Assessment of New Coronary Features on Quantitative Coronary Angiographic Images With Innovative Unsupervised Artificial Adaptive Systems: A Proof-of-Concept Study.

Background and Purpose: The Active Connection Matrixes (ACMs) are unsupervised artificial adaptive systems able to extract from digital images features of interest (edges, tissue differentiation, etc.) unnoticeable with conventional systems. In this proof-of-concept study, we assessed the potentiality of ACMs to increase measurement precision of morphological structures (e.g., stenosis and lumen diameter) and to grasp morphological features (arterial walls) from quantitative coronary angiography (QCA), unnoticeable on the original images. Methods: Archive images of QCA and intravascular ultrasound (IVUS) of 10 patients (8 men, age 69.1 ± 9.7 years) who underwent both procedures for clinical reasons were retrospectively analyzed. Arterial features derived from "IVUS images," "conventional QCA images," and "ACM-reprocessed QCA images" were measured in 21 coronary segments. Portions of 1-mm length (263 for lumen and 526 for arterial walls) were head-to-head compared to assess quali-quantitative between-methods agreement. Results: When stenosis was calculated on "ACM-reprocessed QCA images," the bias vs. IVUS (gold standard) did not improve, but the correlation coefficient of the QCA-IVUS relationship increased from 0.47 to 0.83. When IVUS-derived lumen diameters were compared with diameters obtained on ACM-reprocessed QCA images, the bias (-0.25 mm) was significantly smaller (p < 0.01) than that observed with original QCA images (0.58 mm). ACMs were also able to extract arterial wall features from QCA. The bias between the measures of arterial walls obtained with IVUS and ACMs, although significant (p < 0.01), was small [0.09 mm, 95% CI (0.03, 0.14)] and the correlation was fairly good (r = 0.63; p < 0.0001). Conclusions: This study provides proof of concept that ACMs increase the measurement precision of coronary lumen diameter and allow extracting from QCA images hidden features that mirror well the arterial walls derived by IVUS.

Analysis of COVID-19 pandemic in USA, using Topological Weighted Centroid.

The first case of COVID-19 in USA was reported on January 20, 2020. The number of COVID-19 confirmed cases and death has increased since the first reported case and the outbreak has appeared in all states. This paper analyzes disease outbreak using Topological Weighted Centroid (TWC), which is a data driven intelligent geographical dynamical system that models disease spread in space and time. In this analysis the COVID-19 cases in USA on March 26, 2020 as provided by Johns Hopkins University is used. The COVID-19 outbreak is mapped by the TWC method. We were able to predict and capture some features of the pandemic spread using the early data. Although we have used the geographical distance from the latitude and longitude coordinates, our results indicate that one of the main paths of diseases spread are arguably airline routes. In this analysis, we used a large set of data. A modified version of TWC, is named TWC-Windowing to elaborate the effect of data from all places.

Detection of an Autism EEG Signature From Only Two EEG Channels Through Features Extraction and Advanced Machine Learning Analysis.

BACKGROUND AND OBJECTIVE: In 2 previous studies, we have shown the ability of special machine learning systems applied to standard EEG data in distinguishing children with autism spectrum disorder (ASD) from non-ASD children with an overall accuracy rate of 100% and 98.4%, respectively. Since the equipment routinely available in neonatology units employ few derivations, we were curious to check if just 2 derivations were enough to allow good performance in the same cases of the above-mentioned studies. METHODS: A continuous segment of artifact-free EEG data lasting 1 minute in ASCCI format from C3 and C4 EEG channels present in 2 previous studies, was used for features extraction and subsequent analyses with advanced machine learning systems. A features extraction software package (Python tsfresh) applied to time-series raw data derived 1588 quantitative features. A special hybrid system called TWIST (Training with Input Selection and Testing), coupling an evolutionary algorithm named Gen-D and a backpropagation neural network, was used to subdivide the data set into training and testing sets as well as to select features yielding the maximum amount of information after a first variable selection performed with linear correlation index threshold. RESULTS: After this intelligent preprocessing, 12 features were extracted from C3-C4 time-series of study 1 and 36 C3-C4 time-series of study 2 representing the EEG signature. Acting on these features the overall accuracy predictive capability of the best artificial neural network acting as a classifier in deciphering autistic cases from typicals (study 1) and other neuropsychiatric disorders (study 2) resulted in 100 % for study 1 and 94.95 % for study 2. CONCLUSIONS: The results of this study suggest that also a minor part of EEG contains precious information useful to detect autism if treated with advanced computational algorithms. This could allow in the future to use standard EEG from newborns to check if the ASD signature is already present at birth.

Development of machine learning models to predict RT-PCR results for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) in patients with influenza-like symptoms using only basic clinical data.

BACKGROUND: Reverse Transcription-Polymerase Chain Reaction (RT-PCR) for Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-COV-2) diagnosis currently requires quite a long time span. A quicker and more efficient diagnostic tool in emergency departments could improve management during this global crisis. Our main goal was assessing the accuracy of artificial intelligence in predicting the results of RT-PCR for SARS-COV-2, using basic information at hand in all emergency departments. METHODS: This is a retrospective study carried out between February 22, 2020 and March 16, 2020 in one of the main hospitals in Milan, Italy. We screened for eligibility all patients admitted with influenza-like symptoms tested for SARS-COV-2. Patients under 12 years old and patients in whom the leukocyte formula was not performed in the ED were excluded. Input data through artificial intelligence were made up of a combination of clinical, radiological and routine laboratory data upon hospital admission. Different Machine Learning algorithms available on WEKA data mining software and on Semeion Research Centre depository were trained using both the Training and Testing and the K-fold cross-validation protocol. RESULTS: Among 199 patients subject to study (median [interquartile range] age 65 [46-78] years; 127 [63.8%] men), 124 [62.3%] resulted positive to SARS-COV-2. The best Machine Learning System reached an accuracy of 91.4% with 94.1% sensitivity and 88.7% specificity. CONCLUSION: Our study suggests that properly trained artificial intelligence algorithms may be able to predict correct results in RT-PCR for SARS-COV-2, using basic clinical data. If confirmed, on a larger-scale study, this approach could have important clinical and organizational implications.

Geographical identification of Chianti red wine based on ICP-MS element composition.

Chianti is a precious red wine and enjoys a high reputation for its high quality in the world wine market. Despite this, the production region is small and product needs efficient tools to protect its brands and prevent adulterations. In this sense, ICP-MS combined with chemometrics has demonstrated its usefulness in food authentication. In this study, Chianti/Chianti Classico, authentic wines from vineyard of Toscana region (Italy), together samples from 18 different geographical regions, were analyzed with the objective of differentiate them from other Italian wines. Partial Least Squares-Discriminant Analysis (PLS-DA) identified variables to discriminate wine geographical origin. Rare Earth Elements (REE), major and trace elements all contributed to the discrimination of Chianti samples. General model was not suited to distinguish PDO red wines from samples, with similar chemical fingerprints, collected in some regions. Specific classification models enhanced the capability of discrimination, emphasizing the discriminant role of some elements.

A machine learning approach for differentiating malignant from benign enhancing foci on breast MRI.

BACKGROUND: Differentiate malignant from benign enhancing foci on breast magnetic resonance imaging (MRI) through radiomic signature. METHODS: Forty-five enhancing foci in 45 patients were included in this retrospective study, with needle biopsy or imaging follow-up serving as a reference standard. There were 12 malignant and 33 benign lesions. Eight benign lesions confirmed by over 5-year negative follow-up and 15 malignant histopathologically confirmed lesions were added to the dataset to provide reference cases to the machine learning analysis. All MRI examinations were performed with a 1.5-T scanner. One three-dimensional T1-weighted unenhanced sequence was acquired, followed by four dynamic sequences after intravenous injection of 0.1 mmol/kg of gadobenate dimeglumine. Enhancing foci were segmented by an expert breast radiologist, over 200 radiomic features were extracted, and an evolutionary machine learning method ("training with input selection and testing") was applied. For each classifier, sensitivity, specificity and accuracy were calculated as point estimates and 95% confidence intervals (CIs). RESULTS: A k-nearest neighbour classifier based on 35 selected features was identified as the best performing machine learning approach. Considering both the 45 enhancing foci and the 23 additional cases, this classifier showed a sensitivity of 27/27 (100%, 95% CI 87-100%), a specificity of 37/41 (90%, 95% CI 77-97%), and an accuracy of 64/68 (94%, 95% CI 86-98%). CONCLUSION: This preliminary study showed the feasibility of a radiomic approach for the characterisation of enhancing foci on breast MRI.

Combined use of sonographic and elastosonographic parameters can improve the diagnostic accuracy in thyroid nodules at risk of malignancy at cytological examination.

BACKGROUND: Thyroid nodules diagnosed as Thy3B at fine-needle aspiration biopsy have a relevant risk of malignancy (15-30%) and are usually addressed to surgery. However surgery will result unnecessary in most cases. The present study aims at evaluating the possible increase of diagnostic accuracy for predicting malignancy using novel sonographic and elastographic parameters. METHODS: In fifty patients undergoing thyroidectomy because of a Thy3B thyroid nodule, sonographic and elastosonographic evaluation was carried out by single operator before surgery. Five sonographic parameters (echogenicity, irregular margins, microcalcifications, intra-nodule blood flow and its irregularity) and two elastosonographic parameters (intra-nodule stiffness and its extension to adjacent tissue) were considered. After obtaining histological diagnosis, diagnostic accuracy was calculated. RESULTS: When the two procedures were analyzed separately, sensitivity, specificity, positive (PPV) and negative (NPV) predictive values were 100%, 85%, 63% and 100% for ultrasonography and 60%, 92.5%, 67%, 90% for elastrosonography, respectively. The newly introduced evaluation procedures increased sensitivity. When a combined sonographic and elastosonographic evaluation was introduced, diagnostic accuracy was significantly improved: when ≥4 out of the seven parameters indicated were present, the risk of malignancy was very high (sensitivity 100%, specificity 92.55%, PPV 77%, NPV 100%). CONCLUSIONS: A novel combined sonographic and elastosonographic parameter evaluation improved diagnostic accuracy for identifying thyroid nodules suspicious of malignancy.

The "MS-ROM/IFAST" Model, a Novel Parallel Nonlinear EEG Analysis Technique, Distinguishes ASD Subjects From Children Affected With Other Neuropsychiatric Disorders With High Degree of Accuracy.

Background and Objective. In a previous study, we showed a new EEG processing methodology called Multi-Scale Ranked Organizing Map/Implicit Function As Squashing Time (MS-ROM/IFAST) performing an almost perfect distinction between computerized EEG of Italian children with autism spectrum disorder (ASD) and typically developing children. In this study, we assessed this system in distinguishing ASD subjects from children affected with other neuropsychiatric disorders (NPD). Methods. At a psychiatric practice in Texas, 20 children diagnosed with ASD and 20 children diagnosed with NPD were entered into the study. Continuous segments of artifact-free EEG data lasting 10 minutes were entered in MS-ROM/IFAST. From the new variables created by MS-ROM/IFAST, only 12 has been selected according to a correlation criterion. The selected features represent the input on which supervised machine learning systems (MLS) acted as blind classifiers. Results. The overall predictive capability in distinguishing ASD from other NPD cases ranged from 93% to 97.5%. The results were confirmed in further experiments in which Italian and US data have been combined. In this analysis, the best MLS reached 95.0% global accuracy in 1 out of 3 classes distinction (ASD, NPD, controls). This study demonstrates the value of EEG processing with advanced MLS in the differential diagnosis between ASD and NPD cases. The results were not affected by age, ethnicity and technicalities of EEG acquisition, confirming the existence of a specific EEG signature in ASD cases. To further support these findings, it was decided to test the behavior of already trained neural networks on 10 Italian very young ASD children (25-37 months). In this test, 9 out of 10 cases have been correctly recognized as ASD subjects in the best case. Conclusions. These results confirm the possibility of an early automatic autism detection based on standard EEG.

Directional Relationship Between Vitamin D Status and Prediabetes: A New Approach from Artificial Neural Network in a Cohort of Workers with Overweight-Obesity.

Objective: Despite the increasing literature on the association of diabetes with inflammation, cardiovascular risk, and vitamin D (25(OH)D) concentrations, strong evidence on the direction of causality among these factors is still lacking. This gap could be addressed by means of artificial neural networks (ANN) analysis.Methods: Retrospective observational study was carried out by means of an innovative data mining analysis-known as auto-contractive map (AutoCM)-and semantic mapping followed by Activation and Competition System on data of workers referring to an occupational-health outpatient clinic. Parameters analyzed included weight, height, waist circumference, body mass index (BMI), percentage of fat mass, glucose, insulin, glycated hemoglobin (HbA1c), creatinine, total cholesterol, low- and high-density lipoprotein cholesterol, triglycerides, uric acid, fibrinogen, homocysteine, C-reactive protein (CRP), diastolic and systolic blood pressure, and 25(OH)D.Results: The study included 309 workers. Of these, 23.6% were overweight, 40.5% were classified into the first class of obesity, 23.3% were in the second class, and 12.6% were in the third class (BMI > 40 kg/m ). All mean biochemical values were in normal range, except for total cholesterol, low- and high-density lipoprotein cholesterol, CRP, and 25(OH)D. HbA1c was between 39 and 46 mmol/mol in 51.78%. 25(OH)D levels were sufficient in only 12.6%. Highest inverse correlation for hyperglycemia onset was with BMI and waist circumference, suggesting a protective role of 25(OH)D against their increase. AutoCM processing and the semantic map evidenced direct association of 25(OH)D with high link strength (0.99) to low CRP levels and low high-density lipoprotein cholesterol levels. Low 25(OH)D led to changes in glucose, which affected metabolic syndrome biomarkers, first of which was homeostatic model assessment index and blood glucose, but not 25(OH)D.Conclusions: The use of ANN suggests a key role of 25(OH)D respect to all considered metabolic parameters in the development of diabetes and evidences a causation between low 25(OH)D and high glucose concentrations.

Determinants of metabolic syndrome in obese workers: gender differences in perceived job-related stress and in psychological characteristics identified using artificial neural networks.

OBJECTIVE: The metabolic syndrome (MS) is a multifactorial disorder associated with a higher risk of developing cardiovascular diseases and type 2 diabetes. However, its pathophysiology and risk factors are still poorly understood. In this study, we investigated the associations among gender, psychosocial variables, job-related stress and the presence of MS in a cohort of obese Caucasian workers. METHODS: A total of 210 outpatients (142 women, 68 men) from an occupational medicine service was enrolled in the study. Age, BMI, waist circumference, fasting glucose, blood pressure, triglycerides and HDL cholesterol were collected to define MS. In addition, we evaluated eating behaviors, depressive symptoms, and work-related stress. Data analyses were performed with an artificial neural network algorithm called Auto Semantic Connectivity Map (AutoCM), using all available variables. RESULTS: MS was diagnosed in 54.4 and 33.1% of the men and women, respectively. AutoCM evidenced gender-specific clusters associated with the presence or absence of MS. Men with a moderate occupational physical activity, obesity, older age and higher levels of decision-making freedom at work were more likely to have a diagnosis of MS than women. Women with lower levels of decision-making freedom, and higher levels of psychological demands and social support at work had a lower incidence of MS but showed higher levels of binge eating and depressive symptomatology. CONCLUSION: We found a complex gender-related association between MS, psychosocial risk factors and occupational determinants. The use of these information in surveillance workplace programs might prevent the onset of MS and decrease the chance of negative long-term outcomes. LEVEL OF EVIDENCE: Level V, observational study.

Polycystic Ovary Syndrome: Implication for Drug Metabolism on Assisted Reproductive Techniques-A Literature Review.

Polycystic ovary syndrome (PCOS) affects 6-10% of women and could be considered one of the most common endocrine alterations in women of reproductive age. The syndrome is characterized by several hormonal and metabolic alterations, including insulin resistance and hyperandrogenism, which play a severe detrimental role in the patient's fertility. We aimed to offer an overview about drug metabolism in the PCOS population. Nevertheless, we did not find any study that directly compared drug metabolism between PCOS and healthy women. We therefore decided to summarize briefly how hormonal and insulin sensitizer drugs act differently in healthy and PCOS women, who show altered steroidogenesis by theca cells and metabolic imbalance, focusing especially on assisted reproductive techniques. To date, data about drug metabolism in the PCOS population appears to be extremely limited. This important gap could have significant implications for therapeutic approaches and future perspectives: the dosage of drugs commonly used for the treatment of PCOS women should be tailored according to each patient's characteristics; we should implement new clinical trials in order to identify the best pharmacologic strategy for PCOS patients undergoing in vitro fertilization (IVF); it would be advisable to create an international expert panel to investigate the drug metabolism in the PCOS population.

Diagnosis of autism through EEG processed by advanced computational algorithms: A pilot study.

BACKGROUND: Multi-Scale Ranked Organizing Map coupled with Implicit Function as Squashing Time algorithm(MS-ROM/I-FAST) is a new, complex system based on Artificial Neural networks (ANNs) able to extract features of interest in computerized EEG through the analysis of few minutes of their EEG without any preliminary pre-processing. A proof of concept study previously published showed accuracy values ranging from 94%-98% in discerning subjects with Mild Cognitive Impairment and/or Alzheimer's Disease from healthy elderly people. The presence of deviant patterns in simple resting state EEG recordings in autism, consistent with the atypical organization of the cerebral cortex present, prompted us in applying this potent analytical systems in search of a EEG signature of the disease. AIM OF THE STUDY: The aim of the study is to assess how effectively this methodology distinguishes subjects with autism from typically developing ones. METHODS: Fifteen definite ASD subjects (13 males; 2 females; age range 7-14; mean value = 10.4) and ten typically developing subjects (4 males; 6 females; age range 7-12; mean value 9.2) were included in the study. Patients received Autism diagnoses according to DSM-V criteria, subsequently confirmed by the ADOS scale. A segment of artefact-free EEG lasting 60 seconds was used to compute input values for subsequent analyses. MS-ROM/I-FAST coupled with a well-documented evolutionary system able to select predictive features (TWIST) created an invariant features vector input of EEG on which supervised machine learning systems acted as blind classifiers. RESULTS: The overall predictive capability of machine learning system in sorting out autistic cases from normal control amounted consistently to 100% with all kind of systems employed using training-testing protocol and to 84% - 92.8% using Leave One Out protocol. The similarities among the ANN weight matrixes measured with apposite algorithms were not affected by the age of the subjects. This suggests that the ANNs do not read age-related EEG patterns, but rather invariant features related to the brain's underlying disconnection signature. CONCLUSION: This pilot study seems to open up new avenues for the development of non-invasive diagnostic testing for the early detection of ASD.

Endometrial preparation with Dienogest before hysteroscopic surgery: a systematic review.

PURPOSE: Hysteroscopic surgery is considered the gold standard for the minimal invasive treatment of many endouterine diseases such as endometrial polyps or submucous myomas. Recently, many studies have evaluated the effect of preoperative administration of a number of drugs to reduce endometrial thickness and achieve important intraoperative advantages. The purpose of this systematic review is to summarize the available evidence about the use of Dienogest, an orally administrable progestin, for endometrial preparation before hysteroscopic surgery. METHODS: All studies published on this topic and indexed on PubMed/MEDLINE, Embase or Google scholar databases were retrieved and analysed. RESULTS: We retrieved five studies about this topic. Considered together, the published data analyses allow us to conclude that Dienogest is effective in reducing the thickness of the endometrium, the severity of bleeding and also of operative time, with a lower number of side effects compared with other pharmacological preparations or no treatment. CONCLUSION: Administration of Dienogest may be an effective and safe treatment for endometrial thinning before operative hysteroscopy. However, this conclusion is based on few reports and further studies to prove or disprove it are warranted.

Digging deeper on "deep" learning: A computational ecology approach.

We propose an alternative approach to "deep" learning that is based on computational ecologies of structurally diverse artificial neural networks, and on dynamic associative memory responses to stimuli. Rather than focusing on massive computation of many different examples of a single situation, we opt for model-based learning and adaptive flexibility. Cross-fertilization of learning processes across multiple domains is the fundamental feature of human intelligence that must inform "new" artificial intelligence.

Vitamin B12 among Vegetarians: Status, Assessment and Supplementation.

Cobalamin is an essential molecule for humans. It acts as a cofactor in one-carbon transfers through methylation and molecular rearrangement. These functions take place in fatty acid, amino acid and nucleic acid metabolic pathways. The deficiency of vitamin B12 is clinically manifested in the blood and nervous system where the cobalamin plays a key role in cell replication and in fatty acid metabolism. Hypovitaminosis arises from inadequate absorption, from genetic defects that alter transport through the body, or from inadequate intake as a result of diet. With the growing adoption of vegetarian eating styles in Western countries, there is growing focus on whether diets that exclude animal foods are adequate. Since food availability in these countries is not a problem, and therefore plant foods are sufficiently adequate, the most delicate issue remains the contribution of cobalamin, which is poorly represented in plants. In this review, we will discuss the status of vitamin B12 among vegetarians, the diagnostic markers for the detection of cobalamin deficiency and appropriate sources for sufficient intake, through the description of the features and functions of vitamin B12 and its absorption mechanism.

Metabolism and Ovarian Function in PCOS Women: A Therapeutic Approach with Inositols.

Polycystic ovary syndrome (PCOS) is characterized by chronical anovulation and hyperandrogenism which may be present in a different degree of severity. Insulin-resistance and hyperinsulinemia are the main physiopathological basis of this syndrome and the failure of inositol-mediated signaling may concur to them. Myo (MI) and D-chiro-inositol (DCI), the most studied inositol isoforms, are classified as insulin sensitizers. In form of glycans, DCI-phosphoglycan and MI-phosphoglycan control key enzymes were involved in glucose and lipid metabolism. In form of phosphoinositides, they play an important role as second messengers in several cellular biological functions. Considering the key role played by insulin-resistance and androgen excess in PCOS patients, the insulin-sensitizing effects of both MI and DCI were tested in order to ameliorate symptoms and signs of this syndrome, including the possibility to restore patients' fertility. Accumulating evidence suggests that both isoforms of inositol are effective in improving ovarian function and metabolism in patients with PCOS, although MI showed the most marked effect on the metabolic profile, whereas DCI reduced hyperandrogenism better. The purpose of this review is to provide an update on inositol signaling and correlate data on biological functions of these multifaceted molecules, in view of a rational use for the therapy in women with PCOS.

How to Achieve High-Quality Oocytes? The Key Role of Myo-Inositol and Melatonin.

Assisted reproductive technologies (ART) have experienced growing interest from infertile patients seeking to become pregnant. The quality of oocytes plays a pivotal role in determining ART outcomes. Although many authors have studied how supplementation therapy may affect this important parameter for both in vivo and in vitro models, data are not yet robust enough to support firm conclusions. Regarding this last point, in this review our objective has been to evaluate the state of the art regarding supplementation with melatonin and myo-inositol in order to improve oocyte quality during ART. On the one hand, the antioxidant effect of melatonin is well known as being useful during ovulation and oocyte incubation, two occasions with a high level of oxidative stress. On the other hand, myo-inositol is important in cellular structure and in cellular signaling pathways. Our analysis suggests that the use of these two molecules may significantly improve the quality of oocytes and the quality of embryos: melatonin seems to raise the fertilization rate, and myo-inositol improves the pregnancy rate, although all published studies do not fully agree with these conclusions. However, previous studies have demonstrated that cotreatment improves these results compared with melatonin alone or myo-inositol alone. We recommend that further studies be performed in order to confirm these positive outcomes in routine ART treatment.

Pleiotropic Actions of Peroxisome Proliferator-Activated Receptors (PPARs) in Dysregulated Metabolic Homeostasis, Inflammation and Cancer: Current Evidence and Future Perspectives.

BACKGROUND: Peroxisome proliferator-activated receptors (PPARs) have demonstrated a lot of important effects in the regulation of glucose and lipid metabolism and in the correct functioning of adipose tissue. Recently, many studies have evaluated a possible effect of PPARs on tumor cells. The purpose of this review is to describe the effects of PPARs, their action and their future prospective; METHODS: Narrative review aimed to synthesize cutting-edge evidence retrieved from searches of computerized databases; RESULTS: PPARs play a key role in metabolic diseases, which include several cardiovascular diseases, insulin resistance, type 2 diabetes, metabolic syndrome, impaired immunity and the increasing risk of cancer; in particular, PPARα and PPARß/δ mainly enable energy combustion, while PPARγ contributes to energy storage by enhancing adipogenesis; CONCLUSION: PPAR agonists could represent interesting types of molecules that can treat not only metabolic diseases, but also inflammation and cancer. Additional research is needed for the identification of high-affinity, high-specificity agonists for the treatment of obesity, type 2 diabetes (T2DM) and other metabolic diseases. Further studies are needed also to elucidate the role of PPARs in cancer.

Peroxisome Proliferator-Activated Receptor Modulation during Metabolic Diseases and Cancers: Master and Minions.

The prevalence of obesity and metabolic diseases (such as type 2 diabetes mellitus, dyslipidaemia, and cardiovascular diseases) has increased in the last decade, in both industrialized and developing countries. This also coincided with our observation of a similar increase in the prevalence of cancers. The aetiology of these diseases is very complex and involves genetic, nutritional, and environmental factors. Much evidence indicates the central role undertaken by peroxisome proliferator-activated receptors (PPARs) in the development of these disorders. Due to the fact that their ligands could become crucial in future target-therapies, PPARs have therefore become the focal point of much research. Based on this evidence, this narrative review was written with the purpose of outlining the effects of PPARs, their actions, and their prospective uses in metabolic diseases and cancers.