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Quantifying neurological disorders from voice is a rapidly growing field of research and holds promise for unobtrusive and large-scale disorder monitoring. The data recording setup and data analysis pipelines are both crucial aspects to effectively obtain relevant information from participants. Therefore, we performed a systematic review to provide a high-level overview of practices across various neurological disorders and highlight emerging trends. PRISMA-based literature searches were conducted through PubMed, Web of Science, and IEEE Xplore to identify publications in which original (i.e., newly recorded) datasets were collected. Disorders of interest were psychiatric as well as neurodegenerative disorders, such as bipolar disorder, depression, and stress, as well as amyotrophic lateral sclerosis amyotrophic lateral sclerosis, Alzheimer's, and Parkinson's disease, and speech impairments (aphasia, dysarthria, and dysphonia). Of the 43 retrieved studies, Parkinson's disease is represented most prominently with 19 discovered datasets. Free speech and read speech tasks are most commonly used across disorders. Besides popular feature extraction toolkits, many studies utilise custom-built feature sets. Correlations of acoustic features with psychiatric and neurodegenerative disorders are presented. In terms of analysis, statistical analysis for significance of individual features is commonly used, as well as predictive modeling approaches, especially with support vector machines and a small number of artificial neural networks. An emerging trend and recommendation for future studies is to collect data in everyday life to facilitate longitudinal data collection and to capture the behavior of participants more naturally. Another emerging trend is to record additional modalities to voice, which can potentially increase analytical performance.

Psychology and nutritional science research has highlighted the impact of negative emotions and cognitive load on calorie consumption behaviour using subjective questionnaires. Isolated studies in other domains objectively assess cognitive load without considering its effects on eating behaviour. This study aims to explore the potential for developing an integrated eating behaviour assistant system that incorporates cognitive load factors. Two experimental sessions were conducted using custom-developed experimentation software to induce different stimuli. During these sessions, we collected 30 h of physiological, food consumption, and affective states questionnaires data to automatically detect cognitive load and analyse its effect on food choice. Utilising grid search optimisation and leave-one-subject-out cross-validation, a support vector machine model achieved a mean classification accuracy of 85.12% for the two cognitive load tasks using eight relevant features. Statistical analysis was performed on calorie consumption and questionnaire data. Furthermore, 75% of the subjects with higher negative affect significantly increased consumption of specific foods after high-cognitive-load tasks. These findings offer insights into the intricate relationship between cognitive load, affective states, and food choice, paving the way for an eating behaviour assistant system to manage food choices during cognitive load. Future research should enhance system capabilities and explore real-world applications.

Identification of fatigue in people with Multiple Sclerosis (pwMS) is still mainly based on subjective assessments due to the lack of objective diagnostic tools. We aimed to identify vocal biomarkers to differentiate between pwMS with and without fatigue. This COMMITMENT trial was a prospective, observational study recruiting healthy controls (HCs,  = 20) and relapsing MS (RMS) patients (  = 50, EDSS <4.0) at University Hospital of Bern. The primary objective was to predict fatigue in pwMS by means of automated artificial intelligence (AI)-based speech analysis. An exploratory objective was to differentiate between pwMS and HCs. Participants had a mean age of 36.0 years, and 73% were female, with no significant group differences. Median EDSS in pwMS was 1.0 (range 0-3.0). Motor fatigue affected 50% and cognitive fatigue 40% of pwMS. Five acoustic features were associated with general fatigue, independently of depression and sleepiness. Further, five features were associated with motor-, and 12 with cognitive fatigue. The best-performing classification and regression models [Leave-One-Speaker-Out (LOSO) paradigm] achieved specificities of 0.68-0.94, whereas sensitivity remained lower (0.38-0.90). Speech biomarkers distinguished pwMS from HCs with a specificity of 0.90 but only a sensitivity of 0.3. Speech in pwMS may serve as a potential biomarker for MS-associated fatigue and might help to differentiate between pwMS and HC. Our findings suggest that AI-assisted speech analysis could complement existing fatigue assessments.

INTRODUCTION: Dementia is a syndrome characterised by a decline in memory, language, and problem-solving that affects the ability of patients to perform everyday activities. Patients with dementia tend to experience episodes of anxiety and remain for extended periods, which affects their quality of life.OBJECTIVES: To design AnxiDetector, a system capable of detecting patterns of sounds associated before and during the manifestation of anxiety in patients with dementia.METHODS: We conducted a non-participatory observation of 70 diagnosed patients in-situ, and conducted semi-structured interviews with four caregivers at a residential centre. Using the findings from our observation and caregiver interviews, we developed the AnxiDetector prototype and tested this in an experimental setting where we defined nine classes of audio to represent two groups of sounds: (i) Disturbance which includes audio files that characterise sounds that trigger anxiety in patients with dementia, and (ii) Expression which includes audio files that characterise sounds expressed by the patients during episodes of anxiety. We conducted two experimental classifications of sounds using (i) a Neural Network model trained and (ii) a Support Vector Machine model. The first evaluation consists of a binary discriminating between the two groups of sounds; the second evaluation discriminates the nine classes of audio. The audio resources were retrieved from publicly available datasets.RESULTS: The qualitative results present the views of the caregivers on the adoption of AnxiDetector. The quantitative results from our binary discrimination show a classification accuracy of 98.1% and 99.2% for the Deep Neural Network and Support Vector Machine models, respectively. When classifying the nine classes of sound, our model shows a classification accuracy of 92.2%. Whereas, the Support Vector Machine model yielded an overall classification accuracy of 93.0%.CONCLUSION: In this paper, we presented the outcomes from an observational study in-site at a residential care centre, qualitative findings from interviews with caregivers, the design of AnxiDetector, and preliminary qualitative results of a methodology devised to detect relevant acoustic events associated with anxiety in patients with dementia. We conclude by signalling future plans to conduct in-situ validation of the effectiveness of AnxiDetector for anxiety detection.

Depression commonly co-occurs with neurodegenerative disorders like Multiple Sclerosis (MS), yet the potential of speech-based Artificial Intelligence for detecting depression in such contexts remains unexplored. This study examines the transferability of speech-based depression detection methods to people with MS (pwMS) through cross-corpus and cross-lingual analysis using English data from the general population and German data from pwMS. Our approach implements supervised machine learning models using: 1) conventional speech and language features commonly used in the field, 2) emotional dimensions derived from a Speech Emotion Recognition (SER) model, and 3) exploratory speech feature analysis. Despite limited data, our models detect depressive mood in pwMS with moderate generalisability, achieving a 66% Unweighted Average Recall (UAR) on a binary task. Feature selection further improved performance, boosting UAR to 74%. Our findings also highlight the relevant role emotional changes have as an indicator of depressive mood in both the general population and within PwMS. This study provides an initial exploration into generalising speech-based depression detection, even in the presence of co-occurring conditions, such as neurodegenerative diseases.

The hemodynamic stabilization of patients after complex cardiac surgery is a daily challenge. The use of high doses of catecholamines is common but has potential adverse effects. Glibenclamide, a KATP blocker, seems to attenuate vasoplegia in different animal models of septic shock. Therefore, the aim of this study was to investigate the impact of Glibenclamide on the vasoplegic syndrome after cardiopulmonary bypass in a porcine model. In this experimental study, 20 landrace pigs were randomized into two groups and examined: In the control group, standard medical therapy, including norepinephrine, was used, and in the study group standard medical therapy plus additional Glibenclamide was administered. Following general anesthesia, prolonged cardiopulmonary bypass and aortic cross-clamping was performed. In the study group, Glibenclamide was administered 45 min after weaning from cardiopulmonary bypass. The dosage used was 10 mg/kg as a bolus, followed by a continuous infusion of 10 mg/kg/h. Hemodynamic and laboratory measurements were performed. Glibenclamide had a relevant effect on circulatory parameters. With increasing vascular resistance and blood pressure, norepinephrine was able to be reduced. While the heart rate dropped to physiological levels, the cardiac index decreased as well. The results lead to the conclusion that Glibenclamide was able to break through vasoplegic syndrome and could therefore serve as a potent drug to stabilize patients after cardiac surgery.

Super-solidus hot isostatic pressing (SSHIP) heat treatment has first been developed and applied to the third-generation Ni-base single crystal superalloy CMSX-10 K. This new type of heat treatment aims at significantly reducing the total time required for the solution heat treatment and at enhancing mechanical properties as compared to conventional heat treatment routes. The SSHIP is an innovative, economical and sustainable approach that can be applied to all types of Ni-base SX superalloys. It is especially interesting for alloys with a high content of refractory elements and a large volume fraction of eutectic microstructure in the as-cast state.

Background: A detailed understanding of cardiac anatomy and physiology is crucial in cardiovascular medicine. However, traditional learning methods often fall short in addressing this complexity. Augmented reality (AR) offers a promising tool to enhance comprehension. To assess its potential integration into the Heidelberger Curriculum Medicinale (HeiCuMed), we conducted a needs assessment among medical students and lecturers at Heidelberg University Medical School. Methods: Our survey aimed to evaluate the perceived benefits of AR-based learning compared to conventional methods and to gather expectations regarding an AR course in cardiovascular medicine. Using LimeSurvey, we developed a questionnaire to assess participants’ prior AR experience, preferred learning methods, and interest in a proposed AR-based, 2 × 90-min in-person course. Results: A total of 101 students and 27 lecturers participated. Support for AR in small-group teaching was strong: 96.3% of students and 90.9% of lecturers saw value in a dedicated AR course. Both groups favored its application in anatomy, cardiac surgery, and internal medicine. Students prioritized congenital heart defects, coronary anomalies, and arrhythmias, while lecturers also emphasized invasive valve interventions. Conclusions: There is significant interest in AR-based teaching in cardiovascular education, suggesting its potential to complement and improve traditional methods in medical curricula. Further studies are needed to assess the potential benefits regarding learning outcomes.