Search Results (6,595)

Cancer remains one of the most significant global health challenges, with conventional treatments limited by side effects and resistance to drugs. The unique properties of metal–organic frameworks (MOFs), which offer high surface areas, tunable structures, and biodegradable properties, make them promising candidates for cancer therapy. This review focuses on MOF-based drug delivery systems for cancer treatment in biomedical applications. This article discusses various synthesis methods, drug-loading strategies, and cytotoxicity considerations. The relationship between the basic chemistry of MOFs and their biomedical applications is elucidated by how each feature directly affects MOF performance in cancer drug delivery. Therefore, this review is a practical and complete guide for researchers working to translate MOFs into effective cancer treatments. Moreover, the role of stimuli-responsive MOFs in cancer therapy is highlighted, along with recent studies demonstrating the effectiveness of MOF-based drug delivery systems. Overall, MOFs offer opportunities for advancing cancer treatment and controlled drug delivery. Full article

Hydrogenated amorphous silicon (a-Si:H) is a mature thin-film technology for large-area devices and thin-film sensors, and its low-temperature growth via Plasma-Enhanced Chemical Vapor Deposition (PECVD) makes it particularly suitable for biomedical flexible and wearable platforms. However, the reliable integration of a-Si:H sensors on polymer substrates requires a quantitative assessment of their electrical stability under mechanical stress, since bending-induced variations may affect sensor accuracy. In this work, we provide a quantitative, direction-dependent evaluation of the static-bending robustness of both single-doped a-Si:H layers and complete p-i-n junction stacks on polyimide (Kapton^®), thereby linking material-level strain sensitivity to device-level functionality. First, n- and p-doped a-Si:H layers were deposited on 50 µm thick Kapton^® and then structured as two-terminal thin-film resistors to enable resistivity extraction under bending conditions. Electrical measurements were performed on multiple samples, with the current path oriented either parallel (longitudinal) or perpendicular (transverse) to the bending axis, and resistance profiles were determined as a function of bending radius. While n-type layers exhibited limited and mostly gradual variations, p-type layers showed a stronger sensitivity to mechanical stress, with a critical-radius behavior under transverse bending and a more progressive evolution in the longitudinal one. This directional response identifies a practical bending condition under which doped layers, particularly p-type films, are more susceptible to strain-induced degradation. Subsequently, a linear array of a-Si:H p-i-n sensors was fabricated on Kapton^® substrates with two different thicknesses (25 and 50 µm thick) and characterized under identical bending conditions. Despite the increased strain sensitivity observed in the single-layers, the p-i-n diodes preserved their rectifying behavior down to the smallest radius tested. Indeed, across the investigated radii, the reverse current at −0.5 V remained consistent, confirming stable junction operation under bending. Only minor differences, related to substrate thickness, were observed in the reverse current and in the high-injection regime. Overall, these results demonstrate the mechanical robustness of stacked a-Si:H junctions on polyimide and support their use as sensors for wearable biosensing architectures. By establishing a quantitative, orientation-aware stability benchmark under static bending, this study supports the design of reliable a-Si:H flexible sensor platforms for curved and wearable surfaces. Full article

Background/Objectives: Software is now core infrastructure in biomedical science, yet fragmented workflows across subfields hinder reproducibility and delay the translation of data into actionable decisions. There is a critical need for a cross-disciplinary synthesis to bridge these silos and establish a unified framework for software maturity. This narrative review addresses this gap by synthesizing representative software ecosystems across three major pillars: bioinformatics, molecular modeling/simulations, and epidemiology/public health. Methods: A narrative review of articles indexed in PubMed/NCBI, Web of Science, and Scopus between 2000 and 2025 was conducted. Domain-specific terms related to bioinformatics, molecular modeling, docking, molecular dynamics, epidemiology, public health, and workflow management were combined with software- and algorithm-focused keywords. Studies describing, validating, or applying documented tools with biomedical relevance were included. Results: Across domains, mature data standards and reference resources (e.g., FASTQ, BAM/CRAM, VCF, mzML), widely adopted platforms (e.g., BLAST+ (v2.16.0, NCBI, Bethesda, USA), Bioconductor (v3.20, Bioconductor Foundation, Seattle, USA), AutoDock Vina (v1.2.5, Scripps Research, La Jolla, USA), GROMACS (v2024.3, GROMACS Team, Stockholm, Sweden), Epi Info (v7.2.6, CDC, Atlanta, USA), QGIS (v3.40, QGIS.org, Gossau, Switzerland), and increasing use of workflow engines were identified. Software pipelines routinely transform molecular and surveillance data into interpretable features supporting hypothesis generation. Conclusions: Integrated, standards-based, and validated software pipelines can shorten the path from measurement to decision in biomedicine and public health. Future progress depends on reproducibility practices, benchmarking, user-centered design, portable implementations, and responsible deployment of machine learning. Full article

Background and Objectives: Extracellular matrix (ECM) and collagen remodeling contribute to chronic kidney disease (CKD) progression and vascular access dysfunction. Conventional histological techniques rely on staining and provide limited sensitivity for detecting early or subtle ECM alterations. Nonlinear optical imaging modalities, including second-harmonic generation (SHG), third-harmonic generation (THG), and multiphoton fluorescence (MPF) microscopy, enable label-free, high-resolution visualization of fibrillar collagen and may offer additional structural information. This study aimed to evaluate the added value of nonlinear imaging beyond conventional histology for assessing ECM remodeling in renal and vascular tissues. Materials and Methods: A systematic literature review was conducted in accordance with the PRISMA 2020 guidelines. PubMed and Web of Science were searched for studies published between 1 January 2015, and 4 April 2025, investigating ECM or collagen remodeling in renal or vascular tissues using SHG, THG, or MPF microscopy. After screening 115 records, 10 studies were included in the qualitative synthesis. In addition, representative SHG, THG, and MPF images of excised human arteriovenous fistula (AVF) tissue were acquired as illustrative feasibility examples to demonstrate the application of these imaging modalities. The use of human tissue was approved by the Vilnius Regional Biomedical Research Ethics Committee (approval No. 2022/6-1443-917). Results: The included studies demonstrated that nonlinear microscopy enables label-free assessment of collagen density, organization, and fiber orientation. SHG imaging differentiated healthy from diseased tissues and has been reported to support fibrosis assessment and staging in preclinical and selected clinical studies and revealed microstructural remodeling patterns not readily detected by conventional histology. The illustrative AVF images demonstrated collagen disorganization consistent with patterns reported in the reviewed literature and are presented solely to demonstrate imaging feasibility, without implying disease phenotype or clinical outcome associations. Conclusions: Nonlinear optical microscopy provides complementary structural information on ECM organization that is not accessible with standard histological techniques. Further validation and methodological standardization are required to support its broader application in clinical nephrology and vascular medicine. Full article

Due to the limited data on chemical coding of sympathetic chain ganglia neurons during the prenatal period, this study, for the first time, aimed to characterise noradrenergic and cholinergic neurotransmitter expression in lumbar sympathetic chain ganglia (L SChG) of 5-, 7-, and 10-week-old porcine foetuses as a model increasingly recognised in biomedical research. Double immunohistochemical staining was performed using antibodies against PGP 9.5 (marker of neuronal structures), β-hydroxylase tyrosine (DβH), and vesicular acetylcholine transporter (VAChT). The current findings demonstrated that, in 5-week-old foetuses, approximately 79.83 ± 4.37% of nerve cell bodies were DβH-positive, 25.90 ± 5.60% contained VAChT, and some neurons were DβH/VAChT-positive (12.45 ± 4.36%). In 7-week-old foetuses, the proportion of DβH-positive neurons increased to 82.0 ± 9.7%, while VAChT-positive neurons decreased to 6.5 ± 1.0%, and 9.1 ± 0.7% DβH-positive L SChG perikarya contained VAChT. In 10-week-old foetuses, DβH-positive neurons accounted for 88.5 ± 2.1%, VAChT-positive for 1.98 ± 0.64%, and DβH/VAChT-positive perikarya decreased to 5.4 ± 0.4%. These findings provide new insight into the differentiation of the autonomic nervous system and the timing of neurotransmitter phenotype specification. Understanding the ontogeny of noradrenergic and cholinergic neurons may contribute to a better understanding of developmental disorders affecting the autonomic nervous system and may have implications for regenerative medicine, neurodevelopmental diagnostics, and therapeutic strategies targeting sympathetic dysfunction. Full article

Background: Pharmacology plays a central role in linking biomedical science concepts with their application in clinical practice across medical and healthcare education. Globally, the pharmacological curriculum has evolved, just like other disciplines, through the integration of case-based, problem-based, and hybrid teaching models that led to firm clinical reasoning and long-term learning. Thus, this study aims to evaluate and compare the learning outcomes of pharmacology curricula across the globe by adopting a systematic review and meta-analysis research approach. Methods: This comprehensive review was conducted with transparency and integrity in accordance with Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA 2020) guidelines and was registered with PROSPERO (CRD420251207753). Five electronic databases, including MEDLINE (PubMed), EMBASE, CINAHL, PsycINFO, and the Cochrane Library were searched from January 2000 to October 2025. The Cochrane Library tool was used for the risk of bias assessment of randomised controlled trials, while the Joanna Briggs Institute (JBI) checklist was used for mixed-design, quasi-experimental, and cross-sectional cohorts. Review Manager 5.4 was used for statistical analysis. Results: Out of 3300 identified studies, 11 met the inclusion criteria, spanning 11 countries (published between 2007 and 2025). Integrated and case-based curricula significantly improved pharmacology knowledge compared to traditional lecture-based methods (SMD = 0.35; 95% CI: 0.07–0.64; I² = 75%). Student satisfaction also favours integrated learning (OR = 1.53; 95% CI: 1.16–2.02; I² = 46%). Most included studies were of moderate-to-high methodological quality. Conclusion: Globally, active and integrated pharmacology curricula foster greater cognitive understanding and learner satisfaction than conventional models. However, significant variability persists in resource-limited settings, leading to unequal competency in prescribing and therapeutic reasoning. Australian pharmacology programmes align broadly with international standards but require greater standardisation in assessment and experiential learning. Full article

The analysis of multivariate data is a central issue in biomedical research, where the accurate classification of patients and the extraction of reliable conclusions are of critical importance. Linear Discriminant Analysis (LDA) remains one of the most established methods for both dimensionality reduction and classification of data. In this paper, we examine in detail the theoretical foundations, assumptions, and statistical properties of LDA, and apply the method step by step to real data from the Breast Cancer Wisconsin (Diagnostic) database, which includes cellular features from breast biopsy samples with the aim of distinguishing benign from malignant tumors. Emphasis is placed on the importance of the method’s assumptions, such as multivariate normality, equality of covariance matrices, and absence of multicollinearity, demonstrating that their fulfillment leads to significant improvements in model performance. Specifically, careful preprocessing and strict adherence to these assumptions increase classification accuracy from $95.6\%$ ($94.7\%$ cross-validated) to $97.8\%$ ($97.4\%$ cross-validated). To our knowledge, this study is the first to demonstrate the dual use of LDA as both a dimensionality-reduction tool and a predictive classification model for this medical database within the same biomedical analysis framework. Moreover, we provide, for the first time, a systematic comparison between our assumption-aware LDA model and related studies employing the most accurate machine-learning classifiers reported in the literature for this dataset, showing that classical LDA achieves accuracy comparable to these more complex methods. The resulting discriminant model, which uses 13 variables out of the original 30, can be applied easily by clinical researchers to classify new cases as benign or malignant, while simultaneously providing interpretable coefficients that reveal the underlying relationships among variables. The implementation is carried out in the SPSS environment, following the theoretical steps described in the paper, thus offering a user-friendly and reproducible framework for reliable application. In addition, the study establishes a structured and transparent workflow for the proper application of LDA in biomedical research by explicitly linking assumption verification, preprocessing, dimensionality reduction, and classification. Full article

Past research has consistently demonstrated the negative effects of interpersonal mistreatment on student experiences by employing retrospective studies; however, little is known about the daily effects that could lead to career derailment. The present study advances evidence of the consequences of experiencing multiple forms of interpersonal mistreatment, including sexual harassment, racial harassment and microaggressions, and incivility (collectively labeled HARM) by employing an experience sampling methodology (ESM) to estimate the immediate impact of HARM on career-relevant attitudes among a sample of 202 biomedical health trainees (mentees) funded by a National Institutes of Health fellowship. Grounded in Affective Events Theory, we found that mentees’ daily experiences of HARM were associated with an immediate degradation of their attitudes toward their training program mediated by negative affect. Being racially isolated in a lab or having a racially different mentor increased the prevalence of HARM; moreover, accounting for negative affect, experiences of HARM were positively associated with program attitudes for mentees who were racially well-represented, suggesting that majority status may buffer the negative impact of HARM on attitudes. Understanding these dynamics provides insight into the importance of assessing and addressing daily experiences of mistreatment among graduate and postdoctoral trainees. Full article

Quartz Crystal Microbalance with Dissipation monitoring (QCM-D) systems are widely used for the real-time analysis of mass changes and viscoelastic properties in biological samples, enabling applications such as biomolecular interaction studies, biosensing, and fluid characterization. However, their accessibility has been limited by high acquisition costs. To address this limitation, a low-cost, open-source QCM-D system was developed. Unlike other affordable, open-hardware alternatives, this system is specifically optimized for potential biomedical applications by integrating active thermal control to preserve the physical properties of the samples and dissipation monitoring to characterize their viscoelastic behavior. A 10 MHz quartz crystal with a sensor module and a control and acquisition unit were integrated. The full system was built at a total cost below USD 500. Performance validation showed a temperature stability of ±0.13 °C, a frequency stability of ±2 Hz in air, and a limit of detection (LOD) of 0.46% polyethylene glycol (PEG), thereby enabling stable, reproducible measurements and the sensitive detection of small mass and interfacial changes in low-concentration samples. These results demonstrate that key QCM-D sensing capabilities can be achieved at a fraction of the cost, providing an accessible and reliable platform for potential biomedical research. Full article

With rapid advances in nanotechnology and synthetic biology, biological nanonetworks are emerging for biomedical and environmental applications within the Internet of Bio-NanoThings. While they rely on molecular communication, experimental validation remains limited, especially for non-ideal effects such as molecular accumulation. In this work, we present a novel table-top experimental system that emulates the core functionalities of a biological nanonetwork and is straightforward to reproduce in standard laboratory environments, also making it suitable for educational demonstrations. To the best of our knowledge, this is the first experimental platform that incorporates two end nodes capable of acting interchangeably as transmitter and receiver, thereby enabling true bidirectional molecular communication. Information transfer is realized through controlled release, advection and diffusion of molecules, using molecular concentration coding analogous to concentration shift keying, while the receiver decodes messages by comparing measured concentrations against predefined thresholds. Based on the measurements reported herein, the drop-based algorithm substantially outperforms the threshold-based scheme. Specifically, it reduces first-message latency by more than 2.5× across the tested volumes and reduces latest-message latency by up to 71%, providing approximately 3.7× better message delivery. A key experimental outcome is the observation of channel saturation: beyond a certain operating period, residual molecules accumulate and effectively saturate the medium, inhibiting reliable further message exchange until sufficient clearance occurs. This saturation-induced “channel memory” emerges as a fundamental practical constraint on sustained communication and achievable data rates. Overall, the proposed platform provides a scalable, controllable, and experimentally accessible testbed for systematically studying signal degradation, saturation, clearance dynamics, and throughput limits, thereby bridging the gap between theoretical models and practical implementations in the Internet of Bio-NanoThings era. Full article

This study presents a microfluidic platform for non-Newtonian fluid viscosity sensing, integrating a high-flow-rate flow field stabilizer to mitigate flow uniformity limitations under elevated flow rate conditions. Building upon an established dual-phase laminar flow principle that determines relative viscosity via channel occupancy, this research aimed to extend the measurable viscosity range from 1–10 cP to 1–50 cP, which covers viscosity regimes relevant to biomedical fluids, dairy products during gelation, and low-to-moderate viscosity industrial liquids. A flow stabilizer was developed through computational fluid dynamics simulations, optimizing three key design parameters: blocker position, porosity, and the number of outlet paths. The N5 design proved most effective, providing over 50% reduction in standard deviation for asymmetric velocity distribution in high-flow simulations. The system was validated using simulated blood and dairy samples, achieving over 95% viscosity accuracy with less than 5% sample volume error compared to conventional viscometers. The chip successfully captured viscosity transitions during milk acidification and gelation, demonstrating excellent agreement with standard measurements. This low-volume, high-precision platform offers promising potential for applications in food engineering, biomedical diagnostics, and industrial fluid monitoring, enhancing microfluidic rheometry capabilities. Full article

Understanding electroporation at the molecular level is essential for advancing its biomedical applications, including drug delivery and tumor ablation. Among various influencing factors, the ionic environment, particularly ion concentration and type, plays a crucial role in modulating membrane behavior. In this study, we performed systematic molecular dynamics simulations to investigate how different ions affect the electroporation of phospholipid membranes. While moderate ion concentrations were found to accelerate pore formation by enhancing ion–membrane interactions, our results reveal that excessively high ion concentrations inhibit electroporation. Further analysis shows that this inhibition is primarily due to the formation of an inner potential, induced by the electrophoretic movement of ions under an applied field. This inner potential effectively weakens the transmembrane electric field, delaying or even preventing pore formation. Additionally, we demonstrate a strong correlation between ion charge concentration and electroporation time, regardless of ion species. These findings uncover a concentration-dependent shift in electroporation mechanisms and highlight the critical role of inner potential in regulating membrane permeability. This work provides valuable theoretical insights for the precise control and optimization of electroporation-based therapies. Full article

Alginate is a widely used and versatile biopolymer with an ever-expanding range of applications in the pharmaceutical and biomedical industries. This highlights the importance of developing sustainable and renewable production sources. Conventional extraction methods, although effective, are often energy-intensive and rely on harsh chemicals. In this context, brown algae are a promising alternative due to their abundance and renewability. This study investigated the potential of Saccorhiza polyschides and Sargassum muticum as sources of sodium alginate (SA), thus optimising an extraction process that combines acid treatment with an alkaline step. The extracted biopolymers were characterised using FTIR, H-NMR, STA, SEM/EDX, viscosity measurements, dynamic light scattering, and spectrophotometric assays of residual polyphenols and proteins. The optimised extraction conditions produced yields above 20% of high-purity alginate. When compared with commercial SA, the extracted materials showed comparable quality while relying on a simplified, solvent-reduced protocol that improves process efficiency and reduces the environmental impact. These results demonstrate that S. polyschides and S. muticum are promising, locally available sources of high-quality sodium alginate, and that industrially relevant yields (>20%) can be achieved through an environmentally conscious two-step extraction process. Full article

Background: Electrocardiography (ECG) represents an important noninvasive screening tool for heart disease in preparticipation screening of competitive athletes. However, interpretation of pediatric ECG based on age-specific reference values remains challenging, due to considerable variation among studies, influenced by population characteristics and documentation methodology. The variability of normal values in key pediatric ECG features regarding left ventricular hypertrophy (LVH), QTc prolongation and pre-excitation detection seem to have a significant impact on the efficacy of pediatric ECG as a preparticipation screening tool. Aims and Scope of the Study: This review aims to compare contemporary pediatric ECG reference ranges for key ECG features relevant to LVH, QTc, PR and QRS duration and highlight physiological and methodological sources of observed variability. Methods: A review of the current literature was conducted using common biomedical databases for studies reporting certain quantitative ECG reference values in healthy children from infancy through adolescence regarding the above selected key features. Reported values were summarized descriptively, with emphasis on developmental trends and methodological differences among studies affecting ECG values. Results: Across 16 pediatric studies, ECG parameters demonstrated consistent age-dependent developmental patterns, despite variability in absolute values. R-wave amplitudes in left precordial leads increased from infancy through early childhood and remained stable in older children, whereas S-wave amplitudes in right precordial leads showed greater variation between studies. PR intervals and QRS duration increased progressively with age across all datasets, while QTc values remained relatively stable throughout childhood and adolescence, with minimal sex-related differences. Variability in reported reference ranges was most pronounced for amplitude-based—compared to interval duration—parameters, and was influenced by differences in population characteristics, ECG acquisition techniques, and measurement methodology. Conclusions: This review summarizes contemporary ECG reference data in healthy children for the early detection of LVH, pre-excitation and QT prolongation, which are the main objectives of ECG screening in young athletes. Full article

High-dimensional tabular data are common in biomedical and clinical research, yet conventional machine learning methods often struggle in such settings due to data scarcity, feature redundancy, and limited generalization. In this study, we systematically evaluate Synolitic Graph Neural Networks (SGNNs), a framework that transforms high-dimensional samples into sample-specific graphs by training ensembles of low-dimensional pairwise classifiers and analyzing the resulting graph structure with Graph Neural Networks. We benchmark convolution-based (GCN) and attention-based (GATv2) models across 15 UCI datasets under two training regimes: a foundation setting that concatenates all datasets and a dataset-specific setting with macro-averaged evaluation. We further assess cross-dataset transfer, robustness to limited training data, feature redundancy, and computational efficiency, and extend the analysis to a real-world ovarian cancer proteomics dataset. The results show that topology-aware node feature augmentation provides the dominant performance gains across all regimes. In the foundation setting, GATv2 achieves an ROC-AUC of up to 92.22 (GCN: 91.22), substantially outperforming XGBoost (86.05), $\alpha =0.001$. In the dataset-specific regime, GATv2, combined with minimum-connectivity filtering, achieves a macro ROC-AUC of 83.12, compared to 80.28 for XGBoost. Leave-one-dataset-out evaluation confirms cross-domain transfer, with an ROC-AUC of up to 81.99. SGNNs maintain ROC-AUC around 85% with as little as 10% of the training data and consistently outperform XGBoost in more extreme low-data regimes, $\alpha =0.001$. On ovarian cancer proteomics data, foundation training improves both predictive performance and stability. Efficiency analysis shows that graph filtering substantially reduces training time, inference latency, and memory usage without compromising accuracy. Overall, these findings suggest that SGNNs provide a robust and scalable approach for learning from high-dimensional, heterogeneous tabular data, particularly in biomedical settings with limited sample sizes. Full article