The filament-specific Rep1-1 repellent of the phytopathogen ustilago maydis forms functional surface-active amyloid-like fibrils

Repellents of the maize pathogen Ustilago maydis are involved in formation of hydrophobic aerial hyphae and in cellular attachment. These peptides, called Rep1-1 to Rep1-11, are encoded by the rep1 gene and result from cleavage of the precursor protein Rep1 during passage of the secretion pathway. Using green fluorescent protein as a reporter, we here show that rep1 is expressed in filaments and not in the yeast form of U. maydis. In situ hybridization localized rep1 mRNA in the apex of the filament, which correlates with the expected site of secretion of the repellents into the cell wall. We also produced a synthetic peptide, Rep1-1. This peptide reduced the water surface tension to as low as 36 mJ m-2. In addition, it formed amyloid-like fibrils as was shown by negative staining, by thioflavin T fluorescence, and by x-ray diffraction. These fibrils were not soluble in SDS but could be dissociated with trifluoroacetic acid. The repellents in the hyphal cell wall had a similar solubility and also stained with thioflavin T, strongly indicating that they are present as amyloid fibrils. However, such fibrils could not be observed at the hyphal surface. This can be explained by the fact that the Rep1-1 filaments decrease in length at increasing concentrations. Taken together, we have identified the second class of fungal proteins that form functional amyloid-like filaments at the hyphal surface

Accuracy of continuous photoplethysmography-based 1 min mean heart rate assessment during atrial fibrillation

Aims Although mobile health tools using photoplethysmography (PPG) technology have been validated for the detection of atrial fibrillation (AF), their utility for heart rate assessment during AF remains unclear. Therefore, we aimed to evaluate the accuracy of continuous PPG-based 1 min mean heart rate assessment during AF. Methods and results Persistent AF patients were provided with Holter electrocardiography (ECG) (for >= 24 h) simultaneously with a PPG-equipped smartwatch. Both the PPG-based smartwatch and Holter ECG automatically and continuously monitored patients' heart rate/rhythm. ECG and PPG recordings were synchronized and divided into 1 min segments, from which a PPG-based and an ECG-based average heart rate estimation were extracted. In total, 47 661 simultaneous ECG and PPG 1 min heart rate segments were analysed in 50 patients (34% women, age 73 +/- 8 years). The agreement between ECG-determined and PPG-determined 1 min mean heart rate was high [root mean squared error (RMSE): 4.7 bpm]. The 1 min mean heart rate estimated using PPG was accurate within +/- 10% in 93.7% of the corresponding ECG-derived 1 min mean heart rate segments. PPG-based 1 min mean heart rate estimation was more often accurate during night-time (97%) than day-time (91%, P < 0.001) and during low levels (96%) compared to high levels of motion (92%, P < 0.001). A neural network with a 10 min history of the recording did not further improve the PPG-based 1 min mean heart rate assessment [RMSE: 4.4 (95% confidence interval: 3.5-5.2 bpm)]. Only chronic heart failure was associated with a lower agreement between ECG-derived and PPG-derived 1 min mean heart rates (P = 0.040). Conclusion During persistent AF, continuous PPG-based 1 min mean heart rate assessment is feasible in 60% of the analysed period and shows high accuracy compared with Holter ECG for heart rates <110 bpm

Atrial fibrillation-specific refinement of the STOP-Bang sleep apnoea screening questionnaire: insights from the Virtual-SAFARI study

Background Sleep-disordered breathing (SDB) is prevalent in up to 50% of patients referred for atrial fibrillation (AF) catheter ablation (CA). Currently, it remains unclear how to improve pre-selection for SDB screening in patients with AF.Aim We aimed to (1) assess the accuracy of the STOP-Bang screening questionnaire for detection of SDB within an AF population referred for CA; (2) derive a refined, AF-specific SDB score to improve pre-selection.Methods Consecutive AF patients referred for CA without a history of SDB and/or SDB screening were included. Patients were digitally referred to the previously implemented Virtual-SAFARI SDB screening and management pathway including a home sleep test. An apnoea-hypopnoea index (AHI) of >= 15 was interpreted as moderate-to-severe SDB. Logistic regression analysis was used to assess characteristics associated with moderate-to-severe SDB to refine pre-selection for SDB screening.Results Of 206 included patients, 51% were diagnosed with moderate-to-severe SDB. The STOP-Bang questionnaire performed poorly in detecting SDB, with an area under the receiver operating characteristic curve (AUROC) of 0.647 (95% Confidence-Interval (CI) 0.573-0.721). AF-specific refinement resulted in the BOSS-GAP score. Therein, BMI with cut-off point >= 27 kg/m(2) and previous stroke or transient ischaemic attack (TIA) were added, while tiredness and neck circumference were removed. The BOSS-GAP score performed better with an AUROC of 0.738 (95% CI 0.672-0.805) in the overall population.Conclusion AF-specific refinement of the STOP-Bang questionnaire moderately improved detection of SDB in AF patients referred for CA. Whether questionnaires bring benefits for pre-selection of SDB compared to structural screening in patients with AF requires further studies