Renato Mendes dos Santos

Background: Individuals with attention deficit hyperactivity disorder (ADHD) can exhibit neurocognitive deficits, psychosocial alterations, and changes in sleep regulation. In this sense, non-invasive techniques such as transcranial magnetic stimulation (TMS) can be used to assess and treat people with neurocognitive disorders. Studies using neuromodulation to estimate the timing and regulation of sleep remain scarce, revealing a gap in its understanding and treatment. Aim: To analyze whether the application of 10 Hz TMS modifies time estimation and sleep quality in people with ADHD for characteristics of inattention and hyperactivity. Material and Methods: Twelve adult male participants with high scores on the assessment of ADHD scale underwent a 10 Hz TMS protocol and a visual stimulus time estimation task. Daily rhythmicity was assessed by actigraphy before and after the repetitive transcranial magnetic stimulation (rTMS) intervention. Sleep quality was evaluated using the Pittsburg Sleep Quality Index and the Morning and Evening Questionnaire. Results: The results showed that rTMS modulated the interpretation of the 9 s long interval (p = 0.028). For the ADHD-AD instrument, no statistically significant differences were observed (p > 0.05). In relation to actigraphic variables—sleep time, bedtime, latency, sleep efficiency, and wakefulness after sleep onset—there were no differences after excitatory neuromodulation (p > 0.05). Conclusions: The findings demonstrate that the application of 10 Hz TMS enhances performance in long intervals during the time estimation task for individuals with ADHD, but does not affect sleep quality.

Background: Studies of non‐invasive treatments such as repetitive transcranial magnetic stimulation (rTMS) and transcranial direct current stimulation (tDCS) can contribute in an alternative way to the symptoms treatment and changes in sleep patterns in attention deficit hyperactivity disorder (ADHD), due to some scientific evidence of action on cognition and clinical characteristics.Aim: The aim of this systematic review was to evaluate studies of non‐invasive brain stimulation using rTMS and tDCS on symptoms and sleep parameters in people with ADHD, in order to synthesize the main findings on intervention effects.Methodology: A systematic review was carried out using the PRISMA protocol in the PubMed/Medline, Embase, PsycInfo, CINAHL, PEDro and Central—Cochrane library databases were initially carried out on 14 March 2024 and repeated until September 2024. Randomized clinical trials evaluating the effect of rTMS and tDCS on symptoms and changes in sleep pattern in people with ADHD were included. Primary outcomes were total severity of ADHD symptoms and sleep quality assessed by questionnaires and instrument measures.Results: The review evidenced only positive findings in five studies for tDCS in some cognitive functions, ADHD symptoms and improvement in sleep parameters, being results with reduced efficacy due to the high heterogeneity in the experimental protocols.Conclusion: The findings show limited evidence; however, it is suggested that the combined use of non‐invasive stimulation with other approaches, such as cognitive training, may improve the intervention effect. It is also important to consider limitations such as heterogeneity in clinical protocols, selection of participants with ADHD, including dosage and frequency. These factors should be explored in future research to optimize the application of these therapies.

This research letter presents the co-design process for RG4Face, a mime therapy–based serious game that uses computer vision for human facial movement recognition and estimation to help health care professionals and patients in the facial rehabilitation process.

UNSTRUCTURED This research letter presents the co-design process for RG4Face, a mime therapy–based serious game that uses computer vision for human facial movement recognition and estimation to help health care professionals and patients in the facial rehabilitation process.

Ciência Brasileira: Múltiplos olhares - Medicina, Saúde e Prevenção Volume 02 (978-85-5722-691-3) - Territorialização Em Saúde No Município De Picos-Pi: Relato De Experiência

People at risk of suicide tend to be isolated and cannot share their thoughts. For this reason, suicidal ideation monitoring becomes a hard task. Therefore, people at risk of suicide need to be monitored in a manner capable of identifying if and when they have a suicidal ideation, enabling professionals to perform timely interventions. This study aimed to develop the Boamente tool, a solution that collects textual data from users’ smartphones and identifies the existence of suicidal ideation. The solution has a virtual keyboard mobile application that passively collects user texts and sends them to a web platform to be processed. The platform classifies texts using natural language processing and a deep learning model to recognize suicidal ideation, and the results are presented to mental health professionals in dashboards. Text classification for sentiment analysis was implemented with different machine/deep learning algorithms. A validation study was conducted to identify the model with the best performance results. The BERTimbau Large model performed better, reaching a recall of 0.953 (accuracy: 0.955; precision: 0.961; F-score: 0.954; AUC: 0.954). The proposed tool demonstrated an ability to identify suicidal ideation from user texts, which enabled it to be experimented with in studies with professionals and their patients.