Therapeutic potential of RNA-enriched extracellular vesicles: The next generation in RNA delivery via biogenic nanoparticles.

Exosomes are extracellular vesicles (EVs) (∼50-150 nm) that have emerged as promising vehicles for therapeutic applications and drug delivery. These membrane-bound particles, released by all actively dividing cells, have the ability to transfer effector molecules, including proteins, RNA, and even DNA, from donor cells to recipient cells, thereby modulating cellular responses. RNA-based therapeutics, including microRNAs, messenger RNAs, long non-coding RNAs, and circular RNAs, hold great potential in controlling gene expression and treating a spectrum of medical conditions. RNAs encapsulated in EVs are protected from extracellular degradation, making them attractive for therapeutic applications. Understanding the intricate biology of cargo loading and transfer within EVs is pivotal to unlocking their therapeutic potential. This review discusses the biogenesis and classification of EVs, methods for loading RNA into EVs, their advantages as drug carriers over synthetic-lipid-based systems, and the potential applications in treating neurodegenerative diseases, cancer, and viral infections. Notably, EVs show promise in delivering RNA cargo across the blood-brain barrier and targeting tumor cells, offering a safe and effective approach to RNA-based therapy in these contexts.

Development of crash prediction models by assessing the role of perpetrators and victims: a comparison of ANN & logistic model using historical crash data.

Road traffic injuries cost countries 3% of their annual GDP. In developing countries like India, every year around 150,000 people die on roads. The type of vehicles involved in a crash contribute majorly to the outcome of casualty (injury/death). Barring few studies, literature are less regarding the role of vehicle as perpetrator and victim on road crash fatalities. Historical crash data has been used in the present study to examine the role of vehicles (both as perpetrator & victim). The study reveals that victim's effect is more as compared to perpetrator/accused for determining the outcome of crash. Heavy vehicles as perpetrator, and self-hitting vehicles along with pedestrians as victims have higher fatality rates. Binary logistic regression and artificial neural network (ANN) has been utilized for developing prediction models. Binary logistic model predicted around 75% of outcomes correctly with default cut-off value (0.5). However, based on reported crash data, where 19% of total crashes lead to deaths, 0.19 has been proposed as cut-off value which increases the accuracy of the predictions. Accuracy of ANN technique directly depends on the number of crashes reported for a definite pair of perpetrator and victim and the type of validation technique used (Holdback/K-Fold) along with the type of hidden layer chosen for the study based on different types of sigmoid activation function. ROC curves in ANN suggest that the analysis can predict 75% of the outcomes which can be increased by deleting the pairs of vehicles which are present/have occurred in very less number. A comparison has been made between the two techniques based on their advantages and limitations. The developed models can be used as safety indicators based on composition of traffic flow on urban roads.