Kanyakumari Government Medical College

Budd- chiari syndrome is a rare but serious vascular disorder characterized by obstruction of hepatic venous outflow at the level of hepatic veins, inferior vena cava, or both. This obstruction leads to increased hepatic sinusoidal pressure, hepatomegaly, ascites and progressive hepatic liver dysfunction. The condition may be acute, subacute or chronic and its etiology involves prothrombic states, inherited thrombophilias etc. Diagnosis relies on clinical suspicion supported by doppler ultrasonography, computed tomography or magnetic resonance imaging showing impaired venous drainage. Management includes anticoagulation therapy, endovascular interventions, transjugular intra hepatic portosystemic shunt and in advanced cases, liver transplantation.

This investigation presents a comprehensive investigation into the lubricating wear properties of Ag-Mg alloys, focusing on pin-on-disc wear tests conducted under various lubrication conditions, including gear oil, filtered water, and SAE 80w, as well as Vickers hardness testing for material hardness characterization. The researchers employed cutting-edge techniques such as SEM and XRD to meticulously analyze the size, shape, morphology, and composition of the alloys. To establish connections between the coefficient of friction and wear rate data, the study further examines the underlying wear mechanisms using SEM and XRD. An in-depth examination of surface wear characteristics and the identification of tribo-layers provides crucial insights into the material changes during the wear process. In addition to microstructural investigation, tribo-layer thickness evaluation, and phase identification within the layers, the researchers also explored cross-sectional wear aspects. Furthermore, surface profile metry methods were utilized to create detailed 3D surface maps that accurately capture the post-wear test surface changes. To gain a comprehensive understanding of the tribological behavior of Ag-Mg alloys, this research also undertakes a critical comparative analysis of the acquired data with existing literature on Mg alloys. The findings from this study offer valuable knowledge for enhancing the performance of Ag-Mg alloys in engineering applications by addressing wear-related issues.

Cyberthreats and hazardous application usage have increased due to growing use of internet services and apps over computer networks, endangering customer privacy and service availability. To address this, a hybrid optimized attention residual net security framework for network Intrusion Detection System (IDS) is employed in digital infrastructure to identify abnormal traffic behaviors that conventional firewalls are unable to detect. In IDS, the input data is preprocessed to eliminate noisy and irrelevant data. The raw data is preprocessed by comprising encoding and normalization techniques. It ensures resolution and makes it suitable for the DL model. A novel framework is an attention residual net that focuses on prominent features in the input data and eliminates vanishing gradient issues. The performance of the attention residual net is further enhanced by utilizing the Dragon Fly Optimization (DFO) algorithm that finely tunes its hyperparameters. The hybrid approach is implemented in Python and combines the efficiency of DL-based hybrid Attention ResNet and DFO algorithms to attain a robust and secure IDS, thus making it feasible for safeguarding digital infrastructure.

Cloud computing explosive growth have sparked serious worries about safe transfer, storage and restricted access of private information. Conventional encryption techniques frequently find it difficult to strike a balance between computational overhead, efficiency and confidentiality, especially in multi-user settings where security and performance are equally crucial. This work offers safe and effective cloud architecture by a Blockchain integrated Elliptic Curve–AES Hybrid Model (BI-ECAES) to overcome these difficulties. Blockchain integration guarantees transparent and impenetrable key management and access control, while the proposed paradigm synergistically blends symmetric AES encryption with lightweight ECC-based asymmetric encryption. This hybrid strategy greatly lowers the risks of identity theft, data leakage and unauthorised access by ensuring end-to-end confidentiality with effective key distribution. The proposed BIECAES have the key generation time of 0.6 s, encryption time of 0.01 s and decryption time of 0.12 s respectively. Comparing the BI-ECAES framework to traditional methods, experimental research shows that it achieves improved scalability, lower encryption latency and higher attack resistance.

The rapid advancement of wearable devices has driven the need for highly efficient, low-noise amplifiers (LNAs).This study presents a novel LNA architecture leveraging CMOS inverter topologies to achieve superior performance in gain, noise reduction, and energy efficiency.The key innovation lies in the optimized use of CMOS inverters, which enhance signal amplification while minimizing power consumption.Compared to existing designs, the proposed LNA exhibits a reduced noise figure (NF) of 2.6 dB, a high voltage gain of 30 dB, and operates at ultra-low power levels, making it ideal for energy-constrained applications.Fabricated using standard complementary metal-oxide semiconductor (CMOS) technology, the LNA is optimized for pulsed nanoampere-level currents, such as those generated by Si-based light-emitting sensors.The circuit is meticulously designed to mitigate parasitic effects, employing a layout-aware optimization framework to ensure robust performance under varying conditions.Operating at a low supply voltage of 1.2 V with a current requirement of just 1 A, the LNA is particularly well-suited for biomedical and wearable electronics.To validate its performance, post-layout simulations assess key parameters, demonstrating significant improvements over state-of-the-art designs.Comparative analysis highlights the LNA's efficiency in signal integrity and low-power operation, making it an optimal choice for next-generation sensor networks and medical applications.By addressing critical design challenges, this study provides a comprehensive analysis of an advanced LNA architecture, offering new insights into the development of high-performance amplifiers for modern electronic systems.

Breast cancer remains a major worldwide health concern, with current targeted therapies often showing limited effectiveness. Treatment options vary from invasive procedures like surgery to less intrusive methods such as radiotherapy and chemotherapy. To address these constraints, researchers are engaged in the creation of innovative anti-cancer drugs. One promising compound is (E)-4-(1-(2-(4-(4-chlorophenyl)thiazol-2-yl)hydrazono)ethyl)phenol (CTP), which has demonstrated efficacy as an anti-breast cancer agent. The synthesized compound CTP underwent optimization utilizing the Density Functional Theory (DFT) computation applying 6-311 + G(d,p) basis set. The optimized geometry closely matched experimental data, affirming the accuracy of the computational model. Natural Bond Orbital (NBO) analysis provided through comprehension of both intra- and intermolecular interactions. The vibrational properties of CTP were extensively analyzed using FT-IR and Raman spectroscopy. Through the use of Hirshfeld surface analysis, intermolecular interactions within crystal structures were revealed. The electronic structure and non-covalent interactions within CTP were further examined using Localized Orbital Locator (LOL) analyses and Electron Localization Function (ELF). ADMET profiling evaluated the drug-like properties of CTP, while molecular docking with protein 4ZVM predicted key ligand-protein binding sites. The compound CTP was further evaluated in vitro for its potential anticancer properties, specifically targeting the MCF7 breast cancer cell line. The observed red shift in C-S stretching and the broad, weak C-H band provided compelling evidence for both intra- and intermolecular interactions. Negative binding energies from docking studies indicated potential inhibition of human breast cancer by CTP. This comprehensive analysis systematically elucidated the molecular characteristics and interactions of CTP, highlighting its potential as a therapeutic agent for breast cancer.