30 research outputs found
FungalRV: adhesin prediction and immunoinformatics portal for human fungal pathogens
<p>Abstract</p> <p>Background</p> <p>The availability of sequence data of human pathogenic fungi generates opportunities to develop Bioinformatics tools and resources for vaccine development towards benefitting at-risk patients.</p> <p>Description</p> <p>We have developed a fungal adhesin predictor and an immunoinformatics database with predicted adhesins. Based on literature search and domain analysis, we prepared a positive dataset comprising adhesin protein sequences from human fungal pathogens <it>Candida albicans, Candida glabrata, Aspergillus fumigatus, Coccidioides immitis, Coccidioides posadasii, Histoplasma capsulatum, Blastomyces dermatitidis, Pneumocystis carinii, Pneumocystis jirovecii and Paracoccidioides brasiliensis</it>. The negative dataset consisted of proteins with high probability to function intracellularly. We have used 3945 compositional properties including frequencies of mono, doublet, triplet, and multiplets of amino acids and hydrophobic properties as input features of protein sequences to Support Vector Machine. Best classifiers were identified through an exhaustive search of 588 parameters and meeting the criteria of best Mathews Correlation Coefficient and lowest coefficient of variation among the 3 fold cross validation datasets. The "FungalRV adhesin predictor" was built on three models whose average Mathews Correlation Coefficient was in the range 0.89-0.90 and its coefficient of variation across three fold cross validation datasets in the range 1.2% - 2.74% at threshold score of 0. We obtained an overall MCC value of 0.8702 considering all 8 pathogens, namely, <it>C. albicans, C. glabrata, A. fumigatus, B. dermatitidis, C. immitis, C. posadasii, H. capsulatum </it>and <it>P. brasiliensis </it>thus showing high sensitivity and specificity at a threshold of 0.511. In case of <it>P. brasiliensis </it>the algorithm achieved a sensitivity of 66.67%. A total of 307 fungal adhesins and adhesin like proteins were predicted from the entire proteomes of eight human pathogenic fungal species. The immunoinformatics analysis data on these proteins were organized for easy user interface analysis. A Web interface was developed for analysis by users. The predicted adhesin sequences were processed through 18 immunoinformatics algorithms and these data have been organized into MySQL backend. A user friendly interface has been developed for experimental researchers for retrieving information from the database.</p> <p>Conclusion</p> <p>FungalRV webserver facilitating the discovery process for novel human pathogenic fungal adhesin vaccine has been developed.</p
ΠΠ΅ΠΆΠ΄ΡΠ½Π°ΡΠΎΠ΄Π½ΡΠΉ ΠΌΡΠ·ΡΠΊΠ°Π»ΡΠ½ΡΠΉ ΠΊΠΎΠ½ΠΊΡΡΡ Π² ΡΠΎΡΠΌΠΈΡΠΎΠ²Π°Π½ΠΈΠΈ ΠΈΠΌΠΈΠ΄ΠΆΠ° ΡΡΡΠ°Π½Ρ ΠΏΡΠΎΠ²Π΅Π΄Π΅Π½ΠΈΡ (Π½Π° ΠΏΡΠΈΠΌΠ΅ΡΠ΅ ΠΠ²ΡΠΎΠ²ΠΈΠ΄Π΅Π½ΠΈΡ 2017)
ΠΠ½Π½ΠΎΡΠ°ΡΠΈΡ Π²ΡΠΏΡΡΠΊΠ½ΠΎΠΉ ΠΊΠ²Π°Π»ΠΈΡΠΈΠΊΠ°ΡΠΈΠΎΠ½Π½ΠΎΠΉ ΡΠ°Π±ΠΎΡΡ ΠΡΠ»ΠΎΠ² ΠΠΈΠΊΠΈΡΠ° Π‘Π΅ΡΠ³Π΅Π΅Π²ΠΈΡ Β«ΠΠΠΠΠ£ΠΠΠ ΠΠΠΠ«Π ΠΠ£ΠΠ«ΠΠΠΠ¬ΠΠ«Π ΠΠΠΠΠ£Π Π‘ Π Π€ΠΠ ΠΠΠ ΠΠΠΠΠΠ ΠΠΠΠΠΠ Π‘Π’Π ΠΠΠ« ΠΠ ΠΠΠΠΠΠΠΠ― (ΠΠ ΠΠ ΠΠΠΠ Π ΠΠΠ ΠΠΠΠΠΠΠΠ―-2017)Β» Π. ΡΡΠΊ. - ΠΡΠΊΠΎΠ²Π° ΠΠ»Π΅Π½Π° ΠΠ»Π°Π΄ΠΈΠΌΠΈΡΠΎΠ²Π½Π°, Π΄ΠΎΠΊΡΠΎΡ ΡΠΈΠ»ΠΎΠ»ΠΎΠ³ΠΈΡΠ΅ΡΠΊΠΈΡ
Π½Π°ΡΠΊ, Π΄ΠΎΡΠ΅Π½Ρ ΠΠ°ΡΠ΅Π΄ΡΠ° ΡΠ²ΡΠ·Π΅ΠΉ Ρ ΠΎΠ±ΡΠ΅ΡΡΠ²Π΅Π½Π½ΠΎΡΡΡΡ ΠΡΠ½Π°Ρ ΡΠΎΡΠΌΠ° ΠΎΠ±ΡΡΠ΅Π½ΠΈΡ ΠΠΊΡΡΠ°Π»ΡΠ½ΠΎΡΡΡ: ΠΌΠ΅ΠΆΠ΄ΡΠ½Π°ΡΠΎΠ΄Π½ΡΠΉ ΠΌΡΠ·ΡΠΊΠ°Π»ΡΠ½ΡΠΉ ΠΊΠΎΠ½ΠΊΡΡΡ ΠΠ²ΡΠΎΠ²ΠΈΠ΄Π΅Π½ΠΈΠ΅ ΠΊΠ°ΠΊ ΡΠ°ΠΌΠΎΠ΅ ΠΌΠ°ΡΡΡΠ°Π±Π½ΠΎΠ΅ ΡΠ΅Π³ΡΠ»ΡΡΠ½ΠΎΠ΅ Π²ΡΡΠΎΠΊΠΎΡΠ΅Ρ
Π½ΠΎΠ»ΠΎΠ³ΠΈΡΠ½ΠΎΠ΅ ΡΠ΅Π»Π΅Π²ΠΈΠ·ΠΈΠΎΠ½Π½ΠΎΠ΅ ΠΈ ΠΌΠ΅Π΄ΠΈΠ°-ΡΠΎΠ±ΡΡΠΈΠ΅, ΠΊΠΎΡΠΎΡΠΎΠ΅ . Π Π΅ΠΆΠ΅Π³ΠΎΠ΄Π½ΠΎ Π°ΠΊΡΠ΅Π½ΡΠΈΡΡΠ΅Ρ Π²Π½ΠΈΠΌΠ°Π½ΠΈΠ΅ Π°ΡΠ΄ΠΈΡΠΎΡΠΈΠΈ Π½Π° Π½Π°ΡΠΈΠΎΠ½Π°Π»ΡΠ½ΠΎ-ΠΊΡΠ»ΡΡΡΡΠ½ΡΡ
ΠΎΡΠΎΠ±Π΅Π½Π½ΠΎΡΡΡΡ
ΡΡΡΠ°Π½Ρ-ΠΎΡΠ³Π°Π½ΠΈΠ·Π°ΡΠΎΡΠ° ΠΊΠΎΠ½ΠΊΡΡΡΠ°, ΡΠΎΡΠΌΠΈΡΡΠ΅Ρ Π·Π½Π°ΡΠΈΡΠ΅Π»ΡΠ½ΡΠ΅ ΡΡΡΠΈΡΡΠΈΡΠ΅ΡΠΊΠΈΠ΅ ΠΏΠΎΡΠΎΠΊΠΈ ΠΈ ΡΠ΅ΠΌ ΡΠ°ΠΌΡΠΌ ΡΠΏΠΎΡΠΎΠ±ΡΡΠ²ΡΠ΅Ρ ΡΠΎΡΠΌΠΈΡΠΎΠ²Π°Π½ΠΈΡ ΠΈΠΌΠΈΠ΄ΠΆΠ° ΡΠ΅ΡΡΠΈΡΠΎΡΠΈΠΈ. ΠΠΎΠ»Π΅Π΅ ΡΠΎΠ³ΠΎ, ΠΏΠΎΠ±Π΅Π΄Π° ΡΡΡΠ°Π½Ρ-ΡΡΠ°ΡΡΠ½ΠΈΡΡ ΠΊΠΎΠ½ΠΊΡΡΡΠ° ΠΠ²ΡΠΎΠ²ΠΈΠ΄Π΅Π½ΠΈΡ Π·Π°ΡΠ°ΡΡΡΡ ΠΎΡΡΠ°ΠΆΠ°Π΅Ρ ΠΈΠ΄Π΅ΠΎΠ»ΠΎΠ³ΠΎ-ΠΏΠΎΠ»ΠΈΡΠΈΡΠ΅ΡΠΊΠΈΠΉ Π²Π΅ΠΊΡΠΎΡ ΠΠ²ΡΠΎΠΏΡ ΠΈ ΠΏΠΎ ΡΡΡΠΈ Π΄Π΅Π»Π° Π²ΡΠΏΠΎΠ»Π½ΡΠ΅Ρ ΡΡΠ½ΠΊΡΠΈΡ ΠΏΠΎΠ»ΠΈΡΠΈΡΠ΅ΡΠΊΠΎΠ³ΠΎ PR ΡΡΡΠ°Π½Ρ-ΠΏΠΎΠ±Π΅Π΄ΠΈΡΠ΅Π»Ρ ΠΈ ΡΡΡΠ°Π½Ρ-Ρ
ΠΎΠ·ΡΠΉΠΊΠΈ ΠΌΠ΅ΡΠΎΠΏΡΠΈΡΡΠΈΡ. Π‘Π»Π΅Π΄ΠΎΠ²Π°ΡΠ΅Π»ΡΠ½ΠΎ Π°Π½Π°Π»ΠΈΠ· ΠΈΡΠΏΠΎΠ»ΡΠ·ΡΠ΅ΠΌΡΡ
Π½Π° ΠΌΠ΅ΡΠΎΠΏΡΠΈΡΡΠΈΠΈ ΠΊΠΎΠΌΠΌΡΠ½ΠΈΠΊΠ°ΡΠΈΠ²Π½ΡΡ
ΡΠ΅Ρ
Π½ΠΎΠ»ΠΎΠ³ΠΈΠΉ ΡΠ²Π»ΡΠ΅ΡΡΡ Π°ΠΊΡΡΠ°Π»ΡΠ½ΡΠΌ ΠΈ Π²ΠΎΡΡΡΠ΅Π±ΠΎΠ²Π°Π½Π½ΡΠΌ Π΄Π»Ρ ΡΠΎΠ±ΡΡΠΈΠΉΠ½ΠΎΠ³ΠΎ ΠΈ ΡΡΡΡΠΎΠΈΡΠ΅Π»ΡΠ½ΠΎΠ³ΠΎ PR ΠΠ±ΡΠ΅ΠΊΡ ΠΈΡΡΠ»Π΅Π΄ΠΎΠ²Π°Π½ΠΈΡ: ΠΊΠΎΠΌΠΌΡΠ½ΠΈΠΊΠ°ΡΠΈΠΎΠ½Π½ΡΠ΅ Π°ΠΊΡΠΈΠ²Π½ΠΎΡΡΠΈ ΠΌΠ΅ΠΆΠ΄ΡΠ½Π°ΡΠΎΠ΄Π½ΠΎΠ³ΠΎ ΠΌΡΠ·ΡΠΊΠ°Π»ΡΠ½ΠΎΠ³ΠΎ ΠΊΠΎΠ½ΠΊΡΡΡΠ° (Π½Π° ΠΏΡΠΈΠΌΠ΅ΡΠ΅ ΠΠ²ΡΠΎΠ²ΠΈΠ΄Π΅Π½ΠΈΡ Π² ΠΠΈΠ΅Π²Π΅ Π² 2017 Π³.). ΠΡΠ΅Π΄ΠΌΠ΅Ρ ΠΈΡΡΠ»Π΅Π΄ΠΎΠ²Π°Π½ΠΈΡ: ΡΡΠ½ΠΊΡΠΈΡ ΡΡΠ°ΡΡΡΠ½ΠΎΠ³ΠΎ PR-ΠΌΠ΅ΡΠΎΠΏΡΠΈΡΡΠΈΡ Π² ΡΠΎΡΠΌΠΈΡΠΎΠ²Π°Π½ΠΈΠΈ ΠΈΠΌΠΈΠ΄ΠΆΠ° ΡΡΡΠ°Π½Ρ. Π¦Π΅Π»Ρ ΠΈΡΡΠ»Π΅Π΄ΠΎΠ²Π°Π½ΠΈΡ: Π΄ΠΎΠΊΠ°Π·Π°ΡΡ, ΡΡΠΎ ΠΌΠ΅ΠΆΠ΄ΡΠ½Π°ΡΠΎΠ΄Π½ΡΠΉ ΠΌΡΠ·ΡΠΊΠ°Π»ΡΠ½ΡΠΉ ΠΊΠΎΠ½ΠΊΡΡΡ ΠΠ²ΡΠΎΠ²ΠΈΠ΄Π΅Π½ΠΈΠ΅ ΡΠΏΠΎΡΠΎΠ±ΡΡΠ²ΡΠ΅Ρ ΡΠΎΡΠΌΠΈΡΠΎΠ²Π°Π½ΠΈΡ ΠΈΠΌΠΈΠ΄ΠΆΠ° ΡΡΡΠ°Π½Ρ ΠΏΡΠΎΠ²Π΅Π΄Π΅Π½ΠΈΡ. ΠΠ°Π΄Π°ΡΠΈ ΠΈΡΡΠ»Π΅Π΄ΠΎΠ²Π°Π½ΠΈΡ: ΡΠ°Π·ΡΠ°Π±ΠΎΡΠ°ΡΡ ΡΠ΅ΡΠΌΠΈΠ½ΠΎΠ»ΠΎΠ³ΠΈΡΠ΅ΡΠΊΠΈΠΉ Π°ΠΏΠΏΠ°ΡΠ°Ρ ΠΈΡΡΠ»Π΅Π΄ΠΎΠ²Π°Π½ΠΈΡ Π½Π° ΠΎΡΠ½ΠΎΠ²Π΅ Π½Π°ΡΡΠ½ΠΎΠΉ Π»ΠΈΡΠ΅ΡΠ°ΡΡΡΡ ΠΏΠΎ ΠΈΠΌΠΈΠ΄ΠΆΠΌΠ΅ΠΉΠΊΠΈΠ½Π³Ρ, Π±ΡΠ΅Π½Π΄ΠΈΠ½Π³Ρ ΠΈ ΠΈΠ²Π΅Π½Ρ-ΠΌΠ΅Π½Π΅Π΄ΠΆΠΌΠ΅Π½ΡΡ; ΠΎΠΏΡΠ΅Π΄Π΅Π»ΠΈΡΡ Π°ΠΊΡΡΠ°Π»ΡΠ½ΡΠ΅ ΠΊΠΎΠΌΠΌΡΠ½ΠΈΠΊΠ°ΡΠΈΠΎΠ½Π½ΡΠ΅ ΡΠ΅Ρ
Π½ΠΎΠ»ΠΎΠ³ΠΈΠΈ, ΠΏΡΠΈΠΌΠ΅Π½ΡΠ΅ΠΌΡΠ΅ Π² ΡΠ°ΠΌΠΊΠ°Ρ
ΡΠΏΠ΅ΡΠΈΠ°Π»ΡΠ½ΡΡ
ΡΠΎΠ±ΡΡΠΈΠΉ Π΄Π»Ρ ΡΠΎΡΠΌΠΈΡΠΎΠ²Π°Π½ΠΈΡ ΠΈΠΌΠΈΠ΄ΠΆΠ° ΡΡΡΠ°Π½Ρ; ΠΎΠΏΠΈΡΠ°ΡΡ ΡΠΎΠ»Ρ ΠΠ²ΡΠΎΠΏΠ΅ΠΉΡΠΊΠΎΠ³ΠΎ ΠΠ΅ΡΠ°ΡΠ΅Π»ΡΠ½ΠΎΠ³ΠΎ Π‘ΠΎΡΠ·Π° ΠΊΠ°ΠΊ ΠΎΡΠ³Π°Π½ΠΈΠ·Π°ΡΠΎΡΠ° ΠΠ²ΡΠΎΠ²ΠΈΠ΄Π΅Π½ΠΈΡ Π² ΡΠΎΡΠΌΠΈΡΠΎΠ²Π°Π½ΠΈΠΈ ΠΈΠΌΠΈΠ΄ΠΆΠ° ΡΡΡΠ°Π½Ρ ΠΏΡΠΎΠ²Π΅Π΄Π΅Π½ΠΈΡ ΠΊΠΎΠ½ΠΊΡΡΡΠ°; ΠΎΡΠ΅Π½ΠΈΡΡ ΡΡΡΠ΅ΠΊΡΠΈΠ²Π½ΠΎΡΡΡ ΡΠ΅Π°Π»ΠΈΠ·ΠΎΠ²Π°Π½Π½ΡΡ
ΠΊΠΎΠΌΠΌΡΠ½ΠΈΠΊΠ°ΡΠΈΠ²Π½ΡΡ
ΡΠ΅Ρ
Π½ΠΎΠ»ΠΎΠ³ΠΈΠΉ ΡΠΎΡΠΌΠΈΡΠΎΠ²Π°Π½ΠΈΡ ΠΈΠΌΠΈΠ΄ΠΆΠ° ΡΡΡΠ°Π½Ρ Π² ΡΠ°ΠΌΠΊΠ°Ρ
ΠΠ²ΡΠΎΠ²ΠΈΠ΄Π΅Π½ΠΈΡ; Π΄Π°ΡΡ ΡΠ΅ΠΊΠΎΠΌΠ΅Π½Π΄Π°ΡΠΈΠΈ ΠΏΠΎ ΡΠΎΡΠΌΠΈΡΠΎΠ²Π°Π½ΠΈΡ ΠΈΠΌΠΈΠ΄ΠΆΠ° ΡΡΡΠ°Π½Ρ Ρ ΠΏΠΎΠΌΠΎΡΡΡ ΠΠ²ΡΠΎΠ²ΠΈΠ΄Π΅Π½ΠΈΡ. Π’Π΅ΠΎΡΠ΅ΡΠΈΡΠ΅ΡΠΊΠ°Ρ Π±Π°Π·Π°: Π½Π°ΡΡΠ½ΡΠ΅ ΡΡΡΠ΄Ρ Π. ΠΡΠΊΠΎΠ²ΠΎΠΉ, Π. ΠΠ°Π²ΡΡ, Π. ΠΠ°Π½ΠΊΡΡΡ
ΠΈΠ½Π°, Π. ΠΠΆΠ΅Π½Π΅ΡΠ°, Π. ΠΠ°Π²Π΅ΡΠΈΠ½ΠΎΠΉ, Π£. Π₯Π°Π»ΡΡΠ±Π°ΡΡΠ°, ΠΠΆ. ΠΠΎΠ»Π΄Π±Π»Π°ΡΡΠ° Π° ΡΠ°ΠΊΠΆΠ΅ ΡΡΡΠ΄Ρ Π. ΠΠ°ΡΡΠΌΠ°Π½Π° ΠΎ ΠΌΡΠ·ΡΠΊΠ°Π»ΡΠ½ΠΎΠΌ Π±ΠΈΠ·Π½Π΅ΡΠ΅, Π. ΠΠΆΠΎΡΠ΄Π°Π½Π° ΠΎ ΠΏΡΠΎΠ΄Π²ΠΈΠΆΠ΅Π½ΠΈΠΈ ΠΈΠΌΠΈΠ΄ΠΆΠ° ΡΡΡΠ°Π½ Ρ ΠΏΠΎΠΌΠΎΡΡΡ ΠΠ²ΡΠΎΠ²ΠΈΠ΄Π΅Π½ΠΈΡ ΠΈ Π΄Ρ. ΠΠΌΠΏΠΈΡΠΈΡΠ΅ΡΠΊΠ°Ρ Π±Π°Π·Π°: PR-Π΄ΠΎΠΊΡΠΌΠ΅Π½ΡΡ, ΡΠ°Π·ΠΌΠ΅ΡΠ΅Π½Π½ΡΠ΅ Π½Π° ΡΠ°ΠΉΡΠ΅ ΠΠ²ΡΠΎΠ²ΠΈΠ΄Π΅Π½ΠΈΡ ΠΈ ΠΠ²ΡΠΎΠΏΠ΅ΠΉΡΠΊΠΎΠ³ΠΎ ΠΠ΅ΡΠ°ΡΠ΅Π»ΡΠ½ΠΎΠ³ΠΎ Π‘ΠΎΡΠ·Π°; Π±ΠΎΠ»Π΅Π΅ ΠΏΠΎΠ»ΡΡΠΎΡΠ° ΠΌΠΈΠ»Π»ΠΈΠΎΠ½Π° ΡΡΠ°ΡΠ΅ΠΉ ΠΎΠ± Π£ΠΊΡΠ°ΠΈΠ½Π΅ Π² Π΅Π²ΡΠΎΠΏΠ΅ΠΉΡΠΊΠΈΡ
Π‘ΠΠ, ΡΠ°Π·ΠΌΠ΅ΡΠ΅Π½Π½ΡΠ΅ Π² Π±Π°Π·Π΅ ΠΏΡΠΎΠ΅ΠΊΡΠ° ΠΌΠΎΠ½ΠΈΡΠΎΡΠΈΠ½Π³Π° ΠΌΠ΅ΠΆΠ΄ΡΠ½Π°ΡΠΎΠ΄Π½ΠΎΠ³ΠΎ ΠΈΠΌΠΈΠ΄ΠΆΠ° Π£ΠΊΡΠ°ΠΈΠ½Ρ Β«OkoΒ»; Π΄Π°Π½Π½ΡΠ΅ Π±Π°Π·Ρ ΠΌΠ°ΡΠ΅ΡΠΈΠ°Π»ΠΎΠ² Π‘ΠΠ ΠΈ ΡΠΎΡΠΈΠ°Π»ΡΠ½ΡΡ
ΠΌΠ΅Π΄ΠΈΠ° Factiva; Π΄Π°Π½Π½ΡΠ΅ Google.Analytics. ΠΡΠ°ΠΊΡΠΈΡΠ΅ΡΠΊΠ°Ρ Π·Π½Π°ΡΠΈΠΌΠΎΡΡΡ: ΠΈΡΡΠ»Π΅Π΄ΠΎΠ²Π°Π½ΠΈΠ΅ Π΄ΠΎΠΊΠ°Π·ΡΠ²Π°Π΅Ρ, ΡΡΠΎ ΠΌΠ΅ΠΆΠ΄ΡΠ½Π°ΡΠΎΠ΄Π½ΡΠΉ ΠΌΡΠ·ΡΠΊΠ°Π»ΡΠ½ΡΠΉ ΠΊΠΎΠ½ΠΊΡΡΡ ΠΠ²ΡΠΎΠ²ΠΈΠ΄Π΅Π½ΠΈΠ΅ ΡΠΎΡΠΌΠΈΡΡΠ΅Ρ ΠΈΠΌΠΈΠ΄ΠΆ ΡΡΡΠ°Π½Ρ ΠΏΡΠΎΠ²Π΅Π΄Π΅Π½ΠΈΡ Π½Π΅Π·Π°Π²ΠΈΡΠΈΠΌΠΎ ΠΎΡ ΡΡΠΏΠ΅ΡΠ½ΠΎΡΡΠΈ ΠΈΡΠΏΠΎΠ»ΡΠ·ΠΎΠ²Π°Π½ΠΈΡ ΠΊΠΎΠ½ΠΊΡΠ΅ΡΠ½ΡΡ
ΡΠ΅Ρ
Π½ΠΎΠ»ΠΎΠ³ΠΈΠΉ ΡΠΎΡΠΌΠΈΡΠΎΠ²Π°Π½ΠΈΡ ΠΈΠΌΠΈΠ΄ΠΆΠ° ΡΡΡΠ°Π½Ρ. Π’Π΅Π·ΠΈΡΡ ΠΈΡΡΠ»Π΅Π΄ΠΎΠ²Π°Π½ΠΈΡ Π±ΡΠ»ΠΈ Π°ΠΏΡΠΎΠ±ΠΈΡΠΎΠ²Π°Π½Ρ Π½Π° ΠΌΠ΅ΠΆΠ΄ΡΠ½Π°ΡΠΎΠ΄Π½ΠΎΠΌ Π½Π°ΡΡΠ½ΠΎΠΌ ΡΠΎΡΡΠΌΠ΅ Β«ΠΠ΅Π΄ΠΈΠ° Π² ΡΠΎΠ²ΡΠ΅ΠΌΠ΅Π½Π½ΠΎΠΌ ΠΌΠΈΡΠ΅. 57-Π΅ ΠΠ΅ΡΠ΅ΡΠ±ΡΡΠ³ΡΠΊΠΈΠ΅ ΡΡΠ΅Π½ΠΈΡΒ», ΠΎΠΏΡΠ±Π»ΠΈΠΊΠΎΠ²Π°Π½Ρ Π² ΡΠ±ΠΎΡΠ½ΠΈΠΊΠ΅ ΠΌΠ°ΡΠ΅ΡΠΈΠ°Π»ΠΎΠ² ΡΡΠ°ΡΠ΅ΠΉ ΡΠΎΡΡΠΌΠ° ΠΈ ΠΈΠΌΠ΅ΡΡ ΡΡΠ°ΡΡΡ Π½Π°ΡΡΠ½ΠΎΠΉ ΡΡΠ°ΡΡΠΈ, ΡΠ°Π·ΠΌΠ΅ΡΠ΅Π½Π½ΠΎΠΉ Π² Π±Π°Π·Π΅ Π ΠΠΠ¦. Π‘ΡΡΡΠΊΡΡΡΠ° ΡΠ°Π±ΠΎΡΡ: Π Π°Π±ΠΎΡΠ° ΡΠΎΡΡΠΎΠΈΡ ΠΈΠ· Π²Π²Π΅Π΄Π΅Π½ΠΈΡ, 3 Π³Π»Π°Π²: Β«ΡΡΠ½ΠΊΡΠΈΡ ΡΠΏΠ΅ΡΠΈΠ°Π»ΡΠ½ΠΎΠ³ΠΎ ΡΠΎΠ±ΡΡΠΈΡ Π² ΡΠΎΡΠΌΠΈΡΠΎΠ²Π°Π½ΠΈΠΈ ΠΈΠΌΠΈΠ΄ΠΆΠ° ΡΡΡΠ°Π½ΡΒ», Β«ΠΠ²ΡΠΎΠ²ΠΈΠ΄Π΅Π½ΠΈΠ΅ ΠΊΠ°ΠΊ ΡΠΏΠ΅ΡΠΈΠ°Π»ΡΠ½ΠΎΠ΅ ΡΠΎΠ±ΡΡΠΈΠ΅ ΠΠ²ΡΠΎΠΏΠ΅ΠΉΡΠΊΠΎΠ³ΠΎ ΠΠ΅ΡΠ°ΡΠ΅Π»ΡΠ½ΠΎΠ³ΠΎ Π‘ΠΎΡΠ·Π°Β» ΠΈ Β«ΠΊΠΎΠΌΠΌΡΠ½ΠΈΠΊΠ°ΡΠΈΠΎΠ½Π½ΡΠΉ ΠΏΠΎΡΠ΅Π½ΡΠΈΠ°Π» ΠΠ²ΡΠΎΠ²ΠΈΠ΄Π΅Π½ΠΈΡ ΠΊΠ°ΠΊ ΠΏΠ»ΠΎΡΠ°Π΄ΠΊΠΈ Π΄Π»Ρ ΡΠΎΡΠΌΠΈΡΠΎΠ²Π°Π½ΠΈΡ ΠΈΠΌΠΈΠ΄ΠΆΠ° ΡΡΡΠ°Π½ΡΒ», Π·Π°ΠΊΠ»ΡΡΠ΅Π½ΠΈΡ, ΡΠΏΠΈΡΠΊΠ° ΠΈΡΠΏΠΎΠ»ΡΠ·ΠΎΠ²Π°Π½Π½ΠΎΠΉ Π»ΠΈΡΠ΅ΡΠ°ΡΡΡΡ ΠΈΠ· 67 ΠΏΠΎΠ·ΠΈΡΠΈΠΉ ΠΈ 12 ΠΏΡΠΈΠ»ΠΎΠΆΠ΅Π½ΠΈΠΉ. ΠΠ±ΡΠΈΠΉ ΠΎΠ±ΡΠ΅ΠΌ 76 ΡΡΡΠ°Π½ΠΈΡ.Abstract of graduating qualification thesis Mikita Arlou INTERNATIONAL MUSIC CONTEST IN HOST COUNTRY IMAGE FORMATION (ON THE EXAMPLE OF EUROVISION 2017) Supervisor associate professor Elena Bykova, doctor of philology Department of PR in business full-time study Relevance: the international music contest Eurovision as the most wide scale regular high tech TV and Media event which annually emphasizes audience attention on national cultural features of the host country, forms tourist flows which have huge influence on territorial image formation. Besides the win of a participating in the Eurovision country often shows the ideological and political European vector and in fact serves as political PR of the winning or host country. Consequently the analysis of applied communication technologies is relevant and in-demand for event PR. Research object: communication activities of international music contest (on the example of Eurovision in Kyiv in 2017). Research subject: function of status PR event in country image formation. The aim of research: to prove that international music contest Eurovision contributes host country image formation. The tasks of research: to develop research terminology based on scientific literature on image making, branding and event management; to define actual communication technologies applied in special PR events on country image formation; to describe European Broadcasting Union role in host country image formation; to appreciate effectiveness of applied communication technologies on host country image formation in Eurovision; to give recommendations for host country image formation with the help of Eurovision. Theoretical base: scientific works written by E. Bykova, D. Gavra, A. Pankrukhin, B. Jenes, E. Kaverina, U. Halcbaur, J. Goldblatt and D. PassmanΒ΄s works on music business and P. Jordan on county image building with the help of Eurovision, etc. The empirical base: PR documents from official Eurovision and European Broadcasting Union websites; more than 1.5 million articles on Ukraine in European media stored in the base of international Ukrainian image monitoring project Oko; content of the mass media and social media base Factiva; Google.Analytics data. Practical significance: the research proves that international music contest Eurovision is relevant for the host country image formation independently of the success level of applied country image formation communication technologies. Approbation: General positions of current thesis were aprobated on international scientific forum Media in modern world and were published at the collection of articles of the forum and have the status of a scientific article posted in the RINC database. Thesis structure: Research consists of introduction, 3 chapters: Special event function in country image formation, Eurovision as EBU special event and communication potential of Eurovision as a platform for image formation; conclusion, literature list from 67 positions and 12 attachments. The total volume is 76 pages
Antibiotic Prescriptions and Prophylaxis in Italian Children. Is It Time to Change? Data from the ARPEC Project.
BACKGROUND: Antimicrobials are the most commonly prescribed drugs. Many studies have evaluated antibiotic prescriptions in the paediatric outpatient but few studies describing the real antibiotic consumption in Italian children's hospitals have been published. Point-prevalence survey (PPS) has been shown to be a simple, feasible and reliable standardized method for antimicrobials surveillance in children and neonates admitted to the hospital. In this paper, we presented data from a PPS on antimicrobial prescriptions carried out in 7 large Italian paediatric institutions. METHODS: A 1-day PPS on antibiotic use in hospitalized neonates and children was performed in Italy between October and December 2012 as part of the Antibiotic Resistance and Prescribing in European Children project (ARPEC). Seven institutions in seven Italian cities were involved. The survey included all admitted patients less than 18 years of age present in the ward at 8:00 am on the day of the survey, who had at least one on-going antibiotic prescription. For all patients data about age, weight, underlying disease, antimicrobial agent, dose and indication for treatment were collected. RESULTS: The PPS was performed in 61 wards within 7 Italian institutions. A total of 899 patients were eligible and 349 (38.9%) had an on-going prescription for one or more antibiotics, with variable rates among the hospitals (25.7% - 53.8%). We describe antibiotic prescriptions separately in neonates ( = 30 days to <18 years old). In the neonatal cohort, 62.8% received antibiotics for prophylaxis and only 37.2% on those on antibiotics were treated for infection. Penicillins and aminoglycosides were the most prescribed antibiotic classes. In the paediatric cohort, 64.4% of patients were receiving antibiotics for treatment of infections and 35.5% for prophylaxis. Third generation cephalosporins and penicillin plus inhibitors were the top two antibiotic classes. The main reason for prescribing antibiotic therapy in children was lower respiratory tract infections (LRTI), followed by febrile neutropenia/fever in oncologic patients, while, in neonates, sepsis was the most common indication for treatment. Focusing on prescriptions for LRTI, 43.3% of patients were treated with 3rd generation cephalosporins, followed by macrolides (26.9%), quinolones (16.4%) and carbapenems (14.9%) and 50.1% of LRTI cases were receiving more than one antibiotic. For neutropenic fever/fever in oncologic patients, the preferred antibiotics were penicillins with inhibitors (47.8%), followed by carbapenems (34.8%), aminoglycosides (26.1%) and glycopeptides (26.1%). Overall, the 60.9% of patients were treated with a combination therapy. CONCLUSIONS: Our study provides insight on the Italian situation in terms of antibiotic prescriptions in hospitalized neonates and children. An over-use of third generation cephalosporins both for prophylaxis and treatment was the most worrisome finding. A misuse and abuse of carbapenems and quinolones was also noted. Antibiotic stewardship programs should immediately identify feasible targets to monitor and modify the prescription patterns in children's hospital, also considering the continuous and alarming emergence of MDR bacteria