Th2 cells play a pivotal role in the pathogenesis of allergic diseases, including asthma, but the molecular basis of the Th1/Th2 dichotomy and the precise molecular pathways leading to Th2-dominant immune responses are still unclear. To this end, we have combined suppression subtractive hybridization (SSH) and high throughput analysis of cDNA arrays spotted with IMAGE clones to determine the profile of differential gene expression in human allergen-specific Th2 cells. Allergen-stimulated Th2 cells were used as the tester, and either resting Th2 cells or stimulated Th1 cells were used as the driver. SSH was used to equalize different mRNA levels and remove common sequences between the tester and the driver. Comparison of cDNA arrays probed with subtracted tester and non-subtracted driver provided a profile of Th2-selective gene expression. Analysis of 77 sequence-confirmed and differentially expressed genes in Th2 cells showed predominant EST sequences, representing 80% of sequences analyzed. The pattern of gene expression in 19 selected sequences was further analyzed in additional Th1 and Th2 clones. A total of 15 sequences showed predominant expression in Th2 cells, while the remaining four EST sequences showed no detectable amplification signal. The database containing Th2-selective genes will further our understanding of Th2 cell function and the genetic basis of allergic diseases.
Larynx cancer is one of the most common primary head and neck cancers. For early-stage laryngeal cancer, both surgery and radiotherapy are effective treatment modalities, offering a high rate of local control and cure.
SummaryThis article investigates an adaptive fast finite‐time control problem for a class of nonlinear uncertain systems. First, to reduce the transmission load, an event‐triggering mechanism is introduced into the channel from the controller to the actuator. Second, the observer is employed to estimate the unmeasurable state variables. Third, considering that the nonlinear functions of systems are completely unknown, neural networks are introduced to overcome the obstacles caused by unknown nonlinearities. Finally, an event‐triggered adaptive fast finite‐time output‐feedback control strategy is proposed by means of the fast finite‐time stability criterion and backstepping technique. The theoretical analysis illustrates that under the proposed control strategy, all signals in the closed‐loop systems converge to a bounded domain within a finite time. Furthermore, the Zeno phenomenon can be avoided effectively. The main innovation is to design the adaptive controller from a new perspective. The validity of results is elaborated by numerical simulation.
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