96 research outputs found

    Research of functional properties of nitride ion-plasma coatings

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    This paper considers the influence of ion-plasma coatings with the use of nitrogen (N), zirconium nitride (ZrN), titanium-aluminum nitride (Ti,Al)N and titanium nitride and zirconium nitride by-layer (TiN+ZrN - eight layers) on the properties of steel 65X13. The main functional properties of the coatings are determined: microhardness, nanohardness, Young's modulus and corrosion resistance. It is shown that all the types of coatings allow increasing the physical and mechanical characteristics of instrument steel 65X13. Hardness and wear-resistance, depending on the type of the deposited coating, increase from 1, 5 to 4 times, corrosion resistance increases by tens times

    Π“ΠΠ—ΠžΠ₯Π ΠžΠœΠΠ’ΠžΠ“Π ΠΠ€Π˜Π§Π•Π‘ΠšΠžΠ• ΠžΠŸΠ Π•Π”Π•Π›Π•ΠΠ˜Π• ΠΠ‘ΠšΠΠ Π˜Π”ΠžΠ›Π – ΠŸΠ ΠžΠ”Π£ΠšΠ’Π Π’Π—ΠΠ˜ΠœΠžΠ”Π•Π™Π‘Π’Π’Π˜Π― Π‘Π˜ΠΠ“Π›Π•Π’ΠΠžΠ“Πž ΠšΠ˜Π‘Π›ΠžΠ ΠžΠ”Π Π‘ Ξ±-Π’Π•Π ΠŸΠ˜ΠΠ•ΠΠžΠœ

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    The reaction product of singlet oxygen with Ξ±-terpinene, ascaridole, can be used in an indirect gas chromatographic determination of the singlet oxygen mass concentration in the air. However, ascaridole is a thermally unstable compound, which can isomerize due to high temperature exposure during the analysis.Thermal decomposition products of ascaridole were identified by gas chromatography-mass spectrometry: isoascaridole, 1,2-ethoxy-p-menthane-3-one and 3,4-ethoxy-p-menthane-2-one. The discrepancy between obtained mass spectrum of ascaridole and mass spectra shown in NIST databases was found. The dependence of ascaridole signal intensity from temperature of injector and detector and from column conditioning was established. These conditions were investigated and optimized.Key words: ascaridole, singlet oxygen, gas chromatography, gas chromatography-mass spectrometry(Russian)DOI:Β http://dx.doi.org/10.15826/analitika.2013.17.4.009A.S. Ovechkin1,2, M.D. Reyngeverts2, L.A. Kartsova11St. Petersburg State University,Β Petergof, St. Petersburg, Russian Federation2FSUE Β«RSC Β«Applied ChemistryΒ», St. Petersburg, Russian FederationΠŸΡ€ΠΎΠ΄ΡƒΠΊΡ‚ взаимодСйствия синглСтного кислорода с Ξ±-Ρ‚Π΅Ρ€ΠΏΠΈΠ½Π΅Π½ΠΎΠΌ, аскаридол, ΠΌΠΎΠΆΠ΅Ρ‚ Π±Ρ‹Ρ‚ΡŒ использован ΠΏΡ€ΠΈ косвСнном газохроматографичСском ΠΎΠΏΡ€Π΅Π΄Π΅Π»Π΅Π½ΠΈΠΈ массовой ΠΊΠΎΠ½Ρ†Π΅Π½Ρ‚Ρ€Π°Ρ†ΠΈΠΈ синглСтного кислорода Π² Π²ΠΎΠ·Π΄ΡƒΡ…Π΅. Однако аскаридол – тСрмичСски Π½Π΅ΡΡ‚Π°Π±ΠΈΠ»ΡŒΠ½ΠΎΠ΅ соСдинСниС, способноС ΠΈΠ·ΠΎΠΌΠ΅Ρ€ΠΈΠ·ΠΎΠ²Π°Ρ‚ΡŒΡΡ ΠΏΠΎΠ΄ дСйствиСм Ρ‚Π΅ΠΌΠΏΠ΅Ρ€Π°Ρ‚ΡƒΡ€Ρ‹ Π² Ρ…ΠΎΠ΄Π΅ Π°Π½Π°Π»ΠΈΠ·Π°.ΠœΠ΅Ρ‚ΠΎΠ΄ΠΎΠΌ Ρ…Ρ€ΠΎΠΌΠ°Ρ‚ΠΎ-масс-спСктромСтрии ΠΈΠ΄Π΅Π½Ρ‚ΠΈΡ„ΠΈΡ†ΠΈΡ€ΠΎΠ²Π°Π½Ρ‹ ΠΏΡ€ΠΎΠ΄ΡƒΠΊΡ‚Ρ‹ тСрмичСского разлоТСния аскаридола: изоаскаридол, 1,2-этокси-ΠΏ-ΠΌΠ΅Π½Ρ‚Π°Π½-3-ΠΎΠ½ ΠΈ 3,4-этокси-ΠΏ-ΠΌΠ΅Π½Ρ‚Π°Π½-2-ΠΎΠ½. УстановлСно нСсоотвСтствиС масс-спСктра аскаридола, ΠΏΠΎΠ»ΡƒΡ‡Π΅Π½Π½ΠΎΠ³ΠΎ Π² настоящСй Ρ€Π°Π±ΠΎΡ‚Π΅, масс-спСктрам, ΠΏΡ€ΠΈΠ²Π΅Π΄Π΅Π½Π½Ρ‹ΠΌ Π² Π±Π°Π·Π°Ρ… Π΄Π°Π½Π½Ρ‹Ρ… NIST. Π˜ΡΡΠ»Π΅Π΄ΠΎΠ²Π°Π½Ρ‹ ΠΈ ΠΎΠΏΡ‚ΠΈΠΌΠΈΠ·ΠΈΡ€ΠΎΠ²Π°Π½Ρ‹ условия газохроматографичСского опрСдСлСния аскаридола: Ρ‚Π΅ΠΌΠΏΠ΅Ρ€Π°Ρ‚ΡƒΡ€Ρ‹ испаритСля ΠΈ Π΄Π΅Ρ‚Π΅ΠΊΡ‚ΠΎΡ€Π°, Π½Π΅ΠΎΠ±Ρ…ΠΎΠ΄ΠΈΠΌΠΎΡΡ‚ΡŒ кондиционирования хроматографичСской ΠΊΠΎΠ»ΠΎΠ½ΠΊΠΈ ΠΈ ΠΏΠ°Ρ€Π°ΠΌΠ΅Ρ‚Ρ€Ρ‹ Π΅Π³ΠΎ провСдСния.ΠšΠ»ΡŽΡ‡Π΅Π²Ρ‹Π΅ слова:аскаридол, синглСтный кислород, газовая хроматография, Ρ…Ρ€ΠΎΠΌΠ°Ρ‚ΠΎ-масс-спСктромСтрияDOI:Β http://dx.doi.org/10.15826/analitika.2013.17.4.00

    Prolonged repolarization in the early phase of ischemia is associated with ventricular fibrillation development in a porcine model

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    Background: Repolarization prolongation can be the earliest electrophysiological change in ischemia, but its role in arrhythmogenesis is unclear. The aim of the present study was to evaluate the early ischemic action potential duration (APD) prolongation concerning its causes, expression in ECG and association with early ischemic ventricular fibrillation (phase 1A VF).Methods: Coronary occlusion was induced in 18 anesthetized pigs, and standard 12 lead ECG along with epicardial electrograms were recorded. Local activation time (AT), end of repolarization time (RT), and activation-repolarization interval (ARIc) were determined as dV/dt minimum during QRS-complex, dV/dt maximum during T-wave, and rate-corrected RT–AT differences, respectively. Patch-clamp studies were done in enzymatically isolated porcine cardiomyocytes. IK(ATP) activation and Ito1 inhibition were tested as possible causes of the APD change.Results: During the initial period of ischemia, a total of 11 pigs demonstrated maximal ARIc prolongation >10Β ms at 1 and/or 2.5Β min of occlusion (8 and 6 cases at 1 and 2.5Β min, respectively) followed by typical ischemic ARIc shortening. The maximal ARIc across all leads was associated with VF development (OR 1.024 95% CI 1.003–1.046, p = 0.025) and maximal rate-corrected QT interval (QTc) (B 0.562 95% CI 0.346–0.775, p < 0.001) in logistic and linear regression analyses, respectively. Phase 1A VF incidence was associated with maximal QTc at the 2.5Β min of occlusion in ROC curve analysis (AUC 0.867, p = 0.028) with optimal cut-off 456Β ms (sensitivity 1.00, specificity 0.778). The pigs having maximal QTc at 2.5Β min more and less than 450Β ms significantly differed in phase 1A VF incidence in Kaplan-Meier analysis (log-rank p = 0.007). In the patch-clamp experiments, 4-aminopyridine did not produce any effects on the APD; however, pinacidil activated IK(ATP) and caused a biphasic change in the APD with initial prolongation and subsequent shortening.Conclusion: The transiently prolonged repolarization during the initial period of acute ischemia was expressed in the prolongation of the maximal QTc interval in the body surface ECG and was associated with phase 1A VF. IK(ATP) activation in the isolated cardiomyocytes reproduced the biphasic repolarization dynamics observed in vivo, which suggests the probable role of IK(ATP) in early ischemic arrhythmogenesis

    ΠŸΡƒΡ‚ΠΈ ΡΠΎΠ²Π΅Ρ€ΡˆΠ΅Π½ΡΡ‚Π²ΠΎΠ²Π°Π½ΠΈΡ исслСдования качСства ΠΆΠΈΠ·Π½ΠΈ ΠΏΠ°Ρ†ΠΈΠ΅Π½Ρ‚Π° Π² ΠΎΡ„Ρ‚Π°Π»ΡŒΠΌΠΎΠ»ΠΎΠ³ΠΈΠΈ β€” ΠΌΠΈΡ€ΠΎΠ²Ρ‹Π΅ Ρ‚Π΅Π½Π΄Π΅Π½Ρ†ΠΈΠΈ ΠΈ собствСнный ΠΎΠΏΡ‹Ρ‚

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    Β  Β Improving the methodology for assessment of the "quality of life" (QoL) of patients in ophthalmic practice is a regular process aimed at increasing the efficiency of clinical diagnostic examination. An analysis of how the quality of life assessment methodology is being improved in the practice of foreign ophthalmologists reveals two interrelated areas β€” the introduction of various methods of mathematical analysis in order to confirm the content and constructive validity of the developed questionnaire, as well as the wider distribution of approved questionnaires based on adaptation to a specific state language. The accumulated experience of the authors indicates that the involvement (as experts) of ophthalmologists in the development of a QΠΎL questionnaire, as well as the optimal procedure for scaling patient responses, are effective ways to improve the methodology for assessing QoL, which is confirmed by an assessment of the original QoL questionnaires designed for patients with vitreoretinal pathologies, computer vision syndrome and cataracts.Β  Β Π‘ΠΎΠ²Π΅Ρ€ΡˆΠ΅Π½ΡΡ‚Π²ΠΎΠ²Π°Π½ΠΈΠ΅ ΠΌΠ΅Ρ‚ΠΎΠ΄ΠΈΠΊΠΈ ΠΎΡ†Π΅Π½ΠΊΠΈ «качСства ΠΆΠΈΠ·Π½ΠΈΒ» ΠΏΠ°Ρ†ΠΈΠ΅Π½Ρ‚Π° (ΠšΠ–) Π² ΠΎΡ„Ρ‚Π°Π»ΡŒΠΌΠΎΠ»ΠΎΠ³ΠΈΡ‡Π΅ΡΠΊΠΎΠΉ ΠΏΡ€Π°ΠΊΡ‚ΠΈΠΊΠ΅ являСтся Π·Π°ΠΊΠΎΠ½ΠΎΠΌΠ΅Ρ€Π½Ρ‹ΠΌ процСссом, Π½Π°ΠΏΡ€Π°Π²Π»Π΅Π½Π½Ρ‹ΠΌ Π½Π° ΠΏΠΎΠ²Ρ‹ΡˆΠ΅Π½ΠΈΠ΅ эффСктивности ΠΊΠ»ΠΈΠ½ΠΈΠΊΠΎ-диагностичСского обслСдования ΠΏΠ°Ρ†ΠΈΠ΅Π½Ρ‚Π°. Анализ ΡΠΎΠ²Π΅Ρ€ΡˆΠ΅Π½ΡΡ‚Π²ΠΎΠ²Π°Π½ΠΈΡ ΠΌΠ΅Ρ‚ΠΎΠ΄ΠΈΠΊΠΈ ΠΎΡ†Π΅Π½ΠΊΠΈ ΠšΠ– Π² ΠΏΡ€Π°ΠΊΡ‚ΠΈΠΊΠ΅ Π·Π°Ρ€ΡƒΠ±Π΅ΠΆΠ½Ρ‹Ρ… ΠΎΡ„Ρ‚Π°Π»ΡŒΠΌΠΎΠ»ΠΎΠ³ΠΎΠ² ΡƒΠΊΠ°Π·Ρ‹Π²Π°Π΅Ρ‚ Π½Π° Π΄Π²Π° взаимосвязанных направлСния: Π²Π½Π΅Π΄Ρ€Π΅Π½ΠΈΠ΅ Ρ€Π°Π·Π»ΠΈΡ‡Π½Ρ‹Ρ… ΠΌΠ΅Ρ‚ΠΎΠ΄ΠΎΠ² матСматичСского Π°Π½Π°Π»ΠΈΠ·Π° с Ρ†Π΅Π»ΡŒΡŽ подтвСрТдСния ΡΠΎΠ΄Π΅Ρ€ΠΆΠ°Ρ‚Π΅Π»ΡŒΠ½ΠΎΠΉ ΠΈ конструктивной валидности Ρ€Π°Π·Ρ€Π°Π±ΠΎΡ‚Π°Π½Π½ΠΎΠ³ΠΎ опросника ΠΈ Π±ΠΎΠ»Π΅Π΅ ΡˆΠΈΡ€ΠΎΠΊΠΎΠ΅ распространСниС Π°ΠΏΡ€ΠΎΠ±ΠΈΡ€ΠΎΠ²Π°Π½Π½Ρ‹Ρ… опросников Π½Π° основС Π°Π΄Π°ΠΏΡ‚Π°Ρ†ΠΈΠΈ ΠΊ ΠΊΠΎΠ½ΠΊΡ€Π΅Ρ‚Π½ΠΎΠΌΡƒ государствСнному языку. НакоплСнный авторский ΠΎΠΏΡ‹Ρ‚ ΡƒΠΊΠ°Π·Ρ‹Π²Π°Π΅Ρ‚, Ρ‡Ρ‚ΠΎ ΠΏΡ€ΠΈΠ²Π»Π΅Ρ‡Π΅Π½ΠΈΠ΅ (Π² качСствС экспСртов) Π²Ρ€Π°Ρ‡Π΅ΠΉ-ΠΎΡ„Ρ‚Π°Π»ΡŒΠΌΠΎΠ»ΠΎΠ³ΠΎΠ² ΠΊ Ρ€Π°Π·Ρ€Π°Π±ΠΎΡ‚ΠΊΠ΅ опросника ΠšΠ–, Π° Ρ‚Π°ΠΊΠΆΠ΅ ΠΎΠΏΡ‚ΠΈΠΌΠ°Π»ΡŒΠ½Π°Ρ ΠΏΡ€ΠΎΡ†Π΅Π΄ΡƒΡ€Π° ΡˆΠΊΠ°Π»ΠΈΡ€ΠΎΠ²Π°Π½ΠΈΡ ΠΎΡ‚Π²Π΅Ρ‚ΠΎΠ² ΠΏΠ°Ρ†ΠΈΠ΅Π½Ρ‚Π° ΡΠ²Π»ΡΡŽΡ‚ΡΡ эффСктивными направлСниями ΡΠΎΠ²Π΅Ρ€ΡˆΠ΅Π½ΡΡ‚Π²ΠΎΠ²Π°Π½ΠΈΡ ΠΌΠ΅Ρ‚ΠΎΠ΄ΠΈΠΊΠΈ ΠΎΡ†Π΅Π½ΠΊΠΈ ΠšΠ–, Ρ‡Ρ‚ΠΎ подтвСрТдаСтся ΠΏΡ€ΠΎΠ²Π΅Π΄Π΅Π½Π½ΠΎΠΉ ΠΎΡ†Π΅Π½ΠΊΠΎΠΉ ΠΎΡ€ΠΈΠ³ΠΈΠ½Π°Π»ΡŒΠ½Ρ‹Ρ… опросников ΠšΠ– ΠΏΠ°Ρ†ΠΈΠ΅Π½Ρ‚Π° ΠΏΡ€ΠΈ Π²ΠΈΡ‚Ρ€Π΅ΠΎΡ€Π΅Ρ‚ΠΈΠ½Π°Π»ΡŒΠ½ΠΎΠΉ ΠΏΠ°Ρ‚ΠΎΠ»ΠΎΠ³ΠΈΠΈ, ΠΊΠΎΠΌΠΏΡŒΡŽΡ‚Π΅Ρ€Π½ΠΎΠΌ Π·Ρ€ΠΈΡ‚Π΅Π»ΡŒΠ½ΠΎΠΌ синдромС ΠΈ ΠΊΠ°Ρ‚Π°Ρ€Π°ΠΊΡ‚Π΅

    Risk Factors of Severe Disease and Methods for Clinical Outcome Prediction in Patients with COVID-19 (Review)

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    Large population studies using statistical analysis and mathematical computer modeling could be an effective tool in studying COVID-19. The use of prognostic scales developed using correlation of changes in clinical and laboratory parameters and morphological data, can help in early prediction of disease progression and identification of patients with high risk of unfavorable outcome.Aim of the review. To assess the risk factors for severe course and unfavorable outcome of COVID-19 and to evaluate the existing tools for predicting the course and outcome of the novel coronavirus infection. PubMed, Medline, and Google Scholar were searched for the relevant sources. This review contains information on existing tools for assessing the prognosis and outcome of the disease, along with the brief data on the etiology, pathogenesis of the novel coronavirus infection and the known epidemiological, clinical and laboratory factors affecting its course.Conclusion. It is essential to develop predictive models tailored to specific settings and capable of continuous monitoring of the situation and making the necessary adjustments. The discovery of new and more sensitive early markers and developing marker-based predictive assessment tools could significantly impact improving the outcomes of COVID-19

    Π€Π°ΠΊΡ‚ΠΎΡ€Ρ‹ риска ΠΈ ΠΌΠ΅Ρ‚ΠΎΠ΄Ρ‹ прогнозирования клиничСского исхода COVID-19 (ΠΎΠ±Π·ΠΎΡ€)

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    Large population studies using statistical analysis and mathematical computer modeling could be an effective tool in studying COVID-19. The use of prognostic scales developed using correlation of changes in clinical and laboratory parameters and morphological data, can help in early prediction of disease progression and identification of patients with high risk of unfavorable outcome.Aim of the review. To assess the risk factors for severe course and unfavorable outcome of COVID-19 and to evaluate the existing tools for predicting the course and outcome of the novel coronavirus infection. PubMed, Medline, and Google Scholar were searched for the relevant sources. This review contains information on existing tools for assessing the prognosis and outcome of the disease, along with the brief data on the etiology, pathogenesis of the novel coronavirus infection and the known epidemiological, clinical and laboratory factors affecting its course.Conclusion. It is essential to develop predictive models tailored to specific settings and capable of continuous monitoring of the situation and making the necessary adjustments. The discovery of new and more sensitive early markers and developing marker-based predictive assessment tools could significantly impact improving the outcomes of COVID-19.Одним ΠΈΠ· эффСктивных инструмСнтов изучСния COVID-19 являСтся исслСдованиС Π±ΠΎΠ»ΡŒΡˆΠΈΡ… популяций ΠΏΠ°Ρ†ΠΈΠ΅Π½Ρ‚ΠΎΠ² ΠΈ Π²Ρ‹Π΄Π΅Π»Π΅Π½ΠΈΠ΅ Ρ„Π°ΠΊΡ‚ΠΎΡ€ΠΎΠ², Π²Π»ΠΈΡΡŽΡ‰ΠΈΡ… Π½Π° Ρ‚Π΅Ρ‡Π΅Π½ΠΈΠ΅ ΠΈ ΠΏΡ€ΠΎΠ³Π½ΠΎΠ·, с ΠΏΠΎΠΌΠΎΡ‰ΡŒΡŽ Ρ€Π°Π·Π»ΠΈΡ‡Π½Ρ‹Ρ… ΠΌΠ΅Ρ‚ΠΎΠ΄ΠΎΠ² статистичСского Π°Π½Π°Π»ΠΈΠ·Π° ΠΈ матСматичСского ΠΊΠΎΠΌΠΏΡŒΡŽΡ‚Π΅Ρ€Π½ΠΎΠ³ΠΎ модСлирования. ΠŸΡ€ΠΈΠΌΠ΅Π½Π΅Π½ΠΈΠ΅ прогностичСских шкал, Ρ€Π°Π·Ρ€Π°Π±ΠΎΡ‚Π°Π½Π½Ρ‹Ρ… Π½Π° основании сопоставлСния Π΄ΠΈΠ½Π°ΠΌΠΈΠΊΠΈ клиничСских ΠΈ Π»Π°Π±ΠΎΡ€Π°Ρ‚ΠΎΡ€Π½Ρ‹Ρ… ΠΏΠΎΠΊΠ°Π·Π°Ρ‚Π΅Π»Π΅ΠΉ с морфологичСскими Π΄Π°Π½Π½Ρ‹ΠΌΠΈ, ΠΌΠΎΠΆΠ΅Ρ‚ ΠΏΠΎΠΌΠΎΡ‡ΡŒ Π² своСврСмСнной ΠΎΡ†Π΅Π½ΠΊΠ΅ Π²ΠΎΠ·ΠΌΠΎΠΆΠ½Ρ‹Ρ… Π²Π°Ρ€ΠΈΠ°Π½Ρ‚ΠΎΠ² тСчСния заболСвания ΠΈ Π²Ρ‹Π΄Π΅Π»Π΅Π½ΠΈΠΈ Π±ΠΎΠ»ΡŒΠ½Ρ‹Ρ… Π³Ρ€ΡƒΠΏΠΏΡ‹ высокого риска нСблагоприятного исхода.ЦСль ΠΎΠ±Π·ΠΎΡ€Π°. ΠžΡ†Π΅Π½ΠΈΡ‚ΡŒ Ρ„Π°ΠΊΡ‚ΠΎΡ€Ρ‹ риска тяТСлого тСчСния ΠΈ нСблагоприятного исхода COVID-19, ΡΡƒΡ‰Π΅ΡΡ‚Π²ΡƒΡŽΡ‰ΠΈΠ΅ инструмСнты прогнозирования тСчСния ΠΈ исхода Π½ΠΎΠ²ΠΎΠΉ короновирусной ΠΈΠ½Ρ„Π΅ΠΊΡ†ΠΈΠΈ. Поиск источников осущСствляли Π² Π±Π°Π·Π°Ρ… Π΄Π°Π½Π½Ρ‹Ρ… PubMed, Medline, Google Scholar. Π”Π°Π½Π½Ρ‹ΠΉ Π»ΠΈΡ‚Π΅Ρ€Π°Ρ‚ΡƒΡ€Π½Ρ‹ΠΉ ΠΎΠ±Π·ΠΎΡ€ наряду с ΠΊΡ€Π°Ρ‚ΠΊΠΈΠΌΠΈ Π΄Π°Π½Π½Ρ‹ΠΌΠΈ ΠΎΠ± этиологии, ΠΏΠ°Ρ‚ΠΎΠ³Π΅Π½Π΅Π·Π΅ COVID-19 ΠΈ ΠΎΠ± извСстных эпидСмиологичСских, клиничСских ΠΈ Π»Π°Π±ΠΎΡ€Π°Ρ‚ΠΎΡ€Π½Ρ‹Ρ… Ρ„Π°ΠΊΡ‚ΠΎΡ€Π°Ρ…, Π²Π»ΠΈΡΡŽΡ‰ΠΈΡ… Π½Π° Π΅Π΅ Ρ‚Π΅Ρ‡Π΅Π½ΠΈΠ΅, содСрТит ΠΈΠ½Ρ„ΠΎΡ€ΠΌΠ°Ρ†ΠΈΡŽ ΠΎ ΡΡƒΡ‰Π΅ΡΡ‚Π²ΡƒΡŽΡ‰ΠΈΡ… инструмСнтах ΠΎΡ†Π΅Π½ΠΊΠΈ ΠΏΡ€ΠΎΠ³Π½ΠΎΠ·Π° тСчСния ΠΈ исхода заболСвания.Π—Π°ΠΊΠ»ΡŽΡ‡Π΅Π½ΠΈΠ΅. НСобходима Ρ€Π°Π·Ρ€Π°Π±ΠΎΡ‚ΠΊΠ° прогностичСских ΠΌΠΎΠ΄Π΅Π»Π΅ΠΉ, созданных ΠΏΠΎΠ΄ ΠΊΠΎΠ½ΠΊΡ€Π΅Ρ‚Π½Ρ‹Π΅ условия с Π²ΠΎΠ·ΠΌΠΎΠΆΠ½ΠΎΡΡ‚ΡŒΡŽ постоянного ΠΌΠΎΠ½ΠΈΡ‚ΠΎΡ€ΠΈΠ½Π³Π° ситуации ΠΈ внСсСния ΠΊΠΎΡ€Ρ€Π΅ΠΊΡ‚ΠΈΡ€ΠΎΠ²ΠΎΠΊ ΠΏΡ€ΠΈ нСобходимости. ΠžΠ±Π½Π°Ρ€ΡƒΠΆΠ΅Π½ΠΈΠ΅ Π½ΠΎΠ²Ρ‹Ρ… Π±ΠΎΠ»Π΅Π΅ Ρ‡ΡƒΠ²ΡΡ‚Π²ΠΈΡ‚Π΅Π»ΡŒΠ½Ρ‹Ρ… Π½Π° Ρ€Π°Π½Π½ΠΈΡ… этапах заболСвания ΠΌΠ°Ρ€ΠΊΠ΅Ρ€ΠΎΠ² ΠΈ Ρ€Π°Π·Ρ€Π°Π±ΠΎΡ‚ΠΊΠ° Π½Π° ΠΈΡ… основС инструмСнтов ΠΎΡ†Π΅Π½ΠΊΠΈ ΠΏΡ€ΠΎΠ³Π½ΠΎΠ·Π° ΠΌΠΎΠ³Π»ΠΎ Π±Ρ‹ Π·Π½Π°Ρ‡ΠΈΡ‚Π΅Π»ΡŒΠ½ΠΎ ΡƒΠ»ΡƒΡ‡ΡˆΠΈΡ‚ΡŒ исходы COVID-19
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