ObjectiveTo analyze the evolutionary characteristics and genetic variations of the HA (hemagglutinin) and NA (neuraminidase) genes of influenza A(H1N1) viruses in Shanghai during 2024, to investigate their transmission patterns, and to evaluate their potential impact on vaccine effectiveness. MethodsFrom January to October 2024, throat swab specimens were collected from influenza like illness (ILI) patients at 4 hospitals in Shanghai. Real-time fluorescence ploymerase chain reaction (RT-PCR) was used for virus detection and isolation of H1N1 influenza viruses. Forty influenza A(H1N1) virus strains were sequenced using Illumina NovaSeq 6000 platform, followed by phylogenetic analyses, genetic distance analysis, and amino acid variation analyses of HA and NA genes. ResultsPhylogenetic tree of the HA and NA genes revealed that the 40 influenza A(H1N1) virus strains circulating in Shanghai in 2024 exhibited no significant geographic clustering, with a broad origin of strains and complex transmission chains. Genetic distance analyses demonstrated that the average intra-group genetic distances of HA and NA genes among the Shanghai strains were 0.005 1±0.000 6 and 0.004 6±0.000 6, respectively, which were comparable to or higher than those observed in global surveillance strains. Both HA and NA genes displayed frequent mutations. Compared to the 2023‒2024 and 2024‒2025 Northern Hemisphere A(H1N1) vaccine strains (WHO-recommended), the HA proteins of 40 Shanghai strains exhibited amino acid substitutions at positions 120, 137, 142, 169, 216, 223, 260, 277, 356 and 451, with critical mutations at positions 137 and 142 located within the Ca2 antigenic determinant. Furthermore, mutations in the NA protein were observed at positions 13, 50, 200, 257, 264, 339 and 382. ConclusionThe genetic background of the 2024 Shanghai influenza A(H1N1) virus strains is complex and diverse, and antigenic variation may affect vaccine effectiveness. Therefore, it is recommended to enhance genomic surveillance of influenza viruses, evaluate vaccine suitability, and implement more targeted prevention and control strategies against imported influenza viruses.

AIM To study the chemical constituents from ethyl acetate fraction of Balanophora harlandii Hook.f.and their tyrosinase inhibitory activity.METHODS Separation and purification were performed using silica gel,MCI,ODS,Sephadex LH-20 and semi-preparative HPLC,then the structures of obtained compounds were identified by physicochemical properties and spectral data.The monophenolase inhibitory activity was determined by the tyrosinase-catalyzed oxidation of L-tyrosine.RESULTS Twenty-four compounds were isolated and identified as sesamin(1),methyl caffeate(2),quercetin(3),5,7-dihydroxychromanone(4),methyl 3,4-dihydroxybenzoate(5),esculetin(6),kaempferol(7),naringenin(8),pyrogallic acid(9),pinosylvin(10),methyl propionate(11),caffeic acid(12),saccharinol(13),ferulic acid(14),trans-p-hydroxycinnamic acid(15),cinnamic acid(16),vanillic acid(17),vanillin(18),4-hydroxyacetophenone(19),4-hydroxybenzaldehyde(20),apigenin(21),(-)-isolariciresinol(22),(-)-secoisolariciresinol(23)and meso-2,3-di(3′,4′-methylenedioxybenzyl)butane-1,4-diol(24).The IC50 values of compounds 3,5,7,8,19,and 20 ranged from(0.246 5±0.028 3)to(1.278 2±0.021 3)mmol/L.CONCLUSION Compounds 1-9、11、15、17-21、24 are isolated from this plant for the first time,and 1,6,9,17-19,24 are first isolated from genus Balanophora.Compounds 3、5、7、8、19 and 20 have tyrosinase inhibitory activity.

AIM To study the chemical constituents from ethyl acetate fraction of Balanophora harlandii Hook.f.and their tyrosinase inhibitory activity.METHODS Separation and purification were performed using silica gel,MCI,ODS,Sephadex LH-20 and semi-preparative HPLC,then the structures of obtained compounds were identified by physicochemical properties and spectral data.The monophenolase inhibitory activity was determined by the tyrosinase-catalyzed oxidation of L-tyrosine.RESULTS Twenty-four compounds were isolated and identified as sesamin(1),methyl caffeate(2),quercetin(3),5,7-dihydroxychromanone(4),methyl 3,4-dihydroxybenzoate(5),esculetin(6),kaempferol(7),naringenin(8),pyrogallic acid(9),pinosylvin(10),methyl propionate(11),caffeic acid(12),saccharinol(13),ferulic acid(14),trans-p-hydroxycinnamic acid(15),cinnamic acid(16),vanillic acid(17),vanillin(18),4-hydroxyacetophenone(19),4-hydroxybenzaldehyde(20),apigenin(21),(-)-isolariciresinol(22),(-)-secoisolariciresinol(23)and meso-2,3-di(3′,4′-methylenedioxybenzyl)butane-1,4-diol(24).The IC50 values of compounds 3,5,7,8,19,and 20 ranged from(0.246 5±0.028 3)to(1.278 2±0.021 3)mmol/L.CONCLUSION Compounds 1-9、11、15、17-21、24 are isolated from this plant for the first time,and 1,6,9,17-19,24 are first isolated from genus Balanophora.Compounds 3、5、7、8、19 and 20 have tyrosinase inhibitory activity.

Background: Intravenous immunoglobulin (IVIG)-resistant Kawasaki disease is associated with coronary artery lesion development.Purpose: This study aimed to explore the factors associated with IVIG-resistance and construct and validate an interpretable machine learning (ML) prediction model in clinical practice. Methods: Between December 2014 and November 2022, 602 patients were screened and risk factors for IVIG-resistance investigated. Five ML models are used to establish an optimal prediction model. The SHapley Additive exPlanations (SHAP) method was used to interpret the ML model. Results: Na+, hemoglobin (Hb), C-reactive protein (CRP), and globulin were independent risk factors for IVIG-resistance. A nonlinear relationship was identified between globulin level and IVIG-resistance. The XGBoost model exhibited excellent performance, with an area under the receiver operating characteristic curve of 0.821, accuracy of 0.748, sensitivity of 0.889, and specificity of 0.683 in the testing set. The XGBoost model was interpreted globally and locally using the SHAP method. Conclusion Na+, Hb, CRP, and globulin levels were independently associated with IVIG-resistance. Our findings demonstrate that ML models can reliably predict IVIG-resistance. Moreover, use of the SHAP method to interpret the established XGBoost model's findings would provide evidence of IVIG-resistance and guide the individualized treatment of Kawasaki disease.

Background: Intravenous immunoglobulin (IVIG)-resistant Kawasaki disease is associated with coronary artery lesion development.Purpose: This study aimed to explore the factors associated with IVIG-resistance and construct and validate an interpretable machine learning (ML) prediction model in clinical practice. Methods: Between December 2014 and November 2022, 602 patients were screened and risk factors for IVIG-resistance investigated. Five ML models are used to establish an optimal prediction model. The SHapley Additive exPlanations (SHAP) method was used to interpret the ML model. Results: Na+, hemoglobin (Hb), C-reactive protein (CRP), and globulin were independent risk factors for IVIG-resistance. A nonlinear relationship was identified between globulin level and IVIG-resistance. The XGBoost model exhibited excellent performance, with an area under the receiver operating characteristic curve of 0.821, accuracy of 0.748, sensitivity of 0.889, and specificity of 0.683 in the testing set. The XGBoost model was interpreted globally and locally using the SHAP method. Conclusion Na+, Hb, CRP, and globulin levels were independently associated with IVIG-resistance. Our findings demonstrate that ML models can reliably predict IVIG-resistance. Moreover, use of the SHAP method to interpret the established XGBoost model's findings would provide evidence of IVIG-resistance and guide the individualized treatment of Kawasaki disease.

Background: Intravenous immunoglobulin (IVIG)-resistant Kawasaki disease is associated with coronary artery lesion development.Purpose: This study aimed to explore the factors associated with IVIG-resistance and construct and validate an interpretable machine learning (ML) prediction model in clinical practice. Methods: Between December 2014 and November 2022, 602 patients were screened and risk factors for IVIG-resistance investigated. Five ML models are used to establish an optimal prediction model. The SHapley Additive exPlanations (SHAP) method was used to interpret the ML model. Results: Na+, hemoglobin (Hb), C-reactive protein (CRP), and globulin were independent risk factors for IVIG-resistance. A nonlinear relationship was identified between globulin level and IVIG-resistance. The XGBoost model exhibited excellent performance, with an area under the receiver operating characteristic curve of 0.821, accuracy of 0.748, sensitivity of 0.889, and specificity of 0.683 in the testing set. The XGBoost model was interpreted globally and locally using the SHAP method. Conclusion Na+, Hb, CRP, and globulin levels were independently associated with IVIG-resistance. Our findings demonstrate that ML models can reliably predict IVIG-resistance. Moreover, use of the SHAP method to interpret the established XGBoost model's findings would provide evidence of IVIG-resistance and guide the individualized treatment of Kawasaki disease.

Background: Intravenous immunoglobulin (IVIG)-resistant Kawasaki disease is associated with coronary artery lesion development.Purpose: This study aimed to explore the factors associated with IVIG-resistance and construct and validate an interpretable machine learning (ML) prediction model in clinical practice. Methods: Between December 2014 and November 2022, 602 patients were screened and risk factors for IVIG-resistance investigated. Five ML models are used to establish an optimal prediction model. The SHapley Additive exPlanations (SHAP) method was used to interpret the ML model. Results: Na+, hemoglobin (Hb), C-reactive protein (CRP), and globulin were independent risk factors for IVIG-resistance. A nonlinear relationship was identified between globulin level and IVIG-resistance. The XGBoost model exhibited excellent performance, with an area under the receiver operating characteristic curve of 0.821, accuracy of 0.748, sensitivity of 0.889, and specificity of 0.683 in the testing set. The XGBoost model was interpreted globally and locally using the SHAP method. Conclusion Na+, Hb, CRP, and globulin levels were independently associated with IVIG-resistance. Our findings demonstrate that ML models can reliably predict IVIG-resistance. Moreover, use of the SHAP method to interpret the established XGBoost model's findings would provide evidence of IVIG-resistance and guide the individualized treatment of Kawasaki disease.

OBJECTIVE To investigate the multi-target mechanism of action of Poecilobdella manillensis lyophilized powder(SZ) against hyperuricemia(HUA) based on network pharmacology and transcriptomics approaches, combined with animal experiments. METHODS Utilizing Symmap, SwissTargetPrediction, and Pharmmapper databases, the potential active components and corresponding targets of SZ were obtained. Through the Gene Cards and OMIM databases, HUA-related targets were obtained. By taking the intersection mapping, the common targets of SZ and HUA were identified. Cytoscape 3.9.0 software was used to construct a drug component-disease target interaction network, and in combination with the STRING database, a protein interaction network was built and core targets were screened. The DAVID database was used to perform GO biological function annotation and KEGG pathway enrichment analysis on the intersecting targets. A mouse model of HUA was constructed using potassium oxyzate combined with high purine diet, and the effects of SZ on these mice were examined using ELISA and biochemical index detection. qRT-PCR was used to validate the results of RNA-Seq and network pharmacology enrichment analysis. RESULTS Network pharmacological analysis identified 11 major bioactive substances in SZ and 72 potential targets involved in the treatment of hyperuricemia, involving multiple biological processes and different signaling pathways. It was shown that SZ significantly reduced serum uric acid, creatinine and urea nitrogen levels in hyperuricemic mice by inhibiting xanthineoxidase activity. SZ also reduced the levels of URAT1 while increasing the levels of ABCG2. RNA sequencing analysis revealed that there were 112, 536 and 107 differentially expressed genes in the model group vs treated group, control group vs model group and control group vs treated group, respectively. qRT-PCR results indicated that SZ downregulated the expression of genes related to Th17 cell differentiation as well as mRNA of genes on IL-17 and PI3K/Akt signaling pathways. CONCLUSION SZ has therapeutic effects on hyperuricemia. The mechanism of action maybe related to the inhibition of hepatic xanthineoxidase activity, down-regulation of URAT1 levels, up-regulation of ABCG2 levels, affecting the differentiation of Th17 cells and thus the IL-17 signaling pathway, thereby reducing the inflammatory response.

177Lu-prostate specific membrane antigen(PSMA)radio-ligand therapy has been approved abroad for advanced prostate cancer and has been in several clinical trials in China.Based on domestic clinical practice and experimental data and referred to international experience and viewpoints,the expert group forms a consensus on the clinical application of 177Lu-PSMA radio-ligand therapy in prostate cancer to guide clinical practice.

Objective To explore the analgesic effect of different doses of dezocine combined with ropivacaine for thoracic paravertebral block(TPVB)on patients undergoing thoracoscopic radical resection of lung cancer and the influence on hemodynamics and immune function of patients.Methods Patients with lung cancer who underwent thoracoscopic radical resection were divided into low-dose group and high-dose group according to random number table method.Both groups of patients were given total intravenous anesthesia to complete the surgery.At 15 min before general anesthesia induction,the low-dose group was given TPBV with 0.1 mg·kg-1 dezocine+0.375％ropivacaine for a total of 20 mL,and the high-dose group was given TPBV with 0.15 mg·kg-1 dezocine+0.375％ropivacaine for a total of 20 mL.Comparisons were performed on both groups in terms of analgesic effect,hemodynamic parameters,immune function and occurrence of adverse drug reactions.Results There were 48 cases in low-dose group and 46 cases in high-dose group.In low-dose group,the heart rate values before TPVB,before skin incision,at 5 min after sectioning and at the end of surgery were(78.52±6.54),(70.79±7.07),(74.48±6.68)and(76.69±7.29)beat·min-1,the mean arterial pressure values were(93.16±5.72),(86.38±7.51),(92.15±6.36)and(91.14±6.13)mmHg.In high-dose group,the heart rate values at the above time points were(79.36±7.11),(71.68±6.49),(74.76±7.06)and(76.57±6.52)beat·min-1;the mean arterial pressure values were(93.89±7.18),(85.27±7.41),(90.34±6.52)and(92.43±6.34)mmHg,there were no statistical differences between the two groups(all P＞0.05).The resting state scores at 2,6 and 12 h after surgery were(1.38±0.19),(1.54±0.21)and(1.72±0.16)points,the pain scores at motion state were(1.88±0.15),(2.36±0.37)and(3.26±0.38)points in low-dose group;in high-dose group,the resting state scores were(1.32±0.17),(1.58±0.22)and(1.81±0.18)points,the pain scores at motion state were(1.81±0.13),(2.11±0.31)and(3.03±0.36)points,respectively,there were no statistical differences between the two groups(all P＞0.05).The number of analgesic pump compressions at 24 h after surgery and the number of cases with analgesic remedy were(5.12±1.26)times and 15 cases in low-dose group and were(4.74±1.03)times and 10 cases in high-dose group,with no statistical differences between the groups(all P＞0.05).The percentages of CD3+cells in low-dose group at the end of surgery and at 12 h and 24 h after surgery were(68.51±6.76)％,(54.22±5.43)％and(51.47±6.58)％,the percentages of CD4+cells were(40.29±5.02)％,(34.94±4.79)％and(30.48±5.11)％,CD4+/CD8+ratios were 1.54±0.34,1.36±0.28 and 1.16±0.23;the percentages of CD3+cells in high-dose group were(67.92±7.11)％,(56.58±6.36)％and(54.47±6.89)％,percentages of CD4+cells were(41.33±5.75)％,(35.86±5.21)％and(32.27±4.78)％,the CD4+/CD8+were 1.53±0.35,1.40±0.30 and 1.22±0.26,all with no significant difference(all P＞0.05).The incidence of postoperative adverse drug reactions in high-dose group and low-dose group were 32.61％and 14.58％,with significant difference(P＜0.05).Conclusion When TPVB regimen of dezocine combined with ropivacaine is used in thoracoscopic radical resection of lung cancer,the analgesic effect of low-dose dezocine is comparable to that of high-dose dezocine,with lower risk of adverse drug reactions.