Lithium iron phosphate (LiFePO4, LFP) is severely limited by its intrinsically low electronic conductivity and sluggish lithium-ion diffusion kinetics, particularly under high-rate operating conditions. Therefore, introducing high-performance conductive additives to construct efficient electron transport networks has been considered an effective strategy for improving the rate capability and cycling stability of LFP cathodes. Among various conductive materials, reduced graphene oxide (rGO) and carbon nanotubes (CNT) have attracted extensive attention owing to their excellent electrical conductivity and unique structural advantages. However, the intrinsic tendency of rGO nanosheets to restack and CNT to entangle often hinders the construction of continuous and efficient conductive networks. Herein, an interfacial co-dispersion strategy is developed to regulate the assembly of graphene oxide (GO) and hydroxylated carbon nanotubes (CNT-OH), leading to the formation of an rGO/dCNT conductive architecture. Interfacial interactions associated with oxygen-containing functional groups facilitate the homogeneous co-dispersion of GO and CNT-OH in aqueous media, while partially hydroxylated CNTs are preintercalated between adjacent GO sheets to form an interlayer intercalation-bridging architecture. During the subsequent thermal reduction process, the prefabricated architecture efficiently inhibits the restacking of rGO nanosheets and simultaneously constructs interconnected lithium-ion transport pathways between adjacent layers, thereby forming a robust three-dimensional conductive network. When applied as a conductive additive for LiFePO4 cathodes, the optimized rGO/dCNT composite (L-RO2-1) delivers a discharge capacity of 159 mAh g-1 at 0.2 C and maintains 104.3 mAh g-1 after 300 cycles at 6 C, while exhibiting lower charge-transfer resistance and faster lithium-ion diffusion kinetics. This work provides an effective strategy for constructing stable carbon-based conductive networks for high-rate lithium-ion batteries.
Colorectal cancer remains a leading cause of cancer-related mortality, with resistance to 5-fluorouracil (5-FU) posing a major therapeutic challenge. Thymoquinone (TQ), a bioactive compound derived from Nigella sativa, exhibits anticancer activity; however, its system-level effects in colorectal cancer are not fully understood. RKO colorectal cancer cells were treated with TQ, 5-FU, or their combination for 24 h, followed by genome-wide transcriptomic profiling using oligonucleotide microarrays. Drug interaction effects were assessed using a deviation-from-additivity model. Selected genes were validated by RT-qPCR and Western blotting, and functional relevance was evaluated using bioinformatics analyses. Combined treatment induced extensive network-level reprogramming of pathways associated with apoptosis, cellular stress response, and proliferation. Although no classical transcriptional synergy was observed, the interaction between TQ and 5-FU resulted in coordinated modulation of overlapping signaling networks. Key regulatory genes, including FAS and CYLD, were linked to enhanced pro-apoptotic signaling, whereas BIRC3 and EIF2AK3 were associated with adaptive or resistance-related responses. TQ acts as a context-dependent modulator of chemotherapy response, reshaping cell death and stress-related signaling networks rather than directly enhancing cytotoxicity. These findings highlight the potential of TQ to influence therapeutic responses in fluoropyrimidine-based treatment of colorectal cancer and support further functional and in vivo validation.
To evaluate and compare the efficacy of different preoperative hormonal therapies for preventing postoperative complications after hypospadias repair in pediatric patients through a systematic review and network meta-analysis. PubMed, Web of Science, Cochrane Library, and CNKI were systematically searched (inception to March 15, 2026). Randomized controlled trials (RCTs) enrolling pediatric patients with hypospadias who received preoperative hormone therapy were included. Risk of bias was assessed using the Cochrane RoB 2 tool. The primary outcomes were postoperative urethrocutaneous fistula, glans dehiscence, and meatal stenosis. Bayesian network meta-analysis was performed using the gemtc package in R 4.3.1, and SUCRA was used to rank intervention efficacy. Sensitivity analysis excluding non-distal cohorts was performed to assess robustness. Six RCTs (715 patients) were included. Direct pairwise meta-analysis showed that androgen therapy reduced the odds of urethrocutaneous fistula (pooled OR = 0.474; 95% CI: 0.241-0.934; P = 0.031). In the sensitivity analysis restricted to distal hypospadias only, the protective trend persisted but did not reach statistical significance (OR = 0.613; 95% CI: 0.285-1.319). No significant benefit of androgen was observed for glans dehiscence or meatal stenosis. Estrogen did not significantly reduce any complication. SUCRA analysis identified androgen as the most probable effective intervention for fistula prevention. Preoperative androgen therapy may reduce the odds of postoperative urethrocutaneous fistula in pediatric patients undergoing hypospadias repair, though this benefit was primarily observed in mixed-severity cohorts. No significant protective effect was demonstrated for glans dehiscence or meatal stenosis. Further high-quality RCTs with standardized protocols and long-term follow-up are warranted, particularly in distal hypospadias populations. https://www.crd.york.ac.uk/PROSPERO/view/CRD420261351057, identifier CRD420261351057.
Stroke survivors frequently present with varying degrees of sensory, cognitive, language, and motor impairments. Traditional Chinese exercises have been widely used as adjunctive rehabilitation therapies because they combine low-to-moderate intensity physical training, balance practice, proprioceptive stimulation, motor relearning, respiratory regulation, and cognitive engagement. However, intervention protocols and outcome measures vary substantially across trials, and direct head-to-head comparisons among Tai Chi, Baduanjin, Wuqinxi, Yijinjing, and standard of care remain scarce. This network meta-analysis therefore compared the relative effectiveness of different traditional Chinese exercise modalities for improving post-stroke motor outcomes. We systematically searched Embase, Web of Science, Cochrane Library, PubMed, CNKI, VIP, and Wanfang Data from database inception to January 2026 for RCTs evaluating traditional Chinese exercises for post-stroke motor dysfunction. Eligible participants were adults with clinically diagnosed ischemic or hemorrhagic stroke in the acute, subacute, or chronic stage. The primary outcomes were upper- and lower-extremity motor function assessed using the FMA-UE and FMA-LE. Secondary outcomes were balance function assessed using the BBS and activities of daily living assessed using the BI. Risk of bias was assessed using the revised Cochrane risk-of-bias tool for randomized trials (RoB 2). A Bayesian network meta-analysis was performed, and intervention rankings were estimated using the SUCRA. Fifty RCTs involving 3,718 participants were included. Compared with standard of care, Tai Chi and Wuqinxi showed statistically significant improvements in FMA-UE scores, whereas Baduanjin, Tai Chi, and Wuqinxi improved FMA-LE scores. All four exercise modalities were associated with significant improvements in BBS and BI scores. Ranking results suggested that Tai Chi had the highest probability of improving FMA-UE, Wuqinxi ranked highest for FMA-LE, Yijinjing ranked highest for BBS, and Baduanjin ranked highest for BI. https://www.crd.york.ac.uk/PROSPERO/view/CRD420261323853, CRD420261323853.
This study integrated UPLC-QE Orbitrap-MS/MS, network pharmacology, and experimental validation to investigate the chemical profile and therapeutic mechanisms of the Yueju pill (YJP) in the treatment of alcoholic liver disease (ALD). Chemical analysis identified 91 compounds in the YJP. After SwissADME screening, 45 active ingredients were predicted as potential bioactive compounds. By overlapping the targets of these compounds with ALD-related targets, a "component-target-disease" network was constructed, revealing 183 common targets. Enrichment analysis indicated that YJP exerts its therapeutic effects through multiple pathways, including the HIF-1 signaling pathway. In animal experiments, an ALD mouse model was established using the Lieber-DeCarli ethanol liquid diet. YJP intervention significantly reduced serum TG, AST, and ALT levels, alleviated hepatic lipid deposition and collagen deposition, improved liver mitochondrial homeostasis, and decreased hepatic HIF-1α expression. Moreover, the YJP improved intestinal barrier integrity and upregulated intestinal HIF-1α and occludin expression, reflecting a therapeutic mechanism involving coordinated regulation of the gut-liver axis.
Kawasaki disease (KD) is a systemic vasculitis in children primarily affecting the coronary arteries, and studies suggest that the gut microbiota may be involved in KD pathogenesis, inflammatory responses, and immune regulation. This study employed an integrative multi-omics strategy to systematically investigate gut microbiota-metabolite interactions in KD. Key molecular targets were identified using network-based analyses and machine learning models, with Mendelian randomization providing causal validation. Single-cell transcriptomics and molecular docking further elucidated immune cell interactions and metabolite-protein binding, highlighting critical regulatory pathways. We identified SELP as a core molecular target in KD, predominantly expressed in platelets and involved in immune and inflammatory responses. Gut microbiota-derived metabolites, including palmitoylethanolamide, pantothenic acid, and 1-O-caffeoylglycerol, may regulate immune cell interactions via the RESISTIN signalling pathway. Altered abundances of microbial taxa such as Bacteroides, Parabacteroides, and Bifidobacterium suggest their potential role in inflammation modulation. Activation of IL-17, TNF, MAPK, and PI3K-Akt pathways further contributes to disease progression, highlighting the microbiota-metabolite-SELP axis as a potential therapeutic target in KD. These findings lay the groundwork for subsequent in vitro and in vivo studies, advancing the development of microbiome-based intervention strategies.
Cisplatin (Cis), a commonly used chemotherapy drug, is associated with liver toxicity, which restricts its broader clinical use. This study investigated the potential protective effects of linagliptin (Lina), a DPP-4 inhibitor, in preventing liver damage induced by Cis in rats. There were four groups of male rats: a control group, a Cis group (8 mg/kg, IP), and cotreated groups given Lina (5 and 10 mg/kg, orally) with Cis. Lina was administered daily for 15 days, with Cis injected on Day 8. Liver function, oxidative stress markers, inflammatory mediators, energy metabolism indicators, and key signaling proteins were assessed. Cis administration resulted in significant hepatotoxicity, evidenced by elevated liver enzymes, increased oxidative stress, enhanced inflammatory response, and disrupted energy metabolism. Lina treatment, particularly at the 10-mg/kg dose, demonstrated marked hepatoprotective effects. It significantly reduced liver enzyme levels, improved antioxidant status, attenuated inflammatory markers, and restored energy metabolism indicators. Moreover, Lina positively modulated essential signaling proteins involved in cellular stress response and metabolism, including signal transducer and activator of transcription 3 (STAT3), transforming growth factor beta 1 (TGF-β), silent information regulator 1 (SIRT1), and peroxisome proliferator-activated receptor gamma coactivator 1-alpha (PGC-1α). The results indicate that Lina protects against Cis-induced liver damage by leveraging its antioxidant, anti-inflammatory, and metabolic regulation properties. This study offers new insights into potential strategies for mitigating Cis-induced hepatotoxicity and enhancing its therapeutic index in cancer treatment.
Osteoporosis (OP) is a chronic metabolic bone disorder marked by reduced bone mass, damaged microarchitecture, and increased skeletal fragility. Its pathogenesis is complicated, and the central mechanism involves an imbalance between osteoblastic bone formation and osteoclastic bone resorption, together with several pathological processes, including excessive oxidative stress, persistent inflammatory infiltration, and abnormal apoptosis. The Nuclear Factor Erythroid 2-Related Factor 2 (Nrf2), Nuclear Factor-κB (NF-κB), and Mitogen-Activated Protein Kinase (MAPK) pathways are important signaling networks controlling bone metabolism, oxidative stress, and inflammation. These pathways interact through upstream and downstream regulatory molecules. They establish complex regulatory networks through molecular interactions and crosstalk, collectively promoting the initiation and progression of OP. Traditional Chinese medicine (TCM), based on the holistic concept and "syndrome differentiation and treatment," shows promising clinical value in OP prevention and therapy because of its characteristic multi-target, multi-pathway actions and low toxicity. Extensive pharmacological evidence has confirmed that individual TCM compounds can specifically regulate the Nrf2/NF-κB pathways. By modulating redox homeostasis, inhibiting inflammatory responses, and regulating osteocyte proliferation, differentiation, and apoptosis, these compounds restore bone metabolic equilibrium and produce anti-OP effects. Current reviews mostly focus on individual signaling pathway or single herbal component, lacking systematic sorting of the cross-talk among Nrf2, NF-κB and MAPK networks. This article systematically reviews the major roles of the Nrf2, NF-κB, and MAPK pathways in osteoporosis and discusses the anti-osteoporotic mechanisms of Chinese herbal monomers, thereby providing a theoretical foundation for TCM intervention. Furthermore, it highlights syndrome differentiation and personalized treatment to overcome the limitations of basic research and clarify future clinical translation.
Retinal diseases are a leading cause of visual impairment worldwide, highlighting the need for accurate and scalable automated screening systems. This study introduces RHT-Net (Retinal Hybrid Transformer Network), a novel hybrid deep learning architecture designed for multi-class classification of nine retinal diseases from color fundus images by combining the strengths of convolutional neural networks and transformer encoders. RHT-Net integrates residual convolutional neural networks for local feature extraction with transformer encoders to capture long-range global dependencies. A real-world dataset comprising 5,318 color fundus images collected from Bengali patients was used in this study. Data augmentation techniques, including rotation, flipping, and Gaussian noise addition, expanded the dataset to 21,272 images. Images were preprocessed through resizing to 224 × 224 pixels and contrast enhancement using Contrast Limited Adaptive Histogram Equalization (CLAHE). The augmented dataset was divided into training (80%) and testing (20%) subsets for model development and evaluation. The proposed RHT-Net achieved a training accuracy of 97.93% with an F1-score of 96.10%. On the testing set, the model attained an accuracy of 96.12% and an F1-score of 92.28%. The overall classification performance reached an accuracy of 97.57% and an F1-score of 95.31%. Comprehensive evaluations, including class-wise performance analysis, confusion matrices, and Grad-CAM visualizations, demonstrated the model's strong predictive capability, robustness, and interpretability across the nine retinal disease classes. The findings indicate that RHT-Net is a promising and scalable approach for early retinal disease screening. By effectively capturing both local and global image features, the model achieves high classification performance while providing interpretable predictions. These characteristics support its potential integration into telemedicine and remote diagnostic workflows. However, further external validation and deployment-oriented optimization are necessary before real-world clinical implementation.
Oleogels are innovative lipid-structuring systems that entrap liquid vegetable oils in three-dimensional networks, offering sustainable alternatives to traditional solid fats and reducing dietary trans and saturated fats. As oleogels are predominantly composed of liquid oil, the fatty acid (FA) profile, triacylglycerol composition, minor lipid components, polarity, viscosity, and oxidative stability critically impact gel formation, network structure, and functionality. However, current literature stems from the diverse chemical nature of both oils and gelators, highlighting the necessity to understand oil-gelator compatibility for effective structuring. Furthermore, oils influence interfacial dynamics in derived systems like oleogel-based emulsions, bigels, and oleofoams, where oil-water or oil-air interactions determine stability and performance. Although these interfacial phenomena have attracted increasing attention, the role of oil characteristics in modulating oil-water and oil-air interfacial behavior remains insufficiently clarified. Therefore, this review summarizes oil composition and physicochemical properties, including FA unsaturation, chain length, glyceride structure, minor lipid components, polarity, viscosity, and oxidative stability. It further discusses how oil characteristics affect network formation, rheological behavior, and functional performance in oleogel and its derivatives by regulating oil-gelator interactions and interfacial behavior. This review provides theoretical guidance for the rational design of oleogel and its derived systems.
Brain function emerges from coordinated activity across anatomically connected regions, where structural connectivity (SC)-the network of white matter pathways-provides the physical substrate for functional connectivity (FC), defined as the correlated activity between brain areas. While structural and functional networks exhibit substantial overlap, their relationship involves complex, indirect mechanisms, including the dynamic interplay of direct and indirect pathways. To systematically untangle how structural architecture shapes functional patterns, this work aims to establish a set of rules that decode how direct and indirect structural connections and motifs give rise to FC between brain regions. Specifically, using a generative linear model, we derive explicit rules that predict an individual's resting-state fMRI FC from diffusion-weighted imaging-derived SC, validated against topological null models. Examining the rules reveals distinct classes of brain regions, with integrator hubs acting as structural linchpins promoting synchronization and mediator hubs serving as structural fulcrums orchestrating competing dynamics. Virtual lesion experiments further demonstrate how different cortical and subcortical systems distinctively contribute to global FC. Together, by uncovering how structural architecture governs functional interactions, this framework enables us to predict how alterations in SC, resulting from disease or surgery, propagate through functional networks and contribute to cognitive and behavioral impairments.
Leveraging complementary information from multiple data representations enables richer feature extraction and greater robustness under challenging acquisition conditions. Such an approach enhances the accuracy, reliability, and generalizability of photoacoustic image reconstruction, ultimately leading to improved image quality and stronger performance across diverse photoacoustic imaging (PAI) scenarios. We propose a transformer-based dual SwinUNet architecture that learns features from both the image and sinogram domains to improve PAI within a contrastive learning framework. The developed dual SwinUNet architecture had multiple loss function-wherein noise-to-signal ratio was computed between the predicted output from each SwinUNet model and the ground truth, and the mean square error was estimated between the predicted outputs of both networks. The dual SwinUNet model was fed with two reconstructed images as inputs, generated using different reconstruction algorithms, i.e., backprojection and Tikhonov-regularized reconstruction. These input pairs can either be positive, meaning both inputs share the same ground truth, or negative, meaning they have different ground truths. The model was trained using a contrastive loss in the sinogram domain, enabling the model to learn distinctive features from both positive and negative pairs. The performance of the different networks (ResNet, UNet, FDUNet, TNet, and the proposed network) was evaluated by varying the number of transducers, angular coverage, and noise levels. The data acquired with 100 transducers having a coverage angle of 135 deg have shown that the structural similarity index measure (SSIM) was improved by 7.5% and the universal image quality index improved by 18% compared with FDUNet. For in vivo mice data, SSIM improved by 6.8%, when using data from 100 transducers. The dual SwinUNet architecture demonstrates significant improvement in image quality for PAI by learning features from both the image and sinogram domains. The proposed framework can be extended using different DL architectures alongside different analytical/model-based reconstruction inputs.
Pyruvate kinase deficiency (PKD) is the most common cause of congenital non-spherocytic hemolytic anemia. This study systematically maps the scholarly output and evolving research trends in PKD over the past decade (2015-2025) to identify core contributors and track thematic trajectories. Bibliographic records were retrieved from the Web of Science Core Collection (WoSCC) and analyzed quantitatively using CiteSpace, VOSviewer, and R-bibliometrix. To control for potential selection bias, a parallel supplementary search was conducted in PubMed and Scopus using identical query strings. PubMed yielded 15 unique non-overlapping eligible records, whereas Scopus returned 0. Given metadata disparities and small sample limits, these records were dedicated exclusively to exploratory qualitative thematic concordance verification rather than statistical network synthesis. The primary WoSCC network isolated 90 core publications derived from 649 initial records after rigorous screening. Multi-database cross-validation confirmed a stable thematic alignment. Manual textual extraction of the 15 external PubMed records displayed precise semantic resonance with the core dataset's clusters, tracking a clear focus transition from baseline genotype mapping to targeted pyruvate kinase, R-type (PKR) activators (specifically mitapivat) and preclinical gene-editing interventions, without introducing divergent thematic anomalies. The analytical framework indicates that the PKD research domain is characterized by an evolving therapeutic focus, progressing from foundational descriptive epidemiology and supportive care toward mechanism-based, targeted interventions. Current empirical evidence highlights PKR allosteric activation as the most clinically developed cluster, while gene therapy represents an active, early-stage investigational frontier.
As the most common extracranial solid tumor in children, accurate and consistent pathological diagnosis of neuroblastoma (NB) is critical for clinical decision-making. However, traditional methods relying on manual interpretation are constrained by tumor heterogeneity and inter-observer variability, necessitating the development of objective and quantitative intelligent auxiliary diagnostic technologies. This study introduces a two-stage deep learning framework with hybrid supervision, achieving precise classification of NB through collaborative optimization of cell-level segmentation and multiscale classification. In the first stage, the enhanced Medical Segment Anything Model (MedSAM) uses a cross-attention mechanism to achieve pixel-level tumor cell segmentation, attaining a Dice coefficient of 0.94. In the second stage, an optimized Swin Transformer (ST) is employed to construct the classification network, enabling full slice analysis via a confidence voting strategy. On an independent dataset comprising 185 whole-slide images from 185 patients, the model attained an overall area under the receiver operating characteristic curve (AUC) of 0.864 (95% confidence interval (CI): 0.747 to 0.952), significantly outperforming existing methods (8.7% higher than the second-best model, ResNeXt). Among the three NB subtypes (undifferentiated, poorly differentiated, and differentiating), the recognition accuracy is the highest for the poorly differentiated subtype, which accounts for the largest proportion in clinical practice. This approach effectively addresses issues related to the tumor microenvironment interference and small-sample generalization through a cascaded feature extraction and decision-making mechanism, providing a robust intelligent auxiliary tool for NB pathological diagnosis.
European sea bass (Dicentrarchus labrax) is a seasonal marine teleost of high aquaculture value, yet a comprehensive, stage-resolved molecular description of its spermatogenic cycle has been lacking. Here, we generated an integrated histology-anchored RNA sequencing atlas covering the six canonical testicular stages (Stages I-VI) to reconstruct the temporal sequence of endocrine, proliferative, meiotic, spermiogenic and regression-related processes across the annual reproductive cycle. Early stages (Stages I-III) were characterised by strong activation of spermatogonial maintenance pathways, hormone-responsive programmes, and circadian regulators, along with robust enrichment of DNA replication, kinetochore assembly, and chromosomal segregation mechanisms that prepare germ cells for meiotic entry. Spermiogenesis (Stage IV) represented the major transcriptional shift, with the coordinated upregulation of axonemal motors, intraflagellar transport and BBSome components, outer dense fibres and microtubule regulators, accompanied by extensive chromatin remodelling involving histone variants, transcription factors, ubiquitin/SUMO pathway enzymes, and heterochromatin-associated proteins. Spermiation (Stage V) showed an enrichment in calcium-dependent signalling, cytokine and chemokine networks, and adhesion and extracellular matrix pathways, consistent with Sertoli-germ cell remodelling and sperm release. Regression (Stage VI) was dominated by immune activation, endocytosis, phagocytosis, and vascular remodelling signatures, indicating an active phase of clearance and tissue reorganisation that preserves the spermatogonial reservoir for the next cycle. From 9,327 differentially expressed genes, we identified 132 distinct temporal expression patterns and curated three translational resources: stage-informative markers, a fertility gene set, and a chromatin-associated compendium. Together, this atlas defines the sequential molecular architecture of spermatogenesis in a seasonal marine fish, conserved vertebrate and species-specific regulatory modules, and provides operational biomarkers for broodstock assessment and the mitigation of precocious maturation in aquaculture.
Bone continuously adapts to mechanical forces to maintain structural integrity, yet the molecular sensors that initiate this process have long remained undefined. The identification of the mechanosensitive ion channel Piezo1 has provided a pivotal molecular basis for understanding skeletal mechanotransduction. This review summarizes current advances in elucidating the unique structural features and force-gating mechanisms of Piezo1, and highlights its role as a central mechanoreceptor coordinating mechanical responses within bone tissue. We further delineate the multidimensional downstream signaling networks activated by Piezo1 and discuss the complex crosstalk among these pathways. The pathological consequences of Piezo1 dysregulation in major orthopedic disorders are examined, along with the therapeutic potential and challenges of targeting Piezo1 as a novel "mechanopharmacological" strategy. Collectively, this review provides an integrated framework for understanding the molecular foundations of bone mechanotransduction and identifies Piezo1 as a promising target for developing innovative treatments for orthopedic diseases.
Psoriasis is a chronic, recurrent, inflammatory skin disorder closely associated with dysregulation of the immune microenvironment. Conventional systemic therapies often face limitations in children due to safety concerns, highlighting the urgent need for safer and effective treatment options. Natural products, characterized by their multi-target and multi-pathway actions along with relatively favorable safety profiles, as suggested primarily by adult and preclinical studies, have shown great potential in modulating the immune microenvironment. This review summarizes recent advances in the use of natural products-including plant extracts, active components, and compound formulations-in reshaping the immune microenvironment of psoriatic lesions by regulating T cell subsets, cytokine networks, innate immune cell functions, and skin barrier integrity. We explicitly distinguish between evidence derived from pediatric clinical studies, adult studies, and preclinical models. Furthermore, the article explores the implications of these findings for developing novel therapeutic strategies specifically tailored for pediatric psoriasis, addressing current challenges, and outlining future directions for translational research. By integrating these insights, this review aims to provide a comprehensive understanding of how natural products can contribute to safer and more effective immunomodulatory treatments in children with psoriasis.
Task-agnostic controllers for partial-assist lower-limb exoskeletons aim to reliably mimic biological torque while seamlessly adapting to changing movement patterns. However, current approaches relying on hidden state estimators or neural networks lack explainability and safety guarantees, while force amplification methods risk instability with an inherent trade-off between sensitivity and robustness to control inputs. Energy shaping control uses a kinematic model-based framework to provide predictable, stable assistance, though its traditional passive form limits biomimetic performance. Previous work relaxed the strict passivity requirements to improve biomimicry but reduced the stability guarantees. This paper presents an optimization-based extension of the energy-shaping control framework that combines the stability benefits of energy shaping with the intuitive biomimicry of force amplification. Our framework enables controlled trade-offs between sensitivity to changing human impedance and high performance through adjustable cost contributions of force amplification and model-based terms. We provide theoretical guarantees of closed-loop stability to an invariant set under human joint impedance control, supported by empirical validation of stability characteristics of an ankle exoskeleton under varying controller passivity constraints. A study of ten able-bodied participants using bilateral ankle exoskeletons demonstrates that the biomimetic controller reduced biological ankle torque by 19.1% across various activities of daily life.
Digital health education may help reduce health-information inequality in underdeveloped rural areas, but evidence remains limited on how rural residents encounter health information across different media environments and how digital access, usability, engagement, and self-reported preventive behavior are interrelated. This study examined media-use ecologies and cross-sectional associations among digital access and skills, digital health information engagement, and self-reported preventive behavior among rural adults in Guizhou, China. A cross-sectional survey was conducted among 1,265 adult rural residents recruited from five selected counties/districts in Guizhou Province using a multistage non-probability sampling design. Latent class analysis was used to characterize health-information media-use ecologies based on nine indicators of information channels and social media platforms. Regression-based cross-sectional association models examined associations among digital access and skills, perceived ease of understanding digital health content, lower operational difficulty, digital health information engagement, attitudes and willingness toward health education, and self-reported preventive behavior, adjusting for sex, age, education, income, and media-use ecology. Five media-use ecologies were identified, reflecting different combinations of offline interpersonal/professional channels, traditional media, and digital platforms. Residents in omnichannel and short-video/social-platform-centered ecologies reported higher digital health information engagement, whereas those in the offline village doctor/traditional channels ecology reported the lowest engagement. Higher digital access and skills were associated with stronger engagement, and this association was attenuated after accounting for perceived ease of understanding and lower operational difficulty. Greater engagement was associated with more frequent self-reported preventive behavior, and this association was attenuated after accounting for attitudes toward health education and willingness to adopt new forms of health education. In this non-probability adult sample from selected rural sites in Guizhou, digital health inequality was reflected not only in unequal access to devices and networks, but also in differences in understanding, usability, engagement, and self-reported preventive behavior. The findings should be interpreted as cross-sectional associations among field-feasible indicators rather than evidence of causal mechanisms.
Alzheimer's disease and related dementias remain largely resistant to disease-modifying therapies, despite decades of research focused on linear neuropathological pathways such as beta-amyloid and tau. Persistent paradoxes-including the dissociation between pathology burden and clinical expression, the impact of early-life stress, and the role of systemic factors-indicate the need for integrative theoretical frameworks. This article proposes a multilevel hypothesis conceptualizing dementias as disorders of biological memory and allostatic integrity rather than isolated brain pathologies. The Hierarchical-Circular Model of Biological Memory posits that dementia emerges from progressive disruptions in a circular, multilevel system that encodes and stabilizes biological information across the lifespan. The model is organized around the unifying principle "Signal → Plasticity → Stable State" and integrates five interconnected levels: (1) morphogenetic programming and genetic architecture, (2) epigenetic molecular memory, (3) allostatic load and systemic physiological adaptation, (4) the Psychological-Neurological-Endocrine-Immunological (PNEI) network, and (5) interoceptive-neuronal integration. At any level, perturbation can propagate bidirectionally through the system, establishing maladaptive stable states that manifest clinically as dementia. Through a structured synthesis of longitudinal, mechanistic, and multisystem studies (2010-2025), the model specifies how gene-environment interactions, epigenetic modifications, cumulative allostatic load, neuroimmune dynamics, and altered interoceptive timescales jointly shape vulnerability and resilience. The concept of allostatic integrity is introduced as a dynamic systems-level property-distinct from allostatic load-that explains why similar neuropathological burdens may result in divergent clinical trajectories. Distinct dementia phenotypes are proposed to reflect different patterns of circular reinforcement across the five levels. This framework generates concrete, falsifiable predictions: (1) composite indices of allostatic integrity will outperform single biomarkers in predicting conversion from mild cognitive impairment to dementia; (2) multidomain interventions targeting more than one system level will have multiplicative, rather than additive, effects on slowing cognitive decline; (3) patients with similar amyloid/tau profiles but contrasting allostatic integrity will show markedly different trajectories of clinical progression; and (4) allostatic integrity moderates the protective effect of cognitive reserve, a pattern not predicted by reserve frameworks alone. The Hierarchical-Circular Model of Biological Memory offers a unifying hypothesis for Alzheimer's disease and related dementias that bridges genetic, epigenetic, physiological, neuroimmune, and interoceptive processes across the lifespan. By reframing dementias as failures of biological memory and allostatic integrity, the model provides a conceptual roadmap for mechanistic research, multidomain prevention, and personalized treatment strategies.