neural mass models
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Correct assessment of tissue histopathology is a necessary prerequisite for any clinical diagnosis. Nowadays, classical methods of histochemistry and immunohistochemistry are complemented by various techniques adopted from molecular biology and bioanalytical chemistry. Mass spectrometry profiling or imaging offered a new level of tissue visualization in the last decade, revealing hidden patterns of tissue molecular organization. It can be adapted to diagnostic purposes to improve decisions on complex and morphologically not apparent diagnoses. In this work, we successfully combined tissue profiling by mass spectrometry with analysis by artificial neural networks to classify normal and diseased liver and kidney tissues in a mouse model of primary hyperoxaluria type 1. Lack of the liver l-alanine:glyoxylate aminotransferase catalyzing conversion of l-alanine and glyoxylate to pyruvate and glycine causes accumulation of oxalate salts in various tissues, especially urinary system, resulting in compromised renal function and finally end stage renal disease. As the accumulation of oxalate salts alters chemical composition of affected tissues, it makes it available for examination by bioanalytical methods. We demonstrated that the direct tissue MALDI-TOF MS combined with neural computing offers an efficient tool for diagnosis of primary hyperoxaluria type I and potentially for other metabolic disorders altering chemical composition of tissues.
- Klíčová slova
- MALDI-TOF mass spectrometry,
- MeSH
- játra patologie MeSH
- ledviny patologie MeSH
- myši MeSH
- neuronové sítě * MeSH
- primární hyperoxalurie * diagnóza patologie MeSH
- spektrometrie hmotnostní - ionizace laserem za účasti matrice * statistika a číselné údaje MeSH
- transaminasy nedostatek MeSH
- zvířata MeSH
- Check Tag
- myši MeSH
- zvířata MeSH
- Publikační typ
- práce podpořená grantem MeSH
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 * 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
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 * 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
Hledání optimálního nastavení podmínek chemické analýzy je zpravidla zdlouhavý proces. Tento článek k tomuto účelu navrhuje využití neuronových sítí, zejména ve vztahu k určení optimální podmínek pro analýzu zkoumaných látek s využitím technologie LC/MS/MS a ESI ionizací, a to na základě znalosti jejich základních vlastností, označených jako univerzální deskriptory. Práce se soustředí na nalezení takových podmínek analýzy, kdy dochází k maximalizaci signálu iontu prekurzoru. Práce se zabývá zejména otázkou, zda lze výsledky zjištěné na jednom typu analytu použít k neurální interpolační predikci optimálních podmínek analytů podobných.
The search for the optimal instrumental settings of conditions in chemical analysis is typically a lengthy process. This article proposes the use of neural networks for this purpose, particularly in relation to determining the optimal conditions for the analysis of substances under study using LC/MS/MS and ESI technologies, based on the knowledge of their fundamental properties, referred to as universal descriptors. The work focuses on finding such analysis conditions that maximize the precursor ion signal. The paper specifically addresses the question of whether the results obtained from one type of analyte can be used for neural-interpolated prediction of optimal conditions for similar analytes.
- MeSH
- chemické bojové látky chemie MeSH
- chemické techniky analytické metody MeSH
- chromatografie kapalinová metody MeSH
- hmotnostní spektrometrie s elektrosprejovou ionizací metody MeSH
- hmotnostní spektrometrie metody MeSH
- lidé MeSH
- neuronové sítě MeSH
- organofosfáty * chemie analýza MeSH
- Check Tag
- lidé 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.
The search for the optimal instrumental settings of conditions in chemical analysis is typically a lengthy process. This article proposes the use of neural networks for this purpose, particularly in relation to determining the optimal conditions for the analysis of substances under study using LC/MS/MS and ESI technologies, based on the knowledge of their fundamental properties, referred to as universal descriptors. The work focuses on finding such analysis conditions that maximize the precursor ion signal. The paper specifically addresses the question of whether the results obtained from one type of analyte can be used for neural-interpolated prediction of optimal conditions for similar analytes.
- MeSH
- chemické bojové látky analýza chemie MeSH
- chemické techniky analytické metody MeSH
- chromatografie kapalinová metody MeSH
- hmotnostní spektrometrie s elektrosprejovou ionizací metody MeSH
- hmotnostní spektrometrie metody MeSH
- lidé MeSH
- neuronové sítě MeSH
- organofosfáty * analýza chemie MeSH
- Check Tag
- lidé MeSH
Cross-contamination of eukaryotic cell lines used in biomedical research represents a highly relevant problem. Analysis of repetitive DNA sequences, such as Short Tandem Repeats (STR), or Simple Sequence Repeats (SSR), is a widely accepted, simple, and commercially available technique to authenticate cell lines. However, it provides only qualitative information that depends on the extent of reference databases for interpretation. In this work, we developed and validated a rapid and routinely applicable method for evaluation of cell culture cross-contamination levels based on mass spectrometric fingerprints of intact mammalian cells coupled with artificial neural networks (ANNs). We used human embryonic stem cells (hESCs) contaminated by either mouse embryonic stem cells (mESCs) or mouse embryonic fibroblasts (MEFs) as a model. We determined the contamination level using a mass spectra database of known calibration mixtures that served as training input for an ANN. The ANN was then capable of correct quantification of the level of contamination of hESCs by mESCs or MEFs. We demonstrate that MS analysis, when linked to proper mathematical instruments, is a tangible tool for unraveling and quantifying heterogeneity in cell cultures. The analysis is applicable in routine scenarios for cell authentication and/or cell phenotyping in general.
- MeSH
- analýza hlavních komponent MeSH
- buněčné linie MeSH
- hmotnostní spektrometrie metody MeSH
- kalibrace MeSH
- kokultivační techniky MeSH
- lidé MeSH
- lidské embryonální kmenové buňky fyziologie MeSH
- multivariační analýza MeSH
- myši MeSH
- neuronové sítě * MeSH
- odběr biologického vzorku MeSH
- zvířata MeSH
- Check Tag
- lidé MeSH
- myši MeSH
- zvířata MeSH
- Publikační typ
- časopisecké články MeSH
- práce podpořená grantem MeSH
The application of an internal standard in quantitative analysis is desirable in order to correct for variations in sample preparation and instrumental response. In mass spectrometry of organic compounds, the internal standard is preferably labelled with a stable isotope, such as (18)O, (15)N or (13)C. In this study, a method for the quantification of fructo-oligosaccharides using matrix-assisted laser desorption/ionisation time-of-flight mass spectrometry (MALDI TOF MS) was proposed and tested on raftilose, a partially hydrolysed inulin with a degree of polymeration 2-7. A tetraoligosaccharide nystose, which is chemically identical to the raftilose tetramer, was used as an internal standard rather than an isotope-labelled analyte. Two mathematical approaches used for data processing, conventional calculations and artificial neural networks (ANN), were compared. The conventional data processing relies on the assumption that a constant oligomer dispersion profile will change after the addition of the internal standard and some simple numerical calculations. On the other hand, ANN was found to compensate for a non-linear MALDI response and variations in the oligomer dispersion profile with raftilose concentration. As a result, the application of ANN led to lower quantification errors and excellent day-to-day repeatability compared to the conventional data analysis. The developed method is feasible for MS quantification of raftilose in the range of 10-750 pg with errors below 7%. The content of raftilose was determined in dietary cream; application can be extended to other similar polymers. It should be stressed that no special optimisation of the MALDI process was carried out. A common MALDI matrix and sample preparation were used and only the basic parameters, such as sampling and laser energy, were optimised prior to quantification.
This paper introduces a novel technique to evaluate comfort properties of zinc oxide nanoparticles (ZnO NPs) coated woven fabrics. The proposed technique combines artificial neural network (ANN) and golden eagle optimizer (GEO) to ameliorate the training process of ANN. Neural networks are state-of-the-art machine learning models used for optimal state prediction of complex problems. Recent studies showed that the use of metaheuristic algorithms improve the prediction accuracy of ANN. GEO is the most advanced methaheurstic algorithm inspired by golden eagles and their intelligence for hunting by tuning their speed according to spiral trajectory. From application point of view, this study is a very first attempt where GEO is applied along with ANN to improve the training process of ANN for any textiles and composites application. Furthermore, the proposed algorithm ANN with GEO (ANN-GEO) was applied to map out the complex input-output conditions for optimal results. Coated amount of ZnO NPs, fabric mass and fabric thickness were selected as input variables and comfort properties were evaluated as output results. The obtained results reveal that ANN-GEO model provides high performance accuracy than standard ANN model, ANN models trained with latest metaheuristic algorithms including particle swarm optimizer and crow search optimizer, and conventional multiple linear regression.
- MeSH
- Accipitridae * MeSH
- algoritmy MeSH
- neuronové sítě MeSH
- oxid zinečnatý * MeSH
- propylaminy MeSH
- sulfidy MeSH
- textilie MeSH
- zvířata MeSH
- Check Tag
- zvířata MeSH
- Publikační typ
- časopisecké články MeSH
- práce podpořená grantem MeSH
