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    Beijing Hospital Reports Findings in Atrial Fibrillation (Machine learning-based identification and validation of aging-related genes in cardiomyocytes from pat ients with atrial fibrillation)

    50-50页
    查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – New research on Heart Disorders and Di seases - Atrial Fibrillation is the subject of a report. According to news repor ting originating from Beijing, People’s Republic of China, by NewsRx corresponde nts, research stated, “Aging is a key risk factor for atrial fibrillation (AF), a prevalent cardiac disorder among the elderly. This study aims to elucidate the genetic underpinnings of AF in the context of aging.” Our news editors obtained a quote from the research from Beijing Hospital, “We a nalyzed 12,403 genes from the GSE2240 database and 279 age-related genes from th e CellAge database. Machine learning algorithms, including support vector machin es and random forests, were employed to identify genes significantly associated with AF. Among the genes studied, 76 were found to be potential candidates in th e development of AF. Notably, four genes - PTTG1, AR, RAD21, and YAP1 - stood ou t with a Receiver Operating Characteristic Area Under the Curve (ROC AUC) of 0.9 , signifying high predictive power. Logistic regression, validated through 10-fo ld cross-validation and Bootstrap resampling, was determined as the most suitabl e model for internal validation. The discovery of these four genes could improve diagnostic accuracy for AF in the aged population.”
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    Studies from Concordia University Update Current Data on Machine Learning (Evalu ating the Applicability of a Machine Learning Methodology To Improve Tmy Weather File Generation for Different Canadian Climate Zones)

    51-52页
    查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – Researchers detail new data in Machine Learning. According to news reporting originating from Montreal, Canada, by New sRx correspondents, research stated, “Typical meteorological year (TMY) weather files are used in building energy simulations to represent typical weather condi tions for a location. The conventional approach to generating TMY weather files uses a set of universal weighting factors determined based on expert judgment.” Financial support for this research came from Natural Sciences and Engineering R esearch Council of Canada (NSERC).
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    Nanjing University of Posts and Telecommunications Reports Findings in B-Cell Ly mphoma (Survival prediction in diffuse large B-cell lymphoma patients: multimoda l PET/CT deep features radiomic model utilizing automated machine learning)

    52-53页
    查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – New research on Oncology - B-Cell Lymp homa is the subject of a report. According to news reporting originating in Nanj ing, People’s Republic of China, by NewsRx journalists, research stated, “We sou ght to develop an effective combined model for predicting the survival of patien ts with diffuse large B-cell lymphoma (DLBCL) based on the multimodal PET-CT dee p features radiomics signature (DFR-signature). 369 DLBCL patients from two medi cal centers were included in this study.” The news reporters obtained a quote from the research from the Nanjing Universit y of Posts and Telecommunications, “Their PET and CT images were fused to constr uct the multimodal PET-CT images using a deep learning fusion network. Then the deep features were extracted from those fused PET-CT images, and the DFR-signatu re was constructed through an Automated machine learning (AutoML) model. Combine d with clinical indexes from the Cox regression analysis, we constructed a combi ned model to predict the progression-free survival (PFS) and the overall surviva l (OS) of patients. In addition, the combined model was evaluated in the concord ance index (C-index) and the time-dependent area under the ROC curve (tdAUC). A total of 1000 deep features were extracted to build a DFR-signature. Besides the DFR-signature, the combined model integrating metabolic and clinical factors pe rformed best in terms of PFS and OS. For PFS, the C-indices are 0.784 and 0.739 in the training cohort and internal validation cohort, respectively. For OS, the C-indices are 0.831 and 0.782 in the training cohort and internal validation co hort. DFR-signature constructed from multimodal images improved the classificati on accuracy of prognosis for DLBCL patients.”
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    Hunan Provincial People’s Hospital Reports Findings in Machine Learning (Multiom ics identification of ALDH9A1 as a crucial immunoregulatory molecule involved in calcific aortic valve disease)

    53-54页
    查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – New research on Machine Learning is th e subject of a report. According to news reporting out of Hunan, People’s Republ ic of China, by NewsRx editors, research stated, “Mitochondrial dysfunction and immune cell infiltration play crucial yet incompletely understood roles in the p athogenesis of calcific aortic valve disease (CAVD). This study aimed to identif y immune-related mitochondrial genes critical to the pathological process of CAV D using multiomics approaches.” Our news journalists obtained a quote from the research from Hunan Provincial Pe ople’s Hospital, “The CIBERSORT algorithm was employed to evaluate immune cell i nfiltration characteristics in CAVD patients. An integrative analysis combining weighted gene coexpression network analysis (WGCNA), machine learning, and summa ry data-based Mendelian randomization (SMR) was performed to identify key mitoch ondrial genes implicated in CAVD. Spearman’s rank correlation analysis was also performed to assess the relationships between key mitochondrial genes and infilt rating immune cells. Compared with those in normal aortic valve tissue, an incre ased proportion of M0 macrophages and resting memory CD4 T cells, along with a d ecreased proportion of plasma cells and activated dendritic cells, were observed in CAVD patients. Additionally, eight key mitochondrial genes associated with C AVD, including PDK4, LDHB, SLC25A36, ALDH9A1, ECHDC2, AUH, ALDH2, and BNIP3, wer e identified through the integration of WGCNA and machine learning methods. Subs equent SMR analysis, incorporating multiomics data, such as expression quantitat ive trait loci (eQTLs) and methylation quantitative trait loci (mQTLs), revealed a significant causal relationship between ALDH9A1 expression and a reduced risk of CAVD. Moreover, ALDH9A1 expression was inversely correlated with M0 macropha ges and positively correlated with M2 macrophages. These findings suggest that i ncreased ALDH9A1 expression is significantly associated with a reduced risk of C AVD and that it may exert its protective effects by modulating mitochondrial fun ction and immune cell infiltration. Specifically, ALDH9A1 may contribute to the shift from M0 macrophages to anti-inflammatory M2 macrophages, potentially mitig ating the pathological progression of CAVD.”
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    Study Data from Medical University of Vienna Update Knowledge of Artificial Inte lligence (Role of Artificial Intelligence In Retinal Diseases)

    54-54页
    查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – Investigators discuss new findings in Artificial Intelligence. According to news reporting originating from Vienna, Au stria, by NewsRx correspondents, research stated, “Artificial intelligence (AI) has already found its way into ophthalmology, with the first approved algorithms that can be used in clinical routine. Retinal diseases in particular are provin g to be an important area of application for AI, as they are the main cause of b lindness and the number of patients suffering from retinal diseases is constantl y increasing.” Our news editors obtained a quote from the research from the Medical University of Vienna, “At the same time, regular imaging using high-resolution modalities i n a standardised and reproducible manner generates immense amounts of data that can hardly be processed by human experts. In addition, ophthalmology is constant ly experiencing new developments and breakthroughs that require a re-evaluation of patient management in routine clinical practice. AI is able to analyse these volumes of data efficiently and objectively and also provide new insights into d isease progression and therapeutic mechanisms by identifying relevant biomarkers .”
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    Researchers from Nanjing Forestry University Report New Studies and Findings in the Area of Robotics (Recent Advances of Biomassbased Smart Hydrogel Actuators: a Review)

    55-55页
    查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – A new study on Robotics is now availab le. According to news originating from Nanjing, People’s Republic of China, by N ewsRx correspondents, research stated, “Due to the characteristics of a large am ount of water inside, good softness, and ability to respond to various external stimuli, smart hydrogels have attracted great attention in the field of soft act uates and soft robots, and have a wide range of application prospects in the bio medical field.” Our news journalists obtained a quote from the research from Nanjing Forestry Un iversity, “Biomass materials are biodegradable, renewable, environmentally frien dly and non-toxic, and the hydrogels prepared based on biomass materials have ty pical anisotropy, biocompatibility and biodegradable characteristics, which have become a hot spot for the development of smart actuators. In this review, we br iefly classify and introduce biomass-based smart hydrogel actuators, discuss the ir common actuating modes, and summarize their applications in soft actuators an d soft robots.”
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    Reports Outline Machine Learning Study Results from University of Technology (In sights Into Water-lubricated Transport of Heavy and Extra-heavy Oils: Applicatio n of Cfd, Rsm, and Metaheuristic Optimized Machine Learning Models)

    56-56页
    查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – Fresh data on Machine Learning are pre sented in a new report. According to news reporting originating from Selangor, M alaysia, by NewsRx correspondents, research stated, “With diminishing light crud e oil reserves, the focus shifts to heavy and extra-heavy crude oil, posing chal lenges with high viscosity impeding flow. Water-lubricated technology addresses this issue in oil transmission lines.” Financial support for this research came from Taif University, Saudi Arabia. Our news editors obtained a quote from the research from the University of Techn ology, “This study introduces a novel method integrating response surface method ology (RSM), computational fluid dynamics (CFD), and optimized machine learning (ML) models to analyze pipeline pressure gradients (PG) in oil-water two-phase f lows downstream of T-junctions. The present study uses the D-optimal technique f or simulation design to optimize CFD computational demands efficiently. This stu dy breaks new ground by proposing a framework that leverages support vector mach ines (SVMs). The proposed framework incorporates metaheuristic optimization algo rithms (genetic algorithm (GA) and particle swarm optimization (PSO)) to achieve superior PG prediction accuracy. The optimized ML models outperformed RSM model s for predicting PG. Results indicated that oil-to-water viscosity ratio and oil inlet velocity significantly affect PG, followed by water inlet velocity and su rface tension between phases. In contrast, the oil-to-water density ratio, oil e ntry angle at the T-junction, and wall contact angle have minimal impact. Furthe rmore, statistical metrics and visual comparison tools identified the PSO-optimi zed SVM model based on linear kernel function as the most effective (MAPE = 13.2 % and R = 0.9949).”
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    University of Tubingen Reports Findings in Artificial Intelligence (Artificial i ntelligence-enhanced detection of subclinical coronary artery disease in athlete s: diagnostic performance and limitations)

    57-58页
    查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – New research on Artificial Intelligenc e is the subject of a report. According to news reporting from Tubingen, Germany , by NewsRx journalists, research stated, “This study evaluates the diagnostic p erformance of artificial intelligence (AI)-based coronary computed tomography an giography (CCTA) for detecting coronary artery disease (CAD) and assessing fract ional flow reserve (FFR) in asymptomatic male marathon runners. We prospectively recruited 100 asymptomatic male marathon runners over the age of 45 for CAD scr eening.” Financial support for this research came from Universitatsklinikum Tubingen. The news correspondents obtained a quote from the research from the University o f Tubingen, “CCTA was analyzed using AI models (CorEx and Spimed-AI) on a local server. The models focused on detecting significant CAD ( 50% diam eter stenosis, CAD-RADS 3, 4, or 5) and distinguishing hemodynamically significa nt stenosis (FFR 0.8) from non-significant stenosis (FFR > 0.8). Statistical analysis included sensitivity, specificity, positive predicti ve value (PPV), negative predictive value (NPV), and accuracy. The AI model demo nstrated high sensitivity, with 91.2% for any CAD and 100% for significant CAD, and high NPV, with 92.7% for any CAD and 100% for significant CAD. The diagnostic accuracy was 73.4% for any CAD and 90.4% for significant CAD. However, the PPV was lower, partic ularly for significant CAD (25.0%), indicating a higher incidence o f false positives. AI-enhanced CCTA is a valuable non-invasive tool for detectin g CAD in asymptomatic, low-risk populations. The AI model exhibited high sensiti vity and NPV, particularly for identifying significant stenosis, reinforcing its potential role in screening. However, limitations such as a lower PPV and overe stimation of disease indicate that further refinement of AI algorithms is needed to improve specificity.”
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    Research Reports from Hong Kong University of Science and Technology Provide New Insights into Machine Learning (A Machine Learning Model for Predicting the Pro pagation Rate Coefficient in Free-Radical Polymerization)

    58-58页
    查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – New study results on artificial intell igence have been published. According to news originating from Hong Kong, People ’s Republic of China, by NewsRx correspondents, research stated, “The propagatio n rate coefficient (kp) is one of the most crucial kinetic parameters in free-ra dical polymerization (FRP) as it directly governs the rate of polymerization and the resulting molecular weight distribution.” Financial supporters for this research include Hong Kong Ph.D. Fellowship Scheme ; Hong Kong Research Grants Council Early Career Scheme. Our news journalists obtained a quote from the research from Hong Kong Universit y of Science and Technology: “The kp in FRP can typically be obtained through ex perimental measurements or quantum chemical calculations, both of which can be t ime consuming and resource intensive. Herein, we developed a machine learning mo del based solely on the structural features of monomers involved in FRP, utilizi ng molecular embedding and a Lasso regression algorithm to predict kp more effic iently and accurately. The result shows that the model achieves a mean absolute percentage error (MAPE) of only 5.49% in the predictions for four new monomers, which indicates that the model exhibits strong generalization capa bilities and provides reliable and robust predictions. In addition, this model c an accurately predict the influence of the ester side chain length of (meth)acry lates on kp, aligning well with established scientific knowledge.”
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    Shanxi Medical University Reports Findings in Bioinformatics (Screening core gen es for minimal change disease based on bioinformatics and machine learning appro aches)

    59-59页
    查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – New research on Biotechnology - Bioinf ormatics is the subject of a report. According to news originating from Taiyuan, People’s Republic of China, by NewsRx correspondents, research stated, “Based o n bioinformatics and machine learning methods, we conducted a study to screen th e core genes of minimal change disease (MCD) and further explore its pathogenesi s. First, we obtained the chip data sets GSE108113 and GSE200828 from the Gene E xpression Comprehensive Database (GEO), which contained MCD information.” Our news journalists obtained a quote from the research from Shanxi Medical Univ ersity, “We then used R software to analyze the gene chip data and performed fun ctional enrichment analysis. Subsequently, we employed Cytoscape to screen the c ore genes and utilized machine learning algorithms (random forest and LASSO regr ession) to accurately identify them. To validate and analyze the core genes, we conducted immunohistochemistry (IHC) and gene set enrichment analysis (GSEA). Ou r results revealed a total of 394 highly expressed differential genes. Enrichmen t analysis indicated that these genes are primarily involved in T cell different iation and p13k-akt signaling pathway of immune response. We identified NOTCH1, TP53, GATA3, and TGF-b1 as the core genes. IHC staining demonstrated significant differences in the expression of these four core genes between the normal group and the MCD group. Furthermore, GSEA suggested that their up-regulation may be closely associated with the pathological changes in MCD kidneys, particularly in the glycosaminoglycans signaling pathway.”
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