Magnetic resonance imaging(MRI)is a significant non-invasive diagnostic technique in medical imaging.Due to limitations in MRI hardware and scanning time,some MRI images have relatively low spatial resolution.The rise of deep learning technology offers a new approach to improve the resolution of MRI images.The study outlines the background of MRI super-resolution reconstruction,delves into the applications of various deep learning methods in MRI super-resolution reconstruction and offers a detailed analysis of these methods,evaluating their working principles,advantages,and performance efficiency in image reconstruction.Additionally,it also discusses the key challenges of deep learning technology in MRI super-resolution reconstruction,and provides prospects for future research trends.

During the development of therapeutic microRNAs (miRNAs or miRs), it is essential to define their pharmacological actions. Rather, miRNA research and therapy mainly use miRNA mimics synthesized in vitro. After experimental screening of unique recombinant miRNAs produced in vivo, three lead antiproliferative miRNAs against human NSCLC cells, miR-22-3p, miR-9-5p, and miR-218-5p, were revealed to target folate metabolism by bioinformatic analyses. Recombinant miR-22-3p, miR-9-5p, and miR-218-5p were shown to regulate key folate metabolic enzymes to inhibit folate metabolism and subsequently alter amino acid metabolome in NSCLC A549 and H1975 cells. Isotope tracing studies further confirmed the disruption of one-carbon transfer from serine to folate metabolites by all three miRNAs, inhibition of glucose uptake by miR-22-3p, and reduction of serine biosynthesis from glucose by miR-9-5p and -218-5p in NSCLC cells. With greater activities to interrupt NSCLC cell respiration, glycolysis, and colony formation than miR-9-5p and -218-5p, recombinant miR-22-3p was effective to reduce tumor growth in two NSCLC patient-derived xenograft mouse models without causing any toxicity. These results establish a common antifolate mechanism and differential actions on glucose uptake and metabolism for three lead anticancer miRNAs as well as antitumor efficacy for miR-22-3p nanomedicine, which shall provide insight into developing antimetabolite RNA therapies.

Pharmacokinetics (PK) is the study of the absorption, distribution, metabolism, and excretion (ADME) processes of a drug. Understanding PK properties is essential for drug development and precision medication. In this review we provided an overview of recent research on PK with focus on the following aspects: (1) an update on drug-metabolizing enzymes and transporters in the determination of PK, as well as advances in xenobiotic receptors and noncoding RNAs (ncRNAs) in the modulation of PK, providing new understanding of the transcriptional and posttranscriptional regulatory mechanisms that result in inter-individual variations in pharmacotherapy; (2) current status and trends in assessing drug-drug interactions, especially interactions between drugs and herbs, between drugs and therapeutic biologics, and microbiota-mediated interactions; (3) advances in understanding the effects of diseases on PK, particularly changes in metabolizing enzymes and transporters with disease progression; (4) trends in mathematical modeling including physiologically-based PK modeling and novel animal models such as CRISPR/Cas9-based animal models for DMPK studies; (5) emerging non-classical xenobiotic metabolic pathways and the involvement of novel metabolic enzymes, especially non-P450s. Existing challenges and perspectives on future directions are discussed, and may stimulate the development of new research models, technologies, and strategies towards the development of better drugs and improved clinical practice.