Comparison between tension band and cerclage with X-Plate and lag screws in treatment of comminuted patellar fractures

BackgroundComminuted patellar fractures are not rare, and the ideal treatment method remains controversial. The present study was conducted to evaluate effects and compare complications of two different methods used to treat comminuted patellar fractures.MethodsFrom March 2010 to August 2016, 102 cases of 34-C2 or 34-C3 comminuted patellar fractures were treated at our hospital, wherein patients received two different treatments: titanium cable tension band with cerclage method (group A) and intrafragmentary screws with X-shaped plating technique (group B). At follow-ups, articular step-off, range of motion (ROM), Lysholm scores, time of union, and complications were recorded and analyzed. Radiographic and clinical data as well as rate of complications were statistically analyzed.ResultsIn total, 87 patients were included in the final analysis (n = 47 in group A and n = 40 in group B). No significant differences were noted in terms of cost of implant, age, gender, rate of 34-C3 fractures, rate of layered inferior pole fractures, postoperative articular step-off and union time. At 2-year follow-up, average Lysholm scores, ROM and rate of complications were (89.0 ± 4.5), (122°±12°) and (27.7%) in group A and (90.2 ± 3.9), (124°±11°) and (17.5%) in group B, respectively, with no significant differences (p > 0.05). The mean time of surgery in group B was shorter than that in group A with significant difference (p < 0.05).ConclusionsTreatment using the intrafragmentary screws and plate method for amenable comminuted patellar fractures achieved similar complication rate and favorable functional outcomes at the 2-year follow-up, which was comparable to the titanium cable tension band with cerclage method. Thus, the intrafragmentary screws and plate method is effective, safe and convenient for 34-C2/C3 comminuted patellar fractures, especially appropriate for patients with layered fragments.     

Engraftable neural crest stem cells derived from cynomolgus monkey embryonic stem cells

Neural crest stem cells (NCSCs), a population of multipotent cells that migrate extensively and give rise to diverse derivatives, including peripheral and enteric neurons and glia, craniofacial cartilage and bone, melanocytes and smooth muscle, have great potential for regenerative medicine. Non-human primates provide optimal models for the development of stem cell therapies. Here, we describe the first derivation of NCSCs from cynomolgus monkey embryonic stem cells (CmESCs) at the neural rosette stage. CmESC-derived neurospheres replated on polyornithine/laminin-coated dishes migrated onto the substrate and showed characteristic expression of NCSC markers, including Sox10, AP2α, Slug, Nestin, p75, and HNK1. CmNCSCs were capable of propagating in an undifferentiated state in vitro as adherent or suspension cultures, and could be subsequently induced to differentiate towards peripheral nervous system lineages (peripheral sympathetic neurons, sensory neurons, and Schwann cells) and mesenchymal lineages (osteoblasts, adipocytes, chondrocytes, and smooth muscle cells). CmNCSCs transplanted into developing chick embryos or fetal brains of cynomolgus macaques survived, migrated, and differentiated into progeny consistent with a neural crest identity. Our studies demonstrate that CmNCSCs offer a new tool for investigating neural crest development and neural crest-associated human disease and suggest that this non-human primate model may facilitate tissue engineering and regenerative medicine efforts.     

Deep Phenotyping and Prediction of Long-term Cardiovascular Disease: Optimized by Machine Learning

BackgroundPrediction of cardiovascular disease (CVD) is important in clinical practice. Machine learning (ML) may offer an improved alternative to current CVD risk stratification in individual patients. We aim to identify important predictors and compare ML models with traditional models according to their prediction performance in a large long-term follow-up cohort.MethodsThe Atherosclerosis Risk in Communities (ARIC) study was designed to study the progression of subclinical disease to cardiovascular events over a 25-year follow-up period. All phenotypic variables at visit 1 were obtained. All-cause death, CVD, and coronary heart disease were the outcomes for analysis. The ML framework involved variable selection using the random survival forest (RSF) method, model building, and 5-fold cross-validation. Model performance was evaluated by discrimination using the Harrell concordance index (C-index), accuracy using the Brier score (BS), and interpretability using the number of variables in the model.ResultsOf the 14,842 participants in ARIC, the average age was 54.2 years, with 45.2% male and 26.2% Black participants. Thirty-eight unique variables were selected in the RSF top 20 importance ranking of all 6 outcomes. Aging, hypertension, glucose metabolism, renal function, coagulation, adiposity, and sodium retention dominated the predictions of all outcomes. The ML models outperformed the regression models and established risk scores with a higher C-index, lower BS, and varied interpretability.ConclusionsThe ML framework is useful for identifying important predictors of CVD and for developing models with robust performance compared with existing risk models.