Advances in clinical chemistry最新文献

Autophagy is a complex and highly regulated cellular process essential for maintaining homeostasis in adipose tissue by modulating adipocyte differentiation, lipogenesis, and lipolysis. Although lipids are primarily known as energy-storage compounds, they also play a role in triglyceride transport, intracellular communication via steroid hormones, organ protection, and thermoregulation. Lipid metabolism relies on the dynamic balance between synthesis, storage and degradation of key lipid classes such as: triglycerides, sterols and phospholipids. Among the pathways controlling lipid metabolism, autophagy plays a critical role by facilitating lipid degradation via enzymatic lipolysis and lipophagy. The latter emerges as a central mechanism in selectively degrading lipid droplets (LD), thus preventing lipid overload and lipotoxicity. Dysregulation of autophagy contributes to the onset and progression of various metabolic disorders, including obesity, non-alcoholic fatty liver disease, and lysosomal storage diseases. This review provides a comprehensive overview of the molecular mechanisms underpinning autophagy, its role in lipid metabolism, and its pathological relevance in lipid-associated disorders, offering insights into potential therapeutic strategies targeting autophagic pathways for restoring lipid balance.

Nanopore sensing has developed as a revolutionary analytical tool in clinical chemistry, which facilitates fast, label-free analysis of biomolecules from nucleic acids through proteins and small metabolites. Its potential for converting molecular interaction into measurable ionic current fingerprints makes real-time, high-resolution analysis of clinically relevant targets possible in complex biological matrices. In this chapter, we discuss the changing scene of signal processing methods that augment the diagnostic potential of nanopore platforms. The focus areas are reduction of noise, extraction of features, and integration of machine learning for precise biomarker identification under physiologically noisy environments. The chapter also mentions advances in real-time processing essential for point-of-care diagnostics, such as the adoption of edge AI and application-specific integrated circuits (ASICs). Finally, we present the future application of quantum computing and multimodal sensing in pushing nanopore-based clinical diagnostics forward.

Cholangiocarcinoma (CCA), a highly heterogeneous malignancy of the biliary tract, has emerged as a global health concern due to its rising incidence and poor prognosis. Its pathogenesis is shaped by complex interactions between environmental, infectious, and genetic factors, leading to dysregulation of key cellular pathways and molecular alterations. This review provides an in-depth analysis of the current understanding of CCA oncogenesis, focusing on the anatomical and histological subtypes, cellular sources, and the intricate genetic and epigenetic landscape that drives tumor initiation and progression. Key oncogenic alterations include mutations in KRAS, BRAF, IDH1/2, and PIK3CA, alongside inactivation of tumor suppressors such as TP53, CDKN2A, ARID1A, SMAD4, and BAP1. The dysregulation of core signaling pathways, RTK/PI3K/AKT/mTOR, MAPK/ERK, Wnt/β-catenin, TGF-β, Notch, and Hedgehog, is explored in detail, highlighting their roles in cell proliferation, survival, and tumor progression. Furthermore, the review examines the contribution of chronic inflammation, liver fluke infections, viral hepatitis, and environmental carcinogens in promoting genomic instability and malignant transformation. A global understanding of these molecular mechanisms is essential for advancing precision medicine in CCA and guiding the development of targeted therapies.

Inflammation and oxidative stress play a critical pathophysiological role in the onset and the progression of a wide range of chronic disease states that impose a significant public health burden worldwide, e.g., atherosclerosis, cardiovascular disease, rheumatic diseases, and dementia. Available biomarkers of inflammation and oxidative stress, e.g., C-reactive protein, tumor necrosis factor-alpha, and interleukins, are characterized by relatively poor specificity, significant inter-individual variability, and limited ability to capture the upstream cellular and molecular mechanisms involved in the dysregulation of inflammatory pathways and redox balance. Another biomarker, neopterin, a 2-amino-4-hydroxy-6-(D-erythro-1',2',3'-trihydroxypropyl)-pteridine discovered in the 1960s, has been increasingly studied in various disease states characterized by excess cellular immune response, inflammation, and oxidative stress. This review article initially discusses the complex interplay between inflammation and oxidative stress and atherosclerosis, cardiovascular disease, rheumatic diseases, and dementia. Then, it describes the limitations of current biomarkers, the evidence supporting the role of neopterin as a biomarker of dysregulated inflammation and redox balance, and areas for future research.

MicroRNAs (miRNAs) are experiencing increased interest for their potential as biomarkers and therapeutic agents and targets. In this review, we discuss the various advances in miRNAs detection from sample collection to result interpretation. Emphasis is placed on the necessity for standardized protocols in both collection and analysis to ensure reproducibility and comparability of results across studies. The challenges associated with miRNA detection stem from their small size, low concentration in biofluids, and complexity of their various transport forms. Conventional methods for miRNA measurement, including RT-qPCR, digital PCR, microarrays, and sequencing, are detailed. Others, such as isothermal amplification, have recently been developed to improve sensitivity, specificity, and multiplexing capability. These new methods have contributed to the development of point-of-care testing for near patient miRNA quantification. Furthermore, the evolution of artificial intelligence will enable detailed and more comprehensive analysis of big data thus facilitating miRNA biomarker discovery. Artificial intelligence can also enable the integration of this data with other omics approaches such as transcriptomics and proteomics as well as clinical and imaging data for improved patient management as part of personalized medicine.

Endocrine-disrupting chemicals (EDCs) are external agents that interfere with normal hormone production, release, movement, attachment, activity, and removal. EDCs operate through various mechanisms, including receptor binding, interference with hormone production pathways, modification of hormone-transporting proteins, or disturbance of hormonal metabolic processes. Evidence links EDCs to several health conditions, including insulin resistance, type 2 diabetes mellitus, obesity, and metabolic syndrome. Fortunately, the field of biomarker discovery for metabolic and endocrine disorders is undergoing rapid transformation, driven by advancements in metabolomics, multi-omics integration, and artificial intelligence (AI)-enhanced diagnostics. Cutting-edge analytical techniques, such as mass spectrometry, nuclear magnetic resonance spectroscopy, and next-generation sequencing, have facilitated the identification of novel biomarkers for early disease detection, prognosis, and treatment monitoring. Metabolomics has played a crucial role in unraveling disease mechanisms, identifying biochemical signatures of metabolic dysfunctions, and bridging traditional and modern medical approaches. However, challenges remain in clinical validation, reproducibility, and specificity of these biomarkers. The incorporation of multi-omics technologies such as genomics, proteomics, and transcriptomic has provided a more comprehensive view of metabolic dysregulation. Future advancements in machine learning, AI-driven data analytics, and single-cell omics are expected to revolutionize precision medicine by enabling personalized treatment strategies and improving clinical outcomes. As biomarker research continues to evolve, the convergence of high-throughput technologies, computational approaches, and clinical validation efforts will be critical in ensuring their translation into routine medical practice.

Advancements in clinical chemistry have major implications in terms of public health, prompting many clinicians to seek out chemical information to aid in diagnoses and treatments. While mass spectrometry (MS) and hyphenated-MS techniques such as LC-MS or tandem MS/MS have long been the analytical methods of choice for many clinical applications, these methods routinely demonstrate difficulty in differentiating between isomeric forms in complex matrices. Consequently, ion mobility spectrometry (IM), which differentiates molecules on the basis of size, shape, and charge, has demonstrated unique advantages in the broad application of stand-alone IM and hyphenated IM instruments towards clinical challenges. Here, we highlight representative IM applications and approaches and describe contemporary commercial offerings of IM technology and how these can be, or are currently being, applied to the field of clinical chemistry.

This chapter reviews the emerging role of metabolomics in nephrotic syndrome (NS), a kidney disorder characterized by proteinuria, hypoalbuminemia, edema, and hyperlipidemia. Metabolomics provides valuable insights into the complex metabolic changes associated with NS, including disruptions in lipid, amino acid, and energy metabolism and oxidative stress markers. Through untargeted and targeted approaches, metabolomics enables the discovery of novel potential biomarkers that could enhance diagnosis, monitor disease progression, and personalize treatment strategies. Despite challenges such as methodological variability and the need for extensive computational resources, advancements in metabolomics technology and data integration are poised to improve our understanding of NS. Integrating metabolomics with genomics and proteomics may enable a comprehensive molecular profile of NS, offering new opportunities for precision medicine and improved patient outcomes.