Objective: To build risk prediction models for acute kidney injury (AKI) in severely burned patients, and to compare the prediction performance of machine learning method and logistic regression model. Methods: The clinical data of 157 severely burned patients in August 2nd Kunshan factory aluminum dust explosion accident conforming to the inclusion criteria were collected. Patients suffering AKI within 90 days after admission were enrolled in group AKI, while the others were enrolled in non-AKI group. Single factor analysis was used to choose independent factors associated with AKI, including sex, age, admission time, features of basic injuries, initial score on admission, treatment condition, and mortality on post injury days 30, 60, and 90. Data were processed with Mann-Whitney U test, chi-square test, and Fisher′s exact test. Variables with P<0.1 in single factor analysis and those with possible clinical significance were brought into the establishment of prediction model. Logistic regression and XGBoost machine learning algorithm were used to build the prediction model of AKI. The area under receiver operating characteristic curve (AUC) was calculated, and the sensitivity and specificity for optimal threshold value were also calculated for each model. Nonparametric resampling test was used to compare the significance of difference of AUC of the two models. Results: (1) Eighty-nine (56.7%) patients developed AKI within 90 days from admission. Compared with 68 patients in non-AKI group, 89 patients in group AKI were older (Z=-2.203, P<0.05), with larger total burn area and full-thickness burn area (Z=-5.200, -6.297, P<0.01), worse acute physical and chronic health evaluation (APACHE) Ⅱ score, abbreviated burn severity index score, and sequential organ failure assessment (SOFA) score on admission (Z=-7.485, -4.739, -4.590, P<0.01), higher occurrence rate of sepsis (χ²=33.087, P<0.01), higher rates of accepting tracheotomy, mechanical ventilation, and continuous renal replacement therapy (χ²=12.373, 17.201, 43.763, P<0.01), larger first excision area (Z=-2.191, P<0.05), and higher mortality on post injury days 30, 60, and 90 (χ²=7.483, 37.259, 45.533, P<0.01). There were no statistically significant differences in sex, open decompression, admission time, 24-hour fluid volume after admission, 48-hour fluid volume after admission, the first 24-hour urine volume, the second 24 hour urine volume, the first excision time, and inhalation injury (χ²=0.529, 3.318, Z=-1.746, -0.016, -1.199, -1.824, -0.625, -1.747, P>0.05). The rates of deep vein catheterization of patients in the two groups were both 100%. (2) There were twenty possible prediction variables for preliminary establishment of model according to the difference results of single factor analysis and clinical significance of variables. (3) The logistic regression prediction model had three variables: APACHE Ⅱ score [odds ratio (OR)=1.36, 95% confidence interval (CI)=1.20-1.53, P<0.001], sepsis (OR=2.63, 95% CI=0.90-7.66, P>0.05), and the first 24-hour urine volume (OR=0.71, 95% CI=0.50-1.01, P>0.05). The AUC of the logistic regression prediction model was 0.875 (95% CI=0.821-0.930), with the specificity and sensitivity of optimal threshold value 84.4% and 77.7%, respectively. (4) XGBoost machine learning model had seven main predictive variables: APACHE Ⅱ score, full-thickness burn area, 24-hour fluid volume after admission, sepsis, the first 24-hour urine volume, SOFA score, and 48-hour fluid volume after admission. The AUC of machine learning model was 0.920 (95% CI=0.879-0.962), higher than that of logistic regression model (P<0.001), with the specificity and sensitivity of optimal threshold value 89.7% and 82.0%, respectively. Conclusions Sepsis and fluid resuscitation are two important predictive variables that can be intervened for AKI in severely burned patients. Machine learning method has a better performance and can provide more accurate prediction for individuals than logistic regression prediction model, and therefore has good clinical application prospect.

OBJECTIVETo study the epidemiological characteristics and antibiotic resistance of Salmonella enterica serovar Pomona (S. Pomona).

METHODSAntimicrobial susceptible testing (AST) and pulsed field gel electrophoresis (PFGE) methods were used to analyze on S. Pomona strains that were isolated from diarrhea cases through the diarrhea network monitoring program, environment and food samples in Shanghai as well as from reptiles in Guangxi Zhuang Autonomous Region.

RESULTS4 553 clinic Salmonella (S.) strains were isolated from the Shanghai network laboratories from 2005 to 2012. The top 10 serotypes would include 20 serotypes all belonged to A-F groups, while S. Pomona was next to S. Wandsworth, according to the non- A-F groups. Young children seemed to be susceptible to S. Pomona, and might cause bloody stools and super-infection. The top 10 serotypes from 1 805 foodborne Salmonella strains were significantly more extensive than those from the human S. Pomona strains, followed by those rare serotypes which were mostly isolated from turtle, sea-shellfish and reptiles. Antibiotic resistance of S. Pomona strains from other sources were significantly more severe than those from human samples, and belonged to A and B clones by means of PFGE. Clone A strains were non-epidemic strains which showed multi-drug resistance (MDR) to antimicrobials. Clone B was the main epidemic-causing strain that not resistant to drugs, which consisting B- I from young-age-groups and B-II were from the seniors. B-I strains were homologous to those from shellfish, tortoises and lizards, while B-II strains only showing homology to those from shellfish. One S. Pomona strain-MDR, isolated from human was homologous to 8 antimicrobials.

CONCLUSIONS. Pomona was a quite common serotype among those rare serotypes, which showed higher pathogenicity to infants while genetic evolution might take place when comparing them with the strains isolated from the clinics in 2005. Surveillance programs should be intensified along with the early warnings systems on infections which were from seafood and reptiles.

China ; epidemiology ; Drug Resistance, Multiple, Bacterial ; Humans ; Molecular Epidemiology ; Salmonella Infections ; epidemiology ; microbiology ; Salmonella enterica ; classification ; isolation & purification ; Serogroup

Animals ; Antibodies, Neutralizing/biosynthesis* ; Antibodies, Viral/biosynthesis* ; Body Weight/immunology* ; COVID-19/virology* ; Disease Models, Animal ; Disease Progression ; Humans ; Immunohistochemistry ; Lung/virology* ; Male ; Mesocricetus ; Nasal Cavity/virology* ; RNA, Viral/immunology* ; SARS-CoV-2/pathogenicity* ; Severity of Illness Index ; Viral Load