Deep learning aided measurement of outer retinal layer metrics as biomarkers for inherited retinal degenerations: opportunities and challenges.

PURPOSE OF REVIEW: The purpose of this review was to provide a summary of currently available retinal imaging and visual function testing methods for assessing inherited retinal degenerations (IRDs), with the emphasis on the application of deep learning (DL) approaches to assist the determination of structural biomarkers for IRDs. RECENT FINDINGS: (clinical trials for IRDs; discover effective biomarkers as endpoints; DL applications in processing retinal images to detect disease-related structural changes). SUMMARY: Assessing photoreceptor loss is a direct way to evaluate IRDs. Outer retinal layer structures, including outer nuclear layer, ellipsoid zone, photoreceptor outer segment, RPE, are potential structural biomarkers for IRDs. More work may be needed on structure and function relationship.

Fixation instability during binocular viewing in anisometropic and strabismic children.

PURPOSE: Strabismus or anisometropia disrupts binocularity and results in fixation instability, which is increased with amblyopia. Fixation instability has typically been assessed for each eye individually. Recently, vergence instability was reported in exotropic adults and monkeys during binocular viewing. We evaluated fixation instability during binocular viewing in children treated for anisometropia and/or strabismus. METHODS: 160 children age 4-12 years with treated esotropia and/or anisometropia (98 amblyopic, 62 nonamblyopic) were compared to 46 age-similar controls. Fixation instability was recorded during binocular fixation of a 0.3 deg diameter dot for 20 s using a 500 Hz remote video binocular eye tracker (EyeLink 1000; SR Research). The bivariate contour ellipse area (BCEA; log deg2) for fixation instability was calculated for each eye (nonpreferred, preferred) and for vergence instability (left eye position - right eye position). Best-corrected visual acuity, Randot Preschool stereoacuity, and extent of suppression scotoma (Worth 4-Dot) were also obtained. RESULTS: When binocularly viewing, both amblyopic and nonamblyopic children treated for anisometropia and/or strabismus had larger fixation instability and vergence instability than controls. Amblyopia primarily added to the instability of the nonpreferred eye. Anisometropic children had less nonpreferred eye instability and vergence instability than those with strabismus or combined mechanism. Nonpreferred eye instability and vergence instability were related to poorer stereoacuity and a larger suppression scotoma. Preferred eye instability was not related to any visual outcome measure. No relationships were found with visual acuity. CONCLUSIONS: Fixation instability and vergence instability during binocular viewing suggests that discordant binocular visual experience during childhood, especially strabismus, interferes with ocular motor development. Amblyopia adds to instability of the nonpreferred eye. Vergence instability may limit potential for recovery of binocular vision in these children.

Visual-function tests for self-monitoring of age-related macular degeneration.

Age-related macular degeneration (AMD) is one of the leading causes of severe visual impairment in the United States. Changes in lifestyle can slow the progression of AMD, and new therapies that arrest choroidal neovascularization can preserve vision in patients who progress to the neovascular form of advanced AMD. Appropriate timing is required for these interventions to be optimally effective, which, in turn, depends critically on early diagnosis. Because annual or semiannual eye examinations may not be sufficient to ensure an early diagnosis, the preferred practice for AMD management must include self-monitoring by patients for disease onset or progression. In this review, we discuss a number of visual functions that have been shown to be impaired in eyes with AMD and specify desirable characteristics of visual-function tests that can be used for self-monitoring by populations at risk for AMD.

Remote Monitoring of Visual Function in Patients with Maculopathy: The Aphelion Study.

INTRODUCTION: Remote monitoring of vision, using tools such as the shape discrimination hyperacuity (SDH) test, can detect disease activity in patients with maculopathy. We determined the in-clinic accuracy and repeatability of three myVisionTrack expanded version (mVTx) tests for self-testing of visual acuity (VA) and contrast sensitivity. METHODS: Aphelion, a single-arm, prospective study conducted at two sites in the USA, included adults with any maculopathy and a baseline VA of 0.7 log of minimum angle of resolution (logMAR) (Snellen 20/100) or better. Participants completed the mVTx tests (tumbling E, Landolt C, contrast sensitivity, and SDH) and standard clinical tests (near and distance Early Treatment Diabetic Retinopathy Study [ETDRS] charts and the Pelli-Robson contrast sensitivity chart). Test-retest repeatability and agreement between the mVTx tests and the corresponding clinical test were assessed by Bland-Altman analyses. Participants also completed a usability survey. RESULTS: The mean age of the 122 participants was 67 years. The most common diagnosis was age-related macular degeneration (42% of patients). The tumbling E test had a test-retest 95% limit of agreement (LoA) of ± 0.18 logMAR; the Landolt C test, ± 0.23 logMAR; the SDH test, ± 0.24 logMAR; and the contrast sensitivity test, ± 0.32 log contrast threshold (logCT). Compared with the distance ETDRS chart, the LoA was ± 0.35 logMAR for the tumbling E test (mean difference, - 0.07 logMAR) and ± 0.39 logMAR for the Landolt C test (mean difference, 0.03 logMAR). For the contrast sensitivity test, the LoA compared with the Pelli-Robson chart was ± 0.30 logCT (mean difference, - 0.25 logCT). Most participants (85%) reported that they learned the tests quickly. The tumbling E test scored the highest on ease of use. CONCLUSION: The mVTx tests of VA are accurate and repeatable, supporting their potential use alongside the SDH test to detect disease progression remotely between clinic visits.

Patch-free streaming contrast-rebalanced dichoptic cartoons versus patching for treatment of amblyopia in children aged 3 to 5 years: a pilot, randomized clinical trial.

BACKGROUND: We developed and tested a dichoptic treatment designed for younger children that can be viewed freely and involves a dichoptic manipulation of a popular animation series that enables contrast-rebalancing without disrupting fusion. Our aim was to assess whether this novel amblyopia treatment is superior to patching in children aged 3-5 years. METHODS: A total of 34 children with amblyopia were randomly assigned to contrast-rebalanced dichoptic cartoons (4 hours/week) or patching (14 hours/week) for 2 weeks. Children in the cartoon group continued watching cartoons for an additional 2 weeks. Designed to target the youngest and most treatable children, the dichoptic cartoons presented the entire scene to the amblyopic eye at 100% contrast, while the fellow eye view was presented at reduced contrast with the main character omitted. Best-corrected visual acuity (BCVA), stereoacuity, suppression, and manual dexterity were measured at each visit. RESULTS: After 2 weeks, improvement in amblyopic eye BCVA was greater for dichoptic treatment than for patching, with a mean improvement of 0.11 ± 0.08 versus 0.06 ± 0.09 logMAR, respectively (P = 0.04). Stereoacuity, suppression, and manual dexterity did not improve significantly more in the dichoptic group than the patching group at 2 weeks. After 4 weeks of dichoptic cartoon treatment, mean visual acuity improvement in the dichoptic group was 0.16 logMAR (95% CI, 0.10-0.21). CONCLUSIONS: In our study cohort, a contrast-rebalanced dichoptic cartoon was more effective than patching in treating childhood amblyopia after 2 weeks. Dichoptic cartoons that rebalance contrast to overcome suppression provide an additional treatment option for amblyopia in young children.

Feasibility of a novel remote daily monitoring system for age-related macular degeneration using mobile handheld devices: results of a pilot study.

PURPOSE: This pilot study evaluated the feasibility of the Health Management Tool (HMT), a novel computing system using mobile handheld devices, to remotely monitor retinal visual function daily in patients with neovascular age-related macular degeneration treated with ranibizumab. METHODS: Patients with neovascular age-related macular degeneration in at least 1 eye (newly diagnosed or successfully treated < 1 year) and eligible for ranibizumab therapy were enrolled in this 16-week, prospective, open-label, single-arm study. Patients performed a shape discrimination hyperacuity test (myVisionTrack [mVT]) daily on the HMT device (iPhone 3GS) remotely and at all clinic visits. Data entered into HMT devices were collected in the HMT database, which also sent reminders for patients to take mVT. RESULTS: Among 160 patients from 24 U.S. centers enrolled in the study (103 [64%] ≥ 75 years of age), 84.7% on average complied with daily mVT testing and ≈ 98.9% complied with at least weekly mVT testing. The HMT database successfully uploaded more than 17,000 mVT assessment values and sent more than 9,000 reminders. CONCLUSION: Elderly patients with neovascular age-related macular degeneration were willing and able to comply with daily self-testing of retinal visual function using mobile handheld devices in this novel system of remote vision monitoring.

Pancreatic carcinoma-specific immunotherapy using synthesised alpha-galactosyl epitope-activated immune responders: findings from a pilot study.

BACKGROUND: Dendritic cell (DC)-based and cytokine-induced killer cell (CIK)-based therapy can induce specific antitumor T-cell responses. This clinical pilot study examined the safety, the feasibility, and the outcome of tumor-specific immunotherapy for patients with advanced pancreatic adenocarcinoma. METHODS: Alpha-Gal epitopes were synthesised on pancreatic carcinoma cell membranes with α1,3-galactosyltransferase in vitro. Subsequently, the addition of natural human anti-Gal IgG to the processed membranes resulted in opsonization and effective phagocytosis by DCs, which were co-cultured with newly differentiated CIKs from bone marrow stem cells to generate tumor-specific immune responders ex vivo. Fourteen patients with inoperable stage III/IV pancreatic adenocarcinoma were enrolled in the study; the treatment procedure consisted of injections of DCs and CIKs. RESULTS: Clinical observation showed that the procedure was safe and lacked serious side effects. Tests showed that 12 patients had strong positive delayed-type IV hypersensitivity to the autologous cancer cell lysate; robust systemic cytotoxicity elicited by interferon (IFN)γ expression by peripheral blood mononuclear cells; and significant increases in CD3+CD8+, CD3+CD45RO+, and CD3+CD56+ cells in peripheral blood lymphocytes after 3 injections. During the follow up, the percentages of CD3+CD8+, CD3+CD45RO+, and CD3+CD56+ cells returned to the normal range at 6 to 9 months after the third injection and IFNγ expression in the cells stayed at the higher level from the third injection to 24 months after the treatment. CONCLUSIONS: This new tumor-specific immunotherapy is safe, feasible, and has great potential for pancreatic carcinoma treatment.

Deep Learning-Assisted Measurements of Photoreceptor Ellipsoid Zone Area and Outer Segment Volume as Biomarkers for Retinitis Pigmentosa.

The manual segmentation of retinal layers from OCT scan images is time-consuming and costly. The deep learning approach has potential for the automatic delineation of retinal layers to significantly reduce the burden of human graders. In this study, we compared deep learning model (DLM) segmentation with manual correction (DLM-MC) to conventional manual grading (MG) for the measurements of the photoreceptor ellipsoid zone (EZ) area and outer segment (OS) volume in retinitis pigmentosa (RP) to assess whether DLM-MC can be a new gold standard for retinal layer segmentation and for the measurement of retinal layer metrics. Ninety-six high-speed 9 mm 31-line volume scans obtained from 48 patients with RPGR-associated XLRP were selected based on the following criteria: the presence of an EZ band within the scan limit and a detectable EZ in at least three B-scans in a volume scan. All the B-scan images in each volume scan were manually segmented for the EZ and proximal retinal pigment epithelium (pRPE) by two experienced human graders to serve as the ground truth for comparison. The test volume scans were also segmented by a DLM and then manually corrected for EZ and pRPE by the same two graders to obtain DLM-MC segmentation. The EZ area and OS volume were determined by interpolating the discrete two-dimensional B-scan EZ-pRPE layer over the scan area. Dice similarity, Bland-Altman analysis, correlation, and linear regression analyses were conducted to assess the agreement between DLM-MC and MG for the EZ area and OS volume measurements. For the EZ area, the overall mean dice score (SD) between DLM-MC and MG was 0.8524 (0.0821), which was comparable to 0.8417 (0.1111) between two MGs. For the EZ area > 1 mm2, the average dice score increased to 0.8799 (0.0614). When comparing DLM-MC to MG, the Bland-Altman plots revealed a mean difference (SE) of 0.0132 (0.0953) mm2 and a coefficient of repeatability (CoR) of 1.8303 mm2 for the EZ area and a mean difference (SE) of 0.0080 (0.0020) mm3 and a CoR of 0.0381 mm3 for the OS volume. The correlation coefficients (95% CI) were 0.9928 (0.9892-0.9952) and 0.9938 (0.9906-0.9958) for the EZ area and OS volume, respectively. The linear regression slopes (95% CI) were 0.9598 (0.9399-0.9797) and 1.0104 (0.9909-1.0298), respectively. The results from this study suggest that the manual correction of deep learning model segmentation can generate EZ area and OS volume measurements in excellent agreement with those of conventional manual grading in RP. Because DLM-MC is more efficient for retinal layer segmentation from OCT scan images, it has the potential to reduce the burden of human graders in obtaining quantitative measurements of biomarkers for assessing disease progression and treatment outcomes in RP.

Home vision monitoring in patients with maculopathy: current and future options for digital technologies.

Patients with macular pathology, including that caused by age-related macular degeneration and diabetic macular oedema, must attend frequent in-clinic monitoring appointments to detect onset of disease activity requiring treatment and to monitor progression of existing disease. In-person clinical monitoring places a significant burden on patients, caregivers and healthcare systems and is limited in that it only provides clinicians with a snapshot of the patient's disease status. The advent of remote monitoring technologies offers the potential for patients to test their own retinal health at home in collaboration with clinicians, reducing the need for in-clinic appointments. In this review we discuss visual function tests, both existing and novel, that have the potential for remote use and consider their suitability for discriminating the presence of disease and progression of disease. We then review the clinical evidence supporting the use of mobile applications for monitoring of visual function from clinical development through to validation studies and real-world implementation. This review identified seven app-based visual function tests: four that have already received some form of regulatory clearance and three under development. The evidence included in this review shows that remote monitoring offers great potential for patients with macular pathology to monitor their condition from home, reducing the need for burdensome clinic visits and expanding clinicians' understanding of patients' retinal health beyond traditional clinical monitoring. In order to instil confidence in the use of remote monitoring in both patients and clinicians further longitudinal real-world studies are now warranted.

Deep Learning-Facilitated Study of the Rate of Change in Photoreceptor Outer Segment Metrics in RPGR-Related X-Linked Retinitis Pigmentosa.

Purpose: The aim of this retrospective cohort study was to obtain three-dimensional (3D) photoreceptor outer segment (OS) metrics measurements with the assistance of a deep learning model (DLM) and to evaluate the longitudinal change in OS metrics and associated factors in retinitis pigmentosa GTPase regulator (RPGR) X-linked retinitis pigmentosa (XLRP). Methods: The study included 34 male patients with RPGR-associated XLRP who had preserved ellipsoid zone (EZ) within their spectral-domain optical coherence tomography volume scans and an approximate 2-year or longer follow-up. Volume scans were segmented using a DLM with manual correction for EZ and apical retinal pigment epithelium (RPE). OS metrics were measured from 3D EZ-RPE layers of volume scans. Linear mixed-effects models were used to calculate the rate of change in OS metrics and the associated factors, including baseline age, baseline OS metrics, and follow-up duration. Results: The mean (standard deviation) of progression rates were -0.28 (0.43) µm/y, -0.73 (0.61) mm2/y, and -0.014 (0.012) mm3/y for OS thickness, EZ area, and OS volume, respectively. In multivariable analysis, the progression rates of EZ area and OS volume were strongly associated with their baseline values, with faster decline in eyes with larger baseline values (P ≤ 0.003), and nonlinearly associated with the baseline age (P ≤ 0.003). OS thickness decline was not associated with its baseline value (P = 0.32). Conclusions: These results provide evidence to support using OS metrics as biomarkers to assess the progression of XLRP and as the outcome measures of clinical trials. Given that their progression rates are dependent on their baseline values, the baseline EZ area and OS volume should be considered in the design and statistical analysis of future clinical trials. Deep learning may provide a useful tool to reduce the burden of human graders to analyze OCT scan images and to facilitate the assessment of disease progression and treatment trials for retinitis pigmentosa.

Altered gut microbiota composition in children and their caregivers infected with the SARS-CoV-2 Omicron variant.

BACKGROUND: Gut microbiota alterations have been implicated in the pathogenesis of coronavirus disease 2019 (COVID-19). This study aimed to explore gut microbiota changes in a prospective cohort of COVID-19 children and their asymptomatic caregivers infected with the severe acute respiratory syndrome coronavirus type 2 (SARS-CoV-2) Omicron variant. METHODS: A total of 186 participants, including 59 COVID-19 children, 50 asymptomatic adult caregivers, 52 healthy children (HC), and 25 healthy adults (HA), were recruited between 15 April and 31 May 2022. The gut microbiota composition was determined by 16S rRNA gene sequencing in fecal samples collected from the participants. Gut microbiota functional profiling was performed by using Phylogenetic Investigation of Communities by Reconstruction of Unobserved States (PICRUSt) software. RESULTS: The gut microbiota analysis of beta diversity revealed that the fecal microbial community of COVID-19 children remained far distantly related to HC. The relative abundances of the phyla Actinobacteria and Firmicutes were decreased, whereas Bacteroidetes, Proteobacteria, and Verrucomicrobiota were increased in COVID-19 children. Feces from COVID-19 children exhibited notably lower abundances of the genera Blautia, Bifidobacterium, Fusicatenibacter, Streptococcus, and Romboutsia and higher abundances of the genera Prevotella, Lachnoclostridium, Escherichia-Shigella, and Bacteroides than those from HC. The enterotype distributions of COVID-19 children were characterized by a high prevalence of enterotype Bacteroides. Similar changes in gut microbiota compositions were observed in asymptomatic caregivers. Furthermore, the microbial metabolic activities of KEGG (Kyoto Encyclopedia of Genes and Genomes) and COG (cluster of orthologous groups of proteins) pathways were perturbed in feces from subjects infected with the SARS-CoV-2 Omicron variant. CONCLUSION: Our data reveal altered gut microbiota compositions in both COVID-19 children and their asymptomatic caregivers infected with the SARS-CoV-2 Omicron variant, which further implicates the critical role of gut microbiota in COVID-19 pathogenesis.

Performance of Deep Learning Models in Automatic Measurement of Ellipsoid Zone Area on Baseline Optical Coherence Tomography (OCT) Images From the Rate of Progression of USH2A-Related Retinal Degeneration (RUSH2A) Study.

Purpose: Previously, we have shown the capability of a hybrid deep learning (DL) model that combines a U-Net and a sliding-window (SW) convolutional neural network (CNN) for automatic segmentation of retinal layers from OCT scan images in retinitis pigmentosa (RP). We found that one of the shortcomings of the hybrid model is that it tends to underestimate ellipsoid zone (EZ) width or area, especially when EZ extends toward or beyond the edge of the macula. In this study, we trained the model with additional data which included more OCT scans having extended EZ. We evaluated its performance in automatic measurement of EZ area on SD-OCT volume scans obtained from the participants of the RUSH2A natural history study by comparing the model's performance to the reading center's manual grading. Materials and Methods: De-identified Spectralis high-resolution 9-mm 121-line macular volume scans as well as their EZ area measurements by a reading center were transferred from the management center of the RUSH2A study under the data transfer and processing agreement. A total of 86 baseline volume scans from 86 participants of the RUSH2A study were included to evaluate two hybrid models: the original RP240 model trained on 480 mid-line B-scans from 220 patients with retinitis pigmentosa (RP) and 20 participants with normal vision from a single site, and the new RP340 model trained on a revised RP340 dataset which included RP240 dataset plus an additional 200 mid-line B-scans from another 100 patients with RP. There was no overlap of patients between training and evaluation datasets. EZ and apical RPE in each B-scan image were automatically segmented by the hybrid model. EZ areas were determined by interpolating the discrete 2-dimensional B-scan EZ-RPE layer over the scan area. Dice similarity, correlation, linear regression, and Bland-Altman analyses were conducted to assess the agreement between the EZ areas measured by the hybrid model and by the reading center. Results: For EZ area > 1 mm2, average dice coefficients ± SD between the EZ band segmentations determined by the DL model and the manual grading were 0.835 ± 0.132 and 0.867 ± 0.105 for RP240 and RP340 hybrid models, respectively (p < 0.0005; n = 51). When compared to the manual grading, correlation coefficients (95% CI) were 0.991 (0.987-0.994) and 0.994 (0.991-0.996) for RP240 and RP340 hybrid models, respectively. Linear regression slopes (95% CI) were 0.918 (0.896-0.940) and 0.995 (0.975-1.014), respectively. Bland-Altman analysis revealed a mean difference ± SD of -0.137 ± 1.131 mm2 and 0.082 ± 0.825 mm2, respectively. Conclusion: Additional training data improved the hybrid model's performance, especially reducing the bias and narrowing the range of the 95% limit of agreement when compared to manual grading. The close agreement of DL models to manual grading suggests that DL may provide effective tools to significantly reduce the burden of reading centers to analyze OCT scan images. In addition to EZ area, our DL models can also provide the measurements of photoreceptor outer segment volume and thickness to further help assess disease progression and to facilitate the study of structure and function relationship in RP.

Fecal microbiota transplantation relieves abdominal bloating in children with functional gastrointestinal disorders via modulating the gut microbiome and metabolome.

OBJECTIVE: To evaluate the efficacy and safety of fecal microbiota transplantation (FMT) in functional gastrointestinal disorders (FGIDs) in children with abdominal bloating and changes in their gut microbiome and metabolome. METHODS: Twelve pediatric FGID patients with predominant abdominal bloating who underwent FMT were enrolled in the study. Fourteen healthy controls and four stool donors were included for analysis. Clinical responses were assessed at 8 weeks after FMT. Fecal bacterial composition was determined by 16S rRNA gene sequencing. The fecal metabolome was measured by targeted metabolomics analysis. RESULTS: Median age of the 12 children with FGIDs was 6 years, and nine were boys. Abdominal bloating was relieved in all patients by FMT at 8 weeks. Meanwhile, FMT significantly improved abdominal pain and diarrhea. The a diversity was significantly lower in the FGID patients, while the fecal microbial community (ß diversity) separated from that of healthy control (HCs). The relative abundances of multiple bacterial genera were significantly changed in the feces of the pediatric FGID patients. The levels of several short-chain fatty acids were lower, and lactic acid level was higher in FGID patients than in HCs. Altered bacterial composition was correlated with changes in the fecal metabolite profile and clinical symptoms in FGID patients. FMT modulated fecal microbiome and metabolome in FGID children toward a healthy state. CONCLUSIONS: FMT relieves abdominal bloating and modulates fecal microbiome and metabolome toward a healthy state in children with FGIDs. FMT may provide an alternative therapy for children with FGIDs and abdominal bloating.

Randomized clinical trial of streaming dichoptic movies versus patching for treatment of amblyopia in children aged 3 to 7 years.

Contrast-rebalanced dichoptic movies have been shown to be an effective binocular treatment for amblyopia in the laboratory. Yet, at-home therapy is a more practical approach. In a randomized clinical trial, we compared dichoptic movies, streamed at-home on a handheld 3D-enabled game console, versus patching as amblyopia treatment. Sixty-five amblyopic children (3-7 years; 20/32-125) were randomly assigned to one of two parallel arms, binocular treatment (3 movies/week) or patching (14 h/week). The primary outcome, change in best corrected visual acuity (BCVA) at the 2-week visit was completed by 28 and 30, respectively. After the primary outcome, both groups of children had the option to complete up to 6 weeks of binocular treatment. At the 2-week primary outcome visit, BCVA had improved in the movie (0.07 ± 0.02 logMAR; p < .001) and patching (0.06 ± 0.01 logMAR; p < 0.001) groups. There was no significant difference between groups (CI95%: - 0.02 to 0.04; p = .48). Visual acuity improved in both groups with binocular treatment up to 6 weeks (0.15 and 0.18 logMAR improvement, respectively). This novel, at-home, binocular movie treatment improved amblyopic eye BCVA after 2 weeks (similar to patching), with additional improvement up to 6 weeks. Repeated binocular visual experience with contrast-rebalanced binocular movies provides an additional treatment option for amblyopia.Clincaltrials.gov identifier: NCT03825107 (31/01/2019).

A Hybrid Model Composed of Two Convolutional Neural Networks (CNNs) for Automatic Retinal Layer Segmentation of OCT Images in Retinitis Pigmentosa (RP).

Purpose: We propose and evaluate a hybrid model composed of two convolutional neural networks (CNNs) with different architectures for automatic segmentation of retina layers in spectral domain optical coherence tomography (SD-OCT) B-scans of retinitis pigmentosa (RP). Methods: The hybrid model consisted of a U-Net for initial semantic segmentation and a sliding-window (SW) CNN for refinement by correcting the segmentation errors of U-Net. The U-Net construction followed Ronneberger et al. (2015) with an input image size of 256 × 32. The SW model was similar to our previously reported approach. Training image patches were generated from 480 horizontal midline B-scans obtained from 220 patients with RP and 20 normal participants. Testing images were 160 midline B-scans from a separate group of 80 patients with RP. The Spectralis segmentation of B-scans was manually corrected for the boundaries of the inner limiting membrane, inner nuclear layer, ellipsoid zone (EZ), retinal pigment epithelium, and Bruch's membrane by one grader for the training set and two for the testing set. The trained U-Net and SW, as well as the hybrid model, were used to classify all pixels in the testing B-scans. Bland-Altman and correlation analyses were conducted to compare layer boundary lines, EZ width, and photoreceptor outer segment (OS) length and area determined by the models to those by human graders. Results: The mean times to classify a B-scan image were 0.3, 65.7, and 2.4 seconds for U-Net, SW, and the hybrid model, respectively. The mean ± SD accuracies to segment retinal layers were 90.8% ± 4.8% and 90.7% ± 4.0% for U-Net and SW, respectively. The hybrid model improved mean ± SD accuracy to 91.5% ± 4.8% (P < 0.039 vs. U-Net), resulting in an improvement in layer boundary segmentation as revealed by Bland-Altman analyses. EZ width, OS length, and OS area measured by the models were highly correlated with those measured by the human graders (r > 0.95 for EZ width; r > 0.83 for OS length; r > 0.97 for OS area; P < 0.05). The hybrid model further improved the performance of measuring retinal layer thickness by correcting misclassification of retinal layers from U-Net. Conclusions: While the performances of U-Net and the SW model were comparable in delineating various retinal layers, U-Net was much faster than the SW model to segment B-scan images. The hybrid model that combines the two improves automatic retinal layer segmentation from OCT images in RP. Translational Relevance: A hybrid deep machine learning model composed of CNNs with different architectures can be more effective than either model separately for automatic analysis of SD-OCT scan images, which is becoming increasingly necessary with current high-resolution, high-density volume scans.

Congenital hepatic fibrosis in a young boy with congenital hypothyroidism: A case report.

BACKGROUND: Congenital hepatic fibrosis (CHF) is a rare autosomal recessive disorder characterized by variable degrees of periportal fibrosis and malformation of bile ducts. CHF is generally accompanied by a variety of conditions or syndromes with other organ involvement. CASE SUMMARY: We report a 5-year-4-month-old Chinese boy with congenital hypothyroidism (CH) diagnosed with CHF. The patient was diagnosed with CH by a newborn screening test and has since been taking levothyroxine. He has developed normally without neurocognitive deficits. Abnormal liver function was observed in the patient at the age of 4 years and 11 mo, and elevated levels of liver function indices were persistent for 5 mo. Radiological imaging indicated hepatospleno-megaly without narrowing of the portal vein but dilated splenic vein. A liver biopsy confirmed the pathological features of CHF. Genetic testing revealed two novel homozygous mutations, namely, c.2141-3T>C variant in PKHD1 related to CHF and c.2921G>A (p.R974H) in DUOX2 related to CH. The patient was treated with compound glycyrrhizin tablet, ursodeoxycholic acid, and levothyroxine after diagnosis. The patient achieved a favorable clinical outcome during a follow-up period of over 2 years. CONCLUSION: Herein, we report the first case of a Chinese boy with comorbidity of CHF and CH, carrying both PKHD1 gene and DUOX2 gene novel mutations. Liver biopsy and genetic testing should be considered for the diagnosis of coexistent liver disease in CH patients with unexplained abnormal liver function.

Vernier acuity cards: examination of development and screening validity.

PURPOSE: Because vernier acuity seems to be limited by the visual cortex, it possesses excellent potential as a clinical/screening tool to detect amblyopia in infants and toddlers. Thus, we developed the vernier acuity cards specifically for this age group. We compared developmental data gathered using this new test and the Teller Acuity Cards. In addition, we compared the clinical/screening validity of the two tests by testing children old enough to complete optotype acuity testing (6.2 ± 2.5 years). METHODS: Vernier acuity and grating acuity were assessed in 98 children and 18 adults with normal vision (age range = 2.8 months to 35.8 years). The developmental time course of the two visual functions was compared. In addition, vernier acuity and grating acuity were measured in 43 children with amblyopia and 30 nonamblyopic children with an amblyogenic condition. Each child's grating acuity and vernier acuity were classified as normal/abnormal based on age-appropriate norms. These classifications were compared with amblyopia diagnoses by crowded HOTV or Early Treatment Diabetic Retinopathy Study (ETDRS) testing. RESULTS: Vernier acuity and grating acuity follow different developmental time courses in normal infants and children. Vernier acuity is initially poorer than grating acuity but surpasses it by the age 5 years and is adult-like by the age 8 years. Compared with the Teller Acuity Cards, the vernier acuity cards yielded higher sensitivity (81 vs. 44%) and similar specificity (73 vs. 93%) and were more sensitive to all amblyopia subtypes/levels of severity. CONCLUSIONS: The developmental time course of vernier acuity differed from that of grating acuity, implying that it is not mediated by the retina. Also, the impressive validity of the vernier acuity cards suggests that they are an effective tool for detecting amblyopia.

Application of a Deep Machine Learning Model for Automatic Measurement of EZ Width in SD-OCT Images of RP.

Purpose: We applied a deep convolutional neural network model for automatic identification of ellipsoid zone (EZ) in spectral domain optical coherence tomography B-scans of retinitis pigmentosa (RP). Methods: Midline B-scans having visible EZ from 220 patients with RP and 20 normal subjects were manually segmented for inner limiting membrane, inner nuclear layer, EZ, retinal pigment epithelium, and Bruch's membrane. A total of 2.87 million labeled image patches (33 × 33 pixels) extracted from 480 B-scans were used for training a convolutional neural network model implemented in MATLAB. B-scans from a separate group of 80 patients with RP were used for testing the model. A local connected area searching algorithm was developed to process the model output for reconstructing layer boundaries. Correlation and Bland-Altman analyses were conducted to compare EZ width measured by the model to those by manual segmentation. Results: The accuracy of the trained model to identify inner limiting membrane, inner nuclear layer, EZ, retinal pigment epithelium, and Bruch's membrane patches in the test dataset was 98%, 89%, 91%, 94%, and 96%, respectively. The EZ width measured by the model was highly correlated with that by two graders (r = 0.97; P < 0.0001). Bland-Altman analysis revealed a mean EZ width difference of 0.30 mm (coefficient of repeatability = 0.9 mm) between the model and the graders, comparable to the mean difference of 0.34mm (coefficient of repeatability = 0.8 mm) between two graders. Conclusions: The results demonstrated the capability of a deep machine learning-based method for automatic identification of EZ in RP, suggesting that the method can be used to quantify structural deficits in RP for detecting disease progression and for evaluating treatment effect. Translational Relevance: A deep machine learning model has the potential to replace humans for grading spectral domain optical coherence tomography images in RP.

Ryanodine receptor 1 mediated dexamethasone-induced chondrodysplasia in fetal rats.

BACKGROUND: Osteoarthritis is caused by cartilage dysplasia and has fetal origin. Prenatal dexamethasone exposure (PDE) induced chondrodysplasia in fetal rats by inhibiting transforming growth factor ß (TGFß) signaling. This study aimed to determine the effect of dexamethasone on fetal cartilage development and illustrate the underlying molecular mechanism. METHODS: Dexamethasone (0.2 mg/kg.d) was injected subcutaneously every morning in pregnant rats from gestational day (GD) 9 to GD21. Harvested fetal femurs and tibias at GD21 for immunofluorescence and gene expression analysis. Fetal chondrocytes were treated with dexamethasone (100, 250 and 500 nM), endoplasmic reticulum stress (ERS) inhibitor, and ryanodine receptor 1 (RYR1) antagonist for subsequent analyses. RESULTS: In vivo, prenatal dexamethasone exposure (PDE) decreased the total length of the fetal cartilage, the proportion of the proliferation area and the cell density and matrix content in fetal articular cartilage. Moreover, PDE increased RYR1 expression and intracellular calcium levels and elevated the expression of ERS-related genes, while downregulated the TGFß signaling pathway and extracellular matrix (ECM) synthesis in fetal chondrocytes. In vitro, we verified dexamethasone significantly decreased ECM synthesis through activating RYR 1 mediated-ERS. CONCLUSIONS: PDE inhibited TGFß signaling pathway and matrix synthesis through RYR1 / intracellular calcium mediated ERS, which ultimately led to fetal dysplasia. This study confirmed the molecular mechanism of ERS involved in the developmental toxicity of dexamethasone and suggested that RYR1 may be an early intervention target for fetal-derived adult osteoarthritis.

Recent progress of Ni-Fe layered double hydroxide and beyond towards electrochemical water splitting.

The electrochemical water splitting process including the hydrogen evolution reaction (HER) and oxygen evolution reaction (OER) is considered as one of the most promising methods for high-purity hydrogen production. Ni-Fe based compounds, especially Ni-Fe layered double hydroxide (LDH), have become highly efficient electrocatalysts to expedite the above reactions. During the last decade, great progress has been witnessed in the development of Ni-Fe based electrocatalysts. Diverse regulatory strategies such as morphology modulation, composition control, and defect engineering have been employed to optimize their electrochemical performances for water splitting. In addition, the family of Ni-Fe based compounds has been expanded from LDHs to alloys, sulfides, phosphides and so forth. Deep experimental investigations and theoretical studies have also been carried out to reveal the intrinsic origin of the superior electrocatalytic performances. In this review, we summarise the recent development of Ni-Fe based compounds for electrochemical water splitting with high efficiency. Special focus has been placed on the design principle and synthetic strategies of Ni-Fe based compounds. In the end, remaining challenges and future research directions are briefly discussed.