Preparation and Phytotoxicity Evaluation of Cellulose Acetate Nanoparticles.

The use of biocompatible and low-cost polymeric matrices to produce non-phytotoxic nanoparticles for delivery systems is a promising alternative for good practices in agriculture management and biotechnological applications. In this context, there is still a lack of studies devoted to producing low-cost polymeric nanoparticles that exhibit non-phytotoxic properties. Among the different polymeric matrices that can be used to produce low-cost nanoparticles, we can highlight the potential application of cellulose acetate, a natural biopolymer with biocompatible and biodegradable properties, which has already been used as fibers, membranes, and films in different agricultural and biotechnological applications.

Paracoccidioidomycosis screening diagnosis by FTIR spectroscopy and multivariate analysis.

Paracoccidioidomycosis (PCM) is a systemic mycosis with high incidence in Latin America, caused by species of the genus Paracoccidioides spp. Brazil is considered to be the endemic center of this disease, which is identified as the eighth cause of mortality from chronic infectious disease in the country. There are several specific diagnostic methods in PCM, such as microbiological, immunological, histopathological, and molecular.

A new strategy for canine visceral leishmaniasis diagnosis based on FTIR spectroscopy and machine learning.

Visceral leishmaniasis is a neglected disease caused by protozoan parasites of the genus Leishmania. The successful control of the disease depends on its accurate and early diagnosis, which is usually made by combining clinical symptoms with laboratory tests such as serological, parasitological, and molecular tests. However, early diagnosis based on serological tests may exhibit low accuracy due to lack of specificity caused by cross-reactivities with other pathogens, and sensitivity issues related, among other reasons, to disease stage, leading to misdiagnosis.

Soybean seed vigor discrimination by using infrared spectroscopy and machine learning algorithms.

A novel approach to distinguish soybean seed vigor based on Fourier transform infrared spectroscopy (FTIR) associated with chemometric methods is presented. Batches with high and low vigor soybean seeds were analyzed. Support vector machine (SVM), K-nearest neighbors (KNN), and discriminant analysis were applied to the raw spectral and reduced-dimensionality data from PCA (principal component analysis).