OBJECTIVE To develope a predictive model for medication deviation risks during the hospital-to-home transition period in coronary heart disease （CHD） patients with initial treatment， aiming to assist medical staff in rapidly identifying high-risk groups for medication deviation. METHODS A total of 462 CHD patients with initial treatment from the Affiliated Hospital of North China University of Science and Technology （hereinafter referred to as “our hospital”） between January and July 2024 were enrolled. The patients were randomly divided into a modeling group and an internal validation group. The modeling group was further categorized into a medication deviation group and a non-medication deviation group based on whether medication deviations occurred. Similarly， 57 CHD patients with initial treatment from the cardiology department of our hospital between June and September 2025 were collected as an external validation group. Univariate analysis was used to screen predictive factors， followed by multivariate Logistic regression to construct the predictive model. Internal validation methods were employed to evaluate model performance， while external validation methods were used to test the model’s generalizability. RESULTS The 462 patients were divided into a modeling group （319 cases） and an internal validation group （143 cases）. In the modeling group， the medication deviation group （192 cases， 60.19%） and the non-medication deviation group （127 cases， 39.81%） were identified. Multivariate Logistic regression analysis revealed that age， medication type， medication adherence， and self-efficacy in rational medication use were predictive factors for medication deviations in CHD patients with initial treatment （ P ＜0.05）. The predictive model equation was logit P ＝ln[ P /（1－ P ） ] ＝1.321+1.732×age+4.091×medication type －4.360×medication adherence －3.081×self-efficacy in rational medication use. The model demonstrated good discrimination， with a Hosmer-Lemeshow goodness-of-fit test P -value of 0.439， an area under the receiver operating characteristic curve （AUC） of 0.870， sensitivity of 0.970， and specificity of 0.607. A risk nomogram with a total score of 350 points and a cutoff value of 110 points was plotted. The internal validation group showed an AUC o f 0.787 and a prediction accuracy of 77.6%， while the external validation group exhibited an AUC of 0.802 and a prediction accuracy of 73.7%. CONCLUSIONS This study successfully developed a predictive model for medication deviation risks during the hospital-to-home transition period in CHD patients with initial treatment. The model demonstrates excellent discrimination and predictive accuracy， effectively identifying high-risk populations for medication deviations. Age （＞70 years）， number of drug types≥5， poor medication adherence， and poor self-efficacy in rational medication use are independent risk factors for medication deviations.

: To analyze the prevalence of anemia and iron deficiency among primary and secondary school students in Rural Nutrition Improvement Program areas of Guizhou Province in 2023, and to explore the related factors, so as to provide evidence for Rural Nutrition Improvement Program optimization. Methods: In September 2023, a stratified random cluster sampling strategy was used to select 40 rural compulsory education schools with rural nutrition improvement program in five counties of Guizhou Province. School level questionnaire was employed to collect information of basic characteristics and school meal implementation. A total of 7 826 primary and secondary school students aged 6-16 underwent anthropometry and hemoglobin (Hb) determination; serum ferritin (SF) was additionally measured in a random subsample of 1 795 pupils. Students in Grade 3 and above also completed a questionnaire covering demographic characteristics, dietary behaviours and nutrition knowledge. Group comparisons were conducted by Chi square test or Fisher s exact test, and multivariable Logistic regression models were constructed to identify factors associated with anemia and iron deficiency. Results: The overall Hb level was (133.21±12.95)g/L, with an anemia prevalence of 7.17%. The overall SF level was (69.58±59.01)μg/L, with an iron deficiency prevalence of 2.73%. Multivariable analysis showed that stunting ( OR =1.88), school menus without nutrient calculation ( OR =1.61) and absence of menu planning software in the current semester ( OR =2.34) independently increased anemia risk, whereas obesity reduced it ( OR =0.54) (all P <0.05). Girls ( OR =4.16) and Grades 7-9 ( OR =5.93) increased iron deficiency risk (both P <0.05). Compared with rarely eating fresh vegetables, students with consuming <3 kinds per day ( OR =0.08) or exactly 3 kinds per day ( OR =0.06) had lower iron deficiency risks (both P <0.05). Conclusions Anemia and iron deficiency are prevalent among primary and secondary school students in Guizhou. Targeted intervention measures should be implemented for key populations to enhance the effectiveness of nutrition improvement program.

ObjectiveTo evaluate the clinical efficacy of Shenqi Yangxin decoction in the treatment of ischemic cardiomyopathy （ICM） with Qi and Yin deficiency and blood stasis syndrome and its effect on serum hydrogen sulfide （H₂S） and calcium ion （Ca²⁺）. MethodsA total of 64 ICM patients with Qi and Yin deficiency and blood stasis syndrome who met the inclusion criteria were randomly divided into a control group （n=32） and a treatment group （n=32）. All patients received conventional Western medicine treatment. The treatment group was additionally given Shenqi Yangxin decoction. The TCM syndrome score， Minnesota Living with Heart Failure Questionnaire （MLHFQ） score， left ventricular ejection fraction （LVEF）， N-terminal pro-B-type natriuretic peptide （NT-proBNP）， 6-minute walk test （6MWT）， New York Heart Association （NYHA） cardiac function classification， and serum H₂S and Ca²⁺ levels were compared between the two groups pre- and post-treatment. ResultsTwo cases dropped out from each group during the study. Finally， 30 patients in each group were included in the analysis. There were no significant differences in age， gender， course of coronary heart disease， underlying diseases， and laboratory tests between the two groups. Compared with baseline， the TCM syndrome score， MLHFQ score， and NT-proBNP in both treatment group and control group decreased significantly （P<0.01）， LVEF， 6MWT， and H₂S increased significantly （P<0.01）， and serum Ca²⁺ increased （P<0.05）. Compared with the control group after treatment， the MLHFQ score and NT-proBNP in the treatment group decreased （P<0.05）， the TCM syndrome score decreased significantly （P<0.01）， LVEF， 6MWT， and serum Ca²⁺ increased （P<0.05）， and H₂S increased significantly （P<0.01）. The improvement degree of the NYHA cardiac function classification in the treatment group was higher than that in the control group， but there was no significant difference. ConclusionShenqi Yangxin decoction is effective in treating ICM patients with Qi and Yin deficiency and blood stasis， which could significantly improve cardiac function and quality of life， and its therapeutic effect may be related to the regulation of serum H₂S and Ca²⁺ levels.

This study aimed to investigate the therapeutic effect of Yindan Pinggan capsules (YDPG) on intrahepatic cholestasis (IHC) through animal experiments, while utilizing network pharmacology and molecular docking techniques to explore its potential mechanisms. Initially, the therapeutic effect of YDPG on an α-naphthylisothiocyanate (ANIT)-induced IHC mouse model was assessed through liver function tests, routine blood tests, and liver pathology analysis. Subsequently, network pharmacology tools were employed to predict the active components, core targets, and signaling pathways of YDPG. Molecular docking technology was employed to verify the binding activity of key active components of YDPG with core targets, followed by protein immunoblotting to validate the key targets. Results showed that YDPG significantly improved liver function abnormalities and hepatocyte damage in IHC mice. Network pharmacology analysis revealed that 94 active components in YDPG were associated with 396 targets for the treatment of IHC, and were significantly enriched in pathways such as the phosphatidylinositol 3-kinase (PI3K)-protein kinase B (AKT) signaling pathway, lipid metabolism, and bile secretion. Molecular docking results showed good binding activity between key active components of YDPG and core targets of the PI3K-AKT signaling pathway. Further protein immunoblotting confirmed that YDPG could reduce the phosphorylation levels of PI3K and AKT proteins, core targets of the PI3K-AKT pathway in liver tissue. These findings suggest that YDPG may alleviate biological processes such as oxidative stress and inflammatory responses by regulating the PI3K-AKT signaling pathway, thereby improving liver damage in IHC mice and exerting a therapeutic effect on IHC. This experiment has been approved by the Animal Experiment Ethics Committee of Jinan University (ethical approval number: IACUC-20241011-09).

With the rapid development of social economy and the continuous improvement of human living standard, the incidence, fatality and recurrence rates of cardiovascular disease (CVD) are increasing year by year, which seriously affects people's life and health. Conventional therapeutic drugs have limited improvement on the disability rate, so the search for new therapeutic drugs and action targets has become one of the hotspots of current research. In recent years, the therapeutic role of the natural compound rosmarinic acid (RA) in CVD has attracted much attention, which is capable of preventing CVD by modulating multiple signalling pathways and exerting physiological activities such as antioxidant, anti-apoptotic, anti-inflammatory, anti-platelet aggregation, as well as anti-coagulation and endothelial function protection. In this paper, the role of RA in the prevention of CVD is systematically sorted out, and its mechanism of action is summarised and analysed, with a view to providing a scientific basis and important support for the in-depth exploration of the prevention value of RA in CVD and its further development as a prevention drug.

Water resources are crucial for environmental protection and the health of humans, plants, and animals. Contamination of water by pathogens such as bacteria, viruses, and protozoa can lead to outbreaks of various water-related infectious diseases, posing serious threats to public health and causing significant economic and social losses. Therefore, accurate and timely detection of pathogens in water sources and related substances is vital for preventing water-borne infectious diseases. In recent years, various molecular techniques have been extensively used to address water quality issues, including emerging isothermal amplification techniques such as loop-mediated isothermal amplification (LAMP), nucleic acid sequence-based amplification (NASBA), recombinase polymerase amplification (RPA), and helicase-dependent amplification (HDA). These techniques have significantly enhanced the capacity to detect and monitor pathogens in diverse aquatic systems and wastewater. This review focused on commonly used isothermal amplification techniques in water quality assessment and their recent advancements in environmental pathogens detection.

Growth arrest DNA damage-inducible protein 45 β (GADD45B) has been reported to be a regulatory factor for active DNA demethylation and is implicated in the modulation of synaptic plasticity and chronic stress-related psychopathological processes. However, its precise role and mechanism of action in stress susceptibility remain elusive. In this study, we found a significant reduction in GADD45B expression specifically in the ventral, but not the dorsal hippocampal CA1 (dCA1) of stress-susceptible mice. Furthermore, we demonstrated that GADD45B negatively regulates susceptibility to social stress and NMDA receptor-dependent long-term potentiation (LTP) in the ventral hippocampal CA1 (vCA1). Importantly, through pharmacological inhibition using the NMDA receptor antagonist MK801, we provided further evidence supporting the hypothesis that GADD45B potentially modulates susceptibility to social stress by influencing NMDA receptor-mediated LTP. Collectively, these results suggested that modulation of NMDA receptor-mediated synaptic plasticity is a pivotal mechanism underlying the regulation of susceptibility to social stress by GADD45B.
Animals ; Receptors, N-Methyl-D-Aspartate/antagonists & inhibitors* ; CA1 Region, Hippocampal/drug effects* ; Male ; Stress, Psychological/physiopathology* ; Mice ; Neuronal Plasticity/drug effects* ; Long-Term Potentiation/drug effects* ; Mice, Inbred C57BL ; Antigens, Differentiation/metabolism* ; Dizocilpine Maleate/pharmacology* ; Excitatory Amino Acid Antagonists/pharmacology* ; GADD45 Proteins

Ferroptosis of chondrocytes is a significant contributor to osteoarthritis (OA), for which there is still a lack of safe and effective therapeutic drugs targeting ferroptosis. Here, we screen for anti-ferroptotic drugs in Food and Drug Administration (FDA)-approved drug library via a high-throughput manner in chondrocytes. We identified a group of FDA-approved anti-ferroptotic drugs, among which vitamin K showed the most powerful protective effect. Further study demonstrated that vitamin K effectively inhibited ferroptosis and alleviated the extracellular matrix (ECM) degradation in chondrocytes. Intra-articular injection of vitamin K inhibited ferroptosis and alleviated OA phenotype in destabilization of the medial meniscus (DMM) mouse model. Mechanistically, transcriptome sequencing and knockdown experiments revealed that the anti-ferroptotic effects of vitamin K depended on growth arrest-specific 6 (Gas6). Furthermore, exogenous expression of Gas6 was found to inhibit ferroptosis through the AXL receptor tyrosine kinase (AXL)/phosphatidylinositol 3-kinase (PI3K)/AKT serine/threonine kinase (AKT) axis. Together, we demonstrate that vitamin K inhibits ferroptosis and alleviates OA progression via enhancing Gas6 expression and its downstream pathway of AXL/PI3K/AKT axis, indicating vitamin K as well as Gas6 to serve as a potential therapeutic target for OA and other ferroptosis-related diseases.

OBJECTIVE: To construct machine learning prediction model for sepsis-associated encephalopathy (SAE), and analyze the application value of the model on early identification of SAE risk in elderly septic patients. METHODS: Patients aged over 60 years with a primary diagnosis of sepsis admitted to intensive care unit (ICU) from 2008 to 2023 were selected from Medical Information Mart for Intensive Care-IV 2.2 (MIMIC-IV 2.2). Demographic variables, disease severity scores, comorbidities, interventions, laboratory indicators, and hospitalization details were collected. Key factors associated with SAE were identified using univariate Logistic regression analysis. The data were randomly divided into training and validation sets in a 7 : 3 ratio. Multivariable Logistic regression analysis was conducted in the training set and visualized using a nomogram model for prediction of SAE. The discrimination of the model was evaluated in the validation set using the receiver operator characteristic curve (ROC curve), and its calibration was assessed using calibration curve. Furthermore, multiple machine learning algorithms, including multi-layer perceptron (MLP), support vector machine (SVM), naive bayes (NB), gradient boosting machine (GBM), random forest (RF), and extreme gradient boosting (XGB), were constructed in the training set. Their predictive performance was subsequently evaluated on the validation set. Taking the XGB model as an example, the interpretability of the model through the SHapley Additive exPlanations (SHAP) algorithm was enhanced to identify the key predictive factors and their contributions. RESULTS: A total of 2 204 septic patients were finally enrolled, of whom 840 developed SAE (38.1%). A total of 21 variables associated with SAE were screened through univariate Logistic regression analysis. Multivariable Logistic regression analysis showed that endotracheal intubation [odds ratio (OR) = 0.40, 95% confidence interval (95%CI) was 0.19-0.88, P < 0.001], oxygen therapy (OR = 0.76, 95%CI was 0.53-0.95, P = 0.023), tracheotomy (OR = 0.20, 95%CI was 0.07-0.53, P < 0.001), continuous renal replacement therapy (CRRT; OR = 0.32, 95%CI was 0.15-0.70, P < 0.001), cerebrovascular disease (OR = 0.31, 95%CI was 0.16-0.60, P < 0.001), rheumatic disease (OR = 0.44, 95%CI was 0.19-0.99, P < 0.001), male (OR = 0.68, 95%CI was 0.54-0.86, P = 0.001), and maximum anion gap (AG; OR = 0.95, 95%CI was 0.93-0.97, P < 0.001) were associated with an decreased probability of SAE, and age (OR = 1.05, 95%CI was 1.03-1.06, P < 0.001), acute physiology score III (APSIII; OR = 1.02, 95%CI was 1.01-1.02, P < 0.001), Oxford acute severity of illness score (OASIS; OR = 1.04, 95%CI was 1.03-1.06, P < 0.001), and length of hospital stay (OR = 1.01, 95%CI was 1.01-1.02, P < 0.001) were associated with an increased probability of SAE. A nomogram model was constructed based on these variables. In the validation set, ROC curve analysis showed that the model achieved an area under the ROC curve (AUC) of 0.723, and the calibration curve showed good consistency between the predicted probability of the model and the observed probability. Among the machine learning algorithms, including MLP, SVM, NB, GBM, RF, and XGB, the SVM model and RF model demonstrated relatively good predictive performance, with AUC of 0.748 and 0.739, respectively, and the sensitivity was both exceeding 85%. The predictive performance of the XGB model was explained through SHAP analysis, and the results indicated that APSIII score (SHAP value was 0.871), age (SHAP value was 0.521), and OASIS score (SHAP value was 0.443) were important factors affecting the predictive performance of the model. CONCLUSIONS The machine learning-based SAE prediction model exhibits good predictive capability and holds significant application value for the early identification of SAE risk in elderly septic patients.
Humans ; Machine Learning ; Aged ; Sepsis-Associated Encephalopathy ; Sepsis/complications* ; Intensive Care Units ; Logistic Models ; Middle Aged ; Male ; ROC Curve ; Female ; Bayes Theorem ; Nomograms ; Support Vector Machine ; Algorithms