The innate immune system can be boosted in response to subsequent triggers by pre-exposure to microbes or microbial products, known as “trained immunity”. Compared to classical immune memory, innate trained immunity has several different features. Firstly, the molecules involved in trained immunity differ from those involved in classical immune memory. Innate trained immunity mainly involves innate immune cells (e.g., myeloid immune cells, natural killer cells, innate lymphoid cells) and their effector molecules (e.g., pattern recognition receptor (PRR), various cytokines), as well as some kinds of non-immune cells (e.g., microglial cells). Secondly, the increased responsiveness to secondary stimuli during innate trained immunity is not specific to a particular pathogen, but influences epigenetic reprogramming in the cell through signaling pathways, leading to the sustained changes in genes transcriptional process, which ultimately affects cellular physiology without permanent genetic changes (e.g., mutations or recombination). Finally, innate trained immunity relies on an altered functional state of innate immune cells that could persist for weeks to months after initial stimulus removal. An appropriate inducer could induce trained immunity in innate lymphocytes, such as exogenous stimulants (including vaccines) and endogenous stimulants, which was firstly discovered in bone marrow derived immune cells. However, mature bone marrow derived immune cells are short-lived cells, that may not be able to transmit memory phenotypes to their offspring and provide long-term protection. Therefore, trained immunity is more likely to be relied on long-lived cells, such as epithelial stem cells, mesenchymal stromal cells and non-immune cells such as fibroblasts. Epigenetic reprogramming is one of the key molecular mechanisms that induces trained immunity, including DNA modifications, non-coding RNAs, histone modifications and chromatin remodeling. In addition to epigenetic reprogramming, different cellular metabolic pathways are involved in the regulation of innate trained immunity, including aerobic glycolysis, glutamine catabolism, cholesterol metabolism and fatty acid synthesis, through a series of intracellular cascade responses triggered by the recognition of PRR specific ligands. In the view of evolutionary, trained immunity is beneficial in enhancing protection against secondary infections with an induction in the evolutionary protective process against infections. Therefore, innate trained immunity plays an important role in therapy against diseases such as tumors and infections, which has signature therapeutic effects in these diseases. In organ transplantation, trained immunity has been associated with acute rejection, which prolongs the survival of allografts. However, trained immunity is not always protective but pathological in some cases, and dysregulated trained immunity contributes to the development of inflammatory and autoimmune diseases. Trained immunity provides a novel form of immune memory, but when inappropriately activated, may lead to an attack on tissues, causing autoinflammation. In autoimmune diseases such as rheumatoid arthritis and atherosclerosis, trained immunity may lead to enhance inflammation and tissue lesion in diseased regions. In Alzheimer’s disease and Parkinson’s disease, trained immunity may lead to over-activation of microglial cells, triggering neuroinflammation even nerve injury. This paper summarizes the basis and mechanisms of innate trained immunity, including the different cell types involved, the impacts on diseases and the effects as a therapeutic strategy to provide novel ideas for different diseases.

Objective To analyze the risk factors and effective predictive indicators for postpartum hemor-rhage(PPH)in pregnant women with severe pre-eclampsia(sPE)in singleton pregnancies.The findings will serve as a valuable reference for the clinical prevention and management of PPH in these patients.Methods A retrospective analysis was conducted on 932 pregnant women with sPE at two tertiary hospitals in Guangzhou from January 1,2016,to December 31,2022.Among these,95 cases were complicated by PPH.A comparative analysis was performed between the sPE group and the sPE with PPH group.Results(1)The incidence of assisted reproductive technology,intrapartum blood loss,placental abruption,elevated D-dimer levels,increased monocyte counts,and higher SIRI levels were significantly higher in the PPH group,whereas platelet counts were significantly lower(P<0.05).(2)The results indicated that intrapartum blood loss,D-dimer levels,and platelet counts were inde-pendently associated with PPH in pregnant women with sPE.(3)The area under the curve(AUC)for intrapartum blood loss,D-dimer,and platelet counts were 0.805,0.717,and 0.571,respectively.The optimal cutoff value for D-dimer was determined to be 2.295 μg/mL.The combined AUC for intrapartum blood loss and D-dimer was 0.859.(4)Intrapartum blood loss values were significantly higher in the PPH group for both vaginal delivery and cesarean section(P<0.001).The corresponding optimal cutoff values were 285 mL and 375 mL,respectively.Conclusions Intrapartum haemorrhage,D-dimer levels,and platelet count were identified as independent risk factors for PPH in pregnant women with sPE.Specifically,pregnant women with sPE who experienced blood loss exceeding 285 mL during vaginal delivery or 375 mL during caesarean section,along with a D-dimer level greater than 2.295 μg/mL,demonstrated an increased likelihood of developing PPH.Therefore,it is crucial to enhance clinical monitoring of these relevant indicators in high-risk populations.

Objective To analyze the risk factors and effective predictive indicators for postpartum hemor-rhage(PPH)in pregnant women with severe pre-eclampsia(sPE)in singleton pregnancies.The findings will serve as a valuable reference for the clinical prevention and management of PPH in these patients.Methods A retrospective analysis was conducted on 932 pregnant women with sPE at two tertiary hospitals in Guangzhou from January 1,2016,to December 31,2022.Among these,95 cases were complicated by PPH.A comparative analysis was performed between the sPE group and the sPE with PPH group.Results(1)The incidence of assisted reproductive technology,intrapartum blood loss,placental abruption,elevated D-dimer levels,increased monocyte counts,and higher SIRI levels were significantly higher in the PPH group,whereas platelet counts were significantly lower(P<0.05).(2)The results indicated that intrapartum blood loss,D-dimer levels,and platelet counts were inde-pendently associated with PPH in pregnant women with sPE.(3)The area under the curve(AUC)for intrapartum blood loss,D-dimer,and platelet counts were 0.805,0.717,and 0.571,respectively.The optimal cutoff value for D-dimer was determined to be 2.295 μg/mL.The combined AUC for intrapartum blood loss and D-dimer was 0.859.(4)Intrapartum blood loss values were significantly higher in the PPH group for both vaginal delivery and cesarean section(P<0.001).The corresponding optimal cutoff values were 285 mL and 375 mL,respectively.Conclusions Intrapartum haemorrhage,D-dimer levels,and platelet count were identified as independent risk factors for PPH in pregnant women with sPE.Specifically,pregnant women with sPE who experienced blood loss exceeding 285 mL during vaginal delivery or 375 mL during caesarean section,along with a D-dimer level greater than 2.295 μg/mL,demonstrated an increased likelihood of developing PPH.Therefore,it is crucial to enhance clinical monitoring of these relevant indicators in high-risk populations.

Objective To evaluate the efficacy and safety of Qixian Tongluo Formula in the treatment of post-cerebral infarction paralysis with kidney deficiency and blood stasis syndrome,and to preliminarily explore the molecular mechanism of Qixian Tongluo Formula in improving impaired motor function from the perspective of cross-kingdom regulation of Chinese medicine microRNA(miRNA).Methods A pragmatic randomized controlled trial was conducted with 102 patients in the recovery period of post-cerebral infarction paralysis with kidney deficiency and blood stasis syndrome in our hospital.Patients were randomly divided into trial group and control group,with 51 cases in each group.The control group received standard Western medicine standard treatment,while the trial group received Qixian Tongluo Formula in addition to the standard treatment,with one dose per day,boiled in water,and taken warm after breakfast and dinner for a course of 2 months.The disability rate was used as the main efficacy indicator,and the incidence of adverse reactions was used as a safety indicator.miRNA from patient serum and Qixian Tongluo decoction were extracted respectively,and high-throughput sequencing was performed.The two sequences were compared to screen out the cross-kingdom gene transfer of Chinese medicine miRNA.Finally,its target genes of miRNA were predicted,and GO function and KEGG pathway enrichment analysis were carried out.Results A total of 67 patients completed the clinical trial,including 36 cases in the trial group and 31 cases in the control group;The disability rate in the trial group(13.9%)was lower than that in the control group(35.5%)(P<0.05);The incidence of adverse reactions was similar between the trial group(7.69%)and the control group(6.06%)(P>0.05);A total of 9530 Qixian Tongluo decoction miRNA sequences were screened,with 150 potentially involved in cross-kingdom gene transfer,including families such as miR-15 and miR-17;According to the target gene prediction of the top 10 miRNAs in cross-kingdom gene transfer of Chinese medicine,345 overlapping target genes were obtained;GO functional enrichment analysis revealed 16 biological processes,7 cellular components,and 2 molecular functions among the top 25 enriched functions,while KEGG pathway analysis mainly focused on the transforming growth factor-βsignaling pathway,neurotrophin signaling pathway,which are closely related to neural repair and functional recovery processes such as glial scar formation and synaptic plasticity after cerebral ischemia.Conclusion Qixian Tongluo Formula can significantly improve the functional independence level of patients with kidney deficiency and blood stasis syndrome in the recovery period of paralysis after cerebral infarction,offering a safe and effective treatment option for these patients;There were a large number of miRNAs in Qixian Tongluo decoction,some of which could cross-kingdom transferred into the human blood circulation,and promote the recovery of motor function in patients with cerebral infarction through multi-target,multi link and multi pathway gene network regulation.This study provides a new idea for subsequent clinical and basic research.

OBJECTIVE:Nowadays,machine learning algorithms are gradually being applied to predict stroke and cardiovascular disease.Compared with traditional regression models,machine learning can learn from data to achieve high prediction accuracy by exploring the flexible relationship between a large number of predictive features and outcome variables,providing a new method for the formulation of individualized treatment and rehabilitation programs.This study aims to systematically evaluate stroke functional recovery and prognosis prediction models based on machine learning,comprehensively assessing their predictive performance and clinical application potential to provide references for the development,application,and promotion of related predictive models.METHODS:This review was conducted following the PRISMA(Preferred Reporting Items for Systematic Reviews and Meta-Analyses)guidelines.Relevant literature on stroke prognosis prediction using machine learning methods was selected by searching PubMed,EMbase,Web of Science Core Collection,CNKI,WanFang,and the China Biomedical Literature Database,with the search period from January 1,2014,to July 1,2024.Two researchers independently screened the literature and extracted data based on inclusion and exclusion criteria,using the Prediction model Risk Of Bias ASsessment Tool(PROBAST)to assess model quality.RESULTS:(1)A total of 3 126 articles were obtained in the preliminary search.After screening and exclusion,18 articles were finally included.150 prediction models were constructed using 13 machine learning methods.The three most frequently used methods are Logistic Regression,Random Forest,and Extreme Gradient Boosting(XGBoost).Only one study was externally validated.Eight studies reported how the missing data were handled.(2)In terms of outcome indicators,8 studies used the combination of clinical data and imaging data to build models,9 studies only used clinical data to build models,and 1 study only used imaging data to build models.(3)Each of the 18 studies gave the most important characteristics of the study,with the most mentioned being the National Institute of Health Stroke Scale and age.All studies reported area under curve values ranging from 0.74 to 0.96,with the highest area under curve being 0.96.The overall risk of bias in all models was high.The high risk of bias in the field of model analysis was the main reason for the high risk of overall bias in all models.(4)The results of meta-analysis showed that age and National Institute of Health Stroke Scale score had significant influence on stroke prognosis,with age[MD=8.49,95％CI(6.24,10.75),P＜0.01]and National Institute of Health Stroke Scale score[MD=4.78,95％CI(2.56,7.00),P＜0.01].CONCLUSION:This study systematically evaluated the predictive model of functional recovery and prognosis of stroke based on machine learning,and all the models have good predictive potential.However,future studies should increase the sample size of the included model,adopt prospective studies,and add external validation of the model to improve the stability and prediction accuracy of the model,control the risk of bias,and contribute to the validation and promotion of the model in practical clinical applications.At the same time,the interpolation of missing values is more transparent and accurate.Although existing machine learning models show good predictive performance,it is also important to focus on the functionality and usability of the model,and the inclusion of features will reduce ease of use.We should develop easy to use model interfaces and user-friendly clinical tools to enable medical staff to better apply the model for clinical decision.

Objective To specify the occurrence status,distribution characteristics and risk factors of neglected perforations in surgical gloves used during spinal surgery.Methods A total of 932 surgical gloves used during spinal surgery in our hospital from October 2022 to September 2024 were selected.The occurrence and distribution location of neglected perforations were counted.Univariate and binary Logistic regression analyses were respectively conducted on the factors that might lead to neglected perforations of gloves.Results Among the 932 surgical gloves used in spinal surgery selected in this study,a total of 122 gloves(13.09％)with neglected perforations were detected,of which the proportions of neglected perforations at the index finger of the left and right hands in the gloves were the highest,accounting for 37.50％and 37.93％respectively.The results of univariate and binary Logistic regression analyses showed that grade Ⅲ or Ⅳ surgery,open surgery,combined bone grafting operation,operation time≥120 minutes,use of rotating equipment,and gloves worn by instrument nurses were risk factors for neglected perforations(P<0.05,OR>1.0),while wearing double-layer gloves was a protective factor to avoid the occurrence of neglected perforations(P<0.05,OR<1.0).Conclusion The incidence of neglected perforations in surgical gloves used during spinal surgery is relatively high,especially in the area of the glove index finger.For high-risk spinal surgeries(grade Ⅲ or Ⅳ surgery,open surgeries,combined bone grafting procedures)and requiring rotating equipment,health care workers,especially instrumental nurses,are advised to wear double-layer gloves;furthermore,a 120-minute glove change regime is recommended,in order to reduce the incidence of neglected perforation.

Objective To determine the effects of heated iodophor disinfection on intraoperative body temperature and bleeding in elderly patients undergoing radical mastectomy.Methods A total of 78 elderly patients who underwent unilateral radical mastectomy in Suqian Hospital of Jiangsu Provincial People's Hospital from September 2022 to August 2024 were selected as the study subjects,and they were randomly divided into the control group and the observation group,with 39 cases in each group.The control group and the observation group were disinfected with iodophor at temperature of 23℃and heated to 37℃before surgery,respectively.The qualified skin disinfection,incision infection,body temperature,adverse reactions during the recovery period,intraoperative bleeding volume,postoperative drainage bleeding volume,and hemoglobin levels of patients of the two groups were compared.Results There was no statistically significant difference in the qualified rate of skin disinfection or incision infection rate between of patients the two groups(P>0.05).At 30 minutes after disinfection,60 minutes after disinfection,and at the end of surgery,the body temperature of patients in the observation group was significantly higher than that in the control group(P<0.05).The incidences of intraoperative hypothermia,delayed awakening,and shivering reaction of patients in the observation group were significantly lower than those in the control group(P<0.05).The intraoperative bleeding volume and postoperative drainage bleeding volume of patients in the observation group were significantly lower than those in the control group(P<0.05).The hemoglobin level 3 days after surgery of patients in the observation group was significantly higher than that in the control group(P<0.05).Conclusion Compared with the routine use of 23℃iodophor to disinfect the skin of elderly patients undergoing radical mastectomy,the use of iodophor heated to 37℃does not affect the disinfection effect and incision infection,and the incidence of intraoperative hypothermia in patients is lower,which is conducive to reduce the amount of intraoperative bleeding and the incidence of adverse reactions during the recovery period.

Objective To determine the effects of heated iodophor disinfection on intraoperative body temperature and bleeding in elderly patients undergoing radical mastectomy.Methods A total of 78 elderly patients who underwent unilateral radical mastectomy in Suqian Hospital of Jiangsu Provincial People's Hospital from September 2022 to August 2024 were selected as the study subjects,and they were randomly divided into the control group and the observation group,with 39 cases in each group.The control group and the observation group were disinfected with iodophor at temperature of 23℃and heated to 37℃before surgery,respectively.The qualified skin disinfection,incision infection,body temperature,adverse reactions during the recovery period,intraoperative bleeding volume,postoperative drainage bleeding volume,and hemoglobin levels of patients of the two groups were compared.Results There was no statistically significant difference in the qualified rate of skin disinfection or incision infection rate between of patients the two groups(P>0.05).At 30 minutes after disinfection,60 minutes after disinfection,and at the end of surgery,the body temperature of patients in the observation group was significantly higher than that in the control group(P<0.05).The incidences of intraoperative hypothermia,delayed awakening,and shivering reaction of patients in the observation group were significantly lower than those in the control group(P<0.05).The intraoperative bleeding volume and postoperative drainage bleeding volume of patients in the observation group were significantly lower than those in the control group(P<0.05).The hemoglobin level 3 days after surgery of patients in the observation group was significantly higher than that in the control group(P<0.05).Conclusion Compared with the routine use of 23℃iodophor to disinfect the skin of elderly patients undergoing radical mastectomy,the use of iodophor heated to 37℃does not affect the disinfection effect and incision infection,and the incidence of intraoperative hypothermia in patients is lower,which is conducive to reduce the amount of intraoperative bleeding and the incidence of adverse reactions during the recovery period.

Neonatal sepsis is a global health problem that seriously affects the body health and life safety of newborns. It has a higher incidence in preterm infants,especially for early-onset sepsis (EOS) within 72 hours of birth. The diagnosis of neonatal EOS requires a series of examinations,and early and accurate diagnosis can improve clinical outcomes and reduce antibiotic overuse in a timely manner. At present,the commonly used biomarkers and traditional blood culture methods for EOS diagnosis have certain shortcomings,so it is urgent to find new molecular diagnostic methods. This article summarizes and compares the early and novel diagnostic methods of neonatal EOS,in order to provide a reference for clinical practice.

OBJECTIVE:Nowadays,machine learning algorithms are gradually being applied to predict stroke and cardiovascular disease.Compared with traditional regression models,machine learning can learn from data to achieve high prediction accuracy by exploring the flexible relationship between a large number of predictive features and outcome variables,providing a new method for the formulation of individualized treatment and rehabilitation programs.This study aims to systematically evaluate stroke functional recovery and prognosis prediction models based on machine learning,comprehensively assessing their predictive performance and clinical application potential to provide references for the development,application,and promotion of related predictive models.METHODS:This review was conducted following the PRISMA(Preferred Reporting Items for Systematic Reviews and Meta-Analyses)guidelines.Relevant literature on stroke prognosis prediction using machine learning methods was selected by searching PubMed,EMbase,Web of Science Core Collection,CNKI,WanFang,and the China Biomedical Literature Database,with the search period from January 1,2014,to July 1,2024.Two researchers independently screened the literature and extracted data based on inclusion and exclusion criteria,using the Prediction model Risk Of Bias ASsessment Tool(PROBAST)to assess model quality.RESULTS:(1)A total of 3 126 articles were obtained in the preliminary search.After screening and exclusion,18 articles were finally included.150 prediction models were constructed using 13 machine learning methods.The three most frequently used methods are Logistic Regression,Random Forest,and Extreme Gradient Boosting(XGBoost).Only one study was externally validated.Eight studies reported how the missing data were handled.(2)In terms of outcome indicators,8 studies used the combination of clinical data and imaging data to build models,9 studies only used clinical data to build models,and 1 study only used imaging data to build models.(3)Each of the 18 studies gave the most important characteristics of the study,with the most mentioned being the National Institute of Health Stroke Scale and age.All studies reported area under curve values ranging from 0.74 to 0.96,with the highest area under curve being 0.96.The overall risk of bias in all models was high.The high risk of bias in the field of model analysis was the main reason for the high risk of overall bias in all models.(4)The results of meta-analysis showed that age and National Institute of Health Stroke Scale score had significant influence on stroke prognosis,with age[MD=8.49,95％CI(6.24,10.75),P＜0.01]and National Institute of Health Stroke Scale score[MD=4.78,95％CI(2.56,7.00),P＜0.01].CONCLUSION:This study systematically evaluated the predictive model of functional recovery and prognosis of stroke based on machine learning,and all the models have good predictive potential.However,future studies should increase the sample size of the included model,adopt prospective studies,and add external validation of the model to improve the stability and prediction accuracy of the model,control the risk of bias,and contribute to the validation and promotion of the model in practical clinical applications.At the same time,the interpolation of missing values is more transparent and accurate.Although existing machine learning models show good predictive performance,it is also important to focus on the functionality and usability of the model,and the inclusion of features will reduce ease of use.We should develop easy to use model interfaces and user-friendly clinical tools to enable medical staff to better apply the model for clinical decision.