In vitro culture of embryonic mouse intestinal epithelium: cell differentiation and introduction of reporter genes

BACKGROUND: Study of the normal development of the intestinal epithelium has been hampered by a lack of suitable model systems, in particular ones that enable the introduction of exogenous genes. Production of such a system would advance our understanding of normal epithelial development and help to shed light on the pathogenesis of intestinal neoplasia. The criteria for a reliable culture system include the ability to perform real time observations and manipulations in vitro, the preparation of wholemounts for immunostaining and the potential for introducing genes. RESULTS: The new culture system involves growing mouse embryo intestinal explants on fibronectin-coated coverslips in basal Eagle's medium+20% fetal bovine serum. Initially the cultures maintain expression of the intestinal transcription factor Cdx2 together with columnar epithelial (cytokeratin 8) and mesenchymal (smooth muscle actin) markers. Over a few days of culture, differentiation markers appear characteristic of absorptive epithelium (sucrase-isomaltase), goblet cells (Periodic Acid Schiff positive), enteroendocrine cells (chromogranin A) and Paneth cells (lysozyme). Three different approaches were tested to express genes in the developing cultures: transfection, electroporation and adenoviral infection. All could introduce genes into the mesenchyme, but only to a small extent into the epithelium. However the efficiency of adenovirus infection can be greatly improved by a limited enzyme digestion, which makes accessible the lateral faces of cells bearing the Coxsackie and Adenovirus Receptor. This enables reliable delivery of genes into epithelial cells. CONCLUSION: We describe a new in vitro culture system for the small intestine of the mouse embryo that recapitulates its normal development. The system both provides a model for studying normal development of the intestinal epithelium and also allows for the manipulation of gene expression. The explants can be cultured for up to two weeks, they form the full repertoire of intestinal epithelial cell types (enterocytes, goblet cells, Paneth cells and enteroendocrine cells) and the method for gene introduction into the epithelium is efficient and reliable

Greater myofibrillar protein synthesis following weight-bearing activity in obese old compared with non-obese old and young individuals

The mechanisms through which obesity impacts age-related muscle mass regulation are unclear. In the present study, rates of integrated myofibrillar protein synthesis (iMyoPS) were measured over 48-h prior-to and following a 45-min treadmill walk in 10 older-obese (O-OB, body fat[%]: 33 ± 3%), 10 older-non-obese (O-NO, 20 ± 3%), and 15 younger-non-obese (Y-NO, 13 ± 5%) individuals. Surface electromyography was used to determine thigh muscle “activation”. Quadriceps cross-sectional area (CSA), volume, and intramuscular thigh fat fraction (ITFF) were measured by magnetic resonance imaging. Quadriceps maximal voluntary contraction (MVC) was measured by dynamometry. Quadriceps CSA and volume were greater (muscle volume, Y-NO: 1182 ± 232 cm3; O-NO: 869 ± 155 cm3; O-OB: 881 ± 212 cm3, P 0.271). Equivalent muscle mass in O-OB may be explained by the muscle anabolic response to weight-bearing activity, whereas the age-related decline in indices of muscle quality appears to be exacerbated in O-OB and warrants further exploration

A 4-week, lifestyle-integrated, home-based exercise training programme elicits improvements in physical function and lean mass in older men and women: a pilot study

Background: Developing alternative exercise programmes that can alleviate certain barriers to exercise such as psychological, environmental or socio-economical barriers, but provide similar physiological benefits e.g. increases in muscle mass and strength, is of grave importance. This pilot study aimed to assess the efficacy of an unsupervised, 4-week, whole-body home-based exercise training (HBET) programme, incorporated into daily living activities, on skeletal muscle mass, power and strength. Methods: Twelve healthy older volunteers (63±3 years, 7 men: 5 women, BMI: 29±1 kg/m²) carried out the 4-week “lifestyle-integrated” HBET of 8 exercises, 3x12 repetitions each, every day. Before and after HBET, a number of physical function tests were carried out: unilateral leg extension 1-RM (one- repetition maximum), MVC (maximal voluntary contraction) leg extension, lower leg muscle power (via Nottingham Power Rig), handgrip strength and SPPBT (short physical performance battery test). A D3-Creatine method was used for assessment of whole-body skeletal muscle mass, and ultrasound was used to measure the quadriceps cross-sectional area (CSA) and vastus lateralis muscle thickness. Results: Four weeks HBET elicited significant (p<0.05) improvements in leg muscle power (276.7±38.5 vs. 323.4±43.4 W), maximal voluntary contraction (60°: 154.2±18.4 vs. 168.8±15.2 Nm, 90°: 152.1±10.5 vs. 159.1±11.4 Nm) and quadriceps CSA (57.5±5.4 vs. 59.0±5.3 cm2), with a trend for an increase in leg strength (1-RM: 45.7±5.9 vs. 49.6±6.0 kg, P=0.08). This was despite there being no significant differences in whole-body skeletal muscle mass, as assessed via D3-Creatine. Conclusions: This study demonstrates that increases in multiple aspects of muscle function can be achieved in older adults with just 4-weeks of “lifestyle-integrated” HBET, with a cost-effective means. This training mode may prove to be a beneficial alternative for maintaining and/or improving muscle mass and function in older adults

Hyb:A bioinformatics pipeline for the analysis of CLASH (crosslinking, ligation and sequencing of hybrids) data

Peer reviewedPublisher PD

Investigating risk factors and predicting complications in deep brain stimulation surgery with machine learning algorithms

Background: Deep brain stimulation (DBS) surgery is an option for patients experiencing medically resistant neurological symptoms. DBS complications are rare; finding significant predictors requires a large number of surgeries. Machine learning algorithms may be used to effectively predict these outcomes. The aims of this study were to (1) investigate preoperative clinical risk factors, and (2) build machine learning models to predict adverse outcomes. Methods: This multicenter registry collected clinical and demographic characteristics of patients undergoing DBS surgery (n=501) and tabulated occurrence of complications. Logistic regression was used to evaluate risk factors. Supervised learning algorithms were trained and validated on 70% and 30%, respectively, of both oversampled and original registry data. Performance was evaluated using area under the receiver operating characteristics curve (AUC), sensitivity, specificity and accuracy. Results: Logistic regression showed that the risk of complication was related to the operating institution in which the surgery was performed (OR=0.44, confidence interval [CI]=0.25-0.78), BMI (OR=0.94,CI=0.89-0.99) and diabetes (OR=2.33,CI=1.18-4.60). Patients with diabetes were almost three times more likely to return to the operating room (OR=2.78,CI=1.31-5.88). Patients with a history of smoking were four times more likely to experience postoperative infection (OR=4.20,CI=1.21-14.61). Supervised learning algorithms demonstrated high discrimination performance when predicting any complication (AUC=0.86), a complication within 12 months (AUC=0.91), return to the operating room (AUC=0.88) and infection (AUC=0.97). Age, BMI, procedure side, gender and a diagnosis of Parkinson’s disease were influential features. Conclusions: Multiple significant complication risk factors were identified and supervised learning algorithms effectively predicted adverse outcomes in DBS surgery

A data analysis framework to rank HGV drivers

We report on the details of the methodology applied to support shortlisting the nominees for the Microlise Driver of the Year awards. The aim was to recognise the United Kingdom’s most talented heavy goods vehicle (HGV) drivers, with the list of top 46 drivers across 16 different companies determined through the analysis of telematics data. Initial data for the awards was gathered from over 90,000 drivers engaging with Microlise’s telematics solutions. The data was analysed anonymously in order to identify the best criteria to establish top performing drivers. The initial selection was made based on a minimum number of miles driven across each of the four quarters in 2014. Outlier removal and a consensus clustering framework were subsequently employed to the dataset to identify subgroups of drivers. Three categories of drivers were identified: short, medium and long distance drivers. Each qualifying professional belonging to one of the three categories was then assessed using a range of criteria compared to other drivers from the same category. To determine the final winners, questionnaires for further evidence and indicators that might contribute to a driver being named as a winner was sent down to employers and their responses were evaluated