A nomogram for predicting cerebral white matter lesions in elderly men.

Objective: This study aimed to develop a nomogram tool to predict cerebral white matter lesions (WMLs) in elderly men. Methods: Based on a retrospective cohort from January 2017 to December 2019, a multivariate logistic analysis was performed to construct a nomogram for predicting WMLs. The nomogram was further validated using a follow-up cohort between January 2020 and December 2022. The calibration curve, receiver operating characteristics (ROC) curves, and the decision curves analysis (DCA) were used to evaluate discrimination and calibration of this nomogram. Result: A total of 436 male patients were enrolled in this study, and all 436 patients were used as the training cohort and 163 follow-up patients as the validation cohort. A multivariate logistic analysis showed that age, cystatin C, uric acid, total cholesterol, platelet, and the use of antiplatelet drugs were independently associated with WMLs. Based on these variables, a nomogram was developed. The nomogram displayed excellent predictive power with the area under the ROC curve of 0.951 [95% confidence interval (CI), 0.929-0.972] in the training cohort and 0.915 (95% CI, 0.864-0.966) in the validation cohort. The calibration of the nomogram was also good, as indicated by the Hosmer-Lemeshow test with p-value of 0.594 in the training cohort and 0.178 in the validation cohort. The DCA showed that the nomogram holds good clinical application value. Conclusion: We have developed and validated a novel nomogram tool for identifying elderly men at high risk of WMLs, which exhibits excellent predictive power, discrimination, and calibration.

ChineseEEG: A Chinese Linguistic Corpora EEG Dataset for Semantic Alignment and Neural Decoding.

An Electroencephalography (EEG) dataset utilizing rich text stimuli can advance the understanding of how the brain encodes semantic information and contribute to semantic decoding in brain-computer interface (BCI). Addressing the scarcity of EEG datasets featuring Chinese linguistic stimuli, we present the ChineseEEG dataset, a high-density EEG dataset complemented by simultaneous eye-tracking recordings. This dataset was compiled while 10 participants silently read approximately 13 hours of Chinese text from two well-known novels. This dataset provides long-duration EEG recordings, along with pre-processed EEG sensor-level data and semantic embeddings of reading materials extracted by a pre-trained natural language processing (NLP) model. As a pilot EEG dataset derived from natural Chinese linguistic stimuli, ChineseEEG can significantly support research across neuroscience, NLP, and linguistics. It establishes a benchmark dataset for Chinese semantic decoding, aids in the development of BCIs, and facilitates the exploration of alignment between large language models and human cognitive processes. It can also aid research into the brain's mechanisms of language processing within the context of the Chinese natural language.

Integration of cognitive tasks into artificial general intelligence test for large models.

During the evolution of large models, performance evaluation is necessary for assessing their capabilities. However, current model evaluations mainly rely on specific tasks and datasets, lacking a united framework for assessing the multidimensional intelligence of large models. In this perspective, we advocate for a comprehensive framework of cognitive science-inspired artificial general intelligence (AGI) tests, including crystallized, fluid, social, and embodied intelligence. The AGI tests consist of well-designed cognitive tests adopted from human intelligence tests, and then naturally encapsulates into an immersive virtual community. We propose increasing the complexity of AGI testing tasks commensurate with advancements in large models and emphasizing the necessity for the interpretation of test results to avoid false negatives and false positives. We believe that cognitive science-inspired AGI tests will effectively guide the targeted improvement of large models in specific dimensions of intelligence and accelerate the integration of large models into human society.

Injectable Microgels with Hybrid Exosomes of Chondrocyte-Targeted FGF18 Gene-Editing and Self-Renewable Lubrication for Osteoarthritis Therapy.

Abnormal silencing of fibroblast growth factor (FGF) signaling significantly contributes to joint dysplasia and osteoarthritis (OA); However, the clinical translation of FGF18-based protein drugs is hindered by their short half-life, low delivery efficiency and the need for repeated articular injections. This study proposes a CRISPR/Cas9-based approach to effectively activate the FGF18 gene of OA chondrocytes at the genome level in vivo, using chondrocyte-affinity peptide (CAP) incorporated hybrid exosomes (CAP/FGF18-hyEXO) loaded with an FGF18-targeted gene-editing tool. Furthermore, CAP/FGF18-hyEXO are encapsulated in methacrylic anhydride-modified hyaluronic (HAMA) hydrogel microspheres via microfluidics and photopolymerization to create an injectable microgel system (CAP/FGF18-hyEXO@HMs) with self-renewable hydration layers to provide persistent lubrication in response to frictional wear. Together, the injectable CAP/FGF18-hyEXO@HMs, combined with in vivo FGF18 gene editing and continuous lubrication, have demonstrated their capacity to synergistically promote cartilage regeneration, decrease inflammation, and prevent ECM degradation both in vitro and in vivo, holding great potential for clinical translation.

Remote Ischemic Conditioning and Outcomes in Acute Ischemic Stroke With Versus Without Large Artery Atherosclerosis.

BACKGROUND: RICAMIS trial (The Remote Ischemic Conditioning for Acute Moderate Ischemic Stroke) has demonstrated efficacy of remote ischemic conditioning (RIC) in acute ischemic stroke. We conducted a post hoc analysis of RICAMIS to investigate whether large artery atherosclerosis (LAA) subtype contributed to the outcomes. METHODS: This is a post hoc analysis of the RICAMIS trial. Patients randomized to RIC group and Control group in full analysis set of RICAMIS were classified into LAA and non-LAA subtypes. The primary outcome was excellent functional outcome at 90 days, defined as modified Rankin Scale score of 0 to 1. Compared with patients receiving usual care, we investigated the association of RIC effect with outcomes in stroke subtypes and the interaction between RIC effect and stroke subtypes. The primary analysis was adjusted analysis. RESULTS: Among 1773 patients, 516 were assigned to LAA subtype (229 in the RIC group and 287 in the control group) and 1257 to non-LAA subtype (633 in the RIC group and 624 in the control group). Median age was 65 years, and 34.2% were women. A higher proportion of primary outcome was found to be associated with RIC treatment in LAA subtype (adjusted risk difference, 11.4% [95% CI, 3.6%-19.2%]; P=0.004), but not in non-LAA subtype (adjusted risk difference, 4.1% [95% CI, -1.1% to 9.3%]; P=0.12). There was no significant interaction between RIC effect and stroke subtypes (P=0.12). CONCLUSIONS: Patients with LAA subtype may benefit from RIC after stroke with respect to excellent functional outcome at 90 days. REGISTRATION: URL: https://www.clinicaltrials.gov; Unique identifier: NCT03740971.

Multi-objective optimization via evolutionary algorithm (MOVEA) for high-definition transcranial electrical stimulation of the human brain.

Designing a transcranial electrical stimulation (tES) strategy requires considering multiple objectives, such as intensity in the target area, focality, stimulation depth, and avoidance zone. These objectives are often mutually exclusive. In this paper, we propose a general framework, called multi-objective optimization via evolutionary algorithm (MOVEA), which solves the non-convex optimization problem in designing tES strategies without a predefined direction. MOVEA enables simultaneous optimization of multiple targets through Pareto optimization, generating a Pareto front after a single run without manual weight adjustment and allowing easy expansion to more targets. This Pareto front consists of optimal solutions that meet various requirements while respecting trade-off relationships between conflicting objectives such as intensity and focality. MOVEA is versatile and suitable for both transcranial alternating current stimulation (tACS) and transcranial temporal interference stimulation (tTIS) based on high definition (HD) and two-pair systems. We comprehensively compared tACS and tTIS in terms of intensity, focality, and steerability for targets at different depths. Our findings reveal that tTIS enhances focality by reducing activated volume outside the target by 60%. HD-tTIS and HD-tDCS can achieve equivalent maximum intensities, surpassing those of two-pair tTIS, such as 0.51 V/m under HD-tACS/HD-tTIS and 0.42 V/m under two-pair tTIS for the motor area as a target. Analysis of variance in eight subjects highlights individual differences in both optimal stimulation policies and outcomes for tACS and tTIS, emphasizing the need for personalized stimulation protocols. These findings provide guidance for designing appropriate stimulation strategies for tACS and tTIS. MOVEA facilitates the optimization of tES based on specific objectives and constraints, advancing tTIS and tACS-based neuromodulation in understanding the causal relationship between brain regions and cognitive functions and treating diseases. The code for MOVEA is available at https://github.com/ncclabsustech/MOVEA.

MRI-based risk stratification for recurrent ischemic stroke in embolic stroke of undetermined source.

OBJECTIVE: Leukoaraiosis and other brain MRI-assessed parameters were shown to be associated with recurrent stroke in this population. We aimed to develop an MRI-based predictive tool for risk stratification of ESUS patients. METHODS: We retrospectively assessed consecutive patients who were diagnosed with ESUS and underwent brain MRI and performed a multivariable analysis with the outcome of recurrent stroke/TIA. Based on the coefficient of each covariate, we generated an integer-based point scoring system. The discrimination and calibration of the score were assessed using the area under the receiver operator characteristic curve, net reclassification improvement, integrated discrimination improvement, calibration curve, and decision curve analysis. Also, we compared the new score with a previously published score (ALM score). RESULTS: Among 176 patients followed for an overall period of 902.3 patient-years (median of 74 months), there were 39 recurrent ischemic stroke/TIAs (4.32 per 100 patient-years). Fazekas score (HR: 1.26, 95% CI: 1.03-1.54), enlarged perivascular space (EPVS) (HR: 2.76, 95% CI: 1.12-6.17), NIHSS at admission (HR: 1.11, 95% CI: 1.02-1.18), and infarct subtypes (HR: 2.88, 95% CI: 1.34-6.17) were associated with recurrent stroke/TIA. Accordingly, a score (FENS score) was developed with AUC-ROC values of 0.863, 0.788, and 0.858 for 1, 3, and 5 years, respectively. These were significantly better than the AUC-ROC of ALM score (0.635, 0.695, and 0.705, respectively). The FENS score exhibited better calibration and discrimination ability than the ALM score (Hosmer-Lemeshow test χ2 : 4.402, p = 0.819). CONCLUSION: The MRI-based FENS score can provide excellent predictive performance for recurrent stroke/TIA and may assist in risk stratification of ESUS patients.

Electrospun nanofibrous membranes of recombinant human collagen type III promote cutaneous wound healing.

Cutaneous wound healing is a complex process that strives to re-establish the original structure and functions of the skin. With the development of electrospinning technology, nanofibrous membrane biomaterials have emerged as promising pro-regenerative strategies for recapitulating the structure and composition of the natural extracellular matrix (ECM). Herein, a nanofibrous membrane wound dressing material based on recombinant human collagen type III (rhCol III) crosslinked by EDC/NHS (rhCol III EN NF), which incorporated multiple Gly-Glu-Lys (GEK) and Gly-Leu-Ser-Gly-Glu-Arg (GLSGER) integrin receptors, has been developed utilizing green electrospinning technology. The rhCol III EN NF exhibited excellent flexibility, mechanical properties and water absorption. Amino acid analysis showed that rhCol III EN NF retained integrin receptor-associated amino acids to mediate cell activities and then expedite wound healing. Subsequent in vitro experiments confirmed that the rhCol III EN NF effectively promotes cell adhesion, proliferation and migration. On a mouse full-thickness wound model, rhCol III EN NF dressings expedited wound closure and greatly improved collagen deposition, recovering dermal and epidermal structures as well as skin appendages. Altogether, our research demonstrated that rhCol III EN NF prepared by electrospinning technology could efficiently heal wounds and regenerate skin.

Nanostructured 3D-Printed Hybrid Scaffold Accelerates Bone Regeneration by Photointegrating Nanohydroxyapatite.

Nanostructured biomaterials that replicate natural bone architecture are expected to facilitate bone regeneration. Here, nanohydroxyapatite (nHAp) with vinyl surface modification is acquired by silicon-based coupling agent and photointegrated with methacrylic anhydride-modified gelatin to manufacture a chemically integrated 3D-printed hybrid bone scaffold (75.6 wt% solid content). This nanostructured procedure significantly increases its storage modulus by 19.43-fold (79.2 kPa) to construct a more stable mechanical structure. Furthermore, biofunctional hydrogel with biomimetic extracellular matrix is anchored onto the filament of 3D-printed hybrid scaffold (HGel-g-nHAp) by polyphenol-mediated multiple chemical reactions, which contributes to initiate early osteogenesis and angiogenesis by recruiting endogenous stem cells in situ. Significant ectopic mineral deposition is also observed in subcutaneously implanted nude mice with storage modulus enhancement of 25.3-fold after 30 days. Meanwhile, HGel-g-nHAp realizes substantial bone reconstruction in the rabbit cranial defect model, achieving 61.3% breaking load strength and 73.1% bone volume fractions in comparison to natural cranium 15 weeks after implantation. This optical integration strategy of vinyl modified nHAp provides a prospective structural design for regenerative 3D-printed bone scaffold.

Social conformity is associated with inter-trial electroencephalogram variability.

Human society encompasses diverse social influences, and people experience events differently and may behave differently under such influence, including in forming an impression of others. However, little is known about the underlying neural relevance of individual differences in following others' opinions or social norms. In the present study, we designed a series of tasks centered on social influence to investigate the underlying relevance between an individual's degree of social conformity and their neural variability. We found that individual differences under the social influence are associated with the amount of inter-trial electroencephalogram (EEG) variability over multiple stages in a conformity task (making face judgments and receiving social influence). This association was robust in the alpha band over the frontal and occipital electrodes for negative social influence. We also found that inter-trial EEG variability is a very stable, participant-driven internal state measurement and could be interpreted as mindset instability. Overall, these findings support the hypothesis that higher inter-trial EEG variability may be related to higher mindset instability, which makes participants more vulnerable to exposed external social influence. The present study provides a novel approach that considers the stability of one's endogenous neural signal during tasks and links it to human social behaviors.

Covalently Grafted Biomimetic Matrix Reconstructs the Regenerative Microenvironment of the Porous Gradient Polycaprolactone Scaffold to Accelerate Bone Remodeling.

Integrating a biomimetic extracellular matrix to improve the microenvironment of 3D printing scaffolds is an emerging strategy for bone substitute design. Here, a "soft-hard" bone implant (BM-g-DPCL) consisting of a bioactive matrix chemically integrated on a polydopamine (PDA)-coated porous gradient scaffold by polyphenol groups is constructed. The PDA-coated "hard" scaffolds promoted Ca2+ chelation and mineral deposition; the "soft" bioactive matrix is beneficial to the migration, proliferation, and osteogenic differentiation of stem cells in vitro, accelerated endogenous stem cell recruitment, and initiated rapid angiogenesis in vivo. The results of the rabbit cranial defect model (Φ = 10 mm) confirmed that BM-g-DPCL promoted the integration between bone tissue and implant and induced the deposition of bone matrix. Proteomics confirmed that cytokine adhesion, biomineralization, rapid vascularization, and extracellular matrix formation are major factors that accelerate bone defect healing. This strategy of highly chemically bonded soft-hard components guided the construction of the bioactive regenerative scaffold.

Explainable fMRI-based brain decoding via spatial temporal-pyramid graph convolutional network.

Brain decoding, aiming to identify the brain states using neural activity, is important for cognitive neuroscience and neural engineering. However, existing machine learning methods for fMRI-based brain decoding either suffer from low classification performance or poor explainability. Here, we address this issue by proposing a biologically inspired architecture, Spatial Temporal-pyramid Graph Convolutional Network (STpGCN), to capture the spatial-temporal graph representation of functional brain activities. By designing multi-scale spatial-temporal pathways and bottom-up pathways that mimic the information process and temporal integration in the brain, STpGCN is capable of explicitly utilizing the multi-scale temporal dependency of brain activities via graph, thereby achieving high brain decoding performance. Additionally, we propose a sensitivity analysis method called BrainNetX to better explain the decoding results by automatically annotating task-related brain regions from the brain-network standpoint. We conduct extensive experiments on fMRI data under 23 cognitive tasks from Human Connectome Project (HCP) S1200. The results show that STpGCN significantly improves brain-decoding performance compared to competing baseline models; BrainNetX successfully annotates task-relevant brain regions. Post hoc analysis based on these regions further validates that the hierarchical structure in STpGCN significantly contributes to the explainability, robustness and generalization of the model. Our methods not only provide insights into information representation in the brain under multiple cognitive tasks but also indicate a bright future for fMRI-based brain decoding.

Online Learning Koopman Operator for Closed-Loop Electrical Neurostimulation in Epilepsy.

Electrical neuromodulation as a palliative treatment has been increasingly used in the control of epilepsy. However, current neuromodulations commonly implement predetermined actuation strategies and lack the capability of self-adaptively adjusting stimulation inputs. In this work, rooted in optimal control theory, we propose a Koopman-MPC framework for real-time closed-loop electrical neuromodulation in epilepsy, which integrates i) a deep Koopman operator based dynamical model to predict the temporal evolution of epileptic electroencephalogram (EEG) with an approximate finite-dimensional linear dynamics and ii) a model predictive control (MPC) module to design optimal seizure suppression strategies. The Koopman operator based linear dynamical model is embedded in the latent state space of the autoencoder neural network, in which we can approximate and update the Koopman operator online. The linear dynamical property of the Koopman operator ensures the convexity of the optimization problem for subsequent MPC control. The proposed deep Koopman operator model shows greater predictive capability than the baseline models (e.g., vector autoregressive model, kernel based method and recurrent neural network (RNN)) in both synthetic and real epileptic EEG data. Moreover, compared with the RNN-MPC framework, our Koopman-MPC framework can suppress seizure dynamics with better computational efficiency in both the Jansen-Rit model and the Epileptor model. Koopman-MPC framework opens a new window for model-based closed-loop neuromodulation and sheds light on nonlinear neurodynamics and feedback control policies.

Long-term atorvastatin improves cognitive decline by regulating gut function in naturally ageing rats.

BACKGROUND: Statins have been widely used to prevent cardiovascular disease in middle-aged and elderly populations; however, the effect of long-term treatment on cognitive function is controversial. To simulate clinical conditions, middle-aged rats were given atorvastatin for 9 consecutive months to investigate the effect on natural cognitive decline and the possible mechanisms. RESULTS: The results showed that compared with the control group, long-term atorvastatin treatment naturally improved cognitive decline. Furthermore, long-term treatment regulated intestinal retinoic acid (RA) metabolism and storage by altering retinol dehydrogenase 7 (Rdh7) expression in the intestine, while RA metabolism affected the proliferation of intestinal Treg cells and inhibited IL-17+γδ T-cell function. In addition, long-term atorvastatin increased intestinal flora richness and decreased IL-17 expression in hippocampal tissue. CONCLUSION: Collectively, these findings provide the first evidence that long-term atorvastatin intervention may prevent cognitive decline in naturally ageing rats by inhibiting neuroinflammation via the gut-brain axis.

Single-cell spatiotemporal analysis reveals cell fates and functions of transplanted mesenchymal stromal cells during bone repair.

Mesenchymal stromal cells (MSCs) transplantation could enhance bone repair. However, the cell fate of transplanted MSCs, in terms of their local distribution and spatial associations with other types of cells were poorly understood. Here, we developed a single-cell 3D spatial correlation (sc3DSC) method to track transplanted MSCs based on deep tissue microscopy of fluorescent nanoparticles (fNPs) and immunofluorescence of key proteins. Locally delivered fNP-labeled MSCs enhanced tibial defect repair, increased the number of stem cells and vascular maturity in mice. fNP-MSCs persisted in the defect throughout repair. But only a small portion of transplanted cells underwent osteogenic differentiation (OSX+); a significant portion has maintained their expression of mesenchymal stem cell and skeletal stem cell markers (SCA-1 and PRRX1). Our results contribute to the optimization of MSC-based therapies. The sc3DSC method may be useful in studying cell-based therapies for the regeneration of other tissue types or disease models.

Tailorable 3DP Flexible Scaffolds with Porosification of Filaments Facilitate Cell Ingrowth and Biomineralized Deposition.

Facilitating cell ingrowth and biomineralized deposition inside filaments of 3DP scaffolds are an ideal bone repair strategy. Here, 3D printed PLGA/HA scaffolds with hydroxyapatite content of 50% (P5H5) and 70% (P3H7) were prepared by optimizing 3D printing inks, which exhibited good tailorability and foldability to meet clinical maneuverability. The supercritical CO2 foaming technology further endowed the filaments of P5H5 with a richer interconnected pore structure (P5H5-C). The finite element and computational fluid dynamics simulation analysis indicated that the porosification could effectively reduce the stress concentration at the filament junction and improved the overall permeability of the scaffold. The results of in vitro experiments confirmed that P5H5-C promoted the adsorption of proteins on the surface and inside of filaments, accelerated the release of Ca and P ions, and significantly upregulated osteogenesis (Col I, ALP, and OPN)- and angiogenesis (VEGF)-related gene expression. Subcutaneous ectopic osteogenesis experiments in nude mice further verified that P5H5-C facilitated cell growth inside filaments and biomineralized deposition, as well as significantly upregulated the expression of osteogenesis- and angiogenesis-related genes (Col I, ALP, OCN, and VEGF) and protein secretion (ALP, RUNX2, and VEGF). The porosification of filaments by supercritical CO2 foaming provided a new strategy for accelerating osteogenesis of 3DP implants.

An instantly fixable and self-adaptive scaffold for skull regeneration by autologous stem cell recruitment and angiogenesis.

Limited stem cells, poor stretchability and mismatched interface fusion have plagued the reconstruction of cranial defects by cell-free scaffolds. Here, we designed an instantly fixable and self-adaptive scaffold by dopamine-modified hyaluronic acid chelating Ca2+ of the microhydroxyapatite surface and bonding type I collagen to highly simulate the natural bony matrix. It presents a good mechanical match and interface integration by appropriate calcium chelation, and responds to external stress by flexible deformation. Meanwhile, the appropriate matrix microenvironment regulates macrophage M2 polarization and recruits endogenous stem cells. This scaffold promotes the proliferation and osteogenic differentiation of BMSCs in vitro, as well as significant ectopic mineralization and angiogenesis. Transcriptome analysis confirmed the upregulation of relevant genes and signalling pathways was associated with M2 macrophage activation, endogenous stem cell recruitment, angiogenesis and osteogenesis. Together, the scaffold realized 97 and 72% bone cover areas after 12 weeks in cranial defect models of rabbit (Φ = 9 mm) and beagle dog (Φ = 15 mm), respectively.