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Effects of Paan Extracts on Periodontal Ligament and Osteosarcoma Cells

Venkatachalam et al. | Sep 20, 2018

Effects of Paan Extracts on Periodontal Ligament and Osteosarcoma Cells

In South Asian countries, the major cause of oral cancer is reported to be chewing paan, which is comprised of betel leaf daubed with slaked lime paste and areca nut. To investigate how paan may contribute to the onset of cancer, the authors treated two immortalized cell lines with extracts of betel leaf, areca nut, and lime and evaluated how these treatments affected cell proliferation and cell death. Initial results indicate that while betel leaf alone may inhibit cell growth, areca nut promoted cancer cell survival and proliferation, even when co-treated with betel leaf. These data suggest that areca nut could exacerbate the progression of oral cancer in humans.

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Upregulation of the Ribosomal Pathway as a Potential Blood-Based Genetic Biomarker for Comorbid Major Depressive Disorder (MDD) and PTSD

Ravi et al. | Aug 22, 2018

Upregulation of the Ribosomal Pathway as a Potential  Blood-Based Genetic Biomarker for Comorbid Major Depressive Disorder (MDD) and PTSD

Major Depressive Disorder (MDD), and Post-Traumatic Stress Disorder (PTSD) are two of the fastest growing comorbid diseases in the world. Using publicly available datasets from the National Institute for Biotechnology Information (NCBI), Ravi and Lee conducted a differential gene expression analysis using 184 blood samples from either control individuals or individuals with comorbid MDD and PTSD. As a result, the authors identified 253 highly differentially-expressed genes, with enrichment for proteins in the gene ontology group 'Ribosomal Pathway'. These genes may be used as blood-based biomarkers for susceptibility to MDD or PTSD, and to tailor treatments within a personalized medicine regime.

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Don’t Waste the Medical Waste: Reducing Improperly Classified Hazardous Waste in a Medical Facility

Hemani et al. | Jun 20, 2018

Don’t Waste the Medical Waste: Reducing Improperly Classified Hazardous Waste in a Medical Facility

Hemani et al. tackled the problem of rampant hospital waste by implementing staff training to help inform hospital workers about proper waste disposal. The authors observed a significant increase in proper waste disposal after the training, showing that simple strategies, such as in-person classroom training and posters, can have a profound effect on limiting improper waste handling.

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The Effect of the Stomatal Index on the Net Rate of Photosynthesis in the Leaves of Spinacia oleracea, Vinca minor, Rhododendron spp, Epipremnum aureum, and Hedera spp

Segev et al. | Nov 15, 2015

The Effect of the Stomatal Index on the Net Rate of Photosynthesis in the Leaves of <i>Spinacia oleracea</i>, <i>Vinca minor</i>, <i>Rhododendron spp</i>, <i>Epipremnum aureum</i>, and <i>Hedera spp</i>

The density of stomata, or stomatal index, in plant leaves is correlated with the plant's rate of photosynthesis, and affected by the plant's climate. In this paper, authors measure the stomatal index of five plant species to derive their rates of photosynthesis. These results could help track changes in plants' photosynthetic rates with changing climate.

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Comparative Gamma Radiation Analysis by Geographic Region

Zadan et al. | Jul 20, 2015

Comparative Gamma Radiation Analysis by Geographic Region

Gamma radiation can be produced by both natural and man-made sources and abnormally high exposure levels could lead to an increase in cell damage. In this study, gamma radiation was measured at different locations and any correlation with various geographic factors, such as distance from a city center, elevation and proximity to the nearest nuclear reactor, was determined.

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Groundwater prediction using artificial intelligence: Case study for Texas aquifers

Sharma et al. | Apr 19, 2024

Groundwater prediction using artificial intelligence: Case study for Texas aquifers

Here, in an effort to develop a model to predict future groundwater levels, the authors tested a tree-based automated artificial intelligence (AI) model against other methods. Through their analysis they found that groundwater levels in Texas aquifers are down significantly, and found that tree-based AI models most accurately predicted future levels.

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