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Background The global epidemic of diabetes mellitus continues to grow and affects developed and developing countries alike. Intensive glycemic control is thought to modify the risks for vascular complications, hence the risks for diabetes-related death. We investigated the trend of diabetic vascular complication-related deaths between 2000 and 2016 in the global diabetes landscape. Methods We collected 17 years of death certificates data from 108 countries in the World Health Organization mortality database between 2000 and 2016, with coding for diabetic complications. Crude and age-standardized proportions and rates were calculated. Trend analysis was done with annual average percentage change (AAPC) of rates computed by joinpoint regression. Results From 2000 through 2016, 7,108,145 deaths of diabetes were reported in the 108 countries. Among them, 26.8% (1,904,787 cases) were attributed to vascular complications in damaged organs, including the kidneys (1,355,085 cases, 71.1%), peripheral circulatory (515,293 cases, 27.1%), nerves (28,697 cases, 1.5%) and eyes (5751 cases, 0.3%). Overall, the age-standardized proportion of vascular complication-related mortality was 267.8 [95% confidence interval (95% CI), 267.5–268.1] cases per 1000 deaths and the rate was 53.6 (95% CI 53.5–53.7) cases per 100,000 person-years. Throughout the 17-year period, the overall age-standardized proportions of deaths attributable to vascular complications had increased 37.9%, while the overall age-standardized mortality rates related to vascular complications had increased 30.8% (AAPC = 1.9% [1.4–2.4%, p < 0.05]). These increases were predominantly driven by a 159.8% increase in the rate (AAPC = 2.7% [1.2–4.3%, p < 0.05]) from renal complications. Trends in the rates and AAPC of deaths varied by type of diabetes and of complications, as well as by countries, regions and domestic income. Conclusion Diabetic vascular complication-related deaths had increased substantially during 2000–2016, mainly driven by the increased mortality of renal complications.

Single-cell multi-omics technologies and methods characterize cell states and activities by simultaneously integrating various single-modality omics methods that profile the transcriptome, genome, epigenome, epitranscriptome, proteome, metabolome and other (emerging) omics. Collectively, these methods are revolutionizing molecular cell biology research. In this comprehensive Review, we discuss established multi-omics technologies as well as cutting-edge and state-of-the-art methods in the field. We discuss how multi-omics technologies have been adapted and improved over the past decade using a framework characterized by optimization of throughput and resolution, modality integration, uniqueness and accuracy, and we also discuss multi-omics limitations. We highlight the impact that single-cell multi-omics technologies have had in cell lineage tracing, tissue-specific and cell-specific atlas production, tumour immunology and cancer genetics, and in mapping of cellular spatial information in fundamental and translational research. Finally, we discuss bioinformatics tools that have been developed to link different omics modalities and elucidate functionality through the use of better mathematical modelling and computational methods.Single-cell multi-omics methods are essential for characterizing cell states and types. The past decade has ushered in improvements in spatial resolution and computational data integration and in new omics modalities. Consequently, single-cell multi-omics have advanced fundamental and translational research, including, for example, in production of cell atlases and in tumour immunology therapeutics.

Self-assembling natural drug hydrogels formed without structural modification and able to act as carriers are of interest for biomedical applications. A lack of knowledge about natural drug gels limits there current application. Here, we report on rhein, a herbal natural product, which is directly self-assembled into hydrogels through noncovalent interactions. This hydrogel shows excellent stability, sustained release and reversible stimuli-responses. The hydrogel consists of a three-dimensional nanofiber network that prevents premature degradation. Moreover, it easily enters cells and binds to toll-like receptor 4. This enables rhein hydrogels to significantly dephosphorylate IκBα, inhibiting the nuclear translocation of p65 at the NFκB signalling pathway in lipopolysaccharide-induced BV2 microglia. Subsequently, rhein hydrogels alleviate neuroinflammation with a long-lasting effect and little cytotoxicity compared to the equivalent free-drug in vitro. This study highlights a direct self-assembly hydrogel from natural small molecule as a promising neuroinflammatory therapy. There is interest in the development of drug-based hydrogels for responsive sustained drug release. Here, the authors report on the self-assembly of natural small molecule, rhein, into hydrogels and the application of the hydrogels as stable controlled release agents for neuro-inflammatory therapy

As one of the most common types of pancreatic cancer, pancreatic ductal adenocarcinoma (PDAC) is highly invasive and lethal. This study aims to develop biomarkers and targets for the diagnosis and treatment of PDAC. Differentially expressed genes (DEGs) were screened via GEO2R, protein network was constructed through STRING and Cytoscape. Functional enrichment analysis was performed, followed by survival analysis and expression validation. A total of 115 DEGs were identified, including 108 upregulated and 7 downregulated genes. After enrichment, survival analysis, one potential gene, Cyclin B1 (CCNB1), was selected for further expression verification at the mRNA and protein level. Taker together, CCNB1 may act as a potential biomarker which provided new idea for elucidation of the pathogenesis of PDAC.

We aimed to characterize quality of life (QOL) trajectories among patients with intermediate and advanced hepatocellular carcinoma patients treated with immunotherapy. Barcelona Clinic Liver Cancer (BCLC) stage B-C HCC patients receiving immunotherapy at Guangxi Medical University Cancer Hospital were included. Trajectories of QOL, assessed using the Functional Assessment of Cancer Therapy-Hepatobiliary (FACT-Hep) questionnaire, were identified through iterative estimations of group-based trajectory models. Associations with trajectory group membership were analyzed using multivariable multinomial logistic regression. Three trajectory groups were identified (n=156): excellent (35.3%), poor (43.6%), and deteriorating (21.1%) QOL. The deteriorating trajectory group reported a mean QOL score of 124.79 (95% CI, 116.58-133.00), but then declined significantly at month-2 (estimated QOL score 98.67 [95% CI, 84.33-113.00]), and the lowest mean score is reached at month-6 (estimated QOL score 16.58 [95% CI, 0-46.07]). Factors associated with membership to the deteriorating group included no drinking (odds ratio [OR] yes [95% CI], 3.70 [1.28-11.11]), no received radiotherapy (OR yes [95% CI], 8.33 [1.41-50.00]), diabetes (OR no [95% CI], 6.83 [1.57-29.73]), and extrahepatic metastasis (OR no [95% CI], 3.08 [1.07-8.87]). Factors associated with membership to the poor group also included body mass index ≤24.0 kg/m (OR vs no [95% CI], 4.49 [1.65-12.22]). This latent-class analysis identified a high-risk cluster of patients with severe, persistent post-immunotherapy QOL deterioration. Screening relevant patient-level characteristics may inform tailored interventions to mitigate the detrimental impact of immunotherapy and preserve QOL.

Measuring multiple omics profiles from the same single cell opens up the opportunity to decode molecular regulation that underlies intercellular heterogeneity in development and disease. Here, we present co-sequencing of microRNAs and mRNAs in the same single cell using a half-cell genomics approach. This method demonstrates good robustness (~95% success rate) and reproducibility ( R 2  = 0.93 for both microRNAs and mRNAs), yielding paired half-cell microRNA and mRNA profiles, which we can independently validate. By linking the level of microRNAs to the expression of predicted target mRNAs across 19 single cells that are phenotypically identical, we observe that the predicted targets are significantly anti-correlated with the variation of abundantly expressed microRNAs. This suggests that microRNA expression variability alone may lead to non-genetic cell-to-cell heterogeneity. Genome-scale analysis of paired microRNA-mRNA co-profiles further allows us to derive and validate regulatory relationships of cellular pathways controlling microRNA expression and intercellular variability. Single-cell RNA sequencing allows characterizing cell-to-cell heterogeneity at transcriptome scale. Here, the authors present an approach that enables microRNA and mRNA sequencing in the same single cell, providing insights into the origins of non-genetic cellular variability and mechanisms of miRNA regulation.

Knowledge of drug–target interaction (DTI) plays an important role in discovering new drug candidates. Unfortunately, there are unavoidable shortcomings; including the time-consuming and expensive nature of the experimental method to predict DTI. Therefore, it motivates us to develop an effective computational method to predict DTI based on protein sequence. In the paper, we proposed a novel computational approach based on protein sequence, namely PDTPS (Predicting Drug Targets with Protein Sequence) to predict DTI. The PDTPS method combines Bi-gram probabilities (BIGP), Position Specific Scoring Matrix (PSSM), and Principal Component Analysis (PCA) with Relevance Vector Machine (RVM). In order to evaluate the prediction capacity of the PDTPS, the experiment was carried out on enzyme, ion channel, GPCR, and nuclear receptor datasets by using five-fold cross-validation tests. The proposed PDTPS method achieved average accuracy of 97.73%, 93.12%, 86.78%, and 87.78% on enzyme, ion channel, GPCR and nuclear receptor datasets, respectively. The experimental results showed that our method has good prediction performance. Furthermore, in order to further evaluate the prediction performance of the proposed PDTPS method, we compared it with the state-of-the-art support vector machine (SVM) classifier on enzyme and ion channel datasets, and other exiting methods on four datasets. The promising comparison results further demonstrate that the efficiency and robust of the proposed PDTPS method. This makes it a useful tool and suitable for predicting DTI, as well as other bioinformatics tasks.

Perchlorotrityl radical (PTM), tris (2,4,6-trichlorophenyl) methyl radical (TTM), (3,5-dichloro-4-pyridyl) bis (2,4,6 trichlorophenyl) methyl radical (PyBTM), (N-carbazolyl) bis (2,4,6-trichlorophenyl) methyl radical (CzBTM), and their derivatives are stable organic radicals that exhibit light emissions at room temperature. Since these triarylmethyl radicals have an unpaired electron, their electron spins at the lowest excited state and ground state are both doublets, and the transition from the lowest excited state to the ground state does not pose the problem of a spin-forbidden reaction. When used as OLED layers, these triarylmethyl radicals exhibit unique light-emitting properties, which can increase the theoretical upper limit of the OLED’s internal quantum efficiency (IQE) to 100%. In recent years, research on the luminescent properties of triarylmethyl radicals has attracted increasing attention. In this review, recent developments in these triarylmethyl radicals and their derivatives in OLED devices are introduced.

Emerging spatial technologies, including spatial transcriptomics and spatial epigenomics, are becoming powerful tools for profiling of cellular states in the tissue context 1 , 2 , 3 , 4 – 5 . However, current methods capture only one layer of omics information at a time, precluding the possibility of examining the mechanistic relationship across the central dogma of molecular biology. Here, we present two technologies for spatially resolved, genome-wide, joint profiling of the epigenome and transcriptome by cosequencing chromatin accessibility and gene expression, or histone modifications (H3K27me3, H3K27ac or H3K4me3) and gene expression on the same tissue section at near-single-cell resolution. These were applied to embryonic and juvenile mouse brain, as well as adult human brain, to map how epigenetic mechanisms control transcriptional phenotype and cell dynamics in tissue. Although highly concordant tissue features were identified by either spatial epigenome or spatial transcriptome we also observed distinct patterns, suggesting their differential roles in defining cell states. Linking epigenome to transcriptome pixel by pixel allows the uncovering of new insights in spatial epigenetic priming, differentiation and gene regulation within the tissue architecture. These technologies are of great interest in life science and biomedical research. The authors present two technologies for spatially resolved, genome-wide, joint profiling of the epigenome and transcriptome by cosequencing chromatin accessibility and gene expression, or histone modifications and gene expression on the same tissue section at near-single-cell resolution.