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Background Synonymous mutations do not change the protein sequences. Automatically, they have been regarded as neutral events and are ignored in the mutation-based cancer studies. However, synonymous mutations will change the codon optimality, resulting in altered translational velocity. Methods We fully utilized the transcriptome and translatome of liver cancer and normal tissue from ten patients. We profiled the mutation spectrum and examined the effect of synonymous mutations on translational velocity. Results Synonymous mutations that increase the codon optimality significantly enhanced the translational velocity, and were enriched in oncogenes. Meanwhile, synonymous mutations decreasing codon optimality slowed down translation, and were enriched in tumor suppressor genes. These synonymous mutations significantly contributed to the translational changes in tumor samples compared to normal samples. Conclusions Synonymous mutations might play a role in liver cancer development by altering codon optimality and translational velocity. Synonymous mutations should no longer be ignored in the genome-wide studies.

Endothelial cells (ECs) form the inner linings of blood vessels, and are directly exposed to endogenous hazard signals and metabolites in the circulatory system. The senescence and death of ECs are not only adverse outcomes, but also causal contributors to endothelial dysfunction, an early risk marker of atherosclerosis. The pathophysiological process of EC senescence involves both structural and functional changes and has been linked to various factors, including oxidative stress, dysregulated cell cycle, hyperuricemia, vascular inflammation, and aberrant metabolite sensing and signaling. Multiple forms of EC death have been documented in atherosclerosis, including autophagic cell death, apoptosis, pyroptosis, NETosis, necroptosis, and ferroptosis. Despite this, the molecular mechanisms underlying EC senescence or death in atherogenesis are not fully understood. To provide a comprehensive update on the subject, this review examines the historic and latest findings on the molecular mechanisms and functional alterations associated with EC senescence and death in different stages of atherosclerosis.

Insulin resistance (IR) is the primary pathological mechanism underlying type 2 diabetes mellitus (T2DM). Here, the study aimed to ascertain whether and how exercise mediates IR in T2DM. An in vivo mouse model of high-fat diet-induced IR and an in vitro high-glucose-induced IR model were constructed. High long non-coding RNA (lncRNA) metastasis-associated lung adenocarcinoma transcript 1 (MALAT1) expression was detected in T2MD and was positively correlated with HOMA-IR and resistin levels. Then, short hairpin RNA targeting MALAT1 (sh-MALAT1) or pcDNA-MALAT1 was delivered into human umbilical vein endothelial cells (HUVECs) to knock down or upregulate its expression, respectively. Silencing of MALAT1 resulted in reduced levels of resistin, Ang II, tumor necrosis factor alpha (TNF-α), interleukin-6 (IL-6), soluble intercellular adhesion molecule-1 (sICAM-1), soluble vascular cell adhesion molecule-1 (sVCAM-1), endothelin-1 (ET-1), and p-insulin receptor substrate-1 (p-IRS)/ISR-1, and decreased cell migration, as well as enhanced glucose uptake and levels of nitric oxide (NO) and p-Akt/Akt. In the IR mouse model, exercise was observed to downregulate MALAT1 to reduce resistin, whereby exercise reduced homeostatic model assessment-insulin resistance (HOMA-IR). Besides, exercise also elevated microRNA-382-3p (miR-382-3p) expression in the serum of IR mice. Dual-luciferase reporter and RNA binding protein immunoprecipitation (RIP) assays identified that MALAT1 could bind to miR-382-3p to upregulate resistin. Collectively, the key observations of the study provide evidence that inhibition of MALAT1 elevates miR-382-3p to repress resistin, which consequently underlies the mechanism of exercise protecting against IR, highlighting a direction for T2DM therapy development.

Acute type A aortic dissection (AAAD) has a high probability of postoperative adverse outcomes (PAO) after emergency surgery, so exploring the risk factors for PAO during hospitalization is key to reducing postoperative mortality and improving prognosis. An artificial intelligence approach was used to build a predictive model of PAO by clinical data‐driven machine learning to predict the incidence of PAO after total arch repair for AAAD. This study included 380 patients with AAAD. The clinical features that are associated with PAO were selected using the LASSO regression analysis. Six different machine learning algorithms were tried for modeling, and the performance of each model was analyzed comprehensively using receiver operating characteristic curves, calibration curve, precision recall curve, and decision analysis curves. Explain the optimal model through Shapley Additive Explanation (SHAP) and perform an individualized risk assessment. After comprehensive analysis, the authors believe that the extreme gradient boosting (XGBoost) model is the optimal model, with better performance than other models. The authors successfully built a prediction model for PAO in AAAD patients based on the XGBoost algorithm and interpreted the model with the SHAP method, which helps to identify high‐risk AAAD patients at an early stage and to adjust individual patient‐related clinical treatment plans in a timely manner.

Study purposeDeleterious mutations would be rapidly purged from natural populations along with the extinction of their carriers. The currently observed mutations in existing species are mostly neutral. The inaccessibility of deleterious mutations impedes the functional studies on how these mutations affect the fitness at individual level.Study designThe connection between the deleterious genotype and the non-adaptive phenotype could be bridged by sequencing the genome before extinction. Although this approach is no longer feasible for evolutionary biologists, it is feasible for cancer biologists by profiling the mutations in tumour samples which are so deleterious that the carriers hardly live.ResultsBy comparing the derived mutation profile between normal populations and patients with liver cancer, we found that the shared mutations, which are highly deleterious, are suppressed to low allele frequencies in normal populations and tissues, but show remarkably high frequency in tumours. The density of shared mutations is negatively correlated with gene conservation and expression levels.ConclusionsDeleterious mutations are suppressed in functionally important genes as well as in normal populations. This work deepened our understanding on how natural selection act on deleterious mutations by analogising the cancer evolution to species evolution, which are essentially the same molecular process but at different time scales.

The present study aims to assess the treatment outcome of patients with diabetes and tuberculosis (TB-DM) at an early stage using machine learning (ML) based on electronic medical records (EMRs). A total of 429 patients were included at Chongqing Public Health Medical Center. The random-forest-based Boruta algorithm was employed to select the essential variables, and four models with a fivefold cross-validation scheme were used for modeling and model evaluation. Furthermore, we adopted SHapley additive explanations to interpret results from the tree-based model. 9 features out of 69 candidate features were chosen as predictors. Among these predictors, the type of resistance was the most important feature, followed by activated partial throm-boplastic time (APTT), thrombin time (TT), platelet distribution width (PDW), and prothrombin time (PT). All the models we established performed above an AUC 0.7 with good predictive performance. XGBoost, the optimal performing model, predicts the risk of treatment failure in the test set with an AUC 0.9281. This study suggests that machine learning approach (XGBoost) presented in this study identifies patients with TB-DM at higher risk of treatment failure at an early stage based on EMRs. The application of a convenient and economy EMRs based on machine learning provides new insight into TB-DM treatment strategies in low and middle-income countries.

Stroke is a devastating disease with high morbidity, disability, and mortality, among which ischemic stroke is more common. However, there is still a lack of effective methods to improve the prognosis and reduce the incidence of its complications. At present, there is evidence that peripheral organs are involved in the inflammatory response after stroke. Moreover, the interaction between central and peripheral inflammation includes the activation of resident and peripheral immune cells, as well as the activation of inflammation-related signaling pathways, which all play an important role in the pathophysiology of stroke. In this review, we discuss the mechanisms of inflammatory response after ischemic stroke, as well as the interactions through circulatory pathways between peripheral organs (such as the gut, heart, lung and spleen) and the brain to mediate and regulate inflammation after ischemic stroke. We also propose the potential role of meningeal lymphatic vessels (MLVs)-cervical lymph nodes (CLNs) as a brain-peripheral crosstalk lymphatic pathway in ischemic stroke. In addition, we also summarize the mechanisms of anti-inflammatory drugs in the treatment of ischemic stroke.

Background The transplantation of exosomes derived from human adipose-derived mesenchymal stem cells (hADSCs) has emerged as a prospective cellular-free therapeutic intervention for the treatment of neurodevelopmental disorders (NDDs), as well as autism spectrum disorder (ASD). Nevertheless, the efficacy of hADSC exosome transplantation for ASD treatment remains to be verified, and the underlying mechanism of action remains unclear. Results The exosomal long non-coding RNAs (lncRNAs) from hADSC and human umbilical cord mesenchymal stem cells (hUCMSC) were sequenced and 13,915 and 729 lncRNAs were obtained, respectively. The lncRNAs present in hADSC-Exos encompass those found in hUCMSC-Exos and are associated with neurogenesis. The biodistribution of hADSC-Exos in mouse brain ventricles and organoids was tracked, and the cellular uptake of hADSC-Exos was evaluated both in vivo and in vitro. hADSC-Exos promote neurogenesis in brain organoid and ameliorate social deficits in ASD mouse model BTBR T + tf/J (BTBR). Fluorescence in situ hybridization (FISH) confirmed lncRNA Ifngas1 significantly increased in the prefrontal cortex (PFC) of adult mice after hADSC-Exos intraventricular injection. The lncRNA Ifngas1 can act as a molecular sponge for miR-21a-3p to play a regulatory role and promote neurogenesis through the miR-21a-3p/PI3K/AKT axis. Conclusion We demonstrated hADSC-Exos have the ability to confer neuroprotection through functional restoration, attenuation of neuroinflammation, inhibition of neuronal apoptosis, and promotion of neurogenesis both in vitro and in vivo. The hADSC-Exos-derived lncRNA IFNG-AS1 acts as a molecular sponge and facilitates neurogenesis via the miR-21a-3p/PI3K/AKT signaling pathway, thereby exerting a regulatory effect. Our findings suggest a potential therapeutic avenue for individuals with ASD. Graphical Abstract

The human microbiome is defined as the microorganisms that reside in or on the human body, such as bacteria, viruses, fungi, and protozoa, and their genomes. The human microbiome participates in the modulation of human metabolism by influencing several intricate pathways. The association between specific bacteria or viruses and the efficacy of cancer treatments and the occurrence of treatment-related toxicity in cancer patients has been reported. However, the understanding of the interaction between the host microbiome and the cancer treatment response is limited, and the microbiome potentially plays a greater role in the treatment of cancer than reported to date. Here, we provide a thorough review of the potential role of the gut and locally resident bacterial microbiota in modulating responses to different cancer therapeutics to demonstrate the association between the gut or locally resident bacterial microbiota and cancer therapy. Probable mechanisms, such as metabolism, the immune response and the translocation of microbiome constituents, are discussed to promote future research into the association between the microbiome and other types of cancer. We conclude that the interaction between the host immune system and the microbiome may be the basis of the role of the microbiome in cancer therapies. Future research on the association between host immunity and the microbiome may improve the efficacy of several cancer treatments and provide insights into the cause of treatment-related side effects.

The purposes of this study were to develop and validate a nomogram for predicting postoperative transient neurological dysfunctions (TND) in patients with acute type A aortic dissection (AAAD) who underwent modified triple‐branched stent graft implantation. This retrospective study developed a nomogram‐based model in a consecutive cohort of 146 patients. Patient characteristics, preoperative clinical indices, and operative data were analyzed. Univariate and multivariable analyses were applied to identify the most useful predictive variables for constructing the nomogram. Discrimination and the calibration of the model was evaluated through the receiver operating characteristic curve (ROC), the Hosmer–Lemeshow goodness‐of‐fit test and the decision curve analysis (DCA). At the same time, to identify and compare long‐term cumulative survival rate, Kaplan‐Meier survival curve was plotted. The incidence rate of postoperative TND observed in our cohort were 40.9%. Supra‐aortic dissection with or without thrombosis, creatinine >115 μmol and albumin <39.7 g/L, selective antegrade cerebral perfusion (SACP) time >7 min and total operation time >303 min, were confirmed as independent predictors that enhanced the likelihood of TND. Internal validation showed good discrimination of the model with under the ROC curve (AUC) of 0.818 and good calibration (Hosmer–Lemeshow test, p > .05). DCA revealed that the nomogram was clinically useful. In the long‐term survival there was no significant difference between patients with or without TND history. The results showed the predict model based on readily available predictors has sufficient validity to identify TND risk in this population, that maybe useful for clinical decision‐making.