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Cardiac amyloidosis is an infiltrative cardiomyopathy caused by extracellular deposition of misfolded proteins, most commonly immunoglobulin light chains (AL) or transthyretin (ATTR), with rarer forms occurring less frequently. AL amyloidosis arises from plasma cell-derived light chains and typically follows an aggressive clinical course, whereas ATTR amyloidosis results from misfolded wild-type or variant transthyretin and progresses more indolently. Extracellular vesicles (EVs) have recently been recognized as mediators of amyloid propagation, inflammation, and myocardial remodeling, particularly at later stages of disease. Despite growing evidence, no comprehensive reviews have focused on this relationship. We conducted a structured narrative review (PubMed and Scopus, 2020–2025) following Preferred Reporting Items for Systematic reviews and Meta-Analyses extension for Scoping Reviews (PRISMA-ScR) guidelines to synthesize emerging data. EVs act as scaffolds for transthyretin and serum amyloid A aggregation and carry disease-specific protein and RNA cargo detectable in blood and urine. Experimental models also demonstrate EV-mediated transport of serum amyloid A under conditions of cardiac stress, representing a reactive amyloidogenic pathway rather than a common cause of human cardiac amyloidosis. Preclinical studies show regenerative and anti-fibrotic effects of stem-cell-derived EVs, and early clinical trials demonstrate the feasibility of EV-based cardiac therapy. While methodological and translational challenges persist, EVs represent promising diagnostic and therapeutic tools that could transform the precision management of cardiac amyloidosis. Full article

Parkinson’s disease (PD) is a multifactorial neurodegenerative disorder characterized by complex interactions across molecular, cellular, circuit, and behavioral scales. While experimental and clinical studies have provided critical insights into PD pathology, integrating these heterogeneous data into coherent mechanistic frameworks and translational strategies remains a major challenge. Computational modeling offers a powerful approach to bridge these scales, enabling the systematic investigation of disease mechanisms, candidate biomarkers, and therapeutic strategies. In this review, we survey state-of-the-art computational approaches applied to PD, spanning molecular dynamics and biophysical models, cellular- and circuit-level network models, systems and abstract-level simulations of basal ganglia function, and whole-brain and data-driven models linked to clinical phenotypes. We highlight how multiscale and hybrid modeling strategies connect α-synuclein pathology, mitochondrial dysfunction, oxidative stress, and dopaminergic degeneration to alterations in neural dynamics and motor and non-motor symptoms. We further discuss the role of computational models in biomarker discovery, including imaging, electrophysiological, and digital biomarkers. In particular, eye-movement-based measures are highlighted as quantitative, reproducible behavioral signals that provide principled constraints for individualized computational modeling. We also review the emerging impact of computational approaches on drug discovery, target prioritization, and in silico clinical trials. Finally, we examine future directions toward personalized and precision medicine in PD, emphasizing digital twin frameworks, longitudinal validation, and the integration of patient-specific data with mechanistic and data-driven models. Together, these advances underscore the growing role of computational modeling as an integrative and hypothesis-generating framework, with the long-term goal of supporting data-constrained predictive approaches for biomarker development and translational applications. Full article

Background: Experimental autoimmune thyroiditis is an important animal model for studying Hashimoto’s thyroiditis. Our aim was to develop the model using CBA/J-DR3 mice expressing human HLA-DR3, which is associated with autoimmune thyroiditis in humans, to better simulate human autoimmune thyroiditis. Such a humanized model can be used to test specific antigen therapies for autoimmune thyroiditis. Methods: CBA/J-DR3 mice were produced by back-crossing B6-DR3 mice to the CBA/J background. Female CBA/J-DR3 mice were immunized with human thyroglobulin (Tg) in complete Freund’s adjuvant on days 0 and 7. On day 21, mice were sacrificed, blood collected, spleen and thyroid harvested for analysis. Splenocytes were analyzed for T cell responses to Tg and its major T-cell epitope in human autoimmune thyroiditis, Tg.2098. Serum anti-thyroglobulin antibodies were measured by ELISA, and thyroid-stimulating hormone was measured using the Luminex assay. Thyroid histology and immunohistochemistry were examined. Results: Immunized CBA/J-DR3 mice showed significant T cell proliferation in response to Tg (stimulation index 3.4 ± 4.5) and Tg.2098 (1.5 ± 0.7). Anti-thyroglobulin antibody levels were elevated in immunized mice when compared to control mice (2.05 ± 0.75 vs. 0.15 ± 0.06, p < 0.0001). T cells demonstrated higher reactivity to thyroid antigens by enhanced production of pro-inflammatory cytokines. Thyroid immunohistochemistry revealed mild CD3-positive T-cell infiltration. Conclusions: This novel humanized CBA/J-DR3 mouse model of Hashimoto’s thyroiditis demonstrates key features of human autoimmune thyroiditis. The HLA-DR3 background and the immune response to Tg and Tg.2098 enhance translational relevance, making this a valuable model for studying thyroid disease pathogenesis and testing targeted immune-modifying therapies. Full article

Chronic arthritis following arthritogenic alphavirus infections presents symptoms resembling autoimmune rheumatic diseases, raising questions about the underlying mechanisms, including molecular mimicry and autoantibody production. This systematic review evaluated evidence supporting molecular mimicry and the potential role of autoantibodies as predictive biomarkers in alphavirus-induced chronic arthritis. A comprehensive search of PubMed, Scopus and Web of Science was conducted following PRISMA 2020 guidelines and PECO framework. Thirteen studies met the inclusion criteria: four computational studies assessing peptide homology between viral and human proteins, and nine clinical studies evaluating autoantibodies in chronic post-alphavirus arthritis. Computational analyses identified conserved alphavirus peptides with sequence and structural similarity to human proteins implicated in autoimmunity, supporting the hypothesis of molecular mimicry. However, most lacked experimental validation. Clinical studies showed variable detection of autoantibodies, rheumatoid factors, anti-cyclic citrullinated peptide, and antinuclear antibodies in chronic patients, though seropositivity rates were inconsistent and generally low. Only one study reported a significant association between autoantibody levels and disease chronicity. The findings suggest a potential autoimmune component in post-alphavirus arthritis driven by molecular mimicry, though current evidence remains inconclusive due to methodological heterogeneity and limited validation. Autoantibodies may contribute to pathogenesis but are not reliable predictors of chronicity. Future longitudinal studies with standardized assays and validation of computational findings in human models are needed. Full article

Neurodegenerative disorders such as Alzheimer’s disease (AD), Parkinson’s disease (PD), and amyotrophic lateral sclerosis (ALS) share key molecular features, including neuroinflammation, oxidative stress, mitochondrial dysfunction, and progressive neuronal loss. Increasing evidence indicates that gut dysbiosis and alterations in microbiota-derived metabolites are involved in these processes through multiple pathways along the gut–brain axis. However, while broad compositional changes are well-documented, a critical knowledge gap remains regarding the specific biochemical signal transduction pathways translating dysbiosis into pathology. This narrative review addresses this gap by synthesizing current human and experimental studies addressing gut microbiota alterations in AD, PD, and ALS, with particular emphasis on the biochemical and molecular mechanisms mediated by gut-derived metabolites. Dysbiosis in neurodegenerative diseases is frequently associated with reduced abundance of short-chain fatty acid (SCFA)-producing bacteria and altered metabolism of SCFAs, bile acids, tryptophan-derived indoles, trimethylamine-N-oxide (TMAO), and lipopolysaccharides (LPS). These microbial metabolites have been shown to modulate intestinal and blood–brain barrier integrity, influence Toll-like receptor- and G protein-coupled receptor-dependent signaling, regulate microglial activation, and affect molecular pathways related to protein aggregation in experimental models. In addition, emerging evidence highlights the involvement of oxidative and nitrosative stress, immune–metabolic crosstalk, and altered xenobiotic metabolism in microbiota–host interactions during neurodegeneration. By integrating microbiological, metabolic, and molecular perspectives, this review underscores the important and emerging role of microbiota-derived molecules in neurodegenerative disorders and outlines key chemical and metabolic pathways that may represent targets for future mechanistic studies and therapeutic strategies. Full article

Tick-borne co-infections are an increasingly recognized and clinically important aspect of Lyme borreliosis, particularly in regions where Ixodes ticks transmit a wide range of bacterial, protozoan, and viral pathogens. In addition to Borrelia burgdorferi sensu lato, these ticks frequently harbor microorganisms such as Babesia spp., Anaplasma phagocytophilum, Ehrlichia spp., Borrelia miyamotoi, Bartonella spp., and several tick-borne viruses. Co-infections may increase disease severity, prolong symptom duration, and contribute to atypical or overlapping clinical presentations, thereby complicating diagnosis and management. Growing evidence from epidemiological studies, clinical case series, and experimental in vivo and in vitro models indicates that pathogen–pathogen and pathogen–host interactions can modulate immune responses and influence disease progression. Diagnostic challenges arise from non-specific clinical features and limitations of current laboratory methods. From a therapeutic perspective, although standard antibiotic regimens for Lyme disease are effective against some bacterial co-infections, they do not provide coverage for protozoan or viral agents, necessitating pathogen-specific and, in some cases, combination treatment strategies. This review synthesizes current knowledge on the epidemiology, clinical impact, diagnostic limitations, and treatment approaches for tick-borne co-infections associated with Lyme disease, and highlights critical evidence gaps and future research directions to improve patient outcomes. Full article

Distinguishing Alzheimer’s Disease (AD) from Mild Cognitive Impairment (MCI) is challenging due to their subtle morphological similarities in MRI, yet distinct therapeutic strategies are required. To assist junior clinicians with limited diagnostic experience, this paper proposes Vi-ADiM, a Vision Transformer framework designed for the early differentiation of AD and MCI. Leveraging cross-domain feature adaptation and task-specific data augmentation, the model ensures rapid convergence and robust generalization even in data-limited regimes. By optimizing a two-stage encoding module, Vi-ADiM efficiently extracts both global and local MRI features. Furthermore, by integrating SHAP and Grad-CAM++, the framework offers multi-granular interpretability of pathological regions, providing intuitive visual evidence for clinical decision-making. Experimental results demonstrate that Vi-ADiM outperforms the standard ViT-Base/16, improving accuracy, precision, recall, and F1 score by 0.444%, 0.486%, 0.476%, and 0.482%, respectively, while reducing standard deviations by approximately 0.06–0.29%. Notably, the model achieves these gains with a 48.96% reduction in parameters and a 49.65% decrease in computational cost (FLOPs), offering a reliable, efficient, and interpretable solution for computer-aided diagnosis. Full article

Biportal endoscopic spinal surgery (BESS) is a minimally invasive technique that is widely used in human spinal procedures; however, its standardized methodology and anatomical adaptation for veterinary applications have not yet been established. This study aimed to develop a reproducible experimental framework for performing BESS in dogs and evaluate its technical feasibility. A thoracic intervertebral disc disease model was created by injecting a fluorescently dyed artificial disc material containing methylene blue into the T12–13 intervertebral space of 13 medium-sized canine cadavers. Portal locations were determined using a computed tomography-based measurement method, and instruments specifically designed for BESS were used to perform mini-hemilaminectomies of the accessory process. The artificial disc material was successfully removed in all cases with clear visualization of the spinal cord and nerve roots. The mean portal insertion angle and distance were 31.00 ± 2.79° and 32.95 ± 3.05 mm, respectively, and the average residual material volume was 6.89% ± 1.66% of the initially inserted volume. Surgical time significantly decreased as the surgeon’s experience increased. These results demonstrate the successful methodological standardization of BESS tailored to canine thoracic anatomy and provide foundational data supporting its potential as a minimally invasive spinal surgery technique for future clinical veterinary applications. Full article

Hemostasis and thrombosis reflect a delicate balance, regulated by the interplay between procoagulant and anticoagulant mechanisms. Hemophilia is traditionally viewed as a bleeding disorder, but emerging evidence highlights the paradoxical risks of thrombosis in hemophilia patients. We explore the landscape of hemophilia management, emphasizing challenges of balancing hemostasis in the context of aging, novel non-factor replacement therapies (NRTs), and comorbidity-driven thrombotic complications. Therapeutic approaches, including innovative NRTs, such as emicizumab, or rebalancing agents (e.g., concizumab, marstacimab, fitusiran), offer promising advancements in bleeding prophylaxis but may increase thrombotic risks. Conversely, novel anticoagulants, such as FXI inhibitors, offer potential thrombosis protection with minimal bleeding risk. Our review examines the impact of aging-related comorbidities, including cardiovascular disease, atrial fibrillation, HIV-associated complications, and acute coronary syndromes, on thrombotic risk in hemophilia patients. Evidence-based strategies for balancing hemostasis and thrombosis are outlined alongside experimental models, thrombin generation assays, and advancements in rebalancing coagulation through natural anticoagulant modulation. FXI inhibition emerges as a paradigm shift in thrombosis management, offering reduced bleeding risks while preserving vascular health. Finally, this review highlights the need for global laboratory assays to personalize treatments, emphasizing strategies to optimize safety and efficacy, particularly as hemophilia patients live longer with complex comorbidity profiles. Full article

Background/Objectives: Studying the role of UV-induced metabolic changes in skin physiology, and especially skin diseases, has gained importance in both medicine and cosmetics. With the development of new technologies, a variety of approaches have been implemented to model these metabolic effects. In this study, we explore the reproducibility of the UVA1-induced metabolic changes observed in different in vitro, ex vivo, and in vivo systems with escalating complexity. Our aim is to elaborate on the role of experimental setups in the reliable representation of in vivo data in other systems. Methods: Metabolic profiles post UVA1 treatment were assessed in skin cell culture, skin explants, and intact skin. For cell culture and explants, the metabolites from the culture medium were assessed via 1D-CPMG NMR. Intact skin samples were collected via microdialysis and the resulting dialysate was measured with GC–TOF-MS. Results: Data show that, despite great metabolic variations between the systems, several metabolites, such as glutamic acid, succinic acid, and threonine, change in a similar manner across multiple systems after UVA1 irradiation, including in vivo settings. Some metabolites, like phenylalanine, citric acid, and pyruvic acid, show similar UVA-mediated metabolic patterns between corresponding in vitro and ex vivo systems, but do not overlap well with in vivo data. Conclusions: Our findings emphasize the need for a metabolite-by-metabolite approach when deciding on the proper experimental system to perform UV irradiation experiments with regard to cutaneous physiology. Full article

Early and reliable detection of cardiac disease is crucial for preventing complications and enhancing patient outcomes. Phonocardiogram (PCG) signals, which encode rich information about cardiac function, offer a non-invasive and cost-effective way to identify abnormalities such as valvular disorders, arrhythmias, and other heart pathologies. This study investigates advanced diagnostic methods for heart sound analysis to improve the detection and classification of cardiac abnormalities. In the proposed framework, recurrence plots (RPs) are used for feature extraction, while machine learning algorithms are applied for classification, creating a diagnostic model that can recognize cardiac conditions from composite acoustic signals. This method serves as an efficient alternative to more computationally intensive deep learning methods and other high-dimensional ML-based solutions. Experimental results demonstrate that the multiclass classification task achieves up to 98.4% accuracy, and the binary classification reaches 99.5% accuracy using 2 s signal segments. The techniques assessed in this research demonstrate the potential of automated heart sound analysis as a screening tool in both clinical and remote healthcare settings. Overall, the findings highlight the significance of machine learning in heart sound classification and its potential to facilitate timely, accessible, and cost-effective cardiovascular care. Full article

The development and application of therapeutic oligonucleotides, such as siRNA, miRNA, ASOs and aptamers, is a rapidly growing field in biomedicine. These molecules are undergoing extensive preclinical and clinical testing, and the market for synthetic RNA drugs is expanding. However, several challenges remain, including targeted delivery and high costs associated with development, screening and production. One significant advance has been the creation of GalNAc-conjugates, which selectively target ASGPR and deliver oligonucleotides to hepatocytes. Although these conjugates have shown promising results, their widespread use is limited by the lack of effective synthesis methods. Thus, the development of new methods for the synthesis of ligand-oligonucleotide conjugates is an important task to which this study is devoted. In this study, we created a library of siRNA conjugates with the GalNAc L-96 ligand to suppress the expression of the PCSK9 gene associated with elevated LDL and an increased risk of developing cardiovascular diseases. The selection of the most effective siRNA molecules was carried out using an algorithm previously developed by our research group, which considers thermodynamic stability, predicted specificity and effectiveness. To experimentally confirm the effectiveness of conjugates, an in vitro model based on the cultivation of hepatocyte cells was developed. Optimization of the conjugate synthesis process has significantly reduced the cost of manufacturing technology, which creates the potential for efficient scaling of synthesis for transfer and application in the pharmaceutical industry. The results of the study showed that the development of the siRNA sequence optimized in silico resulted in a significant increase in the inhibitory effect of the GalNAc-siRNA conjugate compared to a compound similar to a commercial drug. Full article

Wheat stripe rust, caused by Puccinia striiformis f. sp. tritici, threatens global wheat production, with climate change intensifying its spread. This meta-analysis, following PRISMA protocol, evaluated chemical and biological control methods through a systematic review of literature (2005–2025), identifying 12 peer-reviewed studies with 156 experimental comparisons under various conditions. Random effects models assessed treatment impacts on disease severity and grain productivity using standardized mean differences (SMDs). Chemical control significantly reduced stripe rust severity (SMD = −1.04) and improved productivity (SMD = 1.30), with low to moderate variability and consistent yield responses. Effectiveness varied by active ingredients and wheat types, with the greatest benefits in highly susceptible varieties. Biological control agents, particularly Bacillus, Pseudomonas, and Trichoderma species, also reduced disease severity (SMD = −2.19) and increased yield (SMD = 2.39), though with greater heterogeneity reflecting strain-specific and environmental effects. Chemical fungicides provided more predictable disease control, while biological agents offered significant yield increases with agroecological benefits. This meta-analysis demonstrates complementary roles for both approaches, strongly supporting integrated disease management combining plant resistance, optimal fungicide use, and strategic biological control to enhance resilience and sustainability of global cereal production systems. Full article

Background: Accurate staging of bladder cancer is critical for guiding clinical management, particularly the distinction between non–muscle-invasive (T1) and muscle-invasive (T2–T4) disease. Although MRI offers superior soft-tissue contrast, image interpretation remains opera-tor-dependent and subject to inter-observer variability. This study proposes an automated deep learning framework for MRI-based bladder cancer staging to support standardized radio-logical interpretation. Methods: A sequential AI-based pipeline was developed, integrating hybrid tumor segmentation using YOLOv11 for lesion detection and DeepLabV3 for boundary refinement, followed by three deep learning classifiers (VGG19, ResNet50, and Vision Transformer) for MRI-based stage prediction. A total of 416 T2-weighted MRI images with radiology-derived stage labels (T1–T4) were included, with data augmentation applied during training. Model performance was evaluated using accuracy, precision, recall, F1-score, and multi-class AUC. Performance un-certainty was characterized using patient-level bootstrap confidence intervals under a fixed training and evaluation pipeline. Results: All evaluated models demonstrated high and broadly comparable discriminative performance for MRI-based bladder cancer staging within the present dataset, with high point estimates of accuracy and AUC, particularly for differentiating non–muscle-invasive from muscle-invasive disease. Calibration analysis characterized the probabilistic behavior of predicted stage probabilities under the current experimental setting. Conclusions: The proposed framework demonstrates the feasibility of automated MRI-based bladder cancer staging derived from radiological reference labels and supports the potential of deep learning for stand-ardizing and reproducing MRI-based staging procedures. Rather than serving as an independent clinical decision-support system, the framework is intended as a methodological and work-flow-oriented tool for automated staging consistency. Further validation using multi-center datasets, patient-level data splitting prior to augmentation, pathology-confirmed reference stand-ards, and explainable AI techniques is required to establish generalizability and clinical relevance. Full article

Hibernation—the ability of some animals to enter prolonged winter sleep—is a natural hypometabolic state that allows them to withstand adverse environmental factors (low temperatures, food and water shortages). The ability to hibernate is a consequence of adaptations accumulated over evolution at various physiological levels, among which molecular adaptation to hypoxia plays a key role, which eliminates not only the negative effect of oxygen deficiency on cells, but also the danger of oxidative stress (OS) after awakening. This aspect of hibernation is medically important because understanding the mechanisms underlying the adaptation of hibernating animals to hypoxia and OS can help address a number of important issues related to preventing post-hypoxic complications in people with chronic neurodegenerative and heart disease. The molecular basis of adaptation to hypoxia in hibernating animals is the presence of an effective antioxidant system (AOC) and regulatory mechanisms that provide extraordinary mitochondrial plasticity, which is especially pronounced when animals emerge from hibernation. This review summarizes and systematizes the latest advances in the study of mitochondria and antioxidant defenses during mammalian hibernation, primarily gophers—a common experimental model of hibernation. Full article