A validated model of passive skeletal muscle to predict force and intramuscular pressure

The passive properties of skeletal muscle are often overlooked in muscle studies, yet they play a key role in tissue function in vivo. Studies analyzing and modeling muscle passive properties, while not uncommon, have never investigated the role of fluid content within the tissue. Additionally, intramuscular pressure (IMP) has been shown to correlate with muscle force in vivo and could be used to predict muscle force in the clinic. In this study, a novel model of skeletal muscle was developed and validated to predict both muscle stress and IMP under passive conditions for the New Zealand White Rabbit tibialis anterior. This model is the first to include fluid content within the tissue and uses whole muscle geometry. A nonlinear optimization scheme was highly effective at fitting model stress output to experimental stress data (normalized mean square error or NMSE fit value of 0.993) and validation showed very good agreement to experimental data (NMSE fit values of 0.955 and 0.860 for IMP and stress, respectively). While future work to include muscle activation would broaden the physiological application of this model, the passive implementation could be used to guide surgeries where passive muscle is stretched.     

Human cell lines for biopharmaceutical manufacturing: history,status, and future perspectives

Biotherapeutic proteins represent a mainstay of treatment for a multitude of conditions, for example, autoimmune disorders, hematologic disorders, hormonal dysregulation, cancers, infectious diseases and genetic disorders. The technologies behind their production have changed substantially since biotherapeutic proteins were first approved in the 1980s. Although most biotherapeutic proteins developed to date have been produced using the mammalian Chinese hamster ovary and murine myeloma (NS0, Sp2/0) cell lines, there has been a recent shift toward the use of human cell lines. One of the most important advantages of using human cell lines for protein production is the greater likelihood that the resulting recombinant protein will bear post-translational modifications (PTMs) that are consistent with those seen on endogenous human proteins. Although other mammalian cell lines can produce PTMs similar to human cells, they also produce non-human PTMs, such as galactose-α1,3-galactose and N-glycolylneuraminic acid, which are potentially immunogenic. In addition, human cell lines are grown easily in a serum-free suspension culture, reproduce rapidly and have efficient protein production. A possible disadvantage of using human cell lines is the potential for human-specific viral contamination, although this risk can be mitigated with multiple viral inactivation or clearance steps. In addition, while human cell lines are currently widely used for biopharmaceutical research, vaccine production and production of some licensed protein therapeutics, there is a relative paucity of clinical experience with human cell lines because they have only recently begun to be used for the manufacture of proteins (compared with other types of cell lines). With additional research investment, human cell lines may be further optimized for routine commercial production of a broader range of biotherapeutic proteins.     

Influence of Phytase Transgenic Corn on the Intestinal Microflora and the Fate of Transgenic DNA and Protein in Digesta and Tissues of Broilers

An experiment was conducted to investigate the effect of phytase transgenic corn (PTC) on intestinal microflora, and the fate of transgenic DNA and protein in the digesta and tissues of broilers. A total of 160 1-day-old Arbor Acres commercial male broilers were randomly assigned to 20 cages (8 chicks per cage) with 10 cages (replicates) for each treatment. Birds were fed with a diet containing either PTC (54.0% during 1–21 days and 61.0% during 22–42 days) or non-transgenic isogenic control corn (CC) for a duration of 42 days. There were no significant differences (P>0.05) between birds fed with the PTC diets and those fed with the CC diets in the quantities of aerobic bacteria, anaerobic bacteria, colibacillus and lactobacilli, or microbial diversities in the contents of ileum and cecum. Transgenic phyA2 DNA was not detected, but phyA2 protein was detected in the digesta of duodenum and jejunum of broilers fed with the PTC diets. Both transgenic phyA2 DNA and protein fragments were not found in the digesta of the ileum and rectum, heart, liver, kidney, and breast or thigh muscles of broilers fed with the PTC diets. It was concluded that PTC had no adverse effect on the quantity and diversity of gut microorganisms; Transgenic phyA2 DNA or protein was rapidly degraded in the intestinal tract and was not transferred to the tissues of broilers.     

How reliable are the Powell–Wetherall plot method and the maximum-length approach? Implications for length-based studies of growth and mortality

Length-based methods are the cornerstone of many population studies and stock assessments. This study tested two widely used methods: the Powell–Wetherall (P–W) plot and the L_max approach (i.e., estimating L_∞ directly from L_max). In most simulations, P–W estimates of the ratio total mortality/growth (Z/K ratio) were biased beyond acceptable limits (bias?>?30%). Bias in Z/K showed a complex behavior, without possible corrections. Estimates of asymptotic length (L_∞) were less biased than Z/K, but were very sensitive to intra-cohort variability in growth and to changes in the occurrence of large individuals in the sample. Exclusion of the largest size classes during the regression procedure or weighing by abundance does not solve these issues. Perfect linearization of the data and extremely narrow confidence intervals for Z/K will lead users to erroneous overconfidence in outputs. Clearly, the P–W method is not suitable for the assessment of Z/K ratios of natural populations. Estimation of L_∞ may be tentatively possible under very specific conditions, with necessary external verifications. Also, this study demonstrates that there is no way to estimate L_∞ directly from L_max, since there is no particular relationship to expect a priori between L_∞ and L_max. Errors in estimating L_∞ directly affect the estimate of the growth constant K and all other subsequent calculations in population studies, stock assessments and ecosystem models. New approaches are urgently needed for length-based studies of body growth (e.g., unconstrained curve fit with subsequent bootstrapping), that consider the inherent uncertainty regarding the underlying data and processes.