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.
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.
Quality indicators (QIs) are widely used in clinical laboratories to support quality management, patient safety, and regulatory compliance. Although regulatory and accreditation frameworks emphasize the use of QIs across the total testing process, published literature has often focused on analytical performance or discipline-specific approaches. Less attention has been given to how QIs are selected, interpreted, and sustained across the full range of clinical pathology subspecialties in routine practice. This critical review examines the role of quality indicators within clinical pathology laboratories, with emphasis on their selection, implementation, interpretation, and integration into quality management systems. Regulatory and accreditation expectations from CLIA, CAP, ISO 15189, and the Joint Commission are used as contextual anchors. We describe a decision-oriented lifecycle for quality indicators that emphasizes risk-based prioritization, feasibility, actionability, and governance integration. Practical examples of quality indicators spanning the pre-analytical, analytical, and post-analytical phases are presented across multiple subspecialties. Approaches to quality indicator data analysis are discussed, with particular focus on longitudinal, within-laboratory monitoring as a practical strategy for identifying meaningful trends and guiding improvement in diverse practice settings. Quality indicators are most effective when used as adaptive management tools rather than static compliance measures. Their value depends on thoughtful selection aligned with patient risk and laboratory scope, consistent interpretation within local context, and sustained linkage to corrective and preventive action. By adopting a discipline-aware, practice-focused approach, clinical pathology laboratories can use quality indicators to support continuous improvement, enhance patient safety, and meet regulatory and accreditation expectations across evolving healthcare environments.
Human preterm premature rupture of membranes (PPROM) refers to the spontaneous rupture of fetal membranes before 37 weeks of gestation and prior to labor. Clinically, the decision to induce labor or continue expectant management must be balanced against the risk of infection, particularly chorioamnionitis. Early diagnosis is critical, as chorioamnionitis is associated with preterm birth and fetal (hypoxia and/or sepsis) and maternal (bacteremia, hemorrhage, and/or death) complications. Currently, no specific blood biomarker is routinely used, with only nonspecific parameters like C-reactive protein (CRP) and white blood cell (WBC) count available. This highlights the need to review and meta-analyze recent studies to summarize new maternal blood biomarkers and improve diagnostic performance. A meta-analysis was performed to assess the prognostic value of some current or new blood biomarkers in predicting histological or clinical chorioamnionitis in women after PPROM. A protocol was designed and registered with PROSPERO (CRD420251058686). Studies were chosen if they included women with PPROM who underwent blood biomarker measurements and histological evaluation of the fetal membranes or clinical assessment of chorioamnionitis. The quality of each study was assessed using the Quality Assessment of Diagnostic Accuracy Studies 2 (QUADAS-2) criteria. Three databases (Medline, Embase, and the Cochrane Central Register of Controlled Trials (CENTRAL)) were consulted. Of the 631 articles screened, 9 were finally selected for inclusion. The overall pooled sensitivity (Se) of CRP, WBC count, procalcitonin (PCT), and neutrophil-lymphocyte ratio (NLR) were respectively 71% (95% CIs of 60%-81%), 73% (95% CIs of 66%-79%), 83% (95% CIs of 68%-94%) and 75% (95% CIs of 68%-81%). The overall pooled specificity (Spe) of CRP, WBC, PCT, and NLR were respectively 75% (95% CIs of 73%-78%), 61% (95% CIs of 52%-63%), 41% (95% CIs of 10%-76%) and 82% (95% CIs of 61%-96). The area under the curve (AUC), obtained from receiver operating characteristic (ROC) curve analysis was 77%, 73%, 74%, and 77% for CRP, WBC count, PCT, and NLR, respectively. Evidently, no single biomarker currently appears capable of accurately predicting the occurrence of chorioamnionitis, as the diagnostic performance of tested biomarkers remains modest. Over the past five years, no new biomarker has been evaluated frequently enough to allow for a meta-analysis of its diagnostic accuracy. This emphasizes the importance of identifying new predictive biomarkers, particularly through large-scale proteomic approaches.
Most flow cytometry diagnostic scores are derived from single-center, retrospective studies, and the reproducibility of their reported performance in validation cohorts remains uncertain. We searched MEDLINE for FC diagnostic scores with at least one validation cohort published in the past 10 years, ultimately focusing on B lymphoid disorders (B-LPD) and myelodysplastic syndrome (MDS) scores, where most validation efforts have been undertaken. Forty-four publications were included: 29 on MDS (10 scores) and 15 on B-LPD (10 scores). Most scores were validated once or twice. For scores with both derivation and validation cohorts, sensitivity and specificity differences ranged from -0.25 to +0.29 (validation minus derivation). Among the three scores with more than five validated cohorts, I2 values were consistently high for sensitivity and, in two cases, also for specificity, indicating high heterogeneity. To explain this variability, we systematically reviewed study methods and identified 31 potential sources of heterogeneity, spanning study design, pre-analytical, analytical, and post-analytical factors. In conclusion, few flow cytometry scores have been validated, most only in limited cohorts, and their reported diagnostic performance varies widely. While numerous potential sources of variability can be identified, the small number of available studies prevents reliable quantification of their impact. Published accuracy estimates should therefore not be accepted at face value, and proposed scores should be validated in-house before being adopted into diagnostic practice.
Over a century since Louis Camille Maillard first described the reaction that bears his name, advanced glycation end products (AGEs) resulting from the advanced stage of the glycation reaction have been widely implicated in the onset and progression of various non-communicable diseases. Although a number of studies on the relationship of AGEs with various diseases have been published, none of them to date has comprehensively and critically assessed the quantification of specific AGEs alongside a description of the analytical methods employed and their clinical relevance as biomarkers. We here provide a review of 116 pertinent articles from the last 10 years and describe the analytical methods, the matrices investigated, and the findings related to specific glycation biomarkers in relation to diagnosis or prognosis of disease-in particular diabetes and its complications, but also cardiovascular and renal diseases, as well as other conditions. Significant trends from the last decade are the diversification of applications beyond diabetes and its complications, an increasing number of innovative, noninvasive approaches to quantifying glycation biomarkers which seek to facilitate large-scale screening and early intervention, and the emergence of approaches combining several markers into a suite of analytes for assessment using Z-scores or machine learning algorithms.
Cancer is a major global public health problem. Epigenetic regulation, such as DNA methylation, histone modifications, and non-coding RNA (ncRNA) dysregulation, is a main driver of tumorigenesis and progression. Recent studies are suggesting that the human microbiota, commonly referred to as a "super-organ," are not only associated with tumors but play an active role in regulating the epigenetic state of the host. The aim of this review is to systematically explain the main regulatory mechanisms of the "microbiota-epigenetic-cancer regulatory axis", their heterogeneous manifestations across various tumors, and the exploration of novel diagnostic biomarkers and therapeutic strategies of this regulatory axis. Microbiota mainly drive tumor epigenetic remodeling through three levels. First, microbial metabolites (e.g., butyrate) can act as natural histone deacetylase inhibitors (HDACis), or tryptophan metabolites can directly regulate the host chromatin state by activating the aryl hydrocarbon receptor (AhR) pathway. Second, bacterial structures such as lipopolysaccharide (LPS) can induce inflammation and disease by activating inflammatory signaling pathways. Third, specific pathogens like HBV and Helicobacter pylori can hijack the host's epigenetic machinery or induce epigenetic reprogramming via virulence factors. The tumor-resident microbiota (TRM) is an emerging and important field. TRM that actively partake in the tumor microenvironment (TME) may promote immune evasion through in situ mechanisms (e.g., lactylation), thereby confirming a direct and causal role for microbes within tumors. The epigenetic therapeutic strategies based on these mechanisms are being rapidly developed, including, for example, the regulation of microbial community structure (e.g., FMT), the targeting of microbial metabolic pathways, and TRM-specific approaches and key pathways (e.g., engineered bacteria). These strategies also have great potential as biomarkers for tumor prognosis prediction and therapy response evaluation. Overall, microbes and tumor epigenetics are part of a network that brings together their metabolism, inflammation, immunity, and gene regulation. Future research will shift from exploring the correlation of the gut microbiota at the macro level to exploring TRM's causality within the TME. By using gnotobiotic mouse models, organoid co-cultures, and multiomics, we will deeply analyze the microenvironment specificity of this network and develop precision interventions targeting TRM that could transform cancer therapy.
Circulating plasma DNA has found important applications in diverse medical fields, including prenatal testing, transplantation, and especially cancer. Many companies have developed products for detecting minimal residual disease, selecting or monitoring therapy, assessing prognosis, and confirming diagnosis. One major application is in screening asymptomatic individuals for the presence of cancer. Screening may facilitate better clinical outcomes through earlier interventions. Collectively, these technologies are widely known as "liquid biopsies". After the extraction of free DNA from the circulation, it is analyzed by various molecular techniques to explore differences between DNA originating from normal cells and cancer cells. Circulating plasma DNA originating from tumors (ctDNA) is expected to harbor the same molecular changes as tumor tissue itself. Thus, ctDNA is considered a surrogate of cancer tissue, but without the need to perform invasive biopsies to obtain it. Many new diagnostic companies have taken advantage of this new biomarker and developed technologies for screening for one or multiple cancers. We previously estimated the amount of ctDNA in circulation, which is admixed with DNA originating from normal cells. We concluded that since only a small fraction of the whole plasma (3 liters) is used for testing (3 to 4 mL), it is possible that the retrieved ctDNA may not be enough for cancer diagnosis in all patients. This problem is more acute with small tumors. Here, we mention some companies in the "liquid biopsy" arena and analyze their clinical data to establish if their tests are close to entering the clinic. We conclude from this analysis that current data do not support the use of these technologies for population screening due to many false negative and false positive results.
Post-translational modifications (PTMs) are critical regulators of protein function. Nearly two-thirds of all human proteins contain at least one PTM. These PTMs introduce covalent modifications, which modulate protein activity, location, and interactions. Further, PTMs are essential for understanding both physiological homeostasis and pathophysiology, and they play a key role in tumorigenesis and cancer development. Tumor immune evasion depends on dysregulated immune homeostasis caused by interactions between tumor cells and immune cells in the tumor microenvironment (TME). In this context, PTMs have emerged as one of the key regulators. From a pan-cancer perspective, PTMs remodel the tumor immune microenvironment through diverse mechanisms. The inability to regulate these processes is a common factor contributing to immune evasion in various cancers. It also facilitates crosstalk between tumor cells and components of TME, which in turn influences the response to immunotherapy. Because PTMs are dysregulated in cancers and can be reversed through drugs, they are attractive therapeutic targets. Small-molecule modulators of PTMs have the potential to reprogram the immune microenvironment and improve immune checkpoint blockade responses. Importantly, wide-ranging signal exchange networks between PTMs collectively increase tumoral immune phenotypic diversity and reveal new shared mechanisms of pan-cancer immune evasion. Recent studies show that the ways tumor cells change their surface proteins are driven by alterations in the tumor-immune environment. Further work could lead to strategies to treat many different cancers. Targeting PTM networks may overcome immune tolerance and significantly improve the clinical prognosis of cancer patients.
The One Health Approach recognizes the interconnectedness of human, animal, and environmental health. Emerging infectious diseases, climate change, and food/water insecurity impact all three. Global health improvement requires collaborative, holistic strategies at all levels to mitigate harmful factors and promote sustainable development. This review defines the One Health approach and illustrates its role in combating antibiotic resistance, emerging infectious diseases, and food/water insecurity. Laboratory medicine for both human and veterinary health, as well as environmental monitoring, are crucial in this context.
Long COVID, or post-acute sequelae of COVID-19 (PASC), is a major global health problem, with cumulative estimates suggesting that around 400 million people worldwide have been affected. It is characterized by persistent or new symptoms such as fatigue, cognitive impairment, and breathlessness lasting beyond four weeks after acute infection. Diverse clinical manifestations, chronic course, and incompletely understood pathophysiology-including hypotheses involving viral persistence, immune dysregulation, autoimmunity, endothelial dysfunction, and metabolic reprogramming-impede the development of diagnostic criteria, biomarkers, and targeted therapies. We conducted a critical review of 101 Long COVID omics studies, focusing on the computational methods used and their methodological quality. Using standardized criteria, we evaluated study design, statistical rigor, reproducibility, and clinical relevance across genomics, epigenomics, transcriptomics, proteomics, metabolomics, and multiomics integration, and mapped these findings onto regulatory and translational frameworks. Despite substantial methodological heterogeneity, convergent biological signals emerged. Genomic studies implicate risk loci in immune and cardiopulmonary pathways. Epigenomic analyses identify differentially methylated regions in immune and circadian genes. Transcriptomic studies reveal persistent dysregulation of innate immune and coagulation pathways, as well as reproducible molecular endotypes. Proteomic studies consistently show abnormalities in the complement cascade and coagulation, with a small panel of complement proteins showing highly reproducible changes across independent cohorts. Metabolomic studies demonstrate sustained mitochondrial dysfunction and altered cellular bioenergetics for up to two years after infection. Multiomics integration supports at least two major endotypes, characterized by predominant inflammatory versus metabolic dysregulation, and provides a basis for patient stratification and computational treatment discovery. Machine learning models frequently achieve high classification performance, but are rarely externally validated. Critical limitations restrict clinical translation. Most studies are underpowered relative to analytical complexity, use heterogeneous case definitions and controls, and report platform-specific signatures with limited overlap. External validation, preregistered analysis plans, and regulatory-aligned assay development are uncommon. To date, no regulatory-approved diagnostic assay or evidence-based therapeutic intervention has directly emerged from these computational findings. Future progress requires harmonized phenotyping protocols, adequately powered longitudinal cohorts with external validation, integration of spatial omics and explainable artificial intelligence, and early engagement with regulatory and health-technology assessment pathways. This review provides a critical assessment and a translational roadmap, outlining how methodologically robust computational omics can be advanced toward clinically actionable tools for Long COVID.
Antiphospholipid antibodies (aPL) may interfere with prothrombin time (PT) and international normalized ratio (INR) assays. Patients with antiphospholipid syndrome (APS) are often treated with vitamin K antagonists and require therapeutic monitoring with the INR. However, it remains unclear to what extent these assays are influenced by aPL and the consequent clinical relevance. This scoping review aimed to map the available evidence on the impact of APS and aPL on PT/INR assays, describe the methods used in research in this area, and identify gaps to guide future investigations. Two databases (MEDLINE and Embase) were searched, in the date range of 1 January 1984 to 10 June 2025, for reports describing clinical or in vitro research involving human subjects with APS or aPL that were analyzed with INR/PT assays. Results were reported according to the Preferred Reporting Items for Systematic Reviews and Meta-analyses extension for Scoping Reviews (PRISMA-Scr) guideline. 3,824 records were retrieved and after deduplication, title/abstract screening, and full-text screening, 39 studies were included in the review. Based on study design, we identified 23 observational clinical studies, 11 case reports/series, and 5 studies reporting in vitro aPL investigations with one report also including a case-control study. Observational studies showed that INR assays utilizing thromboplastin with recombinant human tissue factor (TF), including point-of-care tests (POCT), are more influenced by aPL than assays based on tissue-derived thromboplastins, although not consistently. Case reports described patients, mostly with the presence of multiple aPL, with clinically significant misinterpretation of their anticoagulation status. In vitro studies demonstrated reagent- and antibody-dependent effects, with anti-β2-glycoprotein I and antiprothrombin immunoglobulin G antibodies variably prolonging or shortening the PT. Evidence from both clinical and in vitro studies indicates that INR results in APS patients may be unreliable, particularly with specific recombinant human TF-based and POCT reagents, although not systematically. Interference is antibody profile-, antibody level-, and reagent-dependent. However, studies remain heterogeneous, often small in scale, and methodologically inconsistent with variable evaluation criteria. Well-defined future research should aim to identify in which APS patient subgroups an INR would not be reliable with specific assays.
