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A recent study suggested mortality benefits using vitamin C, hydrocortisone, and thiamine combination therapy (triple therapy) in addition to standard care in patients with severe sepsis and septic shock. In order to further evaluate the effects of triple therapy in real-world clinical practice, we conducted a retrospective observational cohort study at an academic tertiary care hospital. A total of 94 patients (47 in triple therapy group and 47 in standard care group) were included in the analysis. Baseline characteristics in both groups were well-matched. No significant difference in the primary outcome, hospital mortality, was seen between triple therapy and standard care groups (40.4% vs. 40.4%; p = 1.000). In addition, there were no significant differences in secondary outcomes, including intensive care unit (ICU) mortality, requirement for renal replacement therapy for acute kidney injury, ICU length of stay, hospital length of stay, and time to vasopressor independence. When compared to standard care, triple therapy did not improve hospital or ICU mortality in patients with septic shock. A randomized controlled trial evaluating the effects of triple therapy is necessary prior to implementing vitamin C, hydrocortisone, and thiamine combination therapy as a standard of care in patients with septic shock.

Patient-centered structured interdisciplinary bedside rounds provide a venue for increased rounding efficiency, provider satisfaction, and consistent teaching, without impacting patient/family perception.

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Real-noise denoising is a challenging task because the statistics of real-noise do not follow the normal distribution, and they are also spatially and temporally changing. In order to cope with various and complex real-noise, we propose a well-generalized denoising architecture and a transfer learning scheme. Specifically, we adopt an adaptive instance normalization to build a denoiser, which can regularize the feature map and prevent the network from overfitting to the training set. We also introduce a transfer learning scheme that transfers knowledge learned from syntheticnoise data to the real-noise denoiser. From the proposed transfer learning, the synthetic-noise denoiser can learn general features from various synthetic-noise data, and the real-noise denoiser can learn the real-noise characteristics from real data. From the experiments, we find that the proposed denoising method has great generalization ability, such that our network trained with synthetic-noise achieves the best performance for Darmstadt Noise Dataset (DND) among the methods from published papers. We can also see that the proposed transfer learning scheme robustly works for real-noise images through the learning with a very small number of labeled data.

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