Physicochemical properties and cell viability of shrimp chitosan films as affected by film casting solvents. I-potential use as wound dressing

: Chitosan solubility in aqueous organic acids has been widely investigated. However, most of the previous works have been done with plasticized chitosan films and using acetic acid as the film casting solvent. In addition, the properties of these films varied among studies, since they are influenced by different factors such as the chitin source used to produce chitosan, the processing variables involved in the conversion of chitin into chitosan, chitosan properties, types of acids used to dissolve chitosan, types and amounts of plasticizers and the film preparation method. Therefore, this work aimed to prepare chitosan films by the solvent casting method, using chitosan derived from Litopenaeus vannamei shrimp shell waste, and five different organic acids (acetic, lactic, maleic, tartaric, and citric acids) without plasticizer, in order to evaluate the effect of organic acid type and chitosan source on physicochemical properties, degradation and cytotoxicity of these chitosan films. The goal was to select the best suited casting solvent to develop wound dressing from shrimp chitosan films. Shrimp chitosan films were analyzed in terms of their qualitative assessment, thickness, water vapor permeability (WVP), water vapor transmission rate (WVTR), wettability, tensile properties, degradation in phosphate buffered saline (PBS) and cytotoxicity towards human fibroblasts using the resazurin reduction method. Regardless of the acid type employed in film preparation, all films were transparent and slightly yellowish, presented homogeneous surfaces, and the thickness was compatible with the epidermis thickness. However, only the ones prepared with maleic acid presented adequate characteristics of WVP, WVTR, wettability, degradability, cytotoxicity and good tensile properties for future application as a wound dressing material. The findings of this study contributed not only to select the best suited casting solvent to develop chitosan films for wound dressing but also to normalize a solubilization protocol for chitosan, derived from Litopenaeus vannamei shrimp shell waste, which can be used in the pharmaceutical industry.info:eu-repo/semantics/publishedVersio

Human Hantavirus Infection, Brazilian Amazon

Tropical Medicine Foundation of Amazonas. Manaus, AM, Brasil / Amazonas State University. Manaus, AM, Brasil / Nilton Lins University Center. Manaus, AM, Brasil.Tropical Medicine Foundation of Amazonas. Manaus, AM, Brasil / Amazonas State University. Manaus, AM, Brasil / Nilton Lins University Center. Manaus, AM, Brasil / University of Brasília. Brasília, DF, Brasil.Tropical Medicine Foundation of Amazonas. Manaus, AM, Brasil.Tropical Medicine Foundation of Amazonas. Manaus, AM, Brasil / Amazonas State University. Manaus, AM, Brasil / Nilton Lins University Center. Manaus, AM, Brasil.Health Surveillance Foundation. Manaus, AM, Brasil.Ministry of Health. Brasília, DF, Brazil.Ministério da Saúde. Secretaria de Vigilância em Saúde. Instituto Evandro Chagas. Belém, PA, Brasil.Ministério da Saúde. Secretaria de Vigilância em Saúde. Instituto Evandro Chagas. Belém, PA, Brasil.Ministério da Saúde. Secretaria de Vigilância em Saúde. Instituto Evandro Chagas. Belém, PA, Brasil.Tropical Medicine Foundation of Amazonas. Manaus, AM, Brasil / Amazonas State University. Manaus, AM, Brasil / Nilton Lins University Center. Manaus, AM, Brasil / University of Brasília. Brasília, DF, Brasil

Validation of Deep Learning techniques for quality augmentation in diffusion MRI for clinical studies

The objective of this study is to evaluate the efficacy of deep learning (DL) techniques in improving the quality of diffusion MRI (dMRI) data in clinical applications. The study aims to determine whether the use of artificial intelligence (AI) methods in medical images may result in the loss of critical clinical information and/or the appearance of false information. To assess this, the focus was on the angular resolution of dMRI and a clinical trial was conducted on migraine, specifically between episodic and chronic migraine patients. The number of gradient directions had an impact on white matter analysis results, with statistically significant differences between groups being drastically reduced when using 21 gradient directions instead of the original 61. Fourteen teams from different institutions were tasked to use DL to enhance three diffusion metrics (FA, AD and MD) calculated from data acquired with 21 gradient directions and a b-value of 1000 s/mm2. The goal was to produce results that were comparable to those calculated from 61 gradient directions. The results were evaluated using both standard image quality metrics and Tract-Based Spatial Statistics (TBSS) to compare episodic and chronic migraine patients. The study results suggest that while most DL techniques improved the ability to detect statistical differences between groups, they also led to an increase in false positive. The results showed that there was a constant growth rate of false positives linearly proportional to the new true positives, which highlights the risk of generalization of AI-based tasks when assessing diverse clinical cohorts and training using data from a single group. The methods also showed divergent performance when replicating the original distribution of the data and some exhibited significant bias. In conclusion, extreme caution should be exercised when using AI methods for harmonization or synthesis in clinical studies when processing heterogeneous data in clinical studies, as important information may be altered, even when global metrics such as structural similarity or peak signal-to-noise ratio appear to suggest otherwise