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.

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.

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.

The focus of green analytical chemistry(GAC)is to minimize the negative impacts of analytical pro-cedures on human safety,human health,and the environment.Several factors,such as the reagents used,sample collection,sample processing,instruments,energy consumed,and the quantities of hazardous materials and waste generated during analytical procedures,need to be considered in the evaluation of the greenness of analytical assays.In this study,we propose a greenness evaluation metric for analytical methods(GEMAM).The new greenness metric is simple,flexible,and comprehensive.The evaluation criteria are based on both the 12 principles of GAC(SIGNIFICANCE)and the 10 factors of sample prep-aration,and the results are presented on a 0-10 scale.The GEMAM calculation process is easy to perform,and its results are easy to interpret.The output of GEMAM is a pictogram that can provide both qualitative and quantitative information based on color and number.

Objective To investigate the clinical factors affecting the risk of distant metastasis in patients with gallbladder carcinoma(GBC)and to construct a predictive model for metastasis risk.Methods Retrospective analysis was conducted on 4979 GBC patients from the surveillance,epidemiology,and end results(SEER)database from January 2000 to December 2021,which were divided into a training set(n=3485)and an internal validation set(n=1494)in a 7∶3 ratio.Additionally,47 GBC patients from Hefei First People's Hospital from January 2009 to December 2024 were collected as an external validation set.In the training set,univariate and multivariate analyses were performed to identify independent predictors of distant metastasis in GBC and to construct a predictive model.The predictive ability of the model was assessed by using area under the receiver operating characteristic curve.The model's calibration and clinical utility were evaluated through calibration curves and decision curve analysis.Results The results of univariate and multivariate analyses revealed that Caucasian,tumor size,T stage,and N stage were independent risk factors for distant metastasis in patients with GBC(P＜0.05).The predictive model constructed based on these factors had an AUC of 0.727,indicating good predictive performance.In the training set,internal validation set,and external validation set,the predictive results of this model showed good consistency with the actual situation.Conclusion The nomogram established by using SEER database can accurately predict the distant metastasis status in patients with GBC and demonstrates good clinical applicability.It assists clinicians in intuitively assessing the distant metastasis rate in GBC patients,thereby facilitating the formulation of personalized treatment plans.

Objective:To investigate the value of serum D-dimer(DD),C reactive protein(CRP),albumin(ALB),and DD combined CRP/ALB ratio in predicting the severity of acute pancreatitis(AP).Method:A total of 88 AP patients admitted to Hefei First People's Hospital from Jan 2022 to Dec 2023 were enrolled.AP patients were divided into mild AP(MAP)group(39 cases),moderately severe AP(MSAP)group(24 cases)and severe AP(SAP)group(25 cases)according to the revised Atlanta Classification.A total of 25 patients with benign thyroid tumors were selected as the control group.Serum levels of DD,CRP,ALB and CRP/ALB ratio were compared between AP group and the control group.Receiver operating characteristics(ROC)curves were used to evaluate the efficacy of DD,CRP,and ALB alone and the combined DD and CRP/ALB ratio in predicting disease severity of AP.Results:The serum levels of DD and CRP in the control group were signi ficantly lower than those in the AP group,and the level of ALB was significantly higher than that in the AP group(P＜0.01).In addition,in the AP group,the serum levels of DD and CRP in patients admitted≤12 h were significantly lower than those in patients admitted＞12 h,and the ALB level was significantly increased(P＜0.01).The serum levels of DD,CRP and CRP/ALB ratio in MAP group were significantly lower than those in MSAP group and SAP group,and those in MSAP group was lower than those in SAP group(P＜0.05).The serum level of ALB in MAP group were significantly higher than those in MSAP group and SAP group,and the serum level of ALB in MSAP group was significantly higher than that in SAP group(P＜0.05).The areas under ROC curve of serum DD,CRP and ALB and DD combined with CRP/ALB ratio in predicting the severity of AP patients were 0.741,0.737,0.642 and 0.812,respectively.Conclusions:Serum DD,CRP and ALB levels have certain significance in the assessment of the severity of early AP disease.The prediction effect of DD combined with CRP/ALB ratio is better than that of these indicators alone.

Nonobstructive azoospermia (NOA), one of the most severe types of male infertility, etiology often remains unclear in most cases. Therefore, this study aimed to detect four biallelic detrimental variants (0.5%) in the minichromosome maintenance domain containing 2 ( MCMDC2 ) genes in 768 NOA patients by whole-exome sequencing (WES). Hematoxylin and eosin (H&E) demonstrated that MCMDC2 deleterious variants caused meiotic arrest in three patients (c.1360G>T, c.1956G>T, and c.685C>T) and hypospermatogenesis in one patient (c.94G>T), as further confirmed through immunofluorescence (IF) staining. The single-cell RNA sequencing data indicated that MCMDC2 was substantially expressed during spermatogenesis. The variants were confirmed as deleterious and responsible for patient infertility through bioinformatics and in vitro experimental analyses. The results revealed four MCMDC2 variants related to NOA, which contributes to the current perception of the function of MCMDC2 in male fertility and presents new perspectives on the genetic etiology of NOA.
Humans ; Male ; Azoospermia/genetics* ; Meiosis/genetics* ; Spermatogenesis/genetics* ; Adult ; Exome Sequencing ; Microtubule-Associated Proteins/genetics* ; Alleles ; Infertility, Male/genetics*