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Cellulose crystallinity can be described according to the crystal size and the crystallinity index (CI). In this research, using Fourier-transform infrared spectroscopy (FTIR) and X-ray diffraction (XRD) methods, we studied the crystallinity of three different types of cellulose: banana rachis (BR), commercial cellulose (CS), and bacterial cellulose (BC). For each type of cellulose, we analyzed three different crystallization grades. These variations were obtained using three milling conditions: 6.5 h, 10 min, and unmilled (films). We developed a code in MATLAB software to perform deconvolution of the XRD data to estimate CI and full width at half-maximum (FWHM). For deconvolution, crystalline peaks were represented with Voigt functions, and a Fourier series fitted to the amorphous profile was used as the amorphous contribution, which allowed the contribution of the amorphous profile to be more effectively modeled. Comparisons based on the FTIR spectra and XRD results showed there were no compositional differences between the amorphous samples. However, changes associated with crystallinity were observed when the milling time was 10 min. The obtained CI (%) values show agreement with values reported in the literature and confirm the effectiveness of the method used in this work in predicting the crystallization aspects of cellulose samples.

The chemical structure of lycopene has been associated with significant health benefits as an antioxidant, resulting in promising applications as a food additive. However, lycopene is susceptible to chemical changes, such as isomerization and decomposition, when exposed to light and high temperatures. Therefore, lycopene stability during its isolation, food processing, and storage has sparked interest. Isolation includes the extraction of lycopene from vegetable cells by various means and its preservation to protect the biocompound from decomposition. This review explores opportunities to improve lycopene extraction and preservation through the nanotechnology value chain, a framework introduced in 2004. The nanotools explored to extract lycopene are techniques already used in the food industry, such as high-pressure homogenization and microfluidization, which allow the use of food-grade solvents, such as edible oils. Lycopene in oil can be protected by a Pickering emulsion or nanoemulsion, developed with the same nanotools used in lycopene extraction.

Urban gardening is growing as an increasingly therapeutic, social, mental, environmental, and economical alternative to improve the quality of life. One of the options to enhance this activity is the use of mulches. There are several reports on the use of cellulose-based mulches in rural areas. This work is the first successful study on the use of nanocellulose mulches for small-scale urban food production based on bacterial nanocellulose (BNC) pellicules constituted by nanoribbons. In this case, the pellicules were produced by bacteria isolated from the Colombian species Komagataeibacter medellinensis (previously referred to as Gluconacetobacter medellinensis sp. Nov., a new acid-resistant strain that can grow at a pH as low as 3.5) under simple static conditions. A cheap substrate, such as ripened fruit not suitable for human consumption, was used to produce the BNC mulches. The obtained BNC mulches are cheap and environmentally friendly than plastic mulch based on polyethylene. The effectiveness of BNC pellicule mulches was evaluated during the growth of one type of onion plant under urban garden conditions and was compared with an uncovered system and a commercial polyethylene mulch. Progressive degradation of BNC mulches took place during plant growth, indicating the biodegradability of BNC mulches, and thus, the onion plants exhibited healthy physiological development. The results suggest that BNC mulches are environmentally friendly and easy to use for the protection of small-scale food production in urban areas during short plant-growing periods.

The vibration that occurs in vehicles during road transport has an important effect on the damage experienced by agricultural products, such as fruit. Young’s modulus and Poisson’s coefficient are ...

The broad range of applications of cellulose (CNFs) nanofibers derived from plant sources has promoted different strategies for their production and commercialization. Nevertheless, issues concerning its transportation have prompted different projects to achieve a redispersible and dehydrated product in order to allow a cost-effective and safe transport and processing of this material. In this work, different strategies to avoid hornification such as oven drying, freeze-drying, spray-drying and supercritical drying were reviewed in terms of the nanostructural properties and redispersion capability of the obtained product, analyzing the existent literature involving chemical modification of cellulose or additives. Oven drying was reported to have the highest agglomeration rate during dehydration and some of the redispersible products can be used in food and pharmaceutical products, while the structure of freeze-dried CNFs evidenced several morphologies depending on CNF concentration and the presence of charges or steric hindrances. Spray-drying and supercritical drying were the only methods that could not fulfil the aimed industrial requirements due to its agglomeration and cost-efficiency respectively. When considering strategies to keep nanostructural properties upon dehydration it is important to account for the final application of CNFs as some additives or chemical modifications are targeted to specific applications. Further research is still required to clarify if spray drying and supercritical drying are suitable for this nanomaterial, as different additives and drying parameters must be considered to adapt these techniques to industrial requirements

Nanosystems are gaining momentum in pharmaceutical sciences because of the wide variety of possibilities for designing these systems to have specific functions. Specifically, studies of new cancer cotherapy drug-vitamin release nanosystems (DVRNs) including anticancer compounds and vitamins or vitamin derivatives have revealed encouraging results. However, the number of possible combinations of design and synthesis conditions is remarkably high. In addition, a large number of anticancer and vitamin derivatives have been already assayed, but a notably less number of cases of DVRNs were assayed as a whole (with the anticancer compound and the vitamin linked to them). Our approach combines with the perturbation theory and machine learning (PTML) model to predict the probability of obtaining an interesting DVRN by changing the anticancer compound and/or the vitamin present in a DVRN that is already tested for other anticancer compounds or vitamins that have not been tested yet as part of a DVRN. In a previous work, we built a linear PTML model useful for the design of these nanosystems. In doing so, we used information fusion (IF) techniques to carry out data enrichment of DVRN data compiled from the literature with the data for preclinical assays of vitamins from the ChEMBL database. The design features of DVRNs and the assay conditions of nanoparticles (NPs) and vitamins were included as multiplicative PT operators (PTOs) to the system, which indicates the importance of these variables. However, the previous work omitted experiments with nonlinear ML techniques and different types of PTOs such as metric-based PTOs. More importantly, the previous work does not consider the structure of the anticancer drug to be included in the new DVRNs. In this work, we are going to accomplish three main objectives (tasks). In the first task, we found a new model, alternative to the one published before, for the rational design of DVRNs using metric-based PTOs. The most accurate PTML model was the artificial neural network model, which showed values of specificity, sensitivity, and accuracy in the range of 90-95% in training and external validation series for more than 130,000 cases (DVRNs vs ChEMBL assays). Furthermore, in the second task, we used IF techniques to carry out data enrichment of our previous data set. In doing so, we constructed a new working data set of >970,000 cases with the data of preclinical assays of DVRNs, vitamins, and anticancer compounds from the ChEMBL database. All these assays have multiple continuous variables or descriptors dk and categorical variables cj (conditions of the assays) for drugs (dack, cacj), vitamins (dvk, cvj), and NPs (dnk, cnj). These data include >20,000 potential anticancer compounds with >270 protein targets (cac1), >580 assay cell organisms (cac2), and so forth. Furthermore, we include >36,000 assay vitamin derivatives in >6200 types of cells (c2vit), >120 assay organisms (c3vit), >60 assay strains (c4vit), and so forth. The enriched data set also contains >20 types of DVRNs (c5n) with 9 NP core materials (c4n), 8 synthesis methods (c7n), and so forth. We expressed all this information with PTOs and developed a qualitatively new PTML model that incorporates information of the anticancer drugs. This new model presents 96-97% of accuracy for training and external validation subsets. In the last task, we carried out a comparative study of ML and/or PTML models published and described how the models we are presenting cover the gap of knowledge in terms of drug delivery. In conclusion, we present here for the first time a multipurpose PTML model that is able to select NPs, anticancer compounds, and vitamins and their conditions of assay for DVRN design.

Cocoa shell (CS) is a by-product of the chocolate industry with limited economic benefit and a high environmental impact. In this study, a new material for the food industry that consists of nanocellulose fibers with CS fat was successfully isolated (yield of approximately 7.12%). The material was characterized with attenuated total reflection–Fourier transform infrared spectroscopy (ATR–FTIR), solid-state 13C nuclear magnetic resonance (13C NMR), X-ray diffraction (XRD), fluorescence and atomic force microscopy (AFM). The XRD, 13C NMR, and ATR–FTIR results suggest that the structure of the cellulosic CS fibers can be interpreted as cellulose Iβ. The crystallinity index (CrI) of an isolated sample was investigated by different methods with ATR–FTIR, 13C NMR, and XRD. According to the results, 13C NMR and XRD are the most adequate methods for quantifying the CrI of cellulosic samples in the presence of fat. In addition, the XRD results indicate that approximately 65 to 70% of the sample was crystalline. According to the fluorescence microscopy results, the cellulosic sample formed a suspension with fat, and the AFM results show that the cellulosic part of the sample had nanometric diameters between 30–80 nm with high aspect ratios. Consequently, a suspension of nanocellulose, hemicellulose, and fat was isolated from CS by chemical and mechanical treatments. The new material can be called a “suspension of holocellulose nanofibers and fat” owing to its composition and fiber diameters. The high aspect ratio of the nanocellulose fibers in the suspension resulted in an entangled network that stabilized the CS fat.

Aim: Given the current gaps of scientific knowledge and the need of efficient application of food law, this paper makes an analysis of principles of European food law for the appropriateness of applying biological activity Machine Learning prediction models to guarantee public safety. Background: Cheminformatic methods are able to design and create predictive models with high rate of accuracy saving time, costs and animal sacrifice. It has been applied on different disciplines including nanotechnology. Objective: Given the current gaps of scientific knowledge and the need of efficient application of food law, this paper makes an analysis of principles of European food law for the appropriateness of applying biological activity Machine Learning prediction models to guarantee public safety. Results: It is concluded Machine Learning could improve the application of nanotechnology food regulation, especially methods such as Perturbation Theory Machine Learning (PTML), given that it is aligned with principles promoted by the standards of Organization for Economic Co-operation and Development, European Union regulations and European Food Safety Authority. Conclusion: To our best knowledge this is the first study focused on nanotechnology food regulation and it can help to support technical European Food Safety Authority Opinions for complementary information.

Determining the biological activity of vitamin derivatives is needed given that organic synthesis of analogs of vitamins is an active field of interest for medicinal chemistry, pharmaceuticals, and food additives. Accordingly, scientists from different disciplines perform preclinical assays (nij) with a considerable combination of assay conditions (cj). Indeed, the ChEMBL platform contains a database that includes results from 36 220 different biological activity bioassays of 21 240 different vitamins and vitamin derivatives. These assays present are heterogeneous in terms of assay combinations of cj. They are focused on >500 different biological activity parameters (c0), >340 different targets (c1), >6200 types of cell (c2), >120 organisms of assay (c3), and >60 assay strains (c4). It includes a total of >1850 niacin assays, >1580 tretinoin assays, >1580 retinol assays, 857 ascorbic acid assays, etc. Given the complexity of this combinatorial data in terms of being assimilated by researchers, we propose to build a model by combining perturbation theory (PT) and machine learning (ML). Through this study, we propose a PTML (PT + ML) combinatorial model for ChEMBL results on biological activity of vitamins and vitamins derivatives. The linear discriminant analysis (LDA) model presented the following results for training subset a: specificity (%) = 90.38, sensitivity (%) = 87.51, and accuracy (%) = 89.89. The model showed the following results for the external validation subset: specificity (%) = 90.58, sensitivity (%) = 87.72, and accuracy (%) = 90.09. Different types of linear and nonlinear PTML models, such as logistic regression (LR), classification tree (CT), naive Bayes (NB), and random Forest (RF), were applied to contrast the capacity of prediction. The PTML-LDA model predicts with more accuracy by applying combinatorial descriptors. In addition, a PCA experiment with chemical structure descriptors allowed us to characterize the high structural diversity of the chemical space studied. In any case, PTML models using chemical structure descriptors do not improve the performance of the PTML-LDA model based on ALOGP and PSA. We can conclude that the three variable PTML-LDA model is a simplified and adaptable tool for the prediction, for different experiment combinations, the biological activity of derivative vitamins.

Color is an important characteristic of food products. This characteristic is related to consumer acceptability. To use the entire rhizome of

According to the regulations of the United States Food and Drug Administration (FDA), organic solvents should be limited in pharmaceutical and food products due to their inherent toxicity. For this reason, this short paper proposes different mechanical treatments to extract lycopene without organic solvents to produce an edible sunflower oil (SFO) enriched with lycopene from fresh pink guavas (

Emulsion stabilization is a broad and relevant field with applications in oil, polymer and food industries. In recent years, the use of solid particles to stabilize emulsions or Pickering emulsions have been studied for their kinetic and physical properties. Nanomaterials derived from natural sources are an interesting alternative for this application. Cellulose nanofibrils (CNFs) have been widely explored as a Pickering emulsifier with potential food applications, however, in some cases the presence of surfactants is unavoidable, and the literature is devoid of an evaluation of the effect of a non-ionic food-grade surfactant, such as polysorbate 80, in the stabilization of a vegetable oil by CNFs. To better assess the possible interactions between CNFs and this surfactant emulsions containing coconut oil, an emerging and broadly used oil, were processed with and without polysorbate 80 and evaluated in their qualitative stability, morphological and physical properties. Fluorescence microscopy, dynamic light scattering and rheology were used for this assessment. Results indicate in absence of the surfactant, emulsion stability increased at higher CNFs content, creaming was observed at 0.15 and 0.3 wt.% of CNFs, while it was not evidenced when 0.7 wt.% was used. After the addition of surfactant, the droplets are covered by the surfactant, resulting in particles with a smaller diameter, entrapped in the cellulosic structure. Rheology indicates a lower network stiffness after adding polysorbate 80.

Nanoparticles (NPs) decorated with coating agents (polymers, gels, proteins, etc.) form Nanoparticle Drug Delivery Systems (DDNS), which are of high interest in nanotechnology and biomaterials science. There have been increasing reports of experimental data sets of biological activity, toxicity, and delivery properties of DDNS. However, these data sets are still dispersed and not as large as the datasets of DDNS components (NP and drugs). This has prompted researchers to train Machine Learning (ML) algorithms that are able to design new DDNS based on the properties of their components. However, most ML models reported up to date predictions of the specific activities of NP or drugs over a determined target or cell line. In this paper, we combine Perturbation Theory and Machine Learning (PTML algorithm) to train a model that is able to predict the best components (NP, coating agent, and drug) for DDNS design. In so doing, we downloaded a dataset of >30 000 preclinical assays of drugs from ChEMBL. We also downloaded an NP data set formed by preclinical assays of coated Metal Oxide Nanoparticles (MONPs) from public sources. Both the drugs and NP datasets of preclinical assays cover multiple conditions of assays that can be listed as two arrays, namely, cjdrug and cjNP. The cjdrug array includes >504 biological activity parameters (c0drug), >340 target proteins (c1drug), >650 types of cells (c2drug), >120 assay organisms (c3drug), and >60 assay strains (c4drug). On the other hand, the cjNP array includes 3 biological activity parameters (c0NP), 40 types of proteins (c1NP), 10 shapes of nanoparticles (c2NP), 6 assay media (c3NP), and 12 coating agents (c4NP). After downloading, we pre-processed both the data sets by separate calculation PT operators that are able to account for changes (perturbations) in the drug, coating agents, and NP chemical structure and/or physicochemical properties as well as for the assay conditions. Next, we carry out an information fusion process to form a final dataset of above 500 000 DDNS (drug + MONP pairs). We also trained other linear and non-linear PTML models using R studio scripts for comparative purposes. To the best of our knowledge, this is the first multi-label PTML model that is useful for the selection of drugs, coating agents, and metal or metal–oxide nanoparticles to be assembled in order to design new DDNS with optimal activity/toxicity profiles.

The need to have an express regulation covering nanotechnology has been the subject of debate in the scientific literature and identified as one of the main subsets of nanotechnology field research. However, most countries still do not have regulatory framework in order to guarantee consumer safety. This is the case of Costa Rica, one of the most promising countries in Latin America in terms of biotechnology and nanotechnology. This article presents a statistical study about the position of industry, academia and government institutions on the need to expressly regulate nanotechnology in Costa Rica. A qualitative study consisting of a survey of 79 forms was done to individuals representing the community involved with nanotechnology and institutions responsible for ensuring the safety of the citizen’s health, to conclude that the nanotechnology regulation should be created to protect the consumer in Costa Rica. The research also proposes aspects that should be taken into account in its drafting as well as the variables on which decisions should be made to authorize the commercialization of nanomaterials based on the findings of the literature.

Bio-based additives are currently of special interest in the construction industry. The use of biopolymers increases every year owing to the industry's goal to provide non-toxic and emission-free construction products. Therefore, the addition of bio-based nanofibers to cement paste was studied. The bio-based nanofibers were isolated from fique fibers as cellulose nanofibrils (CNF) through mechanical and chemical treatments. The CNF acted as viscosity-modifying agent for the cement paste, thereby increasing its yield point exponentially with small additions. Moreover, the CNF increased the hydration degree and storage modulus of the cement paste, which were measured through a dynamic thermo-mechanical analysis (DMA) at high temperatures. Furthermore, the CNF-modified cement paste presented less microcracking under stress and at high temperatures. Thus, a synergetic interaction occurred between the cement paste and the bio-based nanofibers isolated from fique fibers.

Bacterial nanocellulose (BNC) is a natural polymer composed of glucose units with an important application as a two and three-dimensional scaffold for tissue engineering. However, as a polysaccharide, BNC does not have the biological signals of protein biomaterials. Therefore, this paper aims to develop a novel methodology to biomimic soft extracellular matrix (ECM) chemistry on to 3D BNC using the bioengineering of fibroblasts (the cells responsible for producing and regenerating the ECM) to immobilise adhesion proteins such as collagen and fibronectin. Modified 3D BNC (Mod-BNC) biomaterials were morphologically, thermally, and chemically characterised, and furthermore, the cell response was analysed by adhesion studies using atomic force microscopy (AFM), XTT assay, and confocal microscopy.Cell-derived proteins were deposited on the BNC nanoribbon network to modify its surface. The contact angle was increased from 40 degrees to 60 degrees, reducing the wettability of the biomaterial, and during thermogravimetry, the proteins in Mod-BNC exhibited an enhanced thermal stability because of the interactions between themselves and BNC. Chemical and immunocytochemistry analyses confirmed the presence of collagen type I and fibronectin on 3D BNC. These proteins activate integrin adhesion pathways that generate stronger cell adhesions. AFM experiments showed higher forces and energies on modified biomaterials, and moreover, the cells that adhered on to Mod-BNC exhibited higher mitochondrial activity and higher cell populations per cubic millimetre than non-modified surfaces (NMod-BNC). Accordingly, it was established that this novel methodology is robust and able to biomimic the chemical surface of soft ECM and immobilise cell-derived adhesion proteins from fibroblast; moreover, the Mod-BNC exhibited better cell response than NMod-BNC because of the biological signals in 3D BNC.

The current therapy for the treatment of leishmaniasis is unsatisfactory because it has multiple side effects, and resistance has been reported among the parasites that cause these diseases. Numerous efforts have been made to develop new candidates for vaccines. In recent years, particles of biodegradable polymers have been proposed as vehicles to transport and protect antigens, proteins, drugs and vaccines. In this work, the oil/water (o/w) single emulsion-solvent evaporation technique was used to prepare PLGA biodegradable particles. The encapsulation of two hypothetical proteins from Leishmania panamensis was performed to validate the proposed method. For this validation, different concentrations (50, 100, 150, 200, 250, 500, and 750 μg/ml) of both proteins were encapsulated into PLGA particles, and the particle sizes and shapes were evaluated by optical microscopy and scanning electron microscopy (SEM), respectively. The release of proteins was confirmed by SDS-PAGE and Western blot analyses. The integrity of both proteins was conserved, and they were released from day one until day 15, with a maximum amount of 46 ± 4.25% for the LpanUA.27.1260 protein and 26.19 ± 3.41% for LpanUA.22.1860. Additionally, the protective efficacy of one of these encapsulated proteins was evaluated in vivo using BALB/c mice infected with L. panamensis. Therefore, the encapsulation of proteins is presented here as an excellent alternative to evaluate the antigenicity of proteins from parasites of medical importance such as L. panamensis.

New trends in food are emerging in response to consumer awareness of the relationship between food and health, which has triggered the need to generate new alternatives that meet the expectations of the market. Revolutionary fields such as nanotechnology have been used for the encapsulation of nutritional ingredients and have great potential for the management of food additives derived from fruits and plant species. Turmeric, a spice that has been used as a dyeing agent, is recognized for its properties in Ayurveda medicine. This article aims to provide an overview of the characteristics of turmeric as an ingredient for the food industry, including its properties as a coloring agent, antioxidant, and functional ingredient. This article also highlights the potential of nanotechnology to enhance these properties of turmeric and increase the possibilities for the application of its components, such as cellulose and starch, in the development of nanostructures for food development.

The aim of this paper is to conduct an analysis of the application of the specific rules of nanotechnology incorporated in Regulation No. 1223/2009 of the European Parliament and of the Council of 30 November 2009 on cosmetic products. It has been ten years since the European Commission had issued its proposal to start the co-decision procedure to create Regulation 1223 of 2009. Although it has been praised for noting the regulatory difference of nanomaterials over the rest of the chemicals, what has been the efficacy of the standard? It is concluded that despite what it meant, the regulation has encountered technical obstacles, thus rendering the objectives relating to nanotechnology that were proposed from the European Commission unfulfilled. This finding is inferred through legal dogmatic methodologies and the identification of nanomaterials that have not been expressly approved. Nevertheless, products incorporating nanomaterials still circulate in the European market. The precepts about nanotechnology in the regulation should be reviewed because technical inconsistencies should be avoided in future regulations or applicable regulations in contexts other than Europe. Such inconsistencies exist with respect to the high level of protection of human health that should be ensured and the provisions intended to protect consumer safety. For instance, the catalog of nanomaterials in circulation does not indicate the materials that have been approved or their toxicological profiles. To date, no comparison studies have been presented between the expectations and legislative objectives set as embodied in the regulation and debated in the European Parliament involving the actual efficacy of this regulation.

Cellulose nanofibrils (CNFs) were incorporated into low-fat and standard ice cream formulations (5 wt % and 10 wt %) at two different concentrations each (0.15 and 0.3 wt %) to determine the impact of the CNFs on the structural elements and performance variables of both the pre-frozen mix and the final frozen product. Rheological properties of the aged mixes and fat destabilization, melting rate, hardness, thermo-rheology and ice crystal size of the frozen product were assessed; sensory properties were analyzed for selected formulations. Results indicate that the effects of nanocellulose are related to its adsorption on the fat structure, therefore it varies between 5 and 10 wt % formulations. Sensory properties of low-fat samples were improved after the addition of nanocellulose, even after heat shocking the specimens.

Currently, cellulose nanostructures are among the most promising structures, and extensive work in materials and biotechnology industries is aimed at identifying an efficient process of production. Even when production at the laboratory scale is successful, crucial aspects of increased commercial applications for cellulose nanostructures are linked to large-scale production. Large-scale production requires a balance between the cost of the culture medium and product value. Therefore, in this work, for the optimization and scaling up of bacterial nanocellulose, a culture medium consisting of rotten banana unsuitable for human consumption was used for the first time as an inexpensive feedstock. Initially, the bacterial nanocellulose (BNC) culture medium conditions were optimized, and it was established that a glucose concentration of 26.4 g/L and a V/A ratio of 2.2 cm were the optimal conditions for production reaching a BNC yield of 5 g/L, which was 42.4% higher than the best result initially obtained. Finally, the scale-up process was performed, implementing a regime analysis methodology by comparing the characteristic times of the critical mechanisms involved in BNC production, namely, microbial growth, glucose consumption, BNC production, and glucose diffusion into the BNC membrane, as the first approach for this type of BNC production process. The mechanism underlying the BNC production process is glucose diffusion into the BNC membrane (characteristic time, 675.47 h). Thus, the V/A ratio was selected as the scale-up criterion most suitable for producing BNC under static culture conditions, allowing the production of 16 g of BNC after 12 d of fermentation in a plastic bioreactor, which was 3378% higher than that produced in glass vessels. The results obtained in this study may initiate further improvements in BNC commercial production by exploiting different feedstocks.

Background: Machine Learning (ML) has experienced an increasing use, given the possibilities to expand the scientific knowledge of different disciplines, such as nanotechnology. This has allowed the creation of Cheminformatic models capable of predicting biological activity and physicochemical characteristics of new components with high success rates in training and test partitions. Given the current gaps of scientific knowledge and the need for efficient application of medicines products law, this paper analyzes the position of regulators for marketing medicinal nanoproducts in the European Union and the role of ML in the authorization process. Methods: In terms of methodology, a dogmatic study of the European regulation and the guidance of the European Medicine Agency on the use of predictive models for nanomaterials was carried out. The study has, as the framework of reference, the European Regulation 726/2004 and has focused on the analysis of how ML processes are contemplated in the regulations. Results: As a result, we present a discussion of the information that must be provided for every case for simulation methods. The results show a favorable and flexible position for the development of the use of predictive models to complement the applicant's information. Conclusion: It is concluded that Machine Learning has the capacity to help improve the application of nanotechnology medicine products regulation. Future regulations should promote this kind of information given the advanced state of the art in terms of algorithms that are able to build accurate predictive models. This especially applies to methods, such as Perturbation Theory Machine Learning (PTML), given that it is aligned with principles promoted by the standards of Organization for Economic Co-operation and Development (OECD), European Union regulations, and European Authority Medicine. To our best knowledge, this is the first study focused on nanotechnology medicine products and machine learning used to support technical European public assessment reports (EPAR) for complementary information.

Nano-systems for cancer co-therapy including vitamins or vitamin derivatives have showed adequate results to continue with further research studies to better understand them. However, the number of different combinations of drugs, vitamins, nanoparticle types, coating agents, synthesis conditions, and system types (nanocapsules, micelles, etc.) to be tested is very large generating a high cost in experimentations. In this context, there are reports of large datasets of preclinical assays of compounds (like in the ChEMBL database) and increasing but yet limited reports of experimental measurements of nano-systems per se. On the other hand, Machine Learning is gaining momentum in Nanotechnology and Pharmaceutical Sciences as a tool for rational design of new drugs and drug-release nano-systems. In this work, we propose to combine Perturbation Theory principles and Machine Learning to develop a PTML model for rational selection of the components of cancer co-therapy drug–vitamin release nano-systems (DVRNs). In doing so, we apply information fusion techniques with 2 data sets: (1) a large ChEMBL dataset of >36 000 preclinical assays of vitamin derivatives and a new dataset of >1000 outcomes of DVRNs, collected herein from the literature for the first time. The ChEMBL dataset used covers a considerable number of assay conditions (cjvit) each one with multiple levels. These conditions included >504 biological activity parameters (c0vit), >340 types of proteins (c1vit), >650 types of cells (c2vit), >120 assay organisms (c3vit), >60 assay strains (c4vit). Regarding the DVRNs, there are 25 different types of nano-systems (njn), with up to 16 conditions (cjn) including also different levels such as 8 biological activity parameters (c0n), 9 raw nanomaterials (c4n), 15 assay cells (c11n), etc. In the first stage, we used Moving Average operators to quantify the perturbations (deviations) in all input variables with respect to the conditions. After that, we used multiplicative PT operators to carry out data fusion, and dimension reduction, and Linear Discriminant Analysis (LDA) to seek the PTML model. The best PTML model found showed values of specificity, sensitivity, and accuracy in the range of 83–88% in training and external validation series for >130 000 cases (DVRNs vs. ChEMBL data pairs) formed after data fusion. To the best of our knowledge, this is the first general purpose model for the rational design of DVRNs for cancer co-therapy.

Edible coatings and films are appealing strategies for the postharvest management of blueberries. In the current work, alginate and alginate/cellulose nanofibril (CNF) edible coatings crosslinked with calcium chloride were developed for application on Andean blueberry (a promissory wild blueberry). Cocoa by-products were valorized through the isolation of their CNFs, and these were incorporated in the edible coatings. Edible coating formulations were based on blends of alginate (2% w/v), CNFs (0%, 0.1%, or 0.3%), glycerol, and water. In addition, stand-alone films were prepared, and their light and water vapor barrier properties were studied before applying the coating on the fruit surface. The results show that the addition of CNFs caused a significant decrease in the transparency and the water vapor permeability of the alginate films. After applying on the Andean blueberry fruits, the alginate and alginate/CNF coatings enhanced the appearance and the firmness of the fruits. Moreover, they significantly reduced the respiration rate and the water loss of the Andean blueberries throughout the 21 days of refrigerated storage. Alginate and alginate/CNFs coatings may be considered a useful alternative for the delay of the postharvest deterioration of Andean blueberries.

Despite the efforts focused on manufacturing biological engineering scaffolds for tissue engineering and regenerative medicine, a biomaterial that meets the necessary characteristics for these applications has not been developed to date. Bacterial nanocellulose (BNC) is an outstanding biomaterial for tissue engineering and regenerative medicine; however, BNC's applications have been focused on two-dimensional (2D) medical devices, such as wound dressings. Given the need for three-dimensional (3D) porous biomaterials, this work evaluates two methods to generate (3D) BNC scaffolds. The structural characteristics and physicochemical, mechanical, and cell behaviour properties were evaluated. Likewise, the effects of the pore size and surface area in the mechanical performance of BNC biomaterials and their cell response in a fibroblast cell line are discussed for the first time. In this study, a new method is proposed for the development of 3D BNC scaffolds using paraffin wax. This new method is less time-consuming, more robust in removing the paraffin and less aggressive toward the BNC microstructure. Moreover, the biomaterial had regular porosity with good mechanical behaviour; the cells can adhere and increase in number without overcrowding. Regarding the pore size and surface area, highly interconnected porosities (measuring approximately 60 μm) and high surface area are advantageous for the biomaterial's mechanical properties and cell behaviour. © 2018 Wiley Periodicals, Inc. J Biomed Mater Res Part A: 107A: 348-359, 2019.

The vinyl ester resins and natural composites have emerged as a suitable alternative in tribological application due to mechanical behavior, which relates to the conversion of the double bonds. During tribological test the permanent contact between polymeric sample and counterpart can increase the temperature affecting the crosslinking of the samples. These variations have direct implications in the curing rate and the global conversion. In this work, the FTIR evaluation is used to evaluate possible changes on the global conversion of vinyl ester and their composites reinforced with Musaceae fiber bundles and cured using two hardeners, after a specific tribological test. Increments around 15% on global conversion of styrene double bonds were observed for neat matrix and composites using both hardeners, suggesting that during tribology test some alterations on resin structure takes place. These results open alternatives to manipulate the curing conditions in order to control the tribological behavior.

Bacterial cellulose (BC), which is a hemicellulose- and lignin-free type of cellulose with unique properties, was produced by Komagataeibacter medellinensis, a new acid-resistant bacterial strain, using not suitable human consumption and sub-valorized food supply chain waste (FSCW) and agricultural by-products, namely, rotten banana, rotten mango and cheese whey. The BC was analyzed using Fourier transform infrared (ATR-FTIR) , tensile test, atomic absorption spectroscopy and thermogravimetric analysis. The properties of the BC obtained from each culture medium used were different, ranging from a material with high resistance and stiffness (280.6 MPa, 9.4 GPa) to a material with low resistance and less stiffness (17.7 MPa, 0.8 GPa). These properties provide considerable opportunities for obtaining different materials with multiple applications, such as composite reinforcements, wound dressings and edible films. The highest production of BC was achieved with rotten banana medium, and this BC also had the highest tensile properties. Meanwhile, the BC produced from cheese whey medium had the highest maximum rate of degradation temperature at 368 °C. This research demonstrated that FSCW and agrowaste by-products are advantageous alternative feedstocks in terms of economics and environmental concerns for producing BC with multiple properties and fields of application.

In this study, the effect of bioreactor size was evaluated with respect to the production and characteristics of the nanocellulose membranes produced by two different bioreactors: one with an 1800 cm2 cross-sectional area (BC-B44) and a lab-scale bioreactor with a 41 cm2 cross-sectional area (BC-B1). The culture conditions were kept the same, and the substrate consisted of overripe bananas, which are inexpensive because they are unsuitable for human consumption. The X-ray diffraction pattern showed that the two samples had similar crystalline structures, but changes were observed at the morphological level in the nanofibers that make up the BNC membranes. These changes generated, in turn, variations in the mechanical and thermal properties of the samples. This result represents a novel scale-up effect related to the static mode fermentation of BNC.

Recent advances in materials science and nanotechnology together with ecological awareness have increased interest in the development of green products. The biomedical industry is not an exemption and lignocellulosic biomaterials have become of great interest, given their inherent biocompatibility. Concurrently, they are part of bio-systems and offer great versatility as far as properties and availability. In their native state, lignocellulosic biomaterials, e.g., in wood, typically comprise cellulose (35–50 wt.%), hemicellulose (20–35 wt.%), and lignin (10–25 wt.%). For biomedical applications these three components are promising green resources. Since ancient times cellulose have been used in gauzes for treatment of wounds; presently, it offers a wide range of possibilities that have been opened by nanotechnologies. Cellulose assembly is similar to that of collagen, the major component of extracellular matrices, and its applications are diverse, ranging from three-dimensional scaffolds for tissue engineering, implants for cell regeneration, or cell labeling. Likewise, hemicelluloses bring their own potential, for example, in drug delivery systems where they excel in performance. Likewise, due to its chemical heterogeneity, lignin is an excellent source of bioactive components, displays antioxidant activity, and can be used in pathogen treatment. This chapter discusses some relevant biomedical applications of lignocellulosic biomaterials. It will also highlight applications of bacterial nanocellulose, which has the same chemical structure than vegetable cellulose, but brings some additional properties, especially those critical in biomedical fields. In the medium to long term, lignocellulosic biomaterials will play an important role for improving life quality, for helping in developing tissues and organs, and for accelerating healing processes.

La Vicerrectoria Academica de la Universidad Santo Tomas en Tunja, articulada con el Departamento de Ciencias Basicas, buscan seguir promoviendo espacios academicos que contribuyan a la transformacion del ser humano y que a su vez integren la region dentro del contexto cientifico del mundo, mediante el fomento y divulgacion de la ciencia; razon por la cual en sintonia con el Plan Integral Multicampus se han propuesto incorporar espacios academicos que respondan a los desafios de la globalizacion y que en paralelo involucren novedosas metodologias para el desarrollo del pensamiento y ensenanza de la ciencia. Con este fin se realizo el III Congreso Internacional de Ciencias Basicas, durante los dias 10, 11 y 12 de Abril de 2019 en el Centro de Convenciones de la Camara de Comercio de Tunja, como espacio de visibilidad, impacto nacional y global, que servira como pauta y herramienta fundamental de exploracion hacia nuevos metodos de ensenanza, actualizacion del conocimiento y trascendencia del mismo en el desarrollo del ser humano y su entorno, con enfoque en los problemas de la region, el pais y el mundo. Finalmente, es importante resaltar que el objetivo principal del congreso, fue generar un espacio academico de caracter internacional, que permitiera fortalecer la divulgacion cientifica de trabajos e investigaciones en Ciencias Basicas, que contribuyan al desarrollo del ser humano y su entorno, por lo cual dentro de este documento se encontraran trabajos entorno a la ensenanza de las Ciencias, la Fisica, Quimica y Biologia experimental, al igual que trabajos con respecto a la simulacion en Matematicas, Estadistica y aplicaciones.

Curcuma longa is a rhizome used for the extraction of curcumin, a yellow colorant that only represents 3 wt% of the dried rhizome. To increase the possibility of using the entire rhizome as a food colorant, in the present investigation, the effect of ultra-fine friction grinding (supermasscolloider) to obtain turmeric suspensions was evaluated. To achieve this goal, two distances between the grinding stones or Gap were evaluated (G of -1 and -1.5), and the obtained suspensions were characterized by infrared spectroscopy and through the determination of curcumin content, color, particle size, sedimentation index, serum cloudiness, and microstructure. The results establish that a lower G contributes to an increase in the release of curcumin in the suspension up to 21%, which is related to a greater tendency for yellow coloration, observed in the increase of the * b coordinate of color (from 61.588 to 66.497). Additionally, it was found that a lower G generates smaller particle sizes, which is related to a lower turbidity. PRACTICAL APPLICATION: This research shows that ultra-fine friction grinding (UFFG) has great potential for the development of turmeric suspensions. The results have applications in the food industry sector, because UFFG could be used to produce different types of vegetable suspensions.