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.
- Keywords
- MALDI-TOF mass spectrometry,
- MeSH
- Liver pathology MeSH
- Kidney pathology MeSH
- Mice MeSH
- Neural Networks, Computer * MeSH
- Hyperoxaluria, Primary * diagnosis pathology MeSH
- Spectrometry, Mass, Matrix-Assisted Laser Desorption-Ionization * statistics & numerical data MeSH
- Transaminases deficiency MeSH
- Animals MeSH
- Check Tag
- Mice MeSH
- Animals MeSH
- Publication type
- Research Support, Non-U.S. Gov't 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
- Principal Component Analysis MeSH
- Datasets as Topic MeSH
- Immunoglobulins blood MeSH
- Bone Marrow metabolism pathology MeSH
- Middle Aged MeSH
- Humans MeSH
- Metabolic Networks and Pathways MeSH
- Metabolome * MeSH
- Multiple Myeloma blood diagnosis pathology MeSH
- Neural Networks, Computer * MeSH
- Aged, 80 and over MeSH
- Aged MeSH
- Spectrometry, Mass, Matrix-Assisted Laser Desorption-Ionization MeSH
- Case-Control Studies MeSH
- Artificial Intelligence * statistics & numerical data MeSH
- Check Tag
- Middle Aged MeSH
- Humans MeSH
- Male MeSH
- Aged, 80 and over MeSH
- Aged MeSH
- Female MeSH
- Publication type
- Journal Article MeSH
- Research Support, Non-U.S. Gov't MeSH
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
- Eagles * MeSH
- Algorithms MeSH
- Neural Networks, Computer MeSH
- Zinc Oxide * MeSH
- Propylamines MeSH
- Sulfides MeSH
- Textiles MeSH
- Animals MeSH
- Check Tag
- Animals MeSH
- Publication type
- Journal Article MeSH
- Research Support, Non-U.S. Gov't 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
- Biomarkers blood MeSH
- Chronic Pain blood diagnosis etiology MeSH
- Mice, Inbred C57BL MeSH
- Mice MeSH
- Neuralgia * blood diagnosis etiology MeSH
- Neural Networks, Computer * MeSH
- Spinal Cord Injuries * complications blood MeSH
- Spectrometry, Mass, Matrix-Assisted Laser Desorption-Ionization * methods MeSH
- Animals MeSH
- Check Tag
- Mice MeSH
- Female MeSH
- Animals MeSH
- Publication type
- Journal Article 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
- Chemical Warfare Agents chemistry MeSH
- Chemistry Techniques, Analytical methods MeSH
- Chromatography, Liquid methods MeSH
- Spectrometry, Mass, Electrospray Ionization methods MeSH
- Mass Spectrometry methods MeSH
- Humans MeSH
- Neural Networks, Computer MeSH
- Organophosphates * chemistry analysis MeSH
- Check Tag
- Humans 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
- Principal Component Analysis MeSH
- Cell Line MeSH
- Mass Spectrometry methods MeSH
- Calibration MeSH
- Coculture Techniques MeSH
- Humans MeSH
- Human Embryonic Stem Cells physiology MeSH
- Multivariate Analysis MeSH
- Mice MeSH
- Neural Networks, Computer * MeSH
- Specimen Handling MeSH
- Animals MeSH
- Check Tag
- Humans MeSH
- Mice MeSH
- Animals MeSH
- Publication type
- Journal Article MeSH
- Research Support, Non-U.S. Gov't MeSH
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
- Chemical Warfare Agents analysis chemistry MeSH
- Chemistry Techniques, Analytical methods MeSH
- Chromatography, Liquid methods MeSH
- Spectrometry, Mass, Electrospray Ionization methods MeSH
- Mass Spectrometry methods MeSH
- Humans MeSH
- Neural Networks, Computer MeSH
- Organophosphates * analysis chemistry MeSH
- Check Tag
- Humans 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.
The study of experimental design conjunction with artificial neural networks for optimisation of isocratic ion-pair reverse phase HPLC separation of neuroprotective peptides is reported. Different types of experimental designs (full-factorial, fractional) were studied as suitable input and output data sources for ANN training and examined on mixtures of humanin derivatives. The independent input variables were: composition of mobile phase, including its pH, and column temperature. In case of a simple mixture of two peptides, the retention time of the most retentive component and resolution were used as the dependent variables (outputs). In case of a complex mixture with unknown number of components, number of peaks, sum of resolutions and retention time of ultimate peak were considered as output variables. Fractional factorial experimental design has been proved to produce sufficient input data for ANN approximation and thus further allowed decreasing the number of experiments necessary for optimisation. After the optimal separation conditions were found, fractions with peptides were collected and their analysis using off-line matrix assisted laser desorption/ionisation time of flight mass spectrometry (MALDI-TOF-MS) was performed.
- MeSH
- Chemical Fractionation methods MeSH
- Financing, Organized MeSH
- Intracellular Signaling Peptides and Proteins genetics isolation & purification MeSH
- Neural Networks, Computer MeSH
- Spectrometry, Mass, Matrix-Assisted Laser Desorption-Ionization MeSH
- Amino Acid Substitution MeSH
- Chromatography, High Pressure Liquid methods MeSH
