Molecular detection of SARS-COV-2 in exhaled breath at the point-of-need.

The SARS-CoV-2 pandemic has highlighted the need for improved technologies to help control the spread of contagious pathogens. While rapid point-of-need testing plays a key role in strategies to rapidly identify and isolate infectious patients, current test approaches have significant shortcomings related to assay limitations and sample type. Direct quantification of viral shedding in exhaled particles may offer a better rapid testing approach, since SARS-CoV-2 is believed to spread mainly by aerosols. It assesses contagiousness directly, the sample is easy and comfortable to obtain, sampling can be standardized, and the limited sample volume lends itself to a fast and sensitive analysis. In view of these benefits, we developed and tested an approach where exhaled particles are efficiently sampled using inertial impaction in a micromachined silicon chip, followed by an RT-qPCR molecular assay to detect SARS-CoV-2 shedding. Our portable, silicon impactor allowed for the efficient capture (>85%) of respiratory particles down to 300 nm without the need for additional equipment. We demonstrate using both conventional off-chip and in-situ PCR directly on the silicon chip that sampling subjects' breath in less than a minute yields sufficient viral RNA to detect infections as early as standard sampling methods. A longitudinal study revealed clear differences in the temporal dynamics of viral load for nasopharyngeal swab, saliva, breath, and antigen tests. Overall, after an infection, the breath-based test remains positive during the first week but is the first to consistently report a negative result, putatively signalling the end of contagiousness and further emphasizing the potential of this tool to help manage the spread of airborne respiratory infections.

Exhaled breath SARS-CoV-2 shedding patterns across variants of concern.

OBJECTIVES: We performed exhaled breath (EB) and nasopharyngeal (NP) quantitative polymerase chain reaction (qPCR) and NP rapid antigen testing (NP RAT) of SARS-CoV-2 infections with different variants. METHODS: We included immuno-naïve alpha-infected (n = 11) and partly boosted omicron-infected patients (n = 8) as high-risk contacts. We compared peak NP and EB qPCR cycle time (ct) values between cohorts (Wilcoxon-Mann-Whitney test). Test positivity was compared for three infection phases using Cochran Q test. RESULTS: Peak median NP ct was 11.5 (interquartile range [IQR] 10.1-12.1) for alpha and 12.2 (IQR 11.1-15.3) for omicron infections. Peak median EB ct was 25.2 (IQR 24.5-26.9) and 28.3 (IQR 26.4-30.8) for alpha and omicron infections, respectively. Distributions did not differ between cohorts for NP (P = 0.19) or EB (P = 0.09). SARS-CoV-2 shedding peaked on day 1 in EB (confidence interval [CI] 0.0 - 4.5) and day 3 in NP (CI 1.5 - 6.0). EB qPCR positivity equaled NP qPCR positivity on D0-D1 (P = 0.44) and D2-D6 (P = 1.0). It superseded NP RAT positivity on D0-D1 (P = 0.003) and D2-D6 (P = 0.008). It was inferior to both on D7-D10 (P < 0.001). CONCLUSION: Peak EB and nasopharynx shedding were comparable across variants. EB qPCR positivity matched NP qPCR and superseded NP RAT in the first week of infection.

Color lens-free imaging using multi-wavelength illumination based phase retrieval.

Accurate image reconstruction in color lens-free imaging has proven challenging. The color image reconstruction of a sample is impacted not only by how strongly the illumination intensity is absorbed at a given spectral range, but also by the lack of phase information recorded on the image sensor. We present a compact and cost-effective approach of addressing the need for phase retrieval to enable robust color image reconstruction in lens-free imaging. The amplitude images obtained at transparent wavelength bands are used to estimate the phase in highly absorbed wavelength bands. The accurate phase information, obtained through our iterative algorithm, removes the color artefacts due to twin-image noise in the reconstructed image and improves image reconstruction quality to allow accurate color reconstruction. This could enable the technique to be applied for imaging of stained pathology slides, an important tool in medical diagnostics.

Fast compressive lens-free tomography for 3D biological cell culture imaging.

We present a compressive lens-free technique that performs tomographic imaging across a cubic millimeter-scale volume from highly sparse data. Compared with existing lens-free 3D microscopy systems, our method requires an order of magnitude fewer multi-angle illuminations for tomographic reconstruction, leading to a compact, cost-effective and scanning-free setup with a reduced data acquisition time to enable high-throughput 3D imaging of dynamic biological processes. We apply a fast proximal gradient algorithm with composite regularization to address the ill-posed tomographic inverse problem. Using simulated data, we show that the proposed method can achieve a reconstruction speed ∼10× faster than the state-of-the-art inverse problem approach in 3D lens-free microscopy. We experimentally validate the effectiveness of our method by imaging a resolution test chart and polystyrene beads, demonstrating its capability to resolve micron-size features in both lateral and axial directions. Furthermore, tomographic reconstruction results of neuronspheres and intestinal organoids reveal the potential of this 3D imaging technique for high-resolution and high-throughput biological applications.

Pixel super-resolution for lens-free holographic microscopy using deep learning neural networks.

Lens-free holographic microscopy (LFHM) provides a cost-effective tool for large field-of-view imaging in various biomedical applications. However, due to the unit optical magnification, its spatial resolution is limited by the pixel size of the imager. Pixel super-resolution (PSR) technique tackles this problem by using a series of sub-pixel shifted low-resolution (LR) lens-free holograms to form the high-resolution (HR) hologram. Conventional iterative PSR methods require a large number of measurements and a time-consuming reconstruction process, limiting the throughput of LFHM in practice. Here we report a deep learning-based PSR approach to enhance the resolution of LFHM. Compared with the existing PSR methods, our neural network-based approach outputs the HR hologram in an end-to-end fashion and maintains consistency in resolution improvement with a reduced number of LR holograms. Moreover, by exploiting the resolution degradation model in the imaging process, the network can be trained with a data set synthesized from the LR hologram itself without resorting to the HR ground truth. We validated the effectiveness and the robustness of our method by imaging various types of samples using a single network trained on an entirely different data set. This deep learning-based PSR approach can significantly accelerate both the data acquisition and the HR hologram reconstruction processes, therefore providing a practical solution to fast, lens-free, super-resolution imaging.

Burmese python target reflectivity compared to natural Florida foliage background reflectivity.

The Florida Everglades is infested with Burmese pythons caused by the release of exotic pets in the 1980s. The current estimates are between 30,000 and 300,000 pythons, where the result is a severe decline in Everglade mammals: 90% reductions in raccoon, opossum, bobcats, and foxes. The marsh rabbits are completely gone. The population of the pythons is rapidly increasing exponentially with 20-50 eggs per snake with a life span of up to 20 years. Pythons have been captured in the Everglades with lengths of nearly 6 m. Researchers in the state of Florida are concerned that these pythons are (1) permanently damaging the Everglades, (2) migrating further north into populated areas of Florida, and (3) endangering wildlife, pets, and eventually, people. There have been a number of sensing efforts attempted in the large-area detection of pythons, where limited success has been achieved. For example, infrared sensors have been applied to the problem, but the pythons are cold-blooded, so the infrared bands do not work well. Imec has leveraged its expertise and infrastructure in semiconductor processing to produce highly compact, higher performance, and relatively cheaper hyperspectral image sensors and camera systems. In this work, Imec teamed with the University of Florida and Extended Reality Systems to obtain hyperspectral reflectivity measurements of Burmese pythons along with natural Florida background foliage to determine bands or band combinations that may be exploited in the large-area detection of pythons. The bands investigated are the visible-near infrared (or VisNIR) and the shortwave infrared (SWIR) bands. The results show that there are enough differences in the data collection such that a single band, inexpensive VisNIR band camera may provide reasonable results and a two-band, VisNIR/SWIR combination may provide higher performance results. In this paper, we provide the VisNIR results.

Vignetted-aperture correction for spectral cameras with integrated thin-film Fabry-Perot filters.

Spectral cameras with integrated thin-film Fabry-Perot filters have become increasingly important in many applications. These applications often require the detection of spectral features at specific wavelengths or to quantify small variations in the spectrum. This can be challenging since thin-film filters are sensitive to the angle of incidence of the light. In prior work, we modeled and corrected for the distribution of incident angles for an ideal finite aperture. Many real lenses, however, experience vignetting. Therefore, in this paper, we generalize our model to the more common case of a vignetted aperture, which changes the distribution of incident angles. We propose a practical method to estimate the model parameters and correct undesired shifts in measured spectra. This is experimentally validated for a lens mounted on a visible-to-near-infrared spectral camera.

Finite aperture correction for spectral cameras with integrated thin-film Fabry-Perot filters.

Spectral cameras with integrated thin-film Fabry-Perot filters enable many different applications. Some applications require the detection of spectral features that are only visible at specific wavelengths, and some need to quantify small spectral differences that are undetectable with RGB color cameras. One factor that influences the central wavelength of thin-film filters is the angle of incidence. Therefore, when light is focused from an imaging lens onto the filter array, undesirable shifts in the measured spectra are observed. These shifts limit the use of the sensor in applications that require fast lenses or lenses with large chief ray angles. To increase flexibility and enable new applications, we derive an analytical model that explains and can correct the observed shifts in measured spectra. The model includes the size of the aperture and physical position of each filter on the sensor. We experimentally validate the model with two spectral cameras: one in the visible and near-infrared region and one in the short wave infrared region.

Reflective lens-free imaging on high-density silicon microelectrode arrays for monitoring and evaluation of in vitro cardiac contractility.

The high rate of drug attrition caused by cardiotoxicity is a major challenge for drug development. Here, we developed a reflective lens-free imaging (RLFI) approach to non-invasively record in vitro cell deformation in cardiac monolayers with high temporal (169 fps) and non-reconstructed spatial resolution (352 µm) over a field-of-view of maximally 57 mm2. The method is compatible with opaque surfaces and silicon-based devices. Further, we demonstrated that the system can detect the impairment of both contractility and fast excitation waves in cardiac monolayers. Additionally, the RLFI device was implemented on a CMOS-based microelectrode array to retrieve multi-parametric information of cardiac cells, thereby offering more in-depth analysis of drug-induced (cardiomyopathic) effects for preclinical cardiotoxicity screening applications.