Search Results (2,013)

Glioblastoma (GBM) is the most lethal and aggressive primary brain tumor in adults. Despite a standard-of-care regimen involving surgical resection, radiotherapy and temozolomide (TMZ), median overall survival typically hovers between 12 and 15 months. This poor prognosis is driven by profound intratumoral heterogeneity, glioma stem cell populations, and an immunosuppressive microenvironment that collectively fuel resistance to traditional apoptosis-centric therapies. Ferroptosis—a form of regulated cell death driven by iron-dependent phospholipid peroxidation and the collapse of antioxidant defenses—has emerged as a compelling alternative for eliminating therapy-refractory GBM cells. This review examines the molecular machinery of ferroptosis in glioma and explores how an additional regulatory layer, noncoding RNAs (ncRNAs), modulates this process. We highlight key experimentally validated axes where microRNAs, long noncoding RNAs (lncRNAs), and circular RNAs (circRNAs) orchestrate iron handling and antioxidant thresholds. These include sensitizers like miR-147a and circLRFN5, which promote iron overload, and resistors like circCDK14 and TMEM161B-AS1, which act as “ferroptosis brakes”. Furthermore, we discuss how integrative analyses of TCGA and CGGA cohorts have yielded ferroptosis-related lncRNA signatures that robustly predict patient survival. Finally, we outline the clinical potential of these ncRNAs as biomarkers and therapeutic targets while addressing the delivery challenges, such as the blood–brain barrier, that must be overcome to achieve precision, ferroptosis-oriented GBM therapy. Full article

Microsatellite instability (MSI) testing is frequently used to screen patients for the early detection of Lynch syndrome, the most common hereditary colorectal cancer syndrome. MSI testing compares microsatellite repeat lengths in tumor DNA with those in matched normal tissue from the same patient. Therefore, precise sample identification is critical for obtaining reliable test results. The Penta-C and Penta-D pentanucleotide markers are widely used for sample identification in MSI testing. We investigated instability, defined as allelic mismatches or shifts, discordant fragment sizes, or the appearance of alleles in tumor DNA that were absent in the corresponding normal DNA, in the Penta-C and Penta-D loci across 2609 paired colorectal tumor and matched normal tissue or blood DNA samples. The allele sizes of both markers did not match in 0.3% of microsatellite-stable (MSS) and 12.3% of microsatellite instability-high (MSI-H) patients (p < 0.001, difference in proportions, 12.0% (95% CI, 8.9–15.1%)). Non-matching allele sizes in 12.3% of the MSI-H tumors suggest that other repeat markers may also be unstable and not suitable for sample identification in these tumors. Full article

Toxoplasma gondii (T. gondii) is an intracellular parasite known to modulate host immunity and cellular signaling, raising interest in its potential influence on cancer biology. A systematic review was conducted to evaluate experimental evidence on the antitumor or pro-tumor effects of T. gondii infection and parasite-derived antigens and to categorize the underlying mechanisms. PubMed was searched through 9 September 2024, and 54 eligible experimental studies were included (41 in vivo, 10 in vitro, and three combined). Forty-six studies reported antitumor effects, two pro-tumor effects, one stage-dependent divergent effects (acute infection/antitumor vs. chronic infection/pro-tumor), and five highlighted T. gondii-associated cancer-pertinent signaling pathways. Antitumor effects were observed following acute infection and exposure to parasite antigens, certain recombinant proteins, and exosomal microRNA miR-155-5p. Dominant mechanistic categories included activation of innate and adaptive immunity and reversal of tumor microenvironment immunosuppression (notably Th1-driven IL-12/IFN-γ responses, antitumor M1 macrophage polarization), induction of apoptosis, anti-angiogenesis, molecular mimicry and modulation of cancer-pertinent pathways. Conversely, pro-tumor effects were seen with chronic infection and exposure to ROP18 effector protein and miR-21. Future translational research should focus on rigorous evaluation of the safety and efficacy of attenuated non-replicating T. gondii strains and/or select recombinant antigens for potential cancer T. gondii-based immunotherapy. Full article

Objective: To longitudinally investigate the effect of non-surgical periodontal therapy (NSPT) on the transcriptional and translational levels of Superoxide Dismutase (SOD) and Insulin Receptor Substrate type 2 (IRS2) in individuals with Type 2 Diabetes Mellitus (T2DM) and Periodontitis (P). Methods: This clinical study was registered at the Brazilian Clinical Trials Registry (ReBEC-RBR-5m3yxmb). Saliva, peripheral blood mononuclear cells (PBMCs), and gingival biopsies were collected from 156 individuals, distributed into five groups, each with at least 30 participants: T2DM_poorly_controlled+P, T2DM_well_controlled+P, T2DM_without_P, Periodontitis, and Control. Systemic levels of messenger RNA (mRNA) of Superoxide Dismutase 1 (SOD1) and IRS2 were measured using real-time polymerase chain reaction at baseline, 90, and 180 days after NSPT. SOD enzymatic activity in Saliva and IRS-2 immunohistochemistry in gingival biopsies were also assessed. Results: Higher SOD1 mRNA levels were observed in Control individuals at baseline. In contrast, higher IRS2 mRNA levels were detected in individuals with Periodontitis at baseline, followed by a significant reduction over time. A significant positive longitudinal correlation was identified between IRS2 and SOD1 gene expression in the groups without T2DM, indicating potential functional interaction between the molecules. Salivary SOD enzymatic activity was lower in individuals from the T2DM_poorly_Controlled+P and T2DM_well_Controlled+P groups. SOD concentration (U/g) normalized to the total protein content was higher in the saliva of individuals with Periodontitis. T2DM+P and Periodontitis groups showed extensive inflammatory infiltrate in the gingival biopsies, with predominant IRS-2 immunopositive cells in the T2DM+P groups, independently of the metabolic control. Conclusions: This study shows that non-surgical periodontal therapy (NSPT) is followed by longitudinal changes in IRS2 and SOD1 expression at the mRNA and protein levels in individuals with T2DM+P (poorly/well controlled) and periodontitis, reinforcing the clinical relevance of periodontal treatment in the systemic context of T2DM. Full article

Building on Gutman’s (1987) argument that architectural practice should reflect the nature of the problem, this article explores four eras of architectural practice: the Patronage Model, the Clientage Model, the Transitional Models, and Future Models. Each era is examined in relation to six “Questions of Praxis”: (1) What is the nature of the problem?, (2) What is the nature of the intervention?, (3) What knowledge is valued?, (4) What is the stance toward the problem?, (5) What is the continuity in the relationship?, and (6) What is the prioritization of professional obligations? Through a comparative analysis of questions 2–5—the analytic core of action-taking—alongside four drivers of change in today’s volatile, uncertain, complex, ambiguous world, yields 16 possible futures for architects. Further synthesis identifies five primary roles for architects of the future: systems-thinking designer (embracing complexity), steward (building trust amid volatility), facilitator (reducing ambiguity through shared meaning), curator (making sense of uncertainty), and strategic forecaster (transforming volatility into preparedness). These roles embody a care-based approach—prioritizing ongoing relationships over episodic interventions, collective capacity-building over expert prescriptions, and adaptive readiness over static solutions. This reflects the positioning of architecture as a public good, focused on strengthening social, ecological, and systemic foundations so communities not only withstand disruption but also adapt, learn, and thrive through it. Full article

Background: Chromophobe renal cell carcinoma (ChRCC) is characterized by the accumulation of abnormal mitochondria, a high rate of mitochondrial DNA (mtDNA) mutations, and altered oxidative metabolism. There are no existing circulating biomarkers to distinguish metastatic ChRCC from clear cell renal cell carcinoma (ccRCC). Methods: High-throughput plasma proteomic profiling using the SomaScan platform was performed in 18 ChRCC (including 16 metastatic ChRCC) and 197 metastatic ccRCC patients. Data were harmonized to generate a unified 7K-protein matrix. Results: Differential expression analysis was performed using limma (version 3.62.2). Of 7272 quantified human plasma proteins, 209 were differentially expressed between ChRCC and ccRCC. Upregulated proteins in ChRCC included essential β-oxidation enzymes such as ECH1 (enoyl-CoA hydratase 1) and ECI1 (enoyl-CoA delta-isomerase 1), suggesting increased long-chain fatty acid degradation. Creatine and energy-buffering pathways were also represented, with increased CKMT1A (Creatine Kinase, Mitochondrial 1A) in ChRCC. KIM-1 (Kidney Injury Molecule-1) and leptin were lower in ChRCC, consistent with the known upregulation of these proteins in ccRCC. Pathway enrichment analyses revealed an overrepresentation of mitochondrial protein degradation, fatty acid β-oxidation, and respiratory electron transport in ChRCC, suggesting that ChRCC sheds a unique mitochondrial signature into the peripheral circulation. A bootstrap-based LASSO logistic regression restricted to upregulated mitochondrial proteins in ChRCC vs. ccRCC consistently selected ECI1 and CKMT1A. The LASSO model achieved an AUROC of 0.964. Conclusions: Compared to ccRCC, the plasma proteome of metastatic ChRCC is dominated by mitochondrial metabolic enzymes, revealing a systemic metabolic phenotype strikingly aligned with the known histologic accumulation of abnormal mitochondria in ChRCC cells. Full article

Background and Objectives: Diagnostics for hematologic diseases rely on integrated assessment of clinical manifestation, morphology, flow cytometry, and molecular testing. Current classification systems, including the WHO HAEM5 and the International Consensus Classification, highlight the central role of genomics in defining disease entities and risk. Simultaneously, laboratories face growing case complexity and staffing challenges. Automation, collaborative robots (cobots), digital morphology platforms, and artificial intelligence (AI) have begun to address these issues. Here we examine the application of these technologies in hematopathology and molecular diagnostics and consider their translational potential to improve diagnostic accuracy and, ultimately, patient care. Methods: A review of peer-reviewed literature and technical reports published through December 2025 was performed, focusing on digital morphology platforms, AI for peripheral blood and marrow interpretation, AI-enabled flow cytometry, automated and robotic deployments in clinical laboratories, and machine learning applications in molecular hematopathology. Results: Digital morphology analyzers show strong concordance with manual microscopy and now serve as efficient platforms for AI-assisted differentials, cell classification, and fibrosis quantification. Deep learning applied to multiparameter flow cytometry achieves performance comparable to expert review in distinguishing mature B-cell neoplasms and acute leukemias. Automated solutions, cobot systems and robotic-arm-based slide-scanning clusters have demonstrated substantial gains in throughput and pre-analytic consistency. AI models in molecular hematopathology increasingly assist with variant interpretation, genetic risk stratification, and linking morphologic and genomic findings. Conclusions: AI is beginning to change how hematopathology and molecular diagnostics are practiced. Successful translation will depend on disease-specific validation, the development of multi-modal models aligned with ICC and WHO frameworks, and laboratory governance that maintains expert oversight. Full article

The ontological categorization of the cellular elements of the brain was proposed over a century ago by Santiago Ramón y Cajal (neurons, astroglia) and Pío del Río-Hortega (oligodendroglia, microglia). It combines histochemical observations of morphology with allied inferences about the specialized functions and origins (ectoderm or mesoderm) of each cellular element. This ontology shapes modern neuroscience, with the main non-neuronal cells—astroglia, oligodendroglia and microglia—viewed as having distinct primary roles relating respectively to the metabolic support, myelination and immunoprotection of neurons, the information signaling cells. Yet contemporary techniques, ranging from electrophysiology to single-cell transcriptomics and ultrahigh resolution spectroscopy, are revealing intersecting molecular profiles and functional capacities of these cell groups, for example metabolic support, neuroimmune and signaling functions in oligodendroglia. Here we identify discrepancies in current glial paradigms, from empirical, evolutionary and pragmatic perspectives. We suggest a subset of small, iron-rich glial cells, usually with few processes, often viewed as oligodendroglia with myelin-related primary functions, instead have iron-related primary functions that are central to all aspects of brain activity. We call these ‘ferriglia’. We discuss implications for pathogenesis across the spectrum of neuropsychiatric and neurological disorders, including neurodegenerative conditions such as Alzheimer’s disease and other less common cognitive, movement and neurobehavioral disorders, stroke and cerebrovascular disease, glioblastoma and other brain cancers and neuroimmune conditions. We also briefly address the question of where ferriglia may reside within existing glial compartments and lineages, implications for the ontological classification of other glial cells, and research challenges that must be overcome going forward. Full article

Water levels in the Great Salt Lake (GSL), UT, USA, have been declining overall since 1989, leading to a 70% decrease in surface area. To understand GSL’s future, we seek to image fresh groundwater input and lithologic variation along the lake’s boundary. Determining the amount of groundwater recharge into GSL is crucial for lake management but currently unknown. During the Fall of 2024 and Spring 2025, we conducted 16 electrical resistivity tomography (ERT) and six transient electromagnetic (TEM) surveys along the southern shore of GSL between Burmester Road (to the West), Saltair, and Lee’s Creek (to the East). These measurements indicate a low-resistivity layer consistent with brine pore-water, with variable thickness ranging from 7.1 ± 0.1 m at Burmester to 9.6 ± 0.2 m at Saltair. The Saltair region shows a high-resistivity layer, consistent with a 4.4 ± 0.05 m thick layer of mirabilite. This layer contains vertical conduits that allow saline pore-water to upwell onto the surface forming evaporite deposits. Near Lee’s Creek, we find evidence of high resistivities consistent with fresher groundwater as shallow as 2.8 ± 0.03 m, where increased permeability along the paleo-Jordan River corridor may provide a path for groundwater recharge from the Wasatch Mountains. Full article

This study explores a novel class of polynuclear superhalogen anions featuring heterovalent central atoms from groups 13 (B, Al) and 15 (P, As). The investigated species follow a modified general formula, (X_nY_n’F_{{(3n+5n’)+1}})⁻ where X = B and/or Al, Y = P and/or As, and n + n′ = 2–4. Low-energy isomers were identified using the Coalescence Kick method and subsequently optimized at the MP2/aug-cc-pVDZ level of theory. Electronic stability was assessed via the outer valence Green’s function (OVGF) approach with the same aug-cc-pVDZ basis set. All examined anions exhibit exceptional electronic stability, with vertical electron detachment energies (VDEs) ranging from 10.70 to 12.37 eV, significantly exceeding the superhalogen threshold of 3.65 eV. Thermodynamic analyses indicate that aluminum atoms play a crucial role in stabilizing larger clusters by acting as a structural “glue”, thereby suppressing fragmentation through the loss of neutral XF₃ or YF₅ units. In contrast, larger non-metallic analogs show an increased propensity toward dissociation. The potential of the heterovalent polynuclear superhalogen anions as weakly coordinating anions (WCAs) was further evaluated through molecular electrostatic potential (ESP) analysis. The results demonstrate that combining different central atoms within boron-based frameworks leads to a more homogeneous charge distribution, enhancing weakly coordinating behavior. Full article

Background/Objectives: Upon systemic delivery, macrophages take up a significant portion of nanoparticles and may become saturated. The saturation of macrophages may pose risks to overall immune function and signaling pathways. While some information is available on the survival and functionality of macrophages upon saturation with nanoparticles, there is limited understanding of the molecular-level changes that can occur and their corresponding influences on macrophage phenotypes, gene expression, and immune signaling pathways. Methods: In this study, RAW 264.7 macrophages were saturated with silica nanoparticles (SNPs) of different sizes (50 and 100 nm), porosities (nonporous, mesoporous), densities (solid, mesoporous, and hollow), and surface compositions (hydrophobicity) at their maximum non-toxic concentrations. The saturated macrophages were evaluated for changes in gene expression and immune signaling pathways by RNA sequencing, weighted gene co-expression network analysis (WGCNA), and Hallmark and KEGG pathway analyses. Results: Our results show that in the range studied, the particle size did not have a significant effect on the gene expression profile. Porous SNPs of comparable sizes resulted in increased and unique changes in the gene expression profile compared to nonporous SNPs. Major immune signaling pathways, including TNF-alpha signaling via NF-κB pathways, mTORC1 signaling, and p53 pathways, were modulated in SNP-saturated macrophages. This modulation depended on the physicochemical properties of the particles. The Th1/Th2 multiplex immunoassay revealed that the uptake of SNPs increases the amount of the TNF-alpha cytokine compared to the nontreated controls, whereas no changes in IL-6 and IL-12p70 pro-inflammatory cytokines were observed. Conclusions: Our results demonstrate that physicochemical properties of SNPs, such as porosity, size, surface functionality, and density, influence the modulation of gene expression and macrophage immune signaling pathways. These results, along with others, can provide guidance on the selection of silica nanoparticles for the safe and effective systemic delivery of bioactive agents. Full article

Although ductal carcinoma in situ (DCIS) diagnoses continue to climb, patient management remains constrained by limitations in recurrence prediction. Conventional histopathology and existing prognostic parameters often inadequately predict local recurrence, leading to over- or under-treatment. Additionally, discourse remains over the clinical implications of margin width as a measure of recurrence risk, demonstrating the limitations of a margin-based model, and motivating our proposal that recurrence risk is dynamic and should be defined by patient-specific, spatially resolved diagnostic biomarkers. This review introduces field cancerization as a framework that may illuminate mechanisms underlying DCIS ipsilateral recurrence and improve clinical decision-making. We propose that the potential drivers of ductal field cancerization span two stages: pre-tumorigenesis and post-tumorigenesis. Pre-tumorigenic events include non-biological and biological exposome factors. Post-tumorigenic drivers include intratumoral and microenvironment-mediated remodeling of adjacent tissues that promote malignancy. This review bridges stage-specific molecular mechanisms to potentially actionable strategies for DCIS patient management—particularly margin assessment and recurrence risk prognostication—while highlighting the critical unmet need to identify biomarkers that measure high-risk field changes. We also emphasize the need to move beyond lesion-centric management toward multivariable prognostic models that include distance-mapped field biomarkers, enabling more precise surgery, improved selection of adjuvant therapy, and safer de-escalation for low-risk patients. Full article

In response to growing concerns over global warming and energy sustainability, transitioning from fossil-fuel-based heating systems to renewable alternatives is essential. This study evaluates the economic and environmental performance of geothermal heat pumps for building heating and compares it with conventional coal-fired boilers, natural-gas boilers, and diesel furnaces. Using the heating degree-day (HDD) method, heating energy demand was analyzed for four U.S. cities—Anchorage (AK), San Francisco (CA), Salt Lake City (UT), and Las Vegas (NV)—representing diverse climatic zones. The analysis integrates thermodynamic and economic parameters, including the coefficient of performance (COP = 2–5) and annual fuel-utilization efficiency (AFUE = 80–97%), to evaluate heating-system performance and operational cost across different climatic regions. Sensitivity analysis with ±10% variations in fuel and electricity prices and system efficiencies demonstrates that geothermal heating remains the most stable and emission-efficient option under all scenarios. Results indicate that geothermal systems, despite higher reported initial investment, achieve lower operational and emissions-related costs and offer a robust and sustainable solution for decarbonizing building-heating systems. For example, the estimated seasonal geothermal heating cost is $370.59 in Anchorage compared with $646.48 for coal heating and $3375.65 for diesel systems. Furthermore, policy evaluation indicates that federal and state incentives, such as investment tax credit under the Inflation Reduction Act and rebate programs, can reduce installation costs by 25–40%, improving economic feasibility, particularly in colder regions. The analysis focuses exclusively on energy and emissions-related costs and does not explicitly model capital investment or levelized cost metrics. Full article

Purpose: Tumor location influences survival in bladder cancer, potentially due to genetic heterogeneity driven by distinct embryological origins and structural compositions. We investigate location-specific somatic gene alterations (GAs) and their potential clinical implications in muscle-invasive bladder cancer (MIBC). Methods: We explored the role of the intra-bladder tumor location in determining survival and underlying genetic alterations in MIBC patients using multiple large independent databases. We analyzed the tumor location’s impact on survival using the Surveillance, Epidemiology, and End Results (SEER) database and validated these findings using cBioPortal (CBP), which also contains gene sequencing data, enabling a comparison of GA frequency by tumor location. We investigated GA combinations to identify potential synthetic lethal (SL) combinations and co-occurrence signatures for survival prediction. Using the ROC Plotter database, we explored how significantly altered genes affect the response to immune checkpoint inhibitors (ICI). Results: An analysis of 6712 SEER and 570 CBP patients revealed significant (p < 0.001) differences in overall survival stratified by tumor location, with trigone tumors showing the worst survival. Genomic analysis identified 35 genes with location-specific alteration frequencies. Three of these genes, CDKN2A, SPTAN1, and BIRC6, were significantly predictive of ICI response, and three genes were uniquely associated with a specific location: BPTF (anterior wall), RYR1, and OBSCN (dome). Furthermore, we identified 349 SL pairs from the 35 significantly altered genes, and a co-occurrence analysis revealed two novel gene pairs associated with improved survival. Conclusions: Intra-bladder tumor location determines survival and distinct genetic profiles in MIBC. These location-specific alterations predict ICI response and identify novel synthetic lethal targets, guiding precision oncology. Full article

Measure-While-Drilling (MWD) data provide real-time insight into subsurface conditions and drilling performance, yet their complexity and operational noise often hinder reliable modeling. This study demonstrates the role of Exploratory Data Analysis (EDA) in developing robust machine learning (ML) models for lithology classification and penetration rate (PR) prediction in mining operations. A structured EDA workflow—comprising data integrity assessment, feature distribution analysis, correlation mapping, and depth-wise parameter profiling—was implemented to identify redundant attributes, isolate non-productive intervals, and enhance dataset consistency. Through EDA-informed normalization and feature selection, data consistency and model performance were significantly improved. Machine learning algorithms, including Decision Tree, Random Forest, and Multi-Layer Perceptron, were trained on the refined dataset. The Random Forest Classifier achieved 98.45% accuracy in lithology prediction, while the Random Forest Regressor produced the most accurate PR estimation (R² = 0.83, RMSE = 0.52). These results highlight EDA as a critical foundation for constructing physics-informed, data-driven models that enhance predictive reliability and operational efficiency in mining environments. Full article