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Biomarkers in immunology: Their impact on immune function and response 免疫学中的生物标志物:它们对免疫功能和应答的影响
Pub Date : 2025-01-01 DOI: 10.1016/j.abst.2025.03.001
Deepika Kaushik , Baojun Xu , Mukul Kumar
The immune system is a complex network of organs, tissues, and cells that plays a critical role in defending the body against life-threatening diseases such as infections, cancer, Alzheimer's, and Crohn's disease. Biomarkers serve as valuable tools for assessing immune responses to these threats and evaluating the efficacy of interventions such as vaccines and immunotherapies. They are particularly useful in monitoring immune function in individuals with autoimmune disorders, where the immune system attacks the body's own tissues, or in immunodeficiencies, where immune responses are inadequate. Biomarkers provide a dynamic and comprehensive means of understanding disease mechanisms through observational and analytical epidemiology, randomized clinical trials, screening, diagnosis, and prognosis. However, despite their potential, the clinical application of biomarkers faces challenges, including sensitivity, reproducibility, and the complexity of multi-biomarker panels. Standardization of analytical techniques remains a critical hurdle, as variability in methodologies can impact the reliability and comparability of biomarker data. Addressing these challenges through improved analytical characterization, validation protocols, and integration of advanced technologies is essential to enhance the clinical utility of biomarkers in immune system assessment and disease management. Moreover, biomarkers offer critical insights into disease progression, from early onset to advanced stages, though their sensitivity and specificity may be influenced by various factors. In this review, we focus on the effect of biomarkers on the immune system.
免疫系统是一个由器官、组织和细胞组成的复杂网络,在保护身体免受感染、癌症、阿尔茨海默病和克罗恩病等威胁生命的疾病的侵害方面起着至关重要的作用。生物标志物是评估对这些威胁的免疫反应和评估疫苗和免疫疗法等干预措施有效性的宝贵工具。它们在监测自身免疫性疾病患者(免疫系统攻击人体自身组织)或免疫缺陷患者(免疫反应不足)的免疫功能方面特别有用。生物标志物通过观察和分析流行病学、随机临床试验、筛查、诊断和预后,为了解疾病机制提供了动态和全面的手段。然而,尽管生物标志物具有潜力,但其临床应用仍面临挑战,包括敏感性、可重复性和多生物标志物面板的复杂性。分析技术的标准化仍然是一个关键障碍,因为方法的可变性会影响生物标志物数据的可靠性和可比性。通过改进分析表征、验证方案和整合先进技术来解决这些挑战,对于提高生物标志物在免疫系统评估和疾病管理中的临床应用至关重要。此外,生物标志物提供了从早期发病到晚期疾病进展的关键见解,尽管它们的敏感性和特异性可能受到各种因素的影响。本文就生物标志物对免疫系统的影响作一综述。
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引用次数: 0
Review on zebra fish as an alternative animal model for neurological studies 斑马鱼作为神经学研究替代动物模型的研究进展
Pub Date : 2025-01-01 DOI: 10.1016/j.abst.2025.08.005
Khode Aniket Prakash , Darshana Sunil Nagmoti , Manali Sanjayswami Borkar , Hiray Kuldeep Pannalal , Nagaraju Bandaru
Zebrafish (Danio rerio) have emerged as a powerful alternative animal model in neurological research due to their unique combination of genetic, anatomical, and physiological characteristics. Their transparent embryonic development, high genetic homology with humans, and well-characterized nervous system make them invaluable for studying neurological diseases and disorders. Zebrafish offer practical advantages such as rapid reproduction, cost-effectiveness, and suitability for high-throughput screening. They have been extensively utilized to investigate neurodevelopmental disorders, neurodegenerative diseases like Parkinson's and Alzheimer's, and epilepsy. Moreover, their amenability to genetic manipulation enables precise modeling of human neurological conditions. Behavioral assays in zebrafish provide insights into cognitive, motor, and emotional functions, which can be quantified to study disease phenotypes and therapeutic interventions. Recent advances in imaging techniques, such as live imaging of neuronal activity using calcium indicators, have further enhanced their utility. This review highlights the advantages of zebrafish as an alternative model system, discusses key findings from zebrafish-based neurological studies, and outlines challenges such as translating findings to mammalian systems. By consolidating current knowledge, this article emphasizes the pivotal role of zebrafish in advancing our understanding of neurological mechanisms and in developing novel treatments for brain disorders.
斑马鱼(Danio rerio)由于其独特的遗传、解剖和生理特征组合而成为神经学研究中强有力的替代动物模型。它们透明的胚胎发育,与人类高度的基因同源性,以及良好的神经系统特征,使它们在研究神经系统疾病和障碍方面具有宝贵的价值。斑马鱼具有繁殖迅速、成本效益高、适合高通量筛选等实际优势。它们已被广泛用于研究神经发育障碍,神经退行性疾病,如帕金森病和阿尔茨海默病,以及癫痫。此外,它们对基因操作的适应性使人类神经系统状况的精确建模成为可能。斑马鱼的行为分析提供了对认知、运动和情绪功能的见解,可以量化研究疾病表型和治疗干预。成像技术的最新进展,如利用钙指示剂对神经元活动进行实时成像,进一步增强了它们的实用性。这篇综述强调了斑马鱼作为替代模型系统的优势,讨论了基于斑马鱼的神经学研究的主要发现,并概述了将发现转化为哺乳动物系统等挑战。通过巩固现有知识,本文强调斑马鱼在促进我们对神经机制的理解和开发新的脑部疾病治疗方法方面的关键作用。
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引用次数: 0
Bridging traditional risk factors and genetic insights: A review on polygenic risk scores in cardiovascular diseases 桥接传统危险因素和遗传见解:心血管疾病多基因风险评分综述
Pub Date : 2025-01-01 DOI: 10.1016/j.abst.2025.09.002
Abhishek Gupta , Komal Shah , Aakansha Shukla
Cardiovascular diseases (CVDs) remain the leading cause of global morbidity and mortality, with traditional risk models often falling short in predicting individual susceptibility-especially among diverse populations. Recent advances in genomics have led to the development of polygenic risk scores (PRS), which aggregate the effects of multiple single nucleotide polymorphisms (SNPs) to estimate genetic predisposition to CVD. This review explores the scientific evolution, clinical relevance, and limitations of PRS in CVD prediction. Evidence shows that integrating PRS with conventional risk factors significantly improves risk stratification, aiding in early detection and personalized prevention strategies. Notably, ethnicity-specific PRS models are being developed to enhance predictive accuracy for non-European populations, including South Asians. Despite its promise, PRS implementation faces challenges, such as Eurocentric bias in genome-wide association studies (GWAS), limited accessibility in low- and middle-income countries, and ethical concerns regarding equity and data privacy. Future research should emphasize multi-ethnic datasets, integration with clinical and lifestyle data, and development of equitable policies. As PRS continues to be effective in refining cardiovascular risk stratification, its integration into public health frameworks could revolutionize risk assessment and drive the shift toward precision medicine.
心血管疾病(cvd)仍然是全球发病率和死亡率的主要原因,传统的风险模型在预测个体易感性方面往往不足,特别是在不同人群中。基因组学的最新进展导致了多基因风险评分(PRS)的发展,它汇总了多个单核苷酸多态性(snp)的影响,以估计心血管疾病的遗传易感性。本文综述了PRS在CVD预测中的科学进展、临床相关性和局限性。有证据表明,将PRS与常规风险因素相结合可显著改善风险分层,有助于早期发现和个性化预防策略。值得注意的是,正在开发针对特定种族的PRS模型,以提高对包括南亚人在内的非欧洲人口的预测准确性。尽管有希望,PRS的实施面临着挑战,例如全基因组关联研究(GWAS)中的欧洲中心偏见,低收入和中等收入国家的可及性有限,以及关于公平和数据隐私的伦理问题。未来的研究应强调多民族数据集,与临床和生活方式数据的整合,以及制定公平的政策。由于PRS在完善心血管风险分层方面继续有效,将其纳入公共卫生框架可能会彻底改变风险评估并推动向精准医学的转变。
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引用次数: 0
Network-based discovery of autophagy-regulating miRNA signatures in ovarian carcinoma 基于网络的卵巢癌自噬调节miRNA特征的发现
Pub Date : 2025-01-01 DOI: 10.1016/j.abst.2025.10.001
A. Shriraksha, V.R. Devaraj
Ovarian cancer (OC) remains a leading cause of gynaecologic mortality due to lack of reliable early detection biomarkers. Given the emerging role of autophagy in tumor progression, this study explored the contribution of autophagy-associated miRNAs in OC using integrative bioinformatic approaches. Differential expression analysis of the E-TABM-975 dataset (3 normal, 125 tumor samples) identified 69 significantly altered miRNAs. Functional enrichment revealed their targets were involved in key pathways such as PI3K–AKT, MAPK, endocytosis, and autophagy regulation. Network analysis highlighted hub miRNAs (miR-340-5p, miR-106b-5p, miR-144-5p) interacting with autophagy-related genes (PTEN, MAP1B). A Random Forest model trained on E-TABM-975 achieved 99.22 % accuracy, and independent validation using E-TABM-343 (15 normal, 69 tumor) confirmed strong generalization (100 % accuracy). While most miRNAs exhibited consistent expression trends across datasets, a few discordant cases likely reflect dataset-specific variation. Limited availability of large cohorts with matched normal tissues remains a major constraint. The study provides a computational framework for identifying autophagy-related miRNAs with diagnostic relevance and outlines a phased experimental validation strategy to advance these findings toward translational applicability.
由于缺乏可靠的早期检测生物标志物,卵巢癌(OC)仍然是妇科死亡的主要原因。鉴于自噬在肿瘤进展中的新作用,本研究利用综合生物信息学方法探讨了自噬相关mirna在OC中的作用。E-TABM-975数据集(3个正常样本,125个肿瘤样本)的差异表达分析鉴定出69个显著改变的mirna。功能富集表明它们的靶点参与关键通路,如PI3K-AKT、MAPK、内吞作用和自噬调节。网络分析强调枢纽mirna (miR-340-5p, miR-106b-5p, miR-144-5p)与自噬相关基因(PTEN, MAP1B)相互作用。使用E-TABM-975训练的随机森林模型准确率达到99.22%,使用E-TABM-343(15例正常,69例肿瘤)的独立验证证实了强泛化(100%准确率)。虽然大多数mirna在数据集中表现出一致的表达趋势,但少数不一致的情况可能反映了数据集的特定差异。具有匹配正常组织的大队列的有限可用性仍然是一个主要限制。该研究为识别具有诊断相关性的自噬相关mirna提供了一个计算框架,并概述了一个分阶段的实验验证策略,以推进这些发现的翻译适用性。
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引用次数: 0
Identification of bipolar disorder related biomarkers, signaling pathways and potential therapeutic compounds based on bioinformatics methods and molecular docking technology 基于生物信息学方法和分子对接技术的双相情感障碍相关生物标志物、信号通路和潜在治疗化合物的鉴定
Pub Date : 2025-01-01 DOI: 10.1016/j.abst.2025.08.004
Basavaraj Vastrad , Shivaling Pattanashetti , Chanabasayya Vastrad
Bipolar disorder (BD), also known as psychiatric disorder, affects millions of people all over the world. The aim of this investigation was to screen and verify hub genes involved in BD as well as to explore potential molecular mechanisms. The next generation sequencing (NGS) dataset GSE124326 was downloaded from the Gene Expression Omnibus (GEO) database, which contained 480 samples, including 240 BD and 240 normal controls. Differentially expressed genes (DEGs) were filtered and subjected to gene ontology (GO) and pathway enrichment analyses. A Protein-Protein Interaction (PPI) network and modules were constructed and analyzed. We predicted regulatory miRNAs and TFs of hub-genes through miRNet and NetworkAnalyst online database. Drug predicted for BD treatment was screened out from the DrugBank through NetworkAnalyst. Molecular docking studies were carried out for predicting novel drug molecules. Receiver operating characteristic curve (ROC) curves was drawn to elucidate the diagnostic value of hub genes. In this investigation, total of 957 DEGs, including 477 up regulated and 480 down regulated genes. The GO and pathway enrichment analyses of the DEGs showed that the up regulated genes were enriched in the neutrophil degranulation, immune system, transport, cytoplasm and enzyme regulator activity, and the down regulated genes were enriched in extracellular matrix organization, diseases of metabolism, multicellular organismal process, cell periphery and metal ion binding. We screened hub genes include UBB, UBE2D1, TUBA1A, RPL11, RPS24, NOTCH3, CAV1, CNBD2, CCNA1 and MYH11. We also predicted miRNAs, TFs and drugs include hsa-mir-8085, hsa-mir-4514, HMG20B, STAT3, phenserine and roflumilast. Molecular docking technology screened out three small molecule compounds, including Kakkalide, Divaricatol and Brucine small molecule compounds. The current investigation illustrates a characteristic NGS data in BD, which might contribute to the interpretation of the progression of BD and provide novel biomarkers and therapeutic targets for BD.
双相情感障碍(BD),也被称为精神障碍,影响着全世界数百万人。本研究的目的是筛选和验证参与双相障碍的枢纽基因,并探讨其潜在的分子机制。从Gene Expression Omnibus (GEO)数据库下载下一代测序(NGS)数据集GSE124326,该数据集包含480个样本,其中BD 240个,正常对照240个。对差异表达基因(DEGs)进行筛选,并进行基因本体(GO)和途径富集分析。构建并分析了蛋白质-蛋白质相互作用(PPI)网络和模块。我们通过miRNet和NetworkAnalyst在线数据库预测中心基因的调控mirna和TFs。预测治疗双相障碍的药物是通过NetworkAnalyst从DrugBank中筛选出来的。分子对接研究用于预测新药分子。绘制受试者工作特征曲线(ROC),阐明枢纽基因的诊断价值。共检测到957个基因,其中上调基因477个,下调基因480个。GO和途径富集分析显示,上调基因富集于中性粒细胞脱颗粒、免疫系统、运输、细胞质和酶调节活性,下调基因富集于细胞外基质组织、代谢疾病、多细胞有机体过程、细胞外周和金属离子结合。我们筛选的枢纽基因包括UBB、UBE2D1、TUBA1A、RPL11、RPS24、NOTCH3、CAV1、CNBD2、CCNA1和MYH11。我们还预测了mirna、tf和药物包括hsa-mir-8085、hsa-mir-4514、HMG20B、STAT3、phenserine和roflumilast。分子对接技术筛选出Kakkalide、Divaricatol和马钱子碱三种小分子化合物。目前的研究揭示了双相障碍的特征NGS数据,这可能有助于解释双相障碍的进展,并为双相障碍提供新的生物标志物和治疗靶点。
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引用次数: 0
Application of Generative Adversarial Networks on RNASeq data to uncover COVID-19 severity biomarkers 生成对抗网络在RNASeq数据上的应用揭示COVID-19严重程度生物标志物
Pub Date : 2025-01-01 DOI: 10.1016/j.abst.2025.01.002
Yvette K. Kalimumbalo , Rosaline W. Macharia , Peter W. Wagacha

Background

The COVID-19 pandemic has highlighted the need for reliable biomarkers to predict disease severity and guide treatment strategies. However, the analysis of RNASeq data for biomarker discovery using machine learning is constrained by limited sample sizes, primarily due to cost and privacy considerations. In this study, we applied Generative Adversarial Networks (GANs) to RNASeq data in the process of identifying biomarkers associated with COVID-19 severity.

Methods

RNASeq data from COVID-19 patients, along with severity metadata, were collected from the GEO database. Differential expression analysis was conducted and GAN models were trained to augment the original dataset. This enhanced subsequent machine learning models’ robustness and accuracy for biomarker discovery. Feature selection using Recursive Feature Elimination with Cross-Validation (RFECV) identified key biomarkers on cGAN- and cWGAN-augmented datasets.

Results

Several key biomarkers significantly associated with disease severity were identified. Gene Ontology Enrichment analysis revealed upregulation of neutrophil degranulation and downregulation of T-cell activity, consistent with previous findings. The ROC analysis using a Random Forest machine learning model and the five most important biomarkers (CCDC65, ZNF239, OTUD7A, CEP126, and TCTN2) achieved high accuracy (AUC: 0.98, Acc: 0.94) in predicting disease severity. These genes are associated with processes such as cilium assembly, IFN activation, and NF-kB pathway suppression.

Conclusions

Our results demonstrate that GANs can effectively augment RNASeq data, leading to consistent findings that align with known mechanisms and providing new insights into severe COVID-19 transcriptional responses. Further experimental validation is needed to confirm the applicability of these biomarkers in diverse populations.
2019冠状病毒病大流行凸显了需要可靠的生物标志物来预测疾病严重程度和指导治疗策略。然而,使用机器学习对RNASeq数据进行生物标记物发现分析受到样本量有限的限制,主要是由于成本和隐私方面的考虑。在这项研究中,我们将生成对抗网络(GANs)应用于RNASeq数据,以识别与COVID-19严重程度相关的生物标志物。方法从GEO数据库中收集COVID-19患者的srnaseq数据以及严重程度元数据。进行差异表达分析,并训练GAN模型来增强原始数据集。这增强了后续机器学习模型在生物标志物发现方面的鲁棒性和准确性。使用递归特征消除交叉验证(RFECV)进行特征选择,确定了cGAN和cwgan增强数据集上的关键生物标志物。结果确定了几个与疾病严重程度显著相关的关键生物标志物。基因本体富集分析显示中性粒细胞脱颗粒上调和t细胞活性下调,与先前的研究结果一致。使用随机森林机器学习模型和五个最重要的生物标志物(CCDC65、ZNF239、OTUD7A、CEP126和TCTN2)进行ROC分析,在预测疾病严重程度方面取得了很高的准确性(AUC: 0.98, Acc: 0.94)。这些基因与纤毛组装、IFN激活和NF-kB通路抑制等过程有关。研究结果表明,GANs可以有效地增强RNASeq数据,从而获得与已知机制一致的结果,并为严重的COVID-19转录反应提供新的见解。需要进一步的实验验证来确认这些生物标志物在不同人群中的适用性。
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引用次数: 0
CAR-T and nanotechnology: A comparative Perspective on autoimmune disease and cancer CAR-T和纳米技术:自身免疫性疾病和癌症的比较研究
Pub Date : 2025-01-01 DOI: 10.1016/j.abst.2025.03.002
A. Pavithra , C. Ragavendran
Nanotechnology has emerged as a key transformative force in upgrading Chimeric Antigen Receptor T-cell (CAR-T) from generation to generation in cancer and autoimmune treatment. Nanotechnology-aided CAR-T therapy has shown its ability to overcome various challenges in contemporary CAR-T therapy. The comparative analysis of CAR-T and its applications in cancer and autoimmune diseases focuses on target specificity, with immune cells being used as “chaperones” for nanoparticles, Immunosuppressibility by modifying microarchitecture, prolonged cell viability, and other nanotechnological modifications has been briefly discussed. The latest and smartest innovations, such as nano-enzymes linked CAR, stimuli-responsive nanoparticles, nano-based dual-targeting CAR structures, and nano-cargos, have upgraded CAR-T therapy to its latest advancements. Despite regulatory compliance, and cost of production and utility of Nano-CAR-T can be overcome by the use of green nanotechnology. The aid of advancements in AIs, software, MLs etc offers promising solutions to hurdles in scalability. A number of clinical and preclinical trials in the last few years have been explained to highlight the current status of Nano-CAR-T in present-day treatment and for a promising future. This review emphasizes the novel role of nanotechnology in shaping the future of advanced CAR-T therapy, paving the way for milestones in medical research.
纳米技术已经成为在癌症和自身免疫治疗中一代又一代升级嵌合抗原受体t细胞(CAR-T)的关键变革力量。纳米技术辅助CAR-T疗法已经显示出克服当代CAR-T疗法中各种挑战的能力。CAR-T及其在癌症和自身免疫性疾病中的应用的比较分析侧重于靶特异性,免疫细胞被用作纳米颗粒的“伴侣”,通过修饰微结构的免疫抑制性,延长细胞活力和其他纳米技术修饰已被简要讨论。最新和最聪明的创新,如纳米酶连接CAR,刺激反应纳米颗粒,纳米双靶向CAR结构和纳米货物,使CAR- t疗法升级到最新的进展。尽管遵守法规,纳米car - t的生产成本和实用性可以通过使用绿色纳米技术来克服。人工智能、软件、机器学习等方面的进步为可扩展性障碍提供了有希望的解决方案。在过去的几年里,一些临床和临床前试验已经被解释为强调纳米car - t在当今治疗中的现状和一个充满希望的未来。这篇综述强调了纳米技术在塑造未来先进CAR-T疗法中的新作用,为医学研究的里程碑铺平了道路。
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引用次数: 0
Evaluation of toxicity and antioxidant activities of various crude extracts of leaves and stems of Zygophyllum simplex 小檗叶、茎不同粗提物的毒性及抗氧化活性评价
Pub Date : 2025-01-01 DOI: 10.1016/j.abst.2024.11.003
Noha Maher Galal , Salem Said Al Touby , Yahya Bin Abdullah Alrashdi , Mohammad Amzad Hossain
Zygophyllum simplex (Z. simplex) is a plant that has been used for a long time for the treatment of human diseases. Therefore, this present research study aims to prepare various plant extracts and screen their antioxidant and cytotoxic activities. To attain the present objectives, different crude extracts were prepared from the leaves and stems of Z. simplex by using a maceration method. The activities of antioxidant and cytotoxic were prepared from aerial crude extracts of Z. simplex which were determined by 2,2-diphenyl-1-1-picrylhydrazyl (DPPH) and brine shrimp lethality (BSL) methods, respectively. All the prepared leaves and stems extracts of the selected plant at six different concentrations showed significant antioxidant activity against the DPPH method. The ethyl acetate crude extract showed the highest antioxidant activity and the lowest activity was in butanol extract. However, all the leaves and stems crude extracts of Z. simplex were prepared at different concentrations also showed promising cytotoxic activity against the BSL method. However, based on the antioxidant activity results, the ethyl acetate extract was selected for the isolation of bioactive compounds by using the column chromatographic method. The ethyl acetate was purified by using column chromatography in which different ratios of mobile phase (dichlorometane: methanol) were used. A series of test tubes were collected with a volume of 3 mL and depending on the similar retention mobility (Rf) behavior a total of twelve fractions were prepared. Similarly, the antioxidant activity of the obtained twelve fractions from column chromatography was determined by the same DPPH method. All the fractions showed significant antioxidant activity. Among the fractions from the column, fraction 6 give the highest antioxidant activity and the lowest was fraction 1. In conclusion, all the leaves and stems showed encouraging activities against DPPH and the fraction with the highest antioxidant activity could be used as a natural antioxidant to prevent cell damage.
单纯合子(zz . simplex)是一种长期用于治疗人类疾病的植物。因此,本研究旨在制备各种植物提取物,并对其抗氧化和细胞毒活性进行筛选。为达到本研究目的,采用浸渍法制备了不同粗提物。采用2,2-二苯基-1-1-苦味酰肼(DPPH)法和卤虾致死性(BSL)法分别测定了其抗氧化活性和细胞毒活性。6种不同浓度的叶提取物和茎提取物对DPPH法均表现出显著的抗氧化活性。乙酸乙酯粗提物抗氧化活性最高,丁醇粗提物抗氧化活性最低。然而,不同浓度的单根叶和茎粗提物均表现出良好的细胞毒活性。然而,根据抗氧化活性结果,选择乙酸乙酯提取物,采用柱层析法分离生物活性化合物。采用不同比例的流动相(二氯甲烷:甲醇)柱层析法纯化乙酸乙酯。收集一系列体积为3ml的试管,根据相似的保留迁移率(Rf)行为,共制备了12个馏分。同样,用相同的DPPH法测定柱层析得到的12个组分的抗氧化活性。各组分均表现出显著的抗氧化活性。各馏分中抗氧化活性最高的是6,最低的是1。综上所述,所有叶和茎均表现出较强的抗DPPH活性,抗氧化活性最高的部位可作为天然抗氧化剂保护细胞免受损伤。
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引用次数: 0
Unraveling ankylosing spondylitis: Exploring the genetic and immunological factors and latest treatment innovations 揭示强直性脊柱炎:探索遗传和免疫因素和最新的治疗创新
Pub Date : 2025-01-01 DOI: 10.1016/j.abst.2024.12.002
Nilasree Hazra , Sudeshna Sengupta , Dipannita Burman , Jyoti Sekhar Banerjee , Malavika Bhattacharya
Ankylosing spondylitis (AS) is a chronic inflammatory arthritis primarily affecting the spine and sacroiliac joints. Gut microbiota significantly affects ankylosing spondylitis (AS) pathophysiology. Environmental factors, like smoking, and genetic predispositions can worsen AS. Patients often have altered fecal microbiota, decreased Bacteroides and Lachnospiraceae, and increased Proteobacteria and Enterobacteriaceae. Bacteroides coprophilus and Prevotella copri are particularly enriched in AS. This condition is associated with the HLA-B27 genetic marker and involves various immunological cells and inflammatory cytokines. To develop more effective treatments, research is ongoing to identify specific signaling pathways and genetic markers associated with AS.Gender prevalence of AS is now more evenly distributed, with women experiencing longer diagnostic delays and increased disease activity. Treatment regimens and responses to medication may vary between genders. Some case studies suggest that an Ayurvedic approach, including Panchakarma treatments and specific Ayurvedic medications, may be beneficial in managing AS. HLA-B27 and non-HLA genes such as IL23R, ERAP1, and RUNX3 are linked to AS susceptibility. The Th17 lymphocyte system, associated with IL23R, plays a role in AS pathogenesis, highlighting potential treatment targets. Over 100 genes related to AS were identified in genome-wide association studies, many connected to IL-23-driven inflammation and antigen processing. AS is regulated by various immunological cells, and changes in bone structure are caused by the interaction of immune cells with bone cells. Ankylosing spondylitis (AS) involves inflammatory cytokines like IL-1β IL-17 and IL-23. The immune system plays a crucial role in the disease, with certain proteins linked to AS risk. However, further research is needed to determine the effectiveness of this approach.
强直性脊柱炎(AS)是一种慢性炎症性关节炎,主要影响脊柱和骶髂关节。肠道菌群显著影响强直性脊柱炎(AS)的病理生理。环境因素,如吸烟和遗传倾向会使AS恶化。患者粪便微生物群常发生改变,拟杆菌科和毛螺杆菌科减少,变形杆菌科和肠杆菌科增加。铜proproophilus和copri Prevotella尤其富含AS。这种情况与HLA-B27遗传标记有关,涉及多种免疫细胞和炎症细胞因子。为了开发更有效的治疗方法,正在进行研究以确定与AS相关的特定信号通路和遗传标记。AS的性别患病率现在分布更加均匀,女性经历更长的诊断延迟和疾病活动性增加。治疗方案和对药物的反应可能因性别而异。一些案例研究表明,阿育吠陀的方法,包括Panchakarma治疗和特定的阿育吠陀药物,可能对治疗AS有益。HLA-B27和非hla基因如IL23R、ERAP1和RUNX3与as易感性有关。与IL23R相关的Th17淋巴细胞系统在AS发病机制中发挥作用,突出了潜在的治疗靶点。在全基因组关联研究中发现了100多个与AS相关的基因,其中许多与il -23驱动的炎症和抗原加工有关。AS受多种免疫细胞调节,骨结构的改变是免疫细胞与骨细胞相互作用引起的。强直性脊柱炎(AS)涉及炎性细胞因子如IL-1β IL-17和IL-23。免疫系统在这种疾病中起着至关重要的作用,某些蛋白质与AS风险有关。然而,需要进一步的研究来确定这种方法的有效性。
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引用次数: 0
Clinical features and predictive model for invasive pulmonary fungal infections in hematologic disorders 血液病患者侵袭性肺真菌感染的临床特征和预测模型
Pub Date : 2025-01-01 DOI: 10.1016/j.abst.2025.02.001
Jun Wang , Xuefeng He , Feng Chen , Xiao Ma , Daxiong Zeng , Junhong Jiang

Objective

This study was to investigate the clinical features of hematological disorders complicated by invasive pulmonary fungal infections and identify factors affecting treatment outcomes, with the aim of developing a predictive model.

Methods

Clinical data were collected from patients with hematological disorders and invasive pulmonary fungal infections between January 2020 and June 2023. Based on metagenomics next generation sequencing (mNGS) of bronchoalveolar lavage fluid (BALF), patients were categorized into three groups: Candida, Mucor, and Aspergillus. General conditions, clinical features, treatments, and outcomes were compared. Treatment outcomes were assessed two months after therapy and classified as either improved or not improved. Factors influencing outcomes were analyzed, and a risk prediction model for treatment failure was developed.

Results

A total of 89 patients with hematological diseases and invasive pulmonary fungal infections were included: 26 with Candida, 25 with Mucor, and 38 with Aspergillus. Significant differences were observed between groups in long-term corticosteroid use, hematological disease outcomes, neutropenia duration, treatment duration, central venous catheter placement, galactomannan (GM) test results, CD4+ T-cell count, and clinical manifestations. After two months of antifungal therapy, improvement rates were 96.15 % for Candida, 76.00 % for Mucor, and 63.16 % for Aspergillus. Logistic regression analysis identified elevated platelet count (OR = 0.9823, 95%CI: 0.9663–0.9945), D-dimer (OR = 1.2130, 95%CI: 1.0544–1.4934), C-reactive protein (OR = 1.0066, 95%CI: 1.0026–1.0111) and medication adjustments based on mNGS results (OR = 0.0495, 95%CI: 0.0108–0.1624) as significant prognostic factors. A nomogram prediction model based on these factors demonstrated good discrimination with a C-index of 0.86.

Conclusion

The clinical features and treatment outcomes differ among fungal types in patients with hematological disorders and invasive pulmonary fungal infections. The nomogram prediction model, incorporating platelet count, D-dimer, C-reactive protein and mNGS-guided therapy adjustments, offers robust predictive performance for two-month treatment outcomes.
目的探讨血液学疾病合并侵袭性肺部真菌感染的临床特点及影响治疗效果的因素,建立预测模型。方法收集2020年1月至2023年6月血液病和侵袭性肺部真菌感染患者的临床资料。基于支气管肺泡灌洗液(BALF)的宏基因组学下一代测序(mNGS),将患者分为三组:念珠菌、毛霉菌和曲霉。比较一般情况、临床特征、治疗和结果。治疗后两个月评估治疗结果,并将其分为改善或未改善。分析影响疗效的因素,建立治疗失败的风险预测模型。结果共纳入血液病合并肺部侵袭性真菌感染患者89例,其中念珠菌26例,毛霉25例,曲霉38例。两组间在长期使用皮质类固醇、血液学疾病结局、中性粒细胞减少持续时间、治疗持续时间、中心静脉置管、半乳甘露聚糖(GM)检测结果、CD4+ t细胞计数和临床表现方面均存在显著差异。抗真菌治疗2个月后,念珠菌、毛霉和曲霉菌的治愈率分别为96.15%、76.00%和63.16%。Logistic回归分析发现血小板计数升高(OR = 0.9823, 95%CI: 0.9663-0.9945)、d -二聚体(OR = 1.2130, 95%CI: 1.0544-1.4934)、c -反应蛋白(OR = 1.0066, 95%CI: 1.0026-1.0111)和基于mNGS结果的药物调整(OR = 0.0495, 95%CI: 0.0108-0.1624)是重要的预后因素。基于这些因素的nomogram预测模型具有良好的判别能力,C-index为0.86。结论血液病合并肺部侵袭性真菌感染患者不同类型真菌的临床特点及治疗效果不同。nomogram预测模型,结合血小板计数、d -二聚体、c -反应蛋白和mngs引导的治疗调整,对两个月的治疗结果提供了可靠的预测性能。
{"title":"Clinical features and predictive model for invasive pulmonary fungal infections in hematologic disorders","authors":"Jun Wang ,&nbsp;Xuefeng He ,&nbsp;Feng Chen ,&nbsp;Xiao Ma ,&nbsp;Daxiong Zeng ,&nbsp;Junhong Jiang","doi":"10.1016/j.abst.2025.02.001","DOIUrl":"10.1016/j.abst.2025.02.001","url":null,"abstract":"<div><h3>Objective</h3><div>This study was to investigate the clinical features of hematological disorders complicated by invasive pulmonary fungal infections and identify factors affecting treatment outcomes, with the aim of developing a predictive model.</div></div><div><h3>Methods</h3><div>Clinical data were collected from patients with hematological disorders and invasive pulmonary fungal infections between January 2020 and June 2023. Based on metagenomics next generation sequencing (mNGS) of bronchoalveolar lavage fluid (BALF), patients were categorized into three groups: <em>Candida</em>, <em>Mucor</em>, <em>and Aspergillus</em>. General conditions, clinical features, treatments, and outcomes were compared. Treatment outcomes were assessed two months after therapy and classified as either improved or not improved. Factors influencing outcomes were analyzed, and a risk prediction model for treatment failure was developed.</div></div><div><h3>Results</h3><div>A total of 89 patients with hematological diseases and invasive pulmonary fungal infections were included: 26 with <em>Candida</em>, 25 with <em>Mucor, and</em> 38 with <em>Aspergillus</em>. Significant differences were observed between groups in long-term corticosteroid use, hematological disease outcomes, neutropenia duration, treatment duration, central venous catheter placement, galactomannan (GM) test results, CD4<sup>+</sup> T-cell count, and clinical manifestations. After two months of antifungal therapy, improvement rates were 96.15 % for <em>Candida</em>, 76.00 % for <em>Mucor</em>, and 63.16 % for <em>Aspergillus</em>. Logistic regression analysis identified elevated platelet count (OR = 0.9823, 95%CI: 0.9663–0.9945), D-dimer (OR = 1.2130, 95%CI: 1.0544–1.4934), C-reactive protein (OR = 1.0066, 95%CI: 1.0026–1.0111) and medication adjustments based on mNGS results (OR = 0.0495, 95%CI: 0.0108–0.1624) as significant prognostic factors. A nomogram prediction model based on these factors demonstrated good discrimination with a C-index of 0.86.</div></div><div><h3>Conclusion</h3><div>The clinical features and treatment outcomes differ among fungal types in patients with hematological disorders and invasive pulmonary fungal infections. The nomogram prediction model, incorporating platelet count, D-dimer, C-reactive protein and mNGS-guided therapy adjustments, offers robust predictive performance for two-month treatment outcomes.</div></div>","PeriodicalId":72080,"journal":{"name":"Advances in biomarker sciences and technology","volume":"7 ","pages":"Pages 86-94"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143471421","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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Advances in biomarker sciences and technology
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