The Role of CT Scan in Pediatric Airway Foreign Bodies.

Objective: We aim to determine the utility of CT scan as a method to accurately confirm pediatric airway foreign bodies (AFBs), the current gold standard of which is chest X-ray as the primary imaging modality in the investigation screening of AFBs with progression to microlaryngobronchoscopy. Methods: A retrospective cohort study of children diagnosed with suspected AFBs between July 2019 and June 2020 was conducted. The primary outcome of missed AFBs from radiologic investigations was recorded. Results: A total of 226 children with an average age of 1.94 years were included in this study. One hundred and two children were eventually admitted to the hospital for microlaryngobronchoscopy. A total of 89 cases were initially examined by chest X-ray with the diagnosis confirmed in 26 cases. The initial examination was chest CT scan in 105 cases, of which the diagnosis was confirmed in 46 cases. The initial examination was chest CT scan with airway reconstruction in 32 cases, and the diagnosis was confirmed in 17 cases. Patients with negative chest CT scan with airway reconstruction were observed to have resolution of symptoms with no further need for bronchoscopy. Conclusion: Chest CT scan with airway reconstruction had the highest rate of confirmed diagnosis of pediatric AFBs on initial scanning, followed by chest CT scan, and finally chest X-ray with fluoroscopy; there was no missed diagnosis in chest CT scan with airway reconstruction. Chest CT scan with airway reconstruction can accurately and quickly detect AFBs and reduce unnecessary bronchoscopy.

[Evaluation of hearing and speech rehabilitation after cochlear implantation in children with Waardenburg syndrome].

Objective:By comparing the hearing and speech rehabilitation effects of cochlear implantation （CI） in children with Waardenburg syndrome （WS） and children with common deafness genes （SLC26A4, GJB2） in the Chinese population, and the hearing and speech rehabilitation effects of bilateral CI and unilateral CI in children with WS, to provide a reference for clinical CIin children with WS. Methods:Follow up and return visit 72 pedestrian cochlear implant children with severe and above sensorineural hearing loss and clear gene mutation type diagnosed by Kunming Children's Hospital from 2017 to 2019, including 24 cases in the WS group, 24 cases in the control group （SLC26A4 deafness group and GJB2 deafness group）. All enrolled children were evaluated for auditory and speech ability 12 months after startup. Results:The hearing aid threshold, the correct recognition rate of speech recognition ability evaluation, IT-MAIS / MAIS score rate, CAP score, SIR score, there was no significant difference（P>0.05）. The correct recognition rates of IT-MAIS / MAIS score, SIR score, natural environment sound recognition, vowel recognition, tone recognition, monosyllabic word recognition, disyllabic word recognition and short sentence recognition in children with WS bilateral CI were significantly higher than those in children with WS unilateral CI （P<0.05）. There was no significant difference in CAP score, initial recognition and correct recognition rate of trisyllabic words between children with WS bilateral CI and children with WS unilateral CI （P>0.05）. Conclusion:Common deafness genes in children with WS and Chinese population （SLC26A4, GJB2） the effect of cochlear implantation on hearing and speech rehabilitation of sick children is equivalent. For children with severe and above sensorineural hearing loss associated with this syndrome, CI can be used clinically to improve their hearing and speech ability. WS bilateral CI has advantages in some hearing and speech abilities compared with unilateral CI, so those whomeet the conditions should be encouraged bilateral implantation.

A stochastic model for detecting overlapping and hierarchical community structure.

Community detection is a fundamental problem in the analysis of complex networks. Recently, many researchers have concentrated on the detection of overlapping communities, where a vertex may belong to more than one community. However, most current methods require the number (or the size) of the communities as a priori information, which is usually unavailable in real-world networks. Thus, a practical algorithm should not only find the overlapping community structure, but also automatically determine the number of communities. Furthermore, it is preferable if this method is able to reveal the hierarchical structure of networks as well. In this work, we firstly propose a generative model that employs a nonnegative matrix factorization (NMF) formulization with a l(2,1) norm regularization term, balanced by a resolution parameter. The NMF has the nature that provides overlapping community structure by assigning soft membership variables to each vertex; the l(2,1) regularization term is a technique of group sparsity which can automatically determine the number of communities by penalizing too many nonempty communities; and hence the resolution parameter enables us to explore the hierarchical structure of networks. Thereafter, we derive the multiplicative update rule to learn the model parameters, and offer the proof of its correctness. Finally, we test our approach on a variety of synthetic and real-world networks, and compare it with some state-of-the-art algorithms. The results validate the superior performance of our new method.

Lung metastasis of pancreatic carcinoma is regulated by TGFß signaling.

The molecular regulation of the lung metastasis of pancreatic carcinoma (PCC) is not completely understood. Here, we show that the levels of phosphorylated SMAD3, ZEB1, ZEB2, Snail1, and Snail2 were significantly higher in PCC with lung metastasis than in PCC without lung metastasis. Overexpression of TGFß1 enhanced the invasiveness of PCC cells, while inhibition of TGFß1 decreased the invasiveness of PCC cells, which appeared to be conducted by activated TGFß receptor signaling-induced upregulation of ZEB1, ZEB2, Snail1, and Snail2, suggesting a process of epithelial-mesenchymal transition (EMT). Taken together, our study provides evidence that TGFß receptor signaling-induced EMT may be responsible for the increased PCC invasiveness to enhance its lung metastasis.

Uncovering community structures with initialized Bayesian nonnegative matrix factorization.

Uncovering community structures is important for understanding networks. Currently, several nonnegative matrix factorization algorithms have been proposed for discovering community structure in complex networks. However, these algorithms exhibit some drawbacks, such as unstable results and inefficient running times. In view of the problems, a novel approach that utilizes an initialized Bayesian nonnegative matrix factorization model for determining community membership is proposed. First, based on singular value decomposition, we obtain simple initialized matrix factorizations from approximate decompositions of the complex network's adjacency matrix. Then, within a few iterations, the final matrix factorizations are achieved by the Bayesian nonnegative matrix factorization method with the initialized matrix factorizations. Thus, the network's community structure can be determined by judging the classification of nodes with a final matrix factor. Experimental results show that the proposed method is highly accurate and offers competitive performance to that of the state-of-the-art methods even though it is not designed for the purpose of modularity maximization.