The authors surveyed individuals diagnosed with coronary artery disease about their mental health to study a potential connection between coronary artery disease and depression.
Read More...Examining the prevalence of depression in coronary artery disease patients: a cross-sectional analysis
The authors surveyed individuals diagnosed with coronary artery disease about their mental health to study a potential connection between coronary artery disease and depression.
Read More...Antibacterial activity by Dombeya wallichii plant extracts obtained by ultrasound-assisted extraction
Medicinal plants could be a good source of medication to combat antibiotic resistance. Dombeya wallichii, which is commonly called Pink Ball Tree in the family Sterculiaceae, has been documented to have medicinal potential. We observed the highest antibacterial activity in the stem extracts, followed by leaf and bark extracts. The extracts were more effective against tested Gram-positive bacteria when compared with Gram-negative strains.
Read More...Enhancing activity of antibiotics against Staphylococcus aureus with Shuang-Huang-Lian
Staphylococcus aureus is a major pathogen in both hospitals and the community and can cause systemic infections such as pneumonia. Multi-drug resistant strains, such as Methicillin-resistant S. aureus (MRSA) are particularly worrisome. In order to reduce the development of bacterial resistance, we hypothesized that two selected traditional Chinese medicines, Shuang-Huang-Lian (SHL) and Lan-Qin, would be effective against S. aureus. The results showed that SHL had a synergistic effect with gentamicin as well as additive effects with penicillin and cefazolin against S. aureus compared with using antibiotics alone.
Read More...Discovery of novel targets for diffuse large B-cell lymphoma
In this study, the authors identify new potential targets to treat advanced diffuse large B-cell lymphoma after treatment relapse and loss of CD19 expression.
Read More...EEG study of virtual learning demonstrates worsened learning outcomes and increased mirror neuron activation
In this article, Choi and Rossitto investigated the limitations of virtual learning by examining in-person dance learning compared to virtual dance learning while wearing EEG headsets. They found that in-person learners outperformed virtual learners and that virtual learners had higher mirror neuron activity as assessed by Mu rhythm power.
Read More...Computational Structure-Activity Relationship (SAR) of Berberine Analogs in Double-Stranded and G-Quadruplex DNA Binding Reveals Both Position and Target Dependence
Berberine, a natural product alkaloid, and its analogs have a wide range of medicinal properties, including antibacterial and anticancer effects. Here, the authors explored a library of alkyl or aryl berberine analogs to probe binding to double-stranded and G-quadruplex DNA. They determined that the nature of the substituent, the position of the substituent, and the nucleic acid target affect the free energy of binding of berberine analogs to DNA and G-quadruplex DNA, however berberine analogs did not result in net stabilization of G-quadruplex DNA.
Read More...High school students’ perceptions of third-party tracking and personalization
The authors looked at student perception on various situations involving third-party tracking to personalize recommendations.
Read More...Gender differences in social media, sleep, and cognition in U.S. teens
The authors survey teenagers within the United States regarding the effect of social media use on sleep quality and attention span.
Read More...Transfer Learning with Convolutional Neural Network-Based Models for Skin Cancer Classification
Skin cancer is a common and potentially deadly form of cancer. This study’s purpose was to develop an automated approach for early detection for skin cancer. We hypothesized that convolutional neural network-based models using transfer learning could accurately differentiate between benign and malignant moles using natural images of human skin.
Read More...The precision of machine learning models at classifying autism spectrum disorder in adults
Autism spectrum disorder (ASD) is hard to correctly diagnose due to the very subjective nature of diagnosing it: behavior analysis. Due to this issue, we sought to find a machine learning-based method that diagnoses ASD without behavior analysis or helps reduce misdiagnosis.
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