Sex-related patterns in the electroencephalogram and their relevance in machine learning classifiers.

Deep learning is increasingly being proposed for detecting neurological and psychiatric diseases from electroencephalogram (EEG) data but the method is prone to inadvertently incorporate biases from training data and exploit illegitimate patterns. The recent demonstration that deep learning can detect the sex from EEG implies potential sex-related biases in deep learning-based disease detectors for the many diseases with unequal prevalence between males and females. In this work, we present the male- and female-typical patterns used by a convolutional neural network that detects the sex from clinical EEG (81% accuracy in a separate test set with 142 patients).

Further Dimensions for Sensing in Biofluids: Distinguishing Bioorganic Analytes by the Salt-Induced Adaptation of a Cucurbit[7]uril-Based Chemosensor.

Insufficient binding selectivity of chemosensors often renders biorelevant metabolites indistinguishable by the widely used indicator displacement assay. Array-based chemosensing methods are a common workaround but require additional effort for synthesizing a chemosensor library and setting up a sensing array. Moreover, it can be very challenging to tune the inherent binding preference of macrocyclic systems such as cucurbit[]urils (CB) by synthetic means.

Experiences from treating seven adult 5q spinal muscular atrophy patients with Nusinersen.

Background: The antisense oligonucleotide Nusinersen recently became the first approved drug against spinal muscular atrophy (SMA). It was approved for all ages, albeit the clinical trials were conducted exclusively on children. Hence, clinical data on adults being treated with Nusinersen is scarce.

Influence of tissue conductivity changes on the EEG signal in the human brain: a simulation study.

Tissue disorders due to brain pathologies, like tumors, ischemia, or vasogenic edema, are known to impact the propagation of electrical fields. By using the finite element method the EEG forward problem was solved within an adapted subspace of a simplified human head model. Simulated electric potentials on the scalp revealed strong influences on the magnitude of the signal in almost all cases, even for ischemic tissue in which conductivity is lower than in healthy tissue.