Tristetraprolin (TTP) and let-7 microRNA exhibit suppressive effects on cell growth through down-regulation of oncogenes. Both TTP and let-7 are often repressed in human cancers, thereby promoting oncogenesis by derepressing their target genes. However, the precise mechanism of this repression is unknown. We here demonstrate that p53 stimulated by the DNA-damaging agent doxorubicin (DOX) induced the expression of TTP in cancer cells. TTP in turn increased let-7 levels through down-regulation of Lin28a. Correspondingly, cancer cells with mutations or inhibition of p53 failed to induce the expression of both TTP and let-7 on treatment with DOX. Down-regulation of TTP by small interfering RNAs attenuated the inhibitory effect of DOX on let-7 expression and cell growth. Therefore, TTP provides an important link between p53 activation induced by DNA damage and let-7 biogenesis. These novel findings provide a mechanism for the widespread decrease in TTP and let-7 and chemoresistance observed in human cancers.
Brachial plexus injury (BPI) often causes severe neuropathic pain that becomes chronic and difficult to treat pharmacologically or surgically. Here, we describe two cases of successful treatment of BPI with peripheral nerve stimulation (PNS). Both patients had experienced severe neuropathic pain after incomplete BPI for a long time (32 and 17 years) and did not response to medication, radiofrequency neuroablation, or spinal cord stimulation. After PNS using ultrasound, their pain was relieved by more than 50% over the course of 1 year. Both patients were satisfied with their improved sleep and quality of life. We conclude that PNS could be an alternative therapeutic modality for neuropathic pain after BPI as it provides direct nerve stimulation, has few complications, and is easy to perform.
Background: Immunohistochemistry (IHC) has played an essential role in the diagnosis of hematolymphoid neoplasms. However, IHC interpretations can be challenging in daily practice, and exponentially expanding volumes of IHC data are making the task increasingly difficult. We therefore developed a machine-learning expert-supporting system for diagnosing lymphoid neoplasms. Methods: A probabilistic decision-tree algorithm based on the Bayesian theorem was used to develop mobile application software for iOS and Android platforms. We tested the software with real data from 602 training and 392 validation cases of lymphoid neoplasms and compared the precision hit rates between the training and validation datasets. Results: IHC expression data for 150 lymphoid neoplasms and 584 antibodies was gathered. The precision hit rates of 94.7% in the training data and 95.7% in the validation data for lymphomas were not statistically significant. Results in most B-cell lymphomas were excellent, and generally equivalent performance was seen in T-cell lymphomas. The primary reasons for lack of precision were atypical IHC profiles for certain cases (e.g., CD15-negative Hodgkin lymphoma), a lack of disease-specific markers, and overlapping IHC profiles of similar diseases. Conclusions: Application of the machine-learning algorithm to diagnosis precision produced acceptable hit rates in training and validation datasets. Because of the lack of origin-or disease-specific markers in differential diagnosis, contextual information such as clinical and histological features should be taken into account to make proper use of this system in the pathologic decision-making process.
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