KG Hospital

Introduction: Cardiovascular disease (CVD) is a leading cause of morbidity and mortality worldwide and dyslipidemia is the major risk factor for CVD. Aim: The aim of the study was to assess the real-world treatment patterns associated with the use of rosuvastatin + ezetimibe combination therapy for lipid management. Methods: This retrospective multicentric, noncomparative, nonrandomized, observational real-world study was conducted at multiple healthcare centers in India. Patients aged ≥18 years (either sex), with uncontrolled dyslipidemia, familial hypercholesteremia, and mixed dyslipidemias, with persistently elevated levels of low-density lipoprotein cholesterol (LDL-C) despite receiving statin therapy were included. Fasting blood LDL-C was monitored in high, very high, and extreme-risk patients. Results: A total of 1465 patients were enrolled with a mean age of 60.87 years and men predominance (67.49%). Median (range) duration of hypercholesteremia of patients was 4.00 (0.40–19.50) years. The majority of patients were on moderate-intensity statin therapy (54.68%), followed by high-intensity statin (27.37%) and low-intensity statin (17.95%). The majority of patients taking moderate-intensity statins (47.38%) had LDL-C levels between 151 and 200 mg/dL, compared to those taking low (36.50%). Combination rosuvastatin + ezetimibe therapy showed a significant reduction in mean LDL-C levels of patients using low, moderate, and high-intensity statins with a mean difference of 100.63 mg/dL, 94.24 mg/dL, and 92.00 mg/dL ( P < 0.001 each) as well as a significant reduction in LDL-C levels, total cholesterol levels, and triglycerides levels ( P < 0.001 each). Conclusion: Rosuvastatin and ezetimibe therapy was an effective and well-tolerated lipid-lowering agent, offering higher efficacy, especially in the reduction of LDL-C levels.

Digital Down Conversion (DDC) is a fundamental process in software defined radio (SDR) and digital communications, which transforms intermediate frequency (IF) signals into baseband signals. In conventional DDC designs, multipliers and look-up tables are generally employed, both of which tend to increase hardware size and power consumption. In this paper we present a Dual-Mode CORDIC-Based DDC architecture which uses the CORDIC (Coordinate Rotation Digital Computer) algorithm to eliminate multipliers. This novel architecture combines a CORDIC-based Numerically Controlled Oscillator (NCO), a CORDIC mixer, and a Cascaded Integrator-Comb (CIC) filter with a finite impulse response (FIR) com pentation filter. The NCO provides two precision modes - an 8-stage low-latency mode for speed and a 16-stage high precision mode for accuracy, giving runtime reconfigurability according to design requirements. Simulations and analysis show that the design gives highly accurate frequency translation, is efficient in hardware utilization and fully eliminates the use of digital signal processing (DSP) blocks. Overall, the architecture proposed offers a low-power, low-cost, and recon figural solution ideal for modern SDR-based applications and flexible digital signal processing systems.

The fast-paced development of the data-intensive applications has changed the manner, in which organizations store, process, and analyse data. With the continued generation of large amounts of unstructured and semi-structured data in modern systems, relational databases are commonly not required to be flexible, scalable, and provide real-time responses. With this changing trend, the trend has given way to the emergence of NoSQL technologies with MongoDB as one of the most robust as well as popular document-oriented databases. The book has been developed in such a way that it can be used as a great resource by students, teachers, and practitioners who want to be well-grounded in the concepts of MongoDB and NoSQL. Starting with best tenets of NoSQL databases, the text discusses why non-relational databases are important in modern software structure. The chapters are logically structured to present the basic ideas, shed light on the hidden principles, and develop the practical knowledge at a certain pace. The readers are taken through critical areas such as the data modeling, CRUD operations, indexing, aggregation structures, sharding, replication and performance tuning as factors that are important in building efficient scalable and reliable database-driven applications. Understandings of MongoDB Shell, installation and configuration procedures, and generalized best practices in using the product in real-life situations are also presented in the book. Though the subject matter is quite technical, there are clear explanation of it and it comes with illustration, examples as well as articulated breakdowns. We will do so as we want to make sure that the learners acquire not only theoretical knowledge but also practical knowledge in working with MongoDB in a project, research and industrial setting with a high level of confidence. Hopefully, this book may become a helpful guide that enables a person to become curious, to develop a proper conceptual knowledge, and to become capable of fulfilling the capabilities of MongoDB and the NoSQL technologies. You may be on your way to modern database systems, or you may be expanding your knowledge base, either way, this text is supposed to put you on the right path, with sufficient clarity, accuracy, and intent.

In this big data world, large amount of information is generated regularly leading to major challenges like security and processing. Because of its unparalleled flexibility and scalability, cloud computing has become a crucial component due to the advancement of technology in recent years. Using machine learning techniques is a potential solution for solving these issues. In the constantly evolving world of cloud computing, protecting data and systems from illegal activity and unauthorized access is necessary. But these developments also bring with them serious security risks. A prominent benchmark in the intrusion detection space is the KDDCUP99 dataset. The goal in using XGBoost on this dataset proved to find patterns that could identify security risks. The utilisation of Amazon cloud capabilities and other cloud resources are used to trigger the model scalability and performance. The complexity of the dataset could be handled well due to this integration, which also XGBoost model a stable environment whereby to train and test. The XGBoost model showed outstanding efficiency and effectiveness when identifying significant security risks. Its incredible 99.17% accuracy rate illustrates its capacity to classify between beneficial and malicious behaviour in cloud environment. The model potential as a dependable tool for intrusion detection in cloud computing systems is highlighted by its high accuracy rate.

Disseminated granulomatous diseases, especially in TB-endemic regions, pose diagnostic challenges due to overlapping features with sarcoidosis. This case report highlights a 52-year-old woman managed with anti-tubercular therapy and corticosteroids after extensive investigations. The favorable outcome underscores the need for comprehensive diagnostics and tailored treatment in such cases.