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:
Background: Proenkephalin (PENK) has been suggested as a novel biomarker for kidney function. We investigated the diagnostic and prognostic utility of plasma PENK in comparison with neutrophil gelatinase-associated lipocalin (NGAL) and estimated glomerular filtration rates (eGFR) in septic patients. Methods: A total of 167 septic patients were enrolled: 99 with sepsis, 37 with septic shock, and 31 with suspected sepsis. PENK and NGAL concentrations were measured and GFR was estimated by using the isotope dilution mass spectrometry traceable-Modification of Diet in Renal Disease (MDRD) Study and three Chronic Kidney Disease Epidemiology Collaboration (CKD-EPI) equations: CKD-EPICr, CDK-EPICysC, and CKD-EPICr-CysC. The PENK, NGAL, and eGFR results were compared according to sepsis severity, presence or absence of acute kidney injury (AKI), and clinical outcomes. Results: The PENK, NGAL, and eGFR results were significantly associated with sepsis severity and differed significantly between patients with and without AKI only in the sepsis group (all P < 0.05). PENK was superior to NGAL in predicting AKI (P = 0.022) and renal replacement therapy (RRT) (P = 0.0085). Regardless of the variable GFR category by the different eGFR equations, PENK showed constant and significant associations with all eGFR equations. Unlike NGAL, PENK was not influenced by inflammation and predicted the 30day mortality. Conclusions: PENK is a highly sensitive and objective biomarker of AKI and RRT and is useful for prognosis prediction in septic patients. With its diagnostic robustness and predictive power for survival, PENK constitutes a promising biomarker in critical care settings including sepsis.

Ex Output:
Original Article Clinical Chemistry Proenkephalin, Neutrophil Gelatinase-Associated Lipocalin, and Estimated Glomerular Filtration Rates in Patients With Sepsis


Ex Input:
Community-ARI and inpatient-ARI was also combined with mild and severe cases of influenza from a historical prospective study as mild-ARI and severe-ARI respectively to evaluate the performance of clinical case definitions. We analysed 130 community-ARI and 140 inpatient-ARI episodes (5 inpatient-ARI excluded because multiple pathogens were detected), involving 138 and 207 samples respectively. Detection by PCR declined with days post-onset for influenza virus; decrease was faster for community-ARI than for inpatient-ARI. No such patterns were observed for noninfluenza respiratory virus infections. PathChip added substantially to viruses detected for community-ARI only. Clinical case definitions discriminated influenza from other mild-ARI but performed poorly for severe-ARI and for older participants. Rational strategies for diagnosis and surveillance of influenza and other respiratory virus must acknowledge the differences between ARIs presenting in community and hospital settings.

Ex Output:
Detection of viral respiratory pathogens in mild and severe acute respiratory infections in Singapore To investigate the performance of laboratory methods and clinical case definitions in detecting the viral pathogens for acute respiratory infections (ARIs) from a prospective community cohort and hospital inpatients, nasopharyngeal swabs from cohort members reporting ARIs (community-ARI) and inpatients admitted with ARIs (inpatient-ARI) were tested by Singleplex Real Time-Polymerase Chain Reaction (SRT-PCR), multiplex RT-PCR (MRT-PCR) and pathogen-chip system (PathChip) between OPEN


Ex Input:
Within the ABC-SMC approach [1], particles are first generated from the prior distribution. Particles are then resampled from the obtained sample, and slightly perturbed. From these resampled particles, a new sample is formed, from which again particles are resampled, etc. The threshold value for the summary statistic -below which new particles are accepted -is lowered with every newly obtained sample. As a result, the acceptance rate decreases and the acceptance threshold approaches zero with an increase in the number of iterations (resamplings). Initial -values were estimated as follows. We generated 100 trees and we calculated the summary statistics (indices) and used the standard deviation of this distribution as the initial values. The -values were decreased in an exponential fashion as the sequential ABC scheme progresses. • Set the population indicator t = 1 • Set the particle indicator i = 1 • If t = 1, sample (m , θ ) from the prior π(m, θ) = π(m)π(θ|m) • If t > 1 sample m with probability π t−1 (m ) and perturb m ∼ Km t (m|m ) Sample θ from the previous population {θ(m ) t−1 } with weights w t−1 . Perturb the particle, θ ∼ KP t,m (θ|θ ) where KP t,m is the particle perturbation kernel. If π(m , θ ) = 0, repeat this step. Simulate a candidate dataset x ∼ f (x|m , θ ) If ρ(x , y) > repeat this step. 1/3

Ex Output:

Inference of transmission network structure from HIV phylogenetic trees Supplementary information Approximate Bayesian Computation for model choice