint belt).The duration of surgical interruption due to patient's factors and the incidence of restraint belt-related adverse events were compared between the two groups.Results The duration of surgical interruption of the study group was(0.26±0.12)min,which was significantly shorter than(1.46±0.50)min in the control group,the difference was statistically significant(t=-23.570,P＜0.01).The incidence of restraint belt-related adverse events in the study group and the control group was 0％and 7％respectively,the difference was statistically significant(x2=6.380,P=0.012).Conclusion The protective restraint band used for the interventional catheter bed can reduce the incidence of restraint belt-related adverse events and improve surgical efficiency.

Objective To develop and validate a deep learning model for automatic identification of liver CT contrast-enhanced phases.Methods A total of 766 patients with liver CT contrast-enhanced images were retrospectively collected.A three-phase classification model and an arterial phase(AP)classification model were developed,so as to automatically identify liver CT contrast-enhanced phases as early arterial phase(EAP)or late arterial phase(LAP),portal venous phase(PVP),and equilibrium phase(EP).In addition,221 patients with liver CT contrast-enhanced images in 5 different hospitals were used for external validation.The annotation results of radiologists were used as a reference standard to evaluate the model performances.Results In the external validation datasets,the accuracy in identifying each enhanced phase reached to 90.50%-99.70%.Conclusion The automatic identification model of liver CT contrast-enhanced phases based on residual network may provide an efficient,objective,and unified image quality control tool.

Methods: CHB patients and healthy volunteers were enrolled from Shuguang Hospital Affiliated to Shanghai University of Traditional Chinese Medicine between August 21, 2018 and December 31, 2020. They were divided into three groups: healthy group, LGDHS group, and latent syndrome (LP) group. Proteomic analysis using isobaric tags for relative and absolute quantitation (iTRAQ) was performed to identify differentially expressed proteins (DEPs). Metabolomic profiling via ultra-performance liquid chromatography-tandem mass spectrometry (UPLC-MS/MS) was applied to serum samples to detect differentially regulated metabolites (DMs). Kyoto Encyclopedia of Genes and Genomes (KEGG) and Gene Ontology (GO) enrichment were employed to explore dysregulated pathways. Principal component analysis (PCA) and orthogonal partial least squares discriminant analysis (OPLS-DA) were utilized to visualize group separation and identify key metabolites and proteins contributing to LGDHS differentiation. Receiver operating characteristic (ROC) curve analysis evaluated the diagnostic performance of key biomarkers, while logistic regression models assessed their predictive accuracy. P values were corrected for multiple tests using the Benjamini-Hochberg method to control the false discovery rate (FDR). Validation of potential biomarkers was conducted using independent microarray data and real-time quantitative polymerase chain reaction (RT-qPCR). Results: A total of 150 participants were enrolled, including healthy group (n = 45), LGDHS group (n = 60), and LP group (n = 45). 254 DEPs from proteomics data and 72 DMs from metabolomic profiling were identified by PCA and OPLS-DA. DEPs were mainly enriched in immune and complement pathways, while DMs involved in amino acid and energy metabolism. The integrated analysis identified seven key biomarkers: α1-acid glycoprotein (ORM1), asparagine synthetase (ASNS), solute carrier family 27 member 5 (SLC27A5), glucosidase II alpha subunit (GANAB), hexokinase 2 (HK2), 5-methyltetrahydrofolate-homocysteine methyltransferase (MTR), and maltase-glucoamylase (MGAM). Microarray validation confirmed the diagnostic potential of these genes, with area under the curve (AUC) values for ROC analysis ranging from 0.536 to 0.759. Among these, ORM1, ASNS, and SLC27A5 showed significant differential ability in differentiating LGDHS patients (P = 0.016, P = 0.035, and P < 0.001, respectively), with corresponding AUC of 0.749, 0.743, and 0.759, respectively. A logistic regression model incorporating these three genes demonstrated an AUC of 0.939, indicating a high discriminatory power for LGDHS. RT-qPCR further validated the differential expression of ORM1 and SLC27A5 between LGDHS and LP groups (P = 0.011 and P = 0.034, respectively), with ASNS showing a consistent trend in expression (P = 0.928). Conclusion This study integrates multi-omics approaches to uncover the molecular mechanisms underlying LGDHS in CHB. The identification of biomarkers ORM1, ASNS, and SLC27A5 offers a solid basis for the objective diagnosis of LGDHS, contributing to the standardization and modernization of TCM diagnostic practices.

Objective:To translate the Nurse Delirium Care Competency Scale (NDCCS) into Chinese and test the validity and reliability of Chinese version of NDCCS among ICU nurses.Methods:According to Brislin translation model, the Chinese version of NDCCS was developed by forward translation, back translation, cross-cultural adaptation and pre-survey. From October to December of 2023, 338 ICU nurses from Sir Run Run Shaw Hospital, Zhejiang University School of Medicine were selected by cross-sectional survey to test the reliability and validity of Chinese version of NDCCS.Results:A total of 329 valid questionnaires were collected. There were 50 males and 279 females, aged 20-53 years old. The Chinese version of NDCCS included 6 dimensions and 27 items. The scale-level content validity value was 0.985, and the item-level content validity value was 0.800-1.000. Exploratory factor analysis showed a six-factor structure, the cumulative variance contribution rate was 75.700%, the Cronbach′s α coefficients of the scale amount table were 0.952, and the Cronbach′s α coefficients of the dimensions were 0.811- 0.936,and the retest reliability was 0.932.Conclusions:The Chinese version of the NDCCS has good reliability and validity, and it is suitable for assessing the delirium care competence of ICU nurses in China.

Objective:To optimize the accuracy of the intelligent hand hygiene system to monitor the hand hygiene timing warning, and provide a reference basis for healthcare workers to apply the intelligent hand hygiene system.Methods:Using a single-sample diagnostic pilot study method, 62 clinical nurses wearing smart badges working in the intensive care unit of Sir Run Run Shaw Hospital Affiliated to Zhejiang University School of Medicine in Hangzhou, from December 1, 2020 to December 31, 2021 were selected by convenience sampling methods. Direct observation was used as the gold standard. The accuracy of the warning timing of the intelligent hand hygiene monitoring system was optimized through adjusting the bed sensing rang,adjusting the time setting, adjusting the time settings according to the physical space of the ward and adding posture recognition.Results:The sensitivity of adjusting the bed sensing range was 0.935 (95% CI 0.918-0.949); the specificity was 0.008 (95% CI 0.001-0.074). The sensitivity of the temporal setting based on the physical space of the ward was 0.932(95% CI 0.915-0.946); the specificity was 0.205 (95% CI 0.087-0.410). The false positive rate with gesture recognition turned on was 86.1% higher than the false positive rate without gesture recognition which was 79.5%. The diagnostic OR based on the temporal setting of the physical space of the ward was the largest at 3.517(95% CI 1.213-10.193). Conclusions:The intelligent hand hygiene system exhibits high accuracy in monitoring hand hygiene timing. Adjusting the bed sensing range and individualizing the timing settings according to the physical space of the ward can improve the accuracy. Further optimization is needed for posture recognition to improve the accuracy.

Since the 18th CPC National Congress,General Secretary Xi Jinping has attached great significance to health assistance for foreign countries,and has made a series of important instructions.China's health assistance for foreign countries has entered a new era of development and cooperation,and has made an important contribution to the promotion of building a global community of health for all.The First Affiliated Hospital,Sun Yat-sen University has actively responded to the national strategy and send medical experts to over 11 countries along the"Belt and Road"region,such as Ghana,Equatorial Guinea,Fi-ji,Serbia,Dominica,and so on.The First Affiliated Hospital,Sun Yat-sen University has won awards at home and abroad for its efforts,including the National Advanced Group for health assistance for foreign countries,the National Advanced Individual for health assistance for foreign countries,and the Serbian Government's Gold Medal of Merit.Taking medical experts in Serbia and Dominica as examples,the author focuses on building a global reputation,and summarizes a series of initiatives from The First Affiliated Hospital,Sun Yat-sen University,including the creation of a multi-level and whole-process management system,the cohesion of back-up strength for health assistance for foreign countries.