Regional and Individual Variations in the Function of the Human Eccrine Sweat Gland

Derived values for sodium concentration of the precursor fluid, free water clearance and counts of the number of active sweat glands were determined on the forehead, forearm and back of 14 subjects. Maximal sweat rate per gland and maximal free water clearance per gland were also calculated. The sodium concentration of the precursor fluid averaged 140 mEq/L. The large variations among our subjects in the maximal sweat rate (SR max) per m2 and maximal free water clearance (FWC max) per m2 depended mainly on differences in the functional capacity of individual sweat glands rather than in differences in population. However, regional variations in SR max per m2 and FWC max per m2 in each subject depended largely on differences in the population of active sweat glands. A significant correlation was found between secretory (SR max per gland) and reabsorptive capacity (FWC max per gland)

The Electrolyte Composition of Pharmacologically and Thermally Stimulated Sweat: A Comparative Study**From the Division of Dermatology, University of Oregon Medical School Portland, Oregon 97201.

The sodium and potassium content of sweat induced by pilocarpine iontophoresis, and alter the intracutaneous injection of pilocarpine, acetylcholine or methylcholine, were compared with thermal sweat. In most subjects, sodium concentrations were higher in pharmacologic sweat than in thermal sweat. An increase in potassium content in pharmacologic sweat was seen in all subjects. Unphysiological exposure of the ductal portion, as well as the secretory portion of sweat gland to exogenous cholinergic drugs was assumed as a possible cause of the high sodium and potassium concentrations of pharmacologically-stimulated sweat

Charting a course for smartphones and wearables to transform population health research

The use of data from smartphones and wearable devices has huge potential for population health research, given the high level of device ownership; the range of novel health-relevant data types available from consumer devices; and the frequency and duration with which data are, or could be, collected. Yet, the uptake and success of large-scale mobile health research in the last decade have not met this intensely promoted opportunity. We make the argument that digital person-generated health data are required and necessary to answer many top priority research questions, using illustrative examples taken from the James Lind Alliance Priority Setting Partnerships. We then summarize the findings from 2 UK initiatives that considered the challenges and possible solutions for what needs to be done and how such solutions can be implemented to realize the future opportunities of digital person-generated health data for clinically important population health research. Examples of important areas that must be addressed to advance the field include digital inequality and possible selection bias; easy access for researchers to the appropriate data collection tools, including how best to harmonize data items; analysis methodologies for time series data; patient and public involvement and engagement methods for optimizing recruitment, retention, and public trust; and methods for providing research participants with greater control over their data. There is also a major opportunity, provided through the linkage of digital person-generated health data to routinely collected data, to support novel population health research, bringing together clinician-reported and patient-reported measures. We recognize that well-conducted studies need a wide range of diverse challenges to be skillfully addressed in unison (eg, challenges regarding epidemiology, data science and biostatistics, psychometrics, behavioral and social science, software engineering, user interface design, information governance, data management, and patient and public involvement and engagement). Consequently, progress would be accelerated by the establishment of a new interdisciplinary community where all relevant and necessary skills are brought together to allow for excellence throughout the life cycle of a research study. This will require a partnership of diverse people, methods, and technologies. If done right, the synergy of such a partnership has the potential to transform many millions of people’s lives for the bette

Metagenomic study of the viruses of African straw-coloured fruit bats: detection of a chiropteran poxvirus and isolation of a novel adenovirus

Viral emergence as a result of zoonotic transmission constitutes a continuous public health threat. Emerging viruses such as SARS coronavirus, hantaviruses and henipaviruses have wildlife reservoirs. Characterising the viruses of candidate reservoir species in geographical hot spots for viral emergence is a sensible approach to develop tools to predict, prevent, or contain emergence events. Here, we explore the viruses of Eidolon helvum, an Old World fruit bat species widely distributed in Africa that lives in close proximity to humans. We identified a great abundance and diversity of novel herpes and papillomaviruses, described the isolation of a novel adenovirus, and detected, for the first time, sequences of a chiropteran poxvirus closely related with Molluscum contagiosum. In sum, E. helvum display a wide variety of mammalian viruses, some of them genetically similar to known human pathogens, highlighting the possibility of zoonotic transmission

Late gadolinium enhancement and adverse outcomes in a contemporary cohort of adult survivors of tetralogy of Fallot

Objective: Myocardial fibrosis has been associated with poorer outcomes in tetralogy of Fallot, however only a handful of studies have assessed its significance in the current era. Our aim was to quantify the amount of late gadolinium enhancement in both the LV and RV in a contemporary cohort of adults with surgically repaired tetralogy of Fallot, and assess the relationship with adverse clinical outcomes. Design: Single centre cohort study Setting: National tertiary referral center Patients: One hundred fourteen patients with surgically repaired tetralogy of Fallot with median age 29.5 years (range 17.5-64.2). Prospective follow-up for mean 2.4 years (SD 1.29). Interventions: Cardiovascular magnetic resonance was performed, and late gadolinium enhancement mass was estimated for the LV using the 5-SD remote myocardium method, and for the RV using a segmental scoring system. Cohort characterization was determined through the use of a computerized database. Outcome measures: Survival analysis from time of scan to first adverse event, defined as an episode of atrial arrhythmia, sustained ventricular arrhythmia, hospitalization with heart failure, or implantable cardioverter-defibrillator insertion. Results: Eleven patients experienced an adverse outcome in the follow-up period, although there were no deaths. LV late gadolinium enhancement was associated with adverse outcomes in a univariate model (P = .027). However, when adjusted for age at scan the significant variables included NYHA class (P = .006), peak oxygen uptake (P = .028), number of prior sternotomies (P = .044), and higher indexed RV and LV end diastolic volumes (P = .002 and P < .001), but not RV or LV late gadolinium enhancement. Conclusions: Formal quantification of late gadolinium enhancement is not currently as helpful in ascertaining prognosis compared to other, more easily assessed parameters in a contemporary cohort of tetralogy of Fallot survivors, however assessment particularly of the LV holds promise for the future

Classifying depression symptom severity: Assessment of speech representations in personalized and generalized machine learning models

There is an urgent need for new methods that improve the management and treatment of Major Depressive Disorder (MDD). Speech has long been regarded as a promising digital marker in this regard, with many works highlighting that speech changes associated with MDD can be captured through machine learning models. Typically, findings are based on cross-sectional data, with little work exploring the advantages of personalization in building more robust and reliable models. This work assesses the strengths of different combinations of speech representations and machine learning models, in personalized and generalized settings in a two-class depression severity classification paradigm. Key results on a longitudinal dataset highlight the benefits of personalization. Our strongest performing model set-up utilized self-supervised learning features and convolutional neural network (CNN) and long short-term memory (LSTM) back-end

Use of mass-participation outdoor events to assess human exposure to tickborne pathogens

Mapping the public health threat of tickborne pathogens requires quantification of not only the density of infected host-seeking ticks but also the rate of human exposure to these ticks. To efficiently sample a high number of persons in a short time, we used a mass-participation outdoor event. In June 2014, we sampled ≈500 persons competing in a 2-day mountain marathon run across predominantly tick-infested habitat in Scotland. From the number of tick bites recorded and prevalence of tick infection with Borrelia burgdoferi sensu lato and B. miyamotoi, we quantified the frequency of competitor exposure to the pathogens. Mass-participation outdoor events have the potential to serve as excellent windows for epidemiologic study of tickborne pathogens; their concerted use should improve spatial and temporal mapping of human exposure to infected ticks