Synbiotics, including probiotics and prebiotics, are useful for patients with functional bowel disorders. However, which synbiotics are beneficial for patients with which diseases, especially those with functional diarrhea (FDr) with high fecal calprotectin levels, is currently unknown. FDr is an extension of irritable bowel syndrome with diarrhea (IBS-D). Although fewer studies have been conducted on FDr compared to IBS-D, its importance is increasing as its prevalence increases. The aim of this study was to evaluate the effects of a synbiotic containing a mixture of Lactobacillus and Bifidobacterium and its substrate, fructooligosaccharide, on bowel symptoms, fecal calprotectin levels, fecal microbiota, and safety in FDr patients with high fecal calprotectin levels. Forty patients were randomly assigned to either a synbiotic group or a placebo group. A total of 20 subjects in the synbiotic group and 19 subjects in the placebo group completed the study (8 weeks). Changes in FDr symptoms, fecal calprotectin levels, and gut microbiota were assessed during the intervention period. At 4 and 8 weeks, the number of bowel movements tended to increase in the synbiotic group, with a significant increase in the number of formed stools rather than loose stools (p < 0.05). Bowel movement satisfaction was significantly increased in the synbiotic group, but not in the placebo group. Intestinal flora analysis revealed that Lactobacillales at the order level was increased only in the synbiotic group at the end of the intervention. In contrast, at week 8 of the intervention, log-transformed fecal calprotectin levels were significantly decreased in the synbiotic group, although the change was not significantly different from that of the placebo group. These findings suggest that the intake of a multi-strain-containing synbiotic for 8 weeks could improve gut symptoms and the intestinal microenvironment of FDr patients with high fecal calprotectin levels.

Despite the progress in diagnostics and therapeutics, epithelial ovarian cancer (EOC) remains a fatal disease. Using shallow whole-genome sequencing of plasma cell-free DNA (cfDNA), we investigated biomarkers that could detect EOC and predict survival. Plasma cfDNA from 40 EOC patients and 20 healthy subjects were analyzed by shallow whole-genome sequencing (WGS) to identify copy number variations (CNVs) and determine the Z-scores of genes. In addition, we also calculated the genome-wide scores (Gi scores) to quantify chromosomal instability. We found that the Gi scores could distinguish EOC patients from healthy subjects and identify various EOC histological subtypes (e.g., high-grade serous carcinoma). In addition, we characterized EOC CNVs and demonstrated a relationship between RAB25 amplification (alone or with CA125), and disease-free survival and overall survival. This study identified RAB25 amplification as a predictor of EOC patient survival. Moreover, we showed that Gi scores could detect EOC. These data demonstrated that cfDNA, detected by shallow WGS, represented a potential tool for diagnosing EOC and predicting its prognosis.

Contamination in low-biomass samples, such as urine, presents a major challenge for 16S rRNA gene sequencing, as extraneous DNA from reagents and the environment often obscures microbial signals. Existing in silico decontamination algorithms face limitations in accurately identifying and removing these contaminants. To address this issue, we developed CleanSeqU, a novel decontamination algorithm designed to enhance the accuracy of 16S rRNA gene sequencing data for catheterized urine samples. This approach is grounded in the principle that the compositional pattern of potential contaminant taxa remains similar between biological samples and blank controls. Also, the algorithm identifies potential contaminants based on ecological plausibility and custom blacklist. We evaluated CleanSeqU’s performance using vaginal microbiome dilution experiments as a proxy for low-biomass urine samples and compared it to the Decontam, Microdecon, and SCRuB algorithm. CleanSeqU consistently outperformed Decontam, Microdecon, and SCRuB across various contamination levels, with superior accuracy, F1-scores, and reduced beta-dissimilarity. CleanSeqU improved specificity and positive predictive value by correctly identifying and removing a higher number of contaminant amplicon sequence variants (ASVs). Furthermore, the reduced alpha diversity in the decontaminated datasets suggests more precise contaminant elimination. With its practical use of a single blank extraction control per batch and adjustable decontamination rules, CleanSeqU provides an efficient and scalable solution that delivers accurate microbial profiles. Our findings highlight its potential to significantly advance urine microbiome research by delivering more accurate microbial profiles.