Objective To explore the impact of number of positive regional lymph nodes (nPRLN) in N1 stage on the prognosis of non-small cell lung cancer (NSCLC) patients. Methods Patients with TxN1M0 stage NSCLC who underwent lobectomy and mediastinal lymph node dissection from 2010 to 2015 were screened from SEER database (17 Regs, 2022nov sub). The optimal cutoff value of nPRLN was determined using X-tile software, and patients were divided into 2 groups according to the cutoff value: a nPRLN≤optimal cutoff group and a nPRLN>optimal cutoff group. The influence of confounding factors was minimized by propensity score matching (PSM) at a ratio of 1 : 1. Kaplan-Meier curves and Cox proportional hazards models were used to evaluate overall survival (OS) and lung cancer-specific survival (LCSS) of patients. Results A total of 1316 patients with TxN1M0 stage NSCLC were included, including 662 males and 654 females, with a median age of 67 (60, 73) years. The optimal cutoff value of nPRLN was 3, with 1165 patients in the nPRLN≤3 group and 151 patients in the nPRLN>3 group. After PSM, there were 138 patients in each group. Regardless of before or after PSM, OS and LCSS of patients in the nPRLN≤3 group were superior to those in the nPRLN>3 group (P<0.001). N1 stage nPRLN>3 was an independent prognostic risk factor for OS [HR=1.52, 95%CI (1.22, 1.89), P<0.001] and LCSS [HR=1.72, 95%CI (1.36, 2.18), P<0.001]. Conclusion N1 stage nPRLN>3 is an independent prognostic risk factor for NSCLC patients in TxN1M0 stage, which may provide new evidence for future revision of TNM staging N1 stage subclassification.

Lung cancer is a leading cause of cancer-related morbidity and mortality worldwide. Coupled with the substantial workload, the clinical management of lung cancer is challenged by the critical need to efficiently and accurately process increasingly complex medical information. In recent years, large language models (LLMs) technology has undergone explosive development, demonstrating unique advantages in handling complex medical data by leveraging its powerful natural language processing capabilities, and its application value in the field of lung cancer diagnosis and treatment is continuously increasing. The paper systematically analyzes that the exceptional potential of LLMs in lung cancer auxiliary diagnosis, tumor feature extraction, automatic staging, progression/outcome analysis, treatment recommendations, medical documentation generation, and patient education. However, they face critical technical and ethical challenges including inconsistent performance in complex integrated decision-making (e.g., TNM staging, personalized treatment suggestions) and "black box" opacity issues, along with dilemmas such as training data biases, model hallucinations, data privacy concerns, and cross-lingual adaptation challenges ("data colonization"). Future directions should prioritize constructing high-quality multimodal corpora specific to lung cancer, developing interpretable and compliant specialized models, and achieving seamless integration with existing clinical workflows. Through dual drivers of technological innovation and ethical standardization, LLMs should be prudently advanced for holistic lung cancer management processes, ultimately promoting efficient, standardized, and personalized diagnosis and treatment practices.

Objective To systematically analyze the literature characteristics of Journal of Organ Transplantation since its inception. Methods Using the China National Knowledge Infrastructure (CNKI) academic journal full-text database as the data source, all articles published in the Journal of Organ Transplantation from January 2010 to August 2025 were retrieved. After excluding non-academic papers, a total of 1 568 research papers were included. R language 4.3.0, Bibliometrix package 3.2.1, and Citespace software were used to analyze the number of publications, publishing institutions, authors, keywords and other aspects. Results The number of publications in Journal of Organ Transplantation increased from an average of 82 articles per year in the early years after its inception to 113 articles per year in recent years, a growth of 37.8%. The geographical distribution of publishing institutions covers 32 provinces, cities and autonomous regions nationwide, mainly concentrated in the South China, East China and North China regions, and has now basically covered the central and western regions in recent years. The author collaboration network includes 45 authors distributed across 7 major collaboration clusters, forming a stable multi-level national research system centered on key university-affiliated hospitals. The high-frequency keywords are dominated by "liver transplantation" (425 times) and "kidney transplantation" (396 times). The theme evolution shows a clear three-stage characteristic: initially focusing on clinical technology application, deepening to immune mechanism exploration in the middle stage, and recently (since 2022) focusing on cutting-edge research areas such as xenotransplantation. Conclusions Journal of Organ Transplantation has witnessed the rapid development of China's organ transplantation cause, fully reflecting the research status and trends in China's organ transplantation field, and has provided an important platform for the future development and international cooperation in China's organ transplantation field.

Objective To compare the performance of 7 Chinese-developed GenAI models (DeepSeek, Wenxin Yiyan, Tongyi, Doubao, Kimi, Hunyuan, and iFLYTEK Spark) in responding to common diseases of the pediatric genitourinary system and to explore their potential applications.Methods A total of 80 questions covering basic disease information, diagnosis, treatment, and prognosis related to “hypospadias, hydronephrosis, and cryptorchidism” were developed as prompts.The models were tested under 3 modes:“quick thinking, ”“deep thinking, ” and “deep thinking + internet access.” The results were evaluated in a blinded review by two pediatric urologists.The responses were graded into 4 levels—A, B, C, and D—based on accuracy, completeness, and relevance.The proportions of responses rated as A and A+B were statistically analyzed and compared across the different models/modes.Results The “deep thinking” mode of DeepSeek demonstrated the best performance, with 88% of responses rated as A and 96% rated as A+B.DeepSeek (deep thinking + internet access), Doubao (quick thinking), Hunyuan (deep thinking), and Doubao (deep thinking + internet access) followed closely, also showing excellent response capabilities.Among the top 5 models/modes, significant differences were observed in the proportion of Alevel responses (P＜0.05), while differences in the proportion of A+B-level responses were not statistically significant (P＞0.05).Conclusion Certain Chinese-developed GenAI models, represented by DeepSeek (deep thinking), demonstrate significant application potential.However, they still cannot consistently provide recommendations rated at the A+B level (recommended grade or higher) with 100% accuracy.In clinical practice, both doctors and patients should view GenAI as an auxiliary tool for education and information acquisition rather than as a final decision-making authority.All information must be reviewed and confirmed by professional physicians.

Owing to its inherent advantages—such as being non-destructive, rapid, highly molecule-specific, and minimally interfered with by moisture—Raman spectroscopy has been widely adopted in the fields of skin barrier function assessment, monitoring the transdermal penetration of active cosmetic ingredients, and the identification and quality control of cosmetic products. Despite these strengths, the practical application of this technique faces a significant bottleneck: the strong fluorescence background generated by endogenous skin components and exogenous cosmetic additives. Endogenous skin substances, such as structural proteins (e.g., collagen and elastin), metabolic coenzymes (e.g., nicotinamide adenine dinucleotide), and pigments (e.g., melanin), together with exogenous cosmetic constituents like organic colorants, chemical sunscreens, and fragrances, often possess strong absorption and emission characteristics. When excited by lasers, these components produce a fluorescence background that can be 10⁶ to 10⁸ times stronger than the Raman scattering signals, effectively masking the inherently weak vibrational fingerprint information. In recent years, driven by the rapid development of optoelectronic hardware and artificial intelligence algorithms, fluorescence suppression strategies have evolved from isolated, single-method approaches into comprehensive, multi-level synergistic systems. These systems are categorized into three distinct tiers: sample preparation, signal acquisition, and data processing. At the sample preparation level, techniques such as photobleaching and surface-enhanced Raman spectroscopy (SERS) are employed to eliminate or bypass the generation of fluorescence at the source. At the signal acquisition level, instrumental improvements—including the use of long-wavelength near-infrared excitation (typically 785 nm or 1 064 nm), confocal spatial filtering, and shifted excitation Raman difference spectroscopy (SERDS)— are utilized to physically isolate Raman signals from the fluorescence background. Furthermore, at the data processing level, numerical baseline correction methods such as polynomial fitting, penalized least squares (e.g., airPLS, arPLS), wavelet transform, and derivative algorithms are increasingly integrated into the analytical pipeline to extract Raman spectral features from mixed signals without increasing hardware costs or acquisition time. This review provides a systematic categorization and critical evaluation of these fluorescence suppression methods, detailing their underlying principles, technical advantages, and inherent limitations in diverse experimental setups. By focusing on critical application scenarios—including skin barrier assessment, percutaneous absorption monitoring, the routine quality control of cosmetics, and the emerging field of portable on-site detection—this paper explores the current state of technique selection and optimization. Finally, the article discusses future development trends, emphasizing the necessity of constructing adaptive, tiered suppression strategies, developing intelligent and automated data processing algorithms, and promoting the integration of portable, multi-modal diagnostic devices. The objective of this review is to provide a comprehensive technical reference to facilitate the transition of Raman spectroscopy from a specialized laboratory tool into a routine, robust analytical platform for advancements in skin science and cosmetic research.

Objective To establish an individualized nomogram model and evaluate its efficacy to provide a possible evaluation basis for the prognosis of lower third and abdominal part of oesophageal adenocarcinoma (EAC). Methods Lower third and abdominal part of EAC patients from 2010 to 2015 were chosen from the SEER Research Plus Database (17 Regs, 2022nov sub). The patients were randomly allocated to the training cohort and the internal validation cohort with a ratio of 7∶3 using bootstrap resampling. The Cox proportional hazards regression analysis was used to determine significant contributors to overall survival (OS) in EAC patients, which would be elected to construct the nomogram prediction model. C-index, calibration curve and receiver operating characteristic (ROC) curve were performed to evaluate its efficacy. Finally, the efficacy to evaluate the OS of EAC patients was compared between the nomogram prediction model and TNM staging system. Results In total, 3945 patients with lower third and abdominal part of EAC were enrolled, including 3475 males and 470 females with a median age of 65 (57-72) years. The 2761 patients were allocated to the training cohort and the remaining 1184 patients to the internal validation cohort. In the training and the internal validation cohorts, the C-index of the nomogram model was 0.705 and 0.713, respectively. Meanwhile, the calibration curve also suggested that the nomogram model had a strong capability of predicting 1-, 3-, and 5-year OS rates of EAC patients. The nomogram also had a higher efficacy than the TNM staging system in predicting 1-, 3-, and 5-year OS rates of EAC patients. Conclusion This nomogram prediction model has a high efficiency for predicting OS in the patients with lower third and abdominal part of EAC, which is higher than that of the current TNM staging system.

Objective To establish an acute rejection model of cervical heart transplantation in mice and evaluate the survival and dynamic rejection process post-transplantation. Methods Mice were randomly divided into sham operation group (n=10), syngeneic transplantation group (n=21), and allogeneic transplantation group (n=65). Sham operation, syngeneic cervical heart transplantation, and allogeneic cervical heart transplantation were performed respectively. The survival of recipient mice and grafts, histopathological changes of graft tissues, subpopulations of splenic lymphocytes, and expression of inflammatory factors in serum and grafts were observed. Results The survival rate and graft survival rate of the sham operation group and syngeneic transplantation group were 100% at 7 days after surgery. In the allogeneic transplantation group, 5 cases failed and died on the first day after surgery. The survival rate at 7 days after surgery was 86%, and all surviving mice had grafts that stopped beating at 7 days after surgery. The allogeneic transplantation group showed significant rejection at 7 days after surgery, accompanied by tissue damage and CD8⁺ T cell infiltration. The proportion of CD8⁺ T cells in the spleen continued to rise post-operation, while the proportion of CD4⁺ T cells showed a downward trend. The expression of interferon-γ in serum and grafts peaked at 5 days after surgery, while the expression of tumor necrosis factor-α showed no statistical significance. Conclusions Acute rejection following heart transplantation in mice intensifies between 5 to 7 days after surgery, which may be a critical time window for immunological intervention.

OBJECTIVE To determine the contents of 15 components in Aurantii Fructus Immaturus from different origins （Citrus aurantium， C. junos， C. aurantium Linn.， C. sinensis Osb.， C. sinensis）， screen differential components， and provide references for the quality evaluation of Aurantii Fructus Immaturus. METHODS HPLC method was adopted to determine the contents of synephrine， N-methyltyramine， 5，7-dihydroxychromone-7-neohesperidoside， neoponcirin， narirutin， naringin， hesperidin， neohesperidin， naringenin， hesperetin， sinensetin， nobiletin， tangeretin， 5-demethylnobiletin， and auraptene in 46 batches of Aurantii Fructus Immaturus from different origins. The determination was performed on Waters Symmetry C18 column with mobile phase consisted of acetonitrile-0.1% formic acid （gradient elution） at the flow rate of 1.0 mL/min； column temperature was set at 40 ℃ ， detection wavelength was 284 nm， and sample injection volume was 5 μL. The differences between different origins of Aurantii Fructus Immaturus were analyzed by cluster analysis， principal component analysis （PCA） and orthogonal partial least squares-discriminant analysis （OPLS-DA）， and differential components were screened. RESULTS The linear relationships of the aforementioned 15 components were all good within the tested mass concentration ranges （all r＞0.999 0）. The RSDs for precision， stability （24 h）， and repeatability tests were all less than 2.00%. The average recovery rate ranged from 91.1% to 103.9% （all RSDs＜3.10%）. Cluster analysis， PCA， and OPLS-DA revealed that C. sinensis Osb. and C. sinensis were clustered into one category， while C. aurantium，C. junos and C. aurantium Linn. were clustered into another category. The variable importance projection values for neohesperidin， auraptene， naringin， neoponcirin， tangeretin， hesperidin， sinensetin， and 5，7-dihydroxychromone-7-neohesperidoside were all greater than 1. CONCLUSIONS In this study， the contents of 15 components in Aurantii Fructus Immaturus from different origins are determined， and 8 differential components， including neohesperidin， auraptene， naringin， and neoponcirin， are screened out.

Objective To assess the risk of human Spirometra mansoni infections and investigate the knowledge, attitude and practice (KAP) towards sparganosis mansoni among residents in Henan Province, so as to provide insights into formulation of the sparganosis mansoni control measures. Methods Qinling Village in Fugou County of Zhoukou City, Bali Village in Yancheng District of Luohe City, Duzhai Village in Puyang County of Puyang City and Doushan Village in Luoshan County of Xinyang City were sampled as survey sites in Henan Province from July to August 2023, and more than 40 frogs were sampled from ponds or streams in each survey site for detection of Sparganum mansoni infections. At least 150 residents were sampled using a cluster sampling method from each survey site, and the sero-prevalence of anti-S. mansoni IgG antibody was estimated. In addition, a questionnaire survey was conducted on the KAP towards sparganosis mansoni among participants, and the proportion of eligible KAP, rate of correct KAP and KAP scores were calculated. Results A total 229 frogs were collected from 4 survey sites in 2023, and the overall prevalence of S. mansoni infection was 4.37% (10/229) in frogs, with 7.75% (10/129) prevalence in wild frogs and 0 in farm-bred frogs. A questionnaire survey was performed among 649 residents sampled from 4 survey sites, and 649 serum samples were collected. The seroprevalence of anti-S.mansoni IgG antibody was 0.15% (1/649) and the overall proportion of eligible KAP was 23.73% (154/649) among participants. There were age- (χ² = 30.905, P = 0.000), educational level- (χ² = 41.011, P = 0.000), and occupation-specific proportions of eligible KAP among participants (χ² = 10.721, P = 0.005), and the proportion of eligible KAP decreased with age (χ² _trend = 22.717, P = 0.000) and increased with education levels (χ² _trend = 40.025, P = 0.000). The rates of correct KAP towards sparganosis mansoni were 40.81% (2 119/5 192), 96.66% (1 882/1 947) and 63.81% (3 727/5 841) (χ² = 1 913.731, P = 0.000) among residents, respectively. The rates of correct KAP towards sparganosis mansoni varied significantly among survey sites (χ² = 136.872, 42.347 and 255.157; all P values= 0.000, with the highest rate of correct knowledge (51.94%, 748/1 440) and practices (75.86%, 1 229/1 620) in Yancheng District of Luohe City and the highest rate of correct attitudes in Puyang County of Puyang City (99.11%, 446/450) (all P values< 0.05). Conclusions There is still a high transmission risk of sparganosis mansoni in Henan Province, and the KAP towards sparganosis mansoni is required to be improved among residents.

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.