Cardiovascular Disease Risk Stratification Using Hybrid Deep Learning Paradigm: First of Its Kind on Canadian Trial Data.

BACKGROUND: The risk of cardiovascular disease (CVD) has traditionally been predicted via the assessment of carotid plaques. In the proposed study, AtheroEdge™ 3.0HDL (AtheroPoint™, Roseville, CA, USA) was designed to demonstrate how well the features obtained from carotid plaques determine the risk of CVD. We hypothesize that hybrid deep learning (HDL) will outperform unidirectional deep learning, bidirectional deep learning, and machine learning (ML) paradigms. METHODOLOGY: 500 people who had undergone targeted carotid B-mode ultrasonography and coronary angiography were included in the proposed study. ML feature selection was carried out using three different methods, namely principal component analysis (PCA) pooling, the chi-square test (CST), and the random forest regression (RFR) test. The unidirectional and bidirectional deep learning models were trained, and then six types of novel HDL-based models were designed for CVD risk stratification. The AtheroEdge™ 3.0HDL was scientifically validated using seen and unseen datasets while the reliability and statistical tests were conducted using CST along with p-value significance. The performance of AtheroEdge™ 3.0HDL was evaluated by measuring the p-value and area-under-the-curve for both seen and unseen data. RESULTS: The HDL system showed an improvement of 30.20% (0.954 vs. 0.702) over the ML system using the seen datasets. The ML feature extraction analysis showed 70% of common features among all three methods. The generalization of AtheroEdge™ 3.0HDL showed less than 1% (p-value < 0.001) difference between seen and unseen data, complying with regulatory standards. CONCLUSIONS: The hypothesis for AtheroEdge™ 3.0HDL was scientifically validated, and the model was tested for reliability and stability and is further adaptable clinically.

UltraAIGenomics: Artificial Intelligence-Based Cardiovascular Disease Risk Assessment by Fusion of Ultrasound-Based Radiomics and Genomics Features for Preventive, Personalized and Precision Medicine: A Narrative Review.

Cardiovascular disease (CVD) diagnosis and treatment are challenging since symptoms appear late in the disease's progression. Despite clinical risk scores, cardiac event prediction is inadequate, and many at-risk patients are not adequately categorised by conventional risk factors alone. Integrating genomic-based biomarkers (GBBM), specifically those found in plasma and/or serum samples, along with novel non-invasive radiomic-based biomarkers (RBBM) such as plaque area and plaque burden can improve the overall specificity of CVD risk. This review proposes two hypotheses: (i) RBBM and GBBM biomarkers have a strong correlation and can be used to detect the severity of CVD and stroke precisely, and (ii) introduces a proposed artificial intelligence (AI)-based preventive, precision, and personalized ( aiP 3 ) CVD/Stroke risk model. The PRISMA search selected 246 studies for the CVD/Stroke risk. It showed that using the RBBM and GBBM biomarkers, deep learning (DL) modelscould be used for CVD/Stroke risk stratification in the aiP 3 framework. Furthermore, we present a concise overview of platelet function, complete blood count (CBC), and diagnostic methods. As part of the AI paradigm, we discuss explainability, pruning, bias, and benchmarking against previous studies and their potential impacts. The review proposes the integration of RBBM and GBBM, an innovative solution streamlined in the DL paradigm for predicting CVD/Stroke risk in the aiP 3 framework. The combination of RBBM and GBBM introduces a powerful CVD/Stroke risk assessment paradigm. aiP 3 model signifies a promising advancement in CVD/Stroke risk assessment.

Insulin-like growth factor-1 reduces cardiac autosis through decreasing AMPK/FOXO1 signaling and Na+/K+-ATPase-Beclin-1 interaction.

Introduction: Insulin-like growth factor-1 (IGF-1) promotes survival and inhibits cardiac autophagy disruption. Methods: Male Wistar rats were treated with IGF-1 (50 µg/kg), and 24 h after injection hearts were excised. The level of interaction between Beclin-1 and the α1 subunit of sodium/potassium-adenosine triphosphates (Na+/K+-ATPase), and phosphorylated forms of IGF-1 receptor/insulin receptor (IGF-1R/IR), forkhead box protein O1 (FOXO1) and AMP-activated protein kinase (AMPK) were measured. Results: The results indicate that IGF-1 decreased Beclin-1's association with Na+/K+-ATPase (p < 0.05), increased IGF-1R/IR and FOXO1 phosphorylation (p < 0.05), and decreased AMPK phosphorylation (p < 0.01) in rats' hearts. Conclusions: The new IGF-1 therapy may control autosis and minimize cardiomyocyte mortality.

Impact of different hormones on the regulation of nitric oxide in diabetes.

Polymetabolic syndrome achieved pandemic proportions and dramatically influenced public health systems functioning worldwide. Chronic vascular complications are the major contributors to increased morbidity, disability, and mortality rates in diabetes patients. Nitric oxide (NO) is among the most important vascular bed function regulators. However, NO homeostasis is significantly deranged in pathological conditions. Additionally, different hormones directly or indirectly affect NO production and activity and subsequently act on vascular physiology. In this paper, we summarize the recent literature data related to the effects of insulin, estradiol, insulin-like growth factor-1, ghrelin, angiotensin II and irisin on the NO regulation in physiological and diabetes circumstances.

Artificial intelligence for cardiovascular disease risk assessment in personalised framework: a scoping review.

Background: The field of precision medicine endeavors to transform the healthcare industry by advancing individualised strategies for diagnosis, treatment modalities, and predictive assessments. This is achieved by utilizing extensive multidimensional biological datasets encompassing diverse components, such as an individual's genetic makeup, functional attributes, and environmental influences. Artificial intelligence (AI) systems, namely machine learning (ML) and deep learning (DL), have exhibited remarkable efficacy in predicting the potential occurrence of specific cancers and cardiovascular diseases (CVD). Methods: We conducted a comprehensive scoping review guided by the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) framework. Our search strategy involved combining key terms related to CVD and AI using the Boolean operator AND. In August 2023, we conducted an extensive search across reputable scholarly databases including Google Scholar, PubMed, IEEE Xplore, ScienceDirect, Web of Science, and arXiv to gather relevant academic literature on personalised medicine for CVD. Subsequently, in January 2024, we extended our search to include internet search engines such as Google and various CVD websites. These searches were further updated in March 2024. Additionally, we reviewed the reference lists of the final selected research articles to identify any additional relevant literature. Findings: A total of 2307 records were identified during the process of conducting the study, consisting of 564 entries from external sites like arXiv and 1743 records found through database searching. After 430 duplicate articles were eliminated, 1877 items that remained were screened for relevancy. In this stage, 1241 articles remained for additional review after 158 irrelevant articles and 478 articles with insufficient data were removed. 355 articles were eliminated for being inaccessible, 726 for being written in a language other than English, and 281 for not having undergone peer review. Consequently, 121 studies were deemed suitable for inclusion in the qualitative synthesis. At the intersection of CVD, AI, and precision medicine, we found important scientific findings in our scoping review. Intricate pattern extraction from large, complicated genetic datasets is a skill that AI algorithms excel at, allowing for accurate disease diagnosis and CVD risk prediction. Furthermore, these investigations have uncovered unique genetic biomarkers linked to CVD, providing insight into the workings of the disease and possible treatment avenues. The construction of more precise predictive models and personalised treatment plans based on the genetic profiles of individual patients has been made possible by the revolutionary advancement of CVD risk assessment through the integration of AI and genomics. Interpretation: The systematic methodology employed ensured the thorough examination of available literature and the inclusion of relevant studies, contributing to the robustness and reliability of the study's findings. Our analysis stresses a crucial point in terms of the adaptability and versatility of AI solutions. AI algorithms designed in non-CVD domains such as in oncology, often include ideas and tactics that might be modified to address cardiovascular problems. Funding: No funding received.

The involvement of Akt, mTOR, and S6K in the in vivo effect of IGF-1 on the regulation of rat cardiac Na+/K+-ATPase.

BACKGROUND: We previously demonstrated that insulin-like growth factor-1 (IGF-1) regulates sodium/potassium adenosine triphosphatase (Na+/K+-ATPase) in vascular smooth muscle cells (VSMC) via phosphatidylinositol-3 kinase (PI3K). Taking into account that others' work show that IGF-1 activates the PI3K/protein kinase B (Akt) signaling pathway in many different cells, we here further questioned if the Akt/mammalian target of rapamycin (mTOR)/ribosomal protein p70 S6 kinase (S6K) pathway stimulates Na+/K+-ATPase, an essential protein for maintaining normal heart function. METHODS AND RESULTS: There were 14 adult male Wistar rats, half of whom received bolus injections of IGF-1 (50 µg/kg) for 24 h. We evaluated cardiac Na+/K+-ATPase expression, activity, and serum IGF-1 levels. Additionally, we examined the phosphorylated forms of the following proteins: insulin receptor substrate (IRS), phosphoinositide-dependent kinase-1 (PDK-1), Akt, mTOR, S6K, and α subunit of Na+/K+-ATPase. Additionally, the mRNA expression of the Na+/K+-ATPase α1 subunit was evaluated. Treatment with IGF-1 increases levels of serum IGF-1 and stimulates Na+/K+-ATPase activity, phosphorylation of α subunit of Na+/K+-ATPase on Ser23, and protein expression of α2 subunit. Furthermore, IGF-1 treatment increased phosphorylation of IRS-1 on Tyr1222, Akt on Ser473, PDK-1 on Ser241, mTOR on Ser2481 and Ser2448, and S6K on Thr421/Ser424. The concentration of IGF-1 in serum positively correlates with Na+/K+-ATPase activity and the phosphorylated form of mTOR (Ser2448), while Na+/K+-ATPase activity positively correlates with the phosphorylated form of IRS-1 (Tyr1222) and mTOR (Ser2448). CONCLUSION: These results indicate that the Akt/mTOR/S6K signalling pathway may be involved in the IGF-1 regulating cardiac Na+/K+-ATPase expression and activity.

Deep learning approach for cardiovascular disease risk stratification and survival analysis on a Canadian cohort.

The quantification of carotid plaque has been routinely used to predict cardiovascular risk in cardiovascular disease (CVD) and coronary artery disease (CAD). To determine how well carotid plaque features predict the likelihood of CAD and cardiovascular (CV) events using deep learning (DL) and compare against the machine learning (ML) paradigm. The participants in this study consisted of 459 individuals who had undergone coronary angiography, contrast-enhanced ultrasonography, and focused carotid B-mode ultrasound. Each patient was tracked for thirty days. The measurements on these patients consisted of maximum plaque height (MPH), total plaque area (TPA), carotid intima-media thickness (cIMT), and intraplaque neovascularization (IPN). CAD risk and CV event stratification were performed by applying eight types of DL-based models. Univariate and multivariate analysis was also conducted to predict the most significant risk predictors. The DL's model effectiveness was evaluated by the area-under-the-curve measurement while the CV event prediction was evaluated using the Cox proportional hazard model (CPHM) and compared against the DL-based concordance index (c-index). IPN showed a substantial ability to predict CV events (p < 0.0001). The best DL system improved by 21% (0.929 vs. 0.762) over the best ML system. DL-based CV event prediction showed a ~ 17% increase in DL-based c-index compared to the CPHM (0.86 vs. 0.73). CAD and CV incidents were linked to IPN and carotid imaging characteristics. For survival analysis and CAD prediction, the DL-based system performs superior to ML-based models.

GeneAI 3.0: powerful, novel, generalized hybrid and ensemble deep learning frameworks for miRNA species classification of stationary patterns from nucleotides.

Due to the intricate relationship between the small non-coding ribonucleic acid (miRNA) sequences, the classification of miRNA species, namely Human, Gorilla, Rat, and Mouse is challenging. Previous methods are not robust and accurate. In this study, we present AtheroPoint's GeneAI 3.0, a powerful, novel, and generalized method for extracting features from the fixed patterns of purines and pyrimidines in each miRNA sequence in ensemble paradigms in machine learning (EML) and convolutional neural network (CNN)-based deep learning (EDL) frameworks. GeneAI 3.0 utilized five conventional (Entropy, Dissimilarity, Energy, Homogeneity, and Contrast), and three contemporary (Shannon entropy, Hurst exponent, Fractal dimension) features, to generate a composite feature set from given miRNA sequences which were then passed into our ML and DL classification framework. A set of 11 new classifiers was designed consisting of 5 EML and 6 EDL for binary/multiclass classification. It was benchmarked against 9 solo ML (SML), 6 solo DL (SDL), 12 hybrid DL (HDL) models, resulting in a total of 11 + 27 = 38 models were designed. Four hypotheses were formulated and validated using explainable AI (XAI) as well as reliability/statistical tests. The order of the mean performance using accuracy (ACC)/area-under-the-curve (AUC) of the 24 DL classifiers was: EDL > HDL > SDL. The mean performance of EDL models with CNN layers was superior to that without CNN layers by 0.73%/0.92%. Mean performance of EML models was superior to SML models with improvements of ACC/AUC by 6.24%/6.46%. EDL models performed significantly better than EML models, with a mean increase in ACC/AUC of 7.09%/6.96%. The GeneAI 3.0 tool produced expected XAI feature plots, and the statistical tests showed significant p-values. Ensemble models with composite features are highly effective and generalized models for effectively classifying miRNA sequences.

aiGeneR 1.0: An Artificial Intelligence Technique for the Revelation of Informative and Antibiotic Resistant Genes in Escherichia coli.

BACKGROUND: There are several antibiotic resistance genes (ARG) for the Escherichia coli (E. coli) bacteria that cause urinary tract infections (UTI), and it is therefore important to identify these ARG. Artificial Intelligence (AI) has been used previously in the field of gene expression data, but never adopted for the detection and classification of bacterial ARG. We hypothesize, if the data is correctly conferred, right features are selected, and Deep Learning (DL) classification models are optimized, then (i) non-linear DL models would perform better than Machine Learning (ML) models, (ii) leads to higher accuracy, (iii) can identify the hub genes, and, (iv) can identify gene pathways accurately. We have therefore designed aiGeneR, the first of its kind system that uses DL-based models to identify ARG in E. coli in gene expression data. METHODOLOGY: The aiGeneR consists of a tandem connection of quality control embedded with feature extraction and AI-based classification of ARG. We adopted a cross-validation approach to evaluate the performance of aiGeneR using accuracy, precision, recall, and F1-score. Further, we analyzed the effect of sample size ensuring generalization of models and compare against the power analysis. The aiGeneR was validated scientifically and biologically for hub genes and pathways. We benchmarked aiGeneR against two linear and two other non-linear AI models. RESULTS: The aiGeneR identifies tetM (an ARG) and showed an accuracy of 93% with area under the curve (AUC) of 0.99 (p < 0.05). The mean accuracy of non-linear models was 22% higher compared to linear models. We scientifically and biologically validated the aiGeneR. CONCLUSIONS: aiGeneR successfully detected the E. coli genes validating our four hypotheses.

A Pharmaceutical Paradigm for Cardiovascular Composite Risk Assessment Using Novel Radiogenomics Risk Predictors in Precision Explainable Artificial Intelligence Framework: Clinical Trial Tool.

BACKGROUND: Cardiovascular disease (CVD) is challenging to diagnose and treat since symptoms appear late during the progression of atherosclerosis. Conventional risk factors alone are not always sufficient to properly categorize at-risk patients, and clinical risk scores are inadequate in predicting cardiac events. Integrating genomic-based biomarkers (GBBM) found in plasma/serum samples with novel non-invasive radiomics-based biomarkers (RBBM) such as plaque area, plaque burden, and maximum plaque height can improve composite CVD risk prediction in the pharmaceutical paradigm. These biomarkers consider several pathways involved in the pathophysiology of atherosclerosis disease leading to CVD. OBJECTIVE: This review proposes two hypotheses: (i) The composite biomarkers are strongly correlated and can be used to detect the severity of CVD/Stroke precisely, and (ii) an explainable artificial intelligence (XAI)-based composite risk CVD/Stroke model with survival analysis using deep learning (DL) can predict in preventive, precision, and personalized (aiP3) framework benefiting the pharmaceutical paradigm. METHOD: The PRISMA search technique resulted in 214 studies assessing composite biomarkers using radiogenomics for CVD/Stroke. The study presents a XAI model using AtheroEdgeTM 4.0 to determine the risk of CVD/Stroke in the pharmaceutical framework using the radiogenomics biomarkers. CONCLUSIONS: Our observations suggest that the composite CVD risk biomarkers using radiogenomics provide a new dimension to CVD/Stroke risk assessment. The proposed review suggests a unique, unbiased, and XAI model based on AtheroEdgeTM 4.0 that can predict the composite risk of CVD/Stroke using radiogenomics in the pharmaceutical paradigm.

The Link between miRNAs and PCKS9 in Atherosclerosis.

Cardiovascular disease (CDV) represents the major cause of death globally. Atherosclerosis, as the primary cause of CVD, is a chronic immune-inflammatory disorder with complex multifactorial pathophysiology encompassing oxidative stress, enhanced immune-inflammatory cascade, endothelial dysfunction, and thrombosis. An initiating event in atherosclerosis is the subendothelial accumulation of low-density lipoprotein (LDL), followed by the localization of macrophages to fatty deposits on blood vessel walls, forming lipid-laden macrophages (foam cells) that secrete compounds involved in plaque formation. Given the fact that foam cells are one of the key culprits that underlie the pathophysiology of atherosclerosis, special attention has been paid to the investigation of the efficient therapeutic approach to overcome the dysregulation of metabolism of cholesterol in macrophages, decrease the foam cell formation and/or to force its degradation. Proprotein convertase subtilisin/kexin type 9 (PCSK9) is a secretory serine proteinase that has emerged as a significant regulator of the lipid metabolism pathway. PCSK9 activation leads to the degradation of LDL receptors (LDLRs), increasing LDL cholesterol (LDL-C) levels in the circulation. PCSK9 pathway dysregulation has been identified as one of the mechanisms involved in atherosclerosis. In addition, microRNAs (miRNAs) are investigated as important epigenetic factors in the pathophysiology of atherosclerosis and dysregulation of lipid metabolism. This review article summarizes the recent findings connecting the role of PCSK9 in atherosclerosis and the involvement of various miRNAs in regulating the expression of PCSK9-related genes. We also discuss PCSK9 pathway-targeting therapeutic interventions based on PCSK9 inhibition, miRNA levels manipulation by therapeutic agents, and the most recent advances in PSCK9 gene editing using CRISPR/Cas9 platform, meganuclease, and base editors.

Polygenic Risk Score for Cardiovascular Diseases in Artificial Intelligence Paradigm: A Review.

Cardiovascular disease (CVD) related mortality and morbidity heavily strain society. The relationship between external risk factors and our genetics have not been well established. It is widely acknowledged that environmental influence and individual behaviours play a significant role in CVD vulnerability, leading to the development of polygenic risk scores (PRS). We employed the PRISMA search method to locate pertinent research and literature to extensively review artificial intelligence (AI)-based PRS models for CVD risk prediction. Furthermore, we analyzed and compared conventional vs. AI-based solutions for PRS. We summarized the recent advances in our understanding of the use of AI-based PRS for risk prediction of CVD. Our study proposes three hypotheses: i) Multiple genetic variations and risk factors can be incorporated into AI-based PRS to improve the accuracy of CVD risk predicting. ii) AI-based PRS for CVD circumvents the drawbacks of conventional PRS calculators by incorporating a larger variety of genetic and non-genetic components, allowing for more precise and individualised risk estimations. iii) Using AI approaches, it is possible to significantly reduce the dimensionality of huge genomic datasets, resulting in more accurate and effective disease risk prediction models. Our study highlighted that the AI-PRS model outperformed traditional PRS calculators in predicting CVD risk. Furthermore, using AI-based methods to calculate PRS may increase the precision of risk predictions for CVD and have significant ramifications for individualized prevention and treatment plans.

Nitric oxide, thyroglobulin, and calcitonin: unraveling the nature of thyroid nodules.

Background: Thyroid nodules (TN) are localized morphological changes in the thyroid gland and can be benign or malignant. Objective: The present study investigates the relationships between biochemical markers in serum (s) and their homologs in washout (w) after fine-needle aspiration biopsy (FNAB) of the TN of interest and their correlation with cytology specimen findings. Methods: We investigated the relationships between serum biochemical markers nitric oxide (NO), thyroglobulin (TG), and calcitonin (CT), their homologs in washout after FNAB of the TN of interest, and cytology findings of biopsy samples classified according to the Bethesda system for thyroid cytopathology in this study, which included 86 subjects. Results: Washout TG (TGw) level positively correlates with the cytology finding of the biopsy. A higher level of TGw correlates with higher categories of the Bethesda classification and indicates a higher malignant potential. The levels of serum NO (NOs), serum TG (TGs), serum CT (CTs), and washout CT (CTw) do not correlate with the cytology finding of the biopsy, and the higher levels of washout NO (NOw) correspond to the more suspicious ultrasound findings. Conclusion: The findings of our study suggest that TGw and NOw could be used as potential predictors of malignancy in TN.

Subacute thyroiditis following COVID-19 vaccination: Case presentation.

Background: Subacute thyroiditis (SAT) is an organ-specific disease that various drugs, including COVID-19 vaccines, can trigger. COVID-19 infection has been associated with thyroid gland damage and disease SARS-CoV-2 direct action, euthyroid sick syndrome, and immune-mediated mechanisms are all potential mechanisms of thyroid damage. It denotes thyroid gland inflammation, most commonly of viral origin, and belongs to the transitory, self-limiting thyroid gland diseases group, causing complications in approximately 15% of patients in the form of permanent hypothyroidism. Some authors say SAT is the most common thyroid disease associated with COVID-19.Purpose: The occurrence of SAT many weeks after administering the second COVID-19 vaccine is rare and has limited documentation in academic literature. This study aims to present the occurrence of SAT after administering the COVID-19 vaccine. We present the case of a 37-year-old man who developed SAT 23 days after receiving the second dose of Pfizer BioNTech's COVID-19 mRNA vaccine.Research design and study sample: Due to neck pain and an elevated body temperature (up to 38.2°C), a 37-year-old male subject presented for examination 23 days after receiving the second Pfizer BioNTech mRNA vaccine against SARS-CoV-2 viral infection. The patient denied ever having an autoimmune disease or any other disease. Painful neck palpation and a firm, slightly enlarged thyroid gland with no surrounding lymphadenopathy were identified during the exam. The heart rate was 104 beats per minute. All of the remaining physical findings were normal.Data collection and/or Analysis: Data collected during the disease are integral to the medical record.Results: Hematology and biochemistry analyses at the initial and follow-up visits revealed minor leukocytosis, normocytic anaemia, and thrombocytosis, followed by a mild increase in lactate dehydrogenase and decreased iron levels. The patient's thyroid function and morphology had recovered entirely from post-vaccine SAT.Conclusions: Results from this study emphasise the need for healthcare professionals to promptly report any case of SAT related to COVID-19 vaccination. Further investigation is warranted to understand the immunopathogenesis of COVID-19-associated thyroiditis and the impact of COVID-19 immunization on this condition.

SGLT-2 Inhibitors: The Next-generation Treatment for Type 2 Diabetes Mellitus.

Type 2 diabetes mellitus (T2DM) has become a worldwide concern in recent years, primarily in highly developed Western societies. T2DM causes systemic complications, such as atherosclerotic heart disease, ischemic stroke, peripheral artery disease, kidney failure, and diabetes-related maculopathy and retinopathy. The growing number of T2DM patients and the treatment of long-term T2DM-related complications pressurize and exhaust public healthcare systems. As a result, strategies for combating T2DM and developing novel drugs are critical global public health requirements. Aside from preventive measures, which are still the most effective way to prevent T2DM, novel and highly effective therapies are emerging. In the spotlight of next-generation T2DM treatment, sodium-glucose co-transporter 2 (SGLT-2) inhibitors are promoted as the most efficient perspective therapy. SGLT-2 inhibitors (SGLT2i) include phlorizin derivatives, such as canagliflozin, dapagliflozin, empagliflozin, and ertugliflozin. SGLT-2, along with SGLT-1, is a member of the SGLT family of proteins that play a role in glucose absorption via active transport mediated by Na+ /K+ ATPase. SGLT-2 is only found in the kidney, specifically the proximal tubule, and is responsible for more than 90% glucose absorption. Inhibition of SGLT-2 reduces glucose absorption, and consequently increases urinary glucose excretion, decreasing blood glucose levels. Thus, the inhibition of SGLT-2 activity ultimately alleviates T2DM-related symptoms and prevents or delays systemic T2DM-associated chronic complications. This review aimed to provide a more detailed understanding of the effects of SGLT2i responsible for the acute improvement in blood glucose regulation, a prerequisite for T2DM-associated cardiovascular complications control.

Artificial intelligence-based preventive, personalized and precision medicine for cardiovascular disease/stroke risk assessment in rheumatoid arthritis patients: a narrative review.

The challenges associated with diagnosing and treating cardiovascular disease (CVD)/Stroke in Rheumatoid arthritis (RA) arise from the delayed onset of symptoms. Existing clinical risk scores are inadequate in predicting cardiac events, and conventional risk factors alone do not accurately classify many individuals at risk. Several CVD biomarkers consider the multiple pathways involved in the development of atherosclerosis, which is the primary cause of CVD/Stroke in RA. To enhance the accuracy of CVD/Stroke risk assessment in the RA framework, a proposed approach involves combining genomic-based biomarkers (GBBM) derived from plasma and/or serum samples with innovative non-invasive radiomic-based biomarkers (RBBM), such as measurements of synovial fluid, plaque area, and plaque burden. This review presents two hypotheses: (i) RBBM and GBBM biomarkers exhibit a significant correlation and can precisely detect the severity of CVD/Stroke in RA patients. (ii) Artificial Intelligence (AI)-based preventive, precision, and personalized (aiP3) CVD/Stroke risk AtheroEdge™ model (AtheroPoint™, CA, USA) that utilizes deep learning (DL) to accurately classify the risk of CVD/stroke in RA framework. The authors conducted a comprehensive search using the PRISMA technique, identifying 153 studies that assessed the features/biomarkers of RBBM and GBBM for CVD/Stroke. The study demonstrates how DL models can be integrated into the AtheroEdge™-aiP3 framework to determine the risk of CVD/Stroke in RA patients. The findings of this review suggest that the combination of RBBM with GBBM introduces a new dimension to the assessment of CVD/Stroke risk in the RA framework. Synovial fluid levels that are higher than normal lead to an increase in the plaque burden. Additionally, the review provides recommendations for novel, unbiased, and pruned DL algorithms that can predict CVD/Stroke risk within a RA framework that is preventive, precise, and personalized.

New biomarkers: prospect for diagnosis and monitoring of thyroid disease.

After the metabolic syndrome and its components, thyroid disorders represent the most common endocrine disorders, with increasing prevalence in the last two decades. Thyroid dysfunctions are distinguished by hyperthyroidism, hypothyroidism, or inflammation (thyroiditis) of the thyroid gland, in addition to the presence of thyroid nodules that can be benign or malignant. Thyroid cancer is typically detected via an ultrasound (US)-guided fine-needle aspiration biopsy (FNAB) and cytological examination of the specimen. This approach has significant limitations due to the small sample size and inability to characterize follicular lesions adequately. Due to the rapid advancement of high-throughput molecular biology techniques, it is now possible to identify new biomarkers for thyroid neoplasms that can supplement traditional imaging modalities in postoperative surveillance and aid in the preoperative cytology examination of indeterminate or follicular lesions. Here, we review current knowledge regarding biomarkers that have been reliable in detecting thyroid neoplasms, making them valuable tools for assessing the efficacy of surgical procedures or adjunctive treatment after surgery. We are particularly interested in providing an up-to-date and systematic review of emerging biomarkers, such as mRNA and non-coding RNAs, that can potentially detect thyroid neoplasms in clinical settings. We discuss evidence for miRNA, lncRNA and circRNA dysregulation in several thyroid neoplasms and assess their potential for use as diagnostic and prognostic biomarkers.