Breast cancer is the most commonly diagnosed cancer amongst women worldwide and a leading cause of cancer related deaths among females. It’s the second most common type of cancer overall. According to the International Agency for Research on Cancer Research, there were more than 2 million new cases in 2018.
In industries that depend on bioprocessing, achieving the highest possible yields in the shortest time frame, while keeping costs down and product quality high is often challenging. Meeting these goals requires having a well-designed, well-defined and well-controlled process. And at the core of any effective process control is a set of effective process modeling tools.
In industries ranging from biopharmaceuticals to chemicals, executives in today’s manufacturing marketplace face ever-increasing pressures to grow profit margins, reduce time to market and optimize processes across all aspects of their business. Everything from constraints in the supply of raw materials to multiple steps in a manufacturing process can affect productivity—making process optimization an amorphous target.
Speed and changing market conditions are significant challenges for businesses across many industries and markets. The need for improved efficiency, the ability to adapt to changing market conditions and digitalization are often key drivers of change. By focusing on data, a company can make significant improvements in processes or systems, and gain insights into the driving forces behind business operations. Data analytics is the key to business optimization.
Consumers expect a certain consistency in quality and taste from the food and beverage brands they love. But many factors can influence the way a product tastes when it reaches the consumer – ranging from the manufacturing process to seasonality of ingredients to storage temperatures. Similarly, a number of other factors may influence the overall quality attributes that matter, such as alcohol content of beer or stability of the whiskey aging process.
The natural variability of botanical material often makes it difficult to ensure a consistent quality process for pharmaceuticals made from plant-based products. In addition, botanical drug products (BDPs) are often produced using a series of separate batch processes, which adds even more variability into the manufacturing process.
Many elderly people are afraid of falling – and for good reasons. Falls can have serious consequences for the individual but also the fear of falling could have serious effects on health and independence. A new research project at Luleå University of Technology in Sweden has taken a closer look at fall-related concerns among elderly people, using multivariate data analysis, MVDA, with the ultimate goal of finding diagnostic and training methods that could help reduce falls. Results from the first studies have given some interesting answers.
What’s the secret formula for creating long-lasting bubbles? Is expert knowledge of liquid dynamics needed to optimize the mixture design and develop the best bubble solution? Or can we use design of experiments (DOE) and data analytics to draw conclusions? Let’s a take a look at a fun example of how DOE can be used to optimize a mixture design in order to achieve our goal: create long-lasting bubbles.
Advancements in cell and gene therapy hold promise for the future of personalized medicine, especially for cancer treatments. However, bioprocessing methods for autologous cellular therapies, and CAR-T in particular, often present unique challenges in manufacturing due to the variability of the starting material and unique nature of each batch. Is there a way to create more efficient processes in order to bring down costs and make personalized medicine a viable option for more patients?
Could data analytics aid in the diagnosis of severe neurological diseases? In a recent study, a research group at Umeå University has conducted statistical data analysis of biomarkers from patients suffering from Amyotrophic lateral sclerosis (ALS, also known as Lou Gehrig’s disease) and Parkinson’s disease to investigate whether data analytics could help in the diagnosis of – and help distinguish between – the two diseases.