Objective To study the acupoint selection and medication rules of acupoint application as an advantageous therapy in treating stroke.Methods The clinical literature from CNKI,Wanfang,CBMdisc,and other databases were searched,and Excel 2013 was used to count the frequency of disease,acupoint selection,and medication,and to analyze the acupoint selection rules using SPSS 25.0 and SPSS Modeler.Results Finally,523 articles were included in the literature,among which,the literature on the treatment of post-stroke constipation with acupoint patch was the most,and the related literature was further screened to analyze the acupoint selection and medication rules,and it was concluded that the most frequently applied acupoints of acupoint application for the treatment of post-stroke constipation were Shenque(RN8),Tianshu(ST25),Zhongwan(RN12),Zusanli(ST36),and the acupoints were mainly taken from conception vessel,stomach meridian and gallbladder meridian,and the core prescriptions were Shenque,Tianshu,Zhongwan,Qihai(RN6),Guanyuan(RN4),Zusanli.For the treatment of post-stroke constipation,acupoint application is often used with Rhei Radix et Rhizoma,Aurantii Fructus Immaturus,Cortex Magnoliae Officinalis,Natrii Sulfas Exsiccatus and Borneolum Syntheticum,among which,warm and cold nature drugs are mainly used,and bitter drugs are most frequently used among five flavors,and most frequently enter to the spleen meridian;and the core prescription is Rhei Radix et Rhizoma,Aurantii Fructus Immaturus,Cortex Magnoliae Officinalis,Natrii Sulfas Exsiccatus and Borneolum Syntheticum.Conclusion Acupoint application is an advantageous treatment for treating post-stroke constipation.The selection of acupoints was based on the conception vessel and stomach meridian,and the medication used were mainly focusing on those with functions of unblocking the bowels and directing qi downward,supplemented by strengthening the spleen and benefiting qi,warming the meridians and nourishing the blood.

Objective:To understand the epidemiological characteristics of human respiratory syncytial virus (HRSV) among acute respiratory infection (ARI) cases in 16 provinces of China from 2009 to 2023.Methods:The data of this study were collected from the ARI surveillance data from 16 provinces in China from 2009 to 2023, with a total of 28 278 ARI cases included in the study. The clinical specimens from ARI cases were screened for HRSV nucleic acid from 2009 to 2023, and differences in virus detection rates among cases of different age groups, regions, and months were analyzed.Results:A total of 28 278 ARI cases were enrolled from January 2009 to September 2023. The age of the cases ranged from<1 month to 112 years, and the age M ( Q1, Q3) was 3 years (1 year, 9 years). Among them, 3 062 cases were positive for HRSV nucleic acid, with a total detection rate of 10.83%. From 2009 to 2019, the detection rate of HRSV was 9.33%, and the virus was mainly prevalent in winter and spring. During the Corona Virus Disease 2019 (COVID-19) pandemic, the detection rate of HRSV fluctuated between 6.32% and 18.67%. There was no traditional winter epidemic peak of HRSV from the end of 2022 to the beginning of 2023, and an anti-seasonal epidemic of HRSV occurred from April to May 2023. About 87.95% (2 693/3 062) of positive cases were children under 5 years old, and the difference in the detection rate of HRSV among different age groups was statistically significant ( P<0.001), showing a decreasing trend of HRSV detection rate with the increase of age ( P<0.001). Among them, the HRSV detection rate (25.69%) was highest in children under 6 months. Compared with 2009-2019, the ranking of HRSV detection rates in different age groups changed from high to low between 2020 and 2023, with the age M (Q1, Q3) of HRSV positive cases increasing from 1 year (6 months, 3 years) to 2 years (11 months, 3 years). Conclusion:Through 15 years of continuous HRSV surveillance analysis, children under 5 years old, especially infants under 6 months old, are the main high-risk population for HRSV infection. During the COVID-19 pandemic, the prevalence and patterns of HRSV in China have changed.

Objective:To analyze the current status of the global burden of stroke disease from 1990 to 2019,to predict the development trend of stroke disease burden in the 30 years from 2020 to 2049,and to provide a basis for formulating national health policies on stroke diseases.Methods:The Global Burden of Disease Study 2019(GBD 2019)database was searched to extract global stroke disease incidence,prevalence,case fatality,and disability-adjusted life years(DALYs)disease burden indicators from 1990-2019,the trends over time were modeled using linear,Poisson,and exponential regressions,prediction and study of the relationship between stroke and sociodemographic index(SDI)based on per capita gross domestic product(GDP)were conducted.Results:The global burden of stroke disease increased significantly from 1990-2019 and is predicted to continue to rise over the next 30 years(2020-2049).In 2049,the global stroke incidence,prevalence,case fatality,and DALYs will increase by 8.53 million(63％),119.83 million(109％),7.79 million(118％)and 118.92 million person-years(79％),respectively,compared with 2019,with a significant increase in the burden of stroke in the elderly population.In the next 30 years,the age-standardized incidence rates of stroke in men and women will be similar,while the age-standardized rates of prevalence in women will be relatively higher,and age-standardized case fatality rates and DALYs in men will be relatively higher.The disease burden of stroke was negatively correlated with SDI.The burden of stroke disease was significantly higher in regions with a low SDI than in regions with a high SDI.Conclusion:The global burden of stroke will increase in the next 30 years,which may be related to the aging of population and closely related to the development of economy.It is necessary to strengthen the prevention of stroke and formulate targeted strategies targeted strategies according to different SDI regions.

Patients with severe trauma require an extremely timely treatment and transfusion plays an irreplaceable role in the emergency treatment of such patients. An increasing number of evidence-based medicinal evidences and clinical practices suggest that patients with severe traumatic bleeding benefit from early transfusion of low-titer group O whole blood or hemostatic resuscitation with red blood cells, plasma and platelet of a balanced ratio. However, the current domestic mode of blood supply cannot fully meet the requirements of timely and effective blood transfusion for emergency treatment of patients with severe trauma in clinical practice. In order to solve the key problems in blood supply and blood transfusion strategies for emergency treatment of severe trauma, Branch of Clinical Transfusion Medicine of Chinese Medical Association, Group for Trauma Emergency Care and Multiple Injuries of Trauma Branch of Chinese Medical Association, Young Scholar Group of Disaster Medicine Branch of Chinese Medical Association organized domestic experts of blood transfusion medicine and trauma treatment to jointly formulate Chinese expert consensus on blood support mode and blood transfusion strategies for emergency treatment of severe trauma patients ( version 2024). Based on the evidence-based medical evidence and Delphi method of expert consultation and voting, 10 recommendations were put forward from two aspects of blood support mode and transfusion strategies, aiming to provide a reference for transfusion resuscitation in the emergency treatment of severe trauma and further improve the success rate of treatment of patients with severe trauma.

Objective:To understand the epidemiological characteristics of human respiratory syncytial virus (HRSV) among acute respiratory infection (ARI) cases in 16 provinces of China from 2009 to 2023.Methods:The data of this study were collected from the ARI surveillance data from 16 provinces in China from 2009 to 2023, with a total of 28 278 ARI cases included in the study. The clinical specimens from ARI cases were screened for HRSV nucleic acid from 2009 to 2023, and differences in virus detection rates among cases of different age groups, regions, and months were analyzed.Results:A total of 28 278 ARI cases were enrolled from January 2009 to September 2023. The age of the cases ranged from<1 month to 112 years, and the age M ( Q1, Q3) was 3 years (1 year, 9 years). Among them, 3 062 cases were positive for HRSV nucleic acid, with a total detection rate of 10.83%. From 2009 to 2019, the detection rate of HRSV was 9.33%, and the virus was mainly prevalent in winter and spring. During the Corona Virus Disease 2019 (COVID-19) pandemic, the detection rate of HRSV fluctuated between 6.32% and 18.67%. There was no traditional winter epidemic peak of HRSV from the end of 2022 to the beginning of 2023, and an anti-seasonal epidemic of HRSV occurred from April to May 2023. About 87.95% (2 693/3 062) of positive cases were children under 5 years old, and the difference in the detection rate of HRSV among different age groups was statistically significant ( P<0.001), showing a decreasing trend of HRSV detection rate with the increase of age ( P<0.001). Among them, the HRSV detection rate (25.69%) was highest in children under 6 months. Compared with 2009-2019, the ranking of HRSV detection rates in different age groups changed from high to low between 2020 and 2023, with the age M (Q1, Q3) of HRSV positive cases increasing from 1 year (6 months, 3 years) to 2 years (11 months, 3 years). Conclusion:Through 15 years of continuous HRSV surveillance analysis, children under 5 years old, especially infants under 6 months old, are the main high-risk population for HRSV infection. During the COVID-19 pandemic, the prevalence and patterns of HRSV in China have changed.

Objective To explore the performance of a new multimodal feature fusion classification model based on distance matching and discriminative representation learning for differentiating high-grade glioma(HGG)from solitary brain metastasis(SBM).Methods We collected multi-parametric magnetic resonance imaging(MRI)data from 61 patients with HGG and 60 with SBM,and delineated regions of interest(ROI)on T1WI,T2WI,T2-weighted fluid attenuated inversion recovery(T2_FLAIR)and post-contrast enhancement T1WI(CE_T1WI)images.The radiomics features were extracted from each sequence using Pyradiomics and fused using a multimodal feature fusion classification model based on distance matching and discriminative representation learning to obtain a classification model.The discriminative performance of the classification model for differentiating HGG from SBM was evaluated using five-fold cross-validation with metrics of specificity,sensitivity,accuracy,and the area under the ROC curve(AUC)and quantitatively compared with other feature fusion models.Visual experiments were conducted to examine the fused features obtained by the proposed model to validate its feasibility and effectiveness.Results The five-fold cross-validation results showed that the proposed multimodal feature fusion classification model had a specificity of 0.871,a sensitivity of 0.817,an accuracy of 0.843,and an AUC of 0.930 for distinguishing HGG from SBM.This feature fusion method exhibited excellent discriminative performance in the visual experiments.Conclusion The proposed multimodal feature fusion classification model has an excellent ability for differentiating HGG from SBM with significant advantages over other feature fusion classification models in discrimination and classification tasks between HGG and SBM.

Background and aim:Hepatic ischemia-reperfusion injury(IRI)is a significant challenge in liver trans-plantation,trauma,hypovolemic shock,and hepatectomy,with limited effective interventions available.This study aimed to investigate the role of leukocyte cell-derived chemotaxin 2(LECT2)in hepatic IRI and assess the therapeutic potential of Lect2-short hairpin RNA(shRNA)delivered through adeno-associated virus(AAV)vectors. Materials and methods:This study analyzed human liver and serum samples from five patients under-going the Pringle maneuver.Lect2-knockout and C57BL/6J mice were used.Hepatic IRI was induced by clamping the hepatic pedicle.Treatments included recombinant human LECT2(rLECT2)and AAV-Lect2-shRNA.LECT2 expression levels and serum biomarkers including alanine aminotransferase(ALT),aspartate aminotransferase(AST),creatinine,and blood urea nitrogen(BUN)were measured.Histological analysis of liver necrosis and quantitative reverse-transcription polymerase chain reaction were performed. Results:Serum and liver LECT2 levels were elevated during hepatic IRI.Serum LECT2 protein and mRNA levels increased post reperfusion.Lect2-knockout mice had reduced weight loss;hepatic necrosis;and serum ALT,AST,creatinine,and BUN levels.rLECT2 treatment exacerbated weight loss,hepatic necrosis,and serum biomarkers(ALT,AST,creatinine,and BUN).AAV-Lect2-shRNA treatment significantly reduced weight loss,hepatic necrosis,and serum biomarkers(ALT,AST,creatinine,and BUN),indicating thera-peutic potential. Conclusions:Elevated LECT2 levels during hepatic IRI increased liver damage.Genetic knockout or shRNA-mediated knockdown of Lect2 reduced liver damage,indicating its therapeutic potential.AAV-mediated Lect2-shRNA delivery mitigated hepatic IRI,offering a potential new treatment strategy to enhance clinical outcomes for patients undergoing liver-related surgeries or trauma.

Objective To explore the performance of a new multimodal feature fusion classification model based on distance matching and discriminative representation learning for differentiating high-grade glioma(HGG)from solitary brain metastasis(SBM).Methods We collected multi-parametric magnetic resonance imaging(MRI)data from 61 patients with HGG and 60 with SBM,and delineated regions of interest(ROI)on T1WI,T2WI,T2-weighted fluid attenuated inversion recovery(T2_FLAIR)and post-contrast enhancement T1WI(CE_T1WI)images.The radiomics features were extracted from each sequence using Pyradiomics and fused using a multimodal feature fusion classification model based on distance matching and discriminative representation learning to obtain a classification model.The discriminative performance of the classification model for differentiating HGG from SBM was evaluated using five-fold cross-validation with metrics of specificity,sensitivity,accuracy,and the area under the ROC curve(AUC)and quantitatively compared with other feature fusion models.Visual experiments were conducted to examine the fused features obtained by the proposed model to validate its feasibility and effectiveness.Results The five-fold cross-validation results showed that the proposed multimodal feature fusion classification model had a specificity of 0.871,a sensitivity of 0.817,an accuracy of 0.843,and an AUC of 0.930 for distinguishing HGG from SBM.This feature fusion method exhibited excellent discriminative performance in the visual experiments.Conclusion The proposed multimodal feature fusion classification model has an excellent ability for differentiating HGG from SBM with significant advantages over other feature fusion classification models in discrimination and classification tasks between HGG and SBM.

Purpose::To identify the risk factors for training-related lower extremity muscle injuries in young males by a non-invasive method of body composition analysis.Methods::A total of 282 healthy young male volunteers aged 18 -20 years participated in this cohort study. Injury location, degree, and injury rate were adjusted by a questionnaire based on the overuse injury assessment methods used in epidemiological studies of sports injuries. The occurrence of training injuries is monitored and diagnosed by physicians and treated accordingly. The body composition was measured using the BodyStat QuadScan 4000 multifrequency Bio-impedance system at 5, 50, 100 and 200 kHz to obtain 4 impedance values. The Shapiro-Wilk test was used to check whether the data conformed to a normal distribution. Data of normal distribution were shown as mean ± SD and analyzed by t-test, while those of non-normal distribution were shown as median (Q 1, Q 3) and analyzed by Wilcoxon rank sum test. The receiver operator characteristic curve and logistic regression analysis were performed to investigate risk factors for developing training-related lower extremity injuries and accuracy. Results::Among the 282 subjects, 78 (27.7%) developed training injuries. Lower extremity training injuries revealed the highest incidence, accounting for 23.4% (66 cases). These patients showed higher percentages of lean body mass ( p = 0.001), total body water (TBW, p=0.006), extracellular water ( p=0.020) and intracellular water ( p=0.010) as well as a larger ratio of basal metabolic rate/total weight ( p=0.006), compared with those without lower extremity muscle injuries. On the contrary, the percentage of body fat ( p=0.001) and body fat mass index ( p=0.002) were lower. Logistic regression analysis showed that TBW percentage > 65.35% ( p=0.050, odds ratio =3.114) and 3rd space water > 0.95% ( p=0.045, odds ratio =2.342) were independent risk factors for lower extremity muscle injuries. Conclusion::TBW percentage and 3rd space water measured with bio-impedance method are potential risk factors for predicting the incidence of lower extremity muscle injuries in young males following training.

Purpose To analyze the correlation between quantitative parameters of spectral CT and the mutation status of epidermal growth factor receptor(EGFR)and anaplastic lymphoma kinase(ALK)genes in solid lung adenocarcinoma,in order to predict the expression of gene mutation in solid lung adenocarcinoma by combining spectral parameters with clinical indicators.Materials and Methods The imaging and clinical data of 86 patients in the First Affiliated Hospital of Xinxiang Medical College pathologically confirmed with lung adenocarcinoma who underwent energy spectral CT plain and enhanced scans from May 2020 to May 2022 were retrospectively collected.According to the mutation status of EGFR and ALK genes,all patietns were divided into mutation group(43 cases)and wild group(43 cases).The single-energy CT values(including 40 keV,70 keV and 100 keV),slope of energy spectrum curve,effective atomic number during plain scan,iodine concentration in arterial and venous phases,water concentration and iodine concentration in arteries at the same level were obtained,and standardized iodine base value was also calculated.Statistically significant parameters were used to construct the prediction model,and the receiver operating characteristic curve was performed to evaluate the prediction efficiency of the model.Results There were statistically significant differences in gender and smoking status between the EGFR mutation group and the wild-type group(χ2=5.628,P=0.018;χ2=4.214,P=0.040).There were statistically significant differences in plain scan effective atomic number and energy spectrum slope,arterial phase 40 keV,70 keV and 100 keV single-energy CT values,40 keV single-energy CT values in the venous phase,the slope of the arteriovenous dual-phase energy spectrum and the standardized iodine base value between the two groups(t=2.067-4.394,all P<0.05).Logistic regression analysis showed that normalized iodine base value and energy spectrum slope in venous phase were the independent predictors of EGFR gene mutation status.There were no significant differences in quantitative parameters of plain scan and arterial phase energy spectrum CT between the ALK mutant group and the wild-type group(P>0.05).There was statistically significant difference in water base value of venous phase between the two groups(t=2.058,P=0.043).Conclusion The quantitative features of spectral CT may be correlated with the mutation status of EGFR and ALK genes in solid lung adenocarcinoma,and the Logistic regression model based on the combination of quantitative features of spectral CT and clinical features has certain value in predicting EGFR gene mutation.