DANCE: a deep learning library and benchmark platform for single-cell analysis.

DANCE is the first standard, generic, and extensible benchmark platform for accessing and evaluating computational methods across the spectrum of benchmark datasets for numerous single-cell analysis tasks. Currently, DANCE supports 3 modules and 8 popular tasks with 32 state-of-art methods on 21 benchmark datasets. People can easily reproduce the results of supported algorithms across major benchmark datasets via minimal efforts, such as using only one command line. In addition, DANCE provides an ecosystem of deep learning architectures and tools for researchers to facilitate their own model development. DANCE is an open-source Python package that welcomes all kinds of contributions.

Study on Optimal Extraction and Hypoglycemic Effect of Quercetin.

Quercetin was extracted from Portulaca oleracea L. through biphasic acid hydrolysis to investigate its potential as a suppressor of dipeptidyl peptidase IV (DPP-IV) and its hypoglycemic effect in type 2 diabetic mice. The extraction procedure was optimized utilizing the response surface method (RSM) in a single-factor experimental setting. An extraction efficiency of 0.675% was achieved using the following optimized parameters: 0.064 mol/L vitriol, 1 : 109.155 solid-liquid ratio, and 21.408 min ultrasonication. Overall, findings indicate the effectiveness of quercetin extraction. A mouse model for type 2 diabetes was established to receive oral treatment with various quercetin concentrations for 8 weeks. Fasting blood glucose (FBG) and the DPP-IV activity in the serum were significantly reduced. The weight and insulin levels of the mice in the quercetin group were raised compared to those in the model group (P < 0.01). Quercetin dose-dependently inhibited postprandial blood glucose excursions, as demonstrated by the oral glucose tolerance test. These results confirmed that quercetin has hypoglycemic effects and considerably improves insulin sensitivity via DPP-IV targeting.

Single-Cell Multimodal Prediction via Transformers.

The recent development of multimodal single-cell technology has made the possibility of acquiring multiple omics data from individual cells, thereby enabling a deeper understanding of cellular states and dynamics. Nevertheless, the proliferation of multimodal single-cell data also introduces tremendous challenges in modeling the complex interactions among different modalities. The recently advanced methods focus on constructing static interaction graphs and applying graph neural networks (GNNs) to learn from multimodal data. However, such static graphs can be suboptimal as they do not take advantage of the downstream task information; meanwhile GNNs also have some inherent limitations when deeply stacking GNN layers. To tackle these issues, in this work, we investigate how to leverage transformers for multimodal single-cell data in an end-to-end manner while exploiting downstream task information. In particular, we propose a scMoFormer framework which can readily incorporate external domain knowledge and model the interactions within each modality and cross modalities. Extensive experiments demonstrate that scMoFormer achieves superior performance on various benchmark datasets. Remarkably, scMoFormer won a Kaggle silver medal with the rank of 24/1221 (Top 2%) without ensemble in a NeurIPS 2022 competition. Our implementation is publicly available at Github.

Protective Effect of Raf-1 Kinase Inhibitory Protein on Diabetic Retinal Neurodegeneration through P38-MAPK Pathway.

PURPOSE: This study aimed to investigate the effect of Raf-1 kinase inhibitory protein (RKIP) on diabetic retinal neurodegeneration in streptozotocin-treated rat model and high glucose-treated rat Müller cells. METHODS: Control and streptozotocin-treated rats were intravitreally injected with saline, RKIP gene overexpression lentivirus (oeRKIP) or negative control lentivirus (RKIP-vector). Normal or high glucose-treated Müller cells were transfected with saline, RKIP gene overexpression lentivirus or negative control lentivirus. Western blotting and immunofluorescence assay were utilized to evaluate the function of RKIP on the expression of RKIP, p38 mitogen-activated protein kinase (p38-MAPK), glutamate/aspartate transporter (GLAST), glutamine synthetase (GS), glial fibrillar acidic protein (GFAP) and cysteine-aspartic acid protease-3 (caspase-3). A glutamate assay kit was adopted to detect glutamate level in retina samples. Apoptosis of Müller cells was determined by Annexin-V/PI staining and flow cytometry. RESULTS: High glucose-treated Müller cells exhibited promoted apoptosis, while RKIP overexpression in high glucose-treated Müller cells down-regulated the enhanced apoptosis. Compared with rats injected with saline, streptozotocin-treated hyperglycemic rats displayed enhancement in the immunoreactivities of p38-MAPK and GFAP as well as in the protein expression of p38-MAPK and caspase-3. Strikingly, intravitreal injection of RKIP gene overexpression lentivirus in the hyperglycemic rats reversed the augmented immunoreactivities and protein expression mentioned above. Meanwhile, RKIP overexpression in the hyperglycemic rats improved the immunoreactivities and protein expression of RKIP, GS and GLAST. Besides, RKIP down-regulated the increased level of retinal glutamate in the hyperglycemic rats. CONCLUSIONS: Intravitreal injection of RKIP gene overexpression lentivirus functioned in preventing diabetic retinal neurodegeneration in a rat model of diabetes presumably by inhibiting p38-MAPK pathway.

Shared Genetic Architecture and Causal Relationship Between Asthma and Cardiovascular Diseases: A Large-Scale Cross-Trait Analysis.

Background: Accumulating evidence has suggested that there is a positive association between asthma and cardiovascular diseases (CVDs), implying a common architecture between them. However, the shared genetic architecture and causality of asthma and CVDs remain unclear. Methods: Based on the genome-wide association study (GWAS) summary statistics of recently published studies, our study examined the genetic correlation, shared genetic variants, and causal relationship between asthma (N = 127,669) and CVDs (N = 86,995-521,612). Statistical methods included high-definition likelihood (HDL), cross-trait meta-analyses of large-scale GWAS, transcriptome-wide association studies (TWAS), and Mendelian randomization (MR). Results: First, we observed a significant genetic correlation between asthma and heart failure (HF) (Rg = 0.278, P = 5 × 10-4). Through cross-trait analyses, we identified a total of 145 shared loci between asthma and HF. Fifteen novel loci were not previously reported for association with either asthma or HF. Second, we mapped these 145 loci to a total of 99 genes whose expressions are enriched in a broad spectrum of tissues, including the seminal vesicle, tonsil, appendix, spleen, skin, lymph nodes, breast, cervix and uterus, skeletal muscle, small intestine, lung, prostate, cardiac muscle, and liver. TWAS analysis identified five significant genes shared between asthma and HF in tissues from the hemic and immune system, digestive system, integumentary system, and nervous system. GSDMA, GSDMB, and ORMDL3 are statistically independent genetic effects from all shared TWAS genes between asthma and HF. Third, through MR analysis, genetic liability to asthma was significantly associated with heart failure at the Bonferroni-corrected significance level. The odds ratio (OR) is 1.07 [95% confidence interval (CI): 1.03-1.12; p = 1.31 × 10-3] per one-unit increase in loge odds of asthma. Conclusion: These findings provide strong evidence of genetic correlations and causal relationship between asthma and HF, suggesting a shared genetic architecture for these two diseases.

Prognostic Utility of Fatty Acid-Binding Protein 4 in Patients with Type 2 Diabetes and Acute Ischemic Stroke.

The role of fatty acid-binding proteins (FABPs) in atherosclerosis has been investigated. The aim of this study was to verify the hypothesis that higher levels of serum fatty acid-binding protein 4 (FABP4) could be a prognostic factor in Chinese patients with type 2 diabetes (T2DM) and acute ischemic stroke (AIS). From September 2015 to August 2016, consecutive first-ever AIS patients combined with T2DM were included in this study. FABP4, NIH stroke scale (NIHSS), and conventional risk factors were evaluated to determine their value to predict functional outcomes within 3 months. Multivariate analyses were performed using logistic regression models. We measured FABP4 in 329 patients. The median age of patients included in this study was 63 (IQR, 56-72) years and 45.9% were women. FABP4 serum levels were obtained at a median of 8.5 h (IQR, 4.0-14.0 h) after the stroke onset with a median value of 21.4 ng/ml (IQR, 15.6-28.2 ng/ml). In multivariable models, FABP4 remained an independent stroke severity predictor with an adjusted OR of 1.05 (95% CI, 1.02-1.09). In multivariate models comparing the third (odd ratio (OR), 2.25; 95% confidence interval (CI), 1.59-3.54) and fourth quartiles (OR, 3.75; 95% CI, 2.48-5.03) against the first quartile of the FABP4, levels of FABP4 were associated with poor functional outcome. At 3 months, 38 patients (11.6%; 95%CI, 8.1-15.0%) had died. The mortality distribution across the FABP4 quartiles ranged between 3.7% (first quartile) and 20.7% (fourth quartile). Elevation of FABP4 is associated with an increased risk of death and poor functional outcome events in patients with type 2 diabetes and acute ischemic stroke and is independent of other established clinical risk predictors and biomarkers.