Drug-induced autoimmune-like hepatitis （DI-ALH） is one of the special phenotypes of drug-induced liver injury， and since it has similar autoimmune characteristics and liver histological manifestations as intrinsic autoimmune hepatitis， differential diagnosis of these two diseases may be difficult in some cases. This article reports a case diagnosed with DI-ALH after long-term follow-up and conducts a literature review to provide a reference for clinicians.

Objective:To explore the efficacy of deep learning-based automatic segmentation technology in the segmentation of hepatocellular carcinoma (HCC) lesions using gadoxetate disodium-enhanced MRI (EOB-MRI), and to investigate the prognostic value of radiomics analysis in predicting patient outcomes.Methods:This was a cross-sectional, retrospective study that collected data from 352 patients with solitary HCC who underwent imaging at the Harbin Medical University Cancer Hospital between June 2015 and May 2023. The patients were randomly divided into a training set ( n=213) and a validation set ( n=139) in a 3∶2 ratio using weighted random sampling. Two radiologists manually annotated the lesions. Hepatobiliary-phase EOB-MRI images were standardized, and six deep learning models,nnU-Net, nnFormer, UnetR, Swin-UnetR, UnetR++ and MedNeXt,were trained for automatic segmentation on the training set. The segmentation performance was evaluated on the validation set, and the segmentation efficacy was assessed using the Dice coefficient and 95% Hausdorff distance (HD 95), identifying of the optimal model. Radiomics features were extracted from both manual and automatic segmentation regions, and the radiomics score (Radscore) was calculated to stratify patients into high-risk and low-risk groups. Kaplan-Meier curves and log-rank tests were used to analyze the differences in relapse-free survival (RFS) and overall survival (OS) between the different stratified groups. Results:Among the automatic segmentation models, the MedNeXt model performed best in the validation set, with a Dice coefficient of 76.0%, HD 95 of 7.2, and a segmentation success rate of 90.6% (126/139). The nnFormer model was the second-best, with a Dice coefficient of 75.3%, HD 95 of 10.1, and a segmentation success rate of 89.9% (125/139). Other models showed Dice coefficients ranging from 66.3% to 74.1%. A MedNext-nnF model was established by combining the MedNeXt and nnFormer models, achieving a Dice coefficient of 78.2%, HD 95 of 5.9, and a segmentation success rate of 92.1% (128/139) in the validation group. After constructing the automatic segmentation radiomics prognostic model, patients were stratified by Radscore. Both manual and automatic segmentation models showed statistically significant differences in RFS and OS between different risk groups ( P<0.001). Conclusions:The Mednext-nnF fusion model enables efficient and automated segmentation of HCC lesions in EOB-MRI. The radiomics model constructed based on the automated segmentation demonstrates strong performance in predicting and stratifying prognostic risk.

With the rapid development of generative artificial intelligence(GAI)technologies,their widespread application in academic research and writing is continuously expanding the boundaries of sci-entific inquiry.However,this trend has also raised a series of ethical and regulatory challenges,inclu-ding issues related to authorship,content authenticity,citation accuracy,and accountability.In light of the growing involvement of AI in generating academic content,establishing an open,controllable,and trustworthy ethical governance framework has become a key task for safeguarding research integrity and maintaining trust within the academic community.This expert consensus outlines ethical requirements across key stages of AI-assisted academic writing-including topic selection,data management,citation practices,and authorship attribution.It aims to clarify the boundaries and ethical obligations surrounding AI use in academic writing,ensuring that technological tools enhance efficiency without compromising in-tegrity.The goal is to provide guidance and institutional support for building a responsible and sustainable research ecosystem.

Background: s/Aims: Sarcomatoid hepatocellular carcinoma (HCC) is a rare histological subtype of HCC characterized by extremely poor prognosis; however, its molecular characterization has not been elucidated. Methods: In this study, we conducted an integrated multiomics study of whole-exome sequencing, RNA-seq, spatial transcriptome, and immunohistochemical analyses of 28 paired sarcomatoid tumor components and conventional HCC components from 10 patients with sarcomatoid HCC, in order to identify frequently altered genes, infer the tumor subclonal architectures, track the genomic evolution, and delineate the transcriptional characteristics of sarcomatoid HCCs. Results: Our results showed that the sarcomatoid HCCs had poor prognosis. The sarcomatoid tumor components and the conventional HCC components were derived from common ancestors, mostly accessing similar mutational processes. Clonal phylogenies demonstrated branched tumor evolution during sarcomatoid HCC development and progression. TP53 mutation commonly occurred at tumor initiation, whereas ARID2 mutation often occurred later. Transcriptome analyses revealed the epithelial–mesenchymal transition (EMT) and hypoxic phenotype in sarcomatoid tumor components, which were confirmed by immunohistochemical staining. Moreover, we identified ARID2 mutations in 70% (7/10) of patients with sarcomatoid HCC but only 1–5% of patients with non-sarcomatoid HCC. Biofunctional investigations revealed that inactivating mutation of ARID2 contributes to HCC growth and metastasis and induces EMT in a hypoxic microenvironment. Conclusions We offer a comprehensive description of the molecular basis for sarcomatoid HCC, and identify genomic alteration (ARID2 mutation) together with the tumor microenvironment (hypoxic microenvironment), that may contribute to the formation of the sarcomatoid tumor component through EMT, leading to sarcomatoid HCC development and progression.

Background: s/Aims: Sarcomatoid hepatocellular carcinoma (HCC) is a rare histological subtype of HCC characterized by extremely poor prognosis; however, its molecular characterization has not been elucidated. Methods: In this study, we conducted an integrated multiomics study of whole-exome sequencing, RNA-seq, spatial transcriptome, and immunohistochemical analyses of 28 paired sarcomatoid tumor components and conventional HCC components from 10 patients with sarcomatoid HCC, in order to identify frequently altered genes, infer the tumor subclonal architectures, track the genomic evolution, and delineate the transcriptional characteristics of sarcomatoid HCCs. Results: Our results showed that the sarcomatoid HCCs had poor prognosis. The sarcomatoid tumor components and the conventional HCC components were derived from common ancestors, mostly accessing similar mutational processes. Clonal phylogenies demonstrated branched tumor evolution during sarcomatoid HCC development and progression. TP53 mutation commonly occurred at tumor initiation, whereas ARID2 mutation often occurred later. Transcriptome analyses revealed the epithelial–mesenchymal transition (EMT) and hypoxic phenotype in sarcomatoid tumor components, which were confirmed by immunohistochemical staining. Moreover, we identified ARID2 mutations in 70% (7/10) of patients with sarcomatoid HCC but only 1–5% of patients with non-sarcomatoid HCC. Biofunctional investigations revealed that inactivating mutation of ARID2 contributes to HCC growth and metastasis and induces EMT in a hypoxic microenvironment. Conclusions We offer a comprehensive description of the molecular basis for sarcomatoid HCC, and identify genomic alteration (ARID2 mutation) together with the tumor microenvironment (hypoxic microenvironment), that may contribute to the formation of the sarcomatoid tumor component through EMT, leading to sarcomatoid HCC development and progression.

Background: s/Aims: Sarcomatoid hepatocellular carcinoma (HCC) is a rare histological subtype of HCC characterized by extremely poor prognosis; however, its molecular characterization has not been elucidated. Methods: In this study, we conducted an integrated multiomics study of whole-exome sequencing, RNA-seq, spatial transcriptome, and immunohistochemical analyses of 28 paired sarcomatoid tumor components and conventional HCC components from 10 patients with sarcomatoid HCC, in order to identify frequently altered genes, infer the tumor subclonal architectures, track the genomic evolution, and delineate the transcriptional characteristics of sarcomatoid HCCs. Results: Our results showed that the sarcomatoid HCCs had poor prognosis. The sarcomatoid tumor components and the conventional HCC components were derived from common ancestors, mostly accessing similar mutational processes. Clonal phylogenies demonstrated branched tumor evolution during sarcomatoid HCC development and progression. TP53 mutation commonly occurred at tumor initiation, whereas ARID2 mutation often occurred later. Transcriptome analyses revealed the epithelial–mesenchymal transition (EMT) and hypoxic phenotype in sarcomatoid tumor components, which were confirmed by immunohistochemical staining. Moreover, we identified ARID2 mutations in 70% (7/10) of patients with sarcomatoid HCC but only 1–5% of patients with non-sarcomatoid HCC. Biofunctional investigations revealed that inactivating mutation of ARID2 contributes to HCC growth and metastasis and induces EMT in a hypoxic microenvironment. Conclusions We offer a comprehensive description of the molecular basis for sarcomatoid HCC, and identify genomic alteration (ARID2 mutation) together with the tumor microenvironment (hypoxic microenvironment), that may contribute to the formation of the sarcomatoid tumor component through EMT, leading to sarcomatoid HCC development and progression.

With the rapid development of generative artificial intelligence(GAI)technologies,their widespread application in academic research and writing is continuously expanding the boundaries of sci-entific inquiry.However,this trend has also raised a series of ethical and regulatory challenges,inclu-ding issues related to authorship,content authenticity,citation accuracy,and accountability.In light of the growing involvement of AI in generating academic content,establishing an open,controllable,and trustworthy ethical governance framework has become a key task for safeguarding research integrity and maintaining trust within the academic community.This expert consensus outlines ethical requirements across key stages of AI-assisted academic writing-including topic selection,data management,citation practices,and authorship attribution.It aims to clarify the boundaries and ethical obligations surrounding AI use in academic writing,ensuring that technological tools enhance efficiency without compromising in-tegrity.The goal is to provide guidance and institutional support for building a responsible and sustainable research ecosystem.

Background The adverse effects of long working hours, shift rotation, and job stress on the physical and mental health of occupational populations require urgent attention. Objective To investigate and compare the positive rates of WMSDs between different industries, analyze the exposure status of long working hours, shift rotation, and job stress among key occupational groups, and evaluate the impacts of these factors on WMSDs in the manufacturing and service industries. Methods The study subjects were derived from key occupational populations in Yunnan Province, recruited by the Chinese National Occupational Health Literacy Monitoring Survey in 2022. A cross-sectional design was used for this survey. The key occupational populations were recruited from the secondary industry (manufacturing industry, metal mining and beneficiation industry, and non-metal mining and beneficiation industry) by stratified random sampling and from the tertiary industry (medical and healthcare industry, education industry, environmental sanitation industry, transportation industry, and express/takeaway delivery industry) by proportional probability sampling, and 12014 front-line workers were finally included as survey participants. General information, work-related factors (shift rotation, weekly working hours, job stress, etc.) and WMSDs assessment results were obtained through a web-based self-reported questionnaire, and the associations of long working hours, shift rotation, and job stress with WMSDs were analyzed using χ² test and logistic regression. Results The average positive rate of WMSDs among the key occupational groups in Yunnan Province was 77.2%. The positive rate of WMSDs in the secondary industry group was 69.4%, while it was 81.6% in the tertiary industry group, with a statistically significant difference (P<0.001). Among the eight surveyed sectors, the education sector had the highest positive rate of WMSDs (94.2%). The single-site WMSDs mainly involved the neck, shoulders, and low back. In the secondary industry, WMSDs was reported in 1−2 body parts, while in the tertiary industry, WMSDs was mainly reported in multiple (≥5) body parts. Long working hours (OR: 1.119, 1.165, 1.163, respectively), shift rotation (OR: 1.094, 1.199, 1.230, respectively), and job stress (OR: 1.795, 1.854, 2.006, respectively) were risk factors for WMSDs in the neck, shoulder, or low back, and all three were also risk factors for multi-site WMSDs. Moreover, as the number of involved body parts increased, the effects of long working hours and job stress became more pronounced. For 1-2, 3-4, and 5 or more body parts compared to no pain reported, their OR values were 0.971 (95%CI: 0.871, 1.082), 1.133 (95%CI: 1.004, 1.279), and 1.285 (95%CI: 1.155, 1.429) for long working hours, respectively, and the OR values were 1.009 (95%CI: 0.878, 1.160), 1.360 (95%CI: 1.174, 1.575), and 2.797 (95%CI: 2.471, 3.166) for job stress, respectively. Conclusion The positive rate of WMSDs is higher in the tertiary industry than that in the secondary industry among the key occupational groups in Yunnan Province. Long working hours, rotating shift work, and job stress could significantly increase the risk of WMSDs.

BACKGROUND: Immune checkpoint inhibitors (ICIs) have been included in various neoadjuvant therapy (NAT) regimens for non-small cell lung cancer (NSCLC). However, due to the relatively short period for the use of ICIs in NAT, patients' clinical outcomes with different regimens are uncertain. Our study aims to examine the efficacy of neoadjuvant immunotherapy (NAIT) for NSCLC patients and compare the overall survival (OS) and event-free survival (EFS) of patients receiving different NAT regimens. METHODS: This study retrospectively included 308 NSCLC patients treated with different NAT regimens and subsequent surgery in National Cancer Center between August 1, 2016 and July 31, 2022. Kaplan-Meier survival analysis and Cox proportional hazards regression analysis were conducted to evaluate the prognosis of patients. RESULTS: With a median follow-up of 27.5 months, the 1-year OS rates were 98.8% and 96.2%, and the 2-year OS rates were 96.6% and 85.8% in patients of the NAIT and neoadjuvant chemotherapy (NACT) group, respectively (hazard ratio [HR], 0.339; 95% confidence interval [CI], 0.160-0.720; P = 0.003). The 1-year EFS rates were 96.0% and 88.0%, and the 2-year EFS rates were 92.0% and 77.7% for patients in the NAIT and NACT groups, respectively (HR, 0.438; 95% CI, 0.276-0.846; P = 0.010). For patients who did not achieve pathological complete response (pCR), significantly longer OS ( P = 0.012) and EFS ( P = 0.019) were observed in patients receiving NAIT than those receiving NACT. Different NAT regimens had little effect on surgery and the postoperative length of stay (6 [4, 7] days vs . 6 [4, 7] days, Z = -0.227, P = 0.820). CONCLUSIONS NAIT exhibited superior efficacy to NACT for NSCLC, resulting in longer OS and EFS. The OS and EFS benefits were also observed among patients in the NAIT group who did not achieve pCR.
Humans ; Carcinoma, Non-Small-Cell Lung/mortality* ; Male ; Female ; Lung Neoplasms/mortality* ; Middle Aged ; Immune Checkpoint Inhibitors/therapeutic use* ; Neoadjuvant Therapy/methods* ; Retrospective Studies ; Aged ; Adult ; Kaplan-Meier Estimate ; Treatment Outcome ; Immunotherapy/methods*

This study aims to explore the mechanism of lung and gut protection by Tanreqing Capsules on the mice infected with influenza virus based on "the lung-gut axis". A total of 110 C57BL/6J mice were randomized into control group, model group, oseltamivir group, and low-and high-dose Tanreqing Capsules groups. Ten mice in each group underwent body weight protection experiments, and the remaining 12 mice underwent experiments for mechanism exploration. Mice were infected with influenza virus A/Puerto Rico/08/1934(PR8) via nasal inhalation for the modeling. The lung tissue was collected on day 3 after gavage, and the lung tissue, colon tissue, and feces were collected on day 7 after gavage for subsequent testing. The results showed that Tanreqing Capsules alleviated the body weight reduction and increased the survival rate caused by PR8 infection. Compared with model group, Tanreqing Capsules can alleviate the lung injury by reducing the lung index, alleviating inflammation and edema in the lung tissue, down-regulating viral gene expression at the late stage of infection, reducing the percentage of neutrophils, and increasing the percentage of T cells. Tanreqing Capsules relieved the gut injury by restoring the colon length, increasing intestinal lumen mucin secretion, alleviating intestinal inflammation, and reducing goblet cell destruction. The gut microbiota analysis showed that Tanreqing Capsules increased species diversity compared with model group. At the phylum level, Tanreqing Capsules significantly increased the abundance of Firmicutes and Actinobacteria, while reducing the abundance of Bacteroidota and Proteobacteria to maintain gut microbiota balance. At the genus level, Tanreqing Capsules significantly increased the abundance of unclassified＿f＿Lachnospiraceae while reducing the abundance of Bacteroides, Eubacterium, and Phocaeicola to maintain gut microbiota balance. In conclusion, Tanreqing Capsules can alleviate mouse lung and gut injury caused by influenza virus infection and restore the balance of gut microbiota. Treating influenza from the lung and gut can provide new ideas for clinical practice.
Animals ; Drugs, Chinese Herbal/administration & dosage* ; Mice ; Lung/metabolism* ; Mice, Inbred C57BL ; Capsules ; Orthomyxoviridae Infections/virology* ; Gastrointestinal Microbiome/drug effects* ; Male ; Humans ; Female ; Influenza A virus/physiology* ; Influenza, Human/virology*