AIM:To compare the corneal outcome in Fuchs’endothelial dystrophy(FED)patients between femtosecond laser-assisted cataract surgery(FLACS)and conventional phaco surgery(CPS).METHODS:This was a randomized controlled st...AIM:To compare the corneal outcome in Fuchs’endothelial dystrophy(FED)patients between femtosecond laser-assisted cataract surgery(FLACS)and conventional phaco surgery(CPS).METHODS:This was a randomized controlled study comparing one eye surgery by FLACS and the contralateral eye operated by CPS(stop and chop technique)in FED patients.Central corneal thickness,corneal light backscatter,corneal densitometry,and central corneal endothelial cell count and hexagonality(noncontact endothelial cell microscope),and corrected distance visual acuity(CDVA)were assessed preoperatively and at day 1,40,and 180 postoperatively.RESULTS:Totally 31 patients(16 women)were included.At day 40 postoperatively,the mean endothelial cell loss(ECL)was 23.67%by FLACS and 17.30%by CPS(P=0.53).At day 180 postoperatively,ECL was 25.58%in FLACS and 21.32%in CPS(P=0.69).Densitometry data in all layers and all annuli from anterior layer to posterior layer in annuli 0-2,2-6,6-10 and 10-12,total densitometry with all layers and all annuli was performed.A significant difference was found in 6-10(posterior layer)at day 1 with-1.42 grayscale units(GSU;95%CI:-2.66 to-0.19,P=0.02).In 10-12(anterior layer,central layer and all layers)at day 40 were significant different with 7.7(95%CI:1.89 to 13.50,P=0.009),3.97(95%CI:0.23 to 7.71,P=0.03),4.73 GSU(95%CI:0.71 to 8.75,P=0.02),respectively.In the remaining parameters we found no difference between the two groups(P>0.05).Three CPS eyes suffered from corneal decompensation.CONCLUSION:There is no significant difference in corneal outcome between FLACS and CPS.Endothelial cell density and pentacam corneal outcome may be inadequate as outcome parameters in FED patients.展开更多
Background:The goal of this project was to analyze the relationship between cell morphology and proteases/proteases inhibitors(PIs)secretion profile in fuchs endothelial corneal dystrophy(FECD)corneal endothelial cell...Background:The goal of this project was to analyze the relationship between cell morphology and proteases/proteases inhibitors(PIs)secretion profile in fuchs endothelial corneal dystrophy(FECD)corneal endothelial cells(CECs).Methods:Cell morphology was determined using a circularity index(4π×area/perimeter2)for each CECs population extracted from surgical FECD specimens(N=2)and healthy Eye bank corneas(N=3).CECs were cultured 28 days post-confluency.Supernatant was collected and analysed using Proteome Profiler Array detecting 35 proteases and 32 PIs(R&D Systems).Proteome signal was analyzed using Image Studio Lite and correlated with the population’s circularity index.Results:Calculation of circularity index reported different morphologies among FECD populations(0.59±0.18 and 0.64±0.17)and healthy populations(0.44±0.18,0.66±0.13 and 0.71±0.11).Proteome arrays revealed the presence of 10 proteases(ADAMTS1,Cathepsin A,B,D,and X/Z/P,DPPIV/CD26,MMP-2,3 and 12,uPA/Urokinase)and 10 PIs(Protease Nexin II,Cystatin B and C,EMMPRIN/CD147,Latexin,Lipocalin-1,Serpin E1,TFPI,TFPI-2,TIMP-1,2 and 4).Healthy and FECD specimens showed similar variation patterns according to morphology for secretion of ADAMTS1,MMP-3 and 12.However,opposing patterns between healthy and FECD populations were observed for Cathepsin B and D.Moreover,some proteins did not show variation according to phenotype in healthy CECs,but did in FECD CECs:Cathepsin A,Cystatin C,TFPI-2 and total TIMPs.For the other proteins,secretion did not vary according to morphology or no specific pattern was distinguishable.Conclusions:To conclude,our results suggest that cell phenotype is linked to the secretion of certain proteases/PIs in both groups.However,there seems to be differences in secretion of particular proteases and PIs between FECD and healthy specimens as morphology did not have a similar influence.These differences might initiate an imbalance between proteases and PIs explaining the irregular thickening of the Descemet membrane seen in FECD.展开更多
Dear Sir,Iam Dr.Jaya Kaushik from the Department of Ophthalmology of the Post Graduate Institute of Medical Education and Research,Chandigarh,India.I write to present a case report of phacoemulsification in a rare cas...Dear Sir,Iam Dr.Jaya Kaushik from the Department of Ophthalmology of the Post Graduate Institute of Medical Education and Research,Chandigarh,India.I write to present a case report of phacoemulsification in a rare case of cataract associated with keratoconus and Fuch’s endothelial corneal展开更多
Background:To describe the diagnostic performance of a deep learning algorithm in discriminating early-stage Fuchs'endothelial corneal dystrophy(FECD)without clinically evident corneal edema from healthy and late-...Background:To describe the diagnostic performance of a deep learning algorithm in discriminating early-stage Fuchs'endothelial corneal dystrophy(FECD)without clinically evident corneal edema from healthy and late-stage FECD eyes using high-definition optical coherence tomography(HD-OCT).Methods:In this observational case-control study,104 eyes(53 FECD eyes and 51 healthy controls)received HDOCT imaging(Envisu R2210,Bioptigen,Buffalo Grove,IL,USA)using a 6 mm radial scan pattern centered on the corneal vertex.FECD was clinically categorized into early(without corneal edema)and late stage(with corneal edema).A total of 18,720 anterior segment optical coherence tomography(AS-OCT)images(9180 healthy;5400 early-stage FECD;4140 late-stage FECD)of 104 eyes(81 patients)were used to develop and validate a deep learning classification network to differentiate early-stage FECD eyes from healthy eyes and those with clinical edema.Using 5-fold cross-validation on the dataset containing 11,340 OCT images(63 eyes),the network was trained with 80%of these images(3420 healthy;3060 early-stage FECD;2700 late-stage FECD),then tested with 20%(720 healthy;720 early-stage FECD;720 late-stage FECD).Thereafter,a final model was trained with the entire dataset consisting the 11,340 images and validated with a remaining 7380 images of unseen AS-0CT scans of 41 eyes(5040 healthy;1620 early-stage FECD 720 late-stage FECD).Visualization of learned features was done,and area under curve(AUC),specificity,and sensitivity of the prediction outputs for healthy,early and late-stage FECD were computed.Results:The final model achieved an AUC of 0.997±0.005 with 91%sensitivity and 97%specificity in detecting early-stage FECD;an AUC of 0.974±0.005 with a specificity of 92%and a sensitivity up to 100%in detecting late-stage FECD;and an AUC of 0.998±0.001 with a specificity 98%and a sensitivity of 99%in discriminating healthycorneas fromall FECD.Conclusion:Deep learning algorithm is an accurate autonomous novel diagnostic tool of FECD with very high sensitivity and specificity that can be used to grade FECD severity with high accuracy.展开更多
Background:To describe the diagnostic performance of a deep learning algorithm in discriminating early-stage Fuchs’endothelial corneal dystrophy(FECD)without clinically evident corneal edema from healthy and late-sta...Background:To describe the diagnostic performance of a deep learning algorithm in discriminating early-stage Fuchs’endothelial corneal dystrophy(FECD)without clinically evident corneal edema from healthy and late-stage FECD eyes using high-definition optical coherence tomography(HD-OCT).Methods:In this observational case-control study,104 eyes(53 FECD eyes and 51 healthy controls)received HDOCT imaging(Envisu R2210,Bioptigen,Buffalo Grove,IL,USA)using a 6 mm radial scan pattern centered on the corneal vertex.FECD was clinically categorized into early(without corneal edema)and late-stage(with corneal edema).A total of 18,720 anterior segment optical coherence tomography(AS-OCT)images(9180 healthy;5400 early-stage FECD;4140 late-stage FECD)of 104 eyes(81 patients)were used to develop and validate a deep learning classification network to differentiate early-stage FECD eyes from healthy eyes and those with clinical edema.Using 5-fold cross-validation on the dataset containing 11,340 OCT images(63 eyes),the network was trained with 80%of these images(3420 healthy;3060 early-stage FECD;2700 late-stage FECD),then tested with 20%(720 healthy;720 early-stage FECD;720 late-stage FECD).Thereafter,a final model was trained with the entire dataset consisting the 11,340 images and validated with a remaining 7380 images of unseen AS-OCT scans of 41 eyes(5040 healthy;1620 early-stage FECD 720 late-stage FECD).Visualization of learned features was done,and area under curve(AUC),specificity,and sensitivity of the prediction outputs for healthy,early and late-stage FECD were computed.Results:The final model achieved an AUC of 0.997±0.005 with 91%sensitivity and 97%specificity in detecting early-FECD;an AUC of 0.974±0.005 with a specificity of 92%and a sensitivity up to 100%in detecting late-stage FECD;and an AUC of 0.998±0.001 with a specificity 98%and a sensitivity of 99%in discriminating healthy corneas from all FECD.Conclusion:Deep learning algorithm is an accurate autonomous novel diagnostic tool of FECD with very high sensitivity and specificity that can be used to grade FECD severity with high accuracy.展开更多
Background:Fuchs endothelial corneal dystrophy is a hereditary disease and the most frequent cause of corneal transplantation in the worldwide.Its main clinical signs are an accelerated decrease in the number of endot...Background:Fuchs endothelial corneal dystrophy is a hereditary disease and the most frequent cause of corneal transplantation in the worldwide.Its main clinical signs are an accelerated decrease in the number of endothelial cells,thickening of Descemet’s membrane and formation of guttae in the extracellular matrix.The cornea’s ability to maintain stromal dehydration is impaired,causing painful epithelial bullae and loss of vision at the point when the amount of corneal endothelial cells cannot be compensated.At present,apart from corneal transplantation,there is no other effective treatment that prevents blindness.Main text:In this review,we first summarized the mutations of COL8A2,TCF4,TCF8,SLC4A11 and AGBL1 genes in Fuchs endothelial corneal dystrophy.The molecular mechanisms associated with Fuchs endothelial corneal dystrophy,such as endoplasmic reticulum stress and unfolded protein response pathway,oxidative stress,mitochondrial dysregulation pathway,apoptosis pathway,mitophagy,epithelial-mesenchymal transition pathway,RNA toxicity and repeat-associated non-ATG translation,and other pathogenesis,were then explored.Finally,we discussed several potential treatments related to the pathogenesis of Fuchs endothelial corneal dystrophy,which may be the focus of future research.Conclusions:The pathogenesis of Fuchs endothelial corneal dystrophy is very complicated.Currently,corneal transplantation is an important method in the treatment of Fuchs endothelial corneal dystrophy.It is necessary to continuously explore the pathogenesis of Fuchs endothelial corneal dystrophy and establish the scientific foundations for the development of next-generation corneal therapeutics.展开更多
文摘AIM:To compare the corneal outcome in Fuchs’endothelial dystrophy(FED)patients between femtosecond laser-assisted cataract surgery(FLACS)and conventional phaco surgery(CPS).METHODS:This was a randomized controlled study comparing one eye surgery by FLACS and the contralateral eye operated by CPS(stop and chop technique)in FED patients.Central corneal thickness,corneal light backscatter,corneal densitometry,and central corneal endothelial cell count and hexagonality(noncontact endothelial cell microscope),and corrected distance visual acuity(CDVA)were assessed preoperatively and at day 1,40,and 180 postoperatively.RESULTS:Totally 31 patients(16 women)were included.At day 40 postoperatively,the mean endothelial cell loss(ECL)was 23.67%by FLACS and 17.30%by CPS(P=0.53).At day 180 postoperatively,ECL was 25.58%in FLACS and 21.32%in CPS(P=0.69).Densitometry data in all layers and all annuli from anterior layer to posterior layer in annuli 0-2,2-6,6-10 and 10-12,total densitometry with all layers and all annuli was performed.A significant difference was found in 6-10(posterior layer)at day 1 with-1.42 grayscale units(GSU;95%CI:-2.66 to-0.19,P=0.02).In 10-12(anterior layer,central layer and all layers)at day 40 were significant different with 7.7(95%CI:1.89 to 13.50,P=0.009),3.97(95%CI:0.23 to 7.71,P=0.03),4.73 GSU(95%CI:0.71 to 8.75,P=0.02),respectively.In the remaining parameters we found no difference between the two groups(P>0.05).Three CPS eyes suffered from corneal decompensation.CONCLUSION:There is no significant difference in corneal outcome between FLACS and CPS.Endothelial cell density and pentacam corneal outcome may be inadequate as outcome parameters in FED patients.
文摘Background:The goal of this project was to analyze the relationship between cell morphology and proteases/proteases inhibitors(PIs)secretion profile in fuchs endothelial corneal dystrophy(FECD)corneal endothelial cells(CECs).Methods:Cell morphology was determined using a circularity index(4π×area/perimeter2)for each CECs population extracted from surgical FECD specimens(N=2)and healthy Eye bank corneas(N=3).CECs were cultured 28 days post-confluency.Supernatant was collected and analysed using Proteome Profiler Array detecting 35 proteases and 32 PIs(R&D Systems).Proteome signal was analyzed using Image Studio Lite and correlated with the population’s circularity index.Results:Calculation of circularity index reported different morphologies among FECD populations(0.59±0.18 and 0.64±0.17)and healthy populations(0.44±0.18,0.66±0.13 and 0.71±0.11).Proteome arrays revealed the presence of 10 proteases(ADAMTS1,Cathepsin A,B,D,and X/Z/P,DPPIV/CD26,MMP-2,3 and 12,uPA/Urokinase)and 10 PIs(Protease Nexin II,Cystatin B and C,EMMPRIN/CD147,Latexin,Lipocalin-1,Serpin E1,TFPI,TFPI-2,TIMP-1,2 and 4).Healthy and FECD specimens showed similar variation patterns according to morphology for secretion of ADAMTS1,MMP-3 and 12.However,opposing patterns between healthy and FECD populations were observed for Cathepsin B and D.Moreover,some proteins did not show variation according to phenotype in healthy CECs,but did in FECD CECs:Cathepsin A,Cystatin C,TFPI-2 and total TIMPs.For the other proteins,secretion did not vary according to morphology or no specific pattern was distinguishable.Conclusions:To conclude,our results suggest that cell phenotype is linked to the secretion of certain proteases/PIs in both groups.However,there seems to be differences in secretion of particular proteases and PIs between FECD and healthy specimens as morphology did not have a similar influence.These differences might initiate an imbalance between proteases and PIs explaining the irregular thickening of the Descemet membrane seen in FECD.
文摘Dear Sir,Iam Dr.Jaya Kaushik from the Department of Ophthalmology of the Post Graduate Institute of Medical Education and Research,Chandigarh,India.I write to present a case report of phacoemulsification in a rare case of cataract associated with keratoconus and Fuch’s endothelial corneal
基金supported by a NEI K23 award(K23EY026118)NEI core center grant to the University of Miami(Grant No.P30 EY014801)Research to Prevent Blindness(RPB)。
文摘Background:To describe the diagnostic performance of a deep learning algorithm in discriminating early-stage Fuchs'endothelial corneal dystrophy(FECD)without clinically evident corneal edema from healthy and late-stage FECD eyes using high-definition optical coherence tomography(HD-OCT).Methods:In this observational case-control study,104 eyes(53 FECD eyes and 51 healthy controls)received HDOCT imaging(Envisu R2210,Bioptigen,Buffalo Grove,IL,USA)using a 6 mm radial scan pattern centered on the corneal vertex.FECD was clinically categorized into early(without corneal edema)and late stage(with corneal edema).A total of 18,720 anterior segment optical coherence tomography(AS-OCT)images(9180 healthy;5400 early-stage FECD;4140 late-stage FECD)of 104 eyes(81 patients)were used to develop and validate a deep learning classification network to differentiate early-stage FECD eyes from healthy eyes and those with clinical edema.Using 5-fold cross-validation on the dataset containing 11,340 OCT images(63 eyes),the network was trained with 80%of these images(3420 healthy;3060 early-stage FECD;2700 late-stage FECD),then tested with 20%(720 healthy;720 early-stage FECD;720 late-stage FECD).Thereafter,a final model was trained with the entire dataset consisting the 11,340 images and validated with a remaining 7380 images of unseen AS-0CT scans of 41 eyes(5040 healthy;1620 early-stage FECD 720 late-stage FECD).Visualization of learned features was done,and area under curve(AUC),specificity,and sensitivity of the prediction outputs for healthy,early and late-stage FECD were computed.Results:The final model achieved an AUC of 0.997±0.005 with 91%sensitivity and 97%specificity in detecting early-stage FECD;an AUC of 0.974±0.005 with a specificity of 92%and a sensitivity up to 100%in detecting late-stage FECD;and an AUC of 0.998±0.001 with a specificity 98%and a sensitivity of 99%in discriminating healthycorneas fromall FECD.Conclusion:Deep learning algorithm is an accurate autonomous novel diagnostic tool of FECD with very high sensitivity and specificity that can be used to grade FECD severity with high accuracy.
基金This study was supported by a NEI K23 award(K23EY026118)NEI core center grant to the University of Miami(P30 EY014801)Research to Prevent Blindness(RPB).The funding organization had no role in the design or conduct of this research.
文摘Background:To describe the diagnostic performance of a deep learning algorithm in discriminating early-stage Fuchs’endothelial corneal dystrophy(FECD)without clinically evident corneal edema from healthy and late-stage FECD eyes using high-definition optical coherence tomography(HD-OCT).Methods:In this observational case-control study,104 eyes(53 FECD eyes and 51 healthy controls)received HDOCT imaging(Envisu R2210,Bioptigen,Buffalo Grove,IL,USA)using a 6 mm radial scan pattern centered on the corneal vertex.FECD was clinically categorized into early(without corneal edema)and late-stage(with corneal edema).A total of 18,720 anterior segment optical coherence tomography(AS-OCT)images(9180 healthy;5400 early-stage FECD;4140 late-stage FECD)of 104 eyes(81 patients)were used to develop and validate a deep learning classification network to differentiate early-stage FECD eyes from healthy eyes and those with clinical edema.Using 5-fold cross-validation on the dataset containing 11,340 OCT images(63 eyes),the network was trained with 80%of these images(3420 healthy;3060 early-stage FECD;2700 late-stage FECD),then tested with 20%(720 healthy;720 early-stage FECD;720 late-stage FECD).Thereafter,a final model was trained with the entire dataset consisting the 11,340 images and validated with a remaining 7380 images of unseen AS-OCT scans of 41 eyes(5040 healthy;1620 early-stage FECD 720 late-stage FECD).Visualization of learned features was done,and area under curve(AUC),specificity,and sensitivity of the prediction outputs for healthy,early and late-stage FECD were computed.Results:The final model achieved an AUC of 0.997±0.005 with 91%sensitivity and 97%specificity in detecting early-FECD;an AUC of 0.974±0.005 with a specificity of 92%and a sensitivity up to 100%in detecting late-stage FECD;and an AUC of 0.998±0.001 with a specificity 98%and a sensitivity of 99%in discriminating healthy corneas from all FECD.Conclusion:Deep learning algorithm is an accurate autonomous novel diagnostic tool of FECD with very high sensitivity and specificity that can be used to grade FECD severity with high accuracy.
基金supported by the Heilongjiang Postdoctoral Fund(Grant No.LBH-Z18185)the Heilongjiang Provincial Colleges and Universities Basic Scientific Research Fund(Grant No.2018-KYYWF-0484).
文摘Background:Fuchs endothelial corneal dystrophy is a hereditary disease and the most frequent cause of corneal transplantation in the worldwide.Its main clinical signs are an accelerated decrease in the number of endothelial cells,thickening of Descemet’s membrane and formation of guttae in the extracellular matrix.The cornea’s ability to maintain stromal dehydration is impaired,causing painful epithelial bullae and loss of vision at the point when the amount of corneal endothelial cells cannot be compensated.At present,apart from corneal transplantation,there is no other effective treatment that prevents blindness.Main text:In this review,we first summarized the mutations of COL8A2,TCF4,TCF8,SLC4A11 and AGBL1 genes in Fuchs endothelial corneal dystrophy.The molecular mechanisms associated with Fuchs endothelial corneal dystrophy,such as endoplasmic reticulum stress and unfolded protein response pathway,oxidative stress,mitochondrial dysregulation pathway,apoptosis pathway,mitophagy,epithelial-mesenchymal transition pathway,RNA toxicity and repeat-associated non-ATG translation,and other pathogenesis,were then explored.Finally,we discussed several potential treatments related to the pathogenesis of Fuchs endothelial corneal dystrophy,which may be the focus of future research.Conclusions:The pathogenesis of Fuchs endothelial corneal dystrophy is very complicated.Currently,corneal transplantation is an important method in the treatment of Fuchs endothelial corneal dystrophy.It is necessary to continuously explore the pathogenesis of Fuchs endothelial corneal dystrophy and establish the scientific foundations for the development of next-generation corneal therapeutics.