Universidade Federal Fluminense

To review the state of the art in autism spectrum disorder (ASD), including its etiologic puzzle, clinical features, pathophysiologic mechanisms, differential diagnosis, and therapeutic management.

A search for papers published over the past 10 years in the databases PubMed-MEDLINE, Cochrane Library, and SCIELO was performed using the following terms: "autism" and "clinical features", "differential diagnosis", "pathophysiology", or "management". The search yielded 3240, 590, 6904, and 5023 papers, respectively. A total of 120 most relevant papers were selected based on their title and abstract content.

The current prevalence of ASD is 1 in every 31 eight-year-old children. A genetic defect is found in 10-20% of individuals with ASD. Environmental risk factors that increase the likelihood of ASD include advanced parental age and maternal health conditions. Epigenetic mechanisms may play a crucial role in the interplay between genetic and environmental factors in the pathogenesis of ASD. In addition to language delay, pediatricians should monitor and screen for several early signs of ASD. Differential diagnosis is complex because several neurodevelopmental conditions show clinical features that overlap with ASD. Medications may be used to treat comorbid conditions.

Management is based on a multidisciplinary team, and pediatricians are in a unique position to coordinate this team, given the trustworthy relationship they have with patients and their families.

This work proposes a black-box purely data-driven strategy for structural damage identification concerning localized damaged regions in plate-like structures. The proposed strategy is based on Convolution Neural Networks in the framework of a supervised learning regression task. The relationships between the estimated and target damage parameters are investigated, focusing on the physical interpretability of the damage recovery results. The positional parameters are found to be much more easily estimated than those describing damage size and damage intensity, in accordance with the literature where similar parameterizations are considered. The high accuracy with which it is possible to estimate the positional parameters explains, in part, the many successful approaches found in literature where damage localization is treated as a classification problem. Some numerical analyses are shown for a convolutional neural network architecture with diverse damage scenarios in an elastic plate considering Lamb waves, three actuators and 16 sensors. The first surrogate inverse model training considers homogeneous material properties and the second one considers non-homogeneous material properties. Results are evaluated using a set of overlap metrics, which help identify both the accuracy and the limitations of the inverse surrogates in damage recovery. The inverse surrogate trained with non-homogeneous material properties proved robust with respect to system variability.

The recycling of polystyrene (PS)-based cups for the additive manufacturing of sustainable, cost-effective, and high-performance electrochemical devices is presented here for the first time. In this approach, we integrated graphite (Gpt, a conductive material) within the PS matrix (a thermoplastic component) to design alternative conductive filaments for the production of new electrochemical sensors using a 3D pen (an easy-to-use and transportable tool). Various material ratios were examined, with the Gpt/PS (50:50) composition showing enhanced electrochemical performance without compromising printability. Structural, morphological, and thermal information on the proposed materials was accessed using Raman and infrared spectroscopy, scanning electron microscopy, and thermogravimetric analysis. The electrochemical features of the material were studied by electrochemical impedance spectroscopy and voltammetric measurements using model species such as [Fe(CN)₆]^3-/4-, hexaminruthenium (III) chloride, and dopamine, highlighting a charge transfer resistance (Rct) value of 1.5 kΩ and an elevated electroactive area of 49.3 mm² due to the electrical properties offered by Gpt. Additionally, a differential pulse voltammetry based method was developed for sulfanilamide antibiotic detection, a molecule of interest in various fields (pharmaceutical, food, and environmental). Noteworthy linear ranges (1-10 μmol L^-1 and 10-50 μmol L¹), a limit of detection (LOD) of 0.3 μmol L^-1, high intra- and inter-electrode precisions (RSD < 2.2 %), and selectivity even in the presence of other antibiotics were observed. When applied to real samples, such as pharmaceuticals, water, and milk, recoveries close to 100 % demonstrated the accuracy and reliability of the electrochemical analyses. These results validate the performance of the proposed 3D-printed sensor and underscore a sustainable electrochemical investigation in alignment with circular economy precepts.