Tinnitus: pathology of synaptic plasticity at the cellular and system levels

Despite being more and more common, and having a high impact on the quality of life of sufferers, tinnitus does not yet have a cure. This has been mostly the result of limited knowledge of the biological mechanisms underlying this adverse pathology. However, the last decade has witnessed tremendous progress in our understanding on the pathophysiology of tinnitus. Animal models have demonstrated that tinnitus is a pathology of neural plasticity, and has two main components: a molecular, peripheral component related to the initiation phase of tinnitus; and a system-level, central component-related to the long-term maintenance of tinnitus. Using the most recent experimental data and the molecular/system dichotomy as a framework, we describe here the biological basis of tinnitus. We then discuss these mechanisms from an evolutionary perspective, highlighting similarities with memory. Finally, we consider how these discoveries can translate into therapies, and we suggest operative strategies to design new and effective combined therapeutic solutions using both pharmacological (local and systemic) and behavioral tools (e.g., using tele-medicine and virtual reality settings)

Mermaid health — identifying health issues related to mermaiding

Background: Mermaiding — swimming with a leg-covering monofin mimicking the tail of a mermaid — is an emerging aquatic activity, which has gained a marked popularity over the last few years. However, no study so far has documented the potential health issues or risks of injuries related to this practice. Materials and methods: This study surveyed professional mermaids cumulating an estimated total of 19,147 h of in-water mermaiding, regarding their health issues and injuries. While mermaiding bears some risks, the occurrence of problematic conditions appears limited. Interestingly, the profile of health issues experienced by professional mermaids is unique and specific, and clearly different from both professional swimmers and surfers. Results: Self-reported health issues related to mermaiding could be divided into issues specifically related to mermaiding activities (ear issues, reported by 87.5% of the respondents; sea life encounters, 50%; cold-related issues, 37.5%; compromised access to air, 25%), issues related to the tail and fins (back pain, 50%; lower limbs issues, 37.5%), and issues related to water quality (eye issues, 25%; waterborne diseases, 12.5%). Clear differences appear between professional and recreational mermaiding activities. Conclusions: The results presented here will help to build safer conditions for mermaiding activities and to develop adapted responses from health specialists to help this unique yet growing population of aquatic performers and athletes.

Blockade of Cochlear NMDA Receptors Prevents Long-Term Tinnitus during a Brief Consolidation Window after Acoustic Trauma

Tinnitus, the perception of sound in the absence of external acoustic stimulation, is a common and devastating pathology. It is often a consequence of acoustic trauma or drug toxicity. The neuronal mechanisms of tinnitus are neither yet fully understood nor are effective treatments available. Using a novel behavioral paradigm for measuring tinnitus in the rat based on tone-guided navigation, we show here that the development of long-term noise-induced tinnitus, the most prevalent and clinically important form of human tinnitus, can be abated by local administration of the NMDA antagonist “ifenprodil” into the cochlea in the first 4 days following the noise insult but not afterwards. This suggests that long-term tinnitus undergoes a consolidation-like process, resembling the ontogeny of items in long-term memory. Furthermore, this finding paves the way to potential therapeutic strategies for the prevention of chronic tinnitus once the noise insult had taken place

Reconstruction of eye movements during blinks

In eye movement research in reading, the amount of data plays a crucial role for the validation of results. A methodological problem for the analysis of the eye movement in reading are blinks, when readers close their eyes. Blinking rate increases with increasing reading time, resulting in high data losses, especially for older adults or reading impaired subjects. We present a method, based on the symbolic sequence dynamics of the eye movements, that reconstructs the horizontal position of the eyes while the reader blinks. The method makes use of an observed fact that the movements of the eyes before closing or after opening contain information about the eyes movements during blinks. Test results indicate that our reconstruction method is superior to methods that use simpler interpolation approaches. In addition, analyses of the reconstructed data show no significant deviation from the usual behavior observed in readers

Self-Supervised Learning for Cardiac MR Image Segmentation by Anatomical Position Prediction

In the recent years, convolutional neural networks have transformed the field of medical image analysis due to their capacity to learn discriminative image features for a variety of classification and regression tasks. However, successfully learning these features requires a large amount of manually annotated data, which is expensive to acquire and limited by the available resources of expert image analysts. Therefore, unsupervised, weakly-supervised and self-supervised feature learning techniques receive a lot of attention, which aim to utilise the vast amount of available data, while at the same time avoid or substantially reduce the effort of manual annotation. In this paper, we propose a novel way for training a cardiac MR image segmentation network, in which features are learnt in a self-supervised manner by predicting anatomical positions. The anatomical positions serve as a supervisory signal and do not require extra manual annotation. We demonstrate that this seemingly simple task provides a strong signal for feature learning and with self-supervised learning, we achieve a high segmentation accuracy that is better than or comparable to a U-net trained from scratch, especially at a small data setting. When only five annotated subjects are available, the proposed method improves the mean Dice metric from 0.811 to 0.852 for short-axis image segmentation, compared to the baseline U-net

Age differences in voice evaluation : from auditory-perceptual evaluation to social interactions

Purpose: The factors that influence the evaluation of voice in adulthood, as well as the consequences of such evaluation on social interactions, are not well understood. Here, we examined the effect of listeners' age and the effect of talker age, sex, and smoking status on the auditory-perceptual evaluation of voice, voice-related psychosocial attributions, and perceived speech tempo. We also examined the voice dimensions affecting the propensity to engage in social interactions. Method: Twenty-five younger (age 19-37 years) and 25 older (age 51-74 years) healthy adults participated in this cross-sectional study. Their task was to evaluate the voice of 80 talkers. Results: Statistical analyses revealed limited effects of the age of the listener on voice evaluation. Specifically, older listeners provided relatively more favorable voice ratings than younger listeners, mainly in terms of roughness. In contrast, the age of the talker had a broader impact on voice evaluation, affecting auditory-perceptual evaluations, psychosocial attributions, and perceived speech tempo. Some of these talker differences were dependent upon the sex of the talker and his or her smoking status. Finally, the results also show that voice-related psychosocial attribution was more strongly associated with the propensity of the listener to engage in social interactions with a person than auditory-perceptual dimensions and perceived speech tempo, especially for the younger adults. Conclusions: These results suggest that age has a broad influence on voice evaluation, with a stronger impact for talker age compared with listener age. While voice-related psychosocial attributions may be an important determinant of social interactions, perceived voice quality and speech tempo appear to be less influential

Effects of age on the amplitude, frequency and perceived quality of voice

The manner and extent to which voice amplitude and frequency control mechanisms change with age is not well understood. The related question of whether the assessment of one’s own voice evolves with age, concomitant with the acoustical changes that the voice undergoes, also remains unanswered. In the present study, we characterized the aging of voice production mechanisms (amplitude, frequency), compared the aging voice in different experimental contexts (vowel utterance, connected speech) and examined the relationship between voice self-assessment and age-related voice acoustical changes. Eighty healthy adults (20 to 75 years old) participated in the study, which involved computation of several acoustical measures of voice (including measures of fundamental frequency, voice amplitude, and stability) as well as self-assessments of voice. Because depression is frequent in older adults, depression and anxiety scores were also measured. As was expected, analyses revealed age effects on most acoustical measures. However, there was no interaction between age and the ability to produce high/low voice amplitude/frequency, suggesting that voice amplitude and frequency control mechanisms are preserved in aging. Multiple mediation analyses demonstrated that the relationship between age and voice self-assessment was moderated by depression and anxiety scores. Taken together, these results reveal that while voice production undergoes important changes throughout aging, the ability to increase/decrease the amplitude and frequency of voice are preserved, at least within the age range studied, and that depression and anxiety scores have a stronger impact on perceived voice quality than acoustical changes themselves

eTRIKS Analytical Environment: A Modular High Performance Framework for Medical Data Analysis

Translational research is quickly becoming a science driven by big data. Improving patient care, developing personalized therapies and new drugs depend increasingly on an organization's ability to rapidly and intelligently leverage complex molecular and clinical data from a variety of large-scale partner and public sources. As analysing these large-scale datasets becomes computationally increasingly expensive, traditional analytical engines are struggling to provide a timely answer to the questions that biomedical scientists are asking. Designing such a framework is developing for a moving target as the very nature of biomedical research based on big data requires an environment capable of adapting quickly and efficiently in response to evolving questions. The resulting framework consequently must be scalable in face of large amounts of data, flexible, efficient and resilient to failure. In this paper we design the eTRIKS Analytical Environment (eAE), a scalable and modular framework for the efficient management and analysis of large scale medical data, in particular the massive amounts of data produced by high-throughput technologies. We particularly discuss how we design the eAE as a modular and efficient framework enabling us to add new components or replace old ones easily. We further elaborate on its use for a set of challenging big data use cases in medicine and drug discovery

Single-Step Extraction Coupled with Targeted HILIC-MS/MS Approach for Comprehensive Analysis of Human Plasma Lipidome and Polar Metabolome.

Expanding metabolome coverage to include complex lipids and polar metabolites is essential in the generation of well-founded hypotheses in biological assays. Traditionally, lipid extraction is performed by liquid-liquid extraction using either methyl-tert-butyl ether (MTBE) or chloroform, and polar metabolite extraction using methanol. Here, we evaluated the performance of single-step sample preparation methods for simultaneous extraction of the complex lipidome and polar metabolome from human plasma. The method performance was evaluated using high-coverage Hydrophilic Interaction Liquid Chromatography-ESI coupled to tandem mass spectrometry (HILIC-ESI-MS/MS) methodology targeting a panel of 1159 lipids and 374 polar metabolites. The criteria used for method evaluation comprised protein precipitation efficiency, and relative MS signal abundance and repeatability of detectable lipid and polar metabolites in human plasma. Among the tested methods, the isopropanol (IPA) and 1-butanol:methanol (BUME) mixtures were selected as the best compromises for the simultaneous extraction of complex lipids and polar metabolites, allowing for the detection of 584 lipid species and 116 polar metabolites. The extraction with IPA showed the greatest reproducibility with the highest number of lipid species detected with the coefficient of variation (CV) < 30%. Besides this difference, both IPA and BUME allowed for the high-throughput extraction and reproducible measurement of a large panel of complex lipids and polar metabolites, thus warranting their application in large-scale human population studies