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BackgroundAntibiotic-associated diarrhea (AAD) is a risk factor for exacerbating the outcome of critically ill patients. Dysbiosis induced by the exposure to antibiotics reveals the potential therapeutic role of fecal microbiota transplantation (FMT) in these patients. Herein, we aimed to evaluate the safety and potential benefit of rescue FMT for AAD in critically ill patients.MethodsA series of critically ill patients with AAD received rescue FMT from Chinese fmtBank, from September 2015 to February 2019. Adverse events (AEs) and rescue FMT success which focused on the improvement of abdominal symptoms and post-ICU survival rate during a minimum of 12 weeks follow-up were assessed.ResultsTwenty critically ill patients with AAD underwent rescue FMT, and 18 of them were included for analysis. The mean of Acute Physiology and Chronic Health Evaluation (APACHE) II scores at intensive care unit (ICU) admission was 21.7 ± 8.3 (range 11–37). Thirteen patients received FMT through nasojejunal tube, four through gastroscopy, and one through enema. Patients were treated with four (4.2 ± 2.1, range 2–9) types of antibiotics before and during the onset of AAD. 38.9% (7/18) of patients had FMT-related AEs during follow-up, including increased diarrhea frequency, abdominal pain, increased serum amylase, and fever. Eight deaths unrelated to FMT occurred during follow-up. One hundred percent (2/2) of abdominal pain, 86.7% (13/15) of diarrhea, 69.2% (9/13) of abdominal distention, and 50% (1/2) of hematochezia were improved after FMT. 44.4% (8/18) of patients recovered from abdominal symptoms without recurrence and survived for a minimum of 12 weeks after being discharged from ICU.ConclusionIn this case series studying the use of FMT in critically ill patients with AAD, good clinical outcomes without infectious complications were observed. These findings could potentially encourage researchers to set up new clinical trials that will provide more insight into the potential benefit and safety of the procedure in the ICU.Trial registrationClinicalTrials.gov, Number NCT03895593. Registered 29 March 2019 (retrospectively registered).

Objective To develop high-quality synthetic CT (sCT) generation method from low-dose cone-beam CT (CBCT) images by using attention-guided generative adversarial networks (AGGAN) and apply these images to dose calculations in radiotherapy. Methods The CBCT/planning CT images of 170 patients undergoing thoracic radiotherapy were used for training and testing. The CBCT images were scanned under a fast protocol with 50% less clinical projection frames compared with standard chest M20 protocol. Training with aligned paired images was performed using conditional adversarial networks (so-called pix2pix), and training with unpaired images was carried out with cycle-consistent adversarial networks (cycleGAN) and AGGAN, through which sCT images were generated. The image quality and Hounsfield unit (HU) value of the sCT images generated by the three neural networks were compared. The treatment plan was designed on CT and copied to sCT images to calculated dose distribution. Results The image quality of sCT images by all the three methods are significantly improved compared with original CBCT images. The AGGAN achieves the best image quality in the testing patients with the smallest mean absolute error (MAE, 43.5 ± 6.69), largest structural similarity (SSIM, 93.7 ± 3.88) and peak signal-to-noise ratio (PSNR, 29.5 ± 2.36). The sCT images generated by all the three methods showed superior dose calculation accuracy with higher gamma passing rates compared with original CBCT image. The AGGAN offered the highest gamma passing rates (91.4 ± 3.26) under the strictest criteria of 1 mm/1% compared with other methods. In the phantom study, the sCT images generated by AGGAN demonstrated the best image quality and the highest dose calculation accuracy. Conclusions High-quality sCT images were generated from low-dose thoracic CBCT images by using the proposed AGGAN through unpaired CBCT and CT images. The dose distribution could be calculated accurately based on sCT images in radiotherapy.

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