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Antibacterial activity by Dombeya wallichii plant extracts obtained by ultrasound-assisted extraction

Herur et al. | Nov 13, 2023

Antibacterial activity by <em>Dombeya wallichii</em> 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.

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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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Enhancing activity of antibiotics against Staphylococcus aureus with Shuang-Huang-Lian

Liu et al. | Sep 29, 2022

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.

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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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Methanotrophic bioremediation for the degradation of oceanic methane and chlorinated hydrocarbons

Lee et al. | Oct 08, 2021

Methanotrophic bioremediation for the degradation of oceanic methane and chlorinated hydrocarbons

Seeking an approach to address the increasing levels of methane and chlorinated hydrocarbons that threaten the environment, the authors worked to develop a novel, low-cost biotrickling filter for use as an ex situ method tailored to marine environments. By using methanotrophic bacteria in the filter, they observed methane degradation, suggesting the feasibility of chlorinated hydrocarbon degradation.

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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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