Experiences in the introduction of bedaquiline pretomanid linezolid for drug-resistant tuberculosis in Kyrgyzstan.

Settings: In Kyrgyzstan, drug-resistant tuberculosis poses a significant challenge. Recognizing the potential of the BPaL regimen, the World Health Organization recommended its use for selected drug-resistant TB cases under operational research conditions in 2020.

Objective: This report presents experiences and results from the BPaL operational research under the LIFT-TB project in Kyrgyzstan.

Resting-state oscillations reveal disturbed excitation-inhibition ratio in Alzheimer's disease patients.

An early disruption of neuronal excitation-inhibition (E-I) balance in preclinical animal models of Alzheimer's disease (AD) has been frequently reported, but is difficult to measure directly and non-invasively in humans. Here, we examined known and novel neurophysiological measures sensitive to E-I in patients across the AD continuum. Resting-state magnetoencephalography (MEG) data of 86 amyloid-biomarker-confirmed subjects across the AD continuum (17 patients diagnosed with subjective cognitive decline, 18 with mild cognitive impairment (MCI) and 51 with dementia due to probable AD (AD dementia)), 46 healthy elderly and 20 young control subjects were reconstructed to source-space.

Robin's Viewer: Using deep-learning predictions to assist EEG annotation.

Machine learning techniques such as deep learning have been increasingly used to assist EEG annotation, by automating artifact recognition, sleep staging, and seizure detection. In lack of automation, the annotation process is prone to bias, even for trained annotators. On the other hand, completely automated processes do not offer the users the opportunity to inspect the models' output and re-evaluate potential false predictions.

Improved Manual Annotation of EEG Signals through Convolutional Neural Network Guidance.

The development of validated algorithms for automated handling of artifacts is essential for reliable and fast processing of EEG signals. Recently, there have been methodological advances in designing machine-learning algorithms to improve artifact detection of trained professionals who usually meticulously inspect and manually annotate EEG signals. However, validation of these methods is hindered by the lack of a gold standard as data are mostly private and data annotation is time consuming and error prone.