根管治疗失败病例根管内细菌对根尖周组织致病力的实验研究

目的观察根管治疗失败病例根管内分离的主要微生物对狗牙根尖周组织的影响。方法选择成年健康杂种狗5只,共有40个实验牙,80个实验牙根。实验一组于狗牙根管内接种溶血链球菌、微小消化链球菌、产黑色素类杆菌及具核梭杆菌;实验二组于狗牙根管内接种粪肠球菌及上述4种细菌;对照组不接种细菌。对狗牙完成根管治疗。分别于治疗后3、6、12个月拍摄根尖X线片,并记录牙齿和根尖周组织的临床表现;根管治疗后12个月处死动物,制备根尖周组织病理标本,观察根尖周骨组织破坏情况;动物处死前,根管内进行微生物的取样、培养和鉴定。结果实验组可见狗牙槽骨尖周骨质吸收,牙周膜纤维排列受到破坏,实验二组对根尖周破坏重于实验一组,对照组根尖周骨组织无破坏。结论从根管治疗失败病例根管内分离的主要微生物粪肠球菌、溶血链球菌、微小消化链球菌、产黑色素类杆菌及具核梭杆菌对狗牙根尖周组织有明显的破坏作用。     

Vestibular humanoid postural control

Many of our motor activities require stabilization against external disturbances. This especially applies to biped stance since it is inherently unstable. Disturbance compensation is mainly reactive, depending on sensory inputs and real-time sensor fusion. In humans, the vestibular system plays a major role. When there is no visual space reference, vestibular-loss clearly impairs stance stability. Most humanoid robots do not use a vestibular system, but stabilize upright body posture by means of center of pressure (COP) control. We here suggest using in addition a vestibular sensor and present a biologically inspired vestibular sensor along with a human-inspired stance control mechanism. We proceed in two steps. First, in an introductory review part, we report on relevant human sensors and their role in stance control, focusing on own models of transmitter fusion in the vestibular sensor and sensor fusion in stance control. In a second, experimental part, the models are used to construct an artificial vestibular system and to embed it into the stance control of a humanoid. The robot’s performance is investigated using tilts of the support surface. The results are compared to those of humans. Functional significance of the vestibular sensor is highlighted by comparing vestibular-able with vestibular-loss states in robot and humans. We show that a kinematic body-space sensory feedback (vestibular) is advantageous over a kinetic one (force cues) for dynamic body-space balancing. Our embodiment of human sensorimotor control principles into a robot is more than just bionics. It inspired our biological work (neurorobotics: ‘learning by building’, proof of principle, and more). We envisage a future clinical use in the form of hardware-in-the-loop simulations of neurological symptoms for improving diagnosis and therapy and designing medical assistive devices.     

Fermentation process supervision and strategies for fail‐safe operation: A practical approach

In an industrial production environment, cultivation processes for the production of recombinant proteins run along predefined trajectories. Feedback control is the best way to keep the cultures on track. However, feedback controllers require accurate on‐line values of the controlled variables. To assess whether the measurement signals are correct, process supervision techniques are required. In the case where a process failure has occurred and incorrectly measured variables have been identified, automated fail‐safe techniques must be started. Here, we use the production of a pharmaceutically relevant recombinant protein to compare different approaches to process supervision and fail‐safe routines.