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".

Ex Input:
Paragraph: Porcine epidemic diarrhea virus (PEDV), an enteropathogenic Alphacoronavirus, has caused enormous economic losses in the swine industry. p53 protein exists in a wide variety of animal cells, which is involved in cell cycle regulation, apoptosis, cell differentiation and other biological functions. In this study, we investigated the effects of PEDV infection on the cell cycle of Vero cells and p53 activation. The results demonstrated that PEDV infection induces cell cycle arrest at G0/G1 phase in Vero cells, while UV-inactivated PEDV does not cause cell cycle arrest. PEDV infection up-regulates the levels of p21, cdc2, cdk2, cdk4, Cyclin A protein and down-regulates Cyclin E protein. Further research results showed that inhibition of p53 signaling pathway can reverse the cell cycle arrest in G0/G1 phase induced by PEDV infection and cancel out the up-regulation of p21 and corresponding Cyclin/cdk mentioned above. In addition, PEDV infection of the cells synchronized in various stages of cell cycle showed that viral subgenomic RNA and virus titer were higher in the cells released from G0/G1 phase synchronized cells than that in the cells released from the G1/S phase and G2/M phase synchronized or asynchronous cells after 18 h p.i.. This is the first report to demonstrate that the p53-dependent pathway plays an important role in PEDV induced cell cycle arrest and beneficially contributes to viral infection. 
 Title: Porcine epidemic diarrhea virus through p53-dependent pathway causes cell cycle arrest in the G0/G1 phase

Ex Output:
False


Ex Input:
Paragraph: In recent years, the biomedical community has witnessed a rapid scientific and technological evolution following the development and refinement of high-throughput methodologies. Concurrently and consequentially, the scientific perspective has changed from the reductionist approach of meticulously analyzing the fine details of a single component of biology, to the "holistic" approach of broadmindedly examining the globally interacting elements of biological systems. The emergence of this new way of thinking has brought about a scientific revolution in which genomics, proteomics, metabolomics and other "omics" have become the predominant tools by which large amounts of data are amassed, analyzed and applied to complex questions of biology that were previously unsolvable. This enormous transformation of basic science research and the ensuing plethora of promising data, especially in the realm of human health and disease, have unfortunately not been followed by a parallel increase in the clinical application of this information. On the contrary, the number of new potential drugs in development has been steadily decreasing, suggesting the existence of roadblocks that prevent the translation of promising research into medically relevant therapeutic or diagnostic application. In this paper we will review, in a non-inclusive fashion, several recent scientific advancements in the field of translational research, with a specific focus on how they relate to infectious disease. We will also present a current picture of the limitations and challenges that exist for translational research, as well as ways that have been proposed by the National Institutes of Health to improve the state of this field. 
 Title: Translational research in infectious disease: current paradigms and challenges ahead

Ex Output:
True


Ex Input:
Paragraph: Introduction: Community-acquired pneumonia (CAP) requires prompt treatment, but its diagnosis is complex. Improvement of bacterial CAP diagnosis by biomarkers has been evaluated using chest X-ray infiltrate as the CAP gold standard, producing conflicting results. We analyzed the diagnostic accuracy of biomarkers in suspected CAP adults visiting emergency departments for whom CAP diagnosis was established by an adjudication committee which founded its judgment on a systematic multidetector thoracic CT scan. Methods: In an ancillary study of a multi-center prospective study evaluating the impact of systematic thoracic CT scan on CAP diagnosis, sensitivity and specificity of C-reactive protein (CRP) and procalcitonin (PCT) were evaluated. Systematic nasopharyngeal multiplex respiratory virus PCR was performed at inclusion. An adjudication committee classified CAP diagnostic probability on a 4-level Likert scale, based on all available data. Results: Two hundred patients with suspected CAP were analyzed. The adjudication committee classified 98 patients (49.0 %) as definite CAP, 8 (4.0 %) as probable, 23 (11.5 %) as possible and excluded in 71 (35.5 %, including 29 patients with pulmonary infiltrates on chest X-ray). Among patients with radiological pulmonary infiltrate, 23 % were finally classified as excluded. Viruses were identified by PCR in 29 % of patients classified as definite. Area under the curve was 0.787 [95 % confidence interval (95 % CI), 0.717 to 0.857] for CRP and 0.655 (95 % CI, 0.570 to 0.739) for PCT to detect definite CAP. CRP threshold at 50 mg/L resulted in a positive predictive value of 0.76 and a negative predictive value of 0.75. No PCT cut-off resulted in satisfactory positive or negative predictive values. CRP and PCT accuracy was not improved by exclusion of the 25 (25.5 %) definite viral CAP cases. Conclusions: For patients with suspected CAP visiting emergency departments, diagnostic accuracy of CRP and PCT are insufficient to confirm the CAP diagnosis established using a gold standard that includes thoracic CT scan. Diagnostic accuracy of these biomarkers is also insufficient to distinguish bacterial CAP from viral CAP. Trial registration: ClinicalTrials.gov registry NCT01574066 (February 7, 2012)  
 Title: Diagnostic accuracy of C-reactive protein and procalcitonin in suspected community-acquired pneumonia adults visiting emergency department and having a systematic thoracic CT scan

Ex Output:

False