Arquivos de neuro-psiquiatria最新文献

Background:  Central neuropathic poststroke pain (CNPSP) affects up to 12% of patients with stroke in general and up to 18% of patients with sensory deficits. This pain syndrome is often incapacitating and refractory to treatment. Brain computed tomography and magnetic resonance imaging (MRI) are widely used methods in the evaluation of CNPSP.

Objective:  The present study aims to review the role of neuroimaging methods in CNPSP.

Methods:  We performed a literature review of the main clinical aspects of CNPSP and the contribution of neuroimaging methods to study its pathophysiology, commonly damaged brain sites, and possible differential diagnoses. Lastly, we briefly mention how neuroimaging can contribute to the non-pharmacological CNPSP treatment. Additionally, we used a series of MRI from our institution to illustrate this review.

Results:  Imaging has been used to explain CNPSP pathogenesis based on spinothalamic pathway damage and connectome dysfunction. Imaging locations associated with CNPSP include the brainstem (mainly the dorsolateral medulla), thalamus (especially the ventral posterolateral/ventral posteromedial nuclei), cortical areas such as the posterior insula and the parietal operculum, and, more recently, the thalamocortical white matter in the posterior limb of the internal capsule. Imaging also brings the prospect of helping search for new targets for non-pharmacological treatments for CNPSP. Other neuropathic pain causes identified by imaging include syringomyelia, multiple sclerosis, and herniated intervertebral disc.

Conclusion:  Imaging is a valuable tool in the complimentary evaluation of CNPSP patients in clinical and research scenarios.

Background:  Olfactory dysfunction (OD) represents a frequent manifestation of the coronavirus disease 2019 (COVID-19). Apolipoprotein E (APOE) is a protein that interacts with the angiotensin-converting enzyme receptor, essential for viral entry into the cell. Previous publications have suggested a possible role of APOE in COVID-19 severity. As far as we know, no publications found significant associations between this disease's severity, OD, and APOE polymorphisms (E2, E3, and E4).

Objective:  To analyze the epidemiology of OD and its relationship with APOE polymorphisms in a cohort of Long-COVID patients.

Methods:  We conducted a prospective cohort study with patients followed in a post-COVID neurological outpatient clinic, with OD being defined as a subjective reduction of olfactory function after infection, and persistent OD being defined when the complaint lasted more than 3 months after the COVID-19 infection resolution. This cross-sectional study is part of a large research with previously reported data focusing on the cognitive performance of our sample.

Results:  The final sample comprised 221 patients, among whom 186 collected blood samples for APOE genotyping. The persistent OD group was younger and had a lower hospitalization rate during the acute phase of the disease (p < 0.001). Furthermore, the APOE variant E4 allele frequency was lower in this group (p = 0.035). This study evaluated OD in an outpatient population with COVID-19. In the current literature on this disease, anosmia is associated with better clinical outcomes and the E4 allele is associated with worse outcomes.

Conclusion:  Our study provides new information to these correlations, suggesting APOE E4 as a protective factor for OD.

Background:  The Basic Health Unit (Unidade Básica de Saúde - UBS, in Portuguese) is the first point of contact in the public healthcare system for people with epilepsy. Primary care professionals need to appropriately diagnose, treat, and refer, if necessary, to tertiary services.

Objective:  To evaluate the knowledge of UBS professionals on the management of patients with epilepsy in Rio de Janeiro.

Methods:  Online questionnaires were performed on the topic of epilepsy before and after exposure to classes taught by epileptologists.

Results:  A total of 66 doctors participated, 54.5% of whom were residents or trained in family medicine. The majority had from 1 to 3 years of practice. Insecurity prevailed in the management of pregnant women and the elderly. Around 59.1% of the participants referred patients with seizures without examinations. A total of 78% of the participants did not correctly classify seizure types, and 2/3 did not define drug-resistant epilepsy. Induction and broad-spectrum drugs were common. The therapeutic decision depended on availability in the basic health unit (UBS) (81.8%), dosage (60.6%), side effects (34.8%), and age (36.4%). Comorbidities and sex influenced 1/4 of the sample. For 23% of the participants, the type of crisis did not affect the choice. Regarding typical non-pharmacological options, 75% of the participants were aware of cannabidiol, 40.9% of surgery, 22.7% of ketogenic diet, and 22.8% of deep brain stimulation/vagus nerve stimulation (DBS/VNS). A total of 90.2% indicated the need for training.

Conclusion:  There are deficits in the knowledge of UBS professionals in the management of epilepsy. Specialized training is imperative to optimize the care offered within SUS.

Background:  The delay in recognizing stroke symptoms is a significant obstacle to receiving acute treatment. Therefore, it is essential to understand the gaps in the knowledge about stroke among the general population and promote campaigns based on these gaps.

Objective:  To investigate the general knowledge about stroke in a capital in Northeastern Brazil in a sample of individuals who attended a public hospital and the impacts of the coronavirus disease 2019 (COVID-19) pandemic on the use of emergency services.

Methods:  We included patients older than 18 years of age and their family members and/or companions. After obtaining consent, the researcher presented a typical case of stroke, and the participants filled out a questionnaire divided into 2 sections: sociodemographic data and 15 questions about stroke detection and seeking health services and treatment.

Results:  We included 154 individuals with a mean age of 44.45 ± 16.21 years. After presenting the case, 60.4% mentioned the acronym AVC (acidente vascular cerebral, or cerebrovascular accident [stroke], in Portuguese) as a possible explanation, and 54.5% reported that they would call the Mobile Emergency Care Service. However, 62.9% provided the incorrect telephone number for the Mobile Emergency Care Service or lacked knowledge of the accurate number. Regarding the risk factors for stroke, 27.9% did not know any of them, 65.5% were unaware of any treatment, and no reference was made to thrombolytic therapy. About their chosen conduct in the same case in the context of the COVID-19 pandemic, 98.1% of the participants would not change their behavior.

Conclusion:  These results can assist in the planning of public policies and campaigns emphasizing the issue of risk factors and how to access emergency medical services in the state of Alagoas, Brazil.

Background:  The early diagnosis of Alzheimer's disease (AD) and mild cognitive impairment (MCI) remains a significant challenge in neurology, with conventional methods often limited by subjectivity and variability in interpretation. Integrating deep learning with artificial intelligence (AI) in magnetic resonance imaging (MRI) analysis emerges as a transformative approach, offering the potential for unbiased, highly accurate diagnostic insights.

Objective:  A meta-analysis was designed to analyze the diagnostic accuracy of deep learning of MRI images on AD and MCI models.

Methods:  A meta-analysis was performed across PubMed, Embase, and Cochrane library databases following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, focusing on the diagnostic accuracy of deep learning. Subsequently, methodological quality was assessed using the QUADAS-2 checklist. Diagnostic measures, including sensitivity, specificity, likelihood ratios, diagnostic odds ratio, and area under the receiver operating characteristic curve (AUROC) were analyzed, alongside subgroup analyses for T1-weighted and non-T1-weighted MRI.

Results:  A total of 18 eligible studies were identified. The Spearman correlation coefficient was -0.6506. Meta-analysis showed that the combined sensitivity and specificity, positive likelihood ratio, negative likelihood ratio, and diagnostic odds ratio were 0.84, 0.86, 6.0, 0.19, and 32, respectively. The AUROC was 0.92. The quiescent point of hierarchical summary of receiver operating characteristic (HSROC) was 3.463. Notably, the images of 12 studies were acquired by T1-weighted MRI alone, and those of the other 6 were gathered by non-T1-weighted MRI alone.

Conclusion:  Overall, deep learning of MRI for the diagnosis of AD and MCI showed good sensitivity and specificity and contributed to improving diagnostic accuracy.

Almost two decades before Kanner's and Asperger's works, the original paper by child psychiatrist Grunya Efimovna Sukhareva had already been written. It was published in 1926 by Sukhareva in a German scientific journal of psychiatry and neurology, with a detailed description of children who presented clinical conditions whose characteristics and evolution closely resemble autism, according to current criteria. In the present historical note, we intend to present Sukhareva's pioneering work and retrieve the meaning of her original contribution.