Objective To investigate the association between fibrinogen-to-albumin ratio （FAR） and the overall burden of cerebral small vessel disease （CSVD）， as well as their value in predicting early neurological deterioration （END） in patients with acute ischemic stroke （AIS）. Methods A total of 103 AIS patients who were admitted to our hospital from January 2023 to March 2025 were enrolled. According to the CSVD total burden score， the patients were divided into low burden group （0-2 points） with 58 patients and high burden group （3-4 points） with 45 patients； According to the presence or absence of END， they were divided into END group with 21 patients and non-END group with 82 patients. The weighted generalized additive model combined with smooth curve fitting was used to investigate the correlation between FAR and CSVD total burden score. A logistic regression analysis was used to explore the association of FAR and CSVD total burden score with the prognosis of END in AIS patients. The receiver operating characteristic （ROC） curve was used to assess the value of FAR and CSVD total burden score in predicting END in AIS patients. The restricted cubic spline method was used to analyze the dose-response relationship between FAR and END in AIS patients. The Bootstrap method was used to investigate the mediating effect of CSVD total burden score in the relationship between FAR and END in AIS patients. Results The high burden group had a significantly higher FAR than the low burden group （P<0.05）， and there was a U-shaped relationship between FAR and CSVD total burden score， with an inflection point of 8.14%. Compared with the non-END group， the END group had a significantly higher proportion of patients with a CSVD total burden score of 3-4 points and a significantly higher FAR （P<0.05）. After adjustment for the covariates such as age and sex， FAR （OR=1.918， 95%CI 1.825‒2.157，P<0.05） and CSVD global burden score （OR=2.167，95%CI 2.051‒2.249， P<0.05） were still independently associated with the risk of END in AIS patients. FAR combined with CSVD total load score had a significantly higher predictive value than either indicator alone， with an area under the ROC curve of 0.951. The mediating effect analysis showed that CSVD total burden score played a mediating effect between FAR and AIS patient prognosis END （P<0.05）. Conclusion There is a significant association between FAR and the overall burden of CSVD， and combined measurement of FAR and CSVD total burden score can significantly enhance the performance in predicting END， thereby providing an important basis for developing individualized treatment strategies in clinical practice.

ObjectiveTo investigate the epidemiological and clinical characteristics of human bocavirus (HBoV) in hospitalized children with acute lower respiratory tract infection (ALRTI) at a single-center children’s hospital in Shanghai, thereby providing evidence for the diagnosis, treatment, and prevention of HBoV infection. MethodsA retrospective study was conducted on 19 537 hospitalized children with ALRTI at Shanghai Children’s Hospital from January 2021 to December 2023. Multiplex polymerase chain reaction (PCR) combined with capillary electrophoresis was used to detect HBoV and 12 other common respiratory viruses /atypical pathogens. The positive detection rate, demographic characteristics (sex, age), temporal distribution (year, season) of HBoV, as well as the clinical characteristics of severe and non-severe pneumonia were analyzed. ResultsThe overall HBoV-positive rate was 2.57% (503/19 537), with 59.44% (299/503) being single infections and 40.56% (204/503) being co-infections. The positive detection rate was significantly higher in boys than that in girls (2.78% vs 2.33%, χ²=3.88, P=0.049). The highest infection rate was observed in toddlers, followed by infants (χ²=379.57, P<0.001). The positive rate peaked in 2021 and reached its lowest point in 2023 (χ²=45.49, P<0.001), with epidemics mainly prevalent in summer and autumn. The main clinical symptoms were cough (90.06%, 453/503), fever (75.94%, 382/503), and wheezing (39.96%, 201/503). Children with severe pneumonia showed a higher incidence of wheezing compared with the non-severe group (P<0.001), while underlying diseases and co-infections had no significant association with disease severity (P>0.05). ConclusionHBoV was an important pathogen of ALRTI in children, predominantly affecting infants and toddlers, with higher susceptibility in boys and seasonal peaks in autumn and summer. The main clinical manifestations included cough, fever, and wheezing, with wheezing being more prevalent in children with severe pneumonia.

Liver cirrhosis recompensation refers to the process in which patients with decompensated cirrhosis achieve the disappearance of decompensated complications and restoration of liver function and clinical phenotype to a compensated state after etiological treatment or symptomatic treatment. In recent years， with effective control of the etiology of cirrhosis and treatment or prevention of decompensated complications， the clinical significance of recompensation has gained increasing attention. This article elaborates on recompensation from the aspects of definition， diagnostic criteria， influencing factors， mechanisms affecting long-term prognosis， and clinical management strategies， and with reference to the latest research evidence， this article also discusses the core value of recompensation in improving quality of life and reducing the risk of end-stage events， in order to provide a basis for optimizing the comprehensive management of liver cirrhosis.

Objective:To analyze the distribution of tumor necrosis factor-alpha（TNF-α）308 gene loci polymorphism in children with febrile seizures（FS）and to explore the correlation between TNF-α 308 gene polymorphisms and FS in children.Methods:A total of 320 children diagnosed with FS in the Department of Emergency，Shanghai Children's Hospital from September 1st，2020 to June 30th，2021 were enrolled as the study subjects，which were divided into simple febrile seizures（SFS）group（232 cases）and complex febrile seizures（CFS）group（88 cases）based on their clinical characteristics，and the clinical characteristics and laboratory indexes of the two groups were compared. Children with no history of convulsions were selected as the control group（160 cases）. The high-resolution melting and gene sequencing technology were used to analyze the polymorphism of TNF-α 308 gene in each group and the distribution of different gene types and allele frequencies among the groups was compared. A multivariate Logistic regression model was constructed to analyze the relationship between TNF-α 308 gene polymorphism and FS.Results:The age，mean corpuscular volume，mean corpuscular hemoglobin and platelet distribution width of the CFS group were significantly higher than those in the SFS group，and the difference was statistically significant（all P<0.05）.There was no significant difference in gender distribution，family history of FS，history of FS，body temperature at time of convulsions，WBC，Hb，CRP and PLT between the two groups（all P>0.05）.The genotype frequency distribution of TNF-α 308 polymorphism in the three groups was in line with the Hardy-Weinberg equilibrium（ P>0.05）.The AA genotype of TNF-α 308 locus was not detected in the study.Compared with the control group［17 cases（10.6%）］，the distribution proportion of GA genotype in the CFS group［22cases（25.0%）］and the SFS group［52cases（22.4%）］was increased，and the difference was statistically significant（ χ2＝11.126， P＝0.004）；Compared with the control group［17 frequencies（5.3%）］，the frequency distribution proportion of allele A in the CFS group［22 frequencies（12.5%）］and SFS group［52 frequencies（11.2%）］was also increased，and the difference was statistically significant（ χ2＝9.960， P＝0.007）. Adding control factors such as gender，age，family history of FS，body temperature at time of convulsions and blood routine markers，the multivariate Logistic regression model was constructed to show that there was no statistically significant association between TNF-α 308 genotype and CFS in children（ OR＝1.805，95% CI：0.926~3.519， P＝0.083）. Conclusion:In this study，there was no significant correlation between TNF-α 308 gene loci polymorphism and CFS in children.

Objective To analyze the relationship between clinical drug utilization and the risk of nosocomial infections among hospitalized patients,and provide evidence for the prevention and control of nosocomial infections.Methods This study adopted a retrospective case-control design.The case group included 209 patients with nosocomial infection reported from January 2023 to December 2023 in a tertiary hospital.The control group included 209 patients without nosocomial infection during the same period.The patients in the control group were selected by stratified sampling based on Charlson Comorbidity Index(CCI).Results Univariate analysis showed that proton pump inhibitors,antacids,immunosuppressants and prior antimicrobial combination therapy increased the risk of nosocomial infection(P＜0.05).Multivariate log-binomial regression analysis showed that proton pump inhibitors,immunosuppressive drugs,and prior antimicrobial combination therapy were correlated with nosocomial infection.The corresponding relative risk(RR)was 1.31(95％CI:1.07-1.60),1.40(95％CI:1.02-1.91),and 1.66(95％CI:1.01-2.74),respectively.Further analysis indicated that the patients with nosocomial infection had longer time in use of proton pump inhibitors and prior antimicrobial combination therapy than the patients in the control group(Z=-6.331,P＜0.001;Z=-2.667,P=0.008).The trend Chi-square test showed that there was a dose-response relationship for proton pump inhibitors(x2=73.869,P＜0.001),immunosuppressive drugs(x2=16.530,P＜0.001),and prior antimicrobial combination therapy(x2=35.107,P＜0.001).Conclusions The use of immunosuppressants,proton pump inhibitors and antimicrobial combination therapy increases the risk of nosocomial infections in hospitalized patients.The prolonged use of these drugs will further increase the risk of nosocomial infection.

Alzheimer's disease (AD) is a common neurodegenerative disorder among the elderly, and BuChE has emerged as a potential therapeutic target. In this study, we reported the development of compound 8e, a selective reversible BuChE inhibitor (eqBuChE IC₅₀ = 0.049 μmol/L, huBuChE IC₅₀ = 0.066 μmol/L), identified through extensive virtual screening and lead optimization. Compound 8e demonstrated favorable blood-brain barrier permeability, good drug-likeness property and pronounced neuroprotective efficacy. Additionally, 8e exhibited significant therapeutic effects in zebrafish AD models and scopolamine-induced cognitive impairments in mice. Further, 8e significantly improved cognitive function in APP/PS1 transgenic mice. Proteomics analysis demonstrated that 8e markedly elevated the expression levels of very low-density lipoprotein receptor (VLDLR), offering valuable insights into its potential modulation of the Reelin-mediated signaling pathway. Thus, compound 8e emerges as a novel and potent BuChE inhibitor for the treatment of AD, with significant implications for further exploration into its mechanisms of action and therapeutic applications.

OBJECTIVES: To explore the active components that mediate the therapeutic effect of Centella asiatica on psoriasis and their therapeutic mechanisms. METHODS: TCMSP, TCMIP, PharmMapper, Swiss Target Prediction, GeneCards, OMIM and TTD databases were searched for the compounds in Centella asiatica and their targets and the disease targets of psoriasis. A drug-active component-target network and the protein-protein interaction network were constructed, and DAVID database was used for pathway enrichment analysis. In a RAW264.7 macrophage model of LPS-induced inflammation, the anti-inflammatory effect of 7.5, 15, 30, and 60 μmol/L quercetin, asiaticoside, and asiatic acid, which were identified as the main active components in Centella asiatica, were tested by measuring cellular production of NO, TNF‑α and IL-6 using Griess method and ELISA and by detecting mRNA expressions of IL-23, IL-17A, TNF-α and IL-6 and protein expressions of p-STAT3 (Tyr705) and p-STAT3 (Ser727) with RT-qPCR and Western blotting. RESULTS: A total of 139 targets of Centella asiatica and 4604 targets of psoriasis were obtained, and among them CASP3, EGFR, PTGS2, and ESR1 were identified as the core targets. KEGG analysis suggested that quercetin, asiaticoside, and asiatic acid in Centella asiatica were involved in cancer and IL-17 and MAPK signaling pathways. In the RAW264.7 macrophage model of inflammation, treatment with quercetin significantly reduced cellular production of NO, TNF‑α and IL-6, and lowered mRNA expressions of IL-23, IL-17A, TNF‑α and IL-6 and protein expressions of p-STAT3 (Tyr705) and p-STAT3 (Ser727). CONCLUSIONS Quercetin, asiaticoside and asiatic acid are the main active components in Centella asiatica to mediate the therapeutic effect against psoriasis, and quercetin in particular is capable of suppressing cellular production of NO, TNF‑α and IL-6 and regulating the IL-23/IL-17A inflammatory axis by mediating STAT3 phosphorylation to inhibit inflammatory response.
Quercetin/pharmacology* ; Psoriasis/metabolism* ; STAT3 Transcription Factor/metabolism* ; Mice ; Animals ; Centella/chemistry* ; Triterpenes/pharmacology* ; Phosphorylation ; Interleukin-17/metabolism* ; Interleukin-23/metabolism* ; RAW 264.7 Cells ; Pentacyclic Triterpenes/pharmacology* ; Macrophages/drug effects* ; Signal Transduction ; Plant Extracts

OBJECTIVE: To identify the optimal machine learning algorithm for predicting post-stroke epilepsy (PSE) within one year following acute stroke, establish a nomogram model based on this algorithm, and perform external validation to achieve accurate prediction of secondary epilepsy. METHODS: A total of 870 acute stroke patients admitted to the emergency department of Xiang'an Hospital of Xiamen University from June 2019 to June 2023 were enrolled for model development (model group). An external validation cohort of 435 acute stroke patients admitted to the Fifth Hospital of Xiamen during the same period was used to validate the machine learning algorithms and nomogram model. Patients were classified into control and epilepsy groups based on the development of PSE within one year. Clinical and laboratory data, including baseline characteristics, stroke location, vascular status, complications, hematologic parameters, and National Institutes of Health Stroke Scale (NIHSS) score, were collected for analysis. Nine machine learning algorithms such as logistic regression, CN2 rule induction, K-nearest neighbors, adaptive boosting, random forest, gradient boosting, support vector machine, naive Bayes, and neural network were applied to evaluate predictive performance. The area under the curve (AUC) of receiver operator characteristic curve (ROC curve) was used to identify the optimal algorithm. Logistic regression was used to screen risk factors for PSE, and the top 10 predictors were selected to construct the nomogram model. The predictive performance of the model was evaluated using the ROC curve in both the model and validation groups. RESULTS: Among the 870 patients in the model group, 29 developed PSE within one year. Among the nine algorithms tested, logistic regression demonstrated the best performance and generalizability, with an AUC of 0.923. Univariate logistic regression identified several risk factors for PSE, including platelet count, white blood cell count, red blood cell count, glycated hemoglobin (HbA1c), C-reactive protein (CRP), triglycerides, high-density lipoprotein (HDL), aspartate aminotransferase (AST), alanine aminotransferase (ALT), activated partial thromboplastin time (APTT), thrombin time, D-dimer, fibrinogen, creatine kinase (CK), creatine kinase-MB (CK-MB), lactate dehydrogenase (LDH), serum sodium, lactic acid, anion gap, NIHSS score, brain herniation, periventricular stroke, and carotid artery plaque. Further multivariate logistic regression analysis showed that white blood cell count, HDL, fibrinogen, lactic acid and brain herniation were independent risk factors [odds ratio (OR) were 1.837, 198.039, 47.025, 11.559, 70.722, respectively, all P < 0.05]. In the external validation group, univariate logistic regression analysis showed that platelet count, white blood cell count, CRP, triacylglycerol, APTT, D-dimer, fibrinogen, CK, CK-MB, LDH, NIHSS score, and cerebral herniation were risk factors for PSE one year after acute stroke. Further multiple logistic regression analysis showed that APTT and cerebral herniation were independent predictors (OR were 0.587 and 116.193, respectively, both P < 0.05). The nomogram model, constructed using 10 key variables-brain herniation, periventricular stroke, carotid artery plaque, white blood cell count, triglycerides, thrombin time, D-dimer, serum sodium, lactic acid, and NIHSS score-achieved an AUC of 0.908 in the model group and 0.864 in the external validation group. CONCLUSIONS The logistic regression-based prediction model for epilepsy one year after acute stroke, developed using machine learning algorithms, showed optimal predictive performance. The nomogram model based on the logistic regression-derived predictors showed strong discriminative power and was successfully validated externally, suggesting favorable clinical applicability and generalizability.
Humans ; Machine Learning ; Stroke/complications* ; Nomograms ; Epilepsy/etiology* ; Algorithms ; Male ; Female ; Logistic Models ; Middle Aged ; Aged ; Risk Factors ; Bayes Theorem

Immune checkpoint inhibitors(ICIs)are currently used to treat a variety of tumors.However,there are adverse reactions related to the immune system.Type 1 diabetes mellitus(T1DM)outbreak is a rare endocrine complication,as is ICIs-related myocarditis.This article reports a patient with diabetic ketoacidosis and severe myocarditis after ICIs treatment for lung cancer.After fluid infusion,acid correction,insulin hypoglycemic therapy,and high-dose hormone pulse therapy,the patient showed clinical improvement and was discharged.Through the analysis of the patient's clinical data,we aimed to improve clinicians'understanding of ICIs-associated DM and myocarditis,and to review the literature on possible pathogenesis and treatment.

BACKGROUND:Studies have shown that the vulnerability and elasticity of carotid plaques are related to the presence and degree of neovascularization within the plaque. Ultrasound,as the preferred measure to screen and evaluate vulnerable carotid plaques,is non-invasive,easy to perform,highly reproducible and radiation-free.OBJECTIVE:To investigate the influencing factors of vulnerable carotid plaque in the high-risk stroke population based on clinical baseline characteristics and carotid ultrasound parameters,and to develop a Nomogram prediction model based on independent risk factors.METHODS:A total of 180 patients who were identified to be at high risk of stroke by stroke screening at Fuzhou First People's Hospital from November 2021 to November 2023 were retrospectively selected as the study objects,and the patients were divided into a modeling set (n=126) and a validation set (n=54)at a ratio of 7∶3. According to the results of carotid artery ultrasound,the subjects in the modeling set were divided into a vulnerable plaque group (n=54) and a non-vulnerable plaque group (n=72). Independent risk factors were obtained by multi-factor Logistic regression,and a Nomogram model was constructed. Decision curves were drawn using R language to evaluate the clinical benefit of the model. The predictive efficacy of the model was tested by receiver operating characteristic curve and calibration curve,and the case data of the validation set were analyzed for external validation.RESULTS AND CONCLUSION:Multivariate Logistic regression results showed that age,family history of stroke,maximum carotid plaque thickness,carotid plaque quantity,urine microalbumin,urine microalbumin/creatinine ratio were associated with vulnerable carotid plaques in patients at high risk of stroke (P<0.05). The area under curve of the established Nomogram model was 0.917,and the sensitivity and specificity were 79.6％ and 91.7％,respectively. The results of decision curve showed that the potential clinical benefit of this model was considerable and its usability was high. The calibration curve results showed that the model had good prediction accuracy. The verification set results showed that the external prediction performance of the model was good. To conclude,age,family history of stroke,and maximum carotid plaque thickness in the high-risk population are all factors that influence this prediction model. This Nomogram based on these independent risk factors can provide a powerful reference for the clinical treatment of this high-risk population.