Osteoporosis is a systemic disease, and epidemiological projections indicate that by 2050, approximately 23.43% of the Chinese population over 50 years of age will be affected. Given the poor prognosis associated with osteoporosis, the exploration of safe and effective natural products is of considerable significance. Studies investigating the chemical constituents of traditional Chinese medicine in cellular and/or animal models have demonstrated bone-protective effects. Although most of these compounds lack clinical data, they hold considerable potential as lead candidates for drug development. In-depth study of the structure-activity relationship of these natural products not only contributes to elucidating the mechanisms of action but also provides a theoretical basis for the development of novel antiosteoporosis therapies. This review summarizes natural products with potential antiosteoporotic effects reported between 2020 and 2024. Overall, plant-derived natural compounds exhibit antiosteoporotic effects by regulating bone remodeling, inflammation, and oxidative stress, highlighting their promise as multitarget therapeutic candidates.

ObjectiveThis study aimed to investigate the mechanism by which Luoshi Neiyi prescription treats endometriosis （EMs） through regulating ferroptosis， and to screen key metabolites and analyze their association with ferroptosis. MethodsClinical samples of normal endometrium from patients without EMs and eutopic and ectopic endometrium from EMs patients （10 cases each） were collected and divided into control group， eutopic group， and EMs group. Hematoxylin-eosin （HE） staining was performed to observe ectopic lesions of EMs. Immunohistochemistry was used to detect the expression of solute carrier family 7 member 11 （SLC7A11） and glutathione peroxidase 4 （GPX4）. Enzyme-linked immunosorbent assay （ELISA） was adopted to determine the levels of malondialdehyde （MDA）， ferrous ion （Fe²⁺）， GPX4 and glutathione （GSH） in endometrial tissues， as well as serum levels of Fe²⁺， GPX4 and GSH. Real-time quantitative polymerase chain reaction （Real-time PCR） was used to detect the mRNA expression of SLC7A11， GPX4， transferrin receptor （TFR） and ferritin heavy chain 1 （FTH1）. In the in vitro experiment， primary stromal cells were isolated from ectopic lesions of EMs patients. Cell counting kit-8 （CCK-8） was used to determine the optimal concentration of drug-containing serum for intervention. The level of reactive oxygen species （ROS） was measured， and Real-time PCR was applied to detect ferroptosis-related indicators. In the animal experiments， an EM rat model was established， and the rats were randomly assigned to the sham operation group， EMs group， low-dose Luoshi Neiyi Formula group （7.87 g·kg^-1）， high-dose Luoshi Neiyi prescription group （15.74 g·kg^-1）， and danazol group （42 mg·kg^-1）. Untargeted metabolomics detection and pathway enrichment analysis were conducted on serum samples from patients and rats. Spearman correlation analysis was performed to assess the relationship between differential metabolites and ferroptosis indicators. The correlations between differential metabolites in patient endometrium and serum and key ferroptosis indicators （GPX4， Fe²⁺， MDA， GSH） as well as ferroptosis-related mRNAs （GPX4， SLC7A11， FTH1） were analyzed， and correlation heatmaps were generated accordingly. ResultsCompared with normal eutopic endometrium， ectopic lesions in EMs patients showed glandular disorganization and stromal fibrosis. In ectopic endometrium， the contents of MDA， ROS， and Fe²⁺ decreased， while GPX4 level increased， and the mRNA expression of SLC7A11 and GPX4 was upregulated （P<0.05， P<0.01）. In serum， the levels of GPX4 and Fe²⁺ were elevated， whereas the GSH level declined， suggesting abnormalities in ferroptosis-related pathways in ectopic lesions （P<0.05， P<0.01）. After intervention with Luoshi Neiyi prescription-containing serum， the intracellular ROS level in ectopic endometrial stromal cells was elevated， the mRNA expression of SLC7A11 and GPX4 was downregulated， and TFR mRNA expression was upregulated （P<0.05， P<0.01）. Metabolomics analysis revealed 1104 and 198 differential metabolites in EMs patients and EMs rats， respectively， compared with their corresponding control groups， and both low-dose and high-dose Luoshi Neiyi prescription were found to regulate this metabolic disturbance， with the core regulatory pathways mainly involving arginine and proline metabolism. Correlation analysis showed that the glycerophospholipids including PI（16∶0/17∶0） and PI［18∶2（9Z，12Z）］ were negatively correlated with GPX4 and positively correlated with MDA， while 17α-hydroxyprogesterone was positively correlated with GPX4， SLC7A11， and FTH1 q<0.05）. ConclusionLuoshi Neiyi prescription may systematically ameliorate disease-associated metabolic dysregulation via modulation of the serum arginine and proline metabolism pathway， and may regulate ferroptosis in ectopic lesions through a mechanism potentially linked to the serum glycerophospholipid and steroid metabolism pathways. Collectively， these findings provide experimental evidence for the clinical application of Luoshi Neiyi prescription.

ObjectiveThis study aimed to investigate the mechanism by which Luoshi Neiyi prescription treats endometriosis （EMs） through regulating ferroptosis， and to screen key metabolites and analyze their association with ferroptosis. MethodsClinical samples of normal endometrium from patients without EMs and eutopic and ectopic endometrium from EMs patients （10 cases each） were collected and divided into control group， eutopic group， and EMs group. Hematoxylin-eosin （HE） staining was performed to observe ectopic lesions of EMs. Immunohistochemistry was used to detect the expression of solute carrier family 7 member 11 （SLC7A11） and glutathione peroxidase 4 （GPX4）. Enzyme-linked immunosorbent assay （ELISA） was adopted to determine the levels of malondialdehyde （MDA）， ferrous ion （Fe²⁺）， GPX4 and glutathione （GSH） in endometrial tissues， as well as serum levels of Fe²⁺， GPX4 and GSH. Real-time quantitative polymerase chain reaction （Real-time PCR） was used to detect the mRNA expression of SLC7A11， GPX4， transferrin receptor （TFR） and ferritin heavy chain 1 （FTH1）. In the in vitro experiment， primary stromal cells were isolated from ectopic lesions of EMs patients. Cell counting kit-8 （CCK-8） was used to determine the optimal concentration of drug-containing serum for intervention. The level of reactive oxygen species （ROS） was measured， and Real-time PCR was applied to detect ferroptosis-related indicators. In the animal experiments， an EM rat model was established， and the rats were randomly assigned to the sham operation group， EMs group， low-dose Luoshi Neiyi Formula group （7.87 g·kg^-1）， high-dose Luoshi Neiyi prescription group （15.74 g·kg^-1）， and danazol group （42 mg·kg^-1）. Untargeted metabolomics detection and pathway enrichment analysis were conducted on serum samples from patients and rats. Spearman correlation analysis was performed to assess the relationship between differential metabolites and ferroptosis indicators. The correlations between differential metabolites in patient endometrium and serum and key ferroptosis indicators （GPX4， Fe²⁺， MDA， GSH） as well as ferroptosis-related mRNAs （GPX4， SLC7A11， FTH1） were analyzed， and correlation heatmaps were generated accordingly. ResultsCompared with normal eutopic endometrium， ectopic lesions in EMs patients showed glandular disorganization and stromal fibrosis. In ectopic endometrium， the contents of MDA， ROS， and Fe²⁺ decreased， while GPX4 level increased， and the mRNA expression of SLC7A11 and GPX4 was upregulated （P<0.05， P<0.01）. In serum， the levels of GPX4 and Fe²⁺ were elevated， whereas the GSH level declined， suggesting abnormalities in ferroptosis-related pathways in ectopic lesions （P<0.05， P<0.01）. After intervention with Luoshi Neiyi prescription-containing serum， the intracellular ROS level in ectopic endometrial stromal cells was elevated， the mRNA expression of SLC7A11 and GPX4 was downregulated， and TFR mRNA expression was upregulated （P<0.05， P<0.01）. Metabolomics analysis revealed 1104 and 198 differential metabolites in EMs patients and EMs rats， respectively， compared with their corresponding control groups， and both low-dose and high-dose Luoshi Neiyi prescription were found to regulate this metabolic disturbance， with the core regulatory pathways mainly involving arginine and proline metabolism. Correlation analysis showed that the glycerophospholipids including PI（16∶0/17∶0） and PI［18∶2（9Z，12Z）］ were negatively correlated with GPX4 and positively correlated with MDA， while 17α-hydroxyprogesterone was positively correlated with GPX4， SLC7A11， and FTH1 q<0.05）. ConclusionLuoshi Neiyi prescription may systematically ameliorate disease-associated metabolic dysregulation via modulation of the serum arginine and proline metabolism pathway， and may regulate ferroptosis in ectopic lesions through a mechanism potentially linked to the serum glycerophospholipid and steroid metabolism pathways. Collectively， these findings provide experimental evidence for the clinical application of Luoshi Neiyi prescription.

Objective:Ménière's disease（MD） is a common disorder of the inner ear. The fluctuating clinical symptoms and the absence of gold standards for diagnosis have posed serious problems for clinical diagnosis and treatment over the years. With the development of science and technology, artificial intelligence （AI） has been widely used in the field of medicine, and the potential of AI application to MD is demonstrated. The purpose of this review is to outline the use of AI in MD. Initially, specific instances where AI aids in differentiating MD from other causes of vertigo are presented. Furthermore, the role of AI in the evaluation of Endolymphatic Hydrops （EH）, particularly through imaging and biochemical assays, is highlighted due to its correlation with MD. Additionally, the effectiveness of AI in managing MD patients and forecasting disease progression is examined. In conclusion, the prevalent challenges hindering the clinical integration of AI in MD treatment are discussed, alongside potential strategies to surmount these barriers.
Humans ; Meniere Disease/diagnosis* ; Artificial Intelligence ; Endolymphatic Hydrops/diagnosis*

Allergen specific immunotherapy（AIT）, as an effective treatment for allergic rhinitis, asthma, and other allergic diseases, has received widespread attention in the field of health economic evaluation in recent years. This article reviews the current status and progress of economic research on AIT, mainly discussing the socioeconomic burden of allergic rhinitis, the results of health economic studies from different countries, and the primary methods used in health economic research on allergic rhinitis. Existing studies indicate that, although AIT involves high initial costs, it offers significant long-term economic benefits by reducing healthcare resource utilization, improving patient quality of life, and decreasing medication dependence. Moreover, reducing initial costs, applying standardized assessment tools, and conducting cross-national comparative analyses have become key directions for future research. Overall, AIT demonstrates strong potential in terms of long-term health benefits and cost savings, providing solid economic evidence for the management of allergic diseases.
Humans ; Desensitization, Immunologic/economics* ; Cost-Benefit Analysis ; Rhinitis, Allergic/economics* ; Economics, Medical

In protein engineering, while computational models are increasingly used to predict mutation effects, their evaluations primarily rely on high-throughput deep mutational scanning (DMS) experiments that use surrogate readouts, which may not adequately capture the complex biochemical properties of interest. Many proteins and their functions cannot be assessed through high-throughput methods due to technical limitations or the nature of the desired properties, and this is particularly true for the real industrial application scenario. Therefore, the desired testing datasets, will be small-size (∼10-100) experimental data for each protein, and involve as many proteins as possible and as many properties as possible, which is, however, lacking. Here, we present VenusMutHub, a comprehensive benchmark study using 905 small-scale experimental datasets curated from published literature and public databases, spanning 527 proteins across diverse functional properties including stability, activity, binding affinity, and selectivity. These datasets feature direct biochemical measurements rather than surrogate readouts, providing a more rigorous assessment of model performance in predicting mutations that affect specific molecular functions. We evaluate 23 computational models across various methodological paradigms, such as sequence-based, structure-informed and evolutionary approaches. This benchmark provides practical guidance for selecting appropriate prediction methods in protein engineering applications where accurate prediction of specific functional properties is crucial.

Objective To observe conventional ultrasound and contrast-enhanced ultrasound(CEUS)manifestations of Ewing sarcoma(ES)in children.Methods Fifteen children with pathologically confirmed ES were retrospectively collected.Conventional ultrasound and CEUS characteristics of lesions were analyzed.Results Among 15 cases,ES of bone(ESB)was found in 7 cases,while extraskeletal ES(EES)was observed in the other 8 cases.Solitary tumor was noticed in 14 cases,with a median maximum diameter of 7.50 cm,while multiple abdominal masses were found in 1 case.The tumors had irregular shapes and poorly defined boundaries,with medium echogenicity in 7 cases,low echogenicity in 6 cases,while in other 2 cases present as cystic-solid lesions.CDFI showed sparse blood flow in 11 cases,abundant or slightly abundant blood flow in 2 and 1 case,respectively,while no obvious blood flow was observed in 1 case.Rapid high enhancement and rapid washout were found in all 7 cases underwent CEUS,while patchy no-enhancement areas were detected in 4 cases.Conclusion Conventional ultrasonic manifestations of ES had certain specificities,which demonstrated a rapid enhancement and rapid washout pattern during CEUS and may be accompanied by necrosis.

Objective:To evaluate the association between heatwaves and fall-related mortality.Methods:A total of 61 421 fall-related mortality from 2013 to 2022 in 7 provinces of China were included in a time-stratified case-crossover design, with daily meteorological data derived from the fifth generation European Reanalysis dataset produced by the European Centre for Medium-Range Weather Forecasts. Conditional logistic regression chimeric distributed lag nonlinear model was used to analyze the association between heatwaves and fall-related mortality and stratified analysis was conducted according to gender and age.Results:Heatwaves were associated with an increased risk of fall-related morality. The risk of fall-related mortality during heatwaves was higher than during non-heatwave periods ( OR=1.11, 95% CI: 1.05-1.18). The attributable fraction of fall-related motality due to heatwaves was 10.25% (95% CI: 4.49%-15.36%). For each 1 ℃ increase above the heatwave threshold, the risk of fall-related mortality increased by 34% ( OR=1.34, 95% CI: 1.02-1.76). The effect of heatwave duration on fall-related mortality was not statistically significant. Stratified analyses indicated that women experienced a higher risk of fall-related mortality during heatwaves ( OR=1.13, 95% CI: 1.04-1.22) compared to man ( OR=1.10, 95% CI: 1.04-1.17). Conclusions:Heatwave increases the risk of fall-related mortality, and the intensity of heatwaves modify this risk. Women are vulnerable populations.

The modern Silk Road spirit advocating for win-win cooperative partnerships, aligns with the target of the “Belt and Road” Initiative, which provides new opportunities for collaboration on tropical disease control among countries along the “Belt and Road”. The modern Silk Road spirit may effectively facilitate tropical disease control programmes and improve disease control concepts and approaches through collaborative research, information sharing, infrastructure development, and joint efforts in pharmaceuticals and vaccine development; however, there are still multiple challenges that require to be overcome, including political and cultural differences, and data sharing. Therefore, countries participating in the “Belt and Road” Initiative need to work together with mutual respects, build effective collaborative mechanisms and improve communications to jointly facilitate the sustainable development of global tropical disease control programmes and cultural exchange, so as to contribute to global health and prosperities. This article discusses the contribution of the modern Silk Road spirit to facilitating global tropical disease control programmes in the context of the “Belt and Road” Initiative.

Drug development remains a critical issue in the field of biomedicine. With the rapid advancement of information technologies such as artificial intelligence (AI) and the advent of the big data era, AI-assisted drug development has become a new trend, particularly in predicting drug-target associations. To address the challenge of drug-target prediction, AI-driven models have emerged as powerful tools, offering innovative solutions by effectively extracting features from complex biological data, accurately modeling molecular interactions, and precisely predicting potential drug-target outcomes. Traditional machine learning (ML), network-based, and advanced deep learning architectures such as convolutional neural networks (CNNs), graph convolutional networks (GCNs), and transformers play a pivotal role. This review systematically compiles and evaluates AI algorithms for drug- and drug combination-target predictions, highlighting their theoretical frameworks, strengths, and limitations. CNNs effectively identify spatial patterns and molecular features critical for drug-target interactions. GCNs provide deep insights into molecular interactions via relational data, whereas transformers increase prediction accuracy by capturing complex dependencies within biological sequences. Network-based models offer a systematic perspective by integrating diverse data sources, and traditional ML efficiently handles large datasets to improve overall predictive accuracy. Collectively, these AI-driven methods are transforming drug-target predictions and advancing the development of personalized therapy. This review summarizes the application of AI in drug development, particularly in drug-target prediction, and offers recommendations on models and algorithms for researchers engaged in biomedical research. It also provides typical cases to better illustrate how AI can further accelerate development in the fields of biomedicine and drug discovery.