Aptamer-facilitated biomarker discovery (AptaBiD)

Here we introduce a technology for biomarker discovery in which (i) DNA aptamers to biomarkers differentially expressed on the surfaces of cells being in different states are selected; (ii) aptamers are used to isolate biomarkers from the cells; and (iii) the isolated biomarkers are identified by means of mass spectrometry. The technology is termed aptamer-facilitated biomarker discovery (AptaBiD). AptaBiD was used to discover surface biomarkers that distinguish live mature and immature dendritic cells. We selected in vitro two DNA aptamer pools that specifically bind to mature and immature dendritic cells with a difference in strength of approximately 100 times. The aptamer pools were proven to be highly efficient in flow- and magnetic-bead-assisted separation of mature cells from immature cells. The two aptamer pools were then used to isolate biomarkers from the cells. The subsequent mass spectrometry analysis of the isolated proteins revealed unknown biomarkers of immature and mature dendritic cells.     

Biomarkers for Alzheimer's Disease – An Overview

Alzheimer's disease (AD) is a complex neurodegenerative disorder with a significant global impact on public health. The emergence of atypical clinical phenotypes challenges traditional diagnostic approaches, necessitating a deeper exploration of biomarkers for accurate identification. The US Food and Drug Administration (FDA) classification of biomarkers and their integration into different stages of AD provide a structured framework for their application in research and clinical settings. Within the context of AD drug development, biomarkers are essential for participant selection, target engagement evaluation, and assessment of pathological hallmarks, including Aβ and tau protein abnormalities. The incorporation of nanoparticles with a biodegradable approach introduces innovative strategies to address the complexities of AD. This paper extensively discusses biomarkers associated with synaptic dysfunction, neuroinflammation, and glial activation, recognizing their significance in elucidating disease mechanisms. Common pathologies such as synuclein and TDP-43 further underscore the multifaceted nature of AD. Current biomarkers for AD diagnosis, encompassing cerebral spinal fluid (CSF) biomarkers and various imaging modalities, reflect the ongoing efforts to enhance early detection and monitoring. Intriguingly, novel biomarkers continue to emerge, offering promising avenues for improved understanding and intervention. Current review provides a comprehensive survey of biomarkers for AD, elucidating their diverse roles across different aspects of the disease. By highlighting their contributions to diagnosis, drug development, and mechanistic insights, this overview underscores the importance of biomarker research in the pursuit of effective AD management and treatment strategies.     

Exploring the value of pleural fluid biomarkers for complementary pleural effusion disease examination

ObjectivePleural fluid biomarkers are beneficial for the complementary diagnosis of pleural effusion etiologies. This study focuses on the multidimensional evaluation of deep learning to investigate the pleural effusion biomarkers value and the diagnostic utility of combining these markers, in distinguishing pleural effusion etiologies.MethodsPleural effusion were divided into three groups according to the diagnosis and treatment guidelines: malignant pleural effusion (MPE), parapneumonic effusion (PPE), and congestive heart failure (CHF). First, the value of the biomarker was analyzed by a receiver operating characteristic (ROC) curve. Then by utilizing deep learning and entropy weight method (EWM), the clinical value of biomarkers was computed multidimensionally for complementary diagnosis of pleural effusion diseases.ResultsThere were significant differences in the six biomarkers, TP, ADA, CEA, CYFRA211, NSE, MNC% (p < 0.05) and no significant differences in three physical characteristics including color, transparency, specific gravity and six other biomarkers such as WBC, PNC%, MTC%, pH level, GLU, LDH (p > 0.05) among the three pleural effusion groups. The comprehensive test of pleural fluid biomarkers based on deep learning is of high accuracy. The clinical value of cytomorphology biomarkers WBC, MNC %, PNC %, MTC % was higher among pleural fluid biomarkers.ConclusionThe clinical value of multi-dimensional analysis of biomarkers by deep learning and entropy weight method is different from the ROC curve analysis. It is suggested that during the clinical examination process, more attention should be paid to the cell morphology biomarkers, but the physical properties of the pleural fluid are less clinical significance.     

Phospholipidomic identification of potential plasma biomarkers associated with type 2 diabetes mellitus and diabetic nephropathy

Type 2 diabetes mellitus (T2DM) and its attendant complications, such as diabetic nephropathy (DN), impose a significant societal and economic burden. The investigation of discovering potential biomarkers for T2DM and DN will facilitate the prediction and prevention of diabetes. Phospholipids (PLs) and their metabolisms are closely allied to nosogenesis and aggravation of T2DM and DN. The aim of this study is to characterize the human plasma phospholipids in T2DM and DN to identify potential biomarkers of T2DM and DN. Normal phase liquid chromatography coupled with time of flight mass spectrometry (NPLC-TOF/MS) was applied to the plasma phospholipids metabolic profiling of T2DM and DN. The plasma samples from control (n = 30), T2DM subjects (n = 30), and DN subjects (n = 52) were collected and analyzed. The significant difference in metabolic profiling was observed between healthy control group and DM group as well as between control group and DN group by the help of partial least squares discriminant analysis (PLS-DA). PLS-DA and one-way analysis of variance (ANOVA) were successfully used to screen out potential biomarkers from complex mass spectrometry data. The identification of molecular components of potential biomarkers was performed on Ion trap-MS/MS. An external standard method was applied to quantitative analysis of potential biomarkers. As a result, 18 compounds in 7 PL classes with significant regulation in patients compared with healthy controls were regarded as potential biomarkers for T2DM or DN. Among them, 3 DM-specific biomarkers, 8 DN-specific biomarkers and 7 common biomarkers to DM and DN were identified. Ultimately, 2 novel biomarkers, i.e., PI C18:0/22:6 and SM dC18:0/20:2, can be used to discriminate healthy individuals, T2DM cases and DN cases from each other group.     

北部湾文蛤体内氧化逆境标志物对重金属积累的响应研究

研究目的是通过评价北部湾文蛤（Meretrix meretrix）体内7种氧化逆境标志物对重金属积累的响应, 筛选出能够灵敏指示海域现场重金属污染压力的生物标志物. 2011年秋季, 在北部湾沿海潮间带6个典型站位采集文蛤样品, 测定软组织中重金属含量及鳃、内脏中7种氧化逆境标志物水平, 分析其空间、组织分布特征, 然后对标志物水平与重金属含量进行相关性分析. 结果表明, 文蛤软组织中重金属积累量和氧化逆境标志物水平在站位间存在很大差异. 大多数氧化逆境标志物具有明显的组织差异性, 鳃中的超氧化物歧化酶（SOD）、谷胱甘肽过氧化物酶（GPx）、谷胱甘肽转硫酶（GST）活性以及还原型谷胱甘肽（GSH）、氧化型谷胱甘肽（GSSG）含量均高于内脏, 过氧化氢酶（CAT）在内脏中的活性较高, 而硫代巴比妥酸反应物（TBARs）在两组织中无明显的分布差异. 多种重金属共存的情况下, 鳃的GSH/GSSG与软组织Cd含量有显著相关性; 4种抗氧化酶中, 鳃CAT对Pb具有显著正响应, 内脏CAT、GST对As具有显著负响应. 鳃TBARs分别与Hg、Cd含量呈显著正相关、负相关, 内脏TBARs则与Pb含量呈负相关, 表明这些标志物能够灵敏指示重金属对文蛤抗氧化防御机能的影响, 适于作为评价北部湾重金属污染压力的指标.     

Three aromatic amino acids in gastric juice as potential biomarkers for gastric malignancies

For screening early-stage gastric malignancies, the existing serum biomarkers have limited sensitivity and specificity. Gastric juice biomarkers are scarce and require further investigation. We divided this study on searching potential biomarkers into four parts: (1) detection of differential fluorescence spectrum and peaks in the gastric juice from patients using fluorescence spectroscopy and HPLC, (2) identification and validation of differential peaks using LC/MS and NMR, (3) quantification of potential biomarkers, and (4) establishment of diagnostic detection. The fluorescence intensity (FI), tyrosine, phenylalanine, tryptophan and total protein content were significantly higher in the gastric juice of patients with gastric malignancies (all P<0.01). With all P<0.001, the areas under the receiver operating characteristic curves of the biomarkers were tyrosine, 0.838; phenylalanine, 0.856; and tryptophan, 0.816. At a specificity of 79.4%, the sensitivity for gastric malignancy detection with phenylalanine was 87.9% only. Aromatic amino acids in gastric juices could be used as potential diagnostic biomarkers to screen gastric malignancies. It is a less-invasive and economical method compared to gastric biopsy.     

Mass spectrometry in biodefense

Potential agents for biological attacks include both microorganisms and toxins. In mass spectrometry (MS), rapid identification of potential bioagents is achieved by detecting the masses of unique biomarkers, correlated to each agent. Currently, proteins are the most reliable biomarkers for detection and characterization of both microorganisms and toxins, and MS-based proteomics is particularly well suited for biodefense applications. Confident identification of an organism can be achieved by top-down proteomics following identification of individual protein biomarkers from their tandem mass spectra. In bottom-up proteomics, rapid digestion of intact protein biomarkers is again followed by MS/MS to provide unambiguous bioagent identification and characterization. Bioinformatics obviates the need for culturing and rigorous control of experimental variables to create and use MS fingerprint libraries for various classes of bioweapons. For specific applications, MS methods, instruments and algorithms have also been developed for identification based on biomarkers other than proteins and peptides.     

Enzymatic Methods for Salivary Biomarkers Detection: Overview and Current Challenges

Early detection is a key factor in patient fate. Currently, multiple biomolecules have been recognized as biomarkers. Nevertheless, their identification is only the starting line on the way to their implementation in disease diagnosis. Although blood is the biofluid par excellence for the quantification of biomarkers, its extraction is uncomfortable and painful for many patients. In this sense, there is a gap in which saliva emerges as a non-invasive and valuable source of information, as it contains many of the biomarkers found in blood. Recent technological advances have made it possible to detect and quantify biomarkers in saliva samples. However, there are opportunity areas in terms of cost and complexity, which could be solved using simpler methodologies such as those based on enzymes. Many reviews have focused on presenting the state-of-the-art in identifying biomarkers in saliva samples. However, just a few of them provide critical analysis of technical elements for biomarker quantification in enzymatic methods for large-scale clinical applications. Thus, this review proposes enzymatic assays as a cost-effective alternative to overcome the limitations of current methods for the quantification of biomarkers in saliva, highlighting the technical and operational considerations necessary for sampling, method development, optimization, and validation.     

Simultaneous Quantification of Multiple Cancer Biomarkers in Blood Samples through DNA‐Assisted Nanopore Sensing

Protein biomarkers in blood have been widely used in the early diagnosis of disease. However, simultaneous detection of many biomarkers in a single sample remains challenging. Herein, we show that the combination of a sandwich assay and DNA‐assisted nanopore sensing could unambiguously identify and quantify several antigens in a mixture. We use five barcode DNAs to label different gold nanoparticles that can selectively bind specific antigens. After the completion of the sandwich assay, barcode DNAs are released and subject to nanopore translocation tests. The distinct current signatures generated by each barcode DNA allow simultaneous quantification of biomarkers at picomolar level in clinical samples. This approach would be very useful for accurate and multiplexed quantification of cancer‐associated biomarkers within a very small sample volume, which is critical for non‐invasive early diagnosis of cancer.     

超高效液相色谱-串联质谱法鉴定猪肉的特异性肽生物标志物

以亲缘关系较近的猪、牛和羊3个物种的肌肉组织为研究对象,采用超高效液相色谱-串联质谱(UPLC-MS),筛选并确认了猪物种肉特异性肽生物标志物.3种纯肉样品经蛋白质提取、胰蛋白酶消化和UPLC-TripleTOF-MS分离鉴定,得到的总离子流图谱(TIC)与Uniprot蛋白质数据库对比分析,筛选出3个物种肉的3种高丰度同源蛋白和8种潜在的肽生物标志物;潜在的肽生物标志物经QTRAP-MS质谱的多反应模式(MRM)分析,最终确认了猪物种肉的5种肽生物标志物,其中3种肽生物标志物未见文献报道.     

Ovarian Cancer: Biomarker Proteomic Diagnosis in Progress

Ovarian cancer lacks clear syndromes at an early stage and could result in serious problem in woman's health status. The current diagnostic approach relies on physical examination, ultrasound examination, and blood test for CA125. These approaches could not diagnose early stage ovarian cancer with high sensitivity and specificity. The present paper reviewed the efforts in screening the proteomic biomarkers for ovarian cancer. The sources of biomarkers were discussed. Then, the current techniques in proteomics were introduced. Finally, the biomarkers for ovarian cancer were summarized.     

基于人工智能技术的烟气暴露大鼠代谢生物标志物筛选方法研究

该研究将主成分分析、偏最小二乘判别分析等多元统计分析方法用于烟草血浆、尿液和肺组织代谢组学数据的分析,以揭示暴露于不同烟气中大鼠血浆、尿液和肺组织中内源性生物标志物的整体变化情况,筛选潜在生物标志物;将血样、尿样和肺组织代谢轮廓谱分析得到的生物标志物进行整合,运用神经模糊网络模型对标志物进行缩减,并用人工神经网络评价模型预测能力,确定烟气暴露不同时间(7,14,30 d)以及不同烟气暴露对大鼠内源性代谢物变化影响"因果效应"密切相关的关键生物标志物群,明确不同烟气对大鼠机体损伤机制的异同。     

Nanomaterials‐based Electrochemical Sensing of Cardiac Biomarkers for Acute Myocardial Infarction: Recent Progress

The development of the methods for early and accurate diagnosis of acute myocardial infarction are needed to facilitate immediate treatment of patients. One of the ways to achieve that is the detection of cardiac biomarkers for myocardial infarction, such as thrombin, cardiac troponins (I and T), myoglobin, etc. Nanotechnology has played an important role in the development of sensitive and efficient electrochemical sensors for cardiac biomarkers. In this review, we discuss recent progress on nanomaterial‐based electrochemical sensing of various cardiac biomarkers for acute myocardial infarction.     

Proteomic biomarker discovery: it's more than just mass spectrometry

The previous decade witnessed an enormous number of studies with the singular goal of identifying protein biomarkers for diseases such as cancer. A large majority of these studies have focused on comparative studies of serum or plasma obtained from disease-affected and control patients. In these studies, proteins identified in the samples using MS were compared with the hope that differences between samples would reveal useful biomarkers. Unfortunately, finding clinically relevant biomarkers has often been elusive and frustrating. As with most research efforts, both successes and failures, much has been learned about what strategies work and which do not. Part of the problem can be attributed to underestimating the effort required to discover novel biomarkers and depending too heavily on MS analysis of peripheral blood samples. Fortunately, the future for biomarker discovery still appears bright. MS technology continues to increase in sensitivity, throughput, and accuracy while novel types of samples and clever experimental designs coupled with innovative bioinformatics will make this vision of routine biomarker discovery a reality. To achieve ultimate success is going to require concomitant application of a number of different technologies, all providing the information necessary for discovering and validating clinically useful biomarkers.     

Characterization of ribosomal proteins as biomarkers for matrix-assisted laser desorption/ionization mass spectral identification of Lactobacillus plantarum

For rapid identification of bacteria by matrix-assisted laser desorption/ionization mass spectrometry (MALDI-MS), a bioinformatics approach using ribosomal subunit proteins as biomarkers has been proposed. This method compares the observed masses for biomarkers with calculated masses as predicted from the amino acid sequences registered on protein databases. To evaluate this approach, the expressed ribosomal proteins of a genome-sequenced bacterium, Lactobacillus plantarum NCIMB 8826, were characterized as a model sample. The protein expression of 42 ribosomal subunit proteins, together with 10 ribosome-associated proteins in the isolated ribosome fraction, was confirmed through two-dimensional gel electrophoresis combined with peptide mass fingerprinting. The observed masses of the proteins in the isolated ribosome fraction were then determined by MALDI-MS. We preliminarily selected 44 biomarkers whose observed masses were matched with the calculated masses predicted from the amino acid sequence registered in the protein databases by considering N-terminal methionine loss only. Of these, the finally selected reliable biomarkers were 34 proteins including 31 ribosomal subunit proteins and 3 ribosome-associated proteins that could be observed in the MALDI mass spectra of the cell lysate sample. These biomarkers were usable in MALDI-MS characterization of two industrial L. plantarum cultures.     

A Hydrogel Microneedle Assay Combined with Nucleic Acid Probes for On-Site Detection of Small Molecules and Proteins

Point-of-care testing (POCT) of clinical biomarkers is critical to health monitoring and timely treatment, yet biosensing assays capable of detecting biomarkers without the need for costly external equipment and reagents are limited. Blood-based assays are, specifically, challenging as blood collection is invasive and follow-upprocessing is required. Here, we report a versatile assay that employs hydrogel microneedles (HMNs) to extract interstitial fluid (ISF), in a minimally invasive manner integrated with graphene oxide-nucleic acid (GO.NA)-based fluorescence biosensor to sense the biomarkers of interest in situ. The HMN-GO.NA assay is supplemented with a portable detector, enabling a complete POCT procedure. Our system could successfully measure four clinically important biomarkers (glucose, uric acid (UA), insulin, and serotonin) ex vivo, in addition, to accurately detecting glucose and UA in vivo.     

应用代谢轮廓区分不同致病性副溶血性弧菌

基于tdh, trh和tlh 3个基因区分了不同的致病性副溶血性弧菌. 采用液相色谱(LC)和气相色谱-质谱联用(GC-MS)技术获得不同的致病性副溶血性弧菌的代谢轮廓, 并将其用于区分不同的致病性副溶血性弧菌; 同时以肠杆菌基因间重复共有序列聚合酶链反应技术(ERIC-PCR)及DNA重复序列PCR技术(REP-PCR)为对照, 采用NTsys2.10e软件计算所得结果的相似系数, 并对气相色谱-质谱联用结果进行解析. 结果表明, 根据所得代谢轮廓可以很好地区分不同的致病性副溶血性弧菌; 对气相色谱-质谱联用分析结果解析发现了不同致病性菌株的潜在生物标志物: tdh⁺, trh^-, tlh⁺菌株3种, tdh^-, trh⁺, tlh⁺菌株2种, tdh^-, trh^-, tlh⁺菌株 3种.     

Recent advances in capillary electrophoresis for biomarker discovery

The use of non-invasive methods for detecting biomarkers opens a new era in patient care, since clinical investigators have long been searching for accurate and reproducible measurements of putative biomarkers. There are many factors which make this research challenging, beginning with lack of standardization of sample collection and continuing through the entire analytical procedure. Among the variety of methods so far used for biomarker screening, capillary electrophoresis represents a robust, reliable, and widely used analytical tool. This review will focus on recent applications of CE to the analysis of body fluids and tissues for identification of biomarkers.