Histopathological examination is currently the gold standard for the diagnosis of metabolic associated fatty liver disease （MAFLD）； however， due to its invasiveness， high risks， and low feasibility， application of noninvasive indicators in the staging and classification of MAFLD has become a research hotspot. This article systematically reviews the efficacy of dynamic changes in various noninvasive markers in reflecting histological changes and clinical outcome events in MAFLD patients， in order to provide theoretical support for dynamic monitoring and individualized management of the disease.

ObjectiveTo delineate imaging findings using an imaging platform and investigate the correlation between MRI characteristics of spinal cord atrophy and clinical diagnosis in children with spinal cord injury (SCI). MethodsImaging data of 150 children with SCI admitted to Beijing Bo'ai Hospital, China Rehabilitation Research Center, from January, 2002 to March, 2024 were collected and imported into the imaging platform. The anteroposterior and transverse diameters of the middle part of the spinal cord at the cross-section with the most severe atrophy were measured, and the relevant indicators of the previous normal spinal cord segment were measured as controls; the radiomic features were extracted. Clinical data of the children including gender, age, cause of injury, sensory level, motor level, spinal cord injury level, injury severity and disease course were collected. ResultsSpinal cord atrophy was identified in 81 cases (54%), among which 78 cases (96%) were American Spinal Injury Association Impairment Scale (AIS) grade A and 3 cases (4%) were AIS grade C. The upper boundary of the spinal cord atrophy site strongly correlated with the injury level, motor level and sensory level (r > 0.8, P < 0.001). ConclusionMore than half of children with SCI may develop secondary spinal cord atrophy, the vast majority of whom suffer from complete spinal cord injury; the upper boundary of spinal cord atrophy is correlated with the injury level.

Periodontitis is a chronic inflammatory disease triggered by periodontal pathogens and mediated by immune responses. Traditionally, gingival fibroblasts (GFs) were considered to be primarily responsible for maintaining periodontal matrix homeostasis. However, recent studies reveal that GFs play a significant immunoregulatory role in periodontitis. Through signaling pathways, such as the Toll-like receptor 4 (TLR4) pathway, GFs recognize virulence factors from pathogens, such as Porphyromonas gingivalis, and secrete various inflammatory mediators, thus driving extracellular matrix degradation and osteoclast differentiation. Simultaneously, GFs modulate immune cells, including neutrophils and macrophages, amplifying inflammatory responses and fostering a chronic inflammatory microenvironment. Risk factors, such as hyperglycemia and smoking, exacerbate GFs dysfunction via oxidative stress-mediated activation of the nuclear factor kappa B (NF-κB) pathway and other mechanisms, while inflammation and cellular senescence form a vicious cycle. Senescent GFs further aggravate alveolar bone destruction by activating the mechanistic target of the rapamycin (mTOR) pathway. Therapeutic strategies targeting GFs, such as suppressing NF-κB signaling or modulating mTOR-mediated senescence, may disrupt the link between inflammation and tissue destruction, showing promising therapeutic potential. Future studies should employ advanced technologies such as spatial multi-omics and single-cell proteomics to elucidate the spatial distribution, functional interactomes, and heterogeneity of GFs subsets, in order to deepen our understanding of their roles in periodontitis progression. This review summarizes the multifaceted mechanisms of GFs in periodontitis and explores potential therapeutic strategies targeting GFs, offering novel insights for periodontitis prevention and treatment.

Background: and Purpose Live cell-based assays (LCBA) are increasingly used for serological antibody detection due to their ability to preserve antigen conformation, offering moderately higher sensitivity than fixed cell-based assays. However, the clinical necessity of prioritizing LCBA for the detection of aquaporin-4 immunoglobulin G (AQP4-IgG) in neuromyelitis optica spectrum disorder (NMOSD) remains unclear, especially when compared to its established role in diagnosing myelin oligodendrocyte glycoprotein antibody-associated disease. Methods: We compared the performance of live cell-based assays using immunofluorescence (LCBA-IF) and fixed cell-based assays using immunofluorescence (FCBA-IF) in detecting AQP4-IgG in 90 cases of NMOSD meeting 2015 International Panel for Neuromyelitis Optica Diagnosis criteria, alongside 40 controls. Additionally, we further investigated the relationship between AQP4-IgG titers as measured by LCBA-IF and FCBA-IF and clinical parameters in NMOSD patients. Results: Results showed 96.9% agreement between LCBA-IF and FCBA-IF (Cohen’s κ=0.935), with a strong Spearman correlation (0.977, p<0.001). Both methods demonstrated 100% specificity, with LCBA-IF showing slightly higher sensitivity compared to FCBA-IF. Within LCBAIF-tested groups, statistically significant differences in annualized relapse rates were observed across all pairwise comparisons (low-titer vs. moderate-titer, low-titer vs. high-titer, and moderate-titer vs. high-titer; all p<0.050). However, this association reached statistical significance in some FCBA-IF-tested groups. Conclusions Overall, there is a strong concordance between LCBA-IF and FCBA-IF in detecting AQP4-IgG, where LCBA-IF shows slightly higher sensitivity. Furthermore, there is a potential link between elevated AQP4-IgG titers and an increased risk of relapse, and this correlation may appear more pronounced when using LCBA-IF.

Digital government refers to the wide application of digital technology in government management and services, promoting the optimization of government governance processes, model innovation, and performance enhancement, and building a new form of digital and intelligent government operation. Laboratory animal management is an important national foundational and strategic support work. It is closely related to digital government construction, and is not only an important part of digital government construction but also a typical application of government digital transformation in the field of science and technology. Digital transformation empowering laboratory animal management is a new proposition of government governance in the digital age, and research on its systematic and holistic reform is crucial. Through digital technologies such as artificial intelligence, big data, and cloud computing, the government can optimize approval process, strengthen in-process and post-event supervision, and promote regional coordination, thereby improving laboratory animal management capacity and service levels. In addition, laboratory animal data sharing contributes to government scientific decision-making and industry supervision. This article summarizes the experience and practices of domestic leading regions in recent years, reviews the background and development process of digital transformation of laboratory animal management in Jiangsu Province, describes the current status of such digital transformation in Jiangsu Province across areas including digital infrastructure, data sharing, and government services, analyzes existing challenges in top-level design, data governance, and data sharing, and, focusing on institutional mechanisms, government functions, data governance, and support systems, puts forward corresponding transformation ideas, frameworks, and pathways and provides an outlook. Thereby it provides a reference for the high-quality development of laboratory animal management in Jiangsu Province and other regions.

ObjectiveTo evaluate the effects of drinking chlorinated water on the intestinal flora of rats and mice and to explore differences in the intestinal microecological responses of model animals to chlorine stimulation in chlorinated drinking water systems. MethodsSix 8-week-old male SD rats and six 8-week-old male C57BL/6J mice were acclimated for 5 days. Subsequently, the drinking water for both rats and mice was changed from pure water to water containing free chlorine at a concentration of 25.4–32.4 nmol/L. The intervention lasted for 8 weeks, during which all animals were fed the same diet. Fecal samples were collected before the intervention (week 0, as the control group) and on the last day of week 3 and week 8 (as the chlorinated water group), and microbial composition was analyzed by 16S rRNA sequencing, including α diversity (Chao1 and Shannon indices), β diversity [principal component analysis (PCA)], and linear discriminant analysis effect size (LEfSe) analysis. ResultsAfter 3 weeks of intervention, α diversity of the intestinal flora in both rats and mice in the chlorinated water group was significantly lower than that in the control group (P < 0.01). After 8 weeks of intervention, no significant difference in α diversity was observed between rats in the chlorinated water group and those in the control group, whereas α diversity in mice remained significantly lower than that in the control group (P < 0.01). β diversity analysis showed significant alterations in microbial structure in the chlorinated water group. LEfSe analysis indicated that, compared with the control group, the abundances of Bacteroides and Ruminococcus were significantly reduced in the chlorinated water group, and microbial disturbance was more pronounced in mice [linear discriminant analysis (LDA) > 4.0], with a greater decrease in microbial diversity and a larger number of differentially abundant genera. ConclusionConsumption of chlorinated water can alter the diversity and structure of the intestinal flora in both rats and mice, with mice being more sensitive to this exposure. These findings suggest that the potential effects of chlorine on gut microecology should be considered in drinking water management and model selection for animal experiments.

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.

Tumor lineage plasticity (LP) is an emerging hallmark of cancer progression. Through pharmacologically probing the function of epigenetic regulators in prostate cancer cells and organoids, we identified bromodomain protein BRD4 as a crucial player. Integrated ChIP-seq and RNA-seq analysis of tumors revealed, for the first time, that BRD4 directly activates hundreds of genes in the LP programs which include neurogenesis, axonogenesis, EMT and stem cells and key drivers such as POU3F2 (BRN2), ASCL1/2, NeuroD1, SOX2/9, RUNX1/2 and DLL3. Interestingly, BRD4 genome occupancy is reprogrammed by anti-AR drugs from facilitating AR function in CRPC cells to activating the LP programs and is facilitated by pioneer factor FOXA1. Significantly, we demonstrated that BRD4 inhibitor AZD5153, currently at clinical development, possesses potent activities in complete blockade of tumor growth of both de novo neuroendocrine prostate cancer (NEPC) and treatment-induced NEPC PDXs and that suppression of tumor expression of LP programs through reduction of local chromatin accessibility is the primary mechanism of action (MOA) by AZD5153. Together, our study revealed that BRD4 plays a fundamental role in direct activation of tumor LP programs and that its inhibitor AZD5153 is highly promising in effective treatment of the lethal forms of the diseases.

Abnormal amino acid metabolism promotes tumor progression by inducing malignant behaviors in tumor cells and altering the immune landscape within the tumor microenvironment. However, the underlying mechanisms remain unclear. In this study, we constructed colorectal cancer (CRC) organoids and patient-derived tumor xenograft (PDX) models, performing multifaceted validation to confirm that T-complex protein 1 subunit epsilon (CCT5), mediates the biosynthesis of aspartate and enhances sensitivity to anti-PD-L1 immunotherapy. Mechanistically, CCT5 directly binds to asparagine synthetase (ASNS) and promotes the synthesis of aspartate (Asn). The Asn-mTORC1 axis facilitates tumor cell proliferation while upregulating PD-L1 expression, which leads to a reduction in the number of effector CD8⁺ T cells. Treatment with l-asparaginase (ASNase) combined with anti-PD-L1 therapy effectively reverses the growth of CRC characterized by high CCT5 expression. In summary, we identify CCT5 as a potential biomarker to guide the combined use of ASNase and anti-PD-L1 antibodies in CRC treatment.

The interest in covalent drugs has resurged in recent decades, spurring the development of numerous specialized computational docking tools to facilitate covalent ligand design and screening. Herein, we present CarsiDock-Cov, a new paradigm distinguishing itself as the first deep learning (DL)-guided approach for covalent docking. CarsiDock-Cov retains the core components of its non-covalent predecessor, leveraging a DL model pretrained on millions of docking complexes to predict protein-ligand distance matrices, along with a dedicated-designed geometric optimization procedure to convert these distances into refined binding poses. Additionally, it incorporates several key enhancements specifically tailored to optimize the protocol for covalent docking applications. Our approach has been extensively validated on multiple public datasets regarding the docking and screening of covalent ligands, and the results indicate that our approach not only achieves comparably improved applicability compared to its non-covalent predecessor, but also exhibits competitive performance against various state-of-the-art covalent docking tools. Collectively, our approach represents a significant advance in covalent docking methodology, offering an automated and efficient solution that shows considerable promise for accelerating covalent drug discovery and design.