Objective: To analyze the etiological surveillance results of influenza-like illness (ILI) cases in Jiangsu Province, and investigate the distribution characteristics of different influenza virus types, so as to provide the evidence for improving influenza prevention and control measures. Methods: Influenza laboratory testing data for sentinel surveillance of ILI cases in Jiangsu Province from 2019 to 2024 were collected through the China Influenza Surveillance Information System. The positive detection rate of influenza virus was calculated, and descriptive analysis was performed to characterize the distribution of different influenza virus types. Using the farthest neighbor linkage method, influenza virus positive detection rates clustering was analyzed by year and week. Clusters were defined based on inter-cluster distance, and the intensity of the positive detection rate was visualized through color gradients in the clustering heatmap. Results: From 2019 to 2024, a total of 183 878 ILI specimens were collected in Jiangsu Province. Among them, 20 059 specimens tested positive for influenza virus, corresponding to an overall positive detection rate of 10.91%, and an average annual positive detection rate of 10.89%. The primary circulating influenza virus types were influenza A H3N2 subtype, accounting for 40.92%, followed by influenza B Victoria linage at 34.00%, and influenza A H1N1 subtype at 24.80%. Influenza B Yamagata linage was not detected throughout the five-year period. Influenza A H3N2 subtype predominated during two distinct periods: from January to March 2019, and from June 2022 to December 2023. Influenz B Victoria linage was the dominant type from April 2019 to May 2022 and again from January to April 2024. Influenza A H1N1 subtype emerged as the primary type from May to December 2024. Year-based clustering analysis grouped the annual positive detection rates from 2019 to 2024 into three clusters. The closest cluster distance was observed between 2019 and 2024. The highest annual positive detection rate occurred in 2023. Both influenza A H3N2 and H1N1 subtype each formed a single cluster, with their peak positive detection rates also recorded in 2023. Influenza B Victoria lineage was separated into two clusters, with its highest positive detection rate occurring in 2020. Week-based clustering analysis revealed that influenza virus detection was concentrated in weeks 47 to 52 and weeks 1 to 15. More specifically, the positive detection rates for influenza A H3N2 subtype peaked during weeks 30 to 34 and weeks 42 to 52; for influenza A H1N1 subtype, during weeks 9 to 15 and weeks 51 to 52; and for influenza B Victoria lineage, during weeks 1 to 11 and weeks 50 to 52. Conclusions From 2019 to 2024, the average annual positive detection rate of influenza virus in Jiangsu Province remained relatively low. Influenza activity characterized by the alternating circulation of influenza A H1N1 subtype, influenza A H3N2 subtype, and influenza B Victoria linage. It is necessary to maintain the surveillance sensitivity for the influenza B Yamagata lineage.

OBJECTIVE To analyze the chemical constituents of the active parts of Piper wallichii. METHODS The petroleum ether-extract fraction was prepared from the methanol extract of P. wallichii. Separation and purification were performed using semi-preparative high-performance liquid chromatography. The structures of the compounds were identified by nuclear magnetic resonance spectroscopy. RESULTS Nineteen compounds were isolated from the petroleum ether-extract fraction from the methanol extract of P. wallichii， identified as 3-acetoxybenzyl benzoate （1）， 2-acetoxybenzyl benzoate （2）， 2-methoxybenzyl benzoate （3）， 3-methoxybenzyl benzoate （4）， 4-hydroxy-3-methoxybenzyl benzoate （5）， 3-hydroxybenzyl benzoate（6）， benzyl benzoate （7）， ganschisandrine （8）， lancifolin A （9）， （7R，8R，3′R）-7-acetoxy-3′，4′-dimethoxy-3，4-methylenedioxy-6′-oxo- Δ1′，4′，8′-8.3′-lignan （10）， （7S，8R，3′S）-Δ8′-3′，6′-dihydro-3′-methoxy-3，4-methylenedioxy-6′-oxo-8.3′，7.O.4′-lignan （11）， （7R， 8R，3′S）-Δ8′-3′，6′-dihydro-3′-methoxy-3，4-methylenedioxy-6′-oxo-8.3′，7.O.4′-lignan （12）， isodihydrofutoquinol A （13）， licarin A （14）， licarin B （15）， 2-（2′，5′-dimethoxyphenyl）-3，4- dimethyl-5-（3″，4″-dimethoxyphenyl）- tetrahydrofuran （16）， galgravin （17）， velutin （18）， and piyunin A （19）. CONCLUSIONS Compound 1 is a new benzyl benzoate compound. Compounds 3-5， 8 and 9 are isolated from the Piper genus for the first time， while compounds 2， 6， 10-13 and 15-19 are isolated from P. wallichii for the first time.

Objective: To investigate the Alzheimer's disease (AD) influencing factors among the elderly, so as to provide a basis for early prevention and intervention. Methods: From March to June 2024, participants aged 60 years and above from a sub-district in Haishu District, Ningbo City, Zhejiang Province were selected using a convenience sampling method. Data on demographics, lifestyle, and health status were collected through questionnaire surveys. Depressive symptoms were evaluated using the short-form Geriatric Depression Scale. The Chinese Mini-Mental State Examination (MMSE) was used for the initial screening of AD, and individuals who screened positive were further diagnosed by psychiatrists. Factors affecting AD among the elderly were analyzed using a multivariable logistic regression model. Results: A total of 3 644 individuals were surveyed, comprising 1 526 males (41.88%) and 2 118 females (58.12%). The mean age was (71.85±7.44) years. AD was detected in 200 cases, with a detection rate of 5.49%. Multivariable logistic regression analysis showed that individuals aged ≥65 years (65-<70 years, OR=3.012, 95%CI: 1.007-9.012; 70-<75 years, OR=3.131, 95%CI: 1.059-9.260; 75-<80 years, OR=5.779, 95%CI: 1.989-16.784; ≥80 years, OR=16.810, 95%CI: 5.926-47.685), those who were unmarried, divorced, or widowed (OR=1.973, 95%CI: 1.383-2.815), those with hearing loss (OR=1.573, 95%CI: 1.128-2.193), those with diabetes mellitus (OR=1.958, 95%CI: 1.362-2.814), and those with depressive symptoms (OR=4.143, 95%CI: 2.997-5.728) had a higher risk of AD. Conversely, individuals with an educational level of primary school or above (primary school, OR=0.579, 95%CI: 0.401-0.835; junior high school or above, OR=0.438, 95%CI: 0.259-0.741), and those who engaged in regular physical exercise (OR=0.414, 95%CI: 0.264-0.649) had a lower risk of AD. Conclusions The detection rate of AD was relatively high among the elderly in Haishu District. AD among the elderly was related to age, educational level, marital status, physical exercise, hearing loss, diabetes mellitus, and depressive symptoms.

The continuous emergence of SARS-CoV-2 variants as well as other potential future coronavirus has challenged the effectiveness of current COVID-19 vaccines. Therefore, there remains a need for alternative antivirals that target processes less susceptible to mutations, such as the formation of six-helix bundle (6-HB) during the viral fusion step of host cell entry. In this study, a novel high-throughput screening (HTS) assay employing a yeast-two-hybrid (Y2H) system was established to identify inhibitors of HR1/HR2 interaction. The compound IMB-9C, which achieved single-digit micromolar inhibition of SARS-CoV-2 and its Omicron variants with low cytotoxicity, was selected. IMB-9C effectively blocks the HR1/HR2 interaction in vitro and inhibits SARS-CoV-2-S-mediated cell-cell fusion. It binds to both HR1 and HR2 through non-covalent interaction and influences the secondary structure of HR1/HR2 complex. In addition, virtual docking and site-mutagenesis results suggest that amino acid residues A930, I931, K933, T941, and L945 are critical for IMB-9C binding to HR1. Collectively, in this study, we have developed a novel screening method for HR1/HR2 interaction inhibitors and identified IMB-9C as a potential antiviral small molecule against COVID-19 and its variants.

Recently, diffusion models have emerged as a promising paradigm for molecular design and optimization. However, most diffusion-based molecular generative models focus on modeling 2D graphs or 3D geometries, with limited research on molecular sequence diffusion models. The International Union of Pure and Applied Chemistry (IUPAC) names are more akin to chemical natural language than the Simplified Molecular Input Line Entry System (SMILES) for organic compounds. In this work, we apply an IUPAC-guided conditional diffusion model to facilitate molecular editing from chemical natural language to chemical language (SMILES) and explore whether the pre-trained generative performance of diffusion models can be transferred to chemical natural language. We propose DiffIUPAC, a controllable molecular editing diffusion model that converts IUPAC names to SMILES strings. Evaluation results demonstrate that our model outperforms existing methods and successfully captures the semantic rules of both chemical languages. Chemical space and scaffold analysis show that the model can generate similar compounds with diverse scaffolds within the specified constraints. Additionally, to illustrate the model's applicability in drug design, we conducted case studies in functional group editing, analogue design and linker design.

Objective To investigate the effect and mechanism of melatonin on the anoikis of melanoma cells.Methods The drug concentration of melatonin inhibiting melanoma cell line B16-F10 was optimized based on the effect on CCK-8 assay.An anti-anoikis of melanoma cell model was developed and divided it into four groups:The blank control group,the TrkB activator group,the melatonin group and the melatonin+TrkB activator group.Calce-in AM/EthD-1 fluorescence double staining was used to detect the anoikis of melanoma cells.Reactive oxygen spe-cies were detected using the fluorescent probe DCFH-DA.Western blot was used to detect the expression of Nrf2 protein and TrkB protein in each group.Results Melatonin significantly inhibited the proliferation of melanoma cells in a time-and dose-dependent manner with IC50 of 1×10-7 μmol/L.Its inhibitory effect was found to be related to in-duction of anoikis of melanoma cells.Melatonin could upregulate the generation of cellular reactive oxygen species(P<0.05),while addition of TrkB activator antagonized this effect.Melatonin could reduce the expression of Nrf2 protein and TrkB protein in melanoma cells(P<0.05),and the addition of TrkB activator could inhibite the effect of melatonin on the expression of Nrf2 protein and TrkB protein(P<0.05).Conclusions Melatonin can inhibit the pro-liferation of melanoma cell line B16-F10 through the mechanism of inducing anoikis.

Objective To explore the role of Schlafen4(SLFN4)in acute pneumonia induced by hypervirulent Klebsiella pneumoniae(hvKp)via intratracheal aerosolization.Methods Differential expression gene Slfn4 was identified after infection with hvKp based on RNA sequencing(RNA-seq)and single-cell RNA sequencing(scRNA-seq)data before Slfn4-/-mice were obtained via CRISPR/Cas gene editing technology.Slfn4-/-mice and wild mice were challenged via intratracheal aerosolization.Mortality and weight changes were recorded for 14 d,while pathological changes and expression levels of interleukin-6(IL-6),IL-17A,IL-1β,and tumor necrosis factor-α(TNF-α)were detected at 48 h post-infection.Results SLFN4 expression was significantly increased in wild mice after infection with hvKp.Survival was significantly increased,and weight loss was mitigated before gradual recovery in Slfn4-/-mice after infection.The knockout of SLFN4 attenuated alveolar wall thickening,diminished neutrophil infiltration,and suppressed pro-inflammatory cytokine production(IL-6,IL-17A,IL-1β,TNF-α)in the lung at 48 h post-infection.Conclusion The deletion of SLFN4 may suppress the expression of specific pro-inflammatory cytokines and attenuate neutrophil over-recruitment in the lung,thereby alleviating pneumonia in mice after hvKp infection.

Objective To explore the biological functions of pyroptosis-related genes in pneumonic plague using bioinformatics methods,and to evaluate their potential applicability as diagnostic markers.Methods The pneumonic plague-related dataset GSE220123 was retrieved from the Gene Expression Omnibus(GEO)database and screened for differentially expressed pyroptosis-related genes(DE-PRGs).The functions of DE-PRGs were studied via Gene Ontology(GO),Kyoto Encyclopedia of Genes and Genomes(KEGG)enrichment analyses,and immune infiltration analysis.The hub genes were identified via protein-protein interaction(PPI)network analysis,and further screened for key genes with sustained high expression characteristics based on differential expression analysis.The relative expression levels of the key genes were verified using the reverse transcription real-time quantitative PCR(qPCR)method.Results A total of 17 DE-PRGs were screened,and PPI network analysis revealed 7 Hub genes.Among them,Casp4 continued to be up-regulated during the course of pneumonic plague.The results of reverse transcription qPCR were consistent with the those of bioinformatic analyses.Conclusion DE-PRGs play a crucial role in the immune response of pneumonic plague,especially Casp4,which has significant applications as a diagnostic biomarker and potential therapeutic target for pneumonic plague.

Objective : To identify autophagy-related genes involved in pulmonary infection caused by the highly drug-resistant and virulent methicillin-resistant Staphylococcus aureus strain USA300 ( USA300) ，and to explore the underlying molecular mechanisms ， thereby providing potential targets for immunotherapy． Methods: The GSE220943 dataset of a USA300-induced pulmonary infection mouse model was obtained from the GEO database． Differentially expressed genes ( DEGs ) were identified using the DESeq2 package． Autophagy-related genes ( ARGs) were retrieved from the MSigDB and Autophagy databases．Weighted gene co-expression network analysis ( WGCNA) was performed to construct gene co-expression modules．Genes overlapping among DEGs，ARGs，and WGCNA modules were identified as autophagy-related DEGs．Gene Ontology ( GO) enrichment analysis was con- ducted using the clusterProfiler R package，while Kyoto Encyclopedia of Genes and Genomes ( KEGG) pathway en- richment analysis was performed via the Metascape platform．Immune cell infiltration was analyzed using the Immu- CellAI-mouse website．A protein - protein interaction ( PPI) network was constructed using the STRING database， and hub genes were identified through topological analysis in Cytoscape． Receiver operating characteristic curve ( ROC) curves were plotted via the website https: / /www．bioinformatics．com．cn． Finally，key gene expression was validated in mouse lung tissues by real-time quantitative reverse transcription PCR ( RT-qPCR) ． Results: A total of 6 135，4 075，3 680，and 2 342 differentially expressed genes ( DEGs) were identified at 12，24，48，and 96 hours post-infection，respectively．By integrating DEGs，autophagy-related genes ( ARGs) ，and WGCNA mod- ules，19 autophagy-related DEGs were identified． GO and KEGG enrichment analyses indicated that these genes were mainly involved in CD4 + T cell activation and regulation，innate immune responses，and autophagosome mem- brane formation．Immune infiltration analysis revealed that innate immune cells such as neutrophils and dendritic cells predominated during the early phase of infection，while γδ T cells and M2 macrophages became more promi- nent in the later stages．PPI network analysis identified 12 hub autophagy-related genes，among which three upreg- ulated key genes ( Eif2ak2，Ikbke，and Nfkbiz) were further confirmed．The area under the ROC curve for all three genes was 1. 000．RT-qPCR validation demonstrated significantly elevated expression of these three genes in lung tissues at 24 hours post-infection ( all P＜0. 05) ． Conclusion Eif2ak2，Ikbke，and Nfkbiz may be involved in the pulmonary infection caused by USA300 by promoting autophagy and hold promise as potential targets for immuno- therapy．

Recently,diffusion models have emerged as a promising paradigm for molecular design and optimization.However,most diffusion-based molecular generative models focus on modeling 2D graphs or 3D geom-etries,with limited research on molecular sequence diffusion models.The International Union of Pure and Applied Chemistry(IUPAC)names are more akin to chemical natural language than the simplified molecular input line entry system(SMILES)for organic compounds.In this work,we apply an IUPAC-guided conditional diffusion model to facilitate molecular editing from chemical natural language to chemical language(SMILES)and explore whether the pre-trained generative performance of diffusion models can be transferred to chemical natural language.We propose DiffIUPAC,a controllable molecular editing diffusion model that converts IUPAC names to SMILES strings.Evaluation results demonstrate that our model out-performs existing methods and successfully captures the semantic rules of both chemical languages.Chemical space and scaffold analysis show that the model can generate similar compounds with diverse scaffolds within the specified constraints.Additionally,to illustrate the model's applicability in drug design,we conducted case studies in functional group editing,analogue design and linker design.