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: As infectious disease outbreaks emerge, public health agencies often enact vaccination and social distancing measures to slow transmission. Their success depends on not only strategies and resources, but also public adherence. Individual willingness to take precautions may be influenced by global factors, such as news media, or local factors, such as infected family members or friends. Here, we compare three modes of epidemiological decisionmaking in the midst of a growing outbreak using network-based mathematical models that capture plausible heterogeneity in human contact patterns. Individuals decide whether to adopt a recommended intervention based on overall disease prevalence, the proportion of social contacts infected, or the number of social contacts infected. While all strategies can substantially mitigate transmission, vaccinating (or self isolating) based on the number of infected acquaintances is expected to prevent the most infections while requiring the fewest intervention resources. Unlike the other strategies, it has a substantial herd effect, providing indirect protection to a large fraction of the population. misinformation from the "anti-vaxxer" movement [8, [11] [12] [13] . Recently, there have been calls for a special government commission on vaccine safety, despite overwhelming scientific consensus that vaccines are both safe and effective [12, [14] [15] [16] . As outbreaks unfold, people can take a variety precautionary measures to avoid infection, including immunization and social distancing [1, [17] [18] [19] . They often judge personal risk based on their impressions of overall disease prevalence and severity [2, [20] [21] [22] [23] . When infection risk appears low, small risks of adverse effects from the vaccine can seem relatively important and cause vaccine coverage to drop below levels required to control transmission. A variety of other factors can influence the perceived utility of disease prevention, including epidemiological news and rumors, costs of vaccination and other control measures, trust in health professionals, government agencies, media and non-official information sources, as well as societal pressure to ensure the health of one's children [24] [25] [26] [27] [28] [29] . Studies have shown that media reports about outbreaks that specify numbers of cases, hospitalizations or deaths, can influence avoidance behavior and contact patterns at both individual and community levels. In some cases, oversimplified or erroneous media reports regarding flawed vaccines can trigger panic and increases in vaccine hesitancy [30] [31] [32] [33] . For both seasonal and pandemic influenza, such interactions between vaccination decision-making and transmission dynamics can profoundly shape the course of epidemics [20, [34] [35] [36] . Individual intervention decisions can have far-reaching effects. For example, vaccination protects not only the immunized individual, but also social contacts who might have been infected by the individual. Social distancing decisions may break chains of transmission by protecting the decision-maker and more generally disrupting social dynamics. Following [37, 38] , we refer to this indirect protection as a herd effect. Although previously equated to the reduction in incidence in the unimmunized population [38], we quantify the herd effect of an intervention effort by estimating the number of infections averted per vaccine administered (or per individual social distancing action). The general phenomenon in which individual intervention actions reduce the risk of infection to others has also been called vaccination efficiency, vaccination effectiveness, herd immunity, and indirect protection [37] [38] [39] [40] . The magnitude of the herd effect will critically depend on contact patterns [41, 42]. Measures taken by gregarious individuals may have greater immediate benefits than those taken by solitary individuals, with downstream epidemiological consequences modulated by the full social network [43, 44] . Contact patterns may also influence the decision-making process itself, by constraining epidemiological perspectives. When gauging infection risk, individuals may consider global information (e.g., from news media) or local first-hand encounters with disease (e.g., infected acquaintances, friends or family members) [30, 31] . While traditional compartmental models assume homogeneity in both epidemiological risks and intervention benefits, network-based models provide a tractable framework for studying the complex interplay between contact networks, intervention decision making and disease transmission [34, [45] [46] [47] [48] [49] [50] [51] . Here, we investigate the epidemiological impacts of different decision paradigms using a network-based SIR epidemic model, in which individuals also make vaccination or social distancing choices based on their perceived epidemiological risks. Depending on the decision model, they estimate either overall disease prevalence, their number of infected social contacts, or their fraction of infected social contacts. When the perceived threat is sufficiently high, they take a measure that immediately affords full protection for the duration of the epidemic. We compare the efficacy of these three different paradigms across a range of diseases in a realistically heterogeneous network, and show that the most naive model-simply counting one's infected contacts-affords the most epidemiological protection using the least amount of resources (e.g., vaccinations or economic costs associated with social distancing). Local risk perception enhances epidemic control PLOS ONE | https://doi. 
 Title: Local risk perception enhances epidemic control

A: True
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Q: Paragraph: Apelin is an inotropic and cardioprotective peptide that exhibits beneficial effects through activation of the APJ receptor in the pathology of cardiovascular diseases. Apelin induces the expression of angiotensin-converting enzyme 2 (ACE2) in failing hearts, thereby improving heart function in an angiotensin 1-7-dependent manner. Whether apelin antagonizes the over-activation of the renin-angiotensin system in the heart remains elusive. In this study we show that the detrimental effects of angiotensin II (Ang II) were exacerbated in the hearts of aged apelin-gene-deficient mice. Ang II-mediated cardiac dysfunction and hypertrophy were augmented in apelin knockout mice. The loss of apelin increased the ratio of angiotensin-converting enzyme (ACE) to ACE2 expression in the Ang II-stressed hearts, and Ang II-induced cardiac fibrosis was markedly enhanced in apelin knockout mice. mRNA expression of pro-fibrotic genes, such as transforming growth-factor beta (TGF-β) signaling, were significantly upregulated in apelin knockout hearts. Consistently, treatment with the ACE-inhibitor Captopril decreased cardiac contractility in apelin knockout mice. In vitro, apelin ameliorated Ang II-induced TGF-β expression in primary cardiomyocytes, accompanied with reduced hypertrophy. These results provide direct evidence that endogenous apelin plays a crucial role in suppressing Ang II-induced cardiac dysfunction and pathological remodeling. Int. J. Mol. Sci. 2019, 20, 239 2 of 12 dysfunctions [8, 10, 11] . By contrast, APJ was identified as a dual receptor to mediate cardiac hypertrophy [12, 13] via β-arrestin signaling [14, 15] . Thus, the precise role of endogenous apelin signaling in heart function remains elusive. Cardiac hypertrophy is an adaptive response to maintain cardiac function in the event of an increased workload, although prolonged cardiac hypertrophy can lead to heart failure via the ischemia of cardiomyocytes [16, 17] . In addition, cardiac fibrosis adversely increases tissue stiffness and impairs ventricular function. Consistently, cardiac hypertrophy and the development of fibrosis correlate with the deterioration of myocardial function in heart failure [18] [19] [20] . Thus, cardiac hypertrophy and fibrosis are key features of heart failure, and apelin's inotropic and protective roles in the heart are attractive as a potential therapeutic candidate for treating heart failure. Apelin has also been identified as a substrate for angiotensin-converting enzyme 2 (ACE2), which cleaves apelin with similar efficiency as its primary substrate angiotensin II (Ang II) [21] [22] [23] . ACE2 is a negative regulator of the renin-angiotensin system (RAS) that inactivates Ang II by converting it to angiotensin 1-7 (Ang 1-7) [21] [22] [23] , whereas angiotensin-converting enzyme 1 (or ACE1) activates RAS by converting Ang I to Ang II. ACE2 was also identified as a regulator of heart failure [24-26], diabetic nephropathy [27, 28] , acute lung failure [29, 30] , and the SARS (severe acute respiratory syndrome) coronavirus receptor [31, 32] . Apelin-APJ signaling has been shown to counteract the effects of Ang II on AT1R in defined physiological and pathophysiological settings. For instance, in hypertensive rat models, the continuous infusion of Ang II downregulates apelin and APJ expression [33] . We have shown that Apelin upregulates ACE2 expression. Apelin-induced ACE2 downregulates Ang II signaling while enhancing Ang 1-7-Mas receptor signaling in aged or failing hearts [34] . On the other hand, it has been suggested that the counter-balancing effects of the apelin-APJ axis on Ang II-AT1R signaling are mediated by receptor dimerization. The APJ receptor physically interacts with AT1 receptors, and exogenous apelin negatively regulates Ang II-AT1R signaling in in vitro cell culture systems and in an in vivo atherosclerosis model [35] . However, whether endogenous apelin counteracts the over-activation of RAS in the heart remains unexplored. In this study, we aimed to determine the effects of endogenous apelin on Ang II-induced pathology in the heart. We showed that Ang II-mediated cardiac dysfunction, hypertrophy, and fibrosis were augmented in apelin knockout (Apelin KO) mice. 
 Title: Loss of Apelin Augments Angiotensin II-Induced Cardiac Dysfunction and Pathological Remodeling

A: True
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Q: Paragraph: that were admitted either to a general ward or the intensive care unit compared to 44.3 % (66/149) of single SeasFlu A virus infections (OR 2.5 95 % CI 0.67-9.34, p = 0.17). In a stratified analysis, there was a trend towards higher association between FluA, hCoV and hBoV with increasing age (especially in patients aged 24-45 years and >65 year old). Conclusion Our hCoV RT-PCR protocol appeared to be of adequate analytical sensitivity for diagnosis. More and larger studies are needed to confirm the role of hCoV, hBoV in causing severe disease when they co-infect with influenza A viruses. Influenza A viruses are a significant cause of acute lower respiratory tract infections (ALRI), with 13.0 % of all children with influenza infection developing ALRI-equivalent to 20 million (95 % CI 13-32 million) ALRI cases per year [1]; and according to these authors, in 2008, between 28,000 and 111,500 children younger than 5 years died of influenza-associated ALRI. Among potential co-pathogens, human coronaviruses (hCoV) and human bocavirus (hBoV) are important emerging respiratory viruses [2, 3] . Coronaviruses infect a wide variety of hosts (animals and humans); Alphacoronaviruses and Betacoronaviruses infect mammals, whereas Gammacoronaviruses infect birds [4] . Most of the human coronaviruses (Alphacoronaviruses 229E and NL63 and Betacoronaviruses OC43, HKU1) cause mild influenza like illnesses, however in 2003, the severe acute respiratory syndrome (SARS) Purpose Influenza A viruses, human coronaviruses (hCoV) and human bocavirus (hBoV) are emerging respiratory viruses. This study investigated the association between influenza A viruses co-infection with hBoV and hCoV and severity and the sensitivity of a real-time polymerase chain reaction (RT-PCR) assay for identification of 15 coronaviruses. Methodology Published sequences for the 15 human coronaviruses were used to design a consensus PCR targeting the replicase open reading frame 1b. A previously published PCR targeting the NS1 Gene of all known human bocavirus strains was also utilized. A series of 217 samples from patients aged 37.7 (SD ± 30.4)] with seasonal influenza A viruses (SeasFluA) identified between 06/2011 and 06/2012 in NW England were tested for hCoV and hBoV using RT-PCR. Association between coinfection and disease outcome was assessed using logistic regression. Results The limit of detection of hCoV RT-PCR assay was 2 copies/µl of human coronavirus RNA template, a sensitivity comparable to a previously published SYBR green assay for human coronaviruses. A total of 12 hCoV and 17 hBoV were identified in the 217 influenza A positive samples. A higher proportion (61.5 %; 8/13) of SeasFluA/hBoV co-infections were identified in patients 
 Title: Pan-human coronavirus and human bocavirus SYBR Green and TaqMan PCR assays; use in studying influenza A viruses co-infection and risk of hospitalization

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False
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