Distribution and Drug Resistance Analysis of 2287 Strains of Pathogenic Bacteria in Children’s Blood Culture

Background: Bloodstream infection is a serious infectious disease. In recent years, the drug resistance of pathogenic bacteria to commonly used anti-infective drugs has been widely concerned, which also makes the treatment of bloodstream infection face severe challenges. Objective: To explore the distribution characteristics of blood culture-positive pathogens and the resistance to antibacterial drugs, so as to provide clinicians with accurate laboratory evidence, so as to guide clinicians to rationally apply antibiotics, improve clinical treatment effects, and reduce the emergence of drug-resistant strains. Methods: From January 2019 to June 2022, 2287 positive blood culture specimens of patients in Guangzhou Women and Children’s Medical Center were retrospectively analyzed, and the proportion of different pathogenic bacteria, the distribution of pathogenic bacteria in different departments, and the multi-drug resistance of different pathogenic bacteria were counted. Results: Among the 2287 blood culture positive samples, 1560 strains (68.20%) of gram-positive bacteria and 727 strains (31.80%) of gram-negative bacteria were strained. The top three departments in the distribution of pathogenic bacteria were pediatric intensive care unit (600 strains), pediatric internal medicine (514 strains), and pediatric emergency comprehensive ward (400 strains). The pathogens with high detection rates were: Staphylococcus epidermidis (24.09%), Staphylococcus humans (23.74%), Escherichia coli (13.21%) and Klebsiella pneumoniae (8.71%). The pathogens with high multi-drug resistance rates were: Streptococcus pneumoniae (93%), Staphylococcus epidermidis (83.76%), Enterobacter cloacae (75.61%) and Staphylococcus humans (62.43%). Conclusion: In our hospital, gram-positive bacteria were the main pathogenic bacteria in the blood culture of children patients. The children’s intensive care unit was the department with the largest distribution of pathogenic bacteria, and the multiple drug resistance rate of Streptococcus pneumoniae was the highest.

Meta-analysis of clinical effect of clearing heat and cooling blood on psoriasis and its effect on inflammatory cytokines

Objective:To evaluate the effect of Qingreliangxue method compared with Acitretin Capsules on clinical efficacy of psoriasis and serum inflammatory cytokines.Methods:The computer searches databases such as CNKI,Wanfang,VIP,PubMed,Embase and Cochrane Library.The search time is from the time the library is built until December 2019.According to the criteria for screening and selection of studies,extract data,use risk assessment tools for quality evaluation,and use Revman 5.3 software to perform meta-analysis on the outcome indicators of the included studis.Results:Finally,20 studies were included,with a total of 1592 patients.The analysis results showed that the total effective rate(OR=3.70,95%CI[2.58,5.30],P<0.00001)and cure rate(OR=2.40,95%CI[1.86,3.10],P<0.00001),PASI score(OR=-2.65,95%CI[-3.60,-1.70],P<0.00001),serum inflammatory cytokines(OR=-8.84,95%CI[-10.52,-7.16],P<0.00001),adverse reactions(OR=0.25,95%CI[0.11,0.57],P=0.001)are superior to Acitretin Capsules.Statistics of the top 10 Chinese medicines in clinical used frequency are,in order,habitat,red peony root,paeonol,honeysuckle,comfrey,soil tuckahoe,salvia miltiorrhiza,buffalo horn,heliotrope,angelica.Conclusion:Based on the current evidence,the treatment of psoriasis with clearing heat and cooling blood as the mainstay of Chinese medicine alone or in combination with Acitretin Capsules can better improve the efficacy,and its mechanism may be related to reducing inflammation.Due to the limitation of the included literature,this conclusion needs to be further verified.

Detection of Diabetic Retinopathy from Retinal Images Using DenseNet Models

A prevalent diabetic complication is Diabetic Retinopathy(DR),which can damage the retina’s veins,leading to a severe loss of vision.If treated in the early stage,it can help to prevent vision loss.But since its diagnosis takes time and there is a shortage of ophthalmologists,patients suffer vision loss even before diagnosis.Hence,early detection of DR is the necessity of the time.The primary purpose of the work is to apply the data fusion/feature fusion technique,which combines more than one relevant feature to predict diabetic retinopathy at an early stage with greater accuracy.Mechanized procedures for diabetic retinopathy analysis are fundamental in taking care of these issues.While profound learning for parallel characterization has accomplished high approval exactness’s,multi-stage order results are less noteworthy,especially during beginning phase sickness.Densely Connected Convolutional Networks are suggested to detect of Diabetic Retinopathy on retinal images.The presented model is trained on a Diabetic Retinopathy Dataset having 3,662 images given by APTOS.Experimental results suggest that the training accuracy of 93.51%0.98 precision,0.98 recall and 0.98 F1-score has been achieved through the best one out of the three models in the proposed work.The same model is tested on 550 images of the Kaggle 2015 dataset where the proposed model was able to detect No DR images with 96%accuracy,Mild DR images with 90%accuracy,Moderate DR images with 89%accuracy,Severe DR images with 87%accuracy and Proliferative DR images with 93%accuracy.