Blastocyst telomere length predicts successful implantation after frozen-thawed embryo transfer.

STUDY QUESTION: Do embryos with longer telomere length (TL) at the blastocyst stage have a higher capacity to survive after frozen-thawed embryo transfer (FET)? SUMMARY ANSWER: Digitally estimated TL using low-pass whole genome sequencing (WGS) data from the preimplantation genetic testing for aneuploidy (PGT-A) process demonstrates that blastocyst TL is the most essential factor associated with likelihood of implantation. WHAT IS KNOWN ALREADY: The lifetime TL is established in the early cleavage cycles following fertilization through a recombination-based lengthening mechanism and starts erosion beyond the blastocyst stage. In addition, a telomerase-mediated slow erosion of TL in human fetuses has been observed from a gestational age of 6-11 weeks. Finally, an abnormal shortening of telomeres is likely involved in embryo loss during early development. STUDY DESIGN SIZE DURATION: Blastocyst samples were obtained from patients who underwent PGT-A and FET in an IVF center from March 2015 to May 2018. Digitally estimated mitochondrial copy number (mtCN) and TL were used to study associations with the implantation potential of each embryo. PARTICIPANTS/MATERIALS SETTING AND METHODS: In total, 965 blastocysts from 232 cycles (164 patients) were available to investigate the biological and clinical relevance of TL. A WGS-based workflow was applied to determine the ploidy of each embryo. Data from low-pass WGS-PGT-A were used to estimate the mtCN and TL for each embryo. Single-variant and multi-variant logistic regression, decision tree, and random forest models were applied to study various factors in association with the implantation potential of each embryo. MAIN RESULTS AND THE ROLE OF CHANCE: Of the 965 blastocysts originally available, only 216 underwent FET. While mtCN from the transferred embryos is significantly associated with the ploidy call of each embryo, mtCN has no role in impacting IVF outcomes after an embryo transfer in these women. The results indicate that mtCN is a marker of embryo aneuploidy. On the other hand, digitally estimated TL is the most prominent univariant factor and showed a significant positive association with pregnancy outcomes (P < 0.01, odds ratio 79.1). We combined several maternal and embryo parameters to study the joint effects on successful implantation. The machine learning models, namely decision tree and random forest, were trained and yielded classification accuracy of 0.82 and 0.91, respectively. Taken together, these results support the vital role of TL in governing implantation potential, perhaps through the ability to control embryo survival after transfer. LIMITATIONS REASONS FOR CAUTION: The small sample size limits our study as only 216 blastocysts were transferred. The number was further reduced to 153 blastocysts, where pregnancy outcomes could be accurately traced. The other limitation of this study is that all data were collected from a single IVF center. The uniform and controlled operation of IVF cycles in a single center may cause selection bias. WIDER IMPLICATIONS OF THE FINDINGS: We present novel findings to show that digitally estimated TL at the blastocyst stage is a predictor of pregnancy capacity after a FET cycle. As elective single-embryo transfer has become the mainstream direction in reproductive medicine, prioritizing embryos based on their implantation potential is crucial for clinical infertility treatment in order to reduce twin pregnancy rate and the time to pregnancy in an IVF center. The AI-powered, random forest prediction model established in this study thus provides a way to improve clinical practice and optimize the chances for people with fertility problems to achieve parenthood. STUDY FUNDING/COMPETING INTERESTS: This study was supported by a grant from the National Science and Technology Council, Taiwan (MOST 108-2321-B-006-013 -). There were no competing interests. TRIAL REGISTRATION NUMBER: N/A.

Machine learning for prediction of schizophrenia based on identifying the primary and interaction effects of minor physical anomalies.

In the neurodevelopmental model of schizophrenia, minor physical anomalies (MPAs) are considered neurodevelopmental markers of schizophrenia. To date, there has been no research to evaluate the interaction between MPAs. Our study built and used a machine learning model to predict the risk of schizophrenia based on measurements of MPA items and to investigate the potential primary and interaction effects of MPAs. The study included 470 patients with schizophrenia and 354 healthy controls. The models used are classical statistical model, Logistic Regression (LR), and machine leaning models, Decision Tree (DT) and Random Forest (RF). We also plotted two-dimensional scatter diagrams and three-dimensional linear/quadratic discriminant analysis (LDA/QDA) graphs for comparison with the DT dendritic structure. We found that RF had the highest predictive power for schizophrenia (Full-training AUC = 0.97 and 5-fold cross-validation AUC = 0.75). We identified several primary MPAs, such as the mouth region, high palate, furrowed tongue, skull height and mouth width. Quantitative MPA analysis indicated that the higher skull height and the narrower mouth width, the higher the risk of schizophrenia. In the interaction, we further identified that skull height and mouth width, furrowed tongue and skull height, high palate and skull height, and high palate and furrowed tongue, showed significant two-item interactions with schizophrenia. A weak three-item interaction was found between high palate, skull height, and mouth width. In conclusion, we found that the two machine learning methods showed good predictive ability in assessing the risk of schizophrenia using the primary and interaction effects of MPAs.

Short-term effects of Chlorhexidine mouthwash and Listerine on oral microbiome in hospitalized patients.

Introduction: Chlorhexidine (CHX) and essential oil containing mouthwashes like Listerine® can improve oral hygiene via suppressing oral microbes. In hospitalized patients, CHX mouthwash reduces the incidence of ventilator-associated pneumonia. However, CHX use was also associated with increased mortality, which might be related to nitrate-reducing bacteria. Currently, no study determines oral bacteria targeted by essential oils mouthwash in hospitalized patients using a metagenomic approach. Methods: We recruited 87 hospitalized patients from a previous randomized control study, and assigned them to three mouthwash groups: CHX, Listerine, and normal saline (control). Before and after gargling the mouthwash twice a day for 5-7 days, oral bacteria were examined using a 16S rDNA approach. Results: Alpha diversities at the genus level decreased significantly only for the CHX and Listerine groups. Only for the two groups, oral microbiota before and after gargling were significantly different, but not clearly distinct. Paired analysis eliminated the substantial individual differences and revealed eight bacterial genera (including Prevotella, Fusobacterium, and Selenomonas) with a decreased relative abundance, while Rothia increased after gargling the CHX mouthwash. After gargling Listerine, seven genera (including Parvimonas, Eubacterium, and Selenomonas) showed a decreased relative abundance, and the magnitudes were smaller compared to the CHX group. Fewer bacteria targeted by Listerine were reported to be nitrate-reducing compared to the CHX mouthwash. Discussion: In conclusion, short-term gargling of the CHX mouthwash and Listerine altered oral microbiota in our hospitalized patients. The bacterial genera targeted by the CHX mouthwash and Listerine were largely different and the magnitudes of changes were smaller using Listerine. Functional alterations of gargling CHX and Listerine were also different. These findings can be considered for managing oral hygiene of hospitalized patients.

Machine learning to predict the development of recurrent urinary tract infection related to single uropathogen, Escherichia coli.

Recurrent urinary tract infection (RUTI) can damage renal function and has impact on healthcare costs and patients' quality of life. There were 2 stages for development of prediction models for RUTI. The first stage was a scenario in the clinical visit. The second stage was a scenario after hospitalization for urinary tract infection caused by Escherichia coli. Three machine learning models, logistic regression (LR), decision tree (DT), and random forest (RF) were built for the RUTI prediction. The RF model had higher prediction accuracy than LR and DT (0.700, 0.604, and 0.654 in stage 1, respectively; 0.709, 0.604, and 0.635 in stage 2, respectively). The decision rules constructed by the DT model could provide high classification accuracy (up to 0.92 in stage 1 and 0.94 in stage 2) in certain subgroup patients in different scenarios. In conclusion, this study provided validated machine learning models and RF could provide a better accuracy in predicting the development of single uropathogen (E. coli) RUTI. Both host and bacterial characteristics made important contribution to the development of RUTI in the prediction models in the 2 clinical scenarios, respectively. Based on the results, physicians could take action to prevent the development of RUTI.

Association between Escherichia coli with NotI-restriction resistance and urinary tract infections.

BACKGROUND: Escherichia coli is the most common cause of urinary tract infections (UTIs). It is widely accepted that uropathogenic E. coli (UPEC) mainly emerge from the distal gut microbiota. Identification of bacterial characteristics that are able to differentiate UPEC from fecal commensal strains will facilitate the development of novel strategies to detect and monitor the spread of UPEC. METHODS: Fifty fecal commensal, 83 UTI-associated and 40 biliary tract infection (BTI)-associated E. coli isolates were analyzed. The NotI restriction patterns of chromosomal DNA in the isolates were determined by pulse-field gel electrophoresis. The phylogenetic types and the presence of 9 known virulence genes of each isolate were determined by PCR analyses. Additionally, the susceptibilities of the isolates to antibiotics were revealed. Then the associations of NotI resistance with UTI-associated isolates, phylotypes, and antibiotic resistance were assessed. RESULTS: NotI resistance was correlated with UTI-associated isolates, compared to the fecal isolates. Consistently, NotI-resistant isolates harbored a greater number of virulence factors and mainly belonged to phylotype B2. Additionally NotI resistance was correlated with chloramphenicol resistance among the bacteria. Among the fecal, UTI-associated and BTI-associated groups, the distribution of NotI-resistant group B2 isolates was correlated with UTI-associated bacteria. CONCLUSION: NotI resistance alone is a potential marker for distinguishing fecal strains and UPEC, while the combination of NotI resistance and B2 phylogeny is a candidate marker to differentiate UPEC from fecal and other extraintestinal pathogenic E. coli. Additionally, NotI resistance may be valuable for assessing the potential of chloramphenicol resistance of E. coli.

The Ferric Citrate Uptake System Encoded in a Novel bla CTX-M-3- and bla TEM-1-Harboring Conjugative Plasmid Contributes to the Virulence of Escherichia coli.

Escherichia coli is one major cause of bacterial infections and can horizontally acquire antimicrobial resistance and virulence genes through conjugation. Because conjugative plasmids can rapidly spread among bacteria of different species, the plasmids carrying both antimicrobial resistance and virulence genes may pose a significant threat to public health. Therefore, the identification and characterization of these plasmids may facilitate a better understanding of E. coli pathogenesis and the development of new strategies against E. coli infections. Because iron uptake ability is a potential virulence trait of bacteria, we screened for E. coli conjugative plasmids able to confer both iron uptake ability and ampicillin resistance. The plasmid pEC41, which was derived from the bacteremia clinical isolate EC41, was identified. EC41, which carried the fimH27 allele, belonged to sequence type (ST) 405 and phylogroup D. According to the sequencing analyses, pEC41 was 86 kb in size, and its backbone structure was almost identical to that of another highly conjugative plasmid, pCTX-M3, in which the extended-spectrum ß-lactamase gene bla CTX-M-3 was originally identified. pEC41 carried bla CTX-M-3 and bla TEM-1. The ferric citrate uptake (fec) system was identified in pEC41 and was responsible for conferring iron uptake ability. The fec system contributes to the pathogenesis of EC41 in systemic infections but not in urinary tract infections (UTIs). However, this system promoted competitive fitness of a cystitis-associated clinical isolate to colonize urinary tracts. Additionally, the distribution of the fec system was related to E. coli isolates associated with human bacteremia and UTIs. In summary, the present study identified a novel conjugative plasmid, pEC41, which conferred both antimicrobial resistance and an extra iron uptake ability to E. coli. The iron uptake ability was encoded in the fec system and contributed to E. coli pathogenesis. This study is the first to show that the fec system is a virulence factor in E. coli.

Gene expression analysis of combined RNA-seq experiments using a receiver operating characteristic calibrated procedure.

Because of rapid advancements in sequencing technology, the experimental platforms of RNA-seq are updated frequently. It is quite common to combine data sets from several experimental platforms for analysis in order to increase the sample size and achieve more powerful tests for detecting the presence of differential gene expression. The data sets combined from different experimental platforms will have a complex data distribution, which causes a major problem in statistical modeling as well as in multiple testing. Although plenty of research have studied this problem by modeling the batch effects, there are no general and robust data-driven procedures for RNA-seq analysis. In this paper we propose a new robust procedure which combines the use of popular methods (packages) with a data-driven simulation (DDS). We construct the average receiver operating characteristic curve through the DDS to provide the calibrated levels of significance for multiple testing. Instead of further modifying the adjusted p-values, we calibrated the levels of significance for each specific method and mean effect model. The procedure was demonstrated with several popular RNA-seq analysis methods (edgeR, DEseq2, limma+voom). The proposed procedure relaxes the stringent assumptions of data distributions for RNA-seq analysis methods and is illustrated using colorectal cancer studies from The Cancer Genome Atlas database.

Nasal Microbiome Change During and After Exacerbation in Asthmatic Children.

Airway and gut microbiota are important in asthma pathogenesis. Although several studies have revealed distinct microbiota in asthmatic airways at baseline compared to healthy controls, limited studies compared microbiota during acute exacerbation (AE) and in the recovery phase (RP) in the same asthmatic children. We aim to investigate association between microbiota and asthma status in children and explore their relationship with clinical features of asthma. We recruited 56 asthmatic children and investigated their nasal, throat, and stool microbiota during AE and in the RP. Totally, 320 samples were subjected to 16S rRNA sequencing. Although the microbial communities were clearly separated by body site, within each site the overall communities during AE and in the RP could not be distinguished. Most nasal microbiota were dominated by only one or two of six bacterial genera. The domination was associated with mite allergy and patient age only during AE but not in the RP. When moving into RP, the relative abundance of Staphylococcus increased while that of Moraxella decreased. Throat and stool microbiota were not associated with most of the clinical features. Interestingly, stool microbiota during AE was associated with ABO blood type and stool microbiota in the RP was associated with frequency of the subsequent exacerbations. In summary, the association between nasal microbiota and mite allergy only during AE suggests an altered local immunity and its interplay with nasal microbes. Our work provides a basis for studying microbes, and prevention or therapeutic strategy in childhood asthma, especially during AE.