Exploring Mechanisms of Lang Qing Ata in Non-Alcoholic Steatohepatitis Based on Metabolomics, Network Pharmacological Analysis, and Experimental Validation by Li S, Zhu H, Zhai Q, Hou Y, Yang Y, Lan H, Jiang M, Xuan J

“…These discoveries were further validated in subsequent mouse models. An HFHC-induced NASH mouse model was used to validate the therapeutic effects and potential mechanisms of LQAtta on NASH.Results: From the UHPLC-MS/MS analysis of LQAtta, a total of 1518 chemical components were identified, with 106 of them being absorbed into the bloodstream. …”
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Microarray profile of circular RNAs identifies CBT15_circR_28491 and T helper cells as new regulators for deep vein thrombosis by Weiwei Chen, Ying Zhu, Sihua Niu, Yan Zhou, Jian Chang, Shujie Gan

“…Finally, a DVT rat model was established to verify the expression of critical circRNAs and hub genes using real-time quantitative PCR.ResultsA total of 421 circRNAs and 1,082 mRNAs were differentially expressed in DVT. …”
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The performance of artificial intelligence in image-based prediction of hematoma enlargement: a systematic review and meta-analysis by Wenjing Fan, Zhiping Wu, Wangyang Zhao, Luzhu Jia, Shuze Li, Wei Wei, Xin Chen

“…Two reviewers independently screened the retrieved citations and extracted data. …”
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人工智能融合临床与多组学数据在卒中防治及医药研发中的应用与挑战Applications and Challenges of Integrating Artificial Intelligence with Clinical and Multi-omics Data in Stroke Prevention, Treatment, and Pharmaceut... by 勾岚，姜明慧，姜勇，廖晓凌，李昊，张杰，程丝 (GOU Lan, JIANG Minghui, JIANG Yong, LIAO Xiaoling, LI Hao, ZHANG Jie, CHENG Si)

“…By integrating and analyzing clinical and multi-omics data, AI technology enhances the identification of high-risk populations, optimizes early diagnosis and risk assessment, enables precise subtyping of stroke, facilitates the screening of potential drug targets, and constructs prognostic prediction models. …”
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Utilizing bioinformatics and machine learning to identify CXCR4 gene-related therapeutic targets in diabetic foot ulcers by Hengyan Zhang, Ye Zhou, Heguo Yan, Changxing Huang, Licong Yang, Yangwen Liu

“…We integrated the genes screened by three machine learning models (LASSO, SVM, and Random Forest), and CXCR4 was identified as a key gene with potential therapeutic value in DFUs. …”
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Identification and mechanism analysis of biomarkers related to butyrate metabolism in COVID-19 patients by Wenchao Zhou, Hui Li, Juan Zhang, Changsheng Liu, Dan Liu, Xupeng Chen, Jing Ouyang, Tian Zeng, Shuang Peng, Fan Ouyang, Yunzhu Long, Yukun Li

“…Six machine learning algorithms were employed to determine the best model for identifying biomarkers, and receiver operating characteristic (ROC) curves were plotted to evaluate the diagnostic value of the biomarkers in COVID-19. …”
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Use of artificial intelligence to support prehospital traumatic injury care: A scoping review by Jake Toy, Jonathan Warren, Kelsey Wilhelm, Brant Putnam, Denise Whitfield, Marianne Gausche‐Hill, Nichole Bosson, Ross Donaldson, Shira Schlesinger, Tabitha Cheng, Craig Goolsby

“…Conclusions A small but growing body of literature described AI models based on prehospital features that may support decisions made by dispatchers, Emergency Medical Services clinicians, and trauma teams in early traumatic injury care.…”
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Design, Fabrication, and Application of Large-Area Flexible Pressure and Strain Sensor Arrays: A Review by Xikuan Zhang, Jin Chai, Yongfu Zhan, Danfeng Cui, Xin Wang, Libo Gao

“…The rapid development of flexible sensor technology has made flexible sensor arrays a key research area in various applications due to their exceptional flexibility, wearability, and large-area-sensing capabilities. …”
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Exploring the predictive values of CRP and lymphocytes in coronary artery disease based on a machine learning and Mendelian randomization by Yuan Liu, Yuan Liu, Xin Yuan, Xin Yuan, Yu-Chan He, Yu-Chan He, Zhong-Hai Bi, Zhong-Hai Bi, Si-Yao Li, Si-Yao Li, Ye Li, Ye Li, Yan-Li Liu, Yan-Li Liu, Liu Miao, Liu Miao

“…Techniques employed included propensity score matching (PSM), logistic regression, lasso regression, and random forest algorithms (RF). Risk factors were assessed, and the sensitivity and specificity of the models were evaluated using receiver operating characteristic (ROC) curves. …”
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Implementing Remote Radiotherapy Planning to Increase Patient Flow at a Johannesburg Academic Hospital, South Africa: Protocol for a Prospective Feasibility Study by Duvern Ramiah, Sonwabile Ngcezu, Oluwatosin Ayeni, Okechinyere Achilonu, Mariam Adeleke, Theo Nair, Joseph Otten, Daniel Mmereki

“…Phase 1 (feasibility) encompasses system commissioning, including beam modeling, computed tomography (CT)-to-electron density calibration, multileaf collimator (MLC) optimization, and dose calculations using the anisotropic analytical algorithm. …”
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Estimation of the water content of needles under stress by Erannis jacobsoni Djak. via Sentinel-2 satellite remote sensing by Jiaze Guo, Xiaojun Huang, Xiaojun Huang, Xiaojun Huang, Debao Zhou, Junsheng Zhang, Gang Bao, Gang Bao, Siqin Tong, Siqin Tong, Yuhai Bao, Yuhai Bao, Dashzebeg Ganbat, Dorjsuren Altanchimeg, Davaadorj Enkhnasan, Mungunkhuyag Ariunaa

“…Multiple vegetation indices are screened via recursive feature elimination cross validation (RFECV), and then support vector regression (SVR) and back-propagation neural network (BP) models are used to predict the leaf weight content fresh (LWCF) and leaf weight content dry (LWCD) of needles over a large area. …”
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Locating and quantifying CH₄ sources within a wastewater treatment plant based on mobile measurements by J. Yang, Z. Xu, Z. Xia, Z. Xia, X. Pei, Y. Yang, B. Qiu, B. Qiu, S. Zhao, S. Zhao, Y. Zhang, Y. Zhang, Z. Wang, Z. Wang

“…We utilized a multi-source Gaussian plume model combined with a genetic algorithm inversion framework, designed to locate major sources within the plant and quantify the corresponding <span class="inline-formula">CH₄</span> emission fluxes. …”
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Longitudinal Digital Phenotyping of Multiple Sclerosis Severity Using Passively Sensed Behaviors and Ecological Momentary Assessments: Real-World Evaluation by Zongqi Xia, Prerna Chikersal, Shruthi Venkatesh, Elizabeth Walker, Anind K Dey, Mayank Goel

“…Among the best-performing models with the least sensor data requirement, the ML algorithm predicted depressive symptoms with an accuracy of 80.6% (F1-score=0.76), high global MS symptom burden with an accuracy of 77.3% (F1-score=0.78), severe fatigue with an accuracy of 73.8% (F1-score=0.74), and poor sleep quality with an accuracy of 72.0% (F1-score=0.70). …”
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Comprehensive integration of diagnostic biomarker analysis and immune cell infiltration features in sepsis via machine learning and bioinformatics techniques by Liuqing Yang, Liuqing Yang, Liuqing Yang, Rui Xuan, Rui Xuan, Rui Xuan, Dawei Xu, Dawei Xu, Dawei Xu, Aming Sang, Aming Sang, Aming Sang, Jing Zhang, Jing Zhang, Jing Zhang, Yanfang Zhang, Xujun Ye, Xinyi Li, Xinyi Li, Xinyi Li

“…The utilization of the receiver operating characteristic curve in conjunction with the nomogram model served to authenticate the discriminatory strength and efficacy of the key genes. …”
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Meta-analyses of IL1A polymorphisms and the risk of several autoimmune diseases published in databases. by Hang Su, Na Rei, Lei Zhang, Jiaxiang Cheng

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Epidemiology and Association Rules Analysis for Pulmonary Tuberculosis Cases with Extrapulmonary Tuberculosis from Age and Gender Perspective: A Large-Scale Retrospective Multicent... by Wanli Kang, Jiajia Yu, Chen Liang, Quanhong Wang, Liang Li, Jian Du, Hongyan Chen, Jianxiong Liu, Jinshan Ma, Mingwu Li, Jingmin Qin, Wei Shu, Peilan Zong, Yi Zhang, Xiaofeng Yan, Zhiyi Yang, Zaoxian Mei, Qunyi Deng, Pu Wang, Wenge Han, Meiying Wu, Ling Chen, Xinguo Zhao, Lei Tan, Fujian Li, Chao Zheng, Hongwei Liu, Xinjie Li, Ertai A., Yingrong Du, Fenglin Liu, Wenyu Cui, Song Yang, Xiaohong Chen, Junfeng Han, Qingyao Xie, Yanmei Feng, Wenyu Liu, Peijun Tang, Jianyong Zhang, Jian Zheng, Dawei Chen, Xiangyang Yao, Tong Ren, Yan Li, Yuanyuan Li, Lei Wu, Qiang Song, Mei Yang, Jian Zhang, Yuanyuan Liu, Shuliang Guo, Kun Yan, Xinghua Shen, Dan Lei, Yanli Zhang, Youcai Li, Yongkang Dong, Shenjie Tang

“…The multivariable logistic regression models demonstrated that odds ratios of concurrent PTB-EPTB cases varied by gender and age group. …”
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INTERNATIONAL FORUM “OLD AND NEW MEDIA: ALONG THE PATH TOWARDS A NEW AESTHETICS” / МЕЖДУНАРОДНЫЙ ФОРУМ «СТАРЫЕ И НОВЫЕ МЕДИА: ПУТИ К НОВОЙ ЭСТЕТИКЕ»... by BOGATYRYOVA ELENA A.

“…Along with the formulation of the enumerated questions, the presentations of the participants of the conference concretized the conclusions about the influence of new media on the cognitive capabilities of man, his means of action, “new plasticity” and a new way of living. Notice was made of alarming tendencies of transforming consciousness into that of “gamers,” reacting in correlations with certain algorithms, and a transference from discourse and substantiations towards reactions and evaluations. …”
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Project quality, regulation quality by Elena Mussinelli

“…These tools legitimise choices where conformity to the standard acts as a screen for the assumption of precise responsibilities. …”
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Identification of biomarkers associated with inflammatory response in Parkinson's disease by bioinformatics and machine learning. by Yatan Li, Wei Jia, Chen Chen, Cheng Chen, Jinchao Chen, Xinling Yang, Pei Liu

“…LASSO, SVM-RFE and Random Forest algorithms were used to screen biomarker genes. Then, ROC curves were drawn and PD risk predicting models were constructed on the basis of the biomarker genes. …”
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Identifying potential three key targets gene for septic shock in children using bioinformatics and machine learning methods by Wei Guo, Hao Chen, Feng Wang, Yingjiao Chi, Wei Zhang, Shan Wang, Kezhu Chen, Hong Chen

“…Three kinds of machine learning models were established, and the candidate genes were screened by intersection to obtain the core genes with diagnostic value. …”
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