Femtosecond Laser–Assisted Deep Anterior Lamellar Keratoplasty for Keratoconus: Multi-surgeon Results

PURPOSE: To compare the clinical outcomes in femtosecond laser–assisted deep anterior lamellar keratoplasty (F-DALK) to manual non-laser deep anterior lamellar keratoplasty (M-DALK) for keratoconus in a multi-surgeon public healthcare setting. DESIGN: Single-center, comparative, retrospective interventional case series. METHODS: Population: Consecutive cases of keratoconus treated with big-bubble F-DALK from August 1, 2015, to September 1, 2018 and big-bubble M-DALK from September 1, 2012, to September 30, 2016. Setting: Moorfields Eye Hospital, London. Observations: Data on preoperative status, operative details, intraoperative and postoperative complications, secondary interventions, and visual outcomes were archived on a customized spreadsheet for analysis. Main Outcome Measures: Rate of intraoperative perforation and conversion to penetrating keratoplasty (PK) and the percentage of patients, post removal of sutures (ROS), with corrected distance visual acuity (CDVA) ≥20/40. RESULTS: We analyzed 58 eyes of 55 patients who underwent F-DALK and 326 eyes of 309 patients who underwent M-DALK. Intraoperative perforation of Descemet membrane occurred in 15 of 58 (25.9%) F-DALK cases compared to 148 of 326 (45.4%) M-DALK cases (P = .006). Intraoperative conversion to PK was carried out in 2 of 58 (3.4%) F-DALK cases compared to 80 of 326 (24.5%) M-DALK cases (P = .001). Post ROS, 86.5% of F-DALK eyes had a CDVA of ≥20/40 (15 ± 7.3 months after surgery) compared to 83.7% of M-DALK eyes (24.9 ± 10.6 months) (P = .825). CONCLUSION: Laser automation of some steps in DALK for keratoconus may reduce the rate of intraoperative Descemet perforation and the conversion to PK in a multi-surgeon setting

Amplification of simian retroviral sequences from human recipients of baboon liver transplants

Investigations into the use of baboons as organ donors for human transplant recipients, a procedure called xenotransplantation, have raised the specter of transmitting baboon viruses to humans and possibly establishing new human infectious diseases. Retrospective analysis of tissues from two human transplant recipients with end-stage hepatic disease who died 70 and 27 days after the transplantation of baboon livers revealed the presence of two simian retroviruses of baboon origin, simian foamy virus (SFV) and baboon endogenous virus (BaEV), in multiple tissue compartments. The presence of baboon mitochondrial DNA was also detected in these same tissues, suggesting that xenogeneic 'passenger leukocytes' harboring latent or active viral infections had migrated from the xenografts to distant sites within the human recipients. The persistence of SFV and BaEV in human recipients throughout the posttransplant period underscores the potential infectious risks associated with xenotransplantation

New Insights into the Structure of Nanoporous Carbons from NMR, Raman, and Pair Distribution Function Analysis

The structural characterization of nanoporous carbons is a challenging task as they generally lack long-range order and can exhibit diverse local structures. Such characterization represents an important step toward understanding and improving the properties and functionality of porous carbons, yet few experimental techniques have been developed for this purpose. Here we demonstrate the application of nuclear magnetic resonance (NMR) spectroscopy and pair distribution function (PDF) analysis as new tools to probe the local structures of porous carbons, alongside more conventional Raman spectroscopy. Together, the PDFs and the Raman spectra allow the local chemical bonding to be probed, with the bonding becoming more ordered for carbide-derived carbons (CDCs) synthesized at higher temperatures. The ring currents induced in the NMR experiment (and thus the observed NMR chemical shifts for adsorbed species) are strongly dependent on the size of the aromatic carbon domains. We exploit this property and use computer simulations to show that the carbon domain size increases with the temperature used in the carbon synthesis. The techniques developed here are applicable to a wide range of porous carbons and offer new insights into the structures of CDCs (conventional and vacuum-annealed) and coconut shell-derived activated carbons.A.C.F., J.M.G., C.M., P.K.A, E.K.H., and C.P.G. acknowledge the Sims Scholarship (A.C.F.), EPSRC (via the Supergen consortium, J.M.G.), and the EU ERC (via an Advanced Fellowship to C.P.G.) for funding. C.M. and P.K.A. acknowledge the School of the Physical Sciences of the University of Cambridge for funding through an Oppenheimer Research Fellowship. P.K.A. acknowledges a Junior Research Fellowship from Gonville and Caius College, Cambridge. A.C.F. and J.M.G. thank the NanoDTC Cambridge for travel funding. M.A., M.Z., and V.P. acknowledge funding from the German Federal Ministry for Research and Education (BMBF) in support of the nanoEES3D project (Award Number 03EK3013) as part of the strategic funding initiative energy storage framework and kindly thank Prof. Arzt (INM) for his continuing support. This research used resources of the Advanced Photon Source, a U.S. Department of Energy (DOE) Office of Science User Facility operated for the DOE Office of Science by Argonne National Laboratory under Contract No. DE-AC02-06CH11357. We thank Daan Frenkel for his contributions to this work and Boris Dyatkin for comments on the manuscript.This is the author accepted manuscript. The final version is available from the American Chemical Society via http://dx.doi.org/10.1021/acs.chemmater.5b0321

The SNAPSHOT study protocol : SNAcking, Physical activity, Self-regulation, and Heart rate Over Time

Peer reviewedPublisher PD

The identification of informative genes from multiple datasets with increasing complexity

Background In microarray data analysis, factors such as data quality, biological variation, and the increasingly multi-layered nature of more complex biological systems complicates the modelling of regulatory networks that can represent and capture the interactions among genes. We believe that the use of multiple datasets derived from related biological systems leads to more robust models. Therefore, we developed a novel framework for modelling regulatory networks that involves training and evaluation on independent datasets. Our approach includes the following steps: (1) ordering the datasets based on their level of noise and informativeness; (2) selection of a Bayesian classifier with an appropriate level of complexity by evaluation of predictive performance on independent data sets; (3) comparing the different gene selections and the influence of increasing the model complexity; (4) functional analysis of the informative genes. Results In this paper, we identify the most appropriate model complexity using cross-validation and independent test set validation for predicting gene expression in three published datasets related to myogenesis and muscle differentiation. Furthermore, we demonstrate that models trained on simpler datasets can be used to identify interactions among genes and select the most informative. We also show that these models can explain the myogenesis-related genes (genes of interest) significantly better than others (P < 0.004) since the improvement in their rankings is much more pronounced. Finally, after further evaluating our results on synthetic datasets, we show that our approach outperforms a concordance method by Lai et al. in identifying informative genes from multiple datasets with increasing complexity whilst additionally modelling the interaction between genes. Conclusions We show that Bayesian networks derived from simpler controlled systems have better performance than those trained on datasets from more complex biological systems. Further, we present that highly predictive and consistent genes, from the pool of differentially expressed genes, across independent datasets are more likely to be fundamentally involved in the biological process under study. We conclude that networks trained on simpler controlled systems, such as in vitro experiments, can be used to model and capture interactions among genes in more complex datasets, such as in vivo experiments, where these interactions would otherwise be concealed by a multitude of other ongoing events

Investigating Conceptual Models for the Relationship Between Depression and Condomless Sex Among Gay, Bisexual, and Other Men Who have Sex with Men: Using Structural Equation Modelling to Assess Mediation

The aim of this study is to investigate five hypothesized mechanisms of causation between depression and condomless sex with ≥ 2 partners (CLS2+) among gay, bisexual, and other men who have sex with men (GBMSM), involving alternative roles of self-efficacy for sexual safety and recreational drug use. Data were from the AURAH cross-sectional study of 1340 GBMSM attending genitourinary medicine clinics in England (2013–2014). Structural equation modelling (SEM) was used to investigate which conceptual model was more consistent with the data. Twelve percent of men reported depression (PHQ-9 ≥ 10) and 32% reported CLS2+ in the past 3 months. AURAH data were more consistent with the model in which depression was considered to lead to CLS2+ indirectly via low self-efficacy for sexual safety (indirect Beta = 0.158; p < 0.001) as well as indirectly via higher levels of recreational drug use (indirect Beta = 0.158; p < 0.001). SEM assists in understanding the relationship between depression and CLS among GBMSM

A modified Delphi process to establish research priorities in hernia surgery

Funding British Hernia Society.Peer reviewedPublisher PD

Design and feasibility testing of a novel group intervention for young women who binge drink in groups

BackgroundYoung women frequently drink alcohol in groups and binge drinking within these natural drinking groups is common. This study describes the design of a theoretically and empirically based group intervention to reduce binge drinking among young women. It also evaluates their engagement with the intervention and the acceptability of the study methods.MethodsFriendship groups of women aged 18–35 years, who had two or more episodes of binge drinking (>6 UK units on one occasion; 48g of alcohol) in the previous 30 days, were recruited from the community. A face-to-face group intervention, based on the Health Action Process Approach, was delivered over three sessions. Components of the intervention were woven around fun activities, such as making alcohol free cocktails. Women were followed up four months after the intervention was delivered. Results The target of 24 groups (comprising 97 women) was recruited. The common pattern of drinking was infrequent, heavy drinking (mean consumption on the heaviest drinking day was UK 18.1 units). Process evaluation revealed that the intervention was delivered with high fidelity and acceptability of the study methods was high. The women engaged positively with intervention components and made group decisions about cutting down. Twenty two groups set goals to reduce their drinking, and these were translated into action plans. Retention of individuals at follow up was 87%.ConclusionsThis study successfully recruited groups of young women whose patterns of drinking place them at high risk of acute harm. This novel approach to delivering an alcohol intervention has potential to reduce binge drinking among young women. The high levels of engagement with key steps in the behavior change process suggests that the group intervention should be tested in a full randomised controlled trial