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.

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.

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.

Luteolin is a natural flavonoid compound exists in various fruits and vegetables. Recent studies have indicated that luteolin has variety pharmacological effects, including a wide range of antidepressant properties. Here, we systematically review the preclinical studies and limited clinical evidence on the antidepressant and neuroprotective effects of luteolin to fully explore its antidepressant power. Network pharmacology and molecular docking analyses contribute to a better understanding of the preclinical models of depression and antidepressant properties of luteolin. Seventeen preclinical studies were included that combined network pharmacology and molecular docking analyses to clarify the antidepressant mechanism of luteolin and its antidepressant targets. The antidepressant effects of luteolin may involve promoting intracellular noradrenaline (NE) uptake; inhibiting 5-hydroxytryptamine (5-HT) reuptake; upregulating the expression of synaptophysin, postsynaptic density protein 95, brain-derived neurotrophic factor, B cell lymphoma protein-2, superoxide dismutase, and glutathione S-transferase; and decreasing the expression of malondialdehyde, caspase-3, and amyloid-beta peptides. The antidepressant effects of luteolin are mediated by various mechanisms, including anti-oxidative stress, anti-apoptosis, anti-inflammation, anti-endoplasmic reticulum stress, dopamine transport, synaptic protection, hypothalamic-pituitary-adrenal axis regulation, and 5-HT metabolism. Additionally, we identified insulin-like growth factor 1 receptor (IGF1R), AKT serine/threonine kinase 1 (AKT1), prostaglandin-endoperoxide synthase 2 (PTGS2), estrogen receptor alpha (ESR1), and epidermal growth factor receptor (EGFR) as potential targets, luteolin has an ideal affinity for these targets, suggesting that it may play a positive role in depression through multiple targets, mechanisms, and pathways. However, the clinical efficacy of luteolin and its potential direct targets must be confirmed in further multicenter clinical case-control and molecular targeting studies.

OBJECTIVE To investigate the prevalence of tinnitus and hearing loss in otology patients,and to analyze the characteristics and relationship of tinnitus and hearing loss in different age groups.METHODS A total of 4 716 patients who visited otology clinic were enrolled.Age,gender,and presence or absence of tinnitus were recorded.All patients completed pure tone audiometry.The characteristics and relationship between tinnitus and pure tone audiometry threshold were analyzed after age grouping.RESULTS 1.Among the 4 716 patients,2 772 patients had tinnitus,accounting for 58.78%,and the incidence was highest in the 41-70 years old group.3 521 cases(74.66%)had hearing loss.2.Among the patients with tinnitus,2 227 cases(80.34%)had hearing loss,which was higher than that of patients without tinnitus(66.56%,1 294/1 944 cases).There was a difference in the incidence of hearing loss between those with and without tinnitus in each age group from 11 to 70 years(P<0.05).3.The average hearing threshold of patients with tinnitus was higher than that of patients without tinnitus.There was a difference in the average hearing threshold between patients with and without tinnitus in each age group over 30 years old(P<0.05).4.The incidence of high frequency hearing loss with tinnitus was 24.57%(681/2 772),and the incidence of without tinnitus was 21.66%(421/1 944).The rate of high-frequency hearing loss in the 21-40 years old group with tinnitus was higher than that in the non-tinnitus group(P<0.05).CONCLUSION The incidence of hearing loss is higher and more severe in patients with tinnitus.Particular attention should be paid to tinnitus in young patients.The incidence of high-frequency hearing loss was higher in the young and middle-aged group of patients with tinnitus.Therefore,the evaluation of hearing threshold in patients with tinnitus is very important,which can provide a basis for clinical diagnosis and treatment.

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,of-fering 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 accu-racy 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 summa-rizes the application of AI in drug development,particularly in drug-target prediction,and offers rec-ommendations 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.

Luteolin is a natural flavonoid compound exists in various fruits and vegetables.Recent studies have indicated that luteolin has variety pharmacological effects,including a wide range of antidepressant properties.Here,we systematically review the preclinical studies and limited clinical evidence on the antidepressant and neuroprotective effects of luteolin to fully explore its antidepressant power.Network pharmacology and molecular docking analyses contribute to a better understanding of the preclinical models of depression and antidepressant properties of luteolin.Seventeen preclinical studies were included that combined network pharmacology and molecular docking analyses to clarify the antide-pressant mechanism of luteolin and its antidepressant targets.The antidepressant effects of luteolin may involve promoting intracellular noradrenaline(NE)uptake;inhibiting 5-hydroxytryptamine(5-HT)re-uptake;upregulating the expression of synaptophysin,postsynaptic density protein 95,brain-derived neurotrophic factor,B cell lymphoma protein-2,superoxide dismutase,and glutathione S-transferase;and decreasing the expression of malondialdehyde,caspase-3,and amyloid-beta peptides.The antide-pressant effects of luteolin are mediated by various mechanisms,including anti-oxidative stress,anti-apoptosis,anti-inflammation,anti-endoplasmic reticulum stress,dopamine transport,synaptic protec-tion,hypothalamic-pituitary-adrenal axis regulation,and 5-HT metabolism.Additionally,we identified insulin-like growth factor 1 receptor(IGF1R),AKT serine/threonine kinase 1(AKT1),prostaglandin-endoperoxide synthase 2(PTGS2),estrogen receptor alpha(ESR1),and epidermal growth factor recep-tor(EGFR)as potential targets,luteolin has an ideal affinity for these targets,suggesting that it may play a positive role in depression through multiple targets,mechanisms,and pathways.However,the clinical efficacy of luteolin and its potential direct targets must be confirmed in further multicenter clinical case-control and molecular targeting studies.

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.