In Vitro Inhibitory Effects of Maqian Essential Oil against Ectopic Endometrial Stromal Cells and LPS-Induced Endometrial Epithelial Cells.

Previous studies revealed that MQEO (Maqian fruits essential oil), which is extracted from the fruit of Maqian (Zanthoxylum myriacanthum var. Pubescens), had a good anti-inflammatory effect, but the effect on endometriosis in vitro remains unknown. In the present study, the inhibitory effects of MQEO against the EESCs (ectopic endometrial stromal cells) were investigated. Cells were treated with a concentration gradient (from 0.025 % to 0.15 %) of MQEO for 24 h and cell viability was detected by CCK-8. In addition, apoptotic rates were investigated using flow cytometry. The effect of MQEO on cell migration was determined by wound-healing and transwell assay. The expression of apoptosis-associated and cell adhesion-related proteins was assessed by western blotting. The transcriptional levels of IL-1, IL-6 and TNF-α were determined by Real-time qPCR. RNA-seq was used to identify the DEGs (differentially expressed genes) in MQEO-pretreated EESCs. We found that the MQEO condition dosage-dependently reduced the cell viability of EESCs. Based on flow cytometry results, the number of apoptotic cells increased significantly with dosage. The wound-healing and transwell results showed that MQEO group exhibited a significantly decreased cell motility and migration ability in comparison with the normal group. Western blotting results showed that MQEO down-regulated the expression of Bcl-2, ICAM-1 (intercellular adhesion molecule 1) and CD44, but up-regulated the cleaved caspase-3 expression in EESCs. What's more, MQEO also inhibited the LPS-induced inflammation in human EECs (endometrial epithelial cells). RNA-seq revealed that 221 DEGs were up-regulated genes and 284 DEGs were down-regulated in MQEO-pretreated EESCs. Our data uncovered the beneficial effects of MQEO in endometriosis and provided new insights into the mechanism of the effect of MQEO on EESCs, suggesting MQEO could be a promising new therapeutic agent for endometriosis.

CT Radiomics for the Prediction of Synchronous Distant Metastasis in Clear Cell Renal Cell Carcinoma.

PURPOSE: The aim of this study was to construct and verify a computed tomography (CT) radiomics model for preoperative prediction of synchronous distant metastasis (SDM) in clear cell renal cell carcinoma (ccRCC) patients. METHODS: Overall, 172 patients with ccRCC were enrolled in the present research. Contrast-enhanced CT images were manually sketched, and 2994 quantitative radiomic features were extracted. The radiomic features were then normalized and subjected to hypothesis testing. Least absolute shrinkage and selection operator (LASSO) was applied to dimension reduction, feature selection, and model construction. The performance of the predictive model was validated through analysis of the receiver operating characteristic curve. Multivariate and subgroup analyses were performed to verify the radiomic score as an independent predictor of SDM. RESULTS: The patients randomized into a training (n = 104) and a validation (n = 68) cohort in a 6:4 ratio. Through dimension reduction using LASSO regression, 9 radiomic features were used for the construction of the SDM prediction model. The model yielded moderate performance in both the training (area under the curve, 0.89; 95% confidence interval, 0.81-0.97) and the validation cohort (area under the curve, 0.83; 95% confidence interval, 0.69-0.95). Multivariate analysis showed that the CT radiomic signature was an independent risk factor for clinical parameters of ccRCC. Subgroup analysis revealed a significant connection between the SDM and radiomic signature, except for the lower pole of the kidney subgroup. CONCLUSIONS: The CT-based radiomics model could be used as a noninvasive, personalized approach for SDM prediction in patients with ccRCC.

Radiomics Analysis Based on Ultrasound Images to Distinguish the Tumor Stage and Pathological Grade of Bladder Cancer.

OBJECTIVES: To identify the clinical value of ultrasound radiomic features in the preoperative prediction of tumor stage and pathological grade of bladder cancer (BLCA) patients. METHODS: We retrospectively collected patients who had been diagnosed with BLCA by pathology. Ultrasound-based radiomic features were extracted from manually segmented regions of interest. Participants were randomly assigned to a training cohort and a validation cohort at a ratio of 7:3. Radiomic features were Z-score normalized and submitted to dimensional reduction analysis (including Spearman's correlation coefficient analysis, the random forest algorithm, and statistical testing) for core feature selection. Classifiers for tumor stage and pathological grade prediction were then constructed. Prediction performance was estimated by the area under the curve (AUC) of the receiver operating characteristic curve and was verified by the validation cohort. RESULTS: A total of 5936 radiomic features were extracted from each of the ultrasound images obtained from 157 patients. The BLCA tumor stage and pathological grade prediction models were developed based on 30 and 35 features, respectively. Both models showed good predictive ability. For the tumor stage prediction model, the AUC was 0.94 in the training cohort and 0.84 in the validation cohort. For the pathological grade model, the AUCs obtained were 0.84 in the training cohort and 0.75 in the validation cohort. CONCLUSIONS: The ultrasound-based radiomics models performed well in the preoperative tumor staging and pathological grading of BLCA. These findings should be applied clinically to optimize treatment and to assess prognoses for BLCA.

Ultrasound Image-Based Radiomics: An Innovative Method to Identify Primary Tumorous Sources of Liver Metastases.

OBJECTIVES: To develop radiomic models of B-mode ultrasound (US) signatures for determining the origin of primary tumors in metastatic liver disease. METHODS: A total of 254 patients with a diagnosis of metastatic liver disease were included in this retrospective study. The patients were divided into 3 groups depending on the origin of the primary tumor: group 1 (digestive tract versus non-digestive tract tumors), group 2 (breast cancer versus non-breast cancer), and group 3 (lung cancer versus other malignancies). The patients in each group were allocated to a training or testing set (a ratio of 8:2). The region of interest of liver metastasis was determined through manual differentiation of the tumors, and radiomic signatures were acquired from B-mode US images. Optimal features were selected to develop 3 radiomic models using multiple-dimensionality reduction and classifier screening. The area under the curve (AUC) of the receiver operating characteristic curve was applied to assess each model's performance. RESULTS: A total of 5936 features were extracted, and 40, 6, and 14 optimal features were sequentially identified for the development of radiomic models for groups 1, 2, and 3, respectively, with training set AUC values of 0.938, 0.974, and 0.768 and testing set AUC values of 0.767, 0.768, and 0.750. The differences in age, sex, and number of liver metastatic lesions varied greatly between the 4 primary tumors (P < .050). CONCLUSIONS: B-mode US radiomic models could be effective supplemental means to identify the origin of hepatic metastatic lesions (ie, unknown primary sites).

Magnetic resonance imaging (MRI) radiomics of papillary thyroid cancer (PTC): a comparison of predictive performance of multiple classifiers modeling to identify cervical lymph node metastases before surgery.

PURPOSE: To compare predictive efficiency of multiple classifiers modeling and establish a combined magnetic resonance imaging (MRI) radiomics model for identifying lymph node (LN) metastases of papillary thyroid cancer (PTC) preoperatively. MATERIALS AND METHODS: A retrospective analysis based on the preoperative MRI scans of 109 PTC patients including 77 patients with LN metastases and 32 patients without metastases was conducted, and we divided enroll cases into trained group and validation group. Radiomics signatures were selected from fat-suppressed T2-weighted MRI images, and the optimal characteristics were confirmed by spearman correlation test, hypothesis testing and random forest methods, and then, eight predictive models were constructed by eight classifiers. The receiver operating characteristic (ROC) curves analysis were performed to demonstrate the effectiveness of the models. RESULTS: The area under the curve (AUC) of ROC based on MRI texture diagnosed LN status by naked eye was 0.739 (sensitivity = 0.571, specificity = 0.906). Based on the 5 optimal signatures, the best AUC of MRI radiomics model by logistics regression classifier had a considerable prediction performance with AUCs 0.805 in trained group and 0.760 in validation group, respectively, and a combination of best radiomics model with visual diagnosis of MRI texture had a high AUC as 0.969 (sensitivity = 0.938, specificity = 1.000), suggesting combined model had a preferable diagnostic efficiency in evaluating LN metastases of PTC. CONCLUSION: Our combined radiomics model with visual diagnosis could be a potentially effective strategy to preoperatively predict LN metastases in PTC patients before clinical intervention.

[Application of MRI-based Radiomics Models in the Assessment of Hepatic Metastasis of Rectal Cancer].

OBEJECTIVE: To explore the clinical value of using radiomics models based on different MRI sequences in the assessment of hepatic metastasis of rectal cancer. METHODS: 140 patients with pathologically confirm edrectal cancer were included in the study. They underwent baseline magnetic resonance imaging (MRI) between April 2015 and May 2018 before receiving any treatment. According to the results of liver biopsy, surgical pathology, and imaging, patients were put into two groups, the patients with hepatic metastasis and those without. T2 weighted images (T2WI), diffusion weighted images (DWI) and apparent diffusion coefficient (ADC) images were used to draw the region of interest (ROI) of primary lesions on consecutive slices on ITK-SNAP. 3-D ROIs were generated and loaded into Artificial Intelligent Kit for extraction of radiomics features and 396 features were extracted for each sequence. The feature data were preprocessed on Python and the samples were oversampled, using Support Vector Machine-Synthetic Minority Over-Sampling Technique (SVM-SMOTE) to balance the number of samples in the group with liver metastasis and the group with no liver metastasis at the end of the follow-up. Then, the samples were divided into the training cohort and the test cohort at a ratio of 2∶1. The logistic regression models were developed with selected radionomic features on R software. The receiver operating characteristics (ROC) curves and calibration curves were used to evaluate the performance of the models. RESULTS: In total, 52 patients with liver metastasis and 88 patients without liver metastasis at the end of follow-up were enrolled. Carcinoembryonic antigen (CEA) and T stage and N stage evaluated on the MRI images showed statistically significant difference between the two groups ( P<0.05). After data preprocessing and selecting, except for 17 non-radiomic features, the model combining T2WI, DWI and ADC features, the model of T2WI features alone, the model of DWI features alone and the model of ADC features alone were developed with 32 features, 10 features, 30 features and 15 features, respectively. The combined model (T2WI+DWI+ADC), the T2WI model, and the ADC model can assess hepatic metastasis accurately, with the area under curve ( AUC) on the train set reaching 93.5%, 89.2%, 90.6% and that of the test set reaching 80.8%, 80.5%, 81.4%, respectively. The combined model did not show a higher AUC than those of the T2WI and ADC alone models. Model based on DWI features has a slightly insufficient AUC of 90.3% in the train set and 75.1% in the test set. The calibration curve showed the smallest fluctuation in the combined model, which is closest fit to the diagonal reference line. The fluctuation in the three independent data set models were similar. The calibration curves of all the four models showed that as the risk increased, the prediction of the models turned from an underestimation to an overestimating the risk. In brief, the combined model showed the best performance, with the best fit to the diagonal reference line in calibration curve and high AUC comparable to the AUC of the T2WI model and ADC model. The performance of T2WI and ADC alone models were second to that of the combined model, while the DWI alone model showed relatively poor performance. CONCLUSION: Radiomics models based on MRI could be effectively used in assessing liver metastasis in rectal cancer, which may help determine clinical staging and treatment.

[Application of Automated Machine Learning Based on Radiomics Features of T2WI and RS-EPI DWI to Predict Preoperative T Staging of Rectal Cancer].

OBJECTIVE: To explore the radiomics features of T2 weighted image (T2WI) and readout-segmented echo-planar imaging (RS-EPI) plus difusion-weighted imaging (DWI), to develop an automated mahchine-learning model based on the said radiomics features, and to test the value of this model in predicting preoperative T staging of rectal cancer. METHODS: The study retrospectively reviewed 131 patients who were diagnosed with rectal cancer confirmed by the pathology results of their surgical specimens at West China Hospital of Sichuan University between October, 2017 and December, 2018. In addition, these patients had preoperative rectal MRI. Tumor regions from preoperative MRI were manually segmented by radiologists with the ITK-SNAP software from T2WI and RS-EPI DWI images. PyRadiomics was used to extract 200 features-100 from T2WI and 100 from the apparent diffusion coefficient (ADC) calculated from the RS-EPI DWI. MWMOTE and NEATER were used to resample and balance the dataset, and 13 cases of T 1-2 stage simulation cases were added. The overall dataset was divided into a training set (111 cases) and a test set (37 cases) by a ratio of 3∶1. Tree-based Pipeline Optimization Tool (TPOT) was applied on the training set to optimize model parameters and to select the most important radiomics features for modeling. Five independent T stage models were developed accordingly. Accuracy and the area under the curve ( AUC) of receiver operating characteristic (ROC) were used to pick out the optimal model, which was then applied on the training set and the original dataset to predict the T stage of rectal cancer. RESULTS: The performance of the the five T staging models recommended by automated machine learning were as follows: The accuracy for the training set ranged from 0.802 to 0.838, sensitivity, from 0.762 to 0.825, specificity, from 0.833 to 0.896, AUC, from 0.841 to 0.893, and average precision (AP) from 0.870 to 0.901. After comparison, an optimal model was picked out, with sensitivity, specificity and AUC for the training set reaching 0.810, 0.875, and 0.893, respectively. The sensitivity, specificity and AUC for the test set were 0.810, 0.813, and 0.810, respectively. The sensitivity, specificity and AUC for the original dataset were 0.810, 0.830, and 0.860, respectively. CONCLUSION: Based on the radiomics data of T2WI and RS-EPI DWI, the model established by automated machine learning showed a fairly high accuracy in predicting rectal cancer T stage.

Next-generation sequencing: a follow-up of 36,913 singleton pregnancies with noninvasive prenatal testing in central China.

PURPOSE: To evaluate the noninvasive prenatal testing (NIPT) results of 36,913 cases in Jiangxi province of central China and explore its application value in prenatal screening and diagnosis. METHODS: This retrospective analysis included 36,913 singleton pregnant women who underwent NIPT because of moderate-/high-risk pregnancy or voluntary requirements between January 2017 and December 2019 in our hospital. Chromosomal abnormalities such as trisomies 21, 18, and 13 (T21, T18, T13) and sex chromosome aneuploidies (SCAs) were judged by standard Z-score analysis. Positive NIPT results were confirmed by amniocentesis and karyotyping. Pregnancy outcomes were followed up via telephone interview. RESULTS: A total of 1.01% (371/36,913) positive cases were detected by NIPT, comprising 137, 46, 31, and 157 cases of T21, T18, T13, and SCAs, respectively. A total of 116 of T21, 27 of T18, 13 of T13, and 51 of SCAs were confirmed to be true positive; all normal cases that had been followed up were verified to be true negative. The NIPT sensitivity in T21, T18, T13, and SCAs was 100.00% individually, whereas the specificity was 99.94% (36,488/36,509), 99.95% (36,579/36,598), 99.95% (36,594/36,612), and 99.72% (36,472/36,574), respectively. Furthermore, the negative predictive values of T21, T18, T13, and SCAs were all 100%, while the positive predictive values were 84.67%, 58.70%, 41.94%, and 33.33%, respectively. CONCLUSION: NIPT is highly sensitive and has a low false positive rate in testing clinically significant fetal aneuploidies of general reproductive women. However, this technique cannot substitute for amniocentesis and karyotyping, and detailed genetic counseling is also essential for the high-risk group of NIPT.

Tetra-benzothiadiazole-based [12]Cycloparaphenylene with Bright Emission and Its Supramolecular Assembly.

The radial conjugated π-system of cycloparaphenylenes (CPPs) makes them intriguing fluorophores and unique supramolecular hosts. However, the bright photoluminescence (PL) of CPPs was limited to the blue light and the supramolecular assembly behavior of large CPPs was rarely investigated. Here we present the synthesis of tetra-benzothiadiazole-based [12]cycloparaphenylene (TB[12]CPP), which exhibits a lime to orange PL with an excellent quantum yield up to 82 % in solution. The PL quantum yield of TB[12]CPP can be further improved to 98 % in polymer matrix. Benefiting from its enlarged size, TB[12]CPP can accommodate a fullerene derivative or concave-convex complexes of fullerene and buckybowl through the combined π-π and C-H⋅⋅⋅π interactions. The latter demonstrates the first case of a ternary supramolecule of CPPs.

Correlation between systemic osteoporosis and local bone erosion with rheumatoid arthritis patients in Chinese population.

OBJECTIVE: RA is a systemic auto-immune inflammatory disease that can lead to local bone erosions and generalized osteoporosis (OP). The aim of this study was to investigate the relationship between systemic osteoporosis and local bone erosion with RA patients in the Chinese population. METHODS: In total, 1235 patients with RA and 158 normal subjects were enrolled in this study. Clinical and laboratory features were recorded in detail. Information about functional class and physical activity was collected using specific questionnaires. Dual-energy X-ray absorptiometry was used to measure BMD. The MECALL castor-50-hf model X-ray scanner was used for two-hand (including wrist) photographs. RESULTS: The median Sharp scores differed significantly between the normal bone mass group, osteopenia group and OP group (P < 0.001). There was a modest negative linear correlation between Sharp and HAQ scores and longer disease duration (P < 0.001). There was a clear increasing trend in Sharp score, incidence of OP and HAQ score in the different DAS in 28 joints (DAS28) activity groups (P < 0.001). Spearman's correlation test showed that Sharp and HAQ scores were negatively correlated with BMD at all measured sites (femoral neck, total hip and L1-4) (P < 0.001). Logistic regression indicated that age, female gender, and Sharp and HAQ scores were independent risk factors in the occurrence of OP in RA patients. The use of DMARDs and BMI were protective factors for OP. CONCLUSION: These results suggest that BMD is associated with local bone erosion among Chinese patients with RA. Local bone erosion is closely related to clinical symptoms and BMD in patients with RA.

Osteoporosis Self-Assessment Tool As a Screening Tool for Predicting Osteoporosis in Elderly Chinese Patients With Established Rheumatoid Arthritis.

Osteoporosis Self-Assessment Tool for Asians (OSTA) is an indicator for assessing osteoporosis in postmenopausal women. The aim of this study was to investigate the value of OSTA index on predicting osteoporosis in elderly Chinese patients with established rheumatoid arthritis (RA). A total of 320 patients with RA and 158 normal individuals were recruited from January 2015 to October 2017. Bone mineral density (BMD) at the femur and lumbar spine was measured by dual-energy X-ray absorptiometry. RA group and control group were divided into low risk (values≥-1), medium risk (values between -4 and -1), and high risk (values ≤-4) group according to the value of OSTA index. One-way analysis of variance showed that BMD at all detected regions among the 3 groups were obviously different (p < 0.0001). Incidences of osteoporosis among different OSTA groups were 21.76% (47/216), 56.41% (44/78), and 80.77% (21/26), separately (x2 = 67.389, p < 0.0001). In RA group including premenspausal or postmenspausal female subgroup, prevalences of osteoporosis among different OSTA groups were different (p < 0.05-0.0001). We also found a positive linear correlation between OSTA index and BMD (p < 0.0001) both in RA and in control groups. Logistic regression revealed OSTA index (odds ratio = 0.734, p < 0.0001, 95% confidence interval: 0.657-0.819) was a protective factor for occurrence of RA-induced osteoporosis. OSTA had the highest discriminatory power, with an estimated Area Under Curve (AUC) of 0.750 (95% confidence interval 0.694-0.807, p < 0.0001), sensitivity of 76.9% and specificity of 66.5%. Our findings indicated that OSTA index was closely associated with BMD in RA patients, the degree of correlation was much stronger than age or BMI. OSTA index was a predictor for osteoporosis in RA, but it might have little relationship with disease status in RA.

Effect of anti-tumor necrosis factor α treatment on radiographic progression in patient with ankylosing spondylitis: A systematic review and meta-analysis.

BACKGROUND: Efficacy of anti-tumor necrosis factor (anti-TNF)α treatment in patient with active ankylosing spondylitis (AS) had been proved by many clinical studies. Inflammation and new bone formation in spine were two pivotal aspects in AS. TNF α inhibitor could eliminate inflammation including clinical and laboratory inflammatory manifestation. Paradoxical results whether TNF α antagonist could delay radiographic progression in AS were often been reported simultaneously. OBJECTIVES: To review the literature about the effect of TNF α inhibitor on radiographic progression and disease activity in patient with AS. METHODS: We conducted a comprehensive search including Medline, EMBASE and the Cochrane Library from 1 January 2000 to 15 August 2017. Two reviewers independently supplemented with hand searching for the reference lists of inclusion. All trials focusing on radiographic progression or disease activity in patients with AS treated with anti-TNF α agents. Primary outcomes were modified Stokes AS Spinal Score (mSASSS), as well as Bath AS disease activity index (BASDAI) and Bath AS functional index (BASFI). Two reviewers independently selected studies and analyzed data. Methodological quality was assessed using the Newcastle-Ottawa scale (NOS). We pooled effects recorded on different scales as Standardized mean differences (SMDs) with 95% confidence intervals (CIs) using random-effects models. RESULTS: We included 14 studies of low to moderate risk of bias with 3,186 patients, compared with control group, there was no effect of mSASSS changes (SMD = -0.12, 95% CI: -1.17-0.93, p value = .82, I2 = 95%) and follow-up (SMD = 0.03, 95% CI: 0.21-0.26, p value = .82, I2 = 36%) estimation in anti-TNF α group. However anti-TNF α agent treatment led to remarkable improvements on both Bath AS disease activity index (BASDAI) (SMD = 1.06, 95% CI: 0.22-1.89, p value = .01, I2 = 96%) and Bath AS functional index (BASFI) (SMD = 0.93, 95% CI: 0.24-1.92, p value = .01, I2 = 97%) scores at 12 weeks. CONCLUSION: Our meta-analysis found no significant effect on delaying radiographic progression in AS treated with TNF α inhibitor, although TNF α inhibitor could do improve significantly disease activity and physical function in AS.

Synthesis of 2-morpholinetetraphenylporphyrins and their photodynamic activities.

A series of 2-morpholinetetraphenylporphyrins functionalized with various substituents (Cl, Me, MeO group) at 4-phenyl position were prepared via nucleophilic substitution of 2-nitroporphyrin copper derivatives with morpholine by refluxing under a nitrogen atmosphere and then demetalization. Their basic photophysical properties, intracellular localization, cytotoxicities in vitro and in vivo were also investigated. All synthesized photosensitizers exhibited longer maxima absorption wavelengths than Hematoporphyrin monomethyl ether (HMME). They showed low dark cytotoxicity compared with that of HMME and were more phototoxic than HMME against Eca-109 cells in vitro. M3 also exhibited better photodynamic antitumor efficacy on BALB/c nude mice at a lower concentration. Therefore, M3 is a promising antitumor photosensitizer in photodynamic therapy application.

Synthesis, photophysical properties and biological evaluation of ß-alkylaminoporphyrin for photodynamic therapy.

A series of ß-alkylaminoporphyrins conjugated with different amines at ß position (D1-D3) or with electron-donating and electron-withdrawing substituents at phenyl position (D4-D6) were synthesized. Their photophysical and photochemical properties, intracellular localization, photocytotoxicities in vitro and vivo were also investigated. All target compounds exhibited no cytotoxicities in the dark and excellent photocytotoxicities against HeLa cells. Among them, D6 showed the highest phototoxicity and the lowest dark toxicity, which was more phototoxic than Hematoporphyrin monomethyl ether (HMME). In addition, D6 exhibited best photodynamic antitumor efficacy on BALB/c nude mice bearing HeLa tumor. Therefore, D6 is a powerful and promising antitumor photosensitizer for photodynamic therapy.

[Use of visible and near infrared reflectance spectroscopy to identify the cashmere and wool].

The wool and cashmere samples (n = 130) from different areas of Gansu province were identified by visible and near-infrared reflectance spectroscopy (Vis/NIRs). The result shows that principal component-mahalanobis distance pattern can identify the wool and cashmere, and the boundary between two categories was clear; The calibration set samples were used to establish calibration qualitative model using PCR combined with the best pretreatment of the spectra and math, including multivariate scattering correction (MSC), first derivative, eight for the best principal component factor, one for uncertainty factor, this calibration model of the predicted was the best, and the result of the external validation was correct completely. Results from this experiment indicate that Vis/NIRs can be utilized to identify the wool and cashmere.

Integrating Metabolomics and Network Pharmacology to Explore the Mechanism of Xiao-Yao-San in the Treatment of Inflammatory Response in CUMS Mice.

Depression can trigger an inflammatory response that affects the immune system, leading to the development of other diseases related to inflammation. Xiao-Yao-San (XYS) is a commonly used formula in clinical practice for treating depression. However, it remains unclear whether XYS has a modulating effect on the inflammatory response associated with depression. The objective of this study was to examine the role and mechanism of XYS in regulating the anti-inflammatory response in depression. A chronic unpredictable mild stress (CUMS) mouse model was established to evaluate the antidepressant inflammatory effects of XYS. Metabolomic assays and network pharmacology were utilized to analyze the pathways and targets associated with XYS in its antidepressant inflammatory effects. In addition, molecular docking, immunohistochemistry, Real-Time Quantitative Polymerase Chain Reaction (RT-qPCR), and Western Blot were performed to verify the expression of relevant core targets. The results showed that XYS significantly improved depressive behavior and attenuated the inflammatory response in CUMS mice. Metabolomic analysis revealed the reversible modulation of 21 differential metabolites by XYS in treating depression-related inflammation. Through the combination of liquid chromatography and network pharmacology, we identified seven active ingredients and seven key genes. Furthermore, integrating the predictions from network pharmacology and the findings from metabolomic analysis, Vascular Endothelial Growth Factor A (VEGFA) and Peroxisome Proliferator-Activated Receptor-γ (PPARG) were identified as the core targets. Molecular docking and related molecular experiments confirmed these results. The present study employed metabolomics and network pharmacology analyses to provide evidence that XYS has the ability to alleviate the inflammatory response in depression through the modulation of multiple metabolic pathways and targets.

Altered fear engram encoding underlying conditioned versus unconditioned stimulus-initiated memory updating.

It is known that post-retrieval extinction but not extinction alone could erase fear memory. However, whether the coding pattern of original fear engrams is remodeled or inhibited remains largely unclear. We found increased reactivation of engram cells in the prelimbic cortex and basolateral amygdala during memory updating. Moreover, conditioned stimulus- and unconditioned stimulus-initiated memory updating depends on the engram cell reactivation in the prelimbic cortex and basolateral amygdala, respectively. Last, we found that memory updating causes increased overlapping between fear and extinction cells, and the original fear engram encoding was altered during memory updating. Our data provide the first evidence to show the overlapping ensembles between fear and extinction cells and the functional reorganization of original engrams underlying conditioned stimulus- and unconditioned stimulus-initiated memory updating.

A CT-based radiomics nomogram for predicting early recurrence in patients with high-grade serous ovarian cancer.

PURPOSE: To develop and validate a radiomics nomogram for predicting early recurrence in high-grade serous ovarian cancer (HGSOC) patients. MATERIALS AND METHODS: From May 2008 to December 2019, 256 eligible HGSOC patients were enrolled and divided into training (n = 179) and test cohorts (n = 77) in a 7:3 ratio. A radiomics signature (Radscore) was selected by using recursive feature elimination based on a support vector machine (SVM-RFE) and building a radiomics model for recurrence prediction. Independent clinical risk factors were generated by univariable and multivariable Cox regression analyses. A combined model was developed based on the Radscore and independent clinical risk factors and presented as a radiomics nomogram. Its performance was assessed by AUC, Kaplan-Meier survival analysis and decision curve analysis. RESULTS: Seven radiomics features were selected. The radiomics model yielded AUCs of 0.715 (95% CI: 0.640, 0.790) and 0.717 (95% CI: 0.600, 0.834) in the training and test cohorts, respectively. The clinical model (FIGO stage and residual disease) yielded AUCs of 0.632 and 0.691 in the training and test cohorts, respectively. The combined model demonstrated AUCs of 0.749 (95% CI: 0.678, 0.821) and 0.769 (95% CI: 0.662, 0.877) in the training and test cohorts, respectively. In the combined model, PFS was significantly shorter in the high-risk group than in the low-risk group (P < 0.0001). CONCLUSIONS: The radiomics nomogram performed well for early individualized recurrence prediction in patients with HGSOC and can also be used to differentiate high-risk patients from low-risk patients.

The Development and Validation of a CT-Based Radiomics Nomogram to Preoperatively Predict Lymph Node Metastasis in High-Grade Serous Ovarian Cancer.

PURPOSE: To develop and validate a radiomics model for predicting preoperative lymph node (LN) metastasis in high-grade serous ovarian cancer (HGSOC). MATERIALS AND METHODS: From May 2008 to January 2018, a total of 256 eligible HGSOC patients who underwent tumor resection and LN dissection were divided into a training cohort (n=179) and a test cohort (n=77) in a 7:3 ratio. A Radiomics Model was developed based on a training cohort of 179 patients. A radiomics signature (defined as the Radscore) was selected by using the random forest method. Logistics regression was used as the classifier for modeling. An Integrated Model that incorporated the Radscore and CT_reported LN status (CT_LN_report) was developed and presented as a radiomics nomogram. Its performance was determined by the area under the curve (AUC), calibration, and decision curve. The radiomics nomogram was internally tested in an independent test cohort (n=77) and a CT-LN-report negative subgroup (n=179) using the formula derived from the training cohort. RESULTS: The AUC value of the CT_LN_report was 0.688 (95% CI: 0.626, 0.759) in the training cohort and 0.717 (95% CI: 0.630, 0.804) in the test cohort. The Radiomics Model yielded an AUC of 0.767 (95% CI: 0.696, 0.837) in the training cohort and 0.753 (95% CI: 0.640, 0.866) in the test. The radiomics nomogram demonstrated favorable calibration and discrimination in the training cohort (AUC=0.821), test cohort (AUC=0.843), and CT-LN-report negative subgroup (AUC=0.82), outperforming the Radiomics Model and CT_LN_report alone. CONCLUSIONS: The radiomics nomogram derived from portal phase CT images performed well in predicting LN metastasis in HGSOC and could be recommended as a new, convenient, and non-invasive method to aid in clinical decision-making.

A Comparative Analysis of Two Machine Learning-Based Diagnostic Patterns with Thyroid Imaging Reporting and Data System for Thyroid Nodules: Diagnostic Performance and Unnecessary Biopsy Rate.

Background: The risk stratification system of the American College of Radiology Thyroid Imaging Reporting and Data System (ACR TI-RADS) for thyroid nodules is affected by low diagnostic specificity. Machine learning (ML) methods can optimize the diagnostic performance in medical image analysis. However, it is unknown which ML-based diagnostic pattern is more effective in improving diagnostic performance for thyroid nodules and reducing nodule biopsies. Therefore, we compared ML-assisted visual approaches and radiomics approaches with ACR TI-RADS in diagnostic performance and unnecessary fine-needle aspiration biopsy (FNAB) rate for thyroid nodules. Methods: This retrospective study evaluated a data set of ultrasound (US) and shear wave elastography (SWE) images in patients with biopsy-proven thyroid nodules (≥1 cm) from the Shanghai Tenth People's Hospital (743 nodules in 720 patients from September 2017 to January 2019) and an independent test data set from the Ma'anshan People's Hospital (106 nodules in 102 patients from February 2019 to April 2019). Six US features and five SWE parameters from the radiologists' interpretation were used for building the ML-assisted visual approaches. The radiomics features extracted from the US and SWE images were used with ML methods for developing the radiomics approaches. The diagnostic performance for differentiating thyroid nodules and the unnecessary FNAB rate of the ML-assisted visual approaches and the radiomics approaches were compared with ACR TI-RADS. Results: The ML-assisted US visual approach had the best diagnostic performance than the US radiomics approach and ACR TI-RADS (area under the curve [AUC]: 0.900 vs. 0.789 vs. 0.689 for the validation data set, 0.917 vs. 0.770 vs. 0.681 for the test data set). After adding SWE, the ML-assisted visual approach had a better diagnostic performance than US alone (AUC: 0.951 vs. 0.900 for the validation data set, 0.953 vs. 0.917 for the test data set). When applying the ML-assisted US+SWE visual approach, the unnecessary FNAB rate decreased from 30.0% to 4.5% in the validation data set and from 37.7% to 4.7% in the test data set in comparison to ACR TI-RADS. Conclusions: The ML-assisted dual modalities visual approach can assist radiologists to diagnose thyroid nodules more effectively and considerably reduce the unnecessary FNAB rate in the clinical management of thyroid nodules.