This paper investigates the potential anticancer properties of Uvularia perfoliata by testing its effects on the viability of uveal melanoma cells.
Read More...Investigating the anticancer effects of Uvularia perfoliata
This paper investigates the potential anticancer properties of Uvularia perfoliata by testing its effects on the viability of uveal melanoma cells.
Read More...Levering machine learning to distinguish between optimal and suboptimal basketball shooting forms
The authors looked at different ways to build computational resources that would analyze shooting form for basketball players.
Read More...Identifying 5-hydroxymethylcytosine as a potential cancer biomarker using FFPE DNA samples
This study used an improved CMS-seq method to profile 5hmC in ormalin-fixed and paraffin-embedded (FFPE) samples from HNC tumors and adjacent normal tissues, identifying three genes (PRKD2, HADHA, and AIPL1) with promising potential as biomarkers for Head and neck cancer (HNC) diagnosis.
Read More...Advancing pediatric cancer predictions through generative artificial intelligence and machine learning
Pediatric cancers pose unique challenges due to their rarity and distinct biological factors, emphasizing the need for accurate survival prediction to guide treatment. This study integrated generative AI and machine learning, including synthetic data, to analyze 9,184 pediatric cancer patients, identifying age at diagnosis, cancer types, and anatomical sites as significant survival predictors. The findings highlight the potential of AI-driven approaches to improve survival prediction and inform personalized treatment strategies, with broader implications for innovative healthcare applications.
Read More...Risk factors contributing to Pennsylvania childhood asthma
Asthma is one of the most prevalent chronic conditions in the United States. But not all people experience asthma equally, with factors like healthcare access and environmental pollution impacting whether children are likely to be hospitalized for asthma's effects. Li, Li, and Ruffolo investigate what demographic and environmental factors are predictive of childhood asthma hospitalization rates across Pennsylvania.
Read More...Uncovering the hidden trafficking trade with geographic data and natural language processing
The authors use machine learning to develop an evidence-based detection tool for identifying human trafficking.
Read More...Investigating momentum transfer with gall-forming wasps
The authors use the unique movements of the jumping gall wasp to study momentum transfer with potential applications in robotics and extraterrestrial research.
Read More...Exploring the possibilities for reactions between SiW and alkaline solutions to be renewable energy sources
The authors looked at hydrogen gas production and how reaction temperature, concentration and alkaline solution used impacted the overall reaction with silicon. They found that all alkaline solutions tested would be viable options for using silicon waste to produce hydrogen gas to be used a renewable energy source.
Read More...Investigating cross-cultural emotional responses to world music under simulated hearing loss
The authors survey how emotional responses to music differ across cultures and the impact of hearing loss on these emotional responses.
Read More...Enhancing marine debris identification with convolutional neural networks
Plastic pollution in the ocean is a major global concern. Remotely Operated Vehicles (ROVs) have promise for removing debris from the ocean, but more research is needed to achieve full effectiveness of the ROV technology. Wahlig and Gonzales tackle this issue by developing a deep learning model to distinguish trash from the environment in ROV images.
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