AIM To investigate the protective effects of Shuangyi Qushi Tongluo Capsules(Shuangyi Capsules)on Dexamethasone(Dex)induced osteoporosis in mice.METHODS The C57BL/6J mice were randomly divided into the control group,the model group,the Xianling Gubao Capsules group(1.5 g/kg),and the low-dose,moderate-dose,and high-dose Shuangyi Capsules groups(0.6,1.2,and 2.4 g/kg).The mouse model of osteoporosis was induced by 8-week intraperitoneal injection of Dex sodium phosphate injection(5 mg/kg).The mice had their femur osteogenesis observed with hematoxylin and eosin(HE)staining and tartrate-resistant acid phosphatase(TRAP)staining;their serum alkaline phosphatase(ALP)and osteocalcin(BGP)activities detected by ELISA;their femoral mRNA expressions of Col-Ⅰ,OCN,and OPN detected by RT-qPCR;and their femoral protein expressions of OPG and RANKL detected by Western blot.Upon the MC3T3-E1 cells exposed to Dex and Shuangyi Capsules,their viability was evaluated by CCK-8 assay;their mineralization determined by alkaline phosphatase staining and alizarin red staining(ARS);and their intracellular ROS level detected using DCFH-DA probe.RESULTS Compared with the model group,Shuangyi Capsules groups demonstrated improved fracture of femoral trabeculae and reduced number of osteoclasts;increased serum ALP and BGP activities(P＜0.05,P＜0.01);increased femoral expressions of Col-Ⅰ mRNA and OPG protein(P＜0.05,P＜0.01);and decreased RANKL protein expression(P＜0.05).Compared with the MC3T3-E1 cells stimulated by Dex,those underwent further treatment of Shuangyi Capsules demonstrated increased cell viability and ALP activity(P＜0.05,P＜0.01);increased mineralization and calcium nodule formation;increased expressions of Col-Ⅰ,OCN,OPN mRNA and OPG protein(P＜0.05,P＜0.01);decreased RANKL protein expression(P＜0.05,P＜0.01);and reduced ROS levels.CONCLUSION Shuangyi Capsules ameliorate Dex-induced osteoporosis in mice by suppressing osteoclast overactivation,enhancing osteoblast activity,and stimulating bone formation through modulation of Col-Ⅰ,OCN,OPN mRNA and OPG/RANKL protein levels.

Objective To explore the relationship between dynamic contrast-enhanced magnetic resonance imaging(DCE-MRI)and clinical pathological features of invasive breast cancer and lymphovascular invasion(LVI).Methods Imaging and clinical pathological data were retrospectively collected from 508 patients with invasive breast cancer who underwent breast DCE-MRI at the First Medical Center of Chinese PLA General Hospital from January 2019 to August 2021.Patients were divided into the LVI-positive(LVI+)group(n=79)and LVI-negative(LVI-)group(n=429)based on postoperative pathological results.Univariate and multivariate logistic regression analyses were used to identify risk factors for LVI.Results Compared with LVI-group,LVI+group had a higher proportion of patients aged<45 years(44.3%vs.27.0%,P=0.002),non-mass-like enhancement(NME)(31.7%vs.17.7%,P=0.004),Ki-67 expression rate(40.0%vs.30.0%,P<0.001),high Ki-67 expression(94.9%vs.78.1%,P=0.001),Luminal B subtype(76.0%vs.60.1%,P=0.008),and positive axillary lymph nodes rate(72.2%vs.31.5%,P<0.001),while the proportion of Luminal A subtype was lower(2.5%vs.21.5%,P<0.001).Univariate and multivariate logistic regression analyses showed that age≥45 years(OR=0.468,95%CI 0.280-0.783,P=0.004)was an independent protective factor for LVI,while NME(OR=1.987,95%CI 1.126-3.444,P=0.016)was an independent risk factor.Compared with Luminal A subtype,patients with Luminal B subtype(OR=10.482,95%CI 3.164-64.923,P=0.001),HER-2 overexpression subtype(OR=11.571,95%CI 2.755-79.341,P=0.003)and triple-negative subtypes(OR=8.433,95%CI 1.985-57.908,P=0.009)had a higher risk of LVI.Conclusions Age≥45 years is an independent protective factor for LVI,while NME is an independent risk factor.Among molecular subtypes,patients with Luminal B,HER-2 overexpression and triple-negative subtypes have a higher risk of LVI compared with the Luminal A subtype.

With the rapid development of generative artificial intelligence(GAI)technologies,their widespread application in academic research and writing is continuously expanding the boundaries of sci-entific inquiry.However,this trend has also raised a series of ethical and regulatory challenges,inclu-ding issues related to authorship,content authenticity,citation accuracy,and accountability.In light of the growing involvement of AI in generating academic content,establishing an open,controllable,and trustworthy ethical governance framework has become a key task for safeguarding research integrity and maintaining trust within the academic community.This expert consensus outlines ethical requirements across key stages of AI-assisted academic writing-including topic selection,data management,citation practices,and authorship attribution.It aims to clarify the boundaries and ethical obligations surrounding AI use in academic writing,ensuring that technological tools enhance efficiency without compromising in-tegrity.The goal is to provide guidance and institutional support for building a responsible and sustainable research ecosystem.

Objective To propose a dynamic early warning model based on machine learning methods and validate its predi-ctive efficacy so as to achieve precise assessment and early warning of mortality risk in patients with traumatic hypothermia.Methods Firstly,a total of 480 patients who met inclusion criteria were retrospectively selected from the eICU database and randomly divided into training and test sets at an 8∶2 ratio.Secondly,physiological parameters were extracted from these patients,and five machine learning algorithms including XGBoost,AdaBoost,LightGBM,logistic regression(LR)and random forest(RF)were employed respectively to develop dynamic mortality risk warning models for traumatic hypothermia patients,utilizing a 1-hour observation window.Thirdly,receiver operating characteristic curves(ROC)were plotted using the test set data and the effects of different warning windows on the model performance were analyzed by calculating the AUC.Finally,the interpretability of the models was analyzed using the SHapley Additive exPlanations(SHAP)algorithm to elucidate the contribution of each feature to predictive performance.Results The optimal warning window for the dynamic warning model constructed using the eICU database was 12 hours,and in case of 12-hour warning window the logistic regression model achieved the highest AUC of 0.935 and showed optimal predictive performance.The results of the interpretability analysis by the SHAP algorithm showed that body temperature was the feature that had the greatest impact on the model results,and its reduction was positively correlated with the increased risk of death.Conclusion The machine learning-based dynamic warning model for mortality risk in traumatic hypothermia patients enables real-time dynamic risk assessment,providing robust support for clinicians to identify the patient's condition changes at an early stage and references for the adjustment of clinical treatment programs.[Chinese Medical Equipment Journal,2025,46(3):9-14]

Objective To identify the taste critical quality attribute and design and optimize the flavor-correcting formulation of the traditional Chinese medicine oral preparation Qingre Jiedu Oral Liquid,in order to improve its taste and enhance patient medication adherence.Methods The taste assignment method was employed to identify the taste critical quality attribute of Qingre Jiedu Oral Liquid.Based on human sensory evaluation and the standardized Euclidean distance in electronic tongue analysis,suitable types of corrigent were determined.Subsequently,under constraints such as maximum allowable dosage,solubility,and sweetness,the optimal taste formulation for the sugar-free intermediate of Qingre Jiedu Oral Liquid was determined using Box-Behnken experimental design combined with electronic tongue and human sensory evaluation results.The study was reviewed and approved by the Ethics Committee of Beijing University of Chinese Medicine(Ethics Approval Number 2020BZYLL0609).Results The quantitative score for bitter taste of Qingre Jiedu Oral Liquid accounted for 30.36%,confirming bitterness as the taste critical quality attribute requiring attention.The optimal taste formulation for the sugar-free intermediate of Qingre Jiedu Oral Liquid was determined to be 120 mg·mL?1 erythritol,12 mg·mL?1 acesulfame potassium,and 2.4 mg·mL?1 stevioside.This formulation achieved an 11.75-point improvement in sensory evaluation scores compared to the original commercially available oral liquid.Conclusion This study successfully improved the taste of Qingre Jiedu Oral Liquid and established a comprehensive strategy for flavor-correcting formulation optimization,including a method for identifying taste critical quality attribute.This strategy provides a referential paradigm for palatability enhancement of similar traditional Chinese medicine oral preparations,laying a crucial technical foundation for elevating the clinical value of Chinese herbal medicines and promoting the high-quality development of traditional Chinese medicine(TCM).

Objective To identify the taste critical quality attribute and design and optimize the flavor-correcting formulation of the traditional Chinese medicine oral preparation Qingre Jiedu Oral Liquid,in order to improve its taste and enhance patient medication adherence.Methods The taste assignment method was employed to identify the taste critical quality attribute of Qingre Jiedu Oral Liquid.Based on human sensory evaluation and the standardized Euclidean distance in electronic tongue analysis,suitable types of corrigent were determined.Subsequently,under constraints such as maximum allowable dosage,solubility,and sweetness,the optimal taste formulation for the sugar-free intermediate of Qingre Jiedu Oral Liquid was determined using Box-Behnken experimental design combined with electronic tongue and human sensory evaluation results.The study was reviewed and approved by the Ethics Committee of Beijing University of Chinese Medicine(Ethics Approval Number 2020BZYLL0609).Results The quantitative score for bitter taste of Qingre Jiedu Oral Liquid accounted for 30.36%,confirming bitterness as the taste critical quality attribute requiring attention.The optimal taste formulation for the sugar-free intermediate of Qingre Jiedu Oral Liquid was determined to be 120 mg·mL?1 erythritol,12 mg·mL?1 acesulfame potassium,and 2.4 mg·mL?1 stevioside.This formulation achieved an 11.75-point improvement in sensory evaluation scores compared to the original commercially available oral liquid.Conclusion This study successfully improved the taste of Qingre Jiedu Oral Liquid and established a comprehensive strategy for flavor-correcting formulation optimization,including a method for identifying taste critical quality attribute.This strategy provides a referential paradigm for palatability enhancement of similar traditional Chinese medicine oral preparations,laying a crucial technical foundation for elevating the clinical value of Chinese herbal medicines and promoting the high-quality development of traditional Chinese medicine(TCM).

Diabetic nephropathy(DN)is a serious complication of diabetes mellitus and a leading cause of end-stage renal diseases.In DN patients,key pathological mechanisms include proteinuria,glomerulo-sclerosis,and fibrosis,largely driven by poor glycemic control and oxidative stress caused by prolonged hyperglycemia.This stress damages renal podocytes and triggers inflammatory mesenchymal infiltration of renal tubular cells,exacerbating the progression of proteinuria and fibrosis.Human umbilical cord-de-rived mesenchymal stem cells(hUC-MSCs)offer promising potential for treating DN due to their strong anti-oxidative properties.In this study,we developed a DN mouse model and treated the mouse via tail vein injections of hUC-MSCs(1×106 cells/mouse).The results indicated that hUC-MSCs significantly lowered fasting blood glucose levels(22.5±3.0 vs 14.7±1.1,P＜0.01)and improved glucose toler-ance,as shown by intraperitoneal glucose tolerance test(IPGTT)results(P＜0.05).Additionally,the renal function improved in hUC-MSCs-treated mice,with marked reductions in oxidative stress markers,including blood urea nitrogen(BUN),urinary creatinine(Ucr),urinary protein(PRO),superoxide dismutase(SOD),and malondialdehyde(MDA)(P＜0.05).Histological analyses through hematoxy-lin-eosin(H&E),Periodic Acid-Schiff(PAS),and Sirius red staining demonstrated alleviation of glo-merular mesangial hyperplasia,glomerular hypertrophy,and tubular inflammation.Furthermore,hUC-MSCs treatment downregulated the expression of oxidative stress-related proteins,such as NADPH oxi-dase 4(NOX4)and thioredoxin-interacting protein(TXNIP),and reduced reactive oxygen species(ROS)production(P＜0.05).Meanwhile,human renal cortical proximal tubule epithelial cells(HK-2 cells)were selected for validation in vitro experiments using high glucose treatment followed by super-natants of hUC-MSCs(MSC-CM),and Western blotting showed that the expression of both NOX4 and TXNIP was inhibited(P＜0.05)and ROS expression was reduced.In conclusion,hUC-MSC treatment effectively lowered blood glucose levels and improved renal function in DN mice,likely through the sup-pression of NOX4 expression and TXNIP-mediated oxidative stress.

AIM To investigate the protective effects of Shuangyi Qushi Tongluo Capsules(Shuangyi Capsules)on Dexamethasone(Dex)induced osteoporosis in mice.METHODS The C57BL/6J mice were randomly divided into the control group,the model group,the Xianling Gubao Capsules group(1.5 g/kg),and the low-dose,moderate-dose,and high-dose Shuangyi Capsules groups(0.6,1.2,and 2.4 g/kg).The mouse model of osteoporosis was induced by 8-week intraperitoneal injection of Dex sodium phosphate injection(5 mg/kg).The mice had their femur osteogenesis observed with hematoxylin and eosin(HE)staining and tartrate-resistant acid phosphatase(TRAP)staining;their serum alkaline phosphatase(ALP)and osteocalcin(BGP)activities detected by ELISA;their femoral mRNA expressions of Col-Ⅰ,OCN,and OPN detected by RT-qPCR;and their femoral protein expressions of OPG and RANKL detected by Western blot.Upon the MC3T3-E1 cells exposed to Dex and Shuangyi Capsules,their viability was evaluated by CCK-8 assay;their mineralization determined by alkaline phosphatase staining and alizarin red staining(ARS);and their intracellular ROS level detected using DCFH-DA probe.RESULTS Compared with the model group,Shuangyi Capsules groups demonstrated improved fracture of femoral trabeculae and reduced number of osteoclasts;increased serum ALP and BGP activities(P＜0.05,P＜0.01);increased femoral expressions of Col-Ⅰ mRNA and OPG protein(P＜0.05,P＜0.01);and decreased RANKL protein expression(P＜0.05).Compared with the MC3T3-E1 cells stimulated by Dex,those underwent further treatment of Shuangyi Capsules demonstrated increased cell viability and ALP activity(P＜0.05,P＜0.01);increased mineralization and calcium nodule formation;increased expressions of Col-Ⅰ,OCN,OPN mRNA and OPG protein(P＜0.05,P＜0.01);decreased RANKL protein expression(P＜0.05,P＜0.01);and reduced ROS levels.CONCLUSION Shuangyi Capsules ameliorate Dex-induced osteoporosis in mice by suppressing osteoclast overactivation,enhancing osteoblast activity,and stimulating bone formation through modulation of Col-Ⅰ,OCN,OPN mRNA and OPG/RANKL protein levels.

Objective To propose a dynamic early warning model based on machine learning methods and validate its predi-ctive efficacy so as to achieve precise assessment and early warning of mortality risk in patients with traumatic hypothermia.Methods Firstly,a total of 480 patients who met inclusion criteria were retrospectively selected from the eICU database and randomly divided into training and test sets at an 8∶2 ratio.Secondly,physiological parameters were extracted from these patients,and five machine learning algorithms including XGBoost,AdaBoost,LightGBM,logistic regression(LR)and random forest(RF)were employed respectively to develop dynamic mortality risk warning models for traumatic hypothermia patients,utilizing a 1-hour observation window.Thirdly,receiver operating characteristic curves(ROC)were plotted using the test set data and the effects of different warning windows on the model performance were analyzed by calculating the AUC.Finally,the interpretability of the models was analyzed using the SHapley Additive exPlanations(SHAP)algorithm to elucidate the contribution of each feature to predictive performance.Results The optimal warning window for the dynamic warning model constructed using the eICU database was 12 hours,and in case of 12-hour warning window the logistic regression model achieved the highest AUC of 0.935 and showed optimal predictive performance.The results of the interpretability analysis by the SHAP algorithm showed that body temperature was the feature that had the greatest impact on the model results,and its reduction was positively correlated with the increased risk of death.Conclusion The machine learning-based dynamic warning model for mortality risk in traumatic hypothermia patients enables real-time dynamic risk assessment,providing robust support for clinicians to identify the patient's condition changes at an early stage and references for the adjustment of clinical treatment programs.[Chinese Medical Equipment Journal,2025,46(3):9-14]

This article reports the clinical features and gene mutation types of a large family with Nascimento form of syndromic X-linked intellectual developmental disorder (MRXSN), involving 9 individuals across 3 generations, and a literature review was conducted. In this family, 9 individuals had similar manifestations including mental retardation and unusual facies, and 4 of them had passed away. Genetic testing showed that the proband had the deletion of exons 2-3 of the UBE2A gene, which was inherited from the mother. Fluorescent quantitative polymerase chain reaction showed that the proband and his uncle had the deletion of exons 2-3 of the UBE2A gene; the proband's mother, grandmother, and great-aunt had a heterozygous deletion of exons 2-3 of the UBE2A gene; the proband's father, sister, and aunt had a normal copy number of exons 2-3 of the UBE2A gene. The 34 patients reported in the literature had diverse clinical phenotypes, and UBE2A gene mutations (22/34, 65%) and large fragment deletions (12/34, 35%) were the main mutation types. Moderate to severe mental retardation (34/34, 100%), speech and language impairment (33/34, 97%), and unusual facies (32/34, 94%) were the main clinical manifestations of MRXSN patients. The disease has obvious phenotypic heterogeneity, and early diagnosis facilitates optimal prenatal and postnatal management to improve reproductive outcomes.
Humans ; Male ; Ubiquitin-Conjugating Enzymes/genetics* ; Female ; X-Linked Intellectual Disability/genetics* ; Gene Deletion ; Child ; Pedigree ; Child, Preschool ; Adult