Effects of continuous positive airway pressure therapy on glucose metabolism in patients with obstructive sleep apnoea and type 2 diabetes: a systematic review and meta-analysis

Obstructive sleep apnoea is a highly prevalent chronic disorder and has been shown to be associated with disturbed glucose metabolism and type 2 diabetes. However, the evidence from individual clinical trials on the effect of continuous positive airway pressure (CPAP) treatment on glycaemic control in patients with co-existing obstructive sleep apnoea and type 2 diabetes remains controversial. A systematic review of randomised controlled trials assessing the effect of CPAP on glycaemic control in patients with obstructive sleep apnoea and type 2 diabetes was conducted using the databases MEDLINE, Embase, Cochrane and Scopus up to December 2022. Meta-analysis using a random-effect model was performed for outcomes that were reported in at least two randomised controlled trials. From 3031 records screened, 11 RCTs with a total of 964 patients were included for analysis. CPAP treatment led to a significant reduction in haemoglobin A1c (HbA1c) (mean difference −0.24%, 95% CI −0.43– −0.06%, p=0.001) compared to inactive control groups. Meta-regression showed a significant association between reduction in HbA1c and hours of nightly CPAP usage. CPAP therapy seems to significantly improve HbA1c and thus long-term glycaemic control in patients with type 2 diabetes and obstructive sleep apnoea. The amount of improvement is dependent on the hours of usage of CPAP and thus optimal adherence to CPAP should be a primary goal in these patients

Synthesizing Speech from Intracranial Depth Electrodes using an Encoder-Decoder Framework

Speech Neuroprostheses have the potential to enable communication for people with dysarthria or anarthria. Recent advances have demonstrated high-quality text decoding and speech synthesis from electrocorticographic grids placed on the cortical surface. Here, we investigate a less invasive measurement modality in three participants, namely stereotactic EEG (sEEG) that provides sparse sampling from multiple brain regions, including subcortical regions. To evaluate whether sEEG can also be used to synthesize high-quality audio from neural recordings, we employ a recurrent encoder-decoder model based on modern deep learning methods. We find that speech can indeed be reconstructed with correlations up to 0.8 from these minimally invasive recordings, despite limited amounts of training data