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BackgroundLevothyroxine (LT4) absorption is affected by concomitant ingestion of certain minerals, medications, and foods. It has been hypothesized that metformin may suppress serum thyrotropin (TSH) concentrations by enhancing LT4 absorption or by directly affecting the hypothalamic-pituitary axis. This study examined the effect of metformin ingestion on LT4 absorption, as assessed by serum total thyroxine (TT4) concentrations.MethodsA modified Food and Drug Administration LT4 bioequivalence protocol was applied to healthy, metformin-naïve, euthyroid adult volunteers. Following an overnight fast, 600 μg LT4 was administered orally. Serum TT4 concentrations were measured at baseline and at 0.5, 1, 1.5, 2, 4, and 6 h following LT4 administration. Measurements were performed before and after one week of metformin ingestion (850 mg three times daily). Peak serum TT4 concentrations, time to peak TT4 concentrations, and area under the concentration-time curve (AUC) were calculated.ResultsTwenty-six subjects (54% men, 27% white, age 33 ± 10 years) were studied. There were no significant differences in peak serum TT4 concentrations (p = 0.13) and time to peak TT4 concentrations (p = 0.19) before and after one week of metformin use. A trend toward reduced TT4 AUC was observed after metformin ingestion (pre-metformin 3893 ± 568 μg/dL-min, post-metformin 3765 ± 588 μg/dL-min, p = 0.09).ConclusionsLT4 absorption is unchanged by concomitant metformin ingestion. Mechanisms other than increased LT4 absorption may be responsible for the suppressed TSH concentrations observed in patients ingesting both drugs.

The use of consumer wearable devices (CWDs) to track health and fitness has rapidly expanded over recent years because of advances in technology. The general population now has the capability to continuously track vital signs, exercise output, and advanced health metrics. Although understanding of basic health metrics may be intuitive (eg, peak heart rate), more complex metrics are derived from proprietary algorithms, differ among device manufacturers, and may not historically be common in clinical practice (eg, peak V ˙ O 2 , exercise recovery scores). With the massive expansion of data collected at an individual patient level, careful interpretation is imperative. In this review, we critically analyze common health metrics provided by CWDs, describe common pitfalls in CWD interpretation, provide recommendations for the interpretation of abnormal results, present the utility of CWDs in exercise prescription, examine health disparities and inequities in CWD use and development, and present future directions for research and development. [Display omitted] • CWDs have proliferated, but the heterogeneous health metrics they generate makes interpretation challenging. • To optimize their value for patient assessment and management, physicians should become familiar with the measurement techniques, accuracy, clinical relevance, and potential pitfalls inherent in these devices as they continue to evolve. • Along with technological development, clearer delineation of the indications for and appropriate use of monitoring technologies is needed to ensure safety and accurate application of the information provided by devices. [ABSTRACT FROM AUTHOR]

cellPACK assembles computational models of the biological mesoscale, an intermediate scale (10-100 nm) between molecular and cellular biology scales. cellPACK's modular architecture unites existing and novel packing algorithms to generate, visualize and analyze comprehensive three-dimensional models of complex biological environments that integrate data from multiple experimental systems biology and structural biology sources. cellPACK is available as open-source code, with tools for validation of models and with 'recipes' and models for five biological systems: blood plasma, cytoplasm, synaptic vesicles, HIV and a mycoplasma cell. We have applied cellPACK to model distributions of HIV envelope protein to test several hypotheses for consistency with experimental observations. Biologists, educators and outreach specialists can interact with cellPACK models, develop new recipes and perform packing experiments through scripting and graphical user interfaces at http://cellPACK.org/. [ABSTRACT FROM AUTHOR]