In this task, you're given a paragraph from the research paper and your task is to generate a suitable title for the research paper based on the given paper. Under 100 words is a good title length.

[Q]: Association of targeted multiplex PCR with resequencing microarray for the detection of multiple respiratory pathogens. Front. Microbiol. 6:532. A large number of viral and bacterial organisms are responsible for community-acquired pneumonia (CAP) which contributes to substantial burden on health management. A new resequencing microarray (RPM-IVDC1) associated with targeted multiplex PCR was recently developed and validated for multiple respiratory viruses detection and discrimination. In this study, we evaluated the capability of RPM-IVDC1 for simultaneous identification of multiple viral and bacterial organisms. The nasopharyngeal aspirates (NPAs) of 110 consecutive CAP patients, aged from 1 month to 96 years old, were collected from five distinct general hospitals in Beijing during 1-year period. The samples were subjected to the RPM-IVDC1 established protocol as compared to a real-time PCR (qRT-PCR), which was used as standard. The results of virus detection were consistent with those previously described. A total of 37 of Streptococcus pneumoniae, 14 of Haemophilus influenzae, 10 of Mycoplasma pneumoniae, two of Klebsiella pneumoniae and one of Moraxella catarrhalis were detected by RPM-IVDC1. The sensitivities and specificities were compared with those of qRT-PCR for S. pneumoniae (100, 100%, respectively), H. influenzae (92.3, 97.9%, respectively), M. pneumoniae (69.2, 99.0%, respectively), K. pneumoniae (100, 100%, respectively), and M. catarrhalis (100, 100%, respectively). Additional 22 of Streptococcus spp., 24 of Haemophilus spp. and 16 of Neisseria spp. were identified. In addition, methicillin-resistant and carbapenemases allele were also found in nine of Staphylococcus spp. and one of K. pneumoniae, respectively. These results demonstrated the capability of RPM-IVDC1 for simultaneous detection of broad-spectrum respiratory pathogens in complex backgrounds and the advantage of accessing to the actual sequences, showing great potential use of epidemic outbreak investigation. The detection results should be carefully interpreted when introducing this technique in the clinical diagnostics.
[A]: Association of targeted multiplex PCR with resequencing microarray for the detection of multiple respiratory pathogens


[Q]: Background: Complement proteins and activation products have been found associated with neuropathology in Alzheimer's disease (AD). Recently, a C5a receptor antagonist was shown to suppress neuropathology in two murine models of AD, Tg2576 and 3xTg. Previously, a genetic deficiency of C1q in the Tg2576 mouse model showed an accumulation of fibrillar plaques similar to the complement sufficient Tg2576, but reactive glia were significantly decreased and neuronal integrity was improved suggesting detrimental consequences for complement activation in AD. The goal of this study was to define the role of the classical complement activation pathway in the progression of pathology in the 3xTg mouse that develops tangles in addition to fibrillar plaques (more closely reflecting human AD pathology) and to assess the influence of complement in a model of AD with a higher level of complement hemolytic activity. Methods: 3xTg mice deficient in C1q (3xTgQ-/-) were generated, and both 3xTg and 3xTgQ-/-were backcrossed to the BUB mouse strain which has higher in vitro hemolytic complement activity. Mice were aged and perfused, and brain sections stained for pathological markers or analyzed for proinflammatory marker expression. Results: 3xTgQ-/-mice showed similar amounts of fibrillar amyloid, reactive glia and hyperphosphorylated tau as the C1q-sufficient 3xTg at the ages analyzed. However, 3xTg and 3xTgQ-/-on the BUB background developed pathology earlier than on the original 3xTg background, although the presence of C1q had no effect on neuropathological and pro-inflammatory markers. In contrast to that seen in other transgenic models of AD, C1q, C4 and C3 immunoreactivity was undetectable on the plaques of 3xTg in any background, although C3 was associated with reactive astrocytes surrounding the plaques. Importantly, properdin a component of the alternative complement pathway was associated with plaques in all models. Conclusions: In contrast to previously investigated transgenic models of AD, development of neuropathology in 3xTg mice, which progresses much slower than other murine models, may not be influenced by fibrillar amyloid mediated activation of the classical complement pathway, suggesting that the alternative complement pathway activation or a C3-independent cleavage of C5 could account for the detrimental effects in these mice that are prevented by the C5a receptor antagonist. Furthermore, the paucity of complement activation may be a factor in the slower kinetics of progression of pathology in the 3xTg model of this disease.
[A]: Contribution of complement activation pathways to neuropathology differs among mouse models of Alzheimer's disease


[Q]: Necessary screenings must be performed to control the spread of the Corona Virus in daily life and to make a preliminary diagnosis of suspicious cases. The long duration of pathological laboratory tests and the wrong test results led the researchers to focus on different fields. Fast and accurate diagnoses are essential for effective interventions with COVID-19. The information obtained by using X-ray and Computed Tomography (CT) images is vital in making clinical diagnoses. Therefore it was aimed to develop a machine learning method for the detection of viral epidemics by analyzing X-ray images. In this study, images belonging to 6 situations, including coronavirus images, are classified. Since the number of images in the dataset is deficient and unbalanced, it is more convenient to analyze these images with hand-crafted feature extraction methods. For this purpose, firstly, all the images in the dataset are extracted with the help of four feature extraction algorithms. These extracted features are combined in raw form. The unbalanced data problem is eliminated by producing feature vectors with the SMOTE algorithm. Finally, the feature vector is reduced in size by using a stacked auto-encoder and principal component analysis to remove interconnected features in the feature vector. According to the obtained results, it is seen that the proposed method has leveraging performance, especially in order to make the diagnosis of COVID-19 in a short time and effectively.
[A]:
Classification of Coronavirus Images using Shrunken Features