Experimental comparison of linear regression and LSTM motion prediction models for MLC-tracking on an MRI-linac.

BACKGROUND: Magnetic resonance imaging (MRI)-guided radiotherapy with multileaf collimator (MLC)-tracking is a promising technique for intra-fractional motion management, achieving high dose conformality without prolonging treatment times. To improve beam-target alignment, the geometric error due to system latency should be reduced by using temporal prediction. PURPOSE: To experimentally compare linear regression (LR) and long-short-term memory (LSTM) motion prediction models for MLC-tracking on an MRI-linac using multiple patient-derived traces with different complexities. METHODS: Experiments were performed on a prototype 1.0 T MRI-linac capable of MLC-tracking. A motion phantom was programmed to move a target in superior-inferior (SI) direction according to eight lung cancer patient respiratory motion traces. Target centroid positions were localized from sagittal 2D cine MRIs acquired at 4 Hz using a template matching algorithm. The centroid positions were input to one of four motion prediction models. We used (1) a LSTM network which had been optimized in a previous study on patient data from another cohort (offline LSTM). We also used (2) the same LSTM model as a starting point for continuous re-optimization of its weights during the experiment based on recent motion (offline+online LSTM). Furthermore, we implemented (3) a continuously updated LR model, which was solely based on recent motion (online LR). Finally, we used (4) the last available target centroid without any changes as a baseline (no-predictor). The predictions of the models were used to shift the MLC aperture in real-time. An electronic portal imaging device (EPID) was used to visualize the target and MLC aperture during the experiments. Based on the EPID frames, the root-mean-square error (RMSE) between the target and the MLC aperture positions was used to assess the performance of the different motion predictors. Each combination of motion trace and prediction model was repeated twice to test stability, for a total of 64 experiments. RESULTS: The end-to-end latency of the system was measured to be (389 ± 15) ms and was successfully mitigated by both LR and LSTM models. The offline+online LSTM was found to outperform the other models for all investigated motion traces. It obtained a median RMSE over all traces of (2.8 ± 1.3) mm, compared to the (3.2 ± 1.9) mm of the offline LSTM, the (3.3 ± 1.4) mm of the online LR and the (4.4 ± 2.4) mm when using the no-predictor. According to statistical tests, differences were significant (p-value <0.05) among all models in a pair-wise comparison, but for the offline LSTM and online LR pair. The offline+online LSTM was found to be more reproducible than the offline LSTM and the online LR with a maximum deviation in RMSE between two measurements of 10%. CONCLUSIONS: This study represents the first experimental comparison of different prediction models for MRI-guided MLC-tracking using several patient-derived respiratory motion traces. We have shown that among the investigated models, continuously re-optimized LSTM networks are the most promising to account for the end-to-end system latency in MRI-guided radiotherapy with MLC-tracking.

CT-based deep learning segmentation of ovarian cancer and the stability of the extracted radiomics features.

Background: Radiomics analysis could provide complementary tissue characterization in ovarian cancer (OC). However, OC segmentation required in radiomics analysis is time-consuming and labour-intensive. In this study, we aim to evaluate the performance of deep learning-based segmentation of OC on contrast-enhanced CT images and the stability of radiomics features extracted from the automated segmentation. Methods: Staging abdominopelvic CT images of 367 patients with OC were retrospectively recruited. The training and cross-validation sets came from center A (n=283), and testing set (n=84) came from centers B and C. The tumours were manually delineated by a board-certified radiologist. Four model architectures provided by no-new-Net (nnU-Net) method were tested in this task. The segmentation performance evaluated by Dice score, Jaccard score, sensitivity and precision were compared among 4 architectures. The Pearson correlation coefficient (ρ), concordance correlation coefficient (ρc) and Bland-Altman plots were used to evaluate the volumetric assessment of OC between manual and automated segmentations. The stability of extracted radiomics features was evaluated by intraclass correlation coefficient (ICC). Results: The 3D U-Net cascade architecture achieved highest median Dice score, Jaccard score, sensitivity and precision for OC segmentation in the testing set, 0.941, 0.890, 0.973 and 0.925, respectively. Tumour volumes of manual and automated segmentations were highly correlated (ρ=0.944 and ρc =0.933). 85.0% of radiomics features had high correlation with ICC >0.8. Conclusions: The presented deep-learning segmentation could provide highly accurate automated segmentation of OC on CT images with high stability of the extracted radiomics features, showing the potential as a batch-processing segmentation tool.

Dependency of NELF-E-SLUG-KAT2B epigenetic axis in breast cancer carcinogenesis.

Cancer cells undergo transcriptional reprogramming to drive tumor progression and metastasis. Using cancer cell lines and patient-derived tumor organoids, we demonstrate that loss of the negative elongation factor (NELF) complex inhibits breast cancer development through downregulating epithelial-mesenchymal transition (EMT) and stemness-associated genes. Quantitative multiplexed Rapid Immunoprecipitation Mass spectrometry of Endogenous proteins (qPLEX-RIME) further reveals a significant rewiring of NELF-E-associated chromatin partners as a function of EMT and a co-option of NELF-E with the key EMT transcription factor SLUG. Accordingly, loss of NELF-E leads to impaired SLUG binding on chromatin. Through integrative transcriptomic and genomic analyses, we identify the histone acetyltransferase, KAT2B, as a key functional target of NELF-E-SLUG. Genetic and pharmacological inactivation of KAT2B ameliorate the expression of EMT markers, phenocopying NELF ablation. Elevated expression of NELF-E and KAT2B is associated with poorer prognosis in breast cancer patients, highlighting the clinical relevance of our findings. Taken together, we uncover a crucial role of the NELF-E-SLUG-KAT2B epigenetic axis in breast cancer carcinogenesis.

Computed Tomographic Radiomics in Differentiating Histologic Subtypes of Epithelial Ovarian Carcinoma.

Importance: Epithelial ovarian carcinoma is heterogeneous and classified according to the World Health Organization Tumour Classification, which is based on histologic features and molecular alterations. Preoperative prediction of the histologic subtypes could aid in clinical management and disease prognostication. Objective: To assess the value of radiomics based on contrast-enhanced computed tomography (CT) in differentiating histologic subtypes of epithelial ovarian carcinoma in multicenter data sets. Design, Setting, and Participants: In this diagnostic study, 665 patients with histologically confirmed epithelial ovarian carcinoma were retrospectively recruited from 4 centers (Hong Kong, Guangdong Province of China, and Seoul, South Korea) between January 1, 2012, and February 28, 2022. The patients were randomly divided into a training cohort (n = 532) and a testing cohort (n = 133) with a ratio of 8:2. This process was repeated 100 times. Tumor segmentation was manually delineated on each section of contrast-enhanced CT images to encompass the entire tumor. The Mann-Whitney U test and voted least absolute shrinkage and selection operator were performed for feature reduction and selection. Selected features were used to build the logistic regression model for differentiating high-grade serous carcinoma and non-high-grade serous carcinoma. Exposures: Contrast-enhanced CT-based radiomics. Main Outcomes and Measures: Intraobserver and interobserver reproducibility of tumor segmentation were measured by Dice similarity coefficients. The diagnostic efficiency of the model was assessed by receiver operating characteristic curve and area under the curve. Results: In this study, 665 female patients (mean [SD] age, 53.6 [10.9] years) with epithelial ovarian carcinoma were enrolled and analyzed. The Dice similarity coefficients of intraobserver and interobserver were all greater than 0.80. Twenty radiomic features were selected for modeling. The areas under the curve of the logistic regression model in differentiating high-grade serous carcinoma and non-high-grade serous carcinoma were 0.837 (95% CI, 0.835-0.838) for the training cohort and 0.836 (95% CI, 0.833-0.840) for the testing cohort. Conclusions and Relevance: In this diagnostic study, radiomic features extracted from contrast-enhanced CT were useful in the classification of histologic subtypes in epithelial ovarian carcinoma. Intraobserver and interobserver reproducibility of tumor segmentation was excellent. The proposed logistic regression model offered excellent discriminative ability among histologic subtypes.

A genomic-augmented multivariate prognostic model for the survival of natural-killer/T-cell lymphoma patients from an international cohort.

With lowering costs of sequencing and genetic profiling techniques, genetic drivers can now be detected readily in tumors but current prognostic models for Natural-killer/T cell lymphoma (NKTCL) have yet to fully leverage on them for prognosticating patients. Here, we used next-generation sequencing to sequence 260 NKTCL tumors, and trained a genomic prognostic model (GPM) with the genomic mutations and survival data from this retrospective cohort of patients using LASSO Cox regression. The GPM is defined by the mutational status of 13 prognostic genes and is weakly correlated with the risk-features in International Prognostic Index (IPI), Prognostic Index for Natural-Killer cell lymphoma (PINK), and PINK-Epstein-Barr virus (PINK-E). Cox-proportional hazard multivariate regression also showed that the new GPM is independent and significant for both progression-free survival (PFS, HR: 3.73, 95% CI 2.07-6.73; p < .001) and overall survival (OS, HR: 5.23, 95% CI 2.57-10.65; p = .001) with known risk-features of these indices. When we assign an additional risk-score to samples, which are mutant for the GPM, the Harrell's C-indices of GPM-augmented IPI, PINK, and PINK-E improved significantly (p < .001, χ2 test) for both PFS and OS. Thus, we report on how genomic mutational information could steer toward better prognostication of NKTCL patients.

A composite single-nucleotide polymorphism prediction signature for extranodal natural killer/T-cell lymphoma.

Current prognostic scoring systems based on clinicopathologic variables are inadequate in predicting the survival and treatment response of extranodal natural killer/T-cell lymphoma (ENKTL) patients undergoing nonanthracyline-based treatment. We aimed to construct a classifier based on single-nucleotide polymorphisms (SNPs) for improving predictive accuracy and guiding clinical decision making. Data from 722 patients with ENKTL from international centers were analyzed. A 7-SNP-based classifier was constructed using LASSO Cox regression in the training cohort (n = 336) and further validated in the internal testing cohort (n = 144) and in 2 external validation cohorts (n = 142 and n = 100). The 7-SNP-based classifier showed good prognostic predictive efficacy in the training cohort and the 3 validation cohorts. Patients with high- and low-risk scores calculated by the classifier exhibited significantly different progression-free survival (PFS) and overall survival (OS) (all P < .001). The 7-SNP-based classifier was further proved to be an independent prognostic factor by multivariate analysis, and its predictive accuracy was significantly better than clinicopathological risk variables. Application of the 7-SNP-based classifier was not affected by sample types. Notably, chemotherapy combined with radiotherapy significantly improved PFS and OS vs radiotherapy alone in high-risk Ann Arbor stage I patients, whereas there was no statistical difference between the 2 therapeutic modalities among low-risk patients. A nomogram was constructed comprising the classifier and clinicopathological variables; it showed remarkably better predictive accuracy than either variable alone. The 7-SNP-based classifier is a complement to existing risk-stratification systems in ENKTL, which could have significant implications for clinical decision making for patients with ENKTL.

CT texture analysis in histological classification of epithelial ovarian carcinoma.

OBJECTIVES: The study aimed to compare the ability of morphological and texture features derived from contrast-enhanced CT in histological subtyping of epithelial ovarian carcinoma (EOC). METHODS: Consecutive 205 patients with newly diagnosed EOC who underwent contrast-enhanced CT were included and dichotomised into high-grade serous carcinoma (HGSC) and non-HGSC. Clinical information including age and cancer antigen 125 (CA-125) was documented. The pre-treatment images were analysed using commercial software, TexRAD, by two independent radiologists. Eight qualitative CT morphological features were evaluated, and 36 CT texture features at 6 spatial scale factors (SSFs) were extracted per patient. Features' reduction was based on kappa score, intra-class correlation coefficient (ICC), univariate ROC analysis and Pearson's correlation test. Texture features with ICC ≥ 0.8 were compared by histological subtypes. Patients were randomly divided into training and testing sets by 8:2. Two random forest classifiers were determined and compared: model 1 incorporating selected morphological and clinical features and model 2 incorporating selected texture and clinical features. RESULTS: HGSC showed specifically higher texture features than non-HGSC (p < 0.05). Both models performed highly in predicting histological subtypes of EOC (model 1: AUC 0.891 and model 2: AUC 0.937), and no statistical significance was found between the two models (p = 0.464). CONCLUSION: CT texture analysis provides objective and quantitative metrics on tumour characteristics with HGSC demonstrating specifically high texture features. The model incorporating texture analysis could classify histology subtypes of EOC with high accuracy and performed as well as morphological features. KEY POINTS: • A number of CT morphological and texture features showed good inter- and intra-observer agreements. • High-grade serous ovarian carcinoma showed specifically higher CT texture features than non-high-grade serous ovarian carcinoma. • CT texture analysis could differentiate histological subtypes of epithelial ovarian carcinoma with high accuracy.

The haemoglobinopathy survey: The reality of transfusion practice in sickle cell disease and thalassaemia in England.

OBJECTIVES: To establish, in an unselected population of London haemoglobinopathy patients, transfusion requirements, blood antigens/alloantibodies, transfusion modalities, burden of transfusion reactions and donor exposure. BACKGROUND: Haemoglobinopathy patients are among the most highly transfused patient populations, and the overall population and number of patients on long-term transfusion programmes are increasing. To provide a safe and efficacious transfusion service for patients, it is important to understand current practice, morbidity associated with transfusion, efficacy of different transfusion modalities and geno-/phenotype requirements. METHODS: Data on 4451 transfusion episodes in 760 patients from 12 London hospitals were collected retrospectively over a 6-month period in 2011. RESULTS: Alloimmunisation prevalence was 17% for sickle cell disease (SCD) and 22% for thalassaemia, most commonly anti-Rh/Kell/Kpa /Cw . Rh phenotypes differed between SCD (Ro r 59.8%/R1 r 15.9%/R2 r 15.6%) and thalassaemia (R1 R1 29.6%/R1 r 28.4%/R1 R2 15.4%). Recording of pheno-/genotypes fell below recommendations. A 2-weekly manual exchange and 3-weekly automated exchange came closest to achieving presumptive targets. In adults with thalassaemia, the mean blood requirement was 36 units per year; for SCD, erythrocytapheresis was carried out every 7 weeks with 66 units; for manual exchange, it was 38 units every 4 weeks; and for simple transfusion, it was 30 units p.a. every 4 weeks. CONCLUSION: Transfusion modality choice was influenced by the resources available-children mostly received simple transfusions, and adults received erythrocytapheresis; the relationships between frequency of exchanges/transfusion modality/target HbA% were not simple, possibly reflecting the difference in recipient erythropoiesis and consequent transfusion modality selection bias; adherence to existing and current guidelines regarding geno-/phenotyping was limited; and alloimmunisation had a low incidence and high prevalence in both disorders.

The Challenges of Using UK Renal Registry Data to Audit the Care of Patients with Diabetes on Renal Replacement Therapy.

INTRODUCTION: Diabetes is a major cause of CKD and of mortality in patients on renal replacement therapy (RRT). Auditing the care of patients with diabetes on RRT against published guidelines relies on robust data collection. OBJECTIVE: This article assesses the completeness of data items collected by the UK Renal Registry (UKRR) that are required to audit the care of patients with diabetes on RRT. METHODS: The UKRR receives data on all patients receiving RRT in the UK. Patients with diabetes, diabetes type, and method of renal diagnosis were identified from primary renal disease (PRD) codes and comorbidity data for patients commencing RRT at one of the 57 renal centres in England and Wales between 2010 and 2016. The completeness of demographic and clinical data (blood pressure, cholesterol, glycated haemoglobin [HbA1c], and smoking status) was assessed for the first year of RRT. RESULTS: Ninety-three per cent of all patients on RRT irrespective of diagnosis had a PRD code, but only 28/57 renal centres had comorbidity data completeness ≥70%; 34.9% of patients with diabetic nephropathy (DN) had type 1 diabetes, but this varied between centres (9.2-100%). Overall, 4.2% of DN diagnoses were by biopsy. Data completeness in the first year of RRT for cardiovascular risk factors ranged between 50.0 and 80.0%, with HbA1c data completeness being 63.0%. Of 57 centres, 20 had HbA1c data for ≥70% of patients in the first year of RRT. CONCLUSIONS: There is persistent variation between renal centres in the completeness of data collected on patients with diabetes on RRT, impacting on the ability to undertake robust audit. Data linkages and expanded data permissions could see registry data play a key role in ongoing audit and research into patients with diabetes and CKD, provided adequate data can be collected.

Diffusion-weighted magnetic resonance imaging of primary cervical cancer in the detection of sub-centimetre metastatic lymph nodes.

BACKGROUND: Magnetic resonance imaging (MRI) has limited accuracy in detecting pelvic lymph node (PLN) metastasis. This study aimed to examine the use of intravoxel incoherent motion (IVIM) in classifying pelvic lymph node (PLN) involvement in cervical cancer patients. METHODS: Fifty cervical cancer patients with pre-treatment magnetic resonance imaging (MRI) were examined for PLN involvement by one subspecialist and one non-subspecialist radiologist. PLN status was confirmed by positron emission tomography or histology. The tumours were then segmented by both radiologists. Kruskal-Wallis tests were used to test for differences between diffusion tumour volume (DTV), apparent diffusion coefficient (ADC), pure diffusion coefficient (D), and perfusion fraction (f) in patients with no malignant PLN involvement, those with sub-centimetre and size-significant PLN metastases. These parameters were then considered as classifiers for PLN involvement, and were compared with the accuracies of radiologists. RESULTS: Twenty-one patients had PLN involvement of which 10 had sub-centimetre metastatic PLNs. DTV increased (p = 0.013) while ADC (p = 0.015), and f (p = 0.006) decreased as the nodal status progressed from no malignant involvement to sub-centimetre and then size-significant PLN metastases. In determining PLN involvement, a classification model (DTV + f) had similar accuracies (80%) as the non-subspecialist (76%; p = 0.73) and subspecialist (90%; p = 0.31). However, in identifying patients with sub-centimetre PLN metastasis, the model had higher accuracy (90%) than the non-subspecialist (30%; p = 0.01) but had similar accuracy with the subspecialist (90%, p = 1.00). Interobserver variability in tumour delineation did not significantly affect the performance of the classification model. CONCLUSION: IVIM is useful in determining PLN involvement but the added value decreases with reader experience.

AQP5 enriches for stem cells and cancer origins in the distal stomach.

LGR5 marks resident adult epithelial stem cells at the gland base in the mouse pyloric stomach1, but the identity of the equivalent human stem cell population remains unknown owing to a lack of surface markers that facilitate its prospective isolation and validation. In mouse models of intestinal cancer, LGR5+ intestinal stem cells are major sources of cancer following hyperactivation of the WNT pathway2. However, the contribution of pyloric LGR5+ stem cells to gastric cancer following dysregulation of the WNT pathway-a frequent event in gastric cancer in humans3-is unknown. Here we use comparative profiling of LGR5+ stem cell populations along the mouse gastrointestinal tract to identify, and then functionally validate, the membrane protein AQP5 as a marker that enriches for mouse and human adult pyloric stem cells. We show that stem cells within the AQP5+ compartment are a source of WNT-driven, invasive gastric cancer in vivo, using newly generated Aqp5-creERT2 mouse models. Additionally, tumour-resident AQP5+ cells can selectively initiate organoid growth in vitro, which indicates that this population contains potential cancer stem cells. In humans, AQP5 is frequently expressed in primary intestinal and diffuse subtypes of gastric cancer (and in metastases of these subtypes), and often displays altered cellular localization compared with healthy tissue. These newly identified markers and mouse models will be an invaluable resource for deciphering the early formation of gastric cancer, and for isolating and characterizing human-stomach stem cells as a prerequisite for harnessing the regenerative-medicine potential of these cells in the clinic.

Evaluation of the PIK3 pathway in peripheral T-cell lymphoma and NK/T-cell lymphoma.

Peripheral T-cell lymphomas (PTCL) and natural killer (NK)/T-cell lymphomas (NKTCL) are a heterogeneous group of aggressive malignancies with dismal outcomes and limited treatment options. While the phosphatidylinositol 3-kinase (PIK3) pathway has been shown to be highly activated in many B-cell lymphomas, its therapeutic relevance in PTCL and NKTCL remains unclear. The aim of this study is to investigate the expression of PIK3 and phosphatase and tensin homolog (PTEN) in these subtypes of lymphoma and to identify potential therapeutic targets for clinical testing. Therefore, the expression of PIK3α, PIK3ß, PIK3γ, PIK3δ and PTEN was analyzed in 88 cases of PTCL and NKTCL samples by immunohistochemistry. All PTCL and NKTCL samples demonstrated high expression of PIK3 isoforms. In particular, high PIK3α expression was significantly associated with poor survival, even after adjustment for age, International Prognostic Index (IPI) score and anthracycline-based chemotherapy in first line. Notably, copanlisib, a pan-class I inhibitor with predominant activities towards PIK3α and PIK3δ isoforms, effectively inhibited phosphorylation of AKT, 4E-BP-1 and STAT3, causing G0 /G1 cell cycle arrest and resulting in suppression of tumour cell growth in vitro and in vivo. This study provides evidence that targeting the PIK3 pathway, particularly simultaneous inhibition of PIK3α and δ, could be a promising approach for the treatment of PTCL and NKTCL.

Neonatal Wnt-dependent Lgr5 positive stem cells are essential for uterine gland development.

Wnt signaling is critical for directing epithelial gland development within the uterine lining to ensure successful gestation in adults. Wnt-dependent, Lgr5-expressing stem/progenitor cells are essential for the development of glandular epithelia in the intestine and stomach, but their existence in the developing reproductive tract has not been investigated. Here, we employ Lgr5-2A-EGFP/CreERT2/DTR mouse models to identify Lgr5-expressing cells in the developing uterus and to evaluate their stem cell identity and function. Lgr5 is broadly expressed in the uterine epithelium during embryogenesis, but becomes largely restricted to the tips of developing glands after birth. In-vivo lineage tracing/ablation/organoid culture assays identify these gland-resident Lgr5high cells as Wnt-dependent stem cells responsible for uterine gland development. Adjacent Lgr5neg epithelial cells within the neonatal glands function as essential niche components to support the function of Lgr5high stem cells ex-vivo. These findings constitute a major advance in our understanding of uterine development and lay the foundations for investigating potential contributions of Lgr5+ stem/progenitor cells to uterine disorders.

Overexpression of iASPP is required for autophagy in response to oxidative stress in choriocarcinoma.

BACKGROUND: Gestational trophoblastic disease (GTD) is a heterogeneous group of diseases developed from trophoblasts. ASPP (Ankyrin-repeat, SH3-domain and proline-rich region containing protein) family proteins, ASPP1 and ASPP2, have been reported to be dysregulated in GTD. They modulate p53 activities and are responsible for multiple cellular processes. Nevertheless, the functional role of the ASPP family inhibitory member, iASPP, is not well characterized in GTD. METHODS: To study the functional role of iASPP in GTD, trophoblastic tissues from normal placentas, hydatidiform mole (HM) and choriocarcinoma were used for immunohistochemistry, whereas siRNAs were used to manipulate iASPP expression in choriocarcinoma cell lines and study the subsequent molecular changes. RESULTS: We demonstrated that iASPP was overexpressed in both HM and choriocarcinoma when compared to normal placenta. Progressive increase in iASPP expression from HM to choriocarcinoma suggests that iASPP may be related to the development of trophoblastic malignancy. High iASPP expression in HM was also significantly associated with a high expression of autophagy-related protein LC3. Interestingly, iASPP silencing retarded the growth of choriocarcinoma through senescence instead of induction of apoptosis. LC3 expression decreased once iASPP was knocked down, suggesting a downregulation on autophagy. This may be due to iASPP downregulation rendered decrease in Atg5 expression and concomitantly hindered autophagy in choriocarcinoma cells. Autophagy inhibition per se had no effect on the growth of choriocarcinoma cells but increased the susceptibility of choriocarcinoma cells to oxidative stress, implying a protective role of iASPP against oxidative stress through autophagy in choriocarcinoma. CONCLUSIONS: iASPP regulates growth and the cellular responses towards oxidative stress in choriocarcinoma cells. Its overexpression is advantageous to the pathogenesis of GTD. (266 words).

Oral Supplementation of Tocotrienol-Rich Fraction Alleviates Severity of Ulcerative Colitis in Mice.

Ulcerative colitis (UC) is characterized by damaged colonic mucosa and submucosa layers that are caused by excessive inflammatory reactions and oxidative stress. This study aimed to examine the use of tocotrienol-rich fraction (TRF) in mitigating damages caused by UC on the colon epithelium. Dextran sulfate sodium (DSS)-induced UC mice were treated with vehicle control, TRF, alpha-tocopherol (αTP) and 5-aminosalicylic acid (5-ASA). Observable clinical signs, quality of stool, histopathological scoring, inflammatory and oxidative markers were assessed. Vitamin E levels of colons and plasma were quantified. Oral supplementation of TRF significantly reduced the severity of DSS-induced UC by lowering the disease activity index (DAI) and histopathological inflammatory scoring. TRF also attenuated the DSS-induced enlargement of spleen and shortening of the colon. TRF has demonstrated marked anti-inflammatory and antioxidative properties indicated by the attenuation of DSS-induced upregulation of inflammation and oxidative stress markers including interleukin (IL)-6, IL-17, tumor necrosis factor (TNF)-α, myeloperoxidase (MPO), cyclooxygenase-2 (COX-2), nitric oxide (NO), malondialdehyde (MDA) and pNF-κB. These improvements were similar to that of 5-aminosalicylic acid (5-ASA) treatment. In contrast, αTP did not demonstrate evident clinical and histopathological improvements. The superior protective effect of TRF may be ascribed to the preferential absorption of TRF by the gut mucosa. TRF alleviated the signs and symptoms of acute UC in murine model via the reduction of local inflammatory reactions and oxidative stress. These effects suggested that TRF could serve as a gut health supplement for preventive measures for UC condition in patients.