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Comparing the Biodegradability of Petroleum-based Plastic with a Novel, Sustainable Bio-plastic Alternative

Van Note et al. | Dec 02, 2020

Comparing the Biodegradability of Petroleum-based Plastic with a Novel, Sustainable Bio-plastic Alternative

In this research, a novel bioplastic inclusive of bamboo tannins and chitosan is selected from more than 60 trial formula variations based on resulting strength, fatigue, and transparency attributes. The biodegradability of the finalized bioplastic is compared to that of conventional polyethylene, in addition to investigating its solubility and water absorbance. This research displays the potential of a legitimate, fully biodegradable plastic alternative to current marketplace bioplastics.

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Integrated Ocean Cleanup System for Sustainable and Healthy Aquatic Ecosystems

Anand et al. | Nov 14, 2020

Integrated Ocean Cleanup System for Sustainable and Healthy Aquatic Ecosystems

Oil spills are one of the most devastating events for marine life. Finding ways to clean up oil spills without the need for harsh chemicals could help decrease the negative impact of such spills. Here the authors demonstrate that using a combination of several biodegradable substances can effectively adsorb oil in seawater in a laboratory setting. They suggest further exploring the potential of such a combination as a possible alternative to commonly-used non-biodegradable substances in oil spill management.

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Peptidomimetics Targeting the Polo-box Domain of Polo-like Kinase 1

Jang et al. | Aug 19, 2016

Peptidomimetics Targeting the Polo-box Domain of Polo-like Kinase 1

Polo-like kinase 1 (Plk1) is a master regulator of mitosis, initiating key steps of cell cycle regulation, and its overexpression is associated with certain types of cancer. In this study, the authors carefully designed peptides that were able to bind to Plk1 at a location that is important for its proper localization and function. Future studies could further develop these peptides to selectively target Plk1 in cancer cells and induce mitotic arrest.

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Assessing the Efficacy of NOX Enzyme Inhibitors as Potential Treatments for Ischemic Stroke in silico

Vinay et al. | Sep 18, 2020

Assessing the Efficacy of NOX Enzyme Inhibitors as Potential Treatments for Ischemic Stroke <i>in silico</i>

Ischemic stroke occurs when blood flow to the brain is interrupted, causing brain damage. This study investigated the effectiveness of different NOX inhibitors as treatments for ischemic stroke in silico. The results help corroborate previous in vivo and in vitro studies in an in silico format, and can be used towards developing drugs to treat ischemic stroke.

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Investigating Lymphocytic Involvement in Minimal Change Nephrotic Syndrome

Muncan et al. | Jan 27, 2016

Investigating Lymphocytic Involvement in Minimal Change Nephrotic Syndrome

Minimal Change Disease (MCD) is a degenerative kidney disease. Researchers know very little about the cause of this disorder, however some research has suggested that T lymphocytes may be involved. In this study, the authors measure CD4 and CD8 T cell subpopulations in patients with MCD to investigate whether irregular T lymphocyte populations may be involved in MCD pathogenesis.

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Evaluating Biomarkers and Treatments for Acute Kidney Injury in a Zebrafish Model

Mathew et al. | Aug 11, 2019

Evaluating Biomarkers and Treatments for Acute Kidney Injury in a Zebrafish Model

Coronary Artery Disease (CAD) is the leading cause of death in the United States, and 81% of Acute Kidney Injury (AKI) patients in the renal fibrosis stage later develop CAD. In this study, Mathew and Joykutty aimed to create a cost-effective strategy to treat AKI and thus prevent CAD using a model of the zebrafish, Danio rerio. They first tested whether AKI is induced in Danio rerio upon exposure to environmental toxins, then evaluated nitrotyrosine as an early biomarker for toxin-induced AKI. Finally, they evaluated 4 treatments of renal fibrosis, the last stage of AKI, and found that the compound SB431542 was the most effective treatment (reduced fibrosis by 99.97%). Their approach to treating AKI patients, and potentially prevent CAD, is economically feasible for translation into the clinic in both developing and developed countries.

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The Effects of Barley Straw (Hordeum vulgare) Extract and Barley Straw Pellets on Algal Growth and Water Quality

McHargue et al. | Oct 06, 2020

The Effects of Barley Straw (Hordeum vulgare) Extract and Barley Straw Pellets on Algal Growth and Water Quality

Algal overgrowth often threatens to clog irrigation pipes and drinking water lines when left unchecked, as well as releasing possible toxins that threaten plant and human health. It is thus important to find natural, non-harmful agents that can decrease algal growth without threatening the health of plants and humans. In this paper, the authors test the efficacy of barely extract in either liquid or pellet form in decreasing algal growth. While their results were inconclusive, the experimental set-up allows them to investigate a wider range of agents as anti-algal treatments that could potentially be adopted on a wider scale.

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Zinc-related Treatments Combined with Chloroquine and Gemcitabine for Treating Pancreatic Cancer

Ma et al. | Sep 11, 2021

Zinc-related Treatments Combined with Chloroquine and Gemcitabine for Treating Pancreatic Cancer

Pancreatic cancer is one of the deadliest cancers, with a 10% 5-year survival rate. The authors studied various dosages of TPEN and zinc in combination with Chloroquine and Gemcitabine as treatments to reduce cell proliferation. Results showed that when combined with Chloroquine and Gemcitabine, zinc and TPEN both significantly lowered cell proliferation compared to Gemcitabine, suggesting a synergistic effect that resulted in a more cytotoxic treatment. Further research and clinical trials on this topic are needed to determine whether this could be a viable treatment for pancreatic cancer.

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Using data science along with machine learning to determine the ARIMA model’s ability to adjust to irregularities in the dataset

Choudhary et al. | Jul 26, 2021

Using data science along with machine learning to determine the ARIMA model’s ability to adjust to irregularities in the dataset

Auto-Regressive Integrated Moving Average (ARIMA) models are known for their influence and application on time series data. This statistical analysis model uses time series data to depict future trends or values: a key contributor to crime mapping algorithms. However, the models may not function to their true potential when analyzing data with many different patterns. In order to determine the potential of ARIMA models, our research will test the model on irregularities in the data. Our team hypothesizes that the ARIMA model will be able to adapt to the different irregularities in the data that do not correspond to a certain trend or pattern. Using crime theft data and an ARIMA model, we determined the results of the ARIMA model’s forecast and how the accuracy differed on different days with irregularities in crime.

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