Risk stratification and survival time of patients with gram-negative bacillary pneumonia in the intensive care unit.

Introduction: Pneumonia is a common infection in the intensive care unit (ICU), and gram-negative bacilli are the most common bacterial cause. The purpose of the study was to investigate the risk factors for 30-day mortality in patients with gram-negative bacillary pneumonia in the ICU, construct a predictive model, and stratify patients based on risk to assess their short-term survival. Methods: Patients admitted to the ICU with gram-negative bacillary pneumonia at Fujian Medical University Affiliated First Hospital between January 2018 and September 2020 were selected. Patients were divided into deceased and survivor groups based on whether death occurred within 30 days. Multifactorial logistic regression analysis was used to identify independent risk factors for 30-day mortality in these patients, and a predictive nomogram model was constructed based on these factors. Patients were categorized into low-, medium-, and high-risk groups according to the model's predicted probability, and Kaplan-Meier survival curves were plotted to assess short-term survival. Results: The study included 305 patients. Lactic acid (odds ratio [OR], 1.524, 95% CI: 1.057-2.197), tracheal intubation (OR: 4.202, 95% CI: 1.092-16.169), and acute kidney injury (OR:4.776, 95% CI: 1.632-13.978) were identified as independent risk factors for 30-day mortality. A nomogram prediction model was established based on these three factors. Internal validation of the model showed a Hosmer-Lemeshow test result of X2=5.770, P=0.834, and an area under the ROC curve of 0.791 (95% CI: 0.688-0.893). Bootstrap resampling of the original data 1000 times yielded a C-index of 0.791, and a decision curve analysis indicated a high net benefit when the threshold probability was between 15%-90%. The survival time for low-, medium-, and high-risk patients was 30 (30, 30), 30 (16.5, 30), and 17 (11, 27) days, respectively, which were significantly different. Conclusion: Lactic acid, tracheal intubation, and acute kidney injury were independent risk factors for 30-day mortality in patients in the ICU with gram-negative bacillary pneumonia. The predictive model constructed based on these factors showed good predictive performance and helped assess short-term survival, facilitating early intervention and treatment.

Development of a nomogram for predicting in-hospital mortality in patients with liver cirrhosis and sepsis.

In this study, we aimed to investigate the risk factors associated with in-hospital mortality in patients with cirrhosis and sepsis, establish and validate the nomogram. This retrospective study included patients diagnosed with liver cirrhosis and sepsis in the Medical Information Mart for Intensive Care IV (MIMIC-IV). Models were compared by the area under the curve (AUC), integrated discriminant improvement (IDI), net reclassification index (NRI) and decision curve analysis (DCA). A total of 1,696 patients with cirrhosis and sepsis were included in the final cohort. Our final model included the following 9 variables: age, heartrate, total bilirubin (TBIL), glucose, sodium, anion gap (AG), fungal infections, mechanical ventilation, and vasopressin. The nomogram were constructed based on these variables. The AUC values of the nomograms were 0.805 (95% CI 0.776-0.833), which provided significantly higher discrimination compared to that of SOFA score [0.684 (95% CI 0.647-0.720)], MELD-Na [0.672 (95% CI 0.636-0.709)] and ABIC [0.674(95% CI 0.638-0.710)]. We established the first nomogram for predicting in-hospital mortality in patients with liver cirrhosis and sepsis based on these factors. This nomogram can performs well and facilitates clinicians to identify people at high risk of in-hospital mortality.

New Attempts to Inhibit Methicillin-Resistant Staphylococcus aureus Biofilm? A Combination of Daptomycin and Azithromycin.

Objective: To investigate the antibacterial impact of daptomycin and azithromycin in vitro on methicillin-resistant Staphylococcus aureus (MRSA) biofilm. Methods: (1) Measure the strain growth curve and the biofilm formation curve. (2) Determine the minimum inhibitory concentrations (MICs) of daptomycin and azithromycin. (3) Investigate the antibacterial impact of the combination of daptomycin and azithromycin. (4) Perform the evaluation of the intervention impact of antimicrobial agents on MRSA biofilm. (5) Observe the biofilm after intervention with the antibacterial agent. Results: (1) MRSA exhibited three phases: lag phase (0-4 h), logarithmic growth (4-8 h) and stationary phase after 18 h; its biofilm began to form at 6 h, semi-matured at 24 h, and reached maturity after 48 h. (2) The MICs of daptomycin and azithromycin were 8 µg/mL and greater than 256 µg/mL, respectively. (3) The combination of daptomycin and azithromycin has an additive effect on MRSA (Fractional Inhibitory Concentration Index [FICI] 0.625) (FICI = MIC of drug A in combination/MIC of drug A alone + MIC of drug B in combination/MIC of drug B alone). Evaluation criteria: Synergistic effect is considered when FICI ≤ 0.5; additive effect is considered when 0.5 < FICI ≤ 1; irrelevant effect is considered when 1 < FICI ≤ 2; antagonistic effect is considered when FICI > 2). (4) Daptomycin or azithromycin at MICs inhibited not only the growth of planktonic bacteria but also the formation of biofilm. (5) The combination of both, in which group the ratio of live/dead bacteria is low and the biofilm morphology was incomplete, was more productive than monotherapy in against biofilm. Conclusion: Both daptomycin and azithromycin have anti-MRSA biofilm activity, and daptomycin is dominant. The fact that the combination of both can significantly inhibit the further maturation of MRSA biofilm and destroy already formed biofilm demonstrates the superiority of the combination over the monotherapy.

Spatiotemporal variations of reference crop evapotranspiration in Northern Xinjiang, China.

To set up a reasonable crop irrigation system in the context of global climate change in Northern Xinjiang, China, reference crop evapotranspiration (ET0) was analyzed by means of spatiotemporal variations. The ET0 values from 1962 to 2010 were calculated by Penman-Monteith formula, based on meteorological data of 22 meteorological observation stations in the study area. The spatiotemporal variations of ET0 were analyzed by Mann-Kendall test, Morlet wavelet analysis, and ArcGIS spatial analysis. The results showed that regional average ET0 had a decreasing trend and there was an abrupt change around 1983. The trend of regional average ET0 had a primary period about 28 years, in which there were five alternating stages (high-low-high-low-high). From the standpoint of spatial scale, ET0 gradually increased from the northeast and southwest toward the middle; the southeast and west had slightly greater variation, with significant regional differences. From April to October, the ET0 distribution significantly influenced the distribution characteristic of annual ET0. Among them sunshine hours and wind speed were two of principal climate factors affecting ET0.

[Quantitative analysis of dendritic cell subsets in bone marrow of patients with acute myeloid leukemia].

In order to study the quantity of dendritic cell (DC) subsets of bone marrow in patients with acute myeloid leukemia (AML), the bone marrow aspirate were collected from 77 newly diagnosed AML patients and from 30 healthy persons. The quantity of DC subsets (myeloid dendritic cells, mDC and plasmacytoid dendritic cells, pDC) were detected by flow cytometry and analysed by 3-color and 4-color cytometric gate. Based on the conventional 3-color panel, mDC were identified by Lin-HLA-DR+CD11c+ and pDC were identified by Lin-HLA-DR+CD123+. Based on the 4-color panel, mDC were identified by Lin-HLA-DR+CD11c+ BDCA-1+ and pDC were identified by Lin-HLA-DR+CD123+BDCA-2+. The results showed that a reduction of mDC was found in 74.0% (57/77) and 58.4% (45/77) patients, a reduction of pDC was found in 90.9% (70/77) and 46.8% (36/77) patients respectively by 3-color and 4-color cytometric analysis. Meanwhile an expansion of mDC was showed in 19.5% (15/77) and 22.1% (17/77) patients, an expansion of pDC was showed in 1.3% (1/77) and 27.3% (21/77) patients respectively by 3-color and 4-color cytometric analysis. In subtypes of AML-M2, AML-M3 or AML-M4/5, 81.4%, 100% and 42.1% patients showed mDC decrease and 88.4%, 100% and 89.5% patients showed pDC decrease respectively by 4-color cytometric analysis. It is concluded that the 4-color cytometric gate is better method for detection of mDC and pDC from bone marrow of newly diagnosed AML patients as compared with 3-color cytometric gate, the majority of AML patients showed reduction of mDC and pDC. The percentages of patients with mDC normal or mDC increase in AML-M4/5 subtypes are more than that in AML-M2/3 subtypes, while the pDC does not show difference between AML subtypes.

[Identification and enumeration of dendritic cells in peripheral blood and bone marrow of healthy individuals].

The study was aimed to analyse and enumerate the dendritic cells (DC) subsets in peripheral blood and bone marrow (BM) of healthy individuals in China by using 2 panel 4-color flow cytometry (FCM). The percentage and absolute number of Lin-HLA-DR+CD11chiBDCA1+ myeloid DC (mDC) and Lin-HLA-DR+CD123hiBDCA2+ plasmacytoid DC (pDC) were detected in 35 normal BM (NBM) and 29 normal peripheral blood (NPB), the results were compared with the Lin-HLA-DR+CD11chimDC and Lin-HLA-DR+CD123hi pDC obtained by 3-color FCM. The results indicated that both absolute count of DC subset and relative count of pDC in BM were decreased along with increase of age, the absolute count of DC subset in male BM was higher than that in femoral BM (p<0.05). The DC subsets in NBM and NPB were different whatever by 3 or 4-color cytometric analysis, there were more mDCs than pDCs in PB and the ratio of mDC to pDC was 2.70 and 2.31 respectively. In contrast, pDCs predominated in BM, the ratio of mDC to pDC in BM was 0.90 and 0.71 respectively. The quantity of DC subsets significantly correlated to both frequency (mDC r=0.86; pDC r=0.96, p<0.05) and absolute number (mDC r=0.95; pDC r=0.98, p<0.05) between 3 and 4-color cytometric analysis. The quantity of DC subsets in PB and BM were significantly different, counted by 3 and 4-color cytometric analysis except pDC in PB (p<0.001). The quantity of DC subsets were much higher by 3-color than that by 4-color analysis. Since some Lin-HLA-DR+CD11chimDC and Lin-HLA-DR+CD123hi pDC were BDCA1- and BDCA-2dim/- respectively, that more were in BM than in PB. It is concluded that the DC absolute enumeration is correlated with sample type, gender, age and total nucleated cells (p<0.05). 4-color antibody combination may help to identify the real DC subsets in BM. DC subsets in NBM and NPB are different, that more mDC are in PB whereas more pDC in BM.

[Expression of 5 genes in CD7 positive acute myeloid leukemia stem/progenitor cells from bone marrow].

This study was aimed to investigate abca5, mdr-1, kdr, dapk and irf-1 expressions in leukemia stem/progenitor cells (LSC) from CD7 positive acute myeloid leukemia, the expression of these 5 genes in mononuclear cells (MNC) from 15 normal bone marrow (NBM) and 16 AML patients bone marrow (AML BM) specimen were detected by real-time quantitative PCR (RQ-PCR). CD34(+)CD38(+) progenitor cells and CD34(+)CD38(-)Lin(-) stem cells were sorted by flow cytometry (FCM) from the MNCs of 10 NBM and 21 AML BM specimen. These 5 gene expressions in the sorted cells were detected by small amount cell RQ-PCR. The results showed that these 5 genes above mentioned all expressed in NBM-MNC, in which the expression levels of irf-1 and dapk were highest with the relative expression levels 4.08 and 3.86, the expression levels of abca 5 and mdr-1 were in the middle with the relative expression 0.49 and 0.84 respectively, the kdr expression was lowest with the relative expression level 0.02. In CD34(+)CD38(+) progenitor cells, the expression level of kdr increased dramatically (p < 0.05) while irf-1 and dapk dramatically decreased (p < 0.05). There was no obvious change of expression in the rest 2 genes. In CD34(+)CD38(-) stem cells the expression level of these 5 genes all increased nearly 2 times as much as that in CD34(+)CD38(+) progenitor cells, but kdr increased 3 times as much, and the increase of kdr and irf-1 expressions was of statistical significance (p < 0.05). Compared with the NBM, expression levels of 5 genes in AML-MNC decreased, and out of them abca 5, mdr-1, kdr and dapk were decreased most remarkably (p < 0.05). Comparison between AML CD34(+)CD38(+) cells and AML MNC showed that the expression level of irf-1 and dapk were decreased dramatically (p < 0.05) while the rest 3 genes increased their expression with statistical significance (p < 0.05). The expression levels of these 5 genes were higher in CD34(+)CD38(-) cells than those in CD34(+)CD38(+) stem cells, and the increase of kdr and irf-1 expressions showed statistical difference (p < 0.05). These 5 genes expression levels were all higher than those in CD34(+)CD38(+) cells whether in AML CD34(+)CD38(-)CD7(+) cells or CD34(+)CD38(-)CD7(-) cells. The increase of kdr expression in CD34(+)CD38(-)CD7(+) cells as well as kdr and irf-1 expressions in CD34(+)CD38(-)CD7(-) cells were all of statistical significance (p < 0.05). In conclusion the expression level of kdr in NBM was highest in stem cells while dapk and irf-1 were highest in differentiated cells. The expression levels of these 5 genes in CD34(+)CD38(-)Lin(-) stem cells were higher than those in CD34(+)CD38(+) progenitor cells. The gene expressions in AML CD34(+)CD38(-)CD7(+) cells and CD34(+)CD38(-)CD7(-) cells are in accordance with the characteristics of stem cells.