Understanding Biofilm-Phage Interactions in Cystic Fibrosis Patients Using Mathematical Frameworks

When planktonic bacteria adhere together to a surface, they begin to form biofilms, or communities of bacteria. Biofilm formation in a host can be extremely problematic if left untreated, especially since antibiotics can be ineffective in treating the bacteria. Certain lung diseases such as cystic fibrosis can cause the formation of biofilms in the lungs and can be fatal. With antibiotic-resistant bacteria, the use of phage therapy has been introduced as an alternative or an additive to the use of antibiotics in order to combat biofilm growth. Phage therapy utilizes phages, or viruses that attack bacteria, in order to penetrate and eradicate biofilms. In order to evaluate the effectiveness of phage therapy against biofilm bacteria, we adapt an ordinary differential equation model to describe the dynamics of phage-biofilm combat in the lungs. We then create our own phage-biofilm model with ordinary differential equations and stochastic modeling. Then, simulations of parameter alterations in both models are investigated to assess how they will affect the efficiency of phage therapy against bacteria. By increasing the phage mortality rate, the biofilm growth can be balanced and allow the biofilm to be more vulnerable to antibiotics. Thus, phage therapy is an effective aid in biofilm treatment

Poisoning substances taken by young people: a population-based cohort study

Background: Globally, poisonings account for most medically-attended self-harm. Recent data on poisoning substances are lacking, but needed to inform self-harm prevention. Aim: To assess poisoning substance patterns and trends among 10-24 year olds across England from 1998-2014. Design and Setting: Open cohort study of 1,736,527 young people using linked Clinical Practice Research Datalink, Hospital Episode Statistics and Office for National Statistics mortality data. Method: Poisoning substances were identified by ICD-10 or Read codes. Incidence rates and adjusted incidence rate ratios (aIRR) were calculated for poisoning substances by age, sex, index of multiple deprivation and year. Results: 40,333 poisoning episodes were identified, with 58% specifying the substances involved. The most common substances were paracetamol (39.8%), alcohol (32.7%), NSAIDs (11.6%), antidepressants (10.2%) and opioids (7.6%). Poisoning rates were highest at ages 16-18 for females, 19-24 for males. Opioid poisonings increased 5-fold from 1998-2014 (females: aIRR 5.30, 95%CI 4.08-6.89; males: 5.11, 95%CI 3.37-7.76), antidepressant poisonings 3 to 4-fold (females: aIRR 3.91, 3.18-4.80, males: 2.70, 2.04-3.58), aspirin/NSAID poisonings 3-fold (females: aIRR 2.84, 2.40-3.36, males: 2.76, 2.05-3.72) and paracetamol poisonings 3-fold in females (aIRR 2.87, 2.58-3.20). Across all substances poisoning incidence was higher in more disadvantaged groups, with the strongest gradient for opioid poisonings among males (aIRR 3.46, 2.24-5.36). Conclusion: It is important that GPs raise awareness with families of the substances young people use to self-harm, especially the common use of over-the-counter medications. Quantities of medication prescribed to young people at risk of self-harm and their families should be limited, particularly analgesics and antidepressants

TOWARD A COMPREHENSIVE APPROACH TO YOUTH EMPOWERMENT FOR CLIMATE ACTION

This brief focuses on empowering the youth to act on climate change and environmental protection by strengthening formal and informal environmental and sustainability education. Environmental education is the foundation for problem recognition and solving. The G20 is in a powerful position to exercise environmental and climate leadership by mandating sustainability and environmental education domestically, regularly tracking environmental and climate literacy internationally, investing in infrastructure and educator training for environmental education, promoting games drawing on real-world problems, developing partnerships for environmental education in local communities, and organizing an annual Youth Summit for Climate, Environment, and Sustainable Development Solutions

Feedback modeling of non-esterified fatty acids in rats after nicotinic acid infusions

A feedback model was developed to describe the tolerance and oscillatory rebound seen in non-esterified fatty acid (NEFA) plasma concentrations following intravenous infusions of nicotinic acid (NiAc) to male Sprague-Dawley rats. NiAc was administered as an intravenous infusion over 30 min (0, 1, 5 or 20 μmol kg−1 of body weight) or over 300 min (0, 5, 10 or 51 μmol kg−1 of body weight), to healthy rats (n = 63), and serial arterial blood samples were taken for measurement of NiAc and NEFA plasma concentrations. Data were analyzed using nonlinear mixed effects modeling (NONMEM). The disposition of NiAc was described by a two-compartment model with endogenous turnover rate and two parallel capacity-limited elimination processes. The plasma concentration of NiAc was driving NEFA (R) turnover via an inhibitory drug-mechanism function acting on the formation of NEFA. The NEFA turnover was described by a feedback model with a moderator distributed over a series of transit compartments, where the first compartment (M1) inhibited the formation of R and the last compartment (MN) stimulated the loss of R. All processes regulating plasma NEFA concentrations were assumed to be captured by the moderator function. The potency, IC50, of NiAc was 45 nmol L−1, the fractional turnover rate kout was 0.41 L mmol−1 min−1 and the turnover rate of moderator ktol was 0.027 min−1. A lower physiological limit of NEFA was modeled as a NiAc-independent release (kcap) of NEFA into plasma and was estimated to 0.032 mmol L−1 min−1. This model can be used to provide information about factors that determine the time-course of NEFA response following different modes, rates and routes of administration of NiAc. The proposed model may also serve as a preclinical tool for analyzing and simulating drug-induced changes in plasma NEFA concentrations after treatment with NiAc or NiAc analogues