ObjectiveBased on ultra-performance liquid chromatography-quadrupole-electrostatic field orbitrap high resolution mass spectrometry（UPLC-Q-Orbitrap-MS）， the chemical constituents of Qili Qiangxin capsules was identified， and their distribution in vivo was analyzed. MethodsUPLC-Q-Orbitrap-MS was used to detect the sample solution of Qili Qiangxin capsules， as well as the serum， brain， heart， lung， spleen， liver and kidney tissues of mice after oral administration. Using the Thermo Xcalibur 2.2 software， the compound information database was constructed， and the molecular formulas of compounds corresponding to the quasi-molecular ions were fitted. Based on the information of retention time， accurate relative molecular mass and fragments， the compounds and their distribution in vivo were analyzed by comparing with the data of reference substances and literature. ResultsA total of 233 compounds， including 70 terpenoids， 60 flavonoids， 23 organic acids， 17 alkaloids， 20 steroids， 7 coumarins and 36 others， were identified or predicted from Qili Qiangxin capsules， 73 of which were identified matching with standard substances. Tissue distribution results showed that 71， 17， 38， 33， 32， 58 and 43 migrating components were detected in blood， brain， heart， lung， spleen， liver and kidney， respectively. Thirty-seven components were absorbed into the blood and heart， including quinic acid， benzoylaconitine benzoylmesaconine and so on. Fourteen components were absorbed into the blood and six tissues， including calycosin， methylnissolin， formononetin， alisol B， alisol A and so on. ConclusionThis study comprehensively analyzes the chemical components of Qili Qiangxin capsules and their distribution in vivo. Among them， astragaloside Ⅳ， salvianolic acid B， ginsenoside Rb₁， ginsenoside Rb₃， ginsenoside Rd， ginsenoside Rg₃， calycosin-7-glucoside， and sinapine may be the important components for the treatment of heart failure， which can provide useful reference for its quality control and research on pharmacodynamic material basis.

Traditional Chinese medicine （TCM） offers a rich theoretical foundation and clinical experience for the prevention and treatment of cardiovascular-kidney-metabolic syndrome（CKM）， demonstrating unique advantage. Building on previous work in managing diabetes， its complications， and chronic kidney disease， our team has proposed a five-phase evolution theory of "constraint， heat， deficiency， stasis， and toxin" as the core pathogenesis. These phases correspond to the pathological progression of constraint of phlegm-dampness， constraint transforming into heat， heat damaging qi and yin， stasis accumulated in the collateral vessels， and toxin induced by deficiency and stasis. In the prevention and treatment of CKM by TCM， it is emphasized to integrate the concept of "treating disease before it arises" with constitution theory， and incorporate the "2-5-8" prevention and treatment strategy， which combines prevention with treatment， tailors interventions to different phases， and employs comprehensive treatment modalities. Our goal is to leverage TCM's holistic advantages in preventing and treating CKM.

ObjectiveTo evaluate the clinical efficacy and safety of Qidan Tangshen Granules （芪丹糖肾颗粒， QTG） in the treatment of early diabetic kidney disease （DKD） with qi deficiency， blood stasis， and kidney deficiency syndrome， and to explore its mechanism. MethodsA double-blind， double-simulated method was used to enroll 200 patients with early DKD and qi deficiency， blood stasis， and kidney deficiency syndrome. Patients were randomly assigned in a 1∶1 ratio to the treatment group （100 cases） and the control group （100 cases）. The treatment group received QTG plus a valsartan capsule simulant， while the control group received valsartan capsules plus a QTG simulant， both for 12 weeks. The primary outcome was the urinary albumin-to-creatinine ratio （UACR）. Secondary outcomes included estimated glomerular filtration rate （eGFR）， fasting blood glucose （FBG）， 2-hour postprandial blood glucose （PBG）， glycated hemoglobin （HbA1c）， and traditional Chinese medicine （TCM） syndrome scores （including individual symptom scores for fatigue， dull complexion， soreness and weakness of the waist and knees， headache and chest pain， irritability， spontaneous sweating， thirst and polydipsia， polyphagia， polyuria， numbness of the limbs， and the total TCM syndrome score）. Oxidative stress markers including serum 8-hydroxy-2'-deoxyguanosine （8-OHDG）， 3-nitrotyrosine （3-NT）， and superoxide dismutase （SOD） were also assessed. Clinical efficacy and TCM syndrome efficacy were evaluated after treatment， and routine blood tests， urinalysis， and liver function tests were conducted and adverse reaction during the tria was recorded to assess safety. ResultsA total of 191 patients completed the study （95 in the treatment group and 96 in the control group）. The treatment group showed significant reductions in UACR， FBG， PBG， and HbA1c levels after treatment （P＜0.05 or P＜0.01）. The single TCM symptom scores except for polyphagia and total TCM syndrome scores significantly decreased （P＜0.05 or P＜0.01）. Compared to the control group， the treatment group had signi-ficantly lower UACR， FBG， PBG levels， and total TCM syndrome scores， sinlge symptoms scores except for polyphagia and limb numbness （P＜0.05 or P＜0.01）. Among 40 randomly selected patients （21 cases in the treatment group and 19 cases in the control group） for oxidative stress analysis， there were no significant differences in SOD， 3-NT， and 8-OHDG levels before and after treatment within or between groups （P＞0.05）. The overall effective rate in the treatment group was 64.2% （61/95） and 39.6% （38/96） in the control group， while the TCM syndrome efficacy rates were 80.0% （76/95） and 24.0% （23/96）， respectively， with the treatment group showing superior efficacy （P＜0.01）. No significant differences were observed in routine blood tests， urinalysis， or liver function indices before and after treatment in either group （P＞0.05）. The incidence of adverse reactions was 8.4% （8/95） in the treatment group and 9.4% （9/96） in the control group， with no statistically significant difference （P＞0.05）. ConclusionQTG can effectively reduce UACR and blood glucose levels， alleviate clinical symptoms， and improve clinical efficacy in patients with early DKD with qi deficiency， blood stasis， and kidney deficiency syndrome. The treatment is well-tolerated and safe， with no significant impact on oxidative stress markers.

To summarize the clinical experience of Professor LIU Shangyi in treating pruritus vulvae. It is believed that women have the physiological characteristics of liver and kidney as the root， and their pubic area is easily attacked by wind-dampness pathogenic qi， so the core mechanism of pruritus vulvae is proposed as wind-dampness accumulation and deficiency of liver and kidney. The core treatment method is to dispel wind-dampness and nourish the liver and kidneys， and modify the Danggui Decoction （当归饮子） to form a self-prescribed Yinyang Formula （阴痒方） as the basic prescription to treat pruritus vulvaen.

Objective To explore the feasibility of long non-coding RNAs (lncRNAs) as biomarkers for radiation-induced intestinal injury. Methods Mice were exposed to 15 Gy of ⁶⁰Co γ-rays to the abdominal area. The pathological changes in intestinal tissues were analyzed at 72 h post-irradiation to confirm the successful establishment of the radiation-induced intestinal injury model. Real-time quantitative PCR was conducted to detect the expression of candidate radiation-responsive lncRNAs in the jejunum, jejunal crypts, colon tissues, and plasma of irradiated mice. Human intestinal epithelial cell line HIEC-6 and human colon epithelial cell line NCM460 were exposed to 0, 5, 10, and 15 Gy of ⁶⁰Co γ-rays. The expression levels of candidate lncRNAs were measured at 4, 24, 48, and 72 h post-irradiation to observe their changes with the irradiation dose. Results Pathological analysis showed that abdominal irradiation with 15 Gy successfully established an acute radiation-induced intestinal injury mouse model. Real-time quantitative PCR showed that Dino, Lncpint, Meg3, Dnm3os, Trp53cor1, Pvt1, and Neat1 were significantly upregulated following the occurrence of radiation-induced intestinal injury (P < 0.05). Among them, Meg3 and Dnm3os in mouse plasma were significantly upregulated (P < 0.05), while Gas5 was significantly downregulated (P < 0.05). In HIEC-6 and NCM460 cells, the expression levels of DINO, MEG3, DNM3OS, and GAS5 showed dose-dependent patterns at certain time points (P < 0.05). Conclusion The lncRNAs encoded by MEG3, DNM3OS, and GAS5 in intestinal epithelial cells are responsive to ionizing radiation. Consistent differential expression changes were detected in mouse plasma and intestinal tissues, indicating their potential as biomarkers for radiation-induced intestinal injury.

Objective: This study aimed to explore the correlation between balance function and core muscle activation in patients with adolescent idiopathic scoliosis (AIS), compared to healthy individuals. Methods: A total of 24 AIS patients and 25 healthy controls were recruited. The limits of stability (LOS) test were conducted to assess balance function, while surface electromyography was used to measure the activity of core muscles, including the internal oblique, external oblique, and multifidus. Diaphragm thickness was measured using ultrasound during different postural tasks. Center of pressure (COP) displacement and trunk inclination distance were also recorded during the LOS test. Results: AIS patients showed significantly greater activation of superficial core muscles, such as the internal and external oblique muscles, compared to the control group (p < 0.05). Diaphragm activation was lower in AIS patients during balance tasks (p < 0.01). Although no significant difference was observed in COP displacement between the groups, trunk inclination was significantly greater in the AIS group during certain tasks (p < 0.05). Conclusion These findings suggest distinct postural control patterns in AIS patients, highlighting the importance of targeted interventions to improve balance and core muscle function in this population.

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.

Purpose: The aim of this retrospective study was to evaluate the relationship between admission base excess and clinical outcomes in postoperative patients with aortic dissection. Methods: Clinical data were extracted from the MIMIC-IV (Medical Information Mart for Intensive Care IV) database. The association between admission base excess and mortality in postoperative patients with aortic dissection was assessed using multivariate Cox regression and Kaplan-Meier survival analysis. Subgroup analysis and receiver operating characteristic (ROC) curve analysis were employed to evaluate the predictive performance of base excess for in-hospital, 30-day, 90-day, and 1-year mortality. Results: A total of 196 patients were categorized into the normal base excess (–3 to +3 mmol/L) group and abnormal base excess (<–3 or >+3 mmol/L) group. Multivariate Cox regression analysis revealed that arterial base excess was a significant predictor of all-cause mortality across all periods. Subgroup analyses showed no significant interaction effects.The area under the ROC curve for base excess ranged from 0.640 to 0.745, indicating comparable predictive performance to existing scoring tools. Conclusion Arterial base excess measured at admission is an effective and accessible predictor of mortality in patients with aortic dissection following surgical treatment.

Objective: This study aimed to explore the correlation between balance function and core muscle activation in patients with adolescent idiopathic scoliosis (AIS), compared to healthy individuals. Methods: A total of 24 AIS patients and 25 healthy controls were recruited. The limits of stability (LOS) test were conducted to assess balance function, while surface electromyography was used to measure the activity of core muscles, including the internal oblique, external oblique, and multifidus. Diaphragm thickness was measured using ultrasound during different postural tasks. Center of pressure (COP) displacement and trunk inclination distance were also recorded during the LOS test. Results: AIS patients showed significantly greater activation of superficial core muscles, such as the internal and external oblique muscles, compared to the control group (p < 0.05). Diaphragm activation was lower in AIS patients during balance tasks (p < 0.01). Although no significant difference was observed in COP displacement between the groups, trunk inclination was significantly greater in the AIS group during certain tasks (p < 0.05). Conclusion These findings suggest distinct postural control patterns in AIS patients, highlighting the importance of targeted interventions to improve balance and core muscle function in this population.

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.