The classic formula Mulisan is the 45th of the 93 formulas in the Catalogue of Ancient Classic Formulas （second batch） of Han medicine published by the National Administration of Traditional Chinese Medicine. It consists of Ostreae Concha， Astragali Radix， Ephedrae Radix et Rhizoma， and wheat， with the effect of replenishing qi and stopping sweating. It is a common formula in the clinical treatment with traditional Chinese medicine. This study analyzes the historical evolution， composition， dosage， original plants and their processing methods， decocting method， efficacy， indications， and modern clinical application of Mulisan by tracing， comparative analysis， and bibliometric methods. The results showed that Mulisan firstly appeared in the Pulse Classic written by WANG Shuhe in the Western Jin Dynasty. The formulation idea can be traced back to the Important Prescriptions Worth a Thousand Gold for Emergency in the Tang Dynasty. The herb composition， dosage， efficacy， and indications of Mulisan were first recorded in the Treatise on Diseases， Patterns， and formulas Related to Unification of the Three Etiologies in the Southern Song dynasty. In terms of original plants and their processing methods， Ostreae Concha is the shell of Ostrea rivularis， which should be calcined before use. Astragali Radix and Ephedrae Radix et Rhizoma are the dried roots of Astragalus membranaceus var. mongholicus and Ephedra sinica， respectively， the raw material of which should be used. Wheat is the dried mature fruit of T. aestivum， which can be used without processing， while the stir-fried fruit， being thin and deflated， demonstrates better effect. The composition of Mulisan is Ostreae Concha 8.26 g， Astragali Radix 8.26 g， Ephedrae Radix et Rhizoma 8.26 g， and wheat 7.92 g. The medicinal materials should be ground into coarse powder and decocted with 450 mL water to reach a volume of 240 mL， and the decoction should be taken warm. In modern clinical practice， Mulisan has a wide range of indications， including spontaneous sweating and night sweating caused by Yang deficiency or Qi deficiency. The clinical disease spectrum treated by Mulisan involves endocrine system diseases， neurological diseases， respiratory system diseases， and cancer. This formula plays a significant role in the treatment of internal medicine diseases in traditional Chinese medicine. This study aims to provide a scientific basis for the subsequent research， development， and clinical application of Mulisan.

The classic formula Mulisan is the 45th of the 93 formulas in the Catalogue of Ancient Classic Formulas （second batch） of Han medicine published by the National Administration of Traditional Chinese Medicine. It consists of Ostreae Concha， Astragali Radix， Ephedrae Radix et Rhizoma， and wheat， with the effect of replenishing qi and stopping sweating. It is a common formula in the clinical treatment with traditional Chinese medicine. This study analyzes the historical evolution， composition， dosage， original plants and their processing methods， decocting method， efficacy， indications， and modern clinical application of Mulisan by tracing， comparative analysis， and bibliometric methods. The results showed that Mulisan firstly appeared in the Pulse Classic written by WANG Shuhe in the Western Jin Dynasty. The formulation idea can be traced back to the Important Prescriptions Worth a Thousand Gold for Emergency in the Tang Dynasty. The herb composition， dosage， efficacy， and indications of Mulisan were first recorded in the Treatise on Diseases， Patterns， and formulas Related to Unification of the Three Etiologies in the Southern Song dynasty. In terms of original plants and their processing methods， Ostreae Concha is the shell of Ostrea rivularis， which should be calcined before use. Astragali Radix and Ephedrae Radix et Rhizoma are the dried roots of Astragalus membranaceus var. mongholicus and Ephedra sinica， respectively， the raw material of which should be used. Wheat is the dried mature fruit of T. aestivum， which can be used without processing， while the stir-fried fruit， being thin and deflated， demonstrates better effect. The composition of Mulisan is Ostreae Concha 8.26 g， Astragali Radix 8.26 g， Ephedrae Radix et Rhizoma 8.26 g， and wheat 7.92 g. The medicinal materials should be ground into coarse powder and decocted with 450 mL water to reach a volume of 240 mL， and the decoction should be taken warm. In modern clinical practice， Mulisan has a wide range of indications， including spontaneous sweating and night sweating caused by Yang deficiency or Qi deficiency. The clinical disease spectrum treated by Mulisan involves endocrine system diseases， neurological diseases， respiratory system diseases， and cancer. This formula plays a significant role in the treatment of internal medicine diseases in traditional Chinese medicine. This study aims to provide a scientific basis for the subsequent research， development， and clinical application of Mulisan.

Given the limitations of traditional scientific research methods in revealing the complex and dynamic evolution of disease pathomechanisms， this paper analyzes the current state and challenges of the traditional Chinese medicine （TCM） pathomechanism syndrome differentiation system within the framework of systems thinking. The challenges include insufficient experimental models， low data standardization， complex nonlinear characteristics， and difficulties in integrating expert experience. By leveraging qualitative-quantitative comprehensive integration methods， this paper proposes specific research strategies， including constructing qualitative models of pathomechanism evolution， employing mathematical models for validation and quantitative analysis to reveal pathomechanism patterns， and incorporating a "human-centered" approach to achieve human-machine collaboration. These strategies aim to provide insights for the modernization and development of a new TCM pathomechanism syndrome differentiation system.

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.

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 causal relationship between intestinal flora and benign biliary diseases by genome-wide Mendelian randomization.Methods:This is a retrospective observational study. The data from the genome-wide association study of the gut microbiota from 18 340 samples from the MiBioGen consortium were selected as the exposure group,and the data from the genome-wide association study of biliary tract diseases were obtained from the FinnGen consortium R8 as the outcome group. There were 1 491 cases of primary sclerosing cholangitis,32 894 cases of cholelithiasis,3 770 cases of acalculous cholecystitis,and 34 461 cases of cholecystitis. Single nucleotide polymorphisms were screened as instrumental variables,and the Mendelian randomization method was used to infer the causal relationship between exposures and outcomes. The inverse variance weighting method (IVW) was used as the main basis, supplemented by heterogeneity,pleiotropy and sensitivity tests.Results:Coprococcus 2 was associated with a reduced risk of cholelithiasis (IVW OR=0.88,95% CI:0.80 to 0.97, P=0.012) and cholecystitis (IVW OR=0.88,95% CI:0.80 to 0.97, P=0.011). Coprococcus 3 was associated with cholelithiasis (IVW OR=1.15,95% CI:1.02 to 1.30, P=0.019) and acalculous cholecystitis(IVW OR=1.48, 95% CI: 1.08 to 2.04, P=0.016) and cholecystitis (IVW OR=1.17, 95% CI: 1.02 to 1.33, P=0.020). Peptococcus was associated with an increased risk of cholelithiasis (IVW OR=1.08, 95% CI:1.02 to 1.13, P=0.005) and cholecystitis (IVW CI=1.07, 95% CI:1.02 to 1.13, P=0.010). Clostridiumsensustricto 1 was associated with an increased risk of cholelithiasis (IVW OR=1.16,95% CI:1.02 to 1.31, P=0.020) and cholecystitis (IVW OR=1.16, 95% CI:1.03 to 1.30, P=0.015). Eubacterium hallii was associated with an increased risk of primary sclerosing cholangitis (IVW OR=1.43, 95% CI: 1.03 to 1.99, P=0.033). Eubacterium ruminantium (IVW OR=0.87, 95% CI: 0.76 to 1.00, P=0.043) and Methanobrevibacter (IVW OR=0.81, 95% CI: 0.68 to 0.98, P=0.027) were associated with a reduced risk of acalculous cholecystitis. Conclusions:Eight intestinal bacterial genera maybe play pathogenic roles in benign biliary diseases. Eubacterium hallii can increase the risk of primary sclerosing cholangitis. Peptococcus and Clostridiumsensustricto 1 can increase the risk of cholelithiasis and generalized cholecystitis. Coprococcus 3 have multiple correlations with biliary stones and inflammation.