CLINICAL CHARACTERIZATION OF AQUEOUS AND VITREOUS RETINOL-BINDING PROTEIN 3 CONCENTRATIONS IN RELATION TO DIABETIC RETINOPATHY SEVERITY, RETINAL STRUCTURES, AND SYSTEMIC COMPLICATIONS.

PURPOSE: To evaluate Retinol-Binding Protein 3 (RBP3) from photoreceptors in aqueous and its association with vitreous concentrations, diabetic retinopathy (DR) severity, retinal layer thickness, and clinical characteristics in people with diabetes. METHODS: RBP3 concentration was measured by custom-developed enzyme-linked immunosorbent assay in aqueous and correlated with vitreous concentrations in patients from the 50-Year Medalist study and Beetham Eye Institute at Joslin Diabetes Center. RESULTS: Aqueous RBP3 concentration (N = 131) was elevated in eyes with no to mild DR (mean ± SD 0.7 nM ± 0.2) and decreased in eyes with moderate to severe DR (0.65 nM ± 0.3) and proliferative DR (0.5 nM ± 0.2, P < 0.001) compared to eyes without diabetes. Aqueous and vitreous RBP3 concentrations correlated with each other (r = 0.34, P = 0.001) and between fellow eyes (P < 0.0001). History of retinal surgery did not affect aqueous RBP3 concentrations, but cataract surgery affected both vitreous and aqueous levels. Elevated aqueous RBP3 concentration associated with increased thickness of the outer nuclear layer (P = 0.004) and correlated with hemoglobin A1c, whereas vitreous RBP3 concentrations correlated with diabetic systemic complications. CONCLUSION: These findings suggest that aqueous RBP3 concentration may be an important endogenous clinical retinal protective factor, a biomarker for DR severity, and a promising VEGF-independent clinical intervention target in DR.

A Python-based educational software tool for visualizing bioinformatics alignment algorithms.

Bioinformatics education can be defined as the teaching and learning of how to use software tools, along with mathematical and statistical analysis, to solve biological problems. Although many resources are available, most students still struggle to understand even the simplest sequence alignment algorithms. Applying visualizations to these topics benefits both lecturers and students. Unfortunately, educational software for visualizing step-bystep processes in the user experience of sequence alignment algorithms is rare. In this article, an educational visualization tool for biological sequence alignment is presented, and the source code is released in order to encourage the collaborative power of open-source software, with the expectation of further contributions from the community in the future. Two different modules are integrated to enable a student to investigate the characteristics of alignment algorithms.

Feasibility of Prompt Lumbar Drainage in Patients with Aneurysmal Subarachnoid Hemorrhage.

BACKGROUND: Numerous studies have shown that continuous lumbar drainage (LD) reduces spontaneous subarachnoid hemorrhage (SAH)-related complications, decreasing the incidence of cerebral vasospasm, delayed cerebral ischemia , and hydrocephalus in patients treated with coiling or clipping, but performing LD before securing the aneurysm is still controversial. Our hospital has been implementing prompt LD for several years, and we present the results in this paper. METHODS: Between January 2014 and December 2020, a total of 438 patients with SAH were included in this retrospective study. The indication for prompt LD was aneurysmal SAH of modified Fisher grade III or higher without dense intraventricular hemorrhage with obstructive hydrocephalus requiring extraventricular drainage or large intracranial hemorrhage requiring immediate decompression. Prompt LD was performed for 229 patients with SAH, and the control group included 209 patients. We compared in-hospital mortality and vasospasm or hydrocephalus occurrence and procedure-related complications between the two groups. RESULTS: The in-hospital mortality rate was 7.4% for patients with prompt LD and 14.4% for patients without LD, and the difference was significant (P = 0.019). Vasospasm occurred in 10% of patients with prompt LD and 16.7% of controls (P = 0.039). Hydrocephalus requiring extraventricular drainage occurred in 10.9% of the LD group and 28.7% of the control group (P < 0.001). Rebleeding occurrence was 3.1% in the prompt LD group and 5.7% in the non-LD group (P = 0.168). Cerebrospinal fluid infection occurred in 0.4% of the prompt LD group and 1.4% of controls(P = 0.272). CONCLUSIONS: Prompt LD is a feasible option for treating patients with selective aneurysmal SAH.

Dissemination and Symptoms of Monkeypox Virus Infection.

The monkeypox virus is endemic in Africa. However, the virus has spread worldwide since May 2022, and it has now been confirmed in 109 countries (as of September 22, 2022). Thus, an urgent investigation of the outbreak patterns of confirmed cases and evidence of viral infection is needed. This study investigated the spread of monkeypox in 109 countries, including 15 Asian-Pacific countries, by examining the number of cases, symptoms of infection, and the period between diagnosis and death.

Analysis of the Rate of Confirmed COVID-19 Cases in Seoul and Factors Affecting It Using Big Data Analysis.

Coronavirus disease (COVID-19) is caused by infection with the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and presents with mild to severe symptoms. Vaccines have been developed, but COVID-19 persists. Therefore, it is necessary to analyze big data at an early stage to establish an effective infection prevention strategy. To reduce SARS-CoV-2 infection, this study aimed to analyze the infection factors by region within Seoul, Korea and identify the major factors affecting the infection rate. For ease of data aggregation, the study was conducted after a data refinement operation that organized data in the same group into categories, and classified them in detail by specific keywords. Based on the results of this study, if preventive measures are established after identifying the representative infectious factors, periods, and routes of COVID-19 infection, the infection rate could be effectively reduced in the future.

Elevated Retinol Binding Protein 3 Concentrations Are Associated With Decreased Vitreous Inflammatory Cytokines, VEGF, and Progression of Diabetic Retinopathy.

OBJECTIVE: To correlate inflammatory cytokines and vascular endothelial growth factor (VEGF) in vitreous and plasma with vitreous retinol binding protein 3 (RBP3), diabetic retinopathy (DR) severity, and DR worsening in a population with type 1 and type 2 diabetes. RESEARCH DESIGN AND METHODS: RBP3, VEGF, and inflammatory cytokines were measured in plasma and vitreous samples (n = 205) from subjects of the Joslin Medalist Study and Beetham Eye Institute. RESULTS: Higher vitreous RBP3 concentrations were associated with less severe DR (P < 0.0001) and a reduced risk of developing proliferative DR (PDR) (P < 0.0001). Higher RBP3 correlated with increased photoreceptor segment thickness and lower vitreous interleukin-12 (IL-12), tumor necrosis factor-α (TNF-α), and TNF-ß (P < 0.05). PDR was associated with lower vitreous interferon-γ and IL-10 and higher VEGF, IL-6, and IL-15 (P < 0.05), but was not associated with their plasma concentrations. CONCLUSIONS: Higher vitreous RBP3 concentrations are associated with less severe DR and slower rates of progression to PDR, supporting its potential as a biomarker and therapeutic agent for preventing DR worsening, possibly by lowering retinal VEGF and inflammatory cytokines.

Endothelial Cells Induced Progenitors Into Brown Fat to Reduce Atherosclerosis.

BACKGROUND: Insulin resistance (IR) can increase atherosclerotic and cardiovascular risk by inducing endothelial dysfunction, decreasing nitric oxide (NO) production, and accelerating arterial inflammation. The aim is to determine the mechanism by which insulin action and NO production in endothelial cells can improve systemic bioenergetics and decrease atherosclerosis via differentiation of perivascular progenitor cells (PPCs) into brown adipocytes (BAT). METHODS: Studies used various endothelial transgenic and deletion mutant ApoE-/- mice of insulin receptors, eNOS (endothelial NO synthase) and ETBR (endothelin receptor type B) receptors for assessments of atherosclerosis. Cells were isolated from perivascular fat and micro-vessels for studies on differentiation and signaling mechanisms in responses to NO, insulin, and lipokines from BAT. RESULTS: Enhancing insulin's actions on endothelial cells and NO production in ECIRS1 transgenic mice reduced body weight and increased systemic energy expenditure and BAT mass and activity by inducing differentiation of PPCs into beige/BAT even with high-fat diet. However, positive changes in bioenergetics, BAT differentiation from PPCs and weight loss were inhibited by N(gamma)-nitro-L-arginine methyl ester (L-NAME), an inhibitor of eNOS, in ECIRS1 mice and eNOSKO mice. The mechanism mediating NO's action on PPC differentiation into BAT was identified as the activation of solubilized guanylate cyclase/PKGIα (cGMP protein-dependent kinase Iα)/GSK3ß (glycogen synthase kinase 3ß) pathways. Plasma lipidomics from ECIRS1 mice with NO-induced increased BAT mass revealed elevated 12,13-diHOME production. Infusion of 12,13-diHOME improved endothelial dysfunction and decreased atherosclerosis, whereas its reduction had opposite effects in ApoE-/-mice. CONCLUSIONS: Activation of eNOS and endothelial cells by insulin enhanced the differentiation of PPC to BAT and its lipokines and improved systemic bioenergetics and atherosclerosis, suggesting that endothelial dysfunction is a major contributor of energy disequilibrium in obesity.

Remaining Useful Life Prediction of Lithium-Ion Batteries Using Neural Networks with Adaptive Bayesian Learning.

With smart electronic devices delving deeper into our everyday lives, predictive maintenance solutions are gaining more traction in the electronic manufacturing industry. It is imperative for the manufacturers to identify potential failures and predict the system/device's remaining useful life (RUL). Although data-driven models are commonly used for prognostic applications, they are limited by the necessity of large training datasets and also the optimization algorithms used in such methods run into local minima problems. In order to overcome these drawbacks, we train a Neural Network with Bayesian inference. In this work, we use Neural Networks (NN) as the prediction model and an adaptive Bayesian learning approach to estimate the RUL of electronic devices. The proposed prognostic approach functions in two stages-weight regularization using adaptive Bayesian learning and prognosis using NN. A Bayesian framework (particle filter algorithm) is adopted in the first stage to estimate the network parameters (weights and bias) using the NN prediction model as the state transition function. However, using a higher number of hidden neurons in the NN prediction model leads to particle weight decay in the Bayesian framework. To overcome the weight decay issues, we propose particle roughening as a weight regularization method in the Bayesian framework wherein a small Gaussian jitter is added to the decaying particles. Additionally, weight regularization was also performed by adopting conventional resampling strategies to evaluate the efficiency and robustness of the proposed approach and to reduce optimization problems commonly encountered in NN models. In the second stage, the estimated distributions of network parameters were fed into the NN prediction model to predict the RUL of the device. The lithium-ion battery capacity degradation data (CALCE/NASA) were used to test the proposed method, and RMSE values and execution time were used as metrics to evaluate the performance.

TcellInflamedDetector: an R package to distinguish T cell inflamed tumor types from non-T cell inflamed tumor types.

A major issue in the use of immune checkpoint inhibitors is their lack of efficacy in many patients. Previous studies have reported that the T cell inflamed signature can help predict the response to immunotherapy. Thus, many studies have investigated mechanisms of immunotherapy resistance by defining the tumor microenvironment based on T cell inflamed and non-T cell inflamed subsets. Although methods of calculating T cell inflamed subsets have been developed, valid screening tools for distinguishing T cell inflamed from non-T cell inflamed subsets using gene expression data are still needed, since general researchers who are unfamiliar with the details of the equations can experience difficulties using extant scoring formulas to conduct analyses. Thus, we introduce TcellInflamedDetector, an R package for distinguishing T cell inflamed from non-T cell inflamed samples using cancer gene expression data via bulk RNA sequencing.

Investigation on the Microscopic/Macroscopic Mechanical Properties of a Thermally Annealed Nafion® Membrane.

The Nafion® electrolyte membrane, which provides a proton pathway, is an essential element in fuel cell systems. Thermal treatment without additional additives is widely used to modify the mechanical properties of the membrane, to construct reliable and durable electrolyte membranes in the fuel cell. We measured the microscopic mechanical properties of thermally annealed membranes using atomic force microscopy with the two-point method. Furthermore, the macroscopic property was investigated through tensile tests. The microscopic modulus exceeded the macroscopic modulus over all annealing temperature ranges. Additionally, the measured microscopic modulus increased rapidly near 150 °C and was saturated over that temperature, whereas the macroscopic modulus continuously increased until 250 °C. This mismatched micro/macroscopic reinforcement trend indicates that the internal reinforcement of the clusters is induced first until 150 °C. In contrast, the reinforcement among the clusters, which requires more thermal energy, probably progresses even at a temperature of 250 °C. The results showed that the annealing process is effective for the surface smoothing and leveling of the Nafion® membrane until 200 °C.

Scaffolding protein Gab2 is involved in postnatal development and lipopolysaccharide-induced activation of microglia in the mouse brain.

Grb2-associated-binding protein-2 (Gab2) is a member of the Gab/DOS family and functions as an adapter protein downstream of several growth factor signaling pathways. Gab2 is considered an Alzheimer's disease susceptibility gene. However, the role of Gab2 in the brain is still largely unknown. Herein, we report that Gab2 is involved in the postnatal development of microglia in mice. The Gab2 expression in the brain was detected at postnatal day 1 (P1) and increased until P14 but decreased thereafter. The tyrosine phosphorylation of Gab2 (pGab2) was also detected at P1 and increased until P14. Next, we focused on microglial development in Gab2 knockout and heterozygous mice. Although differences were not detected in the cytoplasmic area of Iba1-labeled microglia between Gab2(±) and Gab2(-/-) mice, the analysis of CD68 and cathepsin D (indicators of microglial lysosomal activation) immunolabeling within Iba1+ cells revealed significant underdevelopment of microglial lysosomes in Gab2(-/-) mice at P60. In addition to the developmental abnormality of microglia in Gab2(-/-) mice, lipopolysaccharide-induced lysosomal activation was selectively suppressed in Gab2(-/-) mice compared to that in Gab2(±) mice. Our findings suggest that Gab2 is involved not only in postnatal development but also in lysosomal activation of microglia, therefore Gab2 dysfunction in microglia might potentially contribute to the development of neurodegenerative diseases.

Estimation of the journal distance of Genomics & Informatics from other bioinformatics-driven journals, 2003-2018.

This study explored the trends of Genomics & Informatics during the period of 2003-2018 in comparison with 11 other scholarly journals: BMC Bioinformatics, Algorithms for Molecular Biology: AMB, BMC Systems Biology, Journal of Computational Biology, Briefings in Bioinformatics, BMC Genomics, Nucleic Acids Research, American Journal of Human Genetics, Oncogenesis, Disease Markers, and Microarrays. In total, 22,423 research articles were reviewed. Content analysis was the main method employed in the current research. The results were interpreted using descriptive analysis, a clustering analysis, word embedding, and deep learning techniques. Trends are discussed for the 12 journals, both individually and collectively. This is an extension of our previous study (PMCID: PMC6808643).

Organizing an in-class hackathon to correct PDF-to-text conversion errors of Genomics & Informatics 1.0.

This paper describes a community effort to improve earlier versions of the full-text corpus of Genomics & Informatics by semi-automatically detecting and correcting PDF-to-text conversion errors and optical character recognition errors during the first hackathon of Genomics & Informatics Annotation Hackathon (GIAH) event. Extracting text from multi-column biomedical documents such as Genomics & Informatics is known to be notoriously difficult. The hackathon was piloted as part of a coding competition of the ELTEC College of Engineering at Ewha Womans University in order to enable researchers and students to create or annotate their own versions of the Genomics & Informatics corpus, to gain and create knowledge about corpus linguistics, and simultaneously to acquire tangible and transferable skills. The proposed projects during the hackathon harness an internal database containing different versions of the corpus and annotations.

Using the PubAnnotation ecosystem to perform agile text mining on Genomics & Informatics: a tutorial review.

The prototype version of the full-text corpus of Genomics & Informatics has recently been archived in a GitHub repository. The full-text publications of volumes 10 through 17 are also directly downloadable from PubMed Central (PMC) as XML files. During the Biomedical Linked Annotation Hackathon 6 (BLAH6), we experimented with converting, annotating, and updating 301 PMC full-text articles of Genomics & Informatics using PubAnnotation, a system that provides a convenient way to add PMC publications based on PMCID. Thus, this review aims to provide a tutorial overview of practicing the iterative task of named entity recognition with the PubAnnotation/PubDictionaries/TextAE ecosystem. We also describe developing a conversion tool between the Genia tagger output and the JSON format of PubAnnotation during the hackathon.

Trends in Genomics & Informatics: a statistical review of publications from 2003 to 2018 focusing on the most-studied genes and document clusters.

Genomics & Informatics (NLM title abbreviation: Genomics Inform) is the official journal of the Korea Genome Organization. Herein, we conduct a statistical analysis of the publications of Genomics & Informatics over the 16 years since its inception, with a particular focus on issues relating to article categories, word clouds, and the most-studied genes, drawing on recent reviews of the use of word frequencies in journal articles. Trends in the studies published in Genomics & Informatics are discussed both individually and collectively.

Quantitative identification of technological paradigm changes using knowledge persistence.

This paper proposes a method to quantitatively identify the changes of technological paradigm over time. Specifically, the method identifies previous paradigms and predicts future paradigms by analyzing a patent citation-based knowledge network. The technological paradigm can be considered as dominantly important knowledge in a specific period. Therefore, we adopted the knowledge persistence which can quantify technological impact of an invention to recent technologies in a knowledge network. High knowledge persistence patents are dominant or paradigmatic inventions in a specific period and so changes of top knowledge persistence patents over time can show paradigm shifts. Moreover, since knowledge persistence of paradigmatic inventions are increasing dramatically faster than other ordinary inventions, recent patents having similar increasing trends in knowledge persistence with previous paradigms are identified as future paradigm inventions. We conducted an empirical case study using patents related to the genome sequencing technology. The results show that the identified previous paradigms are widely recognized as critical inventions in the domain by other studies and the identified future paradigms are also qualitatively significant inventions as promising technologies.

Safety and Efficacy of Minimally Invasive Stereotactic Aspiration with Multicatheter Insertion Compared with Conventional Craniotomy for Large Spontaneous Intracerebral Hemorrhage (≥50 mL).

BACKGROUND: Conventional craniotomy (CC) is generally favored for treating large intracerebral hemorrhage (ICH), but the feasibility of minimally invasive stereotactic aspiration for large ICH is controversial. We investigated the efficacy and safety of stereotactic aspiration with multicatheter insertion (SAMCI) for large ICH (≥50 mL). METHODS: In January 2014, we implemented SAMCI for large ICH. The inclusion criteria for SAMCI were as follows: 1) spontaneous supratentorial ICH, 2) ICH volume ≥50 mL, 3) Glasgow Coma Scale score ≥5, and 4) no bilateral fixed dilated pupils. Forty-seven patients who underwent SAMCI from January 2014 to July 2018 composed the SAMCI group, and 34 patients who underwent CC between January 2010 and December 2013 and retrospectively met the inclusion criteria for SAMCI composed the control group (CC group). RESULTS: The mean preoperative ICH volume in the SAMCI and CC groups was 73.1 ± 22.8 and 72.4 ± 21.5 mL, respectively. There were no significant differences between the groups in baseline characteristics except for ICH location. The deep portion of the ICH was higher in the SAMCI group than in the CC group. Postoperative mortality and rebleeding rates were significantly lower in the SAMCI group than in the CC group (4.3% vs. 26.5% and 0% vs. 14.7%, respectively; P < 0.05). Logistic regression analysis showed that SAMCI contributed to a decrease in the mortality rate (odds ratio, 0.04; P = 0.008). CONCLUSIONS: SAMCI is a feasible therapeutic option for large ICH and has low complication rates.

A Short Report on the Markov Property of DNA Sequences on 200-bp Genomic Units of ENCODE/Broad ChromHMM Annotations: A Computational Perspective.

The non-coding DNA in eukaryotic genomes encodes a language which programs chromatin accessibility, transcription factor binding, and various other activities. The objective of this short report was to determine the impact of primary DNA sequence on the epigenomic landscape across 200-base pair genomic units by integrating nine publicly available ChromHMM Browser Extensible Data files of the Encyclopedia of DNA Elements (ENCODE) project. The nucleotide frequency profiles of nine chromatin annotations with the units of 200 bp were analyzed and integrative Markov chains were built to detect the Markov properties of the DNA sequences in some of the active chromatin states of different ChromHMM regions. Our aim was to identify the possible relationship between DNA sequences and the newly built chromatin states based on the integrated ChromHMM datasets of different cells and tissue types.