Cochlear implantation in adults with acquired single-sided deafness improves cortical processing and comprehension of speech presented to the non-implanted ears: a longitudinal EEG study.

Former studies have established that individuals with a cochlear implant (CI) for treating single-sided deafness experience improved speech processing after implantation. However, it is not clear how each ear contributes separately to improve speech perception over time at the behavioural and neural level. In this longitudinal EEG study with four different time points, we measured neural activity in response to various temporally and spectrally degraded spoken words presented monaurally to the CI and non-CI ears (5 left and 5 right ears) in 10 single-sided CI users and 10 age- and sex-matched individuals with normal hearing.

Review of Outcomes After Peripheral Nerve Transfers for Motor Nerve Injury in the Upper Extremity.

Background: Modern nerve-to-nerve transfers are a significant advancement in peripheral nerve surgery. Nerve transfers involve transferring donor nerves or branches to recipient nerves close to the motor end unit, leading to earlier reinnervation and preservation of the musculotendinous units in proximal nerve injuries. After nerve reinnervation, function may be superior to traditional tendon transfer techniques in terms of strength and independent motion.

Rapid and comprehensive detection of viral antibodies and nucleic acids via an acoustofluidic integrated molecular diagnostics chip: AIMDx.

Precise and rapid disease detection is critical for controlling infectious diseases like COVID-19. Current technologies struggle to simultaneously identify viral RNAs and host immune antibodies due to limited integration of sample preparation and detection. Here, we present acoustofluidic integrated molecular diagnostics (AIMDx) on a chip, a platform enabling high-speed, sensitive detection of viral immunoglobulins [immunoglobulin A (IgA), IgG, and IgM] and nucleic acids.

Utilizing deep learning to predict Alzheimer's disease and mild cognitive impairment with optical coherence tomography.

Introduction: Diagnostic performance of optical coherence tomography (OCT) to detect Alzheimer's disease (AD) and mild cognitive impairment (MCI) remains limited. We aimed to develop a deep-learning algorithm using OCT to detect AD and MCI.

Methods: We performed a cross-sectional study involving 228 Asian participants (173 cases/55 controls) for model development and testing on 68 Asian (52 cases/16 controls) and 85 White (39 cases/46 controls) participants.