With the aging population and the widespread implementation of lung cancer screening programs, an increasing number of elderly patients are undergoing curative lung resection. Due to diminished physiological reserve and complex comorbidities, this demographic exhibits a significantly higher incidence of severe perioperative complications (defined as Clavien-Dindo grade≥Ⅲ), which adversely affects both perioperative safety and long-term prognosis. In recent years, the research paradigm has shifted from univariate analysis toward multidimensional risk integration and the development of predictive models. This article systematically reviews the key risk factors for severe perioperative complications in elderly lung cancer patients, encompassing biological aging processes, frailty and sarcopenia, cardiopulmonary and renal functional reserves, inflammatory-immune and coagulation status, and perioperative interventions. Furthermore, it traces the evolution of risk assessment tools from traditional regression models to machine learning models that integrate multimodal data. The review also discusses common challenges in this field, including the standardization of outcome definitions, external validation, calibration assessment, and clinical translation. Future efforts should prioritize the deep integration of predictive tools with clinical decision support systems to establish a closed-loop care pathway from risk identification to stratified intervention, thereby effectively reducing complication rates and enhancing surgical outcomes for elderly lung cancer patients.

Objective To investigate the current status of multidisciplinary team (MDT) approaches in lung cancer management in China to provide a basis for the further promotion and enhancement of the MDT model. Methods From April 2023 to November 2023, physicians engaged in the clinical diagnosis and treatment of lung cancer were selected as the research objects. Questionnaires were distributed online and offline to investigate the cognition of MDT mode, MDT operating mode of their hospital, challenges faced, and direction of improvement. Descriptive analysis was conducted on the survey results. Results A total of 1137 questionnaires were collected, covering 816 general and cancer hospitals in 31 provinces and cities across the country. A total of 88.1% of doctors believe that the current MDT model is feasible, effective, and is of great value in benefiting patients’ survival, improving diagnosis and treatment efficiency, promoting diagnosis and treatment level and standardization, and increasing clinical research enrollment. Meanwhile, 83.5% of doctors state that their hospital has established an MDT team, including a chair and a coordinator, to ensure the smooth operation of the MDT and follow-up. The frequency of MDT is low and the time is long, usually 1–2 times a month and more than 30 minutes discussion time for each case. In addition, 50.1% of doctors indicate MDT cases account for less than 25% of annual diagnosis and treatment cases. Time coordination challenges and lack of administrative support and incentives are the difficulties affecting the operation of MDT. Having well-defined patient criteria is frequently cited as a key area for optimization that could remarkably enhance MDT efficiency. Conclusion After more than a decade of exploration and practice, the MDT model for lung cancer in China has been widely promoted and applied. Optimizing the selection, diagnosis, and treatment of MDT cases and providing administrative support and incentives are the key to enhancing the efficiency of MDT and expanding their benefits to a great number of patients with cancer.

The integration of artificial intelligence(AI)into medical practice has significantly impacted the field of cerebrovascular disease.AI algorithms are increasingly being employed to enhance the diagnosis and management of cerebrovascular conditions.However,the clinical application and accuracy of these AI tools require further rigorous evaluation.This review probes into the current applications of AI in diagnosis and decision-making in cerebrovascular disease,and explores the potential and challenges associated with their implementation.

The integration of artificial intelligence(AI)into medical practice has significantly impacted the field of cerebrovascular disease.AI algorithms are increasingly being employed to enhance the diagnosis and management of cerebrovascular conditions.However,the clinical application and accuracy of these AI tools require further rigorous evaluation.This review probes into the current applications of AI in diagnosis and decision-making in cerebrovascular disease,and explores the potential and challenges associated with their implementation.

Objective:To develop a nucleic acid detection system for Helicobacter pylori ( H. pylori) based on recombinase-aided isothermal amplification (RAA) and clustered regularly interspaced short palindromic repeats(CRISPR)/CRISPR-associated(Cas13a). Methods:Thirty strains of H. pylori, as well as two strains each of Escherichia coli, Staphylococcus aureus, Enterococcus faecalis, Enterobacter cloacae, and Klebsiella pneumoniae were collected from the Emergency General Hospital between 2021 and 2022. The specific primers and CRISPR RNA (crRNA) required for this newly established method were designed based on the conserved region of the ureC gene of H. pylori. Then, the primer pair that produced the least non-specific products was screened out using agarose gel electrophoresis, and the crRNA sequence with the highest cleavage efficiency was screened according to the fluorescence intensity produced by Cas13a cutting fluorescence probe. The RAA-Cas13a nucleic acid detection system was developed, and the limit of detection and the specificity of which were evaluated by detecting gradient dilutions of H. pylori ATCC 43504 genomic DNA and 5 different clinically common pathogens′ genomic DNA. The consistency with quantitative real-time PCR(qPCR) method was obtained by simultaneously detecting clinical strains using this method and established qPCR method. Two-tailed paired t-test was used to compare the fluorescence results between the two groups, and a P value less than 0.05 indicates a statistically significant difference. Results:The established RAA-Cas13a nucleic acid detection system could detect target DNA as low as 10 copies/μl. within 1 hour ( t=11.05, P<0.01), without cross-reaction with the other 5 clinically common strains. That method also showed good consistency compared to the qPCR method, the kappa coefficient=1. Conclusions:A method combining RAA with CRISPR-Cas13a for detecting H. pylori has been established, which can be used for rapid and sensitive identification of H. pylori infection.

METHODS: We retrospectively collected CT scan data from 276 patients with pathologically confirmed primary bone tumors from 4 medical centers in Guangdong Province between January, 2010 and August, 2021. A convolutional neural network (CNN) was employed as the deep learning architecture. The optimal baseline deep learning model (R-Net) was determined through transfer learning, and an optimized model (S-Net) was obtained through algorithmic improvements. Multivariate logistic regression analysis was used to screen the clinical features such as sex, age, mineralization location, and pathological fractures, which were then connected with the imaging features to construct the deep learning fusion model (SC-Net). The diagnostic performance of the SC-Net model and machine learning models were compared with radiologists' diagnoses, and their classification performance was evaluated using the area under the receiver operating characteristic curve (AUC) and F1 score. RESULTS: In the external test set, the fusion model (SC-Net) achieved the best performance with an AUC of 0.901 (95% CI: 0.803-1.00), an accuracy of 83.7% (95% CI: 69.3%-93.2%) and an F1 score of 0.857, and outperformed the S-Net model with an AUC of 0.818 (95% CI: 0.694-0.942), an accuracy of 76.7% (95% CI: 61.4%-88.2%), and an F1 score of 0.828. The overall classification performance of the fusion model (SC-Net) exceeded that of radiologists' diagnoses. CONCLUSIONS The deep learning fusion model based on multi-center CT images and clinical features is capable of accurate classification of osseous and chondroid matrix mineralization and may potentially improve the accuracy of clinical diagnoses of osteogenic versus chondrogenic primary bone tumors.
Humans ; Deep Learning ; Bone Neoplasms/diagnostic imaging* ; Retrospective Studies ; Tomography, X-Ray Computed/methods* ; Neural Networks, Computer ; Male ; Female ; ROC Curve ; Algorithms

Background: and Purpose Non-high-density lipoprotein cholesterol (non-HDL-C), which represents the total cholesterol content of all pro-atherogenic lipoproteins, has recently been included as a new target for lipid-lowering therapy in high-risk atherosclerotic patients in multiple guidelines. Herein, we aimed to explore the relationship between non-HDL-C level and the efficacy and safety of ticagrelor-aspirin versus clopidogrel-aspirin in preventing stroke recurrence. Methods: This study comprised a post hoc analysis of the CHANCE-2 (Ticagrelor or Clopidogrel in High-Risk Patients with Acute Nondisabling Cerebrovascular Events II) trial, from which 5,901 patients with complete data on non-HDL-C were included and categorized by median non-HDL-C levels, using a cutoff of 3.5 mmol/L. The primary efficacy and safety outcomes were recurrent stroke and severe or moderate bleeding within 90 days. Results: Ticagrelor-aspirin significantly reduced the risk of recurrent stroke in patients with low non-HDL-C (71 [4.8%] vs. 119 [7.7%]; adjusted hazard ratio [HR] 0.54; 95% confidence interval [CI], 0.40–0.74), but not in those with high non-HDL-C (107 [7.3%] vs. 108 [7.6%]; adjusted HR, 0.88; 95% CI, 0.67–1.16), compared with clopidogrel-aspirin (P for interaction=0.010). When analyzed as a continuous variable, the benefit of ticagrelor-aspirin for recurrent stroke decreased as non-HDL-C levels increased. No significant differences in the treatment assignments across the non-HDL-C groups were observed in terms of the rate of severe or moderate bleeding (5 [0.3%] vs. 8 [0.5%] in the low non-HDL-C group; 4 [0.3%] vs. 2 [0.1%] in the high non-HDL-C group; P for interaction=0.425). Conclusion CHANCE-2 participants with low non-HDL-C levels received more clinical benefit from ticagrelor-aspirin versus clopidogrel-aspirin compared to those with high non-HDL-C, following minor ischemic stroke or transient ischemic attack.

With the improved understanding of driver mutations in non-small cell lung cancer (NSCLC), expanding the targeted therapeutic options improved the survival and safety. However, responses to these agents are commonly temporary and incomplete. Moreover, even patients with the same oncogenic driver gene can respond diversely to the same agent. Furthermore, the therapeutic role of immune-checkpoint inhibitors (ICIs) in oncogene-driven NSCLC remains unclear. Therefore, this review aimed to classify the management of NSCLC with driver mutations based on the gene subtype, concomitant mutation, and dynamic alternation. Then, we provide an overview of the resistant mechanism of target therapy occurring in targeted alternations ("target-dependent resistance") and in the parallel and downstream pathways ("target-independent resistance"). Thirdly, we discuss the effectiveness of ICIs for NSCLC with driver mutations and the combined therapeutic approaches that might reverse the immunosuppressive tumor immune microenvironment. Finally, we listed the emerging treatment strategies for the new oncogenic alternations, and proposed the perspective of NSCLC with driver mutations. This review will guide clinicians to design tailored treatments for NSCLC with driver mutations.
Humans ; Carcinoma, Non-Small-Cell Lung/genetics* ; Lung Neoplasms/genetics* ; Mutation ; Tumor Microenvironment/genetics*

Objective:To explore the relationship between thyroid hormone sensitivity and obesity phenotype in people with normal thyroid function.Methods:In this retrospective study, 6155 euthyroid subjects who underwent a health check-up in the First Hospital of China Medical University between January 2017 and December 2018 were included. Participants were categorized into four obesity phenotypes according to body mass index and metabolic status. Thyroid Feedback Quantile-based Index(TFQI), Parametric TFQI, free triiodothyronine to free thyroxine ratio(FT 3/FT 4), and sum activity of peripheral deiodinases(SPINA-GD) were calculated to evaluate thyroid hormone sensitivity. Results:Compared with metabolically healthy non-obese(MHNO) phenotype, the subjects with metabolically healthy obese(MHO) or metabolically unhealthy obese(MUO) phenotype showed higher FT 3/FT 4ratio. Metabolically unhealthy non-obese(MUNO) and MUO subjects showed lower TFQI. After adjusting for confounders, FT 3/FT 4ratio was positively associated with MHO( OR 1.18, 95% CI 1.11-1.26) and MUO phenotype( OR 1.28, 95% CI 1.19-1.39). With 1 s increase of TFQI, the OR for MUNO phenotype was 0.77(95% CI 0.64-0.94). The results of Parametric TFQI and SPINA-GD were similar to TFQI and FT 3/FT 4ratio, respectively. Conclusion:In euthyroid individuals, thyroid hormone sensitivity was positively associated with increased risk for unhealthy obesity phenotypes.

Objective:To analyze the factors affecting the relative height change of the patella after primary robotic-assisted total knee arthroplasty (R-TKA).Methods:The data of 97 patients with osteoarthritis of the knee treated with R-TKA admitted from June 2021 to March 2022 were retrospectively analyzed, including 15 males and 82 females, aged 70.0±5.4 years (range, 58-80 years). The bone resection parameters of the robotic system were recorded intraoperatively, including the bone resection amount of the medial and lateral distal femur, medial and lateral posterior femoral condyle and medial and lateral tibial plateau. The Blackburne-Peel index (BPI) and Caton-Deschamps index (CDI) were used to measure the preoperative and postoperative relative patellar height on lateral radiographs, as well as the posterior tibial slope (PTS) and the change in patellar ligament length before and after surgery. The relationship between the change in relative patellar height and the variables of interest was analyzed using partial correlation and multiple linear regression.Results:The mean postoperative CDI was lower than preoperatively (0.79±0.15 vs. 0.91±0.13, t=9.69, P<0.001), and the percentage of patients with postoperative CDI<0.6 was higher than preoperatively (9.3% vs. 0; χ 2=12.92, P<0.001); the differences in mean postoperative BPI and percentage of patients with postoperative BPI<0.45 were not statistically significant compared to preoperatively (0.69±0.13 vs. 0.71±0.17, t=1.35, P=0.182; 11.3% vs. 17.5%, χ 2=1.50, P=0.220). The mean patellar ligament length on the first postoperative day was 2.29 mm shorter than preoperatively, there was a statistically significant difference ( t=5.90, P<0.001). Partial correlation analysis showed that the amount of patellar ligament length change was positively correlated with the amount of BPI and CDI change ( r=0.84, P<0.001; r=0.70, P<0.001), and the amount of PTS change and the mean distal femoral bone resection were negatively correlated with the amount of BPI ( r= -0.41, P<0.001; r=-0.32, P=0.002) and CDI ( r=-0.23, P=0.029; r=-0.25, P=0.017) change. In contrast, the amount of posterior femoral condyle bone resection and tibial plateau bone resection did not correlate with the change of BPI and CDI. Multiple linear regression analysis showed that the amount of patellar ligament length change, PTS change and the distal femoral bone resection were factors influencing the change of BPI ( β=0.03, P<0.001; β=-0.01, P<0.001; β=-0.02, P=0.021) and CDI ( β=0.02, P<0.001; β=-0.01, P=0.001; β=-0.02, P=0.008). Conclusion:Amount of patellar ligament length change, PTS change and the distal femoral bone resection are factors affecting the BPI. In order to obtain a better relative patellar height in the primary R-TKA, attention should be paid to the adjustment of the distal femoral bone resection and PTS, while taking the necessary measures to reduce the postoperative patellar ligament length changes.