Background: Rubber accelerators are common contact allergens in healthcare personnel, owing to exposures from medical gloves.
Objectives: To analyse glove extracts used for patch testing for the presence of guanidine-type accelerators, and to describe the results of patch testing with triphenylguanidine (TPG) in 2 cases of contact allergy and with TPG added to the rubber series.
Materials And Methods: Gas chromatography-mass spectrometry and liquid chromatography with ultraviolet detection were used for analysis of glove extracts. Patch tests were performed with guanidine accelerators detected in the extracts.
Results: TPG, an accelerator not previously reported as being present in rubber gloves, was found in the glove extracts. Patch testing with TPG showed relevant contact allergic reactions in patients with hand dermatitis caused by rubber gloves.
Conclusions: Chemical analysis of extracts for patch testing is important in the identification of new possible allergens. In this case, a rubber accelerator previously not reported as a possible contact allergen was found in extracts of surgical gloves.
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http://dx.doi.org/10.1111/cod.12276 | DOI Listing |
ACS Appl Bio Mater
January 2025
School of Materials Science and Engineering, Nanyang Technological University, 50 Nanyang Avenue, Singapore 639798, Singapore.
This work leverages the additive antipathogenic effects of natural extracts/essential oils (EOs) and probiotics for the treatment of acne vulgaris associated with () and eczema complicated by secondary infections with (). Six probiotic strains and various extracts/EOs were evaluated in a large screening to evaluate their potential against both pathogens. PCB003 was able to inhibit the growth of both pathogens.
View Article and Find Full Text PDFOphthalmol Sci
November 2024
Casey Eye Institute, Oregon Health & Science University, Portland, Oregon.
Purpose: The diagnosis of fungal keratitis using potassium hydroxide (KOH) smears of corneal scrapings enables initiation of the correct antimicrobial therapy at the point-of-care but requires time-consuming manual examination and expertise. This study evaluates the efficacy of a deep learning framework, dual stream multiple instance learning (DSMIL), in automating the analysis of whole slide imaging (WSI) of KOH smears for rapid and accurate detection of fungal infections.
Design: Retrospective observational study.
Comput Methods Programs Biomed
January 2025
School of Information Science and Technology, Fudan University, Shanghai, 200433, China; Key Laboratory of Medical Imaging, Computing and Computer Assisted Intervention, Shanghai, 200433, China. Electronic address:
Background And Objective: Utilizing AI to mine tumor microenvironment information in whole slide images (WSIs) for glioma molecular subtype and prognosis prediction is significant for treatment. Existing weakly-supervised learning frameworks based on multi-instance learning have potential in WSIs analysis, but the large number of patches from WSIs challenges the effective extraction of key local patch and neighboring patch microenvironment info. Therefore, this paper aims to develop an automatic neural network that effectively extracts tumor microenvironment information from WSIs to predict molecular typing and prognosis of glioma.
View Article and Find Full Text PDFDrug Des Devel Ther
January 2025
State Key Laboratory of Component-Based Chinese Medicine, Tianjin University of Traditional Chinese Medicine, Tianjin, 301617, People's Republic of China.
Purpose: The major cardiac voltage-gated sodium channel Na1.5 (I) is essential for cardiac action potential initiation and subsequent propagation. Compound Chinese medicine Wenxin Keli (WXKL) has been shown to suppress arrhythmias and heart failure.
View Article and Find Full Text PDFMed Biol Eng Comput
January 2025
Pathology Department, Beijing Youan Hospital, Capital Medical University, Beijing, 100000, China.
In the context of chronic liver diseases, where variability in progression necessitates early and precise diagnosis, this study addresses the limitations of traditional histological analysis and the shortcomings of existing deep learning approaches. A novel patch-level classification model employing multi-scale feature extraction and fusion was developed to enhance the grading accuracy and interpretability of liver biopsies, analyzing 1322 cases across various staining methods. The study also introduces a slide-level aggregation framework, comparing different diagnostic models, to efficiently integrate local histological information.
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