A microfluidic immunosensor for automatic detection of carcinoembryonic antigen based on immunomagnetic separation and droplet arrays.

Diagnosis of cancer by biomarkers plays an important role in human health and life. However, current laboratory techniques for detecting cancer biomarkers still require laborious and time-consuming operation by skilled operators and associated laboratory instruments. This work presents a colorimetric biosensor for the rapid and sensitive detection of carcinoembryonic antigen (CEA) based on an automated immunomagnetic separation platform and a droplet array microfluidic chip with the aid of an image analysis system. Immunomagnetic nanoparticles (MNPs) were used to capture CEA in the samples. CEA-detecting antibodies and horseradish peroxidase (HRP) were modified on polystyrene microspheres (PS), catalysing hydrogen peroxide and 3,3',5,5'-tetramethylbenzidine (TMB) as signal outputs. Color reaction data were analyzed to establish a CEA concentration standard curve. The movement of MNPs between droplets in the microfluidic chip is achieved using an automatically programmable magnetic control system. This colorimetric biosensor has been used for the simultaneous detection of six CEA samples ranging from 100 pg mL-1 to 100 ng mL-1 with a detection limit of 14.347 pg mL-1 in 10 min, following the linear equation: y = -4.773 ln(x) + 156.26 with a correlation of R2 = 0.9924, and the entire workflow can be completed within 80 minutes. The microfluidic immunosensor designed in this paper has the advantages of low cost, automation, low sample consumption, high throughput, and promising applications in biochemistry.

Rapid Detection and Analysis of Raman Spectra of Bacteria in Multiple Fields of View Based on Image Stitching Technique.

BACKGROUND: Due to antibiotic abuse, the problem of bacterial resistance is becoming increasingly serious, and rapid detection of bacterial resistance has become an urgent issue. Because under the action of antibiotics, different active bacteria have different metabolism of heavy water, antibiotic resistance of bacteria can be identified according to the existence of a C-D peak in the 2030-2400 cm-1 range in the Raman spectrum. METHODS: To ensure data veracity, a large number of bacteria need to be detected, however, due to the limitation of the field of view of the high magnification objective, the number of single cells in a single field of view is very small. By combining an image stitching algorithm, image recognition algorithm, and processing of Raman spectrum and peak-seeking algorithm, can identify and locate single cells in multiple fields of view at one time and can discriminate whether they are Antimicrobial-resistant bacteria. RESULTS: In experiments 1 and 2, 2706 bacteria in 9 × 11 fields of view and 2048 bacteria in 11 × 11 fields of view were detected. Results showed that in experiment 1, there are 1137 antibiotic-resistant bacteria, accounting for 42%, and 1569 sensitive bacteria, accounting for 58%. In experiment 2, there are 1087 antibiotic-resistant bacteria, accounting for 53%, and 961 sensitive bacteria, accounting for 47%. It showed excellent performance in terms of speed and recognition accuracy as compared to traditional manual detection approaches. And solves the problems of low accuracy of data, a large number of manual experiments, and low efficiency due to the small number of single cells in the high magnification field of view and different peak-seeking parameters of different Raman spectra. CONCLUSIONS: The detection and analysis method of bacterial Raman spectra based on image stitching can be used for unattended, automatic, rapid and accurate detection of single cells at high magnification with multiple fields of view. With the characteristics of automatic, high-throughput, rapid, and accurate identification, it can be used as an unattended, universal and non-invasive means to measure antibiotic-resistant bacteria to screen for effective antibiotics, which is of great importance for studying the persistence and spread of antibiotics in bacterial pathogens.

Decreased expression of CLCA2 and the correlating with immune infiltrates in patients with cervical squamous cell carcinoma: A bioinformatics analysis.

OBJECTIVE: Calcium-activated chloride channel 2 (CLCA2) is closely related to the invasion, metastasis, and prognosis of some common malignant tumors. The present study aimed to evaluate the role of CLCA2 in cervical squamous cell carcinoma (CESC) using bioinformatics analysis. MATERIALS AND METHODS: The mRNA sequencing data and the corresponding clinical data were obtained from Gene Expression Omnibus (GEO) database and The Cancer Genome Atlas (TCGA) database respectively. Then univariate analysis of variance was used to analyze the differential mRNA expression of CLCA2 between normal, cervical Intraepithelial neoplasia (CIN), and CESC tissues and clinicopathological characteristics. The Gene Expression Profiling Interactive Analysis (GEPIA) was used to assess the association between CLCA2 and Disease-Free Survival (DFS), overall survival (OS). The Gene Set Enrichment Analysis (GSEA) was used to explore the associated signaling pathways. The Tumor Immune Estimation Resource (TIMER) was used to predict the potential biological roles of CLCA2 in tumor-immune of CESC. RESULTS: CLCA2 expression was significantly decreased in CESC tissues compared with normal and CIN tissues (P < 0.05). Meanwhile, obese patients had lower levels of CLCA2 expression than normal-weight CESC patients (P < 0.05). However, there was no significant difference in the expression level of CLCA2 in patients with different T stage, lymph node status, metastasis, and FIGO stage in CC(P > 0.05). The survival analysis indicated that for DFS, CESC with high CLCA2 expression was associated with better prognoses compared with those with low expression levels (P < 0.05). But for the OS, there was no difference. GSEA revealed that 4 pathways exhibited significant differential enrichment in the CLCA2 high-expression phenotype, including the P53 signaling pathway, the ERBB signaling pathway, the NOTCH signaling pathway, and the ubiquitin-mediated proteolysis. The TIMER reveals the expression of CLCA2 showed a significant inverse association with the number of B cells, Macrophage cells, and Dendritic Cell infiltration. CONCLUSION: The present study indicates that CLCA2 expression may be a potential prognostic marker for patients with CESC.