Prenatal Cannabinoid Exposure Elicits Memory Deficits Associated with Reduced PSA-NCAM Expression, Altered Glutamatergic Signaling, and Adaptations in Hippocampal Synaptic Plasticity.

Cannabis is now one of the most commonly used illicit substances among pregnant women. This is particularly concerning since developmental exposure to cannabinoids can elicit enduring neurofunctional and cognitive alterations. This study investigates the mechanisms of learning and memory deficits resulting from prenatal cannabinoid exposure (PCE) in adolescent offspring. The synthetic cannabinoid agonist WIN55,212-2 was administered to pregnant rats, and a series of behavioral, electrophysiological, and immunochemical studies were performed to identify potential mechanisms of memory deficits in the adolescent offspring. Hippocampal-dependent memory deficits in adolescent PCE animals were associated with decreased long-term potentiation (LTP) and enhanced long-term depression (LTD) at hippocampal Schaffer collateral-CA1 synapses, as well as an imbalance between GluN2A- and GluN2B-mediated signaling. Moreover, PCE reduced gene and protein expression of neural cell adhesion molecule (NCAM) and polysialylated-NCAM (PSA-NCAM), which are critical for GluN2A and GluN2B signaling balance. Administration of exogenous PSA abrogated the LTP deficits observed in PCE animals, suggesting PSA mediated alterations in GluN2A- and GluN2B- signaling pathways may be responsible for the impaired hippocampal synaptic plasticity resulting from PCE. These findings enhance our current understanding of how PCE affects memory and how this process can be manipulated for future therapeutic purposes.

An Improved Epidemiological Model for the Underprivileged People in the Contemporary Pandemics.

In this work, we introduce an improved form of the basic SEIRD model based on Python simulation for the troublesome people who are oblivious about the contemporary pandemics due to diverse social impediments, especially those economically underprivileged. In the extant epidemiological models, some unorthodox issues are yet to be considered, such as poverty, illiteracy, and carelessness towards health issues, significantly influencing the data modeling. Our focus is to overcome these issues by adding two more branches, for instance, uncovered and apathetic people, which significantly influence the practical purposes. For the data simulation, we have used the Python-based algorithm that trains the desired system based on a set of real-time data with the proposed model and provides predicted data with a certain level of accuracy. Comparative discussions, statistical error analysis, and correlation-regression analysis have been introduced to validate the proposed epidemiological model. To show the numerical evidence, the investigation comprised the figurative and tabular modes for both real-time and predicted data. Finally, we discussed some concluding remarks based on our findings.