COVID-19 and Memory: A Novel Contamination Effect in Memory

A diagnostic model for COVID-19 based on proteomics analysis

BACKGROUND AND OBJECTIVE

Early diagnosis of Coronavirus Disease 2019 (COVID-19) can help save patients' lives before the disease turns severe. This can be achieved through an effective and correct treatment protocol. In this paper, a prediction model is proposed to detect infected cases and determine the severity level of the disease.

METHODS

The proposed model is based on utilizing proteins and metabolites as features for each patient, which are then analyzed using feature selection methods such as Principal Component Analysis (PCA), Information Gain (IG), and analysis of Variance (ANOVA) to select the most significant features. The model employs three classifiers, namely K-Nearest Neighbor (KNN), Support Vector Machine (SVM), and Random Forest (RF), to predict and classify the severity level of the COVID-19 infection. The proposed model is evaluated using four performance measures: accuracy, sensitivity, specificity, and precision.

RESULTS

The experiment results show that the proposed model accuracy can reach 80% using RF classifier with PCA. The PCA selects 22 proteins and 10 metabolites. While ANOVA selects 9 proteins and 5 metabolites. The accuracy reaches 92% after applying RF classifier with the ANOVA. Finally, the accuracy reaches 93% using the RF classifier with only ten features. The selected features are 7 proteins and 3 metabolites. Moreover, it shows that the selected features have a relation to the immune system and respiratory systems.

CONCLUSION

The proposed model uses three classifiers and shows promising results by selecting the important features and maximizing the prediction accuracy.

Adaptive Education: Learning and Remembering with a Stone-Age Brain

Educators generally accept that basic learning and memory processes are a product of evolution, guided by natural selection. Less well accepted is the idea that ancestral selection pressures continue to shape modern memory functioning. In this article, I review evidence suggesting that attention to nature's criterion-the enhancement of fitness-is needed to explain fully how and why people remember. Thinking functionally about memory, and adopting an evolutionary perspective in the laboratory, has led to recent discoveries with clear implications for learning in the classroom. For example, our memory systems appear to be tuned to animacy (the distinction between living and nonliving things) which, in turn, can play a role in enhancing foreign language acquisition. Effective learning management systems need to align with students' prior knowledge, skill, and interest levels, but also with the inherent content biases or "tunings" that are representative of all people.