The authors found that treatment with AS20 suppressed phorbol 12-myristate 13-acetate (PMA) and 5-flurouracil (5-FU) induction of COX2 expression. We also observed AS20 treated cells showed DNA fragmentation in HeLa cells.
Read More...Apoptosis induction and anti-inflammatory activity of polyherbal drug AS20 on cervical cancer cell lines
The authors found that treatment with AS20 suppressed phorbol 12-myristate 13-acetate (PMA) and 5-flurouracil (5-FU) induction of COX2 expression. We also observed AS20 treated cells showed DNA fragmentation in HeLa cells.
Read More...Silver nanoparticle-coated orthopedic screws lead to greater calcium precipitation
The authors test whether coating stainless steel orthopedic screws in silver will promote calcium precipitation to improve orthopedic implant integration into bone.
Read More...A HOG feature extraction and CNN approach to Parkinson’s spiral drawing diagnosis
Parkinson’s disease (PD) is a prevalent neurodegenerative disorder in the U.S., second only to Alzheimer’s disease. Current diagnostic methods are often inefficient and dependent on clinical exams. This study explored using machine and deep learning to enhance PD diagnosis by analyzing spiral drawings affected by hand tremors, a common PD symptom.
Read More...Upregulation of the Ribosomal Pathway as a Potential Blood-Based Genetic Biomarker for Comorbid Major Depressive Disorder (MDD) and PTSD
Major Depressive Disorder (MDD), and Post-Traumatic Stress Disorder (PTSD) are two of the fastest growing comorbid diseases in the world. Using publicly available datasets from the National Institute for Biotechnology Information (NCBI), Ravi and Lee conducted a differential gene expression analysis using 184 blood samples from either control individuals or individuals with comorbid MDD and PTSD. As a result, the authors identified 253 highly differentially-expressed genes, with enrichment for proteins in the gene ontology group 'Ribosomal Pathway'. These genes may be used as blood-based biomarkers for susceptibility to MDD or PTSD, and to tailor treatments within a personalized medicine regime.
Read More...Using text embedding models as text classifiers with medical data
This article describes the classification of medical text data using vector databases and text embedding. Various large language models were used to generate this medical data for the classification task.
Read More...Intra and interspecies control of bacterial growth through extracellular extracts
The study discusses the relationship between bacterial species and the human gut microbiome, emphasizing the role of quorum sensing molecules in bacterial communication and its implications for health. Authors investigated the impact of bacterial supernatants from Escherichia coli (E. coli) on the growth of new E. coli and Enterobacter aerogenes (E. aerogenes) cultures.
Read More...Association of depression and suicidal ideation among adults with the use of H2 antagonists
In this study, the authors investigate associations between use of histamine H2 receptor antagonists and suicidal ideation in different age groups.
Read More...Comparative analysis of the speeds of AES, ChaCha20, and Blowfish encryption algorithms
The authors looked at different algorithms and their ability to encrypt and decrypt text of various lengths.
Read More...Lung cancer AI-based diagnosis through multi-modal integration of clinical and imaging data
Lung cancer is highly fatal, largely due to late diagnoses, but early detection can greatly improve survival. This study developed three models to enhance early diagnosis: an MLP for clinical data, a CNN for imaging data, and a hybrid model combining both.
Read More...A novel approach for predicting Alzheimer’s disease using machine learning on DNA methylation in blood
Here, recognizing the difficulty associated with tracking the progression of dementia, the authors used machine learning models to predict between the presence of cognitive normalcy, mild cognitive impairment, and Alzheimer's Disease, based on blood DNA methylation levels, sex, and age. With four machine learning models and two dataset dimensionality reduction methods they achieved an accuracy of 53.33%.
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