The Impact Of Thymectomy In Thymomatous And Nonthymomatous Myasthenia Gravis - The Experience Of A Tertiary Center.

INTRODUCTION: Thymectomy remains a mainstay of treatment in Thymomatous (T) and Nonthymomatous (nT) Myasthenia Gravis (MG), with improved clinical outcomes and reduced need for medical treatment, however, there is little research regarding long-term follow-up. We aim to assess the impact of surgery on the long-term outcome of patients with MG at our center. METHODS: Retrospective analyses of MG patients submitted to thymectomy between 2007 and 2017 at the thoracic surgery department of CHUC. Clinical assessment was performed according to the MG Foundation of America (MGFA) Clinical Classification (cMGFA). The follow-up was categorized according to the MGFA Post-intervention Status (MGFA-PIS) and cMGFA. Statistical analysis was performed with SPSS, to a significance level of 5%. RESULTS: Thirty-seven patients underwent extended thymectomy and 67.6% were female. Median age at diagnosis was 46.68±19.2 years. Most patients (83.8%) had anti-acetylcholine receptor antibodies and 81.1% had generalized forms of MG. Many patients (67.6%) had surgery less than 12 months after the clinical diagnosis. TMG was present in 19 (51.4%) patients. Compared to nTMG, these patients were older (54.06±17.9 vs 40.17±19.4 years) and most were men (52.9% vs 16.7%). We obtained a good outcome in most patients in the first (81.1%), second (86.1%), and fifth (84.8%) year of follow-up. There was a shift towards better prognosis categories in the good outcome group: 9.1% CSR, 3.0% PR, and 66,7% MM in the fifth year. Preoperative medical treatment did not influence the long-term follow-up outcome. A shorter time to surgery (< 12 months) correlated with better outcomes at year 5 (p=0.016). CONCLUSION: Thymectomy led to a sustained clinical improvement in our cohort, allowing for a reduced need for medication. A shorter time to surgery seems to have a positive influence on long-term prognosis. We expect that an extended follow-up would improve our results.

Neuropsychological Assessment in the Distinction Between Biomarker Defined Frontal-Variant of Alzheimer's Disease and Behavioral-Variant of Frontotemporal Dementia.

BACKGROUND: Frontal-variant of Alzheimer's disease (fvAD) was purposed for patients with AD pathology that, despite the typical amnestic presentation, show early and progressive deterioration of behavior and executive functions, closely resembling the behavioral-variant of frontotemporal dementia (bvFTD). This leads to a challenging differential diagnosis where neuropsychological evaluation and in vivo pathological evidence are essential. OBJECTIVE: To evaluate the contribution of a comprehensive neuropsychological assessment (NP) battery in distinguishing between fvAD-dementia and bvFTD supported by cerebrospinal fluid (CSF) biomarkers. METHODS: We included 40 patients with a baseline NP profile with prominent early executive and/or behavioral dysfunction, who meet both diagnosis of bvFTD and fvAD-dementia, according to international criteria. All patients underwent comprehensive NP assessment and CSF-AD biomarker evaluation. Neuropsychological domains as well as clinical and sociodemographic features, and APOE genotype were compared between groups. RESULTS: 21 patients (52.5%) met the biological criteria for AD (decreased Aß42 together with increased T-tau or P-tau in CSF) and were therefore classified as fvAD (mean age was 64.57, with 47.6% female). There were no differences between groups regarding age/age-at-onset, gender, or educational level. Regarding neuropsychological profile, performances in language and memory functions were equivalent in both groups. Significant differences were found in visuo-constructional abilities (p = 0.004), Trail Making Test A (p < 0.001), and Raven's Colored Progressive Matrices (p = 0.019), with fvAD patients showing worst performances. CONCLUSION: In patients with an early prominent frontal profile, a higher impairment in attention and visuo-spatial functions, signaling additional right hemisphere fronto-parietal dysfunction, point towards a diagnosis of fvAD-dementia and may be useful in clinical practice.

Portuguese version of the CDR plus NACC FTLD: Validation studies.

Introduction: The CDR Dementia Staging Instrument PLUS National Alzheimer's Coordinating Center (CDR plus NACC FTLD) was developed by adding to the standard CDR two extra domains focused on the main features of frontotemporal lobar degeneration (FTLD): language and behavior/personality. We intended to perform the validation studies for the European-Portuguese population. Methods: A total of 105 participants matched for age, education, and disease staging (35 bvFTD, 35 AD, and 35 controls) were included. A translated-version of the CDR and the two added domains was administered by a neuropsychologist who was blinded to the diagnosis. Results: The bvFTD group had higher baseline CDR plus NACC FTLD scores compared to the AD and controls. Only the sum-of-boxes (SB) score, the behavior/personality, and language domains were able to distinguish between clinical groups. Logistic regression analyses showed that adding the behavior/personality domain with or without language significantly enhanced the discriminating ability. Discussion: Results show that the CDR plus NACC FTLD is a reliable tool in the diagnostic process of bvFTD patients and has an added value in distinguishing them from patients with AD.

Acute Myocardial Infarction after Intravenous Thrombolysis for Acute Ischemic Stroke: Case Series and Systematic Review.

BACKGROUND AND OBJECTIVES: Intravenous thrombolysis (IV-rtPA) has been suggested as a potential cause of myocardial infarction (MI) after acute ischemic stroke (AIS), with randomized clinical trials showing a higher number of cardiac events within the thrombolysis group. We assessed the prevalence and MI mechanisms after IV-rtPA for AIS. METHODS: Retrospective review of consecutive AIS patients admitted to six stroke units and systematic literature review searching for AIS patients who suffered a MI less than 24 h after IV-rtPA. In those with available coronary angiography, MI etiology was defined as atherosclerotic or embolic. Patients' characteristics were compared between groups. RESULTS: Fifty-two patients were included. Thirty-two patients (61.5%) derived from hospital cases, after reviewing 6958 patients treated with IV-rtPA [0.5% (95% CI 0.38-0.54) of total hospital cases]. After coronary angiography (n = 25, 48.1%), 14 (54%) patients were considered to have an atherosclerotic MI, and 11 (46%) due to coronary embolism. Patients with an embolic MI more frequently had a cardioembolic AIS (72.7% vs 28.6%; p-value = 0.047) and an intracardiac thrombus (27.3% vs 0.0%; p-value = 0.044). Although not statistically significant, patients with an embolic MI had apparent lower time intervals between starting IV-rtPA infusion and MI occurrence [2 h (0.2-3.0) vs 3 h (1.0-15.0); p-value = 0.134]. CONCLUSIONS: MI within the first 24 h after IV-rtPA for AIS is an infrequent event, and more frequently non-embolic. However, the prevalence of embolic MI was superior to what is found in the general population with MI. There was an association between the pathophysiology of AIS and MI. The low number of events and publication bias may have limited our conclusions.

Crackle and wheeze detection in lung sound signals using convolutional neural networks.

Respiratory diseases are among the leading causes of death worldwide. Preventive measures are essential to avoid and increase the odds of a successful recovery. An important screening tool is pulmonary auscultation, an inexpensive, noninvasive and safe method to assess the mechanics and dynamics of the lungs. On the other hand, it is a difficult task for a human listener since some lung sound events have a spectrum of frequencies outside of the human hearing ability. Thus, computer assisted decision systems might play an important role in the detection of abnormal sounds, such as crackle or wheeze sounds. In this paper, we propose a novel system, which is not only able to detect abnormal lung sound events, but it is also able to classify them. Furthermore, our system was trained and tested using the publicly available ICBHI 2017 challenge dataset, and using the metrics proposed by the challenge, thus making our framework and results easily comparable. Using a Mel Spectrogram as an input feature for our convolutional neural network, our system achieved results in line with the current state of the art, an accuracy of 43%, and a sensitivity of 51%.