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Estimation of cytokines in PHA-activated mononuclear cells isolated from human peripheral and cord blood

Subbiah et al. | Mar 09, 2022

Estimation of cytokines in PHA-activated mononuclear cells isolated from human peripheral and cord blood

In this study, the authors investigated the time-dependent cytokine secretion ability of phyto-hemagglutinin (PHA)-activated T cells derived from human peripheral (PB) and cord blood (CB). They hypothesized that the anti-inflammatory cytokine, IL-10, and pro-inflammatory cytokine, TNFα, levels would be higher in PHA-activated T cells obtained from PB as compared to the levels obtained from CB and would decrease over time. Upon PHA-activation, the IL-10 levels were relatively high while the TNFα levels decreased, making these findings applicable in therapeutic treatments e.g., rheumatoid arthritis, psoriasis, and organ transplantation.

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Breast cancer mammographic screening by different guidelines among women of different races/ethnicities

Wang et al. | Aug 27, 2023

Breast cancer mammographic screening by different guidelines among women of different races/ethnicities

Mammographic screening is a common diagnostic tool for breast cancer among average-risk women. The authors hypothesized that adherence rates for mammographic screening may be lower among minorities (non-Hispanic black (NHB) and Hispanic/Latino) than among non-Hispanic whites (NHW) regardless of the guideline applied. The findings support other studies’ results that different racial/ethnic and socio-demographic factors can affect screening adherence. Therefore, healthcare providers should promote breast cancer screening especially among NHW/Hispanic women and women lacking insurance coverage.

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Transfer learning and data augmentation in osteosarcoma cancer detection

Chu et al. | Jun 03, 2023

Transfer learning and data augmentation in osteosarcoma cancer detection
Image credit: Chu and Khan 2023

Osteosarcoma is a type of bone cancer that affects young adults and children. Early diagnosis of osteosarcoma is crucial to successful treatment. The current methods of diagnosis, which include imaging tests and biopsy, are time consuming and prone to human error. Hence, we used deep learning to extract patterns and detect osteosarcoma from histological images. We hypothesized that the combination of two different technologies (transfer learning and data augmentation) would improve the efficacy of osteosarcoma detection in histological images. The dataset used for the study consisted of histological images for osteosarcoma and was quite imbalanced as it contained very few images with tumors. Since transfer learning uses existing knowledge for the purpose of classification and detection, we hypothesized it would be proficient on such an imbalanced dataset. To further improve our learning, we used data augmentation to include variations in the dataset. We further evaluated the efficacy of different convolutional neural network models on this task. We obtained an accuracy of 91.18% using the transfer learning model MobileNetV2 as the base model with various geometric transformations, outperforming the state-of-the-art convolutional neural network based approach.

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Specific Transcription Factors Distinguish Umbilical Cord Mesenchymal Stem Cells From Fibroblasts

Park et al. | Aug 16, 2019

Specific Transcription Factors Distinguish Umbilical Cord Mesenchymal Stem Cells From Fibroblasts

Stem cells are at the forefront of research in regenerative medicine and cell therapy. Two essential properties of stem cells are self-renewal and potency, having the ability to specialize into different types of cells. Here, Park and Jeong took advantage of previously identified stem cell transcription factors associated with potency to differentiate umbilical cord mesenchymal stem cells (US-MSCs) from morphologically similar fibroblasts. Western blot analysis of the transcription factors Klf4, Nanog, and Sox2 revealed their expression was unique to US-MSCs providing insight for future methods of differentiating between these cell lines.

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The role of CYP46A1 and its metabolic product, 24S-hydroxycholesterol, in Neuro 2A cell death

Ni et al. | May 11, 2021

The role of CYP46A1 and its metabolic product, 24S-hydroxycholesterol, in Neuro 2A cell death

Cholesterol is a major component of neuronal cell membrane and myelin sheath. In this study, the authors either transfected Neuro 2A cells with CYP46A1 cDNA or treated the cells with 24SHC. Cells expressing CYP46A1 had significantly less viability compared to the negative control. Up to 55% reduction in cell viability was also observed in 24S-HC-treated cells. This work supports that CYP46A1 and 24S-HC could directly trigger cell death. The direct involvement of 24S-HC in cell death provides further evidence that 24S-HC can be a promising biomarker for diagnosing brain damage severity.

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