ObjectiveTo establish an animal model of atrial fibrillation（AF） that accurately reflects the phlegm-heat and blood stasis（TRYZ） pathogenesis in traditional Chinese medicine. MethodsForty SPF-grade SD rats were randomly assigned using a random number table to the following groups：the control group， the TRYZ+AF group，the AF group and the TRYZ group， with ten rats in each group. The TRYZ+AF and TRYZ groups underwent a high-fat diet combined with intraperitoneal lipopolysaccharide（LPS） injection to simulate the pathological alterations of TRYZ syndrome. Groups TRYZ+AF and AF were induced with acetylcholine-calcium chloride（Ach-CaCl₂） via caudal vein injection to induce AF. The control group received no intervention and was maintained under normal conditions. The modeling period lasted 3 weeks. Electrocardiography was used to assess AF episodes and duration， echocardiography evaluated left atrial dimensions and cardiac function， fully automated biochemical analyzer measured the levels of total cholesterol（TC）， triglycerides（TG）， high-density lipoprotein cholesterol（HDL-C） and low-density lipoprotein cholesterol（LDL-C）， hemoreometer analyzed the whole blood viscosity， plasma viscosity， and whole blood reduced viscosity， a coagulation analyzer assessed prothrombin time（PT）， activated partial thromboplastin time（APTT）， thrombin time（TT）， and fibrinogen（FIB）， enzyme-linked immunosorbent assay（ELISA） was used to determine the levels of C-reactive protein（CRP）， interleukin（IL）-1β， IL-6， IL-17， tumour necrosis factor（TNF）-α， matrix metalloproteinase-9（MMP-9）， galectin-3（Gal-3）， Collagen Ⅰ， and α-smooth muscle actin（α-SMA）. Hematoxylin-eosin（HE） staining and Masson's trichrome staining were used to analyze pathological changes in atrial myocardium， Western blot was employed to detect MMP-9， Collagen Ⅰ and α-SMA protein expression in myocardial tissue， real-time quantitative polymerase chain reaction（Real-time PCR） evaluated fibrous factor gene expression levels. Changes in the TRYZ syndrome were assessed via body weight， tongue color［red（R）， green（G）， and blue（B）］， and rectal temperature. Ultra-performance liquid chromatography-quadrupole-time-of-flight mass spectrometry（UPLC-Q-TOF-MS） was employed to detect differential metabolites between the control group and the TRYZ+AF group. ResultsFollowing three weeks of sustained modeling， compared with the control group， rats in the TRYZ+AF and the TRYZ groups exhibited reduced body weight， dry faeces， elevated rectal temperature， dark red tongue， decreased RGB values on the tongue surface， and markedly elevated TC and LDL-C levels（P<0.05， P<0.01）. The TRYZ+AF， TRYZ， and AF groups exhibited significantly decreased TT， APTT and PT， along with markedly elevated whole blood viscosity and FIB（P<0.05， P<0.01）. Rats in the TRYZ+AF and AF groups exhibited AF rhythm， markedly decreased heart rate， prolonged RR intervals， enlarged left atrium， and significantly reduced ejection fraction and shortening fraction（P<0.05， P<0.01）. Serum levels of CRP， IL-1β， IL-6， IL-17， TNF-α， MMP-9， Gal-3， Collagen Ⅰ， and α-SMA were elevated in rats from the TRYZ+AF， TRYZ， and AF groups compared to the control group， with the most pronounced increase observed in the TRYZ+AF group（P<0.05， P<0.01）. Histopathology revealed that the collagen fiber deposition in the atrial of rats in the TRYZ+AF， TRYZ and AF groups was higher than that in the control group（P<0.05， P<0.01）. Western blot and Real-time PCR results further demonstrated that the protein and mRNA expression levels of MMP-9， Collagen Ⅰ and α-SMA in the myocardial tissue of the TRYZ+AF group were higher than those in the other three groups（P<0.05， P<0.01）. Metabolomic analysis revealed 173 differentially expressed metabolites in the TRYZ+AF group and the control group， primarily enriched in pathways such as glycerophospholipid metabolism and glycolysis/gluconeogenesis. ConclusionThis study successfully establishes a rat model of AF integrated with the TRYZ syndrome， demonstrating the pathological process where the interactions of phlegm， heat and stasis jointly trigger tremor， this provides a reliable experimental tool for in-depth research into the biological basis of this disease syndrome.

ObjectiveTo investigate the effect of nicotinamide adenine dinucleotide on vascular endothelial injury in hypertension and its molecular mechanism. MethodsC57BL/6J mice were randomly divided into saline group （Saline） and hypertension group （Ang Ⅱ， which were infused with Ang Ⅱ via subcutaneously implanted osmotic pumps）， and supplemented daily with nicotinamide mononucleotide （300 mg/kg）， a precursor of NAD⁺. Blood pressure， endothelial relaxation function and pulse wave velocity were measured after 4 weeks. Wound healing assay and adhesion assay were used to evaluate the function of endothelial cells in vitro. mtROS levels were detected by immunofluorescence staining. RT-PCR was used to detect the mRNA expression of mtDNA， SIRT3 and isocitrate dehydrogenase 2 （IDH2）. 8-hydroxy-2'-deoxyguanosine levels were detected by enzyme-linked immunosorbent assay. The protein expression levels of p-eNOS， eNOS， SIRT3 and IDH2 were detected by Western blot. ResultsNMN supplementation reduced blood pressure （P<0.001） and improved endothelial function and arterial stiffness （P<0.001） in hypertensive mice. In vitro， NMN improved endothelial function in AngII-stimulated endothelial cells （P<0.05） and attenuated mitochondrial oxidative stress levels （P<0.001）. Mechanistically， NMN elevated SIRT3 activity （P<0.001）， which subsequently enhanced IDH activity （P<0.001） and reduced oxidative stress levels in endothelial cells. Conversely， knockdown of IDH2 would reverse the effect of SIRT3 in improving endothelial function （P<0.001）. ConclusionNAD⁺ lowers blood pressure and enhances vascular function in hypertension by reducing the level of oxidative stress in endothelial cells through activation of the SIRT3/IDH2 signal pathway.

Clinical practice guidelines represent the best recommendations for patient care. They are developed through systematically reviewing currently available clinical evidence and weighing the relative benefits and risks of various interventions. However, clinical practice guidelines have to go through a long translation cycle from development and revision to clinical promotion and application, facing problems such as scattered distribution, high duplication rate, and low actual utilization. At present, the clinical practice guideline information platform can directly or indirectly solve the problems related to the lengthy revision cycles, decentralized dissemination and limited application of clinical practice guidelines. Therefore, this paper systematically examines different types of clinical practice guideline information platforms and investigates their corresponding challenges and emerging trends in platform design, data integration, and practical implementation, with the aim of clarifying the current status of this field and providing valuable reference for future research on clinical practice guideline information platforms.

Objective: Machine learning (ML) has been reported to have better predictive capability than traditional statistical techniques. The aim of this study was to assess the efficacy of ML algorithms and logistic regression (LR) for predicting depressive symptoms during the COVID-19 pandemic. Methods: Analyses were carried out in a national cross-sectional study involving 21,916 participants. The ML algorithms in this study included random forest (RF), support vector machine (SVM), neural network (NN), and gradient boosting machine (GBM) methods. The performance indices were sensitivity, specificity, accuracy, precision, F1-score, and area under the receiver operating characteristic curve (AUC). Results: LR and NN had the best performance in terms of AUCs. The risk of overfitting was found to be negligible for most ML models except for RF, and GBM obtained the highest sensitivity, specificity, accuracy, precision, and F1-score. Therefore, LR, NN, and GBM models ranked among the best models. Conclusion Compared with ML models, LR model performed comparably to ML models in predicting depressive symptoms and identifying potential risk factors while also exhibiting a lower risk of overfitting.

Background: Both sodium-glucose cotransporters (SGLTs) and Na+/H+ exchangers (NHEs) rely on a favorable Na-electrochemical gradient. Gastrin, through the cholecystokinin B receptor (CCKBR), can induce natriuresis and diuresis by inhibiting renal NHEs activity. The present study aims to unveil the role of renal CCKBR in diabetes through SGLT2-mediated glucose reabsorption. Methods: Renal tubule-specific Cckbr-knockout (CckbrCKO) mice and wild-type (WT) mice were utilized to investigate the effect of renal CCKBR on SGLT2 and systemic glucose homeostasis under normal diet, high-fat diet (HFD), and HFD with a subsequent injection of a low dose of streptozotocin. The regulation of SGLT2 expression by gastrin/CCKBR and the underlying mechanism was explored using human kidney (HK)-2 cells. Results: CCKBR was downregulated in kidneys of diabetic mice. Compared with WT mice, CckbrCKO mice exhibited a greater susceptibility to obesity and diabetes when subjected to HFD. In vitro experiments using HK-2 cells revealed an upregulation of glucose transporters after incubation with high glucose, a response that was significantly attenuated following gastrin intervention. The glucose uptake from the culture medium of cells was altered accordingly. Moreover, gastrin administration effectively mitigated hyperglycemia in WT diabetic mice by inhibition of SGLT2 mediated glucose reabsorption, but this effect was compromised in the absence of CCKBR, as seen in CckbrCKO mice. Mechanistically, gastrin/CCKBR substantially reduced SGLT2 expression in HK-2 cells exposed to high glucose, via modulating Erkuclear factor-kappa B (NF-κB) pathway. Conclusion Our study underscores the crucial role of renal gastrin/CCKBR in SGLT2 regulation and glucose reabsorption, and renal gastrin/CCKBR can be a promising therapeutic target for diabetes.

Objective: Machine learning (ML) has been reported to have better predictive capability than traditional statistical techniques. The aim of this study was to assess the efficacy of ML algorithms and logistic regression (LR) for predicting depressive symptoms during the COVID-19 pandemic. Methods: Analyses were carried out in a national cross-sectional study involving 21,916 participants. The ML algorithms in this study included random forest (RF), support vector machine (SVM), neural network (NN), and gradient boosting machine (GBM) methods. The performance indices were sensitivity, specificity, accuracy, precision, F1-score, and area under the receiver operating characteristic curve (AUC). Results: LR and NN had the best performance in terms of AUCs. The risk of overfitting was found to be negligible for most ML models except for RF, and GBM obtained the highest sensitivity, specificity, accuracy, precision, and F1-score. Therefore, LR, NN, and GBM models ranked among the best models. Conclusion Compared with ML models, LR model performed comparably to ML models in predicting depressive symptoms and identifying potential risk factors while also exhibiting a lower risk of overfitting.

Objective: Machine learning (ML) has been reported to have better predictive capability than traditional statistical techniques. The aim of this study was to assess the efficacy of ML algorithms and logistic regression (LR) for predicting depressive symptoms during the COVID-19 pandemic. Methods: Analyses were carried out in a national cross-sectional study involving 21,916 participants. The ML algorithms in this study included random forest (RF), support vector machine (SVM), neural network (NN), and gradient boosting machine (GBM) methods. The performance indices were sensitivity, specificity, accuracy, precision, F1-score, and area under the receiver operating characteristic curve (AUC). Results: LR and NN had the best performance in terms of AUCs. The risk of overfitting was found to be negligible for most ML models except for RF, and GBM obtained the highest sensitivity, specificity, accuracy, precision, and F1-score. Therefore, LR, NN, and GBM models ranked among the best models. Conclusion Compared with ML models, LR model performed comparably to ML models in predicting depressive symptoms and identifying potential risk factors while also exhibiting a lower risk of overfitting.

Background: Both sodium-glucose cotransporters (SGLTs) and Na+/H+ exchangers (NHEs) rely on a favorable Na-electrochemical gradient. Gastrin, through the cholecystokinin B receptor (CCKBR), can induce natriuresis and diuresis by inhibiting renal NHEs activity. The present study aims to unveil the role of renal CCKBR in diabetes through SGLT2-mediated glucose reabsorption. Methods: Renal tubule-specific Cckbr-knockout (CckbrCKO) mice and wild-type (WT) mice were utilized to investigate the effect of renal CCKBR on SGLT2 and systemic glucose homeostasis under normal diet, high-fat diet (HFD), and HFD with a subsequent injection of a low dose of streptozotocin. The regulation of SGLT2 expression by gastrin/CCKBR and the underlying mechanism was explored using human kidney (HK)-2 cells. Results: CCKBR was downregulated in kidneys of diabetic mice. Compared with WT mice, CckbrCKO mice exhibited a greater susceptibility to obesity and diabetes when subjected to HFD. In vitro experiments using HK-2 cells revealed an upregulation of glucose transporters after incubation with high glucose, a response that was significantly attenuated following gastrin intervention. The glucose uptake from the culture medium of cells was altered accordingly. Moreover, gastrin administration effectively mitigated hyperglycemia in WT diabetic mice by inhibition of SGLT2 mediated glucose reabsorption, but this effect was compromised in the absence of CCKBR, as seen in CckbrCKO mice. Mechanistically, gastrin/CCKBR substantially reduced SGLT2 expression in HK-2 cells exposed to high glucose, via modulating Erkuclear factor-kappa B (NF-κB) pathway. Conclusion Our study underscores the crucial role of renal gastrin/CCKBR in SGLT2 regulation and glucose reabsorption, and renal gastrin/CCKBR can be a promising therapeutic target for diabetes.

Background: Both sodium-glucose cotransporters (SGLTs) and Na+/H+ exchangers (NHEs) rely on a favorable Na-electrochemical gradient. Gastrin, through the cholecystokinin B receptor (CCKBR), can induce natriuresis and diuresis by inhibiting renal NHEs activity. The present study aims to unveil the role of renal CCKBR in diabetes through SGLT2-mediated glucose reabsorption. Methods: Renal tubule-specific Cckbr-knockout (CckbrCKO) mice and wild-type (WT) mice were utilized to investigate the effect of renal CCKBR on SGLT2 and systemic glucose homeostasis under normal diet, high-fat diet (HFD), and HFD with a subsequent injection of a low dose of streptozotocin. The regulation of SGLT2 expression by gastrin/CCKBR and the underlying mechanism was explored using human kidney (HK)-2 cells. Results: CCKBR was downregulated in kidneys of diabetic mice. Compared with WT mice, CckbrCKO mice exhibited a greater susceptibility to obesity and diabetes when subjected to HFD. In vitro experiments using HK-2 cells revealed an upregulation of glucose transporters after incubation with high glucose, a response that was significantly attenuated following gastrin intervention. The glucose uptake from the culture medium of cells was altered accordingly. Moreover, gastrin administration effectively mitigated hyperglycemia in WT diabetic mice by inhibition of SGLT2 mediated glucose reabsorption, but this effect was compromised in the absence of CCKBR, as seen in CckbrCKO mice. Mechanistically, gastrin/CCKBR substantially reduced SGLT2 expression in HK-2 cells exposed to high glucose, via modulating Erkuclear factor-kappa B (NF-κB) pathway. Conclusion Our study underscores the crucial role of renal gastrin/CCKBR in SGLT2 regulation and glucose reabsorption, and renal gastrin/CCKBR can be a promising therapeutic target for diabetes.

Objective: Machine learning (ML) has been reported to have better predictive capability than traditional statistical techniques. The aim of this study was to assess the efficacy of ML algorithms and logistic regression (LR) for predicting depressive symptoms during the COVID-19 pandemic. Methods: Analyses were carried out in a national cross-sectional study involving 21,916 participants. The ML algorithms in this study included random forest (RF), support vector machine (SVM), neural network (NN), and gradient boosting machine (GBM) methods. The performance indices were sensitivity, specificity, accuracy, precision, F1-score, and area under the receiver operating characteristic curve (AUC). Results: LR and NN had the best performance in terms of AUCs. The risk of overfitting was found to be negligible for most ML models except for RF, and GBM obtained the highest sensitivity, specificity, accuracy, precision, and F1-score. Therefore, LR, NN, and GBM models ranked among the best models. Conclusion Compared with ML models, LR model performed comparably to ML models in predicting depressive symptoms and identifying potential risk factors while also exhibiting a lower risk of overfitting.