ObjectiveTo investigate the mechanism by which Gegen Qinliantang（GQT） improves skeletal muscle insulin resistance. MethodsThe db/m mice were used as the normal group， while db/db mice were assigned to a model group， low-dose （3.12 g·kg^-1）， medium-dose （6.24 g·kg^-1）， and high-dose （12.48 g·kg^-1） GQT groups， and a Western medicine group （semaglutide， 0.045 mg·kg^-1），n=6 in each group. All groups received corresponding interventions. Intraperitoneal glucose tolerance test （IPGTT）， intraperitoneal insulin tolerance test （IPITT）， and hematoxylin-eosin （HE） staining were used to evaluate insulin resistance and therapeutic efficacy. Serum lipid levels were measured using an automatic biochemical analyzer， and apoptosis in skeletal muscle was assessed via TUNEL assay. Transcriptome sequencing combined with gene ontology （GO） and Kyoto Encyclopedia of Genes and Genomes （KEGG） analyses was performed to identify differentially expressed genes （DEGs）. Real-time quantitative polymerase chain reaction （Real-time PCR） was used to validate gene expression. Molecular docking was applied to evaluate the binding patterns between active components of GQT and key regulatory genes to elucidate pharmacological mechanisms. ResultsCompared with the model group， the medium-dose and high-dose GQT groups showed significantly reduced fasting blood glucose （FBG） levels （P<0.01）. Triglycerides （TG）， total cholesterol （TC）， and low-density lipoprotein cholesterol （LDL-C） were markedly decreased （P<0.01）， while high-density lipoprotein cholesterol （HDL-C） was significantly increased （P<0.01）. IPGTT， IPITT， and HE staining demonstrated that GQT enhanced insulin sensitivity and restored skeletal muscle morphology. GQT also alleviated apoptosis in skeletal muscle tissue. Transcriptome analysis revealed that GQT primarily affected biological processes such as oxidative phosphorylation， metabolic pathways， cellular processes， and protein binding. Real-time PCR results showed that CBR2， CDK6， F830016B08Rik， IL-1β， Rab27b， and COLEC12 were key regulatory genes. Molecular docking demonstrated that CBR2， IL-1β， Rab27b， and COLEC12 formed stable binding with the main active components of GQT. The therapeutic effects of high- and medium-dose GQT were comparable to those of the semaglutide group. ConclusionGQT improves skeletal muscle insulin resistance， potentially by regulating apoptosis as part of its underlying biological mechanism.

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.

Hepatocellular carcinoma(HCC),which is essentially primary liver cancer,is closely related to CD8+T cell immune infiltration and immune suppression.We constructed a CD8+T cells related risk score model to pre-dict the prognosis of HCC patients and provided therapeutic guidance based on the risk score.Using integrated bulk RNA sequencing(RNA-seq)and single-cell RNA sequencing(scRNA-seq)datasets,we identified stable CD8+T cell signatures.Based on these signatures,a 3-gene risk score model,comprised of KLRB1,RGS2,and TN-FRSF1B was constructed.The risk score model was well validated through an independent external validation co-hort.We divided patients into high-risk and low-risk groups according to the risk score and compared the differ-ences in immune microenvironment between these two groups.Compared with low-risk patients,high-risk patients have higher M2-type macrophage content(P＜0.0001)and lower CD8+T cells infiltration(P＜0.0001).High-risk patients predict worse response to immunotherapy treatment than low-risk patients(P＜0.01).Drug sensitivity a-nalysis shows that PI3K-β inhibitor AZD6482 and TGFβRII inhibitor SB505124 may be suitable therapies for high-risk patients,while the IGF-1R inhibitor BMS-754807 or the novel pyrimidine-based anti-tumor metabolic drug Gemcitabine could be potential therapeutic choices for low-risk patients.Moreover,expression of these 3-gene mod-el was verified by immunohistochemistry.In summary,the establishment and validation of a CD8+T cell-derived risk model can more accurately predict the prognosis of HCC patients and guide the construction of personalized treatment plans.

In-fusion cloning technology,as a revolutionary and efficient molecular biology tool,has been applied in multiple research fields such as basic biology,biotechnology,and biomedicine.In this article,we introduce a teaching reform project suitable for undergraduate students in the course of"Molecular Bi-ology Laboratory",which utilizes in-Fusion cloning technology to construct a prokaryotic expression vector for alkaline phosphatase mutant genes.Through specific teaching cases,we systematically explored the design and implementation of experimental projects,and focused on analyzing the key and difficult points of the teaching content.Our teaching practice has found that the implementation of this educational re-form project has achieved very good results in enhancing students' core biological literacy,bioinformatics skills,research thinking,and innovation abilities.At the same time,the application of this technology can significantly improve the quality of experimental teaching,providing new ideas and practical refer-ences for promoting the reform and innovation of National First-Class Courses.

Objective:To explore the level of mental health literacy and its relationship with symptoms of de-pression,anxiety,and insomnia in young adults.Methods:A total of 10 273 young adults aged 18-23 in Guang-zhou were selected and assessed with the National Mental Health Literacy Questionnaire,Patient Health Question-naire-9(PHQ-9),General Anxiety Disorder 7-Item Scale(GAD-7)and Insomnia Severity Index(ISI).Results:Totally 1 137(11.1％)participants met the criteria for adequate mental health literacy,2 758(26.8％)participants were with symptoms of depression,1 355(13.2％)with symptoms of anxiety and 4 936(48.0％)experiencing symptoms of insomnia.Logistic regression analyses showed that after adjusting for other factors,substandard mental health literacy was an independent risk factor for symptoms of depression,anxiety and insomnia(OR=2.08,1.93,1.49;95％CI:1.74-2.48,1.52-2.46,1.31-1.70).Conclusion:The level of mental health literacy in young a-dults is insufficient,and the lack of mental health literacy is significantly associated with the symptoms of depres-sion,anxiety and insomnia.

Hepatocellular carcinoma(HCC),which is essentially primary liver cancer,is closely related to CD8+T cell immune infiltration and immune suppression.We constructed a CD8+T cells related risk score model to pre-dict the prognosis of HCC patients and provided therapeutic guidance based on the risk score.Using integrated bulk RNA sequencing(RNA-seq)and single-cell RNA sequencing(scRNA-seq)datasets,we identified stable CD8+T cell signatures.Based on these signatures,a 3-gene risk score model,comprised of KLRB1,RGS2,and TN-FRSF1B was constructed.The risk score model was well validated through an independent external validation co-hort.We divided patients into high-risk and low-risk groups according to the risk score and compared the differ-ences in immune microenvironment between these two groups.Compared with low-risk patients,high-risk patients have higher M2-type macrophage content(P＜0.0001)and lower CD8+T cells infiltration(P＜0.0001).High-risk patients predict worse response to immunotherapy treatment than low-risk patients(P＜0.01).Drug sensitivity a-nalysis shows that PI3K-β inhibitor AZD6482 and TGFβRII inhibitor SB505124 may be suitable therapies for high-risk patients,while the IGF-1R inhibitor BMS-754807 or the novel pyrimidine-based anti-tumor metabolic drug Gemcitabine could be potential therapeutic choices for low-risk patients.Moreover,expression of these 3-gene mod-el was verified by immunohistochemistry.In summary,the establishment and validation of a CD8+T cell-derived risk model can more accurately predict the prognosis of HCC patients and guide the construction of personalized treatment plans.

In-fusion cloning technology,as a revolutionary and efficient molecular biology tool,has been applied in multiple research fields such as basic biology,biotechnology,and biomedicine.In this article,we introduce a teaching reform project suitable for undergraduate students in the course of"Molecular Bi-ology Laboratory",which utilizes in-Fusion cloning technology to construct a prokaryotic expression vector for alkaline phosphatase mutant genes.Through specific teaching cases,we systematically explored the design and implementation of experimental projects,and focused on analyzing the key and difficult points of the teaching content.Our teaching practice has found that the implementation of this educational re-form project has achieved very good results in enhancing students' core biological literacy,bioinformatics skills,research thinking,and innovation abilities.At the same time,the application of this technology can significantly improve the quality of experimental teaching,providing new ideas and practical refer-ences for promoting the reform and innovation of National First-Class Courses.

Purpose To develop a nomogram combined radiomics,clinical and CT morphographic features for the prediction of the acquired T790M mutation in patients with advanced non-small cell lung cancer with resistance after the duration of first-line epidermal growth factor receptor(EGFR)-tyrosine kinase inhibitor(TKI)treatment.Materials and Methods The chest CT images and clinical data of 116 patients who underwent secondary biopsy after disease progression during first-line TKI treatment for advanced non-small cell lung cancer with EGFR sensitive mutations,from December 2016 to December 2022 in Shaoxing People's Hospital were retrospectively analyzed.All patients were randomly divided into a training cohort(n=81)and a validation cohort(n=35),at a ratio of 7︰3.The regions of interest of the lesion were delineated,and radiomics features were extracted.Feature selection was performed via the maximum relevance minimum redundancy and the least absolute shrinkage and selection operator methods.Clinical features were selected via univariate and multivariate Logistic regression.A nomogram combined clinical and radiomics features was subsequently constructed.The predictive ability of the combined model was evaluated via receiver operator characteristic curve,calibration curves and decision curve analysis.Results The progression-free survival of first-line EGFR-TKIs(OR=1.086,P=0.041),initial EGFR profile(OR=0.280,P=0.021),vascular convergence(OR=4.050,P=0.036)and air bronchogram(OR=3.265,P=0.030)were highly correlated with the acquired T790M mutation.The combined model demonstrated good predictive performance for acquired T790M mutation,both in the training set(AUC=0.867,95%CI 0.790-0.944)and the validation set(AUC=0.895,95%CI 0.786-1.000).The calibration curve showed good calibration power,and the decision curve analysis demonstrated a significant net benefit.Conclusion A radiomics-clinical nomogram based on CT radiomics has the potential to predict acquired T790M mutation and could be a complementary tool for T790M mutation detection after resistance to first-or second-generation EGFR-TKIs.

Objective:To explore the level of mental health literacy and its relationship with symptoms of de-pression,anxiety,and insomnia in young adults.Methods:A total of 10 273 young adults aged 18-23 in Guang-zhou were selected and assessed with the National Mental Health Literacy Questionnaire,Patient Health Question-naire-9(PHQ-9),General Anxiety Disorder 7-Item Scale(GAD-7)and Insomnia Severity Index(ISI).Results:Totally 1 137(11.1％)participants met the criteria for adequate mental health literacy,2 758(26.8％)participants were with symptoms of depression,1 355(13.2％)with symptoms of anxiety and 4 936(48.0％)experiencing symptoms of insomnia.Logistic regression analyses showed that after adjusting for other factors,substandard mental health literacy was an independent risk factor for symptoms of depression,anxiety and insomnia(OR=2.08,1.93,1.49;95％CI:1.74-2.48,1.52-2.46,1.31-1.70).Conclusion:The level of mental health literacy in young a-dults is insufficient,and the lack of mental health literacy is significantly associated with the symptoms of depres-sion,anxiety and insomnia.

Demethyleneberberine(DMB),one of the active me-tabolite of berberine,displays versatile pharmacological proper-ties.Numerous studies indicate diverse biological activities,such as actions against infection,inflammation,fibrosis,cancer cells,blood lipids,etc.It presents considerable potential in treating neurodegenerative diseases.This article provides a con-cise review of the pharmacological actions and mechanisms made in demethyleneberberine research to facilitate a deeper under-standing of its pharmacology and provide a scientific foundation for its prudent development.