The advent of RNA-based therapeutics for metabolic syndrome and associated conditions: a comprehensive review of the literature.

Metabolic syndrome (MetS) is a prevalent and intricate health condition affecting a significant global population, characterized by a cluster of metabolic and hormonal disorders disrupting lipid and glucose metabolism pathways. Clinical manifestations encompass obesity, dyslipidemia, insulin resistance, and hypertension, contributing to heightened risks of diabetes and cardiovascular diseases. Existing medications often fall short in addressing the syndrome's multifaceted nature, leading to suboptimal treatment outcomes and potential long-term health risks. This scenario underscores the pressing need for innovative therapeutic approaches in MetS management. RNA-based treatments, employing small interfering RNAs (siRNAs), microRNAs (miRNAs), and antisense oligonucleotides (ASOs), emerge as promising strategies to target underlying biological abnormalities. However, a summary of research available on the role of RNA-based therapeutics in MetS and related co-morbidities is limited. Murine models and human studies have been separately interrogated to determine whether there have been recent advancements in RNA-based therapeutics to offer a comprehensive understanding of treatment available for MetS. In a narrative fashion, we searched for relevant articles pertaining to MetS co-morbidities such as cardiovascular disease, fatty liver disease, dementia, colorectal cancer, and endocrine abnormalities. We emphasize the urgency of exploring novel therapeutic avenues to address the intricate pathophysiology of MetS and underscore the potential of RNA-based treatments, coupled with advanced delivery systems, as a transformative approach for achieving more comprehensive and efficacious outcomes in MetS patients.

Exploring the advances of single-cell RNA sequencing in thyroid cancer: a narrative review.

Thyroid cancer, a prevalent form of endocrine malignancy, has witnessed a substantial increase in occurrence in recent decades. To gain a comprehensive understanding of thyroid cancer at the single-cell level, this narrative review evaluates the applications of single-cell RNA sequencing (scRNA-seq) in thyroid cancer research. ScRNA-seq has revolutionised the identification and characterisation of distinct cell subpopulations, cell-to-cell communications, and receptor interactions, revealing unprecedented heterogeneity and shedding light on novel biomarkers for therapeutic discovery. These findings aid in the construction of predictive models on disease prognosis and therapeutic efficacy. Altogether, scRNA-seq has deepened our understanding of the tumour microenvironment immunologic insights, informing future studies in the development of effective personalised treatment for patients. Challenges and limitations of scRNA-seq, such as technical biases, financial barriers, and ethical concerns, are discussed. Advancements in computational methods, the advent of artificial intelligence (AI), machine learning (ML), and deep learning (DL), and the importance of single-cell data sharing and collaborative efforts are highlighted. Future directions of scRNA-seq in thyroid cancer research include investigating intra-tumoral heterogeneity, integrating with other omics technologies, exploring the non-coding RNA landscape, and studying rare subtypes. Overall, scRNA-seq has transformed thyroid cancer research and holds immense potential for advancing personalised therapies and improving patient outcomes. Efforts to make this technology more accessible and cost-effective will be crucial to ensuring its widespread utilisation in healthcare.

Prospects of evolution-based artificial intelligence models in genome-wide studies to stratify genetic risk variants in nonalcoholic fatty liver disease.

The emergence of genome-wide association studies (GWAS) has identified genetic traits and polymorphisms that are associated with the progression of nonalcoholic fatty liver disease (NAFLD). Phospholipase domain-containing 3 and transmembrane 6 superfamily member 2 are genes commonly associated with NAFLD phenotypes. However, there are fewer studies and replicability in lesser-known genes such as LYPLAL1 and glucokinase regulator (GCKR). With the advent of artificial intelligence (AI) in clinical genetics, studies have utilized AI algorithms to identify phenotypes through electronic health records and utilize convolution neural networks to improve the accuracy of variant identification, predict the deleterious effects of variants, and conduct phenotype-to-genotype mapping. Natural language processing (NLP) and machine-learning (ML) algorithms are popular tools in GWAS studies and connect electronic health record phenotypes to genetic diagnoses using a combination of international classification disease (ICD)-based approaches. However, there are still limitations to machine-learning - and NLP-based models, such as the lack of replicability in larger cohorts and underpowered sample sizes, which prevent the accurate prediction of genetic variants that may increase the risk of NAFLD and its progression to advanced-stage liver fibrosis. This may be largely due to the lack of understanding of the clinical consequence in the majority of pathogenic variants. Though the concept of evolution-based AI models and evolutionary algorithms is relatively new, combining current international classification disease -based NLP models with phylogenetic and evolutionary data can improve prediction accuracy and create valuable connections between variants and their pathogenicity in NAFLD. Such developments can improve risk stratification within clinical genetics and research while overcoming limitations in GWAS studies that prevent community-wide interpretations.