Advances in biomarker sciences and technology最新文献

Exploring the potential of IP-10 and sCD14 as inflammatory biomarkers in HIV–TB co-infection: a systematic review 探索IP-10和sCD14作为HIV-TB合并感染炎症生物标志物的潜力：一项系统综述

Advances in biomarker sciences and technology

Pub Date : 2025-12-02 DOI: 10.1016/j.abst.2025.12.001

Tolutope Adebimpe Oso , Olalekan John Okesanya , Uthman Okikiola Adebayo , Khalifat Boluwatife Obadeyi , Oluwatobi Babajide Ayelaagbe , Mohamed Mustaf Ahmed , Clement Ngele Chukwu , Olaoluwa Joseph Oso , Don Eliseo Lucero-Prisno III

Background

Human Immunodeficiency Virus (HIV) and tuberculosis (TB) are major global health challenges, especially in sub-Saharan Africa and Southeast Asia, where their co-infection greatly increases illness and death. People living with HIV are far more likely to develop TB, and the interaction between the two infections worsens immune dysfunction and complicates management.

Methods

This systematic review followed PRISMA 2020 guidelines to assess the roles of Interferon-γ–induced Protein 10 (IP-10) and Soluble Cluster of Differentiation 14 (sCD14) as inflammatory biomarkers in HIV-TB co-infection. A comprehensive search of PubMed, Scopus, and Google Scholar was conducted for studies published between 2014 and 2024. Eligible interventional and observational studies involving adults with HIV-TB co-infection were included based on predefined inclusion criteria. Data extraction covered study characteristics, biomarker levels, and their associations with inflammation, disease progression, and clinical outcomes.

Results

Six studies met the inclusion criteria, all assessing IP-10, while only one measured sCD14. Most studies reported significantly elevated IP-10 levels in HIV–TB co-infected individuals, linking it to systemic inflammation, disease progression, and poor clinical outcomes, highlighting its potential as a predictive and prognostic biomarker. The single study evaluating sCD14 found increased levels indicating generalized immune activation but limited specificity for distinguishing Tuberculosis-Immune Reconstitution Inflammatory Syndrome , suggesting its broader role as a nonspecific marker of inflammation.

Conclusion

Although the current evidence base is limited, IP-10 shows promise as a diagnostic and prognostic biomarker, and sCD14 may complement its use as a marker of immune activation. Further large-scale, standardized studies are needed to validate these biomarkers in HIV–TB co-infection.

人类免疫缺陷病毒（HIV）和结核病（TB）是主要的全球卫生挑战，特别是在撒哈拉以南非洲和东南亚，它们的合并感染大大增加了疾病和死亡。艾滋病毒感染者患结核病的可能性要大得多，而且这两种感染之间的相互作用加剧了免疫功能障碍并使治疗复杂化。方法本系统综述遵循PRISMA 2020指南，评估干扰素-γ诱导蛋白10 （IP-10）和可溶性分化簇14 （sCD14）作为炎症生物标志物在HIV-TB合并感染中的作用。对2014年至2024年间发表的研究进行了PubMed， Scopus和b谷歌Scholar的综合搜索。根据预先确定的纳入标准纳入了涉及成人HIV-TB合并感染的符合条件的干预性和观察性研究。数据提取包括研究特征、生物标志物水平及其与炎症、疾病进展和临床结果的关联。结果6项研究符合纳入标准，均评估IP-10，而只有1项研究评估sCD14。大多数研究报告了HIV-TB合并感染个体中IP-10水平显著升高，将其与全身性炎症、疾病进展和不良临床结果联系起来，突出了其作为预测和预后生物标志物的潜力。评估sCD14的单一研究发现，sCD14水平升高表明普遍的免疫激活，但在区分结核-免疫重建炎症综合征方面的特异性有限，这表明它作为炎症的非特异性标志物具有更广泛的作用。尽管目前的证据基础有限，但IP-10有望作为一种诊断和预后的生物标志物，而sCD14可能补充其作为免疫激活标志物的用途。需要进一步的大规模标准化研究来验证这些生物标志物在HIV-TB合并感染中的作用。

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引用次数: 0

In silico screening of tripeptide inhibitors reveals potential lead compounds for targeting zika virus NS2B-NS3 protease 三肽抑制剂的计算机筛选揭示了针对寨卡病毒NS2B-NS3蛋白酶的潜在先导化合物

Advances in biomarker sciences and technology

Pub Date : 2025-11-19 DOI: 10.1016/j.abst.2025.11.001

Farhan Ullah , Wajeeha Rahman , Anees Ullah , Shahid Ullah , Sheng Wang

ZIKV is a public health threat causing neurological complications such as microcephaly and Guillain-Barre syndrome, with the additional risk of sexual transmission. The absence of FDA-approved drugs or vaccines for ZIKV, highlight the immediate requirement for potential therapeutics. A potential target for antiviral drug development is the NS2B-NS3 protease which is a critical enzyme in the replication and maturation of ZIKV. Here, we report the inhibitory activity of tripeptide compounds for this protease via molecular docking and molecular dynamics (MD) study. The docking studies were further followed by a 200 ns MD simulation to investigate the stability and binding mode of the inhibitor within the active site of protease. The simulation revealed that, the complex remains stable with lower root mean square deviation RMSD and higher root mean square fluctuation (RMSF) values and showed strong ligand-protein interaction. Further investigation of torsion angles in the ligand and secondary structural changes in the protease support to the viability of these tripeptide inhibitors as anti-viral agents. The results obtained in this study suggest that tripeptide-derived inhibitors of ZIKV may be applied as potential leads for developing novel therapies against the ZIKV, indicating a potential direction for future drug discovery and clinical treatments.

寨卡病毒是一种公共卫生威胁，会导致小头症和格林-巴利综合征等神经系统并发症，并伴有性传播的额外风险。缺乏fda批准的针对寨卡病毒的药物或疫苗，突出了对潜在治疗方法的迫切需求。抗病毒药物开发的潜在靶点是NS2B-NS3蛋白酶，它是ZIKV复制和成熟的关键酶。本文通过分子对接和分子动力学（MD）研究，报道了三肽化合物对该蛋白酶的抑制活性。对接研究之后，进一步进行了200 ns MD模拟，以研究抑制剂在蛋白酶活性位点的稳定性和结合方式。模拟结果表明，该配合物具有较低的均方根偏差RMSD和较高的均方根波动（RMSF）值，并表现出较强的配体-蛋白相互作用。进一步研究配体的扭转角和蛋白酶的二级结构变化支持这些三肽抑制剂作为抗病毒药物的可行性。本研究结果提示，三肽衍生的ZIKV抑制剂可能作为开发新的治疗方法的潜在先导，为未来的药物发现和临床治疗指明了潜在的方向。

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引用次数: 0

An in-depth exploration of CNN-based deep learning models in cervical carcinoma analysis 基于cnn的深度学习模型在宫颈癌分析中的深入探索

Advances in biomarker sciences and technology

Pub Date : 2025-11-17 DOI: 10.1016/j.abst.2025.11.007

P. Karthika , M. Premkumar

Cervical cancer has an extreme effect on women's health worldwide, recognized as the 4th most significant contributor to cancer fatalities among female. World Health Organization (WHO) states that there was on 660,000 new reports and 350,000 death occurred. Detecting the disease early can lead to a significant decrease in the death rate up to 80 %. Currently, doctors diagnose cervical cancer by examining cervical biopsies through Pap smears and colposcopy images. However this techniques is time-intensive, taking up to several hours per case and susceptible to misdiagnosis and diagnostic error between 10 and 30 %.Deep learning has illustrated significant potential for addressing biomedical challenges such as analysis of medical images, disease forecasting, and image partitioning. AI-powered diagnostic methods utilizing deep learning models–such as CNNs, DenseNets, and U-Nets—have achieved classification accuracies exceeding 95 % on datasets like Herlev and SIPaKMeD. This paper surveys diverse deep learning strategies that were implemented for the identification and analysis of cervical carcinoma, emphasizing their performance metrics, datasets and clinical applicability.

宫颈癌对全世界妇女的健康影响极大，被公认为导致女性癌症死亡的第四大因素。世界卫生组织（世卫组织）指出，有66万份新报告，35万人死亡。及早发现该病可使死亡率显著降低，最高可达80%。目前，医生通过子宫颈抹片检查和阴道镜检查来诊断宫颈癌。然而，这种技术是耗时的，每个病例需要花费几个小时，并且容易误诊和诊断错误率在10%到30%之间。深度学习在解决医学图像分析、疾病预测和图像分割等生物医学挑战方面显示了巨大的潜力。利用深度学习模型（如cnn、DenseNets和u - nets）的人工智能诊断方法在Herlev和SIPaKMeD等数据集上的分类准确率超过95%。本文调查了用于宫颈癌识别和分析的各种深度学习策略，强调了它们的性能指标、数据集和临床适用性。

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引用次数: 0

Amoxicillin and cotrimoxazole-driven dysbiosis disrupted blood cell levels, cytokine balance and induced oxido-nitrosative stress in young mice 阿莫西林和复方新恶唑引起的生态失调破坏了年轻小鼠的血细胞水平、细胞因子平衡和诱导氧化-亚硝化应激

Advances in biomarker sciences and technology

Pub Date : 2025-11-15 DOI: 10.1016/j.abst.2025.11.002

Kiptoo K. Cosmas , Silas Kiruki , Olivia A. Njiri , Grace K. Nyambati , John Mokua Mose , Omwenga Isaac , Alfred Orina Isaac , James Nyabuga Nyariki

Amoxicillin and cotrimoxazole are among the most frequently prescribed antibiotics, yet their impact on gut microbiota and systemic physiology, particularly during early life, remains a critical concern. This study investigated the effects of these antibiotics on the gut microbiome and associated physiological and biochemical responses in young male Swiss mice (5 weeks old), serving as a model for infant exposure. Five experimental groups were employed: control, amoxicillin (9.62 mg/kg), cotrimoxazole (15 mg/kg), cotrimoxazole + amoxicillin, and cotrimoxazole + amoxicillin followed by probiotic administration. Parameters assessed included gut microbial composition, hematological indices, organ weights, liver and kidney function, cytokine profiles, oxidative stress markers, and histopathological alterations. Both antibiotics induced marked gut dysbiosis. Cotrimoxazole significantly increased leukocyte, neutrophil, lymphocyte, and monocyte counts, while amoxicillin caused thrombocytosis and cotrimoxazole induced thrombocytopenia; probiotic treatment normalized these effects. Amoxicillin reduced brain glutathione (GSH) levels, whereas cotrimoxazole decreased GSH in both liver and brain. Combined antibiotic exposure exacerbated GSH depletion and elevated nitric oxide (NO) and malondialdehyde (MDA) levels, effects mitigated by probiotics exposure. Co-exposure to cotrimoxazole and amoxicillin upregulated pro-inflammatory cytokines TNF-α and IFN-γ and increased serum markers of hepatic and renal injury (alanine-transaminases, alkaline phosphatases, Aspartate transaminases, creatinine, urea, uric acid). Histopathological analysis confirmed aggravated hepatic and renal damage under combined antibiotic exposure, which was markedly alleviated by probiotics. These findings demonstrate that amoxicillin and cotrimoxazole disrupt gut microbial balance, eliciting systemic oxidative, organ damage and inflammatory responses. Probiotic intervention confers significant protection, underscoring the need for cautious antibiotic use and microbiota-restorative strategies.

阿莫西林和复方新诺明是最常用的抗生素，但它们对肠道微生物群和全身生理的影响，特别是在生命早期，仍然是一个关键问题。本研究调查了这些抗生素对年轻雄性瑞士小鼠（5周龄）肠道微生物组和相关生理生化反应的影响，作为婴儿暴露的模型。5个试验组：对照组、阿莫西林（9.62 mg/kg）、复方新诺明（15 mg/kg）、复方新诺明+阿莫西林、复方新诺明+阿莫西林加益生菌。评估的参数包括肠道微生物组成、血液学指标、器官重量、肝肾功能、细胞因子谱、氧化应激标志物和组织病理学改变。两种抗生素均引起明显的肠道失调。复方新诺明显著增加白细胞、中性粒细胞、淋巴细胞和单核细胞计数，而阿莫西林引起血小板增多，复方新诺明引起血小板减少；益生菌治疗使这些效果正常化。阿莫西林降低脑谷胱甘肽（GSH）水平，而复方新诺明降低肝和脑谷胱甘肽水平。联合抗生素暴露加剧了谷胱甘肽的消耗和一氧化氮（NO）和丙二醛（MDA）水平的升高，益生菌暴露减轻了这种影响。共暴露于复方新诺明和阿莫西林可上调促炎细胞因子TNF-α和IFN-γ，并增加肝脏和肾脏损伤的血清标志物（丙氨酸转氨酶、碱性磷酸酶、天冬氨酸转氨酶、肌酐、尿素、尿酸）。组织病理学分析证实，抗生素联合暴露加重了肝脏和肾脏的损害，而益生菌明显减轻了这种损害。这些发现表明阿莫西林和复方新诺明破坏肠道微生物平衡，引起全身氧化、器官损伤和炎症反应。益生菌干预具有重要的保护作用，强调了谨慎使用抗生素和微生物群恢复策略的必要性。

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引用次数: 0

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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