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AutoChem is a software package consisting of two modules. The first module is a virtual chemical reactor that can generate reaction products starting from inserted reactants and reactions. The process can be iterated, calculating reactions of the products obtained from the previous steps. To avoid a combinatorial explosion of products, constraints can be inserted. AutoChem module can generate 3D structures of the products obtained as well as a list of all the reactions occurred (chemical network).This can be useful for the calculation of reaction free energies by using computational chemistry programs like Gaussian and ORCA. Usually, the computational step involves the geometry optimization of all products followed by the calculation of vibrational frequencies of the optimized structures, to assess if a local minimum is reached. Finally, when products free energies are obtained, the calculation of the reactions free energies can be done.The second module of Autochem, called check, helps to perform the latter step, launching the jobs, collecting products free energies, restarting false minima and calculating the reactions free energies. This module is intended as a general use even outside AutoChem and might be used to perform a large number of free energy calculations with a little effort from the user. This permits to have results at the level of theory needed. AutoChem might also be used for teaching organic chemistry and basic cheminformatics dealing with SMILES and SMARTS by inserting reactants and reactions and analyzing the products obtained.

Background: Elements symbols, groups, periods, and other periodic table-related topics are of fundamental importance in science, but it can be a bit of a taxing subject to learn at times. By combining video games with teaching, the topic could be turned into fun. Methods: In this regard we decided to write a program under the basis of the universally known game snake, called periodic caterpillar (in Italian called BRUCO). The gameplay is intended to help students cooperate. In this purpose, we also designed a joystick interface which is more comfortable for primary-grade students. Use case: Teaching in the classroom by game-based learning is the major usage of this research. We present the usage of this game in two different ways. One student/person play alone to understand their own proficiency with a periodic table. Another one is a group of students/people who can participate and play to exhibit their interpersonal skills. Conclusions: Bruco could prove highly useful for teaching chemistry to school students in a cooperative way. It may also be useful as a refreshment in a research laboratory-like environment and in the classroom.

This research was carried out to comparatively study the impacts of portfolio-based assessment, self-assessment, and scaffolded peer assessment on reading comprehension, vocabulary learning, and grammatical accuracy of Afghan English as a foreign language learners. To accomplish this, 172 learners enrolled at a language institute, through an Oxford Quick Placement Test (OQPT), 120 lower-intermediate learners and 5 higher-intermediate learners were selected. These selected participants were assigned into four groups: portfolio group (N = 30), self-assessment group (N = 30), scaffolded peer assessment group (N = 35), and control group (N = 30). The five higher-intermediate learners were injected into the scaffolded peer assessment group to function as the mediators, hence more participants in the group. After selecting the participants, through a reading-span test developed by Shahnazari (2013), learners' working memory (WM) span was determined. It was discovered that 16 subjects in the portfolio condition, 14 self-assessment learners, 18 participants in the peer assessment group, and 13 participants in the control condition had high WM, while the rest of the participants had low WM. Thereafter, through validated instructor-made tests, subjects' reading comprehension, knowledge of targeted lexical items, and grammatical accuracy at baseline were determined. Then, a ten-session treatment began. After the treatment, a follow-up post-test was administered. The results of three two-way between-group MANOVA disclosed that all three experimental conditions outstripped the comparison group on the second occasion and that high WM learners outstripped low WM learners (with a large effect size on reading comprehension test (partial eta squared = 0.365), a moderate effect size on the same test among high vs. low WM learners (partial eta squared = 0.095), a large effect size on vocabulary post-test (partial eta squared = 0.465), a moderate effect size on the same test among high vs. low WM learners (partial eta squared = 0.083), a large effect size on grammar test (partial eta squared = 0.500), and a moderate effect size on the same test among high vs. low WM learners (partial eta squared = 0.072)). The results further revealed that subjects in the scaffolded peer assessment group outstripped subjects in other experimental conditions, but the difference was non-significant. Additionally, the difference between the portfolio assessment and self-assessment group was not statistically significant. The implications of the study are reported.

Technological advancement in the rise of the Internet of Things (IoT) and computational modelling and application development has revolutionized medical care. Monitoring cardiovascular risks is faster, easier, and more accurate than ever. Using IoT-enabled computational modelling and application development, medical professionals can detect, monitor, and predict certain conditions more efficiently. That paper explores the Internet of Things (IoT) enabled computational model and application development that can be used to monitor cardiovascular risks effectively. Cardiovascular diseases are the leading cause of death globally, and currently, there is a lack of comprehensive and reliable monitoring systems to assess the risk of such diseases. IoT enables the integration of different data sources, such as physical activity, diet, BMI and the environmental context, to form a comprehensive tracking tool that can provide accurate cardiovascular risk assessment. The developed application can offer personalized health coaching, leveraging machine learning algorithms to identify patterns and adapt a user's healthcare journey. Ultimately, this paper assesses the potential of IoT technology for monitoring cardiovascular risks and integrating it into current healthcare systems.

Chronic non-healing wounds impose a significant economic burden on the healthcare system. Among the existing strategies in wound healing therapy, targeting specific enzymes, namely tyrosyl-tRNA synthetase, TGFBR1, IL-1β, and Pseudomonas aeruginosa MvfR, represents an important aspect of the strategy of wound healing. Our research focuses on creating new curcumin hybrids (SV01-SV20) for harnessing wound healing activities and possesses considerable therapeutic implications. The compounds were designed and evaluated through in-silico screening using Molinspiration Cheminformatics, Osiris Property Explorer, AdmetSAR, and SwissADME. The initial filtering process identified the top 19 hybrids, which were then subjected to docking using the AutoDock tool. The results of the molecular docking analysis indicate that the curcumin hybrids, specifically tyrosyl-tRNA synthetase (SV09, SV15), TGFBR1 (SV01, and SV11), IL-1β (SV07, SV08), and Pseudomonas aeruginosa MvfR (SV09, SV13), showed the most significant binding energies (-8.69, -9.01, -8.49, -8.13, -8.48, -6.14, -9.7, and -9.2 kcal/mol, respectively) among the designed compounds. The binding energies were higher than the standard curcumin, indicating a potentially more substantial interaction with the target. Extensive molecular dynamics simulations confirmed the stability of these derivatives throughout the 100 ns simulation; the ligand-protein complexes maintained structural stability. The designed compounds demonstrated favorable pharmacokinetic properties, drug-likeness scores, bioactivity assessments, adherence to Lipinski's rule of five, and low toxicity risks. Overall, results consistently demonstrated that SV01, SV07, SV08, SV09, SV11, SV13, and SV15 compounds hold significant potential as effective agents in treating chronic wounds.

Fungi are an alternative source to animal-based chitin. In fungi, chitin fibrils are strongly interconnected and bound with glucans that justify the unique matrix. The present study aimed to extract chitin and glucans from the mycelium of several wood decay fungal strains in order to obtain flexible materials and to check correlations between chitin content and the mechanical properties of these materials. Five strains were chosen in consideration of their different cell wall chemical composition (high content of α-glucans, β-glucans or chitin) to evaluate how these differences could influence the mechanical and chemical characteristics of the material. The fungal strains were cultivated in liquid-submerged dynamic fermentation (both flasks and bioreactor). Chitin and glucans were crosslinked with acetic acid and plasticized with glycerol to obtain flexible sheets. Abortiporus biennis, Fomitopsis iberica and Stereum hirsutum strains were found to adapt to produce material with adequate flexibility. The obtained materials were characterized by Thermogravimetric analysis (TGA) for the understanding of the material composition. The material obtained from each species was mechanically tested in terms of tear strength, elongation at break, and Young's modulus.

In this paper, Nnce open mapping, Nnce closed mapping, Nnce homeomorphism and Nnce-C homeomorphism are introduced.

Abstract Pancreatic ductal adenocarcinoma (PDAC) cells have a great demand for nutrients in the form of sugars, amino acids, and lipids. Particularly, amino acids are critical for cancer growth and, as intermediates, connect glucose, lipid and nucleotide metabolism. PDAC cells meet these requirements by upregulating selective amino acid transporters. Here we show that SLC38A5 (SN2/SNAT5), a neutral amino acid transporter is highly upregulated and functional in PDAC cells. Using CRISPR/Cas9-mediated knockout of SLC38A5, we show its tumor promoting role in an in vitro cell line model as well as in a subcutaneous xenograft mouse model. Using metabolomics and RNA sequencing, we show significant reduction in many amino acid substrates of SLC38A5 as well as OXPHOS inactivation in response to SLC38A5 deletion. Experimental validation demonstrates inhibition of mTORC1, glycolysis and mitochondrial respiration in KO cells, suggesting a serious metabolic crisis associated with SLC38A5 deletion. Since many SLC38A5 substrates are activators of mTORC1 as well as TCA cycle intermediates/precursors, we speculate amino acid insufficiency as a possible link between SLC38A5 deletion and inactivation of mTORC1, glycolysis and mitochondrial respiration, and the underlying mechanism for PDAC attenuation. Overall, we show that SLC38A5 promotes PDAC, thereby identifying a novel, hitherto unknown, therapeutic target for PDAC.