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Many cases of viral hepatitis are easily preventable if caught early; however, a lack of public awareness regarding often leads to diagnoses near the final stages of disease when it is most lethal. Thus, we wanted to understand to what extent an individual's sex, age, education and country of residence (India or Singapore) impacts disease identification. We sent out a survey and quiz to residents in India (n = 239) and Singapore (n = 130) with questions that test their knowledge and awareness of the disease. We hypothesized that older and more educated individuals would score higher because they are more experienced, but that the Indian population will not be as knowledgeable as the Singaporean population because they do not have as many resources, such as socioeconomic access to schools and accessibility to healthcare, available to them. Additionally, we predicted that there would not be any notable differences between make and females. The results revealed that the accuracy for all groups we looked at was primarily below 50%, demonstrating a severe knowledge gap. Therefore, we concluded that if more medical professionals discussed viral hepatitis during hospital visits and in schools, patients can avoid the end stages of the disease in notable cases.

Glioblastoma Multiforme (GBM) is the most malignant brain tumor with the highest fraction of genome alterations (FGA), manifesting poor disease-free status (DFS) and overall survival (OS). We explored The Cancer Genome Atlas (TCGA) and cBioportal public dataset- Firehose legacy GBM to study DNA repair genes Activating Signal Cointegrator 1 Complex Subunit 3 (ASCC3) and Alpha-Ketoglutarate-Dependent Dioxygenase AlkB Homolog 3 (ALKBH3). To test our hypothesis that these genes have correlations with FGA and can better determine prognosis and survival, we sorted the dataset to arrive at 254 patients. Analyzing using RStudio, both ASCC3 and ALKBH3 demonstrated hypomethylation in 82.3% and 61.8% of patients, respectively. Interestingly, low mRNA expression was observed in both these genes. We further conducted correlation tests between both methylation and mRNA expression of these genes with FGA. ASCC3 was found to be negatively correlated, while ALKBH3 was found to be positively correlated, potentially indicating contrasting dysregulation of these two genes. Prognostic analysis showed the following: ASCC3 hypomethylation is significant with DFS and high ASCC3 mRNA expression to be significant with OS, demonstrating ASCC3’s potential as disease prediction marker.

Here, recognizing the significant growth of electronic cigarettes in recent years, the authors sought to test a hypothesis that three main components of the liquid solutions used in e-cigarettes might affect lung cancer cell viability. In a study performed by exposing A549 cells, human lung cancer cells, to different types of smoke extracts, the authors found that increasing levels of nicotine resulted in improve lung cancer cell viability up until the toxicity of nicotine resulted in cell death. They conclude that these results suggest that contrary to conventional thought e-cigarettes may be more dangerous than tobacco cigarettes in certain contexts.

Here, the authors sought to better understand how and why people experience beauty in music. They explored this fundamental aesthetic response by considering numerous emotional responses of participants to diverse musical excerpts using a 42-item Aesthetic Emotions Scale assessment. They found that the highly nuanced emotional experience of beauty in music includes positive, negative, and knowledge-related feelings.

This study examined the effects of stress and selective serotonin reuptake inhibitors (SSRIs) on a measure of astrocyte reactivity in nonhuman primate (NHP) models of stress. Results showed that chronic separation stress in NHPs leads to increased signs of astrogliosis in the NHP hippocampus. The findings were consistent with the hypotheses that hippocampal astrogliosis is an important mechanism in stress-induced cognitive and behavioral deficits.

In this study, the authors investigate the demographic indicators for voter shift between the 2016 and 2020 presidential elections based on demographic data put through a K-nearest neighbors classification algorithm and Principal Component Analysis.

While remarkable in its ability to mirror human cognition, machine learning and its associated algorithms often require extensive data to prove effective in completing tasks. However, data is not always plentiful, with unpredictable events occurring throughout our daily lives that require flexibility by artificial intelligence utilized in technology such as personal assistants and self-driving vehicles. Driven by the need for AI to complete tasks without extensive training, the researchers in this article use fluid intelligence assessments to develop an algorithm capable of generalization and abstraction. By forgoing prioritization on skill-based training, this article demonstrates the potential of focusing on a more generalized cognitive ability for artificial intelligence, proving more flexible and thus human-like in solving unique tasks than skill-focused algorithms.

In this study, the authors conduct a survey of high school students to evaluate the effects of screen time and habits on motivation.

Here seeking to identify a possible solution to procrastination among college students, the authors used an online experiment that involved the random assignment of study partners that they shared their study time goal with. These partners were classified by status and group membership. The authors found that status and group membership did not significantly affect the likelihood of college students achieving their committed goals, and also suggest the potential of soft commitment devices that take advantage of social relationships to reduce procrastination.

Here the authors investigated the role of agricultural fertilizers as potential contributors to greenhouse gas emissions. In contrast to the typical investigations that consider microbiological processes, the authors considered purely chemical processes. Based on their results they found that as much as 20.41% of all CO₂ emission from land-based activities could be a result of mineral nitrogen fertilizers.