Relationship between hearing function and myasthenia gravis: a contemporary review

There is increasing evidence of a connection between hearing function and myasthenia gravis (MG). Studies of the pathophysiological basis of this relationship suggest that acetylcholine receptors (AChRs) on outer hair cells (OHCs) play a central role. In patients with MG, autoantibodies against AChRs induce a progressive loss of AChRs on OHCs, decreasing their electromotility. The stapedial reflex decay test can be altered in MG patients, and can be used as an additional tool for diagnosis and monitoring. Transient evoked and distortion product otoacoustic emissions are the main diagnostic tool for monitoring OHC functionality in MG patients, and can be used to record subclinical hearing alterations before the onset of clinically evident hearing loss. Understanding the association between MG and hearing dysfunction requires a multidisciplinary approach. Otolaryngologists should take this relationship into account when approaching patients with a diagnosis of myasthenia gravis and "in patients with MG" with ण128;\u9cin MG patients, and the progress of hearing alterations should always be monitored in patients with MG

Next generation sequencing study on RNA viruses of Vespa velutina and Apis mellifera sharing the same foraging area

The predator Asian hornet (Vespa velutina) represents one of the major threats to honeybee survival. Viral spillover from bee to wasp has been supposed in several studies, and this work aims to identify and study the virome of both insect species living simultaneously in the same foraging area. Transcriptomic analysis was performed on V. velutina and Apis mellifera samples, and replicative form of detected viruses was carried out by strand‐specific RT‐PCR. Overall, 6 and 9 different viral types were reported in V. velutina and A. mellifera, respectively, and five of these viruses were recorded in both hosts. Varroa destructor virus‐1 and Cripavirus NB‐1/2011/HUN (now classified as Triato‐like virus) were the most represented viruses detected in both hosts, also in replicative form. In this investigation, Triato‐like virus, as well as Aphis gossypii virus and Nora virus, was detected for the first time in honeybees. Concerning V. velutina, we report for the first time the recently detected honeybee La Jolla virus. A general high homology rate between genomes of shared viruses between V. velutina and A. mellifera suggests the efficient transmission of the virus from bee to wasp. In conclusion, our findings highlight the presence of several known and newly reported RNA viruses infecting A. mellifera and V. velutina. This confirms the environment role as an important source of infection and indicates the possibility of spillover from prey to predator

In Vitro Antibacterial Activity of Manuka (Leptospermum scoparium J.R. et G. Forst) and winter Savory (Satureja montana L.) Essential Oils and Their Blends against Pathogenic E. coli Isolates from Pigs

Neonatal diarrhoea (ND), post-weaning diarrhoea (PWD) and oedema disease (OD) are among the most important diseases affecting pig farming due to economic losses. Among the main aetiological agents, strains of Escherichia coli are identified as the major responsible pathogens involved. Several strategies have been put in place to prevent these infections and, today, research is increasingly studying alternative methods to antibiotics to reduce the antibiotic resistance phenomenon. Essential oils (EOs) are among the alternative tools that are being investigated. In this study, the in vitro effectiveness of winter savory and manuka essential oils and their mixtures in different proportions against strains of E. coli isolated from episodes of disease in pigs was evaluated. The EOs alone demonstrated slight antibacterial effectiveness, whereas the blends, by virtue of their synergistic action, showed remarkable activity, especially the 70%–30% winter savory–manuka blend, showing itself as a potential tool for prevention and therapy

Automated mood disorder symptoms monitoring from multivariate time-series sensory data:getting the full picture beyond a single number

Mood disorders (MDs) are among the leading causes of disease burden worldwide. Limited specialized care availability remains a major bottleneck thus hindering pre-emptive interventions. MDs manifest with changes in mood, sleep, and motor activity, observable in ecological physiological recordings thanks to recent advances in wearable technology. Therefore, near-continuous and passive collection of physiological data from wearables in daily life, analyzable with machine learning (ML), could mitigate this problem, bringing MDs monitoring outside the clinician's office. Previous works predict a single label, either the disease state or a psychometric scale total score. However, clinical practice suggests that the same label may underlie different symptom profiles, requiring specific treatments. Here we bridge this gap by proposing a new task: inferring all items in HDRS and YMRS, the two most widely used standardized scales for assessing MDs symptoms, using physiological data from wearables. To that end, we develop a deep learning pipeline to score the symptoms of a large cohort of MD patients and show that agreement between predictions and assessments by an expert clinician is clinically significant (quadratic Cohen's κ and macro-average F1 score both of 0.609). While doing so, we investigate several solutions to the ML challenges associated with this task, including multi-task learning, class imbalance, ordinal target variables, and subject-invariant representations. Lastly, we illustrate the importance of testing on out-of-distribution samples.</p

Wearable Data From Subjects Playing Super Mario, Taking University Exams, or Performing Physical Exercise Help Detect Acute Mood Disorder Episodes via Self-Supervised Learning:Prospective, Exploratory, Observational Study

BACKGROUND: Personal sensing, leveraging data passively and near-continuously collected with wearables from patients in their ecological environment, is a promising paradigm to monitor mood disorders (MDs), a major determinant of the worldwide disease burden. However, collecting and annotating wearable data is resource intensive. Studies of this kind can thus typically afford to recruit only a few dozen patients. This constitutes one of the major obstacles to applying modern supervised machine learning techniques to MD detection.OBJECTIVE: In this paper, we overcame this data bottleneck and advanced the detection of acute MD episodes from wearables' data on the back of recent advances in self-supervised learning (SSL). This approach leverages unlabeled data to learn representations during pretraining, subsequently exploited for a supervised task.METHODS: We collected open access data sets recording with the Empatica E4 wristband spanning different, unrelated to MD monitoring, personal sensing tasks-from emotion recognition in Super Mario players to stress detection in undergraduates-and devised a preprocessing pipeline performing on-/off-body detection, sleep/wake detection, segmentation, and (optionally) feature extraction. With 161 E4-recorded subjects, we introduced E4SelfLearning, the largest-to-date open access collection, and its preprocessing pipeline. We developed a novel E4-tailored transformer (E4mer) architecture, serving as the blueprint for both SSL and fully supervised learning; we assessed whether and under which conditions self-supervised pretraining led to an improvement over fully supervised baselines (ie, the fully supervised E4mer and pre-deep learning algorithms) in detecting acute MD episodes from recording segments taken in 64 (n=32, 50%, acute, n=32, 50%, stable) patients.RESULTS: SSL significantly outperformed fully supervised pipelines using either our novel E4mer or extreme gradient boosting (XGBoost): n=3353 (81.23%) against n=3110 (75.35%; E4mer) and n=2973 (72.02%; XGBoost) correctly classified recording segments from a total of 4128 segments. SSL performance was strongly associated with the specific surrogate task used for pretraining, as well as with unlabeled data availability.CONCLUSIONS: We showed that SSL, a paradigm where a model is pretrained on unlabeled data with no need for human annotations before deployment on the supervised target task of interest, helps overcome the annotation bottleneck; the choice of the pretraining surrogate task and the size of unlabeled data for pretraining are key determinants of SSL success. We introduced E4mer, which can be used for SSL, and shared the E4SelfLearning collection, along with its preprocessing pipeline, which can foster and expedite future research into SSL for personal sensing.</p

Identifying digital biomarkers of illness activity and treatment response in bipolar disorder with a novel wearable device (TIMEBASE):Protocol for a pragmatic observational clinical study

BackgroundBipolar disorder is highly prevalent and consists of biphasic recurrent mood episodes of mania and depression, which translate into altered mood, sleep and activity alongside their physiological expressions.AimsThe IdenTifying dIgital bioMarkers of illnEss activity and treatment response in BipolAr diSordEr with a novel wearable device (TIMEBASE) project aims to identify digital biomarkers of illness activity and treatment response in bipolar disorder.MethodWe designed a longitudinal observational study including 84 individuals. Group A comprises people with acute episode of mania (n = 12), depression (n = 12 with bipolar disorder and n = 12 with major depressive disorder (MDD)) and bipolar disorder with mixed features (n = 12). Physiological data will be recorded during 48 h with a research-grade wearable (Empatica E4) across four consecutive time points (acute, response, remission and episode recovery). Group B comprises 12 people with euthymic bipolar disorder and 12 with MDD, and group C comprises 12 healthy controls who will be recorded cross-sectionally. Psychopathological symptoms, disease severity, functioning and physical activity will be assessed with standardised psychometric scales. Physiological data will include acceleration, temperature, blood volume pulse, heart rate and electrodermal activity. Machine learning models will be developed to link physiological data to illness activity and treatment response. Generalisation performance will be tested in data from unseen patients.ResultsRecruitment is ongoing.ConclusionsThis project should contribute to understanding the pathophysiology of affective disorders. The potential digital biomarkers of illness activity and treatment response in bipolar disorder could be implemented in a real-world clinical setting for clinical monitoring and identification of prodromal symptoms. This would allow early intervention and prevention of affective relapses, as well as personalisation of treatment

Exploring digital biomarkers of illness activity in mood episodes:Hypotheses generating and model development study

Background: Depressive and manic episodes within bipolar disorder (BD) and major depressive disorder (MDD) involve altered mood, sleep, and activity, alongside physiological alterations wearables can capture. Objective: Firstly, we explored whether physiological wearable data could predict (aim 1) the severity of an acute affective episode at the intra-individual level and (aim 2) the polarity of an acute affective episode and euthymia among different individuals. Secondarily, we explored which physiological data were related to prior predictions, generalization across patients, and associations between affective symptoms and physiological data.Methods: We conducted a prospective exploratory observational study including patients with BD and MDD on acute affective episodes (manic, depressed, and mixed) whose physiological data were recorded using a research-grade wearable (Empatica E4) across 3 consecutive time points (acute, response, and remission of episode). Euthymic patients and healthy controls were recorded during a single session (approximately 48 h). Manic and depressive symptoms were assessed using standardized psychometric scales. Physiological wearable data included the following channels: acceleration (ACC), skin temperature, blood volume pulse, heart rate (HR), and electrodermal activity (EDA). Invalid physiological data were removed using a rule-based filter, and channels were time aligned at 1-second time units and segmented at window lengths of 32 seconds, as best-performing parameters. We developed deep learning predictive models, assessed the channels’ individual contribution using permutation feature importance analysis, and computed physiological data to psychometric scales’ items normalized mutual information (NMI). We present a novel, fully automated method for the preprocessing and analysis of physiological data from a research-grade wearable device, including a viable supervised learning pipeline for time-series analyses.Results: Overall, 35 sessions (1512 hours) from 12 patients (manic, depressed, mixed, and euthymic) and 7 healthy controls (mean age 39.7, SD 12.6 years; 6/19, 32% female) were analyzed. The severity of mood episodes was predicted with moderate (62%-85%) accuracies (aim 1), and their polarity with moderate (70%) accuracy (aim 2). The most relevant features for the former tasks were ACC, EDA, and HR. There was a fair agreement in feature importance across classification tasks (Kendall W=0.383). Generalization of the former models on unseen patients was of overall low accuracy, except for the intra-individual models. ACC was associated with “increased motor activity” (NMI&gt;0.55), “insomnia” (NMI=0.6), and “motor inhibition” (NMI=0.75). EDA was associated with “aggressive behavior” (NMI=1.0) and “psychic anxiety” (NMI=0.52).Conclusions: Physiological data from wearables show potential to identify mood episodes and specific symptoms of mania and depression quantitatively, both in BD and MDD. Motor activity and stress-related physiological data (EDA and HR) stand out as potential digital biomarkers for predicting mania and depression, respectively. These findings represent a promising pathway toward personalized psychiatry, in which physiological wearable data could allow the early identification and intervention of mood episodes