A new biomaterial, a degradable thermoset polymer, was made from simple, economical, biocompatable monomers without the need for a catalyst. Glycerol and citric acid, non-toxic and renewable reagents, were crosslinked by a melt polymerization reaction at temperatures from 90-150°C. Consistent with a condensation reaction, water was determined to be the primary byproduct. The amount of crosslinking was controlled by the reaction conditions, including temperature, reaction time, and ratio between glycerol and citric acid. Also, the amount of crosslinking was inversely proportional to the rate of degradation. As a proof-of-principle for drug delivery applications, gentamicin, an antibiotic, was incorporated into the polymer with preliminary evaluations of antimicrobial activity. The polymers incorporating gentamicin had significantly better bacteria clearing of Staphylococcus aureus compared to non-gentamicin gels for up to nine days.

The melt polymerization of diglycerol with bicyclic anhydride monomers derived from a naturally occurring monoterpene provides an avenue for polyesters with a high degree of sustainability. The hydrophobic anhydrides are synthesized at ambient temperature via a solvent-free Diels-Alder reaction of α-phellandrene with maleic anhydride. Subsequent melt polymerizations with tetra-functional diglycerol are effective under a range of [diglycerol]/[anhydride] ratios. The hydrophobicity of α-phellandrene directly impacts the swelling behavior of the resulting polyesters. The low E factors (<2), large amount of bio-based content (>75%), ambient temperature monomer synthesis, and polymer degradability represent key factors in the design of these sustainable polyesters.

Liver transplantation is the only curative treatment for end-stage liver disease and some forms of liver malignancy, but organ donation has not grown at par with the number of patients in need for liver transplantation. In the United States, there were 8250 liver transplants performed in 2018, yet 12 820 candidates remained on the waiting list by the end of the year. 1 This organ shortage has put living

Background:The increasingly favorable outcomes of live donor liver transplant warrant development of screening techniques to expand current donor pool. Transient elastography (TE) with controlled attenuation parameter (CAP) is accessible and has promising diagnostic performance in non-obese individuals. Here, we demonstrate its utility in grading donor steatosis for risk assessment in living liver donors (LLD). Study Design:In a prospective study of LLD and recipients, accuracy was determined using MRI-derived proton density fat fraction (PDFF) as reference.Results: One hundred and one LLD underwent TE, 95 of whom had available PDFF.Median CAP and MRI-PDFF were 233 dB/m (206-270) and 2.9% (2.3-4.0), respectively. A CAP threshold of 270 dB/m captured all steatosis which was present in 13 (13%) LLD (AUROC .942, 100% sensitivity and 83% specificity). Performance further improved when excluding obese LLD and limiting analysis to M-probe (AUROC .971 and .974, respectively, with 87% specificity). There was no difference in CAP and MRI-PDFF between LLD and nondonors (P = .26 and .21, respectively). Early allograft dysfunction was observed in one recipient (CAP 316, PDFF 9.5%), zero underwent retransplant, and one died from sepsis. Conclusion:The specific role of CAP in living liver donation warrants further study, beginning with its use as screening tool across peripheral clinics.

Anterior cruciate ligament (ACL) is one of the most common injuries associated with sports. Knee osseous morphology can play a role in increased knee instability. Our hypothesis is that the morphological features of the knee, as seen in knee osseous morphology, can contribute to increased knee instability and, thus, increase the likelihood of ACL tear. To test this relationship, it is necessary to segment the femur and tibia bones and extract relevant imaging features. However, manual annotation of 3D medical images, such as on magnetic resonance imaging (MRI) scans, can be a time-consuming and challenging task. In this work, we propose an automated pipeline for creating pseudo-masks of the femur and tibia bones in knee MRI. Our approach involves unsupervised segmentation and deep learning models to classify ACL integrity (intact or torn). Our results demonstrate a high agreement between the automated pseudo-masks and a radiologist's manual segmentation, which also leads to comparable AUC values for the ACL integrity classification.