Traumatic brain injury (TBI) has high morbidity and mortality rates. The mechanisms underlying the TBI are unclear and may include the change in biological material in exosomes. Circular RNAs (circRNAs) are enriched and stable in exosomes, which can function as microRNA (miRNA) sponges to regulate gene expression levels. Therefore, we speculated that circRNAs in exosomes might play an important role in regulating gene expression after TBI and then regulate specific signaling pathways, which may protect the brain. We first isolated exosomes from the brain extracellular space in TBI mice by digestion. We then investigated the alterations in circRNA expression in exosomes by high-throughput whole transcriptome sequencing, analyzed the data by gene ontology (GO) and pathway analysis, and constructed the circRNA-miRNA network. In this study, we identified 231 significantly and differentially expressed circRNAs, including 155 that were up-regulated and 76 that were down-regulated. GO analysis showed that these differentially expressed circRNAs might be related to the growth and repair of neurons, the development of the nervous system, and the transmission of nerve signals. The most highly correlated pathways that we identified were primarily involved with glutamatergic synapse and the cGMP-PKG signaling pathway. The circRNA-miRNA network predicted the potential roles of these differentially expressed circRNAs and the interaction of circRNAs with miRNAs. Our study broadens the horizon of research on gene regulation in exosomes from the brain extracellular space after TBI and provides novel targets for further research on both the molecular mechanisms of TBI and the potential intervention therapy targets.

Background: We have recently showed that atorvastatin (ATO) combined with low dose of dexamethasone (DEX) was more efficacious in treating patients with chronic subdural haematoma (CSDH) than ATO monotherapy. This study was designed to investigate the underlying mechanisms of the improved efficacy of this combined therapy. Methods: Mass spectrometry was performed to quantitatively detect drugs in haematoma fluids and serum samples from CSDH patients and also in cultured macrophages after treatment with either ATO alone or in combination with DEX. The differentiation and apoptosis of macrophages were evaluated using flow cytometry. The expression of cytokines, chemokines and angiogenesis-related proteins was evaluated using proteome profile arrays, immunoblots and ELISA, respectively. Results: ATO was detected in haematoma fluids and serum samples, whose levels were increased significantly in samples collected from patients treated with both ATO and DEX. ATO was also increased in cultured macrophages treated with ATO and DEX. The numbers of M1-polarized macrophages were higher than the M2 phenotype in the haematoma fluids of patients. Cultured macrophages treated with ATO and DEX had reduced numbers of M1-polarized macrophages, increased numbers of M2-polarized macrophages as compared to monotherapies, and decreased rate of apoptosis induced by high-dose DEX. DEX enhanced the anti-inflammatory and anti-angiogenic activity of ATO by suppressing VEGFA and other inflammatory angiogenic factors. Consistent with the finding, patients responded well to the drug treatments had lower serum levels of VEGFA. Conclusions: We have shown for the first time that ATO given orally was detected in CSDH haematoma fluids. DEX enhances the anti-inflammatory and anti-angiogenic effects of ATO, primarily by increasing the presence of ATO in haematoma and macrophages and by regulating the functions of macrophages.

Objective: This study aims to develop a risk prediction model for chemotherapy‐induced nausea and vomiting (CINV) in cancer patients receiving highly emetogenic chemotherapy (HEC) and identify the variables that have the most significant impact on prediction.Methods: Data from Tianjin Medical University General Hospital were collected and subjected to stepwise data preprocessing. Deep learning algorithms, including deep forest, and typical machine learning algorithms such as support vector machine (SVM), categorical boosting (CatBoost), random forest, decision tree, and neural network were used to develop the prediction model. After training the model and conducting hyperparameter optimization (HPO) through cross‐validation in the training set, the performance was evaluated using the test set. Shapley additive explanations (SHAP), partial dependence plot (PDP), and Local Interpretable Model‐Agnostic Explanations (LIME) techniques were employed to explain the optimal model. Model performance was assessed using AUC, F1 score, accuracy, specificity, sensitivity, and Brier score.Results: The deep forest model exhibited good discrimination, outperforming typical machine learning models, with an AUC of 0.850 (95%CI, 0.780–0.919), an F1 score of 0.757, an accuracy of 0.852, a specificity of 0.863, a sensitivity of 0.784, and a Brier score of 0.082. The top five important features in the model were creatinine clearance (Ccr), age, gender, anticipatory nausea and vomiting, and antiemetic regimen. Among these, Ccr had the most significant predictive value. The risk of CINV decreased with increased Ccr and age, while it was higher in the presence of anticipatory nausea and vomiting, female gender, and non‐standard antiemetic regimen.Conclusion: The deep forest model demonstrated good discrimination in predicting the risk of CINV in cancer patients prescribed HEC. Kidney function, as represented by Ccr, played a crucial role in the model's prediction. The clinical application of this predictive tool can help assess individual risks and improve patient care by proactively optimizing the use of antiemetics in cancer patients receiving HEC.

Luteolin, a commonly used traditional Chinese medicine, has been utilized for several decades in the treatment of hepatocellular carcinoma (HCC). Previous research has demonstrated its anti‐tumour efficacy, but its underlying mechanism remains unclear. This study aimed to assess the therapeutic effects of luteolin in H22 tumour‐bearing mice. luteolin effectively inhibited the growth of solid tumours in a well‐established mouse model of HCC. High‐throughput sequencing revealed that luteolin treatment could enhance T‐cell activation, cell chemotaxis and cytokine production. In addition, luteolin helped sustain a high ratio of CD8⁺ T lymphocytes in the spleen, peripheral blood and tumour tissues. The effects of luteolin on the phenotypic and functional changes in tumour‐infiltrating CD8⁺ T lymphocytes were also investigated. Luteolin restored the cytotoxicity of tumour‐infiltrating CD8⁺ T lymphocytes in H22 tumour‐bearing mice. The CD8⁺ T lymphocytes exhibited intensified phenotype activation and increased production of granzyme B, IFN‐γ and TNF‐α in serum. The combined administration of luteolin and the PD‐1 inhibitor enhanced the anti‐tumour effects in H22 tumour‐bearing mice. Luteolin could exert an anti‐tumour immune response by inducing CD8⁺ T lymphocyte infiltration and enhance the anti‐tumour effects of the PD‐1 inhibitor on H22 tumour‐bearing mice.

Atorvastatin is a commonly used cholesterol-lowering drug that possesses non-canonical anti-inflammatory properties. However, the precise mechanism underlying its anti-inflammatory effects remains unclear. The acute phase of ulcerative colitis (UC) was induced using a 5 % dextran sulfate sodium (DSS) solution for 7 consecutive days and administrated with atorvastatin (10 mg/kg) from day 3 to day 7. mRNA-seq, histological pathology, and inflammatory response were determined. Intestinal microbiota alteration, tryptophan, and its metabolites were analyzed through 16S rRNA sequencing and untargeted metabolomics. Atorvastatin relieved the DSS-induced UC in mice, as evidenced by weight loss, colon length, disease activity index score and pathological staining. Atorvastatin treatment reduced the level of pro-inflammatory cytokines interleukin-6 (IL-6) and tumor necrosis factor alpha (TNF-α). Atorvastatin also relieved the intestinal microbiota disorder caused by UC and decreased the proliferation of pernicious microbiota such as Akkermansia and Bacteroides. Atorvastatin dramatically altered tryptophan metabolism and increased the fecal contents of tryptophan, indolelactic acid (ILA), and indole-3-acetic acid (IAA). Furthermore, atorvastatin enhanced the expression level of aryl hydrocarbon receptor (AhR) and interleukin-22 (IL-22) and further promoted the expression level of intestinal tight junction proteins, such as ZO-1 and occluding, in colitis mice with colitis. These findings indicated that atorvastatin could alleviate UC by regulating intestinal flora disorders, promoting microbial tryptophan metabolism, and repairing the intestinal barrier.

The early stages of brain injury can induce acute liver injury, which can be recovered in the short term. Continued medication treatment during hospitalization for brain injury alleviates the prognosis and contributes to a high incidence of drug-induced liver injury (DILI). We hypothesize that there is an interaction between changes in the hepatic environment after brain injury and liver injury produced by intensive drug administration, leading to an upregulation of the organism's sensitivity to DILI. In this study, mice models of TBI were established by controlled cortical impact (CCI) and models of DILI were constructed by acetaminophen (APAP). All mice were divided into four groups: Sham, TBI, APAP, and TBI+APAP, and related liver injury indicators in liver and serum were detected by Western blot, Quantitative real-time PCR (qRT-PCR), and immunohistochemical staining. The results suggested that liver injury induced in the early stages of brain injury recovered in 3 days, but this state could still significantly aggravate DILI, represented by higher liver enzymes (aspartate aminotransferase [AST] and alanine aminotransferase [ALT]), oxidative stress (increase in malondialdehyde [MDA] concentration and deregulation of glutathione [GSH] and superoxide dismutase [SOD] activities), inflammatory response (activation of the HMGB1/TLR4/NF-κB signaling pathway, and increased messenger RNA [mRNA] and protein levels of pro-inflammatory cytokines including tumor necrosis factor alpha [TNF-α], interleukin [IL]-6, and IL-1β), and apoptosis (TUNEL assay, upregulation of Bax protein and deregulation of Bcl-2 protein). In summary, our results suggested that TBI is a potential susceptibility factor for DILI and exacerbates DILI.