Alcohol is known to cause various developmental diseases including Fetal Alcohol Syndrome. Here the authors investigate the effect of ethanol on the development of zebrafish beta cells, the part of the pancreas associated with Type 1 Diabetes. They find that exposure to ethanol does adversely affect beta-cell development, suggesting that alcohol ingestion during pregnancy may be linked to diabetes in newborns.
Cannabidiol, or CBD, is a widely available over the counter treatment used for various medical conditions. However, CBD exerts its effects on the endocannabinoid system, which is involved in neural maturation, and could potentially have adverse effects on brain development. Here, the impact of CBD on the development of brine shrimp (Artemia salina) was assessed. Differences in dose responses were observed.
Plants, and all other multi-cellular organisms, develop through the coordinated action of many sets of genes. The authors here investigate the genes, in a class named KNOX, potentially responsible for organizing a certain part of Aquilegia (columbine) flowers called petal spurs. Through the technique Reverse Transcription-Polymerase Chain Reaction (RT-PCR), they find that certain KNOX genes are expressed non-uniformly in petal spurs, suggesting that they may be involved, perhaps in a cell-specific manner. This research will help guide future efforts toward understanding how many beautiful flowers develop their unique shapes.
This study hypothesizes that nanoparticles derived from corn (cNPs)may have anti-proliferative effects on bone cancer and metastasized bone cancer. It finds that human osteosarcoma and human lung carcinoma metastasized to bone marrow cell viability decreased to 0% when treated with cNPs. Overall, these results indicate that cNPs have anti-proliferative effects on bone cancer cells and cancer cells that metastasize to the bone.
In this study, the authors tested different approaches for removing arsenic from rice. Due to higher arsenic levels in water, some areas grow rice with higher levels as well. This is a health hazard and so developing methods to remove arsenic from the rice will be helpful to many. Using a rapid arsenic kit, the authors found that activated charcoal was the most effective at removing arsenic from rice.
Climate change has contributed to the increasing annual temperatures around the world and poses a grave threat to Maize crops. Two methods proven to help combat plant drought stress effects are presoaking seeds (seeds are soaked in a liquid before planting) and the application of Acetic Acid (vinegar) to soil. The purpose of this experiment was to explore if combining these two methods by presoaking seeds with a vinegar solution can improve the seed development and plant drought tolerance of Maize plants during drought conditions.
The purpose of this investigation is to develop a hydrogel to aid skin regeneration by creating an extracellular matrix for fibroblast growth with antibacterial and infection-detection properties. Authors developed two natural hydrogels based on pectin and potato peels and characterized the gels for fibroblast compatibility through rheology, scanning electron microscopy, swelling, degradation, and cell cytotoxicity assays. Overall, this experiment fabricated various hydrogels capable of acting as skin substitutes and counteracting infections to facilitate wound healing. Following further testing and validation, these hydrogels could help alleviate the 13-billion-dollar financial burden of foot ulcer treatment.
In this study, the authors were interested in developing a hydrophobic surface that will extend the lifespan of metals by reducing water exposure and other damage. The used a zinc coating on steel to pursue this effort.
Many cases of viral hepatitis are easily preventable if caught early; however, a lack of public awareness regarding often leads to diagnoses near the final stages of disease when it is most lethal. Thus, we wanted to understand to what extent an individual's sex, age, education and country of residence (India or Singapore) impacts disease identification. We sent out a survey and quiz to residents in India (n = 239) and Singapore (n = 130) with questions that test their knowledge and awareness of the disease. We hypothesized that older and more educated individuals would score higher because they are more experienced, but that the Indian population will not be as knowledgeable as the Singaporean population because they do not have as many resources, such as socioeconomic access to schools and accessibility to healthcare, available to them. Additionally, we predicted that there would not be any notable differences between make and females. The results revealed that the accuracy for all groups we looked at was primarily below 50%, demonstrating a severe knowledge gap. Therefore, we concluded that if more medical professionals discussed viral hepatitis during hospital visits and in schools, patients can avoid the end stages of the disease in notable cases.
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.