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Before vaccine development during the COVID-19 pandemic, Non-Pharmaceutical Interventions (NPIs) were the only solutions to mitigate COVID-19 infections. Governments continued to use them even after starting vaccine administration. In this research, we review different big data analytics models that assess and optimize the effectiveness of NPIs. These models are categorized into three big data analytics groups: descriptive, which measures the infection rate changes caused by NPIs; predictive, which predicts the future of the pandemic by implementing several NPIs; and data-driven prescriptive, which suggests optimal control policies. We further analyze each method's basic assumptions, limitations, and applicability during different pandemic phases and under different scenarios. This review of COVID-19 NPI evaluation methods will be beneficial for decision-makers to know which model to select for policy-making in possible future pandemics, which are more likely recently due to globalization. Finally, we suggest some future research directions. [ABSTRACT FROM AUTHOR]

Docosahexaenoic acid (DHA) is the predominant omega‐3 long‐chain polyunsaturated fatty acid found in human brain and eyes. There are a number of studies in the literature showing the health benefits of DHA. It is critical throughout all life stages from the need for fetal development, the prevention of preterm birth, and the prevention of cardiovascular disease to the improvements in the cognitive function and the eye health of adults and elderly. These benefits might be related to the modulation of gut microbiota by DHA. In addition, there are some discrepancies in the literature regarding certain health benefits of DHA, and this review is intended to explore and understand these discrepancies. Besides the variations in the DHA contents of different supplement sources, bioavailability is crucial for the efficacy of DHA supplements, which depends on several factors. For example, DHA in phospholipid and triglyceride forms are more readily to be absorbed by the body than that in ethyl ester form. In addition, dietary lipids in meals and emulsification of DHA oil can increase the bioavailability of DHA. Estrogens stimulated the biosynthesis of DHA, whereas testosterone stimulus induced a decrease in DHA. The roles of DHA through human lifespan, the sources, and its recommended daily intake in different countries are also discussed to provide a better understanding of the importance of this review. [ABSTRACT FROM AUTHOR]

Aim: Periodontitis and valvular heart disease (VHD) are common diseases. Both diseases are related to chronic inflammation and share many common risk factors. Previous periodontal studies had focused mainly on atherosclerotic cardiovascular disease. This study aimed to determine whether periodontitis is associated with the development of VHD. Materials and methods: This was a retrospective nationwide cohort study using Taiwan's Longitudinal Health Insurance Database. Using ICD‐9‐CM coding, both the periodontitis and non‐periodontitis groups were matched. Results: There were 8483 cases and 4919 cases of VHD diagnosed in the periodontitis group and non‐periodontitis group, respectively. The cumulative incidence of VHD was significantly higher in the periodontitis group (log‐rank test, p <.001), with the incidence density of 6.44 (95% CI, 6.31–6.58) per 1000 person‐years in the periodontitis group compared to 4.65 (95% CI, 4.52–4.78) in the non‐periodontitis group. The relative risk for VHD was 1.39 (95% CI, 1.34–1.44). After multivariate analysis, periodontitis was independently associated with a risk for VHD (HR, 1.38; 95% CI, 1.33–1.42, p <.001). Intensive treatment of periodontitis significantly lowered the risk for VHD (HR, 0.68; 95% CI, 0.60–0.77, p <.001). Conclusions: Periodontitis was significantly associated with the development of VHD. Treatment of periodontitis reduced the risk for VHD. [ABSTRACT FROM AUTHOR]

Quantifying gait parameters and ambulatory monitoring of changes in these parameters have become increasingly important in epidemiological and clinical studies. Using high-density accelerometry measurements, we propose adaptive empirical pattern transformation (ADEPT), a fast, scalable, and accurate method for segmentation of individual walking strides. ADEPT computes the covariance between a scaled and translated pattern function and the data, an idea similar to the continuous wavelet transform. The difference is that ADEPT uses a data-based pattern function, allows multiple pattern functions, can use other distances instead of the covariance, and the pattern function is not required to satisfy the wavelet admissibility condition. Compared to many existing approaches, ADEPT is designed to work with data collected at various body locations and is invariant to the direction of accelerometer axes relative to body orientation. The method is applied to and validated on accelerometry data collected during a $450$-m outdoor walk of $32$ study participants wearing accelerometers on the wrist, hip, and both ankles. Additionally, all scripts and data needed to reproduce presented results are included in supplementary material available at Biostatistics online. [ABSTRACT FROM AUTHOR]

Implantable medical devices (IMDs) continuously monitor the condition of a patient and directly apply treatments if considered necessary. Because IMDs are highly effective for patients who frequently visit hospitals (e.g., because of chronic illnesses such as diabetes and heart disease), their use is increasing significantly. However, related security concerns have also come to the fore. It has been demonstrated that IMDs can be hacked—the IMD power can be turned off remotely, and abnormally large doses of drugs can be injected into the body. Thus, IMDs may ultimately threaten a patient’s life. In this paper, we propose an energy-efficient key-exchange protocol for securing IMDs. We utilize synchronous interpulse intervals (IPIs) as the source of a secret key. These IPIs enable IMDs to agree upon a secret key with an external programmer in an authenticated and transparent manner without any key material being exposed either before distribution or during initialization. We demonstrate that it is difficult for adversaries to guess the keys established using our method. In addition, we show that the reduced communication overhead of our method enhances battery life, making the proposed approach more energy-efficient than previous methods. [ABSTRACT FROM AUTHOR]

In mobile health care monitoring, compression is an essential tool for solving storage and transmission problems. The important issue is able to recover the original signal from the compressed signal. The main purpose of this paper is compressing the ECG signal with no loss of essential data and also encrypting the signal to keep it confidential from everyone, except for physicians. In this paper, mobile processors are used and there is no need for any computers to serve this purpose. After initial preprocessing such as removal of the baseline noise, Gaussian noise, peak detection and determination of heart rate, the ECG signal is compressed. In compression stage, after 3 steps of wavelet transform (db04), thresholding techniques are used. Then, Huffman coding with chaos for compression and encryption of the ECG signal are used. The compression rates of proposed algorithm is 97.72 %. Then, the ECG signals are sent to a telemedicine center to acquire specialist diagnosis by TCP/IP protocol. [ABSTRACT FROM AUTHOR]

The electrocardiogram (ECG) is a widely used medical test, consisting of voltage versus time traces collected from surface recordings over the heart 1 . Here we hypothesized that a deep neural network (DNN) can predict an important future clinical event, 1-year all-cause mortality, from ECG voltage-time traces. By using ECGs collected over a 34-year period in a large regional health system, we trained a DNN with 1,169,662 12-lead resting ECGs obtained from 253,397 patients, in which 99,371 events occurred. The model achieved an area under the curve (AUC) of 0.88 on a held-out test set of 168,914 patients, in which 14,207 events occurred. Even within the large subset of patients (n = 45,285) with ECGs interpreted as 'normal' by a physician, the performance of the model in predicting 1-year mortality remained high (AUC = 0.85). A blinded survey of cardiologists demonstrated that many of the discriminating features of these normal ECGs were not apparent to expert reviewers. Finally, a Cox proportional-hazard model revealed a hazard ratio of 9.5 (P < 0.005) for the two predicted groups (dead versus alive 1 year after ECG) over a 25-year follow-up period. These results show that deep learning can add substantial prognostic information to the interpretation of 12-lead resting ECGs, even in cases that are interpreted as normal by physicians.

This study describes an integrated approach for fetal heart beat detection from abdominal ECG signals. The signal is first processed by the first‐order difference operation, and then, the envelope of the differentiated signal is given by the Hilbert transform, which indicates both the maternal and fetal heart beats. After that, the non‐linear state‐space projections is applied to the envelope signal to remove the maternal heart beats, obtaining the fetal heart beats envelope. The validity and performance of the described method are confirmed through experiments on real ECG data. A comparison with the well‐known independent component analysis method has also been presented. [ABSTRACT FROM AUTHOR]