Clinical Distribution and Drug Resistance of 224 Strains of Pseudomonas Aeruginosa

Objective: To provide evidence for a rational and effective prevention and treatment of Pseudomonas aeruginosa, the clinical characteristics and the resistance to various antibiotics of were investigated.Methods：A retrospective analysis of 224 strains of Pseudomonas aeruginosa isolated from various specimens from various clinical departments of our hospital (April 1, 2018 to June 31, 2019) were conducted. Identification and drug susceptibility test of isolated strains was performed using a fully automatic bacterial identification analyzer (MicroScan WalkAway-96 plus), and data analysis was performed using WH0NET5.6 software. Result：Among all the bacteria isolated in our hospital during the above period, Pseudomonas aeruginosa accounted for 10.09% of them all and 12.57% of Gram-negative bacilli, respectively. These isolates were mainly derived from sputum specimens (68.75%), mainly from male patients (70.54%), and mostly 61-70 (27.23%) or 51-60 (22.77%) years old. Pseudomonas aeruginosa isolates are mainly from Rehabilitation Ward, ICU, and Liver Transplantation Unit, accounted for 29.91%, 12.95% and 10.27% of all isolates, respectively. The sensitivity of Pseudomonas aeruginosa to various antibacterial drugs, in the order of high to low were carbapenems, aztreonam, quinolones, cephalosporins, piperacillin/ tazobactam, aminoglycoside, with a lowest resistance rate (2.4%) to amikacin and a highest resistance rate to imipenem (33.0%). Conclusion：The isolation rate of Pseudomonas aeruginosa was relatively stable during the study period, and among all the P. aeruginosa detected, most of them were from the respiratory secretions of elderly male patients. The resistance rate of Pseudomonas aeruginosa isolates to various antibiotics is mainly within 30%. Clinical units such as Rehabilitation Ward, ICU, and Liver Transplantation Unit have a high detection rate, therefore, these departments should be monitored in a focused manner. Our research provide a scientific basis for the rational use of antibiotics and a better control of Pseudomonas aeruginosa infection

Combined Detection of Mean Platelet Volume and Immunoglobins as a Strategy for the Diagnosis of Systemic Lupus Erythematosus

Objective:To explore the possibility of diagnosing and monitoring patients with systemic lupus erythematosus (SLE) using the combination of mean platelet volume (MPV) and routine immunoglobulin test. Method:116 patients with SLE were divided into 3 groups according to their clinical characteristics, including 29 patients with renal impairment, 44 cases of active stage and 43 cases of inactive patients. 40 healthy subjects were randomly selected as controls. Subjects were tested for routine blood test and plasma Immunoglobins, such as IgG, IgA, IgM, C3, C4, CH50, CRP. The results were analyzed and the characteristics of each group of subjects were determined, the correlation between test results and diagnosis were studied. Result: In comparison to the control group, the serum level of MPV, C3 and C4 were decreased (P<0.05), and C reactive protein level was elevated (P<0.001) in the three groups of SLE patients. The IgG level in active and inactive SLE patients was increased (P<0.0001), CH50 level was decreased in patients with inactive SLE (P<0.05), IgA level of active SLE subjects was found to be elevated (P<0.05), IgM in patients with renal impairment was decreased (P<0.05). Other than that, no other significant characteristic were found. Conclusion: The pathogenesis of SLE is a complex process involving multiple factors. The changes of MPV, IgG, IgA, IgM, C3, C4, CH50 and CRP in SLE patients are characteristic parameters. The combination of the above indicators can help to determine the diagnosis and staging of SLE. The timely diagnosis and treatment of SLE patients has important clinical significance in protecting the organ function of SLE patients and improving the prognosis

Modelling Polymers as Compressible Elastic Spheres in Couette Flow

A model of polymer chains as compressible elastic spheres in flow is presented. The spherical polymer blobs are assumed to compress in simple Couette flow in accord with recent rheo-optic measurements on semi-dilute solutions. The experimentally determined decrease in radius with increasing shear rate is predicted by the model. Furthermore, the model predicts power law exponents for the viscosity-shear rate within the range of measured values for polymer chains

In Vitro Antibacterial Activity of Galla Chinensis Combined with Different Antibacterial Drugs against Carbapenem-Resistant E.Coli

Objective: To evaluate the antibacterial effects of meropenem and levofloxacin respectively combined with Galla chinensis on carbapenem-resistant Escherichia coli in vitro. Methods： The protocol was designed with checkerboard method and the carbapenem-resistant E.coli was isolated in our hospital. The minimum inhibitory concentrations(MICs) of G. chinensis alone and combined with 2 antimicrobial agents against carbapenem-resistant E.coli were determined by broth dilution method and the fractional inhibitory concentration index (FICI) was calculated according to MICs results. Result： the combined use of G. chinensis and meropenem (or levofloxacin) significantly decreased both MIC50 and MIC90; After the combination of G. chinensis and meropenem, the synergistic effect was 86.7%, and the additive effect was 13.3%, no irrelevant and antagonistic effects. After combined use of G. chinensis and levofloxacin, the synergistic effect was 66.7%, and the additive effect was 33.3%. No irrelevant and antagonistic effects. Conclusion： Galla chinensis combined with meropenem or levofloxacin has synergistic and additive antibacterial effect, with certain combined antibacterial activity

Approximate surface-current distributions of rectangular dipole antennas

Abstract An approximate surface-current distribution of the rectangular dipole antennas, composed of two linear-currents along the antenna edges and a uniform surface-current within the antenna bodies, is proposed. It presents some new insights to planar dipole antennas, and could also be used for fast, explicit and Ultra-wideband predictions of their radiation patterns. The averaged errors between the calculated results based on the proposed distributions and the fullwave results are respectively 0.075 dB on the H-plane and 2.95º on the E-plane. From the explicit results, some design considerations for stable radiation patterns are presented

Survival-Convolution Models for Predicting COVID-19 Cases and Assessing Effects of Mitigation Strategies

Countries around the globe have implemented unprecedented measures to mitigate the coronavirus disease 2019 (COVID-19) pandemic. We aim to predict the COVID-19 disease course and compare the effectiveness of mitigation measures across countries to inform policy decision making using a robust and parsimonious survival-convolution model. We account for transmission during a pre-symptomatic incubation period and use a time-varying effective reproduction number (Rt) to reflect the temporal trend of transmission and change in response to a public health intervention. We estimate the intervention effect on reducing the transmission rate using a natural experiment design and quantify uncertainty by permutation. In China and South Korea, we predicted the entire disease epidemic using only early phase data (2–3 weeks after the outbreak). A fast rate of decline in Rt was observed, and adopting mitigation strategies early in the epidemic was effective in reducing the transmission rate in these two countries. The nationwide lockdown in Italy did not accelerate the speed at which the transmission rate decreases. In the United States, Rt significantly decreased during a 2-week period after the declaration of national emergency, but it declined at a much slower rate afterwards. If the trend continues after May 1, COVID-19 may be controlled by late July. However, a loss of temporal effect (e.g., due to relaxing mitigation measures after May 1) could lead to a long delay in controlling the epidemic (mid-November with fewer than 100 daily cases) and a total of more than 2 million cases

Evaluating effectiveness of public health intervention strategies for mitigating COVID-19 pandemic

Coronavirus disease 2019 (COVID-19) pandemic is an unprecedented global public health challenge. In the United States (US), state governments have implemented various non-pharmaceutical interventions (NPIs), such as physical distance closure (lockdown), stay-at-home order, mandatory facial mask in public in response to the rapid spread of COVID-19. To evaluate the effectiveness of these NPIs, we propose a nested case-control design with propensity score weighting under the quasi-experiment framework to estimate the average intervention effect on disease transmission across states. We further develop a method to test for factors that moderate intervention effect to assist precision public health intervention. Our method takes account of the underlying dynamics of disease transmission and balance state-level pre-intervention characteristics. We prove that our estimator provides causal intervention effect under assumptions. We apply this method to analyze US COVID-19 incidence cases to estimate the effects of six interventions. We show that lockdown has the largest effect on reducing transmission and reopening bars significantly increase transmission. States with a higher percentage of non-White population are at greater risk of increased R t $${R}_t$$ associated with reopening bars

NAT2 Involed in the Susceptibility to Antituberculosis Drug-Induced Liver Injury

Objective: To investigate whether the N-acetyltransferase 2 (NAT2) gene is involved in the development of susceptibility to antituberculosis drug-induced liver damage (ATDLI) in patients with pulmonary tuberculosis in the Han nationality. Methods: We retrospectively analyzed 300 cases of tuberculosis patients without liver damage (control group) and 221 cases of tuberculosis patients with liver damage after antituberculosis treatment (case group). After antituberculosis treatment, genetic polymorphisms of NAT2 were analyzed in those patients using MassARRAY method. Results: Of the 10 tagged SNPs selected, In the promoter area of NAT2, the frequencies of T allele in rs4646243 and A allele in rs4646246 were signifcantly higher in the patients with ATDLI than controls (0.569 vs. 0.483, p=0.0062 and 0.567 vs 0.487, p=0.0103). The A allele of rs1115784 in the intron area showed a significant association with the development of ATDLI (0.389 vs 0.305, p = 0.0043). The frequencies of the mutated genes T and A in rs1041983 and rs1799930 in the second exon region were significantly higher than those in the control group (0.491 vs 0.360, p<0.00001 and 0.336 vs 0.212, respectively; p<0.00001). Two monomer domains were found in the 10 tag SNP sites, haplotype ht [TGAA] in monomeric domain 1 and haplotype ht [TAG] in monomeric domain 2, both were signifcantly more likely to be detected in the liver injury group than in the control group(p=0.0038, p<0.001, respectively). Two haplotypes were also found on the NAT2 gene: haplotype ht [CGGG] in monomeric domain 1 and ht [CGG] in block 2, and their presence means a lower risk of liver damage. Conclusion: NAT2 genotypes might have signifcant association with the risk of ATDLI in the Chinese Han nationality. By detecting the NAT2 gene and its haplotype, we can screen patients with a higher risk of liver damage before anti-TB treatment and take measures for the protection of patients

Changes of serum high mobility group box 1 and soluble triggering receptor expressed on myeloid cells-1 in patients with multiple injuries and their prognostic significance

Objective·To detect the serum levels of high mobility group box 1 (HMGB1) and soluble triggering receptor expressed on myeloid cells-1 (sTREM-1) in patients with multiple injuries at different time points, and to analyze their correlation with disease severity, complications and prognosis.Methods·Ninety-two patients with multiple injuries admitted to the Department of Emergency Medicine of the Suzhou Ninth People′s Hospital from December 2020 to December 2022 were selected. According to the injury severity scores of the patients at admission, the patients were divided into light injury group (n=24), grave injury group (n=58) and severe injury group (n=10). According to whether there was multiple organ dysfunction syndrome (MODS) after admission, the patients were divided into MODS group (n=20) and non-MODS group (n=72). According to the outcome within 28 d after trauma, the patients were divided into death group (n=13) and survival group (n=79). Inflammatory factor indicators in venous blood of patients after admission were detected. Enzyme linked immunosorbent assay (ELISA) was used to detect the serum HMGB1 and sTREM-1 levels at 24 h, 72 h and 7 d after trauma, and the differences of serum HMGB1 and sTREM-1 levels among different groups were analyzed. Multiple Logistic regression was used to analyze the influencing factors of adverse outcomes in patients with multiple injuries. The receiver operating characteristic (ROC) curve was used to evaluate the predictive value of HMGB1 and sTREM-1 for adverse outcomes.Results·The levels of HMGB1 and sTREM-1 in the grave injury and severe injury groups were significantly higher than those in the light injury group (P<0.05). The levels of HMGB1 at 72 h and 7 d, and sTREM-1 at 24 h and 72 h in the severe injury group were significantly higher than those in the grave injury group (P<0.05). There was a positive correlation between HMGB1 and sTREM-1 levels at various time points (r=0.645, r=0.942, r=0.722; all P<0.05). The levels of HMGB1 at 72 h and 7 d, and sTREM-1 at 24 h and 72 h in the MODS group were significantly higher than those in the non-MODS group (all P<0.05). The levels of HMGB1 at 72 h and 7 d, and sTREM-1 at 24 h and 72 h in the death group were significantly higher than those in the survival group (all P<0.05). Logistic regression analysis showed that HMGB1 at 7 d, admission time and hypersensitive C-reactive protein (hs-CRP) were independent factors of adverse outcomes in patients with multiple injuries (all P<0.05). The ROC curve showed that the area under the curve of HMGB1 for predicting poor prognosis at 7 days after trauma was 0.890, the sensitivity was 83.5%, and the specificity was 92.3%.Conclusion·The levels of HMGB1 and sTREM-1 are correlated with MODS and survival outcomes in patients with multiple injuries at different time points after trauma, and HMGB1 at 7 d after trauma is an independent factor affecting adverse outcomes in patients with multiple injuries

Learning Subject-Specific Directed Acyclic Graphs With Mixed Effects Structural Equation Models From Observational Data

The identification of causal relationships between random variables from large-scale observational data using directed acyclic graphs (DAG) is highly challenging. We propose a new mixed-effects structural equation model (mSEM) framework to estimate subject-specific DAGs, where we represent joint distribution of random variables in the DAG as a set of structural causal equations with mixed effects. The directed edges between nodes depend on observed exogenous covariates on each of the individual and unobserved latent variables. The strength of the connection is decomposed into a fixed-effect term representing the average causal effect given the covariates and a random effect term representing the latent causal effect due to unobserved pathways. The advantage of such decomposition is to capture essential asymmetric structural information and heterogeneity between DAGs in order to allow for the identification of causal structure with observational data. In addition, by pooling information across subject-specific DAGs, we can identify causal structure with a high probability and estimate subject-specific networks with a high precision. We propose a penalized likelihood-based approach to handle multi-dimensionality of the DAG model. We propose a fast, iterative computational algorithm, DAG-MM, to estimate parameters in mSEM and achieve desirable sparsity by hard-thresholding the edges. We theoretically prove the identifiability of mSEM. Using simulations and an application to protein signaling data, we show substantially improved performances when compared to existing methods and consistent results with a network estimated from interventional data. Lastly, we identify gray matter atrophy networks in regions of brain from patients with Huntington's disease and corroborate our findings using white matter connectivity data collected from an independent study