Multiple irregularities and thrombus in a patient with COVID-19 presenting with ST-segment elevation myocardial infarction: a case report

Background: Multiple thrombi are likely to develop in Coronavirus Disease 2019 (COVID-19) patients. Hence, achieving successful outcomes following catheterization becomes quite challenging in such patients. Case presentation: We report a challenging case of a 36-year-old female with ST-Segment Elevation Myocardial Infarction (STEMI). Coronary angiography revealed multiple irregularities in the coronary tree as well as thrombi. Although computed tomography imaging of the thorax was normal, reverse transcription-polymerase chain reaction (RT-PCR) confirmed the diagnosis of COVID-19. The outcome was good TIMI flow after a successful Primary Angioplasty in Acute Myocardial Infarction (PAMI), and the patient was isolated and then switched to oral anticoagulants (clopidogrel) for dual antiplatelet therapy (DAPT) therapy.  Conclusion: This case emphasizes the management of a COVID-19 patient for PAMI

GAIT Technology for Human Recognition using CNN

Gait is a distinctive biometric characteristic that can be detected from a distance; as a result, it has several uses in social security, forensic identification, and crime prevention. Existing gait identification techniques use a gait template, which makes it difficult to keep temporal information, or a gait sequence, which maintains pointless sequential limitations and loses the ability to portray a gait. Our technique, which is based on this deep set viewpoint, is immune to frame permutations and can seamlessly combine frames from many videos that were taken in various contexts, such as diversified watching, angles, various outfits, or various situations for transporting something. According to experiments, our single-model strategy obtains an average rank-1 accuracy of 96.1% on the CASIA-B gait dataset and an accuracy of 87.9% on the OU-MVLP gait dataset when used under typical walking conditions. Our model also demonstrates a great degree of robustness under numerous challenging circumstances. When carrying bags and wearing a coat while walking, it obtains accuracy on the CASIA-B of 90.8% and 70.3%, respectively, greatly surpassing the best approach currently in use. Additionally, the suggested method achieves a satisfactory level of accuracy even when there are few frames available in the test samples; for instance, it achieves 85.0% on the CASIA-B even with only 7 frames

A Systematic Study of Stock Markets Using Analytical and AI Techniques

Predicting stock market patterns is seen as a crucial and highly productive activity. Therefore, if investors make wise choices, stock prices will result in significant gains. Investors face a lot of difficulty making predictions about the stock market because of the noisy and stagnating data. As a result, making accurate stock market predictions is difficult for investors who want to put their money to work for them. Predictions of the stock market are made using mathematical techniques and study aids. Out of 30 research papers advocating approaches, this study offers a thorough analysis of each, including computational methodologies, AI algorithms( machine learning and deep learning), performance evaluation parameters, and chosen publications. Research questions are used to choose studies. As a result, these chosen studies contribute to the discovery of ML methods and their corresponding data set for predicting security markets. The majority of Artificial Neural Network and Neural Network techniques are employed for producing precise stock market forecasts. The most recent stock market-related prediction system has significant limitations despite the substantial amount of work that has gone into it. In this survey, one may infer that the stock price forecasting procedure is a comprehensive affair and it is very necessary to look more closely at the typical parameters for the stock market prediction

A Survey on Active Defense Honeypot Mechanism for Information Security

Information security is a rising concern today in this era of the internet because of the rapid development of the new attack techniques. The existing security mechanisms such as traditional intrusion detection systems, firewalls and encryption are the passive defense mechanisms. This has led to growing interest in the active defense technology like honeypots. Honeypots are fake computer Systems which appears vulnerable to attack though it actually prevents access to valuable sensitive data and administrative controls. A well designed and developed Honeypot provide data to the research community to study issues in network and information security. In this paper we examine different Types of Honeypots, Honeypot concepts and approaches in order to determine how we can intend measures to enhance security using these technologies. In this work a web application honeypot architecture is proposed

DEVELOPMENT AND VALIDATION OF A HIGH-PERFORMANCE THIN-LAYER CHROMATOGRAPHY FOR THE DETERMINATION OF TERBUTALINE SULFATE, BROMHEXINE HYDROCHLORIDE, AND ETOPHYLLINE IN PHARMACEUTICAL DOSAGE FORM

Objective: The study aimed to development and validation of simple, precise, and reliable high-performance thin-layer chromatography (HPTLC) for the determination of terbutaline sulfate (TBS), bromhexine hydrochloride (BRH), and etophylline (ETP) in pharmaceutical dosage form. Methods: A simple, precise, rapid, and accurate HPTLC method was developed for the estimation of TBS, BRH, and ETP in pharmaceutical dosage form. Pre-coated silica gel G60 F254 aluminum sheet (10 cm2×10 cm2 and thickness 0.2 mm) was used as stationary phase while mobile phase consisting of benzene: methanol:glacial acetic acid 8:0.5:1.5 v/v/v detection at 275 nm. The present method had validated according to ICH guidelines. Results: Migration distance found 80 mm at 275 nm. The retention factor found to be 0.24, 0.57, and 0.68, respectively. The detector response was linear in the concentration range of 60–210 ng/band, 2400–8400 ng/band, and 96–336 ng/band, respectively. The linear regression equation being Y=32.20x−562.9, Y=11.79x−1711, and Y=1.756x−5636, respectively. The limit of detection for TBS 0.677 μg, for BRH 8.123 μg, and for ETP 57.915 μg and limit of quantification to be 2.053, 24.617, and 175.5 μg, respectively, were found. The developed method validated by ICH guideline, i.e., accuracy, precision, robustness, specificity, and system suitability. Conclusion: In this study, we had developed a simple, fast, and reliable HPTLC method for the determination of TBS, BRH, and ETP in pharmaceutical dosage form