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.

Objective To explore the changes in neural activity in patients with type 2 diabetes mellitus(T2DM)and their corre-lation with executive function,and to analyze the neural mechanisms underlying the decline in executive function in T2DM patients.Methods Thirty-one T2DM patients(T2DM group)and thirty-two healthy controls(HC)(HC group)matched for body mass index(BMI)underwent resting-state functional magnetic resonance imaging(rs-fMRI)scans and N-back task tests were included.Differ-ences in the amplitude of low-frequency fluctuation(ALFF),regional homogeneity(ReHo),and seed-based functional connectivity(FC)between the two groups were compared,and partial correlation analyses were performed between the difference results and N-back task performance.Results The T2DM group showed prolonged reaction time(RT)in the 1-back and 2-back tasks.T2DM patients exhibited increased ALFF in the bilateral caudate nucleus,left medial superior frontal gyrus,and right postcentral gyrus,as well as elevated ReHo in the right putamen.FC analysis revealed significant alterations in FC between the caudate nucleus,putamen,and multiple brain regions in T2DM patients,with some of these FC changes significantly correlated with RT and accuracy(ACC)in the N-back task.Conclusion The decline in executive function in T2DM patients may be associated with abnormal neural activity in brain regions such as the striatum,salience network,and frontoparietal control network.FC further decreases under increased cognitive load.These findings provide evidence for the study of the neural mechanisms of executive function impairment in T2DM patients.

Objective To investigate the effect of closed-chain exercise training on hemiplegic shoulder pain and shoulder joint sta-bility in stroke patients,and to explore the relationship between hemiplegic shoulder pain and shoulder joint sta-bility.Methods A total of 52 stroke patients with hemiplegic shoulder pain in Jiangsu Province Hospital from October,2020 to January,2023 were selected,and were randomly divided into control group(n=26)and experimental group(n=26).Both groups received conventional rehabilitation therapy,while the experimental group additionally under-went closed-chain exercise training for shoulder joint.Pain severity and motor function were assessed using Visu-al Analog Scale(VAS)and Fugl-Meyer Assessment-Upper Extremities(FMA-UE)before and after intervention.Musculoskeletal ultrasound was used to measure acromion-greater tuberosity distance(AGT),acromion-lesser tu-berosity distance(ALT),acromiohumeral distance(AHD)and supraspinatus thickness(SST)to evaluate shoulder joint stability.Correlation analysis was conducted on the improvements in shoulder pain and shoulder joint stabil-ity in the experimental group.Results Two cases in the control group and two in the experimental group dropped down.Both groups showed signifi-cant improvements in VAS and FMA-UE scores after intervention(|t|>5.214,P<0.001),and the scores im-proved more in the experimental group than in the control group(|t|>2.087,P<0.05).The experimental group also showed significant improvements in AGT,ALT,AHD and SST(|t|>4.187,P<0.001),with AGT,ALT and AHD being superior to those in the control group(|t|>2.155,P<0.05).The difference of VAS score in the exper-imental group was correlated with the difference of FMA-UE,AGT and ALT after intervention(r>0.434,P<0.05).Conclusion Closed-chain shoulder exercise training can significantly improve shoulder joint stability while alleviating shoulder pain,and effectively enhancing upper limb function in stroke patients with hemiplegic shoulder pain.Hemiplegic shoulder pain is correlated with shoulder joint stability.

Objective:To develop an artificial intelligence（AI）-based sequential ultrasound-magnetic resonance imaging（US-MRI）diagnostic strategy to optimize the imaging workflow for ovarian masses.Methods:A total of 1 120 patients with pathologically confirmed ovarian masses who underwent both preoperative pelvic ultrasound and MRI between January 2021 and December 2023 at Women's Hospital，Zhejiang University School of Medicine were retrospectively included. Patients were randomly divided into the training（ n=672）and internal test set（ n=448）at a ratio of 6∶4. An external test set（ n=128）was established at the Forth Affiliated Hospital of School of Medicine. Deep learning was used for automated segmentation of MRI lesions，followed by radiomic feature extraction and machine learning classification to construct both a US-MRI multimodal model and sequential US-MRI strategy. Diagnostic performance and potential healthcare cost-saving effects were evaluated across strategies. Results:In the internal test set（ n=448），the AI-based sequential US-MRI strategy achieved a F1 score of 0.863 and a diagnostic accuracy of 82.14%，with no significant difference compared to the US-MRI multi-modal model（ P>0.05）. The sequential strategy identified 82 cases（18.30%，82/448）of patients as low-risk true negatives during initial ultrasound screening，suggesting a potential to reduce the need for MRI examinations in future clinical practice. In the external test set（ n=128），the strategy achieved an F1 score of 0.800 and a confirmed diagnosis rate of 85.94%，with a theoretical reduction of 26.56%（34 cases）in MRI utilization while maintaining a diagnostic accuracy rate higher than that of the multi-modal model（82.18%）. Conclusions:The AI-based US-MRI sequential diagnostic strategy demonstrates favorable diagnostic accuracy while offering the potential to optimize MRI utilization. This approach may enhance the efficiency of imaging resource allocation and reduce healthcare burden in the management of ovarian masses.

A recombinant adenovirus(rAd)for expression of Mycobacterium tuberculosis(M.tb)c-di-AMP phosphodiesterase CnpB was constructed,and its induced humoral immune response was detected.The codon-optimized gene of M.tb CnpB was cloned into the adenoviral plasmid pcADV.The recombinant plasmid pcADV-CnpB was transfected into HEK293T cells,and expression was detected with Western blot.The recombinant plasmid pcADV-CnpB and the backbone plasmid were co-transfected into HEK293T cells to obtain the recombinant adenovirus rAd-CnpB.rAd-CnpB was amplified in HEK293T cells,and the target protein expression of rAd-CnpB was detected with Western blot and immunofluorescence.Mice were immunized with rAd-CnpB intranasally,and their sera and bronchoalveolar lavage fluid(BALF)were collected.ELISA was used to detect levels of antigen-specific antibodies.Restriction enzyme digestion and sequencing indicated that the recombinant plasmid pcADV-CnpB was successfully constructed and led to protein expression in eukaryotic cells.rAd-CnpB was packaged and produced in HEK293T cells.After amplification and purification,rAd-CnpB with a titer of 5.53×1010 PFU/mL was obtained.rAd-CnpB led to CnpB expression in HEK293T cells.Intranasal immunization with rAd-CnpB increased levels of IgG and secretory IgA in BALF and led to high levels of IgG in sera.rAd-CnpB,the recombinant adenovirus for expression of c-di-AMP phosphodiesterase CnpB was successfully constructed,and was found to induce antigen-specific humoral and mucosal immune responses through mucosal immunization.Thus,rAd-CnpB may be used in further research on new TB vaccine strategies.

Objective To investigate the value of gadolinium ethoxybenzyl diethylenetriamine pentaacetic acid(Gd-EOB-DTPA)enhanced MRI radiomics and signal intensity in hepatobiliary phase(HBP)in predicting the pathological differentiation degree of hep-atocellular carcinoma(HCC).Methods The clinical and imaging data of 224 patients pathologically confirmed with HCC were col-lected.All patients were randomly divided into test group(68 cases)and training group(156 cases)at a ratio of 7︰3.The ITK-SNAP software was used to delineate region of interest(ROI)on arterial phase(AP),portal venous phase(PVP)and HBP,the radiomics features of the tumor tissues were extracted and the radiomics models were established using the FAE software.Logistic regression analysis was used to determine the clinical independent predictors associated with the pathological differentiation degree of HCC and to construct clinical model and clinical-radiomics model.Receiver operating characteristic(ROC)curve was plotted for each model and the area under the curve(AUC)was calculated to compare the diagnostic efficacy of the models.Results Age,alpha-fetoprotein(AFP),and r-glutamyltransferase(r-GT)were independent risk factors for predicting the degree of pathological differentiation of HCC.The AUC of the clinical-radiomics model in the training group and test group were 0.825 and 0.779,respectively,which were higher than those of the radiomics model(0.812 and 0.771)and the clinical model(0.687 and 0.666).Conclusion Gd-EOB-DTPA enhanced MRI radiomics have certain value in predicting the degree of pathological differentiation of HCC,while the predictive value of the signal intensity on HBP and the signal intensity ratio(SIR)on HBP is limited.

Objective To investigate the effect of closed-chain exercise training on hemiplegic shoulder pain and shoulder joint sta-bility in stroke patients,and to explore the relationship between hemiplegic shoulder pain and shoulder joint sta-bility.Methods A total of 52 stroke patients with hemiplegic shoulder pain in Jiangsu Province Hospital from October,2020 to January,2023 were selected,and were randomly divided into control group(n=26)and experimental group(n=26).Both groups received conventional rehabilitation therapy,while the experimental group additionally under-went closed-chain exercise training for shoulder joint.Pain severity and motor function were assessed using Visu-al Analog Scale(VAS)and Fugl-Meyer Assessment-Upper Extremities(FMA-UE)before and after intervention.Musculoskeletal ultrasound was used to measure acromion-greater tuberosity distance(AGT),acromion-lesser tu-berosity distance(ALT),acromiohumeral distance(AHD)and supraspinatus thickness(SST)to evaluate shoulder joint stability.Correlation analysis was conducted on the improvements in shoulder pain and shoulder joint stabil-ity in the experimental group.Results Two cases in the control group and two in the experimental group dropped down.Both groups showed signifi-cant improvements in VAS and FMA-UE scores after intervention(|t|>5.214,P<0.001),and the scores im-proved more in the experimental group than in the control group(|t|>2.087,P<0.05).The experimental group also showed significant improvements in AGT,ALT,AHD and SST(|t|>4.187,P<0.001),with AGT,ALT and AHD being superior to those in the control group(|t|>2.155,P<0.05).The difference of VAS score in the exper-imental group was correlated with the difference of FMA-UE,AGT and ALT after intervention(r>0.434,P<0.05).Conclusion Closed-chain shoulder exercise training can significantly improve shoulder joint stability while alleviating shoulder pain,and effectively enhancing upper limb function in stroke patients with hemiplegic shoulder pain.Hemiplegic shoulder pain is correlated with shoulder joint stability.

Objective To investigate the value of gadolinium ethoxybenzyl diethylenetriamine pentaacetic acid(Gd-EOB-DTPA)enhanced MRI radiomics and signal intensity in hepatobiliary phase(HBP)in predicting the pathological differentiation degree of hep-atocellular carcinoma(HCC).Methods The clinical and imaging data of 224 patients pathologically confirmed with HCC were col-lected.All patients were randomly divided into test group(68 cases)and training group(156 cases)at a ratio of 7︰3.The ITK-SNAP software was used to delineate region of interest(ROI)on arterial phase(AP),portal venous phase(PVP)and HBP,the radiomics features of the tumor tissues were extracted and the radiomics models were established using the FAE software.Logistic regression analysis was used to determine the clinical independent predictors associated with the pathological differentiation degree of HCC and to construct clinical model and clinical-radiomics model.Receiver operating characteristic(ROC)curve was plotted for each model and the area under the curve(AUC)was calculated to compare the diagnostic efficacy of the models.Results Age,alpha-fetoprotein(AFP),and r-glutamyltransferase(r-GT)were independent risk factors for predicting the degree of pathological differentiation of HCC.The AUC of the clinical-radiomics model in the training group and test group were 0.825 and 0.779,respectively,which were higher than those of the radiomics model(0.812 and 0.771)and the clinical model(0.687 and 0.666).Conclusion Gd-EOB-DTPA enhanced MRI radiomics have certain value in predicting the degree of pathological differentiation of HCC,while the predictive value of the signal intensity on HBP and the signal intensity ratio(SIR)on HBP is limited.

Postoperative infection of internal fixation of closed fractures the lower limbs in adults represents a devastating complication, characterized by diagnostic challenges, prolonged treatment duration and high disability rates. Current management of these infections faces multiple challenges, such as difficulties in early accurate diagnosis, and various controversies about the treatment plan, leading to poor overall diagnosis and treatment results. To address these issues, based on evidence-based medicine and principles with emphasis on scientific rigor, clinical applicability and innovation, the Trauma Branch of the Chinese Medical Association, Orthopedic Branch of the Chinese Medical Doctor Association, Orthopedics Branch of the Chinese Medical Association, and Trauma Orthopedics and Polytrauma Group of the Resuscitation and Emergency Committee of the Chinese Medical Doctor Association have collaboratively organized a panel of relevant experts to develop the Guideline for diagnosis and treatment of infection after internal fixation of closed lower limb fractures in adults ( version 2025). The guideline proposed 10 recommendations, aiming to provide a foundation for standardized diagnosis and treatment of postoperative infection in adults with closed lower limb fractures.

Acute lateral ankle sprain (ALAS) is one of the most common sport injuries, with high incidence, recurrence and disability rates. Currently, exercise rehabilitation-based non-surgical treatment is the primary management approach for ALAS. However, there remain improper practices such as excessive immobilization or uncontrolled activity, which contribute to recurrent sprains and chronic ankle instability, significantly impairing patients′ athletic function and quality of life. To standardize the non-surgical management of ALAS, improve the cure rates, and reduce the recurrence and disability rates, Chinese Sports Rehabilitation Medicine Training Project of Chinese Medical Association, Foot and Ankle Basics and Orthopedics Group, Orthopedic Branch of Chinese Medical Doctor Association, and Sports Medicine Branch of Jiangsu Medical Association organized relevant experts to formulate Expert consensus on non-surgical treatment for acute lateral ankle sprain ( version 2025), following the principles of scientific vigor, practicality, and innovation. Thirteen recommendations were proposed for standardized treatment protocols across different healing phases, aiming to provide references for standard management of ALAS and improve the therapeutic outcomes.