Chasing Graphene-Based Anticancer Drugs: Where are We Now on the Biomedical Graphene Roadmap?

Graphene and graphene-based materials have attracted growing interest for potential applications in medicine because of their good biocompatibility, cargo capability and possible surface functionalizations. In parallel, prototypic graphene-based devices have been developed to diagnose, imaging and track tumor growth in cancer patients. There is a growing number of reports on the use of graphene and its functionalized derivatives in the design of innovative drugs delivery systems, photothermal and photodynamic cancer therapy, and as a platform to combine multiple therapies. The aim of this review is to introduce the latest scientific achievements in the field of innovative composite graphene materials as potentially applied in cancer therapy. The "Technology and Innovation Roadmap" published in the Graphene Flagship indicates, that the first anti-cancer drugs using graphene and graphene-derived materials will have appeared on the market by 2030. However, it is necessary to broaden understanding of graphene-based material interactions with cellular metabolism and signaling at the functional level, as well as toxicity. The main aspects of further research should elucidate how treatment methods (e.g., photothermal therapy, photodynamic therapy, combination therapy) and the physicochemical properties of graphene materials influence their ability to modulate autophagy and kill cancer cells. Interestingly, recent scientific reports also prove that graphene nanocomposites modulate cancer cell death by inducing precise autophagy dysfunctions caused by lysosome damage. It turns out as well that developing photothermal oncological treatments, it should be taken into account that near-infrared-II radiation (1000-1500 nm) is a better option than NIR-I (750-1000 nm) because it can penetrate deeper into tissues due to less scattering at longer wavelengths radiation.

Microscopic and Biopharmaceutical Evaluation of Emulsion and Self-Emulsifying Oil with Cyclosporine.

Among the currently available commercial eye drops with cyclosporine A (Cs) there is a lack of long-acting dosage forms and products with a concentration of the drug substance higher than 0.1%, although Cs is widely used in ophthalmology. The aim of the research was to conduct the microscopic and biopharmaceutical evaluation of two formulations, an emulsion (EM) and a self-emulsifying oil (SEO), both with 0.5% of Cs, proposed for use in eye drops, and the comparison of both. SEO eye drops with Cs or any other drug substance are currently not available as marketed products, and the highest concentration of Cs in the ocular emulsion is only 0.1%. The microscopic evaluation of the emulsion and the SEO after emulsification with water was carried out using a high-resolution digital microscopy. The properties of both preparations were compared using the high dynamic range function or optical shadow effect mode. Images in the 3D composition mode were also recorded. The in vivo study of the Cs formulations was performed on male albino rabbits. The eye tolerance of the preparations was assessed using the ocular irritation test, which is a modified Draize test. Placebo carriers (without the drug substance) were also subjected to irritation testing. The concentration of Cs in the tissues (cornea and conjunctiva) and fluids (tear fluid and aqueous humor) of the rabbit eye was determined after multiple instillations of Cs-EM or Cs-SEO. The tested preparations were compared using the digital microscopy technique, which highlights the features of the formulations and eliminates the risk of unnoticeable properties that are difficult to observe in classical optical microscopy. Both tested Cs-loaded formulations are classified as practically non-irritating. There were also no significant differences when testing the placebo carriers. After a topical administration, Cs was widely distributed in all tissues (e.g., in cornea 1.3 ng/mg and 1.0 ng/mg) and fluids of the eye (e.g., in tear fluid 11.6 µg/mL and 4.3 µg/mL), after the administration of Cs-SEO and Cs-EM, respectively. The obtained results allow us to recognize both tested formulations, the emulsion and the self-emulsifying oil with 0.5% Cs content, as carriers safe for ophthalmic use and effective in delivering the drug substance to the structures of the eye.

Plasma untargeted metabolomics with proteinase K discloses phospholipid signature associated with pulmonary arterial hypertension.

Pulmonary arterial hypertension is a rare but life-threatening and clinically heterogeneous disease. The diagnostic schedule of this disorder is complex, and no specific indicator of the arterial etiology has been explored. In this study, untargeted plasma metabolomics was applied to evaluate the metabolic fingerprints of pulmonary arterial hypertension patients. Plasma samples were prepared using a new approach, which applies proteinase K during the sample preparation procedure to increase the metabolite coverage. The metabolic fingerprints were determined via LC-MS and subsequently analyzed with the use of both uni- and multivariate statistics. A total of 21 metabolites were discovered to be significantly altered in pulmonary arterial hypertensive patients. The metabolites were mainly related to the phospholipid metabolic pathways. In this study, decreases were found in the phosphatidylcholines (PCs) [PC(32:0), PC(40:7), PC(42:7)], phosphatidylethanolamine PE(18:0/18:2), lysophosphatidylethanolamines (LPEs) [LPE(22:6), LPE(18:2), LPE(18:0), LPE(20:4), LPE(20:1), LPE(20:0)], lysophosphatidylcholine LPC(20:4) and lysophosphatidylserine LPS(19:0), as well as increase of sphingomyelin SM(36:2), in the plasma samples of pulmonary arterial hypertensive patients in comparison to the control group. Besides their function as components of the biological membranes, these metabolites are also involved in the intracellular signaling pathways that are related to cell proliferation and apoptosis. The results obtained during this study confirm the potential of (untargeted) metabolomics to identify the molecular characteristics of the pathophysiology of pulmonary arterial hypertension. The clinical relevance of this study constitutes the selection of a metabolic panel that can potentially detect and properly diagnose the disease.

Searching for the primary metabolic alterations of polycystic ovary syndrome by application of the untargeted metabolomics approach.

Despite a large number of studies, the pathogenesis of polycystic ovary syndrome (PCOS) still remains unexplained. In light of ambiguous observations reported in metabolomics, there is a need to carry out studies focusing on confirming the discriminating power of the proposed metabolomics biomarkers. Our research aimed to perform a validation study of metabolites detected in our previous study from serum samples, on the new set of samples obtained from PCOS women and healthy controls to confirm previously selected compounds. Additionally, the second biological matrix - urine - was used to get a more comprehensive insight into metabolic alterations. We applied two analytical techniques - gas chromatography and liquid chromatography coupled with mass spectrometry to analyze both serum and urine samples obtained from 35 PCOS patients and 35 healthy women. Thank to our approach, we identified and described a comprehensive set of metabolites altered in PCOS patients. Results of our study indicate increased steroid hormone synthesis, alteration in sphingo- and phospholipids metabolism, and disturbed fatty acids metabolism. Moreover, the citric acid cycle, γ-glutamyl cycle, vitamin B metabolism, and a few primary amino acids like tryptophan, phenylalanine, histidine, and alanine are altered.

A multiplatform metabolomics approach for comprehensive analysis of GIST xenografts with various KIT mutations.

Metabolites in biological matrices belong to diverse chemical groups, ranging from non-polar long-chain fatty acids to small polar molecules. The goal of untargeted metabolomic analysis is to measure the highest number of metabolites in the sample. Nevertheless, from an analytical point of view, no single technique can measure such a broad spectrum of analytes. Therefore, we selected a method based on GC-MS and LC-MS with two types of stationary phases for the untargeted profiling of gastrointestinal stromal tumours. The procedure was applied to GIST xenograft samples (n = 71) representing four different mutation models, half of which were treated with imatinib. We aimed to verify the method coverage and advantages of applying each technique. RP-LC-MS measured most metabolites due to a significant fraction of lipid components of the tumour tissue. What is unique and worth noting is that all applied techniques were able to distinguish between different mutation models. However, for detecting imatinib-induced alterations in the GIST metabolome, RP-LC-MS and GC-MS proved to be more relevant than HILIC-LC-MS, resulting in a higher number of significantly changed metabolites in four treated models. Undoubtedly, the inclusion of all mentioned techniques makes the method more comprehensive. Nonetheless, for green chemistry and time and labour saving, we assume that RP-LC-MS and GC-MS analyses are sufficient to cover the global GIST metabolome.

Targeted quantitative metabolomics with a linear mixed-effect model for analysis of urinary nucleosides and deoxynucleosides from bladder cancer patients before and after tumor resection.

In the present study, we developed and validated a fast, simple, and sensitive quantitative method for the simultaneous determination of eleven nucleosides and deoxynucleosides from urine samples. The analyses were performed with the use of liquid chromatography coupled with triple quadrupole mass spectrometry. The sample pretreatment procedure was limited to centrifugation, vortex mixing of urine samples with a methanol/water solution (1:1, v/v), evaporation and dissolution steps. The analysis lasted 20 min and was performed in dynamic multiple reaction monitoring mode (dMRM) in positive polarity. Process validation was conducted to determine the linearity, precision, accuracy, limit of quantification, stability, recovery and matrix effect. All validation procedures were carried out in accordance with current FDA and EMA regulations. The validated method was applied for the analysis of 133 urine samples derived from bladder cancer patients before tumor resection and 24 h, 2 weeks, and 3, 6, 9, and 12 months after the surgery. The obtained data sets were analyzed using a linear mixed-effect model. The analysis revealed that concentration level of 2-methylthioadenosine was decreased, while for inosine, it was increased 24 h after tumor resection in comparison to the preoperative state. The presented quantitative longitudinal study of urine nucleosides and deoxynucleosides before and up to 12 months after bladder tumor resection brings additional prospective insight into the metabolite excretion pattern in bladder cancer disease. Moreover, incurred sample reanalysis was performed proving the robustness and repeatability of the developed targeted method.

Optical method supported by machine learning for urinary tract infection detection and urosepsis risk assessment.

The study presents an optical method supported by machine learning for discriminating urinary tract infections from an infection capable of causing urosepsis. The method comprises spectra of spectroscopy measurement of artificial urine samples with bacteria from solid cultures of clinical E. coli strains. To provide a reliable classification of results assistance of 27 algorithms was tested. We proved that is possible to obtain up to 97% accuracy of the measurement method with the use of use of machine learning. The method was validated on urine samples from 241 patients. The advantages of the proposed solution are the simplicity of the sensor, mobility, versatility, and low cost of the test.

Metabolomic and transcriptomic response to imatinib treatment of gastrointestinal stromal tumour in xenograft-bearing mice.

BACKGROUND: Although imatinib is a well-established first-line drug for treating a vast majority of gastrointestinal stromal tumours (GIST), GISTs acquire secondary resistance during therapy. Multi-omics approaches provide an integrated perspective to empower the development of personalised therapies through a better understanding of functional biology underlying the disease and molecular-driven selection of the best-targeted individualised therapy. In this study, we applied integrative metabolomic and transcriptomic analyses to elucidate tumour biochemical processes affected by imatinib treatment. MATERIALS AND METHODS: A GIST xenograft mouse model was used in the study, including 10 mice treated with imatinib and 10 non-treated controls. Metabolites in tumour extracts were analysed using gas chromatography coupled with mass spectrometry (GC-MS). RNA sequencing was also performed on the samples subset (n=6). RESULTS: Metabolomic analysis revealed 21 differentiating metabolites, whereas next-generation RNA sequencing data analysis resulted in 531 differentially expressed genes. Imatinib significantly changed the profile of metabolites associated mainly with purine and pyrimidine metabolism, butanoate metabolism, as well as alanine, aspartate, and glutamate metabolism. The related changes in transcriptomic profiles included genes involved in kinase activity and immune responses, as well as supported its impact on the purine biosynthesis pathway. CONCLUSIONS: Our multi-omics study confirmed previously known pathways involved in imatinib anticancer activity as well as correlated imatinib-relevant downregulation of expression of purine biosynthesis pathway genes with the reduction of respectful metabolites. Furthermore, considering the importance of the purine biosynthesis pathway for cancer proliferation, we identified a potentially novel mechanism for the anti-tumour activity of imatinib. Based on the results, we hypothesise metabolic modulations aiming at the reduction in purine and pyrimidine pool may ensure higher imatinib efficacy or re-sensitise imatinib-resistant tumours.

Molecular signature of renal cell carcinoma by means of a multiplatform metabolomics analysis.

Renal cell carcinoma (RCC) is a disease with no specific diagnostic method or treatment. Thus, the evaluation of novel diagnostic tools or treatment possibilities is essential. In this study, a multiplatform untargeted metabolomics analysis of urine was applied to search for a metabolic pattern specific for RCC, which could enable comprehensive assessment of its biochemical background. Thirty patients with diagnosed RCC and 29 healthy volunteers were involved in the first stage of the study. Initially, the utility of the application of the selected approach was checked for RCC with no differentiation for cancer subtypes. In the second stage, this approach was used to study clear cell renal cell carcinoma (ccRCC) in 38 ccRCC patients and 38 healthy volunteers. Three complementary analytical platforms were used: reversed-phase liquid chromatography coupled with time-of-flight mass spectrometry (RP-HPLC-TOF/MS), capillary electrophoresis coupled with time-of-flight mass spectrometry (CE-TOF/MS), and gas chromatography triple quadrupole mass spectrometry (GC-QqQ/MS). As a result of urine sample analyses, two panels of metabolites specific for RCC and ccRCC were selected. Disruptions in amino acid, lipid, purine, and pyrimidine metabolism, the TCA cycle and energetic processes were observed. The most interesting differences were observed for modified nucleosides. This is the first time that the levels of these compounds were found to be changed in RCC and ccRCC patients, providing a framework for further studies. Moreover, the application of the CE-MS technique enabled the determination of statistically significant changes in symmetric dimethylarginine (SDMA) in RCC.

Metabolically Doping of 3D Diatomaceous Biosilica with Titanium.

Diatoms represent, in terms of species number, one of the largest groups of microalgae that have the ability to synthesize phenomenal mineral composites characterized by complex hierarchical structures. Their shells, called frustules, create intricately ornamented structures, reminiscent of the most sophisticated, natural mosaics. Ordinated pore systems perforate siliceous walls of the frustules with diameters ranging from nano to micro-scale, forming openwork three-dimensional silica structures. The use of these features is one of the main challenges in developing new technological solutions. In this study we assess the ability of selected diatom species (Pseudostaurosira trainorii) for metabolic insertion of soluble titanium from the culture medium into the structure of amorphous silica cell walls by its cultivation in laboratory conditions. The study is aimed at obtaining new and strengthening the already existing optical properties of diatomaceous biosilica. The physicochemical properties of the obtained materials have been studied using a series of instrumental methods.

Toward the General Mechanistic Model of Liquid Chromatographic Retention.

Large datasets of chromatographic retention times are relatively easy to collect. This statement is particularly true when mixtures of compounds are analyzed under a series of gradient conditions using chromatographic techniques coupled with mass spectrometry detection. Such datasets carry much information about chromatographic retention that, if extracted, can provide useful predictive information. In this work, we proposed a mechanistic model that jointly explains the relationship between pH, organic modifier type, temperature, gradient duration, and analyte retention based on liquid chromatography retention data collected for 187 small molecules. The model was built utilizing a Bayesian multilevel framework. The model assumes (i) a deterministic Neue equation that describes the relationship between retention time and analyte-specific and instrument-specific parameters, (ii) the relationship between analyte-specific descriptors (log P, pKa, and functional groups) and analyte-specific chromatographic parameters, and (iii) stochastic components of between-analyte and residual variability. The model utilizes prior knowledge about model parameters to regularize predictions which is important as there is ample information about the retention behavior of analytes in various stationary phases in the literature. The usefulness of the proposed model in providing interpretable summaries of complex data and in decision making is discussed.

Untargeted Metabolomics Study of Three Matrices: Seminal Fluid, Urine, and Serum to Search the Potential Indicators of Prostate Cancer.

The simultaneous determination of metabolites from biological fluids may provide more accurate information about the current body condition. So far, the metabolomics approach has been successfully applied to study the mechanism of several disorders and to search for novel biomarkers. Urine and plasma are widely accepted matrices for the evaluation of several pathologies, while prostate cancer (CaP) development is still unknown. For this reason, an alternative matrix, the seminal fluid, was proposed to expand the knowledge about the CaP pathomechanism. The main aim of this study was to develop and optimize the sample preparation protocol to ensure the highest coverage of the metabolome of ejaculate samples. Parameters like the type and composition of the solvent mixture, time of extraction, and applied volume of the solvent were tested. The optimized method was applied for the untargeted metabolomics profiling of seminal fluid samples obtained from CaP patients. Moreover, urine and serum samples were also prepared for untargeted metabolomics analysis. Analyses were carried out with the use of two complementary analytical techniques: GC-EI-QqQ/MS and LC-ESI-TOF/MS. Finally, the metabolic signature of seminal fluid (n = 7), urine (n = 7), and plasma (n = 7) samples was compared. Furthermore, the hypothesis of the increased level of metabolites in ejaculate samples related to the CaP development was evaluated. The results indicated that the developed and optimized sample preparation protocol for seminal fluid may be successfully applied for metabolomics study. Untargeted analysis of ejaculate enabled to determine the following classes of compounds: fatty acids, sphingolipids, phospholipids, sugars, and their derivatives, as well as amino acids. Finally, a comparison of the three tested matrices was carried out. To our best knowledge, it is the first time when the metabolic profile of the three matrices, namely, urine, plasma, and seminal fluid, was compared. Based on the results, it can be pointed out that ejaculate comprises the metabolic signature of both matrices (polar compounds characteristic for urine, and non-polar ones present in plasma samples). Compared to plasma, semen samples revealed to have a similar profile; however, determined levels of metabolites were lower in case of ejaculate. In case of urine samples, compared to semen metabolic profiles, the levels of detected metabolites were decreased in the latter ones.

Pre- and Post-Resection Urine Metabolic Profiles of Bladder Cancer Patients: Results of Preliminary Studies on Time Series Metabolomics Analysis.

The incidence of bladder cancer (BCa) has remained high for many years. Nevertheless, its pathomechanism has not yet been fully understood and is still being studied. Therefore, multiplatform untargeted urinary metabolomics analysis has been performed in order to study differences in the metabolic profiles of urine samples collected at three time points: before transurethral resection of bladder tumor (TURBT), the day after the procedure and two weeks after TURBT. Collected samples were analyzed with the use of high-performance liquid chromatography hyphenated with time-of-flight mass spectrometry detection (HPLC-TOF/MS) and gas chromatography coupled with triple quadrupole mass spectrometry detection (GC-QqQ/MS, in a scan mode). Levels of metabolites selected in our previous study were assessed in order to confirm their potential to differentiate the healthy and diseased samples, regardless of the risk factors and individual characteristics. Hippuric acid, pentanedioic acid and uridine confirmed their potential for sample differentiation. Based on the results of statistical analysis for the paired samples (comparison of metabolic profiles of samples collected before TURBT and two weeks after), a set of metabolites belonging to nucleotide metabolism and methylation processes was also selected. Longitudinal studies proved to be useful for the evaluation of metabolic changes in bladder cancer.

Targeted and untargeted metabolomic approach for GDM diagnosis.

Gestational diabetes mellitus (GDM) is a disorder which manifests itself for the first time during pregnancy and is mainly connected with glucose metabolism. It is also known that fatty acid profile changes in erythrocyte membranes and plasma could be associated with obesity and insulin resistance. These factors can lead to the development of diabetes. In the reported study, we applied the untargeted analysis of plasma in GDM against standard glucose-tolerant (NGT) women to identify the differences in metabolomic profiles between those groups. We found higher levels of 2-hydroxybutyric and 3-hydroxybutyric acids. Both secondary metabolites are associated with impaired glucose metabolism. However, they are products of different metabolic pathways. Additionally, we applied lipidomic profiling using gas chromatography to examine the fatty acid composition of cholesteryl esters in the plasma of GDM patients. Among the 14 measured fatty acids characterizing the representative plasma lipidomic cluster, myristic, oleic, arachidonic, and α-linoleic acids revealed statistically significant changes. Concentrations of both myristic acid, one of the saturated fatty acids (SFAs), and oleic acid, which belong to monounsaturated fatty acids (MUFAs), tend to decrease in GDM patients. In the case of polyunsaturated fatty acids (PUFAs), some of them tend to increase (e.g., arachidonic), and some of them tend to decrease (e.g., α-linolenic). Based on our results, we postulate the importance of hydroxybutyric acid derivatives, cholesteryl ester composition, and the oleic acid diminution in the pathophysiology of GDM. There are some evidence suggests that the oleic acid can have the protective role in diabetes onset. However, metabolic alterations that lead to the onset of GDM are complex; therefore, further studies are needed to confirm our observations.

Comprehensive Metabolic Signature of Renal Dysplasia in Children. A Multiplatform Metabolomics Concept.

Renal dysplasia is a severe congenital abnormality of the kidney parenchyma, which is an important cause of end-stage renal failure in childhood and early adulthood. The diagnosis of renal dysplasia relies on prenatal or postnatal ultrasounds as children show no specific clinical symptoms before chronic kidney disease develops. Prompt diagnosis is important in terms of early introduction of nephroprotection therapy and improved long-term prognosis. Metabolomics was applied to study children with renal dysplasia to provide insight into the changes in biochemical pathways underlying its pathology and in search of early indicators for facilitated diagnosis. The studied cohort consisted of 72 children, 39 with dysplastic kidneys and 33 healthy controls. All subjects underwent comprehensive urine metabolic profiling with the use of gas chromatography and liquid chromatography coupled to mass spectrometry, with two complementary separation modes of the latter. Univariate and multivariate statistical calculations identified a total of nineteen metabolites, differentiating the compared cohorts, independent of their estimated glomerular filtration rate. Seven acylcarnitines, xanthine, and glutamine were downregulated in the urine of renal dysplasia patients. Conversely, renal dysplasia was associated with higher urinary levels of dimethylguanosine, threonic acid or glyceric acid. This is the first metabolomic study of subjects with renal dysplasia. The authors define a characteristic urine metabolic signature in children with dysplastic kidneys, irrespective of renal function, linking the condition with altered fatty acid oxidation, amino acid and purine metabolisms.

A Robust Method for Sample Preparation of Gastrointestinal Stromal Tumour for LC/MS Untargeted Metabolomics.

Gastrointestinal stromal tumour has already been well explored at the genome level; however, little is known about metabolic processes occurring in the sarcoma. Sample preparation is a crucial step in untargeted metabolomics workflow, highly affecting the metabolome coverage and the quality of the results. In this study, four liquid-liquid extraction methods for the isolation of endogenous compounds from gastrointestinal stromal tumours were compared and evaluated. The protocols covered two-step or stepwise extraction with methyl-tert-butyl ether (MTBE) or dichloromethane. The extracts were subjected to LC-MS analysis by the application of reversed-phase and hydrophilic interaction liquid chromatography to enable the separation and detection of both polar and nonpolar analytes. The extraction methods were compared in terms of efficiency (total number of detected metabolites) and reproducibility. The method was based on the stepwise extraction with MTBE, methanol, and water proved to be the most reproducible, and thus, its robustness to fluctuations in experimental conditions was assessed employing Plackett-Burman design and hierarchical modelling. While most studied factors had no effect on the metabolite abundance, the highest coefficient value was observed for the volume of MTBE added during extraction. Herein, we demonstrate the application and the feasibility of the selected protocol for the analysis of gastrointestinal stromal tumour samples. The method selected could be considered as a reference for the best characterization of underlying molecular changes associated with complex tissue extracts of GIST.

Effects of perineural administration of ropivacaine combined with perineural or intravenous administration of dexmedetomidine for sciatic and saphenous nerve blocks in dogs.

OBJECTIVE: To evaluate the effects of using ropivacaine combined with dexmedetomidine for sciatic and saphenous nerve blocks in dogs. ANIMALS: 7 healthy adult Beagles. PROCEDURES: In phase 1, dogs received each of the following 3 treatments in random order: perineural sciatic and saphenous nerve injections of 0.5% ropivacaine (0.4 mL/kg) mixed with saline (0.9% NaCl) solution (0.04 mL/kg; DEX0PN), 0.5% ropivacaine mixed with dexmedetomidine (1 µg/kg; DEX1PN), and 0.5% ropivacaine mixed with dexmedetomidine (2 µg/kg; DEX2PN). In phase 2, dogs received perineural sciatic and saphenous nerve injections of 0.5% ropivacaine and an IV injection of diluted dexmedetomidine (1 µg/kg; DEX1IV). For perineural injections, the dose was divided equally between the 2 sites. Duration of sensory blockade was evaluated, and plasma dexmedetomidine concentrations were measured. RESULTS: Duration of sensory blockade was significantly longer with DEX1PN and DEX2PN, compared with DEX0PN; DEX1IV did not prolong duration of sensory blockade, compared with DEX0PN. Peak plasma dexmedetomidine concentrations were reached after 15 minutes with DEX1PN (mean ± SD, 348 ± 200 pg/mL) and after 30 minutes DEX2PN (816 ± 607 pg/mL), and bioavailability was 54 ± 40% and 73 ± 43%, respectively. The highest plasma dexmedetomidine concentration was measured with DEX1IV (1,032 ± 415 pg/mL) 5 minutes after injection. CONCLUSIONS AND CLINICAL RELEVANCE: Results suggested that perineural injection of 0.5% ropivacaine in combination with dexmedetomidine (1 µg/kg) for locoregional anesthesia in dogs seemed to balance the benefit of prolonging sensory nerve blockade while minimizing adverse effects.

Lipidomics as a Diagnostic Tool for Prostate Cancer.

The main goal of this study was to explore the phospholipid alterations associated with the development of prostate cancer (PCa) using two imaging methods: matrix-assisted laser desorption ionization with time-of-flight mass spectrometer (MALDI-TOF/MS), and electrospray ionization with triple quadrupole mass spectrometer (ESI-QqQ/MS). For this purpose, samples of PCa tissue (n = 40) were evaluated in comparison to the controls (n = 40). As a result, few classes of compounds, namely phosphatidylcholines (PCs), lysophosphatidylcholines (LPCs), sphingomyelins (SMs), and phosphatidylethanolamines (PEs), were determined. The obtained results were evaluated by univariate (Mann-Whitney U-test) and multivariate statistical analysis (principal component analysis, correlation analysis, volcano plot, artificial neural network, and random forest algorithm), in order to select the most discriminative features and to search for the relationships between the responses of these groups of substances, also in terms of the used analytical technique. Based on previous literature and our results, it can be assumed that PCa is linked with both the synthesis of fatty acids and lipid oxidation. Among the compounds, phospholipids, namely PC 16:0/16:1, PC 16:0/18:2, PC 18:0/22:5, PC 18:1/18:2, PC 18:1/20:0, PC 18:1/20:4, and SM d18:1/24:0, were assigned as metabolites with the best discriminative power for the tested groups. Based on the results, lipidomics can be found as alternative diagnostic tool for CaP diagnosis.

Metabolic Evaluation of Urine from Patients Diagnosed with High Grade (HG) Bladder Cancer by SPME-LC-MS Method.

Bladder cancer (BC) is a common malignancy of the urinary system and a leading cause of death worldwide. In this work, untargeted metabolomic profiling of biological fluids is presented as a non-invasive tool for bladder cancer biomarker discovery as a first step towards developing superior methods for detection, treatment, and prevention well as to further our current understanding of this disease. In this study, urine samples from 24 healthy volunteers and 24 BC patients were subjected to metabolomic profiling using high throughput solid-phase microextraction (SPME) in thin-film format and reversed-phase high-performance liquid chromatography coupled with a Q Exactive Focus Orbitrap mass spectrometer. The chemometric analysis enabled the selection of metabolites contributing to the observed separation of BC patients from the control group. Relevant differences were demonstrated for phenylalanine metabolism compounds, i.e., benzoic acid, hippuric acid, and 4-hydroxycinnamic acid. Furthermore, compounds involved in the metabolism of histidine, beta-alanine, and glycerophospholipids were also identified. Thin-film SPME can be efficiently used as an alternative approach to other traditional urine sample preparation methods, demonstrating the SPME technique as a simple and efficient tool for urinary metabolomics research. Moreover, this study's results may support a better understanding of bladder cancer development and progression mechanisms.

The potential role of fatty acids in prostate cancer determined by GC-MS analysis of formalin-fixed paraffin-embedded tissue samples.

Prostate cancer (PCa) is one of the leading types of cancer in men. Although the diagnosis of this disease is currently quite effective, there is still a need to search for noninvasive diagnostic and monitoring methods. Consequently, identifying the mechanisms underlying the development and progression of PCa is crucial. It has been confirmed that the hallmarks of PCa include changes in metabolism, particularly that of fatty acids. Therefore, the application of lipidomics with an accurate histopathological assessment can provide the necessary information and reveal the metabolites that are characteristic of the disease. The use of formalin-fixed, paraffin-embedded (FFPE) tissue samples as an alternative matrix in retrospective research makes this approach highly innovative. The main goal of this study was to perform an untargeted lipidomic analysis of FFPE PCa tissue samples (n = 52) using gas chromatography coupled with mass spectrometry (GC-MS), in comparison to controls (n = 50). To our knowledge, this study is the first to simultaneously conduct a metabolic analysis and histopathological assessment. In the latter, the samples were evaluated based on Gleason grading score and pTNM stage. The obtained results were evaluated by univariate (Student's t-test or Mann-Whitney U-test) as well as multivariate statistical analysis (principal component analysis, partial least squares-discriminant analysis, variable importance into projection, and selectivity ratio) in order to select the metabolites with the most discriminative power. Additionally, the correlation between the level of metabolites and pathological characteristics was determined. The results of the analyses confirmed the changes in the lipid metabolism pathway in PCa. It can be assumed that PCa is linked with elevated de novo biosynthesis of steroid hormone-related fatty acids and beta-oxidation of fatty acids. An increased level of three fatty acids, namely 9-octadecanoic acid, 9,12-octadecadienoic acid, and 5, 8, 1,14-eicosatetraenoic acid, was observed in the PCa samples. These fatty acids were assigned as metabolites with the best discriminative power for the two tested groups. In practice, these compounds could be considered as specific biochemical factors that may be implemented in the diagnosis of PCa, but their significance should be validated on a more extensive set of samples. Undoubtedly, these results are valuable as they provide important information on prostate cancerogenesis in the context of a metabolic switch.