Acquired hypertrichosis lanuginosa: a case report.

The authors present a patient with hypertrichosis lanuginosa acquisita without associated malignancy

Erythema gyratum perstans: association with a familial neurologic disease.

Two members of the same family with erythema gyratum perstans and hypertrophic neuritis are reported. The dermatosis could be an expression of localization of neuritis to nerva vasorum with abnormal neurovascular response of cutaneous small vessels to normal stimuli with active erythema followed by cyanosis

Corticobasal syndrome: neuroimaging and neurophysiological advances

Corticobasal degeneration (CBD) is a neurodegenerative condition characterized by 4R-tau protein deposition in several brain regions that clinically manifests itself as a heterogeneous atypical parkinsonism typically expressing in the adulthood. The prototypical clinical phenotype of CBD is corticobasal syndrome (CBS). Important insights into the pathophysiological mechanisms underlying motor and higher cortical symptoms in CBS have been gained by using advanced neuroimaging and neurophysiological techniques. Structural and functional neuroimaging studies often showed asymmetric cortical and subcortical abnormalities, mainly involving perirolandic and parietal regions and basal ganglia structures. Neurophysiological investigations including electroencephalography and somatosensory evoked potentials provided useful information on the origin of myoclonus and on cortical sensory loss. Transcranial magnetic stimulation demonstrated heterogeneous and asymmetric changes in the excitability and plasticity of primary motor cortex and abnormal hemispheric connectivity. Neuroimaging and neurophysiological abnormalities in multiple brain areas reflect the asymmetric neurodegeneration, leading to the asymmetric motor and higher cortical symptoms in CBS. This article is protected by copyright. All rights reserved

Robust and language-independent acoustic features in Parkinson's disease

IntroductionThe analysis of vocal samples from patients with Parkinson's disease (PDP) can be relevant in supporting early diagnosis and disease monitoring. Intriguingly, speech analysis embeds several complexities influenced by speaker characteristics (e.g., gender and language) and recording conditions (e.g., professional microphones or smartphones, supervised, or non-supervised data collection). Moreover, the set of vocal tasks performed, such as sustained phonation, reading text, or monologue, strongly affects the speech dimension investigated, the feature extracted, and, as a consequence, the performance of the overall algorithm.MethodsWe employed six datasets, including a cohort of 176 Healthy Control (HC) participants and 178 PDP from different nationalities (i.e., Italian, Spanish, Czech), recorded in variable scenarios through various devices (i.e., professional microphones and smartphones), and performing several speech exercises (i.e., vowel phonation, sentence repetition). Aiming to identify the effectiveness of different vocal tasks and the trustworthiness of features independent of external co-factors such as language, gender, and data collection modality, we performed several intra- and inter-corpora statistical analyses. In addition, we compared the performance of different feature selection and classification models to evaluate the most robust and performing pipeline.ResultsAccording to our results, the combined use of sustained phonation and sentence repetition should be preferred over a single exercise. As for the set of features, the Mel Frequency Cepstral Coefficients demonstrated to be among the most effective parameters in discriminating between HC and PDP, also in the presence of heterogeneous languages and acquisition techniques.ConclusionEven though preliminary, the results of this work can be exploited to define a speech protocol that can effectively capture vocal alterations while minimizing the effort required to the patient. Moreover, the statistical analysis identified a set of features minimally dependent on gender, language, and recording modalities. This discloses the feasibility of extensive cross-corpora tests to develop robust and reliable tools for disease monitoring and staging and PDP follow-up

White matter alterations in drug-naïve children with Tourette syndrome and obsessive-compulsive disorder

Tourette syndrome (TS) and early-onset obsessive-compulsive disorder (OCD) are frequently associated and conceptualized as distinct phenotypes of a common disease spectrum. However, the nature of their relationship is still largely unknown on a pathophysiological level. In this study, early structural white matter (WM) changes investigated through diffusion tensor imaging (DTI) were compared across four groups of drug-naive children: TS-pure (n = 16), TS+OCD (n = 14), OCD (n = 10), and 11 age-matched controls. We analyzed five WM tracts of interest, i.e., cortico-spinal tract (CST), anterior thalamic radiations (ATR), inferior longitudinal fasciculus (ILF), corpus callosum (CC), and cingulum and evaluated correlations of DTI changes to symptom severity. Compared to controls, TS-pure and TS+OCD showed a comparable pattern of increased fractional anisotropy (FA) in CST, ATR, ILF and CC, with FA changes displaying negative correlation to tic severity. Conversely, in OCD, FA decreased in all WM tracts (except for the cingulum) compared to controls and negatively correlated to symptoms. We demonstrate different early WM microstructural alterations in children with TS-pure/TS+OCD as opposed to OCD. Our findings support the conceptualization of TS+OCD as a subtype of TS while suggesting that OCD is characterized by independent pathophysiological mechanisms affecting WM development

Four Days Are Enough to Provide a Reliable Daily Step Count in Mild to Moderate Parkinson’s Disease through a Commercial Smartwatch

Daily steps could be a valuable indicator of real-world ambulation in Parkinson’s disease (PD). Nonetheless, no study to date has investigated the minimum number of days required to reliably estimate the average daily steps through commercial smartwatches in people with PD. Fifty-six patients were monitored through a commercial smartwatch for 5 consecutive days. The total daily steps for each day was recorded and the average daily steps was calculated as well as the working and weekend days average steps. The intraclass correlation coefficient (ICC) (3,k), standard error of measurement (SEM), Bland–Altman statistics, and minimum detectable change (MDC) were used to evaluate the reliability of the step count for every combination of 2–5 days. The threshold for acceptability was set at an ICC ≥ 0.8 with a lower bound of CI 95% ≥ 0.75 and a SAM < 10%. ANOVA and Mann–Whitney tests were used to compare steps across the days and between the working and weekend days, respectively. Four days were needed to achieve an acceptable reliability (ICC range: 0.84–0.90; SAM range: 7.8–9.4%). In addition, daily steps did not significantly differ across the days and between the working and weekend days. These findings could support the use of step count as a walking activity index and could be relevant to developing monitoring, preventive, and rehabilitation strategies for people with PD

Acoustic analysis in stuttering: a machine-learning study

BackgroundStuttering is a childhood-onset neurodevelopmental disorder affecting speech fluency. The diagnosis and clinical management of stuttering is currently based on perceptual examination and clinical scales. Standardized techniques for acoustic analysis have prompted promising results for the objective assessment of dysfluency in people with stuttering (PWS).ObjectiveWe assessed objectively and automatically voice in stuttering, through artificial intelligence (i.e., the support vector machine – SVM classifier). We also investigated the age-related changes affecting voice in stutterers, and verified the relevance of specific speech tasks for the objective and automatic assessment of stuttering.MethodsFifty-three PWS (20 children, 33 younger adults) and 71 age−/gender-matched controls (31 children, 40 younger adults) were recruited. Clinical data were assessed through clinical scales. The voluntary and sustained emission of a vowel and two sentences were recorded through smartphones. Audio samples were analyzed using a dedicated machine-learning algorithm, the SVM to compare PWS and controls, both children and younger adults. The receiver operating characteristic (ROC) curves were calculated for a description of the accuracy, for all comparisons. The likelihood ratio (LR), was calculated for each PWS during all speech tasks, for clinical-instrumental correlations, by using an artificial neural network (ANN).ResultsAcoustic analysis based on machine-learning algorithm objectively and automatically discriminated between the overall cohort of PWS and controls with high accuracy (88%). Also, physiologic ageing crucially influenced stuttering as demonstrated by the high accuracy (92%) of machine-learning analysis when classifying children and younger adults PWS. The diagnostic accuracies achieved by machine-learning analysis were comparable for each speech task. The significant clinical-instrumental correlations between LRs and clinical scales supported the biological plausibility of our findings.ConclusionAcoustic analysis based on artificial intelligence (SVM) represents a reliable tool for the objective and automatic recognition of stuttering and its relationship with physiologic ageing. The accuracy of the automatic classification is high and independent of the speech task. Machine-learning analysis would help clinicians in the objective diagnosis and clinical management of stuttering. The digital collection of audio samples here achieved through smartphones would promote the future application of the technique in a telemedicine context (home environment)

Pulmonary embolism post-Covid-19 infection. Physiopathological mechanisms and vascular damage biomarkers

Covid-19 infection is characterized by several acute complications, as well long-term sequelae, mostly sustained by endothelial dysfunction; several studies show that complications as pulmonary embolism (PE) are described both in the acute phase and after negativization. Aim of research was to evaluate anthropometric, bio-humoral, instrumental parameters in a group of patients affected by PE after recent Covid-19 infection compared to PE patients without previous Covid-19 infection. We enrolled 72 consecutive patients (35M, 37F) with acute PE, distinguished in relation to previous acute Covid-19 infection: 54 pts without previous acute Covid-19 infection and 18 pts with previous Covid-19 infection within negativity at least 2 months before PE diagnosis; 44 healthy subjects (21M, 23F) were recruited as control group. Patients who had previously developed Covid-19 needed hospitalization in high percentage (84%); this group showed significantly higher prevalence of diabetes mellitus than Covid-19-free PE patients, reduced serum levels of C-reactive protein, sST2 and PESI score. In post-Covid-19 PE group, we observed higher mean IMPROVE risk score, whereas in Covid-19-free group lower P/F ratio, higher radiological severity, and worse PESI score and severity index. Covid-19 infection affects not just the lung parenchyma but also other organs; endothelial damage plays pivotal role in long-term alterations; in high thrombotic risk group (recent hospitalization due to acute Covid-19 infection), we have described thrombotic complications characterized by persistent prothrombotic state after recovery, highlighted by well-known markers as PCR and D-Dimer as well as novel vascular marker (sST2)

Decipher non-canonical SPAST splicing mutations with the help of functional assays in patients affected by spastic paraplegia 4 (SPG4)

Flowchart showing the molecular approach used to decipher the non-canonical splicing mutations