The authors test electrocardiography biofeedback as a treatment for individuals with Postural Orthostatic Tachycardia Syndrome.
Read More...Investigating the impact of electrocardiography biofeedback on POTS symptom management
The authors test electrocardiography biofeedback as a treatment for individuals with Postural Orthostatic Tachycardia Syndrome.
Read More...Determining the impact of caffeine on aggression in Betta splendens
The authors test the effect of caffeine on the behavior of the Siamese fighting fish Betta splendens.
Read More...Surface cleanliness of hydrothermally grown zinc oxide microparticles compared to commercial nanoparticles
The authors test the usefulness of zinc oxide microparticles relative to zinc oxide nanoparticles as antibacterial agents.
Read More...Seed priming with melatonin improves drought tolerance in maize
The authors test whether soaking maize seeds in a solution of melatonin improves seed germination and drought tolerance.
Read More...Computational development of aryl sulfone compounds as potential NNRTIs
Non-nucleoside reverse transcriptase inhibitors (NNRTIs) are allosteric inhibitors that bind to the HIV reverse transcriptase and prevent replication. Indolyl aryl sulfones (IAS) and IAS derivatives have been found to be highly effective against mutant strains of HIV-1 reverse transcriptase. Here, we analyzed molecules designed using aryl sulfone scaffolds paired to cyclic compounds as potential NNRTIs through the computational design and docking of 100 novel NNRTI candidates. Moreover, we explored the specific combinations of functional groups and aryl sulfones that resulted in the NNRTI candidates with the strongest binding affinity while testing all compounds for carcinogenicity. We hypothesized that the combination of an IAS scaffold and pyrimidine would produce the compounds with the best binding affinity. Our hypothesis was correct as the series of molecules with an IAS scaffold and pyrimidine exhibited the best average binding affinity. Additionally, this study found 32 molecules designed in this procedure with higher or equal binding affinities to the previously successful IAS derivative 5-bromo-3-[(3,5-dimethylphenyl)sulfonyl]indole-2-carboxyamide when docked to HIV-1 reverse transcriptase.
Read More...Analyzing honey’s ability to inhibit the growth of Rhizopus stolonifer
Rhizopus stolonifer is a mold commonly found growing on bread that can cause many negative health effects when consumed. Preservatives are the well-known answer to this problem; however, many preservatives are not naturally found in food, and some have negative health effects of their own. We focused on honey as a possible solution because of its natural origin and self-preservation ability. We hypothesized that honey would decrease the growth rate of R. stolonifer . We evaluated the honey with a zone of inhibition (ZOI) test on agar plates. Sabouraud dextrose agar was mixed with differing volumes of honey to generate concentrations between 10.0% and 30.0%. These plates were then inoculated with a solution of spores collected from the mold. The ZOI was measured to determine antifungal effectiveness. A statistically significant difference was found between the means of all concentrations except for 20.0% and 22.5%. Our findings support the hypothesis as we showed a positive correlation between the honey concentration and growth rate of mold. By using this data, progress could be made on an all-natural, honey-based preservative.
Read More...Transfer learning and data augmentation in osteosarcoma cancer detection
Osteosarcoma is a type of bone cancer that affects young adults and children. Early diagnosis of osteosarcoma is crucial to successful treatment. The current methods of diagnosis, which include imaging tests and biopsy, are time consuming and prone to human error. Hence, we used deep learning to extract patterns and detect osteosarcoma from histological images. We hypothesized that the combination of two different technologies (transfer learning and data augmentation) would improve the efficacy of osteosarcoma detection in histological images. The dataset used for the study consisted of histological images for osteosarcoma and was quite imbalanced as it contained very few images with tumors. Since transfer learning uses existing knowledge for the purpose of classification and detection, we hypothesized it would be proficient on such an imbalanced dataset. To further improve our learning, we used data augmentation to include variations in the dataset. We further evaluated the efficacy of different convolutional neural network models on this task. We obtained an accuracy of 91.18% using the transfer learning model MobileNetV2 as the base model with various geometric transformations, outperforming the state-of-the-art convolutional neural network based approach.
Read More...Anti-inflammatory and pro-apoptotic properties of the polyherbal drug, MAT20, in MCF-7 cells
The authors test potential anti-inflammatory and pro-apoptotic effects of a polyherbal extract formulation on cultured breast cancer cells.
Read More...Analysis of Milorganite’s ability to sustain growth of Ocimum basilicum in simulated Martian soil
The authors test whether basil can grow in a simulated Martian soil improved with a waste-based fertilizer called Milorganite.
Read More...Protective effect of bromelain and pineapple extracts on UV-induced damage in human skin cells
In this study, the authors tested whether the compound bromelain extracted from pineapples could protect skin cells from UV damage.
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