The 2025 American Society of Clinical Oncology (ASCO) Annual Meeting was held in Chicago. At the meeting, researches on the treatment of epidermal growth factor receptor (EGFR)-mutant non-small cell lung cancer (NSCLC) once again took the spotlight. Combination therapy strategies have demonstrated the potential to overcome resistance to EGFR tyrosine kinase inhibitor (EGFR-TKI) and prolong survival. Meanwhile, progress has also been made in individualized treatment strategies for young patients and those with fibrotic interstitial lung disease. However, the complexity of resistance mechanisms, special treatment considerations for different populations, and the impact of socioeconomic factors on treatment accessibility remain challenges in the field of EGFR-mutant NSCLC treatment. In the future, it is necessary to further explore more effective treatment regimens and expand the accessibility of precision medicine to maximize patient benefits.

The 26th World Conference on Lung Cancer (WCLC) was held in Barcelona during September 6-9, 2025. As the world's largest and most influential academic meeting in the field of lung cancer, this year's congress unveiled long-term follow-up data from several pivotal studies and significant advances in novel therapeutic strategies. In the realm of targeted therapy, a next-generation combination strategy has been established as the new standard of care for the first-line treatment of patients with advanced epidermal growth factor receptor (EGFR)-mutant non-small cell lung cancer (NSCLC), demonstrating a significant improvement in overall survival. In immunotherapy, novel combination regimens have not only addressed the therapeutic challenge of acquired resistance to EGFR targeted therapies, but also shown clear long-term survival benefits in both the perioperative and locally advanced settings. These findings pave the way for shifting the treatment paradigm to earlier stages for patients with NSCLC. Antibody-drug conjugates have made remarkable strides in this field. They have shown outstanding efficacy in patients with specific resistance mutations and those with brain metastases, and have also demonstrated immense potential in treating patients with HER2-aberrant lung cancer and broader NSCLC populations. This offers new therapeutic options for patients with refractory lung cancer.However, significant challenges remain, including the heterogeneity of resistance mechanisms, the selection of optimal treatment regimens, and management strategies for special populations. Future research should focus on identifying novel precision biomarkers and optimizing therapeutic strategies to ultimately improve clinical outcomes for all patients with lung cancer.

ObjectiveTo explore the driving forces for oncology nurses’ participation in palliative care work， thereby providing a theoretical basis for the improvement of education and training， incentive mechanisms， and other aspects of the palliative care nursing staff. MethodsEmploying a qualitative research method， semi-structured interviews lasting 40-60 minutes were conducted with 14 nurses who had participated in palliative care work. The interview data were analyzed using the Colaizzi seven-step analysis method. ResultsInternal positive driving forces were job interest， empathy， and a sense of professional responsibility， while the negative was low psychological resilience. External positive driving forces included high work support， professional identity， mutual benefits for nurses and patients， and positive patient attitudes， whereas negative driving forces comprised busy routine clinical work， lack of a reward and incentive system， and bland or negative patient attitudes. ConclusionIt is essential to provide a flexible platform for the enhancement of nurses’ professional capabilities in palliative care， intensify the publicity of palliative care and death education； intervene and guide nurses’ negative emotions， improve and implement relevant incentive systems， and standardize the job recognition and scope of responsibilities of palliative care nurses.

Background: Dental pain is the most common chief complaint in oral healthcare and involves multiple disease types. An accurate diagnosis is crucial for treatment decisions. Large language models (LLMs) have great potential for medical diagnosis; however, their accuracy in dental pain diagnosis has not been systematically evaluated.The objectives of this study were to evaluate the diagnostic accuracy of four advanced LLMs for dental pain and analyze the key factors affecting diagnostic performance. Methods: A comparative study was conducted using 320 dental pain cases from authoritative sources covering eight disease categories. Four state-of-the-art LLMs were evaluated: DeepSeek-R1, ChatGPT-4o, Claude-3.5 Sonnet, and Gemini Pro 2.0. A blinded evaluation was performed by senior experts. The primary outcome was overall diagnostic accuracy, and the secondary outcomes included performance across disease types, complexity levels, and confidence levels. Results: DeepSeek-R1 achieved the highest diagnostic accuracy (83.8%), followed by ChatGPT-4o (82.8%), with no significant differences. Pulpal disease had the highest accuracy (88.1%), whereas neurological pain had the lowest (59.4%). The case complexity significantly affects the accuracy. The AI models demonstrated significant time advantages, representing a 94.2% faster diagnostic speed than human experts. Conclusion Latest-generation LLMs, particularly DeepSeek-R1 and ChatGPT-4o, achieved diagnostic levels approaching those of specialist physicians while providing substantial time efficiency gains. These findings support the application of AI in oral medicine by using confidence-based stratified implementation strategies.

In recent years, with the exploration and development of machine learning algorithms in the medical scenario, their potential for application in clinical diagnosis and treatment has been gradually explored. Image-based machine learning algorithms have made a lot of progress in urological cancer-related research, and have been proven to have a positive effect on the diagnosis, treatment and prognosis of urological cancer. Despite the great impact of machine learning algorithms clinically, they are still in the exploratory stage, with many deficiencies and limitations.

Objectives:To analyze whether there is a causal relationship between plasma proteins and the risk of coronary atherosclerosis(CAS)based on a two-sample Mendelian randomization(MR)analysis and to identify potential therapeutic targets for CAS.Methods:Single nucleotide polymorphisms(SNP)associated with plasma proteins from the UK Biobank Pharmacoproteomics Program(UKB-PPP)database were used as instrumental variables and outcome data were obtained from genome-wide association study databases.The Wald ratio method and inverse variance weighted(IVW)method in two-sample MR were employed as the primary approaches to assess the causal relationship between plasma proteins and CAS.Colocalization analysis was conducted as a sensitivity analysis to ensure the robustness of the MR findings.Results:A total of 132 plasma proteins were found to have causal associations with an increased risk of CAS.Colocalization analysis revealed that 12 plasma proteins shared genetic variants with CAS.Among them,Proprotein convertase subtilise/kexin type 9(PCSK9)(OR=1.23,95%CI:1.15-1.32,P<0.01)and neurocan(NCAN)(OR=1.35,95%CI:1.21-1.52,P<0.01)exhibited posterior probability of hypothesis4(PPH4)values<0.80 in the colocalization analysis,indicating strong support for colocalization and suggesting their potential as primary plasma protein drug targets for CAS.Conclusions:PCSK9 is associated with an increased risk of CAS and is confirmed as a therapeutic target for CAS.NCAN emerges as another potential therapeutic target for CAS.

With the increasing emphasis on patient-centered care and psychosocial support in modern healthcare systems,the role of medical social workers has evolved from auxiliary support to comprehensive participation.They serve as crucial liaisons for the healthcare service,delivering humanistic care for patients.Specially,their service has extended beyond traditional psycho-social counseling and doctor-patient communication to encompass whole-course care,community resource coordination,and pa-tient empowerment across multiple domains.As policies promoting humanistic healthcare and holistic well-being are progressively implemented,the scope of work for medical social workers has deepened and broadened in terms of role positioning and service demands.However,they also encounter significant challenges,including role ambiguity,interprofessional collaboration barriers,and insufficient institutional support mechanisms.To address these challenges,enhanced interprofessional education,evidence-based practice implementation,and policy-driven mechanisms are essential for strengthening their clinical competencies,delinea-ting professional boundaries,and optimizing resource allocation.These synergistic approaches collectively reinforce medical social workers'integral role in advancing humanistic care models while elevating their professional stature within the evolving healthcare ecosystem.

Objective:To study the risk factors and the molecular epidemiology characteristics for Carbapenem-resistant Enterobacteriaceae(CRE) colonization in neonatal inpatients in Shenzhen region, China, which provide reference for the prevention and control of clinical CRE infection.Methods:This study is a prospective case-control study.Anal samples from inpatients between January 2023 and December 2023 at Longgang Maternity and Child Institute of Shantou University Medical College and Shenzhen Children's Hospital were collected for screening CRE strain. Drug susceptibility test, modified Carbapenem Inactivation Method (mCIM) test, drug resistance-related gene sequencing and multilocus sequence typing (MLST) were performed for isolated CRE strains.Meanwhile, the clinical data were collected for analyzing the risk factors of CRE intestinal colonization by multivariate regression analysis.Results:A total of 1 517 patients were screened, 26 CRE(1.7%, 26/1 517) were identified which including 14 Escherichia coli(53.8%, 14/26), 11 Klebsiella pneumoniae(42.3%, 11/26), 1 Enterobacter cloacae(3.9%, 1/26). The predominant carbapenemase gene was New Delhi Metallo(NDM) (92.4%, 24/26), followed by Imipenem (IMP) (3.8%, 1/26) and Guiana extended spectrum gene (GES) (3.8%, 1/26).Among the carried NDM resistance genes, New Delhi Metallo 5 (NDM5) was the main one, accounting for 84.6% (22/26).The MLST typing of Escherichia coli was mainly Sequence Type 48 (ST48) (6/14), while that of Klebsiella pneumoniae was mainly Sequence Type 35 (ST35) (10/11). All CRE isolates were resistant to penicillin, penicillinase inhibitors, cephalosporins, ertapenem and imipenem.The resistance rates of Escherichia coli to amikacin, levofloxacin was 1/14, 4/14, respectively. All isolates of Klebsiella pneumoniae were sensitive to amikacin, and the resistance rate to levofloxacin is 1/11. Risk factors for CRE colonization include the older age, length of hospital stay, tracheal intubation, invasive respiration, lumbar puncture, Apgar <7 score, and exposure to antibiotics.Conclusions:NDM5 is the predominant resistant gene in CRE isolated from neonatal patients feces in Shenzhen region.It is necessary to strengthen the screening of CRE colonization in neonate for prevention and control of CRE infection.

To explore the application of sequential analysis in post-market safety dynamic surveillance of vaccines. Under the dynamic monitoring data of vaccines post-market approval, this research introduces the fundamental principles of maximizing sequential probability ratio test (MaxSPRT) and Bayesian sequential analysis, employing R software. Through an example of dynamic safety monitoring data of vaccines post-market approval, we analyze using the MaxSPRT and Bayesian sequential analysis. The MaxSPRT identified a safety signal in week 4 ( P<0.05), while Bayesian sequential analysis indicated that the 95% highest density interval for the RR value at week 4 is 1.13-3.27, suggesting the first appearance of a safety signal at week 4. The MaxSPRT and Bayesian sequential analysis effectively leverage continuously accumulating dynamic monitoring data, thereby serving as a valuable method for post-market safety surveillance of vaccines.

Objectives:To analyze whether there is a causal relationship between plasma proteins and the risk of coronary atherosclerosis(CAS)based on a two-sample Mendelian randomization(MR)analysis and to identify potential therapeutic targets for CAS.Methods:Single nucleotide polymorphisms(SNP)associated with plasma proteins from the UK Biobank Pharmacoproteomics Program(UKB-PPP)database were used as instrumental variables and outcome data were obtained from genome-wide association study databases.The Wald ratio method and inverse variance weighted(IVW)method in two-sample MR were employed as the primary approaches to assess the causal relationship between plasma proteins and CAS.Colocalization analysis was conducted as a sensitivity analysis to ensure the robustness of the MR findings.Results:A total of 132 plasma proteins were found to have causal associations with an increased risk of CAS.Colocalization analysis revealed that 12 plasma proteins shared genetic variants with CAS.Among them,Proprotein convertase subtilise/kexin type 9(PCSK9)(OR=1.23,95%CI:1.15-1.32,P<0.01)and neurocan(NCAN)(OR=1.35,95%CI:1.21-1.52,P<0.01)exhibited posterior probability of hypothesis4(PPH4)values<0.80 in the colocalization analysis,indicating strong support for colocalization and suggesting their potential as primary plasma protein drug targets for CAS.Conclusions:PCSK9 is associated with an increased risk of CAS and is confirmed as a therapeutic target for CAS.NCAN emerges as another potential therapeutic target for CAS.