‘It's a bit of a grey area’: challenges faced by stop smoking practitioners when advising on e-cigarettes

INTRODUCTION: According to UK guidelines, stop smoking practitioners are expected to be open and supportive towards e-cigarette users. As adequate support from practitioners can be instrumental for smokers to successfully quit smoking, it is crucial to explore the challenges that stop smoking practitioners face when advising on e-cigarette use. AIM: This qualitative study explores the challenges that stop smoking practitioners face when advising patients on e-cigarettes. METHODS: A qualitative study was conducted with semi-structured interviews with 10 stop smoking practitioners from four stop smoking services in London. Face to face interviews were recorded and transcribed verbatim. Inductive thematic analysis was conducted to explore practitioners' experiences when advising on e-cigarettes. FINDINGS: Two themes were noted: practitioners' concerns and practitioner–patient interactions. Practitioners were particularly concerned regarding the lack of information, safety issues and the maintenance of addiction linked with e-cigarettes. They emphasised the difficulty of advising on a product that they cannot prescribe. Overall, practitioners expressed the lack of confidence when advising on e-cigarettes since they were often unprepared and not able to answer patients' questions on e-cigarettes. CONCLUSIONS: Stop smoking practitioners' lack of confidence and limited knowledge regarding e-cigarettes emphasises the necessity for training and guidance on e-cigarettes to improve their interactions with patients on this subject. In particular, practitioners need to be provided with clear guidance on how to counsel patients about how and where to buy e-cigarettes

ADDO: a comprehensive toolkit to detect, classify and visualise additive and non-additive Quantitative Trait Loci

MOTIVATION During the past decade, genome-wide association studies (GWAS) have been used to map quantitative trait loci (QTLs) underlying complex traits. However, most GWAS focus on additive genetic effects while ignoring non-additive effects, on the assumption that most QTL act additively. Consequently, QTLs driven by dominance and other non-additive effects could be overlooked. RESULTS We developed ADDO, a highly-efficient tool to detect, classify and visualise quantitative trait loci (QTLs) with additive and non-additive effects. ADDO implements a mixed-model transformation to control for population structure and unequal relatedness that accounts for both additive and dominant genetic covariance among individuals, and decomposes single nucleotide polymorphism (SNP) effects as either additive, partial dominant, dominant and over-dominant. A matrix multiplication approach is used to accelerate the computation: a genome scan on 13 million markers from 900 individuals takes about 5 hours with 10 CPUs. Analysis of simulated data confirms ADDO’s performance on traits with different additive and dominance genetic variance components. We showed two real examples in outbred rat where ADDO identified significant dominant QTL that were not detectable by an additive model. ADDO provides a systematic pipeline to characterize additive and non-additive QTL in whole genome sequence data, which complements current mainstream GWAS software for additive genetic effects. AVAILABILITY AND IMPLEMENTATION ADDO is customizable and convenient to install and provides extensive analytics and visualizations. The package is freely available online at https://github.com/LeileiCui/ADDO

Pathogenic variants in the CYP21A2 gene cause isolated autosomal dominant congenital posterior polar cataracts

Background: Congenital cataracts are the most common cause of visual impairment worldwide. Inherited cataract is a clinically and genetically heterogeneous disease. Here we report disease-causing variants in a novel gene, CYP21A2, causing autosomal dominant posterior polar cataract. Variants in this gene are known to cause autosomal recessive congenital adrenal hyperplasia (CAH). / Methods: Using whole-exome sequencing (WES), we have identified disease-causing sequence variants in two families of British and Irish origin, and in two isolated cases of Asian-Indian and British origin. Bioinformatics analysis confirmed these variants as rare with damaging pathogenicity scores. Segregation was tested within the families using direct Sanger sequencing. / Results: A nonsense variant NM_000500.9 c.955 C > T; p.Q319* was identified in CYP21A2 in two families with posterior polar cataract and in an isolated case with unspecified congenital cataract phenotype. This is the same variant previously linked to CAH and identified as Q318* in the literature. We have also identified a rare missense variant NM_000500.9 c.770 T > C; p.M257T in an isolated case with unspecified congenital cataract phenotype. / Conclusion: This is the first report of separate sequence variants in CYP21A2 associated with congenital cataract. Our findings extend the genetic basis for congenital cataract and add to the phenotypic spectrum of CYP21A2 variants and particularly the CAH associated Q318* variant. CYP21A2 has a significant role in mineralo- and gluco-corticoid biosynthesis. These findings suggest that CYP21A2 may be important for extra-adrenal biosynthesis of aldosterone and cortisol in the eye lens

Extending the phenotypic spectrum of PRPF8, PRPH2, RP1 and RPGR, and the genotypic spectrum of early-onset severe retinal dystrophy

PURPOSE: To present the detailed retinal phenotype of patients with Leber Congenital Amaurosis/Early-Onset Severe Retinal Dystrophy (LCA/EOSRD) caused by sequence variants in four genes, either not (n = 1) or very rarely (n = 3) previously associated with the disease. METHODS: Retrospective case series of LCA/EOSRD from four pedigrees. Chart review of clinical notes, multimodal retinal imaging, electrophysiology, and molecular genetic testing at a single tertiary referral center (Moorfields Eye Hospital, London, UK). RESULTS: The mean age of presentation was 3 months of age, with disease onset in the first year of life in all cases. Molecular genetic testing revealed the following disease-causing variants: PRPF8 (heterozygous c.5804G > A), PRPH2 (homozygous c.620_627delinsTA, novel variant), RP1 (homozygous c.4147_4151delGGATT, novel variant) and RPGR (heterozygous c.1894_1897delGACA). PRPF8, PRPH2, and RP1 variants have very rarely been reported, either as unique cases or case reports, with limited clinical data presented. RPGR variants have not previously been associated with LCA/EOSRD. Clinical history and detailed retinal imaging are presented. CONCLUSIONS: The reported cases extend the phenotypic spectrum of PRPF8-, PRPH2-, RP1-, and RPGR-associated disease, and the genotypic spectrum of LCA/EOSRD. The study highlights the importance of retinal and functional phenotyping, and the importance of specific genetic diagnosis to potential future therapy

Classification of lapses in smokers attempting to stop: A supervised machine learning approach using data from a popular smoking cessation smartphone app

Introduction Smoking lapses after the quit date often lead to full relapse. To inform the development of real-time, tailored lapse prevention support, we used observational data from a popular smoking cessation app to develop supervised machine learning algorithms to distinguish lapse from non-lapse reports. Methods We used data from app users with ≥20 unprompted data entries, which included information about craving severity, mood, activity, social context, and lapse incidence. A series of group-level supervised machine learning algorithms (e.g., Random Forest, XGBoost) were trained and tested. Their ability to classify lapses for out-of-sample i) observations and ii) individuals were evaluated. Next, a series of individual-level and hybrid algorithms were trained and tested. Results Participants (N=791) provided 37,002 data entries (7.6% lapses). The best-performing group-level algorithm had an area under the receiver operating characteristic curve (AUC) of 0.969 (95% CI= 0.961-0.978). Its ability to classify lapses for out-of-sample individuals ranged from poor to excellent (AUC=0.482-1.000). Individual-level algorithms could be constructed for 39/791 participants with sufficient data, with a median AUC of 0.938 (range: 0.518-1.000). Hybrid algorithms could be constructed for 184/791 participants and had a median AUC of 0.825 (range: 0.375-1.000). Discussion Using unprompted app data appeared feasible for constructing a high-performing group-level lapse classification algorithm but its performance was variable when applied to unseen individuals. Algorithms trained on each individual’s dataset, in addition to hybrid algorithms trained on the group plus a proportion of each individual’s data, had improved performance but could only be constructed for a minority of participants. Implications This study used routinely collected data from a popular smartphone app to train and test a series of supervised machine learning algorithms to distinguish lapse from non-lapse events. Although a high-performing group-level algorithm was developed, it had variable performance when applied to new, unseen individuals. Individual-level and hybrid algorithms had somewhat greater performance but could not be constructed for all participants due to lack of variability in the outcome measure. Triangulation of results with those from a prompted study design is recommended prior to intervention development, with real-world lapse prediction likely requiring a balance between unprompted and prompted app data

Artificial intelligence in retinal disease: clinical application, challenges, and future directions

Retinal diseases are a leading cause of blindness in developed countries, accounting for the largest share of visually impaired children, working-age adults (inherited retinal disease), and elderly individuals (age-related macular degeneration). These conditions need specialised clinicians to interpret multimodal retinal imaging, with diagnosis and intervention potentially delayed. With an increasing and ageing population, this is becoming a global health priority. One solution is the development of artificial intelligence (AI) software to facilitate rapid data processing. Herein, we review research offering decision support for the diagnosis, classification, monitoring, and treatment of retinal disease using AI. We have prioritised diabetic retinopathy, age-related macular degeneration, inherited retinal disease, and retinopathy of prematurity. There is cautious optimism that these algorithms will be integrated into routine clinical practice to facilitate access to vision-saving treatments, improve efficiency of healthcare systems, and assist clinicians in processing the ever-increasing volume of multimodal data, thereby also liberating time for doctor-patient interaction and co-development of personalised management plans

Non-Penetrance for Ocular Phenotype in Two Individuals Carrying Heterozygous Loss-of-Function ZEB1 Alleles

ZEB1 loss-of-function (LoF) alleles are known to cause a rare autosomal dominant disorder—posterior polymorphous corneal dystrophy type 3 (PPCD3). To date, 50 pathogenic LoF variants have been identified as disease-causing and familial studies have indicated that the PPCD3 phenotype is penetrant in approximately 95% of carriers. In this study, we interrogated in-house exomes (n = 3616) and genomes (n = 88) for the presence of putative heterozygous LoF variants in ZEB1. Next, we performed detailed phenotyping in a father and his son who carried a novel LoF c.1279C>T; p.(Glu427*) variant in ZEB1 (NM_030751.6) absent from the gnomAD v.2.1.1 dataset. Ocular examination of the two subjects did not show any abnormalities characteristic of PPCD3. GnomAD (n = 141,456 subjects) was also interrogated for LoF ZEB1 variants, notably 8 distinct heterozygous changes presumed to lead to ZEB1 haploinsufficiency, not reported to be associated with PPCD3, have been identified. The NM_030751.6 transcript has a pLI score ≥ 0.99, indicating extreme intolerance to haploinsufficiency. In conclusion, ZEB1 LoF variants are present in a general population at an extremely low frequency. As PPCD3 can be asymptomatic, the true penetrance of ZEB1 LoF variants remains currently unknown but is likely to be lower than estimated by the familial led approaches adopted to date