Search Results (2,879)

Obesity is a chronic disorder associated with serious comorbidities such as diabetes, cardiovascular disease, and cancer. Conventional pharmacological treatments often suffer from limited efficacy, poor selectivity, and undesirable side effects, highlighting the need for more effective alternatives. Nanomedicine offers a promising approach by overcoming these limitations through targeted drug delivery and enhanced therapeutic precision. This review examines key nanotechnological strategies in obesity management, including targeting white adipose tissue (WAT) and the vascular marker prohibitin, promoting WAT browning, and utilizing photothermal therapy and magnetic hyperthermia as nanotheranostic tools. We discuss major nanomedicine platforms—such as liposomes, nanoemulsions, and polymeric nanoparticles—alongside emerging applications in gene nanotherapy and herbal formulations. Potential toxicity concerns are also addressed. In summary, nanomedicine holds substantial potential to revolutionize obesity treatment through targeted, effective, and multifunctional therapeutic strategies. Full article

A wide range of strategies have been developed to modulate dysfunctional brain activities. This narrative review provides a comparative analysis of biophysical, genetic, and biological neuromodulation approaches with an emphasis on their known or unknown molecular targets and translational potential. The review incorporates data from both preclinical and clinical studies covering deep brain stimulation, transcranial electrical and magnetic stimulation, focused ultrasound, chemogenetics, optogenetics, magnetogenetics, and toxin-based neuromodulation. Each method was assessed based on specificity, safety, reversibility, and mechanistic clarity. Biophysical methods are widely used in clinical practice but often rely on empirical outcomes due to undefined molecular targets. Genetic tools offer cell-type precision in experimental systems but face translational barriers related to delivery and safety. Biological agents, such as botulinum neurotoxins, provide long-lasting yet reversible inhibition via well-characterized molecular pathways. However, they require stereotaxic injections and remain invasive. To overcome individual limitations and improve targeting, delivery, and efficacy, there is a growing interest in the synthesis of multiple approaches. This review highlights a critical gap in the mechanistic understanding of commonly used methods. Addressing this gap by identifying molecular targets may help to improve therapeutic precision. This concise review could be valuable for researchers looking to enter the evolving field of the neuromodulation of brain function. Full article

Secondary lymphoma of the prostate is described as the involvement of the prostate gland by lymphomatous spread from a primary site. This condition is exceedingly rare and often presents diagnostic and therapeutic challenges. The symptoms often mimic those of benign prostatic hyperplasia or prostate cancer, including LUTS (lower urinary tract symptoms) and even complete urinary retention. Here, we present a rare case of a 62-year-old male patient undergoing chemotherapy for stage IV mantle cell stomach lymphoma and subsequently secondary prostatic involvement. The patient presented with complete urinary retention, accompanied by biochemical (PSA = 11.7 ng/mL) and imaging (Magnetic Resonance Imaging-PIRADS V lesion) suspicion for prostate cancer. Histopathologic analysis of the MRI-targeted prostate fusion biopsy revealed secondary prostatic lymphoma. The chosen treatment was transurethral resection of the prostate (TUR-P) for relief of symptoms, which significantly improved urinary function (postoperative IPSS = 5 and Qmax = 17 mL/s). This case underscores the importance of considering prostatic lymphoma in the differential diagnosis of bladder outlet obstruction, especially in patients with a known lymphoma history. This report also provides a focused review of the literature on secondary prostatic lymphoma, highlighting the diagnostic challenges, treatment options, and clinical outcomes. Full article

Objectives: To synthesize evidence on structural and functional neuroplasticity in patients after anterior cruciate ligament reconstruction (ACLR) and its clinical implications. Methods: Adhering to the PRISMA guidelines for systematic reviews and meta-analyses, a literature search was conducted using PubMed, Embase, Web of Science, Scopus, and Cochrane CENTRAL (2018–2025) using specific keyword combinations, screening the results based on predetermined inclusion and exclusion criteria. Results: Among the 27 included studies were the following: (1) sensory cortex reorganization with compensatory visual dependence (5 EEG/fMRI studies); (2) reduced motor cortex efficiency evidenced by elevated AMT (TMS, 8 studies) and decreased γ-CMC (EEG, 3 studies); (3) progressive corticospinal tract degeneration (increased radial diffusivity correlating with postoperative duration); (4) enhanced sensory-visual integration correlated with functional recovery. Conclusions: This review provides a novel synthesis of evidence from transcranial magnetic stimulation (TMS), electroencephalography (EEG), functional near-infrared spectroscopy (fNIRS), diffusion tensor imaging (DTI), and functional magnetic resonance imaging (fMRI) studies. It delineates characteristic patterns of post-ACLR structural and functional neural reorganization. Targeting visual–cognitive integration and corticospinal facilitation may optimize rehabilitation. Full article

Background: Charcot–Marie–Tooth (CMT) is a group of inherited diseases impairing the peripheral nervous system. CMT originates from genetic variants that affect proteins fundamental for the myelination of peripheral nerves and survival. Moreover, environmental and humoral factors can impact disease development and evolution. Currently, no therapy is available. Metabolomics is an emerging field of biomedical research that enables the development of novel biomarkers for neurodegenerative diseases by targeting metabolic pathways or metabolites. This study aimed to evaluate the metabolomics profile of CMT disease by comparing patients with healthy individuals. Methods: A total of 22 CMT patients (CMT) were included in this study and were demographically matched with 26 healthy individuals (C). Serum samples were analyzed through Nuclear Magnetic Resonance spectroscopy, and multivariate and univariate statistical analyses were subsequently applied. Results: A supervised model showed a clear separation (R²X = 0.3; R²Y = 0.7; Q² = 0.4; p-value = 0.0004) between the two classes of subjects, and nine metabolites were found to be significantly different (2-hydroxybutyrate, 3-hydroxybutyrate, 3-methyl-2-oxovalerate, choline, citrate, glutamate, isoleucine, lysine, and methyl succinate). The combined ROC curve showed an AUC of 0.94 (CI: 0.9–1). Additional altered metabolic pathways were also identified within the disease context. Conclusion: This study represents a promising starting point, demonstrating the efficacy of metabolomics in evaluating CMT patients and identifying novel potential disease biomarkers. Full article

Cancer metabolic reprogramming plays a critical role in tumor progression and therapeutic resistance, underscoring the need for advanced analytical strategies. Metabolomics, leveraging mass spectrometry and nuclear magnetic resonance (NMR) spectroscopy, offers a comprehensive and functional readout of tumor biochemistry. By enabling both targeted metabolite quantification and untargeted profiling, metabolomics captures the dynamic metabolic alterations associated with cancer. The integration of metabolomics with machine learning (ML) approaches further enhances the interpretation of these complex, high-dimensional datasets, providing powerful insights into cancer biology from biomarker discovery to therapeutic targeting. This review systematically examines the transformative role of ML in cancer metabolomics. We discuss how various ML methodologies—including supervised algorithms (e.g., Support Vector Machine, Random Forest), unsupervised techniques (e.g., Principal Component Analysis, t-SNE), and deep learning frameworks—are advancing cancer research. Specifically, we highlight three major applications of ML–metabolomics integration: (1) cancer subtyping, exemplified by the use of Similarity Network Fusion (SNF) and LASSO regression to classify triple-negative breast cancer into subtypes with distinct survival outcomes; (2) biomarker discovery, where Random Forest and Partial Least Squares Discriminant Analysis (PLS-DA) models have achieved >90% accuracy in detecting breast and colorectal cancers through biofluid metabolomics; and (3) prognostic modeling, demonstrated by the identification of race-specific metabolic signatures in breast cancer and the prediction of clinical outcomes in lung and ovarian cancers. Beyond these areas, we explore applications across prostate, thyroid, and pancreatic cancers, where ML-driven metabolomics is contributing to earlier detection, improved risk stratification, and personalized treatment planning. We also address critical challenges, including issues of data quality (e.g., batch effects, missing values), model interpretability, and barriers to clinical translation. Emerging solutions, such as explainable artificial intelligence (XAI) approaches and standardized multi-omics integration pipelines, are discussed as pathways to overcome these hurdles. By synthesizing recent advances, this review illustrates how ML-enhanced metabolomics bridges the gap between fundamental cancer metabolism research and clinical application, offering new avenues for precision oncology through improved diagnosis, prognosis, and tailored therapeutic strategies. Full article

Rapid screening of foodborne pathogens is critical for food safety, yet current detection techniques often suffer from low efficiency and complexity. In this study, we developed a sliding microfluidic colorimetric biosensor for the fast, sensitive, and multiplex detection of Salmonella. First, the target bacteria were specifically captured by antibody-functionalized magnetic nanoparticles in the microfluidic chip, forming magnetic bead–bacteria complexes. Then, through motor-assisted sliding of the chip, manganese dioxide (MnO₂) nanoflowers conjugated with secondary antibodies were introduced to bind the captured bacteria, generating a dual-antibody sandwich structure. Finally, a second sliding step brought the complexes into contact with a chromogenic substrate, where the MnO₂ nanoflowers catalyzed a colorimetric reaction, and the resulting signal was used to quantify the Salmonella concentration. Under optimized conditions, the biosensor achieved a detection limit of 10 CFU/mL within 20 min. In spiked pork samples, the average recovery rate of Salmonella ranged from 94.9% to 125.4%, with a coefficient of variation between 4.0% and 6.8%. By integrating mixing, separation, washing, catalysis, and detection into a single chip, this microfluidic biosensor offers a user-friendly, time-efficient, and highly sensitive platform, showing great potential for the on-site detection of foodborne pathogens. Full article

Measuring weak magnetic fields proposes significant challenges to the sensing capabilities of magnetic field sensors. The magnetic field detection capacity of tunnel magnetoresistance (TMR) sensors is often insufficient for such applications, necessitating targeted optimization strategies to improve their performance in weak-field measurements. Utilizing magnetic flux concentrators (MFCs) offers an effective approach to enhance TMR sensitivity. In this study, the finite element method was employed to analyze the effects of different MFC geometric structures on the uniformity of the magnetic field in the air gap and the magnetic circuit gain (MCG). It was determined that the MCG of the MFC is not directly related to the absolute values of its parameters but rather to their ratios. Simulation analyses evaluated the impact of these parameter ratios on both the MCG and its spatial distribution uniformity, leading to the formulation of MFC design optimization principles. Building on these simulation-derived principles, several MFCs were fabricated using the 1J85 material, and an experimental platform was established to validate the simulation findings. The fabricated MFCs achieved an MCG of 7.325 times. Based on the previously developed TMR devices, a detection sensitivity of 2.46 nT/$\sqrt{\mathrm{Hz}}$ @1Hz was obtained. By optimizing parameter configurations, this work provides theoretical guidance for further enhancing the performance of TMR sensors in magnetic field measurements. Full article

Background: Oxidative stress is a critical factor contributing to male infertility, impairing spermatogonial stem cells (SSCs) and disrupting normal spermatogenesis. This study aimed to isolate and characterize human SSCs and to investigate oxidative stress-related gene expression, protein interaction networks, and developmental trajectories involved in SSC function. Methods: SSCs were enriched from human orchiectomy samples using CD49f-based magnetic-activated cell sorting (MACS) and laminin-binding matrix selection. Enriched cultures were assessed through morphological criteria and immunocytochemistry using VASA and SSEA4. Transcriptomic profiling was performed using microarray and single-cell RNA sequencing (scRNA-seq) to identify oxidative stress-related genes. Bioinformatic analyses included STRING-based protein–protein interaction (PPI) networks, FunRich enrichment, weighted gene co-expression network analysis (WGCNA), and predictive modeling using machine learning algorithms. Results: The enriched SSC populations displayed characteristic morphology, positive germline marker expression, and minimal fibroblast contamination. Microarray analysis revealed six significantly upregulated oxidative stress-related genes in SSCs—including CYB5R3 and NDUFA10—and three downregulated genes, such as TXN and SQLE, compared to fibroblasts. PPI and functional enrichment analyses highlighted tightly clustered gene networks involved in mitochondrial function, redox balance, and spermatogenesis. scRNA-seq data further confirmed stage-specific expression of antioxidant genes during spermatogenic differentiation, particularly in late germ cell stages. Among the machine learning models tested, logistic regression demonstrated the highest predictive accuracy for antioxidant gene expression, with an area under the curve (AUC) of 0.741. Protein oxidation was implicated as a major mechanism of oxidative damage, affecting sperm motility, metabolism, and acrosome integrity. Conclusion: This study identifies key oxidative stress-related genes and pathways in human SSCs that may regulate spermatogenesis and impact sperm function. These findings offer potential targets for future functional validation and therapeutic interventions, including antioxidant-based strategies to improve male fertility outcomes. Full article

Co-infections pose significant challenges not only clinically, but also in terms of simultaneous diagnoses. The development of sensitive, multiplexed analytical platforms is critical for accurately detecting viral co-infections, particularly in complex biological environments. In this study, we present a mass spectrometry (MS)-based detection strategy employing a target-triggered hybridization chain reaction (HCR) to amplify signals and in situ photocleavable mass tags (PMTs) for the simultaneous detection of multiple targets. Hairpin DNAs modified with PMTs and immobilized loop structures on magnetic particles (Loop@MPs) were engineered for each target, and their hybridization and amplification efficiency was validated using native polyacrylamide gel electrophoresis (PAGE) and laser desorption/ionization MS (LDI-MS), with silica@gold core–shell hybrid (SiAu) nanoparticles being employed as an internal standard to ensure quantitative reliability. The system exhibited excellent sensitivity, with a detection limit of 415.12 amol for the hepatitis B virus (HBV) target and a dynamic range spanning from 1 fmol to 100 pmol. Quantitative analysis in fetal bovine serum confirmed high accuracy and precision, even under low-abundance conditions. Moreover, the system successfully and simultaneously detected multiple targets, i.e., HBV, human immunodeficiency virus (HIV), and hepatitis C virus (HCV), mixed in various ratios, demonstrating clear PMT signals for each. These findings establish our approach as a robust and reliable platform for ultrasensitive multiplexed detection, with strong potential for clinical and biomedical research. Full article

Myocardial hypertrophy (MH) represents a complex and heterogeneous condition in the pediatric and young adult population. While rare in children, MH encompasses a wide spectrum of physiological and pathological entities, ranging from transient hypertrophy in the infants of diabetic mothers to progressive genetic hypertrophic cardiomyopathies (HCM) with significant morbidity and mortality. Differential diagnosis is critical, as many phenocopies—including metabolic, mitochondrial, and syndromic diseases—can mimic HCM. Echocardiography remains the first-line imaging modality, with cardiac magnetic resonance (CMR) and molecular diagnostics increasingly used for detailed characterization. Risk stratification tools, such as the HCM Risk-Kids model, support clinical decision-making but must be integrated with individualized assessment. Advances in prenatal screening and genetic testing have significantly improved outcomes, though long-term management requires multidisciplinary care. Understanding age-specific presentations and the underlying etiologies is essential for accurate diagnosis and targeted treatment. This review provides a comprehensive overview of cardiac hypertrophy from fetal life through young adulthood, with a focus on etiologies, diagnostic approaches, imaging modalities, and therapeutic strategies, and aims to guide clinicians through the evolving landscape of MH, emphasizing early recognition, comprehensive evaluation, and personalized care. Full article

Background/Objectives: In the era of treat-to-target strategies in inflammatory bowel disease (IBD), transmural healing (TH) is gaining recognition as a promising therapeutic goal. TH has been associated with significantly better long-term outcomes, including reduced rates of hospitalization, surgery, and the need for therapy escalation. Cross-sectional imaging techniques, such as intestinal ultrasound (IUS), magnetic resonance imaging (MRI), and computed tomography enterography (CTE), offer a comprehensive, non-invasive means to assess this deeper level of healing. This review explores how TH is currently defined across various imaging modalities and evaluates the feasibility and cost-effectiveness of achieving TH with available therapies. Methods: A literature search was conducted across PubMed, Scopus, and Embase using keywords, including “transmural healing”, “intestinal ultrasonography”, “magnetic resonance imaging”, “computed tomography enterography”, “Crohn’s disease”, “ulcerative colitis”, and “inflammatory bowel disease”. Only English-language studies were considered. Results: Despite growing interest, there is no standardized definition of TH across imaging platforms. Among the modalities, IUS emerges as the most feasible and cost-effective tool, owing to its accessibility, accuracy (sensitivity 62–95.2%, specificity 61.5–100%), and real-time capabilities, though it does have limitations. Current advanced therapies induce TH in roughly 20–40% of patients, with no consistent differences observed between biologics and small molecules. However, TH has only been evaluated as a formal endpoint in a single randomized controlled trial to date. Conclusions: A unified and validated definition of transmural healing is critically needed to harmonize research and guide clinical decision-making. While TH holds promise as a meaningful treatment target linked to improved outcomes, existing therapies often fall short of achieving complete transmural resolution. Further studies are essential to clarify its role and optimize strategies for deep healing in IBD. Full article

Global resource consumption continues to grow each year, exerting increasing pressure on their availability. This trend could lead to a shortage of raw materials in the coming years. Aware of the risks associated with this situation, the European Union has implemented policies and strategies aimed at diversifying its supply sources, including waste recycling. In this context, the present study was conducted with the objective of developing innovative processes to concentrate valuable metals present in the non-recovered fractions of waste electrical and electronic equipment (WEEE). Three types of samples were studied: washing table residues (WTRs), printed circuit boards (PCBs), and powders from cathode-ray tube screens (CRT powders). Several separation techniques, based on the physical properties of the elements, were implemented, including electrostatic separation, magnetic separation, and density and gravity-based separations. The results obtained are promising. For WTRs and PCBs, the recovery rates of targeted metals (Cu, Al, Pb, Zn, Sn) reached approximately 91% and 80%, respectively. In addition to these metals, other valuable metals, present in significant quantities, deserve further exploration. Regarding CRT powders, the performances are also encouraging, with recovery rates of 54.7% for zinc, 57.1% for yttrium, and approximately 71% for europium. Although these results are satisfactory, optimizations are possible to maximize the recovery of these critical elements. The techniques implemented have demonstrated their effectiveness in concentrating target metals in the treated fractions. These results confirm that recycling constitutes a viable alternative to address resource shortages and secure part of the supplies needed for the European Union’s industry. Full article

Aiming at the goal of preparing high-quality coatings, this paper reviews the progress on circular arc source structure and magnetic field arc controlling technology in arc ion plating (AIP), with a focus on design characteristics of the different structures and configuration optimization of the corresponding magnetic fields. The circular arc source, due to its simple structure, convenient installation, flexible target combination, high cooling efficiency, and high ionization rate and deposition rate, has shown significant application potential in AIP technology. In terms of magnetic field arc controlling technology, this paper delves into the design progress of various magnetic field configurations, including fixed magnetic fields generated by permanent magnets, dynamic rotating magnetic fields, axially symmetric magnetic fields, rotating transverse magnetic fields, and multi-mode alternating electromagnetic coupling fields. By designing the magnetic field distribution reasonably, the trajectory and velocity of the arc spot can be controlled precisely, thus reducing the generation of macroparticles, improving target utilization, and enhancing coating uniformity. In particular, the introduction of multi-mode magnetic field coupling technology has broken through the limitations of traditional single magnetic field structures, achieving comprehensive optimization of arc spot motion and plasma transport. Hopefully, these research advances provide an important theoretical basis and technical support for the application of AIP technology in the preparation for high-quality decorative and functional coatings. Full article

Artificial intelligence (AI) has already played a significant role in the healthcare sector, particularly in image-based medical diagnosis. Deep learning models have produced satisfactory and useful results for accurate decision-making. Among the various types of medical images, magnetic resonance imaging (MRI) is frequently utilized in deep learning applications to analyze detailed structures and organs in the body, using advanced intelligent software. However, challenges related to performance and data privacy often arise when using medical data from patients and healthcare institutions. To address these issues, new approaches have emerged, such as federated learning. This technique ensures the secure exchange of sensitive patient and institutional data. It enables machine learning or deep learning algorithms to establish a client–server relationship, whereby specific parameters are securely shared between models while maintaining the integrity of the learning tasks being executed. Federated learning has been successfully applied in medical settings, including diagnostic applications involving medical images such as MRI data. This research introduces an analytical intelligence system based on an Internet of Medical Things (IoMT) framework that employs federated learning to provide a safe and effective diagnostic solution for brain tumor identification. By utilizing specific brain MRI datasets, the model enables multiple local capsule networks (CapsNet) to achieve improved classification results. The average accuracy rate of the CapsNet model exceeds 97%. The precision rate indicates that the CapsNet model performs well in accurately predicting true classes. Additionally, the recall findings suggest that this model is effective in detecting the target classes of meningiomas, pituitary tumors, and gliomas. The integration of these components into an analytical intelligence system that supports the work of healthcare personnel is the main contribution of this work. Evaluations have shown that this approach is effective for diagnosing brain tumors while ensuring data privacy and security. Moreover, it represents a valuable tool for enhancing the efficiency of the medical diagnostic process. Full article