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".

Paragraph: Objective: Increased hepatic expression of dipeptidyl peptidase 4 (DPP4) is associated with non-alcoholic fatty liver disease (NAFLD). Whether this is causative for the development of NAFLD is not yet clarified. Here we investigate the effect of hepatic DPP4 overexpression on the development of liver steatosis in a mouse model of diet-induced obesity. Methods: Plasma DPP4 activity of subjects with or without NAFLD was analyzed. Wild-type (WT) and liver-specific Dpp4 transgenic mice (Dpp4-Liv-Tg) were fed a high-fat diet and characterized for body weight, body composition, hepatic fat content and insulin sensitivity. In vitro experiments on HepG2 cells and primary mouse hepatocytes were conducted to validate cell autonomous effects of DPP4 on lipid storage and insulin sensitivity. Results: Subjects suffering from insulin resistance and NAFLD show an increased plasma DPP4 activity when compared to healthy controls. Analysis of Dpp4-Liv-Tg mice revealed elevated systemic DPP4 activity and diminished active GLP-1 levels. They furthermore show increased body weight, fat mass, adipose tissue inflammation, hepatic steatosis, liver damage and hypercholesterolemia. These effects were accompanied by increased expression of PPARg and CD36 as well as severe insulin resistance in the liver. In agreement, treatment of HepG2 cells and primary hepatocytes with physiological concentrations of DPP4 resulted in impaired insulin sensitivity independent of lipid content. Conclusions: Our results give evidence that elevated expression of DPP4 in the liver promotes NAFLD and insulin resistance. This is linked to reduced levels of active GLP-1, but also to auto-and paracrine effects of DPP4 on hepatic insulin signaling. (A. Schurmann). Abbreviations: Ad, adenovirus; Akt, Akt serineethreonine protein kinase; ALT, alanine aminotransferase; ApoB, apolipoprotein B; AST, aspartate aminotransferase; BAT, brown adipose tissue; CD36, fatty acid translocase; Cpt1a, carnitine palmitoyltransferase 1a; Dgat2, diacylglycerol O-acyltransferase 2; DPP4, dipeptidyl peptidase 4; Dpp4-Liv-Tg, transgenic mice with hepatocyte-specific Dpp4 overexpression; F4/80, adhesion G protein-coupled receptor E1; Gfp, green fluorescent protein; GGT, gammaglutamyl transpeptidase; GLP-1, glucagon-like peptide 1; gWAT, gonadal white adipose tissue; HFD, high-fat diet; HOMA-IR, homeostatic model assessment for insulin resistance; IL6, interleukin 6; MAPK, mitogen-activated protein kinase; MCP1, chemokine (C-C motif) ligand 2; MOGAT1, monoacylglycerol O-acyltransferase 1; NAFLD, nonalcoholic fatty liver disease; NASH, non-alcoholic steatohepatitis; NFkB, nuclear factor-kB; pAkt, phosphorylated Akt serineethreonine protein kinase; WT, wild-type; PPARg, peroxisome proliferator activated receptor gamma; rhDPP4, recombinant human dipeptidyl peptidase 4; rmDPP4, recombinant mouse dipeptidyl peptidase 4; SM, skeletal muscle; Srebf1, sterol regulatory element binding transcription factor 1; sWAT, subcutaneous white adipose tissue; TNFa, tumor necrosis factor a
Title: Elevated hepatic DPP4 activity promotes insulin resistance and non-alcoholic fatty liver disease
False

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
True

Paragraph: Several excellent reviews have been published of the current mechanistic understanding of how viral infections increase susceptibility to secondary bacterial pneumonias. 46-49 Thus, this article provides only a brief overview, with a primary focus on virally mediated effects on pulmonary host defense and subsequent impairment of bacterial clearance (Table 2) . However, the authors acknowledge that microbiologic and epidemiologic factors can contribute to the pathogenesis of viral-bacterial coinfections.
Title: Postviral Complications: Bacterial Pneumonia
False