Dual molecule targeting HDAC6 leads to intratumoral CD4+ cytotoxic lymphocytes recruitment through MHC-II upregulation on lung cancer cells

BACKGROUND

Despite the current therapeutic treatments including surgery, chemotherapy, radiotherapy and more recently immunotherapy, the mortality rate of lung cancer stays high. Regarding lung cancer, epigenetic modifications altering cell cycle, angiogenesis and programmed cancer cell death are therapeutic targets to combine with immunotherapy to improve treatment success. In a recent study, we uncovered that a molecule called QAPHA ((E)-3-(5-((2-cyanoquinolin-4-yl)(methyl)amino)-2-methoxyphenyl)-N-hydroxyacrylamide) has a dual function as both a tubulin polymerization and HDAC inhibitors. Here, we investigate the impact of this novel dual inhibitor on the immune response to lung cancer.

METHODS

To elucidate the mechanism of action of QAPHA, we conducted a chemical proteomics analysis. Using an in vivo mouse model of lung cancer (TC-1 tumor cells), we assessed the effects of QAPHA on tumor regression. Tumor infiltrating immune cells were characterized by flow cytometry.

RESULTS

In this study, we first showed that QAPHA effectively inhibited histone deacetylase 6, leading to upregulation of HSP90, cytochrome C and caspases, as revealed by proteomic analysis. We confirmed that QAPHA induces immunogenic cell death (ICD) by expressing calreticulin at cell surface in vitro and demonstrated its efficacy as a vaccine in vivo. Remarkably, even at a low concentration (0.5 mg/kg), QAPHA achieved complete tumor regression in approximately 60% of mice treated intratumorally, establishing a long-lasting anticancer immune response. Additionally, QAPHA treatment promoted the infiltration of M1-polarized macrophages in treated mice, indicating the induction of a pro-inflammatory environment within the tumor. Very interestingly, our findings also revealed that QAPHA upregulated major histocompatibility complex class II (MHC-II) expression on TC-1 tumor cells both in vitro and in vivo, facilitating the recruitment of cytotoxic CD4+T cells (CD4+CTL) expressing CD4+, NKG2D+, CRTAM+, and Perforin+. Finally, we showed that tumor regression strongly correlates to MHC-II expression level on tumor cell and CD4+ CTL infiltrate.

CONCLUSION

Collectively, our findings shed light on the discovery of a new multitarget inhibitor able to induce ICD and MHC-II upregulation in TC-1 tumor cell. These two processes participate in enhancing a specific CD4+ cytotoxic T cell-mediated antitumor response in vivo in our model of lung cancer. This breakthrough suggests the potential of QAPHA as a promising agent for cancer treatment.

NLRP6 controls pulmonary inflammation from cigarette smoke in a gut microbiota-dependent manner

Chronic obstructive pulmonary disease (COPD) is a major health issue primarily caused by cigarette smoke (CS) and characterized by breathlessness and repeated airway inflammation. NLRP6 is a cytosolic innate receptor controlling intestinal inflammation and orchestrating the colonic host-microbial interface. However, its roles in the lungs remain largely unexplored. Using CS exposure models, our data show that airway inflammation is strongly impaired in Nlrp6-deficient mice with drastically fewer recruited neutrophils, a key cell subset in inflammation and COPD. We found that NLRP6 expression in lung epithelial cells is important to control airway and lung tissue inflammation in an inflammasome-dependent manner. Since gut-derived metabolites regulate NLRP6 inflammasome activation in intestinal epithelial cells, we investigated the link between NLRP6, CS-driven lung inflammation, and gut microbiota composition. We report that acute CS exposure alters gut microbiota in both wild-type (WT) and Nlrp6-deficient mice and that antibiotic treatment decreases CS-induced lung inflammation. In addition, gut microbiota transfer from dysbiotic Nlrp6-deficient mice to WT mice decreased airway lung inflammation in WT mice, highlighting an NLRP6-dependent gut-to-lung axis controlling pulmonary inflammation.

Cigarette smoke-induced gasdermin D activation in bronchoalveolar macrophages and bronchial epithelial cells dependently on NLRP3

Chronic pulmonary inflammation and chronic obstructive pulmonary disease (COPD) are major health issues largely due to air pollution and cigarette smoke (CS) exposure. The role of the innate receptor NLRP3 (nucleotide-binding domain and leucine-rich repeat containing protein 3) orchestrating inflammation through formation of an inflammasome complex in CS-induced inflammation or COPD remains controversial. Using acute and subchronic CS exposure models, we found that Nlrp3-deficient mice or wild-type mice treated with the NLRP3 inhibitor MCC950 presented an important reduction of inflammatory cells recruited into the bronchoalveolar space and of pulmonary inflammation with decreased chemokines and cytokines production, in particular IL-1β demonstrating the key role of NLRP3. Furthermore, mice deficient for Caspase-1/Caspase-11 presented also decreased inflammation parameters, suggesting a role for the NLRP3 inflammasome. Importantly we showed that acute CS-exposure promotes NLRP3-dependent cleavage of gasdermin D in macrophages present in the bronchoalveolar space and in bronchial airway epithelial cells. Finally, Gsdmd-deficiency reduced acute CS-induced lung and bronchoalveolar space inflammation and IL-1β secretion. Thus, we demonstrated in our model that NLRP3 and gasdermin D are key players in CS-induced pulmonary inflammation and IL-1β release potentially through gasdermin D forming-pore and/or pyroptoctic cell death.

What can we say about the impact of teleconsultations on preventing psychiatric inpatient treatment during COVID-19 pandemic?

Introduction

Telepsychiatry was proved effective and satisfactory in settings like the emergency department and mental health services, but its use is historically restricted. Although there are several studies about telepsychiatry pros and cons, more is needed to know about its effect on patient’s follow-up and its influence on inpatient treatment rates, specifically during COVID-19 pandemic.

Objectives

The aim is to look for an eventual change on psychiatric inpatient admissions, during COVID-19 pandemic, when psychiatric patients are in follow-up through teleconsultation.

Methods

We compared the number of hospitalizations for 3 different 6 months periods of time: the 2^nd semester of 2019 with no teleconsultations, March-August 2020 only with teleconsultations (except some few 1^st consultations) and the 1^st semester of 2021 with face-to-face and teleconsultations. A one-way repeated measures ANOVA was conducted on a 1050 patients sample.

Results

The statists showed that the type of approach in consultations didn’t lead to statistically significant differences in hospitalizations (F test-statistic = 0.33086, p = 0.718345).

Conclusions

There is a plethora of advantages about telepsychiatry and it was already shown to be as effective as in-person contact. Some articles show an association of telepsychiatry with a decrease in hospitalization rates, but mostly display similar clinical outcomes. In this study, the authors found that the results follow the latter tendency, although we must consider the COVID-19 pandemic as a possible decompensation and worsening clinical factor. More studies on this matter are important to better understand the potential benefits (and risks) of this treatment setting.

Disclosure

No significant relationships.

A palaeoecological perspective on the transformation of the tropical Andes by early human activity

Palaeoecological records suggest that humans have been in the Andes since at least 14 000 years ago. Early human impacts on Andean ecosystems included an increase in fire activity and the extinction of the Pleistocene megafauna. These changes in Andean ecosystems coincided with rapid climate change as species were migrating upslope in response to deglacial warming. Microrefugia probably played a vital role in the speed and genetic composition of that migration. The period from ca 14 500 to 12 500 years ago was when novel combinations of plant species appeared to form no-analogue assemblages in the Andes. By 12 000 years ago most areas in what are today the Andean grasslands were being burned and modified by human activity. As the vegetation of these highland settings has been modified by human activity for the entirety of the Holocene, they should be regarded as long-term manufactutred landscapes. The sharp tree lines separating Andean forests from grasslands that we see today were probably also created by repeated burning and owe their position more to human-induced fire than climatic constraints. In areas that were readly penetrated by humans on the forested slopes of the Andes, substantial modification and settlement had occurred by the mid-Holocene. In hard-to-reach areas, however, the amount of human modification may always have been minimal, and these slopes can be considered as being close to natural in their vegetation. This article is part of the theme issue 'Tropical forests in the deep human past'.

Protective Role of the Nucleic Acid Sensor STING in Pulmonary Fibrosis

Idiopathic pulmonary fibrosis (IPF) is the most common and severe type of interstitial lung disease for which current treatments display limited efficacy. IPF is largely driven by host-derived danger signals released upon recurrent local tissue damage. Here we explored the roles of self-DNA and stimulator of interferon genes (STING), a protein belonging to an intracellular DNA sensing pathway that leads to type I and/or type III interferon (IFN) production upon activation. Using a mouse model of IPF, we report that STING deficiency leads to exacerbated pulmonary fibrosis with increased collagen deposition in the lungs and excessive remodeling factors expression. We further show that STING-mediated protection does not rely on type I IFN signaling nor on IL-17A or TGF-β modulation but is associated with dysregulated neutrophils. Together, our data support an unprecedented immunoregulatory function of STING in lung fibrosis.

B-Cell Activating Factor Secreted by Neutrophils Is a Critical Player in Lung Inflammation to Cigarette Smoke Exposure

Cigarette smoke (CS) is the major cause of chronic lung injuries, such as chronic obstructive pulmonary disease (COPD). In patients with severe COPD, tertiary lymphoid follicles containing B lymphocytes and B cell-activating factor (BAFF) overexpression are associated with disease severity. In addition, BAFF promotes adaptive immunity in smokers and mice chronically exposed to CS. However, the role of BAFF in the early phase of innate immunity has never been investigated. We acutely exposed C57BL/6J mice to CS and show early BAFF expression in the bronchoalveolar space and lung tissue that correlates to airway neutrophil and macrophage influx. Immunostaining analysis revealed that neutrophils are the major source of BAFF. We confirmed in vitro that neutrophils secrete BAFF in response to cigarette smoke extract (CSE) stimulation. Antibody-mediated neutrophil depletion significantly dampens lung inflammation to CS exposure but only partially decreases BAFF expression in lung tissue and bronchoalveolar space suggesting additional sources of BAFF. Importantly, BAFF deficient mice displayed decreased airway neutrophil recruiting chemokines and neutrophil influx while the addition of exogenous BAFF significantly enhanced this CS-induced neutrophilic inflammation. This demonstrates that BAFF is a key proinflammatory cytokine and that innate immune cells in particular neutrophils, are an unconsidered source of BAFF in early stages of CS-induced innate immunity.

Esculin reduces P2X7 and reverses mitochondrial dysfunction in the renal cortex of diabetic rats

AIMS

To evaluate the effects of esculin treatment on P2X7 receptor and mitochondrial dysfunction in the renal cortex of diabetic rats.

MAIN METHODS

Male Wistar rats, 7 weeks old, were unilaterally nephrectomized. Part of these animals were induced to diabetes using streptozotocin (60 mg/kg). Diabetes was confirmed 48 h after induction, with blood glucose levels ≥200 mg/dL. Part of control and diabetic animals were selected to receive daily doses of esculin (50 mg/kg), during 8 weeks. The animals were placed in metabolic cages at the eighth week of protocol for 24 h urine collection and a small aliquot of blood was collected for biochemical analysis. After this procedure, the animals were euthanized and the remaining kidney was stored for histopathological analysis, Western blotting and mitochondrial high-resolution respirometry.

KEY FINDINGS

Although esculin did not change metabolic parameters, renal biochemical function, neither TBARS in DM rats, esculin reduced P2X7 levels in these animals and restored mitochondrial function via glycolysis substrates and β-oxidation. Besides, at the histological analysis, we observed that esculin reduced inflammatory infiltrates and collagen IV deposits as compared to diabetic group.

SIGNIFICANCE

Esculin attenuated the development of renal injuries caused by hyperglycemia, proinflammatory and oxidative mechanisms mediated by P2X7 receptor, as seen by histological findings and improved mitochondrial function in diabetic animals. This suggests that esculin could be used as an adjuvant therapy to prevent the diabetic nephropathy.

No man is an island: spatial clustering and access to primary care as possible targets for the development of new community mental health approaches

BACKGROUND

to understand if patients seen at Centro Hospitalar Psiquiátrico de Lisboa (CHPL) live in geographical clusters or randomly throughout the city, as well as determine their access to the psychiatric hospital and primary care facilities (PCF).

METHODS

spatial autocorrelation statistics were performed (queen criterion of contiguity), regarding all patients observed at CHPL in 2017 (at the census subsection level), and considering not only their overall number but also main diagnosis, and admission to the psychiatric ward - voluntary or compulsory. Distance to the hospital and to the closest PCF was measured (for each patient and the variables cited above), and the mean values were compared. Finally, the total number of patients around each PCF was counted, considering specified radius sizes of 656 and 1000 m.

RESULTS

All 5161 patients (509 psychiatric admissions) were geolocated, and statistical significance regarding patient clustering was found for the total number (p-0.0001) and specific group of disorders, namely Schizophrenia and related disorders (p-0.007) and depressive disorders (p-0.0002). Patients who were admitted in a psychiatric ward live farther away from the hospital (p-0.002), with the compulsory admissions (versus voluntary ones) living even farther (p-0.004). Furthermore, defining a radius of 1000 m for each PCF allowed the identification of two PCF with more than 1000 patients, and two others with more than 800.

CONCLUSIONS

as patients seem to live in geographical clusters (and considering PCFs with the highest number of them), possible locations for the development of programs regarding mental health treatment and prevention can now be identified.

Inhibition of the P2X7 receptor improves renal function via renin-angiotensin system and nitric oxide on diabetic nephropathy in rats

AIM

To evaluate the effects of P2X₇ receptor blockade on renin-angiotensin system (RAS) in rats with diabetic nephropathy (DN).

MAIN METHODS

Wistar rats were unilaterally nephrectomized and received streptozotocin for diabetes mellitus (DM) induction; control animals (CTL) received the drug vehicle. The animals were submitted to P2X₇ receptor silencing, forming the group (DM + siRNA). The animals were placed in metabolic cages for data collection and evaluation of renal function; at the end of the protocol, the kidney was removed for analysis of P2X₇, renin, angiotensin-converting enzyme (ACE), ACE2, angiotensin, thiobarbituric acid reactive substance levels (TBARS), nitric oxide (NO) and qualitative histological.

KEY FINDINGS

The metabolic profile was attenuated in DM + siRNA vs. DM and there was a significant improvement in creatinine, urea and proteinuria levels in the same group. Renin expression was significantly decreased in DM + siRNA vs. DM. ACE and ACE2 were significantly reduced in DM + siRNA vs. DM. TBARS levels were decreased and NO showed an increase in DM + siRNA vs. DM, both significant. All histological alterations were improved in DM + siRNA vs. DM.

SIGNIFICANCE

Data have shown that although silencing of the P2X₇ receptor did not decrease fasting glucose, it promoted an improvement in the metabolic profile and a significant recovery of renal function, revealing a protective action by the inhibition of this receptor. This effect must have occurred due to the inhibition of RAS and the increase of NO, suggesting that the use of P2X₇ receptors inhibitors could be used as adjuvant therapy against DN progression.

Genetic evaluation for latent variables derived from factor analysis in broilers

1. The aim of this study was to investigate the associations between several carcass, performance and meat quality traits in broilers through factor analysis and use the latent variables (i.e. factors) as pseudo-phenotypes in genetic evaluations.2. Factors were extracted using the principal components method and varimax rotation algorithm. Genetic parameters were estimated via Bayesian inference under a multiple-trait animal model.3. All factors taken together explained 71% of the original variance of the data. The first factor, denominated as 'weight', was associated with carcass and body weight traits; and the second factor, defined as 'tenderness', represented traits related to water-holding capacity and shear force. The third factor, 'colour', was associated with traits related to meat colour, whereas the fourth, referenced as 'viscera', was related to heart, liver and abdominal fat.4. The four biological factors presented moderate to high heritability (ranging from 0.35 to 0.75), which may confer genetic gains in this population.5. In conclusion, it seems possible to reduce the number of traits in the genetic evaluation of broilers using latent variables derived from factor analysis.

Enhanced NMR relaxation of fluids confined to porous media: A proposed theory and experimental tests

We propose a theory to account for the NMR relaxation of water protons in situations in which the fluid is confined to porous structures exhibiting a scarce distribution of paramagnetic centers on their surface. Though much of what is stated and assumed concerns the response of water, the model has sufficiently general features to be able to explain proton relaxation of other polar fluids under similar conditions. One of the main results of the paper is to show that the local anisotropy introduced by the dominant dipolar coupling in the relaxation rates of active surface elements induces a measurable dependence on sample orientation in the overall relaxation rates of the saturating fluid provided its confining structure is not statistically isotropic. Measurements of T_{1} proton relaxation on water saturating microcapillary tubes are performed to reveal the effect.

Hepatic injury induced by thioacetamide causes aortic endothelial dysfunction by a cyclooxygenase-dependent mechanism

Liver cirrhosis is associated with a wide range of cardiovascular abnormalities including hyperdynamic circulation and cirrhotic cardiomyopathy. The pathogenic mechanisms of these cardiovascular changes are multifactorial and include vascular dysregulations.

AIM

The present study tested the hypothesis that the systemic vascular hyporesponsiveness in thioacetamide (TAA)-induced liver injury model is dependent on nitric oxide (NO) and cyclooxygenase (COX) derivatives.

MAIN METHODS

Wistar rats were treated with TAA for eight weeks to induce liver injury.

KEY FINDINGS

The maximal contractile response in concentration-effect curves to phenylephrine was decreased in aorta from TAA-treated rats, but no differences were found in aorta without endothelium, suggesting an endothelium-dependent mechanism in decreased contractile response. There was no difference in the contractile response with and without L-NAME (N(ω)-nitro-l-arginine methyl ester) in rats with liver injury, showing that the TAA treatment impairs NO synthesis. Pre-incubation of the aorta with indomethacin, a COX-inhibitor, normalized the reduced contractile response to phenylephrine in arteries from TAA group. Also, COX-2 and iNOS (inducible nitric oxide syntase) protein expression was increased in aorta from TAA group compared to control group. Animals submitted to TAA treatment had a reduction in systolic blood pressure. Our findings demonstrated that liver injury induced by TAA caused a decrease in aortic contractile response by a COX-dependent mechanism but not by NO release. Also, it was demonstrated an inflammatory process in the aorta of TAA-treated rats by increased expression of COX-2 and iNOS.

SIGNIFICANCE

Therefore, there is an essential contribution of COX-2 activation in extra-hepatic vascular dysfunction and inflammation present in cirrhosis induced by TAA.

The IL-33 Receptor ST2 Regulates Pulmonary Inflammation and Fibrosis to Bleomycin

Idiopathic pulmonary fibrosis is a progressive, devastating, and yet untreatable fibrotic disease of unknown origin. Interleukin-33 (IL-33), an IL-1 family member acts as an alarmin with pro-inflammatory properties when released after stress or cell death. Here, we investigated the role of IL-33 in the bleomycin (BLM)-induced inflammation and fibrosis model using mice IL-33 receptor [chain suppression of tumorigenicity 2 (ST2)] mice compared with C57BL/6 wild-type mice. Unexpectedly, 24 h post-BLM treatment ST2-deficient mice displayed augmented inflammatory cell recruitment, in particular by neutrophils, together with enhanced levels of chemokines and remodeling factors in the bronchoalveolar space and/or the lungs. At 11 days, lung remodeling and fibrosis were decreased with reduced M2 macrophages in the lung associated with M2-like cytokine profile in ST2-deficient mice, while lung cellular inflammation was decreased but with fluid retention (edema) increased. In vivo magnetic resonance imaging (MRI) analysis demonstrates a rapid development of edema detectable at day 7, which was increased in the absence of ST2. Our results demonstrate that acute neutrophilic pulmonary inflammation leads to the development of an IL-33/ST2-dependent lung fibrosis associated with the production of M2-like polarization. In addition, non-invasive MRI revealed enhanced inflammation with lung edema during the development of pulmonary inflammation and fibrosis in absence of ST2.

Regularized quantile regression for SNP marker estimation of pig growth curves

Background

Genomic growth curves are generally defined only in terms of population mean; an alternative approach that has not yet been exploited in genomic analyses of growth curves is the Quantile Regression (QR). This methodology allows for the estimation of marker effects at different levels of the variable of interest. We aimed to propose and evaluate a regularized quantile regression for SNP marker effect estimation of pig growth curves, as well as to identify the chromosome regions of the most relevant markers and to estimate the genetic individual weight trajectory over time (genomic growth curve) under different quantiles (levels).

Results

The regularized quantile regression (RQR) enabled the discovery, at different levels of interest (quantiles), of the most relevant markers allowing for the identification of QTL regions. We found the same relevant markers simultaneously affecting different growth curve parameters (mature weight and maturity rate): two (ALGA0096701 and ALGA0029483) for RQR(0.2), one (ALGA0096701) for RQR(0.5), and one (ALGA0003761) for RQR(0.8). Three average genomic growth curves were obtained and the behavior was explained by the curve in quantile 0.2, which differed from the others.

Conclusions

RQR allowed for the construction of genomic growth curves, which is the key to identifying and selecting the most desirable animals for breeding purposes. Furthermore, the proposed model enabled us to find, at different levels of interest (quantiles), the most relevant markers for each trait (growth curve parameter estimates) and their respective chromosomal positions (identification of new QTL regions for growth curves in pigs). These markers can be exploited under the context of marker assisted selection while aiming to change the shape of pig growth curves.

Artificial neural networks as auxiliary tools for the improvement of bean plant architecture

Classification using a scale of visual notes is a strategy used to select erect bean plants in order to improve bean plant architectures. Use of morphological traits associated with the phenotypic expression of bean architecture in classification procedures may enhance selection. The objective of this study was to evaluate the potential of artificial neural networks (ANNs) as auxiliary tools in the improvement of bean plant architecture. Data from 19 lines were evaluated for 22 traits, in 2007 and 2009 winter crops. Hypocotyl diameter and plant height were selected for analysis through ANNs. For classification purposes, these lines were separated into two groups, determined by the plant architecture notes. The predictive ability of ANNs was evaluated according to two scenarios to predict the plant architecture - training with 2007 data and validating in 2009 data (scenario 1), and vice versa (scenario 2). For this, ANNs were trained and validated using data from replicates of the evaluated lines for hypocotyl diameter individually, or together with the mean height of plants in the plot. In each scenario, the use of data from replicates or line means was evaluated for prediction through previously trained and validated ANNs. In both scenarios, ANNs based on hypocotyl diameter and mean height of plants were superior, since the error rates obtained were lower than those obtained using hypocotyl diameter only. Lower apparent error rates were verified in both scenarios for prediction when data on the means of the evaluated traits were submitted to better trained and validated ANNs.

Affective disorders, psychosis and lipid levels: Is there a connection? Linking psychopathology, clinical exams and neurobiology

Introduction

Published research regarding the relationship between lipid levels in affective disorders has been contradictory. Additionally, most studies correlating psychosis to lipid serum concentrations only concern schizophrenic patients.

Objective

To access the relationship between serum lipid levels with the diagnosis and pathophysiology of affective disorders.

Methods

Diagnostic data (ICD–10: F31–32), including mood and psychotic features, were prospectively collected for all patients admitted at the affective disorder ward at Centro Hospitalar Psiquiátrico de Lisboa (Portugal), during the third trimester of 2016. Serum concentrations of triglycerides and total/HDL/LDL cholesterol were evaluated using standard laboratory tests. Statistical analysis was performed for possible correlations between serum lipid levels and:

– different stages of bipolar disorder (BD);

– elevated versus depressive mood (unipolar and bipolar);

– depressive mood (BD versus non-BD);

– psychotic features.

Results

Sixty-three patients admitted were enrolled in this study: 47 presented with BD (32 manic, 10 depressives and 5 mixed episodes) and 16 presented depressive disorders. Statistical analysis (R software) revealed that depressed bipolar patients had significantly higher triglyceride (P = 0.026), total and LDL cholesterol (P = 0.525) levels than other states; mixed episodes presented higher HDL levels (P = 0.542). Although not significant, manic patients’ HDL levels were consistently elevated compared to depressive ones, whom presented with lower values overall. Finally, when adjusted for age, psychotic patients showed lower levels of total (P = 0.031) and LDL cholesterol (P = 0.052) compared to non-psychotic patients.

Conclusions

There is a potential link between serum lipid levels and diagnosis/psychopathology of affective disorders. Further research is needed to characterize its pathophysiologic relevance.

Disclosure of interest

The authors have not supplied their declaration of competing interest.

Insomnia and aggressiveness in affective disorders: Self-assessment versus clinical examination

Introduction

Although, disturbances of sleep, as well as aggressiveness, have been described in patients with mood disorders, these patients may not be aware of them.

Objectives/aims

To access the personal perception of sleep, disturbances and aggressiveness in patients with mood disorders, admitted to an acute psychiatric ward.

Methods

Diagnostic data (ICD-10: F31–33), including mood evaluation, were prospectively collected for all patients admitted at the affective disorder ward at Centro Hospitalar Psiquiátrico de Lisboa (Portugal), during the third trimester of 2016. Then, 2 auto-questionnaires – Athens insomnia scale (AIS) and Buss and Perry aggression scale (both validated to the Portuguese population) – were applied to these patients. Statistical analysis was performed for possible correlations between patients’ mood and the questionnaires’ scores, using R software.

Results

Thirty-eight patients admitted were enrolled in this study: 28 with bipolar disorder (19 manic, 4 depressive and 5 mixed episodes), and 10 with depressive disorder. Depressed patients presented statistically higher values in the AIS (average = 20), compared to manic (14) and mixed ones (17.2) (P = 0.031). However, there were no statistical differences found between depressed patients (bipolar versus non-bipolar). Even though manic patients presented an increased average score in the Buss and Perry questionnaire (both total–65; but also sub-scores), these values were not significantly different than depressed (60) or mixed patients’ scores (57.4).

Conclusions

Even though some symptoms (like sleep or aggressiveness) seem to be relevant to the clinics, patients with affective disorders do not seem to be aware these disturbances, and therefore are not able to acknowledge their relevance.

Disclosure of interest

The authors have not supplied their declaration of competing interest.

Using artificial neural networks to select upright cowpea (Vigna unguiculata) genotypes with high productivity and phenotypic stability

Cowpea (Vigna unguiculata) is grown in three Brazilian regions: the Midwest, North, and Northeast, and is consumed by people on low incomes. It is important to investigate the genotype x environment (GE) interaction to provide accurate recommendations for farmers. The aim of this study was to identify cowpea genotypes with high adaptability and phenotypic stability for growing in the Brazilian Cerrado, and to compare the use of artificial neural networks with the Eberhart and Russell (1966) method. Six trials with upright cowpea genotypes were conducted in 2005 and 2006 in the States of Mato Grosso do Sul and Mato Grosso. The data were subjected to adaptability and stability analysis by the Eberhart and Russell (1966) method and artificial neural networks. The genotypes MNC99-537F-4 and EVX91-2E-2 provided grain yields above the overall environment means, and exhibited high stability according to both methods. Genotype IT93K-93-10 was the most suitable for unfavorable environments. There was a high correlation between the results of both methods in terms of classifying the genotypes by their adaptability and stability. Therefore, this new approach would be effective in quantifying the GE interaction in upright cowpea breeding programs.

Selection index using the graphical area applied to sugarcane breeding

This study aimed to develop a multivariate selection index based on the graphical area of a polygon formed by standardized values, also known as radar chart. This methodology may be used to assist selection of superior genotypes in sugarcane breeding programs. Seven technological traits in 37 sugarcane genotypes were evaluated. An area index (AI) was constructed and the resulting polygon areas were used to rank genotypes under selection. In this study, we propose the use of restricted maximum likelihood to estimate genetic parameters and mixed model equations to predict genotypic and breeding values. The area of each polygon was calculated for phenotypic, genotypic, and estimated breeding values. Thereby, the genotypes with larger area can be selected based on a detailed a posteriori evaluation of the radar charts. The proposed AI can be adjusted based on the breeders' specific interests, it is perfectly useful in other crops, and may also be applied to studies on genotype-environment interactions. Moreover, AI is a powerful tool that can evaluate trait stability of genotypes based on slight differences in the area formed by each genotype. Hence, this method is easy to apply and shows great potential for use in sugarcane breeding programs as well as in other breeding programs.

Bayesian forecasting of temporal gene expression by using an autoregressive panel data approach

We propose and evaluate a novel approach for forecasting gene expression over non-observed times in longitudinal trials under a Bayesian viewpoint. One of the aims is to cluster genes that share similar expression patterns over time and then use this similarity to predict relative expression at time points of interest. Expression values of 106 genes expressed during the cell cycle of Saccharomyces cerevisiae were used and genes were partitioned into five distinct clusters of sizes 33, 32, 21, 16, and 4. After removing the last observed time point, the agreements of signals (upregulated or downregulated) considering the predicted expression level were 72.7, 81.3, 76.2, 68.8, and 50.0%, respectively, for each cluster. The percentage of credibility intervals that contained the true values of gene expression for a future time was ~90%. The methodology performed well, providing a valid forecast of gene expression values by fitting an autoregressive panel data model. This approach is easily implemented with other time-series models and when Poisson and negative binomial probability distributions are assumed for the gene expression data.

Artificial intelligence in the selection of common bean genotypes with high phenotypic stability

Artificial neural networks have been used for various purposes in plant breeding, including use in the investigation of genotype x environment interactions. The aim of this study was to use artificial neural networks in the selection of common bean genotypes with high phenotypic adaptability and stability, and to verify their consistency with the Eberhart and Russell method. Six trials were conducted using 13 genotypes of common bean between 2002 and 2006 in the municipalities of Aquidauana and Dourados. The experimental design was a randomized block with three replicates. Grain yield data were submitted to individual and joint variance analyses. The data were then submitted to analysis of adaptability and stability through the Eberhart and Russell and artificial neural network methods. There was high concordance between the methodologies evaluated for discrimination of phenotypic adaptability of common bean genotypes, indicating that artificial neural networks can be used in breeding programs. Based on both approaches, the genotypes Aporé, Rudá, and CNFv 8025 are recommended for use in unfavorable, general and favorable environments, respectively by the grain yield above the overall average of environments and high phenotypic stability.

Adaptability and phenotypic stability of common bean genotypes through Bayesian inference

This study used Bayesian inference to investigate the genotype x environment interaction in common bean grown in Mato Grosso do Sul State, and it also evaluated the efficiency of using informative and minimally informative a priori distributions. Six trials were conducted in randomized blocks, and the grain yield of 13 common bean genotypes was assessed. To represent the minimally informative a priori distributions, a probability distribution with high variance was used, and a meta-analysis concept was adopted to represent the informative a priori distributions. Bayes factors were used to conduct comparisons between the a priori distributions. The Bayesian inference was effective for the selection of upright common bean genotypes with high adaptability and phenotypic stability using the Eberhart and Russell method. Bayes factors indicated that the use of informative a priori distributions provided more accurate results than minimally informative a priori distributions. According to Bayesian inference, the EMGOPA-201, BAMBUÍ, CNF 4999, CNF 4129 A 54, and CNFv 8025 genotypes had specific adaptability to favorable environments, while the IAPAR 14 and IAC CARIOCA ETE genotypes had specific adaptability to unfavorable environments.

Evaluation of the efficiency of artificial neural networks for genetic value prediction

Artificial neural networks have shown great potential when applied to breeding programs. In this study, we propose the use of artificial neural networks as a viable alternative to conventional prediction methods. We conduct a thorough evaluation of the efficiency of these networks with respect to the prediction of breeding values. Therefore, we considered eight simulated scenarios, and for the purpose of genetic value prediction, seven statistical parameters in addition to the phenotypic mean in a network designed as a multilayer perceptron. After an evaluation of different network configurations, the results demonstrated the superiority of neural networks compared to estimation procedures based on linear models, and indicated high predictive accuracy and network efficiency.

Bayesian approach increases accuracy when selecting cowpea genotypes with high adaptability and phenotypic stability

This study aimed to verify that a Bayesian approach could be used for the selection of upright cowpea genotypes with high adaptability and phenotypic stability, and the study also evaluated the efficiency of using informative and minimally informative a priori distributions. Six trials were conducted in randomized blocks, and the grain yield of 17 upright cowpea genotypes was assessed. To represent the minimally informative a priori distributions, a probability distribution with high variance was used, and a meta-analysis concept was adopted to represent the informative a priori distributions. Bayes factors were used to conduct comparisons between the a priori distributions. The Bayesian approach was effective for selection of upright cowpea genotypes with high adaptability and phenotypic stability using the Eberhart and Russell method. Bayes factors indicated that the use of informative a priori distributions provided more accurate results than minimally informative a priori distributions.

Incapacity to decide in liaison psychiatry: Analysis of sample of patients admitted in somatic departments of a general hospital

Introduction

Decision capacity (DC) is a complex construct, whose assessment poses huge challenges to Liaison Psychiatrist (LP).

Objectives/aims

Assess factors related to DC in patients with somatic disorders admitted in medical and surgical departments of a general hospital.

Methods

Clinical records of patients who were submitted to a DC assessment at Hospital Fernando Fonseca (Portugal), from 1st January 2012 to 31st December 2014 were retrospectively analysed. Collected data were statistically analysed with SPSS®. Univariable analysis was performed, in order to determine factors related to DC.

Results

Data from 35 patients subject to DC evaluation were considered, of whom 42.4% were considered unable to give consent to medical and/or surgical procedures. Most of these assessments were related to patients who refused treatment. Patients unable to decide were predominantly male and mainly affected by organic mental or neurocognitive disorders (P < 0.05). There were no statistical significant differences in the age of those considered able or unable to decide. After PL intervention, 40% of those considered unable to decide changed their decision. However, it was not significantly related to the ability to give consent.

Conclusions

Neurocognitive disorders are common diagnosis found in patients admitted in somatic departments with no DC. Frequent change in decision after LP intervention may reflect not only cognitive fluctuations, but also a possible influence of LP intervention on patients’ choices. Appropriate standardized measures are useful tools in assessing patients with cognitive impairment, reducing evaluation differences between professionals, and in order to increase LP decisions credibility.

Disclosure of interest

The authors have not supplied their declaration of competing interest.

Comparison of dimensionality reduction methods to predict genomic breeding values for carcass traits in pigs

A significant contribution of molecular genetics is the direct use of DNA information to identify genetically superior individuals. With this approach, genome-wide selection (GWS) can be used for this purpose. GWS consists of analyzing a large number of single nucleotide polymorphism markers widely distributed in the genome; however, because the number of markers is much larger than the number of genotyped individuals, and such markers are highly correlated, special statistical methods are widely required. Among these methods, independent component regression, principal component regression, partial least squares, and partial principal components stand out. Thus, the aim of this study was to propose an application of the methods of dimensionality reduction to GWS of carcass traits in an F2 (Piau x commercial line) pig population. The results show similarities between the principal and the independent component methods and provided the most accurate genomic breeding estimates for most carcass traits in pigs.

Superiority of artificial neural networks for a genetic classification procedure

The correct classification of individuals is extremely important for the preservation of genetic variability and for maximization of yield in breeding programs using phenotypic traits and genetic markers. The Fisher and Anderson discriminant functions are commonly used multivariate statistical techniques for these situations, which allow for the allocation of an initially unknown individual to predefined groups. However, for higher levels of similarity, such as those found in backcrossed populations, these methods have proven to be inefficient. Recently, much research has been devoted to developing a new paradigm of computing known as artificial neural networks (ANNs), which can be used to solve many statistical problems, including classification problems. The aim of this study was to evaluate the feasibility of ANNs as an evaluation technique of genetic diversity by comparing their performance with that of traditional methods. The discriminant functions were equally ineffective in discriminating the populations, with error rates of 23-82%, thereby preventing the correct discrimination of individuals between populations. The ANN was effective in classifying populations with low and high differentiation, such as those derived from a genetic design established from backcrosses, even in cases of low differentiation of the data sets. The ANN appears to be a promising technique to solve classification problems, since the number of individuals classified incorrectly by the ANN was always lower than that of the discriminant functions. We envisage the potential relevant application of this improved procedure in the genomic classification of markers to distinguish between breeds and accessions.