Rapid Noninvasive Skin Monitoring by Surface Mass Recording and Data Learning

Abstract

Skin problems are often overlooked due to a lack of robust and patient-friendly monitoring tools. Herein, we report a rapid, noninvasive, and high-throughput analytical chemical methodology, aiming at real-time monitoring of skin conditions and early detection of skin disorders. Within this methodology, adhesive sampling and laser desorption ionization mass spectrometry are coordinated to record skin surface molecular mass in minutes. Automated result interpretation is achieved by data learning, using similarity scoring and machine learning algorithms. Feasibility of the methodology has been demonstrated after testing a total of 117 healthy, benign-disordered, or malignant-disordered skins. Remarkably, skin malignancy, using melanoma as a proof of concept, was detected with 100% accuracy already at early stages when the lesions were submillimeter-sized, far beyond the detection limit of most existing noninvasive diagnosis tools. Moreover, the malignancy development over time has also been monitored successfully, showing the potential to predict skin disorder progression. Capable of detecting skin alterations at the molecular level in a nonsurgical and time-saving manner, this analytical chemistry platform is promising to build personalized skin care.

Exosome Metabolic Patterns on Aptamer-Coupled Polymorphic Carbon for Precise Detection of Early Gastric Cancer

“…However, it puts forward high demands on exosome sample pretreatment and faces a great challenge when encountering the clinical disease metabolomics toward precision medicine, which requires rapid and high-throughput detection of thousands of biological samples. − Owing to the convenient sample preparation, trace sample consumption, short analysis time, high throughput, and accuracy, laser desorption/ionization mass spectrometry (LDI-MS) is attracting increasing interest in large-scale clinical applications. − LDI-MS is primitively designed for analyzing biological macromolecules with the assistance of traditional organic matrices such as 2,5-dihydroxybenzoic acid (DHB) and α-cyano-4-hydroxycinnamic acid (CHCA). Recently, researchers attempted to separate exosomes beforehand by ultracentrifugation or co-precipitation and then employ traditional organic matrices assisted LDI-MS to profile exosome proteinic patterns, , manifesting the combination potential of LDI-MS and exosomes in diagnostics. However, ultracentrifugation or co-precipitation separation are high-cost and time-consuming .…”

Artificial Intelligence (AI) and Internet of Medical Things (IoMT) Assisted Biomedical Systems for Intelligent Healthcare

“…The algorithm used here is the DL process. Zhu et al developed an analytical chemical methodology to achieve rapid, non-invasive, and high-throughput skin monitoring [ 113 ]. For recording the skin-surface mass profile, an adhesive sampling procedure is combined with matrix-assisted laser desorption ionization time-of-flight (MALDI-TOF) mass spectroscopy.…”

Algorithms push forward the application of MALDI–TOF mass fingerprinting in rapid precise diagnosis

“…49,50 Marker peaks can also be selected by using machine learning algorithms like least absolute shrinkage and selection operator, partial least squares-discriminant analysis, and recursive feature elimination with cross validation. 51,52 To reach a clustering or distinguish of mass fingerprint groups, unsupervised machine learning algorithms like hierarchical clustering analysis (HCA) and principal component analysis (PCA) have been often used. HCA is to group objects in such a way that the objects in the same group are more similar to each other than to those in other groups.…”