Construction and analysis of a prognostic risk scoring model for gastric cancer anoikis-related genes based on LASSO regression by Ai CHEN, Xiaowei CHEN, Yanan WANG, Xiaobing SHEN

“…Gene expression levels in gastric cancer clinical samples and cells were detected by real-time quantitative PCR (RT-qPCR); Kaplan-Meier (KM) survival curves, univariate and multivariate Cox regression analyses were used to verify the predictive efficiency of the prognostic risk scoring model for the prognosis of gastric cancer patients; CIBERSORT and ESTIMATE algorithms were used to analyze the immune cell infiltration levels in patients with different risk groups; the correlation between risk scores and immune checkpoint expression levels in gastric cancer patients was analyzed using the R package "ggplot2" and "ggExtra", and the correlation between tumor mutation burden (TMB) and risk scores was assessed; chemotherapy drug sensitivity analysis was used to evaluate the value of the constructed prognostic risk scoring model in gastric cancer chemotherapy. …”
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Diagnostic accuracy of artificial intelligence for the screening of prostate cancer in biparametric magnetic resonance imaging: a systematic review by Oksana V. Kryuchkova, Elena V. Schepkina, Natalia A. Rubtsova, Boris Y. Alekseev, Anton I. Kuznetsov, Svetlana V. Epifanova, Elena V. Zarya, Ali E. Talyshinskii

“…Accurate and timely detection of prostate cancer is important under the current conditions. …”
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Emerging Diagnostic Approaches for Musculoskeletal Disorders: Advances in Imaging, Biomarkers, and Clinical Assessment by Rahul Kumar, Kiran Marla, Kyle Sporn, Phani Paladugu, Akshay Khanna, Chirag Gowda, Alex Ngo, Ethan Waisberg, Ram Jagadeesan, Alireza Tavakkoli

“…Artificial intelligence and machine learning algorithms now automate image interpretation, predict clinical outcomes, and enhance clinical decision support, complementing conventional clinical evaluations. …”
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Utilizing bioinformatics to identify biomarkers and analyze their expression in relation to immune cell ratios in femoral head necrosis by Dongchen Li, Dongchen Li, Zhilong Huang, Teng Ma, Yu Su, Yu Su, Zhao Li, Zhao Li, Liang Sun, Ming Li, Zhong Li, Yao Li, Qian Wang, Yao Lu

“…BackgroundNecrosis of the Femoral Head (NFH) represents a challenging orthopedic condition, characterized by elusive early detection and rapid progression, predominantly in the middle-aged demographic. …”
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Murine leukemia virus p12 tethers the capsid-containing pre-integration complex to chromatin by binding directly to host nucleosomes in mitosis. by Madushi Wanaguru, David J Barry, Donald J Benton, Nicola J O'Reilly, Kate N Bishop

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Crosstalk between the serine/threonine kinase StkP and the response regulator ComE controls the stress response and intracellular survival of Streptococcus pneumoniae. by Germán E Piñas, Nicolás M Reinoso-Vizcaino, Nubia Y Yandar Barahona, Paulo R Cortes, Rosario Duran, Chandan Badapanda, Ankita Rathore, Dario R Bichara, Melina B Cian, Nadia B Olivero, Daniel R Perez, José Echenique

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Assessment of associations between clinical and immune microenvironmental factors and tumor mutation burden in resected nonsmall cell lung cancer by applying machine learning to wh... by Akira Ono, Yukihiro Terada, Takuya Kawata, Masakuni Serizawa, Mitsuhiro Isaka, Takanori Kawabata, Toru Imai, Keita Mori, Koji Muramatsu, Isamu Hayashi, Hirotsugu Kenmotsu, Keiichi Ohshima, Kenichi Urakami, Takeshi Nagashima, Masatoshi Kusuhara, Yasuto Akiyama, Takashi Sugino, Yasuhisa Ohde, Ken Yamaguchi, Toshiaki Takahashi

“…IME profiles, including PD‐L1 tumor proportion score (TPS), stromal CD8 tumor‐infiltrating lymphocyte (TIL) density, and stromal Foxp3 TIL density, were quantified by digital pathology using a machine learning algorithm. To detect factors associated with TMB, clinical data, and IME factors were assessed by means of a multiple regression model. …”
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Enhancing Sustainable Manufacturing in Industry 4.0: A Zero-Defect Approach Leveraging Effective Dynamic Quality Factors by Rouhollah Khakpour, Ahmad Ebrahimi, Seyed Mohammad Seyed Hosseini

“…Moreover, the gathered data from defect detection can be used in two ways: to prevent defect occurrence in the future (detect-prevent) and to design algorithms for predicting when a defect may occur in the future, hence, to prevent defects before they arise (predict-prevent). …”
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Identification of osteoarthritis-associated chondrocyte subpopulations and key gene-regulating drugs based on multi-omics analysis by Ting Hao, Zhiwei Pei, Sile Hu, Zhenqun Zhao, Wanxiong He, Jing Wang, Liuchang Jiang, jirigala Ariben, Lina Wu, Xiaolong Yang, Leipeng Wang, Yonggang Wu, Xiaofeng Chen, Qiang Li, Haobo Yang, Siqin Li, Xing Wang, Mingqi Sun, Baoxin Zhang

“…To screen for hub genes associated with OA, a combination of 10 machine learning algorithms and 113 algorithm compositions was integrated. …”
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Exposure and infection to Plasmodium knowlesi in case study communities in Northern Sabah, Malaysia and Palawan, The Philippines. by Kimberly M Fornace, Lou S Herman, Tommy R Abidin, Tock Hing Chua, Sylvia Daim, Pauline J Lorenzo, Lynn Grignard, Nor Afizah Nuin, Lau Tiek Ying, Matthew J Grigg, Timothy William, Fe Espino, Jonathan Cox, Kevin K A Tetteh, Chris J Drakeley

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Sorcha: Optimized Solar System Ephemeris Generation by Matthew J. Holman, Pedro H. Bernardinelli, Megan E. Schwamb, Mario Jurić, Drew Oldag, Maxine West, Kevin J. Napier, Stephanie R. Merritt, Grigori Fedorets, Samuel Cornwall, Jacob A. Kurlander, Siegfried Eggl, Jeremy Kubica, Kathleen Kiker, Joseph Murtagh, Shantanu P. Naidu, Colin Orion Chandler

“…Sorcha can swiftly, efficiently, and accurately calculate the on-sky positions for sets of millions of input orbits and surveys with millions of visits, identifying which exposures these objects cross, in order for later stages of the software to make detailed estimates of the apparent magnitude and detectability of those input small bodies. In this paper, we provide the full details of the algorithm and software behind Sorcha ’s ephemeris generator. …”
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A multicenter pragmatic implementation study of AI-ECG-based clinical decision support software to identify low LVEF: Clinical trial design and methods by Francisco Lopez-Jimenez, Heather M. Alger, Zachi I. Attia, Barbara Barry, Ranee Chatterjee, Rowena Dolor, Paul A. Friedman, Stephen J. Greene, Jason Greenwood, Vinay Gundurao, Sarah Hackett, Prerak Jain, Anja Kinaszczuk, Ketan Mehta, Jason O'Grady, Ambarish Pandey, Christopher Pullins, Arjun R. Puranik, Mohan Krishna Ranganathan, David Rushlow, Mark Stampehl, Vinayak Subramanian, Kitzner Vassor, Xuan Zhu, Samir Awasthi

“…Background: Artificial intelligence (AI) enabled algorithms can detect or predict cardiovascular conditions using electrocardiogram (ECG) data. …”
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Epidemiological, molecular, and evolutionary characteristics of G1P[8] rotavirus in China on the eve of RotaTeq application by Rui Peng, Rui Peng, Mengxuan Wang, Saleha Shahar, Guangping Xiong, Qing Zhang, Lili Pang, Hong Wang, Xiangyu Kong, Dandi Li, Zhaojun Duan

“…Neutralizing epitope, amino acid selection pressure, and evolution dynamics analyses on VP7 and VP4 were performed using BioEdit v.7.0.9.0 and PyMOL v.2.5.2, four algorithms (MEME, SLAC, FEL, and FUBAR) in the Datamonkey online software, and the MCMC model in BEAST v. 1.10.4, respectively. …”
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Releasing the octoPus, an open-source digital tool to promote Integrated Pest Management by Simone Bregaglio, Eugenio Rossi, Lorenzo Ascari, Gabriele Mongiano, Eleonora Del Cavallo, Sofia Bajocco, Luisa Maria Manici, Antonio Gerardo Pepe, Chiara Bassi, Rocchina Tiso, Fabio Pietrangeli, Giovanna Cattaneo, Camilla Nigro, Marco Secondo Gerardi, Simone Bussotti, Angela Sanchioni, Danilo Tognetti, Mariangela Sandra, Irene De Lillo, Paolo Framarin, Sandra Di Ferdinando, Riccardo Bugiani

“…Simulated infections served as predictors in a Random Forest algorithm (''brain'') to compute an overall risk level. …”
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A machine learning model to predict neurological deterioration after mild traumatic brain injury in older adults by Daisu Abe, Motoki Inaji, Takeshi Hase, Eiichi Suehiro, Naoto Shiomi, Hiroshi Yatsushige, Shin Hirota, Shu Hasegawa, Hiroshi Karibe, Akihiro Miyata, Kenya Kawakita, Kohei Haji, Hideo Aihara, Shoji Yokobori, Takeshi Maeda, Takahiro Onuki, Kotaro Oshio, Nobukazu Komoribayashi, Michiyasu Suzuki, Taketoshi Maehara

“…Among several machine learning algorithms, eXtreme Gradient Boosting (XGBoost) demonstrated the highest predictive accuracy in cross-validation, with an AUROC of 0.81 (±0.07) and an AUPRC of 0.33 (±0.08). …”
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Polarization Characteristics of the Hyperactive FRB 20240114A by Jin-Tao Xie, Yi Feng, Di Li, Yong-Kun Zhang, Deng-Ke Zhou, Yuanhong Qu, Xiang-han Cui, Jian-Hua Fang, Jia-Ying Xu, Chen-Chen Miao, Mao Yuan, Chao-Wei Tsai, Pei Wang, Chen-Hui Niu, Xianglei Chen, Mengyao Xue, Junshuo Zhang

“…A total of 437 bursts were detected, with a single observation recording 365 bursts over 1.38 hr, corresponding to a burst rate of 264 bursts per hour. …”
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Hepatitis E virus genotypes and subgenotypes causing acute hepatitis, Bulgaria, 2013-2015. by Roberto Bruni, Umbertina Villano, Michele Equestre, Paola Chionne, Elisabetta Madonna, Dilyana Trandeva-Bankova, Maria Peleva-Pishmisheva, Tencho Tenev, Eleonora Cella, Massimo Ciccozzi, Giulio Pisani, Elitsa Golkocheva-Markova, Anna Rita Ciccaglione

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A comparative study of bone density in elderly people measured with AI and QCT by Min Guo, Min Guo, Yu Zhang, Yu Zhang, XinXin Gu, XinXin Gu, Xuhui Liu, Xuhui Liu, Fei Peng, Fei Peng, Zongjun Zhang, Zongjun Zhang, Mei Jing, Mei Jing, Yingxia Fu, Yingxia Fu

“…BackgroundOsteoporosis, a systemic skeletal disorder characterized by deteriorated bone microarchitecture and low bone mass, poses substantial fracture risks to aging populations globally. Early detection of reduced bone mineral density (BMD) through opportunistic screening is critical for preventing fragility fractures. …”
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Persistence of Yellow fever virus outside the Amazon Basin, causing epidemics in Southeast Brazil, from 2016 to 2018. by Izabela Maurício de Rezende, Lívia Sacchetto, Érica Munhoz de Mello, Pedro Augusto Alves, Felipe Campos de Melo Iani, Talita Émile Ribeiro Adelino, Myrian Morato Duarte, Ana Luísa Furtado Cury, André Felipe Leal Bernardes, Tayrine Araújo Santos, Leonardo Soares Pereira, Maria Rita Teixeira Dutra, Dario Brock Ramalho, Benoit de Thoisy, Erna Geessien Kroon, Giliane de Souza Trindade, Betânia Paiva Drumond

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Endothelial cell activation is attenuated by everolimus via transcriptional and post-transcriptional regulatory mechanisms after drug-eluting coronary stenting. by Zsolt Fejes, Zsolt Czimmerer, Tibor Szük, Szilárd Póliska, Attila Horváth, Enikő Balogh, Viktória Jeney, Judit Váradi, Ferenc Fenyvesi, György Balla, István Édes, József Balla, János Kappelmayer, Béla Nagy

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