Background . Continuous wavelet transform (CWT) based scalogram can be used for photoplethysmography (PPG) signal transformation to classify blood pressure (BP) with deep learning. We aimed to investigate the determinants that can improve the accuracy of BP classification based on PPG and deep learning and establish a better algorithm for the prediction. Methods . The dataset from PhysioNet was accessed to extract raw PPG signals for testing and its corresponding BPs as category labels. The BP category of normal or abnormal followed the criteria of the 2017 American College of Cardiology/American Heart Association (ACC/AHA) Hypertension Guidelines. The PPG signals were transformed into 224 ∗ 224 ∗ 3‐pixel scalogram via different CWTs and segment units. All of them are fed into different convolutional neural networks (CNN) for training and validation. The receiver‐operating characteristic and loss and accuracy curves were used to evaluate and compare the performance of different methods. Results . Both wavelet type and segment length could affect the accuracy, and Cgau1 wavelet and segment‐300 revealed the best performance (accuracy 90%) without obvious overfitting. This method performed better than previously reported MATLAB Morse wavelet transformed scalogram on both of our proposed CNN and CNN‐GoogLeNet. Conclusions . We have established a new algorithm with high accuracy to predict BP classification from PPG via matching of CWT type and segment length, which is a promising solution for rapid prediction of BP classification from real‐time processing of PPG signal on a wearable device.

PCSK9 promotes the secretion of pro-inflammatory cytokines by macrophages to aggravate H/R-induced cardiomyocyte injury via activating NF-κB signalling

Background: Atrial fibrillation (AF) is a common arrhythmia that can result in adverse cardiovascular outcomes but is often difficult to detect. The use of machine learning (ML) algorithms for detecting AF has become increasingly prevalent in recent years. This study aims to systematically evaluate and summarize the overall diagnostic accuracy of the ML algorithms in detecting AF in electrocardiogram (ECG) signals. Methods: The searched databases included PubMed, Web of Science, Embase, and Google Scholar. The selected studies were subjected to a meta-analysis of diagnostic accuracy to synthesize the sensitivity and specificity. Results: A total of 14 studies were included, and the forest plot of the meta-analysis showed that the pooled sensitivity and specificity were 97% (95% confidence interval [CI]: 0.94–0.99) and 97% (95% CI: 0.95–0.99), respectively. Compared to traditional machine learning (TML) algorithms (sensitivity: 91.5%), deep learning (DL) algorithms (sensitivity: 98.1%) showed superior performance. Using multiple datasets and public datasets alone or in combination demonstrated slightly better performance than using a single dataset and proprietary datasets. Conclusions: ML algorithms are effective for detecting AF from ECGs. DL algorithms, particularly those based on convolutional neural networks (CNN), demonstrate superior performance in AF detection compared to TML algorithms. The integration of ML algorithms can help wearable devices diagnose AF earlier.

Parkinson's disease (PD) is a neurodegenerative disorder that has a high incidence during the aging process and is characterized by the loss of dopaminergic neurons in the substantia nigra, leading to motor dysfunctions and non‑motor symptoms. Impaired clearance and excessive accumulation of aberrantly modified proteins or damaged organelles, such as aggregated α‑synuclein and dysfunctional mitochondria, are regarded as the main causes of nigrostriatal neurodegeneration. As one of the major degradation pathways, autophagy can recycle these useless or toxic substances to maintain cellular homeostasis and it plays a crucial role in PD progression. MicroRNAs (miRNAs) are a group of small non‑coding RNA molecules that regulate gene expression by silencing targeted mRNAs. Recent studies have illustrated that autophagy‑regulating miRNA has been implicated in pathological processes of PD, including α‑synuclein accumulation, mitochondrial damage, neuroinflammation and neuronal apoptosis, which suggests that targeting autophagy‑regulating miRNAs may provide novel therapeutic strategies for this disease. The present review summarizes the role of autophagy in PD and emphasizes the role of miRNA‑mediated autophagy in PD, for the development of promising interventions in this disease.

Background: Vaccination has been proven to be an effective approach against the coronavirus disease 2019 (COVID-19) pandemic. This study aimed to determine the acceptance rate and factors influencing acceptance of COVID-19 vaccination among people living with HIV (PLWH) in Guangxi, China. Methods: A cross-sectional survey was carried out in five cities in Guangxi, China from May 7 to June 1, 2021. Questionnaires on the acceptance of COVID-19 vaccination and the related factors were conducted among PLWH recruited by simple random sampling. Univariate and multivariate logistic regression analyses were performed to identify factors associated with acceptance of COVID-19 vaccination. Results: Of all valid respondents (n = 903), 72.9% (n = 658) were willing to receive COVID-19 vaccination. Fear of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection was the main reason for being willing to receive vaccination (76.0%), while the main reasons for not willing were the concerns about vaccine safety (54.7%) and the vaccination’s effect on antiretroviral therapy (ART) (50.6%). The most important factors influencing acceptance were the perception that vaccination is unsafe for HIV-infected people (aOR = 0.082, 95% CI = 0.024–0.282) and the poor efficacy in preventing SARS-CoV-2 infection in HIV-infected people (aOR = 0.093, 95% CI = 0.030–0.287). Other factors associated with acceptance included Zhuang ethnicity (aOR = 1.653, 95% CI = 1.109–2.465), highest education level of middle school, high school or above (aOR = 1.747, 95% CI = 1.170–2.608; aOR = 2.492, 95% CI = 1.326–4.682), and the vaccination having little effect on ART efficacy (aOR = 2.889, 95% CI = 1.378–6.059). Conclusions: Acceptance rate of the COVID-19 vaccination is relatively low among PLWH compared to the general population in China, although some patients refused vaccination due to concerns about vaccine safety and vaccination affecting ART efficacy. More research is needed to investigate the impact of the COVID-19 vaccines on ART efficacy and the effectiveness in preventing SARS-CoV-2 infection among PLWH.