Computational and structural biotechnology journal最新文献_第2页

Evidence of quantum-entangled higher states of consciousness

IF 4.4 2区生物学

Computational and structural biotechnology journal Pub Date : 2025-01-01 DOI: 10.1016/j.csbj.2025.03.001

Álex Escolà-Gascón

{"title":"Evidence of quantum-entangled higher states of consciousness","authors":"Álex Escolà-Gascón","doi":"10.1016/j.csbj.2025.03.001","DOIUrl":"10.1016/j.csbj.2025.03.001","url":null,"abstract":"<div><div>What if quantum entanglement could accelerate learning by unlocking <em>higher</em> states of conscious experience? This study provides empirical and statistical evidence of how quantum entanglement influences consciousness at a biophysical level. We analyzed data from 106 monozygotic twin pairs (<em>N</em> = 212), randomly assigned to control and experimental groups. Using a consanguinity-based matching technique, twin pairs (A-B) were formed. Two distinct 2-qubit circuits were designed: C1 (non-entangled) for the control group and E1 (entangled) for the experimental group. These circuits manipulated visual stimulus contingencies during a 144-trial implicit learning experiment conducted under nonlocal conditions, executed via the <em>IBM Brisbane</em> supercomputer. Mental states were assessed with 3D <em>electroencephalography</em> (EEG), while biomarkers—including <em>Brain-Derived Neurotrophic Factor</em> (BDNF) for neuroplasticity, <em>Free Fatty Acids</em> (FFA), and Alpha-Amylase for physiological arousal—were measured. To advance this field, we introduced the <em>Quantum-Multilinear Integrated Coefficient</em> (<em>Q</em>), a groundbreaking metric capable of estimating variance increases attributable to quantum entanglement effects within response matrices. Our findings revealed that the entanglement of qubits in stimulus configurations explained 13.5 % of the variance in accuracy within the experimental group. The <em>Q</em> coefficient captured up to a 31.6 % increase in variance across twin responses, while neuroplasticity markers explained a 26.2 % increase in cognitive performance under entangled conditions. These results provide robust evidence that quantum entanglement enhances conscious experience and facilitates faster, more efficient learning. They point to the existence of anomalous cognitive mechanisms capable of anticipating future, unpredictable stimuli, representing a profound leap in our understanding of consciousness and its quantum underpinnings.</div></div>","PeriodicalId":10715,"journal":{"name":"Computational and structural biotechnology journal","volume":"30 ","pages":"Pages 21-40"},"PeriodicalIF":4.4,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143642387","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Development and validation of a novel AI-derived index for predicting COPD medical costs in clinical practice

IF 4.4 2区生物学

Computational and structural biotechnology journal Pub Date : 2025-01-01 DOI: 10.1016/j.csbj.2025.01.015

Guan-Heng Liu , Chin-Ling Li , Chih-Yuan Yang , Shih-Feng Liu

{"title":"Development and validation of a novel AI-derived index for predicting COPD medical costs in clinical practice","authors":"Guan-Heng Liu , Chin-Ling Li , Chih-Yuan Yang , Shih-Feng Liu","doi":"10.1016/j.csbj.2025.01.015","DOIUrl":"10.1016/j.csbj.2025.01.015","url":null,"abstract":"<div><h3>Background</h3><div>Chronic Obstructive Pulmonary Disease (COPD) is a major contributor to global morbidity and healthcare costs. Accurately predicting these costs is crucial for resource allocation and patient care. This study developed and validated an AI-driven COPD Medical Cost Prediction Index (MCPI) to forecast healthcare expenses in COPD patients.</div></div><div><h3>Methods</h3><div>A retrospective analysis of 396 COPD patients was conducted, utilizing clinical, demographic, and comorbidity data. Missing data were addressed through advanced imputation techniques to minimize bias. The final predictors included interactions such as Age × BMI, alongside Tumor Presence, Number of Comorbidities, Acute Exacerbation frequency, and the DOSE Index. A Gradient Boosting model was constructed, optimized with Recursive Feature Elimination (RFE), and evaluated using 5-fold cross-validation on an 80/20 train-test split. Model performance was assessed with Mean Squared Error (MSE), Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE), and R-squared (R²).</div></div><div><h3>Results</h3><div>On the training set, the model achieved an MSE of 0.049, MAE of 0.159, MAPE of 3.41 %, and R² of 0.703. On the test set, performance metrics included an MSE of 0.122, MAE of 0.258, MAPE of 5.49 %, and R² of 0.365. Tumor Presence, Age, and BMI were identified as key predictors of cost variability.</div></div><div><h3>Conclusions</h3><div>The MCPI demonstrates strong potential for predicting healthcare costs in COPD patients and enables targeted interventions for high-risk individuals. Future research should focus on validation with multicenter datasets and the inclusion of additional socioeconomic variables to enhance model generalizability and precision.</div></div>","PeriodicalId":10715,"journal":{"name":"Computational and structural biotechnology journal","volume":"27 ","pages":"Pages 541-547"},"PeriodicalIF":4.4,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143150060","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

LongReadSum: A fast and flexible quality control and signal summarization tool for long-read sequencing data

IF 4.4 2区生物学

Computational and structural biotechnology journal Pub Date : 2025-01-01 DOI: 10.1016/j.csbj.2025.01.019

Jonathan Elliot Perdomo , Mian Umair Ahsan , Qian Liu , Li Fang , Kai Wang

{"title":"LongReadSum: A fast and flexible quality control and signal summarization tool for long-read sequencing data","authors":"Jonathan Elliot Perdomo , Mian Umair Ahsan , Qian Liu , Li Fang , Kai Wang","doi":"10.1016/j.csbj.2025.01.019","DOIUrl":"10.1016/j.csbj.2025.01.019","url":null,"abstract":"<div><div>While several well-established quality control (QC) tools exist for short-read sequencing data, there is a general paucity of computational tools that efficiently deliver comprehensive metrics across a wide range of long-read sequencing data formats, such as Oxford Nanopore (ONT) POD5, ONT FAST5, ONT basecall summary, Pacific Biosciences (PacBio) unaligned BAM, and Illumina Complete Long Read (ICLR) FASTQ file formats. In addition to nucleotide sequence information, some file formats such as POD5 contain raw signal information used for base calling, while other file formats such as aligned BAM contain alignments to a linear reference genome or transcriptome and may also contain base modification information. There is currently no single available QC tool capable of summarizing each of these features. Furthermore, high-performance tools are required to efficiently process the growing data volumes from long-read sequencing platforms. To address these challenges, here we present LongReadSum, a high-performance tool for generating a summary QC report for major types of long-read sequencing data. We also demonstrate a few examples using LongReadSum to analyze cDNA sequencing, direct RNA sequencing, ONT reduced representation methylation sequencing (RRMS), and whole genome sequencing (WGS) data.</div></div>","PeriodicalId":10715,"journal":{"name":"Computational and structural biotechnology journal","volume":"27 ","pages":"Pages 556-563"},"PeriodicalIF":4.4,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143098553","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Predicting the impact of missense mutations on an unresolved protein’s stability, structure, and function: A case study of Alzheimer’s disease-associated TREM2 R47H variant

IF 4.4 2区生物学

Computational and structural biotechnology journal Pub Date : 2025-01-01 DOI: 10.1016/j.csbj.2025.01.024

Joshua Pillai , Kijung Sung , Chengbiao Wu

{"title":"Predicting the impact of missense mutations on an unresolved protein’s stability, structure, and function: A case study of Alzheimer’s disease-associated TREM2 R47H variant","authors":"Joshua Pillai , Kijung Sung , Chengbiao Wu","doi":"10.1016/j.csbj.2025.01.024","DOIUrl":"10.1016/j.csbj.2025.01.024","url":null,"abstract":"<div><div>AlphaFold2 (AF2) has spurred a revolution in predicting unresolved structures of wild-type proteins with high accuracy. However, AF2 falls short of predicting the effects of missense mutations on unresolved protein structures that may be informative to efforts in personalized medicine. Over the last decade, countless in-silico methods have been developed to predict the pathogenicity of point mutations on resolved structures, but no studies have evaluated their capabilities on unresolved protein structures predicted by AF2. Herein, we investigated Alzheimer's disease (AD)-causing coding variants of the triggering receptor expressed on myeloid cells 2 (TREM2) receptor using in-silico mutagenesis techniques on the AF2-predicted structure. We first demonstrated that the predicted structure retained a high accuracy in critical regions of the extracellular domain and subsequently validated the in-silico mutagenesis methods by evaluating the effects of the strongest risk variant R47H of TREM2. After validation of the R47H variant, we predicted the molecular basis and effects on protein stability and ligand-binding affinity of the R62H and D87N variants that remain unknown in current literature. By comparing it with the R47H variant, our analysis reveals that R62H and D87N variants exert a much less pronounced effect on the structural stability of TREM2. These in-silico findings show the possibility that the R62H and D87N mutations are likely less pathogenic than the R47H AD. Lastly, we investigated the Nasu-Hakola (NHD)-causing Y38C and V126G TREM2 as a comparison and found that they imposed greater destabilization compared to AD-causing variants. We believe that the in-silico mutagenesis methods described here can be applied broadly to evaluate the ever-growing numbers of protein mutations/variants discovered in human genetics study for their potential in diseases, ultimately facilitating personalized medicine.</div></div>","PeriodicalId":10715,"journal":{"name":"Computational and structural biotechnology journal","volume":"27 ","pages":"Pages 564-574"},"PeriodicalIF":4.4,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143098557","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

From homogeneity to heterogeneity: Refining stochastic simulations of gene regulation

IF 4.4 2区生物学

Computational and structural biotechnology journal Pub Date : 2025-01-01 DOI: 10.1016/j.csbj.2025.01.004

Seok Joo Chae , Seolah Shin , Kangmin Lee , Seunggyu Lee , Jae Kyoung Kim

{"title":"From homogeneity to heterogeneity: Refining stochastic simulations of gene regulation","authors":"Seok Joo Chae , Seolah Shin , Kangmin Lee , Seunggyu Lee , Jae Kyoung Kim","doi":"10.1016/j.csbj.2025.01.004","DOIUrl":"10.1016/j.csbj.2025.01.004","url":null,"abstract":"<div><div>Cellular processes are intricately controlled through gene regulation, which is significantly influenced by intrinsic noise due to the small number of molecules involved. The Gillespie algorithm, a widely used stochastic simulation method, is pervasively employed to model these systems. However, this algorithm typically assumes that DNA is homogeneously distributed throughout the nucleus, which is not realistic. In this study, we evaluated whether stochastic simulations based on the assumption of spatial homogeneity can accurately capture the dynamics of gene regulation. Our findings indicate that when transcription factors diffuse slowly, these simulations fail to accurately capture gene expression, highlighting the necessity to account for spatial heterogeneity. However, incorporating spatial heterogeneity considerably increases computational time. To address this, we explored various stochastic quasi-steady-state approximations (QSSAs) that simplify the model and reduce simulation time. While both the stochastic total quasi-steady state approximation (stQSSA) and the stochastic low-state quasi-steady-state approximation (slQSSA) reduced simulation time, only the slQSSA provided an accurate model reduction. Our study underscores the importance of utilizing appropriate methods for efficient and accurate stochastic simulations of gene regulatory dynamics, especially when incorporating spatial heterogeneity.</div></div>","PeriodicalId":10715,"journal":{"name":"Computational and structural biotechnology journal","volume":"27 ","pages":"Pages 411-422"},"PeriodicalIF":4.4,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143098820","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Mapping the 3D genome architecture 绘制三维基因组结构。

IF 4.4 2区生物学

Computational and structural biotechnology journal Pub Date : 2025-01-01 DOI: 10.1016/j.csbj.2024.12.018

Ghazaleh Tavallaee, Elias Orouji

{"title":"Mapping the 3D genome architecture","authors":"Ghazaleh Tavallaee, Elias Orouji","doi":"10.1016/j.csbj.2024.12.018","DOIUrl":"10.1016/j.csbj.2024.12.018","url":null,"abstract":"<div><div>The spatial organization of the genome plays a critical role in regulating gene expression, cellular differentiation, and genome stability. This review provides an in-depth examination of the methodologies, computational tools, and frameworks developed to map the three-dimensional (3D) architecture of the genome, focusing on both ligation-based and ligation-free techniques. We also explore the limitations of these methods, including biases introduced by restriction enzyme digestion and ligation inefficiencies, and compare them to more recent ligation-free approaches such as Genome Architecture Mapping (GAM) and Split-Pool Recognition of Interactions by Tag Extension (SPRITE). These techniques offer unique insights into higher-order chromatin structures by bypassing ligation steps, thus enabling the capture of complex multi-way interactions that are often challenging to resolve with traditional methods. Furthermore, we discuss the integration of chromatin interaction data with other genomic layers through multimodal approaches, including recent advances in single-cell technologies like sci-HiC and scSPRITE, which help unravel the heterogeneity of chromatin architecture in development and disease.</div></div>","PeriodicalId":10715,"journal":{"name":"Computational and structural biotechnology journal","volume":"27 ","pages":"Pages 89-101"},"PeriodicalIF":4.4,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11732852/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143001583","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Investigating plasticity within the interleukin-6 family with AlphaFold-Multimer 利用 AlphaFold-Multimer 研究白细胞介素-6 家族的可塑性

IF 4.4 2区生物学

Computational and structural biotechnology journal Pub Date : 2025-01-01 DOI: 10.1016/j.csbj.2025.02.030

Stefan Düsterhöft , Johannes N. Greve , Christoph Garbers

{"title":"Investigating plasticity within the interleukin-6 family with AlphaFold-Multimer","authors":"Stefan Düsterhöft , Johannes N. Greve , Christoph Garbers","doi":"10.1016/j.csbj.2025.02.030","DOIUrl":"10.1016/j.csbj.2025.02.030","url":null,"abstract":"<div><div>Cytokines are important soluble mediators that are involved in physiological and pathophysiological processes. Among them, members of the interleukin-6 (IL-6) family of cytokines have gained remarkable attention, because especially the name-giving cytokine IL-6 has been shown to be an excellent target to treat inflammatory and autoimmune diseases. The IL-6 family consists of nine members, which activate their target cells via combinations of non-signaling α- and/or signal-transducing β-receptors. While some receptor combinations are exclusively used by a single cytokine, other cytokine receptor combinations are used by multiple cytokines. Research in recent years unraveled another level of complexity: several cytokine cannot only signal via their canonical receptors, but can bind to and signal via additional α- and/or β-receptors, albeit with less affinity. While several examples of such cytokine plasticity have been reported, a systematic analysis of this phenomenon is lacking. The development of artificial intelligence programs like AlphaFold allows the computational analysis of protein complexes in a systematic manner. Here, we develop a analysis pipeline for cytokine:cytokine receptor interaction and show that AlphaFold-Multimer correctly predicts the canonical ligands of the IL-6 family. However, AlphaFold-Multimer does not provide sufficient insight to conclusively predict alternative, low-affinity ligands for receptors within the IL-6 family.</div></div>","PeriodicalId":10715,"journal":{"name":"Computational and structural biotechnology journal","volume":"27 ","pages":"Pages 946-959"},"PeriodicalIF":4.4,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143593725","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

VirusWarn: A mutation-based early warning system to prioritize concerning SARS-CoV-2 and influenza virus variants from sequencing data

IF 4.4 2区生物学

Computational and structural biotechnology journal Pub Date : 2025-01-01 DOI: 10.1016/j.csbj.2025.03.010

Christina Kirschbaum , Kunaphas Kongkitimanon , Stefan Frank , Martin Hölzer , Sofia Paraskevopoulou , Hugues Richard

{"title":"VirusWarn: A mutation-based early warning system to prioritize concerning SARS-CoV-2 and influenza virus variants from sequencing data","authors":"Christina Kirschbaum , Kunaphas Kongkitimanon , Stefan Frank , Martin Hölzer , Sofia Paraskevopoulou , Hugues Richard","doi":"10.1016/j.csbj.2025.03.010","DOIUrl":"10.1016/j.csbj.2025.03.010","url":null,"abstract":"<div><div>The rapid evolution of respiratory viruses is characterized by the emergence of variants with concerning phenotypes that are efficient in antibody escape or show high transmissibility. This necessitates timely identification of such variants by surveillance networks to assist public health interventions. Here, we introduce <em>VirusWarn</em>, a comprehensive system designed for detecting, prioritizing, and warning of emerging virus variants from large genomic datasets. VirusWarn uses both manually-curated rules and machine-learning (ML) classifiers to generate and rank pathogen sequences based on mutations of concern and regions of interest. Validation results for SARS-CoV-2 showed that VirusWarn successfully identifies variants of concern in both assessments, with manual- and ML-derived criteria from positive selection analyses. Although initially developed for SARS-CoV-2, VirusWarn was adapted to Influenza viruses and their dynamics, and provides a robust performance, integrating a scheme that accounts for fixed mutations from past seasons. HTML reports provide detailed results with searchable tables and visualizations, including mutation plots and heatmaps. Because VirusWarn is written in Nextflow, it can be easily adapted to other pathogens, demonstrating its flexibility and scalability for genomic surveillance efforts.</div></div>","PeriodicalId":10715,"journal":{"name":"Computational and structural biotechnology journal","volume":"27 ","pages":"Pages 1081-1088"},"PeriodicalIF":4.4,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143636358","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

AI-driven multi-omics integration for multi-scale predictive modeling of genotype-environment-phenotype relationships

IF 4.4 2区生物学

Computational and structural biotechnology journal Pub Date : 2025-01-01 DOI: 10.1016/j.csbj.2024.12.030

You Wu , Lei Xie

{"title":"AI-driven multi-omics integration for multi-scale predictive modeling of genotype-environment-phenotype relationships","authors":"You Wu , Lei Xie","doi":"10.1016/j.csbj.2024.12.030","DOIUrl":"10.1016/j.csbj.2024.12.030","url":null,"abstract":"<div><div>Despite the wealth of single-cell multi-omics data, it remains challenging to predict the consequences of novel genetic and chemical perturbations in the human body. It requires knowledge of molecular interactions at all biological levels, encompassing disease models and humans. Current machine learning methods primarily establish statistical correlations between genotypes and phenotypes but struggle to identify physiologically significant causal factors, limiting their predictive power. Key challenges in predictive modeling include scarcity of labeled data, generalization across different domains, and disentangling causation from correlation. In light of recent advances in multi-omics data integration, we propose a new artificial intelligence (AI)-powered biology-inspired multi-scale modeling framework to tackle these issues. This framework will integrate multi-omics data across biological levels, organism hierarchies, and species to predict genotype-environment-phenotype relationships under various conditions. AI models inspired by biology may identify novel molecular targets, biomarkers, pharmaceutical agents, and personalized medicines for presently unmet medical needs.</div></div>","PeriodicalId":10715,"journal":{"name":"Computational and structural biotechnology journal","volume":"27 ","pages":"Pages 265-277"},"PeriodicalIF":4.4,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11779603/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143064243","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Artificial intelligence-enhanced electrocardiography improves the detection of coronary artery disease

IF 4.4 2区生物学

Computational and structural biotechnology journal Pub Date : 2025-01-01 DOI: 10.1016/j.csbj.2024.12.032

Chi-Hsiao Yeh , Tsung-Hsien Tsai , Chun-Hung Chen , Yi-Ju Chou , Chun-Tai Mao , Tzu-Pei Su , Ning-I Yang , Chi-Chun Lai , Chien-Tzung Chen , Huey-Kang Sytwu , Ting-Fen Tsai

{"title":"Artificial intelligence-enhanced electrocardiography improves the detection of coronary artery disease","authors":"Chi-Hsiao Yeh , Tsung-Hsien Tsai , Chun-Hung Chen , Yi-Ju Chou , Chun-Tai Mao , Tzu-Pei Su , Ning-I Yang , Chi-Chun Lai , Chien-Tzung Chen , Huey-Kang Sytwu , Ting-Fen Tsai","doi":"10.1016/j.csbj.2024.12.032","DOIUrl":"10.1016/j.csbj.2024.12.032","url":null,"abstract":"<div><div>An AI-assisted algorithm has been developed to improve the detection of significant coronary artery disease (CAD) in high-risk individuals who have normal electrocardiograms (ECGs). This retrospective study analyzed ECGs from patients aged ≥ 18 years who were undergoing coronary angiography to obtain a clinical diagnosis at Chang Gung Memorial Hospital in Taiwan. Utilizing 12-lead ECG datasets, the algorithm integrated features like time intervals, amplitudes, and slope between peaks, a total of 561 features, with the XGBoost model yielding the best performance. The AI-enhanced ECG algorithm demonstrated high sensitivity (0.82–0.84) when detecting CAD in patients with normal ECGs and gave remarkably high prediction rates among those with abnormal ECGs, both with and without ischemia (92 %-95 % and 80 %-83 %, respectively). Notably, the algorithm's top features, mostly related to slope and amplitude differences, are challenging for clinicians to discern manually. Additionally, the study highlights significant sex differences regarding feature prediction and ranking. Comparatively, the AI-enhanced ECG's detection capability matched that of myocardial perfusion scintigraphy, which is a costly nuclear medicine test, and offers a more accessible alternative for identifying significant CAD, especially among patients with atypical ECG readings.</div></div>","PeriodicalId":10715,"journal":{"name":"Computational and structural biotechnology journal","volume":"27 ","pages":"Pages 278-286"},"PeriodicalIF":4.4,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11774641/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143064246","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0