Dehnatkinson9902

Given the role of corneal sensory nerves during epithelial wound repair, we sought to examine the relationship between immune cells and polymodal nociceptors following corneal injury.

Young C57BL/6J mice received a 2 mm corneal epithelial injury. One week later, corneal wholemounts were immunostained using β-tubulin-488, TRPV1 (transient receptor potential ion channel subfamily V member-1, a nonselective cation channel) and immune cell (MHC-II, CD45 and CD68) antibodies. The sum length of TRPV1+ and TRPV1- nerve fibers, and their spatial association with immune cells, was quantified in intact and injured corneas.

TRPV1+ nerves account for ∼40% of the nerve fiber length in the intact corneal epithelium and ∼80% in the stroma. In the superficial epithelial layers, TRPV1+ nerve terminal length was similar in injured and intact corneas. In intact corneas, the density (sum length) of basal epithelial TRPV1+ and TRPV1- nerve fibers was similar, however, in injured corneas, TRPV1+ nerve density was higher comp the cornea.

To investigate the role of elastase in corneal epithelial barrier dysfunction caused by the exoproteins secreted by Pseudomonas aeruginosa.

Exoproteins obtained from Pseudomonas aeruginosa culture supernatant were analyzed by shotgun proteomics approach. In vitro multilayered rabbit corneal epithelial barrier model prepared by air-liquid interface technique (CECs-ALI) were treated with 2 µg/ml exoproteins and/or 8 mM elastase inhibitor. Then the epithelial barrier function was evaluated by transepithelial electrical resistance (TEER) assay and tight junction proteins immunofluorescence. TD-139 Cell viability and the apoptosis rate were examined by CCK8 assay and flow cytometry. TNF-α, IL-6, IL-8, and IL-1β levels were measured by ELISA. Mice cornea treated with exoproteins and/or elastase inhibitor were evaluated in vivo and in vitro.

Elastase (24.2%) is one of the major components of exoproteins. After 2 µg/ml exoproteins were applied to CECs-ALI for two hours, TEER decreased from 323.2 ±  2.7 to 104 ± 6.8 Ω/a reducing virulence and inflammation.

This study investigated the role of limitrin in the pathogenesis of demyelinating optic neuritis using an experimental autoimmune optic neuritis (EAON) model.

EAON was induced in mice via subcutaneous injection with myelin oligodendrocyte glycoprotein peptide. Limitrin protein and mRNA expression were examined in the optic nerve before and after EAON induction. Proinflammatory cytokine expression profiles and degree of glial activation were compared between wild-type (WT) and limitrin knockout mice by real-time PCR and histologic analysis, respectively, after EAON induction. Plasma limitrin levels in patients with optic neuritis and healthy controls were measured by ELISA.

Limitrin expression, observed in astrocytes in the optic nerve of WT mice, was lower in EAON-induced than in naïve WT mice. A comparative analysis of WT and limitrin knockout mice revealed that limitrin deficiency induced more severe neuroinflammation and glial hyperactivation in the optic nerve after EAON induction. Limitrin-deficienma limitrin level may reflect the extent of blood-brain barrier disruption and provide a valuable biomarker reflecting the severity of optic neuritis.

Glaucoma is a multifactorial disease, causing retinal ganglion cells (RGCs) and optic nerve degeneration. The role of diabetes as a risk factor for glaucoma has been postulated but still not unequivocally demonstrated. The purpose of this study is to clarify the effect of diabetes in the early progression of glaucomatous RGC dysfunction preceding intraocular pressure (IOP) elevation, using the DBA/2J mouse (D2) model of glaucoma.

D2 mice were injected with streptozotocin (STZ) obtaining a combined model of diabetes and glaucoma (D2 + STZ). D2 and D2 + STZ mice were monitored for weight, glycemia, and IOP from 3.5 to 6 months of age. In addition, the activity of RGC and outer retina were assessed using pattern electroretinogram (PERG) and flash electroretinogram (FERG), respectively. At the end point, RGC density and astrogliosis were evaluated in flat mounted retinas. In addition, Müller cell reactivity was evaluated in retinal cross-sections. Finally, the expression of inflammation and oxidative stress markers were analyzed.

IOP was not influenced by time or diabetes. In contrast, RGC activity resulted progressively decreased in the D2 group independently from IOP elevation and outer retinal dysfunction. Diabetes exacerbated RGC dysfunction, which resulted independent from variation in IOP and outer retinal activity. Diabetic retinas displayed decreased RGC density and increased glial reactivity given by an increment in oxidative stress and inflammation.

Diabetes can act as an IOP-independent risk factor for the early progression of glaucoma promoting oxidative stress and inflammation-mediated RGC dysfunction, glial reactivity, and cellular death.

Diabetes can act as an IOP-independent risk factor for the early progression of glaucoma promoting oxidative stress and inflammation-mediated RGC dysfunction, glial reactivity, and cellular death.

Image-based deep learning models (DLMs) have been used in other disciplines, but this method has yet to be used to predict surgical outcomes.

To apply image-based deep learning to predict complexity, defined as need for component separation, and pulmonary and wound complications after abdominal wall reconstruction (AWR).

This quality improvement study was performed at an 874-bed hospital and tertiary hernia referral center from September 2019 to January 2020. A prospective database was queried for patients with ventral hernias who underwent open AWR by experienced surgeons and had preoperative computed tomography images containing the entire hernia defect. An 8-layer convolutional neural network was generated to analyze image characteristics. Images were batched into training (approximately 80%) or test sets (approximately 20%) to analyze model output. Test sets were blinded from the convolutional neural network until training was completed. For the surgical complexity model, a separate validation set of computed tomography images was evaluated by a blinded panel of 6 expert AWR surgeons and the surgical complexity DLM.