19 research outputs found
Application of a decision support system in an industrial enterprise
Work at an industrial enterprise is associated with the constant adoption of management decisions at all levels, on which both the timeliness of order fulfilment and the efficiency of production capacity depend. The use of modern information technologies based on mathematical methods, allow you to make a decision knowing what should happen. Β© Published under licence by IOP Publishing Ltd.This work is supported by Act 211 Government of the Russian Federation, contract β 02.A03.21.0006
Multi-agent simulation of the processing shop
Multi-agent model is applied for the transformation of resources used for research companies or parts of companies in the presence of high load or idle assets in production, realized by means of the metallurgical enterprise information system. The following solution has been found as a result of experiments. There are needs in increase the number of heat-treatment furnaces and reduction the number of staff.ΠΡΠ»ΡΡΠΈΠ°Π³Π΅Π½ΡΠ½Π°Ρ ΠΌΠΎΠ΄Π΅Π»Ρ ΠΏΡΠΎΡΠ΅ΡΡΠ° ΠΏΡΠ΅ΠΎΠ±ΡΠ°Π·ΠΎΠ²Π°Π½ΠΈΡ ΡΠ΅ΡΡΡΡΠΎΠ² ΠΏΡΠΈΠΌΠ΅Π½ΡΠ΅ΡΡΡ Π΄Π»Ρ ΠΈΡΡΠ»Π΅Π΄ΠΎΠ²Π°Π½ΠΈΡ ΠΏΡΠ΅Π΄ΠΏΡΠΈΡΡΠΈΠΉ ΠΈΠ»ΠΈ ΡΠ°ΡΡΠ΅ΠΉ ΠΏΡΠ΅Π΄ΠΏΡΠΈΡΡΠΈΠΉ Π½Π° Π½Π°Π»ΠΈΡΠΈΠ΅ ΠΏΡΠΎΡΡΠΎΠ΅Π² ΠΈΠ»ΠΈ Π²ΡΡΠΎΠΊΠΎΠΉ Π·Π°Π³ΡΡΠΆΠ΅Π½Π½ΠΎΡΡΠΈ ΡΡΠ΅Π΄ΡΡΠ² Π² ΠΏΡΠΎΠΈΠ·Π²ΠΎΠ΄ΡΡΠ²Π΅, ΡΠ΅Π°Π»ΠΈΠ·ΠΎΠ²Π°Π½Π½ΠΎΠΉ ΠΏΡΠΈ ΠΏΠΎΠΌΠΎΡΠΈ Π°Π²ΡΠΎΠΌΠ°ΡΠΈΠ·ΠΈΡΠΎΠ²Π°Π½Π½ΠΎΠΉ ΡΠΈΡΡΠ΅ΠΌΡ Π²ΡΠΏΡΡΠΊΠ° ΠΌΠ΅ΡΠ°Π»Π»ΡΡΠ³ΠΈΡΠ΅ΡΠΊΠΎΠΉ ΠΏΡΠΎΠ΄ΡΠΊΡΠΈΠΈ. ΠΠ° ΠΎΡΠ½ΠΎΠ²Π°Π½ΠΈΠΈ ΠΏΠΎΡΡΡΠΎΠ΅Π½Π½ΠΎΠΉ ΠΌΠΎΠ΄Π΅Π»ΠΈ ΠΏΡΠΎΠ²Π΅Π΄Π΅Π½Ρ ΡΠΊΡΠΏΠ΅ΡΠΈΠΌΠ΅Π½ΡΡ ΠΈ ΠΏΡΠ΅Π΄ΡΡΠ°Π²Π»Π΅Π½Ρ ΡΠ΅ΠΊΠΎΠΌΠ΅Π½Π΄Π°ΡΠΈΠΈ ΠΏΠΎ ΠΈΠ·ΠΌΠ΅Π½Π΅Π½ΠΈΡ Π°Π½Π°Π»ΠΈΠ·ΠΈΡΡΠ΅ΠΌΡΡ
ΠΏΡΠΎΡΠ΅ΡΡΠΎΠ²: Π½Π΅ΠΎΠ±Ρ
ΠΎΠ΄ΠΈΠΌΠΎ ΡΠ²Π΅Π»ΠΈΡΠΈΡΡ ΠΊΠΎΠ»ΠΈΡΠ΅ΡΡΠ²ΠΎ ΡΠ΅ΡΠΌΠΈΡΠ΅ΡΠΊΠΈΡ
ΠΏΠ΅ΡΠ΅ΠΉ ΠΈ ΡΠ½ΠΈΠ·ΠΈΡΡ ΠΊΠΎΠ»ΠΈΡΠ΅ΡΡΠ²ΠΎ ΠΏΠ΅ΡΡΠΎΠ½Π°Π»Π° ΡΠ΅Ρ
Π°
ΠΡΠΎΠ±Π΅Π½Π½ΠΎΡΡΠΈ ΠΊΠ»Π΅ΡΠΎΡΠ½ΠΎΠ³ΠΎ ΠΈΠΌΠΌΡΠ½ΠΈΡΠ΅ΡΠ° ΠΈ ΡΠΈΡΠΎΠΊΠΈΠ½ΠΎΠ²ΠΎΠ³ΠΎ ΡΠ΅ΠΏΠ΅ΡΡΡΠ°ΡΠ° Ρ ΠΏΠ°ΡΠΈΠ΅Π½ΡΠΎΠ² Ρ ΠΌΠ΅ΡΠ°Π±ΠΎΠ»ΠΈΡΠ΅ΡΠΊΠΈΠΌ ΡΠΈΠ½Π΄ΡΠΎΠΌΠΎΠΌ
In the formation ofΒ metabolic syndromeΒ there is a changeΒ of the quantitative characteristicsΒ of immunocompetent cellsΒ andΒ blood cytokine profile. According toΒ our study, the elevatedΒ serumΒ content of inflammatory cytokinesΒ (IL-6, IFNΞ³Β andΒ TGF-Ξ²), in the cases of IL-10 decrease, was detected in patients withΒ metabolic syndrome. The level of serumΒ IL-1Β inpatients withΒ metabolic syndromeΒ wasΒ within normal parameters. With it, there was a significant decrease inΒ the percentage of bloodΒ CD3- andΒ CD4-T-lymphocytes,Β and, conversely,Β an increaseΒ in the number of activated TΒ (CD25+) and BΒ (CD23+)-lymphocytes andΒ monocytesΒ (CD14+) has been in patientsΒ withΒ metabolic syndrome. This revealed changes, is characterized byΒ the presence ofΒ subclinical chronic inflammation in metabolic syndromeΒ patients. It can be the result ofΒ compensatoryΒ immune responses developingΒ inΒ the weakening ofΒ adaptive immunity.Π ΠΏΡΠΎΡΠ΅ΡΡΠ΅ ΡΠΎΡΠΌΠΈΡΠΎΠ²Π°Π½ΠΈΡ ΠΌΠ΅ΡΠ°Π±ΠΎΠ»ΠΈΡΠ΅ΡΠΊΠΎΠ³ΠΎ ΡΠΈΠ½Π΄ΡΠΎΠΌΠ° ΠΏΡΠΎΠΈΡΡ
ΠΎΠ΄ΠΈΡ ΠΈΠ·ΠΌΠ΅Π½Π΅Π½ΠΈΠ΅ ΠΊΠΎΠ»ΠΈΡΠ΅ΡΡΠ²Π΅Π½Π½ΡΡ
Ρ
Π°ΡΠ°ΠΊΡΠ΅ΡΠΈΡΡΠΈΠΊ ΠΈΠΌΠΌΡΠ½ΠΎΠΊΠΎΠΌΠΏΠ΅ΡΠ΅Π½ΡΠ½ΡΡ
ΠΊΠ»Π΅ΡΠΎΠΊ ΠΈ ΡΠΈΡΠΎΠΊΠΈΠ½ΠΎΠ²ΠΎΠ³ΠΎ ΠΏΡΠΎΡΠΈΠ»Ρ ΠΊΡΠΎΠ²ΠΈ. Π‘ΠΎΠ³Π»Π°ΡΠ½ΠΎ Π΄Π°Π½Π½ΡΠΌ ΠΏΡΠΎΠ²Π΅Π΄Π΅Π½Π½ΠΎΠ³ΠΎ ΠΈΡΡΠ»Π΅Π΄ΠΎΠ²Π°Π½ΠΈΡ, Ρ Π±ΠΎΠ»ΡΠ½ΡΡ
ΠΌΠ΅ΡΠ°Π±ΠΎΠ»ΠΈΡΠ΅ΡΠΊΠΈΠΌ ΡΠΈΠ½Π΄ΡΠΎΠΌΠΎΠΌ ΡΠ΅Π³ΠΈΡΡΡΠΈΡΠΎΠ²Π°Π»ΠΎΡΡ ΠΏΠΎΠ²ΡΡΠ΅Π½Π½ΠΎΠ΅ ΡΡΠ²ΠΎΡΠΎΡΠΎΡΠ½ΠΎΠ΅ ΡΠΎΠ΄Π΅ΡΠΆΠ°Π½ΠΈΠ΅ ΠΏΡΠΎΠ²ΠΎΡΠΏΠ°Π»ΠΈΡΠ΅Π»ΡΠ½ΡΡ
ΡΠΈΡΠΎΠΊΠΈΠ½ΠΎΠ² (IL-6, IFNΞ³ ΠΈ TGF-Ξ²) ΠΏΡΠΈ ΡΠ½ΠΈΠΆΠ΅Π½ΠΈΠΈ IL-10. Π£ΡΠΎΠ²Π΅Π½Ρ ΡΡΠ²ΠΎΡΠΎΡΠΎΡΠ½ΠΎΠ³ΠΎ IL-1 Ρ Π»ΠΈΡ Ρ ΠΌΠ΅ΡΠ°Π±ΠΎΠ»ΠΈΡΠ΅ΡΠΊΠΈΠΌ ΡΠΈΠ½Π΄ΡΠΎΠΌΠΎΠΌ Π½Π°Ρ
ΠΎΠ΄ΠΈΠ»ΡΡ Π² ΠΏΡΠ΅Π΄Π΅Π»Π°Ρ
Π½ΠΎΡΠΌΡ. ΠΠΌΠ΅ΡΡΠ΅ Ρ ΡΠ΅ΠΌ Ρ ΠΏΠ°ΡΠΈΠ΅Π½ΡΠΎΠ² Ρ ΠΌΠ΅ΡΠ°Π±ΠΎΠ»ΠΈΡΠ΅ΡΠΊΠΈΠΌ ΡΠΈΠ½Π΄ΡΠΎΠΌΠΎΠΌ ΠΈΠΌΠ΅Π»ΠΎ ΠΌΠ΅ΡΡΠΎ Π΄ΠΎΡΡΠΎΠ²Π΅ΡΠ½ΠΎΠ΅ ΡΠΌΠ΅Π½ΡΡΠ΅Π½ΠΈΠ΅ ΡΠΎΠ΄Π΅ΡΠΆΠ°Π½ΠΈΡ Π² ΠΊΡΠΎΠ²ΠΈ Π‘D3- ΠΈ Π‘D4-Π’-Π»ΠΈΠΌΡΠΎΡΠΈΡΠΎΠ² ΠΈ, Π½Π°ΠΏΡΠΎΡΠΈΠ², ΠΏΠΎΠ²ΡΡΠ΅Π½ΠΈΠ΅ ΡΠΈΡΠ»Π° Π°ΠΊΡΠΈΠ²ΠΈΡΠΎΠ²Π°Π½Π½ΡΡ
Π’- (Π‘D25+) ΠΈ Π (Π‘D23+)-Π»ΠΈΠΌΡΠΎΡΠΈΡΠΎΠ², Π° ΡΠ°ΠΊΠΆΠ΅ ΠΌΠΎΠ½ΠΎΡΠΈΡΠΎΠ² (CD14+). ΠΡΡΠ²Π»Π΅Π½Π½ΡΠ΅ ΠΈΠ·ΠΌΠ΅Π½Π΅Π½ΠΈΡ Ρ
Π°ΡΠ°ΠΊΡΠ΅ΡΠΈΠ·ΡΡΡ Π½Π°Π»ΠΈΡΠΈΠ΅ Ρ Π±ΠΎΠ»ΡΠ½ΡΡ
ΠΌΠ΅ΡΠ°Π±ΠΎΠ»ΠΈΡΠ΅ΡΠΊΠΈΠΌ ΡΠΈΠ½Π΄ΡΠΎΠΌΠΎΠΌ ΡΡΠ±ΠΊΠ»ΠΈΠ½ΠΈΡΠ΅ΡΠΊΠΎΠ³ΠΎ Ρ
ΡΠΎΠ½ΠΈΡΠ΅ΡΠΊΠΎΠ³ΠΎ Π²ΠΎΡΠΏΠ°Π»Π΅Π½ΠΈΡ, ΠΊΠΎΡΠΎΡΠΎΠ΅ ΠΌΠΎΠΆΠ΅Ρ Π±ΡΡΡ ΡΠ»Π΅Π΄ΡΡΠ²ΠΈΠ΅ΠΌ ΠΊΠΎΠΌΠΏΠ΅Π½ΡΠ°ΡΠΎΡΠ½ΡΡ
ΠΈΠΌΠΌΡΠ½Π½ΡΡ
ΡΠ΅Π°ΠΊΡΠΈΠΉ, ΡΠ°Π·Π²ΠΈΠ²Π°ΡΡΠΈΡ
ΡΡ Π½Π° ΡΠΎΠ½Π΅ ΠΎΡΠ»Π°Π±Π»Π΅Π½ΠΈΡ Π°Π΄Π°ΠΏΡΠΈΠ²Π½ΠΎΠ³ΠΎ ΠΈΠΌΠΌΡΠ½ΠΈΡΠ΅ΡΠ°
ΠΠΠΠΠΠΠ‘Π’ΠΠ§ΠΠ‘ΠΠΠ ΠΠΠΠ§ΠΠΠΠ Π’Π ΠΠΠΠΠ¦ΠΠ’ΠΠ ΠΠΠΠ Π€ΠΠΠ’ΠΠ Π Π ΠΠ‘Π’Π PDGF-BB Π ST2 ΠΠ Π ΠΠ’Π’ΠΠ ΠΠΠΠΠ Π’Π ΠΠΠ‘ΠΠΠΠΠ’ΠΠ ΠΠΠΠΠΠΠΠ Π‘ΠΠ ΠΠ¦Π
Aim: to determine the association between plasma concentrations of biomarkers (sCD40L, PDGF-BB, PlGF-1,Β ST2) with histochemical and immunohistochemical signs of heart rejection.Materials and methods. The studyΒ included 98 heart recipients aged from 12 to 69 (mean age 43 Β± 14) years, of which 78 men. In 68 patients dilatedΒ cardiomyopathy was diagnosed, 30 recipients were diagnosed with coronary heart disease. The concentrationsΒ of placental growth factor (PlGF-1), platelet-derived growth factor (PDGF-BB), soluble CD40 ligand (sCD40L)Β were measured using xMAP technology. The concentrations of ST2 cardiac biomarker were measured byΒ ELISA.Results. No correlation was found between the levels of biomarkers (sCD40L, PDGF-BB, PlGF-1, ST2)Β and gender, age and diagnosis. The rejection was diagnosed via biopsy in 49 biopsies taken from 37 recipients.Β 1A rejection was found in 25 patients (34 biopsies), 1B rejection was identifi ed in 2 patients (3 biopsies), 3AΒ rejection was diagnosed in 4 patients. Immunohistochemical signs of humoral rejection were identifi ed in 3 patients.Β The combination of acute cellular and humoral rejection was found in 4 patients (5 biopsies). The PDGFBBΒ level was measured at the same day as the biopsy was taken, and it was shown to be signifi cantly higher inΒ patients with rejection (p = 0.02). Rejection frequency was signifi cantly higher in patients with high PDGF-BBΒ level (β₯2473.7 pg/ml, RR = 1.64 Β± 0.23; 95% CI [1.03β2.61]). Rejection frequency increased to 2.11 Β± 0.34Β [95% CI [1.08β4.11]] in recipients with ST2 and PDGF-BB concentration higher than the median value. TheΒ highest predictive value for heart rejection can be reached by a panel of three biomarkers: sCD40L, PlGF-1 andΒ ST2 (RR = 2.51 Β± 0.38; 95% CI [1.18β5.3]).Conclusion. PDGF-BB has moderate predictive value for heartΒ rejection. The highest predictive value for heart rejection was reached by a panel of three biomarkers: sCD40L,Β PlGF-1 and ST2.Π¦Π΅Π»Ρ: ΠΎΠΏΡΠ΅Π΄Π΅Π»ΠΈΡΡ ΡΠ²ΡΠ·Ρ ΠΊΠΎΠ½ΡΠ΅Π½ΡΡΠ°ΡΠΈΠΈ Π±ΠΈΠΎΠΌΠ°ΡΠΊΠ΅ΡΠΎΠ² sCD40L, PDGF-BB, PlGF-1, ST2 Π² ΠΏΠ»Π°Π·ΠΌΠ΅ ΠΊΡΠΎΠ²ΠΈ ΡΠ΅ΡΠΈΠΏΠΈΠ΅Π½ΡΠΎΠ² ΡΠ΅ΡΠ΄ΡΠ° Ρ Π½Π°Π»ΠΈΡΠΈΠ΅ΠΌ ΠΈ Π²ΡΡΠ°ΠΆΠ΅Π½Π½ΠΎΡΡΡΡ Π³ΠΈΡΡΠΎΡ
ΠΈΠΌΠΈΡΠ΅ΡΠΊΠΈΡ
ΠΈ ΠΈΠΌΠΌΡΠ½ΠΎΠ³ΠΈΡΡΠΎΡ
ΠΈΠΌΠΈΡΠ΅ΡΠΊΠΈΡ
ΠΏΡΠΈΠ·Π½Π°ΠΊΠΎΠ²Β ΠΎΡΡΠΎΡΠΆΠ΅Π½ΠΈΡ ΡΠ΅ΡΠ΄Π΅ΡΠ½ΠΎΠ³ΠΎ ΡΡΠ°Π½ΡΠΏΠ»Π°Π½ΡΠ°ΡΠ°.ΠΠ°ΡΠ΅ΡΠΈΠ°Π»Ρ ΠΈ ΠΌΠ΅ΡΠΎΠ΄Ρ. Π ΠΈΡΡΠ»Π΅Π΄ΠΎΠ²Π°Π½ΠΈΠ΅ Π²ΠΊΠ»ΡΡΠ΅Π½Ρ 98 ΠΏΠ°ΡΠΈΠ΅Π½ΡΠΎΠ²Β Ρ ΡΡΠ°Π½ΡΠΏΠ»Π°Π½ΡΠΈΡΠΎΠ²Π°Π½Π½ΡΠΌ ΡΠ΅ΡΠ΄ΡΠ΅ΠΌ Π² Π²ΠΎΠ·ΡΠ°ΡΡΠ΅ ΠΎΡ 12 Π΄ΠΎ 69 (43 Β± 14) Π»Π΅Ρ, ΠΈΠ· Π½ΠΈΡ
78 ΠΌΡΠΆΡΠΈΠ½. Π£ 68 ΡΠ΅ΡΠΈΠΏΠΈΠ΅Π½ΡΠΎΠ² Π΄ΠΎ ΡΡΠ°Π½ΡΠΏΠ»Π°Π½ΡΠ°ΡΠΈΠΈ ΡΠ΅ΡΠ΄ΡΠ° Π±ΡΠ»Π° Π΄ΠΈΠ°Π³Π½ΠΎΡΡΠΈΡΠΎΠ²Π°Π½Π° Π΄ΠΈΠ»Π°ΡΠ°ΡΠΈΠΎΠ½Π½Π°Ρ ΠΊΠ°ΡΠ΄ΠΈΠΎΠΌΠΈΠΎΠΏΠ°ΡΠΈΡ, Ρ 30 β ΠΈΡΠ΅ΠΌΠΈΡΠ΅ΡΠΊΠ°Ρ Π±ΠΎΠ»Π΅Π·Π½Ρ ΡΠ΅ΡΠ΄ΡΠ°. ΠΠΎΠ½ΡΠ΅Π½ΡΡΠ°ΡΠΈΡ ΠΏΠ»Π°ΡΠ΅Π½ΡΠ°ΡΠ½ΠΎΠ³ΠΎ ΡΠ°ΠΊΡΠΎΡΠ° ΡΠΎΡΡΠ° (PlGF-1), ΡΠ°ΠΊΡΠΎΡΠ° ΡΠΎΡΡΠ° ΡΡΠΎΠΌΠ±ΠΎΡΠΈΡΠΎΠ²Β (PDGF-BB), ΡΠ°ΡΡΠ²ΠΎΡΠΈΠΌΠΎΠΉ ΡΠΎΡΠΌΡ Π»ΠΈΠ³Π°Π½Π΄Π° CD40 (sCD40L) ΠΈΠ·ΠΌΠ΅ΡΡΠ»ΠΈ Ρ ΠΈΡΠΏΠΎΠ»ΡΠ·ΠΎΠ²Π°Π½ΠΈΠ΅ΠΌ ΠΌΡΠ»ΡΡΠΈΠΏΠ»Π΅ΠΊΡΠ½ΠΎΠΉΒ ΡΠ΅Ρ
Π½ΠΎΠ»ΠΎΠ³ΠΈΠΈ; ΠΊΠΎΠ½ΡΠ΅Π½ΡΡΠ°ΡΠΈΡ ΡΡΠΈΠΌΡΠ»ΠΈΡΡΡΡΠ΅Π³ΠΎ ΡΠ°ΠΊΡΠΎΡΠ° ΡΠΎΡΡΠ° ST2 β Ρ ΠΏΠΎΠΌΠΎΡΡΡ ΠΈΠΌΠΌΡΠ½ΠΎΡΠ΅ΡΠΌΠ΅Π½ΡΠ½ΠΎΠ³ΠΎ Π°Π½Π°Π»ΠΈΠ·Π°.Π Π΅Π·ΡΠ»ΡΡΠ°ΡΡ. ΠΠΎΠ½ΡΠ΅Π½ΡΡΠ°ΡΠΈΡ ΠΊΠ°ΠΆΠ΄ΠΎΠ³ΠΎ Π±ΠΈΠΎΠΌΠ°ΡΠΊΠ΅ΡΠ° Π½Π΅ Π·Π°Π²ΠΈΡΠ΅Π»Π° ΠΎΡ ΠΏΠΎΠ»Π°, Π²ΠΎΠ·ΡΠ°ΡΡΠ° ΠΈ Π΄ΠΈΠ°Π³Π½ΠΎΠ·Π° Π΄ΠΎ ΡΡΠ°Π½ΡΠΏΠ»Π°Π½ΡΠ°ΡΠΈΠΈ. Π£ 37 ΠΏΠ°ΡΠΈΠ΅Π½ΡΠΎΠ² (ΠΏΠΎ ΡΠ΅Π·ΡΠ»ΡΡΠ°ΡΠ°ΠΌ 49 Π±ΠΈΠΎΠΏΡΠΈΠΉ) Π±ΡΠ»ΠΈ Π΄ΠΈΠ°Π³Π½ΠΎΡΡΠΈΡΠΎΠ²Π°Π½Ρ ΠΏΡΠΈΠ·Π½Π°ΠΊΠΈ ΠΎΡΡΠΎΡΠΆΠ΅Π½ΠΈΡ.Β ΠΠΈΡΡΠΎΡ
ΠΈΠΌΠΈΡΠ΅ΡΠΊΠΈΠ΅ ΠΏΡΠΈΠ·Π½Π°ΠΊΠΈ ΠΎΡΡΡΠΎΠ³ΠΎ ΠΊΠ»Π΅ΡΠΎΡΠ½ΠΎΠ³ΠΎ ΠΎΡΡΠΎΡΠΆΠ΅Π½ΠΈΡ β Ρ 30 ΡΠ΅ΡΠΈΠΏΠΈΠ΅Π½ΡΠΎΠ² (Π² 41 Π±ΠΈΠΎΠΏΡΠ°ΡΠ΅): 1Π β ΡΒ 25 ΠΏΠ°ΡΠΈΠ΅Π½ΡΠΎΠ² (Π² 34 Π±ΠΈΠΎΠΏΡΠ°ΡΠ°Ρ
), 1Π β Ρ Π΄Π²ΡΡ
ΠΏΠ°ΡΠΈΠ΅Π½ΡΠΎΠ² (Π² ΡΡΠ΅Ρ
Π±ΠΈΠΎΠΏΡΠ°ΡΠ°Ρ
), 3Π β Ρ ΡΠ΅ΡΡΡΠ΅Ρ
ΠΏΠ°ΡΠΈΠ΅Π½ΡΠΎΠ².Β ΠΠΌΠΌΡΠ½ΠΎΠ³ΠΈΡΡΠΎΡ
ΠΈΠΌΠΈΡΠ΅ΡΠΊΠΈΠ΅ ΠΏΡΠΈΠ·Π½Π°ΠΊΠΈ Π°Π½ΡΠΈΡΠ΅Π»ΠΎΠΎΠΏΠΎΡΡΠ΅Π΄ΠΎΠ²Π°Π½Π½ΠΎΠ³ΠΎ ΠΎΡΡΠΎΡΠΆΠ΅Π½ΠΈΡ Π²ΡΡΠ²Π»Π΅Π½Ρ Ρ ΡΡΠ΅Ρ
ΠΏΠ°ΡΠΈΠ΅Π½ΡΠΎΠ².Β Π‘ΠΎΡΠ΅ΡΠ°Π½ΠΈΠ΅ ΠΎΡΡΡΠΎΠ³ΠΎ ΠΊΠ»Π΅ΡΠΎΡΠ½ΠΎΠ³ΠΎ ΠΈ Π³ΡΠΌΠΎΡΠ°Π»ΡΠ½ΠΎΠ³ΠΎ ΠΎΡΡΠΎΡΠΆΠ΅Π½ΠΈΡ ΠΎΠ±Π½Π°ΡΡΠΆΠ΅Π½ΠΎ Ρ ΡΠ΅ΡΡΡΠ΅Ρ
ΠΏΠ°ΡΠΈΠ΅Π½ΡΠΎΠ² (Π² ΠΏΡΡΠΈΒ Π±ΠΈΠΎΠΏΡΠ°ΡΠ°Ρ
). ΠΠΎΠ½ΡΠ΅Π½ΡΡΠ°ΡΠΈΡ PDGF-BB, ΠΈΠ·ΠΌΠ΅ΡΠ΅Π½Π½Π°Ρ Π² Π΄Π΅Π½Ρ ΡΠ½Π΄ΠΎΠΌΠΈΠΎΠΊΠ°ΡΠ΄ΠΈΠ°Π»ΡΠ½ΠΎΠΉ Π±ΠΈΠΎΠΏΡΠΈΠΈ, Π±ΡΠ»Π° Π΄ΠΎΡΡΠΎΠ²Π΅ΡΠ½ΠΎΒ Π²ΡΡΠ΅ Ρ ΠΏΠ°ΡΠΈΠ΅Π½ΡΠΎΠ² Ρ ΠΎΡΡΠΎΡΠΆΠ΅Π½ΠΈΠ΅ΠΌ (p = 0,02). Π£ ΡΠ΅ΡΠΈΠΏΠΈΠ΅Π½ΡΠΎΠ² ΡΠ΅ΡΠ΄ΡΠ° Ρ ΡΡΠΎΠ²Π½Π΅ΠΌ PDGF-BB Π²ΡΡΠ΅ ΠΌΠ΅Π΄ΠΈΠ°Π½ΡΒ (2473,7 ΠΏΠ³/ΠΌΠ») ΡΠΈΡΠΊ ΠΎΡΡΠΎΡΠΆΠ΅Π½ΠΈΡ Π±ΡΠ» Π² 1,64 ΡΠ°Π·Π° Π²ΡΡΠ΅, ΡΠ΅ΠΌ Ρ ΠΏΠ°ΡΠΈΠ΅Π½ΡΠΎΠ² Ρ ΡΡΠΎΠ²Π½Π΅ΠΌ Π½ΠΈΠΆΠ΅ ΠΌΠ΅Π΄ΠΈΠ°Π½Ρ. Π£ ΡΠ΅ΡΠΈΠΏΠΈΠ΅Π½ΡΠΎΠ² Ρ ΠΊΠΎΠ½ΡΠ΅Π½ΡΡΠ°ΡΠΈΠ΅ΠΉ ΠΈ ST2, ΠΈ PDGF-BB, ΠΏΡΠ΅Π²ΡΡΠ°ΡΡΠ΅ΠΉ Π·Π½Π°ΡΠ΅Π½ΠΈΡ ΠΌΠ΅Π΄ΠΈΠ°Π½Ρ, ΠΎΡΠ½ΠΎΡΠΈΡΠ΅Π»ΡΠ½ΡΠΉ ΡΠΈΡΠΊΒ ΠΎΡΡΠΎΡΠΆΠ΅Π½ΠΈΡ Π²ΠΎΠ·ΡΠ°ΡΡΠ°Π» Π΄ΠΎ 2,11 Β± 0,34 [95% ΠΠ 1,08β4,11]. ΠΠ°ΠΈΠ±ΠΎΠ»ΡΡΠ΅ΠΉ Π΄ΠΈΠ°Π³Π½ΠΎΡΡΠΈΡΠ΅ΡΠΊΠΎΠΉ Π·Π½Π°ΡΠΈΠΌΠΎΡΡΡΡΒ Π² ΠΎΡΠ½ΠΎΡΠ΅Π½ΠΈΠΈ ΠΎΡΡΠΎΡΠΆΠ΅Π½ΠΈΡ ΡΡΠ°Π½ΡΠΏΠ»Π°Π½ΡΠ°ΡΠ° ΠΎΠ±Π»Π°Π΄Π°Π»Π° ΠΏΠ°Π½Π΅Π»Ρ ΠΈΠ· ΡΡΠ΅Ρ
Π±ΠΈΠΎΠΌΠ°ΡΠΊΠ΅ΡΠΎΠ² (sCD40L, PlGF-1, ST2):Β RR = 2,51 Β± 0,38 [95% ΠΠ 1,18β5,3].ΠΠ°ΠΊΠ»ΡΡΠ΅Π½ΠΈΠ΅. PDGF-BB ΠΎΠ±Π»Π°Π΄Π°Π΅Ρ ΡΠΌΠ΅ΡΠ΅Π½Π½ΠΎΠΉ ΠΏΡΠΎΠ³Π½ΠΎΡΡΠΈΡΠ΅ΡΠΊΠΎΠΉ Π·Π½Π°ΡΠΈΠΌΠΎΡΡΡΡ Π² ΠΎΡΠ½ΠΎΡΠ΅Π½ΠΈΠΈ ΠΎΡΡΠΎΡΠΆΠ΅Π½ΠΈΡ ΡΡΠ°Π½ΡΠΏΠ»Π°Π½ΡΠΈΡΠΎΠ²Π°Π½Π½ΠΎΠ³ΠΎ ΡΠ΅ΡΠ΄ΡΠ°. ΠΠ°ΠΈΠ±ΠΎΠ»ΡΡΠ΅ΠΉ Π΄ΠΈΠ°Π³Π½ΠΎΡΡΠΈΡΠ΅ΡΠΊΠΎΠΉ Π·Π½Π°ΡΠΈΠΌΠΎΡΡΡΡ ΠΎΠ±Π»Π°Π΄Π°Π΅Ρ ΠΏΠ°Π½Π΅Π»Ρ ΠΈΠ· ΡΡΠ΅Ρ
Π±ΠΈΠΎΠΌΠ°ΡΠΊΠ΅ΡΠΎΠ²: sCD40L, PlGF-1, ST2
Π‘ΡΠ°Π²Π½ΠΈΡΠ΅Π»ΡΠ½ΡΠΉ Π°Π½Π°Π»ΠΈΠ· Π΄ΠΈΠ°Π³Π½ΠΎΡΡΠΈΡΠ΅ΡΠΊΠΎΠΉ Π·Π½Π°ΡΠΈΠΌΠΎΡΡΠΈ ΠΏΠ°Π½Π΅Π»Π΅ΠΉ Π±ΠΈΠΎΠΌΠ°ΡΠΊΠ΅ΡΠΎΠ² Ρ ΡΠ΅ΡΠΈΠΏΠΈΠ΅Π½ΡΠΎΠ² ΡΠ΅ΡΠ΄ΡΠ° Π²Β ΠΎΡΠ΄Π°Π»Π΅Π½Π½ΡΠ΅ ΡΡΠΎΠΊΠΈ ΠΏΠΎΡΠ»Π΅ ΡΡΠ°Π½ΡΠΏΠ»Π°Π½ΡΠ°ΡΠΈΠΈ
Aim. To perform comparative analysis of the diagnostic efficacy of sCD40L, PDGF-BB, VEGF-A and ST2 in recipients with cardiac rejection in different periods after transplantation. Materials and methods. The study included 144 cardiac recipients aged from 12 to 71 (mean age 44 Β± 14) years old, among those 112 were men. Venous blood plasma taken on the same day with endomyocardial biopsy was used for the study. The concentrations of soluble CD40 ligand (sCD40L), vascular endothelial growth factor (VEGF-A), platelet-derived growth factor (PDGF-BB) were measured using xMAP technology. The concentrations of ST2 were measured by ELISA. Results. Men had significantly higher levels of ST2 and VEGF-A compared to women (p = 0.03). No correlation was found between the levels of biomarkers (sCD40L, PDGF-BB, VEGF-A, ST2) and age, diagnosis before transplantation, presence of arterial hypertension and diabetes mellitus. Comparative analysis of the biomarkersβ levels didnβt show significant difference between patients with heart transplant rejection and without it in the first month and in the first year after transplantation. The ST2 level was significantly higher in patients with heart rejection (p = 0.01) in the long term period (1β5 years) after transplantation compared to patients without rejection. Relative risk of cardiac transplant rejection was significantly higher in patients with high (>22.8 ng/ml) ST2 level (RR = 2.59 Β± 0.33; Se β 35%, Sp β 93%). However, its combination with other biomarkers improved their diagnostic value. Relative risk for panel including ST2, VEGF-A and PDGF-BB 3.47 Β± 0.55, Se β 57%, Sp β 91%; relative risk for panel including ST2, sCD40L and PDGF-BB was 3.75 Β± 0.59, Se β 50%, Sp β 92%. The highest diagnostic efficacy for the heart transplant rejection was reached by a panel of biomarkers that included ST2 and PDGF-BB (RR = 5.0 Β± 0.56 [95% CI 1.68β14.92], Se β 63%, Sp β 94%). Conclusion. ST2 had the biggest diagnostic value for heart transplant rejection in the long term period after heart transplantation. Its usage as a part of complex tests with other biomarkers improves the sensitivity of noninvasive diagnosis of the cardiac rejection. The highest diagnostic significance for cardiac transplant rejection in the long term period was shown by a panel of ST2 and PDGF-BB.Π¦Π΅Π»Ρ. ΠΡΠΏΠΎΠ»Π½ΠΈΡΡ ΡΡΠ°Π²Π½ΠΈΡΠ΅Π»ΡΠ½ΡΠΉ Π°Π½Π°Π»ΠΈΠ· Π΄ΠΈΠ°Π³Π½ΠΎΡΡΠΈΡΠ΅ΡΠΊΠΎΠΉ ΡΡΡΠ΅ΠΊΡΠΈΠ²Π½ΠΎΡΡΠΈ sCD40L, PDGF-BB, VEGF-A ΠΈ ST2 Π² ΡΠΎΡΡΠ°Π²Π΅ ΠΏΠ°Π½Π΅Π»Π΅ΠΉ Π±ΠΈΠΎΠΌΠ°ΡΠΊΠ΅ΡΠΎΠ² ΠΏΡΠΈ ΠΎΡΡΠΎΡΠΆΠ΅Π½ΠΈΠΈ ΡΡΠ°Π½ΡΠΏΠ»Π°Π½ΡΠΈΡΠΎΠ²Π°Π½Π½ΠΎΠ³ΠΎ ΡΠ΅ΡΠ΄ΡΠ° Π² ΡΠ°Π·Π»ΠΈΡΠ½ΡΠ΅ ΡΡΠΎΠΊΠΈ ΠΏΠΎΡΠ»Π΅ ΡΡΠ°Π½ΡΠΏΠ»Π°Π½ΡΠ°ΡΠΈΠΈ. ΠΠ°ΡΠ΅ΡΠΈΠ°Π»Ρ ΠΈ ΠΌΠ΅ΡΠΎΠ΄Ρ. Π ΠΈΡΡΠ»Π΅Π΄ΠΎΠ²Π°Π½ΠΈΠ΅ Π²ΠΊΠ»ΡΡΠ΅Π½Ρ 144 ΡΠ΅ΡΠΈΠΏΠΈΠ΅Π½ΡΠ° ΡΠ΅ΡΠ΄ΡΠ° Π² Π²ΠΎΠ·ΡΠ°ΡΡΠ΅ ΠΎΡ 12 Π΄ΠΎ 71 (44 Β± 14) Π³ΠΎΠ΄Π°, ΠΈΠ· Π½ΠΈΡ
112 ΠΌΡΠΆΡΠΈΠ½. ΠΠΎΠ½ΡΠ΅Π½ΡΡΠ°ΡΠΈΡ ΡΠΈΡΠΎΠΊΠΈΠ½ΠΎΠ² ΠΈΠ·ΠΌΠ΅ΡΡΠ»ΠΈ Π² ΠΏΠ»Π°Π·ΠΌΠ΅ ΠΏΠ΅ΡΠΈΡΠ΅ΡΠΈΡΠ΅ΡΠΊΠΎΠΉ ΠΊΡΠΎΠ²ΠΈ ΠΏΠ°ΡΠΈΠ΅Π½ΡΠΎΠ², Π²Π·ΡΡΠΎΠΉ Π² Π΄Π΅Π½Ρ ΡΠ½Π΄ΠΎΠΌΠΈΠΎΠΊΠ°ΡΠ΄ΠΈΠ°Π»ΡΠ½ΠΎΠΉ Π±ΠΈΠΎΠΏΡΠΈΠΈ. Π£ΡΠΎΠ²Π΅Π½Ρ ΡΠ°ΡΡΠ²ΠΎΡΠΈΠΌΠΎΠΉ ΡΠΎΡΠΌΡ Π»ΠΈΠ³Π°Π½Π΄Π° CD40 (sCD40L), ΡΠ°ΠΊΡΠΎΡΠ° ΡΠΎΡΡΠ° ΡΠ½Π΄ΠΎΡΠ΅Π»ΠΈΡ ΡΠΎΡΡΠ΄ΠΎΠ² (VEGF-A), ΡΠ°ΠΊΡΠΎΡΠ° ΡΠΎΡΡΠ° ΡΡΠΎΠΌΠ±ΠΎΡΠΈΡΠΎΠ² (PDGF-BB), ΠΈΠ·ΠΌΠ΅ΡΡΠ»ΠΈ Ρ ΠΈΡΠΏΠΎΠ»ΡΠ·ΠΎΠ²Π°Π½ΠΈΠ΅ΠΌ ΠΌΡΠ»ΡΡΠΈΠΏΠ»Π΅ΠΊΡΠ½ΠΎΠΉ ΡΠ΅Ρ
Π½ΠΎΠ»ΠΎΠ³ΠΈΠΈ; ΡΡΠΎΠ²Π΅Π½Ρ ΡΡΠΈΠΌΡΠ»ΠΈΡΡΡΡΠ΅Π³ΠΎ ΡΠ°ΠΊΡΠΎΡΠ° ΡΠΎΡΡΠ° ST2 β Ρ ΠΏΠΎΠΌΠΎΡΡΡ ΠΈΠΌΠΌΡΠ½ΠΎΡΠ΅ΡΠΌΠ΅Π½ΡΠ½ΠΎΠ³ΠΎ Π°Π½Π°Π»ΠΈΠ·Π°. Π Π΅Π·ΡΠ»ΡΡΠ°ΡΡ. Π£ΡΠΎΠ²Π΅Π½Ρ ST2 ΠΈ VEGF-A Π±ΡΠ» Π΄ΠΎΡΡΠΎΠ²Π΅ΡΠ½ΠΎ Π²ΡΡΠ΅ Ρ ΠΌΡΠΆΡΠΈΠ½, ΡΠ΅ΠΌ Ρ ΠΆΠ΅Π½ΡΠΈΠ½ (p = 0,03). Π£ΡΠΎΠ²Π½ΠΈ ΠΈΡΡΠ»Π΅Π΄ΡΠ΅ΠΌΡΡ
Π±ΠΈΠΎΠΌΠ°ΡΠΊΠ΅ΡΠΎΠ² Π½Π΅ Π·Π°Π²ΠΈΡΠ΅Π»ΠΈ ΠΎΡ Π²ΠΎΠ·ΡΠ°ΡΡΠ°, Π΄ΠΈΠ°Π³Π½ΠΎΠ·Π° Π΄ΠΎ ΡΡΠ°Π½ΡΠΏΠ»Π°Π½ΡΠ°ΡΠΈΠΈ ΡΠ΅ΡΠ΄ΡΠ° (Π’Π‘), Π½Π°Π»ΠΈΡΠΈΡ Π°ΡΡΠ΅ΡΠΈΠ°Π»ΡΠ½ΠΎΠΉ Π³ΠΈΠΏΠ΅ΡΡΠΎΠ½ΠΈΠΈ, ΡΠ°Ρ
Π°ΡΠ½ΠΎΠ³ΠΎ Π΄ΠΈΠ°Π±Π΅ΡΠ° II ΡΠΈΠΏΠ°. Π‘ΡΠ°Π²Π½ΠΈΡΠ΅Π»ΡΠ½ΡΠΉ Π°Π½Π°Π»ΠΈΠ· ΡΡΠΎΠ²Π½Π΅ΠΉ Π±ΠΈΠΎΠΌΠ°ΡΠΊΠ΅ΡΠΎΠ² Π½Π΅ ΠΏΠΎΠΊΠ°Π·Π°Π» Π΄ΠΎΡΡΠΎΠ²Π΅ΡΠ½ΠΎΠΉ ΡΠ°Π·Π½ΠΈΡΡ Ρ ΠΏΠ°ΡΠΈΠ΅Π½ΡΠΎΠ² Ρ Π½Π°Π»ΠΈΡΠΈΠ΅ΠΌ ΠΎΡΡΠΎΡΠΆΠ΅Π½ΠΈΡ ΡΠ΅ΡΠ΄Π΅ΡΠ½ΠΎΠ³ΠΎ ΡΡΠ°Π½ΡΠΏΠ»Π°Π½ΡΠ°ΡΠ° ΠΈ Π±Π΅Π· ΡΠ°ΠΊΠΎΠ²ΠΎΠ³ΠΎ Π² ΠΏΠ΅ΡΠ²ΡΠΉ ΠΌΠ΅ΡΡΡ ΠΈ Π² ΠΏΠ΅ΡΠ²ΡΠΉ Π³ΠΎΠ΄ ΠΏΠΎΡΠ»Π΅ Π’Π‘. ΠΠΎΠ½ΡΠ΅Π½ΡΡΠ°ΡΠΈΡ ST2 Π±ΡΠ»Π° Π΄ΠΎΡΡΠΎΠ²Π΅ΡΠ½ΠΎ Π²ΡΡΠ΅ Ρ ΠΏΠ°ΡΠΈΠ΅Π½ΡΠΎΠ² Ρ ΠΎΡΡΠΎΡΠΆΠ΅Π½ΠΈΠ΅ΠΌ ΡΠ΅ΡΠ΄ΡΠ°, ΡΠ΅ΠΌ Π±Π΅Π· ΡΠ°ΠΊΠΎΠ²ΠΎΠ³ΠΎ, Π² ΠΎΡΠ΄Π°Π»Π΅Π½Π½ΡΠ΅ ΡΡΠΎΠΊΠΈ (1β5 Π»Π΅Ρ) ΠΏΠΎΡΠ»Π΅ Π’Π‘ (p = 0,01). ΠΡΠ½ΠΎΡΠΈΡΠ΅Π»ΡΠ½ΡΠΉ ΡΠΈΡΠΊ ΡΠ°Π·Π²ΠΈΡΠΈΡ ΠΎΡΡΠΎΡΠΆΠ΅Π½ΠΈΡ ΡΡΠ°Π½ΡΠΏΠ»Π°Π½ΡΠΈΡΠΎΠ²Π°Π½Π½ΠΎΠ³ΠΎ ΡΠ΅ΡΠ΄ΡΠ° Π² ΠΎΡΠ΄Π°Π»Π΅Π½Π½ΡΠ΅ ΡΡΠΎΠΊΠΈ ΠΏΠΎΡΠ»Π΅ Π’Π‘ Ρ ΠΏΠ°ΡΠΈΠ΅Π½ΡΠΎΠ² Ρ ΠΊΠΎΠ½ΡΠ΅Π½ΡΡΠ°ΡΠΈΠ΅ΠΉ ST2, ΠΏΡΠ΅Π²ΡΡΠ°ΡΡΠ΅ΠΉ Π·Π½Π°ΡΠ΅Π½ΠΈΠ΅ ΠΌΠ΅Π΄ΠΈΠ°Π½Ρ (22,8 Π½Π³/ΠΌΠ»), Π² 2,6 ΡΠ°Π·Π° Π²ΡΡΠ΅, ΡΠ΅ΠΌ Ρ ΠΏΠ°ΡΠΈΠ΅Π½ΡΠΎΠ² Ρ ΠΊΠΎΠ½ΡΠ΅Π½ΡΡΠ°ΡΠΈΠ΅ΠΉ ΡΠΈΡΠΎΠΊΠΈΠ½Π° Π½ΠΈΠΆΠ΅ ΠΌΠ΅Π΄ΠΈΠ°Π½Ρ (ΡΡΠ²ΡΡΠ²ΠΈΡΠ΅Π»ΡΠ½ΠΎΡΡΡ β 35%, ΡΠΏΠ΅ΡΠΈΡΠΈΡΠ½ΠΎΡΡΡ β 93%). ST2 Π² ΡΠΎΡΡΠ°Π²Π΅ ΠΏΠ°Π½Π΅Π»Π΅ΠΉ Π±ΠΈΠΎΠΌΠ°ΡΠΊΠ΅ΡΠΎΠ² ΡΠ²Π΅Π»ΠΈΡΠΈΠ²Π°Π΅Ρ ΠΈΡ
Π΄ΠΈΠ°Π³Π½ΠΎΡΡΠΈΡΠ΅ΡΠΊΡΡ Π·Π½Π°ΡΠΈΠΌΠΎΡΡΡ: Π΄Π»Ρ ΠΏΠ°Π½Π΅Π»ΠΈ, Π²ΠΊΠ»ΡΡΠ°ΡΡΠ΅ΠΉ ST2, PDGF-BB ΠΈ VEGF-A, ΠΎΡΠ½ΠΎΡΠΈΡΠ΅Π»ΡΠ½ΡΠΉ ΡΠΈΡΠΊ ΡΠΎΡΡΠ°Π²Π»ΡΠ» 3,47 Β± 0,55 (ΡΡΠ²ΡΡΠ²ΠΈΡΠ΅Π»ΡΠ½ΠΎΡΡΡ β 57%, ΡΠΏΠ΅ΡΠΈΡΠΈΡΠ½ΠΎΡΡΡ β 91%); Π΄Π»Ρ ΠΏΠ°Π½Π΅Π»ΠΈ, Π²ΠΊΠ»ΡΡΠ°ΡΡΠ΅ΠΉ ST2, PDGF-BB ΠΈ sCD40L β 3,75 Β± 0,59 (ΡΡΠ²ΡΡΠ²ΠΈΡΠ΅Π»ΡΠ½ΠΎΡΡΡ β 50%, ΡΠΏΠ΅ΡΠΈΡΠΈΡΠ½ΠΎΡΡΡ β 92%) Π‘ΠΎΡΠ΅ΡΠ°Π½ΠΈΠ΅ ST2 ΠΈ PDGF-BB ΠΎΠ±Π»Π°Π΄Π°Π»ΠΎ Π½Π°ΠΈΠ±ΠΎΠ»ΡΡΠ΅ΠΉ Π΄ΠΈΠ°Π³Π½ΠΎΡΡΠΈΡΠ΅ΡΠΊΠΎΠΉ Π·Π½Π°ΡΠΈΠΌΠΎΡΡΡΡ (RR = 5,00 Β± 0,56 [95% ΠΠ 1,68β14,92]; ΡΡΠ²ΡΡΠ²ΠΈΡΠ΅Π»ΡΠ½ΠΎΡΡΡ β 63%, ΡΠΏΠ΅ΡΠΈΡΠΈΡΠ½ΠΎΡΡΡ β 94%). ΠΠ°ΠΊΠ»ΡΡΠ΅Π½ΠΈΠ΅. ΠΠ°ΠΈΠ±ΠΎΠ»ΡΡΠ΅ΠΉ Π΄ΠΈΠ°Π³Π½ΠΎΡΡΠΈΡΠ΅ΡΠΊΠΎΠΉ Π·Π½Π°ΡΠΈΠΌΠΎΡΡΡΡ ΠΏΡΠΈ ΠΎΡΡΠΎΡΠΆΠ΅Π½ΠΈΠΈ ΡΡΠ°Π½ΡΠΏΠ»Π°Π½ΡΠΈΡΠΎΠ²Π°Π½Π½ΠΎΠ³ΠΎ ΡΠ΅ΡΠ΄ΡΠ° Π² ΠΎΡΠ΄Π°Π»Π΅Π½Π½ΡΠ΅ ΡΡΠΎΠΊΠΈ ΠΏΠΎΡΠ»Π΅ ΡΡΠ°Π½ΡΠΏΠ»Π°Π½ΡΠ°ΡΠΈΠΈ ΠΎΠ±Π»Π°Π΄Π°Π΅Ρ ST2, ΠΈ Π΅Π³ΠΎ ΠΏΡΠΈΠΌΠ΅Π½Π΅Π½ΠΈΠ΅ Π² ΡΠΎΡΡΠ°Π²Π΅ ΠΊΠΎΠΌΠΏΠ»Π΅ΠΊΡΠ½ΡΡ
ΡΠ΅ΡΡΠΎΠ² Ρ Π΄ΡΡΠ³ΠΈΠΌΠΈ Π±ΠΈΠΎΠΌΠ°ΡΠΊΠ΅ΡΠ°ΠΌΠΈ ΠΏΠΎΠ·Π²ΠΎΠ»ΡΠ΅Ρ ΡΠ»ΡΡΡΠΈΡΡ ΡΡΠ²ΡΡΠ²ΠΈΡΠ΅Π»ΡΠ½ΠΎΡΡΡ Π½Π΅ΠΈΠ½Π²Π°Π·ΠΈΠ²Π½ΠΎΠΉ Π΄ΠΈΠ°Π³Π½ΠΎΡΡΠΈΠΊΠΈ ΠΎΡΡΠΎΡΠΆΠ΅Π½ΠΈΡ. ΠΠ°ΠΈΠ±ΠΎΠ»ΡΡΠ΅ΠΉ Π΄ΠΈΠ°Π³Π½ΠΎΡΡΠΈΡΠ΅ΡΠΊΠΎΠΉ Π·Π½Π°ΡΠΈΠΌΠΎΡΡΡΡ Π² ΠΎΡΠ΄Π°Π»Π΅Π½Π½ΡΠ΅ ΡΡΠΎΠΊΠΈ ΠΏΠΎΡΠ»Π΅ Π’Π‘ ΠΎΠ±Π»Π°Π΄Π°Π΅Ρ ΠΏΠ°Π½Π΅Π»Ρ ΠΈΠ· Π΄Π²ΡΡ
Π±ΠΈΠΎΠΌΠ°ΡΠΊΠ΅ΡΠΎΠ²: ST2 ΠΈ PDGF-BB
THE ROLE OF NEW METHODS OF STATE MANAGEMENT IN PRESERVING AND DEVELOPING THE HUMAN CAPITAL AS THE BASIS OF THE MODERN ECONOMY GROWTH
Objective: to reflect the critical importance of human capital as the basis for economic growth and competitiveness of the country; to identify and justify the importance of new forms and methods of state governance aimed at the preservation and development of human potential in all aspects. Methods: systematic approach; abstract-logic method, method of observation. Results: basing on the study of expert opinions in the field of human capital preservation and development, the necessity is proved to optimize the principles of state management in the sphere of human capital preservation and development. Scientific novelty: the estimation is carried out of new methods and principles of state control in the sphere of human capital preservation and development; approaches are defined to estimate the needs in labour resources , including migration ones, harmonized with the demand in all sectors of the economy. Practical value: possibility for state power executive bodies to use the proposed approaches when calculating the balance of labor resources
Epidemiological significance of chronic pharyngitis, nasopharyngitis, sinusitis, and rhinitis in Moscow and the Russian Federation in 1996 to 2009
Objective: to study trends in the incidence and prevalence of chronic pharyngitis, nasopharyngitis, sinusitis, and rhinitis (CPNSR) in differentage groups in Moscow and the Russian Federation (RF) in the period 1996 to 2009.Materials and methods. The epidemic significance of CPNSR in Moscow and the RF was estimated, by analyzing the records available inΒ the annual official statistical sources (Form No. 12).Results. The incidence of CPNSR in Moscow in the study period remains lower than those in the RF. Its prevalence among the population inΒ the city and in the country as a whole shows a significant increasing trend. Among the adolescents in both Moscow and the RF, the incidenceΒ of CPNSR is higher than that in children and constitutes 24.4 and 31.6 versus 3.3 and 21.8 per 10,000 population, respectively. TheΒ childrenand adolescents in the RF are the highest risk groups in the incidence of CPNSR. In 2009, its prevalence rates in Moscow and theΒ RF were 99.5 and 121.3 per 10,000, respectively; which were well above those in 2007 (81.2 and 117.5 per 10,000, respectively).Conclusion. The high incidence and prevalence rates for CPNSR remain high in all population strata in Moscow and the RF, which may beΒ associated with decreased diagnostic alertness and inadequate treatment for upper airway diseases.</p
Learning motivation of first-year students of a technical university
The concepts βmotiveβ and βmotivationβ and their role in the learning process of the technical university are analyzed. The following types of motives were distinguished: internal learning motivation related to learning activities and their content and external learning motivation not related to learning activities and their content but determined by external factors and circumstances. Learning motivation is associated with various changes that occur in students during the learning process. To identify the motivation in firstyear students, they were tested. Driving motives were identified. The results showed that students prefer real learning motives (to use acquired knowledge in life, to continue successful training in subsequent courses, to get good marks, to acquire deep knowledge and intellectual satisfaction). The authors examined motivating methods and techniques. Motivation is a tool used to improve the learning process
Architecture of the Multi-agent Resource Conversion Processes Extended with Agent Coalitions
Pretty often there is a process visualization in one's mind before the process is implemented in form of a simulation model. The main purpose of this visualization is the certain improvement of an imperfect or ineffective process, or the estimation of the influence of various impacts. Most of the time the simulation systems provide a certain benefit for us, especially when the process develops in a predefined way, no matter how complex it is. But sometimes there are situations when the decision making persons have to interact between each other and make a decision with consideration of the other person's opinion. Moreover, that may have conflicts in case they use the same resources or they are focused on a common goal by using different approaches (and again, same resources). Anyway, this sort of behavior has to be modelled as well. In this work we are presenting the apparatus of the resource conversion processes for the distributed simulation system BPsim.MAS. We will present the advantages of the software, the technologies lying under the hood and some recent additions that were implemented for the definition of agent coalitions. This is something which helps us create the simulation models of the complex systems and behavior scenarios, when multiple agents interact with each other. At the end of the work we suggest a sample implementation of the system on the basis of a network of petrol stations, that relies on presented apparatus. We also compare our results with the ones achieved with the simulation model, based on the networks of requirements and capabilities. Β© 2017 The Authors
Hybrid Agents Implementation for the Control of the Construction Company
Planning the project duration together with separate works is an essential element of managing the construction. The final duration depends on multiple factors, including the funds, customer requests, and capabilities of the construction company. In order to avoid additional costs in penalties or additional expenses, the management needs to estimate the real construction duration in advance, before the contract is signed. Further on, these terms need to be monitored both in whole and for the specific jobs in order to be able to edit further stages with regard of the remaining time, resources and used resources ratio. The development of a decision support system for the construction company is a pressing problem due to the growing demand in decision making persons' labor automation in planning and monitoring the construction processes. The paper presents the model and the application experience for such a system. Β© 2017 The Authors