Progressive auto-segmentation for cone-beam computed tomography-based online adaptive radiotherapy.

Background and purpose: Accurate and automated segmentation of targets and organs-at-risk (OARs) is crucial for the successful clinical application of online adaptive radiotherapy (ART). Current methods for cone-beam computed tomography (CBCT) auto-segmentation face challenges, resulting in segmentations often failing to reach clinical acceptability. Current approaches for CBCT auto-segmentation overlook the wealth of information available from initial planning and prior adaptive fractions that could enhance segmentation precision. Materials and methods: We introduce a novel framework that incorporates data from a patient's initial plan and previous adaptive fractions, harnessing this additional temporal context to significantly refine the segmentation accuracy for the current fraction's CBCT images. We present LSTM-UNet, an innovative architecture that integrates Long Short-Term Memory (LSTM) units into the skip connections of the traditional U-Net framework to retain information from previous fractions. The models underwent initial pre-training with simulated data followed by fine-tuning on a clinical dataset. Results: Our proposed model's segmentation predictions yield an average Dice similarity coefficient of 79% from 8 Head & Neck organs and targets, compared to 52% from a baseline model without prior knowledge and 78% from a baseline model with prior knowledge but no memory. Conclusions: Our proposed model excels beyond baseline segmentation frameworks by effectively utilizing information from prior fractions, thus reducing the effort of clinicians to revise the auto-segmentation results. Moreover, it works together with registration-based methods that offer better prior knowledge. Our model holds promise for integration into the online ART workflow, offering precise segmentation capabilities on synthetic CT images.

Cryo-electron tomography reveals the packaging pattern of RuBisCOs in Synechococcus ß-carboxysome.

Carboxysomes are large self-assembled microcompartments that serve as the central machinery of a CO2-concentrating mechanism (CCM). Biogenesis of carboxysome requires the fine organization of thousands of individual proteins; however, the packaging pattern of internal RuBisCOs remains largely unknown. Here we purified the intact ß-carboxysomes from Synechococcus elongatus PCC 7942 and identified the protein components by mass spectrometry. Cryo-electron tomography combined with subtomogram averaging revealed the general organization pattern of internal RuBisCOs, in which the adjacent RuBisCOs are mainly arranged in three distinct manners: head-to-head, head-to-side, and side-by-side. The RuBisCOs in the outermost layer are regularly aligned along the shell, the majority of which directly interact with the shell. Moreover, statistical analysis enabled us to propose an ideal packaging model of RuBisCOs in the ß-carboxysome. These results provide new insights into the biogenesis of ß-carboxysomes and also advance our understanding of the efficient carbon fixation functionality of carboxysomes.

Risk Factors for Enterococcal Intra-Abdominal Infections and Outcomes in Intensive Care Unit Patients.

Background: To investigate the risk factors for enterococcal intra-abdominal infections (EIAIs) and the association between EIAIs and outcomes in intensive care unit (ICU) patients. Methods: We reviewed retrospectively the records of patients with intra-abdominal infections admitted to the Department of Critical Care Medicine at Nanfang Hospital, Southern Medical University, China, from January 2011 to December 2018. Patients with intra-abdominal infections were divided into enterococcal and non-enterococcal groups based on whether enterococci were isolated from intra-abdominal specimens. Results: A total of 431 patients with intra-abdominal infections were included, of whom 119 were infected with enterococci and 312 were infected with non-enterococci. Enterococci were isolated in 27.6% of patients, accounting for 24.5% (129/527) of all clinical bacterial isolates. Post-operative abdominal infection (adjusted odds ratio [OR], 2.361; p = 0.004), intestinal infection (adjusted OR, 2.703; p < 0.001), Mannheim Peritonitis Index score (MPI; adjusted OR, 1.052; p = 0.015), and use of antibiotic agents within the previous 90 days (adjusted OR, 1.880; p = 0.025) were associated with an increased risk of EIAIs. Compared with patients without enterococcal infection, ICU patients with enterococcal infection had a higher risk of failure of initial clinical therapy (49.6% vs. 24.2%; p < 0.001) and longer hospital stays (33 days [19, 48] vs. 18 days [12, 29]; p < 0.001). Enterococcal infection was associated with increased 28-day mortality, in-hospital mortality, and ICU mortality. However, no difference was found in length of ICU stay between the two groups. Additionally, there was no difference in ICU mortality, hospital mortality, or 28-day mortality in patients infected with enterococcus who did or did not receive empirical anti-enterococcal therapy. Conclusion: Post-operative abdominal infection, intestinal infection, MPI score, and use of antibiotic agents within the previous 90 days were independent risk factors for enterococcal infection. Enterococcal infection was associated with reduced short-term survival in ICU patients.