Decision-Making With Speculative Opponent Models.

Opponent modeling has proven effective in enhancing the decision-making of the controlled agent by constructing models of opponent agents. However, existing methods often rely on access to the observations and actions of opponents, a requirement that is infeasible when such information is either unobservable or challenging to obtain. To address this issue, we introduce distributional opponent-aided multiagent actor-critic (DOMAC), the first speculative opponent modeling algorithm that relies solely on local information (i.e., the controlled agent's observations, actions, and rewards). Specifically, the actor maintains a speculated belief about the opponents using the tailored speculative opponent models that predict the opponents' actions using only local information. Moreover, DOMAC features distributional critic models that estimate the return distribution of the actor's policy, yielding a more fine-grained assessment of the actor's quality. This thus more effectively guides the training of the speculative opponent models that the actor depends upon. Furthermore, we formally derive a policy gradient theorem with the proposed opponent models. Extensive experiments under eight different challenging multiagent benchmark tasks within the MPE, Pommerman, and starcraft multiagent challenge (SMAC) demonstrate that our DOMAC successfully models opponents' behaviors and delivers superior performance against state-of-the-art (SOTA) methods with a faster convergence speed.

Suppressing Deep-Level Trap Toward Over 13% Efficient Solution-Processed Kesterite Solar Cell.

Cu2ZnSn (S,Se)4 (CZTSSe), a promising absorption material for thin-film solar cells, still falls short of reaching the balance limit efficiency due to the presence of various defects and high defect concentration in the thin film. During the high-temperature selenization process of CZTSSe, the diffusion of various elements and chemical reactions significantly influence defect formation. In this study, a NaOH-Se intermediate layer introduced at the back interface can optimize Cu2ZnSnS4 (CZTS)precursor films and subsequently adjust the Se and alkali metal content to favor grain growth during selenization. Through this back interface engineering, issues such as non-uniform grain arrangement on the surface, voids in bulk regions, and poor contact at the back interface of absorber layers are effectively addressed. This method not only optimizes morphology but also suppresses deep-level defect formation, thereby promoting carrier transport at both interfaces and bulk regions of the absorber layer. Consequently, CZTSSe devices with a NaOH-Se intermediate layer improved fill factor, open-circuit voltage, and efficiency by 13.3%. This work initiates from precursor thin films via back interface engineering to fabricate high-quality absorber layers while advancing the understanding regarding the role played by intermediate layers at the back interface of kesterite solar cells.

Adhesion Energy-Assisted Low Contact Thermal Resistance Epoxy Resin-Based Composite.

Although intense efforts have been devoted to the development of thermally conductive epoxy resin composites, most previous works ignore the importance of the contact thermal resistance between epoxy resin composites and mating surfaces. Here, we report on epoxy resin/hexagonal boron nitride (h-BN) composites, which show low contact thermal resistance with the contacting surface by tuning adhesion energy. We found that adhesion energy increases with increasing the ratio of soybean-based epoxy resin/amino silicone oil and h-BN contents. The adhesion energy has a negative correlation with the contact thermal resistance; that is, enhancing the adhesion energy will lead to reduced contact thermal resistance. The contact thermal conductance increases with the h-BN contents and is low to 0.025 mm2·K/W for the epoxy resin/60 wt % h-BN composites, which is consistent with the theoretically calculated value. By investigating the wettability and chain dynamics of the epoxy resin/h-BN composites, we confirm that the low contact thermal resistance stems from the increased intermolecular interaction between the epoxy resin chains. The present study provides a practical approach for the development of epoxy resin composites with enhanced thermal conductivity and reduced contact thermal resistance, aiming for effective thermal management of electronics.

Raman spectroscopic analysis for osteoporosis identification in humans with hip fractures.

Osteoporosis is a significant health concern. While multiple techniques have been utilized to diagnose this condition, certain limitations still persist. Raman spectroscopy has shown promise in predicting bone strength in animal models, but its application to humans requires further investigation. In this study, we present an in vitro approach for predicting osteoporosis in 10 patients with hip fractures using Raman spectroscopy. Raman spectra were acquired from exposed femoral heads collected during surgery. Employing a leave-one-out cross-validated linear discriminant analysis (LOOCV-LDA), we achieved accurate classification (90 %) between osteoporotic and osteopenia groups. Additionally, a LOOCV partial least squares regression (PLSR) analysis based on the complete Raman spectra demonstrated a significant prediction (r2 = 0.84, p < 0.05) of bone mineral density as measured by dual X-ray absorptiometry (DXA). To the best of our knowledge, this study represents the first successful demonstration of Raman spectroscopy correlating with osteoporotic status in humans.

Crystal Structure Regulation of CoSe2 Induced by Fe Dopant for Promoted Surface Reconstitution toward Energetic Oxygen Evolution Reaction.

Most nonoxide catalysts based on transition metal elements will inevitably change their primitive phases under anodic oxidation conditions in alkaline media. Establishing a relationship between the bulk phase and surface evolution is imperative to reveal the intrinsic catalytic active sites. In this work, it is demonstrated that the introduction of Fe facilitates the phase transition of orthorhombic CoSe2 into its cubic counterpart and then accelerates the Co-Fe hydroxide layer generation on the surface during electrocatalytic oxygen evolution reaction (OER). As a result, the Fe-doped cubic CoSe2 catalyst exhibits a significantly enhanced activity with a considerable overpotential decrease of 79.9 and 66.9 mV to deliver 10 mA·cm-2 accompanied by a Tafel slope of 48.0 mV·dec-1 toward OER when compared to orthorhombic CoSe2 and Fe-doped orthorhombic CoSe2, respectively. Density functional theory (DFT) calculations reveal that the introduction of Fe on the surface hydroxide layers will tune electron density around Co atoms and raise the d-band center. These findings will provide deep insights into the surface reconstitution of the OER electrocatalysts based on transition metal elements.

Pathological claustrum activity drives aberrant cognitive network processing in human chronic pain.

Aberrant cognitive network activity and cognitive deficits are established features of chronic pain. However, the nature of cognitive network alterations associated with chronic pain and their underlying mechanisms require elucidation. Here, we report that the claustrum, a subcortical nucleus implicated in cognitive network modulation, is activated by acute painful stimulation and pain-predictive cues in healthy participants. Moreover, we discover pathological activity of the claustrum and a region near the posterior inferior frontal sulcus of the right dorsolateral prefrontal cortex (piDLPFC) in migraine patients during acute pain and cognitive task performance. Dynamic causal modeling suggests a directional influence of the claustrum on activity in this piDLPFC region, and diffusion weighted imaging verifies their structural connectivity. These findings advance understanding of claustrum function during acute pain and provide evidence of a possible circuit mechanism driving cognitive impairments in chronic pain.

Effect of GM1 concentration change on plasma membrane: molecular dynamics simulation and analysis.

Ganglioside GM1 is a class of glycolipids predominantly located in the nervous system. Comprising a ceramide anchor and an oligosaccharide chain containing sialic acid, GM1 plays a pivotal role in various cellular processes, including signal transduction, cell adhesion, and membrane organization. Moreover, GM1 has been implicated in the pathogenesis of several neurological disorders, such as Parkinson's disease, Alzheimer's disease, and stroke. In this study, by creating a neural cell model membrane simulation system and employing rigorous molecular models, we utilize a coarse-grained molecular dynamics approach to explore the structural and dynamic characteristics of multi-component neuronal plasma membranes at varying GM1 ganglioside concentrations. The simulation results reveal that as GM1 concentration increases, a greater number of hydrogen bonds form between GM1 molecules, resulting in the formation of larger clusters, which leads to reduced membrane fluidity, increased lipid ordering, decreased membrane thickness and surface area and higher levels of GM1 dissociation. Through a meticulous analysis, while considering GM1's structural attributes, we offer valuable insights into the structural and dynamic traits of the cell membrane. This study provides a robust methodology for exploring membrane characteristics and enhances our comprehension of GM1 molecules, serving as a resource for both experimental and computational researchers in this field.

Promoting CO2 Electroreduction to Ethane by Iodide-Derived Copper with the Hydrophobic Surface.

Electrochemical reduction of CO2 to value-added products provides a feasible pathway for mitigating net carbon emissions and storing renewable energy. However, the low dimerization efficiency of the absorbed CO intermediate (*CO) and the competitive hydrogen evolution reaction hinder the selective electroreduction of CO2 to ethane (C2H6) with a high energy density. Here, we designed hydrophobic iodide-derived copper electrodes (I-Cu/Nafion) for reducing CO2 to C2H6. The Faradaic efficiency of C2H6 reached 23.37% at -0.7 V vs RHE over the I-Cu/Nafion electrode in an H-type cell, which was about 1.7 times higher than that of the I-Cu electrode. The hydrophobic properties of the I-Cu/Nafion electrodes led to an increase in the local CO2 concentration and stabilized the Cu+ species. In situ Raman characterizations and density functional theory calculations indicate that the enhanced performances could be ascribed to the strong *CO adsorption and decreased the formation energy of *COOH and *COCOH intermediates. This study highlights the effect of the hydrophobic surface on Cu-based catalysts in the electroreduction of CO2 and provides a promising way to adjust the selectivity of C2 products.

Surgical outcomes for non-small cell lung cancer in younger adults: A population-based study.

BACKGROUND: The surgical outcomes for younger patients with non-small cell lung cancer (NSCLC) remain uncertain. The aim of this study was to investigate the clinical features long-term survival outcomes in younger individuals with NSCLC following surgery. METHODS: We queried the Surveillance, Epidemiology, and End Results database from 2010 to 2017, selecting all pathologically confirmed NSCLC cases that underwent cancer-directed surgery. Younger patients were defined as those aged 18-50 years, while older patients were 51-80 years. Propensity score matching (PSM) was implemented to mitigate selection bias. Overall survival (OS) and lung cancer-specific survival (LCSS) were compared using the Kaplan-Meier method. RESULTS: Among the 33 586 treated surgically patients, 2223 (6.6%) were young. Compared to the older group, younger patients had a higher frequency of female gender, non-white ethnicity, carcinoid tumors, stage IV disease, pneumonectomy, and postoperative adjuvant therapies. The 5-year OS rates were significantly higher for younger patients (79.3% vs. 62.0%; p < 0.001), as were the 5-year LCSS rates (82.4% vs. 71.8%; p < 0.001). Post-PSM, younger patients consistently demonstrated significantly better OS and LCSS. Further stage-specific analysis revealed significantly improved 5-year OS rates at each stage and superior 5-year LCSS for stages I-II among younger patients. However, there was no statistically significant difference in LCSS for stages III-IV. CONCLUSIONS: Overall, younger patients with NSCLC treated surgically exhibit superior OS and LCSS compared to their older counterparts, although no statistically significant difference in LCSS for stages III-IV was observed between the two age groups.

Proteomics-derived biomarker panel facilitates distinguishing primary lung adenocarcinomas with intestinal or mucinous differentiation (PAIM) from lung metastatic colorectal cancer (lmCRC).

The diagnosis of primary lung adenocarcinomas with intestinal or mucinous differentiation (PAIM) remains challenging due to the overlapping histomorphological, immunohistochemical and genetic characteristics with lung metastatic colorectal cancer (lmCRC). This study aimed to explore the protein biomarkers that could distinguish between PAIM and lmCRC. To uncover differences between the two diseases, we used tandem mass tagging (TMT)-based shotgun proteomics to characterize proteomes of formalin-fixed paraffin-embedded (FFPE) tumor samples of PAIM (n = 22) and lmCRC (n = 17).Then three machine learning algorithms, namely support vector machine (SVM), random forest and the Least Absolute Shrinkage and Selection Operator (LASSO), were utilized to select protein features with diagnostic significance. These candidate proteins were further validated in an independent cohort (PAIM, n = 11; lmCRC, n = 19) by immunochemistry (IHC) to confirm their diagnostic performance. In total, 105 proteins out of 7871 proteins were significantly dysregulated between PAIM and lmCRC samples and well-separated two groups by Uniform Manifold Approximation and Projection (UMAP). The upregulated proteins in PAIM were involved in actin cytoskeleton organization, platelet degranulation, and regulation of leukocyte chemotaxis, while downregulated ones were involved in mitochondrial transmembrane transport, vasculature development, and stem cell proliferation. A set of 10 candidate proteins (high-level expression in lmCRC: CDH17, ATP1B3, GLB1, OXNAD1, LYST, FABP1; high-level expression in PAIM: CK7 (an established marker), NARR, MLPH, S100A14) was ultimately selected to distinguish PAIM from lmCRC by machine learning algorithms. We further confirmed using IHC that the five protein biomarkers including CDH17, CK7, MLPH, FABP1 and NARR were effective biomarkers for distinguishing PAIM from lmCRC. Our study depicts PAIM-specific proteomic characteristics and demonstrates the potential utility of new protein biomarkers for the differential diagnosis of PAIM and lmCRC. These findings may contribute to improving the diagnostic accuracy and guide appropriate treatments for these patients.

ZS-VAT: Learning Unbiased Attribute Knowledge for Zero-Shot Recognition Through Visual Attribute Transformer.

In zero-shot learning (ZSL), attribute knowledge plays a vital role in transferring knowledge from seen classes to unseen classes. However, most existing ZSL methods learn biased attribute knowledge, which usually results in biased attribute prediction and a decline in zero-shot recognition performance. To solve this problem and learn unbiased attribute knowledge, we propose a visual attribute Transformer for zero-shot recognition (ZS-VAT), which is an effective and interpretable Transformer designed specifically for ZSL. In ZS-VAT, we design an attribute-head self-attention (AHSA) that is capable of learning unbiased attribute knowledge. Specifically, each attribute head in AHSA first transforms the local features into attribute-reinforced features and then accumulates the attribute knowledge from all corresponding reinforced features, reducing the mutual influence between attributes and avoiding information loss. AHSA finally preserves unbiased attribute knowledge through attribute embeddings. We also propose an attribute fusion model (AFM) that learns to recover the correct category knowledge from the attribute knowledge. In particular, AFM takes all features from AHSA as input and generates global embeddings. We carried out experiments to demonstrate that the attribute knowledge from AHSA and the category knowledge from AFM are able to assist each other. During the final semantic prediction, we combine the attribute embedding prediction (AEP) and global embedding prediction (GEP). We evaluated the proposed scheme on three benchmark datasets. ZS-VAT outperformed the state-of-the-art generalized ZSL (GZSL) methods on two datasets and achieved competitive results on the other dataset.

Establishment of a novel microfluidic co-culture system for simultaneous analysis of multiple indicators of gefitinib sensitivity in colorectal cancer cells.

The therapeutic effect of gefitinib on colorectal cancer (CRC) is unclear, but it has been reported that stromal cells in the tumor microenvironment may have an impact on drug sensitivity. Herein, we established a microfluidic co-culture system and explored the sensitivity of CRC cells co-cultured with cancer-associated fibroblasts (CAFs) to gefitinib. The system consisted of a multichannel chip and a Petri dish. The chambers in the chip and dish were designed to continuously supply nutrients for long-term cell survival and create chemokine gradients for driving cell invasion without any external equipment. Using this system, the proliferation and invasiveness of cells were simultaneously evaluated by quantifying the area of cells and the migration distance of cells. In addition, the system combined with live cell workstation could evaluate the dynamic drug response of co-cultured cells and track individual cell trajectories in real-time. When CRC cells were co-cultured with CAFs, CAFs promoted CRC cell proliferation and invasion and reduced the sensitivity of cells to gefitinib through the exosomes secreted by CAFs. Furthermore, the cells that migrated out of the chip were collected, and EMT-related markers were determined by immunofluorescent and western blot assays. The results demonstrated that CAFs affected the response of CRC cells to gefitinib by inducing EMT, providing new ideas for further research on the resistance mechanism of gefitinib. This suggests that targeting CAFs or exosomes might be a new approach to enhance CRC sensitivity to gefitinib, and our system could be a novel platform for investigating the crosstalk between tumor cells and CAFs and understanding multiple biological changes of the tumor cells in the tumor microenvironment.

The rat acute oral toxicity of trifluoromethyl compounds (TFMs): a computational toxicology study combining the 2D-QSTR, read-across and consensus modeling methods.

All areas of the modern society are affected by fluorine chemistry. In particular, fluorine plays an important role in medical, pharmaceutical and agrochemical sciences. Amongst various fluoro-organic compounds, trifluoromethyl (CF3) group is valuable in applications such as pharmaceuticals, agrochemicals and industrial chemicals. In the present study, following the strict OECD modelling principles, a quantitative structure-toxicity relationship (QSTR) modelling for the rat acute oral toxicity of trifluoromethyl compounds (TFMs) was established by genetic algorithm-multiple linear regression (GA-MLR) approach. All developed models were evaluated by various state-of-the-art validation metrics and the OECD principles. The best QSTR model included nine easily interpretable 2D molecular descriptors with clear physical and chemical significance. The mechanistic interpretation showed that the atom-type electro-topological state indices, molecular connectivity, ionization potential, lipophilicity and some autocorrelation coefficients are the main factors contributing to the acute oral toxicity of TFMs against rats. To validate that the selected 2D descriptors can effectively characterize the toxicity, we performed the chemical read-across analysis. We also compared the best QSTR model with public OPERA tool to demonstrate the reliability of the predictions. To further improve the prediction range of the QSTR model, we performed the consensus modelling. Finally, the optimum QSTR model was utilized to predict a true external set containing many untested/unknown TFMs for the first time. Overall, the developed model contributes to a more comprehensive safety assessment approach for novel CF3-containing pharmaceuticals or chemicals, reducing unnecessary chemical synthesis whilst saving the development cost of new drugs.

Ultrasound modification on milk fat globule membrane and soy lecithin to improve the physicochemical properties, microstructure and stability of mimicking human milk fat emulsions.

Starting from the consideration of the structure of human milk fat globule (MFG), this study aimed to investigate the effects of ultrasonic treatment on milk fat globule membrane (MFGM) and soy lecithin (SL) complexes and their role in mimicking human MFG emulsions. Ultrasonic power significantly affected the structure of the MFGM-SL complex, further promoting the unfolding of the molecular structure of the protein, and then increased solubility and surface hydrophobicity. Furthermore, the microstructure of mimicking MFG emulsions without sonication was unevenly distributed, and the average droplet diameter was large. After ultrasonic treatment, the droplets of the emulsion were more uniformly dispersed, the particle size was smaller, and the emulsification properties and stability were improved to varying degrees. Especially when the ultrasonic power was 300 W, the mimicking MFG emulsion had the highest encapsulation rate and emulsion activity index and emulsion stability index were increased by 60.88 % and 117.74 %, respectively. From the microstructure, it was observed that the spherical droplets of the mimicking MFG emulsion after appropriate ultrasonic treatment remain well separated without obvious flocculation. This study can provide a reference for the screening of milk fat globules mimicking membrane materials and the further utilization and development of ultrasound in infant formula.

Hydrogen Sulfide Protects against Rat Ischemic Brain Injury by Promoting RhoA Phosphorylation at Serine 188.

The protective role of hydrogen sulfide against cerebral ischemia-reperfusion injury involves the inhibition of the RhoA-/Rho-associated coiled-coil kinase (ROCK) pathway. However, the specific mechanism remains elusive. This study investigates the impact of hydrogen sulfide on RhoA phosphorylation at serine 188 (Ser188) in vivo, aiming to test the hypothesis that hydrogen sulfide exerts neuroprotection by enhancing RhoA phosphorylation at Ser188, subsequently inhibiting the RhoA/ROCK pathway. Recombinant RhoAwild-pEGFP-N1 and RhoAS188A-pEGFP-N1 plasmids were constructed and administered via stereotaxic injection into the rat hippocampus. A rat global cerebral ischemia-reperfusion model was induced by bilateral carotid artery ligation to elucidate the neuroprotective mechanisms of hydrogen sulfide. Both RhoAwild-pEGFP-N1 and RhoAS188A-pEGFP-N1 plasmids expressed RhoAwild and RhoAS188A proteins, respectively, in rat hippocampal tissues, alongside the intrinsic RhoA protein. Systemic administration of the exogenous hydrogen sulfide donor sodium hydrosulfide led to an increase in Ser188 phosphorylation of transfected RhoAwild and intrinsic RhoA protein within the hippocampus. However, this effect was not observed in tissues transfected with RhoAS188A. Sodium hydrosulfide-mediated RhoA phosphorylation correlated with decreased RhoA and ROCK2 activity in rat hippocampal tissues. Furthermore, sodium hydrosulfide administration reduced cerebral ischemia-reperfusion-induced neuronal damage and apoptosis in rat hippocampal tissues transfected with RhoAwild. However, this neuroprotective effect was attenuated in rats transfected with RhoAS188A. These findings suggest that the neuroprotective mechanism of hydrogen sulfide against cerebral ischemia/reperfusion injury involves increased RhoA phosphorylation at Ser188. Promoting this phosphorylation may represent a potential intrinsic therapeutic target for ischemic stroke.

The Role of Leukocytes in Myeloproliferative Neoplasms Thromboinflammation.

Classic myeloproliferative neoplasms lacking the Philadelphia chromosome are stem cell disorders characterized by the proliferation of myeloid cells in the bone marrow and increased counts of peripheral blood cells. The occurrence of thrombotic events is a common complication in myeloproliferative neoplasms. The heightened levels of cytokines play a substantial role in the morbidity and mortality of these patients, establishing a persistent proinflammatory condition that culminates in thrombosis. The etiology of thrombosis remains intricate and multifaceted, involving blood cells and endothelial dysfunction, the inflammatory state, and the coagulation cascade, leading to hypercoagulability. Leukocytes play a pivotal role in the thromboinflammatory process of myeloproliferative neoplasms by releasing various proinflammatory and prothrombotic factors as well as interacting with other cells, which contributes to the amplification of the clotting cascade and subsequent thrombosis. The correlation between increased leukocyte counts and thrombotic risk has been established. However, there is a need for an accurate biomarker to assess leukocyte activation. Lastly, tailored treatments to address the thrombotic risk in myeloproliferative neoplasms are needed. Therefore, this review aims to summarize the potential mechanisms of leukocyte involvement in myeloproliferative neoplasm thromboinflammation, propose potential biomarkers for leukocyte activation, and discuss promising treatment options for controlling myeloproliferative neoplasm thromboinflammation.

The mediating effect of self-directed learning ability between professional identity and burnout among nursing students.

Objectives: Burnout influences students' academic performance and mental health. This study analyzed the relationship between professional identity, self-directed learning ability, and burnout, and examined the mediating effect of self-directed learning ability between professional identity and burnout among nursing students. Methods: 884 nursing students were recruited at two medical universities in China. Demographic information, Academic Burnout Scale, Professional Identity Questionnaire for Nursing Students, and Self-directed Learning Instrument were distributed to collect data. Results: Both professional identity (ß = -0.17) and self-directed learning ability (ß = -0.43) showed negative associations with students' burnout. Meanwhile, there was a partial mediating effect of self-directed learning ability between professional identity and burnout (-0.24, 95% CI = -0.30, -0.20). Conclusions: Nursing educators should emphasize on developing effective strategies to improve nursing students' professional identity and self-directed learning ability to prevent or reduce their burnout.

Back Interface and Absorber Bulk Deep-Level Trap Optimization Enables Highly Efficient Flexible Antimony Triselenide Solar Cell.

The unique 1D crystal structure of Antimony Triselenide (Sb2Se3) offers notable potential for use in flexible, lightweight devices due to its excellent bending characteristics. However, fabricating high-efficiency flexible Sb2Se3 solar cells is challenging, primarily due to the suboptimal contact interface between the embedded Sb2Se3 layer and the molybdenum back-contact, compounded by complex intrinsic defects. This study introduces a novel Molybdenum Trioxide (MoO3) interlayer to address the back contact interface issues in flexible Sb2Se3 devices. Further investigations indicate that incorporating a MoO3 interlayer not only enhances the crystalline quality but also promotes a favorable [hk1] growth orientation in the Sb2Se3 absorber layer. It also reduces the barrier height at the back contact interface and effectively passivates harmful defects. As a result, the flexible Sb2Se3 solar cell, featuring a Mo-foil/Mo/MoO3/Sb2Se3/CdS/ITO/Ag substrate structure, demonstrates exceptional flexibility and durability, enduring large bending radii and multiple bending cycles while achieving an impressive efficiency of 8.23%. This research offers a straightforward approach to enhancing the performance of flexible Sb2Se3 devices, thereby expanding their application scope in the field of photovoltaics.

Plasma metabolites and risk of myocardial infarction: a bidirectional Mendelian randomization study.

BACKGROUND: Myocardial infarction (MI) is a critical cardiovascular event with multifaceted etiology, involving several genetic and environmental factors. It is essential to understand the function of plasma metabolites in the development of MI and unravel its complex pathogenesis. METHODS: This study employed a bidirectional Mendelian randomization (MR) approach to investigate the causal relationships between plasma metabolites and MI risk. We used genetic instruments as proxies for plasma metabolites and MI and conducted MR analyses in both directions to assess the impact of metabolites on MI risk and vice versa. In addition, the large-scale genome-wide association studies datasets was used to identify genetic variants associated with plasma metabolite (1400 metabolites) and MI (20,917 individuals with MI and 440,906 individuals without MI) susceptibility. Inverse variance weighted was the primary method for estimating causal effects. MR estimates are expressed as beta coefficients or odds ratio (OR) with 95% CI. RESULTS: We identified 14 plasma metabolites associated with the occurrence of MI (P < 0.05), among which 8 plasma metabolites [propionylglycine levels (OR = 0.922, 95% CI: 0.881-0.965, P < 0.001), gamma-glutamylglycine levels (OR = 0.903, 95% CI: 0.861-0.948, P < 0.001), hexadecanedioate (C16-DC) levels (OR = 0.941, 95% CI: 0.911-0.973, P < 0.001), pentose acid levels (OR = 0.923, 95% CI: 0.877-0.972, P = 0.002), X-24546 levels (OR = 0.936, 95% CI: 0.902-0.971, P < 0.001), glycine levels (OR = 0.936, 95% CI: 0.909-0.964, P < 0.001), glycine to serine ratio (OR = 0.930, 95% CI: 0.888-0.974, P = 0.002), and mannose to trans-4-hydroxyproline ratio (OR = 0.912, 95% CI: 0.869-0.958, P < 0.001)] were correlated with a decreased risk of MI, whereas the remaining 6 plasma metabolites [1-palmitoyl-2-arachidonoyl-GPE (16:0/20:4) levels (OR = 1.051, 95% CI: 1.018-1.084, P = 0.002), behenoyl dihydrosphingomyelin (d18:0/22:0) levels (OR = 1.076, 95% CI: 1.027-1.128, P = 0.002), 1-stearoyl-2-docosahexaenoyl-GPE (18:0/22:6) levels (OR = 1.067, 95% CI: 1.027-1.109, P = 0.001), alpha-ketobutyrate levels (OR = 1.108, 95% CI: 1.041-1.180, P = 0.001), 5-acetylamino-6-formylamino-3-methyluracil levels (OR = 1.047, 95% CI: 1.019-1.076, P < 0.001), and N-acetylputrescine to (N (1) + N (8))-acetylspermidine ratio (OR = 1.045, 95% CI: 1.018-1.073, P < 0.001)] were associated with an increased risk of MI. Furthermore, we also observed that the mentioned relationships were unaffected by horizontal pleiotropy (P > 0.05). On the contrary, MI did not lead to significant alterations in the levels of the aforementioned 14 plasma metabolites (P > 0.05 for each comparison). CONCLUSIONS: Our bidirectional MR study identified 14 plasma metabolites associated with the occurrence of MI, among which 13 plasma metabolites have not been reported previously. These findings provide valuable insights for the early diagnosis of MI and potential therapeutic targets.

Multi-modal imaging for dynamic visualization of osteogenesis and implant degradation in 3D bioprinted scaffolds.

In situ monitoring of bone regeneration enables timely diagnosis and intervention by acquiring vital biological parameters. However, an existing gap exists in the availability of effective methodologies for continuous and dynamic monitoring of the bone tissue regeneration process, encompassing the concurrent visualization of bone formation and implant degradation. Here, we present an integrated scaffold designed to facilitate real-time monitoring of both bone formation and implant degradation during the repair of bone defects. Laponite (Lap), CyP-loaded mesoporous silica (CyP@MSNs) and ultrasmall superparamagnetic iron oxide nanoparticles (USPIO@SiO2) were incorporated into a bioink containing bone marrow mesenchymal stem cells (BMSCs) to fabricate functional scaffolds denoted as C@M/GLU using 3D bioprinting technology. In both in vivo and in vitro experiments, the composite scaffold has demonstrated a significant enhancement of bone regeneration through the controlled release of silicon (Si) and magnesium (Mg) ions. Employing near-infrared fluorescence (NIR-FL) imaging, the composite scaffold facilitates the monitoring of alkaline phosphate (ALP) expression, providing an accurate reflection of the scaffold's initial osteogenic activity. Meanwhile, the degradation of scaffolds was monitored by tracking the changes in the magnetic resonance (MR) signals at various time points. These findings indicate that the designed scaffold holds potential as an in situ bone implant for combined visualization of osteogenesis and implant degradation throughout the bone repair process.