Artificial Intelligence-Driven Nanosensing to Identify Circulating Bacterial DNA: A Novel Approach to Breast Cancer Risk Profiling
- 1ICMR National Institute for Research in Environmental Health
Abstract
Perovskite quantum dots (PQDs) are exceptionally promising next-generation optical materials for bioimaging applications. This paper presents an optical PQDs nanocomposite method to identify bacterial species associated with breast cancer, employing a LSTM deep learning model. The results indicate that the developed PQD-based nano-sensor offers impressive sensitivity, selectivity, and practical application. The four bacterial species identified in the study, Pseudomonas aeruginosa, Vibrio parvula, Acinetobacter baumannii, and Streptococcus vestibularis, were considered the most relevant indicators of breast cancer. With rapid detection, an intuitive design, and improved reliability, the PQD sensor could be an excellent breast cancer diagnostic tool for a point-of-care application. Furthermore, it has the potential to be integrated into healthcare systems, specifically in resource-limited settings, thereby improving the accessibility and efficiency of early breast cancer detection.
Related
- Multiparametric Extracellular Vesicle Profiling Identifies a Distinct Circulating Signature of Bicuspid Aortic Valve Disease
- An artificial macrophage reveals the impact of nitric oxide microenvironments on bacterial viability
- Profiling Systemic IgG-Targeted Gut Microbiota Reveals Disease Activity-Associated Bacterial Signatures in Ulcerative Colitis
- Genome mining, antibacterial activity, and metabolomic profiling reveal the biosynthetic potential of Burkholderia glumae isolates from Thailand with activity against clinically important bacterial pathogens
- Crowdsourcing functional cell therapy binders from generative artificial intelligence
BibTeX
@misc{nazeer2026artificial,
title = {{Artificial Intelligence-Driven Nanosensing to Identify Circulating Bacterial DNA: A Novel Approach to Breast Cancer Risk Profiling}},
author = {N. Nazeer and P. Ratre and V. Gurjar and O. Goryacheva and S. Lodhi and A. Bhargava and I. Y. Goryacheva and P. K. Mishra},
howpublished = {bioRxiv},
year = {2026},
month = sep,
doi = {10.64898/2026.09.28.755235},
url = {https://www.biorxiv.org/content/10.64898/2026.09.28.755235v1},
note = {CC BY},
}
RIS
TY - UNPB
TI - Artificial Intelligence-Driven Nanosensing to Identify Circulating Bacterial DNA: A Novel Approach to Breast Cancer Risk Profiling
AU - N. Nazeer
AU - P. Ratre
AU - V. Gurjar
AU - O. Goryacheva
AU - S. Lodhi
AU - A. Bhargava
AU - I. Y. Goryacheva
AU - P. K. Mishra
PY - 2026
DA - 2026/09/28
PB - bioRxiv
DO - 10.64898/2026.09.28.755235
UR - https://www.biorxiv.org/content/10.64898/2026.09.28.755235v1
AB - Perovskite quantum dots (PQDs) are exceptionally promising next-generation optical materials for bioimaging applications. This paper presents an optical PQDs nanocomposite method to identify bacterial species associated with breast cancer, employing a LSTM deep learning model. The results indicate that the developed PQD-based nano-sensor offers impressive sensitivity, selectivity, and practical application. The four bacterial species identified in the study, Pseudomonas aeruginosa, Vibrio parvula, Acinetobacter baumannii, and Streptococcus vestibularis, were considered the most relevant indicators of breast cancer. With rapid detection, an intuitive design, and improved reliability, the PQD sensor could be an excellent breast cancer diagnostic tool for a point-of-care application. Furthermore, it has the potential to be integrated into healthcare systems, specifically in resource-limited settings, thereby improving the accessibility and efficiency of early breast cancer detection.
ER -