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 This study reports on the obligatory parasitism of water mites Arrenurus sp. on Anopheles messeae at the Manzhouli Port, Inner Mongolia, China. Methods Duing July 2024, a survey on the mosquito diversity was conducted at the Manzhouli Port. Captured mosquitoes and their ectoparasites were identified to species level. Results A total of 1840 adult mosquitoes were collected, representing species from three genera: Culex(Cx. modestus, Cx. pipiens pallens), Aedes(Ae. dorsalis, Ae. flavidorsalis, Ae. flavescens), and Anopheles (An. messeae). Among all the mosqutioes specimens,3 out of 150 captured An. messeae were found to carry ectoparasitic mites, with number of 2,4,27 mites separately. Morphological and molecular identification reached the same result as water mites(Hydrachnidiae, Hydracrina). COI gene sequence showed 94% similarity with the closest species Arrenurus truncatellus. Conlusions Literature review suggests water mites are host-specific parasitism of mosquito species and herein with the first record of Arrenurus sp. parasiting on An. Messeae in the most high-latitude region globally.

With the widespread adoption of low-dose CT screening and the extensive application of high-resolution CT, the detection rate of sub-centimeter lung nodules has significantly increased. How to scientifically manage these nodules while avoiding overtreatment and diagnostic delays has become an important clinical issue. Among them, lung nodules with a consolidation tumor ratio less than 0.25, dominated by ground-glass shadows, are particularly worthy of attention. The therapeutic challenge for this group is how to achieve precise and complete resection of nodules during surgery while maximizing the preservation of the patient's lung function. The "watershed topography map" is a new technology based on big data and artificial intelligence algorithms. This method uses Dicom data from conventional dose CT scans, combined with microscopic (22-24 levels) capillary network anatomical watershed features, to generate high-precision simulated natural segmentation planes of lung sub-segments through specific textures and forms. This technology forms fluorescent watershed boundaries on the lung surface, which highly fit the actual lung anatomical structure. By analyzing the adjacent relationship between the nodule and the watershed boundary, real-time, visually accurate positioning of the nodule can be achieved. This innovative technology provides a new solution for the intraoperative positioning and resection of lung nodules. This consensus was led by four major domestic societies, jointly with expert teams in related fields, oriented to clinical practical needs, referring to domestic and foreign guidelines and consensus, and finally formed after multiple rounds of consultation, discussion, and voting. The main content covers the theoretical basis of the "watershed topography map" technology, indications, operation procedures, surgical planning details, and postoperative evaluation standards, aiming to provide scientific guidance and exploration directions for clinical peers who are currently or plan to carry out lung nodule resection using the fluorescent microscope watershed analysis method.

In recent decades,the incidence of human papillomavirus(HPV)-associated oropharyngeal squamous cell carcinoma(OPSCC)has shown a marked increase.Significant changes have also occurred in the OPSCC diagnosis and treatment paradigm.Deter-mining HPV status prior to treatment is now essential,and radiotherapy/chemotherapy,immunotherapy,and minimally invasive surgical techniques have progressively emerged as key modalities for managing OPSCC.However,alongside these paradigm shifts,a comprehen-sive technical consensus guiding the entire diagnostic and therapeutic process for OPSCC patients is currently lacking.Given China's large population base and the rising incidence of OPSCC,an expert panel convened to develop a clinical technical consensus on OPSCC diagno-sis and management tailored to China's specific context.This consensus aims to further enhance and standardize understanding of OPSCC management techniques among relevant healthcare professionals.

Objective:To explore the effectiveness of generative artificial intelligence (GAI) -empowered interprofessional collaborative learning model in nursing psychology practical teaching for undergraduate nursing students.Methods:From November to December 2024, 109 second-year nursing students from the Xiangya School of Nursing, Central South University were selected as research subjects using convenience sampling. Two classes were randomly divided into an experimental group (55 students) and a control group (54 students) using a random draw method. Control group received a traditional teaching model based primarily on case discussion and scenario simulation teaching, while experimental group used the GAI-empowered interprofessional collaborative learning model on the basis of control group. The teaching effectiveness was evaluated using the Psychological Nursing Practice Competency Assessment Scale, Caring Ability Inventory (CAI), Readiness for Interprofessional Learning Scale (RIPLS), and Satisfaction Survey Questionnaire.Results:After teaching, experimental group's practical course assessment scores, CAI total scores, and scores in each dimension were all higher than those of control group, with statistically significant differences ( P<0.01). The RIPLS score of nursing students in experimental group after teaching was higher than that before teaching, and the difference was statistically significant ( P<0.01). 90.9% of nursing students supported the introduction of GAI into the classroom, and 90.9% of nursing students recognized the auxiliary role of GAI in achieving learning objectives, and the teaching satisfaction was 96.4%. Conclusions:This study validates the effectiveness of GAI-empowered interprofessional collaborative learning model in nursing psychology practical teaching for undergraduate nursing students, providing new ideas for nursing education reform.

Objective:To explore the application effect of a student-faculty collaborative dynamic question bank based on outcome based education (OBE) in the teaching of maternal and infant nursing.Methods:A total of 204 junior nursing undergraduates (Grade 2021 and Grade 2022) enrolled at Shantou University Medical College between September 2023 and January 2025 were selected by convenience sampling. Ninety-two students from Grade 2021 were assigned to the control group, and 112 students from Grade 2022 were assigned to the intervention group. The control group received the traditional teaching syllabus before the course, used the People's Medical Publishing House question bank for self-testing, and the original static self-constructed exam question bank in the Kaoyi system for assessment. The intervention group practiced and was assessed using a student-faculty collaborative dynamic question bank based on OBE. The intervention lasted for one semester. Course performance and satisfaction were compared between the two groups.Results:The number of questions in the bank increased from 347 to 2 058. After course completion, the intervention group had higher stage test scores, final exam scores, and overall course scores than the control group ( P<0.05). The intervention group also reported higher levels of overall course satisfaction, perceived personal ability improvement, and satisfaction with course content and instructors than the control group ( P<0.05) . Conclusions:The student-faculty collaborative dynamic question bank based on OBE can effectively improve students' academic performance and satisfaction, and is worthy of wider application.

Objective To elucidate the mechanisms of trauma-induced coagulopathy(TIC),clarify the specific pathogenic factors and pathophysiological processes,and discover the effective diagnostic indicators and therapeutic targets.Methods Transcriptomic data of traumatic hemorrhagic shock patients were obtained from the Gene Expression Omnibus(GEO)to identify differentially expressed genes(DEGs).Coagulation-related genes(CRGs)from the Kyoto Encyclopedia of Genes and Genomes(KEGG)were intersected with DEGs.Machine learning algorithms,including least absolute shrinkage and selection operator(LASSO)and random forest(RF),were applied to identify key genes.The CIBERSORT algorithm was used to analyze the correlation between key genes and immune cell infiltration.Through consensus clustering,subtype analysis was conducted on trauma patients to compare the infiltration of immune cells.A rat model of traumatic hemorrhagic shock was established to validate coagulation function and the expression of key genes.Results The dataset included samples from 17 healthy controls and 478 patients with traumatic hemorrhagic shock.A total of 6 315 DEGs were identified under the screening criterion of corrected P<0.05.Gene set enrichment analysis(GSEA)showed that the up-regulated DEGs were significantly enriched in the glucose metabolism pathway,while the down-regulated DEGs were enriched in the immune reaction-related pathways.Through cross-analysis of DEGs and CRGs,a total of 65 differentially expressed coagulation-related genes(DE-CRGs)were screened out.GO functional enrichment showed that these genes were mainly located in secreting granular membranes and platelet α-granules,and were involved in physiological processes such as blood coagulation,regulation of body fluid levels,and wound healing.KEGG pathway analysis revealed that these genes were significantly enriched in pathways such as platelet activation,complement and coagulation cascade reactions,Rap1 signaling pathway,and human cytomegalovirus infection.Six key DE-CRGs were identified through machine learning.Receiver operating characteristic(ROC)curve analysis indicated that these genes had good diagnostic efficacy.CIBERSORT analysis revealed a significant correlation between key genes and immune cell infiltration.Patients were classified into two subtypes based on the six key genes:subtype A was rich in CD8+T cells and activated NK cells,presented an immune-active state;subtype B was mainly composed of monocytes and resting NK cells,with insufficient activation of immune pathways.Animal experiments on rats showed that hemorrhagic shock can lead to coagulation dysfunction.The results of qRT-PCR further confirmed that the expression trend of key genes was consistent with the results of bioinformatics analysis.Conclusion In this study,through transcriptomics and machine learning methods,six key genes closely related to TIC were systematically screened out,namely GNA13,PIK3R3,ITGAM,MAPK14,PPP1CC and LYN,and their close connections with coagulation function and immune infiltration were revealed.Animal experiments have further verified the value of these genes as potential diagnostic and therapeutic targets.

Objective:Through the comparative analysis of the payment program for the therapeutic value of Traditional Chinese Medicine dominant diseases in 8 provinces,we found the shortcomings of the existing program and put forward the perfect policy suggestions.Methods:Comparative analysis of the core content of the programs in various places,the selection of disease types,efficacy evaluation indexes,the application of the payment link and the protection mechanism.Results:The fragmentation of existing programs is an obvious problem,the specific content settings of each item are quite different,reflecting differences in the understanding of the core content of the program,the existence of inconsistent understanding of Chinese medicine's therapeutic value,the failure to link the payment standard to the results of the therapeutic value evaluation,and the lack of recognition of the value of Chinese medicine's technical labor,among other problems.Conclusions:We can select disease types based on the prominent advantages of Traditional Chinese medicine,learn from the evaluation framework of western medicine value-based medical care,construct the evaluation system of Chinese medicine therapeutic value,and carry out"equal price"payment based on the"same effect"of health results,set performance indicators and payment standards in stages,realize the whole process management of disease,and enhance the Traditional Chinese Medicine's therapeutic value,and promote the development of Traditional Chinese Medicine inheritance and innovation.

Purpose To investigate the role of ultrasound-guided fine needle aspiration cytology(US-FNAC)in the diagnosis of breast cancer.Methods Fine needle aspiration cytology(FNAC)samples of 203 patients with breast cancer were selected and prepared by liquid-based cytology.The results of histopathology after neoadjuvant therapy were used as the gold standard to compare with the diagnostic results of FNAC samples,and then evaluated the diagnos-tic coincidence rate of FNAC samples.Results Of 203 axillary lymph node FNAC samples,111 cases were diagnosed as positive,38 cases were diagnosed as suspicious for positivity,and 54 cases were diagnosed as negative.The diag-nostic accuracy of FNAC was 85.2％,the sensitivity of FNAC was 98.0％,and the specificity of FNAC was 90.0％.Conclusion FNAC has high sensitivity and accuracy in the diagnosis of axillary lymph node metastasis.Sentinel lymph node biopsy is recommended for patients with negative FNAC but clinically and radiographically suspected lymph node metastasis.

Objective:To investigate the application of the conjugate gradient (CG) algorithm to treatment plan optimization for intensity-modulated brachytherapy (IMBT).Methods:The general Monte Carlo software TOPAS was utilized to simulate the 192Ir source of IMBT, and the unit dose contribution matrix was calculated. An objective function was established using the weighted least squares method and was solved using the CG algorithm to achieve optimized IMBT treatment plans. The optimization was validated using five clinical cervical cancer cases under modulation width 60°. The dose distributions of IMBT treatment plans under 45°, 60°, 90°, 120°, and 180° modulation widths were compared using the Wilcoxon test to determine the optimal IMBT treatment plan for cervical cancer treatment. Results:The CG algorithm successfully optimized IMBT treatment plans under modulation width 60° for five cases within 22.2 s on average. On the premise of sufficient target dose coverage, the average D2 cm 3 values of the bladder and rectum in IMBT treatment plans were 3.66 and 1.97 Gy, respectively, representing reductions of 0.54 and 0.69 Gy compared to traditional brachytherapy plans. For the five modulation widths, the D90% values of all IMBT treatment plans reached 6 Gy, without statistically significant differences ( P > 0.05). The average D2 cm 3 values of the bladder in IMBT treatment plans were significantly lower than those in the traditional brachytherapy plans( P<0.05), with modulation width 60° associated with the greatest reduction of 0.61 Gy. In contrast, the average D2 cm 3 values of the rectum under 45°, 60°, and 90° modulation widths decreased by 0.63, 0.54, and 0.45 Gy, respectively, compared to traditional plans, with statistically significant differences( P<0.05). Conclusions:The CG method enables rapid achievement of optimized IMBT treatment plans that meet clinical requirements, and modulation width 60° contributes to valid dosimetric optimization. This study can serve as a guide for the clinical implementation of IMBT.