Formation of marine oil snow by soot particles generated from burning of oils.

This study aims to investigate the interactions between marine oil snow (MOS) formation and soot particles derived from two distinct oils: condensate and heavy oil. Experimental findings demonstrate that the properties of oil droplets and soot particles play a key role in MOS formation. Peak MOS formation is observed within the initial days for condensate, while for heavy oil, peak formation occurs at a later stage. Furthermore, the addition of oils and soot particles influences the final concentrations of polycyclic aromatic hydrocarbons (PAHs) in MOS. Remarkably, the ranking order of PAHs with different rings in various MOS samples remains consistent: 4- > 3- > 5- > 2- > 6-ring. Specific diagnostic ratios such as Phe/Ant, Ant/(Ant + Phe), BaA/(Chr + BaA), and LMW/HMW effectively differentiate petrogenic and pyrogenic sources of PAHs in MOS. And stable ratios like Flu/(Pyr + Flu), InP/(InP + BghiP), and BaF/BkF are identified for source analysis of soot MOS.

Spiral-Pipe Gas Anaerobic Digester.

The reduction of carbon dioxide to methane using hydrogen is an important process in biogas production. However, designing gas anaerobic digesters (GADs) based on this reaction presents several challenges. In this study, we developed an innovative spiral-pipe gas anaerobic digester (SGAD) to increase the displacement distance between the bubbles, thus prolonging the gas retention time and facilitating the reduction of CO2 to CH4 via H2. The process was successfully demonstrated by using a CO2/H2 ratio of 1:3 and a gas-feeding rate of 3.9 L Lr -1 d-1. During the experiment, more than 98% of the CO2 and 96% of the H2 were consumed, resulting in biogas containing ca. 86-96% CH4. Additionally, we applied our proposed evaluation methodology for assessing GAD performance to evaluate the performance of the SGAD. This methodology serves as a reference for evaluating and designing GAD systems. The innovative design of the SGAD and the corresponding evaluation methodology offer new insights into the design of reactors.

Lysophosphatidic Acid Receptor 1 (LPA1) Antagonists as Potential Migrastatics for Triple Negative Breast Cancer.

Metastasis is responsible for about 90 % of cancer deaths. Anti-metastatic drugs, termed as migrastatics, offer a distinctive therapeutic approach to address cancer migration and invasion. However, therapeutic exploitation of metastasis-specific targets remains limited, and the effective prevention and suppression of metastatic cancer continue to be elusive. Lysophosphatidic acid receptor 1 (LPA1) is activated by an endogenous lipid molecule LPA, leading to a diverse array of cellular activities. Previous studies have shown that the LPA/LPA1 axis supports the progression of metastasis for many types of cancer. In this study, we report the synthesis and biological evaluation of fluorine-containing triazole derivatives as potent LPA1 antagonists, offering potential as migrastatic drugs for triple negative breast cancer (TNBC). In particular, compound 12 f, the most potent and highly selective in this series with an IC50 value of 16.0 nM in the cAMP assay and 18.4 nM in the calcium mobilization assay, inhibited cell survival, migration, and invasion in the TNBC cell line. Interestingly, the compound did not induce apoptosis in TNBC cells and demonstrated no cytotoxic effects. These results highlight the potential of LPA1 as a migrastatic target. Consequently, the LPA1 antagonists developed in this study hold promise as potential migrastatic candidates for TNBC.

[Microtubule-associated tumor suppressor 1 inhibits hemin-induced apoptosis of vascular endothelial cells via hemeoxygenase 1].

This study aimed to investigate the effects of microtubule associated tumor suppressor 1 (MTUS1) on hemeoxygenase 1 (HMOX1) expression and hemin-induced apoptosis of vascular endothelial cells and its regulatory mechanism. RNA sequencing, RT-qPCR and Western blot were used to assess altered genes of hemin binding proteins, the expression of cAMP response element-binding protein (CREB) and nuclear respiratory factor 2 (NRF2), hemin-induced HMOX1 expression in MTUS1 knockdown human umbilical vein endothelial cells (HUVEC), and the effect of overexpression of CREB and NRF2 on HMOX1 expression in MTUS1 knockdown 293T cells. The effect of MTUS1 or HMOX1 knockdown on hemin-induced apoptosis in HUVEC, and the overexpression of NRF2 on hemin-induced apoptosis in MTUS1 knockdown 293T cells were assayed with CCK8 and Western blot. The results showed that MTUS1 was knocked down significantly in HUVEC by siRNA (P < 0.01), accompanied by decreased HMOX1 expression (P < 0.01). The increased HMOX1 expression induced by hemin was also inhibited by MTUS1 knockdown (P < 0.01). And the apoptosis of HUVEC induced by hemin was amplified by MTUS1 or HMOX1 knockdown (P < 0.01). Moreover the expression of CREB and NRF2 were both inhibited by MTUS1 knockdown in HUVEC (P < 0.01). The decreased HMOX1 regulated by MTUS1 knockdown could be rescued partly by overexpression of NRF2 (P < 0.01), however, not by overexpression of CREB. And the MTUS1 knockdown mediated decreased 293T cells viability induced by hemin could be partly rescued by NRF2 overexpression (P < 0.01). These results suggest that MTUS1 can inhibit hemin-induced apoptosis of HUVEC, and the mechanism maybe related to MTUS1/NRF2/HMOX1 pathway.

Dose-Incorporated Deep Ensemble Learning for Improving Brain Metastasis Stereotactic Radiosurgery Outcome Prediction.

PURPOSE: To develop a novel deep ensemble learning model for accurate prediction of brain metastasis (BM) local control outcomes after stereotactic radiosurgery (SRS). METHODS AND MATERIALS: A total of 114 brain metastases (BMs) from 82 patients were evaluated, including 26 BMs that developed biopsy-confirmed local failure post-SRS. The SRS spatial dose distribution (Dmap) of each BM was registered to the planning contrast-enhanced T1 (T1-CE) magnetic resonance imaging (MRI). Axial slices of the Dmap, T1-CE, and planning target volume (PTV) segmentation (PTVseg) intersecting the BM center were extracted within a fixed field of view determined by the 60% isodose volume in Dmap. A spherical projection was implemented to transform planar image content onto a spherical surface using multiple projection centers, and the resultant T1-CE/Dmap/PTVseg projections were stacked as a 3-channel variable. Four Visual Geometry Group (VGG-19) deep encoders were used in an ensemble design, with each submodel using a different spherical projection formula as input for BM outcome prediction. In each submodel, clinical features after positional encoding were fused with VGG-19 deep features to generate logit results. The ensemble's outcome was synthesized from the 4 submodel results via logistic regression. In total, 10 model versions with random validation sample assignments were trained to study model robustness. Performance was compared with (1) a single VGG-19 encoder, (2) an ensemble with a T1-CE MRI as the sole image input after projections, and (3) an ensemble with the same image input design without clinical feature inclusion. RESULTS: The ensemble model achieved an excellent area under the receiver operating characteristic curve (AUCROC: 0.89 ± 0.02) with high sensitivity (0.82 ± 0.05), specificity (0.84 ± 0.11), and accuracy (0.84 ± 0.08) results. This outperformed the MRI-only VGG-19 encoder (sensitivity: 0.35 ± 0.01, AUCROC: 0.64 ± 0.08), the MRI-only deep ensemble (sensitivity: 0.60 ± 0.09, AUCROC: 0.68 ± 0.06), and the 3-channel ensemble without clinical feature fusion (sensitivity: 0.78 ± 0.08, AUCROC: 0.84 ± 0.03). CONCLUSIONS: Facilitated by the spherical image projection method, a deep ensemble model incorporating Dmap and clinical variables demonstrated excellent performance in predicting BM post-SRS local failure. Our novel approach could improve other radiation therapy outcome models and warrants further evaluation.

Prognostic value of different discretization parameters in 18fluorodeoxyglucose positron emission tomography radiomics of oropharyngeal squamous cell carcinoma.

Purpose: We aim to interrogate the role of positron emission tomography (PET) image discretization parameters on the prognostic value of radiomic features in patients with oropharyngeal cancer. Approach: A prospective clinical trial (NCT01908504) enrolled patients with oropharyngeal squamous cell carcinoma (N=69; mixed HPV status) undergoing definitive radiotherapy and evaluated intra-treatment 18fluorodeoxyglucose PET as a potential imaging biomarker of early metabolic response. The primary tumor volume was manually segmented by a radiation oncologist on PET/CT images acquired two weeks into treatment (20 Gy). From this, 54 radiomic texture features were extracted. Two image discretization techniques-fixed bin number (FBN) and fixed bin size (FBS)-were considered to evaluate systematic changes in the bin number ({32, 64, 128, 256} gray levels) and bin size ({0.10, 0.15, 0.22, 0.25} bin-widths). For each discretization-specific radiomic feature space, an LASSO-regularized logistic regression model was independently trained to predict residual and/or recurrent disease. The model training was based on Monte Carlo cross-validation with a 20% testing hold-out, 50 permutations, and minor-class up-sampling to account for imbalanced outcomes data. Performance differences among the discretization-specific models were quantified via receiver operating characteristic curve analysis. A final parameter-optimized logistic regression model was developed by incorporating different settings parameterizations into the same model. Results: FBN outperformed FBS in predicting residual and/or recurrent disease. The four FBN models achieved AUC values of 0.63, 0.61, 0.65, and 0.62 for 32, 64, 128, and 256 gray levels, respectively. By contrast, the average AUC of the four FBS models was 0.53. The parameter-optimized model, comprising features joint entropy (FBN = 64) and information measure correlation 1 (FBN = 128), achieved an AUC of 0.70. Kaplan-Meier analyses identified these features to be associated with disease-free survival (p=0.0158 and p=0.0180, respectively; log-rank test). Conclusions: Our findings suggest that the prognostic value of individual radiomic features may depend on feature-specific discretization parameter settings.

TMEM158 functions as an oncogene and promotes lung adenocarcinoma progression through the PI3K/AKT pathway via interaction with TWIST1.

Lung adenocarcinoma (LUAD) is a common and deadly form of lung cancer, with high rates of metastasis and unsatisfactory clinical outcomes. Herein, we examined the influence of TMEM158 on the LUAD progression. A combination of bioinformatic analyses was used to assess the TMEM158 expression pattern, prognostic implications, and potential function in LUAD. The levels of TMEM158 and TWIST1 were evaluated in clinical samples from LUAD patients using Western blot analysis and qRT-PCR. To discover the function and underlying molecular pathways of TMEM158 in LUAD, we employed a combination of experimental approaches in vitro, such as flow cytometry analysis and colony formation, Co-IP, CCK-8, Transwell, and wound-healing assays. Elevated expression of TMEM158 in LUAD is associated with increased cancer aggressiveness and a poor prognosis. In vitro experiments demonstrated that high levels of TMEM158 promote cell proliferation, progression through the cell cycle, migration, and invasion while suppressing apoptosis. Knockdown of TMEM158 produced opposite effects. The underlying mechanism involves TMEM158 and TWIST1 directly interacting, stimulating the PI3K/AKT signaling pathway in LUAD cells. This investigation emphasizes the molecular functions of TMEM158 in LUAD progression and proposes targeting it as a promising treatment approach for managing LUAD.

Are Radiomic Spleen Features Useful for Assessing the Response to Infliximab in Patients With Crohn's Disease? A Multicenter Study.

INTRODUCTION: To develop and validate a radiomics nomogram for assessing the response of patients with Crohn's disease (CD) to infliximab. METHODS: Radiomics features of the spleen were extracted from computed tomography enterography images of each patient's arterial phase. The feature selection process was performed using the least absolute shrinkage and selection operator algorithm, and a radiomics score was calculated based on the radiomics signature formula. Subsequently, the radiomic model and the clinical risk factor model were separately established based on the radiomics score and clinically significant features, respectively. The performance of both models was evaluated using receiver operating characteristic curves, decision curve analysis curves, and clinical impact curves. RESULTS: Among the 175 patients with CD, 105 exhibited a clinical response, and 60 exhibited clinical remission after receiving infliximab treatment. Our radiomic model, comprising 20 relevant features, demonstrated excellent predictive performance. The radiomic nomogram for predicting clinical response showed good calibration and discrimination in the training cohort (area under the curve [AUC] 0.909, 95% confidence interval [CI] 0.840-0.978), the validation cohort (AUC 0.954, 95% CI 0.889-1), and the external cohort (AUC = 0.902, 95% CI 0.83-0.974). Accordingly, the nomogram was also suitable for predicting clinical remission. Decision curve analysis and clinical impact curves highlighted the clinical utility of our nomogram. DISCUSSION: Our radiomics nomogram is a noninvasive predictive tool constructed from radiomic features of the spleen. It also demonstrated good predictive accuracy in evaluating CD patients' response to infliximab treatment. Multicenter validation provided high-level evidence for its clinical application.

Radiomics on spatial-temporal manifolds via Fokker-Planck dynamics.

BACKGROUND: Delta radiomics is a high-throughput computational technique used to describe quantitative changes in serial, time-series imaging by considering the relative change in radiomic features of images extracted at two distinct time points. Recent work has demonstrated a lack of prognostic signal of radiomic features extracted using this technique. We hypothesize that this lack of signal is due to the fundamental assumptions made when extracting features via delta radiomics, and that other methods should be investigated. PURPOSE: The purpose of this work was to show a proof-of-concept of a new radiomics paradigm for sparse, time-series imaging data, where features are extracted from a spatial-temporal manifold modeling the time evolution between images, and to assess the prognostic value on patients with oropharyngeal cancer (OPC). METHODS: To accomplish this, we developed an algorithm to mathematically describe the relationship between two images acquired at time t = 0 $t = 0$ and t > 0 $t > 0$ . These images serve as boundary conditions of a partial differential equation describing the transition from one image to the other. To solve this equation, we propagate the position and momentum of each voxel according to Fokker-Planck dynamics (i.e., a technique common in statistical mechanics). This transformation is driven by an underlying potential force uniquely determined by the equilibrium image. The solution generates a spatial-temporal manifold (3 spatial dimensions + time) from which we define dynamic radiomic features. First, our approach was numerically verified by stochastically sampling dynamic Gaussian processes of monotonically decreasing noise. The transformation from high to low noise was compared between our Fokker-Planck estimation and simulated ground-truth. To demonstrate feasibility and clinical impact, we applied our approach to 18F-FDG-PET images to estimate early metabolic response of patients (n = 57) undergoing definitive (chemo)radiation for OPC. Images were acquired pre-treatment and 2-weeks intra-treatment (after 20 Gy). Dynamic radiomic features capturing changes in texture and morphology were then extracted. Patients were partitioned into two groups based on similar dynamic radiomic feature expression via k-means clustering and compared by Kaplan-Meier analyses with log-rank tests (p < 0.05). These results were compared to conventional delta radiomics to test the added value of our approach. RESULTS: Numerical results confirmed our technique can recover image noise characteristics given sparse input data as boundary conditions. Our technique was able to model tumor shrinkage and metabolic response. While no delta radiomics features proved prognostic, Kaplan-Meier analyses identified nine significant dynamic radiomic features. The most significant feature was Gray-Level-Size-Zone-Matrix gray-level variance (p = 0.011), which demonstrated prognostic improvement over its corresponding delta radiomic feature (p = 0.722). CONCLUSIONS: We developed, verified, and demonstrated the prognostic value of a novel, physics-based radiomics approach over conventional delta radiomics via data assimilation of quantitative imaging and differential equations.

Independent Monte Carlo dose calculation identifies single isocenter multi-target radiosurgery targets most likely to fail pre-treatment measurement.

PURPOSE: For individual targets of single isocenter multi-target (SIMT) Stereotactic radiosurgery (SRS), we assess dose difference between the treatment planning system (TPS) and independent Monte Carlo (MC), and demonstrate persistence into the pre-treatment Quality Assurance (QA) measurement. METHODS: Treatment plans from 31 SIMT SRS patients were recalculated in a series of scenarios designed to investigate sources of discrepancy between TPS and independent MC. Targets with > 5% discrepancy in DMean[Gy] after progressing through all scenarios were measured with SRS MapCHECK. A matched pair analysis was performed comparing SRS MapCHECK results for these targets with matched targets having similar characteristics (volume & distance from isocenter) but no such MC dose discrepancy. RESULTS: Of 217 targets analyzed, individual target mean dose (DMean[Gy]) fell outside a 5% threshold for 28 and 24 targets before and after removing tissue heterogeneity effects, respectively, while only 5 exceeded the threshold after removing effect of patient geometry (via calculation on StereoPHAN geometry). Significant factors affecting agreement between the TPS and MC included target distance from isocenter (0.83% decrease in DMean[Gy] per 2 cm), volume (0.15% increase per cc), and degree of plan modulation (0.37% increase per 0.01 increase in modulation complexity score). SRS MapCHECK measurement had better agreement with MC than with TPS (2%/1 mm / 10% threshold gamma pass rate (GPR) = 99.4 ± 1.9% vs. 93.1 ± 13.9%, respectively). In the matched pair analysis, targets exceeding 5% for MC versus TPS also had larger discrepancies between TPS and measurement with no GPR (2%/1 mm / 10% threshold) exceeding 90% (71.5% ± 16.1%); whereas GPR was high for matched targets with no such MC versus TPS difference (96.5% ± 3.3%, p = 0.01). CONCLUSIONS: Independent MC complements pre-treatment QA measurement for SIMT SRS by identifying problematic individual targets prior to pre-treatment measurement, thus enabling plan modifications earlier in the planning process and guiding selection of targets for pre-treatment QA measurement.

Quantifying U-Net uncertainty in multi-parametric MRI-based glioma segmentation by spherical image projection.

BACKGROUND: Uncertainty quantification in deep learning is an important research topic. For medical image segmentation, the uncertainty measurements are usually reported as the likelihood that each pixel belongs to the predicted segmentation region. In potential clinical applications, the uncertainty result reflects the algorithm's robustness and supports the confidence and trust of the segmentation result when the ground-truth result is absent. For commonly studied deep learning models, novel methods for quantifying segmentation uncertainty are in demand. PURPOSE: To develop a U-Net segmentation uncertainty quantification method based on spherical image projection of multi-parametric MRI (MP-MRI) in glioma segmentation. METHODS: The projection of planar MRI data onto a spherical surface is equivalent to a nonlinear image transformation that retains global anatomical information. By incorporating this image transformation process in our proposed spherical projection-based U-Net (SPU-Net) segmentation model design, multiple independent segmentation predictions can be obtained from a single MRI. The final segmentation is the average of all available results, and the variation can be visualized as a pixel-wise uncertainty map. An uncertainty score was introduced to evaluate and compare the performance of uncertainty measurements. The proposed SPU-Net model was implemented on the basis of 369 glioma patients with MP-MRI scans (T1, T1-Ce, T2, and FLAIR). Three SPU-Net models were trained to segment enhancing tumor (ET), tumor core (TC), and whole tumor (WT), respectively. The SPU-Net model was compared with (1) the classic U-Net model with test-time augmentation (TTA) and (2) linear scaling-based U-Net (LSU-Net) segmentation models in terms of both segmentation accuracy (Dice coefficient, sensitivity, specificity, and accuracy) and segmentation uncertainty (uncertainty map and uncertainty score). RESULTS: The developed SPU-Net model successfully achieved low uncertainty for correct segmentation predictions (e.g., tumor interior or healthy tissue interior) and high uncertainty for incorrect results (e.g., tumor boundaries). This model could allow the identification of missed tumor targets or segmentation errors in U-Net. Quantitatively, the SPU-Net model achieved the highest uncertainty scores for three segmentation targets (ET/TC/WT): 0.826/0.848/0.936, compared to 0.784/0.643/0.872 using the U-Net with TTA and 0.743/0.702/0.876 with the LSU-Net (scaling factor = 2). The SPU-Net also achieved statistically significantly higher Dice coefficients, underscoring the improved segmentation accuracy. CONCLUSION: The SPU-Net model offers a powerful tool to quantify glioma segmentation uncertainty while improving segmentation accuracy. The proposed method can be generalized to other medical image-related deep-learning applications for uncertainty evaluation.

P450-Modified Ribosomally Synthesized Peptides with Aromatic Cross-Links.

Cyclization of linear peptides is an effective strategy to convert flexible molecules into rigid compounds, which is of great significance for enhancing the peptide stability and bioactivity. Despite significant advances in the past few decades, Nature and chemists' ability to macrocyclize linear peptides is still quite limited. P450 enzymes have been reported to catalyze macrocyclization of peptides through cross-linkers between aromatic amino acids with only three examples. Herein, we developed an efficient workflow for the identification of P450-modified RiPPs in bacterial genomes, resulting in the discovery of a large number of P450-modified RiPP gene clusters. Combined with subsequent expression and structural characterization of the products, we have identified 11 novel P450-modified RiPPs with different cross-linking patterns from four distinct classes. Our results greatly expand the structural diversity of P450-modified RiPPs and provide new insights and enzymatic tools for the production of cyclic peptides.

Development of a multi-feature-combined model: proof-of-concept with application to local failure prediction of post-SBRT or surgery early-stage NSCLC patients.

Objective: To develop a Multi-Feature-Combined (MFC) model for proof-of-concept in predicting local failure (LR) in NSCLC patients after surgery or SBRT using pre-treatment CT images. This MFC model combines handcrafted radiomic features, deep radiomic features, and patient demographic information in an integrated machine learning workflow. Methods: The MFC model comprised three key steps. (1) Extraction of 92 handcrafted radiomic features from the GTV segmented on pre-treatment CT images. (2) Extraction of 512 deep radiomic features from pre-trained U-Net encoder. (3) The extracted handcrafted radiomic features, deep radiomic features, along with 4 patient demographic information (i.e., gender, age, tumor volume, and Charlson comorbidity index), were concatenated as a multi-dimensional input to the classifiers for LR prediction. Two NSCLC patient cohorts from our institution were investigated: (1) the surgery cohort includes 83 patients with segmentectomy or wedge resection (7 LR), and (2) the SBRT cohort includes 84 patients with lung SBRT (9 LR). The MFC model was developed and evaluated independently for both cohorts, and was subsequently compared against the prediction models based on only handcrafted radiomic features (R models), patient demographic information (PI models), and deep learning modeling (DL models). ROC with AUC was adopted to evaluate model performance with leave-one-out cross-validation (LOOCV) and 100-fold Monte Carlo random validation (MCRV). The t-test was performed to identify the statistically significant differences. Results: In LOOCV, the AUC range (surgery/SBRT) of the MFC model was 0.858-0.895/0.868-0.913, which was higher than the three other models: 0.356-0.480/0.322-0.650 for PI models, 0.559-0.618/0.639-0.682 for R models, and 0.809/0.843 for DL models. In 100-fold MCRV, the MFC model again showed the highest AUC results (surgery/SBRT): 0.742-0.825/0.888-0.920, which were significantly higher than PI models: 0.464-0.564/0.538-0.628, R models: 0.557-0.652/0.551-0.732, and DL models: 0.702/0.791. Conclusion: We successfully developed an MFC model that combines feature information from multiple sources for proof-of-concept prediction of LR in patients with surgical and SBRT early-stage NSCLC. Initial results suggested that incorporating pre-treatment patient information from multiple sources improves the ability to predict the risk of local failure.

CircUCP2 promotes the tumor progression of non-small cell lung cancer through the miR-149/UCP2 pathway.

Non-small cell lung cancer (NSCLC) is a highly lethal cancer, and better treatments are urgently needed. Many studies have implicated circular RNAs (circRNAs) in the progression of multiple malignant tumors. Nonetheless, the functions of circRNAs in NSCLC remain unclear. To study new targets for the treatment of NSCLC, circRNA expression profiling was performed on NSCLC tissues and para-carcinoma nonmalignant tissues. RNA was isolated and used for circRNA sequencing. Biological studies were performed in vitro and in vivo to determine the functions of circRNAs in NSCLC, including their functions in cell proliferation and migration. How circRNAs function in NSCLC was explored to clarify the underlying regulatory mechanisms. We found that circUCP2 was upregulated in NSCLC tissues compared with neighboring nonmalignant tissues. circUCP2 promoted the proliferation and metastasis of NSCLC cells. circUCP2 promoted NSCLC progression by sponging miR-149 and upregulating UCP2. The circUCP2/miR-149/UCP2 axis accelerates the progression of NSCLC, and circUCP2 may therefore be a novel diagnostic biomarker for the progression of NSCLC.

A radiomics-incorporated deep ensemble learning model for multi-parametric MRI-based glioma segmentation.

Objective.To develop a deep ensemble learning (DEL) model with radiomics spatial encoding execution for improved glioma segmentation accuracy using multi-parametric magnetic resonance imaging (mp-MRI).Approach.This model was developed using 369 glioma patients with a four-modality mp-MRI protocol: T1, contrast-enhanced T1 (T1-Ce), T2, and FLAIR. In each modality volume, a 3D sliding kernel was implemented across the brain to capture image heterogeneity: 56 radiomic features were extracted within the kernel, resulting in a fourth-order tensor. Each radiomic feature can then be encoded as a 3D image volume, namely a radiomic feature map (RFM). For each patient, all RFMs extracted from all four modalities were processed using principal component analysis for dimension reduction, and the first four principal components (PCs) were selected. Next, a DEL model comprised of four U-Net sub-models was trained for the segmentation of a region-of-interest: each sub-model utilizes the mp-MRI and one of the four PCs as a five-channel input for 2D execution. Last, four softmax probability results given by the DEL model were superimposed and binarized using Otsu's method as the segmentation results. Three DEL models were trained to segment the enhancing tumor (ET), tumor core (TC), and whole tumor (WT), respectively. The segmentation results given by the proposed ensemble were compared to the mp-MRI-only U-Net results.Main Results.All three radiomics-incorporated DEL models were successfully implemented: compared to the mp-MRI-only U-net results, the dice coefficients of ET (0.777 → 0.817), TC (0.742 → 0.757), and WT (0.823 → 0.854) demonstrated improvement. The accuracy, sensitivity, and specificity results demonstrated similar patterns.Significance.The adopted radiomics spatial encoding execution enriches the image heterogeneity information that leads to the successful demonstration of the proposed DEL model, which offers a new tool for mp-MRI-based medical image segmentation.

Case report: a successful treatment with immune checkpoint inhibitors was associated with severe dermatologic toxicities in a patient with double primary malignancies.

Dermatological toxicities are well-recognized immune-related adverse events (irAEs) secondary to immune checkpoint inhibitor (ICI) use. Corticosteroids are considered the first-line therapy for grade 3 or grade 4 skin irAEs, but long-term usage of corticosteroids may abolish the effect of ICIs. Multiple antitumor therapies might be an influencing factor in an increased incidence of skin irAEs. The safety and prognostic value in resuming ICIs after irAEs has been inconsistently reported, especially the severe skin irAE. We report a case of a 75-year-old man with non-small cell lung cancer (NSCLC) and prostate cancer with a Stevens-Johnson syndrome (SJS)-like eruption. The severe rash might have been induced by resuming pembrolizumab was successfully treated with a combination of corticosteroids, gamma globulin, and immunosuppressants. Early detection of dermatologic toxicity is crucial, especially for patients receiving multiple antitumor treatments. We should treat ICI resumption seriously after skin irAE.

Modulation of Intratumoral Fusobacterium nucleatum to Enhance Sonodynamic Therapy for Colorectal Cancer with Reduced Phototoxic Skin Injury.

Intratumoral pathogens can contribute to cancer progression and affect therapeutic response. Fusobacterium nucleatum, a core pathogen of colorectal cancer (CRC), is an important cause of low therapeutic efficacy and metastasis. Thus, the modulation of intratumoral pathogens may provide a target for cancer therapy and metastasis inhibition. Herein, we propose an intratumoral F. nucleatum-modulating strategy for enhancing the therapeutic efficacy of CRC and inhibiting lung metastasis by designing an antibacterial nanoplatform (Au@BSA-CuPpIX), which produced reactive oxygen species (ROS) under ultrasound and exhibited strong antibacterial activity. Importantly, Au@BSA-CuPpIX reduced the levels of apoptosis-inhibiting proteins by inhibiting intratumoral F. nucleatum, thereby enhancing ROS-induced apoptosis. In vivo results demonstrated that Au@BSA-CuPpIX effectively eliminated F. nucleatum to enhance the therapeutic efficacy of sonodynamic therapy (SDT) for orthotopic CRC and inhibit lung metastasis. Notably, entrapped gold nanoparticles reduced the phototoxicity of metalloporphyrin accumulated in the skin during tumor treatment, preventing severe inflammation and damage to the skin. Therefore, this study proposes a strategy for the elimination of F. nucleatum in CRC to enhance the therapeutic effect of SDT, thus providing a promising paradigm for improving cancer treatment with fewer toxic side effects and promoting the clinical translational potential of SDT.

Pathogenesis and therapy of radiation enteritis with gut microbiota.

Radiotherapy is widely used in clinic due to its good effect for cancer treatment. But radiotherapy of malignant tumors in the abdomen and pelvis is easy to cause radiation enteritis complications. Gastrointestinal tract contains numerous microbes, most of which are mutualistic relationship with the host. Abdominal radiation results in gut microbiota dysbiosis. Microbial therapy can directly target gut microbiota to reverse microbiota dysbiosis, hence relieving intestinal inflammation. In this review, we mainly summarized pathogenesis and novel therapy of the radiation-induced intestinal injury with gut microbiota dysbiosis and envision the opportunities and challenges of radiation enteritis therapy.