A Spatially Stable Crystal-Particle Gel to Trap Patchouli Oil for Efficient Colonic Drug Delivery.

Patchouli oil has exhibited remarkable efficacy in the treatment of colitis. However, its volatility and potential irritancy are often drawbacks when extensively used in clinical applications. Oil gel is a semisolid and thermoreversible system that has received extensive interest for its solubility enhancement, inhibition of bioactive component recrystallization, and the facilitation of controlled bioactive release. Therefore, we present a strategy to develop an oil gel formulation that addresses this multifaceted problem. Notably, a patchouli oil gel formulation was designed to solidify and trap patchouli oil into a spatially stable crystal-particle structure and colonic released delivery, which has an advantage of the stable structure and viscosity. The patchouli oil gel treatment of zebrafish with colitis improved goblet cells and decreased macrophages. Additionally, patchouli oil gel showed superior advantages for restoring the tissue barrier. Furthermore, our investigative efforts unveiled patchouli oil's influence on TRP channels, providing evidence for its potential role in mechanisms of anti-inflammatory action. While the journey continues, these preliminary revelations provide a robust foundation for considering the adoption of patchouli oil gel as a pragmatic intervention for managing colitis.

A CT-based integrated model for preoperative prediction of occult lymph node metastasis in early tongue cancer.

Background: Occult lymph node metastasis (OLNM) is an essential prognostic factor for early-stage tongue cancer (cT1-2N0M0) and a determinant of treatment decisions. Therefore, accurate prediction of OLNM can significantly impact the clinical management and outcomes of patients with tongue cancer. The aim of this study was to develop and validate a multiomics-based model to predict OLNM in patients with early-stage tongue cancer. Methods: The data of 125 patients diagnosed with early-stage tongue cancer (cT1-2N0M0) who underwent primary surgical treatment and elective neck dissection were retrospectively analyzed. A total of 100 patients were randomly assigned to the training set and 25 to the test set. The preoperative contrast-enhanced computed tomography (CT) and clinical data on these patients were collected. Radiomics features were extracted from the primary tumor as the region of interest (ROI) on CT images, and correlation analysis and the least absolute shrinkage and selection operator (LASSO) method were used to identify the most relevant features. A support vector machine (SVM) classifier was constructed and compared with other machine learning algorithms. With the same method, a clinical model was built and the peri-tumoral and intra-tumoral images were selected as the input for the deep learning model. The stacking ensemble technique was used to combine the multiple models. The predictive performance of the integrated model was evaluated for accuracy, sensitivity, specificity, and the area under the receiver operating characteristic curve (AUC-ROC), and compared with expert assessment. Internal validation was performed using a stratified five-fold cross-validation approach. Results: Of the 125 patients, 41 (32.8%) showed OLNM on postoperative pathological examination. The integrated model achieved higher predictive performance compared with the individual models, with an accuracy of 84%, a sensitivity of 100%, a specificity of 76.5%, and an AUC-ROC of 0.949 (95% CI [0.870-1.000]). In addition, the performance of the integrated model surpassed that of younger doctors and was comparable to the evaluation of experienced doctors. Conclusions: The multiomics-based model can accurately predict OLNM in patients with early-stage tongue cancer, and may serve as a valuable decision-making tool to determine the appropriate treatment and avoid unnecessary neck surgery in patients without OLNM.

Artesunate-loaded solid lipid nanoparticles resist esophageal squamous cell carcinoma by inducing Ferroptosis through inhibiting the AKT/mTOR signaling.

Esophageal squamous cell carcinoma (ESCC) is a severe malignancy with high incidence and mortality rate in China, while the application of standard chemotherapeutic drugs for ESCC meets the barriers of high toxicity and multiple drug resistance (MDR). In recent years, the anticancer effects of artesunate (ART), a Chinese medicine monomer have gained extensive attentions due to its characteristics of low toxicity, high potency, and reversal of MDR. In this study, we develop the artesunate-loaded solid lipid nanoparticles (SLNART) to overcome the poor water solubility and bioavailability of ART, further improving the efficiency of ART on ESCC treatment. Especially mentioned, SLNART is shown to present marked inhibitory effects on ESCC development based on the induction of ferroptosis by two pathways included upregulating TFR to increase Fe2+ ions and inhibiting the AKT/mTOR signaling to downregulate GPX4. Collectively, this study is the first to pave a promising approach for ESCC therapy based on a strategy of developing SLNART to induce ferroptosis by mediating Fe2+ ions and AKT/mTOR signaling.

Could IL-25 be a potential therapeutic target for intestinal inflammatory diseases?

As a member of the IL-17 cytokine family, IL-25 (also called IL-17E) induces and sustains type 2 immunity. IL-25, which is mainly produced by intestinal epithelial cells, has been gradually investigated in recent years for its function in intestinal inflammation but is not yet fully understood. This review summarizes the expression and function of IL-25 in the intestine, especially the progression of its regulatory role on type 2 immunity-related cells. Finally, we discuss the dual role of IL-25 based on inflammatory bowel disease to inform research on targeting IL-25 for the treatment of intestinal inflammatory diseases.

Identification of diagnostic and prognostic lncRNA biomarkers in oral squamous carcinoma by integrated analysis and machine learning.

BACKGROUND: Patients with oral squamous carcinoma (OSCC) present difficulty in precise diagnosis and poor prognosis. OBJECTIVE: We aimed to identify the diagnostic and prognostic indicators in OSCC and provide basis for molecular mechanism investigation of OSCC. METHODS: We collected sequencing data and clinical data from TCGA database and screened the differentially expressed mRNAs (DEmRNAs) and lncRNAs (DElncRNAs) in OSCC. Machine learning and modeling were performed to identify the optimal diagnostic markers. In order to determine lncRNAs with prognostic value, survival analysis was performed through combing the expression profiles with the clinical data. Finally, co-expressed DEmRNAs of lncRNAs were identified by interacted network construction and functional annotated by GO and KEGG analysis. RESULTS: A total of 1114 (345 up- and 769 down-regulated) DEmRNAs and 156 (86 up- and 70 down-regulated) DElncRNAs were obtained in OSCC. Following the machine learning and modeling, 15 lncRNAs were identified to be the optimal diagnostic indicators of OSCC. Among them, FOXD2.AS1 was significantly associated with survival rate of patients with OSCC. In addition, Focal adhesion and ECM-receptor interaction pathways were found to be involved in OSCC. CONCLUSIONS: FOXD2.AS1 might be a prognostic marker for OSCC and our study may provide more information to the further study in OSCC.

Metabolic modulation via mTOR pathway and anti-angiogenesis remodels tumor microenvironment using PD-L1-targeting codelivery.

Tumor microenvironment (TME) closely affects cancer progression by promoting cancer cell survival and proliferation, drug resistance, metastasis, and immunosuppression as well. Remodeling TME is a promising therapeutic strategy for anticancer. mTOR signaling is an essential regulator for cellular metabolism and tumor-associated macrophages (TAMs) repolarization. There is an integrated crosstalk among mTOR/metabolism/immunity. Angiogenesis can also regulate metabolism and immunity. Based on these, a potential therapeutic avenue was developed by targeting mTOR and angiogenesis to remodel tumor immune microenvironment (TIME). A dual-targeting delivery liposomal system was designed with dual-modification of PD-L1 nanobody and mannose ligands for co-delivering an mTOR inhibitor (rapamycin) and an anti-angiogenic drug (regorafenib). The liposomes were able to target both TAMs and cancer cells that overexpressed PD-L1 and mannose receptors. The liposomes efficiently reduced glycolysis, repolarized TAMs, inhibited angiogenesis, reprogrammed immune cells, and consequently arrested tumor growth.

Pharmacokinetic comparison of five xanthones in rat plasma after oral administration of crude and processed Garcinia hanburyi extracts.

Gamboge, a dried resin secreted by Garcinia hanburyi Hook. f. (Guttiferae), possesses remarkable anticancer activity. However, due to toxicity, it must be processed before use in clinics. Xanthones are the main bioactive ingredients in gamboge. In order to elucidate the influence of processing technology on pharmacological properties of gamboge, an efficient, sensitive, and selective ultra-performance liquid chromatography coupled with triple quadruple mass spectrometry (UPLC-MS/MS) method of five critical xanthones, including ß-morellic acid (ß-MA), isogambogenic acid (IGNA), gambogenic acid (GNA), R-gambogic acid (GA), and S-GA in rat plasma was established for a comparative pharmacokinetics study of these xanthones after oral administration of crude and processed G. hanburyi extracts. The chromatographic separation of these five xanthones along with an internal standard (I.S.) was carried out on a Waters Acquity UPLC BEH C8 column with a gradient elution method using acetonitrile/0.1% formic acid-water as mobile phases. The eluate was detected by multiple-reaction monitoring (MRM) scanning with an electrospray ionization source operating in the positive ionization mode. Sample preparation involved a liquid-liquid extraction of the five analytes with ethyl acetate. Deoxyschizandrin was employed as an internal standard. This assay method was validated for selectivity, linearity, intra-day and inter-day precision, accuracy, recovery, matrix effect, and stability. The results revealed that the calibration curves displayed good linear regression (r > 0.995), and the lower limit of quantification (LLOQ) was <5.52 ng/mL for each analyte. The intra-day and inter-day precision (RSD) of the five xanthones at low, medium, and high levels was <10.58%, and the bias of the accuracy ranged from -8.54 to 10.2%. All other parameters fulfilled the FDA criteria for bioanalytical validation. In addition, the assay was successfully applied to the determination and pharmacokinetic study of these five xanthones after oral administration of crude and processed gamboge. Furthermore, Cmax of GNA and AUC0-t of IGNA were increased significantly (P < 0.05) after processing, while AUC0-t of ß-MA, R-GA, and S-GA decreased remarkably (P < 0.05), which suggested that processing exerted different effects on the absorption of xanthones. The results might be valuable for the clinical reasonable application and understanding the processing mechanism of gamboge.

The MR radiomic signature can predict preoperative lymph node metastasis in patients with esophageal cancer.

PURPOSE: To assess the role of the MR radiomic signature in preoperative prediction of lymph node (LN) metastasis in patients with esophageal cancer (EC). PATIENTS AND METHODS: A total of 181 EC patients were enrolled in this study between April 2015 and September 2017. Their LN metastases were pathologically confirmed. The first half of this cohort (90 patients) was set as the training cohort, and the second half (91 patients) was set as the validation cohort. A total of 1578 radiomic features were extracted from MR images (T2-TSE-BLADE and contrast-enhanced StarVIBE). The lasso and elastic net regression model was exploited for dimension reduction and selection of the feature space. The multivariable logistic regression analysis was adopted to identify the radiomic signature of pathologically involved LNs. The discriminating performance was assessed with the area under receiver-operating characteristic curve (AUC). The Mann-Whitney U test was adopted for testing the potential correlation of the radiomic signature and the LN status in both training and validation cohorts. RESULTS: Nine radiomic features were selected to create the radiomic signature significantly associated with LN metastasis (p < 0.001). AUC of radiomic signature performance in the training cohort was 0.821 (95% CI: 0.7042-0.9376) and in the validation cohort was 0.762 (95% CI: 0.7127-0.812). This model showed good discrimination between metastatic and non-metastatic lymph nodes. CONCLUSION: The present study showed MRI radiomic features that could potentially predict metastatic LN involvement in the preoperative evaluation of EC patients. KEY POINTS: • The role of MRI in preoperative staging of esophageal cancer patients is increasing. • MRI radiomic features showed the ability to predict LN metastasis in EC patients. • ICCs showed excellent interreader agreement of the extracted MR features.

Building CT Radiomics Based Nomogram for Preoperative Esophageal Cancer Patients Lymph Node Metastasis Prediction.

PURPOSE: To build and validate a radiomics-based nomogram for the prediction of pre-operation lymph node (LN) metastasis in esophageal cancer. PATIENTS AND METHODS: A total of 197 esophageal cancer patients were enrolled in this study, and their LN metastases have been pathologically confirmed. The data were collected from January 2016 to May 2016; patients in the first three months were set in the training cohort, and patients in April 2016 were set in the validation cohort. About 788 radiomics features were extracted from computed tomography (CT) images of the patients. The elastic-net approach was exploited for dimension reduction and selection of the feature space. The multivariable logistic regression analysis was adopted to build the radiomics signature and another predictive nomogram model. The predictive nomogram model was composed of three factors with the radiomics signature, where CT reported the LN number and position risk level. The performance and usefulness of the built model were assessed by the calibration and decision curve analysis. RESULTS: Thirteen radiomics features were selected to build the radiomics signature. The radiomics signature was significantly associated with the LN metastasis (P<0.001). The area under the curve (AUC) of the radiomics signature performance in the training cohort was 0.806 (95% CI: 0.732-0.881), and in the validation cohort it was 0.771 (95% CI: 0.632-0.910). The model showed good discrimination, with a Harrell's Concordance Index of 0.768 (0.672 to 0.864, 95% CI) in the training cohort and 0.754 (0.603 to 0.895, 95% CI) in the validation cohort. Decision curve analysis showed our model will receive benefit when the threshold probability was larger than 0.15. CONCLUSION: The present study proposed a radiomics-based nomogram involving the radiomics signature, so the CT reported the status of the suspected LN and the dummy variable of the tumor position. It can be potentially applied in the individual preoperative prediction of the LN metastasis status in esophageal cancer patients.

3.0T MRI for long-term observation of lung nodules post cryoablation: a pilot study.

BACKGROUND: The purpose of this study was to use serial magnetic resonance imaging (MRI) examinations to observe changes in malignant lung tumors over time post-cryoablation. METHODS: The study protocol was approved by Institutional Review Board, and written informed consent was obtained from each participant in accordance with the Declaration of Helsinki. Patients with primary or metastatic lung tumors eligible for cryoablation were included in this prospective study. Cryoablation was performed according to standard procedures. Unenhanced and dynamic contrast-enhanced MRI scans were performed pre-cryoablation and at 1 day, 1 week, and 3-, 6-, and 12 months after cryoablation. At each time point, the signal intensity of the ablated zone on both T1WI and T2WI images, and volume and characteristics of the ablation zone were examined, and changes over time analyzed. RESULTS: A total of 26 nodules in 23 patients were included in the study. The mean patient age was 53.7 ± 13.6 years, and 57.7% were males. Ablation zone volume increased to 1 week after the procedure, and then returned to baseline by 3 months. Cavitation post-cryoablation was found in 34.6% (9/26) of the nodules 1 month after treatment. Two types of time-signal intensity curves post-cryoablation were found: a straight line representing no definite enhancement from 1-day to 1-month, and an inflow curve representing mild delayed enhancement from month 3 to month 12. Local progression was associated with an incomplete hypointense rim around the ablation zone and absence of cavitation post-treatment. CONCLUSIONS: Characteristic changes are present on MRI after cryoablation of lung tumors. A complete hypointense rim and cavitation may be signs of adequate treatment and that local tumor progression is less likely.

MicroRNA-377 Downregulates Bcl-xL and Increases Apoptosis in Hepatocellular Carcinoma Cells.

Aberrantly expressed microRNAs (miRNAs/miRs) and their role in cancer development have recently gained more attention. However, the potential role of miRNAs in hepatocellular carcinoma (HCC) remains largely unknown. In this study, we demonstrated that miR-377 was markedly downregulated in HCC cell lines and primary human HCC tissues. The decreased expression of miR-377 contributes to the upregulation of Bcl-xL expression by targeting its 3'-untranslated region (3'-UTR). Functionally, knockdown of miR-377 noticeably increased HCC cell growth and colony formation and inhibited apoptosis. In contrast, overexpression of miR-377 suppressed cell proliferation and increased apoptosis. This study provides new insights for the use of miR-377 as a potential molecular target in HCC therapy.