Artificial intelligence (AI) has the potential to transform every facet of cardiovascular practice and research. The exponential rise in technology powered by AI is defining new frontiers in cardiovascular care, with innovations that span novel diagnostic modalities, new digital native biomarkers of disease, and high-performing tools evaluating care quality and prognosticating clinical outcomes. These digital innovations promise expanded access to cardiovascular screening and monitoring, especially among those without access to high-quality, specialized care historically. Moreover, AI is propelling biological and clinical discoveries that will make future cardiovascular care more personalized, precise, and effective. The review brings together these diverse AI innovations, highlighting developments in multimodal cardiovascular AI across clinical practice and biomedical discovery, and envisioning this new future backed by contemporary science and emerging discoveries. Finally, we define the critical path and the safeguards essential to realizing this AI-enabled future that helps achieve optimal cardiovascular health and outcomes for all.

UNASSIGNED: The cytotoxic T-lymphocyte-associated protein 4 (CTLA4) is involved in the progression of various cancers, but its biological roles in breast cancer (BRCA) remain unclear. Therefore, we performed a systematic multiomic analysis to expound on the prognostic value and underlying mechanism of CTLA4 in BRCA.
UNASSIGNED: We assessed the effect of CTLA4 expression on BRCA using a variety of bioinformatics platforms, including Oncomine, GEPIA, UALCAN, PrognoScan database, Kaplan-Meier plotter, and R2: Kaplan-Meier scanner.
UNASSIGNED: CTLA4 was highly expressed in BRCA tumor tissue compared to normal tissue (P < 0.01). The CTLA4 messenger RNA levels in BRCA based on BRCA subtypes of Luminal, human epidermal growth factor receptor 2, and triple-negative BRCA were considerably higher than in normal tissues (P < 0.001). However, the overexpression of CTLA4 was associated with a better prognosis in BRCA (P < 0.001) and was correlated with clinicopathological characteristics including age, T stage, estrogen receptors, progesterone receptors, and prediction analysis of microarray 50 (P < 0.01). The infiltration of multiple immune cells was associated with increased CTLA4 expression in BRCA (P < 0.001). CTLA4 was highly enriched in antigen binding, immunoglobulin complexes, lymphocyte-mediated immunity, and cytokine-cytokine receptor interaction.
UNASSIGNED: This study provides suggestive evidence of the prognostic role of CTLA4 in BRCA, which may be a therapeutic target for BRCA. Furthermore, CTLA4 may influence BRCA prognosis through antigen binding, immunoglobulin complexes, lymphocyte-mediated immunity, and cytokine-cytokine receptor interaction. These findings help us understand how CTLA4 plays a role in BRCA and set the stage for more research.

Male infertility is a reproductive disorder, accounting for 40-50% of infertility. Currently, in about 70% of infertile men, the cause remains unknown. With the introduction of novel omics and advancement in high-throughput technology, potential biomarkers are emerging. The main purpose of our work was to overview different aspects of omics approaches in association with idiopathic male infertility and highlight potential genes, transcripts, non-coding RNA, proteins, and metabolites worth further exploring. Using the Gene Ontology (GO) analysis, we aimed to compare enriched GO terms from each omics approach and determine their overlapping. A PubMed database screening for the literature published between February 2014 and June 2022 was performed using the keywords: male infertility in association with different omics approaches: genomics, epigenomics, transcriptomics, ncRNAomics, proteomics, and metabolomics. A GO enrichment analysis was performed using the Enrichr tool. We retrieved 281 global studies: 171 genomics (DNA level), 21 epigenomics (19 of methylation and two histone residue modifications), 15 transcriptomics, 31 non-coding RNA, 29 proteomics, two protein posttranslational modification, and 19 metabolomics studies. Gene ontology comparison showed that different omics approaches lead to the identification of different molecular factors and that the corresponding GO terms, obtained from different omics approaches, do not overlap to a larger extent. With the integration of novel omics levels into the research of idiopathic causes of male infertility, using multi-omic systems biology approaches, we will be closer to finding the potential biomarkers and consequently becoming aware of the entire spectrum of male infertility, their cause, prognosis, and potential treatment.

The neuroscience and neurobiology of gene editing to enhance learning and memory is of paramount interest to the scientific community. The advancements of CRISPR system have created avenues to treat neurological disorders by means of versatile modalities varying from expression to suppression of genes and proteins. Neurodegenerative disorders have also been attributed to non-canonical DNA secondary structures by affecting neuron activity through controlling gene expression, nucleosome shape, transcription, translation, replication, and recombination. Changing DNA regulatory elements which could contribute to the fate and function of neurons are thoroughly discussed in this review. This study presents the ability of CRISPR system to boost learning power and memory, treat or cure genetically-based neurological disorders, and alleviate psychiatric diseases by altering the activity and the irritability of the neurons at the synaptic cleft through DNA manipulation, and also, epigenetic modifications using Cas9. We explore and examine how each different OMIC techniques can come useful when altering DNA sequences. Such insight into the underlying relationship between OMICs and cellular behaviors leads us to better neurological and psychiatric therapeutics by intelligently designing and utilizing the CRISPR/Cas9 technology.

Neuroblastoma, a paediatric malignancy with high rates of cancer-related morbidity and mortality, is of significant interest to the field of paediatric cancers. High-risk NB tumours are usually metastatic and result in survival rates of less than 50%. Machine learning approaches have been applied to various neuroblastoma patient data to retrieve relevant clinical and biological information and develop predictive models. Given this background, this study will catalogue and summarise the literature that has used machine learning and statistical methods to analyse data such as multi-omics, histological sections, and medical images to make clinical predictions. Furthermore, the question will be turned on its head, and the use of machine learning to accurately stratify NB patients by risk groups and to predict outcomes, including survival and treatment response, will be summarised. Overall, this study aims to catalogue and summarise the important work conducted to date on the subject of expression-based predictor models and machine learning in neuroblastoma for risk stratification and patient outcomes including survival, and treatment response which may assist and direct future diagnostic and therapeutic efforts.

In recent years, plastic pollution has become a global environmental problem, posing a potential threat to agricultural ecosystems and human health, and may further exacerbate global food security problems. Studies have revealed that exposure to micro/nano-plastics (MPs/NPs) might cause various aspects of physiological toxicities, including plant biomass reduction, intracellular oxidative stress burst, photosynthesis inhibition, water and nutrient absorption reduction, cellular and genotoxicity, seed germination retardation, and that the effects were closely related to MP/NP properties (type, particle size, functional groups), exposure concentration, exposure duration and plant characteristics (species, tissue, growth stage). Based on a brief review of the physiological toxicity of MPs/NPs to plant growth, this paper comprehensively reviews the potential molecular mechanism of MPs/NPs on plant growth from perspectives of multi-omics, including transcriptome, metabolome, proteome and microbiome, thus to reveal the role of MPs/NPs in plant transcriptional regulation, metabolic pathway reprogramming, protein translational and post-translational modification, as well as rhizosphere microbial remodeling at multiple levels. Meanwhile, this paper also provides prospects for future research, and clarifies the future research directions and the technologies adopted.

Triple-Negative Breast Cancer (TNBC) presents a significant challenge in oncology due to its aggressive behavior and limited therapeutic options. This review explores the potential of immunotherapy, particularly vaccine-based approaches, in addressing TNBC. It delves into the role of immunoinformatics in creating effective vaccines against TNBC. The review first underscores the distinct attributes of TNBC and the importance of tumor antigens in vaccine development. It then elaborates on antigen detection techniques such as exome sequencing, HLA typing, and RNA sequencing, which are instrumental in identifying TNBC-specific antigens and selecting vaccine candidates. The discussion then shifts to the in-silico vaccine development process, encompassing antigen selection, epitope prediction, and rational vaccine design. This process merges computational simulations with immunological insights. The role of Artificial Intelligence (AI) in expediting the prediction of antigens and epitopes is also emphasized. The review concludes by encapsulating how Immunoinformatics can augment the design of TNBC vaccines, integrating tumor antigens, advanced detection methods, in-silico strategies, and AI-driven insights to advance TNBC immunotherapy. This could potentially pave the way for more targeted and efficacious treatments.

To review the application of omics technologies in the field of cariology research and provide critical insights into the emerging opportunities and challenges.
Publications on the application of omics technologies in cariology research up to December 2022 were sourced from online databases, including PubMed, Web of Science and Scopus. Two independent reviewers assessed the relevance of the publications to the objective of this review.
Studies that employed omics technologies to investigate dental caries were selected from the initial pool of identified publications. A total of 922 publications with one or more omics technologies adopted were included for comprehensive bibliographic analysis. (Meta)genomics (676/922, 73 %) is the predominant omics technology applied for cariology research in the included studies. Other applied omics technologies are metabolomics (108/922, 12 %), proteomics (105/922, 11 %), and transcriptomics (76/922, 8 %).
This study identified an emerging trend in the application of multiple omics technologies in cariology research. Omics technologies possess significant potential in developing strategies for the detection, staging evaluation, risk assessment, prevention, and management of dental caries. Despite the numerous challenges that lie ahead, the integration of multi-omics data obtained from individual biological samples, in conjunction with artificial intelligence technology, may offer potential avenues for further exploration in caries research.
This review presented a comprehensive overview of the application of omics technologies in cariology research and discussed the advantages and challenges of using these methods to detect, assess, predict, prevent, and treat dental caries. It contributes to steering research for improved understanding of dental caries and advancing clinical translation of cariology research outcomes.

Biofilm formation by Pseudomonas aeruginosa is primarily responsible for chronic wound and lung infections in humans. These infections are persistent owing to the biofilm\'s high tolerance to antimicrobials and constantly changing environmental factors. Understanding the mechanism governing biofilm formation can help to develop therapeutics explicitly directed against the molecular markers responsible for this process. After numerous years of research, many genes responsible for both in vitro and in vivo biofilm development remain unidentified. However, there is no \"all in one\" complete in vivo or in vitro biofilm model. Recent findings imply that the shift from planktonic bacteria to biofilms is a complicated and interrelated differentiation process. Research on the applications of omics technologies in P. aeruginosa biofilm development is ongoing, and these approaches hold great promise for expanding our knowledge of the mechanisms of biofilm formation. This review discusses the different factors that affect biofilm formation and compares P. aeruginosa biofilm formation using the omics approaches targeting essential biological macromolecules, such as DNA, RNA, Protein, and metabolome. Furthermore, we have outlined the application of currently available omics tools, such as genomics, proteomics, metabolomics, transcriptomics, and integrated multi-omics methodologies, to understand the differential gene expression (biofilm vs. planktonic bacteria) of P. aeruginosa biofilms.

BACKGROUND: The term \"vascular cognitive impairment\" (VCI) describes various cognitive conditions that include vascular elements. It increases the risk of morbidity and mortality in the elderly population and is the most common cognitive impairment associated with cerebrovascular disease. Understanding the etiology of VCI may aid in identifying approaches to target its possible therapy for the condition. Treatment of VCI has focused on vascular risk factors. There are no authorized conventional therapies available right now. The medications used to treat VCI are solely approved for symptomatic relief and are not intended to prevent or slow the development of VCI.
OBJECTIVE: The function of Chinese medicine in treating VCI has not yet been thoroughly examined. This review evaluates the preclinical and limited clinical evidence to comprehend the \"multi-component, multi-target, multi-pathway\" mechanism of Traditional Chinese medicine (TCM). It investigates the various multi-omics approaches in the search for the pathological mechanisms of VCI, as well as the new research strategies, in the hopes of supplying supportive evidence for the clinical treatment of VCI.
METHODS: This review used the Preferred Reporting Items for Preferred reporting items for systematic reviews and meta-analyses (PRISMA) statements. Using integrated bioinformatics and network pharmacology approaches, a thorough evaluation and analysis of 25 preclinical studies published up to July 1, 2023, were conducted to shed light on the mechanisms of TCM for vascular cognitive impairment. The studies for the systematic review were located using the following databases: PubMed, Web of Science, Scopus, Cochrane, and ScienceDirect.
RESULTS: We discovered that the multi-omics analysis approach would hasten the discovery of the role of TCM in the treatment of VCI. It will explore components, compounds, targets, and pathways, slowing the progression of VCI from the perspective of inhibiting oxidative stress, stifling neuroinflammation, increasing cerebral blood flow, and inhibiting iron deposition by a variety of molecular mechanisms, which have significant implications for the treatment of VCI.
CONCLUSIONS: TCM is a valuable tool for developing dementia therapies, and further research is needed to determine how TCM components may affect the operation of the neurovascular unit. There are still some limitations, although several research have offered invaluable resources for searching for possible anti-dementia medicines and treatments. To gain new insights into the molecular mechanisms that precisely modulate the key molecules at different levels during pharmacological interventions-a prerequisite for comprehending the mechanism of action and determining the potential therapeutic value of the drugs-further research should employ more standardized experimental methods as well as more sophisticated science and technology. Given the results of this review, we advocate integrating chemical and biological component analysis approaches in future research on VCI to provide a more full and objective assessment of the standard of TCM. With the help of bioinformatics, a multi-omics analysis approach will hasten the discovery of the role of TCM in the treatment of VCI, which has significant implications for the treatment of VCI.