Detection of nuclear biomarkers such as nucleic acids and nuclear proteins is critical for early-stage cancer diagnosis and prognosis. Conventional methods relying on morphological assessment of cell nuclei in histopathology slides may be subjective, whereas colorimetric immunohistochemical and fluorescence-based imaging are limited by strong light absorption, broad-emission bands and low contrast. Here, we describe the development and use of a scanning laser-emission-based microscope that maps lasing emissions from nuclear biomarkers in human tissues. 41 tissue samples from 35 patients labelled with site-specific and biomarker-specific antibody-conjugated dyes were sandwiched in a Fabry-Pérot microcavity while an excitation laser beam built a laser-emission image. We observed multiple sub-cellular lasing emissions from cancer cell nuclei, with a threshold of tens of μJ/mm2, sub-micron resolution (<700 nm), and a lasing band in the few-nanometre range. Different lasing thresholds of nuclei in cancer and normal tissues enabled the identification and multiplexed detection of nuclear proteomic biomarkers, with a high sensitivity for early-stage cancer diagnosis. Laser-emission-based cancer screening and immunodiagnosis might find use in precision medicine and facilitate research in cell biology.

Non-dispersive infrared (NDIR) spectroscopy analyzes the concentration of target gases based on their characteristic infrared absorption. In conventional NDIR gas sensors, an infrared detector has to pair with a bandpass filter to select the target gas. However, multiplexed NDIR gas sensing requires multiple pairs of bandpass filters and detectors, which makes the sensor bulky and expensive. Here, we propose a multiplexed NDIR gas sensing platform consisting of a narrowband infrared detector array as read-out. By integrating plasmonic metamaterial absorbers with pyroelectric detectors at the pixel level, the detectors exhibit spectrally tunable and narrowband photoresponses, circumventing the need for separate bandpass filter arrays. We demonstrate the sensing of H2S, CH4, CO2, CO, NO, CH2O, NO2, SO2. The detection limits of common gases such as CH4, CO2, and CO are 63 ppm, 2 ppm, and 11 ppm, respectively. We also demonstrate the deduction of the concentrations of two target gases in a mixture.

Convalescent serum with a high abundance of neutralization IgG is a promising therapeutic agent for rescuing COVID-19 patients in the critical stage. Knowing the concentration of SARS-CoV-2 S1-specific IgG is crucial in selecting appropriate convalescent serum donors. Here, we present a portable microfluidic ELISA technology for rapid (15 min), quantitative, and sensitive detection of anti-SARS-CoV-2 S1 IgG in human serum with only 8 μL sample volume. We first identified a humanized monoclonal IgG that has a high binding affinity and a relatively high specificity towards SARS-CoV-2 S1 protein, which can subsequently serve as the calibration standard of anti-SARS-CoV-2 S1 IgG in serological analyses. We then measured the abundance of anti-SARS-CoV-2 S1 IgG in 16 convalescent COVID-19 patients. Due to the availability of the calibration standard and the large dynamic range of our assay, we were able to identify “qualified donors” for convalescent serum therapy with only one fixed dilution factor (200 ×). Finally, we demonstrated that our technology can sensitively detect SARS-CoV-2 antigens (S1 and N proteins) with pg/mL level sensitivities in 40 min. Overall, our technology can greatly facilitate rapid, sensitive, and quantitative analysis of COVID-19 related markers for therapeutic, diagnostic, epidemiologic, and prognostic purposes.

This paper presents a new method for determining the widths of the power and ground routes in integrated circuits so that the area required by the routes is minimized subject to the reliability constraints. The basic idea is to transform the resulting constrained nonlinear programming problem into a sequence of linear programs. Theoretically, we show that the sequence of linear programs always converges to the optimum solution of the relaxed convex problem. Experimental results demonstrate that the sequence-of-linear-programming method is orders of magnitude faster than the best-known method based on conjugate gradients, with constantly better optimization solutions.