A hierarchical opportunistic screening model for osteoporosis using machine learning applied to clinical data and CT images

Background: Osteoporosis is a common metabolic skeletal disease and usually lacks obvious symptoms. Many individuals are not diagnosed until osteoporotic fractures occur. Bone mineral density (BMD) measured by dual-energy X-ray absorptiometry (DXA) is the gold standard for osteoporosis detection. However, only a limited percentage of people with osteoporosis risks undergo the DXA test. As a result, it is vital to develop methods to identify individuals at-risk based on methods other than DXA. Results: We proposed a hierarchical model with three layers to detect osteoporosis using clinical data (including demographic characteristics and routine laboratory tests data) and CT images covering lumbar vertebral bodies rather than DXA data via machine learning. 2210 individuals over age 40 were collected retrospectively, among which 246 individuals’ clinical data and CT images are both available. Irrelevant and redundant features were removed via statistical analysis. Consequently, 28 features, including 16 clinical data and 12 texture features demonstrated statistically significant differences (p < 0.05) between osteoporosis and normal groups. Six machine learning algorithms including logistic regression (LR), support vector machine with radial-basis function kernel, artificial neural network, random forests, eXtreme Gradient Boosting and Stacking that combined the above five classifiers were employed as classifiers to assess the performances of the model. Furthermore, to diminish the influence of data partitioning, the dataset was randomly split into training and test set with stratified sampling repeated five times. The results demonstrated that the hierarchical model based on LR showed better performances with an area under the receiver operating characteristic curve of 0.818, 0.838, and 0.962 for three layers, respectively in distinguishing individuals with osteoporosis and normal BMD. Conclusions: The proposed model showed great potential in opportunistic screening for osteoporosis without additional expense. It is hoped that this model could serve to detect osteoporosis as early as possible and thereby prevent serious complications of osteoporosis, such as osteoporosis fractures

Genetic diversity of 23 Y-STR loci of the Lisu ethnic minority residing in Chuxiong Yi Autonomous Prefecture, Yunnan province, Southwest China

Background The Lisu group is a unique minority in Yunnan province. However, there is a lack of Y-STR population data for Chinese Lisu and the genetic structure of the Lisu group and other populations is unclear. Aim To provide genetic data for 23 Y-STRs in the Chinese Lisu population from Chuxiong Yi Autonomous Prefecture, as well as to analyse population genetic relationships between Chinese Lisu ethnic minority and other reference groups. Subjects and methods 423 unrelated healthy Lisu males were genotyped using the PowerPlex® Y23 system. Forensic parameters were calculated according to the previously published studies. Genetic structure analysis among Chinese Lisu and other populations was conducted using the YHRD’s AMOVA tools. Results Gene diversity (GD) ranged from 0.2,466 (DYS438) to 0.8,945 (DYS385a/b) among the 23 Y-STR loci. According to haplotype analysis, 323 different haplotypes were obtained, out of which 271 were unique. The haplotype diversity (HD) and discrimination capacity (DC) were 0.9,977 and 0.7,636, respectively. MDS plot indicated that the Chuxiong Lisu group is genetically related to the Yunnan Yi group. Conclusions This is the first report on Y-STR population data for the Chinese Lisu population. These data would be valuable for forensic applications

Evolutionary algorithm using surrogate models for solving bilevel multiobjective programming problems.

A bilevel programming problem with multiple objectives at the leader's and/or follower's levels, known as a bilevel multiobjective programming problem (BMPP), is extraordinarily hard as this problem accumulates the computational complexity of both hierarchical structures and multiobjective optimisation. As a strongly NP-hard problem, the BMPP incurs a significant computational cost in obtaining non-dominated solutions at both levels, and few studies have addressed this issue. In this study, an evolutionary algorithm is developed using surrogate optimisation models to solve such problems. First, a dynamic weighted sum method is adopted to address the follower's multiple objective cases, in which the follower's problem is categorised into several single-objective ones. Next, for each the leader's variable values, the optimal solutions to the transformed follower's programs can be approximated by adaptively improved surrogate models instead of solving the follower's problems. Finally, these techniques are embedded in MOEA/D, by which the leader's non-dominated solutions can be obtained. In addition, a heuristic crossover operator is designed using gradient information in the evolutionary procedure. The proposed algorithm is executed on some computational examples including linear and nonlinear cases, and the simulation results demonstrate the efficiency of the approach

Finite-Time Stabilization for a Class of Nonlinear Singular Systems

The finite-time stabilization problem for a class of nonlinear singular systems is studied. Under the assumption that the considered system is impulse controllable, a sufficient condition is provided for the design of a state feedback control law guaranteeing the finite-time stability of the closed-loop system, and an explicit expression of the state feedback gain is also given. The proposed criterion is expressed in terms of strict matrix inequalities which is easy to be verified numerically. A numerical example is given to illustrate the effectiveness of the proposed method

Expression and significant roles of the lncRNA NEAT1/miR‐493‐5p/Rab27A axis in ulcerative colitis

Abstract Background Long noncoding RNAs (lncRNAs) and microRNAs (miRNAs) have been reported to play regulatory roles in ulcerative colitis (UC). In this study, we aimed to determine the specific roles and action mechanism of the nuclear paraspeckle assembly transcript 1 (NEAT1) in UC. Methods Reverse transcription‐quantitative polymerase chain reaction (RT‐qPCR) was used to determine the lncRNA NEAT1 and miR‐493‐5p expression levels in patients with UC and healthy volunteers. We determine the forecast linkage points of NEAT1 and miR‐493‐5p using Starbase and those of miR‐493‐5p and Rab27A using TargetScan, and further verified them using a double luciferase gene reporter kit. RT‐qPCR and Western blot analysis were used to determine the lncRNA NEAT1, miR‐493‐5p, and Rab27A expression levels in lipopolysaccharide (LPS)‐induced Caco‐2 cells. Flow cytometry and cell counting kit‐8 were used to assess Caco‐2 cell viability. Tumor necrosis factor‐α, interleukin (IL)‐6, IL‐8, and IL‐1β levels were determined via an enzyme‐linked immunosorbent assay. Results Expression levels of NEAT1 were upregulated and those of miR‐493‐5p were downregualted in 10 ng/mL LPS‐treated Caco‐2 cells and patients with UC. Dual‐luciferase gene reporter assay revealed that miR‐493‐5p is linked to NEAT1, and Rab27A is a downstream target of miR‐493‐5p. Overexpression of miR‐493‐5p inhibited the apoptosis and inflammation in LPS‐treated Caco‐2 cells. Moreover, downregulation of lncRNA NEAT1 expression also inhibited the apoptosis and inflammation in LPS‐treated Caco‐2 cells, which was reversed by Rab27A plasmid cotransfection. Conclusion Our results revealed that NEAT1 participates in UC progression by inhibiting miR‐493‐5p expression

Histogram of the median values on 5-dimensional problems.

Histogram of the median values on 5-dimensional problems.</p

Identification and Structure-Activity Studies of 1,3-Dibenzyl-2-aryl imidazolidines as Novel Hsp90 Inhibitors

Hsp90 (Heat shock protein 90) is involved in various processes in cancer occurrence and development, and therefore represents a promising drug target for cancer therapy. In this work, a virtual screening strategy was employed, leading to the identification of a series of compounds bearing a scaffold of 1,3-dibenzyl-2-aryl imidazolidine as novel Hsp90 inhibitors. Compound 4a showed the highest binding affinity to Hsp90&alpha; (IC50 = 12 nM) in fluorescence polarization (FP) competition assay and the strongest anti-proliferative activity against human breast adenocarcinoma cell line (MCF-7) and human lung epithelial cell line (A549) with IC50 values of 21.58 &mu;M and 31.22 &mu;M, respectively. Western blotting assays revealed that these novel Hsp90 inhibitors significantly down-regulated the expression level of Her2, a client protein of Hsp90, resulting in the cytotoxicity of these novel Hsp90 inhibitors. The molecular docking study showed that these novel Hsp90 inhibitors bound to the adenosine triphosphate (ATP) binding site at the N-terminus of Hsp90. Furthermore, structure&ndash;activity relationship studies indicated that the N-benzyl group is important for the anti-cancer activity of 1,3-dibenzyl-2-aryl imidazolidines

The frame diagram of TCEA.

The frame diagram of TCEA.</p