Oral administration of a recombinant modified RBD antigen of SARS-CoV-2 as a possible immunostimulant for the care of COVID-19.

BACKGROUND: Developing effective vaccines against SARS-CoV-2 that consider manufacturing limitations, equitable access, and acceptance is necessary for developing platforms to produce antigens that can be efficiently presented for generating neutralizing antibodies and as a model for new vaccines. RESULTS: This work presents the development of an applicable technology through the oral administration of the SARS-CoV-2 RBD antigen fused with a peptide to improve its antigenic presentation. We focused on the development and production of the recombinant receptor binding domain (RBD) produced in E. coli modified with the addition of amino acids extension designed to improve antigen presentation. The production was carried out in shake flask and bioreactor cultures, obtaining around 200 mg/L of the antigen. The peptide-fused RBD and peptide-free RBD proteins were characterized and compared using SDS-PAGE gel, high-performance chromatography, and circular dichroism. The peptide-fused RBD was formulated in an oil-in-water emulsion for oral mice immunization. The peptide-fused RBD, compared to RBD, induced robust IgG production in mice, capable of recognizing the recombinant RBD in Enzyme-linked immunosorbent assays. In addition, the peptide-fused RBD generated neutralizing antibodies in the sera of the dosed mice. The formulation showed no reactive episodes and no changes in temperature or vomiting. CONCLUSIONS: Our study demonstrated the effectiveness of the designed peptide added to the RBD to improve antigen immunostimulation by oral administration.

Behavioral domains in compulsive rats: implications for understanding compulsive spectrum disorders.

Introduction: Compulsive behavior has been proposed as a transdiagnostic trait observed in different neuropsychiatric disorders, such as obsessive-compulsive disorder, autism, and schizophrenia. Research Domain Criteria (RDoC) strategy could help to disentangle the neuropsychological basis of compulsivity for developing new therapeutic and preventive approaches. In preclinical research, the selection of high-drinker (HD) vs. low-drinker (LD) animals by schedule-induced polydipsia (SIP) is considered a putative model of compulsivity, which includes a well-differentiated behavioral pattern. Methods: The purpose of this research was to assess the cognitive control and the negative valence system domains in a phenotype of compulsive HD rats. After the selection of animals as HD or LD, we assessed behavioral inflexibility by probabilistic spatial reversal learning (PSRL), motor and cognitive impulsivity by variable delay-to-signal (VDS), and risky decision-making by rodent gambling task (rGT). Results: HD rats performed fewer reversals and showed less probability of pressing the same lever that was previously reinforced on PSRL, more premature responses after the exposure to longer delays on VDS, and more disadvantageous risky choices on rGT. Moreover, HD animals performed more perseverative responses under the punishment period on rGT. Discussion: These results highlight that HD compulsive phenotype exhibits behavioral inflexibility, insensitivity to positive feedback, waiting impulsivity, risky decision-making, and frustrative non-reward responsiveness. Moreover, these findings demonstrate the importance of mapping different behavioral domains to prevent, treat, and diagnose compulsive spectrum disorders correctly.

Biases associated with database structure for COVID-19 detection in X-ray images.

Several artificial intelligence algorithms have been developed for COVID-19-related topics. One that has been common is the COVID-19 diagnosis using chest X-rays, where the eagerness to obtain early results has triggered the construction of a series of datasets where bias management has not been thorough from the point of view of patient information, capture conditions, class imbalance, and careless mixtures of multiple datasets. This paper analyses 19 datasets of COVID-19 chest X-ray images, identifying potential biases. Moreover, computational experiments were conducted using one of the most popular datasets in this domain, which obtains a 96.19% of classification accuracy on the complete dataset. Nevertheless, when evaluated with the ethical tool Aequitas, it fails on all the metrics. Ethical tools enhanced with some distribution and image quality considerations are the keys to developing or choosing a dataset with fewer bias issues. We aim to provide broad research on dataset problems, tools, and suggestions for future dataset developments and COVID-19 applications using chest X-ray images.

Cov-caldas: A new COVID-19 chest X-Ray dataset from state of Caldas-Colombia.

The emergence of COVID-19 as a global pandemic forced researchers worldwide in various disciplines to investigate and propose efficient strategies and/or technologies to prevent COVID-19 from further spreading. One of the main challenges to be overcome is the fast and efficient detection of COVID-19 using deep learning approaches and medical images such as Chest Computed Tomography (CT) and Chest X-ray images. In order to contribute to this challenge, a new dataset was collected in collaboration with "S.E.S Hospital Universitario de Caldas" ( https://hospitaldecaldas.com/ ) from Colombia and organized following the Medical Imaging Data Structure (MIDS) format. The dataset contains 7,307 chest X-ray images divided into 3,077 and 4,230 COVID-19 positive and negative images. Images were subjected to a selection and anonymization process to allow the scientific community to use them freely. Finally, different convolutional neural networks were used to perform technical validation. This dataset contributes to the scientific community by tackling significant limitations regarding data quality and availability for the detection of COVID-19.

Towards the development of an epitope-focused vaccine for SARS-CoV-2.

The rapid spread of COVID-19 on all continents and the mortality induced by SARS-CoV-2 virus, the cause of the pandemic coronavirus disease 2019 (COVID-19) has motivated an unprecedented effort for vaccine development. Inactivated viruses as well as vaccines focused on the partial or total sequence of the Spike protein using different novel platforms such us RNA, DNA, proteins, and non-replicating viral vectors have been developed. The high global need for vaccines, now and in the future, and the emergence of new variants of concern still requires development of accessible vaccines that can be adapted according to the most prevalent variants in the respective regions. Here, we describe the immunogenic properties of a group of theoretically predicted RBD peptides to be used as the first step towards the development of an effective, safe and low-cost epitope-focused vaccine. One of the tested peptides named P5, proved to be safe and immunogenic. Subcutaneous administration of the peptide, formulated with alumina, induced high levels of specific IgG antibodies in mice and hamsters, as well as an increase of IFN-γ expression by CD8+ T cells in C57 and BALB/c mice upon in vitro stimulation with P5. Neutralizing titers of anti-P5 antibodies, however, were disappointingly low, a deficiency that we will attempt to resolve by the inclusion of additional immunogenic epitopes to P5. The safety and immunogenicity data reported in this study support the use of this peptide as a starting point for the design of an epitope restricted vaccine.

Identifying General Tumor and Specific Lung Cancer Biomarkers by Transcriptomic Analysis.

The bioinformatic pipeline previously developed in our research laboratory is used to identify potential general and specific deregulated tumor genes and transcription factors related to the establishment and progression of tumoral diseases, now comparing lung cancer with other two types of cancer. Twenty microarray datasets were selected and analyzed separately to identify hub differentiated expressed genes and compared to identify all the deregulated genes and transcription factors in common between the three types of cancer and those unique to lung cancer. The winning DEGs analysis allowed to identify an important number of TFs deregulated in the majority of microarray datasets, which can become key biomarkers of general tumors and specific to lung cancer. A coexpression network was constructed for every dataset with all deregulated genes associated with lung cancer, according to DAVID's tool enrichment analysis, and transcription factors capable of regulating them, according to oPOSSUM´s tool. Several genes and transcription factors are coexpressed in the networks, suggesting that they could be related to the establishment or progression of the tumoral pathology in any tissue and specifically in the lung. The comparison of the coexpression networks of lung cancer and other types of cancer allowed the identification of common connectivity patterns with deregulated genes and transcription factors correlated to important tumoral processes and signaling pathways that have not been studied yet to experimentally validate their role in lung cancer. The Kaplan-Meier estimator determined the association of thirteen deregulated top winning transcription factors with the survival of lung cancer patients. The coregulatory analysis identified two top winning transcription factors networks related to the regulatory control of gene expression in lung and breast cancer. Our transcriptomic analysis suggests that cancer has an important coregulatory network of transcription factors related to the acquisition of the hallmarks of cancer. Moreover, lung cancer has a group of genes and transcription factors unique to pulmonary tissue that are coexpressed during tumorigenesis and must be studied experimentally to fully understand their role in the pathogenesis within its very complex transcriptomic scenario. Therefore, the downstream bioinformatic analysis developed was able to identify a coregulatory metafirm of cancer in general and specific to lung cancer taking into account the great heterogeneity of the tumoral process at cellular and population levels.

Socioemotional deficit and HPA axis time response in high compulsive rats selected by schedule-induced polydipsia.

Compulsivity is a failure to stop an ongoing behavior that has become inappropriate to the situation and is recognized as a transdiagnostic trait present in different neuropsychiatric disorders. The implication of motivation and emotion, as well as the stress response in compulsive population has not been fully understood. We assessed the motivation to reward and cues, the emotional response in different contexts and the hypothalamic-pituitary-adrenal (HPA) axis response in rats selected by a preclinical model of compulsive behavior. Firstly, high (HD) or low (LD) drinkers were selected according to their drinking behavior on schedule-induced polydipsia (SIP). Then, we assessed motivation by the propensity to attribute incentive salience to rewards on Pavlovian Conditioned Approach (PavCA) and motivation to gain reward on Progressive Ratio Schedule of Reinforcement (PRSR). Emotion was measured by Social Dominance on the Tube Test (SDTT) and emotional memory on Passive Avoidance (PA). Plasma corticosterone (CORT) levels in response to SIP were assessed. HD rats showed a socioemotional deficit by fewer victories on the SDTT, and an increased latency to enter the dark compartment on the PA. No differences were found between groups regarding to motivational assessment. Moreover, HD rats revealed a blunted time response in the increase of CORT levels at 45 min after SIP compared to LD rats. The findings show that the compulsive phenotype of HD rats exhibit less social dominance, more resistance to extinction and a differential CORT time response to SIP. These findings may contribute to highlight the relevance of assessing socioemotional behaviors and stress response for a better characterization of the vulnerability to compulsive spectrum disorders.

COVID-19 detection in X-ray images using convolutional neural networks.

COVID-19 global pandemic affects health care and lifestyle worldwide, and its early detection is critical to control cases' spreading and mortality. The actual leader diagnosis test is the Reverse transcription Polymerase chain reaction (RT-PCR), result times and cost of these tests are high, so other fast and accessible diagnostic tools are needed. Inspired by recent research that correlates the presence of COVID-19 to findings in Chest X-ray images, this papers' approach uses existing deep learning models (VGG19 and U-Net) to process these images and classify them as positive or negative for COVID-19. The proposed system involves a preprocessing stage with lung segmentation, removing the surroundings which does not offer relevant information for the task and may produce biased results; after this initial stage comes the classification model trained under the transfer learning scheme; and finally, results analysis and interpretation via heat maps visualization. The best models achieved a detection accuracy of COVID-19 around 97%.

Sensitivity of deep learning applied to spatial image steganalysis.

In recent years, the traditional approach to spatial image steganalysis has shifted to deep learning (DL) techniques, which have improved the detection accuracy while combining feature extraction and classification in a single model, usually a convolutional neural network (CNN). The main contribution from researchers in this area is new architectures that further improve detection accuracy. Nevertheless, the preprocessing and partition of the database influence the overall performance of the CNN. This paper presents the results achieved by novel steganalysis networks (Xu-Net, Ye-Net, Yedroudj-Net, SR-Net, Zhu-Net, and GBRAS-Net) using different combinations of image and filter normalization ranges, various database splits, different activation functions for the preprocessing stage, as well as an analysis on the activation maps and how to report accuracy. These results demonstrate how sensible steganalysis systems are to changes in any stage of the process, and how important it is for researchers in this field to register and report their work thoroughly. We also propose a set of recommendations for the design of experiments in steganalysis with DL.

Strategy to improve the accuracy of convolutional neural network architectures applied to digital image steganalysis in the spatial domain.

In recent years, Deep Learning techniques applied to steganalysis have surpassed the traditional two-stage approach by unifying feature extraction and classification in a single model, the Convolutional Neural Network (CNN). Several CNN architectures have been proposed to solve this task, improving steganographic images' detection accuracy, but it is unclear which computational elements are relevant. Here we present a strategy to improve accuracy, convergence, and stability during training. The strategy involves a preprocessing stage with Spatial Rich Models filters, Spatial Dropout, Absolute Value layer, and Batch Normalization. Using the strategy improves the performance of three steganalysis CNNs and two image classification CNNs by enhancing the accuracy from 2% up to 10% while reducing the training time to less than 6 h and improving the networks' stability.

Secreted IgD Amplifies Humoral T Helper 2 Cell Responses by Binding Basophils via Galectin-9 and CD44.

B cells thwart antigenic aggressions by releasing immunoglobulin M (IgM), IgG, IgA, and IgE, which deploy well-understood effector functions. In contrast, the role of secreted IgD remains mysterious. We found that some B cells generated IgD-secreting plasma cells following early exposure to external soluble antigens such as food proteins. Secreted IgD targeted basophils by interacting with the CD44-binding protein galectin-9. When engaged by antigen, basophil-bound IgD increased basophil secretion of interleukin-4 (IL-4), IL-5, and IL-13, which facilitated the generation of T follicular helper type 2 cells expressing IL-4. These germinal center T cells enhanced IgG1 and IgE but not IgG2a and IgG2b responses to the antigen initially recognized by basophil-bound IgD. In addition, IgD ligation by antigen attenuated allergic basophil degranulation induced by IgE co-ligation. Thus, IgD may link B cells with basophils to optimize humoral T helper type 2-mediated immunity against common environmental soluble antigens.

mTOR intersects antibody-inducing signals from TACI in marginal zone B cells.

Mechanistic target of rapamycin (mTOR) enhances immunity in addition to orchestrating metabolism. Here we show that mTOR coordinates immunometabolic reconfiguration of marginal zone (MZ) B cells, a pre-activated lymphocyte subset that mounts antibody responses to T-cell-independent antigens through a Toll-like receptor (TLR)-amplified pathway involving transmembrane activator and CAML interactor (TACI). This receptor interacts with mTOR via the TLR adapter MyD88. The resulting mTOR activation instigates MZ B-cell proliferation, immunoglobulin G (IgG) class switching, and plasmablast differentiation through a rapamycin-sensitive pathway that integrates metabolic and antibody-inducing transcription programs, including NF-κB. Disruption of TACI-mTOR interaction by rapamycin, truncation of the MyD88-binding domain of TACI, or B-cell-conditional mTOR deficiency interrupts TACI signaling via NF-κB and cooperation with TLRs, thereby hampering IgG production to T-cell-independent antigens but not B-cell survival. Thus, mTOR drives innate-like antibody responses by linking proximal TACI signaling events with distal immunometabolic transcription programs.

Human Secretory IgM Emerges from Plasma Cells Clonally Related to Gut Memory B Cells and Targets Highly Diverse Commensals.

Secretory immunoglobulin A (SIgA) enhances host-microbiota symbiosis, whereas SIgM remains poorly understood. We found that gut IgM+ plasma cells (PCs) were more abundant in humans than mice and clonally related to a large repertoire of memory IgM+ B cells disseminated throughout the intestine but rare in systemic lymphoid organs. In addition to sharing a gut-specific gene signature with memory IgA+ B cells, memory IgM+ B cells were related to some IgA+ clonotypes and switched to IgA in response to T cell-independent or T cell-dependent signals. These signals induced abundant IgM which, together with SIgM from clonally affiliated PCs, recognized mucus-embedded commensals. Bacteria recognized by human SIgM were dually coated by SIgA and showed increased richness and diversity compared to IgA-only-coated or uncoated bacteria. Thus, SIgM may emerge from pre-existing memory rather than newly activated naive IgM+ B cells and could help SIgA to anchor highly diverse commensal communities to mucus.

[Prevalence and Associated Factors of Mental Disorders in Colombian Child Population, the 2015 National Mental Health Survey]. / Prevalencia y factores asociados a trastornos mentales en la población de niños colombianos, Encuesta Nacional de Salud Mental (ENSM) 2015.

INTRODUCTION: The 2015 National Mental Health Survey aimed to expand our knowledge about the real mental state of children in Colombia, taking into account the fact that most mental disorders in adults begin during childhood or adolescence. It is essential to have an improved knowledge of the magnitude of this issue and to design timely interventions that reduce long term complications. OBJECTIVE: The aim of the study was to determine the prevalence of the disorders in the last 12 months and 30 days according to the DSM-IV, as well as to collect data about social and demographic variables. METHODS: The structured Diagnostic Interview Schedule for Children (DISC-P), which provides DSM-IV diagnoses, was applied to carers of non-institutionalised children between 7 and 11 years old. The disorders evaluated included: major depressive disorder, dysthymia, generalised anxiety disorder, separation anxiety disorder, attention deficit hyperactivity disorder in its three kinds (mixed, inattentive, and hyperactive), oppositional defiant disorder, and conduct disorder. The instrumentation was computer-assisted. RESULTS: Prevalences of the disorders are present both in the last 30 days and in the last 12 months. In general, there is a prevalence of any of the disorders of 3% (95% CI, 2.2-4.0) in the last 30 days, and 4.7% (95% CI, 3.6-6.2) in the last 12 months. When evaluated individually, attention deficit hyperactivity disorder is the most frequent disorder, with a prevalence of 2.3% and 3.0% in the last 30 days and the last 12 months, respectively. In addition, the disorders that are known to frequently begin during childhood are the most common disorders in the age group studied, with a prevalence of 2.5% in the last 30 days and 3.2% in the last year. CONCLUSIONS: The 2015 National Mental Health Survey provides precise information about the real mental situation in children between the ages of 7 and 11 years in Colombia, compared with past epidemiological studies in the country, which were restricted to specific populations. By improving the reliability on knowledge about mental disorders in children, it will be possible to design more appropriate and precise strategies for prevention, diagnosis, and treatment.

Prevalencia y factores asociados a trastornos mentales en la población de niños colombianos, Encuesta Nacional de Salud Mental (ENSM) 2015 / Prevalence and Associated Factors of Mental Disorders in Colombian Child Population, the 2015 National Mental Health Survey

Resumen Introducción: La Encuesta Nacional de Salud Mental (ENSM) de 2015 busca profundizar en la realidad de la situación mental de los menores de edad de Colombia, teniendo en cuenta la carga de enfermedad que representan y que algunos trastornos mentales del adulto tienen su origen en la niñez o en la adolescencia, lo cual exige un mayor conocimiento del problema para diseñar intervenciones oportunas y disminuir las complicaciones a largo plazo. Objetivo: Determinar la prevalencia y los factores asociados de los trastornos en los últimos 30 días y los últimos 12 meses. Métodos: Se aplicó la entrevista estructurada Diagnostic Interview Schedule for Children (DISCP), que provee diagnósticos del DSM-IV a los cuidadores de niños no institucionalizados de 7-11 años. Se evaluaron los siguientes trastornos: depresión mayor, distimia, ansiedad generalizada, ansiedad de separación, déficit de atención e hiperactividad en sus tres presentaciones (mixto, inatento e hiperactivo), negativista desafiante y de conducta. El instrumento fue asistido por computador. Resultados: Se presentan las prevalencias de los trastornos tanto en los últimos 30 días como en los últimos 12 meses. En general se observa una prevalencia de cualquiera de los trastornos medidos del 3% (intervalo de confianza del 95% [IC95%], 2,2%-4,0%) en los últimos 30 días y del 4,7% (IC95%, 3,6%-6,2%) en los últimos 12 meses. Son más frecuentes en las mujeres. El trastorno por déficit de atención e hiperactividad es el más frecuente (el 2,3 y el 3,0%). Además, los trastornos de la conducta de inicio habitual en la infancia son los más frecuentes en este grupo etario, con prevalencias del 2,5% en los últimos 30 días y el 3,2% en el último año. Se presentan factores de riesgo asociados. Conclusiones: La ENSM de 2015 provee una información sobre la realidad de la situación mental de los niños de Colombia que permitirá diseños de 7-11 añar estrategias de prevención, diagnóstico y tratamiento más apropiados y precisos.

Abstract Introduction: The 2015 National Mental Health Survey aimed to expand our knowledge about the real mental state of children in Colombia, taking into account the fact that most mental disorders in adults begin during childhood or adolescence. It is essential to have an improved knowledge of the magnitude of this issue and to design timely interventions that reduce long term complications. Objective: The aim of the study was to determine the prevalence of the disorders in the last 12 months and 30 days according to the DSM-IV, as well as to collect data about social and demographic variables. Methods: The structured Diagnostic Interview Schedule for Children (DISC-P), which provides DSM-IV diagnoses,was applied to carers of non-institutionalised children between 7 and 11 years old. The disorders evaluated included: major depressive disorder, dysthymia, generalised anxiety disorder, separation anxiety disorder, attention deficit hyperactivity disorder in its three kinds (mixed, inattentive, and hyperactive), oppositional defiant disorder, and conduct disorder. The instrumentation was computer-assisted. Results: Prevalences of the disorders are present both in the last 30 days and in the last 12 months. In general, there is a prevalence of any of the disorders of 3% (95% CI, 2.2-4.0) in the last 30 days, and 4.7% (95% CI, 3.6-6.2) in the last 12 months. When evaluated individually, attention deficit hyperactivity disorder is the most frequent disorder, with a prevalence of 2.3% and 3.0% in the last 30 days and the last 12 months, respectively. In addition, the disorders that are known to frequently begin during childhood are the most common disorders in the age group studied, with a prevalence of 2.5% in the last 30 days and 3.2% in the last year. Conclusions: The 2015 National Mental Health Survey provides precise information about the real mental situation in children between the ages of 7 and 11 years in Colombia, compared with past epidemiological studies in the country, which were restricted to specific populations. By improving the reliability on knowledge about mental disorders in children, it will be possible to design more appropriate and precise strategies for prevention, diagnosis, and treatment.

Novel Approaches in Astrocyte Protection: from Experimental Methods to Computational Approaches.

Astrocytes are important for normal brain functioning. Astrocytes are metabolic regulators of the brain that exert many functions such as the preservation of blood-brain barrier (BBB) function, clearance of toxic substances, and generation of antioxidant molecules and growth factors. These functions are fundamental to sustain the function and survival of neurons and other brain cells. For these reasons, the protection of astrocytes has become relevant for the prevention of neuronal death during brain pathologies such as Parkinson's disease, Alzheimer's disease, stroke, and other neurodegenerative conditions. Currently, different strategies are being used to protect the main astrocytic functions during neurological diseases, including the use of growth factors, steroid derivatives, mesenchymal stem cell paracrine factors, nicotine derivatives, and computational biology tools. Moreover, the combined use of experimental approaches with bioinformatics tools such as the ones obtained through system biology has allowed a broader knowledge in astrocytic protection both in normal and pathological conditions. In the present review, we highlight some of these recent paradigms in assessing astrocyte protection using experimental and computational approaches and discuss how they could be used for the study of restorative therapies for the brain in pathological conditions.

[Historical trauma. Systematic review of a different approach to armed conflict]. / Trauma histórico. Revisión sistemática de un abordaje diferente al conflicto armado.

INTRODUCTION: Historical trauma (HT) is a collective trauma inflicted on a group of people who share an identity or affiliation, and is often characterized by the transgenerational legacy of traumatic experiences, and expressed through various psychological and social responses. This construct is proposed in contrast to post-traumatic stress disorder (PTSD) due to limitations identified with the latter diagnostic category when addressing collective trauma, especially in situations of political and social violence. The purpose of this article is to review the literature published so far on HT. METHODS: A search was performed using the terms "historical trauma" and "mental health" or "trauma histórico" and "salud mental" in the scientific databases, EMBASE, Ebscohost, JSTOR, ProQuest, LILACS, SciELO, PsycARTICLES, ISI Web of Science and PubMed. RESULTS: The authors reviewed HT definition, paramount characteristics of its traumatic experience, and several theories of on the transgenerational succession if these experiences occur, as well as possible consequences of traumatic events at individual, family and social level. Common characteristics of different therapeutic models are highlighted, in addition to some recommendations for their application. CONCLUSIONS: PTSD has clear limitations in addressing community and cumulative traumatic experiences related to specific social and historical contexts. The authors discuss the potential utility of HT in this task. Finally, several gaps in current knowledge regarding this construct are mentioned, and some recommendations for future research are indicated.