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Imaging-based total bacterial count and type identification of bacteria play crucial roles in clinical diagnostics, public health, biological and medical science, and environmental protection. Herein, we designed and synthesized a series of tetraphenylethenes (TPEs) functionalized with one or two aldehyde, carboxylic acid, and quaternary ammonium groups, which were successfully used as fluorescent materials for rapid and efficient staining of eight kinds of representative bacterial species, including pathogenic bacteria Vibrio cholera, Klebsiella pneumoniae, and Listeria monocytogenes and potential bioterrorism agent Yersinia pestis. By comparing the fluorescence intensity changes of the aggregation-induced-emission (AIE) materials before and after bacteria incubation, the sensing mechanisms (electrostatic versus hydrophobic interactions) were simply discussed. Moreover, the designed AIE materials were successfully used as an efficient artificial tongue for bacteria discrimination, and all of the bacteria tested were identified via linear discriminant analysis. Our current work provided a general method for simultaneous broad-spectrum bacterial imaging and species discrimination, which is helpful for bacteria surveillance in many fields.
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http://dx.doi.org/10.1021/acsami.7b09848 | DOI Listing |
Head Neck
September 2025
Department of Oral Oncology, Hiroshima University Graduate School of Biomedical and Health Sciences, Hiroshima, Japan.
Background: Reconstruction of head and neck mucosal defects presents unique challenges due to the anatomical complexity and functional demands of the region. Artificial biomaterials such as collagen and polyglycolic acid (PGA) sheets have gained clinical traction owing to their ease of use and reduced surgical burden. However, limitations such as local inflammation, degradation-related complications, and mechanical instability-particularly in highly mobile areas like the tongue-continue to hinder their broader application.
View Article and Find Full Text PDFSci Rep
August 2025
Department of Computer Science, Hanyang University, Seoul, 04763, South Korea.
This study aimed to develop and evaluate deep convolutional neural network (DCNN) models with Grad-CAM visualization for the automated classification with interpretability of tongue conditions-specifically glossitis and oral squamous cell carcinoma (OSCC)-using clinical tongue photographs, with a focus on their potential for early detection and telemedicine-based diagnostics. A total of 652 tongue images were categorized into normal control (n = 294), glossitis (n = 340), and OSCC (n = 17). Four pretrained DCNN architectures (VGG16, VGG19, ResNet50, ResNet152) were fine-tuned using transfer learning.
View Article and Find Full Text PDFBiomedicines
July 2025
Department of Oral Medicine, School of Dentistry, Chosun University, Gwangju 61452, Republic of Korea.
: Tongue squamous cell carcinoma (TSCC) is an aggressive oral malignancy characterized by early submucosal invasion and a high risk of cervical lymph node metastasis. Accurate and timely diagnosis is essential, but it remains challenging when relying solely on conventional imaging and histopathology. This systematic review aimed to evaluate studies applying artificial intelligence (AI) in the diagnostic imaging of TSCC.
View Article and Find Full Text PDFCereb Cortex
August 2025
Laboratory for Neuro- and Psychophysiology, Department of Neurosciences, KU Leuven, ON2 Herestraat 49, box 1021, 3000 Leuven, Belgium.
High Gamma Band (HGB) and Slow Wave Oscillations (SWOs) have been identified as significant features in movement neurophysiology. HGB reflects local neuronal activity, while SWOs inform on the temporal characteristics of movement, especially during repetitive tasks. However, to date, they have mostly been studied separately, leaving details on their interaction largely unknown.
View Article and Find Full Text PDFJMIR Med Inform
August 2025
Department of Traditional Chinese Medicine for Liver Diseases, Fifth Medical Center of the Chinese People's Liberation Army General Hospital, No. 100 West Fourth Ring Middle Road, Fengtai District, Beijing, 1000039, China, 1 13811050593.
Background: Human adenoviruses (HAdVs) and COVID-19 are prominent respiratory pathogens with overlapping clinical presentations, including fever, cough, and sore throat, posing significant diagnostic challenges without viral testing. Tongue image diagnosis, a noninvasive method used in traditional Chinese medicine, has shown correlations with specific respiratory infections, but its application remains underexplored in differentiating HAdVs from COVID-19. Advances in artificial intelligence offer opportunities to enhance tongue image analysis for more objective and accurate diagnostics.
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