Search

Abstract Purpose To report the rate of retinal redetachment after silicone oil removal following rhegmatogenous retinal detachment surgery and to determine potential risk factors. Methods Retrospective observational case series of 161 eyes who underwent rhegmatogenous retinal detachment surgery and subsequent silicone oil removal. Pre- and intraoperative risk factors were evaluated using univariate and multivariate logistic regression. We also evaluated the effect of tamponade duration on anatomical outcomes. Results The median tamponade duration was 5.9 [4.3;7.6] months. Seventeen (10.6%) eyes underwent silicone oil removal within 3 months of surgery, with a median delay of 2.3 [2.0;2.8] months. The rate of retinal detachment after silicone oil removal was 14.9%. A history of previous unsuccessful surgery was the only significant risk factor for retinal redetachment after silicone oil removal (OR 4.8, 95%CI [1.5;19.0], p = 0.02). The use of 360° laser retinopexy and concomitant air or gas tamponade during silicone oil removal were not found to affect the redetachment rate. Eyes with silicone oil tamponade ≤ 3 months showed an increased, albeit not significant, risk of developing recurrent rhegmatogenous retinal detachment after silicone oil removal (35.3% versus 12.5%, p = 0.06). Conclusion A retinal redetachment occurred in 14.9% of eyes undergoing silicone oil removal following rhegmatogenous retinal detachment surgery. Previous failed surgery was associated with a 4.8-fold increased risk of developing recurrent rhegmatogenous retinal detachment after silicone oil removal. Eyes with silicone oil tamponade ≤ 3 months tended to have a higher redetachment rate. Trial registration number ID NCT05647928 (12th April 2022)

Abstract Background A standard task in pharmacogenomics research is identifying genes that may be involved in drug response variability, i.e., pharmacogenes. Because genomic experiments tended to generate many false positives, computational approaches based on the use of background knowledge have been proposed. Until now, only molecular networks or the biomedical literature were used, whereas many other resources are available. Method We propose here to consume a diverse and larger set of resources using linked data related either to genes, drugs or diseases. One of the advantages of linked data is that they are built on a standard framework that facilitates the joint use of various sources, and thus facilitates considering features of various origins. We propose a selection and linkage of data sources relevant to pharmacogenomics, including for example DisGeNET and Clinvar. We use machine learning to identify and prioritize pharmacogenes that are the most probably valid, considering the selected linked data. This identification relies on the classification of gene–drug pairs as either pharmacogenomically associated or not and was experimented with two machine learning methods –random forest and graph kernel–, which results are compared in this article. Results We assembled a set of linked data relative to pharmacogenomics, of 2,610,793 triples, coming from six distinct resources. Learning from these data, random forest enables identifying valid pharmacogenes with a F-measure of 0.73, on a 10 folds cross-validation, whereas graph kernel achieves a F-measure of 0.81. A list of top candidates proposed by both approaches is provided and their obtention is discussed.

Abstract Background Adverse drug reactions (ADRs) are statistically characterized within randomized clinical trials and postmarketing pharmacovigilance, but their molecular mechanism remains unknown in most cases. This is true even for hepatic or skin toxicities, which are classically monitored during drug design. Aside from clinical trials, many elements of knowledge about drug ingredients are available in open-access knowledge graphs, such as their properties, interactions, or involvements in pathways. In addition, drug classifications that label drugs as either causative or not for several ADRs, have been established. Methods We propose in this paper to mine knowledge graphs for identifying biomolecular features that may enable automatically reproducing expert classifications that distinguish drugs causative or not for a given type of ADR. In an Explainable AI perspective, we explore simple classification techniques such as Decision Trees and Classification Rules because they provide human-readable models, which explain the classification itself, but may also provide elements of explanation for molecular mechanisms behind ADRs. In summary, (1) we mine a knowledge graph for features; (2) we train classifiers at distinguishing, on the basis of extracted features, drugs associated or not with two commonly monitored ADRs: drug-induced liver injuries (DILI) and severe cutaneous adverse reactions (SCAR); (3) we isolate features that are both efficient in reproducing expert classifications and interpretable by experts (i.e., Gene Ontology terms, drug targets, or pathway names); and (4) we manually evaluate in a mini-study how they may be explanatory. Results Extracted features reproduce with a good fidelity classifications of drugs causative or not for DILI and SCAR (Accuracy = 0 .74 and 0 .81, respectively). Experts fully agreed that 7 3% and 3 8% of the most discriminative features are possibly explanatory for DILI and SCAR, respectively; and partially agreed (2/3) for 9 0% and 7 7% of them. Conclusion Knowledge graphs provide sufficiently diverse features to enable simple and explainable models to distinguish between drugs that are causative or not for ADRs. In addition to explaining classifications, most discriminative features appear to be good candidates for investigating ADR mechanisms further.