The PA-PB1 interface of the influenza polymerase is an attractive site for antiviral drug design. In this study, we designed and synthesized a mini-library of indazole-containing compounds based on rational structure-based design to target the PB1-binding interface on PA. Biological evaluation of these compounds through a viral yield reduction assay revealed that compounds 27 and 31 both had a low micromolar range of the half maximal effective concentration (EC₅₀) values against A/WSN/33 (H1N1) (8.03 μmol/L for 27; 14.6 μmol/L for 31), while the most potent candidate 24 had an EC₅₀ value of 690 nM. Compound 24 was effective against different influenza strains including a pandemic H1N1 strain and an influenza B strain. Mechanistic studies confirmed that compound 24 bound PA with a K _d which equals to 1.88 μmol/L and disrupted the binding of PB1 to PA. The compound also decreased the lung viral titre in mice. In summary, we have identified a potent anti-influenza candidate with potency comparable to existing drugs and is effective against different viral strains. The therapeutic options for influenza infection have been limited by the occurrence of antiviral resistance, owing to the high mutation rate of viral proteins targeted by available drugs. To alleviate the public health burden of this issue, novel anti-influenza drugs are desired. In this study, we present our discovery of a novel class of indazole-containing compounds which exhibited favourable potency against both influenza A and B viruses. The EC₅₀ of the most potent compounds were within low micromolar to nanomolar concentrations. Furthermore, we show that the mouse lung viral titre decreased due to treatment with compound 24. Thus our findings identify promising candidates for further development of anti-influenza drugs suitable for clinical use.

OBJECTIVE: The relationship between fish consumption and stroke is inconsistent, and it is uncertain whether this association varies across predicted stroke risks. METHODS: A cohort study comprising 95,800 participants from the Prediction for Atherosclerotic Cardiovascular Disease Risk in China project was conducted. A standardized questionnaire was used to collect data on fish consumption. Participants were stratified into low- and moderate-to-high-risk categories based on their 10-year stroke risk prediction scores. Hazard ratios ( HRs) and 95% confidence intervals ( CIs) were estimated using Cox proportional hazard models and additive interaction by relative excess risk due to interaction (RERI), attributable proportion (AP), and synergy index (SI). RESULTS: During 703,869 person-years of follow-up, 2,773 incident stroke events were identified. Higher fish consumption was associated with a lower risk of stroke, particularly among moderate-to-high-risk individuals ( HR = 0.53, 95% CI: 0.47-0.60) than among low-risk individuals ( HR = 0.64, 95% CI: 0.49-0.85). A significant additive interaction between fish consumption and predicted stroke risk was observed (RERI = 4.08, 95% CI: 2.80-5.36; SI = 1.64, 95% CI: 1.42-1.89; AP = 0.36, 95% CI: 0.28-0.43). CONCLUSION Higher fish consumption was associated with a lower risk of stroke, and this beneficial association was more pronounced in individuals with moderate-to-high stroke risk.
Humans ; China/epidemiology* ; Male ; Female ; Stroke/etiology* ; Middle Aged ; Prospective Studies ; Incidence ; Aged ; Animals ; Fishes ; Risk Factors ; Diet ; Seafood ; Adult ; Cohort Studies

Objective To design a nurse-driven intelligent individualized medication management system to enhance the whole-course patient management efficiency.Methods The system consisted of an in-hospital PC terminal,a mobile patient terminal and a mobile medical terminal.The in-hospital PC terminal was developed with B/S architecture and.NET;the patient and medical terminals both were realized with Java.The patient terminal was made up of a personal information module,a data reporting module and a message center module,and the in-hospital PC and medical terminals were composed of several modules for patient management,data management,statistical analysis and system management.Results The system developed provided whole-course intelligent management for out-of-hospital patients comprehensively,continuously and actively,lowering acute morbidity and nurses'workload for follow-up.Conclusion The system developed enhances patients'awareness of self-health management and improves nurses'efficiency for individualized medication management.[Chinese Medical Equipment Journal,2024,45(11):44-48]

Objective The causes of bleeding after endoscopic duodenal papilloma resection were analyzed and discussed,and the prediction model of nomogram was established.Methods A total of 233 patients who underwent endoscopic duodenal papilloma resection in our hospital from January 2018 to December 2023 were retrospectively analyzed,and they were divided into bleeding group(n=31 cases)and non-bleeding group(n=202 cases)according to whether postoperative bleeding occurred.The clinical data of the two groups were compared,the independent risk factors for postoperative bleeding were analyzed by multi-factor logistic regression,the risk nomogram prediction model was constructed,and the Bootstrap method was used for 1000 repeated samples to carry out internal verification.Results Anticoagulant drugs(OR=9.063,95％CI:2.132-38.525),lesion diameter ≥2 cm(OR=2.802,95％CI:1.073-7.321),intraoperative fragment resection(OR=27.653,95％CI:3.055～619.174)and pancreatic complications(OR=6.859,95％CI:1.930～24.377)were independent risk factors for postoperative bleeding after endoscopic duodenal papilloma resection(P＜0.05).A risk prediction nomogram model was constructed according to the Logistic regression analysis results.The samples were repeatedly sampled 1000 times through Bootstrap method for internal verification.The area under the ROC curve was 0.850,and the 95％CI was 0.780-0.913,indicating good differentiation ability of the model.Calibration curve analysis indicated that the prediction probability of postoperative bleeding predicted by the nomogram prediction model was in good agreement with the actual probability of postoperative bleeding,and Hosmer-Lemeshow showed good goodness of fit(x2=3.304 9,P=0.913 8).Conclusion Taking anticoagulant drugs,lesion diameter ≥2 cm,intraoperative segmentary resection,and postoperative combination of pancreas were independent risk factors for bleeding after endoscopic duodenal papilloma resection.A nomogram prediction model was established to help clinical assessment of postoperative bleeding risk in patients and improve decision-making basis for early prevention.

Objective To evaluate the bioequivalence of test product and reference product in a single dose of amoxicillin clavulanate potassium tablet under fasting and fed conditions in healthy volunteers.Methods An open label,randomized,single dose,four-period,crossover bioequivalence study was designed.Fasting and postprandial tests were randomly divided into 2 administration sequence groups according to 1:1 ratio,amoxicillin clavulanate potassium tablet test product or reference product 375 mg,oral administration separately,liquid chromatography tanden mass spectrometry was applied to determine the concentration of amoxicillin and clavulanate potassium in plasma of healthy subjects after fasting or fed administration,while Phoenix WinNonlin 8.2 software were used for pharmacokinetics(PK)parameters calculation and bioequivalence analysis.Results Healthy subjects took the test product and the reference product under fasting condition,the main PK parameters of amoxicillin are as follows:Cmax were(5 075.57±1 483.37)and(5 119.86±1 466.73)ng·mL-1,AUC0_twere(1.32 × 104±2 163.76)and(1.30 × 104±1 925.11)ng·mL-1,AUC0-∞were(1.32 × 104±2 175.40)and(1.31 ×104±1 935.86)ng·mL-1;the main PK parameters of clavulanic acid are as follows:Cmax were(3 298.27±1 315.23)and(3 264.06±1 492.82)ng·mL-1,AUC0-twere(7 690.06±3 053.40)and(7 538.39±3 155.89)ng·mL-1,AUC0-∞were(7 834.81±3 082.61)and(7 671.67±3 189.31)ng·mL-1;the 90％confidence intervals of Cmax,AUC0-tand AUC0-∞ after logarithmic conversion of amoxicillin and clavulanate potassium of the two products were all within 80.00％-125.00％.Healthy subjects took the test and reference product under fed condition,the main PK parameters of amoxicillin are as follows:Cmax were(4 514.08±1 324.18)and(4 602.82±1 366.48)ng·mL-1,AUC0-twere(1.15 × 104±1 637.95)and(1.15 × 104±1 665.69)ng·mL-1,AUC0-∞ were(1.16 × 104±1 646.26)and(1.15 × 104±1 607.20)ng·mL-1;the main PK parameters of clavulanic acid are as follows:Cmax were(2 654.75±1 358.29)and(2 850.51±1 526.31)ng·mL-1,AUC0-twere(5 882.82±2 930.06)and(6 161.28±3 263.20)ng·mL-1,AUC0-∞ were(6 022.70±2 965.05)and(6 298.31±3 287.63)ng·mL-1;the 90％confidence intervals of Cmax,AUC0-t and AUC0-∞ after logarithmic conversion of amoxicillin and clavulanate potassium of the two products were all within 80.00％-125.00％.Conclusion The two formulations were bioequivalent to healthy adult volunteers under fasting and fed conditions.

Objective: This study aims to overcome challenges in lumbar spine imaging, particularly lumbar spinal stenosis, by developing an automated segmentation model using advanced techniques. Traditional manual measurement and lesion detection methods are limited by subjectivity and inefficiency. The objective is to create an accurate and automated segmentation model that identifies anatomical structures in lumbar spine magnetic resonance imaging scans. Methods: Leveraging a dataset of 539 lumbar spinal stenosis patients, the study utilizes the residual U-Net for semantic segmentation in sagittal and axial lumbar spine magnetic resonance images. The model, trained to recognize specific tissue categories, employs a geometry algorithm for anatomical structure quantification. Validation metrics, like Intersection over Union (IOU) and Dice coefficients, validate the residual U-Net’s segmentation accuracy. A novel rotation matrix approach is introduced for detecting bulging discs, assessing dural sac compression, and measuring yellow ligament thickness. Results: The residual U-Net achieves high precision in segmenting lumbar spine structures, with mean IOU values ranging from 0.82 to 0.93 across various tissue categories and views. The automated quantification system provides measurements for intervertebral disc dimensions, dural sac diameter, yellow ligament thickness, and disc hydration. Consistency between training and testing datasets assures the robustness of automated measurements. Conclusion Automated lumbar spine segmentation with residual U-Net and deep learning exhibits high precision in identifying anatomical structures, facilitating efficient quantification in lumbar spinal stenosis cases. The introduction of a rotation matrix enhances lesion detection, promising improved diagnostic accuracy, and supporting treatment decisions for lumbar spinal stenosis patients.

Objective:To explore the expressions of zinc homeostasis-related proteins,G protein-coupled receptor 39(GPR39)and ANO1 mRNA in the sperm of patients with asthenozoospermia(AS),and analyze their correlation with sperm motility.Methods:We collected semen samples from 82 male subjects with PR+NP＜40％,PR＜32％and sperm concentration＞15 × 106/ml(the AS group,n=40)or PR+NP≥40％,PR≥32％and sperm concentration＞15 × 106/ml(the normal control group,n=42).We analyzed the routine semen parameters and measured the zinc content in the seminal plasma using the computer-assisted sperm analysis system,detected the expressions of zinc transporters(ZIP13,ZIP8 and ZNT10),metallothioneins(MT1G,MT1 and MTF),GPR39,and calcium-dependent chloride channel protein(ANO1)in the sperm by real-time quantitative PCR(RT qPCR),examined free zinc distribution in the sperm by laser confocal microscopy,and determined the expressions of GPR39 and MT1 proteins in the sperm by immunofluorescence staining,followed by Spearman rank correlation analysis of their correlation with semen parameters.Results:There was no statistically significant difference in the zinc concentration in the seminal plasma between the AS and normal control groups(P＞0.05).Compared with the controls,the AS patients showed a significantly reduced free zinc level(P＜0.05),relative expressions of MT1G,MTF,ZIP13,GPR39 and ANO1 mRNA(P＜0.05),and that of the GPR39 protein in the AS group(P＜0.05).No statistically significant differences were observed in the relative expression levels of ZIP8,ZNT10 and MT1 mRNA between the two groups(P＞0.05).The relative expression levels of GPR39,ANO1,MT1G and MTF mRNA were positively correlated with sperm motility and the percentage of progressively motile sperm(P＜0.05).Conclusion:The expressions of zinc homeostasis proteins(MT1G,MTF and ZIP13),GPR39 and ANO1 mRNA are downregulated in the sperm of asthenozoospermia pa-tients,and positively correlated with sperm motility.

Objective: This study aims to overcome challenges in lumbar spine imaging, particularly lumbar spinal stenosis, by developing an automated segmentation model using advanced techniques. Traditional manual measurement and lesion detection methods are limited by subjectivity and inefficiency. The objective is to create an accurate and automated segmentation model that identifies anatomical structures in lumbar spine magnetic resonance imaging scans. Methods: Leveraging a dataset of 539 lumbar spinal stenosis patients, the study utilizes the residual U-Net for semantic segmentation in sagittal and axial lumbar spine magnetic resonance images. The model, trained to recognize specific tissue categories, employs a geometry algorithm for anatomical structure quantification. Validation metrics, like Intersection over Union (IOU) and Dice coefficients, validate the residual U-Net’s segmentation accuracy. A novel rotation matrix approach is introduced for detecting bulging discs, assessing dural sac compression, and measuring yellow ligament thickness. Results: The residual U-Net achieves high precision in segmenting lumbar spine structures, with mean IOU values ranging from 0.82 to 0.93 across various tissue categories and views. The automated quantification system provides measurements for intervertebral disc dimensions, dural sac diameter, yellow ligament thickness, and disc hydration. Consistency between training and testing datasets assures the robustness of automated measurements. Conclusion Automated lumbar spine segmentation with residual U-Net and deep learning exhibits high precision in identifying anatomical structures, facilitating efficient quantification in lumbar spinal stenosis cases. The introduction of a rotation matrix enhances lesion detection, promising improved diagnostic accuracy, and supporting treatment decisions for lumbar spinal stenosis patients.

Aim To observe the effects of Wenfei Jiangzhuo formula(WFJZF)on rats with vascular de-mentia and investigate its possible mechanism of ac-tion.Methods Thirty-six healthy male SD rats were randomly divided into the sham group,model group,donepezil group,and low-dose,medium-dose and high-dose groups of Wenfei Jiangzhuo formula,with six rats per group.Except for the sham group,the other groups were prepared as VaD models,and each group was gavaged with the corresponding drugs after suc-cessful modeling,and tests were performed after three weeks of treatment.Behavioral,hippocampal CA1 area morphology,neural dendrites and mitochondrial chan-ges were observed in all groups of rats,and phospho-glycerate mutase 5(PGAM5),dynamics-related pro-teins1(Drp1),opticatrophyprotein-1(OPA1),and other proteins were detected in each group.Results Compared with the sham group,rats in the model group and each intervention group had prolonged es-cape latency(P＜0.05),a shorter number of travers-als across the platforms(P＜0.05),a sparse morphol-ogy of hippocampal neurons,a reduction in the number of secondary dendritic spines,and a rupture of the out-er membrane of the mitochondria;the expression of the PGAM5 and Drp1 proteins in hippocampal tissues was elevated(P＜0.05),and the expression of the OPA1 and Mfn1/2 protein expression decreased(P＜0.05);compared with the model group,donepezil group and Wenfei Jiangzhuo formula high-dose group of rats had shorter evasion latency(P＜0.05),increased number of times to traverse the platform(P＜0.05),increased number of hippocampal neurons,tightly packed,more secondary dendritic structures,and reduced mitochon-drial damage;the expression of PGAM5 and Drp1 pro-teins was reduced(P＜0.05),and the expression of OPA1 and Mfn1/2 proteins was elevated(P＜0.05).Conclusions Wenfei Jiangzhuo formula can regulate the PGAM5-Drp1 signaling axis to improve the balance of mitochondrial homeostasis,thus improving the cog-nitive condition of the brain and exerting cerebroprotec-tive effects.

Objective: This study aims to overcome challenges in lumbar spine imaging, particularly lumbar spinal stenosis, by developing an automated segmentation model using advanced techniques. Traditional manual measurement and lesion detection methods are limited by subjectivity and inefficiency. The objective is to create an accurate and automated segmentation model that identifies anatomical structures in lumbar spine magnetic resonance imaging scans. Methods: Leveraging a dataset of 539 lumbar spinal stenosis patients, the study utilizes the residual U-Net for semantic segmentation in sagittal and axial lumbar spine magnetic resonance images. The model, trained to recognize specific tissue categories, employs a geometry algorithm for anatomical structure quantification. Validation metrics, like Intersection over Union (IOU) and Dice coefficients, validate the residual U-Net’s segmentation accuracy. A novel rotation matrix approach is introduced for detecting bulging discs, assessing dural sac compression, and measuring yellow ligament thickness. Results: The residual U-Net achieves high precision in segmenting lumbar spine structures, with mean IOU values ranging from 0.82 to 0.93 across various tissue categories and views. The automated quantification system provides measurements for intervertebral disc dimensions, dural sac diameter, yellow ligament thickness, and disc hydration. Consistency between training and testing datasets assures the robustness of automated measurements. Conclusion Automated lumbar spine segmentation with residual U-Net and deep learning exhibits high precision in identifying anatomical structures, facilitating efficient quantification in lumbar spinal stenosis cases. The introduction of a rotation matrix enhances lesion detection, promising improved diagnostic accuracy, and supporting treatment decisions for lumbar spinal stenosis patients.