In this task, you're given a paragraph from the research paper and your task is to generate a suitable title for the research paper based on the given paper. Under 100 words is a good title length.

Example Input: Infectious bronchitis virus (IBV) infection has been a major threat to the poultry industry worldwide. Current commercially available ELISA kits detect group-specific antibodies; however, to understand the status of field infection, a monoclonal antibody (mAb) blocking ELISA (b-ELISA) against local IBVs was developed. The selected mAb showed specificity to Taiwan IBV strains but had no cross-reactivity with the vaccine strain H120. Using the hemagglutination inhibition (HI) test as a standard, the cut-off value, sensitivity, and specificity of a b-ELISA using this mAb were evaluated in 390 field samples. The typespecificity of detection was validated using a panel of chicken hyperimmune sera. The results showed that the b-ELISA demonstrated high sensitivity (98.0%) and specificity (97.2%) of detection. The agreement between the results of the b-ELISA and the HI test was statistically significant (Kappa = 0.95), and there was no significant difference between these two methods (McNemar p = 0.72). The b-ELISA specifically detected Taiwan IBV serotypes but not three non-Taiwan IBV serotypes nor sera against other avian pathogens. This b-ELISA provides type-specific antibody detection of local IBV strains. It has the potential to serve as a rapid and reliable diagnostic method for IBV clinical infections in the field in Taiwan.
Example Output: A type-specific blocking ELISA for the detection of infectious bronchitis virus antibody

Example Input: Background-The complement system is activated in liver diseases including acute liver failure (ALF); however, the role of the lectin pathway of complement has scarcely been investigated in ALF. The pathway is initiated by soluble pattern recognition molecules: mannan-binding lectin (MBL), M-, L-and H-ficolin and collectin-liver-1 (CL-L1), which are predominantly synthesised in the liver. Methods-Serum samples from 75 patients enrolled by the U.S. ALF Study Group were collected on days 1 and 3. We included 75 healthy blood donors and 20 cirrhosis patients as controls. Analyses were performed using sandwich-type immunoassays (ELISA, TRIFMA). Results-At day 1, the MBL level in ALF patients was 40% lower compared with healthy controls ((median(interquartile range) 0.72 μg/ml(0.91) vs. 1.15(1.92)(p=0.02)), and increased significantly by day 3 (0.83 μg/ml(0.94)(p=0.01)). The M-ficolin level was 60% lower (0.54 μg/ ml(0.50) vs. 1.48(1.01)(p<0.0001)). The CL-L1 level at day 1 was slightly higher compared with healthy controls (3.20 μg/ml(2.37) vs. 2.64(0.72)(p=0.11)); this was significant at day 3 (3.35(1.84)(p=0.006)). H-and L-ficolin levels were similar to healthy controls. Spontaneous ALF survivors had higher levels of MBL at day 1 (0.96 μg/ml(1.15) vs. 0.60(0.60)(p=0.02)) and lower levels of L-ficolin by day 3 compared with patients who died or were transplanted (1.61 μg/ ml(1.19) vs. 2.17(2.19)(p=0.02)).
Example Output: Circulating mannan-binding lectin, M-, L-, H-ficolin, and collectin-liver-1 levels in patients with acute liver failure

Example Input: Background An outbreak of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has led to 95 333 confirmed cases as of March 5, 2020. Understanding the early transmission dynamics of the infection and evaluating the effectiveness of control measures is crucial for assessing the potential for sustained transmission to occur in new areas. Combining a mathematical model of severe SARS-CoV-2 transmission with four datasets from within and outside Wuhan, we estimated how transmission in Wuhan varied between December, 2019, and February, 2020. We used these estimates to assess the potential for sustained human-to-human transmission to occur in locations outside Wuhan if cases were introduced. Wuhan and international cases that originated in Wuhan to estimate how transmission had varied over time during January, 2020, and February, 2020. Based on these estimates, we then calculated the probability that newly introduced cases might generate outbreaks in other areas. To estimate the early dynamics of transmission in Wuhan, we fitted a stochastic transmission dynamic model to multiple publicly available datasets on cases in Wuhan and internationally exported cases from Wuhan. The four datasets we fitted to were: daily number of new internationally exported cases (or lack thereof), by date of onset, as of Jan 26, 2020; daily number of new cases in Wuhan with no market exposure, by date of onset, between Dec 1, 2019, and Jan 1, 2020; daily number of new cases in China, by date of onset, between Dec 29, 2019, and Jan 23, 2020; and proportion of infected passengers on evacuation flights between Jan 29, 2020, and Feb 4, 2020. We used an additional two datasets for comparison with model outputs: daily number of new exported cases from Wuhan (or lack thereof) in countries with high connectivity to Wuhan (ie, top 20 most at-risk countries), by date of confirmation, as of Feb 10, 2020; and data on new confirmed cases reported in Wuhan between Jan 16, 2020, and Feb 11, 2020. Findings We estimated that the median daily reproduction number (R t ) in Wuhan declined from 2·35 (95% CI 1·15-4·77) 1 week before travel restrictions were introduced on Jan 23, 2020, to 1·05 (0·41-2·39) 1 week after. Based on our estimates of R t , assuming SARS-like variation, we calculated that in locations with similar transmission potential to Wuhan in early January, once there are at least four independently introduced cases, there is a more than 50% chance the infection will establish within that population. Interpretation Our results show that COVID-19 transmission probably declined in Wuhan during late January, 2020, coinciding with the introduction of travel control measures. As more cases arrive in international locations with similar transmission potential to Wuhan before these control measures, it is likely many chains of transmission will fail to establish initially, but might lead to new outbreaks eventually.
Example Output:
Articles Early dynamics of transmission and control of COVID-19: a mathematical modelling study

