Attention Classification Based on Biosignals during Standard Cognitive Tasks for Occupational Domains

Studying the effect of Chlamydia trachomatis, Helicobacter pylori, and Varicella zoster microorganisms in stimulating the expression of cytokines TNFα, IFNɤ, TGFβ, IL-10 in Alzheimer and non-Alzheimer patients

OBJECTIVES

This study aimed to use the real-time RT-PCR method to detect the gene expression cytokines IL-10, TNFα, IFN-γ, and TGF-β in the serum of Alzheimer's patients.

METHODS

This study was conducted on 100 serum samples of Alzheimer's patients. DNA extraction was performed on the samples with the Cinnaclone kit and PCR techniques were used to detect the presence of Helicobacter pylori, Chlamydia trachomatis, and Varicella zoster virus. Real-time RT-PCR was performed to measure the expression of TNFα, IFNɤ, TGFβ, and IL-10 genes with a Smobio kit.

RESULTS

The relative changes in the expression of TNFα, IFNɤ, TGFβ, and IL-10 genes were observed in Alzheimer's patients compared to the control samples without microorganisms, and a significant increase was observed (P < 0.05).

CONCLUSION

This study showed that the cytokines TNFα, IFNɤ, TGFβ, and IL-10, have an increase in Alzheimer's patients(P < 0.05). Therefore, the presence of the microorganisms accompanied by the rise and inducing the expression of cytokines compared to the groups without the mentioned microorganisms causes a significant increase in the production of cytokines effective in the occurrence or exacerbation of Alzheimer's disease.

EgoActive: Integrated Wireless Wearable Sensors for Capturing Infant Egocentric Auditory-Visual Statistics and Autonomic Nervous System Function 'in the Wild'

There have been sustained efforts toward using naturalistic methods in developmental science to measure infant behaviors in the real world from an egocentric perspective because statistical regularities in the environment can shape and be shaped by the developing infant. However, there is no user-friendly and unobtrusive technology to densely and reliably sample life in the wild. To address this gap, we present the design, implementation and validation of the EgoActive platform, which addresses limitations of existing wearable technologies for developmental research. EgoActive records the active infants' egocentric perspective of the world via a miniature wireless head-mounted camera concurrently with their physiological responses to this input via a lightweight, wireless ECG/acceleration sensor. We also provide software tools to facilitate data analyses. Our validation studies showed that the cameras and body sensors performed well. Families also reported that the platform was comfortable, easy to use and operate, and did not interfere with daily activities. The synchronized multimodal data from the EgoActive platform can help tease apart complex processes that are important for child development to further our understanding of areas ranging from executive function to emotion processing and social learning.

Automatic Cognitive Fatigue Detection Using Wearable fNIRS and Machine Learning

Wearable sensors have increasingly been applied in healthcare to generate data and monitor patients unobtrusively. Their application for Brain-Computer Interfaces (BCI) allows for unobtrusively monitoring one's cognitive state over time. A particular state relevant in multiple domains is cognitive fatigue, which may impact performance and attention, among other capabilities. The monitoring of this state will be applied in real learning settings to detect and advise on effective break periods. In this study, two functional near-infrared spectroscopy (fNIRS) wearable devices were employed to build a BCI to automatically detect the state of cognitive fatigue using machine learning algorithms. An experimental procedure was developed to effectively induce cognitive fatigue that included a close-to-real digital lesson and two standard cognitive tasks: Corsi-Block task and a concentration task. Machine learning models were user-tuned to account for the individual dynamics of each participant, reaching classification accuracy scores of around 70.91 ± 13.67 %. We concluded that, although effective for some subjects, the methodology needs to be individually validated before being applied. Moreover, time on task was not a particularly determining factor for classification, i.e., to induce cognitive fatigue. Further research will include other physiological signals and human-computer interaction variables.