The oncoimmunology research has witnessed notable advancements in recent years. Reshaping the tumor microenvironment (TME) approach is an effective method to improve antitumor immune response. The T cell-mediated antitumor response is crucial for favorable therapeutic outcomes in several cancers.
View Article and Find Full Text PDFAllylic diboronates are highly valuable reagents in organic synthesis. Existing methods predominantly yield alkyl-substituted allylic diboronates, while the incorporation of electrophilic carbonyl groups conjugated to these allylic systems remains unknown. We present a strain-release promoted cycloaddition-based strategy that enabled access to unprecedented carbonyl conjugated secondary allylic diborons.
View Article and Find Full Text PDFLong non-coding RNAs (lncRNAs) are critical regulators of physiological and pathological processes, with their dysregulation increasingly implicated in aging and Alzheimer's disease (AD). Using spatial transcriptomics, we analyzed 78 postmortem brain sections from 21 ROSMAP participants to map the spatial expression of lncRNAs in the dorsolateral prefrontal cortex of aged human brains. Compared to mRNAs, lncRNAs exhibited greater subregion-specific expression, with enrichment in antisense and lincRNA biotypes.
View Article and Find Full Text PDFBackground: The COVID-19 pandemic overburdened the healthcare system and affected the mental health of healthcare workers. Yoga has proven to improve mental health correlates, within diverse population groups, including healthcare workers. Considering the pandemic-imposed restrictions, this trial was designed to study the feasibility and effect of tele-yoga intervention on burnout, sleep quality, depression, anxiety, stress, mindfulness, and immune markers of healthcare workers on COVID-19 duty.
View Article and Find Full Text PDFThe gene signatures of Alzheimer's Disease (AD) brains reflect an output of a complex interplay of genetic, epigenetic, epi-transcriptomic, and post-transcriptional regulations. To identify the most significant factor that shapes the AD brain signature, we developed a machine learning model (DEcode-tree) to integrate cellular and molecular factors explaining differential gene expression in AD. Our model indicates that YTHDF proteins, the canonical readers of N6-methyladenosine RNA modification (m6A), are the most influential predictors of the AD brain signature.
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