Autoimmune pancreatitis is a special type of chronic pancreatitis that can lead to abnormal pancreatic exocrine function in patients. Autoimmune pancreatitis comorbid with pancreatic exocrine insufficiency has a complex pathogenesis， and there is limited research on this topic， leading to the lack of understanding of such patients in clinical practice. This article introduces the epidemiology of autoimmune pancreatitis， briefly describes the pathogenesis of pancreatic exocrine insufficiency caused by autoimmune pancreatitis， and summarizes the various detection methods for pancreatic exocrine function， nutritional assessments， lifestyle management， and drug therapy， in order to strengthen the understanding of autoimmune pancreatitis comorbid with pancreatic exocrine insufficiency and improve the clinical diagnosis and treatment of pancreatic exocrine insufficiency.

Intraoperative cell salvage (IOCS) has been widely applied as an important blood conservation measure in surgical operations. However, there is currently a lack of clinical practice guidelines for the implementation of IOCS in patients with malignant tumors. This report aims to provide clinicians with recommendations on the use of IOCS in patients with malignant tumors based on the review and assessment of the existed evidence. Data were derived from databases such as PubMed, Embase, the Cochrane Library and Wanfang. The guideline development team formulated recommendations based on the quality of evidence, balance of benefits and harms, patient preferences, and health economic assessments. This study constructed seven major clinical questions. The main conclusions of this guideline are as follows: 1) Compared with no perioperative allogeneic blood transfusion (NPABT), perioperative allogeneic blood transfusion (PABT) leads to a more unfavorable prognosis in cancer patients (Recommended); 2) Compared with the transfusion of allogeneic blood or no transfusion, IOCS does not lead to a more unfavorable prognosis in cancer patients (Recommended); 3) The implementation of IOCS in cancer patients is economically feasible (Recommended); 4) Leukocyte depletion filters (LDF) should be used when implementing IOCS in cancer patients (Strongly Recommended); 5) Irradiation treatment of autologous blood to be reinfused can be used when implementing IOCS in cancer patients (Recommended); 6) A careful assessment of the condition of cancer patients (meeting indications and excluding contraindications) should be conducted before implementing IOCS (Strongly Recommended); 7) Informed consent from cancer patients should be obtained when implementing IOCS, with a thorough pre-assessment of the patient's condition and the likelihood of blood loss, adherence to standardized internally audited management procedures, meeting corresponding conditions, and obtaining corresponding qualifications (Recommended). In brief, current evidence indicates that IOCS can be implemented for some malignant tumor patients who need allogeneic blood transfusion after physician full evaluation, and LDF or irradiation should be used during the implementation process.

Hepatocellular carcinoma (HCC) is a leading cause of cancer-associated death globally. Liver transplantation (LT) has emerged as a key treatment for patients with HCC, and the Milan criteria have been adopted as the cornerstone of the selection policy. To allow more patients to benefit from LT, a number of expanded criteria have been proposed, many of which use radiologic morphological characteristics with larger and more tumors as surrogates to predict outcomes. Other groups developed indices incorporating biological variables and dynamic markers of response to locoregional treatment. These expanded selection criteria achieved satisfactory results with limited liver supplies. In addition, a number of prognostic models have been developed using clinicopathological characteristics, imaging radiomics features, genetic data, and advanced techniques such as artificial intelligence. These models could improve prognostic estimation, establish surveillance strategies, and bolster long-term outcomes in patients with HCC. In this study, we reviewed the latest findings and achievements regarding the selection criteria and post-transplant prognostic models for LT in patients with HCC.

Hepatocellular carcinoma (HCC) is a leading cause of cancer-associated death globally. Liver transplantation (LT) has emerged as a key treatment for patients with HCC, and the Milan criteria have been adopted as the cornerstone of the selection policy. To allow more patients to benefit from LT, a number of expanded criteria have been proposed, many of which use radiologic morphological characteristics with larger and more tumors as surrogates to predict outcomes. Other groups developed indices incorporating biological variables and dynamic markers of response to locoregional treatment. These expanded selection criteria achieved satisfactory results with limited liver supplies. In addition, a number of prognostic models have been developed using clinicopathological characteristics, imaging radiomics features, genetic data, and advanced techniques such as artificial intelligence. These models could improve prognostic estimation, establish surveillance strategies, and bolster long-term outcomes in patients with HCC. In this study, we reviewed the latest findings and achievements regarding the selection criteria and post-transplant prognostic models for LT in patients with HCC.

Hepatocellular carcinoma (HCC) is a leading cause of cancer-associated death globally. Liver transplantation (LT) has emerged as a key treatment for patients with HCC, and the Milan criteria have been adopted as the cornerstone of the selection policy. To allow more patients to benefit from LT, a number of expanded criteria have been proposed, many of which use radiologic morphological characteristics with larger and more tumors as surrogates to predict outcomes. Other groups developed indices incorporating biological variables and dynamic markers of response to locoregional treatment. These expanded selection criteria achieved satisfactory results with limited liver supplies. In addition, a number of prognostic models have been developed using clinicopathological characteristics, imaging radiomics features, genetic data, and advanced techniques such as artificial intelligence. These models could improve prognostic estimation, establish surveillance strategies, and bolster long-term outcomes in patients with HCC. In this study, we reviewed the latest findings and achievements regarding the selection criteria and post-transplant prognostic models for LT in patients with HCC.

ObjectiveTo improve the quality and service performance of community visual health services in Shanghai, and to establish a set of reasonable and effective evaluation index system for community visual health services. MethodsCentered on the national and Shanghai-based visual health policies and based on the current status and development trends of community visual health service program in Shanghai, the candidate indicators were formed through literature review and expert interviews, firstly. The framework of an evaluation index system was formulated through qualitative research successively, which was further revised and perfected using the Delphi method. Coefficient weights were calculated using the analytic hierarchy process (AHP), culminating in the establishment of the community visual health evaluation index system, lastly. ResultsA total of 22 visual health experts from district-level center for disease control, hospital ophthalmology and leaders in charging of visual health service in community health centers participated in the Delphi questionnaire survey, with a questionnaire recovery rate of 100% and an expert authority coefficient of 0.86, indicating high credibility. After a round of correspondence to experts’ importance ratings and discussions, a comprehensive evaluation index system comprising 3 primary indicators, 12 secondary indicators, and 47 tertiary indicators, along with 5 additional indicators, was finalized. ConclusionAn index system tailored to effective evaluation for community visual health initiatives was drawn up in this study, which can promote the capacity building in community eye health services, facilitating the high-quality development of visual health courses, and enhancing residents’ eye health.

Background Temporal kurtosis (without frequency weighting, i.e., Z-weighted kurtosis) can evaluate noise-induced hearing loss (NIHL). However, few studies have considered the function of frequency weighting (A- or C-weighted) kurtosis on NIHL. Objective To study the significance of A- and C-weighted kurtosis adjustment for equivalent sound level (L'_{EX,8 h}) in evaluating occupational hearing loss. Methods A cross-sectional survey was used to select 973 noise-exposed workers in seven industries as the subjects. The noise exposure of all workers was assessed by distributions of A-, C-, and Z-weighted kurtosis (e.g., K_A, K_C, and K_Z) and respective adjusted equivalent sound level (e.g., L'_{EX,8 h}-K_A, L'_{EX,8 h}-K_C, and L'_{EX,8 h}-K_Z). The significance of A- and C-weighted kurtosis in evaluating NIHL was evaluated by correlations between three types of L'_{EX,8 h} and NIHL, and improvement of noise-induced permanent threshold shift (NIPTS) underestimation predicted by the ISO prediction model (Acoustics—Estimation of noise-induced hearing loss, ISO 1999-2013). Results The median K_A, K_C, and K_Z were 68.33, 28.22, and 19.82, respectively. The binary logistic regression showed that L_{EX, 8 h}-K_A, L_{EX, 8 h}-K_C, and L'_{EX, 8 h}-K_Z were risk factors for NIHL (OR>1, P<0.001). The receiver operating characteristic (ROC) curve showed that when the outcome variable was noise-induced hearing impairment (NIHI), the areas under the curves corresponding to L'_{EX,8 h}-K_A, L'_{EX,8 h}-K_C, and L'_{EX,8 h}-K_Z were 0.625, 0.628, and 0.625, respectively. When the outcome variable was high-frequency noise-induced hearing loss (HFNIHL), the areas under the curves corresponding to L'_{EX,8 h}-K_A, L'_{EX, 8 h}-K_C, and L'_{EX,8 h}-K_Z were 0.624, 0.623, and 0.622, respectively (P<0.05). The order of underestimation improvement values predicted by L'_{EX,8 h} for NIPTS₁₂₃₄ was: L'_{EX,8 h}-K_A (4.68 dB HL)>L'_{EX,8 h}-K_C (4.38 dB HL)>L'_{EX,8 h}-K_Z (4.28 dB HL) (P<0.001). The order of underestimation improvement values predicted by L'_{EX,8 h}-K for NIPTS₃₄₆ was: L'_{EX,8 h}-K_A (7.20 dB HL)>L'_{EX,8 h}-K_C (6.83 dB HL)>L'_{EX,8 h}-K_Z (6.71 dB HL) (P<0.001). Conclusion The adjustment of A- and C-weighted kurtosis to equivalent sound level L_{EX,8 h} can effectively improve the accuracy of the ISO 1999 prediction model in NIPTS prediction, and compared with the C-weighted, the A-weighted kurtosis can improve the result of the ISO 1999 prediction model in terms of underestimating NIPTS.

The objective of this study was to analyze the effects on cell viability, apoptosis, and cell cycle of non-small cell lung cancer (NSCLC) A549 cells after intervention with Agrimonia pilosa (AP) and investigate Agrimonia pilosa anti-tumor activity in vitro. Meanwhile, liquid chromatography mass spectrometry (LC-MS) metabolomics technology was used to analyze the changes of cellular metabolites and metabolic pathways. The results of this study will provide a theoretical and experimental basis for investigating the mechanism of the effect of Agrimonia pilosa on non-small cell lung cancer A549 cells. The results showed that the cell nucleus of A549 cells crumpled and apoptosis occurred with the increase of drug concentration. The survival rate of the cells decreased, and the inhibition rate reached 21.5% and 91.74% under the low and high dose conditions, respectively. Lactate dehydrogenase (LDH) content increased (P < 0.05). Metabolomics results showed significant differences in metabolism between groups, thirty-three distinct metabolites including LysoPC(24:0/0:0), LysoPC(17:0/0:0) and PC(O-40:5) were deduced. The pathway enrichment showed that the Agrimonia pilosa plays an anti-tumor role mainly by regulating the metabolism of glycerophosphate and purine in A549 cells, in which the effect on glycerophosphate metabolism pathway was most significant. The results of combined pharmacodynamics suggested that Agrimonia pilosa might induce apoptosis and inhibit the growth of A549 cells by regulating LysoPC(24:0/0:0), LysoPC(17:0/0:0) and PC(O-40:5) metabolites in A549 cells.

Endo-beta-N-acetylglucosaminidase (ENGase) is widely distributed in various organisms. The first reported ENGase activity was detected in Diplococcus pneumoniae in 1971. The protein (Endo D) was purified and its peptide sequence was determined in 1974. Three ENGases (Endo F1-F3) were discovered in Flavobacterium meningosepticum from 1982 to 1993. After that, the activity was detected from different species of bacteria, yeast, fungal, plant, mice, human, etc. Multiple ENGases were detected in some species, such as Arabidopsis thaliana and Trichoderma atroviride. The first preliminary crystallographic analysis of ENGase was conducted in 1994. But to date, only a few ENGases structures have been obtained, and the structure of human ENGase is still missing. The currently identified ENGases were distributed in the GH18 or GH85 families in Carbohydrate-Active enZyme (CAZy) database. GH18 ENGase only has hydrolytic activity, but GH85 ENGase has both hydrolytic and transglycosylation activity. Although ENGases of the two families have similar (β/α)8-TIM barrel structures, the active sites are slightly different. ENGase is an effective tool for glycan detection andglycan editing. Biochemically, ENGase can specifically hydrolyze β‑1,4 glycosidic bond between the twoN-acetylglucosamines (GlcNAc) on core pentasaccharide presented on glycopeptides and/or glycoproteins. Different ENGases may have different substrate specificity. The hydrolysis products are oligosaccharide chains and a GlcNAc or glycopeptides or glycoproteins with a GlcNAc. Conditionally, it can use the two products to produce a new glycopeptides or glycoprotein. Although ENGase is a common presentation in cell, its biological function remains unclear. Accumulated evidences demonstrated that ENGase is a none essential gene for living and a key regulator for differentiation. No ENGase gene was detected in the genomes of Saccharomyces cerevisiae and three other yeast species. Its expression was extremely low in lung. As glycoproteins are not produced by prokaryotic cells, a role for nutrition and/or microbial-host interaction was predicted for bacterium produced enzymes. In the embryonic lethality phenotype of the Ngly1-deficient mice can be partially rescued by Engase knockout, suggesting down regulation of Engase might be a solution for stress induced adaptation. Potential impacts of ENGase regulation on health and disease were presented. Rabeprazole, a drug used for stomach pain as a proton inhibitor, was identified as an inhibitor for ENGase. ENGases have been applied in vitro to produce antibodies with a designated glycan. The two step reactions were achieved by a pair of ENGase dominated for hydrolysis of substrate glycoprotein and synthesis of new glycoprotein with a free glycan of designed structure, respectively. In addition, ENGase was also been used in cell surface glycan editing. New application scenarios and new detection methods for glycobiological engineering are quickly opened up by the two functions of ENGase, especially in antibody remodeling and antibody drug conjugates. The discovery, distribution, structure property, enzymatic characteristics and recent researches in topical model organisms of ENGase were reviewed in this paper. Possible biological functions and mechanisms of ENGase, including differentiation, digestion of glycoproteins for nutrition and stress responding were hypothesised. In addition, the role of ENGase in glycan editing and synthetic biology was discussed. We hope this paper may provide insights for ENGase research and lay a solid foundation for applied and translational glycomics.

Objective To investigate the feasibility of developing a grading diagnostic model for schistosomiasis-induced liver fibrosis based on B-mode ultrasonographic images and clinical laboratory indicators. Methods Ultrasound images and clinical laboratory testing data were captured from schistosomiasis patients admitted to the Second People’s Hospital of Duchang County, Jiangxi Province from 2018 to 2022. Patients with grade I schistosomiasis-induced liver fibrosis were enrolled in Group 1, and patients with grade II and III schistosomiasis-induced liver fibrosis were enrolled in Group 2. The machine learning binary classification tasks were created based on patients’radiomics and clinical laboratory data from 2018 to 2021 as the training set, and patients’radiomics and clinical laboratory data in 2022 as the validation set. The features of ultrasonographic images were labeled with the ITK-SNAP software, and the features of ultrasonographic images were extracted using the Python 3.7 package and PyRadiomics toolkit. The difference in the features of ultrasonographic images was compared between groups with t test or Mann-Whitney U test, and the key imaging features were selected with the least absolute shrinkage and selection operator (LASSO) regression algorithm. Four machine learning models were created using the Scikit-learn repository, including the support vector machine (SVM), random forest (RF), linear regression (LR) and extreme gradient boosting (XGBoost). The optimal machine learning model was screened with the receiver operating characteristic curve (ROC), and features with the greatest contributions to the differentiation features of ultrasound images in machine learning models with the SHapley Additive exPlanations (SHAP) method. Results The ultrasonographic imaging data and clinical laboratory testing data from 491 schistosomiasis patients from 2019 to 2022 were included in the study, and a total of 851 radiomics features and 54 clinical laboratory indicators were captured. Following statistical tests (t = −5.98 to 4.80, U = 6 550 to 20 994, all P values < 0.05) and screening of key features with LASSO regression, 44 features or indicators were included for the subsequent modeling. The areas under ROC curve (AUCs) were 0.763 and 0.611 for the training and validation sets of the SVM model based on clinical laboratory indicators, 0.951 and 0.892 for the training and validation sets of the SVM model based on radiomics, and 0.960 and 0.913 for the training and validation sets of the multimodal SVM model. The 10 greatest contributing features or indicators in machine learning models included 2 clinical laboratory indicators and 8 radiomics features. Conclusions The multimodal machine learning models created based on ultrasound-based radiomics and clinical laboratory indicators are feasible for intelligent identification of schistosomiasis-induced liver fibrosis, and are effective to improve the classification effect of one-class data models.