The Evolution and Future Trends of Unsaturated Polyester Biocomposites: A Bibliometric Analysis.

Unsaturated polyester resin (UPR) is one of the first commercialized polymer matrices for composites reinforced with glass fibers, but has remained popular to this day. To reduce their environmental impact, natural fibers have been used as reinforcements. Researchers all over the world are still interested in these composites, and numerous papers have been published in the last four decades. Using bibliometric analysis, this work provides compiled, structured, and relevant information about the evolution and current state of these materials. This first study on UPR biocomposites based on bibliometric analysis examined 531 published papers identified in the Scopus database from 1982 to July 2022. An analysis of the most active states, leading institutions, and leading authors is followed by the identification of key areas such as the most common natural fibers used as reinforcements, fiber treatments, and composite design parameters such as processing techniques; recently, composite testing; and technological applications. The findings emphasize the importance of staying active in this global field and provide information on novel promising topics for future research.

The Effects of Adding a Gel-Alike Curcuma longa L. Suspension as Color Agent on Some Quality and Sensory Properties of Yogurt.

Color is an important characteristic of food products. This characteristic is related to consumer acceptability. To use the entire rhizome of Curcuma longa (CL) as a food colorant, a novel gel alike stable suspension (CLS) was previously developed using cellulose nanofibers (CNFs). Therefore, the present study was conducted to evaluate the CLS as a color additive on a stirred yogurt. Three concentrations of CLS were studied (0.1, 0.125, and 0.15 wt. %) and compared to yogurt without CLS. The obtained yogurts were characterized through the determination of pH, titratable acidity, syneresis, color and curcumin content after 1, 7, 14, and 21 days of storage. Additionally, rheological and sensory measurements were performed on the samples after one day of storage. Results show that the addition of CLS does not affect the pH and titratable acidity of the samples, but all the yogurts showed an increase in their syneresis during the storage time, showing a breakdown of the gel structure. Furthermore, the CLS suspension has the ability to impart a yellow color to yogurts, a characteristic that was stable during storage. Finally, the addition of 1 wt. % or 1.25 wt. % of CLS allows the development of a yogurt with adequate sensory perception.

An Edible Oil Enriched with Lycopene from Pink Guava (Psidium guajava L.) Using Different Mechanical Treatments.

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 (Psidium guajava L.) (FPGs). The methodology involves the use of SFO and a combination of mechanical treatments: a waring blender (WB), WB+ high-shear mixing (HSM) and WB+ ultrafine friction grinding (UFFG). The solid:solvent (FPG:SFO) ratios used in all the techniques were 1:5, 1:10 and 1:20. The results from optical microscopy and UV-vis spectroscopy showed a correlation between the concentration of lycopene in SFO, vegetable tissue diameters and FPG:SFO ratio. The highest lycopene concentration, 18.215 ± 1.834 mg/g FPG, was achieved in WB + UFFG with an FPG:SFO ratio of 1:20. The yield of this treatment was 66% in comparison to the conventional extraction method. The maximal lycopene concentration achieved in this work was significantly higher than the values reported by other authors, using high-pressure homogenization for tomato peel and several solvents such as water, SFO, ethyl lactate and acetone.

Influence of a Non-Ionic Surfactant in the Microstructure and Rheology of a Pickering Emulsion Stabilized by Cellulose Nanofibrils.

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.

Nanocelluloses Reinforced Bio-Waterborne Polyurethane.

The aim of this work was to evaluate the influence of two kinds of bio- nano-reinforcements, cellulose nanocrystals (CNCs) and bacterial cellulose (BC), on the properties of castor oil-based waterborne polyurethane (WBPU) films. CNCs were obtained by the acidolysis of microcrystalline cellulose, while BC was produced from Komagataeibacter medellinensis. A WBPU/BC composite was prepared by the impregnation of a wet BC membrane and further drying, while the WBPU/CNC composite was obtained by casting. The nanoreinforcement was adequately dispersed in the polymer using any of the preparation methods, obtaining optically transparent compounds. Thermal gravimetric analysis, Fourier-transform infrared spectroscopy, field emission scanning electron microscopy, dynamical mechanical analysis, differential scanning calorimetry, contact angle, and water absorption tests were carried out to analyze the chemical, physical, and thermal properties, as well as the morphology of nanocelluloses and composites. The incorporation of nanoreinforcements into the formulation increased the storage modulus above the glass transition temperature of the polymer. The thermal stability of the BC-reinforced composites was slightly higher than that of the CNC composites. In addition, BC allowed maintaining the structural integrity of the composites films, when they were immersed in water. The results were related to the relatively high thermal stability and the particular three-dimensional interconnected reticular morphology of BC.

Characterization of Chitosan Extracted from Fish Scales of the Colombian Endemic Species Prochilodus magdalenae as a Novel Source for Antibacterial Starch-Based Films.

Scales of Prochilodus magdalenae, a Colombian endemic fish species, were used to obtain chitosan for application as an antibacterial agent integrated into starch-based films. Analysis of its composition during the demineralization and deproteinization process indicated that minerals and protein were both removed successfully. At this point, mild conditions for the deacetylation process were employed, namely, 2, 4, and 6 wt.% NaOH at room temperature for 16 h. Chitosan processed under 2 wt.% NaOH had low molecular weight, with the lowest value of 107.18 ± 24.99 kDa, which was closely related to its antibacterial activity. Finally, this chitosan was integrated into a banana starch-based film, and its antibacterial activity was assayed in Escherichia coli and Staphylococcus aureus cultures, with positive results in the former culture, especially due to the low-molecular-weight characteristic of chitosan.

The Role of Machine Learning in Centralized Authorization Process of Nanomedicines in European Union.

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.

Predicting coated-nanoparticle drug release systems with perturbation-theory machine learning (PTML) models.

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.

Effect of production process scale-up on the characteristics and properties of bacterial nanocellulose obtained from overripe Banana culture medium.

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.

PTML Model for Selection of Nanoparticles, Anticancer Drugs, and Vitamins in the Design of Drug-Vitamin Nanoparticle Release Systems for Cancer Cotherapy.

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.

PTML Model of ChEMBL Compounds Assays for Vitamin Derivatives.

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), näive 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.

Machine Learning as a Proposal for a Better Application of Food Nanotechnology Regulation in the European Union.

AIMS: 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. METHODS: A systematic study of the regulation and the incorporation of predictive models of biological activity of nanomaterials was carried out through the analysis of the express nanotechnology regulation on foods, applicable in European Union. 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.

Designing nanoparticle release systems for drug-vitamin cancer co-therapy with multiplicative perturbation-theory machine learning (PTML) models.

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.

Development of novel three-dimensional scaffolds based on bacterial nanocellulose for tissue engineering and regenerative medicine: Effect of processing methods, pore size, and surface area.

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.

Effects of alternative energy sources on bacterial cellulose characteristics produced by Komagataeibacter medellinensis.

Bacterial cellulose (BC) was produced by Komagataeibacter medellinensis using Hestrin and Schramm modified medium in the presence of alternative energy sources (AES), such as ethanol and acetic acid, to explore the effect of AES on the characteristics and properties of the resulting BC. In this study, the physicochemical and structural characteristics of the obtained BC were determined using Fourier-transform infrared spectroscopy, X-ray diffraction spectrometry, thermogravimetric analysis, and mechanical testing analysis. Ethanol and acetic acid (at 0.1 wt%) were proven to improve the BC yield by K. medellinensis by 279% and 222%, respectively. However, the crystallinity index (%), the degree of polymerization, and maximum rate of degradation temperatures decreased by 9.2%, 36%, and 4.96%, respectively, by the addition of ethanol and by 7.2%, 27%, and 4.21%, respectively, by the addition of acetic acid. The significance of this work, lies on the fact that there is not any report about how BC properties change when substances like ethanol or acetic acid are added to culture medium, and which is the mechanism that provokes those changes, that in our case we could demonstrate the relationship of a higher BC production rate (provoked by ethanol and acetic acid adding) and changes in BC properties.

Effect of Different Carbon Sources on Bacterial Nanocellulose Production and Structure Using the Low pH Resistant Strain Komagataeibacter Medellinensis.

Bacterial cellulose (BC) is a polymer obtained by fermentation with microorganism of different genera. Recently, new producer species have been discovered, which require identification of the most important variables affecting cellulose production. In this work, the influence of different carbon sources in BC production by a novel low pH-resistant strain Komagataeibacter medellinensis was established. The Hestrin-Schramm culture medium was used as a reference and was compared to other media comprising glucose, fructose, and sucrose, used as carbon sources at three concentrations (1, 2, and 3% w/v). The BC yield and dynamics of carbon consumption were determined at given fermentation times during cellulose production. While the carbon source did not influence the BC structural characteristics, different production levels were determined: glucose > sucrose > fructose. These results highlight considerations to improve BC industrial production and to establish the BC property space for applications in different fields.

In-situ glyoxalization during biosynthesis of bacterial cellulose.

A novel method to synthesize highly crosslinked bacterial cellulose (BC) is reported. The glyoxalization is started in-situ, in the culture medium during biosynthesis of cellulose by Gluconacetobacter medellensis bacteria. Strong crosslinked networks were formed in the contact areas between extruded cellulose ribbons by reaction with the glyoxal precursors. The crystalline structure of cellulose was preserved while the acidic component of the surface energy was reduced. As a consequence, its predominant acidic character and the relative contribution of the dispersive component increased, endowing the BC network with a higher hydrophobicity. This route for in-situ crosslinking is expected to facilitate other modifications upon biosynthesis of cellulose ribbons by microorganisms and to engineer the strength and surface energy of their networks.

Rheological and physical properties of gelatin suspensions containing cellulose nanofibers for potential coatings.

Rheological and physical properties of edible coating formulations containing gelatin, cellulose nanofibers (CNFs), and glycerol are characterized. Measured properties are analyzed in order to optimize edible coating thickness. Results show that coating formulations density increases linearly with gelatin concentration in presence of CNFs. Surface tension decreases with either gelatin or CNF concentration increases. Power law model well described the rheological behavior of edible coating formulations since determination coefficient was high (R(2 )> 0.98) and standard error was low (SE < 0.0052). Formulations showed pseudoplastic (shear-thinning) flow behavior and no time-dependent features were observed. The flow behavior index was not significantly affected by any factor. Consistency coefficient increases with gelatin concentrations but it decreases with glycerol concentrations.

Gluconacetobacter medellinensis sp. nov., cellulose- and non-cellulose-producing acetic acid bacteria isolated from vinegar.

The phylogenetic position of a cellulose-producing acetic acid bacterium, strain ID13488, isolated from commercially available Colombian homemade fruit vinegar, was investigated. Analyses using nearly complete 16S rRNA gene sequences, nearly complete 16S-23S rRNA gene internal transcribed spacer (ITS) sequences, as well as concatenated partial sequences of the housekeeping genes dnaK, groEL and rpoB, allocated the micro-organism to the genus Gluconacetobacter, and more precisely to the Gluconacetobacter xylinus group. Moreover, the data suggested that the micro-organism belongs to a novel species in this genus, together with LMG 1693(T), a non-cellulose-producing strain isolated from vinegar by Kondo and previously classified as a strain of Gluconacetobacter xylinus. DNA-DNA hybridizations confirmed this finding, revealing a DNA-DNA relatedness value of 81 % between strains ID13488 and LMG 1693(T), and values <70 % between strain LMG 1693(T) and the type strains of the closest phylogenetic neighbours. Additionally, the classification of strains ID13488 and LMG 1693(T) into a single novel species was supported by amplified fragment length polymorphism (AFLP) and (GTG)5-PCR DNA fingerprinting data, as well as by phenotypic data. Strains ID13488 and LMG 1693(T) could be differentiated from closely related species of the genus Gluconacetobacter by their ability to produce 2- and 5-keto-d-gluconic acid from d-glucose, their ability to produce acid from sucrose, but not from 1-propanol, and their ability to grow on 3 % ethanol in the absence of acetic acid and on ethanol, d-ribose, d-xylose, sucrose, sorbitol, d-mannitol and d-gluconate as carbon sources. The DNA G+C content of strains ID13488 and LMG 1693(T) was 58.0 and 60.7 mol%, respectively. The major ubiquinone of LMG 1693(T) was Q-10. Taken together these data indicate that strains ID13488 and LMG 1693(T) represent a novel species of the genus Gluconacetobacter for which the name Gluconacetobacter medellinensis sp. nov. is proposed. The type strain is LMG 1693(T) ( = NBRC 3288(T) = Kondo 51(T)).

Bacterial cellulose produced by a new acid-resistant strain of Gluconacetobacter genus.

A bacterial strain isolated from the fermentation of Colombian homemade vinegar, Gluconacetobacter medellensis, was investigated as a new source of bacterial cellulose (BC). The BC produced from substrate media consisting of various carbon sources at different pH and incubation times was quantified. Hestrin-Schramm (HS) medium modified with glucose led to the highest BC yields followed by sucrose and fructose. Interestingly, the microorganisms are highly tolerant to low pH: an optimum yield of 4.5 g/L was achieved at pH 3.5, which is generally too low for other bacterial species to function. The cellulose microfibrils produced by the new strain were characterized by scanning and transmission electron microscopy, infrared spectroscopy X-ray diffraction and elemental analysis. The morphological, structural and chemical characteristics of the cellulose produced are similar to those expected for BC.