Search Results (599)

Spina bifida (SB) is a congenital malformation of the central nervous system (CNS), resulting from incomplete closure of the neural tube (NT) during early embryogenesis. Myelomeningocele (MMC), the most severe form of SB, leads to progressive neurological, orthopedic, and urological dysfunctions due to both NT developmental failure and secondary intrauterine injury (“two-hit hypothesis”). Prenatal repair of MMC has progressed considerably since the Management of Myelomeningocele Study (MOMS, 2011) trial, which showed that open fetal surgery can decrease the need for shunting and improve motor function, although it carries significant maternal risks. To address these limitations, minimally invasive techniques have been developed, with the goal of achieving similar benefits for the fetus while reducing maternal morbidity. Recent research has shifted toward regenerative strategies, integrating mesenchymal stem cells (MSCs), bioengineered scaffolds, and cell-derived products to move beyond mere mechanical protection toward true NT repair. Preclinical studies in rodent and ovine models have shown that amniotic- and placenta-derived MSCs exert neuroprotective and immunomodulatory paracrine effects, promoting angiogenesis, modulating inflammation, and supporting tissue regeneration. Minimally invasive, cell-based interventions such as Transamniotic Stem Cell Therapy (TRASCET), in preclinical rodent models, offer the possibility of very early treatment without hysterotomy, although translation remains limited by the lack of large-animal validation and long-term safety data. In parallel, advances in biomaterials, nanostructured scaffolds, and exosome-based therapies reinforce a regenerative paradigm that may improve neurological outcomes and quality of life in affected children. Ongoing translational studies are essential to optimize these approaches and define their safety and efficacy in clinical settings. This review provides an integrated overview of embryological mechanisms, diagnostic strategies, and prenatal therapeutic advances in SB treatment, with emphasis on prenatal repair, fetal surgery and emerging regenerative approaches. Full article

Contemporary neural and generative architectures are deficient in self-preservation mechanisms and sustainable stability. In uncertain or noisy situations, they frequently demonstrate oscillatory learning, overconfidence, and structural deterioration, indicating a lack of biological regulatory principles in artificial systems. We present Bio-RegNet, a meta-homeostatic Bayesian neural network architecture that integrates T-regulatory-cell-inspired immunoregulation with autophagic structural optimization. The model integrates three synergistic subsystems: the Bayesian Effector Network (BEN) for uncertainty-aware inference, the Regulatory Immune Network (RIN) for Lyapunov-based inhibitory control, and the Autophagic Optimization Engine (AOE) for energy-efficient regeneration, thereby establishing a closed energy–entropy loop that attains adaptive equilibrium among cognition, regulation, and metabolism. This triadic feedback achieves meta-homeostasis, transforming learning into a process of ongoing self-stabilization instead of static optimization. Bio-RegNet routinely outperforms state-of-the-art dynamic GNNs across twelve neuronal, molecular, and macro-scale benchmarks, enhancing calibration and energy efficiency by over 20% and expediting recovery from perturbations by 14%. Its domain-invariant equilibrium facilitates seamless transfer between biological and manufactured systems, exemplifying a fundamental notion of bio-inspired, self-sustaining intelligence—connecting generative AI and biomimetic design for sustainable, living computation. Bio-RegNet consistently outperforms the strongest baseline HGNN-ODE, improving ARI from 0.77 to 0.81 and NMI from 0.84 to 0.87, while increasing equilibrium coherence $\kappa$ from 0.86 to 0.93. Full article

Spinal cord injury (SCI) is a debilitating condition resulting in a range of neurological impairments up to complete loss of function below the level of injury. With current clinical management limited to decompression and stabilisation of the injury, there is urgent need to develop effective restorative treatments. In animal models, cell transplantation therapies are being tested that utilise different cell types including olfactory ensheathing cells (OECs), a type of glial cell, to support and promote regeneration. While OECs have a unique combination of properties highly suitable for SCI repair, their efficacy and consistency need to be improved. Evidence suggests a combinational approach using growth factors or compounds alongside OECs may stimulate their innate properties and alter the internal milieu of an injury site in favour of neural repair. Naturally, there is intricate interplay between various growth factors and OECs during development of the olfactory system, and in injury and repair events, which regulate their migration, phagocytosis, and proliferation. Therefore, exploiting different growth factors to selectively enhance OECs’ therapeutic potential could lead to restorative treatment of SCI. While some studies have already explored using growth factors to treat SCI in animal models, an optimal ‘cocktail’ has yet to be identified. In seeking to identify such a cocktail, this review presents the current understanding of SCI and the therapeutic potential of OECs and explores combined use of growth factors and OECs to improve treatment outcomes. Full article

The model of a liquid desiccant dehumidification air conditioning (LDAC) system is one of the key foundations for achieving efficient cooling, dehumidification and regeneration, and saving energy consumption. The data-driven modeling method does not need to understand the complex heat and mass transfer mechanism and equipment physical information, thus the modeling complexity is greatly reduced. This paper proposes a temperature and humidity prediction model integrating the Black Kite Algorithm (BKA), Bidirectional Temporal Convolutional Network (BiTCN), Bidirectional Long Short-Term Memory (BiLSTM), and Self-Attention mechanism (SA). The model extracts local spatiotemporal features from sequence data through BiTCN, enhances the understanding of contextual dependencies in temporal data using BiLSTM, and employs the SA to assign dynamic weights to different time steps. Furthermore, BKA is adopted to optimize the hyperparameter combinations of the neural network, thereby improving prediction accuracy. To validate the model performance, an experimental platform for an LDAC system was established to collect operational data under multiple working conditions, constructing a comprehensive dataset for simulation analysis. Experimental results demonstrate that compared to conventional time-series prediction models, the proposed model achieves higher accuracy in predicting outlet temperature and humidity across various operating conditions, providing reliable technical support for system real-time control and performance optimization. Full article

Accurately predicting the remaining useful life (RUL) of lithium-ion batteries is critical for ensuring the safe operation of equipment, optimizing industrial cost management, and promoting the sustainable development of the renewable energy sector. Although various deep learning-based approaches for RUL prediction have been proposed, their performance is highly dependent on the availability of large training datasets. As a result, these methods generally achieve satisfactory accuracy only when sufficient training samples are available. To address this limitation, this study proposes a novel hybrid strategy that combines a parameter-optimized signal decomposition algorithm with an enhanced neural network architecture, aiming to improve RUL prediction reliability under small-sample conditions. Specifically, we develop a lithium-ion battery capacity prediction method that integrates the Triangle Topology Aggregation Optimizer (TTAO), Variational Mode Decomposition (VMD), and a Bidirectional Long Short-Term Memory (BiLSTM) network. First, the TTAO algorithm is used to optimize the number of modes and the quadratic penalty factor in VMD, enabling the decomposition of battery capacity data into multiple intrinsic mode functions (IMFs) while minimizing the impact of phenomena such as capacity regeneration. Key features highly correlated with battery life are then extracted as inputs for prediction. Subsequently, a BiLSTM network is employed to capture subtle variations in the capacity degradation process and to predict capacity based on the decomposed sequences. The prediction results are effectively integrated, and comprehensive experiments are conducted on the NASA and CALCE lithium-ion battery aging datasets. The results show that the proposed TTAO-VMD-BiLSTM model exhibits a small number of parameters, low memory consumption, high prediction accuracy, and fast convergence. The root mean square error (RMSE) does not exceed 0.8%, and the maximum mean absolute error (MAE) is less than 0.5%. Full article

Bombyx mori silk fibroin (BMSF) has developed from a textile fibre into a mature biomaterial with broad utility in regenerative medicine, owing to its unique hierarchical molecular structure. Its excellent biocompatibility, tuneable mechanical properties, optical property, and controllable biodegradability arise from its protein conformation, which can be precisely regulated through processing and fabrication strategies. Recent advances in bioengineering have further expanded the capabilities of BMSF, enabling the development of biomaterials with engineered architectures, tailored microtopographies, and enhanced bioactivity. These technological developments have facilitated the design of scaffolds that more effectively guide tissue regeneration and enhance functional outcomes. Such constructs have demonstrated promising outcomes in the regeneration of bone, cartilage, vascular, neural, corneal, and skin tissues. This review summarises current progress while emphasising emerging trends that couple BMSF’s unique molecular features with immune-responsive design, instructive microarchitectures that guide cell behaviour, composite scaffold design, and functionalisation with bioactive molecules. BMSF has been positioned as a structurally adaptable and biologically instructive platform whose continued progression will depend on integrating advanced fabrication, mechanistic understanding, and translational standardisation. Full article

As the last critical hydrological control station on the Lancang River before it flows out of China, the daily runoff variations at the Yunjinghong Hydrological Station are directly linked to agricultural irrigation, hydropower development, and ecological security in downstream Mekong River riparian countries such as Laos, Myanmar, and Thailand. Aiming at the core issues of the runoff sequence in the Lancang–Mekong Basin, which is characterized by prominent nonlinearity, non-stationarity, and coupling of multi-scale features, this study proposes a synergistic prediction framework of “multi-scale decomposition-model improvement-parameter optimization”. Firstly, Regenerated Phase-Shifted Sine-Assisted Empirical Mode Decomposition (RPSEMD) is adopted to adaptively decompose the daily runoff data. On this basis, a Convolutional Sparse Attention Transformer (CSAT) model is constructed. A one-dimensional convolutional neural network (1D-CNN) module is embedded in the input layer to enhance local feature perception, making up for the deficiency of traditional Transformers in capturing detailed information. Meanwhile, the sparse attention mechanism replaces the multi-head attention, realizing efficient focusing on key time-step correlations and reducing computational costs. Additionally, an Improved Multi-Verse Optimizer (IMVO) is introduced, which optimizes the hyperparameters of CSAT through a spiral update mechanism, exponential Travel Distance Rate (T_DR), and adaptive compression factor, thereby improving the model’s accuracy in capturing short-term abrupt patterns such as flood peaks and drought transition points. Experiments are conducted using measured daily runoff data from 2010 to 2022, and the proposed model is compared with mainstream models such as LSTM, GRU, and standard Transformer. The results show that the RPSEMD-IMVO-CSAT model reduces the Mean Absolute Error (MAE) and Root Mean Square Error (RMSE) by 15.3–28.7% and 18.6–32.4%, respectively, compared with the comparative models. Full article

Background/Objectives: Facial nerve injury from conditions such as Bell’s palsy, trauma, surgery, and infection leads to facial asymmetry and motor deficits. Axotomy models reproduce peripheral nerve disruption and consequent motor impairment. To compare the effects of forward versus reverse autologous nerve suturing on neural regeneration and motor recovery within the facial nucleus after axotomy. Methods: In rats subjected to facial nerve axotomy, motor recovery was assessed at 8 weeks using whisker movement and blink reflex tests. Immunohistochemistry quantified choline acetyltransferase (ChAT), sirtuin 1 (SIRT1), and Iba-1 as indices of cholinergic function, cellular stress/inflammation modulation, and microglial activation in the facial nucleus. Results: Axotomy significantly reduced whisker and blink scores compared with sham. Both forward and reverse suturing significantly improved these behavioral outcomes versus axotomy. Within the facial nucleus, axotomy decreased ChAT- and SIRT1-positive cells and increased Iba-1 expression, while both suturing techniques increased ChAT and SIRT1 and reduced Iba-1. These changes suggest enhanced cholinergic function, mitigation of stress/inflammatory responses, and attenuation of microglial activation following repair. Conclusions: Forward and reverse suturing were each associated with improved motor function and favorable molecular and cellular changes in the facial nucleus after facial nerve axotomy. These findings support the utility of surgical repair irrespective of graft orientation and highlight involvement of key pathways—cholinergic signaling, SIRT1-related regulation, and microglial activity—in nerve restoration. This work extends our previous study, which focused on peripheral nerve regeneration after forward and reverse suturing, by elucidating how graft orientation affects central facial nucleus responses. By integrating behavioral outcomes with ChAT, Iba-1, and SIRT1 expression, the present study provides novel insight into the central mechanisms underlying motor recovery after facial nerve repair and helps explain why comparable functional outcomes are achieved regardless of graft polarity. Full article

A thorough understanding of the dispersion characteristics of graphene oxide (GO), its micro-pore enhancement mechanisms, and correlations with mechanical properties are crucial for advancing high-strength, durable green concrete. Introducing recycled concrete powder (RCP) can weaken the interfacial transition zone (ITZ) and inhibit hydration reactions, degrading the pore structure and affecting mechanical strength and durability. However, traditional methods struggle to accurately characterize and quantitatively analyze GO-modified pore structures due to their nanoscale size, microstructural diversity, and characterization technique limitations. To address these challenges, this study integrates deep learning-based backscattered electron image analysis with deep Taylor decomposition feature extraction. This innovative method systematically analyzes pore characteristic evolution and the correlation between porosity and mechanical strength. The results indicate that GO promotes Calcium Silicate Hydrate gel growth, refines pores, and reduces pore connectivity, decreasing the maximum pore size by 33.4–45.2%. Using a Convolutional Neural Network architecture, BSE images are efficiently processed and analyzed, achieving an average recognition accuracy of 94.3–96.9%. The optimized degree of GO coating on enhanced regions reaches 30.2%. Fitting porosity with mechanical strength and chloride ion permeability coefficients reveals that enhanced regions exhibit the highest correlation with mechanical strength and durability in regenerated cementitious materials, with R² values ranging from 0.79 to 0.99. The deep learning-assisted pore structure characterization method demonstrates high accuracy and efficiency, providing a critical theoretical basis and data support for performance optimization and engineering applications of recycled cementitious materials. This research expands the application of deep learning in building materials and offers new insights into the relationship between the microstructural and macroscopic properties of recycled cementitious materials. Full article

Cognitive deterioration and the transition to neurodegenerative disease does not develop through simple, linear regression; it develops as rapid and global transitions from one state to another within the neural network. Developing understanding and control over these events is among the largest tasks facing contemporary neuroscience. This paper will discuss a conceptual reframing of cognitive decline as a transitional phase of the functional state of complex neural networks resulting from the intertwining of molecular degradation, vascular dysfunction and systemic disarray. The paper will integrate the latest findings that have demonstrated how the disruptive changes in glymphatic clearance mechanisms, aquaporin-4 polarity, venous output, and neuroimmune signaling increasingly correlate with the neurophysiologic homeostasis landscape, ultimately leading to the destabilization of the network attraction sites of memory, consciousness, and cognitive resilience. Furthermore, the destabilizing processes are exacerbated by epigenetic silencing; neurovascular decoupling; remodeling of the extracellular matrix; and metabolic collapse that result in accelerating the trajectory of neural circuits towards the pathological tipping point of various neurodegenerative diseases including Alzheimer’s disease; Parkinson’s disease; traumatic brain injury; and intracranial hypertension. New paradigms in systems neuroscience (connectomics; network neuroscience; and critical transition theory) provide an intellectual toolkit to describe and predict these state changes at the systems level. With artificial intelligence and machine learning combined with single cell multi-omics; radiogenomic profiling; and digital twin modeling, the predictive biomarkers and early warnings of impending collapse of the system are beginning to emerge. In terms of therapeutic intervention, the possibility of reprogramming the circuitry of the brain into stable attractor states using precision neurointervention (CRISPR-based neural circuit reprogramming; RNA guided modulation of transcription; lineage switching of glia to neurons; and adaptive neuromodulation) represents an opportunity to prevent further progression of neurodegenerative disease. The paper will address the ethical and regulatory implications of this revolutionary technology, e.g., algorithmic transparency; genomic and other structural safety; and equity of access to advanced neurointervention. We do not intend to present a list of the many vertices through which the mechanisms listed above instigate, exacerbate, or maintain the neurodegenerative disease state. Instead, we aim to present a unified model where the phenomena of molecular pathology; circuit behavior; and computational intelligence converge in describing cognitive decline as a translatable change of state, rather than an irreversible succumbing to degeneration. Thus, we provide a framework for precision neurointervention, regenerative brain medicine, and adaptive intervention, to modulate the trajectory of neurodegeneration. Full article

Objectives: Spinal cord injury (SCI) disrupts the microstructure of the spinal cord, triggers reorganization of the brain network, and causes motor deficits. However, the temporal dynamics and interrelationships of these alterations remain unclear. Methods: Eight monkeys underwent spinal cord hemisection and were randomly assigned to either the SCI-only group or the treatment group that received neurotrophin-3-chitosan implants. Longitudinal brain structural/resting-state magnetic resonance imaging and spinal cord diffusion tensor imaging (DTI) were conducted. Concurrently, hindlimb motor function was assessed. The brain network topology was characterized through graph theory. The generalized additive mixed model (GAMM) was employed to analyze the longitudinal trajectories of network metrics, while the linear mixed-effects model (LMM) was used to evaluate the moderating effect of treatment on correlations between network metrics and motor/DTI parameters. Results: The SCI-only group exhibited sustained functional network segregation, aberrant structural topology, and lower fractional anisotropy (FA). These findings collectively reflect chronic maladaptive plasticity. In the treatment group, the therapy not only enhanced white matter integrity, reflected by increased FA values, but also reduced the clustering coefficient (Cp) in brain structural network, indicating a shift away from maladaptive segregation. Critically, the LMMs further revealed that treatment moderated the pathological correlations between global efficiency (Eg), local efficiency, Cp, and locomotor parameters. Moreover, spinal FA exerted a significant main effect on Eg of brain functional networks. Conclusions: These findings suggest that treatment-induced brain reorganization underlies motor function following SCI, and progressive brain reorganization correlates with changes in spinal cord microstructure, revealing a systems-level mechanism of neural repair. Full article

The spice Ferula assa-foetida L., also known as asafoetida, is widely recognized for its medicinal and culinary applications. The non-native status of the plant and the prolonged dormancy of its seeds pose significant challenges for large-scale cultivation in India. In vitro organogenesis offers an effective solution to these obstacles. Establishing reliable in vitro regeneration protocols requires standardized statistical methods to evaluate univariate and multivariate data for optimizing specific traits. However, these methods have limitations when handling complex, nonlinear inputs, often producing large prediction errors that reduce the reliability of trait optimization. This study developed an in vitro regeneration system for F. assa-foetida L. and identified optimal PGRs for somatic embryogenesis and shoot organogenesis through image-based morphological analysis. Predictive models were created using DL and ML algorithms. Calli induced from leaf explants was cultured on the Murashige and Skoog medium supplemented with various combinations and concentrations of thidiazuron (TDZ), 6-benzylaminopurine (BAP), and α-naphthaleneacetic acid (NAA), as experimental variables. Seven ML approaches, namely random forest (RF), support vector machine (SVM), k-nearest neighbours (kNN), decision tree (DT), extreme gradient boosting (XG Boost), naïve bayes, and logistic regression, alongside five DL models—convolutional neural network (CNN), MobileNet, region-based convolutional neural network (RCNN), residual neural network (ResNet), and visual geometry group (VGG19)—were employed to predict the best PGRs for somatic embryogenesis and shoot organogenesis. Among them, the convolutional neural network (CNN) achieved the highest accuracy (87%), outperforming baseline ML models such as logistic regression and decision tree (82%). This pioneering study in F. assa-foetida L. presents an AI-driven, image-based framework for predicting optimal PGRs, offering a scalable approach to enhance micropropagation in endangered medicinal plants. Full article

Ibrutinib (IB), a Bruton’s tyrosine kinase (BTK) inhibitor, is widely used against B-cell malignancies. However, its adverse effects on stem cell-dependent processes and tissue homeostasis remain incompletely understood. Freshwater planarians possess pluripotent stem cells (neoblasts), which enable remarkable regeneration of various tissues, including the central nervous system. This makes them ideal in vivo models for studying chemical toxicity within a whole-organism context. Here, we utilized planarian Dugesia constrictiva to assess IB toxicity and elucidate its mechanisms, focusing on its impact on stem cell dynamics and regeneration. Our results demonstrated that exposure to IB at concentrations as low as 0.9 mg/L, far below clinical plasma levels, led to severe morphological and regenerative impairments, including disrupted neural regeneration. Mechanistically, IB disrupted stem cell dynamics by suppressing proliferation and differentiation and by inducing oxidative stress via ROS overproduction. Notably, IB exposure significantly downregulated BTK expression. Crucially, BTK RNAi caused the key toxic effects of IB exposure, including morphological and regenerative defects, suppression of stem cell proliferation and differentiation, and increased apoptosis. Therefore, we conclude that IB may exert its toxicity in planarians primarily through BTK inhibition. This finding provides direct functional evidence linking BTK inhibition to stem cell dysfunction and regenerative defects in a novel in vivo context, offering critical insights for refining the clinical safety profile of IB. Full article

The increasing contamination of neonicotinoid pesticides in the environment has become a growing concern, and biochar is considered a promising strategy for removing these pollutants. This study converted waste rice husks into biochar (RHB) via pyrolysis at 400–600 °C under anaerobic conditions, using dinotefuran (DIN) as a representative neonicotinoid. The physicochemical properties of RHB and its adsorption mechanisms for DIN were systematically investigated. Results showed that higher pyrolysis temperatures increased the specific surface area, microporosity, and aromaticity of biochar, while altering the distribution of surface functional groups. RHB prepared at 600 °C (RHB600) exhibited the highest adsorption capacity. The adsorption process followed the Sips isotherm and pseudo-second-order kinetic models, indicating a spontaneous and endothermic process involving heterogeneous physic–chemical adsorption. The primary mechanisms included pore filling, π–π interactions, and hydrogen bonding. The sequence of functional group response during DIN adsorption was C–O > C=C > C=O > –OH. Environmental factors such as solution pH and humic acid concentration significantly influenced adsorption, while phosphate ions caused strong competitive inhibition. An artificial neural network model accurately predicted adsorption under multiple interacting factors, and RHB600 demonstrated good regeneration after ethanol elution. This study confirms that RHB is an effective and practical adsorbent, providing a technical reference for agricultural waste valorization and pesticide-polluted water remediation. Full article