Depression and Psychotherapy: The Importance of a Psychotherapeutic Approach Focused on Logical Reasoning and Functioning

Many patients who show up with depressive and anxious symptomology have, or have had, interpersonal conflicts that triggered or contributed to the aggravation of the symptomology herein explained. Clinical experience has taught us that many people have difficulty in maintaining Faultless Logical Reasoning (FLR) and, even if FLR is present, they have difficulty in maintaining Faultless Logical Functioning (FLF). In clinical practice, psychotherapists saw people that in consequence of their difficulty in FLR/FLF involves in conflicts that brought them interpersonal problems in relationships, in business, work, and in other areas of their life. Consequently, these problems will be followed by anxious and depressive symptomatology. Almost always, this symptomology is accompanied by intense emotional changes. With this clinical case of a patient with depression, and its treatment, is demonstrated the importance to investigate the capacity of patients to function and think, respectively, with FLF and FLR. This work has proved very exciting because Logic-Based Psychotherapy (LBP) provide pedagogy to think better, to improve emotional processing, introspection, and more profound and rigorous analysis and responses. If the responses of the subject are more logical, it will result in fewer conflicts, less ill will, and fewer disagreements, which will lead to fewer cases of depression

Communication in Clinical Practice, the Perspective of Patients with Cancer: Translation of the PACE (Patient Assessment of Cancer Communication Experiences) Questionnaire to European Portuguese

Introduction: Communication in clinical practice is essential to healthcare quality, especially in Oncology. The Patient Assessment of Communication Experiences questionnaire evaluates the perspective of cancer patients towards communication and identifies areas that can be improved. This study consists in its translation and validation to European Portuguese, to identify these areas. Material and methods: We performed a descriptive, observational, cross-sectional study. The translation was conducted according to the World Health Organization's guidelines. We applied the questionnaires to a convenience sample, in patients under systemic antineoplastic treatment at the Day Hospital of Centro Hospitalar Universitário do Porto, between January and March 2020. We calculated the Cronbach's Alpha for each phase of care, the bivariate and multiple correlations and, for each question, the percentage of "non applicable" and most positive answers. Results: We had 100 participants. The instrument we obtained ha good internal consistency, but the classification of some questions does not correlate sufficiently with the global opinion about the experiences with communication in the respective phase. The diagnosis phase revealed a lower proportion of positive experiences, particularly in terms of receiving the bad news. Conclusion: This study translates and validates part of the communication assessment instrument PACE to the Portuguese language and elicits the necessity to invest in the phase of diagnosis and disclosure of bad news.ste trabalho não recebeu qualquer tipo de suporte financeiro de nenhuma entidade no domínio público ou privado

Identification of hot-spot residues in protein-protein interactions by computational docking

<p>Abstract</p> <p>Background</p> <p>The study of protein-protein interactions is becoming increasingly important for biotechnological and therapeutic reasons. We can define two major areas therein: the structural prediction of protein-protein binding mode, and the identification of the relevant residues for the interaction (so called 'hot-spots'). These hot-spot residues have high interest since they are considered one of the possible ways of disrupting a protein-protein interaction. Unfortunately, large-scale experimental measurement of residue contribution to the binding energy, based on alanine-scanning experiments, is costly and thus data is fairly limited. Recent computational approaches for hot-spot prediction have been reported, but they usually require the structure of the complex.</p> <p>Results</p> <p>We have applied here normalized interface propensity (<it>NIP</it>) values derived from rigid-body docking with electrostatics and desolvation scoring for the prediction of interaction hot-spots. This parameter identifies hot-spot residues on interacting proteins with predictive rates that are comparable to other existing methods (up to 80% positive predictive value), and the advantage of not requiring any prior structural knowledge of the complex.</p> <p>Conclusion</p> <p>The <it>NIP </it>values derived from rigid-body docking can reliably identify a number of hot-spot residues whose contribution to the interaction arises from electrostatics and desolvation effects. Our method can propose residues to guide experiments in complexes of biological or therapeutic interest, even in cases with no available 3D structure of the complex.</p

Deriving a mutation index of carcinogenicity using protein structure and protein interfaces

With the advent of Next Generation Sequencing the identification of mutations in the genomes of healthy and diseased tissues has become commonplace. While much progress has been made to elucidate the aetiology of disease processes in cancer, the contributions to disease that many individual mutations make remain to be characterised and their downstream consequences on cancer phenotypes remain to be understood. Missense mutations commonly occur in cancers and their consequences remain challenging to predict. However, this knowledge is becoming more vital, for both assessing disease progression and for stratifying drug treatment regimes. Coupled with structural data, comprehensive genomic databases of mutations such as the 1000 Genomes project and COSMIC give an opportunity to investigate general principles of how cancer mutations disrupt proteins and their interactions at the molecular and network level. We describe a comprehensive comparison of cancer and neutral missense mutations; by combining features derived from structural and interface properties we have developed a carcinogenicity predictor, InCa (Index of Carcinogenicity). Upon comparison with other methods, we observe that InCa can predict mutations that might not be detected by other methods. We also discuss general limitations shared by all predictors that attempt to predict driver mutations and discuss how this could impact high-throughput predictions. A web interface to a server implementation is publicly available at http://inca.icr.ac.uk/

Prediction of hot spot residues at protein-protein interfaces by combining machine learning and energy-based methods

Background: Alanine scanning mutagenesis is a powerful experimental methodology for investigating the structural and energetic characteristics of protein complexes. Individual aminoacids are systematically mutated to alanine and changes in free energy of binding (Delta Delta G) measured. Several experiments have shown that protein-protein interactions are critically dependent on just a few residues ("hot spots") at the interface. Hot spots make a dominant contribution to the free energy of binding and if mutated they can disrupt the interaction. As mutagenesis studies require significant experimental efforts, there is a need for accurate and reliable computational methods. Such methods would also add to our understanding of the determinants of affinity and specificity in protein-protein recognition.Results: We present a novel computational strategy to identify hot spot residues, given the structure of a complex. We consider the basic energetic terms that contribute to hot spot interactions, i.e. van der Waals potentials, solvation energy, hydrogen bonds and Coulomb electrostatics. We treat them as input features and use machine learning algorithms such as Support Vector Machines and Gaussian Processes to optimally combine and integrate them, based on a set of training examples of alanine mutations. We show that our approach is effective in predicting hot spots and it compares favourably to other available methods. In particular we find the best performances using Transductive Support Vector Machines, a semi-supervised learning scheme. When hot spots are defined as those residues for which Delta Delta G >= 2 kcal/mol, our method achieves a precision and a recall respectively of 56% and 65%.Conclusion: We have developed an hybrid scheme in which energy terms are used as input features of machine learning models. This strategy combines the strengths of machine learning and energy-based methods. Although so far these two types of approaches have mainly been applied separately to biomolecular problems, the results of our investigation indicate that there are substantial benefits to be gained by their integration

The impact of large-scale deployment of Wolbachia mosquitoes on dengue and other Aedes-borne diseases in Rio de Janeiro and Niterói, Brazil: study protocol for a controlled interrupted time series analysis using routine disease surveillance data.

Background: Rio de Janeiro and Niterói are neighbouring cities in southeastern Brazil which experience large dengue epidemics every 2 to 5 years, with >100,000 cases notified in epidemic years. Costs of vector control and direct and indirect costs due to the Aedes-borne diseases dengue, chikungunya and Zika were estimated to total $650 million USD in 2016, but traditional vector control strategies have not been effective in preventing mosquito-borne disease outbreaks. The Wolbachia method is a novel and self-sustaining approach for the biological control of Aedes-borne diseases, in which the transmission potential of Aedes aegypti mosquitoes is reduced by stably transfecting them with the Wolbachia bacterium ( wMel strain). This paper describes a study protocol for evaluating the effect of large-scale non-randomised releases of Wolbachia--infected mosquitoes on the incidence of dengue, Zika and chikungunya in the two cities of Niterói and Rio de Janeiro. This follows a lead-in period since 2014 involving intensive community engagement, regulatory and public approval, entomological surveys, and small-scale pilot releases. Method: The Wolbachia releases during 2017-2019 covered a combined area of 170 km 2 with a resident population of 1.2 million, across Niterói and Rio de Janeiro. Untreated areas with comparable historical dengue profiles and demographic characteristics have been identified a priori as comparative control areas in each city. The proposed pragmatic epidemiological approach combines a controlled interrupted time series analysis of routinely notified suspected and laboratory-confirmed dengue and chikungunya cases, together with monitoring of Aedes-borne disease activity utilising outbreak signals routinely used in public health disease surveillance. Discussion: If the current project is successful, this model for control of mosquito-borne disease through Wolbachia releases can be expanded nationally and regionally

Prediction of binding hot spot residues by using structural and evolutionary parameters

In this work, we present a method for predicting hot spot residues by using a set of structural and evolutionary parameters. Unlike previous studies, we use a set of parameters which do not depend on the structure of the protein in complex, so that the predictor can also be used when the interface region is unknown. Despite the fact that no information concerning proteins in complex is used for prediction, the application of the method to a compiled dataset described in the literature achieved a performance of 60.4%, as measured by F-Measure, corresponding to a recall of 78.1% and a precision of 49.5%. This result is higher than those reported by previous studies using the same data set