Search Results (2,313)

Growing evidence indicates that myocardial infarction (MI) is the clinical manifestation of heterogeneous plaque substrates with distinct molecular, cellular, and biomechanical mechanisms. Acute coronary thrombosis (ACT) most commonly arises from plaque rupture (PR), plaque erosion (PE), and calcified nodules (CNs), each associated with different inflammatory profiles, thrombus composition, clinical presentation, and prognosis. This comprehensive review provides a clinician-oriented synthesis of the pathophysiological mechanisms underlying these three principal plaque phenotypes and discusses their implications for the contemporary management of acute coronary syndromes (ACS). We examine the molecular and cellular determinants of plaque instability and highlight how systemic factors such as plaque burden, impaired healing responses, and myocardial jeopardy modulate clinical risk. The role of intracoronary and non-invasive imaging is discussed primarily as a tool to elucidate plaque biology with direct clinical relevance rather than merely as a procedural guide. Building on these insights, we propose a conceptual framework for integrating plaque biology into clinical decision-making across the acute phase, secondary prevention, and long-term follow-up. In particular, recognizing the biological heterogeneity of plaque substrates may support more personalized therapeutic strategies, enabling clinicians to tailor pharmacological and interventional approaches according to the underlying plaque phenotype and patient-specific risk profile. Finally, we briefly address emerging perspectives, including the potential role of artificial intelligence (AI) in refining plaque characterization, risk stratification, and precision cardiovascular prevention. Overall, recognition of PR, PE, and CNs as biologically distinct entities supports a shift toward mechanism-informed and personalized management of MI, aligning advances in plaque biology with the principles of precision cardiovascular medicine. Full article

Depression is a highly heterogeneous psychiatric disorder with its pathogenesis increasingly linked to dysregulated neuroinflammation. Microglia, as the resident immune cells of the central nervous system (CNS), play a pivotal role in the initiation and progression of the neuroinflammation and the pathophysiology of depression. These cells exhibit a dual role in pro- and anti-inflammatory processes, dynamically regulating immune responses through immunometabolic reprogramming in response to environmental cues. This review elaborates how metabolic remodeling in microglia, particularly within glucose, lipid, and amino acid pathways, drives their polarization toward a pro-inflammatory phenotype. This shift promotes depression pathogenesis via the release of inflammatory factors, disruption of synaptic plasticity, and mediation of neurotoxicity. We further discuss the impact of existing antidepressants on cellular metabolism and highlight the promise and challenges of targeting specific microglial metabolic pathways as a novel therapeutic strategy. This synthesis provides new insights into the immunometabolic mechanisms of depression and outlines directions for developing targeted treatments. Full article

Background: Recent advances in spatial transcriptomic technologies enable in situ gene expression profiling while preserving spatial context. This capability is particularly important for studying the tumor microenvironment (TME), where diverse and admixed cell populations interact within highly organized spatial niches that influence tumor progression and therapeutic response. However, the limited resolution of early spatial transcriptomic platforms results in each spatial spot capturing transcripts from multiple cell types, making accurate spot deconvolution or annotation a critical yet challenging step in downstream data analysis. The level of complexity will be particularly prominent in heterogeneous samples like the tumor microenvironments where multiple cell types are highly admixed and reliable single-cell reference atlases may usually be unavailable. Methods: In this paper, we developed our method called STEA, which is a novel and accurate reference-independent enrichment-based annotation algorithm for major cell type. Unlike the existing approaches, STEA does not require single-cell RNA sequencing datasets as reference, offering both flexibility and computational efficiency in execution. Results: We performed comprehensive benchmarking using a variety of simulated datasets across different platforms and scenarios and demonstrated the superior accuracy of STEA. Apart from synthetic data, we also evaluated multiple real datasets to further exemplify its practical applicability on both oncology-related and oncology-unrelated data. More importantly, we could confidently demonstrate the high concordance between prediction of STEA and histological classification by experienced pathologist. Conclusion: Our STEA algorithm provides a practical reference-independent framework to complement the cutting-edge spatial transcriptomics in genomics studies, facilitating accurate downstream high-dimensional spatial characterization of cellular and molecular landscapes, reconstruction of tissue architecture as well as cell–cell communication in malignant and non-malignant scenarios. Taken together, our comprehensive evaluation demonstrates the robustness and reliability of STEA, highlighting its potential as a valuable tool for studying complex tissue organization, particularly within heterogeneous TME. Full article

Extracellular vesicles (EVs) can disseminate replication-competent viral genomes complexed with selected host proteins, enabling stealth cell-to-cell transfer within lipid membrane-enclosed bubbles. In addition to complementing free-virion spread, EV-associated genomes can be protected from neutralizing antibodies and persist under conditions in which classical virion production decreases. Here, we propose a route-resolved framework in which interconnected cellular secretory pathways, including endoplasmic reticulum (ER) remodeling, multivesicular body (MVB) biogenesis, secretory autophagy, and plasma-membrane budding, jointly generate EV heterogeneity and create discrete opportunities for the capture, protection, and export of infectious cargo. We highlight reticulon-3 (RTN3), an ER-shaping protein, as an upstream regulator that can couple infection-induced ER microdomains to endosomal docking and to autophagy-linked trafficking decisions that bias intermediates toward secretion rather than degradation. Supporting this view, transmission electron microscopy of dengue virus-infected cells reveals extensive vesicular remodeling, including irregular MVBs adjacent to the plasma membrane and autophagosome-like double-membrane structures, consistent with altered vesicular routing following RTN3 perturbation. Collectively, these route-resolved, spatially organized spatio-organelle changes support a pathomechanistic model in which RTN3-mediated ER remodeling reshapes ER-endosome-autophagy trafficking interfaces, creating regulated decision points that can be leveraged to stratify infectious EV subsets (with infectivity-linked single-vesicle and quantitative proteomics approaches) and to inform host-directed strategies that curb non-lytic viral dissemination. Full article

Fetal skeletal muscle development involves coordinated interactions among myogenic, stromal, vascular, and immune compartments, yet the cellular and molecular programs guiding tissue maturation remain incompletely understood. To address this, we generated a high-resolution single-cell atlas of fetal female goat skeletal muscle and performed trajectory analysis, transcription factor activity profiling, and intercellular communication mapping. Unsupervised clustering identified RUNX2 mesenchymal progenitors, fibro-adipogenic progenitors (FAPs), myofibroblasts, endothelial cells, macrophages, differentiating myocytes, and mature skeletal muscle fibers, revealing a heterogeneous ecosystem in which stromal populations support myogenic progression and vascular and immune cells contribute to tissue organization. Pseudotime analysis traced a maturation continuum from differentiation-competent myocytes to contractile fibers, marked by sequential activation of extracellular matrix remodeling, cytoskeletal stabilization, and sarcomere assembly. KEGG and GO enrichment highlighted stage-specific engagement of ErbB, Hedgehog, and Hippo signaling, as well as cell cycle and ubiquitin-mediated proteolysis pathways, linking proliferation, differentiation, and structural maturation. Transcription factor profiling revealed early-stage proliferative and morphogenetically permissive states driven by E2F4/5, HMGA2, and HAND2, transitioning to late-stage differentiation, ECM remodeling, and tissue stabilization orchestrated by CEBPB, CREB3L1, ELK1, and E2F2. Cell–cell communication analysis showed a developmental redistribution of signaling authority, from ECM-driven, progenitor-centered networks to modular, structurally stabilized interactions. These findings define the cellular, transcriptional, and signaling framework orchestrating fetal skeletal muscle maturation. Full article

Stress has been prevalent and has become an epidemic health burden, loaded with chronic disorders. The stress response is an adaptive mechanism that prepares an individual to respond to threats or other stressors in a fight-or-flight situation. The stress response involves the induction of neurological and hormonal networks and is usually resolved when stress subsides; however, persistent stress leads to permanent and detrimental impacts on health. With the rise of advanced single-cell analysis technologies, a wave of basic and translational research aimed at elucidating stress has shed light on the underlying mechanisms. Among 80 studies in this review, stressors are classified into acute/chronic physical, physiological, and psychological groups, whereas some studies have more than one stress source. Single-cell RNA-seq was the dominant technology utilized in these studies. This advanced technique systematically reveals cellular heterogeneity in gene expression patterns and the differential transcriptomic landscape of stress response in a wide array of tissues and organ systems, e.g., the nervous system, the endocrine system, the immune system, and others. Bioinformatics identified a single-cell atlas of stress-specific cell subtypes, cell-to-cell interactions, and enriched pathways, showing promise for stress syndrome biomarkers, attenuation, and targeted therapy. The limits of these stress studies were mainly focused on transcriptomics, so future studies using multi-omics approaches across multiple organ systems will yield insights into stress disorders and novel therapeutic strategies. Full article

The papaya aril is a specialized seed appendage that has been reported to contain germination-inhibiting substances and usually requires removal before seed germination, thereby limiting breeding efficiency. However, the cellular origin and candidate molecular regulators of papaya aril development remain poorly understood. To investigate the early developmental process and candidate regulatory genes of the papaya aril, we combined histological analysis, bulk RNA-seq, and single-cell RNA-seq. Histological observations suggested that aril differentiation begins around 10 days after pollination (DAP) in the funiculus region. Based on this initiation stage, bulk RNA-seq profiling of seeds at 5, 10, and 15 DAP identified genes with initiation-stage-specific expression and prioritized candidate genes potentially related to seed appendage development, including CpRING-like, CpMBR2, and CpNDR8. Single-cell RNA-seq of seeds at 10 and 15 DAP annotated a putative aril cell population and reconstructed its developmental trajectory, revealing five trajectory-associated genes: CpATJ3, CpDYL1, CpGRP-like, CpHIRD11, and CpERD15. Integrative analysis of bulk and single-cell transcriptomic datasets further identified three candidate genes potentially involved in aril development: CpFER3, CpUVI4, and CpCEP1. These findings support the funiculus region as the likely anatomical origin of the papaya aril and provide candidate genes for future functional validation. Full article

Background: Critical illness is characterized by profound and rapidly evolving metabolic derangements driven by systemic inflammation, hypercatabolism, fluid shifts, and endocrine dysregulation. These dynamic changes markedly limit the accuracy of predictive equations, increasing the risk of both underfeeding and overfeeding. Indirect Calorimetry Energy represents the gold standard for measuring energy expenditure, while bioelectrical impedance vector analysis (BIVA) provides complementary insights into hydration status, cellular integrity, and body cell mass. In palliative care, AI-supported integration of indirect calorimetry and BIVA enables goal-concordant artificial nutrition by aligning energy delivery with real-time metabolic status while minimizing symptom burden. Artificial intelligence (AI) has emerged as a promising tool to integrate these heterogeneous data streams and support adaptive nutritional strategies. Methods: We conducted a structured narrative review of the literature published between 2000 and 2025 using PubMed, Scopus, Embase, and Web of Science. Artificial intelligence was not used to perform the literature search or study selection. Instead, AI was analyzed as a clinical and technological component within the included studies and explored as a future enabling strategy. Eligible publications involved adult critically ill patients and addressed indirect calorimetry, BIVA-derived parameters, or AI-based metabolic modeling applied to nutritional support. Given the heterogeneity of study designs and outcomes, findings were synthesized qualitatively. Results: Predictive equations showed substantial inaccuracy in unstable metabolic states, with errors frequently exceeding ±20–40%. Indirect calorimetry enabled individualized assessment of energy expenditure but remained limited by intermittent availability. Serial BIVA assessments consistently identified clinically relevant alterations in hydration status, body cell mass, and phase angle, the latter being strongly associated with adverse outcomes. Studies incorporating AI demonstrated improved integration of calorimetry, BIVA, and clinical variables, allowing identification of metabolic phenotypes, anticipation of metabolic shifts, and generation of adaptive nutritional recommendations. Conclusions: This narrative review highlights the complementary roles of Indirect Calorimetry and BIVA in characterizing metabolic needs in critical illness. Artificial intelligence does not replace these tools but enhances their clinical utility by integrating multidimensional data into dynamic, patient-specific nutritional strategies. The combined AI–IC–BIVA approach represents a promising framework for metabolic precision nutrition in the ICU, warranting prospective validation. Full article

This study theoretically investigates the role of plant moisture content and its spatial heterogeneity in wildfire dynamics using Cellular Automata models. The model incorporates varying moisture levels and ignition probabilities across different grid configurations, including homogeneous moisture grids and heterogeneous setups with elliptical and segmented high-moisture zones. The relationship between moisture content and ignition probability is modeled using a nonlinear formulation, reflecting threshold-like combustion dynamics observed in real ecosystems. Simulation results show that introducing high-moisture zones significantly reduces the rate of fire spread, with segmented configurations providing the most effective firebreaks. In this context, the ‘suppression effect’ denotes the reductions in forward spread and total burned area attributable to high-moisture regions acting as low-ignitability barriers. The effect is more pronounced when ignition probability depends nonlinearly on moisture, since the nonlinear mapping produces a steeper decline in ignitability above a critical moisture range, which reduces successful transmission across the barrier and increases the likelihood of fire isolation. In particular, the results highlight how modeling can be used as a decision-support tool for the strategic placement of firebreaks. By evaluating alternative spatial configurations of moisture, the approach helps identify barrier designs that maximize containment effectiveness while minimizing ecological and economic costs. This positions the methodology not only as a theoretical contribution but also as a practical framework for guiding firebreak planning and wildfire prevention policies. While the model successfully captures critical fire dynamics, its assumptions of static moisture content and simplified environmental conditions warrant further investigation. Future work will focus on integrating real-time moisture data and refining parameters with observational wildfire data to enhance the model’s predictive capabilities. This study provides valuable insights into the interplay between moisture content and wildfire spread, contributing to the development of decision-support tools for effective wildfire management. Full article

Breast cancer stem-like cells (bCSCs) fundamentally represent a highly dynamic “immune-adaptive functional state” rather than a fixed cellular lineage, serving as the core engine driving tumor recurrence, metastasis, and therapeutic resistance. Despite rapid advances, the heterogeneity of bCSC states and their intricate interactions with the immune microenvironment lack systematic integration. This review centers on the dynamic evolution and niche adaptation of bCSCs. First, we systematically dissect the multilayered regulatory network maintaining stemness, encompassing core transcription factors, epigenetic–metabolic coupling, and the synergistic mechanisms of critical signaling pathways such as Wnt and Notch. Second, we propose a trinary “stemness–immune–spatial” feedback model, elucidating how bCSCs achieve active immune evasion by downregulating antigen presentation, secreting immunosuppressive factors, and embedding within perivascular “immune-cold niches.” Finally, leveraging a multi-omics integration perspective, we reconstruct precision intervention strategies, exploring the synergistic potential of targeting stemness pathways in conjunction with immunotherapies like PD-1/PD-L1 blockade and STING agonists. Furthermore, we highlight the pivotal role of integrating organoids, PDX models, and AI-assisted decision systems in overcoming heterogeneity and enabling personalized treatment. By establishing a closed-loop framework spanning mechanistic insight to spatially precise intervention, this review aims to provide novel theoretical foundations and translational pathways to surmount the bottleneck of therapeutic resistance in breast cancer. Full article

Background: Glioblastoma (GBM) exhibits marked cellular heterogeneity and resistance to therapy. Calcium (Ca²⁺) signaling at endoplasmic reticulum (ER)–mitochondria contact sites has emerged as a key regulator of mitochondrial function and cell fate; however, its lineage-specific role and therapeutic relevance in GBM remain unclear. Methods: ITPR1 expression was analyzed using single-cell and bulk RNA sequencing (RNA-seq) datasets and validated by immunohistochemistry and survival analyses. Functional studies were conducted using genetic silencing or CRISPR-mediated activation of ITPR1, combined with DRP1 knockdown, Ca²⁺ imaging, transmission electron microscopy, co-immunoprecipitation, mitochondrial fractionation, and mitochondrial functional assays. Therapeutic efficacy was evaluated in orthotopic GBM xenograft models treated with 2-aminoethoxydiphenyl borate (2-APB), temozolomide (TMZ), or their combination. Results: ITPR1 was enriched in mesenchymal-like malignant cell states and associated with higher tumor grade, recurrence, and poor prognosis. ITPR1 knockdown suppressed GBM cell proliferation and tumor growth while promoting intrinsic apoptosis. Mechanistically, loss of ITPR1 impaired ER-to-mitochondria Ca²⁺ transfer, disrupted ER–mitochondria contacts, and altered mitochondrial ultrastructure. This was accompanied by reduced DRP1 Ser616 phosphorylation and mitochondrial recruitment, as well as decreased autophagy and mitophagy activity. Consequently, ITPR1 knockdown led to mitochondrial depolarization, increased mitochondrial reactive oxygen species (ROS) accumulation, and activation of mitochondria-dependent apoptosis. Conversely, DRP1 knockdown attenuated the mitochondrial and pro-survival effects induced by ITPR1 overexpression. In vivo, combined treatment with 2-APB and TMZ resulted in greater tumor suppression and prolonged survival compared with either treatment alone, accompanied by increased apoptosis and reduced proliferation in tumor tissues. Conclusions: ITPR1 promotes GBM progression by sustaining ER–mitochondria Ca²⁺ coupling and DRP1-dependent mitochondrial quality control, thereby maintaining mitochondrial homeostasis and cell survival. Targeting inositol 1,4,5-trisphosphate receptor (IP₃R)-mediated Ca²⁺ signaling with 2-APB enhances the therapeutic efficacy of TMZ, suggesting that ITPR1-centered Ca²⁺ signaling may represent a potential therapeutic vulnerability in aggressive GBM. Full article

Imaging-based multi-omics derived from digital histopathology provides a valuable approach for characterizing tumor heterogeneity from routine clinical specimens. However, robust multi-cancer histopathological analysis remains challenging due to pronounced intra-tumor variability, inter-organ morphological overlap, and sensitivity to staining and acquisition variations, which can limit the generalizability of deep learning models. These limitations are largely driven by insufficient representation learning, particularly in multi-organ and multi-class diagnostic settings. In this study, we propose a hierarchically regularized representation learning framework for multi-cancer histopathological image analysis that models imaging-based features across multiple organs and diagnostic categories. The framework integrates complementary mechanisms to capture fine-grained cellular morphology, long-range tissue architecture, and organ-aware diagnostic semantics within a unified computational model. A hierarchical supervision strategy guides the network to reduce entanglement between organ-level structural characteristics and disease-specific diagnostic patterns in the learned representations. The method operates without pixel-level annotations or handcrafted morphological priors, supporting scalable experimental evaluation. We demonstrate the approach on balanced lung and colon cancer histopathology cohorts, achieving 96.5% accuracy on lung cancer classification and 96.8% accuracy on colon cancer classification. Ablation and robustness analyses further validate the contributions of hierarchical regularization and consistency learning. Overall, this work provides a demonstrated proof-of-concept framework for representation-centric imaging-based analysis in multi-organ histopathology under the evaluated dataset conditions. Full article

Background: The epiligament (EL) of the medial collateral ligament (MCL) has recently attracted increasing attention as a biologically active structure. Emerging evidence suggests that it may contribute to ligament healing by providing progenitor cells, vascular components, and signaling mediators. However, its cellular composition and possible regional variability remain insufficiently characterized. Aim: This study evaluated the expression of vimentin, S100 protein, and epithelial membrane antigen (EMA) to better characterize the EL compared with the ligament proper (LP). Methods: Twelve human MCLs obtained from twelve deceased donors were divided into proximal, middle, and distal segments. Thirty-six paraffin blocks were prepared, from which 180 sections were obtained and equally assigned for immunohistochemical staining of vimentin, S100 protein, and EMA (60 slides for each marker). Systematic quantification of seven to eight non-overlapping microscopic fields per slide generated 900 standardized observations for each investigated marker. This sampling strategy provided 150 measurements for each sub-region (EL and LP across the three anatomical segments). Immunoreactivity was quantified using ImageJ software. Statistical differences were analyzed using a robust two-way analysis of variance (ANOVA), while biological associations between markers were assessed using Spearman’s rank correlation analysis. Results: Vimentin and S100 expression were consistently higher in the EL than in the LP across all anatomical regions (p < 0.0001). The highest vimentin values were observed in the proximal region (median 17.34 vs. 10.14) and distal region (median 19.34 vs. 11.23), whereas S100 showed the greatest expression in the proximal (median 16.9 vs. 7.2) and distal regions (median 14.1 vs. 8.9). EMA expression was generally lower overall; however, it remained significantly higher in the EL than in the LP within the proximal (median 6.87 vs. 5.77) and middle regions (median 4.80 vs. 3.26). No significant difference was identified in the distal region. Spearman rank correlation analysis demonstrated significant positive associations among all investigated markers (p < 0.001), with the strongest relationship observed between vimentin and S100 protein (Spearman correlation coefficient = 0.430). Conclusions: The EL of the MCL is a structurally and biologically distinct component, characterized by significantly higher expressions of vimentin, S100, and EMA than the LP. The significant positive correlations observed among these markers support the concept that the EL functions as an integrated biological microenvironment with clear regional heterogeneity, particularly within the proximal and distal segments. Further studies are warranted to clarify the functional relevance of these findings and their potential implications for clinical management and ligament healing strategies. Full article

Background: Somatostatin receptors (SSTRs) are pivotal diagnostic and therapeutic targets in well-differentiated neuroendocrine neoplasms (NENs). SSTR-directed treatment strategies rely on sufficient SSTR2 expression. Thus, receptor loss during dedifferentiation limits therapeutic efficacy. Preclinical data suggest pharmacologic modulation of SSTRs’ expression. However, there are no robust data on the effect of NEN-specific systemic treatments on SSTRs’ expression and function. Methods: We systematically evaluated the effects of six systemic agents commonly used in NEN therapy—isplatin, etoposide, 5-fluorouracil (5-FU), streptozotocin (STZ), temozolomide (TMZ), and everolimus—on SSTR2 and SSTR5 expression, as well as on uptake of ⁶⁸Ga-DOTATOC, in BON-1 and QGP-1 cells, as well as the MS-18 cell line. Analyses included qRT-PCR, Western blotting, immunohistochemistry, and radiopeptide uptake assays. Results: Systemic agents modulated SSTR expression and radioligand uptake in a drug- and cell line-dependent manner. Etoposide consistently upregulated SSTR2 expression and significantly increased radioligand uptake across all three cell lines. TMZ enhanced SSTR2 expression and uptake in BON-1 cells, but reduced uptake in QGP-1 and MS-18 cells. In contrast, 5-FU, STZ, cisplatin, and everolimus showed heterogeneous, compound- and cell line-specific effects on SSTR2 expression and ⁶⁸Ga-DOTATOC uptake, including both up- and downregulation depending on the model. Conclusions: All agents under investigation affect SSTR expression in vitro, while etoposide is identified as the most consistent enhancer of SSTR2’s expression and function across cellular NEN models. Our findings highlight both the potential and the risks of systemic therapy-induced receptor modulation and therefore support further investigation of treatment sequencing strategies to optimize SSTR-targeted approaches. However, further studies are required to translate these observations to a clinical setting. Full article

Background: Intervertebral disc degeneration (IVDD) is driven by the interplay between inflammatory signaling, extracellular matrix (ECM) degradation, and impaired cellular adaptation. Although several nutraceutical compounds have been reported to exert protective effects in IVDD-related models, their multitarget mechanisms within integrated molecular networks remain incompletely characterized. Methods: An in silico framework integrating molecular docking with network-based analyses was employed to evaluate resveratrol, quercetin, melatonin, curcumin, and baicalein against a predefined panel of IVDD-associated targets, within an exploratory in silico framework. Binding affinities and interaction profiles were assessed using molecular docking, followed by protein–protein interaction (PPI) network construction, Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses, and hub gene identification. Results: Docking analyses revealed binding energies ranging from −4.59 to −13.25 kcal/mol, with curcumin and quercetin showing plausible docking poses across a subset of selected targets under the applied protocol. Network analysis showed a highly interconnected structure centered on key inflammatory regulators, including NFKB1, IL6, TNF, IL1B, STAT3, and NLRP3, together with ECM-associated components such as ACAN, COL2A1, SOX9, MMP13, and ADAMTS5. Enrichment analyses further suggested significant associations with inflammatory signaling pathways, cytokine regulation, and ECM organization. Conclusions: These findings are compatible with a distributed, multitarget interaction pattern of nutraceutical compounds within IVDD-associated molecular networks. By integrating molecular docking with network-based analyses, this study offers a system-level framework for interpreting previously reported effects within a disease-specific context. Docking-derived interaction patterns should be interpreted as qualitative and exploratory observations, as docking scores represent model-dependent estimates and do not establish comparable pharmacological effects across heterogeneous targets. The results should be considered hypothesis-generating and require experimental validation. Full article