Biomedical subjects

Eija Vesti

Publications and source records attributed to Eija Vesti.

3 recordsLinked to original sources

PubMed2006

Levels and activation of matrix metalloproteinases in aqueous humor are elevated in uveitis-related secondary glaucoma.

PURPOSE: To measure the levels of matrix metalloproteinase (MMP)-2 and tissue inhibitors of metalloproteinases (TIMP)-2 and to study the expression pattern and molecular forms of MMP-2, 8, 9, 13, and 14 and TIMP-1 and 2 in aqueous humor samples in cases of uveitis-related secondary glaucoma (USG) with a history of up to 20 years by comparison with primary open-angle glaucoma (POAG) and cataracts. METHODS: 33 aqueous humor samples were collected during intraocular surgery. MMP-2 and TIMP-2 levels were analyzed by enzyme-linked immunosorbent assay. Molecular forms and activation degrees of MMPs and TIMPs were analyzed by Western immunoblotting and zymography. The results were related to the clinical data. RESULTS: Enzyme-linked immunosorbent assay measurements of both MMP-2 and TIMP-2 were statistically significantly increased in the USG samples relative to POAG and cataracts (P=0.002). In Western blotting all the MMPs showed increased expression and conversion to their active forms in USG, whereas in the POAG and cataract samples MMPs were found mainly in their latent forms. MMP-8, 9, 13, and 14 showed statistically significantly elevated expression in USG relative to POAG and cataracts on densitometric scanning of Western blots. On zymography, MMP-2 and 9 activation was significantly enhanced in USG compared with POAG and cataracts. CONCLUSIONS: Increased expression of MMPs and their conversion to active forms is characteristics of the aqueous humor in USG, even with a very long history. This emphasizes the fact that increased MMP expression reflects inflammatory disease activity and is probably associated with the development of USG and its complications. Although intraocular pressure is elevated in both glaucoma types, MMP expression in POAG more closely resembles that in cataracts, and therefore the role of MMPs in USG differs very markedly from that in POAG.

Adolescent↗

PubMed2003

Comparison of different methods for detecting glaucomatous visual field progression.

PURPOSE: To compare the performance characteristics of seven methods for analyzing glaucomatous visual field progression, using a combination of real patient data and computer simulation techniques. METHODS: The initial and final visual field results, separated by 7 years and measured with the full-threshold 30-2 program of the Humphrey Field Analyzer (Carl Zeiss Meditec, Dublin, CA) of 76 patients with open-angle glaucoma were used. A computer simulation program generated 14 interim semiannual visual fields under conditions of high, moderate, and no variability. Progression was analyzed using the methods of the Advanced Glaucoma Intervention Study (AGIS), the Collaborative Initial Glaucoma Treatment Study (CIGTS), three criteria based on the Glaucoma Change Probability (GCP) analysis, and two criteria based on point-wise linear regression analysis (PLRA). Specificities were calculated by using the same visual field of each patient as both the initial and final field (no progression) under conditions of moderate and high variability. RESULTS: Under the no-variability condition, progression rates were 18% for the AGIS, 36% for CIGTS, 47% to 62% for the three GCP methods, and 72% and 84% for the two PLRA methods. Progression rates increased with greater variability with the three GCP methods and decreased with all other methods. The time to detect confirmed progression was longest for the PLRA methods and shortest for the CIGTS and GCP methods. Under the moderate-variability condition, all methods yielded high specificity. The AGIS, CIGTS, and one of the GCP and PLRA methods were relatively resistant to high variability and maintained high specificities. CONCLUSIONS: The AGIS and CIGTS methods had high specificity, but classified fewer cases of progression than the other methods. The GCP methods determined progression earliest; however, they were generally not as specific. Methods based on PLRA were specific but times to confirmed progression were the longest.

Computer Simulation↗

PubMed2002

Sensitivity differences between real-patient and computer-stimulated visual fields.

PURPOSE: The authors sought to verify computer simulation of visual fields by comparing thresholds of real and corresponding simulated visual fields. METHODS: Four patients with stable glaucomatous visual fields and three patients with progressing glaucomatous visual fields were chosen for the study. Visual fields had been recorded at 6-month intervals for 5 to 7.5 years. A previously described computer simulation program was used to generate a corresponding simulated visual field for each of the real fields. Twenty different levels of response variability and long-term variability were used in the simulations. Pointwise sensitivity differences between real and simulated fields were calculated. The average difference and 95% interval of the differences were analyzed for the different simulation conditions, for the pointwise sensitivities in the real patient fields, and to determine whether the field was stable or progressing. RESULTS: In almost all simulation conditions, the average pointwise sensitivity differences ranged from -1 to 1 dB and were not significantly different among different simulation conditions. The 95% interval of the average difference increased significantly with response variability, whereas long-term variability failed to show any apparent effect. Average pointwise differences and the 95% intervals were greatest in locations where the real-patient field had reduced sensitivity of 14 dB or worse. CONCLUSION: The simulation program provided good estimates of visual field sensitivities. Increasing amounts of response, but not long-term variability, produced a linear increase in the variability of threshold sensitivities. This finding implies that short-term rather than long-term fluctuation is the most important factor determining the variability of thresholds.

Aged↗