Single-cell mosaic integration and cell state transfer with auto-scaling self-attention mechanism.

The integration of data from multiple modalities generated by single-cell omics technologies is crucial for accurately identifying cell states. One challenge in comprehending multi-omics data resides in mosaic integration, in which different data modalities are profiled in different subsets of cells, as it requires simultaneous batch effect removal and modality alignment. Here, we develop Multi-omics Mosaic Auto-scaling Attention Variational Inference (mmAAVI), a scalable deep generative model for single-cell mosaic integration.

A novel subtype based on driver methylation-transcription in lung adenocarcinoma.

Aims: To identify driver methylation genes and a novel subtype of lung adenocarcinoma (LUAD) by multi-omics and elucidate its molecular features and clinical significance.

Methods: We collected LUAD patients from public databases, and identified driver methylation genes (DMGs) by MethSig and MethylMix algrothms. And novel driver methylation multi-omics subtypes were identified by similarity network fusion (SNF).

Improving the classification of multiple sclerosis and cerebral small vessel disease with interpretable transfer attention neural network.

As an autoimmune-mediated inflammatory demyelinating disease of the central nervous system, multiple sclerosis (MS) is often confused with cerebral small vessel disease (cSVD), which is a regional pathological change in brain tissue with unknown pathogenesis. This is due to their similar clinical presentations and imaging manifestations. That misdiagnosis can significantly increase the occurrence of adverse events.

Assessing the health risk of hyperuricemia in participants with persistent organic pollutants exposure - a systematic review and meta-analysis.

Based on a systematic review and meta-analysis of articles published in PubMed, Embase, Cochrane, and Web of Science, we identified nine articles that provide evidence of the relationship between persistent organic pollutants and hyperuricemia. Our researchers assess the quality of the included studies and their risk of bias using the recommended method and tool. This study uses meta-analyses of the random effects of each exposure and outcome to estimate combined odds ratios (ORs) and 95% confidence intervals (CIs).

Assessing the risk of diabetes in participants with DDT DDE exposure- A systematic review and meta-analysis.

We have performed a systematic review and meta-analysis of the association between DDT/DDE and diabetes, searching PubMed, Embase, and Cochrane for relevant articles published up to August 30, 2021, and eventually including 43 publications. Our researchers evaluate included studies' quality and risk of bias via the recommended tool. This study uses meta-analyses of random effects of each exposure and outcome to estimate combined odds ratios (ORs) and 95% confidence intervals (CIs).