Background: The Association of British Neurologists (ABN) 2015 guidelines suggested classifying multiple sclerosis therapies according to their average relapse reduction. We sought to classify newer therapies (cladribine, ocrelizumab, ofatumumab, ozanimod) based on these guidelines. Materials & methods: Therapies were classified by using direct comparative trial results as per ABN guidelines and generating classification probabilities for each therapy based on comparisons versus placebo in a network meta-analysis for annualized relapse rate. Results: For both approaches, cladribine and ofatumumab were classified as high efficacy. Ocrelizumab and ozanimod (1.0 mg) were classified as moderate or high efficacy depending on the approach used. Conclusion: Cladribine and ofatumumab have an efficacy comparable with therapies classified in the ABN guidelines as high efficacy.

Aim: To compare the efficacy of ofatumumab to other disease-modifying therapies (DMTs) for relapsing multiple sclerosis (RMS). Materials & methods: A network meta-analysis was conducted to determine the relative effect of ofatumumab on annualized relapse rate and confirmed disability progression at 3 months and 6 months. Results: For each outcome, ofatumumab was as effective as other highly efficacious monoclonal antibody DMTs (i.e., alemtuzumab, natalizumab and ocrelizumab). Conclusion: Ofatumumab offers beneficial outcomes for RMS by reducing relapse and disability progression risk.

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The highly adaptive nature of prokaryotic communities in the face of changing environmental conditions reflects in part their ability to share advantageous genetic information through horizontal gene transfer (HGT). Natural freshwater lacustrine (lake) systems are a vital and finite resource, and the influence of HGT on their quality (e.g. enabling the spread of antibiotic resistance and xenobiotic catabolism genes) is likely significant. Laboratory and in situ studies indicate that the dynamic physical, chemical, and biological conditions that structure freshwater systems can influence HGT within freshwater prokaryotic communities. Thus, understanding how biogeochemical parameters impact HGT in freshwater lakes is an emerging knowledge gap with potential implications for ecosystem and human health on a global scale. In this review, we provide a general synopsis of what is known about HGT in freshwater prokaryotic communities, followed by an integrated summary of current knowledge identifying how biogeochemical factors may influence prokaryotic HGT in freshwater lacustrine systems

Public Health Ontario is working to estimate the burden of disease from environmental hazards in Ontario, Canada. As part of this effort, we estimated deaths and health care utilization resulting from exposure to pathogens and toxic substances in food. We applied fractions for the proportion of illness attributable to foodborne transmission to the annual (2008–2012) counts of deaths, hospitalizations, emergency department (ED) visits, and physician office visits for 15 diseases (13 pathogen-specific diseases and 2 nonspecific syndromes) captured by administrative health data. Nonspecific gastroenteritis (causative agent unknown) was the dominant disease, accounting for 98% of ED visits, 94% of hospitalizations, and 88% of deaths annually attributed to the 15 diseases. We estimated that foodborne nonspecific gastroenteritis results in ∼137,000 physician office visits (1000/100,000 population), 40,000 ED visits (310/100,000), 6200 hospitalizations (47/100,000), and 59 deaths (0.45/100,000) in Ontario per year (mean estimates). Our results indicate that pathogen-specific approaches to foodborne disease surveillance can substantially underestimate the deaths and illness resulting from exposure to foodborne pathogens and other causes of foodborne illness.

Background: Quantifying the potential cancer cases associated with environmental carcinogen exposure can help inform efforts to improve population health. This study developed an approach to estimate the environmental burden of cancer and applied it to Ontario, Canada. The purpose was to identify environmental carcinogens with the greatest impact on cancer burden to support evidence-based decision making. Methods: We conducted a probabilistic assessment of the environmental burden of cancer in Ontario. We selected 23 carcinogens that we defined as "environmental" (e.g., pollutants) and were relevant to the province, based on select classifications provided by the International Agency for Research on Cancer. We evaluated population exposure to the carcinogens through inhalation of indoor/outdoor air; ingestion of food, water, and dust; and exposure to radiation. We obtained or calculated concentration-response functions relating carcinogen exposure and the risk of developing cancer. Using both human health risk assessment and population attributable fraction models in a Monte Carlo simulation, we estimated the annual cancer cases associated with each environmental carcinogen, reporting the simulation summary (e.g., mean and percentiles). Results: We estimated between 3540 and 6510 annual cancer cases attributable to exposure to 23 environmental carcinogens in Ontario. Three carcinogens were responsible for over 90% of the environmental burden of cancer: solar ultraviolet (UV) radiation, radon in homes, and fine particulate matter (PM 2.5) in outdoor air. Eight other carcinogens had an estimated mean burden of at least 10 annual cancer cases: acrylamide, arsenic, asbestos, chromium, diesel engine exhaust particulate matter, dioxins, formaldehyde, and secondhand smoke. The remaining 12 carcinogens had an estimated mean burden of less than 10 annual cancer cases in Ontario.

Selective adaptation of biofilm-forming bacteria to the nutrient-rich but environmentally challenging conditions of the surface microlayer (SML) or neuston layer was evident in littoral regions of two physically and geochemically contrasting freshwater lakes. SML bacterial communities (bacterioneuston) in these systems were depleted in Actinobacteria, enriched in either Betaproteobacteria or Gammaproteobacteria, and either unicellular Cyanobacteria were absent or microbial mat forming Cyanobacteria enriched relative to communities in the underlying shallow water column (0.5 m depth). Consistent with the occurrence of biofilm-hosted, geochemically distinct microhabitats, As-, Fe-, and S-metabolizing bacteria including anaerobic taxa were detected only in the SML in both systems. Over diurnal time scales, higher wind speeds resulted in the generation of floc from SML biofilms, identifying a transport mechanism entraining SML accumulated microorganisms, nutrients, and contaminants into the underlying water column. The energy regime experienced by the SML was more important to floc generation as larger flocs were more abundant in the larger, oligotrophic lake (higher relative energy regime) compared to the sheltered, smaller lake, despite relatively higher concentrations of bacteria, organic carbon, Fe, and PO4(3-) in the latter system.

Road traffic noise can adversely impact the health of city residents, particularly when it occurs at night. The objective of this study was to evaluate nighttime traffic ambient noise in Toronto, Canada using measured and model-estimated noise levels. Road traffic noise was measured at 767 locations over 3 seasonal sampling campaigns between June 2012 and October 2013 to fully capture noise variability in Toronto. Temporal and campaign-specific spatial models, developed using the noise measurements, were used to build a final predictive surface. The surface was capable of estimating noise across the city over a 24-hr time frame. Measured and surface-estimated noise levels were compared with guidelines from the World Health Organization and the Province of Ontario to identify areas where noise may pose a health risk. Measured mean nighttime noise in Toronto exceeded World Health Organization (40 dBA) guidelines and mean daytime noise exceeded provincial (55 dBA) guidelines. The final predictive surface, incorporating spatial variables and daily cycles in noise levels, provides noise estimates geocoded for the entire study area. This tool could be used for epidemiological studies and to inform noise mitigation efforts. Based on surface-estimated noise levels during the quietest time of night (2 a.m.-2:30 a.m.), 100% of Toronto has nighttime noise exceeding 40 dBA (mean = 57 dBA, range = 49-110 dBA). A predictive surface was developed to estimate geocoded noise levels and facilitate further study of noise in Toronto. This tool can be used to assess road traffic noise, particularly at night, as an environmental health hazard.