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The development of compostable packages that maintain fresh meat quality, is an important achievement for the poultry industry. The objective of this study was to evaluate the feasibility of using a starch-based composite foam (SCF) in the packaging of fresh chicken meat during refrigerated storage. SCF was prepared using extrusion process. Nisin (2%) was added as antimicrobial agent (SCFN). Commercial expanded polystyrene (EPS) was used as control. Physical characterization, antimicrobial analysis and storage of fresh chicken meat were carried out. No differences were observed in SEM images between SFC and SCFN samples. Water uptake of SCF were higher than SCFN (p < 0.05). SCFN exhibited higher Young´s modulus and flexural strength (p < 0.05), and antimicrobial effect against foodborne pathogens. During the storage of chicken meat, the starch-based composite foam showed a higher capacity to retain liquid than EPS. The color of chicken meat had slight variations at day 4 compared with the raw meat. Nisin did not retard lipid oxidation of chicken meat, however, the aerobic plate count was lower. Therefore, the starch-based composite foam is suitable for fresh meat storage, being improved with the incorporation of nisin as antimicrobial agent. Supplementary Information: The online version contains supplementary material available at 10.1007/s13197-022-05538-6.
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Drug Discovery is an active research area that demands great investments and generates low returns due to its inherent complexity and great costs. To identify potential therapeutic candidates more effectively, we propose protein-ligand with adversarial augmentations network (PLA-Net), a deep learning-based approach to predict target-ligand interactions. PLA-Net consists of a two-module deep graph convolutional network that considers ligands' and targets' most relevant chemical information, successfully combining them to find their binding capability. Moreover, we generate adversarial data augmentations that preserve relevant biological backgrounds and improve the interpretability of our model, highlighting the relevant substructures of the ligands reported to interact with the protein targets. Our experiments demonstrate that the joint ligand-target information and the adversarial augmentations significantly increase the interaction prediction performance. PLA-Net achieves 86.52% in mean average precision for 102 target proteins with perfect performance for 30 of them, in a curated version of actives as decoys dataset. Lastly, we accurately predict pharmacologically-relevant molecules when screening the ligands of ChEMBL and drug repurposing Hub datasets with the perfect-scoring targets.
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Redes Neurales de la Computación , Proteínas , Ligandos , Preparaciones Farmacéuticas , Poliésteres , Proteínas/metabolismoRESUMEN
The nutritional value and biological properties of 24 samples of Chilean edible mushrooms were evaluated. The nutritional value was determined by measuring moisture, protein, fat, ash and carbohydrate contents. The biological activity was determined by using antibacterial, antifungal and antioxidant tests. The mushrooms showed high total carbohydrate (83.65-62.97 g/100 g dw) and crude protein (23.88-8.56 g/100 g dw) contents, but low fat contents (6.09-1.05 g/100 g dw). Ch2Cl2-extracts were more active against bacteria and fungi than MeOH-extracts. Ch2Cl2-extracts of B. loyo, C. lebre, L. edodes, M. conica and R. flava inhibited the growth of Gram-positive bacteria. The Ch2Cl2-extracts of A. cylindracea, B. loyo, and G. gargal showed strong effects against fungi. R. flava showed the highest phenolic content and antioxidant activity. The Chilean species B. loyo, C. lebre and G. gargal exhibited interesting nutritional value and biological properties, showing potential to be used as a dietary nutritional supplement.
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Agaricales/química , Antibacterianos/farmacología , Antioxidantes/farmacología , Chile , Carbohidratos de la Dieta/análisis , Viabilidad Microbiana/efectos de los fármacos , Valor Nutritivo , Fenoles/análisisRESUMEN
The discovery and development of novel pharmaceuticals is an area of active research mainly due to the large investments required and long payback times. As of 2016, the development of a novel drug candidate required up to $ USD 2.6 billion in investment for only 10% rate of approval by the FDA. To help decreasing the costs associated with the process, a number of in silico approaches have been developed with relatively low success due to limited predicting performance. Here, we introduced a machine learning-based algorithm as an alternative for a more accurate search of new pharmacological candidates, which takes advantage of Recurrent Neural Networks (RNN) for active molecule prediction within large databases. Our approach, termed PharmaNet was implemented here to search for ligands against specific cell receptors within 102 targets of the DUD-E database, which contains 22886 active molecules. PharmaNet comprises three main phases. First, a SMILES representation of the molecule is converted into a raw molecular image. Second, a convolutional encoder processes the data to obtain a fingerprint molecular image that is finally analyzed by a Recurrent Neural Network (RNN). This approach enables precise predictions of the molecules' target on the basis of the feature extraction, the sequence analysis and the relevant information filtered out throughout the process. Molecule Target prediction is a highly unbalanced detection problem and therefore, we propose that an adequate evaluation metric of performance is the area under the Normalized Average Precision (NAP) curve. PharmaNet largely surpasses the previous state-of-the-art method with 97.7% in the Receiver Operating Characteristic curve (ROC-AUC) and 65.5% in the NAP curve. We obtained a perfect performance for human farnesyl pyrophosphate synthase (FPPS), which is a potential target for antimicrobial and anticancer treatments. We decided to test PharmaNet for activity prediction against FPPS by searching in the CHEMBL data set. We obtained three (3) potential inhibitors that were further validated through both molecular docking and in silico toxicity prediction. Most importantly, one of this candidates, CHEMBL2007613, was predicted as a potential antiviral due to its involvement on the PCDH17 pathway, which has been reported to be related to viral infections.
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Preparaciones Farmacéuticas/química , Algoritmos , Bases de Datos Factuales , Aprendizaje Profundo , Humanos , Ligandos , Aprendizaje Automático , Simulación del Acoplamiento Molecular/métodos , Redes Neurales de la Computación , Curva ROCRESUMEN
Antecedentes: Los accidentes ocupacionales de riesgo biológico tienen como mayor riesgo postexposición la seroconversión para el virus de la inmunodeficiencia humana (VIH) y virus de la hepatitis C (VHC) y B (VHB). En la literatura latinoamericana aún faltan estudios que aporten información al respecto. Objetivo: Describir las características epidemiológicas de los accidentes ocupacionales de riesgo biológico. Metodología: Estudio descriptivo longitudinal. Resultados: Se describen 231 episodios de riesgo biológico. La mediana de edad fue 30 años. Un 65,8% fueron mujeres. Las principales actividades laborales fueron: auxiliares de enfermería (22,9%), aseo hospitalario (16,5%), estudiantes (14,3%), recolección de basuras (5,2%) y médicos (4,8%). El mecanismo del accidente fue: punción (77%), herida cortante (11,3%) y contacto con mucosas (9,1%). En 24% la fuente fue conocida y de estas fueron positivas para VIH un 62,5%, para VHB un 3,5% y para VHC un 5,3%. Recibieron profilaxis postexposición (PPE) un 75,8% de los 231. Entre los expuestos a fuente VIH positiva, recibieron PPE biconjugada 85,1% y terapia triple 14,8% De los que recibieron profilaxis, 40% presentaron reacciones adversas, siendo las gastrointestinales (77,1%) y las neurológicas (45,7%) las más frecuentes. Al ingreso, un 67,1% tenían anticuerpos protectores para VHB. Durante el seguimiento se confirmó una seroconversión postexposición para VIH. Conclusión: El riesgo de adquirir infecciones postexposición ocupacional es una realidad en nuestro medio; se debe hacer énfasis en estrategias de prevención de exposición, introyectar la cultura del reporte y el manejo adecuado de la profilaxis postexposición.
Background: Occupational biohazard exposure can increase the risk of postexposure seroconversion of human immunodeficiency virus (HIV) and hepatitis C (HCV) and B virus (HBV). In Latin America, the literature lack of studies on this topic. Objective: To describe the epidemiological characteristics of occupational biohazard exposure. Methodology: A descriptive, longitudinal study. Results: A total of 231 episodes of biological risk exposure are described. The median age was 30 years, and 65.8% were women. The major occupational activities were: nursing assistants 22.9%, hospital cleaning 16.5%, students 14.3%, garbage collection 5.2% and physicians 4.8%. The mechanisms of the accidents were: needle stick 77%, cutting wound 11.3% and contact with mucous membranes 9.1%. In 24% the source was known and of these, 62.5% were positive for HIV 3.5% for HBV and 5.3% for HCV. A total of 75.8% of the 231 received postexposure prophylaxis (PEP). In those exposed to an HIV-positive source, 85.1% received a two-drug conjugate for PPE, and 14.8% received triple therapy. Of those who received prophylaxis, 40% reported adverse events with being the most frequent the gastrointestinal (77.1%) and neurological (45.7%). At admission, 67.1% had protective antibodies to HBV. During program monitoring, HIV seroconversion was confirmed in one patient. Conclusion: The risk of acquiring occupational infections postexposure is a reality in our country. This emphasizes the importance of exposure prevention strategies, introjecting the reporting culture and proper management of postexposure prophylaxis.