AIMC Topic: Spectrum Analysis, Raman

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AI-assisted SERS imaging method for label-free and rapid discrimination of clinical lymphoma.

Journal of nanobiotechnology
BACKGROUND: Lymphoma is a malignant tumor of the immune system and its incidence is increasing year after year, causing a major threat to people's health. Conventional diagnosis of lymphoma basically depends on histological images consuming long-time...

Rapid diagnosis of membranous nephropathy based on kidney tissue Raman spectroscopy and deep learning.

Scientific reports
Membranous nephropathy (MN) is one of the most common glomerular diseases. Although the diagnostic method based on serum PLA2R antibodies has gradually been applied in clinical practice, only 52-86% of PLA2R-associated MN patients show positive anti-...

Nasopharyngeal cancer screening and immunotherapy efficacy evaluation based on plasma separation combined with label-free SERS technology.

Analytica chimica acta
BACKGROUND: In recent years, significant progress has been made in the treatment of nasopharyngeal carcinoma (NPC). The application of immunotherapy, especially the use of Programmed Cell Death Protein 1 inhibitors, has demonstrated excellent therape...

Rapid, label-free detection of colorectal cancer with an aberration-free line scanning confocal Raman imager.

Spectrochimica acta. Part A, Molecular and biomolecular spectroscopy
A self-developed aberration-free line scanning confocal Raman imager (AFLSCRI) with a spectral resolution of 0.12 nm and a spatial resolution of 2 μm is utilized to diagnose colorectal cancer. The tissues were categorized into four subgroups (typical...

DeepATsers: a deep learning framework for one-pot SERS biosensor to detect SARS-CoV-2 virus.

Scientific reports
The integration of Artificial Intelligence (AI) techniques with medical kits has revolutionized disease diagnosis, enabling rapid and accurate identification of various conditions. We developed a novel deep learning model, namely DeepATsers based on ...

Raman spectral band imaging for the diagnostics and classification of canine and feline cutaneous tumors.

The veterinary quarterly
This study introduces Raman imaging technique for diagnosing skin cancer in veterinary oncology patients (dogs and cats). Initially, Raman spectral bands (with specificity to certain molecular structures and functional groups) were identified in form...

Intermediate data fusion improves the accuracy of near-infrared spectroscopy and Raman spectroscopy for the detection of aflatoxin B1 in peanuts.

Spectrochimica acta. Part A, Molecular and biomolecular spectroscopy
This study developed a convolutional neural network (CNN) model based on feature-level data fusion for quantitatively detecting aflatoxin B1 (AFB1) in peanuts. Using a portable near-infrared (NIR) spectrometer and a Raman spectrometer, NIR and Raman ...

Interpretable Multiscale Convolutional Neural Network for Classification and Feature Visualization of Weak Raman Spectra of Biomolecules at Cell Membranes.

ACS sensors
Raman spectroscopy in biological applications faces challenges due to complex spectra, characterized by peaks of varying widths and significant biological background noise. Convolutional neural networks (CNNs) are widely used for spectrum classificat...

Cell Response to Nanoplastics and Their Carrier Effects Tracked Real-Timely with Machine Learning-Driven Smart Surface-Enhanced Raman Spectroscopy Slides.

Analytical chemistry
Research on nanoplastic (NP) toxicity and their "carrier effects" on human health remains nascent, especially for real-time, in situ monitoring of metabolic reactions in live cells. Herein, we developed smart surface-enhanced Raman spectroscopy (SERS...

Deep Learning-Powered Colloidal Digital SERS for Precise Monitoring of Cell Culture Media.

Nano letters
Maintaining consistent quality in biomanufacturing is essential for producing high-quality complex biologics. Yet, current process analytical technologies (PAT) often fall short in achieving rapid and accurate monitoring of small-molecule critical pr...