Nanogels as novel drug nanocarriers for CNS drug delivery.

Nanogels are highly recognized as adaptable drug delivery systems that significantly contribute to improving various therapies and diagnostic examinations for different human diseases. These three-dimensional, hydrophilic cross-linked polymers have the ability to absorb large amounts of water or biological fluids. Due to the growing demand for enhancing current therapies, nanogels have emerged as the next-generation drug delivery system. They effectively address the limitations of conventional drug therapy, such as poor stability, large particle size, and low drug loading efficiency. Nanogels find extensive use in the controlled delivery of therapeutic agents, reducing adverse drug effects and enabling lower therapeutic doses while maintaining enhanced efficacy and patient compliance. They are considered an innovative drug delivery system that highlights the shortcomings of traditional methods. This article covers several topics, including the involvement of nanogels in the nanomedicine sector, their advantages and limitations, ideal properties like biocompatibility, biodegradability, drug loading capacity, particle size, permeability, non-immunological response, and colloidal stability. Additionally, it provides information on nanogel classification, synthesis, drug release mechanisms, and various biological applications. The article also discusses barriers associated with brain targeting and the progress of nanogels as nanocarriers for delivering therapeutic agents to the central nervous system.

A cognitive IoT-based framework for effective diagnosis of COVID-19 using multimodal data.

The COVID-19 emerged at the end of 2019 and has become a global pandemic. There are many methods for COVID-19 prediction using a single modality. However, none of them predicts with 100% accuracy, as each individual exhibits varied symptoms for the disease. To decrease the rate of misdiagnosis, multiple modalities can be used for prediction. Besides, there is also a need for a self-diagnosis system to narrow down the risk of virus spread in testing centres. Therefore, we propose a robust IoT and deep learning-based multi-modal data classification method for the accurate prediction of COVID-19. Generally, highly accurate models require deep architectures. In this work, we introduce two lightweight models, namely CovParaNet for audio (cough, speech, breathing) classification and CovTinyNet for image (X-rays, CT scans) classification. These two models were identified as the best unimodal models after comparative analysis with the existing benchmark models. Finally, the obtained results of the five independently trained unimodal models are integrated by a novel dynamic multimodal Random Forest classifier. The lightweight CovParaNet and CovTinyNet models attain a maximum accuracy of 97.45% and 99.19% respectively even with a small dataset. The proposed dynamic multimodal fusion model predicts the final result with 100% accuracy, precision, and recall, and the online retraining mechanism enables it to extend its support even in a noisy environment. Furthermore, the computational complexity of all the unimodal models is minimized tremendously and the system functions effectively with 100% reliability even in the absence of any one of the input modalities during testing.

Peristaltic-Like Motion of the Human Fetal Right Ventricle and its Effects on Fluid Dynamics and Energy Dynamics.

In both adult human and canine, the cardiac right ventricle (RV) is known to exhibit a peristaltic-like motion, where RV sinus (inflow region) contracts first and the infundibulum (outflow region) later, in a wave-like contraction motion. The delay in contraction between the sinus and infundibulum averaged at 15% of the cardiac cycle and was estimated to produce an intra-ventricular pressure difference of 15 mmHg. However, whether such a contractile motion occurs in human fetuses as well, its effects on hemodynamics remains unknown, and are the subject of the current study. Hemodynamic studies of fetal hearts are important as previous works showed that healthy cardiac development is sensitive to fluid mechanical forces. We performed 4D clinical ultrasound imaging on eight 20-weeks old human fetuses. In five fetal RVs, peristaltic-like contractile motion from the sinus to infundibulum ("forward peristaltic-like motion") was observed, but in one RV, peristaltic-like motion was observed from the infundibulum to sinus ("reversed peristaltic-like motion"), and two RVs contraction delay could not be determined due to poor regression fit. Next, we performed dynamic-mesh computational fluid dynamics simulations with varying extents of peristaltic-like motions for three of the eight RVs. Results showed that the peristaltic-like motion did not affect flow patterns significantly, but had significant influence on energy dynamics: increasing extent of forward peristaltic-like motion reduced the energy required for movement of fluid out of the heart during systolic ejection, while increasing extent of reversed peristaltic-like motion increased the required energy. It is currently unclear whether the peristaltic-like motion is an adaptation to reduce physiological energy expenditure, or merely an artefact of the cardiac developmental process.