Recent advances in large models demonstrate significant prospects for transforming the field of medical imaging. These models, including large language models, large visual models, and multimodal large models, offer unprecedented capabilities in processing and interpreting complex medical data across various imaging modalities. By leveraging self-supervised pretraining on vast unlabeled datasets, cross-modal representation learning, and domain-specific medical knowledge adaptation through fine-tuning, large models can achieve higher diagnostic accuracy and more efficient workflows for key clinical tasks. This review summarizes the concepts, methods, and progress of large models in medical imaging, highlighting their potential in precision medicine. The article first outlines the integration of multimodal data under large model technologies, approaches for training large models with medical datasets, and the need for robust evaluation metrics. It then explores how large models can revolutionize applications in critical tasks such as image segmentation, disease diagnosis, personalized treatment strategies, and real-time interactive systems, thus pushing the boundaries of traditional imaging analysis. Despite their potential, the practical implementation of large models in medical imaging faces notable challenges, including the scarcity of high-quality medical data, the need for optimized perception of imaging phenotypes, safety considerations, and seamless integration with existing clinical workflows and equipment. As research progresses, the development of more efficient, interpretable, and generalizable models will be critical to ensuring their reliable deployment across diverse clinical environments. This review aims to provide insights into the current state of the field and provide directions for future research to facilitate the broader adoption of large models in clinical practice.
Humans ; Diagnostic Imaging/methods* ; Precision Medicine/methods* ; Image Processing, Computer-Assisted/methods*

OBJECTIVE: This study aimed to explore the association between body mass index (BMI) and mortality based on the 10-year population-based multicenter prospective study. METHODS: A general population-based multicenter prospective study was conducted at four sites in rural China between 2013 and 2023. Multivariate Cox proportional hazards models and restricted cubic spline analyses were used to assess the association between BMI and mortality. Stratified analyses were performed based on the individual characteristics of the participants. RESULTS: Overall, 19,107 participants with a sum of 163,095 person-years were included and 1,910 participants died. The underweight (< 18.5 kg/m ²) presented an increase in all-cause mortality (adjusted hazards ratio [ aHR] = 2.00, 95% confidence interval [ CI]: 1.66-2.41), while overweight (≥ 24.0 to < 28.0 kg/m ²) and obesity (≥ 28.0 kg/m ²) presented a decrease with an aHR of 0.61 (95% CI: 0.52-0.73) and 0.51 (95% CI: 0.37-0.70), respectively. Overweight ( aHR = 0.76, 95% CI: 0.67-0.86) and mild obesity ( aHR = 0.72, 95% CI: 0.59-0.87) had a positive impact on mortality in people older than 60 years. All-cause mortality decreased rapidly until reaching a BMI of 25.7 kg/m ² ( aHR = 0.95, 95% CI: 0.92-0.98) and increased slightly above that value, indicating a U-shaped association. The beneficial impact of being overweight on mortality was robust in most subgroups and sensitivity analyses. CONCLUSION This study provides additional evidence that overweight and mild obesity may be inversely related to the risk of death in individuals older than 60 years. Therefore, it is essential to consider age differences when formulating health and weight management strategies.
Humans ; Body Mass Index ; China/epidemiology* ; Male ; Female ; Middle Aged ; Prospective Studies ; Rural Population/statistics & numerical data* ; Aged ; Follow-Up Studies ; Adult ; Mortality ; Cause of Death ; Obesity/mortality* ; Overweight/mortality*

This study aimed to elucidate the mechanism of action of metformin (MET) in inhibiting the malignant progression of hepatocellular carcinoma (HCC) by regulating the degradation of aldo-keto reductase family 1 member C3 (AKR1C3). The correlation between the sensitivity of different hepatocellular carcinoma cell lines to MET and their basal expression levels of AKR1C3 was firstly evaluated. MET was found to significantly reduce the level and accelerate the degradation rate of AKR1C3 protein by Western blot. The interaction between MET and AKR1C3 protein was confirmed by cellular thermal shift assay (CETSA). Proteasome inhibitor MG132 and the lysosomal inhibitor chloroquine (CQ) were used to screen the degradation pathway, and confirm, in combination with the HBSS starvation-induced autophagy model, that MET mediated the degradation of AKR1C3 through the autophagy lysosome pathway. Ubiquitylation assay showed that MET specifically enhanced the K63-linked polyubiquitylation modification of AKR1C3. Sequestosome 1 (SQSTM1/p62) knockdown, immunoprecipitation, and immunofluorescence co-localization analyses confirmed that the autophagy receptor p62 plays a key role in mediating MET-induced degradation of AKR1C3. The adenosine 5'-monophosphate (AMP)-activated protein kinase (AMPK) inhibitor compound C was used to demonstrate that the regulatory effect of MET on AKR1C3 is independent of the classical AMPK signaling pathway. The experimental results showed that metformin promoted the ubiquitination modification of AKR1C3 by targeting AKR1C3, enhanced the binding of AKR1C3 to autophagy receptor p62, then degraded the AKR1C3 protein through selective autophagy-like pathway, and ultimately inhibited the malignant phenotypes of hepatocellular carcinoma cells, which is a regulatory mechanism free of the classical AMPK activation pathway of metformin.

Objective:To explore the correlations between the cerebral infarction area and cytokines and immune status in patients with acute ischemic stroke,and to provide the theoretical basis for immunotherapy of the patients with different degrees of cerebral infarction.Methods:Sixty-seven patients with acute ischemic stroke within 72 h of the onset were randomly selected according to the inclusion and exclusion criteria,and were divided into large-area cerebral infarction group(n=34)and non-large-area cerebral infarction group(n=33)on the basis of the biggest infarction area in the sequences of magnetic resonance diffusion-weighted imaging(CDWI).Clinical baseline characteristics such as gender,age,and medical history were collected from the patients in two groups,the serum levels of interleukin(IL)-2,IL-6,IL-10,and IL-17A,tumor necrosis factor-α(TNF-α),and interferon-γ(IFN-γ)were measured using flow cytometry;the absolute values of lymphocytes(LYM#),lymphocyte percentages(LYM％),and neutrophil/lymphocy ratios(NLR)in peripheral blood of the patients caiculated,and the ratios of IFN-γ/IL-4,TNF-α/IL-4,and TNF-α/IL-10 rations were also calculated.The values of National Institutes of Health Stroke Scale(NIHSS)scores of the patients were evaluatd on the basis of the assessment of clinical neurological signs.The correlations of the cerebral infarction area and NIHSS score,cytokines and immune status groups of the patients in two were tested by rank correlation analysis.Results:Compared with non-large-area cerebral infarction group,the serum levels of IL-2,IL-6,IL-10,IL-17A,TNF-α,and IFN-γ as well as the NLR in the peripheral blood of the patients in large-area cerebral infarction group were significantly increased(P＜0.01),while the LYM#,LYM％and TNF-α/IL-4 were significantly decreased(P＜0.01).There was a positive correlation between cerebral infarction area and NIHSS score in the patients in large-area cerebral infarction group(rs=0.521,P＜0.05),and there was a significantly positive correlation between cerebral infarct area and NIHSS score in the patients in non-large-area cerebral infarction group(rs=0.721,P＜0.001).The NIHSS scores were positively correlated with serum IL-6(rs=0.306,P=0.005),IL-4(rs=0.252,P＜0.001),IL-2(rs=0.109,P=0.025),IL-17A(rs=0.405,P＜0.001),and IFN-γ(rs=0.146,P＜0.001)levels in two groups;no correlations were found between NIHSS scores and TNF-α(rs=0.039,P=0.726)and IL-10(rs=0.121,P=0.192)levels.NIHSS scores of the patients in two groups had negative correlatious with the serum level of LYM#(rs=-0.026,P=0.036)and LYM％(rs=-0.008,P=0.002),and had positive correlated with NLR(rs=0.315,P=0.009).Conclusion:The infarction area of the patients with actue cerebral infarction is correlated with the NIHSS score,the inflammatory response,the degree of adaptive immune injury,and the immune status.The have positive correlation with cytokines and immune markers and the overall size of the infarction area.Compared with the patients with non-large-acea cerebral infarction,the immunosuppression of the patients with large-area infarcted areas is more likely to occure.

ObjectiveBased on ultra-high performance liquid chromatography-quadrupole-electrostatic field orbital trap-linear ion-trap mass spectrometry（UPLC-Orbitrap Fusion Lumos Tribrid-MS）， the plasma concentration of ziyuglycoside Ⅰ was determined at different time points after oral administration， and its pharmacokinetic characteristics in normal rats and rats with acute kidney injury were compared. MethodsRats were randomly divided into normal group and model group， the model group received intraperitoneal cisplatin（10 mg·kg^-1） to establish the acute kidney injury model， the normal group was given the same volume of saline. After successful modeling， rats in the normal and model groups were randomly divided into the normal low， medium and high dose groups（2.5， 5， 7.5 mg·kg^-1） and the model low， medium and high dose groups（2.5， 5， 7.5 mg·kg^-1）， 6 rats in each group， and the plasma was collected at different time points after receiving the corresponding dose of ziyuglycoside Ⅰ. Then， the concentration of ziyuglycoside Ⅰ in rat plasma was determined by UPLC-Orbitrap Fusion Lumos Tribrid-MS， and the drug-time curve was poltted. The pharmacokinetic parameters were calculated by Kinetica 5.1 software， and the differences in pharmacokinetic parameters between different administration groups were compared by independent sample t-test with SPSS 22.0. ResultsThe pharmacokinetic results showed that after receiving the different doses of ziyuglycoside Ⅰ， its concentration increased first and then decreased， and all of them reached the maximum plasma concentration at about 0.5 h. The area under the curve（AUC_0-t） and mean retention time（MRT_0-t） of normal and model rats increased with the increased dose， and the clearance（CL） decreased with the increasing dose. Compared with the normal group， the AUC_0-t was significantly increased（P<0.01）， peak concentration（C_max） and CL decreased in model rats at different doses， indicating that the physiological state of the rats could affect the absorption and elimination of ziyuglycoside Ⅰ in vivo. ConclusionThe pharmacokinetic characteristics of ziyuglycoside Ⅰ are quite different in normal rats and acute kidney injury model rats， which may be due to the change of the body environment in the pathological state， then lead to changes in absorption and metabolic processes.

This article systematically analyzes the historical evolution of the name， origin， quality evaluation， harvesting， processing and other aspects of Picrorhizae Rhizoma by referring to the medical books， prescription books， and other documents of the past dynasties， combined with relevant modern research materials， in order to provide a basis for the development and utilization of famous classical formulas containing this medicinal herb. The research results indicate that Picrorhizae Rhizoma was first recorded in New Revised Materia Medica from the Tang dynasty. Throughout history， Huhuanglian has been used as its official name， and there are also aliases such as Gehu Luze， Jiahuanglian and Hulian. The main source of past dynasties is the the rhizomes of Picrorhiza kurrooa and P. scrophulariiflora. In ancient times， Picrorhizae Rhizoma was mainly imported by foreign traders via Guangzhou and other regions， and also produced in China， mainly in Xizang. In ancient times， it was harvested and dried in early August of the lunar calendar， while in modern times， it is mostly harvested from July to September， with the best quality being those with thick and crispy rhizomes without impurities， and bitter taste. Throughout history， Picrorhizae Rhizoma was collected， washed， sliced， and dried before being used as a raw material for medicine， it has a bitter and cold taste， mainly used to treat bone steaming， hot flashes， infantile chancre fever， and dysentery. There is no significant difference in taste and efficacy between ancient and modern times. Based on the research results， it is recommended that the rhizomes of P. scrophulariiflora in the 2020 edition of Chinese Pharmacopoeia， or the rhizomes of P. kurrooa， can be used in famous classical formulas containing this medicinal herb， which can be processed according to the processing requirements marked by the original formula. For those without clear processing requirements， the dried raw products are used as medicine.

Clinical practice guidelines represent the best recommendations for patient care. They are developed through systematically reviewing currently available clinical evidence and weighing the relative benefits and risks of various interventions. However, clinical practice guidelines have to go through a long translation cycle from development and revision to clinical promotion and application, facing problems such as scattered distribution, high duplication rate, and low actual utilization. At present, the clinical practice guideline information platform can directly or indirectly solve the problems related to the lengthy revision cycles, decentralized dissemination and limited application of clinical practice guidelines. Therefore, this paper systematically examines different types of clinical practice guideline information platforms and investigates their corresponding challenges and emerging trends in platform design, data integration, and practical implementation, with the aim of clarifying the current status of this field and providing valuable reference for future research on clinical practice guideline information platforms.

Background: Salvia miltiorrhiza Bunge, commonly known as “Danshen” in China due to the distinctive red color of its roots, is one of the most widely used traditional Chinese medicines. It is cultivated in various regions across China, and environmental differences among these regions can affect the secondary metabolites of plants, thereby influencing the quality of S. miltiorrhiza. In recent years, increasing demand for S. miltiorrhiza has exacerbated the problem of origin fraud. Therefore, ensuring the authenticity of its geographical origin is crucial for the sustainable development of the industry. Objective: The red coloration of S. miltiorrhiza is closely associated with the content of its primary active compounds, particularly tanshinones. Therefore, both its internal chemical composition and external color characteristics serve as key indicators for quality assessment. This study utilized hyperspectral imaging technology to evaluate its potential in classifying the geographical origin of S. miltiorrhiza. Methods: Spectral data reflecting the internal chemical properties of S. miltiorrhiza were integrated with color information representing its external features through 3 levels of data fusion. These fused datasets were then combined with deep learning algorithms to achieve accurate origin classification. Results: The results demonstrated that the Transformer model combined with soft-voting decision-level fusion achieved the highest classification accuracy of 98.72% by integrating image color and short-wave infrared spectral data. Conclusion: This study demonstrates that integrating hyperspectral imaging spectral data with color information provides a reliable and innovative approach for verifying the authenticity and traceability of S. miltiorrhiza.

Background: Optimizing cultivation techniques for traditional Chinese medicine has become a crucial means to improve the quality of medicinal materials. Microbial agents, as environmentally friendly and efficient plant growth promoters and soil conditioners, have increasingly attracted attention in eco-agriculture research. Objective: Our understanding remains limited regarding how the application of microbial agents, alone or in combination, affects changes in the rhizosphere microbiome and its association with the bioactive components of medicinal materials. Methods: In this study, Epimedium pubescens Maxim. was employed as a model plant to examine the effects of 2 microbial agents(Paenibacillus mucilaginosus and Bacillus subtilis) applied individually and in combination on plant growth and the accumulation of bioactive components. Additionally, this study explored the relationship between the rhizosphere microbiome and plant development. Results: The application of microbial agents increased the yield of E. pubescens leaves by 20.30% to 33.66% and enhanced the total flavonol glycosides content by 11.40% to 29.94%. Meanwhile, microbial treatments reshaped the rhizosphere microbiome, promoted the enrichment of beneficial microorganisms (e.g., Frankia and Paenibacillus), suppressed phytopathogenic fungi such as Didymella and Scytalidium, and enhanced the stability of the soil microbial co-occurrence network. The partial least squares path model suggested that microbial agents not only directly impact the quality of medicinal herbs but also indirectly alter the accumulation of bioactive components by modulating the soil microbiome. Conclusion: These findings deepen our understanding of the relationship between medicinal plant quality and rhizosphere microbiomes as mediated by microbial agents. They also provide a basis for designing and manipulating synthetic microbial communities to promote sustainable development in eco-agriculture.

Background:Salvia miltiorrhiza Bunge,commonly known as"Danshen"in China due to the distinctive red color of its roots,is one of the most widely used traditional Chinese medicines.It is cultivated in various regions across China,and environmental differences among these regions can affect the secondary metabolites of plants,thereby influencing the quality of S.miltiorrhiza.In recent years,increasing demand for S.miltiorrhiza has exacerbated the problem of origin fraud.Therefore,ensuring the authenticity of its geo-graphical origin is crucial for the sustainable development of the industry.Objective:The red coloration of S.miltiorrhiza is closely associated with the content of its primary active compounds,particularly tanshinones.Therefore,both its internal chemical composition and external color characteristics serve as key indicators for quality assessment.This study utilized hyperspectral imaging technology to evaluate its potential in classifying the geographical origin of S.miltiorrhiza.Methods:Spectral data reflecting the internal chemical properties of S.miltiorrhiza were integrated with color information represent-ing its external features through 3 levels of data fusion.These fused datasets were then combined with deep learning algorithms to achieve accurate origin classification.Results:The results demonstrated that the Transformer model combined with soft-voting decision-level fusion achieved the highest classification accuracy of 98.72％by integrating image color and short-wave infrared spectral data.Conclusion:This study demonstrates that integrating hyperspectral imaging spectral data with color information provides a reliable and innovative approach for verifying the authenticity and traceability of S.miltiorrhiza.