Metabolomic machine learning predictor for diagnosis and prognosis of gastric cancer.

Gastric cancer (GC) represents a significant burden of cancer-related mortality worldwide, underscoring an urgent need for the development of early detection strategies and precise postoperative interventions. However, the identification of non-invasive biomarkers for early diagnosis and patient risk stratification remains underexplored. Here, we conduct a targeted metabolomics analysis of 702 plasma samples from multi-center participants to elucidate the GC metabolic reprogramming. Our machine learning analysis reveals a 10-metabolite GC diagnostic model, which is validated in an external test set with a sensitivity of 0.905, outperforming conventional methods leveraging cancer protein markers (sensitivity < 0.40). Additionally, our machine learning-derived prognostic model demonstrates superior performance to traditional models utilizing clinical parameters and effectively stratifies patients into different risk groups to guide precision interventions. Collectively, our findings reveal the metabolic landscape of GC and identify two distinct biomarker panels that enable early detection and prognosis prediction respectively, thus facilitating precision medicine in GC.

A data-driven model for power system operating costs based on different types of wind power fluctuations.

The stochastic and intermittent features of wind power as well as the high percentage of wind power grid-connected significantly increase the additional operating costs of the power system. It is difficult to accurately calculate the impact of complex fluctuations in wind power on additional operating costs. To solve the above problems, a power system operating cost model adapted to various wind power fluctuation processes is established. Firstly, based on a two-layer clustering strategy, different types of wind power fluctuations are obtained. Then, a production simulation model of the power system with renewable energy is established. The production simulation model costs include thermal plant operating costs, energy storage system operating costs, positive reserve costs and negative reserve costs. With the optimization objective of minimizing the total operating cost of the power system, realistic and representative system operating parameters and cost samples are obtained for various wind power fluctuations and different wind power grid-connected scenarios. Finally, a data-driven approach based on a deep neural network algorithm is proposed to achieve precise mapping between wind energy fluctuations and the operating costs of power systems and thermal power units, and the operating costs of the power system during the four seasons with different types of wind power fluctuations can be precisely analyzed. The results demonstrate that the method proposed in this paper has high simulation accuracy for the overall simulation operating cost of the power system and the operating cost of thermal power plants. The simulation errors are 4%-18% and 3%-13%, respectively, which verified the effectiveness of the method.

Self-Assembly of Bi2Sn2O7/ß-Bi2O3 S-Scheme Heterostructures for Efficient Visible-Light-Driven Photocatalytic Degradation of Tetracycline.

Fabrication of S-scheme heterojunctions with enhanced redox capability offers an effective approach to address environmental remediation. In this study, high-performance Bi2Sn2O7/ß-Bi2O3 S-scheme heterojunction photocatalysts were fabricated via the in situ growth of Bi2Sn2O7 on ß-Bi2O3 microspheres. The optimized Bi2Sn2O7/ß-Bi2O3 (BSO/BO-0.4) degradation efficiency for tetracycline hydrochloride was 95.5%, which was 2.68-fold higher than that of ß-Bi2O3. This improvement originated from higher photoelectron-hole pair separation efficiency, more exposed active sites, excellent redox capacity, and efficient generation of ·O2 - and ·OH. Additionally, Bi2Sn2O7/ß-Bi2O3 exhibited good stability against photocatalytic degradation, and the degradation efficiency remained >89.7% after five cycles. The photocatalytic mechanism of Bi2Sn2O7/ß-Bi2O3 S-scheme heterojunctions was elucidated. In this study, we design and fabricate high-performance heterojunction photocatalysts for environmental remediation using S-scheme photocatalysts.

Tech-economic modeling and analysis of agricultural photovoltaic-water systems for irrigation in arid areas.

Affected by the shortage of water resources and land degradation, the sustainable development of agriculture in more and more arid areas will face serious obstacles. The combinations of agricultural photovoltaic, water transportation and irrigation systems are considered as a potential choice to solve above problem. This study aims to investigate the competitiveness of various system configurations to transport water from water resource to agricultural irrigation systems driven by the output power of agricultural photovoltaic. Including the levelized cost of electricity and net present value, a comprehensive techno-economic assessment model is proposed to analyze the agricultural photovoltaic and irrigation systems in arid areas for six scenarios. The applicability of the proposed model in managing regional water and renewable energy nexus systems was tested through application to a real-world case study in the Gansu province, China. Assuming that the baseline transportation distance is 50 km, the results show that exporting water to farmland through electric water trucks shows the best economic performance with the net present value of 13.71 MU$, and every 10 km increase in the transportation distance can decrease the net present value by 1.32 MU$. An important finding is that when the transportation distance was greater than 100 km, pipeline transportation mode was more economical than electric water truck transportation mode. Finally, a sensitivity analysis was carried out to analyze the electricity and water prices, farmland size, photovoltaic efficiency on the economic performance of these systems. Results show that only when the electricity price was greater than 0.08 $/kWh, pipeline transport mode yielded positive benefits, and every 0.1$/m3 increase in the water price can increase the net present value by 0.2 MU$.

Metabolomics Identifies a Panel of Diagnostic Biomarkers for Early Human Embryonic Development Arrest.

Early embryonic development arrest (EEDA) is a unique form of early spontaneous abortion in pregnant women, which is previously suggested to be associated with metabolic abnormalities. Noninvasive biomarkers would significantly improve its diagnosis and clinical outcome. Here, we performed a targeted metabolomics study in plasma from EEDA patients (n = 27) and normal pregnant women (NPW, n = 27) using liquid chromatography coupled with mass spectrometry (LC-MS) to identify potential diagnostic marker metabolites. Our results showed significantly different plasma metabolic profiles between EEDA patients and NPW. Particularly, EEDA patients showed significant alterations in amino acid, carbohydrate, and vitamin metabolism, which were characterized by 21 significantly increased metabolites and five decreased metabolites in plasma. Further receiver operating characteristic analysis showed that an optimal combination of S-methyl-5'-thioadenosine, kynurenine, leucine, and malate could be used as a panel of metabolites for EEDA diagnosis. The area under the curve of the metabolite panel was 0.941, suggesting a better performance than any single metabolite for the diagnosis of EEDA. In summary, our study identifies a panel of differential metabolites in plasma that could act as potential biomarkers for the diagnosis of EEDA in clinical settings.

Visible-light driven p-n heterojunction formed between α-Bi2O3 and Bi2O2CO3 for efficient photocatalytic degradation of tetracycline.

To improve the efficiency of photocatalytic oxidative degradation of antibiotic pollutants, it is essential to develop an efficient and stable photocatalyst. In this study, a polymer-assisted facile synthesis strategy is proposed for the polymorph-controlled α-Bi2O3/Bi2O2CO3 heterojunction retained at elevated calcination temperatures. The p-n heterojunction can effectively separate and migrate electron-hole pairs, which improves visible-light-driven photocatalytic degradation from tetracycline (TC). The BO-400@PAN-140 photocatalyst achieves the highest pollutant removal efficiency of 98.21% for photocatalytic tetracycline degradation in 1 h (λ > 420 nm), and the degradation efficiency was maintained above 95% after 5 cycles. The morphology, crystal structure, and chemical state of the composites were analysed by scanning electron microscopy, X-ray diffraction, and X-ray photoelectron spectroscopy. Ultraviolet-visible diffuse reflection, transient photocurrent response, and electrochemical impedance spectroscopy were adopted to identify the charge transfer and separation efficiency of photogenerated electron-hole pairs. The EPR results verified h+ and ˙OH radicals as the primary active species in the photocatalytic oxidation reactions. This observation was also consistent with the results of radical trapping experiments. In addition, the key intermediate products of the photocatalytic degradation of TC over BO-400@PAN-140 were identified via high-performance liquid chromatography-mass spectrometry, which is compatible with two possible photocatalytic reaction pathways. This work provides instructive guidelines for designing heterojunction photocatalysts via a polymer-assisted semiconductor crystallographic transition pathway for TC degradation into cleaner production.

Characteristics and Statistical Analysis of Large and above Hazardous Chemical Accidents in China from 2000 to 2020.

To investigate the occurrence and development pattern of large-scale hazardous chemicals emergencies, a statistical analysis of 195 large and above accidents of hazardous chemicals in China during 2000-2020 was conducted. A general description of the characteristics of larger and above accidents based on statistical data was analyzed, and then the system risk of the hazardous chemical industry was calculated and evaluated by the entropy weight method and the TOPSIS method comprehensively. Results show that: (1) The geographical distribution of large and above hazardous chemical accidents (LAHCA) varies significantly; (2) The high-temperature season has high probabilities of having large and above accidents; (3) Human factors and management factors are the main causes of LAHCA; (4) During the period from 2000 to 2020, due to the rapid development of the chemical industry, the overall risk of accidents involving hazardous chemicals were upswing accompanied by volatility, and the risk of serious accidents remains high. The development history of safety regulations in China's hazardous chemical sector and the industry's projected course for future growth were then discussed. Finally, based on the findings of the aforementioned statistics and research, specific recommendations were provided for the safety management of the hazardous chemical sector. This study expects to provide a practical and effective reference for the construction of safety management as well as accident prevention in the hazardous chemical industry.

Protected Health Information Recognition of Unstructured Code-Mixed Electronic Health Records in Taiwan.

Electronic health records (EHRs) at medical institutions provide valuable sources for research in both clinical and biomedical domains. However, before such records can be used for research purposes, protected health information (PHI) mentioned in the unstructured text must be removed. In Taiwan's EHR systems the unstructured EHR texts are usually represented in the mixing of English and Chinese languages, which brings challenges for de-identification. This paper presented the first study, to the best of our knowledge, of the construction of a code-mixed EHR de-identification corpus and the evaluation of different mature entity recognition methods applied for the code-mixed PHI recognition task.

A graphical approach for mixed ratio optimisation in the binary mixed amine solution.

Chemical absorption method plays an important role in the process of CO2 separation. One major problem for chemical absorption is huge energy consumption, which is affected by the performance of absorbents. Developing a type of absorbent with high absorption capacity and low regenerative energy consumption is a research topic that attracts attention. The combination of two or more amines is one way to develop new solvents. However, the change of amine liquid ratio can cause a series of complex nonlinear changes in absorption capacity, absorption heat, the heat of vaporisation and sensible heat. It is of interest to visualise the amine solution mixing ratio optimisation to help reduce the energy consumption and increase the absorption capacity. Derivative analysis of standardised vs variables diagram (DSVD), a kind of graphical method based on maximum benefit and minimum consumption, is proposed to determine the optimal mixing ratio of binary amine solution. This novel approach helps to visualise what kind of amines are not suitable for compounding, what kind of amines have the best compounding ratio, and how to determine the optimal compounding ratio. The optimal mixing ratio of the Methyldiethanolamine (MDEA) - Piperazine (PZ) system and MDEA - Monoethanolamine (MEA) were optimised by this method. The optimal ratio of MDEA - PZ and MDEA - MEA are 0.6 (PZ: MDEA = 0.6:0.4, wt.%) and 0.8 (MEA: MDEA = 0.8:0.2, wt.%).

Simultaneous 3-Nitrophenylhydrazine Derivatization Strategy of Carbonyl, Carboxyl and Phosphoryl Submetabolome for LC-MS/MS-Based Targeted Metabolomics with Improved Sensitivity and Coverage.

Metabolomics is a powerful and essential technology for profiling metabolic phenotypes and exploring metabolic reprogramming, which enables the identification of biomarkers and provides mechanistic insights into physiology and disease. However, its applications are still limited by the technical challenges particularly in its detection sensitivity for the analysis of biological samples with limited amount, necessitating the development of highly sensitive approaches. Here, we developed a highly sensitive liquid chromatography tandem mass spectrometry method based on a 3-nitrophenylhydrazine (3-NPH) derivatization strategy that simultaneously targets carbonyl, carboxyl, and phosphoryl groups for targeted metabolomic analysis (HSDccp-TM) in biological samples. By testing 130 endogenous metabolites including organic acids, amino acids, carbohydrates, nucleotides, carnitines, and vitamins, we showed that the derivatization strategy resulted in significantly improved detection sensitivity and chromatographic separation capability. Metabolic profiling of merely 60 oocytes and 5000 hematopoietic stem cells primarily isolated from mice demonstrated that this method enabled routine metabolomic analysis in trace amounts of biospecimens. Moreover, the derivatization strategy bypassed the tediousness of inferring the MS fragmentation patterns and simplified the complexity of monitoring ion pairs of metabolites, which greatly facilitated the metabolic flux analysis (MFA) for glycolysis, the tricarboxylic acid (TCA) cycle, and pentose phosphate pathway (PPP) in cell cultures. In summary, the novel 3-NPH derivatization-based method with high sensitivity, good chromatographic separation, and broad coverage showed great potential in promoting metabolomics and MFA, especially in trace amounts of biospecimens.

Total Site Heat Integration benefiting from geothermal energy for heating and cooling implementations.

Geothermal energy is a promising renewable energy source that has been developed by many countries in recent years. It can be utilised to meet various energy demand. This paper studies the performance of integrating geothermal energy in the Locally Integrated Energy Sector (LIES). The heating and cooling demand of various processes should be satisfied, and heat among processes should be recovered. This is done by using Grand Composite Curves and Total Site Profiles to visually illustrate how much load is required for utility systems. The geothermal utility system and steam utility system are compared. The integration plan for geothermal energy under different temperatures are studied. An illustrative case shows that by using this type of renewable energy under a specific and favourable condition, above 70% of steam utility load can be saved. The working cycle of using a geothermal utility system is studied by using the Time Slice model. The heat recovery plan for normal operation, mineral scaling, and cleaning periods are optimised. The minimum temperature for heat storage can also be identified.

Gluconeogenic enzyme PCK1 deficiency promotes CHK2 O-GlcNAcylation and hepatocellular carcinoma growth upon glucose deprivation.

Although cancer cells are frequently faced with a nutrient- and oxygen-poor microenvironment, elevated hexosamine-biosynthesis pathway (HBP) activity and protein O-GlcNAcylation (a nutrient sensor) contribute to rapid growth of tumor and are emerging hallmarks of cancer. Inhibiting O-GlcNAcylation could be a promising anticancer strategy. The gluconeogenic enzyme phosphoenolpyruvate carboxykinase 1 (PCK1) is downregulated in hepatocellular carcinoma (HCC). However, little is known about the potential role of PCK1 in enhanced HBP activity and HCC carcinogenesis under glucose-limited conditions. In this study, PCK1 knockout markedly enhanced the global O-GlcNAcylation levels under low-glucose conditions. Mechanistically, metabolic reprogramming in PCK1-loss hepatoma cells led to oxaloacetate accumulation and increased de novo uridine triphosphate synthesis contributing to uridine diphosphate-N-acetylglucosamine (UDP-GlcNAc) biosynthesis. Meanwhile, deletion of PCK1 also resulted in AMPK-GFAT1 axis inactivation, promoting UDP-GlcNAc synthesis for elevated O-GlcNAcylation. Notably, lower expression of PCK1 promoted CHK2 threonine 378 O-GlcNAcylation, counteracting its stability and dimer formation, increasing CHK2-dependent Rb phosphorylation and HCC cell proliferation. Moreover, aminooxyacetic acid hemihydrochloride and 6-diazo-5-oxo-L-norleucine blocked HBP-mediated O-GlcNAcylation and suppressed tumor progression in liver-specific Pck1-knockout mice. We reveal a link between PCK1 depletion and hyper-O-GlcNAcylation that underlies HCC oncogenesis and suggest therapeutic targets for HCC that act by inhibiting O-GlcNAcylation.

A Heat and Power Pinch for Process Integration targeting in hybrid energy systems.

Hybrid energy systems have been widely used for residential and industrial purposes. In this system, the total energy requirement of each unit can be met with heat and electricity. Pinch Analysis becomes a widely used tool for Process Integration, and using Pinch Analysis for Heat Integration is well-established. However, for the combined heat and power system, the theory and the corresponding tool deserve some more development. This paper extended the Pinch Analysis concept and proposed a Heat and Power Pinch Analysis to target the amount of heat that should be recovered from the hybrid energy system. Heat and Power Composite Curve (HPCC) is developed to visualise the total energy and the separated heat and power (electricity) requirement of a hybrid energy system in a working time period. The amount of outsourced electricity that should be purchased, and stored electricity at the startup period, and the extra electricity generated by the system at the end of the working period can be demonstrated. A case is studied to illustrate the steps of using this tool, two scenarios are discussed, and the targets are shown.

Integrated cytokine and metabolite analysis reveals immunometabolic reprogramming in COVID-19 patients with therapeutic implications.

Cytokine release syndrome (CRS) is a major cause of the multi-organ injury and fatal outcome induced by SARS-CoV-2 infection in severe COVID-19 patients. Metabolism can modulate the immune responses against infectious diseases, yet our understanding remains limited on how host metabolism correlates with inflammatory responses and affects cytokine release in COVID-19 patients. Here we perform both metabolomics and cytokine/chemokine profiling on serum samples from healthy controls, mild and severe COVID-19 patients, and delineate their global metabolic and immune response landscape. Correlation analyses show tight associations between metabolites and proinflammatory cytokines/chemokines, such as IL-6, M-CSF, IL-1α, IL-1ß, and imply a potential regulatory crosstalk between arginine, tryptophan, purine metabolism and hyperinflammation. Importantly, we also demonstrate that targeting metabolism markedly modulates the proinflammatory cytokines release by peripheral blood mononuclear cells isolated from SARS-CoV-2-infected rhesus macaques ex vivo, hinting that exploiting metabolic alterations may be a potential strategy for treating fatal CRS in COVID-19.

Untargeted Defining Protein-Metabolites Interaction Based on Label-Free Kinetic Size Exclusion Chromatography-Mass Spectrometry.

The specific interactions between protein and metabolites (PMIs) are closely related to many cellular processes and play a vital role in signal transduction and regulating material and energy metabolism. However, most of the available analytical strategies for PMIs involve chemical modification of metabolites or immobilization of protein, which has restricted current PMIs study mainly to lipid-protein and hydrophobic metabolites. In this work, a label-free online kinetic size exclusion chromatography-mass spectrometry (KSEC-MS) method combined with untargeted metabolomics was developed to define PMIs in a complex system. The metabolite mixture and target protein were injected into the SEC column sequentially without preincubation, and the separation results of KSEC were monitored by global metabolite profiling with mass spectrometry. The potential ligands in the metabolite mixture can be discovered if their migration patterns were affected by the target protein and the variation was positively correlated with the concentration of target protein. To verify this approach, carbonic anhydrase was first selected as a test protein, and acetazolamide as its known inhibitor was successfully defined. Furthermore, human serum albumin (HSA) as the common transport carrier of metabolites was selected as a target protein to demonstrate the usefulness of this approach. Multiple endogenous ligands of HSA were simultaneously defined from the extracted metabolites of human serum; most of them are polar metabolites rather than nonpolar lipids. This approach can provide a novel way for mapping and identifying unknown PMIs in a complex system, especially for polar metabolites-protein interactions.

Which is the urine sample material of choice for metabolomics-driven biomarker studies?

Urine-based metabolomics-driven strategies for the discovery of biomarkers are increasingly developed and applied in analytical chemistry. But valid, data-based recommendations for a urine sample material of choice are lacking. We investigated first and second morning urine (MU), which are the most commonly used urine specimens. Potential major factors biasing metabolomics biomarker results in these sample materials were studied. First, 35 1st and 2nd MU samples were collected from healthy, young men after an overnight fast. Subsequently, two subgroups were built, one having fast food at lunch and dinner (n = 17), the other vegetarian meals (n = 18). Again 1st and 2nd MU were collected. Non-targeted liquid chromatography-mass spectrometry was applied for analyses. More than half of the >5400 urinary ion features showed a significant difference between 1st and 2nd MU. Just two fast food meals on previous day significantly affected around 30% of all metabolites in 1st and 2nd MU. In contrast, the effects of two vegetarian meals in 2nd MU were only minor. Additionally, we describe 47 metabolites in urine, possible hits in biomarker studies, which are susceptible to the diet the day before sample collection. They should be handled with caution until validation in diet-controlled studies. Based on our results we think the second MU, ideally collected after standardized vegetarian meals and drinking only water on the previous day, is most suitable for valid analysis of biomarkers in urine.

Agonism activities of lyso-phosphatidylcholines (LPC) Ligands binding to peroxisome proliferator-activated receptor gamma (PPARγ).

PPARγ is an isoform of peroxisome proliferator-activated receptor (PPAR) belonging to a super family of nuclear receptors and is a primary target of the effective drug to treat the type II diabetes. The experiments found that Lyso-phosphatidylcholines (LPC) could bind to PPARγ, but the binding modes remain unknown. We used the Molecular Docking and Molecular Dynamic (MD) simulations to study the binding of four LPC ligands (LPC16:0, LPC18:0, LPC18:1-1 and LPC18:1-2) to PPARγ. The two-step MD simulations were employed to determine the final binding modes. The 20 ns MD simulations for four final LPC-PPARγ complexes were performed to analyze their structures, the binding key residues, and agonism activities. The results reveal that three LPC ligands (LPC16:0, LPC18:0 and LPC18:1-1) bind to Arm II and III regions of the Ligand Binding Domain (LBD) pocket, whereas they do not interact with Tyr473 of Helix 12 (H12). In contrast, LPC18:1-2 can form the hydrogen bonds with Tyr473 and bind into Arm I and II regions. Comparing with the paradigm systems of the full agonist (Rosiglitazone-PPARγ) and the partial agonist (MRL24-PPARγ), our results indicate that LPC16:0, LPC18:0 and LPC18:1-1 could be the potential partial agonists and LPC18:1-2 could be a full agonist. The in-depth analysis of the residue fluctuations and structure alignment confirm the present prediction of the LPC agonism activities.Communicated by Ramaswamy H. Sarma.

Parallel derivatization strategy coupled with liquid chromatography-mass spectrometry for broad coverage of steroid hormones.

Steroid hormones are a type of crucial substances that mediate numerous vital physiological functions. The comprehensive detection of steroid hormones can help understand the physiopathologic mechanism of steroid hormone-related diseases. It is very difficult to determine steroid hormones in biological samples due to their low endogenous concentrations and poor ionization efficiency. In this study, an efficient and sensitive approach was developed for profiling steroid hormones by combining liquid-liquid extraction and parallel derivatization with liquid chromatography-tandem mass spectrometry. Methoxyamine and dansyl chloride were used to derivatize steroid hormones containing carbonyl and phenolic hydroxyl groups, respectively. Our established method achieved simultaneous analysis of carbonyl and phenolic hydroxyl-containing steroid hormones and could cover estrogens, androgens, corticoids and progestogens. Twenty-nine steroid hormones were detected at pg/mL levels with the sensitivity enhanced by three orders of magnitude after derivatization. The linearity (with linear range of 2-4 orders of magnitude), precision (less than 15%) and recovery (71.1-128.7%) were satisfactory for quantitative analysis of steroid hormones. Finally, the established method was successfully employed to the determination of steroid hormones in serum samples of healthy males and females as well as ovarian cancer patients. The results showed that this approach was suitable and reliable for routine test of steroid hormones containing carbonyl and phenolic hydroxyl groups.

A comprehensive strategy for studying protein-metabolite interactions by metabolomics and native mass spectrometry.

Protein-metabolite interactions play important roles in many cellular and physiological processes in biological systems. However, the lack of effective research approaches impedes the understanding of the protein-metabolite interactions. In this study, a novel comprehensive strategy by combining metabolomics platform with native mass spectrometry was developed for investigating the protein-metabolite interactions. Peroxisome proliferator-activated receptors gamma (PPARγ) is a lipid-binding nuclear receptors that plays a key role in regulating fatty-acid oxidation and lipid metabolism, which was selected as the model protein. Seven metabolites including lyso-phosphatidylcholine (LPC) 16:0, LPC18:0, LPC18:1, arachidonic acid, oleic acid, linoleic acid and palmitoleic acid (p < 0.05) were found to have the possible interactions with the PPARγ, these LPCs were discovered as candidate ligands for the first time by using untargeted metabolomics method. Native mass spectrometry based on 15 T Fourier transform ion cyclotron resonance mass spectrometer was employed to directly detect the PPARγ-LPCs complexes to obtain their stoichiometry and kinetic constants. Isothermal titration calorimetry, circular dichroism spectrum and molecular modeling were further utilized to investigate the thermodynamics, conformation and binding mechanism of the interaction between PPARγ and LPCs. It was found that the PPARγ-LPC interaction was an endothermic process, and these LPCs have similar binding constants with stoichiometric number of 1:1. The novel strategy can provide a very useful approach for mapping and identifying unknown protein-metabolite interactions in biological systems.

Carboxymethylated polyethylenimine modified magnetic nanoparticles specifically for purification of His-tagged protein.

Employing immobilized metal-ion affinity chromatography and magnetic separation could ideally provide a useful analytical strategy for purifying His-tagged protein. In the current study, a facile route was designed to prepare CMPEI-Ni2+ @SiO2 @Fe3 O4 (CMPEI=carboxymethylated polyethyleneimine) magnetic nanoparticles composed of a strong magnetic core of Fe3 O4 and a Ni2+ -immobilized carboxymethylated polyethyleneimine coated outside shell, which was formed by electrostatic interactions between polyanionic electrolyte of carboxymethylated polyethyleneimine and positively charged surface of 3-(trimethoxysilyl)propylamin modified SiO2 @Fe3 O4 . The resulting CMPEI-Ni2+ @SiO2 @Fe3 O4 composite nanoparticles displayed well-uniform structure and high magnetic responsiveness. Hexa His-tagged peptides and purified His-tagged recombinant retinoid X receptor alpha were chosen as the model samples to evaluate the adsorption, capacity, and reusability of the composite nanoparticles. The results demonstrated the CMPEI-Ni2+ @SiO2 @Fe3 O4 nanoparticles possessed rapid adsorption, large capacity, and good recyclability. The obtained nanoparticles were further used to purify His-tagged protein in practical environment. It was found that the nanoparticles could selectively capture His-tagged recombinant retinoid X receptor protein from complex cell lysate. Owing to its easy synthesis, large binding capacity, and good reusability, the prepared CMPEI-Ni2+ @SiO2 @Fe3 O4 magnetic nanoparticles have great potential for application in biotechnological fields.