Is Exposure to BMAA a Risk Factor for Neurodegenerative Diseases? A Response to a Critical Review of the BMAA Hypothesis.

In a literature survey, Chernoff et al. (2017) dismissed the hypothesis that chronic exposure to ß-N-methylamino-L-alanine (BMAA) may be a risk factor for progressive neurodegenerative disease. They question the growing scientific literature that suggests the following: (1) BMAA exposure causes ALS/PDC among the indigenous Chamorro people of Guam; (2) Guamanian ALS/PDC shares clinical and neuropathological features with Alzheimer's disease, Parkinson's disease, and ALS; (3) one possible mechanism for protein misfolds is misincorporation of BMAA into proteins as a substitute for L-serine; and (4) chronic exposure to BMAA through diet or environmental exposures to cyanobacterial blooms can cause neurodegenerative disease. We here identify multiple errors in their critique including the following: (1) their review selectively cites the published literature; (2) the authors reported favorably on HILIC methods of BMAA detection while the literature shows significant matrix effects and peak coelution in HILIC that may prevent detection and quantification of BMAA in cyanobacteria; (3) the authors build alternative arguments to the BMAA hypothesis, rather than explain the published literature which, to date, has been unable to refute the BMAA hypothesis; and (4) the authors erroneously attribute methods to incorrect studies, indicative of a failure to carefully consider all relevant publications. The lack of attention to BMAA research begins with the review's title which incorrectly refers to BMAA as a "non-essential" amino acid. Research regarding chronic exposure to BMAA as a cause of human neurodegenerative diseases is emerging and requires additional resources, validation, and research. Here, we propose strategies for improvement in the execution and reporting of analytical methods and the need for additional and well-executed inter-lab comparisons for BMAA quantitation. We emphasize the need for optimization and validation of analytical methods to ensure that they are fit-for-purpose. Although there remain gaps in the literature, an increasingly large body of data from multiple independent labs using orthogonal methods provides increasing evidence that chronic exposure to BMAA may be a risk factor for neurological illness.

[Evaluation and treatment of PVC's]. / Utredning och behandling av ventrikulära extraslag.

Premature ventricular complex (PVC) is common in the general population. Symptoms vary from none to pronounced. The prognostic significance of PVC's depends on the presence of underlying structural heart disease. The clinical evaluation in patients with PVC aims at excluding structural heart disease and usually involves transthoracic echocardiogram and Holter. Patients without structural heart disease usually have a good prognosis. Frequent PVC's may cause impaired left ventricular function, which usually is reversible after treatment with drugs or ablation. A 12-lead ECG provides important information about PVC localization, however anatomical factors such as the heart's localization in the thorax as well as electrode placement and pharmacological treatment may affect the ECG appearance. In symptomatic patients with or without left ventricular impairment, pharmacological treatment or catheter ablation is indicated. However, in most cases the main goal is to reasure the patient of the good prognosis. To summarize, treatment of choice depends on symptoms, comorbidities, left ventricular function and patient's choice.

Standard Error of Empirical Bayes Estimate in NONMEM(R) VI

The pharmacokinetics/pharmacodynamics analysis software NONMEM(R) output provides model parameter estimates and associated standard errors. However, the standard error of empirical Bayes estimates of inter-subject variability is not available. A simple and direct method for estimating standard error of the empirical Bayes estimates of inter-subject variability using the NONMEM(R) VI internal matrix POSTV is developed and applied to several pharmacokinetic models using intensively or sparsely sampled data for demonstration and to evaluate performance. The computed standard error is in general similar to the results from other post-processing methods and the degree of difference, if any, depends on the employed estimation options.