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566 results on '"Zhou, Xiaoshuang"'

6. Mitochondria transcription and cancer

Author

Tang Lei, Yu Rui, Zhou Xiaoshuang, Zhang Jinglan, and Zhang Jihong

Subjects
Neoplasms. Tumors. Oncology. Including cancer and carcinogens, RC254-282, Cytology, QH573-671
Abstract

Abstract Mitochondria are major organelles involved in several processes related to energy supply, metabolism, and cell proliferation. The mitochondria function is transcriptionally regulated by mitochondria DNA (mtDNA), which encodes the key proteins in the electron transport chain that is indispensable for oxidative phosphorylation (OXPHOS). Mitochondrial transcriptional abnormalities are closely related to a variety of human diseases, such as cardiovascular diseases, and diabetes. The mitochondria transcription is regulated by the mtDNA, mitochondrial RNA polymerase (POLRMT), two transcription factors (TFAM and TF2BM), one transcription elongation (TEFM), and one known transcription termination factor (mTERFs). Dysregulation of these factors directly leads to altered expression of mtDNA in tumor cells, resulting in cellular metabolic reprogramming and mitochondrial dysfunction. This dysregulation plays a role in modulating tumor progression. Therefore, understanding the role of mitochondrial transcription in cancer can have implications for cancer diagnosis, prognosis, and treatment. Targeting mitochondrial transcription or related pathways may provide potential therapeutic strategies for cancer treatment. Additionally, assessing mitochondrial transcriptional profiles or biomarkers in cancer cells or patient samples may offer diagnostic or prognostic information.

Published
2024
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11. Multi-objective optimization and explanation for stroke risk assessment in Shanxi province

Author

Ma, Jing, Sun, Yiyang, Liu, Junjie, Huang, Huaxiong, Zhou, Xiaoshuang, and Xu, Shixin

Subjects
Computer Science - Machine Learning, 68T10
Abstract

Stroke is the top leading causes of death in China (Zhou et al. The Lancet 2019). A dataset from Shanxi Province is used to identify the risk of each patient's at four states low/medium/high/attack and provide the state transition tendency through a SHAP DeepExplainer. To improve the accuracy on an imbalance sample set, the Quadratic Interactive Deep Neural Network (QIDNN) model is first proposed by flexible selecting and appending of quadratic interactive features. The experimental results showed that the QIDNN model with 7 interactive features achieve the state-of-art accuracy $83.25\%$. Blood pressure, physical inactivity, smoking, weight and total cholesterol are the top five important features. Then, for the sake of high recall on the most urgent state, attack state, the stroke occurrence prediction is taken as an auxiliary objective to benefit from multi-objective optimization. The prediction accuracy was promoted, meanwhile the recall of the attack state was improved by $24.9\%$ (to $84.83\%$) compared to QIDNN (from $67.93\%$) with same features. The prediction model and analysis tool in this paper not only gave the theoretical optimized prediction method, but also provided the attribution explanation of risk states and transition direction of each patient, which provided a favorable tool for doctors to analyze and diagnose the disease., Comment: 22pages, 8 figures

Published
2021

12. Analysis and classification of main risk factors causing stroke in Shanxi Province

Author

Liu, Junjie, Sun, Yiyang, Ma, Jing, Tu, Jiachen, Deng, Yuhui, He, Ping, Huang, Huaxiong, Zhou, Xiaoshuang, and Xu, Shixin

Subjects
Computer Science - Machine Learning, 92C50
Abstract

In China, stroke is the first leading cause of death in recent years. It is a major cause of long-term physical and cognitive impairment, which bring great pressure on the National Public Health System. Evaluation of the risk of getting stroke is important for the prevention and treatment of stroke in China. A data set with 2000 hospitalized stroke patients in 2018 and 27583 residents during the year 2017 to 2020 is analyzed in this study. Due to data incompleteness, inconsistency, and non-structured formats, missing values in the raw data are filled with -1 as an abnormal class. With the cleaned features, three models on risk levels of getting stroke are built by using machine learning methods. The importance of "8+2" factors from China National Stroke Prevention Project (CSPP) is evaluated via decision tree and random forest models. Except for "8+2" factors the importance of features and SHAP1 values for lifestyle information, demographic information, and medical measurement are evaluated and ranked via a random forest model. Furthermore, a logistic regression model is applied to evaluate the probability of getting stroke for different risk levels. Based on the census data in both communities and hospitals from Shanxi Province, we investigate different risk factors of getting stroke and their ranking with interpretable machine learning models. The results show that Hypertension (Systolic blood pressure, Diastolic blood pressure), Physical Inactivity (Lack of sports), and Overweight (BMI) are ranked as the top three high-risk factors of getting stroke in Shanxi province. The probability of getting stroke for a person can also be predicted via our machine learning model., Comment: 13 pages, 9 figures

Published
2021

19. MSA-MIL: A deep residual multiple instance learning model based on multi-scale annotation for classification and visualization of glomerular spikes

Author

Chen, Yilin, Li, Ming, Wu, Yongfei, Liu, Xueyu, Hao, Fang, Zhou, Daoxiang, Zhou, Xiaoshuang, and Wang, Chen

Subjects
Computer Science - Computer Vision and Pattern Recognition, Electrical Engineering and Systems Science - Image and Video Processing, Quantitative Biology - Quantitative Methods
Abstract

Membranous nephropathy (MN) is a frequent type of adult nephrotic syndrome, which has a high clinical incidence and can cause various complications. In the biopsy microscope slide of membranous nephropathy, spikelike projections on the glomerular basement membrane is a prominent feature of the MN. However, due to the whole biopsy slide contains large number of glomeruli, and each glomerulus includes many spike lesions, the pathological feature of the spikes is not obvious. It thus is time-consuming for doctors to diagnose glomerulus one by one and is difficult for pathologists with less experience to diagnose. In this paper, we establish a visualized classification model based on the multi-scale annotation multi-instance learning (MSA-MIL) to achieve glomerular classification and spikes visualization. The MSA-MIL model mainly involves three parts. Firstly, U-Net is used to extract the region of the glomeruli to ensure that the features learned by the succeeding algorithm are focused inside the glomeruli itself. Secondly, we use MIL to train an instance-level classifier combined with MSA method to enhance the learning ability of the network by adding a location-level labeled reinforced dataset, thereby obtaining an example-level feature representation with rich semantics. Lastly, the predicted scores of each tile in the image are summarized to obtain glomerular classification and visualization of the classification results of the spikes via the usage of sliding window method. The experimental results confirm that the proposed MSA-MIL model can effectively and accurately classify normal glomeruli and spiked glomerulus and visualize the position of spikes in the glomerulus. Therefore, the proposed model can provide a good foundation for assisting the clinical doctors to diagnose the glomerular membranous nephropathy.

Published
2020

47. Direct Catalysis‐Driven Yarn Artificial Muscles: Chemically Induced Actuation.

Author

Zhou, Xiaoshuang, Li, Mingxia, Cao, Xiaoting, Dong, Xu, Fang, Shaoli, Li, Lvzhou, Yuan, Ningyi, Ding, Jianning, and Baughman, Ray H.

Subjects
*CARBON fibers, *CARBON nanotubes, *CHEMICAL energy, *ENERGY conversion, *MECHANICAL energy
Abstract

Yarn artificial muscles offer an exciting avenue to replicate the extraordinary efficiency of biological muscles, converting chemical energy directly into mechanical work. Nevertheless, realizing the chemical‐mechanical energy conversion has posed significant challenges. In this study, a novel approach for harnessing direct catalysis to power yarn artificial muscles within a one‐compartment aqueous system is introduced. This research distinguishes itself through an innovative actuation mechanism using nanoscale catalytic particles. These nanoparticles synthesized and integrated onto the yarn surface act as a chemical trigger for muscle actuation. Notably, the resulting yarn muscle demonstrates a reversible tensile stroke of nearly 4% in ≈20 s. By bridging the gap between chemical catalysis and mechanical performance, this study paves the way for innovative applications in fields ranging from robotics to biomedical devices. [ABSTRACT FROM AUTHOR]

Published
2024
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48. Empirical likelihood based heteroscedasticity diagnostics for varying coefficient partially nonlinear models.

Author

Wang, Cuiping, Zhou, Xiaoshuang, and Zhao, Peixin

Subjects
INFERENTIAL statistics, EMPIRICAL research, DATA analysis, HYPOTHESIS
Abstract

Heteroscedasticity diagnostics of error variance is essential before performing some statistical inference work. This paper is concerned with the statistical diagnostics for the varying coefficient partially nonlinear model. We propose a novel diagnostic approach for heteroscedasticity of error variance in the model by combining it with the empirical likelihood method. Under some mild conditions, the nonparametric version of the Wilks theorem is obtained. Furthermore, simulation studies and a real data analysis are implemented to evaluate the performances of our proposed approaches. [ABSTRACT FROM AUTHOR]

Published
2024
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49. β-Hydroxybutyrate Protects Against Cisplatin-Induced Renal Damage via Regulating Ferroptosis.

Author

Tian, Ruixue, Tang, Shuqin, Zhao, Jingyu, Hao, Yajie, Zhao, Limei, Han, Xiutao, Wang, Xingru, Zhang, Lijun, Li, Rongshan, and Zhou, Xiaoshuang

Subjects
CISPLATIN, BUTYRATES, CALCIUM-dependent protein kinase, BLOOD urea nitrogen, ACUTE kidney failure, IMMUNOSTAINING, METABOLIC regulation
Abstract

Cisplatin is a particularly potent antineoplastic drug. However, its usefulness is restricted due to the induction of nephrotoxicity. More recent research has indicated that β-hydroxybutyrate (β-HB) protects against acute or chronic organ damage as an efficient healing agent. Nonetheless, the therapeutic mechanisms of β-HB in acute kidney damage caused by chemotherapeutic drugs remain unclear. Our study developed a model of cisplatin-induced acute kidney injury (AKI), which involved the administration of a ketogenic diet or β-HB. We analyzed blood urea nitrogen (BUN) and creatinine (Cr) levels in serum, and used western blotting and immunohistochemical staining to assess ferroptosis and the calcium/calmodulin-dependent kinase kinase 2 (Camkk2)/AMPK pathway. The mitochondrial morphology and function were examined. Additionally, we conducted in vivo and in vitro experiments using selective Camkk2 inhibitor or activator to investigate the protective mechanism of β-HB on cisplatin-induced AKI. Exogenous or endogenous β-HB effectively alleviated cisplatin-induced abnormally elevated levels of BUN and Cr and renal tubular necrosis in vivo. Additionally, β-HB reduced ferroptosis biomarkers and increased the levels of anti-ferroptosis biomarkers in the kidney. β-HB also improved mitochondrial morphology and function. Moreover, β-HB significantly attenuated cisplatin-induced cell ferroptosis and damage in vitro. Furthermore, western blotting and immunohistochemical staining indicated that β-HB may prevent kidney injury by regulating the Camkk2-AMPK pathway. The use of the Camkk2 inhibitor or activator verified the involvement of Camkk2 in the renal protection by β-HB. This study provided evidence of the protective effects of β-HB against cisplatin-induced nephrotoxicity and identified inhibited ferroptosis and Camkk2 as potential molecular mechanisms. Cisplatin induces disruptions in mitochondrial energy metabolism and lipid peroxidation. Both endogenous and exogenous β-hydroxybutyrate can protected against cisplatin-induced renal damage by repairing mitochondrial energy homeostasis and preventing ferroptosis, and this protective effect might be attributed to Camkk2 regulation. β-HB protects against cisplatin-induced renal damage both in vivo and in vitro. Moreover, β-HB is effective in attenuating cisplatin-induced lipid peroxidation and ferroptosis. The regulation of energy metabolism, as well as the treatment involving β-HB, is associated with Camkk2. [ABSTRACT FROM AUTHOR]

Published
2024
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