Fluorescence and Photoacoustic Spectroscopy-Based Assessment of Mitochondrial Dysfunction in Oral Cancer Together with Machine Learning: A Pilot Study

Label-free visualization of unfolding and crosslinking mediated protein aggregation in nonenzymatically glycated proteins

Nonenzymatic glycation (NEG) unfolds and crosslinks proteins, resulting in aggregation. Label-free evaluation of such structural changes, without disturbing molecular integrity, would be beneficial for understanding the fundamental mechanisms of protein aggregation. The current study demonstrates the assessment of NEG-induced protein aggregation by combining autofluorescence (AF) spectroscopy and imaging. The methylglyoxal (MG) induced protein unfolding and the formation of cross-linking advanced glycation end-products (AGEs) leading to aggregation were evaluated using deep-UV-induced-autofluorescence (dUV-AF) spectroscopy in proteins with distinct structural characteristics. Since the AGEs formed on proteins are fluorescent, the study demonstrated the possibility of autofluorescence imaging of NEG-induced protein aggregates. Autofluorescence spectroscopy can potentially reveal molecular alterations such as protein unfolding and cross-linking. In contrast, AGE-based autofluorescence imaging offers a means to visually explore the structural arrangement of aggregates, regardless of whether they are amyloid or non-amyloid in nature.

Machine Learning Enabled Photoacoustic Spectroscopy for Noninvasive Assessment of Breast Tumor Progression In Vivo: A Preclinical Study

Breast cancer is a dreaded disease affecting women the most in cancer-related deaths over other cancers. However, early diagnosis of the disease can help increase survival rates. The existing breast cancer diagnosis tools do not support the early diagnosis of the disease. Therefore, there is a great need to develop early diagnostic tools for this cancer. Photoacoustic spectroscopy (PAS), being very sensitive to biochemical changes, can be relied upon for its application in detecting breast tumors in vivo. With this motivation, in the current study, an aseptic chamber integrated photoacoustic (PA) probe was designed and developed to monitor breast tumor progression in vivo, established in nude mice. The device served the dual purpose of transporting tumor-bearing animals to the laboratory from the animal house and performing PA experiments in the same chamber, maintaining sterility. In the current study, breast tumor was induced in the nude mice by MCF-7 cells injection and the corresponding PA spectra at different time points (day 0, 5, 10, 15, and 20) of tumor progression in vivo in the same animals. The recorded photoacoustic spectra were subsequently preprocessed, wavelet-transformed, and subjected to filter-based feature selection algorithm. The selected top 20 features, by minimum redundancy maximum relevance (mRMR) algorithm, were then used to build an input feature matrix for machine learning (ML)-based classification of the data. The performance of classification models demonstrated 100% specificity, whereas the sensitivity of 95, 100, 92.5, and 85% for the time points, day 5, 10, 15, and 20, respectively. These results suggest the potential of PA signal-based classification of breast tumor progression in a preclinical model. The PA signal contains information on the biochemical changes associated with disease progression, emphasizing its translational strength toward early disease diagnosis.

Protein classification by autofluorescence spectral shape analysis using machine learning

Depending on the relative numbers and spatial arrangement of Tryptophan (Trp; W) and Tyrosine (Tyr; Y) residues, different proteins produce distinct autofluorescence (AF) spectral shapes when excited at ∼280 nm. Yet, considering the vast number and heterogeneous forms in nature, visual analysis and precise identification of proteins based on their AF spectra is challenging and further compounded in cases when different proteins produce substantially similar AF spectral shapes. There is, thus, a serious need to develop a methodology to address this problem. The current study proposes a practical technology to quickly identify proteins using machine learning (ML) algorithms based on their AF spectra. Specifically, AF spectra of fifteen different standard proteins of varying origin with distinct structural and Trp/Tyr compositions were recorded; based on the spectral features selected by the Minimum-Redundancy-Maximum-Relevance (mRMR) algorithm, a multiclass Support Vector Machine (SVM) learning model with Radial Basis Function (RBF), Polynomial, and Linear kernels classified the proteins with high accuracy of 99.06%, 99.03%, and 98.29% respectively. Since protein identification is the key to understand biological functions and disease diagnosis, the proposed methodology could offer a viable alternative to and improve the existing protein identification techniques.

Intraoperative Assessment of Resection Margin in Oral Cancer: The Potential Role of Spectroscopy

In parallel with the increasing number of oncological cases, the need for faster and more efficient diagnostic tools has also appeared. Different diagnostic approaches are available, such as radiological imaging or histological staining methods, but these do not provide adequate information regarding the resection margin, intraoperatively, or are time consuming. The purpose of this review is to summarize the current knowledge on spectrometric diagnostic modalities suitable for intraoperative use, with an emphasis on their relevance in the management of oral cancer. The literature agrees on the sensitivity, specificity, and accuracy of spectrometric diagnostic modalities, but further long-term prospective, multicentric clinical studies are needed, which may standardize the intraoperative assessment of the resection margin and the use of real-time spectroscopic approaches.

Tensor product based 2-D correlation data preprocessing methods for Raman spectroscopy of Chinese handmade paper

The paper introduces two new methods, namely the cross correlation method (CCM) and two-dimensional correlation method (TDCM), for preprocessing Raman spectroscopy data for analyzing Chinese handmade paper samples. CCM expands the spectral dimension from 1×N to 1×2N-1 by taking cross-correlation between two spectral data of the same category. TDCM includes two-dimensional synchronous correlation method (TDSCM) and two-dimensional asynchronous correlation method (TDACM), which expand the spectral dimension from 1×N to N×N by taking tensor products between two spectral data and between one spectral data and the Hilbert transformation of the other spectral data of the same category, respectively. The experimental data were preprocessed using baseline removal, CCM, TDSCM, and TDACM methods. Four machine learning models were employed to evaluate the effects of these methods: principal component analysis (PCA) combined with linear regression (LR), support vector machine (SVM) combined with LR, k-Nearest Neighbors (KNN), and random forest (RF). The results show that the R-squared values for the PCA model were nearly 1 for all types of data, indicating high accuracy. However, for SVM-LR, KNN, and RF models, the R-squared values were sorted in the order of raw data, baseline removal data, CCM, TDSCM, and TDACM preprocessed data. The R-squared values of KNN and RF machine learning models for TDACM preprocessed data were approaching 1, indicating that the accuracy of machine learning was significantly improved by nearly 100%. This has led to a remarkable improvement in the accuracy of supervised models such as KNN and RF, bringing them closer to the level of unsupervised models such as PCA.

Covalent organic framework linked with amination luminol derivative as enhanced ECL luminophore for ultrasensitive analysis of cytochrome c

Cytochrome c (cyt c) plays a critical role in mitochondrial respiratory chain, whose absence is detrimental to electron transport and reduce adenosine triphosphate. For ultrasensitive detection of cyt c, sheet-like covalent organic frameworks (COFs) were prepared by orderly accumulation of 1,3,5-benzenetricarboxaldehyde (BTA) and p-phenylenediamine (PDA), and further grafted with N-(4-aminobutyl)-N-ethylisoluminol (ABEI) - an electrochemiluminescence (ECL) emitter. Specifically, the morphology and structure of the COFs-ABEI were mainly characterized by transmission electron microscopy (TEM), X-ray diffraction (XRD) analysis, and X-ray photoelectron spectroscopy (XPS). In parallel, the optical properties of the emitter were certified by UV-vis absorbance spectroscopy, Fourier infrared spectroscopy (FTIR), fluorescence (FL), and ECL measurements, showing 2.25-time enhanced ECL efficiency over pure ABEI, coupled by illustrating the interfacial electron transport mechanism. On the above foundation, a label-free "signal off" ECL biosensor was constructed by virtue of the specific immune recognition between the aptamer of the target cyt c with its capture DNA (cDNA) anchored on the biosensing platform, exhibiting a wider linear range of 1.00 fg mL^-1-0.10 ng mL^-1 (R² = 0.998) and a lower limit of detection (LOD) down to 0.73 fg mL^-1. This work offers some constructive guidelines for sensitive bioassays of disease-related biomarkers in the clinical field.