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The rate of death or major bleeding was significantly higher among preterm infants with severe thrombocytopenia assigned to transfusions at higher platelet-count thresholds (50,000 per cubic millimeter) than among those assigned to lower thresholds (25,000 per cubic millimeter).

Autism is a developmental disorder defined by social and communication impairments. Current theoretical approaches and research studies however conceptualise autism as both static and independent from the social context in which it develops. Two lines of research stand out from this general trend. First, research from the neuroconstructivist approach of Karmiloff-Smith (Hum Brain Mapp 31:934–941, 2010 ) aims to establish developmental trajectories of cognitive impairments in autism over time. Second, studies from intersubjective approaches such as that of Hobson (The cradle of thought, Macmillan, London, 2002 ) focus on the influence of emotional engagement in cognitive impairments. Although these two lines of research have made an invaluable contribution towards our understanding of autism, both offer only partial explanations: Intersubjective approaches fail to provide a developmental perspective and the neuroconstructivist model neglects the role of the social context. This paper argues that the nature of autism demands the theoretical and methodological integration of these two approaches so that developmental and social aspects are investigated in tandem.

Electroencephalography (EEG) is a widely used non-invasive technique for monitoring brain activity, offering valuable insights into neurological disorders. Feature extraction methods based on signal processing approaches have been shown to be effective, but they tend to overlook the statistical properties of EEG signals. This study proposes a decile-based feature extraction method for EEG signal analysis, aimed at improving classification performance while maintaining simplicity and interpretability. The method was evaluated across multiple tasks, including the classification of Alzheimer’s disease (AD), frontotemporal dementia (FTD), Parkinson’s disease (PD), and seizure detection, using three machine learning models: Random Forest (RF), K-Nearest Neighbors (KNN), and LightGBM. Experimental results demonstrate that the decile-based approach, particularly when paired with RF and KNN, achieves competitive classification accuracy. Furthermore, the proposed method showed robustness to reduced channel counts, suggesting its potential relevance for low-cost, wearable EEG systems. While model performance varied across datasets, particularly for LightGBM, the results indicate that decile-based features provide a useful and interpretable representation for diverse EEG classification tasks. Further studies in larger and more heterogeneous EEG populations are needed to assess generalizability and establish the potential clinical applicability of early diagnosis and real-time monitoring of neurological conditions, especially in resource-constrained or ambulatory settings.

Obesity has become a major epidemic in the 21st century. It increases the risk of dyslipidemia, hypertension, and type 2 diabetes, which are known cardiometabolic risk factors and components of the metabolic syndrome. Although overt cardiovascular (CV) diseases such as stroke or myocardial infarction are the domain of adulthood, it is evident that the CV continuum begins very early in life. Recognition of risk factors and early stages of CV damage, at a time when these processes are still reversible, and the development of prevention strategies are major pillars in reducing CV morbidity and mortality in the general population. In this review, we will discuss the role of well-known but also novel risk factors linking obesity and increased CV risk from prenatal age to adulthood, including the role of perinatal factors, diet, nutrigenomics, and nutri-epigenetics, hyperuricemia, dyslipidemia, hypertension, and cardiorespiratory fitness. The importance of ‘tracking’ of these risk factors on adult CV health is highlighted and the economic impact of childhood obesity as well as preventive strategies are discussed.

Background Few studies have quantitatively estimated the income elasticity of demand of voluntary private health insurance (VPHI) in countries with a universal National Health Service. Most studies to date have uses cross-sectional data. Methods In this paper we used a longitudinal database from the Bank of Spain to analyse the financial behaviour of approximately six thousand families per wave. We used three waves (2008, 2011 and 2014). We estimated income and wealth semi-elasticities of VPHI in Spain considering personal and family characteristics (age, sex, level of health, education, composition of the household), i.e. changes in the probability of buying VPHI as result of 1% change in income or wealth. We estimated cross-sectional models for each wave and longitudinal models for families remaining for at least two waves, taking account of possible selection bias due to attrition. Results Cross-sectional models suggest that the income effect on the probability of buying a VPHI increased from 2008 to 2014. The positive impact was observed for, wealth. In 2008 a 1% increase in income is associated with an increase in the probability of having VPHI of 0.064 [95%-CI: 0.023; 0.104] - on the probability scale (0.1) – whereas in 2014, this effect is of 0.116 [95%-CI, 0.094; 0.139]. In 2011 and 2014 the wealth effect is not significant at 5%. The estimation of the longitudinal model leads to different results where both, income and wealth are associated with non- significant results. Conclusion Our three main conclusions are: 1) Cross-sectional estimates of semi-elasticities of VPHI might be biased upwards; 2) Wealth is alongside income are economic determinants, of the decision to buy VPHI in high-income countries; 3) The effects of income and wealth on the probability of buying VHPI are neither linear nor log-linear. There are no significant differences among 60% of the most disadvantaged families, while the families of the two upper wealth quintiles show clearly differentiated behaviour with a higher probability of insurance.

Current views suggest that autism spectrum disorders (ASDs) are characterised by enhanced low-level auditory discrimination abilities. Little is known, however, about whether enhanced abilities are universal in ASD and how they relate to symptomatology. We tested auditory discrimination for intensity, frequency and duration in 21 adults with ASD and 21 IQ and age-matched controls. Contrary to predictions, there were significant deficits in ASD on all acoustic parameters. The findings suggest that low-level auditory discrimination ability varies widely within ASD and this variability relates to IQ level, and influences the severity of restricted and repetitive behaviours (RRBs). We suggest that it is essential to further our understanding of the potential contributing role of sensory perception ability on the emergence of RRBs.

Government employees in Spain are covered by public Mutual Funds that purchase a uniform basket of benefits, equal to the ones served to the general population, from private companies. Companies apply as private bidders for a fixed per capita premium hardly adjusted by age. Our hypothesis is that this premium does not cover risks, and companies have incentives for risk selection, which are more visible in high-cost patients. We focus on a particularly costly disease, cancer, whose prevalence is similar among government employees and the general population. We compare hospitalisations in the public hospitals of the government employees that have chosen public provision and the general population. We analysed a database of hospital discharges in the Valencian Community from 2010 to 2015 (3 million episodes). Using exact matching and logistic models, we find significant risk selection; thus, in hospitalised government employees, the likelihood for a solid metastatic carcinoma and non-metastatic cancer to appear in the registry is 31% higher than in the general population. Lymphoma shows the highest odds ratio of 2.64. We found quantitatively important effects. This research provides indirect evidence of risk selection within Spanish Mutual Funds for government employees, prompting action to reduce incentives for such a practice. More research is needed to figure out if what we have observed with cancer patients occurs in other conditions.

The rise of drug-resistant strains is a major challenge to containing the tuberculosis (TB) pandemic. Yet, little is known about the extent of resistance in early years of chemotherapy and when transmission of resistant strains on a larger scale became a major public health issue. Here we reconstruct the timeline of the acquisition of antimicrobial resistance during a major ongoing outbreak of multidrug-resistant TB in Argentina. We estimate that the progenitor of the outbreak strain acquired resistance to isoniazid, streptomycin and rifampicin by around 1973, indicating continuous circulation of a multidrug-resistant TB strain for four decades. By around 1979 the strain had acquired additional resistance to three more drugs. Our results indicate that Mycobacterium tuberculosis ( Mtb ) with extensive resistance profiles circulated 15 years before the outbreak was detected, and about one decade before the earliest documented transmission of Mtb strains with such extensive resistance profiles globally. The early origin and evolution of multidrug resistant strains of Mycobacterium tuberculosis are poorly understood. Here, the authors perform genomic and phylogenetic analyses of 252 clinical isolates from a tuberculosis outbreak in Argentina and reconstruct the timeline of the acquisition of antibiotic resistance.

Anomaly detection methods have a great potential to assist the detection of diseases in animal production systems. We used sequence data of Porcine Reproductive and Respiratory Syndrome (PRRS) to define the emergence of new strains at the farm level. We evaluated the performance of 24 anomaly detection methods based on machine learning, regression, time series techniques and control charts to identify outbreaks in time series of new strains and compared the best methods using different time series: PCR positives, PCR requests and laboratory requests. We introduced synthetic outbreaks of different size and calculated the probability of detection of outbreaks (POD), sensitivity (Se), probability of detection of outbreaks in the first week of appearance (POD1w) and background alarm rate (BAR). The use of time series of new strains from sequence data outperformed the other types of data but POD, Se, POD1w were only high when outbreaks were large. The methods based on Long Short-Term Memory (LSTM) and Bayesian approaches presented the best performance. Using anomaly detection methods with sequence data may help to identify the emergency of cases in multiple farms, but more work is required to improve the detection with time series of high variability. Our results suggest a promising application of sequence data for early detection of diseases at a production system level. This may provide a simple way to extract additional value from routine laboratory analysis. Next steps should include validation of this approach in different settings and with different diseases.

Many studies have demonstrated the existence of simple, unidimensional socioeconomic gradients in body mass index (BMI). However, in the present paper we move beyond such traditional analyses by simultaneously considering multiple demographic and socioeconomic dimensions. Using the Spanish National Health Survey 2011-2012, we apply intersectionality theory and multilevel analysis of individual heterogeneity and discriminatory accuracy (MAIHDA) to analyze 14,190 adults nested within 108 intersectional strata defined by combining categories of gender, age, income, educational achievement and living situation. We develop two multilevel models to obtain information on stratum-specific BMI averages and the degree of clustering of BMI within strata expressed by the intra-class correlation coefficient (ICC). The first model is a simple variance components analysis that provides a detailed mapping of the BMI disparities in the population and measures the accuracy of stratum membership to predict individual BMI. The second model includes the variables used to define the intersectional strata as a way to identify stratum-specific interactions. The first model suggests moderate but meaningful clustering of individual BMI within the intersectional strata (ICC = 12.4%). Compared with the population average (BMI = 26.07 Kg/m2), the stratum of cohabiting 18-35-year-old females with medium income and high education presents the lowest BMI (-3.7 Kg/m2), while cohabiting 36-64-year-old females with low income and low education show the highest BMI (+2.6 Kg/m2). In the second model, the ICC falls to 1.9%, suggesting the existence of only very small stratum specific interaction effects. We confirm the existence of a socioeconomic gradient in BMI. Compared with traditional analyses, the intersectional MAIHDA approach provides a better mapping of socioeconomic and demographic inequalities in BMI. Because of the moderate clustering, public health policies aiming to reduce BMI in Spain should not solely focus on the intersectional strata with the highest BMI, but should also consider whole population polices.