Aplastic anemia (AA) is a rare hematological disorder characterized by bone marrow failure and pancytopenia, with viral infections implicated as a significant acquired cause. This scoping review evaluates the pathophysiological mechanisms linking viral infections to AA and assesses current therapeutic strategies for managing virus-associated cases. Following PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines, a scoping review was conducted from April to May 2024 using PubMed, Google Scholar, Web of Science, and ScienceDirect. Eligible studies included those reporting viral infections associated with AA, diagnostic approaches, or therapeutic interventions. Data were extracted on study design, type of virus, diagnostic methods, and treatment strategies. A total of 48 studies were included, identifying parvovirus B19, Epstein-Barr virus, cytomegalovirus, human immunodeficiency virus, hepatitis viruses, human T-cell lymphotropic virus type 1, and SARS-CoV-2 as key pathogens. Rare cases of post-vaccination complications were also reported. Identified mechanisms include direct bone marrow suppression, immune-mediated destruction, and viral reactivation during immunosuppression. Antiviral therapies and hematopoietic stem cell transplantation constitute the primary treatments, and virological monitoring is essential to prevent reactivation. Viral infections play a significant role in AA etiology and progression. Tailored antiviral regimens and proactive virological monitoring are essential for optimizing outcomes. Future research should focus on longitudinal studies to clarify causal relationships and refine therapeutic approaches.

Proteases are central to cancer pathobiology and attractive targets for noninvasive diagnosis. Diagnostic use of urinary proteases has developed along two methodologically distinct streams: measurement of protein abundance by immunoassay and increasingly by mass spectrometry, and direct measurement of enzymatic activity using peptide substrates. However, no common terminology unifies these approaches. This narrative review synthesizes evidence published between 2016 and 2026, concentrating on urine and, for the transferability of the methodology, on fecal water and serum. Saliva and seminal fluid are surveyed only to show that protease biomarkers there are measured by abundance alone. It adopts, from its existing use in nephrology, the term proteasuria as a unifying construct for the urinary protease diagnostic field, proposing a wider scope than the pathophysiological sense in which it was introduced. Two operational subtypes are defined: concentration-based and catalytic proteasuria. The distinction rests on the parameter measured rather than on the detection chemistry. Within the catalytic subtype single-enzyme detection using a selective substrate is distinguished from disease profiling using a multi-substrate panel. Evidence was identified by a PubMed search supplemented by citation and semantic search and screened by a single reviewer without protocol registration or formal risk-of-bias assessment. Concentration-based proteasuria is the clinically established stream, yet no urinary analyte has displaced cystoscopy, and approved assays remain insufficient as stand-alone rule-out or triage tests. The catalytic stream rests mainly on small single-center case-control studies reporting activity qualitatively; only one reports formal diagnostic performance, with a single elastase-specific substrate in 12 cases and 24 controls, and no head-to-head comparison with concentration-based assays has been published. Analytical validity is partial: substrate selectivity is established by combinatorial screening and confirmed with inhibitor and orthogonal controls, but precision, limits of quantification in urine and inter-laboratory reproducibility are not systematically reported, and no reference material or external quality-assessment scheme exists for this assay class. A largely independent exogenous stream of synthetic urinary biomarkers and activity-based nanosensors reports high accuracy, but all published diagnostic-accuracy data derive from animal models. On a translational-readiness scale the urinary catalytic stream is proof-of-concept, research-grade work, and any realistic target is therefore not cystoscopy but the margin at which established assays fail, principally low-grade noninvasive disease. The same reasoning identifies the detection of advanced adenoma, for which the fecal immunochemical test performs poorly, as the concrete benchmark for extending the framework to fecal protease profiling in colorectal cancer.

Heart failure (HF) is a heterogeneous clinical syndrome that has pathophysiologic manifestations of hemodynamic abnormalities, downstream neurohumoral activation and inflammation, myocardial fibrosis, metabolic dysfunction, and cardiomyocyte injury. Relevant to the issue of B-type natriuretic peptide (BNP) and N-terminal pro-B-type natriuretic peptide (NT-proBNP) as clinically established biomarkers, their interpretation may be influenced by confounding factors such as obesity, renal dysfunction, atrial fibrillation, and HF phenotype. Recent data highlight a broad spectrum of non-coding RNA (ncRNA) types, including microRNAs (miRNAs), long non-coding RNAs (lncRNAs), and circular RNAs (circRNAs), which can be detected in circulating samples and participate in regulatory networks associated with myocardial remodeling, supporting their investigation as potential complementary biomarkers. A structured literature search of PubMed/MEDLINE, Web of Science Core Collection, and Scopus from database inception to August 6, 2026, was conducted according to predefined eligibility, study-selection, and evidence-appraisal criteria. Here, HF-associated ncRNAs are assessed from perspectives including biological relevance, pre-analytical variables, detection platforms, quality control (QC), analytical and clinical validation, clinical-use scenarios, and implementation pathways. The available evidence so far rather supports a role in which ncRNAs serve as adjunctive rather than stand-alone biomarkers. The field is highlighted by the fact that presently no ncRNA-based assay is ready for routine clinical use in HF. Harmonized pre-analytical and analytical workflows, independent multicenter validation, predefined clinical-use scenarios, as well as demonstration of incremental clinical value and cost-effectiveness beyond established biomarkers, imaging, and clinical variables will be needed for future progress.

Urinalysis comprises test strip examination of physicochemical parameters together with microscopic or automated analysis of formed urine elements. The test strip covers pH, relative density (specific gravity, SG), protein, glucose, ketone bodies, urobilinogen, bilirubin, blood, and leukocyte esterase. In selected settings, it is supplemented by quantitative indices such as the urine protein-to-creatinine and albumin-to-creatinine ratios. This review evaluates age- and sex-specific reference intervals and clinical decision limits for urinalysis parameters in children, integrating developmental renal physiology with preanalytical and analytical determinants of test performance. A search of PubMed identified 148 publications published between 1982 and 2026. Pediatric urine composition is shaped by immature glomerular and tubular function, changing muscle mass, hormonal changes during puberty, and anatomical differences between sexes. Preanalytical variables, particularly the collection technique, materially affect reliability contamination rates range from approximately 1% for suprapubic aspiration to 26-80% for adhesive bag specimens. Automated systems improve reproducibility and require smaller specimen volumes, but pediatric validation data remain limited, and reference limits are not transferable between analyzer generations. Critically, values reported in the literature comprise both reference limits derived from healthy populations and clinical decision limits selected for diagnostic use; these are not interchangeable, and several widely cited pediatric values originate from screen-positive or hospitalized cohorts. Laboratories serving pediatric populations should report reference intervals together with the method, analyzer generation, and source population; define diagnostic decision limits locally rather than adopting adult-derived defaults; and confirm any positive bag-collected specimen by a less contamination-prone method. No reference intervals for formed elements have been published for preterm neonates, which remains the most urgent gap.

Continuous glucose monitoring (CGM) has revolutionized the landscape of diabetes management both in adults and pediatrics. CGM devices are now standard-of-care for pediatric diabetes management and are increasingly used in hospitalized patients. In the outpatient setting, studies show increasing analytical and clinical performance of CGM devices with improved diabetes outcomes. However, there are several inpatient factors that impact the accuracy of CGM devices relevant to hospitalized patients. The pediatric population is also the most vulnerable, and changes in management based on a false CGM reading can cause adverse events. Therefore, it is imperative to critically assess the accuracy and clinical performance of CGM devices in hospitalized pediatric patients. In this review, we assess the literature and summarize recent studies on the accuracy of CGM devices in pediatric inpatients, discuss regulatory requirements for their use, safety considerations, and procedures for confirmatory testing using standard-of-care glucose tests.

Laboratory medicine plays an important role in clinical decision-making. The timely notification of results requiring medical attention is therefore essential to ensure patient safety. Since its introduction, the concept of critical result notification has expanded to include both critical decision limits and significant abnormal results. To avoid overlooking such results, most laboratories use alert lists consisting of predefined thresholds programmed into the laboratory information system. Despite widespread adoption, considerable variability persists in alert lists used across laboratories. Differences in patient populations and healthcare settings partly explain variation in alert thresholds, acceptable notification delays, communication modalities, and the designated recipients of notifications. Telephone communication remains the predominant modality for notifying critical results, although electronic medical record-based alerts are increasingly being implemented. Persistent challenges include notification fatigue, communication delays, and inconsistent follow-up actions. Quality indicators should therefore address not only the timeliness of notification but also the timeliness of the clinical response. Looking ahead, the integration of structured clinical and laboratory data, together with emerging artificial intelligence tools, holds promise for developing more personalized and clinically meaningful notification policies.

Metabolic dysfunction-associated steatotic liver disease (MASLD) is the leading cause of chronic liver disease worldwide and includes a spectrum ranging from simple steatosis to metabolic dysfunction-associated steatohepatitis (MASH), advanced fibrosis, and cirrhosis. Given the heterogeneous nature of disease progression and the central role of fibrosis predicting local or systemic clinical outcomes (e.g. liver cirrhosis, hepatocarcinoma, cardiovascular diseases), early identification and longitudinal monitoring of individuals at risk is crucial. However, current diagnostic tools present important limitations, particularly across diverse metabolic profiles such as individuals living with obesity or type 2 diabetes. Therefore, this review provides a comprehensive overview of current diagnostic and monitoring strategies for MASLD and MASH, including routine biochemical markers, noninvasive steatosis and fibrosis scores, imaging modalities, and liver biopsy. Multimodal and individualized diagnostic approaches integrating clinical, metabolic, imaging, and molecular data appear essential to improve early detection, refine longitudinal monitoring, and support precision-based management of MASLD.

The laboratory information system (LIS) is an indispensable component of the modern clinical laboratory, underpinning day-to-day operations and playing a critical role in delivering quality patient care. Clinical laboratories are challenged to introduce new technologies within steady-state staffing, including navigating the complex process of implementing a new LIS. In recent years, a growing number of laboratories are transitioning to systems integrated within the EHR. The complexity increases further when LIS and EHR go live simultaneously, a scenario that is particularly challenging in integrated health systems. This review synthesizes the current literature as well as real-world experiences and lessons from the planning, build/validation, rollout readiness, stabilization, and optimization phases to offer practical strategies and operational insights for clinical laboratories undertaking LIS implementation, with particular attention to the unique considerations of concurrent EHR/LIS rollouts. Opportunities for future developments are also highlighted.

Chronic inflammation is now recognized as an important factor linking tissue damage to the development of cancer. Epidemiological studies have suggested that 20%-25% of cancers are related to chronic inflammation, as exemplified by chronic hepatitis B infection inducing hepatocellular carcinoma (HCC) or Helicobacter pylori infection inducing the development of gastric cancer. In chronic inflammation, an overexpression of signaling pathways such as NF-κB, JAK/STAT3, MAPK and NLRP3 at the molecular level leads to continuous upregulation of the secretion of pro-inflammatory cytokines and immune-metabolic reprogramming. These processes result in DNA damage, genetic mutations and epigenetic changes, promoting the initiation and progression of tumors. Interactions between pro-inflammatory cytokines and different types of cellular constituents reshape the tumor microenvironment (TME), leading to fibrosis, chaotic angiogenesis, and immune suppression-favoring further immune escape by tumors and perpetuating cancer stemness. Furthermore, through epigenetic as well as other molecular mechanisms, chronic inflammation fosters tumor drug resistance. So far, anti-inflammatory agents (e.g. anti-inflammatory drugs and natural bioactive compounds) as well as forms of dietary intervention have been associated with a reduced risk of cancer development in selected inflammation-associated settings, emphasizing that inflammation control may contribute to cancer prevention when supported by appropriate patient selection and clinical validation. In addition, new anti-inflammatory drug interventions targeting the IL-6/STAT3, NF-κB and NLRP3 pathways have shown good prospects for inhibiting tumors while modulating the immune microenvironment. Inflammatory biomarkers and epigenetic inflammation scores may support cancer risk stratification, early detection, prognostic assessment and treatment monitoring; however, most should be interpreted as complementary laboratory tools rather than stand-alone diagnostic tests, and their clinical implementation requires standardized analytical platforms, harmonized cutoff values, demonstration of incremental utility and prospective validation.

Traumatic brain injury (TBI) is a major cause of mortality and long-term disability, and mild TBI accounts for more than 90% of hospital-presenting cases. Diagnostic management of mild TBI relies on clinical assessment and neuroimaging, with non-contrast head computed tomography (CT) representing the diagnostic standard for detecting acute intracranial abnormalities. Nevertheless, approximately 90% of CT scans performed in mild TBI show no intracranial injury. Blood biomarkers have emerged as objective measures of brain injury for identifying patients at low probability of CT-detectable injury. We conducted a systematic review and diagnostic test accuracy meta-analysis to evaluate blood biomarkers measured using automated analytical platforms for excluding CT-detectable intracranial abnormalities in adults with mild TBI. The review was conducted according to PRISMA-DTA guidelines and registered in PROSPERO (CRD420251087456). PubMed/MEDLINE, Embase, Web of Science, Cochrane Library, Scopus, Science Citation Index and Google Scholar were searched for studies published from January 2010 to December 2025. Eligible studies enrolled adults with mild TBI (Glasgow Coma Scale score 13-15), biomarkers measured within 24 h of injury using commercially available automated assays and non-contrast head CT used as the reference standard. Methodological quality was assessed using QUADAS-2. Pooled sensitivity and specificity were estimated using bivariate random-effects models, and heterogeneity was explored using meta-regression. Thirty studies including more than 15,000 patients were included. Eligible evidence was available only for S100B, glial fibrillary acidic protein (GFAP), ubiquitin C-terminal hydrolase-L1 (UCH-L1), and combined biomarkers GFAP and UCH-L1. No eligible studies evaluating neuron-specific enolase, neurofilament light chain or other biomarkers met the predefined criteria. S100B showed high pooled sensitivity of 92% (95% CI 89-94%) but low specificity of 31% (95% CI 27-35%). GFAP alone showed pooled sensitivity of 89% (95% CI 83-93%) and specificity of 37% (95% CI 27-48%), whereas UCH-L1 alone showed lower sensitivity of 72% (95% CI 62-80%) and specificity of 53% (95% CI 47-59%). The combination of GFAP and UCH-L1 demonstrated the highest pooled sensitivity of 94% (95% CI 92-95%) but lowest specificity of 28% (95% CI 23-33%). Specificity showed substantial heterogeneity across biomarkers. Meta-regression suggested that earlier sampling improved S100B sensitivity, while age was associated with specificity for the combined biomarkers GFAP and UCH-L1. Sensitivity analyses excluding high risk of bias studies did not substantially change the pooled estimates, and Deeks' funnel plot did not indicate significant small-study effects. Blood biomarkers measured using automated platforms showed clinically meaningful rule-out potential for CT-detectable intracranial abnormalities in adults with mild TBI. Specifically, the results of our diagnostic test accuracy meta-analysis support the clinical utility of S100B and the combination of GFAP and UCH-L1 as adjunctive triage tools in acute care settings for identifying adult patients with mild TBI (GCS 13-15) and low probability head CT abnormalities. Their safest and most effective application is likely to be within validated clinical algorithms that account for patient characteristics, timing of sampling, assay-specific cutoffs, high-risk clinical features and local acute care practice.

Colorectal cancer (CRC) remains a leading cause of cancer-related mortality worldwide, driven largely by pronounced molecular heterogeneity and delayed clinical detection. Although high-throughput sequencing technologies have substantially advanced the understanding of CRC biology, their routine clinical implementation remains constrained by high costs, infrastructural requirements, and limited accessibility. This review addresses these translational barriers by systematically synthesizing circulating transcriptomic and proteomic biomarkers within a clinically scalable framework. Particular emphasis is placed on biomolecules detectable using reverse transcription-polymerase chain reaction (RT-PCR) and enzyme-linked immunosorbent assay (ELISA), two widely accessible platforms that underwent extensive global optimization during the COVID-19 pandemic and are readily adaptable to liquid biopsy workflows. Through a stage-resolved analysis, we identify 9 genomic and 7 proteomic biomarkers associated with early-stage (I-II) CRC, alongside 9 genomic and 4 proteomic biomarkers linked to advanced-stage (III-IV) disease progression. Beyond biomarker cataloging, these molecules are integrated with Cancer Hallmark pathways, clinical-stage associations, and available clinical trial evidence to evaluate their biological relevance and translational readiness. In addition, we summarize standardized operating procedure (SOP) considerations and multiplex detection strategies to improve assay reproducibility, scalability, and cross-border clinical implementation. Collectively, this review bridges molecular discovery with clinically deployable laboratory workflows and provides a translational roadmap for the development of affordable, liquid biopsy-based diagnostic strategies aimed at improving CRC detection, patient stratification, longitudinal monitoring, and early therapeutic intervention.

Vitamin D has multiple functions in most organs and tissues of the human body. In addition to its canonical role in bone and mineral metabolism, vitamin D impacts cell growth and differentiation, immunity, glucose homeostasis, cognition, and endocrine pathways. Mounting evidence links vitamin D deficiency to a broad spectrum of diseases including cardiovascular disease (CVD). Considering that both vitamin D deficiency and CVD are highly prevalent conditions, it is important to understand the potential interplay between these two. Observational studies consistently demonstrate an inverse relationship between the inactive prohormone 25-hydroxyvitamin D (25[OH]D) in serum, which represents the body's vitamin D reservoir, and CVD risk. Specifically, lower serum 25-(OH)D levels are associated with a higher risk of CVD events and CVD mortality. Putative mechanisms that mediate the pathophysiologic effects of vitamin D deficiency comprise oxidative stress, systemic inflammation, activation of the renin-angiotensin-aldosterone system, endothelial dysfunction, hypertension, and myocardial fibrosis. However, vitamin D supplementation failed to demonstrate significant CVD-related benefits. Although existing randomized, placebo-controlled supplementation studies yielded neutral results, most of these studies did not specifically target CVD outcomes. Another limitation of previous randomized controlled studies is the application of fixed vitamin D dosing regimens, regardless of the actual serum 25-(OH)D level. Studies that escalate the vitamin D dose until serum 25-(OH)D reaches a prespecified target range are largely lacking. This article reviews the existing literature on the role of vitamin D deficiency in CVD incidence, progression, and mortality.

Despite the obligation imposed by national and international accreditation standards to periodically verify reference intervals against local conditions, this task is performed infrequently in most clinical laboratories due to the perceived complexity relative to the resources available. The CLSI/IFCC C28-A3 standard recommends a simple 20-sample binomial procedure whose statistical power is, however, fundamentally inadequate: it cannot reliably detect intervals that are too wide, requires sample sizes approaching n = 100 to achieve acceptable sensitivity for shifted or too narrow limits, and offers laboratories a false sense of compliance. Fifty years after the foundational conceptual framework for reference intervals was established, a critical and timely reassessment is therefore warranted. This review evaluates the limitations of the CLSI/IFCC 20-sample verification approach and systematically examines methodological advances published since the last comprehensive review in 2018, with emphasis on indirect statistical methods capable of exceeding guideline performance. A structured literature search identified 22 publications meeting inclusion criteria, and about the same number of references were added from the authors' literature collections to ensure complete coverage of the topic, especially for more recent literature that may not yet be fully indexed. Four converging lines of evidence support replacing the binomial procedure with indirect estimation methods applied to routine laboratory data. Two open-source R packages (reflimR and refineR) implement quantitative acceptance criteria based on equivalence limits (EL) and uncertainty margins (UM), respectively. Independent multi-center and multi-method studies confirm robust performance and comparable results of indirect methods over a wide range of analytes. Machine learning techniques, particularly Gaussian mixture deconvolution and regression tree modeling, extend verification capability to datasets with high pathological prevalence or complex biological confounders. Web-based platforms now make these tools accessible without programming expertise. In conclusion, indirect verification methods have reached sufficient methodological maturity for guideline endorsement. A stepwise workflow that includes rapid screening with reflimR, confirmatory analysis with refineR, and machine learning-assisted decomposition for complex cases is proposed as a practical, statistically defensible standard for routine laboratory verification. This workflow has been implemented in a new web tool at trillium.de/VeRIf.

Hospitals are major generators of waste, producing an estimated 5.9 million tons of solid waste annually in the United States alone. This burden contributes to environmental pollution, rising operational costs, and downstream health impacts. While operating rooms, food services, and imaging departments have received increasing scrutiny as waste-intensive sectors, clinical laboratories, despite their high energy use, dependence on single-use plastics, and central role in every diagnostic decision, remain comparatively understudied in sustainability research. In this review, we argue that integrating environmental impact into laboratory practice represents the next evolution of quality in laboratory medicine. We propose a workflow-based framework to evaluate waste generation across the full testing continuum, including test ordering, specimen collection, transport, analysis, post-analytical practices, procurement, and accreditation. Drawing on current evidence, case studies, and emerging sustainability programs, we identify practical intervention points and critical knowledge gaps, including the lack of standardized environmental metrics such as waste-per-test, energy-per-test and water-per-test. By treating environmental performance as measurable, comparable and improvable, clinical laboratories can help lead healthcare's broader transition toward high-value, environmentally responsible care while maintaining diagnostic quality and patient-centered care.

Influenza virus remains a major global health challenge due to its genetic variability and frequent emergence of novel strains. Subtyping is determined by the diverse arrangements of hemagglutinin (HA) and neuraminidase (NA) glycoproteins, with HA frequently serving as the molecular target for diagnostic assays. Conventional detection methods, however, are labor-intensive, require specialized expertise, and often lack adaptability to rapidly evolving viral variants. Aptamer-based biosensing technologies have emerged as promising alternatives. Aptamers, synthetic single-stranded DNA or RNA oligonucleotides generated via the Systematic Evolution of Ligands by Exponential Enrichment (SELEX) process, exhibit exceptional specificity and high binding affinity, enabling precise recognition of viral proteins. This systematic and critical review synthesizes recent advances in aptamer-integrated biosensing platforms, focusing on electrochemical and optical detection strategies for influenza virus. By comparing sensitivity, specificity, operational simplicity, and translational potential, this review highlights that electrochemical platforms are better suited for point-of-care use due to their speed and simpler instrumentation, while optical platforms offer superior sensitivity for reference laboratory settings. A key finding is that despite two decades of research and remarkable analytical sensitivity (reaching fg/mL levels), the vast majority of platforms have not been validated on authentic clinical specimens or compared with gold-standard molecular methods, explaining why none have entered routine diagnostic workflows. The analysis provides evidence-based insights into which biosensing strategies are most suitable for the development of rapid, reliable, and scalable diagnostic tools against influenza virus, thereby informing future research directions and clinical translation.