Exploration of Potential Gut Microbiota-Derived Biomarkers to Predict the Success of Fecal Microbiota Transplantation in Ulcerative Colitis: A Prospective Cohort in Korea.

Background/Aims: Although fecal microbiota transplantation (FMT) has been proven as one of the promising treatments for patients with ulcerative colitis (UC), potential prognostic markers regarding the clinical outcomes of FMT remain elusive. Methods: We collected fecal samples of 10 participants undergoing FMT to treat UC and those from the corresponding donors. We categorized them into two groups: responders and nonresponders. Sequencing of the bacterial 16S rRNA gene was conducted on the samples to explore bacterial composition. Results: Analyzing the gut microbiota of patients who showed different outcomes in FMT presented a distinct microbial niche. Source tracking analysis showed the nonresponder group had a higher rate of preservation of donor microbiota, underscoring that engraftment degrees are not one of the major drivers for the success of FMT. At the phylum level, Bacteroidetes bacteria were significantly depleted (p<0.003), and three genera, including Enterococcus, Rothia, and Pediococcus, were enriched in the responder group before FMT (p=0.003, p=0.025, and p=0.048, respectively). Furthermore, we applied a machine learning algorithm to build a prediction model that might allow the prediction of FMT outcomes, which yielded an area under the receiver operating characteristic (ROC) curve of 0.844. Notably, the microbiota-based model was much better at predicting outcomes than the clinical features model (area under the ROC curve=0.531). Conclusions: This study is the first to suggest the significance of indigenous microbiota of recipients as a critical factor. The result highlights that bacterial composition should be evaluated before FMT to select suitable patients and achieve better efficiency.

Alterations and Prediction of Functional Profiles of Gut Microbiota After Fecal Microbiota Transplantation for Iranian Recurrent Clostridioides difficile Infection with Underlying Inflammatory Bowel Disease: A Pilot Study.

BACKGROUND AND PURPOSE: Fecal microbiota transplantation (FMT) has emerged for the therapeutic treatment of recurrent Clostridioides difficile infection (rCDI) with concurrent inflammatory bowel disease (IBD). As the first Iranian population cohort, we examined how gut microbiota and their functional profiles change in Iranian rCDI patients with underlying IBD before and after FMT. PATIENTS AND METHODS: FMT was performed to eight IBD patients via colonoscopy. Profiles of gut microbiota from donors and recipients were investigated using 16S rRNA gene sequence analysis. RESULTS: Patients experienced no IBD flare-ups or other adverse effects, and all recovered to full health. Moreover, all rCDI patients lacked the Bacteroidetes present in donor samples. After FMT, the proportion of Bacteroidetes increased until a normal range was achieved. More specifically, the relative abundance of Prevotella was found to increase significantly following FMT. Prevotella was also found to correlate negatively with inflammation metrics, suggesting that Prevotella may be a key factor for resolving CDI and IBD. Gut microbiota diversity was found to increase following FMT, while dysbiosis decreased. However, the similarity of microbial communities of host and recipients did not increase, and wide variation in the extent of donor stool engraftment indicated that the gut bacterial communities of recipients do not shift towards those of donors. CONCLUSION: FMT leads to significant alterations of the community structure of gut bacteria in rCDI patients with IBD. The change in relative abundance of Proteobacteria and bacterial diversity indicated that FMT promotes recovery from intestinal permeability and inflammation in rCDI patients. Moreover, strong negative correlation between Prevotella and inflammation index, and decreased dysbiosis index advocate that the improvement of CDI is possibly due to gut microbiome alteration. Collectively, our findings show that FMT would be a promising therapy to help reprogram the gut microbiome of Iranian rCDI patients with IBD.

Pre-injection ultrasound scanning for treating temporal hollowing.

BACKGROUND: Hyaluronic acid filler injection is commonly administered to correct temple hollowness, typically through deep temporal injection. Since the vascular distribution at the injection site can be diverse, studies on avoiding damage to the corresponding blood vessels are needed. AIMS: To assess the commonly used hyaluronic acid filler injection site in the temple region, 1cm lateral and 1 cm above from the end of eyebrow, using a Doppler ultrasound to detect any anatomic variations in the blood vessels. PATIENTS/METHODS: Thirty patients (60 temples, right and left) were examined using Doppler ultrasonography. An 8-17 MHz ultrasound probe was used to discriminate between the anatomic layers of the temple. Blood vessels found in each anatomical layer were subsequently investigated. RESULTS: Among the 30 patients included in this study, we found temporal region arteries 1 cm above and 1 cm lateral to the distal end of the eyebrow in 9 patients; However, no arteries were detected in the temples of 21 patients. The presence or absence of arteries was bilateral in all patients. CONCLUSIONS: The anatomical layers with blood vessels varied among patients. The variability could give rise to complications. Possible anatomic variations at the temple should be carefully identified using pre-injection ultrasonography, and harming blood vessels should be avoided while injecting hyaluronic acid filler for temple augmentation.

Alteration of the Soil Microbiota in Ginseng Rusty Roots: Application of Machine Learning Algorithm to Explore Potential Biomarkers for Diagnostic and Predictive Analytics.

Conceptualization to utilize microbial composition as a prediction tool has been widely applied in human cohorts, yet the potential capacity of soil microbiota as a diagnostic tool to predict plant phenotype remains unknown. Here, we collected 130 soil samples which are 54 healthy controls and 76 ginseng rusty roots (GRRs). Alpha diversities including Shannon, Simpson, Chao1, and phylogenetic diversity were significantly decreased in GRR (P < 0.05). Moreover, we identified 30 potential biomarkers. The optimized markers were obtained through fivefold cross-validation on a support vector machine and yielded a robust area under the curve of 0.856. Notably, evaluation of multi-index classification performance including accuracy, F1-score, and Kappa coefficient also showed robust discriminative capability (90.99%, 0.903, and 0.808). Taken together, our results suggest that the disease affects the microbial community and offers the potential ability of soil microbiota to identifying farms at the risk of GRR.

Vaginal Microbiome-Based Bacterial Signatures for Predicting the Severity of Cervical Intraepithelial Neoplasia.

Although emerging evidence revealed that the gut microbiome served as a tool and as biomarkers for predicting and detecting specific cancer or illness, it is yet unknown if vaginal microbiome-derived bacterial markers can be used as a predictive model to predict the severity of CIN. In this study, we sequenced V3 region of 16S rRNA gene on vaginal swab samples from 66 participants (24 CIN 1-, 42 CIN 2+ patients) and investigated the taxonomic composition. The vaginal microbial diversity was not significantly different between the CIN 1- and CIN 2+ groups. However, we observed Lactobacillus amylovorus dominant type (16.7%), which does not belong to conventional community state type (CST). Moreover, a minimal set of 33 bacterial species was identified to maximally differentiate CIN 2+ from CIN 1- in a random forest model, which can distinguish CIN 2+ from CIN 1- (area under the curve (AUC) = 0.952). Among the 33 bacterial species, Lactobacillus iners was selected as the most impactful predictor in our model. This finding suggests that the random forest model is able to predict the severity of CIN and vaginal microbiome may play a role as biomarker.