Heart Rate Response Predicts 6-Minutes Walking Distance in Pulmonary Arterial Hypertension.

Because the 6-minute walking test (6MWT) is a self-paced submaximal test, the 6-minute walking distance (6MWD) is substantially influenced by individual effort level and physical condition, which is difficult to quantify. We aimed to explore the optimal indicator reflecting the perceived effort level during 6MWT. We prospectively enrolled 76 patients with pulmonary arterial hypertension and 152 healthy participants; they performed 2 6MWTs at 2 different speeds: (1) at leisurely speed, as performed in daily life without extra effort (leisure 6MWT) and (2) an increased walking speed, walking as the guideline indicated (standard 6MWT). The factors associated with 6MWD during standard 6MWT were investigated using a multiple linear regression analysis. The heart rate (HR) and Borg score increased and oxygen saturation (SpO2) decreased after walking in 2 6MWTs in both groups (all p <0.001). The ratio of difference in HR before and after each test (ΔHR) to HR before walking (HRat rest) and the difference in SpO2 (ΔSpO2) and Borg (ΔBorg) before and after each test were all significantly higher in both groups after standard 6MWT than after leisure 6MWT (all p <0.001). Multiple linear regression analysis revealed that ΔHR/HRat rest was an independent predictor of 6MWD during standard 6MWT in both groups (both p <0.001, adjusted R2 = 0.737 and 0.49, respectively). 6MWD and ΔHR/HRat rest were significantly lower in patients than in healthy participants (both p <0.001) and in patients with cardiac functional class III than in patients with class I/II (both p <0.001). In conclusion, ΔHR/HRat rest is a good reflector of combined physical and effort factors. HR response should be incorporated into 6MWD to better assess a participant's exercise capacity.

CircRNAFisher: a systematic computational approach for de novo circular RNA identification.

Circular RNAs (circRNAs) are emerging species of mRNA splicing products with largely unknown functions. Although several computational pipelines for circRNA identification have been developed, these methods strictly rely on uniquely mapped reads overlapping back-splice junctions (BSJs) and lack approaches to model the statistical significance of the identified circRNAs. Here, we reported a systematic computational approach to identify circRNAs by simultaneously utilizing BSJ overlapping reads and discordant BSJ spanning reads to identify circRNAs. Moreover, we developed a novel procedure to estimate the P-values of the identified circRNAs. A computational cross-validation and experimental validations demonstrated that our method performed favorably compared to existing circRNA detection tools. We created a standalone tool, CircRNAFisher, to implement the method, which might be valuable to computational and experimental scientists studying circRNAs.