Ultrasmall Fe3O4 nanoparticles self-assembly induced dual-mode T1/T2-weighted magnetic resonance imaging and enhanced tumor synergetic theranostics.

Individual theranostic agents with dual-mode MRI responses and therapeutic efficacy have attracted extensive interest due to the real-time monitor and high effective treatment, which endow the providential treatment and avoid the repeated medication with side effects. However, it is difficult to achieve the integrated strategy of MRI and therapeutic drug due to complicated synthesis route, low efficiency and potential biosafety issues. In this study, novel self-assembled ultrasmall Fe3O4 nanoclusters were developed for tumor-targeted dual-mode T1/T2-weighted magnetic resonance imaging (MRI) guided synergetic chemodynamic therapy (CDT) and chemotherapy. The self-assembled ultrasmall Fe3O4 nanoclusters synthesized by facilely modifying ultrasmall Fe3O4 nanoparticles with 2,3-dimercaptosuccinic acid (DMSA) molecule possess long-term stability and mass production ability. The proposed ultrasmall Fe3O4 nanoclusters shows excellent dual-mode T1 and T2 MRI capacities as well as favorable CDT ability due to the appropriate size effect and the abundant Fe ion on the surface of ultrasmall Fe3O4 nanoclusters. After conjugation with the tumor targeting ligand Arg-Gly-Asp (RGD) and chemotherapy drug doxorubicin (Dox), the functionalized Fe3O4 nanoclusters achieve enhanced tumor accumulation and retention effects and synergetic CDT and chemotherapy function, which serve as a powerful integrated theranostic platform for cancer treatment.

Microalgae Film-Derived Water Evaporation-Induced Electricity Generator with Negative Carbon Emission.

Water evaporation-induced electricity generators (WEGs) are regarded as one of the most promising solutions for addressing the increasingly severe environmental pollution and energy crisis. Owing to the potential carbon emission in the preparation process of WEGs, whether WEG represents a clean electricity generation technology is open to question. Here, a brand-new strategy is proposed for manufacturing negative carbon emission WEG (CWEG). In this strategy, the microalgae film is used as the electricity generation interface of WEG, which achieves a stable open-circuit voltage (Voc) of 0.25 V and a short-circuit current (Isc) of 3.3 µA. Since microalgae can capture carbon dioxide during its growing process, CWEG holds great promise to generate electricity without carbon emissions in the full life cycle compared with other WEGs. To the best of the author's knowledge, this is the first work using microalgae films to fabricate WEG. Therefore, it is believed that this work not only provides a new direction for designing high-efficiency and eco-friendly WEG but also offers an innovative approach to the resource utilization of microalgae.

Comparation of the effectiveness of conventional needle irrigation and photon-induced photoacoustic streaming with sodium hypochorite in the treatment of teeth with apical periodontitis: a randomized clinical trial.

Photon-initiated photoacoustic streaming (PIPS) with an Er: YAG laser has been introduced in root canal treatment to improve irrigation and facilitate the removal of bacteria in the root canal system. This study aimed to compare the antibacterial effectiveness of two different root canal irrigation techniques, conventional needle irrigation (CNI) and PIPS, using 1% sodium hypochlorite (NaOCl), in the treatment of teeth with apical periodontitis. Sixty patients with a total of sixty teeth affected by apical periodontitis were included in this study. The teeth underwent root canal therapy, and after mechanical instrumentation, they were randomly assigned to two groups (n = 30) based on the final irrigation protocol: CNI or PIPS with 1% NaOCl. Bacterial suspensions in the root canals were evaluated using Adenosine 5'-triphosphate (ATP) assay kit after mechanical instrumentation and after final irrigation. Then, a follow-up was conducted after 7 days. The results revealed that final irrigation significantly reduced ATP values in both the CNI and PIPS groups (P < 0.001). The ATP values after final irrigation was greater in the CNI group compared to the PIPS group (P < 0.001). After a 7-day follow-up, percussion tenderness and fistula were significantly resolved in both groups (P < 0.05). A multivariate linear regression model was used to identify the factors that influence post irrigation ATP values. The analysis demonstrated that pre-operative percussion tenderness (P = 0.006), the presence of a fistula (P < 0.001) and the method used in the final irrigation (P < 0.001) had a significant impact on the ATP value after final irrigation. These results indicate that employing PIPS with 1% NaOCl as the final irrigation protocol exhibited superior antibacterial effectiveness and has the potential to enhance clinical outcomes in the treatment of teeth afflicted with apical periodontitis.

Modulation of Cerebrospinal Fluid Dysregulation via a SPAK and OSR1 Targeted Framework Nucleic Acid in Hydrocephalus.

Hydrocephalus is one of the most common brain disorders and a life-long incurable condition. An empirical "one-size-fits-all" approach of cerebrospinal fluid (CSF) shunting remains the mainstay of hydrocephalus treatment and effective pharmacotherapy options are currently lacking. Macrophage-mediated ChP inflammation and CSF hypersecretion have recently been identified as a significant discovery in the pathogenesis of hydrocephalus. In this study, a pioneering DNA nano-drug (TSOs) is developed by modifying S2 ssDNA and S4 ssDNA with SPAK ASO and OSR1 ASO in tetrahedral framework nucleic acids (tFNAs) and synthesis via a one-pot annealing procedure. This construct can significantly knockdown the expression of SPAK and OSR1, along with their downstream ion channel proteins in ChP epithelial cells, thereby leading to a decrease in CSF secretion. Moreover, these findings indicate that TSOs effectively inhibit the M0 to M1 phenotypic switch of ChP macrophages via the MAPK pathways, thus mitigating the cytokine storm. In in vivo post-hemorrhagic hydrocephalus (PHH) models, TSOs significantly reduce CSF secretion rates, alleviate ChP inflammation, and prevent the onset of hydrocephalus. These compelling results highlight the potential of TSOs as a promising therapeutic option for managing hydrocephalus, with significant applications in the future.

Association of sex hormones with non-alcoholic fatty liver disease: An observational and Mendelian randomization study.

BACKGROUND AND AIMS: Sex-specific associations of sex hormone-binding globulin (SHBG) and bioavailable testosterone (BAT) with NAFLD remain indeterminate. We aimed to explore observational and genetically determined relationships between each hormone and NAFLD. METHODS: We included 187 395 men and 170 193 women from the UK Biobank. Linear and nonlinear Cox regression models and Mendelian randomization (MR) analysis were used to test the associations. RESULTS: During 12.49 years of follow-up, 2209 male and 1886 female NAFLD cases were documented. Elevated SHBG levels were linearly associated with a lower risk of NAFLD in women (HR (95% CI), .71 (.63, .79)), but not in men (a "U" shape, pnon-linear < .001). Higher BAT levels were associated with a lower NAFLD risk in men (HR (95% CI), .81 (.71, .93)) but a higher risk in women (HR (95% CI): 1.25 (1.15, 1.36)). Genetically determined SHBG and BAT levels were linearly associated with NAFLD risk in women (OR (95% CI): .57 (.38, .87) and 2.21 (1.41, 3.26) respectively); in men, an "L-shaped" MR association between SHBG levels and NAFLD risk was found (pnon-linear = .016). The bidirectional MR analysis further revealed the effect of NAFLD on SHBG and BAT levels in both sexes. CONCLUSIONS: Consistently, linear associations of lower SHBG and higher BAT levels with increased NAFLD risk were both conventionally and genetically found in women, while in men, SHBG acts in a nonlinear manner. In addition, NAFLD may affect SHBG and BAT levels.

DNA framework signal amplification platform-based high-throughput systemic immune monitoring.

Systemic immune monitoring is a crucial clinical tool for disease early diagnosis, prognosis and treatment planning by quantitative analysis of immune cells. However, conventional immune monitoring using flow cytometry faces huge challenges in large-scale sample testing, especially in mass health screenings, because of time-consuming, technical-sensitive and high-cost features. However, the lack of high-performance detection platforms hinders the development of high-throughput immune monitoring technology. To address this bottleneck, we constructed a generally applicable DNA framework signal amplification platform (DSAP) based on post-systematic evolution of ligands by exponential enrichment and DNA tetrahedral framework-structured probe design to achieve high-sensitive detection for diverse immune cells, including CD4+, CD8+ T-lymphocytes, and monocytes (down to 1/100 µl). Based on this advanced detection platform, we present a novel high-throughput immune-cell phenotyping system, DSAP, achieving 30-min one-step immune-cell phenotyping without cell washing and subset analysis and showing comparable accuracy with flow cytometry while significantly reducing detection time and cost. As a proof-of-concept, DSAP demonstrates excellent diagnostic accuracy in immunodeficiency staging for 107 HIV patients (AUC > 0.97) within 30 min, which can be applied in HIV infection monitoring and screening. Therefore, we initially introduced promising DSAP to achieve high-throughput immune monitoring and open robust routes for point-of-care device development.

Identification macrophage signatures in prostate cancer by single-cell sequencing and machine learning.

BACKGROUND: The tumor microenvironment (TME) encompasses a variety of cells that influence immune responses and tumor growth, with tumor-associated macrophages (TAM) being a crucial component of the TME. TAM can guide prostate cancer in different directions in response to various external stimuli. METHODS: First, we downloaded prostate cancer single-cell sequencing data and second-generation sequencing data from multiple public databases. From these data, we identified characteristic genes associated with TAM clusters. We then employed machine learning techniques to select the most accurate TAM gene set and developed a TAM-related risk label for prostate cancer. We analyzed the tumor-relatedness of the TAM-related risk label and different risk groups within the population. Finally, we validated the accuracy of the prognostic label using single-cell sequencing data, qPCR, and WB assays, among other methods. RESULTS: In this study, the TAM_2 cell cluster has been identified as promoting the progression of prostate cancer, possibly representing M2 macrophages. The 9 TAM feature genes selected through ten machine learning methods and demonstrated their effectiveness in predicting the progression of prostate cancer patients. Additionally, we have linked these TAM feature genes to clinical pathological characteristics, allowing us to construct a nomogram. This nomogram provides clinical practitioners with a quantitative tool for assessing the prognosis of prostate cancer patients. CONCLUSION: This study has analyzed the potential relationship between TAM and PCa and established a TAM-related prognostic model. It holds promise as a valuable tool for the management and treatment of PCa patients.

Transfer of inflammatory mitochondria via extracellular vesicles from M1 macrophages induces ferroptosis of pancreatic beta cells in acute pancreatitis.

Extracellular vesicles (EVs) exert a significant influence not only on the pathogenesis of diseases but also on their therapeutic interventions, contingent upon the variances observed in their originating cells. Mitochondria can be transported between cells via EVs to promote pathological changes. In this study, we found that EVs derived from M1 macrophages (M1-EVs), which encapsulate inflammatory mitochondria, can penetrate pancreatic beta cells. Inflammatory mitochondria fuse with the mitochondria of pancreatic beta cells, resulting in lipid peroxidation and mitochondrial disruption. Furthermore, fragments of mitochondrial DNA (mtDNA) are released into the cytosol, activating the STING pathway and ultimately inducing apoptosis. The potential of adipose-derived stem cell (ADSC)-released EVs in suppressing M1 macrophage reactions shows promise. Subsequently, ADSC-EVs were utilized and modified with an F4/80 antibody to specifically target macrophages, aiming to treat ferroptosis of pancreatic beta cells in vivo. In summary, our data further demonstrate that EVs secreted from M1 phenotype macrophages play major roles in beta cell ferroptosis, and the modified ADSC-EVs exhibit considerable potential for development as a vehicle for targeted delivery to macrophages.

Can performance status mediate the relationship between therapy-related symptoms and health-related quality of life in children with cancer? A mediation model.

PURPOSE: Understanding the interplay among health-related quality of life (HRQoL), therapy-related symptoms, and performance status can offer insights into potential strategies to enhance HRQoL for pediatric cancer patients. This study aimed to examine the mediating effect of performance status on the relationship between symptom burden and HRQoL in children and adolescents with cancer. METHOD: A cross-sectional study was conducted. Participants were recruited from two tertiary hospitals located in Guangzhou, China. HRQoL, therapy-related symptoms, and performance status were assessed using the DISAKIDS Chronic Generic Measure (DCGM-37), Therapy-Related Symptom Checklist for Children (TRSC-C), and Lansky Play Performance Scale (LPPS), respectively. RESULTS: A total of 287 children with cancer (aged 11.08 ± 2.34 years) were included. The DCGM-37 scores were 59.70 ± 9.64. Emotion (56.45 ± 14.56) and physical limitations (58.59 ± 15.38) were the most affected domains. The number of symptoms experienced was 12.49 ± 5.95. The DCGM-37 demonstrated strong negative correlations with the TRSC-C (r = -0.60, P < 0.001) and number of symptoms (r = -0.62, P < 0.001), but mild-to-moderate negative correlations (r -0.16∼ -0.42, P < 0.05) with individual symptoms. The TRSC-C demonstrated an indirect effect on the DCGM-37 via the LPPS (Bootstrap-corrected standardized ß = -0.05, 95 % CI -0.10∼ -0.01; SE = 0.02). Additional analysis showed that tripping/falling (OR = 4.02, 95 % CI 2.02-7.98; P < 0.001) and sore mouth (OR = 2.38, 95 % CI 1.56-3.64; P < 0.001) were associated factors for presenting poor performance status in children undergoing acute chemotherapy. CONCLUSIONS: The accumulated symptom burden, rather than individual symptoms, weighs heavily on the HRQoL. Performance status partially mediated the relationship between symptom burdens and HRQoL among these patients.

Development and validation of a sensitive liquid chromatography-tandem mass spectrometry method for the assay of 12 substances in rat plasma and its application to rat pharmacokinetics of Epimedium and Psoraleae Fructus herb pair after oral administration.

Epimedium (EM) and Psoraleae Fructus (PF) are a traditional herb combination often used as a fixed form to treat osteoporosis disease in the clinic. However, the intricate interactions of this pair remain unknown. In our study, we undertook a comprehensive examination of their compatibility behaviors. Concurrently, a precise and sensitive quantitation method was successfully developed and validated using liquid chromatography-tandem mass spectrometry for the determination of 12 components. This method was applied in analyzing herbal extracts and biological samples (both in the portal vein and systemic plasma), which was also used to study the pharmacokinetics of the herb pair. The results indicated that the combination of EM and PF enhanced the dissolution of chemical components from PF in extracts, but it had a negligible influence on the contents of the components from EM. On the contrary, the in vivo exposure of the lowly exposed EM flavonoids significantly increased following the combination of EM and PF, whereas the highly exposed psoralen and isopsoralen were greatly reduced. These interactions might be crucial for the synergy and toxicity reduction of the herbal pair in disease treatment, which pave the way for further exploration into the clinical application and pharmacological mechanisms of EM and PF.

Brain-inspired bodily self-perception model for robot rubber hand illusion.

The core of bodily self-consciousness involves perceiving ownership of one's body. A central question is how body illusions like the rubber hand illusion (RHI) occur. Existing theoretical models still lack satisfying computational explanations from connectionist perspectives, especially for how the brain encodes body perception and generates illusions from neuronal interactions. Moreover, the integration of disability experiments is also neglected. Here, we integrate biological findings of bodily self-consciousness to propose a brain-inspired bodily self-perception model by which perceptions of bodily self are autonomously constructed without any supervision signals. We successfully validated the model with six RHI experiments and a disability experiment on an iCub humanoid robot and simulated environments. The results show that our model can not only well-replicate the behavioral and neural data of monkeys in biological experiments but also reasonably explain the causes and results of RHI at the neuronal level, thus contributing to the revelation of mechanisms underlying RHI.

Meet the authors: Yuxuan Zhao, Enmeng Lu, and Yi Zeng.

Yuxuan Zhao, associate professor, Enmeng Lu, research engineer, and Yi Zeng, professor and lab director, have proposed a brain-inspired bodily self-perception model based on biological findings on monkeys and humans. This model can reproduce various rubber hand illusion (RHI) experiments, which helps reveal the RHI's computational and biological mechanisms. They talk about their view of data science and research plans for brain-inspired robot self-modeling and ethical robots.

Personalized radiomics signature to screen for KIT-11 mutation genotypes among patients with gastrointestinal stromal tumors: a retrospective multicenter study.

OBJECTIVES: Gastrointestinal stromal tumors (GISTs) carrying different KIT exon 11 (KIT-11) mutations exhibit varying prognoses and responses to Imatinib. Herein, we aimed to determine whether computed tomography (CT) radiomics can accurately stratify KIT-11 mutation genotypes to benefit Imatinib therapy and GISTs monitoring. METHODS: Overall, 1143 GISTs from 3 independent centers were separated into a training cohort (TC) or validation cohort (VC). In addition, the KIT-11 mutation genotype was classified into 4 categories: no KIT-11 mutation (K11-NM), point mutations or duplications (K11-PM/D), KIT-11 557/558 deletions (K11-557/558D), and KIT-11 deletion without codons 557/558 involvement (K11-D). Subsequently, radiomic signatures (RS) were generated based on the arterial phase of contrast CT, which were then developed as KIT-11 mutation predictors using 1408 quantitative image features and LASSO regression analysis, with further evaluation of its predictive capability. RESULTS: The TC AUCs for K11-NM, K11-PM/D, K11-557/558D, and K11-D ranged from 0.848 (95% CI 0.812-0.884), 0.759 (95% CI 0.722-0.797), 0.956 (95% CI 0.938-0.974), and 0.876 (95% CI 0.844-0.908), whereas the VC AUCs ranged from 0.723 (95% CI 0.660-0.786), 0.688 (95% CI 0.643-0.732), 0.870 (95% CI 0.824-0.918), and 0.830 (95% CI 0.780-0.878). Macro-weighted AUCs for the KIT-11 mutant genotype ranged from 0.838 (95% CI 0.820-0.855) in the TC to 0.758 (95% CI 0.758-0.784) in VC. TC had an overall accuracy of 0.694 (95%CI 0.660-0.729) for RS-based predictions of the KIT-11 mutant genotype, whereas VC had an accuracy of 0.637 (95%CI 0.595-0.679). CONCLUSIONS: CT radiomics signature exhibited good predictive performance in estimating the KIT-11 mutation genotype, especially in prediction of K11-557/558D genotype. RS-based classification of K11-NM, K11-557/558D, and K11-D patients may be an indication for choice of Imatinib therapy.

A Mitochondrial Nanoguard Modulates Redox Homeostasis and Bioenergy Metabolism in Diabetic Peripheral Neuropathy.

As a major late complication of diabetes, diabetic peripheral neuropathy (DPN) is the primary reason for amputation. Nevertheless, there are no wonder drugs available. Regulating dysfunctional mitochondria is a key therapeutic target for DPN. Resveratrol (RSV) is widely proven to guard mitochondria, yet the unsatisfactory bioavailability restricts its clinical application. Tetrahedral framework nucleic acids (tFNAs) are promising carriers due to their excellent cell entrance efficiency, biological safety, and structure editability. Here, RSV was intercalated into tFNAs to form the tFNAs-RSV complexes. tFNAs-RSV achieved enhanced stability, bioavailability, and biocompatibility compared with tFNAs and RSV alone. With its treatment, reactive oxygen species (ROS) production was minimized and reductases were activated in an in vitro model of DPN. Besides, respiratory function and adenosine triphosphate (ATP) production were enhanced. tFNAs-RSV also exhibited favorable therapeutic effects on sensory dysfunction, neurovascular deterioration, demyelination, and neuroapoptosis in DPN mice. Metabolomics analysis revealed that redox regulation and energy metabolism were two principal mechanisms that were impacted during the process. Comprehensive inspections indicated that tFNAs-RSV inhibited nitrosation and oxidation and activated reductase and respiratory chain. In sum, tFNAs-RSV served as a mitochondrial nanoguard (mito-guard), representing a viable drilling target for clinical drug development of DPN.

Advanced Insights into Walnut Protein: Structure, Physiochemical Properties and Applications.

Facing extreme pressure from an increasing population and climate degeneration, it is important to explore a green, safe and environmentally sustainable food source, especially for protein-enriched diets. Plant proteins have gained much attention in recent years, ascribing to their high nutritional value and environmental friendliness. In this review, we summarized recent advances in walnut protein with respect to its geographical distribution, structural and physiochemical properties and functional attributes. As a worldwide cultivated and largely consumptive crop, allergies and some physicochemical limitations have also led to a few concerns about walnut protein. Through comprehensive analysis and discussion, some strategies may be useful for future research, extraction and processing of walnut protein.

[Application of Deep Learning Algorithm in the Grading Assessment of Corneal Fluorescein Staining].

Objective: To explore the application value of applying deep learning (DL) algorithm in the grading assessment of corneal fluorescein staining. Methods: A cross-sectional study was carried out, covering 600 corneal fluorescein staining photos acquired in the Contact Lens Clinic, West China Hospital, Sichuan University between 2020 and 2022. Out of the 600 photos, 500 were used to construct the algorithm and the remaining 100 were used for the validation of the algorithm and a comparative analysis of the difference in grading accuracy (ACC) and the length of diagnostic time between artificial intelligence (AI) and optometry students. One month after finishing the first grading analysis, assessment by AI and optometry students was conducted for a second time and results from the two rounds of assessment were compared to examine the intrarater agreement ( kappa value) of the two analyses. The grading analysis results of 3 experienced optometrists were used as the gold standard in the study. Results: Findings of the cross validation with the complete dataset, the training dataset, and the test dataset showed that ResNet34 had the highest predictive accuracy among four DL models. ResNet34 DL model achieved an accuracy of 93.0%, sensitivity of 89.5%, and specificity of 89.6% in the grading of corneal staining. In the comparison of the grading accuracy of AI and two optometry students, AI showed better accuracy, with the respective grading accuracy being 87.0%, 78.0%, and 52.0% for AI, student 1, and student 2 ( P ACC=0.001). In addition, the average diagnostic time of AI was shorter than that of optometry students ( t AI=1.00 s, t S1=11.86 s, t S2=13.25 s, P t =0.001). In the comparative analysis of the intrarater agreement between the two assessments, AI ( kappa AI=0.658, P AI=0.001) achieved better consistency than the two optometry students did （ kappa S1=0.575, P S1=0.001; kappa S2=0.609, P S2=0.001). Conclusion: Applying deep learning algorithms in the grading assessment of corneal fluorescein staining has considerable feasibility and clinical value. In the performance comparison between AI and optometry students, AI achieved higher accuracy and better consistency, which indicates that AI has potential application value for assisting optometrists to make clinical decisions with speed and accuracy.

Development and External Validation of a Nomogram Including Body Composition Parameters for Predicting Early Recurrence of Hepatocellular Carcinoma After Hepatectomy.

RATIONALE AND OBJECTIVES: Body composition, including adipose and muscle tissues, evaluated by computer tomography is correlated with the prognosis of hepatocellular carcinoma (HCC). However, its relationship with early recurrence (ER) remains unclear. This study aimed at establishing and validating a nomogram based on body composition and clinicopathological indices to predict ER of HCC. MATERIALS AND METHODS: One hundred ninety-five patients from institution A formed the training cohort and internal validation cohort, and 50 patients from institution B formed the external validation cohort. Independent predictors of ER were identified using LASSO and Cox regression analyses. The performance of nomogram was evaluated using the calibration curve, concordance index (C-index), area under the curve (AUC), and decision curve analysis (DCA). RESULTS: After data screening, the nomogram was constructed using eight independent predictors of ER, including the tumor size, alpha fetoprotein, body mass index, Edmondson Steiner grade, visceral adipose tissue radiodensity, intermuscular adipose tissue index, intramuscular adipose tissue content, and skeletal muscle area. The calibration curve exhibited excellent concordances, with C-indices of 0.808 (95%CI: 0.771-0.860), 0.802 (95%CI: 0.747-0.942), and 0.804 (95%CI: 0.701-0.861) in training, internal validation, and external validation cohorts, respectively. In addition, compared to conventional staging systems and pure clinical model, the nomogram exhibited a higher AUC and wider range of threshold probabilities in DCA, which indicated better discriminative ability and greater clinical benefit. Finally, patients with nomogram scores of <183.07, 183.07-243.09, and >243.09 were considered to have low, moderate, and high risks of ER, respectively. CONCLUSION: The nomogram exhibits excellent ER predictive ability for patients with HCC who underwent hepatectomy.

A comparative study of compartmental models for COVID-19 transmission in Ontario, Canada.

The number of confirmed COVID-19 cases reached over 1.3 million in Ontario, Canada by June 4, 2022. The continued spread of the virus underlying COVID-19 has been spurred by the emergence of variants since the initial outbreak in December, 2019. Much attention has thus been devoted to tracking and modelling the transmission of COVID-19. Compartmental models are commonly used to mimic epidemic transmission mechanisms and are easy to understand. Their performance in real-world settings, however, needs to be more thoroughly assessed. In this comparative study, we examine five compartmental models-four existing ones and an extended model that we propose-and analyze their ability to describe COVID-19 transmission in Ontario from January 2022 to June 2022.

A brain-inspired theory of mind spiking neural network improves multi-agent cooperation and competition.

During dynamic social interaction, inferring and predicting others' behaviors through theory of mind (ToM) is crucial for obtaining benefits in cooperative and competitive tasks. Current multi-agent reinforcement learning (MARL) methods primarily rely on agent observations to select behaviors, but they lack inspiration from ToM, which limits performance. In this article, we propose a multi-agent ToM decision-making (MAToM-DM) model, which consists of a MAToM spiking neural network (MAToM-SNN) module and a decision-making module. We design two brain-inspired ToM modules (Self-MAToM and Other-MAToM) to predict others' behaviors based on self-experience and observations of others, respectively. Each agent can adjust its behavior according to the predicted actions of others. The effectiveness of the proposed model has been demonstrated through experiments conducted in cooperative and competitive tasks. The results indicate that integrating the ToM mechanism can enhance cooperation and competition efficiency and lead to higher rewards compared with traditional MARL models.

BrainCog: A spiking neural network based, brain-inspired cognitive intelligence engine for brain-inspired AI and brain simulation.

Spiking neural networks (SNNs) serve as a promising computational framework for integrating insights from the brain into artificial intelligence (AI). Existing software infrastructures based on SNNs exclusively support brain simulation or brain-inspired AI, but not both simultaneously. To decode the nature of biological intelligence and create AI, we present the brain-inspired cognitive intelligence engine (BrainCog). This SNN-based platform provides essential infrastructure support for developing brain-inspired AI and brain simulation. BrainCog integrates different biological neurons, encoding strategies, learning rules, brain areas, and hardware-software co-design as essential components. Leveraging these user-friendly components, BrainCog incorporates various cognitive functions, including perception and learning, decision-making, knowledge representation and reasoning, motor control, social cognition, and brain structure and function simulations across multiple scales. BORN is an AI engine developed by BrainCog, showcasing seamless integration of BrainCog's components and cognitive functions to build advanced AI models and applications.