Proteomic analysis of blood plasma as a tool for personalized diagnosis of lung adenocarcinoma by D. N. Korobkov, A. S. Kononikhin, S. D. Semenov, H. L. Kordzaya, A. G. Brzhozovskiy, A. E. Bugrova, E. Yu. Vasilieva, D. Yu. Kanner, E. N. Nikolaev, A. A. Komissarov

“…Classifiers developed based on these protein panels make it possible to distinguish between patients with LAC and healthy controls, as well as to detect the presence of metastases among patients with LAC, with sensitivity and specificity of more than 90 %.Conclusion. The data obtained can be used to develop new tests for LAC screening and predicting disease outcomes based on the blood plasma proteome. …”
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Deaths ascribed to non-communicable diseases among rural Kenyan adults are proportionately increasing: evidence from a health and demographic surveillance system, 2003-2010. by Penelope A Phillips-Howard, Kayla F Laserson, Nyaguara Amek, Caryl M Beynon, Sonia Y Angell, Sammy Khagayi, Peter Byass, Mary J Hamel, Anne M van Eijk, Emily Zielinski-Gutierrez, Laurence Slutsker, Kevin M De Cock, John Vulule, Frank O Odhiambo

“…<h4>Background</h4>Non-communicable diseases (NCDs) result in more deaths globally than other causes. Monitoring systems require strengthening to attribute the NCD burden and deaths in low and middle-income countries (LMICs). …”
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Ethical and social issues in prediction of risk of severe mental illness: a scoping review and thematic analysis by Ivars Neiders, Signe Mežinska, Neeltje E. M. van Haren

“…First, there are issues that should deserve more attention than they have received thus far (clinical utility, extensive or mandatory use). …”
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Amyloid Cardiomyopathy: Review of A Fatal Case Report by O. V. Soldatova, I. Ya. Goryanskaya

“…To date, it has been proven that amyloid cardiomyopathy is an important and often undiagnosed cause of chronic heart failure and cardiac arrhythmias, especially in the elderly. There are more than 15 types of precursor proteins capable of causing systemic amyloidosis, but only 2 of them accumulate in the interstitium of the heart: light chains of clonal immunoglobulin (AL) and tetrameric protein transthyretin (TTR). …”
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Generative and predictive neural networks for the design of functional RNA molecules by Aidan T. Riley, James M. Robson, Aiganysh Ulanova, Alexander A. Green

“…We pair these predictive models with generative adversarial RNA design networks (GARDN), allowing the generative modelling of a diverse range of functional RNA molecules with targeted experimental attributes. …”
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The systemic oxidative stress index predicts clinical outcomes of esophageal squamous cell carcinoma receiving neoadjuvant immunochemotherapy by Jifeng Feng, Jifeng Feng, Liang Wang, Xun Yang, Qixun Chen, Qixun Chen

“…Then, a new staging that included TNM and SOSI based on RPA algorithms was produced. In terms of prognostication, the RPA model performed significantly better than TNM classification.ConclusionSOSI is a simple and useful score based on available SOS-related indices. …”
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Machine learning identification of key genes in cardioembolic stroke and atherosclerosis: their association with pan-cancer and immune cells by Tianxiang Zhang, Chunhui Yuan, Mo Chen, Jinjiang Liu, Wei Shao, Ning Cheng

“…Two machine learning algorithms, Least Absolute Shrinkage and Selection Operator (LASSO) regression and Support Vector Machine Recursive Feature Elimination (SVM-RFE), were used to screen for overlapping FRDEGs in CS and AS. …”
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Elucidating the dynamic tumor microenvironment through deep transcriptomic analysis and therapeutic implication of MRE11 expression patterns in hepatocellular carcinoma by Ruiqiu Chen, Chaohui Xiao, Zizheng Wang, Guineng Zeng, Shaoming Song, Gong Zhang, Lin Zhu, Penghui Yang, Rong Liu

“…Publicly available single-cell RNA sequencing (scRNA-seq) data and spatial transcriptomics were utilized to explore MRE11’s dynamic mechanisms in the tumor microenvironment (TME) of both primary and post-immunotherapy cases. We also screened for differentially expressed genes and constructed a robust HCC prognosis model using 101 machine-learning algorithms. …”
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Unraveling shared diagnostic genes and cellular microenvironmental changes in endometriosis and recurrent implantation failure through multi-omics analysis by Dongxu Qin, Yongquan Zheng, Libo Wang, Zhenyi Lin, Yao Yao, Weidong Fei, Caihong Zheng

“…Differential expression analysis and weighted gene co-expression network analysis (WGCNA) were employed to identify key genes. Machine learning algorithms, including Random Forest (RF) and XGBoost, were utilized to screen for shared diagnostic genes, which were subsequently validated through receiver operating characteristic (ROC) analysis and clinical prediction models. …”
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Integrating bioinformatics and machine learning for comprehensive analysis and validation of diagnostic biomarkers and immune cell infiltration characteristics in pediatric septic... by Peng Lyu, Na Xie, Xu-peng Shao, Shuai Xing, Xiao-yue Wang, Li-yun Duan, Xue Zhao, Jia-min Lu, Rong-fei Liu, Duo Zhang, Wei Lu, Kai-liang Fan

“…Abstract This study aims to predict and diagnose pediatric septic shock through the screening of immune infiltration-related biomarkers. …”
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APPLICATION OF NEURAL NETWORKS IN DIAGNOSTICS OF BREAST CANCER ACCORDING TO THE DATA OF MICROWAVE RADIO THERMOMETRY by A. Losev, D. Medvedev

“…However, the analysis of microwave radiothermometry data is a very complex task, which prevents the widespread use of this method in screening. This problem can be solved by creating an effective expert system based on the use of mathematical and computer modeling methods, the capabilities of modern information technologies and, above all, machine learning algorithms. …”
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Artificial intelligence in breast oncology by Alexander Mundinger

“…Abstract Artificial intelligence (AI) is based on complex artificial neural networks, characterized by layered network architecture, parallel processing of large data sets and iterative algorithms for processing large data sets. AI-assisted screening studies have demonstrated non-inferior diagnostic performance, reduced human workload by up to 70%, and reduced recall rates by 25% compared to human double reading. …”
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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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Poor Nutritional Status Increases Risk of Postoperative Adverse Events after Surgical Management of Traumatic Ankle Fracture by Sanket Mehta MD, Nicholas Danford MD

“…Patients with poor nutritional status were older (56 vs. 51 years), more likely to present in shock (7.5% vs. 2.3%) and due to motor vehicle collision mechanism (all P< 0.001). …”
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EEG Signal Analysis for Numerical Digit Classification: Methodologies and Challenges by Augoustos Tsamourgelis, Adam Adamopoulos

“…We achieve strong differentiation capabilities between digit and non-digit values in all classification algorithms. However, our study also highlights the profound neurological challenges encountered in distinguishing between the digit values, as our model, inspired by the related bibliography, was unable to differentiate between digit values 0 and 1. …”
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Active Learning for Medical Article Classification with Bag of Words and Bag of Concepts Embeddings by Radosław Pytlak, Paweł Cichosz, Bartłomiej Fajdek, Bogdan Jastrzębski

“…Systems supporting systematic literature reviews often use machine learning algorithms to create classification models to assess the relevance of articles to study topics. …”
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DETERMINATION OF THE BASIC PARAMETERS OF SENSOR DEVICES FOR THE IMPLEMENTATION OF PSYCHONEUROLOGICAL RESEARCH WITH THE INTRODUCTION OF MULTITOUCH TECHNOLOGY by Karina Selivanova, Oleg Avrunin, Nikita Kazimirov

“…The scientific novelty and practical significance of the obtained results is the development of specialized software and algorithmic software for dynamic testing on sensory devices with the ability to video record the movements of the upper extremities in the mode "Rapid". …”
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Will Artificial Intelligence Replace Physicians or Augment Their Capabilities? by Sara Rahmati Roodsari, Alireza Zali, Mohammad Rahmati-Roodsari, Behina Forouzanmehr

“…In medical imaging, deep learning algorithms, especially those trained with genetic algorithms, have demonstrated immense promise to improve the accuracy of pneumonia and COVID-19 diagnosis from chest X-rays. …”
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EDUCATIONAL TRAINING SIMULATOR FOR MONITORING READING TECHNIQUE AND SPEED BASED ON SPEECH-TO-TEXT (STT) METHODS by Olesia Barkovska, Heorhii Ivashchenko, Dmytro Rosinskiy, Daniil Zakharov

“…The results of testing children with dyslalia, meaning the rearrangement of sounds in words when reading, also showed that the Jaro algorithm has a shorter line comparison time for large text arrays (by 7%). …”
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Artificial intelligence applications in pediatric ophthalmology: A comprehensive review by Anupama Janardhanan, Meenakshi Ravindran, Allapitchai Fathima, Neelam Pawar

“…Furthermore, it explores existing AI models and their applicability in diagnosing refractive errors and strabismus. …”
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