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5. A real-world study and network pharmacology analysis of EGFR-TKIs combined with ZLJT to delay drug resistance in advanced lung adenocarcinoma

Author

Xue Han, Lan Liang, Chenming He, Qinyou Ren, Jialin Su, Liang Cao, and Jin Zheng

Subjects
Lung adenocarcinoma, ZiLongJin Tablet, Epidermal growth factor receptor-tyrosine kinase inhibitor, Real-world study, Network pharmacology, Molecular docking simulation, Other systems of medicine, RZ201-999
Abstract

Abstract Objective This study aimed to explore the efficacy and safety of combining epidermal growth factor receptor tyrosine kinase inhibitors (EGFR-TKIs) with ZiLongJin Tablet (ZLJT) in delaying acquired resistance in advanced EGFR-mutant lung adenocarcinoma (LUAD) patients. Furthermore, we employed network pharmacology and molecular docking techniques to investigate the underlying mechanisms. Methods A retrospective comparative study was conducted on stage IIIc/IV LUAD patients treated with EGFR-TKIs alone or in combination with ZLJT at the Second Affiliated Hospital of the Air Force Medical University between January 1, 2017, and May 1, 2023. The study evaluated the onset of TKI resistance, adverse reaction rates, safety indicators (such as aspartate aminotransferase, alanine aminotransferase, and creatinine), and inflammatory markers (neutrophil-to-lymphocyte ratio and platelet-to-lymphocyte ratio) to investigate the impact of EGFR-TKI combined with ZLJT on acquired resistance and prognostic indicators. Additionally, we utilized the Traditional Chinese Medicine Systems Pharmacology Database and Analysis Platform, the Bioinformatics Analysis Tool for Molecular Mechanism of Traditional Chinese Medicine, PubChem, UniProt, and Swiss Target Prediction databases to identify the active ingredients and targets of ZLJT. We obtained differentially expressed genes related to EGFR-TKI sensitivity and resistance from the Gene Expression Omnibus database using the GSE34228 dataset, which included sensitive (n = 26) and resistant (n = 26) PC9 cell lines. The "limma" package in R software was employed to detect DEGs. Based on this, we constructed a protein‒protein interaction network, performed gene ontology and KEGG enrichment analyses, and conducted pathway network analysis to elucidate the correlation between the active ingredients in ZLJT and signaling pathways. Finally, molecular docking was performed using AutoDockVina, PYMOL 2.2.0, and Discovery Studio Client v19.1.0 software to simulate spatial and energy matching during the recognition process between predicted targets and their corresponding compounds. Results (1) A total of 89 patients were included, with 40 patients in the EGFR-TKI combined with ZLJT group (combination group) and 49 patients in the EGFR-TKI alone group (monotherapy group). The baseline characteristics of the two groups were comparable. There was a significant difference in the onset of resistance between the combination group and the monotherapy group (P 0.05). There were no statistically significant differences in serum AST, ALT, CREA, TBIL, ALB and BUN levels between the two groups after medication (P > 0.05). (3) Regarding inflammatory markers, there were no statistically significant differences in the changes in neutrophil-to-lymphocyte Ratio(NLR) and Platelet-to-lymphocyte Ratio(PLR) values before and after treatment between the two groups (P > 0.05). (4) Network pharmacology analysis identified 112 active ingredients and 290 target genes for ZLJT. From the GEO database, 2035 differentially expressed genes related to resistant LUAD were selected, and 39 target genes were obtained by taking the intersection. A "ZLJT-compound-target-disease" network was successfully constructed using Cytoscape 3.7.0. GO enrichment analysis revealed that ZLJT mainly affected biological processes such as adenylate cyclase-modulating G protein-coupled receptor. In terms of cellular components, ZLJT was associated with the cell projection membrane. The molecular function primarily focused on protein heterodimerization activity. KEGG enrichment analysis indicated that ZLJT exerted its antitumor and anti-drug resistance effects through pathways such as the PI3K-Akt pathway. Molecular docking showed that luteolin had good binding activity with FOS (-9.8 kJ/mol), as did tanshinone IIA with FOS (-9.8 kJ/mol) and quercetin with FOS (-8.7 kJ/mol). Conclusion ZLJT has potential antitumor progression effects. For patients with EGFR gene-mutated non-small cell LUAD, combining ZLJT with EGFR-TKI treatment can delay the occurrence of acquired resistance. The underlying mechanisms may involve altering signal transduction pathways, blocking the tumor cell cycle, inhibiting tumor activity, enhancing cellular vitality, and improving the bioavailability of combination therapy. The combination of EGFR-TKI and ZLJT represents an effective approach for the treatment of tumors using both Chinese and Western medicine.

Published
2023
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6. Memory-Efficient Factorization Machines via Binarizing both Data and Model Coefficients

Author

Geng, Yu and Lan, Liang

Subjects
Computer Science - Machine Learning
Abstract

Factorization Machines (FM), a general predictor that can efficiently model feature interactions in linear time, was primarily proposed for collaborative recommendation and have been broadly used for regression, classification and ranking tasks. Subspace Encoding Factorization Machine (SEFM) has been proposed recently to overcome the expressiveness limitation of Factorization Machines (FM) by applying explicit nonlinear feature mapping for both individual features and feature interactions through one-hot encoding to each input feature. Despite the effectiveness of SEFM, it increases the memory cost of FM by $b$ times, where $b$ is the number of bins when applying one-hot encoding on each input feature. To reduce the memory cost of SEFM, we propose a new method called Binarized FM which constraints the model parameters to be binary values (i.e., 1 or $-1$). Then each parameter value can be efficiently stored in one bit. Our proposed method can significantly reduce the memory cost of SEFM model. In addition, we propose a new algorithm to effectively and efficiently learn proposed FM with binary constraints using Straight Through Estimator (STE) with Adaptive Gradient Descent (Adagrad). Finally, we evaluate the performance of our proposed method on eight different classification datasets. Our experimental results have demonstrated that our proposed method achieves comparable accuracy with SEFM but with much less memory cost.

Published
2021

10. Effective and Sparse Count-Sketch via k-means clustering

Author

Wang, Yuhan, Lei, Zijian, and Lan, Liang

Subjects
Computer Science - Machine Learning
Abstract

Count-sketch is a popular matrix sketching algorithm that can produce a sketch of an input data matrix X in O(nnz(X))time where nnz(X) denotes the number of non-zero entries in X. The sketched matrix will be much smaller than X while preserving most of its properties. Therefore, count-sketch is widely used for addressing high-dimensionality challenge in machine learning. However, there are two main limitations of count-sketch: (1) The sketching matrix used count-sketch is generated randomly which does not consider any intrinsic data properties of X. This data-oblivious matrix sketching method could produce a bad sketched matrix which will result in low accuracy for subsequent machine learning tasks (e.g.classification); (2) For highly sparse input data, count-sketch could produce a dense sketched data matrix. This dense sketch matrix could make the subsequent machine learning tasks more computationally expensive than on the original sparse data X. To address these two limitations, we first show an interesting connection between count-sketch and k-means clustering by analyzing the reconstruction error of the count-sketch method. Based on our analysis, we propose to reduce the reconstruction error of count-sketch by using k-means clustering algorithm to obtain the low-dimensional sketched matrix. In addition, we propose to solve k-mean clustering using gradient descent with -L1 ball projection to produce a sparse sketched matrix. Our experimental results based on six real-life classification datasets have demonstrated that our proposed method achieves higher accuracy than the original count-sketch and other popular matrix sketching algorithms. Our results also demonstrate that our method produces a sparser sketched data matrix than other methods and therefore the prediction cost of our method will be smaller than other matrix sketching methods., Comment: 8 pages, 8 figures

Published
2020

13. Compressing Deep Convolutional Neural Networks by Stacking Low-dimensional Binary Convolution Filters

Author

Lan, Weichao and Lan, Liang

Subjects
Computer Science - Computer Vision and Pattern Recognition, Computer Science - Machine Learning
Abstract

Deep Convolutional Neural Networks (CNN) have been successfully applied to many real-life problems. However, the huge memory cost of deep CNN models poses a great challenge of deploying them on memory-constrained devices (e.g., mobile phones). One popular way to reduce the memory cost of deep CNN model is to train binary CNN where the weights in convolution filters are either 1 or -1 and therefore each weight can be efficiently stored using a single bit. However, the compression ratio of existing binary CNN models is upper bounded by around 32. To address this limitation, we propose a novel method to compress deep CNN model by stacking low-dimensional binary convolution filters. Our proposed method approximates a standard convolution filter by selecting and stacking filters from a set of low-dimensional binary convolution filters. This set of low-dimensional binary convolution filters is shared across all filters for a given convolution layer. Therefore, our method will achieve much larger compression ratio than binary CNN models. In order to train our proposed model, we have theoretically shown that our proposed model is equivalent to select and stack intermediate feature maps generated by low-dimensional binary filters. Therefore, our proposed model can be efficiently trained using the split-transform-merge strategy. We also provide detailed analysis of the memory and computation cost of our model in model inference. We compared the proposed method with other five popular model compression techniques on two benchmark datasets. Our experimental results have demonstrated that our proposed method achieves much higher compression ratio than existing methods while maintains comparable accuracy.

Published
2020

14. Memory and Computation-Efficient Kernel SVM via Binary Embedding and Ternary Model Coefficients

Author

Lei, Zijian and Lan, Liang

Subjects
Computer Science - Machine Learning
Abstract

Kernel approximation is widely used to scale up kernel SVM training and prediction. However, the memory and computation costs of kernel approximation models are still too high if we want to deploy them on memory-limited devices such as mobile phones, smartwatches, and IoT devices. To address this challenge, we propose a novel memory and computation-efficient kernel SVM model by using both binary embedding and binary model coefficients. First, we propose an efficient way to generate compact binary embedding of the data, preserving the kernel similarity. Second, we propose a simple but effective algorithm to learn a linear classification model with ternary coefficients that can support different types of loss function and regularizer. Our algorithm can achieve better generalization accuracy than existing works on learning binary coefficients since we allow coefficient to be $-1$, $0$, or $1$ during the training stage, and coefficient $0$ can be removed during model inference for binary classification. Moreover, we provide a detailed analysis of the convergence of our algorithm and the inference complexity of our model. The analysis shows that the convergence to a local optimum is guaranteed, and the inference complexity of our model is much lower than other competing methods. Our experimental results on five large real-world datasets have demonstrated that our proposed method can build accurate nonlinear SVM models with memory costs less than 30KB.

Published
2020

15. Improved Subsampled Randomized Hadamard Transform for Linear SVM

Author

Lei, Zijian and Lan, Liang

Subjects
Computer Science - Machine Learning, Statistics - Machine Learning
Abstract

Subsampled Randomized Hadamard Transform (SRHT), a popular random projection method that can efficiently project a $d$-dimensional data into $r$-dimensional space ($r \ll d$) in $O(dlog(d))$ time, has been widely used to address the challenge of high-dimensionality in machine learning. SRHT works by rotating the input data matrix $\mathbf{X} \in \mathbb{R}^{n \times d}$ by Randomized Walsh-Hadamard Transform followed with a subsequent uniform column sampling on the rotated matrix. Despite the advantages of SRHT, one limitation of SRHT is that it generates the new low-dimensional embedding without considering any specific properties of a given dataset. Therefore, this data-independent random projection method may result in inferior and unstable performance when used for a particular machine learning task, e.g., classification. To overcome this limitation, we analyze the effect of using SRHT for random projection in the context of linear SVM classification. Based on our analysis, we propose importance sampling and deterministic top-$r$ sampling to produce effective low-dimensional embedding instead of uniform sampling SRHT. In addition, we also proposed a new supervised non-uniform sampling method. Our experimental results have demonstrated that our proposed methods can achieve higher classification accuracies than SRHT and other random projection methods on six real-life datasets., Comment: AAAI-20

Published
2020
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16. A new variant of the optical trapezoid model (OPTRAM) for remote sensing of soil moisture and water bodies

Author

Morteza Sadeghi, Neda Mohamadzadeh, Lan Liang, Uditha Bandara, Marcellus M. Caldas, and Tyler Hatch

Subjects
Soil moisture, Water body, Reflectance, Landsat, Google earth engine, Physical geography, GB3-5030, Science
Abstract

Over the past few years, the Optical Trapezoid Model (OPTRAM) has been widely used as a means for high-resolution mapping of surface soil moisture using optical satellite data. In this paper, we propose a new variant of OPTRAM that can map not only soil moisture, but also water bodies such as lakes and rivers. The proposed variant was tested using laboratory experimental data as well as Landsat-8 reflectance observations. Results showed the new OPTRAM variant has greater skill than the original variant in separating land and water pixels. In addition, the new variant showed less sensitivity to the model parameters, and hence, is less user dependent. To quantitatively examine the user-dependency of the model, we analyzed OPTRAM soil moisture based on Landsat-8 satellite images in California, where we varied the model parameters in a plausible range. The correlations of the resulting maps in terms of R2 between two largely different sets of parameters were found in the range of 0.47-0.52 for the original variant and 0.67-0.76 for the new variant. Because some OPTRAM parameters can be quite uncertain, particularly in wet regions, the reduced sensitivity promises more consistent soil moisture estimates across the range of parameter choices.

Published
2023
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18. Peroxymonosulfate Activation by Bi-Fe Oxide Co-Doped Graphitic Carbon Nitride for Degradation of Sulfamethoxazole: Performance and Mechanism

Author

Zhili Wang, Lan Liang, Ning Li, Shuang Wu, Zhanjun Cheng, Beibei Yan, and Guanyi Chen

Subjects
carbon nitride, peroxymonosulfate, degradation, sulfamethoxazole, Bi2Fe4O9, mechanism, Technology, Engineering (General). Civil engineering (General), TA1-2040, Biology (General), QH301-705.5, Physics, QC1-999, Chemistry, QD1-999
Abstract

Graphite carbon nitride (g-C3N4) has been employed as an emerging metal-free catalyst in heterogeneous catalysis. However, the catalyst has a poor activation property for peroxymonosulfate (PMS). In this study, Bi-Fe oxide co-doped g-C3N4 (Bi@Fe/CN) was synthesized for PMS activation to degrade sulfamethoxazole (SMX). In particular, Bi@Fe/CN-3 presented remarkable catalytic performance with 99.7% removal of SMX within 60 min in the PMS system. Additionally, Bi@Fe/CN-3 presented good stability and recyclability through the cycling experiments. Moreover, it was shown that free radicals (O2•−, •OH, and SO4•−) and non-free radicals (1O2) were the primary active species in the Bi@Fe/CN-3/PMS system. Bi, Fe, and surface lattice oxygen were confirmed to be the main contributors to the active species. This work elucidates the mechanism of activation of PMS by Bi@Fe/CN-3, which is beneficial to promote the application of bimetallic oxide-modified g-C3N4/PMS systems in wastewater treatment.

Published
2024
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19. Clinical evaluation on automatic segmentation results of convolutional neural networks in rectal cancer radiotherapy

Author

Jing Li, Ying Song, Yongchang Wu, Lan Liang, Guangjun Li, and Sen Bai

Subjects
automatic segmentation, deep learning, rectal neoplasms, radiotherapy, CNN, Neoplasms. Tumors. Oncology. Including cancer and carcinogens, RC254-282
Abstract

PurposeImage segmentation can be time-consuming and lacks consistency between different oncologists, which is essential in conformal radiotherapy techniques. We aimed to evaluate automatic delineation results generated by convolutional neural networks (CNNs) from geometry and dosimetry perspectives and explore the reliability of these segmentation tools in rectal cancer.MethodsForty-seven rectal cancer cases treated from February 2018 to April 2019 were randomly collected retrospectively in our cancer center. The oncologists delineated regions of interest (ROIs) on planning CT images as the ground truth, including clinical target volume (CTV), bladder, small intestine, and femoral heads. The corresponding automatic segmentation results were generated by DeepLabv3+ and ResUNet, and we also used Atlas-Based Autosegmentation (ABAS) software for comparison. The geometry evaluation was carried out using the volumetric Dice similarity coefficient (DSC) and surface DSC, and critical dose parameters were assessed based on replanning optimized by clinically approved or automatically generated CTVs and organs at risk (OARs), i.e., the Planref and Plantest. Pearson test was used to explore the correlation between geometric metrics and dose parameters.ResultsIn geometric evaluation, DeepLabv3+ performed better in DCS metrics for the CTV (volumetric DSC, mean = 0.96, P< 0.01; surface DSC, mean = 0.78, P< 0.01) and small intestine (volumetric DSC, mean = 0.91, P< 0.01; surface DSC, mean = 0.62, P< 0.01), ResUNet had advantages in volumetric DSC of the bladder (mean = 0.97, P< 0.05). For critical dose parameters analysis between Planref and Plantest, there was a significant difference for target volumes (P< 0.01), and no significant difference was found for the ResUNet-generated small intestine (P > 0.05). For the correlation test, a negative correlation was found between DSC metrics (volumetric, surface DSC) and dosimetric parameters (δD95, δD95, HI, CI) for target volumes (P< 0.05), and no significant correlation was found for most tests of OARs (P > 0.05).ConclusionsCNNs show remarkable repeatability and time-saving in automatic segmentation, and their accuracy also has a certain potential in clinical practice. Meanwhile, clinical aspects, such as dose distribution, may need to be considered when comparing the performance of auto-segmentation methods.

Published
2023
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30. Discovery and validation of Ferroptosis-related molecular patterns and immune characteristics in Alzheimer’s disease

Author

Yi-Jie He, Lin Cong, Song-Lan Liang, Xu Ma, Jia-Nan Tian, Hui Li, and Yun Wu

Subjects
Alzheimer’s disease, immune characteristics, Ferroptosis, biomarkers, cluster, machine learning, Neurosciences. Biological psychiatry. Neuropsychiatry, RC321-571
Abstract

BackgroundTo date, the pathogenesis of Alzheimer’s disease is still not fully elucidated. Much evidence suggests that Ferroptosis plays a crucial role in the pathogenesis of AD, but little is known about its molecular immunological mechanisms. Therefore, this study aims to comprehensively analyse and explore the molecular mechanisms and immunological features of Ferroptosis-related genes in the pathogenesis of AD.Materials and methodsWe obtained the brain tissue dataset for AD from the GEO database and downloaded the Ferroptosis-related gene set from FerrDb for analysis. The most relevant Hub genes for AD were obtained using two machine learning algorithms (Least absolute shrinkage and selection operator (LASSO) and multiple support vector machine recursive feature elimination (mSVM-RFE)). The study of the Hub gene was divided into two parts. In the first part, AD patients were genotyped by unsupervised cluster analysis, and the different clusters’ immune characteristics were analysed. A PCA approach was used to quantify the FRGscore. In the second part: we elucidate the biological functions involved in the Hub genes and their role in the immune microenvironment by integrating algorithms (GSEA, GSVA and CIBERSORT). Analysis of Hub gene-based drug regulatory networks and mRNA-miRNA-lncRNA regulatory networks using Cytoscape. Hub genes were further analysed using logistic regression models.ResultsBased on two machine learning algorithms, we obtained a total of 10 Hub genes. Unsupervised clustering successfully identified two different clusters, and immune infiltration analysis showed a significantly higher degree of immune infiltration in type A than in type B, indicating that type A may be at the peak of AD neuroinflammation. Secondly, a Hub gene-based Gene-Drug regulatory network and a ceRNA regulatory network were successfully constructed. Finally, a logistic regression algorithm-based AD diagnosis model and Nomogram diagram were developed.ConclusionOur study provides new insights into the role of Ferroptosis-related molecular patterns and immune mechanisms in AD, as well as providing a theoretical basis for the addition of diagnostic markers for AD.

Published
2022
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31. A High Efficiency, Low Resistance Antibacterial Filter Formed by Dopamine‐Mediated In Situ Deposition of Silver onto Glass Fibers

Author

Sun, Zhaoxia, primary, Kong, Ying, additional, Lan, Liang, additional, Meng, Yingchao, additional, You, Tianle, additional, Pauer, Robin, additional, Wang, Hao, additional, Zhang, Yizhou, additional, Tang, Min, additional, deMello, Andrew, additional, Liang, Yun, additional, Hu, Jian, additional, and Wang, Jing, additional

Published
2024
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32. Artificial intelligence: A powerful paradigm for scientific research

Author

Xu, Yongjun, Liu, Xin, Cao, Xin, Huang, Changping, Liu, Enke, Qian, Sen, Liu, Xingchen, Wu, Yanjun, Dong, Fengliang, Qiu, Cheng-Wei, Qiu, Junjun, Hua, Keqin, Su, Wentao, Wu, Jian, Xu, Huiyu, Han, Yong, Fu, Chenguang, Yin, Zhigang, Liu, Miao, Roepman, Ronald, Dietmann, Sabine, Virta, Marko, Kengara, Fredrick, Zhang, Ze, Zhang, Lifu, Zhao, Taolan, Dai, Ji, Yang, Jialiang, Lan, Liang, Luo, Ming, Liu, Zhaofeng, An, Tao, Zhang, Bin, He, Xiao, Cong, Shan, Liu, Xiaohong, Zhang, Wei, Lewis, James P., Tiedje, James M., Wang, Qi, An, Zhulin, Wang, Fei, Zhang, Libo, Huang, Tao, Lu, Chuan, Cai, Zhipeng, Wang, Fang, and Zhang, Jiabao

Published
2021
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34. Discussion of tumor mutation burden as an indicator to predict efficacy of immune checkpoint inhibitors: A case report

Author

Mingrui Wu, Lan Liang, and Xiaotian Dai

Subjects
immune checkpoint inhibitors, prognostic biomarker, treatment efficacy, tumor mutation burden (TMB), immunotherapy, Neoplasms. Tumors. Oncology. Including cancer and carcinogens, RC254-282
Abstract

There are many treatment options for advanced lung cancer, among which immunotherapy has developed rapidly and benefited a lot of patients. However, immunotherapy can only benefit a subgroup of patients, and how to select patients suitable for this therapy is critical. Tumor mutation burden (TMB) is one of the important reference indicators for immune checkpoint inhibitors (ICIs). However, there are many factors influencing the usage of this indicator, which will lead to considerable consequences if not treated well. In this study, we performed a case study on a male advanced lung squamous cell carcinoma patient of age 83. The patient suffered from “cough and sputum”, and did chest CT scans on 24 October 2018, which showed “a mass-like mass in the anterior segment of the right lung upper lobe, about 38mm×28mm”. He was treated with systemic chemotherapy; however, the tumor was still under progression. Although PD-L1 was not tested in gene testing, he had a TMB value of 10.26 mutations/Mb with a quantile value 88.63%. Thus, “toripalimab injection” was added as immunotherapy and the size of the lesion decreased. In summary, we adopted a clinical case as the basis to explore the value and significance of TMB in immunotherapy in this study. We hope that more predictive molecular markers will be discovered, which will bring more treatment methods for advanced lung cancer.

Published
2022
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35. Impact of maternal neuraxial labor analgesia exposure on offspring's neurodevelopment: A longitudinal prospective cohort study with propensity score matching

Author

Chun-Mei Deng, Ting Ding, Zhi-Hua Liu, Shu-Ting He, Jia-Hui Ma, Ming-Jun Xu, Lei Wang, Ming Li, Wei-Lan Liang, Xue-Ying Li, Daqing Ma, and Dong-Xin Wang

Subjects
labor, obstetric [MeSH], analgesia obstetric, depression postpartum, child development, cognition, Public aspects of medicine, RA1-1270
Abstract

BackgroundNeuraxial analgesia is widely used to relieve labor pain; its effects on long-term neurodevelopment of offspring remain unclear. This study was designed to investigate the influence of maternal neuraxial labor analgesia on offspring mental development.MethodsThis was a predefined secondary analysis of a 2-year prospective longitudinal study. Nulliparous women with single-term cephalic pregnancy preparing for vaginal delivery self-selected neuraxial analgesia or not during labor. Mothers and their offspring were followed up 2 years later. children's mental development was assessed with the bayley scales of infant development. A multivariable logistic model was used to identify factors associated with below-average mental development (Mental Development Index

Published
2022
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37. Guidance for identification and treatment of individuals with attention deficit/hyperactivity disorder and autism spectrum disorder based upon expert consensus

Author

Susan Young, Jack Hollingdale, Michael Absoud, Patrick Bolton, Polly Branney, William Colley, Emily Craze, Mayuri Dave, Quinton Deeley, Emad Farrag, Gisli Gudjonsson, Peter Hill, Ho-lan Liang, Clodagh Murphy, Peri Mackintosh, Marianna Murin, Fintan O’Regan, Dennis Ougrin, Patricia Rios, Nancy Stover, Eric Taylor, and Emma Woodhouse

Subjects
hyperactivity disorder/hyperactivity disorder (ADHD), Autism spectrum disorder (ASD), Comorbidity, Assessment, Treatment, Guidance, Medicine
Abstract

Abstract Background Individuals with co-occurring hyperactivity disorder/hyperactivity disorder (ADHD) and autism spectrum disorder (ASD) can have complex presentations that may complicate diagnosis and treatment. There are established guidelines with regard to the identification and treatment of ADHD and ASD as independent conditions. However, ADHD and ASD were not formally recognised diagnostically as co-occurring conditions until the Diagnostic and Statistical Manual of Mental Disorders 5 (DSM-5) was published in 2013. Hence, awareness and understanding of both conditions when they co-occur is less established and there is little guidance in the clinical literature. This has led to uncertainty among healthcare practitioners when working with children, young people and adults who present with co-existing ADHD and ASD. The United Kingdom ADHD Partnership (UKAP) therefore convened a meeting of professional experts that aimed to address this gap and reach expert consensus on the topic that will aid healthcare practitioners and allied professionals when working with this complex and vulnerable population. Method UK experts from multiple disciplines in the fields of ADHD and ASD convened in London in December 2017. The meeting provided the opportunity to address the complexities of ADHD and ASD as a co-occurring presentation from different perspectives and included presentations, discussion and group work. The authors considered the clinical challenges of working with this complex group of individuals, producing a consensus for a unified approach when working with male and female, children, adolescents and adults with co-occurring ADHD and ASD. This was written up, circulated and endorsed by all authors. Results The authors reached a consensus of practical recommendations for working across the lifespan with males and females with ADHD and ASD. Consensus was reached on topics of (1) identification and assessment using rating scales, clinical diagnostic interviews and objective supporting assessments; outcomes of assessment, including standards of clinical reporting; (2) non-pharmacological interventions and care management, including psychoeducation, carer interventions/carer training, behavioural/environmental and Cognitive Behavioural Therapy (CBT) approaches; and multi-agency liaison, including educational interventions, career advice, occupational skills and training, and (3) pharmacological treatments. Conclusions The guidance and practice recommendations (Tables 1, 4, 5, 7, 8 and 10) will support healthcare practitioners and allied professionals to meet the needs of this complex group from a multidisciplinary perspective. Further research is needed to enhance our understanding of the diagnosis, treatment and management of individuals presenting with comorbid ADHD and ASD.

Published
2020
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38. A Binocular Vision Motion Capture System Based on Passive Markers for Rehabilitation

Author

Mei, Zhang, Lin, Lin, Lei, Yang, Lan, Liang, Xianglin, Fang, Kacprzyk, Janusz, Series editor, Pal, Nikhil R., Advisory editor, Bello Perez, Rafael, Advisory editor, Corchado, Emilio S., Advisory editor, Hagras, Hani, Advisory editor, Kóczy, László T., Advisory editor, Kreinovich, Vladik, Advisory editor, Lin, Chin-Teng, Advisory editor, Lu, Jie, Advisory editor, Melin, Patricia, Advisory editor, Nedjah, Nadia, Advisory editor, Nguyen, Ngoc Thanh, Advisory editor, Wang, Jun, Advisory editor, Qiao, Feng, editor, Patnaik, Srikanta, editor, and Wang, John, editor

Published
2018
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39. Inductive Kernel Low-rank Decomposition with Priors: A Generalized Nystrom Method

Author

Zhang, Kai, Lan, Liang, Liu, Jun, Rauber, andreas, and Moerchen, Fabian

Subjects
Computer Science - Learning
Abstract

Low-rank matrix decomposition has gained great popularity recently in scaling up kernel methods to large amounts of data. However, some limitations could prevent them from working effectively in certain domains. For example, many existing approaches are intrinsically unsupervised, which does not incorporate side information (e.g., class labels) to produce task specific decompositions; also, they typically work "transductively", i.e., the factorization does not generalize to new samples, so the complete factorization needs to be recomputed when new samples become available. To solve these problems, in this paper we propose an"inductive"-flavored method for low-rank kernel decomposition with priors. We achieve this by generalizing the Nystr\"om method in a novel way. On the one hand, our approach employs a highly flexible, nonparametric structure that allows us to generalize the low-rank factors to arbitrarily new samples; on the other hand, it has linear time and space complexities, which can be orders of magnitudes faster than existing approaches and renders great efficiency in learning a low-rank kernel decomposition. Empirical results demonstrate the efficacy and efficiency of the proposed method., Comment: ICML2012

Published
2012

42. Short-term efficacy of pranoprofen combined with sodium hyaluronate in the treatment of moderate to severe dry eye

Author

Mao-Jun Yang, Lan Liang, Kang-Sheng Luo, Zhen-Jun Fang, Zhi-Yong Wu, and Dan Qiao

Subjects
moderate and severe dry eye, Pranoprofen, short-term efficacy, psychological status, anxiety, Ophthalmology, RE1-994
Abstract

AIM: To evaluate the short-term clinical efficacy of pranoprofen eye drops combined with sodium hyaluronate eye drops in the treatment of moderate to severe dry eye and its influence on the psychological status of patients.METHODS: From January to December 2018, 318 patients with moderate to severe dry eye were selected and treated in ophthalmology department of our hospital. The patients were divided into experimental group(238 patients)and control group(80 patients)by random number table method, and the control group was treated with sodium hyaluronate eye drops, while the test group was treated with pranoprofen eye drops combined with sodium hyaluronate eye drops. The general data of the patients were statistically analyzed. Before treatment, 7d, 14d, 28d after treatment, the patients' scores of conscious symptoms and signs were investigated by questionnaire, the degree of conjunctival congestion, tear breakup time, corneal fluorescein staining and tear secretion were examined, and the degree of anxiety and depression were evaluated.RESULTS: There was no significant difference in general data between the two groups(P>0.05). In terms of patients' scores of conscious symptoms and signs, degree of conjunctival congestion, tear breakup time, corneal fluorescein staining and tear secretion, the experimental group was significantly improved 7d after treatment, and the effect was more significant with the prolonged treatment, while the control group was gradually improved 14 or 28d after treatment. At the same treatment time point, the efficacy of the experimental group was significantly better than that of the control group, and the difference was statistically significant(PPCONCLUSION: Pranoprofen eye drops combined with sodium hyaluronate eye drops for treatment of dry eye syndrome had fast and significant effect, patients' self-consciousness symptoms and signs are significantly improved, conjunctival congestion and corneal fluorescein staining are reduced, tear breakup time is prolonged, tear secretion is increased, and anxiety or depression are significantly improved.

Published
2019
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43. Analysis of nickel distribution by synchrotron radiation X-ray fluorescence in nickel-induced early- and late-phase allergic contact dermatitis in Hartley guinea pigs

Author

Shan-Qun Jiang, Xiang-Yu Wu, Jin-Lyu Sun, Guang Chen, Rui Tang, Zhi Li, Ruo-Yao Wei, Lan Liang, Xian-Jie Zhou, Dong-Liang Chen, Jun Li, Hong Gao, Jing Zhang, Zuo-Tao Zhao, and Yuan-Yuan Ji

Subjects
Medicine
Abstract

Abstract. Background:. Nickel-induced allergic contact dermatitis (Ni-ACD) is a global health problem. More detailed knowledge on the skin uptake of haptens is required. This study aimed to investigate the penetration process and distribution of nickel in skin tissues with late phase and early phase of Ni-ACD to understand the mechanisms of metal allergy. Methods:. Forty Hartley guinea pigs were divided into four groups according to the NiSO4 sensitizing concentration and the NiSO4 challenged concentration: the 5% NiSO4-group, 5% to 10% (sensitization-challenge; late phase group); 10% NiSO4-group, 10% to 10% (sensitization-challenge; early-phase group); and the positive and negative controls. Pathological biopsies were performed on each group. The depth profile of nickel element concentration in the skin of guinea pigs was detected by synchrotron radiation micro X-ray fluorescence spectroscopy (SR-μ-XRF) and micro X-ray absorption near-edge spectroscopy (μ-XANES). Results:. In each section, the nickel element concentration in both the 5% NiSO4-group and 10% NiSO4-group was significantly higher than that in the negative control group. In the upper 300-μm section of skin for the early phase group, the nickel element concentration was significantly higher than that in the lower section of skin. In deeper sections (>200 μm) of skin, the concentration of nickel in the early phase group was approximately equal to that in the late phase group. The curve of the late phase group was flat, which means that the nickel element concentration was distributed uniformly by SR-μ-XRF. According to the XANES data for the 10% NiSO4 metal salt solution, structural changes occurred in the skin model sample, indicating that nickel was not present in the Ni2+ aqueous ionic state but in the nickel-binding protein. Conclusions:. This study showed that the distribution of the nickel element concentration in ACD skin tissue was different between the early phase and late phase groups. The nickel element was not present in the Ni2+ aqueous ionic state but bound with certain proteins to form a complex in the stratum corneum in ACD model tissue.

Published
2019
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44. Correlation between intracranial carotid artery stenosis and ocular arterial blood flow and retinal vascular morphology

Author

Mao-Jun Yang, Lan Liang, Kang-Sheng Luo, Zhen-Jun Fang, Zhi-Yong Wu, and Dan Qiao

Subjects
intracranial segment, internal carotid artery stenosis, retina, CTA, ultrasonic, Ophthalmology, RE1-994
Abstract

AIM: To investigate the correlation between intracranial internal carotid artery stenosis and ocular arterial blood flow and retinal vascular morphology.METHODS: Totally, 251 patients admitted to our hospital due to suspected cerebral ischemia from January 2017 to June 2018 were selected. According to the degree of internal carotid artery stenosis, the patients were divided into non-stenosis group(39 cases), mild stenosis group(80 cases), moderate stenosis group(83 cases), and severe stenosis group(49 cases). The ocular hemodynamic indexes and retinal vascular diameters of the four groups were compared. The correlation and diagnostic value were analyzed between eye blood flow parameters and severe intracranial internal carotid artery stenosis.RESULTS: In the severe stenosis group, the peak systolic velocity(PSV)and diastolic peak velocity(EDV)of ophthalmic artery(OA), central retinal artery(CRA)and posterior ciliary artery(PCA)were significantly lower than those of the other three groups. In the blood flow parameters of OA, CRA and PCA, PSV and EDV were significantly negatively correlated with severe intracranial internal carotid artery stenosis. The optimal diagnostic thresholds of PCA PSV and PCA EDV for severe intracranial internal carotid artery stenosis were 11.26cm/s and 5.21cm/s, respectively.CONCLUSION: Intracranial internal carotid artery stenosis was significantly negatively correlated with PSV and EDV in the ocular arteries. PCA PSV and PCA EDV were most sensitive to intracranial internal carotid artery stenosis under the same index.

Published
2019
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45. Metformin induces apoptosis in mesenchymal stromal cells and dampens their therapeutic efficacy in infarcted myocardium

Author

Xiao He, Meng-Wei Yao, Ming Zhu, Dong-Lan Liang, Wei Guo, Yi Yang, Rong-Seng Zhao, Ting-Ting Ren, Xiang Ao, Wei Wang, Chun-Yu Zeng, Hua-Ping Liang, Dong-po Jiang, Jian Yu, and Xiang Xu

Subjects
Mesenchymal stromal cells, Metformin, Myocardial infarction, Apoptosis, Diabetes mellitus, Medicine (General), R5-920, Biochemistry, QD415-436
Abstract

Abstract Background Cardiovascular complications, especially myocardial infarctions (MIs), are the leading mortality cause in diabetic patients. The transplantation of stem cells into damaged hearts has had considerable success as a treatment for MI, although whether antidiabetic drugs affect the therapeutic efficacy of stem cell transplantation is still unknown. This study aims to understand whether and how metformin, one of the first-line drugs used to treat type 2 diabetes mellitus (T2DM), induces mesenchymal stromal cell (MSC) apoptosis and dampens their cardioprotective effect after transplantation into infarcted hearts. Methods A mouse MI model was generated via permanent ligation of the left anterior descending (LAD) coronary artery. MSCs with or without metformin treatment were transplanted after MI in diabetic mice. Echocardiography was used to assess cardiac function and determine cardiac remodeling, and TTC staining was performed to evaluate infarction size. A mouse gavage model was performed to evaluate bone marrow MSCs for flow cytometry assay. Results Metformin dampened MSC therapeutic efficacy, which increased infarct size and restricted functional cardiac recovery. Specifically, metformin induced the activation of AMP-activated protein kinase (AMPK)-mediated apoptosis through the inhibition of S6K1-Bad-Bcl-xL cell survival signaling, resulting in the upregulated expression of apoptosis-associated proteins and increased MSC apoptosis. Accordingly, counteracting AMPK attenuated metformin-induced apoptosis in MSCs and partially restored their cardioprotective effects in diabetic mice with MI. Furthermore, a decrease in peripheral blood MSCs was found in patients with T2DM who had a metformin medication history. Conclusions Our results highlight an unexpected adverse effect of metformin-induced MSC apoptosis through AMPK-mediated mTOR suppression, which is attenuated by an AMPK inhibitor. Moreover, AMPK inhibition may be a novel strategy for enhancing the effectiveness of stem cell therapy after MI in diabetes.

Published
2018
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47. Recurrence Rate of Proximal Contact Loss Between Implant Restorations and Adjacent Teeth After Proximal Contact Repair: A Retrospective Study.

Author

Chao-Hua Liang, Chung-Yi Nien, Hsiao-Lan Liang, Chih-Chien Hsiao, Ying-Chin Peng, and Kuang-Wei Hsu

Subjects
DISEASE relapse, RISK of prosthesis complications, SURGICAL complication risk factors, DENTAL implants, PATIENT aftercare, STATISTICS, CONFIDENCE intervals, BRIDGES (Dentistry), RETROSPECTIVE studies, FISHER exact test, RISK assessment, T-test (Statistics), DESCRIPTIVE statistics, DENTAL fillings, LOGISTIC regression analysis, ODDS ratio, COMPLICATIONS of prosthesis
Abstract

Purpose: Proximal contact loss (PCL) between implant-supported fixed dental prostheses (FDPs) and adjacent teeth has been reported as a common complication of implant therapy. The prevalence of PCL and its potential risk factors have been extensively studied. However, few studies have discussed the recurrent PCL after intervention to restore the proximal contacts. Thus, this retrospective study aimed to evaluate the recurrence rate of PCL and its potential risk factors. Materials and Methods: This study included 41 patients (with 45 implants in the posterior region), who had experienced PCL between implant restorations and adjacent teeth and had received contact repair. Recurrent PCL was recorded and evaluated during routine follow-ups with an interval of 6 to 12 months. The recurrence rates and time were measured. The potential influential factors of PCL were also assessed. Fisher exact test, t test, univariate logistic regression analyses, and multivariate logistic regression model were utilized to identify factors influencing PCL. Results: The recurrence rates of mesial PCL were high (> 50%) and the recurrence time became progressively rapid after each repair (5, 3.2, and 2.2 years). Implants with the first PCL recurrence were more likely to be splinted than those implants without recurrence (54.5% vs 18.8%; P = .032). In addition, patients with the first recurrence were slightly older than those without recurrence (55.8 vs 50.1 years; P = .087). Age, implant restoration (splinted vs single), frequent use of interdental brushes, and time to first complaint were the candidate factors associated with recurrent PCL in the univariate logistic regression analysis. The multivariate logistic regression model revealed that only splinted implant restoration was independently associated with a higher risk of recurrence (odds ratio 4.99; 95% confidence interval 1.02--24.31; P = .047). Conclusion: The recurrence rates of mesial PCL were high and associated with the splinted-type design. Also, the recurrence time of PCL accelerated after each repair. Therefore, routine follow-up monitoring PCL and carefully assessing patient compliance after implant therapy are recommended. [ABSTRACT FROM AUTHOR]

Published
2022
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48. A large-scale evaluation of computational protein function prediction

Author

Radivojac, Predrag, Clark, Wyatt T, Oron, Tal Ronnen, Schnoes, Alexandra M, Wittkop, Tobias, Sokolov, Artem, Graim, Kiley, Funk, Christopher, Verspoor, Karin, Ben-Hur, Asa, Pandey, Gaurav, Yunes, Jeffrey M, Talwalkar, Ameet S, Repo, Susanna, Souza, Michael L, Piovesan, Damiano, Casadio, Rita, Wang, Zheng, Cheng, Jianlin, Fang, Hai, Gough, Julian, Koskinen, Patrik, Törönen, Petri, Nokso-Koivisto, Jussi, Holm, Liisa, Cozzetto, Domenico, Buchan, Daniel WA, Bryson, Kevin, Jones, David T, Limaye, Bhakti, Inamdar, Harshal, Datta, Avik, Manjari, Sunitha K, Joshi, Rajendra, Chitale, Meghana, Kihara, Daisuke, Lisewski, Andreas M, Erdin, Serkan, Venner, Eric, Lichtarge, Olivier, Rentzsch, Robert, Yang, Haixuan, Romero, Alfonso E, Bhat, Prajwal, Paccanaro, Alberto, Hamp, Tobias, Kaßner, Rebecca, Seemayer, Stefan, Vicedo, Esmeralda, Schaefer, Christian, Achten, Dominik, Auer, Florian, Boehm, Ariane, Braun, Tatjana, Hecht, Maximilian, Heron, Mark, Hönigschmid, Peter, Hopf, Thomas A, Kaufmann, Stefanie, Kiening, Michael, Krompass, Denis, Landerer, Cedric, Mahlich, Yannick, Roos, Manfred, Björne, Jari, Salakoski, Tapio, Wong, Andrew, Shatkay, Hagit, Gatzmann, Fanny, Sommer, Ingolf, Wass, Mark N, Sternberg, Michael JE, Škunca, Nives, Supek, Fran, Bošnjak, Matko, Panov, Panče, Džeroski, Sašo, Šmuc, Tomislav, Kourmpetis, Yiannis AI, van Dijk, Aalt DJ, Braak, Cajo JF ter, Zhou, Yuanpeng, Gong, Qingtian, Dong, Xinran, Tian, Weidong, Falda, Marco, Fontana, Paolo, Lavezzo, Enrico, Di Camillo, Barbara, Toppo, Stefano, Lan, Liang, Djuric, Nemanja, Guo, Yuhong, Vucetic, Slobodan, Bairoch, Amos, Linial, Michal, Babbitt, Patricia C, Brenner, Steven E, Orengo, Christine, and Rost, Burkhard

Subjects
Generic health relevance, Algorithms, Animals, Computational Biology, Databases, Protein, Exoribonucleases, Forecasting, Humans, Molecular Biology, Molecular Sequence Annotation, Proteins, Species Specificity, Biological Sciences, Technology, Medical and Health Sciences, Developmental Biology
Abstract

Automated annotation of protein function is challenging. As the number of sequenced genomes rapidly grows, the overwhelming majority of protein products can only be annotated computationally. If computational predictions are to be relied upon, it is crucial that the accuracy of these methods be high. Here we report the results from the first large-scale community-based critical assessment of protein function annotation (CAFA) experiment. Fifty-four methods representing the state of the art for protein function prediction were evaluated on a target set of 866 proteins from 11 organisms. Two findings stand out: (i) today's best protein function prediction algorithms substantially outperform widely used first-generation methods, with large gains on all types of targets; and (ii) although the top methods perform well enough to guide experiments, there is considerable need for improvement of currently available tools.

Published
2013

49. A Randomized, Open‐Label, Controlled Clinical Trial of Azvudine Tablets in the Treatment of Mild and Common COVID‐19, a Pilot Study

Author

Zhigang Ren, Hong Luo, Zujiang Yu, Jingchao Song, Lan Liang, Ling Wang, Haiyu Wang, Guangying Cui, Yong Liu, Jin Wang, Qingquan Li, Zhaohai Zeng, Shengkun Yang, Guangzhong Pei, Yonghui Zhu, Wenbin Song, Wenquan Yu, Chuanjun Song, Lihong Dong, Chuansong Hu, Jinfa Du, and Junbiao Chang

Subjects
azvudine, COVID‐19, SARS‐CoV‐2, Science
Abstract

Abstract Coronavirus disease 2019 (COVID‐19) has spread worldwide. To date, no specific drug for COVID‐19 has been developed. Thus, this randomized, open‐label, controlled clinical trial (ChiCTR2000029853) was performed in China. A total of 20 mild and common COVID‐19 patients were enrolled and randomly assigned to receive azvudine and symptomatic treatment (FNC group), or standard antiviral and symptomatic treatment (control group). The mean times of the first nucleic acid negative conversion (NANC) of ten patients in the FNC group and ten patients in the control group are 2.60 (SD 0.97; range 1–4) d and 5.60 (SD 3.06; range 2–13) d, respectively (p = 0.008). The mean times of the first NANC of four newly diagnosed subjects in the FNC group and ten subjects in the control group are 2.50 (SD 1.00; range 2–4) d and 9.80 (SD 4.73; range 3–19) d, respectively (starting from the initial treatment) (p = 0.01). No adverse events occur in the FNC group, while three adverse events occur in the control group (p = 0.06). The preliminary results show that FNC treatment in the mild and common COVID‐19 may shorten the NANC time versus standard antiviral treatment. Therefore, clinical trials of FNC treating COVID‐19 with larger sample size are warranted.

Published
2020
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50. Performance Test of Dry Ice Phase Change Generator

Author

GAO Peng, HU Lan, LIANG Shuo, ZHAO Chunxiang

Subjects
coal spontaneous combustion, dry-ice phase transformation generator, hydraulic characteristics, carbon dioxide flow, heat exchange quantity, copper tube freezing risk, Mining engineering. Metallurgy, TN1-997
Abstract

To solve the problem that the sublimation of dry-ice is too slow when it was used for fire prevention in goaf, a new device, dry-ice phase transformation generator, has been designed, which can improve the speed of dry-ice sublimation by the heat supply of water. In order to make the device successfully applied to underground coal mines, its performance was tested. Through the laboratory experiments, its hydraulic characteristics of the hot water supply part and gas production change were analyzed, the heat transfer was calculated, and the risk of copper tube freezing was evaluated. The results show that the device can produce CO2 stably and efficiently for 4 hours, and the water in the copper pipe does not have the risk of freezing under normal conditions. Finally, it is concluded that the device has good fire-fighting performance and can be used in goaf fire-fighting.

Published
2020
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