Properties Of Networks Research Articles

Dynamics of convolutional recurrent neural networks near their critical point

We examine the dynamical properties of a single-layer convolutional recurrent network with a smooth sigmoidal activation function, for small values of the inputs and when the convolution kernel is unitary, so all eigenvalues lie exactly at the unit circle. Such networks have a variety of hallmark properties: the outputs depend on the inputs via compressive nonlinearities such as cubic roots and both the and the depend sensitively on the inputs as power laws, both diverging as the input →0. The basic dynamical mechanism is that inputs to the network generate ongoing activity, which in turn controls how additional inputs or signals propagate spatially or attenuate in time. We present analytical solutions for the steady states when the network is forced with a single oscillation and when a background value creates a steady state of “ongoing activity” and derive the relationships shaping the value of the temporal decay and spatial propagation length as a function of this background value. Published by the American Physical Society 2024

Integrated cerebellar radiomic-network model for predicting mild cognitive impairment in Alzheimer's disease.

Pathological and neuroimaging alterations in the cerebellum of Alzheimer's disease (AD) patients have been documented. However, the role of cerebellum-derived radiomic and structural connectome modeling in the prediction of AD progression remains unclear. Radiomic features were extracted from magnetic resonance imaging (MRI) in the Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset (n=1319) and an in-house dataset (n=308). Integrated machine learning models were developed to predict the conversion risk of normal cognition (NC) to mild cognitive impairment (MCI) over a 6-year follow-up. The cerebellar models outperformed hippocampal models in distinguishing MCI from NC and in predicting transitions from NC to MCI across both cohorts. Key predictors included textural features in the right III and left I and II lobules, and network properties in Vermis I and II, which were associated with cognitive decline in AD. Cerebellum-derived radiomic-network modeling shows promise as a tool for early identification and prediction of disease progression during the preclinical stage of AD. Altered cerebellar radiomic features and topological networks were identified in the subjects with mild cognitive impairment (MCI). The cerebellar radiomic-network integrated models outperformed hippocampal models in distinguishing MCI from normal cognition. The cerebellar radiomic model effectively predicts MCI risk and can stratify individuals into distinct risk categories. Specific cerebellar radiomic features are associated with cognitive impairment across various stages of amyloid beta and tau pathology.

Characterization of entropy measures with connection number based indices of boric acid hydrogen-bonded 2D lattice sheets.

The cut method is a computational approach utilized to predict the fundamental activities of physicochemical properties of chemical networks, also called topological indices. The connection number is a new idea, that gives interesting and good results of the topological indices (TIs) and entropy measures (EMs) for structural representation of chemical compounds and networks. The physical density of chemical networks is characterized by these indices. In this paper, we determined the computational results for indices based on connection numbers for a two-dimensional lattice sheet of hydrogen-bonded boric acid. Boric acid, an inorganic compound, is not very harmful when applied to the skin or consumed. Finally, graphical and numerical comparisons of topological numbers including the number of borate hydrogen-bonded double lattice forms are also included in this study.

Efficient coding in biophysically realistic excitatory-inhibitory spiking networks.

The principle of efficient coding posits that sensory cortical networks are designed to encode maximal sensory information with minimal metabolic cost. Despite the major influence of efficient coding in neuroscience, it has remained unclear whether fundamental empirical properties of neural network activity can be explained solely based on this normative principle. Here, we derive the structural, coding, and biophysical properties of excitatory-inhibitory recurrent networks of spiking neurons that emerge directly from imposing that the network minimizes an instantaneous loss function and a time-averaged performance measure enacting efficient coding. We assumed that the network encodes a number of independent stimulus features varying with a time scale equal to the membrane time constant of excitatory and inhibitory neurons. The optimal network has biologically-plausible biophysical features, including realistic integrate-and-fire spiking dynamics, spike-triggered adaptation, and a non-specific excitatory external input. The excitatory-inhibitory recurrent connectivity between neurons with similar stimulus tuning implements feature-specific competition, similar to that recently found in visual cortex. Networks with unstructured connectivity cannot reach comparable levels of coding efficiency. The optimal ratio of excitatory vs inhibitory neurons and the ratio of mean inhibitory-to-inhibitory vs excitatory-to-inhibitory connectivity are comparable to those of cortical sensory networks. The efficient network solution exhibits an instantaneous balance between excitation and inhibition. The network can perform efficient coding even when external stimuli vary over multiple time scales. Together, these results suggest that key properties of biological neural networks may be accounted for by efficient coding.

Physics-inspired machine learning detects “unknown unknowns” in networks: discovering network boundaries from observable dynamics

Abstract Dynamics on networks is often only partially observable in experiment, with many nodes being inaccessible or indeed the existence and properties of a larger unobserved network being unknown. This limits our ability to reconstruct the topology of the network and the strength of the interactions among even the observed nodes. Here, we show how machine learning inspired by physics can be utilized on noisy time series of such partially observed networks to determine which nodes of the observed part of a network form its boundary, i.e. have significant interactions with the unobserved part. This opens a route to reliable network reconstruction. We develop the method for arbitrary network dynamics and topologies and demonstrate it on a broad range of dynamics including non- linear coupled oscillators and chaotic attractors. Beyond these we focus in particular on biochemical reaction networks, where we apply the approach to the dynamics of the Epidermal Growth Factor Receptor (EGFR) network and show that it works even for substantial noise levels.

Capturing Dynamic Assembly of Nanoscale Proteins During Network Formation.

The structural evolution of hierarchical structures of nanoscale biomolecules is crucial for the construction of functional networks in vivo and in vitro. Despite the ubiquity of these networks, the physical mechanisms behind their formation and self-assembly remains poorly understood. Here, this study uses photochemically cross-linked folded protein hydrogels as a model biopolymer network system, with a combined time-resolved rheology and small-angle x-ray scattering (SAXS) approach to probe both the load-bearing structures and network architectures respectively thereby providing a cross-length scale understanding of the network formation. Combining SAXS, rheology, and kinetic modeling, a dual formation mechanism consisting of a primary formation phase is proposed, where monomeric folded proteins create the preliminary protein network scaffold; and a subsequent secondary formation phase, where both additional intra-network cross-links form and larger oligomers diffuse to join the preliminary network, leading to a denser more mechanically robust structure. Identifying this as the origin of the structural and mechanical properties of protein networks creates future opportunities to understand hierarchical biomechanics in vivo and develop functional, designed-for-purpose, biomaterials.

Structural covariance alterations reveal motor damage in periventricular leukomalacia

Abstract Periventricular leukomalacia is a common neuroimaging finding in patients with spastic cerebral palsy. Myelin damage disrupts neuronal connectivity. However, specific alterations in the gray matter structure and their impact on the whole brain remain unclear, particularly when differentiating between preterm and full-term periventricular leukomalacia. This study investigated the gray matter network alterations following early white matter injury in infants and young children. High-resolution T1-weighted 3T brain magnetic resonance imaging, clinical data, and motor function scores were collected from 42 children with periventricular leukomalacia and 38 age- and sex-matched healthy controls. Based on gestational age, the periventricular leukomalacia group was stratified into preterm (n = 27) and full-term (n = 15) groups. Voxel-based morphometry was used to analyze whole-brain structural metrics, and motor-related regions were selected as nodes for network construction. Structural covariance analysis was used to quantify the strength of the structural connections between gray matter regions, and graph theory metrics were used to assess network properties. Motor assessments included gross and fine motor skills, and their associations with brain regions were analyzed. Both preterm and full-term periventricular leukomalacia groups exhibited abnormal motor networks. Preterm periventricular leukomalacia showed more extensive central gray matter nuclei atrophy, whereas full-term periventricular leukomalacia was predominantly localized to the motor cortex. Children with periventricular leukomalacia displayed decreased connectivity between the central gray matter nuclei and other regions, coupled with increased connectivity between the motor cortex and cerebellar hemispheres. Thalamic volume correlated with gross motor scores in preterm infants. These findings suggest that ischemic-hypoxic injury disrupts motor gray matter networks, with preterm infants being more severely affected. This study highlights the potential of structural covariance patterns for monitoring brain development and advancing our understanding of aberrant brain development in children with periventricular leukomalacia.

NIMG-11. DISCOVERING POSTOPERATIVE APHASIA RECOVERY IN GLIOMA PATIENTS: THE ROLE OF THE RIGHT BROCA’S HOMOLOG AND VERIFICATION

Abstract OBJECTIVE This study aimed to elucidate the neurological mechanisms that support the recovery of postoperative aphasia by examining alterations in language and extended networks in patients with glioma. Additionally, we sought to determine the causal relationship between the right Broca’s homolog and language recovery using neuro-navigated repetitive transcranial magnetic stimulation (nrTMS). METHODS Sixty-two patients with left hemisphere gliomas were retrospectively analyzed and categorized into aphasia and non-aphasia groups based on Aphasia Quotient (AQ) scores. Resting-state functional MRI (rs-fMRI) data were collected and analyzed to investigate differences in functional connectivity (FC) and topological properties of language-related networks. Additionally, eight patients with surgery-induced aphasia were prospectively treated with nrTMS, targeting the node identified from the retrospective analysis. RESULTS Significant increases in FC between the left auditory cortex and the right inferior frontal gyrus in the Executive Control Network were observed in the non-aphasia group. Nodal properties, such as the nodal efficiency of the right Broca’s homolog, were independently associated with postoperative language function. The nrTMS treatment group demonstrated significant improvements in AQ scores, with correlations between increased nodal efficiency and positive language outcomes. CONCLUSION The right hemisphere, specifically the right inferior frontal gyrus, plays a compensatory role in maintaining language function after glioma resection. These findings suggest the therapeutic potential of nrTMS for enhancing postoperative language recovery, offering insights into individualized patient care and rehabilitation strategies for patients with glioma.

INTERCONNECTION LINK ADDITIONS TOWARDS COMMUNICATION NETWORK CONSOLIDATION

The ever-increasing demand for modern communication services and the rising cost of capital and operating expenditures have led to interest in multi-operator network collaborations. These network operators manage different network domains and infrastructures, such that their topologies may be disjointed from each other, and require the addition of intermediate interconnection links for the domains to communicate with each other. The main contribution of this paper is the proposal of an integer linear program for finding the optimum set of interconnection links to be added to the multi-domain networks such that the multi-domain networks become a connected graph, while minimizing either (1) the total length of added interconnection links, (2) the total shortest path length between all the border nodes of the multi-domain networks, or (3) the total number of shortest hop counts between all the border nodes of the multi-domain networks. The proposed integer linear program is shown to find the optimum solution under various properties for the multi-domain networks, albeit with increased computation time as the size of the problem increases. Simulation results using randomly generated multi-domain networks show a trade-off in terms of length of added links, shortest path length and shortest path hop count for each specific objective function.

Digital Government Transformation and Sustainable Development Goals: To What Extent Are They Interconnected? Bibliometric Analysis Results

The increasing prevalence of digital technologies has sparked interest in the relationship between digital transformation and sustainable development, leading to a surge in theoretical and practical research on this topic. This study conducts a bibliometric analysis of publications in the Web of Science database to examine the extent to which sustainable development goals are integrated into digital government transformation discourses. Using statistical properties of the citation network and the main path approach to identify key publication clusters, the analysis reveals limited research explicitly focusing on the link between digital government transformation and sustainable development goals. Furthermore, articles exploring this interconnection are notably scarce in public administration journals, indicating a significant gap in the literature that requires further investigation.

Metrics of physiological network topology are novel biomarkers to capture functional disability and health.

Physiological networks are highly complex, integrating connections among multiple organ systems and their dynamic changes underlying human aging. It is unknown whether individual-level network could serve as robust biomarkers for health and aging. We used personalized network analysis to construct single sample network and examine the associations between network properties and functional disability in the Rugao Longevity and Aging Study (RuLAS), the China Health and Retirement Longitudinal Study (CHARLS), the Chinese Longitudinal Healthy Longevity Survey (CLHLS), and the National Health and Nutrition Examination Survey (NHANES). We observed impairments in interconnected physiological systems among long-lived adults in RuLAS. Single sample network analysis was applied to reflect the co-occurrence of these multi-system impairments at the individual level. The ADL-disabled individuals' networks exhibited notably increased connectivity among various biomarkers. Significant associations were found between network topology and functional disability across RuLAS, CHARLS, CLHLS and NHANES. Additionally, network topology served as novel biomarkers to capture risks of incident ADL disability in CHARLS. Furthermore, these metrics of physiological network topology predicted mortality across four cohorts. Sensitivity analysis demonstrated that prediction performance of network topology remained robust, regardless of the chosen biomarkers and parameters. These findings showed that metrics of network topology were sensitive and robust biomarkers to capture risks of functional disability and mortality, highlighting the role of single sample physiological networks as novel biomarker for health and aging.

Subthalamic nucleus deep brain stimulation modulates speech graphs in patients with Parkinson’s disease

Graph theory enables a direct quantification of topological properties of any arbitrary network. Its application in neuroscience has unveiled topological changes of brain networks associated with various neurodegenerative diseases. This study used the graph theory to understand speech deficits in patients with Parkinson’s disease (PD). In particular, this study investigated the effect of subthalamic nucleus deep brain stimulation (STN-DBS) on the topology of speech graphs. Sixty patients with PD completed a standard semantic fluency test with DBS switched ON and OFF. A control group of sixty matched nonsurgical PD patients completed the test once. All verbal responses were recorded, transcripted, and transformed into directed speech graphs. Volumes of tissue activated (VTA) were estimated for three STN subregions, including sensorimotor, associative, and limbic parts. First, the patients with DBS OFF produced smaller and denser speech graphs than nonsurgical patients, showing fewer nodes, higher density, shorter diameter, and shorter average shortest path. Second, DBS partially reversed the effect of surgery, leading to larger and sparser speech graphs with more nodes, lower density, longer diameter, and longer average shortest path (ON versus OFF). Third, however, the left associative VTA negatively correlated with the DBS-induced diameter and average shortest path changes (ON versus OFF), suggesting that the patients with greater left associative STN stimulation tended to produce smaller and denser speech graphs. This study demonstrates that STN-DBS can partially restore the topological structure of speech graphs in PD patients. However, stimulating the left associative STN appears to disrupt speech graphs.

Flying by night: Comparing nocturnal pollinator networks over time in the Colorado Rocky Mountains

Abstract Where climate is seasonal, such as in high‐altitude ecosystems, the occurrence and activity patterns of insect pollinators may vary with the varying occurrence of floral resources. This high turnover of species occurrence and interactions may lead to changes in the structure and properties of plant–pollinator networks. Here, we systematically compared the properties of moth pollen‐transport networks in Colorado, USA, over the course of a season, and tested for a link between moth species specialisation and morphology. We predicted that network properties would change through the season as the abundance and diversity of available floral resources increased. We also predicted that there would be a link between moth specialisation and morphology, as moth proboscis length can restrict their ability to utilise different floral resources. We sampled moths for pollen over 9 weeks in June through August 2021. We then constructed pollen‐transport networks, and used mixed models to test for differences in network indices between different parts of the 2021 season; we also tested for a relationship between moth proboscis length, the duration of moth species' presence across the season and the number of flower species pollinated. As expected, we found high rates of species turnover and high rates of plant–pollinator interactions over the summer. However, linkage density and robustness were the only overall network metrics that changed. We also found that longer proboscis lengths and more persistent species occurrence over the season were predictive of higher generalisation among moth species. We suggest that even with high rates of species turnover, nocturnal moth pollen‐transport networks may maintain their overall structure over a season, with variations in the availability of floral resources driving limited variation in some network metrics. Our study highlights the necessity of continuing to research moth pollen‐transport networks over different temporal scales, to better understand how ecological factors can lead to changes in network structure over time.

NMDA receptors regulate the firing rate set point of hippocampal circuits without altering single-cell dynamics.

Understanding how neuronal circuits stabilize their activity is a fundamental yet poorly understood aspect of neuroscience. Here, we show that hippocampal network properties, such as firing rate distribution and dimensionality, are actively regulated, despite perturbations and single-cell drift. Continuous inhibition of N-methyl-D-aspartate receptors (NMDARs) exvivo lowers the excitation/inhibition ratio and network firing rates while preserving resilience to perturbations. This establishes a new network firing rate set point via NMDAR-eEF2K signaling pathway. NMDARs' capacity to modulate and stabilize network firing is mediated by excitatory synapses and the intrinsic excitability of parvalbumin-positive neurons, respectively. In behaving mice, continuous NMDAR blockade in CA1 reduces network firing without altering single-neuron drift or triggering a compensatory response. These findings expand NMDAR function beyond their canonical role in synaptic plasticity and raise the possibility that some NMDAR-dependent behavioral effects are mediated by their unique regulation of population activity set points.

Bioprinting of Aptamer-Based Programmable Bioinks to Modulate Multiscale Microvascular Morphogenesis in 4D.

Dynamic growth factor presentation influences how individual endothelial cells assemble into complex vascular networks. Here, programmable bioinks are developed that facilitate dynamic vascular endothelial growth factor (VEGF) presentation to guide vascular morphogenesis within 3D-bioprinted constructs. Aptamer's high affinity is leveraged for rapid VEGF sequestration in spatially confined regions and utilized aptamer-complementary sequence (CS) hybridization to tune VEGF release kinetics temporally, days after bioprinting. It is shown that spatial resolution of programmable bioink, combined with CS-triggered VEGF release, significantly influences the alignment, organization, and morphogenesis of microvascular networks in bioprinted constructs. The presence of aptamer-tethered VEGF and the generation of instantaneous VEGF gradients upon CS-triggering restricted hierarchical network formation to the printed aptamer regions at all spatial resolutions. Network properties improved as the spatial resolution decreased, with low-resolution designs yielding the highest network properties. Specifically, CS-treated low-resolution designs exhibited significant vascular network remodeling, with an increase in vessel density(1.35-fold), branching density(1.54-fold), and average vessel length(2.19-fold) compared to non-treated samples. The results suggest that CS acts as an external trigger capable of inducing time-controlled changes in network organization and alignment on-demand within spatially localized regions of a bioprinted construct. It is envisioned that these programmable bioinks will open new opportunities for bioengineering functional, hierarchically self-organized vascular networks within engineered tissues.

Data-driven fire modeling: Learning first arrival times and model parameters with neural networks

Data-driven techniques are increasingly being applied to complement physics-based models in fire science. However, the lack of sufficiently large datasets continues to hinder the application of certain machine learning techniques. In this paper, we use simulated data to investigate the ability of neural networks to parameterize dynamics in fire science. In particular, we investigate neural networks that map five key parameters in fire spread to the first arrival time, and the corresponding inverse problem. By using simulated data, we are able to characterize the error, the required dataset size, and the convergence properties of these neural networks. For the inverse problem, we quantify the network’s sensitivity in estimating each of the key parameters. The findings demonstrate the potential of machine learning in fire science, highlight the challenges associated with limited dataset sizes, and quantify the sensitivity of neural networks to estimate key parameters governing fire spread dynamics.

Altered structural covariance of the cortex and hippocampal formation in patients with lung cancer after chemotherapy

ObjectiveIn this retrospective study, we aimed to investigate changes in brain morphology and structural topological networks in patients with lung cancer (LC) with or without chemotherapy. MethodsWe retrospectively recruited 191 participants for this cross-sectional study, including 113 patients with LC without chemotherapy (Ch-), 38 patients with LC with chemotherapy (Ch+), and 40 healthy controls (HC) matched for age, sex, and education. The gray matter volume (GMV) and cortical properties were compared among the three groups. We constructed the structural covariant network (SCN) based on cortical thickness, volumes of subcortical structures, and volumes of hippocampal subfields and the amygdala in all participants. The global and nodal topological properties of SCN were compared among groups. In addition, 23 patients with LC (8 Ch+ and 15 Ch-) who received two identical brain magnetic resonance scans were enrolled in the follow-up study. The paired t-test was used to compare group differences in brain morphology and topological properties in the structural network. ResultsThe GMV of the bilateral caudate and thalamus were smaller in the Ch- and Ch+ groups compared to the HC group using threshold-free cluster enhancement and permutation (P < 0.05, 5000 times permutations) for multiple comparison correction. The cortical SCN analysis suggested multiple enhanced nodal properties in several brain areas in Ch+ and Ch- compared to HC, mainly in the temporal gyrus, using permutations test and false discovery rate (FDR) (P < 0.05) corrections. Moreover, an increased sigma was found in the Ch+ compared with HC (P = 0.0238). The reduced nodal degree (P = 0.0002) and betweenness (P = 0.0008) in the right amygdala of Ch+ compared to HC were detected by subcortical SCN analysis. Furthermore, reduced gamma (P = 0.0342) and sigma (P < 0.0001) were found in Ch- compared with HC in the SCN analysis of subfields of the amygdala-hippocampal complex. In the follow-up study, reduced nodal degree (P < 0.0001) was found in the right anterior amygdala, and reduced clustering coefficient and local efficiency were found in patients with LC after the permutation test. ConclusionsOur study showed GMV defects and structural topological property abnormalities related to LC and chemotherapy. Such morphological changes associated with LC and chemotherapy could be used as imaging markers for clinical assessments and pathological indicators.

Transition metal-modified armchair graphene network for high-performance hydrogen evolution reaction catalysts

This study explores the structural, electronic, and catalytic properties of armchair hexagonal graphene networks (AHGN) modified with single transition metal (TM) atoms from Period V. Substitutions of Ag, Mo, Nb, Pd, Rh, Ru, Tc, Y, and Zr were investigated, revealing significant changes in bond lengths, bond angles, and dihedral angles. The binding energies indicate that TM substitutions do not drastically destabilize AHGN, suggesting potential applications requiring modified electronic properties. Electronic investigations showed that TM substitutions influence the partial density of states (PDOS) and energy gaps (ΔE), with Tc significantly reducing ΔE, indicating a shift towards metallic behavior. Mulliken charge analysis highlighted the electron density redistribution around TM atoms and edge carbons, enhancing electronic conductivity and chemical reactivity. Based on the results, AHGN-Rh-H, AHGN-Pd-H, and AHGN-Mo-H demonstrate the most efficient catalytic performance for the hydrogen evolution reaction (HER), with ΔG values comparable to platinum, underscoring their potential for practical hydrogen production applications. Conversely, materials like AHGN-Nb-H and AHGN-Zr-H exhibit higher ΔG values, indicating lower efficiency for HER. Global reactivity parameters (GRPs) analysis revealed AHGN-Tc with the most negative chemical potential (−4.010 eV) and highest electronegativity (4.010 eV), indicating strong electron-accepting and electron-attracting abilities. These findings underscore the potential of TM-substituted AHGN for advanced electronic applications and efficient hydrogen production.

Sequential decline in cyanobacterial, total prokaryotic, and eukaryotic responses to backward flow in a river connected to Lake Taihu

River ecosystems face escalating challenges due to altered flow regimes from human activities, such as urbanization with hydrological modifications. Understanding the role of microbial communities for ecosystems with changing flow regimes is still incomplete and remains at the frontier of aquatic microbial ecology. In particular, influences of riverine backward flow on the aquatic biota remain largely unknown. Therefore, we examined the impact of backward flow on the cyanobacterial, total prokaryotic, and eukaryotic communities in the Changdougang River, which naturally flows into Lake Taihu, through environmental DNA metabarcoding. We analyzed the differences in community diversity, assembly, and ecological network stability among groups under backward, weak, and forward flow direction conditions. Non-metric multidimensional scaling showed higher variations in communities of groups across flow direction conditions than seasonal groups. Variations in alpha and beta diversity showed that cyanobacterial and total prokaryotic communities experienced strong homogenization under backward flow conditions, whereas the ecological uniqueness of the eukaryotic community decreased. Assembly of the three flow-related communities was primarily governed by drift and dispersal limitation in stochastic processes. However, in the cyanobacterial community, homogeneous selection in deterministic processes increased from 22.79% to 42.86% under backward flow, aligning with trends observed in the checkerboard score (C-score). More importantly, the topological properties of ecological networks and the degree of average variation revealed higher stability in the cyanobacterial community compared to total prokaryotic and eukaryotic communities. Considering the variations in cohesion, the network stability in the cyanobacterial community decreased under backward flow. Our findings emphasize the distinct and sequentially diminishing responses of cyanobacterial, total prokaryotic, and eukaryotic communities to backward flowing rivers. This knowledge is crucial for maintaining ecological health of rivers, assessing the complex ecological impacts on hydrological engineering, and formulating sustainable water management strategies.

Miconazole-splitomicin combined β-glucan hydrogel for effective prevention of Candida albicans Periprosthetic Joint Infection

As one of the most common and serious infections caused by Candida albicans (C. albicans), periprosthetic joint infection (PJI) increasingly concerns surgeons and scientists. Generally, biofilms shield C. albicans from antifungal agents and immune clearance and induce drug-resistant strains. Developing novel strategies for PJI to get rid of current drug-resistant problems is highly needed. In our study, splitomicin (SP) can inhibit the mycelium formation of C. albicans and enhance the drug sensitivity of C. albicans to miconazole nitrate (MCZ). The combination of SP and MCZ significantly inhibited the viability, proliferation and adhesion of C. albicans, reduced the yeast to hyphae transition and biofilm formation. When SP and MCZ were coloaded in the β-glucan hydrogel, a viscoelastic solid with porous 3D network, sustained release and erosion properties was obtained. In the in vivo PJI mice model, SP-MCZ-β-glucan hydrogel effectively reduced the colonization and aggregation of C. albicans around the implant, reduced the pathological changes caused by C. albicans in the femur tissue. Therefore, SP-MCZ-β-glucan hydrogel holds a great promise for the management of C. albicans infection around joint prosthesis.