[Objective] To resolve the ambiguities of HLA genotyping generated by next generation sequencing (NGS) using nanopore sequencing technology. [Methods] A total of 38 samples with ambiguous HLA genotyping by NGS in our laboratory were collected, and HLA-A, -B, -C, -DRB1, -DRB3/4/5, -DQA1, -DQB1, -DPA1 and -DPB1 loci in these samples were amplified using primers in the same commercial NGS HLA genotyping kit, then subjected to third-generation library construction, and sequenced on the nanopore sequencer. The sequencing data were converted into Fastq files and analyzed by software, and the genotypes of 11 HLA loci were obtained. The ambiguities were counted directly. [Results] The high-resolution genotyping at the second domain of 11 HLA loci of 38 samples using the third generation sequencing (TGS) were consistent with the results of the NGS method at a rate of 100%. The genotypes for the HLA-A, -B, -C, -DRB3, -DRB4, -DQA1 and -DPA1 loci by TGS were all only one result, and the discrimination rate for ambiguities of the HLA-A, -B, -C, and -DQA1 loci (all caused by the difficulty in phasing due to the short NGS read length) was 100%. Among the HLA-DRB1, -DRB5, -DQB1 and -DPB1 loci, the discrimination rate of TGS for the ambiguities caused by non-amplification of exon 1 was 0% and by the short NGS read length was 100%. [Conclusion] Nanopore technology was used to identify the ambiguities of 11 HLA loci in this study, and the ambiguities caused by the short read length disadvantage of the NGS method could be solved effectively and the accuracy of HLA genotyping would be improved.

Background: s/Aims: Non-invasive models stratifying clinically significant portal hypertension (CSPH) are limited. Herein, we developed a new non-invasive model for predicting CSPH in patients with compensated cirrhosis and investigated whether carvedilol can prevent hepatic decompensation in patients with high-risk CSPH stratified using the new model. Methods: Non-invasive risk factors of CSPH were identified via systematic review and meta-analysis of studies involving patients with hepatic venous pressure gradient (HVPG). A new non-invasive model was validated for various performance aspects in three cohorts, i.e., a multicenter HVPG cohort, a follow-up cohort, and a carvediloltreating cohort. Results: In the meta-analysis with six studies (n=819), liver stiffness measurement and platelet count were identified as independent risk factors for CSPH and were used to develop the new “CSPH risk” model. In the HVPG cohort (n=151), the new model accurately predicted CSPH with cutoff values of 0 and –0.68 for ruling in and out CSPH, respectively. In the follow-up cohort (n=1,102), the cumulative incidences of decompensation events significantly differed using the cutoff values of <–0.68 (low-risk), –0.68 to 0 (medium-risk), and >0 (high-risk). In the carvediloltreated cohort, patients with high-risk CSPH treated with carvedilol (n=81) had lower rates of decompensation events than non-selective beta-blockers untreated patients with high-risk CSPH (n=613 before propensity score matching [PSM], n=162 after PSM). Conclusions Treatment with carvedilol significantly reduces the risk of hepatic decompensation in patients with high-risk CSPH stratified by the new model.

Background: s/Aims: Non-invasive models stratifying clinically significant portal hypertension (CSPH) are limited. Herein, we developed a new non-invasive model for predicting CSPH in patients with compensated cirrhosis and investigated whether carvedilol can prevent hepatic decompensation in patients with high-risk CSPH stratified using the new model. Methods: Non-invasive risk factors of CSPH were identified via systematic review and meta-analysis of studies involving patients with hepatic venous pressure gradient (HVPG). A new non-invasive model was validated for various performance aspects in three cohorts, i.e., a multicenter HVPG cohort, a follow-up cohort, and a carvediloltreating cohort. Results: In the meta-analysis with six studies (n=819), liver stiffness measurement and platelet count were identified as independent risk factors for CSPH and were used to develop the new “CSPH risk” model. In the HVPG cohort (n=151), the new model accurately predicted CSPH with cutoff values of 0 and –0.68 for ruling in and out CSPH, respectively. In the follow-up cohort (n=1,102), the cumulative incidences of decompensation events significantly differed using the cutoff values of <–0.68 (low-risk), –0.68 to 0 (medium-risk), and >0 (high-risk). In the carvediloltreated cohort, patients with high-risk CSPH treated with carvedilol (n=81) had lower rates of decompensation events than non-selective beta-blockers untreated patients with high-risk CSPH (n=613 before propensity score matching [PSM], n=162 after PSM). Conclusions Treatment with carvedilol significantly reduces the risk of hepatic decompensation in patients with high-risk CSPH stratified by the new model.

Background: s/Aims: Non-invasive models stratifying clinically significant portal hypertension (CSPH) are limited. Herein, we developed a new non-invasive model for predicting CSPH in patients with compensated cirrhosis and investigated whether carvedilol can prevent hepatic decompensation in patients with high-risk CSPH stratified using the new model. Methods: Non-invasive risk factors of CSPH were identified via systematic review and meta-analysis of studies involving patients with hepatic venous pressure gradient (HVPG). A new non-invasive model was validated for various performance aspects in three cohorts, i.e., a multicenter HVPG cohort, a follow-up cohort, and a carvediloltreating cohort. Results: In the meta-analysis with six studies (n=819), liver stiffness measurement and platelet count were identified as independent risk factors for CSPH and were used to develop the new “CSPH risk” model. In the HVPG cohort (n=151), the new model accurately predicted CSPH with cutoff values of 0 and –0.68 for ruling in and out CSPH, respectively. In the follow-up cohort (n=1,102), the cumulative incidences of decompensation events significantly differed using the cutoff values of <–0.68 (low-risk), –0.68 to 0 (medium-risk), and >0 (high-risk). In the carvediloltreated cohort, patients with high-risk CSPH treated with carvedilol (n=81) had lower rates of decompensation events than non-selective beta-blockers untreated patients with high-risk CSPH (n=613 before propensity score matching [PSM], n=162 after PSM). Conclusions Treatment with carvedilol significantly reduces the risk of hepatic decompensation in patients with high-risk CSPH stratified by the new model.

Recent advances in large models demonstrate significant prospects for transforming the field of medical imaging. These models, including large language models, large visual models, and multimodal large models, offer unprecedented capabilities in processing and interpreting complex medical data across various imaging modalities. By leveraging self-supervised pretraining on vast unlabeled datasets, cross-modal representation learning, and domain-specific medical knowledge adaptation through fine-tuning, large models can achieve higher diagnostic accuracy and more efficient workflows for key clinical tasks. This review summarizes the concepts, methods, and progress of large models in medical imaging, highlighting their potential in precision medicine. The article first outlines the integration of multimodal data under large model technologies, approaches for training large models with medical datasets, and the need for robust evaluation metrics. It then explores how large models can revolutionize applications in critical tasks such as image segmentation, disease diagnosis, personalized treatment strategies, and real-time interactive systems, thus pushing the boundaries of traditional imaging analysis. Despite their potential, the practical implementation of large models in medical imaging faces notable challenges, including the scarcity of high-quality medical data, the need for optimized perception of imaging phenotypes, safety considerations, and seamless integration with existing clinical workflows and equipment. As research progresses, the development of more efficient, interpretable, and generalizable models will be critical to ensuring their reliable deployment across diverse clinical environments. This review aims to provide insights into the current state of the field and provide directions for future research to facilitate the broader adoption of large models in clinical practice.
Humans ; Diagnostic Imaging/methods* ; Precision Medicine/methods* ; Image Processing, Computer-Assisted/methods*

This study aims to integrate data mining techniques of single cell transcriptomics and spatial transcriptomics, along with animal experiment validation, so as to systematically characterize the protective effects of Jianpi Tongluo Formula(JTF) on the cartilage in knee osteoarthritis(KOA) and elucidate the underlying molecular mechanisms. Single cell transcriptomics and spatial transcriptomics datasets(GSE254844 and GSE255460) of the cartilage tissue obtained from KOA patients were analyzed to map the single cell-spatial heterogeneity and identify key pathogenic factors. After that, a KOA rat model was established via knee joint injection of papain. The intervention effects of JTF on the expression features of these key factors were assessed through real-time quantitative polymerase chain reaction(PCR), Western blot, and immunohistochemical staining. As a result, the integrated single cell and spatial transcriptomics data identified distinct cell subsets with different pathological changes in different regions of the inflamed cartilage tissue in KOA, and their differentiation trajectories were closely related to the inflammatory fibrosis-like pathological changes of chondrocytes. Accordingly, the expression levels of the two key effect targets, namely nuclear receptor coactivator 4(NCOA4) and high mobility group box 1(HMGB1) were significantly reduced in the articular surface and superficial zone of the inflamed joints when JTF effectively alleviated various pathological changes in KOA rats, thus reversing the abnormal chondrocyte autophagy level, relieving the inflammatory responses and fibrosis-like pathological changes, and promoting the repair of chondrocyte function. Collectively, this study revealed the heterogeneous characteristics and dynamic changes of inflamed cartilage tissue in different regions and different cell subsets in KOA patients. It is worth noting that NCOA4 and HMGB1 were crucial in regulating chondrocyte autophagy and inflammatory reaction, while JTF could reverse the regulation of NCOA4 and HMGB1 and correct the abnormal molecular signal axis in the target cells of the inflamed joints. The research can provide a new research idea and scientific basis for developing a personalized therapeutic schedule targeting the spatiotemporal heterogeneity characteristics of KOA.
Animals ; Drugs, Chinese Herbal/administration & dosage* ; Rats ; Osteoarthritis, Knee/pathology* ; Humans ; Male ; Cartilage, Articular/metabolism* ; Chondrocytes/metabolism* ; Rats, Sprague-Dawley ; Female ; Protective Agents/administration & dosage* ; Single-Cell Analysis ; Middle Aged ; HMGB1 Protein/metabolism*

Continuous manufacturing, as an innovative pharmaceutical production model, offers advantages such as high production efficiency and ease of control compared to traditional batch production, aligning with the future trend of drug production moving toward greater efficiency and intelligence. However, the development of continuous manufacturing technology in wet granulation has been slow. On one hand, this is closely related to its high technical complexity, substantial equipment investment costs, and stringent process control requirements. On the other hand, the long-term use of the traditional batch production model has created strong path dependence, and the lack of mature standardized processes further increases the difficulty of technological transformation. To promote the deep integration of wet granulation technology with continuous manufacturing, this review systematically outlines the current application of wet granulation in continuous manufacturing. It focuses on the development of key technologies such as online detection, process modeling, and process scale-up, with the aim of providing a reference for process innovation and application in wet granulation.
Drug Compounding/instrumentation* ; Technology, Pharmaceutical/methods* ; Drugs, Chinese Herbal/chemistry* ; Models, Theoretical

This paper aimed to use non-targeted urine metabolomics to reveal the potential biomarkers of toxicity in rats with hepatic injury induced by aqueous extracts of Dictamni Cortex(ADC). Forty-eight SD rats were randomly assigned to a blank group and high-dose, medium-dose, and low-dose ADC groups, with 12 rats in each group(half male and half female), and they were administered orally for four weeks. The hepatic injury in SD rats was assessed by body weight, liver weight/index, biochemical index, L-glutathione(GSH), malondialdehyde(MDA), and pathological alterations. The qPCR was utilized to determine the expression of metabolic enzymes in the liver and inflammatory factors. Differential metabolites were screened using principal component analysis(PCA) and partial least squares-discriminant analysis(PLS-DA), followed by a metabolic pathway analysis. The Mantel test was performed to assess differential metabolites and abnormally expressed biochemical indexes, obtaining potential biomarkers. The high-dose ADC group showed a decrease in body weight and an increase in liver weight and index, resulting in hepatic inflammatory cell infiltration and hepatic steatosis. In addition, this group showed elevated levels of MDA, cytochrome P450(CYP) 3A1, interleukin-1β(IL-1β), and tumor necrosis factor-α(TNF-α), as well as lower levels of alanine transaminase(ALT) and GSH. A total of 76 differential metabolites were screened from the blank and high-dose ADC groups, which were mainly involved in the pentose phosphate pathway, tryptophan metabolism, purine metabolism, pentose and glucuronic acid interconversion, galactose metabolism, glutathione metabolism, and other pathways. The Mantel test identified biomarkers of hepatotoxicity induced by ADC in SD rats, including glycineamideribotide, dIDP, and galactosylglycerol. In summary, ADC induced hepatotoxicity by disrupting glucose metabolism, ferroptosis, purine metabolism, and other pathways in rats, and glycineamideribotide, dIDP, and galactosylglycerol could be employed as the biomarkers of its toxicity.
Animals ; Male ; Rats, Sprague-Dawley ; Rats ; Metabolomics ; Biomarkers/metabolism* ; Liver/metabolism* ; Drugs, Chinese Herbal/adverse effects* ; Female ; Chemical and Drug Induced Liver Injury/metabolism* ; Glutathione/metabolism* ; Humans