Corrigendum: Dynamic risk prediction of BK polyomavirus reactivation after renal transplantation.

[This corrects the article DOI: 10.3389/fimmu.2022.971531.].

Microbiota of long-term indwelling hemodialysis catheters during renal transplantation perioperative period: a cross-sectional metagenomic microbial community analysis.

Background: Catheter-related infection (CRI) is a major complication in patients undergoing hemodialysis. The lack of high-throughput research on catheter-related microbiota makes it difficult to predict the occurrence of CRI. Thus, this study aimed to delineate the microbial structure and diversity landscape of hemodialysis catheter tips among patients during the perioperative period of kidney transplantation (KTx) and provide insights into predicting the occurrence of CRI.Methods: Forty patients at the Department of Transplantation undergoing hemodialysis catheter removal were prospectively included. Samples, including catheter tip, catheter outlet skin swab, catheter blood, peripheral blood, oropharynx swab, and midstream urine, from the separate pre- and post-KTx groups were collected and analyzed using metagenomic next-generation sequencing (mNGS). All the catheter tips and blood samples were cultured conventionally.Results: The positive detection rates for bacteria using mNGS and traditional culture were 97.09% (200/206) and 2.65% (3/113), respectively. Low antibiotic-sensitivity biofilms with colonized bacteria were detected at the catheter tip. In asymptomatic patients, no statistically significant difference was observed in the catheter tip microbial composition and diversity between the pre- and post-KTx group. The catheter tip microbial composition and diversity were associated with fasting blood glucose levels. Microorganisms at the catheter tip most likely originated from catheter outlet skin and peripheral blood.Conclusions: The long-term colonization microbiota at the catheter tip is in a relatively stable state and is not readily influenced by KTx. It does not act as the source of infection in all CRIs, but could reflect hematogenous infection to some extent.

Dynamic risk prediction of BK polyomavirus reactivation after renal transplantation.

Purpose: To construct a dynamic prediction model for BK polyomavirus (BKV) reactivation during the early period after renal transplantation and to provide a statistical basis for the identification of and intervention for high-risk populations. Methods: A retrospective study of 312 first renal allograft recipients with strictly punctual follow-ups was conducted between January 2015 and March 2022. The covariates were screened using univariable time-dependent Cox regression, and those with P<0.1 were included in the dynamic and static analyses. We constructed a prediction model for BKV reactivation from 2.5 to 8.5 months after renal transplantation using dynamic Cox regression based on the landmarking method and evaluated its performance using the area under the curve (AUC) value and Brier score. Monte-Carlo cross-validation was done to avoid overfitting. The above evaluation and validation process were repeated in the static model (Cox regression model) to compare the performance. Two patients were presented to illustrate the application of the dynamic model. Results: We constructed a dynamic prediction model with 18 covariates that could predict the probability of BKV reactivation from 2.5 to 8.5 months after renal transplantation. Elder age, basiliximab combined with cyclophosphamide for immune induction, acute graft rejection, higher body mass index, estimated glomerular filtration rate, urinary protein level, urinary leukocyte level, and blood neutrophil count were positively correlated with BKV reactivation, whereas male sex, higher serum albumin level, and platelet count served as protective factors. The AUC value and Brier score of the static model were 0.64 and 0.14, respectively, whereas those of the dynamic model were 0.79 ± 0.05 and 0.08 ± 0.01, respectively. In the cross-validation, the AUC values of the static and dynamic models decreased to 0.63 and 0.70 ± 0.03, respectively, whereas the Brier score changed to 0.11 and 0.09 ± 0.01, respectively. Conclusion: Dynamic Cox regression based on the landmarking method is effective in the assessment of the risk of BKV reactivation in the early period after renal transplantation and serves as a guide for clinical intervention.

Self-selective attention using correlation between instances for distant supervision relation extraction.

Distant supervision relation extraction methods are widely used to extract relational facts in text. The traditional selective attention model regards instances in the bag as independent of each other, which makes insufficient use of correlation information between instances and supervision information of all correctly labeled instances, affecting the performance of relation extractor. Aiming at this problem, a distant supervision relation extraction method with self-selective attention is proposed. The method uses a layer of convolution and self-attention mechanism to encode instances to learn the better semantic vector representation of instances. The correlation between instances in the bag is used to assign a higher weight to all correctly labeled instances, and the weighted summation of instances in the bag is used to obtain a bag vector representation. Experiments on the NYT dataset show that the method can make full use of the information of all correctly labeled instances in the bag. The method can achieve better results as compared with baselines.