Influenza virus pneumonia （IVP） is an acute inflammatory disease of the lung with high incidence rate and infectivity caused by invading of the virus into the lower respiratory tract. At present, the treatment of IVP is mainly based on anti-influenza virus infection strategies, including the use of influenza vaccines and anti-influenza virus drugs. Due to the strong variability of viral antigens, it is difficult to obtain long-lasting immunity through vaccination. Commonly used chemical//biological antiviral drugs usually target a single specific viral protein. The mutation and evolution of the virus can reduce its efficacy or render it ineffective, which may lead to drug resistance, limiting the clinical application of these treatment options. Traditional Chinese Medicines （TCMs） have a long history in the prevention and treatment of IVP and are widely used in clinical practice due to their unique advantages and clear therapeutic effects. The research progress on the pathogenesis of IVP, effective prevention and treatment of TCMs for IVP, and the mechanism of action of its active ingredients were reviewed, which could provide new ideas for the treatment of IVP and reference for the development of new multi-target and low toxicity drugs.

Objectives:To develop a machine learning prediction model for poor outcome of acute minor ischemic stroke (AMIS) at 90 days after onset and to explain the importance of various risk factors.Methods:Patients with AMIS admitted to the Second People's Hospital of Hefei from June 2022 to December 2023 were included retrospectively. AMIS was defined as the National Institutes of Health Stroke Scale (NIHSS) score ≤5 at admission. According to the modified Rankin Scale score at 90 days after onset, the patients were divided into a good outcome group (<2) and a poor outcome group (≥2). Recursive feature elimination (RFE) method was used to screen characteristic variables of poor outcome. Based on logistic regression (LR), supported vector machine (SVM), and extreme Gradient Boosting (XGBoost) machine learning algorithms, prediction models for poor outcome of AMIS were developed, and the predictive performance of the models was compared by the area under the curve (AUC) of receiver operating characteristic (ROC) curve and the calibration curve. Shapley Additive exPlanations (SHAP) algorithm was used to explain the role of characteristic variables in the optimal prediction model. Results:A total of 225 patients with AMIS were included, of which 152 (67.56%) had good outcome and 73 (32.44%) had poor outcome. Multivariate analysis showed that baseline NIHSS score, baseline systolic blood pressure, hypertension, diabetes, low-density lipoprotein cholesterol, homocysteine, body mass index, D-dimer, and age were the characteristic variables associated with poor outcome in patients with AMIS. The ROC curve analysis shows that the LR model had the best predictive performance (AUC=0.888, 95% confidence interval [ CI] 0.807-0.970), the next was the XGBoost model (AUC=0.888, 95% CI 0.796-0.980), while the SVM model had the lowest performance (AUC=0.849, 95% CI 0.754-0.944). The calibration curve showed that the LR model performed the best in terms of calibration accuracy. SHAP showed that baseline systolic blood pressure, baseline NIHSS score, diabetes, hypertension and body mass index were the top five risk factors for poor outcome of patients with AMIS. Conclusions:The LR algorithm has stable and superior performance in predicting poor outcome of patients with AMIS. Baseline systolic blood pressure, baseline NIHSS score, diabetes, hypertension and body mass index are the important risk factors for poor outcome of patients with AMIS.

Objective:To explore the association between polymorphisms of transforming growth factor-β(TGF-β)signaling pathway and non-syndromic cleft lip with or without cleft palate(NSCL/P)among Asian populations,while considering gene-gene interaction and gene-environment interaction.Methods:A total of 1 038 Asian NSCL/P case-parent trios were ascertained from an international consortium,which conducted a genome-wide association study using a case-parent trio design to investigate the genes affec-ting risk to NSCL/P.After stringent quality control measures,343 single nucleotide polymorphism(SNP)spanning across 10 pivotal genes in the TGF-β signaling pathway were selected from the original genome-wide association study(GWAS)dataset for further analysis.The transmission disequilibrium test(TDT)was used to test for SNP effects.The conditional Logistic regression models were used to test for gene-gene interaction and gene-environment interaction.Environmental factors collected for the study in-cluded smoking during pregnancy,passive smoking during pregnancy,alcohol intake during pregnancy,and vitamin use during pregnancy.Due to the low rates of exposure to smoking during pregnancy and al-cohol consumption during pregnancy(＜3％),only the interaction between maternal smoking during pregnancy and multivitamin supplementation during pregnancy was analyzed.The threshold for statistical significance was rigorously set at P=1.46 × 10-4,applying Bonferroni correction to account for multiple testing.Results:A total of 23 SNPs in 4 genes yielded nominal association with NSCL/P(P＜0.05),but none of these associations was statistically significant after Bonferroni's multiple test correction.How-ever,there were 6 pairs of SNPs rs4939874(SMAD2)and rs1864615(TGFBR2),rs2796813(TGFB2)and rs2132298(TGFBR2),rs4147358(SMAD3)and rs1346907(TGFBR2),rs4939874(SMAD2)and rs1019855(TGFBR2),rs4939874(SMAD2)and rs12490466(TGFBR2),rs2009112(TGFB2)and rs4075748(TGFBR2)showed statistically significant SNP-SNP interaction(P＜1.46 × 10-4).In contrast,the analysis of gene-environment interactions did not yield any significant results after being cor-rected by multiple testing.Conclusion:The comprehensive evaluation of SNP associations and interac-tions within the TGF-β signaling pathway did not yield any direct associations with NSCL/P risk in Asian populations.However,the significant gene-gene interactions identified suggest that the genetic architec-ture influencing NSCL/P risk may involve interactions between genes within the TGF-β signaling path-way.These findings underscore the necessity for further investigations to unravel these results and further explore the underlying biological mechanisms.

Objectives:To develop a machine learning prediction model for post-stroke depression (PSD) in patients with acute ischemic stroke (AIS) at 3 months after onset.Methods:Patients with AIS admitted to the Second People's Hospital of Hefei from January 2021 to December 2023 were included retrospectively. According to the 17-item Hamilton Depression Rating Scale (HAMD) evaluation results at 3 months after onset, they were divided into PSD group and non-PSD group. The recursive feature elimination (RFE) method was used to screen the characteristic variables of PSD. A PSD prediction model for patients with AIS was developed based on three machine learning algorithms: logistic regression (LR), random forest (RF), and supported vector machine (SVM). The area under a receiver operating characteristic (ROC) curve (AUC) and calibration curve were used to evaluate the performance of the model. The SHapley Additive exPlanations (SHAP) algorithm was used to analyze the contribution of each risk factor. Results:A total of 243 patients with AIS were included, including 159 males (64.6%), aged 64.32±11.54 years, the median years of schooling was 6 years, and 13 males (5.3%) lived alone. 105 patients (42.7%) had a history of stroke. The median baseline National Institutes of Health Stroke Scale (NIHSS) score was 3, and the median baseline Modified Rankin Scale (mRS) score was 2. 33 patients (13.4%) received intravenous thrombolysis treatment. 93 patients (38.27%) had PSD at 3 months after onset. RFE showed that the optimal number of features was 11, including baseline NIHSS score, baseline mRS score, C-reactive protein, intravenous thrombolysis, low-density lipoprotein cholesterol, small vessel occlusion, D-dimer, total cholesterol, alcohol consumption, right side infarction, and baseline systolic blood pressure. ROC curve analysis shows that the RF model had the best predictive performance (AUC=0.831, 95% confidence interval 0.730-0.931), followed by the SVM model (AUC=0.827, 95% confidence interval 0.713-0.941), and the LR model has the lowest predictive performance (AUC=0.771, 95% confidence interval 0.658-0.885). The calibration curve shows that the RF model fits well with the ideal curve, making it the final advantageous model. SHAP showed that the contribution of baseline NIHSS score, baseline mRS score, low-density lipoprotein cholesterol, total cholesterol, and intravenous thrombolysis ranked among the top 5.Conclusions:The RF model can effectively predict the risk of PSD. The baseline NIHSS score, baseline mRS score, low-density lipoprotein cholesterol, and total cholesterol, as well as intravenous thrombolysis are the key predictive factors.

Objective: To investigate the incidence and possible risk factors of small intestinal bacterial overgrowth(SIBO) in patients with chronic kidney disease(CKD). Methods : 92 patients with CKD were selected as subjects.SIBO was assessed by lactulose hydrogen methane breath test(LHMBT),fat tissue index(FTI) was determined using multifrequency bioelectrical impedance analysis, and gastrointestinal symptoms were evaluated by gastrointestinal symptom rating scale(GSRS).According to the results of LHMBT,the patients were divided into SIBO group and non-SIBO group.The demographic data, laboratory and clinical indicators, gastrointestinal symptoms and other differences between the two groups were compared.Binomial logistic stepwise regression was used to analyze the possible risk factors of SIBO in CKD patients. Results : The incidence of SIBO in these 92 patients was 47.8%.Compared with the non-SIBO group, the GSRS score of the SIBO group was higher, and the difference was statistically significant [(23.27±3.97)vs(21.13±4.39),t=2.451,P=0.016].Multivariate stepwise logistic regression analysis showed that low serum potassium(OR=0.396,95%CI0.176-0.893,P=0.025) and high FTI(OR=1.182,95%CI1.037-1.348,P=0.013) were independent risk factors for SIBO in patients with CKD. Conclusion The incidence of SIBO is high in CKD patients.Symptoms of dyspepsia are more prominent in SIBO positive patients.Low serum potassium and high FTI are independent risk factors for SIBO in patients with CKD.

In recent years, the concept of " microbiome-gut-brain axis" has been proposed to reveal the wide connection between gut microbiome and nervous system diseases. As a common and frequently occurring disease of nervous system, the occurrence and outcome of ischemic stroke are closely related to gut microbiome. This article reviews the relationship between gut microbiome and risk factors of ischemic stroke and immune inflammation after stroke.

Recent studies have shown that there is a mutual influence between gut microbiota and stroke. Stroke-associated pneumonia (SAP) is a common complication of stroke, which is closely associated with death and poor prognosis of patients. Gut microbiota translocation may be the source of infection of SAP, but the specific mechanism of gut microbiota and SAP remains unclear. This article reviews the relationship between gut microbiota and SAP in order to provide reference for the prevention and treatment of SAP.

Objective To study the effect of high rosuvastatin dose on outcome in patients with large artery atherosclerotic stroke (LAAS).Methods Eighty-two LAAS patients were randomly divided into high rosuvastatin dose group (n=39) and routine rosuvastatin dose group (n=43).Their serum blood lipid level and inflammatory indexes were measured and their clinical outcome was assessed.Results No significant difference was found in mortality,recurrence and hemorrhagic transformation between the two groups (P＞0.05).The rate of improved outcome was significantly higher in high rosuvastatin dose group than in routine rosuvastatin dose group (84.62％ vs 65.12％,P=0.04).The serum hs-CRP level was significantly lower in routine rosuvastatin dose group and high rosuvastatin dose group after treatment than before treatment (0.56±0.60 mg/L vs 0.70±0.68 mg/L,P=0.01;0.22±0.29 mg/L vs 0.69±0.58mg/L,P=0.00) and in high rosuvastatin dose group than in routine rosuvastatin dose group after treatment than before treatment (0.22±0.29 mg/L vs 0.56±0.60 mg/L,P=0.00).The rate of LDL C＜1.8 mmol/L and non HDL-C＜2.6 mmol/L was significantly higher in high rosuvastatin dose group than in rosuvastatin dose group after treatment (69.23％ vs 46.51％,P=0.04;66.67％ vs 41.86％,P=0.03).No significant adverse reactions occurred in both groups.Conclusion High rosuvastatin dose can effectively increase the blood lipid level,reduce the serum hs-CRP level,and improve the clinical outcome in LAAS patients.

β2-Microglobulin is a small molecule protein, consisting of a polypeptide chain.Previous studies have confirmed that serum β2-microglobulin is a biomarker that reflects early renal function injury, and renal function injury is closely correlated with ischemic stroke.Studies in recent years have shown that the level of serum β2-microglobulin increases significantly in patients with ischemic stroke.Thus, it can be used as a biomarker for the risk of ischemic stroke.

Massive open online courses (MOOCs) , which has caused a large scale of medical edu-cation in recent years, has led to an exploration boom in medical education. By the end of November 2014, on four large domestic and foreign MOOCs platform, a total of 178 medical related MOOCs were found, accounting for 12.2% of the total number of courses, among which public health MOOCs accounted for 44.9%. In terms of medical education, MOOCs are not only a powerful supplement of existing medical ed-ucation and can assist dissemination of medical knowledge, they can also promote pedagogy innovation and improve teaching quality to a certain extent. Moreover, the huge amounts of data collected by MOOCs can also be used to develop research of students' learning behavior. In addition, by recruiting study objects, the researchers have begun to use MOOCs supporting scientific research. As a novel educational development, MOOCs face many challenges while they bring opportuni-ties for medical education. However, active prac-tice and exploration will bring more powerful vitality for its development in the medical field.