Advances in biomarker sciences and technology最新文献_第3页

Implementation of a disease trigger prediction model using AIML for early diagnosis of epilepsy 基于AIML的疾病触发预测模型在癫痫早期诊断中的实现

Advances in biomarker sciences and technology

Pub Date : 2025-01-01 DOI: 10.1016/j.abst.2025.07.001

Aarohi Deshpande , Aarohi Gherkar , Avni Bhambure , Girish Shivhare , Shreyash Kolhe , Bhupendra Prajapati , Shama Mujawar

Epilepsy is one of the most prevalent neurological disorders that negatively impacts patients' quality of life and poses a severe health risk. It is often characterized by recurrent brain seizures. A current method that involves monitoring these seizures is Electroencephalography, which allows for the scientific investigation of electrical impulses within the brain. In this research, we have used Artificial Intelligence and Machine Learning in the management of Epilepsy to evaluate electrical impulses within the brain, emphasizing the potential to significantly improve the quality of life of those who suffer from this disorder. The goal of this study is to propose a Deep Neural Network model that can predict early seizure detection of Epilepsy using Electroencephalography data from a control group in order to anticipate the frequency of episodes of the patient and provide accurate insights into when they might experience their symptoms. Additionally, our research aims to identify particular genes of interest with specific protein targets that are directly responsible for the changes in EEG values in the epileptic patients. After thorough examination of these proteins' therapeutic targets and ligands, a suitable ligand and protein were identified and docked. The purpose of the docking studies in the Machine Learning model gains valuable information about the genetic origin for the change in EEG values in Epileptic patients.

The integration of predictive modeling with in-silico drug discovery enhances both the diagnostic and therapeutic dimensions of epilepsy care. This dual-layered approach not only supports early warning systems but also opens avenues for personalized treatment strategies. Our study thus represents a step toward a more holistic, computationally driven framework for neurological disorder management. By bridging data-driven seizure prediction with molecular-level therapeutic exploration, this research contributes to precision medicine and highlights the potential of interdisciplinary computational approaches in tackling complex, treatment-resistant forms of epilepsy.

癫痫是最常见的神经系统疾病之一，对患者的生活质量产生负面影响，并构成严重的健康风险。它通常以反复发作的脑痉挛为特征。目前监测这些癫痫发作的一种方法是脑电图，它允许对大脑内的电脉冲进行科学研究。在这项研究中，我们在癫痫管理中使用人工智能和机器学习来评估大脑内的电脉冲，强调了显著改善这种疾病患者生活质量的潜力。本研究的目的是提出一个深度神经网络模型，该模型可以使用来自对照组的脑电图数据预测癫痫的早期发作检测，以便预测患者发作的频率，并提供准确的见解，当他们可能会出现症状。此外，我们的研究旨在确定具有特定蛋白质靶点的特定基因，这些基因直接导致癫痫患者脑电图值的变化。在对这些蛋白质的治疗靶点和配体进行彻底的检查后，确定了合适的配体和蛋白质并进行了对接。机器学习模型对接研究的目的是获得癫痫患者脑电图值变化的遗传来源的有价值信息。预测模型与计算机药物发现的集成提高了癫痫护理的诊断和治疗维度。这种双层方法不仅支持早期预警系统，而且为个性化治疗策略开辟了道路。因此，我们的研究代表了朝着更全面、计算驱动的神经系统疾病管理框架迈出的一步。通过将数据驱动的癫痫发作预测与分子水平的治疗探索相结合，这项研究为精准医学做出了贡献，并突出了跨学科计算方法在解决复杂的、难治性癫痫方面的潜力。

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引用次数: 0

Urine PD-L1 as a non-invasive biomarker for immune checkpoint inhibitor (ICI) therapy in bladder cancer 尿PD-L1作为免疫检查点抑制剂（ICI）治疗膀胱癌的非侵入性生物标志物

Advances in biomarker sciences and technology

Pub Date : 2025-01-01 DOI: 10.1016/j.abst.2025.05.001

Qianyun Ge , Peng Wang , Shang-jui Wang , Akshay Sood , Lingbin Meng , Cheryl Lee , Anil V. Parwani , Jenny Li , Xuefeng Liu

Bladder cancer (BCa) is a common urological malignancy with a high recurrence rate, often within 2 years of initial diagnosis and treatment. Due to this high recurrence, near all patients require cystoscopic surveillance, which is invasive, uncomfortable, and costly. The cost of surveillance makes this cancer the most expensive cancer per case among all cancer types in the US. Therefore, early detection of recurrence or assessment of patients’ response to treatment, particularly through non-invasive methods, is urgently needed. Since immune checkpoint inhibitors (ICIs) are widely used in many clinical trials for BCa treatment, having non-invasive and reliable biomarkers to select appropriate patients for ICI therapies or predict their treatment responses would be invaluable. Here we summarized the potential applications of programmed death-ligand 1 (PD-L1) from urine or urine BCa cell samples in BCa clinical settings. We discuss the use of both the free form of PD-L1 in urine samples and the expression levels of PD-L1 on the BCa cells shed in urine samples. Free PD-L1 can be measured with flow cytometry or ELISA-based approaches, while detecting PD-L1 on BCa cell surface requires isolating the urine-derived cancer cells and analyzing them via flow cytometry. Furthermore, we discuss the promising future research areas of urinary PD-L1 (uPD-L1) in bladder cancer, with a particular focus on the combination of conditional reprogramming cells (CRCs) technology and uPD-L1 studies, followed by an overview of several ongoing research topics. Based on current findings, uPD-L1 shows great potential as a versatile biomarker; however, further research is urgently needed to facilitate its translation into clinical applications.

膀胱癌（BCa）是一种常见的泌尿系统恶性肿瘤，复发率高，通常在最初诊断和治疗后2年内。由于这种高复发率，几乎所有患者都需要进行膀胱镜检查，这是一种侵入性的、不舒服的、昂贵的检查。监测费用使这种癌症成为美国所有癌症类型中每个病例最昂贵的癌症。因此，迫切需要早期发现复发或评估患者对治疗的反应，特别是通过非侵入性方法。由于免疫检查点抑制剂（ICI）广泛应用于BCa治疗的许多临床试验中，因此拥有非侵入性和可靠的生物标志物来选择适合ICI治疗的患者或预测其治疗反应将是非常宝贵的。在这里，我们总结了尿液或尿液BCa细胞样本中程序性死亡配体1 （PD-L1）在BCa临床环境中的潜在应用。我们讨论了尿液样本中PD-L1的自由形式和尿液样本中BCa细胞脱落上PD-L1的表达水平。游离PD-L1可以通过流式细胞术或基于elisa的方法进行检测，而检测BCa细胞表面的PD-L1需要分离尿源性癌细胞并通过流式细胞术进行分析。此外，我们讨论了尿PD-L1 （uPD-L1）在膀胱癌中有前景的未来研究领域，特别关注条件重编程细胞（CRCs）技术与uPD-L1研究的结合，随后概述了几个正在进行的研究课题。根据目前的研究结果，uPD-L1显示出作为一种多功能生物标志物的巨大潜力；然而，为了使其转化为临床应用，还需要进一步的研究。

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引用次数: 0

Corrigendum to “Paper based molecularly imprinted SERS substrate for early detection of lysophosphatidic acid in ovarian cancer” [Advan Biomarker Sci Technol. 6 (2024) 46–58 https://doi.org/10.1016/j.abst.2024.03.001] “基于纸张的分子印迹SERS底物用于卵巢癌溶血磷脂酸的早期检测”的勘误表[Advan生物标志物科学技术，6 (2024)46-58 https://doi.org/10.1016/j.abst.2024.03.001]

Advances in biomarker sciences and technology

Pub Date : 2025-01-01 DOI: 10.1016/j.abst.2025.09.003

Nazia Tarannum , Deepak Kumar , Akanksha Yadav , Anil K. Yadav

引用次数: 0

A mini review: Role of novel biomarker for kidney disease of future study 综述：未来研究中新型肾脏疾病生物标志物的作用

Advances in biomarker sciences and technology

Pub Date : 2025-01-01 DOI: 10.1016/j.abst.2025.02.002

Palash Mitra , Sahadeb Jana , Suchismita Roy

In the world, kidney disease is most common cause of death. Primary care physicians must conduct appropriate diagnosis, and management in order to avoid detrimental consequences linked to death as well as end-stage kidney disease. In this scenario biomarkers can detect renal pathology more accurately and early than currently known biomarkers, including serum creatinine, estimated glomerular filtration rate and urine albumin, they hold out hope for bettering the care of individuals with kidney illnesses. Nowadays, nephrology is concentrating extensively on finding novel indicators of acute stage of kidney disease in order to prevent further complications from chronic kidney disease as well as end-stage renal disease. The best treatment targets for a particular patient or illness context may also be determined with the use of proteomic and genomic biomarkers. Therefore, current advancements in the study of important biomarkers including tumor necrosis factor, transforming growth factor, interleukin −1, interleukin-18, nephrin, uromodulin, collagen, osteopontin, NGAL and Dickkopf-3 are linked to different aspects of renal injury. Prognosis and risk classification can be enhanced by a variety of proteome and genome biomarkers that are linked to different pathophysiological processes that follow renal damage.

在世界上，肾脏疾病是最常见的死亡原因。初级保健医生必须进行适当的诊断和管理，以避免与死亡和终末期肾脏疾病相关的有害后果。在这种情况下，生物标志物可以比目前已知的生物标志物（包括血清肌酐、肾小球滤过率和尿白蛋白）更准确、更早地检测肾脏病理，它们有望更好地治疗肾脏疾病患者。为了预防慢性肾脏疾病和终末期肾脏疾病的进一步并发症，肾脏病学正在广泛关注寻找肾脏疾病急性期的新指标。针对特定患者或疾病背景的最佳治疗靶点也可以通过使用蛋白质组学和基因组生物标志物来确定。因此，肿瘤坏死因子、转化生长因子、白细胞介素-1、白细胞介素-18、肾素、尿调素、胶原蛋白、骨桥蛋白、NGAL和Dickkopf-3等重要生物标志物的研究进展与肾损伤的不同方面有关。预后和风险分类可以通过与肾损伤后不同病理生理过程相关的各种蛋白质组和基因组生物标志物来增强。

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引用次数: 0

The diagnostic value of miRNAs combination for Kurdish NAFLD patients miRNAs组合对库尔德NAFLD患者的诊断价值

Advances in biomarker sciences and technology

Pub Date : 2025-01-01 DOI: 10.1016/j.abst.2025.11.004

Sarah Esmaeilpour , Farshad Sheikhesmaili , Mohammad Moradzad , Bijan Noori , Mohammad Abdi , Zakaria Vahabzadeh

Background and objectives

Non-alcoholic fatty liver disease (NAFLD) encompasses a spectrum of liver disorders ranging from simple steatosis to steatohepatitis. The development of non-invasive diagnostic tools is crucial for management of liver diseases. MicroRNAs (miRNAs) have emerged as potential biomarkers for NAFLD diagnostic. This case–control pilot study included 30 patients with NAFLD and 30 healthy controls. Our findings indicate promising potential of miR-34a, miR-192, and miR-122 as non-invasive biomarkers for NAFLD.

Materials and methods

We enrolled 30 confirmed NAFLD patients (grade 3) and 30 healthy individuals as controls. General laboratory tests were assessed in both groups. MicroRNA expression levels were quantified using RT-qPCR, and data were analyzed using R software. Diagnostic table was assessed using the area under the ROC curve and 95 % confidence intervals.

Results

Significantly elevated serum levels of miR-34a and miR-192 were observed in NAFLD patients compared to controls (P = 0.002 and P < 0.0001, respectively), whereas miR-122 was downregulated (P < 0.001). The combination of miR-34a, miR-192, and miR-122 showed a high apparent diagnostic performance, which should be interpreted with caution given the limited sample size.

Conclusion

This pilot study suggests that serum miR-34a, miR-192, and miR-122 may serve as promising indicator for NAFLD patients.

背景和目的非酒精性脂肪性肝病（NAFLD）包括一系列肝脏疾病，从单纯脂肪变性到脂肪性肝炎。非侵入性诊断工具的发展对肝脏疾病的治疗至关重要。MicroRNAs （miRNAs）已成为NAFLD诊断的潜在生物标志物。本病例对照先导研究包括30名NAFLD患者和30名健康对照者。我们的研究结果表明，miR-34a、miR-192和miR-122作为NAFLD的非侵入性生物标志物具有很大的潜力。材料和方法我们招募了30例确诊的NAFLD患者（3级）和30例健康个体作为对照。对两组患者进行一般实验室检查。采用RT-qPCR定量分析MicroRNA表达水平，并使用R软件分析数据。诊断表采用ROC曲线下面积和95%置信区间评估。结果与对照组相比，NAFLD患者血清miR-34a和miR-192水平显著升高（P = 0.002和P <； 0.0001），而miR-122水平下调（P < 0.001）。miR-34a， miR-192和miR-122的组合显示出很高的明显诊断性能，考虑到有限的样本量，应谨慎解释。结论本初步研究提示血清miR-34a、miR-192和miR-122可能是NAFLD患者有希望的指标。

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引用次数: 0

Genomic analysis identifies an incipient signature to forecast imatinib resistance before start of treatment in patients with chronic myeloid leukemia 基因组分析确定了在慢性髓性白血病患者开始治疗前预测伊马替尼耐药性的早期特征

Advances in biomarker sciences and technology

Pub Date : 2025-01-01 DOI: 10.1016/j.abst.2025.01.004

Rahul Mojidra , Nilesh Gardi , Bhausaheb Bagal , Navin Khattry , Anant Gokarn , Sachin Punatar , Rukmini Govekar

The unprecedented success of tyrosine kinase inhibitor (TKI), imatinib, to induce remission in 86 % of chronic phase (CP) patients of chronic myeloid leukemia (CML) is undermined by drug resistance. Few patients have primary resistance and do not respond to imatinib, while majority of them who respond must continue treatment to sustain the remission. This continued treatment increases the possibility of developing secondary resistance and these resistant patients progress to the acute phase of blast crisis (BC) wherein the survival is 7–11 months. However, if the patients who are at risk of developing resistance, can be identified before start of treatment with imatinib, they can be assisted with better treatment strategies. To identify markers to forecast imatinib resistance we chose to study chromosomal aberrations (CAs), as they are associated with causation, progression as well as drug resistance in CML. In this study, genomic DNA from CD34⁺ cells, isolated from healthy controls and CML patients in CP and BC before start of treatment, were subjected to array comparative genomic hybridization (aCGH). The number of CAs on distinct chromosomes identified by genomic analysis in CML-CP and -BC patients, were able to segregate the patients as imatinib-sensitive and -resistant in cluster analysis. The CP patients who misclassified into predominantly imatinib-resistant BC cluster were found to develop resistance during treatment. We thus report an incipient genomic signature which can forecast development of secondary resistance and upon validation in large cohort of patients has the potential for clinical application.

酪氨酸激酶抑制剂（TKI）伊马替尼（imatinib）在86%的慢性髓性白血病（CML）慢慢期（CP）患者中诱导缓解的前所未有的成功被耐药性破坏了。很少有患者有原发性耐药，对伊马替尼无反应，而大多数有反应的患者必须继续治疗以维持缓解。这种持续治疗增加了发生继发性耐药的可能性，这些耐药患者进展到细胞危象（BC）的急性期，其中生存期为7-11个月。然而，如果能够在开始伊马替尼治疗之前确定有产生耐药性风险的患者，则可以为他们提供更好的治疗策略。为了确定预测伊马替尼耐药性的标志物，我们选择研究染色体畸变（CAs），因为它们与CML的病因、进展以及耐药性有关。在这项研究中，从治疗开始前的健康对照和CP和BC的CML患者中分离的CD34+细胞的基因组DNA进行了阵列比较基因组杂交（aCGH）。基因组分析在CML-CP和-BC患者中鉴定出的不同染色体上的CAs数量，能够在聚类分析中将患者区分为伊马替尼敏感和耐药。被错误分类为主要耐伊马替尼BC群的CP患者在治疗期间发现出现耐药性。因此，我们报告了一个早期的基因组特征，它可以预测继发性耐药的发展，并在大量患者中进行验证，具有临床应用的潜力。

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引用次数: 0

Machine learning and IoT in healthcare: Recent advancements, challenges & future direction 医疗保健中的机器学习和物联网：最新进展、挑战和未来方向

Advances in biomarker sciences and technology

Pub Date : 2025-01-01 DOI: 10.1016/j.abst.2025.08.006

Md Zonayed , Rumana Tasnim , Sayma Sultana Jhara , Mariam Akter Mimona , Molla Rashied Hussein , Md Hosne Mobarak , Umme Salma

Background

The integration of Machine Learning and Deep Learning with IoT-enabled devices for real-time health monitoring has significantly revolutionized healthcare. These technologies facilitate the analysis of intricate medical datasets, yielding actionable insights that promote evidence-based clinical decision-making. Although significant advancements have been made, there is still an absence of a thorough synthesis regarding current applications, primary challenges, and prospective research directions. This review aims to synthesize recent applications, identify significant gaps, and propose clear direction for future research.

Methodology

A comprehensive narrative review was performed where a systematic literature search was conducted in PubMed and Scopus for studies published between 2020 and 2025. A total of 300 pertinent papers on ML and IoT's applications in healthcare were selected and analyzed to synthesize technological advancements, trade-offs, practical implications, challenges, and potential directions for future research.

Key findings

Neural network models, such as CNNs and ANNs, along with ensemble methods like Random Forest and XGBoost, often attain predictive accuracies ranging from 85 % to 95 %. Advanced technique, like generative imaging models, reinforcement learning, and transformer-based architectures, improve diagnostics, chronic disease management, robotic-assisted surgery, and predictive analytics, while explainable AI promotes clinical trust. Cloud-edge integration utilizing lightweight machine learning models enables real-time, energy-efficient applications, enhancing diagnosis, decision support, personalization, and cost-effectiveness, notwithstanding current challenges.

Conclusion

To conclude, the integration of ML and IoT is transforming healthcare through enhanced monitoring, improved predictive capabilities, and tailored treatment approaches. Addressing persistent limitations is crucial for fully realizing its potential and directing future research in this evolving field.

机器学习和深度学习与支持物联网的设备的集成，用于实时健康监测，极大地改变了医疗保健。这些技术有助于分析复杂的医疗数据集，产生可操作的见解，促进循证临床决策。尽管已经取得了重大进展，但仍然缺乏对当前应用，主要挑战和未来研究方向的全面综合。本文综述了该技术的最新应用，指出了存在的不足，并为今后的研究提出了明确的方向。方法在PubMed和Scopus中进行系统文献检索，对2020年至2025年间发表的研究进行全面的叙述性回顾。本文选取并分析了300篇关于机器学习和物联网在医疗保健领域应用的相关论文，以综合技术进步、权衡、实际影响、挑战和未来研究的潜在方向。神经网络模型，如cnn和ann，以及像随机森林和XGBoost这样的集成方法，通常可以达到85%到95%的预测精度。先进的技术，如生成成像模型、强化学习和基于变压器的架构，改善了诊断、慢性疾病管理、机器人辅助手术和预测分析，而可解释的人工智能促进了临床信任。尽管目前面临挑战，但利用轻量级机器学习模型的云边缘集成可以实现实时、节能的应用程序，增强诊断、决策支持、个性化和成本效益。总之，机器学习和物联网的整合通过增强监测、提高预测能力和定制治疗方法正在改变医疗保健。解决持续存在的局限性对于充分发挥其潜力和指导这一不断发展的领域的未来研究至关重要。

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引用次数: 0

Corrigendum regarding “Declaration of Competing Interest statements in previously published articles” 关于“先前发表的文章中的竞争利益声明”的勘误表

Advances in biomarker sciences and technology

Pub Date : 2025-01-01 DOI: 10.1016/j.abst.2025.11.006

引用次数: 0

Bringing lab to the field: Exploring innovations in point-of-care diagnostics for the rapid detection and management of tropical diseases in resource-limited settings 将实验室带到实地：探索在资源有限的环境中快速检测和管理热带病的即时诊断创新

Advances in biomarker sciences and technology

Pub Date : 2025-01-01 DOI: 10.1016/j.abst.2025.01.001

Abdullahi Tunde Aborode , Ridwan Olamilekan Adesola , Godfred Yawson Scott , Emele Arthur-Hayford , Oche Joseph Otorkpa , Somuah Daniel Kwaku , Emmanuel Ebuka Elebesunu , Eghaghe Osadebamwen Nibokun , Ibude Jane Aruorivwooghene , Adetolase A. Bakre , Oluwaseun Adeolu Ogundijo , Olamilekan Gabriel Banwo , Oluwatobiloba Ige , Ibrahim O. Adelakun , Isreal Ayobami Onifade , Segun E. Ogungbemi , Boluwatife T. Dosunmu , Oluwaseunayo Deborah Ayando , Nike Idowu , Grace A. Adegoye , Olusegun Oluwaseun Jimoh

Tropical diseases present major health challenges in regions with limited resources, where access to advanced laboratory facilities is often scarce. This study explores the innovative techniques emerging in point-of-care (POC) diagnostics that are transforming the detection and treatment of tropical diseases. These advancements aim to provide faster diagnosis, better medical care, and ongoing monitoring in areas where traditional diagnostic methods are not practical, combining the precision of laboratory testing with the accessibility of field-based solutions. The review focuses on rapid diagnostic tests, molecular diagnostic tools, and smartphone-based applications, analyzing their advantages, limitations, and potential impact on healthcare delivery. It also addresses the challenges and opportunities involved in deploying these technologies in resource-constrained environments. Key to their success is the need for interdisciplinary collaboration, sustainable funding models, and strong regulatory frameworks to ensure their effective integration into healthcare systems. The review emphasizes how point-of-care diagnostics can play a crucial role in reducing the burden of tropical diseases and advancing health equity on a global scale.

在资源有限的地区，热带病是重大的卫生挑战，在这些地区，获得先进实验室设施的机会往往很少。本研究探讨了正在改变热带病检测和治疗的即时诊断（POC）中出现的创新技术。这些进步的目的是在传统诊断方法不实用的领域提供更快的诊断、更好的医疗护理和持续监测，将实验室检测的精确性与实地解决方案的可及性相结合。这篇综述的重点是快速诊断测试、分子诊断工具和基于智能手机的应用，分析了它们的优势、局限性和对医疗保健服务的潜在影响。它还解决了在资源受限的环境中部署这些技术所涉及的挑战和机遇。它们成功的关键是需要跨学科合作、可持续的筹资模式和强有力的监管框架，以确保它们有效地融入卫生保健系统。该审查强调了即时诊断如何能够在全球范围内减轻热带病负担和促进卫生公平方面发挥关键作用。

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引用次数: 0

Etiological connections between initial COVID-19 and two rare infectious diseases 初始COVID-19与两种罕见传染病之间的病因学联系

Advances in biomarker sciences and technology

Pub Date : 2025-01-01 DOI: 10.1016/j.abst.2024.12.001

Zhengjun Zhang

The origin of COVID-19 remains unclear despite extensive research. Theoretical models can simplify complex epigenetic landscapes by reducing vast methylation sites into manageable sets, revealing fundamental pathogen interactions that leap medical advances for the first time in tracing virus origin in the literature and practices. In our study, a max-logistic intelligence classifier analyzed 865,859 Infinium MethylationEPIC sites (CpGs), identifying eight CpGs that achieved 100 % accuracy in distinguishing COVID-19 patients from other respiratory disease patients and healthy controls. One CpG, cg07126281, linked to the SAMM50 gene, shares genetic ties with rare infectious diseases like Sennetsu fever and glanders, suggesting a potential connection between COVID-19 and these diseases, possibly transmitted through contaminated seafood or glanders-infected individuals. Identifying such links among 865,859 CpG sites is challenging, with a random correlation probability of less than one in ten million. However, the likelihood of finding meaningful associations with rare diseases lowers this probability to one in one hundred million, reinforcing the credibility of our findings. These results highlight the importance of investigating seafood markets and global supply chains in tracing COVID-19's origins and emphasize the need for ongoing biosafety and biosecurity measures to prevent future outbreaks.

尽管进行了广泛的研究，COVID-19的起源仍不清楚。理论模型可以通过将大量甲基化位点减少到可管理的集合来简化复杂的表观遗传景观，揭示了在文献和实践中首次在追踪病毒起源方面飞跃医学进步的基本病原体相互作用。在我们的研究中，一个最大逻辑智能分类器分析了865,859个Infinium MethylationEPIC位点（CpGs），确定了8个CpGs，在区分COVID-19患者与其他呼吸系统疾病患者和健康对照方面达到100%的准确率。与SAMM50基因相关的CpG cg07126281与Sennetsu热和腺体等罕见传染病具有遗传联系，这表明COVID-19与这些疾病之间存在潜在联系，可能通过受污染的海鲜或腺体感染的个体传播。在865,859个CpG位点中识别这样的链接是具有挑战性的，随机相关概率不到千万分之一。然而，发现与罕见疾病有意义的关联的可能性将这种可能性降低到一亿分之一，从而加强了我们研究结果的可信度。这些结果突出了调查海鲜市场和全球供应链对于追踪COVID-19起源的重要性，并强调需要采取持续的生物安全和生物保障措施，以防止未来爆发疫情。

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引用次数: 0