The Use of Big Data in Personalized Healthcare to Reduce Inventory Waste and Optimize Patient Treatment.

Precision medicine is emerging as an integral component in delivering care in the health system leading to better diagnosis and optimizing the treatment of patients. This growth is due to the new technologies in the data science field that have led to the ability to model complex diseases. Precision medicine is based on genomics and omics facilities that provide information about molecular proteins and biomarkers that could lead to discoveries for the treatment of patients suffering from various diseases. However, the main problems related to precision medicine are the ability to analyze, interpret, and integrate data. Hence, there is a lack of smooth transition from conventional to precision medicine. Therefore, this work reviews the limitations and discusses the benefits of overcoming them if big data tools are utilized and merged with precision medicine. The results from this review indicate that most of the literature focuses on the challenges rather than providing flexible solutions to adapt big data to precision medicine. As a result, this paper adds to the literature by proposing potential technical, educational, and infrastructural solutions in big data for a better transition to precision medicine.

Classification of Mental Stress Levels using EEG Connectivity and Convolutional Neural Networks.

Classifying mental stress is important as it helps in identifying the type and severity of stress, which can inform the most appropriate treatment or intervention. In this study, we propose utilizing electroencephalography (EEG) signals with convolutional neural networks (CNNs) to classify four mental states: rest, control-alert, stress and stress mitigation. The mental stress state was induced using Stroop color word test (SCWT) with time constrains and was then mitigated using 16 Hz Binaural beat stimulation (BBs). We quantified the four mental states using the reaction time (RT) to stimuli, accuracy of target detection, subjective score, and functional connectivity images of EEG estimated by Phase Locking Value (PLV). Our results show that, the SCWT reduced the accuracy of target detection by 70% with (F= 24.56, p = .00001), and the BBs improved the accuracy by 28% (F= 4.54, p = .00470). The functional connectivity network showed different patterns between the frontal/occipital and parietal regions, under the four mental states. The proposed CNNs with PLV images differentiated between the four mental states with highest classification performance at beta frequency band with 80.95% accuracy, 80.36% sensitivity, 94.75% specificity, 83.63% precision and 81.96% F-score. The overall results suggest that 16 Hz BBs can be used as an effective method to mitigate stress and the proposed CNNs with EEG-PLV images as a promising method for classifying different mental states.

Mental Stress Detection and Mitigation using Machine Learning and Binaural Beat Stimulation.

Stress is an inevitable problem experienced by people worldwide. Continuous exposure to stress can greatly impact mental activity as well as physical health thereby leading to several diseases. In this study, we investigate the effectiveness of audio binaural beat stimulation (BBs) in mitigating mental stress. We developed an experimental protocol to induce four mental states: rest, control, stress, and stress mitigation. The stress was induced by utilizing Stroop Color Word Test (SCWT) with time constraints and mitigated, by listening to 16 Hz of BBs. The four mental states were assessed using behavioral responses (accuracy of target detection), a perceived stress state questionnaire (PSS-10), and electroencephalography (EEG). The mean spectral power of four frequency bands was estimated using Power Spectral Density (PSD), and five different machine learning classifiers were used to classify the four mental states. Our results show that SCWT reduced the detection accuracy by 59.58% while listening to 16-Hz BBs significantly increased the accuracy of detection by 27.08%, (p = .00392). Furthermore, the support vector machine (SVM) significantly outperformed other classifiers achieving the highest accuracy of 82.5 ± 2.0 % using the beta band information. Similarly, the PSD topographical maps showed different patterns between the four mental states, where the temporal region's PSD was mostly affected by stress. Nevertheless, under mitigation, there was a noticeable restoration in the temporal activity. Overall, our results demonstrate that BBs at 16 Hz can be used to mitigate stress levels.

Attractive study of the antimicrobial, antiviral, and cytotoxic activity of novel synthesized silver chromite nanocomposites.

Antibiotic resistance is a global problem. This is the reason why scientists search for alternative treatments. In this regard, seven novel silver chromite nanocomposites were synthesized and assayed to evaluate their antimicrobial, antiviral, and cytotoxic activity. Five bacterial species were used in this study: three Gram-positive (Bacillus subtilis, Micrococcus luteus, and Staphylococcus aureus) and two Gram-negative (Escherichia coli and Salmonella enterica). Three fungal species were also tested: Candida albicans, Aspergillus niger, and A. flavus. The MIC of the tested compounds was determined using the bifold serial dilution method. The tested compounds showed good antibacterial activity. Maximum antibacterial activity was attained in the case of 15 N [Cobalt Ferrite (0.3 CoFe2O4) + Silver chromite (0.7 Ag0.5Cr2.5O4)] against M. luteus. Concerning antifungal activity, C. albicans was the most susceptible fungal species. The maximum inhibition was recorded also in case of 15 N [Cobalt Ferrite (0.3 CoFe2O4) + Silver chromite (0.7 Ag0.5Cr2.5O4)]. The most promising antimicrobial compound 15 N [Cobalt Ferrite (0.3 CoFe2O4) + Silver chromite (0.7 Ag0.5Cr2.5O4)] was assayed for its antiviral and cytotoxic activity. The tested compound showed weak antiviral activity. The cytotoxic activity against Mammalian cells from African Green Monkey Kidney (Vero) cells was detected. The inhibitory effect against Hepatocellular carcinoma cells was detected using a MTT assay. The antimicrobial effect of the tested compounds depends on the tested microbial species. The tested compounds could be attractive and alternative antibacterial compounds that open a new path in chemotherapy.