Various researches have reported on the relationship between homocysteine levels and adverse pregnancy outcomes. Researchers are increasingly focusing on the impact of homocysteine on female reproductive health and figuring out the potential positive effects of lowering homocysteine levels on women fertility. Our review aims to systematically summarize the possible roles of homocysteine in female reproductive disorders based on relevant studies from the past 15 years and therapeutic prospects targeting homocysteine to improve the reproductive health of women.

OBJECTIVE To establish a extraction process of Changyan Heji Ⅱ(CYHJ-Ⅱ)based on UPLC-Q-TOF-MS/MS technology combined with response surface analysis,and to optimize the extraction process.METHODS The chemical components in CYHJ-Ⅱ were qualitatively analyzed by UPLC-Q-TOF-MS/MS technology,and the chemical components with good linear relation-ship in mass spectrometry response were selected as process investigation indicators;the extraction process parameters(water addition amount,extraction time and soaking time)were investigated by Box-Behnken design;the comprehensive score was obtained by princi-pal component analysis(PCA),and the optimal process was determined by the comprehensive score combined with response surface a-nalysis.RESULTS Through qualitative analysis,110 components were inferred and identified from CYHJ-Ⅱ,including 2 organic acids,82 flavonoids,13 terpenoids,and 13 alkaloids.Based on the results of qualitative analysis,48 index components with good lin-ear relationships were derived by UPLC-Q-TOF-MS/MS combined with Masshunter mass spectrometry data analysis software.PCA was performed and the comprehensive score was calculated.Response surface analysis was performed with the comprehensive score as an indicator.The optimal extraction process obtained by combining the response surface prediction results and actual production was:soaking for 45 min,8 times the amount of solvent,2 extractions,each time for 120 min.CONCLUSION This study provides a new idea for the investigation of the extraction process of traditional Chinese medicine compound prescriptions and expands a new method for the development of traditional Chinese medicine compound prescriptions.

OBJECTIVE To establish a extraction process of Changyan Heji Ⅱ(CYHJ-Ⅱ)based on UPLC-Q-TOF-MS/MS technology combined with response surface analysis,and to optimize the extraction process.METHODS The chemical components in CYHJ-Ⅱ were qualitatively analyzed by UPLC-Q-TOF-MS/MS technology,and the chemical components with good linear relation-ship in mass spectrometry response were selected as process investigation indicators;the extraction process parameters(water addition amount,extraction time and soaking time)were investigated by Box-Behnken design;the comprehensive score was obtained by princi-pal component analysis(PCA),and the optimal process was determined by the comprehensive score combined with response surface a-nalysis.RESULTS Through qualitative analysis,110 components were inferred and identified from CYHJ-Ⅱ,including 2 organic acids,82 flavonoids,13 terpenoids,and 13 alkaloids.Based on the results of qualitative analysis,48 index components with good lin-ear relationships were derived by UPLC-Q-TOF-MS/MS combined with Masshunter mass spectrometry data analysis software.PCA was performed and the comprehensive score was calculated.Response surface analysis was performed with the comprehensive score as an indicator.The optimal extraction process obtained by combining the response surface prediction results and actual production was:soaking for 45 min,8 times the amount of solvent,2 extractions,each time for 120 min.CONCLUSION This study provides a new idea for the investigation of the extraction process of traditional Chinese medicine compound prescriptions and expands a new method for the development of traditional Chinese medicine compound prescriptions.

Plant-derived bioactive peptides have become a research hotspot in the fields of food and medicine due to their high source safety,easy absorption and utilization by the human body,and potential edible and medicinal value.Bioactive peptides can be classified into antihypertensive,hypoglycemic,hypolipidemic,anticancer,antioxidant,antimicrobial,and anti-inflammatory peptides according to their functions.Among these,antihypertensive,hypoglycemic,and hypolipidemic peptides are collectively referred to as cardiovascular active peptides,which can be used for the treatment and prevention of cardiovascular diseases and have an important role in the development of modem biomedicine.This review focused on the preparation methods,separation,purification,and identification techniques of bioactive peptides,as well as their mechanisms of action and applications in regulating cardiovascular diseases,aiming to provide a reference for further development and application of plant-derived cardiovascular peptides.

Various researches have reported on the relationship between homocysteine levels and adverse pregnancy outcomes. Researchers are increasingly focusing on the impact of homocysteine on female reproductive health and figuring out the potential positive effects of lowering homocysteine levels on women fertility. Our review aims to systematically summarize the possible roles of homocysteine in female reproductive disorders based on relevant studies from the past 15 years and therapeutic prospects targeting homocysteine to improve the reproductive health of women.

Objective:To test the difficulty, discrimination, and reliability of the ICU Nurse Pressure Injury Prevention and Care Knowledge Questionnaire.Methods:Two researchers independently translated the ICU Nurse Pressure Injury Prevention and Care Knowledge Questionnaire and cross-checked it to form a Chinese version of the questionnaire. The Chinese version of the ICU Nurse Pressure Injury Prevention and Care Knowledge Questionnaire consists of 24 items, with correct answers scoring one point and incorrect answers scoring zero points, with a total score of 24 points. Convenience sampling was used to select ICU nurses from 14 GradeⅢ Class A hospitals in five provinces/autonomous regions and two municipalities in China for the survey between April and July 2023. The difficulty index, discrimination index, and Cronbach's α coefficient of the questionnaire were analyzed.Results:A total of 1 121 questionnaires were distributed, with 1 020 valid responses, yielding a valid response rate of 90.99%. The mean score of the 1 020 ICU nurses on the Chinese version of the ICU Nurse Pressure Injury Prevention and Care Knowledge Questionnaire was (16.10±5.58) , with a minimum score of 4.00 and a maximum score of 24.00. The Cronbach's α coefficient of the Chinese version of the ICU Nurse Pressure Injury Prevention and Care Knowledge Questionnaire was 0.91. The questionnaire's overall difficulty and discrimination indexes were 0.67 and 0.59, respectively.Conclusions:The Chinese version of the ICU Nurse Pressure Injury Prevention and Care Knowledge Questionnaire has appropriate difficulty, moderate discrimination, and strong reliability, making it a valuable tool for assessing ICU nurses' knowledge of pressure injury-related topics.

Objective To construct a machine learning prediction model for diabetic kidney disease(DKD)in type 2 diabetes mellitus(T2DM)patients in the plain-sand and loess hilly areas of Gansu Province,and analyze the interpretability of the model.Methods A multi-stage stratified random sampling method was used to collect the data of T2DM patients in the two areas.After key feature screening,eight ML prediction models were constructed for the risk of DKD in the two areas.The receiver operating characteristic(ROC)curve,accuracy and F1 index were used to evaluate the model,and Shapley additive explanation(SHAP)algorithm was used for model interpretation.Results A total of 1599 patients with T2DM were enrolled in this study.After feature screening,ten variables were selected for model construction in the plain-sand areas.Among the eight models,the gradient boosting decision tree(GBDT)model had the highest prediction efficiency.The area under the curve(AUC)of the test dataset was 0.972,the accuracy was 0.949,and the F1 index was 0.884.In the loess hilly region,12 variables were included in the model,and the best model was the random forest(RF).The AUC of the test set was 0.966,the accuracy was 0.951,and the F1 index was 0.861.SHAP analysis showed that in addition to serum creatinine,age,LDL-C,HbA1c,DM duration,serum uric acid and urinary microalbumin were also closely related to the high risk of DKD.Conclusions The GBDT and RF models have good predictive efficiency for the occurrence of DKD in the two areas,which can be used for the screening of DKD high-risk populations and the in-depth exploration of potential risk factors in the two areas.

Plant-derived bioactive peptides have become a research hotspot in the fields of food and medicine due to their high source safety,easy absorption and utilization by the human body,and potential edible and medicinal value.Bioactive peptides can be classified into antihypertensive,hypoglycemic,hypolipidemic,anticancer,antioxidant,antimicrobial,and anti-inflammatory peptides according to their functions.Among these,antihypertensive,hypoglycemic,and hypolipidemic peptides are collectively referred to as cardiovascular active peptides,which can be used for the treatment and prevention of cardiovascular diseases and have an important role in the development of modem biomedicine.This review focused on the preparation methods,separation,purification,and identification techniques of bioactive peptides,as well as their mechanisms of action and applications in regulating cardiovascular diseases,aiming to provide a reference for further development and application of plant-derived cardiovascular peptides.

Objective To construct a machine learning prediction model for diabetic kidney disease(DKD)in type 2 diabetes mellitus(T2DM)patients in the plain-sand and loess hilly areas of Gansu Province,and analyze the interpretability of the model.Methods A multi-stage stratified random sampling method was used to collect the data of T2DM patients in the two areas.After key feature screening,eight ML prediction models were constructed for the risk of DKD in the two areas.The receiver operating characteristic(ROC)curve,accuracy and F1 index were used to evaluate the model,and Shapley additive explanation(SHAP)algorithm was used for model interpretation.Results A total of 1599 patients with T2DM were enrolled in this study.After feature screening,ten variables were selected for model construction in the plain-sand areas.Among the eight models,the gradient boosting decision tree(GBDT)model had the highest prediction efficiency.The area under the curve(AUC)of the test dataset was 0.972,the accuracy was 0.949,and the F1 index was 0.884.In the loess hilly region,12 variables were included in the model,and the best model was the random forest(RF).The AUC of the test set was 0.966,the accuracy was 0.951,and the F1 index was 0.861.SHAP analysis showed that in addition to serum creatinine,age,LDL-C,HbA1c,DM duration,serum uric acid and urinary microalbumin were also closely related to the high risk of DKD.Conclusions The GBDT and RF models have good predictive efficiency for the occurrence of DKD in the two areas,which can be used for the screening of DKD high-risk populations and the in-depth exploration of potential risk factors in the two areas.

Objective:To establish a rapid and accurate duplex real-time fluorescent reverse transcription-recombinase polymerase amplification/recombinase-aided amplification（RT-RPA/RAA）detection method for identification and differentiation of Nipah virus（NiV），Langya virus（LayV），Mojiang virus（MoJV），and Cedar virus（CedV）.Methods:First，specific primers and probes were designed targeting the conserved L gene regions of NiV and LayV，as well as the conserved N gene regions of MoJV and CedV，respectively. The four viruses were divided into two groups for duplex detection. Subsequently，the optimal primer and probe combinations were screened by comparing the amplification efficiency of different primer pair combinations（F1/R1，F1/R2，F2/R1，F2/R2）. The reaction temperature was optimized through temperature gradient settings from 37 ℃ to 42 ℃，and the amounts of primers and probes were optimized to establish the duplex real-time fluorescent RT-RPA/RAA detection system. Finally，the detection performance was evaluated through specificity，sensitivity，and stability tests，as well as clinical sample validation.Results:The selected primer pairs（NiV primer pair F2/R1，LayV primer pair F1/R1，MoJV primer pair F2/R2，and CedV primer pair F2/R2）all demonstrated optimal amplification efficiency when combined with their corresponding probes. The optimal annealing temperature was 39 ℃，and the minimum detection limit was 101-102 copies/μl. The method could effectively distinguish target viruses from other non-target viruses，and repeated experiments showed good stability（ R2> 0.90）. Additionally，detection results for Malaysian NiV strains and various clinical samples were consistent with the Taqman multiplex qRT-PCR method. Conclusion:The duplex real-time fluorescent RT-RPA/RAA detection method successfully established in this study can rapidly and accurately identify and differentiate four important henipavirus-like viruses：NiV，LayV，MoJV，and CedV. It features simple operation，rapid reaction，high specificity，and good stability，providing an effective molecular detection tool for rapid field diagnosis，surveillance，and control of these zoonotic viruses.