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

Molecular biomarkers and nano-immunopharmacology in inflammatory carcinoma: Bridging mechanisms and therapeutic translation 炎症性癌的分子生物标志物和纳米免疫药理学：桥接机制和治疗翻译

Advances in biomarker sciences and technology

Pub Date : 2026-11-01 Epub Date: 2025-12-29 DOI: 10.1016/j.abst.2025.12.009

Kamlesh Sahu, Trilochan Satapathy, Poonam Sahu, Om Chandrakar

This section delineates the mechanistic framework linking chronic inflammation to carcinogenesis and critically explains how molecular biomarkers can be rationally exploited through nano-immunopharmacological strategies to enable precision therapy in inflammation-driven cancers. Chronic inflammation serves as a central driver of carcinogenesis, orchestrating tumor initiation, progression, metastasis and therapy resistance through highly intricate molecular networks. Inflammatory carcinomas such as inflammatory breast carcinoma, hepatocellular carcinoma and cholangiocarcinoma exhibit distinct gender- and region-specific prevalence, highlighting the dynamic interplay between host biology and tumor-promoting inflammatory micro-environments. At the molecular level, persistent pro-inflammatory cytokine signaling, notably IL-6 and TNF-α, in conjunction with activation of transcription factors NF-κB and STAT3, induces genomic instability, epigenetic reprogramming and epithelial-mesenchymal transition, collectively driving malignant transformation and aggressive phenotypes. The tumor micro-environment, enriched with immune subsets including tumor-associated macrophages, neutrophils and regulatory T cells, potentiates oncogenic signaling and fosters immune evasion. Emerging molecular biomarkers spanning cytokine signatures, immune checkpoints (PD-L1, CTLA-4) and epigenetic indicators offer critical prognostic value and therapeutic guidance. Cutting-edge nano-immunopharmacology enables precise modulation of these inflammatory axes by employing nanocarriers for cytokine inhibitors, immune modulators, RNA therapeutics and CRISPR-based interventions while minimizing systemic toxicity. By integrating mechanistic insights with translational strategies, receptor-guided nano-therapeutics emerge as a transformative approach to precision oncology, promising to redefine treatment paradigms, enhance therapeutic efficacy and overcome resistance in cancers fueled by chronic inflammation.

本节描述了将慢性炎症与致癌联系起来的机制框架，并批判性地解释了如何通过纳米免疫药理学策略合理地利用分子生物标志物来实现炎症驱动癌症的精确治疗。慢性炎症是癌症发生的核心驱动因素，通过高度复杂的分子网络协调肿瘤的发生、进展、转移和治疗耐药性。炎性癌如炎性乳腺癌、肝细胞癌和胆管癌表现出明显的性别和地区特异性患病率，突出了宿主生物学和促肿瘤炎症微环境之间的动态相互作用。在分子水平上，持续的促炎细胞因子信号，特别是IL-6和TNF-α，与转录因子NF-κB和STAT3的激活一起，诱导基因组不稳定、表观遗传重编程和上皮-间质转化，共同驱动恶性转化和侵袭性表型。肿瘤微环境富含免疫亚群，包括肿瘤相关巨噬细胞、中性粒细胞和调节性T细胞，增强致癌信号并促进免疫逃避。新兴的分子生物标志物跨越细胞因子特征、免疫检查点（PD-L1、CTLA-4）和表观遗传指标，提供了关键的预后价值和治疗指导。尖端的纳米免疫药理学通过使用细胞因子抑制剂、免疫调节剂、RNA疗法和基于crispr的干预的纳米载体，可以精确调节这些炎症轴，同时最大限度地减少全身毒性。通过将机制见解与转化策略相结合，受体引导的纳米治疗成为精确肿瘤学的一种变革性方法，有望重新定义治疗范式，提高治疗效果并克服由慢性炎症引起的癌症的耐药性。

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引用次数: 0

Bioscreening and phytochemical profiling of Phyllanthus niruri endophytic fungi with potential Anti-HCV applications 具有潜在抗hcv应用价值的余甘子内生真菌的生物筛选和植物化学分析

Advances in biomarker sciences and technology

Pub Date : 2026-11-01 Epub Date: 2025-12-19 DOI: 10.1016/j.abst.2025.12.006

Kalyanasundaram Parvatham, Joysingh V. Kishonika Sri, Rajendiran Dharanika, Alagarsamy Atsaya, Velu Rajesh Kannan

This study proposes a novel strategy for antiviral drug discovery by exploring endophytic fungi associated with Phyllanthus niruri, a medicinal plant traditionally recognized for its therapeutic value in liver disorders and viral infections. The research underscores the critical demand for cost-effective therapies for Hepatitis C Virus (HCV), particularly in low source settings which represents a major global health challenge with severe consequences like cirrhosis and hepatocellular carcinoma. The research focuses on identifying bioactive fungal metabolites with antiviral potential. Endophytic fungi were isolated from different tissues of P. niruri, followed by solvent extraction of the obtained isolates. Comprehensive phytochemical analysis was carried out to det_ect antiviral compounds such as lignans, flavonoids, alkaloids, tannins, coumarins, and saponins and the antioxidant property was evaluated by DPPH and ABTS assays demonstrated strong free-radical scavenging activity, supporting the therapeutic relevance of the extracts. The cytotoxicity assessment of the most promising fungal extract on HepG2 liver cell lines exhibited moderate effects at elevated concentrations, indicating a potential safety margin at lower doses for therapeutic applications. This investigation emphasizes the promise of endophytic fungi from Phyllanthus niruri as a significant source of natural antiviral agents, facilitating the pursuit of alternative treatments for HCV, offering a valuable lead toward the development of cost-effective therapeutic candidates.

本研究提出了一种新的抗病毒药物发现策略，即通过探索与Phyllanthus niruri相关的内生真菌，这种药用植物传统上被认为具有治疗肝脏疾病和病毒感染的价值。该研究强调了对具有成本效益的丙型肝炎病毒（HCV）治疗方法的迫切需求，特别是在低来源环境中，这是一项重大的全球卫生挑战，具有肝硬化和肝细胞癌等严重后果。研究的重点是鉴定具有抗病毒潜力的生物活性真菌代谢物。从尼鲁假单胞菌的不同组织中分离内生真菌，并对分离物进行溶剂提取。全面的植物化学分析检测了木脂素、类黄酮、生物碱、单宁、香豆素和皂苷等抗病毒化合物，并通过DPPH和ABTS测试评估了抗氧化性能，显示出强大的自由基清除活性，支持提取物的治疗相关性。最有希望的真菌提取物对HepG2肝细胞系的细胞毒性评估显示，浓度升高时效果适中，表明低剂量治疗应用具有潜在的安全边际。这项研究强调了Phyllanthus niruri内生真菌作为天然抗病毒药物的重要来源，促进了对HCV替代治疗的追求，为开发具有成本效益的治疗候选药物提供了有价值的线索。

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引用次数: 0

Methanol extract of Smritisagara Rasa attenuates neurobehavior, neurochemical and stress indicators in rotenone-induced Drosophila model of Parkinson's disease 鱼藤酮诱导的帕金森病果蝇模型的神经行为、神经化学和应激指标减弱

Advances in biomarker sciences and technology

Pub Date : 2026-11-01 Epub Date: 2025-11-28 DOI: 10.1016/j.abst.2025.11.003

Gopinath G , Ramesha Hanumanthappa , Raifa Abdul Aziz , P.C. Kiran , Hemalatha Nanjaiah , Iranna B. Kotturshetty , Shamprasad Varija Raghu , Manjunath Ajanal , Devaraju Kuramkote Shivanna

Smritisagara Rasa (SSR), a traditional Ayurvedic herbo-mineral formulation, has long been prescribed for the treatment of Parkinson's disease (PD) since ancient times. Nevertheless, the underlying molecular mechanisms and treatment strategies remain unclear due to a lack of adequate experimental data. This study focused on formulating SSR and extracting its components using different solvent systems, including water and methanol. SSR, along with its water extract (SSR-WEX) and methanol extract (SSR-MEX), was tested for efficacy in mitigating PD phenotypes using a Rotenone (ROT)-induced Drosophila melanogaster PD model. SSR was prepared according to the Ayurvedic Formulary of India and subsequently characterized, followed by solvent extraction and phytoconstituent profiling. Different concentrations of SSR, SSR-WEX, and SSR-MEX were administered to control flies to evaluate their potential toxicity. Flies were then co-exposed to 500 μM ROT and 75 mg/kg diet of SSR, SSR-WEX, and SSR-MEX for seven days. Behavioral, neurochemical, and oxidative stress parameters were assessed, with pramipexole (PPX) used as a positive control. ROT exposure markedly impaired climbing ability, reduced dopamine and acetylcholinesterase (AChE) activity, and elevated oxidative stress markers, including malondialdehyde (MDA), protein carbonyl content (PCC), hydrogen peroxide, and nitric oxide. Co-treatment with SSR and its extracts significantly restored locomotor function, dopamine, and AChE levels, while enhancing antioxidant enzymes (glutathione-S-transferase and catalase) and reducing oxidative damage indices. Among the formulations, SSR-MEX demonstrated superior neuroprotective and antioxidative efficacy, comparable to the standard drug pramipexole. This effect is likely due to its enriched profile of flavonoids, coumarins, and phenolic acids. These findings substantiate traditional claims regarding SSR's role in mitigating neurodegenerative symptoms and highlight SSR-MEX as a promising phytopharmaceutical candidate for managing PD-like pathologies through attenuation of oxidative stress and restoration of dopaminergic and cholinergic systems.

Smritisagara Rasa （SSR）是一种传统的阿育吠陀草药矿物配方，自古以来就被用于治疗帕金森病（PD）。然而，由于缺乏足够的实验数据，潜在的分子机制和治疗策略仍不清楚。本研究的重点是制备SSR，并利用不同的溶剂体系，包括水和甲醇提取其成分。利用鱼藤酮（ROT）诱导的果蝇PD模型，研究了SSR及其水提取物（SSR- wex）和甲醇提取物（SSR- mex）对PD表型的缓解效果。根据印度阿育吠陀处方制备了SSR，并对其进行了表征，随后进行了溶剂提取和植物成分分析。用不同浓度的SSR、SSR- wex和SSR- mex对照蝇，评价其潜在毒性。然后将果蝇分别暴露于500 μM ROT和75 mg/kg的SSR、SSR- wex和SSR- mex中7天。以普拉克索（PPX）作为阳性对照，评估行为、神经化学和氧化应激参数。暴露于ROT环境中会显著损害攀爬能力，降低多巴胺和乙酰胆碱酯酶（AChE）活性，并升高氧化应激标志物，包括丙二醛（MDA）、蛋白质羰基含量（PCC）、过氧化氢和一氧化氮。与SSR及其提取物共处理可显著恢复运动功能、多巴胺和AChE水平，同时增强抗氧化酶（谷胱甘肽- s -转移酶和过氧化氢酶），降低氧化损伤指标。在这些制剂中，SSR-MEX表现出与标准药物普拉克索相当的优越的神经保护和抗氧化功效。这种效果可能是由于其富含类黄酮、香豆素和酚酸。这些发现证实了SSR在缓解神经退行性症状方面的作用，并强调了SSR- mex作为一种有前途的植物药物候选物，可以通过降低氧化应激和恢复多巴胺能和胆碱能系统来治疗pd样病理。

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引用次数: 0

Multicenter evaluation of M2BPGi as a biomarker for liver fibrosis and hepatocellular carcinoma in chronic hepatitis B and C patients in Malaysia M2BPGi作为马来西亚慢性乙型和丙型肝炎患者肝纤维化和肝细胞癌生物标志物的多中心评估

Advances in biomarker sciences and technology

Pub Date : 2026-11-01 Epub Date: 2026-02-04 DOI: 10.1016/j.abst.2026.01.011

Huan Keat Chan , Ibtisam Ismail , Faizah Ahmad , Siti Maisarah Md Ali , Norizati Razali , Nurul Husna Qaiyimah Ibrahim , Muhammad Radzi Abu Hassan

Background

Chronic hepatitis B (HBV) and C (HCV) cause major global morbidity, especially in settings with limited diagnostic access. Liver biopsy is not routinely used for fibrosis assessment because of its invasive nature, whereas elastography, a reliable non-invasive alternative, is often limited by high cost. In this context, Mac-2 binding protein glycosylation isomer (M2BPGi) has emerged as a promising non-invasive biomarker for liver fibrosis and hepatocellular carcinoma (HCC), but data from Southeast Asia remain limited. This study assessed its diagnostic accuracy among chronic HBV and HCV patients in Malaysia.

Methods

A cross-sectional study was conducted among 154 participants from seven Malaysian public hospitals: 62 HCV patients, 52 HBV patients, and 40 healthy controls. Liver stiffness was measured using transient elastography, and serum biomarkers (M2BPGi, AFP, FIB-4, GPR) were analyzed. Diagnostic performance for fibrosis, cirrhosis, and HCC was evaluated using ROC curve analysis.

Results

M2BPGi levels, expressed as cutoff index (COI), rose with increasing disease severity, with median values of 0.4 COI (controls), 0.8 COI (non-cirrhotic), 4.9 COI (cirrhotic), and 3.9 COI (HCC) (p < 0.001). For cirrhosis detection, M2BPGi showed excellent accuracy (AUC 0.989; 92% sensitivity; 100% specificity). For HCC screening, it achieved an AUC of 0.962 (85.3% sensitivity; 97.5% specificity). However, it performed poorly in differentiating cirrhosis from HCC (AUC 0.594, p = 0.220).

Conclusion

M2BPGi demonstrates strong diagnostic accuracy for liver fibrosis and cirrhosis in chronic HBV and HCV patients, supporting its use as a non-invasive screening tool. Its limited ability to distinguish HCC from cirrhosis highlights the need for multimodal biomarker strategies and further evaluation in Malaysia.

慢性乙型肝炎（HBV）和丙型肝炎（HCV）是全球主要发病率，特别是在诊断途径有限的地区。肝活检由于其侵入性而不常用于纤维化评估，而弹性成像是一种可靠的非侵入性替代方法，但往往受到高成本的限制。在这种背景下，Mac-2结合蛋白糖基化异构体（M2BPGi）已成为肝纤维化和肝细胞癌（HCC）的一种有前途的非侵入性生物标志物，但来自东南亚的数据仍然有限。本研究评估了其在马来西亚慢性HBV和HCV患者中的诊断准确性。方法对来自马来西亚7家公立医院的154名参与者进行横断面研究：62名HCV患者，52名HBV患者和40名健康对照。采用瞬时弹性图测量肝脏硬度，并分析血清生物标志物（M2BPGi、AFP、FIB-4、GPR）。使用ROC曲线分析评估纤维化、肝硬化和HCC的诊断性能。结果sm2bpgi水平，以截断指数（COI）表示，随着疾病严重程度的增加而升高，中位数为0.4 COI（对照组），0.8 COI（非肝硬化），4.9 COI（肝硬化）和3.9 COI (HCC) （p < 0.001）。对于肝硬化的检测，M2BPGi具有很好的准确性（AUC 0.989，灵敏度92%，特异性100%）。对于HCC筛查，AUC为0.962（敏感性85.3%，特异性97.5%）。然而，它在区分肝硬化和HCC方面表现不佳（AUC 0.594, p = 0.220）。结论m2bpgi对慢性HBV和HCV患者肝纤维化和肝硬化具有较强的诊断准确性，支持其作为无创筛查工具的应用。它区分HCC和肝硬化的能力有限，这突出了马来西亚对多模式生物标志物策略和进一步评估的需求。

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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 Epub Date: 2025-07-16 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 Epub Date: 2025-05-20 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 Epub Date: 2025-10-11 DOI: 10.1016/j.abst.2025.09.003

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

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 Epub Date: 2025-11-13 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

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 Epub Date: 2025-02-11 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

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 Epub Date: 2025-09-01 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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