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.

The chemical composition of Paeoniae Radix Alba(PRA) is complex, with primary secondary metabolites including monoterpenoids, tannins, triterpenoids, and flavonoids. In previous studies on the material basis of PRA, it was found that, in addition to the widely studied characteristic monoterpene glycosides, tannic acid components also play an important role in the efficacy of PRA. However, their pharmacological effects have not been thoroughly investigated. This paper reviews the tannic acid components in PRA, including pentagaloyl glucose(PGG), tetragaloyl glucose(TGG), trigaloyl glucose(TriGG), and gallic acid, along with their structures, properties, and characteristics to provide a detailed discussion of their pharmacological activities and related mechanisms, aiming to offer a theoretical basis for the material basis research and clinical application of PRA.
Paeonia/chemistry* ; Tannins/chemistry* ; Humans ; Drugs, Chinese Herbal/chemistry* ; Animals ; Plant Extracts

PURPOSE: This study aims to identify the prevalence and risk factors of military training-related abdominal injuries and help plan and conduct training properly. METHODS: This questionnaire survey study was conducted from October 2021 to May 2022 among military personnel from 6 military units and 8 military medical centers and participants' medical records were consulted to identify the training-related abdominal injuries. All the military personnel who ever participated in military training were included. Those who refused to participate in this study or provided an incomplete questionnaire were excluded. The questionnaire collected demographic information, type of abdominal injury, frequency, training subjects, triggers, treatment, and training disturbance. Chi-square test and t-test were used to compare baseline information. Univariate and multivariate regression analyses were used to explore the risk factors associated with military training-related abdominal injuries. RESULTS: A total of 3058 participants were involved in this study, among which 1797 (58.8%) had suffered training-related abdominal injuries (the mean age was 24.3 years and the service time was 5.6 years), while 1261 (41.2%) had no training-related abdominal injuries (the mean age was 23.1 years and the service time was 4.3 years). There were 546 injured patients (30.4%) suspended the training and 84 (4.6%) needed to be referred to higher-level hospitals. The most common triggers included inadequate warm-up, fatigue, and intense training. The training subjects with the most abdominal injuries were long-distance running (589, 32.8%). Civil servants had the highest rate of abdominal trauma (17.1%). Age ≥ 25 years, military service ≥ 3 years, poor sleep status, and previous abdominal history were independent risk factors for training-related abdominal injury. CONCLUSION More than half of the military personnel have suffered military training-related abdominal injuries. Inadequate warm-up, fatigue, and high training intensity are the most common inducing factors. Scientific and proper training should be conducted according to the factors causing abdominal injuries.
Humans ; Military Personnel ; Risk Factors ; Prevalence ; Male ; Abdominal Injuries/etiology* ; Female ; Adult ; Surveys and Questionnaires ; Young Adult

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*

Fifty whitespotted bamboo sharks (Chiloscyllium playgiosum) of both sexes were used to establish a large capacity variable domain of the new antigen receptor (VNAR) library with a total capacity of over 10⁹ colony-forming units (CFU). It was applied to screen VNARs against human serum albumin (HSA) and human transcription factor EB (TFEB), respectively. Meanwhile, VNAR libraries specific to HSA and TFEB with capacities above 10⁸ CFU were obtained following conventional immunization. These two approaches were systematically studied in terms of VNAR yield and composition. By comparing the VNAR sequences obtained from naïve and antigen-immunized libraries, we found that the complementary-determining region 3 (CDR3) of the former differs in composition from that of the latter. It shares a higher degree of homology with the naïve library. Meanwhile, the binding efficiency assessed by ELISA is also different between the naïve and antigen-immunized libraries. The binding of VNARs from the TFEB-immunized library appeared to surpass that observed with the naïve libraries, whereas the performance of VNARs from the HSA-immunized library indicated that both the immunized and naïve libraries for HSA had positive binding responses in polyclonal and monoclonal ELISA. The results are useful to develop novel diagnostic and therapeutic products based on shark VNARs.

Drug repurposing offers a promising alternative to traditional drug development and significantly reduces costs and timelines by identifying new therapeutic uses for existing drugs. However, the current approaches often rely on limited data sources and simplistic hypotheses, which restrict their ability to capture the multi-faceted nature of biological systems. This study introduces adaptive multi-view learning (AMVL), a novel methodology that integrates chemical-induced transcriptional profiles (CTPs), knowledge graph (KG) embeddings, and large language model (LLM) representations, to enhance drug repurposing predictions. AMVL incorporates an innovative similarity matrix expansion strategy and leverages multi-view learning (MVL), matrix factorization, and ensemble optimization techniques to integrate heterogeneous multi-source data. Comprehensive evaluations on benchmark datasets (Fdataset, Cdataset, and Ydataset) and the large-scale iDrug dataset demonstrate that AMVL outperforms state-of-the-art (SOTA) methods, achieving superior accuracy in predicting drug-disease associations across multiple metrics. Literature-based validation further confirmed the model's predictive capabilities, with seven out of the top ten predictions corroborated by post-2011 evidence. To promote transparency and reproducibility, all data and codes used in this study were open-sourced, providing resources for processing CTPs, KG, and LLM-based similarity calculations, along with the complete AMVL algorithm and benchmarking procedures. By unifying diverse data modalities, AMVL offers a robust and scalable solution for accelerating drug discovery, fostering advancements in translational medicine and integrating multi-omics data. We aim to inspire further innovations in multi-source data integration and support the development of more precise and efficient strategies for advancing drug discovery and translational medicine.

Pharmacokinetics of traditional Chinese medicine(TCM)is a discipline that adopts pharmacokinetic research methods and techniques under the guidance of TCM theories to elucidate the dynamic changes in the absorption,distribution,metabolism and excretion of active ingredients,active sites,single-flavour Chinese medicinal and compounded formulas of TCM in vivo.However,the sources and components of TCM are complex,and the pharmacodynamic substances and mechanisms of action of the majority of TCM are not yet clear,so the pharmacokinetic study of TCM is later than that of chemical medicines,and is far more complex than that of chemical medicines,and its development also confronts with challenges.The pharmacokinetic study of TCM originated in the 1950s and has experienced more than 70 years of development from the initial in vivo study of a single active ingredient,to the pharmacokinetic and pharmacodynamic study of active ingredients,to the pharmacokinetic study of compound and multi-component of Chinese medicine.In recent years,with the help of advanced extraction,separation and analysis technologies,gene-editing animals and cell models,multi-omics technologies,protein purification and structure analysis technologies,and artificial intelligence,etc.,the pharmacokinetics of TCM has been substantially applied in revealing and elucidating the pharmacodynamic substances and mechanisms of action of Chinese medicines,research and development of new drugs of TCM,scientific and technological upgrading of large varieties of Chinese patent medicines,as well as guiding the rational use of medicines in clinics.Pharmacokinetic studies of TCM have made remarkable breakthroughs and significant development in theory,methodology,technology and application.In this paper,the history of the development of pharmacokinetics of TCM and the progress of cutting-edge research was reviewed,with the aim of providing ideas and references for the pharmacokinetics of TCM and related research.

Objective To evaluate the perioperative safety of lung surgery for patients with corona virus disease 2019 (COVID-19). Methods We retrospectively analyzed the clinical data of the patients recovered from COVID-19 infection and received lung surgery from December 2022 to February 2023 in the Department of Thoracic Surgery at Beijing Hospital. Patients who received lung surgery and without COVID-19 at the same time were selected as a control group. Perioperative data between the two groups were compared. Results A total of 103 patients were included with 44 males and 49 females at an average age of (62.2±12.1) years. All surgeries were performed by uniportal video-assisted thoracoscopic surgery (VATS). Among patients who recovered from COVID-19, 53 (51.5%) received lobectomy, 30 (29.1%) received segmentectomy, and 20 (19.4%) received wedge resection. The interval between diagnosis of infection and lung surgery was ≤1 month in 32 (31.1%) patients, and >1 month in 71 (68.9%) patients. The results of virus nucleic acid test for all patients before surgery were negative. A total of 13 (12.6%) patients had positive IgM, and 100 (97.1%) patients had positive IgG. A total of 20 patients experienced perioperative complications (13 patients with pulmonary air leakage, 3 patients with chylothorax, 2 patients with atrial fibrillation, and 2 patients with severe pulmonary complications). There was one perioperative death. Comparing the patients who recovered from COVID-19 with those without COVID-19, we found no statistical difference in perioperative outcomes including surgical duration, postoperative drainage, duration of thoracic tube, and duration of postoperative stay (P>0.05). There was no significant difference in perioperative complications between the two groups (P>0.05). Multivariable logistical regression analysis demonstrated that positive IgM before surgery (OR=7.319, 95%CI 1.669 to 32.103, P=0.008), and longer duration of surgery (OR=1.016, 95%CI 1.003 to 1.028, P=0.013) were independent risk factors of perioperative complications for patients who recovered from COVID-19. Conclusion It is safe for patients recover from COVID-19 to receive lung surgery when symptoms disappear and the nucleic acid test turn negative. However, positive COVID-19 IgM is an independent risk factor for perioperative complications. We suggest that lung surgery could be performed when the nucleic acid test and COVID-19 IgM are both negative for patients recover from COVID-19 infection.