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I cordially invite you to visit the Stegmann Systems booth at the CASSS: Bioassays: Scientific Approaches & Regulatory Strategies conference in Gaithersburg, MD, USA. Our renowned CEO, Dr. Ralf Stegmann, will be on-site to introduce and discuss our most recent releases with you, including Biological Assay Package version 27, our new PLA 3.0 Academy, and the state-of-the-art PLA 3.0 Knowledge Center.

You can now trend values of test results like the p value from the F-test on parallelism. Additional curve characteristics are also available, now, together with their confidence intervals. Create fully customizable charts with the Control Chart Package for PLA 3.0, and discover conspicuous patterns in your assay data!

We're continuing our journey through the Biological Assay Package Version 27, we now explore the elaborated analysis capabilities by the latest update.

Exciting news! Our new Training Center, the PLA 3.0 Academy, is now open. To celebrate, we're offering exclusive introductory rates on all our PLA 3.0 Super User Trainings and PLA 3.0 Advanced Analysis Workshops. This is a limited-time opportunity to experience our cutting-edge courses at a reduced cost. Don't miss out on the chance to enhance your skills and knowledge with our expert-led programs. Register now and be among the first to benefit from our special launch prices!

We are thrilled to announce that Stegmann Systems is not only a sponsor of the BEBPA Conference, taking place from March 11-13 in Long Beach, California. Our esteemed CEO, Dr. Ralf Stegmann, will be a speaker in Interest Group 3: Handling Dose-Response Curves. This year’s conference promises an engaging blend of presentations, workshops, and round-table discussions.

We have uploaded new PLA 3.0 Shorts.

The Biological Assay Package 27 provides new data processing tools to support block-based adjustments and normalization, replicate averaging, and multiple response columns!

Adjust and normalize data within blocks based on your assay layout. For example, shift data to an established baseline, create block-adjusted data sets, and account for plate or litter effects. You can also calculate the replicate mean for more accurate results when using pseudoreplicates.