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: 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-) 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- 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 -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: 1. Predicting infectious disease dynamics is a central challenge in disease ecology. Models 36 that can assess which individuals are most at risk of being exposed to a pathogen not only 37 provide valuable insights into disease transmission and dynamics but can also guide 38 management interventions. Constructing such models for wild animal populations, 39 however, is particularly challenging; often only serological data is available on a subset 40 of individuals and non-linear relationships between variables are common. 41 42 2. Here we take advantage of the latest advances in statistical machine learning to construct 43 pathogen-risk models that automatically incorporate complex non-linear relationships 44 with minimal statistical assumptions from ecological data with missing values. Our 45 approach compares multiple machine learning algorithms in a unified environment to 46 find the model with the best predictive performance and uses game theory to better 47 interpret results. We apply this framework on two major pathogens that infect African 48 lions: canine distemper virus (CDV) and feline parvovirus. 49 50 3. Our modelling approach provided enhanced predictive performance compared to more 51 traditional approaches, as well as new insights into disease risks in a wild population. We 52 were able to efficiently capture and visualise strong non-linear patterns, as well as model 53 complex interactions between variables in shaping exposure risk from CDV and feline 54 parvovirus. For example, we found that lions were more likely to be exposed to CDV at a 55 young age but only in low rainfall years. 56 57 . CC-BY-NC-ND 4.0 International license is made available under a The copyright holder for this preprint (which was not peer-reviewed) is the author/funder. It . https://doi.org/10.1101/569012 doi: bioRxiv preprint 3 4. When combined with our data calibration approach, our framework helped us to answer 58 questions about risk of pathogen exposure which are difficult to address with previous 59 methods. Our framework not only has the potential to aid in predicting disease risk in 60 animal populations, but also can be used to build robust predictive models suitable for 61 other ecological applications such as modelling species distribution or diversity patterns.
Title: How to make more from exposure data? An integrated machine Author Contributions Data Accessibility Statement Running Title Machine learning and pathogen exposure risk

A: True
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Q: Paragraph: Health care is the fastest-growing sector of the US economy, employing more than 18 million persons. 1 HCP face a range of noninfectious hazards on the job, including back injuries, strains and sprains, latex allergy, violence, and stress. 1 HCP are also commonly exposed to infectious agents via sharp injuries (eg, hepatitis C virus [HCV], hepatitis B virus [HBV], and human immunodeficiency virus [HIV]), direct patient
Title: Occupational Health Update Focus on Preventing the Acquisition of Infections with Pre-exposure Prophylaxis and Postexposure Prophylaxis

A:
True
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