ObjectiveTo investigate the role of 4-week high-intensity interval training (HIIT) in modulating the metabolic homeostasis of the pyruvate-lactate axis in the hippocampus of rats with chronic unpredictable mild stress (CUMS) to improve their depressive-like behavior. MethodsForty-eight SPF-grade 8-week-old male SD rats were randomly divided into 4 groups: the normal quiet group (C), the CUMS quiet group (M), the normal exercise group (HC), and the CUMS exercise group (HM). The M and HM groups received 8 weeks of CUMS modeling, while the HC and HM groups were exposed to 4 weeks of HIIT starting from the 5th week (3 min (85%-90%) S_max+1 min (50%-55%) S_max, 3-5 cycles, S_max is the maximum movement speed). A lactate analyzer was used to detect the blood lactate concentration in the quiet state of rats in the HC and HM groups at week 4 and in the 0, 2, 4, 8, 12, and 24 h after exercise, as well as in the quiet state of rats in each group at week 8. Behavioral indexes such as sucrose preference rate, number of times of uprightness and number of traversing frames in the absenteeism experiment, and other behavioral indexes were used to assess the depressive-like behavior of the rats at week 4 and week 8. The rats were anesthetized on the next day after the behavioral test in week 8, and hippocampal tissues were taken for assay. LC-MS non-targeted metabolomics, target quantification, ELISA and Western blot were used to detect the changes in metabolite content, lactate and pyruvate concentration, the content of key metabolic enzymes in the pyruvate-lactate axis, and the protein expression levels of monocarboxylate transporters (MCTs). Results4-week HIIT intervention significantly increased the sucrose preference rate, the number of uprights and the number of traversed frames in the absent field experiment in CUMS rats; non-targeted metabolomics assay found that 21 metabolites were significantly changed in group M compared to group C, and 14 and 11 differential metabolites were significantly dialed back in the HC and HM groups, respectively, after the 4-week HIIT intervention; the quantitative results of the targeting showed that, compared to group C, lactate concentration in the hippocampal tissues of M group, compared with group C, lactate concentration in hippocampal tissue was significantly reduced and pyruvate concentration was significantly increased, and 4-week HIIT intervention significantly increased the concentration of lactate and pyruvate in hippocampal tissue of HM group; the trend of changes in blood lactate concentration was consistent with the change in lactate concentration in hippocampal tissue; compared with group C, the LDHB content of group M was significantly increased, the content of PKM2 and PDH, as well as the protein expression level of MCT2 and MCT4 were significantly reduced. The 4-week HIIT intervention upregulated the PKM2 and PDH content as well as the protein expression levels of MCT2 and MCT4 in the HM group. ConclusionThe 4-week HIIT intervention upregulated blood lactate concentration and PKM2 and PDH metabolizing enzymes in hippocampal tissues of CUMS rats, and upregulated the expression of MCT2 and MCT4 transport carrier proteins to promote central lactate uptake and utilization, which regulated metabolic homeostasis of the pyruvate-lactate axis and improved depressive-like behaviors.

ObjectiveThis study aimed to investigate the effects of 4-week high-intensity interval training (HIIT) on synaptic plasticity in the prefrontal cortex (PFC) of rats exposed to chronic unpredictable mild stress (CUMS), and to explore its potential mechanisms. MethodsA total of 48 male Sprague-Dawley rats were randomly divided into 4 groups: control (C), model (M), control plus HIIT (HC), and model plus HIIT (HM). Rats in groups M and HM underwent 8 weeks of CUMS to establish depression-like behaviors, while groups HC and HM received HIIT intervention beginning from the 5th week for 4 consecutive weeks. The HIIT protocol consisted of repeated intervals of 3 min at high speed (85%-90% maximal training speed, S_max) alternated with one minute at low speed (50%-55% S_max), with 3 to 5 sets per session, conducted 5 d per week. Behavioral assessments and tail-vein blood lactate levels were measured at the end of the 4th and 8th weeks. After the intervention, rat PFC tissues were collected for Golgi staining to analyze synaptic morphology. Enzyme-linked immunosorbent assays (ELISA) were employed to detect brain-derived neurotrophic factor (BDNF), monocarboxylate transporter 1 (MCT1), lactate, and glutamate levels in the PFC, as well as serotonin (5-HT) levels in serum. Additionally, Western blot analysis was conducted to quantify the expression of synaptic plasticity-related proteins, including c-Fos, activity-regulated cytoskeleton-associated protein (Arc), and N-methyl-D-aspartate receptor 1 (NMDAR1). ResultsCompared to the control group (C), the CUMS-exposed rats (group M) exhibited significant reductions in sucrose preference rates, number of grid crossings, frequency of upright postures, and entries into and duration spent in open arms of the elevated plus maze, indicating marked depressive-like behaviors. Additionally, the group M showed significantly reduced dendritic spine density in the PFC, along with elevated levels of c-Fos, Arc, NMDAR1 protein expression, and increased concentrations of lactate and glutamate. Conversely, BDNF and MCT1 contents in the PFC and 5-HT levels in serum were significantly decreased. Following HIIT intervention, rats in the group HM displayed considerable improvement in behavioral indicators compared with the group M, accompanied by significant elevations in PFC MCT1 and lactate concentrations. Furthermore, HIIT notably normalized the expression levels of c-Fos, Arc, NMDAR1, as well as glutamate and BDNF contents in the PFC. Synaptic spine density also exhibited significant recovery. ConclusionFour weeks of HIIT intervention may alleviate depressive-like behaviors in CUMS rats by increasing lactate levels and reducing glutamate concentration in the PFC, thereby downregulating the overexpression of NMDAR, attenuating excitotoxicity, and enhancing synaptic plasticity.

The COVID-19 epidemic has spread to the whole world for three years and has had a serious impact on human life, health and economic activities. China's epidemic prevention and control has gone through the following stages: emergency unconventional stage, emergency normalization stage, and the transitional stage from the emergency normalization to the "Category B infectious disease treated as Category B" normalization, and achieved a major and decisive victory. The designated hospitals for prevention and control of COVID-19 epidemic in Tianjin has successfully completed its tasks in all stages of epidemic prevention and control, and has accumulated valuable experience. This article summarizes the experience of constructing a hospital infection prevention and control system during the "Category B infectious disease treated as Category A" period in designated hospital. The experience is summarized as the "Cluster" hospital infection prevention and control system, namely "three rings" outside, middle and inside, "three districts" of green, orange and red, "three things" before, during and after the event, "two-day pre-purification" and "two-director system", and "one zone" management. In emergency situations, we adopt a simplified version of the cluster hospital infection prevention and control system. In emergency situations, a simplified version of the "Cluster" hospital infection prevention and control system can be adopted. This system has the following characteristics: firstly, the system emphasizes the characteristics of "cluster" and the overall management of key measures to avoid any shortcomings. The second, it emphasizes the transformation of infection control concepts to maximize the safety of medical services through infection control. The third, it emphasizes the optimization of the process. The prevention and control measures should be comprehensive and focused, while also preventing excessive use. The measures emphasize the use of the least resources to achieve the best infection control effect. The fourth, it emphasizes the quality control work of infection control, pays attention to the importance of the process, and advocates the concept of "system slimming, process fattening". Fifthly, it emphasizes that the future development depends on artificial intelligence, in order to improve the quality and efficiency of prevention and control to the greatest extent. Sixth, hospitals need to strengthen continuous training and retraining. We utilize diverse training methods, including artificial intelligence, to ensure that infection control policies and procedures are simple. We have established an evaluation and feedback mechanism to ensure that medical personnel are in an emergency state at all times.

Objective To investigate the current status and influencing factors of nurses'missed nursing care in infection prevention and control,and provide reference for establishing targeted programs to improve nursing care situation in clinical infection prevention and control.Methods From October to November 2023,369 nurses from two tertiary first-class hospitals in Zhejiang Province were selected as surveyed subjects by convenience sampling methods.General information questionnaire,missed nursing care in infection prevention and control survey,hospi-tal ethical climate survey,and safety attitudes questionnaire were used for questionnaire survey.Univariate analy-sis,Pearson correlation analysis,and multiple linear regression analysis were adopted to analyze the influencing fac-tors.Results A total of 364 valid questionnaires were collected,with a respond rate of 98.64％.The scores of 364 nurses'missed nursing care in infection prevention and control survey B and C were(48.94±15.06)and(47.88±14.54)points,respectively.The score of hospital ethical climate was(103.00±16.01)points,and the score of safety attitude was(128.50±17.00)points.Multiple linear regression analysis showed that the influencing factors for missed nursing care in infection control and prevention were temporary employment modes,work at pre-hospital emergency departments,and low scores in hospital ethical climate(all P＜0.05).Conclusion Nurses'missed nursing care in infection prevention and control is at a lower-middle level,there is still room for improvement.Nur-sing managers should reduce missed nursing care in infection prevention and control by improving the hospital ethical climate and taking appropriate intervention measures for nurses in different departments and employment styles.

Objective:To construct classification prediction models for gamma passing rate using radiomics-based machine learning approaches and data from multiple radiotherapy institutions and evaluate the models′ performance.Methods:The data from 572 volumetric-modulated arc therapy (VMAT) patients across three radiotherapy institutions (514 for training and 58 for testing)were retrospectively collected. Additionally, 45 VMAT plans were collected from a single institution as an independent external validation set. For all the data, a three-dimensional dose validation approach based on actual measurements of phantoms was utilized, and gamma analysis was performed at the 3%/2 mm criterion using a dose threshold of 10%, absolute doses, and global normalization. After radiomic features were extracted from dose files, feature selection was performed using the random forest (RF) method and RF combined with Shapley Additive exPlanation (SHAP). Then, feature subsets of varying sizes (10, 20, 30, 40, and 50) were selected based on feature rankings. Using these subsets as inputs, data training was conducted using the Extreme Gradient Boosting (XGBoost) algorithm. Finally, the models′ classification performance was assessed using the area under the curve (AUC) values and F1-score.Results:Under the 3%/2 mm criterion, all models performed the best in the case of 20 feature subsets. The optimal prediction model established based on the feature selection using RF exhibited AUC and F1-score of 0.88 and 0.89, respectively on the testing set and 0.82 and 0.90, respectively, on the validation set. The optimal prediction model built based on the feature selection using RF combined with SHAP yielded AUC and F1-score of 0.86 and 0.92 on the testing set and 0.87 and 0.89, respectively, on the validation set, along with superior robustness. Therefore, the second model possessed certain advantages over the first model.Conclusions:For multicenter dose verification result, it is feasible to construct a machine learning prediction model with high classification performance using radiomic features derived from dose files, combined with feature selection based on SHAP. This approach can assist in advancing the clinical applications and implementation of gamma passing rate prediction models.

Objective:To construct classification prediction models for gamma passing rate using radiomics-based machine learning approaches and data from multiple radiotherapy institutions and evaluate the models′ performance.Methods:The data from 572 volumetric-modulated arc therapy (VMAT) patients across three radiotherapy institutions (514 for training and 58 for testing)were retrospectively collected. Additionally, 45 VMAT plans were collected from a single institution as an independent external validation set. For all the data, a three-dimensional dose validation approach based on actual measurements of phantoms was utilized, and gamma analysis was performed at the 3%/2 mm criterion using a dose threshold of 10%, absolute doses, and global normalization. After radiomic features were extracted from dose files, feature selection was performed using the random forest (RF) method and RF combined with Shapley Additive exPlanation (SHAP). Then, feature subsets of varying sizes (10, 20, 30, 40, and 50) were selected based on feature rankings. Using these subsets as inputs, data training was conducted using the Extreme Gradient Boosting (XGBoost) algorithm. Finally, the models′ classification performance was assessed using the area under the curve (AUC) values and F1-score.Results:Under the 3%/2 mm criterion, all models performed the best in the case of 20 feature subsets. The optimal prediction model established based on the feature selection using RF exhibited AUC and F1-score of 0.88 and 0.89, respectively on the testing set and 0.82 and 0.90, respectively, on the validation set. The optimal prediction model built based on the feature selection using RF combined with SHAP yielded AUC and F1-score of 0.86 and 0.92 on the testing set and 0.87 and 0.89, respectively, on the validation set, along with superior robustness. Therefore, the second model possessed certain advantages over the first model.Conclusions:For multicenter dose verification result, it is feasible to construct a machine learning prediction model with high classification performance using radiomic features derived from dose files, combined with feature selection based on SHAP. This approach can assist in advancing the clinical applications and implementation of gamma passing rate prediction models.

Objective To investigate the current status and influencing factors of nurses'missed nursing care in infection prevention and control,and provide reference for establishing targeted programs to improve nursing care situation in clinical infection prevention and control.Methods From October to November 2023,369 nurses from two tertiary first-class hospitals in Zhejiang Province were selected as surveyed subjects by convenience sampling methods.General information questionnaire,missed nursing care in infection prevention and control survey,hospi-tal ethical climate survey,and safety attitudes questionnaire were used for questionnaire survey.Univariate analy-sis,Pearson correlation analysis,and multiple linear regression analysis were adopted to analyze the influencing fac-tors.Results A total of 364 valid questionnaires were collected,with a respond rate of 98.64％.The scores of 364 nurses'missed nursing care in infection prevention and control survey B and C were(48.94±15.06)and(47.88±14.54)points,respectively.The score of hospital ethical climate was(103.00±16.01)points,and the score of safety attitude was(128.50±17.00)points.Multiple linear regression analysis showed that the influencing factors for missed nursing care in infection control and prevention were temporary employment modes,work at pre-hospital emergency departments,and low scores in hospital ethical climate(all P＜0.05).Conclusion Nurses'missed nursing care in infection prevention and control is at a lower-middle level,there is still room for improvement.Nur-sing managers should reduce missed nursing care in infection prevention and control by improving the hospital ethical climate and taking appropriate intervention measures for nurses in different departments and employment styles.

Two new ursane triterpenoids along with twelve known compounds were isolated from 80% ethanol extract of Agastache rugosa (Fisch. et. Mey.) O. Kuntze by using silica gel column, MCI column, ODS column and HPLC. The structures of the new compounds were identified as 2α,3α-dihydroxy-24-nor-urs-4(23),12(13)-dien-28-oic acid (1) and 2α,3α-dihydroxy-24-nor-urs-4(23),12(13),20(30) -trien-28-oic acid (2) by HR-ESI-MS, NMR and ECD spectral data, named agasursacid A and agasursacid B. In addition, compounds 3, 4, 6, 8 showed anti-coxsackievirus B3 (CVB3) activities with a IC₅₀ as 4.77, 1.59, 11.11 and 25.87 μmol·L^-1, resepectively.

This study aimed to evaluate the efficacy of Qi-supplementing and Yin-nourishing Chinese patent medicine in the treatment of early diabetic nephropathy(DN) by network Meta-analysis to explore the Chinese patent medicine with optimal efficacy and provide references for preventing renal deterioration and delaying the progression of early DN. Eight databases, including CNKI, Wanfang, VIP, SinoMed, PubMed, EMbase, Cochrane Library, and Web of Science, were searched for clinical randomized controlled trial(RCT) of Qi-supplementing and Yin-nourishing Chinese patent medicines in the treatment of early DN. After the literature mee-ting the inclusion criteria was screened, the quality of the literature was evaluated using the Cochrane risk-of-bias tool, and network Meta-analysis was performed using the BUGSnet package in R 4.2.1. Seventy-two research articles with a sample size of 6 344 cases were included, involving eight Chinese patent medicines and seven outcome indicators. The results of the network Meta-analysis showed that(1)in terms of improving urinary albumin excretion rate(UAER), Chinese patent medicines combined with conventional treatment were superior to conventional treatment, and Qiyao Xiaoke Capsules + conventional treatment was optimal.(2)In terms of reducing serum crea-tinine(Scr), Bailing Capsules + conventional treatment had superior efficacy.(3)In terms of reducing 24-hour urine total protein(24hUTP), Shenyan Kangfu Tablets + conventional treatment and Jinshuibao Capsules + conventional treatment had equivalent efficacy, and Shenyan Kangfu Tablets + conventional treatment was superior.(4)In terms of improving fasting blood glucose(FBG), Shenyan Kangfu Tablets + conventional treatment had superior efficacy.(5)In terms of improving total cholesterol(TC), Qiyao Xiaoke Capsules +conventional treatment had superior efficacy.(6)In terms of reducing triglyceride(TG), Bailing Capsules + conventional treatment had superior efficacy.(7)In terms of safety, the occurrence of adverse reactions was reported in seven interventions, but due to the large clinical heterogeneity, the quantitative analysis could not be performed. Overall, Qi-supplementing and Yin-nourishing Chinese patent medicines combined with conventional treatment were superior to conventional treatment alone in the treatment of early DN. The results showed that Qi-supplementing and Yin-nourishing Chinese patent medicines combined with conventional treatment had good clinical efficacy, and they could significantly reduce renal function indicators such as UAER, Scr, and 24hUTP, and reduce blood sugar and blood lipid, which can provide evidence-based support for the treatment of early DN. However, due to the differences in the quantity and quality of the included research articles, large-sample, multi-center, high-quality studies are still needed for further verification.
Humans ; Diabetic Nephropathies/drug therapy* ; Nonprescription Drugs/therapeutic use* ; Qi ; Network Meta-Analysis ; Capsules ; Drugs, Chinese Herbal/therapeutic use* ; Tablets ; Diabetes Mellitus/drug therapy*

The mechanisms underlying autophagic defects in nonalcoholic steatohepatitis (NASH) remain largely unknown. We aimed to elucidate the roles of hepatic cyclooxygenase 1 (COX1) in autophagy and the pathogenesis of diet-induced steatohepatitis in mice. Human nonalcoholic fatty liver disease (NAFLD) liver samples were used to examine the protein expression of COX1 and the level of autophagy. Cox1^Δhepa mice and their wildtype littermates were generated and fed with 3 different NASH models. We found that hepatic COX1 expression was increased in patients with NASH and diet-induced NASH mice models accompanied by impaired autophagy. COX1 was required for basal autophagy in hepatocytes and liver specific COX1 deletion exacerbated steatohepatitis by inhibiting autophagy. Mechanistically, COX1 directly interacted with WD repeat domain, phosphoinositide interacting 2 (WIPI2), which was crucial for autophagosome maturation. Adeno-associated virus (AAV)-mediated rescue of WIPI2 reversed the impaired autophagic flux and improved NASH phenotypes in Cox1^Δhepa mice, indicating that COX1 deletion-mediated steatohepatitis was partially dependent on WIPI2-mediated autophagy. In conclusion, we demonstrated a novel role of COX1 in hepatic autophagy that protected against NASH by interacting with WIPI2. Targeting the COX1-WIPI2 axis may be a novel therapeutic strategy for NASH.