Treg and TH17 link to immune response in individuals with peri-implantitis: a preliminary report.

BACKGROUND AND OBJECTIVES: Treg and TH17 cells influence the inflammatory process in periodontal diseases and could also play in a similar pattern, an essential role in immune-inflammatory mechanisms involved in the destruction of the peri-implant tissues, peri-implantitis. Therefore, this study evaluated the levels of RORγT and FOXP3 gene expression in subjects with peri-implantitis and healthy peri-implant tissues. METHODS: A total of 35 subjects with implant-supported restorations in both diseased and healthy clinical conditions (n = 15 healthy; n = 20 peri-implantitis) were included in this study. Peri-implantitis was defined as probing depth > 5 mm, bleeding on probing and/or suppuration, and peri-implant bone loss >4 mm. Peri-implant tissue biopsies were collected for analysis of the mRNA, RORγT, and FOXP3 expression levels. The samples were submitted to total RNA extraction, treatment with DNAse, and cDNA synthesis. Subsequently, real-time PCR reaction was performed to evaluate the levels of RORγT and FOXP3 gene expression to the reference gene. These were analyzed by the non-parametric Mann-Whitney method with a level of significance of 5%. RESULTS: Higher gene expression levels of the transcription factors RORγT and FOXP3 were detected in the tissues affected by peri-implantitis when compared with healthy tissues (p < 0.05). CONCLUSIONS: The present study demonstrated the possible existence of a hybrid TH17-Treg profile, based on the gene expression of transcription factors inducing differentiation of these cells. Further studies must be designed to gain a better understanding of the immunological mechanisms involved in the pathogenesis of peri-implantitis. CLINICAL RELEVANCE: The levels of RORγT and FOXP3 transcription factors that were linked to cells with the FOXP3+RORγT+ phenotype could be used as a predictor of peri-implantitis progression.

Comparative Finite Element Analysis of Short Implants and Lateralization of the Inferior Alveolar Nerve With Different Prosthesis Heights.

The lateralization of the inferior alveolar nerve (LIAN) and short implants are efficient options for rehabilitation of the posterior atrophic mandible. However, the loss of bone leads to prosthesis with greater height and lever effect that in turn can have different impact on treatments. Through the finite element method, the present study tests the hypothesis that conventional implants placed under LIAN and short implants have similar risk of bone loss regarding variable height of the crown and that crown-to-implant ratio is not a reliable resource to evaluate risk in these treatments. Computed tomography scans of mandibles were processed and implants and prosthetic components were reverse engineered for reconstruction of three-dimensional models to simulate 3 elements fixed partial dentures supported by 2 osseointegrated implants. The models of implants were based on MK III implants (Nobel Biocare, Zurich, Switzerland) with 4 mm in diameter by 7 mm in length representing short implants, and 15 mm in length representing implants used in LIAN. The implant/crown ratio for short implants was 1:1.5, 1:2, and 1:2.5 and LIAN models were modeled with exactly the same prosthesis, resulting in implant/crown ratios of 1:0.67, 1:0.89, and 1:1.12. The results partially rejected the hypothesis that LIAN and short implants have similar risk of bone loss, showing that although LIAN results were better in the models evaluated, the variations in height had proportionally similar impact on both treatments and accepted the hypothesis that crown-to-implant ratio was not a reliable resource to evaluate risk.

Application of genetic algorithms and constructive neural networks for the analysis of microarray cancer data.

BACKGROUND: Extracting relevant information from microarray data is a very complex task due to the characteristics of the data sets, as they comprise a large number of features while few samples are generally available. In this sense, feature selection is a very important aspect of the analysis helping in the tasks of identifying relevant genes and also for maximizing predictive information. METHODS: Due to its simplicity and speed, Stepwise Forward Selection (SFS) is a widely used feature selection technique. In this work, we carry a comparative study of SFS and Genetic Algorithms (GA) as general frameworks for the analysis of microarray data with the aim of identifying group of genes with high predictive capability and biological relevance. Six standard and machine learning-based techniques (Linear Discriminant Analysis (LDA), Support Vector Machines (SVM), Naive Bayes (NB), C-MANTEC Constructive Neural Network, K-Nearest Neighbors (kNN) and Multilayer perceptron (MLP)) are used within both frameworks using six free-public datasets for the task of predicting cancer outcome. RESULTS: Better cancer outcome prediction results were obtained using the GA framework noting that this approach, in comparison to the SFS one, leads to a larger selection set, uses a large number of comparison between genetic profiles and thus it is computationally more intensive. Also the GA framework permitted to obtain a set of genes that can be considered to be more biologically relevant. Regarding the different classifiers used standard feedforward neural networks (MLP), LDA and SVM lead to similar and best results, while C-MANTEC and k-NN followed closely but with a lower accuracy. Further, C-MANTEC, MLP and LDA permitted to obtain a more limited set of genes in comparison to SVM, NB and kNN, and in particular C-MANTEC resulted in the most robust classifier in terms of changes in the parameter settings. CONCLUSIONS: This study shows that if prediction accuracy is the objective, the GA-based approach lead to better results respect to the SFS approach, independently of the classifier used. Regarding classifiers, even if C-MANTEC did not achieve the best overall results, the performance was competitive with a very robust behaviour in terms of the parameters of the algorithm, and thus it can be considered as a candidate technique for future studies.

The generalization complexity measure for continuous input data.

We introduce in this work an extension for the generalization complexity measure to continuous input data. The measure, originally defined in Boolean space, quantifies the complexity of data in relationship to the prediction accuracy that can be expected when using a supervised classifier like a neural network, SVM, and so forth. We first extend the original measure for its use with continuous functions to later on, using an approach based on the use of the set of Walsh functions, consider the case of having a finite number of data points (inputs/outputs pairs), that is, usually the practical case. Using a set of trigonometric functions a model that gives a relationship between the size of the hidden layer of a neural network and the complexity is constructed. Finally, we demonstrate the application of the introduced complexity measure, by using the generated model, to the problem of estimating an adequate neural network architecture for real-world data sets.

Pattern of recurrence of early breast cancer is different according to intrinsic subtype and proliferation index.

INTRODUCTION: Recurrence risk in breast cancer varies throughout the follow-up time. We examined if these changes are related to the level of expression of the proliferation pathway and intrinsic subtypes. METHODS: Expression of estrogen and progesterone receptor, Ki-67, human epidermal growth factor receptor 2 (HER2), epidermal growth factor receptor (EGFR) and cytokeratin 5/6 (CK 5/6) was performed on tissue-microarrays constructed from a large and uniformly managed series of early breast cancer patients (N = 1,249). Subtype definitions by four biomarkers were as follows: luminal A (ER + and/or PR+, HER2−, Ki-67 <14), luminal B (ER + and/or PR+, HER2−, Ki-67 ≥14), HER2-enriched (any ER, any PR, HER2+, any Ki-67), triple-negative (ER−, PR−, HER2−, any Ki-67). Subtype definitions by six biomarkers were as follows: luminal A (ER + and/or PR+, HER2−, Ki-67 <14, any CK 5/6, any EGFR), luminal B (ER + and/or PR+, HER2−, Ki-67 ≥14, any CK 5/6, any EGFR), HER2-enriched (ER−, PR−, HER2+, any Ki-67, any CK 5/6, any EGFR), Luminal-HER2 (ER + and/or PR+, HER2+, any Ki-67, any CK 5/6, any EGFR), Basal-like (ER−, PR−, HER2−, any Ki-67, CK5/6+ and/or EGFR+), triple-negative nonbasal (ER−, PR−, HER2−, any Ki-67, CK 5/6−, EGFR−). Each four- or six-marker defined intrinsic subtype was divided in two groups, with Ki-67 <14% or with Ki-67 ≥14%. Recurrence hazard rate function was determined for each intrinsic subtype as a whole and according to Ki-67 value. RESULTS: Luminal A displayed a slow risk increase, reaching its maximum after three years and then remained steady. Luminal B presented most of its relapses during the first five years. HER2-enriched tumors show a peak of recurrence nearly twenty months post-surgery, with a greater risk in Ki-67 ≥14%. However a second peak occurred at 72 months but the risk magnitude was greater in Ki-67 <14%. Triple negative tumors with low proliferation rate display a smooth risk curve, but with Ki-67 ≥14% show sharp peak at nearly 18 months. CONCLUSIONS: Each intrinsic subtype has a particular pattern of relapses over time which change depending on the level of activation of the proliferation pathway assessed by Ki-67. These findings could have clinical implications both on adjuvant treatment trial design and on the recommendations concerning the surveillance of patients.

CaviDB: a database of cavities and their features in the structural and conformational space of proteins.

Proteins are the structural, functional and evolutionary units of cells. On their surface, proteins are shaped into numerous depressions and protrusions that provide unique microenvironments for ligand binding and catalysis. The dynamics, size and chemical properties of these cavities are essential for a mechanistic understanding of protein function. Here, we present CaviDB, a novel database of cavities and their features in known protein structures. It integrates the results of commonly used cavity detection software with protein features derived from sequence, structural and functional analyses. Each protein in CaviDB is linked to its corresponding conformers, which also facilitates the study of conformational changes in cavities. Our initial release includes ∼927 773 distinct proteins, as well as the characterization of 36 136 869 cavities, of which 1 147 034 were predicted to be drug targets. The structural focus of CaviDB provides the ability to compare cavities and their properties from different conformational states of the protein. CaviDB not only aims to provide a comprehensive database that can be used for various aspects of drug design and discovery but also contributes to a better understanding of the fundamentals of protein structure-function relationships. With its unique approach, CaviDB represents an indispensable resource for the large community of bioinformaticians in particular and biologists in general. Database URL https://www.cavidb.org.

Application of data augmentation techniques towards metabolomics.

Niemann-Pick Class 1 (NPC1) disease is a rare and debilitating neurodegenerative lysosomal storage disease (LSD). Metabolomics datasets of NPC1 patients available to perform this type of analysis are often limited in the number of samples and severely unbalanced. In order to improve the predictive capability and identify new biomarkers in an NPC1 disease urinary dataset, data augmentation (DA) techniques based on computational intelligence have been employed to create synthetic samples, i.e. the addition of noise, oversampling techniques and conditional generative adversarial networks. These techniques have been used to evaluate their predictive capacities on a set of urine samples donated by 13 untreated NPC1 disease and 47 heterozygous (parental) carrier control participants. Results on the prediction have also been obtained using different machine learning classification models and the partial least squares techniques. These results provide strong evidence for the ability of DA techniques to generate good quality synthetic data. Results acquired show increases in sensitivity of 20%-50%, an F1 score of 6%-30%, and a predictive capacity of 0.3 (out of 1). Additionally, more conventional forms of multivariate data analysis have been employed. These have allowed the detection of unusual urinary metabolite profiles, and the identification of biomarkers through the use of synthetically augmented datasets. Results indicate that urinary branched-chain amino acids such as valine, 3-aminoisobutyrate and quinolinate, may be employable as valuable biomarkers for the diagnosis and prognostic monitoring of NPC1 disease.

OBI: A computational tool for the analysis and systematization of the positive selection in proteins.

There are multiple tools for positive selection analysis, including vaccine design and detection of variants of circulating drug-resistant pathogens in population selection. However, applying these tools to analyze a large number of protein families or as part of a comprehensive phylogenomics pipeline could be challenging. Since many standard bioinformatics tools are only available as executables, integrating them into complex Bioinformatics pipelines may not be possible. We have developed OBI, an open-source tool aimed to facilitate positive selection analysis on a large scale. It can be used as a stand-alone command-line app that can be easily installed and used as a Conda package. Some advantages of using OBI are:•It speeds up the analysis by automating the entire process•It allows multiple starting points and customization for the analysis•It allows the retrieval and linkage of structural and evolutive data for a protein throughWe hope to provide with OBI a solution for reliably speeding up large-scale protein evolutionary and structural analysis.

Integration of a Passive Exoskeleton and a Robotic Supernumerary Finger for Grasping Compensation in Chronic Stroke Patients: The SoftPro Wearable System.

Upper-limb impairments are all-pervasive in Activities of Daily Living (ADLs). As a consequence, people affected by a loss of arm function must endure severe limitations. To compensate for the lack of a functional arm and hand, we developed a wearable system that combines different assistive technologies including sensing, haptics, orthotics and robotics. The result is a device that helps lifting the forearm by means of a passive exoskeleton and improves the grasping ability of the impaired hand by employing a wearable robotic supernumerary finger. A pilot study involving 3 patients, which was conducted to test the capability of the device to assist in performing ADLs, confirmed its usefulness and serves as a first step in the investigation of novel paradigms for robotic assistance.

Transfer learning with convolutional neural networks for cancer survival prediction using gene-expression data.

Precision medicine in oncology aims at obtaining data from heterogeneous sources to have a precise estimation of a given patient's state and prognosis. With the purpose of advancing to personalized medicine framework, accurate diagnoses allow prescription of more effective treatments adapted to the specificities of each individual case. In the last years, next-generation sequencing has impelled cancer research by providing physicians with an overwhelming amount of gene-expression data from RNA-seq high-throughput platforms. In this scenario, data mining and machine learning techniques have widely contribute to gene-expression data analysis by supplying computational models to supporting decision-making on real-world data. Nevertheless, existing public gene-expression databases are characterized by the unfavorable imbalance between the huge number of genes (in the order of tenths of thousands) and the small number of samples (in the order of a few hundreds) available. Despite diverse feature selection and extraction strategies have been traditionally applied to surpass derived over-fitting issues, the efficacy of standard machine learning pipelines is far from being satisfactory for the prediction of relevant clinical outcomes like follow-up end-points or patient's survival. Using the public Pan-Cancer dataset, in this study we pre-train convolutional neural network architectures for survival prediction on a subset composed of thousands of gene-expression samples from thirty-one tumor types. The resulting architectures are subsequently fine-tuned to predict lung cancer progression-free interval. The application of convolutional networks to gene-expression data has many limitations, derived from the unstructured nature of these data. In this work we propose a methodology to rearrange RNA-seq data by transforming RNA-seq samples into gene-expression images, from which convolutional networks can extract high-level features. As an additional objective, we investigate whether leveraging the information extracted from other tumor-type samples contributes to the extraction of high-level features that improve lung cancer progression prediction, compared to other machine learning approaches.

Strontium ranelate improves alveolar bone healing in estrogen-deficient rats.

BACKGROUND: This study evaluated the impact of strontium ranelate on tooth-extraction wound healing in estrogen-deficient and estrogen-sufficient rats. METHODS: Ninety-six Wistar rats (90 days of age) were allocated into one of the following groups: sham-surgery+water (estrogen-sufficient); ovariectomy+water (estrogen-deficient), sham-surgery+strontium ranelate (625 mg/kg/d) (strontium/estrogen-sufficient); ovariectomy+strontium ranelate (625 mg/kg/d) (strontium/estrogen-deficient). Water or strontium ranelate were administrated from the 14th day post-ovariectomy/sham surgery until euthanasia. Maxillary first molars were extracted at 21 days after sham/ovariectomy surgery. Rats were euthanized at 10, 20, and 30 days post-extractions. The following parameters were analyzed inside tooth-extraction wound: proportion of newly formed bone (bone healing/BH), number of cells stained for tartrate-resistant acid phosphatase (TRAP) and immunohistochemical staining for five bone metabolism-related markers (osteocalcin [OCN], osteopontin [OPN], bone sialoprotein [BSP], osteoprotegerin [OPG] and receptor activator of NF-КB ligand [RANKL]). RESULTS: The estrogen-deficient group presented lower BH than all other groups at 20 and 30 days post-extraction (P < 0.05). The number of TRAP-stained cells was higher in the estrogen-deficient group than in estrogen-sufficient group at 30 days post-extraction (P < 0.05). The strontium /estrogen-sufficient group exhibited stronger staining for OCN, when compared to the estrogen-sufficient and estrogen-deficient groups (P < 0.05). Both strontium ranelate-treated groups presented higher staining of OPN and BSP than both untreated groups (P < 0.05). The strontium/estrogen-sufficient group demonstrated stronger staining for OPG than the estrogen-deficient group (P < 0.05). The estrogen-sufficient group and both groups treated with strontium ranelate showed lower expression of RANKL than the estrogen-deficient group (P < 0.05). CONCLUSIONS: Strontium ranelate benefited BH and the expression of bone markers in tooth-extraction wound in estrogen-deficient rats whereas its benefits in estrogen-sufficient rats were modest.

BLASSO: integration of biological knowledge into a regularized linear model.

BACKGROUND: In RNA-Seq gene expression analysis, a genetic signature or biomarker is defined as a subset of genes that is probably involved in a given complex human trait and usually provide predictive capabilities for that trait. The discovery of new genetic signatures is challenging, as it entails the analysis of complex-nature information encoded at gene level. Moreover, biomarkers selection becomes unstable, since high correlation among the thousands of genes included in each sample usually exists, thus obtaining very low overlapping rates between the genetic signatures proposed by different authors. In this sense, this paper proposes BLASSO, a simple and highly interpretable linear model with l1-regularization that incorporates prior biological knowledge to the prediction of breast cancer outcomes. Two different approaches to integrate biological knowledge in BLASSO, Gene-specific and Gene-disease, are proposed to test their predictive performance and biomarker stability on a public RNA-Seq gene expression dataset for breast cancer. The relevance of the genetic signature for the model is inspected by a functional analysis. RESULTS: BLASSO has been compared with a baseline LASSO model. Using 10-fold cross-validation with 100 repetitions for models' assessment, average AUC values of 0.7 and 0.69 were obtained for the Gene-specific and the Gene-disease approaches, respectively. These efficacy rates outperform the average AUC of 0.65 obtained with the LASSO. With respect to the stability of the genetic signatures found, BLASSO outperformed the baseline model in terms of the robustness index (RI). The Gene-specific approach gave RI of 0.15±0.03, compared to RI of 0.09±0.03 given by LASSO, thus being 66% times more robust. The functional analysis performed to the genetic signature obtained with the Gene-disease approach showed a significant presence of genes related with cancer, as well as one gene (IFNK) and one pseudogene (PCNAP1) which a priori had not been described to be related with cancer. CONCLUSIONS: BLASSO has been shown as a good choice both in terms of predictive efficacy and biomarker stability, when compared to other similar approaches. Further functional analyses of the genetic signatures obtained with BLASSO has not only revealed genes with important roles in cancer, but also genes that should play an unknown or collateral role in the studied disease.

Expression Levels of Semaphorins 3A, 3B, 4A, and 4D on Human Peri-implantitis.

PURPOSE: To evaluate the gene expression levels of semaphorins 3A, 3B, 4A, and 4D in both healthy and diseased implants. MATERIALS AND METHODS: Subjects with peri-implantitis presented clinical attachment loss, probing depth ≥ 5 mm, bleeding on probing and/or suppuration, and radiographic bone loss > 4 mm. Peri-implant tissue biopsy specimens were sampled for analysis of the mRNA expression levels for semaphorins 3A, 3B, 4A, and 4D. A real-time polymerase chain reaction was performed, and the gene expression levels of semaphorins in relation to the housekeeping gene were analyzed by using the nonparametric Mann-Whitney test (P < .05). RESULTS: Thirty-five subjects (16 men, 19 women; mean age: 54.12 ± 2.34 years) with implant-supported restorations, using screw-shaped dental implants with internal or external hexagon were enrolled in this study. Higher levels of semaphorins 3A and 4D were detected in the peri-implantitis compared with the healthy tissues (P = .0011 and P = .0404, respectively), whereas Sem4A levels were significantly higher in the control group (P < .0001). Differences between groups in the expression levels of Sem3B were not significant. CONCLUSION: Advanced peri-implantitis lesions showed higher levels of gene expression for Sem3A and Sem4D and lower levels of Sem4A in comparison to tissues obtained from a healthy dental implant.

Information in the first spike, the order of spikes, and the number of spikes provided by neurons in the inferior temporal visual cortex.

Information theoretic analyses showed that for single inferior temporal neurons and neuronal populations, more information was encoded in 20 or more ms by all the spikes available than just by the first spike in the same time window about which of 20 objects or faces was shown. Further, the temporal order in which the first spike arrived from different simultaneously recorded neurons did not encode more information than was present in the first spike or the spike counts. Thus information transmission in the inferior temporal cortex by the number of spikes in even short time windows is fast, and provides more information than only the first spike, or the spike order from different neurons.

The influence of oppositely classified examples on the generalization complexity of Boolean functions.

In this paper, we analyze Boolean functions using a recently proposed measure of their complexity. This complexity measure, motivated by the aim of relating the complexity of the functions with the generalization ability that can be obtained when the functions are implemented in feed-forward neural networks, is the sum of a number of components. We concentrate on the case in which we use the first two of these components. The first is related to the "average sensitivity" of the function and the second is, in a sense, a measure of the "randomness" or lack of structure of the function. In this paper, we investigate the importance of using the second term in the complexity measure, and we consider to what extent these two terms suffice as an indicator of how difficult it is to learn a Boolean function. We also explore the existence of very complex Boolean functions, considering, in particular, the symmetric Boolean functions.

Supervised discretization can discover risk groups in cancer survival analysis.

Discretization of continuous variables is a common practice in medical research to identify risk patient groups. This work compares the performance of gold-standard categorization procedures (TNM+A protocol) with that of three supervised discretization methods from Machine Learning (CAIM, ChiM and DTree) in the stratification of patients with breast cancer. The performance for the discretization algorithms was evaluated based on the results obtained after applying standard survival analysis procedures such as Kaplan-Meier curves, Cox regression and predictive modelling. The results show that the application of alternative discretization algorithms could lead the clinicians to get valuable information for the diagnosis and outcome of the disease. Patient data were collected from the Medical Oncology Service of the Hospital Clínico Universitario (Málaga, Spain) considering a follow up period from 1982 to 2008.

Efficient Implementation of the Backpropagation Algorithm in FPGAs and Microcontrollers.

The well-known backpropagation learning algorithm is implemented in a field-programmable gate array (FPGA) board and a microcontroller, focusing in obtaining efficient implementations in terms of a resource usage and computational speed. The algorithm was implemented in both cases using a training/validation/testing scheme in order to avoid overfitting problems. For the case of the FPGA implementation, a new neuron representation that reduces drastically the resource usage was introduced by combining the input and first hidden layer units in a single module. Further, a time-division multiplexing scheme was implemented for carrying out product computations taking advantage of the built-in digital signal processor cores. In both implementations, the floating-point data type representation normally used in a personal computer (PC) has been changed to a more efficient one based on a fixed-point scheme, reducing system memory variable usage and leading to an increase in computation speed. The results show that the modifications proposed produced a clear increase in computation speed in comparison with the standard PC-based implementation, demonstrating the usefulness of the intrinsic parallelism of FPGAs in neurocomputational tasks and the suitability of both implementations of the algorithm for its application to the real world problems.