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: 1057
Function: getPubMedXML
File: /var/www/html/application/helpers/my_audit_helper.php
Line: 3175
Function: GetPubMedArticleOutput_2016
File: /var/www/html/application/controllers/Detail.php
Line: 575
Function: pubMedSearch_Global
File: /var/www/html/application/controllers/Detail.php
Line: 489
Function: pubMedGetRelatedKeyword
File: /var/www/html/index.php
Line: 316
Function: require_once
Anterior talofibular ligament (ATFL) sprain is one of the most prevalent sports-related injuries, so proper evaluation of ligament sprains is critical for treatment options. However, existing tests suffer from a lack of standardized quantitative evaluation criteria, interindividual variability, incompatible materials, or risks of infection. Although advanced medical diagnostic methods already have been using noninvasive, portable, and wearable diagnostic electronics, these devices have insufficient adhesion to accurately respond to internal body injuries. Therefore, we propose a high-adhesive hydrogel-based strain sensor made from gelatin, cellulose nanofiber (CNF), and cross-linked poly(acrylic acid) grafted with -hydrosuccinimide ester. The adhesive strain sensor, with excellent conformability and stretchability, firmly adheres to the skin, making it suitable for accurately evaluating the severity of anterior talofibular ligament sprain. Its strong adhesive (up to 192 kPa) can adapt to the surface characterization of ankles. The high-adhesive hydrogel-based strain sensor has a high tensile strength (680%) and achieves a high gauge factor (GF) of 8.29. Simultaneously, it also presents a 40 μm ultralow detection limit. Additionally, after a deep learning model was integrated to improve sensing accuracy, the system achieved a diagnostic accuracy of 95%, significantly surpassing the magnetic resonance imaging (MRI) gold standard of 81.1%.
Download full-text PDF |
Source |
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http://dx.doi.org/10.1021/acssensors.4c03472 | DOI Listing |
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