Search Results (1,014)

Prostate cancer (PCa) progression is critically driven by androgen receptor (AR) signaling, which integrates hormonal cues with metabolic programs supporting tumor growth, survival, and therapy resistance. Emerging evidence suggests that intermittent fasting (IF) and related dietary interventions—such as time-restricted eating (TRE), alternate-day fasting (ADF), and fasting-mimicking diet (FMD)—modulate systemic metabolism, including reductions in insulin and insulin-like growth factor 1 (IGF-1), and induce intracellular nutrient stress that can influence AR activity, splice variant expression (e.g., AR-V7), and downstream metabolic pathways. This systematic literature review (Scopus, PubMed, Web of Science; publications up to December 2025; search terms: “prostate cancer,” “androgen receptor,” “AR splice variants,” “intermittent fasting,” “fasting mimicking diet”, “metabolism,” “therapy resistance”) summarizes preclinical and clinical studies addressing the impact of IF on AR signaling, lipogenesis, mitochondrial function, redox homeostasis, and therapy response. Preclinical studies indicate that IF can reduce AR expression, impair nuclear translocation, modulate AR splice variants such as AR-V7 via nutrient-sensitive splicing mechanisms, and enhance sensitivity to androgen deprivation therapy and AR-targeted agents. Mechanistically, IF-induced metabolic stress engages AMP-activated protein kinase (AMPK), mechanistic target of rapamycin (mTOR), and sirtuin pathways, alters lipid and mitochondrial metabolism, and transiently increases reactive oxygen species (ROS), creating vulnerabilities in prostate tumor cells. Translational evidence suggests potential benefits of integrating IF with standard therapy, but effects may depend on fasting regimen, caloric intake, macronutrient composition, and patient metabolic context, including risk of lean mass loss. This review highlights the metabolic crosstalk between IF and AR signaling and emphasizes the need for future clinical studies incorporating biomarker-guided approaches and body composition monitoring to fully exploit this intersection for improved therapeutic outcomes in prostate cancer. 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

Emotions and affective responses are core intervention targets in music therapy. Through acoustic elements, music can evoke emotional responses at physiological and neurological levels, influencing cognition and behavior while providing an important dimension for evaluating therapeutic efficacy. However, emotions are inherently abstract and difficult to represent directly. Artificial intelligence models therefore provide a promising tool for modeling and quantifying such abstract affective states from physiological signals. In this paper, we propose a structured and explicitly factorized multi-modal representation learning framework for joint affective state and preference inference. Instead of entangling heterogeneous dynamics within monolithic encoders, the framework decomposes representation learning into cross-channel interaction modeling and intra-channel temporal–spectral organization modeling. The framework integrates electroencephalography (EEG), peripheral physiological signals (GSR, BVP, EMG, respiration, and temperature), and eye-movement data (EOG) within a unified temporal modeling paradigm. At its core, a Dynamic Token Feature Extractor (DTFE) transforms raw time series into compact token representations and explicitly factorizes representation learning into (i) explicit channel-wise cross-series interaction modeling and (ii) temporal–spectral refinement via learnable frequency-domain gating. These complementary structural modules are implemented through Cross-Series Intersection (CSI) and Intra-Series Intersection (ISI), which perform low-rank channel dependency learning and adaptive spectral modulation, respectively. A hierarchical cross-modal fusion strategy integrates modality-level tokens in a representation-consistent and interaction-aware manner, enabling coordinated modeling of neural, autonomic, and attentional responses. The entire framework is optimized under a unified multi-task objective for valence, arousal, and liking prediction. Experiments on the DEAP dataset demonstrate consistent improvements over state-of-the-art methods. The model achieves 98.32% and 98.45% accuracy for valence and arousal prediction, 97.96% for quadrant classification in single-task evaluation, and 92.8%, 91.8%, and 93.6% accuracy for valence, arousal, and liking in joint multi-task settings. Overall, this work establishes a structure-aware and factorized multi-modal representation learning framework for robust affective decoding and intelligent music therapy systems. Full article

The integration of Artificial Intelligence (AI) into Internet of Things (IoT) medical devices has revolutionized arrhythmia monitoring. However, the high computational and power demands of traditional Deep Learning (DL) models pose significant challenges for long-term, battery-operated smart electronics. Spiking Neural Networks (SNNs), inspired by the biological efficiency of the human brain, offer a promising solution. This paper reviews the intersection of SNNs, low-power IoT hardware, and biomedical signal processing. I examine the transition from frame-based to event-driven processing, and discuss the hardware–software co-design necessary for next-generation cardiac wearables. Full article

Endometriosis is a heterogeneous chronic inflammatory disorder associated with substantial diagnostic delay and limited therapeutic options, highlighting the need of robust non-invasive biomarkers and actionable molecular targets to complement existing low-sensitivity tests. To identify conserved pathogenic mechanisms with translational potential, here, we uniformly reprocessed three independent the Gene Expression Omnibus (GEO) microarray cohorts (GSE7305, GSE25628, and GSE11691) and applied a strict, directionally consistent intersection strategy to identify conserved transcriptional signals. We identified 262 consensus differentially expressed genes enriched for immunity/inflammation, cell adhesion and migration, and angiogenesis, consistent with key biological hallmarks of lesion establishment and persistence. Protein–protein interaction topology prioritized 11 highly connected hub genes (VCAM1, CCL2, MCAM, CD14, CD24, FGFR1, SIRPA, CSF1R, S100A9, S100A8, and LY96) that likely act as an integrated immune-adhesion-angiogenesis axis. Notably, 63/262 (24%) of the consensus genes were annotated to the extracellular exosome compartment, supporting their translational relevance as liquid-biopsy candidates. Finally, connectivity mapping using the LINCS L1000 framework nominated small-molecule perturbagens predicted to reverse the endometriosis-associated signature, providing a rational starting point for drug-repurposing experiments. In conclusion, this study elucidates a conserved immune–adhesion–angiogenesis axis driven by an 11-gene hub network in endometriosis. These core regulators represent promising candidates for the development of non-invasive liquid biopsies and precision, non-hormonal therapeutics. Full article

Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) infection induces heterogeneous immune responses that influence both acute disease severity and long-term immune remodeling. A key question in the context of infection and vaccination is whether SARS-CoV-2 exerts direct oncogenic effects or instead acts as a transient immunological stressor capable of reinforcing tumor-permissive pathways. Current evidence does not support classical viral oncogenesis. Rather, severe infection is characterized by early interferon (IFN) imbalance followed by NF-κB-dominant inflammatory amplification, promoting sustained IL-6/JAK–STAT3 and MAPK signaling, chronic cytokine production, metabolic reprogramming, and impaired antitumor immune surveillance. At the molecular level, viral structural proteins modulate host signaling networks. The spike (S1) protein engages TLR2/TLR4–MyD88 pathways, activating NF-κB and MAPK cascades, while the membrane (M) protein reinforces NF-κB–STAT3 circuits linked to epithelial–mesenchymal transition and inflammatory gene expression. These mechanisms intensify pre-existing oncogenic signaling without initiating malignant transformation. Tissue-specific responses are further shaped by IFN competence, renin–angiotensin system balance, and metabolic context. In parallel, immune evasion programs shared by chronic viral infection and cancer, including checkpoint upregulation, impaired antigen presentation, and suppressive myeloid expansion, may be transiently reinforced following severe infection. In contrast, SARS-CoV-2 vaccination induces spatially restricted, self-limited innate activation without sustained inflammatory signaling or persistent antigen exposure. By preventing severe disease and chronic immune dysregulation, vaccination interrupts pathways hypothesized to intersect with cancer biology, with no evidence of increased cancer incidence. Ongoing longitudinal studies are required to clarify the long-term oncologic implications of post-infectious immune remodeling. Full article

Accurate short-term traffic flow prediction is vital for effective signal control and sustainable urban mobility. Deep learning models, such as the Gated Recurrent Unit (GRU) and Long Short-Term Memory (LSTM) networks, have demonstrated strong capability in modelling temporal traffic dynamics. However, the influence of their architectural and hyperparameter configurations remains underexplored. This study proposes a systematic methodology to assess the impact of hyperparameter optimization on GRU and LSTM models for predicting traffic flow at a signalized intersection. The methodology is evaluated using minute-level traffic data from the Globe Roundabout in Jeddah, Saudi Arabia. Bayesian optimization is applied to identify the best-performing hyperparameters. The results show that the optimized GRU model achieves a Root Mean Square Error (RMSE) of 0.0953, representing a 90.2% improvement compared to the baseline GRU (RMSE ≈ 0.969). Likewise, the optimized LSTM model attains an RMSE of 0.0960, corresponding to an 85.2% improvement relative to its baseline (RMSE ≈ 0.648). Similar gains are observed for the Mean Absolute Error. Visual analysis further shows that optimized models reduce smoothing bias, enhance the tracking of transient fluctuations, and produce stable, low-variance residuals. The findings demonstrate that hyperparameter optimization substantially improves predictive accuracy while preserving computational efficiency, enabling lightweight recurrent architectures to perform at a level comparable to more complex models. Full article

Talking Head Generation (THG) is a rapidly advancing field at the intersection of computer vision, deep learning, and speech synthesis, enabling the creation of animated human-like heads that can produce speech and express emotions with high visual realism. The core objective of THG systems is to synthesize coherent and natural audio–visual outputs by modeling the intricate relationship between speech signals, facial dynamics, and emotional cues. These systems find widespread applications in virtual assistants, interactive avatars, video dubbing for multilingual content, educational technologies, and immersive virtual and augmented reality environments. Moreover, the development of THG has significant implications for accessibility technologies, cultural preservation, and remote healthcare interfaces. This survey paper presents a comprehensive and systematic overview of the technological landscape of Talking Head Generation. We begin by outlining the foundational methodologies that underpin the synthesis process, including generative adversarial networks (GANs), motion-aware recurrent architectures, and attention-based models. A taxonomy is introduced to organize the diverse approaches based on the nature of input modalities and generation goals. We further examine the contributions of various domains such as computer vision, speech processing, and human–robot interaction, each of which plays a critical role in advancing the capabilities of THG systems. The paper also provides a detailed review of datasets used for training and evaluating THG models, highlighting their coverage, structure, and relevance. In parallel, we analyze widely adopted evaluation metrics, categorized by their focus on image quality, motion accuracy, synchronization, and semantic fidelity. Operating parameters such as latency, frame rate, resolution, and real-time capability are also discussed to assess deployment feasibility. Special emphasis is placed on the integration of generative artificial intelligence (GenAI), which has significantly enhanced the adaptability and realism of talking head systems through more powerful and generalizable learning frameworks. Full article

Background: Mitochondrial dysfunction is central to the pathogenesis of acute myocardial infarction (AMI), but mitochondria-related molecular biomarkers and mechanisms remain incompletely defined. This study aimed to identify mitochondria-associated biomarkers in AMI and elucidate their functional roles in mitochondrial dynamics, extracellular matrix (ECM) remodeling, and cardiac protection. Methods: Two GEO datasets (GSE19322, GSE71906) were analyzed to identify mitochondria-related differentially expressed genes (DE-MRGs) by intersecting DEGs with MitoCarta3.0 genes. Functional enrichment (GO/KEGG), LASSO regression, ROC curves, and nomogram modeling were employed to screen biomarkers. Immune infiltration profiling, GeneMANIA, GSEA, TF-mRNA and ceRNA network construction, and drug prediction analyses were performed. Expression validation was conducted via RT-qPCR, Western blot (WB), and immunohistochemistry (IHC) in murine AMI models and hypoxia-induced cardiomyocytes. Functional assays assessed cardiac performance (echocardiography), infarct size (TTC staining), fibrosis (Masson/Sirius red), oxidative stress (ROS), and ECM remodeling (MMP9/TIMP1 axis). Results: We identified 295 DE-MRGs, enriched in oxidative phosphorylation and mitochondrial metabolic pathways. Machine learning and validation analyses pinpointed MTFP1 and DNAJC28 as AMI biomarkers with strong diagnostic accuracy. In vivo and in vitro studies confirmed marked downregulation of MTFP1 post-AMI and under hypoxia. AAV9-mediated MTFP1 overexpression improved cardiac function, reduced infarct size, attenuated fibrosis, and decreased ROS levels. Mechanistically, MTFP1 upregulated phosphorylated DRP1 (Ser616) without altering total DRP1, balanced MMP9/TIMP1 activity, and suppressed fibrosis markers (COL1A1, α-SMA). Gelatin zymography indicated that MMP9 activation remained restrained despite elevated pro-MMP9, consistent with TIMP1-mediated regulation. Hypoxia-induced cardiomyocytes showed similar antifibrotic and antioxidative responses following MTFP1 overexpression. Conclusions: Our study identified MTFP1 as a novel mitochondria-related biomarker and therapeutic modulator in AMI. MTFP1 exerts cardioprotective effects by restoring mitochondrial fission balance and ECM remodeling through the p-DRP1/MMP9/TIMP1 signaling axis, attenuating fibrosis and oxidative stress. These findings provide mechanistic insight into mitochondria-targeted cardioprotection and highlight MTFP1 as a promising diagnostic and therapeutic target for AMI. Full article

Social media has become a key venue where PhD graduates seek career information, compare experiences, and negotiate uncertainty. Drawing on information behavior and sense-making perspectives, this study examines how returnee PhDs from non-core study destinations discuss employment challenges in China’s academic labor market when credential signals are contested. Using Korean-trained PhDs as a theoretically motivated exemplary case, we collected 1149 publicly available posts from Xiaohongshu, a Chinese social media platform, and applied BERTopic to identify latent themes, followed by qualitative close reading of representative posts to interpret discourse functions. The model yielded ten topics, and semantic association analysis indicates substantial overlap among high-frequency topics, suggesting intertwined concerns rather than neatly separated issue domains. The four most prevalent topics account for 72.06% of the corpus, centering on credential recognition, job-search pathways, informal screening rules, and intersecting age- and gender-related pressures. Qualitative readings further reveal recurring discursive moves, including exposing tacit hiring heuristics, contesting stigmatizing labels (e.g., “water PhD,” a derogatory term implying low-quality credentials), and exchanging actionable strategies across regions and career tracks. Overall, the findings point to discursive convergence under evaluation uncertainty: when formal criteria are ambiguous and institutional signals are unreliable, participants turn to social media to stabilize expectations by triangulating cases and iteratively refining shared interpretations of the job market. This study contributes empirical evidence on uncertainty-driven information practices in highly educated labor markets and demonstrates the value of combining topic modeling with qualitative interpretation to capture online collective sense-making. Full article

Background/Objectives: Breast implant-associated anaplastic large cell lymphoma (BIA-ALCL) is the sentinel implant-associated malignancy, illustrating how long-lived biomaterials can reshape local tissue–immune ecology. Although textured (high-surface-area) implants show the strongest epidemiologic association, the rarity of disease despite widespread exposure suggests additional host modifiers. We synthesize evidence supporting a gene–environment (G × E) framework and critically appraise emerging host-susceptibility signals (including BRCA1/BRCA2 and HLA associations). Methods: We conducted a narrative, evidence-based synthesis of peer-reviewed epidemiologic and registry studies, peri-implant niche biology (biofilm/foreign-body response and cytokine milieu), tumor genomic profiling, and current guidelines/regulatory communications, prioritizing primary studies for key claims. Results: Textured exposure dominates risk attribution, whereas absolute-risk estimates vary with denominators, exposure ascertainment, and follow-up duration. Mechanistic studies support a chronically inflamed capsule niche. Genomic analyses repeatedly converge on JAK/STAT pathway activation with frequent co-alterations in epigenetic regulators and recurrent copy-number changes, consistent with stepwise evolution under sustained selection. Immune-evasion features—including frequent PD-L1 expression and CD274 (9p24.1) copy-number alterations—provide a plausible checkpoint route, while host-susceptibility signals remain preliminary and require multi-center, multi-ancestry replication. Conclusions: BIA-ALCL is a multistep, context-dependent lymphoma in which implant-mediated inflammation intersects with host susceptibility to enable somatic evolution and immune escape. Clinically, prevention currently relies on exposure mitigation, standardized risk communication, and symptom-driven evaluation; precision prevention will require integrative cohorts linking verified device exposure, immunogenetics, microenvironment profiling, and tumor multi-omics. Full article

Intelligent Transportation Systems (ITS) primarily rely on flow rate and occupancy to estimate traffic states. However, in heterogeneous traffic conditions characterized by weak lane discipline and diverse vehicle classes, these conventional metrics fail to capture the true operational efficiency of signalized intersections. High flow rates can mask underlying inefficiencies, while low flow rates do not necessarily indicate free-flow conditions. This paper introduces a novel computer vision-based metric, the Effective Flow Ratio (EFR), designed to quantify the actual discharge efficiency of mixed traffic. By leveraging Bird’s-Eye View (BEV) vehicle tracking using You Only Look Once version 11 (YOLOv11) and ByteTrack, EFR distinguishes between kinematic movement and effective discharge, resolving the ambiguity of “moving but not clearing” states. We analyze 21 days of continuous footage from a rooftop-mounted camera overlooking a congested intersection in Dhaka, Bangladesh, exhibiting distinct non-linear behaviors compared to raw flow counts. Our results demonstrate that: (i) Flow rate and discharge efficiency are dynamically decoupled, evidenced by significant variance in EFR within identical flow bins; (ii) Temporal rolling correlations reveal transient regimes where traditional signal control logic would misinterpret congestion severity; and (iii) EFR provides a more robust proxy for intersection performance than occupancy or volume alone. The proposed metric offers a granular, physics-informed input for next-generation adaptive traffic signal control in developing urban environments. Full article

Plastic pollutants, including phthalates, bisphenol A (BPA), per- and polyfluoroalkyl substances (PFAS), and microplastics (MPs), are increasingly recognized as emerging environmental cofactors that intersect with infectious disease dynamics. These compounds, once considered inert, can alter immune function, reshape host–pathogen interactions, and directly influence viral survival and transmission. In this review, we compile current evidence on the chemistry, environmental occurrence, and biological activity of major plastic-associated pollutants with emphasis on their role in viral infections. Phthalates such as di(2-ethylhexyl) phthalate (DEHP) and its metabolite MEHP modulate innate immune signaling and have been shown to exacerbate infections, including Dengue and Coxsackievirus B3. Other DEHP-like phthalates, such as dibutyl phthalate (DBP), exhibit consistent infection-enhancing effects, while high molecular weight or cyclical phthalates such as polyvinyl acetate phthalate (PVAP) display conflicting results in their modulation of viral infections. BPA, widely detected in human tissues, acts through endocrine and immune disruption, worsening viral myocarditis, and altering influenza outcomes. PFAS, persistent “forever chemicals,” reshape adaptive immune responses and are associated with increased susceptibility, viral persistence, or severity of infection of herpesvirus (HCMV, EBV, HSV-1), hepatitis virus, and influenza infection. Microplastics represent a distinct risk by acting as physical carriers for viruses and bacteria, stabilizing viral RNA, enhancing host cell uptake, and skewing immune responses. Together, these pollutants extend beyond toxicology into virology, providing novel insights into how environmental exposures converge with viral pathogenesis. We highlight mechanistic advances and critical knowledge gaps and propose future directions for integrating environmental health and infectious disease research. Full article

Transit Signal Priority (TSP) enhances transit reliability by minimizing delays at signalized intersections, but its broader implementation is often hindered by organizational and procedural challenges. Although many studies have examined the technical performance of TSP systems, fewer have explored the organizational, regulatory, and procedural factors that affect their successful implementation. This study investigates TSP business procedures across multiple U.S. states by conducting structured interviews with key stakeholders and performing a SWOT (Strengths, Weaknesses, Opportunities, Threats) analysis. The study identified recurring barriers, including unclear leadership, staffing shortages, inconsistent permitting processes, incompatible equipment, and outdated infrastructure. In contrast, successful programs relied on regular interagency coordination, assigned TSP staff, centralized management, and simplified funding processes. We propose strategies such as assigning a lead agency, streamlining permitting through blanket procedures, shifting to cloud-based control systems, and linking grant funding to performance data. Full article

Given the increasing hepatotoxicity risk associated with pyrrolizidine alkaloids (PAs) in Emilia sonchifolia (L.) DC., this study aimed to systematically characterize its PA components and identify the material basis responsible for liver injury. Using solid-phase extraction (SPE) enrichment coupled with ultra-high-performance liquid chromatography–quadrupole/orbitrap high-resolution mass spectrometry (UHPLC-HRMS), efficient annotation of PAs was achieved through building-block-based molecular network (BBMN) cluster analysis. A total of 76 PAs were identified (66 otonecine-type and 10 retronecine-type PAs), including 35 known compounds (e.g., senkirkine and petasitenine) and 41 potentially novel compounds. Semi-quantitative analysis revealed that senkirkine accounted for 86% of the total PAs. As an otonecine-type diester alkaloid, it serves as the core toxic substance triggering hepatic sinusoidal obstruction syndrome (HSOS). Network toxicology analysis identified 52 intersecting targets between senkirkine and hepatotoxicity. A protein–protein interaction (PPI) network was constructed, revealing 44 connected nodes with MAPK1, AKT1, and PIK3CA as key hub targets. Enrichment analysis indicated that these targets are primarily involved in the PI3K-Akt signaling pathway and focal adhesion. Molecular docking further validated that senkirkine exhibits strong binding affinities with these core targets, with binding energies ranging from −26.33 to −51.50 kcal/mol, stabilized by robust hydrogen-bonding networks. Consequently, senkirkine was identified as the critical safety indicator for quality control, and processing techniques were applied to reduce its content, balancing efficacy and toxicity risks. Full article