To develop and validate a model for predicting intensive care unit (ICU) mortality risk in patients with malignant tumors complicated by acute respiratory failure (ARF) based on an explainable machine learning framework.

Based on the theory of "disease of both blood and water" in Essentials from the Golden Cabinet （《金匮要略》）， and in combination with the dynamic syndrome evolution of heart failure with preserved ejection fraction （HFpEF）， this paper systematically clarifies the pathomechanism of HFpEF， characterized by yang deficiency as the root， blood stasis as the pivotal factor and water retention as the manifestation. Accordingly， the therapeutic principles have been proposed， which are warming yang and banking up original qi to consolidate the root， activating blood and unblocking collaterals to smooth the mechanism， and promoting urination and regulating pivot to remove the branch. On this basis， a compound formula structure of "one monarch， one minister and one assistant" is established， forming an integrated intervention strategy that synergistically combines the three methods of warming yang， activating blood， and promoting urination through combined classical formulas. Zhenwu Decoction （真武汤）， which warms yang and dissolves rheum， is used to consolidate the root and directly target the source of yang deficiency， serving as the monarch； Guizhi Fuling Pills （桂枝茯苓丸）， which activates blood， promotes urination and unblocks the pivot， assists in interrupting the binding of blood stasis and water retention， serving as the minister； Tingli Dazao Xiefei Decoction （葶苈大枣泻肺汤）， which regulates qi， disperses retained fluids， and eliminates the manifestation， alleviates acute water-retention symptoms， serving as the assistant. This compound formula is warming without being drying， diuretic without being drastic， and dispels stasis without consuming blood， thereby achieving the therapeutic effects of warming yang， activating blood， and promoting urination.

Objective: To analyze the independent and interaction effects of daytime outdoor light exposure and moderate to vigorous physical activity (MVPA) duration on sleep quality of primary school students, so as to provide scientific evidence for interventions on children s sleep health. Methods: From April to June 2024, a total of 444 students from grades 3 and 4 in 2 primary schools in Jiading District, Shanghai were selected using stratified random cluster sampling method for continuous 7 day monitoring. Wearable devices "Clouclip" were used to monitor daytime outdoor activity time (represented by time with light intensity ≥ 1 000 lx ), and accelerometers were used to monitor MVPA time and sleep quality related indicators. Multiple linear regression was used to analyze the associations of daytime outdoor activity and MVPA with sleep quality. Results: Both daytime outdoor light exposure and MVPA duration(longer actual sleep duration per night,longer time in bed,fewer awakening and shorter post sleep awakening shic) were independently associated with multiple sleep indicators( β =0.52, 0.46, -0.83, -2.19, all P <0.05), with no significant interaction between the associations ( P >0.05). After controlling for MVPA, more daytime outdoor light exposure was significantly and independently associated with longer actual sleep time ( β =0.50, 95% CI =0.21-0.79, P <0.05). After controlling for light exposure, longer MVPA duration was independently associated with shorter post-sleep awakening duration ( β=-4.15, 95% CI = -6.33 to -1.96, P <0.05). Conclusion Increased daytime outdoor activity and MVPA are both associated with better sleep quality in primary school students.

OBJECTIVE To evaluate the cost-effectiveness of culmerciclib combined with fulvestrant as second-line treatment for patients with hormone receptor-positive（HR+）/human epidermal growth factor receptor 2-negative （HER2–） locally advanced or metastatic breast cancer， within the context of the Chinese healthcare system. METHODS A partitioned survival model was established based on the CULMATE-1 study， with a simulation time horizon set at 15 years and a cycle length of 28 days. The incremental cost-effectiveness ratio （ICER） of culmerciclib combined with fulvestrant versus fulvestrant monotherapy as second-line treatment for HR+/HER2– breast cancer was calculated. One-way sensitivity analysis and probabilistic sensitivity analysis were performed to assess the robustness of the model. Meanwhile， scenario analysis of culmerciclib price reduction was conducted； the required price reduction and price to reach the willingness-to-pay （WTP） threshold in this study were calculated. RESULTS The results of the base-case analysis indicated that， compared with the fulvestrant monotherapy regimen， culmerciclib combined with fulvestrant yielded an additional 0.823 quality-adjusted life year （QALY）， with a corresponding ICER of 371 696.26 yuan/QALY， which exceeded the WTP threshold （199 330 yuan/QALY）. The results of the univariate sensitivity analysis indicated that the cost of culmerciclib， the discount rate， the utility values for progression disease and progression free survival status were significant factors influencing the ICER； both the univariate sensitivity analysis and the probabilistic sensitivity analysis validated the robustness of the model results. Scenario analysis indicated that when the price of culmerciclib was reduced by 30%， 55% and 85% respectively， the corresponding ICER values fell below 3， 2， and 1 times China’s per capita GDP in 2025， with the probability of cost-effectiveness being 3.00%， 94.90%， 100%. When the cost of culmerciclib （60 mg） was reduced by 52.6% to 50.96 yuan， the ICER value met the WTP threshold established in this study. CONCLUSIONS When the WTP threshold is set at twice China’s per capita GDP in 2025， second-line treatment with culmerciclib combined with fulvestrant for HR+/HER2– locally advanced or metastatic breast cancer does not exhibit cost-effectiveness advantage over fulvestrant monotherapy. Therefore， a reasonable price reduction is required to alleviate the financial burden on patients.

The rupture of carotid vulnerable plaques is the core pathological basis for major cardiovascular and cerebrovascular events. However， the insufficient alignment between existing animal models and the clinical disease and syndrome characteristics of traditional Chinese and western medicine has limited research progress. In this study， biomedical databases in China and abroad were systematically searched， and the modeling mechanisms and evaluation systems of carotid vulnerable plaque animal models were systematically assessed based on diagnostic criteria of both traditional Chinese and western medicine. Analysis of the clinical correspondence indicated that existing animal models can be categorized into four types： simple high-fat diet， surgical induction combined with high-fat feeding， genetic engineering combined with high-fat feeding， and drug induction combined with high-fat feeding. Among these， the compound strategy of surgical induction combined with high-fat feeding has become the current mainstream approach， showing good concordance with western medicine. The study found that the double balloon injury rabbit model and the ApoE^-/- mouse carotid artery tandem constriction combined with high-fat feeding model demonstrated a high degree of clinical correspondence with both traditional Chinese and western medicine in terms of vulnerable plaque imaging and pathological features. Nevertheless， existing models still face significant technical limitations in faithfully simulating plaque pathology and in translating findings to clinical applications. To address these challenges， integrating complex comorbidity mechanism construction， multimodal dynamic mechanism monitoring， and collaborative evaluation systems of traditional Chinese and western medicine could enable the development of highly concordant carotid vulnerable plaque disease-syndrome combination animal models. Such models would provide a reproducible experimental platform for targeted drug development to regulate plaque stability and for individualized precision treatment， as well as a theoretical basis for innovation in clinical diagnostic and therapeutic strategies.

OBJECTIVE: To propose a machine learning-based method for predicting the trajectories during manual acupuncture manipulation (MAM), aiming to improve the precision and consistency of acupuncture practitioner' operation and provide the real-time suggestions on MAM error correction. METHODS: Computer vision technology was used to analyze the hand micromotion when holding needle during acupuncture, and provide a three-dimensional coordinate description method of the index finger joints of the holding hand. Focusing on the 4 typical motions of MAM, a machine learning-based MAM trajectory predictive model was designed. By integrating the changes of phalangeal joint angle and hand skeletal information of acupuncture practitioner, the motion trajectory of the index finger joint was predicted accurately. Besides, the roles of machine learning-based MAM trajectory predictive model in the skill transmission of acupuncture manipulation were verified by stratified randomized controlled trial. RESULTS: The performance of MAM trajectory predictive model, based on the long short-term memory network (LSTM), obtained the highest stability and precision, up to 98%. The learning effect was improved when the model applied to the skill transmission of acupuncture manipulation. CONCLUSION The machine learning-based MAM predictive model provides acupuncture practitioner with precise action prediction and feedback. It is valuable and significant for the inheritance and error correction of manual operation of acupuncture.
Humans ; Acupuncture Therapy/instrumentation* ; Machine Learning ; Adult ; Male ; Female

BACKGROUND: Lung adenocarcinoma (LUAD) is the predominant subtype of non-small cell lung cancer (NSCLC). Damage-specific DNA binding protein 1 (DDB1), as a core protein of the CUL4-DDB1 ubiquitin ligase complex, is involved in the regulation of DNA damage repair, epigenetic modification, and cell cycle checkpoint activation. While the involvement of DDB1 in tumour progression through DNA repair and RNA transcriptional regulation has been reported, its expression and role in LUAD remain to be elucidated. This study aims to investigate the expression and role of DDB1 in LUAD. METHODS: The expression, clinicopathological features and prognosis of DDB1 in LUAD were analysed using databases such as UALCAN, Kaplan-Meier Plotter and GEPIA; The interaction network and enriched functional pathways were constructed by GeneMANIA and Metascape; the correlation between DDB1 and immune cells by combining with TISIDB infiltration was evaluated, and the clustering results of cell subtypes and the expression of DDB1 in different immune cell subpopulations were analysed by single-cell sequencing; finally, tissue microarrays were used to further verify the expression and prognostic value of DDB1 in LUAD. RESULTS: The mRNA and protein expression of DDB1 in LUAD tissues were significantly higher than those in normal tissues (P<0.01), and the high expression correlated with later clinical stage (P<0.001), lymph node metastasis (P<0.001) and poor prognosis (P<0.001). Functional enrichment showed that DDB1 was involved in DNA repair and RNA transcriptional regulation, and TISIDB evaluation revealed that DDB1 was negatively correlated with the expression level of immune cells, suggesting the potential regulation of the immune microenvironment. Single cell analysis showed that DDB1 was mainly expressed in T cells, alveolar macrophages and dendritic cells. Tissue microarrays confirmed that overall survival was shorter in the DDB1 high expression group (P<0.001), and Cox multifactorial analysis showed that DDB1 was an independent predictor of LUAD prognosis. CONCLUSIONS DDB1 is highly expressed in LUAD, which is associated with poor prognosis, and is closely related to tumor immune cell infiltration, and is involved in tumourigenesis and development through DNA repair and RNA transcriptional regulation. DDB1 can be used as a potential prognostic marker and therapeutic target for LUAD.
Humans ; Adenocarcinoma of Lung/immunology* ; DNA-Binding Proteins/metabolism* ; Lung Neoplasms/diagnosis* ; Gene Expression Regulation, Neoplastic ; Prognosis ; Male ; Female ; Middle Aged

Traditional Chinese medicine (TCM) demonstrates distinctive advantages in disease prevention and treatment. However, analyzing its biological mechanisms through the modern medical research paradigm of "single drug, single target" presents significant challenges due to its holistic approach. Network pharmacology and its core theory of network targets connect drugs and diseases from a holistic and systematic perspective based on biological networks, overcoming the limitations of reductionist research models and showing considerable value in TCM research. Recent integration of network target computational and experimental methods with artificial intelligence (AI) and multi-modal multi-omics technologies has substantially enhanced network pharmacology methodology. The advancement in computational and experimental techniques provides complementary support for network target theory in decoding TCM principles. This review, centered on network targets, examines the progress of network target methods combined with AI in predicting disease molecular mechanisms and drug-target relationships, alongside the application of multi-modal multi-omics technologies in analyzing TCM formulae, syndromes, and toxicity. Looking forward, network target theory is expected to incorporate emerging technologies while developing novel approaches aligned with its unique characteristics, potentially leading to significant breakthroughs in TCM research and advancing scientific understanding and innovation in TCM.
Artificial Intelligence ; Medicine, Chinese Traditional ; Humans ; Network Pharmacology/methods* ; Drugs, Chinese Herbal/pharmacology* ; Animals ; Multiomics

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*