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The non-nutritive sweeteners acesulfame potassium and neotame slow the regeneration rate of planaria

Russo et al. | Nov 29, 2023

The non-nutritive sweeteners acesulfame potassium and neotame slow the regeneration rate of planaria
Image credit: Russo et al. 2023

The consumption of sugar substitute non-nutritive sweeteners (NNS) has dramatically increased in recent years. Despite being advertised as a healthy alternative, NNS have been linked to adverse effects on the body, such as neurodegenerative diseases (NDs). In NDs, neural stem cell function is impaired, which inhibits neuron regeneration. The purpose of this study was to determine if the NNS acesulfame potassium (Ace-K) and neotame affect planaria neuron regeneration rates. Since human neurons may regenerate, planaria, organisms with extensive regenerative capabilities due to stem cells called neoblasts, were used as the model organism. The heads of planaria exposed to either a control or non-toxic concentrations of NNS were amputated. The posterior regions of the planaria were observed every 24 hours to see the following regeneration stages: (1) wound healing, (2) blastema development, (3) growth, and (4) differentiation. The authors hypothesized that exposure to the NNS would slow planaria regeneration rates. The time it took for the planaria in the Ace-K group and the neotame group to reach the second, third, and fourth regeneration stage was significantly greater than that of the control. The results of this study indicated that exposure to the NNS significantly slowed regeneration rates in planaria. This suggests that the NNS may adversely impact neoblast proliferation rates in planaria, implying that it could impair neural stem cell proliferation in humans, which plays a role in NDs. This study may provide insight into the connection between NNS, human neuron regeneration, and NDs.

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Prediction of preclinical Aβ deposit in Alzheimer’s disease mice using EEG and machine learning

Igarashi et al. | Nov 29, 2022

Prediction of preclinical Aβ deposit in Alzheimer’s disease mice using EEG and machine learning

Alzheimer’s disease (AD) is a common disease affecting 6 million people in the U.S., but no cure exists. To create therapy for AD, it is critical to detect amyloid-β protein in the brain at the early stage of AD because the accumulation of amyloid-β over 20 years is believed to cause memory impairment. However, it is difficult to examine amyloid-β in patients’ brains. In this study, we hypothesized that we could accurately predict the presence of amyloid-β using EEG data and machine learning.

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Autologous transplantation of fresh ovarian tissue in the ICR mice model

Wang et al. | Oct 24, 2022

Autologous transplantation of fresh ovarian tissue in the ICR mice model

In this study, we performed orthotopic auto-transplantation of fresh ovarian tissues by transplanting unilateral half ovarian tissue to the contralateral ovary in the ICR (Institute of Cancer Research) strain of outbred, heterogeneous mice to determine if the transplanted tissue could be functional. We found that the freshly transplanted mouse ovarian tissue survived and functional, as histochemical and immunofluorescence assays have shown that not only both follicles at different developing stages and corpus luteum are available, but the morphology of them are properly maintained within the transplanted tissue.

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POC-MON: A Novel and Cost-Effective Pocket Lemon Sniff Test (PLST) for Early Detection of Major Depressive Disorder

Cruz et al. | Jul 07, 2020

POC-MON: A Novel and Cost-Effective Pocket Lemon Sniff Test (PLST) for Early Detection of Major Depressive Disorder

Effective treatment of depression requires early detection. Depressive symptoms overlap with olfactory regions, which led to several studies of the correlation between sense of smell and depression. The alarming rise of depression, its related crimes, suicides, and lack of inexpensive, quick tools in detecting early depression — this study aims in demonstrating decreased olfaction and depression correlation. Forty-two subjects (ages 13-83) underwent POC-MON (Pocket Lemon) assessment — an oven-dried lemon peel sniff test, subjected to distance measurement when odor first detected (threshold) and completed Patient Health Questionnaires (PHQ-9). POC-MON and PHQ-9 scores yielded a correlation of 20% and 18% for the right and left nostrils, respectively. Among male (n=17) subjects, the average distance of POC-MON and PHQ-9 scores produced a correlation of 14% and 16% for the right and left nostrils, respectively. Females (n=25) demonstrated a correlation of 28% and 21% for the right and left nostrils, respectively. These results suggest the correlation between olfaction and depression in diagnosing its early-stage, using a quick, inexpensive, and patient-friendly tool — POC-MON.

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A new therapy against MDR bacteria by in silico virtual screening of Pseudomonas aeruginosa LpxC inhibitors

Liu et al. | Apr 27, 2022

A new therapy against MDR bacteria by <em>in silico</em> virtual screening of <em>Pseudomonas aeruginosa</em> LpxC inhibitors

Here, seeking to address the growing threat of multidrug-resistant bacteria (MDR). the authors used in silico virtual screening to target MDR Pseudomonas aeruginosa. They considered a key protein in its biosynthesis and virtually screened 20,000 candidates and 30 derivatives of brequinar. In the end, they identified a possible candidate with the highest degree of potential to inhibit the pathogen's lipid A synthesis.

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The Cosmic Microwave Background: Galactic Foregrounds and Faraday Rotation

Connelly et al. | Nov 20, 2017

The Cosmic Microwave Background: Galactic Foregrounds and Faraday Rotation

The cosmic microwave background (CMB) is faint electromagnetic radiation left over from early stages in the formation of the universe. In order to analyze the CMB, scientists need to remove from electromagnetic data foreground radiation that contaminates CMB datasets. In this study, students utilize extensive updated datasets to analyze the correlation between CMB maps and Faraday RM and WMAP sky maps.

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The Inhibitory Effect of Probiotics on the Growth and Biofilm Formation of Salmonella Sp.

Lee et al. | Jan 26, 2019

The Inhibitory Effect of Probiotics on the Growth and Biofilm Formation of Salmonella Sp.

Salmonella is a genus of bacteria responsible for over 90 million cases of intestinal illnesses yearly. Like many bacteria, Salmonella can create a biofilm matrix, which confers stronger resistance against antibiotics. However, there has been relatively little research on the inhibition of Salmonella biofilm formation, which is a crucial factor in its widespread growth. In this study, Lee and Kim quantitatively measure the effectiveness of several common probiotics in inhibiting Salmonella bacterial growth. They found concentration-dependent antibacterial effects varied among the probiotics tested, indicating the possibility of probiotic species-specific mechanisms of Salmonella growth inhibition.

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Utilizing a novel T1rho method to detect spinal degeneration via magnetic resonance imaging

Wang et al. | Oct 04, 2023

Utilizing a novel T1rho method to detect spinal degeneration via magnetic resonance imaging

Spinal degeneration has been linked to critical conditions such as osteoarthritis in adults aged 40+; while this condition is considered to be irreversible, we took interest in magnetic resonance imaging (MRI) for early detection of the condition. Ultimately, our purpose was to determine the effectiveness of a relatively novel T1rho method in the early detection of spinal degeneration, and we hypothesized that the early to mild progression of spinal degeneration would affect T1rho values following an MRI scan.

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A machine learning approach for abstraction and reasoning problems without large amounts of data

Isik et al. | Jun 25, 2022

A machine learning approach for abstraction and reasoning problems without large amounts of data

While remarkable in its ability to mirror human cognition, machine learning and its associated algorithms often require extensive data to prove effective in completing tasks. However, data is not always plentiful, with unpredictable events occurring throughout our daily lives that require flexibility by artificial intelligence utilized in technology such as personal assistants and self-driving vehicles. Driven by the need for AI to complete tasks without extensive training, the researchers in this article use fluid intelligence assessments to develop an algorithm capable of generalization and abstraction. By forgoing prioritization on skill-based training, this article demonstrates the potential of focusing on a more generalized cognitive ability for artificial intelligence, proving more flexible and thus human-like in solving unique tasks than skill-focused algorithms.

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