ObjectiveThis study aims to systematically review the marketed traditional Chinese patent medicines for treating chronic gastritis （CG） in China. By analyzing their variety characteristics and prescription patterns， it seeks to provide references for clinical syndrome differentiation-based drug selection， treatment method optimization， and the design of high-quality clinical research. MethodsInformation on marketed traditional Chinese patent medicines for treating CG was collected. Microsoft Excel software was used to collate and statistically analyze representative drugs for each pathological stage， market status， syndrome types， and other contents. The Ancient and Modern Medical Case Cloud Platform （V2.3.9） was employed to analyze the formula composition patterns of standardized prescriptions. ResultsA total of 141 marketed traditional Chinese patent medicines for treating CG in China were included. Based on the disease's pathological progression， they can be classified into drugs for non-atrophic gastritis， atrophic gastritis， and precancerous lesions. Post-marketing research reveals that relevant evaluation is only conducted on 17 drugs， of which 2 involve pharmacoeconomic studies and 14 possess standardized evidence-based evidence. The primary dosage forms were capsules， granules， and tablets. From the 100 prescriptions screened according to inclusion/exclusion criteria， the varieties indicated for the stomach collateral stasis syndrome in atrophic gastritis accounted for the highest proportion. The main efficacy distributions were clearing heat， detoxifying， and relieving pain by promoting Qi circulation. Core drugs included Glycyrrhizae Radix et Rhizome， Paeoniae Radix Alba， and Aucklandiae Radix. Medicinal properties were predominantly warm and neutral. Flavors were mainly bitter， pungent， and sweet. The drugs primarily entered the spleen and stomach meridians. Analysis of the package inserts reveals that 67 products list "contraindications"， 110 include "precautions"， and 23 explicitly state "adverse reactions". ConclusionTraditional Chinese patent medicines for treating CG hold unique value in clinical practice. However， currently there are challenges such as insufficient clarity in syndrome type descriptions within package inserts and a relative lack of high-level evidence-based medical evidence， as well as pharmacoeconomic evaluations. Future efforts should focus on addressing these shortcomings by advancing research on syndrome characteristics and medication patterns based on syndrome differentiation， systematically conducting pharmacoeconomic evaluations， strengthening the accumulation of high-level evidence-based evidence， and， on this basis， improving patient medication adherence. This will comprehensively enhance the clinical application value and scientific connotation of this category of drugs.

Objective To investigate the expression of ferritinophagy-related gene ELAV-like RNA binding protein 1(ELAVL1)in multiple myeloma(MM)and elucidate its diagnostic and prognostic value for MM.Methods First,we analyzed ELAVL1 expression level in healthy controls and MM patients using data from the GEO and TCGA databases.Subsequently,bone marrow specimens were collected from 28 newly diagnosed MM patients and 20 healthy controls,and qRT-PCR was employed to validate ELAVL1 expression.The diagnostic and prognostic potential of ELAVL1 was assessed using ROC curve analysis and Kaplan-Meier survival curves.Additionally,univariate and multivariate COX regression analyses were performed to identify independent risk factors for MM prognosis.Finally,KEGG and GO enrichment analyses were performed using the DAVID online platform.Results The level of ELAVL1 expression was significantly higher in newly diagnosed MM patients and refractory/relapsed MM patients than in the healthy controls(P＜0.001).Moreover,ELAVL1 expression was positively correlated with the International Staging System(ISS)stage of MM(P＜0.01).Furthermore,qRT-PCR validation confirmed that ELAVL1 expression was elevated in the 28 newly diagnosed MM patients compared to the 20 healthy controls(P＜0.001).ROC curve analysis demonstrated that ELAVL1 could effectively differentiate between newly diagnosed MM patients,healthy controls,and MGUS patients(P＜0.001 and P=0.000 2,respectively).Survival analysis revealed that high ELAVL1 expression was associated with shorter progression-free survival(P=0.0141)and overall survival(P=0.008 0).Univariate and multivariate COX regression analyses identified high ELAVL1 expression as an independent risk factor for poor MM prognosis(P=0.005 0).KEGG analysis suggested that ELAVL1 might be involved in the Hippo and MAPK signaling pathways.Conclusion High ELAVL1 expression in MM may serve as a biomarker for diagnosis and poor prognosis.ELAVL1 may promote MM initiation and progression via the Hippo and MAPK signaling pathways.

Peri-implant diseases have a relatively high incidence,and immune-inflammatory re-sponses are closely associated with peri-implant mucositis and peri-implantitis.The complement sys-tem is a vital component of the immune system.Its activation not only regulates the initiation and pro-gression of inflammatory responses but also plays a crucial role in bone remodeling.This review sum-marizes the roles of the core complement components C3 and C5 in various stages of bone remodeling after implant placement,as well as in peri-implant mucositis and peri-implantitis.The aim of the pa-per was to provide novel insights for the prevention and treatment of peri-implantitis through targeted regulation of complement protein levels.

Objective To investigate the role and possible mechanism of celastrol(Cel)in sodium oxalate(NaOx)-induced acute kidney injury(AKI)and crystal deposition in the kidney tissues in mice.Methods Male C57BL/6 mice(aged 8～12 weeks,weighing 22～24 g)were randomly divided into 3 groups.Saline group(control group,intraperitoneal injection with normal saline and drinking water freely),NaOx group(injured group,intraperitoneal injection of 75 mg/kg NaOx,and drinking water containing 50 μmol/L NaOx),and NaOx+Cel group(treatment group,intraperitoneal injection of 1 mg/kg Cel firstly and then 75 mg/kg NaOx in 24 h later,drinking water containing 50 μmol/L NaOx).All specimens were collected in 24 h after NaOx injection.HK-2 cells were randomly divided into 4 groups:Medium group(no treatment),NaOx group(500 μmol/L NaOx),NaOx+Cel group(400 nmol/L Cel pre-treatment for 2 h followed by 500 μmol/L NaOx treatment),and NaOx+Cel+BA group[8 μmol/L betulinic acid(BA,NF-κB agonist)after the interventions as the NaOx+Cel group].Cells of each group were collected in 24 h after corresponding treatments.Von Koosa and cell adhesion assays were used to observe crystal deposition.HE staining was employed to observe renal histopathology and score the damage.CCK-8 assay was utilized to detect cell viability to obtain the optimal concentrations of NaOx and Cel.Serum urea and creatinine levels were detected.Immunohisotochemical assay was conducted to detect the expression of OPN,CD44,KIM-1,NGAL,p65,IL-1β,BAX,and Caspase-3,and Western blotting was performed for protein levels of OPN,CD44,KIM-1,p65,P-p65 and IL-1β.Results The mice in the NaOx+Cel group showed reduced crystal deposition(P<0.0001),attenuated renal tubular damage(P<0.01),decreased serum urea and creatinine levels(P<0.05),and declined expression levels of the renal adhesion molecules OPN and CD44,the kidney injury molecules KIM-1 and NGAL,the inflammation-associated molecules p65 and IL-1β,and the apoptosis related molecules BAX and Caspase-3 when compared with the NaOx group(P<0.05).In in vitro study,the NaOx+Cel group showed reduced crystal adhesion(P<0.0001),decreased expression of the adhesion molecules OPN and CD44(P<0.05),down-regulation of the inflammatory molecule IL-1β and P-p65/p65 ratio(P<0.05),and down-regulation of the renal injury molecule KIM-1(P<0.05)when compared with the NaOx group.In the NaOx+Cel+BA group,crystal adhesion was significantly increased(P<0.0001),the inflammatory molecule IL-1β and the ratio of P-p65/p65 were increased(P<0.05),and the kidney injury molecule KIM-1 was increased when compared with the NaOx+Cel group(P<0.05).Conclusion Cel may reduce NaOx-induced crystal deposition and AKI by inhibiting NF-κB activation.

BACKGROUND: The sirtuin family is well recognized for its crucial involvement in various cellular processes. Nevertheless, studies on its role in the human endometrium are limited. This study aimed to explore the expression and localization of the sirtuin family in the human endometrium, focusing on sirtuin 3 (SIRT3) and its potential role in the oxidative imbalance of the endometrium in polycystic ovary syndrome (PCOS). METHODS: Endometrial specimens were collected from both patients with PCOS and controls undergoing hysteroscopy at the Center for Reproductive Medicine, Peking University Third Hospital, from July to August 2015 and used for cell culture. The protective effects of SIRT3 were investigated, and the mechanism of SIRT3 in improving endometrial receptivity of patients with PCOS was determined using various techniques, including cellular bioenergetic analysis, small interfering ribonucleic acid (siRNA) silencing, real-time quantitative polymerase chain reaction, Western blot, immunofluorescence, immunohistochemistry, and flow cytometry analysis. RESULTS: The sirtuin family was widely expressed in the human endometrium, with SIRT3 showing a significant increase in expression in patients with PCOS compared with controls ( P <0.05), as confirmed by protein and gene assays. Concurrently, endometrial antioxidant levels were elevated, while mitochondrial respiratory capacity was reduced, in patients with PCOS ( P <0.05). An endometrial oxidative stress (OS) model revealed that the downregulation of SIRT3 impaired the growth and proliferation status of endometrial cells and reduced their receptivity to day 4 mouse embryos. The results suggested that SIRT3 might be crucial in maintaining normal cellular state by regulating antioxidants, cell proliferation, and apoptosis, thereby contributing to enhanced endometrial receptivity. CONCLUSIONS Our findings proposed a significant role of SIRT3 in improving endometrial receptivity in patients with PCOS by alleviating OS and regulating the balance between cell proliferation and apoptosis. Therefore, SIRT3 could be a promising target for predicting and improving endometrial receptivity in this patient population.
Humans ; Female ; Polycystic Ovary Syndrome/metabolism* ; Endometrium/metabolism* ; Sirtuin 3/genetics* ; Oxidative Stress/genetics* ; Adult ; Animals ; Mice ; Apoptosis/physiology* ; Immunohistochemistry ; Cell Proliferation/physiology*

BACKGROUND: Postoperative pulmonary complications (PPCs) are major adverse events in neurosurgical patients. This study aimed to develop and validate machine learning models predicting PPCs after neurosurgery. METHODS: PPCs were defined according to the European Perioperative Clinical Outcome standards as occurring within 7 postoperative days. Data of cases meeting inclusion/exclusion criteria were extracted from the anesthesia information management system to create three datasets: The development (data of Huashan Hospital, Fudan University from 2018 to 2020), temporal validation (data of Huashan Hospital, Fudan University in 2021) and external validation (data of other three hospitals in 2023) datasets. Machine learning models of six algorithms were trained using either 35 retrievable and plausible features or the 11 features selected by Lasso regression. Temporal validation was conducted for all models and the 11-feature models were also externally validated. Independent risk factors were identified and feature importance in top models was analyzed. RESULTS: PPCs occurred in 712 of 7533 (9.5%), 258 of 2824 (9.1%), and 207 of 2300 (9.0%) patients in the development, temporal validation and external validation datasets, respectively. During cross-validation training, all models except Bayes demonstrated good discrimination with an area under the receiver operating characteristic curve (AUC) of 0.840. In temporal validation of full-feature models, deep neural network (DNN) performed the best with an AUC of 0.835 (95% confidence interval [CI]: 0.805-0.858) and a Brier score of 0.069, followed by Logistic regression (LR), random forest and XGBoost. The 11-feature models performed comparable to full-feature models with very close but statistically significantly lower AUCs, with the top models of DNN and LR in temporal and external validations. An 11-feature nomogram was drawn based on the LR algorithm and it outperformed the minimally modified Assess respiratory RIsk in Surgical patients in CATalonia (ARISCAT) and Laparoscopic Surgery Video Educational Guidelines (LAS VEGAS) scores with a higher AUC (LR: 0.824, ARISCAT: 0.672, LAS: 0.663). Independent risk factors based on multivariate LR mostly overlapped with Lasso-selected features, but lacked consistency with the important features using the Shapley additive explanation (SHAP) method of the LR model. CONCLUSIONS: The developed models, especially the DNN model and the nomogram, had good discrimination and calibration, and could be used for predicting PPCs in neurosurgical patients. The establishment of machine learning models and the ascertainment of risk factors might assist clinical decision support for improving surgical outcomes. TRIAL REGISTRATION ChiCTR 2100047474; https://www.chictr.org.cn/showproj.html?proj=128279 .
Adult ; Aged ; Female ; Humans ; Male ; Middle Aged ; Algorithms ; Lung Diseases/etiology* ; Machine Learning ; Neurosurgical Procedures/adverse effects* ; Postoperative Complications/diagnosis* ; Risk Factors ; ROC Curve

Objective:To preliminarily establish a sample clot warning model for coagulation screening tests using 5 machine learning methods.Methods:This cross-sectional study collected 7 401 routine screening test samples from Peking Union Medical College Hospital, Chinese Academy of Medical Sciences, from January 1st, 2015, to August 18th, 2024, including 4 786 clotted (positive) and 2 615 qualified (negative) samples for model development. The dataset was divided into Dataset 1 and Dataset 2 based on a reagent change for APTT in December 2018, with separate models developed for each. An additional 2 493 samples (October 31st to November 8th, 2024) were used to evaluate consistency between the model and manual assessment, while 23 200 samples (October 17th to December 31st, 2024) were used for assessing real-world predictive performance. Five machine learning algorithms were employed to develop the clot prediction model: logistic regression (LR), random forest (RF), extreme gradient boosting (XGBoost), naive bayes (NB), and artificial neural network (ANN), with the ANN model constructed using two different hidden layer and neuron parameter settings. Model selection was based on AUC, accuracy, sensitivity, specificity, F1-score, PPV, and NPV, with the optimal model integrated into the LIS for validation.Results:Among the six models using 5 machine learning algorithms, XGBoost demonstrated the highest performance (AUC=0.961, sensitivity=0.945, F1-score=0.934) and robustness to reagent changes ( Z=-1.333, P=0.113). When deployed, the differences between the model's predictions and manual pre-judgment were statistically significant ( Z=-5.289 to 8.933, all P<0.01). The predictive efficacy indices AUC (95% CI), sensitivity, specificity, and accuracy of the XGBoost model deployed in real-world operation of the LIS were 0.939 (0.918—0.960), 0.958, 0.921, and 0.921 respectively. Conclusion:In this study, a clot warning model for coagulation screening samples was established based on the XGBoost algorithm, and its prediction efficacy is good, providing a foundation for intelligent pre-analytical quality control for coagulation screening tests.

The immune microenvironment plays a crucial role in bone remodeling around implants.The complement system,a key com-ponent of the immune system,significantly influences bone formation and resorption.However,the activation of the complement system varies depending on the stages of implant placement and the types of implant materials.This review discusses the complement system,its activation pathways,its effects on bone remodeling,and the impact of various stages of implant placement and different materials on com-plement system activation.These insights would help develop strategies for promoting bone remodeling around implants through targeted regulation of the complement microenvironment in the vicinity of the implanted material.

Objective:To preliminarily establish a sample clot warning model for coagulation screening tests using 5 machine learning methods.Methods:This cross-sectional study collected 7 401 routine screening test samples from Peking Union Medical College Hospital, Chinese Academy of Medical Sciences, from January 1st, 2015, to August 18th, 2024, including 4 786 clotted (positive) and 2 615 qualified (negative) samples for model development. The dataset was divided into Dataset 1 and Dataset 2 based on a reagent change for APTT in December 2018, with separate models developed for each. An additional 2 493 samples (October 31st to November 8th, 2024) were used to evaluate consistency between the model and manual assessment, while 23 200 samples (October 17th to December 31st, 2024) were used for assessing real-world predictive performance. Five machine learning algorithms were employed to develop the clot prediction model: logistic regression (LR), random forest (RF), extreme gradient boosting (XGBoost), naive bayes (NB), and artificial neural network (ANN), with the ANN model constructed using two different hidden layer and neuron parameter settings. Model selection was based on AUC, accuracy, sensitivity, specificity, F1-score, PPV, and NPV, with the optimal model integrated into the LIS for validation.Results:Among the six models using 5 machine learning algorithms, XGBoost demonstrated the highest performance (AUC=0.961, sensitivity=0.945, F1-score=0.934) and robustness to reagent changes ( Z=-1.333, P=0.113). When deployed, the differences between the model's predictions and manual pre-judgment were statistically significant ( Z=-5.289 to 8.933, all P<0.01). The predictive efficacy indices AUC (95% CI), sensitivity, specificity, and accuracy of the XGBoost model deployed in real-world operation of the LIS were 0.939 (0.918—0.960), 0.958, 0.921, and 0.921 respectively. Conclusion:In this study, a clot warning model for coagulation screening samples was established based on the XGBoost algorithm, and its prediction efficacy is good, providing a foundation for intelligent pre-analytical quality control for coagulation screening tests.

With the changes in the lifestyle of the Chinese in recent decades, hypertriglyceridemia (HTG) has surpassed alcohol as the second leading cause of acute pancreatitis. Hypertriglyceridemia-associated acute pancreatitis (HTG-AP) is characterized with severe disease, numerous complications and poor prognosis. However, the pathogenesis of HTG-AP has not yet been fully understood. This paper, based on the gene research on HTG-AP conducted by our team, elaborates on the latest research progress and pathogenesis of HTG-AP and discusses potential therapeutic targets, aiming to provide innovative ideas and methods for clinical treatment.