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 Patients with lung cancer often experience interrelated symptoms such as fatigue, pain, and sleep disturbances during chemotherapy, forming a core symptom cluster that significantly impacts daily functioning, treatment experience, and quality of life. Currently, evidence on non-pharmacological management of this core symptom cluster is scattered, and there is a lack of standardized, patient- and caregiver-friendly guidance tools that are easy to understand and implement. This patient guideline aims to provide decision support for self-management of the core symptom cluster in patients undergoing chemotherapy for lung cancer. Methods Guided by evidence-based methodology, qualitative interviews and on-site questionnaire surveys were conducted to identify the health-related needs of stakeholders. Domestic and international guideline websites, relevant professional association websites, and comprehensive databases were systematically searched according to the inclusion and exclusion criteria. The included literature was evaluated for quality, and recommendations were extracted, integrated, and graded to form a summary of the best evidence. Based on the identified health issues and evidence summary, preliminary recommendations for the patient guideline were developed. The recommendations were then revised through two rounds of Delphi expert consultation, and consensus was reached on the strength of recommendations, forming the final version of the patient guideline. Results The patient guideline covered four major sections: screening and assessment of core symptom clusters, daily management, intervention measures, and timing of referral. The core content included nine aspects: screening of core symptom clusters, assessment, lifestyle habits, dietary interventions, sleep environment, exercise interventions, psychological interventions, Traditional Chinese Medicine interventions, and referral indications. In total, the guidelines addressed 16 health issues and provided 25 recommendations. Conclusion The development process of this patient guideline is rigorous and fully considered the needs of patients. Expert review concludes that the recommendations are highly feasible and appropriate.

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.

This study aims to investigate the antipyretic effects and mechanisms of ethanol extracts from Arisaematis Rhizoma fermented with bile from different sources on a rat model of fever induced by a dry-yeast suspension. The rat model of fever was established by subcutaneous injection of 20% dry-yeast suspension into the rat back. The levels of tumor necrosis factor-α(TNF-α), interleukin-1β(IL-1β), interleukin-6(IL-6) in the serum, as well as prostaglandin E_2(PGE_2) and cyclic adenosine monophosphate(cAMP) in the hypothalamus, were determined by ELISA. Metabolomics analysis was then performed on serum and hypothalamus samples based on UPLC-Q-TOF MS to explore the potential biomarkers and metabolic pathways. The results showed that the body temperatures of rats significantly rose 4 h after modeling. After oral administration of high-dose ethanol extracts of Arisaematis Rhizoma fermented with bovine bile(NCH) and porcine bile(ZCH), the body temperatures of rats declined(P<0.05), and the NCH group showed better antipyretic effect than the ZCH group. Additionally, compared with the model group, the NCH and ZCH groups showed lowered levels of IL-1β, IL-6, TNF-α, PGE_2, and cAMP(P<0.01). The results of serum and hypothalamus metabolomics analysis indicated that both NCH and ZCH exerted antipyretic effects by regulating phenylalanine metabolism, sphingolipid metabolism, arachidonic acid metabolism, and steroid hormone biosynthesis. Collectively, both NCH and ZCH can play an obvious antipyretic role in the rat model of dry yeast-induced fever, and the underlying mechanism might be closely associated with inhibiting inflammation and regulating metabolic disorders. Moreover, NCH demonstrates better antipyretic effect.
Animals ; Rats ; Male ; Fermentation ; Rats, Sprague-Dawley ; Rhizome/metabolism* ; Drugs, Chinese Herbal/chemistry* ; Bile/chemistry* ; Antipyretics/chemistry* ; Fever/metabolism* ; Cattle ; Swine ; Tumor Necrosis Factor-alpha/metabolism* ; Ethanol/chemistry* ; Interleukin-6/blood* ; Interleukin-1beta/blood*

Based on the high-fat diet-induced hyperlipidemia rat model, this study aimed to evaluate the lipid-lowering effect of Arisaema Cum Bile and explore its mechanisms, providing experimental evidence for its clinical application. Biochemical analysis was used to detect serum levels of alanine aminotransferase(ALT), aspartate aminotransferase(AST), high-density lipoprotein cholesterol(HDL-C), low-density lipoprotein cholesterol(LDL-C), triglycerides(TG), and total cholesterol(TC) to assess the lipid-lowering activity of Arisaema Cum Bile. Additionally, 16S rDNA sequencing and metabolomics techniques were employed to jointly elucidate the lipid-lowering mechanisms of Arisaema Cum Bile. The experimental results showed that high-dose Arisaema Cum Bile(PBA-H) significantly reduced serum ALT, AST, LDL-C, TG, and TC levels(P<0.01), and significantly increased HDL-C levels(P<0.01). The effect was similar to that of fenofibrate, with no significant difference. Furthermore, Arisaema Cum Bile significantly alleviated hepatocyte ballooning and mitigated fatty degeneration in liver tissues. As indicated by 16S rDNA sequencing results, PBA-H significantly enhanced both alpha and beta diversity of the gut microbiota in the model rats, notably increasing the relative abundance of Akkermansia and Subdoligranulum species(P<0.01). Liver metabolomics analysis revealed that PBA-H primarily regulated pathways involved in arachidonic acid metabolism, vitamin B_6 metabolism, and steroid biosynthesis. In summary, Arisaema Cum Bile significantly improved abnormal blood lipid levels and liver pathology induced by a high-fat diet, regulated hepatic metabolic disorders, and improved the abundance and structural composition of gut microbiota, thereby exerting its lipid-lowering effect. The findings of this study provide experimental evidence for the clinical application of Arisaema Cum Bile and the treatment of hyperlipidemia.
Animals ; Gastrointestinal Microbiome/drug effects* ; Rats ; Male ; Metabolomics ; Hyperlipidemias/microbiology* ; Drugs, Chinese Herbal/administration & dosage* ; Rats, Sprague-Dawley ; Hypolipidemic Agents/pharmacology* ; Liver/metabolism* ; Humans ; Alanine Transaminase/metabolism* ; Triglycerides/metabolism* ; Aspartate Aminotransferases/metabolism*

Objective To evaluate the value of postoperative radiotherapy (PORT) in patients with stage ⅢA-N2 non-small cell lung cancer who received complete resection and chemotherapy. Methods Patients with stage ⅢA-N2 non-small cell lung cancer who received complete resection and chemotherapy were chosen from the SEER Research Plus Database [17 Registries, November 2012 Submission (2000-2019)]. The patients were divided into a PORT group and a non-PORT group according to whether the PORT was used. To balance baseline characteristics between non-PORT and PORT groups, R software was used to conduct a propensity score matching (PSM) with a ratio of 1 : 1 and a matching tolerance of 0.01. Both the Cox regression analysis and Kaplan-Meier survival analysis were conducted to evaluate the value of PORT in terms of overall survival (OS) and disease-specific survival (DSS). Results In total, 2468 patients with stage ⅢA-N2 non-small cell lung cancer were enrolled, including 1078 males and 1390 females with a median age of 65 (58-71) years. There were 1336 patients in the PORT group, and 1132 patients in the non-PORT group. Cox regression analysis showed that PORT was not significantly associated with OS (multivariate analysis: HR=1.051, 95%CI 0.949-1.164, P=0.338) and DSS (multivariate analysis: HR=1.094, 95%CI 0.976-1.225, P=0.123). No statistical difference was found in the OS or DSS between non-PORT group and PORT group after PSM analysis (P＞0.05). Conclusion PORT does not have a survival benefit for patients with stage ⅢA-N2 non-small cell lung cancer who received complete resection and chemotherapy.