Machine learning analysis of a large set of homopolymers to predict glass transition temperatures.

Glass transition temperature of polymers, Tg, is an important thermophysical property, which sometimes can be difficult to measure experimentally. In this regard, data-driven machine learning approaches are important alternatives to assess Tg values, in a high-throughput way. In this study, a large dataset of more than 900 polymers with reported glass transition temperature (Tg) was assembled from various public sources in order to develop a predictive model depicting the structure-property relationships. The collected dataset was curated, explored via cluster analysis, and then split into training and test sets for validation purposes and then polymer structures characterized by molecular descriptors. To find the models, several machine learning techniques, including multiple linear regression (MLR), k-nearest neighbor (k-NN), support vector machine (SVM), random forest (RF), gaussian processes for regression (GPR), and multi-layer perceptron (MLP) were explored. As result, a model with the subset of 15 descriptors accurately predicting the glass transition temperatures was developed. The electronic effect indices were determined to be important properties that positively contribute to the Tg values. The SVM-based model showed the best performance with determination coefficients (R2) of 0.813 and 0.770, for training and test sets, respectively. Also, the SVM model showed the lowest estimation error, RMSE = 0.062. In addition, the developed structure-property model was implemented as a web app to be used as an online computational tool to design and evaluate new homopolymers with desired glass transition profiles.

Design and evaluation of nanoscale materials with programmed responsivity towards epigenetic enzymes.

Self-assembled materials capable of modulating their assembly properties in response to specific enzymes play a pivotal role in advancing 'intelligent' encapsulation platforms for biotechnological applications. Here, we introduce a previously unreported class of synthetic nanomaterials that programmatically interact with histone deacetylase (HDAC) as the triggering stimulus for disassembly. These nanomaterials consist of co-polypeptides comprising poly(acetyl L-lysine) and poly(ethylene glycol) blocks. Under neutral pH conditions, they self-assemble into particles. The hydrodynamic diameters of particles were typically withing the range of 108-190 nm, depending on degree of acetylation of the hydrophobic block. However, their stability is compromised upon exposure to HDACs, depending on enzyme concentration and exposure time. Our investigation, utilizing HDAC8 as the model enzyme, revealed that the primary mechanism behind disassembly involves a decrease in amphiphilicity within the block copolymer due to the deacetylation of lysine residues within the particles' hydrophobic domains. To elucidate the response mechanism, we encapsulated a fluorescent dye within these nanoparticles. Upon incubation with HDAC, the nanoparticle structure collapsed, leading to controlled release of the dye over time. Notably, this release was not triggered by denatured HDAC8, other proteolytic enzymes like trypsin, or the co-presence of HDAC8 and its inhibitor. We also demonstrated the biocompatibility and cellular effects of these materials in the context of drug delivery in different types of anticancer cell lines, such as MIA PaCa-2, PANC-1, cancer like stem cells (CSCs), and non-cancerous HPNE cells. We observed that the release of a model drug (such as a STAT3 pathway inhibitor, Napabucasin) can be loaded into these nanoparticles, with >90% of the dosage can be released over 3 h under the influence of HDAC8 enzyme in a controlled fashion. Further, we conducted a comprehensive computational study to unveil the possible interaction mechanism between enzymes and particles. By drawing parallels to the mechanism of naturally occurring histone proteins, this research represents a pioneering step toward developing functional materials capable of harnessing the activity of epigenetic enzymes such as HDACs.

Design and Evaluation of Nanoscale Materials with Programmed Responsivity towards Epigenetic Enzymes.

Self-assembled materials capable of modulating their assembly properties in response to specific enzymes play a pivotal role in advancing 'intelligent' encapsulation platforms for biotechnological applications. Here, we introduce a previously unreported class of synthetic nanomaterials that programmatically interact with histone deacetylase (HDAC) as the triggering stimulus for disassembly. These nanomaterials consist of co-polypeptides comprising poly (acetyl L-lysine) and poly(ethylene glycol) blocks. Under neutral pH conditions, they self-assemble into particles. However, their stability is compromised upon exposure to HDACs, depending on enzyme concentration and exposure time. Our investigation, utilizing HDAC8 as the model enzyme, revealed that the primary mechanism behind disassembly involves a decrease in amphiphilicity within the block copolymer due to the deacetylation of lysine residues within the particles' hydrophobic domains. To elucidate the response mechanism, we encapsulated a fluorescent dye within these nanoparticles. Upon incubation with HDAC, the nanoparticle structure collapsed, leading to controlled release of the dye over time. Notably, this release was not triggered by denatured HDAC8, other proteolytic enzymes like trypsin, or the co-presence of HDAC8 and its inhibitor. We further demonstrated the biocompatibility and cellular effects of these materials and conducted a comprehensive computational study to unveil the possible interaction mechanism between enzymes and particles. By drawing parallels to the mechanism of naturally occurring histone proteins, this research represents a pioneering step toward developing functional materials capable of harnessing the activity of epigenetic enzymes such as HDACs.

Improved synthesis, molecular modeling and anti-inflammatory activity of new fluorinated dihydrofurano-naphthoquinone compounds.

A series of new fluorinated dihydrofurano-napthoquinone compounds were sucessfully synthesized in good yields using microwave-assisted multi-component reactions of 2-hydroxy-1,4-naphthoquinone, fluorinated aromatic aldehydes, and pyridinium bromide. The products were fully characterized using spectroscopic techniques and evaluated for their anti-inflammatory activity using lipopolysaccharide (LPS)-stimulated RAW264.7 macrophage cells. Among 12 new compounds, compounds 8b, 8d, and 8e showed high potent NO inhibitory activity in lipopolysaccharide (LPS)-stimulated RAW264.7 macrophage cells with IC50 values ranging from 1.54 to 3.92 µM. The levels of pro-inflammatory cytokines IL-1ß and IL-6 in LPS-stimulated RAW264.7 macrophages were remarkably decreased after the application of 8b, 8d, 8e and 8k. Molecular docking simulations revealed structure-activity relationships of 8b, 8d, and 8e toward NO synthase, cyclooxygenase (COX-2 over COX-1), and prostaglandin E synthase-1 (mPGES-1). Further physicochemical and pharmacokinetic computations also demonstrated the drug-like characteristics of synthesized compounds. These findings demonstrated the importance of fluorinated dihydrofurano-napthoquinone moieties in the development of potential anti-inflammatory agents.

Synthesis, molecular docking analysis and in vitro evaluation of new heterocyclic hybrids of 4-aza-podophyllotoxin as potent cytotoxic agents.

Two different synthetic approaches to novel heterocyclic hybrid compounds of 4-azapodophyllotoxin were investigated. The obtained products were characterized by infrared spectroscopy, nuclear magnetic resonance spectroscopy, and high-resolution mass spectrometry. MTT protocol was then performed to examine the cytotoxic activity of these products against KB, HepG2, A549, MCF7, and Hek-293 cell lines. The cytotoxic assessment indicated that all products displayed moderate to high cytotoxicity against all tested cancer cell lines. The most active compound 13k containing the 2-methoxypyridin-4-yl group exhibited selective cytotoxicity against KB, A549, and HepG2 cell lines with the IC50 values ranging from 0.23 to 0.27 µM, which were between 5- to 10-fold more potent than the positive control ellipticine. Compounds 13a (HetAr = thiophen-3-yl) and 13d (HetAr = 5-bromofuran-2-yl) displayed high cytotoxic selectivity for A549 and HepG2 cancer cell lines when compared to the other cancer cell lines and low toxicity to the normal Hek-293 cell line. Molecular docking study was conducted to evaluate the interaction of new synthesized compounds with the colchicine-binding-site of tubulin. Besides that, physicochemical and pharmacokinetic properties of the most active compounds 13h,k were predicted.

Stability Constant and Potentiometric Sensitivity of Heavy Metal-Organic Fluorescent Compound Complexes: QSPR Models for Prediction and Design of Novel Coumarin-like Ligands.

Industrial wastewater often consists of toxic chemicals and pollutants, which are extremely harmful to the environment. Heavy metals are toxic chemicals and considered one of the major hazards to the aquatic ecosystem. Analytical techniques, such as potentiometric methods, are some of the methods to detect heavy metals in wastewaters. In this work, the quantitative structure-property relationship (QSPR) was applied using a range of machine learning techniques to predict the stability constant (logßML) and potentiometric sensitivity (PSML) of 200 ligands in complexes with the heavy metal ions Cu2+, Cd2+, and Pb2+. In result, the logßML models developed for four ions showed good performance with square correlation coefficients (R2) ranging from 0.80 to 1.00 for the training and 0.72 to 0.85 for the test sets. Likewise, the PSML displayed acceptable performance with an R2 of 0.87 to 1.00 for the training and 0.73 to 0.95 for the test sets. By screening a virtual database of coumarin-like structures, several new ligands bearing the coumarin moiety were identified. Three of them, namely NEW02, NEW03, and NEW07, showed very good sensitivity and stability in the metal complexes. Subsequent quantum-chemical calculations, as well as physicochemical/toxicological profiling were performed to investigate their metal-binding ability and developability of the designed sensors. Finally, synthesis schemes are proposed to obtain these three ligands with major efficiency from simple resources. The three coumarins designed clearly demonstrated capability to be suitable as good florescent chemosensors towards heavy metals. Overall, the computational methods applied in this study showed a very good performance as useful tools for designing novel fluorescent probes and assessing their sensing abilities.

Development of Activity Rules and Chemical Fragment Design for In Silico Discovery of AChE and BACE1 Dual Inhibitors against Alzheimer's Disease.

Multi-target drug development has become an attractive strategy in the discovery of drugs to treat of Alzheimer's disease (AzD). In this study, for the first time, a rule-based machine learning (ML) approach with classification trees (CT) was applied for the rational design of novel dual-target acetylcholinesterase (AChE) and ß-site amyloid-protein precursor cleaving enzyme 1 (BACE1) inhibitors. Updated data from 3524 compounds with AChE and BACE1 measurements were curated from the ChEMBL database. The best global accuracies of training/external validation for AChE and BACE1 were 0.85/0.80 and 0.83/0.81, respectively. The rules were then applied to screen dual inhibitors from the original databases. Based on the best rules obtained from each classification tree, a set of potential AChE and BACE1 inhibitors were identified, and active fragments were extracted using Murcko-type decomposition analysis. More than 250 novel inhibitors were designed in silico based on active fragments and predicted AChE and BACE1 inhibitory activity using consensus QSAR models and docking validations. The rule-based and ML approach applied in this study may be useful for the in silico design and screening of new AChE and BACE1 dual inhibitors against AzD.

A Review of Computational Approaches Targeting SARS-CoV-2 Main Protease to the Discovery of New Potential Antiviral Compounds.

The new pandemic caused by the coronavirus (SARS-CoV-2) has become the biggest challenge that the world is facing today. It has been creating a devastating global crisis, causing countless deaths and great panic. The search for an effective treatment remains a global challenge owing to controversies related to available vaccines. A great research effort (clinical, experimental, and computational) has emerged in response to this pandemic, and more than 125000 research reports have been published in relation to COVID-19. The majority of them focused on the discovery of novel drug candidates or repurposing of existing drugs through computational approaches that significantly speed up drug discovery. Among the different used targets, the SARS-CoV-2 main protease (Mpro), which plays an essential role in coronavirus replication, has become the preferred target for computational studies. In this review, we examine a representative set of computational studies that use the Mpro as a target for the discovery of small-molecule inhibitors of COVID-19. They will be divided into two main groups, structure-based and ligand-based methods, and each one will be subdivided according to the strategies used in the research. From our point of view, the use of combined strategies could enhance the possibilities of success in the future, permitting to development of more rigorous computational studies in future efforts to combat current and future pandemics.

Chemical Profile and Biological Activities of Fungal Strains Isolated from Piper nigrum Roots: Experimental and Computational Approaches.

The current report describes the chemical investigation and biological activity of extracts produced by three fungal strains Fusarium oxysporum, Penicillium simplicissimum, and Fusarium proliferatum isolated from the roots of Piper nigrum L. growing in Vietnam. These fungi were namely determined by morphological and DNA analyses. GC/MS identification revealed that the EtOAc extracts of these fungi were associated with the presence of saturated and unsaturated fatty acids. These EtOAc extracts showed cytotoxicity towards cancer cell lines HepG2, inhibited various microbacterial organisms, especially fungus Aspergillus niger and yeast Candida albicans (the MIC values of 50-100 µg/mL). In α-glucosidase inhibitory assay, they induced the IC50 values of 1.00-2.53 µg/mL were better than positive control acarbose (169.80 µg/mL). The EtOAc extract of F. oxysporum also showed strong anti-inflammatory activity against NO production and PGE-2 level. Four major compounds linoleic acid (37.346 %), oleic acid (27.520 %), palmitic acid (25.547 %), and stearic acid (7.030 %) from the EtOAc extract of F. oxysporum were selective in molecular docking study, by which linoleic and oleic acids showed higher binding affinity towards α-glucosidase than palmitic and stearic acids. In subsequent docking assay with inducible nitric oxide synthase (iNOS), palmitic acid, oleic acid and linoleic acid could be moderate inhibitors.

Comparison of the University Pharmacy Education Programs in Germany and Vietnam.

During the global COVID pandemic, the importance of professionals in the health care sector has been put in a new light, including pharmacists. In this context, the focus is also on how pharmacists are trained in different countries. Through an exchange of pharmacy teaching staff from a German to a Vietnamese university, the pharmacy education programs in both countries were compared. Aspects such as access to studies, structure of studies, and further training opportunities were considered. Differences and similarities emerged. In both countries, students first acquire basic knowledge and then delve deeper into pharmaceutical content in main studies. There is, expectedly, a great overlap in the content of the courses. Overall, the education at Vietnamese universities seems to be more practice-oriented due to a large number of placements. This also allows a specialization, which can be pursued in Germany with self-interest after graduation. There, the preparation for everyday work in the community pharmacy is separated from the university by a mandatory practical year. For the future, efforts are being made in both countries to strengthen the importance of clinical pharmacy in the curriculum. To this end, the Vietnamese are taking their inspiration from abroad in many cases, including Germany.

Synthesis and biological activity, and molecular modelling studies of potent cytotoxic podophyllotoxin-naphthoquinone compounds.

A new approach for the synthesis of podophyllotoxin-naphthoquinone compounds using microwave-assisted three-component reactions is reported in this study. Novel podophyllotoxin-naphthoquinone derivatives with modification on ring E were synthesized. All the synthetic compounds were assessed in terms of their cytotoxicity profile against four cancer cell lines (KB, HepG2, A549, and MCF7), and noncancerous Hek-293 cell lines. Notably, treatment of SK-LU-1 cells with compounds 5a and 5b resulted in G2/M phase arrest of the cell cycle, caspase-3/7 activation, and apoptosis. Additionally, molecular docking studies were performed and showed important interaction of two compounds against residues in the colchicine-binding-site of tubulin as well. Taken together, compounds 5a and 5b were identified as potent anticancer agents.

Essential oils of Uvaria boniana - chemical composition, in vitro bioactivity, docking, and in silico ADMET profiling of selective major compounds.

Phytochemical investigation applying GC (gas chromatography)-MS (mass spectrometry)/GC-FID (flame ionization detection) on the hydro-distilled essential oils of the Vietnamese medicinal plant Uvaria boniana leaf and twig lead to the detection of 35 constituents (97.36%) in the leaf oil and 52 constituents (98.75%) in the twig oil. Monoterpenes, monoterpenoids, sesquiterpenes, and sesquiterpenoids were characteristic of U. boniana essential oils. The leaf oil was represented by major components (E)-caryophyllene (16.90%), bicyclogermacrene (15.95%), α-humulene (14.96%), and linalool (12.40%), whereas four compounds α-cadinol (16.16%), epi-α-muurolol (10.19%), α-pinene (11.01%), and ß-pinene (8.08%) were the main ones in the twig oil. As compared with the leaf oil, the twig oil was better in antimicrobial activity. With the same MIC value of 40 mg/mL, the twig oil successfully controlled the growth of Gram (+) bacterium Bacillus subtilis, Gram (-) bacterium Escherichia coli, fungus Aspergillus niger, and yeast Saccharomyces cerevisiae. In addition, both two oil samples have induced antiinflammatory activity with the IC50 values of 223.7-240.6 mg/mL in NO productive inhibition when BV2 cells had been stimulated by LPS. Docking simulations of four major compounds of U. boniana twig oil on eight relevant antibacterial targets revealed that epi-α-muurolol and α-cadinol are moderate inhibitors of E. coli DNA gyrase subunit B, penicillin binding protein 2X and penicillin binding protein 3 of Pseudomonas aeruginosa with similar free binding energies of -30.1, -29.3, and -29.3 kJ/mol, respectively. Furthermore, in silico ADMET studies indicated that all four docked compounds have acceptable oral absorption, low metabolism, and appropriated toxicological profile to be considered further as drug candidates.

Synthesis and biological evaluation of novel quinazoline-triazole hybrid compounds with potential use in Alzheimer's disease.

A library of twelve quinazoline-triazole hybrid compounds were designed, synthesized and evaluated as a novel class of acetylcholinesterase inhibitors to treat Alzheimer's disease (AD). The biological assay results demonstrated the ability of several hybrid compounds to inhibit AChE enzyme (IC50 range = 0.2-83.9 µM). To understand the high potential activity of these compounds, molecular docking simulations were performed to get better insights into the mechanism of binding of quinazoline-triazole hybrid compounds. As expected, compounds 8a and 9a-b bind to both catalytic anionic site (CAS) and peripheral anionic site (PAS) in the active site of AChE enzyme, which implicates that these compounds could act as dual binding site inhibitors. These compounds were not cytotoxic and they also displayed appropriated physicochemical as well as pharmacokinetic profile to be developed as novel anti-AD drug candidates.

Design, Synthesis and Bioevaluation of Two Series of 3-[(1-Benzyl-1H-1,2,3-triazol-4-yl)methyl]quinazolin-4(3H)-ones and N-(1-Benzylpiperidin-4-yl)quinazolin-4-amines.

Two series of 3-[(1-benzyl-1H-1,2,3-triazol-4-yl)methyl]quinazolin-4(3H)-ones and N-(1-benzylpiperidin-4-yl)quinazolin-4-amines were designed initially as potential acetylcholine esterase inhibitors. Biological evaluation demonstrated that N-(1-benzylpiperidin-4-yl)quinazolin-4-amines significantly inhibited AChE activity. Especially, two compounds of them were found to be the most potent with relative AChE inhibition percentages of 87 % in comparison to donepezil. The docking studies with AChE showed similar interactions between donepezil and four derivatives. N-(1-Benzylpiperidin-4-yl)quinazolin-4-amines also exhibited significant DPPH scavenging effects. The two series of compound also exerted moderate to good cytotoxicity against three human cancer cell lines, including SW620 (human colon cancer), PC-3 (prostate cancer), and NCI-H23 (lung cancer), with 3-[(1-benzyl-1H-1,2,3-triazol-4-yl)methyl]quinazolin-4(3H)-one being the most cytotoxic agent. 3-[(1-Benzyl-1H-1,2,3-triazol-4-yl)methyl]quinazolin-4(3H)-one significantly induced early apoptosis and arrested the SW620 cells at G2/M phase. From this study, two compounds of N-(1-benzylpiperidin-4-yl)quinazolin-4-amines could serve as new leads for further design and AChE inhibitors, while 3-[(1-benzyl-1H-1,2,3-triazol-4-yl)methyl]quinazolin-4(3H)-one could serve as a new lead for the design and development of more potent anticancer agents.

Emerging Role of Circulating Tumor Cells in Gastric Cancer.

With over 1 million incidence cases and more than 780,000 deaths in 2018, gastric cancer (GC) was ranked as the 5th most common cancer and the 3rd leading cause of cancer deaths worldwide. Though several biomarkers, including carcinoembryonic antigen (CEA), cancer antigen 19-9 (CA19-9), and cancer antigen 72-4 (CA72-4), have been identified, their diagnostic accuracies were modest. Circulating tumor cells (CTCs), cells derived from tumors and present in body fluids, have recently emerged as promising biomarkers, diagnostically and prognostically, of cancers, including GC. In this review, we present the landscape of CTCs from migration, to the presence in circulation, biologic properties, and morphologic heterogeneities. We evaluated clinical implications of CTCs in GC patients, including diagnosis, prognosis, and therapeutic management, as well as their application in immunotherapy. On the one hand, major challenges in using CTCs in GC were analyzed, from the differences of cut-off values of CTC positivity, to techniques used for sampling, storage conditions, and CTC molecular markers, as well as the unavailability of relevant enrichment and detection techniques. On the other hand, we discussed future perspectives of using CTCs in GC management and research, including the use of circulating tumor microembolies; of CTC checkpoint blockade in immunotherapy; and of organoid models. Despite the fact that there are remaining challenges in techniques, CTCs have potential as novel biomarkers and/or a non-invasive method for diagnostics, prognostics, and treatment monitoring of GC, particularly in the era of precision medicine.

Higher-Order and Mixed Discrete Derivatives such as a Novel Graph- Theoretical Invariant for Generating New Molecular Descriptors.

BACKGROUND: Recently, some authors have defined new molecular descriptors (MDs) based on the use of the Graph Discrete Derivative, known as Graph Derivative Indices (GDI). This new approach about discrete derivatives over various elements from a graph takes as outset the formation of subgraphs. Previously, these definitions were extended into the chemical context (N-tuples) and interpreted in structural/physicalchemical terms as well as applied into the description of several endpoints, with good results. OBJECTIVE: A generalization of GDIs using the definitions of Higher Order and Mixed Derivative for molecular graphs is proposed as a generalization of the previous works, allowing the generation of a new family of MDs. METHODS: An extension of the previously defined GDIs is presented, and for this purpose, the concept of Higher Order Derivatives and Mixed Derivatives is introduced. These novel approaches to obtaining MDs based on the concepts of discrete derivatives (finite difference) of the molecular graphs use the elements of the hypermatrices conceived from 12 different ways (12 events) of fragmenting the molecular structures. The result of applying the higher order and mixed GDIs over any molecular structure allows finding Local Vertex Invariants (LOVIs) for atom-pairs, for atoms-pairs-pairs and so on. All new families of GDIs are implemented in a computational software denominated DIVATI (acronym for Discrete DeriVAtive Type Indices), a module of KeysFinder Framework in TOMOCOMD-CARDD system. RESULTS: QSAR modeling of the biological activity (Log 1/K) of 31 steroids reveals that the GDIs obtained using the higher order and mixed GDIs approaches yield slightly higher performance compared to previously reported approaches based on the duplex, triplex and quadruplex matrix. In fact, the statistical parameters for models obtained with the higher-order and mixed GDI method are superior to those reported in the literature by using other 0-3D QSAR methods. CONCLUSION: It can be suggested that the higher-order and mixed GDIs, appear as a promissory tool in QSAR/QSPRs, similarity/dissimilarity analysis and virtual screening studies.

Beyond model interpretability using LDA and decision trees for α-amylase and α-glucosidase inhibitor classification studies.

In this report are used two data sets involving the main antidiabetic enzyme targets α-amylase and α-glucosidase. The prediction of α-amylase and α-glucosidase inhibitory activity as antidiabetic is carried out using LDA and classification trees (CT). A large data set of 640 compounds for α-amylase and 1546 compounds in the case of α-glucosidase are selected to develop the tree model. In the case of CT-J48 have the better classification model performances for both targets with values above 80%-90% for the training and prediction sets, correspondingly. The best model shows an accuracy higher than 95% for training set; the model was also validated using 10-fold cross-validation procedure and through a test set achieving accuracy values of 85.32% and 86.80%, correspondingly. Additionally, the obtained model is compared with other approaches previously published in the international literature showing better results. Finally, we can say that the present results provided a double-target approach for increasing the estimation of antidiabetic chemicals identification aimed by double-way workflow in virtual screening pipelines.

(E)-N'-Arylidene-2-(4-oxoquinazolin-4(3H)-yl) acetohydrazides: Synthesis and evaluation of antitumor cytotoxicity and caspase activation activity.

In our search for novel small molecules activating procaspase-3, we have designed and synthesised a series of novel acetohydrazides incorporating quinazolin-4(3H)-ones (5, 6, 7). Biological evaluation revealed eight compounds with significant cytotoxicity against three human cancer cell lines (SW620, colon cancer; PC-3, prostate cancer; NCI-H23, lung cancer). The most potent compound 5t displayed cytotoxicity up to 5-fold more potent than 5-FU. Analysis of structure-activity relationships showed that the introduction of different substituents at C-6 position on the quinazolin-4(3H)-4-one moiety, such as 6-chloro or 6-methoxy potentially increased the cytotoxicity of the compounds. In term of caspase activation activity, several compounds were found to exhibit potent effects, (e.g. compounds 7 b, 5n, and 5l). Especially, compound 7 b activated caspases activity by almost 200% in comparison to that of PAC-1. Further docking simulation also revealed that this compound potentially is a potent allosteric inhibitor of procaspase-3.

Radicular cyst in a primary molar following pulp therapy with gutta percha: A case report and literature review.

A radicular cyst (RC) in deciduous dentition is relatively rare. This clinical report presents a case of RC that condition derived from a primary molar undergone an endodontic treatment with gutta-percha approximately one year ago. In addition, we also considered whether intracanal medicaments and gutta-percha filling material related to the formation and development of the cyst or not. Key words:Primary tooth, radicular cyst, pulp therapy, gutta-percha filling material, intracanal medicament.

Computational Identification of Chemical Compounds with Potential Activity against Leishmania amazonensis using Nonlinear Machine Learning Techniques.

Leishmaniasis is a poverty-related disease endemic in 98 countries worldwide, with morbidity and mortality increasing daily. All currently used first-line and second-line drugs for the treatment of leishmaniasis exhibit several drawbacks including toxicity, high costs and route of administration. Consequently, the development of new treatments for leishmaniasis is a priority in the field of neglected tropical diseases. The aim of this work is to develop computational models those allow the identification of new chemical compounds with potential anti-leishmanial activity. A data set of 116 organic chemicals, assayed against promastigotes of Leishmania amazonensis, is used to develop the theoretical models. The cutoff value to consider a compound as active one was IC50≤1.5µM. For this study, we employed Dragon software to calculate the molecular descriptors and WEKA to obtain machine learning (ML) models. All ML models showed accuracy values between 82% and 91%, for the training set. The models developed with k-nearest neighbors and classification trees showed sensitivity values of 97% and 100%, respectively; while the models developed with artificial neural networks and support vector machine showed specificity values of 94% and 92%, respectively. In order to validate our models, an external test-set was evaluated with good behavior for all models. A virtual screening was performed and 156 compounds were identified as potential anti-leishmanial by all the ML models. This investigation highlights the merits of ML-based techniques as an alternative to other more traditional methods to find new chemical compounds with anti-leishmanial activity.