Neonatal sepsis: epidemiology, clinical spectrum, recent antimicrobial agents and their antibiotic susceptibility pattern

Verma, Prateek; Berwal, Pramod Kumar; Nagaraj, Niranjan; Swami, Sarika; Jivaji, Prathusha; Narayan, Satya

doi:10.18203/2349-3291.ijcp20150523

Cited by 59 publications

(55 citation statements)

References 5 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Similar results were obtained in the study by Pradeep et al (60.94% being LBW). 14 Comparable results were also found in study by Shitayee et al (60%). 26 This may be due to the fact that Lbw babies have low IgG stores leading to lower immunity status.…”

Section: Discussionsupporting

confidence: 64%

“…11 A study done in SP medical college, Bikaner also showed Klebsiella as the most common (48.21%) micro-organism associated with sepsis in inborn unit. 14 In an another study done in Indore, Madhya Pradesh Klebsiella was found to be the most commonly associated organism. 13 Whereas in a study done in GMCH Chandigarh from 2008-12 the most common organism was found to be Staph.…”

Section: Discussionmentioning

confidence: 93%

See 1 more Smart Citation

Study of current status of bacteriological prevalence and profile in an inborn unit of SNCU in central India

Agrawal

Awasthi

Ghanghoriya

et al. 2018

Int J Contemp Pediatr

View full text Add to dashboard Cite

Background: About 43% of the under-five child mortality is contributed by neonatal death. According to National Neonatal Perinatal Database (NNPD) in inborn births, Klebsiella pneumonia was the most commonly associated pathogen, followed by Staphylococcus aureus. Hence to know the prevalence and profile of bacterial infection in the inborn unit of an SNCU in Central India,this study was done.Methods: This is a cross sectional study done in the Inborn Unit of SNCU, Department of Paediatrics, Netaji Subhash Chandra Bose Medical College and Hospital, Jabalpur (M.P.), INDIA done between 1st March 2015 to 31st August 2016 where the blood sample and data collection of the suspected patients was done. Samples were then sent for culture and sensitivity testing. All the details then entered in Microsoft Excel Sheet and data was analysed using SPSS v 20.Results: The prevalence of bacteriologically positive sepsis was found to be 5.06% (in 43patients out of 850 examined cases). It was more prevalent among males that is in 24 among total 43 culture positives (55.8%), low birth weight were 37 (86% of culture positive patients)and preterm were 35(81.4% of culture positive patients). The most common pathogen associated was found to be Klebsiella pneumonia which was detected in 16 patients (37.2% of all bacteriologically positive patients) followed by E. coli and Pseudomonas, each in 7 patients (16.28%).Conclusions: Though we are on the track of minimising morbidities still we have a high prevalence of neonatal sepsis in inborn unit so sepsis related morbidities can be prevented if timely interventions are done.

show abstract

Section: Discussionsupporting

confidence: 64%

Section: Discussionmentioning

confidence: 93%

Study of current status of bacteriological prevalence and profile in an inborn unit of SNCU in central India

Agrawal

Awasthi

Ghanghoriya

et al. 2018

Int J Contemp Pediatr

View full text Add to dashboard Cite

show abstract

“…РОССИЙСКИЙ ВЕСТНИК ПЕРИНАТОЛОГИИ И ПЕДИАТРИИ, 2017; 62: (5) ROSSIYSKIY VESTNIK PERINATOLOGII I PEDIATRII, 2017; 62: (5) Н еонатальный сепсис -актуальная инфекци-онная патология новорожденных [1,2]. Риск его развития, как правило, ассоциируется с недо-ношенностью и низкой массой тела при рождении, что связывают с незрелостью иммунной системы но-ворожденного ребенка [3][4][5].…”

Section: перинатология и неонатологияunclassified

Proinflammatory cytokine profile in children with neonatal sepsis

Хаертынов¹,

Анохин²,

Rizvanov³

et al. 2017

Ross. vestn. perinatol. pediatr.

View full text Add to dashboard Cite

“…In addition, the empirical P value of each module was evaluated as the probability of the module with the smaller score by chance. The Formula 2 was shown as followed: (2) P V alue = Sum (HR > HD) HR…”

Section: Significant Analysismentioning

confidence: 99%

“…Neonatal sepsis, a systemic infection occurring in infants at ≤ 28 days of life (1) is a major cause of morbidity and mortality in newborns, especially in the developing countries (2). Even though there has been an improvement in neonatal care in the past few years, infection remains a leading cause of morbidity and mortality in neonates worldwide by currently causing about 1.6 million deaths per year (3).…”

Section: Introductionmentioning

confidence: 99%

Determination of Biomarkers for Neonatal Sepsis Based on Differential Modules

Wang

Luan

et al. 2016

Iran Red Crescent Med J

View full text Add to dashboard Cite

Background: The exact interacting factor that response to the infection for neonatal sepsis is still needed to urgently to be disclosed. Objectives: This research was aimed to explore the potential biomarkers and illuminate the underlying molecular mechanisms associated with neonatal sepsis via identifying differential modules (DMs). Methods: This is a case-control bioinformatics analysis using already published microarray data of neonatal sepsis. This study was conducted in Qingdao, China from September 2015 to May 2016. We recruited the gene expression profile of neonatal sepsis from the Array Express database (http://www.ebi.ac.uk/arrayexpress) under the accessing number of E-GEOD-25504, which included 27 neonatal samples with a confirmed blood culture-positive test for sepsis (bacterial infected cases) as well as 35 matched controls. Meanwhile, the human protein-protein interaction (PPI) data was collected from the database of Search Tool for the Retrieval of Interacting Genes/Proteins (STRING, http://string-db.org). All of the data was preprocessed. Then, the differential co-expression network (DCN) was constructed by integrating co-expression analysis and differential expression analysis. Next, a systemic module searching strategy, which contained seed genes selection, module searching and refinement of modules, was performed by select DMs. Results: Starting from the gene expression data and PPI data, the DCN that included 430 edges (covering 324 nodes) was constructed, in which each edge was assigned a weight value. From the DCN, we selected a total of 16 seed genes. Starting from these seed genes, a total of 3 modules were identified from the DCN based on the systemic module algorithm. Of them, only one module (Module 3) was considered as DM under P < 0.05. This DM was involved in the progress of ribosome biogenesis in eukaryotes. Conclusions:In the present study, we identified a key gene RPS16 and a significant module involved in ribosome biogenesis in eukaryotes that were related to neonatal sepsis, which might be potential biomarkers for early detection and therapy for neonatal sepsis.

show abstract

Neonatal sepsis: epidemiology, clinical spectrum, recent antimicrobial agents and their antibiotic susceptibility pattern

Cited by 59 publications

References 5 publications

Study of current status of bacteriological prevalence and profile in an inborn unit of SNCU in central India

Study of current status of bacteriological prevalence and profile in an inborn unit of SNCU in central India

Proinflammatory cytokine profile in children with neonatal sepsis

Determination of Biomarkers for Neonatal Sepsis Based on Differential Modules

Contact Info

Product

Resources

About