Multi-branch attention Raman network and surface-enhanced Raman spectroscopy for the classification of neurological disorders.

Surface-enhanced Raman spectroscopy (SERS), a rapid, low-cost, non-invasive, ultrasensitive, and label-free technique, has been widely used in-situ and ex-situ biomedical diagnostics questions. However, analyzing and interpreting the untargeted spectral data remains challenging due to the difficulty of designing an optimal data pre-processing and modelling procedure. In this paper, we propose a Multi-branch Attention Raman Network (MBA-RamanNet) with a multi-branch attention module, including the convolutional block attention module (CBAM) branch, deep convolution module (DCM) branch, and branch weights, to extract more global and local information of characteristic Raman peaks which are more distinctive for classification tasks.

Identification of late-life depression and mild cognitive impairment via serum surface-enhanced Raman spectroscopy and multivariate statistical analysis.

Identification of age-related neuropsychiatric disorders, i.e., late-life depression (LDD) and mild cognitive impairment (MCI) is of imperative clinical value considering the large probability of misdiagnosis and current lack of sensitive, non-invasive and low-cost diagnostic approaches.

A hybrid SERS sensing platform constructed by porous carbon/Ag nanoparticles for efficient imatinib detection in bio-environment.

Surface enhanced Raman scattering (SERS) is a rapid and non-destructive spectral detection technique, and has been widely implemented on trace-level molecule detection. In this work, a hybrid SERS substrate constructed by porous carbon film and silver nanoparticles (PCs/Ag NPs) was developed and then used for imatinib (IMT) detection in bio-environment. The PCs/Ag NPs was prepared by direct carbonizing the gelatin-AgNO film in the air atmosphere, and an enhancement factor (EF) of 10 was achieved with R6G as the Raman reporter.

Rapid and precise detection of cancers via label-free SERS and deep learning.

Early, express, and reliable detection of cancer can provide a favorable prognosis and decrease mortality. Tumor biomarkers have been proven to be closely related to tumor occurrence and development. Conventional tumor biomarker detection based on genomic, proteomic, and metabolomic methods is time and equipment-consuming and always needs a specific target marker.