Vitamins and Minerals for Blood Pressure Reduction in the General, Normotensive Population: A Systematic Review and Meta-Analysis of Six Supplements.

Hypertension is the leading preventable risk factor for cardiovascular disease and all-cause mortality worldwide. However, studies have shown increased risk of mortality from heart disease and stroke even within the normal blood pressure (BP) range, starting at BPs above 110-115/70-75 mm Hg. Nutraceuticals, such as vitamins and minerals, have been studied extensively for their efficacy in lowering BP and may be of benefit to the general, normotensive population in achieving optimal BP. Our study investigated the effects of six nutraceuticals (Vitamins: C, D, E; Minerals: Calcium, Magnesium, Potassium) on both systolic blood pressure (SBP) and diastolic blood pressure (DBP) in this population. We performed a systematic review and pairwise meta-analysis for all six supplements versus placebo. Calcium and magnesium achieved significant reductions in both SBP and DBP of -1.37/-1.63 mm Hg and -2.79/-1.56 mm Hg, respectively. Vitamin E and potassium only yielded significant reductions in SBP with values of -1.76 mm Hg and -2.10 mm Hg, respectively. Vitamins C and D were not found to significantly lower either SBP or DBP. Future studies should determine optimal dosage and treatment length for these supplements in the general, normotensive population.

Determination of doxepin hydrochloride in spiked human plasma and capsule after derivatization with eosin Y using resonance Rayleigh scattering, second-order scattering and frequency doubling scattering methods.

Doxepin hydrochloride (DOX) is a tricyclic antidepressant drug. Three sensitive spectrofluorimetric methods, namely resonance Rayleigh scattering (RRS), frequency doubling scattering (FDS) and second-order scattering (SOS), were developed and validated for their estimation of doxepin in spiked human plasma and formulation using liquid-liquid extraction method through the formation of an ion pair complex with eosin Y at a pH of 4.5. Various factors affecting fluorescence intensity were optimized, and the reaction kinetics was determined using the Arrhenius equation method. Different scattering methods such as RRS, FDS and SOS were developed at maximum scattering wavelengths λex /λem = 567/567 nm for RRS, 720/360 nm for SOS and 260/520 nm for FDS, respectively. The methods exhibited high sensitivities, and the detection limits for DOX were found to be 0.82, 1.20 and 1.03 ng/ml for RRS, FDS and SOS methods, respectively. The FDS method exhibited the highest sensitivity. The methods were validated using the International Council for Harmonisation of Technical Requirements for Pharmaceuticals for Human Use guidelines and applied to determine DOX in capsule and spiked human plasma samples.

Monitoring Cardiovascular Problems in Heart Patients Using Machine Learning.

The World Health Organization reports that heart disease is the most common cause of death globally, accounting for 17.9 million fatalities annually. The fundamentals of a cure, it is thought, are important symptoms and recognition of the illness. Traditional techniques are facing many challenges, ranging from delayed or unnecessary treatment to incorrect diagnoses, which can affect treatment progress, increase the bill, and give the disease more time to spread and harm the patient's body. Such errors could be avoided and minimized by employing ML and AI techniques. Many significant efforts have been made in recent years to increase computer-aided diagnosis and detection applications, which is a rapidly growing area of research. Machine learning algorithms are especially important in CAD, which is used to detect patterns in medical data sources and make nontrivial predictions to assist doctors and clinicians in making timely decisions. This study aims to develop multiple methods for machine learning using the UCI set of data based on individuals' medical attributes to aid in the early detection of cardiovascular disease. Various machine learning techniques are used to evaluate and review the results of the UCI machine learning heart disease dataset. The proposed algorithms had the highest accuracy, with the random forest classifier achieving 96.72% and the extreme gradient boost achieving 95.08%. This will assist the doctor in taking appropriate actions. The proposed technology will only be able to determine whether or not a person has a heart issue. The severity of heart disease cannot be determined using this method.

Formulation and optimization of potassium iodide tablets.

The use of potassium iodide (KI) as a protective agent against accidental radioactive exposure is well established. In this study, we aimed to prepare a KI tablet formulation using a direct compression method. We utilized Design of Experiment (DoE)/mixture design to define the best formulation with predetermined physical qualities as to its dissolution, hardness, assay, disintegration, and angle of repose. Based on the results from the DoE, the formulation had the following components (%w/w): Avicel 48.70%, silicon dioxide 0.27%, stearic acid (1.00%), magnesium stearate 2.45%, and dicalcium phosphate 18.69%, in addition to potassium iodide 28.89% (130 mg/tablet). This formulation was scaled-up using two tablet presses, a single-punch press and a rotary mini tablet press. The final scaled-up formulation was subjected to a variety of quality control tests, including photo-stability testing. The results indicate that potassium iodide tablets prepared by a rotary mini tablet press had good pharmaceutical characteristics and a shelf-life of 25 days when stored at room temperature protected from light.