The freeze-thaw cycle effect on blood serum autofluorescence, Raman spectroscopy and SERS: implications for sample classification and disease diagnostics.

The issue of variability introduced into blood plasma and serum analysis by preanalytical procedures is the major obstacle to obtaining accurate and reproducible results. While the question of how to overcome this issue has been discussed in biochemical detection of analytes and omics technologies, its relevance to the field of optical spectroscopy remains mostly unexplored. In this work, we evaluated the freeze-thaw cycle (FTC)-induced alternations in blood serum optical properties by means of autofluorescence and Raman spectroscopy, including surface-enhanced Raman spectroscopy (SERS).

Comment on "Raman spectroscopy combined with multivariate statistical algorithms for the simultaneous screening of cervical and breast cancers".

This article discusses current research on the detection of cervical and breast cancer using in vitro Raman spectral analysis of human serum by Cao et al. (2024) which was published in the Lasers in Medical Science journal. Despite the high accuracy of the suggested approach (93%), the demonstrated findings could be treated unclear due to possible overestimation of the classification models.

Raman spectroscopy in chronic heart failure diagnosis based on human skin analysis.

This work aims at studying Raman spectroscopy in combination with chemometrics as an alternative fast noninvasive method to detect chronic heart failure (CHF) cases. Optical analysis is focused on the changes in the spectral features associated with the biochemical composition changes of skin tissues. A portable spectroscopy setup with the 785 nm excitation wavelength was used to record skin Raman features.

Comment on "Serum Raman spectroscopy combined with multiple classification models for rapid diagnosis of breast cancer".

This paper comments recent findings about Raman spectroscopy application for breast cancer detection published in the Photodiagnosis and Photodynamic Therapy journal by Li et al. Despite the high performance of the proposed approach for breast cancer detection by means of Raman spectroscopy analysis of serum, the proposed results may be ambiguous due to overestimation of classification models.

Comment on "Feasibility of Raman spectroscopy as a potential in vivo tool to screen for pre-diabetes and diabetes".

This paper comments recent findings about Raman spectroscopy application for in vivo noninvasive diabetes detection, published in the Journal of Biophotonics by E. Guevara et al. (J.

Analyzing the serum of hemodialysis patients with end-stage chronic kidney disease by means of the combination of SERS and machine learning.

The aim of this paper is a multivariate analysis of SERS characteristics of serum in hemodialysis patients, which includes constructing classification models (PLS-DA, CNN) by the presence/absence of end-stage chronic kidney disease (CKD) with dialysis and determining the most informative spectral bands for identifying dialysis patients by variable importance distribution. We found the spectral bands that are informative for detecting the hemodialysis patients: the 641 cm, 724 cm, 1094 cm and 1393 cm bands are associated with the degree of kidney function inhibition; and the 1001 cm band is able to demonstrate the distinctive features of hemodialysis patients with end-stage CKD.

Classification of skin cancer using convolutional neural networks analysis of Raman spectra.

Background And Objective: Skin cancer is the most common malignancy in whites accounting for about one third of all cancers diagnosed per year. Portable Raman spectroscopy setups for skin cancer "optical biopsy" are utilized to detect tumors based on their spectral features caused by the comparative presence of different chemical components. However, low signal-to-noise ratio in such systems may prevent accurate tumors classification.

Comment on "Finding reduced Raman spectroscopy fingerprint of skin samples for melanoma diagnosis through machine learning".

This paper comments on the article "Finding reduced Raman spectroscopy fingerprint of skin samples for melanoma diagnosis through machine learning" by D. C. Araújo et al.

Raman spectroscopy based diagnosis of dermatofibrosarcoma protuberans: Case report.

Dermatofibrosarcoma protuberans is a rare disease and this pathology provokes insufficient oncological alertness among clinicians. A possible way to increase the accuracy of early diagnosis of rare skin neoplasms is "optical biopsy" using Raman spectroscopy tissue response. This case report of a 32-year-old woman with a dermatofibrosarcoma protuberans demonstrates that Raman spectroscopy based "optical biopsy" can help to diagnose rare tumors.

In vivo diagnosis of skin cancer with a portable Raman spectroscopic device.

In this study, we performed in vivo diagnosis of skin cancer based on implementation of a portable low-cost spectroscopy setup combining analysis of Raman and autofluorescence spectra in the near-infrared region (800-915 nm). We studied 617 cases of skin neoplasms (615 patients, 70 melanomas, 122 basal cell carcinomas, 12 squamous cell carcinomas and 413 benign tumors) in vivo with a portable setup. The studies considered the patients examined by GPs in local clinics and directed to a specialized Oncology Dispensary with suspected skin cancer.

Comment on "Combining derivative Raman with autofluorescence to improve the diagnosis performance of echinococcosis".

This paper comments on the article "Combining derivative Raman with autofluorescence to improve the diagnosis performance of echinococcosis" by X. Zheng et al. The authors put forward an idea to apply Raman spectroscopy and autofluorescence to measure spectral characteristics of human serum and diagnose echinococcosis.

Raman spectroscopy of human skin for kidney failure detection.

The object of this paper is in vivo study of skin spectral-characteristics in patients with kidney failure by conventional Raman spectroscopy in near infrared region. The experimental dataset was subjected to discriminant analysis with the projection on latent structures (PLS-DA). Application of Raman spectroscopy to investigate the forearm skin in 85 adult patients with kidney failure (90 spectra) and 40 healthy adult volunteers (80 spectra) has yielded the accuracy of 0.

Use of Raman spectroscopy to screen diabetes mellitus with machine learning tools: comment.

This paper comments on the article "Use of Raman spectroscopy to screen diabetes mellitus with machine learning tools" by E. Guevara et al. The authors propose an optical method for noninvasive automated screening of type 2 diabetes mellitus.