The application of metagenomic next-generation sequencing in diagnosing Chlamydia psittaci pneumonia: a report of five cases.

BACKGROUND: Chlamydia psittaci pneumonia is a zoonotic infectious disease caused by Chlamydia psittaci. Diagnostic tools, including culture, serologic test and PCR-based methods, are available but prone to false negative results. CASE PRESENTATION: This report included five cases of Chlamydia psittaci pneumonia. Symptoms and signs common to all 5 cases included fever, coughing, generalized muscle ache, and most notably, inflammatory infiltration of the lungs upon chest CT and X-ray. Metagenomic next-generation sequencing (mNGS) revealed the presence of Chlamydia psittaci in biopsy lung tissue in 3 cases and bronchoalveolar lavage fluid in the remaining 2 cases. Three patients responded to doxycycline plus moxifloxacin; two patients responded to moxifloxacin alone. CONCLUSIONS: mNGS could be used to diagnose Chlamydia psittaci pneumonia.

Deep Learning for Sensor-Based Rehabilitation Exercise Recognition and Evaluation.

In this paper, a multipath convolutional neural network (MP-CNN) is proposed for rehabilitation exercise recognition using sensor data. It consists of two novel components: a dynamic convolutional neural network (D-CNN) and a state transition probability CNN (S-CNN). In the D-CNN, Gaussian mixture models (GMMs) are exploited to capture the distribution of sensor data for the body movements of the physical rehabilitation exercises. Then, the input signals and the GMMs are screened into different segments. These form multiple paths in the CNN. The S-CNN uses a modified Lempel⁻Ziv⁻Welch (LZW) algorithm to extract the transition probabilities of hidden states as discriminate features of different movements. Then, the D-CNN and the S-CNN are combined to build the MP-CNN. To evaluate the rehabilitation exercise, a special evaluation matrix is proposed along with the deep learning classifier to learn the general feature representation for each class of rehabilitation exercise at different levels. Then, for any rehabilitation exercise, it can be classified by the deep learning model and compared to the learned best features. The distance to the best feature is used as the score for the evaluation. We demonstrate our method with our collected dataset and several activity recognition datasets. The classification results are superior when compared to those obtained using other deep learning models, and the evaluation scores are effective for practical applications.