Hypoxia-induced NLRP3 inflammasome activation via the HIF-1α/NF-κB signaling pathway in human dental pulp fibroblasts.

BACKGROUND: Previous studies have reported the link between hypoxic conditions and NLRP3 inflammasome-mediated pulpal inflammation in the progression of pulpitis. However, the underlying mechanism has not been fully elucidated. This study aimed to investigate the role of HIF-1α in the regulation of NLRP3 inflammasome pathway via NF-κB signaling under hypoxic conditions with or without LPS in human dental pulp fibroblasts (HDPFs) during the progression of pulpitis. METHODS: HIF-1α plasmids or siRNAs were used to upregulate or downregulate HIF-1α in HDPFs, respectively. The effect of hypoxia with or without LPS on the NF-κB signaling and NLRP3 inflammasome pathway was analyzed by immunofluorescence staining, qRT-PCR, western blotting and ELISA. RESULTS: The hypoxic conditions alone induced ASC oligomerization and NLRP3/CASP1 inflammasome pathway activation via NF-κB signaling in a time-dependent manner in HDPFs. The upregulation of HIF-1α further promoted hypoxia-induced ASC oligomerization and NLRP3/CASP1 inflammasome pathway activation via NF-κB signaling compared to the hypoxia-induced group. In comparison, downregulation of HIF-1α inhibited ASC oligomerization and NLRP3/CASP1 inflammasome pathway activation via NF-κB signaling compared to the hypoxia-induced group. Additionally, LPS plus hypoxia further promoted HIF-1α expression and NLRP3/ASC/CASP1 inflammasome pathway activation via NF-κB signaling compared to the hypoxia-induced group. CONCLUSIONS: HIF-1α served as a positive regulator of NLRP3/ASC/CASP1 inflammasome pathway activation via NF-κB signaling in HDPFs in the sterile pulpal inflammation and caries-related pulpitis microenvironment. The finding of a novel functional HIF-1α-NF-κB-NLRP3 axis provides insight into the link between the hypoxic microenvironment and pulpal inflammation, thus supporting a promising therapeutic strategy for the control of pulpal inflammation.

Development and validation of a novel nomogram to avoid unnecessary biopsy in patients with PI-RADS category ≥ 4 lesions and PSA ≤ 20 ng/ml.

OBJECTIVES: To develop and validate a prediction model for identifying non-prostate cancer (non-PCa) in biopsy-naive patients with PI-RADS category ≥ 4 lesions and PSA ≤ 20 ng/ml to avoid unnecessary biopsy. PATIENTS AND METHODS: Eligible patients who underwent transperineal biopsies at West China Hospital between 2018 and 2022 were included. The patients were randomly divided into training cohort (70%) and validation cohort (30%). Logistic regression was used to screen for independent predictors of non-PCa, and a nomogram was constructed based on the regression coefficients. The discrimination and calibration were assessed by the C-index and calibration plots, respectively. Decision curve analysis (DCA) and clinical impact curves (CIC) were applied to measure the clinical net benefit. RESULTS: A total of 1580 patients were included, with 634 non-PCa. Age, prostate volume, prostate-specific antigen density (PSAD), apparent diffusion coefficient (ADC) and lesion zone were independent predictors incorporated into the optimal prediction model, and a corresponding nomogram was constructed ( https://nomogramscu.shinyapps.io/PI-RADS-4-5/ ). The model achieved a C-index of 0.931 (95% CI, 0.910-0.953) in the validation cohort. The DCA and CIC demonstrated an increased net benefit over a wide range of threshold probabilities. At biopsy-free thresholds of 60%, 70%, and 80%, the nomogram was able to avoid 74.0%, 65.8%, and 55.6% of unnecessary biopsies against 9.0%, 5.0%, and 3.6% of missed PCa (or 35.9%, 30.2% and 25.1% of foregone biopsies, respectively). CONCLUSION: The developed nomogram has favorable predictive capability and clinical utility can help identify non-PCa to support clinical decision-making and reduce unnecessary prostate biopsies.

A machine learning model utilizing delphian lymph node characteristics to predict contralateral central lymph node metastasis in papillary thyroid carcinoma: A prospective multicenter study.

BACKGROUND: This study aimed to use artificial intelligence (AI) to integrate various radiological and clinical pathological data to identify effective predictors of contralateral cervical lymph node metastasis (CCLNM) in patients with papillary thyroid carcinoma (PTC) and to establish a clinically applicable model to guide the extent of surgery. METHODS: This prospective cohort study included 603 patients with PTC from three centers. Clinical, pathological, and ultrasonographic data were collected and utilized to develop a machine learning (ML) model for predicting CCLNM. Model development at the internal center utilized logistic regression along with other ML algorithms. Diagnostic efficacy was compared among these methods, leading to the adoption of the final model (random forest). This model was subject to AI interpretation and externally validated at other centers. RESULTS: CCLNM was associated with multiple pathological factors. The Delphian lymph node metastasis ratio, ipsilateral cervical lymph node metastasis number, and presence of ipsilateral cervical lymph node metastasis were independent risk factors for CCLNM. Following feature selection, a Delphian lymph node-CCLNM (D-CCLNM) model was established using the Random forest algorithm based on five attributes. The D-CCLNM model demonstrated the highest area under the curve (AUC; 0.9273) in the training cohort and exhibited high predictive accuracy, with AUCs of 0.8907 and 0.9247 in the external and validation cohorts, respectively. CONCLUSIONS: We developed a new, effective method that uses ML to predict CCLNM in patients with PTC. This approach integrates data from Delphian lymph nodes and clinical characteristics, offering a foundation for guiding surgical decisions, and is conveniently applicable in clinical settings.

Functional Semi-Interpenetrating Polymer Networks.

Semi-interpenetrating polymer networks (SIPNs) have garnered significant interest due to their potential applications in self-healing materials, drug delivery systems, electrolytes, functional membranes, smart gels and, toughing. SIPNs combine the characteristics of physical cross-linking with advantageous chemical properties, offering broad application prospects in materials science and engineering. This perspective introduces the history of semi-interpenetrating polymer networks and their diverse applications. Additionally, the ongoing challenges associated with traditional semi-interpenetrating polymer materials are discussed and provide an outlook on future advancements in novel functional SIPNs.

Genome-Wide Identification and Bioinformatics Analysis of the FK506 Binding Protein Family in Rice.

The FK506 Binding Protein (FKBP), ubiquitously present across diverse species, is characterized by its evolutionarily conserved FK506 binding domain (FKBd). In plants, evidence suggests that this gene family plays integral roles in regulating growth, development, and responses to environmental stresses. Notably, research on the identification and functionality of FKBP genes in rice remains limited. Therefore, this study utilized bioinformatic tools to identify 30 FKBP-encoding genes in rice. It provides a detailed analysis of their chromosomal locations, evolutionary relationships with the Arabidopsis thaliana FKBP family, and gene structures. Further analysis of the promoter elements of these rice FKBP genes revealed a high presence of stress-responsive elements. Quantitative PCR assays under drought and heat stress conditions demonstrated that genes OsFKBP15-2, OsFKBP15-3, OsFKBP16-3, OsFKBP18, and OsFKBP42b are inducible by these adverse conditions. These findings suggest a significant role for the rice FKBP gene family in stress adaptation. This research establishes a critical foundation for deeper explorations of the functional roles of the OsFKBP genes in rice.

Hypoglycemic Effect of Polysaccharides from Physalis alkekengi L. in Type 2 Diabetes Mellitus Mice.

Type 2 diabetes mellitus (T2DM) is a common metabolic disease that adversely impacts patient health. In this study, a T2DM model was established in ICR mice through the administration of a high-sugar and high-fat diet combined with the intraperitoneal injection of streptozotocin to explore the hypoglycemic effect of polysaccharides from Physalis alkekengi L. After six weeks of treatment, the mice in the high-dosage group (800 mg/kg bw) displayed significant improvements in terms of fasting blood glucose concentration, glucose tolerance, serum insulin level, insulin resistance, and weight loss (p < 0.05). The polysaccharides also significantly regulated blood lipid levels by reducing the serum contents of total triglycerides, total cholesterol, and low-density lipoproteins and increasing the serum content of high-density lipoproteins (p < 0.05). Furthermore, they significantly enhanced the hepatic and pancreatic antioxidant capacities, as determined by measuring the catalase and superoxide dismutase activities and the total antioxidant capacity (p < 0.05). The results of immunohistochemistry showed that the P. alkekengi polysaccharides can increase the expression of GPR43 in mice colon epithelial cells, thereby promoting the secretion of glucagon-like peptide-1. In summary, P. alkekengi polysaccharides can help to regulate blood glucose levels in T2DM mice and alleviate the decline in the antioxidant capacities of the liver and pancreas, thus protecting these organs from damage.

Uncovering the differentiated impacts of carbon neutrality and clean air policies in multi-provinces of China.

Ambitious action plans have been launched to address climate change and air pollution. Through coupling the IMED|CGE, GAINS, and IMED|HEL models, this study investigate the impacts of implementing carbon neutrality and clean air policies on the energy-environment-health-economy chain in the Beijing-Tianjin-Hebei-Henan-Shandong-Shanxi region of China. Results show that Shandong holds the largest reduction in energy consumption and carbon emissions toward the 1.5°C target. Shandong, Henan, and Hebei are of particularly prominent pollutant reduction potential. Synergistic effects of carbon reduction on decreasing PM2.5 concentration will increase in the future, specifically in energy-intensive regions. Co-deployment of carbon reduction and end-of-pipe technologies are beneficial to decrease PM2.5-related mortalities and economic loss by 4.7-12.9% in 2050. Provincial carbon reduction cost will be higher than monetary health benefits after 2030, indicating that more zero-carbon technologies should be developed. Our findings provide scientific enlightenment on policymaking toward achieving carbon reduction and pollution mitigation from multiple perspectives.

Erratum: Multi-Gene Single Nucleotide Polymorphism Detection in Gastric Cancer Based on Ion Semiconductor Sequencing Platform.

This corrects the article 10.3791/66058.

Hearing loss prevalence and burden of disease in China: Findings from provincial-level analysis.

BACKGROUND: Without timely and effective rehabilitation, hearing loss may profoundly affect human life quality. China has a large population of hearing-impaired individuals, which imposes a heavy health burden on society. Moreover, this population is projected to increase rapidly owing to China's aging society. METHODS: We used data from a population-representative epidemiological investigation of hearing loss and ear diseases in four Chinese provinces. We estimated the national prevalence using multiple linear regression of the age-group proportions and prevalence in 31 provinces with clustering analysis. We used years lived with disability (YLDs) to analyze the disease burden and forecasted the prevalence of hearing loss by 2060 in China. RESULTS: An estimated 115 million people had moderate-to-complete hearing loss in 2015 across the 31 provinces of China (8.4% of 1.37 billion people). Of these, 85.7% were older than age 50 years (99 million people) and 2.4% were younger than 20 years old (2.8 million people). Of all YLDs attributable to hearing loss, 68.9% were attributable to moderate-to-complete cases. By 2060, a projected 242 million people in China will have moderate-to-complete hearing loss, a 110.0% increase from 2015. CONCLUSIONS: The hearing loss prevalence in China is high. Population aging and socioeconomic factors substantially affect the prevalence and severity of hearing loss and the disease burden. The prevalence and severity of hearing loss are unevenly distributed across different provinces. Future public health policies should take these trends and regional variations into account.

The Effects of Live Streamer's Facial Attractiveness and Product Type on Consumer Purchase Intention: An Exploratory Study with Eye Tracking Technology.

As a booming branch of online retailing, live-streaming e-commerce can present abundant information dimensions and diverse forms of expression. Live-streaming e-commerce has enabled online retailers to interact with customers face-to-face, resulting in widespread instances of emotional and impulse buying behavior. Prior research in live-streaming e-commerce has suggested that live streamers' characteristics, especially the live streamer's face, can affect customers' purchase intentions. The present research used questionnaire surveys and an eye tracking experiment to investigate the impact of live streamer's facial attractiveness on consumer purchase intention for search-based and experience-based products. The questionnaire survey analyzed 309 valid questionnaires and revealed that attractive faces are the key influencing factor driving consumers' impulse purchase intentions. Moreover, consumers' emotional experience plays a partial mediating role in the process of live streamers' faces influencing purchase intention. The eye tracking experiment further explored the mechanism of a live streamer's facial attractiveness on consumers' purchase intentions of search-based products and experience-based products from the perspective of visual attention by analyzing 64 valid sets of data. The results showed that attractive faces attract more consumers' attention and, therefore, increase their purchase intention. Furthermore, there is a significant interaction between product type, the live streamer's facial attractiveness, and consumers' purchase intentions. In the case of unattractive live streamers, consumers are more likely to buy search-based products than experience-based products, while the purchase intention does not vary between search-based products and experience-based products in the case of attractive live streamers. The present study provides evidence for 'beauty premium' in live-streaming e-commerce and sheds light on the design of the match between live streamers and different types of products.

Determining toxicity of europium oxide nanoparticles in immune cell components and hematopoiesis in dominant organs in mice: Role of lysosomal fluid interaction.

Extensive application of rare earth element oxide nanoparticles (REE NPs) has raised a concern over the possible toxic health effects after human exposure. Once entering the body, REE NPs are primarily processed by phagocytes in particular macrophages and undergo biotic phosphate complexation in lysosomal compartment. Such biotransformation affects the target organs and in vivo fate of REE NPs after escaping the lysosomes. However, the immunomodulatory effects of intraphagolysosomal dissolved REE NPs remains insufficient. Here, europium oxide (Eu2O3) NPs were pre-incubated with phagolysosomal simulant fluid (PSF) to mimic the biotransformation of europium oxide (p-Eu2O3) NPs under acid phagolysosome conditions. We investigated the alteration in immune cell components and the hematopoiesis disturbance on adult mice after intravenous administration of Eu2O3 NPs and p-Eu2O3 NPs. Our results indicated that the liver and spleen were the main target organs for Eu2O3 NPs and p-Eu2O3 NPs. Eu2O3 NPs had a much higher accumulative potential in organs than p-Eu2O3 NPs. Eu2O3 NPs induced more alterations in immune cells in the spleen, while p-Eu2O3 NPs caused stronger response in the liver. Regarding hematopoietic disruption, Eu2O3 NPs reduced platelets (PLTs) in peripheral blood, which might be related to the inhibited erythrocyte differentiation in the spleen. By contrast, p-Eu2O3 NPs did not cause significant disturbance in peripheral PLTs. Our study demonstrated that the preincubation with PSF led to a distinct response in the immune system compared to the pristine REE NPs, suggesting that the potentially toxic effects induced by the release of NPs after phagocytosis should not be neglected, especially when evaluating the safety of NPs application in vivo.

Multi-Gene Single Nucleotide Polymorphism Detection in Gastric Cancer Based on Ion Semiconductor Sequencing Platform.

Gastric cancer is a common heterogeneous tumor. Most patients have advanced gastric cancer at the time of diagnosis and often need chemotherapy. Although 5-fluorouracil (5-FU) is widely used for treatment, its therapeutic sensitivity and drug tolerance still need to be determined, which emphasizes the importance of individualized administration. Pharmacogenetics can guide the clinical implementation of individualized treatment. Single nucleotide polymorphisms (SNPs), as a genetic marker, contribute to the selection of appropriate chemotherapy regimens and dosages. Some SNPs are associated with folate metabolism, the therapeutic target of 5-FU. Methylenetetrahydrofolate reductase (MTHFR) rs1801131 and rs1801133, dihydrofolate reductase (DHFR) rs1650697 and rs442767, methionine synthase (MTR) rs1805087, gamma-glutamyl hydrolase (GGH) rs11545078 and solute carrier family 19 member 1 (SLC19A1) rs1051298 have been investigated in different kinds of cancers and antifolate antitumor drugs, which have potential forecasting and guiding significance for application of 5-FU. The ion torrent next-generation semiconductor sequencing technology can rapidly detect gastric cancer-related SNPs. Each time a base is extended in a DNA chain, an H+ will be released, causing local pH changes. The ionic sensor detects pH changes and converts chemical signals into digital signals, achieving sequencing by synthesis. This technique has low sample requirement, simple operation, low cost, and fast sequencing speed, which is beneficial for guiding individualized chemotherapy by SNPs.

Using UAV images and deep learning in investigating potential breeding sites of Aedes albopictus.

Aedes albopictus (Diptera: Culicidae) plays a crucial role as a vector for mosquito-borne diseases like dengue and zika. Given the limited availability of effective vaccines, the prevention of Aedes-borne diseases mainly relies on extensive efforts in vector surveillance and control. In multiple mosquito control methods, the identification and elimination of potential breeding sites (PBS) for Aedes are recognized as effective methods for population control. Previous studies utilizing unmanned aerial vehicles (UAVs) and deep learning to identify PBS have primarily focused on large, regularly-shaped containers. However, there has been a small amount of empirical research into their practical application in the field. We have thus constructed a PBS dataset specifically tailored for Ae. albopictus, including items such as buckets, bowls, bins, aquatic plants, jars, lids, pots, boxes, and sinks that were common in the Yangtze River Basin in China. Then, a YOLO v7 model for identifying these PBS was developed. Finally, we recognized and labeled the area with the highest PBS density, as well as the subarea with the most urgent need for source reduction in the empirical region, by calculating the kernel density value. Based on the above research, we proposed a UAV-AI-based methodological framework to locate the spatial distribution of PBS, and conducted empirical research on Jinhulu New Village, a typical model community. The results revealed that the YOLO v7 model achieved an excellent result on the F1 score and mAP(both above 0.99), with 97% of PBS correctly located. The predicted distribution of different PBS categories in each subarea was completely consistent with true distribution; the five houses with the most PBS were correctly located. The results of the kernel density map indicate the subarea 4 with the highest density of PBS, where PBS needs to be removed or destroyed with immediate effect. These results demonstrate the reliability of the prediction results and the feasibility of the UAV-AI-based methodological framework. It can minimize repetitive labor, enhance efficiency, and provide guidance for the removal and destruction of PBS. The research can shed light on the investigation of mosquito PBS investigation both methodologically and practically.

Strong Interaction between Titanium Carbonitride Embedded in Mesoporous Carbon Nanofibers and Pt Enables Durable Oxygen Reduction.

Platinum (Pt) supported on high surface area carbon has been the most widely used electrocatalyst in proton exchange membrane fuel cell (PEMFC). However, conventional carbon supports are susceptible to corrosion at high potentials, leading to severe degradation of electrochemical performance. In this work, titanium carbonitride embedded in mesoporous carbon nanofibers (m-TiCN NFs) are reported as a promising alternative to address this issue. Benefiting from the interpenetrating conductive pathways inside the one-dimensional (1D) nanostructures and the embedded TiCN nanoparticles (NPs), m-TiCN NFs exhibit excellent stability at high potentials and interact strongly with Pt NPs. Subsequently, m-TiCN NFs-supported Pt NPs deliver remarkably enhanced oxygen reduction reaction (ORR) activity and durability, with negligible activity decay and less than 5% loss of electrochemical surface area（ECSA） after 50 000 cycles. Moreover, the fuel cell assembled by this catalyst delivers a maximum power density of 1.22 W cm-2 and merely 3% loss after 30 000 cycles of accelerated durability tests under U.S. Department of Energy (DOE) protocols. The improved ORR activity and durability are attributed to the superior corrosion resistance of the m-TiCN NF support and the strong interaction between Pt and m-TiCN NFs.

Spectrum-efficient user grouping and resource allocation based on deep reinforcement learning for mmWave massive MIMO-NOMA systems.

Millimeter-wave (mmWave) massive multiple-input multiple-output non-orthogonal multiple access (MIMO-NOMA) is proven to be a primary technique for sixth-generation (6G) wireless communication networks. However, the great increase in users and antennas brings challenges for interference suppression and resource allocation for mmWave massive MIMO-NOMA systems. This study proposes a spectrum-efficient and fast convergence deep reinforcement learning (DRL)-based resource allocation framework to optimize user grouping and allocation of subchannel and power. First, an enhanced K-means grouping algorithm is proposed to reduce the multi-user interference and accelerate the convergence. Then, a dueling deep Q-network (DQN) structure is proposed to perform subchannel allocation, which further improves the convergence speed. Moreover, a deep deterministic policy gradient (DDPG)-based power resource allocation algorithm is designed to avoid the performance loss caused by power quantization and improve the system's achievable sum-rate. The simulation results demonstrate that our proposed scheme outperforms other neural network-based algorithms in terms of convergence performance, and can achieve higher system capacity compared with the greedy algorithm, the random algorithm, the RNN algorithm, and the DoubleDQN algorithm.

The impact of air pollution on regional innovation: empirical evidence based on 267 cities in China.

Based on the spatially correlated effects of air pollution on regional innovation, theoretical hypotheses are proposed, and this paper employs a spatial Durbin model to conduct empirical tests using panel data from 267 Chinese cities from 2003 to 2019, and investigates the mediating effect of human capital. Research has shown that (1) air pollution significantly reduces regional innovation output and has a negative spatial spillover effect significantly in the short term; (2) in the process of regional innovation impacted by air pollution, human capital acts as a mediator role; and (3) analysis of heterogeneity reveals that, from the regional perspective, air pollution has significantly damaged regional innovation in eastern and middle cities, but not significantly influences western cities, and in terms of innovation types, there is a stronger detrimental effect on invention patents exerted by air pollution compared to non-innovation patents. The study's findings provide theoretical and empirical evidence to strengthen environmental governance, enhance regional innovation and promote the coordinated development of regional innovation.

Major influencing factors identification and probabilistic health risk assessment of soil potentially toxic elements pollution in coal and metal mines across China: A systematic review.

Deposition of potentially toxic elements (PTEs) in soils due to different types of mining activities has been an increasingly important concern worldwide. Quantitative differences of soil PTEs contamination and related health risk among typical mines remain unclear. Herein, data from 110 coal mines and 168 metal mines across China were analyzed based on 265 published literatures to evaluate pollution characteristics, spatial distribution, and probabilistic health risks of soil PTEs. The results showed that PTE levels in soil from both mine types significantly exceeded background values. The geoaccumulation index (Igeo) revealed metal-mine soil pollution levels exceeded those of coal mines, with average Igeo values for Cd, Hg, As, Pb, Cu, and Zn being 3.02-15.60 times higher. Spearman correlation and redundancy analysis identified natural and anthropogenic factors affecting soil PTE contamination in both mine types. Mining activities posed a significant carcinogenic risk, with metal-mine soils showing a total carcinogenic risk an order of magnitude higher than in coal-mine soils. This study provides policymakers a quantitative foundation for developing differentiated strategies for sustainable remediation and risk-based management of PTEs in typical mining soils.

Helicobacter pylori CagA-mediated ether lipid biosynthesis promotes ferroptosis susceptibility in gastric cancer.

Helicobacter pylori, particularly cytotoxin-associated gene A (CagA)-positive strains, plays a key role in the progression of gastric cancer (GC). Ferroptosis, associated with lethal lipid peroxidation, has emerged to play an important role in malignant and infectious diseases, but the role of CagA in ferroptosis in cancer cells has not been determined. Here, we report that CagA confers GC cells sensitivity to ferroptosis both in vitro and in vivo. Mechanistically, CagA promotes the synthesis of polyunsaturated ether phospholipids (PUFA-ePLs), which is mediated by increased expression of alkylglycerone phosphate synthase (AGPS) and 1-acylglycerol-3-phosphate O-acyltransferase 3 (AGPAT3), leading to susceptibility to ferroptosis. This susceptibility is mediated by activation of the MEK/ERK/SRF pathway. SRF is a crucial transcription factor that increases AGPS transcription by binding to the AGPS promoter region. Moreover, the results demonstrated that CagA-positive cells are more sensitive to apatinib than are CagA-negative cells, suggesting that detecting the H. pylori CagA status may aid patient stratification for treatment with apatinib.

Method for Estimating Road Impulse Ahead of Vehicles in Urban Environment with Microelectromechanical System Three-Dimensional Sensor.

Most automated vehicles (AVs) are equipped with abundant sensors, which enable AVs to improve ride comfort by sensing road elevation, such as speed bumps. This paper proposes a method for estimating the road impulse features ahead of vehicles in urban environments with microelectromechanical system (MEMS) light detection and ranging (LiDAR). The proposed method deploys a real-time estimation of the vehicle pose to solve the problem of sparse sampling of the LiDAR. Considering the LiDAR error model, the proposed method builds the grid height measurement model by maximum likelihood estimation. Moreover, it incorporates height measurements with the LiDAR error model by the Kalman filter and introduces motion uncertainty to form an elevation weight method by confidence eclipse. In addition, a gate strategy based on the Mahalanobis distance is integrated to handle the sharp changes in elevation. The proposed method is tested in the urban environment. The results demonstrate the effectiveness of our method.

Universal and Energy-Efficient Approach to Synthesize Pt-Rare Earth Metal Alloys for Proton Exchange Membrane Fuel Cell.

Traditional synthesis methods of platinum-rare earth metal (Pt-RE) alloys usually involve harsh conditions and high energy consumption because of the low standard reduction potentials and high oxophilicity of RE metals. In this work, a one-step strategy is developed by rapid Joule thermal-shock (RJTS) to synthesize Pt-RE alloys within tens of seconds. The method can not only realize the regulation of alloy size, but also a universal method for the preparation of a family of Pt-RE alloys (RE = Ce, La, Gd, Sm, Tb, Y). In addition, the energy consumption of the Pt-RE alloy preparation is only 0.052 kW h, which is 2-3 orders of magnitude lower than other reported methods. This method allows individual Pt-RE alloy to be embedded in the carbon substrate, endowing the alloy catalyst excellent durability for oxygen reduction reaction (ORR). The performance of alloy catalyst shows negligible decay after 20k accelerated durability testing (ADT) cycles. This strategy offers a new route to synthesize noble/non-noble metal alloys with diversified applications besides ORR.