Correlation between endometriomas volume and Raman spectra. Attempting to use Raman spectroscopy in the diagnosis of endometrioma

Detection of primary myelofibrosis in blood serum via Raman spectroscopy assisted by machine learning approaches; correlation with clinical diagnosis

Primary myelofibrosis (PM) is one of the myeloproliferative neoplasm, where stem cell-derived clonal neoplasms was noticed. Diagnosis of this disease is based on: physical examination, peripheral blood findings, bone marrow morphology, cytogenetics, and molecular markers. However, the molecular marker of PM, which is a mutation in the JAK2V617F gene, was observed also in other myeloproliferative neoplasms such as polycythemia vera and essential thrombocythemia. Therefore, there is a need to find methods that provide a marker unique to PM and allow for higher accuracy of PM diagnosis and consequently the treatment of the disease. Continuing, in this study, we used Raman spectroscopy, Principal Components Analysis (PCA), and Partial Least Squares (PLS) analysis as helpful diagnostic tools for PM. Consequently, we used serum collected from PM patients, which were classified using clinical parameters of PM such as the dynamic international prognostic scoring system (DIPSS) for primary myelofibrosis plus score, the JAK2V617F mutation, spleen size, bone marrow reticulin fibrosis degree and use of hydroxyurea drug features. Raman spectra showed higher amounts of C-H, C-C and C-C/C-N and amide II and lower amounts of amide I and vibrations of CH3 groups in PM patients than in healthy ones. Furthermore, shifts of amides II and I vibrations in PM patients were noticed. Machine learning methods were used to analyze Raman regions: (i) 800 cm-1 and 1800 cm-1, (ii) 1600 cm-1-1700 cm-1, and (iii) 2700 cm-1-3000 cm-1 showed 100 % accuracy, sensitivity, and specificity. Differences in the spectral dynamic showed that differences in the amide II and amide I regions were the most significant in distinguishing between PM and healthy subjects. Importantly, until now, the efficacy of Raman spectroscopy has not been established in clinical diagnostics of PM disease using the correlation between Raman spectra and PM clinical prognostic scoring. Continuing, our results showed the correlation between Raman signals and bone marrow fibrosis, as well as JAKV617F. Consequently, the results revealed that Raman spectroscopy has a high potential for use in medical laboratory diagnostics to quantify multiple biomarkers simultaneously, especially in the selected Raman regions.

An application of raman spectroscopy in combination with machine learning to determine gastric cancer spectroscopy marker

BACKGROUND AND OBJECTIVE

Globally, gastric carcinoma (Gca) ranks fifth in terms of incidence and third in terms of mortality. Higher serum tumor markers (TMs) than those from healthy individuals, led to TMs clinical application as diagnostic biomarkers for Gca. Actually, there is no accurate blood test to diagnose Gca.

METHODS

Raman spectroscopy is applied as an efficient, credible, minimally invasive technique to evaluate the serum TMs levels in blood samples. After curative gastrectomy, serum TMs levels are important in predicting the recurrence of gastric cancer, which must be detected early. The experimentally assesed TMs levels using Raman measurements and ELİSA test were used to develop a prediction model based on machine learning techniques. A total of 70 participants diagnosed with gastric cancer after surgery (n = 26) and healthy (n = 44) were comrpised in this study.

RESULTS

In the Raman spectra of gastric cancer patients, an additional peak at 1182 cm^-1 was observed and, the Raman intensity of amide III, II, I, and CH₂ proteins as well as lipids functional group was higher. Furthermore, Principal Component Analysis (PCA) showed, that it is possible to distinguish between the control and Gca groups using the Raman range between 800 and 1800 cm^-1, as well as between 2700 and 3000 cm^-1. The analysis of Raman spectra dynamics in gastric cancer and healthy patients showed, that the vibrations at 1302 and 1306 cm^-1 were characteristic for cancer patients. In addition, the selected machine learning methods showed classification accuracy of more than 95%, while obtaining an AUROC of 0.98. Such results were obtained using Deep Neural Networks and the XGBoost algorithm.

CONCLUSIONS

The obtained results suggest, that Raman shifts at 1302 and 1306 cm^-1 could be spectroscopic markers of gastric cancer.

Increased levels of nerve growth factor accompany oxidative load in recurrent pregnancy loss. Machine learning applied to FT-Raman spectra study

The presented article is focused on developing and validating an efficient, credible, minimally invasive technique based on spectral signatures of blood serum samples in patients with diagnosed recurrent pregnancy loss (RPL) versus healthy individuals who were followed at the Gynecology department. A total of 120 participants, RPL disease (n = 60) and healthy individuals (n = 60), participated in the study. First, we investigated the effect of circulating nerve growth factor (NGF) in RPL and healthy groups. To show NGF's effect, we measured the level of oxidative loads such as Total Antioxidant Level (TAS), Total Oxidant Level (TOS), and Oxidative Stress Index (OSI) with Beckman Coulter AU system and biochemical assays. We find a correlation between oxidative load and NGF level. Oxidative load mainly causes structural changes in the blood. Therefore, we obtained Raman measurements of the participant's serum. Then we selected two Raman regions, 800 and 1800 cm^-1, and between 2700 cm^-1 and 3000 cm^-1, to see chemical changes. We noted that Raman spectra obtained for RPL and healthy women differed. The findings confirm that the imbalance between reactive oxygen species and antioxidants has important implications for the pathogenesis of RPL and that NGF levels accompany the level of oxidative load in the RPL state. Biomolecular structure and composition were determined using Raman spectroscopy and machine learning methods, and the correlation of these parameters was studied alongside machine learning technologies to advance toward clinical translation. Here we determined and validated the development of instrumentation for the Analysis of RPL patients' serum that can differentiate from control individuals with an accuracy of 100% using the Raman region corresponding to structural changes. Furthermore, this study found a correlation between traditional biochemical parameters and Raman data. This suggests that Raman spectroscopy is a sensitive tool for detecting biochemical changes in serum caused by RPL or other diseases.

Correlation between human colon cancer specific antigens and Raman spectra. Attempting to use Raman spectroscopy in the determination of tumor markers for colon cancer

Colorectal cancer is the second most common cause of cancer-related deaths worldwide. To follow up on the progression of the disease, tumor markers are commonly used. Here, we report serum analysis based on Raman spectroscopy to provide a rapid cancer diagnosis with tumor markers and two new cell adhesion molecules measured using the ELİSA method. Raman spectra showed higher Raman intensities at 1447 cm^-1 1560 cm^-1, 1665 cm^-1, and 1769 cm^-1, which originated from CH₂ proteins and lipids, amide II and amide I, and CO lipids vibrations. Furthermore, the correlation test showed, that only the CEA colon cancer marker correlated with the Raman spectra. Importantly, machine learning methods showed, that the accuracy of the Raman method in the detection of colon cancer was around 95 %. Obtained results suggest, that Raman shifts at 1302 cm^-1 and 1306 cm^-1 can be used as spectroscopy markers of colon cancer.

Application of the Dynamical Network Biomarker Theory to Raman Spectra

The dynamical network biomarker (DNB) theory detects the early warning signals of state transitions utilizing fluctuations in and correlations between variables in complex systems. Although the DNB theory has been applied to gene expression in several diseases, destructive testing by microarrays is a critical issue. Therefore, other biological information obtained by non-destructive testing is desirable; one such piece of information is Raman spectra measured by Raman spectroscopy. Raman spectroscopy is a powerful tool in life sciences and many other fields that enable the label-free non-invasive imaging of live cells and tissues along with detailed molecular fingerprints. Naïve and activated T cells have recently been successfully distinguished from each other using Raman spectroscopy without labeling. In the present study, we applied the DNB theory to Raman spectra of T cell activation as a model case. The dataset consisted of Raman spectra of the T cell activation process observed at 0 (naïve T cells), 2, 6, 12, 24 and 48 h (fully activated T cells). In the DNB analysis, the F-test and hierarchical clustering were used to detect the transition state and identify DNB Raman shifts. We successfully detected the transition state at 6 h and related DNB Raman shifts during the T cell activation process. The present results suggest novel applications of the DNB theory to Raman spectra ranging from fundamental research on cellular mechanisms to clinical examinations.

The ultrasonography, colour Doppler ultrasonography and sonoelastography findings of scar endometriosis in comparison with menstrual phases

We aimed to evaluate ultrasonography (US), colour Doppler US (CDUS) and sonoelastography (SEL) findings of histopathologically proven abdominal wall scar endometriosis in comparison with menstrual phases. A total of 24 female patients with scar endometriosis were included. Lesion size, volume, echogenicity, solid/cystic or complex appearance, contour and location on US, vascularisation on CDUS, and elasticity on SEL were recorded in both menstrual and ovulatory phases. Hypoechoic heterogeneous lesions with increased peripheral echogenicity were observed in all lesions. The mean ± standard deviation (SD) volume of the lesions in the menstrual and ovulatory phases of the lesions was 4.36 ± 3.01 cm³ and 4.63 ± 7.61 cm³ (p = .316). The mean ± SD resistive index values on CDUS in the menstrual and ovulatory phases were 0.96 ± 0.09 and 0.97 ± 0.07, respectively (p = .667). The SEL examination demonstrated a hard coding pattern in all cases with no statistically significant difference between menstrual and ovulatory phases (p = .176). We found no significant difference in terms of US, CDUS and SEL findings of scar endometriosis in comparison with menstrual phases which suggests there is no need to evaluate the patient in a specific menstrual phase.Impact StatementWhat is already known on this subject? Scar endometriosis is the endometriosis located at the abdominal wall around the scar area with a very rare incidence. The typical sonographic findings of scar endometriosis are a hypoechoic solid mass with irregular contours. High resistive index on colour Doppler ultrasonography (CDUS) and hard coding pattern on sonoelastography (SEL) are seen in the lesion.What do the results of this study add? This is the first study that evaluates sonographic features of scar endometriosis lesions in the menstrual phase. We found that scar endometriosis lesions did not have a significant difference in terms of B-mode US, CDUS and SEL in menstrual and ovulatory phases.What are the implications of these findings for clinical practice and/or further research? Our findings suggest that there is no need to evaluate the patient in a specific menstrual phase.

Biochemistry and Nanomechanical Properties of Human Colon Cells upon Simvastatin, Lovastatin, and Mevastatin Supplementations: Raman Imaging and AFM Studies

One of the most important areas of medical science is oncology, which is responsible for both the diagnostics and treatment of cancer diseases. Over the years, there has been an intensive development of cancer diagnostics and treatment. This paper shows the comparison of normal (CCD-18Co) and cancerous (CaCo-2) cell lines of the human gastrointestinal tract on the basis of nanomechanical and biochemical properties to obtain information on cancer biomarkers useful in oncological diagnostics. The research techniques used were Raman spectroscopy and imaging and atomic force microscopy (AFM). In addition, the studies also included the effect of the statin compounds—mevastatin, lovastatin, and simvastatin—and their influence on biochemical and nanomechanical changes of cell properties using Raman imaging and AFM techniques. The cytotoxicity of statins was determined using XTT tests.

Determination of idiopathic female infertility from infrared spectra of follicle fluid combined with gonadotrophin levels, multivariate analysis and machine learning methods

By in vitro fertilization, oocytes can be removed and the embryo can be cultured, and then trans cervically replaced when they reach cleavage or when the blastocyst stage. The characterization of the follicular fluid is important for the treatment process. Women who applied to the Academic Hospital in vitro fertilization (IVF) Center diagnosed with idiopathic female infertility (IFI) were sought in the patient group. Demographics and clinical gonadotropin measurements of the study population were recorded. Of the 116 follicular fluid samples (n=58 male-induced infertility; n=58 control) were analyzed using the FTIR system. To identify FTIR spectral characteristics of follicular fluids associated with an ovarian reserve and reproductive hormone levels from control and IFI, six machine learning methods and multivariate analysis were used. To assess the quantitative information about the total biochemical composition of a follicular fluid across various diagnoses. FTIR spectra showed a higher level of vibrations corresponding to lipids and a lower level of amide vibrations in the IFI group. Furthermore, the T square plot from Partial Last Square (PLS) analysis showed, that these vibrations can be used to distinguish IFI from the control group which was obtained by principal component analysis (PCA). Proteins and lipids play an important role in the development of IFI. The absorption dynamics of FTIR spectra showed wavenumbers with around 100% discrimination probability, which means, that the presented wavenumbers can be used as a spectroscopic marker of IFI. Also, six machine learning methods showed, that classification accuracy for the original set was from 93.75% to 100% depending on the learning algorithm used. These results can inform about IFI women's follicular fluid has biomacromolecular differentiation in their follicular fluid. By using a safe and effective tool for the characterization of changes in follicular fluid during in vitro fertilization, this study builds upon a comprehensive examination of the idiopathic female infertility remodeling process in human studies. We anticipate that this technology will be a valuable adjunct for clinical studies.