ObjectiveThis study aimed to investigate the action mechanism by which Banxia Xiexintang （BXT） inhibits epithelial-mesenchymal transition （EMT） in chronic atrophic gastritis （CAG） rats by regulating the interleukin-17（IL-17）/extracellular regulated protein kinases（ERK）/CCAAT enhancer binding protein β（C/EBPβ）signaling pathway， thereby providing new theoretical evidence for the treatment of CAG with classic traditional Chinese medicine formulas. MethodsA CAG rat model was established by using the combined factor method. After successful modeling， the rats were randomly divided into the model group， low-， medium-， and high-dose groups （0.549， 1.098， 2.196 g·kg^-1， respectively） of BXT， and the positive drug group （vitacoenzyme， 0.3 g·kg^-1）. A normal control group was also set up. After 8 weeks of intervention， the pathological changes of gastric tissue were evaluated. The enzyme-linked immunosorbent assay （ELISA） was used to detect the contents of IL-17， tumor necrosis factor-α （TNF-α）， cyclooxygenase-2 （COX-2）， and C/EBPβ in serum， as well as the contents of EMT markers in gastric mucosal tissue including E-cadherin， N-cadherin， and vimentin. The immunohistochemistry method was employed to determine the localization and protein expression levels of IL-17， p-ERK， and C/EBPβ in gastric mucosal tissue. Western blot was used to detect the protein expressions of C/EBPβ， ERK， and its phosphorylated form （p）-ERK in gastric mucosa. Real-time polymerase chain reaction （Real-time PCR） was applied to measure the mRNA expression levels of ERK， COX-2， and C/EBPβ in gastric mucosa. ResultsCompared with those in the normal control group， the rats in the model group showed gastric mucosal glandular atrophy and inflammatory cell infiltration. The protein and their related mRNA expressions of C/EBPβ， ERK， and p-ERK in gastric mucosa were significantly increased （P<0.05，P<0.01）. The levels of IL-17， TNF-α， COX-2， and C/EBPβ in serum were significantly increased （P<0.01）. The contents of N-cadherin and vimentin in gastric mucosal tissue were significantly increased， while the content of E-cadherin was significantly decreased （P<0.01）. Compared with the model group， after intervention with different doses of BXT， the pathological damage of the gastric mucosa was improved to varying degrees. The protein and mRNA expressions of C/EBPβ， ERK， and p-ERK in gastric mucosa were significantly reduced （P<0.05，P<0.01）. The levels of IL-17， TNF-α， COX-2， and C/EBP β in serum were significantly decreased （P<0.01）. The contents of N-cadherin and vimentin in gastric mucosa tissue were decreased， while the content of E-cadherin was increased （P<0.05，P<0.01）. ConclusionBXT can effectively improve the pathological damage of gastric mucosal tissue in CAG rats. Its action mechanism may be related to reducing the levels of IL-17 and TNF-α in serum， regulating the IL-17/ERK/C/EBPβ signaling pathway and inhibiting the EMT process.

ObjectiveTo elucidate the critical role of rehabilitation medical records (including electronic records) in rehabilitation medicine's clinical practice and management, comprehensively analyzed the structure, core content and data standards of rehabilitation medical records, to develop a standardized medical record data architecture and core dataset suitable for rehabilitation medicine and to explore the application of rehabilitation data in performance evaluation and payment. MethodsBased on the regulatory documents Basic Specifications for Medical Record Writing and Basic Specifications for Electronic Medical Records (Trial) issued by National Health Commission of China, and referencing the World Health Organization (WHO) Family of International Classifications (WHO-FICs) classifications, International Classification of Diseases (ICD-10/ICD-11), International Classification of Functioning, Disability and Health (ICF), and International Classification of Health Interventions (ICHI Beta-3), this study constructed the data architecture, core content and data standards for rehabilitation medical records. Furthermore, it explored the application of rehabilitation record summary sheets (home page) data in rehabilitation medical statistics and payment methods, including Diagnosis-related Groups (DRG), Diagnosis-Intervention Packet (DIP) and Case Mix Index. ResultsThis study proposed a systematic standard framework for rehabilitation medical records, covering key components such as patient demographics, rehabilitation diagnosis, functional assessment, rehabilitation treatment prescriptions, progress evaluations and discharge summaries. The research analyzed the systematic application methods and data standards of ICD-10/ICD-11, ICF and ICHI Beta-3 in the fields of medical record terminology, coding and assessment. Constructing a standardized data structure and data standards for rehabilitation medical records can significantly improve the quality of data reporting based on the medical record summary sheet, thereby enhancing the quality control of rehabilitation services, effectively supporting the optimization of rehabilitation medical insurance payment mechanisms, and contributing to the establishment of rehabilitation medical performance evaluation and payment based on DRG and DIP. ConclusionStructured rehabilitation records and data standardization are crucial tools for quality control in rehabilitation. Systematically applying the three reference classifications of the WHO-FICs, and aligning with national medical record and electronic health record specifications, facilitate the development of a standardized rehabilitation record architecture and core dataset. Standardizing rehabilitation care pathways based on the ICF methodology, and developing ICF- and ICD-11-based rehabilitation assessment tools, auxiliary diagnostic and therapeutic systems, and supporting terminology and coding systems, can effectively enhance the quality of rehabilitation records and enable interoperability and sharing of rehabilitation data with other medical data, ultimately improving the quality and safety of rehabilitation services.

ObjectiveTransfer RNA-derived fragments (tRFs) are a recently characterized and rapidly expanding class of small non-coding RNAs, typically ranging from 13 to 50 nucleotides in length. They are derived from mature or precursor tRNA molecules through specific cleavage events and have been implicated in a wide range of cellular processes. Increasing evidence indicates that tRFs play important regulatory roles in gene expression, primarily by interacting with target messenger RNAs (mRNAs) to induce transcript degradation, in a manner partially analogous to microRNAs (miRNAs). However, despite their emerging biological relevance and potential roles in disease mechanisms, there remains a significant lack of computational tools capable of systematically predicting the interaction landscape between tRFs and their target mRNAs. Existing databases often rely on limited interaction features and lack the flexibility to accommodate novel or user-defined tRF sequences. The primary goal of this study was to develop a machine learning based prediction algorithm that enables high-throughput, accurate identification of tRF:mRNA binding events, thereby facilitating the functional analysis of tRF regulatory networks. MethodsWe began by assembling a manually curated dataset of 38 687 experimentally verified tRF:mRNA interaction pairs and extracting seven biologically informed features for each pair: (1) AU content of the binding site, (2) site pairing status, (3) binding region location, (4) number of binding sites per mRNA, (5) length of the longest consecutive complementary stretch, (6) total binding region length, and (7) seed sequence complementarity. Using this dataset and feature set, we trained 4 distinct machine learning classifiers—logistic regression, random forest, decision tree, and a multilayer perceptron (MLP)—to compare their ability to discriminate true interactions from non-interactions. Each model’s performance was evaluated using overall accuracy, receiver operating characteristic (ROC) curves, and the corresponding area under the ROC curve (AUC). The MLP consistently achieved the highest AUC among the four, and was therefore selected as the backbone of our prediction framework, which we named tRF Prospect. For biological validation, we retrieved 3 high-throughput RNA-seq datasets from the gene expression omnibus (GEO) in which individual tRFs were overexpressed: AS-tDR-007333 (GSE184690), tRF-3004b (GSE197091), and tRF-20-S998LO9D (GSE208381). Differential expression analysis of each dataset identified genes downregulated upon tRF overexpression, which we designated as putative targets. We then compared the predictions generated by tRF Prospect against those from three established tools—tRFTar, tRForest, and tRFTarget—by quantifying the number of predicted targets for each tRF and assessing concordance with the experimentally derived gene sets. ResultsThe proposed algorithm achieved high predictive accuracy, with an AUC of 0.934. Functional validation was conducted using transcriptome-wide RNA-seq datasets from cells overexpressing specific tRFs, confirming the model’s ability to accurately predict biologically relevant downregulation of mRNA targets. When benchmarked against established tools such as tRFTar, tRForest, and tRFTarget, tRF Prospect consistently demonstrated superior performance, both in terms of predictive precision and sensitivity, as well as in identifying a higher number of true-positive interactions. Moreover, unlike static databases that are limited to precomputed results, tRF Prospect supports real-time prediction for any user-defined tRF sequence, enhancing its applicability in exploratory and hypothesis-driven research. ConclusionThis study introduces tRF Prospect as a powerful and flexible computational tool for investigating tRF:mRNA interactions. By leveraging the predictive strength of deep learning and incorporating a broad spectrum of interaction-relevant features, it addresses key limitations of existing platforms. Specifically, tRF Prospect: (1) expands the range of detectable tRF and target types; (2) improves prediction accuracy through multilayer perceptron model; and (3) allows for dynamic, user-driven analysis beyond database constraints. Although the current version emphasizes miRNA-like repression mechanisms and faces challenges in accurately capturing 5'UTR-associated binding events, it nonetheless provides a critical foundation for future studies aiming to unravel the complex roles of tRFs in gene regulation, cellular function, and disease pathogenesis.

BACKGROUND: The available evidence regarding the benefits of percutaneous coronary intervention (PCI) on patients receiving dialysis with coronary artery disease (CAD) is limited and inconsistent. This study aimed to evaluate the association between PCI and clinical outcomes as compared with medical therapy alone in patients undergoing dialysis with CAD in China. METHODS: This multicenter, retrospective study was conducted in 30 tertiary medical centers across 12 provinces in China from January 2015 to June 2021 to include patients on dialysis with CAD. The primary outcome was major adverse cardiovascular events (MACE), defined as a composite of cardiovascular death, non-fatal myocardial infarction, and non-fatal stroke. Secondary outcomes included all-cause death, the individual components of MACE, and Bleeding Academic Research Consortium criteria types 2, 3, or 5 bleeding. Multivariable Cox proportional hazard models were used to assess the association between PCI and outcomes. Inverse probability of treatment weighting (IPTW) and propensity score matching (PSM) were performed to account for potential between-group differences. RESULTS: Of the 1146 patients on dialysis with significant CAD, 821 (71.6%) underwent PCI. After a median follow-up of 23.0 months, PCI was associated with a 43.0% significantly lower risk for MACE (33.9% [ n  = 278] vs . 43.7% [ n  = 142]; adjusted hazards ratio 0.57, 95% confidence interval 0.45-0.71), along with a slightly increased risk for bleeding outcomes that did not reach statistical significance (11.1% vs . 8.3%; adjusted hazards ratio 1.31, 95% confidence interval, 0.82-2.11). Furthermore, PCI was associated with a significant reduction in all-cause and cardiovascular mortalities. Subgroup analysis did not modify the association of PCI with patient outcomes. These primary findings were consistent across IPTW, PSM, and competing risk analyses. CONCLUSION This study indicated that PCI in patients on dialysis with CAD was significantly associated with lower MACE and mortality when comparing with those with medical therapy alone, albeit with a slightly increased risk for bleeding events that did not reach statistical significance.
Humans ; Percutaneous Coronary Intervention/methods* ; Male ; Female ; Coronary Artery Disease/drug therapy* ; Retrospective Studies ; Renal Dialysis/methods* ; Middle Aged ; Aged ; China ; Proportional Hazards Models ; Treatment Outcome

The neonatal mammalian heart has a remarkable regenerative capacity, while the adult heart has difficulty to regenerate. A metabolic reprogramming from glycolysis to fatty acid oxidation occurs along with the loss of cardiomyocyte proliferative capacity shortly after birth. In this study, we sought to determine if and how metabolic reprogramming regulates cardiomyocyte proliferation. Reversing metabolic reprogramming by carnitine palmitoyltransferase 1 (CPT1) inhibition, using cardiac-specific Cpt1a and Cpt1b knockout mice promoted cardiomyocyte proliferation and improved cardiac function post-myocardial infarction. The inhibition of CPT1 is of pharmacological significance because those protective effects were replicated by etomoxir, a CPT1 inhibitor. CPT1 inhibition, by decreasing poly(ADP-ribose) polymerase 1 expression, reduced ADP-ribosylation of dual-specificity phosphatase 1 in cardiomyocytes, leading to decreased p38 MAPK phosphorylation, and stimulation of cardiomyocyte proliferation. Our present study indicates that reversing metabolic reprogramming is an effective strategy to stimulate adult cardiomyocyte proliferation. CPT1 is a potential therapeutic target for promoting heart regeneration and myocardial infarction treatment.

Pyroptosis is an identified programmed cell death that has been highly linked to endoplasmic reticulum (ER) dynamics. However, the crucial proteins for modulating dynamic ER membrane curvature change that trigger pyroptosis are currently not well understood. In this study, a biotin-labeled chemical probe of potent pyroptosis inducer α-mangostin (α-MG) was synthesized. Through protein microarray analysis, reticulon-4 (RTN4/Nogo), a crucial regulator of ER membrane curvature, was identified as a target of α-MG. We observed that chemically induced proteasome degradation of RTN4 by α-MG through recruiting E3 ligase UBR5 significantly enhances the pyroptosis phenotype in cancer cells. Interestingly, the downregulation of RTN4 expression significantly facilitated a dynamic remodeling of ER membrane curvature through a transition from tubules to sheets, consequently leading to rapid fusion of the ER with the cell plasma membrane. In particular, the ER-to-plasma membrane fusion process is supported by the observed translocation of several crucial ER markers to the "bubble" structures of pyroptotic cells. Furthermore, α-MG-induced RTN4 knockdown leads to pyruvate kinase M2 (PKM2)-dependent conventional caspase-3/gasdermin E (GSDME) cleavages for pyroptosis progression. In vivo, we observed that chemical or genetic RTN4 knockdown significantly inhibited cancer cells growth, which further exhibited an antitumor immune response with anti-programmed death-1 (anti-PD-1). In translational research, RTN4 high expression was closely correlated with the tumor metastasis and death of patients. Taken together, RTN4 plays a fundamental role in inducing pyroptosis through the modulation of ER membrane curvature remodeling, thus representing a prospective druggable target for anticancer immunotherapy.
Pyroptosis/immunology* ; Humans ; Endoplasmic Reticulum/immunology* ; Animals ; Nogo Proteins/antagonists & inhibitors* ; Mice ; Cell Line, Tumor ; Xanthones/pharmacology* ; Neoplasms/pathology* ; Mice, Nude

In this study,a sensitive lateral flow immunoassay(LFIA)platform based on carbon dots-mesoporous silica nanocomposite(CD-MSNs)fluorescent probes was constructed for high-performance detection of inflammatory marker interleukin-6(IL-6).Green fluorescent carbon dots(CDs)were prepared by hydrothermal method with 3,9-perylenic acid and 3-aminopropyltriethoxysilane(APTES)as raw materials,and highly fluorescent CD-MSNs composites were then constructed by encapsulating the prepared CDs in mesoporous silica nanoparticles(MSNs).Fluorescent probes were prepared by covalent coupling of CD-MSNs with IL-6 antibody.Fluorescent immunochromatographic test strips were constructed by spraying IL-6 capture antibody and goat anti-mouse IgG on nitrocellulose membrane as detection line(T-line)and quality control line(C-line),respectively.The fluorescence immunoassay analyzer was used to quantitatively detect the fluorescence intensity of T-line,and the experimental results showed that the LFIA platform based on this probe had a good linear relationship in IL-6 concentration range of 102-106 pg/mL,and the detection limit was 64 pg/mL,which was two orders of magnitude more sensitive than that of the traditional colloidal gold test strips.This method effectively solved the issue of insufficient sensitivity of traditional LFIA technique,and provided a rapid and highly sensitive detection method for early diagnosis of inflammatory diseases.

Objective To understand the molecular epidemiological characteristics of Norovirus outbreaks and the genome evolution of Norovirus epidemic strains in Hainan Province from 2020 to 2022.Methods The information and samples have been collected from the norovirus outbreaks from 2020 to 2022.Norovirus was detected by using the real-time PCR in these samples,then the detected sequences were amplified the analyzed.The Norovirus se-quences of 8 strains had been amplified and analyzed.Results From 2020 to 2022,39 gastroenteritis outbreaks were reported,and 25 outbreaks caused by Norovirus which mainly occurred in childcare institutions and schools(20/25,80％).The Norovirus outbreaks were mainly concentrated in counties around Haikou(northeast),which including Ding'an(5 cases),Wenchang(4 cases),Chengmai(4 cases),and Lingao(3 cases);following by western regions which included Baisha(2 cases),Ledong(2 cases),and Dongfang(3 cases).1 case was in Wanning in the southeast.Among individuals aged 2-17,the positive proportion of Norovirus in males was higher than that in females.Among individuals aged over 55,the proportion of Norovirus positive in females was higher than that in males.The gender of positive samples among individuals aged 18-40 was related to their profession.According to RT-PCR typing and sequencing,GⅡ group Norovirus were classified in13 outbreaks.There were 4 genotypes detected.GⅡ.2[P1 6]was the main epidemic strain with 60％(9/13),and the other three genotypes were GⅡ.4 Sydney[P31](15.4％,2/13)GⅡ.4 Sydney[P16](7.7％,1/13)and GⅡ.3[P12](7.7％,1/13).Further genic analysis of 8 Norovirus strains showed that all of them were still in the same branch as the previ-ous strain,and all exhibited a certain amount of amino acid variation.Conclusion Norovirus is the main pathogen of gastroenteritis outbreaks in Hainan province,and the main epidemic strain is GⅡ.2[P16].It is necessary to continue to strengthen the monitoring that provides scientific evidence for the prevention and control of norovirus out-breaks in Hainan region.