The thyroid gland is the largest endocrine organ in the human body， and its dysfunction can cause varying degrees of liver injury， leading to liver failure in severe cases. Patients with hyperthyroidism have a relatively high incidence rate of liver dysfunction， manifesting as hepatocellular injury or cholestatic liver injury， while hypothyroidism is closely associated with metabolic dysfunction-associated fatty liver disease. Autoimmune thyroid diseases， including Hashimoto’s thyroiditis and subacute thyroiditis， are commonly comorbid with autoimmune liver disease. In addition， medications such as antithyroid drugs， amiodarone， and immune checkpoint inhibitors can cause severe liver injury through direct toxicity or immune-mediated mechanisms. Although significant progress has been achieved in related diagnosis and treatment techniques in recent years， there are still many challenges in pathogenesis， individualized treatment strategies， early warning， and prognostic evaluation. This article systematically reviews the research advances in liver injury associated with thyroid dysfunction and proposes the directions for future research， in order to provide guidance for clinical diagnosis and treatment.

Fresh-cut processing constitutes a pivotal technique in the origin processing of Chinese medicinal materials， with a long history documented in multiple materia medica. In recent years， it has garnered national policy support for its ability to prevent component loss and low processing efficiency associated with traditional drying-before-cutting methods. As of August 2025， 26 provinces and municipalities nationwide have cumulatively published 789 species for fresh-cut processing. Among these， 78 were included in the 2025 edition of the Pharmacopoeia of the People's Republic of China. However， the practice continues to face common challenges and difficulties， including ambiguous scientific understanding， fragmented standards， limited quality control approaches， and poor process stability. Based on this， this paper synthesises years of research findings to systematically elucidate the core mechanisms of fresh-cut processing. These encompass alterations to herbal tissue structure during cutting， post-processing changes in constituents， and physiological-biochemical processes such as plant stress responses and shifts in endogenous enzyme activity. It also summarises influencing factors， including inherent herbal properties， cutting timing and methods， and environmental conditions like temperature， humidity， and microbial presence. Based on this overview of fresh-cutting mechanisms， subsequent research should advance in four directions：Clarifying the scientific principles of fresh-cutting， overcoming technical bottlenecks， upgrading intelligent equipment， and establishing quality standards and evaluation systems. This study provides a theoretical foundation and scientific basis for future research on fresh-cutting in traditional Chinese medicine（TCM）， promoting its deeper practical application within the industry and contributing to the high-quality development of TCM industry and the modernization of TCM.

The circadian system maintains physiological homeostasis by precisely orchestrating 24-hour cycles of metabolic and cardiovascular functions. Circadian disruption has been substantiated as a contributor to cardiometabolic dysfunction, significantly elevating the risk of obesity, type 2 diabetes, hypertension, and cardiovascular diseases, thereby emerging as a crucial target for global chronic disease prevention. In 2025, the American Heart Association (AHA) issued a dedicated scientific statement that systematically reviewed the regulatory mechanisms of circadian rhythms and their intrinsic links to cardiometabolic health. It highlighted the pivotal role of modifiable behavioral factors, including light exposure, sleep-wake patterns, meal timing, and timing of physical activity, and proposed multi-faceted intervention strategies centered on chronotherapy. Considering the distinct circadian characteristics and chronic disease epidemiology in the Chinese population compared to Western counterparts, this article, grounded in the statement's core framework and incorporating evidence from localized Chinese research, provides a systematic interpretation of the biological basis of circadian rhythms, the pathophysiological mechanisms underlying disruption-related diseases, and potential intervention pathways. It particularly discusses the applicability of the statement's conclusions to clinical practice and public health strategies in China, aiming to offer an evidence-based reference for developing a cardiometabolic health support system tailored to the national context.

Diabetic retinopathy （DR） is a microvascular complication of diabetes and one of its most common complications. Prolonged hyperglycemia induces oxidative stress， inflammatory responses， apoptosis， and pathological angiogenesis， ultimately disrupting the blood-retinal barrier（BRB） and leading to visual impairment or even blindness. Recent studies show that the nuclear factor erythroid 2-related factor 2 （Nrf2） signaling pathway plays an important role in the development of DR's pathological changes. Meanwhile， Chinese herbal monomers have been shown to modulate the Nrf2 signaling pathway， thereby intervening in the development of DR. In terms of inhibiting oxidative stress， saponin compounds such as platycodin-D and ginsenoside Rb1 downregulate the expression of malondialdehyde （MDA）， thereby ameliorating retinal oxidative stress. Flavonoids such as total flavonoids from Pueraria lobata flower and puerarin upregulate the expression of superoxide dismutase （SOD） and glutathione peroxidase （GPx）， effectively clearing lipid peroxides. Regarding the suppression of inflammation， phenolic compounds like resveratrol and chlorogenic acid inhibit the nuclear factor kappa B （NF-κB） pathway， reducing the release of tumor necrosis factor-alpha （TNF-α） and mitigating inflammatory responses. In the context of inhibiting apoptosis， polysaccharides such as Polygonatum sibiricum polysaccharide and Angelica sinensis polysaccharide downregulate the expression of the pro-apoptotic protein Bcl-2-associated X protein （Bax） and suppress the activity of the executioner Caspase-3， thereby reducing the apoptosis rate. As for the inhibition of neovascularization， compounds including bilobalide and physcion significantly decrease the protein expression of vascular endothelial growth factor （VEGF）， leading to a reduction in retinal pathological angiogenesis. Furthermore， Chinese herbal compound prescriptions such as Tongluo Zhujing pills， Yiqi Huoxue Yangyin decoction， Qiming granules， and Danlou tablets can also intervene in the onset and progression of DR through the mechanisms described above. In summary， both Chinese herbal monomers and Chinese herbal compound prescriptions can modulate the Nrf2 signaling pathway to inhibit oxidative stress， alleviate inflammation， and participate in maintaining BRB integrity， suppressing retinal neovascularization， and preventing neurodegeneration， thereby delaying the progression of DR. Therefore， this paper reviews and summarizes recent studies at home and abroad on how traditional Chinese medicine （TCM） works to treat DR， and the relationship between the Nrf2 pathway and DR. It aims to provide research ideas for preventing and treating DR.

ObjectiveTo evaluate the antitumor activity of Periplaneta americana extract（PAE） against human-derived lung adenocarcinoma organoids（LUAD-PDOs） and to elucidate its potential mechanism based on transcriptomics. MethodsFresh tumor and adjacent normal tissues from patients with LUAD were collected to construct LUAD-PDOs and normal lung organoid（Nor-PDOs） models using 3D organoid culture technology. The effective intervention concentration of PAE was determined using the cell counting kit-8（CCK-8） assay. Experimental groups included the model group（LUAD-PDOs）， normal group， model administration group（LUAD-PDOs+PAE）， and normal administration group（Nor-PDOs+PAE）. Hematoxylin-eosin（HE） staining was used to observe the pathological structures of PDOs， immunohistochemistry（IHC） was performed to detect the expressions of the proliferation marker Ki-67 and lung adenocarcinoma differentiation markers cytokeratin-7（CK-7） and Napsin A， TUNEL staining was applied to detect cell apoptosis. RNA sequencing（RNA-Seq） was conducted to identify differentially expressed genes（DEGs）， followed by Gene Ontology（GO）， Kyoto Encyclopedia of Genes and Genomes（KEGG）， and Gene Set Enrichment Analysis（GSEA）， alongside protein-protein interaction（PPI） network analysis to screen core mechanisms. Finally， key targets were validated by integrating external database analysis with immunofluorescence（IF）. ResultsNor-PDOs and LUAD-PDOs that highly recapitulated the pathological characteristics of the primary tissues were successfully established. The CCK-8 assay determined that the effective intervention concentration of PAE was 16 g·L^-1. Morphological observation showed that Nor-PDOs exhibited lumen-forming structures， whereas LUAD-PDOs displayed dense， solid structures. CCK-8 and TUNEL assays revealed that， compared with the model group， PAE intervention inhibited the proliferation of LUAD-PDOs and promoted apoptosis in LUAD cells， while showing no significant effect on the viability of Nor-PDOs. Transcriptomic analysis identified 719 DEGs that were significantly reversed after PAE intervention（347 up-regulated and 372 down-regulated）（P<0.05）. GO enrichment analysis indicated that DEGs in the model administration group were significantly enriched in biological processes related to cell cycle regulation compared to the model group. KEGG pathway analysis revealed that PAE affected pathways related to proliferation and metabolism， including pathways in cancer and the p53 signaling pathway. GSEA further confirmed that PAE significantly enhanced the activity of the p53 signaling pathway（P<0.05）. PPI network analysis indicated that breast cancer type 1 susceptibility protein（BRCA1） and checkpoint kinase 1（CHEK1） were the core down-regulated targets in the p53 pathway. IF verified the high expression of BRCA1 and CHEK1 in LUAD-PDOs and their significant downregulation after PAE intervention（P<0.05）. Furthermore， survival analysis based on The Cancer Genome Atlas（TCGA） database indicated that low expression of BRCA1 and CHEK1 was significantly associated with prolonged overall survival in patients with LUAD（P<0.05）. ConclusionPAE effectively inhibits proliferation of LUAD-PDOs and promotes their apoptosis， its anti-tumor mechanism is potentially associated with the activation of the p53 signaling pathway， with BRCA1 and CHEK1 genes likely serving as key downstream targets for the effects of PAE.

Objective To explore the application value of three mainstream large language models in the diagnosis, differential diagnosis, and treatment decision support of the primary diseases related to pediatric liver transplantation. Methods Seventy-nine cases of pediatric liver transplantation-related diseases diagnosed through pathological or clinical follow-up data were collected from Renji Hospital, Shanghai Jiao Tong University School of Medicine or published high-quality case reports. These cases covered 25 types of primary diseases such as cholestatic liver disease, metabolic diseases, and tumors. Standardized prompts were used to input the case information into the DeepSeek-R1, ChatGPT-4o and Grok-3 models, and the accuracy of their preliminary diagnosis and differential diagnosis based on basic clinical data was evaluated. The final diagnosis accuracy and the response time after supplementary examination were also assessed, as well as the completeness and rationality of their analysis of disease treatment principles. Results In the initial diagnosis and differential diagnosis stage, the comprehensive accuracy of DeepSeek-R1 was the highest [72.1%, 95% confidence interval (CI) 61.4% - 80.8%], and there was a statistically significant difference in the comprehensive accuracy of the three models for initial diagnosis (P = 0.008). After adding further examination information, the final diagnosis accuracy of the three models increased, with DeepSeek-R1 at 88.6% (95% CI 79.7% - 93.9%), ChatGPT-4o at 87.3% (95% CI 78.2% - 93.0%), and Grok-3 at 78.5% (95% CI 68.2% - 86.1%). There was no statistically significant difference among the three models (P = 0.05). The scores given by experts for the treatment principles showed good consistency (Kappa = 0.769). In addition, the response time of ChatGPT-4o is shorter than that of the other two models [(24 ± 7) s]. Conclusions Large language models demonstrate good efficacy in the diagnosis and treatment decision-making process of various pediatric liver diseases, have a good application prospect for auxiliary diagnosis and decision support, and are expected to help improve the accuracy and efficiency of clinical diagnosis and treatment of pediatric liver transplantation-related primary diseases.

Antibody-drug conjugates (ADCs) have emerged as a standard treatment modality for previously treated driver gene-positive advanced non-small cell lung cancer (NSCLC). Sacituzumab tirumotecan (sac-TMT), a novel TROP-2-targeting ADC, has been the focus of the phase Ⅲ clinical trial OptiTROP-Lung04. This trial demonstrated, for the first time, that single-agent sac-TMT significantly improves progression-free survival and overall survival compared with conventional platinum-based chemotherapy in patients with epidermal growth factor receptor (EGFR) mutant advanced NSCLC following resistance to EGFR tyrosine kinase inhibitors. This review combines existing consensus from literature with the latest research evidence to systematically analyze OptiTROP-Lung04 from multiple perspectives, including molecular mechanisms, study design, clinical significance, and safety management. Furthermore, it explores future development directions in an effort to provide new insights into precision therapy for advanced NSCLC.

Objective: To develop a RHCE genotyping assay based on kompetitive allele-specific PCR (KASP) and assess its clinical accuracy for RhCE blood group determination. Methods: KASP primers were designed to interrogate three RHCE loci: the 109 bp insertion/deletion in intron 2, c. 307T>C, and c. 676C>G. A total of 1 194 RhD-positive inpatients from Chengdu were typed by both KASP genotyping and manual tube serology. Discordant samples (n=10) were retested by both methods and further resolved by Sanger sequencing. An additional 377 cases were tested for the c. 48C>G locus to evaluate the predictive accuracy of individual loci and combined locus testing for RhC antigen. Results: Genotyping concordance with serology was 100.0% for both the c. 676C>G locus (RhE/Rhe) and the c. 307T>C locus (Rhc). For RhC prediction using the 109 bp insertion, overall accuracy was 99.7% (1 191/1 194); the 3 discordant cases were confirmed by Sanger sequencing to be false negatives attributable to 109 bp deletion in intron 2. Testing the c. 48C>G allele for RhC prediction yielded 7 false positives, with an accuracy of 98.1% (370/377). RhC antigen status was determined by combining the 109 bp insertion and the c. 48C allele. After excluding 10 samples with inconsistent results between the two loci, the accuracy reached 100% in the remaining 367 samples. When both loci were applied in combination, accuracy reached 100% in the 367 cases with concordant results. Among the 1 194 patients, CCee (45.8%) and CcEe (31.7%) were the most common RhCE phenotypes. The e antigen had the highest positivity rate (92.2%), and the Ce haplotype was the most frequent (66.9%). Conclusion: The KASP-based RHCE genotyping method achieves high accuracy for clinical RhCE typing. Combining the 109 bp insertion/deletion with the c. 48C allele significantly improves RhC antigen prediction compared with either locus alone. This method was applied to RhCE genotyping of 1 194 RhD-positive inpatients in Chengdu, providing local RhCE phenotype and haplotype distribution data to support RhCE-matched transfusion practice.

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.

Effective symptom management can alleviate the physical and psychological distress experienced by patients with cancer, improve quality of life, and contribute to treatment adherence and improve clinical outcomes. However, most existing guidelines are developed for healthcare professionals, and patients and the public have limited access to standardized and comprehensible guidance on symptom management. To address this gap and to facilitate effective communication and shared decision-making, this study proposes the development of a patient version of the cancer symptom management guideline. The development process will adhere to the methodological framework recommended by the Guidelines International Network and the World Health Organization. The GRADE approach will be employed to assess the certainty of evidence and to formulate recommendations. In addition, the process will be informed by the Appraisal of Guidelines for Research and Evaluation Ⅱ (AGREE Ⅱ) instrument and the Reporting Items for Practice Guidelines in Healthcare-Public or Patient Versions of Guidelines (RIGHT-PVG). This protocol outlines the establishment of the guideline working group, the identification and prioritization of key questions, evidence retrieval and appraisal, and the formulation of recommendations, with the aim of ensuring methodological rigor and transparency in the development of the patient guideline and providing methodological reference for similar guideline initiatives.