Pub Date : 2025-01-01DOI: 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.
{"title":"Biomarkers in immunology: Their impact on immune function and response","authors":"Deepika Kaushik , Baojun Xu , Mukul Kumar","doi":"10.1016/j.abst.2025.03.001","DOIUrl":"10.1016/j.abst.2025.03.001","url":null,"abstract":"<div><div>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.</div></div>","PeriodicalId":72080,"journal":{"name":"Advances in biomarker sciences and technology","volume":"7 ","pages":"Pages 95-110"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143610950","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}
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.
{"title":"Review on zebra fish as an alternative animal model for neurological studies","authors":"Khode Aniket Prakash , Darshana Sunil Nagmoti , Manali Sanjayswami Borkar , Hiray Kuldeep Pannalal , Nagaraju Bandaru","doi":"10.1016/j.abst.2025.08.005","DOIUrl":"10.1016/j.abst.2025.08.005","url":null,"abstract":"<div><div>Zebrafish (<em>Danio rerio</em>) 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.</div></div>","PeriodicalId":72080,"journal":{"name":"Advances in biomarker sciences and technology","volume":"7 ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145010287","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-01-01DOI: 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.
{"title":"Bridging traditional risk factors and genetic insights: A review on polygenic risk scores in cardiovascular diseases","authors":"Abhishek Gupta , Komal Shah , Aakansha Shukla","doi":"10.1016/j.abst.2025.09.002","DOIUrl":"10.1016/j.abst.2025.09.002","url":null,"abstract":"<div><div>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.</div></div>","PeriodicalId":72080,"journal":{"name":"Advances in biomarker sciences and technology","volume":"7 ","pages":"Pages 365-377"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145048295","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-01-01DOI: 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.
{"title":"Network-based discovery of autophagy-regulating miRNA signatures in ovarian carcinoma","authors":"A. Shriraksha, V.R. Devaraj","doi":"10.1016/j.abst.2025.10.001","DOIUrl":"10.1016/j.abst.2025.10.001","url":null,"abstract":"<div><div>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.</div></div>","PeriodicalId":72080,"journal":{"name":"Advances in biomarker sciences and technology","volume":"7 ","pages":"Pages 380-393"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145319636","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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。
{"title":"Identification of bipolar disorder related biomarkers, signaling pathways and potential therapeutic compounds based on bioinformatics methods and molecular docking technology","authors":"Basavaraj Vastrad , Shivaling Pattanashetti , Chanabasayya Vastrad","doi":"10.1016/j.abst.2025.08.004","DOIUrl":"10.1016/j.abst.2025.08.004","url":null,"abstract":"<div><div>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.</div></div>","PeriodicalId":72080,"journal":{"name":"Advances in biomarker sciences and technology","volume":"7 ","pages":"Pages 261-319"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144925605","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-01-01DOI: 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.
{"title":"Application of Generative Adversarial Networks on RNASeq data to uncover COVID-19 severity biomarkers","authors":"Yvette K. Kalimumbalo , Rosaline W. Macharia , Peter W. Wagacha","doi":"10.1016/j.abst.2025.01.002","DOIUrl":"10.1016/j.abst.2025.01.002","url":null,"abstract":"<div><h3>Background</h3><div>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.</div></div><div><h3>Methods</h3><div>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.</div></div><div><h3>Results</h3><div>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.</div></div><div><h3>Conclusions</h3><div>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.</div></div>","PeriodicalId":72080,"journal":{"name":"Advances in biomarker sciences and technology","volume":"7 ","pages":"Pages 44-58"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143098984","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}
Pub Date : 2025-01-01DOI: 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.
{"title":"CAR-T and nanotechnology: A comparative Perspective on autoimmune disease and cancer","authors":"A. Pavithra , C. Ragavendran","doi":"10.1016/j.abst.2025.03.002","DOIUrl":"10.1016/j.abst.2025.03.002","url":null,"abstract":"<div><div>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.</div></div>","PeriodicalId":72080,"journal":{"name":"Advances in biomarker sciences and technology","volume":"7 ","pages":"Pages 124-137"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143790815","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}
Pub Date : 2025-01-01DOI: 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.
{"title":"Evaluation of toxicity and antioxidant activities of various crude extracts of leaves and stems of Zygophyllum simplex","authors":"Noha Maher Galal , Salem Said Al Touby , Yahya Bin Abdullah Alrashdi , Mohammad Amzad Hossain","doi":"10.1016/j.abst.2024.11.003","DOIUrl":"10.1016/j.abst.2024.11.003","url":null,"abstract":"<div><div><em>Zygophyllum simplex</em> (<em>Z</em>. <em>simplex</em>) 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 <em>Z. simplex</em> by using a maceration method. The activities of antioxidant and cytotoxic were prepared from aerial crude extracts of <em>Z. simplex</em> 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 <em>Z. simplex</em> 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.</div></div>","PeriodicalId":72080,"journal":{"name":"Advances in biomarker sciences and technology","volume":"7 ","pages":"Pages 1-7"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143099099","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}
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.
{"title":"Unraveling ankylosing spondylitis: Exploring the genetic and immunological factors and latest treatment innovations","authors":"Nilasree Hazra , Sudeshna Sengupta , Dipannita Burman , Jyoti Sekhar Banerjee , Malavika Bhattacharya","doi":"10.1016/j.abst.2024.12.002","DOIUrl":"10.1016/j.abst.2024.12.002","url":null,"abstract":"<div><div>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. <em>Bacteroides coprophilus</em> and <em>Prevotella copri</em> 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.</div></div>","PeriodicalId":72080,"journal":{"name":"Advances in biomarker sciences and technology","volume":"7 ","pages":"Pages 21-27"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143099100","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}
Pub Date : 2025-01-01DOI: 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.
{"title":"Clinical features and predictive model for invasive pulmonary fungal infections in hematologic disorders","authors":"Jun Wang , Xuefeng He , Feng Chen , Xiao Ma , Daxiong Zeng , 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}