A Multicenter Randomized Controlled Trial Of Microbiome-Based Artificial Intelligence-Assisted Personalized Diet Vs Low Fodmap Diet: A Novel Approach for the Management of Irritable Bowel Syndrome.

BACKGROUND AND AIMS: Personalized management strategies are pivotal in addressing Irritable Bowel Syndrome (IBS). This multicenter randomized controlled trial focuses on comparing the efficacy of a microbiome-based artificial intelligence (AI)-assisted personalized diet (PD) with a low FODMAP diet (LFD) for IBS management. METHODS: One hundred and twenty-one patients participated, with 70 assigned to the PD group and 51 to the LFD group. IBS subtypes, demographics, symptom severity (IBS-SSS), anxiety, depression, and quality of life (IBS-QOL) were evaluated. Both interventions spanned 6 weeks. The trial's primary outcome was the within-individual difference in IBS-SSS compared between intervention groups. RESULTS: For the primary outcome, there was a change in IBS-SSS of -112.7 for those in the PD group vs -99.9 for those in the LFD group (p: 0.29). Significant improvement occurred in IBS-SSS scores (p<.001), frequency (p<.001), abdominal distension (p<.001), and life interference (p<.001) in both groups. Additionally, there were significant improvements in anxiety levels and IBS-QOL scores for both groups (p<.001). Importantly, PD was effective in reducing IBS SSS scores across all IBS subtypes IBS-C (p<.001), IBS-D (p=.01), and IBS-M (p<.001) subtypes, while LFD exhibited comparable improvements in IBS-C (p=.004) and IBS-M (p<.001). PD intervention significantly improved IBS-QOL scores for all subtypes (IBS-C (p<.001), IBS-D (p<.001), and IBS-M (p=.008)), while the LFD did so for the IBS-C (p=.004) and IBS-D (p=.022). Notably, PD intervention led to significant microbiome diversity shifts (p<0.05) and taxa alterations compared to LFD. CONCLUSIONS: The AI-assisted personalized diet emerges as a promising approach for comprehensive IBS management. With its ability to address individual variation, the PD approach demonstrates significant symptom relief, enhanced quality of life, and notable diversity shifts in the gut microbiome, making it a valuable strategy in the evolving landscape of IBS care.

16S rRNA sequencing reveals microbiota differences in orthopedic implants between aseptic loosening and prosthetic joint infection cases.

Prosthetic joint infection (PJI) and aseptic loosening (AL) are common complications of total joint arthroplasty. An accumulation of evidence indicates the presence of microbial communities on prosthetic implants, but the overall microbial profile is unclear. In this study, we aimed to investigate the differences in the microbial composition of prosthetic implants obtained from PJI and AL patients using the 16S rRNA sequencing method. Patients who underwent revision hip, knee, or shoulder arthroplasty caused by PJI (n = 20) or AL (n = 10) were enrolled in the study. 16S rRNA sequencing targeting the V3-V4 region was performed on the microbial specimens collected from synovial fluid, periprosthetic deep-tissue, and biofilm during the revision surgery. The sequenced raw data were analysed for microbial composition and ecological and differential abundance analyses using bioinformatics tools. The AL group had relatively balanced and higher diversity, with Staphylococcus, Streptococcus, and Veillonella being prominent. In the PJI group, Staphylococcus and Pseudomonas were predominant, especially in deep-tissue samples and biofilm samples, respectively. The differential abundance analysis identified 15 and 2 distinctive taxa in the AL and PJI groups, respectively. Our findings provided preliminary insights supporting the existence of periprosthetic microbiota in orthopedic implants and explaining the differences in microbial composition between the AL and PJI groups.

A toolbox of machine learning software to support microbiome analysis.

The human microbiome has become an area of intense research due to its potential impact on human health. However, the analysis and interpretation of this data have proven to be challenging due to its complexity and high dimensionality. Machine learning (ML) algorithms can process vast amounts of data to uncover informative patterns and relationships within the data, even with limited prior knowledge. Therefore, there has been a rapid growth in the development of software specifically designed for the analysis and interpretation of microbiome data using ML techniques. These software incorporate a wide range of ML algorithms for clustering, classification, regression, or feature selection, to identify microbial patterns and relationships within the data and generate predictive models. This rapid development with a constant need for new developments and integration of new features require efforts into compile, catalog and classify these tools to create infrastructures and services with easy, transparent, and trustable standards. Here we review the state-of-the-art for ML tools applied in human microbiome studies, performed as part of the COST Action ML4Microbiome activities. This scoping review focuses on ML based software and framework resources currently available for the analysis of microbiome data in humans. The aim is to support microbiologists and biomedical scientists to go deeper into specialized resources that integrate ML techniques and facilitate future benchmarking to create standards for the analysis of microbiome data. The software resources are organized based on the type of analysis they were developed for and the ML techniques they implement. A description of each software with examples of usage is provided including comments about pitfalls and lacks in the usage of software based on ML methods in relation to microbiome data that need to be considered by developers and users. This review represents an extensive compilation to date, offering valuable insights and guidance for researchers interested in leveraging ML approaches for microbiome analysis.

The role of the Mediterranean diet in modulating the gut microbiome: A review of current evidence.

The Mediterranean diet (MedDiet) is recognized as one of the United Nations Educational, Scientific and Cultural Organization Intangible Cultural Heritage assets associated with lower rates of cardiometabolic diseases; lower prevalence of cancer, Alzheimer's disease, depression, and onset of inflammatory bowel disease; and more generally low-grade inflammation and mortality risks. Beyond being an input source of beneficial micronutrients, it recently has been discovered that the MedDiet plays a role in a more complex human microbiome-mediated mechanism. An interesting hypothesis suggests a bidirectional relationship between the MedDiet and the gut microbiome, where gut microbiota assembly and biosynthetic capacity are responsive to the diet; in return, the microbiome-reachable nutrients shape and modulate the microbiome toward a characteristic probiotic state. It can be speculated that that primary health benefits of the MedDiet exerted via the gut microbiome are mediated by the bioactive compounds transformed by the microbiome. Furthermore, it is possible that additional probiotic properties of the organisms promoted by diet adherence have secondary benefits. As more detailed omic-based studies take place, more evidence on the MedDiet as a core generic probiotic microbiome modulation strategy surface. However, individual-specific microbiome compositions might impose personal variations on the diet outcome. Therefore, a prospective strategy of a fine-tuned precision nutrition approach might deliver optimized benefits of the MedDiet.

Potential associations between alterations in gut microbiome and obesity-related traits after the bariatric surgery.

BACKGROUND: This study aimed to examine the effects of both obesity and bariatric surgery on gut microbiome, dietary intake, as well as metabolic and inflammatory parameters. METHODS: All participants (15 with morbid obesity who had bariatric surgery, 8 with morbid obesity and 11 non-obese) were followed up for a 6-month period with interviews at baseline (M0), at the end of 3 (M3) and 6 months (M6). Dietary assessment was done, and blood and faecal samples were collected. RESULTS: Dietary energy and nutrient intakes as well as serum glucose levels, total cholesterol, low-density lipoprotein (LDL)-cholesterol and high sensitivity C-reactive protein (hs-CRP) levels decreased after surgery (p < 0.05, for each). Participants with morbid obesity had higher levels of Firmicutes and lower levels of Bacteroidetes at M0 compared to non-obese participants. The abundances of Bacteroidetes increased (p = 0.02), whereas that of Firmicutes decreased (p > 0.05) after the surgery, leading to a significant decrease in Firmicutes/Bacteroidetes ratio (p = 0.01). At sub-phylum level, the abundances of Lactobacillus and Bifidobacterium decreased, whereas those of Akkermansia increased after the surgery (p < 0.01, for each). Although participants who were morbidly obese had a distinct profile according to ß-diversity indices at M0, it became similar with the profile of non-obese participants (p > 0.05) at M3 and M6. Similarly, α-diversity indices were lower in subjects with morbid obesity at M0, but became similar to levels in non-obese controls at M6. CONCLUSION: This study confirmed that bariatric surgery has substantial impacts on gut microbiome's composition and diversity, as well as anthropometrical measurements and biochemical parameters, which were associated with the alterations in dietary intake patterns.

A comparative study of the clonal diversity and virulence characteristics of uropathogenic Escherichia coli isolated from Australian and Turkish (Turkey) children and adults with urinary tract infections.

Introduction: The virulence-associated gene (VAG) repertoire and clonal organization of uropathogenic Escherichia coli (UPEC) strains is influenced by host demographic, geographic locale, and the setting of urinary tract infection (UTI). Nevertheless, a direct comparison of these features among Australian and Turkish UPEC remains unexplored. Accordingly, this study investigated the clonal composition and virulence characteristics of a collection of UPEC isolated from Australian and Turkish UTI patients. Methods: A total of 715 UPEC strains isolated from Australian (n=361) and Turkish (n=354) children and adults with hospital (HA)- and community-acquired (CA)-UTIs were included in this study. Typing of the strains using RAPD-PCR and PhPlate fingerprinting grouped all strains into 25 clonal groups (CGs). CG representatives were phylogrouped and screened for the presence of 18 VAGs associated with extraintestinal pathogenic E. coli. Results: Turkish UPEC strains were characterized by high clonal diversity and predominance of the phylogroup D, while few distinct clonal groups with phylogenetic group B2 backgrounds dominated among the Australian strains. Twelve identical CGs were shared between ≥1 patient group from either country. Australian strains, particularly those isolated from children with HA-UTI, showed higher virulence potential than their Turkish counterparts, carrying significantly more genes associated with adhesion, iron acquisition and capsule biosynthesis. Conclusions: This study identified identical CGs of UPEC causing HA- and CA-UTIs among Australian and Turkish UTI patients. These CGs frequently carried VAGs associated with adhesion, iron acquisition, immune evasion, and toxin production, which may contribute to their ability to disseminate internationally and to cause UTI.

Efficacy of AI-Assisted Personalized Microbiome Modulation by Diet in Functional Constipation: A Randomized Controlled Trial.

Background: Currently, medications and behavioral modifications have limited success in the treatment of functional constipation (FC). An individualized diet based on microbiome analysis may improve symptoms in FC. In the present study, we aimed to investigate the impacts of microbiome modulation on chronic constipation. Methods: Between December 2020−December 2021, 50 patients fulfilling the Rome IV criteria for functional constipation were randomized into two groups. The control group received sodium picosulfate plus conventional treatments (i.e., laxatives, enemas, increased fiber, and fluid intake). The study group underwent microbiome analysis and received an individualized diet with the assistance of a soft computing system (Enbiosis Biotechnology®, Sariyer, Istanbul). Differences in patient assessment constipation−quality of life (PAC-QoL) scores and complete bowel movements per week (CBMpW) were compared between groups after 6-weeks of intervention. Results: The mean age of the overall cohort (n = 45) was 31.5 ± 10.2 years, with 88.9% female predominance. The customized diet developed for subjects in the study arm resulted in a 2.5-fold increase in CBMpW after 6-weeks (1.7 vs. 4.3). The proportion of the study group patients with CBMpW > 3 was 83% at the end of the study, and the satisfaction score was increased 4-fold from the baseline (3.1 to 10.7 points). More than 50% improvement in PAC-QoL scores was observed in 88% of the study cohort compared to 40% in the control group (p = 0.001). Conclusion: The AI-assisted customized diet based on individual microbiome analysis performed significantly better compared to conventional therapy based on patient-reported outcomes in the treatment of functional constipation.

Artificial intelligence-based personalized diet: A pilot clinical study for irritable bowel syndrome.

We enrolled consecutive IBS-M patients (n = 25) according to Rome IV criteria. Fecal samples were obtained from all patients twice (pre-and post-intervention) and high-throughput 16S rRNA sequencing was performed. Six weeks of personalized nutrition diet (n = 14) for group 1 and a standard IBS diet (n = 11) for group 2 were followed. AI-based diet was designed based on optimizing a personalized nutritional strategy by an algorithm regarding individual gut microbiome features. The IBS-SSS evaluation for pre- and post-intervention exhibited significant improvement (p < .02 and p < .001 for the standard IBS diet and personalized nutrition groups, respectively). While the IBS-SSS evaluation changed to moderate from severe in 78% (11 out of 14) of the personalized nutrition group, no such change was observed in the standard IBS diet group. A statistically significant increase in the Faecalibacterium genus was observed in the personalized nutrition group (p = .04). Bacteroides and putatively probiotic genus Propionibacterium were increased in the personalized nutrition group. The change (delta) values in IBS-SSS scores (before-after) in personalized nutrition and standard IBS diet groups are significantly higher in the personalized nutrition group. AI-based personalized microbiome modulation through diet significantly improves IBS-related symptoms in patients with IBS-M. Further large-scale, randomized placebo-controlled trials with long-term follow-up (durability) are needed.

SERS-based sensor with a machine learning based effective feature extraction technique for fast detection of colistin-resistant Klebsiella pneumoniae.

Colistin-resistant Klebsiella pneumoniae (ColR-Kp) causes high mortality rates since colistin is used as the last-line antibiotic against multi-drug resistant Gram-negative bacteria. To reduce infections and mortality rates caused by ColR-Kp fast and reliable detection techniques are vital. In this study, we used a label-free surface-enhanced Raman scattering (SERS)-based sensor with machine learning algorithms to discriminate colistin-resistant and susceptible strains of K. pneumoniae. A total of 16 K. pneumoniae strains were incubated in tryptic soy broth (TSB) for 4 h. Collected SERS spectra of ColR-Kp and colistin susceptible K. pneumoniae (ColS-Kp) have shown some spectral differences that hard to discriminate by the naked eye. To extract discriminative features from the dataset, autoencoder and principal component analysis (PCA) that extract features in a non-linear and linear manner, respectively were performed. Extracted features were fed into the support vector machine (SVM) classifier to discriminate K. pneumoniae strains. Classifier performance was evaluated by using features extracted by each feature extraction techniques. Classification results of SVM classifier with extracted features by an autoencoder (autoencoder-SVM) has shown better performance than SVM classifier with extracted features by PCA (PCA-SVM). The accuracy, sensitivity, specificity, and area under curve (AUC) value of the autoencoder-SVM model were found as 94%, 94.2%, 93.8%, and 0.98, respectively. Furthermore, the autoencoder-SVM model has demonstrated statistically significantly better classifier performance than PCA-SVM in terms of accuracy and AUC values. These results illustrate that non-linear features can be more discriminative than linear ones to determine SERS spectral data of antibiotic-resistant and susceptible bacteria. Our methodological approach enables rapid and high accuracy detection of ColR-Kp and ColS-Kp, suggesting that this can be a promising tool to limit colistin resistance.

Acromegaly is associated with a distinct oral and gut microbiota.

PURPOSE: Our aim was to investigate the changes in the composition of oral and gut microbiota in patients with newly diagnosed acromegaly and their relationship with IGF-1 levels. METHODS: Oral and fecal samples were collected from patients with newly diagnosed acromegaly without comorbidities and from healthy controls. The composition of the microbiota was analyzed. The general characteristics, oral and stool samples of the patients and healthy control subjects were compared. The changes in microbiota composition in both habitats, their correlations and associations with IGF-1 were statistically observed using machine learning models. RESULTS: Fifteen patients with newly diagnosed acromegaly without comorbidities and 15 healthy controls were included in the study. There was good agreement between fecal and oral microbiota in patients with acromegaly (p = 0.03). Oral microbiota diversity was significantly increased in patients with acromegaly (p < 0.01). In the fecal microbiota, the Firmicutes/Bacteroidetes ratio was lower in patients with acromegaly than in healthy controls (p = 0.011). Application of the transfer learned model to the pattern of microbiota allowed us to identify the patients with acromegaly with perfect accuracy. CONCLUSIONS: Patients with acromegaly have their own oral and gut microbiota even if they do not have acromegaly-related complications. Moreover, the excess IGF-1 levels could be correctly predicted based on the pattern of the microbiome.

Stratification of the Gut Microbiota Composition Landscape across the Alzheimer's Disease Continuum in a Turkish Cohort.

Alzheimer's disease (AD) is a heterogeneous disorder that spans a continuum with multiple phases, including preclinical, mild cognitive impairment, and dementia. Unlike for most other chronic diseases, human studies reporting on AD gut microbiota in the literature are very limited. With the scarcity of approved drugs for AD therapies, the rational and precise modulation of gut microbiota composition using diet and other tools is a promising approach to the management of AD. Such an approach could be personalized if an AD continuum can first be deconstructed into multiple strata based on specific microbiota features by using single or multiomics techniques. However, stratification of AD gut microbiota has not been systematically investigated before, leaving an important research gap for gut microbiota-based therapeutic approaches. Here, we analyze 16S rRNA amplicon sequencing of stool samples from 27 patients with mild cognitive impairment, 47 patients with AD, and 51 nondemented control subjects by using tools compatible with the compositional nature of microbiota. To stratify the AD gut microbiota community, we applied four machine learning techniques, including partitioning around the medoid clustering and fitting a probabilistic Dirichlet mixture model, the latent Dirichlet allocation model, and we performed topological data analysis for population-scale microbiome stratification based on the Mapper algorithm. These four distinct techniques all converge on Prevotella and Bacteroides stratification of the gut microbiota across the AD continuum, while some methods provided fine-scale resolution in stratifying the community landscape. Finally, we demonstrate that the signature taxa and neuropsychometric parameters together robustly classify the groups. Our results provide a framework for precision nutrition approaches aiming to modulate the AD gut microbiota. IMPORTANCE The prevalence of AD worldwide is estimated to reach 131 million by 2050. Most disease-modifying treatments and drug trials have failed, due partly to the heterogeneous and complex nature of the disease. Recent studies demonstrated that gut dybiosis can influence normal brain function through the so-called "gut-brain axis." Modulation of the gut microbiota, therefore, has drawn strong interest in the clinic in the management of the disease. However, there is unmet need for microbiota-informed stratification of AD clinical cohorts for intervention studies aiming to modulate the gut microbiota. Our study fills in this gap and draws attention to the need for microbiota stratification as the first step for microbiota-based therapy. We demonstrate that while Prevotella and Bacteroides clusters are the consensus partitions, the newly developed probabilistic methods can provide fine-scale resolution in partitioning the AD gut microbiome landscape.

Temporal overexpression of IL-22 and Reg3γ differentially impacts the severity of experimental autoimmune encephalomyelitis.

IL-22 is an alpha-helical cytokine which belongs to the IL-10 family of cytokines. IL-22 is produced by RORγt+ innate and adaptive lymphocytes, including ILC3, γδ T, iNKT, Th17 and Th22 cells and some granulocytes. IL-22 receptor is expressed primarily by non-haematopoietic cells. IL-22 is critical for barrier immunity at the mucosal surfaces in the steady state and during infection. Although IL-22 knockout mice were previously shown to develop experimental autoimmune encephalomyelitis (EAE), a murine model of multiple sclerosis (MS), how temporal IL-22 manipulation in adult mice would affect EAE course has not been studied previously. In this study, we overexpressed IL-22 via hydrodynamic gene delivery or blocked it via neutralizing antibodies in C57BL/6 mice to explore the therapeutic impact of IL-22 modulation on the EAE course. IL-22 overexpression significantly decreased EAE scores and demyelination, and reduced infiltration of IFN-γ+IL-17A+Th17 cells into the central nervous system (CNS). The neutralization of IL-22 did not alter the EAE pathology significantly. We show that IL-22-mediated protection is independent of Reg3γ, an epithelial cell-derived antimicrobial peptide induced by IL-22. Thus, overexpression of Reg3γ significantly exacerbated EAE scores, demyelination and infiltration of IFN-γ+IL-17A+ and IL-17A+GM-CSF+Th17 cells to CNS. We also show that Reg3γ may inhibit IL-2-mediated STAT5 signalling and impair expansion of Treg cells in vivo and in vitro. Finally, Reg3γ overexpression dramatically impacted intestinal microbiota during EAE. Our results provide novel insight into the role of IL-22 and IL-22-induced antimicrobial peptide Reg3γ in the pathogenesis of CNS inflammation in a murine model of MS.

Gut microbiota in patients with newly diagnosed acromegaly: a pilot cross-sectional study.

PURPOSE: Microbiota has crucial biological importance for human well-being. Bidirectional interaction exists between microbiota and the host, and there have been no studies investigating this interaction in patients with acromegaly. We aimed to analyze the composition of microbiota in patients with newly diagnosed acromegaly. METHOD: Stool samples were obtained from the patients with newly diagnosed acromegaly in the Endocrinology Clinic of Erciyes University Medical School. The composition of microbiota was analyzed, and the results were compared to healthy volunteers matched to the patients in terms of age, gender and body mass index. RESULTS: Seven patients (three male, four female) with a mean age of 48 ± 17.6 years were included in the study. The stool analysis revealed a significantly lower bacterial diversity in the patients with acromegaly. Bacteroidetes phylum was predominating in the patient group, and Firmicutes/Bacteroidetes ratio was altered significantly. Bifidobacterium, Collinsella, Bacteroides, Butyricimonas, Clostridium, Oscillospira, and Dialister were predominating in the control group. CONCLUSION: The gut microbiota is significantly altered in patients with newly diagnosed acromegaly. Further prospective studies are needed to elucidate the causative relationship between acromegaly, colorectal pathologies, and microbial alterations.

Statistical and Machine Learning Techniques in Human Microbiome Studies: Contemporary Challenges and Solutions.

The human microbiome has emerged as a central research topic in human biology and biomedicine. Current microbiome studies generate high-throughput omics data across different body sites, populations, and life stages. Many of the challenges in microbiome research are similar to other high-throughput studies, the quantitative analyses need to address the heterogeneity of data, specific statistical properties, and the remarkable variation in microbiome composition across individuals and body sites. This has led to a broad spectrum of statistical and machine learning challenges that range from study design, data processing, and standardization to analysis, modeling, cross-study comparison, prediction, data science ecosystems, and reproducible reporting. Nevertheless, although many statistics and machine learning approaches and tools have been developed, new techniques are needed to deal with emerging applications and the vast heterogeneity of microbiome data. We review and discuss emerging applications of statistical and machine learning techniques in human microbiome studies and introduce the COST Action CA18131 "ML4Microbiome" that brings together microbiome researchers and machine learning experts to address current challenges such as standardization of analysis pipelines for reproducibility of data analysis results, benchmarking, improvement, or development of existing and new tools and ontologies.

Effect of essential oil supplementation to diet on meat quality, fatty acid composition, performance parameters and intestinal microbiota of Japanese quails.

The effect of essential oil (EO) supplementation on carcass characteristics of Japanese quails and interactions between ingredients and intestinal morphology were investigated in this study. A total of 250 quails were fed different diet: D1, basal diet (BD); D2, BD plus palmarosa oil (PO; 100 µg/kg diet); D3, BD plus lemon myrtle oil (LMO; 100 µg/kg diet); D4, BD plus α-Tops (mixture of α-terpineol, cineole and terpinene-4-ol; 100 µg/kg diet); and D5, BD plus cyclodextrin. Overall growth performance was determined at multiple time points during 35 days of experiment. Carcass characteristics (fatty acid, pH and colour), intestinal morphology and the expression levels of meat quality-related genes including the insulin-like growth factor (IGF-1), myogenin and avian uncoupling protein (avUCP) were examined at the end of the trial. Additionally, intestinal microbiome of quails was studied by next-generation sequencing-based culture-independent analysis. Although the inclusion of EOs into the diet had no effect on the growth performance of quails and the microbial profile, the significant changes in pH24 and colour (a*) of the quail's breast muscle (p < .05) in the group receiving PO were observed. Additionally, oleic acid content in the breast muscle was significantly higher in the EOs supplemented groups (p < .01). Quails fed the PO supplemented diet had higher villus and relatively rich in oleic acid. The expression levels of IGF-1 and myogenin genes in quail's muscle were not affected, but the expression of avUCP gene was significantly lower in quails fed with LMO and α-Tops (p < .05). The results demonstrated variable effects of these treatments on intestinal morphology. Taken together, dietary inclusion of EOs is found to be beneficial and hence can be recommended for improving the quality of poultry meat.

Presence of OXA-48 Gene in a Clinical Isolate of Lactobacillus rhamnosus.

Lactobacilli are part of the microbiota and are also used as probiotics. However, in recent years they have been associated with invasive infections, especially bacteremia. Lactobacillus spp. are usually susceptible to penicillins, macrolides, and carbapenems, but Lactobacillus rhamnosus is intrinsically resistant to glycopeptides. The aim of this study was to determine the antimicrobial susceptibility profile and resistance mechanism of a clinical isolate of L. rhamnosus isolated from 10 sets of blood cultures of the same patient. The isolate was identified by Matrix-Assisted Laser Desorption/Ionization Time-of-Flight Mass Spectrometry (Bruker Daltonics; BD, Bremen, Germany) and 16S rRNA gene sequencing. In vitro susceptibilities to penicillin, ampicillin, imipenem, vancomycin, erythromycin, clindamycin, and linezolid were determined with gradient test strips (bioMérieux, France) on Mueller-Hinton agar plates supplemented with 5% defibrinated horse blood and 20 mg/L ß-NAD. The isolate was resistant to vancomycin and imipenem. Polymerase chain reaction test was positive for blaOXA-48 and the presence of this carbapenemase was confirmed by gene sequencing. Although plasmid analysis suggested that the blaOXA-48 is chromosomal in this isolate, it is still an alarming finding for potential transmission of antibiotic resistance genes to other bacteria in the gut. To our knowledge, this is the first report of the presence of blaOXA-48 in a Lactobacillus spp. and has utmost importance as these bacteria are used as probiotics. The isolation of these bacteria from sterile body sites should not go unnoticed.

An outbreak of bloodstream infection due to extensively resistant Acinetobacter baumannii among neonates.

BACKGROUND: Extensively resistant Acinetobacter baumannii has emerged and spread worldwide as a significant cause of health care-associated infections and outbreaks. It also causes life-threatening infections among neonates, including bacteremia. The aim of this study was to investigate an outbreak of A baumannii bacteremia (ABB) among neonates. MATERIALS AND METHODS: A retrospective, case-control study was conducted from July 2014 to July 2015 in a neonatal intensive care unit (NICU). Risk factors associated with ABB in univariate and multivariate analysis with logistic regression was performed. Molecular typing by pulsed field gel electrophoresis was used to confirm relatedness of bacteremic A baumannii strains. RESULTS: During the 5-year period (2011-2016), 68 patients in our NICU were diagnosed with BSI due to A baumannii. The case-control study included 41 case patients within the outbreak caused by a major epidemic clone and 108 control patients. Risk factors (by univariate analysis) associated with ABB were intubation, 14-day mortality, and use of peritoneal dialysis and an umbilical catheter. Multivariate analysis identified 14-day mortality (odds ratio, 5.75; 95% confidence interval, 2.58-12.79) and umbilical catheter use (odds ratio, 2.44; 95% confidence interval, 1.1-5.4) as independent risk factors for ABB. CONCLUSIONS: This outbreak of bacteremia due to resistant A baumannii affected 41 infants and was associated with 58% mortality. Control of the outbreak was achieved by implementing long-term sustained infection control measures within the unit.

Could Frequent Carbapenem Use Be a Risk Factor for Colistin Resistance?

AIMS: The antibiotic colistin, which had been previously abandoned, is being brought back as a last line of defense against bacterial infection. However, colistin resistance was reported shortly after its reintroduction. This study evaluated the risk factors for colonization/infections due to colistin-resistant Acinetobacter baumannii (ColR-Ab) and Klebsiella pneumoniae (ColR-Kp) strains and characterized the molecular epidemiology of these two strains. RESULTS: Age, previous hospitalization duration, and previous use of carbapenem and colistin were risk factors for ColR-Kp, whereas previous use of carbapenem and colistin was a risk factor for ColR-Ab. According to pulsed-field gel electrophoresis analysis, most ColR-Kp strains could be grouped into two major pulsotypes. This appears to be an indicator of cross contamination of ColR-Kp strain, since different isolates appeared to be belonging to the same clones. The existence of colistin-susceptible (ColS) and colistin-resistant (ColR) strains in the same pulsotypes might also be an indicator of the recent emergence of resistance mechanisms. CONCLUSIONS: The results highlight the emergence of ColR pathogens in Turkey, which is considered to be developing country, and that carbapenem use coupled with insufficient infection control measures might increase the risk of ColR outbreaks.