Application of the PAMONO-Sensor for Quantiﬁcation of Microvesicles and Determination of Nano-Particle Size Distribution

Shpacovitch, Victoria; Sidorenko, Irina; Lenssen, Jan Eric; Temchura, Vladimir; Weichert, Frank; Müller, Heinrich; Überla, Klaus; Zybin, A.; Schramm, Alexander; Hergenröder, Roland

doi:10.3390/s17020244

Cited by 25 publications

(26 citation statements)

References 32 publications

(51 reference statements)

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“…The PAMONO sensor is an optical biosensor that is used to detect nanoparticles, such as viruses or microvesicles, in liquid samples [2,10,11]. Nanoparticles are too small to be detected using light directly.…”

Section: Materials and Methodsmentioning

confidence: 99%

Nanoparticle Classification Using Frequency Domain Analysis on Resource-Limited Platforms

Yayla

Toma

Chen

et al. 2019

Sensors

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A mobile system that can detect viruses in real time is urgently needed, due to the combination of virus emergence and evolution with increasing global travel and transport. A biosensor called PAMONO (for Plasmon Assisted Microscopy of Nano-sized Objects) represents a viable technology for mobile real-time detection of viruses and virus-like particles. It could be used for fast and reliable diagnoses in hospitals, airports, the open air, or other settings. For analysis of the images provided by the sensor, state-of-the-art methods based on convolutional neural networks (CNNs) can achieve high accuracy. However, such computationally intensive methods may not be suitable on most mobile systems. In this work, we propose nanoparticle classification approaches based on frequency domain analysis, which are less resource-intensive. We observe that on average the classification takes 29 μs per image for the Fourier features and 17 μs for the Haar wavelet features. Although the CNN-based method scores 1–2.5 percentage points higher in classification accuracy, it takes 3370 μs per image on the same platform. With these results, we identify and explore the trade-off between resource efficiency and classification performance for nanoparticle classification of images provided by the PAMONO sensor.

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Section: Materials and Methodsmentioning

confidence: 99%

Nanoparticle Classification Using Frequency Domain Analysis on Resource-Limited Platforms

Yayla

Toma

Chen

et al. 2019

Sensors

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“…hospitals, retirement homes, bigger social gatherings) is necessary. A medical biosensor which complies with these requirements and is able to detect virus-like particles in the range between 80 nm and 200 nm is the PAMONO sensor [6]. It is a modified SPR (Surface Plasmon Resonance) sensor which can detect individual particles attaching to the antibody coated surface.…”

Section: Introductionmentioning

confidence: 99%

“…Wang et al [16] or Srinidhi et al [17]. For a detailed explanation of the state of the art and the technology of the PAMONO sensor we refer to previous work [6], [18], [19]. The following sections detail our spatiotemporal CNN approach for virus detection.…”

Section: Introductionmentioning

confidence: 99%

An Automated Rapid Test for Viral Nanoparticles Based on Spatiotemporal Deep Learning

Wüstefeld

Weichert

2020

2020 Ieee Sensors

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The recent outbreak of the COVID-19 pandemic has shown the importance of medical testing for an early detection of regional disease hot spots. The PAMONO sensor (Plasmon-Assisted Microscopy of Nano-Objects) can detect virus-like particles. It utilizes the surface plasmon resonance of particles attaching to an antibody coated gold plate to generate a stream of noisy images containing characteristic spatiotemporal signals. These signals are then analyzed via a deep neural network based architecture which targets a parameter-free usage on-site. It is able to detect individual (virus) particles within ten seconds and to reach a count exactness of higher than 80% in a minute.

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“…The schematics of the sensing platform can be seen in Figure 27. By monitoring the reflected beam intensity over the whole beam area with a CCD camera, the SPR response for each sensing spot was The presentation of the PAMONO-sensor (plasmon assisted microscopy of nano-objects) for examination of EVs, showed a potential alternative way for size determination and quantification of EVs by plasmonic sensors [172]. This method is also based on SPR imaging, but uses an image processing software to identify and count EVs that bind to the sensor surface and trigger a SPR response.…”

Section: Plasmonic Biosensors For Ev Analysismentioning

confidence: 99%

“…Image of EV bound to the PAMONO-sensor surface and the corresponding increase in the SPR signal.Figures reprinted from[172].…”

mentioning

confidence: 99%

Extracellular vesicles and their detection with plasmonic bionsensors for the diagnosis of ovarian cancer

Reiner¹

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Ovarian cancer is the deadliest gynecological cancer, due to its late diagnosis resulting in unsuccessful treatment. An earlier diagnosis at a stage, when the disease is still treatable, would improve the chances of survival immensely. To achieve such an early diagnosis extracellular vesicles (EVs) have been proposed as a new class of biomarkers, but the currently used methods for their isolation and analysis lack in specificity and sensitivity. Hence this project aimed to identify new EV-based biomarkers for an improved diagnosis of ovarian cancer by usage of lipidbinding proteins for EV isolation and to develop new highly sensitive biosensors for EV detection based on surface plasmon resonance (SPR). We achieved our aim by identifying a subpopulation of EVs that carries a distinct lipid and protein composition as candidate biomarker for ovarian cancer and will validate its value for clinical application in subsequent studies. Furthermore, we developed a plasmonic biosensor platform that was capable of detecting trace amounts of EVs by making use of magnetic nanoparticles for signal enhancement. The specific detection of cancer-derived EVs with this sensing platform will be studied in future.

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Application of the PAMONO-Sensor for Quantiﬁcation of Microvesicles and Determination of Nano-Particle Size Distribution

Cited by 25 publications

References 32 publications

Nanoparticle Classification Using Frequency Domain Analysis on Resource-Limited Platforms

Nanoparticle Classification Using Frequency Domain Analysis on Resource-Limited Platforms

An Automated Rapid Test for Viral Nanoparticles Based on Spatiotemporal Deep Learning

Extracellular vesicles and their detection with plasmonic bionsensors for the diagnosis of ovarian cancer

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