The Research Progress on the Interaction between Mammalian Gut Microbiota and the Host's Metabolism Homeostasis during Hibernation.

Hibernating mammals confront seasonal and harsh environmental shifts, prompting a cycle of pre-hibernation feeding and subsequent winter fasting. These adaptive practices induce diverse physiological adjustments within the animal's body. With the gut microbiota's metabolic activity being heavily reliant on the host's diet, this cycle's primary impact is on this microbial community. When the structure and composition of the gut microbiota changes, corresponding alterations in the interactions occur between these microorganisms and their host. These successive adaptations significantly contribute to the host's capacity to sustain relatively stable metabolic and immune functions in severe environmental conditions. A thorough investigation into the reciprocal interplay between the host and gut microbiota during hibernation-induced adaptive changes holds promise for unveiling new insights. Understanding the underlying mechanisms driving these interactions may potentially unlock innovative approaches to address extreme pathological conditions in humans.

Deep Learning for Patient-Specific Quality Assurance: Predicting Gamma Passing Rates for IMRT Based on Delivery Fluence Informed by log Files.

Objectives: In this study, we propose a deep learning-based approach to predict Intensity-modulated radiation therapy (IMRT) quality assurance (QA) gamma passing rates using delivery fluence informed by log files. Methods: A total of 112 IMRT plans for chest cancers were planned and measured by portal dosimetry equipped on TrueBeam linac. The convolutional neural network (CNN) based learning model was trained using delivery fluence as inputs and gamma passing rates (GPRs) of 4 different criteria (3%/3 mm, 2%/3 mm, 3%/2 mm, and 2%/2 mm) as outputs. Model performance for both validation and test sets was assessed using mean absolute error (MAE), mean squared error (MSE), root MSE (RMSE), Spearman rank correlation coefficients (Sr), and Determination coefficient (R2) between the measured and predicted GPR values. Results: In the test set, the MAE of the prediction model were 0.402, 0.511, 1.724, and 2.530, the MSE were 0.640, 0.986, 6.654, and 9.508, the RMSE were 0.800, 0.993, 2.580, and 3.083, the Sr were 0.643, 0.684, 0.821, and 0.824 (P < .001) and the R2 were 0.4110, 0.4666, 0.6677, and 0.6769 for 3%/3 mm, 3%/2 mm, 2%/3 mm, and 2%/2 mm, respectively. The MAE and RMSE of the prediction model decreased with stricter gamma criteria while the Sr and R2 between measured and predicted GPR values increased. Conclusions: The CNN prediction model based on delivery fluence informed by log files could accurately predict IMRT QA passing rates for different gamma criteria. It could reduce QA workload and improve efficiency in pretreatment QA. Our results suggest that the CNN prediction model based on delivery fluence informed by log files may be a promising tool for the gamma evaluation of IMRT QA.