The authors use blood smears from individuals with sickle cell disease to correlate sickle cell frequency with the occurrence of vaso-occlusive crises.
Read More...Predicting sickle cell vaso-occlusion by microscopic imaging and modeling
The authors use blood smears from individuals with sickle cell disease to correlate sickle cell frequency with the occurrence of vaso-occlusive crises.
Read More...The correlation between the phase of the moon and the number of psychiatric patients admitted to the hospital
The authors looked at if there was any correlation between the phase of the moon and admissions for psychiatric concerns.
Read More...Study of neural network parameters in detecting heart disease
The authors looked at the ability to detect heart disease before the onset of severe clinical symptoms.
Read More...Incorporating graphite from pencils as a component of lithium-ion batteries
The authors looked at the ability to use graphite from pencils in anodes of lithium-anode batteries.
Read More...Using advanced machine learning and voice analysis features for Parkinson’s disease progression prediction
The authors looked at the ability to use audio clips to analyze the progression of Parkinson's disease.
Read More...In silico screening of DEAB analogues as ALDH1 isoenzymes inhibitors in cancer treatment
The authors computationally screened potential ALDH1 inhibitors, for use as potential cancer therapeutics.
Read More...Predictive modeling of cardiovascular disease using exercise-based electrocardiography
The authors looked factors that could lead to earlier diagnosis of cardiovascular disease thereby improving patient outcomes. They found that advances in imaging and electrocardiography contribute to earlier detection of cardiovascular disease.
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...Advancements in glioma segmentation: comparing the U-Net and DeconvNet models
This study compares the performance of two deep learning models, U-Net and DeconvNet, for segmenting gliomas from MRI scans.
Read More...Genetic Bioaugmentation of Oryza sativa to Facilitate Self-Detoxification of Arsenic In-Situ
Arsenic contamination in rice, caused by the use of arsenic-laden groundwater for irrigation, is a growing global concern, affecting over 150 million people. To address this, researchers hypothesized that genetically modifying rice plants with arsenic-resistant genes could reduce arsenic uptake and allow the plants to detoxify arsenic, making them safer to consume.
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