Titre : Lyophilisation

Lyophilisation : Questions médicales fréquentes

Termes MeSH sélectionnés :

Data Analysis
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"acceptedAnswer": { "@type": "Answer", "text": "Conservez-les dans un endroit frais, sec et à l'abri de la lumière pour préserver leur qualité." } }, { "@type": "Question", "name": "Quelles conditions sont idéales pour stocker des produits lyophilisés ?", "position": 6, "acceptedAnswer": { "@type": "Answer", "text": "Un environnement à faible humidité et température stable est idéal pour le stockage." } }, { "@type": "Question", "name": "Comment réhydrater un produit lyophilisé ?", "position": 7, "acceptedAnswer": { "@type": "Answer", "text": "Il suffit d'ajouter de l'eau à température ambiante ou chaude selon le produit." } }, { "@type": "Question", "name": "Quels produits sont souvent lyophilisés ?", "position": 8, "acceptedAnswer": { "@type": "Answer", "text": "Les fruits, légumes, viandes et médicaments sont couramment lyophilisés pour la conservation." } }, { "@type": "Question", "name": "Quels risques si un produit lyophilisé est mal stocké ?", "position": 9, "acceptedAnswer": { "@type": "Answer", "text": "Un mauvais stockage peut entraîner une perte de qualité, de goût et de valeur nutritionnelle." } }, { "@type": "Question", "name": "Peut-on consommer un produit lyophilisé périmé ?", "position": 10, "acceptedAnswer": { "@type": "Answer", "text": "Il est déconseillé de consommer un produit périmé, même s'il semble intact." } }, { "@type": "Question", "name": "Quels facteurs influencent la qualité de la lyophilisation ?", "position": 11, "acceptedAnswer": { "@type": "Answer", "text": "La température, la pression et la durée du processus de lyophilisation influencent la qualité." } }, { "@type": "Question", "name": "Quels produits sont plus sensibles à la lyophilisation ?", "position": 12, "acceptedAnswer": { "@type": "Answer", "text": "Les produits riches en graisses ou en sucres peuvent être plus sensibles à la dégradation." } } ] } ] }

Sources (10000 au total)

A novel dynamic Bayesian network approach for data mining and survival data analysis.

Censorship is the primary challenge in survival modeling, especially in human health studies. The classical methods have been limited by applications like Kaplan-Meier or restricted assumptions like t... We proposed a two-slice temporal Bayesian network model for the survival data, introducing the survival and censorship status in each observed time as the dynamic states. A score-based algorithm learn... The simulation study shows the superiority of DBN in bias reduction for many scenarios compared with Cox regression and Kaplan-Meier, especially in the late survival times. In the real-world data, the... Our proposed dynamic Bayesian network model could be used as a data mining technique in the context of survival data analysis. The advantages of this approach are feature selection ability, straightfo...

Measuring concordance of data sources used for infectious disease research in the USA: a retrospective data analysis.

As highlighted by the COVID-19 pandemic, researchers are eager to make use of a wide variety of data sources, both government-sponsored and alternative, to characterise the epidemiology of infectious ... Retrospective descriptive analysis.... Yearly number of national-level and state-level disease-specific case counts and disease clusters for three diseases (measles, mumps and varicella) during a 5-year study period (2013-2017) across four... Our study demonstrated drastic differences in reported infectious disease incidence across data sources. When compared with the other three sources of interest, Optum data showed substantially higher,... Researchers should consider data source limitations when attempting to characterise the epidemiology of infectious diseases. Some data sources, such as billing claims data, may be unsuitable for epide...