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.

Ex Input:
A nested PCR assay was used to determine the viral RNA and proviral DNA status of naturally infected cats. Selected samples that were FeLV-positive by PCR were subjected to sequencing, phylogenetic analysis, and motifs search. Of the 39 samples that were positive for FeLV p27 antigen, 87.2% (34/39) were confirmed positive with nested PCR. FeLV proviral DNA was detected in 38 (97.3%) of p27-antigen negative samples. Malaysian FeLV isolates are found to be highly similar with a homology of 91% to 100%. Phylogenetic analysis revealed that Malaysian FeLV isolates divided into two clusters, with a majority (86.2%) sharing similarity with FeLV-K01803 and fewer isolates (13.8%) with FeLV-GM1 strain. Different enhancer motifs including NF-GMa, Krox-20/WT1I-del2, BAF1, AP-2, TBP, TFIIF-beta, TRF, and TFIID are found to occur either in single, duplicate, triplicate, or sets of 5 in different positions within the U3-LTR-gag region. The present result confirms the occurrence of FeLV viral RNA and provirus DNA in naturally infected cats. Malaysian FeLV isolates are highly similar, and a majority of them are closely related to a UK isolate. This study provides the first molecular based information on FeLV in Malaysia. Additionally, different enhancer motifs likely associated with FeLV related pathogenesis have been identified.

Ex Output:
Molecular Detection, Phylogenetic Analysis, and Identification of Transcription Motifs in Feline Leukemia Virus from Naturally Infected Cats in Malaysia

Ex Input:
Objectives: We aimed to develop a prospective prediction tool on CrimeaneCongo haemorrhagic fever (CCHF) to identify geographic regions at risk. The tool could support public health decision-makers in implementation of an effective control strategy in a timely manner. Methods: We used monthly surveillance data between 2004 and 2015 to predict case counts between 2016 and 2017 prospectively. The Turkish nationwide surveillance data set collected by the Ministry of Health contained 10 411 confirmed CCHF cases. We collected potential explanatory covariates about climate, land use, and animal and human populations at risk to capture spatiotemporal transmission dynamics. We developed a structured Gaussian process algorithm and prospectively tested this tool predicting the future year's cases given past years' cases. Results: We predicted the annual cases in 2016 and 2017 as 438 and 341, whereas the observed cases were 432 and 343, respectively. Pearson's correlation coefficient and normalized root mean squared error values for 2016 and 2017 predictions were (0.83; 0.58) and (0.87; 0.52), respectively. The most important covariates were found to be the number of settlements with fewer than 25 000 inhabitants, latitude, longitude and potential evapotranspiration (evaporation and transpiration). Conclusions: Main driving factors of CCHF dynamics were human population at risk in rural areas, geographical dependency and climate effect on ticks. Our model was able to prospectively predict the numbers of CCHF cases. Our proof-of-concept study also provided insight for understanding possible mechanisms of infectious diseases and found important directions for practice and policy to combat against emerging infectious diseases. C. Ak, Clin Microbiol Infect 2020;26:123.e1e123.e7

Ex Output:
A prospective prediction tool for understanding CrimeaneCongo haemorrhagic fever dynamics in Turkey

Ex Input:
HIV infected individuals have been shown to be pre-disposed to pulmonary infections even while receiving anti-retroviral therapy. Alveolar macrophages (AMs) play a critical role in lung innate immunity, but contradictory results have been reported regarding their functionality following HIV infection. Here, using the SIV rhesus macaque model, we document the effect of SIV infection on the phenotypic and functional properties of AMs. Following infection with SIV mac251 , AMs in bronchoalveolar lavage (BAL) sampled over 2-to 20-weeks post-infection (wpi) were compared to those in BAL samples from naive macaques. AM expression of proinflammatory cytokines TNF-, IL-6, IL-1, and chemokine RANTES drastically increased 2-wpi compared to AMs of naive macaques (p < 0.0001 for all), but dropped significantly with progression to chronic infection. Phagocytic activity of AMs 2-and 4-wpi was elevated compared to AMs of naive animals (p = 0.0005, p = 0.0004, respectively) but significantly decreased by 12-wpi (p = 0.0022, p = 0.0019, respectively). By 20-wpi the ability of AMs from chronically infected animals to perform SIV-specific antibody-dependent phagocytosis (ADP) was also diminished (p = 0.028). Acute SIV infection was associated with increased FcRIII expression which subsequently declined with disease progression. Frequency of FcRIII + AMs showed a strong trend toward correlation with SIV-specific ADP, and at 2-wpi FcRIII expression negatively correlated with viral load (r = 0.6819; p = 0.0013), suggesting a contribution to viremia control. Importantly, PD-1 was found to be expressed on AMs and showed a strong trend toward correlation with plasma viral load (r = 0.8266; p = 0.058), indicating that similar to over-expression on T-cells, PD-1 expression on AMs may also be associated with disease progression. Further, AMs predominantly expressed PD-L2, which remained consistent over the course of infection. PD-1 blockade enhanced SIV-specific ADP by AMs from chronic infection indicating that the PD-1/PD-L2 pathway may modulate functional activity of AMs at that stage. These findings provide new insight into the dynamics of SIV infection leading to AM dysfunction and alteration of pulmonary innate immunity. Our results suggest new pathways to exploit in developing therapies targeting pulmonary disease susceptibility in HIV-infected individuals.

Ex Output:
Alveolar Macrophage Dysfunction and Increased PD-1 Expression During Chronic SIV Infection of Rhesus Macaques