Epidemiological insights into Childhood vernal keratoconjunctivitis in India: Unravelling clinical presentation and environmental influences - The EPIC VKC study - Fourth report of the ICMR EYE SEE study group.

Purpose: To study the epidemiology and clinical presentation of allergic eye diseases (AEDs) and Vernal Keratoconjunctivitis (VKC).

Methods: A cross-sectional- cum-cohort study was conducted in rural and urban areas in different geographical locations (plains, hilly, high-altitude and coastal) in India. Children (5-15 years) were included, information on exposure to environmental factors gathered, participants screened for AED and VKC on torch light, followed by a comprehensive eye examination.

Tralokinumab Treatment of Atopic Dermatits Induces A Progressive Transcriptomic Response.

Atopic dermatitis (AD) is characterized by a complex epidermal barrier deficiency and exaggerated immune responses dominated by type-2-mechanisms with variable contributions of additional immune axes. Interleukin (IL)-13 is overexpressed in AD skin and a key driver of both barrier dysfunction and inflammation. We here prospectively studied the effects of IL-13 inhibition with tralokinumab on cutaneous transcriptome profiles using RNA sequencing of biopsies from 16 moderate-to-severe AD patients obtained at baseline, week 2 and week 16.

Evaluation of topical 1% posaconazole therapy in refractory fungal keratitis.

Purpose: The primary objective was to evaluate the clinical response of refractory cases of fungal keratitis to topical 1% posaconazole therapy.

Methods: Prospective longitudinal non-randomized open label dual-cohort study of 70 eyes of refractory fungal keratitis, 35 were recruited as posaconazole treatment (PCZ) group for topical 1% posaconazole therapy and compared to 35 eyes on conventional antifungal therapy. Study parameters included demographic and treatment details, visual acuity, comprehensive slit-lamp biomicroscopy, clinical photography, ASOCT at recruitment and weekly (week 1, 2, 3 and 4 after treatment initiation).

EmbedGEM: a framework to evaluate the utility of embeddings for genetic discovery.

Summary: Machine learning-derived embeddings are a compressed representation of high content data modalities. Embeddings can capture detailed information about disease states and have been qualitatively shown to be useful in genetic discovery. Despite their promise, embeddings have a major limitation: it is unclear if genetic variants associated with embeddings are relevant to the disease or trait of interest.