This study aimed to investigate the effects of sweet potato leaf extract on production per-formance,systematic and mammary gland oxidative stress status and rumen microbiota of dairy goats fed high concentrate diets.Twenty Guanzhong dairy goats with same parity,similar lactation period(120±15)d and healthy body condition were selected and randomly divided into four groups:low-concentrate(LC),low-concentrate supplemented with 1％sweet potato leaf extract(LCS),high-concentrate(HC)and high-concentrate supplemented with 1％sweet potato leaf ex-tract(HCS).The experimental period was 35 days.The results showed that in the third week,milk yield in the HCS group was significantly higher than that in the LC and LCS groups(P＜0.05).The content of lipopolysaccharide in the rumen fluid of the HC group was significantly higher than that of the other three groups(P＜0.05),the content of malondialdehyde in the serum of the HC group was significantly higher than that of the LCS group(P＜0.05),the content of reactive oxygen species,protein carbonyl,8-hydroxydeoxyguanosine in the milk of the HC group was sig-nificantly higher than that of the LC and LCS groups(P＜0.05),GSH-Px in HCS group was sig-nificantly higher than that in the other three groups(P＜0.05).After the addition of sweet potato leaf extract,there was an increasing trend in the content of Anabaena phylum at the phylum level.In the joint analysis of genera,rumen fluid LPS showed highly significant negative correlation with Succiniclasticum(P＜0.01)and negative correlation with Prevotella(P＜0.05).Valeric acid was negatively correlated with Prevotella(P＜0.05).The pH value was negatively correlated with Treponema(P＜0.05).Butyric acid was positively correlated with Anaeroplasma(P＜0.05).In conclusion,the addition of sweet potato leaf extract to the diet can increase milk production and al-leviate the state of mammary gland oxidative stress,as well as improving rumen microbial diversi-ty of dairy goats.

This study aimed to investigate the effects of sweet potato leaf extract on production per-formance,systematic and mammary gland oxidative stress status and rumen microbiota of dairy goats fed high concentrate diets.Twenty Guanzhong dairy goats with same parity,similar lactation period(120±15)d and healthy body condition were selected and randomly divided into four groups:low-concentrate(LC),low-concentrate supplemented with 1％sweet potato leaf extract(LCS),high-concentrate(HC)and high-concentrate supplemented with 1％sweet potato leaf ex-tract(HCS).The experimental period was 35 days.The results showed that in the third week,milk yield in the HCS group was significantly higher than that in the LC and LCS groups(P＜0.05).The content of lipopolysaccharide in the rumen fluid of the HC group was significantly higher than that of the other three groups(P＜0.05),the content of malondialdehyde in the serum of the HC group was significantly higher than that of the LCS group(P＜0.05),the content of reactive oxygen species,protein carbonyl,8-hydroxydeoxyguanosine in the milk of the HC group was sig-nificantly higher than that of the LC and LCS groups(P＜0.05),GSH-Px in HCS group was sig-nificantly higher than that in the other three groups(P＜0.05).After the addition of sweet potato leaf extract,there was an increasing trend in the content of Anabaena phylum at the phylum level.In the joint analysis of genera,rumen fluid LPS showed highly significant negative correlation with Succiniclasticum(P＜0.01)and negative correlation with Prevotella(P＜0.05).Valeric acid was negatively correlated with Prevotella(P＜0.05).The pH value was negatively correlated with Treponema(P＜0.05).Butyric acid was positively correlated with Anaeroplasma(P＜0.05).In conclusion,the addition of sweet potato leaf extract to the diet can increase milk production and al-leviate the state of mammary gland oxidative stress,as well as improving rumen microbial diversi-ty of dairy goats.

Objective To investigate the expression characteristics and clinical diagnostic value of programmed death receptor 1(PD-1)and its corresponding ligand(PD-L1)in patients with acute exacerbation of chronic obstructive pulmonary disease(AECOPD).Methods One hundred and sixty COPD patients who visited Dongzhimen Hospital of Beijing University of Chinese Medicine from April 2024 to November 2024 were included and divided into an acute exacerbation group of 100 cases and a stable group of 60 cases according to the severity of the disease.Additionally,40 healthy volunteers during the same period were recruited as the control group.The general clinical data of the patients were collected.Chronic Obstructive Pulmonary Disease Assessment Test(CAT)and Modified Medical Research Council Dyspnea Questionnaire(mMRC)Scale were used to test the severity of the disease;respiratory function testing was performed and fasting venous blood was collected for serum PD-1 and PD-L1 testing.Pearson correlation was used to analyze the correlation between serum PD-1,PD-L1,CAT,and mMRC,and multiple logistic regression analysis to identify the influencing factors of AECOPD.Receiver operating characteristic(ROC)curve was drawn to evaluate the diagnostic value of serum PD-1 and PD-L1 level for AECOPD.Results Serum PD-1 level in the stable COPD group and AECOPD group was significantly increased compared with that in the control group,while serum PD-L1 level was significantly decreased,showing statistical significance(P<0.05);The level of PD-1 gradually increased with the grading of lung function and the deterioration of AECOPD,with statistical significance(P<0.05);Pearson correlation showed that serum PD-1 level was positively correlated with CAT scores in COPD patients,while negatively with CAT scores,showing statistical significance(P<0.05);Multiple logistic regression analysis showed that elevated levels of serum inter-leukin-6(IL-6),neutrophil to lymphocyte ratio(NLR),and PD-1 were risk factors for AECOPD,while elevated level of PD-L1 was protective factor for AECOPD(P<0.05);ROC curve showed that the levels of PD-1,PD-L1,IL-6,NLR,and the area under the ROC curve(AUC)for their combined prediction of AECOPD diagnosis were 0.884,0.867,0.868,0.802,and 0.995,respectively.Conclusion Serum PD-1 and PD-L1 in AECOPD patients have presented certain expression characteristics,with elevated PD-1 level while decreased PD-L1 level.Both have good clinical diagnostic value for AECOPD.

Objective To develop a predictive model for guiding blood transfusion decisions in pediatric patients with traumatic brain injury(TBI)by identifying and analyzing key factors that influence blood transfusion requirements.Methods A retrospective analysis was conducted on the clinical data of 1,535 pediatric patients with TBI admitted to four medical institutions from January 1,2015,to December 31,2022.Patients were divided into two groups:those who received red blood cell transfusions during hospitalization and those who did not.Comparative analyses were performed on demographic,clinical,and laboratory data between these two groups.Logistic regression analysis was used to identify risk factors associated with in-hospital blood transfusion,and a predictive model was developed using a nomogram.The performance of this model was evaluated using a receiver operating characteristic(ROC)curve.Results Significant differences were observed between the blood transfusion and non-blood transfusion groups in terms of baseline demographics,clinical indicators,and laboratory test results(all P<0.05).Patients in the blood transfusion group exhibited significantly higher in-hospital mortality,compli-cation rates,use of mechanical ventilation,ICU admission rates,and length of stay compared to those in the non-blood transfusion group(all P<0.05).Multivariate logistic regression analysis identified heart rate,presence of other fractures,treatment methods,hemoglobin(Hb),platelet count(Plt),activated partial thromboplastin time(APTT),and D-dimer levels as independent risk factors for blood transfusion in TBI patients.The area under the ROC curve for the blood transfusion prediction model,based on these independent risk factors,was 0.95(95%CI:0.94～0.97),indicating excellent predictive accuracy.Calibration and decision curves further validated the robust-ness and reliability of the model's predictive capacity.Conclusions Heart rate,presence of other fractures,treatment methods,Hb,Plt count,APTT,and D-dimer levels serve as independent risk factors for blood transfusion in TBI patients.The prediction model developed based on these factors demonstrates excellent predictive performance,thereby guiding clinicians in making informed blood transfusion decisions and enhancing the success rate of patient outcomes.

Objective To investigate the expression characteristics and clinical diagnostic value of programmed death receptor 1(PD-1)and its corresponding ligand(PD-L1)in patients with acute exacerbation of chronic obstructive pulmonary disease(AECOPD).Methods One hundred and sixty COPD patients who visited Dongzhimen Hospital of Beijing University of Chinese Medicine from April 2024 to November 2024 were included and divided into an acute exacerbation group of 100 cases and a stable group of 60 cases according to the severity of the disease.Additionally,40 healthy volunteers during the same period were recruited as the control group.The general clinical data of the patients were collected.Chronic Obstructive Pulmonary Disease Assessment Test(CAT)and Modified Medical Research Council Dyspnea Questionnaire(mMRC)Scale were used to test the severity of the disease;respiratory function testing was performed and fasting venous blood was collected for serum PD-1 and PD-L1 testing.Pearson correlation was used to analyze the correlation between serum PD-1,PD-L1,CAT,and mMRC,and multiple logistic regression analysis to identify the influencing factors of AECOPD.Receiver operating characteristic(ROC)curve was drawn to evaluate the diagnostic value of serum PD-1 and PD-L1 level for AECOPD.Results Serum PD-1 level in the stable COPD group and AECOPD group was significantly increased compared with that in the control group,while serum PD-L1 level was significantly decreased,showing statistical significance(P<0.05);The level of PD-1 gradually increased with the grading of lung function and the deterioration of AECOPD,with statistical significance(P<0.05);Pearson correlation showed that serum PD-1 level was positively correlated with CAT scores in COPD patients,while negatively with CAT scores,showing statistical significance(P<0.05);Multiple logistic regression analysis showed that elevated levels of serum inter-leukin-6(IL-6),neutrophil to lymphocyte ratio(NLR),and PD-1 were risk factors for AECOPD,while elevated level of PD-L1 was protective factor for AECOPD(P<0.05);ROC curve showed that the levels of PD-1,PD-L1,IL-6,NLR,and the area under the ROC curve(AUC)for their combined prediction of AECOPD diagnosis were 0.884,0.867,0.868,0.802,and 0.995,respectively.Conclusion Serum PD-1 and PD-L1 in AECOPD patients have presented certain expression characteristics,with elevated PD-1 level while decreased PD-L1 level.Both have good clinical diagnostic value for AECOPD.

Objective To develop a predictive model for guiding blood transfusion decisions in pediatric patients with traumatic brain injury(TBI)by identifying and analyzing key factors that influence blood transfusion requirements.Methods A retrospective analysis was conducted on the clinical data of 1,535 pediatric patients with TBI admitted to four medical institutions from January 1,2015,to December 31,2022.Patients were divided into two groups:those who received red blood cell transfusions during hospitalization and those who did not.Comparative analyses were performed on demographic,clinical,and laboratory data between these two groups.Logistic regression analysis was used to identify risk factors associated with in-hospital blood transfusion,and a predictive model was developed using a nomogram.The performance of this model was evaluated using a receiver operating characteristic(ROC)curve.Results Significant differences were observed between the blood transfusion and non-blood transfusion groups in terms of baseline demographics,clinical indicators,and laboratory test results(all P<0.05).Patients in the blood transfusion group exhibited significantly higher in-hospital mortality,compli-cation rates,use of mechanical ventilation,ICU admission rates,and length of stay compared to those in the non-blood transfusion group(all P<0.05).Multivariate logistic regression analysis identified heart rate,presence of other fractures,treatment methods,hemoglobin(Hb),platelet count(Plt),activated partial thromboplastin time(APTT),and D-dimer levels as independent risk factors for blood transfusion in TBI patients.The area under the ROC curve for the blood transfusion prediction model,based on these independent risk factors,was 0.95(95%CI:0.94～0.97),indicating excellent predictive accuracy.Calibration and decision curves further validated the robust-ness and reliability of the model's predictive capacity.Conclusions Heart rate,presence of other fractures,treatment methods,Hb,Plt count,APTT,and D-dimer levels serve as independent risk factors for blood transfusion in TBI patients.The prediction model developed based on these factors demonstrates excellent predictive performance,thereby guiding clinicians in making informed blood transfusion decisions and enhancing the success rate of patient outcomes.

[Abstract] [Objective] To explore the risk factors of blood transfusion in patients with isolated traumatic brain injury (iTBI) based on multiple machine learning methods, so as to establish a predictive model to provide reasonable guidance for blood transfusion in patients with iTBI. [Methods] A total of 2 273 patients with iTBI from the First Affiliated Hospital of Nanchang University from January 1, 2015 to June 30, 2021 were included to compare and analyze the differences in variables such as vital signs, clinical indicators and laboratory testing indicators between transfusion and non transfusion patients. Furthermore, six machine learning models were established to compare the performance of different models through cross validation, accuracy, specificity, recall, f1 value and area under the ROC curve. The SHAP plot was used to explain the influencing factors of blood transfusion in iTBI patients. [Results] This study included 2 273 iTBI patients, with a total of 301 patients receiving blood transfusions. There were significant differences (P<0.05) in gender, age, HR, clinical diagnosis, skull fracture, treatment methods, hemorrhagic shock, GCS, K, Ca, PT, APTT, INR, RBC, Hct, Hb and Plt between transfusion and non transfusion patients; Moreover, the LOS, incidence of complications, mechanical ventilation rate, ICU admission rate, readmission rate within 90 days and in-hospital mortality rate of transfusion patients were all higher than those of the non transfusion group (P<0.05). Six machine learning algorithms were used for model construction, and the validation results on the test set showed that the CatBoost model performed the best with an AUC of 0.911. Furthermore, the SHAP framework was used to explain and visualize the optimal model CatBoost, showing that surgical treatment, lower GCS, higher INR, lower Hct, lower K, lower Ca, age ≥60 years, skull fractures and hemorrhagic shock increase the risk of blood transfusion in patients. [Conclusion] This study established a machine learning model for predicting blood transfusion in iTBI patients, and the CatBoost model performed the best. This model may be useful and beneficial for identifying transfusion risks in this population, making clinical transfusion decisions and monitoring progress.

Objective Explore the comprehensive emotion adjustment pattern that combines non-contact physiological and psychological detection methods in fasting and low metabolism scenarios.This study aims to verify the accuracy of non-contact physiological and psychological detection algorithms and evaluate the effectiveness of multimodal emotion adjustment schemes for addressing negative emotional states such as depression and anxiety.Methods Deploy non-contact physiological and psychological detection algorithms and emotion adjustment plans to build a multimodal emotion adjustment system.Collect physiological and psychological data from volunteers participating in the 15-days complete fasting human low metabolism experiment of"Green Star Travel Ⅷ".Utilize finger clip oximeters and scales to verify the accuracy of existing non-contact physiological and psychological methods within the system.Design an emotion adjustment experiment featuring four groups:sound adjustment,acupoints adjustment,magnetism adjustment,and combination adjustment.Compare the volunteers'scale scores before and after the adjustments to verify the effectiveness of the system's emotion adjustment capabilities.Results The experimental results demonstrate that the average difference in the Bland-Altman plot for the non-contact heart rate detection model is ﹣0.497 bpm,with 95.3%of the error values falling within the 95%consistency interval.The non-contact psychological detection model achieved an accuracy rate of over 80%in identifying stress,anxiety,and depression,and an accuracy rate of over 70%in identifying fatigue and anger.Following emotion adjustment,the stress levels of the subjects significantly improved(P?

Objective In the future,during the landing and exploration of near-Earth planets(e.g.,Mars,Jupiter,etc.),astronauts may take the initiative to start fasting to reduce the amount of load;and it is even more likely that astronauts will take the initiative to fast in the process of manned deep-space exploration in the future,or they may enter a dormant low-metabolism state to save the amount of load to enable the spaceship to fly for a longer period,and to locate in a deeper and farther position.The ability of an individual to maintain cognitive ability and respond appropriately over a period in a long-term fasting state is important for survival.Therefore,the present study focused on investigating the effects of 15 days of complete fasting on dual cognitive control function and its neural mechanisms.Methods Twenty-four healthy volunteers were recruited to participate in the fasting experiment.Behavioral and electroencephalographic data from the AX-CPT Task were collected with event-related potentials(ERP)to assess the effects of 15 days of complete fasting on dual cognitive control in 16 volunteers.Results(1)Behavioral outcomes had significant main effects on response time[F(1,15)=99.41,P?