Oral squamous cell carcinoma (OSCC) is a common head and neck malignancy. Approximately 50% to 60% of patients with OSCC are diagnosed at a locally advanced stage (clinical staging III-IVa). Even with comprehensive and sequential treatment primarily based on surgery, the 5-year overall survival rate remains below 50%, and patients often suffer from postoperative functional impairments such as difficulties with speaking and swallowing. Programmed death receptor-1 (PD-1) inhibitors are increasingly used in the neoadjuvant treatment of locally advanced OSCC and have shown encouraging efficacy. However, clinical practice still faces key challenges, including the definition of indications, optimization of combination regimens, and standards for efficacy evaluation. Based on the latest research advances worldwide and the clinical experience of the expert group, this expert consensus systematically evaluates the application of PD-1 inhibitors in the neoadjuvant treatment of locally advanced OSCC, covering combination strategies, treatment cycles and surgical timing, efficacy assessment, use of biomarkers, management of special populations and immune related adverse events, principles for immunotherapy rechallenge, and function preservation strategies. After multiple rounds of panel discussion and through anonymous voting using the Delphi method, the following consensus statements have been formulated: 1) Neoadjuvant therapy with PD-1 inhibitors can be used preoperatively in patients with locally advanced OSCC. The preferred regimen is a PD-1 inhibitor combined with platinum based chemotherapy, administered for 2-3 cycles. 2) During the efficacy evaluation of neoadjuvant therapy, radiographic assessment should follow the dual criteria of Response Evaluation Criteria in Solid Tumors (RECIST) version 1.1 and immune RECIST (iRECIST). After surgery, systematic pathological evaluation of both the primary lesion and regional lymph nodes is required. For combination chemotherapy regimens, PD-L1 expression and combined positive score need not be used as mandatory inclusion or exclusion criteria. 3) For special populations such as the elderly (≥ 70 years), individuals with stable HIV viral load, and carriers of chronic HBV/HCV, PD-1 inhibitors may be used cautiously under the guidance of a multidisciplinary team (MDT), with close monitoring for adverse events. 4) For patients with a poor response to neoadjuvant therapy, continuation of the original treatment regimen is not recommended; the subsequent treatment plan should be adjusted promptly after MDT assessment. Organ transplant recipients and patients with active autoimmune diseases are not recommended to receive neoadjuvant PD-1 inhibitor therapy due to the high risk of immune related activation. Rechallenge is generally not advised for patients who have experienced high risk immune related adverse events such as immune mediated myocarditis, neurotoxicity, or pneumonitis. 5) For patients with a good pathological response, individualized de escalation surgery and function preservation strategies can be explored. This consensus aims to promote the standardized, safe, and precise application of neoadjuvant PD-1 inhibitor strategies in the management of locally advanced OSCC patients.

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.

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.

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.

ObjectiveTo investigate the expression patterns of key steroidogenic enzymes （CYP11A1， HSD3B1， and CYP19A1） in placental tissues from preeclampsia （PE） patients and evaluate​ their potential as biomarkers for PE. MethodsA case-control study was conducted using placental tissues from PE patients （PE group， n=3） and normotensive pregnant women （control group， n=3）. RNA sequencing was performed to identify differentially expressed genes （DEGs）， followed by validation of target gene expression using Western blot， qRT-PCR， and immunohistochemistry. Steroid hormone levels in placental tissues and serum were measured by ELISA. Spearman correlation analysis was used to assess associations between protein expression and clinical parameters， and receiver operating characteristic （ROC） curve analysis was performed to evaluate the predictive value of target genes for PE. ResultsThe results showed that 501 DEGs （P<0.05 and |Log₂Fold Change| >1） were identified in PE placental tissues， with KEGG enrichment analysis revealing significant involvement in steroid hormone biosynthesis pathways. Compared with the control group， PE placental tissues exhibited significantly decreased protein and mRNA expression of HSD3B1 and CYP19A1 （P<0.01）， while CYP11A1 expression was upregulated （P<0.05）. Serum estradiol （E2） and progesterone （P4） levels were significantly reduced in the PE group （P<0.01）， whereas testosterone （T） levels were elevated （P<0.05）. Correlation analysis demonstrated that CYP11A1 expression was positively associated with systolic blood pressure （SBP）， diastolic blood pressure （DBP）， and soluble fms-like tyrosine kinase-1 （sFlt-1） （P<0.01）， but negatively correlated with placental growth factor （PLGF） （P<0.01）. Conversely， HSD3B1 and CYP19A1 expression showed inverse correlations with these clinical parameters （P<0.01）. ROC analysis revealed that CYP11A1， HSD3B1， and CYP19A1 each had an area under the curve （AUC） of 0.778 for predicting PE， with 95%CI ranging from 0.614 to 0.942 （P=0.004）. The sensitivity was 94.4%， and the specificity was 66.7%. ConclusionThese findings suggest that dysregulated expression of steroidogenic enzymes in PE placental tissues contributes to hormonal imbalance， thereby playing a role in PE pathogenesis. Furthermore， CYP11A1， HSD3B1， and CYP19A1 may serve as potential diagnostic biomarkers for PE.

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.

Objective: To investigate the correlation among α2, 6-sialyltransferase (ST6Gal-Ⅰ), CD36 desialylation, and caveolin-1 (Cav-1) in phorbol ester (PMA)-induced MEG-01 cell model, as well as their potential mechanism in immune thrombocytopenia (ITP). Methods: MEG-01 cells were treated with 10 ng/mL PMA for 48 hours (control group: 0.1% DMSO). Flow cytometry was used to detect cell surface markers: desialylation (CD41 RCA ) and α2, 6-sialylation (CD41 SNA ). Western blot was performed to analyze the protein expressions of ST6Gal-Ⅰ, CD36, and Cav-1. Results: Flow cytometry analysis revealed that, compared with the control group (set as 100%), the proportion of CD41 RCA positive cells in the MEG-01 cells after PMA intervention significantly increased to (127.79±2.01)%, while the proportion of CD41 SNA positive cells significantly decreased to (78.09±1.76)% (both P<0.05). Western blot analysis results showed that, compared with the control group, PMA intervention significantly downregulated the expression of ST6Gal-Ⅰ protein (0.602±0.023 vs 0.768±0.068) and Cav-1 protein (1.012±0.028 vs 1.253±0.068) (both P<0.05), while significantly upregulating the expression of CD36 protein (0.936±0.033 vs 0.694±0.070, P<0.05). Conclusion: PMA can significantly inhibit the expression of ST6Gal-Ⅰ, accompanied by increased desialylation (β-galactose exposure), elevated CD36, and downregulated Cav-1. These changes suggest that the increased exposure of CD36 antigen and the disorder of membrane microenvironment may be involved in the pathological process of ITP, providing a new direction for mechanism research.

ObjectiveRecent studies have highlighted the critical role of NUDT19 in the initiation, progression, and prognosis of specific cancer types. However, its involvement in pan-cancer analysis has not been fully characterized. This study aims to systematically explore the expression patterns, clinical significance, and immune-related functions of NUDT19 in various cancer types through multi-omics analysis, further revealing its potential role in cancer, particularly its functional and therapeutic target value in leukemia. MethodsTo achieve this goal, various bioinformatics approaches were employed to evaluate the expression patterns, clinical significance, and immune-related functions of NUDT19 in tumors and normal tissues. Additionally, we analyzed the mutation characteristics of NUDT19 and its relationship with epigenetic modifications. Using the single-cell analysis tool SingleCellBase, we explored the distribution of NUDT19 across different cell subpopulations in tumors. To validate these findings, qRT-PCR was used to measure NUDT19 expression levels in specific tumor cell lines, and we established acute myeloid leukemia (AML) cell lines (HL-60 and THP-1) to conduct NUDT19 knockdown and overexpression experiments, assessing its effects on leukemia cell proliferation, apoptosis, and invasion. ResultsPan-cancer analysis revealed the dysregulated expression of NUDT19 across multiple cancer types, which was closely associated with poor prognosis, clinical staging, and diagnostic markers. Furthermore, NUDT19 was significantly correlated with tumor biomarkers, immune-related genes, and immune cell infiltration in different cancers. Mutation analysis showed that multiple mutations in NUDT19 were significantly associated with epigenetic changes. Single-cell analysis revealed the heterogeneity of NUDT19 expression in cancer cells, suggesting its potentially diverse functional roles in different cell subpopulations. qRT-PCR experiments confirmed the significant upregulation of NUDT19 in various tumor cell lines. In AML cell lines, NUDT19 knockdown led to reduced cell proliferation and invasion, with increased apoptosis, while NUDT19 overexpression significantly enhanced cell proliferation and invasion while reducing apoptosis. ConclusionThis study demonstrates the diverse roles of NUDT19 in various cancer types, with a particularly prominent functional role in leukemia. NUDT19 is not only associated with tumor initiation and progression but may also influence cancer progression through the regulation of immune microenvironment and epigenetic mechanisms. Our research highlights the potential of NUDT19 as a therapeutic target, particularly for targeted therapies in malignancies such as leukemia, with significant clinical application prospects.

INTRODUCTION: Achieving low-density lipoprotein cholesterol (LDL-C) levels is key to preventing atherosclerotic cardiovascular events. However, many high-risk cardiovascular patients still experience poor LDL-C goal attainment and receive suboptimal lipid-lowering therapy (LLT) prescriptions. Herein, we evaluated LLT prescription patterns, LDL-C goal attainment and cardiovascular mortality among this population group in Singapore. METHODS: This prospective observational cohort study included 555 patients with ischaemic heart disease (IHD) admitted to the hospital in 2020. The LLT prescriptions, corresponding LDL-C levels and cardiovascular outcomes were assessed over a 24-month period. RESULTS: Most participants were male (82.3%), with 48.5% identified as Chinese. High-intensity statin prescriptions increased from 45.4% at hospital admission to 87.1% at discharge and remained stable at approximately 80% at 6, 12, and 24 months post-discharge. Combination LLT prescriptions increased from 12.3% at discharge to 33.8% by 24 months. Ezetimibe was the most commonly prescribed second-line LLT (40.8%), followed by inclisiran (1.09%) and anti-proprotein convertase subtilisin/kexin type 9 monoclonal antibody therapies (0.87%). Over 24 months, LDL-C goal attainment rates were 22.1% for LDL-C < 1.4 mmol/L and 47.2% for LDL-C < 1.8 mmol/L. Multivariable Cox proportional hazards regression indicated that achieving LDL-C < 1.8 mmol/L goal was associated with a reduction in all-cause mortality at 24 months (hazard ratio 0.53, 95% confidence interval 0.30-0.94, P = 0.030). CONCLUSION Treatment gaps in lipid management persist in 80% of the study population, indicating that statin monotherapy alone is insufficient to achieve LDL-C goals. Greater efforts to improve LDL-C goal attainment rates in high-risk cardiovascular patients are imperative.
Humans ; Male ; Cholesterol, LDL/blood* ; Female ; Myocardial Ischemia/drug therapy* ; Middle Aged ; Prospective Studies ; Aged ; Singapore/epidemiology* ; Hydroxymethylglutaryl-CoA Reductase Inhibitors/therapeutic use* ; Ezetimibe/therapeutic use* ; Anticholesteremic Agents/therapeutic use* ; Treatment Outcome