ECG classification or heartbeat classification is an extremely valuable tool in cardiology. Deep learning-based techniques for the analysis of ECG signals assist human experts in the timely diagnosis of cardiac diseases and help save precious lives. This research aims at digitizing a dataset of images of ECG records into time series signals and then applying deep learning (DL) techniques on the digitized dataset. State-of-the-art DL techniques are proposed for the classification of the ECG signals into different cardiac classes. Multiple DL models, including a convolutional neural network (CNN), a long short-term memory (LSTM) network, and a self-supervised learning (SSL)-based model using autoencoders are explored and compared in this study. The models are trained on the dataset generated from ECG plots of patients from various healthcare institutes in Pakistan. First, the ECG images are digitized, segmenting the lead II heartbeats, and then the digitized signals are passed to the proposed deep learning models for classification. Among the different DL models used in this study, the proposed CNN model achieves the highest accuracy of ∼92%. The proposed model is highly accurate and provides fast inference for real-time and direct monitoring of ECG signals that are captured from the electrodes (sensors) placed on different parts of the body. Using the digitized form of ECG signals instead of images for the classification of cardiac arrhythmia allows cardiologists to utilize DL models directly on ECG signals from an ECG machine for the real-time and accurate monitoring of ECGs.

Background: Acute heart failure (AHF) is one of the most common cardiovascular diseases. Early diagnosis and prognosis are essential, as they can eventually lead to a fatal condition. Recently, brain natriuretic peptide (BNP) has been recognized as one of the most popular biomarkers for AHF. Changes in glomerular filtration rate (GFR) are often observed in AHF. Methods: We searched PubMed, Google Scholar, and ScienceDirect between March and June 2023. Original case control studies written in English that assessed levels oh BNP in AHF were included. Systematic reviews, letters to editor, correspondence, comprehensive reviews, and duplicated studies were excluded. Funnel plots were constructed to assess publication bias. Results: A total of 9 studies were selected and we obtained the mean difference (MD) of BNP level to be 2.57 (95% CI: 1.35, 3.78), and GFR to be −15.52, (95% CI: −23.35, −7.70) in AHF patients. Sensitivity analyses supported the robustness of the outcome. Conclusion: Results indicated that BNP was a promising prognostic biomarker of AHF, whereas GFR was found to be negatively correlated with AHF.

Introduction: The lipid profile has been reported high in people suffering from hyperlipidemias. Garlic can act as a shield against cancer, aging and chronic disorders like cardiovascular disorder. Methodology: Randomized Control Trial (RCT) approach was used; patients with CVDS along with other diseases like diabetes and arthritis were excluded. 50 members were selected for each group. Results: The mean values of lipid profile calculated for the two groups are 356.46 mg/dl and 257.46 mg/dl respectively. BMI differed significantly between the garlic (P < 0.03), and non-garlic (P < 0.06) groups. According to t-test the value of t is 5.66, and the critical values of t are 1.67 and 2.00 for t-critical (one tailed and two tailed respectively). Conclusion: The study suggests that consuming raw garlic has a positive effect on lipid profiles in patients with hyperlipidemias. The results showed a significant reduction in blood cholesterol after consuming raw garlic for 42 days. Due to the smaller size of the study, we were unable to generalize the results. To determine optimal garlic consumption, further research is needed.

Introduction Primary hyperparathyroidism is a common endocrine condition requiring parathyroidectomy for curative management. Localization of parathyroid gland by ultrasound and Tc99m-SESTAMIBI is important to opt for less invasive and comparatively lower complication risk surgery minimal invasive parathyroidectomy (MIP) instead of four-gland exploration surgery. Aim To evaluate ultrasound and Tc99m-SESTAMIBI in localization of abnormal parathyroid gland before surgery. Method and materials All patients of primary hyperparathyroidism (PHPT) that presented to a secondary care hospital (endocrinology department) from 2015-2019 were recruited retrospectively from electronic fusion system of hospital. Results of ultrasound parathyroid and Tc99m-SESTAMIBI done for localization of abnormal parathyroid gland were analyzed. Results Total PHPT patients recruited were 59, mean age 64.2 years, male 11 (18.64%) and female 48 (81.3%). Ultrasound parathyroid was done in 44 patients, Tc 99m-SESTAMIBI was done in 31, both tests were done in 31 patients. Combined concordant adenoma in both tests was seen in 11 (35%) cases which can opt for minimal invasive parathyroidectomy (MIP) with confidence whereas 65% of cases would require either four-gland exploration or further testing like single-photon emission computed tomography-computed tomography (SPECT-CT) or intraoperative parathyroid hormone measurement to opt for MIP. Conclusion Combined ultrasound parathyroid and Tc99m-SESTAMIBI scan was useful in localization of parathyroid adenoma in 11 (35%) patients that can opt for MIP which is a lower complication risk surgery whereas 20 (65%) patients would need further investigation with SPECT-CT or intraoperative parathyroid hormone measurement or four-gland exploration surgery. Recommendation Third modality of investigation such as SPECT-CT or intraoperative parathyroid hormone measurement needs evaluation so that more patients can benefit from MIP instead of four-gland exploration surgery.