The impact of educational interventions on COVID-19 and vaccination attitudes among patients in Michigan: A prospective study.

Background: Mass vaccination serves as an effective strategy to combat the COVID-19 pandemic. Vaccine hesitancy is a recognized impediment to achieving a vaccination rate necessary to protect communities. However, solutions and interventions to address this issue are limited by a lack of prior research. Methods: Over 200 patients from 18 Michigan counties participated in this study. Each participant received an initial survey, including demographical questions and knowledge and opinion questions regarding COVID-19 and vaccines. Participants were randomly assigned an educational intervention in either video or infographic format. Patients received a post-survey to assess changes in knowledge and attitudes. Paired sample t-tests and ANOVA were used to measure the effectiveness of the educational interventions. Participants also elected to complete a 3-month follow-up survey. Results: Patients showed increased knowledge after the educational intervention in six out of seven COVID-19 topics (p < 0.005). There was increased vaccine acceptance after the intervention but no difference in the effectiveness between the two intervention modalities. Post-intervention, more patients believed in CDC recommendations (p = 0.005), trusted the vaccine (p = 0.001), believed the vaccines had adequate testing (p = 0.019), recognized prior mistreatment in the medical care system (p = 0.005), agreed that a source they trust told them to receive a vaccine (p = 0.015), and were worried about taking time off of work to get a vaccine (p = 0.023). Additionally, post-intervention, patients were less concerned about mild reactions of the virus (p = 0.005), the rapid development of the vaccines (p < 0.001), and vaccine side effects (p = 0.031). Data demonstrated that attitude and knowledge improved when comparing pre-educational intervention to follow-up but decreased from post-intervention to follow-up. Conclusion: The findings illustrate that educational interventions improved COVID-19 and vaccine knowledge among patients and that the knowledge was retained. Educational interventions serve as powerful tools to increase knowledge within communities and address negative views on vaccination. Interventions should be continually utilized to reinforce information within communities to improve vaccination rates.

Untangling Classification Methods for Melanoma Skin Cancer.

Skin cancer is the most common cancer in the USA, and it is a leading cause of death worldwide. Every year, more than five million patients are newly diagnosed in the USA. The deadliest and most serious form of skin cancer is called melanoma. Skin cancer can affect anyone, regardless of skin color, race, gender, and age. The diagnosis of melanoma has been done by visual examination and manual techniques by skilled doctors. It is a time-consuming process and highly prone to error. The skin images captured by dermoscopy eliminate the surface reflection of skin and give a better visualization of deeper levels of the skin. However, the existence of many artifacts and noise such as hair, veins, and water residue make the lesion images very complex. Due to the complexity of images, the border detection, feature extraction, and classification process are challenging. Without a proper mechanism, it is hard to identify and predict melanoma at an early stage. Therefore, there is a need to provide precise details, identify early skin cancer, and classify skin cancer with appropriate sensitivity and precision. This article aims to review and analyze two deep neural network-based classification algorithms (convolutional neural network, CNN; recurrent neural network, RNN) and a decision tree-based algorithm (XG-Boost) on skin lesion images (ISIC dataset) and find which of these provides the best classification performance metric. Also, the performance of algorithms is compared using six different metrics-loss, accuracy, precision, recall, F1 score, and ROC.

The autophagic response to polystyrene nanoparticles is mediated by transcription factor EB and depends on surface charge.

BACKGROUND: A number of engineered nanoparticles induce autophagy, the main catabolic pathway that regulates bulk degradation of cytoplasmic material by the lysosomes. Depending on the specific physico-chemical properties of the nanomaterial, however, nanoparticle-induced autophagy may have different effects on cell physiology, ranging from enhanced autophagic degradation to blockage of autophagic flux. To investigate the molecular mechanisms underlying the impact of nanoparticle charge on the nature of the autophagic response, we tested polystyrene nanoparticles (50 nm) with neutral, anionic, and cationic surface charges. RESULTS: We found all polystyrene nanoparticles investigated in this study to activate autophagy. We showed that internalization of polystyrene nanoparticles results in activation of the transcription factor EB, a master regulator of autophagy and lysosome biogenesis. Autophagic clearance, however, was observed to depend specifically on the charge of the nanoparticles. Particularly, we found that the autophagic response to polystyrene nanoparticles presenting a neutral or anionic surface involves enhanced clearance of autophagic cargo. Cell exposure to polystyrene nanoparticles presenting a cationic surface, on the other hand, results in transcriptional upregulation of the pathway, but also causes lysosomal dysfunction, ultimately resulting in blockage of autophagic flux. CONCLUSIONS: This study furthers our understanding of the molecular mechanisms that regulate the autophagic response to nanoparticles, thus contributing essential design criteria for engineering benign nanomaterials.