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".

Example Input: Paragraph: Objective: The aim of this study was to further clarify clinical characteristics and predict mortality risk among patients with viral pneumonia. Methods: A total of 528 patients with viral pneumonia at RuiJin hospital in Shanghai from May 2015 to May 2019 were recruited. Multiplex real-time RT-PCR was used to detect respiratory viruses. Demographic information, comorbidities, routine laboratory examinations, immunological indexes, etiological detections, radiological images and treatment were collected on admission. Results: 76 (14.4%) patients died within 90 days in hospital. A predictive MuLBSTA score was calculated on the basis of a multivariate logistic regression model in order to predict mortality with a weighted score that included multilobular infiltrates (OR = 5.20, 95% CI 1.41-12.52, p = 0.010; 5 points), lymphocyte ≤ 0.8 * 10 9 /L (OR = 4.53, 95% CI 2.55-8.05, p < 0.001; 4 points), bacterial coinfection (OR = 3.71, 95% CI 2.11-6.51, p < 0.001; 4 points), acute-smoker (OR = 3.19, 95% CI 1.34-6.26, p = 0.001; 3 points), quit-smoker (OR = 2.18, 95% CI 0.99-4.82, p = 0.054; 2 points), hypertension (OR = 2.39, 95% CI 1.55-4.26, p = 0.003; 2 points) and age ≥60 years (OR = 2.14, 95% CI 1.04-4.39, p = 0.038; 2 points). 12 points was used as a cut-off value for mortality risk stratification. This model showed sensitivity of 0.776, specificity of 0.778 and a better predictive ability than CURB-65 (AUROC = 0.773 vs. 0.717, p < 0.001). Conclusion: Here, we designed an easy-to-use clinically predictive tool for assessing 90-day mortality risk of viral pneumonia. It can accurately stratify hospitalized patients with viral pneumonia into relevant risk categories and could provide guidance to make further clinical decisions. 
 Title: Citation: Clinical Features Predicting Mortality Risk in Patients With Viral Pneumonia: The MuLBSTA Score
Example Output: False

Example Input: Paragraph: Mechanistic hypotheses about airborne infectious disease transmission have traditionally emphasized the role of coughing and sneezing, which are dramatic expiratory events that yield both easily visible droplets and large quantities of particles too small to see by eye. Nonetheless, it has long been known that normal speech also yields large quantities of particles that are too small to see by eye, but are large enough to carry a variety of communicable respiratory pathogens. Here we show that the rate of particle emission during normal human speech is positively correlated with the loudness (amplitude) of vocalization, ranging from approximately 1 to 50 particles per second (0.06 to 3 particles per cm 3 ) for low to high amplitudes, regardless of the language spoken (english, spanish, Mandarin, or Arabic). Furthermore, a small fraction of individuals behaves as "speech superemitters," consistently releasing an order of magnitude more particles than their peers. our data demonstrate that the phenomenon of speech superemission cannot be fully explained either by the phonic structures or the amplitude of the speech. these results suggest that other unknown physiological factors, varying dramatically among individuals, could affect the probability of respiratory infectious disease transmission, and also help explain the existence of superspreaders who are disproportionately responsible for outbreaks of airborne infectious disease. It has long been recognized that particles expelled during human expiratory events, such as sneezing, coughing, talking, and breathing, serve as vehicles for respiratory pathogen transmission 1-6 . The relative contribution of each expiratory activity in transmitting infectious microorganisms, however, remains unclear 4 . Much previous research has focused on coughing 7-12 and sneezing 11,13,14 activities that yield relatively large droplets (approximately 50 μm or larger) easily visible to the naked eye. Less noticeable, but arguably more infectious for some diseases, are the smaller particles emitted during sneezing and coughing as well as during breathing 15-17 and talking 16, 18, 19 . These small particles are believed to be generated during breathing and talking from the mucosal layers coating the respiratory tract via a combination of a "fluid-film burst" mechanism within the bronchioles and from vocal folds adduction and vibration within the larynx 6,20,21 . The particles emitted during breathing and typical speech predominantly average only 1 μm in diameter 15-17 and are thus too small to see without specialized equipment; most people outside of the community of bioaerosol researchers are less aware of them. Despite their small size, however, these micron-scale particles are sufficiently large to carry a variety of respiratory pathogens such as measles virus (50-500 nm) 22 , influenza virus (100 nm-1 µm) 23 , and Mycobacterium tuberculosis (1-3 µm) 24 . Indeed, recent work by Yan et al. has confirmed that significant amounts of influenza viral RNA are present in small particles (<5 μm) emitted by influenza-infected individuals during natural breathing, without coughing or sneezing 25 . These small particles are potentially more infectious than larger sneeze-or 
 Title: Aerosol emission and superemission during human speech increase with voice loudness
Example Output: False

Example Input: Paragraph: B cells are one of the key players in the pathogenesis of multiple sclerosis (MS). The peripheral B cell distributions are similar in healthy persons and MS patients. In healthy controls, B cells are rarely present in the cerebrospinal fluid (CSF) while in MS patients, a clonally expanded B cell population is detected. This consists of memory B cells, centroblasts and antibody-secreting plasma blasts and plasma cells that are responsible for intrathecal immunoglobulin G production and oligoclonal band formation in more than 90% of MS patients. Unfortunately, the targets of the autoreactive B cells and antibodies remain largely unknown. Various candidate antigens have been identified but often their involvement in the disease process is still unclear. Most studies characterizing these target antigens examined autoantibodies by analyzing sera or CSF of MS patients. An alternative approach is focusing on the clonally expanded B cells. In this way B cells directed against myelin, astroglia and axons have been denoted in MS patients. B cell immortalization, that is based on the antibody-producing potential of Epstein-Barr virus (EBV) transformed B cells, can be used to expand B cells from MS patients for the production of antibodies, that ultimately can be analysed for target identification. 
 Title: B cell characterization and reactivity analysis in multiple sclerosis
Example Output:
False