Disruption of the precise balance of positive and negative molecular regulators of blood and lymphatic vessels can lead to myriad diseases that affect one in four people worldwide. Although dozens of natural inhibitors of hemangiogenesis have been identified, an endogenous selective inhibitor of lymphatic vessels has not yet been described. We report the existence of a secreted, splice variant of vascular endothelial growth factor receptor-2 (sVegfr-2) that inhibits developmental and reparative lymphangiogenesis by blocking Vegf-c. Tissue-specific loss of sVegfr-2 in mice induced, at birth, spontaneous lymphatic invasion of the normally alymphatic cornea and hyperplasia of skin lymphatics without accompanying changes in blood vasculature. sVegfr-2 inhibited lymphangiogenesis but not hemangiogenesis induced by corneal suture injury or transplantation, enhanced corneal allograft survival, and suppressed lymphangioma cellular proliferation. Naturally occurring sVegfr-2 is a molecular uncoupler of blood and lymphatic vessels whose modulation might have a therapeutic role in lymphatic vascular malformations, transplantation, and potentially in tumor lymphangiogenesis and lymphedema.

We developed an unsupervised machine learning algorithm and applied it to big corneal parameters to identify and monitor keratoconus stages. A big dataset of corneal swept source optical coherence tomography (OCT) images of 12,242 eyes acquired from SS-1000 CASIA OCT Imaging Systems in multiple centers across Japan was assembled. A total of 3,156 eyes with valid Ectasia Status Index (ESI) between zero and 100% were selected for the downstream analysis. Four hundred and twenty corneal topography, elevation, and pachymetry parameters (excluding ESI Keratoconus indices) were selected. The algorithm included three major steps. 1) Principal component analysis (PCA) was used to linearly reduce the dimensionality of the input data from 420 to eight significant principal components. 2) Manifold learning was used to further reducing the selected principal components nonlinearly to two eigen-parameters. 3) Finally, a density-based clustering was applied to the eigen-parameters to identify eyes with keratoconus. Visualization of clusters in 2-D space was used to validate the quality of learning subjectively and ESI was used to assess the accuracy of the identified clusters objectively. The proposed method identified four clusters; I: a cluster composed of mostly normal eyes (224 eyes with ESI equal to zero, 23 eyes with ESI between five and 29, and nine eyes with ESI greater than 29), II: a cluster composed of mostly healthy eyes and eyes with forme fruste keratoconus (1772 eyes with ESI equal to zero, 698 eyes with ESI between five and 29, and 117 eyes with ESI greater than 29), III: a cluster composed of mostly eyes with mild keratoconus stage (184 eyes with ESI greater than 29, 74 eyes with ESI between five and 29, and 6 eyes with ESI equal to zero), and IV: a cluster composed of eyes with mostly advanced keratoconus stage (80 eyes had ESI greater than 29 and 1 eye had ESI between five and 29). We found that keratoconus status and severity can be well identified using unsupervised machine learning algorithms along with linear and non-linear corneal data transformation. The proposed method can better identify and visualize the keratoconus stages.

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We showed that clinical phenotype in early phase of BD was different depending on onset age and sex.

Background/AimsTo analyse graft detachments prior to rebubbling, the influence of rebubbling on the postoperative outcome after Descemet membrane endothelial keratoplasty (DMEK) and the need for rebubbling on the contralateral eye.MethodsIn this retrospective cohort study, out of 1541 DMEKs, optical coherence tomography scans and clinical records of 499 eyes undergoing rebubbling after DMEK at the University Hospital of Cologne, Cologne, Germany, were examined. Main Outcome measures were (a) number, localisation and size of graft detachments; (b) influence of rebubbling/s on postoperative outcome after 12 months; and (c) rebubbling risk of the contralateral eye after DMEK.ResultsMean number of detachment areas was 2.02±0.9. Mean lateral diameter of all detachments was 4534.76±1920.83 μm. Mean axial diameter was 382.53±282.02 μm. Detachments were equally distributed over all regions of the cornea. Best spectacle corrected visual acuity ( BSCVA) after 12 months was 0.197±0.23 logarithm of the minimum angle of resolution, endothelial cell density (ECD) was 1575.21±397.71 cells/mm2 and mean central corneal thickness (CCT) was 566.37±68.11 μm. BSCVA, CCT, ECD or endothelial cell loss of all rebubbled patients were not influenced by the number of rebubblings or the time between DMEK and rebubbling. Of the rebubbled patients, which received a DMEK subsequently on the other eye, 193 (58.8%) also received a rebubbling, which was significantly higher, when compared to the overall rebubbling rate of 32.3% (p=0.000).ConclusionsThe overall number of rebubblings has no influence on the postoperative outcome after DMEK, if a rebubbling becomes necessary. Patients who received a rebubbling on one eye have an elevated risk for a rebubbling on the fellow eye.

The achievement of CR was independently associated with appropriate-for-stage therapy for HL, with HAART, and with a baseline CD4 count > or =100 cells/microL. The only variable independently associated with OS was the achievement of CR.

Purpose: To describe several essential surgical techniques that overcome difficulties in performing Descemet membrane endothelial keratoplasty (DMEK) for inexperienced surgeons, especially those who perform DMEK on eyes of Asian patients.Methods: Nine eyes of 9 Asian patients with bullous keratopathy who underwent DMEK were analyzed retrospectively. All patients were given a diuretic such as D-mannitol or acetazolamide shortly before surgery, with retrobulbar anesthesia and a Nadbath facial nerve block. Core vitrectomy before DMEK was performed in several cases in which a high vitreous pressure during surgery was predicted. The donor graft was stained with trypan blue, and a 25-G anterior chamber maintenance cannula was used to maintain the anterior chamber depth during graft insertion in all eyes. Results:The cornea became clear in all eyes. The best spectaclecorrected visual acuity had improved significantly 6 months after the surgery compared with preoperative values (P = 0.026). The corneal endothelial cell density was 1371 cells per square millimeter at postoperative 6 months. Conclusions:Although DMEK is technically difficult, especially for inexperienced surgeons who operate on eyes of Asian patients, controlling anterior chamber pressure using various manipulations may help to prevent iatrogenic primary graft failure and lead to successful DMEK.

Purpose: To evaluate the ability of deep learning (DL) models to detect obstructive meibomian gland dysfunction (MGD) using in vivo laser confocal microscopy images. Methods: For this study, we included 137 images from 137 individuals with obstructive MGD (mean age, 49.9 ± 17.7 years; 44 men and 93 women) and 84 images from 84 individuals with normal meibomian glands (mean age, 53.3 ± 19.6 years; 29 men and 55 women). We constructed and trained 9 different network structures and used single and ensemble DL models and calculated the area under the curve, sensitivity, and specificity to compare the diagnostic abilities of the DL. Results: For the single DL model (the highest model; DenseNet-201), the area under the curve, sensitivity, and specificity for diagnosing obstructive MGD were 0.966%, 94.2%, and 82.1%, respectively, and for the ensemble DL model (the highest ensemble model; VGG16, DenseNet-169, DenseNet-201, and InceptionV3), 0.981%, 92.1%, and 98.8%, respectively. Conclusions: Our network combining DL and in vivo laser confocal microscopy learned to differentiate between images of healthy meibomian glands and images of obstructive MGD with a high level of accuracy that may allow for automatic obstructive MGD diagnoses in patients in the future.