Rhizopus stolonifer is a mold commonly found growing on bread that can cause many negative health effects when consumed. Preservatives are the well-known answer to this problem; however, many preservatives are not naturally found in food, and some have negative health effects of their own. We focused on honey as a possible solution because of its natural origin and self-preservation ability. We hypothesized that honey would decrease the growth rate of R. stolonifer . We evaluated the honey with a zone of inhibition (ZOI) test on agar plates. Sabouraud dextrose agar was mixed with differing volumes of honey to generate concentrations between 10.0% and 30.0%. These plates were then inoculated with a solution of spores collected from the mold. The ZOI was measured to determine antifungal effectiveness. A statistically significant difference was found between the means of all concentrations except for 20.0% and 22.5%. Our findings support the hypothesis as we showed a positive correlation between the honey concentration and growth rate of mold. By using this data, progress could be made on an all-natural, honey-based preservative.

Osteosarcoma is a type of bone cancer that affects young adults and children. Early diagnosis of osteosarcoma is crucial to successful treatment. The current methods of diagnosis, which include imaging tests and biopsy, are time consuming and prone to human error. Hence, we used deep learning to extract patterns and detect osteosarcoma from histological images. We hypothesized that the combination of two different technologies (transfer learning and data augmentation) would improve the efficacy of osteosarcoma detection in histological images. The dataset used for the study consisted of histological images for osteosarcoma and was quite imbalanced as it contained very few images with tumors. Since transfer learning uses existing knowledge for the purpose of classification and detection, we hypothesized it would be proficient on such an imbalanced dataset. To further improve our learning, we used data augmentation to include variations in the dataset. We further evaluated the efficacy of different convolutional neural network models on this task. We obtained an accuracy of 91.18% using the transfer learning model MobileNetV2 as the base model with various geometric transformations, outperforming the state-of-the-art convolutional neural network based approach.

Currently there is no early dehydration detection system using temperature and pH as indicators. A sensor could alert the wearer and others of low hydration levels, which would normally be difficult to catch prior to more serious complications resulting from dehydration. In this study, a protein fluorophore, green fluorescent protein (GFP), and a chemical fluorophore, fluorescein, were tested for a change in fluorescence in response to increased temperature or decreased pH. Reversing the pH change did not restore GFP fluorescence, but that of fluorescein was re-established. This finding suggests that fluorescein could be used as a reusable sensor for a dehydration-related pH change.

Anticholinergics are used in treating asthma, a chronic inflammation of the airways. These drugs block human M1 and M2 muscarinic acetylcholine receptors, inhibiting bronchoconstriction. However, studies have reported complications of anticholinergic usage, such as exacerbated eosinophil production and worsened urinary retention. Modification of known anticholinergics using bioisosteric replacements to increase efficacy could potentially minimize these complications. The present study focuses on identifying viable analogs of anticholinergics to improve binding energy to the receptors compared to current treatment options. Glycopyrrolate (G), ipratropium (IB), and tiotropium bromide (TB) were chosen as parent drugs of interest, due to the presence of common functional groups within the molecules, specifically esters and alcohols. Docking score analysis via AutoDock Vina was used to evaluate the binding energy between drug analogs and the muscarinic acetylcholine receptors. The final results suggest that G-A3, IB-A3, and TB-A1 are the most viable analogs, as binding energy was improved when compared to the parent drug. G-A4, IB-A4, IB-A5, TB-A3, and TB-A4 are also potential candidates, although there were slight regressions in binding energy to both muscarinic receptors for these analogs. By researching the effects of bioisosteric replacements of current anticholinergics, it is evident that there is a potential to provide asthmatics with more effective treatment options.

This paper hypothesized that the tumor microenvironment mediates cancer’s response to oxidative stress by delivering extracellular vesicles to cancer cells. Breast and lung cancer cells were treated with EVs, reavealing that EVs extracted from oxidatively stressed adipocytes increased the cell proliferation of breast cancer cells. These findings present a novel way that the TME influences cancer progression.

A primary cause of diabetes is insulin resistance, which is caused by disruption of insulin signal transduction. The objective of this study was to maximize insulin sensitivity by creating a more effective, early intervention-based treatment to avert severe T2D. This treatment combined metformin, “the insulin sensitizer”, and medicinal plants, curcumin, fenugreek, and nettle.

As digital tools become more prevalent in medicine, the ability for individuals to understand and take actions based on what they read on the internet is crucial. eHealth literacy is defined as as the ability to seek, find, understand, and evaluate health information from electronic sources and apply the knowledge gained to addressing or solving a health problem. In general, Americans have low eHealth literacy rates. However, limited research has been conducted to understand the eHealth literacy level among older Chinese adult immigrants in the U.S. To determine the eHealth literacy of elderly Chinese immigrants, we sent out an eHealth survey and relevant computer skills survey using a modified version of the eHEALS (eHealth Literacy Scale) health literacy test. We hypothesized that elders who consumed more electronic health content would have a higher eHealth literacy score. The results of this survey showed that there was a positive correlation between the frequency of electronic health information consumption and the participant's eHealth literacy rate. In addition, the results of our computer literacy test show that the frequency of consumption and computer literacy are positively correlated as well. There is a strong positive correlation between the level of computer skills and eHealth literacy of participants. These results reveal possible steps individuals can take to reduce health misinformation and improve their own health by attaining, understanding, and taking action on health material on the internet.

In this study, the authors use bioinformatic approaches to characterize the mirror neurons, which are active when performing and seeing certain actions. They also investigated whether mirror neuron impairment was connected to neural degenerative diseases and psychiatric disorders.

Smoking generates free radicals and reactive oxygen species which induce cell damage and lipid peroxidation. This is linked to the development of oral cancer in chronic smokers. The authors of this study developed Quitpuff, simple colorimetric test to measure the extent of lipid peroxidation in saliva samples. This test detected salivary lipid peroxidation with 96% accuracy in test subjects and could serve as an inexpensive, non-invasive test for smokers to measure degree of salivary lipid peroxidation and potential risk of oral cancer.

The authors test potential anti-inflammatory and pro-apoptotic effects of a polyherbal extract formulation on cultured breast cancer cells.