Cytoplasmic abundant heat-soluble (CAHS) proteins, a stress-responsive intrinsically disordered protein from tardigrades, have been discovered to form gel-like networks providing structural support during dehydration, thus enabling anhydrobiosis. However, the mechanism by which CAHS proteins protect the dehydrating cellular membrane remains enigmatic. Using giant unilamellar vesicles (GUVs) as a model membrane system, here we show that encapsulated CAHS12 undergoes a reversible structural transformation that reinforces membrane integrity and preserves encapsulated components, mimicking natural anhydrobiosis. CAHS12-containing GUVs demonstrated stability for weeks and mechanical robustness under dehydration, elevated temperature, and osmotic stresses. Molecular simulations suggest that CAHS12 forms a filamentous network within the vesicle lumen that mitigates membrane collapse and preserves compartmental architecture. Synthetic cells with cell-free transcription-translation capabilities withstand desiccation and recover biochemical activities, akin to the tun state of the tardigrade. This discovery opens up synthetic cell applications in bioengineering, cold-chain-independent biomanufacturing, and adaptive biointerfaces.
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.
Core needle biopsy is the gold standard procedure for sampling tissues for pathology-based diagnostics. However, it produces significant tissue damage and may lead to undue pain and risk of complications such as infections. Alternatives such as fine needle aspiration and liquid biopsy have not yet achieved the same widespread utility owing to the limited abundance of cells and relevant biomarkers in extracted samples. Here we introduce a shock-scattering micro-histotripsy-aided fine needle aspiration technology which uses cavitation to liquefy nano-liter to micro-liter volumes to produce tissue homogenates with both intact and lysed cells. It permits not only conventional cytopathology with high success but sufficient high-quality tissue homogenates to enable reliable ancillary testing such as genetic biomarker profiling and even whole genome sequencing with improved quality compared to formalin-fixed samples. Our approach represents an advance in tissue diagnostics with orders of magnitude less damage than core-needle biopsy procedures.
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Type 1 diabetes is caused by immune-mediated destruction of beta cells. Interestingly, individuals with long-standing type 1 diabetes have residual beta cells, suggesting regenerative mechanisms may help beta cell survival. Islet-resident macrophages have an important role in diabetes, and can adopt a tissue-regenerating phenotype that may support beta cells. However, the roles of macrophages in beta cell survival, function, and proliferation remain poorly defined. This study aimed to elucidate how macrophage subtypes influence beta cell survival, function, and proliferation. Mouse and human islets were isolated from the pancreas and co-cultured in vitro with macrophages. To investigate whether macrophages enhance beta cell survival and function, apoptosis was measured using flow cytometry, and insulin secretion was assessed via glucose-stimulated insulin secretion assays. We also examined whether macrophages increased beta cell proliferation in the presence of harmine, a DYRK1A inhibitor. Finally, we evaluated the effect of islet co-culture on macrophage phenotype by flow cytometry and cytokine secretion analysis. We found that regenerative, but not pro-inflammatory, macrophages enhanced beta cell survival and function through mechanisms that did not require direct cell contact. Direct contact between macrophages and islets promoted a macrophage regenerative phenotype characterized by increased CD206 expression and secretion of anti-inflammatory factors. Additionally, regenerative macrophages promoted beta cell proliferation in the presence of harmine. Our findings demonstrate that regenerative macrophages support pancreatic beta cell survival, function, and proliferation. Harnessing the regenerative properties of macrophages could offer a novel strategy to promote beta cell survival and function.
Press-fit acetabular components achieve long-term fixation through osseointegration, yet the extent of bone ingrowth necessary for durable stability in well-functioning implants remains unclear. Postmortem retrievals provide a unique opportunity to directly assess the bone-cup interface in clinically successful total hip arthroplasties (THAs). This study evaluated osseointegration and biomechanical fixation strength in deceased-donor acetabular components to better define the characteristics of stable long-term fixation. Cadaver pelvis specimens containing uncemented THAs from a single institution were evaluated. There were 29 acetabular components that underwent axial pull-out testing using a universal testing machine. A total of seven of these were additionally processed for histologic evaluation, including dehydration, acrylic embedding, thin-sectioning, staining, and digital imaging. Osseointegration was quantified by bone-area fraction occupancy (%BAFO), representing the proportion of bone occupying the porous thread spaces of the cup. All 29 specimens failed through fracture of the ilium rather than at the bone-cup interface, indicating that the mechanical integrity of the osseointegrated construct exceeded that of the surrounding bone under axial tension. Among the seven histologically analyzed components, %BAFO ranged from 4.2 to 27.0% (mean 15.1%), despite all implants being clinically stable at the time of death. There were no significant linear correlations observed between %BAFO and time implanted, fracture load, or body mass index. A significant quadratic relationship between %BAFO and age was identified, peaking near 81 years. Cementless acetabular components exhibited strong fixation despite modest osseointegration, with failure occurring through host bone on axial testing. Durable biological fixation appears achievable with limited, but mechanically favorable bone ingrowth.
Magnesium-titanium (Mg-Ti) composites have the potential in applications to bone repair and orthopedic implants due to their mechanical properties derived from Ti and biological functions derived from Mg. However, the degradation rate of Mg in the composite is accelerated due to the effect of galvanic corrosion, which has affected its clinical application. In order to regulate the degradation behavior of Mg-Ti composites,3D printing technology was employed to prepare Ti scaffolds with porous structures of different pore areas and pore shapes (square, hexagonal, and triangular), then Mg was infiltrated into the porous Ti scaffold to obtain the Mg-Ti composites. The results showed that the groups with smaller pore areas had a faster initial degradation rate and could reach a stable stage earlier. The degradation of the square structure composite was more controllable compared to the other two composites, so it was selected for the subsequent study on osteogenic properties. The addition of Mg in the composite enhanced the apatite-forming ability, upregulated the expression of osteogenic-related genes such as RUNX2, ALP, and OPN, and promoted the generation of bone tissue and collage. The results indicated that the Mg-Ti composite could regulate the content and the degradation rate of Mg through structure, thereby improving the osteogenic properties by regulating the release of Mg2+ in the surrounding microenvironment. This study explored the degradation mechanism and systematically established a relationship between structure, degradation, and osteogenesis of Mg-Ti composites, thereby providing ideas for the structural optimization of Mg-Ti composites and offering references for applications in medical bone repair.
As the global population ages rapidly, delaying and preventing age-related diseases have become urgent priorities in public health and biomedical research. During aging, mitochondrial dysfunction is a core molecular hallmark and a common pathogenic mechanism underlying multiple age-related disorders. Age-related mitochondrial dysfunction typically manifests as diminished metabolic capacity, impaired organelle renewal, and disrupted redox homeostasis. These factors interact to form a feedback loop constraining mitochondrial adaptability. Specifically, the interdependent decline in NAD+ availability, impaired mitochondrial biogenesis, and excessive oxidative stress render single-pathway interventions ineffective in mitigating systemic functional impairments triggered by aging. To address this complex mechanism, this review presents a novel tri-axis anti-aging model encompassing three key compounds: nicotinamide mononucleotide/nicotinamide riboside (NMN/NR), pyrroloquinoline quinone (PQQ), and l-ergothioneine (EGT). Within this framework, NMN/NR serves as a broad NAD+-dependent regulator of mitochondrial homeostasis, with its most immediate effects on metabolic activation, while PQQ and EGT may further strengthen mitochondrial remodeling and redox resilience, respectively. While each compound has distinct functional emphases, they are highly mechanistically coupled, collectively forming a closed-loop network regulating mitochondrial number, function, and homeostasis. This review synthesizes preclinical and emerging clinical evidence supporting the standalone or combined use of NMN/NR, PQQ, and EGT across various diseases. Collectively, by conceptualizing mitochondrial aging as a systemic imbalance rather than isolated molecular defects, this paper highlights a three-axis model of NMN/NR, PQQ, and EGT. This framework offers a theoretical foundation for mitochondrial-targeted anti-aging interventions while laying the groundwork for future clinical research, nutritional interventions, and the development of multi-target combination strategies.
Specimen-specific Finite Element (FE) models support personalized biomechanics but commercial software costs limit accessibility. Open-source tools offer alternatives, though complete workflows remain difficult. This study evaluated feasibility using primarily open-source software by reproducing a vertebral body FE study. CT images of a porcine cervical vertebra were segmented in 3D Slicer, refined in MeshMixer, converted in FreeCAD, and meshed in Ansys due to BoneMat dependencies. Material properties were mapped from Hounsfield Units. The model reproduced geometry, distribution, and mechanical response with <8% differences and no significant load-displacement variation. Limitations include tool incompatibility, yet the workflow reduces cost and improves accessibility.
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.
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.
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.
The cyanobacterium Prochloron didemni produces macrocyclic octapeptides with thiazole and oxazoline heterocycles, known as patellamides. An interesting observation is that Cu2+ binding to the patellamides is likely to be related to their biological function. First, we show that Cu2 + injection into Lissoclinum patella increases patG gene expression and patellamide levels in the ascidians. Second, x-ray absorption spectroscopy shows that biological extracts of specimen from the Great Barrier Reef match structurally synthetic carbonato-bridged dicopper(II)-patellamide complexes. Third, patellamides exhibit very high membrane permeability (PAMPA, Caco-2). Combined with intracellular pH data, patellamide-Cu2 + bioactivity in algae, and the absence of many of the typical CO2 uptake mechanisms in Prochloron, we propose that patellamides facilitate carbonate transport from the ascidians to the cyanobacteria. This provides unprecedented evidence for a link between cyanobactin metal binding and their production and function, suggesting possible novel metal-related roles for marine cyclic peptides.
Near-infrared fluorescence (NIRF) can deliver high-contrast, video-rate, non-contact imaging of tumor-targeted contrast agents with the potential to guide surgeries excising solid tumors. However, it has been met with skepticism for wide-margin excision due to sensitivity and resolution limitations at depths larger than ~ 5 mm in tissue. To address this limitation, fast-sweep photoacoustic-ultrasound (PAUS) imaging is proposed to complement NIRF. In an exploratory in vitro feasibility study using dark-red bovine muscle tissue, we observed that PAUS scanning can identify tozuleristide, a clinical stage investigational imaging agent, at a concentration of 20 µM from the background at depths estimated to be of up to ~ 34 mm, highly extending the capabilities of NIRF alone. The capability of spectroscopic PAUS imaging was tested by direct injection of 20 µM tozuleristide into bovine muscle tissue at a depth of ~ 8 mm. Experimental results demonstrate that multi-point laser fluence compensation and strong clutter suppression enabled by the unique capabilities of the fast-sweep approach greatly improve spectroscopic accuracy and the PA detection limit and strongly reduce image artifacts. Thus, the complementary NIRF-PAUS approach can be promising for comprehensive pre- (with PA) and intra- (with NIRF) operative solid tumor detection and wide-margin excision in optically guided solid tumor surgery.
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 ≤ 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.
This study presents a hybrid actuation framework for enhanced and automated control of Janus particle microrobots across multiple surfaces. Under various magnetic and electric field combinations, Janus particles act as active microscale agents with diverse propulsion and navigation modes on a single surface, while their electric response additionally provides frequency-modulated functionalities such as reversible cargo loading. Here, we show that by further leveraging magnetic levitation and electrostatic trapping at a microchamber ceiling against gravity, these particles can transition between surfaces on demand, extending their mobility beyond the limitations of many traditional microrobots which are surface-bound. This approach grants access to three-dimensional workspaces without continuous surface contact, enabling capabilities such as vertical obstacle crossing, navigation of elevated surfaces, discrete surface patterning, and cargo transport between surfaces. Together, these developments establish a versatile micro-robotic platform applicable in microfluidic and lab-on-a-chip systems, and may further inspire microrobot designs utilizing controlled inter-surface transitions.
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.
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.
Rapid eye movement (REM) sleep behaviour disorder (RBD), particularly its idiopathic/isolated form (iRBD), is a prodromal marker for α-synucleinopathies, including Parkinson's disease, dementia with Lewy bodies and multiple system atrophy. Machine learning (ML) offers opportunities to improve diagnosis and risk stratification in this high-risk group. We conducted a systematic review of PubMed, Embase (Ovid) and Medline (Ovid) from 2014 to September 2025, following PRISMA guidelines. From 335 records identified, 202 remained after duplicate removal and 75 studies on adult humans with clinically diagnosed RBD or iRBD that applied and validated an ML model were included. Fifty-eight studies addressed diagnosis, four studied RBD phenotypes, and thirteen evaluated prediction of phenoconversion to overt α-synucleinopathy. Across diagnostic studies, reported accuracies ranged from ∼63% to ∼99.7%, with median values around 90%, using polysomnography, EEG, neuroimaging, molecular and behavioural markers. Phenoconversion models (often using dopaminergic imaging or multimodal features) achieved AUCs up to ∼0.94, but frequently relied on small, single-centre cohorts with heterogeneous definitions of phenoconversion and limited external validation. A wide variety of ML algorithms was used (n ~ 30), most commonly support vector machines, random forests and logistic regression. Overall, ML approaches show promise for scalable diagnosis and risk stratification in iRBD, but progress is constrained by methodological bias, inconsistent endpoints, data imbalance and a lack of explainable, externally validated models. We outline methodological priorities to make future ML tools clinically interpretable and translatable.