Critical Biomarkers Research Articles

Fusogenic Nanoreactor-Based Detection of Extracellular Vesicle-derived miRNAs for Diagnosing Atherosclerosis.

Extracellular vesicle (EV) microRNAs (miRNAs) are critical liquid-biopsy biomarkers that facilitate noninvasive clinical diagnosis and disease monitoring. However, conventional methods for detecting these miRNAs require EV lysis, which is expensive, labor-intensive, and time-consuming. Inspired by natural viral infection mechanisms, a novel strategy is developed for detecting EV miRNAs in situ via vesicle fusion mediated by viral fusion proteins. A padlock probe encapsulated within fusogenic liposomes is activated by target miRNAs, thereby initiating a highly sensitive and specific rolling circle amplification (RCA) reaction. Three EV miRNAs associated with atherosclerosis are successfully analyzed using this method, thereby enabling clear differentiation of healthy and diseased mice at several disease stages. Overall, the developed platform offers a simple approach for detecting EV miRNAs and demonstrates significant potential for broad use in applications involving disease diagnosis and monitoring.

Abstract 2420: Deep learning enables automated detection of circulating tumor cell-immune cell interactions with prognostic insights in cancer

Abstract Background: Circulating tumor cells (CTCs) are critical biomarkers for predicting therapy response and survival in breast cancer patients. Multicellular CTC clusters exhibit enhanced metastatic potential, yet their detection and characterization are constrained by low frequency in blood samples and reliance on labor-intensive manual analysis. Advancing these methods could significantly improve prognostic evaluation and therapeutic strategies. Methods: Leveraging FDA-approved CellSearch technology and single-cell sequencing, we analyzed 2, 853 blood specimens, longitudinally collected from 1358 patients with advanced cancer (breast, prostate, etc) and other diseases. Integrating machine learning and deep learning tools, we developed a novel CTCpose platform to automate detection and analysis of CTCs, immune cells, and their interactions. Using artificial intelligence (AI)-driven image analysis, we extracted over 270 cellular and nuclear features including intensity, morphometry, fourier shape, gradient/edge, and haralick of cytokeratin, CD45, and DAPI expression patterns, enabling precise characterization of CTCs, white blood cells (WBCs), CTC clusters, and their interactions with immune cells (WBCs). Results: The CTCpose platform enabled automated identification of CTCs, WBCs, homotypic CTC clusters, heterogenous CTC-WBC clusters, and immune cell clusters, providing comprehensive insights into cell morphology, biomarker expression, and spatial organization. These features correlated with patient survival, disease progression, and treatment response. Our findings highlight the clinical significance of CTC-immune cell interactions and dynamic alterations of CTCs (singles and clusters) and underscore their potential in stratifying patients into distinct risk categories. Conclusions: This study demonstrates the transformative potential of deep learning in overcoming limitations of traditional CTC detection methods and integrating imaging data with large cohorts of patient data. By automating and enhancing the analysis of CTC-immune cell interactions, we present a robust framework for developing predictive models with direct clinical relevance. This work opens avenues for personalized treatment strategies, underscoring the impact of AI in advancing precision oncology. Citation Format: Yuanfei Sun, Joshua R. Squires, Andrew Hoffmann, Youbin Zhang, Allegra Minor, Anmol Singh, David Scholten, Chengsheng Mao, Yuan Luo, Deyu Fang, William J. Gradishar, Massimo Cristofanilli, Carsen Stringer, Huiping Liu. Deep learning enables automated detection of circulating tumor cell-immune cell interactions with prognostic insights in cancer [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2025; Part 1 (Regular Abstracts); 2025 Apr 25-30; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2025;85(8_Suppl_1):Abstract nr 2420.

Integrative bioinformatics analysis reveals STAT1, ORC2, and GTF2B as critical biomarkers in lupus nephritis with Monkeypox virus infection

The monkeypox virus (MPXV) is currently spreading rapidly around the world, but the mechanisms by which it interacts with lupus nephritis (LN) are unknown. The aim of this study was to investigate the role and mechanism of lupus nephritis combined with monkeypox virus infection. The data comes from GEO and GeneCards.Through Limma and Weighted Gene Co-expression Network Analysis (WGCNA) analysis, differential expression genes (DEGs) and module genes were identified, and KEGG and GO enrichment analysis was carried out.In addition, a protein-protein interaction (PPI) network was constructed and LASSO regression was used to screen genes related to senescence. The diagnostic effectiveness was evaluated using a Nomogram and the receiver operating characteristic (ROC) curve and verified using GSE99967.Immune infiltration and gene set enrichment analysis (GSEA) Were also included in the study.In the end, miRNet was used to construct a miRNA-mRNA-TF network and screen targeted drugs through DGIdb. 5707 DEGs were identified in the lupus nephritis and 737 in the monkeypox data. WGCNA and Lasso regression analyses screened for three important targets (STAT1, ORC2, and GTF2B) .Predictive modeling and ROC of STAT1, ORC2 and GTF2B by Nomogram showed good diagnostic value .Immune infiltration analysis showed immune cell disorders and related pathway activation.The miRNA-mRNA-TF network covers 516 miRNAs and 15 transcription factors, and enrichment analysis shows that it plays an important role in senescence and inflammation.Potential Target Drugs Screened Include Guttiferone K And Silicon Phthalocyanine 4. This study identifies STAT1, ORC2, and GTF2B as key factors in cellular senescence and immune dysregulation associated with lupus nephritis and monkeypox infection, suggesting they may serve as important predictive targets.

Identification of key therapeutic targets in nicotine-induced intracranial aneurysm through integrated bioinformatics and machine learning approaches

BackgroundIntracranial aneurysm (IA) is a critical cerebrovascular condition, and nicotine exposure is a known risk factor. This study delves into the toxicological mechanisms of nicotine in IA, aiming to identify key biomarkers and therapeutic targets.MethodsGene Set Variation Analysis (GSVA), Weighted Gene Co-Expression Network Analysis (WGCNA), and enrichment analyses were conducted on differentially expressed genes (DEGs) from the GSE122897 dataset. Additionally, nicotine-related targets were identified using CTD, SwissTargetPrediction, and Super-PRED databases. Integrative machine learning approaches, such as Random Forest (RF) and Support Vector Machine (SVM), were employed to pinpoint key toxicity targets. Molecular docking and immune cell infiltration analyses were also performed.ResultsDEGs in IA showed significant alterations in metabolic, secretory, signaling, and homeostatic pathways. Several immune and metabolic response pathways were notably disrupted. WGCNA identified 1127 DEGs with 37 overlapping toxic targets between IA and nicotine. ssGSEA revealed substantial upregulation in immune response and inflammation-related processes. Integrative analyses highlighted TGFB1, MCL1, and CDKN1A as core toxicity targets, confirmed via molecular docking studies. Immune cell infiltration analysis indicated significant correlations between these core targets and various immune cell populations.ConclusionThis study uncovers significant disruptions in metabolic and immune pathways in IA under nicotine influence, identifying TGFB1, MCL1, and CDKN1A as critical biomarkers. These findings offer a deeper understanding of IA’s molecular mechanisms and potential therapeutic targets for nicotine-related toxicity.

Saliva-Sensing Dental Floss: An Innovative Tool for Assessing Stress via On-Demand Salivary Cortisol Measurement with Molecularly Imprinted Polymer and Thread Microfluidics Integration.

On-demand dental-floss-based point-of-care platform is developed for the noninvasive and real-time quantification of salivary cortisol utilizing redox-molecule embedded molecularly imprinted polymer structures and thread microfluidics. Herein, we explore the high-surface-area graphene-based electrode substrate for electrochemically synthesizing selective cortisol MIPs and integrate it with thread microfluidics to build a highly sensitive cortisol-sensing platform for stress monitoring. This platform uses flossing to collect and transport saliva to a flexible electrochemical sensor via capillary microfluidics, where cortisol, a stress biomarker, is measured. This strategy allowed us to detect cortisol as low as 0.048 pg mL-1 in real-time with a detection range of 0.10-10,000 pg mL-1 (R2 = 0.9916). The saliva-sensing dental floss provides results within 11-12 min. The thread-based microfluidic design minimizes interference and ensures consistent repeatability when testing both artificial and actual human saliva samples, yielding 98.64-102.4% recoveries with a relative standard deviation of 5.01%, demonstrating high accuracy and precision. For the human saliva sample (as part of the stress study), the platform showed a high correlation (r = 0.9910) against conventional ELISA assays. Combined with a wireless readout, this saliva floss offers a convenient way to monitor daily stress levels. It can be extended to detect other critical salivary biomarkers with high sensitivity and selectivity in complex environments.

Unraveling the roles of BMS1, ZNF749 and miR-6726-5p in MODS progression via integrated multiomics and ML-based approach

BackgroundMultiple organ dysfunction syndrome (MODS) is a severe, life-threatening condition characterized by progressive dysfunction of multiple organ systems, commonly observed in critically ill patients within intensive care units (ICUs). Despite intensive medical interventions, MODS continues to be a major cause of mortality due to its multifactorial pathophysiology and the absence of effective targeted therapies. The significant burden on patients, families, and healthcare systems underscores the critical need for a deeper molecular understanding of MODS pathophysiology to inform the development of targeted therapeutic interventions.AimThis study aims to elucidate the molecular mechanisms of MODS through a comprehensive integration of transcriptomic profiling and network-based approaches.MethodsDifferential expression analysis (DEA) was followed by the identification of hub modules via weighted gene co-expression network (WGCN), establishment of protein interaction network (PPIN), gene ontology (GO) term & pathway enrichment analysis of PPIN hub module, and feed-forward loop (FFL) analysis.ResultsUsing the GSE13205 dataset, we identified 2365 differentially expressed genes (DEGs) followed by obtaining a total of 179 hub DEGs from the WGCN hub modules. The PPIN highlighted key regulatory nodes such as BMS1, FBL, and NOP14, emphasizing the disruption of ribosomal function and cellular stress responses in MODS. Furthermore, a MODS-specific 3-node FFL comprising 325 nodes and 784 edges revealed a tightly interconnected regulatory motif involving ZNF749 (transcription factor), miR-6726-5p (miRNA), and BMS1 (mRNA).DiscussionThis motif underscores the central role of transcriptional and post-transcriptional regulation in MODS pathophysiology. Functional enrichment analyses linked these hub genes to significant pathways, including rRNA processing and ribonucleoprotein complex biogenesis. Through advanced multiomics approaches, this study unveils groundbreaking insights into the molecular landscape of MODS, identifying critical biomarkers and therapeutic targets.ConclusionThis study lays the groundwork for precision medicine solutions by revealing important facets of MODS's molecular landscape using sophisticated multiomics techniques. One important step in improving critical care for patients with MODS is identifying important regulatory networks and molecular targets. Future research should concentrate on confirming these results using clinical trials and experimental models, opening the door for focused therapies that could raise survival rates and lessen the toll that MODS takes on patients, families, and healthcare systems around the globe.

Photoacoustic Imaging in Visualization of Acupuncture Mechanisms

Photoacoustic imaging (PAI) has emerged as a transformative modality for bridging traditional Chinese medicine (TCM) theory and contemporary biomedical research in acupuncture mechanism studies. This review assesses PAI’s capacity to decode acupuncture-induced neuromodulatory and hemodynamic effects, with dual focus on the central nervous system (CNS) responses and acupoint-specific microcirculatory dynamics. Leveraging the photoacoustic effect coupled with ultrasonic detection, PAI enables non-invasive, high-resolution mapping of cerebral hemodynamic parameters, including blood flow, oxygen saturation and hemoglobin concentrations, in real time. Experimental evidence from murine models of cerebral hypoperfusion and ischemic stroke demonstrates acupoint-specific spatiotemporal activation patterns, particularly at Yongquan (KI1) and Yanglingquan (GB34), revealing cortical hemodynamic reorganization and angiogenesis. At the microcirculatory level, PAI identifies functional transitions from quiescent to activated vascular states during disease progression, characterized by altered perfusion dynamics and vascular permeability. While structural metrics (e.g., microvascular density and curvature) show no significant differences in knee osteoarthritis models, functional parameters such as hemoglobin flux and oxygen metabolism emerge as critical biomarkers of acupoint specificity. PAI further enhances treatment precision through standardized acupoint localization, as evidenced by electrostimulation studies at Hegu (LI4) and Zhongwan (CV12). This synthesis highlights PAI’s dual contributions: (1) validating CNS-mediated systemic regulation via acupoint-brain functional correlations, and (2) providing multimodal quantification of microcirculatory dynamics. Future directions emphasize integration of molecular probes for neuroendocrine pathway visualization and multimodal imaging to address unresolved thermal/optical interactions. By synergizing TCM principles with advanced biophotonics, PAI establishes a paradigm for mechanistic acupuncture research and clinical translation.

Continuous Monitoring of Sleep-Related Biomarkers via a Nearable Solution Based on Fiber Bragg Grating Technology.

This study explores the innovative application of a nearable solution (i.e., mattress) based on fiber Bragg grating (FBG) technology for continuously monitoring of critical sleep-related biomarkers. Based on biocompatible silicone compounds, the mattress embeds thirteen strategically positioned FBG sensors to detect bed occupancy, sleeping posture, respiratory rate (RR), and heart rate (HR). Our experimental protocol involves ten participants who underwent simulated sleeping conditions to evaluate the mattress's performance across different postures and respiratory patterns. Employing traditional machine learning algorithms, including decision tree, support vector machine (SVM), and Naïve-Bayes classifiers, the mattress achieves 100$\%$ accuracy in bed occupancy detection. It also effectively distinguishes between axial and lateral sleeping positions, with SVM achieving the highest accuracy of 78.4$\%$ for axial versus lateral differentiation and convolutional neural networks achieving 75.9$\%$ in distinguishing left from right positions. Additionally, for most participants, the system successfully estimates RR and HR with mean absolute errors of less than 0.7 breaths per minute and 4 bpm, respectively, across various breathing patterns in terms of frequencies and amplitudes employing different algorithms (frequency and time-domain approaches). The promising findings highlight the potential of the proposed system for a comprehensive evaluation of sleep-related breathing disorders in clinical and home settings.

Sensitive detection of small extracellular vesicles using a gold nanostar-based SERS assay.

Small extracellular vesicles (sEVs) are lipid bilayer-bound vesicles that carry critical biomarkers for disease detection. However, the inherent heterogeneity and complexity of sEV molecular characteristics pose significant challenges for accurate and comprehensive molecular profiling. Traditional analytical methods, including immunoblotting, enzyme-linked immunosorbent assay (ELISA), and flow cytometry, exhibit several limitations, such as being time-consuming, requiring large sample volumes, and demonstrating relatively low sensitivity. Therefore, there is an urgent need to develop a highly sensitive and specific assay for the reliable detection of sEVs. Surface-enhanced Raman scattering (SERS) assays have emerged as a promising approach for sEV detection, offering advantages including high sensitivity and specificity. In the proposed SERS assay, SERS nanotags - comprising nanoparticles coated with Raman-active molecules and conjugated with antibodies - are employed to label surface-bound molecules on sEVs. This approach facilitates the generation of a high-intensity signal from molecules present in low abundance. Recently, anisotropic nanoparticles, such as star-shaped nanostructures, have garnered interest due to their ability to significantly amplify generated SERS signals for ultra-sensitive biomarker detection. In this study, we explore the application of gold nanostars (AuNSs) as SERS nanotags for the detection of sEVs. In principle, AuNS-based SERS nanotags were used to label the EpCAM protein, which can be found on the surface of cancer cell derived sEVs, and then sEV labelled SERS nanotags were captured by CD9-conjugated magnetic beads to form an immunocomplex, which exhibits a SERS signal. Our results demonstrate that the proposed SERS assay utilizing AuNSs provides high specificity and sensitivity, with a detection limit as low as 2.47 × 103 particles per μL. Furthermore, the assay was tested with spiked plasma samples (cancer cell-derived sEVs spiked into healthy plasma), showing that its specificity remains unaffected by the presence of plasma. These findings suggest that the SERS assay incorporating AuNSs holds significant promise as an effective and reliable detection method for potential clinical applications.

A Smart DNA Network-Based Diagnostic System for Enrichment and Detection of Circulating Tumor Cells in Cancer Liquid Biopsy.

Circulating tumor cells (CTCs) have emerged as critical biomarkers in liquid biopsy for noninvasive tumor diagnosis and real-time monitoring of cancer progression. However, the isolation of CTCs is often required before detection due to their ultralow abundance in peripheral blood. These isolation processes are typically time-consuming and prone to cell loss, which limits the utility of CTC-based liquid biopsy. Herein, we present a DNA network-based diagnostic system that enables specific recognition, selective enrichment, and accurate detection of CTCs directly from blood samples. The DNA network comprises ultralong DNA chains embedded with polyvalent aptamers and fluorescence detection modules. The polyvalent aptamers selectively bind to the epithelial cell adhesion molecule (EpCAM) on a CTC membrane, facilitating their enrichment through base pairing-driven DNA network formation. This system semiquantitatively detects the expression level of cancer-associated microRNA within CTCs using ratiometric fluorescence imaging based on the chemical assembly of two fluorescence modules. In clinical blood samples, this diagnostic system achieves 100% precision and 96% accuracy in distinguishing breast cancer patients from healthy donors, highlighting its promising potential for clinical breast cancer diagnosis.

Pembrolizumab versus bevacizumab plus modified FOLFOX6 in metastatic MSI-H/dMMR colorectal cancer: a multicenter retrospective study with CT evaluation.

The optimal therapeutic strategy for metastatic microsatellite instability-high/mismatch repair-deficient (MSI-H/dMMR) colorectal cancer (CRC) remains uncertain. This multicenter retrospective study compared the efficacy and safety of pembrolizumab monotherapy versus bevacizumab combined with modified FOLFOX6 (mFOLFOX6) in this molecularly defined population. Consecutive patients with metastatic MSI-H/dMMR CRC treated with pembrolizumab or bevacizumab plus mFOLFOX6 at two tertiary centers (2017-2024) were analyzed. Dual primary endpoints included overall survival (OS) and progression-free survival (PFS); secondary endpoints encompassed incidence of grade ≥3 treatment-emergent adverse events (AEs). Among 58 eligible patients (PE: n=30; BF: n=28), the PE cohort demonstrated a significantly higher objective response rate (ORR) compared to the BF cohort (XX% vs XX%, p=0.030) after a median follow-up of 18.0 months (IQR: 1.0-24.0). Survival analyses revealed superior outcomes in the PE cohort, with a median OS of 12.0 months (95% CI: 10.2-14.1) versus 8.8 months (95% CI: 7.1-9.6) in the BF cohort (HR=0.55, 95% CI: 0.29-0.56; p=0.02). Similarly, median PFS was prolonged in the PE cohort (7.0 months, 95% CI: 5.3-9.3) relative to the BF cohort (3.7 months, 95% CI: 2.2-5.4; HR=0.46, 95% CI: 0.24-0.89; p<0.001). No statistically significant intergroup differences were observed in grade ≥3 treatment-emergent AE rates. Pembrolizumab monotherapy significantly improved survival over bevacizumab-based chemotherapy in metastatic MSI-H/dMMR CRC, with a manageable safety profile. These results reinforce PD-1 inhibitors as first-line therapy for this population, while highlighting tumor mutation burden (TMB) and tumor burden as critical biomarkers for personalized strategies.

Subtle behavioral alterations in the spontaneous behaviors of MPTP mouse model of Parkinson’s disease

Midbrain dopamine (DA) neurons are essential for regulating movement, emotion, and reward, with their dysfunction closely linked to Parkinson’s disease (PD). While DA neurons in the substantia nigra pars compacta (SNc) and ventral tegmental area (VTA) have known overlapping roles in behaviors such as depression and reward, their distinct contributions to subtle spontaneous behaviors remain insufficiently understood. In this study, we utilized a 3D behavioral analysis platform powered by machine learning to explore motor and nuanced behavioral changes in a subacute MPTP mouse model of PD. This investigative approach was combined with cell-type-specific genetic ablation of DA neurons in both the SNc and VTA. Our findings highlight significant deficits in rearing, walking, and hunching behaviors correlated with the loss of SNc DA neurons, but not VTA DA neurons, alongside increased overall movement, reduced movement precision, and pronounced right-sided lateralization. These subtle features, particularly rearing deficits and lateralization, emerge as critical behavioral biomarkers of SNc DA neuron loss, thereby enhancing the translational relevance of PD models.

An Updated Review on Dysregulated lncRNAs and their Contribution to the Various Molecular Types of Lung Carcinoma

Lung cancer is correlated with a high death rate, with approximately 1.8 million mortality cases reported worldwide in 2022. Despite development in the control of lung cancer, most cases are detected at higher stages with short survival rates. This reveals a need to recognize novel techniques to treat malignancy and decrease the burden of lung cancer. Long noncoding RNAs (lncRNAs) manage vital cellular and biochemical functions. lncRNAs play crucial roles in transcriptional and translational processes and signaling cascades. Recently, lncRNAs have been reported to be associated with malignancy where their expression is deregulated, leading to abnormal cellular activities and signaling pathways. In various malignancies, including lung cancer, lncRNA deregulation disrupts normal cellular function, promoting tumorigenesis and influencing patient outcomes and treatment responses. Studies have shown that lncRNAs can act as both oncogenes and tumor suppressors, depending on the lung cancer subtype, specifically in Non-small Cell Lung Cancer (NSCLC) and Small Cell Lung Cancer (SCLC). This dual role of lncRNAs as critical biomarkers might provide insights into lung cancer development and progression. lncRNAs have been discussed as key biomarkers in lung cancer. A comprehensive understanding of the biological activities of lncRNAs in NSCLC and SCLC may improve prognosis, diagnosis, and therapeutic methods. Researchers are increasingly interested in lncRNAs as potential diagnostic biomarkers and therapeutic targets in cancer treatment. As researchers continue to explore lncRNAs, their pivotal roles in lung cancer become increasingly evident. This review highlights the function of lncRNAs in lung carcinogenesis and discusses their molecular mechanisms of function.

Amelioration of Isoproterenol‐Induced Myocardial Infarction by the Phytochemical Koenigicine via Modulation of NF‐κB/HO‐1/NQO‐1 Pathways: An In Vivo Analysis

ABSTRACTPhytochemicals exhibit diverse cardioprotective properties that contribute to the prevention and management of myocardial infarction (MI). In our study, we examined the potency of the phytochemical Koenigicine, which belongs to the carbazole alkaloid, in alleviating MI in an animal model. The animals were supplemented with Koenigicine before MI induction using isoproterenol, with supplementation continuing during the MI induction period. The impact of Koenigicine on mitigating the onset of MI was evaluated by quantifying lipid levels and arterial blood pressure. Its ameliorative potential against isoproterenol‐induced cardiac damage was assessed by measuring antioxidant levels and critical biomarkers of MI in the experimental animals. Protein, C‐reactive protein (CRP), and uric acid levels were assessed to determine the effect of Koenigicine on immune function and inflammation. Additionally, the impact of Koenigicine on cardiac muscle function and its role in healing ischemic‐induced cardiac tissues were examined in MI‐induced rats. The effect of Koenigicine treatment on post‐ischemic injury was analyzed by quantifying NF‐κB, HO‐1, and NQO‐1 levels, and the findings were confirmed through cardiac histopathological analysis. Koenigicine administration effectively mitigated MI induction by regulating lipid levels and arterial blood pressure. It enhanced the antioxidant defense system, attenuated inflammatory signaling, and thereby prevented MI‐induced cardiac tissue damage. The results of MI biomarker analysis confirmed the ameliorative potential of Koenigicine against isoproterenol‐induced cardiac inflammation. Furthermore, it demonstrated a positive effect on cardiac function and facilitated the healing process following MI induction. Overall, our findings suggest that Koenigicine provides preventive, suppressive, and ameliorative effects at all stages of MI, addressing gaps in the efficacy of currently available treatments.

The diagnostic value of citrullinated antigens with multiple citrulline similar motif in patients with rheumatoid arthritis.

The diagnostic value of citrullinated antigens with multiple citrulline similar motif in patients with rheumatoid arthritis.

Machine learning-based transcriptmics analysis reveals BMX, GRB10, and GADD45A as crucial biomarkers and therapeutic targets in sepsis.

Sepsis is a life-threatening condition characterized by a dysregulated host response to infection, resulting in high mortality rates and complex clinical management. This study leverages transcriptomics and machine learning (ML) to identify critical biomarkers and therapeutic targets in sepsis. Analyzing microarray data from the Gene Expression Omnibus (GEO) datasets GSE28750, GSE26440, GSE13205, and GSE9960, we discovered three pivotal biomarkers that BMX (bone marrow tyrosine kinase gene on chromosome X), GRB10 (growth factor receptor bound protein 10), and GADD45A (growth arrest and DNA damage inducible alpha), exhibiting exceptional diagnostic accuracy (AUC >0.9). Functional enrichment analyses revealed that these genes play key roles in reactive oxygen species metabolism and immune response regulation. Specifically, GADD45A was positively correlated with eosinophils and inversely associated with activated NK cells, CD8 T cells, and activated memory CD4 T cells. BMX showed positive correlations with eosinophils, mast cells, and neutrophils, while GRB10 was linked to eosinophils and M2 macrophages. Additionally, we constructed a comprehensive mRNA-miRNA-lncRNA regulatory network, identifying key interactions that may drive sepsis pathogenesis. Molecular docking and dynamics simulations validated Bendroflumethiazide, Cianidanol, and Hexamidine as promising therapeutic agents targeting these biomarkers. In conclusion, this integrated approach provides profound insights into the molecular mechanisms underlying sepsis, pinpointing BMX, GRB10, and GADD45A as pivotal biomarkers and therapeutic targets. These findings significantly enhance our understanding of sepsis pathophysiology and lay the groundwork for developing personalized diagnostic and therapeutic strategies aimed at improving patient outcomes.

Archaeal Lipids: Extraction, Separation, and Identification via Natural Product Chemistry Perspective.

Archaeal lipids, defining a primordial life domain alongside Bacteria and Eukarya, are distinguished by their unique glycerol-1-phosphate backbone and ether-linked isoprenoid chains. Serving as critical geochemical biomarkers, archaeal lipids like glycerol dialkyl glycerol tetraethers (GDGTs) underpin paleoclimate proxies, while their phylum-specific distributions illuminate phylogenetic divergence. Despite the maturity of Mass Spectrometry-based quantitative biomarkers-predominantly those with established structures-becoming well-established in geochemical research, systematic investigation of archaeal lipids as natural products has notably lagged. This deficit manifests across three key dimensions: (1) Extraction methodology lacks universal protocols adapted to diverse archaeal taxa and sample matrices. While comparative studies exist, theoretical frameworks guiding method selection remain underexplored. (2) Purification challenges persist due to the unique structures and complex isomerization profiles of archaeal lipids, hindering standardized separation protocols. (3) Most critically, structural characterization predominantly depends on decades-old foundational studies. However, the existing reviews prioritize chemical structural, biosynthetic, and applied aspects of archaeal lipids over analytical workflows. This review addresses this gap by adopting a natural product chemistry perspective, integrating three key aspects: (1) the clarification of applicable objects, scopes, and methodological mechanisms of various extraction technologies for archaeal lipids, encompassing both cultured and environmental samples; (2) the elucidation of separation principles underlying polar-gradient lipid fractionation processes, leveraging advanced chromatographic technologies; (3) the detailed exploration of applications for NMR in resolving complex lipid structures, with specialized emphasis on determining the stereochemical configuration. By synthesizing six decades of methodological evolution, we establish a comprehensive analytical framework, from lipids extraction to structural identification. This integrated approach constructs a systematic methodological paradigm for archaeal lipid analysis, bridging theoretical principles with practical implementation.

Integrating CRISPR-Cas12a with Aptamer as a Logic Gate Biosensing Platform for the Detection of CD33 and CD123.

Molecular logic gates, as biomolecule-based computational systems, are highly suitable for multitarget detection due to their programmability and modularity. However, existing systems are primarily limited to nucleic acid detection and have not been widely applied to disease-related sensing, particularly for disease antigens. CD33 and CD123 are critical biomarkers for acute myeloid leukemia (AML), yet conventional detection methods rely on expensive equipment and complex procedures, limiting their accessibility and practicality. This study designs a DNA logic gate system integrating nucleic acid aptamers, catalytic hairpin assembly (CHA), and CRISPR-Cas12a, pioneering its use for AML antigen detection. The system comprises three modules: input recognition, signal amplification, and signal transduction. Nucleic acid aptamers specifically identify CD33 and CD123, while CHA enables efficient signal amplification and CRISPR-Cas12a generates a fluorescent output via trans-cleavage activity. The system operates stably at room temperature and implements multiple logic gate models, including YES, OR, AND, NOR, and INHIBIT, enabling the simultaneous detection of CD33 and CD123. Experimental results are visually distinguishable under blue light, and the system requires only standard fluorescence detection instruments. In serum samples, it exhibits excellent selectivity and stability, with a detection limit of 0.5 ng/mL. This study pioneers the application of logic gate technology for disease antigen detection, addressing a critical gap in AML biomarker sensing. Our study indicates that this logic detection platform, characterized by its simplicity in operation, high sensitivity, and versatility in logic functions, holds promise as a potent sensing system for the intelligent multiplex target detection of disease antigens, environmental pollutants, and heavy metals.

Beyond Targeted Newborn Screening: A Nontargeted Metabolomics Workflow to Investigate Birthweight-Metabolome Correlations.

Newborn screening (NBS) is one of the United States' largest, most successful preventative public health initiatives, improving outcomes for newborns with inborn errors of metabolism. Most disorders on the Recommended Uniform Screening Panel are screened using triple-quadrupole mass spectrometry and flow injection analysis. While these methods are sensitive and well suited for high-throughput quantitative applications, the breadth of measured analytes is limited to a relatively small number of biomarkers, which often have considerable overlaps between healthy and diseased populations. High-resolution liquid chromatography-mass spectrometry (LC-MS)-based metabolomics is now capable of profiling thousands of metabolites, making it well suited for exploratory and biomarker discovery studies. To this end, we developed a robust workflow for performing nontargeted LC-MS analysis on dried bloodspot (DBS) specimens with coverage across many metabolic pathways relevant to NBS. HILIC chromatography enabled quantitation of amino acid and acylcarnitine species while also retaining lipid species, such as lyso-phosphatidylcholines. We analyzed 810 newborn-derived DBS samples across a wide range of newborn birthweights, identifying correlations with metabolites that help to better account for the lower accuracy observed for some NBS markers (e.g., isovalerylcarnitine). Additionally, we leveraged this nontargeted workflow to capture new biomarkers and metabolic phenotypes in newborns associated with parenteral nutrition administration and maternal nicotine exposure. Two critical biomarkers were identified as useful additions to targeted screening panels: N-acetyltyrosine as a qualitative marker for parenteral nutrition administration and N-acetylputrescine as a quantitative marker for controlling birthweight variability.

Proinflammatory cytokines, oxidative stress, and organ function as biomarkers of soman (GD) chronic neurotoxicity

Organophosphate (OP) nerve agents, such as soman (GD), pose great risk to neurological health by inhibiting acetylcholinesterase, leading to seizures, epilepsy, and behavioral deficits. While acute treatment may alleviate immediate symptoms, the long-term consequences, particularly those involving neuroinflammation and systemic toxicity, remain poorly understood. This study used adult male and female Sprague Dawley rats to investigate the chronic effects of a single acute exposure to soman (132 µg/kg, s.c., 1.2 × LD50) on neuroinflammation, behavioral comorbidity, and systemic toxicity. Following exposure, animals were treated with atropine sulfate (2 mg/kg, i.m.) and oxime HI-6 (125 mg/kg, i.m.) to mitigate peripheral cholinergic effects, and with midazolam (3 mg/kg, i.m., 1 h post-exposure) to control seizures. Spontaneously recurring seizures were monitored during handling and with video electroencephalogram (vEEG). Neurobehavioral deficits were assessed 4–8 weeks post-exposure. At 18 weeks post-exposure, brain, serum, and cerebrospinal fluid (CSF) were analyzed for inflammatory and nitro-oxidative stress markers, and the liver and kidney function biomarkers were evaluated. Soman-exposed animals developed epilepsy, confirmed by handling-induced seizures and/or continuous vEEG monitoring. Behavioral assessments revealed significant memory deficits following soman exposure. Proinflammatory cytokines (TNF-α, IL-6, IL-1α, IL-18, IL-17A, and MCP-1) were significantly elevated in both serum and CSF, alongside corresponding increases in their gene expression in the brain. Elevated reactive oxygen/nitrogen species were detected in the serum. Although hematological parameters remained unchanged, a significant increase in total bilirubin and an upward trend in serum blood urea nitrogen (BUN) levels and BUN: Creatinine ratio indicated potential liver and kidney dysfunction. However, no significant structural changes in these organs at the cellular level were observed in histological analyses. This study identifies critical chronic biomarkers of soman exposure affecting the brain, serum, CSF, liver, and kidney. The findings highlight the critical need to monitor systemic and neurological impacts, as well as organ function, to develop effective diagnostic and therapeutic strategies for survivors of nerve agent exposure or OP pesticide poisoning. Behavioral deficits and EEG changes in soman-exposed animals further emphasize the long-term neurological consequences of exposure.