This study applied the grading of recommendations assessment, development and evaluation(GRADE) system and the integrated evidence chain-based effectiveness evaluation of traditional Chinese medicine(Eff-iEC) to evaluate the evidence for 12 commonly used Chinese patent medicines for the treatment of osteoporosis, which are frequently recommended in guidelines or expert consensuses. The results showed that Xianling Gubao Capsules/Tablets were rated as C(low-level evidence) according to the GRADE system, and as BA~+B~+(intermediate evidence) according to the Eff-iEC system. Jintiange Capsules were rated as C(low-level evidence) by the GRADE system, and as AA~+B(high-level evidence) by the Eff-iEC system. Gushukang Granules/Capsules were rated as C(low-level evidence) by GRADE system, and as BA~+B~+(intermediate evidence) by Eff-iEC system. Zuogui Pills were rated as C(low-level evidence) by GRADE system, and as AA~(++)B~+(high-level evidence) by Eff-iEC system. Qianggu Capsules were rated as D(extremely low-level evidence) by GRADE system, and as AA~+B~+(high-level evidence) by Eff-iEC system. Zhuanggu Zhitong Capsules were rated as D(extremely low-level evidence) by GRADE system, and as BA~+B(intermediate evidence) by Eff-iEC system. Jingui Shenqi Pills were rated as D(extremely low-level evidence) by GRADE system, and as AA~+B(high-level evidence) by Eff-iEC system. Quanduzhong Capsules were rated as D(extremely low-level evidence) by GRADE system, and as AD~+B~+(low-level evidence) by Eff-iEC system. Epimedium Total Flavones Capsules were rated as D(extremely low-level evidence) by GRADE system, and as AAB~+(high-level evidence) by Eff-iEC system. Yougui Pills were rated as D(extremely low-level evidence) by GRADE system, and as AA~(++)B~(+ )(high-level evidence) by Eff-iEC system. Qigu Capsules were rated as D(extremely low-level evidence) by GRADE system, and as BB~+B(intermediate evidence) by Eff-iEC system. Liuwei Dihuang Pills were rated as C(low-level evidence) by GRADE system, and as AA~(++)B~+(high-level evidence) by Eff-iEC system. Overall, the Eff-iEC system provides a more comprehensive assessment of the effectiveness evidence for traditional Chinese medicine(TCM) than the GRADE system. However, it still has certain limitations that hinder its wider promotion and application. In terms of clinical evidence evaluation, both the Eff-iEC and GRADE systems reflect that the current clinical research quality on Chinese patent medicines for the treatment of osteoporosis is generally low. High-quality clinical trials are still needed in the future to further validate clinical efficacy.
Drugs, Chinese Herbal/therapeutic use* ; Osteoporosis/drug therapy* ; Humans ; Nonprescription Drugs/therapeutic use* ; Evidence-Based Medicine ; Medicine, Chinese Traditional

Iron metabolism is a critical factor in tumorigenesis and development. Although TP53 mutations are prevalent in glioblastoma (GBM), the mechanisms by which TP53 regulates iron metabolism remain elusive. We reveal an imbalance iron homeostasis in GBM via TCGA database analysis. TP53 mutations disrupted iron homeostasis in GBM, characterized by elevated total iron levels and reduced ferritin (FTH). The gain-of-function effect triggered by TP53 mutations upregulates itchy E3 ubiquitin-protein ligase (ITCH) protein expression in astrocytes, leading to FTH degradation and an increase in free iron levels. TP53-mut astrocytes were more tolerant to the high iron environment induced by exogenous ferric ammonium citrate (FAC), but the increase in intracellular free iron made them more sensitive to Erastin-induced ferroptosis. Interestingly, we found that Erastin combined with FAC treatment significantly increased ferroptosis. These findings provide new insights for drug development and therapeutic modalities for GBM patients with TP53 mutations from iron metabolism perspectives.
Ferroptosis/drug effects* ; Humans ; Iron/metabolism* ; Glioblastoma/metabolism* ; Tumor Suppressor Protein p53/metabolism* ; Homeostasis/physiology* ; Ferritins/metabolism* ; Brain Neoplasms/genetics* ; Mutation ; Astrocytes/drug effects* ; Cell Line, Tumor ; Piperazines/pharmacology* ; Quaternary Ammonium Compounds/pharmacology* ; Ferric Compounds

Objective To propose a novel identification algorithm based on ensemble learning for assessing the severity of acute respiratory distress syndrome(ARDS)to achieve continuous monitoring of the disease severity.Methods Firstly,leve-raging the open-source MIMIC-Ⅳ database,a variety of non-invasive physiological parameters of patients were extracted and subjected to preliminary preprocessing.A multivariate feature selection algorithm was employed to rank these parameters and calculate feature importance scores through weighted computation.Secondly,based on the feature importance scores,a subset search algorithm was utilized to identify the subset of features that could yield optimal performance across four machine learning algorithms:neural networks,logistic regression,AdaBoost and XGBoost.Finally,a soft voting ensemble method was designed using a generalized linear regression model to integrate the results of each single machine learning algorithm,and a multivariate ensemble learning algorithm was proposed by combining the optimal feature subsets.The algorithm proposed when used to identify the severity of ADRS was evaluated with MIMIC-Ⅳ database,and compared with the traditional algorithms.Results The sensitivity,specificity,accuracy and AUC of the algorithm were 87.15％,89.23％,88.34％and 0.923 4,respectively,all of which outperformed those of the traditional algorithms.Conclusion The ARDS severity identification algorithm based on ensemble learning is capable of achieving continuous and real-time monitoring of the severity of ARDS,thereby offering robust support for the early identification and warning of ARDS in patients.[Chinese Medical Equipment Journal,2025,46(2):1-9]

Lung cancer is one of the most lethal tumors in the world with a 5-year overall survival rate of less than 20%, mainly including lung adenocarcinoma (LUAD). Tumor microenvironment (TME) has become a new research focus in the treatment of lung cancer. The TME is heterogeneous in composition and consists of cellular components, growth factors, proteases, and extracellular matrix. The various cellular components exert a different role in apoptosis, metastasis, or proliferation of lung cancer cells through different pathways, thus contributing to the treatment of adenocarcinoma and potentially facilitating novel therapeutic methods. This review summarizes the research progress on different cellular components with cell-cell interactions in the TME of LUAD, along with their corresponding drug candidates, suggesting that targeting cellular components in the TME of LUAD holds great promise for future theraputic development.
Humans ; Tumor Microenvironment/drug effects* ; Adenocarcinoma of Lung/drug therapy* ; Lung Neoplasms/pathology* ; Adenocarcinoma/metabolism* ; Animals ; Apoptosis/physiology*

Cancer represents a major worldwide disease burden marked by escalating incidence and mortality. While therapeutic advances persist, developing safer and precisely targeted modalities remains imperative. Nanomedicines emerges as a transformative paradigm leveraging distinctive physicochemical properties to achieve tumor-specific drug delivery, controlled release, and tumor microenvironment modulation. By synergizing passive enhanced permeation and retention effect-driven accumulation and active ligand-mediated targeting, nanoplatforms enhance pharmacokinetics, promote tumor microenvironment enrichment, and improve cellular internalization while mitigating systemic toxicity. Despite revolutionizing cancer therapy through enhanced treatment efficacy and reduced adverse effects, translational challenges persist in manufacturing scalability, longterm biosafety, and cost-efficiency. This review systematically analyzes cutting-edge nanoplatforms, including polymeric, lipidic, biomimetic, albumin-based, peptide engineered, DNA origami, and inorganic nanocarriers, while evaluating their strategic advantages and technical limitations across three therapeutic domains: immunotherapy, chemotherapy, and radiotherapy. By assessing structure-function correlations and clinical translation barriers, this work establishes mechanistic and translational references to advance oncological nanomedicine development.
Humans ; Neoplasms/radiotherapy* ; Immunotherapy/methods* ; Nanoparticles/chemistry* ; Animals ; Nanomedicine/methods* ; Drug Delivery Systems/methods* ; Drug Carriers/chemistry* ; Radiotherapy/methods*

Surgical robot(SR),which integrates mechanical control,multimodal image navigation and artificial intelligence(AI)algorithms,is reshaping modern surgical paradigm with its advantages of minimally invasive operation,high precision and intelligent capabilities.The evolution,classification and representative commercial platforms of SR both domestically and internationally were systematically reviewed in this article,mainly focused on in-depth comparison of core parameters and key technological breakthroughs across different manufacturers and future development trends toward semi-autonomous and fully autonomous surgical systems.

Objective To investigate the relationship between the level of hand grip strength(HGS)-based cachexia index(H-CXI)and all-cause mortality in a population with abnormal liver function.Methods The study was based on the America National Health and Nutrition Examination Survey(NHANES)data from 2011～2014,which included 2 752 cases of people with abnormal liver function and followed up until December 31,2019,using Kaplan-Meier survival analysis and COX regression modeling to assess the relationship between H-CXI levels and all-cause mortality,and restricted cubic spline analysis to explore the nonlinear relationship.Results There were 244 all-cause deaths during a mean follow-up time of 82.70±3.86 months.H-CXI was ana-lyzed as a categorical variable(quartiles),and Cox regression analyses showed that in model 2,the risk of all-cause mortality was reduced in groups Q2,Q3 and Q4 compared with group Q1(HR=0.41,95%CI:0.21～0.82,P=0.012 1;HR=0.34,95%CI:0.16～0.69,P=0.003 1;HR=0.24,95%CI:0.09～0.67,P=0.006 3).Subgroup analyses revealed an interaction between stroke and all-cause mortality,and restricted cubic spline revealed a nonlinear relationship between H-CXI levels and all-cause mortality.Con-clusion H-CXI levels are negatively associated with the risk of all-cause mortality in people with abnormal liver function,and attention to H-CXI levels is needed in clinical practice to prevent adverse health outcomes.

Objective To investigate the relationship between the level of hand grip strength(HGS)-based cachexia index(H-CXI)and all-cause mortality in a population with abnormal liver function.Methods The study was based on the America National Health and Nutrition Examination Survey(NHANES)data from 2011～2014,which included 2 752 cases of people with abnormal liver function and followed up until December 31,2019,using Kaplan-Meier survival analysis and COX regression modeling to assess the relationship between H-CXI levels and all-cause mortality,and restricted cubic spline analysis to explore the nonlinear relationship.Results There were 244 all-cause deaths during a mean follow-up time of 82.70±3.86 months.H-CXI was ana-lyzed as a categorical variable(quartiles),and Cox regression analyses showed that in model 2,the risk of all-cause mortality was reduced in groups Q2,Q3 and Q4 compared with group Q1(HR=0.41,95%CI:0.21～0.82,P=0.012 1;HR=0.34,95%CI:0.16～0.69,P=0.003 1;HR=0.24,95%CI:0.09～0.67,P=0.006 3).Subgroup analyses revealed an interaction between stroke and all-cause mortality,and restricted cubic spline revealed a nonlinear relationship between H-CXI levels and all-cause mortality.Con-clusion H-CXI levels are negatively associated with the risk of all-cause mortality in people with abnormal liver function,and attention to H-CXI levels is needed in clinical practice to prevent adverse health outcomes.

Objective To propose a novel identification algorithm based on ensemble learning for assessing the severity of acute respiratory distress syndrome(ARDS)to achieve continuous monitoring of the disease severity.Methods Firstly,leve-raging the open-source MIMIC-Ⅳ database,a variety of non-invasive physiological parameters of patients were extracted and subjected to preliminary preprocessing.A multivariate feature selection algorithm was employed to rank these parameters and calculate feature importance scores through weighted computation.Secondly,based on the feature importance scores,a subset search algorithm was utilized to identify the subset of features that could yield optimal performance across four machine learning algorithms:neural networks,logistic regression,AdaBoost and XGBoost.Finally,a soft voting ensemble method was designed using a generalized linear regression model to integrate the results of each single machine learning algorithm,and a multivariate ensemble learning algorithm was proposed by combining the optimal feature subsets.The algorithm proposed when used to identify the severity of ADRS was evaluated with MIMIC-Ⅳ database,and compared with the traditional algorithms.Results The sensitivity,specificity,accuracy and AUC of the algorithm were 87.15％,89.23％,88.34％and 0.923 4,respectively,all of which outperformed those of the traditional algorithms.Conclusion The ARDS severity identification algorithm based on ensemble learning is capable of achieving continuous and real-time monitoring of the severity of ARDS,thereby offering robust support for the early identification and warning of ARDS in patients.[Chinese Medical Equipment Journal,2025,46(2):1-9]

Surgical robot(SR),which integrates mechanical control,multimodal image navigation and artificial intelligence(AI)algorithms,is reshaping modern surgical paradigm with its advantages of minimally invasive operation,high precision and intelligent capabilities.The evolution,classification and representative commercial platforms of SR both domestically and internationally were systematically reviewed in this article,mainly focused on in-depth comparison of core parameters and key technological breakthroughs across different manufacturers and future development trends toward semi-autonomous and fully autonomous surgical systems.