Detection of ovarian cancer via the spectral fingerprinting of quantum-defect-modified carbon nanotubes in serum by machine learning

Kim, Mijin; Chen, Chen; Wang, Peng; Yang, Yoona; Wun, Christopher; Antman‐Passig, Merav; Luo, Hong-Bin; Cho, Sun; Long-Roche, Kara C.; Ramanathan, Lakshmi; Jagota, Anand; Zheng, Ming; Wang, YuHuang; Heller, Daniel A.

doi:10.1038/s41551-022-00860-y

Cited by 107 publications

(88 citation statements)

References 48 publications

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“…A key to all these applications is versatile non-covalent and recently covalent surface chemistry 53,94,95 . It enabled imaging of neurotransmitters 96-100 , reactive oxygen species [101][102][103][104][105][106][107] , polyphenols 108 , disease markers 33,53,109,110 and many other important analytes and biological targets. Translated to EB-NS, the versatile surface chemistry presented by us in this work will enable similar applications.…”

Section: Discussionmentioning

confidence: 99%

“…and nanomaterials (e.g. quantum dots 47 , silicon nanocrystals 48 , single-walled carbon nanotubes [49][50][51][52][53][54][55] , etc.) have been successfully implemented for numerous biological studies, their number is quite limited and in most cases they are affected by low quantum yield, low photostability and/or biocompatibility issues 40,42,47,[56][57][58][59] .…”

Section: Introductionmentioning

confidence: 99%

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Covalently Functionalized Egyptian Blue Nanosheets for Near-Infrared Bioimaging

Selvaggio

Herrmann

Hill

et al. 2022

Preprint

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Fluorophores emitting in the near-infrared (NIR) wavelength region present optimal characteristics for photonics and especially bioimaging. Unfortunately, only few NIR fluorescent materials are known and even fewer are biocompatible. For this reason, the scientific interest in designing novel NIR fluorophores is very high. Egyptian Blue (CaCuSi4O10, EB) is a NIR fluorescent layered silicate that can be exfoliated into fluorescent nanosheets (EB-NS). So far, its surface chemistry has not been tailored but this is crucial for colloidal stability and biological targeting. Here, we demonstrate covalent surface functionalization of EB nanosheets (EBfunc) via Si-H activation using hydrosilanes with variable functionalities. EB-NS were first grafted with the visible fluorescent pyrene (Pyr) moieties to prove conjugation by colocalization of the Vis/NIR fluorescence on the (single) EB-NS level. The same procedure was performed and validated with carboxyl group (COOH)-containing hydrosilanes. These groups can serve as generic handle for further (bio)functionalization of the EB-NS surface. Finally, folic acid (FA) was conjugated to these COOH-functionalized EB-NS to target folic acid receptor-expressing cancer cells. These results highlight the potential of this surface chemistry approach to modify EB-NS and enable targeted NIR imaging for biomedical applications.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Covalently Functionalized Egyptian Blue Nanosheets for Near-Infrared Bioimaging

Selvaggio

Herrmann

Hill

et al. 2022

Preprint

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“…So-called “perception-based” approaches offer an additional new tool for classifying disease states, whereby an array of non-specific responses can be combined and processed with machine learning to produce a singular response that is highly specific and accurate. In one example, Kim et al generated a nanosensing array that consisted of modified semiconducting single-walled carbon nanotubes (SWCNTs) ( Figure 3 ) [ 138 ]. SWCNTs are similar to Ag 2 S QD’s in that they also exhibit intrinsic near-infrared fluorescence detectable even down to the single-molecule level.…”

Section: Protein Diagnosticsmentioning

confidence: 99%

“… Molecular model and array construction of OCC-DNA nanosensor elements created by Kim et al [ 138 ]. ( A ) Fabricated molecular model of a single OCC-DNA nanosensor with ssDNA wrapped around a semi-conducting single-walled nanotube (SWNCT) modified with organic color sensors (OCC).…”

Section: Figurementioning

confidence: 99%

Current and Emerging Methods for Ovarian Cancer Screening and Diagnostics: A Comprehensive Review

Liberto

Chen

Shih

et al. 2022

Cancers

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With a 5-year survival rate of less than 50%, ovarian high-grade serous carcinoma (HGSC) is one of the most highly aggressive gynecological malignancies affecting women today. The high mortality rate of HGSC is largely attributable to delays in diagnosis, as most patients remain undiagnosed until the late stages of -disease. There are currently no recommended screening tests for ovarian cancer and there thus remains an urgent need for new diagnostic methods, particularly those that can detect the disease at early stages when clinical intervention remains effective. While diagnostics for ovarian cancer share many of the same technical hurdles as for other cancer types, the low prevalence of the disease in the general population, coupled with a notable lack of sensitive and specific biomarkers, have made the development of a clinically useful screening strategy particularly challenging. Here, we present a detailed review of the overall landscape of ovarian cancer diagnostics, with emphasis on emerging methods that employ novel protein, genetic, epigenetic and imaging-based biomarkers and/or advanced diagnostic technologies for the noninvasive detection of HGSC, particularly in women at high risk due to germline mutations such as BRCA1/2. Lastly, we discuss the translational potential of these approaches for achieving a clinically implementable solution for screening and diagnostics of early-stage ovarian cancer as a means of ultimately improving patient outcomes in both the general and high-risk populations.

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“…Thus, we explored the possibility of using machine learning (ML) to build a prognostic model for CRC. ML is a branch of arti cial intelligence (AI) in which a computer generates rules underlying or based on raw data (Mitchell, 2003); the use of ML in medicine has gradually become common (Grimm et al 2022;Kim et al 2022;Metsky et al 2022; Xie and Zhuang and Niu and Ai et al 2022). ML can be used to directly compare the accuracy of two or more quantitative tests for the same disease/condition (Tripepi et al 2009).…”

Section: Introductionmentioning

confidence: 99%

Prognostic Prediction Models for Postoperative Patients with Stage I to III Colorectal Cancer: A Retrospective Study Based on Machine Learning Methods

et al. 2022

Preprint

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Purpose To utilize the patient, tumor, and treatment features and compare the performance of machine learning algorithms, develop and validate models to predict overall, disease-free, recurrence-free, and distant metastasis-free survival, and screen important variables to improve the prognosis of patients in clinical settings.Methods More than 1000 colorectal cancer patients who underwent curative resection were grouped according to 4 endpoints and divided into testing sets and training sets (9:1). We applied 4 machine learning algorithms to predict 1-, 3-, and 5-year survival times. The area under the receiver operating characteristic curve (AUC) and average precision (AP) were our accuracy indicators. Vital parameters were screened by multivariate regression models. To achieve better prediction of longitudinal oncological outcomes, we performed 10-fold cross-validation except for the recurrence-free survival model (3-fold cross-validation). We iterated 3000 times after hyperparameter optimization and assessed the internal testing sets.Results The best AP values were greater than 80%, except for the overall survival model (69.5%). The best AUCs were all greater than 0.70 except for the recurrence free survival model (0.61). The models performed well. Variables that were widely correlated with prognoses, such as the TNM stage, were selected as important features; however, indirectly related indicators, such as Ki-67 level, were also selected.Conclusion We constructed an independent, high-accuracy "white-box" machine learning system for predicting survival times. This system may help in determining managing strategies for colorectal cancer patients and has future utility in personalized medicine and monitoring.

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Detection of ovarian cancer via the spectral fingerprinting of quantum-defect-modified carbon nanotubes in serum by machine learning

Cited by 107 publications

References 48 publications

Covalently Functionalized Egyptian Blue Nanosheets for Near-Infrared Bioimaging

Covalently Functionalized Egyptian Blue Nanosheets for Near-Infrared Bioimaging

Current and Emerging Methods for Ovarian Cancer Screening and Diagnostics: A Comprehensive Review

Prognostic Prediction Models for Postoperative Patients with Stage I to III Colorectal Cancer: A Retrospective Study Based on Machine Learning Methods

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