Randomised short-term trial of high-span versus low-span APAP for treating sleep apnoea

PURPOSE: Auto-titrating continuous positive airway pressure (APAP) devices were developed to improve treatment efficacy and compliance in patients with obstructive sleep apnoea syndrome (OSAS). Since there are insufficient data on the optimal pressure range setting, we aimed to compare the adherence, efficacy and tolerability of treatment with high-span versus low-span APAP. METHODS: Seventy-six newly diagnosed OSAS patients fulfilling the treatment criteria were randomised to receive high-span (HS, range 4-15cmH2O, n?=?38) or low-span (LS, range 8-12cmH2O, n?=?38) APAP. Patients were assessed at 1 and 3 months. RESULTS: Median Epworth sleepiness scale (ESS) was 13 (IQR, 6-16) and median apnoea-hypopnoea index (AHI) was 35.9 (IQR, 27.6-56.3). There were no significant differences in baseline demographic and clinical characteristics between groups. Overall, no significant differences were found at the first month assessment. After 3 months of therapy, we found again no differences in residual AHI or ESS. However, the group HS proved less adherent than group LS, respectively, with median 87 % (IQR, 60.5-97.5) versus 94 % (IQR, 80.0-98.3) of the nights using =4 h (P?=?0.014) and mean (±SD) usage 5.7?±?1.6 versus 6.4?±?1.2 h/night (P?=?0.049). The group HS reported more frequently nasal congestion, excessive oronasal dryness and nocturnal awakenings of at least moderate intensity, the latter with statistical significance (P?=?0.005). CONCLUSIONS: Both pressure ranges appear to be equally effective to correct AHI and to improve symptoms. Though, patients with high-span APAP were less compliant to treatment, raising issues about the tolerability of wide pressure range settings of these devices.T Pinto has received financial support from Linde and Vitalaire (Healthcare Providers) for attending symposia and honoraria for speaking at symposia from Philips. After the conclusion of the study, JC Winck has started working in a global position for Linde. The remaining authors declare that they have no conflict of interest

In Vitro Micropropagation of Vernonia cinerea (L.) Less. - An important medicinal herb

In vitro micropropagation was achieved from Nodal and leaf explants of Vernonia cinerea L. on MS medium supplemented with different concentrations of BAP (1.0 to 3.0 mg/l), GA3 (1.0 to 5.0 mg/l) and KN (0.5 mg/l) for direct shoot induction. Maximum numbers of shoots were obtained from nodal and leaf explants at BAP (2.5 mg/l) GA3 (2.0 mg/l) and KN (0.5 mg/l) respectively. The multiple shoots elongation and their development were better on MS medium supplemented with 1.5 mg/l BAP and 3.0 mg/l GA3 for both the explants. The regenerated shoots were successfully rooted on MS medium supplemented with IBA 2.0 mg/1, after sequential hardening, survival rate was 91%. The in vitro raised plantlets were acclimatized and established successfully in the field

Enhancing the electrochemical kinetics of high voltage olivine LiMnPO 4 by isovalent co-doping

10.1039/c3cp52311jPhysical Chemistry Chemical Physics154017240-17249PPCP

Influence of nanosize and thermodynamics on lithium storage in insertion and conversion reactions

10.1117/12.921157Proceedings of SPIE - The International Society for Optical Engineering8377-PSIS

Classification of EEG signals on SEED dataset using improved CNN

The proposed research introduces an Improved Convolutional Neural Network (ICNN) to construct EEG-based emotion detection models. This study has utilized an EEG dataset of 15 subjects available from a BCMI laboratory. In our work, differential entropy characteristics obtained from multichannel EEG data are used to train the Improved CNN. The best classification accuracy is 95.67% which is significantly higher than that of the original 62 channels. The most important channels and frequency bands are identified by Improved CNN. The outcomes of our study also demonstrate the existence of neuronal signatures linked to various emotions, which are consistent between sessions and people. Finally, the effectiveness of deep and shallow models are compared and also the performance of improved CNN is compared with benchmark algorithms