ACS Publications. Most Trusted. Most Cited. Most Read
My Activity
CONTENT TYPES

Patients, Here Comes More Nanotechnology

View Author Information
Institute of Biomaterials and Biomedical Engineering, Donnelly Center for Biomolecular Research, Department of Chemistry, Department of Chemical Engineering, Department of Materials Science and Engineering, University of Toronto, 164 College St., Toronto, Ontario M5T 2R5, Canada
Sunnybrook Health Sciences Center, 2075 Bayview Ave., Toronto, Ontario M4N 3M5, Canada
§ ∥ §California NanoSystems Institute, Department of Chemistry and Biochemistry, and Department of Materials Science and Engineering, University of California, Los Angeles, Los Angeles, California 90095, United States
Fachbereich Physik, Philipps Universität Marburg, Marburg D-35032, Germany
# CIC biomaGUNE, Donostia-San Sebastián 20009, Spain
Cite this: ACS Nano 2016, 10, 9, 8139–8142
Publication Date (Web):August 30, 2016
https://doi.org/10.1021/acsnano.6b05610

Copyright © 2016 American Chemical Society. This publication is licensed under these Terms of Use.

  • Open Access
  • Editors Choice

Article Views

6445

Altmetric

-

Citations

LEARN ABOUT THESE METRICS
PDF (638 KB)

Abstract

We describe the current difference in reporting the performance of nanotechnology diagnostic devices between technologists and clinicians. This perspective specifies the “metrics” used to evaluate these devices and describes strategies to bridge the gap between these two communities in order to accelerate the translation from academic bench to the clinic. We use two recently published ACS Nano articles to highlight the evaluation of silicon nanowire and surface-enhanced Raman spectroscopy-breath diagnostic tests for patients afflicted with cancer and asthma. These studies represent some of the earliest studies of emerging nanotechnology devices utilizing clinical parameters to assess performance.

Throughout time, doctors have aimed to determine appropriate treatments for patients based on symptoms such as a fever, cough, or fatigue. In the last 30 years, researchers have started to focus on developing technologies that can provide molecular precision in determining the cause of a disease. This transition enables physicians to manage patient care more appropriately based on objective data. This approach has fueled the development of immunoassays to identify distinct protein biomarkers that are unique to a disease, as well as polymerase chain reaction to detect host genetic variabilities or human pathogens. However, despite contributions to improved patient outcomes, long turnaround times and the need for expensive reagents/instruments have limited the accessibility of these technologies to a broader pool of patients. Nanotechnology is a promising avenue to overcome some of these issues and will play a significant role in developing technologies to improve patient diagnoses.

Where Are We with Nanotechnology-Based Diagnostics?

ARTICLE SECTIONS
Jump To

Using nanoparticles to improve diagnostic devices was considered long before nanotechnology became popular. For example, gold nanoparticles were employed in lateral flow immunoassays (better known as a “dipstick”) as early as the 1960s. The dipstick is a widely used form of capillary-driven diagnostics for biological samples, where an antigen interacts with immobilized primary antibodies and antibody-coated gold nanoparticle labels, to provide a red line indicating a positive result. Such a simple diagnostic device has leveraged nanotechnology for diagnosing pregnancy, infectious pathogens, as well as diabetic and cardiovascular diseases. Similarly, over the last 15 years, the research community has focused on incorporating nanoparticles in diagnostic assays or devices. (1-5) Diagnostics can exploit many desirable properties of nanotechnology including tunable optical, electrical, and magnetic properties. Gold nanoparticles, for example, are used in dipstick tests because of their high molar absorptivity coefficient contributing to an intense red color. Iron oxide nanoparticles are also used in many bead-based assays, as their magnetic properties simplify the purification and isolation of biomarkers.

Nanotechnology offers diagnostics many desirable properties, such as tunable optical, electrical, and magnetic properties.

Due to the broad applications of nanoparticles in diagnostics and other medical applications, methods to synthesize and to characterize a variety of nanoparticle types, including semiconductor, organic nanoparticles, and other metal nanoparticles, have been developed. (6) Tools that are aimed at measuring the properties and functions of these nanoparticles have also been developed and applied. Additionally, strategies to modify the nanoparticles’ surfaces to enhance their water solubility and to coat them with nucleic acids, proteins, and other molecules to capture target biomarkers have been optimized for a number of applications. (7) These research activities have created a foundation for the use of nanoparticles as important building blocks to engineer diagnostic technologies. As a result, nanoparticles are used as colorimetric, fluorescent, magnetic, or thermal probes to detect biological molecules and can be incorporated into larger metal structures or polymeric beads to create barcoded platforms for the simultaneous detection of multiple biological molecules.

Clinical Evaluation of Nanotechnology-Based Diagnostics

ARTICLE SECTIONS
Jump To

Most researchers who are developing nanotechnology-based diagnostics come from engineering, chemistry, physics, or material sciences backgrounds. (8) When a diagnostic device is built, one typically determines the limit of detection and linear dynamic range of the device—all standard metrics used by engineers and physical scientists. In these experiments, the analyte to be detected is serially diluted, and the device measures the signal at each concentration. This is expressed as a graph of concentration versus signal, where the limit of detection is defined as three standard deviations above the mean background signal. The linear dynamic range is defined as the concentration range where the signal responds linearly to changes in concentration, and this range becomes an important metric for quantification of biological molecules. These analyses are often carried out on samples spiked in buffer, blood, plasma, urine, and other biological fluids. Performance characteristics of these assays are then reported. Although these analytical characteristics are fundamental in the development of new diagnostic assays, additional metrics are needed for clinical implementation. Commonly used terminologies, definitions, and equations related to clinical evaluation are listed in Table 1. (9) Currently, “technology” developers within academia rarely use clinical metrics or samples to evaluate the performance of diagnostic tests. In contrast to spiked samples, which are often used as a substitute for clinical samples, biological samples are complex and contain different types of molecules that can adversely influence the performance of the diagnostic assay under evaluation. This complexity makes it extremely difficult to recapitulate actual patient samples using spiked samples. By analyzing the performance of the device with patient samples, the researcher can obtain enough information to make the appropriate engineering changes to ensure adequate performance when used on patients. In our opinion, to expedite clinical translation, these “technology” developers at the academic level need to start using clinical samples and nomenclature to evaluate and to describe the performance of diagnostic tests.
Table 1. Commonly Used Metrics and Terminologies for Clinical Assessment of Diagnostic Technologiesa
termdescription
true positivea positive test result given by the diagnostic assay under evaluation that matches that of the reference standard
true negativea negative test result given by the diagnostic assay under evaluation that matches that of the reference standard
false positivea positive test result given by the diagnostic assay under evaluation that does not match that of the reference standard
false negativea negative test result given by the diagnostic assay under evaluation that does not match that of the reference standard
sensitivitythe predicted percent of true positives among all positive test results obtained by the reference standard
specificitythe predicted percent of true negatives among all negative test results obtained by the reference standard
positive predictive valuethe predicted percent of true positives among all positive test results obtained by the diagnostic assay under evaluation
negative predictive valuethe predicted percent of true negatives among all negative test results obtained by the diagnostic assay under evaluation
positive likelihood ratiothe odds of the test under evaluation to produce a positive test result when the disease is present versus a positive test result when the disease is absent
negative likelihood ratiothe odds of the test under evaluation to produce a positive test result when the disease is absent versus a negative test result when the disease is absent
receiver operator characteristic curvesthe curve produced when true positives on the y-axis are compared against false positives on the x-axis, for a range of cutoff values for the diagnostic test under evaluation
a

For in-depth reading on these metrics, please see refs 9, 14, and 15.

Examples of Clinical Advancement for Breath Tests

ARTICLE SECTIONS
Jump To

ACS Nano has recently published a number of manuscripts that have demonstrated the successful use of nanotechnology-based diagnostics systems on clinical samples. (10, 11) These studies include the use of silicon nanowire sensors to diagnose patients with gastric cancer, lung cancer, and asthma (as described by Shehada et al.) (12) as well as the use of surface-enhanced Raman scattering (as described by Chen et al.) (13) to distinguish between early and advanced gastric cancer. In both studies, the authors used the patient’s exhaled breath to make the diagnosis, a method that is noninvasive and simple to conduct. The breath was analyzed for volatile organic compounds (VOCs), where the composition of the VOCs is used as a “breath-print” to differentiate between patients with and without cancer at different stages. Both studies shared similar approaches, where the surfaces of their sensors were chemically modified to increase the interaction with VOCs. When the patient breathes into the device, the VOCs bind to the sensor’s surfaces and change the electrical signal or Raman signature. The generated signals are then deconvolved and compared between patient cohorts. The study by Shehada et al. included 374 subjects and achieved an 80% accuracy in differentiating between patients with and without cancer or asthma. Chen et al. tested 200 patient samples and achieved >83% clinical sensitivity and 92% clinical specificity for patients with early and late gastric cancer. These two reports demonstrate the clinical feasibility of emerging technologies through the use of patient samples and illustrate their diagnostic accuracy levels in comparison to reference tests.

ACS Nano has recently published a number of manuscripts that have demonstrated the successful use of nanotechnology-based diagnostics systems on clinical samples.

Where Should the Clinical Testing Occur?

ARTICLE SECTIONS
Jump To

The implementation of novel diagnostic tests in the clinical setting is a lengthy process that involves (i) research and development, (ii) laboratory validation and evaluation, (iii) clinical evaluation and implementation, and (iv) ongoing proficiency testing. The depth and robustness of clinical evaluations are highly variable. In comparing new technology with the gold standard technology, new diagnostic tests must offer some advantage over established methods, whether it is in clinical sensitivity, clinical specificity, turnaround time, or cost.
It remains a challenge to understand where in the development cycle the clinical analysis of an emerging technology should occur. There are currently two research silos in the development of diagnostic technologies. The first is the “technology” developers, where a study is considered to be complete when researchers have characterized the analytical performance of the diagnostic device. The second is the “clinical” evaluators, where research activities of the diagnostic device occur after a company has built a black-box system. This community is mostly focused on clinical evaluation of the technology. In between these two research communities is the “entrepreneurial” community, where companies are started based on the technology developer’s concept and analytical data. Founding a company can be risky, however, as the performance claims made by the technology community may not hold up when applied to patient samples. (6) Connecting to clinical needs and conducting a small amount of “clinical” testing early on in the development cycle can help predict clinical applicability of the device and guide the engineering process. Few published studies have combined the efforts of both the “technology” and “clinical” communities. Having these two communities work together early in the development process is important and can be critical to the successful translation of the technology.
After these issues have been addressed, the next path to translation will involve the identification of larger partners for full clinical evaluation. These partners are generally diagnostic laboratories in large tertiary care centers, reference centers, or other centers of excellence in a given diagnostic sphere. The clinical impact (or lack thereof) for some assays has been more extensively studied (e.g., prostate specific antigen or PSA) than others (e.g., the utility of 16S ribosomal sequencing in culture-negative sterile fluids). In all evaluations, the new technology has to be compared to the current gold standard techniques. Typically, this process involves evaluating clinical sensitivity, clinical specificity, diagnostic accuracy, turnaround times, and operational feasibility in the clinical setting to measure the clinical effectiveness of diagnostic tests and to determine a patient’s prospective status. Other issues, such as downtimes due to instrument or reagent issues, complex/time-consuming steps, long turnaround times, or even incompatibilities with laboratory workflow need to be considered. Hence, a new test may not ultimately succeed, despite promising analytical performance characteristics.

Conclusion and Outlook

ARTICLE SECTIONS
Jump To

The nanotechnology community has made significant strides in the development of diagnostic devices. Unlike in vivo applications of nanoparticles, diagnostic devices are unlikely to have the stigma of nanoparticle toxicity since the analysis is done outside the body. This distinction would likely ensure that nanotechnology-based diagnostic devices advance more rapidly for clinical use. Many of these technologies remain at the academic stage and analytical performance is typically validated with spiked samples to convey diagnostic success. Here, we suggest a move toward clinical testing during the technology-development phase. Early testing of real samples will help engineer the device and should lead to more likely and more rapid translation of the technology.

Author Information

ARTICLE SECTIONS
Jump To

  • Corresponding Author
    • Warren C. W. Chan - Institute of Biomaterials and Biomedical Engineering, Donnelly Center for Biomolecular Research, Department of Chemistry, Department of Chemical Engineering, Department of Materials Science and Engineering, University of Toronto, 164 College St., Toronto, Ontario M5T 2R5, Canada Email: [email protected]
  • Authors
    • Buddhisha Udugama - Institute of Biomaterials and Biomedical Engineering, Donnelly Center for Biomolecular Research, Department of Chemistry, Department of Chemical Engineering, Department of Materials Science and Engineering, University of Toronto, 164 College St., Toronto, Ontario M5T 2R5, Canada
    • Pranav Kadhiresan - Institute of Biomaterials and Biomedical Engineering, Donnelly Center for Biomolecular Research, Department of Chemistry, Department of Chemical Engineering, Department of Materials Science and Engineering, University of Toronto, 164 College St., Toronto, Ontario M5T 2R5, Canada
    • Jisung Kim - Institute of Biomaterials and Biomedical Engineering, Donnelly Center for Biomolecular Research, Department of Chemistry, Department of Chemical Engineering, Department of Materials Science and Engineering, University of Toronto, 164 College St., Toronto, Ontario M5T 2R5, Canada
    • Samira Mubareka - Sunnybrook Health Sciences Center, 2075 Bayview Ave., Toronto, Ontario M4N 3M5, Canada
    • Paul S. Weiss - Institute of Biomaterials and Biomedical Engineering, Donnelly Center for Biomolecular Research, Department of Chemistry, Department of Chemical Engineering, Department of Materials Science and Engineering, University of Toronto, 164 College St., Toronto, Ontario M5T 2R5, CanadaSunnybrook Health Sciences Center, 2075 Bayview Ave., Toronto, Ontario M4N 3M5, Canada§California NanoSystems Institute, ∥Department of Chemistry and Biochemistry, and Department of Materials Science and Engineering, University of California, Los Angeles, Los Angeles, California 90095, United StatesFachbereich Physik, Philipps Universität Marburg, Marburg D-35032, GermanyCIC biomaGUNE, Donostia-San Sebastián 20009, Spain
    • Wolfgang J. Parak - Fachbereich Physik, Philipps Universität Marburg, Marburg D-35032, GermanyCIC biomaGUNE, Donostia-San Sebastián 20009, Spain
  • Notes
    The authors declare no competing financial interest.

Acknowledgment

ARTICLE SECTIONS
Jump To

W.C.W.C. would like to acknowledge Canadian Institute of Health Research and Natural Sciences and Engineering Research Council of Canada (NSERC) for funding support. B.U. and J.K. acknowledge NSERC for graduate fellowships. W.J.P. is grateful to the European Commission (grant FutureNanoNeeds) for financial support.

References

ARTICLE SECTIONS
Jump To

This article references 15 other publications.

  1. 1
    Zhou, W.; Gao, X.; Liu, D.; Chen, X. Gold Nanoparticles for In Vitro Diagnostics Chem. Rev. 2015, 115, 10575 10636 DOI: 10.1021/acs.chemrev.5b00100
  2. 2
    Nash, M. A.; Waitumbi, J. N.; Hoffman, A. S.; Yager, P.; Stayton, P. S. Multiplexed Enrichment and Detection of Malarial Biomarkers Using a Stimuli-Responsive Iron Oxide and Gold Nanoparticle Reagent System ACS Nano 2012, 6, 6776 6785 DOI: 10.1021/nn3015008
  3. 3
    Qin, Z.; Chan, W. C. W.; Boulware, D. R.; Akkin, T.; Butler, E. K.; Bischof, J. C. Significantly Improved Analytical Sensitivity of Later Flow Immunoassays By Using Thermal Contrast Angew. Chem., Int. Ed. 2012, 51, 4358 4361 DOI: 10.1002/anie.201200997
  4. 4
    Piraino, F.; Volpetti, F.; Watson, C.; Maerkl, S. J. A Digital-Analog Microfluidic Platform for Patient-Centric Multiplexed Biomarker Diagnostics of Ultralow Volume Samples ACS Nano 2016, 10, 1699 1710 DOI: 10.1021/acsnano.5b07939
  5. 5
    Li, Y.; Xuan, J.; Song, Y.; Qi, W.; He, B.; Wang, P.; Qin, L. Nanoporous Glass Integrated in Volumetric Bar-Chart Chip for Point-of-Care Diagnostics of Non-Small Cell Lung Cancer ACS Nano 2016, 10, 1640 1647 DOI: 10.1021/acsnano.5b07357
  6. 6
    Parak, W. J. Complex Colloidal Assembly Science 2011, 334, 1359 1360 DOI: 10.1126/science.1215080
  7. 7
    Sperling, R. A.; Parak, W. J. Surface Modification, Functionalization and Bioconjugation of Colloidal Inorganic Nanoparticles Philos. Trans. R. Soc., A 2010, 368, 1333 1383 DOI: 10.1098/rsta.2009.0273
  8. 8
    Oklu, R.; Khademhosseini, A.; Weiss, P. S. Patient-Inspired Engineering and Nanotechnology ACS Nano 2015, 9, 7733 7734 DOI: 10.1021/acsnano.5b05000
  9. 9
    Lalkhen, A. G.; McCluskey, A. Clinical Tests: Sensitivity and Specificity Contin. Educ. Anaesthesia Crit. Care Pain 2008, 8, 221 223 DOI: 10.1093/bjaceaccp/mkn041
  10. 10
    Berg, B.; Cortazar, B.; Tseng, D.; Ozkan, H.; Feng, S.; Wei, Q.; Chan, R.; Burbano, J.; Farooqui, Q.; Lewinski, M.; Di Carlo, D.; Garner, O. B.; Ozcan, A. Cellphone-Based Hand-Held Microplate Reader for Point-of-Care Testing of Enzyme-Linked Immunosorbent Assays ACS Nano 2015, 9, 7857 7866 DOI: 10.1021/acsnano.5b03203
  11. 11
    Kim, J.; Biondi, M. J.; Feld, J. J.; Chan, W. C. W. Clinical Validation of Quantum Dot Barcode Diagnostics ACS Nano 2016, 10, 4742 4753 DOI: 10.1021/acsnano.6b01254
  12. 12
    Shehada, N.; Cancilla, J. C.; Torrecilla, J. S.; Pariente, E. S.; Bronstrup, G.; Christiansen, S.; Johnson, D. W.; Leja, M.; Davies, M. P. A.; Liran, O.; Peled, N.; Haick, H. Silicon Nanowire Sensors Enable Diagnosis of Patients via Exhaled Breath ACS Nano 2016, 10, 7047 7057 DOI: 10.1021/acsnano.6b03127
  13. 13
    Chen, Y.; Zhang, Y.; Pan, F.; Liu, J.; Wang, K.; Zhang, C.; Cheng, S.; Lu, L.; Zhang, Z.; Zhi, X.; Zhang, Q.; Zhang, W.; Chen, D.; Alfranca, G.; De La Fuente, J. M.; Cui, D. Breath Analysis Based on Surface-Enhanced Raman Scattering Sensors Distinguishes Early and Advanced Gastric Cancer Patients from Healthy Persons. ACS Nano 2016, 10, DOI:  DOI: 10.1021/acsnano.6b01441 .
  14. 14
    Banoo, S.; Bell, D.; Bossuyt, P.; Herring, A.; Mabey, D.; Poole, F.; Smith, P.G.; Sriram, N.; Wongsrichanalai, C.; Linke, R.; O'Brien, R.; Perkins, M.; Cunningham, J.; Matsoso, P.; Nathanson, C. M.; Olliaro, P.; Peeling, R. W.; Ramsay, A. Nature Reviews Microbiology 2010, 8, S17 S29
  15. 15
    Borysiak, M. D.; Thompson, M. J.; Posner, J. D. Translating Diagnostic Assays from the Laboratory to the Clinic: Analytical and Clinical Metrics for Device Development and Evaluation Lab Chip 2016, 16, 1293 1313 DOI: 10.1039/C6LC00015K

Cited By

ARTICLE SECTIONS
Jump To

This article is cited by 43 publications.

  1. Ming Hui Chua, Kang Le Osmund Chin, Xian Jun Loh, Qiang Zhu, Jianwei Xu. Aggregation-Induced Emission-Active Nanostructures: Beyond Biomedical Applications. ACS Nano 2023, 17 (3) , 1845-1878. https://doi.org/10.1021/acsnano.2c10826
  2. Junnan He, Kangkai Xia, Binggong Zhao, Wangze Song, Yubin Zheng, Guishan Xiao, Huijian Wu, Nan Zheng. Codelivery of High-Molecular-Weight Poly-porphyrins and HIF-1α Inhibitors for In Vivo Synergistic Anticancer Therapy. Biomacromolecules 2021, 22 (11) , 4783-4793. https://doi.org/10.1021/acs.biomac.1c01073
  3. Vellingiri Yasothamani, Laxmanan Karthikeyan, Namratha Partha Sarathy, Raju Vivek. Targeted Designing of Multimodal Tumor-Seeking Nanomedicine for Breast Cancer-Specific Triple-Therapeutic Effects. ACS Applied Bio Materials 2021, 4 (8) , 6575-6588. https://doi.org/10.1021/acsabm.1c00740
  4. Vellingiri Yasothamani, Laxmanan Karthikeyan, Selvaraj Shyamsivappan, Yuvaraj Haldorai, Dayakar Seetha, Raju Vivek. Synergistic Effect of Photothermally Targeted NIR-Responsive Nanomedicine-Induced Immunogenic Cell Death for Effective Triple Negative Breast Cancer Therapy. Biomacromolecules 2021, 22 (6) , 2472-2490. https://doi.org/10.1021/acs.biomac.1c00244
  5. Dionysios C. Christodouleas, Balwinder Kaur, Parthena Chorti. From Point-of-Care Testing to eHealth Diagnostic Devices (eDiagnostics). ACS Central Science 2018, 4 (12) , 1600-1616. https://doi.org/10.1021/acscentsci.8b00625
  6. Samuel M. Stavis, Jeffrey A. Fagan, Michael Stopa, J. Alexander Liddle. Nanoparticle Manufacturing – Heterogeneity through Processes to Products. ACS Applied Nano Materials 2018, 1 (9) , 4358-4385. https://doi.org/10.1021/acsanm.8b01239
  7. Kevin M. Koo, Jing Wang, Renée S. Richards, Aine Farrell, John W. Yaxley, Hema Samaratunga, Patrick E. Teloken, Matthew J. Roberts, Geoffrey D. Coughlin, Martin F. Lavin, Paul N. Mainwaring, Yuling Wang, Robert A. Gardiner, Matt Trau. Design and Clinical Verification of Surface-Enhanced Raman Spectroscopy Diagnostic Technology for Individual Cancer Risk Prediction. ACS Nano 2018, 12 (8) , 8362-8371. https://doi.org/10.1021/acsnano.8b03698
  8. Edward J. Sayers, Johannes P. Magnusson, Paul R. Moody, Francesca Mastrotto, Claudia Conte, Chiara Brazzale, Paola Borri, Paolo Caliceti, Peter Watson, Giuseppe Mantovani, Jonathan Aylott, Stefano Salmaso, Arwyn T. Jones, Cameron Alexander. Switching of Macromolecular Ligand Display by Thermoresponsive Polymers Mediates Endocytosis of Multiconjugate Nanoparticles. Bioconjugate Chemistry 2018, 29 (4) , 1030-1046. https://doi.org/10.1021/acs.bioconjchem.7b00704
  9. Buddhisha Udugama, Pranav Kadhiresan, Amila Samarakoon, and Warren C. W. Chan . Simplifying Assays by Tableting Reagents. Journal of the American Chemical Society 2017, 139 (48) , 17341-17349. https://doi.org/10.1021/jacs.7b07055
  10. Warren C. W. Chan (Associate Editor), Ali Khademhosseini (Associate Editor), Wolfgang Parak (Associate Editor), and Paul S. Weiss (Editor-in-Chief). Cancer: Nanoscience and Nanotechnology Approaches. ACS Nano 2017, 11 (5) , 4375-4376. https://doi.org/10.1021/acsnano.7b03308
  11. Srijit Nair, Carlos Escobedo, and Ribal Georges Sabat . Crossed Surface Relief Gratings as Nanoplasmonic Biosensors. ACS Sensors 2017, 2 (3) , 379-385. https://doi.org/10.1021/acssensors.6b00696
  12. Warren W. C. Chan (Associate Editor), Manish Chhowalla (Associate Editor), Sharon Glotzer (Associate Editor), Yury Gogotsi (Associate Editor), Jason H. Hafner (Associate Editor), Paula T. Hammond (Associate Editor), Mark C. Hersam (Associate Editor), Ali Javey (Associate Editor), Cherie R. Kagan (Associate Editor), Ali Khademhosseini (Associate Editor), Nicholas A. Kotov (Associate Editor), Shuit-Tong Lee (Associate Editor), Yan Li (Associate Editor), Helmuth Möhwald (Associate Editor), Paul A. Mulvaney (Associate Editor), Andre E. Nel (Associate Editor), Peter J. Nordlander (Associate Editor), Wolfgang J. Parak (Associate Editor), Reginald M. Penner (Associate Editor), Andrey L. Rogach (Associate Editor), Raymond E. Schaak (Associate Editor), Molly M. Stevens (Associate Editor), Andrew T. S. Wee (Associate Editor), C. Grant Willson (Associate Editor), Laura E. Fernandez (Managing Editor), Paul S. Weiss (Editor-in-Chief). Nanoscience and Nanotechnology Impacting Diverse Fields of Science, Engineering, and Medicine. ACS Nano 2016, 10 (12) , 10615-10617. https://doi.org/10.1021/acsnano.6b08335
  13. Mattias Björnmalm, Matthew Faria, and Frank Caruso . Increasing the Impact of Materials in and beyond Bio-Nano Science. Journal of the American Chemical Society 2016, 138 (41) , 13449-13456. https://doi.org/10.1021/jacs.6b08673
  14. Yuguang Lu, Yuling Wu, Zhe Tang, Yike Hou, Mingyue Cui, Shuqi Huang, Binghua Long, Zhangsen Yu, Muhammad Zubair Iqbal, Xiangdong Kong. Synthesis of Multifunctional Mn3O4-Ag2S Janus Nanoparticles for Enhanced T1-Magnetic Resonance Imaging and Photo-Induced Tumor Therapy. Sensors 2023, 23 (21) , 8930. https://doi.org/10.3390/s23218930
  15. Tina Naghdi, Sina Ardalan, Zeinab Asghari Adib, Amir Reza Sharifi, Hamed Golmohammadi. Moving toward smart biomedical sensing. Biosensors and Bioelectronics 2023, 223 , 115009. https://doi.org/10.1016/j.bios.2022.115009
  16. Behnaz Ghaemi, Mohammad Javad Hajipour. Tumor acidic environment directs nanoparticle impacts on cancer cells. Journal of Colloid and Interface Science 2023, 634 , 684-692. https://doi.org/10.1016/j.jcis.2022.12.019
  17. Hannah N. Kozlowski, Shrey Sindhwani, Warren C. W. Chan. The Impact of Patient Characteristics on Diagnostic Test Performance. Small Methods 2022, 6 (2) https://doi.org/10.1002/smtd.202101233
  18. Navid Rabiee, Yousef Fatahi, Mohsen Asadnia, Hossein Daneshgar, Mahsa Kiani, Amir Mohammad Ghadiri, Monireh Atarod, Amin Hamed Mashhadzadeh, Omid Akhavan, Mojtaba Bagherzadeh, Eder C. Lima, Mohammad Reza Saeb. Green porous benzamide-like nanomembranes for hazardous cations detection, separation, and concentration adjustment. Journal of Hazardous Materials 2022, 423 , 127130. https://doi.org/10.1016/j.jhazmat.2021.127130
  19. T.J. MacCormack, M.-V. Meli, J.D. Ede, K.J. Ong, J.L. Rourke, C.A. Dieni. Commentary: Revisiting nanoparticle-assay interference: There's plenty of room at the bottom for misinterpretation. Comparative Biochemistry and Physiology Part B: Biochemistry and Molecular Biology 2021, 255 , 110601. https://doi.org/10.1016/j.cbpb.2021.110601
  20. Hui Huang, Wei Feng, Yu Chen, Jianlin Shi. Inorganic nanoparticles in clinical trials and translations. Nano Today 2020, 35 , 100972. https://doi.org/10.1016/j.nantod.2020.100972
  21. Da Huo, Xiqun Jiang, Yong Hu. Recent Advances in Nanostrategies Capable of Overcoming Biological Barriers for Tumor Management. Advanced Materials 2020, 32 (27) https://doi.org/10.1002/adma.201904337
  22. Xiangjun Chen, Lixue Song, Xiliang Li, Lingyu Zhang, Lu Li, Xiuping Zhang, Chungang Wang. Co-delivery of hydrophilic/hydrophobic drugs by multifunctional yolk-shell nanoparticles for hepatocellular carcinoma theranostics. Chemical Engineering Journal 2020, 389 , 124416. https://doi.org/10.1016/j.cej.2020.124416
  23. Carolina Ramos Hurtado, Cristiane da Costa Wachesk, Rafaela Campos Queiroz, Erenilda Ferreira de Macedo, Rebeca Falcão Borja de Oliveira Correia, Thalita Sani Taiariol, Milton Faria Diniz, Alexandre Martins Isaias dos Santos, Thais Larissa do Amaral Montanheiro, Gabriela Ramos Hurtado, Vladimir Jesus Trava-Airoldi, Dayane Batista Tada. A simple procedure to obtain nanodiamonds from leftover of HFCVD system for biological application. SN Applied Sciences 2020, 2 (3) https://doi.org/10.1007/s42452-020-1967-1
  24. Shin-Chen Pan, Yao-Hung Tsai, Chin-Chuan Chuang, Chao-Min Cheng. Preliminary Assessment of Burn Depth by Paper-Based ELISA for the Detection of Angiogenin in Burn Blister Fluid—A Proof of Concept. Diagnostics 2020, 10 (3) , 127. https://doi.org/10.3390/diagnostics10030127
  25. Archana Upadhyay, Huan Yang, Bilal Zaman, Lei Zhang, Yundi Wu, Jinhua Wang, Jianguo Zhao, Chenghong Liao, Qian Han. ZnO Nanoflower-Based NanoPCR as an Efficient Diagnostic Tool for Quick Diagnosis of Canine Vector-Borne Pathogens. Pathogens 2020, 9 (2) , 122. https://doi.org/10.3390/pathogens9020122
  26. Arghya Bandyopadhyay, Priya Yadav, Keka Sarkar, Sayan Bhattacharyya. The destructive spontaneous ingression of tunable silica nanosheets through cancer cell membranes. Chemical Science 2019, 10 (24) , 6184-6192. https://doi.org/10.1039/C9SC00076C
  27. Yi Zeng, Kevin M. Koo, Matt Trau, Ai-Guo Shen, Ji-Ming Hu. Watching SERS glow for multiplex biomolecular analysis in the clinic: A review. Applied Materials Today 2019, 15 , 431-444. https://doi.org/10.1016/j.apmt.2019.03.005
  28. Aihua Qu, Maozhong Sun, Liguang Xu, Changlong Hao, Xiaoling Wu, Chuanlai Xu, Nicholas A. Kotov, Hua Kuang. Quantitative zeptomolar imaging of miRNA cancer markers with nanoparticle assemblies. Proceedings of the National Academy of Sciences 2019, 116 (9) , 3391-3400. https://doi.org/10.1073/pnas.1810764116
  29. Alexander P. Jankowski, Caroline Pao, Gilbert C. Walker. Photonic Nanoparticles for Cellular and Tissular Labeling. 2019, 147-170. https://doi.org/10.1016/B978-0-12-803581-8.10446-1
  30. Jun Yang, Shaodong Zhai, Huan Qin, He Yan, Da Xing, Xianglong Hu. NIR-controlled morphology transformation and pulsatile drug delivery based on multifunctional phototheranostic nanoparticles for photoacoustic imaging-guided photothermal-chemotherapy. Biomaterials 2018, 176 , 1-12. https://doi.org/10.1016/j.biomaterials.2018.05.033
  31. Qiong Dai, Nadja Bertleff‐Zieschang, Julia A. Braunger, Mattias Björnmalm, Christina Cortez‐Jugo, Frank Caruso. Particle Targeting in Complex Biological Media. Advanced Healthcare Materials 2018, 7 (1) https://doi.org/10.1002/adhm.201700575
  32. Ruben Lanche, Vivek Pachauri, Walid-Madhat Munief, Achim Müller, Miriam Schwartz, Patrick Wagner, Ronald Thoelen, Sven Ingebrandt. Graphite oxide electrical sensors are able to distinguish single nucleotide polymorphisms in physiological buffers. FlatChem 2018, 7 , 1-9. https://doi.org/10.1016/j.flatc.2017.12.001
  33. Aihua Qu, Liguang Xu, Maozhong Sun, Liqiang Liu, Hua Kuang, Chuanlai Xu. Photoactive Hybrid AuNR‐Pt@Ag 2 S Core–Satellite Nanostructures for Near‐Infrared Quantitive Cell Imaging. Advanced Functional Materials 2017, 27 (46) https://doi.org/10.1002/adfm.201703408
  34. Seung Won Shin, Byoung Sang Lee, Kisuk Yang, Lunjakorn Amornkitbamrung, Min Su Jang, Bo Mi Ku, Seung-Woo Cho, Jung Heon Lee, Hojae Bae, Byung-Keun Oh, Myung-Ju Ahn, Yong Taik Lim, Soong Ho Um. Fluorescence-coded DNA Nanostructure Probe System to Enable Discrimination of Tumor Heterogeneity via a Screening of Dual Intracellular microRNA Signatures in situ. Scientific Reports 2017, 7 (1) https://doi.org/10.1038/s41598-017-13456-3
  35. Jisung Kim, Mohamed A. Abdou Mohamed, Kyryl Zagorovsky, Warren C.W. Chan. State of diagnosing infectious pathogens using colloidal nanomaterials. Biomaterials 2017, 146 , 97-114. https://doi.org/10.1016/j.biomaterials.2017.08.013
  36. Mijeong Kang, Eunkyoung Kim, Thomas E. Winkler, George Banis, Yi Liu, Christopher A. Kitchen, Deanna L. Kelly, Reza Ghodssi, Gregory F. Payne. Reliable clinical serum analysis with reusable electrochemical sensor: Toward point-of-care measurement of the antipsychotic medication clozapine. Biosensors and Bioelectronics 2017, 95 , 55-59. https://doi.org/10.1016/j.bios.2017.04.008
  37. Xiangjie Su, Feifei Zhao, Yuhui Wang, Xiaosa Yan, Shaona Jia, Bin Du. CuS as a gatekeeper of mesoporous upconversion nanoparticles-based drug controlled release system for tumor-targeted multimodal imaging and synergetic chemo-thermotherapy. Nanomedicine: Nanotechnology, Biology and Medicine 2017, 13 (5) , 1761-1772. https://doi.org/10.1016/j.nano.2017.03.008
  38. K Turcheniuk, Vadym N Mochalin. Biomedical applications of nanodiamond (Review). Nanotechnology 2017, 28 (25) , 252001. https://doi.org/10.1088/1361-6528/aa6ae4
  39. Freya Joris, Daniel Valdepérez, Beatriz Pelaz, Tianqiang Wang, Shareen H. Doak, Bella B. Manshian, Stefaan J. Soenen, Wolfgang J. Parak, Stefaan C. De Smedt, Koen Raemdonck. Choose your cell model wisely: The in vitro nanoneurotoxicity of differentially coated iron oxide nanoparticles for neural cell labeling. Acta Biomaterialia 2017, 55 , 204-213. https://doi.org/10.1016/j.actbio.2017.03.053
  40. Margaret M. Billingsley, Rachel S. Riley, Emily S. Day, . Antibody-nanoparticle conjugates to enhance the sensitivity of ELISA-based detection methods. PLOS ONE 2017, 12 (5) , e0177592. https://doi.org/10.1371/journal.pone.0177592
  41. Leoni A. Kunz-Schughart, Anna Dubrovska, Claudia Peitzsch, Alexander Ewe, Achim Aigner, Samuel Schellenburg, Michael H. Muders, Silke Hampel, Giuseppe Cirillo, Francesca Iemma, Rainer Tietze, Christoph Alexiou, Holger Stephan, Kristof Zarschler, Orazio Vittorio, Maria Kavallaris, Wolfgang J. Parak, Lutz Mädler, Suman Pokhrel. Nanoparticles for radiooncology: Mission, vision, challenges. Biomaterials 2017, 120 , 155-184. https://doi.org/10.1016/j.biomaterials.2016.12.010
  42. Eden Morales‐Narváez, Luis Baptista‐Pires, Alejandro Zamora‐Gálvez, Arben Merkoçi. Graphene‐Based Biosensors: Going Simple. Advanced Materials 2017, 29 (7) https://doi.org/10.1002/adma.201604905
  43. Zhiqiang Shen, Huilin Ye, Martin Kröger, Ying Li. Self-assembled core–polyethylene glycol–lipid shell nanoparticles demonstrate high stability in shear flow. Physical Chemistry Chemical Physics 2017, 19 (20) , 13294-13306. https://doi.org/10.1039/C7CP01530E
  • Abstract

  • References

    ARTICLE SECTIONS
    Jump To

    This article references 15 other publications.

    1. 1
      Zhou, W.; Gao, X.; Liu, D.; Chen, X. Gold Nanoparticles for In Vitro Diagnostics Chem. Rev. 2015, 115, 10575 10636 DOI: 10.1021/acs.chemrev.5b00100
    2. 2
      Nash, M. A.; Waitumbi, J. N.; Hoffman, A. S.; Yager, P.; Stayton, P. S. Multiplexed Enrichment and Detection of Malarial Biomarkers Using a Stimuli-Responsive Iron Oxide and Gold Nanoparticle Reagent System ACS Nano 2012, 6, 6776 6785 DOI: 10.1021/nn3015008
    3. 3
      Qin, Z.; Chan, W. C. W.; Boulware, D. R.; Akkin, T.; Butler, E. K.; Bischof, J. C. Significantly Improved Analytical Sensitivity of Later Flow Immunoassays By Using Thermal Contrast Angew. Chem., Int. Ed. 2012, 51, 4358 4361 DOI: 10.1002/anie.201200997
    4. 4
      Piraino, F.; Volpetti, F.; Watson, C.; Maerkl, S. J. A Digital-Analog Microfluidic Platform for Patient-Centric Multiplexed Biomarker Diagnostics of Ultralow Volume Samples ACS Nano 2016, 10, 1699 1710 DOI: 10.1021/acsnano.5b07939
    5. 5
      Li, Y.; Xuan, J.; Song, Y.; Qi, W.; He, B.; Wang, P.; Qin, L. Nanoporous Glass Integrated in Volumetric Bar-Chart Chip for Point-of-Care Diagnostics of Non-Small Cell Lung Cancer ACS Nano 2016, 10, 1640 1647 DOI: 10.1021/acsnano.5b07357
    6. 6
      Parak, W. J. Complex Colloidal Assembly Science 2011, 334, 1359 1360 DOI: 10.1126/science.1215080
    7. 7
      Sperling, R. A.; Parak, W. J. Surface Modification, Functionalization and Bioconjugation of Colloidal Inorganic Nanoparticles Philos. Trans. R. Soc., A 2010, 368, 1333 1383 DOI: 10.1098/rsta.2009.0273
    8. 8
      Oklu, R.; Khademhosseini, A.; Weiss, P. S. Patient-Inspired Engineering and Nanotechnology ACS Nano 2015, 9, 7733 7734 DOI: 10.1021/acsnano.5b05000
    9. 9
      Lalkhen, A. G.; McCluskey, A. Clinical Tests: Sensitivity and Specificity Contin. Educ. Anaesthesia Crit. Care Pain 2008, 8, 221 223 DOI: 10.1093/bjaceaccp/mkn041
    10. 10
      Berg, B.; Cortazar, B.; Tseng, D.; Ozkan, H.; Feng, S.; Wei, Q.; Chan, R.; Burbano, J.; Farooqui, Q.; Lewinski, M.; Di Carlo, D.; Garner, O. B.; Ozcan, A. Cellphone-Based Hand-Held Microplate Reader for Point-of-Care Testing of Enzyme-Linked Immunosorbent Assays ACS Nano 2015, 9, 7857 7866 DOI: 10.1021/acsnano.5b03203
    11. 11
      Kim, J.; Biondi, M. J.; Feld, J. J.; Chan, W. C. W. Clinical Validation of Quantum Dot Barcode Diagnostics ACS Nano 2016, 10, 4742 4753 DOI: 10.1021/acsnano.6b01254
    12. 12
      Shehada, N.; Cancilla, J. C.; Torrecilla, J. S.; Pariente, E. S.; Bronstrup, G.; Christiansen, S.; Johnson, D. W.; Leja, M.; Davies, M. P. A.; Liran, O.; Peled, N.; Haick, H. Silicon Nanowire Sensors Enable Diagnosis of Patients via Exhaled Breath ACS Nano 2016, 10, 7047 7057 DOI: 10.1021/acsnano.6b03127
    13. 13
      Chen, Y.; Zhang, Y.; Pan, F.; Liu, J.; Wang, K.; Zhang, C.; Cheng, S.; Lu, L.; Zhang, Z.; Zhi, X.; Zhang, Q.; Zhang, W.; Chen, D.; Alfranca, G.; De La Fuente, J. M.; Cui, D. Breath Analysis Based on Surface-Enhanced Raman Scattering Sensors Distinguishes Early and Advanced Gastric Cancer Patients from Healthy Persons. ACS Nano 2016, 10, DOI:  DOI: 10.1021/acsnano.6b01441 .
    14. 14
      Banoo, S.; Bell, D.; Bossuyt, P.; Herring, A.; Mabey, D.; Poole, F.; Smith, P.G.; Sriram, N.; Wongsrichanalai, C.; Linke, R.; O'Brien, R.; Perkins, M.; Cunningham, J.; Matsoso, P.; Nathanson, C. M.; Olliaro, P.; Peeling, R. W.; Ramsay, A. Nature Reviews Microbiology 2010, 8, S17 S29
    15. 15
      Borysiak, M. D.; Thompson, M. J.; Posner, J. D. Translating Diagnostic Assays from the Laboratory to the Clinic: Analytical and Clinical Metrics for Device Development and Evaluation Lab Chip 2016, 16, 1293 1313 DOI: 10.1039/C6LC00015K

Pair your accounts.

Export articles to Mendeley

Get article recommendations from ACS based on references in your Mendeley library.

Pair your accounts.

Export articles to Mendeley

Get article recommendations from ACS based on references in your Mendeley library.

You’ve supercharged your research process with ACS and Mendeley!

STEP 1:
Click to create an ACS ID

Please note: If you switch to a different device, you may be asked to login again with only your ACS ID.

Please note: If you switch to a different device, you may be asked to login again with only your ACS ID.

Please note: If you switch to a different device, you may be asked to login again with only your ACS ID.

MENDELEY PAIRING EXPIRED
Your Mendeley pairing has expired. Please reconnect