Auditory feedback control mechanisms do not contribute to cortical hyperactivity within the voice production network in adductor spasmodic dysphonia

Adductor spasmodic dysphonia (ADSD), the most common form of spasmodic dysphonia, is a debilitating voice disorder characterized by hyperactivity and muscle spasms in the vocal folds during speech. Prior neuroimaging studies have noted excessive brain activity during speech in ADSD participants compared to controls. Speech involves an auditory feedback control mechanism that generates motor commands aimed at eliminating disparities between desired and actual auditory signals. Thus, excessive neural activity in ADSD during speech may reflect, at least in part, increased engagement of the auditory feedback control mechanism as it attempts to correct vocal production errors detected through audition. To test this possibility, functional magnetic resonance imaging was used to identify differences between ADSD participants and age-matched controls in (i) brain activity when producing speech under different auditory feedback conditions, and (ii) resting state functional connectivity within the cortical network responsible for vocalization. The ADSD group had significantly higher activity than the control group during speech (compared to a silent baseline task) in three left-hemisphere cortical regions: ventral Rolandic (sensorimotor) cortex, anterior planum temporale, and posterior superior temporal gyrus/planum temporale. This was true for speech while auditory feedback was masked with noise as well as for speech with normal auditory feedback, indicating that the excess activity was not the result of auditory feedback control mechanisms attempting to correct for perceived voicing errors in ADSD. Furthermore, the ADSD group had significantly higher resting state functional connectivity between sensorimotor and auditory cortical regions within the left hemisphere as well as between the left and right hemispheres, consistent with the view that excessive motor activity frequently co-occurs with increased auditory cortical activity in individuals with ADSD.First author draf

Skin Autofluorescence and Glycemic Variability

Background: Accumulation of advanced glycation end products (AGEs) is accelerated during glycemic and oxidative stress and is an important predictor of complications in diabetes mellitus (DM). Study Design: Here we both review and present original data on the relationship between skin autofluorescence (SAF), a noninvasive measure of AGEs, and short-and intermediate-term glycemic variations. Results: Acute changes in glucose levels during an oral glucose tolerance test in 56 persons with varying degrees of glucose tolerance did not influence SAF. AGE-rich meals result in a transient postprandial rise in SAF of 10% 2-4 h later. This could not be attributed to meal-induced glycemic changes and is probably caused by the AGE content of the meal. In type 1DM major intermediate-term improvements of glycemic control as depicted by multiple hemoglobin A1c (HbA1c) measurements were associated with lower skin AGE levels. In a well-controlled, stable type 2DM cohort, only a weak correlation was found between SAF and HbA1c. In both studies skin AGE/SAF levels predicted complications of diabetes with an accuracy superior to that of HbA1c. SAF has also been proposed as a new tool in diagnosing impaired glucose tolerance (IGT) and DM. It proved to be more sensitive than either fasting glucose or HbA1c. Conclusions: SAF is not influenced by short-term glycemic variations. AGE-rich meals may, however, cause a transient rise postprandially. There is a weak correlation between SAF or skin AGEs and current or time-integrated HbA1c levels. SAF has strong added value in risk prediction of complications of diabetes and is a promising tool for early detection of diabetes and IGT

Attrition of certified teachers in secondary education during the induction phase

Teacher attrition is generally considered problematically high, with attrition rates of beginning teachers up to 50%. This study shows the problem is not as big as has been claimed before. Previous studies have often focused on the attrition within 5 years, showing a quarter or more of beginning teachers leaving the profession. However, this disregards the fact that teachers leave at later stages as well, and the fact that some beginning teachers are not qualified to work as a teacher. Using administrative data from payroll administrations of schools in the Netherlands for secondary education a reliable measure of teacher attrition was made. Administrative data on diplomas in higher education were used to establish if teachers are certified at the start of their career. The results of this study show that the attrition rate of beginning teachers is only high within the first year of their career. The attrition rate within 1 year of experience is around 12% until the early 2000s, rising to close to 20% in more recent years. In comparison with other countries this seems relatively modest. However, after the first year, a base rate of attrition (retirement excluded) remains fairly constant at approximately 3% to 5% every year, explaining the gap with high attrition rates found in earlier studies. Attrition of certified teachers within one year is about 9%, with very little variation over time, versus the 12% to 20% of all beginning teachers. This 9% attrition rate of certified teachers is much lower than many earlier studies suggest

Psychogenic periodic fever

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Integration of Sensor-Based Technology in Mental Healthcare:A Systematic Scoping Review

Sensor-based technologies can collect objective and real-time data on physiological, behavioral, and contextual factors related to mental disorders. This not only holds potential for mental healthcare but also comes with challenges, such as handling large amounts of data and supporting the integration of sensors in clinical practice. This systematic scoping review aims to provide an overview of studies explicitly addressing the integration of sensor-based technology in mental healthcare by reporting on the way that therapists and patients work with sensors. In addition, we explore barriers and facilitators for the integration of sensor-based technology in clinical practice. Four databases were searched on April 5, 2023. Studies on sensor-based technology integrated in mental healthcare were included. A total of 14 studies were included. In these studies, a variety of sensor-based technologies were used. All studies were conducted between 2016 and 2022. Most studies showed that sensor-based technologies are accepted by patients and that their use is associated with symptom reduction. However, most studies did not systematically report on barriers and facilitators and mainly focused on the technology itself rather than on the broader context of its intended use. Also, sensor-based technologies are not yet embedded in clinical protocols. From the current review, we can conclude that sensor-based technologies are sufficiently accepted and feasible, and that sensors are promising for enhancing clinical outcomes. However, sensors are not properly integrated in treatment protocols yet. Therefore, we propose a next phase in research on sensor-based technology in mental healthcare treatment. This next phase asks for a multifaceted approach consisting of (1) embedding sensor-based technology in treatment protocols in co-creation with patients and clinicians, (2) examining the feasibility of these interventions together with small-scale evidence studies, and (3) systematically examining the implementation of sensor-based technology in clinical practice using existing frameworks for technology implementation. Open Science Framework: https://doi.org/10.17605/OSF.IO/XQHSY.</p