Depression as is Seen by Molecular Spectroscopy. Phospholipid- Protein Balance in Affective Disorders and Dementia

Pogocki, Dariusz; Kisała, Joanna; Cebulski, J.

doi:10.2174/1566524020666191219102746

Cited by 3 publications

(1 citation statement)

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“…Previous studies have indicated that there are close associations between neuropsychiatric disorders and metabolism, including mitochondrial energy metabolism [ 50 ], glucose metabolism [ 51 ] and amino acid metabolism [ 52 ]. These studies have also found that phospholipids and proteins [ 53 , 54 ], cortisol levels [ 55 ] or vitamins [ 56 , 57 ] in blood serum can be used as potential diagnostic biomarkers in neuropsychiatric disorders. The abnormal changes of these substances may be linked to symptoms of psychiatric disorders.…”

Section: Methodsmentioning

confidence: 99%

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

Xiong,

Zhong,

Yan

et al. 2024

Biomed. Opt. Express

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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. CBAM, including channel and spatial aspects, is adopted to enhance the distinctive global information on Raman peaks. DCM is used to supplement local information of Raman peaks. Autonomously trained branch weights are applied to fuse the features of each branch, thereby optimizing the global and local information of the characteristic Raman peaks for identifying diseases. Extensive experiments are performed for two different neurological disorders classification tasks via untargeted serum SERS data. The results demonstrate that MBA-RamanNet outperforms commonly used CNN methods with an accuracy of 88.24% for the classification of healthy controls, mild cognitive impairment, Alzheimer’s disease, and Non-Alzheimer’s dementia; an accuracy of 90% for the classification of healthy controls, elderly depression, and elderly anxiety.

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