Adsorption processes are responsible for detection of cancer biomarkers in biosensors (and immunosensors), which can be captured with various principles of detection. In this study, we used a biosensor made with nanostructured films of polypyrrole and p53 antibodies, and image analysis of scanning electron microscopy data made it possible to correlate morphological changes of the biosensor with the concentration of cells containing the cancer biomarker p53. The selectivity of the biosensor was proven by distinguishing images obtained with exposure of the biosensor to cells containing the biomarker from those acquired with cells that did not contain it. Detection was confirmed with cyclic voltammetry measurements, while the adsorption of the p53 biomarker was probed with polarization-modulated infrared reflection absorption (PM-IRRAS) and a quartz crystal microbalance (QCM). Adsorption is described using the Langmuir-Freundlich model, with saturation taking place at a concentration of 100 Ucells/mL. Taken together, our results point to novel ways to detect biomarkers or any type of analyte for which detection is based on adsorption as is the case of the majority of biosensors.
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http://dx.doi.org/10.1021/acsami.6b16105 | DOI Listing |
J Med Microbiol
January 2025
Institute of Advanced Study in Science and Technology (IASST), Guwahati 781035, Assam, India.
Cold atmospheric plasma (CAP) has emerged as a promising technology for neutralizing microbes, including multidrug-resistant strains. This study investigates CAP's potential as an alternative to traditional antimicrobial drugs for microbial inactivation. In the era of increasing antimicrobial resistance, there is a persistent need for alternative antimicrobial strategies.
View Article and Find Full Text PDFACS Appl Bio Mater
January 2025
Departamento de Física, Universidade Federal de Viçosa, Viçosa, Minas Gerais 36570-900, Brasil.
This study investigates the functionalization of gold-coated magnetoelastic sensors with thionine molecules, focusing on resonance frequency shifts. The functionalization process was characterized by using Raman spectroscopy and analyzed via scanning electron microscopy and atomic force microscopy, revealing the progressive formation of molecular clusters over time. Our results demonstrate that longer functionalization time leads to saturation of surface coverage and cluster formation, impacting the sensor's resonance frequency shifts.
View Article and Find Full Text PDFClin Oral Investig
January 2025
Stomatology Hospital, School of Stomatology, Zhejiang University School of Medicine, Clinical Research Center for Oral Diseases of Zhejiang Province, Key Laboratory of Oral Biomedical Research of Zhejiang Province, Cancer Center of Zhejiang University, Hangzhou, 310006, China.
Objectives: To evaluate recent advances in the automatic multimodal registration of cone-beam computed tomography (CBCT) and intraoral scans (IOS) and their clinical significance in dentistry.
Methods: A comprehensive literature search was conducted in October 2024 across the PubMed, Web of Science, and IEEE Xplore databases, including studies that were published in the past decade. The inclusion criteria were as follows: English-language studies, randomized and nonrandomized controlled trials, cohort studies, case-control studies, cross-sectional studies, and retrospective studies.
Mol Biol Rep
January 2025
Department of Biology, Central Tehran Branch, Islamic Azad University, Tehran, Iran.
Background: Breast carcinoma stands out as the most widespread invasive cancer and the top contributor to cancer-related mortality in women. Nanoparticles have emerged as promising tools in cancer detection, diagnosis, and prevention. In this study, the antitumor and apoptotic capability of silver nanoparticles synthesized through Scrophularia striata extract (AgNPs-SSE) was investigated toward breast cancer cells.
View Article and Find Full Text PDFPhysiol Rep
February 2025
Motion and Exercise Science, University of Stuttgart, Stuttgart, Germany.
The maintenance of an appropriate ratio of body fat to muscle mass is essential for the preservation of health and performance, as excessive body fat is associated with an increased risk of various diseases. Accurate body composition assessment requires precise segmentation of structures. In this study we developed a novel automatic machine learning approach for volumetric segmentation and quantitative assessment of MRI volumes and investigated the efficacy of using a machine learning algorithm to assess muscle, subcutaneous adipose tissue (SAT), and bone volume of the thigh before and after a strength training.
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