Search Results (14,065)

A remarkably well-preserved skeleton of a male Mammuthus meridionalis, approximately 60 years old, from the Early Pleistocene that is housed at the Castle of L’Aquila (Italy) exhibits a fractured left tusk with severe bone erosion of the alveolus and premaxillary bone, as well as marked spinal deformities. The cranial region underwent ultrasonographic, radiological, and histological examinations, while morphological and biomechanical analyses were conducted on the vertebral column. Microscopic analysis revealed intra vitam lesions, including woven bone fibers indicative of early bone remodeling and lamellar bone with expanded and remodeled Haversian systems. These findings are consistent with osteomyelitis and bone sequestration, likely resulting from chronic pulpitis following the tusk fracture, possibly due to an accident or interspecific combat. The vertebral column shows cervical scoliosis, compensatory curves, fusion between the first cervical vertebrae, and asymmetric articular facets, suggesting postural adaptations. Evidence of altered molar wear and masticatory function also support long-term survival post-trauma. Additionally, lesions compatible with spondyloarthropathy, an inflammatory spinal condition not previously documented in Mammuthus meridionalis, were identified. These findings provide new insights into the pathology and adaptive responses of extinct proboscideans, demonstrating the critical role of (paleo)histological methods in reconstructing trauma, disease, and aspects of life history in fossil vertebrates. Full article

Cardiovascular diseases remain a leading cause of mortality in the United States, driving the need for advanced cardiovascular devices and pharmaceuticals. Mock Circulatory Loops (MCLs) have emerged as essential tools for in vitro testing, replicating pulsatile pressure and flow to simulate various physiological and pathological conditions. While many studies focus on custom MCL designs tailored to specific applications, few have systematically reviewed their use in device testing, and none have assessed their broader utility across diverse biomedical domains. This comprehensive review categorizes MCL designs into three types: mechanical, computational, and hybrid. Applications are classified into four major areas: Cardiovascular Devices Testing, Clinical Training and Education, Hemodynamics and Blood Flow Studies, and Disease Modeling. Most existing MCLs are complex, highly specialized, and difficult to reproduce, highlighting the need for simplified, standardized, and programmable hybrid systems. Improved validation and waveform fidelity—particularly through incorporation of the dicrotic notch and other waveform parameters—are critical for advancing MCL reliability. Furthermore, integration of machine learning and artificial intelligence holds significant promise for enhancing waveform analysis, diagnostics, predictive modeling, and personalized care. In conclusion, the development of MCLs should prioritize standardization, simplification, and broader accessibility to expand their impact across biomedical research and clinical translation. Full article

Background: This systematic review evaluates the efficacy of rehabilitation-focused exercise interventions for lumbar degenerative disc disease (DDD), a leading cause of chronic low back pain. Methods: Following PRISMA guidelines, a comprehensive search was conducted across international and regional databases (PubMed, Scopus, Web of Science, Magiran, SID, and Noormags) covering the period from January 2010 to January 2025. The review protocol was registered with the International Prospective Register of Systematic Reviews (PROSPERO) under registration number CRD420251088811. Using keywords such as “lumbar DDD,” “exercise therapy,” and “rehabilitation,” a total of 2495 records were identified. After screening, 20 studies—including clinical trials, quasi-experimental, and experimental designs—met the inclusion criteria and were assessed using the McMaster Critical Review Form for Quantitative Studies. Results: Interventions such as hydrotherapy, core stability training, Pilates, and suspension exercises were found to significantly reduce pain and improve functional outcomes. While multimodal approaches (e.g., aquatic exercise combined with acupuncture) showed positive effects, the comparative studies revealed no significant differences between modalities. Suspension training demonstrated superior efficacy in pain reduction compared to isolated core stability exercises. The methodological quality of included studies ranged from good to excellent, with the majority rated as very good or excellent (McMaster scores: 8 “excellent,” 7 “very good,” and 5 “good”). Common limitations among the studies included methodological heterogeneity, small sample sizes (n = 14–30), and insufficient long-term follow-up. Conclusions: Exercise-based rehabilitation is an effective strategy for managing lumbar DDD. Evidence particularly supports the use of suspension training and aquatic therapy for superior improvements in pain and functional outcomes. Future research should aim to adopt standardized protocols, recruit larger sample sizes, and include extended follow-up periods to produce more robust and generalizable findings. Full article

Alkyladenine DNA glycosylase (AAG) is a critical enzyme in the base excision repair (BER) pathway, responsible for removing a broad spectrum of alkylated DNA lesions. While AAG maintains genomic stability, dysregulated activity has been implicated in cancer development, drug resistance, and neurodegenerative diseases. This review synthesizes the current knowledge on AAG’s structure, catalytic mechanism, and polymorphic variants, highlighting their potential roles in disease pathogenesis. A comprehensive bioinformatics analysis of over 370 AAG single-nucleotide polymorphisms (SNPs) is presented, identifying ~40% as high-risk variants likely to impair enzymatic function. Notably, 151 SNPs were predicted to be damaging by multiple algorithms, including substitutions at catalytic residues and non-conserved sites with unknown functional consequences. Analysis of cancer databases (COSMIC, cBioPortal, NCBI) revealed 93 tumor-associated AAG variants, with 18 classified as high-impact mutations. This work underscores the need for mechanistic studies of AAG variants using structural biology, cellular models, and clinical correlation analyses. Deciphering AAG’s polymorphic landscape may unlock personalized strategies for cancer prevention and treatment. Full article

Recent advances in computational biology have provided powerful tools for analyzing, modeling, and optimizing probiotic microorganisms, thereby supporting their development as promising agents for improving human health. The essential role of the microbiota in regulating physiological processes and preventing disease has driven interest in the rational design of next-generation probiotics. This review highlights progress in in silico approaches for enhancing the functionality of probiotic strains. Particular attention is given to genome-scale metabolic models, advanced simulation algorithms, and AI-driven tools that provide deeper insight into microbial metabolism and enable precise probiotic optimization. The integration of these methods with multi-omics data has greatly improved our ability to predict strain behavior and design probiotics with specific health benefits. Special focus is placed on modeling probiotic–prebiotic interactions and host–microbiome dynamics, which are essential for the development of functional food products. Despite these achievements, key challenges remain, including limited model accuracy, difficulties in simulating complex host–microbe systems, and the absence of unified standards for validating in silico-optimized strains. Addressing these gaps requires the development of integrative modeling platforms and clear regulatory frameworks. This review provides a critical overview of current advances, identifies existing barriers, and outlines future directions for the application of computational strategies in probiotic research. Full article

This study presents an approach based on machine learning (ML) techniques to analyze the relationship between emergency room (ER) admissions for cardiorespiratory diseases (CRDs) and environmental factors. The aim of this study is the development and verification of an interpretable machine learning framework applied to environmental and health data to assess the relationship between environmental factors and daily emergency room admissions for cardiorespiratory diseases. The model’s predictive accuracy was evaluated by comparing simulated values with observed historical data, thereby identifying the most influential environmental variables and critical exposure thresholds. This approach supports public health surveillance and healthcare resource management optimization. The health and environmental data, collected through meteorological sensors and air quality monitoring stations, cover eleven years (2013–2023), including meteorological conditions and atmospheric pollutants. Four ML models were compared, with XGBoost showing the best predictive performance (R² = 0.901; MAE = 0.047). A 10-fold cross-validation was applied to improve reliability. Global model interpretability was assessed using SHAP, which highlighted that high levels of carbon monoxide and relative humidity, low atmospheric pressure, and mild temperatures are associated with an increase in CRD cases. The local analysis was further refined using LIME, whose application—followed by experimental verification—allowed for the identification of the critical thresholds beyond which a significant increase in the risk of hospital admission (above the 95th percentile) was observed: CO > 0.84 mg/m³, P_atm ≤ 1006.81 hPa, Tavg ≤ 17.19 °C, and RH > 70.33%. The findings emphasize the potential of interpretable ML models as tools for both epidemiological analysis and prevention support, offering a valuable framework for integrating environmental surveillance with healthcare planning. Full article

As people with cystic fibrosis (PwCF) live longer, kidney disease is emerging as a significant comorbidity that is increasingly linked to cardiovascular complications and progression to end-stage kidney disease. In our recent review, we proposed the unifying term CF-related kidney disease (CFKD) to encompass the spectrum of kidney dysfunction observed in this population. Early detection of kidney injury is critical for improving long-term outcomes, yet remains challenging due to the limited sensitivity of conventional laboratory tests, particularly in individuals with altered muscle mass and unique CF pathophysiology. Emerging approaches, including novel blood and urinary biomarkers, urinary extracellular vesicles, and genetic risk profiling, offer promising avenues for identifying subclinical kidney damage. When integrated with machine learning algorithms, these tools may enable the development of personalized risk stratification models and targeted therapeutic strategies. This precision medicine approach has the potential to transform kidney disease management in PwCF, shifting care from reactive treatment of late-stage disease to proactive monitoring and early intervention. Full article

Motor Neuron Diseases (MNDs) such as Amyotrophic Lateral Sclerosis (ALS), Primary Lateral Sclerosis (PLS), Hereditary Spastic Paraplegia (HSP), Spinal Muscular Atrophy with Respiratory Distress Type 1 (SMARD1), Multisystem Proteinopathy (MSP), Spinal and Bulbar Muscular Atrophy (SBMA), and ALS associated to Frontotemporal Dementia (ALS-FTD), have traditionally been studied as distinct entities, each one with unique genetic and clinical characteristics. However, emerging research reveals that these seemingly disparate conditions converge on shared molecular mechanisms that drive progressive neuroaxonal degeneration. This narrative review addresses a critical gap in the field by synthesizing the most recent findings into a comprehensive, cross-disease mechanisms framework. By integrating insights into RNA dysregulation, protein misfolding, mitochondrial dysfunction, DNA damage, kinase signaling, axonal transport failure, and immune activation, we highlight how these converging pathways create a common pathogenic landscape across MNDs. Importantly, this perspective not only reframes MNDs as interconnected neurodegenerative models but also identifies shared therapeutic targets and emerging strategies, including antisense oligonucleotides, autophagy modulators, kinase inhibitors, and immunotherapies that transcend individual disease boundaries. The diagnostic and prognostic potential of Neurofilament Light Chain (NfL) biomarkers is also emphasized. By shifting focus from gene-specific to mechanism-based approaches, this paper offers a much-needed roadmap for advancing both research and clinical management in MNDs, paving the way for cross-disease therapeutic innovations. Full article

Schizophrenia (SCZ) is a complex psychiatric disorder with heterogeneous molecular underpinnings that remain poorly resolved by conventional single-omics approaches, limiting biomarker discovery and mechanistic insights. To address this gap, we applied an artificial intelligence (AI)-driven multi-omics framework to an open access dataset comprising plasma proteomics, post-translational modifications (PTMs), and metabolomics to systematically dissect SCZ pathophysiology. In a cohort of 104 individuals, comparative analysis of 17 machine learning models revealed that multi-omics integration significantly enhanced classification performance, reaching a maximum AUC of 0.9727 (95% CI: 0.8889–1.000) using LightGBMXT, compared to 0.9636 (95% CI: 0.8636–1.0000) with CNNBiLSTM for proteomics alone. Interpretable feature prioritization identified carbamylation at immunoglobulin-constant region sites IGKC_K20 and IGHG1_K8, alongside oxidation of coagulation factor F10 at residue M8, as key discriminative molecular events. Functional analyses identified significantly enriched pathways including complement activation, platelet signaling, and gut microbiota-associated metabolism. Protein interaction networks further implicated coagulation factors F2, F10, and PLG, as well as complement regulators CFI and C9, as central molecular hubs. The clustering of these molecules highlights a potential axis linking immune activation, blood coagulation, and tissue homeostasis, biological domains increasingly recognized in psychiatric disorders. These results implicate immune–thrombotic dysregulation as a critical component of SCZ pathology, with PTMs of immune proteins serving as quantifiable disease indicators. Our work delineates a robust computational strategy for multi-omics integration into psychiatric research, offering biomarker candidates that warrant further validation for diagnostic and therapeutic applications. Full article

Cyclin-dependent kinase (CDK)4/6 inhibitors have become a key component of adjuvant treatment for patients with hormone receptor-positive (HR+), human epidermal growth factor receptor 2-negative (HER2−) early breast cancer who are at high risk of recurrence. The addition of abemaciclib and ribociclib to standard endocrine therapy has demonstrated clinically meaningful improvements in invasive disease-free survival, supported by the monarchE and NATALEE trials, respectively. With expansion of patient eligibility for CDK4/6 inhibitors, multidisciplinary coordination among medical oncologists, surgeons, nurses, pharmacists, and other health care providers is critical to optimizing patient identification, monitoring, and management of adverse events. This expert guidance document provides practical recommendations for implementing adjuvant CDK4/6 inhibitor therapy in routine clinical practice, incorporating insights from multiple specialties and with patient advocacy representation. Key considerations include patient selection based on clinical trial data, treatment duration, dosing schedules, adverse event profiles, monitoring requirements, drug–drug interactions, and patient-specific factors such as tolerability, cost, and quality of life. This guidance aims to support Canadian clinicians in effectively integrating CDK4/6 inhibitors into clinical practice, ensuring optimal patient outcomes through a multidisciplinary and patient-centric approach. Full article

The retina is highly sensitive to oxygen and blood supply, and hypoxia plays a key role in retinal diseases such as diabetic retinopathy (DR) and age-related macular degeneration (AMD). Müller glial cells, which are essential for retinal homeostasis, respond to injury and hypoxia with reactive gliosis, characterized by the upregulation of the glial fibrillary acidic protein (GFAP) and vimentin, cellular hypertrophy, and extracellular matrix changes, which can impair retinal function and repair. The retinal pigment epithelium (RPE) supports photoreceptors, forms part of the blood–retinal barrier, and protects against oxidative stress; its dysfunction contributes to retinal degenerative diseases such as AMD, retinitis pigmentosa (RP), and Stargardt disease (SD). Extracellular vesicles (EVs) play a crucial role in intercellular communication, protein homeostasis, and immune modulation, and have emerged as promising diagnostic and therapeutic tools. Understanding the role of extracellular vesicles’ (EVs’) signaling machinery of glial cells and the retinal pigment epithelium (RPE) is critical for developing effective treatments for retinal degeneration. In this study, we investigated the bidirectional EV-mediated crosstalk between RPE and Müller cells under hypoxic conditions and its impact on cellular metabolism and retinal cell integrity. Our findings demonstrate that RPE-derived extracellular vesicles (RPE EVs) induce time-dependent metabolic reprogramming in Müller cells. Short-term exposure (24 h) promotes pathways supporting neurotransmitter cycling, calcium and mineral absorption, and glutamate metabolism, while prolonged exposure (72 h) shifts Müller cell metabolism toward enhanced mitochondrial function and ATP production. Conversely, Müller cell-derived EVs under hypoxia influenced RPE metabolic pathways, enhancing fatty acid metabolism, intracellular vesicular trafficking, and the biosynthesis of mitochondrial co-factors such as ubiquinone. Proteomic analysis revealed significant modulation of key regulatory proteins. In Müller cells, hypoxic RPE-EV exposure led to reduced expression of Dyskerin Pseudouridine Synthase 1 (DKc1), Eukaryotic Translation Termination Factor 1 (ETF1), and Protein Ser/Thr phosphatases (PPP2R1B), suggesting alterations in RNA processing, translational fidelity, and signaling. RPE cells exposed to hypoxic Müller cell EVs exhibited elevated Ribosome-binding protein 1 (RRBP1), RAC1/2, and Guanine Nucleotide-Binding Protein G(i) Subunit Alpha-1 (GNAI1), supporting enhanced endoplasmic reticulum (ER) function and cytoskeletal remodeling. Functional assays also revealed the compromised barrier integrity of the outer blood–retinal barrier (oBRB) under hypoxic co-culture conditions. These results underscore the adaptive but time-sensitive nature of retinal cell communication via EVs in response to hypoxia. Targeting this crosstalk may offer novel therapeutic strategies to preserve retinal structure and function in ischemic retinopathies. Full article

Intravascular biosensors have become a crucial and novel class of devices in healthcare, enabling the constant real-time monitoring of essential physiological parameters directly within the circulatory system. Recent developments in micro- and nanotechnology have relevantly improved the sensitivity, miniaturization, and biocompatibility of these devices, thereby enabling their application in precision medicine. This review summarizes the latest advances in intravascular biosensor technologies, with a special focus on glucose and oxygen level monitoring, blood pressure and heart rate assessment, and early disease diagnostics, as well as modern approaches to drug therapy monitoring and delivery systems. Key challenges such as long-term biostability, signal accuracy, and regulatory approval processes are critical considerations. Innovative strategies, including biodegradable implants, nanomaterial-functionalized surfaces, and integration with artificial intelligence, are regarded as promising avenues to overcome current limitations. This review provides a comprehensive roadmap for upcoming research and the clinical translation of advanced intravascular biosensors with a strong emphasis on their transformative impact on personalized healthcare. Full article

The widespread use of pesticides in modern agriculture has significantly enhanced food production by managing pests and diseases; however, their degradation in soil can lead to unintended consequences for soil fertility and nutrient cycling. This review explores the mechanisms of pesticide degradation, both abiotic and biotic, and the soil factors influencing these processes. It critically examines how degradation products impact soil microbial communities, organic matter decomposition, and key nutrient cycles, including nitrogen, phosphorus, potassium, and micronutrients. This review highlights emerging evidence linking pesticide residues with altered enzymatic activity, disrupted microbial populations, and reduced nutrient bioavailability, potentially compromising soil structure, water retention, and long-term productivity. Additionally, it discusses the broader environmental and agricultural implications, including decreased crop yields, biodiversity loss, and groundwater contamination. Sustainable management strategies such as bioremediation, the use of biochar, eco-friendly pesticides, and integrated pest management (IPM) are evaluated for mitigating these adverse effects. Finally, this review outlines future research directions emphasizing long-term studies, biotechnology innovations, and predictive modeling to support resilient agroecosystems. Understanding the intricate relationship between pesticide degradation and soil health is crucial to ensuring sustainable agriculture and food security. Full article

Parkinson’s disease (PD) is a progressive, multisystemic α-synucleinopathy, recognized as the second most prevalent neurodegenerative disorder globally. Its neuropathology is characterized by the degeneration of dopaminergic neurons, particularly in the substantia nigra pars compacta (SNpc), and the intraneuronal accumulation of α-synuclein-forming Lewy bodies. Oxidative stress is a key contributor to PD pathogenesis. Thioredoxin-interacting protein (TXNIP) is a crucial regulator of cellular redox balance, inhibiting the antioxidant function of thioredoxin. This pilot study aimed to investigate the protein expression and localization of TXNIP in the SNpc of PD patients compared to healthy controls. We performed immunohistochemical analyses on 12 post-mortem human brain sections (formalin-fixed, paraffin-embedded) from six subjects with PD and six healthy controls. The study was performed on PD subjects with Braak stage 6. Our findings revealed that in control samples, TXNIP protein was distinctly and closely associated with neuromelanin (NM) pigment within the cytoplasm of SNpc dopaminergic neurons. Conversely, in PD samples, there was a markedly weak cytoplasmic expression of TXNIP, and critically, this association with NM pigment was absent. Furthermore, PD samples exhibited a significant reduction in both dopaminergic neurons and NM content, consistent with advanced disease. These findings, which mirror previous transcriptomic data showing TXNIP gene under-expression in the same subjects, suggest that altered TXNIP expression and localization in SNpc dopaminergic neurons are features of late-stage PD, potentially reflecting neuronal dysfunction and loss. Full article

Vitamin D has emerged as a modulatory factor in the pathogenesis and management of diabetes mellitus due to its influence on pancreatic β-cell function, immune regulation, and inflammatory pathways. This narrative review critically examines mechanistic and clinical evidence linking vitamin D status with type 1 diabetes (T1DM), type 2 diabetes (T2DM), and gestational diabetes (GDM). In T1DM, vitamin D’s immunomodulatory effects are thought to protect β-cells from autoimmune destruction; epidemiological studies associate vitamin D sufficiency with lower T1DM incidence and improved glycemic control, although causality remains under investigation. In T2DM, vitamin D deficiency is associated with worsened metabolic control and may contribute to disease development in at-risk individuals; however, it does not influence the initial onset of T2DM in patients who are already diagnosed. Intervention trials indicate that correcting the deficiency can modestly improve insulin sensitivity, β-cell function, and metabolic parameters. GDM has similarly been linked to hypovitaminosis D, with low maternal vitamin D levels associated with higher GDM risk and adverse perinatal outcomes; mechanistic insights suggest that adequate vitamin D supports glucose homeostasis in pregnancy, and emerging trials demonstrate improved insulin resistance with maternal vitamin D supplementation. Across these diabetes subtypes, maintaining sufficient vitamin D levels appears to confer metabolic benefits and may serve as an adjunct to current preventive and therapeutic strategies. However, definitive evidence from large-scale trials is required to establish optimal vitamin D supplementation protocols and confirm its efficacy in diabetes care. Full article