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Those who suffer from aphasia benefit from digital speech and language therapy solutions, and automatic speech recognition (ASR) has been already used for giving feedback on the correctness of the answers in naming exercises. AphaDIGITAL application is to provide German-speaking users with detailed feedback on phonemic/phonetic and semantic errors, based on automatic speech and language processing. For this purpose, open-source ASR solutions for German were evaluated on different corpora of atypical speech, including two small datasets with aphasic speech samples. Character error rate, the number of precisely recognized items and empty outputs served as evaluation metrics. The four selected models are generally robust to the deteriorated condition of speech and audio quality and consistently outperform commercial models in atypical speech recognition. Applying error acceptance threshold, additional use of phonemic error rate, and other valuable insights for ASR implementation in aphaDIGITAL are discussed.
Background
Accurate and rapid diagnosis of rheumatic diseases is essential for further treatment decision. Different rheumatic diseases present characteristic patterns (image features) in fluorescence optical imaging (FOI). We developed an atlas of FOI image features and tested its ability to differentiate various rheumatic diseases.
Methods
FOI images from patients with rheumatoid arthritis (RA), psoriatic arthritis (PsA), connective tissue diseases (CTD) and osteoarthritis (OA) were analysed by two readers blinded for diagnosis and calibrated against each other, using the prima vista mode (PVM) and an automated 5-phase model. Twenty-six different reoccurring typical signal enhancement patterns (features) indicating inflamed joints, nail or skin were defined and all FOI images were scored accordingly. The feature frequency in each patient cohort and phase (PVM, 5-phase) was counted. Contingency tables were created with categorical variable counts and diagnosis using common formulae.
Findings
Four hundred thirty-eight patients with RA (n=117), PsA (n=110), CTD (n=121) and OA (n=90) were included. Once the data had been categorised, a two-step diagnostic pathway was developed: in the first step, OA was best distinguished from the other diseases with high specificity by five patterns (specificity >0.9, diagnostic OR between 2.34 and 8.24). In a second step, the remaining autoimmune diseases were differentiated from each other by a certain number of features (five for RA, 12 for PsA and four for CTD).
Interpretation
This was the first study to show that feature analysis in FOI helps to differentiate typical rheumatic diseases from each other, potentially simplifying and speeding up the diagnostic process. Therefore, FOI could be considered an additional component of a wider range of imaging techniques used in rheumatology.
In mammals, proper storage and distribution of lipids in and between tissues is essential for the maintenance of energy homeostasis. In contrast, aberrantly high levels of triglycerides in the blood ("hypertriglyceridemia") represent a hallmark of the metabolic syndrome and type 2 diabetes. As hypertriglyceridemia has been identified as an important risk factor for cardiovascular complications, in this study we aimed to identify molecular mechanisms in aberrant triglyceride elevation under these conditions.
To determine the importance of hepatic lipid handling for systemic dyslipidemia, we profiled the expression patterns of various hepatic lipid transporters and receptors under healthy and type 2 diabetic conditions. A differentially expressed lipoprotein receptor was functionally characterized by generating acute, liver-specific loss- and gain-of-function animal models.
We show that the hepatic expression of lipid transporter lipolysis-stimulated lipoprotein receptor (LSR) is specifically impaired in mouse models of obesity and type 2 diabetes and can be restored by leptin replacement. Experimental imitation of this pathophysiological situation by liver-specific knockdown of LSR promotes hypertriglyceridemia and elevated apolipoprotein (Apo)B and E serum levels in lean wild-type and ApoE knockout mice. In contrast, genetic restoration of LSR expression in obese animals to wild-type levels improves serum triglyceride levels and serum profiles in these mice.
The dysregulation of hepatic LSR under obese and diabetic conditions may provide a molecular rationale for systemic dyslipidemia in type 2 diabetes and the metabolic syndrome and represent a novel target for alternative treatment strategies in these patients.
Linguistic and extralinguistic factors in automatic speech recognition of German atypical speech
(2024)
Automatic speech recognition (ASR) has been already used in speech and language therapy, including diagnostic tasks and practice exercises for people with aphasia (PWA). The lack of relevant data makes it difficult to evaluate the algorithms’ suitability for German-speaking PWA. For the current project, four open-source ASR models were selected based on their performance on other types of atypical speech, and the details of their evaluation are presented in this paper. The four selected models are generally robust to speakers’ gender and age. The one-word recognition yields better results for words of moderate length. Speech rate should be neither too slow nor too quick for lower error rates both in words and phrases, and the latter should be also of moderate length.
Stress is an important factor affecting human health, and its timely detection can significantly improve quality of life. This study addresses the current issue of identifying stress states using photoplethysmography signals obtained from the wearable Shimmer 3 sensor. The purpose of the study was to establish an effective approach for stress detection, specifically through the analysis of heart rate variability changes during different states: rest and stress. A distinctive feature of the research is the use of the air raid siren sound as a stress factor. The research methodology includes the collection and analysis of biosignals, allowing for the assessment of cardiovascular system functioning under the impact of stress factors. The results of the study demonstrated significant differences in heart rhythm indicators depending on the person's state, highlighting the potential of these metrics as a tool for health monitoring and stress detection. The paper proposes a new approach to using photoplethysmography for assessing stress responses, which may contribute to the development of personalized stress management methods.
This study investigates stress recognition using galvanic skin response (GSR) and photoplethysmography (PPG) data and machine learning, with a new focus on air raid sirens as a stressor. It bridges laboratory and real-world conditions and highlights the reliability of wearable sensors in dynamic, high-stress environments such as war and conflict zones. The study involves 37 participants (20 men, 17 women), aged 20–30, who had not previously heard an air raid siren. A 70 dB “S-40 electric siren” (400–450 Hz) was delivered via headphones. The protocol included a 5 min resting period, followed by 3 min “no-stress” phase, followed by 3 min “stress” phase, and finally a 3 min recovery phase. GSR and PPG signals were recorded using Shimmer 3 GSR+ sensors on the fingers and earlobes. A single session was conducted to avoid sensitization. The workflow includes signal preprocessing to remove artifacts, feature extraction, feature selection, and application of different machine learning models to classify the “stress “and “no-stress” states. As a result, the best classification performance was shown by the k-Nearest Neighbors model, achieving 0.833 accuracy. This was achieved by using a particular combination of heart rate variability (HRV) and GSR features, which can be considered as new indicators of siren-induced stress.
Identification of a bone density is crucial in all branches of medicine. Assessing bone density is a labor-intensive process. Bone density is quite unstable and depends on many factors, both physiological (aging, hormonal balance) and pathological (medication use, various underlying conditions).
The aim of our study is to determine bone density in patients with malignant breast tumors undergoing anti-cancer therapy.
Material and Methods. The study included 50 women aged 60-70 years who were diagnosed with infiltrating intraductal carcinoma. According to established protocols, MSCT is recommended for this category of women at intervals of once every six months. The first MSCT scan was performed immediately after the diagnosis was made, before the start of treatment, and the second scan was conducted six months later.
Results. The maximum radiological density was 75.8954±37.9477 Hu in the group of women who had been receiving treatment for six months, compared to 93.9388±46.9694 Hu in the group of patients who did not take the drug. Meanwhile, the minimum density showed a slight increase from 29.7295±14.8647 Hu to 38.6919±19.3460 Hu, which can be attributed to the compensatory mechanisms of the body.
Conclusions. In the course of this study, bone density in patients with infiltrating intraductal carcinoma undergoing anti-cancer therapy was determined using uncertainty estimation. It was found that after six months, the first to respond to changes in density was the maximum bone density.
One of the most prevalent harmful habits that affect people daily is smoking. The aim of our study was to investigate changes in the bone density of the human paranasal sinuses under the influence of smoking using uncertainty calculation.
Material and Methods. The study involved 150 male and female individuals aged 20-40 years, which helps to exclude other factors that could influence the reduction in bone radiodensity (e.g., menopause in women, age-related changes, etc.). The participants were divided into three groups based on the duration of their smoking habits.
Results. Smoking has almost no effect on the maximum bone density. All groups show nearly identical values. In the first group, consisting of individuals who smoked for up to 5 years, the maximum density is 75.22 ± 37.61 Hu. In the second group, those who smoked for up to 10 years have a density of 84.22 ± 42.11 Hu, while in the third control group, the density is 74.29 ± 37.15 Hu. On the other hand, smoking has a greater impact on the minimum radiological bone density of the upper wall of the maxillary sinus. The lowest values are found in the group of individuals who smoked for 10 or more years, with a minimum density of 23.86 ± 11.93 Hu. For those who smoked for up to 5 years, the radiological bone density was 52.65 ± 26.32 Hu. In the control group, the density values were slightly higher than those of individuals who smoked for up to 5 years, with the highest values being 58.12 ± 29.06 Hu.
Conclusion. The study investigated changes in the bone density of the human paranasal sinuses under the influence of smoking, utilizing uncertainty calculation. It was found that the most sensitive indicator to nicotine exposure is the minimum radiological density, which can significantly increase the risk of developing complications. Specifically, the lowest radiological density was observed in the group of individuals who smoked for 10 years or more, with a value of 23.86 ± 11.93 Hu.
Recently, sonodynamic therapy (SDT) has emerged as a promising non-invasive approach for treating cancer by activating sensitizers with ultrasound (US). In this context, we investigated C60 fullerene (C60) as a nanocarrier for the promising drug Berberine (Ber)—both potential aromatic sonosensitizers. The preferential mitochondrial accumulation of C60 and the proapoptotic effects of Ber also make the C60-Berberine nanocomplex (C60-Ber) a good candidate for direct induction of the intrinsic apoptotic cell death under US action. The in vitro research on C60-Ber can provide insights into novel, non-invasive cancer treatments. These findings lead to the development of targeted therapies with reduced side effects, inspire interdisciplinary collaboration, and open new avenues for drug delivery and cancer therapy research.
This paper introduces aphaDIGITAL – a mobile application for speech and language therapy (SLT) support of German-speaking people with aphasia. The app includes automatic speech recognition and text analysis components, which allows giving detailed feedback to the users on different types of errors. Furthermore, the exercises are accompanied by an avatar-based SLT assistant, which is created for this specific purpose. The user performs the exercises, individually configured for her by the SLT practitioner, on a mobile device. Data collection and processing are carried out on the server.

