We report a label-free surface-enhanced Raman scattering method for identification of methicillin-susceptible Staphylococcus aureus and methicillin-resistant Staphylococcus aureus based on aptamer-guided AgNP enhancement and convolutional neural network classification.

Accurate detection and timely treatment of component defects in substations is an important measure to ensure the safe operation of power systems. In this study, taking substation meters as an example, a dataset of common meter defects, such as a fuzzy or damaged dial on the meter and broken meter housing, is constructed from the images of manual inspection in power systems. There are several challenges involved in accurately detecting defects in substation meter images, such as the complex background, different meter sizes and large differences in the shapes of meter defects. Therefore, this paper proposes the PHAM-YOLO (Parallel Hybrid Attention Mechanism You Only Look Once) network for automatic detection of substation meter defects. In order to make the network pay attention to the key areas against the complex background of the meter defect images and the differences between different defect features, a Parallel Hybrid Attention Mechanism (PHAM) module is designed and added to the backbone of YOLOv5. PHAM integration of local and non-local correlation information can highlight these differences while remaining focused on the meter defect features. To improve the expressive ability of the feature map, a Spatial Pyramid Pooling Fast (SPPF) module is introduced, which pools the input feature map using a continuous fixed convolution kernel, fusing the feature maps of different receptive fields. Bounding box regression (BBR) is the key way to determine object positioning performance in defect detection. EIOU (Efficient Intersection over Union) is, therefore, introduced as a boundary loss function to solve the ambiguity of the CIOU (Complete Intersection Over Union) loss function, making the BBR regression more accurate. The experimental results show that the Average Precision Mean (mAP), Precision (P) and Recall (R) of the proposed PHAM-YOLO network in the dataset are 78.3%, 78.3%, and 79.9%, respectively, with mAP being improved by 2.7% compared to the original model and higher than SSD, Fast R-CNN, etc.

A colorimetric-fluorescent dual-signal lateral flow assay was proposed for the sensitive detection of S. aureus by using vancomycin-modified SiO ₂ –Au-QD tags.

Acinetobacter baumannii (A. baumannii) is among the main pathogens that cause nosocomial infections. The ability to rapidly and accurately detect A. baumannii and its drug resistance is essential for blocking secondary infections and guiding treatments. In this study, we reported a nucleic acid fluorescent lateral flow assay (NFLFA) to identify A. baumannii and carbapenem-resistant A. baumannii (CRAB) in a rapid and quantitative manner by integrating loop-mediated isothermal amplification (LAMP) and silica–based multilayered quantum dot nanobead tag (Si@MQB). First, a rapid LAMP system was established and optimised to support the effective amplification of two bacterial genes in 35 min. Then, the antibody-modified Si@MQB was introduced to capture the two kinds of amplified DNA sequences and simultaneously detect them on two test lines of a LFA strip, which greatly improved the detection sensitivity and stability of the commonly used AuNP-based nucleic acid LFA. With these strategies, the established LAMP-NFLFA achieved detection limits of 199 CFU/mL and 287 CFU/mL for the RecA (house-keeping gene) and bla_OXA-23 (drug resistance gene) genes, respectively, within 43 min. Furthermore, the assay exhibited good repeatability and specificity for detecting target pathogens in real complex specimens and environments; thus, the proposed assay undoubtedly provides a promising and low-cost tool for the on-site monitoring of nosocomial infections.

Fluorescence lateral flow assays (FLFA) based on quantum dot probes have attracted significant attention in recent years due to their high sensitivity and quantitative detection capabilities. FLFA requires the use of a straightforward fluorescence reader for quantitative detection. Most fluorescence readers employ narrowband filters for auxiliary imaging, which facilitates the acquisition of high-contrast signals. However, during trace detection, the weak signal from FLFA can be easily lost due to optical flux loss associated with narrowband filters, thereby indirectly diminishing detection sensitivity. To address this issue, we developed a fluorescence signal reader that employs CMOS imaging without optical filters and proposed a highly sensitive signal detection algorithm based on continuous wavelet transform (CWT) to identify weak fluorescence signals with low contrast. Experimental results demonstrate that the method achieves a fluorescence detection sensitivity for quantum dots of 10⁻¹⁰ mol/L, with a relative standard deviation (RSD) of < 1.45%. The designed filter-free detection system and CWT analysis algorithm were applied to various FLFA systems (including the sandwich method and the competition method), with the correlation coefficient (R²) between all detection results and sample concentration exceeding 0.997. The findings of this study offer a highly sensitive signal detection method for the precise quantification of FLFA.