The human gastrointestinal tract (GIT) is inhabited by a dense microbial community of symbionts. Enterococci are among the earliest members of this community and remain core members of the GIT microbiota throughout life. Enterococci have also recently emerged as opportunistic pathogens and major causes of nosocomial infections. Although recognized as a prerequisite for infection, colonization of the GIT by enterococci remains poorly understood. One way that bacteria adapt to dynamic ecosystems like the GIT is through the use of their surface proteins to sense and interact with components of their immediate environment. In Grampositive bacteria, a subset of surface proteins relies on an enzyme called sortase for covalent attachment to the cell wall. Here, we show that the housekeeping sortase A (SrtA) enzyme promotes intestinal colonization by enterococci. Furthermore, we show that the enzymatic activity of SrtA is key to the ability of Enterococcus faecalis to bind mucin (a major component of the GIT mucus). We also report the GIT colonization phenotypes of E. faecalis mutants lacking selected sortase-dependent proteins (SDPs). Further examination of the mucin binding ability of these mutants suggests that adhesion to mucin contributes to intestinal colonization by E. faecalis.

Enterococci are colonizers of the mammalian gastrointestinal tract (GIT) and normally live in healthy association with their human host. However, enterococci are also major causes of healthcare-acquired infections, prompting the US Centers for Disease Control and Prevention to declare vancomycin-resistant enterococci (VRE) a serious threat to public health. Because of both intrinsic and acquired antibiotic resistance, enterococci proliferate in the GIT during antibiotic therapy, leading to dissemination and disease. The recognition that colonization of the GIT is a prerequisite for enterococcal infections has prompted research to study mechanisms used by enterococci to colonize this niche. This review discusses major findings of recent research to understand GIT colonization by enterococci using diverse experimental models, each of which exhibits unique strengths. This work has revealed enterococcal transcriptional reprogramming in the GIT, contributions of specific enterococcal genes encoded by the core genome to GIT colonization, the impact of genome plasticity, and roles for intra-and inter-species interactions in modulation of GIT colonization.

BackgroundIn Togo, the prevalence of Hepatitis B Virus Surface Antigen (HBsAg) among young people aged 15–24 years was estimated at 16.4% in 2010; however, risk factors for HBsAg carriage are poorly documented. We sought to identify risk factors for HBsAg carriage and the serological profile of HBsAg carriers in Lomé (capital city of Togo).MethodWe conducted a case control study from October 2016 to March 2017 in Lomé. Cases and controls were randomly selected from a database of Institut National d’Hygiène (INH) of Lomé during a free screening campaign for hepatitis B. We calculated means, frequencies, proportions, odds ratios (OR), and 95% confidence interval (CI) and performed logistic regression.ResultsWe included 83 confirmed cases and 249 controls. The median age was 31 years among cases and 30 years among the controls. The sex ratios (M/F) were 11/6 among cases and 4/3 for the controls. The independent risk factors for HBsAg carriage were the awareness of hepatitis B serological status (OR = 3.56, 95% CI [1.80–7.04]) and Kabyè-tem ethnic group (OR = 3.56, 95% CI [1.98–6.39]). Among HBsAg carriers, 13.3% were at the viral replication stage (all of whom were between 30 and 45 years of age) and 1.2% were at the acute stage of the disease. The prevalence of co-infection with hepatitis B and C was 4.80%. All co-infections were in women aged 24–28 years.ConclusionThe Kabyè-tem ethnic group is at risk of HBsAg carriage in Lomé. Of note, most HBsAg carriers in this ethnic group are aware of their HBsAg serological status. Furthermore, the prevalence of Hepatitis among adults of reproductive age is high and is cause for concern. We therefore recommend screening and vaccination campaigns at subsidized prices among people aged 30 years and older.

To develop a deep-learning based 3D tumor motion prediction algorithm for non-invasive intra-fractional tumor-tracked radiotherapy (nifteRT). NifteRT is a novel RT technique being developed to treat mobile tumors (thoracic and abdominal) on a hybrid linac-MR system. Linac-MR provides fast, intra-fractional MR images (concurrently with irradiation) of tumor region with sufficient soft tissue contrast. NifteRT aims to automatically segment the mobile tumor in the continuously acquired, free-breathing MR images, and to adapt the shape and position of the therapeutic radiation beam on-the-fly via multi-leaf collimator (MLC) control. Motion prediction is a critical component of nifteRT to account for the tumor motion during the system delay; the time interval between the detection of current tumor position and the beam delivery upon the MLC reaching its desired leaf position. Materials/Methods: We implemented a long short-term memory network (LSTM) in our algorithm. LSTM predicted a future (at a system delay of 280 ms) 3D tumor position using the current and previous tumor positions. LSTM network is capable of selectively memorizing previous inputs (i.e. positions), and in combination with current position, produces a future position. At each prediction step, our network takes the 3D coordinates of current and 13 previous tumor positions as inputs, and it outputs a 3D future tumor position. The network has 2 LSTM layers (each with 256 hidden units) followed by a fully connected layer. Adam optimizer was used for network training (100000 epochs, 0.8 dropout). We incorporated adaptive learning by continuously updating the network weights prior to each prediction, immediately after the input of a current tumor position. Our algorithm was trained and validated using 3D tumor motion data obtained from 158 RT fractions (46 patients with lung, liver, pancreatic cancer). Each fraction record consists of 8 min long, 3D tumor positions at 40 ms intervals, which defines the standard tumor positions. The first 0.5 min of data was used for algorithm training, and the remaining 7.5 min of data was used to validate the motion prediction. A common network structure was used for all fractions; however, it was newly trained for each fraction. We report prediction accuracy as the root-mean-squared-error (RMSE) between the standard and predicted tumor positions. Results: The maximum amplitude of standard tumor motion ranged 0.6-51.2 mm in the patient data set. Our algorithm achieved mean RMSE of 0.9 mm (Range 0.0-3.5 mm, STD 0.6 mm) from 158 data sets. Conclusion: We developed a deep-learning based 3D tumor motion prediction algorithm, and evaluated its performance with in-vivo tumor motion data from 46 patients. Our algorithm showed promising results (< 1 mm prediction error on average) suggesting the feasibility of nifteRT at a system delay of 280 ms.

Results: A total of 2278 SE and DE image frames were analyzed. DE imaging resulted in significantly improved tracking vs. SE on 884/2278 (38.8%) of image frames. In those frames, the average tracking error was 1.78 +/-2.02 mm vs. 2.21 +/-2.38 mm, for DE vs. SE imaging, respectively (P < 0.001). For 4/10 patients, the average reduction in tracking error with DE vs. SE tracking was > 0.7 mm. No correlation was observed between tumor volume/location and improved tracking accuracy with DE imaging. Additionally, the fraction of images that could not be tracked with SE was 91/2278 (4.0%). The addition of DE imaging decreased this number to 33/2278 (1.4%) (P < 0.001). Conclusion: This is the first prospective study to evaluate fast-kV switching DE imaging for MTT in a cohort of SBRT lung cancer patients. This study has demonstrated that by removal of overlapping bony anatomy, DE imaging increases tracking accuracy and decreases the number of images where tracking fails. Future work includes optimization of template parameters to further improve DE tracking accuracy.