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Results for “Non-invasive monitoring”

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At least 19 recordsLinked to original sources

PubMed2014

Gene transfer of arginine kinase to skeletal muscle using adeno-associated virus.

In this study, we tested the feasibility of non-invasively measuring phosphoarginine (PArg) after gene delivery of arginine kinase (AK) using an adeno-associated virus (AAV) to murine hindlimbs. This was achieved by evaluating the time course, regional distribution and metabolic flux of PArg using (31)phosphorus magnetic resonance spectroscopy ((31)P-MRS). AK gene was injected into the gastrocnemius of the left hindlimb of C57Bl10 mice (age 5 weeks, male) using self-complementary AAV, type 2/8 with desmin promoter. Non-localized (31)P-MRS data were acquired over 9 months after injection using 11.1-T and 17.6-T Bruker Avance spectrometers. In addition, (31)P two-dimensional chemical shift imaging and saturation transfer experiments were performed to examine the spatial distribution and metabolic flux of PArg, respectively. PArg was evident in each injected mouse hindlimb after gene delivery, increased until 28 weeks, and remained elevated for at least 9 months (P<0.05). Furthermore, PArg was primarily localized to the injected posterior hindimb region and the metabolite was in exchange with ATP. Overall, the results show the viability of AAV gene transfer of AK gene to skeletal muscle, and provide support of PArg as a reporter that can be used to non-invasively monitor the transduction of genes for therapeutic interventions.

Animals↗

PubMed2026

The Potential Link Between Eosinophilic Esophagitis and Food Allergy: Inflammatory Pathogenesis and Management.

Eosinophilic esophagitis (EoE) has transitioned from an isolated gastrointestinal disorder to a recognized type 2 immune-mediated allergic disease, most likely representing a late manifestation of the atopic march. This comprehensive review examines the complex inflammatory pathogenesis linking EoE and food allergy, and critically discusses the mechanisms of disease induction, dietary treatments, and emerging clinical challenges. While genome-wide association studies identify shared susceptibility loci with classic atopy, EoE exhibits distinct tissue-specific pathways, particularly dominated by the local interleukin (IL)-13 axis and highly esophagus-selective proteases like Calpain-14. This unique immunological interplay is clinically epitomized by food oral immunotherapy (OIT)-induced EoE. During OIT, systemic immune reprogramming successfully drives immune tolerance-marked by a robust increase in plasma food-specific IgG4-but fails at the local level, due to the persistence of pathogenic Th2 cells and aberrant mucosal IgG4 immune complex deposition within the esophageal lamina propria. Regarding therapeutic management, conventional skin and serum allergy testing remain highly inaccurate in identifying dietary triggers, rendering test-guided diets ineffective. Conversely, empiric elimination diets achieve robust histological remission, ranging from standardized six-food restrictions to pragmatic single-food approaches targeting cow's milk. Furthermore, novel insights into industrial milk processing (such as UHT sterilization and homogenization) and specific beta-casein genetic variants (A1 vs A2) highlight how altered protein structures generate neoantigens that accelerate esophageal immunogenicity. In conclusion, decoding the divergent immunological mechanisms operating in the refractory esophagus is essential to move beyond trial-and-error dietary interventions towards non-invasive monitoring tools, precision medicine, and optimized biological therapies in EoE management.

Calpain14↗

PubMed2025

Biomarkers of inflammation in sweat after myocardial infarction.

ST-elevation myocardial infarction (STEMI) triggers a significant inflammatory response. Sweat may offer a novel, non-invasive medium for monitoring inflammation. In this prospective study, we characterized the inflammatory signatures in plasma and sweat collected from the skin surface of two patient groups: (1) 18 STEMI patients immediately following percutaneous coronary intervention (exposure) and (2) six patients who underwent outpatient angiography without subsequent intervention (control). Levels of 92 biomarkers were measured using a high-throughput proteomic assay and reassessed after 4-6 weeks in STEMI patients. Adjusting for patient group, sweat biomarkers did not show significant changes over time. In plasma, hepatocyte growth factor and interleukin-6 showed a significant decrease from the acute phase to follow-up, adjusted for patient group. STAM binding protein was significantly higher in the sweat of STEMI patients compared to controls, adjusted for time effects. While sweat was less sensitive than plasma for detecting biomarker levels in the setting of STEMI, its longitudinal analysis via wearable sensors holds promise for detecting specific markers.Trial registration: The trial is registered on www.clinicaltrials.gov with the trial registration number NCT05843006.

Aged↗

PubMed2026

Evaluating sampling strategies for the detection of avian influenza viruses in the environment.

Highly pathogenic avian influenza (HPAI) viruses pose an increasing threat to wildlife, livestock and human health, underscoring the need for scalable and early-warning surveillance systems. Environmental RNA (eRNA) monitoring offers a non-invasive, cost-effective alternative to traditional host-based sampling by detecting viral genetic material shed into the environment. Despite its utility, the relative performance of different environmental sampling approaches for avian influenza virus (AIV) detection remains poorly resolved. Here, we conducted a longitudinal study with monthly sampling over approximately one year across two urban waterfowl ponds in Aotearoa New Zealand to evaluate four eRNA sampling strategies - fresh faeces, sediment, active-filtered water and passive-filtered water - for their ability to detect AIV. Using a combination of metagenomic sequencing and RT-qPCR, we show that all sample types can detect AIV, although detections were highly inconsistent across sampling methods, locations and time points. While metagenomic sequencing provided valuable genomic data, including subtype identification and phylogenetic context, RT-qPCR exhibited greater sensitivity, with active-filtered water yielding the highest detection rates, and is currently the more cost-effective approach for large-scale surveillance. Notably, AIV detections were asynchronous among sample types and frequently lacked temporal concordance, suggesting that environmental heterogeneity, RNA persistence, and methodological detection limits strongly influence surveillance outcomes. Despite these inconsistencies, phylogenetic analyses revealed that detected viruses belong to established Australasian lineages, highlighting the ability of environmental surveillance to capture ecologically relevant viral diversity. Our findings demonstrate that while eRNA-based surveillance holds substantial promise as a complementary tool for AIV monitoring, its effectiveness is highly dependent on the environmental sampling strategies and laboratory detection methods used.

Ducks↗

PubMed2026

Blood-based proteomic profiling reveals context-dependent changes in BCL2-associated signaling during taxane therapy in breast cancer patients.

The quality of life for many cancer survivors is compromised due to severe, long-lasting side effects of chemotherapy. As part of a pilot, prospective, non-interventional study to examine the side effects of chemotherapy in breast cancer patients, we examined the change in protein expression in blood collected from patients before and after treatment with taxanes for 12 weeks. Protein expression was measured with reverse phase proteomic arrays (RPPA), which revealed divergent changes in apoptosis, senescence, and calcium signaling-related proteins depending on treatment setting (neoadjuvant vs. adjuvant). The largest change identified was BCL2 (B-cell lymphoma 2), a founding member of the BCL2 family of proteins that regulate apoptosis. Other proteins regulated by BCL2, including RB1 (retinoblastoma protein 1) and NLRP3 (NLR family pyrin domain containing 3) changed significantly over the course of treatment. These differences are consistent with intracellular calcium signaling dysregulation and activation of stress-response pathways that overlap with senescent-associated secretory phenotype (SASP)-like signaling, which has been implicated in cancer recurrence. To contextualize these observations, we generated Kaplan-Meier survival curves using publicly available proteomics data from The Cancer Proteome Atlas (TCPA). This work aims to demonstrate how blood-based proteomics can serve as a non-invasive method to monitor systemic physiological shifts during cancer therapy, offering a framework for generating hypotheses about chemotherapy timing and long-term outcomes.

Humans↗

PubMed2026

Human DNA levels in feces reflect gut inflammation and associate with presence of gut species in IBD patients across the age spectrum.

BACKGROUND: Feces represent a complex biological matrix that provides valuable information about intestinal physiology and gut microbial activity. Comprehensive fecal DNA sequencing is mostly utilized as a non-invasive way to profile the gut microbiome, and both clinical practice and research on inflammatory bowel diseases (IBD) would greatly benefit from accurate and non-invasive methods to monitor gut inflammation in IBD patients. In IBD, excessive immune cell recruitment and epithelial cell shedding in the gut increase the amount of human DNA in feces, making fecal DNA profiling a desirable approach to monitor gut inflammation dynamics. METHODS: We used a combination of sequencing techniques to comprehensively characterize the fecal DNA diversity in a newly established cohort of pediatric IBD patients and controls (Pediatric cohort, N = 134 children, Israel). We performed methylation-based human cell-specific profiling together with shotgun metagenomics to characterize the human and the microbial DNA content in feces, respectively. Moreover, we included a large complementary external cohort including adult IBD patients and controls (Adult cohort, N = 689 adults, the Netherlands), not only to compare microbial patterns across the age spectrum, but also to extend our findings from the methylation-based profiling to the more broadly-available quantification of human DNA in metagenomic sequencing. RESULTS: We found that neutrophil DNA dominates fecal human DNA content in IBD patients, and our measurements were highly correlated with fecal calprotectin levels. Combining neutrophil and other cell type DNA fractions in one metric was able to distinguish between remissive and active cases of IBD. Human reads percentage by metagenomics was well correlated with disease severity and species richness, which had distinct trends in CD and UC over time. We used a combination of species richness, human DNA percentage, and microbiome composition data to predict IBD and distinguish CD from UC in both adult and pediatric IBD cohorts. CONCLUSIONS: The comprehensive characterization of human and microbiome fecal DNA is a useful approach to track immune response level and investigate the interaction that the immune system has with gut microbiome richness and composition over time, enriching opportunities for better disease monitoring and thus better treatment of IBD patients. Video Abstract.

Humans↗

PubMed2026

Multi-omics dynamic profiling reveals predictive biomarkers for first-line immunochemotherapy in extensive-stage small-cell lung cancer.

BACKGROUND: Extensive-stage small-cell lung cancer (ES-SCLC) is associated with a poor prognosis. Although first-line immunochemotherapy improves clinical outcomes, robust prognostic biomarkers for this treatment modality remain unavailable. The aim of this study was to identify non-invasive, easily accessible, and dynamically monitored biomarkers of ES-SCLC by machine learning integrating serum metabolomics, lipidomics, and proteomics at multiple time points. METHODS: A total of 816 serum samples were collected from ES-SCLC patients receiving first-line immunotherapy combined with chemotherapy or first-line chemotherapy for metabolomics, lipidomics, and proteomics analysis. The immunochemotherapy cohort was randomly divided into training and validation subsets at a 6:4 ratio. Biomarkers were identified using machine learning algorithms, and their prognostic significance was evaluated through receiver operating characteristic (ROC) analysis, Kaplan–Meier survival analysis, and multivariate Cox regression. Potential metabolic pathways and mechanisms were further explored via integrated multi-omic analysis. RESULTS: The immunochemotherapy exhibited a prolonged median progression-free survival (PFS) and higher objective response rate (ORR) compared to the chemotherapy group. A total of 5 serum metabolites (uric acid, L-aspartate-semialdehyde, dimethisterone, xanthine, L-cysteine), 6 lipids (Cer d18:1/26:0, Cer d18:2/25:0, SM d18:1/20:1, SM d17:1/25:1, DG O-18:1_16:0, PS 18:0_24:0), and 3 proteins (ACIN1, ACSL4, PHGDH) were identified and constructed into independent prognostic models. Among patients receiving immunochemotherapy, those categorized as low-risk based on the model demonstrated significantly longer PFS compared with those in the high-risk group. These prognostic signatures also retained predictive value in patients who underwent second-line treatment with anlotinib plus immunochemotherapy. Integrated analysis revealed that glycine, serine, and threonine metabolism was the commonly enriched pathway across all three omics layers. Notably, PHGDH (protein), L-aspartate-semialdehyde and L-cysteine (metabolites), and PS (18:0_24:0) (lipid), key elements in this pathway, were all incorporated in the predictive model. In addition, models of the composition of these substances after one cycle of treatment can still predict the prognosis of patients. CONCLUSION: In this study, we constructed and validated a set of non-invasive, dynamically monitorable prognostic models (containing 5 metabolites, 6 lipids, and 3 proteins) using machine learning by integrating multiple time point data from the serum metabolome, lipid panel, and proteome to accurately distinguish the prognostic risk of patients with ES-SCLC receiving immunochemotherapy. PFS was significantly prolonged in patients in the low-risk group, and this model remains predictive in the subsequent second-line treatment with anlotinib in combination with immunochemotherapy. Glycine-serine-threonine metabolic pathway may be the key mechanism, of which PHGDH, L-aspartate semialdehyde, L-cysteine and PS (18:0_24:0) are the core predictors. This study provides the first multi-omics dynamic prognostic tool for ES-SCLC immunochemotherapy and reveals potential therapeutic targets.

Humans↗

PubMed2026

Quality assessment, prognostic factors, and biomarkers for brain tumor analysis: a comprehensive systematic review.

The brain tumors possess different causative factors and properties, making their diagnosis and treatment difficult. Growth of these cancers usually leads to compression of the adjacent nerves and obstruction of the flow of cerebrospinal fluid, thus leading to increase in intracranial pressure. This affects the working of brain in many ways; thus, the difficulty involved in its treatment. With the improvements in technology in neuroimaging, including Diffusion Tensor Imaging (DTI), Positron Emission Tomography (PET), and multiparametric Magnetic Resonance Imaging (mpMRI), the diagnosis process has become easy. The effectiveness of any form of therapy in such patients depends primarily on their prognosis. While it is a common practice that physicians determine the prognosis of the disease by considering the age of the patient, histological grade of the tumor, and resection status, now this method has become more comprehensive by adding molecular signature and genetic analyses to the list of criteria. Next-generation sequencing (NGS) allows a reliable molecular classification. It increases the level of risk stratification, facilitating the application of therapies tailored to individual patients. Thus, molecular oncology has greatly changed our views on brain tumors' pathology and prognosis while neoadjuvant treatments aim at increasing the survival rate. On the other hand, radiogenomics is a field of study that combines non-invasive imaging phenotypes and genomic information in order to find unique molecular signatures of tumors without collecting samples from tumors. Molecular biomarkers are absolutely essential in the diagnosis of cancer, treatment monitoring, and recurrence of cancer. Advances in liquid biopsy technology, particularly the methods for circulating tumor DNA (ctDNA) and Extracellular Vesicle (EV) based analysis, have enabled the possibility of non-invasive monitoring of the progression of the tumors over time. This review highlights key studies and important scientific works about imaging technologies, biomarkers, and prognostic factors of malignant brain tumors.

Humans↗

PubMed2026

Novel insights into retinoblastoma: From oncogenic circuitry to precision diagnosis and eye-preserving therapies.

Retinoblastoma (RB) represents the most common primary intraocular malignancy in childhood and stands as a paradigm for translating molecular oncology into precision clinical management. This review synthesizes the comprehensive evolution in the understanding and treatment of RB. First, we deconstruct the intricate oncogenic circuitry that extends far beyond Knudson's classic "two-hit" RB1 inactivation model, describing non-classical MYCN-driven pathogenesis, multi-layered epigenetic reprogramming (including chromatin, RNA and histone changes), and distinct histological subtypes with defined clinical correlates, such as the favorable-prognosis cavitary RB. Single-cell genomics has elucidated the cellular origin from cone precursor cells and intratumoral heterogeneity. Risk stratification has been refined through well-defined classification systems, from the therapy-guiding International Intraocular Retinoblastoma Classification (IIRC) to the comprehensive American Joint Committee on Cancer Tumor-Node-metastasis (AJCC TNM) staging. Furthermore, the diagnostic paradigm has advanced from conventional anatomical imaging to liquid biopsies, enabling non-invasive molecular staging and monitoring via tumor-derived cell-free DNA analysis. Concurrently, the therapeutic landscape has undergone a radical shift, moving from enucleation and external-beam radiotherapy to an era dominated by local sight-preserving strategies. We provide a critical synthesis of the evidence for intravenous chemotherapy and the transformative role of super-selective intra-arterial chemotherapy (IAC), and describe essential randomized controlled trials, technical innovations, and optimized drug regimens. Finally, we explore emerging targeted molecular therapies and future directions. By integrating cutting-edge molecular insights with robust, high-level clinical evidence, this review offers the framework for achieving patient and eye survival as well as vision preservation in children with Retinoblastoma.

Intra-arterial chemotherapy↗

PubMed2026

Small extracellular vesicles are the key players in ochratoxin A-induced kidney toxicity.

BACKGROUND: Despite growing evidence of ochratoxin A (OTA)-induced kidney toxicity, the underlying mechanisms remain elusive. Emerging evidence suggests that small extracellular vesicles (sEVs) act as mediators of intercellular communication to recipient cells during various physiological and pathological conditions. Given the distinctive properties of sEVs, it is hypothesized that OTA-induced sEVs might mediate the OTA-induced kidney pathogenesis. METHODS: To explore the involvement of sEVs in OTA-induced kidney toxicity, sEVs were isolated and characterized from OTA-exposed rat kidney epithelial cells (NRK52E). Later, these sEVs were used to treat NRK52E cells and Wistar rats to assess the impact of OTA-induced sEVs on kidney toxicity. Label-free proteomics was also performed on OTA-induced sEVs, and key proteins were identified and validated. The biodistribution of sEVs in rats was also assessed using live imaging. The role of validated protein/s in kidney toxicity was further confirmed via a gene silencing and overexpression study. RESULTS: OTA exposure increased sEV secretion into conditioned media of NRK52E cells and into the urine of Wistar rats. Interestingly, we found that OTA-induced sEVs cause similar kidney toxicity in vitro and in vivo systems as OTA exposure, and blocking of sEV secretion markedly alleviated OTA-mediated kidney toxicity. Proteomics analysis identified annexin A2 and fibrinogen-ɣ as common proteins detected in sEVs derived from OTA-exposed NRK52E cells or rat urine. However, immunoblotting validated that annexin A2 was the only sEV-associated protein, expressed significantly in both NRK52E and rat urine following OTA exposure. Notably, silencing of annexin A2 attenuated the ability of OTA-induced sEVs to cause kidney toxicity, whereas overexpression exacerbates it. CONCLUSIONS: Our findings identify the annexin A2-enriched sEVs as key mediators of OTA-induced kidney toxicity. Annexin A2, along with other kidney injury markers, offers a promising non-invasive translational biomarker for early detection and monitoring of OTA-induced kidney toxicity.

Ochratoxins↗

PubMed2026

Towards a Robust cell-free DNA Isolation Protocol for NGS Applications in a Clinical Molecular Diagnostics Setting.

Cell-free DNA (cfDNA), released from apoptotic and necrotic cells into body fluids, is a non-invasive source of genetic information for disease prediction, diagnosis, and monitoring. However, its low abundance makes cfDNA highly susceptible to various pre-analytical influences, potentially increasing high molecular weight (HMW) or genomic DNA (gDNA) compromising downstream cfDNA analyses. This study evaluated the impact of different cfDNA-stabilizing blood collection tubes (BCT; Cell-Free DNA BCT, Streck; S-Monovette cfDNA Exact, Sarstedt) stored at room temperature for 1, 5, or 10 days, prior to plasma isolation using different isolation methods (magnetic bead-based or silica column-based) on cfDNA stability and yield. DNA quantity and quality were assessed by fluorometric quantification, automated fragment analysis, and gene-specific quantitative PCR. Streck-based workflows maintained stable cfDNA yields and characteristic mononucleosomal fragmentation profiles across all storage times. In contrast, Sarstedt tubes showed reduced cfDNA concentrations after 5 days and a pronounced increase at 10 Days, accompanied by high-molecular weight DNA patterns consistent with white-blood cells (WBC) lysis. These trends were largely independent of the extraction method. Overall, the results demonstrate that blood collection tube chemistry critically influences cfDNA integrity during delayed processing. Streck tubes, particularly when combined with silica column-based isolation method, provided the most robust and reproducible workflow for routine molecular diagnostics, whereas Sarstedt tubes produced physiologically implausible results after extended storage.

blood collection tubes↗

PubMed2025

Molecular profiling of exhaled breath condensate in respiratory diseases.

BACKGROUND: Respiratory disorders, , continue to pose a major global health burden. Their complexity and heterogeneity challenge accurate diagnosis, effective monitoring, and therapeutic decision-making. Exhaled breath condensate (EBC) provides a reliable, non-invasive means of sampling the molecular environment of the airways. AIM: This review presents the state-of-the-art in EBC-based omics approaches-particularly metabolomics and proteomics-to characterize molecular signatures associated with chronic respiratory (e.g. asthma, chronic obstructive pulmonary disease, and rhinitis) and infectious diseases (e.g. COVID-19). RESULTS: We critically examine findings from studies applying nuclear magnetic resonance (NMR), mass spectrometry (MS), and sensor-based technologies to analyze EBC across various respiratory conditions. NMR, valued for its reproducibility and minimal sample preparation, consistently discriminates among disease phenotypes, identifies distinct metabotypes, and monitors treatment response over time. MS-based approaches afford enhanced sensitivity and specificity, enabling detailed profiling of inflammatory mediators, such as lipid-derived eicosanoids and amino acid derivatives. Proteomic studies reveal protein-level alterations associated with inflammation and tissue remodeling. In COVID-19 and long COVID, metabolomic and volatile compound profiling distinguishes affected individuals from healthy controls suggesting clinical potential. However, inconsistent sample processing and lack of analytical standardization remain limiting factors. CONCLUSIONS: EBC profiling shows clear promise for improving diagnosis, monitoring, and stratification in respiratory medicine. Yet, translation into clinical practice is hindered by limited standardization and validation. Broader, longitudinal studies will be essential to establish robust molecular signatures across disease states. This review underscores the timely need to implement breathomics investigations to gain mechanistic insight into the underlying biology of respiratory diseases.

Humans↗

PubMed2026

Decoding cancer with artificial intelligence: Transforming research, diagnosis, and therapy with future insights.

Cancer remains one of the leading global health burdens, with increasing complexity in genomic, imaging, and clinical datasets presenting significant challenges for effective management. Artificial intelligence (AI) has emerged as a powerful tool to address these challenges by enabling pattern recognition, knowledge integration, and data-driven decision-making. This review highlights recent advances in the application of AI across cancer research, diagnosis, and therapy. In research, AI accelerates drug discovery and repurposing, enhances genomic data interpretation, and facilitates biomarker identification through multi-omics integration. In diagnosis, AI has demonstrated high technical performance in radiology for lesion detection and image segmentation, in pathology for tumour grading and molecular prediction, and in liquid biopsy for non-invasive biomarker analysis. In therapy, AI supports precision medicine by predicting treatment responses, monitoring disease progression, and optimizing clinical trial design. Despite these advances, barriers such as data heterogeneity, algorithmic bias, interpretability, and regulatory challenges remain. Future directions, including explainable AI, federated learning, multimodal modelling, and digital twins, hold promise for translating AI-driven innovations into routine oncology practice. Significance Statement This review provides a timely synthesis of recent (2020-2025) advances in artificial intelligence across cancer research, diagnosis, and therapy, highlighting applications in drug discovery, genomics, multi-omics biomarker identification, and clinical decision-making. By integrating technological progress with translational and clinical relevance, this work serves as a valuable resource for bridging AI innovation with precision oncology practice. As a narrative review, the literature was identified through targeted PubMed, Scopus, and Google Scholar searches, combining terms for artificial intelligence, machine learning, and deep learning with cancer-related keywords, with priority given to peer-reviewed studies published between 2020 and 2025, seminal earlier works, and official regulatory or guideline documents. Within each domain, representative studies were selected to illustrate methodological diversity, clinical context, and current translational readiness rather than to provide exhaustive coverage of an extremely rapidly evolving field.

Artificial intelligence↗

PubMed2026

Urine Proteomics as a Source of Biological Information and Outcome Predictor in Living Kidney Transplantation.

Kidney transplantation (KTx) is the preferred treatment for kidney failure. However, post-transplant management is challenging due to the limited lifespan of transplanted organs. Current methods for monitoring post-transplant complications are invasive and have limitations. Therefore, there is an urgent need for novel non-invasive biomarkers. This study investigates the proteomic composition of urine to understand renal biology during the process of transplantation and to identify potential markers for outcome prediction. Urine samples were collected from donors before transplantation and from recipients 4 weeks and 1 year after transplantation. Proteomic analysis was performed using mass spectrometry and label-free quantification. Statistical analyses included principal component analysis (PCA) and enrichment analysis. The resulting key findings were confirmed in an independent validation cohort. In addition, correlative regression models to evaluate the relationship between protein abundance and clinical outcomes in the further course after transplantation were performed. 106 urine samples in the setting of 70 kidney transplantations were analyzed. PCA revealed distinct clustering of donor and recipient samples, indicating significant proteomic changes after transplantation. Hierarchical clustering and gene ontology analysis identified molecular changes as a response to transplantation and showed an over-representation of relevant pathways related to inflammation, cell immune response and coagulation in both the original and validation cohorts. Multivariate regression analysis, including linear and logistic regression, identified 11 potential protein biomarkers, including ORM2, IL1RAP, APP, and FABP4 as predictors of eGFR 12 months after transplantation and 1 HP as a predictor of infections within the first year after transplantation, respectively. This study underscores the potential of non-invasive urine proteomics for identifying biological processes involved in kidney transplantation and for enhancing post-transplant monitoring and outcome prediction. We identified 12 potential biomarkers with added value to standard clinical parameters linked to transplant outcomes, which will be promising candidates for future outcome monitoring after KTx.

Humans↗

PubMed2026

Body mass index-specific nanoparticle protein corona signatures in late pregnancy.

The protein corona (PC) formed on the surface of nanoparticles (NPs) upon exposure to human biofluids is a dynamic interface that reflects the physiological and pathological status of the host. In this study, we investigated how the maternal body mass index (BMI) influences the composition of the NPs' PC during late pregnancy. Polystyrene NPs were incubated with plasma samples collected from third-trimester pregnant individuals across normal weight, overweight, and obese BMI categories. Comprehensive characterization using dynamic light scattering (DLS), zeta potential measurements, and transmission electron microscopy (TEM) confirmed BMI-dependent differences in PC thickness and colloidal stability. SDS-PAGE and label-free quantitative proteomics revealed distinct molecular compositions: PCs from obese individuals were enriched in inflammatory and lipid metabolism-associated proteins (e.g., APOE and CRP), while normal weight-derived PCs showed higher levels of complementary regulators and extracellular matrix proteins. Principal component analysis (PCA) demonstrated clear clustering of proteomic profiles by the BMI group, suggesting BMI-specific PC fingerprints. These findings indicate that the maternal metabolic phenotype shapes nano-bio interactions at the proteomic level and highlight the potential of PC profiling as a non-invasive approach for assessing maternal health and metabolic status. This work lays the foundation for integrating NP-based proteomics into precision nanomedicine for maternal-fetal health monitoring.

Female↗

PubMed2026

Granger connectivity and graph-theoretical analysis of scalp EEG across the preictal to ictal transition for presurgical evaluation.

OBJECTIVE: To assess the feasibility of estimating lateralization and localization of the epileptogenic zone (EZ) in temporal and extratemporal lobe epilepsy by combining Electric Source Imaging (ESI) with functional connectivity analysis of high-density EEG from the preictal to the ictal phase. METHODS: Adults with drug-resistant focal epilepsy and at least one recorded seizure during 40- or 64 channels EEG monitoring were retrospectively included. Granger causality and hubness centrality were computed over the 10-s preictal interval and the first 5 s of the ictal period, with ictal onset defined as the first EEG change identified by experienced epileptologists. The reference standard for EZ localization was based on resective surgical outcome or stereo-EEG findings. RESULTS: Thirteen patients (7 females; median age 35 years) were included. Connectivity analyses showed higher concordance with clinical findings during the preictal phase than during the ictal phase for both lateralization (91% vs 46%) and localization (73% vs 27%). Performance was highest in temporal (7/7 lateralization; 6/7 localization) and frontal lobe epilepsy (2/2 for both), and lower in parieto-occipital epilepsy (1/2 and 0/2, respectively). In two cases with poor surgical outcome or no surgical indication, connectivity findings were discordant with clinical estimates. CONCLUSIONS: Connectivity analysis across the preictal to ictal transition provides relevant lateralizing and localizing information, particularly in temporal and frontal lobe epilepsy, and may reveal clinically meaningful discordance. SIGNIFICANCE: Integrating high-density EEG, ESI, and functional connectivity during the phase preceding the first EEG change may support non-invasive presurgical evaluation.

Humans↗

PubMed2026

Canine Tear Proteomics: A New Frontier in Veterinary Ophthalmology.

Canine tear proteomics is an emerging field with significant potential for advancing both veterinary ophthalmology and comparative biomedical research. Tears are a readily accessible, non-invasive biofluid that contain a mixture of proteins involved in ocular surface protection, immune defense, and intercellular signaling. In dogs, tear proteomics studies have revealed biomarkers associated with various ocular and non-ocular diseases including keratoconjunctivitis sicca (KCS), glaucoma, neoplasia, and diabetes mellitus. This review compiles all previous studies conducted on the comprehensive canine tear proteome and highlights some of the key tear proteomic studies in human biomedical research. Tear film composition, study design, technological advancements, and select tear proteins are discussed along with key protein alterations and their use as potential biomarkers of disease. Fundamental challenges, clinical implications, and future directions of this rapidly growing field are discussed in detail. As proteomic technology and analytical techniques continue to evolve, canine tear proteomics will become a valuable tool for the veterinary ophthalmologist, enabling the early identification and diagnosis of ocular disease as well as providing a means for monitoring treatment outcomes, disease progression, and overall prognosis for the canine patient.

Animals↗

PubMed2026

Circulating Tumor DNA in Breast Cancer: A Liquid Biopsy Revolution for Non-Invasive Genomic Profiling and Clinical Decision-Making.

Breast cancer remains the most frequently diagnosed cancer and a leading cause of cancer-related mortality among women worldwide, underscoring the need for accurate, minimally invasive biomarkers to support precision oncology. Conventional tissue biopsy remains the standard for molecular characterization but is limited by its invasiveness, inability to capture spatial and temporal tumor heterogeneity, and challenges in serial monitoring. Circulating tumor DNA (ctDNA), a tumor-derived fraction of cell-free DNA, has emerged as a promising liquid biopsy biomarker capable of providing real-time genomic information throughout disease progression. This narrative review examines recent advances in ctDNA biology, analytical technologies, clinical applications, current limitations, and future directions in breast cancer management. A structured literature search of PubMed/MEDLINE, Scopus, Embase, Web of Science, and Google Scholar identified relevant English-language publications from 2015 to 2026. Current evidence indicates that highly sensitive platforms, including digital PCR, BEAMing, and next-generation sequencing, can detect clinically actionable alterations in genes such as PIK3CA, ESR1, TP53, ERBB2, AKT1, and BRCA1/2. ctDNA has demonstrated particular utility in identifying minimal residual disease, monitoring therapeutic response, detecting emerging resistance mechanisms, and guiding targeted treatment selection in advanced breast cancer. However, applications in early cancer detection, population screening, and artificial intelligence-assisted clinical decision-making remain investigational. Widespread clinical implementation is constrained by low ctDNA abundance in early-stage disease, analytical variability, limited assay standardization, and cost considerations. Continued technological innovation, prospective multicenter validation, standardized testing protocols, and evidence-based clinical guidelines are essential to fully integrate ctDNA into routine precision breast cancer care.

breast cancer↗