Influenza Virus in Human Exhaled Breath: An Observational Study

Background: Recent studies suggest that humans exhale fine particles during tidal breathing but little is known of their composition, particularly during infection. Methodology/Principal Findings: We conducted a study of influenza infected patients to characterize influenza virus and particle concentrations in their exhaled breath. Patients presenting with influenza-like-illness, confirmed influenza A or B virus by rapid test, and onset within 3 days were recruited at three clinics in Hong Kong, China. We collected exhaled breath from each subject onto Teflon filters and measured exhaled particle concentrations using an optical particle counter. Filters were analyzed for influenza A and B viruses by quantitative polymerase chain reaction (qPCR). Twelve out of thirteen rapid test positive patients provided exhaled breath filter samples (7 subjects infected with influenza B virus and 5 subjects infected with influenza A virus). We detected influenza virus RNA in the exhaled breath of 4 (33%) subjects-three (60%) of the five patients infected with influenza A virus and one (14%) of the seven infected with influenza B virus. Exhaled influenza virus RNA generation rates ranged from <3.2 to 20 influenza virus RNA particles per minute. Over 87% of particles exhaled were under 1 μm in diameter. Conclusions: These findings regarding influenza virus RNA suggest that influenza virus may be contained in fine particles generated during tidal breathing, and add to the body of literature suggesting that fine particle aerosols may play a role in influenza transmission. © 2008 Fabian et al.published_or_final_versio

Genetic Sharing with Cardiovascular Disease Risk Factors and Diabetes Reveals Novel Bone Mineral Density Loci.

Bone Mineral Density (BMD) is a highly heritable trait, but genome-wide association studies have identified few genetic risk factors. Epidemiological studies suggest associations between BMD and several traits and diseases, but the nature of the suggestive comorbidity is still unknown. We used a novel genetic pleiotropy-informed conditional False Discovery Rate (FDR) method to identify single nucleotide polymorphisms (SNPs) associated with BMD by leveraging cardiovascular disease (CVD) associated disorders and metabolic traits. By conditioning on SNPs associated with the CVD-related phenotypes, type 1 diabetes, type 2 diabetes, systolic blood pressure, diastolic blood pressure, high density lipoprotein, low density lipoprotein, triglycerides and waist hip ratio, we identified 65 novel independent BMD loci (26 with femoral neck BMD and 47 with lumbar spine BMD) at conditional FDR < 0.01. Many of the loci were confirmed in genetic expression studies. Genes validated at the mRNA levels were characteristic for the osteoblast/osteocyte lineage, Wnt signaling pathway and bone metabolism. The results provide new insight into genetic mechanisms of variability in BMD, and a better understanding of the genetic underpinnings of clinical comorbidity

AI is a viable alternative to high throughput screening: a 318-target study

: High throughput screening (HTS) is routinely used to identify bioactive small molecules. This requires physical compounds, which limits coverage of accessible chemical space. Computational approaches combined with vast on-demand chemical libraries can access far greater chemical space, provided that the predictive accuracy is sufficient to identify useful molecules. Through the largest and most diverse virtual HTS campaign reported to date, comprising 318 individual projects, we demonstrate that our AtomNet® convolutional neural network successfully finds novel hits across every major therapeutic area and protein class. We address historical limitations of computational screening by demonstrating success for target proteins without known binders, high-quality X-ray crystal structures, or manual cherry-picking of compounds. We show that the molecules selected by the AtomNet® model are novel drug-like scaffolds rather than minor modifications to known bioactive compounds. Our empirical results suggest that computational methods can substantially replace HTS as the first step of small-molecule drug discovery

Pseudomonas aeruginosa transmission is infrequent in New Zealand cystic fibrosis clinics

Pseudomonas aeruginosa is an important pathogen in cystic fibrosis (CF). Although most patients harbour unique P. aeruginosa isolates, some clinics report patients sharing common strains. The overall importance of person-to-person transmission in P. aeruginosa acquisition and whether routine patient segregation is necessary remains uncertain. The present authors therefore investigated the extent of P. aeruginosa transmission in New Zealand CF clinics. New Zealand’s seven major CF centres were assessed, combining epidemiological data with computer-assisted SalI DNA fingerprinting of 496 isolates from 102 patients. One cluster of related isolates was significantly more prevalent in the largest clinic than expected by chance. The seven patients with isolates belonging to this cluster had more contact with each other than the remaining patients attending this centre. No other convincing evidence of transmission was found in any of the other smaller clinics. Three P. aeruginosa strains believed to be transmissible between patients in Australian and British CF clinics are present in New Zealand, but there was no definite evidence they had spread. Pseudomonas aeruginosa transmission is currently infrequent in New Zealand cystic fibrosis clinics. This situation could change rapidly and ongoing surveillance is required. The current results confirm that computer-assisted SalI DNA fingerprinting is ideally suited for such surveillance