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Herbal formulation, HF1 diminishes tumorigenesis: a cytokine study between MCF-7 and BM-MSCs.

Guru et al. | Apr 11, 2022

Herbal formulation, HF1 diminishes tumorigenesis: a cytokine study between MCF-7 and BM-MSCs.

The authors use HF-1, an herbal formation, on bone marrow derived cells as well as breast cancer cells to assess HF-1's ability to prevent tumorigenesis. As metastasis requires coordination of multiple cells in the tumor microenvironment, their findings that HF-1 augments cytokine expression such as VEGF & TGF-B show that HF-1 has potential application to therapeutics.

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Anticancer, anti-inflammatory, and apoptotic activities of MAT20, a poly-herbal formulation.

Kashyap Jha et al. | Mar 29, 2022

Anticancer, anti-inflammatory, and apoptotic activities of MAT20, a poly-herbal formulation.

Kashyap Jha et al. look at the formulation of MAT20, a crude extract of the moringa, amla, and tulsi leaves, as a potential complementary and alternative medicine. Using HeLa cells, they find MAT20 up-regulates expression of inflammation and cell cytotoxicity markers. Their data is important for understanding the anti-cancer and anti-inflammatory properties of MAT20.

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A study on the stretching behavior of rubber bands

Davuluri et al. | Jan 18, 2022

A study on the stretching behavior of rubber bands

Here, the authors considered the stretching behavior of rubber bands by exposing the rubber bands to increasing loads and measuring their stretch response. They found that a linear stretch response was observed for intermediate loading steps, but this behavior was lost at lower or higher loads, deviating from Hooke's Law. The authors suggest that studies such as these can be used to evaluate other visco-elastic structures.

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The Role of a Mask - Understanding the Performance of Deep Neural Networks to Detect, Segment, and Extract Cellular Nuclei from Microscopy Images

Dasgupta et al. | Jul 06, 2021

The Role of a Mask - Understanding the Performance of Deep Neural Networks to Detect, Segment, and Extract Cellular Nuclei from Microscopy Images

Cell segmentation is the task of identifying cell nuclei instances in fluorescence microscopy images. The goal of this paper is to benchmark the performance of representative deep learning techniques for cell nuclei segmentation using standard datasets and common evaluation criteria. This research establishes an important baseline for cell nuclei segmentation, enabling researchers to continually refine and deploy neural models for real-world clinical applications.

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