Simultaneous noninvasive ultrasensitive detection of prostate specific antigen and lncRNA PCA3 using multiplexed dual optical microfibers with strong plasmonic nanointerfaces.

Low accuracy of diagnosing prostate cancer (PCa) was easily caused by only assaying single prostate specific antigen (PSA) biomarker. Although conventional reported methods for simultaneous detection of two specific PCa biomarkers could improve the diagnostic efficiency and accuracy, low detection sensitivity restrained their use in extreme early-stage PCa clinical assay applications. In order to overcome above drawbacks, this paper herein proposed a multiplexed dual optical microfibers separately functionalized with gold nanorods (GNRs) and Au nanobipyramids (Au NBPs) nanointerfaces with strong localized surface plasmon resonance (LSPR) effects. The sensors could simultaneously detect PSA protein biomarker and long noncoding RNA prostate cancer antigen 3 (lncRNA PCA3) with ultrahigh sensitivity and remarkable specificity. Consequently, the proposed dual optical microfibers multiplexed biosensors could detect the PSA protein and lncRNA PCA3 with ultra-low limit-of-detections (LODs) of 3.97 × 10-15 mol/L and 1.56 × 10-14 mol/L in pure phosphorus buffer solution (PBS), respectively, in which the obtained LODs were three orders of magnitude lower than existed state-of-the-art PCa assay technologies. Additionally, the sensors could discriminate target components from complicated physiological environment, that showing noticeable biosensing specificity of the sensors. With good performances of the sensors, they could successfully assay PSA and lncRNA PCA3 in undiluted human serum and urine simultaneously, respectively. Consequently, our proposed multiplexed sensors could real-time high-sensitivity simultaneously detect complicated human samples, that providing a novel valuable approach for the high-accurate diagnosis of early-stage PCa individuals.

Fault Diagnosis for Reducers Based on a Digital Twin.

A new method based on a digital twin is proposed for fault diagnosis, in order to compensate for the shortcomings of the existing methods for fault diagnosis modeling, including the single fault type, low similarity, and poor visual effect of state monitoring. First, a fault diagnosis test platform is established to analyze faults under constant and variable speed conditions. Then, the obtained data are integrated into the Unity3D platform to realize online diagnosis and updated with real-time working status data. Finally, an industrial test of the digital twin model is conducted, allowing for its comparison with other advanced methods in order to verify its accuracy and application feasibility. It was found that the accuracy of the proposed method for the entire reducer was 99.5%, higher than that of other methods based on individual components (e.g., 93.5% for bearings, 96.3% for gear shafts, and 92.6% for shells).