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.

BioBBC: a multi-feature model that enhances the detection of biomedical entities.

The rapid increase in biomedical publications necessitates efficient systems to automatically handle Biomedical Named Entity Recognition (BioNER) tasks in unstructured text. However, accurately detecting biomedical entities is quite challenging due to the complexity of their names and the frequent use of abbreviations. In this paper, we propose BioBBC, a deep learning (DL) model that utilizes multi-feature embeddings and is constructed based on the BERT-BiLSTM-CRF to address the BioNER task. BioBBC consists of three main layers; an embedding layer, a Long Short-Term Memory (Bi-LSTM) layer, and a Conditional Random Fields (CRF) layer. BioBBC takes sentences from the biomedical domain as input and identifies the biomedical entities mentioned within the text. The embedding layer generates enriched contextual representation vectors of the input by learning the text through four types of embeddings: part-of-speech tags (POS tags) embedding, char-level embedding, BERT embedding, and data-specific embedding. The BiLSTM layer produces additional syntactic and semantic feature representations. Finally, the CRF layer identifies the best possible tag sequence for the input sentence. Our model is well-constructed and well-optimized for detecting different types of biomedical entities. Based on experimental results, our model outperformed state-of-the-art (SOTA) models with significant improvements based on six benchmark BioNER datasets.

A deep learning model predicts the presence of diverse cancer types using circulating tumor cells.

Circulating tumor cells (CTCs) are cancer cells that detach from the primary tumor and intravasate into the bloodstream. Thus, non-invasive liquid biopsies are being used to analyze CTC-expressed genes to identify potential cancer biomarkers. In this regard, several studies have used gene expression changes in blood to predict the presence of CTC and, consequently, cancer. However, the CTC mRNA data has not been used to develop a generic approach that indicates the presence of multiple cancer types. In this study, we developed such a generic approach. Briefly, we designed two computational workflows, one using the raw mRNA data and deep learning (DL) and the other exploiting five hub gene ranking algorithms (Degree, Maximum Neighborhood Component, Betweenness Centrality, Closeness Centrality, and Stress Centrality) with machine learning (ML). Both workflows aim to determine the top genes that best distinguish cancer types based on the CTC mRNA data. We demonstrate that our automated, robust DL framework (DNNraw) more accurately indicates the presence of multiple cancer types using the CTC gene expression data than multiple ML approaches. The DL approach achieved average precision of 0.9652, recall of 0.9640, f1-score of 0.9638 and overall accuracy of 0.9640. Furthermore, since we designed multiple approaches, we also provide a bioinformatics analysis of the gene commonly identified as top-ranked by the different methods. To our knowledge, this is the first study wherein a generic approach has been developed to predict the presence of multiple cancer types using raw CTC mRNA data, as opposed to other models that require a feature selection step.

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.

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.

MicroRNA networks linked with BRCA1/2, PTEN, and common genes for Alzheimer's disease and breast cancer share highly enriched pathways that may unravel targets for the AD/BC comorbidity treatment.

MicroRNAs (miRNAs) are involved in the regulation of various cellular processes including pathological conditions. MiRNA networks have been extensively researched in age-related degenerative diseases, such as cancer, Alzheimer's disease (AD), and heart failure. Thus, miRNA has been studied from different approaches, in vivo, in vitro, and in silico including miRNA networks. Networks linking diverse biomedical entities unveil information not readily observable by other means. This work focuses on biological networks related to Breast cancer susceptibility 1 (BRCA1) in AD and breast cancer (BC). Using various bioinformatics approaches, we identified subnetworks common to AD and BC that suggest they are linked. According to our results, miR-107 was identified as a potentially good candidate for both AD and BC treatment (targeting BRCA1/2 and PTEN in both diseases), accompanied by miR-146a and miR-17. The analysis also confirmed the involvement of the miR-17-92 cluster, and miR-124-3p, and highlighted the importance of poorly researched miRNAs such as mir-6785 mir-6127, mir-6870, or miR-8485. After filtering the in silico analysis results, we found 49 miRNA molecules that modulate the expression of at least five genes common to both BC and AD. Those 49 miRNAs regulate the expression of 122 genes in AD and 93 genes in BC, from which 26 genes are common genes for AD and BC involved in neuron differentiation and genesis, cell differentiation and migration, regulation of cell cycle, and cancer development. Additionally, the highly enriched pathway was associated with diabetic complications, pointing out possible interplay among molecules underlying BC, AD, and diabetes pathology.

OncoRTT: Predicting novel oncology-related therapeutic targets using BERT embeddings and omics features.

Late-stage drug development failures are usually a consequence of ineffective targets. Thus, proper target identification is needed, which may be possible using computational approaches. The reason being, effective targets have disease-relevant biological functions, and omics data unveil the proteins involved in these functions. Also, properties that favor the existence of binding between drug and target are deducible from the protein's amino acid sequence. In this work, we developed OncoRTT, a deep learning (DL)-based method for predicting novel therapeutic targets. OncoRTT is designed to reduce suboptimal target selection by identifying novel targets based on features of known effective targets using DL approaches. First, we created the "OncologyTT" datasets, which include genes/proteins associated with ten prevalent cancer types. Then, we generated three sets of features for all genes: omics features, the proteins' amino-acid sequence BERT embeddings, and the integrated features to train and test the DL classifiers separately. The models achieved high prediction performances in terms of area under the curve (AUC), i.e., AUC greater than 0.88 for all cancer types, with a maximum of 0.95 for leukemia. Also, OncoRTT outperformed the state-of-the-art method using their data in five out of seven cancer types commonly assessed by both methods. Furthermore, OncoRTT predicts novel therapeutic targets using new test data related to the seven cancer types. We further corroborated these results with other validation evidence using the Open Targets Platform and a case study focused on the top-10 predicted therapeutic targets for lung cancer.

Diabetic cardiomyopathy: The role of microRNAs and long non-coding RNAs.

Diabetes mellitus (DM) is on the rise, necessitating the development of novel therapeutic and preventive strategies to mitigate the disease's debilitating effects. Diabetic cardiomyopathy (DCMP) is among the leading causes of morbidity and mortality in diabetic patients globally. DCMP manifests as cardiomyocyte hypertrophy, apoptosis, and myocardial interstitial fibrosis before progressing to heart failure. Evidence suggests that non-coding RNAs, such as long non-coding RNAs (lncRNAs) and microRNAs (miRNAs), regulate diabetic cardiomyopathy-related processes such as insulin resistance, cardiomyocyte apoptosis and inflammation, emphasizing their heart-protective effects. This paper reviewed the literature data from animal and human studies on the non-trivial roles of miRNAs and lncRNAs in the context of DCMP in diabetes and demonstrated their future potential in DCMP treatment in diabetic patients.

Exploiting machine learning models to identify novel Alzheimer's disease biomarkers and potential targets.

We still do not have an effective treatment for Alzheimer's disease (AD) despite it being the most common cause of dementia and impaired cognitive function. Thus, research endeavors are directed toward identifying AD biomarkers and targets. In this regard, we designed a computational method that exploits multiple hub gene ranking methods and feature selection methods with machine learning and deep learning to identify biomarkers and targets. First, we used three AD gene expression datasets to identify 1/ hub genes based on six ranking algorithms (Degree, Maximum Neighborhood Component (MNC), Maximal Clique Centrality (MCC), Betweenness Centrality (BC), Closeness Centrality, and Stress Centrality), 2/ gene subsets based on two feature selection methods (LASSO and Ridge). Then, we developed machine learning and deep learning models to determine the gene subset that best distinguishes AD samples from the healthy controls. This work shows that feature selection methods achieve better prediction performances than the hub gene sets. Beyond this, the five genes identified by both feature selection methods (LASSO and Ridge algorithms) achieved an AUC = 0.979. We further show that 70% of the upregulated hub genes (among the 28 overlapping hub genes) are AD targets based on a literature review and six miRNA (hsa-mir-16-5p, hsa-mir-34a-5p, hsa-mir-1-3p, hsa-mir-26a-5p, hsa-mir-93-5p, hsa-mir-155-5p) and one transcription factor, JUN, are associated with the upregulated hub genes. Furthermore, since 2020, four of the six microRNA were also shown to be potential AD targets. To our knowledge, this is the first work showing that such a small number of genes can distinguish AD samples from healthy controls with high accuracy and that overlapping upregulated hub genes can narrow the search space for potential novel targets.

New insights on the cardiovascular effects of IGF-1.

Introduction: Cardiovascular (CV) disorders are steadily increasing, making them the world's most prevalent health issue. New research highlights the importance of insulin-like growth factor 1 (IGF-1) for maintaining CV health. Methods: We searched PubMed and MEDLINE for English and non-English articles with English abstracts published between 1957 (when the first report on IGF-1 identification was published) and 2022. The top search terms were: IGF-1, cardiovascular disease, IGF-1 receptors, IGF-1 and microRNAs, therapeutic interventions with IGF-1, IGF-1 and diabetes, IGF-1 and cardiovascular disease. The search retrieved original peer-reviewed articles, which were further analyzed, focusing on the role of IGF-1 in pathophysiological conditions. We specifically focused on including the most recent findings published in the past five years. Results: IGF-1, an anabolic growth factor, regulates cell division, proliferation, and survival. In addition to its well-known growth-promoting and metabolic effects, there is mounting evidence that IGF-1 plays a specialized role in the complex activities that underpin CV function. IGF-1 promotes cardiac development and improves cardiac output, stroke volume, contractility, and ejection fraction. Furthermore, IGF-1 mediates many growth hormones (GH) actions. IGF-1 stimulates contractility and tissue remodeling in humans to improve heart function after myocardial infarction. IGF-1 also improves the lipid profile, lowers insulin levels, increases insulin sensitivity, and promotes glucose metabolism. These findings point to the intriguing medicinal potential of IGF-1. Human studies associate low serum levels of free or total IGF-1 with an increased risk of CV and cerebrovascular illness. Extensive human trials are being conducted to investigate the therapeutic efficacy and outcomes of IGF-1-related therapy. Discussion: We anticipate the development of novel IGF-1-related therapy with minimal side effects. This review discusses recent findings on the role of IGF-1 in the cardiovascular (CVD) system, including both normal and pathological conditions. We also discuss progress in therapeutic interventions aimed at targeting the IGF axis and provide insights into the epigenetic regulation of IGF-1 mediated by microRNAs.

MetastaSite: Predicting metastasis to different sites using deep learning with gene expression data.

Deep learning has massive potential in predicting phenotype from different omics profiles. However, deep neural networks are viewed as black boxes, providing predictions without explanation. Therefore, the requirements for these models to become interpretable are increasing, especially in the medical field. Here we propose a computational framework that takes the gene expression profile of any primary cancer sample and predicts whether patients' samples are primary (localized) or metastasized to the brain, bone, lung, or liver based on deep learning architecture. Specifically, we first constructed an AutoEncoder framework to learn the non-linear relationship between genes, and then DeepLIFT was applied to calculate genes' importance scores. Next, to mine the top essential genes that can distinguish the primary and metastasized tumors, we iteratively added ten top-ranked genes based upon their importance score to train a DNN model. Then we trained a final multi-class DNN that uses the output from the previous part as an input and predicts whether samples are primary or metastasized to the brain, bone, lung, or liver. The prediction performances ranged from AUC of 0.93-0.82. We further designed the model's workflow to provide a second functionality beyond metastasis site prediction, i.e., to identify the biological functions that the DL model uses to perform the prediction. To our knowledge, this is the first multi-class DNN model developed for the generic prediction of metastasis to various sites.

DES-Amyloidoses "Amyloidoses through the looking-glass": A knowledgebase developed for exploring and linking information related to human amyloid-related diseases.

More than 30 types of amyloids are linked to close to 50 diseases in humans, the most prominent being Alzheimer's disease (AD). AD is brain-related local amyloidosis, while another amyloidosis, such as AA amyloidosis, tends to be more systemic. Therefore, we need to know more about the biological entities' influencing these amyloidosis processes. However, there is currently no support system developed specifically to handle this extraordinarily complex and demanding task. To acquire a systematic view of amyloidosis and how this may be relevant to the brain and other organs, we needed a means to explore "amyloid network systems" that may underly processes that leads to an amyloid-related disease. In this regard, we developed the DES-Amyloidoses knowledgebase (KB) to obtain fast and relevant information regarding the biological network related to amyloid proteins/peptides and amyloid-related diseases. This KB contains information obtained through text and data mining of available scientific literature and other public repositories. The information compiled into the DES-Amyloidoses system based on 19 topic-specific dictionaries resulted in 796,409 associations between terms from these dictionaries. Users can explore this information through various options, including enriched concepts, enriched pairs, and semantic similarity. We show the usefulness of the KB using an example focused on inflammasome-amyloid associations. To our knowledge, this is the only KB dedicated to human amyloid-related diseases derived primarily through literature text mining and complemented by data mining that provides a novel way of exploring information relevant to amyloidoses.

Role of C-Reactive Protein in Diabetic Inflammation.

Even though type 2 diabetes mellitus (T2DM) represents a worldwide chronic health issue that affects about 462 million people, specific underlying determinants of insulin resistance (IR) and impaired insulin secretion are still unknown. There is growing evidence that chronic subclinical inflammation is a triggering factor in the origin of T2DM. Increased C-reactive protein (CRP) levels have been linked to excess body weight since adipocytes produce tumor necrosis factor α (TNF-α) and interleukin 6 (IL-6), which are pivotal factors for CRP stimulation. Furthermore, it is known that hepatocytes produce relatively low rates of CRP in physiological conditions compared to T2DM patients, in which elevated levels of inflammatory markers are reported, including CRP. CRP also participates in endothelial dysfunction, the production of vasodilators, and vascular remodeling, and increased CRP level is closely associated with vascular system pathology and metabolic syndrome. In addition, insulin-based therapies may alter CRP levels in T2DM. Therefore, determining and clarifying the underlying CRP mechanism of T2DM is imperative for novel preventive and diagnostic procedures. Overall, CRP is one of the possible targets for T2DM progression and understanding the connection between insulin and inflammation may be helpful in clinical treatment and prevention approaches.

Combining biomedical knowledge graphs and text to improve predictions for drug-target interactions and drug-indications.

Biomedical knowledge is represented in structured databases and published in biomedical literature, and different computational approaches have been developed to exploit each type of information in predictive models. However, the information in structured databases and literature is often complementary. We developed a machine learning method that combines information from literature and databases to predict drug targets and indications. To effectively utilize information in published literature, we integrate knowledge graphs and published literature using named entity recognition and normalization before applying a machine learning model that utilizes the combination of graph and literature. We then use supervised machine learning to show the effects of combining features from biomedical knowledge and published literature on the prediction of drug targets and drug indications. We demonstrate that our approach using datasets for drug-target interactions and drug indications is scalable to large graphs and can be used to improve the ranking of targets and indications by exploiting features from either structure or unstructured information alone.

Affinity2Vec: drug-target binding affinity prediction through representation learning, graph mining, and machine learning.

Drug-target interaction (DTI) prediction plays a crucial role in drug repositioning and virtual drug screening. Most DTI prediction methods cast the problem as a binary classification task to predict if interactions exist or as a regression task to predict continuous values that indicate a drug's ability to bind to a specific target. The regression-based methods provide insight beyond the binary relationship. However, most of these methods require the three-dimensional (3D) structural information of targets which are still not generally available to the targets. Despite this bottleneck, only a few methods address the drug-target binding affinity (DTBA) problem from a non-structure-based approach to avoid the 3D structure limitations. Here we propose Affinity2Vec, as a novel regression-based method that formulates the entire task as a graph-based problem. To develop this method, we constructed a weighted heterogeneous graph that integrates data from several sources, including drug-drug similarity, target-target similarity, and drug-target binding affinities. Affinity2Vec further combines several computational techniques from feature representation learning, graph mining, and machine learning to generate or extract features, build the model, and predict the binding affinity between the drug and the target with no 3D structural data. We conducted extensive experiments to evaluate and demonstrate the robustness and efficiency of the proposed method on benchmark datasets used in state-of-the-art non-structured-based drug-target binding affinity studies. Affinity2Vec showed superior and competitive results compared to the state-of-the-art methods based on several evaluation metrics, including mean squared error, rm2, concordance index, and area under the precision-recall curve.

Type 2 Diabetes Mellitus and its comorbidity, Alzheimer's disease: Identifying critical microRNA using machine learning.

MicroRNAs (miRNAs) are critical regulators of gene expression in healthy and diseased states, and numerous studies have established their tremendous potential as a tool for improving the diagnosis of Type 2 Diabetes Mellitus (T2D) and its comorbidities. In this regard, we computationally identify novel top-ranked hub miRNAs that might be involved in T2D. We accomplish this via two strategies: 1) by ranking miRNAs based on the number of T2D differentially expressed genes (DEGs) they target, and 2) using only the common DEGs between T2D and its comorbidity, Alzheimer's disease (AD) to predict and rank miRNA. Then classifier models are built using the DEGs targeted by each miRNA as features. Here, we show the T2D DEGs targeted by hsa-mir-1-3p, hsa-mir-16-5p, hsa-mir-124-3p, hsa-mir-34a-5p, hsa-let-7b-5p, hsa-mir-155-5p, hsa-mir-107, hsa-mir-27a-3p, hsa-mir-129-2-3p, and hsa-mir-146a-5p are capable of distinguishing T2D samples from the controls, which serves as a measure of confidence in the miRNAs' potential role in T2D progression. Moreover, for the second strategy, we show other critical miRNAs can be made apparent through the disease's comorbidities, and in this case, overall, the hsa-mir-103a-3p models work well for all the datasets, especially in T2D, while the hsa-mir-124-3p models achieved the best scores for the AD datasets. To the best of our knowledge, this is the first study that used predicted miRNAs to determine the features that can separate the diseased samples (T2D or AD) from the normal ones, instead of using conventional non-biology-based feature selection methods.

The protective role of nutritional antioxidants against oxidative stress in thyroid disorders.

An imbalance between pro-oxidative and antioxidative cellular mechanisms is oxidative stress (OxS) which may be systemic or organ-specific. Although OxS is a consequence of normal body and organ physiology, severely impaired oxidative homeostasis results in DNA hydroxylation, protein denaturation, lipid peroxidation, and apoptosis, ultimately compromising cells' function and viability. The thyroid gland is an organ that exhibits both oxidative and antioxidative processes. In terms of OxS severity, the thyroid gland's response could be physiological (i.e. hormone production and secretion) or pathological (i.e. development of diseases, such as goitre, thyroid cancer, or thyroiditis). Protective nutritional antioxidants may benefit defensive antioxidative systems in resolving pro-oxidative dominance and redox imbalance, preventing or delaying chronic thyroid diseases. This review provides information on nutritional antioxidants and their protective roles against impaired redox homeostasis in various thyroid pathologies. We also review novel findings related to the connection between the thyroid gland and gut microbiome and analyze the effects of probiotics with antioxidant properties on thyroid diseases.

Predicting Bone Metastasis Using Gene Expression-Based Machine Learning Models.

Bone is the most common site of distant metastasis from malignant tumors, with the highest prevalence observed in breast and prostate cancers. Such bone metastases (BM) cause many painful skeletal-related events, such as severe bone pain, pathological fractures, spinal cord compression, and hypercalcemia, with adverse effects on life quality. Many bone-targeting agents developed based on the current understanding of BM onset's molecular mechanisms dull these adverse effects. However, only a few studies investigated potential predictors of high risk for developing BM, despite such knowledge being critical for early interventions to prevent or delay BM. This work proposes a computational network-based pipeline that incorporates a ML/DL component to predict BM development. Based on the proposed pipeline we constructed several machine learning models. The deep neural network (DNN) model exhibited the highest prediction accuracy (AUC of 92.11%) using the top 34 featured genes ranked by betweenness centrality scores. We further used an entirely separate, "external" TCGA dataset to evaluate the robustness of this DNN model and achieved sensitivity of 85%, specificity of 80%, positive predictive value of 78.10%, negative predictive value of 80%, and AUC of 85.78%. The result shows the models' way of learning allowed it to zoom in on the featured genes that provide the added benefit of the model displaying generic capabilities, that is, to predict BM for samples from different primary sites. Furthermore, existing experimental evidence provides confidence that about 50% of the 34 hub genes have BM-related functionality, which suggests that these common genetic markers provide vital insight about BM drivers. These findings may prompt the transformation of such a method into an artificial intelligence (AI) diagnostic tool and direct us towards mechanisms that underlie metastasis to bone events.

Levothyroxine Treatment and the Risk of Cardiac Arrhythmias - Focus on the Patient Submitted to Thyroid Surgery.

Levothyroxine (LT4) is used to treat frequently encountered endocrinopathies such as thyroid diseases. It is regularly used in clinical (overt) hypothyroidism cases and subclinical (latent) hypothyroidism cases in the last decade. Suppressive LT4 therapy is also part of the medical regimen used to manage thyroid malignancies after a thyroidectomy. LT4 treatment possesses dual effects: substituting new-onset thyroid hormone deficiency and suppressing the local and distant malignancy spreading in cancer. It is the practice to administer LT4 in less-than-high suppressive doses for growth control of thyroid nodules and goiter, even in patients with preserved thyroid function. Despite its approved safety for clinical use, LT4 can sometimes induce side-effects, more often recorded with patients under treatment with LT4 suppressive doses than in unintentionally LT4-overdosed patients. Cardiac arrhythmias and the deterioration of osteoporosis are the most frequently documented side-effects of LT4 therapy. It also lowers the threshold for the onset or aggravation of cardiac arrhythmias for patients with pre-existing heart diseases. To improve the quality of life in LT4-substituted patients, clinicians often prescribe higher doses of LT4 to reach low normal TSH levels to achieve cellular euthyroidism. In such circumstances, the risk of cardiac arrhythmias, particularly atrial fibrillation, increases, and the combined use of LT4 and triiodothyronine further complicates such risk. This review summarizes the relevant available data related to LT4 suppressive treatment and the associated risk of cardiac arrhythmia.