Empirically Supported Psychological Treatments for Children and Adolescents: State of the Art. BACKGROUNDThe empirical evidence accumulated on the efficacy, effectiveness, and efficiency of ...psychotherapeutic treatments in children and adolescents calls for an update. The main goal of this paper objective was to carry out a selective review of empirically supported psychological treatments for a variety of common psychological disorders and problems in childhood and adolescence. METHODA review was carried out of the psychological treatments for different psychological disorders and problems in social-emotional or behavioral adjustment in the child-adolescent population according to the Spanish National Health System (Clinical Practice Guidelines) levels of evidence and degrees of recommendation. RESULTSThe findings suggest that psychological treatments have empirical support for addressing a wide range of psychological problems in these developmental stages. The degree of empirical support ranges from low to high depending on the phenomenon analyzed. The review suggests unequal progress in the different fields of intervention. CONCLUSIONSFrom this update, psychologists will be able to make informed decisions when implementing those empirically supported treatments to address the problems that occur in childhood and adolescence.
Generalization and Regularization for Inverse Cardiac Estimators Melgarejo Meseguer, Francisco Manuel; Everss Villalba, Estrella; Gutierrez-Fernandez-Calvillo, Miriam ...
IEEE transactions on bio-medical engineering/IEEE transactions on biomedical engineering,
10/2022, Letnik:
69, Številka:
10
Journal Article
Recenzirano
Odprti dostop
Electrocardiographic Imaging (ECGI) aims to estimate the intracardiac potentials noninvasively, hence allowing the clinicians to better visualize and understand many arrhythmia mechanisms. Most of ...the estimators of epicardial potentials use a signal model based on an estimated spatial transfer matrix together with Tikhonov regularization techniques, which works well specially in simulations, but it can give limited accuracy in some real data. Based on the quasielectrostatic potential superposition principle, we propose a simple signal model that supports the implementation of principled out-of-sample algorithms for several of the most widely used regularization criteria in ECGI problems, hence improving the generalization capabilities of several of the current estimation methods. Experiments on simple cases (cylindrical and Gaussian shapes scrutinizing fast and slow changes, respectively) and on real data (examples of torso tank measurements available from Utah University, and an animal torso and epicardium measurements available from Maastricht University, both in the EDGAR public repository) show that the superposition-based out-of-sample tuning of regularization parameters promotes stabilized estimation errors of the unknown source potentials, while slightly increasing the re-estimation error on the measured data, as natural in non-overfitted solutions. The superposition signal model can be used for designing adequate out-of-sample tuning of Tikhonov regularization techniques, and it can be taken into account when using other regularization techniques in current commercial systems and research toolboxes on ECGI.
Failure in glucose response to insulin is a common pathology associated with obesity. In this study, we analyzed the genome wide DNA methylation profile of visceral adipose tissue (VAT) samples in a ...population of individuals with obesity and assessed whether differential methylation profiles are associated with the presence of type 2 diabetes (T2D).
More than 485,000 CpG genome sites from VAT samples from women with obesity undergoing gastric bypass (n = 18), and classified as suffering from type 2 diabetes (T2D) or not (no type 2 diabetes, NT2D), were analyzed using DNA methylation arrays.
We found significant differential methylation between T2D and NT2D samples in 24 CpGs that map with sixteen genes, one of which, HOOK2, demonstrated a significant correlation between differentially hypermethylated regions on the gene body and the presence of type 2 diabetes. This was validated by pyrosequencing in a population of 91 samples from both males and females with obesity. Furthermore, when these results were analyzed by gender, female T2D samples were found hypermethylated at the cg04657146-region and the cg 11738485-region of HOOK2 gene, whilst, interestingly, male samples were found hypomethylated in this latter region.
The differential methylation profile of the HOOK2 gene in individuals with T2D and obesity might be related to the attendant T2D, but further studies are required to identify the potential role of HOOK2 gene in T2D disease. The finding of gender differences in T2D methylation of HOOK2 also warrants further investigation.
To estimate the infection fatality risk for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), based on deaths with confirmed coronavirus disease 2019 (covid-19) and excess deaths from all ...causes.
Nationwide seroepidemiological study.
First wave of covid-19 pandemic in Spain.
Community dwelling individuals of all ages.
The main outcome measure was overall, and age and sex specific, infection fatality risk for SARS-CoV-2 (the number of covid-19 deaths and excess deaths divided by the estimated number of SARS-CoV-2 infections) in the community dwelling Spanish population. Deaths with laboratory confirmed covid-19 were obtained from the National Epidemiological Surveillance Network (RENAVE) and excess all cause deaths from the Monitoring Mortality System (MoMo), up to 15 July 2020. SARS-CoV-2 infections in Spain were derived from the estimated seroprevalence by a chemiluminescent microparticle immunoassay for IgG antibodies in 61 098 participants in the ENE-COVID nationwide seroepidemiological survey between 27 April and 22 June 2020.
The overall infection fatality risk was 0.8% (19 228 of 2.3 million infected individuals, 95% confidence interval 0.8% to 0.9%) for confirmed covid-19 deaths and 1.1% (24 778 of 2.3 million infected individuals, 1.0% to 1.2%) for excess deaths. The infection fatality risk was 1.1% (95% confidence interval 1.0% to 1.2%) to 1.4% (1.3% to 1.5%) in men and 0.6% (0.5% to 0.6%) to 0.8% (0.7% to 0.8%) in women. The infection fatality risk increased sharply after age 50, ranging from 11.6% (8.1% to 16.5%) to 16.4% (11.4% to 23.2%) in men aged 80 or more and from 4.6% (3.4% to 6.3%) to 6.5% (4.7% to 8.8%) in women aged 80 or more.
The increase in SARS-CoV-2 infection fatality risk after age 50 appeared to be more noticeable in men than in women. Based on the results of this study, fatality from covid-19 was greater than that reported for other common respiratory diseases, such as seasonal influenza.
To assess the value of machine learning approaches in the development of a multivariable model for early prediction of ICU death in patients with acute respiratory distress syndrome (ARDS).
A ...development, testing, and external validation study using clinical data from four prospective, multicenter, observational cohorts.
A network of multidisciplinary ICUs.
A total of 1,303 patients with moderate-to-severe ARDS managed with lung-protective ventilation.
None.
We developed and tested prediction models in 1,000 ARDS patients. We performed logistic regression analysis following variable selection by a genetic algorithm, random forest and extreme gradient boosting machine learning techniques. Potential predictors included demographics, comorbidities, ventilatory and oxygenation descriptors, and extrapulmonary organ failures. Risk modeling identified some major prognostic factors for ICU mortality, including age, cancer, immunosuppression, Pa o2 /F io2 , inspiratory plateau pressure, and number of extrapulmonary organ failures. Together, these characteristics contained most of the prognostic information in the first 24 hours to predict ICU mortality. Performance with machine learning methods was similar to logistic regression (area under the receiver operating characteristic curve AUC, 0.87; 95% CI, 0.82-0.91). External validation in an independent cohort of 303 ARDS patients confirmed that the performance of the model was similar to a logistic regression model (AUC, 0.91; 95% CI, 0.87-0.94).
Both machine learning and traditional methods lead to promising models to predict ICU death in moderate/severe ARDS patients. More research is needed to identify markers for severity beyond clinical determinants, such as demographics, comorbidities, lung mechanics, oxygenation, and extrapulmonary organ failure to guide patient management.
Alternaria conidia have high allergenic potential and they can trigger important respiratory diseases. Due to that and to their extensive detection period, airborne Alternaria spores are considered ...as a relevant airborne allergenic particle. Several studies have been developed in order to predict the human exposure to this aeroallergen and to prevent their negative effects on sensitive population. These studies revealed that some sampling locations usually have just one single Alternaria spore season while other locations generally have two seasons within the same year. However, the reasons of these two different seasonal patterns remain unclear. To understand them better, the present study was carried out in order to determine if there are any weather conditions that influence these different behaviours at different sampling locations. With this purpose, the airborne Alternaria spore concentrations of 18 sampling locations in a wide range of latitudinal, altitudinal and climate ranges of Spain were studied. The aerobiological samples were obtained by means of Hirst-Type volumetric pollen traps, and the seasonality of the airborne Alternaria spores were analysed. The optimal weather conditions for spore production were studied, and the main weather factor affecting Alternaria spore seasonality were analysed by means of random forests and regression trees. The results showed that the temperature was the most relevant variable for the Alternaria spore dispersion and it influenced both the spore integrals and their seasonality. The water availability was also a very significant variable. Warmer sampling locations generally have a longer period of Alternaria spore detection. However, the spore production declines during the summer when the temperatures are extremely warm, what splits the favourable period for Alternaria spore production and dispersion into two separate ones, detected as two Alternaria spore seasons within the same year.
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•Temperature is the main driving factor for the seasonality of Alternaria spores.•Warmer sampling locations have longer Alternaria spore seasons.•The water availability is also a relevant variable for Alternaria spore production.•Warm summers split in two the Alternaria spore season.
Syndemics are a framework that documents health inequities and vulnerabilities in populations with rheumatic diseases. Compared with other approaches, syndemics are able to conjunctly consider ...epidemiological, biological, sociodemographic and economic factors, and their interactions.ObjectiveTo estimate health inequity and vulnerability among Indigenous and non-Indigenous populations with rheumatic and musculoskeletal diseases (RMD) in Latin America using the syndemic approach.DesignThis is a secondary analysis of a previously published large-scale study on the prevalence of RMD.SettingStudies carried out in five Latin American countries (Argentina, Colombia, Ecuador, Mexico and Venezuela). Health inequity and vulnerability in RMD were identified through a syndemic approach using network and cluster analysis.ParticipantsA total of 44 560 individuals were studied: 29.78% self-identified as Indigenous, 60.92% were female, the mean age was 43.25 years. Twenty clusters were identified in the Indigenous population and 17 in the non-Indigenous population.ResultsThe variables associated with RMD among Indigenous populations were rurality, public health system, high joint biomechanical stress, greater pain, disability and alcoholism; and among non-Indigenous people they were being a woman, urban origin, older age, private health system, joint biomechanical stress, greater pain and disability. We identified different health inequities among patients with RMD (ie, lower educational attainment, more comorbidities), associated with factors such as Indigenous self-identification and rural residence.ConclusionsA syndemic approach enables us to identify health inequities in RMD, as shown by higher prevalence of comorbidities, disability and socioeconomic factors like lower educational attainment. These inequities exist for the overall population of patients with RMD, although it is more evident in Indigenous groups with added layers of vulnerability.
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