Retinal Neuroprotective Effects of Flibanserin, an FDA-Approved Dual Serotonin Receptor Agonist-Antagonist

Purpose: To assess the neuroprotective effects of flibanserin (formerly BIMT-17), a dual 5-HT1A agonist and 5-HT2A antagonist, in a light-induced retinopathy model. Methods: Albino BALB/c mice were injected intraperitoneally with either vehicle or increasing doses of flibanserin ranging from 0.75 to 15 mg/kg flibanserin. To assess 5-HT1A-mediated effects, BALB/c mice were injected with 10 mg/kg WAY 100635, a 5-HT1A antagonist, prior to 6 mg/kg flibanserin and 5-HT1A knockout mice were injected with 6 mg/kg flibanserin. Injections were administered once immediately prior to light exposure or over the course of five days. Light exposure lasted for one hour at an intensity of 10,000 lux. Retinal structure was assessed using spectral domain optical coherence tomography and retinal function was assessed using electroretinography. To investigate the mechanisms of flibanserin-mediated neuroprotection, gene expression, measured by RT-qPCR, was assessed following five days of daily 15 mg/kg flibanserin injections. Results: A five-day treatment regimen of 3 to 15 mg/kg of flibanserin significantly preserved outer retinal structure and function in a dose-dependent manner. Additionally, a single-day treatment regimen of 6 to 15 mg/kg of flibanserin still provided significant protection. The action of flibanserin was hindered by the 5-HT1A antagonist, WAY 100635, and was not effective in 5-HT1A knockout mice. Creb, c-Jun, c-Fos, Bcl-2, Cast1, Nqo1, Sod1, and Cat were significantly increased in flibanserin-injected mice versus vehicle-injected mice. Conclusions: Intraperitoneal delivery of flibanserin in a light-induced retinopathy mouse model provides retinal neuroprotection. Mechanistic data suggests that this effect is mediated through 5-HT1A receptors and that flibanserin augments the expression of genes capable of reducing mitochondrial dysfunction and oxidative stress. Since flibanserin is already FDA-approved for other indications, the potential to repurpose this drug for treating retinal degenerations merits further investigation

A generalized framework to predict continuous scores from medical ordinal labels

Many variables of interest in clinical medicine, like disease severity, are recorded using discrete ordinal categories such as normal/mild/moderate/severe. These labels are used to train and evaluate disease severity prediction models. However, ordinal categories represent a simplification of an underlying continuous severity spectrum. Using continuous scores instead of ordinal categories is more sensitive to detecting small changes in disease severity over time. Here, we present a generalized framework that accurately predicts continuously valued variables using only discrete ordinal labels during model development. We found that for three clinical prediction tasks, models that take the ordinal relationship of the training labels into account outperformed conventional multi-class classification models. Particularly the continuous scores generated by ordinal classification and regression models showed a significantly higher correlation with expert rankings of disease severity and lower mean squared errors compared to the multi-class classification models. Furthermore, the use of MC dropout significantly improved the ability of all evaluated deep learning approaches to predict continuously valued scores that truthfully reflect the underlying continuous target variable. We showed that accurate continuously valued predictions can be generated even if the model development only involves discrete ordinal labels. The novel framework has been validated on three different clinical prediction tasks and has proven to bridge the gap between discrete ordinal labels and the underlying continuously valued variables

Photography-based taxonomy is inadequate, unnecessary, and potentially harmful for biological sciences

The question whether taxonomic descriptions naming new animal species without type specimen(s) deposited in collections should be accepted for publication by scientific journals and allowed by the Code has already been discussed in Zootaxa (Dubois & Nemésio 2007; Donegan 2008, 2009; Nemésio 2009a–b; Dubois 2009; Gentile & Snell 2009; Minelli 2009; Cianferoni & Bartolozzi 2016; Amorim et al. 2016). This question was again raised in a letter supported by 35 signatories published in the journal Nature (Pape et al. 2016) on 15 September 2016. On 25 September 2016, the following rebuttal (strictly limited to 300 words as per the editorial rules of Nature) was submitted to Nature, which on 18 October 2016 refused to publish it. As we think this problem is a very important one for zoological taxonomy, this text is published here exactly as submitted to Nature, followed by the list of the 493 taxonomists and collection-based researchers who signed it in the short time span from 20 September to 6 October 2016

Automated Fundus Image Quality Assessment in Retinopathy of Prematurity Using Deep Convolutional Neural Networks

Accurate image-based ophthalmic diagnosis relies on fundus image clarity. This has important implications for the quality of ophthalmic diagnoses and for emerging methods such as telemedicine and computer-based image analysis. The purpose of this study was to implement a deep convolutional neural network (CNN) for automated assessment of fundus image quality in retinopathy of prematurity (ROP). Experimental study. Retinal fundus images were collected from preterm infants during routine ROP screenings. Six thousand one hundred thirty-nine retinal fundus images were collected from 9 academic institutions. Each image was graded for quality (acceptable quality [AQ], possibly acceptable quality [PAQ], or not acceptable quality [NAQ]) by 3 independent experts. Quality was defined as the ability to assess an image confidently for the presence of ROP. Of the 6139 images, NAQ, PAQ, and AQ images represented 5.6%, 43.6%, and 50.8% of the image set, respectively. Because of low representation of NAQ images in the data set, images labeled NAQ were grouped into the PAQ category, and a binary CNN classifier was trained using 5-fold cross-validation on 4000 images. A test set of 2109 images was held out for final model evaluation. Additionally, 30 images were ranked from worst to best quality by 6 experts via pairwise comparisons, and the CNN's ability to rank quality, regardless of quality classification, was assessed. The CNN performance was evaluated using area under the receiver operating characteristic curve (AUC). A Spearman's rank correlation was calculated to evaluate the overall ability of the CNN to rank images from worst to best quality as compared with experts. The mean AUC for 5-fold cross-validation was 0.958 (standard deviation, 0.005) for the diagnosis of AQ versus PAQ images. The AUC was 0.965 for the test set. The Spearman's rank correlation coefficient on the set of 30 images was 0.90 as compared with the overall expert consensus ranking. This model accurately assessed retinal fundus image quality in a comparable manner with that of experts. This fully automated model has potential for application in clinical settings, telemedicine, and computer-based image analysis in ROP and for generalizability to other ophthalmic diseases

Federated Learning for Multicenter Collaboration in Ophthalmology: Implications for Clinical Diagnosis and Disease Epidemiology

To utilize a deep learning (DL) model trained via federated learning (FL), a method of collaborative training without sharing patient data, to delineate institutional differences in clinician diagnostic paradigms and disease epidemiology in retinopathy of prematurity (ROP). Evaluation of a diagnostic test or technology. We included 5245 patients with wide-angle retinal imaging from the neonatal intensive care units of 7 institutions as part of the Imaging and Informatics in ROP study. Images were labeled with the clinical diagnoses of plus disease (plus, preplus, no plus), which were documented in the chart, and a reference standard diagnosis was determined by 3 image-based ROP graders and the clinical diagnosis. Demographics (birth weight, gestational age) and clinical diagnoses for all eye examinations were recorded from each institution. Using an FL approach, a DL model for plus disease classification was trained using only the clinical labels. The 3 class probabilities were then converted into a vascular severity score (VSS) for each eye examination, as well as an "institutional VSS," in which the average of the VSS values assigned to patients' higher severity ("worse") eyes at each examination was calculated for each institution. We compared demographics, clinical diagnoses of plus disease, and institutional VSSs between institutions using the McNemar-Bowker test, 2-proportion Z test, and 1-way analysis of variance with post hoc analysis by the Tukey-Kramer test. Single regression analysis was performed to explore the relationship between demographics and VSSs. We found that the proportion of patients diagnosed with preplus disease varied significantly between institutions (P < 0.001). Using the DL-derived VSS trained on the data from all institutions using FL, we observed differences in the institutional VSS and the level of vascular severity diagnosed as no plus (P < 0.001) across institutions. A significant, inverse relationship between the institutional VSS and mean gestational age was found (P = 0.049, adjusted R  = 0.49). A DL-derived ROP VSS developed without sharing data between institutions using FL identified differences in the clinical diagnoses of plus disease and overall levels of ROP severity between institutions. Federated learning may represent a method to standardize clinical diagnoses and provide objective measurements of disease for image-based diseases

A 5-day time course of flibanserin preserves retinal function in a dose-dependent manner, as measured by ERG.

<p>(<b>A</b>) Representative ERG traces at the 3.55 log cd•s/m² light intensity demonstrating dose-dependent improvements in ERG responses as compared to vehicle-injected mice. (<b>B</b>) Mice treated with doses of 3 mg/kg or greater of flibanserin showed significantly higher ERG b-wave and a-wave responses at the highest ERG flash intensity (3.55 log cds/m²) as compared to vehicle-treated mice. Individual ERGs were averaged with ERGs for mice within its respective group and are represented as mean ± standard error. * indicates a significant difference from both the vehicle-treated group and the naïve group, <i>P</i> < 0.05. n.s. indicates non-significance with <i>P</i> > 0.05. The responses at the ERG b_(max,rod) light intensity, indicated by the “†”, were statistically evaluated and are represented in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0159776#pone.0159776.s001" target="_blank">S1A Fig</a>.</p

Flibanserin increases anti-apoptotic and antioxidant gene expression.

<p><b>(A)</b> RT-qPCR shows that 48 hours following light-exposure, expression of <i>Creb</i>, <i>Bcl-2</i>, <i>Cast1</i>, <i>Sod1</i>, and <i>Cat</i> are significantly increased in flibanserin-injected mice versus vehicle-injected mice. <b>(B)</b> After 72 hours, expression of <i>Creb</i>, <i>c-Jun</i>, <i>c-Fos</i>, <i>Bcl-2</i>, <i>Cast1</i>, <i>Nqo1</i>, and <i>Sod1</i> are significantly increased in flibanserin-injected mice versus vehicle-injected mice. Analysis was performed using the ΔΔCt method with β-actin as the internal control. Significance was determined using a multiple t-test analysis (* indicates <i>P</i> < 0.05). cAMP Response Binding-element Protein (<i>Creb</i>), Cyclin D1 (<i>Cd1</i>), B-cell lymphoma 2 (<i>Bcl-2</i>), Calpastatin (<i>Cast1</i>), Nitric Oxide Synthase (<i>Nos1</i>), NAD(P)H quinone dehydrogenase 1 (<i>Nqo1</i>), Superoxide Dismutase 1 (<i>Sod1</i>), Catalase (<i>Cat</i>), Metallothionein 1 (<i>Mt1</i>).</p

Summary of OCT findings.

<p>Summary of OCT findings.</p

A one-day time course of flibanserin can preserve retinal morphology and function.

<p><b>(A i)</b> A spider graph representing average right-eye receptor plus values demonstrates that a single dose of 6 mg/kg flibanserin or greater can significantly preserve retinal morphology to thicknesses comparable to naïve mice. The gray area indicates ± 2 SD of the naïve averaged data. (<b>A ii, iii</b>) Receptor plus thicknesses, with each dot representing average right and left eye thickness from one mouse, demonstrate that 6 mg/kg flibanserin provides morphological protection in the <b>(A ii)</b> temporal quadrant and the <b>(A iii)</b> nasal quadrant. Group averages are represented as mean ± standard error bar. <b>(B)</b> Mice treated with doses of 6 mg/kg and greater of flibanserin showed significantly higher ERG b-wave and a-wave responses at the highest flash intensity (3.55 log cd•s/m²) as compared to vehicle-treated mice. “†” indicates the greatest flash intensity that elicits a rod-only response (b_(max,rod)). Group differences at the “†” flash intensity were statistically evaluated and are represented in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0159776#pone.0159776.s001" target="_blank">S1B Fig</a>. Individual ERGs were averaged with ERGs for mice within group and are represented as mean ± standard error. * indicates a significant difference from both the vehicle-treated group and the naïve group, <i>P</i> < 0.05. n.s. indicates non-significance with <i>P</i> > 0.05.</p