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Analysis of complement system gene expression and outcome across the subtypes of glioma

Mudda et al. | May 17, 2023

Analysis of complement system gene expression and outcome across the subtypes of glioma
Image credit: National Cancer Institute

Here the authors sought to better understand glioma, cancer that occurs in the glial cells of the brain with gene expression profile analysis. They considered the expression of complement system genes across the transcriptional and IDH-mutational subtypes of low-grade glioma and glioblastoma. Based on their results of their differential gene expression analysis, they found that outcomes vary across different glioma subtypes, with evidence suggesting that categorization of the transcriptional subtypes could help inform treatment by providing an expectation for treatment responses.

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Isolation of Microbes From Common Household Surfaces

Gajanan et al. | Jan 27, 2013

Isolation of Microbes From Common Household Surfaces

Microorganisms such as bacteria and fungi live everywhere in the world around us. The authors here demonstrate that these predominantly harmless microbes can be isolated from many household locations that appear "clean." Further, they test the cleaning power of 70% ethanol and suggest that many "clean" surfaces are not in fact "sterile."

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Effects of Temperature on Hand Sanitizer Efficiency

Molom-Ochir et al. | May 11, 2022

Effects of Temperature on Hand Sanitizer Efficiency

Here, recognizing the widespread use of hand sanitizers, the authors investigated their effectiveness in relation to storage temperature. They applied hand sanitizer before and after touching a cell phone and used LB-agar plates to monitor the growth of bacteria following this process. They found that 70% ethyl-alcohol-based sanitizers are least effective at temperatures above 107.27 °F and most effective at 96.17 °F.

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Antibiotic Residues Detected in Commercial Cow’s Milk

Memili et al. | Mar 18, 2015

Antibiotic Residues Detected in Commercial Cow’s Milk

Antibiotics are oftentimes used to treat mastitis (infection of the mammary gland) in dairy cows. Regulations require that milk from these cows be discarded until the infection has cleared and antibiotic residues are no longer detectable in the cow's milk. These regulations are in place to protect consumers and to help prevent the rise of antibiotic resistant bacteria. In this study, the authors test milk samples from 10 milk suppliers in the Greensboro, NC to see if they contain detectable levels of antibiotic residues.

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Honey Bee Pollen in Allergic Rhinitis Healing

Bjelajac et al. | Jun 24, 2020

Honey Bee Pollen in Allergic Rhinitis Healing

The most common atopic disease of the upper respiratory tract is allergic rhinitis. It is defined as a chronic inflammatory condition of nasal mucosa due to the effects of one or more allergens and is usually a long-term problem. The purpose of our study was to test the efficiency of apitherapy in allergic rhinitis healing by the application of honey bee pollen. Apitherapy is a branch of alternative medicine that uses honey bee products. Honey bee pollen can act as an allergen and cause new allergy attacks for those who suffer from allergic rhinitis. Conversely, we hoped to prove that smaller ingestion of honey bee pollen on a daily basis would desensitize participants to pollen and thus reduce the severity of allergic rhinitis.

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Machine learning-based enzyme engineering of PETase for improved efficiency in plastic degradation

Gupta et al. | Jan 31, 2023

 Machine learning-based enzyme engineering of PETase for improved efficiency in plastic degradation
Image credit: Markus Spiske

Here, recognizing the recognizing the growing threat of non-biodegradable plastic waste, the authors investigated the ability to use a modified enzyme identified in bacteria to decompose polyethylene terephthalate (PET). They used simulations to screen and identify an optimized enzyme based on machine learning models. Ultimately, they identified a potential mutant PETases capable of decomposing PET with improved thermal stability.

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Machine learning on crowd-sourced data to highlight coral disease

Narayan et al. | Jul 26, 2021

Machine learning on crowd-sourced data to highlight coral disease

Triggered largely by the warming and pollution of oceans, corals are experiencing bleaching and a variety of diseases caused by the spread of bacteria, fungi, and viruses. Identification of bleached/diseased corals enables implementation of measures to halt or retard disease. Benthic cover analysis, a standard metric used in large databases to assess live coral cover, as a standalone measure of reef health is insufficient for identification of coral bleaching/disease. Proposed herein is a solution that couples machine learning with crowd-sourced data – images from government archives, citizen science projects, and personal images collected by tourists – to build a model capable of identifying healthy, bleached, and/or diseased coral.

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The effects of the cancer metastasis promoting gene CD151 in E. coli

Burgess et al. | Jun 11, 2023

The effects of the cancer metastasis promoting gene <i>CD151</i> in <i>E. coli</i>
Image credit: qimono

The independent effects of metastasis-promoting gene CD151 in the process of metastasis are not known. This study aimed to isolate CD151 to discover what its role in metastasis would be uninfluenced by potential interactions with other components and pathways in human cells. Results showed that CD151 significantly increased the adhesion of the cells and decreased their motility. Thus, it may be that CD151 is upregulated in cancer cells for the last step of metastasis, and it increases the chances of success of metastasis by aiding in implantation of the cancer cells. Targeting CD151 in chemotherapeutic modalities could therefore potentially slow or prevent metastasis.

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Using machine learning to develop a global coral bleaching predictor

Madireddy et al. | Feb 21, 2023

Using machine learning to develop a global coral bleaching predictor
Image credit: Madireddy, Bosch, and McCalla

Coral bleaching is a fatal process that reduces coral diversity, leads to habitat loss for marine organisms, and is a symptom of climate change. This process occurs when corals expel their symbiotic dinoflagellates, algae that photosynthesize within coral tissue providing corals with glucose. Restoration efforts have attempted to repair damaged reefs; however, there are over 360,000 square miles of coral reefs worldwide, making it challenging to target conservation efforts. Thus, predicting the likelihood of bleaching in a certain region would make it easier to allocate resources for conservation efforts. We developed a machine learning model to predict global locations at risk for coral bleaching. Data obtained from the Biological and Chemical Oceanography Data Management Office consisted of various coral bleaching events and the parameters under which the bleaching occurred. Sea surface temperature, sea surface temperature anomalies, longitude, latitude, and coral depth below the surface were the features found to be most correlated to coral bleaching. Thirty-nine machine learning models were tested to determine which one most accurately used the parameters of interest to predict the percentage of corals that would be bleached. A random forest regressor model with an R-squared value of 0.25 and a root mean squared error value of 7.91 was determined to be the best model for predicting coral bleaching. In the end, the random model had a 96% accuracy in predicting the percentage of corals that would be bleached. This prediction system can make it easier for researchers and conservationists to identify coral bleaching hotspots and properly allocate resources to prevent or mitigate bleaching events.

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