Articular cartilage, as a mechanosensitive tissue, supports and distributes various mechanical forces-including compression, shear, hydrostatic pressure, and tensile strain-during joint loading and motion. These external forces deform not only the chondrocytes but also their pericellular matrix and the surrounding extracellular matrix (ECM). Those mechanical cues are detected by mechanosensors on the plasma membrane (e.g., integrins) and transmitted through the cytoskeleton, ultimately being converted into biochemical signals. These signals activate key mechanoresponsive intracellular pathways-including TGF-β-induced SMAD, Rho-GTPase, MAPKs (ERK, JNK, p38), PI3K/AKT/mTOR, MRTF-SRF, and YAP/TAZ-that regulate chondrogenic differentiation and cartilage-specific matrix synthesis. This field of study is known as mechanobiology. Over the past decades, it has gained increasing recognition, particularly with the emergence of tissue-engineering constructs as a novel strategy for cartilage repair. However, progress in chondrogenic mechanobiology has primarily centred on intrinsic substrate- or matrix-derived cues, while overlooking the role of extrinsic mechanical forces. This review therefore provides an updated perspective on chondrogenic mechanobiology, with a particular focus on the cellular responses to external mechanical stimuli. It also emphasizes the therapeutic potential of incorporating mechanical stimulation into tissue-engineering strategies for cartilage repair, an emerging filed referred to as Regenerative Rehabilitation (RR). Since this concept has so far been investigated mainly in vitro, we highlight only those studies and refer to it as In vitro Regenerative Rehabilitation. Moreover, this review also addresses post-traumatic osteoarthritis (PTOA), a common joint disorder that frequently results from traumatic cartilage damage. It explores the mechanobiological mechanisms underlying OA and discusses in vitro regenerative rehabilitation studies, highlighting how external forces could serve as an alternative to conventional biochemical treatments for preventing OA progression.
Nanoconfined fluid is central to many engineering applications such as shale energy production, carbon sequestration, and molecular separations. While classical molecular dynamics (MD) simulation provides essential atomistic detail, its prohibitive computational cost severely limits accessible time and length scales. Hybrid MD-Monte Carlo (MDMC) methods accelerate sampling but lack generality beyond their trained conditions. In this work, we introduce an AI-assisted MDMC framework that overcomes this limitation by learning local, conditional transition statistics directly from MD trajectories. Our method encodes molecular motion into a compact set of neural network-predicted displacement actions, preserving MD-level accuracy within a drastically reduced dimensionality. This approach enables efficient sampling with robust generality. We systematically demonstrate the framework's accuracy and transferability across diverse thermodynamic conditions (temperature, pressure), spatial scales, and complex nano-scale geometries, establishing a versatile path for simulating confined fluid phenomena relevant to engineering applications.
The development of mechanically tunable hydrogels that replicate the dynamic mechanoelastic properties of native extracellular matrices (ECMs) is essential for advancing 3D tissue engineering. DNA, with its precise, programmable architecture and exceptional control at the nanometre scale, offers a valuable platform for designing ECM-mimicking scaffolds. This study presents stiffness-tuneable DNA supramolecular hydrogels with different branching architectures for programming cellular and organellar states. Utilizing precise DNA motifs-including DX (Double Crossovers), PX (Paranemic Crossovers), and Tensegrity architectures-we engineer hydrogels with widely adjustable mechanical properties (50-185 kPa) without chemical additives or enzymatic crosslinking. These hydrogels exhibit excellent strain-bearing and load-bearing capacity, making them suitable for biomedical applications. Additionally, these DNA hydrogels influence cellular behaviour in retinal pigment epithelial (RPE1) cells by enhancing cellular adhesion, encouraging elongation (a 3-8-fold increase in area compared to control), and improving viability (dependent on concentration, 1-8-fold increase vs. control), while also maintaining organellar homeostasis, including mitochondrial fragmentation and ER stress reduction. This work presents a framework for automating the production of stiffness-tunable DNA hydrogel scaffolds, aligning with the mechanical needs of various cells and tissues, thereby advancing personalized, high-throughput tissue engineering platforms.
Large bone defects remain a significant clinical challenge due to the limitations of autografts and allografts, prompting interest in bioactive scaffolds for bone tissue engineering. This study aimed to develop and evaluate electrospun nanofibrous scaffolds composed of polycaprolactone (PCL) and polylactic acid (PLA), incorporating titanium dioxide nanoparticles (TiO₂) and melatonin (Mel), to enhance their osteogenic differentiation potential. PCL/PLA, PCL/PLA/TiO₂, PCL/PLA/Mel, and PCL/PLA/TiO₂/Mel scaffolds were fabricated via electrospinning and characterized for morphology, fiber diameter, and mechanical properties. Biological performance was evaluated using adipose-derived mesenchymal stem cells (ADMSCs) through MTT assay, calcium content, alkaline phosphatase (ALP) activity, and osteogenic gene expression. TEM confirmed uniform TiO₂ nanoparticle distribution without agglomeration. All scaffolds exhibited continuous cylindrical fibers without defects. TiO₂ incorporation reduced tensile strength but increased elongation, while melatonin enhanced tensile strength. MTT assays confirmed biocompatibility and higher proliferation in TiO₂- and Mel-containing scaffolds. Calcium content and ALP activity were significantly higher in TiO₂- and/or Mel-modified scaffolds, with the PCL/PLA/TiO₂/Mel group showing the most significant osteogenic differentiation potential. Gene expression analysis revealed upregulated osteopontin, osteonectin, and osteocalcin in TiO₂- and Mel-containing scaffolds. Co-incorporation of TiO₂ nanoparticles and melatonin into electrospun PCL/PLA scaffolds synergistically enhances osteogenic differentiation and may serve as a promising strategy for bone tissue regeneration.
Ordered phases give rise to collective modes and quasiparticles, such as spin waves and magnons emerging from magnetic order. Extending this paradigm to ferroelectrics suggests the existence of polarization waves and their fundamental quanta, ferrons. A coherent ferron-that is, a polarization wave-modulates the magnitude of the electric polarization and is thus an amplitude (Higgs) mode of the ferroelectric order. Here we observe coherent ferrons from the pulsed laser excitation of van der Waals ferroelectrics, NbOI2 and WO2Br2. We demonstrate two complementary manifestations of coherent ferrons: intense narrow-band terahertz emission at the ferroelectric transverse optical phonon frequency, and uniaxial propagation along the polar axis as hyperbolic phonon polaritons with exceptionally long coherence times. These long-lived, uniaxial and dipole-carrying polarization waves may find applications in narrow-band terahertz emission, ferronic information processing and coherent electric control.
Computed tomography pulmonary angiography (CTPA) is the gold standard for pulmonary embolism (PE) diagnosis, but patients with iodinated contrast allergies or renal insufficiency are often ineligible. CT-derived perfusion (CTP) is a novel, non-contrast method to quantify pulmonary perfusion from an inhale/exhale CT image pair (4DCT). The resulting CT-P information can be used to identify hypo-perfused regions associated with PE. This pilot study introduces a thresholding approach that estimates the number of lung lobes with perfusion deficits according to optimally selected CTP thresholds. The number of lobes indicated as low-functioning provides a score to categorize patients as PE-positive, negative, or inconclusive. We trained and validated the model on a retrospective dataset of 123 suspected PE patients, achieving 72% accuracy, 75% sensitivity, and 69% specificity, with 17% of cases inconclusive. To our knowledge, this is the first PE diagnostic model from non-contrast 4DCT, showing the feasibility of non-contrast PE diagnosis strategies.
The transition from well-fed to food-deprived conditions in C. elegans triggers a stereotypic exploration behaviour characterised by a temporal decrease in reorientation frequency. In this study we conduct a screen of neuropeptide mutants and identify several candidates involved in modulating this behaviour. Among these, the neuropeptide FLP-15 emerges as a key regulator. Our observations reveal that FLP-15 regulates the frequency of reversals during foraging through the I2 pharyngeal neuron via the G protein-coupled receptor NPR-3. Mutants lacking either flp-15, npr-3 or both display a significant defect in reversal frequency which does not decline temporally unlike in wild-type animals. This study also describes the expression pattern of NPR-3 in a subset of head neurons, predominantly comprising of dopaminergic neurons. Finally, flp-15 expression studies and exogenous dopamine supplementation assays reveal that FLP-15 may regulate exploratory search by modulating dopamine transmission, highlighting a novel neuropeptide-dopamine interaction involved in the control of foraging behaviours.
This special article synthesizes transformative insights from a recent international neonatal cell therapy symposium (held in Noosa, Australia) where leading experts convened to explore regenerative solutions for serious perinatal and neonatal conditions. The discussions highlighted pioneering cell-based therapies targeting preterm brain injury, bronchopulmonary dysplasia, fetal growth restriction, hypoplastic left heart syndrome and congenital diaphragmatic hernia; neonatal conditions that present both neonatal challenges and long-term morbidities, demanding innovation beyond conventional medical, surgical and supportive care. Advances in regenerative medicine, particularly those leveraging umbilical cord blood-derived cells, mesenchymal stromal cells from various sources, amniotic fluid and human amnion epithelial derived cells and extracellular vesicles, are redefining therapeutic possibilities through paracrine signaling, immunomodulation, and tissue repair to counteract shared mechanisms of inflammation, oxidative stress, apoptosis, and impaired regeneration. This article integrates the symposium's key clinical and translational perspectives, emphasizing system-specific developments across cardiovascular, pulmonary, neurological, and systemic domains, with a particular focus on scalable production strategies, and the importance of multidisciplinary collaboration. IMPACT: Synthesizes global evidence from preclinical and clinical studies to define the current translational trajectory of cell therapies across major neonatal conditions. Highlights integrative frameworks combining advanced preclinical modeling, clinical trials, scalable manufacturing and stakeholder collaboration to accelerate translation in neonatal regenerative medicine.
Haematopoietic stem cells (HSCs) represent a well-established system for studying stem cell maintenance. While RNA regulators have been reported in HSCs, a systematic characterization and how they define transcript fate remains outstanding. Here we profile RNA characteristics of HSC-essential genes and uncover a notable feature in both human and mouse: they have extended 3' untranslated regions specifically enriched with AU-rich elements (AREs). These AREs are crucial for the expression of HSC genes, primarily through NAT10, which stabilizes their mRNAs. Notably, Nat10 deficiency markedly disrupts HSCs self-renewal and long-term reconstitution capacity. Mechanistically, NAT10 recruits ribosomes to the 3' untranslated region AREs of HSC-essential mRNAs, sheltering them from degradation-an effect independent of NAT10's ac4C catalytic activity. Moreover, NAT10 dysregulations were associated with multiple human haematological malignancies. Collectively, our findings uncover a specific mechanism of RNA turnover control mediated by specific RNA ARE motifs and identify a non-catalytic role of NAT10 in maintaining HSC homeostasis.
Weed adaptability to environmental conditions poses a major challenge in agricultural production, often leading to yield reduction or even complete crop loss. Effective weed detection requires algorithms tailored to the unique visual and biological characteristics of each crop. However, despite the agricultural importance of garlic, no studies have developed optimized state-of-the-art deep learning models for weed identification in garlic fields. Addressing this gap, the present study focuses on optimizing two prominent object detection frameworks-YOLOv5 and YOLOv8-for accurate weed detection under real field conditions. A dataset of 600 RGB images containing garlic plants and five weed species was collected directly from the field. Different versions of YOLOv5 and YOLOv8 were optimized and evaluated based on mAP@0.5 and inference speed. Experimental results showed that YOLOv8n achieved superior performance, yielding an mAP@0.5 of 87.0% with an average inference time of 16 ms, compared to YOLOv5m with an mAP@0.5 of 85.0% and 50 ms. The findings highlight that YOLOv8n, with an input resolution of 320 × 320 pixels and optimized using the SGD optimizer, provides the best trade-off between detection accuracy and computational efficiency. These results demonstrate the potential of YOLOv8n for real-time weed detection and its application in precision agriculture machinery.
The terahertz frequency band, ranging from 0.1 to 10 THz, offers extensive spectral resources for next-generation wireless communications systems. To compensate for the limited transmit power of terahertz transceivers and severe propagation losses, high-gain directional transmission is essential, making dynamic beam manipulation a key enabler for practical terahertz communications. The stringent gain requirements further extend the Fresnel region, necessitating efficient beam manipulation across both near-field and far-field conditions. The increasing reliance on beam manipulation reflects a paradigm shift in terahertz communications, where performance scaling is achieved through architecture-level innovation rather than solely through incremental hardware or algorithmic improvements. This article provides a comprehensive overview of terahertz beam manipulation techniques. It begins with an introduction of diffraction theory as the foundational propagation model for beam manipulation. Detailed examples tailored to specific communication scenarios are then presented. Experimental verifications using lenses and metasurfaces are included for three distinct beam manipulation cases. Alternative approaches for achieving beam manipulation, such as reconfigurable intelligent surfaces, are briefly discussed.
The development of mines creates substantial amounts of soil-rock mixture (S-RM) waste that is accumulated as waste dumps. These dumps pose significant safety hazards that require management. In this study, S-RM was sampled from a waste dump at a copper mine in Jiangxi Province, China. Consolidated-undrained triaxial tests were conducted on four gradations of S-RM specimens. Numerical simulations of dump slope stability were performed by considering factors such as groundwater level, rainfall intensity, and root reinforcement depth. The findings are as follows: (1) cohesion and friction angle are sensitive to rock content; (2) the power-law model outperforms the Duncan-Chang model in fitting triaxial data; (3) the stability of the dump deteriorates as the groundwater level gets shallower; (4) the factors of safety decrease with increasing rainfall duration and intensity with an approximately exponential decay pattern; (5) vegetation root reinforcement improves slope stability; however, extreme rainfall can trigger perched water accumulation, which can compromise stability. These findings provide valuable indicators that can assist in managing the safety hazard posed by such waste dumps.
Polygenic risk scores (PRS) have emerged as important tools for quantifying inherited susceptibility to cancer, and are increasingly combined with environmental and lifestyle factors into composite risk scores (CRS). In this context, environmental inputs should be understood not as isolated covariates, but as components of the human exposome, encompassing cumulative, time-varying, and interacting exposures across the life course, which fundamentally shape cancer risk alongside inherited susceptibility. These approaches are often discussed as candidates for precision prevention and screening, yet their evidentiary basis spans heterogeneous study designs, outcomes, and methodological assumptions. Here, we provide an integrated review of genetic, environmental, and composite cancer risk models, explicitly distinguishing etiologic association from predictive performance and clinical translation. We synthesize evidence from large genome-wide association studies, cohort and case-control analyses, and recent CRS evaluations using both narrative assessment and structured quantitative summaries. Across cancer sites, PRS and CRS consistently stratify relative risk, with monotonic increases in odds ratios across score percentiles. However, gains in discrimination metrics such as the area under the curve or C-index are generally modest and heterogeneous, and calibration performance varies substantially across populations and settings. External validation and multi-ancestry evaluations remain limited, and methodological challenges, including overfitting, population stratification, and model transportability, are frequently under-reported. We argue that current evidence supports the use of PRS and CRS primarily as tools for risk stratification, prioritization, and risk-enriched research designs, rather than as stand-alone clinical decision systems. The most near-term translational value lies in targeted screening strategies, prevention trials, and population-level risk assessment, provided that calibration, governance, and equity considerations are explicitly addressed. We conclude by outlining key methodological and data requirements needed to advance CRS from exploratory models toward robust, population-appropriate tools in cancer prevention.
To assess the non-inferiority of topical intrapericardial tranexamic acid (TXA) versus intravenous TXA for efficacy and safety in patients undergoing cardiac surgery with cardiopulmonary bypass (CPB). In this single-center randomized trial, 492 patients were assigned 1:1 to topical TXA (2.5 g in 50 mL saline via pericardial drain, clamp 30 min) or standard intravenous TXA. allogeneic red blood cell (RBC) transfusion rate (postoperative to discharge); 30-day composite adverse events (mortality, renal dysfunction, stroke, myocardial infarction, thromboembolism, seizures). Secondary endpoints included drainage volume, coagulation, and thromboelastography (TEG). The non-inferiority threshold was established at 10% for sensitivity. RBC transfusion rate was 35.0% (topical) vs. 27.6% (intravenous; 95%CI - 1.2% to 16.0%, P = 0.080). Composite adverse events were 9.8% vs. 15.4% (95%CI - 11.2% to 0.0%, P = 0.057). Topical TXA showed delayed coagulation initiation, lower fibrinogen, and higher 24-hour drainage (all P < 0.05). At 10% margin, efficacy non-inferiority was not confirmed, but safety non-inferiority was verified for all endpoints. Topical intrapericardial TXA is non-inferior to intravenous TXA in safety but fails strict efficacy non-inferiority. Intravenous TXA remains first-line; topical TXA is a reasonable alternative for patients intolerant to systemic administration. Dose and timing optimization is needed to improve hemostasis. http://www.chictr.org.cn, ChiCTR2500113718, Registration date: 2 December 2025. Retrospectively registratered.
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White light endoscopy is the clinical gold standard for detecting diseases in the gastrointestinal tract. Most applications involve identifying visual abnormalities in tissue color, texture, and shape. Unfortunately, the contrast of these features is often subtle, causing many clinically relevant cases to go undetected. To overcome this challenge, we introduce Multi-contrast Laser Endoscopy (MLE): a platform for widefield clinical imaging with rapidly tunable spectral, coherent, and directional illumination. We demonstrate three capabilities of MLE: enhancing tissue chromophore contrast with multispectral diffuse reflectance, quantifying blood flow using laser speckle contrast imaging, and characterizing mucosal topography using photometric stereo. We validate MLE with benchtop models, then demonstrate MLE in vivo during clinical colonoscopies. MLE images from 31 polyps demonstrate an approximate three-fold improvement in contrast and a five-fold improvement in color difference compared to white light and narrow band imaging. With the ability to reveal multiple complementary types of tissue contrast while seamlessly integrating into the clinical environment, MLE shows promise as an investigative tool to improve gastrointestinal imaging.
Metabolic dysfunction-associated steatotic liver disease (MASLD) is an increasing global health concern. This cross-sectional study aimed to evaluate the association between the AGAHR index [calculated as (alanine aminotransferase (ALT)×plasma fasting glucose (PFG)) to (aspartate aminotransferase (AST)×high-density lipoprotein cholesterol (HDL-C)) ratio] and MASLD. Data from 2508 participants in the NHANES 2017-2020 cycle were analyzed. Weighted multivariable logistic regression, restricted cubic spline (RCS), receiver operating characteristic (ROC), and decision curve analysis (DCA) were performed. The findings were further assessed in an independent local cohort (n = 56). After adjustment for potential confounders, AGAHR was positively associated with MASLD [odds ratio (OR): 1.17, 95% confidence interval (CI): 1.10-1.24, P < 0.001]. RCS analysis suggested a nonlinear association pattern. In the NHANES cohort, AGAHR demonstrated good discriminative ability [area under the curve (AUC): 0.752, 95% CI: 0.733-0.771], outperforming the PFG/HDL-C ratio (GHR) and ALT/AST ratio (AAR). These findings were consistent in the validation cohort (AUC: 0.756, 95% CI: 0.624-0.889). DCA indicated that AGAHR provided a greater net benefit across a range of threshold probabilities compared with the reference indicators. AGAHR is significantly associated with MASLD and may serve as a simple indicator reflecting metabolic features related to hepatic steatosis. Further studies in larger and more diverse populations are warranted to confirm these findings.
The global surge in antimicrobial resistance has intensified the search for novel therapeutic strategies that can overcome the limitations of conventional antibiotics. Antimicrobial peptides (AMPs), particularly those derived from arthropod venoms, have emerged as promising alternatives due to their unique ability to target conserved microbial structures and minimize resistance development. Among these, Lasioglossin-III (LL-III) stands out for its remarkable potency and versatility. LL-III exhibits a broad spectrum of activity encompassing antibacterial, antifungal, and anticancer properties, attributed to its structural features that facilitate selective interactions with microbial and cancer cell membranes. Despite its promising therapeutic profile, comprehensive reviews focusing specifically on LL-III remain scarce, with most existing literature addressing lasioglossins only in general terms. This review provides the first in-depth and consolidated discussion on LL-III by examining its structural characteristics, mechanisms of action, and pharmacological applications, alongside insights from computational modeling and experimental studies. By integrating current knowledge on the structure-function relationship of LL-III, this article underscores its translational potential as a next-generation therapeutic. Notably, the peptide's dual mechanism of action, combining membrane disruption with intracellular targeting, together with its multifunctional properties, including antibacterial, antifungal, anticancer, and immunomodulatory activities, positions LL-III as a promising, versatile peptide-based therapeutic candidate.
This paper introduces the Type II Exponentiated Half-Logistic Exponential (TIIEHLEx) distribution, a three-parameter model designed for enhanced flexibility in modeling positive-valued data. We derive the distribution's core mathematical properties, including the probability density function, cumulative distribution function, and quantile function. A key focus of this work is the evaluation of fourteen non-Bayesian estimation methods to identify the most robust approach for parameter estimation. Through a comprehensive simulation study, we demonstrate that the Minimum Spacing Absolute Distance Estimator (MSADE) consistently outperforms other methods, yielding the lowest bias and mean square error across various sample sizes. The practical utility of the TIIEHLEx model is illustrated using March precipitation and insurance service exports data. Goodness-of-fit tests, including AIC and Kolmogorov-Smirnov statistics, reveal that the TIIEHLEx distribution provides a superior or highly competitive fit compared to several well-known heavy-tailed distributions, particularly in capturing complex hazard rate profiles.
Accurate classification of renal masses before treatment is crucial for therapeutic decision-making and patient outcome. This study developed and validated Multi-Phase Attention Network (MPANet), a multimodal deep learning model integrating multiphase contrast-enhanced CT and clinical information, which can utilize both complete-phase and missing-phase CT data for multiclass classification of four common and easily confusable renal tumors-clear cell renal cell carcinoma (ccRCC), papillary renal cell carcinoma (pRCC), oncocytic neoplasms (including chromophobe renal cell carcinoma (chRCC) and renal oncocytoma (RO)), and fat-poor angiomyolipoma (fpAML). A total of 1688 multi-center cases were enrolled. Across all test sets, MPANet consistently outperformed single-phase models. In the internal test set, MPANet achieved a macro-average AUC of 0.850, a micro-average AUC of 0.865, and an accuracy of 73.3%. These results compared favorably to assessments by four radiologists based on CT (accuracies 43.6-62.4%) and two radiologists using MRI with clear cell likelihood score (ccLS) system (accuracies 52.5% and 49.5%). The net improvement rate of MPANet over radiologist assessment ranged from 10.9% to 29.7%. In the two external test sets, macro-average AUCs were 0.811 and 0.813, and micro-average AUCs were 0.867 and 0.909, respectively. MPANet shows potential as a clinical decision-support tool for personalized renal tumor diagnosis.