Objective To study the etiology and antibiotic resistance of bloodstream infections in low birth weight preterm infants .Methods A total of 95 cases of bloodstream infections in low birth weight preterm infants were treated in our hospital from January 2011 to April 2014 .The clinical data of these patients were analyzed retrospectively .Results A total of 96 pathogens were isolated ,including 57 strains of gram‐negative bacilli ,38 strains of gram‐positive cocci ,and 1 strains of Trichosporon asahii .The most frequently isolated pathogens were Klebsiella pneumoniae (40 strains)and coagulase‐negative Staphylococcus(31 strains).All gram‐negative bacilli were sensitive to carbapenems such as imipenem and panipenem . Streptococcus isolates were sensitive to most antibiotics .Most Staphylococcus isolates were methicillin‐resistant ,which were highly resistant to common antibiotics but all sensitive to linezolid , vancomycin and teicoplanin . Conclusions The most important pathogens responsible for bloodstream infections in low birth weight preterm infants in our hospital are K lebsiella pneumoniae and coagulase‐negative Staphylococcus . Early identification of responsible pathogen and rational antimicrobial therapy are critical for good prognosis of bloodstream infections in low birth weight preterm infants .

The purpose of this paper is to achieve a measurement of temperature prediction for artificial heart without sensor, for which the research briefly describes the application of back propagation neural network as well as the optimized, by genetic algorithm, BP network. Owing to the limit of environment after the artificial heart implanted, detectable parameters out of body are taken advantage of to predict the working temperature of the pump. Lastly, contrast is made to demonstrate the prediction result between BP neural network and genetically optimized BP network, by which indicates that the probability is 1.84% with the margin of error more than 1%.
Heart, Artificial ; Neural Networks (Computer) ; Temperature

The purpose of this paper is to achieve a measurement of temperature prediction for artificial heart without sensor, for which the research briefly describes the application of back propagation neural network as wel as the optimized, by genetic algorithm, BP network. Owing to the limit of environment after the artificial heart implanted, detectable parameters out of body are taken advantage of to predict the working temperature of the pump. Lastly, contrast is made to demonstrate the prediction result between BP neural network and genetical y optimized BP network, by which indicates that the probability is 1.84%with the margin of error more than 1%.

Objective:This study aims to evaluate the performance of digital PCR (dPCR) detecting multiple and single copies genes of the Epstein-Barr virus (EBV) for nucleic acid quantification and explore their applicability in clinical settings.Methods:Compared the sensitivity, specificity, precision, lower limit of detection (LoD), and linearity for multicopy BamHI-W dPCR and single-copy EBNA1 dPCR systems. Linear regression analysis using the least squares method was employed to evaluate the linearity. Additionally, we analyzed plasma samples from 182 patients with suspected EBV-related diseases between January and July 2022 at the Southern Medical University Southern Hospital, using both dPCR and quantitative PCR (qPCR) for EBV DNA quantification. Linear regression analysis using the least squares method was conducted to assess their quantitative correlation.Results:The dPCR systems for both multicopy and single-copy genes showed excellent linearity ( R 2 values of 0.992 and 0.997, respectively, both P<0.001). The LoD were 188 IU/ml for BamHI-W gene and 358 IU/ml for EBNA1 gene dPCR systems. The logarithmic coefficient of variation ( CV) values for high-concentration samples (1 000 000 IU/ml) were 0.34% and 0.21% for the BamHI-W gene and EBNA1 gene dPCR assays, respectively, while for low-concentration samples (5 000 IU/ml) were 0.98% and 0.64%, respectively. In the detection of seven common clinical infectious pathogens and EBV positive samples, only EBV-positive samples yielded positive signals in the dPCR detection system, with no cross-reaction with other pathogens. In 182 samples, the positive detection rates were 47.80% (87/182) for BamHI-W gene and 35.16% (64/182) for EBNA1 gene dPCR, compared to 43.41% (79/182) for qPCR. Linear correlation analysis with qPCR showed R2 values of 0.837 for BamHI-W gene and 0.763 for EBNA1 gene dPCR (both P<0.001). The BamHI-W gene copy number ranged from 3 to 18 copies per clinical sample, with patient-specific variations. There was a high consistency in viral load trends between the multicopy BamHI-W gene and single-copy EBNA1 gene dPCR systems within individual patients. Conclusions:The dPCR methods detecting EBV multiple and single copies genes showed high sensitivity, specificity, precision, and quantitative accuracy, suitable for clinical sample analysis. The multicopy BamHI-W gene dPCR method notably enhances detection sensitivity and can be used as a supplement to current EBV DNA load detection methods, especially in low-concentration samples. For within-patient EBV DNA monitoring, the multicopy gene method proves more effective, while inter-patient comparisons might necessitate single-copy gene methods or normalize them using the same standard.