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. Here, we provided a simple and low-cost route to produce cellulose acetate nanoparticles (CA-NPs), by modified emulsification solvent evaporation technique, with a main diameter of around 200 nm and a spherical and smooth morphology for potential use as agrochemical nanocarriers. The non-phytotoxic properties of the produced cellulose acetate nanoparticles were proved by performing a plant toxic test by Allium cepa assay. The cytotoxicity and genotoxicity tests allowed us to evaluate the mitotic process, chromosomal abnormalities, inhibition/delay in root growth, and micronucleus induction. In summary, the results demonstrated that CA-NPs did not induce phytotoxic, cytotoxic, or genotoxic effects, and they did not promote changes in the root elongation, germination or in the mitotic, chromosomal aberration, and micronucleus indices. Consequently, the present findings indicated that CA-NPs can be potentially used as environmentally friendly nanoparticles.

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. However, the standard laboratory diagnosis depends mostly on fungus direct observation - the gold standard of PCM diagnosis. The implementation of new technologies, such as Fourier Transform Infrared (FTIR), can contribute to the clinical diagnosis trial of this disease. Here, we evaluated a new strategy for the diagnosis of PCM by using blood serum FTIR spectra from 20 patients with PCM and 20 healthy individuals. Machine learning algorithms were able to provide an overall accuracy of 91.67% by using Cubic SVM in the PCA data from FTIR results.

PVDF/KNO3 Composite Sub-Microfibers Produced by Solution Blow Spinning as a Hydrophobic Matrix for Fertilizer Delivery System.

Nutrient supplementation is a common practice in agriculture to increase crop productivity in the field. This supplementation is usually excessive, causing nutrient leaching in periods of rainfall leading to environmental problems. To overcome such issues, many studies have been devoted to developing polymeric matrices for the controlled and continuous release of nutrients, reducing losses, and keeping plants nourished for as long as possible. However, the release mechanism of these matrices is based on water diffusion. They start immediately for swellable polymeric matrices, which is not interesting and also may cause some waste, because the plant only needs nutrition only after the germination process. Here, as proof of concept, we tested a hydrophobic polymeric matrix based on sub-microfibers mats, produced by solution blow spinning, filled with potassium nitrate (KNO3) for the controlled release of nutrients to plants. In this work, we used the polyvinylidene fluoride (PVDF) polymer to produce composite nanofibers containing pure potassium nitrate in the proportion of 10% weight. PVDF/KNO sub-microfibers mats were obtained with 370 nm average diameter and high occurrence of beads. We performed a release test using PVDF/KNO3 mats in a water bath. The release kinetic tests showed an anomalous delivery mechanism, but the composite polymeric fibrous mat showed itself to be a promising alternative to delay the nutrient delivery for the plants.

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. In this study was investigated the use of mid-infrared spectroscopy and multivariate analysis to perform a fast, accurate, and easy canine visceral leishmaniasis diagnosis. Canine blood sera of 20 noninfected, 20 Leishmania infantum, and eight Trypanosoma evansi infected dogs were studied. The data demonstrate that principal component analysis with machine learning algorithms achieved an overall accuracy above 85% in the diagnosis.

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). Proteins, fatty acids, and amides were identified as the main molecules responsible for the discrimination of the batches. The cross-validation tests pointed out that high vigor soybean seeds were successfully discriminated from low vigor ones with an accuracy of 100%. These findings indicate FTIR spectroscopy associated with multivariate analysis as a new alternative approach to discriminate seed vigor.