Clinical progression of ocular injury following arsenical vesicant lewisite exposure

<p>Ocular injury by lewisite (LEW), a potential chemical warfare and terrorist agent, results in edema of eyelids, inflammation, massive corneal necrosis and blindness. To enable screening of effective therapeutics to treat ocular injury from LEW, useful clinically-relevant endpoints are essential. Hence, we designed an efficient exposure system capable of exposing up to six New-Zealand white rabbits at one time, and assessed LEW vapor-induced progression of clinical ocular lesions mainly in the cornea. The right eye of each rabbit was exposed to LEW (0.2 mg/L) vapor for 2.5, 5.0, 7.5 and 10.0 min and clinical progression of injury was observed for 28 days post-exposure (dose–response study), or exposed to same LEW dose for 2.5 and 7.5 min and clinical progression of injury was observed for up to 56 days post-exposure (time–response study); left eye served as an unexposed control. Increasing LEW exposure caused corneal opacity within 6 h post-exposure, which increased up to 3 days, slightly reduced thereafter till 3 weeks, and again increased thereafter. LEW-induced corneal ulceration peaked at 1 day post-exposure and its increase thereafter was observed in phases. LEW exposure induced neovascularization starting at 7 days which peaked at 22–35 days post-exposure, and remained persistent thereafter. In addition, LEW exposure caused corneal thickness, iris redness, and redness and swelling of the conjunctiva. Together, these findings provide clinical sequelae of ocular injury following LEW exposure and for the first time establish clinically-relevant quantitative endpoints, to enable the further identification of histopathological and molecular events involved in LEW-induced ocular injury.</p

Automated syndrome diagnosis by three-dimensional facial imaging.

PurposeDeep phenotyping is an emerging trend in precision medicine for genetic disease. The shape of the face is affected in 30-40% of known genetic syndromes. Here, we determine whether syndromes can be diagnosed from 3D images of human faces.MethodsWe analyzed variation in three-dimensional (3D) facial images of 7057 subjects: 3327 with 396 different syndromes, 727 of their relatives, and 3003 unrelated, unaffected subjects. We developed and tested machine learning and parametric approaches to automated syndrome diagnosis using 3D facial images.ResultsUnrelated, unaffected subjects were correctly classified with 96% accuracy. Considering both syndromic and unrelated, unaffected subjects together, balanced accuracy was 73% and mean sensitivity 49%. Excluding unrelated, unaffected subjects substantially improved both balanced accuracy (78.1%) and sensitivity (56.9%) of syndrome diagnosis. The best predictors of classification accuracy were phenotypic severity and facial distinctiveness of syndromes. Surprisingly, unaffected relatives of syndromic subjects were frequently classified as syndromic, often to the syndrome of their affected relative.ConclusionDeep phenotyping by quantitative 3D facial imaging has considerable potential to facilitate syndrome diagnosis. Furthermore, 3D facial imaging of "unaffected" relatives may identify unrecognized cases or may reveal novel examples of semidominant inheritance

Automated syndrome diagnosis by three-dimensional facial imaging.

PurposeDeep phenotyping is an emerging trend in precision medicine for genetic disease. The shape of the face is affected in 30-40% of known genetic syndromes. Here, we determine whether syndromes can be diagnosed from 3D images of human faces.MethodsWe analyzed variation in three-dimensional (3D) facial images of 7057 subjects: 3327 with 396 different syndromes, 727 of their relatives, and 3003 unrelated, unaffected subjects. We developed and tested machine learning and parametric approaches to automated syndrome diagnosis using 3D facial images.ResultsUnrelated, unaffected subjects were correctly classified with 96% accuracy. Considering both syndromic and unrelated, unaffected subjects together, balanced accuracy was 73% and mean sensitivity 49%. Excluding unrelated, unaffected subjects substantially improved both balanced accuracy (78.1%) and sensitivity (56.9%) of syndrome diagnosis. The best predictors of classification accuracy were phenotypic severity and facial distinctiveness of syndromes. Surprisingly, unaffected relatives of syndromic subjects were frequently classified as syndromic, often to the syndrome of their affected relative.ConclusionDeep phenotyping by quantitative 3D facial imaging has considerable potential to facilitate syndrome diagnosis. Furthermore, 3D facial imaging of "unaffected" relatives may identify unrecognized cases or may reveal novel examples of semidominant inheritance