Validation of venous thromboembolism predictive model in hematologic malignancies.

Although several scores stratify venous thromboembolism (VTE) risk in solid tumors, hematologic malignancies (HM) are underrepresented. To develop an internal and external validation of a logistic regression model to predict VTE risk in hospitalized HM patients. Validation of the existing VTE predictive model was performed through a prospective case-control study in 496 hospitalized HM patients between December 2010 and 2020 at the Arnaldo Milián University Hospital, Cuba. The predictive model designed with data from 285 patients includes 5 predictive factors: hypercholesterolemia, tumoral activity, use of thrombogenic drugs, diabetes mellitus, and immobilization. The model was internally validated using bootstrap analysis. External validation was realized in a prospective cohort of 211 HM patients. The predictive model had a 76.4% negative predictive value (NPV) and an 81.7% positive predictive value (PPV) in the bootstrapping validation. The area under curve (AUC) in the bootstrapping set was 0.838. Accuracy was 80.1% and 82.9% in the internal and external validation, respectively. In the external validation, the model produced 89.7% of NPV, 67.7% of PPV, 74.6% of sensitivity, and 86.2% of specificity. The AUC in the external validation was 0.900. VTE predictive model is a reproducible and simple tool with good accuracy and discrimination.

A Novel Automated Framework for QSAR Modeling of Highly Imbalanced Leishmania High-Throughput Screening Data.

In silico prediction of antileishmanial activity using quantitative structure-activity relationship (QSAR) models has been developed on limited and small datasets. Nowadays, the availability of large and diverse high-throughput screening data provides an opportunity to the scientific community to model this activity from the chemical structure. In this study, we present the first KNIME automated workflow to modeling a large, diverse, and highly imbalanced dataset of compounds with antileishmanial activity. Because the data is strongly biased toward inactive compounds, a novel strategy was implemented based on the selection of different balanced training sets and a further consensus model using single decision trees as the base model and three criteria for output combinations. The decision tree consensus was adopted after comparing its classification performance to consensuses built upon Gaussian-Naïve-Bayes, Support-Vector-Machine, Random-Forest, Gradient-Boost, and Multi-Layer-Perceptron base models. All these consensuses were rigorously validated using internal and external test validation sets and were compared against each other using Friedman and Bonferroni-Dunn statistics. For the retained decision tree-based consensus model, which covers 100% of the chemical space of the dataset and with the lowest consensus level, the overall accuracy statistics for test and external sets were between 71 and 74% and 71 and 76%, respectively, while for a reduced chemical space (21%) and with an incremental consensus level, the accuracy statistics were substantially improved with values for the test and external sets between 86 and 92% and 88 and 92%, respectively. These results highlight the relevance of the consensus model to prioritize a relatively small set of active compounds with high prediction sensitivity using the Incremental Consensus at high level values or to predict as many compounds as possible, lowering the level of Incremental Consensus. Finally, the workflow developed eliminates human bias, improves the procedure reproducibility, and allows other researchers to reproduce our design and use it in their own QSAR problems.

Classifier ensemble based on feature selection and diversity measures for predicting the affinity of A(2B) adenosine receptor antagonists.

A(2B) adenosine receptor antagonists may be beneficial in treating diseases like asthma, diabetes, diabetic retinopathy, and certain cancers. This has stimulated research for the development of potent ligands for this subtype, based on quantitative structure-affinity relationships. In this work, a new ensemble machine learning algorithm is proposed for classification and prediction of the ligand-binding affinity of A(2B) adenosine receptor antagonists. This algorithm is based on the training of different classifier models with multiple training sets (composed of the same compounds but represented by diverse features). The k-nearest neighbor, decision trees, neural networks, and support vector machines were used as single classifiers. To select the base classifiers for combining into the ensemble, several diversity measures were employed. The final multiclassifier prediction results were computed from the output obtained by using a combination of selected base classifiers output, by utilizing different mathematical functions including the following: majority vote, maximum and average probability. In this work, 10-fold cross- and external validation were used. The strategy led to the following results: i) the single classifiers, together with previous features selections, resulted in good overall accuracy, ii) a comparison between single classifiers, and their combinations in the multiclassifier model, showed that using our ensemble gave a better performance than the single classifier model, and iii) our multiclassifier model performed better than the most widely used multiclassifier models in the literature. The results and statistical analysis demonstrated the supremacy of our multiclassifier approach for predicting the affinity of A(2B) adenosine receptor antagonists, and it can be used to develop other QSAR models.

Antileishmanial activity of 5-nitroindazole derivatives.

Background: Currently, there is no safe and effective vaccine against leishmaniasis and existing therapies are inadequate due to high toxicity, cost and decreased efficacy caused by the emergence of resistant parasite strains. Some indazole derivatives have shown in vitro and in vivo activity against Trichomonas vaginalis and Trypanosoma cruzi. On that basis, 20 indazole derivatives were tested in vitro against Leishmania amazonensis. Objective: To evaluate the in vitro activity of twenty 2-benzyl-5-nitroindazolin-3-one derivatives against L. amazonensis. Design: For the selection of promising compounds, it is necessary to evaluate the indicators for in vitro activity. For this aim, a battery of studies for antileishmanial activity and cytotoxicity were implemented. These results enabled the determination of the substituents in the indazole derivatives responsible for activity and selectivity, through the analysis of the structure-activity relationship (SAR). Methods: In vitro cytotoxicity against mouse peritoneal macrophages and growth inhibitory activity in promastigotes were evaluated for 20 compounds. Compounds that showed adequate selectivity were tested against intracellular amastigotes. The SAR from the results in promastigotes was represented using the SARANEA software. Results: Eight compounds showed selectivity index >10% and 50% inhibitory concentration <1 µM against the promastigote stage. Against intracellular amastigotes, four were as active as Amphotericin B. The best results were obtained for 2-(benzyl-2,3-dihydro-5-nitro-3-oxoindazol-1-yl) ethyl acetate, with 50% inhibitory concentration of 0.46 ± 0.01 µM against amastigotes and a selectivity index of 875. The SAR study showed the positive effect on the selectivity of the hydrophilic fragments substituted in position 1 of 2-benzyl-5- nitroindazolin-3-one, which played a key role in improving the selectivity profile of this series of compounds. Conclusion: 2-bencyl-5-nitroindazolin-3-one derivatives showed selective and potent in vitro activity, supporting further investigations on this family of compounds as potential antileishmanial hits.

TI2BioP: Topological Indices to BioPolymers. Its practical use to unravel cryptic bacteriocin-like domains.

Bacteriocins are proteinaceous toxins produced and exported by both gram-negative and gram-positive bacteria as a defense mechanism. The bacteriocin protein family is highly diverse, which complicates the identification of bacteriocin-like sequences using alignment approaches. The use of topological indices (TIs) irrespective of sequence similarity can be a promising alternative to predict proteinaceous bacteriocins. Thus, we present Topological Indices to BioPolymers (TI2BioP) as an alignment-free approach inspired in both the Topological Substructural Molecular Design (TOPS-MODE) and Markov Chain Invariants for Network Selection and Design (MARCH-INSIDE) methodology. TI2BioP allows the calculation of the spectral moments as simple TIs to seek quantitative sequence-function relationships (QSFR) models. Since hydrophobicity and basicity are major criteria for the bactericide activity of bacteriocins, the spectral moments ((HP)µ(k)) were derived for the first time from protein artificial secondary structures based on amino acid clustering into a Cartesian system of hydrophobicity and polarity. Several orders of (HP)µ(k) characterized numerically 196 bacteriocin-like sequences and a control group made up of 200 representative CATH domains. Subsequently, they were used to develop an alignment-free QSFR model allowing a 76.92% discrimination of bacteriocin proteins from other domains, a relevant result considering the high sequence diversity among the members of both groups. The model showed a prediction overall performance of 72.16%, detecting specifically 66.7% of proteinaceous bacteriocins whereas the InterProScan retrieved just 60.2%. As a practical validation, the model also predicted successfully the cryptic bactericide function of the Cry 1Ab C-terminal domain from Bacillus thuringiensis's endotoxin, which has not been detected by classical alignment methods.

Animal model of implant capsular contracture: effects of chitosan.

BACKGROUND: The mechanism(s) responsible for breast capsular contracture (CC) remain unknown, but inflammatory pathways play a role. Various molecules have been attached to implant shells in the hope of modifying or preventing CC. The intrinsic antibacterial and antifungal activities of chitosan and related oligochitosan molecules lend themselves well to the study of the infectious hypothesis; chitosan's ability to bind to growth factors, its hemostatic action, and its ability to activate macrophages, cause cytokine stimulation, and increase the production of transforming growth factor (TGF)-ß1 allow study of the hypertrophic scar hypothesis. OBJECTIVE: The authors perform a comprehensive evaluation, in a rabbit model, of the relationship between CC and histological, microbiological, and immunological characteristics in the presence of a chitooligosaccharide (COS) mixture and a low molecular weight chitosan (LMWC). METHODS: Eleven adult New Zealand rabbits were each implanted with three silicone implants: a control implant, one impregnated with COS, and one impregnated with LMWC. At four-week sacrifice, microdialysates were obtained in the capsule-implant interfaces for tumor necrosis factor alpha (TNF-α) and interleukin-8 (IL-8) level assessment. Histological and microbiological analyses were performed. RESULTS: Baker grade III/IV contractures were observed in the LMWC group, with thick capsules, dense connective tissue, and decreased IL-8 levels (p < .05) compared to control and COS groups. Capsule tissue bacterial types and microdialysate TNF-α levels were similar among all groups. CONCLUSIONS: Chitosan-associated silicone implantation in a rabbit model resulted in Baker grade III/IV CC. This preclinical study may provide a model to test various mechanistic hypotheses of breast capsule formation and subsequent CC.

Effects of coagulase-negative staphylococci and fibrin on breast capsule formation in a rabbit model.

BACKGROUND: The etiology and ideal clinical treatment of capsular contracture (CC) remain unresolved. Bacteria, especially coagulase-negative staphylococci, have been previously shown to accelerate the onset of CC. The role of fibrin in capsule formation has also been controversial. OBJECTIVE: The authors investigate whether fibrin and coagulase-negative staphylococci (CoNS) modulate the histological, microbiological, and clinical outcomes of breast implant capsule formation in a rabbit model and evaluate contamination during the surgical procedure. METHODS: Thirty-one New Zealand white female rabbits were each implanted with one tissue expander and two breast implants. The rabbits received (1) untreated implants and expanders (control; n = 10), (2) two implants sprayed with 2 mL of fibrin and one expander sprayed with 0.5 mL of fibrin (fibrin; n = 11), or (3) two implants inoculated with 100 µL of a CoNS suspension (10(8)CFU/mL-0.5 density on the McFarland scale) and one expander inoculated with a CoNS suspension of 2.5 × 10(7) CFU/mL (CoNS; n = 10). Pressure/volume curves and histological and microbiological evaluations were performed. Operating room air samples and contact skin samples were collected for microbiological evaluation. The rabbits were euthanized at four weeks. RESULTS: In the fibrin group, significantly decreased intracapsular pressures, thinner capsules, loose/dense (<25%) connective tissue, and negative/mild angiogenesis were observed. In the CoNS group, increased capsular thicknesses and polymorph-type inflammatory cells were the most common findings. Similar bacteria in capsules, implants, and skin were cultured from all the study groups. One Baker grade IV contracture was observed in an implant infected with Micrococcus spp. CONCLUSIONS: Fibrin was associated with reduced capsule formation in this preclinical animal model, which makes fibrin an attractive potential therapeutic agent in women undergoing breast augmentation procedures. Clinical strategies for preventing bacterial contamination during surgery are crucial, as low pathogenic agents may promote CC.

Effects of fibrin, thrombin, and blood on breast capsule formation in a preclinical model.

BACKGROUND: The root cause of capsular contracture (CC) associated with breast implants is unknown. Recent evidence points to the possible role of fibrin and bacteria in CC formation. OBJECTIVES: The authors sought to determine whether fibrin, thrombin, and blood modulated the histological and microbiological outcomes of breast implant capsule formation in a rabbit model. METHODS: The authors carried out a case-control study to assess the influence of fibrin, thrombin, and blood on capsule wound healing in a rabbit model. Eighteen New Zealand white rabbits received four tissue expanders. One expander acted as a control, whereas the other expander pockets received one of the following: fibrin glue, rabbit blood, or thrombin sealant. Intracapsular pressure/volume curves were compared among the groups, and histological and microbiological evaluations were performed (capsules, tissue expanders, rabbit skin, and air). The rabbits were euthanized at two or four weeks. RESULTS: At four weeks, the fibrin and thrombin expanders demonstrated significantly decreased intracapsular pressure compared to the control group. In the control and fibrin groups, mixed inflammation correlated with decreased intracapsular pressure, whereas mononuclear inflammation correlated with increased intracapsular pressure. The predominant isolate in the capsules, tissue expanders, and rabbit skin was coagulase-negative staphylococci. For fibrin and thrombin, both cultures that showed an organism other than staphylococci and cultures that were negative were associated with decreased intracapsular pressure, whereas cultures positive for staphylococci were associated with increased intracapsular pressure. CONCLUSIONS: Fibrin application during breast implantation may reduce rates of CC, but the presence of staphylococci is associated with increased capsule pressure even in the presence of fibrin, so care should be taken to avoid bacterial contamination.

Development of Bioinspired Functional Chitosan/Cellulose Nanofiber 3D Hydrogel Constructs by 3D Printing for Application in the Engineering of Mechanically Demanding Tissues.

Soft tissues are commonly fiber-reinforced hydrogel composite structures, distinguishable from hard tissues by their low mineral and high water content. In this work, we proposed the development of 3D printed hydrogel constructs of the biopolymers chitosan (CHI) and cellulose nanofibers (CNFs), both without any chemical modification, which processing did not incorporate any chemical crosslinking. The unique mechanical properties of native cellulose nanofibers offer new strategies for the design of environmentally friendly high mechanical performance composites. In the here proposed 3D printed bioinspired CNF-filled CHI hydrogel biomaterials, the chitosan serves as a biocompatible matrix promoting cell growth with balanced hydrophilic properties, while the CNFs provide mechanical reinforcement to the CHI-based hydrogel. By means of extrusion-based printing (EBB), the design and development of 3D functional hydrogel scaffolds was achieved by using low concentrations of chitosan (2.0-3.0% (w/v)) and cellulose nanofibers (0.2-0.4% (w/v)). CHI/CNF printed hydrogels with good mechanical performance (Young's modulus 3.0 MPa, stress at break 1.5 MPa, and strain at break 75%), anisotropic microstructure and suitable biological response, were achieved. The CHI/CNF composition and processing parameters were optimized in terms of 3D printability, resolution, and quality of the constructs (microstructure and mechanical properties), resulting in good cell viability. This work allows expanding the library of the so far used biopolymer compositions for 3D printing of mechanically performant hydrogel constructs, purely based in the natural polymers chitosan and cellulose, offering new perspectives in the engineering of mechanically demanding hydrogel tissues like intervertebral disc (IVD), cartilage, meniscus, among others.

Biodegradation of Crystalline Cellulose Nanofibers by Means of Enzyme Immobilized-Alginate Beads and Microparticles.

Recent advances in nanocellulose technology have revealed the potential of crystalline cellulose nanofibers to reinforce materials which are useful for tissue engineering, among other functions. However, the low biodegradability of nanocellulose can possess some problems in biomedical applications. In this work, alginate particles with encapsulated enzyme cellulase extracted from Trichoderma reesei were prepared for the biodegradation of crystalline cellulose nanofibers, which carrier system could be incorporated in tissue engineering biomaterials to degrade the crystalline cellulose nanoreinforcement in situ and on-demand during tissue regeneration. Both alginate beads and microparticles were processed by extrusion-dropping and inkjet-based methods, respectively. Processing parameters like the alginate concentration, concentration of ionic crosslinker Ca2+, hardening time, and ionic strength of the medium were varied. The hydrolytic activity of the free and encapsulated enzyme was evaluated for unmodified (CNFs) and TEMPO-oxidized cellulose nanofibers (TOCNFs) in suspension (heterogeneous conditions); in comparison to solubilized cellulose derivatives (homogeneous conditions). The enzymatic activity was evaluated for temperatures between 25-75 °C, pH range from 3.5 to 8.0 and incubation times until 21 d. Encapsulated cellulase in general displayed higher activity compared to the free enzyme over wider temperature and pH ranges and for longer incubation times. A statistical design allowed optimizing the processing parameters for the preparation of enzyme-encapsulated alginate particles presenting the highest enzymatic activity and sphericity. The statistical analysis yielded the optimum particles characteristics and properties by using a formulation of 2% (w/v) alginate, a coagulation bath of 0.2 M CaCl2 and a hardening time of 1 h. In homogeneous conditions the highest catalytic activity was obtained at 55 °C and pH 4.8. These temperature and pH values were considered to study the biodegradation of the crystalline cellulose nanofibers in suspension. The encapsulated cellulase preserved its activity for several weeks over that of the free enzyme, which latter considerably decreased and practically showed deactivation after just 10 d. The alginate microparticles with their high surface area-to-volume ratio effectively allowed the controlled release of the encapsulated enzyme and thereby the sustained hydrolysis of the cellulose nanofibers. The relative activity of cellulase encapsulated in the microparticles leveled-off at around 60% after one day and practically remained at that value for three weeks.

Convenient QSAR model for predicting the complexation of structurally diverse compounds with beta-cyclodextrins.

This paper reports a QSAR study for predicting the complexation of a large and heterogeneous variety of substances (233 organic compounds) with beta-cyclodextrins (beta-CDs). Several different theoretical molecular descriptors, calculated solely from the molecular structure of the compounds under investigation, and an efficient variable selection procedure, like the Genetic Algorithm, led to models with satisfactory global accuracy and predictivity. But the best-final QSAR model is based on Topological descriptors meanwhile offering a reasonable interpretation. This QSAR model was able to explain ca. 84% of the variance in the experimental activity, and displayed very good internal cross-validation statistics and predictivity on external data. It shows that the driving forces for CD complexation are mainly hydrophobic and steric (van der Waals) interactions. Thus, the results of our study provide a valuable tool for future screening and priority testing of beta-CDs guest molecules.

Long-term follow-up of breast capsule contracture rates in cosmetic and reconstructive cases.

BACKGROUND: Silicone gel breast implants are associated with long-term adverse events, including capsular contracture, with reported incidence rates as high as 50 percent. However, it is not clear how long the follow-up period should be and whether there is any association with estrogen or menopausal status. In addition, the placement of Baker grade II subjects in the majority of reports has been in data sets of controls instead of capsular contracture. METHODS: A retrospective medical study (1998 to 2004) was performed in women (n = 157) who received textured silicone breast implants for aesthetic or reconstructive procedures at the Hospital of S. João (Portugal). Medical data were collected that included the following: patient demographics, history, lifestyle factors, surgical procedures, and postoperative complications. Statistical analyses included Pearson chi-square testing, logistic regression modeling, and chi-squared automatic interaction detection (CHAID) methods. RESULTS: The reconstructive cohort had a great incidence of capsular contracture compared with the cosmetic cohort. If one considered no capsular contracture versus capsular contracture, the follow-up period should be longer than 42 months. However, if considering no capsular contracture and grade II subjects versus grade III or IV subjects, a longer follow-up period of 64 months was determined. There was no association between capsular contracture and menopause/estrogen status. CONCLUSIONS: Increased frequencies of capsular contracture were recorded in breast reconstruction that were not attributable to estrogen or menopausal status. On the basis of these results, the authors propose a follow-up period longer than 42 months and the inclusion of Baker grade II subjects.

Prediction of telomerase inhibitory activity for acridinic derivatives based on chemical structure.

Telomerase is a reverse transcriptase enzyme that activates in more than 85% of cancer cells and it is associated with the acquisition of a malignant phenotype. Some experimental strategies have been suggested in order to avoid the enzyme effect on unstopped telomere elongation. One of them, the stabilization of the G-quartet structure, has been widely studied. Nevertheless, no QSAR studies to predict this activity have been developed. In the present study a classification model was carried out to identify, through molecular descriptors with structural fragments and groups information, those acridinic derivatives with better inhibitory concentration on telomerase enzyme. A linear discriminant model was developed to classify a data set of 90 acridinic derivatives (48 more potent derivatives with IC(50) < 1 microM and 42 less potent with IC(50) > or = 1 microM). The final model fit the data with sensitivity of 87.50% and specificity of 82.85%, for a final accuracy of 85.33%. The predictive ability of the model was assessed by a prediction set (15 compounds of 90% and 82.29% of prediction accuracy); a tenfold full cross-validation procedure (removing 15 compounds in each cycle, 84.80% of good prediction) and the prediction of inhibitory concentration on telomerase enzyme for external data of 10 novel acridines (90% of good prediction). The results of this study suggest that the established model has a strong predictive ability and can be prospectively used in the molecular design and action mechanism analysis of this kind of compounds with anticancer activity.

Quantitative structure-carcinogenicity relationship for detecting structural alerts in nitroso compounds: species, rat; sex, female; route of administration, gavage.

Chemical carcinogenicity is of primary interest because it drives much of the current regulatory actions regarding new and existing chemicals and conventional experimental tests take around 3 years to design, conduct, and interpret in addition to costing hundreds of millions of dollars, millions of skilled personnel hours, and millions of animal lives. Thus, theoretical approaches such as the one proposed here, quantitative structure-activity relationship (QSAR), are increasingly used for assessing the risks of environmental chemicals, since they can markedly reduce costs, avoid animal testing, and speed up policy decisions. This paper reports a QSAR study based on the TOPological Substructural MOlecular DEsign (TOPS-MODE) approach, aimed at predicting the rodent carcinogenicity of a set of nitroso compounds selected from the Carcinogenic Potency Data Base (CPDB). The set comprises 26 nitroso compounds, divided into N-nitrosoureas, N-nitrosamines, and C-nitroso compounds, which have been bioassayed in female rats using gavage as a route of administration. Here, we are especially concerned in discerning the role of structural parameters on the carcinogenic activity of this family of compounds. First, the regression model derived, upon removal of two identified nitrosamine outliers, is able to account for more than 86% of the variance in the experimental activity. Second, TOPS-MODE afforded the bond contributions (expressed as fragment contributions to the carcinogenic activity) that can be interpreted and provided tools for better understanding of the mechanisms of carcinogenesis. Finally and, most importantly, we demonstrate the potential use of this approach toward the recognition of structural alerts for carcinogenicity predictions.

TOPS-MODE versus DRAGON descriptors to predict permeability coefficients through low-density polyethylene.

The TOPological Sub-Structural MOlecular DEsign (TOPS-MODE) approach has been applied to the study of the permeability coefficient of various compounds through low-density polyethylene at 0 degrees C. A model able to describe more than 92% of the variance in the experimental permeability of 38 organic compounds was developed with the use of the mentioned approach. In contrast, none of eight different approaches, including the use of constitutional, topological, BCUT, 2D autocorrelations, geometrical, RDF, 3D Morse, and GETAWAY descriptors was able to explain more than 75% of the variance in the mentioned property with the same number of descriptors. In addition, the TOPS-MODE approach permitted to find the contribution of different fragments to the permeability coefficients, giving to the model a straightforward structural interpretability.