Clinica Chimica Acta最新文献

Hepatic fibrosis is a dynamic and progressive condition that can lead to cirrhosis and hepatocellular carcinoma (HCC) if left untreated. Appropriate assessment of the disease progression of fibrosis is critical for early intervention and individualized treatment regimens. Traditional biopsy techniques are invasive and prone to sampling errors, highlighting the need for less invasive predictive techniques. Non-coding RNAs (ncRNAs), including microRNAs (miRNAs), long ncRNAs (lncRNAs), and circular RNAs (circRNAs), have emerged as key regulators of hepatic fibrogenesis and as a possible biomarker for disease staging and prognosis. The emergence of artificial intelligence (AI), particularly machine learning (ML) and deep learning (DL), has revolutionized the comprehensive large-scale analysis of transcriptomic data, enhancing the identification of ncRNA biomarkers and predictive modeling. The AI-based algorithms have been found to be more precise in anticipating fibrosis progression by means of integrating multi-omics data, ncRNA interaction networks, and by improving non-invasive diagnostic tools. This review involves the analysis of AI and ncRNA research in hepatic fibrosis, highlighting recent discoveries, possible challenges, and future opportunities. We address the necessity of standardization of data and clinical validation, as well as discuss the role of AI in identifying biomarkers of ncRNA, predicting the stage of fibrosis and risk stratification. ncRNA analysis with AI has a tremendous potential of transforming the diagnostics and prognostics of hepatic fibrosis, enabling precision hepatology.

In vitro diagnostic measurement procedures (IVD-MPs) are central to clinical decision-making. Consistent interpretation across different IVD-MPs and laboratories depends on equivalence of measurement results. Equivalence of measurement results is achieved by establishing metrological traceability to higher-order references for IVD-MPs. Commutable secondary certified reference materials (CRMs) play an essential role in the metrological traceability of many IVD-MPs. However, due to their instability and inhomogeneity, whole blood (WB) samples are often not suited as source material for preparation of CRMs making secondary CRMs commutable with WB clinical samples (CSs) very challenging to produce. This report describes scenarios for establishing metrological traceability of IVD-MPs intended to measure WB CSs: i) When secondary CRMs are available and can be measured by the WB IVD-MP. If these CRMs are not commutable due to differences in matrix with WB CSs, a noncommutability bias correction may be established and implemented in the calibration hierarchy; ii) When no secondary CRM is available but WB CSs with a value assigned by a reference or other measurement procedure (MP) can be obtained, these WB CSs can be used directly in the place of a commutable secondary CRM in the calibration hierarchy; iii) When no secondary CRM is available and WB CSs with a reference value or other MP assigned value cannot be obtained. The guidance provided supports laboratories, manufacturers and other stakeholders in selecting and implementing appropriate calibration hierarchies, thereby improving the reliability of measurements on WB CSs.

Extracellular vesicles (EVs) have emerged as pivotal mediators of intercellular communication in cardiovascular disease (CVD), influencing inflammation, thrombosis, fibrosis, angiogenesis, and cardiac remodeling through the transfer of proteins, lipids, and RNAs. The presence of EVs in virtually all biofluids and their cargo's close linkage to cellular activation or injury make EVs attractive noninvasive biomarkers for CVD diagnosis, prognosis, and therapy monitoring, with circulating EV signatures reported in myocardial infarction, heart failure, atherosclerosis, and valvular and cardiomyopathic disorders. Parallel advances highlight EVs as cell-free therapeutic agents, recapitulating key paracrine benefits of stem and progenitor cell therapies while avoiding issues of low engraftment and arrhythmogenic risk. In addition to their endogenous activity, both native and engineered EVs are being actively developed as drug delivery platforms, offering biocompatibility, immune stealth, and the capacity to cross biological barriers, with promising data for targeted delivery to the ischemic myocardium and atherosclerotic plaques. Engineering strategies, including surface functionalization, controlled cargo loading, and combination with biomaterials such as hydrogels, can increase cardiac homing, prolong circulation, and improve on-target efficacy. Despite this promise, major hurdles remain: heterogeneity of EV subtypes, lack of standardized isolation and characterization workflows, low production yields, incomplete pharmacokinetic understanding, and unresolved regulatory classification. Addressing these limitations through multi-omics, advanced bioengineering, scalable bioprocessing, and rigorously designed clinical trials will be critical to integrate EV-based biomarkers, therapeutics, and delivery systems into cardiovascular precision medicine.

Cardiovascular disease (CVD) remains the leading cause of morbidity and mortality worldwide, and increasing evidence has demonstrated that inflammation is a central driver of CVD initiation, progression, and clinical complications. While cholesterol-lowering therapies have transformed CVD prevention, a substantial residual inflammatory risk persists even in optimally treated patients, underscoring the need to move beyond lipid-centric paradigms. This narrative review synthesizes current knowledge on key inflammatory biomarkers including high-sensitivity C-reactive protein (hsCRP), interleukin-6 (IL-6), tumor necrosis factor-α (TNF-α), myeloperoxidase (MPO), and emerging multi-omics-derived signatures and examines their mechanistic roles in atherosclerotic plaque formation, endothelial dysfunction, and thromboinflammatory pathways. We highlight evidence from major clinical trials demonstrating that targeted modulation of inflammatory pathways, such as IL-1β inhibition and colchicine therapy, can reduce cardiovascular events independent of lipid lowering. Additionally, we discuss the translational challenges and opportunities in integrating inflammatory biomarkers into risk stratification, precision therapeutics, and clinical decision-making. By highlighting CVD as a lipid-inflammatory disorder, this review emphasizes the potential of inflammation-targeted strategies to complement existing therapies and reshape future cardiovascular prevention.