This study aims to systematically review and evaluate the quality of clinical research on the treatment of adenomyosis(AM) with traditional Chinese medicine(TCM) in recent ten years, using evidence graphs. Computer searches were conducted on eight Chinese and English databases, commonly used guideline databases, and guideline-related websites, covering the period from January 1, 2014, to October 1, 2024. Two researchers independently screened, extracted information, and evaluated the quality of the evidence. The distribution and quality of the clinical research evidence were presented using both text and charts. A total of 565 articles were included in the study, comprising 523 intervention studies, 23 observational studies, 18 systematic reviews/Meta-analysis, and 1 guideline. The overall publication volume has shown a downward trend in past two years. The sample sizes of the intervention and observational studies primarily focused on 60 to 120 cases. The intervention schemes mainly involved multi-therapy combinations, including 33 classic prescriptions and 25 Chinese patent medicines. Among these, 48 studies related to 17 classic prescriptions and 45 studies related to 10 types of Chinese patent medicines involved TCM syndrome types. Randomized controlled trial(RCT) tended to focus on overall clinical efficacy and the degree of dysmenorrhea as key outcome measures. Methodological quality issues were found in 97 RCTs related to TCM decoctions and 131 RCTs related to Chinese patent medicines, primarily involving unclear explanations of some information. The AMSTAR scores for the 18 systematic reviews/Meta-analysis ranged from 1 to 8 points, with 16 studies suggesting "evidence of potential therapeutic efficacy". The recommended level for the one included guideline was B-level. TCM shows significant advantages in treating AM. Future clinical research should further standardize study designs, reference relevant reporting guidelines, improve the quality of clinical research, generate higher-level evidence-based results, and promote the high-quality development of clinical research on TCM for treating AM.
Humans ; Adenomyosis/drug therapy* ; Drugs, Chinese Herbal/therapeutic use* ; Female ; Medicine, Chinese Traditional ; Randomized Controlled Trials as Topic

In this study, lipopolysaccharide(LPS), ovalbumin(OVA), and compound 48/80(C48/80) were administered to establish non-infectious pneumonia models under simulated clinical conditions, and the correlation between their pathological characteristics and traditional Chinese medicine(TCM) syndromes was compared, providing the basis for the selection of appropriate animal models for TCM efficacy evaluation. An acute pneumonia model was established by nasal instillation of LPS combined with intraperitoneal injection for intensive stimulation. Three doses of OVA mixed with aluminum hydroxide adjuvant were injected intraperitoneally on days one, three, and five and OVA was administered via endotracheal drip for excitation on days 14-18 to establish an OVA-induced allergic pneumonia model. A single intravenous injection of three doses of C48/80 was adopted to establish a C48/80-induced pneumonia model. By detecting the changes in peripheral blood leukocyte classification, lung tissue and plasma cytokines, immunoglobulins(Ig), histamine levels, and arachidonic acid metabolites, the multi-dimensional analysis was carried out based on pathological evaluation. The results showed that the three models could cause pulmonary edema, increased wet weight in the lung, and obvious exudative inflammation in lung tissue pathology, especially for LPS. A number of pyrogenic cytokines, inclading interleukin(IL)-6, interferon(IFN)-γ, IL-1β, and IL-4 were significantly elevated in the LPS pneumonia model. Significantly increased levels of prostacyclin analogs such as prostaglandin E2(PGE2) and PGD2, which cause increased vascular permeability, and neutrophils in peripheral blood were significantly elevated. The model could partly reflect the clinical characteristics of phlegm heat accumulating in the lung or dampness toxin obstructing the lung. The OVA model showed that the sensitization mediators IgE and leukotriene E4(LTE4) were increased, and the anti-inflammatory prostacyclin 6-keto-PGF2α was decreased. Immune cells(lymphocytes and monocytes) were decreased, and inflammatory cells(neutrophils and basophils) were increased, reflecting the characteristics of "deficiency", "phlegm", or "dampness". Lymphocytes, monocytes, and basophils were significantly increased in the C48/80 model. The phenotype of the model was that the content of histamine, a large number of prostacyclins(6-keto-PGE1, PGF2α, 15-keto-PGF2α, 6-keto-PGF1α, 13,14-D-15-keto-PGE2, PGD2, PGE2, and PGH2), LTE4, and 5-hydroxyeicosatetraenoic acid(5S-HETE) was significantly increased, and these indicators were associated with vascular expansion and increased vascular permeability. The pyrogenic inflammatory cytokines were not increased. The C48/80 model reflected the characteristics of cold and damp accumulation. In the study, three non-infectious pneumonia models were constructed. The LPS model exhibited neutrophil infiltration and elevated inflammatory factors, which was suitable for the efficacy study of TCM for clearing heat, detoxifying, removing dampness, and eliminating phlegm. The OVA model, which took allergic inflammation as an index, was suitable for the efficacy study of Yiqi Gubiao formulas. The C48/80 model exhibited increased vasoactive substances(histamine, PGs, and LTE4), which was suitable for the efficacy study and evaluation of TCM for warming the lung, dispersing cold, drying dampness, and resolving phlegm. The study provides a theoretical basis for model selection for the efficacy evaluation of TCM in the treatment of pneumonia.
Animals ; Disease Models, Animal ; Mice ; Pneumonia/genetics* ; Medicine, Chinese Traditional ; Male ; Humans ; Cytokines/immunology* ; Female ; Lipopolysaccharides/adverse effects* ; Lung/drug effects* ; Drugs, Chinese Herbal ; Ovalbumin ; Mice, Inbred BALB C

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.

OBJECTIVES: To study the clinical characteristics of pediatric Behçet syndrome (BS). METHODS: A retrospective review was conducted on the medical records of children hospitalized in the Department of Pediatrics at the Second Hospital of Hebei Medical University between December 2014 and December 2024 who met diagnostic criteria for BS. RESULTS: Among 45 children with BS, 26 (58%) were male. Oral aphthous ulcers were the most common manifestation (43/45, 96%), followed by genital ulcers (23/45, 51%) and gastrointestinal involvement (18/45, 40%). Genital ulcers were more frequent in girls, whereas ocular involvement was more common in boys (P<0.05). The pathergy test was positive in 10 (22%), and HLA-B51 was positive in 13 (29%). Fecal calprotectin (FC) was elevated in 16 (36%); gastrointestinal involvement was more frequent in children with elevated FC than in those with normal FC (P<0.05). According to the respective criteria, 17 (38%) patients met the International Study Group criteria (1990), 33 (73%) met the International Criteria for Behçet Disease (2014), and 13 (29%) met the Pediatric Behçet Disease criteria (2015). CONCLUSIONS Pediatric BS shows marked clinical heterogeneity. HLA-B51 is associated with disease susceptibility.
Humans ; Behcet Syndrome/genetics* ; Male ; Female ; Child ; Retrospective Studies ; Adolescent ; Child, Preschool ; Leukocyte L1 Antigen Complex/analysis* ; HLA-B51 Antigen

mRNA therapy is an emerging treatment that has become a frontier and hot topic in the field of biomedicine.To explore the trend in the development of mRNA therapy,this paper conducts an analysis from the perspectives of papers and patents,examining multiple dimensions including development trend,research areas,and high-value research.The study reveals the following findings:Global research in mRNA therapy is growing rapidly.Basic research mainly focuses on oncology,chemistry-multidisciplinary,biochemistry and molecular biology,while applied research centers on mRNA concerning genetic engineering,isolation,synthesis,purification,and the development of medicines.High-value research mainly centers on topics such as mRNA delivery,composition,manufacture,modification,and the development of various mRNA-based therapies.

Objective:This study aims to analyze the trends,hotspots,and interrelations in research on retroperito-neal tumors through bibliometric methods,providing the latest scientific information support for clinicians and research-ers.Methods:Data were sourced from the SCI-expanded database of the Web of Science Core Collection,covering the period from 2004 to 2023.Statistical analysis and visualization of the number of publications,total citations,average citations per article,countries,institutions,journals,and keywords were conducted using Microsoft Excel 2019,VOS-viewer,and CiteSpace.Results:A total of 6,842 relevant articles were retrieved,with a total of 113 753 citations and an average of 16.63 citations per article.The number of publications had been increasing annually,peaking in 2022.The United States,China,and Japan are the major research countries,with the United States contributing the most.Memo-rial Sloan Kettering Cancer Center and the University of Texas MD Anderson Cancer Center are the leading research in-stitutions.The journal with the most publications was the Cureus Journal of Medical Science.Gronchi Alessandro was the most prolific author.The ain keywords were"Management","Surgery",and"Tumor",and the most cited papers focus on surgery and multicenter studies.Conclusion:Research on retroperitoneal tumors is increasing annually,with hot-spots focusing on treatment methods and prognosis analysis.The United States is the main contributor to this field,with significant international collaboration.Future research should further explore the pathogenesis of retroperitoneal tumors and more effective treatment strategies.

Objective To design a combat rescue specialized physical training simulator to solve the problems of the existing combat rescue physical traing in multifunctionality and simulation vividness.Methods The simulator was divided into three types for stretcher handling,land combat rescue and marine rescue based on the application scenerio and functional positioning,and into three grades of basic level,intensive level and ultra intensive level based on the loaded mass and additional weight object.The main components of the simulator included a manikin,a bionic joint and addtional weight objects.The manikin was made up of outer skin,inner liner and skeleton;the bionic joint was made of stainless steel with surface electrophoresis treatment,and was composed of high-strength medal bearing shafts with multiple disc springs and damping mechanisms;the additional weight objects involued in high-intensity cast iron or lead blocks,which were pre-embedded,mounted or srtapped into the simulator.The simulator was verified with body shape and mass detection,drop test,waterproof test and drag test.Results It's proved the simulator gained advantages in vividness for body shape and mass,bionic joint structure and adaptability to training environments and could be used for graded physical training in typical combat rescue scenerios.Conclusion The simulator developed solves the problems of the combat rescue specialized physical training equipment,and facilitates the enhancement of physical training of combat rescue personnel.[Chinese Medical Equipment Journal,2025,46(1):33-37]

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.

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.