MONOCLORAL ANTIBODY KILLS HUMAN PLURIPOTENT STEM CELLS BY ONCOSIS VIA EXCESS REACTIVE OXYGEN SPECIES PRODUCTION

Ph.DDOCTOR OF PHILOSOPH

An in vitro human lens capsular bag model adopting a graded culture regime to assess putative impact of IOLs on PCO formation

Purpose: To develop a culture regime for the in vitro human lens capsular bag model that better reflects clinical events following cataract surgery and to use this refined model to evaluate the putative impact of IOLs on PCO formation. Methods: Capsulorhexis and lens extraction were performed on human donor eyes to generate capsular bags attached to the ciliary body by the zonules. Preparations were secured by pinning the ciliary body to a silicone ring and maintaining in 6 mL serum-free EMEM for 28 days or in a graded culture system (Days 1-3, 5% Human serum and 10ng/ml TGFβ2; Days 4-7, 2% Human serum and 1ng/ml TGFβ2; Days 8-14, 1% Human serum and 0.1ng/ml TGFβ2; Days 15-28, Serum-free EMEM), which better mimics clinical changes. Preparations were monitored with phase-contrast and modified-dark-field microscopy. Cell coverage and light scatter were quantified using image analysis software. The trans-differentiation marker, α- SMA and matrix component, fibronectin were assessed by immunocytochemistry. To assess IOLs in the model, Alcon Acrysof or Hoya Vivinex IOLs were implanted in match-paired capsular bags. Results: Match-paired experiments showed that graded culture enhanced growth, facilitated matrix contraction, increased trans-differentiation and promoted matrix deposition relative to serum-free culture. The graded culture protocol was applied to match-paired bags implanted with a Hoya Vivinex or an Alcon Acrysof IOL. The Vivinex demonstrated a lag in growth across the posterior capsule. However, by day 28, coverage was similar, but light-scatter was greater with Acrysof implanted. Cell growth on the Acrysof IOL anterior surface was significantly greater than Vivinex. Conclusions: The graded culture human capsular bag model serves as an excellent system to evaluate and develop intraocular lenses. The Hoya Vivinex IOL showed an overall better level of performance against post-surgical wound-healing and PCO than the Alcon Acrysof using this model

The voltage-gated proton channel Hv1 promotes microglia-astrocyte communication and neuropathic pain after peripheral nerve injury

Activation of spinal cord microglia contributes to the development of peripheral nerve injury-induced neuropathic pain. However, the molecular mechanisms underlying microglial function in neuropathic pain are not fully understood. We identified that the voltage-gated proton channel Hv1, which is functionally expressed in spinal microglia, was significantly increased after spinal nerve transection (SNT). Hv1 mediated voltage-gated proton currents in spinal microglia and mice lacking Hv1 (Hv1 KO) display attenuated pain hypersensitivities after SNT compared with wildtype (WT) mice. In addition, microglial production of reactive oxygen species (ROS) and subsequent astrocyte activation in the spinal cord was reduced in Hv1 KO mice after SNT. Cytokine screening and immunostaining further revealed that IFN-γ expression was compromised in spinal astrocytes in Hv1 KO mice. These results demonstrate that Hv1 proton channel contributes to microglial ROS production, astrocyte activation, IFN-γ upregulation, and subsequent pain hypersensitivities after SNT. This study suggests Hv1-dependent microglia-astrocyte communication in pain hypersensitivities and identifies Hv1 as a novel therapeutic target for alleviating neuropathic pain.The work was supported by the National Institutes of Health grants (R01NS110825 and R01NS088627) to L.J.W and National Research Founda‑tion of Korea grants (NRF-2017M3C7A1025602, 2018R1A5A2024418 and 2021R1A2C3003334) from Korean government MSIT (Ministry of Science and ICT) to S.B.O

B and T Lymphocyte Attenuator Down-regulation by HIV-1 Depends on Type I Interferon and Contributes to T-Cell Hyperactivation

Background. Nonspecific T-cell hyperactivation is the main driving force for human immunodeficiency virus (HIV)–1 disease progression, but the reasons why the excess immune response is not properly shut off are poorly defined

Large expert-curated database for benchmarking document similarity detection in biomedical literature search

Document recommendation systems for locating relevant literature have mostly relied on methods developed a decade ago. This is largely due to the lack of a large offline gold-standard benchmark of relevant documents that cover a variety of research fields such that newly developed literature search techniques can be compared, improved and translated into practice. To overcome this bottleneck, we have established the RElevant LIterature SearcH consortium consisting of more than 1500 scientists from 84 countries, who have collectively annotated the relevance of over 180 000 PubMed-listed articles with regard to their respective seed (input) article/s. The majority of annotations were contributed by highly experienced, original authors of the seed articles. The collected data cover 76% of all unique PubMed Medical Subject Headings descriptors. No systematic biases were observed across different experience levels, research fields or time spent on annotations. More importantly, annotations of the same document pairs contributed by different scientists were highly concordant. We further show that the three representative baseline methods used to generate recommended articles for evaluation (Okapi Best Matching 25, Term Frequency-Inverse Document Frequency and PubMed Related Articles) had similar overall performances. Additionally, we found that these methods each tend to produce distinct collections of recommended articles, suggesting that a hybrid method may be required to completely capture all relevant articles. The established database server located at https://relishdb.ict.griffith.edu.au is freely available for the downloading of annotation data and the blind testing of new methods. We expect that this benchmark will be useful for stimulating the development of new powerful techniques for title and title/abstract-based search engines for relevant articles in biomedical research.Peer reviewe

Load-Identification Method for Flexible Multiple Corrugated Skin Using Spectra Features of FBGs

Flexible corrugated skins are ideal structures for morphing wings, and the associated load measurements are of great significance in structural health monitoring. This paper proposes a novel load-identification method for flexible corrugated skins based on improved Fisher discrimination dictionary learning (FDDL). Several fiber Bragg grating sensors are pasted on the skin to monitor the load on multiple corrugated crests. The loads on different crests cause nonuniform strain fields, and these discriminative spectra are recorded and used as training data. The proposed method involves load-positioning and load-size identification. In the load-size-identification stage, a classifier is trained for every corrugated crest. An interleaved block grouping of samples is introduced to enhance the discrimination of dictionaries, and a two-resolution load-size classifier is introduced to improve the performance and resolution of the grouping labels. An adjustable weight is introduced to the FDDL classification scheme to optimize the contribution from different sensors for different load-size classifiers. With the proposed method, the individual loads on eight crests can be identified by two fiber Bragg grating sensors. The positioning accuracy is 100%, and the mean error of the load-size identification is 0.2106 N, which is sufficiently precise for structural health monitoring

Low-Velocity Impact Localization on a Honeycomb Sandwich Panel Using a Balanced Projective Dictionary Pair Learning Classifier

Carbon-fiber aluminum honeycomb sandwich panels are vulnerable to low-velocity impacts, which can cause structural damage and failures that reduce the bearing performance and reliability of the structure. Therefore, a method for locating such impacts through a sensor network is very important for structural health monitoring. Unlike composite laminates, the stress wave generated by an impact is damped rapidly in a sandwich panel, meaning that the signal qualities measured by different sensors vary greatly, thereby making it difficult to locate the impact. This paper presents a method for locating impacts on carbon-fiber aluminum honeycomb sandwich panels utilizing fiber Bragg grating sensors. This method is based on a projective dictionary pair learning algorithm and uses structural sparse representation for impact localization. The measurement area is divided into several sub-areas, and a corresponding dictionary is trained separately for each sub-area. For each dictionary, the sensors are grouped into main sensors within the sub-area and auxiliary sensors outside the sub-area. A balancing weight factor is added to optimize the proportion of the two types of sensor in the recognition model, and the algorithm for determining the balancing weight factor is designed to suppress the negative effects on the positioning of the sensors with poor signal quality. The experimental results show that on a 300 mm × 300 mm × 15 mm sandwich panel, the impact positioning accuracy of this method is 96.7% and the average positioning error is 0.85 mm, which are both sufficient for structural health monitoring