Use of biologics for inflammatory bowel disease in Hong Kong: consensus statement

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Imaging of X-Ray-Excited Emissions from Quantum Dots and Biological Tissue in Whole Mouse

© The Author(s) 2019. This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.Optical imaging in clinical and preclinical settings can provide a wealth of biological information, particularly when coupled with targetted nanoparticles, but optical scattering and absorption limit the depth and resolution in both animal and human subjects. Two new hybrid approaches are presented, using the penetrating power of X-rays to increase the depth of optical imaging. Foremost, we demonstrate the excitation by X-rays of quantum-dots (QD) emitting in the near-infrared (NIR), using a clinical X-ray system to map the distribution of QDs at depth in whole mouse. We elicit a clear, spatially-resolved NIR signal from deep organs (brain, liver and kidney) with short (1 second) exposures and tolerable radiation doses that will permit future in vivo applications. Furthermore, X-ray-excited endogenous emission is also detected from whole mouse. The use of keV X-rays to excite emission from QDs and tissue represent novel biomedical imaging technologies, and exploit emerging QDs as optical probes for spatial-temporal molecular imaging at greater depth than previously possible.Peer reviewe

Preserving Differential Privacy in Convolutional Deep Belief Networks

The remarkable development of deep learning in medicine and healthcare domain presents obvious privacy issues, when deep neural networks are built on users' personal and highly sensitive data, e.g., clinical records, user profiles, biomedical images, etc. However, only a few scientific studies on preserving privacy in deep learning have been conducted. In this paper, we focus on developing a private convolutional deep belief network (pCDBN), which essentially is a convolutional deep belief network (CDBN) under differential privacy. Our main idea of enforcing epsilon-differential privacy is to leverage the functional mechanism to perturb the energy-based objective functions of traditional CDBNs, rather than their results. One key contribution of this work is that we propose the use of Chebyshev expansion to derive the approximate polynomial representation of objective functions. Our theoretical analysis shows that we can further derive the sensitivity and error bounds of the approximate polynomial representation. As a result, preserving differential privacy in CDBNs is feasible. We applied our model in a health social network, i.e., YesiWell data, and in a handwriting digit dataset, i.e., MNIST data, for human behavior prediction, human behavior classification, and handwriting digit recognition tasks. Theoretical analysis and rigorous experimental evaluations show that the pCDBN is highly effective. It significantly outperforms existing solutions

Tgf-β signaling : from tissue fibrosis to tumor microenvironment

202110 bcvcVersion of RecordPublishe

Single-cell RNA sequencing uncovers a neuron-like macrophage subset associated with cancer pain

202307 bcchVersion of RecordRGCOthersDepartment of Anatomical and Cellular Pathology; Chinese University of Hong KongPublishe

Low-dose azathioprine is effective in maintaining remission in steroid-dependent ulcerative colitis: results from a territory-wide Chinese population-based IBD registry

BACKGROUND: Whether low-dose azathioprine (AZA) is effective in maintaining remission in patients with steroid-dependent ulcerative colitis (UC) remains unclear. We assessed the efficacy and safety of low-dose AZA in a Chinese population with UC. METHODS: We identified steroid-dependent UC patients in clinical remission on AZA maintenance therapy from a territory-wide IBD Registry. Standard- and low-dose AZA were defined as at least 2 mg/kg/day and less than 2 mg/kg/day, respectively. Relapse rates were analyzed by Kaplan-Meier analysis and compared using log-rank test. RESULTS: Among 1226 UC patients, 128 (53% male, median duration on AZA 44 months) were included. Median maintenance AZA dose was 1.3 mg/kg/day. 97.7% of the patients were on concomitant oral 5-aminosalicylic acid. Cumulative relapse-free rates in patients on standard-dose and low-dose AZA were 71.2%, 52.8% and 45.2%, and 71.8%, 55.3% and 46.2% at 12, 24 and 36 months, respectively (p = 0.871). Relapse rate within 12 months was higher in patients who withdrew compared with those who maintained on AZA (52.6% versus 29.4%; p = 0.045). Mean corpuscular volume increased after AZA therapy in both of the low-dose [median (interquartile range, IQR): 88.2 (81.4-92.2) versus 95.1 (90.1-100.9) fl, p < 0.001] and standard-dose subgroups [median (IQR) 86.8 (76.9-89.9) versus 94.7 (85.9-99.7) fl, p < 0.001]. Leukopenia occurred in 21.1% of the patients. Patients on standard dose had a higher risk for leukopenia than those on low-dose AZA [odds ratio (OR) 3.9, 95% CI 1.9-8.2, p < 0.001]. CONCLUSIONS: In the Chinese population, low-dose AZA is effective for maintaining remission in steroid-dependent UC patients. Standard-dose AZA was associated with more than threefold increased risk of leukopenia

Hair-based rapid analyses for multiple drugs in forensics and doping : application of dynamic multiple reaction monitoring with LC-MS/MS

This is the first report of the application of this proprietary system to investigate the presence of drugs in human hair samples. The method is selective, sensitive and robust for the screening and confirmation of multiple drugs in a single analysis and has potential as a very useful tool for the analysis of large array of controlled substances and drugs of abuse