Severity: Warning
Message: file_get_contents(https://...@gmail.com&api_key=61f08fa0b96a73de8c900d749fcb997acc09&a=1): Failed to open stream: HTTP request failed! HTTP/1.1 429 Too Many Requests
Filename: helpers/my_audit_helper.php
Line Number: 197
Backtrace:
File: /var/www/html/application/helpers/my_audit_helper.php
Line: 197
Function: file_get_contents
File: /var/www/html/application/helpers/my_audit_helper.php
Line: 271
Function: simplexml_load_file_from_url
File: /var/www/html/application/helpers/my_audit_helper.php
Line: 3165
Function: getPubMedXML
File: /var/www/html/application/controllers/Detail.php
Line: 597
Function: pubMedSearch_Global
File: /var/www/html/application/controllers/Detail.php
Line: 511
Function: pubMedGetRelatedKeyword
File: /var/www/html/index.php
Line: 317
Function: require_once
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Surface-enhanced Raman scattering (SERS) has emerged as a potent spectroscopic technique for the detection of single cells. However, it is difficult to achieve label-free detection at the single-cell level in dynamic liquids because nanoprobe aggregation in biological fluids and the low combination of nanoprobes and cells reduce the sensitivity of SERS detection. Herein, a dynamic liquid integrated single-cell SERS (DLISC-SERS) platform is developed for the label-free detection of single cancer cells. DLISC-SERS consists of three components, including a twisted mixing microfluidic chip to achieve an efficient combination of nanoprobes and cells, a commercial coaxial needle to accomplish 3D dynamic liquid focusing by annular sheath flow, and a quartz capillary to offer a SERS detection area with low noise. The mixing intensity of the twisted mixing microfluidic chip is almost 3.67-fold higher than that of straight mixing. The multifunctionally modified nanoprobe, Ag NSs@PEG@3COOH, can be stably dispersed in biological fluids for at least 30 min. The segment weighting similarity-based KNN model can classify single-cell spectra with sensitivity, specificity, and accuracy up to 100, 99.4, and 99.5%, respectively. The accuracy of the model for three-way classification is 95.2%. The DLISC-SERS platform is a powerful tool for detecting cancer cells at the single-cell level.
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http://dx.doi.org/10.1021/acs.analchem.4c06051 | DOI Listing |