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.

ObjectiveTo investigate the role of interleukin （IL）-17A in acute inhalational pneumonia induced by the highly drug-resistant and hypervirulent Staphylococcus aureus strain USA300-R in mice. MethodsAn acute inhalational pneumonia model was established in mice using an aerosolized pulmonary delivery technique. RNA sequencing （RNA-seq） and enzyme-linked immunosorbent assay （ELISA） were employed to examine the expression dynamics of Il17a mRNA and IL-17A protein， respectively， in the lungs of infected mice. Il17a knockout （Il17a^-/-） mice were generated using CRISPR/Cas9 gene editing technology. The survival rate， body weight， bacterial load in lung tissue， and histopathological changes were compared between Il17a^-/- and wild-type （WT） mice following inhalational infection with USA300-R. Results12 hours after USA300-R infection， compared to pre-infection， the expression level of Il17a mRNA in lung tissue and the level of IL-17A protein in bronchoalveolar lavage fluid （BALF） increased by approximately 50-fold （P<0.01） and 6-fold （P<0.001）， respectively. Compared to WT mice， Il17a^-/- mice exhibited approximately 10-fold higher bacterial loads in lung tissue at both 12 and 24 hours post-infection （P<0.001， P<0.05）. However， they showed significantly attenuated lung histopathological injury， reduced alveolar wall thickening， markedly decreased neutrophil infiltration， and an approximately 50% improvement in survival rate （P<0.05）. ConclusionIn acute Staphylococcus aureus USA300-R inhalational pneumonia， IL-17A contributes to bacterial clearance by recruiting neutrophils； however， excessive neutrophil infiltration exacerbates pulmonary inflammation and injury， reduces survival rates， and represents a potential therapeutic target.

ObjectiveTo analyze the epidemiological characteristics of 21 respiratory pathogens in influenza-like illness (ILI) cases in Jing’an District, Shanghai in 2024, and to provide a scientific basis for the prevention and control of respiratory infectious diseases. MethodsData of1 907 ILI cases at four sentinel hospitals in Jing’an District were collected from January to December 2024. Nasopharyngeal swab samples were collected and tested for 21 respiratory pathogens using polymerase chain reaction (PCR) methods. Chi-square test and Cochran-Armitage trend test were used for data analyses. ResultsAmong the 1 907 ILI cases, 1 340 were tested positive (70.27%), including 1 160 (60.83%) virus-positive cases, 424 (22.23%) bacteria-positive cases , and 86 (4.51%) positive cases of other pathogens (fungi, mycoplasma, and chlamydia). The top five viruses by detection rate were: influenza virus (14.84%), SARS-CoV-2 (14.47%), rhinovirus (12.69%), adenovirus (7.08%), and parainfluenza virus (6.71%). The top two bacteria by detection rate were Streptococcus pneumoniae (14.47%) and Haemophilus influenzae (10.33%). Among other pathogens (fungi, mycoplasma, and chlamydia), Mycoplasma pneumoniae showed the highest detection rate (4.30%). In terms of age distribution, statistically significant differences were observed in the detection rates of SARS-CoV-2, Legionella, and Klebsiella pneumoniae (P<0.05), with the highest rates found in individuals aged 65 years and above. Statistically significant differences were also found in the detection rates of rhinovirus, adenovirus, enterovirus, common coronavirus, respiratory syncytial virus, bocavirus, parainfluenza virus, human metapenu-movirus, Streptococcus pneumoniae, Haemophilus influenzae, and Mycoplasma pneumoniae among different age groups (P<0.05), all showing the highest detection rates in the 0‒<15 years age group. In terms of seasonal distribution, SARS-CoV-2, adenovirus, parainfluenza virus, enterovirus, Streptococcus pneumoniae, Haemophilus influenzae, and Mycoplasma pneumoniae showed epidemic peaks in summer; rhinovirus, common coronavirus, bocavirus, and Klebsiella pneumoniae had higher detection rates in autumn. Influenza virus exhibited a peak incidence during winter, while human metapenu-movirus peaked in winter and spring. Significant differences in co-infection detection rates were observed among age groups, with the rate in children aged 0‒<15 years (34.81%) being the highest. The co-infection detection rate was higher in males than in females (P=0.019). Both the single-pathogen detection rate and the co-infection detection rate (P<0.001) varied significantly across seasons: the single-pathogen detection rate was highest in winter (62.06%), while the co-infection detection rate peaked in summer (31.20%) and was lowest in winter (14.52%). ConclusionBased on detection rates, the main pathogens in the ILI population of Jing’an District, Shanghai, 2024 were influenza virus, SARS-CoV-2, rhinovirus, adenovirus, parainfluenza virus, common coronavirus, enterovirus, Human metapenu-movirus, Streptococcus pneumoniae, Haemophilus influenzae, and Mycoplasma pneumoniae. Pathogen detection rates varied by age and season. Coinfection rates were much higher in children than in adults, higher in males than in females, and peaked in summer while being lowest in winter.

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 evaluate the application of metagenomic next-generation sequencing（mNGS）in the identification of non-tuberculous mycobacteria（NTM）.Methods:A retrospective analysis was conducted on mNGS results of 358 bronchoalveolar lavage fluid（BALF）samples positive for NTM collected at the First Affiliated Hospital of Zhejiang University School of Medicine from February 2021 to January 2024. The analysis included the distribution of NTM species，the detection of mixed pathogens，and the performance of conventional mycobacterial detection methods.Results:The results showed that 362 strains of 15 NTM species were identified from 350 specimens，8 specimens were not precise to the species level. The most frequently detected species were Mycobacterium intracellulare（37.3%，135/362）， Mycobacterium abscessus（26.8%，97/362），followed by Mycobacterium avium（11.0%，40/362）， Mycobacterium kansasii（8.0%，29/362）and Mycobacterium chelonae（7.7%，28/362）. Single NTM species were detected in 339 specimens，while two or three NTM species were simultaneously detected in 11 specimens（3.1%，11/358）. Non-NTM microorganisms co-infected were detected in 53.4%（191/358）of NTM-positive BALF samples，including common pathogens such as Pseudomonas aeruginosa， Staphylococcus aureus，and Aspergillus fumigatus；and difficult-to-identify pathogens such as Legionella pneumophila and Talaromyces marneffei. In NTM-positive patients detected by mNGS，the results supported the diagnosis of NTM infection in 298 cases（298/358，83.2%）and 105 cases（105/358，29.3%）initiated anti-NTM treatment accordingly；while in 60 cases（60/358，16.8%）the positive results were considered as colonization or unrelated to clinical infection. For samples tested with acid-fast staining，mycobacterial liquid culture，and DNA microarray，the positivity rates for NTM were 31.5%（73/232），48.7%（57/117），and 43.0%（46/107），respectively. Conclusions:mNGS demonstrates advantages in identification of NTM. However，the test may detect multiple microorganisms，in that case，the interpretation with clinical and radiological results is requried to determine the main pathogens.

OBJECTIVE T o compare the biological characteristics,drug resistance and pathogenicity between two strains of mucoid Pseudomonas aeruginosa and the standard strain PAO1.METHODS The strains were identified,and biofilms were detected by 96-well plates method.The bacterial drug resistance was detected by fully automatic drug susceptibility analysis system,the expression levels of RNA of virulence factors were detected by real-time fluorescent quantitative polymerase chain reaction(RT-PCR);the models of rats with pneumonia infection were established through liquid aerosol lung delivery method,the survival status of the rats was observed,and the lev-els of cytokines in bronchoalveolar lavage fluid(BALF)were detected.RESULTS NY4593,NY4605 and PAO1 strains were successfully isolated and identified.NY4593 and NY4605 showed high-yield biofilms,while PAO1 showed low-yield biofilms.The drug resistance rates of NY4593 and NY4605 were remarkably higher than those of the PAO1.The expression levels of exoT and exoY gene RNA of the NY4593 and NY4605 strains were higher than those of the PAO1 strains(P＜0.05);the expression level of exoS gene RNA of the NY4605 was lower than that of the PAO1(P＜0.05).Under the same infection dose,the PAO1 showed more powerful pathogenicity,and the secretion volumes of inflammatory factors interleukin-6(IL-6),interleuki-1β(IL-1β)and interleukin-17A(IL-17A)of the PAO1 were(2858.00±150.30)pg/ml,(7821.00±761.20)pg/ml and(1079.00±225.40)pg/ml respectively,remarkably higher than those of the NY4593 and NY4605(P＜0.05).CONCLUSION The clini-cal mucoid NY4593 and NY4605 remarkably differ from PAO1 in biology and pathogenicity.The study may facilitate deep understanding of the mechanisms of PA infection and provide guidance for clinical treatment,prevention and control.

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.

Sacroiliac complex injuries are commonly seen in high-energy pelvic fractures. The injuries make a big difference in treatment patterns due to the diverse injury types, posing considerable challenges in formulating optimal treatment strategies, and hence are persistent clinical difficulties in orthopedic trauma. The clinical management of sacroiliac complex injuries presents several key challenges such as a non-negligible rate of missed diagnoses in associated vascular and visceral injuries, absence of standardized protocols for surgical approaches and reduction-fixation strategies across different injury patterns, and ongoing controversies regarding surgical indications and optimal timing for patients combined with concomitant lumbosacral plexus injuries. Currently, no systematic clinical guidelines are available for the diagnosis and treatment of sacroiliac complex injuries both domestically and internationally. To this end, the Pelvic and Acetabular Surgery Group, Orthopedic Branch, China International Exchange and Promotive Association for Medical and Health Care and Orthopedic Physician Branch, Chinese Medical Doctor Association organized a panel of domestic experts in the field to develop the Clinical guideline for the diagnosis and treatment of sacroiliac complex injuries ( version 2025), based on evidence-based medicine and adhering to the principles of scientific rigor, clinical applicability, and innovation. These guidelines provided 11 recommendations covering diagnosis, therapeutic principles and techniques, management protocols for lumbosacral plexus injuries, outcome evaluation, and postoperative rehabilitation pathways, etc., aiming to standardize the clinical management of sacroiliac complex injuries.

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.