In Vitro and In Vivo Biocompatibility of Bacterial Cellulose.

Bacterial cellulose is a unique biomaterial produced by various species of bacteria that offers a range of potential applications in the biomedical field. To provide a cost-effective alternative to soft-tissue implants used in cavity infills, remodeling, and subdermal wound healing, in vitro cytotoxicity and in vivo biocompatibility of native bacterial cellulose were investigated. Cytotoxicity was assessed using a metabolic assay on Swiss 3T3 fibroblasts and INS-1832/13 rat insulinoma. Results showed no cytotoxicity, whether the cells were seeded over or under the bacterial cellulose scaffolds. Biocompatibility was performed on Sprague-Dawley rats (males and females, 8 weeks old) by implanting bacterial cellulose membranes subcutaneously for 1 or 12 weeks. The explanted scaffolds were then sliced and stained with hematoxylin and eosin for histological characterization. The first series of results revealed acute and chronic inflammation persisting over 12 weeks. Examination of the explants indicated a high number of granulocytes within the periphery of the bacterial cellulose, suggesting the presence of endotoxins within the membrane, confirmed by a Limulus amebocyte lysate test. This discovery motivated the development of non-pyrogenic bacterial cellulose scaffolds. Following this, a second series of animal experiments was done, in which materials were implanted for 1 or 2 weeks. The results revealed mild inflammation 1 week after implantation, which then diminished to minimal inflammation after 2 weeks. Altogether, this study highlights that unmodified, purified native bacterial cellulose membranes may be used as a cost-effective biomedical device provided that proper endotoxin clearance is achieved.

An objective metric of individual health and aging for population surveys.

BACKGROUND: We have previously developed and validated a biomarker-based metric of overall health status using Mahalanobis distance (DM) to measure how far from the norm of a reference population (RP) an individual's biomarker profile is. DM is not particularly sensitive to the choice of biomarkers; however, this makes comparison across studies difficult. Here we aimed to identify and validate a standard, optimized version of DM that would be highly stable across populations, while using fewer and more commonly measured biomarkers. METHODS: Using three datasets (the Baltimore Longitudinal Study of Aging, Invecchiare in Chianti and the National Health and Nutrition Examination Survey), we selected the most stable sets of biomarkers in all three populations, notably when interchanging RPs across populations. We performed regression models, using a fourth dataset (the Women's Health and Aging Study), to compare the new DM sets to other well-known metrics [allostatic load (AL) and self-assessed health (SAH)] in their association with diverse health outcomes: mortality, frailty, cardiovascular disease (CVD), diabetes, and comorbidity number. RESULTS: A nine- (DM9) and a seventeen-biomarker set (DM17) were identified as highly stable regardless of the chosen RP (e.g.: mean correlation among versions generated by interchanging RPs across dataset of r = 0.94 for both DM9 and DM17). In general, DM17 and DM9 were both competitive compared with AL and SAH in predicting aging correlates, with some exceptions for DM9. For example, DM9, DM17, AL, and SAH all predicted mortality to a similar extent (ranges of hazard ratios of 1.15-1.30, 1.21-1.36, 1.17-1.38, and 1.17-1.49, respectively). On the other hand, DM9 predicted CVD less well than DM17 (ranges of odds ratios of 0.97-1.08, 1.07-1.85, respectively). CONCLUSIONS: The metrics we propose here are easy to measure with data that are already available in a wide array of panel, cohort, and clinical studies. The standardized versions here lose a small amount of predictive power compared to more complete versions, but are nonetheless competitive with existing metrics of overall health. DM17 performs slightly better than DM9 and should be preferred in most cases, but DM9 may still be used when a more limited number of biomarkers is available.