Lipopolysaccharides, major molecules in the outer membrane of Gram-negative bacteria, play important roles on membrane integrity of the cell. However, how the core oligosaccharide of lipopolysaccharide affect the membrane behavior is not well understood. In this study, the relationship between the core oligosaccharide of lipopolysaccharide and the membrane behavior was investigated using a series of Escherichia coli mutants defective in genes to affect the biosynthesis of core oligosaccharide of lipopolysaccharide. Cell surface hydrophobicity, outer membrane permeability, biofilm formation and auto-aggregation of these mutant cells were compared. Compared to the wild type W3110, cell surface hydrophobicities of mutant ΔwaaC, ΔwaaF, ΔwaaG, ΔwaaO, ΔwaaP, ΔwaaY and ΔwaaB were enhanced, outer membrane permeabilities of ΔwaaC, ΔwaaF, ΔwaaG and ΔwaaP were significantly increased, abilities of biofilm formation by ΔwaaC, ΔwaaF, ΔwaaG, ΔwaaO, ΔwaaR, ΔwaaP, ΔwaaQ and ΔwaaY decreased, and auto-aggregation abilities of ΔwaaC, ΔwaaF, ΔwaaG, ΔwaaO, ΔwaaR, ΔwaaU, ΔwaaP and ΔwaaY were strongly enhanced. These results give new insight into the influence of core oligosaccharide of lipopolysaccharide on bacterial cell membrane behavior.

When 10 Escherichia coli mutant strains with defects in lipopolysaccharide (LPS) core biosynthesis were grown on agar medium at 30°C, four of them, the Δ waaF , Δ waaG , Δ waaP , and Δ waaB strains, formed mucoid colonies, while the other six, the Δ waaU , Δ waaR , Δ waaO , Δ waaC , Δ waaQ , and Δ waaY strains, did not. Using light microscopy with tannin mordant staining, the presence of exopolysaccharide around the cells of the mutants that formed mucoid colonies could be discerned. The Δ waaF mutant produced the largest amounts of exopolysaccharide, regardless of whether it was grown on agar or in liquid medium. The exopolysaccharide was isolated from the liquid growth medium of Δ waaF cells, hydrolyzed, and analyzed by high-performance liquid chromatography with an ion-exchange column, and the results indicated that the exopolysaccharide was consistent with colanic acid. When the key genes related to the biosynthesis of colanic acid, i.e., wza , wzb , wzc , and wcaA , were deleted in the Δ waaF background, the exopolysaccharide could not be produced any more, further confirming that it was colanic acid. Colanic acid could not be produced in strains in which rcsA , rcsB , rcsD , or rcsF was deleted in the Δ waaF background, but a reduced level of colanic acid production was detected when the rcsC gene was deleted, suggesting that a change of lipopolysaccharide structure in Δ waaF cells might be sensed by the RcsCDB phosphorelay system, leading to the production of colanic acid. The results demonstrate that E. coli cells can activate colanic acid production through the RcsCDB phosphorelay system in response to a structural deficiency of lipopolysaccharide. IMPORTANCE Lipopolysaccharide and colanic acid are important forms of exopolysaccharide for Escherichia coli cells. Their metabolism and biological significance have been investigated, but their interrelation with the cell stress response process is not understood. This study demonstrates, for the first time, that E. coli cells can activate colanic acid production through the RcsCDB phosphorelay system in response to a structural change of lipopolysaccharide, suggesting that bacterial cells can monitor the outer membrane integrity, which is essential for cell survival and damage repair.

Background: Using high robust radiomic features in modeling is recommended, yet its impact on radiomic model is unclear. This study evaluated the radiomic model’s robustness and generalizability after screening out low-robust features before radiomic modeling. The results were validated with four datasets and two clinically relevant tasks.Materials and methods: A total of 1,419 head-and-neck cancer patients’ computed tomography images, gross tumor volume segmentation, and clinically relevant outcomes (distant metastasis and local-regional recurrence) were collected from four publicly available datasets. The perturbation method was implemented to simulate images, and the radiomic feature robustness was quantified using intra-class correlation of coefficient (ICC). Three radiomic models were built using all features (ICC > 0), good-robust features (ICC > 0.75), and excellent-robust features (ICC > 0.95), respectively. A filter-based feature selection and Ridge classification method were used to construct the radiomic models. Model performance was assessed with both robustness and generalizability. The robustness of the model was evaluated by the ICC, and the generalizability of the model was quantified by the train-test difference of Area Under the Receiver Operating Characteristic Curve (AUC).Results: The average model robustness ICC improved significantly from 0.65 to 0.78 (P< 0.0001) using good-robust features and to 0.91 (P< 0.0001) using excellent-robust features. Model generalizability also showed a substantial increase, as a closer gap between training and testing AUC was observed where the mean train-test AUC difference was reduced from 0.21 to 0.18 (P< 0.001) in good-robust features and to 0.12 (P< 0.0001) in excellent-robust features. Furthermore, good-robust features yielded the best average AUC in the unseen datasets of 0.58 (P< 0.001) over four datasets and clinical outcomes.Conclusions: Including robust only features in radiomic modeling significantly improves model robustness and generalizability in unseen datasets. Yet, the robustness of radiomic model has to be verified despite building with robust radiomic features, and tightly restricted feature robustness may prevent the optimal model performance in the unseen dataset as it may lower the discrimination power of the model.

Purpose: Deep learning model has shown the feasibility of providing spatial lung perfusion information based on CT images. However, the performance of this method on lung cancer patients is yet to be investigated. This study aims to develop a transfer learning framework to evaluate the deep learning based CT-to-perfusion mapping method specifically on lung cancer patients.Methods: SPECT/CT perfusion scans of 33 lung cancer patients and 137 non-cancer patients were retrospectively collected from two hospitals. To adapt the deep learning model on lung cancer patients, a transfer learning framework was developed to utilize the features learned from the non-cancer patients. These images were processed to extract features from three-dimensional CT images and synthesize the corresponding CT-based perfusion images. A pre-trained model was first developed using a dataset of patients with lung diseases other than lung cancer, and subsequently fine-tuned specifically on lung cancer patients under three-fold cross-validation. A multi-level evaluation was performed between the CT-based perfusion images and ground-truth SPECT perfusion images in aspects of voxel-wise correlation using Spearman’s correlation coefficient (R), function-wise similarity using Dice Similarity Coefficient (DSC), and lobe-wise agreement using mean perfusion value for each lobe of the lungs.Results: The fine-tuned model yielded a high voxel-wise correlation (0.8142 ± 0.0669) and outperformed the pre-trained model by approximately 8%. Evaluation of function-wise similarity indicated an average DSC value of 0.8112 ± 0.0484 (range: 0.6460-0.8984) for high-functional lungs and 0.8137 ± 0.0414 (range: 0.6743-0.8902) for low-functional lungs. Among the 33 lung cancer patients, high DSC values of greater than 0.7 were achieved for high functional volumes in 32 patients and low functional volumes in all patients. The correlations of the mean perfusion value on the left upper lobe, left lower lobe, right upper lobe, right middle lobe, and right lower lobe were 0.7314, 0.7134, 0.5108, 0.4765, and 0.7618, respectively.Conclusion: For lung cancer patients, the CT-based perfusion images synthesized by the transfer learning framework indicated a strong voxel-wise correlation and function-wise similarity with the SPECT perfusion images. This suggests the great potential of the deep learning method in providing regional-based functional information for functional lung avoidance radiation therapy.

Purpose: This study aimed to develop and evaluate CTVISVD, a super-voxel-based method for surrogate computed tomography ventilation imaging (CTVI).Methods and Materials: The study used four-dimensional CT (4DCT) and single-photon emission computed tomography (SPECT) images and corresponding lung masks from 21 patients with lung cancer obtained from the Ventilation And Medical Pulmonary Image Registration Evaluation dataset. The lung volume of the exhale CT for each patient was segmented into hundreds of super-voxels using the Simple Linear Iterative Clustering (SLIC) method. These super-voxel segments were applied to the CT and SPECT images to calculate the mean density values (D_mean) and mean ventilation values (Vent_mean), respectively. The final CT-derived ventilation images were generated by interpolation from the D_mean values to yield CTVISVD. For the performance evaluation, the voxel- and region-wise differences between CTVISVD and SPECT were compared using Spearman’s correlation and the Dice similarity coefficient index. Additionally, images were generated using two deformable image registration (DIR)-based methods, CTVIHU and CTVIJac, and compared with the SPECT images.Results: The correlation between the D_mean and Vent_mean of the super-voxel was 0.59 ± 0.09, representing a moderate-to-high correlation at the super-voxel level. In the voxel-wise evaluation, the CTVISVD method achieved a stronger average correlation (0.62 ± 0.10) with SPECT, which was significantly better than the correlations achieved with the CTVIHU (0.33 ± 0.14, p < 0.05) and CTVIJac (0.23 ± 0.11, p < 0.05) methods. For the region-wise evaluation, the Dice similarity coefficient of the high functional region for CTVISVD (0.63 ± 0.07) was significantly higher than the corresponding values for the CTVIHU (0.43 ± 0.08, p < 0.05) and CTVIJac (0.42 ± 0.05, p < 0.05) methods.Conclusion: The strong correlation between CTVISVD and SPECT demonstrates the potential usefulness of this novel method of ventilation estimation for surrogate ventilation imaging.