Desorption Separation Ionization Mass Spectrometry (DSI-MS) for Rapid Analysis of COVID-19.

During the coronavirus disease 2019 (COVID-19) pandemic, which has witnessed over 772 million confirmed cases and over 6 million deaths globally, the outbreak of COVID-19 has emerged as a significant medical challenge affecting both affluent and impoverished nations. Therefore, there is an urgent need to explore the disease mechanism and to implement rapid detection methods. To address this, we employed the desorption separation ionization (DSI) device in conjunction with a mass spectrometer for the efficient detection and screening of COVID-19 urine samples. The study encompassed patients with COVID-19, healthy controls (HC), and patients with other types of pneumonia (OP) to evaluate their urine metabolomic profiles. Subsequently, we identified the differentially expressed metabolites in the COVID-19 patients and recognized amino acid metabolism as the predominant metabolic pathway involved. Furthermore, multiple established machine learning algorithms validated the exceptional performance of the metabolites in discriminating the COVID-19 group from healthy subjects, with an area under the curve of 0.932 in the blind test set. This study collectively suggests that the small-molecule metabolites detected from urine using the DSI device allow for rapid screening of COVID-19, taking just three minutes per sample. This approach has the potential to expand our understanding of the pathophysiological mechanisms of COVID-19 and offers a way to rapidly screen patients with COVID-19 through the utilization of machine learning algorithms.

Abnormal upregulation of NUBP2 contributes to cancer progression in colorectal cancer.

Colorectal cancer (CRC), a digestive tract malignancy with high mortality and morbidity, lacks effective biomarkers for clinical prognosis due to its complex molecular pathogenesis. Nucleotide binding protein 2 (NUBP2) plays a vital role in the assembly of cytosolic Fe/S protein and has been implicated in cancer progression. In this study, we found that NUBP2 was highly expressed in CRC by TCGA database analysis. Subsequently, we verified the expression of NUBP2 in CRC tumor tissues and para-carcinoma tissues using IHC staining, and further investigated its association with clinicopathological parameters. In vitro cell experiments were conducted to assess the role of NUBP2 in CRC by evaluating cell proliferation, migration, and apoptosis upon NUBP2 dysregulation. Furthermore, we established a subcutaneous CRC model to evaluate the impact of NUBP2 on tumor growth in vivo. Additionally, we performed mechanistic exploration using a Human Phospho-Kinase Array-Membrane. Our results showed higher expression of NUBP2 in CRC tissues, which positively correlated with the pathological stage, indicating its involvement in tumor malignancy. Functional studies demonstrated that NUBP2 knockdown reduced cell proliferation, increased apoptosis, and impaired migration ability. Moreover, NUBP2 knockdown inhibited tumor growth in mice. We also observed significant changes in the phosphorylation level of GSK3ß upon NUBP2 knockdown or overexpression. Additionally, treatment with CHIR-99021 HCl, an inhibitor of GSK3ß, reversed the malignant phenotype induced by NUBP2 overexpression. Overall, this study elucidated the functional role of NUBP2 in CRC progression both in vitro and in vivo, providing insights into the molecular mechanisms underlying CRC and potential implications for targeted therapeutic strategies.

Correction: Machine learning encodes urine and serum metabolic patterns for autoimmune disease discrimination, classification and metabolic dysregulation analysis.

Correction for 'Machine learning encodes urine and serum metabolic patterns for autoimmune disease discrimination, classification and metabolic dysregulation analysis' by Qiuyao Du et al., Analyst, 2023, https://doi.org/10.1039/d3an01051a.

Machine learning encodes urine and serum metabolic patterns for autoimmune disease discrimination, classification and metabolic dysregulation analysis.

There is a wide variety of autoimmune diseases (ADs) with complex pathogenesis and their accurate diagnosis is difficult to achieve because of their vague symptoms. Metabolomics has been proven to be an efficient tool in the analysis of metabolic disorders to provide clues about the mechanism and diagnosis of diseases. Previous studies of the metabolomics analysis of ADs were not competent in their discrimination. Herein, a liquid chromatography tandem mass spectrometry (LC-MS) strategy combined with machine learning is proposed for the discrimination and classification of ADs. Urine and serum samples were collected from 267 subjects consisting of 127 healthy controls (HC) and 140 AD patients, including those with rheumatoid arthritis (RA), systemic lupus erythematosus (SLE), sicca syndrome (SS), ankylosing spondylitis (AS), systemic scleroderma (SSc) and connective tissue disease (CTD). Machine learning algorithms were encoded for the discrimination and classification of ADs with metabolomic patterns obtained by LC-MS, and satisfactory results were achieved. Notably, urine samples exhibited higher accuracy for disease differentiation and triage than serum samples. Apart from that, differential metabolites were selected and metabolite panels were evaluated to demonstrate their representativeness. Metabolic dysregulations were also investigated to gain more knowledge about the pathogenesis of ADs. This research provides a promising method for the application of metabolomics combined with machine learning in precision medicine.

Chilling injury monitoring and intensity identification of dryland maize in Heilongjiang.

BACKGROUND: Accurate and timely access to large-scale crop damage information provides an essential reference for responding to agricultural disaster prevention and mitigation needs and ensuring food production security. The present study aimed to reveal the new characteristics of low-temperature cold damage to maize in the context of climate warming. Heilongjiang, one of the provinces with the highest latitude, the most significant climate change and the largest maize production in China, was taken as the study area. We combined meteorological stations and MODIS remote sensing data to spatially identify the occurrence and intensity of cold damage to maize based on the growing season temperature distance level index, as well as to assess the extent of cold damage. RESULTS: The main findings are: (i) The frequency and intensity range of cold damage in the growing season (May to September) in Heilongjiang Province from 1991 to 2020 against climate warming showed a decreasing trend. The average temperature from 1991 to 2000 was 17.777 °C, with seven occurrences of maize cold damage years, of which 5 years comprised widespread cold damage and 2 years comprised regional cold damage. The average temperature from 2000 to 2010 was 18.137 °C, with cold damage three times, of which 2 years comprised regional cold damage and 1 year comprised widespread cold damage. The average temperature from 2010 to 2020 was 18.130 °C, with one maize cold damage year occurring, which comprised regional cold damage. The frequency of maize chilling injury decreased significantly from 1991 to 2020, from 0.23 in 1991-2000 to 0.1 in 2000-2010 and, finally, to 0.03 in 2010-2020. (ii) The good consistency between MODIS_LST data and temperature data from meteorological stations suggests that MODIS_LST data can be used to build a temperature remote sensing estimation model for spatially extensive cold damage monitoring and intensity discrimination. (iii) Taking 2009 as an example of a large-scale cold damage year, the spatial discrimination of maize cold damage intensity shows that the spatial distribution of chilling injury intensity has no obvious geographical features. The intensity of cold damage was mainly mild cold damage. According to administrative regions, the scope of chilling injury was the largest in Mudanjiang City, Heihe City, and Jixi City, accounting for 91.56%, 86.25%, and 84.91%, respectively. The areas with the most extensive range of severe chilling injuries were the Great Khingan Mountains region, Heihe City, Mudanjiang City, Yichun City, and Jixi City. CONCLUSION: In the context of climate warming, the frequency and intensity range of maize cold damage showed a decreasing trend from 1991 to 2020 in Heilongjiang Province. The results of cold damage identification based on MODIS_LST data are accurate and can improve the spatial accuracy. The results of the present study provide a reference and guidance for dealing with the occurrence and defence of spatially refined cold damage. © 2023 Society of Chemical Industry.

Influence of blended instruction on students' learning effectiveness: the role of Flow.

The rapid development and extensive application of information and communication technologies has facilitated blended instruction, which is regarded as the "new normal" in the field of modern education and has become the focus of academic research. This study thus explored the influencing mechanism of blended instruction on students' learning effectiveness from the perspective of complementarity and conflict with the support of flow. This study collected 349 survey data from universities in Southwest China that adopted a blended instruction mode and analyzed them using the structural equation model. The results demonstrated that complementary advantages and practical conflicts in blended instruction influenced students' flow experience during the learning process. Flow experience plays an important role in blended instruction and influences positively students' cognitive, emotional, and behavioral engagement. Learning engagement impacted positively students' learning effectiveness. In addition, self-efficacy positively moderated the relationship between students' learning engagement and learning effectiveness in blended instruction. These findings contribute to related research on blended instruction. The implications and limitations of this study are discussed.

Profiling of Urine Carbonyl Metabolic Fingerprints in Bladder Cancer Based on Ambient Ionization Mass Spectrometry.

The diagnosis of bladder cancer (BC) is currently based on cystoscopy, which is invasive and expensive. Here, we describe a noninvasive profiling method for carbonyl metabolic fingerprints in BC, which is based on a desorption, separation, and ionization mass spectrometry (DSI-MS) platform with N,N-dimethylethylenediamine (DMED) as a differential labeling reagent. The DSI-MS platform avoids the interferences from intra- and/or intersamples. Additionally, the DMED derivatization increases detection sensitivity and distinguishes carboxyl, aldehyde, and ketone groups in untreated urine samples. Carbonyl metabolic fingerprints of urine from 41 BC patients and 41 controls were portrayed and 9 potential biomarkers were identified. The mechanisms of the regulations of these biomarkers have been tentatively discussed. A logistic regression (LR) machine learning algorithm was applied to discriminate BC from controls, and an accuracy of 85% was achieved. We believe that the method proposed here may pave the way toward the point-of-care diagnosis of BC in a patient-friendly manner.

Alloy-Type Anodes for High-Performance Rechargeable Batteries.

Alloy-type anodes are one of the most promising classes of next-generation anode materials due to their ultrahigh theoretical capacity (2-10 times that of graphite). However, current alloy-type anodes have several limitations: huge volume expansion, high tendency to fracture and disintegrate, an unstable solid-electrolyte interphase (SEI) layer, and low Coulombic efficiency. Efforts to overcome these challenges are ongoing. This Review details recent progress in the research of batteries based on alloy-type anodes and discusses the direction of their future development. We conclude that improvements in structural design, the introduction of a protective interface, and the selection of suitable electrolytes are the most effective ways to improve the performance of alloy-type anodes. Furthermore, future studies should direct more attention toward analyzing their synergistic promoting effect.

Betaine prevented high-fat diet-induced NAFLD by regulating the FGF10/AMPK signaling pathway in ApoE-/- mice.

PURPOSE: Nonalcoholic fatty liver disease (NAFLD) is currently the leading cause of chronic liver disease in developing countries. The pathogenesis is complex, and there is currently no effective treatment. Betaine is an essential intermediate in choline catabolism and an important component of the methionine cycle. Betaine deficiency is associated with NAFLD severity, and its mechanism needs to be further elaborated. METHODS: In this study, an NAFLD mouse model was established by feeding ApoE-/- mice a high-fat diet. The effects of betaine on NAFLD were investigated, including its mechanism. RESULTS: In this study, after treatment with betaine, blood lipid levels and liver damage were significantly decreased in the NAFLD mouse model. The fat infiltration of the liver tissues of high-fat diet (HFD)-fed mice after betaine administration was significantly improved. Betaine treatment significantly upregulated AMP-activated protein kinase (AMPK), fibroblast growth factor 10 (FGF10), and adipose triglyceride lipase (ATGL) protein levels both in vivo and in vitro and suppressed lipid metabolism-related genes. Furthermore, the overexpression of FGF10 increased the protein level of AMPK and decreased lipid accumulation in HepG2 cells. CONCLUSION: Taken together, the data strongly suggest that betaine significantly prevents high-fat diet-induced NAFLD through the FGF10/AMPK signaling pathway in ApoE-/- mice.

The use of acupuncture for advanced cancer care: Protocol for a systematic review and meta-analysis.

AIM: To assess the up-to-date evidence of acupuncture for the management of cancer-related and cancer treatment-related outcomes among people with advanced cancer. DESIGN: Systematic review with meta-analyses involving multidimensional outcomes. METHODS: The protocol of this systematic review has been registered in PROSPERO with the registration number CRD42020212982. Six databases (including Pubmed, EMBASE, Cochrane Library, SinoMed, ClinicalTrials.gov and Chinese Clinical Trial Registry) will be searched from inception through November 2020 to identify relevant interventional trials examining acupuncture management on multidimensional outcomes in patients with advanced cancer. Main outcomes will include cancer and treatment-related symptoms, quality of life, sleep quality and adverse events. DerSimonian & Laird random-effects meta-analysis will be applied to calculate pooled relative risks for binary data and pooled weighted mean differences (WMDs) or standardized mean differences (SMDs) for continuous data. Trial quality ratings and risk of bias will be evaluated using the Cochrane Risk of Bias Tool and the Grading of Recommendations Assessment, Development and Evaluation approach. DISCUSSION: The efficacy of acupuncture on advanced cancer care and outcomes has not yet been determined. Palliative care for patients with advanced cancer may involve multiple challenges that include physical and mental health care. This systematic review will offer updated and comprehensive evidence of acupuncture on specific outcomes induced by advanced cancer and cancer-related treatment, which can give high level clinical recommendations to improve patient care and clinical outcomes.

Regulatory analysis of the C. elegans genome with spatiotemporal resolution.

Discovering the structure and dynamics of transcriptional regulatory events in the genome with cellular and temporal resolution is crucial to understanding the regulatory underpinnings of development and disease. We determined the genomic distribution of binding sites for 92 transcription factors and regulatory proteins across multiple stages of Caenorhabditis elegans development by performing 241 ChIP-seq (chromatin immunoprecipitation followed by sequencing) experiments. Integration of regulatory binding and cellular-resolution expression data produced a spatiotemporally resolved metazoan transcription factor binding map. Using this map, we explore developmental regulatory circuits that encode combinatorial logic at the levels of co-binding and co-expression of transcription factors, characterizing the genomic coverage and clustering of regulatory binding, the binding preferences of, and biological processes regulated by, transcription factors, the global transcription factor co-associations and genomic subdomains that suggest shared patterns of regulation, and identifying key transcription factors and transcription factor co-associations for fate specification of individual lineages and cell types.

Comparative analysis of regulatory information and circuits across distant species.

Despite the large evolutionary distances between metazoan species, they can show remarkable commonalities in their biology, and this has helped to establish fly and worm as model organisms for human biology. Although studies of individual elements and factors have explored similarities in gene regulation, a large-scale comparative analysis of basic principles of transcriptional regulatory features is lacking. Here we map the genome-wide binding locations of 165 human, 93 worm and 52 fly transcription regulatory factors, generating a total of 1,019 data sets from diverse cell types, developmental stages, or conditions in the three species, of which 498 (48.9%) are presented here for the first time. We find that structural properties of regulatory networks are remarkably conserved and that orthologous regulatory factor families recognize similar binding motifs in vivo and show some similar co-associations. Our results suggest that gene-regulatory properties previously observed for individual factors are general principles of metazoan regulation that are remarkably well-preserved despite extensive functional divergence of individual network connections. The comparative maps of regulatory circuitry provided here will drive an improved understanding of the regulatory underpinnings of model organism biology and how these relate to human biology, development and disease.

Association Between ESR1 PvuII, XbaI, and P325P Polymorphisms and Breast Cancer Susceptibility: A Meta-Analysis.

BACKGROUND: Breast cancer is one of the leading causes of cancer-related deaths for women. Numerous studies have shown that single-nucleotide polymorphisms (SNPs) on the ESR1 gene are associated to this disease. However, data and conclusions are inconsistent and controversial. MATERIAL AND METHODS: To investigate the association between PvuII (rs2234693), XbaI (rs9340799) and P325P (rs1801132) polymorphisms of ESR1 gene with the risk of breast cancer under different population categorizations, we searched multiple databases for data collection, and performed the meta-analysis on a total of 25 case-control studies. Three different comparison models - dominant model, recessive model, and homozygote comparison model - were applied to evaluate the association. RESULTS: Our results indicated that people with TT+TC or TT genotype were at a greater risk of developing breast cancer than those with CC genotype in the PvuII polymorphism. While for XbaI and P325P polymorphisms, no significance was found using any of the 3 models. Furthermore, the data were also stratified into different subgroups according to the ethnicity (white or Asian) and source of controls (hospital-based or population-based), and separate analyses were conducted to assess the association. The ethnicity subgroup assessment showed that the higher risk of breast cancer for TT genotype of PvuII polymorphism than CC genotype only occurred in Asian people, but not in white populations. For the source-stratified subgroup analysis, significant association suggested that people with TT + TC genotype were at a greater risk of developing breast cancer than those with CC genotype in the hospital-based subgroup. CONCLUSIONS: Thus, this meta-analysis clarified the inconsistent conclusions from previous studies, conducted analyses for the entire population as well as for different subgroups using diverse population categorization strategies, and has the potential to help provide a personalized risk estimate for breast cancer susceptibility.

[Comparison of echocardiographic parameters in healthy Chinese children born and living at high altitude or at sea-level].

OBJECTIVE: Chronic hypoxia at high altitude might result in cardiopulmonary adaptations including pulmonary arterial hypertension and cardiac remodeling. But little is known about the adaptive changes in healthy children born and living at high altitude. We compared the echocardiographic measurements between the healthy children living at 16 m (Shanghai, n = 220) and 3 700 m (Qinghai, total 257, Han children 117, Tibetan children 140). METHODS: Children's age ranged from 15 d to 14 years. Echocardiography measurements were performed, values of the left and right ventricular dimensions and wall thickness, systolic and diastolic function including cardiac output index (CI), as well as mean pulmonary arterial pressure (mPAP) were obtained. RESULTS: mPAP and right heart dimension, CI, right ventricular anterior wall thickness were significantly higher while ventricular systolic and diastolic function were significantly lower in 3 700 m group than in 16 m group (all P < 0.05). Left ventricular dimension was similar between the two groups. There were no differences on above measured parameters between the Han and Tibetan children from 3 700 m group (all P > 0.05). CONCLUSIONS: Children born and living at high altitude in China have significantly higher mPAP, dilated right heart and lower systolic and diastolic function of both ventricles and higher CI compared to children born and living at see-level. Above parameters were similar between the Han and the Tibetan childen born and living at high altitude. Present study provides reference values for the healthy children living at high altitude.

Diverse transcription factor binding features revealed by genome-wide ChIP-seq in C. elegans.

Regulation of gene expression by sequence-specific transcription factors is central to developmental programs and depends on the binding of transcription factors with target sites in the genome. To date, most such analyses in Caenorhabditis elegans have focused on the interactions between a single transcription factor with one or a few select target genes. As part of the modENCODE Consortium, we have used chromatin immunoprecipitation coupled with high-throughput DNA sequencing (ChIP-seq) to determine the genome-wide binding sites of 22 transcription factors (ALR-1, BLMP-1, CEH-14, CEH-30, EGL-27, EGL-5, ELT-3, EOR-1, GEI-11, HLH-1, LIN-11, LIN-13, LIN-15B, LIN-39, MAB-5, MDL-1, MEP-1, PES-1, PHA-4, PQM-1, SKN-1, and UNC-130) at diverse developmental stages. For each factor we determined candidate gene targets, both coding and non-coding. The typical binding sites of almost all factors are within a few hundred nucleotides of the transcript start site. Most factors target a mixture of coding and non-coding target genes, although one factor preferentially binds to non-coding RNA genes. We built a regulatory network among the 22 factors to determine their functional relationships to each other and found that some factors appear to act preferentially as regulators and others as target genes. Examination of the binding targets of three related HOX factors--LIN-39, MAB-5, and EGL-5--indicates that these factors regulate genes involved in cellular migration, neuronal function, and vulval differentiation, consistent with their known roles in these developmental processes. Ultimately, the comprehensive mapping of transcription factor binding sites will identify features of transcriptional networks that regulate C. elegans developmental processes.

Design of a hemispherical shell shaped natural ventilation temperature observation instrument.

Atmospheric temperature is fundamental information for various industries, such as production, life, and scientific research. The temperature error induced by the solar rays can reach 1 °C or even higher. A hemispherical shell-shaped atmospheric temperature measuring instrument that can reduce heat pollution and increase air velocity was designed. First, the instrument was optimized using computational fluid dynamics (CFD) software packages. Then, the CFD software packages were employed to quantify the temperature errors of the instrument with varying situations. A neural network model was employed to develop a temperature error correction model that can be targeted for multi-variable changes. This model provides accurate correction data when the influencing factors change continuously. Finally, field experiments were performed. The experimental data analysis indicates that the mean temperature error and the maximum error of the instrument before correction are 0.08 and 0.25 °C, respectively. The root mean square error, the mean absolute error, and the correlation coefficient between measured temperature errors from experiments and corrected temperature errors from the correction model are 0.099, 0.016, and 0.952 °C, respectively. By utilizing a temperature error correction model, the measuring error of the instrument can be minimized to a range between -0.05 and 0.04 °C. Consequently, the instrument is anticipated to enhance temperature measurement accuracy to ∼0.1 °C.

Construction of yield loss indicators for cold vortex, light-temperature-water combined stress during the flowering period of rice in Northeast China.

The construction of a yield loss evaluation index for the cold vortex type light-temperature-water composite adversity during rice flowering period in Northeast China is important for elucidating the impacts of cold vortex type composite disasters on rice yield loss in middle and high latitude areas. Moreover, it can provide meteorological support to ensure safe production of high-quality japonica rice in China and contribute to regional disaster reduction and efficiency improvement. By combining growth period data, meteorological data, and yield data, we delineated and constructed the composite stress occurrence index of cold vortex type light-temperature-water at the flowering stage of japonica. We analyzed the relationship between factors causing disasters and yield structure, as well as the relationship between different yield structures and yield by employing BP neural network method. We further dissected the processes involved in the causation of combined disasters. Based on the K-means clustering method and historical typical disaster years, we quantified the critical thresholds and disaster grades, and established an evaluation index and model for assessing yield loss caused by combined stress from cold vortex type light-temperature-water. Finally, we examined the spatial and temporal variations of low temperature, abundant rainfall, and reduced sunlight during the flowering period in the three provinces of Northeast China. Results showed that the critical thresholds for light, temperature, and water stress index during the flowering stage of mild, moderate, and severe cold vortex types were [0, 0.21), [0.21, 0.32), and [0.32, 0.64], respectively. The rates of yield loss were [0, 0.03), [0.03, 0.08), and [0.08, 0.096], respectively. Based on the verification results of a total of 751 samples in 11 random years from 1961 to 2020, the percentage of stations for which the production reduction grade, as calculated by the composite index developed in this study, aligning with the actual production reduction grade was 63.7%, consistently exceeding 58.0% annually. Moreover, the proportion of sites with a similarity or difference level of 1 stood at 88.3%, surpassing 85.0% in each year. The index could effectively assess the extent of rice yield loss caused by cold vortex disasters in Northeast China.

Urine Metabolic Profiling for Rapid Lung Cancer Screening: A Strategy Combining Rh-Doped SrTiO3-Assisted Laser Desorption/Ionization Mass Spectrometry and Machine Learning.

Lung cancer ranks among the cancers with the highest global incidence rates and mortality. Swift and extensive screening is crucial for the early-stage diagnosis of lung cancer. Laser desorption/ionization mass spectrometry (LDI-MS) possesses clear advantages over traditional analytical methods for large-scale analysis due to its unique features, such as simple sample processing, rapid speed, and high-throughput performance. As n-type semiconductors, titanate-based perovskite materials can generate charge carriers under ultraviolet light irradiation, providing the capability for use as an LDI-MS substrate. In this study, we employ Rh-doped SrTiO3 (STO/Rh)-assisted LDI-MS combined with machine learning to establish a method for urine-based lung cancer screening. We directly analyzed urine metabolites from lung cancer patients (LCs), pneumonia patients (PNs), and healthy controls (HCs) without employing any pretreatment. Through the integration of machine learning, LCs are successfully distinguished from HCs and PNs, achieving impressive area under the curve (AUC) values of 0.940 for LCs vs HCs and 0.864 for LCs vs PNs. Furthermore, we identified 10 metabolites with significantly altered levels in LCs, leading to the discovery of related pathways through metabolic enrichment analysis. These results suggest the potential of this method for rapidly distinguishing LCs in clinical applications and promoting precision medicine.

Drug Resistance Genes and Molecular Epidemiological Characteristics of Carbapenem-Resistant Klebsiella pneumonia.

Background: The global prevalence of carbapenem-resistant Klebsiella pneumoniae (CRKP) has become a serious challenge for nosocomial infection and attracted worldwide attention. This study explored the drug resistance genes and molecular characteristics for CRKP, providing a reference for nosocomial prevention and control. Methods: A total of 42 CRKP isolates were collected from the First Affiliated Hospital of Gannan Medical University (Ganzhou, China) from January 2018 to February 2021. The drug resistance of CRKP was tested by the VitekII Compact system. Drug resistance gene expression was detected by poly-merase chain reaction (PCR), and molecular typing was performed by pulsed-field gel electrophoresis (PFGE) and multi-locus sequence typing (MLST). Results: All the 42 CRKP isolates were multi-drug resistant. Among them, 35 isolates (83.3%) produced blaKPC-2 and 12 isolates (28.6%) produced blaNDM-1. The detection rate of blaIMP-4 and blaOXA-48 was 2.4% (1/42), respectively. Twelve isolates (28.6%) carried both blaKPC-2 and blaNDM-1, one isolate (2.4%) carried both blaKPC-2 and blaIMP-4, and one isolate (2.4%) carried blaKPC-2, blaNDM-1 and blaOXA-48. A variety of other extended-spectrum beta-lactamases (ESBLs) were also detected. All 42 isolates carried blaSHV and blaCTX-M-1, 27 isolates (64.3%) carried blaTEM and 12 isolates (28.6%) carried blaCTX-M-9. The MLST data classified the 42 CRKP isolates into 11 sequence types, mainly ST11, accounting for 61.9% (26/42), of which 92.3% of isolates (24/26) carrying blaKPC-2. The PFGE results demonstrated that the 42 CRKP isolates could be divided into 20 clusters A-T, with cluster A (26.2%, 11/42) and cluster H (21.4%, 9/42) dominating, which were all ST11. Conclusion: The CRKP isolates were severely multi-drug resistant, and the main resistant gene was blaKPC-2 production, carrying multiple ESBLs genes simultaneously. The MLST and PFGE revealed that the ST11-blaKPC-2 Klebsiella pneumoniae was the main clonotype. Our findings may offer help to antibiotics selection and nosocomial infection prevention and control.