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 (počítačové) * MeSH
- odběr biologického vzorku MeSH
- zvířata MeSH
- Check Tag
- lidé MeSH
- myši MeSH
- zvířata MeSH
- Publikační typ
- časopisecké články MeSH
- práce podpořená grantem MeSH
Metal-based coordination compounds have been used throughout the history of human medicine to treat various diseases, including cancer. Since the discovery of cisplatin in 1965, a great number of metal coordination complexes, such as platinum, ruthenium, gold or copper have been designed, synthesized and tested in order to develop clinically effective and safe drugs. Currently, many reviews cover applications of cytostatic metal complexes pointing out the most promising examples of platinum- and non-platinum-based compounds in preclinical and clinical trials. However, recent comprehensive reviews covering chemical and biological aspects of metal-based coordination compounds in cancer therapy are still rare. In this review we wish to provide an overview of the coordination chemistry of current and novel cytostatic compounds, including an outline of their design and rationale of synthesis, and summarize bio-chemical reactivity and physicochemical properties of candidate metal complexes.
- MeSH
- antitumorózní látky * farmakologie terapeutické užití MeSH
- cisplatina dějiny farmakologie terapeutické užití MeSH
- galium dějiny farmakologie terapeutické užití MeSH
- genomika metody trendy MeSH
- individualizovaná medicína metody trendy využití MeSH
- kobalt dějiny farmakologie terapeutické užití MeSH
- komplexní sloučeniny * farmakologie terapeutické užití MeSH
- lidé MeSH
- měď farmakologie terapeutické užití MeSH
- metabolomika metody trendy MeSH
- mezioborová komunikace MeSH
- proteomika metody trendy MeSH
- sloučeniny ruthenia dějiny farmakologie terapeutické užití MeSH
- sloučeniny železa dějiny farmakologie terapeutické užití MeSH
- sloučeniny zlata dějiny farmakologie terapeutické užití MeSH
- statistika jako téma MeSH
- Check Tag
- lidé MeSH
- Publikační typ
- práce podpořená grantem MeSH
- přehledy MeSH
RATIONALE: Silver tellurides find applications in the development of infrared detection, imaging, magnetics, sensors, memory devices, and optic materials. However, only a limited number of silver tellurides have been described to date. Laser ablation synthesis (LAS) was selected to generate new Ag-Te clusters. METHODS: Isothermal adsorption was used to study the formation of silver nano-particles-tellurium aggregates. Laser desorption ionization quadrupole ion trap time-of-flight mass spectrometry (LDI-QIT-TOFMS) was used for the generation and analysis of Agm Ten clusters. Scanning electron microscopy and energy-dispersive X-ray spectroscopy were used to visualize the structure of materials. The stoichiometry of the generated clusters was determined by computer modeling of isotopic patterns. RESULTS: A simple, one-pot method for the preparation of Ag-Te nano-composite was developed and found suitable for LAS of silver tellurides. The LDI of Ag-Te nano-composite leads to the formation of 11 unary and 52 binary clusters. The stoichiometry of the 34 novel Agm Ten clusters is reported here for the first time. CONCLUSIONS: LAS with TOFMS detection was proven to be a powerful technique for the generation of silver telluride clusters. Knowledge of the stoichiometry of the generated clusters might facilitate the further development of novel high-tech silver tellurium nano-materials.
An extensive amount of information is currently available to clinical specialists, ranging from details of clinical symptoms to various types of biochemical data and outputs of imaging devices. Each type of data provides information that must be evaluated and assigned to a particular pathology during the diagnostic process. To streamline the diagnostic process in daily routine and avoid misdiagnosis, artificial intelligence methods (especially computer aided diagnosis and artificial neural networks) can be employed. These adaptive learning algorithms can handle diverse types of medical data and integrate them into categorized outputs. In this paper, we briefly review and discuss the philosophy, capabilities, and limitations of artificial neural networks in medical diagnosis through selected exampl
- MeSH
- databáze jako téma MeSH
- diabetes mellitus diagnóza MeSH
- diagnostické techniky a postupy * trendy MeSH
- kardiovaskulární nemoci diagnóza MeSH
- nádory diagnóza MeSH
- neuronové sítě (počítačové) * MeSH
- rozhodovací teorie MeSH
- umělá inteligence MeSH
- Publikační typ
- práce podpořená grantem MeSH
- úvodní články MeSH
The possibilities of artificial neural networks (ANNs) "soft" computing to evaluate chemical kinetic data have been studied. In the first stage, a set of "standard" kinetic curves with known parameters (rate constants and/or concentrations of the reactants), which is some kind of "normalized maps", is prepared. The database should be built according to a suitable experimental design (ED). In the second stage, such data set is then used for ANNs "learning". Afterwards, in the second stage, experimental data are evaluated and parameters of "other" kinetic curves are computed without solving anymore the system of differential equations. The combined ED-ANNs approach has been applied to solve several kinetic systems. It was also demonstrated that using ANNs, the optimization of complex chemical systems can be achieved even not knowing or determining the values of the rate constants. Moreover, the solution of differential equations is here not necessary, as well. Using ED the number of experiments can be reduced substantially. Methodology of ED-ANNs applied to multicomponent analysis shows advantages over classical methods while the knowledge of kinetic reactions is not needed. ANNs computation in kinetics is robust as shown evaluating the effect of experimental errors and it is of general applicability.
- Publikační typ
- časopisecké články MeSH
- práce podpořená grantem MeSH
