PERTUSSIS - RE-EMERGIGNG DISEASE

Profese online（2021）

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Abstract

VĂ˝chodiska: OÄkovĂĄnĂ proti pertusi v ÄeskĂŠ republice (ÄR) bylo zahĂĄjeno na konci roku 1958. ProoÄkovanost se dlouhodobÄ udrĹžovala na velmi dobrĂŠ Ăşrovni a hlĂĄĹĄeno bylo pouze nÄkolik pĹĂpadĹŻ roÄnÄ. PĹesto je v ÄR od 90. let minulĂŠho stoletĂ pertuse na vzestupu (re-emerging disease) podobnÄ jako v jinĂ˝ch zemĂch. CĂl: SeznĂĄmit s aktuĂĄlnĂ epidemiologickou situacĂ a faktory, kterĂŠ ji ovlivĹujĂ. Metodika: PĹehled shrnujĂcĂ publikovanĂŠ studie a ÄlĂĄnky k danĂŠ problematice. VĂ˝sledky: Nemocnost pertusĂ stoupĂĄ v zemĂch nejen ve stĂĄtech, kde se oÄkuje acelulĂĄrnĂ pertusovou vakcĂnou, ale i tam, kde se pouĹžĂvĂĄ celobunÄÄnĂĄ pertusovĂĄ vakcĂna. ZejmĂŠna v oblastech s dobrou prooÄkovanostĂ je pozorovĂĄna zmÄna ve vÄkovĂŠ distribuci onemocnÄnĂ se shiftem do skupiny adolescentĹŻ a dospÄlĂ˝ch. Pertuse je vĂ˝znamnou pĹĂÄinou nemocnosti a Ăşmrtnosti u nejmenĹĄĂch dÄtĂ. AcelulĂĄrnĂ vakcĂny jsou povaĹžovĂĄny za bezpeÄnĂŠ, ale pĹibĂ˝vĂĄ dĹŻkazĹŻ, Ĺže acelulĂĄrnĂ vakcĂny nejsou schopny onemocnÄnĂ pertusĂ dostateÄnÄ kontrolovat. ZĂĄvÄry: Pertusi lze pĹedchĂĄzet oÄkovĂĄnĂm. PĹes vysokou prooÄkovanost je pertuse povaĹžovĂĄna za znovu se objevujĂcĂ onemocnÄnĂ. Je nutnĂŠ zlepĹĄit vĹĄechny prvky surveillance vÄetnÄ ÄasnĂŠ a sprĂĄvnĂŠ diagnostiky. Je dĹŻleĹžitĂŠ zavĂŠst opatĹenĂ, kterĂĄ snĂĹžĂ pĹenos pertuse na nejmenĹĄĂ dÄti. Vzhledem k nĂĄrĹŻstu pertuse v ÄR je zejmĂŠna v populaci ve zvĂ˝ĹĄenĂŠm riziku onemocnÄnĂ nezbytnĂŠ udrĹžet i nadĂĄle co nejvyĹĄĹĄĂ prooÄkovanost.

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Pretraining has recently greatly promoted the development of natural language processing (NLP)
We show that M6 outperforms the baselines in multimodal downstream tasks, and the large M6 with 10 parameters can reach a better performance
We propose a method called M6 that is able to process information of multiple modalities and perform both single-modal and cross-modal understanding and generation
The model is scaled to large model with 10 billion parameters with sophisticated deployment, and the 10 -parameter M6-large is the largest pretrained model in Chinese
Experimental results show that our proposed M6 outperforms the baseline in a number of downstream tasks concerning both single modality and multiple modalities We will continue the pretraining of extremely large models by increasing data to explore the limit of its performance

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