Quantum Dots-Based Point-of-Care Measurement of Procalcitonin in Finger-Prick Blood and Venous Whole Blood Specimens.

OBJECTIVE: To determine whether the performance of a new quantum dots-based point-of-care test (POCT) devices is qualified for procalcitonin testing. METHODS: Finger-prick and venous blood specimens from 153 patients were measured with a quantum dots-based POCT device; the results were compared with those from the reference method. RESULTS: The quantum dots-based POCT device correlated well with the reference method in measuring plasma, venous whole blood, and finger-prick blood. No significant bias was observed (-0.08 ng/mL). At 0.5 ng per mL cutoff value, the concordances were 96.6%, 94.6%, and 90.5% for plasma, venous whole blood, and finger-prick blood, respectively. And at 2 ng per mL cutoff value, the concordances were 98.0%, 96.6%, and 95.3%, respectively. CONCLUSIONS: The quantum dots-based POCT device measured procalcitonin with multiple specimen types, high sensitivity, wide detection range, and short turnaround time. It would allow a more widespread use of procalcitonin and help lessen the burden of overcrowding in healthcare facilities in China.

Correcting Artifacts in Single Molecule Localization Microscopy Analysis Arising from Pixel Quantum Efficiency Differences in sCMOS Cameras.

Optimal analysis of single molecule localization microscopy (SMLM) data acquired with a scientific Complementary Metal-Oxide-Semiconductor (sCMOS) camera relies on statistical compensation for its pixel-dependent gain, offset and readout noise. In this work we show that it is also necessary to compensate for differences in the relative quantum efficiency (RQE) of each pixel. We found differences in RQE on the order of 4% in our tested sCMOS sensors. These differences were large enough to have a noticeable effect on analysis algorithm results, as seen both in simulations and biological imaging data. We discuss how the RQE differences manifest themselves in the analysis results and present the modifications to the Poisson maximum likelihood estimation (MLE) sCMOS analysis algorithm that are needed to correct for the RQE differences.