With the introduction of fiber-guided radiation at 1 µ wavelength emitting in the milti-kW range at better beam quality than CO2-lasers the most established application in laser processing, namely laser fusion cutting, came back into the industrial and scientific focus. Laser sources with extraordinary optical and economical properties - disk and fiber lasers - in a stormy way enter the market of cutting machines so far reserved for the 10 µ radiation source and led to a volatile situation. The new laser sources can already address a market-relevant class of applications, namely, fusion cutting of steel up to a sheet thickness of 2 mm with pronounced advantages in productivity. However, there is a significant lack of cut quality for larger sheet thickness. The main reason for the drawback and its physical background are given. With the availability of cutting machines with 1 µ fiber-guided radiation the race for the worldwide market regarding the larger sheet thickness is opened and the priority issues to improve the cut quality are related to the three levels: wavelength, beam delivery and the application stage of the machine. The stability model called QuCut is presented which for the first time allows to analyze stability of cutting with fiber-guided radiation. Experimental ripple patterns and ripple spectra resolved with respect to the cutting depth are well reproduced by the new stability model. A number of different experimental methods towards an improved understanding of the dynamics in laser drilling are developed, however, there are gaps related to in-situ observation which is obscured by the hole walls. There are four novel experimental methods resolving the dynamics from a µms-down to a ns-time scale having a spatial resolution with respect to transient drilling depth on the µm scale. As result, the different mechanisms contributing to recast formation and dynamical features of drilling are revealed in more detail. In particular, the action of double pulses and its changes depending on the evolving drill are investigated
A recombinant vaccinia virus in which the transcription of the human immunodeficiency virus type 1 (BRU isolate) env gene is driven by the 11K late vaccinia promoter yields about 10-fold higher amounts of gp160 env protein upon infection of monkey cells than does a recombinant in which gp160 is expressed using the 7.5K early-late promoter. The gp160 was purified from detergent lysates of infected cells by lentil lectin affinity chromatography followed by immunoaffinity chromatography, and was obtained in yields of 1-2 mg/10(9) cells of material estimated to be about 70% pure. Pairs of rabbits were immunized with purified gp160 using either one of five different adjuvants or an immunostimulating complex. In all cases a substantial humoral immune response was obtained after boosting, including an activity that neutralized the homologous (BRU) isolate of HIV-1. In some cases, this activity also neutralized two distantly related isolates, SF2 and MN.
CAR-T cell therapy is a promising treatment for acute leukemia and lymphoma. CAR-T cell therapies take a pioneering role in autologous gene therapy with three EMA-approved products. However, the chance of clinical success remains relatively low as the applicability of CAR-T cell therapy suffers from long, labor-intensive manufacturing and a lack of comprehensive insight into the bioprocess. This leads to high manufacturing costs and limited clinical success, preventing the widespread use of CAR-T cell therapies. New manufacturing approaches are needed to lower costs to improve manufacturing capacity and shorten provision times. Semi-automated devices such as the Miltenyi Prodigy® were developed to reduce hands-on production time. However, these devices are not equipped with the process analytical technology necessary to fully characterize and control the process. An automated AI-driven CAR-T cell manufacturing platform in smart manufacturing hospitals (SMH) is being developed to address these challenges. Automation will increase the cost-effectiveness and robustness of manufacturing. Using Artificial Intelligence (AI) to interpret the data collected on the platform will provide valuable process insights and drive decisions for process optimization. The smart integration of automated CAR-T cell manufacturing platforms into hospitals enables the independent manufacture of autologous CAR-T cell products. In this perspective, we will be discussing current challenges and opportunities of the patient-specific but highly automated, AI-enabled CAR-T cell manufacturing. A first automation concept will be shown, including a system architecture based on current Industry 4.0 approaches for AI integration.
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