Genetic basis of channelopathies and cardiomyopathies in Hong Kong Chinese patients: a 10-year regional laboratory experience.

INTRODUCTION: Hereditary channelopathies and cardiomyopathies are potentially lethal and are clinically and genetically heterogeneous, involving at least 90 genes. Genetic testing can provide an accurate diagnosis, guide treatment, and enable cascade screening. The genetic basis among the Hong Kong Chinese population is largely unknown. We aimed to report on 28 unrelated patients with positive genetic findings detected from January 2006 to December 2015. METHODS: Sanger sequencing was performed for 28 unrelated patients with a clinical diagnosis of channelopathies or cardiomyopathies, testing for the following genes: KCNQ1,KCNH2,KCNE1,KCNE2, and SCN5A, for long QT syndrome; SCN5A for Brugada syndrome; RYR2 for catecholaminergic polymorphic ventricular tachycardia; MYH7 and MYBPC3 for hypertrophic cardiomyopathy; LMNA for dilated cardiomyopathy; and PKP2 and DSP for arrhythmogenic right ventricular dysplasia/cardiomyopathy. RESULTS: There were 17 males and 11 females; their mean age at diagnosis was 39 years (range, 1-80 years). The major clinical presentations included syncope, palpitations, and abnormal electrocardiography findings. A family history was present in 13 (46%) patients. There were 26 different heterozygous mutations detected, of which six were novel-two in SCN5A (NM_198056.2:c.429del and c.2024-11T>A), two in MYBPC3 (NM_000256.3:c.906-22G>A and c.2105_2106del), and two in LMNA (NM_170707.3:c.73C>A and c.1209_1213dup). CONCLUSIONS: We have characterised the genetic heterogeneity in channelopathies and cardiomyopathies among Hong Kong Chinese patients in a 10-year case series. Correct interpretation of genetic findings is difficult and requires expertise and experience. Caution regarding issues of non-penetrance, variable expressivity, phenotype-genotype correlation, susceptibility risk, and digenic inheritance is necessary for genetic counselling and cascade screening.

The first pilot study of expanded newborn screening for inborn errors of metabolism and survey of related knowledge and opinions of health care professionals in Hong Kong.

INTRODUCTION: Newborn screening is important for early diagnosis and effective treatment of inborn errors of metabolism (IEM). In response to a 2008 coroners' report of a 14-year-old boy who died of an undiagnosed IEM, the OPathPaed service model was proposed. In the present study, we investigated the feasibility of the OPathPaed model for delivering expanded newborn screening in Hong Kong. In addition, health care professionals were surveyed on their knowledge and opinions of newborn screening for IEM. METHODS: The present prospective study involving three regional hospitals was conducted in phases, from 1 October 2012 to 31 August 2014. The 10 steps of the OPathPaed model were evaluated: parental education, consent, sampling, sample dispatch, dried blood spot preparation and testing, reporting, recall and counselling, confirmation test, treatment and monitoring, and cost-benefit analysis. A fully automated online extraction system for dried blood spot analysis was also evaluated. A questionnaire was distributed to 430 health care professionals by convenience sampling. RESULTS: In total, 2440 neonates were recruited for newborn screening; no true-positive cases were found. Completed questionnaires were received from 210 respondents. Health care professionals supported implementation of an expanded newborn screening for IEM. In addition, there is a substantial need of more education for health care professionals. The majority of respondents supported implementing the expanded newborn screening for IEM immediately or within 3 years. CONCLUSION: The feasibility of OPathPaed model has been confirmed. It is significant and timely that when this pilot study was completed, a government-led initiative to study the feasibility of newborn screening for IEM in the public health care system on a larger scale was announced in the Hong Kong Special Administrative Region Chief Executive Policy Address of 2015.

Convolutional neural network based proton stopping-power-ratio estimation with dual-energy CT: a feasibility study.

Dual-energy computed tomography (DECT) has shown a great potential for lowering range uncertainties, which is necessary for truly leveraging the Bragg peak in proton therapy. However, analytical stopping-power-ratio (SPR) estimation methods have limitations in resolving the influence from the beam-hardening artifact, i.e. CT number variation of the same object scanned under different imaging conditions, such as different patient size and location in the field-of-view (FOV). We present a convolutional neural network (CNN)-based framework to estimate proton SPR that accounts for patient geometry variation and addresses CT number variation. The proposed framework was tested on both prostate and head-and-neck (HN) patient datasets. Simulated CT images were used in order to have a well-defined ground-truth SPR for evaluation. Two training scenarios were evaluated: training with patient CT images (ideal scenario) and training with computational phantoms (realistic scenario). For the training in ideal scenario, computational phantoms were created based on 120 kVp patient CT images using a custom-defined density and material translation curve. Then, 80 kVp and 150 kVp Sn DECT image pairs were obtained using ray-tracing simulation, and their corresponding SPR was calculated from the known density and elemental compositions. For the training in realistic scenario, computational phantoms were created based on the geometry of calibration phantoms. For both scenarios, evaluation was performed on the phantoms created from patient CT images. Compared to a conventional parametric model, U-net trained with computational phantoms (realistic scenario) reduced the SPR estimation uncertainty (95th percentile) of the prostate patient from 1.10% to 0.71%, and HN patient from 2.11% to 1.20%. With the U-net trained with patient images (ideal scenario) uncertainty values were 0.32% and 0.42% for prostate and HN patients, respectively. These results suggest that CNN has great potential to improve the accuracy of SPR estimation in proton therapy by incorporating individual patient geometry information.