In situ cellular hitchhiking of nanoparticles for drug delivery.

Since the inception of the concept of "magic bullet", nanoparticles have evolved to be one of the most effective carriers in drug delivery. Nanoparticles improve the therapeutic efficacy of drugs offering benefits to treating various diseases. Unlike free drugs which freely diffuse and distribute through the body, nanoparticles protect the body from the drug by reducing non-specific interactions while also improving the drug's pharmacokinetics. Despite acquiring some FDA approvals, further clinical application of nanoparticles is majorly hindered by its limited ability to overcome biological barriers resulting in uncontrolled biodistribution and high clearance. The use of cell-inspired systems has emerged as a promising approach to overcome this challenge as cells are biocompatible and have improved access to tissues and organs. One of such is the hitchhiking of nanoparticles to circulating cells such that they are recognized as 'self' components evading clearance and resulting in site-specific drug delivery. In this review, we discuss the concept of nanoparticle cellular hitchhiking, highlighting its advantages, the principles governing the process and the challenges currently limiting its clinical translation. We also discuss in situ hitchhiking as a tool for overcoming these challenges and the considerations to be taken to guide research efforts in advancing this promising technology.

COVID-19 and its impacts: The situation in Niger republic.

COVID-19 being a public health emergency of international concern has emerged in most African countries including Niger. Niger, a landlocked country, is tasked with controlling the pandemic. However, of the big challenges the country faced is the fragility of healthcare system which posed limitations to the fight against the virus. The virus overwhelmed the fragile healthcare system which led to inaccessibility of quality healthcare to the citizens coupled with issues of flooding and economic recession that happened during the pandemic. The healthcare sectored has further been crippled by exposure and infection of the already insufficient healthcare workers. In addition to this, there was the burden of NTDs and other communicable and non-communicable diseases that subverted the country in the depths of difficulties. As per the predictions of World Bank, the poverty curve is likely to escalate due to the outrageous impacts of COVID-19. Adding on to this, the occurrence of natural disasters such as flooding has further stretched the country. It's no coincidence that the country would confront plethora of challenges amidst the second wave. Therefore, timely decision and necessary interventions are needed to strengthen the country's fight against the pandemic. However, this is only feasible when Nigerien government, international allies and other wealthy nations work closely to ensure that the challenges faced by the healthcare system are tackled.

Processed HIV prognostic dataset for control experiments.

This paper provides a control dataset of processed prognostic indicators for analysing drug resistance in patients on antiretroviral therapy (ART). The dataset was locally sourced from health facilities in Akwa Ibom State of Nigeria, West Africa and contains 14 attributes with 1506 unique records filtered from 3168 individual treatment change episodes (TCEs). These attributes include sex, before and follow-up CD4 counts (BCD4, FCD4), before and follow-up viral load (BRNA, FRNA), drug type/combination (DTYPE), before and follow-up body weight (Bwt, Fwt), patient response to ART (PR), and classification targets (C1-C5). Five (5) output membership grades of a fuzzy inference system ranging from very high interaction to no interaction were constructed to model the influence of adverse drug reaction (ADR) and subsequently derive the PR attribute (a non-fuzzy variable). The PR attribute membership clusters derived from a universe of discourse table were then used to label the classification targets as follows: C1=no interaction, C2=very low interaction, C3=low interaction, C4=high interaction, and C5=very high interaction. The classification targets are useful for building classification models and for detecting patients with ADR. This data can be exploited for the development of expert systems, for useful decision support to treatment failure classification [1] and effectual drug regimen prescription.