ALL-Net: integrating CNN and explainable-AI for enhanced diagnosis and interpretation of acute lymphoblastic leukemia.

This article presents a new model, ALL-Net, for the detection of acute lymphoblastic leukemia (ALL) using a custom convolutional neural network (CNN) architecture and explainable Artificial Intelligence (XAI). A dataset consisting of 3,256 peripheral blood smear (PBS) images belonging to four classes-benign (hematogones), and the other three Early B, Pre-B, and Pro-B, which are subtypes of ALL, are utilized for training and evaluation. The ALL-Net CNN is initially designed and trained on the PBS image dataset, achieving an impressive test accuracy of 97.

Enhancing machine learning-based forecasting of chronic renal disease with explainable AI.

Chronic renal disease (CRD) is a significant concern in the field of healthcare, highlighting the crucial need of early and accurate prediction in order to provide prompt treatments and enhance patient outcomes. This article presents an end-to-end predictive model for the binary classification of CRD in healthcare, addressing the crucial need for early and accurate predictions to enhance patient outcomes. Through hyperparameter optimization using GridSearchCV, we significantly improve model performance.

An intranasal nanoparticle STING agonist protects against respiratory viruses in animal models.

Respiratory viral infections cause morbidity and mortality worldwide. Despite the success of vaccines, vaccination efficacy is weakened by the rapid emergence of viral variants with immunoevasive properties. The development of an off-the-shelf, effective, and safe therapy against respiratory viral infections is thus desirable.

A Machine Learning-Based Anomaly Prediction Service for Software-Defined Networks.

Software-defined networking (SDN) has gained tremendous growth and can be exploited in different network scenarios, from data centers to wide-area 5G networks. It shifts control logic from the devices to a centralized entity (programmable controller) for efficient traffic monitoring and flow management. A software-based controller enforces rules and policies on the requests sent by forwarding elements; however, it cannot detect anomalous patterns in the network traffic.

Blockchain Empowered Federated Learning Ecosystem for Securing Consumer IoT Features Analysis.

Resource constraint Consumer Internet of Things (CIoT) is controlled through gateway devices (e.g., smartphones, computers, etc.

Development and On-Field Deployment of a Mobile-Based Application 'MoSQuIT' for Malaria Surveillance in International Border Districts of Northeast India-Challenges and Opportunities.

The conventional paper-based system for malaria surveillance is time-consuming, difficult to track and resource-intensive. Few digital platforms are in use but wide-scale deployment and acceptability remain to be seen. To address this issue, we created a malaria surveillance mobile app that offers real-time data to stakeholders and establishes a centralised data repository.

Controlling the Outbreak of COVID-19: A Noncooperative Game Perspective.

is a global epidemic. Till now, there is no remedy for this epidemic. However, isolation and social distancing are seemed to be effective preventive measures to control this pandemic.

Single-dose intranasal vaccination elicits systemic and mucosal immunity against SARS-CoV-2.

Despite remarkable progress in the development and authorization of vaccines against severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), there is a need to validate vaccine platforms for broader application. The current intramuscular vaccines are designed to elicit systemic immunity without conferring mucosal immunity in the nasal compartment, which is the first barrier that SARS-CoV-2 virus breaches before dissemination to the lung. We report the development of an intranasal subunit vaccine that uses lyophilized spike protein and liposomal STING agonist as an adjuvant.

FcεRIγ-negative NK cells persist in vivo and enhance efficacy of therapeutic monoclonal antibodies in multiple myeloma.

Monoclonal antibodies (mAbs) are a central component of therapy for hematologic malignancies. Widely used mAb agents in multiple myeloma (MM) include daratumumab and elotuzumab. However, not all patients respond to these agents, and resistance is a significant clinical issue.

Nasal delivery of an IgM offers broad protection from SARS-CoV-2 variants.

Resistance represents a major challenge for antibody-based therapy for COVID-19. Here we engineered an immunoglobulin M (IgM) neutralizing antibody (IgM-14) to overcome the resistance encountered by immunoglobulin G (IgG)-based therapeutics. IgM-14 is over 230-fold more potent than its parental IgG-14 in neutralizing SARS-CoV-2.

Single-dose intranasal vaccination elicits systemic and mucosal immunity against SARS-CoV-2.

A safe and durable vaccine is urgently needed to tackle the COVID19 pandemic that has infected >15 million people and caused >620,000 deaths worldwide. As with other respiratory pathogens, the nasal compartment is the first barrier that needs to be breached by the SARS-CoV-2 virus before dissemination to the lung. Despite progress at remarkable speed, current intramuscular vaccines are designed to elicit systemic immunity without conferring mucosal immunity.

Simulating yield response of rice to salinity stress with the AquaCrop model.

The FAO AquaCrop model has been widely applied throughout the world to simulate crop responses to deficit water applications. However, its application to saline conditions is not yet reported, though saline soils are common in coastal areas. In this study, we parameterized and tested AquaCrop to simulate rice yield under different salinity regimes.