Due the need to recruit new volunteer blood donors and consequently keep the blood bags stock in collection centers of several countries, especially those in development, Technology Course in Aesthetics and Cosmetics coordination of the Faculdades Unidas do Vale do Araguaia (UNIVAR), Barra do Garças, MT, Brazil, created the "Red June - Use Your Inner Beauty" program, which aims to motivate and educate people about the importance of donating blood voluntarily to save lives. The program aims to cover several countries and will take to the community various activities, such as motivational lectures and informative pamphlets distribution, as well as other actions. The program is intended to draw the attention of the whole world to the need for voluntary unpaid blood donation.

The present work proposes a procedure to evaluate the economic impact of recycling cellulolytic enzymes in the process of ethanol production from sugarcane bagasse. The proposed procedure stems from the experimental results reported about the yield changes obtained at laboratory level when the cellulose from Aspergillus niger, Sigma is used when it is recycled in one or two times. From the technological demands of enzymatic quality, some necessary mixtures are established for the technological requirements and with that the levels of addition of original enzyme are evaluated for different levels of recycling of enzymes. The procedure then includes possible scenarios for recirculating the enzymes one or several times and it establishes the economic impacts regarding reduction of raw materials. Since the process of recycling enzymes is planned for an industrial installation, economic estimates of the investment are made for a given capacity with and without recycling of enzymes. For this, starting from the material and energy balances, the investment and production costs are estimated, as well as the investments required to be able to recycle the enzymes in the enzymatic hydrolysis stage. Finally, economic technical analysis are carried out to evaluate the effectiveness of the enzyme recycling by measuring the recovery of investments required for this activity in industrial conditions. The economic benefits of recycling enzymes increase as installed production capacity increases.

Over-expression of the Epidermial Growth Factor Receptor (EGFR) is usually present in more than 90% of the Head and Neck Squamous Cell Cancer (HNSCC), due to this the selection of more selective and powefull inhibitors is a major point to threat this type of cancer. In fact, has been demonstrated that this over-expression is responsible of a more aggressive disease, increased resistance to chemotherapy and radiotherapy, increased metastasis, inhibition of apoptosis, promotion of neoplastic angiogenesis, and, finally, poor prognosis and decreased survival. Computational methods are a major tool while looking for new EGFR inhibitors since should help researchers selecting new and enhanced inhibitors in this area. In this contest, Quantitative structure activity relationship (QSAR) is one of the most and widely used computational technique to select new EGFR inhibitors. Here we will present a new QSAR approach aimed at the prediction of new EGFR inhibitors drugs using 1D molecular descriptors.

Prediction of an endpoint for new query chemical without having any experimental response data is one of the important applications of Quantitative structure-activity relationship (QSAR) models. Usually a QSAR model is developed based on chemical information of a properly designed training set and corresponding experimental response data while the model is validated using one or more test set(s) for which the experimental response data are available. However, it is interesting to estimate the reliability of predictions when the model is applied to a completely new data set (true external set) even when the new data points are within applicability domain (AD) of the developed model. In the present study, we have developed a tool “Prediction Reliability Indicator” to indicate or categorize the quality of predictions for the test set or true external set into three groups: good (with composite score 3), moderate (with composite score 2) and bad (with composite score 1). Here, we have used three criteria [1) Mean absolute error of leave-one-out predictions for 10 most close training compounds for each query molecule (J Chemom 2018, http://dx.doi.org/10.1002/cem.2992 ); 2) Applicability domain in terms of similarity based on the standardization approach (Chemom Intell Lab Sys, 145, 2015, 22-29, http://dx.doi.org/10.1016/j.chemolab.2015.04.013); 3) Proximity of the predicted value of the query compound to the experimental mean training response (Chemom Intell Lab Sys, 162, 2017, 44-54, https://authors.elsevier.com/a/1UOpFcc6LvBdv )] in different weightage schemes for making a composite score of predictions. The tool can automatically find the optimum weightage based on % correct prediction score computed using a test set with known observed response and thus known quality of predictions. However, the user also has an option to select the weightage manually. It was found that using the most frequently appearing weightage scheme 0.5:0:0.5, the composite score based categorization showed concordance with absolute prediction error based categorization for more than 80% test data points while working with 5 different data sets with 15 models for each set derived in three different splitting techniques. These observations were also confirmed with two external sets suggesting applicability of the scheme to judge the reliability of predictions for new data sets. The tool is available free of charge at http://dtclab.webs.com/software-tools .

With increasing population of the world, the uses of needful chemicals are also increasing day by day. In the last 20 years, thousands of research papers have been published, reporting different aspects of chemicals known as contaminants of emerging concern (CEC), and more than 40,000 chemicals are identified as CECs. The pharmaceuticals and personal care products (PPCPs), endocrine disrupting chemicals (EDCs), UV filters etc. belong to the CEC class and their incorrect disposal procedure has made them as emerging contaminants. CECs are detected in the groundwater which may produce undesirable effects to human health and aquatic organisms. Unfortunately, very less amount of data are available on the environmental behavior and ecotoxicity of pharmaceuticals and other CECs; therefore, we need computational models for ecotoxicological risk assessment of chemicals with speed and accuracy that may fill the data gap by utilizing fewer resources and experimental animals. New approaches like Quantitative Structure-Activity Relationship (QSAR) may be able to generate valuable information and could help to meet these challenges. In this present study, we have developed Quantitative Structure-Toxicity Relationship (QSTR) models for the prediction of aquatic ecotoxicity of CECs on fresh water planarian (Dugesia japonica) by partial least squares (PLS) regression algorithm using simple molecular descriptors selected by genetic algorithm approach. Furthermore, interspecies quantitative structure toxicity-toxicity relationship (QSTTR) models were developed between planarian and daphnia (Daphnia magna) as well as between planarian and fish (Pimephales promelas) which can extrapolate data from one toxicity endpoint to another toxicity endpoint. The descriptors were calculated from PaDEL-Descriptor and Dragon software. Both QSTR and QSTTR models have desirable statistical qualities, meeting rigorous criteria of different validation metrics and OECD guidelines. Applicability domain assessment was also carried out to define the scope of the developed models and to highlight the compounds which are falling outside the domain. Consensus predictions were also performed based on multiple models generated in this study by using the Intelligent Consensus Predictor (ICP) tool http://teqip.jdvu.ac.in/QSAR_Tools/DTCLab/) to enhance the prediction quality for external set compounds. This study shows how in silico models can be applied for the toxicity assessment of CECs in aquatic organisms and indicating what are the structural features involved in their toxicity.

In recent years, an increased attention has been paid towards the use of chemometric modeling tools in predicting properties of various synthetic organic materials including polymers. In silico tools such as quantitative structure-property relationships (QSPRs) have become very much popular in designing organic molecules with desired physicochemical properties. These approaches save man power, cost of instrumentation, time and chemical wastages. In the current work, predictive QSPR models have been developed for predicting refractive index (RI) of a set of 221 diverse organic polymers by using theoretical 2D molecular descriptors computed from the monomer units of the polymers. Double cross-validation (DCV) followed by partial least squares (PLS) regression methodology was adopted for the generation of QSPR models using genetic algorithm (GA) as the descriptor selection technique. The predictive performance of models was judged by using cross-validation (leave-one-out or LOO), Y randomization, prediction for a test set and applicability domain analysis followed by comparison with the quality of previously reported QSPR models. Presence of polarizability, aromatic ring and different functional groups were the main contributing factors that influence the change of refractive index. An “Intelligent consensus predictor” (http://teqip.jdvu.ac.in/QSAR_Tools/DTCLab/) was employed on the final models to improve the quality of predictions for the external dataset. We have used the final selected models for the prediction of the refractive index of five small virtual libraries of monomers recently reported (Computational Materials Science, 2017, 137:215-224), and finally the predicted values were compared with the previous model derived predictions. Additionally, a true external data set of 98 diverse monomer units with the experimental RI values of the corresponding polymers were used to check the predictability of the obtained QSPR models. The good predictive ability of the derived QSPR models was also reflected from very good external predicted variance for the true external set.

The occurrence and risk associated with pharmaceuticals in aquatic flora is insufficiently understood due to a huge lack of documented resources. However, in recent decades, an enhanced focus has been made towards testing of the level of pharmaceuticals in water bodies due to several reports on accumulation of these ingredients to the range of not less than 0.1µg/L in surface waters. Due to involvement of time, cost, labor and animal sacrifice associated with experimental testing of toxicity, Quantitative Structure-Toxicity Relationship (QSTR) models have been recommended to bridge data gaps by European Union Commission’s Scientific Committee on Toxicity, Ecotoxicity, and Environment (CSTEE). In the current work, QSTR models have been developed for the toxicity of pharmaceuticals against Pseudokirchneriella subcapitata, Daphnia magna, Oncorhynchus mykiss and Pimephales promelas. The acute experimental toxicity data were mainly taken from the ECOTOX database. Genetic algorithm followed by partial least squares technique was used for model development whereas validation was performed using stringent internal and external validation metrics, namely cross-validated leave-one-out predicted variance for the training set while average rm2test and MAE95% for the test set, following strict OECD guidelines. Selected 2D descriptors namely extended topochemical atom (ETA) descriptors, constitutional indices, functional group count, atom-centered and CATS-2D were calculated from DRAGON and PaDEL-Descriptor software for model development. The frequent repetition and higher VIP value of 2D atom pair descriptors namely (B01[C-X], F08 [O-O]) suggest their significant impact on toxicity of pharmaceuticals against different aquatic organisms followed by the number of rotatable bonds and presence of lone pair electrons involved in resonance with aromatic structures as defined by the 2nd generation ETA indices. Models were developed with and without LogP/lipophilicity descriptors separately to understand the impact of LogP on the model quality. The applicability domain study was carried out using different techniques namely DModX and standardization approach in order to set a predefined chemical zone of applicability for the obtained QSTR models; the external test set compounds falling outside the domain were not taken for further analysis while making a prioritized list of most potent emerging contaminants. An additional comparison was made with predictions from ECOSAR, an online expert system, using calculated RMSE values obtained fromour models and those calculated by ECOSAR, in order to prove predictability of the obtained models. Finally, the obtained robust models were utilized to rank a large dataset of approximately 1267 drug like molecules in order of their potential for emerging contaminants following a scaling technique.

The aim of drug delivery is primarily focused on the optimum bioavailability at the targeted site of action over a defined period of time. Nanoparticle plays significant role in the drug delivery as it can be designed as target based, with improved stability, increased drug stability as well as can offer constant rate in the drug delivery. Nanoparticles can be created via carbamate, thiourea and amide linkage as well as via electrostatic interaction, hydrophobic entrapment and chemisorptions. Literature also supported the profound antibacterial and antiviral activity of silver nanoparticles. On the basis of methodology adopted for the preparation, nanoparticles, nanospheres or nanocapsules can be prepared. For the nanoparticles, methods like dispersion of preformed polymers, polymerization of monomers and ionic gelation/ coecervation of hydrophilic polymer technology were usually adopted.

Caffeine (1,3,7 - trimethylxanthine) is a natural molecule present in a variety of plants, seeds or fruits standing out in coffee, tea, mate, cola nuts, cocoa and guarana. It is widely used in different industries, acting as a stimulant for respiratory and central nervous systems. Ilex guayusa L., is located in the Amazon region of Colombia, Ecuador and Peru. Plant leaves present alkaloids such as caffeine and theobromine. In this work, extraction of caffeine from Ilex guayusa L. leaves using supercritical carbon dioxide (SCCO₂) was studied. The overall caffeine recovery from plant matrix was determined as a function of time (0.17, 0.5, 1, 2 and 4 h) at the same extraction conditions (23 MPa and 328 K). Ethanol as cosolvent was introduced in the extraction vessel to soak the vegetable material before SCCO₂ was pumped. Cosolvent to solvent ratio remained constant in each experiment (3.5 g of ethanol / 100 g of SCCO₂). The highest caffeine recovery 89.7% was obtained after four hours of dynamic extraction. Barton´s model was used to analyze the extraction kinetics, data was successfully fitted (R2=0.974) and diffusion coefficient was determined using model assumptions. Information here is presented for the very first time, is useful to predict extraction yields and to promote further research with this natural material.

Hydrolysis of cellulose is a fundamental step related to the amount of glucose obtained for ethanol production. The aim of this work has been to improve the conditions of enzymatic hydrolysis of the study performed by Mesa, 2010. The same factors used in the study were taken into account to be improved, which are: temperature, solid percentage, Tween 80 surfactant, agitation speed, amount of cellulase and time. The Plackett-Bürman method was used for 8 experiments, to discard variables that do not influence the enzymatic hydrolysis process, in order to subsequently adjust the model and use the Box-Hunter factorial optimization design. The glucose yield was improved, obtaining results of 25.90%, unlike the first study in which 24.33% were obtained; this data for every 100 grams of bagasse.