Camelid single-domain antibody fragments (“nanobodies”) provide the remarkable specificity of antibodies within a single 15 kDa immunoglobulin VHH domain. This unique feature has enabled applications ranging from use as biochemical tools to therapeutic agents. Nanobodies have emerged as especially useful tools in protein structural biology, facilitating studies of conformationally dynamic proteins such as G protein-coupled receptors (GPCRs). Nearly all nanobodies available to date have been obtained by animal immunization, a bottleneck restricting many applications of this technology. To solve this problem, we report a fully in vitro platform for nanobody discovery based on yeast surface display. We provide a blueprint for identifying nanobodies, demonstrate the utility of the library by crystallizing a nanobody with its antigen, and most importantly, we utilize the platform to discover conformationally-selective nanobodies to two distinct human GPCRs. To facilitate broad deployment of this platform, the library and associated protocols are freely available for non-profit research.
Purpose To quantitatively characterize macrophage-like cells (MLCs) at the vitreoretinal interface in different severity stages of diabetic retinopathy (DR) using optical coherence tomography angiography (OCTA). Methods The study included 72 eyes of 72 subjects: 18 healthy controls, 22 diabetes mellitus (DM) without DR, 17 nonproliferative DR (NPDR), and 15 proliferative DR (PDR). We obtained repeated (average, 6.5; range, 3–10) macular OCTA scans for each eye. We registered and averaged the 3-µm OCT slab above the vitreoretinal interface to visualize MLCs. Using a semiautomated method, we binarized and quantified MLCs and compared MLC densities among groups. We also evaluated MLC distribution relative to underlying superficial capillary plexus vasculature and quantified MLCs overlying blood vessels within the perivascular 30-µm watershed region and within ischemic zones (defined as >30 µm from the nearest vessel). Results MLC density was 2.8- to 3.8-fold higher in PDR compared with all other groups ( P < 0.05 for all). MLC density in PDR was most increased in perivascular areas (3.3- to 4.2-fold; P < 0.05 vs. all) and on blood vessels (3.0- to 4.0-fold; P < 0.05 vs. all), and elevated to a lesser extent in ischemic areas (2.3- to 3.4-fold; P < 0.05 vs. all). MLCs were more likely to localize on blood vessels in DM without DR, NPDR, and PDR ( P < 0.05 for all), but not healthy eyes. Conclusions MLC density was significantly increased in PDR. MLCs clustered on blood vessels in diabetic but not in healthy eyes. Further studies are needed to confirm the origin, identity, and function of MLCs during DR.
Purpose To identify objective optical coherence tomography angiography (OCTA) parameters that characterize the spectrum of non-proliferative diabetic retinopathy (NPDR), especially those that distinguish moderate from severe NPDR. Methods Sixty eyes of 60 patients with treatment-naïve NPDR (mild: 21, moderate: 21, severe: 18), 23 eyes with diabetes and no retinopathy, and 24 healthy control eyes were enrolled. OCTA slabs were segmented into superficial (SCP), middle (MCP), and deep capillary plexus (DCP) and thresholded by a new method based on DCP skeletonized vessel length. The foveal avascular zone (FAZ) area, parafoveal vessel density (VD), and adjusted flow index (AFI) from all three capillary layers and the vessel length density (VLD) of the SCP were compared between each severity group, after adjusting for age and image quality. Results All vessel density markers decreased with increasing severity of NPDR. SCP VD and VLD demonstrated significant differences between eyes with diabetes with no retinopathy and mild NPDR ( p = 0.001 and p < 0.001, respectively), as well as between moderate vs. severe NPDR ( p = 0.004 and p = 0.009, respectively). MCP VD significantly decreased between moderate and severe NPDR ( p = 0.01). AFI significantly increased in the SCP and showed a decreasing trend in the MCP and DCP with increasing NPDR severity. Conclusions Changes in the SCP VD, SCP VLD, and MCP VD can distinguish severe NPDR from lower-risk stages. SCP changes may be more reliable due to their lower susceptibility to noise and projection artifacts. Thresholding OCTA images based on DCP skeletonized vessel length showed less variability in moderate and severe NPDR. Additional studies are warranted to validate this new thresholding method.
OBJECTIVE: To create a prediction model for external cephalic version (ECV) success using objective patient characteristics. METHODS: This retrospective study included pregnant individuals of at least 18 years of age with a nonanomalous, singleton gestation who underwent an ECV attempt between 2006 and 2016 at a single quaternary care hospital. Variables assessed included maternal age, height, weight, body mass index (BMI), parity, fetal sex, gestational age, estimated fetal weight, type of fetal malpresentation, placental location, and amniotic fluid volume. Univariable and multivariable logistic regression models were used to determine the association of patient characteristics with ECV success. Estimated odds ratios and corresponding 95% CIs were calculated for each variable, and backward elimination and bootstrapping were used to find a parsimonious model for ECV success with the highest discriminatory capacity (as determined by the area under the receiver operating characteristic curve [AUC]). This model was evaluated with a calibration curve across deciles of success. RESULTS: A total of 1,138 individuals underwent an ECV attempt and were included in this analysis. The overall ECV success frequency was 40.6%. Factors significantly associated with ECV success were maternal age, parity, placental location, estimated fetal weight, and type of fetal malpresentation. A final model with BMI, parity, placental location, and type of fetal malpresentation had the highest AUC (0.667 [95% CI 0.634–0.701]), resulted in good calibration, and is represented by the following equation: 1/[1+e-x] where x=1.1726–0.0314 (BMI)−0.9299 (nulliparity)+1.0218 (transverse or oblique presentation at ECV)−0.5113 (anterior placenta). An interactive version of this equation was created and can be accessed at www.ecvcalculator.com. CONCLUSION: A prediction model that estimates the probability of ECV success was created and internally validated. This model incorporates easily obtainable and objective patient factors known before ECV and may be used in decision making and patient counseling about ECV.
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