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Electrospinning is a straightforward approach for efficiently creating continuous fibers within the submicron to nanometer size range. Electrospun fibers possess excellent properties like high porosity, large specific surface area, tunable morphology, small diameter, etc., making them desirable in various applications. Because of its various properties, polymer is one of the most used materials as the spinning solution in electrospinning. Electrospun polymeric fibers, by themselves, may serve limited applications. Therefore, they are usually mixed with other materials to serve many applications. There are many ways in which these other materials are mixed with polymers in electrospinning, like doping, surface treatment, functionalization, etc. There are several studies published that report on the various composite fibers produced using electrospinning. However, a review focused solely on the production of heterogeneous fibers, where the electrospun fibers are intrinsically made of more than one material, is lacking. Herein, we review different heterogeneous fibers synthesized using electrospinning and their fabrication methods. 

Abstract BackgroundBreast cancer poses a significant health risk to women worldwide, with approximately 30% being diagnosed annually in the United States. The identification of cancerous mammary tissues from non-cancerous ones during surgery is crucial for the complete removal of tumors. ResultsOur study innovatively utilized machine learning techniques (Random Forest (RF), Support Vector Machine (SVM), and Convolutional Neural Network (CNN)) alongside Raman spectroscopy to streamline and hasten the differentiation of normal and late-stage cancerous mammary tissues in mice. The classification accuracy rates achieved by these models were 94.47% for RF, 96.76% for SVM, and 97.58% for CNN, respectively. To our best knowledge, this study was the first effort in comparing the effectiveness of these three machine-learning techniques in classifying breast cancer tissues based on their Raman spectra. Moreover, we innovatively identified specific spectral peaks that contribute to the molecular characteristics of the murine cancerous and non-cancerous tissues. ConclusionsConsequently, our integrated approach of machine learning and Raman spectroscopy presents a non-invasive, swift diagnostic tool for breast cancer, offering promising applications in intraoperative settings.