Search Results (16,236)

Electromagnetic fields (EMFs), increasingly prevalent due to technological advancements, have raised significant concerns regarding their potential biological effects on living organisms. While much attention has focused on human health, growing evidence suggests that EMFs can also affect invertebrates, which play vital ecological roles. This study investigates the biochemical and cell death biomarker responses to EMF exposure for 24 h or 72 h in Parasteatoda tepidariorum. The focus is placed on the 10 MHz frequency, which is relevant to environmental exposure scenarios. Biochemical biomarkers include heat shock proteins (HSP70) and the percentage of apoptotic and living cells in individuals at their embryonic, young and adult stages. Results indicate that exposure to EMFs can induce measurable stress responses at the biochemical level, with variations depending on developmental stage and protective structures. Embryos outside of the egg sac exhibited significantly elevated levels of HSP70 and apoptosis markers compared to those within the sac, suggesting a partial protective effect of the cocoons. Furthermore, differences in biomarker sensitivity were observed across all the developmental stages and increased with prolonged exposure. These findings contribute to the understanding of EMF-induced biological effects in invertebrates and support the use of P. tepidariorum as a model species for environmental electromagnetic pollution. Full article

In Saccharomyces cerevisiae (S. cerevisiae), sister kinetochores are mono-oriented during meiosis I to ensure accurate homolog segregation, a process dependent on Hrr25 kinase activity. However, its direct interactors remain poorly defined. To address this, we performed a yeast two-hybrid (Y2H) screen using Hrr25 as bait. HRR25 was cloned into a Y2H vector and functionally validated by complementation of a temperature-sensitive hrr25-ts mutant. Screening across three reading frames identified three putative interactors: Hed1, Cyr1, and Rep1. Additional open reading frames (ORFs), including DAD1, SYS1, and YDR015C were identified but were oppositely oriented to the GAL4 activation domain. Structural modeling and phosphorylation prediction identified high-confidence Hrr25 target residues, including S70/T73 on Hed1, S323 on Rep1, and S198/S527 on Cyr1, whereas Sys1 and YDR015C lacked favorable sites. Although Dad1 was not validated as a direct interactor from Y2H, S63 was identified as a favorable phosphorylation site, and its full-length ORF in the interacting clone and known biological role supported its inclusion. Among the meiotic candidates, Hed1 may link Hrr25 activity to homologous recombination, while Dad1 represents a plausible target for regulating kinetochore–microtubule interactions. Collectively, these findings identify new candidate interactors and substrates of Hrr25 and suggest a broader role in coordinating recombination and kinetochore function during meiosis, warranting further experimental validation. Full article

High-resolution LiDAR and individual-tree modeling are generating increasing volumes of tree-level spatial data, including coordinates, tree height, and diameter at breast height (DBH). However, the lack of scalable and spatially explicit identifiers still limits the organization and integration of tree records in digital twin forest systems. This paper presents a GeoSOT-based framework for assigning position-linked identifiers to standardized tree observation records. The proposed code is used as a spatial anchor for record organization, candidate retrieval, and lifecycle-oriented management, rather than as a direct label of biological tree identity. The framework is implemented through a Yukon-based workflow for spatial storage and GeoSOT-code attachment, with a Bigtable-style schema described for time-stamped record organization. In a Mengjiagang forest farm case study, 604 treetop observations were extracted from airborne-LiDAR-derived canopy height models. Perturbation tests, boundary stress testing, controlled candidate matching, and a prototype retrieval benchmark show that fine-level GeoSOT codes are sensitive to positional uncertainty, whereas coarser levels combined with target-cell and adjacent-cell retrieval provide more stable candidate filtering with compact candidate sets under controlled experimental conditions. These results suggest that GeoSOT-based coding can support tree-observation record organization and candidate matching in digital twin forest systems. Independent cross-source identity validation and deployed database-level benchmarking should be addressed using real multi-source datasets and operational database environments. Full article

Background/Objectives: Oral squamous cell carcinoma (OSCC) exhibits substantial biological heterogeneity, and current clinicopathological risk stratification incompletely reflects tumor metabolic behavior. Stable isotope ratio mass spectrometry enables the quantitative assessment of carbon and nitrogen isotopic composition, potentially capturing cumulative metabolic reprogramming associated with tumor aggressiveness. This study evaluated whether isotopic signatures of tumor tissue and surgical margins are associated with lymph node metastasis and survival outcomes in OSCC. Methods: In this prospective study, 54 consecutive patients undergoing primary surgical treatment for OSCC were enrolled. Paired samples derived from tumor tissue and surgical margins were analyzed using isotope ratio mass spectrometry to determine the relative abundance of nitrogen-15 and carbon-13 isotopes. The primary endpoint was pathological lymph node metastasis. Secondary endpoints included disease-free survival and overall survival. Paired comparisons were performed using Wilcoxon signed-rank tests with false discovery rate correction. Logistic regression models for nodal metastasis were constructed using Firth penalization with bootstrap internal validation, while survival outcomes were evaluated using Cox proportional hazards models with model complexity restricted according to the number of events. Results: Tumor tissues demonstrated significantly lower δ¹³C and δ¹⁵N values and higher nitrogen-to-carbon ratios compared with surgical margins (all adjusted p < 0.05). In multivariable analysis, tumor δ¹⁵N was independently associated with lymph node metastasis and modestly improved model discrimination. However, it was not independently associated with disease-free or overall survival. Exploratory analyses indicated that higher δ¹³C values in surgical margins were independently associated with shorter disease-free survival. Conclusions: These findings suggest that isotope ratio mass spectrometry-based isotopic profiling identifies reproducible metabolic differences between tumor and margin tissues in OSCC. Tumor δ¹⁵N is associated with lymph node metastasis, whereas margin δ¹³C may reflect recurrence risk and potentially capture metabolic field effects. These findings are hypothesis-generating and warrant validation in larger, independent cohorts. Full article

Skin aging is a complex, multifactorial process driven by intrinsic biological mechanisms and environmental exposures, resulting in progressive functional and structural decline. Chronological age does not adequately capture this variability, highlighting the need for molecular biomarkers that reflect biological aging. In this context, transcriptomic aging clocks have emerged as a promising approach, as gene-expression profiles provide a dynamic representation of cellular and tissue states. This narrative review is based on a targeted literature search in PubMed and IEEE Xplore and focuses on transcriptomic aging clocks in human skin, with emphasis on gene-expression signatures, key biological pathways, and computational modeling strategies. These models consistently capture coordinated alterations in processes such as cellular senescence, DNA damage response, inflammation, and extracellular matrix remodeling. Representative transcriptomic frameworks, including models such as SkinAGE, illustrate the ability of gene-expression-based approaches to quantify biologically meaningful and dynamic aging states in the skin. Advances in machine-learning approaches, including deep learning and pathway-guided models, are critically evaluated, alongside the role of explainable artificial intelligence in enhancing model transparency and biological interpretability. Future developments are expected to integrate multi-omics data and digital twin frameworks, enabling the transition from static biomarkers toward dynamic, predictive, and personalized models of skin aging Full article

Background: Migraine is a complex neurovascular disorder with a substantial genetic component, yet many contributing loci remain poorly characterised. Methods: This study investigated the association between 21 biologically prioritised single nucleotide variants (SNVs) and migraine susceptibility in a case-control cohort of 548 individuals of European ancestry, of whom 304 (164 cases, 140 controls) remained after quality control and principal component analysis (PCA). Genotyping was performed using a targeted Sequenom MassARRAY platform, and substantial missingness (mean 30.3% per SNV) was addressed using multiple imputation by chained equations (MICE). Association testing was conducted using three complementary logistic regression frameworks: unadjusted single-variant analysis, covariate-adjusted marginal models, and a multivariable joint model incorporating all SNVs with L2 regularisation. Results: Across analyses, two variants in ASTN2 (rs1052053 and rs6478241) showed the most robust associations with migraine, surviving Bonferroni correction in the joint model (p = 0.001 and p = 0.002, respectively) and false discovery rate (FDR) correction in marginal models (q = 0.003 for both). A third variant, rs7304841 (12p12), demonstrated a risk-increasing effect that reached FDR significance in marginal analysis (q = 0.035) and remained nominally significant in the joint model. In contrast, rs62624978 in CTC1 showed a strong signal in unadjusted analysis (OR = 0.217, p = 0.0014) and remained nominally significant after adjustment (p = 0.011), although it did not survive multiple-testing correction in imputed models. The joint model demonstrated good discriminatory performance (AUC = 0.822), though this is not intended as a predictive tool. Biologically, implicated loci suggest contributions from both neuronal circuit organisation (ASTN2) and telomere and vascular maintenance pathways (CTC1), supporting a broader neurovascular model of migraine susceptibility. Conclusions: These findings are consistent with shared genetic architecture between migraine and microvascular dysfunction, potentially involving endothelial integrity, neurovascular coupling, and cortical excitability mechanisms. Full article

CAR-T cell therapy remains ineffective in most solid tumours because effector cells infiltrate poorly, undergo exhaustion, and face antigen escape within an immunosuppressive microenvironment. To address this, we developed a hybrid framework that combines a mechanistic spatiotemporal model with machine learning for limited individual-level mechanistic personalisation under data constraints. At its core, we employed a reaction–diffusion–chemotaxis model describing functional and exhausted CAR-T cells, antigen-positive and antigen-negative tumour subpopulations, a chemoattractant, an immunosuppressive factor, and hypoxia. Gradient boosting combined with nested cross-validation was used to recover model-consistent latent-parameter pseudo-labels generated by a limited inverse problem. Within this surrogate-target setting, parameters characterising the tumour microenvironment and CAR-T cell exhaustion were reproduced most robustly, whereas antigen escape and individualised initial conditions were substantially less well constrained. As an auxiliary reference point, we also considered a direct empirical baseline for binary clinical outcomes. This baseline indicated that the observed clinical features contained a more stable signal for disease control than for objective response. A favourable response was associated with high CAR-T cell infiltration and cytotoxic potency, whereas resistance was linked to exhaustion, antigen escape, and a suppressive microenvironment. Overall, the proposed approach should be interpreted as an internally validated, hypothesis-generating proof-of-concept platform for mapping clinical features to mechanistically interpretable surrogate latent targets, rather than as evidence for validated recovery of true patient-specific biological parameters. Full article

Prolonged hospitalization in cardiac patients is associated with increased morbidity and healthcare resource utilization, yet early biological factors linked to extended length of stay remain insufficiently defined. This study aimed to explore an integrative framework combining oral health parameters and targeted gut microbial markers to identify factors associated with prolonged hospitalization in cardiac patients. A comparative observational design was applied, including patients with short-term hospitalization (1–4 days) and prolonged hospitalization (≥25 days). Oral health status was evaluated using a standardized dental protocol at admission and longitudinally in patients with prolonged hospitalization. Targeted qRT-PCR-based quantification of selected gut bacterial markers was performed at admission and reassessed after one and two weeks. Temporal changes were calculated relative to baseline, and multivariate logistic regression models adjusted for age, sex, and major cardiac diagnoses were used to explore associations with prolonged hospitalization. Short-term hospitalized patients (n = 27) exhibited minimal oral health variation (+2%) and stable marker profiles. In contrast, patients with prolonged hospitalization (n = 30 for oral health; n = 18 for microbial markers) showed progressive changes over time. Oral health impairment increased by 3% after one week and 16% after two weeks, while targeted microbial marker variation showed modest directional changes. Integrative models combining oral health parameters and targeted microbial markers suggested potential complementary information alongside clinical variables, within the limits of an exploratory framework and limited sample size. These findings support the relevance of multidomain clinical and biological monitoring in the early identification of patients at risk for prolonged hospitalization. Full article

Background/Objectives: Both adiposity and nutritional–inflammatory status influence glucose metabolism; however, their longitudinal associations with glycemic changes in non-diabetic populations remain unclear. We examined the independent, interactive, and joint associations of body mass index (BMI) and prognostic nutritional index (PNI) with the 3-year change in HbA1c (ΔHbA1c). PNI, a composite marker of serum albumin and peripheral lymphocyte count, reflects both protein nutritional status and systemic immune competence. We hypothesized that BMI and PNI would each independently predict ΔHbA1c and that their joint profiling would identify higher-risk subgroups. Methods: A total of 9414 non-diabetic adults from the Shanghai Suburban Adult Cohort were included. Participants with diabetes at baseline (defined as fasting plasma glucose ≥ 7.0 mmol/L, 2-h post-load glucose ≥ 11.1 mmol/L, HbA1c ≥ 6.5%, or self-reported physician diagnosis of diabetes or use of glucose-lowering medications) were excluded. BMI was measured, and PNI was calculated as serum albumin + 5 × lymphocyte count. ΔHbA1c was assessed over a 3-year period. Multivariable linear regression, interaction testing, and joint stratification were performed. Covariate selection was guided by prior biological plausibility, and model adequacy was evaluated using the Akaike Information Criterion (AIC). Results: Both BMI (β = 0.013% per kg/m², 95% CI: 0.011–0.016, p < 0.001) and PNI (β = 0.002% per unit, 95% CI: 0.000–0.004, p = 0.019) were independently and positively associated with ΔHbA1c. No significant interaction was observed (p = 0.431). High BMI (≥24 kg/m²) was associated with glycemic worsening irrespective of PNI level (β ≈ 0.075%, p < 0.001). Among normal-weight individuals, higher PNI was associated with a modest increase in ΔHbA1c (β = 0.031%, p = 0.007). Conclusions: Although the absolute effect sizes were modest at the individual level, BMI was consistently and independently associated with glycemic deterioration therefore, even small per-unit increases may translate into meaningful risk at the population level given the high prevalence of overweight and obesity. PNI showed a small positive association, suggesting that in relatively healthy populations a higher PNI may partly capture subtle pro-glycemic factors—such as low-grade inflammation or higher protein intake—rather than representing unambiguous nutritional benefit. The absence of interaction suggests that BMI and PNI act through largely independent pathways. These findings extend prior evidence by demonstrating that PNI provides modest additional glycemic information beyond BMI in non-diabetic community-dwelling adults, particularly among those of normal weight. Full article

Background: Primary stability and long-term osseointegration depend on bone healing surrounding dental implants. Implant macrogeometry is crucial for controlling insertion torque and the biological reaction of peri-implant bone. This study assessed the impact of an implant design meant to lessen cortical bone compression on early bone healing. Methods: Forty titanium prototype implants (3 × 6 mm) were equally divided into Control (standard macrogeometry) and Test (macrogeometry with healing chambers) groups. Initial insertion torque was measured in vitro using synthetic bone blocks. Subsequently, an in vivo rabbit tibia model was used (n = 10 implants per group) to assess early healing. At 21 days, histological sections were analyzed for bone-to-implant contact (BIC%) at three cervical positions (C1, C2, and C3). Additionally, digital radiographs of the cervical region were evaluated using RGB color mapping, where distinct color channels quantified varying degrees of bone density. Results: The in vitro insertion torque for the Control group was significantly greater than the Test group (8.01 vs. 5.70 Ncm). The in vivo histomorphometric analysis indicated improved integration for the Test design, showing substantially higher BIC% at the C2 (59.30% vs. 40.30%) and C3 (42.10% vs. 17.90%) positions. Furthermore, radiographic RGB analysis revealed that the Test group possessed a higher blue channel contribution, indicating greater mineralized tissue density. Conclusions: These results imply that modifying implant macrogeometry to lower insertion torque and minimize cortical bone compression favorably enhances early cervical bone healing and osseointegration. Full article

Epigenetic mechanisms profoundly regulate gene expression, developmental trajectories, and phenotypic variation, extending biological influence beyond DNA sequence alone. A growing body of evidence suggests that environmental exposures, including pollutants, drugs, stress, and diet, can induce germline and early embryonic epimutations that alter developmental programs with lasting consequences for neurodevelopmental and cognitive outcomes. However, the fields most relevant to these processes have largely developed independently. These include germline epigenetics, early embryonic patterning, neurodevelopment and cognitive regulation, and intergenerational or transgenerational inheritance. Each field has its own conceptual frameworks and mechanistic models. This fragmentation obscures the biological reality that these systems are tightly interconnected: environmentally induced epigenetic perturbations in gametes can reshape the epigenetic landscape of the early embryo, influence lineage allocation during gastrulation, and ultimately modify the molecular architecture of the developing central nervous system. A systems–biology perspective capable of linking germline epimutations and early embryonic epigenetic instability to later neurodevelopmental and cognitive phenotypes and their potential inheritance is therefore required. This review synthesizes current evidence across these traditionally isolated domains and proposes a coherent mechanistic framework linking germ cell epimutations and early embryonic epigenetic instability to the emergence of neurodevelopmental and cognitive phenotypes. By bridging these conceptual gaps, we aim to establish a cohesive foundation for understanding how early epigenetic disruptions generate long-lasting and in some cases heritable effects on brain development and cognitive function. Full article

Pathological cardiac hypertrophy is an important risk factor for cardiovascular disease. Ganoderic acid A (GAA), the primary bioactive constituent of Ganoderma lucidum (G. lucidum), is known for its stable chemical properties and diverse biological activities. It has been shown to confer protection against myocardial ischemia–reperfusion injury in rat models, potentially through modulating inflammatory responses and inhibiting protein expression linked to both NF-κB and apoptosis pathways. Nevertheless, the role of GAA in cardiac hypertrophy has not yet been fully elucidated. Using transverse aortic constriction (TAC)-induced cardiac hypertrophy in mice, we analyzed the degree of hypertrophy using echocardiography and at the pathology and molecular levels. Our results demonstrate that GAA effectively attenuates Ang II-induced cardiomyocyte hypertrophy in vitro and reduces pressure overload-induced cardiac hypertrophy in vivo. Further investigation revealed that GAA exerts its anti-hypertrophic effects by downregulating the mRNA expression of hypertrophic and fibrotic markers and attenuating inflammatory responses, and that the protective effects of GAA may involve NF-κB signaling. This study provides valuable theoretical support for the potential therapeutic application of GAA in treating pathological myocardial hypertrophy and heart failure. Full article

This paper extends the metacybernetic framework by grounding its conceptual descriptions in first principles of information physics. We demonstrate that for living systems to organise efficiently under uncertainty, they must adhere to a strict recursive pattern, a “fractal seed” originating in the third-order interaction between potential and action. By utilising Fisher Information Field Theory (FIFT) within an Informational Realism paradigm, we formalise this process through variational analysis on an implicate–explicate manifold. Under a rigorous informational parsimony constraint (a functional analogue of the holographic principle), we treat the J-field as the dispositional reservoir of latent potential and the I-field as the operative field of structured configurations, and show how their autopoietic coupling generates the system’s Potential–Actuation trait poles as a scale-invariant viability structure This coupling reveals that the boundary substructure, which encodes the holographic content, directly conditions the emergent superstructure through a deterministic parity rule inherited from the dyadic logic of the minimal generic living system represented by ${\widehat{\theta }}_2$. Drawing on the application of Fisher Information, we show that maintaining informational parsimony requires the system’s architecture to oscillate: odd-numbered orders express two traits (dyads), whereas even-numbered orders express three (triads). This produces a canonical 2–3–2–3–2 sequence, preventing a combinatorial explosion of traits as systemic depth increases. We present the Cogitor5 model as a complete fifth-order exemplar of this rule, demonstrating how this rhythmic structural pattern enables self-evolution, systemic coherence, and collective intelligence in both biological and artificial agencies. Full article

Introduction: It is accepted that nano- and micro-plastic (NMP) pollutants threaten ecosystems and human health by their bioaccumulation but, interestingly, their toxicity is shape-dependent. However, a clear definition of irregular NMPs, as the dominant shape in environmental and biological samples, is currently lacking when compared to spherical and fibrous NMPs. Objectives: This study quantifies morphometric descriptors in order to develop a standardized definition for irregular NMPs. Methods: Hyperspectral images of 34 spherical, 50 fibrous, and 45 irregular NMPs were collected from the literature. All shape-related features reported previously were analyzed using a machine learning model. Using five-fold cross-validation, a decision tree-based ensemble classifier with fixed parameters and Gini coefficient was established to screen key morphometric descriptors and their optimal interval ranges. The model was independently validated, enabling the accurate distinction of irregular NMPs from spherical and fibrous NMPs. Results: Three morphometric descriptors, including circularity, roundness, and perimeter-to-area ratio, were identified using five-fold cross-validation as optimal indicators for NMP shape classification. Optimal interval ranges for irregular NMPs were as follows: circularity (0.388 ± 0.004–0.768 ± 0.004), roundness (0.248 ± 0.01–0.752 ± 0.06) and perimeter-to-area ratio (>11.608 ± 1.39). This approach generated a 96.0% macro-averaged accuracy across these NMPs, with 100% precision and 89.0% recall. Conclusions: Irregular NMPs may be characterized using three morphometric descriptors, such as circularity, roundness, and perimeter-to-area ratio. The three-descriptor combination has highly accurate discrimination from spherical and fibrous NMPs. Full article

Functional antibody-dependent enhancement (ADE) represents a fundamental and context-dependent characteristic of antiviral antibody responses, reflecting the dual capacity of antibodies to mediate both the neutralization and Fc receptor-dependent enhancement of infection. In flavivirus research, this duality complicates the interpretation of conventional serological metrics and limits the reliability of single-parameter correlates of immunity, particularly in populations with complex exposure histories. Over the past decade, functional ADE assays have evolved from specialized mechanistic tools into integrated immune assessment platforms supporting translational immunology, vaccine evaluation, and population-level immune surveillance. These platforms incorporate Fcγ receptor-relevant target cell systems, standardized viral inputs, dilution series-based profiling, quantitative enhancement metrics, and structured quality control frameworks to enable reproducible, comparable, and context-aware functional measurements across cohorts and laboratories. A central concept emerging from these developments is that ADE reflects a dynamic functional immune state rather than an intrinsic property of antibodies or a direct indicator of pathological risk. Accordingly, functional ADE platforms support the contextual interpretation of antibody activity across physiologically relevant conditions, facilitating discrimination between transient functional enhancement and clinically meaningful immunological risk. By integrating functional ADE metrics with serological, cellular, and epidemiological data, these platforms provide a structured framework for interpreting immune profiles in vaccine evaluation, booster strategy design, and population-level risk stratification. This review synthesizes the development, standardization, and global dissemination of functional ADE platforms and discusses key principles governing biological relevance, analytical robustness, and inter-site transferability. Emerging directions integrating functional ADE profiling with systems immunology, immunogenomics, and computational modeling are highlighted as pathways toward predictive, decision-support-oriented frameworks. By positioning ADE platforms as immune assessment infrastructures rather than isolated assays, this review underscores their value for mechanistic inquiry, translational interpretation, and preparedness-oriented responses to emerging viral threats in the absence of definitive correlates of protection. Full article