206 research outputs found
Using a hierarchical decision-making system in e-learning
Itβs clear that not all factors affecting the effectiveness of training can be taken into account using only the information about the content. According to this there has been implemented a two-level decision making system (DMS). The first level of DMS is responsible for the choice of further training strategy and execution of an action related to the chosen strategy (reaction). The second level, if it required by the reaction, selects the desired content based on a model of knowledge and skills of the user.
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ΠΠ·Π°ΠΈΠΌΠΎΠ΄Π΅ΠΉΡΡΠ²ΠΈΠ΅ ΠΏΡΠΎΡΠΈΠ²ΠΎΠΎΠΏΡΡ ΠΎΠ»Π΅Π²ΠΎΠ³ΠΎ ΠΏΡΠ΅ΠΏΠ°ΡΠ°ΡΠ° Π°ΡΠ΅ΡΠ°ΡΠ° Π°Π±ΠΈΡΠ°ΡΠ΅ΡΠΎΠ½Π° Ρ Π΄ΡΠΠΠ
The electroanalytical characteristics of double-stranded DNA (dsDNA) and the complex of dsDNA and the antitumor drug abiraterone acetate (AA) were studied by differential pulse voltammetry. The effect of abiraterone acetate on dsDNA was shown, which was registered by alteration the intensity of electrochemical oxidation of purine heterocyclic bases guanine and adenine using screen printed electrodes modified with functionalized carbon nanotubes. The binding constants (Kb) of the [dsDNA-AA] complex for guanine and adenine were 1.63Γ104 M-1 and 1.93Γ104 M-1, respectively. The electrochemical coefficients of the toxic effect were calculated as the ratio of the intensity of the electrochemical oxidation signals of guanine and adenine, in the presence of abiraterone acetate to the intensity of the electrooxidation signals of these nucleobases Β without drug (%). At concentrations of abiraterone acetate exceeding 60 ΞΌM, a decrease in the currents of electrochemical oxidation of guanine and adenine by 50% or more is recorded. Based on the analysis of electrochemical parameters and values ββof binding constants, an assumption was made about the mechanism of interaction of abiraterone acetate with DNA, mainly due to the formation of hydrogen bonds with the minor groove. An electrochemical DNA biosensor was first used to study the mechanism of interaction of the anticancer drug abiraterone acetate with dsDNA.ΠΠ΅ΡΠΎΠ΄ΠΎΠΌ Π΄ΠΈΡΡΠ΅ΡΠ΅Π½ΡΠΈΠ°Π»ΡΠ½ΠΎ-ΠΈΠΌΠΏΡΠ»ΡΡΠ½ΠΎΠΉ Π²ΠΎΠ»ΡΡΠ°ΠΌΠΏΠ΅ΡΠΎΠΌΠ΅ΡΡΠΈΠΈ ΠΈΡΡΠ»Π΅Π΄ΠΎΠ²Π°Π½Ρ ΡΠ»Π΅ΠΊΡΡΠΎΠ°Π½Π°Π»ΠΈΡΠΈΡΠ΅ΡΠΊΠΈΠ΅ Ρ
Π°ΡΠ°ΠΊΡΠ΅ΡΠΈΡΡΠΈΠΊΠΈ Π΄Π²ΡΡ
ΡΠ΅ΠΏΠΎΡΠ΅ΡΠ½ΠΎΠΉ ΠΠΠ (Π΄ΡΠΠΠ) ΠΈ ΠΊΠΎΠΌΠΏΠ»Π΅ΠΊΡΠ° Π΄ΡΠΠΠ ΠΈ ΠΏΡΠΎΡΠΈΠ²ΠΎΠΎΠΏΡΡ
ΠΎΠ»Π΅Π²ΠΎΠ³ΠΎ ΠΏΡΠ΅ΠΏΠ°ΡΠ°ΡΠ° Π°ΡΠ΅ΡΠ°ΡΠ° Π°Π±ΠΈΡΠ°ΡΠ΅ΡΠΎΠ½Π° (ΠΠ). ΠΠΎΠΊΠ°Π·Π°Π½ΠΎ Π²Π»ΠΈΡΠ½ΠΈΠ΅ Π°ΡΠ΅ΡΠ°ΡΠ° Π°Π±ΠΈΡΠ°ΡΠ΅ΡΠΎΠ½Π° Π½Π° Π΄ΡΠΠΠ, ΡΠ΅Π³ΠΈΡΡΡΠΈΡΡΠ΅ΠΌΠΎΠ΅ ΠΏΠΎ ΠΈΠ·ΠΌΠ΅Π½Π΅Π½ΠΈΡ ΠΈΠ½ΡΠ΅Π½ΡΠΈΠ²Π½ΠΎΡΡΠΈ ΡΠ»Π΅ΠΊΡΡΠΎΡ
ΠΈΠΌΠΈΡΠ΅ΡΠΊΠΎΠ³ΠΎ ΠΎΠΊΠΈΡΠ»Π΅Π½ΠΈΡ ΠΏΡΡΠΈΠ½ΠΎΠ²ΡΡ
Π³Π΅ΡΠ΅ΡΠΎΡΠΈΠΊΠ»ΠΈΡΠ΅ΡΠΊΠΈΡ
Π°Π·ΠΎΡΠΈΡΡΡΡ
ΠΎΡΠ½ΠΎΠ²Π°Π½ΠΈΠΉ Π³ΡΠ°Π½ΠΈΠ½Π° ΠΈ Π°Π΄Π΅Π½ΠΈΠ½Π° Ρ ΠΈΡΠΏΠΎΠ»ΡΠ·ΠΎΠ²Π°Π½ΠΈΠ΅ΠΌ ΠΏΠ΅ΡΠ°ΡΠ½ΡΡ
ΡΠ»Π΅ΠΊΡΡΠΎΠ΄ΠΎΠ², ΠΌΠΎΠ΄ΠΈΡΠΈΡΠΈΡΠΎΠ²Π°Π½Π½ΡΡ
ΡΡΠ½ΠΊΡΠΈΠΎΠ½Π°Π»ΠΈΠ·ΠΈΡΠΎΠ²Π°Π½Π½ΡΠΌΠΈ ΡΠ³Π»Π΅ΡΠΎΠ΄Π½ΡΠΌΠΈ Π½Π°Π½ΠΎΡΡΡΠ±ΠΊΠ°ΠΌΠΈ. ΠΠΎΠ½ΡΡΠ°Π½ΡΡ ΡΠ²ΡΠ·ΡΠ²Π°Π½ΠΈΡ (Πb) ΠΊΠΎΠΌΠΏΠ»Π΅ΠΊΡΠ° [Π΄ΡΠΠΠ-ΠΠ], Π΄Π»Ρ Π³ΡΠ°Π½ΠΈΠ½Π° ΠΈ Π°Π΄Π΅Π½ΠΈΠ½Π°, ΡΠΎΡΡΠ°Π²ΠΈΠ»ΠΈ 1.63Γ104 Π-1 ΠΈ 1.93Γ104 Π-1, ΡΠΎΠΎΡΠ²Π΅ΡΡΡΠ²Π΅Π½Π½ΠΎ. Π Π°ΡΡΡΠΈΡΠ°Π½Ρ ΡΠ»Π΅ΠΊΡΡΠΎΡ
ΠΈΠΌΠΈΡΠ΅ΡΠΊΠΈΠ΅ ΠΊΠΎΡΡΡΠΈΡΠΈΠ΅Π½ΡΡ ΡΠΎΠΊΡΠΈΡΠ΅ΡΠΊΠΎΠ³ΠΎ ΡΡΡΠ΅ΠΊΡΠ° ΠΊΠ°ΠΊ ΠΎΡΠ½ΠΎΡΠ΅Π½ΠΈΠ΅ ΠΈΠ½ΡΠ΅Π½ΡΠΈΠ²Π½ΠΎΡΡΠΈ ΡΠΈΠ³Π½Π°Π»ΠΎΠ² ΡΠ»Π΅ΠΊΡΡΠΎΠΎΠΊΠΈΡΠ»Π΅Π½ΠΈΡ Π³ΡΠ°Π½ΠΈΠ½Π° ΠΈ Π°Π΄Π΅Π½ΠΈΠ½Π°, Π²Ρ
ΠΎΠ΄ΡΡΠΈΡ
Π² ΡΠΎΡΡΠ°Π² Π΄ΡΠΠΠ, Π² ΠΏΡΠΈΡΡΡΡΡΠ²ΠΈΠΈ Π°ΡΠ΅ΡΠ°ΡΠ° Π°Π±ΠΈΡΠ°ΡΠ΅ΡΠΎΠ½Π° ΠΊ ΠΈΠ½ΡΠ΅Π½ΡΠΈΠ²Π½ΠΎΡΡΠΈ ΡΠΈΠ³Π½Π°Π»ΠΎΠ² ΡΠ»Π΅ΠΊΡΡΠΎΠΎΠΊΠΈΡΠ»Π΅Π½ΠΈΡ ΡΡΠΈΡ
Π°Π·ΠΎΡΠΈΡΡΡΡ
ΠΎΡΠ½ΠΎΠ²Π°Π½ΠΈΠΉ Π±Π΅Π· Π»Π΅ΠΊΠ°ΡΡΡΠ²Π° (%). ΠΡΠΈ ΠΊΠΎΠ½ΡΠ΅Π½ΡΡΠ°ΡΠΈΡΡ
Π°ΡΠ΅ΡΠ°ΡΠ° Π°Π±ΠΈΡΠ°ΡΠ΅ΡΠΎΠ½Π°, ΠΏΡΠ΅Π²ΡΡΠ°ΡΡΠΈΡ
60 ΠΌΠΊΠ, ΡΠ΅Π³ΠΈΡΡΡΠΈΡΡΠ΅ΡΡΡ ΡΠ½ΠΈΠΆΠ΅Π½ΠΈΠ΅ ΡΠΎΠΊΠΎΠ² ΡΠ»Π΅ΠΊΡΡΠΎΡ
ΠΈΠΌΠΈΡΠ΅ΡΠΊΠΎΠ³ΠΎ ΠΎΠΊΠΈΡΠ»Π΅Π½ΠΈΡ Π³ΡΠ°Π½ΠΈΠ½Π° ΠΈ Π°Π΄Π΅Π½ΠΈΠ½Π° Π½Π° 50% ΠΈ Π±ΠΎΠ»Π΅Π΅. ΠΠ° ΠΎΡΠ½ΠΎΠ²Π°Π½ΠΈΠΈ Π°Π½Π°Π»ΠΈΠ·Π° ΡΠ»Π΅ΠΊΡΡΠΎΡ
ΠΈΠΌΠΈΡΠ΅ΡΠΊΠΈΡ
ΠΏΠ°ΡΠ°ΠΌΠ΅ΡΡΠΎΠ² ΠΈ Π·Π½Π°ΡΠ΅Π½ΠΈΠΉ ΠΊΠΎΠ½ΡΡΠ°Π½Ρ ΡΠ²ΡΠ·ΡΠ²Π°Π½ΠΈΡ ΡΠ΄Π΅Π»Π°Π½ΠΎ ΠΏΡΠ΅Π΄ΠΏΠΎΠ»ΠΎΠΆΠ΅Π½ΠΈΠ΅ ΠΎ ΠΌΠ΅Ρ
Π°Π½ΠΈΠ·ΠΌΠ΅ Π²Π·Π°ΠΈΠΌΠΎΠ΄Π΅ΠΉΡΡΠ²ΠΈΡ Π°ΡΠ΅ΡΠ°ΡΠ° Π°Π±ΠΈΡΠ°ΡΠ΅ΡΠΎΠ½Π° Ρ ΠΠΠ ΠΏΡΠ΅ΠΈΠΌΡΡΠ΅ΡΡΠ²Π΅Π½Π½ΠΎ Π·Π° ΡΡΠ΅Ρ ΠΎΠ±ΡΠ°Π·ΠΎΠ²Π°Π½ΠΈΡ Π²ΠΎΠ΄ΠΎΡΠΎΠ΄Π½ΡΡ
ΡΠ²ΡΠ·Π΅ΠΉ Ρ ΠΌΠ°Π»ΠΎΠΉ Π±ΠΎΡΠΎΠ·Π΄ΠΊΠΎΠΉ. ΠΠ»Π΅ΠΊΡΡΠΎΡ
ΠΈΠΌΠΈΡΠ΅ΡΠΊΠΈΠΉ ΠΠΠ-Π±ΠΈΠΎΡΠ΅Π½ΡΠΎΡ Π±ΡΠ» Π²ΠΏΠ΅ΡΠ²ΡΠ΅ ΠΈΡΠΏΠΎΠ»ΡΠ·ΠΎΠ²Π°Π½ Π΄Π»Ρ ΠΈΡΡΠ»Π΅Π΄ΠΎΠ²Π°Π½ΠΈΡ ΠΌΠ΅Ρ
Π°Π½ΠΈΠ·ΠΌΠ° Π²Π·Π°ΠΈΠΌΠΎΠ΄Π΅ΠΉΡΡΠ²ΠΈΡ ΠΏΡΠΎΡΠΈΠ²ΠΎΠΎΠΏΡΡ
ΠΎΠ»Π΅Π²ΠΎΠ³ΠΎ ΠΏΡΠ΅ΠΏΠ°ΡΠ°ΡΠ° Π°ΡΠ΅ΡΠ°ΡΠ° Π°Π±ΠΈΡΠ°ΡΠ΅ΡΠΎΠ½Π° Ρ Π΄ΡΠΠΠ
Enhancing functional efficiency in information-extreme machine learning with logistic regression ensembles
The subject matter of the article is the application of supervised machine learning for the task of object class recognition. The goal is enhancing functional efficiency in information-extreme technology (IET) for object class recognition. The tasks to be solved are: to analyse possible ways of increasing the functional efficiency IET approach; implement an ensemble of models that include logistic regression for prioritizing recognition features and an IEI learning algorithm; compare the functional efficiency of the resulting ensemble of models on well-known dataset with classic approach and results of other researchers. The methods: The method is developed within the framework of the functional approach to modelling natural intelligence applied to the problem of object classification. The following results were obtained: The study tries to augment existing IET to support feature prioritization as part of the object class recognition algorithm. The classical information-extreme algorithm treats all input features equivalently important in forming the decisive rule. As a result, the object features with strong correlation are not prioritized by the algorithm's decisive mechanism β resulting in decreasing functional efficiency in exam mode. The proposed approach is solving this problem by applying a two-stage approach. In the first stage the multiclass logistic regression applied to the input training features vectors of objects to be classified β formed the normalized training matrix. To prevent overfitting of the logistic regression, a model the L2(ridge) regularization method was used. On the second stage, the information-extreme method as input takes the result of the first stage. The geometrical parameters of class containers and the control tolerances on the recognition features were considered as the optimization parameters. Conclusions. The proposed approach increases MNIST (Modified National Institute of Standards and Technology) dataset classification accuracy compared with the classic information-extreme method by 26,44%. The proposed approach has a 3.77% lower accuracy compared to neural-like approaches but uses fewer resources in the training phase and allows retraining the model, as well as expanding the dictionary of recognition classes without model retraining
ΠΠΎΠ½ΡΡΡΡΠΈΡΠΎΠ²Π°Π½ΠΈΠ΅ ΠΌΠ΅ΡΠΎΠ΄ΠΈΠΊΠΈ ΠΈΡΡΠ»Π΅Π΄ΠΎΠ²Π°Π½ΠΈΡ ΡΠ°ΠΌΠΎΡΠ°Π·Π²ΠΈΡΠΈΡ Π»ΠΈΡΠ½ΠΎΡΡΠΈ
Π£ ΡΡΠ°ΡΡΡ ΡΠΎΠ·Π³Π»ΡΠ΄Π°ΡΡΡΡΡ Π°Π²ΡΠΎΡΡΡΠΊΠΈΠΉ ΠΏΡΠ΄Ρ
ΡΠ΄ Π΄ΠΎ ΡΠΎΠ·ΡΠΎΠ±ΠΊΠΈ Π΄ΡΠ°Π³Π½ΠΎΡΡΠΈΡΠ½ΠΎΠ³ΠΎ ΡΠ½ΡΡΡΡΠΌΠ΅Π½ΡΠ°ΡΡΡ Π²ΠΈΠ²ΡΠ΅Π½Π½Ρ ΠΎΡΠΎΠ±Π»ΠΈΠ²ΠΎΡΡΠ΅ΠΉ ΡΠ°ΠΌΠΎΡΠΎΠ·Π²ΠΈΡΠΊΡ ΠΎΡΠΎΠ±ΠΈΡΡΠΎΡΡΡ. ΠΠ½Π°Π»ΡΠ·ΡΡΡΡΡΡ Π·ΠΌΡΡΡΠΎΠ²Ρ ΠΊΠΎΠΌΠΏΠΎΠ½Π΅Π½ΡΠΈ (ΠΏΡΠΈΡ
ΠΎΠ»ΠΎΠ³ΡΡΠ½Ρ ΡΠ΅ΡΡΡΡΠΈ) ΠΎΡΠΎΠ±ΠΈΡΡΡΡΠ½ΠΎΠ³ΠΎ ΡΠ°ΠΌΠΎΡΠΎΠ·Π²ΠΈΡΠΊΡ. ΠΠ°Π²ΠΎΠ΄ΠΈΡΡΡΡ ΡΠΎΠ·ΡΠΎΠ±Π»Π΅Π½Π° Π΄ΡΠ°Π³Π½ΠΎΡΡΠΈΡΠ½Π° ΠΌΠ΅ΡΠΎΠ΄ΠΈΠΊΠ° ΠΠ₯Π‘Π. ΠΠΏΠΈΡΡΡΡΡΡΡ ΠΏΡΠΎΡΠ΅Π΄ΡΡΠ° ΠΏΠ΅ΡΠ΅Π²ΡΡΠΊΠΈ Π²Π°Π»ΡΠ΄Π½ΠΎΡΡΡ Ρ Π½Π°Π΄ΡΠΉΠ½ΠΎΡΡΡ, Π° ΡΠ°ΠΊΠΎΠΆ ΡΡΠ°Π½Π΄Π°ΡΡΠΈΠ·Π°ΡΡΡ ΡΠ΅Π·ΡΠ»ΡΡΠ°ΡΡΠ².In the article the author's approach to the development of diagnostic tools study features self-identity. Analyzed a substantial of components (psychological resources) personal self-development. A developed of diagnostic the technique DCSEP. A procedure of checking the validity and reliability, as well as standardization of results.Π ΡΡΠ°ΡΡΠ΅ ΡΠ°ΡΡΠΌΠ°ΡΡΠΈΠ²Π°Π΅ΡΡΡ Π°Π²ΡΠΎΡΡΠΊΠΈΠΉ ΠΏΠΎΠ΄Ρ
ΠΎΠ΄ ΠΊ ΡΠ°Π·ΡΠ°Π±ΠΎΡΠΊΠ΅ Π΄ΠΈΠ°Π³Π½ΠΎΡΡΠΈΡΠ΅ΡΠΊΠΎΠ³ΠΎ ΠΈΠ½ΡΡΡΡΠΌΠ΅Π½ΡΠ°ΡΠΈΡ ΠΈΠ·ΡΡΠ΅Π½ΠΈΡ ΠΎΡΠΎΠ±Π΅Π½Π½ΠΎΡΡΠ΅ΠΉ ΡΠ°ΠΌΠΎΡΠ°Π·Π²ΠΈΡΠΈΡ Π»ΠΈΡΠ½ΠΎΡΡΠΈ. ΠΠ½Π°Π»ΠΈΠ·ΠΈΡΡΡΡΡΡ ΡΠΎΠ΄Π΅ΡΠΆΠ°ΡΠ΅Π»ΡΠ½ΡΠ΅ ΠΊΠΎΠΌΠΏΠΎΠ½Π΅Π½ΡΡ (ΠΏΡΠΈΡ
ΠΎΠ»ΠΎΠ³ΠΈΡΠ΅ΡΠΊΠΈΠ΅ ΡΠ΅ΡΡΡΡΡ) Π»ΠΈΡΠ½ΠΎΡΡΠ½ΠΎΠ³ΠΎ ΡΠ°ΠΌΠΎΡΠ°Π·Π²ΠΈΡΠΈΡ. ΠΡΠΈΠ²ΠΎΠ΄ΠΈΡΡΡ ΡΠ°Π·ΡΠ°Π±ΠΎΡΠ°Π½Π½Π°Ρ Π΄ΠΈΠ°Π³Π½ΠΎΡΡΠΈΡΠ΅ΡΠΊΠ°Ρ ΠΌΠ΅ΡΠΎΠ΄ΠΈΠΊΠ° ΠΠ₯Π‘Π. ΠΠΏΠΈΡΡΠ²Π°Π΅ΡΡΡ ΠΏΡΠΎΡΠ΅Π΄ΡΡΠ° ΠΏΡΠΎΠ²Π΅ΡΠΊΠΈ Π²Π°Π»ΠΈΠ΄Π½ΠΎΡΡΠΈ ΠΈ Π½Π°Π΄Π΅ΠΆΠ½ΠΎΡΡΠΈ, Π° ΡΠ°ΠΊΠΆΠ΅ ΡΡΠ°Π½Π΄Π°ΡΡΠΈΠ·Π°ΡΠΈΠΈ ΡΠ΅Π·ΡΠ»ΡΡΠ°ΡΠΎΠ²
Π‘ΡΡΠ°ΡΠ½ΠΈΠΉ ΡΡΠ°Π½ ΡΠ° Π½Π°ΠΏΡΡΠΌΠΈ ΡΠΎΠ·Π²ΠΈΡΠΊΡ Education Data Mining Π² Π‘ΡΠΌΡΡΠΊΠΎΠΌΡ Π΄Π΅ΡΠΆΠ°Π²Π½ΠΎΠΌΡ ΡΠ½ΡΠ²Π΅ΡΡΠΈΡΠ΅ΡΡ
ΠΠ΄Π½ΡΡΡ Π· ΠΏΠ΅ΡΠ΅Π²Π°Π³ Π°Π²ΡΠΎΠΌΠ°ΡΠΈΠ·Π°ΡΡΡ Π±ΡΠ·Π½Π΅Ρ ΠΏΡΠΎΡΠ΅ΡΡΠ² Ρ ΠΌΠΎΠΆΠ»ΠΈΠ²ΡΡΡΡ ΡΡ
Π³Π»ΠΈΠ±ΠΎΠΊΠΎΠ³ΠΎ Π°Π½Π°Π»ΡΠ·Ρ Π· ΠΌΠ΅ΡΠΎΡ ΠΏΠΎΡΡΠΊΡ ΠΏΡΠΈΡ
ΠΎΠ²Π°Π½ΠΈΡ
Π·Π²βΡΠ·ΠΊΡΠ² ΠΌΡΠΆ ΠΏΠ°ΡΠ°ΠΌΠ΅ΡΡΠ°ΠΌΠΈ ΠΌΠΎΠ΄Π΅Π»Ρ Ρ Π΅ΡΠ΅ΠΊΡΠΈΠ²Π½ΡΡΡΡ ΠΏΡΠΎΡΠ΅ΡΡΠ². ΠΠΎΡΠ»ΡΠ΄ΠΆΠ΅Π½Π½Ρ Ρ ΡΡΠΎΠΌΡ Π½Π°ΠΏΡΡΠΌΠΊΡ Π½Π΅ ΠΏΡΠΎΠΉΡΠ»ΠΈ ΠΎΡΡΠΎΡΠΎΠ½Ρ ΡΠΈΡΡΠ΅ΠΌ Π΄ΠΈΡΡΠ°Π½ΡΡΠΉΠ½ΠΎΠ³ΠΎ Π½Π°Π²ΡΠ°Π½Π½Ρ, ΠΎΡΠΎΠ±Π»ΠΈΠ²ΠΎ Π· ΠΏΠΎΡΠ²ΠΎΡ Massive Open Online Courses (MOOC). ΠΠ° ΡΡΠ΄Ρ ΡΠ· ΡΠ΅ΡΠΌΡΠ½ΠΎΠΌ Data Mining β Π²ΠΈΠ΄ΠΎΠ±ΡΡΠΎΠΊ Π·Π½Π°Π½Ρ Π· ΠΌΠ°ΡΠΈΠ²ΡΠ² ΡΠ½ΡΠΎΡΠΌΠ°ΡΡΡ, ΡΠΎΠ·Π³Π»ΡΠ΄Π°ΡΡΡ ΠΏΠΎΠ½ΡΡΡΡ Β«Education Data MiningΒ» (EDM)
ΠΠ»Π΅ΠΊΡΡΠΎΡ ΠΈΠΌΠΈΡΠ΅ΡΠΊΠΈΠΉ Π°Π½Π°Π»ΠΈΠ· ΠΌΠ΅ΡΠ°Π±ΠΎΠ»ΠΈΡΠΎΠ² ΠΊΠ°ΠΊ ΠΌΠ΅ΡΠΎΠ΄ ΠΎΠΏΡΠ΅Π΄Π΅Π»Π΅Π½ΠΈΡ Π°ΠΊΡΠΈΠ²Π½ΠΎΡΡΠΈ ΡΠΈΡΠΎΡ ΡΠΎΠΌΠΎΠ² P450
The review deals with the electrochemical methods for determination of metabolites of cytochromes P450 catalyzed reactions. We have focused on the electrochemical determination of metabolites of drugs and some endogenous compounds. We have reviewed bielectrode systems for determination of cytochrome P450 activity, where one electrode serves as a matrix for enzyme immobilization and a source of electrons for heme iron ion reduction and initialization of the catalytic reaction towards a substrate and the second one is being used for quantification of the products formed by their electrochemical oxidation. Such systems allow one to elude additional steps of separation of reaction substrates and products. The review also includes discussion of the ways to increase the analytical sensitivity and decrease the limit of detection of the investigated metabolites by chemical modification of electrodes. We demonstrate the possibilities of these systems for cytochrome P450 kinetics analysis and the perspectives of their further improvement, such as increasing the sensitivity of metabolite electrochemical determination by modern electrode modificators, including carbon-based, and construction of devices for automatic monitoring of the products.Π ΠΎΠ±Π·ΠΎΡΠ΅ ΡΠ°ΡΡΠΌΠΎΡΡΠ΅Π½Ρ ΠΌΠ΅ΡΠΎΠ΄Ρ ΡΠ»Π΅ΠΊΡΡΠΎΡ
ΠΈΠΌΠΈΡΠ΅ΡΠΊΠΎΠ³ΠΎ ΠΎΠΏΡΠ΅Π΄Π΅Π»Π΅Π½ΠΈΡ ΠΌΠ΅ΡΠ°Π±ΠΎΠ»ΠΈΡΠΎΠ² ΡΠ΅Π°ΠΊΡΠΈΠΉ, ΠΊΠ°ΡΠ°Π»ΠΈΠ·ΠΈΡΡΠ΅ΠΌΡΡ
ΡΠΈΡΠΎΡ
ΡΠΎΠΌΠ°ΠΌΠΈ P450. ΠΡΠ½ΠΎΠ²Π½ΠΎΠΉ Π°ΠΊΡΠ΅Π½Ρ ΡΠ΄Π΅Π»Π°Π½ Π½Π° ΡΠ»Π΅ΠΊΡΡΠΎΡ
ΠΈΠΌΠΈΡΠ΅ΡΠΊΠΎΠΌ ΠΎΠΏΡΠ΅Π΄Π΅Π»Π΅Π½ΠΈΠΈ ΠΌΠ΅ΡΠ°Π±ΠΎΠ»ΠΈΡΠΎΠ² Π»Π΅ΠΊΠ°ΡΡΡΠ²Π΅Π½Π½ΡΡ
ΠΏΡΠ΅ΠΏΠ°ΡΠ°ΡΠΎΠ² ΠΈ Π½Π΅ΠΊΠΎΡΠΎΡΡΡ
ΡΠ½Π΄ΠΎΠ³Π΅Π½Π½ΡΡ
ΡΠΎΠ΅Π΄ΠΈΠ½Π΅Π½ΠΈΠΉ. Π Π°ΡΡΠΌΠΎΡΡΠ΅Π½Ρ Π±ΠΈΡΠ»Π΅ΠΊΡΡΠΎΠ΄Π½ΡΠ΅ ΡΠΈΡΡΠ΅ΠΌΡ Π΄Π»Ρ ΠΎΠΏΡΠ΅Π΄Π΅Π»Π΅Π½ΠΈΡ Π°ΠΊΡΠΈΠ²Π½ΠΎΡΡΠΈ ΡΠΈΡΠΎΡ
ΡΠΎΠΌΠΎΠ² P450, Π² ΠΊΠΎΡΠΎΡΡΡ
ΠΎΠ΄ΠΈΠ½ ΡΠ»Π΅ΠΊΡΡΠΎΠ΄ Π²ΡΡΡΡΠΏΠ°Π΅Ρ Π² ΠΊΠ°ΡΠ΅ΡΡΠ²Π΅ ΠΌΠ°ΡΡΠΈΡΡ Π΄Π»Ρ ΠΈΠΌΠΌΠΎΠ±ΠΈΠ»ΠΈΠ·Π°ΡΠΈΠΈ ΡΠ΅ΡΠΌΠ΅Π½ΡΠ° ΠΈ Π΄ΠΎΠ½ΠΎΡΠ° ΡΠ»Π΅ΠΊΡΡΠΎΠ½ΠΎΠ² Π΄Π»Ρ Π²ΠΎΡΡΡΠ°Π½ΠΎΠ²Π»Π΅Π½ΠΈΡ ΠΈΠΎΠ½Π° ΠΆΠ΅Π»Π΅Π·Π° Π³Π΅ΠΌΠ° ΠΈ ΠΈΠ½ΠΈΡΠΈΠ°ΡΠΈΠΈ ΠΊΠ°ΡΠ°Π»ΠΈΡΠΈΡΠ΅ΡΠΊΠΎΠΉ ΡΠ΅Π°ΠΊΡΠΈΠΈ ΠΏΠΎ ΠΎΡΠ½ΠΎΡΠ΅Π½ΠΈΡ ΠΊ ΡΡΠ±ΡΡΡΠ°ΡΡ, Π° Π²ΡΠΎΡΠΎΠΉ β Π΄Π»Ρ ΠΊΠΎΠ»ΠΈΡΠ΅ΡΡΠ²Π΅Π½Π½ΠΎΠ³ΠΎ ΠΎΠΏΡΠ΅Π΄Π΅Π»Π΅Π½ΠΈΡ ΠΎΠ±ΡΠ°Π·ΡΡΡΠΈΡ
ΡΡ ΠΏΡΠΎΠ΄ΡΠΊΡΠΎΠ² ΠΏΡΡΠ΅ΠΌ ΠΈΡ
ΡΠ»Π΅ΠΊΡΡΠΎΡ
ΠΈΠΌΠΈΡΠ΅ΡΠΊΠΎΠ³ΠΎ ΠΎΠΊΠΈΡΠ»Π΅Π½ΠΈΡ. Π’Π°ΠΊΠΈΠ΅ ΡΠΈΡΡΠ΅ΠΌΡ Π² ΠΈΠ΄Π΅Π°Π»Π΅ ΠΏΠΎΠ·Π²ΠΎΠ»ΡΡΡ ΠΈΠ·Π±Π΅ΠΆΠ°ΡΡ Π΄ΠΎΠΏΠΎΠ»Π½ΠΈΡΠ΅Π»ΡΠ½ΡΡ
ΡΡΠ°Π΄ΠΈΠΉ ΡΠ°Π·Π΄Π΅Π»Π΅Π½ΠΈΡ ΡΡΠ±ΡΡΡΠ°ΡΠΎΠ² ΠΈ ΠΏΡΠΎΠ΄ΡΠΊΡΠΎΠ² ΡΠ΅Π°ΠΊΡΠΈΠΉ. Π ΠΎΠ±Π·ΠΎΡΠ΅ ΡΠ°ΠΊΠΆΠ΅ ΠΎΠ±ΡΡΠΆΠ΄Π΅Π½Ρ ΡΠΏΠΎΡΠΎΠ±Ρ ΠΏΠΎΠ²ΡΡΠ΅Π½ΠΈΡ Π°Π½Π°Π»ΠΈΡΠΈΡΠ΅ΡΠΊΠΎΠΉ ΡΡΠ²ΡΡΠ²ΠΈΡΠ΅Π»ΡΠ½ΠΎΡΡΠΈ ΠΈ ΡΠ½ΠΈΠΆΠ΅Π½ΠΈΡ ΠΏΡΠ΅Π΄Π΅Π»Π° ΠΎΠΏΡΠ΅Π΄Π΅Π»ΡΠ΅ΠΌΡΡ
ΠΊΠΎΠ½ΡΠ΅Π½ΡΡΠ°ΡΠΈΠΉ ΠΈΡΡΠ»Π΅Π΄ΡΠ΅ΠΌΡΡ
ΠΌΠ΅ΡΠ°Π±ΠΎΠ»ΠΈΡΠΎΠ² Π·Π° ΡΡΠ΅Ρ Ρ
ΠΈΠΌΠΈΡΠ΅ΡΠΊΠΎΠΉ ΠΌΠΎΠ΄ΠΈΡΠΈΠΊΠ°ΡΠΈΠΈ ΡΠ»Π΅ΠΊΡΡΠΎΠ΄ΠΎΠ². ΠΠΎΠΊΠ°Π·Π°Π½Ρ Π²ΠΎΠ·ΠΌΠΎΠΆΠ½ΠΎΡΡΠΈ ΡΠ°ΠΊΠΈΡ
ΡΠΈΡΡΠ΅ΠΌ Π΄Π»Ρ Π°Π½Π°Π»ΠΈΠ·Π° ΠΊΠΈΠ½Π΅ΡΠΈΠΊΠΈ ΡΠ΅Π°ΠΊΡΠΈΠΉ, ΠΊΠ°ΡΠ°Π»ΠΈΠ·ΠΈΡΡΠ΅ΠΌΡΡ
ΡΠΈΡΠΎΡ
ΡΠΎΠΌΠ°ΠΌΠΈ P450, ΠΈ ΠΏΠ΅ΡΡΠΏΠ΅ΠΊΡΠΈΠ²Ρ ΠΈΡ
Π΄Π°Π»ΡΠ½Π΅ΠΉΡΠ΅Π³ΠΎ ΡΠ°Π·Π²ΠΈΡΠΈΡ, ΡΠ°ΠΊΠΈΠ΅ ΠΊΠ°ΠΊ ΠΏΠΎΠ²ΡΡΠ΅Π½ΠΈΠ΅ ΡΡΠ²ΡΡΠ²ΠΈΡΠ΅Π»ΡΠ½ΠΎΡΡΠΈ ΡΠ»Π΅ΠΊΡΡΠΎΡ
ΠΈΠΌΠΈΡΠ΅ΡΠΊΠΎΠ³ΠΎ ΠΎΠΏΡΠ΅Π΄Π΅Π»Π΅Π½ΠΈΡ ΠΌΠ΅ΡΠ°Π±ΠΎΠ»ΠΈΡΠΎΠ² Ρ ΠΏΠΎΠΌΠΎΡΡΡ ΡΠΎΠ²ΡΠ΅ΠΌΠ΅Π½Π½ΡΡ
ΠΌΠ°ΡΠ΅ΡΠΈΠ°Π»ΠΎΠ² Π΄Π»Ρ ΠΌΠΎΠ΄ΠΈΡΠΈΠΊΠ°ΡΠΈΠΈ ΡΠ»Π΅ΠΊΡΡΠΎΠ΄ΠΎΠ², Π² ΡΠΎΠΌ ΡΠΈΡΠ»Π΅ Π½Π° ΠΎΡΠ½ΠΎΠ²Π΅ ΡΠ³Π»Π΅ΡΠΎΠ΄Π°, Π° ΡΠ°ΠΊΠΆΠ΅ ΡΠΎΠ·Π΄Π°Π½ΠΈΠ΅ ΡΡΡΡΠΎΠΉΡΡΠ², ΠΏΠΎΠ·Π²ΠΎΠ»ΡΡΡΠΈΡ
Π² Π°Π²ΡΠΎΠΌΠ°ΡΠΈΡΠ΅ΡΠΊΠΎΠΌ ΡΠ΅ΠΆΠΈΠΌΠ΅ ΠΏΡΠΎΠ²ΠΎΠ΄ΠΈΡΡ ΡΠ΅Π³ΠΈΡΡΡΠ°ΡΠΈΡ ΠΎΠ±ΡΠ°Π·ΡΡΡΠΈΡ
ΡΡ ΠΏΡΠΎΠ΄ΡΠΊΡΠΎΠ²
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