Multiple sclerosis (MS) is a neurodegenerative disorder characterized by periodic neuronal demyelination, which leads to a range of symptoms and eventually to disability. The goal of this research was to use UPLC‐Orbitrap/MS to identify validated biomarkers and explore the metabolic mechanisms of MS in mice. Thirty‐two C57BL/6 male mice were randomized into two groups that were fed either normal food or 0.2% CPZ for 11 weeks. The mouse demyelination model was assessed by LFB and the expression of MBP by immunofluorescence and immunohistochemistry. The metabolites of the corpus callosum were quantified using UPLC‐Orbitrap/MS. The mouse pole climbing experiment was used to assess coordination ability. Multivariate statistical analysis was adopted for screening differential metabolites, and the ingenuity pathway analysis (IPA) was used to reveal the metabolite interaction network. We successfully established the demyelination model. The CPZ group slowly lost weight and showed an increased pole climbing time during feeding compared to the CON group. A total of 81 metabolites (VIP > 1 and P < 0.05) were determined to be enriched in 24 metabolic pathways; 41 metabolites were markedly increased, while 40 metabolites were markedly decreased in the CPZ group. The IPA results revealed that these 81 biomarker metabolites were associated with neuregulin signaling, PI3K‐AKT signaling, mTOR signaling, and ERK/MAPK signaling. KEGG pathway analysis showed that two significantly different metabolic pathways were enriched, namely, the glycerophospholipid and sphingolipid metabolic pathways, comprising a total of nine biomarkers. Receiver operating characteristic analysis showed that the metabolites (e.g., PE (16 : 0/22 : 6(4Z, 7Z, 10Z, 13Z, 16Z, 19Z)), PC (18 : 0/22 : 4(7Z, 10Z, 13Z, 16Z)), cytidine 5 ^′‐diphosphocholine, PS (18 : 0/22 : 6(4Z, 7Z, 10Z, 13Z, 16Z, 19Z)), glycerol 3‐phosphate, SM (d18 : 0/16 : 1(9Z)), Cer (d18:1/18 : 0), galabiosylceramide (d18:1/18 : 0), and GlcCer (d18:1/18 : 0)) have good discrimination ability for the CPZ group. In conclusion, the differential metabolites have great potential to serve as biomarkers of demyelinating diseases. In addition, we identified metabolic pathways associated with CPZ‐induced demyelination pathogenesis, which provided a new perspective for understanding the relationship between metabolites and CNS demyelination pathogenesis.

Objective: To develop and validate an admission warning strategy that incorporates the general emergency department indicators for predicting the hospital discharge outcome of patients with traumatic brain injury (TBI) in China. Methods: This admission warning strategy was developed in a primary cohort that consisted of 605 patients with TBI who were admitted within 6 h of injury. The least absolute shrinkage and selection operator and multivariable logistic regression analysis were used to develop the early warning strategy of selected indicators. Two sub-cohorts consisting of 180 and 107 patients with TBI were used for the external validation. Results: Indicators of the strategy included three categories: baseline characteristics, imaging and laboratory indicators. This strategy displayed good calibration and good discrimination. A high C-index was reached in the internal validation. The multicenter external validation cohort still showed good discrimination C-indices. Decision curve analysis (DCA) showed the actual needs of this strategy when the possibility threshold was 0.01 for the primary cohort, and at thresholds of 0.02–0.83 and 0.01–0.88 for the two sub-cohorts, respectively. In addition, this strategy exhibited a significant prognostic capacity compared to the traditional single predictors, and this optimization was also observed in two external validation cohorts. Conclusions: We developed and validated an admission warning strategy that can be quickly deployed in the emergency department. This strategy can be used as an ideal tool for predicting hospital discharge outcomes and providing objective evidence for early informed consent of the hospital discharge outcome to the family members of TBI patients.

Bile acid reflux and subsequent caudal-related homeobox 2 (CDX2) activation contribute to gastric intestinal metaplasia (IM), a precursor of gastric cancer; however, the mechanism underlying this phenomenon is unclear. Here, we demonstrate that alkylation repair homolog protein 5 (ALKBH5), a major RNA N⁶-adenosine demethylase, is required for bile acid-induced gastric IM. Mechanistically, we revealed the N⁶-methyladenosine (m⁶A) modification profile in gastric IM for the first time and identified ZNF333 as a novel m⁶A target of ALKBH5. ALKBH5 was shown to demethylate ZNF333 mRNA, leading to enhanced ZNF333 expression by abolishing m⁶A-YTHDF2-dependent mRNA degradation. In addition, ALKBH5 activated CDX2 and downstream intestinal markers by targeting the ZNF333/CYLD axis and activating NF-κB signaling. Reciprocally, p65, the key transcription factor of the canonical NF-κB pathway, enhanced the transcription activity of ALKBH5 in the nucleus, thus forming a positive feedforward circuit. Furthermore, ALKBH5 levels were positively correlated with ZNF333 and CDX2 levels in IM tissues, indicating significant clinical relevance. Collectively, our findings suggest that an m⁶A modification-associated positive feedforward loop between ALKBH5 and NF-κB signaling is involved in generating the IM phenotype of gastric epithelial cells. Targeting the ALKBH5/ZNF333/CYLD/CDX2 axis may be a useful therapeutic strategy for gastric IM in patients with bile regurgitation.

Background: PYGL has been reported as a glycogen degradation-related gene, which is up-regulated in many tumors. This study was designed to investigate the predictive value of high PYGL expression in patients with gliomas through bioinformatics analysis of the gene transcriptome and the single-cell sequencing data.Methods: The gene transcriptome data of 595 glioma patients from the TCGA database and the single-cell RNA sequencing data of 7,930 GBM cells from the GEO database were included in the study. Differential analysis was used to find the distribution of expression of PYGL in different groups of glioma patients. OS analysis was used to assess the influence of the high expression of PYGL on the prognosis of patients. The reliability of its prediction was evaluated by the AUC of ROC and the C-index. The GSEA be used to reveal potential mechanisms. The single-cell analysis was used to observe the high expression of PYGL in different cell groups to further analyze the mechanism of its prediction.Results: Differential analysis identified the expression level of PYGL is positively associated with glioma malignancy. OS analysis and Cox regression analyses showed high expression of PYGL was an independent factor for poor prognosis of gliomas (p < 0.05). The AUC values were 0.838 (1-year ROC), 0.864 (3-year ROC) and 0.833 (5-year ROC). The C index was 0.81. The GSEA showed that gene sets related to MTORC1 signaling, glycolysis, hypoxia, PI3K/AKT/mTOR signaling, KRAS signaling up and angiogenesis were differentially enriched in the high PYGL expression phenotype. The single-cell sequencing data analysis showed TAMs and malignant cells in GBM tissues expressed a high level of PYGL.Conclusion: The high expression of PYGL is an independent predictor of poor prognosis in patients with glioma.

Background and Aims. Cancer-specific survival (CSS) of rectal cancer (RC) is associated with several factors. We aimed to build an efficient competing-risk nomogram based on log odds of positive lymph nodes (LODDS) to predict RC survival. Methods. Medical records of 8754 patients were collected from the Surveillance, Epidemiology, and End Results (SEER) database, of 4895 patients from SEER during 20112014 and of 478 patients from an Eastern center as a development cohort, validation cohort, and test cohort, respectively. Univariate and multivariate competing-risk analyses were performed to build competing-risk nomogram for predicting the CSS of RC patients. Prediction efficacy was evaluated and compared with reference to the 8^th TNM classification using the factor areas under the receiver operating characteristic curve (AUC) and Brier score. Results. The competing-risk nomogram was based on 6 variables: size, M stage, LODDS, T stage, grade, and age. The competing-risk nomogram showed a higher AUC value in predicting the 5-year death rate due to RC than the 8^th TNM stage in the development cohort (0.81 vs. 0.76), validation cohort (0.85 vs. 0.82), and test cohort (0.71 vs. 0.66). The competing-risk nomogram also showed a higher Brier score in predicting the 5-year death rate due to RC than the 8^th TNM stage in the development cohort (0.120 vs. 0.127), validation cohort (0.123 vs. 0.128), and test cohort (0.202 vs. 0.226). Conclusion. We developed and validated a competing-risk nomogram for RC death, which could provide the probability of survival averting competing risk to facilitate clinical decision-making.