p53 exerts anticancer effects by regulating enhancer formation and activity.

The abnormality of p53 tumor suppressor is crucial in lung cancer development, and p53 may regulate target gene promoters to combat cancer. Recent studies have shown extensive p53 binding to enhancer elements. However, whether p53 exerts a tumor suppressor role by shaping the enhancer landscape remains poorly understood. In the current study, we employed several functional genomics approaches to assess the enhancer activity at p53 binding sites throughout the genome based on our established TP53 knockout human bronchial epithelial cells (BEAS-2B). A total of 943 active regular enhancers and 370 super-enhancers (SEs) disappeared upon the deletion of p53, indicating that p53 modulates the activity of hundreds of enhancer elements. We found that one p53-dependent SE, located on chromosome 9 and designated as KLF4-SE, regulated the expression of the Krüppel-like factor 4 ( KLF4) gene. Furthermore, deletion of p53 significantly decreased the KLF4-SE enhancer activity and the KLF4 expression, but increased colony formation ability in the nitrosamines 4-(methylnitrosamino)-1-(3-pyridyl)-1-butanone-induced cell transformation model. Subsequently, in TP53 knockout cells, the overexpression of KLF4 partially reversed the increased clonogenic capacity caused by p53 deficiency. Consistently, KLF4 expression also decreased in lung cancer tissues and cell lines. Overexpression of KLF4 significantly suppressed lung cancer cell proliferation and migration. Collectively, our results suggest that the regulation of enhancer formation and activity by p53 is an integral component of the p53 tumor suppressor function. Therefore, our findings offer novel insights into the regulation mechanism of p53 in lung oncogenesis and introduce a new strategy for screening therapeutic targets.

TNFRSF19 within the 13q12.12 Risk Locus Functions as a Lung Cancer Suppressor by Binding Wnt3a to Inhibit Wnt/ß-Catenin Signaling.

Cancer risk loci provide special clues for uncovering pathogenesis of cancers. The TNFRSF19 gene located within the 13q12.12 lung cancer risk locus encodes TNF receptor superfamily member 19 (TNFRSF19) protein and has been proved to be a key target gene of a lung tissue-specific tumor suppressive enhancer, but its functional role in lung cancer pathogenesis remains to be elucidated. Here we showed that the TNFRSF19 gene could protect human bronchial epithelial Beas-2B cells from pulmonary carcinogen nicotine-derived nitrosamine ketone (NNK)-induced malignant transformation. Knockout of the TNFRSF19 significantly increased NNK-induced colony formation rate on soft agar. Moreover, TNFRSF19 expression was significantly reduced in lung cancer tissues and cell lines. Restoration of TNFRSF19 expression in A549 lung cancer cell line dramatically suppressed the tumor formation in xenograft mouse model. Interestingly, the TNFRSF19 protein that is an orphan membrane receptor could compete with LRP6 to bind Wnt3a, thereby inhibiting the Wnt/ß-catenin signaling pathway that is required for NNK-induced malignant transformation as indicated by protein pulldown, site mutation, and fluorescence energy resonance transfer experiments. Knockout of the TNFRSF19 enhanced LRP6-Wnt3a interaction, promoting ß-catenin nucleus translocation and the downstream target gene expression, and thus sensitized the cells to NNK carcinogen. In conclusion, our study demonstrated that the TNFRSF19 inhibited lung cancer carcinogenesis by competing with LRP6 to combine with Wnt3a to inhibit the Wnt/ß-catenin signaling pathway. IMPLICATIONS: These findings revealed a novel anti-lung cancer mechanism, highlighting the special significance of TNFRSF19 gene within the 13q12.12 risk locus in lung cancer pathogenesis.

An explainable artificial intelligence framework for risk prediction of COPD in smokers.

BACKGROUND: Since the inconspicuous nature of early signs associated with Chronic Obstructive Pulmonary Disease (COPD), individuals often remain unidentified, leading to suboptimal opportunities for timely prevention and treatment. The purpose of this study was to create an explainable artificial intelligence framework combining data preprocessing methods, machine learning methods, and model interpretability methods to identify people at high risk of COPD in the smoking population and to provide a reasonable interpretation of model predictions. METHODS: The data comprised questionnaire information, physical examination data and results of pulmonary function tests before and after bronchodilatation. First, the factorial analysis for mixed data (FAMD), Boruta and NRSBoundary-SMOTE resampling methods were used to solve the missing data, high dimensionality and category imbalance problems. Then, seven classification models (CatBoost, NGBoost, XGBoost, LightGBM, random forest, SVM and logistic regression) were applied to model the risk level, and the best machine learning (ML) model's decisions were explained using the Shapley additive explanations (SHAP) method and partial dependence plot (PDP). RESULTS: In the smoking population, age and 14 other variables were significant factors for predicting COPD. The CatBoost, random forest, and logistic regression models performed reasonably well in unbalanced datasets. CatBoost with NRSBoundary-SMOTE had the best classification performance in balanced datasets when composite indicators (the AUC, F1-score, and G-mean) were used as model comparison criteria. Age, COPD Assessment Test (CAT) score, gross annual income, body mass index (BMI), systolic blood pressure (SBP), diastolic blood pressure (DBP), anhelation, respiratory disease, central obesity, use of polluting fuel for household heating, region, use of polluting fuel for household cooking, and wheezing were important factors for predicting COPD in the smoking population. CONCLUSION: This study combined feature screening methods, unbalanced data processing methods, and advanced machine learning methods to enable early identification of COPD risk groups in the smoking population. COPD risk factors in the smoking population were identified using SHAP and PDP, with the goal of providing theoretical support for targeted screening strategies and smoking population self-management strategies.

Diabetes mellitus early warning and factor analysis using ensemble Bayesian networks with SMOTE-ENN and Boruta.

Diabetes mellitus (DM) has become the third chronic non-infectious disease affecting patients after tumor, cardiovascular and cerebrovascular diseases, becoming one of the major public health issues worldwide. Detection of early warning risk factors for DM is key to the prevention of DM, which has been the focus of some previous studies. Therefore, from the perspective of residents' self-management and prevention, this study constructed Bayesian networks (BNs) combining feature screening and multiple resampling techniques for DM monitoring data with a class imbalance in Shanxi Province, China, to detect risk factors in chronic disease monitoring programs and predict the risk of DM. First, univariate analysis and Boruta feature selection algorithm were employed to conduct the preliminary screening of all included risk factors. Then, three resampling techniques, SMOTE, Borderline-SMOTE (BL-SMOTE) and SMOTE-ENN, were adopted to deal with data imbalance. Finally, BNs developed by three algorithms (Tabu, Hill-climbing and MMHC) were constructed using the processed data to find the warning factors that strongly correlate with DM. The results showed that the accuracy of DM classification is significantly improved by the BNs constructed by processed data. In particular, the BNs combined with the SMOTE-ENN resampling improved the most, and the BNs constructed by the Tabu algorithm obtained the best classification performance compared with the hill-climbing and MMHC algorithms. The best-performing joint Boruta-SMOTE-ENN-Tabu model showed that the risk factors of DM included family history, age, central obesity, hyperlipidemia, salt reduction, occupation, heart rate, and BMI.

Mo2TiC2 MXene-Supported Ru Clusters for Efficient Photothermal Reverse Water-Gas Shift.

Driving metal-cluster-catalyzed high-temperature chemical reactions by sunlight holds promise for the development of negative-carbon-footprint industrial catalysis, which has yet often been hindered by the poor ability of metal clusters to harvest and utilize the full spectrum of solar energy. Here, we report the preparation of Mo2TiC2 MXene-supported Ru clusters (Ru/Mo2TiC2) with pronounced broadband sunlight absorption ability and high sintering resistance. Under illumination of focused sunlight, Ru/Mo2TiC2 can catalyze the reverse water-gas shift (RWGS) reaction to produce carbon monoxide from the greenhouse gas carbon dioxide and renewable hydrogen with enhanced activity, selectivity, and stability compared to their nanoparticle counterparts. Notably, the CO production rate of MXene-supported Ru clusters reached 4.0 mol·gRu-1·h-1, which is among the best reported so far for photothermal RWGS catalysts. Detailed studies suggest that the production of methane is kinetically inhibited by the rapid desorption of CO from the surface of the Ru clusters.

[Treatment of severe medial tibial bone defect in primary total knee arthroplasty with autogenous bone graft and plate fixation].

OBJECTIVE: To explore the technique of autogenous bone graft combined with plate fixation in total knee arthroplasty(TKA) with severe proximal medial tibial bone defect. METHODS: From March 2012 to October 2018, 21 patients (9 males and 12 females) with severe bone defects in the proximal medial tibia during primary total knee arthroplasty were treated with autogenous structural bone grafting and steel plate fixation, with an age of 61 to 77 years old with an average of (69.6±9.1) years and a course of 64 to 257 months with an average of (73.6±170.7) months. According to Rand classification, there were 13 cases of type Ⅲb and 8 cases of type Ⅳb. Postoperative complications were observed, and knee joint function was evaluated by the Hospital for Special Surgery (HSS) score and SF-36 quality of life score. RESULTS: All 21 patients were followed up for 37 to 64 months with an average of (49.5±13.7) months. The incisions of all patients healed smoothly, and 2 patients developed lower limb intermuscular venous plexus thrombosis after operation. There were no periprosthetic infection, loosening of prosthesis and other complications. The autogenous bone grafts of all patients achieved bony healing during postoperative X-ray follow-up, and the healing time was 8 to 13 months with an average of (10.1±2.3) months. The HSS score of patients increased significantly from 30 to 48 with an average of (53.4±4.2) before operation to 75 to 92 with an average of (81.2±8.4) at the final follow-up (P<0.05). The SF-36 quality of life score of patients after operation was significantly different from that before operation (P<0.05). CONCLUSION: The technique of autogenous bone graft combined with steel plate fixation can achieve satisfactory osseointegration effect in the treatment of severe proximal tibial bone defects in primary knee arthroplasty, with less complications and obvious improvement in knee function.

A Novel ATM Antisense Transcript ATM-AS Positively Regulates ATM Expression in Normal and Breast Cancer Cells.

OBJECTIVE: The ataxia telangiectasia mutated (ATM) gene is a master regulator in cellular DNA damage response. The dysregulation of ATM expression is frequent in breast cancer, and is known to be involved in the carcinogenesis and prognosis of cancer. However, the underlying mechanism remains unclear. The bioinformatic analysis predicted a potential antisense transcript ATM-antisense (AS) from the opposite strand of the ATM gene. The purpose of this study was to identify ATM-AS and investigate the possible effect of ATM-AS on the ATM gene regulation. METHODS: Single strand-specific RT-PCR was performed to verify the predicted antisense transcript ATM-AS within the ATM gene locus. qRT-PCR and Western blotting were used to detect the expression levels of ATM-AS and ATM in normal and breast cancer cell lines as well as in tissue samples. Luciferase reporter gene assays, biological mass spectrometry, ChIP-qPCR and RIP were used to explore the function of ATM-AS in regulating the ATM expression. Immunofluorescence and host-cell reactivation (HCR) assay were performed to evaluate the biological significance of ATM-AS in ATM-mediated DNA damage repair. Breast cancer tissue samples were used for evaluating the correlation of the ATM-AS level with the ATM expression as well as prognosis of the patients. RESULTS: The ATM-AS significantly upregulated the ATM gene activity by recruiting KAT5 histone acetyltransferase to the gene promoter. The reduced ATM-AS level led to the abnormal downregulation of ATM expression, and impaired the ATM-mediated DNA damage repair in normal breast cells in vitro. The ATM-AS level was positively correlated with the ATM expression in the examined breast cancer tissue samples, and the patient prognosis. CONCLUSION: The present study demonstrated that ATM-AS, an antisense transcript located within the ATM gene body, is an essential positive regulator of ATM expression, and functions by mediating the binding of KAT5 to the ATM promoter. These findings uncover the novel mechanism underlying the dysregulation of the ATM gene in breast cancer, and enrich our understanding of how an antisense transcript regulates its host gene.

Exploring influencing factors of chronic obstructive pulmonary disease based on elastic net and Bayesian network.

This study aimed to construct Bayesian networks (BNs) to analyze the network relationships between COPD and its influencing factors, and the strength of each factor's influence on COPD was reflected through network reasoning. Elastic Net and Max-Min Hill-Climbing (MMHC) algorithm were adopted to screen the variables on the surveillance data of COPD among residents in Shanxi Province, China from 2014 to 2015, and construct BNs respectively. 10 variables finally entered the model after screening by Elastic Net. The BNs constructed by MMHC showed that smoking status, household air pollution, family history, cough, air hunger or dyspnea were directly related to COPD, and Gender was indirectly linked to COPD through smoking status. Moreover, smoking status, household air pollution and family history were the parent nodes of COPD, and cough, air hunger or dyspnea represented the child nodes of COPD. In other words, smoking status, household air pollution and family history were related to the occurrence of COPD, and COPD would make patients' cough, air hunger or dyspnea worse. Generally speaking, BNs could reveal the complex network linkages between COPD and its relevant factors well, making it more convenient to carry out targeted prevention and control of COPD.

The enhancer rare germline variation rs548071605 contributes to lung cancer development.

Rare germline variations contribute to the missing heritability of human complex diseases including cancers. Given their very low frequency, discovering and testing disease-causing rare germline variations remains challenging. The tag-single nucleotide polymorphism rs17728461 in 22q12.2 is highly associated with lung cancer risk. Here, we identified a functional rare germline variation rs548071605 (A>G) in a p65-responsive enhancer located within 22q12.2. The enhancer significantly promoted lung cancer cell proliferation in vitro and in a xenograft mouse model by upregulating the leukemia inhibitory factor (LIF) gene via the formation of a chromatin loop. Differential expression of LIF and its significant correlation with first progression survival time of patients further supported the lung cancer-driving effects of the 22q-Enh enhancer. Importantly, the rare variation was harbored in the p65 binding sequence and dramatically increased the enhancer activity by increasing responsiveness of the enhancer to p65 and B-cell lymphoma 3 protein, an oncoprotein that assisted the p65 binding. Our study revealed a regulatory rare germline variation with a potential lung cancer-driving role in the 22q12.2 risk region, providing intriguing clues for investigating the "missing heritability" of cancers, and also offered a useful experimental model for identifying causal rare variations.

Heritable Variants in the Chromosome 1q22 Locus Increase Gastric Cancer Risk via Altered Chromatin Looping and Increased UBAP2L Expression.

Genome-wide association studies (GWAS) have implicated the 1q22 gastric cancer risk locus in disease, but little is known about its underlying oncogenic functions. This study represents a systematic investigation of the biological significance and potential mechanism associated with the gastric cancer risk of SNP rs2075570(C>T) in 1q22. We identified two functional germline variations (rs2049805-C and rs2974931-G) in an active enhancer in a 64.8 kb high-linkage disequilibrium block of rs2075570. The enhancer upregulated ubiquitin associated protein 2 like (UBAP2L) gene expression over a 960 kb distance by chromatin looping. Gastric cancer tissues expressed significantly higher levels of UBAP2L than was observed in the matched noncancerous tissues, and the UBAP2L expression was negatively correlated with patient survival. Downregulation of UBAP2L inhibited the proliferation and invasion of human gastric cancer cells in vitro and in a xenograft mouse model. Notably, the two mutant variations significantly enforced the enhancer activity and UBAP2L expression. In conclusion, this study revealed two causal variations in the 1q22 region using tag-SNP rs2075570 as a genetic marker. These variations may affect the occurrence and progression of gastric cancer by reinforcing the expression of the 1q22-Enh enhancer-regulated UBAP2L target gene. IMPLICATIONS: Our study provides an important clue of how noncoding germline variations contribute to gastric cancer, which gives a novel insight into understanding the genetic mechanism of gastric cancer.

Application of a novel hybrid algorithm of Bayesian network in the study of hyperlipidemia related factors: a cross-sectional study.

BACKGROUND: This article aims to understand the prevalence of hyperlipidemia and its related factors in Shanxi Province. On the basis of multivariate Logistic regression analysis to find out the influencing factors closely related to hyperlipidemia, the complex network connection between various variables was presented through Bayesian networks(BNs). METHODS: Logistic regression was used to screen for hyperlipidemia-related variables, and then the complex network connection between various variables was presented through BNs. Since some drawbacks stand out in the Max-Min Hill-Climbing (MMHC) hybrid algorithm, extra hybrid algorithms are proposed to construct the BN structure: MMPC-Tabu, Fast.iamb-Tabu and Inter.iamb-Tabu. To assess their performance, we made a comparison between these three hybrid algorithms with the widely used MMHC hybrid algorithm on randomly generated datasets. Afterwards, the optimized BN was determined to explore to study related factors for hyperlipidemia. We also make a comparison between the BN model with logistic regression model. RESULTS: The BN constructed by Inter.iamb-Tabu hybrid algorithm had the best fitting degree to the benchmark networks, and was used to construct the BN model of hyperlipidemia. Multivariate logistic regression analysis suggested that gender, smoking, central obesity, daily average salt intake, daily average oil intake, diabetes mellitus, hypertension and physical activity were associated with hyperlipidemia. BNs model of hyperlipidemia further showed that gender, BMI, and physical activity were directly related to the occurrence of hyperlipidemia, hyperlipidemia was directly related to the occurrence of diabetes mellitus and hypertension; the average daily salt intake, daily average oil consumption, smoking, and central obesity were indirectly related to hyperlipidemia. CONCLUSIONS: The BN of hyperlipidemia constructed by the Inter.iamb-Tabu hybrid algorithm is more reasonable, and allows for the overall linking effect between factors and diseases, revealing the direct and indirect factors associated with hyperlipidemia and correlation between related variables, which can provide a new approach to the study of chronic diseases and their associated factors.

Suppressing Dehydroisomerization Boosts n-Butane Dehydrogenation with High Butadiene Selectivity.

Butadiene (BD) is a critical raw material in chemical industry, which is conventionally produced from naphtha cracking. The fast-growing demand of BD and the limited oil reserve motivate chemists to develop alternative methods for BD production. Shale gas, which mainly consists of light alkanes, has been considered as cheap raw materials to replace oil for BD production via n-butane direct dehydrogenation (n-BDH). However, the quest for highly-efficient catalysts for n-BDH is driven by the current drawback of low BD selectivity. Here, we demonstrate a strategy for boosting the selectivity of BD by suppressing dehydroisomerization, an inevitable step in the conventional n-BDH process which largely reduces the selectivity of BD. Detailed investigations show that the addition of alkali-earth metals (e. g., Mg and Ca) into Pt-Ga2 O3 /S10 catalysts increases Pt dispersity, suppresses coke deposition and dehydroisomerization, and thus leads to the significant increase of BD selectivity. The optimized catalyst displays an initial BD selectivity of 34.7 % at a n-butane conversion of 82.1 % at 625 °C, which outperforms the reported catalysts in literatures. This work not only provides efficient catalysts for BD production via n-BDH, but also promotes the researches on catalyst design in heterogeneous catalysis.

Exploratory study on classification of diabetes mellitus through a combined Random Forest Classifier.

BACKGROUND: Diabetes Mellitus (DM) has become the third chronic non-communicable disease that hits patients after tumors, cardiovascular and cerebrovascular diseases, and has become one of the major public health problems in the world. Therefore, it is of great importance to identify individuals at high risk for DM in order to establish prevention strategies for DM. METHODS: Aiming at the problem of high-dimensional feature space and high feature redundancy of medical data, as well as the problem of data imbalance often faced. This study explored different supervised classifiers, combined with SVM-SMOTE and two feature dimensionality reduction methods (Logistic stepwise regression and LAASO) to classify the diabetes survey sample data with unbalanced categories and complex related factors. Analysis and discussion of the classification results of 4 supervised classifiers based on 4 data processing methods. Five indicators including Accuracy, Precision, Recall, F1-Score and AUC are selected as the key indicators to evaluate the performance of the classification model. RESULTS: According to the result, Random Forest Classifier combining SVM-SMOTE resampling technology and LASSO feature screening method (Accuracy = 0.890, Precision = 0.869, Recall = 0.919, F1-Score = 0.893, AUC = 0.948) proved the best way to tell those at high risk of DM. Besides, the combined algorithm helps enhance the classification performance for prediction of high-risk people of DM. Also, age, region, heart rate, hypertension, hyperlipidemia and BMI are the top six most critical characteristic variables affecting diabetes. CONCLUSIONS: The Random Forest Classifier combining with SVM-SMOTE and LASSO feature reduction method perform best in identifying high-risk people of DM from individuals. And the combined method proposed in the study would be a good tool for early screening of DM.

A Review of the Current State of Hospice Care in China.

PURPOSE OF REVIEW: This review was undertaken to analyze the main reasons behind the limited development of hospice care in China, and to put forward some suggestions. RECENT FINDINGS: Although the Chinese government has increased its support for hospice care in recent years, however, owing to the lack of education around hospice care and the heavy influence of the traditional Chinese Confucian concept of "filial piety," many individuals resist hospice care. Moreover, due to impaired patient rights, inadequate composition of hospice care teams, unbalanced geographical distribution, and limited service range, the development of hospice care in China is hindered. Hospice care education and continued training should be popularized and the government should strengthen the legal structure of the medical system to protect the rights of patients, families, and medical staff to promoting social support for hospice care. Through graded diagnosis and referral systems in medical institutions to integrate medical resources and expand the range of hospice care services.

The inherited variations of a p53-responsive enhancer in 13q12.12 confer lung cancer risk by attenuating TNFRSF19 expression.

BACKGROUND: Inherited factors contribute to lung cancer risk, but the mechanism is not well understood. Defining the biological consequence of GWAS hits in cancers is a promising strategy to elucidate the inherited mechanisms of cancers. The tag-SNP rs753955 (A>G) in 13q12.12 is highly associated with lung cancer risk in the Chinese population. Here, we systematically investigate the biological significance and the underlying mechanism behind 13q12.12 risk locus in vitro and in vivo. RESULTS: We characterize a novel p53-responsive enhancer with lung tissue cell specificity in a 49-kb high linkage disequilibrium block of rs753955. This enhancer harbors 3 highly linked common inherited variations (rs17336602, rs4770489, and rs34354770) and six p53 binding sequences either close to or located between the variations. The enhancer effectively protects normal lung cell lines against pulmonary carcinogen NNK-induced DNA damages and malignant transformation by upregulating TNFRSF19 through chromatin looping. These variations significantly weaken the enhancer activity by affecting its p53 response, especially when cells are exposed to NNK. The effect of the mutant enhancer alleles on TNFRSF19 target gene in vivo is supported by expression quantitative trait loci analysis of 117 Chinese NSCLC samples and GTEx data. Differentiated expression of TNFRSF19 and its statistical significant correlation with tumor TNM staging and patient survival indicate a suppressor role of TNFRSF19 in lung cancer. CONCLUSION: This study provides evidence of how the inherited variations in 13q12.12 contribute to lung cancer risk, highlighting the protective roles of the p53-responsive enhancer-mediated TNFRSF19 activation in lung cells under carcinogen stress.

Fast response two-photon fluorogenic probe based on Schiff base derivatives for monitoring nitric oxide levels in living cells and zebrafish.

Numerous excellent fluorogenic probes for NO detection based on NO-mediated reactions have been developed. However, some of them still suffer from limitations such as low selectivity, slow response, and a short excitation wavelength (<500 nm). Herein, a novel two-photon fluorogenic probe (XNO1) based on a Schiff base derivative has been reported for the first time. This new mechanism with a Schiff base structure as the specific response moiety towards NO endows the probe with fast responsibility, high selectivity and pH-independent properties. Furthermore, XNO1 has been demonstrated to be lysosome-targeted and to successfully monitor exogenous/endogenous NO in living cells and zebrafishes with one- and two-photon fluorescence imaging.

Encapsulated Silver Nanoparticles Can Be Directly Converted to Silver Nanoshell in the Gas Phase.

We report, for the first time, that an encapsulated silver nanoparticle can be directly converted to a silver nanoshell through a nanoscale localized oxidation and reduction process in the gas phase. Silver can be etched when exposed to a mixture of NH3/O2 gases through a mechanism analogous to the formation of aqueous Tollens' reagent, in which a soluble silver-ammonia complex was formed. Starting with Ag@resorcinol-formaldehyde (RF) resin core-shell nanoparticles, we demonstrate that RF-core@Ag-shell nanoparticles can be prepared successfully when the etching rate and RF thickness were well controlled. Due to the strong surface plasmon resonance (SPR) coupling effect among neighboring silver nanoparticles, the RF@Ag nanoparticle showed great SPR and SERS performance. This process provides a general route to the conversion of Ag-core to Ag-shell nanostructures and might be extended to other systems.

One-Pot Synthesis of Monodisperse Noble Metal @ Resorcinol-Formaldehyde (M@RF) and M@Carbon Core-Shell Nanostructure and Their Catalytic Applications.

We demonstrate that noble metal @ RF core-shell nanostructures can be obtained through a facile one-pot synthesis approach in the absence of any additional surfactants. Monodisperse metal@RF core-shell nanostructures can be produced within 1 h on a large scale. Both the core size and shell thickness can be readily tuned by altering the reaction parameters. Systematic studies reveal that resorcinol could have several functions: it could act as a reactant to form RF resin, and it also could passivate the surface of metallic nanoparticles to prevent them from aggregating. Additionally, for the first time, our results suggest that resorcinol may act as a reducing agent that can reduce metal salts to form metal nanoparticles. The core-shell nanoparticles can be carbonized into M@carbon nanostructures, which have shown great performance in the catalytic hydrogenation of chlorobenzene. This work not only will help to achieve the controllable synthesis of noble metal@RF resin and M@carbon core-shell nanostructures but also will promote research into other RF-based nanostructures and their catalytic applications.