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PURPOSE To assess the performance of artificial intelligence in the automated classification of images taken with a tablet device of patients with blepharoptosis and subjects with normal eyelid. METHODS This is a prospective and observational study. A total of 1276 eyelid images (624 images from 347 blepharoptosis cases and 652 images from 367 normal controls) from 606 participants were analyzed. In order to obtain a sufficient number of images for analysis, 1 to 4 eyelid images were obtained from each participant. We developed a model by fully retraining the pre-trained MobileNetV2 convolutional neural network. Subsequently, we verified whether the automatic diagnosis of blepharoptosis was possible using the images. In addition, we visualized how the model captured the features of the test data with Score-CAM. k-fold cross-validation (k = 5) was adopted for splitting the training and validation. Sensitivity, specificity, and the area under the curve (AUC) of the receiver operating characteristic curve for detecting blepharoptosis were examined. RESULTS We found the model had a sensitivity of 83.0% (95% confidence interval [CI], 79.8-85.9) and a specificity of 82.5% (95% CI, 79.4-85.4). The accuracy of the validation data was 82.8%, and the AUC was 0.900 (95% CI, 0.882-0.917). CONCLUSION Artificial intelligence was able to classify with high accuracy images of blepharoptosis and normal eyelids taken using a tablet device. Thus, the diagnosis of blepharoptosis with a tablet device is possible at a high level of accuracy. TRIAL REGISTRATION Date of registration: 2021-06-25. TRIAL REGISTRATION NUMBER UMIN000044660. Registration site: https://upload.umin.ac.jp/cgi-open-bin/ctr/ctr_view.cgi?recptno=R000051004.

AimTo compare the preoperative biometric data and the refractive accuracy of cataract surgery among major surgical sites in a nationwide multicentre study.MethodsWe prospectively obtained the preoperative biometric data of 2143 eyes of 2143 consecutive patients undergoing standard cataract surgery at major 12 facilities and compared the preoperative biometry as well as the postoperative refractive accuracy among them.ResultsWe found significant differences in most preoperative variables, such as axial length (one-way analysis of variance, p=0.003), anterior chamber depth (pConclusionsRegional divergences of the preoperative biometry were not necessarily negligible, and the optimised intraocular lens power calculation formula was individually different among the 12 facilities. Our findings highlight the importance of individual optimisation of these formulas at each facility, especially in consideration of these biometric variations.Trial registration numberClinical Trial Registry; 000039976.

ImportanceThere is no widespread effective treatment to halt the progression of retinitis pigmentosa. Consequently, adequate assessment and estimation of residual visual function are important clinically.ObjectiveTo examine whether deep learning can accurately estimate the visual function of patients with retinitis pigmentosa by using ultra-widefield fundus images obtained on concurrent visits.Design, Setting, and ParticipantsData for this multicenter, retrospective, cross-sectional study were collected between January 1, 2012, and December 31, 2018. This study included 695 consecutive patients with retinitis pigmentosa who were examined at 5 institutions. Each of the 3 types of input images—ultra-widefield pseudocolor images, ultra-widefield fundus autofluorescence images, and both ultra-widefield pseudocolor and fundus autofluorescence images—was paired with 1 of the 31 types of ensemble models constructed from 5 deep learning models (Visual Geometry Group–16, Residual Network–50, InceptionV3, DenseNet121, and EfficientNetB0). We used 848, 212, and 214 images for the training, validation, and testing data, respectively. All data from 1 institution were used for the independent testing data. Data analysis was performed from June 7, 2021, to December 5, 2022.Main Outcomes and MeasuresThe mean deviation on the Humphrey field analyzer, central retinal sensitivity, and best-corrected visual acuity were estimated. The image type–ensemble model combination that yielded the smallest mean absolute error was defined as the model with the best estimation accuracy. After removal of the bias of including both eyes with the generalized linear mixed model, correlations between the actual values of the testing data and the estimated values by the best accuracy model were examined by calculating standardized regression coefficients and P values.ResultsThe study included 1274 eyes of 695 patients. A total of 385 patients were female (55.4%), and the mean (SD) age was 53.9 (17.2) years. Among the 3 types of images, the model using ultra-widefield fundus autofluorescence images alone provided the best estimation accuracy for mean deviation, central sensitivity, and visual acuity. Standardized regression coefficients were 0.684 (95% CI, 0.567-0.802) for the mean deviation estimation, 0.697 (95% CI, 0.590-0.804) for the central sensitivity estimation, and 0.309 (95% CI, 0.187-0.430) for the visual acuity estimation (all P Conclusions and RelevanceResults of this study suggest that the visual function estimation in patients with retinitis pigmentosa from ultra-widefield fundus autofluorescence images using deep learning might help assess disease progression objectively. Findings also suggest that deep learning models might monitor the progression of retinitis pigmentosa efficiently during follow-up.

To develop an artificial intelligence (AI) algorithm enabling corneal surgeons to predict the probability of rebubbling after Descemet membrane endothelial keratoplasty (DMEK) from images obtained using optical coherence tomography (OCT).Anterior segment OCT data of patients undergoing DMEK by 2 different DMEK surgeons (C.C. and B.B.; University of Cologne, Cologne, Germany) were extracted from the prospective Cologne DMEK database. An AI algorithm was trained by using a data set of C.C. to detect graft detachments and predict the probability of a rebubbling. The architecture of the AI model used in this study was called EfficientNet. This algorithm was applied to OCT scans of patients, which were operated by B.B. The transferability of this algorithm was analyzed to predict a rebubbling after DMEK.The algorithm reached an area under the curve of 0.875 (95% confidence interval: 0.880-0.929). The cutoff value based on the Youden index was 0.214, and the sensitivity and specificity for this value were 78.9% (67.6%-87.7%) and 78.6% (69.5%-86.1%).The development of AI algorithms allows good transferability to other surgeons reaching a high accuracy in predicting rebubbling after DMEK based on OCT image data.

KEGG (https://www.kegg.jp/) is a manually curated resource integrating eighteen databases categorized into systems, genomic, chemical and health information. It also provides KEGG mapping tools, which enable understanding of cellular and organism-level functions from genome sequences and other molecular datasets. KEGG mapping is a predictive method of reconstructing molecular network systems from molecular building blocks based on the concept of functional orthologs. Since the introduction of the KEGG NETWORK database, various diseases have been associated with network variants, which are perturbed molecular networks caused by human gene variants, viruses, other pathogens and environmental factors. The network variation maps are created as aligned sets of related networks showing, for example, how different viruses inhibit or activate specific cellular signaling pathways. The KEGG pathway maps are now integrated with network variation maps in the NETWORK database, as well as with conserved functional units of KEGG modules and reaction modules in the MODULE database. The KO database for functional orthologs continues to be improved and virus KOs are being expanded for better understanding of virus-cell interactions and for enabling prediction of viral perturbations.

PURPOSE: Branch retinal vein occlusion (BRVO) causes macular edema (ME), which can be controlled with anti-VEGF treatments. However, these treatments are not curative, necessitating additional anti-VEGF treatments at recurrence. Long-term results, optimal anti-VEGF treatment regimens, and the comprehensive effects of ME recurrence are largely unknown. Thus, we aimed to examine the effects of foveal thickness (FT) fluctuation (FTF) on the visual and morphologic outcomes of anti-VEGF treatments for BRVO-ME administered via a pro re nata regimen. DESIGN: A retrospective, observational case series. SUBJECTS: This study analyzed 309 treatment-naïve patients (309 eyes) with BRVO-ME between 2012 and 2021 at a multicenter retinal practice. METHODS: The FT was assessed using OCT at each study visit. MAIN OUTCOME MEASURES: We evaluated the logarithm of the minimal angle of resolution (logMAR) best corrected visual acuity (BCVA) and the defect length of the foveal ellipsoid zone (EZ) band using OCT. RESULTS: At baseline, the mean logMAR BCVA was 0.30 ± 0.30 and the mean FT was 503 ± 162 μm. The number of anti-VEGF injections for BRVO-ME was 5.8 ± 4.6 during the mean follow-up period (50.6 ± 22.2 months). At the final examination, the mean logMAR BCVA and FT values were significantly improved compared with those at the baseline. Multiple regression analyses showed that age, baseline logMAR BCVA, and FTF were significantly associated with the final logMAR BCVA (β= 0.20, 0.35, and 0.30, respectively). Foveal thickness fluctuation (divided into groups 0-3 in ascending order of FTF) was significantly associated with logMAR BCVA and the defect length of the foveal EZ band at the final examination. The defect lengths of the foveal EZ band were longitudinally shortened in groups 0 and 1 and were slightly prolonged in groups 2 and 3. The logMAR BCVA showed improvements in groups 0 and 1 and worsened slightly in groups 2 and 3. CONCLUSIONS: Foveal thickness fluctuation was significantly associated with visual acuity and foveal photoreceptor status. Thus, the morphologic and functional prognoses of eyes with BRVO may improve with the identification of the characteristics of eyes with greater FTF and consequently controlling the FTF more strictly.

We aimed to predict keratoconus progression and the need for corneal crosslinking (CXL) using deep learning (DL). Two hundred and seventy-four corneal tomography images taken by Pentacam HR® (Oculus, Wetzlar, Germany) of 158 keratoconus patients were examined. All patients were examined two times or more, and divided into two groups, the progression group and the non-progression group. An axial map of the frontal corneal plane, a pachymetry map, and a combination of these two maps at the initial examination were assessed according to the patients’ age. Training with a convolutional neural network on these learning data objects was conducted. Ninety eyes showed progression and 184 eyes showed no progression. The axial map, the pachymetry map, and their combination combined with patients’ age showed mean AUC values of 0.783, 0.784, and 0.814 (95% confidence interval (0.721–0.845) (0.722–0.846), and (0.755–0.872), respectively), with sensitivities of 87.8%, 77.8%, and 77.8% ((79.2–93.7), (67.8–85.9), and (67.8–85.9)) and specificities of 59.8%, 65.8%, and 69.6% ((52.3–66.9), (58.4–72.6), and (62.4–76.1)), respectively. Using the proposed DL neural network model, keratoconus progression can be predicted on corneal tomography maps combined with patients’ age.

KEGG (http://www.kegg.jp/ or http://www.genome.jp/kegg/) is an encyclopedia of genes and genomes. Assigning functional meanings to genes and genomes both at the molecular and higher levels is the primary objective of the KEGG database project. Molecular-level functions are stored in the KO (KEGG Orthology) database, where each KO is defined as a functional ortholog of genes and proteins. Higher-level functions are represented by networks of molecular interactions, reactions and relations in the forms of KEGG pathway maps, BRITE hierarchies and KEGG modules. In the past the KO database was developed for the purpose of defining nodes of molecular networks, but now the content has been expanded and the quality improved irrespective of whether or not the KOs appear in the three molecular network databases. The newly introduced addendum category of the GENES database is a collection of individual proteins whose functions are experimentally characterized and from which an increasing number of KOs are defined. Furthermore, the DISEASE and DRUG databases have been improved by systematic analysis of drug labels for better integration of diseases and drugs with the KEGG molecular networks. KEGG is moving towards becoming a comprehensive knowledge base for both functional interpretation and practical application of genomic information.

KEGG (Kyoto Encyclopedia of Genes and Genomes; https://www.kegg.jp/ or https://www.genome.jp/kegg/) is a reference knowledge base for biological interpretation of genome sequences and other high-throughput data. It is an integrated database consisting of three generic categories of systems information, genomic information and chemical information, and an additional human-specific category of health information. KEGG pathway maps, BRITE hierarchies and KEGG modules have been developed as generic molecular networks with KEGG Orthology nodes of functional orthologs so that KEGG pathway mapping and other procedures can be applied to any cellular organism. Unfortunately, however, this generic approach was inadequate for knowledge representation in the health information category, where variations of human genomes, especially disease-related variations, had to be considered. Thus, we have introduced a new approach where human gene variants are explicitly incorporated into what we call ‘network variants’ in the recently released KEGG NETWORK database. This allows accumulation of knowledge about disease-related perturbed molecular networks caused not only by gene variants, but also by viruses and other pathogens, environmental factors and drugs. We expect that KEGG NETWORK will become another reference knowledge base for the basic understanding of disease mechanisms and practical use in clinical sequencing and drug development.

KEGG (http://www.kegg.jp/ or http://www.genome.jp/kegg/) is an integrated database resource for biological interpretation of genome sequences and other high-throughput data. Molecular functions of genes and proteins are associated with ortholog groups and stored in the KEGG Orthology (KO) database. The KEGG pathway maps, BRITE hierarchies and KEGG modules are developed as networks of KO nodes, representing high-level functions of the cell and the organism. Currently, more than 4000 complete genomes are annotated with KOs in the KEGG GENES database, which can be used as a reference data set for KO assignment and subsequent reconstruction of KEGG pathways and other molecular networks. As an annotation resource, the following improvements have been made. First, each KO record is re-examined and associated with protein sequence data used in experiments of functional characterization. Second, the GENES database now includes viruses, plasmids, and the addendum category for functionally characterized proteins that are not represented in complete genomes. Third, new automatic annotation servers, BlastKOALA and GhostKOALA, are made available utilizing the non-redundant pangenome data set generated from the GENES database. As a resource for translational bioinformatics, various data sets are created for antimicrobial resistance and drug interaction networks.

Kyoto Encyclopedia of Genes and Genomes (KEGG, http://www.genome.jp/kegg/ or http://www.kegg.jp/) is a database resource that integrates genomic, chemical and systemic functional information. In particular, gene catalogs from completely sequenced genomes are linked to higher-level systemic functions of the cell, the organism and the ecosystem. Major efforts have been undertaken to manually create a knowledge base for such systemic functions by capturing and organizing experimental knowledge in computable forms; namely, in the forms of KEGG pathway maps, BRITE functional hierarchies and KEGG modules. Continuous efforts have also been made to develop and improve the cross-species annotation procedure for linking genomes to the molecular networks through the KEGG Orthology system. Here we report KEGG Mapper, a collection of tools for KEGG PATHWAY, BRITE and MODULE mapping, enabling integration and interpretation of large-scale data sets. We also report a variant of the KEGG mapping procedure to extend the knowledge base, where different types of data and knowledge, such as disease genes and drug targets, are integrated as part of the KEGG molecular networks. Finally, we describe recent enhancements to the KEGG content, especially the incorporation of disease and drug information used in practice and in society, to support translational bioinformatics.

Most human diseases are complex multi-factorial diseases resulting from the combination of various genetic and environmental factors. In the KEGG database resource (http://www.genome.jp/kegg/), diseases are viewed as perturbed states of the molecular system, and drugs as perturbants to the molecular system. Disease information is computerized in two forms: pathway maps and gene/ molecule lists. The KEGG PATHWAY database contains pathway maps for the molecular systems in both normal and perturbed states. In the KEGG DISEASE database, each disease is represented by a list of known disease genes, any known environmental factors at the molecular level, diagnostic markers and therapeutic drugs, which may reflect the underlying molecular system. The KEGG DRUG database contains chemical structures and/or chemical components of all drugs in Japan, including crude drugs and TCM (Traditional Chinese Medicine) formulas, and drugs in the USA and Europe. This database also captures knowledge about two types of molecular networks: the interaction network with target molecules, metabolizing enzymes, other drugs, etc. and the chemical structure transformation network in the history of drug development. The new disease/drug information resource named KEGG MEDICUS can be used as a reference knowledge base for computational analysis of molecular networks, especially, by integrating large-scale experimental datasets.

Inositol 1,4,5-trisphosphate (IP 3 ) is a second messenger that elicits complex spatiotemporal patterns of calcium ion (Ca 2+ ) mobilization and has essential roles in the regulation of many cellular functions. In Madin-Darby canine kidney epithelial cells, green fluorescent protein–tagged pleckstrin homology domain translocated from the plasma membrane to the cytoplasm in response to increased concentration of IP 3 . The detection of translocation enabled monitoring of IP 3 concentration changes within single cells and revealed spatiotemporal dynamics in the concentration of IP 3 synchronous with Ca 2+ oscillations and intracellular and intercellular IP 3 waves that accompanied Ca 2+ waves. Such changes in IP 3 concentration may be fundamental to Ca 2+ signaling.

The level of aggressiveness and the weight of preputial gland and testis in male mice (Mus musculus) were influenced by housing condition, especially by the presence of cohabitant males. In this study, the relation between aggressiveness and the preputial gland and testis weight was studied for various housing conditions. The mouse individually housed in a cage that was linked to another cage containing another male separated by wire net was more aggressive than isolated or paired mice. The preputial gland weight also showed the same tendency, suggesting that the odor from other males promotes pituitary-gonadal activity in males, and that long-term cohabitance inhibits it.

KEGG (Kyoto Encyclopedia of Genes and Genomes) is a bioinformatics resource for understanding the functions and utilities of cells and organisms from both high-level and genomic perspectives. It is a self-sufficient, integrated resource consisting of genomic, chemical, and network information, with cross-references to numerous outside databases. The genomic and chemical information is a complete set of building blocks (genes and molecules) and the network information includes molecular wiring diagrams (interaction/reaction networks) and hierarchical classifications (relation networks) to represent high-level functions. This unit describes protocols for using KEGG, focusing on molecular network information in KEGG PATHWAY, KEGG BRITE, and KEGG MODULE, perturbed molecular networks in KEGG DISEASE and KEGG DRUG, molecular building block information in KEGG GENES and KEGG LIGAND, and a mechanism for linking genomes to molecular networks in KEGG ORTHOLOGY (KO). All of these many protocols enable the user to take advantage of the full breadth of the functionality provided by KEGG. Curr. Protoc. Bioinform. 38:1.12.1-1.12.43. © 2012 by John Wiley & Sons, Inc. Keywords: KEGG pathway map; BRITE functional hierarchy; molecular network; genome annotation; chemical information analysis

Signaling pathways are often represented by networks where each node corresponds to a protein and each edge corresponds to a relationship between nodes such as activation, inhibition and binding. However, such signaling pathways in a cell may be affected by genetic and epigenetic alteration. Some edges may be deleted and some edges may be newly added. The current knowledge about known signaling pathways is available on some public databases, but most of the signaling pathways including changes upon the cell state alterations remain largely unknown. In this paper, we develop an integer programming-based method for inferring such changes by using gene expression data. We test our method on its ability to reconstruct the pathway of colorectal cancer in the KEGG database.

RNA interference (RNAi) induced by small interfering (siRNA) or short hairpin RNA (shRNA) is an important research approach in mammalian genetics. Here we describe a technology called enzymatic production of RNAi library (EPRIL) by which cDNAs are converted by a sequence of enzymatic treatments into an RNAi library consisting of a vast array of different shRNA expression constructs. We applied EPRIL to a single cDNA source and prepared an RNAi library consisting of shRNA constructs with various RNAi efficiencies. High-throughput screening allowed us to rapidly identify the best shRNA constructs from the library. We also describe a new selection scheme using the thymidine kinase gene for obtaining efficient shRNA constructs. Furthermore, we show that EPRIL can be applied to constructing an RNAi library from a cDNA library, providing a basis for future whole-genome phenotypic screening of genes.

Protein transduction domains (PTDs) derived from human immunodeficiency virus Tat protein and herpes simplex virus VP22 protein are useful for the delivery of non-membrane-permeating polar or large molecules into living cells. In the course of our study aiming at evaluating PTD, we unexpectedly found that the fluorescent-dye-labeled glutathione S-transferase (GST) from Schistosoma japonicum without known PTDs was delivered into COS7 cells. The intracellular transduction of GST was also observed in HeLa, NIH3T3, and PC12 cells, as well as in hippocampal primary neurons, indicating that a wide range of cell types is permissive for GST transduction. Furthermore, we showed that the immunosuppressive peptide VIVIT fused with GST successfully inhibits NFAT activation. These results suggest that GST is a novel PTD which may be useful in the intracellular delivery of biologically active molecules, such as small-molecule drugs, bioactive peptides, or proteins.

Characteristic vibrational signals are suggested to be exchanged between the sexes during the spawning behavior in the hime salmon (landlocked red salmon, Oncorhynchus nerka). To check whether the lateral line is used to detect and process these vibrational signals, we examined how Co2+, which is known to block the mechano-electrical transduction in the lateral line detector, affects both the spawning behavior and lateral line response of the male hime salmon. The results showed that Co2+ blocked both the spawning behavior towards the vibrating model (Fig. 2) and the lateral line response to the vibrational stimuli (Figs. 5, 6), if the fish were forced to swim in the water containing 1.0 mM Co2+ for 1 to 1.5 h or longer in the presence of 0.25 mM Ca2+. 0.1 mM Co2+ had similar but weaker effects. These results indicate that the vibrational signals from the vibrating model are detected and processed by the lateral line system to elicit the spawning behavior. These are the first experimental evidences that the lateral line sense is involved in the communicational behavior of the fish.