Left ventricular assist devices (LVADs) are emerging innovations that provide a feasible alternative treatment for heart failure (HF) patients to enhance their quality of life. In this work, a novel physiological control system to optimize LVAD pump speed using an H-infinity controller was developed. The controller regulates the calculated target pump flow vs. measured pump flow to meet the changes in metabolic demand. The method proposes the implementation of the Frank–Starling mechanism (FSM) approach to control the speed of an LVAD using the left ventricle end-diastolic volume (V_lved) parameter (preload). An operating point was proposed to move between different control lines within the safe area to achieve the FSM. A proportional–integral (PI) controller was used to control the gradient angle between control lines to obtain the flow target. A lumped parameter model of the cardiovascular system was used to evaluate the proposed method. Exercise and rest scenarios were assessed under multi-physiological conditions of HF patients. Simulation results demonstrated that the control system was stable and feasible under different physiological states of the cardiovascular system (CVS). In addition, the proposed controller was able to keep hemodynamic variables within an acceptable range of the mean pump flow (Qp) (max = 5.2 L/min and min = 3.2 L/min) during test conditions.

Tamoxifen (TAM) is a hormonal drug and is mainly used as an anti-estrogen in breast cancer patients. TAM binds to estrogen receptors (ERs), resulting in inhibition of estrogen signaling pathways and thus, a downregulation of cell proliferation. Cancer cells with negative or low ER expression will not uptake TAM and will show low response. Poly (methyl methacrylate) (PMMA) nanoparticles were prepared using surfactant-free emulsion polymerization, then were loaded with Nile red (NR), which resulted in PMMA-NR. To enhance TAM delivery to cervical cancer cells (HELA), which is considered ER-negative, we loaded TAM and polymethyl methacrylate nanoparticles-Nile-red into silica (PMMA-NR-Si-TAM). The uptake and intracellular distribution were visualized by confocal laser scanning microscopy, and the in vitro cytotoxic activity was evaluated by MTT (3-(4,5-Dimethylthiazol-2-yl)-2,5-Diphenyltetrazolium Bromide) assay using HELA and non-tumorigenic cell line HFF-1. The sensitivity of HELA (LC50: 207.31 µg/mL) and HFF-1 (LC50: 234.08 µg/mL) to free TAM was very low. However, after the encapsulation of TAM with PMMA-NR, the sensitivity significantly increased HELA (LC50: 71.83 µg/mL) and HFF-1 (LC50: 37.36 µg/mL). This indicates that TAM can be used for the treatment of ER-negative cervical cancer once conjugated to PMMA-NR nanoparticles. In addition, the PMMA-NR formulation appears to be highly suitable for cancer imaging and drug delivery.

Airborne droplets and contaminated surfaces are the main routes for the epidemic virus outbreak that causes the novel coronavirus. To reduce virus spread, people wear face masks and gloves daily, which massively increases the amount of waste generated in the environment. Also, the inappropriate disposal of used masks and gloves in communities may boost the spread of the novel coronavirus. However, no studies have been conducted to evaluate a public perception regarding the management of generated masks and gloves during the global health threat. Therefore, this paper proposes a study of public awareness, attitudes, and practices towards gloves and masks generated during the novel coronavirus pandemic in Saudi Arabia. The study showed that most of the participants (74%) have adequate awareness regarding the proper way to dispose of used masks and gloves, with 76% showing positive attitudes toward waste management. Gender shows a significant correlation with the regulations of medical waste collection and disposal (r = 0.169, p = 0.0001), and the consideration of the effective disposal of COVID-19 waste management as a collective responsibility of the community (r = 0.158, p = 0.0001). Also, the study shows that the majority of participants were aware on the availability of regulations for the collection and disposal of biomedical waste. Moreover, it demonstrates that public awareness regarding the attitude and measures taken by regional municipalities to reduce the spread of coronavirus has a significant positive correlation (r = 0.279, p = 0.0001). The study further recognized that the effective management of contagious wastes significantly protects the public against the improper practices of wastes disposal generated during the novel coronavirus pandemic.

Synchrotron X-ray radiation (SXR) has been widely studied to explore the structure of matter. Recently, there has been an intense focus on the medical application of SXR in imaging. This review is intended to explore the latest applications of SXR in medical imaging and to shed light on the advantages and drawbacks of this modality. The article highlights the latest developments in other fields that can greatly enhance the capability and applicability of SXR. The potentials of using machine and deep learning (DL)-based methods to generate synthetic images to use in regular clinics along with the use of photon counting X-ray detectors for spectral medical imaging with SXR are also discussed.

Cardiovascular disease is the main cause of death worldwide. An electrocardiogram (ECG) signals is typically used as the first diagnosis tool to detect abnormality in the heart signal. Reliable detection of R-peak in the ECG signal indicates various heart malfunctions (e.g., arrhythmia) and allows for proactive prevention of death due to cardiovascular disease. Though several R-peak detection methods have been proposed, the existence of noise in ECG signals and changes in QRS morphology compromise the robustness and reliability of these methods. Therefore, the need for a reliable detection of R-peak is crucial for diagnosis of heart abnormalities. This paper introduces a time-efficient and novel continuous wavelet transform (CWT) based method for R-peak detection. The proposed method first transforms the ECG signal in to time-frequency spectrum. The contributions of different frequencies at every time point are then calculated from the time-frequency spectrum to efficiently reduce the impact of noise and generate a summed frequency signal. A threshold technique is also proposed to detect the R-peak from the newly generated signal allows. The MIT-BIH arrhythmia database is used as a reference for validation and comparison of the proposed method with the results of other existing R-peak detection methods. The experimental results prove the efficiency and robustness of the developed method on noisy ECG signals with changes in QRS morphology with 99.87% sensitivity, 99.76% positive prediction value and a detection error rate of only 0.37%. In addition to the high accuracy in detecting R-peaks, the ease-to-use and fast-processing make the proposed method an efficient and reliable tool for real-time abnormality detection in ECG signal.