Chitosan Nanoparticles as a Mucoadhesive Drug Delivery System for Ocular Administration.

Pharmaceutical approaches based on nanotechnologies and the development of eye drops composed of the mucoadhesive polymers chitosan and hyaluronic acid are emerging strategies for the efficient treatment of ocular diseases. These innovative nanoparticulate systems aim to increase drugs' bioavailability at the ocular surface. For the successful development of these systems, the evaluation of mucoahesiveness (the interaction between the ocular delivery system and mucins present on the eye) is of utmost importance. In this context, the aim of the present work was to investigate the mucoadhesivity of a novel nanoparticle eye drop formulation containing an antibiotic (ceftazidime) intended to treat eye infections. Eye drop formulations comprised a polymer (hydroxypropyl) methyl cellulose (HPMC) 0.75% (w/v) in an isotonic solution incorporating chitosan/sodium tripolyphosphate (TPP)-hyaluronic acid-based nanoparticles containing ceftazidime. The viscosity of the nanoparticles, and the gels incorporating the nanoparticles were characterized in contact with mucin at different mass ratios, allowing the calculation of the rheological synergism parameter (∆η). Results showed that at different nanoparticle eye formulation:mucin weight ratios, a minimum in viscosity occurred which resulted in a negative rheological synergism. Additionally, the results highlighted the mucoadhesivity of the novel ocular formulation and its ability to interact with the ocular surface, thus increasing the drug residence time in the eye. Moreover, the in vitro release and permeation studies showed a prolonged drug release profile from the chitosan/TPP-hyaluronic acid nanoparticles gel formulation. Furthermore, the gel formulations were not cytotoxic on ARPE-19 and HEK293T cell lines, evaluated by the metabolic and membrane integrity tests. The formulation was stable and the drug active, as shown by microbiological studies. In conclusion, chitosan/TPP-hyaluronic acid nanoparticle eye drop formulations are a promising platform for ocular drug delivery with enhanced mucoadhesive properties.

Covid-19 rapid test by combining a Random Forest-based web system and blood tests.

The disease caused by the new type of coronavirus, Covid-19, has posed major public health challenges for many countries. With its rapid spread, since the beginning of the outbreak in December 2019, the disease transmitted by SARS-CoV-2 has already caused over 2 million deaths to date. In this work, we propose a web solution, called Heg.IA, to optimize the diagnosis of Covid-19 through the use of artificial intelligence. Our system aims to support decision-making regarding to diagnosis of Covid-19 and to the indication of hospitalization on regular ward, semi-ICU or ICU based on decision a Random Forest architecture with 90 trees. The main idea is that healthcare professionals can insert 41 hematological parameters from common blood tests and arterial gasometry into the system. Then, Heg.IA will provide a diagnostic report. The system reached good results for both Covid-19 diagnosis and to recommend hospitalization. For the first scenario we found average results of accuracy of 92.891%±0.851, kappa index of 0.858 ± 0.017, sensitivity of 0.936 ± 0.011, precision of 0.923 ± 0.011, specificity of 0.921 ± 0.012 and area under ROC of 0.984 ± 0.003. As for the indication of hospitalization, we achieved excellent performance of accuracies above 99% and more than 0.99 for the other metrics in all situations. By using a computationally simple method, based on the classical decision trees, we were able to achieve high diagnosis performance. Heg.IA system may be a way to overcome the testing unavailability in the context of Covid-19.Communicated by Ramaswamy H. Sarma.