Molecular epidemiology and comparative genomics of carbapenemase-producing Escherichia coli isolates from 19 tertiary hospitals in China from 2019 to 2020

BackgroundThe clinical use of carbapenems is facing challenges due to increased carbapenemase-producing Escherichia coli (CP-EC) infections over the past decade. Meanwhile, whole-genome sequencing (WGS) is an important method for bacterial epidemiological research. We aim to provide more gene-based surveys to explore the genomics and occurrence of CP-EC in China.MethodsA total of 780 Escherichia coli isolates were collected by the China Antimicrobial Resistance Surveillance Trial (CARST) from 2019 to 2020. An antibacterial susceptibility test was performed by using the agar dilution method. CP-EC were detected by the modified carbapenem inactivation method (mCIM), EDTA-modified carbapenem inactivation method (eCIM), and polymerase chain reaction (PCR). Homology analysis was performed by multilocus sequence typing (MLST). A conjugation experiment was performed to verify the transferability of plasmids carrying carbapenemase genes. WGS was conducted to explore the gene-environment of the carbapenemase gene.ResultOf the 780 Escherichia coli isolates, 31 isolates were insensitive to carbapenem with a rate of 4%. Among them, 13 CP-EC isolates had transferability of the blaNDM gene. These isolates belonged to nine distinct sequence types (STs), with some correlation. We found that two (2/13, 15.4%) of the CP-EC isolates that were collected from blood specimens were highly pathogenic and also showed high transferability of the blaNDM gene. In addition, eight (8/13, 61.5%) of the CP-EC isolates were found to be multidrug-resistant.ConclusionWith the increasing use of carbapenem, CP-EC isolates accounted for nearly half of the total carbapenem-insensitive Escherichia coli isolates. Our findings highlight the urgent need to pay attention to CP-EC isolates in bloodstream infections and ESBL-producing CP-EC isolates. Based on the One Health concept, we suggest various measures, including the development of bacterial vaccines, antibiotic management, and establishment of better medical environments, to avoid the outbreak of CP-EC

Gestational Folic Acid Administration Alleviated Maternal Postpartum Emotional and Cognitive Dysfunction in Mice

Gestational folic acid (FA) supplementation has been widely recognized for its benefits in preventing offspring defects, but its effect on postpartum females has not yet been adequately assessed. The occurrence of emotional and cognitive dysfunction is common in postpartum women, and its treatment remains limited. Considering the promising results of FA in various psychiatric disorders both in human and redents, we tested the effect of gestational FA administration on postpartum psychiatric behavioral phenotypes and the implicated brain-related mechanisms in a murine model. FA was administered orally in both the hormone-stimulated-pregnancy (HSP) model and pregnant mice at doses of 1 and 5 mg/kg. Postpartum behavioral results showed that the disorders of cognitive performance, depressive, and anxiety-related behaviors were all alleviated in the 5 mg/kg FA group. However, the general development of their offspring remained unaffected. Immunofluorescence and immunoblot results revealed that FA pretreatment significantly activated the maternal hippocampal BDNF-related pathway. Morphological studies have confirmed that FA promotes hippocampal neurogenesis. Moreover, synaptic plasticity and synaptic transmission are enhanced. All of these hippocampal changes play critical roles in rescuing neuronal function and behaviors. Thus, our data suggest that gestational FA administration has a therapeutic effect that improves cognition and reduces depression and anxiety in a murine postpartum model. This may be developed as a preventive and adjuvant therapeutic option for pregnant women

Table_1_Comparison of cumulative live birth rates between progestin-primed ovarian stimulation protocol and gonadotropin-releasing hormone antagonist protocol in different populations.docx

ObjectiveTo compare cumulative live birth rate (LBR) between progestin-primed ovarian stimulation (PPOS) and GnRH antagonist protocols of preimplantation genetic testing (PGT) cycles in different populations.MethodsThis was a retrospective cohort study. A total of 865 patients were enrolled and separate analyses were performed for three populations: 498 patients with predicted normal ovarian response (NOR), 285 patients with PCOS, and 82 patients with predicted poor ovarian response (POR). The primary outcome was cumulative LBR for one oocyte retrieval cycle. The results of response to ovarian stimulation were also investigated, including numbers of oocytes retrieved, MII oocytes, 2PN, blastocysts, good-quality blastocysts, and usable blastocysts after biopsy, as well as rates of oocyte yield, blastocyst formation, good-quality blastocysts, and moderate or severe OHSS. Univariable and multivariable logistic regression analyses were used to identify potential confounders that may be independently associated with cumulative live birth.ResultsIn NOR, the cumulative LBR of PPOS protocol was significantly lower than that of GnRH antagonists (28.4% vs. 40.7%; P=0.004). In multivariable analysis, the PPOS protocol was negatively associated with cumulative LBR (adjusted OR=0.556; 95% CI, 0.377-0.822) compared to GnRH antagonists after adjusting for potential confounders. The number and ratio of good-quality blastocysts were significantly reduced in PPOS protocol compared to GnRH antagonists (2.82 ± 2.83 vs. 3.20 ± 2.79; P=0.032 and 63.9% vs. 68.5%; P=0.021), while numbers of oocytes, MII oocytes and 2PN did not show any significant difference between GnRH antagonist and PPOS protocols. PCOS patients had similar outcomes as NOR. The cumulative LBR of PPOS group appeared to be lower than that of GnRH antagonists (37.4% vs. 46.1%; P=0.151), but not significantly. Meanwhile, the proportion of good-quality blastocysts in PPOS protocol was also lower compared to GnRH antagonists (63.5% vs. 68.9%; P=0.014). In patients with POR, the cumulative LBR of PPOS protocol was comparable to that of GnRH antagonists (19.2% vs. 16.7%; P=0.772). There was no statistical difference in the number and rate of good-quality blastocysts between the two protocols in POR, while the proportion of good-quality blastocysts appeared to be higher in PPOS group compared to GnRH antagonists (66.7% vs. 56.3%; P=0.182). In addition, the number of usable blastocysts after biopsy was comparable between the two protocols in three populations.ConclusionThe cumulative LBR of PPOS protocol in PGT cycles is lower than that of GnRH antagonists in NOR. In patients with PCOS, the cumulative LBR of PPOS protocol appears to be lower than that of GnRH antagonists, albeit lacking statistical difference, whereas in patients with diminished ovarian reserve, the two protocols were comparable. Our findings suggest the need for caution when choosing PPOS protocol to achieve live births, especially for normal and high ovarian responders.</p

Non-invasively identifying candidates of active surveillance for prostate cancer using magnetic resonance imaging radiomics

Abstract Active surveillance (AS) is the primary strategy for managing patients with low or favorable-intermediate risk prostate cancer (PCa). Identifying patients who may benefit from AS relies on unpleasant prostate biopsies, which entail the risk of bleeding and infection. In the current study, we aimed to develop a radiomics model based on prostate magnetic resonance images to identify AS candidates non-invasively. A total of 956 PCa patients with complete biopsy reports from six hospitals were included in the current multicenter retrospective study. The National Comprehensive Cancer Network (NCCN) guidelines were used as reference standards to determine the AS candidacy. To discriminate between AS and non-AS candidates, five radiomics models (i.e., eXtreme Gradient Boosting (XGBoost) AS classifier (XGB-AS), logistic regression (LR) AS classifier, random forest (RF) AS classifier, adaptive boosting (AdaBoost) AS classifier, and decision tree (DT) AS classifier) were developed and externally validated using a three-fold cross-center validation based on five classifiers: XGBoost, LR, RF, AdaBoost, and DT. Area under the receiver operating characteristic curve (AUC), accuracy (ACC), sensitivity (SEN), and specificity (SPE) were calculated to evaluate the performance of these models. XGB-AS exhibited an average of AUC of 0.803, ACC of 0.693, SEN of 0.668, and SPE of 0.841, showing a better comprehensive performance than those of the other included radiomic models. Additionally, the XGB-AS model also presented a promising performance for identifying AS candidates from the intermediate-risk cases and the ambiguous cases with diagnostic discordance between the NCCN guidelines and the Prostate Imaging-Reporting and Data System assessment. These results suggest that the XGB-AS model has the potential to help identify patients who are suitable for AS and allow non-invasive monitoring of patients on AS, thereby reducing the number of annual biopsies and the associated risks of bleeding and infection

Combining Machine-detected EEG Epileptiform Abnormalities and Quantitative EEG Spectral Features Predicts Post Traumatic Epilepsy (S7.002)

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Quantitative epileptiform burden and electroencephalography background features predict post-traumatic epilepsy

BACKGROUND: Post-traumatic epilepsy (PTE) is a severe complication of traumatic brain injury (TBI). Electroencephalography aids early post-traumatic seizure diagnosis, but its optimal utility for PTE prediction remains unknown. We aim to evaluate the contribution of quantitative electroencephalograms to predict first-year PTE (PTE(1)). METHODS: We performed a multicentre, retrospective case-control study of patients with TBI. 63 PTE(1) patients were matched with 63 non-PTE(1) patients by admission Glasgow Coma Scale score, age and sex. We evaluated the association of quantitative electroencephalography features with PTE(1) using logistic regressions and examined their predictive value relative to TBI mechanism and CT abnormalities. RESULTS: In the matched cohort (n=126), greater epileptiform burden, suppression burden and beta variability were associated with 4.6 times higher PTE(1) risk based on multivariable logistic regression analysis (area under the receiver operating characteristic curve, AUC (95% CI) 0.69 (0.60 to 0.78)). Among 116 (92%) patients with available CT reports, adding quantitative electroencephalography features to a combined mechanism and CT model improved performance (AUC (95% CI), 0.71 (0.61 to 0.80) vs 0.61 (0.51 to 0.72)). CONCLUSIONS: Epileptiform and spectral characteristics enhance covariates identified on TBI admission and CT abnormalities in PTE(1) prediction. Future trials should incorporate quantitative electroencephalography features to validate this enhancement of PTE risk stratification models

Quantitative epileptiform burden and electroencephalography background features predict post-traumatic epilepsy

Background Post-traumatic epilepsy (PTE) is a severe complication of traumatic brain injury (TBI). Electroencephalography aids early post-traumatic seizure diagnosis, but its optimal utility for PTE prediction remains unknown. We aim to evaluate the contribution of quantitative electroencephalograms to predict first-year PTE (PTE1). Methods We performed a multicentre, retrospective case-control study of patients with TBI. 63 PTE1 patients were matched with 63 non-PTE1 patients by admission Glasgow Coma Scale score, age and sex. We evaluated the association of quantitative electroencephalography features with PTE1 using logistic regressions and examined their predictive value relative to TBI mechanism and CT abnormalities. Results In the matched cohort (n=126), greater epileptiform burden, suppression burden and beta variability were associated with 4.6 times higher PTE1 risk based on multivariable logistic regression analysis (area under the receiver operating characteristic curve, AUC (95% CI) 0.69 (0.60 to 0.78)). Among 116 (92%) patients with available CT reports, adding quantitative electroencephalography features to a combined mechanism and CT model improved performance (AUC (95% CI), 0.71 (0.61 to 0.80) vs 0.61 (0.51 to 0.72)). Conclusions Epileptiform and spectral characteristics enhance covariates identified on TBI admission and CT abnormalities in PTE1 prediction. Future trials should incorporate quantitative electroencephalography features to validate this enhancement of PTE risk stratification models