- 作者列表："Kammili N","Rani M","Styczynski A","Latha M","Pavuluri PR","Reddy V","Alsan M
:With the growing threat of antimicrobial resistance worldwide, uncovering the molecular epidemiology is critical for understanding what is driving this crisis. We aimed to evaluate the prevalence of plasmid-mediated-quinolone-resistance (PMQR) and extended-spectrum beta-lactamase- (ESBL) producing gram-negative organisms among primigravid women with bacteriuria. We collected urine specimens from primigravid women attending their first antenatal visit at Gandhi Hospital during October 1, 2015 to September 30, 2016. We determined antimicrobial susceptibility and ESBL and quinolone resistance using VITEK-2. We performed polymerase chain reaction amplification on resistant isolates for detection of ESBL-encoding genes (TEM, SHV, CTX-M) and PMQR genes (qnrA, qnrB, qnrD, qnrS, aac (6')-Ib-cr). Of 1,841 urine samples, 133 demonstrated significant bacterial growth with gram-negative bacilli accounting for 85% of isolates, including Escherichia coli (n = 79), Klebsiella pneumoniae (n = 29), Sphingomonas (n = 3), Enterobacter (n = 1), and Citrobacter (n = 1). We found 65% of E. coli isolates and 41% of K. pneumoniae isolates were ESBL positive. Of ESBL-positive isolates, the most common genes conferring resistance were TEM-1 (66.7%) followed by CTX-M-15 (33.3%). Fifty-seven percent of ESBL-positive E. coli also demonstrated resistance to quinolones with the most common PMQR genes being qnr-S (62.5%) and aac (6')-Ib-cr (37.5%). We did not find any resistance to quinolones among ESBL-positive K. pneumoniae isolates. Across different classes of antibiotics we found a strong clustering of multi-drug resistance in E. coli with over 45% of ESBL-positive isolates demonstrating resistance to at least three classes of antibiotics. This study emphasizes the high prevalence of plasmid-mediated ESBL and quinolone resistance in community-acquired urinary tract infections of primigravid women. The overall abundance of multi-drug-resistant isolates in this population is alarming and may present therapeutic challenges.
: 随着全球抗菌药物耐药性的威胁日益增加，揭示分子流行病学学对于了解是什么导致了这场危机至关重要。我们的目的是评估患有菌尿的初产妇中质粒介导的喹诺酮耐药 (PMQR) 和产超广谱 β-内酰胺酶 (ESBL) 的革兰氏阴性细菌的患病率。我们收集了2015年10月1日至20 16年9月30日期间在甘地医院首次产前就诊的初产妇的尿液标本。我们使用VITEK-2确定了抗菌药物敏感性和ESBL和喹诺酮耐药性。我们对耐药分离株进行聚合酶链反应扩增，以检测ESBL编码基因 (TEM，SHV，ctx-m) 和PMQR基因 (qnrA，qnrB，qnrD，qnrS，aac (6 ')-Ib-cr)。在1,841份尿液样本中，133份显示出显著的细菌生长，革兰阴性杆菌占分离株的85%，包括大肠埃希菌 (n = 79) 、肺炎克雷伯菌 (n = 29) 、鞘氨醇单胞菌 (n = 3) 、肠杆菌 (n = 1) 和柠檬酸杆菌 (n = 1)。我们发现65% 的大肠杆菌分离株和41% 的肺炎克雷伯菌分离株为ESBL阳性。在ESBL阳性分离株中，最常见的赋予抗性的基因是TEM-1 (66.7%)，其次是CTX-M-15 (33.3%)。7% 的ESBL阳性大肠杆菌也表现出对喹诺酮类药物的耐药性，最常见的PMQR基因是qnr-s (62.5%) 和aac (6 ')-Ib-cr (37.5%)。在ESBL阳性肺炎克雷伯菌分离株中，我们未发现对喹诺酮类药物有任何耐药性。在不同类别的抗生素中，我们发现大肠杆菌中多重耐药性的强烈聚类，超过45% 的ESBL阳性分离株表现出对至少三类抗生素的耐药性。本研究强调了初产妇社区获得性尿路感染中质粒介导的ESBL和喹诺酮耐药性的高患病率。该人群中多重耐药分离株的总体丰度令人担忧，并可能带来治疗挑战。
METHODS:PURPOSE:In utero myelomeningocele closure is a valid alternative to postnatal repair with unclear benefits to bladder function. We compared bladder status in patients who underwent fetal myelomeningocele surgery versus postnatal repair. MATERIALS AND METHODS:We retrospectively reviewed our database, with group 1 consisting of in utero surgery and group 2 consisting of postnatal repair. Group 3 was a subgroup of group 2, including patients initially presenting at age less than 12 months. We recorded medical history, radiological investigation with renal ultrasonography, voiding cystourethrography, urodynamic evaluation and clinical outcome of the bladder pattern after treatment. RESULTS:We identified 88 patients in group 1, 86 in group 2 and 38 in group 3. The incidence of urinary tract infection was higher in the postnatal period (45% vs 20%). Hydronephrosis occurred in 20.7%, 22.6% and 28.9% of patients in groups 1, 2 and 3, respectively. Vesicoureteral reflux was diagnosed in 15% in all groups. Urodynamic data showed a higher prevalence of detrusor overactivity in group 1 and no difference in other urodynamic parameters. The high risk bladder pattern at initial evaluation occurred in 56%, 50% and 46% of patients in groups 1, 2 and 3, respectively. There was a trend to decrease the percentages of the high risk bladder pattern and to increase the normal pattern after treatment in all groups. CONCLUSIONS:In utero repair did not improve urological parameters compared to repair in the postnatal period.
METHODS::The development and evolution of antimicrobial resistance (AMR) in pathogens has been reported to be one of the major issues confronting the global health community. The aim of this study was to examine the period prevalence of antibiotic resistance, as well as the trends and patterns in sensitivity profile of enteric bacteria isolated from urine samples of patients with UTIs in a teaching Hospital in south west Nigeria. Urine samples were collected from 77 patients with UTIs from February 2017 to October 2018. Standard laboratory methods were used for urine sample culture and bacterial identification. The Kirby-Bauer disk diffusion method was used to evaluate antimicrobial sensitivity. Predominant enteric bacteria isolates were Escherichia coli (24, 39.3%), Salmonella species (12, 19.7%), Klebsiella species (4, 6.6%), Providencia species (6, 9.8%), Proteus species (8, 13.1%), Serratia species (2, 3.3%), Yersinia species (1, 1.6%) and Morganella species (4, 6.6%). A large proportion (90.2%) of isolates obtained were multi-drug resistant. High resistance in amoxycillin-clavulanate (98%), cefuroxime (92%), erythromycin (90%) and ceftazidime (84%) were recorded. These results emphasize the importance of continuous screening and surveillance programmes for detection of AMR in enteric bacteria of public health importance.
METHODS::Massive generation of health-related data has been key in enabling the big data science initiative to gain new insights in healthcare. Nursing can benefit from this era of big data science, as there is a growing need for new discoveries from large quantities of nursing data to provide evidence-based care. However, there are few nursing studies using big data analytics. The purpose of this article is to explain a knowledge discovery and data mining approach that was employed to discover knowledge about hospital-acquired catheter-associated urinary tract infections from multiple data sources, including electronic health records and nurse staffing data. Three different machine learning techniques are described: decision trees, logistic regression, and support vector machines. The decision tree model created rules to interpret relationships among associated factors of hospital-acquired catheter-associated urinary tract infections. The logistic regression model showed what factors were related to a higher risk of hospital-acquired catheter-associated urinary tract infections. The support vector machines model was included to compare performance with the other two interpretable models. This article introduces the examples of cutting-edge machine learning approaches that will advance secondary use of electronic health records and integration of multiple data sources as well as provide evidence necessary to guide nursing professionals in practice.