Phytoremediation as an effective tool to handle emerging contaminants

Phytoremediation is a process which effectively uses plants as a tool to remove, detoxify or immobilize contaminants. It has been an eco-friendly and cost-effective technique to clean contaminated environments. The contaminants from various sources have caused an irreversible damage to all the biotic factors in the biosphere. Bioremediation has become an indispensable strategy in reclaiming or rehabilitating the environment that was damaged by the contaminants. The process of bioremediation has been extensively used for the past few decades to neutralize toxic contaminants, but the results have not been satisfactory due to the lack of cost-effectiveness, production of byproducts that are toxic and requirement of large landscape. Phytoremediation helps in treating chemical pollutants on two broad categories namely, emerging organic pollutants (EOPs) and emerging inorganic pollutants (EIOPs) under in situ conditions. The EOPs are produced from pharmaceutical, chemical and synthetic polymer industries, which have potential to pollute water and soil environments. Similarly, EIOPs are generated during mining operations, transportations and industries involved in urban development. Among the EIOPs, it has been noticed that there is pollution due to heavy metals, radioactive waste production and electronic waste in urban centers. Moreover, in recent times phytoremediation has been recognized as a feasible method to treat biological contaminants. Since remediation of soil and water is very important to preserve natural habitats and ecosystems, it is necessary to devise new strategies in using plants as a tool for remediation. In this review, we focus on recent advancements in phytoremediation strategies that could be utilized to mitigate the adverse effects of emerging contaminants without affecting the environment.

Efficient Extraction of the RuBisCO Enzyme from Spinach Leaves Using Aqueous Solutions of Biocompatible Ionic Liquids

Ribulose-1,5-biphosphate carboxylase/oxygenase (RuBisCO) is the most abundant protein on the planet, being present in plants, algae and various species of bacteria, with application in the pharmaceutical, chemical, cosmetic and food industries. However, current extraction methods of RuBisCO do not allow high yields of extraction. Therefore, the development of an efficient and selective RuBisCOs’ extraction method is required. In this work, aqueous solutions of biocompatible ionic liquids (ILs), i.e., ILs derived from choline and analogues of glycine-betaine, were applied in the RuBisCO’s extraction from spinach leaves. Three commercial imidazolium-based ILs were also investigated for comparison purposes. To optimize RuBisCO’s extraction conditions, response surface methodology was applied. Under optimum extraction conditions, extraction yields of 10.92 and 10.57 mg of RuBisCO/g of biomass were obtained with the ILs cholinium acetate ([Ch][Ac]) and cholinium chloride ([Ch]Cl), respectively. Circular dichroism (CD) spectroscopy results show that the secondary structure of RuBisCO is better preserved in the IL solutions when compared to the commonly used extraction solvent. The obtained results indicate that cholinium-based ILs are a promising and viable alternative for the extraction of RuBisCO from vegetable biomass.

Production of bacterial cellulose films by Gluconoacetobacter xylinus for lipase immobilization

Bacterial cellulose (BC), a microbial polysaccharide, has chemically equivalent structure to plant cellulose with unbranched pellicle structure of only glucose monomers. Due to the unique nanostructure, BC has great potential in enzyme immobilization. In this study, the effects of different cultivation conditions including rotational speed, initial inoculum concentration and medium pH on the film-like cellulosic biomass formation of Gluconacetobacter xylinus JCM 9730 were examined. The resultant BC films were then studied for its feasibility in the immobilization of lipase, a widely used enzyme in biotechnological and industrial processes including food, pharmaceutical, chemical and paper industries. Results showed that increasing in rotational speed from 0 rpm to 200 rpm converted cellulose-producing cells to non-cellulose-producing ones, leading to a significant decline in BC film formation. The increase in initial inoculum size from 0.01 g/L to 0.1 g/L reduced sugar concentration and surface area of the medium, and therefore inhibiting the formation of film-like cellulosic biomass. In addition, the optimum pH range of Acetobacter species from 5.4 – 6.3 was found not optimal for BC film formation. The highest amount of film-like cellulosic biomass of 19.01 g/L was obtained under static condition (0 rpm) with initial cell concentration of 0.04 g/L and initial pH of 4.0. The BC film samples were then acetylated with acetic anhydride/iodine system to convert the hydroxyl groups to less hydrophilic acetyl groups and were used for lipase immobilization. Results showed that lipase immobilized on acetylated BC still maintained its lipid hydrolytic activity. It can be hence concluded that BC films produced by G. xylinus JCM 9730 were potential for lipase immobilization.

Lipid-Based Nanovesicular Drug Delivery Systems

In designing a new drug, considering the preferred route of administration, various requirements must be fulfilled. Active molecules pharmacokinetics should be reliable with a valuable drug profile as well as well-tolerated. Over the past 20 years, nanotechnologies have provided alternative and complementary solutions to those of an exclusively pharmaceutical chemical nature since scientists and clinicians invested in the optimization of materials and methods capable of regulating effective drug delivery at the nanometer scale. Among the many drug delivery carriers, lipid nano vesicular ones successfully support clinical candidates approaching such problems as insolubility, biodegradation, and difficulty in overcoming the skin and biological barriers such as the blood–brain one. In this review, the authors discussed the structure, the biochemical composition, and the drug delivery applications of lipid nanovesicular carriers, namely, niosomes, proniosomes, ethosomes, transferosomes, pharmacosomes, ufasomes, phytosomes, catanionic vesicles, and extracellular vesicles.

Analyzing transfer learning impact in biomedical cross-lingual named entity recognition and normalization

Background The volume of biomedical literature and clinical data is growing at an exponential rate. Therefore, efficient access to data described in unstructured biomedical texts is a crucial task for the biomedical industry and research. Named Entity Recognition (NER) is the first step for information and knowledge acquisition when we deal with unstructured texts. Recent NER approaches use contextualized word representations as input for a downstream classification task. However, distributed word vectors (embeddings) are very limited in Spanish and even more for the biomedical domain. Methods In this work, we develop several biomedical Spanish word representations, and we introduce two Deep Learning approaches for pharmaceutical, chemical, and other biomedical entities recognition in Spanish clinical case texts and biomedical texts, one based on a Bi-STM-CRF model and the other on a BERT-based architecture. Results Several Spanish biomedical embeddigns together with the two deep learning models were evaluated on the PharmaCoNER and CORD-19 datasets. The PharmaCoNER dataset is composed of a set of Spanish clinical cases annotated with drugs, chemical compounds and pharmacological substances; our extended Bi-LSTM-CRF model obtains an F-score of 85.24% on entity identification and classification and the BERT model obtains an F-score of 88.80% . For the entity normalization task, the extended Bi-LSTM-CRF model achieves an F-score of 72.85% and the BERT model achieves 79.97%. The CORD-19 dataset consists of scholarly articles written in English annotated with biomedical concepts such as disorder, species, chemical or drugs, gene and protein, enzyme and anatomy. Bi-LSTM-CRF model and BERT model obtain an F-measure of 78.23% and 78.86% on entity identification and classification, respectively on the CORD-19 dataset. Conclusion These results prove that deep learning models with in-domain knowledge learned from large-scale datasets highly improve named entity recognition performance. Moreover, contextualized representations help to understand complexities and ambiguity inherent to biomedical texts. Embeddings based on word, concepts, senses, etc. other than those for English are required to improve NER tasks in other languages.

Applications of Autoencoders along with Deep Learning Techniques to generate valid molecules

From the moment of identifying the fundamental cause of an illness to its availability in the marketplace, it takes an average of 10 years and almost $2.6 billion dollars to develop a medication. We’re actually hunting for a needle in a haystack, which takes a lot of time, effort, and money. In a solution space of between 1030 and 10100 synthetically viable compounds, we’re seeking for the one molecule that can turn off a disease at the molecular level. The chemical solution space is just too large to adequately screen for the desired molecule. Only a small percentage of the synthetically viable compounds for wet lab research are stored in pharmaceutical chemical repositories. Computational de novo drug design can be used to explore this vast chemical space and develop previously undesigned compounds. Computational drug design can cut the amount of time spent in the discovery phase in half, resulting in a shorter time to market and lower drug prices. Deep learning and artificial intelligence (AI) have opened up new perspectives in cheminformatics, especially in molecules generative models. Recurrent neural networks (RNNs) trained with molecules in the SMILES text format, in particular, are very good at exploring the chemical space. Two baseline models were created for generating molecules, one of the model includes an encoder that takes SMILES as input and then develops a deep generative LSTM model which acts as a hidden layer and the output from layers acts as an input to the decoder. The other baseline model acts the same as the above-mentioned model but it includes latent space, it is simply a representation of compressed data that bring related data points closer together physically. To learn data properties and find simpler data representations for analysis, and weights which are obtained from the previous model to generate more efficient molecules. Then created a custom function to play with the temperature of the softmax activation function which creates a threshold value for the valid molecules to generate. This model enables us to produce new molecules through successful exploration.

PHYSICOCHEMICAL COMPOSITION AND HEAVY METAL DETERMINATION OF SELECTED INDUSTRIAL EFFLUENTS OF IBADAN CITY, NIGERIA

The rise of heavy metal presence in environmental waters has made it necessary to continuously examine industrial effluents to maintain the quality of the environment. The focus of this study is centered on determining the heavy metal concentrations and some physicochemical parameters in twelve industrial effluents samples collected from various locations across Ibadan city. A composite sampling method was utilized to obtain representative effluent samples of the 12 Industries (categorized into food, beverage, tobacco, plastic, Pharmaceutical, chemical, and allied industries) and borehole samples from around the city were used as control. The effluent samples were digested by nitric acid (HNO3) and analyzed for cobalt (Co), chromium (Cr), copper (Cu), iron (Fe), and lead (Pb) using the atomic absorption spectrophotometric method (AAS). Some physicochemical parameters such as pH (Jenway 3510 pH meter), total dissolved solids (Hanna TDS meter), total suspended solids, and phosphate were determined. The heavy metal mean values were compared with Federal Environment Protection Agency (FEPA) and the United States Environmental Protection Agency (USEPA) standard values shown in table 1. The mean concentrations of heavy metal in the industrial effluent samples were Cu (0.32 mg/L), Pb (0.037 mg/L), Ni (0.50 mg/L), Co (0.037 mg/L), Cd (0.016 mg/L), Fe (54.0 mg/L) and Cr (0.44 mg/L). It was found that Chemical and allied industries have the highest concentration for metals such as Fe (128 mg/L), Ni (1.1 mg/L), and Cu (0.27 mg/L) while Cr (0.0067 mg/L) and Co (0.08 mg/L) were obtained in the Food/Beverage and pharmaceutical industries respectively. Conclusively, the industries around the Ibadan city stand as potential contributors to pollution, hence a periodical and continuous assessment effort are recommended.

Assessment of Job Risks in the Chemical Laboratory of the Pharmacy Study Program with Job Safety Analysis (JSA) Techniques

Chemistry laboratory organizes practicum, one of them quantitative pharmaceutical chemistry which uses a variety of chemicals and tools where if not careful, not following instructions or procedures even underestimate will cause work accidents and/ or occupational illness. Is observational descriptive research on the job risk assessments in chemical laboratories prodi pharmacy with JSA techniques aimed at finding out the risk of work performed. Population is type practicum work in chemistry laboratory and the sample is quantitative pharmaceutical chemistry practicum. Data collection techniques i.e. the work that has been selected is further determined with the working procedure and the working steps and then determines the findings of errors from each step of the work then further identify the potential hazards of each step of the work. Data analysis techniques is data from each finding of errors determined potential injury / danger / loss and its impact is then calculated risk value (risk matrix) i.e. the value of consequences multiplied by opportunity value where the results are could grouped into risk extreme, high, medium or low that continued with to determine safeguards measures that can be done to control the such danger. The results showed that performs the titration step has high very risk level value compared to another work step other of pharmaceutical chemical practicum quantitative. Titration activities carry an extreme or significant risk of harm when performed incompatible with working measures and unsafe with potential danger (disadvantage) is liquid evaporates (inhaled), disturbances The End Point of the Titration: change in color and determination of concentration, and perform movements manual repetitive continuously namely mixing the liquid or rotating the container (erlenmeyer) contains a chemical liquid by hand continuously. Chemistry laboratory organizes practicum, one of them quantitative pharmaceutical chemistry which uses a variety of chemicals and tools where if not careful, not following instructions or procedures even underestimate will cause work accidents and/ or occupational illness. Is observational descriptive research on the job risk assessments in chemical laboratories prodi pharmacy with JSA techniques aimed at finding out the risk of work performed. Population is type practicum work in chemistry laboratory and the sample is quantitative pharmaceutical chemistry practicum. Data collection techniques i.e. the work that has been selected is further determined with the working procedure and the working steps and then determines the findings of errors from each step of the work then further identify the potential hazards of each step of the work. Data analysis techniques is data from each finding of errors determined potential injury / danger / loss and its impact is then calculated risk value (risk matrix) i.e. the value of consequences multiplied by opportunity value where the results are could grouped into risk extreme, high, medium or low that continued with to determine safeguards measures that can be done to control the such danger. The results showed that performs the titration step has high very risk level value compared to another work step other of pharmaceutical chemical practicum quantitative. Titration activities carry an extreme or significant risk of harm when performed incompatible with working measures and unsafe with potential danger (disadvantage) is liquid evaporates (inhaled), disturbances The End Point of the Titration: change in color and determination of concentration, and perform movements manual repetitive continuously namely mixing the liquid or rotating the container (erlenmeyer) contains a chemical liquid by hand continuously.