The Development of Multi-modal, pH-sensitive Quantum Dots for the Detection of Breast Cancer
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Keywords

pH-lemon sensor
Breast cancer
Quantum dots
Progesterone
Multimodal

DOI

10.26689/ijgpn.v2i4.8544

Submitted : 2024-11-04
Accepted : 2024-11-19
Published : 2024-12-04

Abstract

In the current decade, a significant amount of women have been diagnosed with breast cancer. There is a one in eight chance that women will develop breast cancer. It consists of a high fatality rate and tragic death due to the existing barriers set by current methods that are accessible as a treatment for breast cancer. For example, most treatment plans include a combination of surgery, radiation, hormone therapy, chemotherapy, and targeted therapies. However, due to the collateral damage from multiple follow-up surgeries and subsequent infections, some patients are even averse to starting or continuing treatment. Chemotherapy can result in fatigue, pain in the fingers and feet, increased risk of infection, and more. Hormone therapy can result in similar symptoms but also include nausea, muscle and joint pain, and headaches. Especially breast biopsy, the removal of a sample of breast tissue for diagnosis, is very painful for patients. It takes multiple days to get a diagnosis. It also results in soreness, swelling, or bruising at the biopsy site. This article will attempt to address the current limitations of breast cancer diagnosis by suggesting an innovative method on multimodal pH and progesterone-sensitive quantum dots to detect breast cancer faster and cheaper. Just a sample of blood is needed from the patient, and the quantum dot sensor will detect the cancerous cells through the emission of wavelengths of about 475 and 1300 nm with a cyan color and infrared radiation. These wavelengths can be translated to quantitative graphs with a spectrophotometer. With its optical sensor, the quantum dot will significantly reduce the price of getting a diagnosis, and it will be able to diagnose the patient almost immediately. Further, it does not require a trained professional to diagnose, which is a significant improvement over the current techniques.

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