Cancer is a malignancy engendering enormous global mortality, steering extensive research for early diagnosis and efficacious prognosis leading to emergence of cancer sensing technologies for multitudinous biomarkers. In this context, nanofibers, imparting high surface area, facile production, morphology control, and synergistic properties attainable, are poised to be inevitable in futuristic sensing devices for predictive diagnostics when integrated with artificial intelligence and machine learning. To this end, fundamentals governing the sensor response and their analytical performance have been discussed. The headways in organic and inorganic nanofibers for biomarker gas sensing, fluid sample sensing and imaging have been supplemented with discussions on materials for nanofiber formation, along with sensitizing materials, and formation of sensing elements by processes like surface deposition on nanofibers, immobilising, calcination, etc. and their effect on final sensing device properties. The review culminates by summarising the conceptual understanding of the hitherto progress leading to achievement of excellent analytical performance giving detection limits to the order of 1.6 pM concentration and response time of as low as 0.5 s. Current bottlenecks in this state of the art have been delineated and pathways for future research are discussed.
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http://dx.doi.org/10.1016/j.ijpharm.2020.119364 | DOI Listing |
BMC Chem
January 2025
Department of Pharmaceutical Analytical Chemistry, Faculty of Pharmacy, University of Alexandria, Elmessalah, Alexandria, 21521, Egypt.
A simple, rapid, and reproducible high-performance liquid chromatography (HPLC) method has been developed and validated for the determination of β-sitosterol in the pharmaceutical dosage form of moist exposed burn ointment (MEBO). This method involved an effective sample procedure for extraction of β-sitosterol from MEBO using an alkali saponification agent composed of 0.8 N ethanolic NaOH and diethyl ether.
View Article and Find Full Text PDFBMC Med Inform Decis Mak
January 2025
Institute of Mathematical Sciences Centre for Health Analytics and Modelling (CHaM), Strathmore University, Nairobi, Kenya.
Background: Measures of diagnostic test accuracy provide evidence of how well a test correctly identifies or rules-out disease. Commonly used diagnostic accuracy measures (DAMs) include sensitivity and specificity, predictive values, likelihood ratios, area under the receiver operator characteristic curve (AUROC), area under precision-recall curves (AUPRC), diagnostic effectiveness (accuracy), disease prevalence, and diagnostic odds ratio (DOR) etc. Most available analysis tools perform accuracy testing for a single diagnostic test using summarized data.
View Article and Find Full Text PDFEnviron Sci Pollut Res Int
January 2025
Laboratory of Coordination and Analytical Chemistry (LCCA), Department of Chemistry, Faculty of Sciences, Chouaïb Doukkali University, Ben Maachou Road, B.P: 20, 24000, El Jadida, Morocco.
This work is focused on the synthesis and performance of Ni(PO)-based catalysts doped with Cu, Co, Mn, Ce, Zr, and Mg for the complete oxidation of ethanol, aiming at reducing emissions from ethanol-blended gasoline. Nickel phosphate was prepared via the co-precipitation method, followed by impregnation with the specified dopants. The catalysts were thoroughly characterized by XRD, N-physisorption, XRF, FTIR and Raman spectroscopy, FESEM, NH-TPD, CO-TPD, and H-TPR to explain their performance.
View Article and Find Full Text PDFSurg Endosc
January 2025
Colorectal Surgery Unit, Department of Digestive Surgery, Pontificia Universidad Católica de Chile, Uc-Christus Health Network, Santiago, Chile.
Background: The benefits of the totally laparoscopic right hemicolectomy have been established, but its adoption has been limited by the challenges of intracorporeal suturing. While simulation is effective for training advanced surgical skills, no dedicated simulation-based course exists for intracorporeal ileo-transverse anastomosis (ICA). This study aimed to develop and validate a simulation module for training in ICA.
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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