Targeting Neutrophil Extracellular Trap Formation: Exploring Promising Pharmacological Strategies for the Treatment of Preeclampsia.

Neutrophils, which constitute the most abundant leukocytes in human blood, emerge as crucial players in the induction of endothelial cell death and the modulation of endothelial cell responses under both physiological and pathological conditions. The hallmark of preeclampsia is endothelial dysfunction induced by systemic inflammation, in which neutrophils, particularly through the formation of neutrophil extracellular traps (NETs), play a pivotal role in the development and perpetuation of endothelial dysfunction and the hypertensive state. Considering the potential of numerous pharmaceutical agents to attenuate NET formation (NETosis) in preeclampsia, a comprehensive assessment of the extensively studied candidates becomes imperative. This review aims to identify mechanisms associated with the induction and negative regulation of NETs in the context of preeclampsia. We discuss potential drugs to modulate NETosis, such as NF-κß inhibitors, vitamin D, and aspirin, and their association with mutagenicity and genotoxicity. Strong evidence supports the notion that molecules involved in the activation of NETs could serve as promising targets for the treatment of preeclampsia.

Longitudinal In-Bed Pressure Signals Decomposition and Gradients Analysis for Pressure Injury Monitoring.

Pressure injury (PI) is a major problem for patients that are bound to a wheelchair or bed, such as seniors or people with spinal cord injuries. This condition can be life threatening in its later stages. It can be very costly to the healthcare system as well. Fortunately with proper monitoring and assessment, PI development can be prevented. The major factor that causes PI is prolonged interface pressure between the body and the support surface. A possible solution to reduce the chance of developing PI is changing the patient's in-bed pose at appropriate times. Monitoring in-bed pressure can help healthcare providers to locate high-pressure areas, and remove or minimize pressure on those regions. The current clinical method of interface pressure monitoring is limited by periodic snapshot assessments, without longitudinal measurements and analysis. In this paper we propose a pressure signal analysis pipeline to automatically eliminate external artefacts from pressure data, estimate a person's pose, and locate and track high-risk regions over time so that necessary attention can be provided.

ECG Signal Denoising and Features Extraction Using Unbiased FIR Smoothing.

Methods of the electrocardiography (ECG) signal features extraction are required to detect heart abnormalities and different kinds of diseases. However, different artefacts and measurement noise often hinder providing accurate features extraction. One of the standard techniques developed for ECG signals employs linear prediction. Referring to the fact that prediction is not required for ECG signal processing, smoothing can be more efficient. In this paper, we employ the p-shift unbiased finite impulse response (UFIR) filter, which becomes smooth by p < 0. We develop this filter to have an adaptive averaging horizon: optimal for slow ECG behaviours and minimal for fast excursions. It is shown that the adaptive UFIR algorithm developed in such a way provides better denoising and suboptimal features extraction in terms of the output signal-noise ratio (SNR). The algorithm is developed to detect durations and amplitudes of the P-wave, QRS-complex, and T-wave in the standard ECG signal map. Better performance of the algorithm designed is demonstrated in a comparison with the standard linear predictor, UFIR filter, and UFIR predictive filter based on real ECG data associated with normal heartbeats.