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.

Infrared Spectroscopy as a Potential Diagnostic Tool for Medulloblastoma.

INTRODUCTION: Medulloblastoma (MB) is the most common malignant tumor of the central nervous system in childhood. FTIR spectroscopy provides a holistic view of the chemical composition of biological samples, including the detection of molecules such as nucleic acids, proteins, and lipids. This study evaluated the applicability of FTIR spectroscopy as a potential diagnostic tool for MB. MATERIALS AND METHODS: FTIR spectra of MB samples from 40 children (boys/girls: 31/9; age: median 7.8 years, range 1.5-21.5 years) treated in the Oncology Department of the Children's Memorial Health Institute in Warsaw between 2010 and 2019 were analyzed. The control group consisted of normal brain tissue taken from four children diagnosed with causes other than cancer. Formalin-fixed and paraffin-embedded tissues were sectioned and used for FTIR spectroscopic analysis. The sections were examined in the mid-infrared range (800-3500 cm-1) by ATR-FTIR. Spectra were analysed using a combination of principal component analysis, hierarchical cluster analysis, and absorbance dynamics. RESULTS: FTIR spectra in MB were significantly different from those of normal brain tissue. The most significant differences related to the range of nucleic acids and proteins in the region 800-1800 cm-1. Some major differences were also revealed in the quantification of protein conformations (α-helices, ß-sheets, and others) in the amide I band, as well as in the absorbance dynamics in the 1714-1716 cm-1 range (nucleic acids). It was not, however, possible to clearly distinguish between the various histological subtypes of MB using FTIR spectroscopy. CONCLUSIONS: MB and normal brain tissue can be distinguished from one another to some extent using FTIR spectroscopy. As a result, it may be used as a further tool to hasten and enhance histological diagnosis.

Blood serum lipid profiling may improve the management of recurrent miscarriage: a combination of machine learning of mid-infrared spectra and biochemical assays.

The present article is focused on developing and validating an efficient, credible, minimally invasive technique based on spectral signatures of blood samples of women with recurrent miscarriage vs. those of healthy individuals who were followed in the Department of Obstetrics and Gynecology for 2 years. For this purpose, blood samples from a total of 120 participants, including healthy women (n=60) and women with diagnosed recurrent miscarriage (n=60), were obtained. The lipid profile (high-density lipoprotein, low-density lipoprotein, triglyceride, and total cholesterol levels) and lipid peroxidation (malondialdehyde and glutathione levels) were evaluated with a Beckman Coulter analyzer system for chemical analysis. Biomolecular structure and composition were determined using an attenuated total reflectance sampling methodology with Fourier transform infrared spectroscopy alongside machine learning technology to advance toward clinical translation. Here, we developed and validated instrumentation for the analysis of recurrent miscarriage patient serum that was able to differentiate recurrent miscarriage and control patients with an accuracy of 100% using a Fourier transform infrared region corresponding to lipids. We found that predictors of lipid profile abnormalities in maternal serum could significantly improve this patient pathway. The study also presents preliminary results from the first prospective clinical validation study of its kind.

Development of novel spectroscopic and machine learning methods for the measurement of periodic changes in COVID-19 antibody level.

In this research, blood samples of 47 patients infected by COVID were analyzed. The samples were taken on the 1st, 3rd and 6th month after the detection of COVID infection. Total antibody levels were measured against the SARS-CoV-2 N antigen and surrogate virus neutralization by serological methods. To differentiate COVID patients with different antibody levels, Fourier Transform InfraRed (FTIR) and Raman spectroscopy methods were used. The spectroscopy data were analyzed by multivariate analysis, machine learning and neural network methods. It was shown, that analysis of serum using the above-mentioned spectroscopy methods allows to differentiate antibody levels between 1 and 6 months via spectral biomarkers of amides II and I. Moreover, multivariate analysis showed, that using Raman spectroscopy in the range between 1317 cm-1 and 1432 cm-1, 2840 cm-1 and 2956 cm-1 it is possible to distinguish patients after 1, 3, and 6 months from COVID with a sensitivity close to 100%.

A Preliminary Study of FTIR Spectroscopy as a Potential Non-Invasive Screening Tool for Pediatric Precursor B Lymphoblastic Leukemia.

Early detection of the most common pediatric neoplasm, B-cell precursor lymphoblastic leukemia (BCP-ALL), is challenging and requires invasive bone marrow biopsies. The purpose of this study was to establish new biomarkers for early screening to detect pediatric leukemia. In this small cohort study, Fourier transform infrared (FTIR) spectra were obtained from blood sera of 10 patients with BCP-ALL and were compared with the control samples from 10 children with some conditions other than neoplasm. Using various analytical approaches, including a new physical model, some significant differences were observable. The most important include: the different peak area ratio 2965/1645 cm-1 (p = 0.002); the lower average percentage of both ß-sheet and ß-turn protein structures in the sera of BCP-ALL patients (p = 0.03); an AdaBoost-based predictive model for classifying healthy vs. BCP-ALL patients with 85% accuracy; and the phase shift of the first derivative in the spectral range 1050-1042 cm-1 correlating with white blood cell (WBC) and blast cell count in BCP-ALL patients contrary to the samples obtained from healthy controls. Although verification in larger groups of patients will be necessary, these promising results suggest that FTIR spectroscopy may have future potential for the early screening of BCP-ALL.

Simultaneous FTIR and Raman Spectroscopy in Endometrial Atypical Hyperplasia and Cancer.

Currently, endometrial carcinoma (EC) is the most common genital cancer in high-income countries. Some types of endometrial hyperplasia (EH) may be progressing to this malignancy. The diagnosis of EC and EH is based on time consuming histopathology evaluation, which is subjective and causes discrepancies in reassessment. Therefore, there is a need to create methods of objective evaluation allowing the diagnosis of early changes. The study aimed to simultaneously asses Fourier Transform Infrared (FTIR) and Raman spectroscopy combined with multidimensional analysis to identify the tissues of endometrial cancer, atypical hyperplasia and the normal control group, and differentiate them. The results of FTIR and Raman spectroscopy revealed quantitative and qualitative changes in the nucleic acid and protein in the groups of cancer and atypical hyperplasia, in comparison with the control group. Changes in the lipid region were also observed in Raman spectra. Pearson correlation coefficient demonstrated a statistically significant correlation between Raman spectra for the cancer and atypical hyperplasia groups (0.747, p < 0.05) and for atypical hyperplasia and the controls (0.507, p < 0.05), while FTIR spectra demonstrated a statistically significant positive correlation for the same group as in Raman data and for the control and cancer groups (0.966, p < 0.05). To summarize, the method of spectroscopy enables differentiation of atypical hyperplasia and endometrial cancer tissues from the physiological endometrial tissue.

The Spectroscopic Similarity between Breast Cancer Tissues and Lymph Nodes Obtained from Patients with and without Recurrence: A Preliminary Study.

Lymph nodes (LNs) play a very important role in the spread of cancer cells. Moreover, it was noticed that the morphology and chemical composition of the LNs change in the course of cancer development. Therefore, finding and monitoring similarities between these characteristics of the LNs and tumor tissues are essential to improve diagnostics and therapy of this dreadful disease. In the present study, we used Raman and Fourier transform infrared (FTIR) spectroscopies to compare the chemical composition of the breast cancer tissues and LNs collected from women without (I group-4 patients) and with (II group-4 patients) recurrence. It was shown that the similarity of the chemical composition of the breast tissues and LNs is typical for the II group of the patients. The average Raman spectrum of the breast cancer tissues from the I group was not characterized by vibrations in the 800-1000 cm-1 region originating from collagen and carbohydrates, which are typical for tumor-affected breast tissues. At the same time, this spectrum contains peaks at 1029 cm-1, corresponding to PO2- from DNA, RNA and phospholipids, and 1520 cm-1, which have been observed in normal breast tissues before. It was shown that Raman bands of the average LN spectrum of the II group associated with proteins and carbohydrates are more intensive than those of the breast tissues spectrum. The intensity of the Raman spectra collected from the samples of the II group is almost three times higher compared to the I group. The vibrations of carbohydrates and amide III are much more intensive in the II group's case. The Raman spectra of the breast cancer tissues and LNs of the II group's samples do not contain bands (e.g., 1520 cm-1) found in the Raman spectra of the normal breast tissues elsewhere. FTIR spectra of the LNs of the I group's women showed a lower level of vibrations corresponding to functional group building nucleic acid, collagen, carbohydrates, and proteins in comparison with the breast cancer tissues. Pearson's correlation test showed positive and more significant interplay between the nature of the breast tissues and LN spectra obtained for the II group of patients than that in the I group's spectra. Moreover, principal component analysis (PCA) showed that it is possible to distinguish Raman and FTIR spectra of the breast cancer tissues and LNs collected from women without recurrence of the disease.

Body Mass Index (BMI) and Infectious/Febrile Episodes in Children with Intermediate Risk Acute Lymphoblastic Leukemia (IR ALL).

The incidence of treatment related mortality in children with acute lymphoblastic leukemia (ALL) is reported to be between 2% and 4% with infections being the leading cause. AIM: To establish a relationship between body mass index at diagnosis (BMI 0), after protocol I therapy completion (BMI I) and the incidence rate ratio (IRR) of infectious/febrile episodes in children with ALL intermediate risk. METHODS: Thirty one consecutive patients (2-18 years old, with a male to female ratio of 19/12) with newly diagnosed ALL that were treated uniformly according to ALL IC 2009 protocol were included in this analysis. RESULTS: A BMI decrease of at least 5% during protocol I therapy and BMI 1 under 15th percentile score corresponds significantly with higher IRR (with P-values 0.04 and 0.006 respectively) during the whole intensive therapy. CONCLUSION: Some relationships between BMI reduction and higher IRR in ALL patients were found, but their significance is limited by the size of the group analyzed.

Predicting Ewing Sarcoma Treatment Outcome Using Infrared Spectroscopy and Machine Learning.

BACKGROUND: Improved outcome prediction is vital for the delivery of risk-adjusted, appropriate and effective care to paediatric patients with Ewing sarcoma-the second most common paediatric malignant bone tumour. Fourier transform infrared (FTIR) spectroscopy of tissues allows the bulk biochemical content of a biological sample to be probed and makes possible the study and diagnosis of disease. METHODS: In this retrospective study, FTIR spectra of sections of biopsy-obtained bone tissue were recorded. Twenty-seven patients (between 5 and 20 years of age) with newly diagnosed Ewing sarcoma of bone were included in this study. The prognostic value of FTIR spectra obtained from Ewing sarcoma (ES) tumours before and after neoadjuvant chemotherapy were analysed in combination with various data-reduction and machine learning approaches. RESULTS: Random forest and linear discriminant analysis supervised learning models were able to correctly predict patient mortality in 92% of cases using leave-one-out cross-validation. The best performing model for predicting patient relapse was a linear Support Vector Machine trained on the observed spectral changes as a result of chemotherapy treatment, which achieved 92% accuracy. CONCLUSION: FTIR spectra of tumour biopsy samples may predict treatment outcome in paediatric Ewing sarcoma patients with greater than 92% accuracy.

Time-of-flight secondary ion mass spectroscopy with bismuth primary ions of clean and air-exposed surfaces of tellurium.

The regularity of Bi(+), Bi(3+) and Bi(3++) primary ions in the time- of-flight secondary ion mass spectroscopy fragment pattern of air oxidized Te and Bi(+) direct-current scan cleaned Te is discussed. The most intensive fragments for a cleaned Te surface are positive and negative Tex and BiTex clusters. The sequence of secondary ion cluster formation is Bi-Te alloying followed by sputtering and ionization. For oxidized Te the chemical composition of the produced TexOy fragments satisfies the relation y=2x for positive fragments and y=2x+1 for negative ones. Experimental findings are in a good agreement with the results predicted by Plog's model for TeO2.

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.

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.

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.

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.

Equimolar As4S4/Fe3O4 Nanocomposites Fabricated by Dry and Wet Mechanochemistry: Some Insights on the Magnetic-Fluorescent Functionalization of an Old Drug.

Multifunctional nanocomposites from an equimolar As4S4/Fe3O4 cut section have been successfully fabricated from coarse-grained bulky counterparts, employing two-step mechanochemical processing in a high-energy mill operational in dry- and wet-milling modes (in an aqueous solution of Poloxamer 407 acting as a surfactant). As was inferred from the X-ray diffraction analysis, these surfactant-free and surfactant-capped nanocomposites are ß-As4S4-bearing nanocrystalline-amorphous substances supplemented by an iso-compositional amorphous phase (a-AsS), both principal constituents (monoclinic ß-As4S4 and cubic Fe3O4) being core-shell structured and enriched after wet milling by contamination products (such as nanocrystalline-amorphous zirconia), suppressing their nanocrystalline behavior. The fluorescence and magnetic properties of these nanocomposites are intricate, being tuned by the sizes of the nanoparticles and their interfaces, dependent on storage after nanocomposite fabrication. A specific core-shell arrangement consisted of inner and outer shell interfaces around quantum-confined nm-sized ß-As4S4 crystallites hosting a-AsS, and the capping agent is responsible for the blue-cyan fluorescence in as-fabricated Poloxamer capped nanocomposites peaking at ~417 nm and ~442 nm, while fluorescence quenching in one-year-aged nanocomposites is explained in terms of their destroyed core-shell architectures. The magnetic co-functionalization of these nanocomposites is defined by size-extended heterogeneous shells around homogeneous nanocrystalline Fe3O4 cores, composed by an admixture of amorphous phase (a-AsS), nanocrystalline-amorphous zirconia as products of contamination in the wet-milling mode, and surfactant.

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.

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%.

Phase-change materials based on amorphous equichalcogenides.

Phase-change materials, demonstrating a rapid switching between two distinct states with a sharp contrast in electrical, optical or magnetic properties, are vital for modern photonic and electronic devices. To date, this effect is observed in chalcogenide compounds based on Se, Te or both, and most recently in stoichiometric Sb2S3 composition. Yet, to achieve best integrability into modern photonics and electronics, the mixed S/Se/Te phase change medium is needed, which would allow a wide tuning range for such important physical properties as vitreous phase stability, radiation and photo-sensitivity, optical gap, electrical and thermal conductivity, non-linear optical effects, as well as the possibility of structural modification at nanoscale. In this work, a thermally-induced high-to-low resistivity switching below 200 °C is demonstrated in Sb-rich equichalcogenides (containing S, Se and Te in equal proportions). The nanoscale mechanism is associated with interchange between tetrahedral and octahedral coordination of Ge and Sb atoms, substitution of Te in the nearest Ge environment by S or Se, and Sb-Ge/Sb bonds formation upon further annealing. The material can be integrated into chalcogenide-based multifunctional platforms, neuromorphic computational systems, photonic devices and sensors.

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 at 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.

Characterisation of breast cancer molecular signature and treatment assessment with vibrational spectroscopy and chemometric approach.

Triple negative breast cancer (TNBC) is regarded as the most aggressive breast cancer subtype with poor overall survival and lack of targeted therapies, resulting in many patients with recurrent. The insight into the detailed biochemical composition of TNBC would help develop dedicated treatments. Thus, in this study Fourier Transform Infrared microspectroscopy combined with chemometrics and absorbance ratios investigation was employed to compare healthy controls with TNBC tissue before and after chemotherapy within the same patient. The primary spectral differences between control and cancer tissues were found in proteins, polysaccharides, and nucleic acids. Amide I/Amide II ratio decrease before and increase after chemotherapy, whereas DNA, RNA, and glycogen contents increase before and decrease after the treatment. The chemometric results revealed discriminatory features reflecting a clinical response scheme and proved the chemotherapy efficacy assessment with infrared spectroscopy is possible.