Kaluza C Analysis Software

Purpose-built for clinical laboratories, Kaluza C Flow Cytometry Analysis Software helps you turn critical data into meaningful patient reports.

With the power to processes multicolor files of up to 20 million events in real time, Kaluza C software streamlines quality control (QC) reporting requirements and enables you to analyze any flow cytometry standard (FCS) compliant file.

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Features Explore Kaluza C Models Documentation Documents techniques Produits associés

Fonctionnalités

La philosophie Kaluza

Les menus radiaux contextuels mettent les fonctions à votre disposition
Quatre panneaux de commande permettent d’accéder à tous les aspects de vos données
Réglages en temps réel de l’affichage des données

Fonctions de déclaration clinique

Répertorié auprès de la FDA
Standardiser les rapports avec des feuilles de rapport, y compris la signature électronique pour les approbations
Transmettre les résultats au système d’information du laboratoire

Fonctions de contrôle qualité

Détecter les problèmes qui pourraient produire des résultats inexacts
Surveiller les performances de l’instrument et les contrôles de test avec des graphes de Levey-Jennings
Utilise le protocole de CQ et des fichiers de données multiples

Content and Resources

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Kaluza C Analysis Software Brochure Kaluza C Analysis Software streamlines clinical QC reporting requirements and addresses flow cytometry standardization issues

Offline Flow Cytometry Analysis Software: Importance of Scaling and Data Visualization This whitepaper discussed the flow cytometry open platform data structure and how it facilitates for robust offline analysis, the importance of data scaling and display features in ensuring your data looks consistent with the native environment, and illustrates specific examples comparing the native display to an offline data analysis software across a variety of platforms and manufacturers.

Using Standardized Dry Antibody Panels for Flow Cytometry in the Assessment of Altered Immune Profiles in Response to SARS-CoV2 Infection In order to identify early indicators of disease severity in SARS-CoV2-infection, the proportions of well-established immune cell phenotypes have been subject to extensive research, utilizing flow cytometry as a core technology1-3. In order to ensure comparable and consistent results in the massively multi-institutional research setting of a global pandemic, the use of standardized antibody panels and procedures, as demonstrated by The ONE Study4-6, is a promising approach that also can lower technical barriers