Background: Herpes simplex virus (HSV) is the leading cause of infectious corneal blindness in the developed world. Eighty percent of the general population develop asymptomatic viral latency in the trigeminal ganglion following orofacial inoculation, but only 0.2% of all such orofacial inoculations are followed by recurrent corneal reactivation. Recurrences still threaten graft survival following penetrating keratoplasty, so that advance identification of patients at increased risk would be helpful in aftercare following penetrating keratoplasty. The HLA B27 phenotype is associated with increased susceptibility to genital HSV. However, no such association has been reported for herpetic eye disease.
Methods: The HLA phenotypes of 129 patients who underwent penetrating keratoplasties for herpetic corneal scars were available for retrospective analysis. Four of these patients were positive for HLA-B27. The 125 HLA-B27-negative patients served as controls. The mean follow-up was 2.2 years. We compared the frequencies of herpetic recurrence and graft failure in the two groups using the Kaplan-Meier method and applied log-rank statistics.
Results: After the average period of follow-up, 75% (three patients) of the HLA B27-positive patients experienced either graft failure or at least one reversible recurrence of the herpetic eye disease, as against only 25% of controls. This difference was highly statistically significant (p<0.01).
Conclusion: This retrospective analysis suggests that the HLA B27 phenotype promotes recurrence of herpetic eye disease following penetrating keratoplasty. HLA B27-positive patients should be closely monitored, and the indications for prophylactic antiviral therapy should be viewed liberally in this group.
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http://dx.doi.org/10.1007/s00347-007-1550-9 | DOI Listing |
Chin Med J (Engl)
January 2025
Department of Cardiovascular Surgery, Fuwai Hospital, National Center for Cardiovascular Diseases, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100037, China.
J Hepatol
January 2025
Department of Hepatology, Sanjay Gandhi Postgraduate institute of medical sciences, Lucknow, UP, INDIA. Electronic address:
EBioMedicine
January 2025
Institute of Immunology, Hannover Medical School, Hannover, Germany; Cluster of Excellence RESIST (EXC 2155), Hannover Medical School, Hannover, Germany; German Centre for Infection Research, Partner Site Hannover-Braunschweig, Hannover, Germany. Electronic address:
Background: Aging increases disease susceptibility and reduces vaccine responsiveness, highlighting the need to better understand the aging immune system and its clinical associations. Studying the human immune system, however, remains challenging due to its complexity and significant inter-individual variability.
Methods: We conducted an immune profiling study of 550 elderly participants (≥60 years) and 100 young controls (20-40 years) from the RESIST Senior Individuals (SI) cohort.
Graefes Arch Clin Exp Ophthalmol
January 2025
Division of Ophthalmology, Department of Visual Sciences, Nihon University School of Medicine, 30-1 Oyaguchi-kamicho, Itabashi-ku, Tokyo, 173-8610, Japan.
Purpose: To investigate the correlation between intracellular dark endothelial spots (IDESs) detected by specular microscopy and the incidence of graft failure after Descemet's membrane endothelial keratoplasty (DMEK).
Methods: We reviewed 100 consecutive DMEK patients performed by a single surgeon at two centres between January 2015 and July 2022. Central corneal thickness was evaluated using anterior segment optical coherence tomography (SS-1000; Tomey, Aichi, Japan), and endothelial cell density was measured using specular microscopy.
J Clin Med
January 2025
Guthrie Cortland Medical Center, Cortland, NY 13045, USA.
Artificial intelligence (AI) in echocardiography represents a transformative advancement in cardiology, addressing longstanding challenges in cardiac diagnostics. Echocardiography has traditionally been limited by operator-dependent variability and subjective interpretation, which impact diagnostic reliability. This study evaluates the role of AI, particularly machine learning (ML), in enhancing the accuracy and consistency of echocardiographic image analysis and its potential to complement clinical expertise.
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