Entropy Measures of Electroencephalograms towards the Diagnosis of Psychogenic Non-Epileptic Seizures

Psychogenic non-epileptic seizures (PNES) may resemble epileptic seizures but are not caused by epileptic activity. However, the analysis of electroencephalogram (EEG) signals with entropy algorithms could help identify patterns that differentiate PNES and epilepsy. Furthermore, the use of machine learning could reduce the current diagnosis costs by automating classification. The current study extracted the approximate sample, spectral, singular value decomposition, and Renyi entropies from interictal EEGs and electrocardiograms (ECG)s of 48 PNES and 29 epilepsy subjects in the broad, delta, theta, alpha, beta, and gamma frequency bands. Each feature-band pair was classified by a support vector machine (SVM), k-nearest neighbour (kNN), random forest (RF), and gradient boosting machine (GBM). In most cases, the broad band returned higher accuracy, gamma returned the lowest, and combining the six bands together improved classifier performance. The Renyi entropy was the best feature and returned high accuracy in every band. The highest balanced accuracy, 95.03%, was obtained by the kNN with Renyi entropy and combining all bands except broad. This analysis showed that entropy measures can differentiate between interictal PNES and epilepsy with high accuracy, and improved performances indicate that combining bands is an effective improvement for diagnosing PNES from EEGs and ECGs

Positive airway pressure longer than 24 h is associated with histopathological volutrauma in severe COVID-19 pneumonia—an ESGFOR based narrative case-control review

SARS-CoV-2; Post-mortem microbiology; VolutraumaSARS-CoV-2; Microbiología post mortem; VolutraumaSARS-CoV-2; Microbiologia post mortem; VolutraumaBackground and Objective: A thorough understanding of the pathogenic mechanisms elicited by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) still requires further research. Until recently, only a restricted number of autopsies have been performed, therefore limiting the accurate knowledge of the lung injury associated with SARS-CoV-2. A multidisciplinary European Society of Clinical Microbiology and Infectious Diseases (ESCMID) Study Group of Forensic and Post-mortem Microbiology-ESGFOR team conducted a non-systematic narrative literature review among coronavirus 2019 disease (COVID-19) pneumonia cases assessing the histopathological (HP) effects of positive airways pressure. HP lung features were recorded and compared between mechanically ventilated (>24 hours) and control (ventilation <24 hours) patients. A logistic regression analysis was performed to identify associations between mechanical ventilation (MV) and HP findings. Methods: A PubMed and MEDLINE search was conducted in order to identify studies published between March 1st 2020 and June 30th 2021. Key Content and Findings: Seventy patients (median age: 69 years) from 24 studies were analysed, among whom 38 (54.2%) underwent MV longer than 24 hours. Overall, main HP features were: diffuse alveolar damage (DAD) in 53 (75.7%), fibrosis (interstitial/intra-alveolar) in 43 (61.4%), vascular damage—including thrombosis/emboli- in 41 (58.5%), and endotheliitis in only 8 (11.4%) patients. Association of DAD, fibrosis and vascular damage was detected in 30 (42.8%) patients. Multivariate analysis, adjusted by age and gender, identified MV >24 hours as an independent variable associated with DAD (OR =5.40, 95% CI: 1.48–19.62), fibrosis (OR =3.88, 95% CI: 1.25–12.08), vascular damage (OR =5.49, 95% CI: 1.78–16.95) and association of DAD plus fibrosis plus vascular damage (OR =6.99, 95% CI: 2.04–23.97). Conclusions: We identified that patients mechanically ventilated >24 hours had a significantly higher rate of pulmonary injury on histopathology independently of age and gender. Our findings emphasize the importance of maintaining a protective ventilator strategy when subjects with COVID-19 pneumonia undergo intubation

Management of work-relevant upper limb disorders: a review

Background Upper limb disorders (ULDs) are clinically challenging and responsible for considerable work loss. There is a need to determine effective approaches for their management. Aim To determine evidence-based management strategies for work-relevant ULDs and explore whether a biopsychosocial approach is appropriate. Methods Literature review using a best evidence synthesis. Data from articles identified through systematic searching of electronic databases and citation tracking were extracted into evidence tables. The information was synthesized into high-level evidence statements, which were ordered into themes covering classification/diagnosis, epidemiology, associations/risks and management/treatment, focusing on return to work or work retention and taking account of distinctions between non-specific complaints and specific diagnoses. Results Neither biomedical treatment nor ergonomic workplace interventions alone offer an optimal solution; rather, multimodal interventions show considerable promise, particularly for occupational outcomes. Early return to work, or work retention, is an important goal for most cases and may be facilitated, where necessary, by transitional work arrangements. The emergent evidence indicates that successful management strategies require all the players to be onside and acting in a coordinated fashion; this requires engaging employers and workers to participate. Conclusions The biopsychosocial model applies: biological considerations should not be ignored, but psychosocial factors are more influential for occupational outcomes. Implementation of interventions that address the full range of psychosocial issues will require a cultural shift in the way the relationship between upper limb complaints and work is conceived and handled. Dissemination of evidence-based messages can contribute to the needed cultural shift

Investigating neuromagnetic brain responses against chromatic flickering stimuli by wavelet entropies

BACKGROUND: Photosensitive epilepsy is a type of reflexive epilepsy triggered by various visual stimuli including colourful ones. Despite the ubiquitous presence of colorful displays, brain responses against different colour combinations are not properly studied. METHODOLOGY/PRINCIPAL FINDINGS: Here, we studied the photosensitivity of the human brain against three types of chromatic flickering stimuli by recording neuromagnetic brain responses (magnetoencephalogram, MEG) from nine adult controls, an unmedicated patient, a medicated patient, and two controls age-matched with patients. Dynamical complexities of MEG signals were investigated by a family of wavelet entropies. Wavelet entropy is a newly proposed measure to characterize large scale brain responses, which quantifies the degree of order/disorder associated with a multi-frequency signal response. In particular, we found that as compared to the unmedicated patient, controls showed significantly larger wavelet entropy values. We also found that Renyi entropy is the most powerful feature for the participant classification. Finally, we also demonstrated the effect of combinational chromatic sensitivity on the underlying order/disorder in MEG signals. CONCLUSIONS/SIGNIFICANCE: Our results suggest that when perturbed by potentially epileptic-triggering stimulus, healthy human brain manages to maintain a non-deterministic, possibly nonlinear state, with high degree of disorder, but an epileptic brain represents a highly ordered state which making it prone to hyper-excitation. Further, certain colour combination was found to be more threatening than other combinations

Gender differences in disability after sickness absence with musculoskeletal disorders: five-year prospective study of 37,942 women and 26,307 men

<p>Abstract</p> <p>Background</p> <p>Gender differences in the prevalence and occupational consequences of musculoskeletal disorders (MSDs) are consistently found in epidemiological studies. The study investigated whether gender differences also exist with respect to chronicity, measured as the rate of transition from sickness absence into permanent disability pension (DP).</p> <p>Methods</p> <p>Prospective national cohort study in Norway including all cases with a spell of sickness absence > eight weeks during 1997 certified with a MSD, 37,942 women and 26,307 men. The cohort was followed-up for five years with chronicity measured as granting of DP as the endpoint. The effect of gender was estimated in the full sample adjusting for sociodemographic factors and diagnostic distribution. Gender specific analyses were performed with the same explanatory variables. Finally, the gender difference was estimated for nine diagnostic subgroups.</p> <p>Results</p> <p>The crude rate of DP was 22% for women and 18% for men. After adjusting for all sociodemographic variables, a slightly higher female risk of DP remained. However, additional adjustment for diagnostic distribution removed the gender difference completely. Having children and working full time decreased the DP risk for both genders, whereas low socioeconomic status increased the risk similarly. There was a different age effect as more women obtained a DP below the age of 50. Increased female risk of chronicity remained for myalgia/fibromyalgia, back disorders and "other/unspecified" after relevant adjustments, whereas men with neck disorders were at higher risk of chronicity.</p> <p>Conclusions</p> <p>Women with MSDs had a moderately increased risk of chronicity compared to men, when including MSDs with a traumatic background. Possible explanations are lower income, a higher proportion belonging to diagnostic subgroups with poor prognosis, and a younger age of chronicity among women. When all sociodemographic and diagnostic variables were adjusted for, no gender difference remained, except for some diagnostic subgroups.</p

Nonlinear complexity analysis of brain fMRI signals in schizophrenia

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