Influence of substrate on corneal epithelial cell viability within ocular surface models

Corneal tissue engineering has improved dramatically over recent years. It is now possible to apply these technological advancements to the development of superior in vitro ocular surface models to reduce animal testing. We aim to show the effect different substrates can have on the viability of expanded corneal epithelial cells and that those which more accurately mimic the stromal surface provide the most protection against toxic assault. Compressed collagen gel as a substrate for the expansion of a human epithelial cell line was compared against two well-known substrates for modeling the ocular surface (polycarbonate membrane and conventional collagen gel). Cells were expanded over 10 days at which point cell stratification, cell number and expression of junctional proteins were assessed by electron microscopy, immunohistochemistry and RT-PCR. The effect of increasing concentrations of sodium lauryl sulphate on epithelial cell viability was quantified by MTT assay. Results showed improvement in terms of stratification, cell number and tight junction expression in human epithelial cells expanded upon either the polycarbonate membrane or compressed collagen gel when compared to a the use of a conventional collagen gel. However, cell viability was significantly higher in cells expanded upon the compressed collagen gel. We conclude that the more naturalistic composition and mechanical properties of compressed collagen gels produces a more robust corneal model

ETP: Learning Transferable ECG Representations via ECG-Text Pre-training

In the domain of cardiovascular healthcare, the Electrocardiogram (ECG) serves as a critical, non-invasive diagnostic tool. Although recent strides in self-supervised learning (SSL) have been promising for ECG representation learning, these techniques often require annotated samples and struggle with classes not present in the fine-tuning stages. To address these limitations, we introduce ECG-Text Pre-training (ETP), an innovative framework designed to learn cross-modal representations that link ECG signals with textual reports. For the first time, this framework leverages the zero-shot classification task in the ECG domain. ETP employs an ECG encoder along with a pre-trained language model to align ECG signals with their corresponding textual reports. The proposed framework excels in both linear evaluation and zero-shot classification tasks, as demonstrated on the PTB-XL and CPSC2018 datasets, showcasing its ability for robust and generalizable cross-modal ECG feature learning.Comment: under revie

The mechanical properties of amniotic membrane influence its effect as a biomaterial for ocular surface repair

The human amniotic membrane (AM) is a tissue of fetal origin and has proven to be clinically useful as a biomaterial in the management of various ocular surface disorders including corneal stem cell transplantation. However, its success rate displays a degree of clinical unpredictability. We suggest that the measured variability inAMstiffness offers an explanation for the poor clinical reproducibility when it is used as a substrate for stem cell expansion and transplantation. Corneal epithelial stem cells were expanded upon AM samples possessing different mechanical stiffness. To investigate further the importance of biological substrate stiffness on cell phenotype we replaced AM with type I collagen gels of known stiffness. Substrate stiffness was measured using shear rheometry and surface topography was characterized using scanning electron microscopy and atomic force microscopy. The differentiation status of epithelial cells was examined using RT-PCR, immunohistochemistry and Western blotting. The level of corneal stem cell differentiation was increased in cells expanded upon AM with a high dynamic elastic shear modulus and cell expansion on type I collagen gels confirmed that the level of corneal epithelial stem cell differentiation was related to the substrate’s mechanical properties. In this paper we provide evidence to show that the preparatory method of AM for clinical use can affect its mechanical properties and that these measured differences can influence the level of differentiation within expanded corneal epithelial stem cells

Tissue engineering a fetal membrane

The aim of this study was to construct an artificial fetal membrane (FM) by combination of human amniotic epithelial stem cells (hAESCs) and a mechanically enhanced collagen scaffold containing encapsulated human amniotic stromal fibroblasts (hASFs). Such a tissue-engineered FM may have the potential to plug structural defects in the amniotic sac after antenatal interventions, or to prevent preterm premature rupture of the FM. The hAESCs and hASFs were isolated from human fetal amniotic membrane (AM). Magnetic cell sorting was used to enrich the hAESCs by positive ATP-binding cassette G2 selection. We investigated the use of a laminin/fibronectin (1:1)-coated compressed collagen gel as a novel scaffold to support the growth of hAESCs. A type I collagen gel was dehydrated to form a material mimicking the mechanical properties and ultra-structure of human AM. hAESCs successfully adhered to and formed a monolayer upon the biomimetic collagen scaffold. The resulting artificial membrane shared a high degree of similarity in cell morphology, protein expression profiles, and structure to normal fetal AM. This study provides the first line of evidence that a compacted collagen gel containing hASFs could adequately support hAESCs adhesion and differentiation to a degree that is comparable to the normal human fetal AM in terms of structure and maintenance of cell phenotype

Comparative Genomics Reveals Adaptation by Alteromonas sp. SN2 to Marine Tidal-Flat Conditions: Cold Tolerance and Aromatic Hydrocarbon Metabolism

Alteromonas species are globally distributed copiotrophic bacteria in marine habitats. Among these, sea-tidal flats are distinctive: undergoing seasonal temperature and oxygen-tension changes, plus periodic exposure to petroleum hydrocarbons. Strain SN2 of the genus Alteromonas was isolated from hydrocarbon-contaminated sea-tidal flat sediment and has been shown to metabolize aromatic hydrocarbons there. Strain SN2's genomic features were analyzed bioinformatically and compared to those of Alteromonas macleodii ecotypes: AltDE and ATCC 27126. Strain SN2's genome differs from that of the other two strains in: size, average nucleotide identity value, tRNA genes, noncoding RNAs, dioxygenase gene content, signal transduction genes, and the degree to which genes collected during the Global Ocean Sampling project are represented. Patterns in genetic characteristics (e.g., GC content, GC skew, Karlin signature, CRISPR gene homology) indicate that strain SN2's genome architecture has been altered via horizontal gene transfer (HGT). Experiments proved that strain SN2 was far more cold tolerant, especially at 5°C, than the other two strains. Consistent with the HGT hypothesis, a total of 15 genomic islands in strain SN2 likely confer ecological fitness traits (especially membrane transport, aromatic hydrocarbon metabolism, and fatty acid biosynthesis) specific to the adaptation of strain SN2 to its seasonally cold sea-tidal flat habitat

Med-UniC: Unifying Cross-Lingual Medical Vision-Language Pre-Training by Diminishing Bias

The scarcity of data presents a critical obstacle to the efficacy of medical visionlanguage pre-training (VLP). A potential solution lies in the combination of datasets from various language communities. Nevertheless, the main challenge stems from the complexity of integrating diverse syntax and semantics, language-specific medical terminology, and culture-specific implicit knowledge. Therefore, one crucial aspect to consider is the presence of community bias caused by different languages. This paper presents a novel framework named Unifying Cross-Lingual Medical Vision-Language Pre-Training (Med-UniC), designed to integrate multimodal medical data from the two most prevalent languages, English and Spanish. Specifically, we propose Cross-lingual Text Alignment Regularization (CTR) to explicitly unify cross-lingual semantic representations of medical reports originating from diverse language communities. CTR is optimized through latent language disentanglement, rendering our optimization objective to not depend on negative samples, thereby significantly mitigating the bias from determining positive-negative sample pairs within analogous medical reports. Furthermore, it ensures that the cross-lingual representation is not biased toward any specific language community. Med-UniC reaches superior performance across 5 medical image tasks and 10 datasets encompassing over 30 diseases, offering a versatile framework for unifying multi-modal medical data within diverse linguistic communities. The experimental outcomes highlight the presence of community bias in cross-lingual VLP. Reducing this bias enhances the performance not only in vision-language tasks but also in uni-modal visual tasks.Comment: NeurIPS 2023 Main trac

Enhanced viability of corneal epithelial cells for efficient transport/storage using a structurally-modified calcium alginate hydrogel

Aims: Therapeutic limbal epithelial stem cells could be managed more efficiently if clinically validated batches were transported for ‘on-demand’ use. Materials & methods: In this study, corneal epithelial cell viability in calcium alginate hydrogels was examined under cell culture, ambient and chilled conditions for up to 7 days. Results: Cell viability improved as gel internal pore size increased, and was further enhanced with modification of the gel from a mass to a thin disc. Ambient storage conditions were optimal for supporting cell viability in gel discs. Cell viability in gel discs was significantly enhanced with increases in pore size mediated by hydroxyethyl cellulose. Conclusion: Our novel methodology of controlling alginate gel shape and pore size together provides a more practical and economical alternative to established corneal tissue/cell storage methods

UPLC-MS/MS method for Icariin and metabolites in whole blood of C57 mice: development, validation, and pharmacokinetics study

Icariin, a Chinese medicinal herb with significant effects on Alzheimer’s disease, lacks pharmacokinetic data in mice. To address this, a UPLC-MS/MS method was developed and validated for quantifying Icariin and its metabolites, Icariside I and Icariside II, in the whole blood of mice. The method processed micro-whole blood from serial collections of the same C57 mouse, with well-fitted linearity (0.25–800 ng mL−1) and intra- and inter-day precision and accuracy within 15%. Short-time and autosampler stability were verified, with acceptable extraction recoveries and matrix effects over 74.55%. After intravenous administration (15 mg kg−1) of Icariin in C57 mice, Icariside I and Icariside II were detected within 2 min. However, after the intragastric administration (30, 90, and 150 mg kg−1) of Icariin in C57 mice, Icariin and Icariside I were not detected, and Icariin was rapidly converted into Icariside II. Furthermore, the Cmax and AUC0-t of three doses (30, 90, and 150 mg kg-1) of Icariside II increased as the dose increased. In conclusion, this method improves the traditional method of collecting only one blood sample from each mouse, detecting Icariin and its metabolites in the whole blood of mice, especially for serial collection of micro-whole blood