Ulcerative colitis（UC） is a chronic non-specific inflammatory bowel disease characterized by inflammation and ulceration of the colonic mucosa and submucosa， and its complex pathogenesis involves immune abnormality， oxidative stress and other factors. The nuclear transcription factor E₂-related factor 2（Nrf2）， encoded by the Nfe212 gene， plays a central role in antioxidant responses. It not only activates various antioxidant response elements such as heme oxygenase-1（HO-1） and quinone oxidoreductase 1（NQO1）， but also enhances the activity of glutathione-S-transferase（GST） and superoxide dismutase 1（SOD1）， effectively eliminating reactive oxygen species（ROS） accumulated in the body， and mitigating oxidative stress-induced damage to intestinal mucosa. In addition， Nrf2 can reduce the release of inflammatory factors and infiltration of immune cells by regulating immune response， cell apoptosis and autophagy pathways， thereby alleviating intestinal inflammation and promoting the repair and regeneration of damaged mucosa. Based on this， this paper reviews the research progress of Chinese materia medica in the prevention and treatment of UC by modulating the Nrf2 signaling pathway. It deeply explores the physiological role of Nrf2， the molecular mechanism of activation， the protective effect in the pathological process of UC， and how active ingredients in Chinese materia medica regulate the Nrf2 signaling pathway through multiple pathways to exert their potential mechanisms. These studies have revealed in depth that Chinese materia medica can effectively combat oxidative stress by regulating the Nrf2 signaling pathway. It can also play a role in anti-inflammatory， promoting autophagy， inhibiting apoptosis， protecting the intestinal mucosal barrier， and promoting intestinal mucosal repair， providing new ideas and methods for the multi-faceted treatment of UC.

Two-dimensional nuclear magnetic resonance (2D NMR) is a widely used technique for structural analysis of small molecular compounds. It can obtain information about the hydrogen-hydrogen correlation, hydrogen-carbon single bond correlation, hydrogen-carbon remote correlation, and hydrogen-hydrogen spatial arrangement of compounds. Thus, 2D NMR has an irreplaceable role in the structure elucidation of small molecular products. However, the sample amount of trace components in phytochemical research is very low, and the traditional sampling method (uniform sampling) has problems of poor spectral quality and too long measure time. Increasing the number of scans results in several hours of the acquisition time for a single two-dimensional spectrum, which in turn causes strain on the NMR machine. The non-uniform sampling (NUS) technique can shorten the acquisition time to a large extent and not affect the quality of 2D NMR data, which greatly improves the efficiency of 2D NMR acquisition. In this paper, fuziline, a small molecular compound in the lateral roots of Aconitum carmichaelii was selected as the research object. Its ¹H-¹³C HSQC, ¹H-¹H COSY, HMBC, and NOESY spectra were acquired by US and NUS methods, respectively. By comparing the integral values of NMR signals of three chemical groups in fuziline, it is confirmed that the NUS technique has the advantages of improving the quality of 2D NMR spectra and shortening the acquisition time in structure elucidation of small molecule compounds. In HMBC spectrum, it was further confirmed that NUS technology can improve the quality of the 2D spectra and the signal resolution. This indicates that NUS technology can improve the efficiency and reliability of the structure elucidation of small molecule compounds.

Background: and Purpose Symptomatic intracranial hemorrhage (sICH) following endovascular thrombectomy (EVT) is a severe complication associated with adverse functional outcomes and increased mortality rates. Currently, a reliable predictive model for sICH risk after EVT is lacking. Methods: This study used data from patients aged ≥20 years who underwent EVT for anterior circulation stroke from the nationwide Taiwan Registry of Endovascular Thrombectomy for Acute Ischemic Stroke (TREAT-AIS). A predictive model including factors associated with an increased risk of sICH after EVT was developed to differentiate between patients with and without sICH. This model was compared existing predictive models using nationwide registry data to evaluate its relative performance. Results: Of the 2,507 identified patients, 158 developed sICH after EVT. Factors such as diastolic blood pressure, Alberta Stroke Program Early CT Score, platelet count, glucose level, collateral score, and successful reperfusion were associated with the risk of sICH after EVT. The TREAT-AIS score demonstrated acceptable predictive accuracy (area under the curve [AUC]=0.694), with higher scores being associated with an increased risk of sICH (odds ratio=2.01 per score increase, 95% confidence interval=1.64–2.45, P<0.001). The discriminatory capacity of the score was similar in patients with symptom onset beyond 6 hours (AUC=0.705). Compared to existing models, the TREAT-AIS score consistently exhibited superior predictive accuracy, although this difference was marginal. Conclusions The TREAT-AIS score outperformed existing models, and demonstrated an acceptable discriminatory capacity for distinguishing patients according to sICH risk levels. However, the differences between models were only marginal. Further research incorporating periprocedural and postprocedural factors is required to improve the predictive accuracy.

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.

Background: and Purpose Symptomatic intracranial hemorrhage (sICH) following endovascular thrombectomy (EVT) is a severe complication associated with adverse functional outcomes and increased mortality rates. Currently, a reliable predictive model for sICH risk after EVT is lacking. Methods: This study used data from patients aged ≥20 years who underwent EVT for anterior circulation stroke from the nationwide Taiwan Registry of Endovascular Thrombectomy for Acute Ischemic Stroke (TREAT-AIS). A predictive model including factors associated with an increased risk of sICH after EVT was developed to differentiate between patients with and without sICH. This model was compared existing predictive models using nationwide registry data to evaluate its relative performance. Results: Of the 2,507 identified patients, 158 developed sICH after EVT. Factors such as diastolic blood pressure, Alberta Stroke Program Early CT Score, platelet count, glucose level, collateral score, and successful reperfusion were associated with the risk of sICH after EVT. The TREAT-AIS score demonstrated acceptable predictive accuracy (area under the curve [AUC]=0.694), with higher scores being associated with an increased risk of sICH (odds ratio=2.01 per score increase, 95% confidence interval=1.64–2.45, P<0.001). The discriminatory capacity of the score was similar in patients with symptom onset beyond 6 hours (AUC=0.705). Compared to existing models, the TREAT-AIS score consistently exhibited superior predictive accuracy, although this difference was marginal. Conclusions The TREAT-AIS score outperformed existing models, and demonstrated an acceptable discriminatory capacity for distinguishing patients according to sICH risk levels. However, the differences between models were only marginal. Further research incorporating periprocedural and postprocedural factors is required to improve the predictive accuracy.

Background: and Purpose Symptomatic intracranial hemorrhage (sICH) following endovascular thrombectomy (EVT) is a severe complication associated with adverse functional outcomes and increased mortality rates. Currently, a reliable predictive model for sICH risk after EVT is lacking. Methods: This study used data from patients aged ≥20 years who underwent EVT for anterior circulation stroke from the nationwide Taiwan Registry of Endovascular Thrombectomy for Acute Ischemic Stroke (TREAT-AIS). A predictive model including factors associated with an increased risk of sICH after EVT was developed to differentiate between patients with and without sICH. This model was compared existing predictive models using nationwide registry data to evaluate its relative performance. Results: Of the 2,507 identified patients, 158 developed sICH after EVT. Factors such as diastolic blood pressure, Alberta Stroke Program Early CT Score, platelet count, glucose level, collateral score, and successful reperfusion were associated with the risk of sICH after EVT. The TREAT-AIS score demonstrated acceptable predictive accuracy (area under the curve [AUC]=0.694), with higher scores being associated with an increased risk of sICH (odds ratio=2.01 per score increase, 95% confidence interval=1.64–2.45, P<0.001). The discriminatory capacity of the score was similar in patients with symptom onset beyond 6 hours (AUC=0.705). Compared to existing models, the TREAT-AIS score consistently exhibited superior predictive accuracy, although this difference was marginal. Conclusions The TREAT-AIS score outperformed existing models, and demonstrated an acceptable discriminatory capacity for distinguishing patients according to sICH risk levels. However, the differences between models were only marginal. Further research incorporating periprocedural and postprocedural factors is required to improve the predictive accuracy.

Traditional Chinese medicine (TCM) has historically played a pivotal role in the prevention and treatment of warm diseases, establishing a comprehensive theoretical framework that underpins its practices. The distinctive and indispensable contributions of aromatic Chinese herbs in dispelling harmful influences and mitigating the spread of these diseases are well recognized; however, further investigation is warranted to elucidate their systematic properties and regularities, and the theory of aromatic Chinese herbs in preventing and treating warm diseases still needs to be comprehensively summarized. This study employs the principles rooted in TCM, with particular emphasis on the framework for warm diseases. An analysis of the disease mechanisms, transmission dynamics, and preventive strategies is conducted during the early stage of infection, throughout the course of the disease, and in the post-illness phase. Furthermore, the characteristics and applications of aromatic Chinese herbs are integrated with insights drawn from modern pharmacological research to explore their specific roles in the prevention and management of warm diseases. The utilization of aromatic Chinese herbs manifests in a variety of therapeutic effects: aromatic medicinals purging filth and dispelling pathogens for preventing epidemic disease, aromatic medicinals regulation for relieving superficies syndrome and dispersing evils, aromatic medicinals ventilation the lung to relieve cough and asthma, aromatic medicinals resolving the dampness to awaken the spleen and stomach, aromatic medicinals opening the orifices to restore consciousness, aromatic and pungent medicinals to regulate qi, aromatic medicinals dredging the vessels to activate blood circulation and dissipate blood stasis, and aromatic medicinals clearing latent heat from the yin level. These properties facilitate tailored approaches to address the diverse manifestations of warm diseases and their associated symptoms, providing clear guidance for clinical application to achieve pre-disease prevention, active disease treatment, complication prevention, and post-recovery relapse avoidance. The use of aromatic Chinese herbs in preventing and treating warm diseases demonstrates theoretical, practical, systematic, and regular characteristics. The theory of the properties of aromatic Chinese herbs has been expanded and sublimated in clinical practice, and its scientific connotation has been expounded in modern research. Under the guidance of the theory of treatment based on syndrome differentiation, and by taking into account the distinct stages and pathologies of warm diseases, the rational selection of aromatic Chinese herbs can improve the clinical efficacy.

Objective To establish a dynamic prediction model of fatal massive hemorrhage in trauma based on the vital signs time series data and machine learning algorithms.Methods Retrospectively analyze the vital signs time series data of 7522 patients with trauma in the Medical Information Mart for Intensive Care-Ⅳ(MIMIC-Ⅳ)database from 2008 to 2019.According to the occurrence of posttraumatic fatal massive hemorrhage,the patients were divided into two groups:fatal massive hemorrhage group(n=283)and non-fatal massive hemorrhage group(n=7239).Six machine learning algorithms,including logistic regression(LR),support vector machine(SVM),random forests(RF),adaptive boosting(AdaBoost),gated recurrent unit(GRU),and GRU-D were used to develop a dynamic prediction models of fatal massive hemorrhage in trauma.The probability of fatal massive hemorrhage in the following 1,2,and 3 h was dynamically predicted.The performance of the models was evaluated by accuracy,sensitivity,specificity,positive predictive value,negative predictive value,Youden index,and area under receiver operating characteristic curve(AUC).The models were externally validated based on the trauma database of the Chinese PLA General Hospital.Results In the MIMIC-Ⅳ database,the set of dynamic prediction models based on the GRU-D algorithm was the best.The AUC for predicting fatal major bleeding in the next 1,2,and 3 h were 0.946±0.029,0.940±0.032,and 0.943±0.034,respectively,and there was no significant difference(P=0.905).In the trauma dataset,GRU-D model achieved the best external validation effect.The AUC for predicting fatal major bleeding in the next 1,2,and 3 h were 0.779±0.013,0.780±0.008,and 0.778±0.009,respectively,and there was no significant difference(P=0.181).This set of models was deployed in a public web calculator and hospital emergency department information system,which is convenient for the public and medical staff to use and validate the model.Conclusion A set of dynamic prediction models has been successfully developed and validated,which is greatly significant for the early diagnosis and dynamic prediction of fatal massive hemorrhage in trauma.

Objective To investigate the effect of varying blood pressure stratification on renal function in the diabetic population.Methods A prospective cohort study was conducted,enrolling 9 489 diabetic patients from a total of 101 510 Kailuan Group employees who underwent health examinations between July 2006 and October 2007.The follow-up period was(8.6±4.0)years.Participants were categorized into four groups based on their baseline blood pressure levels:normal blood pressure(systolic blood pressure<120 mmHg and diastolic blood pressure<80 mmHg),elevated blood pressure(systolic blood pressure 120-130 mmHg and diastolic blood pressure<80 mmHg),stage 1 hypertension(systolic blood pressure 130-140 mmHg and/or diastolic blood pressure 80-90 mmHg),and stage 2 hypertension(systolic blood pressure≥140 mmHg and/or diastolic blood pressure≥90 mmHg).The incidence density of chronic kidney disease(CKD)was compared among these groups.A multivariate Cox proportional hazards regression model was employed to assess the effects of different blood pressure levels on renal function in diabetic patients,with the stability of the results confirmed using a multivariate time-dependent Cox proportional hazards model.Sensitivity analysis was conducted after excluding cases of cardiovascular disease(CVD)during follow-up,and cases using antihypertensive and antidiabetic medications at baseline.Results(1)At baseline,stage 1 hypertension patients demonstrated statistically significant higher differences with age and body mass index(BMI)compared to normal blood pressure group(P<0.05).(2)By the end of the follow-up,2 294 cases of CKD were identified,including 1 117 cases of estimated glomerular filtration rate(eGFR)decline and 1 575 cases of urinary protein.The incidences density of CKD,eGFR decline and urinary protein for stage 1 hypertension group were 39.4,16.3 and 25.5 per thousand person-years,respectively,all of which were statistically significant different from normal blood pressure group(log-rank test,P<0.01).(3)Multivariate Cox regression analysis revealed that,compared to the normal blood pressure group,stage 1 hypertension was associated with a 29%increased risk of CKD(HR=1.29,95%CI 1.09-1.52)and a 40%increased risk of eGFR decline(HR=1.40,95%CI 1.08-1.80)in diabetic individuals.Conclusion Stage 1 hypertension significantly increases the risk of CKD and eGFR decline in diabetic individuals,with a particularly notable effect on the risk of eGFR decline.