Background and purpose: Although crizotinib could manifest marked antitumor activity in anaplastic lymphoma kinase (ALK)-rearrangement-positive non-small cell lung cancer (NSCLC) patients, but brain metastases is always occured in such patients. This study aimed to explore the efifcacy and treatment mode of crizotinib for brain metastases in ALK-rearrangement-positive NSCLC. Methods: The clinical data of 6 patients with brain metastases in ALK-rearrangement-positive NSCLC treated in 81 Hospital of PLA from Jan. 2011 to Aug. 2014 were analyzed retrospectively. Results: Three patients had brain metastases before crizotinib administration, 1 obtained partial response (PR) and 2 obtained stable disease (SD) in intracraninal tumors. The median progression free survival (PFS)for the ifrst period of crizotinib administration were 5.7 months, and the sites of ifrst disease progression were brains. All the 6 patients continued to receive crizotinib after radiotherapy with the median PFS of 4 months. One patient even experienced a median PFS of 23.3 months for the second period of crizotinib administration, and her brain tumors obtained complete response (CR). Conclusion:The data of this study suggest that crizotinib is effective for brain metastases in ALK-rearrangement-positive NSCLC, and continued administration of crizotinib after radiotherapy for isolated intracraninal tumor progression is a elective treatment option for such patients.

Objective To evaluate the value of simultaneous amplification and testing method for diagnosing in patients with pulmonary tuberculosis.Methods Total of 277 sputum samples were detected by SAT,Lowenstein-Jensen (L-J) culture and Ziehl-neelsen staining.Chi-square test was used to compare and analysis the statistical difference in positive detection rates.The sensitivity,specificity,positive predictive value (PPV) and negative predictive value (NPV) of SAT for early diagnosing and judging curative effect were calculated respectively when the clinical diagnosis and L-J culture used as its reference standard.Results The positive detection rates of the three methods for detecting Mycobacterium tuberculosis from the 277 sputum samples were 47.3％ (131/277),30.3％ (84/277),29.6％ (82/277),there was a significant difference between SAT and L-J culture(x2 =16.8,P ＜ 0.05)and Ziehl-neelsen(X2 =18.3,P ＜ 0.05)by chi-square test.Before treatment was commenced,using clinical diagnosis as its reference,the sensitivity,specificity,PPV and NPV of SAT were 59.0％ (95/161),97.4％ (37/38)、99.0％ (95/96) and 35.9％ (37/103) ; After intensive treatment was over,taking culture as its standard,the results were 11/11,64.2％ (43/67),31.4％ (11/35),100％ (43/43).Conclusions SAT can be useful for early diagnosis of clinically suspicious TB case,it has a more sensitive detecting values of SAT for living bacilli and a shorter test period,which implied it can help judging the response to anti-TB treatment.

Objective To develop an intelligent recognition model based on deep learning algorithms of unmanned aerial vehicle (UAV) images, and to preliminarily explore the value of this model for remote identification, monitoring and management of cattle, a source of Schistosoma japonicum infection. Methods Oncomelania hupensis snail-infested marshlands around the Poyang Lake area were selected as the study area. Image datasets of the study area were captured by aerial photography with UAV and subjected to augmentation. Cattle in the sample database were annotated with the annotation software VGG Image Annotator to create the morphological recognition labels for cattle. A model was created for intelligent recognition of livestock based on deep learning-based Mask R-convolutional neural network (CNN) algorithms. The performance of the model for cattle recognition was evaluated with accuracy, precision, recall, F1 score and mean precision. Results A total of 200 original UAV images were obtained, and 410 images were yielded following data augmentation. A total of 2 860 training samples of cattle recognition were labeled. The created deep learning-based Mask R-CNN model converged following 200 iterations, with an accuracy of 88.01%, precision of 92.33%, recall of 94.06%, F1 score of 93.19%, and mean precision of 92.27%, and the model was effective to detect and segment the morphological features of cattle. Conclusion The deep learning-based Mask R-CNN model is highly accurate for recognition of cattle based on UAV images, which is feasible for remote intelligent recognition, monitoring, and management of the source of S. japonicum infection.