Heart Attack Treatment Research Articles

OPTIMIZING HEART ATTACK DIAGNOSIS USING RANDOM FOREST WITH BAT ALGORITHM AND GREEDY CROSSOVER TECHNIQUE

Cardiovascular disease stands as one of the primary contributors to global mortality, with the World Health Organization (WHO) reporting approximately 17.9 million deaths annually. Swift and accurate diagnosis of heart attacks is crucial to ensure timely and specialized intervention for patients afflicted by this ailment. A machine learning algorithm that can be employed for addressing such issues is the Random Forest algorithm. However, the efficacy of the model is significantly influenced by the features selected during the training phase. To mitigate this, the Binary Bat Algorithm (BBA) with greedy crossover has been utilized to enhance feature selection within the model. This approach is particularly adept at preventing convergence issues often associated with local minima. The optimal parameters for BBA with greedy crossover are determined to be , , , and . With these parameters, the proposed algorithm identifies the most relevant features, including age, gender, cp, chol, thalach, oldpeak, slope, and ca, achieving an accuracy of 94.19% on the training data and 91.8% on the test data. Furthermore, the precision and recall values for both classes range from 0.87 to 0.96, contributing to an approximate -score of 0.92. The proposed method has increased its -score by 0.05 if compared with the regular Random Forest model. These results underscore the effectiveness of the proposed algorithm in providing accurate and reliable predictions for heart disease diagnosis. As such, this model makes diagnosing heart attack more convenient and effective because it does not require too much medical features or patient data. Hopefully, the results of this research help medical practitioners make better and timely decisions in the diagnosis and treatment of heart attacks, as well as assist in planning more effective public health programs for heart attack prevention.

A novel LC-QTOF-MS/MS method for trace level identification and quantification of potential genotoxic impurity, 5-Nitro-2-(propylthio)pyrimidine-4,6-diol in ticagrelor drug substance

An important objective of the present work is to develop and validate a novel LC-QTOF-MS/MS method to identify and quantify 5-Nitro-2-(propylthio) pyrimidine-4,6-diol genotoxic impurity at trace level (∼8.33 ppm) in ticagrelor drug substance, is an antiplatelet medicine used in the treatment of heart attacks and strokes. The potential genotoxic impurities must be controlled below acceptance limit during process of ticagrelor synthesis and release. In reversed-phase mode, the ACQUITY UPLC CSH C18 (2.1 mm x 100 mm, 1.7 μm) with ambient column temperature was used. 0.1% formic acid in Milli-Q water and methanol in the ratio of 30:70 v/v used as mobile phase A and mobile phase B with isocratic elution mode and the run time was 8.0 min. The flow rate was maintained at 0.25 mL/min, autosampler temperature was 5°C, Injection volume was 20 μL. The Retention time of 5-Nitro-2-(propylthio)pyrimidine-4,6-diol was found at 1.345 minutes. Limit of detection (LOD) and limit of quantification (LOQ) were found at 0.56 ppm and 1.67 ppm, respectively. The 5-Nitro-2-(propylthio)pyrimidine-4,6-diol was linear (R2=0.9986) over the concentration range 1.666 to 12.495 ppm. The method was accurate and precise, it was demonstrated by calculating the %mean recovery values ranging from 98.0-106.9%.

The Study on Timolol and Its Potential Phototoxicity Using Chemical, In Silico and In Vitro Methods.

Timolol (TIM) is a non-selective ß-adrenergic receptor antagonist used orally for the treatment of hypertension and heart attacks, and topically for treating glaucoma; lately, it has also been used in some specific dermatological problems. In the present study, its photodegradation and potential risk of phototoxicity were examined using chemical, in silico and in vitro methods. The UV/VIS irradiated solutions of TIM at pH 1-13 were subjected to LC-UV and UPLC-HRMS/MS analyses showing pseudo first-order kinetics of degradation and several degradation products. The structures of these photodegradants were elucidated by fragmentation path analysis based on high resolution (HR) fragmentation mass spectra, and then used for toxicity evaluation using OSIRIS Property Explorer and Toxtree. Potential risk of phototoxicity was also studied using chemical tests for detecting ROS under UV/VIS irradiation and in vitro tests on BALB/c 3T3 mouse fibroblasts (MTT, NRU and Live/Dead tests). TIM was shown to be potentially phototoxic because of its UV/VIS absorptive properties and generation ROS during irradiation. As was observed in the MTT and NRU tests, the co-treatment of fibroblasts with TIM and UV/VIS light inhibited cell viability, especially when concentrations of the drug were higher than 50 µg/mL.

Multiclass Classification of Myocardial Infarction Based on Phonocardiogram Signals Using Ensemble Learning

Myocardial infarction (MI) is a serious cardiovascular disease with a high mortality rate worldwide. Early detection and consistent treatment can significantly reduce mortality from cardiovascular diseases. However, there is a need for efficient models that can enable the early detection of heart disease without relying on trained clinical experts. MI studies using phonocardiogram (PCG) signals and implementing ensemble learning models are still relatively scarce, often resulting in poor accuracy and low detection rates. This study aims to implement an ensemble learning model for the classification of MI using PCG signals into different classes. In this stage of research, several classification algorithms, including Random Forest and Logistic Regression, serve as basic models for ensemble learning, utilizing features extracted from audio signals. Evaluation of the model's performance reveals that the stacking model achieves an accuracy of 96%. These results demonstrate that our system can appropriately and accurately classify MI within PCG data. We believe that the findings of this study will enhance the diagnosis and treatment of heart attacks, making them more effective and accurate.

Development and Validation of RP-HPLC Method for the Determination of Enoxaparin Sodium in Dry Injection Formulation.

Enoxaparin sodium is an anticoagulant medication that is used as a blood thinning agent. It is mostly used for the treatment and prevention of deep vein thrombosis (DVT) and pulmonary embolism (PE). It is also used in certain surgeries and during pregnancy. For the treatment of acute coronary syndrome (ACS) and heart attacks, it may be used. Enoxaparin sodium was validated by the RP-HPLC method. A simple RP-HPLC method was developed in a single HPLC run in a dry powder injection formulation. All injections of HPLC sample were 20 μL volume. The chromatographic separation was completed in the isocratic mode. The used column was USP-L8 (250 mm × 4.6 mm) of BDS type of 10 μm meters in the same mobile phase throughout the analysis by using methanol and ultrapure water with a ratio of 7:93, respectively. The flow rate was 1.0 mL/min. The mobile phase was filtered through 0.45 μm filter paper, and isocratic elution was performed. The refractive index (RI) detector was used to analyze this sample. The specific peak of enoxaparin sodium was observed at 5:56 min. The calculated detection limit (LOD) was 0.351 ppm, and the calculated quantitation limit was 1.063 ppm. In repeatability of precision, the average calculated assay (%) was 100.85%, and the calculated RSD (%) was 0.01. In the accuracy test, the RSD (%) was 0.50, and the mean recovery (%) was 100.35. The system's suitability was within the limit. This newly developed method is proposed according to ICH guidelines, and rules and can be applied effectively for the exact estimation of enoxaparin sodium in injection formulation. This newly developed methodology is affordable in cost as long as less time is taken and the consumption of samples is in smaller quantities for every investigation. In medicinal chemistry, the USP (United States Pharmacopeia) and BP (British Pharmacopeia) are directly involved in production as well as in quality testing.

QSPR Analysis of Some Important Drugs Used in Heart Attack Treatment via Degree-Based Topological Indices and Regression Models

Degree-based topological indices are very useful tools to model and characterize the molecular structure of drugs in order to predict their physicochemical properties without going into laborious and time-consuming laboratory experiments. These indices are numerical descriptors derived for the molecular structures using the principles of graph theory. Degree-based topological indices play a vital role in the QSPR analysis of heart attack drugs by providing molecular descriptors to predict their properties. The main goal of this paper is to compute six degree-based topological indices and a regression model for seven heart attack drugs. These drugs are nitroglycerin, clopidogrel, beta-blockers (metoprolol), ACE inhibitors (lisinopril), statins (atorvastatin), (ARBs) losartan, and beta-adrenergic blockers (propranolol). Regression analysis and degree-based indices correlate with various physicochemical properties related to drug activities, such as molecular weight, complexity, melting point, and boiling point. Correlations provide insights into how the molecular structure influences these properties, helping design and optimize new drugs. In the results, various statistical parameters are used to analyze heart attack drugs.

An estimation of physicochemical properties of heart attack treatment medicines by using molecular descriptor's

A topological index is a numerical quantity associate with a graph describes its topology by applying molecular structure of a chemical compound, we obtain its properties theoretically. The chemical structures of anti-heart attack drugs are investigated and analyzed by using the several degree-based topological indices. Heart attacks, also known as myocardial infarctions, are extremely prevalent in the world. In the QSAR model, degree-based topological indices computed through the edge partitioning approach, while correlation coefficients determined by a linear regression model and line graphs used to compare the correlations. The statistical analysis of various characteristics including boiling point, vapor pressure, enthalpy, flash point, molar refraction, surface tension, and molar volume in anti-heart attack medicines, suggests a strong correlation with topological indices which signify the importance of all computations. All positive correlation values indicate a direct correlation to the molecular descriptors. The characteristics of the anti-heart attack medicines computed by our simple mathematical method are better as compare to experimental values of the heart attack drugs obtained with time-consuming and expensive laboratory experiments.

Regeneration Progress in Heart Attack

Heart attack, also known as myocardial infarction, is a common and fatal cardiovascular disease all around the world. Not only is there the high death rate, but is there the increased risk of further heart failure, which makes the researches on heart regeneration of great importance and worthy of continuing. Although great progress has been made in recent years, current treatment of heart attack cannot perfectly eliminate the potential risks of future heart problems. In order to find an ultimate solution to the disease, scientists have been studying the heart regeneration abilities on newts, zebrafish, and neonatal mice, etc, and have understood various mechanisms of animal heart regeneration; however, heart regeneration possibility on humans is still not studied enough and the side effects on humans are the main obstacles for the regeneration techniques to be medically used, but this technology still has great potentials and might ultimately eliminate the underlying risks of heart attack.

HEART ATTACK PREDICTION USING MACHINE LEARNING

Early detection is essential for both the treatment and prevention of heart attacks, which account for the majority of deaths worldwide. A machine learning model for predicting a person's risk of having a heart attack based on various medical factors is the goal of this project. Patients' medical histories, lifestyle, and physical characteristics, such as temperature, humidity, heart rate (in beats per minute), and spo2 (blood oxygen levels) by corresponding sensors. Using machine learning algorithms like the naive Bayes classifier, SVM classifier, and KNN classifier, the data will be cleaned, pre-processed, and analyzed. Using metrics like precision, recall, and the F1-score, the model will be trained and tested on a dataset of patients who are known to be at risk for heart attacks. The final model will be put into use as a web application that lets users enter their medical information to get a customized heart attack risk score. This project aims to provide individuals and healthcare professionals with an easy-to-use, wearable device and accurate tool for identifying heart attack risk factors and taking preventative measures. Key words – Body Temperature, Heart rate, breathing rates, blood oxygen level, smart wearable system

Therapeutic hypercapnia. Review

In recent years, interest in hypercapnia and its practical applications has grown significantly. An analysis of literature data shows a wide range of systemic and local applications. Due to its powerful effect on blood circulation, vascular elasticity, activation of angiogenesis, and inhibition of pro-inflammatory factors, hypercapnia is already used in dermatology, phlebology, and therapy. Wide opportunities open up for practical use in neurology, given the powerful neuroprotective effect of carbon dioxide, which not only increases tolerance to ischemia, preventing the development of diseases but can also become a tool for the treatment of stroke and heart attack. The antitumor effect and the ability to reduce the level of metabolic processes also make hypercapnia an attractive geroprotector that will help in solving the issue of life extension. Keywords: hypercapnia; hypoxia; cancer; diabetes; neuroprotection; longevity; carboxytherapy; ischemia; stroke

Stent Wear Analysis for Percutaneous Coronary Interventions

The given paper deals with the issue of materials used in the treatment of heart attacks. In more de-tail it focuses on individual types of stents and their specific properties. The article describes selected groups of biocompatible metal-based materials. Our attention is mainly paid to magnesium alloys. The introduction explains the concept of biocompa-tibility and the distribution of materials accord-ing to their biological tolerance. In the experiment, we performed an ana-lysis of the chemical com-position of individual metal stents and focused on examining their wear resistance. Since it is too difficult to imitate human body environment, we opted for corrosion test for the experiment. Stents were exposed to the given concentration of the solution and temperature. The time period during which the stents were in solution was also important. In the final part, we focused on the microscopic evaluation of the surface.

Early Contributions to the Design and Conduct of Clinical Trials From a Largely Unknown 1960s Pilot Trial of Physical Activity and a Well-Known Diet-Heart Feasibility Study for the Primary Prevention of Coronary Heart Disease.

This historical note focuses on contributions to the concepts, design, methods, and conduct of clinical trials from the 1960s planning and findings of a feasibility study that was little reported and largely unrecognized: the Pilot Study of Prescribed Physical Activity for the Primary Prevention of Ischemic Heart Disease (1963-1968). In contrast, complementary findings from the 1960s National Diet-Heart Feasibility Study (1962-1968) were fully reported, widely recognized, and well-analyzed by historians in context of the rapid evolution in chronic disease epidemiology and clinical trials following World War II. The experience of these thoughtfully planned and rigorously conducted pilot studies changed the career paths of their planners and investigators and shaped the form and direction of subsequent research on the causes, treatment, and prevention of heart attack. The 2 studies were both fruit and seed of a consortium of experts that evolved in reaction to new questions and findings about the midcentury coronary disease epidemic. The contributions of each study, and of this broader academy of pioneer thinkers, are essential elements in the history of cardiovascular disease epidemiology, prevention research, and health policy.

Are Medical Care Prices Still Declining? A Re‐Examination Based on Cost‐Effectiveness Studies

More than two decades ago, a well‐known study on heart attack treatments provided evidence suggesting that, when appropriately adjusted for quality, medical care prices were actually declining (Cutler, McClellan, Newhouse, and Remler (1998)). Our paper revisits this subject by leveraging estimates from more than 8000 cost‐effectiveness studies across a broad range of conditions and treatments. We find large quality‐adjusted price declines associated with treatment innovations. To incorporate these quality‐adjusted indexes into an aggregate measure of inflation, we combine an unadjusted medical‐care price index, quality‐adjusted price indexes from treatment innovations, and proxies for the diffusion rate of new technologies. In contrast to official statistics that suggest medical care prices increased by 0.53 percent per year relative to economy‐wide inflation from 2000 to 2017, we find that quality‐adjusted medical care prices declined by 1.33 percent per year over the same period.

Research into the genesis of innovative products and professions of the future in the context of technological revolutions

A survey method was used to investigate the genetic relationships between innovative products and professions of the future potential technological revolution of the cyber-physical, biomedical, and related spheres. It is shown, that products of biomedical orientation lead in the field of high estimates, with 8 products having a C2-5 score of 4.13-4.08, which is higher than the leading product in cyberphysical area (compact supercomputers – 4.02). In the area of lower scores, the difference in scores between the two areas increases. The leaders among the products of the biomedical field are: successful treatment of heart attack and stroke, vision restoration, successful treatment of 95% of cancer patients, prevention of genetic diseases, treatment of drug and alcohol addiction, hearing restoration, human organ regeneration, and physical organ rehabilitation (C2-5=4.13 to 4.05). Comparison of the specialties from the “Atlas of New Professions” of cyberphysical and biomedical orientation with the corresponding innovative products indicated the presence of genetic links between them. The key requirement for the development of professional skills of human capital in conditions of technological revolutions is the orientation on the professions required for the most demanded innovative products, primarily medical and biological, cyber-physical and cyber-medical specializations. The results obtained can be used for investment forecasts, as well as for long-term planning of professional training of specialists in the period of the emerging technological revolution.

Effect of Hydroxypropyl Methylcellulose and Microcrystalline Cellulose in Design and Optimization of Nebivolol Hydrochloride Immediate Release Tablets by Response Surface Methodology

The goal of the research is to design and optimize Nebivolol Hydrochloride immediate-release tablet using response surface methodology. Nebivolol Hydrochloride immediate-release tablets used in the treatment of heart attacks, myocardial infarction. Response surface methodology calculations for this optimization study were performed utilizing Minitab 17. Different formulations of immediate-release were prepared by applying 2 factors 3 levels full factorial design using Minitab 17, which gave 9 formulations by using the wet granulation method. Independent variables like the amount of hydroxypropyl methylcellulose (X1), and microcrystalline cellulose (X2) and dependent variables like the per cent drug release at 45 minutes (Y1), disintegration (Y2) were selected for optimization. The prepared batches of Nebivolol Hydrochloride immediate-release tablets were evaluated for the pre-compression and post-compression parameters like weight variation, thickness, hardness, and friability, disintegration, and in-vitro drug release studies. All the Physico-chemical parameters were found satisfactory for prepared tablets. The optimized formulation F7 showed disintegrated in 83 sec, percentage dissolution release 97.85 at the end of 45th minute. The results shows that formulated immediate-release tablets of Nebivolol HCl were better to meet patient compliance with respect to effectiveness.

Maternal Cardiac Function in Women at High Risk for Pre-eclampsia Treated With 150 mg Aspirin or Placebo: An Observational Study

(BJOG. 2020;127:1018–1025) The Aspirin for Prevention of Preterm Pre-eclampsia (ASPRE) trial recently reported that aspirin (150 mg/d) may reduce the risk of preterm and early pre-eclampsia (PE) by 60% and 90%, respectively, in pregnant women screened in their first trimester. Although commonly used for the prevention and treatment of heart attacks or strokes in the general population, aspirin has also been shown to cause high blood pressure and exacerbate pre-existing heart failure. This may be explained in part by the inhibition of PGI2, a compensatory vasodilatory prostacyclin which results in vasoconstriction. In pregnant women at high risk for preterm PE, aspirin could lead to unfavorable cardiac outcomes. The aim of this study was to compare the hemodynamic effect of aspirin versus placebo in women at high-risk for preterm PE and to compare this effect in women at low risk versus high risk for developing this condition.

Learning-by-Doing and Productivity Growth Among High-Skilled Workers: Evidence from the Treatment of Heart Attacks

Learning-by-doing is a fundamental concept in economics but a challenging one to document in high-skilled settings due to non-random assignment of workers to tasks and lacking performance measures. Our paper overcomes these challenges in the context of heart attack treatments in Sweden, where we exploit quasirandom assignment of physicians to patients. We document long learning curves, where physicians keep learning over the first 1000 treatments performed, affecting both proficiency and decision-making skills. These learning effects translate into effects on patient health, but only over the first 150 treatments performed, corresponding to one year of experience. Learning rates are higher for physicians who have worked with more experienced colleagues and who have gained more experience in treating complicated cases. Experienced physicians are more responsive to patient characteristics when deciding on treatments and experience from more recent heart attack treatments is more valuable than experience from more distant ones, suggesting that human capital depreciates. We also show that productivity growth keeps pace with wage growth over the first four years of the career but flattens out thereafter. Our results provide rare evidence on the existence of prolonged learning curves in high-skilled tasks and support the notion that learning-by-doing can be a powerful mechanism for productivity growth.

Pharmaco-Invasive Therapy for Acute ST-Elevation Myocardial Infarction. - A Viable Alternative to Primary Percutaneous Coronary Intervention

Primary percutaneous coronary intervention (pPCI) is considered as the preferred treatment for acute ST-Elevation myocardial infarction (STEMI). However, its availability is limited to less than 10% in rural and sub urban population in India (1). Therefore, Pharmaco-Invasive Therapy (PIT) (Thrombolysis first followed by planned coronary stenting) as an alternative to pPCI for acute STEMI has more recently been explored. A retrospective observational study of 60 patients with acute STEMI treated at the Dedicated Heart Attack Treatment Centre of SKY Hospital & Research Centre, Imphal, India over a period of 2 years has shown that Pharmaco-Invasive Therapy improved survival of patients with acute STEMI to 100% and increased Left Ventricular Ejection Fraction (LVEF) by 5.08% at the time of discharge and may be used as a viable alternative to pPCI in the treatment of patients with acute STEMI when pPCI cannot be performed within recommended time.

Peter Sleight: visionary cardiologist and inspiring leader

Photo credit: Mervyn George Bishop/Fairfax Media/Getty Images Peter Sleight, a professor at Oxford University, helped to transform heart attack treatment and prevent cardiovascular disease with angiotensin converting enzyme inhibitors and...

Venous cerebral infarction in patient with thrombophilia while taking hormonal drugs

A clinical case of venous cerebral infarction in a patient with congenital thrombophilia without thrombosis of cerebral venous sinuses is presented. Etiology, diagnosis and treatment of venous heart attacks are considered. Particular attention is paid to methods of diagnosis and therapy.