Deciphering coral disease dynamics: integrating host, microbiome, and the changing environment

Diseases of tropical reef organisms is an intensive area of study, but despite significant advances in methodology and the global knowledge base, identifying the proximate causes of disease outbreaks remains difficult. The dynamics of infectious wildlife diseases are known to be influenced by shifting interactions among the host, pathogen, and other members of the microbiome, and a collective body of work clearly demonstrates that this is also the case for the main foundation species on reefs, corals. Yet, among wildlife, outbreaks of coral diseases stand out as being driven largely by a changing environment. These outbreaks contributed not only to significant losses of coral species but also to whole ecosystem regime shifts. Here we suggest that to better decipher the disease dynamics of corals, we must integrate more holistic and modern paradigms that consider multiple and variable interactions among the three major players in epizootics: the host, its associated microbiome, and the environment. In this perspective, we discuss how expanding the pathogen component of the classic host-pathogen-environment disease triad to incorporate shifts in the microbiome leading to dysbiosis provides a better model for understanding coral disease dynamics. We outline and discuss issues arising when evaluating each component of this trio and make suggestions for bridging gaps between them. We further suggest that to best tackle these challenges, researchers must adjust standard paradigms, like the classic one pathogen-one disease model, that, to date, have been ineffectual at uncovering many of the emergent properties of coral reef disease dynamics. Lastly, we make recommendations for ways forward in the fields of marine disease ecology and the future of coral reef conservation and restoration given these observations

Nicotine delivery to users from cigarettes and from different types of e-cigarettes

BACKGROUND: Delivering nicotine in the way smokers seek is likely to be the key factor in e-cigarette (EC) success in replacing cigarettes. We examined to what degree different types of EC mimic nicotine intake from cigarettes. METHODS: Twelve participants (‘dual users’ of EC and cigarettes) used their own brand cigarette and nine different EC brands. Blood samples were taken at baseline and at 2-min intervals for 10 min and again at 30 min. RESULTS: Eleven smokers provided usable data. None of the EC matched cigarettes in nicotine delivery (C (max) = 17.9 ng/ml, T (max) = 4 min and AUC(0–>30) = 315 ng/ml/min). The EC with 48 mg/ml nicotine generated the closest PK profile (C (max) = 13.6 ng/ml, T (max) = 4 min, AUC(0–>30) = 245 ng/ml/min), followed by a third generation EC using 20 mg/ml nicotine (C (max) = 11.9 ng/ml, T (max) = 6 min, AUC(0–>30) = 232 ng/ml/min), followed by the tank system using 20 mg/ml nicotine (C (max) = 9.9 ng/ml, T (max) = 6 min, AUC(0–>30) = 201 ng/ml/min). Cig-a-like PK values were similar, ranging from C (max) 7.5 to 9.7 ng/ml, T (max) 4-6 min, and AUC(0–>30) 144 to 173 ng/ml/min. Moderate differences in e-liquid nicotine concentrations had little effect on nicotine delivery, e.g. the EC with 24 mg/ml cartridge had the same PK profile as ECs with 16 mg/ml cartridges. Using similar strength e-liquid, the tank EC provided significantly more nicotine than cig-a-like ECs. CONCLUSIONS: EC brands we tested do not deliver nicotine as efficiently as cigarettes, but newer EC products deliver nicotine more efficiently than cig-a-like brands. Moderate variations in nicotine content of e-liquid have little effect on nicotine delivery. Smokers who are finding cig-a-like EC unsatisfactory should be advised to try more advanced systems