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Respiratory syncytial virus (RSV) remains a leading cause of infant morbidity and mortality, with the highest burden concentrated in low- and middle-income countries (LMICs). Existing preventive options, including long-acting monoclonal antibodies, can be constrained by cost, logistics, and access, leaving many high-risk infants unprotected. This article is a structured narrative review summarizing clinical efficacy, immunological mechanisms, safety and public health implications of maternal RSV immunization for preventing infant RSV lower respiratory tract infection (LRTI), with an explicit implementation focus for LMICs. Evidence was identified through targeted searches of major biomedical databases and prioritized by clinical relevance and policy importance, including phase III efficacy trials, regulatory and technical documents, post-authorization safety signals, and modelling studies evaluating potential impact in LMICs. Across the evidence base, maternal vaccination induces robust RSV-neutralizing IgG responses and efficient transplacental antibody transfer, providing passive protection during the first months of life when RSV risk is highest. In phase III data, maternal RSV vaccination demonstrated high efficacy against severe medically attended RSV LRTI in early infancy (e.g., up to 81.8
Internet of Medical Things (IoMT) continues to be increasingly integrated in modern healthcare systems as a form of realtime specialist healthcare along with data exchange among medical equipment. Nevertheless, the high rate of interconnected medical equipment development also preconditions the emergence of a range of cybersecurity threats that can alarm patient safety and confidentiality of their data. This study suggests a hybrid intrusion detection agenda to solve these concerns, a pool of machine learning and deep learning processes. The suggested system combines the feature selection based on a Grey Wolf Optimizer (GWO), a Random Forest classifier to determine known attacks, and an autoencoder model to detect unknown attacks or zero-day attacks. GWO algorithm eliminates redundant features in the network traffic dataset without losing significant information needed to do the accurate detection. Random Forest model is applied to classify known patterns of attack with categorized data, and the autoencoder is trained to learn the normal work of the IoMT traffic and identify anomalies without the necessity of labelled attack samples. On the whole, the hybrid framework advances the reliability and effectiveness of intrusion detection on the IoMT setting through the fusion of the feature optimization and machine learning and anomaly detection algorithms. The suggested method can assist in enhancing the security of healthcare networks and assist in safer disposition of IoMT systems
Localized cellular transport and micro-vessel friction are difficult to map using conventional single-phase and two-phase blood flow models, particularly in pathological situations like dengue virus infection. In order to study cellular interactions and pressure decreases, this research proposes a comprehensive three-phase computational framework to model hemodynamics in micro-vessels, especially renal capillaries. Three interacting layers make up the model of whole blood: a pure Newtonian plasma wall layer (a≤r≤R), a Newtonian leukocyte/platelet intermediate phase (a≤r≤b), and a central non-Newtonian power-law Red Blood Cell (RBC) core (0≤r≤a). Under steady-state, axisymmetric laminar flow assumptions, cylindrical coordinates are used to translate and solve the governing equations of continuity and motion. Due to the no-slip situation, the velocity shows a blunt shape in the central core, a steeper decrease in the WBC-rich layer, and a quick drop to zero at the capillary wall. The highest Wall Shear Stress 𝜏𝑤=107.94 Pascal is driven by a strain rate that is almost negligible at the centerline and rises dramatically in the plasma layer close to the wall.An increase in hematocrit H raises the effective viscosity, causing a proportional decrease in axial velocity at all radial locations. An 8-day patient timeline shows that Hematocrit H, Mean Blood Pressure Drop (MBPD), and Capillary Drop (RCBPD) move in perfect synchronization, all peaking on Day 5 (H=43.59%), (MBPD = 2923.91 Pascal), (RCBPD= 2392.53 Pascal).
Face recognition systems have been propelled to new heights by the deep learning. You can now see them everywhere in regulating access and policing crowds, monitoring attendance. The majority of studies narrow down to making such systems more precise and solid. However, frankly, the security and integrity of the training data upon which they work is not discussed sufficiently by people. Deep learning systems are heavily reliant on their data, and, therefore, in case an individual alters or poisons the data, the entire machine can silently crash. Credibility flies down the drain. DeepAudit comes at that point. It is a framework that ensures that face recognition data is not tampered and poisoned. As opposed to tampering with the model or altering its learning process, DeepAudit places tiny, imperceptible watermarks in the facial embeddings during enrollment. These cues do not disrupt performance of recognition in any way. However, when you have to verify the integrity of your data or audit your dataset in the future, those watermarks are available. Here is the mechanism: First during enrollment, a neural net converts facial images into numberical representations. DeepAudit watermarks each of them and stores them safely. Then when recognition is required, the system simply compares live faces to such stored embeddings no muss no messing with the watermark. The watermark will not interfere until it is necessary to check data integrity or audit it. The tests indicate that these embedded watermarks do not affect recognition accuracy or similarity scores. Nevertheless, DeepAudit can detect tampering of the data by an individual. The main takeaway? To be serious about biometric security, it is essential that you are concerned about the integrity of data, and not plain accuracy. DeepAudit provides you with a scalable and practical means of ensuring that face recognition systems are trustworthy on the inside
This project focuses on the creation and implementation of river cleaning systems, aiming to address the issue of vine and leaf accumulation commonly observed on river surfaces globally. Solar-based river cleaning system is an eco-friendly solution designed to remove floating waste from water bodies using renewable energy. It operates using solar panels, reducing electricity costs and making it suitable for rural and farming areas. The system helps maintain clean water in farm ponds, improving water quality for irrigation and livestock. It requires low maintenance and works automatically, making it farmer-friendly. Overall, it promotes sustainable agriculture and environmental protection. In cities the problem of water thyme is getting more problematic for Municipal Corporation, hence this project will help in resolving the problem. The primary objective of this project is the design and deployment of a solar powered river cleaning system to combat pollution and decrease reliance on conventional electricity sources. The machine's functionality hinges on solar panels, ensuring both pollution reduction and a decreased demand for electricity. Various adjustments and enhancements to the existing river cleaning system form a crucial aspect of this project, with a particular emphasis on the pivotal role played by motors and conveyor belts in facilitating the cleaning process.
