Search Results (483)

Osteoporosis and chronic conditions such as type 2 diabetes mellitus, cardiovascular disease, heart failure, and chronic kidney disease share several common biological mechanisms, including chronic inflammation, oxidative stress, hormonal dysregulation, and metabolic alterations. In this context, multimorbidity presents an increasing clinical challenge, particularly in older populations, where osteoporosis remains frequently underdiagnosed and undertreated. This review aims to explore the complex interplay between skeletal fragility and cardiometabolic diseases, emphasizing the role of nutritional deficiencies (such as iron and vitamin C), shared molecular pathways (advanced glycation end-products, Renin–Angiotensin–Aldosterone System, RANK Ligand, RANK), and the systemic impact of chronic inflammation and tissue hypoperfusion. The review also addresses the effects of various drug classes—antidiabetics, antihypertensives, anticoagulants, and anti-osteoporotic agents—on bone metabolism and cardiovascular risk. Special focus is given to the implementation of integrated and personalized care models, particularly multidisciplinary team-based approaches, which have demonstrated significant reductions in mortality and refracture rates, despite their still limited adoption in clinical practice. In conclusion, this review highlights the shared mechanisms between osteoporosis and cardiometabolic conditions in the context of multimorbidity, underscoring persistent clinical challenges related to diagnosis, drug interactions, and care fragmentation that warrant further research into integrated care models. Full article

Background/Objectives: Cardiovascular diseases (CVDs) remain the leading cause of morbidity and mortality worldwide and increasingly are recognized as a continuum of interconnected conditions rather than isolated entities. Methods: A structured narrative literature search was performed in PubMed, Scopus, and Google Scholar for publications from 2015 to 2025 using combinations of different keywords: “cardiovascular disease spectrum”, “multi-omics”, “precision cardiology”, “machine learning”, and “artificial intelligence in cardiology”. Results: Evidence was synthesized across seven major clusters of cardiovascular conditions, and across these domains, common biological pathways were mapped onto heterogeneous clinical phenotypes, and we summarize how multi-omics integration, AI-enabled imaging and digital tools contribute to improved risk prediction and more informed clinical decision-making within this spectrum. Conclusions: Interpreting cardiovascular conditions as components of a shared disease spectrum clarifies cross-disease interactions and supports a shift from organ- and syndrome-based classifications toward mechanism- and data-driven precision cardiology. The convergence of multi-omics, and AI offers substantial opportunities for earlier detection, individualized prevention, and tailored therapy, but requires careful attention to data quality, equity, interpretability, and practical implementation in routine care. Full article

Multiple Myeloma (MM) is an incurable malignancy that progresses from asymptomatic precursor stages—Monoclonal Gammopathy of Undetermined Significance (MGUS) and Smouldering Multiple Myeloma (SMM)—to active disease. Despite ongoing research, the molecular mechanisms driving this progression remain poorly understood. In this study, we aimed to uncover key regulatory factors involved in MM progression by integrating single-cell RNA sequencing (scRNA-seq) data with curated a priori biological knowledge of MM. To this end, we first integrated a priori knowledge from databases in a synthetic gene network map to play the role of an MM-related backbone to project findings from scRNA analysis on CD138⁺ Plasma Cells. This was followed by stage-specific regulatory network construction and analysis using Integrated Value of Influence (IVI) metrics to identify the most influential genes across disease stages. Our findings revealed GSK3B, RELA, CDKN1A, and PCK2 as central regulators shared across multiple stages of the disease. Notably, several of these genes had not previously been included in established MM gene sets, highlighting them as prime candidates for biomarkers and drug targets. Full article

Marine oil spill detection using Synthetic Aperture Radar (SAR) is crucial but challenged by dynamic marine conditions, diverse spill scales, and limitations in existing algorithms regarding model size and real-time performance. To address these challenges, we propose LSFE-YOLO, a YOLOv8s-optimized (You Only Look Once version 8) lightweight model with an original, domain-tailored synergistic integration of FasterNet, GN-LSC Head (GroupNorm Lightweight Shared Convolution Head), and C2f_MBE (C2f Mobile Bottleneck Enhanced). FasterNet serves as the backbone (25% neck width reduction), leveraging partial convolution (PConv) to minimize memory access and redundant computations—overcoming traditional lightweight backbones’ high memory overhead—laying the foundation for real-time deployment while preserving feature extraction. The proposed GN-LSC Head replaces YOLOv8’s decoupled head: its shared convolutions reduce parameter redundancy by approximately 40%, and GroupNorm (Group Normalization) ensures stable accuracy under edge computing’s small-batch constraints, outperforming BatchNorm (Batch Normalization) in resource-limited scenarios. The C2f_MBE module integrates EffectiveSE (Effective Squeeze and Excitation)-optimized MBConv (Mobile Inverted Bottleneck Convolution) into C2f: MBConv’s inverted-residual design enhances multi-scale feature capture, while lightweight EffectiveSE strengthens discriminative oil spill features without extra computation, addressing the original C2f’s scale variability insufficiency. Additionally, an SE (Squeeze and Excitation) attention mechanism embedded upstream of SPPF (Spatial Pyramid Pooling Fast) suppresses background interference (e.g., waves, biological oil films), synergizing with FasterNet and C2f_MBE to form a cascaded feature optimization pipeline that refines representations throughout the model. Experimental results show that LSFE-YOLO improves mAP (mean Average Precision) by 1.3% and F1 score by 1.7% over YOLOv8s, while achieving substantial reductions in model size (81.9%), parameter count (82.9%), and computational cost (84.2%), alongside a 20 FPS (Frames Per Second) increase in detection speed. LSFE-YOLO offers an efficient and effective solution for real-time marine oil spill detection. Full article

Pervasive microplastics (MPs) and per- and polyfluoroalkyl substances (PFAS) frequently co-occur across aquatic and terrestrial environments due to shared sources, transport pathways, and persistence, yet their interaction-driven effects on environmental fate, bioavailability, and toxicity remain incompletely resolved. This review critically synthesizes current knowledge on the environmental co-occurrence of MPs and PFAS, the physicochemical mechanisms governing their interactions, and the resulting ecological and toxicological consequences across aquatic, terrestrial, and biological systems. Emphasis is placed on sorption and desorption processes; environmental modifiers such as pH, salinity, dissolved organic matter (DOM), and aging; and biological responses under combined exposure scenarios. Across laboratory and field studies, MPs–PFAS co-exposure is frequently associated with altered PFAS partitioning and enhanced organismal uptake, with reported bioaccumulation increases of up to ~2.5-fold relative to PFAS-only exposures. These changes are often accompanied by amplified oxidative stress, immune dysregulation, metabolic disturbance, and reproductive impairment, particularly in aquatic invertebrates and early life stages of fish. Evidence further indicates that the magnitude and direction of combined effects depend on polymer type, particle size, surface aging, and biological context, underscoring the highly system-specific nature of MPs–PFAS interactions. By integrating findings from environmental monitoring, laboratory toxicology, and mechanistic and modeling studies, this review identifies key knowledge gaps related to nanoplastics detection, environmentally realistic exposure conditions, sorption reversibility, and mixture toxicity assessment. Collectively, these insights highlight limitations in current single-contaminant risk frameworks and underscore the importance of incorporating MPs-mediated PFAS transport and bioavailability into exposure assessment and regulatory evaluation. Full article

Objective: Asthma is a complex and heterogeneous syndrome, making it hard to predict disease progression and suitable treatments. One strategy for reducing this uncertainty is to define genetic subtypes, or endophenotypes, that capture shared biological mechanisms. Most genome-wide studies, however, compare one subgroup against all others within a single cohort and rarely replicate their findings. We aimed to determine whether simultaneously modeling all asthma endophenotypes improves the discovery and replication of genetic associations compared with the standard one-versus-rest approach. Methods: We analyzed common single-nucleotide polymorphisms (SNPs) in the Childhood Asthma Management Program (CAMP) using an analysis of covariance (ANCOVA) across all severity-related endophenotypes, adjusting for age, sex, and ancestry principal components. SNPs showing genome-wide significance were tested for replication in the Genetics of Asthma in Costa Rican Children Study (GACRS). For comparison, we performed traditional one-versus-rest logistic regression analyses within each cohort, using identical covariates and endophenotype labels. Results: The ANCOVA identified 244 genome-wide significant SNPs in CAMP, of which six unique loci replicated in GACRS. In contrast, logistic regression recovered only four significant contrasts from those six loci in CAMP and replicated just one in GACRS. Conclusions: Our findings highlight genetic variants that are associated with asthma severity endophenotypes and demonstrate that modeling all clinical subtypes simultaneously can reveal biologically meaningful signals that are missed by standard pairwise design. Full article

Aluminum (Al), an element that has no biological function described in plants, is commonly found in acidic soils, reducing plant growth, despite some beneficial effects reported in the literature. Iron (Fe) is an essential nutrient for plants, and Fe deficiency causes leaf interveinal chlorosis. Remarkably, rice (Oryza sativa), a C₃ crop considered tolerant to Al, shows alleviation of Fe deficiency chlorosis when exposed to Al, suggesting that Al can positively impact Fe homeostasis. However, whether this effect is observed only in rice or is common to other plant species is unknown. The rice wild progenitor Oryza rufipogon is closely related to the domesticated species, sharing several traits such as a semi-aquatic habit and use of the combined strategy for Fe uptake. Maize (Zea mays), on the other hand, is a C₄ plant, adapted to well-aerated soils, and uses a classic chelation-based strategy for Fe uptake. Here we used these two Poaceae representatives to determine whether Al excess could alleviate Fe deficiency chlorosis in species other than rice. Although Al caused toxicity irrespective of Fe levels, its addition essentially abolished chlorosis in Fe-deficient plants. The expression of Fe deficiency-induced marker genes was reduced to control levels in both species, suggesting that the Al alleviation effect leads to systemic signaling and down-regulation of Fe uptake mechanisms. Al alleviation partially rescued photosynthetic machinery inhibited by Fe deficiency, suggesting that leaves are maintaining photosynthetic activity when Al is present even under low Fe conditions. Taken together, our data show that the Al alleviation effect is shared by two other Poaceae species in addition to O. sativa and suggest that it might not be directly linked to domestication, changes in C₃/C₄ metabolism, or Al tolerance levels found in different species. Full article

Obesity-associated inflammation underlies much of cardiometabolic pathology, reflecting the convergence of chronic, low-grade systemic immune activation with region-specific maladaptation of adipose depots. Among these, epicardial adipose tissue (EAT)—a visceral fat layer contiguous with the myocardium and sharing its microvasculature—functions as a cardio-proximal immunometabolic interface that influences atrial fibrillation, heart failure with preserved ejection fraction, and coronary atherogenesis through paracrine crosstalk. These relationships extend beyond crude measures of adiposity, emphasizing the primacy of local inflammatory signaling, adipokine flux, and fibro-inflammatory remodeling at the EAT–myocardium interface. Of importance, substantial weight reduction only partially reverses obesity-imprinted transcriptional and epigenetic programs across subcutaneous, visceral, and epicardial depots, supporting the concept of an enduring adipose memory that sustains cardiovascular (CV) risk despite metabolic improvement. Accordingly, therapeutic strategies should move beyond weight-centric management toward mechanism-guided interventions. Resolution pharmacology—leveraging specialized pro-resolving mediators and their cognate G-protein-coupled receptors—offers a biologically plausible means to terminate inflammation and reprogram immune–stromal interactions within adipose and CV tissues. Although preclinical studies report favorable effects on vascular remodeling, myocardial injury, and arrhythmic vulnerability, clinical translation is constrained by pharmacokinetic liabilities of native mediators and by incomplete validation of biomarkers for target engagement. This review integrates mechanistic, depot-resolved, and therapeutic evidence to inform the design of next-generation anti-inflammatory strategies for obesity-related CV disease. Full article

(1) Background: Wound healing is a highly coordinated process encompassing hemostasis, inflammation, angiogenesis, keratinocyte migration, collagen deposition, and extracellular matrix remodeling. Successful repair also requires adequate nutrient and oxygen delivery through a well-developed vascular supply. Disruption of these processes can occur through aberrations in diverse biological pathways, including extracellular matrix organization, cellular adhesions, angiogenesis, and immune regulation. (2) Methods: We reviewed mechanisms of impaired tissue repair in monogenic disorders by focusing on three categories—connective tissue, hematological/immunological, and aging-related disorders—to illustrate how single-gene defects disrupt inflammation, cellular proliferation, and matrix remodeling. Additionally, we reviewed various polygenic disorders—chronic kidney disease, diabetes mellitus, hypertension, and obesity—to contrast complex multifactorial pathologies with single-gene defects. (3) Results: This review establishes that genetic impediments, despite their distinct etiologies, monogenic and polygenic disorders share critical downstream failures in the wound healing cascade. While monogenic diseases illustrate direct causal links between specific protein deficits and repair failure, polygenic diseases demonstrate how multifactorial stressors overwhelm the body’s regenerative capacity. (4) Conclusions: This review synthesizes current evidence on both monogenic diseases and polygenic contributions to impaired wound healing. These findings highlight that genetic susceptibility is a decisive factor in the ability to restore tissue homeostasis. This underscores the profound impact of genetic background on the efficacy of hemostasis, inflammation, and remodeling. Full article

For the first time, a comparative analysis has been conducted to elucidate the biosynthesis of three families of natural products—staurosporines/rebeccamycins, cladoniamides, and fascaplysins. Based on the available data, a well-founded hypothesis was formed that these metabolites arise through a shared biosynthetic pathway. A comparative evaluation of biological activity profiles and molecular mechanisms of action of the major representatives of these alkaloid families and their derivatives shows that, despite an apparent similarity between the activity spectra of indolo[2,3-a]pyrrolo[3,4-c]carbazoles and fascaplysins, they operate through different mechanisms. The biological effects of fascaplysin are driven primarily by the induction of metabolic stress rather than by the inhibition of DNA topoisomerase I or of a broad-spectrum protein kinases. The successful optimization of natural indolo[2,3-a]pyrrolo[3,4-c]carbazoles—compounds with initially poorer pharmacokinetic properties than those of fascaplysin—to drug-like candidates underscores the substantial pharmaceutical potential of the fascaplysin scaffold. Several existing fascaplysin derivatives, after the improvement of their pharmacokinetic characteristics, may serve as promising leads for the development of a new class of antibiotics. Full article

The Coronaviridae family represents a broad group of RNA-containing viruses that infect humans and animals. This family belongs to the order Nidovirales and is divided into four main genera: α-CoV, β-CoV, γ-CoV and δ-CoV. It is particularly noteworthy that representatives of β-CoV have caused serious epidemics in humans, such as the outbreaks of SARS-CoV, MERS-CoV, and COVID-19 caused by SARS-CoV-2. Although the clinical manifestations of CoVs can range from mild cold-like symptoms to severe respiratory diseases, they share common features in their structure, modes of transmission, and natural reservoirs. Identifying natural reservoirs, as well as establishing intermediate hosts, is crucial for understanding the mechanisms of interspecies transmission of CoVs. These processes are often mediated by molecular interactions between viral spike (S) proteins and cellular receptors of different species, which contribute to zoonotic outbreaks. Thus, the interaction of various species and the study of these processes of viral spread, cross-species transmission, and pathogen evolution play a key role in ensuring global biological safety. Therefore, we conducted this review to summarize the data from existing studies focused on the taxonomy of CoVs, their main types, natural reservoirs, intermediate hosts, pathways of interspecies transmission, and the significance of the One Health concept as an interdisciplinary approach to monitoring, prevention and control of CoV infections at the intersection of human, animal, and environmental health. We examined databases such as PubMed, Science Direct, Web of Science, and Google Scholar to identify relevant scientific articles in English available for such a review. The aim of this work is to study the taxonomy and classification of coronaviruses, as well as to identify their natural reservoirs, intermediate hosts, and applicable control measures. A review of human and animal coronaviruses has revealed their evolutionary diversity, their main natural reservoirs, their intermediate hosts, and their interactions with cellular receptors. This information allows for a better understanding of the mechanisms by which the viruses are transmitted from animals to humans. The concept of One Health demonstrated the interconnections between human, animal and environmental factors. Full article

Background: Loneliness (LON) is a heritable psychosocial trait that frequently co-occurs with major depressive disorder (MDD) and sleep traits. Despite known genetic contributions, the shared genetic architecture and molecular mechanisms underlying their co-occurrence remain largely unknown. This study aimed to uncover novel genetic risk loci and cross-trait gene expression effects. Methods: Large-scale genome-wide association study (GWAS) datasets were analyzed using the causal mixture model (MiXeR) to estimate polygenicity and shared genetic architecture. Genetic correlation analyses were performed using linkage disequilibrium score regression (LDSC) and local analysis of [co]variant annotation (LAVA). Conditional and conjunctional FDR methods further identified single nucleotide polymorphisms (SNPs). FUMA was used for gene mapping and annotation, and transcriptome-wide association studies (TWAS) assessed cross-trait gene expression effects. Results: Analyses revealed extensive polygenic overlap between LON, MDD, and sleep-related traits, with concordant and discordant effects. Several novel loci were identified, and cross-trait gene expression effects were observed in multiple brain-expressed genes, including WNT3, ARHGAP27, PLEKHM1, and FOXP2. These findings provide insight into the shared genetic architecture and relevance of these traits. Conclusions: This study demonstrates a significant shared polygenic architecture among LON, MDD, and sleep traits, providing new biological insights. It advances our understanding of cross-trait genetic mechanisms and identifies potential targets for future research, offering broader implications for trait co-occurrence. Full article

Non-invasive brain stimulation (NIBS) techniques—including repetitive transcranial magnetic stimulation (rTMS), theta-burst stimulation (TBS), paired associative stimulation (PAS), transcranial direct current stimulation (tDCS), and transcranial alternating current stimulation (tACS)—have emerged as valuable tools for modulating neural activity and promoting plasticity. Traditionally, their effects have been interpreted within a binary framework of long-term potentiation (LTP)-like and long-term depression (LTD)-like plasticity, largely inferred from changes in motor evoked potentials (MEPs). However, existing models do not fully capture the complexity of the biological processes engaged by these techniques and despite extensive clinical application, the cellular and molecular mechanisms underlying NIBS remain only partially understood. This systematic review, conducted in accordance with the PRISMA 2020 guidelines, synthesizes evidence from in vivo, in vitro, and ex vivo studies to delineate how NIBS influences neurotransmission through intracellular signaling, gene expression, and protein synthesis at the cellular level. Emphasis is placed on the roles of classical synaptic models, grounded in Ca²⁺-dependent glutamatergic signaling and receptor phosphorylation dynamics, as well as broader forms of plasticity involving BDNF–TrkB signaling, epigenetic modifications, neuroimmune and glial interactions, anti-inflammatory pathways, and apoptosis- and survival-related cascades. By integrating findings in humans with those in animal and cellular models, we identify both shared and technique-specific molecular mechanisms underlying NIBS-induced effects, highlighting emerging evidence for multi-pathway, non-binary plasticity mechanisms. Understanding these convergent pathways provides a mechanistic foundation for refining stimulation paradigms and improving their translational relevance for treatment of neurological and psychiatric disorders. Full article

Sparse Distributed Memory (SDM) provides a biologically inspired mechanism for associative and online learning. Transformer architectures, despite exceptional inference performance, remain static and vulnerable to catastrophic forgetting. This work introduces Continual Associative Learning Model (CALM), a conceptual framework that defines the theoretical base and integration logic for the cognitive model seeking to establish continual, lifelong adaptation without retraining by combining SDM system with lightweight dual-transformer modules. The architecture proposes an always-online associative memory for episodic storage (System 1), as well as a pair of asynchronous transformer consolidate experience in the background for uninterrupted reasoning and gradual model evolution (System 2). The framework remains compatible with standard transformer benchmarks, establishing a shared evaluation basis for both reasoning accuracy and continual learning stability. Preliminary experiments using the SDMPreMark benchmark evaluate algorithmic behavior across multiple synthetic sets, confirming a critical radius-threshold phenomenon in SDM recall. These results represent deterministic characterization of SDM dynamics in the component level, preceding the integration in the model level with transformer-based semantic tasks. The CALM framework provides a reproducible foundation for studying continual memory and associative learning in hybrid transformer architectures, although future work should involve experiments with non-synthetic, high-load data to confirm scalable behavior in high interference. Full article

Cutaneous lymphomas are a heterogeneous group of extranodal non-Hodgkin lymphomas with distinct clinical and biological features, broadly classified into cutaneous T-cell lymphomas (CTCL) and cutaneous B-cell lymphomas (CBCL). With improved survival due to early detection and therapeutic advances, the emergence of second primary malignancies (SPMs) has become a clinical concern. SPMs, defined as new, distinct malignant neoplasms arising synchronously or metachronously with the index cancer, can significantly impair prognosis and quality of life. In this narrative review, we meticulously examine the current literature, to synthesize evidence on SPMs’ incidence and risk factors in patients with primary cutaneous lymphomas. Evidence from population-based and institutional studies consistently demonstrates elevated risks of hematologic and solid tumors in CTCL. By contrast, data on CBCL remain limited, though recent population-based analyses suggest increased risks of certain hematologic malignancies and solid tumors. We further propose development mechanisms for SPMs, including treatment-related mutagenesis, shared genetic susceptibilities, chronic antigenic stimulation, and immune dysregulation. Lastly, we highlight the clinical implications of these findings, underscoring the need for vigilant surveillance, patient education, and tailored screening strategies. Future research should prioritize large-scale, prospective, and molecularly integrated studies to refine risk stratification and guide personalized survivorship care of this vulnerable population. Full article