Advances in clinical chemistry最新文献

Preeclampsia (PE), a pregnancy-related syndrome, has motivated extensive research to understand its pathophysiology and develop early diagnostic methods. 'Omic' technologies, focusing on genes, mRNA, proteins, and metabolites, have revolutionized biological system studies. Urine emerges as an ideal non-invasive specimen for omics analysis, offering accessibility, easy collection, and stability, making it valuable for identifying biomarkers. A comprehensive exploration of urinary omics in preeclampsia is discussed in this review. Proteomic studies identified biomarkers such as SERPINA-1 and uromodulin, showing promise for early diagnosis and severity assessment. Metabolomic analyses revealed alterations in metabolites like glycine and hippurate, providing insights into molecular mechanisms underlying PE. Challenges include methodological inconsistencies and the need for standardized protocols. Urinary omics technologies have significantly advanced our understanding of PE pathophysiology and hold promise for improved diagnosis and management. Biomarkers identified through these approaches offer potential for early detection, severity stratification, and elucidation of underlying mechanisms.

Periodontitis is the infectious-inflammatory disease of tooth-supporting tissues. Periodontal treatment, either non-surgical or surgical, aims to remove infection, reduce inflammation, eliminate tissue loss, and gain clinical attachment. Clinical and radiographic recordings are widely used and accepted as gold-standard methods in periodontal diagnostics. While these traditional methods allow clinicians to monitor and diagnose periodontitis, they cannot be used to estimate the course of periodontal healing, or predict the disease recurrence or estimate the treatment outcome. Early prediction of the long-term consequences of periodontal treatment would be a crucial and valuable information not only for the clinicians, but also for the patients. Rapid advancements during past few decades boosted the periodontal biomarker studies and various microbe- or host-derived biochemical markers have been suggested as diagnostic biomarkers of periodontitis. Yet, there is no consensus regarding the accuracy of diagnostic biomarkers to monitor treatment response or to predict prognosis. The aim of this chapter will be to describe the healing patterns of periodontal tissues after treatment and present the available evidence on biomarkers that can indicate or predict successful treatment outcomes.

The advent of multiomics has ushered in a new era of cancer research characterized by integrated genomic, transcriptomic and proteomic analysis to unravel the complexities of cancer biology and facilitate the discovery of novel biomarkers. This chapter provides a comprehensive overview of the concept of multiomics, detailing the significant advances in the underlying technologies and their contributions to our understanding of cancer. It delves into the evolution of genomics and transcriptomics, breakthroughs in proteomics, and overarching progress in multiomic methodologies, highlighting their collective impact on cancer biomarker discovery. Furthermore, this chapter explores the computational methods essential for multiomic studies, including clustering techniques for delineating cancer subtypes, strategies for estimating molecular features and activities, and utility of pathway enrichment analyses for interpreting multiomic datasets. Particular focus has been placed on the application of these methods for identifying distinct cancer subtypes, thereby enabling a more personalized approach to cancer treatment. Through a detailed discussion of the scientific principles, technological advancements, and practical applications of multiomics, this chapter aims to underscore the pivotal role of multiomics in advancing cancer research and paving the way for personalized medicine. The insights provided herein not only illuminate the current landscape of cancer biomarker discovery, but also forecast future directions of multiomics research in oncology, advocating for a more integrated and nuanced approach to understanding and combating cancer.

A key factor in the progression of Alzheimer's disease (AD) is internalization of extracellular Tau oligomers (ecTauOs) by neuroglial cells. Aberrant hyperphosphorylation of Tau results in their dissociation from microtubules and formation of toxic intracellular Tau oligomers (icTauOs). These are subsequently released to the extracellular space following neuronal dysfunction and death. Although receptor mediated internalization of these ecTauOs by other neurons, microglia and astrocytes can facilitate elimination, incomplete degradation thereof promotes inflammation, exacerbates pathologic spread and accelerates neurodegeneration. Targeting Tau oligomer degradation pathways, blocking internalization receptors, and mitigating neuroinflammation are proposed as therapeutic strategies to control Tau propagation and toxicity. This review highlights the urgent need for innovative approaches to prevent the spread of Tau pathology, emphasizing its implications for AD and related neurodegenerative diseases.

A dynamic extracellular matrix (ECM) surrounds individual cellular units and fills the intercellular space to provide physical support, structural organization and a medium for signaling mechanisms. This article focuses on the ECM in skeletal muscles, which exhibits a diverse and dynamic protein composition in the endomysium, perimysium and epimysium. The muscle matrisome plays a key role in providing tissue integrity during embryonic myogenesis, muscle repair and myofiber regeneration. Mass spectrometry-based proteomics has been instrumental in the systematic cataloguing of the muscle ECM, including collagens, glycoproteins, proteoglycans, matricellular proteins and adhesion complexes, and the elucidation of their pathophysiological role in neuromuscular disorders.

Cancer therapy is a rapidly evolving and constantly expanding field. Current approaches include surgery, conventional chemotherapy and novel biologic agents as in immunotherapy, that together compose a wide armamentarium. The plethora of choices can, however, be clinically challenging in prescribing the most suitable treatment for any given patient. Fortunately, biomarkers can greatly facilitate the most appropriate selection. In recent years, RNA-based biomarkers have proven most promising. These molecules that range from small noncoding RNAs to protein coding gene transcripts can be valuable in cancer management and especially in cancer therapeutics. Compared to their DNA counterparts which are stable throughout treatment, RNA-biomarkers are dynamic. This allows prediction of success prior to treatment start and can identify alterations in expression that could reflect response. Moreover, improved nucleic acid technology allows RNA to be extracted from practically every biofluid/matrix and evaluated with exceedingly high analytic sensitivity. In addition, samples are largely obtained by minimally invasive procedures and as such can be used serially to assess treatment response real-time. This chapter provides the reader insight on currently known RNA biomarkers, the latest research employing Artificial Intelligence in the identification of such molecules and in clinical decisions driving forward the era of personalized oncology.

Reproductive success consists of a sequential events chronology, starting with the ovum fertilization, implantation of the embryo, placentation, and cellular processes like proliferation, apoptosis, angiogenesis, endocrinology, or metabolic changes, which taken together finally conduct the birth of healthy offspring. Currently, many factors are known that affect the regulation and proper maintenance of pregnancy in humans, domestic animals, or rodents. Among the determinants of reproductive success should be distinguished: the maternal microenvironment, genes, and proteins as well as numerous pregnancy hormones that regulate the most important processes and ensure organism homeostasis. It is well known that white adipose tissue, as the largest endocrine gland in our body, participates in the synthesis and secretion of numerous hormones belonging to the adipokine family, which also may regulate the course of pregnancy. Unfortunately, overweight and obesity lead to the expansion of adipose tissue in the body, and its excess in both women and animals contributes to changes in the synthesis and release of adipokines, which in turn translates into dramatic changes during pregnancy, including those taking place in the organ that is crucial for the proper progress of pregnancy, i.e. the placenta. In this chapter, we are summarizing the current knowledge about levels of adipokines and their role in the placenta, taking into account the physiological and pathological conditions of pregnancy, e.g. gestational diabetes mellitus, preeclampsia, or intrauterine growth restriction in humans, domestic animals, and rodents.

High density lipoprotein (HDL) functions are mostly mediated through a complex proteome, particularly its enzymes. HDL can provide a scaffold for the assembly of several proteins that affect each other's function. HDL particles, particularly small, dense HDL3, are rich in paraoxonase 1 (PON1), which is an important enzyme in the functionality of HDL, so the antioxidant and antiatherogenic properties of HDL are largely attributed to this enzyme. There is an increasing need to represent a valid, reproducible, and reliable method to assay HDL function in routine clinical laboratories. In this context, HDL-associated proteins may be key players; notably PON1 activity (its arylesterase activity) may be a proper candidate because its decreased activity can be considered an important risk factor for HDL dysfunctionality. Of note, automated methods have been developed for the measurement of serum PON1 activity that facilitates its assay in large sample numbers. Arylesterase activity is proposed as a preferred activity among the different activities of PON1 for its assay in epidemiological studies. The binding of PON1 to HDL is critical for the maintenance of its activity and it appears apolipoprotein A-I plays an important role in HDL-PON1 interaction as well as in the biochemical and enzymatic properties of PON1. The interrelationships between HDL, PON1, and HDL's other components are complex and incompletely understood. The purpose of this review is to discuss biochemical and clinical evidence considering the interactions of PON1 with HDL and the role of this enzyme as an appropriate biomarker for HDL function as well as a potential therapeutic target.