Posttherapy I-131 Thymic Uptake Demonstrated with SPECT/CT in a Young Girl with Papillary Thyroid Carcinoma

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/63174/1/thy.2007.0394.pd

All\u27s right with the world = 歌舞昇平

Film Director: Cheung King Wai (張經緯) Film Release Year: 2007https://commons.ln.edu.hk/ccs_worksheet/1002/thumbnail.jp

Lingual Thyroid Ectopia: Diagnostic SPECT/CT Imaging and Radioactive Iodine Treatment

Background: Lingual thyroid is a rare abnormality of thyroid development that is usually treated conservatively with levothyroxine replacement. Rarely, it becomes large enough to cause obstructive symptoms in the oral cavity, requiring definitive treatment. Patient Findings: This study reports on three patients with lingual thyroid treated with radioactive iodine-131 (131I) with successful radioablation of their ectopic thyroid tissues. Measurement of 24-hour radioactive iodine uptake within thyroidal tissues and hybrid single-photon emission computed tomography/computed tomography imaging using either iodine-123 or technetium-99m pertechnetate scans were performed in all patients demonstrating the location and size of lingual thyroid and absence of an orthotopic thyroid gland. Summary: The aim of this study was to describe nonsurgical management of obstructive lingual thyroid tissue with 131I therapy for lingual thyroid radioablation. Patients were prepared with a low-iodine diet and levothyroxine withdrawal prior to radioablation for optimizing 131I uptake in ectopic thyroid tissues. Hybrid single-photon emission computed tomography/computed tomography measurement of anatomic size of lingual thyroid tissue and radioactive iodine uptake guided the selection of therapeutic doses, resulting in administration of 10.7, 17.5, and 15.4 mCi of 131I, respectively. There were no post-therapy complications, and clinical follow-up demonstrated resolution of obstructive oropharyngeal symptoms. Conclusions: Ectopic lingual thyroid tissue is rarely associated with obstructive oropharyngeal symptoms due to progressive enlargement. Radioiodine therapy with 131I is an effective treatment modality for ablation of ectopic thyroid tissue as an alternative to surgery.Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/140270/1/thy.2015.0396.pd

Patent applications for using DNA technologies to authenticate medicinal herbal material

Herbal medicines are used in many countries for maintaining health and treating diseases. Their efficacy depends on the use of the correct materials, and life-threatening poisoning may occur if toxic adulterants or substitutes are administered instead. Identification of a medicinal material at the DNA level provides an objective and powerful tool for quality control. Extraction of high-quality DNA is the first crucial step in DNA authentication, followed by a battery of DNA techniques including whole genome fingerprinting, DNA sequencing and DNA microarray to establish the identity of the material. New or improved technologies have been developed and valuable data have been collected and compiled for DNA authentication. Some of these technologies and data are patentable. This article provides an overview of some recent patents that cover the extraction of DNA from medicinal materials, the amplification of DNA using improved reaction conditions, the generation of DNA sequences and fingerprints, and the development of high-throughput authentication methods. It also briefly explains why these patents have been granted

Energy-Efficient Resource Allocation in SWIPT Enabled NOMA Systems

In this paper, we investigate joint power allocation and time switching (TS) control for energy efficiency (EE) optimization in a TS-based simultaneous wireless information and power transfer (SWIPT) non-orthogonal multiple access (NOMA) system. Our aim is to optimize the EE of the system whilst satisfying the constraints on maximum transmit power, minimum data rate and minimum harvested energy per-terminal. The considered EE optimization problem is formulated and then transformed according to the duality of broadcast channels (BC) and multiple access channels (MAC). The corresponding non-linear and non-convex optimization problem, involving joint optimization of power allocation and time switching factor, is difficult to solve directly. In order to tackle this problem, we develop a dual-layer algorithm where a convex programming-based Dinkelbach's method is proposed to optimize the power allocation in the inner-layer and an efficient search method is then applied to optimize the TS factor in the outer-layer. Numerical results validate the theoretical findings and demonstrate that significant performance gain over orthogonal multiple access (OMA) scheme in terms of EE can be achieved by the proposed algorithm in a SWIPT-enabled NOMA system

Joint 3D Trajectory Design and Time Allocation for UAV-Enabled Wireless Power Transfer Networks

This paper considers a rotary-wing unmanned aerial vehicle (UAV)-enabled wireless power transfer system, where a UAV is dispatched as an energy transmitter (ET), transferring radio frequency (RF) signals to a set of energy receivers (ERs) periodically. We aim to maximize the energy harvested at all ERs by jointly optimizing the UAV's three-dimensional (3D) placement, beam pattern and charging time. However, the considered optimization problem taking into account the drone flight altitude and the wireless coverage performance is formulated as a non-convex problem. To tackle this problem, we propose a low-complexity iterative algorithm to decompose the original problem into four sub-problems in order to optimize the variables sequentially. In particular, we first use the sequential unconstrained convex minimization based algorithm to find the globally optimal UAV two-dimensional (2D) position. Subsequently, we can directly obtain the optimal UAV altitude as the objective function of problem is monotonic decreasing with respect to UAV altitude. Then, we propose the multiobjective evolutionary algorithm based on decomposition (MOEA/D) based algorithm to control the phase of antenna array elements, in order to achieve high steering performance of multi-beams. Finally, with the above solved variables, the original problem is reformulated as a single-variable optimization problem where charging time is the optimization variable, and can be solved using the standard convex optimization techniques. Furthermore, we use the branch and bound method to design the UAV trajectory which can be constructed as traveling salesman problem (TSP) to minimize flight distance. Numerical results validate the theoretical findings and demonstrate that significant performance gain in terms of sum received power of ERs can be achieved by the proposed algorithm in UAV-enabled wireless power transfer networks

Development of a predictive risk model for all-cause mortality in patients with diabetes in Hong Kong

Introduction Patients with diabetes mellitus are risk of premature death. In this study, we developed a machine learning-driven predictive risk model for all-cause mortality among patients with type 2 diabetes mellitus using multiparametric approach with data from different domains. Research design and methods This study used territory-wide data of patients with type 2 diabetes attending public hospitals or their associated ambulatory/outpatient facilities in Hong Kong between January 1, 2009 and December 31, 2009. The primary outcome is all-cause mortality. The association of risk variables and all-cause mortality was assessed using Cox proportional hazards models. Machine and deep learning approaches were used to improve overall survival prediction and were evaluated with fivefold cross validation method. Results A total of 273 678 patients (mean age: 65.4±12.7 years, male: 48.2%, median follow-up: 142 (IQR=106–142) months) were included, with 91 155 deaths occurring on follow-up (33.3%; annualized mortality rate: 3.4%/year; 2.7 million patient-years). Multivariate Cox regression found the following significant predictors of all-cause mortality: age, male gender, baseline comorbidities, anemia, mean values of neutrophil-to-lymphocyte ratio, high-density lipoprotein-cholesterol, total cholesterol, triglyceride, HbA1c and fasting blood glucose (FBG), measures of variability of both HbA1c and FBG. The above parameters were incorporated into a score-based predictive risk model that had a c-statistic of 0.73 (95% CI 0.66 to 0.77), which was improved to 0.86 (0.81 to 0.90) and 0.87 (0.84 to 0.91) using random survival forests and deep survival learning models, respectively. Conclusions A multiparametric model incorporating variables from different domains predicted all-cause mortality accurately in type 2 diabetes mellitus. The predictive and modeling capabilities of machine/deep learning survival analysis achieved more accurate predictions