Sleep Quality through Vocal Analysis: a Telemedicine Application

Voice is a reservoir of valuable health data. Recent studies highlighted its efficacy in predicting sleep quality, and its potential as biomarker of neurodegeneration. This study assesses the feasibility of a Telemedicine system for the evaluation of sleep quality through brief vocal recordings. Machine Learning models were employed in the binary classification between good and poor sleepers, with great performance in scoring poor sleep quality - 88% and 85% F-1 score on a 5-fold Cross Validation (CV) for females and males, respectively. Moreover, the correlation between perceived sleep quality and a validated global score was studied, as well as the influence of external factors and sleep-wake schedule

Single-channel EEG classification of sleep stages based on REM microstructure

Rapid-eye movement (REM) sleep, or paradoxical sleep, accounts for 20–25% of total night-time sleep in healthy adults and may be related, in pathological cases, to parasomnias. A large percentage of Parkinson's disease patients suffer from sleep disorders, including REM sleep behaviour disorder and hypokinesia; monitoring their sleep cycle and related activities would help to improve their quality of life. There is a need to accurately classify REM and the other stages of sleep in order to properly identify and monitor parasomnias. This study proposes a method for the identification of REM sleep from raw single-channel electroencephalogram data, employing novel features based on REM microstructures. Sleep stage classification was performed by means of random forest (RF) classifier, K-nearest neighbour (K-NN) classifier and random Under sampling boosted trees (RUSBoost); the classifiers were trained using a set of published and novel features. REM detection accuracy ranges from 89% to 92.7%, and the classifiers achieved a F-1 score (REM class) of about 0.83 (RF), 0.80 (K-NN), and 0.70 (RUSBoost). These methods provide encouraging outcomes in automatic sleep scoring and REM detection based on raw single-channel electroencephalogram, assessing the feasibility of a home sleep monitoring device with fewer channels

Impaired immunogenicity to COVID-19 vaccines in autoimmune systemic diseases. High prevalence of non-response in different patients’ subgroups

Autoimmune systemic diseases (ASD) may show impaired immunogenicity to COVID-19 vaccines. Our prospective observational multicenter study aimed to evaluate the seroconversion after the vaccination cycle and at 6-12-month follow-up, as well the safety and efficacy of vaccines in preventing COVID-19. The study included 478 unselected ASD patients (mean age 59 ± 15 years), namely 101 rheumatoid arthritis (RA), 38 systemic lupus erythematosus (SLE), 265 systemic sclerosis (SSc), 61 cryoglobulinemic vasculitis (CV), and a miscellanea of 13 systemic vasculitis. The control group included 502 individuals from the general population (mean age 59 ± 14SD years). The immunogenicity of mRNA COVID-19 vaccines (BNT162b2 and mRNA-1273) was evaluated by measuring serum IgG-neutralizing antibody (NAb) (SARS-CoV-2 IgG II Quant antibody test kit; Abbott Laboratories, Chicago, IL) on samples obtained within 3 weeks after vaccination cycle. The short-term results of our prospective study revealed significantly lower NAb levels in ASD series compared to controls [286 (53–1203) vs 825 (451–1542) BAU/mL, p < 0.0001], as well as between single ASD subgroups and controls. More interestingly, higher percentage of non-responders to vaccine was recorded in ASD patients compared to controls [13.2% (63/478), vs 2.8% (14/502); p < 0.0001]. Increased prevalence of non-response to vaccine was also observed in different ASD subgroups, in patients with ASD-related interstitial lung disease (p = 0.009), and in those treated with glucocorticoids (p = 0.002), mycophenolate-mofetil (p < 0.0001), or rituximab (p < 0.0001). Comparable percentages of vaccine-related adverse effects were recorded among responder and non-responder ASD patients. Patients with weak/absent seroconversion, believed to be immune to SARS-CoV-2 infection, are at high risk to develop COVID-19. Early determination of serum NAb after vaccination cycle may allow to identify three main groups of ASD patients: responders, subjects with suboptimal response, non-responders. Patients with suboptimal response should be prioritized for a booster-dose of vaccine, while a different type of vaccine could be administered to non-responder individuals

Role of MRI in staging and follow-up of endometrial and cervical cancer:pitfalls and mimickers

Abstract MRI plays important roles in endometrial and cervical cancer assessment, from detection to recurrent disease evaluation. Endometrial cancer (EC) is the most common malignant tumor of the female genital tract in Western countries. EC patients are divided into risk categories based on histopathological tumor type, grade, and myometrial invasion depth. EC is surgically staged using the International Federation of Gynecology and Obstetrics (FIGO) system. Since FIGO (2009) stage correlates with prognosis, preoperative staging is essential for tailored treatment. MRI reveals myometrial invasion depth, which correlates with tumor grade and lymph node metastases, and thus correlates with prognosis. Cervical cancer (CC) is the second most common cancer, and the third leading cause of cancer-related death among females in developing countries. The FIGO Gynecologic Oncology Committee recently revised its CC staging guidelines, allowing staging based on imaging and pathological findings when available. The revised FIGO (2018) staging includes node involvement and thus enables both therapy selection and evaluation, prognosis estimation, and calculation of end results. MRI can accurately assess prognostic indicators, e.g., tumor size, parametrial invasion, pelvic sidewall, and lymph node invasion. Despite these important roles of MRI, radiologists still face challenges due to the technical and interpretation pitfalls of MRI during all phases of endometrial and cervical cancer evaluation. Awareness of mimics that can simulate both cancers is critical. With careful application, functional MRI with DWI and DCE sequences can help establish a correct diagnosis, although it is sometimes necessary to perform biopsy and histopathological analysis