Evaluating the efficacy and safety of mavacamten in hypertrophic cardiomyopathy: A systematic review and meta-analysis focusing on qualitative assessment, biomarkers, and cardiac imaging.

BACKGROUND: Hypertrophic Cardiomyopathy (HCM) is a complex cardiac condition characterized by hypercontractility of cardiac muscle leading to a dynamic obstruction of left ventricular outlet tract (LVOT). Mavacamten, a first-in-class cardiac myosin inhibitor, is increasingly being studied in randomized controlled trials. In this meta-analysis, we aimed to analyse the efficacy and safety profile of Mavacamten compared to placebo in patients of HCM. METHOD: We carried out a comprehensive search in PubMed, Cochrane, and clinicaltrials.gov to analyze the efficacy and safety of mavacamten compared to placebo from 2010 to 2023. To calculate pooled odds ratio (OR) or risk ratio (RR) at 95% confidence interval (CI), the Mantel-Haenszel formula with random effect was used and Generic Inverse Variance method assessed pooled mean difference value at a 95% CI. RevMan was used for analysis. P<0.05 was considered significant. RESULTS: We analyzed five phase 3 RCTs including 609 patients to compare mavacamten with a placebo. New York Heart Association (NYHA) grade improvement and KCCQ score showed the odds ratio as 4.94 and 7.93 with p<0.00001 at random effect, respectively. Cardiac imaging which included LAVI, LVOT at rest, LVOT post valsalva, LVOT post-exercise, and reduction in LVEF showed the pooled mean differences for change as -5.29, -49.72, -57.45, -36.11, and -3.00 respectively. Changes in LVEDV and LVMI were not statistically significant. The pooled mean difference for change in NT-proBNP and Cardiac troponin-I showed 0.20 and 0.57 with p<0.00001. The efficacy was evaluated in 1) A composite score, which was defined as either 1·5 mL/kg per min or greater increase in peak oxygen consumption (pVO2) and at least one NYHA class reduction, or a 3·0 mL/kg per min or greater pVO2 increase without NYHA class worsening and 2) changes in pVO2, which was not statistically significant. Similarly, any treatment-associated emergent adverse effects (TEAE), treatment-associated serious adverse effects (TSAE), and cardiac-related adverse effects were not statistically significant. CONCLUSION: Mavacamten influences diverse facets of HCM comprehensively. Notably, our study delved into the drug's impact on the heart's structural and functional aspects, providing insights that complement prior findings. Further large-scale trials are needed to evaluate the safety profile of Mavacamten.

Prospective of Pancreatic Cancer Diagnosis Using Cardiac Sensing.

Pancreatic carcinoma (Ca Pancreas) is the third leading cause of cancer-related deaths in the world. The malignancies of the pancreas can be diagnosed with the help of various imaging modalities. An endoscopic ultrasound with a tissue biopsy is so far considered to be the gold standard in terms of the detection of Ca Pancreas, especially for lesions <2 mm. However, other methods, like computed tomography (CT), ultrasound, and magnetic resonance imaging (MRI), are also conventionally used. Moreover, newer techniques, like proteomics, radiomics, metabolomics, and artificial intelligence (AI), are slowly being introduced for diagnosing pancreatic cancer. Regardless, it is still a challenge to diagnose pancreatic carcinoma non-invasively at an early stage due to its delayed presentation. Similarly, this also makes it difficult to demonstrate an association between Ca Pancreas and other vital organs of the body, such as the heart. A number of studies have proven a correlation between the heart and pancreatic cancer. The tumor of the pancreas affects the heart at the physiological, as well as the molecular, level. An overexpression of the SMAD4 gene; a disruption in biomolecules, such as IGF, MAPK, and ApoE; and increased CA19-9 markers are a few of the many factors that are noted to affect cardiovascular systems with pancreatic malignancies. A comprehensive review of this correlation will aid researchers in conducting studies to help establish a definite relation between the two organs and discover ways to use it for the early detection of Ca Pancreas.

Digital Pulmonology Practice with Phonopulmography Leveraging Artificial Intelligence: Future Perspectives Using Dual Microwave Acoustic Sensing and Imaging.

Respiratory disorders, being one of the leading causes of disability worldwide, account for constant evolution in management technologies, resulting in the incorporation of artificial intelligence (AI) in the recording and analysis of lung sounds to aid diagnosis in clinical pulmonology practice. Although lung sound auscultation is a common clinical practice, its use in diagnosis is limited due to its high variability and subjectivity. We review the origin of lung sounds, various auscultation and processing methods over the years and their clinical applications to understand the potential for a lung sound auscultation and analysis device. Respiratory sounds result from the intra-pulmonary collision of molecules contained in the air, leading to turbulent flow and subsequent sound production. These sounds have been recorded via an electronic stethoscope and analyzed using back-propagation neural networks, wavelet transform models, Gaussian mixture models and recently with machine learning and deep learning models with possible use in asthma, COVID-19, asbestosis and interstitial lung disease. The purpose of this review was to summarize lung sound physiology, recording technologies and diagnostics methods using AI for digital pulmonology practice. Future research and development in recording and analyzing respiratory sounds in real time could revolutionize clinical practice for both the patients and the healthcare personnel.

Machine Learning-Based Classification of Abnormal Liver Tissues Using Relative Permittivity.

The search for non-invasive, fast, and low-cost diagnostic tools has gained significant traction among many researchers worldwide. Dielectric properties calculated from microwave signals offer unique insights into biological tissue. Material properties, such as relative permittivity (εr) and conductivity (σ), can vary significantly between healthy and unhealthy tissue types at a given frequency. Understanding this difference in properties is key for identifying the disease state. The frequency-dependent nature of the dielectric measurements results in large datasets, which can be postprocessed using artificial intelligence (AI) methods. In this work, the dielectric properties of liver tissues in three mouse models of liver disease are characterized using dielectric spectroscopy. The measurements are grouped into four categories based on the diets or disease state of the mice, i.e., healthy mice, mice with non-alcoholic steatohepatitis (NASH) induced by choline-deficient high-fat diet, mice with NASH induced by western diet, and mice with liver fibrosis. Multi-class classification machine learning (ML) models are then explored to differentiate the liver tissue groups based on dielectric measurements. The results show that the support vector machine (SVM) model was able to differentiate the tissue groups with an accuracy up to 90%. This technology pipeline, thus, shows great potential for developing the next generation non-invasive diagnostic tools.