An automated heart rate-based algorithm for sleep stage classification: Validation using conventional polysomnography and an innovative wearable electrocardiogram device.

Background: The rapid advancement in wearable solutions to monitor and score sleep staging has enabled monitoring outside of the conventional clinical settings. However, most of the devices and algorithms lack extensive and independent validation, a fundamental step to ensure robustness, stability, and replicability of the results beyond the training and testing phases. These systems are thought not to be feasible and reliable alternatives to the gold standard, polysomnography (PSG). Materials and methods: This validation study highlights the accuracy and precision of the proposed heart rate (HR)-based deep-learning algorithm for sleep staging. The illustrated solution can perform classification at 2-levels (Wake; Sleep), 3-levels (Wake; NREM; REM) or 4- levels (Wake; Light; Deep; REM) in 30-s epochs. The algorithm was validated using an open-source dataset of PSG recordings (Physionet CinC dataset, n = 994 participants, 994 recordings) and a proprietary dataset of ECG recordings (Z3Pulse, n = 52 participants, 112 recordings) collected with a chest-worn, wireless sensor and simultaneous PSG collection using SOMNOtouch. Results: We evaluated the performance of the models in both datasets in terms of Accuracy (A), Cohen's kappa (K), Sensitivity (SE), Specificity (SP), Positive Predictive Value (PPV), and Negative Predicted Value (NPV). In the CinC dataset, the highest value of accuracy was achieved by the 2-levels model (0.8797), while the 3-levels model obtained the best value of K (0.6025). The 4-levels model obtained the lowest SE (0.3812) and the highest SP (0.9744) for the classification of Deep sleep segments. AHI and biological sex did not affect scoring, while a significant decrease of performance by age was reported across the models. In the Z3Pulse dataset, the highest value of accuracy was achieved by the 2-levels model (0.8812), whereas the 3-levels model obtained the best value of K (0.611). For classification of the sleep states, the lowest SE (0.6163) and the highest SP (0.9606) were obtained for the classification of Deep sleep segment. Conclusion: The results of the validation procedure demonstrated the feasibility of accurate HR-based sleep staging. The combination of the proposed sleep staging algorithm with an inexpensive HR device, provides a cost-effective and non-invasive solution deployable in the home environment and robust across age, sex, and AHI scores.

Quantification of Simian Immunodeficiency Virus by SYBR Green RT-PCR Technique / 中国病毒学·英文版

Plasma viral RNA load is widely accepted as the most relevant parameter to assess the status and progression of Simian immunodeficiency virus (SIV) infections. To accurately measure RNA levels of the virus, a one-step fluorescent quantitative assay was established based on the SYBR green Real-time reverse transcription-polymerase chain reaction (RT-PCR). The lower detection limit of the assay was 10 copies per reaction for the virus. This method was successfully applied to quantify SIVmac251 and SIVmac239 viruses produced in CEM×174 cells. Additionally, the performance of the SYBR green RT-PCR was assessed in a SIVmac251 infected rhesus macaque. The result demonstrated that the method could detect as little as 215 copies per milliliter of plasma and the dynamic pattern of viral load was highly consistent with previous results. With regard to convenience, sensitivity and accuracy our assay represents a realistic alternative to both branched-chain DNA (b-DNA) assays or real-time PCR assays based on TaqMan probes.