Cystic fibrosis (CF) airways harbour complex and dynamic polymicrobial communities that include many oral bacteria. Despite increased knowledge of CF airway microbiomes the interaction between established CF pathogens and other resident microbes and resulting impact on disease progression is poorly understood. Previous studies have demonstrated that oral commensal streptococci of the Anginosus group (AGS) can establish chronic pulmonary infections and become numerically dominant in CF sputa indicating that they play an important role in CF microbiome dynamics. In this study a strain of Pseudomonas aeruginosa (DWW2) of the mucoid alginate overproducing phenotype associated with chronic CF airway infection and a strain of the oral commensal AGS species Streptococcus anginosus (3a) from CF sputum were investigated for their ability to co-exist and their responses to biofilm co-culture. Bacteria in biofilms were quantified, pyocyanin expression by DWW2 was measured and the effect of AGS strain 3a on reversion of DWW2 to a non-mucoidal phenotype investigated. The virulence of DWW2, 3a and colony variant phenotypes of DWW2 in mono- and co-culture were compared in a Galleria mellonella infection model. Co-culture biofilms were formed in normoxic, hypercapnic (10% CO2) and anoxic atmospheres with the streptococcus increasing in number in co-culture, indicating that these bacteria would be able to co-exist and thrive within the heterogeneous microenvironments of the CF airway. The streptococcus caused increased pyocyanin expression by DWW2 and colony variants by stimulating reversion of the mucoid phenotype to the high pyocyanin expressing non-mucoid phenotype. The latter was highly virulent in the infection model with greater virulence when in co-culture with the streptococcus. The results of this study demonstrate that the oral commensal S. anginosus benefits from interaction with P. aeruginosa of the CF associated mucoid phenotype and modulates the behaviour of the pseudomonad in ways that may be clinically relevant.
Human body exposure to radiofrequency electromagnetic waves emitted from smart meters was assessed using various exposure configurations. Specific energy absorption rate distributions were determined using three anatomically realistic human models. Each model was assigned with age- and frequency-dependent dielectric properties representing a collection of age groups. Generalized exposure conditions involving standing and sleeping postures were assessed for a home area network operating at 868 and 2,450 MHz. The smart meter antenna was fed with 1 W power input which is an overestimation of what real devices typically emit (15 mW max limit). The highest observed whole body specific energy absorption rate value was 1.87 mW kg , within the child model at a distance of 15 cm from a 2,450 MHz device. The higher values were attributed to differences in dimension and dielectric properties within the model. Specific absorption rate (SAR) values were also estimated based on power density levels derived from electric field strength measurements made at various distances from smart meter devices. All the calculated SAR values were found to be very small in comparison to International Commission on Non-Ionizing Radiation Protection limits for public exposure. Bioelectromagnetics. 39:200-216, 2018. © 2017 Wiley Periodicals, Inc.
Background: A noninvasive, wearable continuous glucose monitor would be a major advancement in diabetes therapy. This trial investigated a novel noninvasive glucose monitor which analyzes spectral variations in radio frequency/microwave signals reflected from the wrist. Methods: A single-arm, open-label, experimental study compared glucose values from a prototype investigational device with laboratory glucose measurements from venous blood samples (Super GL Glucose Analyzer, Dr. Müller Gerätebau GmbH) at varying levels of glycemia. The study included 29 male participants with type 1 diabetes (age range = 19-56 years). The study comprised three stages with the following aims: (1) demonstrate initial proof-of-principle, (2) test an improved device design, and (3) test performance on two consecutive days without device recalibration. The co-primary endpoints in all trial stages were median and mean absolute relative difference (ARD) calculated across all data points. Results: In stage 1, the median and mean ARDs were 30% and 46%, respectively. Stage 2 produced marked performance improvements with a median and mean ARD of 22% and 28%, respectively. Stage 3 showed that, without recalibration, the device performed as well as the initial prototype (stage 1) with a median and mean ARD of 35% and 44%, respectively. Conclusion: This proof-of-concept study shows that a novel noninvasive continuous glucose monitor was capable of detecting glucose levels. Furthermore, the ARD results are comparable to first models of commercially available minimally invasive products without the need to insert a needle. The prototype has been further developed and is being tested in subsequent studies. Trial registration number: NCT05023798.
Background and Aims: To determine accuracy, safety and specificity of a novel non-invasive wrist-worn continuous BGM device which analyses resonance shifts in the microwave spectrum using AI. We present results from an ongoing study in patients with T1D and T2D from an expanded dataset (cohorts 1-3). Methods: In this open, pilot, adaptive design study, subjects (N=5/cohort) attended 4 test occasions (n=2/session), each ≤7 days apart. Devices automatically collected data every 60 secs for 500 msec over 3 hours/session, with plasma glucose measured every 5 mins. A global AI model was evaluated by MARD using venous blood glucose. Interim results from every 5 completed patients informed the next device iteration with 10 iterations possible across the study. Results: Data from each cohort was used to train a neural net algorithm to predict a new trial. Each cohort improved overall MARD prediction accuracy. All analyses followed a leave-one-trial-out methodology where the data for the omitted trial was predicted for each analysis cycle. Using the first cohort, the MARD was 21%, adding the second reduced MARD to 15% and with all 3 cohorts the predictive MARD was 13%. Improvement in the SEG plots showed data falling into low risk SEG categories. Conclusions: We have shown that by giving the neural network an expanded dataset, the MARD decreased to nearer commercially available minimally-invasive BGMs by 38% from a predictive MARD of 21% to 13%. Disclosure M.S.Chaudhry: None. G.J.Dunseath: None. J.Ryan: None. L.Barlow: None. I.C.Carrillo masso: Employee; Afon Technology Ltd. J.H.Crane: None. M.R.A.Qureshi: Employee; Afon Technology Ltd. S.C.Bain: Advisory Panel; Novo Nordisk, Sanofi, Lilly, Boehringer Ingelheim Inc., AstraZeneca. S.D.Luzio: None. C.M.Handy: None. B.Love: Consultant; Afon Technologies LTD. N.M.M.Silva: None. L.M.Ferreira: None. K.Wareham: None.
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