Practical deployment of automation to expedite aqueous two-phase extraction.

The feasibility of bioprocess development relies heavily on the successful application of primary recovery and purification techniques. Aqueous two-phase extraction (ATPE) disrupts the definition of "unit operation" by serving as an integrative and intensive technique that combines different objectives such as the removal of biomass and integrated recovery and purification of the product of interest. The relative simplicity of processing large samples renders this technique an attractive alternative for industrial bioprocessing applications. However, process development is hindered by the lack of easily predictable partition behaviours, the elucidation of which necessitates a large number of experiments to be conducted. Liquid handling devices can assist to address this problem; however, they are configured to operate using low viscosity fluids such as water and water-based solutions as opposed to highly viscous polymeric solutions, which are typically required in ATPE. In this work, an automated high throughput ATPE process development framework is presented by constructing phase diagrams and identifying the binodal curves for PEG6000, PEG3000, and PEG2000. Models were built to determine viscosity- and volume-independent transfer parameters. The framework provided an appropriate strategy to develop a very precise and accurate operation by exploiting the relationship between different liquid transfer parameters and process error. Process accuracy, measured by mean absolute error, and device precision, evaluated by the coefficient of variation, were both shown to be affected by the mechanical properties, particularly viscosity, of the fluids employed. For PEG6000, the mean absolute error improved by six-fold (from 4.82% to 0.75%) and the coefficient of variation improved by three-fold (from 0.027 to 0.008) upon optimisation of the liquid transfer parameters accounting for the viscosity effect on the PEG-salt buffer utilising ATPE operations. As demonstrated here, automated liquid handling devices can serve to streamline process development for APTE enabling wide adoption of this technique in large scale bioprocess applications.

Pb/Pb3O4 Metal-Semiconductor Nanocomposite Obtained on 4A Zeolite-Optical and Structural Properties.

This work describes a controlled and low-cost synthesis method to obtain Pb/Pb3O4 nanocomposites using synthetic zeolite 4A. The nanostructures obtained have a core-shell configuration with 5-25 nm diameters. High-resolution transmission electron microscopy (HRTEM), BF, high-angle annular dark-field annular scanning transmission electron microscopy (HAADF-STEM), energy-dispersive X-ray spectroscopy (EDS), X-ray photoelectron spectroscopy (XPS), and ultraviolet-visible (UV-vis) characterization techniques were used. Crystallographic planes (111), (200), and (220) for the core and planes (110) and (211) for the shell, corresponding to FCC and tetragonal structures for Pb and Pb3O4, respectively, were determined using HRTEM. The HAADF-STEM images allowed the analysis of intensity contrast images proportional to the number of atoms. XPS spectral analysis showed a 4.8 eV difference in binding energy between Pb 4f7/2 and Pb 4f5/2 for lead and lead oxide. EDS elemental mapping, XPS, and UV-vis spectroscopy analyses revealed the simultaneous presence of lead and lead oxide in the same structure. The band gap obtained for the shell was determined to be 4.50 eV. Consequently, Pb/Pb3O4 nanocomposites show a higher response to high-energy photons, making them suitable for UV photocatalysis applications.

Rationalising development of classification systems describing livestock production systems for disease burden analysis within the Global Burden of Animal Diseases programme.

The heterogeneity that exists across the global spectrum of livestock production means that livestock productivity, efficiency, health expenditure and health outcomes vary across production systems. To ensure that burden of disease estimates are specific to the represented livestock population and people reliant upon them, livestock populations need to be systematically classified into different types of production system, reflective of the heterogeneity across production systems. This paper explores the data currently available of livestock production system classifications and animal health through a scoping review as a foundation for the development of a framework that facilitates more specific estimates of livestock disease burdens. A top-down framework to classification is outlined based on a systematic review of existing classification methods and provides a basis for simple grouping of livestock at global scale. The proposed top-down classification framework, which is dominated by commodity focus of production along with intensity of resource use, may have less relevance at the sub-national level in some jurisdictions and will need to be informed and adapted with information on how countries themselves categorize livestock and their production systems. The findings in this study provide a foundation for analysing animal health burdens across a broad level of production systems. The developed framework will fill a major gap in how livestock production and health are currently approached and analysed.

Influence of Carboxymethyl Cellulose on the Green Synthesis of Gold Nanoparticles Using Gliricidia sepium and Petiveria alliacea Extracts: Surface-Enhanced Raman Scattering Effect Evaluation.

Gold nanoparticles (AuNPs) were synthesized and stabilized using ecological strategies: the extracts of the leaves of the plants Gliricidia sepium (GS) and Petiveria alliacea (PA) reduced the metallic Au ions to AuNPs. The AuNPs were analyzed as surface-enhanced Raman scattering (SERS) substrates for pyridoxine detection (vitamin B6). UV-vis spectroscopy was carried out to assess the stability of the AuNPs. As a result, absorption bands around 530 and 540 nm were obtained for AuNPs-PA and AuNPs-GS, respectively. Both cases associated it with localized surface plasmon resonance (LSPR). In the final stage of the synthesis, to stabilize the AuNPs, carboxymethyl cellulose (CMC) was added; however, LSPR bands do not exhibit bathochromic or hypsochromic shifts with the addition of CMC. Transmission electron microscopy (TEM) micrographs show relatively spherical morphologies; the particle diameters were detected around 7.7 and 12.7 nm for AuNPs-PA and AuNPs-GS, respectively. The nanomaterials were evaluated as SERS substrates on pyridoxine, revealing an intensification in the vibrational mode centered at 688 cm-1 associated with the pyridinic ring. Complementarily, different density functional theory functionals were included to obtain molecular descriptors on the Aun-cluster-pyridoxine interaction to study the SERS behavior.

Segregationally stabilised plasmids improve production of commodity chemicals in glucose-limited continuous fermentation.

BACKGROUND: The production of chemicals via bio-based routes is held back by limited easy-to-use stabilisation systems. A wide range of plasmid stabilisation mechanisms can be found in the literature, however, how these mechanisms effect genetic stability and how host strains still revert to non-productive variants is poorly understood at the single-cell level. This phenomenon can generate difficulties in production-scale bioreactors as different populations of productive and non-productive cells can arise. To understand how to prevent non-productive strains from arising, it is vital to understand strain behaviour at a single-cell level. The persistence of genes located on plasmid vectors is dependent on numerous factors but can be broadly separated into structural stability and segregational stability. While structural stability refers to the capability of a cell to resist genetic mutations that bring about a loss of gene function in a production pathway, segregational stability refers to the capability of a cell to correctly distribute plasmids into daughter cells to maintain copy number. A lack of segregational stability can rapidly generate plasmid-free variants during replication, which compromises productivity. RESULTS: Citramalate synthase expression was linked in an operon to the expression of a fluorescent reporter to enable rapid screening of the retention of a model chemical synthesis pathway in a continuous fermentation of E. coli. Cells without additional plasmid stabilisation started to lose productivity immediately after entering the continuous phase. Inclusion of a multimer resolution site, cer, enabled a steady-state production period of 58 h before a drop in productivity was detected. Single-cell fluorescence measurements showed that plasmid-free variants arose rapidly without cer stabilisation and that this was likely due to unequal distribution of plasmid into daughter cells during cell division. The addition of cer increased total chemical yield by more than 50%. CONCLUSIONS: This study shows the potential remains high for plasmids to be used as pathway vectors in industrial bio-based chemicals production, providing they are correctly stabilised. We demonstrate the need for accessible bacterial 'toolkits' to enable rapid production of known, stabilised bacterial production strains to enable continuous fermentation at scale for the chemicals industry.

Characterization and optimization of polymer-polymer aqueous two-phase systems for the isolation and purification of CaCo2 cell-derived exosomes.

Exosomes are cell-derived vesicles that present attractive characteristics such as nano size and unique structure for their use as drug delivery systems for drug therapy, biomarkers for prognostic, diagnostic and personalized treatments. So far, one of the major challenges for therapeutic applications of exosomes is the development of optimized isolation methods. In this context, aqueous two-phase systems (ATPS) have been used as an alternative method to isolate biological molecules and particles with promising expectations for exosomes. In this work, fractionation of exosomes obtained from CaCo2 cell line and culture media contaminants were individually performed in 20 polymer-polymer ATPS. The effect of design parameters such as polymer composition, molecular weight, and tie-line length (TLL) on polyethylene glycol (PEG)-Dextran, Dextran-Ficoll and PEG-Ficoll systems was studied. After partition analysis, 4 of the 20 systems presented the best exosome fractionation from contaminants under initial conditions, which were optimized via salt addition (NaCl) to a final concentration of 25 mM, to improve collection efficiency. The PEG 10,000 gmol-1 -Dextran 10,000 gmol-1 system at TLL 25% w/w with NaCl, showed the best potential isolation efficiency. Following this proposed strategy, an exosome purification factor of 2 in the top PEG-rich phase can be expected furtherly demonstrating that ATPS have the potential for the selective recovery of these promising nanovesicles.

Are AI systems biased against the poor? A machine learning analysis using Word2Vec and GloVe embeddings.

Among the myriad of technical approaches and abstract guidelines proposed to the topic of AI bias, there has been an urgent call to translate the principle of fairness into the operational AI reality with the involvement of social sciences specialists to analyse the context of specific types of bias, since there is not a generalizable solution. This article offers an interdisciplinary contribution to the topic of AI and societal bias, in particular against the poor, providing a conceptual framework of the issue and a tailor-made model from which meaningful data are obtained using Natural Language Processing word vectors in pretrained Google Word2Vec, Twitter and Wikipedia GloVe word embeddings. The results of the study offer the first set of data that evidences the existence of bias against the poor and suggest that Google Word2vec shows a higher degree of bias when the terms are related to beliefs, whereas bias is higher in Twitter GloVe when the terms express behaviour. This article contributes to the body of work on bias, both from and AI and a social sciences perspective, by providing evidence of a transversal aggravating factor for historical types of discrimination. The evidence of bias against the poor also has important consequences in terms of human development, since it often leads to discrimination, which constitutes an obstacle for the effectiveness of poverty reduction policies.

Application of Machine Learning Techniques to Help in the Feature Selection Related to Hospital Readmissions of Suicidal Behavior.

Suicide was the main source of death from external causes in Spain in 2020, with 3,941 cases. The importance of identifying those mental disorders that influenced hospital readmissions will allow us to manage the health care of suicidal behavior. The feature selection of each hospital in this region was carried out by applying Machine learning (ML) and traditional statistical methods. The results of the characteristics that best explain the readmissions of each hospital after assessment by the psychiatry specialist are presented. Adjustment disorder, alcohol abuse, depressive syndrome, personality disorder, and dysthymic disorder were selected for this region. The most influential methods or characteristics associated with suicide were benzodiazepine poisoning, suicidal ideation, medication poisoning, antipsychotic poisoning, and suicide and/or self-harm by jumping. Suicidal behavior is a concern in our society, so the results are relevant for hospital management and decision-making for its prevention.

Combination of Genome-Scale Models and Bioreactor Dynamics to Optimize the Production of Commodity Chemicals.

The current production of a number of commodity chemicals relies on the exploitation of fossil fuels and hence has an irreversible impact on the environment. Biotechnological processes offer an attractive alternative by enabling the manufacturing of chemicals by genetically modified microorganisms. However, this alternative approach poses some important technical challenges that must be tackled to make it competitive. On the one hand, the design of biotechnological processes is based on trial-and-error approaches, which are not only costly in terms of time and money, but also result in suboptimal designs. On the other hand, the manufacturing of chemicals by biological processes is almost exclusively carried out by batch or fed-batch cultures. Given that batch cultures are expensive and not easy to scale, technical means must be developed to make continuous cultures feasible and efficient. In order to address these challenges, we have developed a mathematical model able to integrate in a single model both the genome-scale metabolic model for the organism synthesizing the chemical of interest and the dynamics of the bioreactor in which the organism is cultured. Such a model is based on the use of Flexible Nets, a modeling formalism for dynamical systems. The integration of a microscopic (organism) and a macroscopic (bioreactor) model in a single net provides an overall view of the whole system and opens the door to global optimizations. As a case study, the production of citramalate with respect to the substrate consumed by E. coli is modeled, simulated and optimized in order to find the maximum productivity in a steady-state continuous culture. The predicted computational results were consistent with the wet lab experiments.

Executive Functioning in Adults with Down Syndrome: Machine-Learning-Based Prediction of Inhibitory Capacity.

The study of executive function decline in adults with Down syndrome (DS) is important, because it supports independent functioning in real-world settings. Inhibitory control is posited to be essential for self-regulation and adaptation to daily life activities. However, cognitive domains that most predict the capacity for inhibition in adults with DS have not been identified. The aim of this study was to identify cognitive domains that predict the capacity for inhibition, using novel data-driven techniques in a sample of adults with DS (n = 188; 49.47% men; 33.6 ± 8.8 years old), with low and moderate levels of intellectual disability. Neuropsychological tests, including assessment of memory, attention, language, executive functions, and praxis, were submitted to Random Forest, support vector machine, and logistic regression algorithms for the purpose of predicting inhibition capacity, assessed with the Cats-and-Dogs test. Convergent results from the three algorithms show that the best predictors for inhibition capacity were constructive praxis, verbal memory, immediate memory, planning, and written verbal comprehension. These results suggest the minimum set of neuropsychological assessments and potential intervention targets for individuals with DS and ID, which may optimize potential for independent living.

Multi-product biorefinery from Arthrospira platensis biomass as feedstock for bioethanol and lactic acid production.

With the aim to reach the maximum recovery of bulk and specialty bioproducts while minimizing waste generation, a multi-product biorefinery for ethanol and lactic acid production from the biomass of cyanobacterium Arthrospira platensis was investigated. Therefore, the residual biomass resulting from different pretreatments consisting of supercritical fluid extraction (SF) and microwave assisted extraction with non-polar (MN) and polar solvents (MP), previously applied on A. platensis to extract bioactive metabolites, was further valorized. In particular, it was used as a substrate for fermentation with Saccharomyces cerevisiae LPB-287 and Lactobacillus acidophilus ATCC 43121 to produce bioethanol (BE) and lactic acid (LA), respectively. The maximum concentrations achieved were 3.02 ± 0.07 g/L of BE by the MN process at 120 rpm 30 °C, and 9.67 ± 0.05 g/L of LA by the SF process at 120 rpm 37 °C. An economic analysis of BE and LA production was carried out to elucidate the impact of fermentation scale, fermenter costs, production titer, fermentation time and cyanobacterial biomass production cost. The results indicated that the critical variables are fermenter scale, equipment cost, and product titer; time process was analyzed but was not critical. As scale increased, costs tended to stabilize, but also more product was generated, which causes production costs per unit of product to sharply decrease. The median value of production cost was US$ 1.27 and US$ 0.39, for BE and LA, respectively, supporting the concept of cyanobacterium biomass being used for fermentation and subsequent extraction to obtain ethanol and lactic acid as end products from A. platensis.

Primary recovery of hyaluronic acid produced in Streptococcus equi subsp. zooepidemicus using PEG-citrate aqueous two-phase systems.

Given its biocompatibility, rheological, and physiological properties, hyaluronic acid (HA) has become a biomaterial of increasing interest with multiple applications in medicine and cosmetics. In recent decades, microbial fermentations have become an important source for the industrial production of HA. However, due to its final applications, microbial HA must undergo critical and long purification processes to ensure clinical and cosmetic grade purity. Aqueous two-phase systems (ATPS) have proven to be an efficient technique for the primary recovery of high-value biomolecules. Nevertheless, their implementation in HA downstream processing has been practically unexplored. In this work, polyethylene glycol (PEG)-citrate ATPS were used for the first time for the primary recovery of HA produced with an engineered strain of Streptococcus equi subsp. zooepidemicus. The effects of PEG molecular weight (MW), tie-line length (TLL), volume ratio (VR), and sample load on HA recovery and purity were studied with a clarified fermentation broth as feed material. HA was recovered in the salt-rich bottom phase, and its recovery increased when a PEG MW of 8000 g mol-1 was used. Lower VR values (0.38) favoured HA recovery, whereas purity was enhanced by a high VR (3.50). Meanwhile, sample load had a negative impact on both recovery and purity. The ATPS with the best performance was PEG 8000 g mol-1, TLL 43% (w/w), and VR 3.50, showing 79.4% HA recovery and 74.5% purity. This study demonstrated for the first time the potential of PEG-citrate ATPS as an effective primary recovery strategy for the downstream process of microbial HA.

Comparative Economic Analysis Between Endogenous and Recombinant Production of Hyaluronic Acid.

Hyaluronic acid (HA) is a biopolymer with a wide range of applications, mainly in the cosmetic and pharmaceutical sectors. Typical industrial-scale production utilizes organisms that generate HA during their developmental cycle, such as Streptococcus equi sub. zooepidemicus. However, a significant disadvantage of using Streptococcus equi sub. zooepidemicus is that it is a zoonotic pathogen, which use at industrial scale can create several risks. This creates opportunities for heterologous, or recombinant, production of HA. At an industrial scale, the recovery and purification of HA follow a series of precipitation and filtration steps. Current recombinant approaches are developing promising alternatives, although their industrial implementation has yet to be adequately assessed. The present study aims to create a theoretical framework to forecast the advantages and disadvantages of endogenous and recombinant strains in production with the same downstream strategy. The analyses included a selection of the best cost-related recombinant and endogenous production strategies, followed by a sensitivity analysis of different production variables in order to identify the three most critical parameters. Then, all variables were analyzed by varying them simultaneously and employing multiple linear regression. Results indicate that, regardless of HA source, production titer, recovery yield and bioreactor scale are the parameters that affect production costs the most. Current results indicate that recombinant production needs to improve current titer at least 2-fold in order to compete with costs of endogenous production. This study serves as a platform to inform decision-making for future developments and improvements in the recombinant production of HA.

Sentiment Analysis Techniques Applied to Raw-Text Data from a Csq-8 Questionnaire about Mindfulness in Times of COVID-19 to Improve Strategy Generation.

The use of artificial intelligence in health care has grown quickly. In this sense, we present our work related to the application of Natural Language Processing techniques, as a tool to analyze the sentiment perception of users who answered two questions from the CSQ-8 questionnaires with raw Spanish free-text. Their responses are related to mindfulness, which is a novel technique used to control stress and anxiety caused by different factors in daily life. As such, we proposed an online course where this method was applied in order to improve the quality of life of health care professionals in COVID 19 pandemic times. We also carried out an evaluation of the satisfaction level of the participants involved, with a view to establishing strategies to improve future experiences. To automatically perform this task, we used Natural Language Processing (NLP) models such as swivel embedding, neural networks, and transfer learning, so as to classify the inputs into the following three categories: negative, neutral, and positive. Due to the limited amount of data available-86 registers for the first and 68 for the second-transfer learning techniques were required. The length of the text had no limit from the user's standpoint, and our approach attained a maximum accuracy of 93.02% and 90.53%, respectively, based on ground truth labeled by three experts. Finally, we proposed a complementary analysis, using computer graphic text representation based on word frequency, to help researchers identify relevant information about the opinions with an objective approach to sentiment. The main conclusion drawn from this work is that the application of NLP techniques in small amounts of data using transfer learning is able to obtain enough accuracy in sentiment analysis and text classification stages.

Melanoma diagnosis using deep learning techniques on dermatoscopic images.

BACKGROUND: Melanoma has become more widespread over the past 30 years and early detection is a major factor in reducing mortality rates associated with this type of skin cancer. Therefore, having access to an automatic, reliable system that is able to detect the presence of melanoma via a dermatoscopic image of lesions and/or skin pigmentation can be a very useful tool in the area of medical diagnosis. METHODS: Among state-of-the-art methods used for automated or computer assisted medical diagnosis, attention should be drawn to Deep Learning based on Convolutional Neural Networks, wherewith segmentation, classification and detection systems for several diseases have been implemented. The method proposed in this paper involves an initial stage that automatically crops the region of interest within a dermatoscopic image using the Mask and Region-based Convolutional Neural Network technique, and a second stage based on a ResNet152 structure, which classifies lesions as either "benign" or "malignant". RESULTS: Training, validation and testing of the proposed model was carried out using the database associated to the challenge set out at the 2017 International Symposium on Biomedical Imaging. On the test data set, the proposed model achieves an increase in accuracy and balanced accuracy of 3.66% and 9.96%, respectively, with respect to the best accuracy and the best sensitivity/specificity ratio reported to date for melanoma detection in this challenge. Additionally, unlike previous models, the specificity and sensitivity achieve a high score (greater than 0.8) simultaneously, which indicates that the model is good for accurate discrimination between benign and malignant lesion, not biased towards any of those classes. CONCLUSIONS: The results achieved with the proposed model suggest a significant improvement over the results obtained in the state of the art as far as performance of skin lesion classifiers (malignant/benign) is concerned.

A novel MHC-II targeted BVDV subunit vaccine induces a neutralizing immunological response in guinea pigs and cattle.

Bovine viral diarrhoea virus (BVDV) is a major cause of economic loss in the cattle industry, worldwide. Infection results in reduced productive performance, growth retardation, reduced milk production and increased susceptibility to other diseases leading to early culling of animals. There are two primary methods used to control the spread of BVDV: the elimination of persistently infected (PI) animals and vaccination. Currently, modified live or inactivated vaccines are used in BVDV vaccination programmes, but there are safety risks or insufficient protection, respectively, with these vaccines. Here, we report the development and efficacy of the first targeted subunit vaccine against BVDV. The core of the vaccine is the fusion of the BVDV structural protein, E2, to a single-chain antibody, APCH, together termed, APCH-E2. The APCH antibody targets the E2 antigen to the major histocompatibility type II molecule (MHC-II) present on antigen-presenting cells. Industrial production of the vaccine is carried out using the baculovirus expression vector system (BEVS) using single-use manufacturing technologies. This new subunit vaccine induces strong BVDV-specific neutralizing antibodies in guinea pigs and cattle. Importantly, in cattle with low levels of natural BVDV-specific neutralizing antibodies, the vaccine induced strong neutralizing antibody levels to above the protective threshold, as determined by a competition ELISA. The APCH-E2 vaccine induced a rapid and sustained neutralizing antibody response compared with a conventional vaccine in cattle.

Economic evaluation of the development of a phage therapy product for the control of Salmonella in poultry.

Poultry products are one of the major transmission media of Salmonella enteritidis to humans. A promising alternative to reduce the load of Salmonella in poultry are bacteriophages. Elsewhere, a mixture of six bacteriophages has been used successfully, but large-scale production would be necessary to supply potential poultry market and costs analyses have not been calculated yet. For this, a powerful tool to predict production costs is bioprocess modeling coupled with economic analyses. This work aims to model the scaled-up production of a six bacteriophages mixture based on a laboratory/pilot-scale production using Biosolve Process. For the model construction, a combination of experimental and reported data was applied, in which different production alternatives and the range of 1-100% of the Colombian poultry market (at broiler's farm and slaughterhouse) were analyzed. Results indicate that the best cost-effective process configuration/scale is to use one bioreactor (156 L) for the six bacteriophages, then a 0.45 µm filtration for removal of biomass, and a 0.22 µm filtration for sterility; this to supply the 35% of the market size for broiler farms (equivalent to 210 million chickens). This configuration gives a production cost per chicken of US$ 0.02. Additionally, a sensitivity analysis and a theoretical contrast for understanding the impact that titer and recovery have on production scale determined that titer affects the most the cost and requires optimization. The present works serves as a first, and required, approach for the development of phage therapy products that are alternatives to present-day pathogens control strategies.

Ex vivo Manufactured Neutrophils for Treatment of Neutropenia-A Process Economic Evaluation.

Neutropenia is a common side-effect of acute myeloid leukemia (AML) chemotherapy characterized by a critical drop in neutrophil blood concentration. Neutropenic patients are prone to infections, experience poorer clinical outcomes, and require expensive medical care. Although transfusions of donor neutrophils are a logical solution to neutropenia, this approach has not gained clinical traction, primarily due to challenges associated with obtaining sufficiently large numbers of neutrophils from donors whilst logistically managing their extremely short shelf-life. A protocol has been developed that produces clinical-scale quantities of neutrophils from hematopoietic stem and progenitor cells (HSPC) in 10 L single-use bioreactors (1). This strategy could be used to mass produce neutrophils and generate sufficient cell numbers to allow decisive clinical trials of neutrophil transfusion. We present a bioprocess model for neutrophil production at relevant clinical-scale. We evaluated two production scenarios, and the impact on cost of goods (COG) of multiple model parameters including cell yield, materials costs, and process duration. The most significant contributors to cost were consumables and raw materials, including the cost of procuring HSPC-containing umbilical cord blood. The model indicates that the most cost-efficient culture volume (batch size) is ~100 L in a single bioreactor. This study serves as a framework for decision-making and optimization strategies when contemplating the production of clinical quantities of cells for allogeneic therapy.

Aqueous Two-Phase Systems at Large Scale: Challenges and Opportunities.

Aqueous two-phase systems (ATPS) have proved to be an efficient and integrative operation to enhance recovery of industrially relevant bioproducts. After ATPS discovery, a variety of works have been published regarding their scaling from 10 to 1000 L. Although ATPS have achieved high recovery and purity yields, there is still a gap between their bench-scale use and potential industrial applications. In this context, this review paper critically analyzes ATPS scale-up strategies to enhance the potential industrial adoption. In particular, large-scale operation considerations, different phase separation procedures, the available optimization techniques (univariate, response surface methodology, and genetic algorithms) to maximize recovery and purity and economic modeling to predict large-scale costs, are discussed. ATPS intensification to increase the amount of sample to process at each system, developing recycling strategies and creating highly efficient predictive models, are still areas of great significance that can be further exploited with the use of high-throughput techniques. Moreover, the development of novel ATPS can maximize their specificity increasing the possibilities for the future industry adoption of ATPS. This review work attempts to present the areas of opportunity to increase ATPS attractiveness at industrial levels.

Economic evaluation of the primary recovery of tetracycline with traditional and novel aqueous two-phase systems.

Antibiotics are a key pharmaceutical to inhibit growth or kill microorganisms. They represent a profitable market and, in particular, tetracycline has been listed as an essential medicine by the WHO. Therefore it is important to improve their production processes. Recently novel and traditional aqueous two-phase systems for the extraction have been developed with positive results. The present work performs an economic analysis of the production and recovery of tetracycline through the use of several ATPS through bioprocess modeling using specialized software (BioSolve, Biopharm Services Ltd, UK) to determine production costs per gram (CoG/g). First, a virtual model was constructed using published data on the recovery of tetracycline and extended to incorporate uncertainties. To determine how the model behaved, a sensitivity analysis and Monte Carlo simulations were performed. Results showed that ATPS formed by cholinium chloride/K3PO4 was the best option to recover tetracycline, as it had the lowest CoG/g (US$ 672.83/g), offered the highest recovery yield (92.42%), second best sample input capacity (45% of the ATPS composition) and one of the lowest materials contribution to cost. The ionic liquid-based method of ATPS is a promising alternative for recovering tetracycline from fermentation broth.