Classification of Plant Endogenous States Using Machine Learning-Derived Agricultural Indices.

Leaf color patterns vary depending on leaf age, pathogen infection, and environmental and nutritional stresses; thus, they are widely used to diagnose plant health statuses in agricultural fields. The visible-near infrared-shortwave infrared (VIS-NIR-SWIR) sensor measures the leaf color pattern from a wide spectral range with high spectral resolution. However, spectral information has only been employed to understand general plant health statuses (e.g., vegetation index) or phytopigment contents, rather than pinpointing defects of specific metabolic or signaling pathways in plants. Here, we report feature engineering and machine learning methods that utilize VIS-NIR-SWIR leaf reflectance for robust plant health diagnostics, pinpointing physiological alterations associated with the stress hormone, abscisic acid (ABA). Leaf reflectance spectra of wild-type, ABA2-overexpression, and deficient plants were collected under watered and drought conditions. Drought- and ABA-associated normalized reflectance indices (NRIs) were screened from all possible pairs of wavelength bands. Drought associated NRIs showed only a partial overlap with those related to ABA deficiency, but more NRIs were associated with drought due to additional spectral changes within the NIR wavelength range. Interpretable support vector machine classifiers built with 20 NRIs predicted treatment or genotype groups with an accuracy greater than those with conventional vegetation indices. Major selected NRIs were independent from leaf water content and chlorophyll content, 2 well-characterized physiological changes under drought. The screening of NRIs, streamlined with the development of simple classifiers, serves as the most efficient means of detecting reflectance bands that are highly relevant to characteristics of interest.

Label-free detection of MiRNA biomarkers using broadband multi-resonant infrared metasurfaces for early breast cancer diagnosis.

Breast cancer is the most prevalent cancer globally. Early detection is crucial and can be achieved by detecting cancer biomarkers in blood, such as circulating miRNAs (microRNAs). In this study, we present a label-free detection method based on broadband multi-resonant infrared metasurface for surface-enhanced infrared absorption (SEIRA) spectroscopy to detect miRNAs. The SEIRA resonances were optimized to match the miRNA biomarker fingerprint regions in the range of 800 to 2000 cm-1 and 2800 to 3500 cm-1, resulting in a simulated resonance enhancement of up to 103 times. Nine patient samples (six cancerous and three non-cancerous) were measured using SEIRA multi-well sensor chips. A novel analysis method, SEIRA-AR, was also developed to benchmark the results against industry standards, such as quantitative reverse transcription polymerase chain reaction (RT-qPCR) and next-generation sequencing (NGS). Our results showed an excellent linear correlation with a Pearson's r value of up to 0.99 and an R Squared value of up to 0.98. This study represents the first use of a SEIRA sensor for biomarker detection on clinical breast cancer samples and introduces an analysis method that produces results comparable to industry standards. Our findings pave the way for routine cancer diagnosis in the future. Additionally, the method discussed can be generalized to other biosensing activities involving two-step binding processes with complementary molecule-capturing agents.

Highly sensitive SERS detection of cancer proteins in low sample volume using hollow core photonic crystal fiber.

Enzyme-linked immunosorbent assays (ELISA) are commonly used for detecting cancer proteins at concentration in the range of about ng-µg/mL. Hence it often fails to detect tumor markers at the early stages of cancer and other diseases where the amount of protein is extremely low. Herein, we report a novel photonic crystal fiber (PCF) based surface enhanced Raman scattering (SERS) sensing platform for the ultrasensitive detection of cancer proteins in an extremely low sample volume. As a proof of concept, epidermal growth factor receptors (EGFRs) in a lysate solution from human epithelial carcinoma cells were immobilized into the hollow core PCF. Highly sensitive detection of protein was achieved using anti-EGFR antibody conjugated SERS nanotag. This SERS nanotag probe was realized by anchoring highly active Raman molecules onto the gold nanoparticles followed by bioconjugation. The proposed sensing method can detect low amount of proteins at ∼100 pg in a sample volume of ∼10 nL. Our approach may lead to the highly sensitive protein sensing methodology for the early detection of diseases.