Diego Cristos

Preserving minimally processed fruits represents a technological challenge. Therefore, non-thermal plasma (NTP) is proposed as one of the preservation methods. The aim of this work was to evaluate the effect of the application of NTP before and after packaging sliced apples in two films: One with high and another with low oxygen barrier features. Different parameters were evaluated during 14 days at 4 °C. Samples treated before packaging showed lower crunchiness and browning development, as indicated by chromatic and textural parameters. NTP reduced mesophiles, psychrotrophs, and yeasts and molds on the slices by 1–2 log units, although it had no effect on the antioxidant content of the apple slices, which were better preserved with high-barrier packaging. Samples treated with NTP, and low barrier packaging showed lower sugar content on day one. When applied after packaging, NTP contributed to better preservation of slice microstructure and tissue viability. Results showed that the combination of NTP applied after packaging and high-barrier film was the most suitable for maintaining fruit quality, mainly by better preserving slice color. In turn, tissue microstructure, texture analysis, and viability tests also supported this conclusion.

The study determined the optimal conditions for extracting antioxidant phenols from Mutisia friesiana (Asteraceae) using ultrasound-assisted extraction. We evaluated extraction time, ethanol concentration, and solid/solvent ratio, applying a Box-Behnken experimental design. Response variables were total phenolic content (TPC), DPPH free radical scavenging activity (EC50), and ferric reducing antioxidant power (FRAP). All extraction variables were optimized by the response surface methodology. The optimal conditions resulted in 15 minutes of extraction, 65% ethanol, and a solid/solvent ratio of 5 g/100 mL. Under these conditions, TPC, EC50, and FRAP values were 54.24 mg GAE/g dw, 31.40 µg/mL, and 965.32 µmol EFe(II)/g dw, respectively. Extract HPLC–MS analysis under optimal conditions identified four phenolic compounds, previously reported for their antioxidant properties. This study provides valuable insights into optimal extraction parameters of bioactive compounds from M. friesiana, positioning the species as a promising natural antioxidants source for the food industry, particularly meat products. Highlights: Ultrasound-assisted extraction (UAE) was optimized to recover antioxidant phenols from Mutisia friesiana using response surface methodology. Optimal conditions yielded an extract with high antioxidant activity. HPLC-MS identified four antioxidant phenols in the optimized extract.

Neurodegenerative diseases, including Alzheimer’s disease (AD), represent one of the main global health challenges. Cannabis sativa synthesizes spermidine-type alkaloids, whose potential biological activities have been little studied. This study aimed to isolate bioactive alkaloids from an alkaloid-enriched extract (AEE) of C. sativa roots throughout a bioguided approach using conventional chromatographic techniques based on AChE and BuChE inhibitory activities. A qualitative and semiquantitative analysis by UPLC-ESI-MS/MS as well as molecular modeling simulations were performed. In addition, predictive in silico analyses were conducted to assess toxicity properties. The alkaloids cannabisativine (CS) and anhydrocannabisativine (ACS) were isolated, and showed highly selective BuChE inhibitory activity. The molecular modeling study revealed a conserved interaction profile across both alkaloids, indicating the amino acids TRP82, GLU197, TYR440, and HIS438 as the major contributors involved in the complex formation. Finally, CS and ACS exhibited low in silico predictive toxicity values. In conclusion, CS and ACS alkaloids emerge as new selective BuChE inhibitors with therapeutic potential that deserves the attention from the field of pharmacology in neurodegenerative disease research. Additionally, this approach promotes innovation and environmental sustainability through the use of C. sativa roots.

This study assessed the herbicides atrazine (ATZ) and glyphosate (GLP) concentration in water and sediment of Argentine Pampas shallow lakes with agriculture activity (AA) mainly transgenic crops of soybeans and corn on the coast near the water (Cochicó and Guaminí), and in a lake with extensive cattle activity (CA) close to their coasts (La Salada de Monasterio). Also, the concentration of ATZ in the muscle and the frequency of testicular histopathological lesions (THL) of pejerrey fish (Odontesthes bonariensis) were studied. In the water, maximum concentrations of ATZ (0.279 µg l^− 1) and GLP (0.015 µg l^− 1 were found in Guaminí and Cochicó shallow lakes. For sediments, the maximum concentrations were 32.6, 45.2, and 14.9 µg kg^− 1 for ATZ, GLP, and its metabolite aminomethylphosphonic acid (AMPA), respectively in the same shallow lakes. The results showed that 30.6% of the pejerrey sampled had THL being fish from Guaminí (60%) the most affected. Of the pejerrey analyzed for ATZ, 80.82% of them had this herbicide in the muscle. The highest percentage of THL was found again in pejerrey from Guaminí (68.75). with sterility as dominant (59.38%). These findings suggest a possible adverse effect on the gonads related to the herbicides identified in the water, sediments and in muscle which is an alarm signal in using agrochemicals and protecting the fish biota.

Beef, as a nutrient-rich food, is widely favored by consumers. The production region significantly influences the nutritional value and quality of beef. However, current methods for tracing the origin of beef are still under development, necessitating effective approaches to ensure food safety and meet consumer demand for high-quality beef. This study aims to establish a classification model for beef origin prediction by analyzing elemental content and stable isotopes in beef samples from two countries. The concentrations of elements in beef were analyzed using ICP-MS and ICP-OES, while the stable carbon isotope ratio was determined using EA-IRMS. Machine learning algorithms were employed to construct classification prediction models. A total of 83 beef samples were analyzed for the concentrations of 52 elements and the stable carbon isotope ratio. The classification accuracy of the PLS-DA model built on these results was 98.8%, while the prediction accuracy was 94.12% for the convolutional neural network (CNN) and 82.35% for the Random Forest algorithm. The PLS-DA model demonstrated higher classification accuracy compared to CNN and Random Forest, with an explanatory power (R²) of 0.924 and predictive ability (Q²) of 0.787. Combining the analysis of 52 elements and the stable carbon isotope ratio with machine learning algorithms enables effective tracing and origin prediction of beef from different regions. Key factors influencing beef origin were identified as Fe, Cs, As, δ¹³C, Co, V, Sc, Rb, and Ru.

Background/Objectives: The aims of this work were to formulate cannabidiol in different lipid carriers for skin delivery after topical application and to study their stability, interaction with the skin, and antibacterial activity. Methods: Solid lipid nanoparticles and nanostructured lipid carriers loaded with cannabidiol were prepared and characterized in terms of their physicochemical properties, colloidal stability, protection of the antioxidant capacity of cannabidiol, as well as their retention over time. Skin penetration was assessed using an in vitro model with human skin. The antibacterial activity was tested against Staphylococcus aureus and compared to free cannabidiol. Results: Three nanoformulations exhibited the best size and reproducibility values and were selected for further studies. The formulations were stable, protected the active ingredient, succeeded in delivering it to deep skin layers, and demonstrated antibacterial activity. Conclusions: These cannabidiol nanoformulations show potential for use in skin diseases and conditions, as they protect the active ingredient, enhance its delivery to the skin, and exhibit antibacterial effects.

During storage, soybean kernels can be attacked by insects, which are preventively controlled with insecticides. Information on the dissipation of insecticide residues is crucial to know their final concentrations in food and establish the waiting periods necessary for consumption without health risks, and to determine the minimum waiting period necessary to comply with national and international standards. The aims of this study were to quantify the residue levels of organophosphorus insecticides (dichlorvos, chlorpyrifos-methyl and pirimiphos-methyl) in stored soybean, establish the effect during the storage period, and model the dissipation dynamics. Insecticide residues in soybeans were analyzed at 2, 30, 60, 90 and 120 days after application. An analytical method based on QuEChERS extraction followed by gas chromatography mass spectrometry (GC-MS/MS) determination was validated, with mean recoveries of 82-105%, depending on the spiking levels. Residues decreased below 80% of the initial concentration at 60 days after application and below quantifiable levels at 120 days. Residues followed a pseudo-first-order dissipation dynamics [Ct = C0×exp(-k.t)], with the dissipation constant (k) and half-lives being 0.538 and 1.3 days for dichlorvos, 0.018 and 38.8 days for chlorpyrifos-methyl, and 0.023 and 30.1 days for pirimiphos-methyl, respectively. These results allow concluding that, at the recommended dosage, these insecticides are safe for use on soybean grains stored under standard conditions commonly nowadays used in Argentina.