Improved Cognitive Vigilance Assessment after Artifact Reduction with Wavelet Independent Component Analysis.

Vigilance level assessment is of prime importance to avoid life-threatening human error. Critical working environments such as air traffic control, driving, or military surveillance require the operator to be alert the whole time. The electroencephalogram (EEG) is a very common modality that can be used in assessing vigilance. Unfortunately, EEG signals are prone to artifacts due to eye movement, muscle contraction, and electrical noise. Mitigating these artifacts is important for an accurate vigilance level assessment. Independent Component Analysis (ICA) is an effective method and has been extensively used in the suppression of EEG artifacts. However, in vigilance assessment applications, it was found to suffer from leakage of the cerebral activity into artifacts. In this work, we show that the wavelet ICA (wICA) method provides an alternative for artifact reduction, leading to improved vigilance level assessment results. We conducted an experiment in nine human subjects to induce two vigilance states, alert and vigilance decrement, while performing a Stroop Color-Word Test for approximately 45 min. We then compared the performance of the ICA and wICA preprocessing methods using five classifiers. Our classification results showed that in terms of features extraction, the wICA method outperformed the existing ICA method. In the delta, theta, and alpha bands, we obtained a mean classification accuracy of 84.66% using the ICA method, whereas the mean accuracy using the wICA methodwas 96.9%. However, no significant improvement was observed in the beta band. In addition, we compared the topographical map to show the changes in power spectral density across the brain regions for the two vigilance states. The proposed method showed that the frontal and central regions were most sensitive to vigilance decrement. However, in this application, the proposed wICA shows a marginal improvement compared to the Fast-ICA.

Perception, Preventive Practice, and Attitude Towards Vaccine Against COVID-19 Among Health Care Professionals in Bangladesh.

BACKGROUND: Healthcare professionals (HCPs) are one of the vital and persuading means of information, prevention and control, and incentive of vaccination to content a pandemic. Therefore, knowing the status of HCPs' perception about symptoms, transmission, preventive measures, and attitudes towards a vaccine against COVID-19 is crucial. METHODS: This multi-center cross-sectional descriptive study was one of the first and foremost ones in Bangladesh among the HCPs - doctors, nurses, and other subordinates (MLSS) engaged at COVID-dedicated hospitals. The study was conducted from February 5, 2021, to March 7, 2021, using a convenience sampling method among 550 HCPs using structured questionnaire with twenty-five questions on a three-point scale of responses. RESULTS: The age range of the respondents were 18 to 64 years with the mean 36.17 ±10.94 years of 524 HCPs, of which the majority of the respondents were female 323 (61.6%) and 201 (38.4%) were male with the 95.27% response rate. Almost all participants had "high" or more than sufficient perception (94.34%) about the symptoms of COVID-19. But all categories of HCPs expressed their poor or fair practice about restraining from shaking hands, and only 6.84% of nurses, 8.33% doctors, and 11.59% of MLSS avoided crowded public gatherings as a practice of prevention. A majority (95.99%) of the HCPs showed positive attitudes about the availability of vaccines free of cost, and 87.40% showed trust in the efficacy and safety of the vaccine against COVID-19. CONCLUSION: The majority of the HCPs from the COVID-dedicated hospitals have a good perception and positive attitude towards vaccination; nevertheless, have a poor practice of prevention toward COVID-19. This may play a vital role in motivation and wide acceptance of vaccine among the general population and contribute in comprehensive strategic planning to fight back against the pandemic in the country with the restricted resource.

Modeling the Effects of Chemotherapy and Immunotherapy on Tumor Growth.

Mathematical modeling has been used to simulate the interaction of chemotherapy and immunotherapy drugs intervention with the dynamics of tumor cells growth. This work studies the interaction of cells in the immune system, such as the natural killer, dendritic, and cytotoxic CD8+ T cells, with chemotherapy. Four different cases were considered in the simulation: no drug intervention, independent interventions (either chemotherapy or immunotherapy), and combined interventions of chemotherapy and immunotherapy. The system of ordinary differential equations was initially solved using the Runge-Kutta method and compared with two additional methods: the Explicit Euler and Heun's methods. Results showed that the combined intervention is more effective compared to the other cases. In addition, when compared with Runge-Kutta, the Heun's method presented a better accuracy than the Explicit Euler technique. The proposed mathematical model can be used as a tool to improve cancer treatments and targeted therapy.