Raman Spectroscopy of Blood Serum for Essential Thrombocythemia Diagnosis: Correlation with Genetic Mutations and Optimization of Laser Wavelengths.

Essential thrombocythemia (ET) is a type of myeloproliferative neoplasm that increases the risk of thrombosis. To diagnose this disease, the analysis of mutations in the Janus Kinase 2 (JAK2), thrombopoietin receptor (MPL), or calreticulin (CALR) gene is recommended. Disease poses diagnostic challenges due to overlapping mutations with other neoplasms and the presence of triple-negative cases. This study explores the potential of Raman spectroscopy combined with machine learning for ET diagnosis. We assessed two laser wavelengths (785, 1064 nm) to differentiate between ET patients and healthy controls. The PCR results indicate that approximately 50% of patients in our group have a mutation in the JAK2 gene, while only 5% of patients harbor a mutation in the ASXL1 gene. Additionally, only one patient had a mutation in the IDH1 and one had a mutation in IDH2 gene. Consequently, patients having no mutations were also observed in our group, making diagnosis challenging. Raman spectra at 1064 nm showed lower amide, polysaccharide, and lipid vibrations in ET patients, while 785 nm spectra indicated significant decreases in amide II and C-H lipid vibrations. Principal Component Analysis (PCA) confirmed that both wavelengths could distinguish ET from healthy subjects. Support Vector Machine (SVM) analysis revealed that the 800-1800 cm-1 range provided the highest diagnostic accuracy, with 89% for 785 nm and 72% for 1064 nm. These findings suggest that FT-Raman spectroscopy, paired with multivariate and machine learning analyses, offers a promising method for diagnosing ET with high accuracy by detecting specific molecular changes in serum. Principal Component Analysis (PCA) confirmed that both wavelengths could distinguish ET from healthy subjects. Support Vector Machine (SVM) analysis revealed that the 800-1800 cm-1 range provided the highest diagnostic accuracy, with 89% for 785 nm and 72% for 1064 nm. These findings suggest that FT-Raman spectroscopy, paired with multivariate and machine learning analyses, offers a promising method for diagnosing ET with high accuracy by detecting specific molecular changes in serum.

Lipids balance as a spectroscopy marker of diabetes. Analysis of FTIR spectra by 2D correlation and machine learning analyses.

The number of people suffering from type 2 diabetes has rapidly increased. Taking into account, that elevated intracellular lipid concentrations, as well as their metabolism, are correlated with diminished insulin sensitivity, in this study we would like to show lipids spectroscopy markers of diabetes. For this purpose, serum collected from rats (animal model of diabetes) was analyzed using Fourier Transformed Infrared-Attenuated Total Reflection (FTIR-ATR) spectroscopy. Analyzed spectra showed that rats with diabetes presented higher concentration of phospholipids and cholesterol in comparison with non-diabetic rats. Moreover, the analysis of second (IInd) derivative spectra showed no structural changes in lipids. Machine learning methods showed higher accuracy for IInd derivative spectra (from 65 % to 89 %) than for absorbance FTIR spectra (53-65 %). Moreover, it was possible to identify significant wavelength intervals from IInd derivative spectra using random forest-based feature selection algorithm, which further increased the accuracy of the classification (up to 92 % for phospholipid region). Moreover decision tree based on the selected features showed, that peaks at 1016 cm-1 and 2936 cm-1 can be good candidates of lipids marker of diabetes.

Investigation of the structural changes in the hippocampus and prefrontal cortex using FTIR spectroscopy in sleep deprived mice.

Sleep is a basic, physiological requirement for living things to survive and is a process that covers one third of our lives. Melatonin is a hormone that plays an important role in the regulation of sleep. Sleep deprivation affect brain structures and functions. Sleep deprivation causes a decrease in brain activity, with particularly negative effects on the hippocampus and prefrontal cortex. Despite the essential role of protein and lipids vibrations, polysaccharides, fatty acid side chains functional groups, and ratios between amides in brain structures and functions, the brain chemical profile exposed to gentle handling sleep deprivation model versus Melatonin exposure remains unexplored. Therefore, the present study, aims to investigate a molecular profile of these regions using FTIR spectroscopy measurement's analysis based on lipidomic approach with chemometrics and multivariate analysis to evaluate changes in lipid composition in the hippocampus, prefrontal regions of the brain. In this study, C57BL/6J mice were randomly assigned to either the control or sleep deprivation group, resulting in four experimental groups: Control (C) (n = 6), Control + Melatonin (C + M) (n = 6), Sleep Deprivation (S) (n = 6), and Sleep Deprivation + Melatonin (S + M) (n = 6). Interventions were administered each morning via intraperitoneal injections of melatonin (10 mg/kg) or vehicle solution (%1 ethanol + saline), while the S and S + M groups underwent 6 h of daily sleep deprivation from using the Gentle Handling method. All mice were individually housed in cages with ad libitum access to food and water within a 12-hour light-dark cycle. Results presented that the brain regions affected by insomnia. The structure of phospholipids, changed. Yet, not only changes in lipids but also in amides were noticed in hippocampus and prefrontal cortex tissues. Additionally, FTIR results showed that melatonin affected the lipids as well as the amides fraction in cortex and hippocampus collected from both control and sleep deprivation groups.

Modeling Absorption Dynamics of Differently Shaped Gold Glioblastoma and Colon Cells Based on Refractive Index Distribution in Holotomographic Imaging.

Herein, it is demonstrated that the toxic effect of gold nanoparticles (Au NPs) on three different cancer cell lines (U-118 and LN-299 glioblastoma and HCT-116 colon) depends on their absorption dynamics by cells, related to the shapes of the NPs. This hypothesis is confirmed by showing that i) based on refractive index (RI) values, typical for cell components and gold nanoparticles, it is possible to show the absorption dynamics and accumulation locations of the latter ones inside and outside of the cells. Moreover, ii) the saturation of the accumulated Au NPs volume in the cells depends on the nanoparticle shape and is reached in the shortest time for star-shaped Au NPs (AuS NPs) and in the longest time for spherical Au NPs (AuSph NPs) and on the cancer cells, where the longest and the shortest saturation are noticed for HCT-116 and LN-229 cells, respectively. A physical model of Au NPs absorption dynamics is proposed, where the diameter and shape of the Au NPs are used as parameters. The obtained theoretical data are consistent with experimental data in 85-98%.

FT-Raman and FTIR spectroscopy as a tools showing marker of platinum-resistant phenomena in women suffering from ovarian cancer.

Platinum-resistant phenomena in ovarian cancer is very dangerous for women suffering from this disease, because reduces the chances of complete recovery. Unfortunately, until now there are no methods to verify whether a woman with ovarian cancer is platinum-resistant. Importantly, histopathology images also were not shown differences in the ovarian cancer between platinum-resistant and platinum-sensitive tissues. Therefore, in this study, Fourier Transform InfraRed (FTIR) and FT-Raman spectroscopy techniques were used to find chemical differences between platinum-resistant and platinum-sensitive ovarian cancer tissues. Furthermore, Principal Component Analysis (PCA) and machine learning methods were performed to show if it possible to differentiate these two kind of tissues as well as to propose spectroscopy marker of platinum-resistant. Indeed, obtained results showed, that in platinum-resistant ovarian cancer tissues higher amount of phospholipids, proteins and lipids were visible, however when the ratio between intensities of peaks at 1637 cm-1 (FTIR) and at 2944 cm-1 (Raman) and every peaks in spectra was calculated, difference between groups of samples were not noticed. Moreover, structural changes visible as a shift of peaks were noticed for C-O-C, C-H bending and amide II bonds. PCA clearly showed, that PC1 can be used to differentiate platinum-resistant and platinum-sensitive ovarian cancer tissues, while two-trace two-dimensional correlation spectra (2T2D-COS) showed, that only in amide II, amide I and asymmetric CH lipids vibrations correlation between two analyzed types of tissues were noticed. Finally, machine learning algorithms showed, that values of accuracy, sensitivity and specificity were near to 100% for FTIR and around 95% for FT-Raman spectroscopy. Using decision tree peaks at 1777 cm-1, 2974 cm-1 (FTIR) and 1714 cm-1, 2817 cm-1 (FT-Raman) were proposed as spectroscopy marker of platinum-resistant.

Quantitative or qualitative biomolecular changes in blood serum composition induced by childhood obesity: A Fourier transform infrared examination.

Childhood obesity (CO) negatively affects one in three children and stands as the fourth most common risk factor of health and well-being. Clarifying the molecular and structural modifications that transpire during the development of obesity is crucial for understanding its progression and devising effective therapies. The study was indeed conducted as part of an ongoing CO treatment trial, where data were collected from children diagnosed with CO before the initiation of non-drug treatment interventions. Our primary aim was to analyze the biochemical changes associated with childhood obesity, specifically focusing on concentrations of lipids, lipoproteins, insulin, and glucose. By comparing these parameters between the CO group (n = 60) and a control group of healthy children (n = 43), we sought to elucidate the metabolic differences present in individuals with CO. Our biochemical analyses unveiled lower LDL (low-density lipoproteins) levels and higher HDL (high-density lipoproteins), cholesterol, triglycerides, insulin, and glucose levels in CO individuals compared to controls. To scrutinize these changes in more detail, we employed Fourier transform infrared (FTIR) spectroscopy on the serum samples. Our results indicated elevated levels of lipids and proteins in the serum of CO, compared to controls. Additionally, we noted structural changes in the vibrations of glucose, ß-sheet, and lipids in CO group. The FTIR technique, coupled with principal component analysis (PCA), demonstrated a marked differentiation between CO and controls, particularly in the FTIR region corresponding to amide and lipids. The Pearson test revealed a stronger correlation between biochemical data and FTIR spectra than between 2nd derivative FTIR spectra. Overall, our study provides valuable insights into the molecular and structural changes occurring in CO.

FT-Raman spectra in combination with machine learning and multivariate analyses as a diagnostic tool in brain tumors.

Brain tumors are one of the most dangerous, because the position of these are in the organ that governs all life processes. Moreover, a lot of brain tumor types were observed, but only one main diagnostic method was used - histopathology, for which preparation of sample was long. Consequently, a new, quicker diagnostic method is needed. In this paper, FT-Raman spectra of brain tissues were analyzed by Principal Component Analysis (PCA), Hierarchical Cluster Analysis (HCA), four different machine learning (ML) algorithms to show possibility of differentiating between glioblastoma G4 and meningiomas, as well as two different types of meningiomas (atypical and angiomatous). Obtained results showed that in meningiomas additional peak around 1503 cm-1 and higher level of amides was noticed in comparison with glioblastoma G4. In the case of meningiomas differentiation, in angiomatous meningiomas tissues lower level of lipids and polysaccharides were visible than in atypical meningiomas. Moreover, PCA analyses showed higher distinction between glioblastoma G4 and meningiomas in the FT-Raman range between 800 cm-1 and 1800 cm-1 and between two types of meningiomas in the range between 2700 cm-1 and 3000 cm-1. Decision trees showed, that the most important peaks to differentiate glioblastoma and meningiomas were at 1151 cm-1 and 2836 cm-1 while for angiomatous and atypical meningiomas - 1514 cm-1 and 2875 cm-1. Furthermore, the accuracy of obtained results for glioblastoma G4 and meningiomas was 88 %, while for meningiomas - 92 %. Consequently, obtained data showed possibility of using FT-Raman spectroscopy in diagnosis of different types of brain tumors.

FT-Raman data analyzed by multivariate and machine learning as a new methods for detection spectroscopy marker of platinum-resistant women suffering from ovarian cancer.

The phenomenon of platinum resistance is a very serious problem in the treatment of ovarian cancer. Unfortunately, no molecular, genetic marker that could be used in assigning women suffering from ovarian cancer to the platinum-resistant or platinum-sensitive group has been discovered so far. Therefore, in this study, for the first time, we used FT-Raman spectroscopy to determine chemical differences and chemical markers presented in serum, which could be used to differentiate platinum-resistant and platinum-sensitive women. The result obtained showed that in the serum collected from platinum-resistant women, a significant increase of chemical compounds was observed in comparison with the serum collected from platinum-sensitive woman. Moreover, a decrease in the ratio between amides vibrations and shifts of peaks, respectively, corresponding to C-C/C-N stretching vibrations from proteins, amide III, amide II, C = O and CH lipids vibrations suggested that in these compounds, structural changes occurred. The Principal Component Analysis (PCA) showed that using FT-Raman range, where the above-mentioned functional groups were present, it was possible to differentiate the serum collected from both analyzed groups. Moreover, C5.0 decision tree clearly showed that Raman shifts at 1224 cm-1 and 2713 cm-1 could be used as a marker of platinum resistance. Importantly, machine learning methods showed that the accuracy, sensitivity and specificity of the FT-Raman spectroscopy were from 95 to 100%.

Fe3O4 Magnetic Nanoparticles Obtained by the Novel Aerosol-Based Technique for Theranostic Applications.

This work is aimed at presenting a novel aerosol-based technique for the synthesis of magnetite nanoparticles (Fe3O4 NPs) and to assess the potential medical application of their dispersions after being coated with TEA-oleate. Refinement of the processing conditions led to the formation of monodispersed NPs with average sizes of ∼5-6 nm and narrow size distribution (FWHM of ∼3 nm). The NPs were coated with Triethanolammonium oleate (TEA-oleate) to stabilize them in water dispersion. This allowed obtaining the dispersion, which does not sediment for months, although TEM and DLS studies have shown the formation of small agglomerates of NPs. The different behaviors of cancer and normal cell lines in contact with NPs indicated the diverse mechanisms of their interactions with Fe3O4 NPs. Furthermore, the studies allowed assessment of the prospective theranostic application of magnetite NPs obtained using the aerosol-based technique, particularly magnetic hyperthermia and magnetic resonance imaging (MRI).

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.

Application of Fourier Transform InfraRed spectroscopy of machine learning with Support Vector Machine and principal components analysis to detect biochemical changes in dried serum of patients with primary myelofibrosis.

Primary myelofibrosis (PM) is a myeloproliferative neoplasm characterized by stem cell-derived clonal neoplasms. Several factors are involved in diagnosing PM, including physical examination, peripheral blood findings, bone marrow morphology, cytogenetics, and molecular markers. Commonly gene mutations are used. Also, these gene mutations exist in other diseases, such as polycythemia vera and essential thrombocythemia. Hence, understanding the molecular mechanism and finding disease-related biomarker characteristics only for PM is crucial for the treatment and survival rate. For this purpose, blood samples of PM (n = 85) vs. healthy controls (n = 45) were collected for biochemical analysis, and, for the first time, Fourier Transform InfraRed (FTIR) spectroscopy measurement of dried PM and healthy patients' blood serum was analyzed. A Support Vector Machine (SVM) model with optimized hyperparameters was constructed using the grid search (GS) method. Then, the FTIR spectra of the biomolecular components of blood serum from PM patients were compared to those from healthy individuals using Principal Components Analysis (PCA). Also, an analysis of the rate of change of FTIR spectra absorption was studied. The results showed that PM patients have higher amounts of phospholipids and proteins and a lower amount of H-O=H vibrations which was visible. The PCA results indicated that it is possible to differentiate between dried blood serum samples collected from PM patients and healthy individuals. The Grid Search Support Vector Machine (GS-SVM) model showed that the prediction accuracy ranged from 0.923 to 1.00 depending on the FTIR range analyzed. Furthermore, it was shown that the ratio between α-helix and ß-sheet structures in proteins is 1.5 times higher in PM than in control people. The vibrations associated with the CO bond and the amide III region of proteins showed the highest probability value, indicating that these spectral features were significantly altered in PM patients compared to healthy ones' spectra. The results indicate that the FTIR spectroscope may be used as a technique helpful in PM diagnostics. The study also presents preliminary results from the first prospective clinical validation study.

FTIR- based serum structure analysis in molecular diagnostics of essential thrombocythemia disease.

Essential thrombocythemia (ET) reflects the transformation of a multipotent hematopoietic stem cell, but its molecular pathogenesis remains obscure. Nevertheless, tyrosine kinase, especially Janus kinase 2 (JAK2), has been implicated in myeloproliferative disorders other than chronic myeloid leukaemia. FTIR analysis was performed on the blood serum of 86 patients and 45 healthy volunteers as control with FTIR spectra-based machine learning methods and chemometrics. Thus, the study aimed to determine biomolecular changes and separation of ET and healthy control groups illustration by applying chemometrics and ML techniques to spectral data. The FTIR-based results showed that in ET disease with JAK2 mutation, there are alterations in functional groups associated with lipids, proteins and nucleic acids significantly. Moreover, in ET patients the lower amount of proteins with simultaneously higher amount of lipids was noted in comparison with the control one. Furthermore, the SVM-DA model showed 100% accuracy in calibration sets in both spectral regions and 100.0% and 96.43% accuracy in prediction sets for the 800-1800 cm-1 and 2700-3000 cm-1 spectral regions, respectively. While changes in the dynamic spectra showed that CH2 bending, amide II and CO vibrations could be used as a spectroscopy marker of ET. Finally, it was found a positive correlation between FTIR peaks and first bone marrow fibrosis degree, as well as the absence of JAK2 V617F mutation. The findings of this study contribute to a better understanding of the molecular pathogenesis of ET and identifying biomolecular changes and may have implications for early diagnosis and treatment of this disease.

Chemical changes in childhood obesity blood as a marker of the disease. A Raman-based machine learning study.

Obesity in children is a global problem, leading to different medical conditions that may contribute to metabolic syndrome and increase the risk of diabetes, dyslipidemia, hypertension, and cardiovascular diseases in future health. Metabolic disorders are the results of the body's chemical process. The changes in the chemical compositions could be determined by Raman spectroscopy. Therefore, in this study, we measured blood collected from children with obesity to show chemical changes caused by obesity disease. Moreover, we will also show characteristic Raman peaks/regions, which could be used as a marker of obesity, not other metabolic syndromes. Children with obesity had higher glucose levels, proteins, and lipids than the control ones. Furthermore, it was noticed that the ratio between CO and C-H is 0.23 in control patients and 0.31 in children with obesity, as well as the ratio between amide II and amide I was 0.72 in control and 1.15 in obesity, which suggests an imbalance in these two fractions in childhood obesity. PCA with discrimination analyses showed that the accuracy, selectivity, and specificity of Raman spectroscopy in differentiation between childhood obesity and healthy children was between 93% and 100%. There is an increased risk of metabolic changes in childhood obesity with higher glucose levels, lipids, and proteins in children with obesity. Also, there were differences in the ratio between proteins and lipids functional groups and glucose, amide II, and amide I vibrations as a marker of obesity. The results of the study offer valuable insights into potential alterations in protein structure and lipid composition in children with obesity, emphasizing the importance of considering metabolic changes beyond traditional anthropometric, measurements.

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 ELISA 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.

Fourier transform infrared spectroscopic marker of glioblastoma obtained from machine learning and changes in the spectra.

BACKGROUND: Glioblastoma is among the most malignant brain cancer with an average survival rate measured in months. In neurosurgical practice, it is considered impossible to completely remove a glioblastoma because of difficulties in the intraoperative assessment of the boundaries between healthy brain tissue and glioblastoma cells. Therefore, it is important to find a new, quick, cost-effective and useful neurosurgical practice method for the intraoperative differentiation of glioblastoma from healthy brain tissue. METHODS: Herein, the features of absorbance at specific wavenumbers considered characteristic of glioblastoma tissues could be markers of this cancer. We used Fourier transform infrared spectroscopy to measure the spectra of tissues collected from control and patients suffering from glioblastoma. RESULTS: The spectrum obtained from glioblastoma tissues demonstrated an additional peak at 1612 cm-1 and a shift of peaks at 1675 cm-1 and 1637 cm-1. Deconvolution of amide I vibrations showed that in the glioblastoma tissue, the percentage amount of ß-sheet is around 20% higher than that in the control. Moreover, the principal component analysis showed that using fingerprint and amide I regions it is possible to distinguish cancer and non-cancer samples. Machine learning methods presented that the accuracy of the results is around 100%. Finally, analysis of the differences in the rate of change of Fourier transform infrared spectroscopy spectra showed that absorbance features between 1053 cm-1 and 1056 cm-1 as well as between 1564 cm-1 and 1588 cm-1 are characteristic of glioblastoma. CONCLUSION: Calculated features of absorbance at specific wavenumbers could be used as a spectroscopic marker of glioblastoma which may be useful in the future for neuronavigation.

Raman spectroscopy-based biomarker screening by studying the fingerprint and lipid characteristics of Polycythem..a Vera cases blood serum.

This study aimed to develop a novel approach for diagnosing Polycythemia Vera (PV), a stem cell-derived neoplasm of the myeloid lineage. The approach utilized Raman spectroscopy coupled with multivariate analysis to analyze blood serum samples collected from PV patients. The results showed that PV serum exhibited lower protein and lipid levels and structural changes in the functional groups that comprise proteins and lipids. The study also demonstrated differences in lipid biosynthesis and protein levels in PV serum. Using the Partial Least Square Discriminant Analysis (PLS-DA) model, Raman-based multivariate analysis achieved high accuracy rates of 96.49 and 93.04% in the training sets and 93.10% and 89.66% in the test sets for the 800-1800 cm-1 and 2700-3000 cm-1 ranges, respectively. The area under the curve (AUC) values of the test datasets were calculated as 0.92 and 0.89 in the 800-1800 cm-1 and 2700-3000 cm-1 spectral regions, respectively, demonstrating the effectiveness of the PLS-DA models for the diagnosis of PV. This study highlights the potential of Raman spectroscopy-based analysis in the early and accurate diagnosis of PV, enabling the application of effective treatment strategies.

Application of Fourier transform infrared spectroscopy to detect biochemical changes in blood serum of obese patients.

Obesity is frequently a significant risk factor for multiple obesity-associated diseases that have been increasing in prevalence worldwide. Anthropometric data such as body mass index, fat, and fat mass values are assessed for obesity. Therefore, we aimed to propose two Fourier transform infrared (FT-IR) spectral regions, 800-1800 cm-1 and 2700-3000 cm-1 , as sensitive potential band assignments for obesity-related biochemical changes. A total of 134 obese (n = 89) and controls (n = 45) biochemical characteristics and clinical parameters indicative of obesity were evaluated. The FT-IR spectra of dried blood serum were measured. Anthropometric data of the obese have the highest body mass index, %fat, and fat mass values compared to the healthy group (p < 0.01). Also, the triglyceride and high-density lipoprotein cholesterol levels were higher than in healthy subjects (p < 0.01). Principal component analysis (PCA) technique successfully distinguished obese and control groups in the fingerprint, accounting for 98.5% and 99.9% of the total variability (800-1800 cm-1 ) and lipids (2700-3000 cm-1 ) regions presented as 2D and 3D score plots. The loading results indicated that peaks corresponding to phosphonate groups, glucose, amide I, and lipid groups were shifted in the obese group, indicating their potential as biomarkers of obesity. This study suggests that FTIR analysis based on PCA can provide a detailed and reliable method for the analysis of blood serum in obese patients.

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 ELISA 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.

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.

Improving the Effect of Cancer Cells Irradiation with X-rays and High-Energy Protons Using Bimetallic Palladium-Platinum Nanoparticles with Various Nanostructures.

Nano-sized radiosensitizers can be used to increase the effectiveness of radiation-based anticancer therapies. In this study, bimetallic, ~30 nm palladium-platinum nanoparticles (PdPt NPs) with different nanostructures (random nano-alloy NPs and ordered core-shell NPs) were prepared. Scanning transmission electron microscopy (STEM), selected area electron diffraction (SAED), energy-dispersive X-ray spectroscopy (EDS), zeta potential measurements, and nanoparticle tracking analysis (NTA) were used to provide the physicochemical characteristics of PdPt NPs. Then, PdPt NPs were added to the cultures of colon cancer cells and normal colon epithelium cells in individually established non-toxic concentrations and irradiated with the non-harmful dose of X-rays/protons. Cell viability before and after PdPt NPs-(non) assisted X-ray/proton irradiation was evaluated by MTS (3-(4,5-dimethylthiazol-2-yl)-5-(3-carboxymethoxyphenyl)-2-(4-sulfophenyl)-2H-tetrazolium) assay. Flow cytometry was used to assess cell apoptosis. The results showed that PdPt NPs significantly enhanced the effect of irradiation on cancer cells. It was noticed that nano-alloy PdPt NPs possess better radiosensitizing properties compared to PtPd core-shell NPs, and the combined effect against cancer cells was c.a. 10% stronger for X-ray than for proton irradiation. Thus, the radio-enhancing features of differently structured PdPt NPs indicate their potential application for the improvement of the effectiveness of radiation-based anticancer therapies.