Exercise induces systemic biological adaptations across multiple tissues and cell types. Single-cell and multi-omic approaches now allow researchers to resolve these cell-type-specific responses and establish a translational framework for exercise medicine. This paper outlines a pipeline linking the discovery of exercise-responsive cell types and states, mechanism-informed candidate biomarkers, validation using accessible assays, and integration of human and animal evidence to support precision exercise applications. By highlighting how these approaches can guide biomarker discovery and translational validation, the article provides a conceptual roadmap for advancing single-cell exercise research. In exercise medicine, single-cell approaches can clarify how different exercise modes and doses influence muscle adaptation, immune regulation, ageing-related cellular states, and tissue-specific stress responses. Their near-term translational value lies in discovering cell-derived biomarkers that can later be measured using more accessible assays, including flow cytometry, targeted transcript panels, proteomics, methylation assays, and routine laboratory platforms. Future studies should combine clear exercise protocols, longitudinal sampling, spatial context, physiological outcomes, and rigorous validation.
Foundation and multimodal models now match expert-level performance on many diagnostic, prognostic, and biomarker tasks in pathology. Yet only a handful of AI systems are used in routine clinical practice, and the products that have reached patients differ substantially from research prototypes. We define this mismatch as the adoption paradox of computational pathology.We first survey the technical landscape from task-specific deep learning to large unimodal foundation models, multimodal systems, and early agentic architectures. We then examine what has actually entered the clinic, identifying four product archetypes (digital pathology platforms, population scale cytology screening, assistive detection in surgical pathology, and quantitative immunohistochemistry scoring). Using a three stage maturity model algorithmic capability (Stage 1), system integration (Stage 2), and institutional adoption (Stage 3), we analyze the structural barriers that gate each transition.Three interconnected barriers explain most of the gap: (1) Data and infrastructure fragility [(pre-analytical variability, scanner-induced domain shift, format fragmentation, annotation scarcity, manual quality control (QC)]; (2) Workflow misalignment (cognitive rhythm of pathologists, automation bias, scenario-dependent latency); (3) Institutional trust deficits (shallow interpretability, incomplete prospective validation, unclear reimbursement, unsettled liability, and regulatory gaps for generative/adaptive systems).We outline system-level pathways for each stage, including infrastructure first, AI, workflow-embedded intelligence, and adaptive governance. Our central claim is that the next phase of progress will depend less on architectural novelty than on the slower institutional work that turns capability into clinical benefit. The framework provides an actionable lens for regulators, developers, and healthcare organizations to diagnose why a given AI system remains a prototype and what is needed to move it into routine use.
Coronary artery disease (CAD) and heart failure (HF) remain the leading causes of mortality worldwide. Small-molecule metabolites have emerged as promising biomarkers for CAD and HF management, yet their accurate and rapid detection in complex biological samples is challenging due to their low abundance. Laser desorption/ionization mass spectrometry (LDI-MS) offers a powerful platform for metabolic analysis, but conventional organic matrices often generate strong background signals and exhibit limited reproducibility. Metal-based nanomaterials have recently emerged as potential alternative matrices of LDI-MS owing to their unique optical and physicochemical properties. This minireview summarizes recent advances in metal-based nanomaterials for LDI-MS toward the detection of small-molecule biomarkers in CAD and HF. Composition engineering strategies are discussed from single-component systems (noble metals and metal oxides) to multi-component nanocomposites [noble metal-, metal oxide-, and metal-organic framework (MOF)-based materials], while structural design principles and their influence on LDI performance are highlighted. Furthermore, their applications in CAD and HF, including stable CAD, acute coronary syndrome (ACS), and myocardial infarction (MI) within the CAD spectrum, are outlined, together with current challenges and future perspectives, and the potential for extending these approaches to other cardiovascular disorders.
Sleep disorders, including obstructive sleep apnea (OSA), chronic insomnia, and sleep deprivation, are increasingly linked to dysfunction of the blood-brain barrier (BBB), a key regulator of central nervous system homeostasis. Growing evidence demonstrates that BBB injury is associated with cognitive impairment, including Alzheimer’s disease (AD), white matter injury, cerebral small vessel disease, and vascular cognitive impairment. This review summarizes evidence that disturbed sleep impairs BBB integrity and discusses the underlying mechanisms. BBB injury may increase para-cellular permeability, alter endothelial transport, and impair metabolic clearance pathways, thereby facilitating the entry of circulating toxins into the brain and amplifying glial activation, cerebrovascular dysfunction, and neurodegenerative processes. Furthermore, BBB breakdown in the hippocampus may represent an early marker of cognitive dysfunction, which is partly independent of classical amyloid-β and tau pathology. However, this finding requires further validation in larger, longitudinal cohorts. Finally, we discuss the future of therapeutic strategies aimed at both sleep restoration and BBB-related mechanisms and potential biomarkers for assessing BBB dysfunction. We identify key knowledge gaps, including the lack of validated BBB biomarkers in sleep disorders and limited evidence for reversibility of BBB injury after treatment.
Antibody-oligonucleotide conjugates (AOCs) couple antibody-mediated recognition with oligonucleotide-based amplification, barcoding, or gene manipulation. By combining the targeting specificity of antibodies and the programmability of nucleic acids, AOCs translate antigen binding into nucleic acid signals for protein profiling while enabling the targeted delivery of oligonucleotide therapeutics. This article reviews major conjugation strategies for AOCs, ranging from random conjugation to site-specific approaches, and further explores the technological evolution of AOC-based platforms, highlighting their dual biomedical utility in protein analysis and RNA therapeutics. In protein analysis, AOCs have transitioned from simple detection agents to comprehensive proteomic platforms, facilitating high-sensitivity detection, high-throughput screening, and spatially resolved profiling. In therapeutics, AOCs have emerged as targeted RNA delivery systems for antisense oligonucleotides (ASOs), small interfering RNAs (siRNAs), and other synthetic analogs. However, AOC development remains hindered by multi-faceted challenges, including chemical limitations (conjugation heterogeneity and imprecise payload-to-antibody ratio control), diagnostic hurdles (steric interference and background noise), and translational bottlenecks (suboptimal intracellular delivery and scalability issues). This review systematically covers (1) conjugation strategies for AOCs, a class of chimeric biomolecular constructs; (2) the technical evolution and recent advances in AOC-based protein analysis; and (3) the application value and emerging impact of AOCs in precision therapy. By surveying recent developments in both diagnostic and therapeutic AOCs, this review argues for an integrated overview that connects molecular design to functional performance across these applications.
While immune checkpoint inhibitors (ICIs) have revolutionized oncology, microsatellite stable (MSS) colorectal cancer (CRC) remains immunologically “cold” and resistant. Ferroptosis, an iron-dependent form of cell death triggered by radiotherapy via reactive oxygen species (ROS) and acyl-CoA synthetase long-chain family member 4 (ACSL4), represents a critical vulnerability. Radiotherapy-induced ferroptosis promotes CD8+ T-cell recruitment; these T cells secrete interferon-γ (IFN-γ) to further repress solute carrier family 7 member 11 (SLC7A11), forming a reciprocal positive feedback loop that amplifies tumor killing. Crucially, we propose a paradigm shift by introducing an inherent “immunosuppressive brake” within this process. Recent evidence has revealed that ferroptotic cancer cells selectively release extracellular glutathione peroxidase 4 (eGPX4). As a novel damage-associated molecular pattern (DAMP), eGPX4 binds to the zona pellucida glycoprotein 3 (ZP3) receptor on dendritic cells (DCs), activating the cyclic adenosine monophosphate–protein kinase A (cAMP‒PKA) signaling cascade to inhibit 6-phosphofructo-2-kinase/fructose-2,6-bisphosphatase 2 (PFKFB2)-mediated glycolysis. This metabolic hijacking impairs DC maturation and subsequent T-cell priming, limiting the systemic efficacy of ferroptosis-inducing therapies. Therefore, we argue that successfully converting “cold” MSS CRC into a “hot” phenotype requires a dual strategy: maximizing ferroptosis induction while simultaneously neutralizing the eGPX4-ZP3 axis. This integrated approach provides a novel translational framework to overcome immunotherapy resistance, offering fresh hope for the majority of CRC patients.
Clonal hematopoiesis (CH) is the clonal expansion of hematopoietic stem and progenitor cell lineages carrying acquired somatic mutations, a common feature of hematopoietic aging. Its clinical relevance extends beyond hematologic malignancy risk to inflammation-linked comorbidities and emerging implications for cancer therapy response. CH detection faces a depth-dependent trade-off: cohort-scale whole-genome and whole-exome sequencing often miss small clones, whereas ultra-deep targeted panels increase exposure to technical artifacts that mimic low-frequency variants. We summarize approaches to stabilize low-variant allele fraction calls, including UMI-based error suppression, background modeling with panels of normals, and consensus calling across complementary variant callers. We also note how deep learning can improve artifact discrimination and variant prioritization when rule-based filters become brittle. Finally, we outline an interpretation framework integrating orthogonal evidence, including non-coding risk variants, routine clinical measures, and proteomic/metabolomic readouts, and we emphasize the need for better harmonization of detection and reporting standards.
Objectives Deep-brain brain‒computer interfaces (BCI) are currently used in clinical neuromodulation but mostly rely on population-level signals that limit decoding precision and the ability to target high-level cognition, facing challenges of noninterpretability and limited temporal‒spatial specificity. This review, informed by the human single-neuron recording technique and new evidence of concept cells from Ruijin Hospital, proposes a closed-loop deep-brain single-neuron brain‒computer interface framework to address these limitations. Methods We summarize the methodology enabling human single-neuron recordings via Behnke-Fried macromicro electrodes implanted for stereoelectroencephalography monitoring. To illustrate feasibility, we present single-unit data from four patients at Ruijin Hospital and describe the procedures for stimulus design, spike detection, and neuronal response identification. This evidence, together with current deep-brain BCI clinical applications, informs the proposed framework. Results Recordings from the hippocampus and amygdala revealed highly selective single-neuron responses to personally meaningful visual stimuli, demonstrating concept-specific firing patterns consistent with those previously described in the human medial temporal lobe. These findings confirm that concept cells can be reliably identified in clinical settings in China via the single-neuron recording technique. In parallel, current deep-brain BCIs that use local field potential signals have shown therapeutic value in epilepsy, Parkinson’s disease, depression, and memory modulation but remain limited by the use of coarse biomarkers and noninterpretability. By integrating these clinical advances with single-neuron recording, we outline two closed-loop strategies: (1) adaptive neural feedback systems that accelerate concept cell identification and (2) adaptive neuromodulation systems that adjust stimulation parameters on the basis of single-neuron biomarkers relevant to memory processing. Conclusions Human single-neuron recordings provide a unique opportunity to link deep-brain neuronal activity with high-level cognitive representations. Our findings demonstrate that concept cells can be reliably identified in clinical settings and offer a powerful substrate for next-generation deep-brain BCIs. A closed-loop framework informed by a single-neuron response may enhance both cognitive research and therapeutic neuromodulation. Achieving clinical translation will require advances in long-term signal stability, decoding robustness, and scalable integration with existing deep-brain stimulation technologies.
Objectives Antithrombin (AT) is a critical anticoagulant whose deficiency, which is common in many clinical conditions, requires precise management, although accurate laboratory measurements of low AT levels are challenging. This study aims to evaluate the precision and comparability of AT activity assays across different analytical systems, particularly at low activity levels, with the goal of developing validated optimization strategies to improve performance and interlaboratory harmonization. Methods Standardized lyophilized plasma materials (with AT activities of 10%, 15%, 35% and 100% of normal) were distributed to nine clinical laboratories via Systems A and B, with bovine thrombin as the reagent, and System C, with bovine activated coagulation factor Ⅹ as the reagent. Baseline performance was assessed through a laboratory evaluation and external quality assessment style comparison. Optimization measures (standardized reagent reconstitution, calibration curves at low levels, enhanced quality control protocols) were then applied in laboratories with better baseline performance, followed by a re-evaluation study to assess improvements. Results The initial evaluation confirmed significant variability at low AT activity levels: Systems A and B demonstrated closer agreement with target levels, whereas System C exhibited significant bias and poor reproducibility and was consequently excluded from optimization. After implementation of the optimization protocols, low-level AT activity measurements showed markedly reduced bias and improved precision. At the 10% and 15% levels, both the interlaboratory coefficients of variation and the recovery rates significantly improved. In the re-evaluation study, Systems A and B demonstrated enhanced performance across all participating laboratories. Conclusions: This multicenter evaluation established the feasibility of significantly improving AT activity assay performance through targeted methodological optimization. Through the use of quality control materials and harmonized procedures, the method achieved reliable low-level activity measurements, thereby paving the way for the reliable clinical application of AT activity measurements.
As a multiparameter, high-throughput single-cell analysis technology, flow cytometry has become a core tool for biomedical research and clinical diagnosis because of its ability to detect the physical properties (such as size and complexity) and biochemical characteristics (such as surface markers and intracellular proteins) of cells. This article reviews its technical development history, application fields and future challenges. At the technical level, flow cytometry has gradually developed from early single-parameter detection to innovative technologies such as multicolor fluorescence, mass spectrometry flow and imaging flow, realizing the simultaneous analysis and dynamic visualization of over 40 parameters of single cells. In clinical applications, flow cytometry is widely used in the fields of immune diseases and blood tumors, significantly improving diagnostic accuracy and treatment monitoring efficiency. In addition, it also plays a key role in cell biology and drug development. Although flow cytometry faces technical challenges such as high cost, complex data processing and multicolor fluorescence interference, future development directions should focus on intelligence (AI-assisted analysis), portability and multiomics integration, which is expected to further promote the development of precision medicine and personalized treatment. This article systematically reviews the technical evolution and application value of flow cytometry, providing a reference for understanding its core position and future potential in modern medicine.
Objectives EGFR mutation testing is key for the management of lung adenocarcinoma. Here, we aimed to examine the contribution of liquid biopsy versus tissue biopsy and formalin-fixed paraffin-embedded (FFPE) tissue to EGFR mutation testing in lung adenocarcinoma in a resource-limited setting. Methods This study included 54 patients with confirmed lung adenocarcinoma, 31 patients from a retrospective cohort and 23 from a prospective cohort. The Therascreen EGFR RGQ PCR Kit (Qiagen) was used for the identification of EGFR mutations from DNA samples extracted from tissue biopsy or FFPE tissue. A Therascreen EGFR plasma RGQ PCR Kit (Qiagen) was used for the detection of EGFR mutations from DNA samples extracted from liquid biopsy. Results The DNA extracted from the FFPE tissues was significantly degraded, whereas the DNA extracted from the tissue biopsies or liquid biopsies was of good quality. EGFR mutations were detected in circulating tumor DNA (ctDNA) samples extracted from liquid biopsies in 17.4% of the patients in the prospective cohort. Moreover, a dual mutation (exon 19 deletion and the T790M resistance mutation conferring resistance to first- and second-generation EGFR-tyrosine kinase inhibitors (EGFR-TKIs)) was detected among the positive cases. However, no EGFR mutations were detected in the DNA samples extracted from tissue biopsies from all patients, notably even in cases where EGFR mutations had been detected in ctDNA samples, suggesting that in these cases, the tissue biopsies collected for molecular analysis did not contain cancerous tissue. Conclusions: To our knowledge, this is the first study conducted in Morocco on EGFR testing in lung adenocarcinoma using ctDNA. Our results clearly demonstrated the utility of liquid biopsy versus tissue biopsy for EGFR mutation testing, especially when DNA extracted from FFPE tissues is degraded or in cases of limited tissue material. Our findings also showed that liquid biopsy can overcome spatial tumor heterogeneity by identifying multiple mutations. Therefore, this study supports the clinical integration of this noninvasive method for assessing tumor characteristics and monitoring disease progression when obtaining a tissue biopsy is challenging.
Adrenocortical carcinoma (ACC) is a rare and highly aggressive endocrine malignancy with limited therapeutic options and a substantial risk of postoperative recurrence. Mitotane remains the only US Food and Drug Administration (FDA) and the European Medicines Agency (EMA) approved agent specifically for ACC. Its antitumor activity involves disruption of cholesterol trafficking, inhibition of steroidogenic enzymes, and mitochondrial dysfunction, leading to selective cytotoxicity in adrenocortical cells. Current evidence indicates that maintaining therapeutic plasma concentrations between 14–20 mg/L is critical for clinical benefit, underscoring the importance of therapeutic drug monitoring. Mitotane is used in both adjuvant and advanced disease settings; however, its highly variable pharmacokinetics, broad endocrine disruptions, and frequent gastrointestinal and neurological toxicities require individualized dosing strategies and comprehensive supportive care. This review summarizes recent advances in the understanding of mitotane’s molecular mechanisms, pharmacogenetic determinants, clinical efficacy, and adverse-effect profile, with an emphasis on laboratory monitoring and practical considerations for optimizing treatment in ACC.
Objective Carbohydrate antigen 125 (CA125), which is traditionally used in ovarian cancer diagnostics, is increasingly recognized as a marker of congestion and inflammation in heart failure (HF). This study compared the analytical performance of N-(4-aminobutyl)-N-ethylisoluminol (ABEI)-based CA125 and N-terminal pro-B-type natriuretic peptide (NT-proBNP) assays on the Maglumi® X6 analyzer with that of the Roche Cobas e602 system and explored the relationship of CA125 with biomarkers of adverse remodeling in HF with reduced ejection fraction (HFrEF). Methods Imprecision testing and method comparison were performed on matched serum samples from 108 HFrEF patients. CA125 concentrations were evaluated in relation to the New York Heart Association (NYHA) class, left ventricular ejection fraction (LVEF), galectin-3, and soluble suppression of tumorigenicity 2 (sST2) levels. Prognostic value was assessed by Kaplan-Meier survival analysis using the 35 U/mL threshold. Results The ABEI based CA125 assay showed low imprecision [coefficient of variation (CV)≤4.5%] and strong agreement with the Cobas e602 assay (R=0.97, slope=1.06, P<0.001). CA125 levels increased progressively with NYHA class (P=0.02), correlated negatively with LVEF (R=–0.38, P<0.001) and positively with galectin-3 (R=0.21, P=0.03) and sST2 (R=0.57, P<0.001). Elevated CA125 levels (≥35 U/mL) were associated with significantly increased cardiovascular mortality (P<0.001). Conclusions ABEI-based CA125 measurement provides an analytical performance comparable to that of Cobas e602. In HFrEF, CA125 is correlated with clinical severity, fibrosis/inflammation biomarkers, and prognosis. Its integration into multimarker strategies, particularly alongside NT-proBNP and sST2, may enhance risk stratification and therapeutic monitoring, including the response to sodium–glucose cotransporter-2 (SGLT2) inhibitor therapy.
