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.

Homeostasis and energy and substance metabolism reprogramming shape various tumor microenvironment to sustain cancer stemness, self-plasticity and treatment resistance. Aiming at them, a lipid-based pharmaceutical loaded with CaO₂ and glucose oxidase (GOx) (LipoCaO₂/GOx, LCG) has been obtained to disrupt calcium homeostasis and interfere with glycometabolism. The loaded GOx can decompose glucose into H₂O₂ and gluconic acid, thus competing with anaerobic glycolysis to hamper lactic acid (LA) secretion. The obtained gluconic acid further deprives CaO₂ to produce H₂O₂ and release Ca²⁺, disrupting Ca²⁺ homeostasis, which synergizes with GOx-mediated glycometabolism interference to deplete glutathione (GSH) and yield reactive oxygen species (ROS). Systematical experiments reveal that these sequential multifaceted events unlocked by Ca²⁺ homeostasis disruption and glycometabolism interference, ROS production and LA inhibition, successfully enhance cancer immunogenic deaths of breast cancer cells, hamper regulatory T cells (Tregs) infiltration and promote CD8⁺ T recruitment, which receives a considerably-inhibited outcome against breast cancer progression. Collectively, this calcium homeostasis disruption glycometabolism interference strategy effectively combines ion interference therapy with starvation therapy to eventually evoke an effective anti-tumor immune environment, which represents in the field of biomedical research.

Total knee arthroplasty(TKA)is mainly used for the treatment of advanced knee osteoarthritis.Accurate preoperative planning and rapid prosthesis recognition are essential for smooth operation and postoperative rehabilitation.However,manual prosthesis recognition rely on doctors'experience,easily leading to misdiagnosis or missed diagnosis.Recent years,artificial intelligence(AI)had been integrated with medical imaging,represented by machine learning(ML)and its branch deep learning(DL),and displayed relatively powerful auxiliary functions in TKA.The research status and application progresses of AI combined with imaging in TKA were reviewed in this article.

Objective To observe the value of enhanced MRI"strawberry sign"for differentiating solitary predominantly cystic brain metastasis and glioma.Methods Thirty-four patients with solitary predominantly cystic(cystic proportion greater than 50％)brain metastasis(metastasis group)and 43 with solitary predominantly cystic glioma(glioma group)were retrospectively enrolled,and the value of"strawberry sign"showed on contrast enhanced T1WI(CE-T1WI)for differentiation was analyzed.Results The detection rate of"strawberry sign"in metastasis group was 44.12％(15/34),and the primary cancer was lung adenocarcinoma in 6 cases(6/15,40.00％),small cell lung cancer in 3 cases(3/15,20.00％),as well as lung squamous cell carcinoma,breast cancer,colon adenocarcinoma,endometrioid carcinoma,fallopian tube adenocarcinoma and rectal melanoma each in 1 case(1/15,6.67％).Meanwhile,the detection rate of"strawberry sign"in glioma group was 18.60％(8/43),and all were observed in WHO grade 4 gliomas.The detection rate of"strawberry sign"in metastasis group was higher than that in glioma group,which was not related to patients' gender(P=0.442).The sensitivity,specificity,accuracy,positive predictive value and negative predictive value of"strawberry sign"for differentiating solitary predominantly cystic brain metastasis and glioma was 44.12％,81.40％,64.94％,65.22％and 64.81％,respectively.Conclusion"Strawberry sign"showed on CE-T1WI was helpful for differentiating solitary predominantly cystic brain metastasis and glioma.

Objective:To evaluate the accuracy and feasibility of an automated scanning and uptake system to assist pathologists with hu-man epidermal growth factor receptor 2(HER2)FISH interpretation.Methods:HER2 gene amplification is detected using FISH,and"result interpretation by independent pathologists"is regarded as the"gold standard."The consistency of"human-machine dialogue results"(use of a CytoVision* system combined with manual interpretation)and"CytoVision*-based automated interpretation"with the"gold standard"was assessed.Results:Consistency between"human-machine dialogue results"and the"gold standard"can surpass 91%,with the former method saving up to 50%of the manual operation time.The tendency of each cell nucleus's HER2 copy number to be"underestimated"is the main reason for the low sensitivity observed in cases with low copy number amplification and HER2 heterogeneous expression cases in"human-machine dialogue interpretation."Conclusions:Automatic FISH image analysis and uptake systems simulate the process of manu-ally interpreted cell selection,ensure random cell selection,and improve work efficiency.With its accurate selection of the hybridization re-gion and"human-computer dialogue,"the system is expected to"replace"interpretation by independent pathologists.

Pre-eruptive intra-coronal resorption is a rare intra-coronal dental disorder that typically emerges prior to tooth eruption. The radiological images show a well-demarcated low-density translucent area within the dentin adjacent to the enamel-dentin junction. This lesion is often accidentally discovered by radiological examinations. It may even progress to enamel and the dentin of the root. Currently, there is a considerable variation in the reported prevalence of this lesion. Its pathogenic mechanisms have not been fully revealed, and there is lack of systematic clinical management strategies. This article provides a comprehensive review of the pre-eruptive intra-coronal resorption, and aims as a reference for dentists to improve their understanding of this lesion, and to facilitate its early detection, early diagnosis and timely treatment.

Background: s/Aims: Non-invasive models stratifying clinically significant portal hypertension (CSPH) are limited. Herein, we developed a new non-invasive model for predicting CSPH in patients with compensated cirrhosis and investigated whether carvedilol can prevent hepatic decompensation in patients with high-risk CSPH stratified using the new model. Methods: Non-invasive risk factors of CSPH were identified via systematic review and meta-analysis of studies involving patients with hepatic venous pressure gradient (HVPG). A new non-invasive model was validated for various performance aspects in three cohorts, i.e., a multicenter HVPG cohort, a follow-up cohort, and a carvediloltreating cohort. Results: In the meta-analysis with six studies (n=819), liver stiffness measurement and platelet count were identified as independent risk factors for CSPH and were used to develop the new “CSPH risk” model. In the HVPG cohort (n=151), the new model accurately predicted CSPH with cutoff values of 0 and –0.68 for ruling in and out CSPH, respectively. In the follow-up cohort (n=1,102), the cumulative incidences of decompensation events significantly differed using the cutoff values of <–0.68 (low-risk), –0.68 to 0 (medium-risk), and >0 (high-risk). In the carvediloltreated cohort, patients with high-risk CSPH treated with carvedilol (n=81) had lower rates of decompensation events than non-selective beta-blockers untreated patients with high-risk CSPH (n=613 before propensity score matching [PSM], n=162 after PSM). Conclusions Treatment with carvedilol significantly reduces the risk of hepatic decompensation in patients with high-risk CSPH stratified by the new model.

Background: s/Aims: Non-invasive models stratifying clinically significant portal hypertension (CSPH) are limited. Herein, we developed a new non-invasive model for predicting CSPH in patients with compensated cirrhosis and investigated whether carvedilol can prevent hepatic decompensation in patients with high-risk CSPH stratified using the new model. Methods: Non-invasive risk factors of CSPH were identified via systematic review and meta-analysis of studies involving patients with hepatic venous pressure gradient (HVPG). A new non-invasive model was validated for various performance aspects in three cohorts, i.e., a multicenter HVPG cohort, a follow-up cohort, and a carvediloltreating cohort. Results: In the meta-analysis with six studies (n=819), liver stiffness measurement and platelet count were identified as independent risk factors for CSPH and were used to develop the new “CSPH risk” model. In the HVPG cohort (n=151), the new model accurately predicted CSPH with cutoff values of 0 and –0.68 for ruling in and out CSPH, respectively. In the follow-up cohort (n=1,102), the cumulative incidences of decompensation events significantly differed using the cutoff values of <–0.68 (low-risk), –0.68 to 0 (medium-risk), and >0 (high-risk). In the carvediloltreated cohort, patients with high-risk CSPH treated with carvedilol (n=81) had lower rates of decompensation events than non-selective beta-blockers untreated patients with high-risk CSPH (n=613 before propensity score matching [PSM], n=162 after PSM). Conclusions Treatment with carvedilol significantly reduces the risk of hepatic decompensation in patients with high-risk CSPH stratified by the new model.

Background: s/Aims: Non-invasive models stratifying clinically significant portal hypertension (CSPH) are limited. Herein, we developed a new non-invasive model for predicting CSPH in patients with compensated cirrhosis and investigated whether carvedilol can prevent hepatic decompensation in patients with high-risk CSPH stratified using the new model. Methods: Non-invasive risk factors of CSPH were identified via systematic review and meta-analysis of studies involving patients with hepatic venous pressure gradient (HVPG). A new non-invasive model was validated for various performance aspects in three cohorts, i.e., a multicenter HVPG cohort, a follow-up cohort, and a carvediloltreating cohort. Results: In the meta-analysis with six studies (n=819), liver stiffness measurement and platelet count were identified as independent risk factors for CSPH and were used to develop the new “CSPH risk” model. In the HVPG cohort (n=151), the new model accurately predicted CSPH with cutoff values of 0 and –0.68 for ruling in and out CSPH, respectively. In the follow-up cohort (n=1,102), the cumulative incidences of decompensation events significantly differed using the cutoff values of <–0.68 (low-risk), –0.68 to 0 (medium-risk), and >0 (high-risk). In the carvediloltreated cohort, patients with high-risk CSPH treated with carvedilol (n=81) had lower rates of decompensation events than non-selective beta-blockers untreated patients with high-risk CSPH (n=613 before propensity score matching [PSM], n=162 after PSM). Conclusions Treatment with carvedilol significantly reduces the risk of hepatic decompensation in patients with high-risk CSPH stratified by the new model.

BACKGROUND:Finite element analysis is an advanced computer-based engineering technique that uses mathematical approximations to simulate the human body.This method accurately reflects the biomechanical characteristics within the knee,providing a powerful tool for understanding knee disease pathogenesis,optimizing surgical protocols,and developing new implant materials.OBJECTIVE:To review the establishment of finite element modelling of the knee joint and its application in the study of knee joint diseases,and look forward to the future development trend.METHODS:The first author searched the PubMed and EI databases in April 2024 by applying a computer with English search terms"finite element analysis,FEA,knee joint,finite element model,knee biomechanics,knee osteoarthritis,knee prosthesis,knee ligaments,meniscus"and searched CNKI and WanFang databases with Chinese search terms"finite element analysis,finite element model,knee joint,biomechanics,osteoarthritis,computational model,knee prosthesis,knee ligament,meniscus."Finally,75 papers were included in the analysis.RESULTS AND CONCLUSION:(1)Finite element analysis method uses medical imaging data to obtain a three-dimensional human model,simplifies the complex human joint structure into finite and interconnected units,and visually displays the internal stress distribution of the knee joint by applying external loads to the model.(2)The researchers deeply study the internal stress and strain distribution of the knee joint under different working conditions by means of finite element analysis,revealing the overloading of the articular cartilage and the decrease of load in some areas when the balance of the internal load distribution of the knee joint is changed,and that such long-term abnormal stresses cause deformation,wear and tear,and eventual loss of cartilage,which is crucial for understanding how biomechanical factors cause degenerative changes of the knee joint.(3)The effect of physical therapy methods such as Tai Chi and gait adjustment in patients with osteoarthritis of the knee joint was evaluated by finite element analysis,and the results showed that these treatments reduced the overloading of the cartilage,which provided a scientific theoretical basis for clinical treatment.(4)Clinicians are able to optimize surgical treatment strategies by performing three-dimensional reconstruction,data measurement,and simulation of surgery before surgery through finite element analysis.Furthermore,the mechanical characteristics of different prostheses can be simulated to improve the shape,material,and fixation of the prostheses,reduce patient complications,and improve patient outcomes.(5)The combination of artificial intelligence and finite element analysis makes the construction of finite element models more accurate and easy to operate,greatly contributing to the efficiency of clinicians'medical practice and patient outcomes.(6)Finite element analysis is only a digital simulation,which is still somewhat different from the real physical state.