The theory of constitution in traditional Chinese medicine （TCM） has emerged as a new discipline in recent years. Constitution plays a vital role in the onset，progression，transformation，and prognosis of diseases. At present，some clinical scholars have adopted a novel diagnostic and treatment model of "constitution differentiation-disease identification-syndrome differentiation"，in which constitution is regarded as a core element throughout the diagnostic and therapeutic process. Constitution is closely associated with etiology，onset，pathogenesis，syndrome differentiation，and treatment. Against this background，the construction of animal models based on constitution holds far-reaching significance for advancing clinical research. This paper focuses on the construction and evaluation of rodent models with Qi-deficiency constitution，aiming to explore how to further induce Qi-deficiency syndromes and related disease states on the basis of Qi-deficiency constitution models，thereby developing an integrated animal model that embodies the trinity of "constitution-disease-syndrome". The establishment of this model not only provides a solid experimental foundation for the development of new therapies and drugs in TCM targeting specific constitutions，diseases，and syndromes，but also greatly promotes the modernization and scientific advancement of TCM theory. By comprehensively applying multidisciplinary technologies and methods，the study evaluates the model's validity，reliability，and practicality，with the aim of opening new avenues for future research in TCM and promoting the development of the field.

The theory of constitution in traditional Chinese medicine （TCM） has emerged as a new discipline in recent years. Constitution plays a vital role in the onset，progression，transformation，and prognosis of diseases. At present，some clinical scholars have adopted a novel diagnostic and treatment model of "constitution differentiation-disease identification-syndrome differentiation"，in which constitution is regarded as a core element throughout the diagnostic and therapeutic process. Constitution is closely associated with etiology，onset，pathogenesis，syndrome differentiation，and treatment. Against this background，the construction of animal models based on constitution holds far-reaching significance for advancing clinical research. This paper focuses on the construction and evaluation of rodent models with Qi-deficiency constitution，aiming to explore how to further induce Qi-deficiency syndromes and related disease states on the basis of Qi-deficiency constitution models，thereby developing an integrated animal model that embodies the trinity of "constitution-disease-syndrome". The establishment of this model not only provides a solid experimental foundation for the development of new therapies and drugs in TCM targeting specific constitutions，diseases，and syndromes，but also greatly promotes the modernization and scientific advancement of TCM theory. By comprehensively applying multidisciplinary technologies and methods，the study evaluates the model's validity，reliability，and practicality，with the aim of opening new avenues for future research in TCM and promoting the development of the field.

With the continuous deepening of the concept of precision diagnosis and treatment for lung cancer, how to achieve higher efficiency and accuracy in the screening, diagnosis, and treatment pathways in clinical practice has become an important issue that urgently needs to be overcome. The current clinical difficulty lies in the fact that despite continuous advancements in imaging and molecular diagnostic technologies, there are still limitations in manual efficiency and subjective experience when it comes to massive data analysis and multi-scale feature extraction. Artificial intelligence (AI), especially algorithm systems based on deep learning, is an innovative technology capable of deeply empowering medical big data. This method utilizes algorithms such as convolutional neural networks, combined with radiomics, pathomics, and multi-modal data fusion analysis, demonstrating immense potential in early precise detection and benign-malignant differentiation of pulmonary nodules, digital pathological subtype recognition and non-invasive prediction of driver genes, precise 3D surgical planning and automatic delineation of radiotherapy target volumes, as well as dynamic risk warning during follow-up. This innovative technology provides a brand-new solution for realizing intelligent and individualized lung cancer diagnosis and treatment models. This consensus, based on the latest evidence from evidence-based medicine and combined with the development trends in the AI field and real-world clinical needs, was ultimately formed by gathering the consensus opinions of multidisciplinary experts in radiology, pathology, thoracic surgery, and other fields. The main content covers the application specifications of AI in the three core scenarios of lung cancer screening, diagnosis, and treatment, the technical standards for data collection and algorithm validation, as well as the ethical and regulatory challenges faced at the current stage. It aims to clarify the applicable boundaries of AI as a clinical auxiliary decision support tool, providing scientific guidance and standardized exploration directions for peers currently engaged in or planning to carry out AI-assisted clinical diagnosis, treatment, and translation of lung cancer.

Organoid technology has become a highly promising in vitro model in biomedical research, but its traditional culture relies on animal-derived basement membrane matrices, which suffer from batch-to-batch variability, undefined biochemical composition, and poorly controllable mechanical properties. Recent studies have confirmed that matrix stiffness, viscoelasticity, topography, and temporal mechanical dynamics play regulatory roles equally as critical as biochemical signals in stem cell self-organization, lineage specification, and functional maturation of organoids. This review systematically summarizes the progress in understanding how extracellular matrix (ECM) mechanical properties regulate organoid formation. Focusing on the YAP/TAZ factors and the integrin receptors, which engage in crosstalk with biochemical signals to synergistically determine cell fate. We then elaborate how specific mechanical parameters, such as stiffness, viscoelasticity, and topography, influence each distinct stage of organoid development, from initial aggregation and compaction, through proliferative expansion, to lineage-committed differentiation and terminal functional maturation. Appropriate stiffness facilitates efficient aggregation and symmetry breaking, while excessive rigidity impairs proliferation and accelerates cellular senescence. In hepatic organoid expansion where only stiffness matching liver tissue(~6.2 kPa) supported robust growth. Viscoelastic stress relaxation promotes integrin clustering, enhances focal adhesion maturation, and stimulates endogenous ECM deposition, thereby supporting self-organized crypt-villus architecture and basement membrane assembly. Notably, dynamic softening of hydrogels enables optimal crypt budding, whereas rapid stress relaxation permits nephron segment coiling and drives vascular organoid maturation toward arteriole differentiation, highlighting stress relaxation as a key temporal parameter. Topographical cues provide contact guidance that directs polarized outgrowth, lumen formation, and neuronal orientation, all of which are essential for achieving organotypic architecture. The embedded 3D printing of complex neural networks enables precise control over branching geometries, facilitating studies of morphogenesis under spatial constraints. The engineered mechanical microenvironments have been successfully applied to recapitulate disease phenotypes: stiff matrices induce fibrosis and epithelial-mesenchymal transition in liver organoids, promote chemoresistance in cholangiocarcinoma. Furthermore, we summarize engineering strategies for both natural and synthetic hydrogels aimed at creating mechanically defined culture platforms. Natural biomaterial modifications achieve spatial separation of mechanical constraints from biochemical support, enabling self-limited compressive stimulation within a defined time window. Synthetic polymers allow independent tuning of stiffness, ligand density, and viscoelasticity through adjustable crosslinking and dynamic non-covalent interactions, thus providing orthogonal control over multiple mechanical parameters. Dynamic microenvironments that mimic progressive stiffening as observed in development, tumor evolution, or fibrosis can be constructed using composites that undergo spontaneous structural transitions. In the future, critical challenges include deciphering the synergistic rules governing mechanical-biochemical interplay, developing spatiotemporally programmable hydrogels that enable on-demand modulation of local mechanics, establishing comprehensive databases of tissue-specific mechanical parameter windows, and advancing standardized high-throughput screening platforms to accelerate preclinical validation and clinical translation. Moreover, integrating real-time mechanical sensors and non-invasive imaging techniques would allow dynamic monitoring of intracellular forces and tissue-level stress during culture. Ultimately, this review aims to provide a theoretical foundation for deepening our understanding of mechano-biological coupling mechanisms and for rationally designing high-fidelity, physiologically relevant organoid culture platforms that fully harness the regulatory power of mechanical cues.

Objective:To analyze the epidemiological and clinical characteristics of respiratory pathogens in China.Methods:This study was a cross-sectional study, which encompassed 19 core units of the clinical pathogen network and established a three-tiered clinical pathogen surveillance system. Thirty respiratory samples were collected every two weeks from various units from January to December 2024, and the clinical and pathogen diagnostic information were gathered. A total of 11 864 samples were tested using this system. The tier-1 clinical pathogen surveillance system covered influenza A virus (Flu-A), influenza B virus (Flu-B), respiratory syncytial virus (RSV), and severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). The tier-2 clinical pathogen surveillance system focused on 18 key respiratory pathogens. The tier-3 clinical pathogen surveillance system further clarified whether any emerging infectious diseases had occurred.Results:The tier-1 clinical pathogen surveillance system showed Flu-A predominated in December, Flu-B predominated in January, SARS-CoV-2 peaked in March and August, whereas RSV circulated sporadically throughout the year. Geographic trends were broadly consistent across the seven major regions, although Flu-A detection in December was notably higher in Northeast China (48.1%(111/231)) and East China (36.2%(148/409)), and RSV detection was concentrated in the Northwest and South China from January to March. Data from the tier-2 clinical pathogen surveillance system indicated that Streptococcus pneumoniae, Mycoplasma pneumoniae, rhinovirus, and adenovirus were detected year-round, of these, Streptococcus pneumoniae and rhinovirus showed elevated positive detection rates from August to September, while adenovirus peaked in January. Legionella pneumophila was not detected throughout the year, and other pathogens fluctuated throughout the year without a consistent pattern. The predominant etiologic agents of pediatric pneumonia were Mycoplasma pneumoniae (35.0%(105/300)), rhinovirus (25.7%(77/300)), and adenovirus (17.3%(52/300)), whereas adult pneumonia was mainly caused by Streptococcus pneumoniae (10.5%(29/277)), Staphylococcus aureus (6.9%(19/277)), Mycoplasma pneumoniae (6.9%(19/277)), and Flu-A (6.1%(17/277)). The tier-3 clinical pathogen surveillance system did not identify any emerging respiratory pathogens. Conclusion:Respiratory pathogens in China in 2024 exhibit distinct temporal and spatial distribution patterns and vary among different populations.

This study was aimed at identifying the pathogenic bacteria responsible for the death of a young tiger at the Fujian Meihua Mountain South China Tiger Breeding Research Institute.Tissue samples from the lungs,liver,and intestines of the deceased tiger were collected,and the bacteria were cultured inasterile environment.The bacterial strains were characterized according to their morphological and molecular biological properties,including assessment of virulence genes and antibiotic resistance genes,mouse lethality tests,and antibiotic susceptibility evaluations.A predominant bacterial strain isolated from the liver of the deceased tiger was identified as enterotoxigenic Escherichia coli(ETEC)strain Tiger22513F.Phylogenetic analysis of the 16S rRNA gene revealed that the Tiger22513F strain exhibited close genetic similarity to the reference strain ETEC(MF919609.1),with 99.9%nucleotide similarity,and resided on the same evolutionary branch.The Tiger22513F strain contained 11 antibiotic resistance genes(tetA,sul1,sul3,cmlA,floR,blaTEM,blaSHV,blaCMY-2,qnrA,qnrS,and qnrD)along with five virulence genes(VT1,fyuA,tsh,iucD,and ST).Mouse lethality tests indicated significant pathogenicity toward mice,affecting primarily the lungs,liver,and intestines.Antibiotic susceptibility testing demonstrated that this strain exhibited resistance to various classes of beta-lactam antibiotics,as well as quinolones and aminoglycosides.This investigation successfully isolated a multi-drug resistant enterotoxigenic Escherichia coli strain with pronounced pathogenicity from the liver of a deceased tiger;thus providing valuable scientific insights for clinical diagnosis,as well as prevention and control measures,against ETEC infections in South China tigers.

Objective:To investigate the additional prognostic value of coronary CT angiography (CCTA)-based flow reserve fraction (CT-FFR) over semi-quantitative CCTA risk scores in predicting the occurrence of major adverse cardiovascular events (MACE) in type 2 diabetic patients.Methods:A total of 231 patients with type 2 diabetes mellitus who underwent CCTA at Lanzhou University from May 2020 to April 2021 were retrospectively enrolled. Clinical baseline data were collected, and patients were divided into a MACE-positive group (20 cases) and a MACE-negative group (211 cases) based on follow-up results. The CCTA images of all patients were analyzed by semi-quantitative CCTA risk score, which included coronary artery disease reporting and data system classification, segment involvement score, segmental stenosis score, Leaman score, and Leiden score. CT-FFR measurements of CCTA data of all patients were performed using Coronary Analysis software. t-test, U-test, and χ2 test were used to compare baseline parameters between MACE-positive and MACE-negative groups. The Cox proportional hazards regression model was used to analyze the relationship between semi-quantitative CCTA risk score and CT-FFR with the occurrence of MACE, and the area under the curve (AUC) of the receiver operating characteristic (ROC) was used to calculate the efficacy of the prediction model established by the semi-quantitative CCTA risk score combined with CT-FFR. Results:There was no statistically significant difference in baseline data between patients in the MACE-positive and MACE-negative groups ( P>0.05), and there were significant differences in semi-quantitative CCTA risk scores and CT-FFR ( P<0.05). Multivariate Cox proportional risk regression analysis of CT-FFR≤0.80 ( HR=3.860, 95% CI 1.477-10.087, P=0.006) and Leaman score≥5 ( HR=5.210, 95% CI 1.136-23.908, P=0.029) were the best and independent predictors for the occurrence of MACE events. The combined CT-FFR and Leaman score prediction model (AUC=0.791, 95% CI 0.733-0.842, P<0.001) was a better predictor of MACE than CT-FFR alone (AUC=0.718, 95% CI 0.656-0.775, P<0.001) and Leaman score alone (AUC=0.711, 95% CI 0.648-0.768, P<0.001) both had better predictive efficacy ( Z=2.62, 1.98, P=0.009, 0.047). Conclusion:CT-FFR independently predict the occurrence of MACE in patients with type 2 diabetes mellitus and significantly improve the predictive capacity of semi-quantitative CCTA risk score for MACE.

Objective:To analyze the clinical efficacy and learning curve of robotic-assisted Warshaw procedure using the da Vinci system.Methods:Clinical data of 91 consecutive patients with pancreatic body and tail lesions undergoing robotic-assisted Warshaw procedure at Qingdao University Affiliated Hospital from October 2021 to April 2023 were retrospectively analyzed, including 21 males and 70 females, aged (50.2±14.3) years. Patient characteristics, operative time, and intraoperative blood loss were recorded. The learning curve was constructed using cumulative sum (CUSUM) analysis, with case number on the x-axis and CUSUM values on the y-axis. Linear fitting was performed, and the model with the highest determination coefficient was selected as the optimal fitting model. The learning process was divided into two phases based on the inflection point of the CUSUM learning curve: the learning phase and the proficiency phase. Perioperative outcomes were compared between these two phases.Results:All 91 procedures were successfully completed using the da Vinci robotic system with R0 resection margins. There were no perioperative mortalities or reoperations due to postoperative splenic infarction. The operative time was (227.84±76.68) min. The optimal fitting equation for the CUSUM learning curve was: CUSUM=0.005 640X 3-1.501X 2+ 92.59X-183.1. The CUSUM learning curve showed an inflection point at case 39, dividing the learning process into the learning phase (cases 1-39) and proficiency phase (cases 40-91). Compared to the learning phase, the proficiency phase demonstrated significantly shorter operative time [(203.0±75.6) min vs. (260.9±65.5) min], less intraoperative blood loss [50.0 (20.0, 50.0) ml vs. 100.0 (50.0, 100.0) ml], and reduced postoperative drainage duration [(8.7±2.4) d vs. (10.8±3.2) d] (all P<0.05). Conclusion:The robotic-assisted Warshaw procedure feasible for patients with pancreatic body and tail lesions. Surgeons require approximately 39 cases to complete the learning curve and achieve proficiency in this procedure.

This study aims to investigate the antipyretic effects and mechanisms of ethanol extracts from Arisaematis Rhizoma fermented with bile from different sources on a rat model of fever induced by a dry-yeast suspension. The rat model of fever was established by subcutaneous injection of 20% dry-yeast suspension into the rat back. The levels of tumor necrosis factor-α(TNF-α), interleukin-1β(IL-1β), interleukin-6(IL-6) in the serum, as well as prostaglandin E_2(PGE_2) and cyclic adenosine monophosphate(cAMP) in the hypothalamus, were determined by ELISA. Metabolomics analysis was then performed on serum and hypothalamus samples based on UPLC-Q-TOF MS to explore the potential biomarkers and metabolic pathways. The results showed that the body temperatures of rats significantly rose 4 h after modeling. After oral administration of high-dose ethanol extracts of Arisaematis Rhizoma fermented with bovine bile(NCH) and porcine bile(ZCH), the body temperatures of rats declined(P<0.05), and the NCH group showed better antipyretic effect than the ZCH group. Additionally, compared with the model group, the NCH and ZCH groups showed lowered levels of IL-1β, IL-6, TNF-α, PGE_2, and cAMP(P<0.01). The results of serum and hypothalamus metabolomics analysis indicated that both NCH and ZCH exerted antipyretic effects by regulating phenylalanine metabolism, sphingolipid metabolism, arachidonic acid metabolism, and steroid hormone biosynthesis. Collectively, both NCH and ZCH can play an obvious antipyretic role in the rat model of dry yeast-induced fever, and the underlying mechanism might be closely associated with inhibiting inflammation and regulating metabolic disorders. Moreover, NCH demonstrates better antipyretic effect.
Animals ; Rats ; Male ; Fermentation ; Rats, Sprague-Dawley ; Rhizome/metabolism* ; Drugs, Chinese Herbal/chemistry* ; Bile/chemistry* ; Antipyretics/chemistry* ; Fever/metabolism* ; Cattle ; Swine ; Tumor Necrosis Factor-alpha/metabolism* ; Ethanol/chemistry* ; Interleukin-6/blood* ; Interleukin-1beta/blood*

The main active compound, 3,4-dihydroxyacetophenone(3,4-DHAP), in the leaves of Ilex pubescens var. glaber, exhibits various pharmacological activities, including vasodilation and heart protection. Currently, natural extraction and chemical synthesis are the primary methods for obtaining 3,4-DHAP, but both approaches have inherent challenges. To address these problems, this study explored the whole-cell bioconversion of 1-(4-hydroxyphenol)-ethanol to 3,4-DHAP using recombinant Escherichia coli, cultivated in a green, cost-effective medium at room temperature and atmospheric pressure. Firstly, this study successfully constructed recombinant E. coli S1, which contained only the HpaBC gene, and recombinant E. coli S3, which contained both the Hped and HpaBC genes. The ability of S1 and S3 to synthesize 3,4-DHAP from their respective substrates was then evaluated through whole-cell bioconversion. Based on these results, the effects of four factors, i.e., substrate concentration, IPTG concentration, induction temperature, and transformation temperature, on the whole-cell bioconversion yield of S3 were investigated using an orthogonal experiment. The results showed that the factors influenced the yield in the following order: transformation temperature > induction temperature > IPTG concentration > substrate concentration. The optimal conditions were found to be a transformation temperature of 35 ℃, IPTG concentration of 0.1 mmol·L~(-1), induction temperature of 25 ℃, and substrate concentration of 10 mmol·L~(-1). Finally, the effect of transformation time on the yield of 3,4-DHAP was further examined under the optimal conditions. The results indicated that as the transformation time increased, the yield of 3,4-DHAP steadily increased. The highest yield of 260 mg·L~(-1) with a productivity of 17% was achieved after 72 hours of transformation. In conclusion, this study successfully achieved the whole-cell bioconversion of 1-(4-hydroxyphenol)-ethanol to 3,4-DHAP using recombinant E. coli for the first time, laying the groundwork for further optimization and development of the biosynthesis of 3,4-DHAP.
Escherichia coli/genetics* ; Acetophenones/chemistry* ; Ethanol/chemistry* ; Drugs, Chinese Herbal/chemistry* ; Biotransformation