AND-Logic Cascade Rolling Circle Amplification for Optomagnetic Detection of Dual Target SARS-CoV-2 Sequences.

DNA logic operations are accurate and specific molecular strategies that are appreciated in target multiplexing and intelligent diagnostics. However, most of the reported DNA logic operation-based assays lack amplifiers prior to logic operation, resulting in detection limits at the subpicomolar to nanomolar level. Herein, a homogeneous and isothermal AND-logic cascade amplification strategy is demonstrated for optomagnetic biosensing of two different DNA inputs corresponding to a variant of concern sequence (containing spike L452R) and a highly conserved sequence from SARS-CoV-2. With an "amplifiers-before-operator" configuration, two input sequences are recognized by different padlock probes for amplification reactions, which generate amplicons used, respectively, as primers and templates for secondary amplification, achieving the AND-logic operation. Cascade amplification products can hybridize with detection probes grafted onto magnetic nanoparticles (MNPs), leading to hydrodynamic size increases and/or aggregation of MNPs. Real-time optomagnetic MNP analysis offers a detection limit of 8.6 fM with a dynamic detection range spanning more than 3 orders of magnitude. The accuracy, stability, and specificity of the system are validated by testing samples containing serum, salmon sperm, a single-nucleotide variant, and biases of the inputs. Clinical samples are tested with both quantitative reverse transcription-PCR and our approach, showing highly consistent measurement results.

Optomagnetic biosensors: Volumetric sensing based on magnetic actuation-induced optical modulations.

In comparison to alternative nanomaterials, magnetic micron/nano-sized particles show unique advantages, e.g., easy manipulation, stable signal, and high contrast. By applying magnetic actuation, magnetic particles exert forces on target objects for highly selective operation even in non-purified samples. We herein describe a subgroup of magnetic biosensors, namely optomagnetic biosensors, which employ alternating magnetic fields to generate periodic movements of magnetic labels. The optical modulation induced by the dynamics of magnetic labels is then analyzed by photodetectors, providing information of, e.g., hydrodynamic size changes of the magnetic labels. Optomagnetic sensing mechanisms can suppress the noise (by performing lock-in detection), accelerate the reaction (by magnetic force-enhanced molecular collision), and facilitate homogeneous/volumetric detection. Moreover, optomagnetic sensing can be performed using a low magnetic field (<10 mT) without sophisticated light sources or pickup coils, further enhancing its applicability for point-of-care tests. This review concentrates on optomagnetic biosensing techniques of different concepts classified by the magnetic actuation strategy, i.e., magnetic field-enhanced agglutination, rotating magnetic field-based particle rotation, and oscillating magnetic field-induced Brownian relaxation. Optomagnetic sensing principles applied with different actuation strategies are introduced as well. For each representative optomagnetic biosensor, a simple immunoassay strategy-based application is introduced (if possible) for methodological comparison. Thereafter, challenges and perspectives are discussed, including minimization of nonspecific binding, on-chip integration, and multiplex detection, all of which are key requirements in point-of-care diagnostics.

Detection and recognition of ultrasound breast nodules based on semi-supervised deep learning: a powerful alternative strategy.

BACKGROUND: The successful recognition of benign and malignant breast nodules using ultrasound images is based mainly on supervised learning that requires a large number of labeled images. However, because high-quality labeling is expensive and time-consuming, we hypothesized that semi-supervised learning could provide a low-cost and powerful alternative approach. This study aimed to develop an accurate semi-supervised recognition method and compared its performance with supervised methods and sonographers. METHODS: The faster region-based convolutional neural network was used for nodule detection from ultrasound images. A semi-supervised classifier based on the mean teacher model was proposed to recognize benign and malignant nodule images. The general performance of the proposed method on two datasets (8,966 nodules) was reported. RESULTS: The detection accuracy was 0.88±0.03 and 0.86±0.02, respectively, on two testing sets (1,350 and 2,220 nodules). When 800 labeled training nodules were available, the proposed semi-supervised model plus 4,396 unlabeled nodules performed better than the supervised learning model (area under the curve (AUC): 0.934±0.026 vs. 0.83±0.050; 0.916±0.022 vs. 0.815±0.049). The performance of the semi-supervised model trained on 800 labeled and 4,396 unlabeled nodules was close to that of the supervised learning model trained on a massive number of labeled nodules (n=5,196) (AUC: 0.934±0.026 vs. 0.952±0.027; 0.916±0.022 vs. 0.918±0.017). Moreover, the semi-supervised model was better than the average accuracy of five human sonographers (AUC: 0.922 vs. 0.889). CONCLUSIONS: The semi-supervised model can achieve excellent performance for nodule recognition and be useful for medical sciences. The method reduced the number of labeled images required for training, thus significantly alleviating the difficulty in data preparation of medical artificial intelligence.