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.

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 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.

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.

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.

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.

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.

Apocynin reduces cytotoxic effects of monosodium glutamate in the brain: A spectroscopic, oxidative load, and machine learning study.

Herein, we examined the modulatory effects ofApocynum (APO) on Monosodium Glutamate (MSG)-induced oxidative damage on the brain tissue of rats after long-term consumption of blood serum components by biochemical assays, Fourier transform infrared spectroscopy(FTIR), and machine learning methods. Sprague-Dawley male rats were randomly divided into the Control, Control + APO, MSG, and MSG + APO groups (n = 8 per group). All administrations were made by oral gavage saline, MSG, or APO and they were repeated for 28 days of the experiments. Brain tissue and blood serum samples were collected and analyzed for measurement levels ofmalondialdehyde (MDA),glutathione (GSH),myeloperoxidase (MPO), superoxide dismutase (SOD) activity, and Spectroscopic analysis. After 29 days, the results were evaluated using machine learning (ML). The levels of MDA and MPO showed changes in the MSG and MSG + APO groups, respectively. Changes in the proteins and lipids were observed in the FTIR spectra of the MSG groups. Additionally, APO in these animals improved the FTIR spectra to be similar to those in the Control group. The accuracy of the FTIR results calculated by ML was 100%. The findings of this study demonstrate that Apocynin treatment protectsagainst MSG-induced oxidative damage by inhibitingreactive oxygen speciesand upregulatingantioxidant capacity, indicating its potential in alleviatingthe toxic effects of MSG.

Applying spectrochemical analyses on venous disease patients treated by N-Butyl Cyanoacrylate Ablation Surgery.

BACKGROUND: The venous disease of the legs is a common disease among adults that may lead to a deterioration in the structure and concentration of biomolecules. N-Butyl Cyanoacrylate Ablation Surgery (NBCA) or cyanoacrylate embolization (CAE) technique to adhesive the saphenous vein is an alternative method for the treatment of venous disease. OBJECTIVE: We aimed to show what kind of changes occurs after CAE surgery using FTIR spectroscopy combined chemometrics. We compared before and after surgery blood sera of patients to find whether a correlation between spectral data and laboratory indexes. We studied the blood sera of those who suffered from varicose veins and treated them by CAE technique. METHODS: In order to examine the molecular profiles in blood sera who underwent the CAE technique of the great saphenous vein for the treatment we used Fourier Transform InfraRed spectroscopy (FTIR) spectroscopy of blood samples of patients before and after surgery as a fast diagnostic technique. To obtain information about the spectra variation among the types of samples Principal component analysis (PCA) was performed for fingerprint, amide II with amide I regions. To find normality among variations Partial Least Square P-P plot of residual was performed. RESULTS: Absorbance values were statistically significant only in amide II, amide I, and OH vibrations. In the blood collected before surgery, higher peaks area of α-helix and ß-harmonica were noticed. However, in both groups of samples, a higher amount of ß-harmonica was visible. Pearson correlation analysis showed that the value of white blood cells (WBC) correlate with absorbance at 2858 cm-1 wavenumber. Moreover, a correlation between neutrophil (NEU) and OH vibrations, and between hematocrit (HCT) and 1082 cm-1, were found. Furthermore, a high correlation Platelets (PLT) and FTIR peak at 1165 cm-1, was noticed. CONCLUSIONS: This methodology suggests with PCA analysis CAE caused structural and quantitative chemical changes in blood samples of patients.

N-Acetyl-Cysteine Increases Activity of Peanut-Shaped Gold Nanoparticles Against Biofilms Formed by Clinical Strains of Pseudomonas aeruginosa Isolated from Sputum of Cystic Fibrosis Patients.

Background: Extracellular polymeric substances (EPS) produced by bacteria, as they form a biofilm, determine the stability and viscoelastic properties of biofilms and prevent antibiotics from penetrating this multicellular structure. To date, studies demonstrated that an appropriate optimization of the chemistry and morphology of nanotherapeutics might provide a favorable approach to control their interaction with EPS and/or diffusion within the biofilm matrix. Targeting the biofilms' EPS, which in certain conditions can adopt liquid crystal structure, was demonstrated to improve the anti-biofilm activity of antibiotics and nanoparticles. A similar effect is achievable by interfering EPS' production by mucoactive agents, such as N-acetyl-cysteine (NAC). In our previous study, we demonstrated the nanogram efficiency of non-spherical gold nanoparticles, which due to their physicochemical features, particularly morphology, were noted to be superior in antimicrobial activity compared to their spherical-shaped counterparts. Methods: To explore the importance of EPS matrix modulation in achieving a suitable efficiency of peanut-shaped gold nanoparticles (AuP NPs) against biofilms produced by Pseudomonas aeruginosa strains isolated from cystic fibrosis patients, fluorescence microscopy, as well as resazurin staining were employed. Rheological parameters of AuP NPs-treated biofilms were investigated by rotational and creep-recovery tests using a rheometer in a plate-plate arrangement. Results: We demonstrated that tested nanoparticles significantly inhibit the growth of mono- and mixed-species biofilms, particularly when combined with NAC. Notably, gold nanopeanuts were shown to decrease the viscosity and increase the creep compliance of Pseudomonas biofilm, similarly to EPS-targeting NAC. Synergistic activity of AuP NPs with tobramycin was also observed, and the AuP NPs were able to eradicate bacteria within biofilms formed by tobramycin-resistant isolates. Conclusion: We propose that peanut-shaped gold nanoparticles should be considered as a potent therapeutic agent against Pseudomonas biofilms.

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

The formation of the uterus lining, i.e. the endometrium, outside the uterus (ex. in the abdominal cavity,ovaries,or anywhere in the body) is called endometriosis. The presence of endometrial tissue present in the ovaries, thickens after menstruation, leading to menstrual-like bleeding and to the formation of chocolate cyst (Endometrioma) because of the accumulation of old, brown blood in the ovary. It is still unknown, what triggers the development ofendometrioma. However,it leads to excessive bleeding during menstrual periods or abnormal bleeding between periods and infertility. Endometriosis is often first diagnosed in those who seek medical attention for infertility. Therefore, new markers of endometrioma as well as new methods of its diagnosis are sought. In this study we used Raman spectra of serum collected from 50 healthy women and 50 women suffering from endometriosis. The obtained Raman data were used in multivariateanalysis to determine the Raman range, which can be used for endometriomadiagnostics. Partial Least Square (PLS), Principal Component Analysis (PCA) and Hierarchical Component Analysis (HCA) showed, that it is possible to distinguish between the serum collected from healthy and un-healthy women using the Raman range between 800 cm-1 and 1800 cm-1 and between 2956 cm-1 and 2840 cm-1, while the first range corresponds to the fingerprint region and the second one to lipids vibrations. Consequently, the Pearson correlation test showeda significantpositive correlation betweenvaluesoflipidintensity in Raman spectra and volume of endometriomas. Summarizing, Raman spectroscopy can be a helpful tool in endometrioma diagnosis and the lipid vibrations are candidates for being a spectroscopic marker of the disease being studied.

Identification of polycystic ovary syndrome from blood serum using hormone levels via Raman spectroscopy and multivariate analysis.

Polycystic ovarian syndrome (PCOS) is a disease, which causes infertility in women. The factors for the development of the disease are still not well understood and diagnostic methods need to be improved. Therefore, in this study, Raman spectroscopy as a potential diagnostic tool, was investigated and spectra of blood serum were collected from PCOS and healthy women. The obtained spectra showed distinct changes in intensities as well as shift of peaks for the blood serum collected from PCOS compared to healthy individuals. Partial Last Square (PLS) analysis and Principal Component Analysis (PCA) allowed to determine that Raman shifts of amides (1500 - 1700 cm-1) and CH2, CH3 lipid groups (2700 - 3000 cm-1), could be thus used as potential PCOS markers. Furthermore, the Pearson correlation test showed a strong correlation between hormones (lutropin (LH), prolactin (PRL), follicle-stimulating (FSH), dehydroepiandrosterone (DHEAS), thyroid-stimulating (TSH), Estradiol) and lipids, as well as between hormones and protein functional groups in PCOS women, compared to the control. These results show, that the lipid and protein balance could be potentially applied as a helpful PCOS marker in Raman spectra.

Differential of cholangiocarcinoma disease using Raman spectroscopy combined with multivariate analysis.

Cholangiocarcinoma (CCA) is a type of cancer, which 5-year survival is lower than 20 %, and which is detected mostly in advanced stage of the disease. Unfortunately, there are no diagnostic tools, which could show changes in the body indicating the development of the disease. Therefore, in this study, we investigate Raman spectroscopy as a promising analytical tool in medical diagnostics and as a method, which would allow to distinguish between healthy patients and patients suffering from cholangiocarcinoma. The obtained Raman spectra showed, that lower intensities of peaks corresponding to amino acids and proteins, as well as higher intensities of peaks originating from lipids vibrations were observed in healthy individuals in comparison with cancer patients. Moreover, Partial Last Square (PLS), Principal Component Analysis (PCA) and Hierarchical Component Analysis (HCA) of Raman spectra indicate that the ranges between 800 cm-1 and 1800 cm-1, 3477 cm-1 -3322 cm-1 and 1394 cm-1 -1297 cm-1 allow to distinguish cancer patients from healthy ones. The obtained results showed, that Raman spectroscopy is a good candidate, to become in future one of the diagnostic tools of Cholangiocarcinoma.

Peanut-Shaped Gold Nanoparticles with Shells of Ceragenin CSA-131 Display the Ability to Inhibit Ovarian Cancer Growth In Vitro and in a Tumor Xenograft Model.

Gold nanoparticles-assisted delivery of antineoplastics into cancerous cells is presented as an effective approach for overcoming the limitations of systemic chemotherapy. Although ceragenins show great potential as anti-cancer agents, in some tumors, effective inhibition of cancer cells proliferation requires application of ceragenins at doses within their hemolytic range. For the purpose of toxicity/efficiency ratio control, peanut-shaped gold nanoparticles (AuP NPs) were functionalized with a shell of ceragenin CSA-131 and the cytotoxicity of AuP@CSA-131 against ovarian cancer SKOV-3 cells and were then analyzed. In vivo efficiency of intravenously and intratumorally administered CSA-131 and AuP@CSA-131 was examined using a xenograft ovarian cancer model. Serum parameters were estimated using ELISA methods. Comparative analysis revealed that AuP@CSA-131 exerted stronger anti-cancer effects than free ceragenin, which was determined by enhanced ability to induce caspase-dependent apoptosis and autophagy processes via reactive oxygen species (ROS)-mediated pathways. In an animal study, AuP@CSA-131 was characterized by delayed clearance and prolonged blood circulation when compared with free ceragenin, as well as enhanced anti-tumor efficiency, particularly when applied intratumorally. Administration of CSA-131 and AuP@CSA-131 prevented the inflammatory response associated with cancer development. These results present the possibility of employing non-spherical gold nanoparticles as an effective nanoplatform for the delivery of antineoplastics for the treatment of ovarian malignancy.