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588 results on '"Chiquet C"'

2. French recommendations for the management of non-infectious chronic uveitis

Author

Abad, S., Bayen, M., Bielefeld, P., Chalumeau, M., Chiquet, C., Cohen, J.-D., Despert, V., Devilliers, H., Fardeau, C., Georgin-Lavialle, S., Guex-Crosier, Y., Guillaume Czitrom, S., Heron, E., Hofer, M., Agbo Kpati, K.P., Labalette, P., Lemelle, I., Nouar, D., Pugnet, G., Sellam, J., Sene, D., Terrier, B., Trad, G.S., Quartier, P., Saadoun, D., Belot, A., Errera, M.-H., Kaplanski, G., Kodjikian, L., Kone-Paut, I., Miceli-Richard, C., Monnet, D., Audouin-Pajot, C., Seve, P., Uettwiller, F., Weber, M., and Bodaghi, B.

Published
2023
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3. Prise en charge de la toxoplasmose oculaire en France : résultats d’une étude Delphi modifiée

Author

Schaeffer, M., Ballonzoli, L., Gaucher, D., Arndt, C., Angioi-Duprez, K., Baudonnet, R., Bodaghi, B., Bron, A., Chiambaretta, F., Cimon, B., Chiquet, C., Creuzot-Garcher, C., Daien, V., Deleplanque, A.-S., Fricker-Hidalgo, H., Hadjadj, E., Houze, S., Ifrah, T., Korobelnik, J.-F., Labalette, P., Le Lez, M.-Laure, L’Ollivier, C., Mercie, M., Mouriaux, F., Paris, L., Pelloux, H., Pomares, C., Quintyn, J.-C., Rougier, M.-B., Rousseau, A., Soler, V., Talmud, M., Villena, I., Villard, O., Speeg-Schatz, C., Bourcier, T., and Sauer, A.

Published
2022
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12. French recommendations for the management of non-infectious chronic uveitis

Author

Quartier, P., primary, Saadoun, D., additional, Belot, A., additional, Errera, M.-H., additional, Kaplanski, G., additional, Kodjikian, L., additional, Kone-Paut, I., additional, Miceli-Richard, C., additional, Monnet, D., additional, Audouin-Pajot, C., additional, Seve, P., additional, Uettwiller, F., additional, Weber, M., additional, Bodaghi, B., additional, Abad, S., additional, Bayen, M., additional, Bielefeld, P., additional, Chalumeau, M., additional, Chiquet, C., additional, Cohen, J.-D., additional, Despert, V., additional, Devilliers, H., additional, Fardeau, C., additional, Georgin-Lavialle, S., additional, Guex-Crosier, Y., additional, Guillaume Czitrom, S., additional, Heron, E., additional, Hofer, M., additional, Agbo Kpati, K.P., additional, Labalette, P., additional, Lemelle, I., additional, Nouar, D., additional, Pugnet, G., additional, Sellam, J., additional, Sene, D., additional, Terrier, B., additional, and Trad, G.S., additional

Published
2023
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24. Concordance between SIVA, IVAN, and VAMPIRE Software Tools for Semi-Automated Analysis of Retinal Vessel Caliber.

Author

Mautuit, T, Cunnac, P, Cheung, CY, Wong, TY, Hogg, S, Trucco, E, Daien, V, MacGillivray, TJ, Labarère, J, Chiquet, C, Mautuit, T, Cunnac, P, Cheung, CY, Wong, TY, Hogg, S, Trucco, E, Daien, V, MacGillivray, TJ, Labarère, J, and Chiquet, C

Abstract

We aimed to compare measurements from three of the most widely used software packages in the literature and to generate conversion algorithms for measurement of the central retinal artery equivalent (CRAE) and central retinal vein equivalent (CRVE) between SIVA and IVAN and between SIVA and VAMPIRE. We analyzed 223 retinal photographs from 133 human participants using both SIVA, VAMPIRE and IVAN independently for computing CRAE and CRVE. Agreement between measurements was assessed using Bland-Altman plots and intra-class correlation coefficients. A conversion algorithm between measurements was carried out using linear regression, and validated using bootstrapping and root-mean-square error. The agreement between VAMPIRE and IVAN was poor to moderate: The mean difference was 20.2 µm (95% limits of agreement, LOA, -12.2-52.6 µm) for CRAE and 21.0 µm (95% LOA, -17.5-59.5 µm) for CRVE. The agreement between VAMPIRE and SIVA was also poor to moderate: the mean difference was 36.6 µm (95% LOA, -12.8-60.4 µm) for CRAE, and 40.3 µm (95% LOA, 5.6-75.0 µm) for CRVE. The agreement between IVAN and SIVA was good to excellent: the mean difference was 16.4 µm (95% LOA, -4.25-37.0 µm) for CRAE, and 19.3 µm (95% LOA, 0.09-38.6 µm) for CRVE. We propose an algorithm converting IVAN and VAMPIRE measurements into SIVA-estimated measurements, which could be used to homogenize sets of vessel measurements obtained with different software packages.

Published
2022

28. Utilité d’un questionnaire standardisé dans la prise en charge et l’étude épidémiologique des kératites infectieuses sous lentilles de contact

Author

Sauer, A., Abry, F., Berrod, J.-P., Bron, A., Burillon, C., Chiquet, C., Colin, J., Creuzot-Garcher, C., Delbosc, B., Hoffart, L., Kodjikian, L., Labetoulle, M., Malet, F., Merle, H., Robert, P.-Y., Vabres, B., Beynat, J., Brisard, M., Combey de Lambert, A., Donnio, A., Gendron, G., Pagot, R., Saleh, M., Gaucher, D., Speeg-Schatz, C., and Bourcier, T.

Published
2010
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41. Mechanisms of staurosporine induced apoptosis in a human corneal endothelial cell line. (Laboratory Science)

Author

Thuret, G., Chiquet, C., Herrag, S., Dumollard, J-M., Boudard, D., Bednarz, J., Campos, L., and Gain, P.

Subjects
Research, Cornea -- Research, Cell death -- Research
Abstract

Background: Apoptosis very probably plays a key part in endothelial cell loss during corneal storage in organ culture as well as hypothermic storage. However, the mechanisms underlying endothelial apoptosis are [...]

Published
2003

44. Sensitivity and rapidity of blood culture bottles in the detection of cornea organ culture media contamination by bacteria and fungi. (Laboratory Science)

Author

Thuret, G., Carricajo, A., Chiquet, C., Vautrin, A.C., Celle, N., Boureille, M., Acquart, S., Aubert, G., Maugery, J., and Gain, P.

Subjects
Physiological aspects, Research, Cornea -- Physiological aspects -- Research, Bacteria -- Physiological aspects -- Research, Organ culture -- Physiological aspects -- Research, Blood culture -- Physiological aspects -- Research, Fungi -- Physiological aspects -- Research, Organs, Culture of -- Physiological aspects -- Research
Abstract

Aims: To test the bactericidal activity of standard organ culture medium, and to compare the sensitivity and rapidity of blood culture bottles with conventional microbiological methods for detection of bacteria [...]

Published
2002

45. Cornea procurement from very old donors: Post organ culture cornea outcome and recipient graft outcome. (Clinical Science)

Author

Gain, P., Thuret, G., Chiquet, C., Rizzi, P., Pugniet, J.L., Acquart, S., Colpart, J.J., Petit, J.C. Le, and Maugery, J.

Subjects
Physiological aspects, Cornea -- Physiological aspects, Organ preservation -- Physiological aspects, Endothelium -- Physiological aspects, Cytology -- Physiological aspects, Preservation of organs, tissues, etc. -- Physiological aspects
Abstract

Aim: To study the suitability of corneas from very old donors for graft after banking and their clinical and endothelial outcomes in recipients. Methods: 419 corneas stored in organ culture [...]

Published
2002

46. Value of two mortality assessment techniques for organ cultured cornea I endothelium: Trypan blue versus TUNEL technique. (Clinical Science)

Author

Gain, P., Thuret, G., Chiquet, C., Dumollard, J.M., Mosnier, J.F., Burillon, C., Delbosc, B., Herve, P., and Campos, L.

Subjects
Diagnosis, Physiological aspects, Case studies, Methods, Cornea -- Case studies -- Physiological aspects -- Methods, Diagnosis -- Methods -- Case studies -- Physiological aspects, Endothelium -- Physiological aspects -- Case studies -- Methods, Cell death -- Diagnosis -- Case studies -- Methods -- Physiological aspects
Abstract

Background/aim: It is known that trypan blue staining is not a good predictor of loss of corneal endothelial cells (ECs) during organ culture. As it is primarily an indicator of [...]

Published
2002

48. Artificial Intelligence to Detect Papilledema from Ocular Fundus Photographs

Author

MILEA, D., NAJJAR, R. P., ZHUBO, J., TING, D., VASSENEIX, C., Xu, X., AGHSAEI FARD, M., FONSECA, P., VANIKIETI, K., LAGREZE, W. A., LA MORGIA, C., Cheung, C. Y., Hamann, S., Chiquet, C., SANDA, N., Yang, H., MEJICO, L. J., Rougier, Marie-Benedicte, KHO, R., THI HA CHAU, T., Singhal, S., GOHIER, P., CLERMONT-VIGNAL, C., CHENG, C. Y., JONAS, J. B., YU-WAI-MAN, P., FRASER, C. L., Chen, J. J., AMBIKA, S., MILLER, N. R., Liu, Y., NEWMAN, N. J., WONG, T. Y., BIOUSSE, V., Bordeaux population health (BPH), and Université de Bordeaux (UB)-Institut de Santé Publique, d'Épidémiologie et de Développement (ISPED)-Institut National de la Santé et de la Recherche Médicale (INSERM)

Subjects
genetic structures, [SDV.SPEE]Life Sciences [q-bio]/Santé publique et épidémiologie, sense organs, eye diseases, LEHA
Abstract

BackgroundNonophthalmologist physicians do not confidently perform direct ophthalmoscopy. The use of artificial intelligence to detect papilledema and other optic-disk abnormalities from fundus photographs has not been well studied.MethodsWe trained, validated, and externally tested a deep-learning system to classify optic disks as being normal or having papilledema or other abnormalities from 15,846 retrospectively collected ocular fundus photographs that had been obtained with pharmacologic pupillary dilation and various digital cameras in persons from multiple ethnic populations. Of these photographs, 14,341 from 19 sites in 11 countries were used for training and validation, and 1505 photographs from 5 other sites were used for external testing. Performance at classifying the optic-disk appearance was evaluated by calculating the area under the receiver-operating-characteristic curve (AUC), sensitivity, and specificity, as compared with a reference standard of clinical diagnoses by neuro-ophthalmologists.ResultsThe training and validation data sets from 6779 patients included 14,341 photographs: 9156 of normal disks, 2148 of disks with papilledema, and 3037 of disks with other abnormalities. The percentage classified as being normal ranged across sites from 9.8 to 100%; the percentage classified as having papilledema ranged across sites from zero to 59.5%. In the validation set, the system discriminated disks with papilledema from normal disks and disks with nonpapilledema abnormalities with an AUC of 0.99 (95% confidence interval [CI], 0.98 to 0.99) and normal from abnormal disks with an AUC of 0.99 (95% CI, 0.99 to 0.99). In the external-testing data set of 1505 photographs, the system had an AUC for the detection of papilledema of 0.96 (95% CI, 0.95 to 0.97), a sensitivity of 96.4% (95% CI, 93.9 to 98.3), and a specificity of 84.7% (95% CI, 82.3 to 87.1).ConclusionsA deep-learning system using fundus photographs with pharmacologically dilated pupils differentiated among optic disks with papilledema, normal disks, and disks with nonpapilledema abnormalities. (Funded by the Singapore National Medical Research Council and the SingHealth Duke–NUS Ophthalmology and Visual Sciences Academic Clinical Program.)

Published
2020
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