Recent advancements in the delivery of therapeutics for retinal diseases include the development of injectable hydrogels, networks of one or more hydrophilic polymers that contain a high-volume fraction of water. These systems are of particular interest due to their biocompatibility, permeability to water-soluble metabolites, and function as minimally invasive injectable delivery vehicles. Recently, hydrogels for ophthalmic applications have been developed that display a controlled release of factors necessary for cellular survival and proliferation. Understanding the relationship between the volume water fraction and the physical, chemical, and diffusion properties of the hydrogel scaffold could aid in the improvement of existing drug delivery treatments for retinal regeneration. In this study, we compared the diffusion and release of human epidermal growth factor (hEGF) encapsulated in different injectable homogenous and heterogenous hydrogels, namely gelatin-hydroxyphenyl propionic acid (Gtn-HPA) and hyaluronic acid-tyramine (HA-Tyr)-based hydrogels. These experimental results were compared with the measured stiffness and water content of these hydrogels and applied to different diffusion theories of polymers to determine the model of best fit. We find that the normalized diffusion and release of hEGF increases with free water content in injectable hydrogels ranging from 0.176 at 41% free water in HA-Tyr to 0.2 at 53% free water in Gtn-HPA, whereas it decreases with hydrogel stiffness 600 Pa for Gtn-HPA and 1440 Pa for HA-Tyr. Further, we compared our experimental data with theoretical diffusion models. We found that homogeneous theoretical models, notably the hydrodynamic model (giving a normalized diffusion close to 0.2), provide the most suitable explanation for the measured solute diffusion coefficient.Near-peer learning at undergraduate level has the potential to introduce students to a career in general practice. The Wass Report suggested the need to provide enthusiastic role models within general practice, and this was the stimulus for the introduction of a near-peer general practitioner (GP) mentoring scheme at University College London (UCL) Medical School.The UCL Medical School GP mentoring scheme was introduced in the academic year of 2019 enlisting UCL GP training scheme doctors to pair up with UCL medical students to meet face-to-face and discuss a career in general practice. Following the end of the scheme, a mix of focus groups and semi-structured interviews were used to provide an insight into the students’ experiences of the mentoring scheme.This evaluation focused on the perceptions, experiences and insights of the medical students who participated in the GP near-peer mentoring scheme and considered their views about a career within general practice. The evaluation considered the students’ perceptions of their trainee as a role-model figure.One common goal of subgroup analyses is to determine the subgroup of the population for which a given treatment is effective. Like most problems in subgroup analyses, this benefiting subgroup identification requires careful attention to multiple testing considerations, especially Type I error inflation. To partially address these concerns, the credible subgroups approach provides a pair of bounding subgroups for the benefiting subgroup, constructed so that with high posterior probability one is contained by the benefiting subgroup while the other contains the benefiting subgroup. To date, this approach has been presented within the Bayesian paradigm only, and requires sampling from the posterior of a Bayesian model. FPH1 Additionally, in many cases, such as regulatory submission, guarantees of frequentist operating characteristics are helpful or necessary. We present Monte Carlo approaches to constructing confidence subgroups, frequentist analogues to credible subgroups that replace the posterior distribution with an estimate of the joint distribution of personalized treatment effect estimates, and yield frequentist interpretations and coverage guarantees. The estimated joint distribution is produced using either draws from asymptotic sampling distributions of estimated model parameters, or bootstrap resampling schemes. The approach is applied to a publicly available dataset from randomized trials of Alzheimer’s disease treatments.

To report our clinical experience with IVUS-guided percutaneous deep vein arterialization (pDVA) to treat chronic critical limb ischemia (cCLI) patients with no-endovascular or surgical options approach due to creation of an arteriovenous fistula (AVF).

In a 2 years period, 14 no-option cCLI patients were treated with percutaneous deep vein arterialization (pDVA) by creating an AVF with a IVUS-guided system between posterior tibial artery and its satellite deep vein. Technical success was defined as successful AVF creation and venous perfusion of the wound site. Patients’ characteristics, procedure details, mortality and wound outcomes were assessed prospectively.

Successful pDVA was successfully performed in all patients (mean age 82 years) without any procedural complications. Clinical improvement was achieved in all patients with resolution of rest pain, tissue formation of granulation tissue or both; only 3 major amputations were performed within the study period with a limb salvage rate of 78%. Median wound healing time was 4.8 months.

pDVA is a safe and feasible revascularization technique alternative in no-option cCLI patients.

pDVA is a safe and feasible revascularization technique alternative in no-option cCLI patients.In reliability theory, diagnostic accuracy, and clinical trials, the quantity P ( X > Y ) + 1 / 2 P ( X = Y ) , also known as the Probabilistic Index (PI), is a common treatment effect measure when comparing two groups of observations. The quantity P ( X > Y ) – P ( Y > X ) , a linear transformation of PI known as the net benefit, has also been advocated as an intuitively appealing treatment effect measure. Parametric estimation of PI has received a lot of attention in the past 40 years, with the formulation of the Uniformly Minimum-Variance Unbiased Estimator (UMVUE) for many distributions. However, the non-parametric Mann-Whitney estimator of the PI is also known to be UMVUE in some situations. To understand this seeming contradiction, in this paper a systematic comparison is performed between the non-parametric estimator for the PI and parametric UMVUE estimators in various settings. We show that the Mann-Whitney estimator is always an unbiased estimator of the PI with univariate, completely observed data, while the parametric UMVUE is not when the distribution is misspecified.