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Background Increasing evidence has underscored the role of long non-coding RNAs (lncRNAs) acting as competing endogenous RNAs (ceRNAs) in the development and progression of tumors. Nevertheless, lncRNA biomarkers in lncRNA-related ceRNA network that can predict the prognosis of breast cancer (BC) are still lacking. The aim of our study was to identify potential lncRNA signatures capable of predicting overall survival (OS) of BC patients. Methods The RNA sequencing data and clinical characteristics of BC patients were obtained from the Cancer Genome Atlas database, and differentially expressed lncRNA (DElncRNAs), DEmRNAs, and DEmiRNAs were then identified between BC and normal breast tissue samples. Subsequently, the lncRNA–miRNA–mRNA ceRNA network of BC was established, and the gene oncology enrichment analyses for the DEmRNAs interacting with lncRNAs in the ceRNA network was implemented. Using univariate and multivariate Cox regression analyses, a four-lncRNA signature was developed and used for predicting the survival in BC patients. We applied receiver operating characteristic analysis to assess the performance of our model. Results A total of 1061 DElncRNAs, 2150 DEmRNAs, and 82 DEmiRNAs were identified between BC and normal breast tissue samples. A lncRNA–miRNA–mRNA ceRNA network of BC was established, which comprised of 8 DEmiRNAs, 48 DElncRNAs, and 10 DEmRNAs. Further gene oncology enrichment analyses revealed that the DEmRNAs interacting with lncRNAs in the ceRNA network participated in cell leading edge, protease binding, alpha-catenin binding, gamma-catenin binding, and adenylate cyclase binding. A univariate regression analysis of the DElncRNAs revealed 7 lncRNAs (ADAMTS9-AS1, AC061992.1, LINC00536, HOTAIR, AL391421.1, TLR8-AS1 and LINC00491) that were associated with OS of BC patients. A multivariate Cox regression analysis demonstrated that 4 of those lncRNAs (ADAMTS9-AS1, LINC00536, AL391421.1 and LINC00491) had significant prognostic value, and their cumulative risk score indicated that this 4-lncRNA signature independently predicted OS in BC patients. Furthermore, the area under the curve of the 4-lncRNA signature associated with 3-year survival was 0.696. Conclusions The current study provides novel insights into the lncRNA-related ceRNA network in BC and the 4 lncRNA biomarkers may be independent prognostic signatures in predicting the survival of BC patients.

Novel diagnostic and prognostic biomarkers of cancers are needed to improve precision medicine. Circular RNAs act as important regulators in cancers at the transcriptional and posttranscriptional levels. The circular RNA circMAN1A2 is highly expressed in nasopharyngeal carcinoma according to our previous RNA sequencing data; however, the expression and functions of circMAN1A2 in cancers are still obscure. Therefore, in this study, we evaluated the expression of circMAN1A2 in the sera of patients with nasopharyngeal carcinoma and other malignant tumors and analyzed its correlations with clinical features and diagnostic values. The expression levels of circMAN1A2 were detected by quantitative real‐time PCR, and the correlations of clinical features with circMAN1A2 expression were analyzed by χ2 tests. Receiver operating characteristic curves were used to evaluate the clinical applications of circMAN1A2. The results showed that circMAN1A2 was upregulated in nasopharyngeal carcinoma, oral cancer, thyroid cancer, ovarian cancer, and lung cancer, with areas under the curves of 0.911, 0.779, 0.734, 0.694, and 0.645, respectively, indicating the good diagnostic value of circMAN1A2. Overall, our findings suggested that circMAN1A2 could be a serum biomarker for malignant tumors, providing important insights into diagnostic approaches for malignant tumors. Further studies are needed to elucidate the mechanisms of circMAN1A2 in the pathogenesis of cancer. We verified that circMAN1A2 was significantly upregulated in the sera of patients with NPC, oral cancer, thyroid cancer, ovarian cancer, and lung cancer and had good clinical diagnostic value. We speculate that circMAN1A2 could be a serum biomarker for malignant cancers and provide effective clues for the early diagnosis of malignant cancers.

In order to seriously impact the global burden of heart failure (HF) and coronary artery disease (CAD), identifying at-risk individuals as early as possible is vital. Risk calculator tools in wide clinical use today are informed by traditional statistical methods that have historically yielded only modest prediction accuracy. This study uses machine learning algorithms to generate predictions models for the development and progression of severe HF and CAD. Participants (~485,000 followed in the UK Biobank over 7 years) were stratified by cardiac status at the time of enrollment (asymptomatic, high-risk and affected); separate prediction models were built for each stratum. Participants were split between a training set (80%) and holdout dataset (20%), all performance metrics are reported for the holdout dataset. Out of 6 machine learning algorithms screened, artificial neural networks (ANN) most successfully predicted future disease across the various strata (area under the curve: 0.77-0.86 for 10/12 models), results were very consistent between methodologies. Models trained using ANN showed excellent calibration in all strata and across the entire spectrum of risk (0.4-1.2% average observed/predicted difference across 10 deciles of risk). Key predictive features included age, frailty, adiposity, history of hypertension and diabetes, tobacco use and family history of heart disease and were consistent between models for HF and CAD. When deployed as a patient-facing application, the prediction models presented here will be able to provide both user-specific predictions and simulate the effect of changes in lifestyle and of prophylaxis interventions, thus resulting in an individualized patient counselling and management tool.

Organic light-emitting diodes (OLEDs) exploiting simple binary emissive layers (EMLs) blending only emitters and hosts have natural advantages in low-cost commercialization. However, previously reported OLEDs based on binary EMLs hardly simultaneously achieved desired comprehensive performances, e.g., high efficiency, low efficiency roll-off, narrow emission bands, and high operation stability. Here, we report a molecular-design strategy. Such a strategy leads to a fast reverse intersystem crossing rate in our designed emitter h -BNCO-1 of 1.79×10 5  s −1 . An OLED exploiting a binary EML with h -BNCO-1 achieves ultrapure emission, a maximum external quantum efficiency of over 40% and a mild roll-off of 14% at 1000 cd·m −2 . Moreover, h -BNCO-1 also exhibits promising operational stability in an alternative OLED exploiting a compact binary EML (the lifetime reaching 95% of the initial luminance at 1000 cd m −2 is ~ 137 h). Here, our work has thus provided a molecular-design strategy for OLEDs with promising comprehensive performance. Multi-resonance thermally activated delayed fluorescent emitters composed of only period-2 elements are important for achieving comprehensive performances. Here, authors report hybridization of organoboron-nitrogen and carbonyl groups in the emitter to achieve a long device operational stability.

Dendrite growth of alkali metal anodes limited their lifetime for charge/discharge cycling. Here, we report near-perfect anodes of lithium, sodium, and potassium metals achieved by electrochemical polishing, which removes microscopic defects and creates ultra-smooth ultra-thin solid-electrolyte interphase layers at metal surfaces for providing a homogeneous environment. Precise characterizations by AFM force probing with corroborative in-depth XPS profile analysis reveal that the ultra-smooth ultra-thin solid-electrolyte interphase can be designed to have alternating inorganic-rich and organic-rich/mixed multi-layered structure, which offers mechanical property of coupled rigidity and elasticity. The polished metal anodes exhibit significantly enhanced cycling stability, specifically the lithium anodes can cycle for over 200 times at a real current density of 2 mA cm –2 with 100% depth of discharge. Our work illustrates that an ultra-smooth ultra-thin solid-electrolyte interphase may be robust enough to suppress dendrite growth and thus serve as an initial layer for further improved protection of alkali metal anodes. The dendrite growth of alkali metal anodes leads to charge/discharge cycling instability. Here, the authors show that electrochemical polishing can yield near-perfect anodes of three alkali metals by constructing smooth and thin solid-electrolyte interphase layers.

A large set of Paleozoic volcaniclastic rocks is exposed in the northwestern margin of the Junggar Basin from the southern part of the Central Asian Orogenic Belt. The Carboniferous volcaniclastic strata in this area have been studied in depth, and an accurate chronostratigraphic framework of these strata has been established. However, there is a lack of sufficient geochronological data for the deposition times of the other Paleozoic volcaniclastic strata. In this study, zircon U–Pb dating of the Ordovician, Silurian, and Devonian volcaniclastic strata in the area reveals that the youngest age of the tuffite sample collected from the originally defined Ordovician strata is 398 ± 11 Ma, which represents the age of volcanic activity during the period of tuffite deposition. Based on this finding, the originally defined Ordovician strata are redefined as the Lower Devonian. The youngest ages of the silty tuff samples collected from the originally defined Silurian strata peak are 445–418 Ma, so its age is Upper Silurian. The youngest ages of the tuffaceous sandstone samples collected from the originally defined Devonian strata peak are 346–342 Ma, so these Devonian are redefined as the Early Carboniferous strata. Two Archean ages (2,501 ± 12 and 3,193 ± 8 Ma) were obtained in Silurian strata, thus confirming the existence of metamorphic rock basement in the provenance areas from which the sediments were derived.

ObjectivesTo synthesise evidence on longer term unmet needs perceived by stroke survivors, and psychometric properties of the tools used to evaluate unmet care needs after stroke.DesignSystematic review.SettingCommunity or patients’ home.ParticipantsStroke survivors.MethodsWe searched PubMed, PsycINFO, CINAHL, EMBASE from inception to 31 March 2018 to identify survey studies that evaluated unmet needs perceived by stroke survivors after hospital discharge. Reported unmet needs were categorised under three domains: body functioning, activity/participation and environmental factors. Ranges of prevalence rates of unmet needs reported in studies were presented.ResultsWe included 19 eligible studies, with considerable heterogeneity in patients, survey methods and results. Psychometric properties of two stroke-specific tools were formally evaluated, indicating their moderate reliability and content/concurrent validity. The median number of reported unmet needs per stroke survivor was from two to five, and the proportion of stroke survivors with at least one unmet needs was on average 73.8% (range 19.8%– 91.7%). Unmet needs perceived by stroke survivors included 55 records of unmet body functioning needs, 47 records of unmet activities/participatory needs and 101 records of unmet environmental needs. Common unmet service needs were unmet information needs (3.1%– 65.0%), transport (5.4%–53.0%), home help/personal care (4.7%–39.3%) and therapy (2.0%–35.7%).ConclusionsThe prevalence of unmet long-term needs is high among stroke survivors, and there is considerable heterogeneity in type and frequency of specific unmet needs. More research is required to link regular assessment of long-term unmet needs of stroke survivors with the provision of cost-effective patient-centred health and social care services.

Cancer patients with psychotic disorders have occasionally exhibited reduced tumor sizes following long-term antipsychotic treatment. Previous studies have shown that antipsychotic drugs, such as clozapine, could inhibit cancer cell proliferation, but the underlying mechanisms remain unclear. This study investigates the anti-tumor effects of clozapine on breast cancer cells and explores its mechanisms of action. We used clonogenic and MTT assays to assess cell proliferation, flow cytometry and western blotting analyses to evaluate cell cycle distribution, apoptosis, and autophagy following clozapine exposure. The results show that clozapine downregulates Cyclin D1, CDK4, and CDK6, while upregulating p21 and p27 in MCF-7 cells, leading to G0/G1 phase arrest. Clozapine exposure also increases reactive oxygen species (ROS), apoptosis and autophagy levels. Notably, treatment with the antioxidant α-Tocopherol restores cell viability and reduces ROS and autophagy, indicating that ROS plays a central role in clozapine-induced cytotoxicity. Additionally, inhibition of autophagy using chloroquine enhances clozapine-induced apoptosis and further reduces cell viability. These findings suggest that clozapine induces apoptosis and autophagy through ROS generation and that combining clozapine with autophagy inhibitors could sensitize MCF-7 cells to treatment. Furthermore, clozapine induces significant cytotoxicity in MDA-MB-231 cells, an aggressive, ER-negative breast cancer model, through similar ROS- and autophagy-mediated mechanisms. The addition of α-Tocopherol similarly rescued these cells from clozapine-induced cell death. Overall, our study demonstrates that clozapine suppresses the growth of both MCF-7 and MDA-MB-231 breast cancer cells by inducing cytotoxicity via ROS and autophagy, highlighting its potential as a therapeutic agent, especially in combination with autophagy inhibitors.

In recent years, genome-wide association study (GWAS) sample sizes have become larger, the statistical power has improved and thousands of trait-associated variants have been uncovered, offering new insights into the genetic etiology of complex human traits and disorders. However, a large fraction of the polygenic architecture underlying most complex phenotypes still remains undetected. We here review the conditional false discovery rate (condFDR) method, a model-free strategy for analysis of GWAS summary data, which has improved yield of existing GWAS and provided novel findings of genetic overlap between a wide range of complex human phenotypes, including psychiatric, cardiovascular, and neurological disorders, as well as psychological and cognitive traits. The condFDR method was inspired by Empirical Bayes approaches and leverages auxiliary genetic information to improve statistical power for discovery of single-nucleotide polymorphisms (SNPs). The cross-trait condFDR strategy analyses separate GWAS data, and leverages overlapping SNP associations, i.e., cross-trait enrichment, to increase discovery of trait-associated SNPs. The extension of the condFDR approach to conjunctional FDR (conjFDR) identifies shared genomic loci between two phenotypes. The conjFDR approach allows for detection of shared genomic associations irrespective of the genetic correlation between the phenotypes, often revealing a mixture of antagonistic and agonistic directional effects among the shared loci. This review provides a methodological comparison between condFDR and other relevant cross-trait analytical tools and demonstrates how condFDR analysis may provide novel insights into the genetic relationship between complex phenotypes.

Recent studies have shown that on one hand, tumors need to obtain a sufficient energy supply, and on the other hand they must evade the body’s immune surveillance. Because of their metabolic reprogramming characteristics, tumors can modify the physicochemical properties of the microenvironment, which in turn affects the biological characteristics of the cells infiltrating them. Regulatory T cells (Tregs) are a subset of T cells that regulate immune responses in the body. They exist in large quantities in the tumor microenvironment and exert immunosuppressive effects. The main effect of tumor microenvironment on Tregs is to promote their differentiation, proliferation, secretion of immunosuppressive factors, and chemotactic recruitment to play a role in immunosuppression in tumor tissues. This review focuses on cell metabolism reprogramming and the most significant features of the tumor microenvironment relative to the functional effects on Tregs, highlighting our understanding of the mechanisms of tumor immune evasion and providing new directions for tumor immunotherapy.