A disposable paper-based electrochemical biosensor decorated by electrospun cellulose acetate nanofibers for highly sensitive bio-detection.

Paper-based electrochemical sensors have the characteristics of flexibility, biocompatibility, environmental protection, low cost, wide availability, and hydropathy, which make them very suitable for the development and application of biological detection. This work proposes electrospun cellulose acetate nanofiber (CA NF)-decorated paper-based screen-printed (PBSP) electrode electrochemical sensors. The CA NFs were directly collected on the PBSP electrode through an electrospinning technique at an optimized voltage of 16 kV for 10 min. The sensor was functionalized with different bio-sensitive materials for detecting different targets, and its sensing capability was evaluated by CV, DPV, and chronoamperometry methods. The test results demonstrated that the CA NFs enhanced the detection sensitivity of the PBSP electrode, and the sensor showed good stability, repeatability, and specificity (p < 0.01, N = 3). The electrochemical sensing of the CA NF-decorated PBSP electrode exhibited a short detection duration of ∼5-7 min and detection ranges of 1 nmol mL-1-100 µmol mL-1, 100 fg mL-1-10 µg mL-1, and 1.5 × 102-106 CFU mL-1 and limits of detection of 0.71 nmol mL-1, 89.1 fg mL-1, and 30 CFU mL-1 for glucose, Ag85B protein, and E. coli O157:H7, respectively. These CA NF-decorated PBSP sensors can be used as a general electrochemical tool to detect, for example, organic substances, proteins, and bacteria, which are expected to achieve point-of-care testing of pathogenic microorganisms and have wide application prospects in biomedicine, clinical diagnosis, environmental monitoring, and food safety.

A novel nanoparticle-based fluorescent sandwich immunoassay for specific detection of Salmonella Typhimurium.

The diseases caused by foodborne pathogens have a serious impact on human health and social stability. Conventional detection methods can involve long assay times and complex pretreatment steps, making them unsuitable for rapid, large-scale analysis of food samples. We constructed a novel nano-fluorescence sandwich immunosorbent immunoassay (nano-FSIA) to rapidly detect Salmonella Typhimurium in food, based on strong covalent binding between streptavidin and biotin. We used antibodies coupled to large particle-size fluorescent microspheres as fluorescent probes for direct quantitative analysis of S. typhimurium in milk. The optimized parameters were determined, and specificity and sensitivity were validated in phosphate-buffered saline (PBS) and milk. The results demonstrated a wide dynamic detection range for S. typhimurium (103-108 colony forming units [CFU]/mL), with the limit of detection in PBS and milk at 234 and 346 CFU/mL, respectively. The results of nano-FSIA were consistent with those of plate counts and enzyme-linked immunosorbent assays, providing an effective and promising single-bacterium counting method for the rapid detection of Salmonella.

Highly sensitive digital detection of SARS-CoV-2 nucleocapsid protein through single-molecule counting.

Nucleocapsid protein (NP) is one of the structural proteins of SARS-CoV-2 which is stable, well-conserved, highly immunogenic, and abundantly expressed due to the host's adaptive immune response, making it a promising antigenic biomarker for the early and rapid identification and diagnosis of SARS-CoV-2. Traditional antigen analytical methods with NP as the detection marker often have insufficient sensitivity. To achieve rapid and highly sensitive detection of NP, we constructed a novel single-molecule (digital) fluorescence-linked immunosorbent assay (FLISA) based on streptavidin-modified transparent 96-well microplates. Streptavidin was immobilized on the microplate under optimized conditions with a 15 mM carbonate buffer solution (pH 9.6) as the coating solution, biotinylated antibodies conjugated with streptavidin as capture probes, and carboxylated fluorescent microsphere-conjugated monoclonal antibodies (FMs-mAbs) as fluorescent probes. Individual sandwich immunolabeled complexes of the SARS-CoV-2 diagnostic marker NP were detected and counted though wide-field inverted fluorescence microscopy (1.1 × 1.4 mm2). FLISA had a linear detection range of 0.2 pg/mL to 200 ng/mL and a limit of detection (LOD) of 0.73 fg/mL and 8 fg/mL for NP in phosphate buffer saline and spiked nasal swab samples, respectively. The sensitivity was much higher than commercial antigen detection kits, providing wide detection prospects in future clinical diagnosis, environmental monitoring, and other fields.

Overview and Future Perspectives of Microfluidic Digital Recombinase Polymerase Amplification (dRPA).

Digital recombinase polymerase amplification (dRPA) aims to quantify the initial amount of nucleic acid by dividing nucleic acid and all reagents required for the RPA reaction evenly into numerous individual reaction units, such as chambers or droplets. dRPA turns out to be a prominent technique for quantifying the absolute quantity of target nucleic acid because of its advantages including low equipment requirements, short time consumption, as well as high sensitivity and specificity. dRPA combined with microfluidics are recognized as simple, various, and high-throughput nucleic acid quantization systems. This paper classifies the microfluidic dRPA systems over the last decade. We analyze and summarize the vital technologies of various microfluidic dRPA systems (e.g., chip preparation process, segmentation principle, microfluidic control, and statistical analysis methods), and major efforts to address limitations (e.g., prevention of evaporation and contamination, accurate initiation, and reduction of manual operation). In addition, this paper summarizes key factors and potential constraints to the success of the microfluidic dRPA to help more researchers, and possible strategies to overcome the mentioned challenges. Lastly, actual suggestions and strategies are proposed for the subsequent development of microfluidic dRPA.

Test Article for automation purposes.

Digital recombinase polymerase amplification (dRPA) aims to quantify the initial amount of nucleic acid by dividing nucleic acid and all reagents required for the RPA reaction evenly into numerous individual reaction units, such as chambers or droplets. dRPA turns out to be a prominent technique for quantifying the absolute quantity of target nucleic acid because of its advantages including low equipment requirements, short time consumption, as well as high sensitivity and specificity. dRPA combined with microfluidics are recognized as simple, various, and high-throughput nucleic acid quantization systems. This paper classifies the microfluidic dRPA systems over the last decade. We analyze and summarize the vital technologies of various microfluidic dRPA systems (e.g., chip preparation process, segmentation principle, microfluidic control, and statistical analysis methods), and major efforts to address limitations (e.g., prevention of evaporation and contamination, accurate initiation, and reduction of manual operation). In addition, this paper summarizes key factors and potential constraints to the success of the microfluidic dRPA to help more researchers, and possible strategies to overcome the mentioned challenges. Lastly, actual suggestions and strategies are proposed for the subsequent development of microfluidic dRPA.

Using Deep Learning for Classification of Lung Nodules on Computed Tomography Images.

Lung cancer is the most common cancer that cannot be ignored and cause death with late health care. Currently, CT can be used to help doctors detect the lung cancer in the early stages. In many cases, the diagnosis of identifying the lung cancer depends on the experience of doctors, which may ignore some patients and cause some problems. Deep learning has been proved as a popular and powerful method in many medical imaging diagnosis areas. In this paper, three types of deep neural networks (e.g., CNN, DNN, and SAE) are designed for lung cancer calcification. Those networks are applied to the CT image classification task with some modification for the benign and malignant lung nodules. Those networks were evaluated on the LIDC-IDRI database. The experimental results show that the CNN network archived the best performance with an accuracy of 84.15%, sensitivity of 83.96%, and specificity of 84.32%, which has the best result among the three networks.