AcrAB-TolC efflux pump overexpression and tet(A) gene mutation increase tigecycline resistance in Klebsiella pneumoniae.

Tigecycline-non-susceptible Klebsiella pneumoniae (TNSKP) is increasing and has emerged as a global public health issue. However, the mechanism of tigecycline resistance remains unclear. The objective of this study was to investigate the potential role of efflux pump system in tigecycline resistance. 29 tigecycline-non-susceptible Klebsiella pneumoniae (TNSKP) strains were collected and their minimum inhibitory concentrations (MIC) were determined by the broth microdilution method. The ramR, acrR, rpsJ, tet(A), and tet(X) were amplified by polymerase chain reaction (PCR). The mRNA expression of different efflux pump genes and regulator genes were analyzed by real-time PCR. Additionally, KP14 was selected for genome sequencing. KP14 genes without acrB, oqxB, and TetA were modified using suicide plasmids and MIC of tigecycline of KP14 with target genes knocked out was investigated. It was found that MIC of tigecycline of 20 out of the 29 TNSKP strains decreased by over four folds once combined with phenyl-arginine-ß-naphthylamide dihydrochloride (PaßN). Most strains exhibited upregulation of AcrAB and oqxAB efflux pumps. The strains with acrB, oqxB, and tetA genes knocked out were constructed, wherein the MIC of tigecycline of KP14∆acrB and KP14∆tetA was observed to be 2 µg/mL (decreased by 16 folds), the MIC of tigecycline of KP14ΔacrBΔTetA was 0.25 µg/mL (decreased by 128 folds), but the MIC of tigecycline of KP14∆oqxB remained unchanged at 32 µg/mL. The majority of TNSKP strains demonstrated increased expression of AcrAB-TolC and oqxAB, while certain strains showed mutations in other genes associated with tigecycline resistance. In KP14, both overexpression of AcrAB-TolC and tet(A) gene mutation contributed to the mechanism of tigecycline resistance.

Significance of non-DLVO interactions on the co-transport of levofloxacin and titanium dioxide nanoparticles in porous media.

With the application of engineered nanomaterials and antibiotics in the fields of medicine, aerospace, new energy and agriculture, the associated contamination is detected widely in soil-groundwater systems. It is of great scientific and practical significance to deeply explore the environmental interface process between nanoparticles and antibiotics for the scientific assessment of environmental fate and ecological environmental risks, as well as the development of new composite pollution control technologies. In this study, the co-transport behaviors of positively charged titanium dioxide nanoparticles (TiO2-NPs) and negatively charged levofloxacin (LEV) in quartz sand (QS) are investigated in this study. The results show that TiO2-NPs hardly flow out when transported alone in the column because of its positive charge, which creates a strong attraction with the negatively charged quartz sand on the surface. When TiO2-NPs co-migrate with LEV in porous media, the presence of LEV promotes the transport of TiO2-NPs, while the presence of TiO2-NPs inhibits LEV transport. Non-XDLVO interactions based on molecular dynamics (MD) simulations can help explain the observed promotion and inhibition phenomena as well as the correlation between TiO2-NPs and LEV. The results indicate that TiO2-LEV complexes or aggregates can be formed during the co-transportation process of TiO2-NPs and LEV in porous media. As flow velocity increases from 0.204 cm min-1 to 1.630 cm min-1, both the transport capacities of TiO2-NPs and LEV are enhanced significantly. Under the condition of high citric acid (CA) concentration (15 mmol L-1), the transport capacity of TiO2-NPs is slightly inhibited, while the transport capacity of LEV is enhanced. This study provides new insights into the transport of nanometallic oxides and antibiotics in porous media, which suggests that non-XDLVO interactions should be considered together when assessing the environmental risks and fate of nanometallic oxides and antibiotics in soil-groundwater systems.

Emergence of ceftazidime-avibactam resistance in blaKPC-33-harbouring ST11 Klebsiella pneumoniae in a paediatric patient.

Carbapenem-resistant Klebsiella pneumoniae (CRKP) poses immense threats to the health of infected patients worldwide, especially children. This study reports the infection caused by CRKP in a paediatric intensive care unit (PICU) child and its drug-resistant mutation during the treatment. Twelve Klebsiella pneumoniae carbapenemase (KPC)-producing K. pneumoniae strains were isolated from the child. Broth microdilution method, plasmid transformation assay, and whole genome sequencing (WGS) were performed to investigate the antimicrobial susceptibility, resistance mechanisms, and genetic structural features of CRKPs. The results showed that 12 strains were highly resistant to most available antimicrobial agents. Among them, K. pneumoniae FD11 and K. pneumoniae FD12 were resistant to ceftazidime-avibactam (CZA, MIC >64 mg/L) and restored the carbapenem susceptibility (Imipenem, MIC =0.25 mg/L; Meropenem, MIC =2 mg/L). The patient improved after treatment with CZA in combination with aztreonam. Plasmid transformation assay demonstrated that the blaKPC-33-positive transformant increased MICs of CZA by at least 33-fold and 8-fold compared with the recipient Escherichia coli DH5α and blaKPC-2-positive transformants. WGS analysis revealed that all strains belonged to the ST11-KL64 type and showed highly homologous (3-26 single nucleotide polymorphisms [SNPs]). A single base mutation (G532T) of blaKPC-2 resulted in a tyrosine to aspartic acid substitution at Ambler amino acid position 179 (D179Y), which conferred CZA resistance in K. pneumoniae. This is the first report of a drug-resistant mutation evolving into blaKPC-33 during the treatment of blaKPC-2-positive CRKP in paediatric-infected patients. It advises clinicians that routine sequential antimicrobial susceptibility testing and KPC genotyping are critical during CZA therapy in children infected with CRKP.

Antimicrobial resistance prediction by clinical metagenomics in pediatric severe pneumonia patients.

BACKGROUND: Antimicrobial resistance (AMR) is a major threat to children's health, particularly in respiratory infections. Accurate identification of pathogens and AMR is crucial for targeted antibiotic treatment. Metagenomic next-generation sequencing (mNGS) shows promise in directly detecting microorganisms and resistance genes in clinical samples. However, the accuracy of AMR prediction through mNGS testing needs further investigation for practical clinical decision-making. METHODS: We aimed to evaluate the performance of mNGS in predicting AMR for severe pneumonia in pediatric patients. We conducted a retrospective analysis at a tertiary hospital from May 2022 to May 2023. Simultaneous mNGS and culture were performed on bronchoalveolar lavage fluid samples obtained from pediatric patients with severe pneumonia. By comparing the results of mNGS detection of microorganisms and antibiotic resistance genes with those of culture, sensitivity, specificity, positive predictive value, and negative predictive value were calculated. RESULTS: mNGS detected bacterial in 71.7% cases (86/120), significantly higher than culture (58/120, 48.3%). Compared to culture, mNGS demonstrated a sensitivity of 96.6% and a specificity of 51.6% in detecting pathogenic microorganisms. Phenotypic susceptibility testing (PST) of 19 antibiotics revealed significant variations in antibiotics resistance rates among different bacteria. Sensitivity prediction of mNGS for carbapenem resistance was higher than penicillins and cephalosporin (67.74% vs. 28.57%, 46.15%), while specificity showed no significant difference (85.71%, 75.00%, 75.00%). mNGS also showed a high sensitivity of 94.74% in predicting carbapenem resistance in Acinetobacter baumannii. CONCLUSIONS: mNGS exhibits variable predictive performance among different pathogens and antibiotics, indicating its potential as a supplementary tool to conventional PST. However, mNGS currently cannot replace conventional PST.

Exploring the Correlation between Azido-Tetrazolo Tautomerizations and Isomer Structures: Electron Density of Bonding N Atoms and N-N Bond Polarity.

Azido-tetrazolo tautomerizations between azido N-heteroaromatic compounds and tetrazole-fused energetic materials can produce a new generation of high-energy density compounds. Density functional theory (DFT) computations are performed to explore the relationship between reaction barriers and electron densities of bonding N atoms, i.e., the terminal N1 and heterocyclic N2 atoms, for six reported tautomerizations. The results reveal four linear correlations between reverse reaction barriers (Gr) and the electron densities of N1 and N2 atoms in the product. N1 electron density (ρN1) and N-N bond polarity, as measured by the difference between the electron densities on the two N atoms (ΔρN = ρN1 - ρN2) in products, are inversely proportional to the reverse reaction barriers. They are also proportional to the energy barrier differences between the forward and reverse reactions (ΔG = Gf - Gr). Polar solvents, including DMSO, water, and acetone, can effectively increase the reverse reaction barriers (Gr) by improving the stability of products. This regularity is further confirmed by its application to four additional tautomerizations and can be used to screen out unfavorable azido-tetrazolo tautomerization reactions and increase the success rate of such synthesis.

S-Doping Regulated Iron Spin States in Fe-N-C Single-Atom Material for Enhanced Peroxidase-Mimicking Activity at Neutral pH.

Designing biomimetic nanomaterials with peroxidase (POD)-like activity at neutral pH remains a significant challenge. An S-doping strategy is developed to afford an iron single-atom nanomaterial (Fe1@CN-S) with high POD-like activity under neutral conditions. To the best of knowledge, there is the first example on the achievement of excellent POD-like activity under neutral conditions by regulating the active site structure. S-doping not only promotes the dissociation of the N─H bond in 3,3″,5,5″-tetramethylbenzidine (TMB), but also facilitates the desorption of OH* by the transformation of iron species' spin states from middle-spin (MS FeII) to low-spin (LS FeII). Meanwhile, LS FeII sites typically have more unfilled d orbitals, thereby exhibiting stronger interactions with H2O2 than MS FeII, which can enhance POD-like activity. Finally, a one-pot visual detection of glucose at pH 7 is performed, demonstrating the best selectivity and sensitivity than previous reports.

Nanozyme sensor array based on Fe, Se co-doped carbon material for the discrimination of Sulfur-containing compounds.

Developing methods for the accurate identification and analysis of sulfur-containing compounds (SCCs) is of great significance because of their essential roles in living organisms and the diagnosis of diseases. Herein, Se-doping improved oxidase-like activity of iron-based carbon material (Fe-Se/NC) was prepared and applied to construct a four-channel colorimetric sensor array for the detection and identification of SCCs (including biothiols and sulfur-containing metal salts). Fe-Se/NC can realize the chromogenic oxidation of 3,3',5,5'-tetramethylbenzidine (TMB) by activating O2 without relying on H2O2, which can be inhibited by different SCCs to diverse degrees to produce different colorimetric response changes as "fingerprints" on the sensor array. Principal component analysis (PCA) and hierarchical cluster analysis (HCA) revealed that nine kinds of SCCs could be well discriminated. The sensor array was also applied for the detection of SCCs with a linear range of 1-50 µM and a limit of detection of 0.07-0.2 µM. Moreover, colorimetric sensor array inspired by the different levels of SCCs in real samples were used to discriminate cancer cells and food samples, demonstrating its potential application in the field of disease diagnosis and food monitoring. ENVIRONMENTAL IMPLICATIONS: In this work, a four-channel colorimetric sensor array for accurate SCCs identification and detection was successfully constructed. The colorimetric sensor array inspired by the different levels of SCCs in real samples were also used to discriminate cancer cells and food samples. Therefore, this Fe-Se/NC based sensor array is expected to be applied in the field of environmental monitoring and environment related disease diagnosis.

Malat1 regulates PMN-MDSC expansion and immunosuppression through p-STAT3 ubiquitination in sepsis.

Myeloid-derived suppressor cells (MDSCs) expand during sepsis and contribute to the development of persistent inflammation-immunosuppression-catabolism syndrome. However, the underlying mechanism remains unclear. Exploring the mechanisms of MDSCs generation may provide therapeutic targets for improving immune status in sepsis. Here, a sepsis mouse model is established by cecal ligation and perforation. Bone marrow cells at different sepsis time points are harvested to detect the proportion of MDSCs and search for differentially expressed genes by RNA-sequence. In lethal models of sepsis, polymorphonuclear-MDSCs (PMN-MDSCs) decrease in early but increase and become activated in late sepsis, which is contrary to the expression of metastasis-associated lung adenocarcinoma transcript 1 (Malat1). In vivo, Malat1 inhibitor significantly increases the mortality in mice with late sepsis. And in vitro, Malat1 down-regulation increases the proportion of PMN-MDSCs and enhanced its immunosuppressive ability. Mechanistically, Malat1 limits the differentiation of PMN-MDSCs by accelerating the degradation of phosphorylated STAT3. Furthermore, Stattic, an inhibitor of STAT3 phosphorylation, improves the survival of septic mice by inhibiting PMN-MDSCs. Overall, the study identifies a novel insight into the mechanism of sepsis-induced MDSCs and provides more evidence for targeting MDSCs in the treatment of sepsis.

Establishing a pulmonary aspergillus fumigatus infection diagnostic platform based on RPA-CRISPR-Cas12a.

In this study, we devised a diagnostic platform harnessing a combination of recombinase polymerase amplification (RPA) and the clustered regularly interspaced short palindromic repeats (CRISPR)/Cas12a system. Notably, this platform obviates the need for intricate equipment and finds utility in diverse settings. Two result display methods were incorporated in this investigation: the RPA-Cas12a-fluorescence method and the RPA-Cas12a-LFS (lateral flow strip). Upon validation, both display platforms exhibited no instances of cross-reactivity, with seven additional types of fungal pathogens responsible for respiratory infections. The established detection limit was ascertained to be as low as 102 copies/µL. In comparison to fluorescence quantitative PCR, the platform demonstrated a sensitivity of 96.7%, a specificity of 100%, and a consistency rate of 98.0%.This platform provides expeditious, precise, and on-site detection capabilities, thereby rendering it a pivotal diagnostic instrument amenable for deployment in primary healthcare facilities and point-of-care settings.

Brain development in newborns and infants after ECMO.

BACKGROUND: Extracorporeal membrane oxygenation (ECMO) not only significantly improves survival rates in severely ill neonates but also is associated with long-term neurodevelopmental issues. To systematically review the available literature on the neurodevelopmental outcomes of neonates and infants who have undergone ECMO treatment, with a focus on motor deficits, cognitive impairments, sensory impairments, and developmental delays. This review aims to understand the incidence, prevalence, and risk factors for these problems and to explore current nursing care and management strategies. DATA SOURCES: A comprehensive literature search was performed across PubMed, EMBASE, and Web of Science using a wide array of keywords and phrases pertaining to ECMO, neonates, infants, and various facets of neurodevelopment. The initial screening involved reviewing titles and abstracts to exclude irrelevant articles, followed by a full-text assessment of potentially relevant literature. The quality of each study was evaluated based on its research methodology and statistical analysis. Moreover, citation searches were conducted to identify potentially overlooked studies. Although the focus was primarily on neonatal ECMO, studies involving children and adults were also included due to the limited availability of neonate-specific literature. RESULTS: About 50% of neonates post-ECMO treatment exhibit varying degrees of brain injury, particularly in the frontal and temporoparietal white matter regions, often accompanied by neurological complications. Seizures occur in 18%-23% of neonates within the first 24 hours, and bleeding events occur in 27%-60% of ECMO procedures, with up to 33% potentially experiencing ischemic strokes. Although some studies suggest that ECMO may negatively impact hearing and visual development, other studies have found no significant differences; hence, the influence of ECMO remains unclear. In terms of cognitive, language, and intellectual development, ECMO treatment may be associated with potential developmental delays, including lower composite scores in cognitive and motor functions, as well as potential language and learning difficulties. These studies emphasize the importance of early detection and intervention of potential developmental issues in ECMO survivors, possibly necessitating the implementation of a multidisciplinary follow-up plan that includes regular neuromotor and psychological evaluations. Overall, further multicenter, large-sample, long-term follow-up studies are needed to determine the impact of ECMO on these developmental aspects. CONCLUSIONS: The impact of ECMO on an infant's nervous system still requires further investigation with larger sample sizes for validation. Fine-tuned management, comprehensive nursing care, appropriate patient selection, proactive monitoring, nutritional support, and early rehabilitation may potentially contribute to improving the long-term outcomes for these infants.

Identification and validation of an explainable prediction model of acute kidney injury with prognostic implications in critically ill children: a prospective multicenter cohort study.

Background: Acute kidney injury (AKI) is a common and serious organ dysfunction in critically ill children. Early identification and prediction of AKI are of great significance. However, current AKI criteria are insufficiently sensitive and specific, and AKI heterogeneity limits the clinical value of AKI biomarkers. This study aimed to establish and validate an explainable prediction model based on the machine learning (ML) approach for AKI, and assess its prognostic implications in children admitted to the pediatric intensive care unit (PICU). Methods: This multicenter prospective study in China was conducted on critically ill children for the derivation and validation of the prediction model. The derivation cohort, consisting of 957 children admitted to four independent PICUs from September 2020 to January 2021, was separated for training and internal validation, and an external data set of 866 children admitted from February 2021 to February 2022 was employed for external validation. AKI was defined based on serum creatinine and urine output using the Kidney Disease: Improving Global Outcome (KDIGO) criteria. With 33 medical characteristics easily obtained or evaluated during the first 24 h after PICU admission, 11 ML algorithms were used to construct prediction models. Several evaluation indexes, including the area under the receiver-operating-characteristic curve (AUC), were used to compare the predictive performance. The SHapley Additive exPlanation method was used to rank the feature importance and explain the final model. A probability threshold for the final model was identified for AKI prediction and subgrouping. Clinical outcomes were evaluated in various subgroups determined by a combination of the final model and KDIGO criteria. Findings: The random forest (RF) model performed best in discriminative ability among the 11 ML models. After reducing features according to feature importance rank, an explainable final RF model was established with 8 features. The final model could accurately predict AKI in both internal (AUC = 0.929) and external (AUC = 0.910) validations, and has been translated into a convenient tool to facilitate its utility in clinical settings. Critically ill children with a probability exceeding or equal to the threshold in the final model had a higher risk of death and multiple organ dysfunctions, regardless of whether they met the KDIGO criteria for AKI. Interpretation: Our explainable ML model was not only successfully developed to accurately predict AKI but was also highly relevant to adverse outcomes in individual children at an early stage of PICU admission, and it mitigated the concern of the "black-box" issue with an undirect interpretation of the ML technique. Funding: The National Natural Science Foundation of China, Jiangsu Province Science and Technology Support Program, Key talent of women's and children's health of Jiangsu Province, and Postgraduate Research & Practice Innovation Program of Jiangsu Province.

Cluster Synchronization of Finite-Field Networks.

This technical paper investigates the cluster synchronization of finite-field networks (FFNs) based on the algebraic state space representation. By resorting to the semi-tensor product of matrices, the cluster synchronization of an FFN can be completely converted into the set stability of state trajectory. Then, a necessary and sufficient condition for the cluster synchronization of FFNs is obtained based on the invariant subset and one-step transition matrix. In particular, the obtained results are applicable to check the leader-follower synchronization and group consensus of FFNs. Finally, a numerical example is given to illustrate the feasibility of the obtained results.

EEGProgress: A fast and lightweight progressive convolution architecture for EEG classification.

Because of the intricate topological structure and connection of the human brain, extracting deep spatial features from electroencephalograph (EEG) signals is a challenging and time-consuming task. The extraction of topological spatial information plays a crucial role in EEG classification, and the architecture of the spatial convolution greatly affects the performance and complexity of convolutional neural network (CNN) based EEG classification models. In this study, a progressive convolution CNN architecture named EEGProgress is proposed, aiming to efficiently extract the topological spatial information of EEG signals from multi-scale levels (electrode, brain region, hemisphere, global) with superior speed. To achieve this, the raw EEG data is permuted using the empirical topological permutation rule, integrating the EEG data with numerous topological properties. Subsequently, the spatial features are extracted by a progressive feature extractor including prior, electrode, region, and hemisphere convolution blocks, progressively extracting the deep spatial features with reduced parameters and speed. Finally, the comparison and ablation experiments under both cross-subject and within-subject scenarios are conducted on a public dataset to verify the performance of the proposed EEGProgress and the effectiveness of the topological permutation. The results demonstrate the superior feature extraction ability of the proposed EEGProgress, with an average increase of 4.02% compared to other CNN-based EEG classification models under both cross-subject and within-subject scenarios. Furthermore, with the obtained average testing time, FLOPs, and parameters, the proposed EEGProgress outperforms other comparison models in terms of model complexity.

Molecular Evolution and Increasing Macrolide Resistance of Bordetella pertussis, Shanghai, China, 2016-2022.

Resurgence and spread of macrolide-resistant Bordetella pertussis (MRBP) threaten global public health. We collected 283 B. pertussis isolates during 2016-2022 in Shanghai, China, and conducted 23S rRNA gene A2047G mutation detection, multilocus variable-number tandem-repeat analysis, and virulence genotyping analysis. We performed whole-genome sequencing on representative strains. We detected pertussis primarily in infants (0-1 years of age) before 2020 and older children (>5-10 years of age) after 2020. The major genotypes were ptxP1/prn1/fhaB3/ptxA1/ptxC1/fim2-1/fim3-1 (48.7%) and ptxP3/prn2/fhaB1/ptxA1/ptxC2/fim2-1/fim3-1 (47.7%). MRBP increased remarkably from 2016 (36.4%) to 2022 (97.2%). All MRBPs before 2020 harbored ptxP1, and 51.4% belonged to multilocus variable-number tandem-repeat analysis type (MT) 195, whereas ptxP3-MRBP increased from 0% before 2020 to 66.7% after 2020, and all belonged to MT28. MT28 ptxP3-MRBP emerged only after 2020 and replaced the resident MT195 ptxP1-MRBP, revealing that 2020 was a watershed in the transformation of MRBP.

The nuclear cytokine IL-37a controls lethal cytokine storms primarily via IL-1R8-independent transcriptional upregulation of PPARγ.

Cytokine storms are crucial in the development of various inflammatory diseases, including sepsis and autoimmune disorders. The immunosuppressive cytokine INTERLEUKIN (IL)-37 consists of five isoforms (IL-37a-e). We identified IL-37a as a nuclear cytokine for the first time. Compared to IL-37b, IL-37a demonstrated greater efficacy in protecting against Toll-like receptor-induced cytokine hypersecretion and lethal endotoxic shock. The full-length (FL) form of IL-37a and the N-terminal fragment, which is processed by elastase, could translocate into cell nuclei through a distinctive nuclear localization sequence (NLS)/importin nuclear transport pathway. These forms exerted their regulatory effects independent of the IL-1R8 receptor by transcriptionally upregulating the nuclear receptor peroxisome proliferator-activated receptor (PPARγ). This process involved the recruitment of the H3K4 methyltransferase complex WDR5/MLL4/C/EBPß and H3K4me1/2 to the enhancer/promoter of Pparg. The receptor-independent regulatory pathway of the nuclear IL-37a-PPARγ axis and receptor-dependent signaling by secreted IL-37a maintain homeostasis and are potential therapeutic targets for various inflammatory diseases, including sepsis.

2023 China Guidelines for Lipid Management.

Atherosclerotic cardiovascular disease (ASCVD) is the leading cause of death among urban and rural residents in China, and elevated low-density lipoprotein cholesterol (LDL-C) is a risk factor for ASCVD. Considering the increasing burden of ASCVD, lipid management is of the utmost importance. In recent years, research on blood lipids has made breakthroughs around the world, hence a revision of China guidelines for lipid management is imperative, especially since the target lipid levels in the general population vary in respect to the risk of ASCVD. The level of LDL-C, which can be regarded as appropriate in a population without frisk factors, can be considered abnormal in people at high risk of developing ASCVD. As a result, the "Guidelines for the prevention and treatment of dyslipidemia" were adapted into the "China Guidelines for Lipid Management" (henceforth referred to as the new guidelines) by an Experts' committee after careful deliberation. The new guidelines still recommend LDL-C as the primary target for lipid control, with CVD risk stratification to determine its target value. These guidelines recommend that moderate intensity statin therapy in adjunct with a heart-healthy lifestyle, be used as an initial line of treatment, followed by cholesterol absorption inhibitors or/and proprotein convertase subtilisin/kexin type 9 (PCSK9) inhibitors, as necessary. The new guidelines provide guidance for lipid management across various age groups, from children to the elderly. The aim of these guidelines is to comprehensively improve the management of lipids and promote the prevention and treatment of ASCVD by guiding clinical practice.

Shift in the dominant sequence type of carbapenem-resistant Klebsiella pneumonia infection from ST278-NDM-1 to ST11-KPC-2 in neonatal patients in a children's hospital in Shanghai, China, 2017-2021.

OBJECTIVES: To investigate the clinical characteristics and molecular epidemiology of CRKP infection in neonatal patients in a children's hospital in China from 2017 to 2021. METHODS: Species identification and antibiotic susceptibilities were tested with matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF MS) and VITEK 2 systems. The clinical data were collected from medical records. Carbapenem-resistant Klebsiella pneumoniae (CRKP) isolates were investigated by antimicrobial susceptibility testing, carbapenemase genes and multilocus sequence typing. RESULTS: Six kinds of resistant genes and 23 STs were detected. BlaNDM-1 (n=83, 55.3%) was the predominant carbapenemase gene, followed by blaKPC-2 (n=45, 30.0%), blaNDM-5 (n=7, 4.7%), blaIMP-38 (n=6, 4.0%). BlaNDM-1 was predominant in 2017 and 2018, whereas blaKPC-2 increased in 2019 and became the predominant gene from 2020 to 2021. ST11 accounted for most infections (n=35, 23.3%), followed by ST278 (n=23, 15.3%), ST17 (n=17, 11. 3%) and ST2735 (n=16, 10.7%). ST278 and ST17 were predominant in 2017 and 2018, whereas ST11 increased in 2019 and became the predominant sequence type from 2020 to 2021. Compared with blaNDM-1, the CRKP strains producing blaKPC-2 were characterized by high resistance to gentamicin, amikacin and levofloxacin and the change trend of drug resistance rate before and after COVID-19 was consistent with that of blaNDM-1 and blaKPC-2. CONCLUSIONS: The main sequence type of CRKP infection changed dynamically from ST278-NDM-1 to ST11-KPC-2 during the years 2017-2021 in the newborns. Antibiotic exposure and the prevalence of COVID-19 since 2020 may have led to changes in hospital population and lead to the changes.

Utilizing metagenomic next-generation sequencing for diagnosis and lung microbiome probing of pediatric pneumonia through bronchoalveolar lavage fluid in pediatric intensive care unit: results from a large real-world cohort.

Background: Metagenomic next-generation sequencing (mNGS) is a powerful method for pathogen detection in various infections. In this study, we assessed the value of mNGS in the pathogen diagnosis and microbiome analysis of pneumonia in pediatric intensive care units (PICU) using bronchoalveolar lavage fluid (BALF) samples. Methods: A total of 104 pediatric patients with pneumonia who were admitted into PICU between June 2018 and February 2020 were retrospectively enrolled. Among them, 101 subjects who had intact clinical information were subject to parallel comparison of mNGS and conventional microbiological tests (CMTs) for pathogen detection. The performance was also evaluated and compared between BALF-mNGS and BALF-culture methods. Moreover, the diversity and structure of all 104 patients' lung BALF microbiomes were explored using the mNGS data. Results: Combining the findings of mNGS and CMTs, 94.06% (95/101) pneumonia cases showed evidence of causative pathogenic infections, including 79.21% (80/101) mixed and 14.85% (15/101) single infections. Regarding the pathogenesis of pneumonia in the PICU, the fungal detection rates were significantly higher in patients with immunodeficiency (55.56% vs. 25.30%, P =0.025) and comorbidities (40.30% vs. 11.76%, P=0.007). There were no significant differences in the α-diversity either between patients with CAP and HAP or between patients with and without immunodeficiency. Regarding the diagnostic performance, the detection rate of DNA-based BALF-mNGS was slightly higher than that of the BALF-culture although statistically insignificant (81.82% vs.77.92%, P=0.677) and was comparable to CMTs (81.82% vs. 89.61%, P=0.211). The overall sensitivity of DNA-based mNGS was 85.14% (95% confidence interval [CI]: 74.96%-92.34%). The detection rate of RNA-based BALF-mNGS was the same with CMTs (80.00% vs 80.00%, P>0.999) and higher than BALF-culture (80.00% vs 52.00%, P=0.045), with a sensitivity of 90.91% (95%CI: 70.84%-98.88%). Conclusions: mNGS is valuable in the etiological diagnosis of pneumonia, especially in fungal infections, and can reveal pulmonary microecological characteristics. For pneumonia patients in PICU, the mNGS should be implemented early and complementary to CMTs.

Diagnosis of mixed infection and a primary immunodeficiency disease using next-generation sequencing: a case report.

Major Histocompatibility Complex Class II (MHC II) deficiency is a rare primary immunodeficiency disorder (PID) with autosomal recessive inheritance pattern. The outcome is almost fatal owing to delayed diagnosis and lacking of effective therapy. Therefore, prompt diagnosis, timely and effective treatment are critical. Here, we report a 117-day-old boy with diarrhea, cough, cyanosis and tachypnea who was failed to be cured by empiric antimicrobial therapy initially and progressed to severe pneumonia and respiratory failure. The patient was admitted to the pediatric intensive care unit (PICU) immediately and underwent a series of tests. Blood examination revealed elevated levels of inflammatory markers and cytomegalovirus DNA. Imaging findings showed signs of severe infection of lungs. Finally, the diagnosis was obtained mainly through next-generation sequencing (NGS). We found out what pathogenic microorganism he was infected via repeated conventional detection methods and metagenomic next-generation sequencing (mNGS) of sputum and bronchoalveolar lavage fluid (BALF). And his whole exome sequencing (WES) examination suggested that CIITA gene was heterozygous mutation, a kind of MHC II deficiency diseases. After aggressive respiratory support and repeated adjustment of antimicrobial regimens, the patient was weaned from ventilator on the 56th day of admission and transferred to the immunology ward on the 60th day. The patient was successful discharged after hospitalizing for 91 days, taking antimicrobials orally to prevent infections post-discharge and waiting for stem cell transplantation. This case highlights the potential importance of NGS in providing better diagnostic testing for unexplained infection and illness. Furthermore, pathogens would be identified more accurately if conventional detection techniques were combined with mNGS.