Irritable bowel syndrome with diarrhea （IBS-D）， as a prototypical disorder involving the microbiota-gut-brain axis， remains poorly understood in terms of its pathogenesis， posing ongoing challenges for clinical diagnosis. Omics technologies， leveraging their high-throughput detection and systematic analysis advantages， has emerged as a critical tool for deciphering the complex mechanisms underlying traditional Chinese medicine （TCM） treatment of IBS-D. This systematic review summarizes the research progress of transcriptomics， proteomics， metabolomics， microbiomics， and multi-omics integration techniques in TCM interventions for IBS-D. In single-omics studies， transcriptomics using techniques like RNA-seq， reveals the regulatory mechanisms of TCM on IBS-related signaling pathways. Proteomics， leveraging quantitative technologies such as 2D-difference gel electrophoresis and tandem mass tag， identifies differentially expressed proteins and elucidates the action targets of TCM in treating IBS-D. Metabolomics， employing methods like UPLC-Q-TOF-MS and LC-MS/MS， discovers metabolic pathways regulated by TCM to improve metabolic disturbances in IBS-D. Microbiomics， based on 16S rRNA sequencing， confirms that TCM can reshape the gut microbiota structure and restore the intestinal microecological balance， thereby improving IBS-D. Multi-omics integration further overcomes the limitations of single-omics approaches by synthesizing information from transcriptomics， proteomics， metabolomics， and microbiomics， enabling a more comprehensive and systematic elucidation of the complex mechanisms underlying TCM treatment for IBS-D. In the future， research related to IBS-D should be advanced from three aspects： stratified clinical research based on TCM syndrome types， multi-omics integration verification mechanisms， and emerging omics to explore new mechanisms， providing more innovative ideas for the precise diagnosis and treatment of this disease.

Objective: To develop a depression recognition model by integrating the spirit-expression diagnostic framework of traditional Chinese medicine (TCM) with machine learning algorithms. The proposed model seeks to establish a TCM-informed tool for early depression screening, thereby bridging traditional diagnostic principles with modern computational approaches. Methods: The study included patients with depression who visited the Shanghai Pudong New Area Mental Health Center from October 1, 2022 to October 1, 2023, as well as students and teachers from Shanghai University of Traditional Chinese Medicine during the same period as the healthy control group. Videos of 3 – 10 s were captured using a Xiaomi Pad 5, and the TCM spirit and expressions were determined by TCM experts (at least 3 out of 5 experts agreed to determine the category of TCM spirit and expressions). Basic information, facial images, and interview information were collected through a portable TCM intelligent analysis and diagnosis device, and facial diagnosis features were extracted using the Open CV computer vision library technology. Statistical analysis methods such as parametric and non-parametric tests were used to analyze the baseline data, TCM spirit and expression features, and facial diagnosis feature parameters of the two groups, to compare the differences in TCM spirit and expression and facial features. Five machine learning algorithms, including extreme gradient boosting (XGBoost), decision tree (DT), Bernoulli naive Bayes (BernoulliNB), support vector machine (SVM), and k-nearest neighbor (KNN) classification, were used to construct a depression recognition model based on the fusion of TCM spirit and expression features. The performance of the model was evaluated using metrics such as accuracy, precision, and the area under the receiver operating characteristic (ROC) curve (AUC). The model results were explained using the Shapley Additive exPlanations (SHAP). Results: A total of 93 depression patients and 87 healthy individuals were ultimately included in this study. There was no statistically significant difference in the baseline characteristics between the two groups (P > 0.05). The differences in the characteristics of the spirit and expressions in TCM and facial features between the two groups were shown as follows. (i) Quantispirit facial analysis revealed that depression patients exhibited significantly reduced facial spirit and luminance compared with healthy controls (P < 0.05), with characteristic features such as sad expressions, facial erythema, and changes in the lip color ranging from erythematous to cyanotic. (ii) Depressed patients exhibited significantly lower values in facial complexion L, lip L, and a values, and gloss index, but higher values in facial complexion a and b, lip b, low gloss index, and matte index (all P < 0.05). (iii) The results of multiple models show that the XGBoost-based depression recognition model, integrating the TCM “spirit-expression” diagnostic framework, achieved an accuracy of 98.61% and significantly outperformed four benchmark algorithms—DT, BernoulliNB, SVM, and KNN (P < 0.01). (iv) The SHAP visualization results show that in the recognition model constructed by the XGBoost algorithm, the complexion b value, categories of facial spirit, high gloss index, low gloss index, categories of facial expression and texture features have significant contribution to the model. Conclusion This study demonstrates that integrating TCM spirit-expression diagnostic features with machine learning enables the construction of a high-precision depression detection model, offering a novel paradigm for objective depression diagnosis.

Purpose: This study aimed to evaluate the safety and efficacy of gemcitabine and cisplatin (GP) regimen in combination with immune checkpoint inhibitor sintilimab as neoadjuvant therapy for muscle-invasive bladder cancer (MIBC) patients and the feasibility of the following selective bladder sparing surgery. Materials and Methods: Patients with histopathologically confirmed urothelial carcinoma without distant metastases (T2-4a, N ≤ 1, M0, American Joint Committee of Cancer 8th) and with adequate organ function will be enrolled. The therapeutic regimen was sintilimab 200 mg once on day 8, gemcitabine 1,000 mg/m2 and cisplatin 35 mg/m2 once on days 1 and 8, every 21 days for four cycles. The primary endpoint was pathologic complete response (pCR, pT0N0) rate. The secondary end points were ypT < 2 rate, R0 resection rate, event-free survival, and safety. Results: From May 4, 2020, to May 20, 2023, 55 patients were enrolled. Forty-six patients were evaluated for efficacy. Among the 42 patients who underwent surgery, 16 patients (38.0%) achieved pCR. Thirty-three patients (78.6%) achieved pT < 2. With a median follow-up of 15.7 months, the 1-year event-free survival was 91.3%. Notwithstanding the poor pathological baseline characteristic of a high T3-T4a proportion (39.1%), a promising bladder preservation (including 22 patients transurethral resection of bladder tumor, 5 patients partial cystectomy, and 4 surveillances) rate was achieved (67.4%). The most common grade ≥ 3 treatment-related adverse events was neutropenia (n=15, 27.3%), which was related to chemotherapy. There were no grade 3 immune-related adverse events. Conclusion Neoadjuvant GP plus sintilimab is a promising regimen for MIBC patients, with relatively high pT < 2 rate and triggering the emerging roles for the multi-disciplinary team decision-making for bladder sparing surgery.

Background/Aims: Cholangiocarcinoma (CCA) is a primary malignant neoplasm with an extremely poor prognosis. While combined chemoradiotherapy has been demonstrated to delay CCA progression to a certain extent, the absence of specific molecular biomarkers or targets significantly hinders the diagnosis and treatment of CCA. Methods: Through cross-analysis of proteomics and ADMA modificationomics, we identified DDX1 overexpressed in CCA with elevated R602-ADMA modifications. HPLC-MS/MS identified PRMT1 as the methyltransferase and USP10 as the deubiquitinating enzyme for DDX1. Immunofluorescence and nuclear-cytoplasmic partitioning experiments confirmed DDX1’s nuclear localization. GO and KEGG analyses clarify the biological functions of DDX1 in response to hypoxia. RNA-seq transcriptomics analyzed key pathways influenced by DDX1. A hydrodynamic in situ CCA mouse model was established to validate the chemopreventive effects of the PRMT1-specific inhibitor GSK715 on CCA development. Results: DDX1 promotes CCA progression both in vivo and in vitro and can be inhibited by GSK715. Mechanistically, PRMT1 mediates ADMA modification at position R602 of DDX1. This modification promotes DDX1 nuclear localization by recruiting USP10 to deubiquitinate DDX1, while simultaneously inhibiting PRMT1 degradation. DDX1 promotes the transcription of PRMT1 and USP10 by binding to the mRNA 3’UTR region, establishing a positive feedback regulatory pathway. This mechanism promotes the occurrence and development of CCA and can serve as a target for the inhibitor GSK715 to suppress CCA progression. Conclusions Our study identified DDX1-R602-ADMA modification as a novel ADMA modification in CCA. It further confirmed its pivotal role in CCA progression. Targeting the USP10-PRMT1-DDX1 axis may represent a significant therapeutic approach for CCA.

OBJECTIVE To investigate the prevalence and influencing factors of potentially inappropriate medication （PIM） among the elderly in nursing homes in Urumqi based on three PIM screening tools， and to compare the applicability of different tools. METHODS A cross-sectional study design was adopted. Elderly individuals from six nursing homes in Urumqi were selected as the research subjects from May 2021 to September 2023. Demographic characteristics， disease burden and medication records of the elderly were collected. PIM screening was performed using the Chinese PIM criteria （2024 edition）， the American Geriatrics Society （AGS） Beers criteria （2023 edition）， and the Screening Tool of Older Person’S Prescriptions（STOPP）/Screening Tool to Alert to Right Treatment（START） （2023 edition）. Fleiss’s Kappa test was used to assess the consistency among the three screening tools， and multivariate Logistic regression was used to analyze the influencing factors of PIM. RESULTS The prevalence of PIM screened by the Chinese PIM criteria was 47.4% （323/682）， by the AGS Beers criteria was 43.4% （296/682）， and by the STOPP criteria was 50.9% （347/682）. The consistency among the three screening tools was low （Fleiss’s Kappa＝0.12， P ＜0.001）. Multivariate Logistic regression analysis showed that all three screening tools indicated that polypharmacy， aspirin use and psychotropic drug use were independent risk factors for PIM among elderly in nursing houses （all OR＞1， P ＜0.05）. CONCLUSIONS The prevalence of PIM among the elderly in nursing homes in Urumqi is relatively high. Polypharmacy， aspirin use and psychotropic drug use are common independent risk factors for PIM. There are differences in screening results among the three tools with low consistency. The Chinese PIM criteria can serve as a first-line tool for PIM screening in nursing homes.

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.

Background Coal workers' pneumoconiosis is a serious occupational disease in China. Exhaled volatile organic compounds (VOCs) can serve as the "breath fingerprint" of internal pathological processes, which provides a theoretical basis for exhaled VOCs to be used as potential non-invasive biomarkers for early diagnosis of coal workers' pneumoconiosis. Objective To screen out the characteristic VOCs and important characteristic VOCs of exhaled air in patients with coal workers' pneumoconiosis, and to explore the potential of these VOCs as biomarkers for early non-invasive diagnosis of the disease. Methods In this study, 27 VOCs in the exhaled breath of 22 patients with stage I coal workers' pneumoconiosis, 77 workers exposed to dust, and 92 healthy controls were quantitatively detected by thermal desorption gas chromatography-mass spectrometry (TD-GC-MS). Substances with P<0.05 in univariate analysis and variable importance projection (VIP) >1 in supervised orthogonal partial least squares discriminant analysis (OPLS-DA) model were selected as the characteristic VOCs for early diagnosis of coal workers' pneumoconiosis. Age was included in the LASSO regression model as a covariate to screen out important characteristic VOCs, and the diagnostic performance was evaluated by receiver operating characteristic (ROC) curve. Spearman correlation was further used to explore the correlation between important characteristic VOCs and clinical lung function indicators. Results Through univariate analysis and OPLS-DA modeling, 8 VOCs were selected, including 2-methylpentane, 3-methylpentane, n-hexane, methylcyclopentane, n-heptane, methylcyclohexane, 4-methyl-2-pentanone, and 2-hexanone, in exhaled breath of patients with coal workers' pneumoconiosis. The concentrations of 4 VOCs, including 3-methylpentane, n-hexane, 4-methyl-2-pentanone, and 2-hexanone, showed a decreasing trend with the increase of dust exposure years. By LASSO regression, the important characteristic VOCs of the coal workers' pneumoconiosis group and the dust exposure group were n-hexane, methylcyclohexane and 4-methyl-2-pentanone, and the important characteristic VOCs of the coal workers' pneumoconiosis group and the healthy group were 2-methyl-pentane and 4-methyl-2-pentanone. The ROC analysis showed that the area under the curve (AUC) of n-hexane, methylcyclohexane, and 4-methyl-2-pentanone were 0.969, 0.909, and 0.956, respectively, and the AUC of combined diagnosis was 0.988 and its Youden index was 0.961, suggesting that these results can serve as a valuable reference for further research on early diagnosis. The Correlation analysis found that there was a positive correlation between n-hexane and lung function indicators in the important characteristic VOCs, indicating that it could indirectly reflect the obstruction of lung function ventilation, further proving that important characteristic VOCs have the potential to monitor lung function decline. Conclusion Three important characteristic VOCs selected in this study have the potential to be used as non-invasive biomarkers for early diagnosis and disease monitoring of coal workers' pneumoconiosis, and are worthy of further study and verification.

Methods: The clinical data of 26 patients diagnosed with irreducible atlantoaxial dislocation complicated by atlantodental bony obstruction were analyzed retrospectively. All patients underwent anterior retropharyngeal atlantodentoplasty, followed by posterior occipitocervical fusion. Details including surgical duration and blood loss volume were recorded. Radiographic data such as the anterior atlantodental interval, O–C2 angle, space available for the cord, clivus–canal angle, and cervical medullary angle, and clinical data including the Japanese Orthopedic Association (JOA) score were assessed. The fusion time of the grafted bone and the development of complications were examined. Results: In patients undergoing anterior retropharyngeal atlantodentoplasty, the surgical duration and blood loss volume were 120.1±16.4 minutes and 100.6±33.5 mL, respectively. The anterior atlantodental interval decreased significantly after the surgery (p <0.001). The O–C2 angle, space available for the cord, clivus–canal angle, and cervical medullary angle increased significantly after the surgery (p <0.001). The JOA score during the latest follow-up significantly increased compared with that before the surgery (p <0.001). The improvement rate of the JOA score was 80.8%±18.1%. The fusion time of the grafted bone was 3–8 months, with an average of 5.7±1.5 months. In total, 11 patients presented with postoperative dysphagia and three with irritating cough. However, none of them exhibited other major complications. Conclusions Anterior retropharyngeal atlantodentoplasty can anatomically reduce the atlantoaxial joint with a satisfactory clinical outcome in patients with irreducible atlantoaxial dislocation with atlantodental bony obstruction.

As new evidence emerges, treatment strategies toward the functional cure of chronic hepatitis B are evolving. In 2019, a panel of national hepatologists published a Consensus Statement on the functional cure of chronic hepatitis B. Currently, an international group of hepatologists has been assembled to evaluate research since the publication of the original consensus, and to collaboratively develop the updated statements. The 2.0 Consensus was aimed to update the original consensus with the latest available studies, and provide a comprehensive overview of the current relevant scientific literatures regarding functional cure of hepatitis B, with a particular focus on issues that are not yet fully clarified. These cover the definition of functional cure of hepatitis B, its mechanisms and barriers, the effective strategies and treatment roadmap to achieve this endpoint, in particular new surrogate biomarkers used to measure efficacy or to predict response, and the appropriate approach to pursuing a functional cure in special populations, the development of emerging antivirals and immunomodulators with potential for curing hepatitis B. The statements are primarily intended to offer international guidance for clinicians in their practice to enhance the functional cure rate of chronic hepatitis B.

Objective To analyze the relationship between the expression of ferroptosis markers in the tumor microenvironment(TME)and the prognosis of transcatheter arterial chemoembolization(TACE)for hepatocellular carcinoma(HCC).Methods This prospective observational study included 100 HCC patients who received TACE treatment at Langfang Hospital of Traditional Chinese Medicine from March 2019 to June 2021 as the study subjects.The levels of 8-isoprostaglandin F2α(8-iso-PGF2α),4-hydroxy-2-nonenal(4-HNE),8-hydroxy-2'deoxyguanosine(8-OH-dG)and hepcidin in plasma were evaluated by ELISA kit at baseline(1 day before TACE),1 day after TACE and 4～8 weeks.The changes of ferroptosis related markers during TACE treatment were compared.The difference between the level of 8-iso-PGF2α,4-HNE and the baseline 1 day after TACE treatment was recorded as △8-iso-PGF2α,△4-HNE.Results Compared with the baseline,the levels of 8-iso-PGF2α and 4-HNE increased significantly and the level of hepcidin decreased significantly one day after TACE treatment,and the differences were statistically significant(t=8.03,16.29,2.92,all P<0.05).Compared with 1 day after treatment,the levels of 8-iso-PGF2α,4-HNE decreased and the level of 8-OH-dG increased at 4～8 weeks after TACE treatment,and the differences were statistically significant(t=9.12,17.17,2.63,all P<0.05).Multivariate COX analysis showed that △8-iso-PGF2α,△4-HNE and 8-iso-PGF2α 1 day after TACE treatment were independent factors affecting the overall survival after TACE(Wald χ2=5.205,13.801,6.054,all P<0.05).The survival time of patients with △4-HNE>2.01 μg/ml was significantly longer than that of patients with △4-HNE≤2.01 μg/ml(Log-rank=5.718,P=0.017),and that of patients with△8-iso-PGF2α>1.75ng/ml was sig-nificantly longer than that of patients with△8-iso-PGF2≤1.75ng/ml(Log-rank=4.163,P=0.041).Conclusion The prognosis of HCC patients who are in a state of high ferroptosis(4-HNE and 8-iso-PGF2 increased)at 1 day after TACE treatment is better,which indicate that ferroptosis mediated HCC death induced by TACE treatment.