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: Background-Patients with the 2019 coronavirus disease have different degrees of psychological pain, such as anxiety and depression, which may related to their prognosis. Psychological intervention can be conducted in different ways to improve psychological pain and improve the treatment effect. Objective-The present study aimed to investigate and analyze the psychological status of patients with COVID-19 during the course of illness, and to evaluate the effect and influencing factors of psychological crisis intervention, so as to explore the effective mode of clinical psychological intervention in acute patients under isolation environment. Methods-A total of 143 persons participated in the study, including 26 patients diagnosed with COVID-19 in the isolation ward (COVID-19 group), 87 patients with general pneumonia in the observation ward (General Pneumonia group) and 30 healthy volunteers (Normal group). All the patients in the ward received comprehensive psychological intervention, including telephone psychological counseling (active and passive), self-adjustment of written materials and one-to-one psychological crisis intervention. Hamilton depression scale (HAMD) and Hamilton anxiety scale (HAMA) were used to evaluate the mental health status of all patients on the day of admission and 1 week after treatment. Results-The scores of HAMA and HAMD of all patients (including isolation ward and observation ward) were significantly higher than the healthy volunteers at the time of admission. The total score of HAMA and HAMD in CVOID-19 group were both higher than that General Pneumonia group. After 1 week`s hospitalization with comprehensive psychological intervention, the scores of HAMA and HAMD in CVOID-19 group were significantly decreased. Conclusion-Patients those who diagnosed with COVID-19 in the isolation ward and/or general pneumonia in observation ward have different degrees of anxiety, depression and sleep problems. While receiving antiviral treatment, patients also need psychological intervention. Comprehensive psychological intervention model has been proved to be effective. 
 Title: Analysis of psychological state and clinical psychological intervention model of patients with COVID-19 Running Title: Psychological state and intervention model in COVID-19 patients
Example Output: True

Example Input: Paragraph: Some stem cell lines may contain an endogenous virus or can be contaminated with exogenous viruses (even of animal origin) and may secrete viral particles or express viral antigens on their surface. Moreover, certain biotechnological products (e.g. bovine fetal serum, murine feeder cells) may contain prion particles. Viral and prion contamination of cell cultures and ''feeder'' cells, which is a common risk in all biotechnological products derived from the cell lines, is the most challenging and potentially serious outcome to address, due to the difficulty involved in virus and prion detection and the potential to cause serious disease in recipients of these cell products. Stem cell banks should introduce adequate quality assurance programs like the microbiological control program and can provide researchers with valuable support in the standardization and safety of procedures and protocols used for the viral and prion testing and in validation programs to assure the quality and safety of the cells. 
 Title: Diagnostic approaches for viruses and prions in stem cell banks
Example Output: True

Example Input: Paragraph: In spite of the success of computational methods for predicting RNA secondary structure, the problem of predicting RNA tertiary structure folding remains. Low-resolution structural models show promise as they allow for rigorous statistical mechanical computation for the conformational entropies, free energies, and the coarse-grained structures of tertiary folds. Molecular dynamics refinement of coarse-grained structures leads to all-atom 3D structures. Modeling based on statistical mechanics principles also has the unique advantage of predicting the full free energy landscape, including local minima and the global free energy minimum. The energy landscapes combined with the 3D structures form the basis for quantitative predictions of RNA functions. In this chapter, we present an overview of statistical mechanical models for RNA folding and then focus on a recently developed RNA statistical mechanical model --the Vfold model. The main emphasis is placed on the physics underpinning the models, the computational strategies, and the connections to RNA biology. shown success in predicting RNA 3D structures. However, RNA function is determined not only by the minimum free energy state of the RNA, but also by the its folding stability and the potentially large conformational changes it can undergo. Understanding RNA function requires models that predict the full free energy landscape. Recent developments in statistical mechanical modeling of RNA folding have led to successes in predicting RNA structures, folding stabilities and folding kinetics for structures with increasing complexity. The models provide quantitative predictions and novel insights for a variety of experiments and RNA functions such as programmed ribosomal frameshifting (Cao and Chen 2009), mRNA splicing (Cao and Chen 2006a), and microRNA gene regulation (Kertesz et al. 2007; Long et al. 2007) . Despite the success of this approach, several key issues remain. These issues include the computation of the entropy for RNA tertiary folds and the extraction of the energy/entropy parameters for non-canonical tertiary interactions from thermodynamic data and known structures. The primary focus of this chapter is the application of methods based on statistical mechanics to predict RNA 3D structures and folding energy landscapes and to gain quantitative understanding of RNA functions. An RNA structure, defined by the nested Watson-Crick base pairs and the tertiary contacts contained in the 3D structure, can be conveniently represented by a polymer graph (Fig. 1) . Such a graph (= a 2D structure) usually corresponds to many 3D conformations due to the flexible conformations of the single-stranded regions. Following Chastain and Tinoco (Chastain and Tinoco 1991), tertiary and secondary (2D) structures can be classified as the polymer graphs with and without cross-links, respectively. Chemical and enzymatic reagents (Ehresmann et al. 1987 ) are highly effective structural probes for nucleic acids because the reactivity of a nucleotide can be sensitive to its local conformation and interactions, including base pairing and stacking, which are reflected in its solvent accessibility. Structure-probing experiments based on chemical and enzymatic reagents, such as the Selective 2′-Hydroxyl Acylation analyzed by Primer Extension (SHAPE) analysis (Watts et al. 2009 ) and synchrotron-generated hydroxyl radical footprinting (Petri and Brenowitz 1997) , give direct information about base pairing and local structures. The experimental data provide useful input as structural constraints for the computational modeling of complete 3D structures. In parallel with these the experimental developments, de novo computational modeling of RNA folding shows continuous improvements in the accuracy of the predictions of RNA structures, including the structures for long RNA sequences. Table 1 shows a list of the computational models for RNA structure prediction from single sequence input. For structure predictions from sequence CAO and CHEN The free energy for a pseudoknot is equal to the sum of the free energies for the helical stems and loops: ΔG pk = ΔG S 1 + ΔG S 2 + ΔG CS − TΔS loops , where ΔG S 1 and ΔG S 2 are the free energies of stems S 1 and S 2 and ΔG CS is the coaxial stacking energy between stems S 1 and S 2 . While the helix free energies can be evaluated from the nearest-neighbor model based on the empirical thermodynamic parameters for base stacks, to determine the loop entropy ΔS loops requires a physical model. CAO and CHEN  
 Title: Statistical mechanical modeling of RNA folding: from free energy landscape to tertiary structural prediction
Example Output:
True