Artificial intelligence for diagnostic and prognostic neuroimaging in dementia: A systematic review.

INTRODUCTION: Artificial intelligence (AI) and neuroimaging offer new opportunities for diagnosis and prognosis of dementia. METHODS: We systematically reviewed studies reporting AI for neuroimaging in diagnosis and/or prognosis of cognitive neurodegenerative diseases. RESULTS: A total of 255 studies were identified. Most studies relied on the Alzheimer's Disease Neuroimaging Initiative dataset. Algorithmic classifiers were the most commonly used AI method (48%) and discriminative models performed best for differentiating Alzheimer's disease from controls. The accuracy of algorithms varied with the patient cohort, imaging modalities, and stratifiers used. Few studies performed validation in an independent cohort. DISCUSSION: The literature has several methodological limitations including lack of sufficient algorithm development descriptions and standard definitions. We make recommendations to improve model validation including addressing key clinical questions, providing sufficient description of AI methods and validating findings in independent datasets. Collaborative approaches between experts in AI and medicine will help achieve the promising potential of AI tools in practice. HIGHLIGHTS: There has been a rapid expansion in the use of machine learning for diagnosis and prognosis in neurodegenerative disease Most studies (71%) relied on the Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset with no other individual dataset used more than five times There has been a recent rise in the use of more complex discriminative models (e.g., neural networks) that performed better than other classifiers for classification of AD vs healthy controls We make recommendations to address methodological considerations, addressing key clinical questions, and validation We also make recommendations for the field more broadly to standardize outcome measures, address gaps in the literature, and monitor sources of bias.

Current views on in vivo models for breast cancer research and related drug development.

INTRODUCTION: Animal models play a crucial role in breast cancer research, in particular mice and rats, who develop mammary tumors that closely resemble their human counterparts. These models allow the study of mechanisms behind breast carcinogenesis, as well as the efficacy and safety of new, and potentially more effective and advantageous therapeutic approaches. Understanding the advantages and disadvantages of each model is crucial to select the most appropriate one for the research purpose. AREA COVERED: This review provides a concise overview of the animal models available for breast cancer research, discussing the advantages and disadvantages of each one for searching new and more effective approaches to treatments for this type of cancer. EXPERT OPINION: Rodent models provide valuable information on the genetic alterations of the disease, the tumor microenvironment, and allow the evaluation of the efficacy of chemotherapeutic agents. However, in vivo models have limitations, and one of them is the fact that they do not fully mimic human diseases. Choosing the most suitable model for the study purpose is crucial for the development of new therapeutic agents that provide better care for breast cancer patients.

Realistic Aspects of Cardiac Ultrasound in Rats: Practical Tips for Improved Examination.

Echocardiography is a reliable and non-invasive method for assessing cardiac structure and function in both clinical and experimental settings, offering valuable insights into disease progression and treatment efficacy. The successful application of echocardiography in murine models of disease has enabled the evaluation of disease severity, drug testing, and continuous monitoring of cardiac function in these animals. However, there is insufficient standardization of echocardiographic measurements for smaller animals. This article aims to address this gap by providing a guide and practical tips for the appropriate acquisition and analysis of echocardiographic parameters in adult rats, which may also be applicable in other small rodents used for scientific purposes, like mice. With advancements in technology, such as ultrahigh-frequency ultrasonic transducers, echocardiography has become a highly sophisticated imaging modality, offering high temporal and spatial resolution imaging, thereby allowing for real-time monitoring of cardiac function throughout the lifespan of small animals. Moreover, it allows the assessment of cardiac complications associated with aging, cancer, diabetes, and obesity, as well as the monitoring of cardiotoxicity induced by therapeutic interventions in preclinical models, providing important information for translational research. Finally, this paper discusses the future directions of cardiac preclinical ultrasound, highlighting the need for continued standardization to advance research and improve clinical outcomes to facilitate early disease detection and the translation of findings into clinical practice.

Interplay Between Western Diet and Mammary Cancer: Data from a Chemically-induced Model in Wistar Rats.

BACKGROUND/AIM: This study aimed to investigate the influence of Western diet on mammary cancer in Wistar female rats, focusing on systemic responses and tumor development. MATERIALS AND METHODS: Twenty-eight Wistar female rats were acclimatized and divided into four experimental groups (n=7 each): Western diet (WD), Western diet with N-methyl-N-nitrosourea (MNU) administration (WD+MNU), standard diet (CTR), and standard diet with MNU administration (CTR+MNU). MNU was administered intraperitoneally at 50 mg/kg at seven weeks of age to induce mammary cancer. The 20-week experiment involved monitoring animal weight, food and water intake. At the end of the study, rats were euthanized, and blood samples and organs were collected for hematological and plasma biochemical analysis, oxidative stress, and histo-pathological and immunobiological evaluations of the tumors. RESULTS: No significant differences were found in body weight, composition, or organ weights, but the WD group showed reduced food and water intake and lower cholesterol levels. Leptin and adiponectin levels were higher in the WD+MNU group, suggestive of changes in appetite regulation. Histopathological analysis showed malignant tumors in both MNU-induced groups. However, WD groups had fewer tumors compared to the CTR+MNU group. CONCLUSION: WD led to higher feed efficiency and increased visceral adipose tissue but decreased systemic cholesterol and triglyceride levels. While this diet resulted in lower tumor incidence, the volume and weight of the tumors were higher. Additionally, the WD decreased ERα and progesterone receptor immunoexpression, while Ki-67 immunoexpression was elevated.

Identifying healthy individuals with Alzheimer's disease neuroimaging phenotypes in the UK Biobank.

BACKGROUND: Identifying prediagnostic neurodegenerative disease is a critical issue in neurodegenerative disease research, and Alzheimer's disease (AD) in particular, to identify populations suitable for preventive and early disease-modifying trials. Evidence from genetic and other studies suggests the neurodegeneration of Alzheimer's disease measured by brain atrophy starts many years before diagnosis, but it is unclear whether these changes can be used to reliably detect prediagnostic sporadic disease. METHODS: We trained a Bayesian machine learning neural network model to generate a neuroimaging phenotype and AD score representing the probability of AD using structural MRI data in the Alzheimer's Disease Neuroimaging Initiative (ADNI) Cohort (cut-off 0.5, AUC 0.92, PPV 0.90, NPV 0.93). We go on to validate the model in an independent real-world dataset of the National Alzheimer's Coordinating Centre (AUC 0.74, PPV 0.65, NPV 0.80) and demonstrate the correlation of the AD-score with cognitive scores in those with an AD-score above 0.5. We then apply the model to a healthy population in the UK Biobank study to identify a cohort at risk for Alzheimer's disease. RESULTS: We show that the cohort with a neuroimaging Alzheimer's phenotype has a cognitive profile in keeping with Alzheimer's disease, with strong evidence for poorer fluid intelligence, and some evidence of poorer numeric memory, reaction time, working memory, and prospective memory. We found some evidence in the AD-score positive cohort for modifiable risk factors of hypertension and smoking. CONCLUSIONS: This approach demonstrates the feasibility of using AI methods to identify a potentially prediagnostic population at high risk for developing sporadic Alzheimer's disease.

Spotting people with dementia early is challenging, but important to identify people for trials of treatment and prevention. We used brain scans of people with Alzheimer's disease, the commonest type of dementia, and applied an artificial intelligence method to spot people with Alzheimer's disease. We used this to find people in the Healthy UK Biobank study who might have early Alzheimer's disease. The people we found had subtle changes in their memory and thinking to suggest they may have early disease, and we also found they had high blood pressure and smoked for longer. We have demonstrated an approach that could be used to select people at high risk of future dementia for clinical trials.

Cross-cultural adaptation of the web-based executive functioning questionnaire for Brazilian sample (Webexec-BR).

The Web-based Executive Functioning Questionnaire (Webexec) is a brief scale developed to assess executive functions via online format. It has been used in different contexts, but its adaptation to other cultures is still restricted. This study aimed to perform a cross-cultural adaptation of the Webexec for a Brazilian sample considering the psychometric properties of the scale. This study used a sample of 295 Brazilian participants, with a mean age equal to 20.69 (SD = 6.030). This is a longitudinal study with reapplication of the scale six weeks after the test phase. Classical and contemporary methods were applied to analyze the psychometric properties of the Webexec. The results showed that the scale presented excellent psychometric properties for the Brazilian version, considering validity evidence based on the content and internal structure of Webexec, as well as reliability and precision. However, it is considered that other relational and experimental studies should be carried out with a larger sample size and for different population groups.

Studying and Analyzing Humane Endpoints in the Fructose-Fed and Streptozotocin-Injected Rat Model of Diabetes.

This work aimed to define a humane endpoint scoring system able to objectively identify signs of animal suffering in a rat model of type 2 diabetes. Sprague-Dawley male rats were divided into control and induced group. The induced animals drink a 10% fructose solution for 14 days. Then, received an administration of streptozotocin (40 mg/kg). Animals' body weight, water and food consumption were recorded weekly. To evaluate animal welfare, a score sheet with 14 parameters was employed. Blood glucose levels were measured at three time points. After seven weeks of initiating the protocol, the rats were euthanized. The induced animals showed weight loss, polyuria, polyphagia, and polydipsia. According to our humane endpoints table, changes in animal welfare became noticeable after the STZ administration. None of the animals hit the critical score limit (four). Data showed that the most effective parameters to assess welfare in this type 2 diabetes rat induction model were dehydration, grooming, posture, abdominal visualization, and stool appearance. The glycemia was significantly higher in the induced group when compared to the controls (p < 0.01). Induced animals' murinometric and nutritional parameters were significantly lower than the controls (p < 0.01). Our findings suggest that in this rat model of type 2 diabetes with STZ-induced following fructose consumption, our list of humane endpoints is suitable for monitoring the animals' welfare.

A deep graph neural network architecture for modelling spatio-temporal dynamics in resting-state functional MRI data.

Resting-state functional magnetic resonance imaging (rs-fMRI) has been successfully employed to understand the organisation of the human brain. Typically, the brain is parcellated into regions of interest (ROIs) and modelled as a graph where each ROI represents a node and association measures between ROI-specific blood-oxygen-level-dependent (BOLD) time series are edges. Recently, graph neural networks (GNNs) have seen a surge in popularity due to their success in modelling unstructured relational data. The latest developments with GNNs, however, have not yet been fully exploited for the analysis of rs-fMRI data, particularly with regards to its spatio-temporal dynamics. In this paper, we present a novel deep neural network architecture which combines both GNNs and temporal convolutional networks (TCNs) in order to learn from both the spatial and temporal components of rs-fMRI data in an end-to-end fashion. In particular, this corresponds to intra-feature learning (i.e., learning temporal dynamics with TCNs) as well as inter-feature learning (i.e., leveraging interactions between ROI-wise dynamics with GNNs). We evaluate our model with an ablation study using 35,159 samples from the UK Biobank rs-fMRI database, as well as in the smaller Human Connectome Project (HCP) dataset, both in a unimodal and in a multimodal fashion. We also demonstrate that out architecture contains explainability-related features which easily map to realistic neurobiological insights. We suggest that this model could lay the groundwork for future deep learning architectures focused on leveraging the inherently and inextricably spatio-temporal nature of rs-fMRI data.

Early downregulation of hsa-miR-144-3p in serum from drug-naïve Parkinson's disease patients.

Advanced age represents one of the major risk factors for Parkinson's Disease. Recent biomedical studies posit a role for microRNAs, also known to be remodelled during ageing. However, the relationship between microRNA remodelling and ageing in Parkinson's Disease, has not been fully elucidated. Therefore, the aim of the present study is to unravel the relevance of microRNAs as biomarkers of Parkinson's Disease within the ageing framework. We employed Next Generation Sequencing to profile serum microRNAs from samples informative for Parkinson's Disease (recently diagnosed, drug-naïve) and healthy ageing (centenarians) plus healthy controls, age-matched with Parkinson's Disease patients. Potential microRNA candidates markers, emerging from the combination of differential expression and network analyses, were further validated in an independent cohort including both drug-naïve and advanced Parkinson's Disease patients, and healthy siblings of Parkinson's Disease patients at higher genetic risk for developing the disease. While we did not find evidences of microRNAs co-regulated in Parkinson's Disease and ageing, we report that hsa-miR-144-3p is consistently down-regulated in early Parkinson's Disease patients. Moreover, interestingly, functional analysis revealed that hsa-miR-144-3p is involved in the regulation of coagulation, a process known to be altered in Parkinson's Disease. Our results consistently show the down-regulation of hsa-mir144-3p in early Parkinson's Disease, robustly confirmed across a variety of analytical and experimental analyses. These promising results ask for further research to unveil the functional details of the involvement of hsa-mir144-3p in Parkinson's Disease.

Deep Learning Enables Fast and Accurate Imputation of Gene Expression.

A question of fundamental biological significance is to what extent the expression of a subset of genes can be used to recover the full transcriptome, with important implications for biological discovery and clinical application. To address this challenge, we propose two novel deep learning methods, PMI and GAIN-GTEx, for gene expression imputation. In order to increase the applicability of our approach, we leverage data from GTEx v8, a reference resource that has generated a comprehensive collection of transcriptomes from a diverse set of human tissues. We show that our approaches compare favorably to several standard and state-of-the-art imputation methods in terms of predictive performance and runtime in two case studies and two imputation scenarios. In comparison conducted on the protein-coding genes, PMI attains the highest performance in inductive imputation whereas GAIN-GTEx outperforms the other methods in in-place imputation. Furthermore, our results indicate strong generalization on RNA-Seq data from 3 cancer types across varying levels of missingness. Our work can facilitate a cost-effective integration of large-scale RNA biorepositories into genomic studies of disease, with high applicability across diverse tissue types.

Multilayer modelling of the human transcriptome and biological mechanisms of complex diseases and traits.

Here, we performed a comprehensive intra-tissue and inter-tissue multilayer network analysis of the human transcriptome. We generated an atlas of communities in gene co-expression networks in 49 tissues (GTEx v8), evaluated their tissue specificity, and investigated their methodological implications. UMAP embeddings of gene expression from the communities (representing nearly 18% of all genes) robustly identified biologically-meaningful clusters. Notably, new gene expression data can be embedded into our algorithmically derived models to accelerate discoveries in high-dimensional molecular datasets and downstream diagnostic or prognostic applications. We demonstrate the generalisability of our approach through systematic testing in external genomic and transcriptomic datasets. Methodologically, prioritisation of the communities in a transcriptome-wide association study of the biomarker C-reactive protein (CRP) in 361,194 individuals in the UK Biobank identified genetically-determined expression changes associated with CRP and led to considerably improved performance. Furthermore, a deep learning framework applied to the communities in nearly 11,000 tumors profiled by The Cancer Genome Atlas across 33 different cancer types learned biologically-meaningful latent spaces, representing metastasis (p < 2.2 × 10-16) and stemness (p < 2.2 × 10-16). Our study provides a rich genomic resource to catalyse research into inter-tissue regulatory mechanisms, and their downstream consequences on human disease.

A geroscience approach for Parkinson's disease: Conceptual framework and design of PROPAG-AGEING project.

Advanced age is the major risk factor for idiopathic Parkinson's disease (PD), but to date the biological relationship between PD and ageing remains elusive. Here we describe the rationale and the design of the H2020 funded project "PROPAG-AGEING", whose aim is to characterize the contribution of the ageing process to PD development. We summarize current evidences that support the existence of a continuum between ageing and PD and justify the use of a Geroscience approach to study PD. We focus in particular on the role of inflammaging, the chronic, low-grade inflammation characteristic of elderly physiology, which can propagate and transmit both locally and systemically. We then describe PROPAG-AGEING design, which is based on the multi-omic characterization of peripheral samples from clinically characterized drug-naïve and advanced PD, PD discordant twins, healthy controls and "super-controls", i.e. centenarians, who never showed clinical signs of motor disability, and their offspring. Omic results are then validated in a large number of samples, including in vitro models of dopaminergic neurons and healthy siblings of PD patients, who are at higher risk of developing PD, with the final aim of identifying the molecular perturbations that can deviate the trajectories of healthy ageing towards PD development.

A deep spatiotemporal graph learning architecture for brain connectivity analysis.

In recent years, the conceptualisation of the brain as a "connectome" as summary measures derived from graph theory analyses, has become increasingly popular. Still, such approaches are inherently limited by the need to condense and simplify temporal fMRI dynamics and architecture into a purely spatial representation. We formulate a novel architecture based on Geometric Deep Learning which is specifically tailored to the one-step integration of spatial relationship between nodes and single-node temporal dynamics. We compare different spatiotemporal modelling mechanisms and demonstrate the effectiveness of our architecture in a binary prediction task based on a large homogeneous fMRI dataset made publicly available by the Human Connectome Project (HCP). As the idea of e.g. a dynamical network connectivity is beginning to make its way into the more mainstream toolset which neuroscientists commonly employ with neuroimaging data, our model can contribute to laying the groundwork for explicitly incorporating spatiotemporal information into every association and prediction problem in neuroscience.

A novel Graph Attention Network Architecture for modeling multimodal brain connectivity.

While Deep Learning methods have been successfully applied to tackle a wide variety of prediction problems, their application has been mostly limited to data structured in a grid-like fashion. However, the study of the human brain "connectome" involves the representation of the brain as a graph with interacting nodes. In this paper, we extend the Graph Attention Network (GAT), a novel neural network (NN) architecture acting on the features of the nodes of a binary graph, to handle a set of graphs provided with node features and non-binary edge weights. We demonstrate the effectiveness of our architecture by training it multimodal data collected from a large homogeneous fMRI dataset (n=1003 individuals with multiple fMRI sessions per subject) made publicly available by the Human Connectome Project (HCP), demonstrating good performance and seamless integration of multimodal neuroimaging data. Our adaptation provides a powerful and flexible deep learning tool to integrate multimodal neuroimaging connectomics data in a predictive context.

Droplet precautions on site instead of single room isolation for respiratory tract infections.

We introduced a pragmatic concept of on site droplet precautions instead of single room isolation for rural hospitals in a tiered network. A survey among healthcare workers revealed that this approach was considered comprehensive, safe, and acceptable. This concept could be an alternative for hospitals with few single rooms available for isolation.

Integration of Machine Learning Methods to Dissect Genetically Imputed Transcriptomic Profiles in Alzheimer's Disease.

The genetic component of many common traits is associated with the gene expression and several variants act as expression quantitative loci, regulating the gene expression in a tissue specific manner. In this work, we applied tissue-specific cis-eQTL gene expression prediction models on the genotype of 808 samples including controls, subjects with mild cognitive impairment, and patients with Alzheimer's Disease. We then dissected the imputed transcriptomic profiles by means of different unsupervised and supervised machine learning approaches to identify potential biological associations. Our analysis suggests that unsupervised and supervised methods can provide complementary information, which can be integrated for a better characterization of the underlying biological system. In particular, a variational autoencoder representation of the transcriptomic profiles, followed by a support vector machine classification, has been used for tissue-specific gene prioritizations. Interestingly, the achieved gene prioritizations can be efficiently integrated as a feature selection step for improving the accuracy of deep learning classifier networks. The identified gene-tissue information suggests a potential role for inflammatory and regulatory processes in gut-brain axis related tissues. In line with the expected low heritability that can be apportioned to eQTL variants, we were able to achieve only relatively low prediction capability with deep learning classification models. However, our analysis revealed that the classification power strongly depends on the network structure, with recurrent neural networks being the best performing network class. Interestingly, cross-tissue analysis suggests a potentially greater role of models trained in brain tissues also by considering dementia-related endophenotypes. Overall, the present analysis suggests that the combination of supervised and unsupervised machine learning techniques can be used for the evaluation of high dimensional omics data.

Preservative spleen surgery and hyperbaric oxygen therapy.

PURPOSE: To assess functional and morphological aspects of spleen auto-implants and of the splenic inferior pole of rats, post-operatively treated or not with hyperbaric oxygen, as well as the survival of these animals, were studied. METHODS: Seventy-eight male Wistar rats, weighing between 192 and 283 g ( 238,3 +/- 9,6g), were randomly distributed into three groups: Group 1--(n=20), spleen manipulation; group 2--(n=36), spleen auto-implantation; group 3--(n= 22), subtotal splenectomy preserving the inferior pole. Each group was subdivided as follows: subgroup a, not submitted to hyperbaric oxygen therapy: 1a(n=10), 2a(n=21), 3a(n= 13); subgroup b, submitted to the therapy: 1b(n=10), 2b(n=15), 3b(n=9). Blood was collected pre-operatively and 11 days after surgery, for the estimation of lipids and immunoglobulins and the counting of platelets and Howell-Jolly corpuscles. The spleen and remains were taken for histological study. RESULTS: The number of surviving animals was significantly higher in groups 1(p<0,01) and 3(p<0,05) relative to those of subgroup 2a. Total cholesterol and the LDL fraction increased significantly in subgroup 2a (p<0,01) and 3a (p<0,05), and remained unaltered in subgroups 2b e 3b. IgM decreased more significantly in subgroup 2 than in subgroup 3 (p<0,001 vs p<0,01). The increase of platelet numbers and the appearance of Howell Jolly corpuscles was smaller in subgroup 2b compared to subgroup 2a , and in group 3 compared to group aqui-> 2. The macro and microscopic appearance in subgroup 2b were more viable than in subgroup 2a, and that of group 3 more viable than in group 2. The survival of the animals carrying their whole spleen or its inferior pole was more frequent than that of the auto-implanted animals. CONCLUSION: Functionality and viability of the whole spleen or of its inferior pole, were better than in the auto-implanted animals. Hyperbaric oxygen-therapy contributed to increased survival frequency of auto-implanted animals, and to improve the functionality and viability of the auto-implants and the function of the inferior splenic pole, and did not interfere in animals carrying their whole spleen.

Evidências de Validade de Conteúdo da Beck Cognitive Insight Scale, Versão Brasileira (BCIS-BR) / Content Validity Evidence of the Beck Cognitive Insight Scale Brazilian Version (BCIS) / Evidencia de Validez de Contenido de la Beck Cognitive Insight Scale, Versión Brasileña (BCIS-BR)

O insight cognitivo ou clínico refere-se à capacidade de atribuição de sentido aos sintomas presentes em transtornos psicóticos, especialmente naqueles relacionados ao espectro da esquizofrenia. A Beck Cognitive Insight Scale (BCIS) foi desenvolvida com a finalidade de auxiliar o tratamento de pessoas com o insight comprometido. No entanto, este instrumento ainda não foi devidamente adaptado ao contexto brasileiro. Este estudo teve como objetivo fazer a adaptação transcultural da BCIS para o português do Brasil por meio da evidência de validade de conteúdo. Seis juízes avaliaram os itens desta escala quanto à clareza da linguagem, pertinência prática, relevância teórica e relação item-dimensão. A BCIS apresentou concordância satisfatória nos índices de validade de conteúdo e homogeneidade das respostas referente à análise da clareza da linguagem, pertinência prática e relevância teórica e concordância entre item-dimensão teórica, com reformulação de alguns itens. A retrotradução da escala recebeu aval positiva de uma das autoras originais. No entanto, ainda é necessária a verificação das propriedades psicométricas desta versão da BCIS.

Cognitive or clinical insight refers to the ability to assign meaning to symptoms present in psychotic disorders, especially those related to the schizophrenia spectrum. The Beck Cognitive Insight Scale (BCIS) was developed to support the treatment of people with impaired insight. However, this instrument has not yet been adapted to the Brazilian context. This study aimed to make the cross-cultural adaptation of the BCIS into Brazilian Portuguese through evidence of content validity. Six judges evaluated the items of this scale regarding clarity of language, practical relevance, theoretical relevance and item-dimension relatedness. The BCIS presented satisfactory agreement in the indices of content validity and homogeneity of responses regarding the analysis of clarity of language, practical relevance and theoretical relevance and agreement between item-theoretical dimension, reformulating some items. The back-translation of the scale received a positive endorsement from one of the original authors. However, it is still necessary to verify the psychometric properties for this version of the BCIS.

El insight cognitivo o clínico se refiere a la capacidad de asignar un significado a los síntomas presentes en los trastornos psicóticos, especialmente aquellos relacionados con el espectro de la esquizofrenia. La Escala de Insight Cognitivo de Beck (BCIS) fue desarrollada para apoyar el tratamiento de personas con insight comprometido. Sin embargo, este instrumento todavía no ha sido adaptado al contexto brasileño. Este estudio tuvo como objetivo realizar la adaptación transcultural de la BCIS al portugués brasileño mediante pruebas de validez de contenido. Seis jueces evaluaron los ítems de esta escala en cuanto a la claridad del lenguaje, la relevancia práctica, la relevancia teórica y la relación ítem-dimensión. La BCIS presentó una concordancia satisfactoria en los índices de validez de contenido y homogeneidad de respuestas en cuanto al análisis de la claridad del lenguaje, relevancia práctica y relevancia teórica, y concordancia entre ítem-dimensión teórica, reformulando algunos de los ítems. La retraducción de la escala recibió una aprobación positiva de una de las autoras. Todavía es necesario verificar las propiedades psicométricas de esta versión de la BCIS.

Hepatitis C in HIV-infected individuals: a systematic review and meta-analysis of estimated prevalence in Africa.

INTRODUCTION: Although hepatitis C virus (HCV) screening is recommended for all HIV-infected patients initiating antiretroviral therapy, data on epidemiologic characteristics of HCV infection in resource-limited settings are scarce. METHODS: We searched PubMed and EMBASE for studies assessing the prevalence of HCV infection among HIV-infected individuals in Africa and extracted data on laboratory methods used. Prevalence estimates from individual studies were combined for each country using random-effects meta-analysis. The importance of study design, population and setting as well as type of test (anti-HCV antibody tests and polymerase chain reactions) was examined with meta-regression. RESULTS: Three randomized controlled trials, 28 cohort studies and 121 cross-sectional analyses with 108,180 HIV-infected individuals from 35 countries were included. The majority of data came from outpatient populations (55%), followed by blood donors (15%) and pregnant women (14%). Based on estimates from 159 study populations, anti-HCV positivity prevalence ranged between 3.3% (95% confidence interval (CI) 1.8-4.7) in Southern Africa and 42.3% (95% CI 4.1-80.5) in North Africa. Study design, type of setting and age distribution did not influence this prevalence significantly. The prevalence of replicating HCV infection, estimated from data of 29 cohorts, was 2.0% (95% CI 1.5-2.6). Ten studies from nine countries reported the HCV genotype of 74 samples, 53% were genotype 1, 24% genotype 2, 14% genotype 4 and 9% genotypes 3, 5 or 6. CONCLUSIONS: The prevalence of anti-HCV antibodies is high in HIV-infected patients in Africa, but replicating HCV infection is rare and varies widely across countries.

Impactos do avanço da pandemia de COVID-19 na saúde mental de profissionais de saúde / Impacts of COVID-19 pandemic advancement on healthcare workers mental health / Impactos del avance de la pandemia COVID-19 en la salud mental de los profesionales sanitarios

O estudo verificou a associação e a influência entre variáveis sociodemográficas, laborais, impactos da pandemia (desesperança, contaminação, óbito na família), traços de personalidade e de saúde mental em profissionais de saúde brasileiros em dois tempos distintos da pandemia de COVID-19. Foram incluídos 155 profissionais que responderam um questionário online. Foi utilizado o modelo de redes para a análise dos dados. Os sintomas de depressão, ansiedade e estresse foram os mais influentes no modelo testado e apresentaram associações com a desesperança nos dois tempos. No tempo 1, o estigma foi uma das variáveis mais influentes. No tempo 2, o estigma e a ansiedade reduziram sua influência, enquanto o estresse e a desesperança tornaram-se mais influentes. Os alvos das intervenções para os profissionais de saúde podem ser diferenciados no início e no avanço do contexto pandêmico, mas cabe contínua focalização do estresse e da desesperança

The study verified the association and influence between sociodemographic and labor variables, pandemic impacts (hopelessness, contamination, death in the family), personality and mental health traits in Brazilian health professionals at two different times of the COVID-19 pandemic. There were 155 professionals included who replied to an online questionnaire. The network model was used for data analysis. Symptoms of depression, anxiety, and stress were the most influential variables in the model tested and showed connections with hopelessness at both times. At time 1, stigma was one of the most influential variables. At time 2, stigma and anxiety reduced their influence, while stress and hopelessness became more prominent. The targets of interventions for health professionals can be differentiated in relation to the onset and progression of the pandemic context, but with a continuous focus on the level of stress and hopelessness for intervention

El estudio verificó la asociación e influencia entre variables sociodemográficas y laborales, impactos de la pandemia (desesperanza, contaminación, muerte en la familia), rasgos de personalidad y salud mental en trabajadores sanitarios brasileños en dos momentos de la pandemia del COVID-19. Se incluyeron 155 profesionales que respondieron a un cuestionario en línea. Se utilizó el modelo de red para el análisis. Los síntomas de depresión, ansiedad y estrés fueron las variables más influyentes y mostraron conexiones con la desesperanza en ambos momentos. En el momento 1, el estigma fue una de las variables más influyentes. En el momento 2, el estigma y la ansiedad redujeron su influencia y el estrés y la desesperanza se hicieron más prominentes. Los objetivos de las intervenciones para los trabajadores sanitarios se pueden diferenciar en el inicio y en el avance de la pandemia, pero es necesario un enfoque continuo en el estrés y desesperanza