The authors use machine learning on MRI images of brain tissue to predict tumor onset as an avenue for early detection of brain cancer.
Read More...A comparative analysis of machine learning approaches to predict brain tumors using MRI
The authors use machine learning on MRI images of brain tissue to predict tumor onset as an avenue for early detection of brain cancer.
Read More...High-performance liquid chromatography insight in pH-dependent hydrolysis of andrographolide acetonide
Andrographolide, a natural compound with anti-inflammatory, antidepressant, and anti-cancer properties, can be chemically modified by adding an acetonide group to form andrographolide acetonide, which is more potent and acts as a pH-dependent prodrug. Researchers investigated the hydrolysis of this acetonide group under mildly acidic conditions.
Read More...The utilization of Artificial Intelligence in enabling the early detection of brain tumors
AI analysis of brain scans offers promise for helping doctors diagnose brain tumors. Haider and Drosis explore this field by developing machine learning models that classify brain scans as "cancer" or "non-cancer" diagnoses.
Read More...Identification of microwave-related changes in tissue using an ultrasound scan
Microwave energy (ME) is used in the medical field to denature protein structures, resulting in inactivation or destruction of abnormal cells. Identifying the extent of destruction of abnormal tissue (cancer tissue or tissue with abnormal electrical activity) is essential for accomplishing successful therapy and reducing collateral damage. Our study was an ex vivo assessment of the changes on ultrasound scans (US) in chicken tissue exposed to ME. We hypothesized that any changes in tissue structures would be recognized on the reflected ultrasound waves. Ultrasound scans of tissues change with exposure to microwaves with increasing reflection of ultrasound waves. With exposure to microwaves, surface level brightness on the ultrasound scans increases statistically significantly. The findings could be used in heat related (ME and radiofrequency) procedures where clinicians would be able to actively assess lesions in real-time. Further studies are required to assess changes in tissue during active exposure to different types of energies.
Read More...Effect of pH on the antibacterial properties of turmeric
Some spices have antimicrobial or antibacterial properties that people have already tested. Turmeric has a wide variety of uses and has even been implemented in alternative medicine as a treatment for cancer, inflammation, osteoarthritis, and other diseases. We tested the antimicrobial effects of turmeric under two different pHs to characterize this effect in vitro. Decreasing the pH of a solution of turmeric may increase antibacterial properties.
Read More...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.
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...The anticancer and anti-inflammatory effects of polyherbal drug AS20 on HeLa cells resistant to 5-Fluorouracil
The authors looked at 5-FU resistant HeLa cells and the ability of an herbal extract to show anti-inflammatory properties.
Read More...Governing Glioblastoma: A novel therapy to restore motor control and mitigate glioblastoma proliferation
The authors looked at ways that seizures in patients with glioblastoma could be treated using C. elegans as a model system.
Read More...A novel CNN-based machine learning approach to identify skin cancers
In this study, the authors developed and assessed the accuracy of a machine learning algorithm to identify skin cancers using images of biopsies.
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