Cardiovascular diseases are the most common cause of death worldwide and thyroid diseases are frequently associated with cardiac function impairment. Hypothyroidism is related to dilation of the ventricles, fibrosis and consequent reduction in cardiac contractility, as well as an increased risk of heart failure. Recent studies have demonstrated the participation of the NLRP3 inflammasome, an important mechanism of the innate immune response, in cardiovascular diseases, such as myocardial infarction and atherosclerosis. There is evidence in the literature that hypothyroidism is accompanied by increased levels of cytokines and oxidative stress, processes that can activate the inflammasome. The aim of the present study is to evaluate the role of the NLRP3 inflammasome in the heart of hypothyroid mice. For this, Wild Type (WT) mice treated with methimazole (0.05% added to drinking water for 12 weeks) were used (N=4-6). All protocols used in this study were approved by the Animal Ethics Committee of the Institute of Biomedical Sciences (9768300119). Thyroid hormones were quantified by bead-based immunoassay Luminex to confirm the efficacy of experimental model. Fibrosis was analyzed by histology, using picrosirius red staining. Cardiac function was evaluated in mice by plethysmography and in isolated hearts after ischemia/reperfusion by the Langendorff method. Protein expression of NLRP3 inflammasome components was evaluated by Western Blotting. Data were analyzed by one-way ANOVA or Student's t test. Results are presented as mean ± standard error of the mean, and p<0.05 was considered statistically significant. Increased TSH levels (control=92±33, hypo=369±62 (pg/ml)) and reduced T4 (control=1.16±0.38, hypo=0.03±0.01 (ng/ml)) confirmed the hypothyroid status of mice. The heart weight/tibial length ratio and fibrosis were not altered by hypothyroidism. Heart rate was reduced in hypothyroid mice (control=663±27, hypo=541±28 (bpm)). No differences were detected in the cardiac function parameters after Ischemia-Reperfusion injury: left ventricular developed pressure (LVDP), maximum (dP/dtmax) or minimum first derivative (dP/dtmin). Concerning to inflammasome, NLRP3 expression was reduced (control=100±16, hypo=29±14 (%)) and pro-caspase 1 increased (control=100±17, hypo=259±43 (%)) in hypothyroid hearts. These preliminary results showed characterization data for experimental hypothyroidism and that this condition can modulate inflammasome expression. Additional experiments are underway to confirm the role of the NLRP3 inflammasome on cardiac function in hypothyroid mice. FAPESP (grants nº 2021/06151-7 and 2019/17031-2) This is the full abstract presented at the American Physiology Summit 2023 meeting and is only available in HTML format. There are no additional versions or additional content available for this abstract. Physiology was not involved in the peer review process.

Background Our experience in creating innovative Artificial Intelligence-guided single lead EKG methodologies for ST-Elevation Myocardial Infarction (STEMI) detection within complex EKG records has been previously validated. Purpose By expanding the intricate variables of our previously tested algorithm input, we seek to further improve our STEMI detecting tool. Methods 11,567 12-lead EKG records (10-s length, 500 Hz sample frequency) derived from the Latin America Telemedicine Infarct Network database from April 2014 to December 2019. From these records, we included the following balanced classes: angiographically confirmed and unconfirmed STEMI (divided by wall affected), branch blocks, non-specific ST-T changes, normal, and abnormal (Remaining 200+ CPT codes). Cardiologist annotations ensured precision (Ground truth). Determined classes were “STEMI” and “Not-STEMI”. A 1-D Convolutional Neural Network model was trained and tested for each lead with dataset proportions of 90/10, respectively. The last dense layer outputs a probability for each record being STEMI/Not-STEMI. The analysis also included performance metrics and false-negative reports. Results Overall, the most promising Single lead for STEMI detection was V2 (91.2% Accuracy, 89.6% Sensitivity, and 92.9% Specificity). 55% of false negatives were inferior wall STEMI (Table 1). Conclusion Appreciable progress of our new methodology compared to our previous experiences in AI-guided Single Lead for STEMI detection, especially for lead V2. By performing a thorough analysis of false-negative reports, we aspire to identify potential areas of STEMI detection weakness which will become the focus of future ventures. Funding Acknowledgement Type of funding sources: None.

Background Our experience in creating innovative Artificial Intelligence-guided single lead EKG methodologies for ST-Elevation Myocardial Infarction (STEMI) detection within complex EKG records has been previously validated. Purpose By expanding the intricate variables of our previously tested algorithm input, we seek to further improve our STEMI detecting tool. Methods 11,567 12-lead EKG records (10-s length, 500 Hz sample frequency) derived from the Latin America Telemedicine Infarct Network database from April 2014 to December 2019. From these records, we included the following balanced classes: angiographically confirmed and unconfirmed STEMI (divided by wall affected), branch blocks, non-specific ST-T changes, normal, and abnormal (Remaining 200+ CPT codes). Cardiologist annotations ensured precision (Ground truth). Determined classes were “STEMI” and “Not-STEMI”. A 1-D Convolutional Neural Network model was trained and tested for each lead with dataset proportions of 90/10, respectively. The last dense layer outputs a probability for each record being STEMI/Not-STEMI. The analysis also included performance metrics and false-negative reports. Results Overall, the most promising Single lead for STEMI detection was V2 (91.2% Accuracy, 89.6% Sensitivity, and 92.9% Specificity). 55% of false negatives were inferior wall STEMI (Table 1). Conclusion Appreciable progress of our new methodology compared to our previous experiences in AI-guided Single Lead for STEMI detection, especially for lead V2. By performing a thorough analysis of false-negative reports, we aspire to identify potential areas of STEMI detection weakness which will become the focus of future ventures. Funding Acknowledgement Type of funding sources: None.