A systematic review of clinical value of three-dimensional printing in renal disease

The aim of this systematic review is to analyse current literature related to the clinical value of three-dimensional (3D) printed models in renal disease. A literature search of PubMed and Scopus databases was performed to identify studies reporting the clinical application and usefulness of 3D printed models in renal disease. Fifteen studies were found to meet the selection criteria and were included in the analysis. Eight of them provided quantitative assessments with five studies focusing on dimensional accuracy of 3D printed models in replicating renal anatomy and tumour, and on measuring tumour volume between 3D printed models and original source images and surgical specimens, with mean difference less than 10%. The other three studies reported that the use of 3D printed models significantly enhanced medical students and specialists’ ability to identify anatomical structures when compared to two-dimensional (2D) images alone; and significantly shortened intraoperative ultrasound duration compared to without use of 3D printed models. Seven studies provided qualitative assessments of the usefulness of 3D printed kidney models with findings showing that 3D printed models improved patient’s understanding of renal anatomy and pathology; improved medical trainees’ understanding of renal malignant tumours when compared to viewing medical images alone; and assisted surgical planning and simulation of renal surgical procedures with significant reductions of intraoperative complications. The cost and time associated with 3D printed kidney model production was reported in 10 studies, with costs ranging from USD$100 to USD$1,000, and duration of 3D printing production up to 31 h. The entire process of 3D printing could take up to a few days. This review shows that 3D printed kidney models are accurate in delineating renal anatomical structures and renal tumours with high accuracy. Patient-specific 3D printed models serve as a useful tool in preoperative planning and simulation of surgical procedures for treatment of renal tumours. Further studies with inclusion of more cases and with a focus on reducing the cost and 3D model production time deserve to be investigated

DeepMed: Semiparametric Causal Mediation Analysis with Debiased Deep Learning

Causal mediation analysis can unpack the black box of causality and is therefore a powerful tool for disentangling causal pathways in biomedical and social sciences, and also for evaluating machine learning fairness. To reduce bias for estimating Natural Direct and Indirect Effects in mediation analysis, we propose a new method called DeepMed that uses deep neural networks (DNNs) to cross-fit the infinite-dimensional nuisance functions in the efficient influence functions. We obtain novel theoretical results that our DeepMed method (1) can achieve semiparametric efficiency bound without imposing sparsity constraints on the DNN architecture and (2) can adapt to certain low dimensional structures of the nuisance functions, significantly advancing the existing literature on DNN-based semiparametric causal inference. Extensive synthetic experiments are conducted to support our findings and also expose the gap between theory and practice. As a proof of concept, we apply DeepMed to analyze two real datasets on machine learning fairness and reach conclusions consistent with previous findings.Comment: Accepted by NeurIPS 202

ERStruct: An Eigenvalue Ratio Approach to Inferring Population Structure from Sequencing Data

Inference of population structure from genetic data plays an important role in population and medical genetics studies. The traditional EIGENSTRAT method has been widely used for computing and selecting top principal components that capture population structure information (Price et al., 2006). With the advancement and decreasing cost of sequencing technology, whole-genome sequencing data provide much richer information about the underlying population structures. However, the EIGENSTRAT method was originally developed for analyzing array-based genotype data and thus may not perform well on sequencing data for two reasons. First, the number of genetic variants $p$ is much larger than the sample size $n$ in sequencing data such that the sample-to-marker ratio $n/p$ is nearly zero, violating the assumption of the Tracy-Widom test used in the EIGENSTRAT method. Second, the EIGENSTRAT method might not be able to handle the linkage disequilibrium (LD) well in sequencing data. To resolve those two critical issues, we propose a new statistical method called ERStruct to estimate the number of latent sub-populations based on sequencing data. We propose to use the ratio of successive eigenvalues as a more robust testing statistic, and then we approximate the null distribution of our proposed test statistic using modern random matrix theory. Simulation studies found that our proposed ERStruct method has outperformed the traditional Tracy-Widom test on sequencing data. We further use two public data sets from the HapMap 3 and the 1000 Genomes Projects to demonstrate the performance of our ERStruct method. We also implement our ERStruct in a MATLAB toolbox which is now publicly available on github through https://github.com/bglvly/ERStruct

Using contemporary education strategies to improve teaching and learning in a Botany course at Beijing Forestry University

This paper introduces some contemporary education strategies and recent trends in teaching and learning. The report then reviews the general condition of a botany course in Beijing Forestry University. Based on the weakness of conventional teaching methods and the advantages of contemporary education strategies, five possible approaches are used to modify the botany course: concept mapping; PBL (problem-based learning); case study; web-based learning and changing assessment. Also discussed are possible problems with implementation

Multislice CT virtual endoscopy in pre-aortic stent grafting: optimization of scanning protocals

The purpose of this study was to investigate the optimal scanning protocols of multislice CT (MSCT) angiography in pre-aortic stent grafting, visualized on virtual endoscopy (VE). A series of scans were performed on a human aorta phantom with a 16-slice multislice CT scanner with the scanning protocols as follows: section thickness of 1.0/1.5/2.0/3.0 mm, pitch value of 1.0/1.25/1.5, and reconstruction interval of 50% overlap. Signal to noise ratio and standard deviation (SD) of the signal intensity on VE images were measured to determine the image quality in relation to MSCT scanning protocols. Subjective assessment was performed by two observers evaluating the degree of artefacts and the configuration of the renal ostium visualized on VE images. Our results showed that the scanning protocol with a section thickness of 2.0 mm resulted in the highest SNR and lowest SD compared to other protocols (p<0.05). Subjective assessment demonstrated that VE image quality was determined by section thickness, but independent of pitch values. We recommended the scanning protocol of section thickness 2.0 mm, pitch 1.5 with a reconstruction interval of 1.0 mm as the optimal one since it allows optimal visualization of VE images of aortic ostia, fewer artefacts and less radiation dose