Influence of PLLA/PCL/HA scaffold fiber orientation on mechanical properties and osteoblast behavior

Scaffolds based on aligned and non-aligned poly (L-lactic acid) (PLLA)/polycaprolactone (PCL) fibers obtained by electrospinning, associated to electrosprayed hydroxyapatite (HA) for tissue engineering applications were developed and their performance was compared in terms of their morphology and biological and mechanical behaviors. The morphological results assessed by scanning electron microscopy showed a mesh of PLLA/PCL fibers (random and perfectly aligned) associated with aggregates of nanophased HA. Fourier transform infrared spectrometry confirmed the homogeneity in the blends and the presence of nanoHA in the scaffold. As a result of fiber alignment a 15-fold increase in Young's Modulus and an 8-fold increase in tensile strength were observed when compared to non-aligned fibers. In PLLA/PCL/HA scaffolds, the introduction of nanoHA caused a remarkable improvement of the mechanical strength of this material acting as a reinforcement, enhancing the response of these constructs to tensile stress. In vitro testing was evaluated using osteoblast (MC3T3-E1) cells. The results showed that both fibrous scaffolds were able to support osteoblast cell adhesion and proliferation and that fiber alignment induced increased cellular metabolic activity. In addition, the adhesion and proliferation of Staphylococcus aureus were evaluated and a lower number of colony forming units (CFUs) was obtained in the scaffolds with aligned fibers.Project UID/BIM/04293/2019 by FCT/MCTES through Portuguese Funds

A Novel Plant-Based Protein Has Similar Effects Compared to Whey Protein on Body Composition, Strength, Power, and Aerobic Performance in Professional and Semi-Professional Futsal Players

IntroductionThe effects of dietary protein on body composition and physical performance seemingly depend on the essential amino acid profile of the given protein source, although controversy exists about whether animal protein sources may possess additional anabolic properties to plant-based protein sources. PurposeTo compare the effects of a novel plant-based protein matrix and whey protein supplementation on body composition, strength, power, and endurance performance of trained futsal players. MethodsFifty male futsal players were followed during 8 weeks of supplementation, with 40 completing the study either with plant-based protein (N = 20) or whey protein (N = 20). The following measures were assessed: bone mineral content, lean body mass, and fat mass; muscle thickness of the rectus femoris; total body water; blood glucose, hematocrit, C-reactive protein, aspartate aminotransferase, alanine aminotransferase, creatine kinase, creatinine, and estimated glomerular filtration rate; salivary cortisol; maximal strength and 1-RM testing of the back squat and bench press exercises; muscle power and countermovement jump; VO2max and maximal aerobic speed. Subjects were asked to maintain regular dietary habits and record dietary intake every 4 weeks through 3-day food records. ResultsNo differences in any variable were observed between groups at baseline or pre- to post-intervention. Moreover, no time*group interaction was observed in any of the studied variables, and a time effect was only observed regarding fat mass reduction. ConclusionsSupplementing with either a novel plant-based protein matrix or whey protein did not affect any of the variables assessed in high-level futsal players over 8 wks. These results suggest that whey protein does not possess any unique anabolic properties over and above those of plant-based proteins when equated to an essential amino acid profile in the population studied. Furthermore, when consuming a daily protein intake >1.6 g/kg BW.day(-1), additional protein supplementation does not affect body composition or performance in trained futsal players, regardless of protein type/source

Predictors of linkage to care following community-based HIV counseling and testing in rural Kenya

Despite innovations in HIV counseling and testing (HCT), important gaps remain in understanding linkage to care. We followed a cohort diagnosed with HIV through a community-based HCT campaign that trained persons living with HIV/AIDS (PLHA) as navigators. Individual, interpersonal, and institutional predictors of linkage were assessed using survival analysis of self-reported time to enrollment. Of 483 persons consenting to follow-up, 305 (63.2%) enrolled in HIV care within 3 months. Proportions linking to care were similar across sexes, barring a sub-sample of men aged 18–25 years who were highly unlikely to enroll. Men were more likely to enroll if they had disclosed to their spouse, and women if they had disclosed to family. Women who anticipated violence or relationship breakup were less likely to link to care. Enrolment rates were significantly higher among participants receiving a PLHA visit, suggesting that a navigator approach may improve linkage from community-based HCT campaigns.Vestergaard Frandse

Evaluation of random forest and ensemble methods at predicting complications following cardiac surgery

Cardiac patients undergoing surgery face increased risk of postoperative complications, due to a combination of factors, including higher risk surgery, their age at time of surgery and the presence of co-morbid conditions. They will therefore require high levels of care and clinical resources throughout their perioperative journey (i.e. before, during and after surgery). Although surgical mortality rates in the UK have remained low, postoperative complications on the other hand are common and can have a significant impact on patients’ quality of life, increase hospital length of stay and healthcare costs. In this study we used and compared several machine learning methods – random forest, AdaBoost, gradient boosting model and stacking – to predict severe postoperative complications after cardiac surgery based on preoperative variables obtained from a surgical database of a large acute care hospital in Scotland. Our results show that AdaBoost has the best overall performance (AUC = 0.731), and also outperforms EuroSCORE and EuroSCORE II in other studies predicting postoperative complications. Random forest (Sensitivity = 0.852, negative predictive value = 0.923), however, and gradient boosting model (Sensitivity = 0.875 and negative predictive value = 0.920) have the best performance at predicting severe postoperative complications based on sensitivity and negative predictive value

An updated PREDICT breast cancer prognostication and treatment benefit prediction model with independent validation

Background PREDICT is a breast cancer prognostic and treatment benefit model implemented online. The overall fit of the model has been good in multiple independent case series, but PREDICT has been shown to underestimate breast cancer specific mortality in women diagnosed under the age of 40. Another limitation is the use of discrete categories for tumour size and node status resulting in ‘step’ changes in risk estimates on moving between categories. We have refitted the PREDICT prognostic model using the original cohort of cases from East Anglia with updated survival time in order to take into account age at diagnosis and to smooth out the survival function for tumour size and node status. Methods Multivariable Cox regression models were used to fit separate models for ER negative and ER positive disease. Continuous variables were fitted using fractional polynomials and a smoothed baseline hazard was obtained by regressing the baseline cumulative hazard for each patients against time using fractional polynomials. The fit of the prognostic models were then tested in three independent data sets that had also been used to validate the original version of PREDICT. Results In the model fitting data, after adjusting for other prognostic variables, there is an increase in risk of breast cancer specific mortality in younger and older patients with ER positive disease, with a substantial increase in risk for women diagnosed before the age of 35. In ER negative disease the risk increases slightly with age. The association between breast cancer specific mortality and both tumour size and number of positive nodes was non-linear with a more marked increase in risk with increasing size and increasing number of nodes in ER positive disease. The overall calibration and discrimination of the new version of PREDICT (v2) was good and comparable to that of the previous version in both model development and validation data sets. However, the calibration of v2 improved over v1 in patients diagnosed under the age of 40. Conclusions The PREDICT v2 is an improved prognostication and treatment benefit model compared with v1. The online version should continue to aid clinical decision making in women with early breast cancer.The BCOS was funded by the Netherlands Cancer Institute (NKI2007-3839). Funding for the POSH study was provided by Cancer Research UK (C1275/A9896, C1275/A11699, and C1275/A15956) and Breast Cancer Now (2005Nov63). PDPP is supported by the National Institute for Health Research Biomedical Research Centre at the University of Cambridge