A Multicenter Randomized Controlled Trial of Microbiome-Based Artificial Intelligence-Assisted Personalized Diet vs Low-Fermentable Oligosaccharides, Disaccharides, Monosaccharides, and Polyols Diet: A Novel Approach for the Management of Irritable Bowel Syndrome.

INTRODUCTION: 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-assisted personalized diet (PD) with a low-fermentable oligosaccharides, disaccharides, monosaccharides, and polyols diet (FODMAP) for IBS management. METHODS: One hundred twenty-one patients participated, with 70 assigned to the PD group and 51 to the FODMAP diet 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 FODMAP diet group ( P = 0.29). Significant improvement occurred in IBS-SSS scores ( P < 0.001), frequency ( P < 0.001), abdominal distension ( P < 0.001), and life interference ( P < 0.001) in both groups. In addition, there were significant improvements in anxiety levels and IBS-QOL scores for both groups ( P < 0.001). Importantly, PD was effective in reducing IBS SSS scores across all IBS subtypes IBS-Constipation (IBS-C; P < 0.001), IBS-Diarrhea (IBS-D; P = 0.01), and IBS-Mixed (IBS-M; P < 0.001) while FODMAP diet exhibited comparable improvements in IBS-C ( P = 0.004) and IBS-M ( P < 0.001). PD intervention significantly improved IBS-QOL scores for all subtypes (IBS-C [ P < 0.001], IBS-D [ P < 0.001], and IBS-M [ P = 0.008]) while the FODMAP diet did so for the IBS-C ( P = 0.004) and IBS-D ( P = 0.022). Notably, PD intervention led to significant microbiome diversity shifts ( P < 0.05) and taxa alterations compared with FODMAP diet. DISCUSSION: The artificial intelligence-assisted PD emerges as a promising approach for comprehensive IBS management. With its ability to address individual variation, the PD approach demonstrates significant symptom relief, enhanced QOL, 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 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.

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.

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.