Publications by authors named "G P H Leung"

Myeloproliferative neoplasms (MPN) are a heterogeneous group of clonal hematopoietic stem cell disorders characterized clinically by the proliferation of one or more hematopoietic lineage(s). The classical Philadelphia-chromosome (Ph)-negative MPNs include polycythemia vera (PV), essential thrombocythemia (ET) and primary myelofibrosis (PMF). The Asian Myeloid Working Group (AMWG) comprises representatives from fifteen Asian centers experienced in the management of MPN. This consensus from the AMWG aims to review the current evidence in the risk stratification and treatment of Ph-negative MPN, to identify management gaps for future improvement, and to offer pragmatic approaches for treatment commensurate with different levels of resources, drug availabilities and reimbursement policies in its constituent regions. The management of MPN should be patient-specific and based on accurate diagnostic and prognostic tools. In patients with PV, ET and early/prefibrotic PMF, symptoms and risk stratification will guide the need for early cytoreduction. In younger patients requiring cytoreduction and in those experiencing resistance or intolerance to hydroxyurea, recombinant interferon-α preparations (pegylated interferon-α 2A or ropeginterferon-α 2b) should be considered. In myelofibrosis, continuous risk assessment and symptom burden assessment are essential in guiding treatment selection. Allogeneic hematopoietic stem cell transplantation (allo-HSCT) in MF should always be based on accurate risk stratification for disease-risk and post-HSCT outcome. Management of classical Ph-negative MPN entails accurate diagnosis, cytogenetic and molecular evaluation, risk stratification, and treatment strategies that are outcome-oriented (curative, disease modification, improvement of quality-of-life).

Target discovery is crucial for the development of innovative therapeutics and diagnostics. However, current approaches often face limitations in efficiency, specificity, and scalability, necessitating the exploration of novel strategies for identifying and validating disease-relevant targets. Advances in natural language processing have provided new avenues for predicting potential therapeutic targets for various diseases. Here, we present a novel approach for predicting therapeutic targets using a large language model (LLM). We trained a domain-specific BioGPT model on a large corpus of biomedical literature consisting of grant text and developed a pipeline for generating target prediction. Our study demonstrates that pre-training of the LLM model with task-specific texts improves its performance. Applying the developed pipeline, we retrieved prospective aging and age-related disease targets and showed that these proteins are in correspondence with the database data. Moreover, we propose CCR5 and PTH as potential novel dual-purpose anti-aging and disease targets which were not previously identified as age-related but were highly ranked in our approach. Overall, our work highlights the high potential of transformer models in novel target prediction and provides a roadmap for future integration of AI approaches for addressing the intricate challenges presented in the biomedical field.

Wastewater surveillance is considered as a powerful tool in providing cost-effective, population-wide and near real-time surveillance results for controlling infectious diseases (i.e., SARS-CoV-2, influenza virus), complementary to clinical surveillance. To facilitate the utility of this emerging tool, we developed two preanalytical protocols (supernatant-based and pellet-based) for influenza A/B virus (IAV/IBV) wastewater surveillance and applied them to the established wastewater surveillance network for large-scale longitudinal monitoring in Hong Kong. We tested 724 wastewater samples from 24 stationary sites for weekly surveillance for 8 months and 458 wastewater samples from 11 wastewater treatment plants (WWTPs) for more frequent (three times per week) city-wide surveillance for 4 months when influenza season commenced. We found the city-wide IAV virus concentration in wastewater were associated with the detection rate and influenza-like illness plus rates (ILI+) of clinical respiratory specimens and increased significantly after the cancelling of mask mandate that was in place for COVID-19. IBV was at low detection rates and low virus concentration levels, consistent with the low detection rates observed by clinical surveillance. In addition, we conducted virus subtype identification in selected wastewater samples, and observed the H1pdm was the major circulation subtype. Moreover, the obtained virus signals were confirmed by Sanger sequencing of PCR products, suggesting the feasibility and applicability of established methods for rapid detection of influenza virus types and subtypes in wastewater surveillance. This study demonstrates the applicability of IAV/IBV wastewater surveillance to current wastewater infrastructures and it could be used as a rapid and cost-effective surveillance strategy to track virus transmission patterns in the community for timely public health actions in the future.

In recent years, there has been an intensive development of portable ultralow-field magnetic resonance imaging (MRI) for low-cost, shielding-free, and point-of-care applications. However, its quality is poor and scan time is long. We propose a fast acquisition and deep learning reconstruction framework to accelerate brain MRI at 0.055 tesla. The acquisition consists of a single average three-dimensional (3D) encoding with 2D partial Fourier sampling, reducing the scan time of T1- and T2-weighted imaging protocols to 2.5 and 3.2 minutes, respectively. The 3D deep learning leverages the homogeneous brain anatomy available in high-field human brain data to enhance image quality, reduce artifacts and noise, and improve spatial resolution to synthetic 1.5-mm isotropic resolution. Our method successfully overcomes low-signal barrier, reconstructing fine anatomical structures that are reproducible within subjects and consistent across two protocols. It enables fast and quality whole-brain MRI at 0.055 tesla, with potential for widespread biomedical applications.

Purpose: This cross-sectional study aimed to investigate the sectoral variance of optical coherence tomography (OCT) and OCT angiography (OCTA) glaucoma diagnostic parameters across eyes with varying degrees of refractive error.

Methods: Healthy participants, including individuals with axial ametropia, enrolled in the Hong Kong FAMILY cohort were imaged using the Avanti/AngioVue OCT/OCTA system. The OCT and OCTA parameters obtained include peripapillary nerve fiber layer thickness (NFLT), peripapillary nerve fiber layer plexus capillary density (NFLP-CD), and macular ganglion cell complex thickness (GCCT). Sectoral measurements of NFLT, NFLP-CD, and GCCT were based on sectors and hemispheres.

Results: A total of 1339 eyes from 791 participants were stratified based on spherical equivalent refraction: high myopia (<-6 D), low myopia (-6 D to -1 D), emmetropia (-1 D to 1 D), and hyperopia (>1 D). Multivariable broken stick regression models, accounting for age, sex, and signal strength, showed that all NFLT sectors except temporally, the inferior GCCT hemisphere, and half of the NFLP-CD sectors were more affected by ametropia-related covariates than the corresponding global parameters. As expected, the false-positive rates in those sectors were elevated. Finally, sector-specific axial length (AL) and spherical equivalent (SE) adjustments helped reduce the elevated false-positive rates.

Conclusions: The effect of optical magnification is even more prominent among sectors than the global parameters. AL- and SE-based adjustments should be individualized to each sector to mitigate this magnification bias effectively.

Translational Relevance: Identifying sectoral differences among diagnostic parameters and adopting these sector-based adjustments into commercial OCT systems will hopefully reduce false-positive rates related to refractive error.