Esophageal carcinoma is a malignant disease with a high incidence rate and poor prognosis. The mitochondrial apoptosis pathway plays a pivotal role in the regulation of cell death and has become a focal point in current cancer therapeutics research. Various active components from traditional Chinese medicine （TCM） can target the mitochondrial apoptosis pathway to inhibit esophageal carcinoma， presenting as potential therapeutic agents for this disease. This paper summarizes relevant research on the inhibition of esophageal carcinoma by active components in TCM via targeting the mitochondrial apoptosis pathway. It has been found that flavonoids （casticin， icariin， luteolin， kaempferol， hesperetin， deguelin， etc.）， terpenoids （oridonin， Jaridonin， artesunate， ethyl acetate fraction of pleurotus ferulatus triterpenoid， etc.）， alkaloids （matrine， swainsonine， etc.）， polyphenols （curcumin， epigallocatechin-3-gallate， corilagin， etc.）， steroids （α-hederin， polyphyllin Ⅵ， etc.）， phenols （optimized scorpion venom peptide CT-K3K7， gecko active polypeptide， etc.）， volatile oils （cinnamaldehyde， α -asarone， etc.） and other active components from TCM can target the intrinsic mitochondrial apoptosis pathway， induce apoptosis in esophageal carcinoma cells， and inhibit their proliferation， invasion and migration by regulating oxidative stress， blocking the cell cycle， regulating signaling pathways such as PI3K/Akt and MAPK.

Objective To investigate the influence of type 2 diabetes mellitus(T2DM)on cognitive function and the effective connectivity with in the default mode network(DMN)in the brain.Methods A total of 93 hospitalized patients diagnosed with T2DM were enrolled in this study as T2DM group from The First Affiliated Hospital of Guangzhou University of Chinese Medicine during September 2021 to December 2022.Simultaneously,108 healthy individuals were recruited from the community as normal control(NC)group.The cognitive functions were evaluated in the two groups.A random dynamic causal modeling approach was employed to analyze the effective connectivity within DMN in both groups.Additionally,Pearson correlation analysis was performed to examine the association between differential connectivity,clinical indicators,and cognitive scores in both groups.Results In comparison to the NC group,T2DM individuals exhibited statistically significant reductions in scores in the auditory verbal learning test(AVLT)for immediate recall and the digit symbol substitution test(DSST)(P＜0.05).Additionally,they displayed a notable decrease in effective connectivity from the left lateral parietal cortex(LLPC)to the posterior cingulate cortex(PCC),as well as from the LLPC to the right lateral parietal cortex(RLPC)within the DMN(P＜0.05).Pearson correlation analysis unveiled a negative association between HbA1c levels and the strength of effective connectivity from LLPC to PCC.Conversely,a positive correlation was observed between AVLT(immediate)scores and the strength of effective connectivity from LLPC to PCC and LLPC to RLPC.Additionally,DSST scores displayed a positive correlation with the strength of effective connectivity from LLPC to PCC(P＜0.05).Conclusion Patients with T2DM display compromised effective connectivity from LLPC to PCC and LLPC to RLPC within the DMN network,and this alteration may associated with cognitive impairment.

Objective To evaluate the quality of prediction model on healthcare-associated infections in China,so as to standardize research process and reporting methods.Methods It performed a literature search for healthcare-associated infections prediction model studies published using the following databases by the end of 2022.After independently screening the literature and cross-checking the extracted data according to the inclusion and exclusion criteria,the research team applied the prediction model risk of bias assessment tool(PROBAST)to evaluate the methodological quality,and the transparent reporting of a multivariable prediction model for individual prognosis or diagnosis(TRIPOD)statement to evaluate the quality of study reporting.Results A total of 81 healthcare-associated infections prediction studies were identified.Their median PROBAST overall adherence were 58.11%±13.88%,median TRIPOD adherence were 56.11%±16.35%.The main methodological flaws involved participants defined,ignored complexities in data,and omitted missing data.The reporting flaws lay in the items of risk groups,sample size,and supplementary information.Conclusion There are methodological deficiencies and incomplete reporting of domestic hospital infection prediction modelling studies,which limit the reliability and applicability of the results and leave much room for improvement.

Objective To evaluate the quality of prediction model on healthcare-associated infections in China,so as to standardize research process and reporting methods.Methods It performed a literature search for healthcare-associated infections prediction model studies published using the following databases by the end of 2022.After independently screening the literature and cross-checking the extracted data according to the inclusion and exclusion criteria,the research team applied the prediction model risk of bias assessment tool(PROBAST)to evaluate the methodological quality,and the transparent reporting of a multivariable prediction model for individual prognosis or diagnosis(TRIPOD)statement to evaluate the quality of study reporting.Results A total of 81 healthcare-associated infections prediction studies were identified.Their median PROBAST overall adherence were 58.11%±13.88%,median TRIPOD adherence were 56.11%±16.35%.The main methodological flaws involved participants defined,ignored complexities in data,and omitted missing data.The reporting flaws lay in the items of risk groups,sample size,and supplementary information.Conclusion There are methodological deficiencies and incomplete reporting of domestic hospital infection prediction modelling studies,which limit the reliability and applicability of the results and leave much room for improvement.

Objective To evaluate the quality of prediction model on healthcare-associated infections in China,so as to standardize research process and reporting methods.Methods It performed a literature search for healthcare-associated infections prediction model studies published using the following databases by the end of 2022.After independently screening the literature and cross-checking the extracted data according to the inclusion and exclusion criteria,the research team applied the prediction model risk of bias assessment tool(PROBAST)to evaluate the methodological quality,and the transparent reporting of a multivariable prediction model for individual prognosis or diagnosis(TRIPOD)statement to evaluate the quality of study reporting.Results A total of 81 healthcare-associated infections prediction studies were identified.Their median PROBAST overall adherence were 58.11%±13.88%,median TRIPOD adherence were 56.11%±16.35%.The main methodological flaws involved participants defined,ignored complexities in data,and omitted missing data.The reporting flaws lay in the items of risk groups,sample size,and supplementary information.Conclusion There are methodological deficiencies and incomplete reporting of domestic hospital infection prediction modelling studies,which limit the reliability and applicability of the results and leave much room for improvement.

Objective To evaluate the quality of prediction model on healthcare-associated infections in China,so as to standardize research process and reporting methods.Methods It performed a literature search for healthcare-associated infections prediction model studies published using the following databases by the end of 2022.After independently screening the literature and cross-checking the extracted data according to the inclusion and exclusion criteria,the research team applied the prediction model risk of bias assessment tool(PROBAST)to evaluate the methodological quality,and the transparent reporting of a multivariable prediction model for individual prognosis or diagnosis(TRIPOD)statement to evaluate the quality of study reporting.Results A total of 81 healthcare-associated infections prediction studies were identified.Their median PROBAST overall adherence were 58.11%±13.88%,median TRIPOD adherence were 56.11%±16.35%.The main methodological flaws involved participants defined,ignored complexities in data,and omitted missing data.The reporting flaws lay in the items of risk groups,sample size,and supplementary information.Conclusion There are methodological deficiencies and incomplete reporting of domestic hospital infection prediction modelling studies,which limit the reliability and applicability of the results and leave much room for improvement.

Objective To evaluate the quality of prediction model on healthcare-associated infections in China,so as to standardize research process and reporting methods.Methods It performed a literature search for healthcare-associated infections prediction model studies published using the following databases by the end of 2022.After independently screening the literature and cross-checking the extracted data according to the inclusion and exclusion criteria,the research team applied the prediction model risk of bias assessment tool(PROBAST)to evaluate the methodological quality,and the transparent reporting of a multivariable prediction model for individual prognosis or diagnosis(TRIPOD)statement to evaluate the quality of study reporting.Results A total of 81 healthcare-associated infections prediction studies were identified.Their median PROBAST overall adherence were 58.11%±13.88%,median TRIPOD adherence were 56.11%±16.35%.The main methodological flaws involved participants defined,ignored complexities in data,and omitted missing data.The reporting flaws lay in the items of risk groups,sample size,and supplementary information.Conclusion There are methodological deficiencies and incomplete reporting of domestic hospital infection prediction modelling studies,which limit the reliability and applicability of the results and leave much room for improvement.

Objective To evaluate the quality of prediction model on healthcare-associated infections in China,so as to standardize research process and reporting methods.Methods It performed a literature search for healthcare-associated infections prediction model studies published using the following databases by the end of 2022.After independently screening the literature and cross-checking the extracted data according to the inclusion and exclusion criteria,the research team applied the prediction model risk of bias assessment tool(PROBAST)to evaluate the methodological quality,and the transparent reporting of a multivariable prediction model for individual prognosis or diagnosis(TRIPOD)statement to evaluate the quality of study reporting.Results A total of 81 healthcare-associated infections prediction studies were identified.Their median PROBAST overall adherence were 58.11%±13.88%,median TRIPOD adherence were 56.11%±16.35%.The main methodological flaws involved participants defined,ignored complexities in data,and omitted missing data.The reporting flaws lay in the items of risk groups,sample size,and supplementary information.Conclusion There are methodological deficiencies and incomplete reporting of domestic hospital infection prediction modelling studies,which limit the reliability and applicability of the results and leave much room for improvement.

Objective To evaluate the quality of prediction model on healthcare-associated infections in China,so as to standardize research process and reporting methods.Methods It performed a literature search for healthcare-associated infections prediction model studies published using the following databases by the end of 2022.After independently screening the literature and cross-checking the extracted data according to the inclusion and exclusion criteria,the research team applied the prediction model risk of bias assessment tool(PROBAST)to evaluate the methodological quality,and the transparent reporting of a multivariable prediction model for individual prognosis or diagnosis(TRIPOD)statement to evaluate the quality of study reporting.Results A total of 81 healthcare-associated infections prediction studies were identified.Their median PROBAST overall adherence were 58.11%±13.88%,median TRIPOD adherence were 56.11%±16.35%.The main methodological flaws involved participants defined,ignored complexities in data,and omitted missing data.The reporting flaws lay in the items of risk groups,sample size,and supplementary information.Conclusion There are methodological deficiencies and incomplete reporting of domestic hospital infection prediction modelling studies,which limit the reliability and applicability of the results and leave much room for improvement.

Objective To evaluate the quality of prediction model on healthcare-associated infections in China,so as to standardize research process and reporting methods.Methods It performed a literature search for healthcare-associated infections prediction model studies published using the following databases by the end of 2022.After independently screening the literature and cross-checking the extracted data according to the inclusion and exclusion criteria,the research team applied the prediction model risk of bias assessment tool(PROBAST)to evaluate the methodological quality,and the transparent reporting of a multivariable prediction model for individual prognosis or diagnosis(TRIPOD)statement to evaluate the quality of study reporting.Results A total of 81 healthcare-associated infections prediction studies were identified.Their median PROBAST overall adherence were 58.11%±13.88%,median TRIPOD adherence were 56.11%±16.35%.The main methodological flaws involved participants defined,ignored complexities in data,and omitted missing data.The reporting flaws lay in the items of risk groups,sample size,and supplementary information.Conclusion There are methodological deficiencies and incomplete reporting of domestic hospital infection prediction modelling studies,which limit the reliability and applicability of the results and leave much room for improvement.