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AimTo generate data on blindness and visual impairment for planning and monitoring a comprehensive eye care programme in Katsina state of Nigeria.MethodA rapid assessment of avoidable blindness (RAAB) survey methodology was used to select 3120 persons aged 50 years and over. The sample was selected using a multistage cluster randomised sampling. Each participant had visual acuity and lens assessment. Persons with vision less than 6/12 in any eye were assessed for the cause of visual impairment. Persons with cataract were asked why they had not had surgery. Data were captured electronically with the mRAAB Android-based software and analysed with STATA V.14 software.ResultsA response rate of 90.1% was achieved. The age-sex adjusted blindness prevalence was 5.3% (95% CI 5.2% to 5.3%). Women were 30% more likely to be blind (OR 1.3, 95% CI 1.2 to 1.3). The principal causes of blindness were cataract (70%), other posterior segment (12%) and glaucoma (7%); 86.7% of blindness was avoidable. The prevalence of cataract blindness is 2.6% (95% CI 2.5% to 2.6%) with higher odds in women (OR 1.2, 95% CI 1.2 to 1.3, p<0.005). The cataract surgical coverage <6/60 for persons was 28.2% and women were 45% less likely to have had cataract surgery (OR 0.55, 95% CI 0.34 to 0.78, p<0.005). The major barriers to cataract surgery are lack of felt need and the cost of services.ConclusionKatsina state of Nigeria has high burden of avoidable blindness affecting more women. The state eye care programme should have cataract services that are more accessible, affordable and gender sensitive.

The PML::RARA fusion protein is the hallmark driver of Acute Promyelocytic Leukemia (APL) and disrupts retinoic acid signaling, leading to wide-scale gene expression changes and uncontrolled proliferation of myeloid precursor cells. While known to be recruited to binding sites across the genome, its impact on gene regulation and expression is under-explored. Using integrated multi-omics datasets, we characterize the influence of PML::RARA binding on gene expression and regulation in an inducible PML::RARA cell line model and APL patient ex vivo samples. We find that genes whose regulatory elements recruit PML::RARA are not uniformly transcriptionally repressed, as commonly suggested, but also may be upregulated or remain unchanged. We develop a computational machine learning implementation called Regulatory Element Behavior Extraction Learning to deconvolute the complex, local transcription factor binding site environment at PML::RARA bound positions to reveal distinct signatures that modulate how PML::RARA directs the transcriptional response. The PML-RARA gene fusion is the characteristic driver of Acute Promyelocytic Leukaemia (APL) and is known to bind to the genome. Here, the authors characterise the impact of PML-RARA on gene regulation in APL cell lines and patient samples using transcriptomics, epigenomics, and machine learning.