Targeted de novo phasing and long-range assembly by template mutagenesis.

Short-read sequencers provide highly accurate reads at very low cost. Unfortunately, short reads are often inadequate for important applications such as assembly in complex regions or phasing across distant heterozygous sites. In this study, we describe novel bench protocols and algorithms to obtain haplotype-phased sequence assemblies with ultra-low error for regions 10 kb and longer using short reads only. We accomplish this by imprinting each template strand from a target region with a dense and unique mutation pattern. The mutation process randomly and independently converts ∼50% of cytosines to uracils. Sequencing libraries are made from both mutated and unmutated templates. Using de Bruijn graphs and paired-end read information, we assemble each mutated template and use the unmutated library to correct the mutated bases. Templates are partitioned into two or more haplotypes, and the final haplotypes are assembled and corrected for residual template mutations and PCR errors. With sufficient template coverage, the final assemblies have per-base error rates below 10-9. We demonstrate this method on a four-member nuclear family, correctly assembling and phasing three genomic intervals, including the highly polymorphic HLA-B gene.

Safety of routine protamine in the reversal of heparin in percutaneous coronary intervention: A systematic review and meta-analysis.

OBJECTIVES: To determine the safety and efficacy of protamine in the reversal of heparin in percutaneous coronary intervention (PCI). BACKGROUND: Heparin is routinely used for anticoagulation in PCI. Protamine is not used routinely to reverse heparin's effects in PCI, partly due to the perceived risk of stent thrombosis. METHODS: Relevant studies published in English were searched for in PubMed, Embase, and Cochrane databases from inception to April 26th, 2023. Our primary outcome of interest was stent thrombosis in patients receiving PCI for all indications. Secondary outcomes included mortality, major bleeding complications, and hospitalization length. Dichotomous outcomes were analyzed using a Mantel-Haenszel random-effects model and expressed as odds ratios (OR) with their 95% confidence intervals (CI), while continuous outcomes were analyzed using an inverse variance random-effects model expressed as mean differences (MD) with their 95% CI. RESULTS: 11 studies were included in our analysis. Protamine use was not associated with stent thrombosis: OR 0.58, 95% CI: 0.33, 1.01 (p = 0.05) nor with mortality (p = 0.89). Protamine administration was associated with a decreased incidence of major bleeding complications: OR 0.48; 95% CI: 0.25, 0.95 (p = 0.03) and decreased length of hospitalization (p < 0.0001). CONCLUSIONS: In patients pre-treated with dual antiplatelet therapy (DAPT), protamine may be a safe and efficacious option to facilitate earlier sheath removal, reduce major bleeding complications, and reduce length of hospitalization without increased risk of stent thrombosis.

Implementation of deep learning artificial intelligence in vision-threatening disease screenings for an underserved community during COVID-19.

INTRODUCTION: Age-related macular degeneration, diabetic retinopathy, and glaucoma are vision-threatening diseases that are leading causes of vision loss. Many studies have validated deep learning artificial intelligence for image-based diagnosis of vision-threatening diseases. Our study prospectively investigated deep learning artificial intelligence applications in student-run non-mydriatic screenings for an underserved, primarily Hispanic community during COVID-19. METHODS: Five supervised student-run community screenings were held in West New York, New Jersey. Participants underwent non-mydriatic 45-degree retinal imaging by medical students. Images were uploaded to a cloud-based deep learning artificial intelligence for vision-threatening disease referral. An on-site tele-ophthalmology grader and remote clinical ophthalmologist graded images, with adjudication by a senior ophthalmologist to establish the gold standard diagnosis, which was used to assess the performance of deep learning artificial intelligence. RESULTS: A total of 385 eyes from 195 screening participants were included (mean age 52.43 ± 14.5 years, 40.0% female). A total of 48 participants were referred for at least one vision-threatening disease. Deep learning artificial intelligence marked 150/385 (38.9%) eyes as ungradable, compared to 10/385 (2.6%) ungradable as per the human gold standard (p < 0.001). Deep learning artificial intelligence had 63.2% sensitivity, 94.5% specificity, 32.0% positive predictive value, and 98.4% negative predictive value in vision-threatening disease referrals. Deep learning artificial intelligence successfully referred all 4 eyes with multiple vision-threatening diseases. Deep learning artificial intelligence graded images (35.6 ± 13.3 s) faster than the tele-ophthalmology grader (129 ± 41.0) and clinical ophthalmologist (68 ± 21.9, p < 0.001). DISCUSSION: Deep learning artificial intelligence can increase the efficiency and accessibility of vision-threatening disease screenings, particularly in underserved communities. Deep learning artificial intelligence should be adaptable to different environments. Consideration should be given to how deep learning artificial intelligence can best be utilized in a real-world application, whether in computer-aided or autonomous diagnosis.