The detection and analysis of circulating tumor cells (CTCs) would be of aid in a precise cancer diagnosis and an efficient prognosis assessment. However, traditional methods that rely heavily on the isolation of CTCs based on their physical or biological features suffer from intensive labor, thus being unsuitable for rapid detection. Furthermore, currently available intelligent methods are short of interpretability, which creates a lot of uncertainty during diagnosis. Therefore, we propose here an automated method that takes advantage of bright-field microscopic images with high resolution, so as to take an insight into cell patterns. Specifically, the precise identification of CTCs was achieved by using an optimized single-shot multi-box detector (SSD)–based neural network with integrated attention mechanism and feature fusion modules. Compared to the conventional SSD system, our method exhibited a superior detection performance with the recall rate of 92.2%, and the maximum average precision (AP) value of 97.9%. To note, the optimal SSD-based neural network was combined with advanced visualization technology, i.e., the gradient-weighted class activation mapping (Grad-CAM) for model interpretation, and the t-distributed stochastic neighbor embedding (T-SNE) for data visualization. Our work demonstrates for the first time the outstanding performance of SSD-based neural network for CTCs identification in human peripheral blood environment, showing great potential for the early detection and continuous monitoring of cancer progression.

Currently,most of the available technologies for medical diagnosis rely on separation based on cell biological properties,which suffer from problems such as low sensitivity,complex technical procedures,low cost-effectiveness,and unsuitability for continuous monitoring.Therefore,this work proposes a new microfluidic technology based on deep learning to adapt to the accurate detection of circulating tumor cells in a high-flow environment.To assist in positioning the detection frame,a finite element analysis software was used to simulate the generation process of microfluidic droplets in the water phase(dispersion phase)and oil phase(continuous phase)at different flow rates,so as to select the most stable flow rate(dispersion phaseat 1 μL/min and continuous phase at 16 μL/min,accordingly).To verify the results of simulation,positioning tests were conducted on videos with different flow rates,and the relative error was controlled within 1%via the pixel and scale optimization of the detection frame.On this basis,the YOLOv5 algorithm structure was optimized by adding an attention mechanism and a multi-scale feature fusion algorithm module,converting the pixels and scales of the detection frame,thereby achieving precise droplet detection and size prediction.In the experiment,lung cancer and breast cancer cells were added into the water phase to construct a data set(15 min,20 F/s)for algorithm model training.Finally,the improved YOLO micro-total analysis system could accurately measure the position(mean average precision of 98.92%)and size(relative error of 0.49%)of the droplets,and precisely identify circulating tumor cells in the video stream with an accuracy of 72.49%.This work not only provides a new technology for the intelligent detection of components in microfluidic droplets,but also provides a potential strategy for real-time monitoring of circulating tumor cells in real blood environment.

Correction for 'A molecularly imprinted antibiotic receptor on magnetic nanotubes for the detection and removal of environmental oxytetracycline' by Jixiang Wang et al., J. Mater. Chem. B, 2022, 10, 6777-6783, https://doi.org/10.1039/D2TB00497F.

In this work, we propose an automated detection system for circulating tumor cells (CTCs) identification in spiked samples based on the bright-field microscope images. Specifically, the precise identification of CTCs relied on the modified single-shot multibox detector (SSD)–based neural network. Moreover, we choose attention mechanism and feature fusion for the performance improvement. With this method, the detection performance was considerably boosted with the mean average precision (mAP) value of 91.54% with respect to 85.00% in case of general SSD. It turns out that our model has stronger generalization ability and higher small target detection ability, equipped with a cell counting function, which can assist pathologists in qualitative and quantitative analysis of CTCs in blood visually and accurately.

The detection and elimination of antibiotic contaminants, such as oxytetracycline (OTC), a broad-spectrum tetracycline antibiotic, would be of help in efficient environmental monitoring, agriculture and food safety tests. Nevertheless, currently available methodologies, which mostly rely on the chromatographic separation of OTC, suffer from low sensitivity and complicated processes. Thus, we report here on the design and synthesis of a fluorescent sensor based on molecularly imprinted magnetic halloysite nanotubes (referred to as MHNTs@FMIPs) for the effective detection and purification of OTC in actual environmental samples. The fluorescence of the MHNTs@FMIPs was quenched obviously upon loading with OTC, covering a linear concentration range of 10-300 nM with a limit of detection (LOD) as low as 8.1 nM. The imprinting factor is 4.47, indicating an excellent specificity. Furthermore, the MHNTs@FMIPs can be applied to the quantitative detection of OTC (5 cycles of 300 nM) in aquaculture wastewater and Yangtze River water, demonstrating their immense application potential.