In this task, you're given a paragraph and title from the research paper. Your task is to classify whether the given title is suitable or not for the research paper based on the given paragraph. Return "True" if title is proper according to paragraph else "False".

Q: Paragraph: medRxiv preprint [Abstract]Objective Coronavirus disease 2019 (COVID-19) has become pandemic in the world. The need for IgG-IgM combined antibody test is booming, but data on diagnostic indexes evaluation was inadequate. The aim of this study was to evaluate diagnostic indexes of a rapid IgG-IgM combined antibody test for SARS-CoV-2. Methods A total of 179 patients were enrolled. Serum were collected for IgG-IgM combined antibody test and corresponding nasal and pharyngeal swab specimens were collected for SARS-CoV-2 RT-PCR. According to SARS-CoV-2 RT-PCR results, patients under study were categorized as PCR positive group in 90 patients and PCR negative group in 89 patients. Results 1. Of the 90 PCR positive samples, 77 were tested positive by SARS-CoV-2 IgG-IgM test kit, yielding a sensitivity of 85.6%. Meanwhile, of the 89 PCR negative sample, 8 samples were detected positive, resulting in a specificity of 91%. Positive predictive value, negative predictive value and accuracy of this test kit was 95.1%, 82.7%, and 88.3%, respectively. Kappa efficiency between IgG/IgM test kit and RT-PCR were 0.75. 2. Accuracy in mild/common and severe/critical subgroup were 73.9% and 97.7%, respectively. Accuracy in clinical confirmed, suspected cases and other disease subgroups were 70%, 60%, and 100%, respectively. 3. Patients were further divided into '0 -7', '8 -15' and '>= 16' groups according to the time from illness onset to sample collection. Sensitivity, specificity and accuracy in these three groups were 18.8%, 77.8% and 40%; 100%, 50% and 87.5%; 100%, 64.3%, and 93.9, respectively. Conclusion The sensitivity and specificity of this ease-of-use IgG/IgM combined test kit were adequate, plus short turnaround time, no specific requirements for additional equipment or skilled technicians, all of these collectively contributed to its competence for mass testing. At the current stage, it cannot take the place of SARA-CoV-2 nucleic acid RT-PCR, but can be served as a complementary option for RT-PCR. The combination of RT-PCR and IgG-IgM combined test kit could provide further insight into SARS-CoV-2 infection diagnosis. 
 Title: Diagnostic Indexes of a Rapid IgG/IgM Combined Antibody Test for SARS-CoV-2

A: True
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Q: Paragraph: We aimed to test the hypothesis that gene silencing of tumor necrosis factor alpha converting enzyme (TACE) may attenuate lesion inflammation and positive vascular remodeling and enhance plaque stability in a rabbit model of atherosclerosis. Lentivirus-mediated TACE shRNA was injected into the abdominal aortic plaques of rabbits which effectively down-regulated TACE expression and activities from week 8 to week 16. TACE gene silencing reduced remodeling index and plaque burden, and diminished the content of macrophages and lipids while increased that of smooth muscle cells and collagen in the aortic plaques. In addition, TACE gene silencing attenuated the local expression of P65, iNOS, ICAM-1, VEGF and Flt-1 and activities of MMP9 and MMP2 while increased the local expression of TGF-β1 together with reduced number of neovessels in the aorta. TACE shRNA treatment resulted in down-regulated expression of TACE in macrophages and blunted ERK-P38 phosphorylation and tube formation of co-cultured mouse vascular smooth muscle cells or human umbilical vein endothelial cells. In conclusion, gene silencing of TACE enhanced plaque stability and improved vascular positive remodeling. The mechanisms may involve attenuated local inflammation, neovascularization and MMP activation, as well as enhanced collagen production probably via down-regulated ERK-NF-κB and upregulated TGF-β1 signaling pathways. Tumor necrosis factor alpha converting enzyme (TACE), also known as ADAM17 (A disintegrin and A metalloproteinase 17), was initially discovered as a protease that cleaves the 26-kDa precursor of TNF-α and sheds transmembrane TNF-α to generate a soluble form of TNF-α that can bind to TNF-α receptors to induce inflammatory response 1 . Recently it has been recognized that TACE is a type I transmembrane protein and a member of a superfamily of Zn dependent metalloproteases. The major physiological role of TACE is to regulate the proteolytic release of a number of growth factors, cytokines, adhesion molecules and cleavage enzymes from cellular membrane 2,3 . The major pro-inflammatory cytokine processed by TACE is TNF-α which is produced by macrophages, monocytes and T-cells, and acts as a major player in the pathogenesis of inflammation. It has been increasingly recognized that TACE-mediated shedding is involved in a variety of diseases such as ischemia, heart failure, arthritis, atherosclerosis, diabetes, cancer, neurological and immune diseases 3-5 . Ashley EA et al. provided functional evidence for TACE as a candidate gene of atherosclerosis susceptibility and found that polymorphisms of the TACE genes had relation to cardiovascular mortality in humans 6 . Canault M et al. found that TACE expression was associated with lesion formation in atherosclerosis-prone sites of apolipoprotein E-knockout (ApoE -/-) mice 7 . Oksala N demonstrated that TACE was upregulated in advanced human atherosclerotic lesions in samples from carotid, aortic, and femoral arteries compared to samples from internal thoracic artery free of atherosclerotic plaques 8 . In addition, Stoehr R et al. showed that loss of TIMP3, an endogenous inhibitor of TACE, exacerbated atherosclerosis 1 
 Title: Gene silencing of TACE enhances plaque stability and improves vascular remodeling in a rabbit model of atherosclerosis

A: False
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Q: Paragraph: Background: Ribonucleic acid (RNA) molecules play important roles in many biological processes including gene expression and regulation. Their secondary structures are crucial for the RNA functionality, and the prediction of the secondary structures is widely studied. Our previous research shows that cutting long sequences into shorter chunks, predicting secondary structures of the chunks independently using thermodynamic methods, and reconstructing the entire secondary structure from the predicted chunk structures can yield better accuracy than predicting the secondary structure using the RNA sequence as a whole. The chunking, prediction, and reconstruction processes can use different methods and parameters, some of which produce more accurate predictions than others. In this paper, we study the prediction accuracy and efficiency of three different chunking methods using seven popular secondary structure prediction programs that apply to two datasets of RNA with known secondary structures, which include both pseudoknotted and non-pseudoknotted sequences, as well as a family of viral genome RNAs whose structures have not been predicted before. Our modularized MapReduce framework based on Hadoop allows us to study the problem in a parallel and robust environment. Results: On average, the maximum accuracy retention values are larger than one for our chunking methods and the seven prediction programs over 50 non-pseudoknotted sequences, meaning that the secondary structure predicted using chunking is more similar to the real structure than the secondary structure predicted by using the whole sequence. We observe similar results for the 23 pseudoknotted sequences, except for the NUPACK program using the centered chunking method. The performance analysis for 14 long RNA sequences from the Nodaviridae virus family outlines how the coarse-grained mapping of chunking and predictions in the MapReduce framework exhibits shorter turnaround times for short RNA sequences. However, as the lengths of the RNA sequences increase, the fine-grained mapping can surpass the coarse-grained mapping in performance. Conclusions: By using our MapReduce framework together with statistical analysis on the accuracy retention results, we observe how the inversion-based chunking methods can outperform predictions using the whole sequence. Our chunk-based approach also enables us to predict secondary structures for very long RNA sequences, which is not feasible with traditional methods alone. Figure 1 Basic elements in RNA secondary structures. The stem loop (a) and pseudoknot (b). Zhang et al. BMC Structural Biology 2013, 13(Suppl 1):S3 
 Title: Enhancement of accuracy and efficiency for RNA secondary structure prediction by sequence segmentation and MapReduce

A:
False
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