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The synergistic use of silver nanoparticles (AgNPs) and photosensitizer's offers promise biomedical improvements. This study assesses and creates the potential for photosensitizers (Chlorine e6 (Ce6), Methylene Blue (MB)) and Silver Nanoparticles to work together to enhance biological activity. AgNPs were created by the laser ablation method and characterized using methods including scanning electron microscopy (SEM), Fourier transform infrared (FTIR) spectroscopy, and X-ray diffraction (XRD).The antibacterial and anticancer properties of these nanoparticles, both individually and in combination with photosensitizers, were further examined. AgNPs were combined with Methylene Blue and Chlorine e6 to enhance their antibacterial activity against Gram-negative bacteria, such as Salmonella enteritidis, Pseudomonas aeruginosa, and Acinetobacter baumannii, resulting in inhibition zones of up to as large as 0.66 ± 057 mm. The anticancer properties of the combination therapy were also examined against MCF-7 breast cancer cells, where Chlorine e6 alone had an IC50 of approximately 231.2%. Another photosensitizer, Methylene blue, showed a dose-dependent reduction in cell viability, with an IC50 of around 6.52 ± 3.26%. When AgNPs and Methylene Blue combined, the IC50 decreased to 11.42 ± 5.71, indicating a synergistic increase in cytotoxicity. Similarly, Chlorine e6 and AgNPs together significantly decreased the IC50 to 80µM to 100 µM. These findings show that the combined use of Methylene Blue or Chlorine e6 with AgNPs greatly improves anticancer and antibacterial efficacy compared to their individual applications. This research highlights how AgNPs and photosensitizers have the ability to change treatment approaches by providing improved specificity and efficacy in biomedical applications.

Ferritins are vital macromolecules that have been widely used in a number of biotechnological fields. Ferritin-based hybrid nanoparticles, composed of different types of subunits and conjugates, represent a next generation of tools, which can significantly enhance their efficiency and expand the range of existing applications. This review outlines the application landscape of these hybrids in developing recombinant vaccines, drug delivery and imaging systems. We highlight the increasing trend towards the development of ferritin-based mosaic vaccines and some of them are already in the first or second phases of clinical studies. In comparison, drug delivery research, which is mostly focused on cancer theranostics, to our knowledge, has not progressed beyond the preclinical stage. Herein, we describe the key limitations and challenges of ferritin-based drug delivery systems development, suggest strategies that address these limitations and discuss promising future research directions. We conclude that engineered ferritin hybrids hold significant potential as useful tools for immunology, theranostics and other biomedical applications.

The increasing application of time-series analysis in fields like biomedical engineering or telecommunications emphasizes the need for high-quality data to train and evaluate advanced machine learning models. Acquiring temporal data at suitable resolutions is often limited by ethical, economic, or practical constraints. We introduce CoSiBD (Complex Signal Benchmark Dataset for Super-Resolution), a synthetic dataset designed for reproducible time-series super-resolution research. CoSiBD provides 2,500 high-resolution signals (N = 5, 000 samples each over a reference domain τ ∈ [0, 4π]) with aligned low-resolution versions at four levels (150, 250, 500, and 1,000 samples) obtained via uniform decimation. Signals are generated with diverse non-stationary behaviors through piecewise frequency modulation and spline-based amplitude envelopes, and provides both clean and noisy variants. Signals are distributed as NumPy arrays, plain text, and JSON, with comprehensive metadata describing segment structure, generation parameters, and seeds for full reproducibility. Technical validation analyzes spectral properties and reports baseline SR benchmarking and transfer experiments on EEG and speech data.

Access to large, diverse biomedical datasets is critical for advancing medical research, yet privacy regulations severely restrict data sharing. We present an end-to-end framework for privacy-preserving health data synthesis that integrates advanced deep generative models (DGMs) with robust preprocessing, formal differential privacy (DP) training for select DGMs, empirical privacy risk evaluation, data-sufficiency analysis, domain-guided quality control, and biobank visualization tools. Released as open-source containerized software, the framework ensures reproducible deployment while preserving statistical fidelity, machine learning (ML) utility, and privacy guarantees. Empirical evaluations across diverse biobank datasets demonstrate that TabSyn-a transformer-based diffusion model-combined with our correlation-and distribution-aware CorrDst loss function achieves superior performance balancing fidelity, privacy, and computational efficiency. The tailored preprocessing pipeline effectively handles high missingness rates, substantially improving distributional accuracy and clinical plausibility. Across 26 biobank datasets spanning three regulatory levels, the framework shows that TabSyn with correlation- and distribution-aware loss function consistently achieves superior performance in terms of fidelity, privacy, and computational efficiency.

This article presents the design and the numerical analysis of a smart label-free Surface Plasmon Resonance (SPR) sensor to detect the concentration of haemoglobin in blood and the concentration of glucose in the urine samples. The suggested sensor uses a thin film of silver (Ag) on the prism's surface to excite the surface plasmons. The finite element method (FEM) was used to do numerical simulations to optimize the layer thickness and to analyse the sensor's performance in terms of the sensitivity and the Figure of merit (FOM). The results of the simulation showed that there is a linear correlation between resonance wavelength shift and change in analyte refractive index. The optimised design obtained a sensitivity of 288.29 °/RIU, QF of 780.80 [Formula: see text], SNR of 15.62, FoM of 492.51 [Formula: see text] and CSF of 539.20. The label-free methodology involves no chemical tagging and thus allows biosensing that is quick, real-time and economical. The suggested SPR sensor has great possibilities to be implemented in the non-invasive biomedical applications, diagnostics and point-of-care monitoring. In addition, machine learning models were employed to predict sensor sensitivity based on structural and optical parameters, demonstrating the strong capability of data-driven approaches for rapid performance estimation and design optimization.

Parkinson's Disease (PD) is a progressive neurodegenerative disorder that causes motor and cognitive impairments, affecting approximately 1% of individuals over 60 years of age. Speech impairments are among the earliest and most accessible biomarkers, making voice-based assessment a promising avenue for remote PD monitoring. However, existing speech-based PD prediction methods suffer from feature redundancy that degrades model performance, non-Gaussian data distributions that violate model assumptions, and limited systematic feature grouping strategies. This study introduces an adaptive approach to improve PD diagnostic precision by predicting the Motor Unified PD Rating Scale (UPDRS) and Total-UPDRS scores from biomedical voice measurements. The proposed framework addresses these challenges through three integrated components: (1) Box-Cox transformation to stabilize variance, reduce skewness, and normalize features; (2) a clustering-based feature selection method that groups correlated features via K-Means and selects the most informative representative per cluster using mutual information, thereby eliminating redundancy without losing discriminative power; and (3) an Extra Trees Regressor (ETR) whose extreme randomization in node splitting provides computational efficiency and reduced variance. To ensure rigorous evaluation, a subject-independent data splitting strategy is adopted to prevent data leakage, and k-fold cross-validation is employed to assess model stability. The proposed method is compared against multiple feature selection techniques-mutual information, recursive feature elimination, Lasso regression, and autoencoders-paired with nine regression models including Ridge, Lasso, Linear, Decision Tree, k-Nearest Neighbors, Random Forest, Gradient Boosting, AdaBoost, and Extra Trees Regressors. The clustering-based feature selection combined with ETR yielded the best performance, achieving [Formula: see text] scores of 0.999 for Motor-UPDRS and 0.997 for Total-UPDRS on the test set. These results are further supported by cross-validation analysis and feature importance evaluation, demonstrating the effectiveness and robustness of the proposed framework for speech-based PD telemonitoring.

Targeted delivery of drugs and hyperthermia in cardiovascular disease demand the accurate delivery of nanoparticles in complex arterial geometries. This paper introduces combined hybrid computational model that concomitantly examines the combined impact of nanoparticle radius and interparticle spacing on the thermal and mass transport characteristics of ternary bio-nanofluid flow under magnetohydrodynamic (MHD) effect. The ternary fluid is composed of blood fluid with suspended nanoparticles such as gold (Au), silver (Ag) silica (SiO2). The mathematical model accounts for geometric properties of nanoparticles such as nanoparticles radius and interparticle spacing for their practical utility for several medical interventions. The numerical analysis is based on hybrid computational strategy, where the solutions are determined through the bvp4c numerical solver and then a novel supervised multi hidden layers Artificial neural network (ANN) is integrated. The proposed model has a high predictive capability with an exceptionally high accuracy with the lowest Mean squared error and ideal regression coefficient MSE=9.6327×10-11, Gradient=9.5681e-08, Mu=1e-09, and R2=1.0. Some of the main findings indicate that less spacing between particles (h=0.1) leads to continuous networks of thermal percolation, which enhance the thermal conductivity by up to 35% to improve the efficiency of hyperthermia, whereas the larger nanoparticles (radius ≥1.5) offer a higher drug-loading capacity, yet the rate of heat transfer decreases by 15-20%. Optimization of the magnetic parameter (M=0.1-0.7) also decreases flow velocity by 28% and extends the nanoparticle residence time at the stenosis by 35% which allows sustained drug delivery, results directly applicable to clinical-strength (1.5-3T) MRI-guided interventions. Radiation parameter (Rd=0.5-2.5) increases temperature of the arteries by 15-20% giving controllable thermal modulation to applications of hyperthermia. The proposed model predicts that optimal nanoparticle preparations (50 nm radius, 20 nm spacing) have to potential to lower the rate of restenosis by 30-40% in relation to traditional drug-eluting stents. The purpose of such an integrated computational-machine learning systems is to give quantitative advice to stent coating design, nanoparticle formulation, and optimization of treatment protocols, and has been directly used in biomedical interventions. The results can be used to offer practical advice to stent manufactures, interventional radiologist and pharmaceutical developers to create evidence-based cardiovascular therapy of the next generation.

Segmentation of spinal nerve rootlets is relevant for spinal level estimation, lesion classification, neuromodulation therapy, and group-level analyses. The aim of this study was to develop a deep learning method for the automatic segmentation of C2-T1 dorsal and ventral spinal nerve rootlets on various MRI scans. The study included MRI scans from two open-access and one private dataset, consisting of 3D isotropic 3T turbo spin echo T2-weighted (T2w) and 7T MP2RAGE (T1-weighted [T1w] INV1 and INV2, and UNIT1) MRI scans. A deep learning model, RootletSeg, was developed on 93 MRI scans from 50 healthy adults (mean age, 28.70 years ± 6.53 [SD]; 28 [56%] males, 22 [44%] females) and achieved a mean ± SD Dice score of 0.67 ± 0.09 for T1w-INV2, 0.65 ± 0.11 for UNIT1, 0.64 ± 0.08 for T2w, and 0.62 ± 0.10 for T1w-INV1 contrasts. RootletSeg accurately segmented C2-T1 spinal rootlets across MRI contrasts, enabling the determination of spinal levels directly from MRI scans. The method is open-source and can be used for a variety of downstream analyses.

Bone remodelling is essential for maintaining skeletal integrity by preserving the balance between bone formation and resorption, with excessive osteoclast activity contributing to osteoporosis. Osteocytes act as central regulators of osteoclastogenesis through mechanically sensitive paracrine signals, yet the influence of osteoblasts and their mesenchymal precursors remains less defined. Extracellular vesicles (EVs) have recently emerged as mediators of bone cell communication, although their role in osteoclast regulation are still underexplored. This study demonstrates that mesenchymal-derived bone cells inhibit osteoclastogenesis through an EV-dependent mechanism shaped by their differentiation stage and mechanical environment. Mechanically stimulated osteocyte-derived EVs showed the strongest anti-catabolic response. Notably, we identify miR-150-5p as a mechano-responsive miRNA enriched within osteocyte EVs, capable of inducing a dose-dependent reduction in osteoclastogenesis. Transcriptomic analyses reveal that EV treatment and miR-150-5p delivery induce substantial transcriptional changes in osteoclast precursors, including downregulation of shared target genes linked to bone remodelling. Overall, we highlight mechanically activated osteocytes as key regulators of osteoclastogenesis through an EV-mediated mechanism, in which miR-150-5p represents a promising candidate contributor within the broader EV cargo landscape, highlighting their potential for future cell-free therapeutic strategies.

Pyogenic liver abscess (PLA) is a life-threatening infection rising in East Asia, especially among patients with type 2 diabetes. Although SGLT2 inhibitors improve glycemic control and offer extraglycemic benefits, their effect on PLA risk is unknown. Using Taiwan's National Health Insurance Research Database, we conducted a nationwide retrospective cohort study of adults with T2DM. After 1:1 propensity score matching, 258,800 SGLT2i users and 258,800 non-users were included. The primary outcome was incident PLA. Incidence rates were calculated per 1,000 person-years, and adjusted hazard ratios (aHRs) with 95% confidence intervals (CIs) were estimated using multivariable Cox proportional hazards models. Additional analyses included subgroup analyses with interaction testing, a time-dependent Cox model, a competing-risks model, and a negative-control outcome analysis using fracture. During follow-up, 1,275 PLA events were identified. The incidence rate of PLA was 0.75 per 1,000 person-years in SGLT2i users and 0.83 per 1,000 person-years in non-users. In the primary multivariable Cox model, SGLT2i use was associated with a lower risk of PLA compared with nonuse (aHR, 0.88; 95% CI, 0.79-0.99). This inverse association was generally consistent across most subgroups. In the time-dependent analysis, SGLT2i use remained associated with a lower PLA risk (aHR, 0.72; 95% CI, 0.64-0.81). SGLT2i therapy was independently associated with reduced PLA risk in T2DM patients, particularly with prolonged exposure. These findings suggest an inverse association between SGLT2i use and the risk of pyogenic liver abscess in patients with T2DM.

Digital phenotyping-the moment-by-moment quantification of human behavior using data from smartphones and wearables-offers new pathways for mental health research and care. This review summarizes current trends, tools, and applications of digital phenotyping, highlighting its growing clinical relevance in early detection, symptom monitoring, and personalized interventions. Although studies increasingly demonstrate its feasibility and clinical utility across conditions such as depression, anxiety, and schizophrenia, challenges persist. These challenges include inconsistent data quality, small and nonrepresentative samples, lack of methodological standardization, and pressing ethical considerations about privacy and transparency.

Epicardial radiofrequency ablation can fail when lesions are not sufficiently deep or transmural, yet intraoperative feedback remains largely indirect. This study presents a fiber-based, side-viewing near-infrared spectroscopy (NIRS) probe with multiple source-detector separations (SDS) to enable depth-sensitive mapping of lesions on the porcine left-ventricular epicardium. Monte Carlo simulations predicted progressively deeper sampling with increasing SDS, motivating the use of multi-separation acquisition for depth-resolved contrast. Experiments were performed on 11 porcine hearts with 133 irrigated epicardial lesions spanning a wide depth range, with lesion depth ground truth reconstructed from post-stain gross section measurements. SDS-dependent spectral signatures were observed across lesions with depths greater than 4 mm, lesions with depths ≤ 4 mm, adipose tissue, and untreated epicardial muscle, and optical indices capturing these patterns were identified for lesion and adipose classification, as well as for lesion-depth sensitivity. Lesion and adipose indices achieved strong receiver operating characteristic (ROC) performance across SDS (lesion AUC 0.87-0.91; adipose AUC 0.94-0.97), and depth-sensitive indices exhibited monotonic trends with lesion depth (R² up to 0.97). Applying a random forest lesion mask enabled depth-sensitive maps that were consistent with variations in the ground truth.

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Resistance to third-generation EGFR-TKIs such as naquotinib and osimertinib remains a major obstacle in the treatment of non-small cell lung cancer (NSCLC). Epithelial-mesenchymal transition (EMT) is a key mechanism driving drug resistance and metastatic progression. Cilengitide, a cyclic RGD peptide targeting integrins, has shown potential in suppressing EMT-associated signaling. This study examined the effects of cilengitide and its derivatives on TGF-β1-induced EMT, migration, and invasion in EGFR-TKI-resistant NSCLC cells. Naquotinib- and osimertinib-resistant HCC827 cell lines were established and analyzed using 2D and 3D culture models. EMT marker expression, cell viability, migration, and invasion were assessed following treatment with cilengitide derivatives (R-1, R-7, R-8). Combination treatment with dovitinib, an FGFR inhibitor, was also evaluated. Experimental approaches included qRT-PCR, western blotting, wound-healing assays, invasion assays, and whole-mount organoid staining. Resistant cells exhibited reduced epithelial markers and increased mesenchymal markers, along with enhanced migration and invasion upon TGF-β1 stimulation. Cilengitide and its derivatives significantly inhibited TGF-β1-induced EMT, migration, and invasion, with stronger effects observed in 3D organoid models. Among the derivatives, cilengitide (R-8) most effectively suppressed vimentin expression and ERK1/2 phosphorylation. Combination treatment with dovitinib further enhanced the inhibition of migration and invasion, suggesting synergistic potential.

Neuroimaging studies have revealed altered functional connectome dynamics in autism spectrum disorder (ASD) and linked these alterations to clinical symptoms. However, most studies have emphasized population-level contrasts, leaving interindividual variability in connectome dynamics and its structural underpinnings poorly understood. To address this gap, we analyzed resting-state functional and structural MRI data from 939 male participants (440 with ASD, 499 typically developing controls) across 18 sites in the Autism Brain Imaging Data Exchange (ABIDE). Whole-brain functional state dynamics was characterized using five leading activity modes and their expressions via eigen-microstate analysis. Age-related trajectories of mode expressions were constructed for typically developing controls using normative modeling, enabling quantification of individual-level deviations in functional dynamics. Compared with controls, ASD individuals showed greater interindividual variability in functional deviation profiles. Unsupervised clustering of these profiles identified two robust ASD subtypes with distinct mode-specific dysfunctions. One subtype primarily involved the visual, default-mode, frontoparietal, and dorsal attention networks, whereas the other subtype primarily involved the somatomotor, visual, frontoparietal, and ventral attention networks. These subtypes were clinically dissociable, differing in restricted and repetitive behaviors and social impairments, and exhibited mode-specific brain-symptom associations. Furthermore, the subtypes exhibited distinct cortical thickness alterations, and individual subtype membership was predicted with high accuracy (83%) using a random forest classifier based on cortical thickness. The main findings were replicated in an independent cohort outside ABIDE. This study delineates two reproducible and clinically dissociable ASD subtypes and links functional connectome dynamics to structural substrates, offering novel insights into the neurobiological basis behind ASD heterogeneity.

The development of sustainable and highly sensitive diagnostic platforms is critical for rapid pathogen identification and effective disease management. Here, a green, magneto-electrochemical biosensing strategy is reported for the selective detection of Streptococcus pneumoniae based on pathogen-specific nuclease activity. Uniform organic-inorganic hybrid polyhedral oligomeric silsesquioxane (POSS) nanoparticles were synthesized via an ultrafast UV-initiated emulsion polymerization within 5 min using an eco-friendly approach. The nanoparticles were sequentially functionalized by in situ deposition of superparamagnetic iron oxide nanoparticles and biomimetic polydopamine coating, enabling robust and high-density immobilization of nuclease-responsive oligonucleotide probes. The resulting PDA@SPION/POSS nanohybrids exhibit controlled size, preserved structural integrity, and strong superparamagnetic behavior, allowing efficient magnetic manipulation and electrochemical signal transduction. Upon exposure to S. pneumoniae, nuclease-mediated probe cleavage produces a pronounced electrochemical response, enabling label-free detection over a wide dynamic range (102-10⁸ CFU mL⁻¹) with a detection limit of 102 CFU mL⁻¹. High selectivity against non-target bacteria highlights the specificity of the enzymatic recognition mechanism. This work establishes a sustainable and amplification-free biosensing platform with strong potential for rapid clinical diagnostics.

Complex tissue architecture is achieved through multiple rounds of morphological transitions. Here, we analyzed cellular flows and tissue mechanics during avian skin development by employing chicken and transgenic quail skin explant models. We demonstrate how novel cellular flows initiate chemo-mechanical circuits that guide epithelial protrusion, folding, invagination, and spatial cell fate specification. During initial feather bud formation, stiff dermal condensates protrude vertically from the locally softened epithelial sheet. As the bud elongates, it stretches the epithelial cells at the base, thus mechanically activating YAP, which causes the epithelial sheet to fold downward and form a stiff cylindrical wall that invaginates into the skin. This stiff epithelial tongue is essential for the compaction and formation of the tightly packed dermal papillae. These topological transformational events are mechanically interconnected, and the completion of one circuit initiates the next. In contrast, during scale development, the rigid epithelial sheet restricts dermal cell flows, preventing further topological transformation. Based on these findings, we developed a topological transformation model describing how this process enabled the evolution of feather follicles from scales.

Human papillomavirus (HPV) is a well-established oncogenic virus implicated in the development of several epithelial cancers, most notably cervical, anogenital, and oropharyngeal carcinomas. In contrast, neuroendocrine neoplasms (NENs)-a heterogeneous group of malignancies arising from neuroendocrine cells across various organ systems-have not traditionally been linked to HPV infection. In this study, we performed extensive genomic and transcriptomic profiling to compare HPV-positive NENs to HPV-positive non-NENs across anatomical sites, aiming to uncover biologically and clinically actionable differences. HPV16- and HPV18-positive tumors were identified from 101,343 solid tumors profiled at Caris Life Sciences (Phoenix, AZ) with DNA and RNA sequencing. Prevalence of pathogenic mutations and copy number amplifications were calculated. Fisher's exact/&#x3c7;2 tests were applied appropriately with p-values adjusted for multiple comparisons (p&#x2009;<&#x2009;0.05). HPV positivity was most frequent in cervical carcinomas (70%, 1200/1716). Importantly, 6% (96/1620) of NENs from all tissues were positive for HPV16 or HPV18. Among HPV-positive NENs, 93% were high-grade compared to 54% observed in HPV-negative NENs (p&#x2009;<&#x2009;0.001), highlighting a strong association between HPV and tumor aggressiveness in this subset. Analysis of HPV-associated sites (cervix, anorectal region, and head and neck) revealed that HPV-positive NENs possess distinct genomic and transcriptomic landscapes compared to HPV-positive non-NENs. Notably, interferon signaling was significantly suppressed in HPV-positive NENs, suggesting a unique tumor-immune microenvironment. Our findings indicate that HPV-positive NENs form a distinct subset with unique genomic features, including reduced interferon signaling, compared to HPV-positive non-NENs. Thus, future studies focused on evaluating HPV status, along with genomic and transcriptomic characteristics, may uncover biologically and clinically actionable distinctions for this rare yet clinically significant tumor subgroup. Not applicable.

Identifying robust biomarkers for early cancer detection remains challenging, particularly when working with limited or heterogeneous datasets. Here, we present a proof-of-concept deep learning framework for cancer classification using blood-based proteomic profiles. Our approach leverages sample type transfer and synthetic data augmentation to improve performance and generalization across sample types. Models were trained on plasma proteome data from 13,208 pan-cancer cases and 39,806 controls in the UK Biobank. To address class imbalance and enrich the feature space, a convolutional neural network (CNN-Synth) was trained to detect cancer cases using data augmented with synthetic pan-cancer samples generated via a variational autoencoder. Performance was evaluated in an independent saliva-based dataset from a head and neck cancer case-control study (n = 156). CNN-Synth (AUC = 0.88) surpassed models trained without synthetic data (AUC &#x2264; 0.77). SHapley Additive explanations identified well-known cancer markers as key features. These results highlight the use of sample type transfer and synthetic data augmentation, with further validation needed.