Association of indoleamine 2,3 dioxygenase, brain derived neurotrophic factor and cellular senescence in type 2 diabetes mellitus with depression: a clinical approach.

BACKGROUND AND AIM: Type 2 diabetes mellitus (T2DM) and depression are often linked. Several studies have reported the role of molecular markers either in diabetes or depression. The present study aimed at molecular level profiling of Indoleamine-2,3-dioxygenase (IDO), brain-derived neurotrophic factor (BDNF) and cellular senescence in patients with type 2 diabetes with and without depression compared to individuals with healthy controls. METHODS: A total of 120 individuals diagnosed with T2DM were enlisted for the study, with a subset of participants with and without exhibiting depression. The gene expression analysis was done using quantitative real-time PCR. RESULTS: Indoleamine 2,3 dioxygenase (p < 0.001) and senescence genes (p < 0.001) were significantly upregulated, while brain derived neurotrophic factor (p < 0.01) was significantly downregulated in T2DM patients comorbid with and without depression when compared to healthy controls. CONCLUSION: Indoleamine 2,3 dioxygenase, Brain derived neurotrophic factor and cellular senescence may play a role in the progression of the disease. The aforementioned discoveries offer significant contributions to our understanding of the molecular mechanisms that underlie T2DM with depression, potentially aiding in the advancement of prediction and diagnostic methods for this particular ailment.

Hypothyroidism in acute coronary syndrome - A prospective Indian study.

BACKGROUND: Evidence suggests that hypothyroidism may be associated with an increased risk of acute coronary syndrome (ACS). The data regarding the influence of hypothyroidism on cardiovascular disease in the Asian population is conflicting. Therefore, we undertook this study to assess the overall prevalence of hypothyroidism in Acute Coronary Syndrome (ACS) patients and determine if there is a relationship between hypothyroidism, both sub-clinical and overt and other significant risk factors of ACS in an Indian population. METHODS: We studied 487 hospitalized patients between March 2018 and February 2021 with a diagnosis of ACS to determine the prevalence of hypothyroidism, both clinical and sub-clinical and their relationship with other known coronary risk factors. Thyroid function Tests - free T3, free T4 and TSH were collected from all the patients within 24 h of their admission to the coronary care unit (CCU) of 2 major hospitals in New Delhi and Imphal (Manipur). RESULTS: Subclinical hypothyroidism was prevalent in 44 (9 %), followed by overt hypothyroidism in 25 (5.2 %). Subclinical hypothyroidism was more common in females, whereas overt hypothyroidism was more common in males. ST Elevation Myocardial Infarction (STEMI) (52 %), followed by Non-ST-Elevation Myocardial Infarction (NSTEMI) (25 %), was the commonest diagnosis at presentation. Patients with overt hypothyroidism showed a higher proportion of increased triglyceride levels. Patients with hypothyroidism had no differences in the prevalence of concomitant diabetes hypertension and other coronary risk factors. CONCLUSIONS: Patients with ACS without known thyroid disorders should be screened for hypothyroidism since it is found frequently. There might be a case to treat their thyroid dysfunction appropriately.

Secure and privacy-preserving automated machine learning operations into end-to-end integrated IoT-edge-artificial intelligence-blockchain monitoring system for diabetes mellitus prediction.

Diabetes Mellitus, one of the leading causes of death worldwide, has no cure to date and can lead to severe health complications, such as retinopathy, limb amputation, cardiovascular diseases, and neuronal disease, if left untreated. Consequently, it becomes crucial to be able to monitor and predict the incidence of diabetes. Machine learning approaches have been proposed and evaluated in the literature for diabetes prediction. This paper proposes an IoT-edge-Artificial Intelligence (AI)-blockchain system for diabetes prediction based on risk factors. The proposed system is underpinned by blockchain to obtain a cohesive view of the risk factors data from patients across different hospitals and ensure security and privacy of the user's data. We provide a comparative analysis of different medical sensors, devices, and methods to measure and collect the risk factors values in the system. Numerical experiments and comparative analysis were carried out within our proposed system, using the most accurate random forest (RF) model, and the two most used state-of-the-art machine learning approaches, Logistic Regression (LR) and Support Vector Machine (SVM), using three real-life diabetes datasets. The results show that the proposed system predicts diabetes using RF with 4.57% more accuracy on average in comparison with the other models LR and SVM, with 2.87 times more execution time. Data balancing without feature selection does not show significant improvement. When using feature selection, the performance is improved by 1.14% for PIMA Indian and 0.02% for Sylhet datasets, while it is reduced by 0.89% for MIMIC III.

Exosomal microRNAs in breast cancer: towards theranostic applications.

Breast cancer is one of the top two reproductive cancers responsible for high rates of morbidity and mortality among women globally. Despite the advancements in the treatment of breast cancer, its early diagnosis remains a challenge. Recent evidence indicates that despite the adroit use of numerous strategies to facilitate rapid and precision-oriented screening of breast cancer at the community level through the use of mammograms, Fine-needle aspiration cytology (FNAC) and biomarker tracking, no strategy has been unequivocally accepted as a gold standard for facilitating rapid screening for disease. This necessitates the need to identify novel strategies for the detection and triage of breast cancer lesions at higher rates of specificity, and sensitivity, whilst taking into account the epidemiologic and social-demographic features of the patients. Recent shreds of evidence indicate that exosomes could be a robust source of biomaterial for the rapid screening of breast cancer due to their high stability and their presence in body fluids. Increasing evidence indicates that the Exosomal microRNAs- play a significant role in modifying the tumour microenvironment of breast cancers, thereby potentially aiding in the proliferation, invasion and metastasis of breast cancer. In this review, we summarize the role of ExomiRs in the tumour microenvironment in breast cancer. These ExomiRs can also be used as candidate biomarkers for facilitating rapid screening and triaging of breast cancer patients for clinical intervention.

Endocrine disrupting chemicals: gestational diabetes and beyond.

Gestational Diabetes Mellitus (GDM) has been on the rise for the last two decades along with the growing incidence of obesity. The ubiquitous use of Endocrine-Disrupting Chemicals (EDCs) worldwide has been associated with this increase in GDM incidence. Epigenetic modifications such as DNA methylation, histone acetylation, and methylation have been associated with prenatal exposure to EDCs. EDC exposure can also drive a sustained disruption of the hypothalamus-pituitary-thyroid axis and various other signaling pathways such as thyroid signaling, PPARγ signaling, PI3K-AKT signaling. This disruption leads to impaired glucose metabolism, insulin resistance as well as ß-cell dysfunction, which culminate into GDM. Persistent EDC exposure in pregnant women also increases adipogenesis, which results in gestational weight gain. Importantly, pregnant mothers transfer these EDCs to the fetus via the placenta, thus leading to other pregnancy-associated complications such as intrauterine growth restriction (IUGR), and large for gestational age neonates. Furthermore, this early EDC exposure of the fetus increases the susceptibility of the infant to metabolic diseases in early life. The transgenerational impact of EDCs is also associated with higher vascular tone, cognitive aberrations, and enhanced susceptibility to lifestyle disorders including reproductive health anomalies. The review focuses on the impact of environmental toxins in inducing epigenetic alterations and increasing the susceptibility to metabolic diseases during pregnancy needs to be extensively studied such that interventions can be developed to break this vicious cycle. Furthermore, the use of EDC-associated ExomiRs from the serum of patients can help in the early diagnosis of GDM, thereby leading to triaging of patients based on increasing risk factor of the clinicopathological condition.

Prospecting the theragnostic potential of the psycho-neuro-endocrinological perturbation of the gut-brain-immune axis for improving cardiovascular diseases outcomes.

Biological derivatives and their effective influence on psychological parameters are increasingly being deciphered to better understand body-mind perspectives in health. Recent evidence suggests that the gut-brain immune axis is an attractive theragnostic target due to its innate capacity to excite the immune system by activating monocyte exosomes. These exosomes induce spontaneous alterations in the microRNAs within the brain endothelial cells, resulting in an acute inflammatory response with physiological and psychological sequelae, evidenced by anxiety and depression. Exploring the role of the stress models that influence anxiety and depression may reflect on the effect and role of exosomes, shedding light on various physiological responses that explain the contributing factors of cardiovascular disorders. The pathophysiological effects of gut-microbiome dysbiosis are further accentuated by alterations in the glucose metabolism, leading to type 2 diabetes, which is known to be a risk factor for cardiovascular disorders. Understanding the role of exosomes and their implications for cell-to-cell communication, inflammatory responses, and neuronal stress reactions can easily provide insight into the gut-brain immune axis and downstream cardiovascular sequelae.