Transcriptomics Analysis Identifies the Decline in the Alveolar Type II Stem Cell Niche in Aged Human Lungs.

Aging poses a global public health challenge, which is linked to the rise of age-related lung diseases. The precise understanding of the molecular and genetic changes in the aging lung that elevate the risk of acute and chronic lung diseases remains incomplete. Alveolar type II (AT2) cells are stem cells that maintain epithelial homeostasis and repair the lung after injury. AT2 progenitor function decreases with aging. The maintenance of AT2 function requires niche support from other cell types, but little has been done to characterize alveolar alterations with aging in the AT2 niche. To systematically profile the genetic changes associated with age, we present a single-cell transcriptional atlas comprising nearly half a million cells from the healthy lungs of human subjects spanning various ages, sexes, and smoking statuses. Most annotated cell lineages in aged lungs exhibit dysregulated genetic programs. Specifically, the aged AT2 cells demonstrate loss of epithelial identities, heightened inflammaging characterized by increased expression of AP-1 (Activator Protein-1) transcription factor and chemokine genes, and significantly increased cellular senescence. Furthermore, the aged mesenchymal cells display a remarkable decrease in collagen and elastin transcription and a loss of support to epithelial cell stemness. The decline of the AT2 niche is further exacerbated by a dysregulated genetic program in macrophages and dysregulated communications between AT2 and macrophages in aged human lungs. These findings highlight the dysregulations observed in both AT2 stem cells and their supportive niche cells, potentially contributing to the increased susceptibility of aged populations to lung diseases.

Lipid Deficiency Contributes to Impaired Alveolar Progenitor Cell Function in Aging and Idiopathic Pulmonary Fibrosis.

Idiopathic pulmonary fibrosis (IPF) is an aging-associated interstitial lung disease resulting from repeated epithelial injury and inadequate epithelial repair. Alveolar type II cells (AEC2s) are progenitor cells that maintain epithelial homeostasis and repair the lung after injury. In the current study, we assessed lipid metabolism in AEC2s from human lungs of patients with IPF and healthy donors, as well as AEC2s from bleomycin-injured young and old mice. Through single-cell RNA sequencing, we observed that lipid metabolism-related genes were downregulated in IPF AEC2s and bleomycin-injured mouse AEC2s. Aging aggravated this decrease and hindered recovery of lipid metabolism gene expression in AEC2s after bleomycin injury. Pathway analyses revealed downregulation of genes related to lipid biosynthesis and fatty acid ß-oxidation in AEC2s from IPF lungs and bleomycin-injured, old mouse lungs compared with the respective controls. We confirmed decreased cellular lipid content in AEC2s from IPF lungs and bleomycin-injured, old mouse lungs using immunofluorescence staining and flow cytometry. Futhermore, we show that lipid metabolism was associated with AEC2 progenitor function. Lipid supplementation and PPARγ (peroxisome proliferator activated receptor γ) activation promoted progenitor renewal capacity of both human and mouse AEC2s in three-dimensional organoid cultures. Lipid supplementation also increased AEC2 proliferation and expression of SFTPC in AEC2s. In summary, we identified a lipid metabolism deficiency in AEC2s from lungs of patients with IPF and bleomycin-injured old mice. Restoration of lipid metabolism homeostasis in AEC2s might promote AEC2 progenitor function and offer new opportunities for therapeutic approaches to IPF.

Multiple Fibroblast Subtypes Contribute to Matrix Deposition in Pulmonary Fibrosis.

Progressive pulmonary fibrosis results from a dysfunctional tissue repair response and is characterized by fibroblast proliferation, activation, and invasion and extracellular matrix accumulation. Lung fibroblast heterogeneity is well recognized. With single-cell RNA sequencing, fibroblast subtypes have been reported by recent studies. However, the roles of fibroblast subtypes in effector functions in lung fibrosis are not well understood. In this study, we incorporated the recently published single-cell RNA-sequencing datasets on murine lung samples of fibrosis models and human lung samples of fibrotic diseases and analyzed fibroblast gene signatures. We identified and confirmed the novel fibroblast subtypes we reported recently across all samples of both mouse models and human lung fibrotic diseases, including idiopathic pulmonary fibrosis, systemic sclerosis-associated interstitial lung disease, and coronavirus disease (COVID-19). Furthermore, we identified specific cell surface proteins for each fibroblast subtype through differential gene expression analysis, which enabled us to isolate primary cells representing distinct fibroblast subtypes by flow cytometry sorting. We compared matrix production, including fibronectin, collagen, and hyaluronan, after profibrotic factor stimulation and assessed the invasive capacity of each fibroblast subtype. Our results suggest that in addition to myofibroblasts, lipofibroblasts and Ebf1+ (Ebf transcription factor 1+) fibroblasts are two important fibroblast subtypes that contribute to matrix deposition and also have enhanced invasive, proliferative, and contraction phenotypes. The histological locations of fibroblast subtypes are identified in healthy and fibrotic lungs by these cell surface proteins. This study provides new insights to inform approaches to targeting lung fibroblast subtypes to promote the development of therapeutics for lung fibrosis.

Molecular features and evolutionary trajectory of ASCL1+ and NEUROD1+ SCLC cells.

BACKGROUND: Small cell lung cancer (SCLC) is the most aggressive subtype of lung cancer without recognised morphologic or genetic heterogeneity. Based on the expression of four transcription factors, ASCL1, NEUROD1, POU2F3, and YAP1, SCLCs are classified into four subtypes. However, biological functions of these different subtypes are largely uncharacterised. METHODS: We studied intratumoural heterogeneity of resected human primary SCLC tissues using single-cell RNA-Seq. In addition, we undertook a series of in vitro and in vivo functional studies to reveal the distinct features of SCLC subtypes. RESULTS: We identify the coexistence of ASCL1+ and NEUROD1+ SCLC cells within the same human primary SCLC tissue. Compared with ASCL1+ SCLC cells, NEUROD1+ SCLC cells show reduced epithelial features and lack EPCAM expression. Thus, EPCAM can be considered as a cell surface marker to distinguish ASCL1+ SCLC cells from NEUROD1+ SCLC cells. We further demonstrate that NEUROD1+ SCLC cells exhibit higher metastatic capability than ASCL1+ SCLC cells and can be derived from ASCL1+ SCLC cells. CONCLUSIONS: Our studies unveil the biology and evolutionary trajectory of ASCL1+ and NEUROD1+ SCLC cells, shedding light on SCLC tumourigenesis and progression.

Effect of intercalated molybdenum atoms on structure and electrochemical properties of Mo1+xS2synthesized by hydrothermal method.

As an alternative anode to graphene, molybdenum disulfide (MoS2) has attracted much attention due to its layered structure and high specific capacity. Moreover, MoS2can be synthesized by hydrothermal method with low cost and the size of its layer spacing can be controlled. In this work, the results of experiment and calculation proved that the presence of intercalated Mo atoms, leading to the expansion of MoS2layer spacing and weakening of Mo-S bonding. For the electrochemical properties, the presence of intercalated Mo atoms causes the lower reduction potentials for the Li+intercalation and Li2S formation. In addition, the effective reduction of diffusion resistance and charge transfer resistance in Mo1+xS2leads to the acquisition of high specific capacity for battery applications.

Cluster-Delay Mean Square Consensus of Stochastic Multi-Agent Systems with Impulse Time Windows.

This paper investigates the cluster-delay mean square consensus problem of a class of first-order nonlinear stochastic multi-agent systems with impulse time windows. Specifically, on the one hand, we have applied a discrete control mechanism (i.e., impulsive control) into the system instead of a continuous one, which has the advantages of low control cost, high convergence speed; on the other hand, we considered the existence of impulse time windows when modeling the system, that is, a single impulse appears randomly within a time window rather than an ideal fixed position. In addition, this paper also considers the influence of stochastic disturbances caused by fluctuations in the external environment. Then, based on algebraic graph theory and Lyapunov stability theory, some sufficiency conditions that the system must meet to reach the consensus state are given. Finally, we designed a simulation example to verify the feasibility of the obtained results.

Prediction of the clinicopathological subtypes of breast cancer using a fisher discriminant analysis model based on radiomic features of diffusion-weighted MRI.

BACKGROUND: The clinicopathological classification of breast cancer is proposed according to therapeutic purposes. It is simplified and can be conducted easily in clinical practice, and this subtyping undoubtedly contributes to the treatment selection of breast cancer. This study aims to investigate the feasibility of using a Fisher discriminant analysis model based on radiomic features of diffusion-weighted MRI for predicting the clinicopathological subtypes of breast cancer. METHODS: Patients who underwent breast magnetic resonance imaging were confirmed by retrieving data from our institutional picture archiving and communication system (PACS) between March 2013 and September 2017. Five clinicopathological subtypes were determined based on the status of ER, PR, HER2 and Ki-67 from the immunohistochemical test. The radiomic features of diffusion-weighted imaging were derived from the volume of interest (VOI) of each tumour. Fisher discriminant analysis was performed for clinicopathological subtyping by using a backward selection method. To evaluate the diagnostic performance of the radiomic features, ROC analyses were performed to differentiate between immunohistochemical biomarker-positive and -negative groups. RESULTS: A total of 84 radiomic features of four statistical methods were included after preprocessing. The overall accuracy for predicting the clinicopathological subtypes was 96.4% by Fisher discriminant analysis, and the weighted accuracy was 96.6%. For predicting diverse clinicopathological subtypes, the prediction accuracies ranged from 92 to 100%. According to the cross-validation, the overall accuracy of the model was 82.1%, and the accuracies of the model for predicting the luminal A, luminal BHER2-, luminal BHER2+, HER2 positive and triple negative subtypes were 79, 77, 88, 92 and 73%, respectively. According to the ROC analysis, the radiomic features had excellent performance in differentiating between different statuses of ER, PR, HER2 and Ki-67. CONCLUSIONS: The Fisher discriminant analysis model based on radiomic features of diffusion-weighted MRI is a reliable method for the prediction of clinicopathological breast cancer subtypes.

Radiomics nomogram of contrast-enhanced spectral mammography for prediction of axillary lymph node metastasis in breast cancer: a multicenter study.

OBJECTIVE: This study aims to establish and validate a radiomics nomogram based on contrast-enhanced spectral mammography (CESM) for prediction of axillary lymph node (ALN) metastasis in breast cancer. METHODS: This retrospective study included 394 patients with breast cancer who underwent CESM examination in two hospitals. The least absolute shrinkage and selection operator (LASSO) logistic regression was established for feature selection and utilized to construct radiomics signature. The nomogram model included the radiomics signature and independent clinical factors. The receiver operating characteristic (ROC) curves were used to confirm the performance of the nomogram in training and validation sets. RESULTS: The nomogram model, which includes the radiomics signature and the CESM-reported lymph node status, has areas under the ROC curves of 0.774 (95% confidence interval (CI) 0.689-0.858), 0.767 (95% CI 0.583-0.857), and 0.79 (95% CI 0.63-0.94) in the training, internal validation, and external validation sets, respectively. We identified the cutoff score in the radiomics nomogram as - 1.49, which corresponded to a total point of 49 that could diagnose ALN metastasis with a sensitivity of > 95%. CONCLUSIONS: The CESM-based radiomics nomogram is a noninvasive predictive tool that shows good application prospects in the preoperative prediction of ALN metastasis in breast cancer. KEY POINTS: • The CESM-based radiomics nomogram shows good performance in predicting ALN metastasis in breast cancer. • The application of radiomics nomogram in this study provides a new approach for establishing a prediction model with multiple characteristics. • The nomogram has good application prospects in assisting clinical decision makers.

The effect of heat treatment on the microstructure and functional properties of whey protein from goat milk.

This work investigated the effects of thermal processing methods commonly used in the dairy industry and prolonged treatment at different temperatures on the denaturation, microstructure, and functional properties of whey proteins (WP) from goat milk. The complete denaturation of WP was observed in goat milk treated at 85°C for 30 min, and at a higher temperature (>85°C), a considerable amount of WP was easily denatured. The low temperature, long time treatment had the least effect on the secondary structure, whereas ultra-high temperature treatment had the greatest effect, and the amount of regular structures decreased gradually with prolonged time. The most serious morphological damage occurred after treatment at 85°C for 30 min, which was consistent with the denaturation results. This result indicated that the denaturation degree, particle size, surface hydrophobicity, and microstructure had a strong influence on the functional properties of WP from goat milk after heat treatment. The heat treatment of goat milk at 65°C for 30 min and 85°C for 15 s increased the particle size, turbidity, zeta potential, and surface hydrophobicity of WP, and these increases ensured that the WP had a good emulsifying activity index, water-holding capacity, oil-holding capacity, foaming capacity, and foam stability. This study simulated the heat treatment conditions used in actual production, aiming to provide a theoretical basis for industry.

Using Deep Learning to Forecast Maritime Vessel Flows.

Forecasting vessel flows is important to the development of intelligent transportation systems in the maritime field, as real-time and accurate traffic information has favorable potential in helping a maritime authority to alleviate congestion, mitigate emission of GHG (greenhouse gases) and enhance public safety, as well as assisting individual vessel users to plan better routes and reduce additional costs due to delays. In this paper, we propose three deep learning-based solutions to forecast the inflow and outflow of vessels within a given region, including a convolutional neural network (CNN), a long short-term memory (LSTM) network, and the integration of a bidirectional LSTM network with a CNN (BDLSTM-CNN). To apply those solutions, we first divide the given maritime region into M × N grids, then we forecast the inflow and outflow for all the grids. Experimental results based on the real AIS (Automatic Identification System) data of marine vessels in Singapore demonstrate that the three deep learning-based solutions significantly outperform the conventional method in terms of mean absolute error and root mean square error, with the performance of the BDLSTM-CNN-based hybrid solution being the best.

Benign or Malignant Characterization of Soft-Tissue Tumors by Using Semiquantitative and Quantitative Parameters of Dynamic Contrast-Enhanced Magnetic Resonance Imaging.

PURPOSE: To evaluate the efficacy of the semiquantitative and quantitative parameters of dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) in differentiating between benign and malignant soft-tissue tumors. METHODS: A total of 45 patients with pathologically confirmed soft-tissue tumors (15 benign and 30 malignant tumors) underwent DCE-MRI. The semiquantitative parameters assessed were as follows: time to peak (TTP), maximum concentration (MAX Conc), area under the curve of time-concentration curve (AUC-TC), and maximum rise slope (MAX Slope). Quantitative DCE-MRI was analyzed with the extended Tofts-Kety model to assess the following quantitative parameters: volume transfer constant (Ktrans), microvascular permeability reflux constant (Kep), and distribute volume per unit tissue volume (Ve). Data were evaluated using the independent t test or Mann-Whitney U test and receiver operating characteristic (ROC) curves. RESULTS: The TTP (P = .0035), MAX Conc (P = .0018), AUC-TC (P = .0018), MAX Slope (P = .0018), Ktrans (P = .0018), and Kep (P = .0035) were significantly different between the benign and malignant soft-tissue tumors. The AUC of the ROC curve demonstrated the diagnostic potential of TTP (0.778), MAX Conc (0.849), AUC-TC (0.831), MAX Slope (0.847), Ktrans (0.836), Kep (0.778), and Ve (0.638). CONCLUSIONS: The use of semiquantitative and quantitative parameters of DCE-MRI enabled differentiation between benign and malignant soft-tissue tumors. The values of TTP were lower, while those of MAX Conc, AUC-TC, MAX Slope, Ktrans, and Kep were higher in malignant than in benign tumors.

Computerized Image Analysis to Differentiate Benign and Malignant Breast Tumors on Magnetic Resonance Diffusion Weighted Image: A Preliminary Study.

PURPOSE: This work aims to determine the feasibility of using a computer-aided diagnosis system to differentiate benign and malignant breast tumors on magnetic resonance diffusion-weighted image (DWI). MATERIALS AND METHODS: Institutional review board approval was obtained. This retrospective study included 76 patients who underwent breast magnetic resonance imaging before neoadjuvant chemotherapy from March 10, 2017, to October 12, 2017, with a total of 80 breast tumors including 40 cases of breast cancers and 40 cases of benign breast tumors. The textural features of DWI images were analyzed. The area under the receiver operating characteristic curve was calculated to evaluate the diagnostic efficiency of texture parameters. Multiple linear regression analysis was used to determine the efficiency of texture parameters for distinguishing the 2 types of breast tumors. RESULTS: Computer vision algorithms were applied to extract 67 imaging features from lesions indicated by a breast radiologist on DWI images. A total of 19 texture feature parameters, such as variance, standard deviation, intensity, and entropy, out of 67 texture parameters were statistically significant in the 2 sets of data (P < 0.05). By comparing the receiver operating characteristic curves, we found that the mean and relative deviations exhibited high diagnostic values in differentiating between benign and malignant tumors. The accuracy of Fisher discriminant analysis for the 2 types of breast tumors was 92.5%. CONCLUSIONS: Breast lesions exhibit certain characteristic features in DWI images that can be captured and quantified with computer-aided diagnosis, which enables good discrimination of benign and malignant breast tumors.

[Study on the Anisotropy of Collagenours Fibers in Articular with Infrared Imaging].

Formalin solution has been widely used to solidify the organization of articular cartilage and prevent tissue decomposition in long-time measurement. However, it was rarely investigated that the structural anisotropy changes of collagen fiber (fixation) of articular cartilage when it was immersed in formalin. In this paper, Fourier transform infrared spectroscopic imaging with polarization technique was used to investigate the anisotropic structure change of collagen fiber of articular cartilage fixed in formalin through the absorbance change of Amide I and Amide II with immersing time and polarization direction. The degree of anisotropy of collagen fiber in cartilage was characterized with fitting related coefficient of absorbance. The anisotropy of Amide I and Amide II became stronger with immersing extension of articular cartilage in formalin, and the amide I showed more remarkable anisotropy. It was concluded that the formalin solution induced new crosslinks of collagen, which gradually strengthened the collagen fiber anisotropy and was helpful for the structural analysis of the articular cartilage. The study will be significant for the preparation, preservation and anisotropy research of cartilage specimen.

Large Cyclability of Elastocaloric Effect in Highly Porous Ni-Fe-Ga Foams.

Solid-state refrigeration based on elastocaloric materials (eCMs) requires reversibility and repeatability. However, the intrinsic intergranular brittleness of ferromagnetic shape memory alloys (FMSMAs) limits fatigue life and, thus, is the crucial bottleneck for its industrial applications. Significant cyclic stability of elastocaloric effects (eCE) via 53% porosity in Ni-Fe-Ga FMSMA has already been proven. Here, Ni-Fe-Ga foams (single-/hierarchical pores) with high porosity of 64% and 73% via tailoring the material's architecture to optimize the eCE performances are studied. A completely reversible superelastic behavior at room temperature (297 K) is demonstrated in high porosity (64-73%) Ni-Fe-Ga foams with small stress hysteresis, which is greatly conducive to durable fatigue life. Consequentially, hierarchical pore foam with 64% porosity exhibits a maximum reversible ∆Tad of 2.0 K at much lower stress of 45 MPa with a large COPmat of 34. Moreover, it shows stable elastocaloric behavior (ΔTad = 2.0 K) over >300 superelastic cycles with no significant deterioration. The enhanced eCE cyclability can be attributed to the pore hierarchies, which remarkably reduce the grain boundary constraints and/or limit the propagation of cracks to induce multiple stress-induced martensitic transformations (MTs). Therefore, this work paves the way for designing durable fatigue life FMSMAs as promising eCMs by manipulating the material architectures.

Aging-Associated Molecular Changes in Human Alveolar Type I Cells.

Human alveolar type I (AT1) cells are specialized epithelial cells that line the alveoli in the lungs where gas exchange occurs. The primary function of AT1 cells is not only to facilitate efficient gas exchange between the air and the blood in the lungs, but also to contribute to the structural integrity of the alveoli to maintain lung function and homeostasis. Aging has notable effects on the structure, function, and regenerative capacity of human AT1 cells. However, our understanding of the molecular mechanisms driving these age-related changes in AT1 cells remains limited. Leveraging a recent single-cell transcriptomics dataset we generated on healthy human lungs, we identified a series of significant molecular alterations in AT1 cells from aged lungs. Notably, the aged AT1 cells exhibited increased cellular senescence and chemokine gene expression, alongside diminished epithelial features such as decreases in cell junctions, endocytosis, and pulmonary matrisome gene expression. Gene set analyses also indicated that aged AT1 cells were resistant to apoptosis, a crucial mechanism for turnover and renewal of AT1 cells, thereby ensuring alveolar integrity and function. Further research on these alterations is imperative to fully elucidate the impact on AT1 cells and is indispensable for developing effective therapies to preserve lung function and promote healthy aging.

Cu- and Fe-Doped Ni-Mn-Sn Shape Memory Alloys with Enhanced Mechanical and Magnetocaloric Properties.

Ni-Mn-Sn-based ferromagnetic shape memory alloys (FSMAs) are multifunctional materials that are promising for solid-state refrigeration applications based on the magnetocaloric effect (MCE) and elastocaloric effect (eCE). However, a combination of excellent multi-caloric properties, suitable operating temperatures, and mechanical properties cannot be well achieved in these materials, posing a challenge for their practical application. In this work, we systematically study the phase transformations and magnetic properties of Ni50-xMn38Sn12Cux (x = 0, 2, 3, 4, 5, and 6) and Ni50-yMn38Sn12Fey (y = 0, 1, 2, 3, 4, and 5) alloys, and the magnetic-structural phase diagrams of these alloy systems are reported. The influences of the fourth-element doping on the phase transitions and magnetic properties of the alloys are elucidated by first-principles calculations. This work demonstrates that the fourth-element doping of Ni-Mn-Sn-based FSMA is effective in developing multicaloric refrigerants for practical solid-state refrigeration.

Model-Informed individualized dosage regimen of sirolimus in pediatric patients with intractable lymphatic malformations.

Intractable lymphatic malformations (iLM) pose a significant threat to affected children, demonstrating limited responses to conventional treatments. Sirolimus, effectively inhibiting endothelial cell proliferation in lymphatic vessels, plays a crucial role in iLM treatment. However, the drug's narrow therapeutic window and substantial interindividual variability necessitate customized dosing strategies. This study aims to establish a Population Pharmacokinetic Model (PopPK model) for sirolimus in pediatric iLM patients, identifying quantitative relationships between covariates and sirolimus clearance and volume of distribution. Initial dosages are recommended based on a target concentration range of 5-15 ng/mL. Retrospective data from our institution, encompassing 53 pediatric patients with 275 blood concentration results over the past five years (average age: 4.64 ± 4.19 years), constituted the foundation of this analysis. The final model, adopting a first-order absorption and elimination single-compartment model, retained age as the sole covariate. Results indicated a robust correlation between apparent clearance (CL/F) at 5.56 L/h, apparent volume of distribution (V/F) at 292.57 L, and age. Monte Carlo simulation guided initial dosages for patients aged 0-18 years within the target concentration range. This study presents the first PopPK model using a large Therapeutic Drug Monitoring (TDM) database to describe personalized sirolimus dosing for pediatric iLM patients, contributing to pharmacokinetic guidance and potentially improving long-term clinical outcomes.

Strength-Plasticity Relationship and Intragranular Nanophase Distribution of Hybrid (GNS + SiCnp)/Al Composites Based on Heat Treatment.

The distribution of reinforcements and interfacial bonding state with the metal matrix are crucial factors in achieving excellent comprehensive mechanical properties for aluminum (Al) matrix composites. Normally, after heat treatment, graphene nanosheets (GNSs)/Al composites experience a significant loss of strength. Here, better performance of GNS/Al was explored with a hybrid strategy by introducing 0.9 vol.% silicon carbide nanoparticles (SiCnp) into the composite. Pre-ball milling of Al powders and 0.9 vol.% SiCnp gained Al flakes that provided a large dispersion area for 3.0 vol.% GNS during the shift speed ball milling process, leading to uniformly dispersed GNS for both as-sintered and as-extruded (0.9 vol.% SiCnp + 3.0 vol.% GNS)/Al. High-temperature heat treatment at 600 °C for 60 min was performed on the as-extruded composite, giving rise to intragranular distribution of SiCnp due to recrystallization and grain growth of the Al matrix. Meanwhile, nanoscale Al4C3, which can act as an additional reinforcing nanoparticle, was generated because of an appropriate interfacial reaction between GNS and Al. The intragranular distribution of both nanoparticles improves the Al matrix continuity of composites and plays a key role in ensuring the plasticity of composites. As a result, the work hardening ability of the heat-treated hybrid (0.9 vol.% SiCnp + 3.0 vol.% GNS)/Al composite was well improved, and the tensile elongation increased by 42.7% with little loss of the strength. The present work provides a new strategy in achieving coordination on strength-plasticity of Al matrix composites.

Accelerated First-Principles Calculations Based on Machine Learning for Interfacial Modification Element Screening of SiCp/Al Composites.

SiCp/Al composites offer the advantages of lightweight construction, high strength, and corrosion resistance, rendering them extensively applicable across various domains such as aerospace and precision instrumentation. Nonetheless, the interfacial reaction between SiC and Al under high temperatures leads to degradation in material properties. In this study, the interface segregation energy and interface binding energy subsequent to the inclusion of alloying elements were computed through a first-principle methodology, serving as a dataset for machine learning. Feature descriptors for machine learning undergo refinement via feature engineering. Leveraging the theory of machine-learning-accelerated first-principle computation, six machine learning models-RBF, SVM, BPNN, ENS, ANN, and RF-were developed to train the dataset, with the ANN model selected based on R2 and MSE metrics. Through this model, the accelerated computation of interface segregation energy and interface binding energy was achieved for 89 elements. The results indicate that elements including B, Si, Fe, Co, Ni, Cu, Zn, Ga, and Ge exhibit dual functionality, inhibiting interfacial reactions while bolstering interfacial binding. Furthermore, the atomic-scale mechanism elucidates the interfacial modulation of these elements. This investigation furnishes a theoretical framework for the compositional design of SiCp/Al composites.