Peña-Méndez, E M* Dotaz Zobrazit nápovědu
Simple molecular descriptors of extensive series of 1,3,5-triazinyl sulfonamide derivatives, based on the structure of sulfonamides and their physicochemical properties, were designed and calculated. These descriptors were successfully applied as inputs for artificial neural network (ANN) modelling of the relationship between the structure and biological activity. The optimized ANN architecture was applied to the prediction of the inhibition activity of 1,3,5-triazinyl sulfonamides against human carbonic anhydrase (hCA) II, tumour-associated hCA IX, and their selectivity (hCA II/hCA IX).
- MeSH
- antigeny nádorové metabolismus MeSH
- inhibitory karboanhydras chemie metabolismus MeSH
- karboanhydrasa II antagonisté a inhibitory metabolismus MeSH
- karboanhydrasa IX antagonisté a inhibitory metabolismus MeSH
- lidé MeSH
- neuronové sítě (počítačové) * MeSH
- racionální návrh léčiv MeSH
- sulfonamidy chemie metabolismus MeSH
- triaziny chemie MeSH
- Check Tag
- lidé MeSH
- Publikační typ
- časopisecké články MeSH
Silver nanoparticles (AgNP) are emerging pollutants. The use of novel materials such as Cu-(benzene 1,3,5-tricarboxylate, BTC) Metal-Organic Framework (MOFs), for AgNP adsorption and their removal from aqueous solutions has been studied. The effect of different parameters was followed and isotherm model was suggested. MOFs adsorbed fast and efficiently AgNP in the range C0 < 10 mg L(-1), being Freundlich isotherm (R = 0.993) these data fitted to. Among studied parameters a remarkable effect of chloride on sorption was found, thus their possible interactions were considered. The high adsorption efficiency of AgNP was achieved and it was found to be very fast. The feasibility of adsorption on Cu-(BTC) was proved in spiked waters. The results showed the potential interest of new material as adsorbent for removing AgNP from environment.
The magnetic metal-organic framework Fe3O4@(Fe-(benzene-1,3,5-tricarboxylic acid) (MMOF) was prepared, characterized and studied as a magnetic sorbent for the dispersive solid-phase extraction (DSPE) of several widely used blood lipid regulators (i.e., bezafibrate, clofibric acid, clofibrate, gemfibrozil and fenofibrate) from water samples. Characterization of the synthesized Fe3O4@Fe-BTC magnetic nanomaterial was performed by Fourier transform infrared spectroscopy, powder X-ray diffractometry, thermogravimetric analysis, scanning electron microscopy and transmission electron microscopy. The magnetic nanocomposite was found to be chemically stable and to possess a large surface area (803.62 m2/g) and pore volume (0.59 cm³/g). The concentrations of fibrates in different water samples were determined using HPLC-UV-Vis and confirmed by UPLC-MS/MS. Parameters affecting the extraction efficiency of magnetic-DSPE were studied and optimized. The maxima absorption capacities (Qmax) were determined to be (in mg/g) 197.0 for bezafibrate, 620.3 for clofibric acid, 537.6 for clofibrate, 288.7 gemfibrozil and 223.2 for fenofibrate. Validations of the optimized magnetic DSPE method for analyses at two fibrate concentrations in spiked water samples produced relative recovery values ≤ 70% for clofibrate and within the range of 80-100% for bezafibrate, clofibric acid, gemfibrozil and fenofibrate. LODs ranging from 4 μg/L for fenofibrate to 99 μg/L for gemfibrozil were obtained. The validated methodology produced recovery values ranging from 70 to 112% (relative standard deviations < 7%).
- MeSH
- benzen chemie MeSH
- chemické látky znečišťující vodu izolace a purifikace MeSH
- extrakce na pevné fázi metody MeSH
- kyseliny trikarboxylové chemie MeSH
- látky regulující metabolismus lipidů krev izolace a purifikace MeSH
- magnetické nanočástice chemie MeSH
- porézní koordinační polymery chemie MeSH
- voda chemie MeSH
- železo chemie MeSH
- Publikační typ
- časopisecké články MeSH
Spinal cord injury (SCI) often leads to central neuropathic pain, a condition associated with significant morbidity and is challenging in terms of the clinical management. Despite extensive efforts, identifying effective biomarkers for neuropathic pain remains elusive. Here we propose a novel approach combining matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF MS) with artificial neural networks (ANNs) to discriminate between mass spectral profiles associated with chronic neuropathic pain induced by SCI in female mice. Functional evaluations revealed persistent chronic neuropathic pain following mild SCI as well as minor locomotor disruptions, confirming the value of collecting serum samples. Mass spectra analysis revealed distinct profiles between chronic SCI and sham controls. On applying ANNs, 100% success was achieved in distinguishing between the two groups through the intensities of m/z peaks. Additionally, the ANNs also successfully discriminated between chronic and acute SCI phases. When reflexive pain response data was integrated with mass spectra, there was no improvement in the classification. These findings offer insights into neuropathic pain pathophysiology and underscore the potential of MALDI-TOF MS coupled with ANNs as a diagnostic tool for chronic neuropathic pain, potentially guiding attempts to discover biomarkers and develop treatments.
- MeSH
- biologické markery krev MeSH
- chronická bolest krev diagnóza etiologie MeSH
- myši inbrední C57BL MeSH
- myši MeSH
- neuralgie * krev diagnóza etiologie MeSH
- neuronové sítě (počítačové) * MeSH
- poranění míchy * komplikace krev MeSH
- spektrometrie hmotnostní - ionizace laserem za účasti matrice * metody MeSH
- zvířata MeSH
- Check Tag
- myši MeSH
- ženské pohlaví MeSH
- zvířata MeSH
- Publikační typ
- časopisecké články MeSH
Pathological pain subtypes can be classified as either neuropathic pain, caused by a somatosensory nervous system lesion or disease, or nociplastic pain, which develops without evidence of somatosensory system damage. Since there is no gold standard for the diagnosis of pathological pain subtypes, the proper classification of individual patients is currently an unmet challenge for clinicians. While the determination of specific biomarkers for each condition by current biochemical techniques is a complex task, the use of multimolecular techniques, such as matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF MS), combined with artificial intelligence allows specific fingerprints for pathological pain-subtypes to be obtained, which may be useful for diagnosis. We analyzed whether the information provided by the mass spectra of serum samples of four experimental models of neuropathic and nociplastic pain combined with their functional pain outcomes could enable pathological pain subtype classification by artificial neural networks. As a result, a simple and innovative clinical decision support method has been developed that combines MALDI-TOF MS serum spectra and pain evaluation with its subsequent data analysis by artificial neural networks and allows the identification and classification of pathological pain subtypes in experimental models with a high level of specificity.
Multiple myeloma (MM) is a highly heterogeneous disease of malignant plasma cells. Diagnosis and monitoring of MM patients is based on bone marrow biopsies and detection of abnormal immunoglobulin in serum and/or urine. However, biopsies have a single-site bias; thus, new diagnostic tests and early detection strategies are needed. Matrix-Assisted Laser Desorption/Ionization Time-of Flight Mass Spectrometry (MALDI-TOF MS) is a powerful method that found its applications in clinical diagnostics. Artificial intelligence approaches, such as Artificial Neural Networks (ANNs), can handle non-linear data and provide prediction and classification of variables in multidimensional datasets. In this study, we used MALDI-TOF MS to acquire low mass profiles of peripheral blood plasma obtained from MM patients and healthy donors. Informative patterns in mass spectra served as inputs for ANN that specifically predicted MM samples with high sensitivity (100%), specificity (95%) and accuracy (98%). Thus, mass spectrometry coupled with ANN can provide a minimally invasive approach for MM diagnostics.
- MeSH
- analýza hlavních komponent MeSH
- datové soubory jako téma MeSH
- imunoglobuliny krev MeSH
- kostní dřeň metabolismus patologie MeSH
- lidé středního věku MeSH
- lidé MeSH
- metabolické sítě a dráhy MeSH
- metabolom * MeSH
- mnohočetný myelom krev diagnóza patologie MeSH
- neuronové sítě (počítačové) * MeSH
- senioři nad 80 let MeSH
- senioři MeSH
- spektrometrie hmotnostní - ionizace laserem za účasti matrice MeSH
- studie případů a kontrol MeSH
- umělá inteligence * statistika a číselné údaje MeSH
- Check Tag
- lidé středního věku MeSH
- lidé MeSH
- mužské pohlaví MeSH
- senioři nad 80 let MeSH
- senioři MeSH
- ženské pohlaví MeSH
- Publikační typ
- časopisecké články MeSH
- práce podpořená grantem MeSH
