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Hepatocellular carcinoma (HCC) is a disease with high morbidity, high mortality, and low cure rate. Hyaluronic acid (HA) is widely adopted in tissue engineering and drug delivery. 5-(4-Hydroxyphenyl)-3H-1, 2-dithiol-3-thione (ADT-OH) is one of commonly used H2S donors. In our previous study, HA-ADT was designed and synthesized via coupling of HA and ADT-OH. In this study, compared with sodium hydrosulfide (NaHS, a fast H2S-releasing donor) and morpholin-4-ium (4-methoxyphenyl)-morpholin-4-ylsulfanylidenesulfido-λ5-phosphane (GYY4137, a slow H2S-releasing donor), HA-ADT showed stronger inhibitory effect on the proliferation, migration, invasion, and cell cycle of human HCC cells. HA-ADT promoted apoptosis by suppressing the expressions of phospho (p)-protein kinase B (PKB/AKT), p‐glycogen synthase kinase‐3β (GSK-3β), p‐β‐catenin, and also inhibited autophagy via the downregulation of the protein levels of p‐Smad2, p‐Smad3, and transforming growth factor‐β (TGF‐β) in human HCC cells. Moreover, HA-ADT inhibited HCC xenograft tumor growth more effectively than both NaHS and GYY4137. Therefore, HA-ADT can suppress the growth of HCC cells by blocking the AKT/GSK-3β/β-catenin and TGF‐β/Smad2/3 signaling pathways. HA-ADT and its derivatives may be developed as promising antitumor drugs.

Cucurbitacin IIa is a triterpene isolated exclusively from Hemsleya plants, which is non-steroidal anti-inflammatory drug that function as the main ingredient of Hemslecin capsules and Supplemental Tablets in China. In this study, the biosynthetic pathway of cucurbitacin IIa was elucidated by characterization of squalene epoxidases (HcSE1, HcSE2), cucurbitenol synthases (HcOSC6) and acyltransferases (HcAT1) in Hemsleya chinensis. Meanwhile, cycloartenol synthases (HcOSC1), isomultiflorenol synthases (HcOSC5) and β-amyrin synthase (HcOSC2-4) involved in sterol and triterpenes biosynthesis were functionally illustrated. The high-level production of yeast the key cucurbitacin precursor, cucurbitadienol, was constructed to produce 296.37 mg/L cucurbitadienol and 722.99 mg/L total triterpenoid which is the highest yield cucurbitadienol from known engineered microbes. Moreover, production of cucurbitenol in transient expression of tobacco was employed to achieve 94.8 mg/g dry weight (dw) cucurbitenol from leaves. In this study, the key genes involved in cucurbitacin IIa biosynthesis were identified to facilitate its medical applications via biosynthetic strategy. Meanwhile, the high-level production of cucurbitadienol chassis yeast and tobacco transient expression offered a robust and sSupplemental Table substrate for pharmaceutical cucurbitacin production and screening platform for candidate genes involved in cucurbitacin biosynthesis.

PURPOSE: To establish a machine-learning (ML) model based on coronary computed tomography angiography (CTA) images for evaluating myocardial ischemia in patients diagnosed with coronary atherosclerosis. METHODS: This retrospective analysis includes CTA images acquired from 110 patients. Among them, 58 have myocardial ischemia and 52 have normal myocardial blood supply. The patients are divided into training and test datasets with a ratio 7 : 3. Deep learning model-based CQK software is used to automatically segment myocardium on CTA images and extract texture features. Then, seven ML models are constructed to classify between myocardial ischemia and normal myocardial blood supply cases. Predictive performance and stability of the classifiers are determined by receiver operating characteristic curve with cross validation. The optimal ML model is then validated using an independent test dataset. RESULTS: Accuracy and areas under ROC curves (AUC) obtained from the support vector machine with extreme gradient boosting linear method are 0.821 and 0.777, respectively, while accuracy and AUC achieved by the neural network (NN) method are 0.818 and 0.757, respectively. The naive Bayes model yields the highest sensitivity (0.942), and the random forest model yields the highest specificity (0.85). The k-nearest neighbors model yields the lowest accuracy (0.74). Additionally, NN model demonstrates the lowest relative standard deviations (0.16 for accuracy and 0.08 for AUC) indicating the high stability of this model, and its AUC applying to the independent test dataset is 0.72. CONCLUSION: The NN model demonstrates the best performance in predicting myocardial ischemia using radiomics features computed from CTA images, which suggests that this ML model has promising potential in guiding clinical decision-making.

Objective To investigate the value of CT-based radiomics signature for preoperatively discriminating mucinous adenocarcinoma (MA) from nomucinous adenocarcinoma (NMA) in rectal cancer and compare with conventional CT values. Method A total of 225 patients with histologically confirmed MA or NMA of rectal cancer were retrospectively enrolled. Radiomics features were computed from the entire tumor volume segmented from the post-contrast phase CT images. The maximum relevance and minimum redundancy (mRMR) and LASSO regression model were performed to select the best preforming features and build the radiomics models using a training cohort of 155 cases. Then, predictive performance of the models was validated using a validation cohort of 70 cases and receiver operating characteristics (ROC) analysis method. Meanwhile, CT values in post- and pre-contrast phase, as well as their difference (D-values) of tumors in two cohorts were measured by two radiologists. ROC curves were also calculated to assess diagnostic efficacies. Results One hundred and sixty-three patients were confirmed by pathology as NMA and 62 cases were MA. The radiomics signature comprised 19 selected features and showed good discrimination performance in both the training and validation cohorts. The areas under ROC curves (AUC) are 0.93 (95% confidence interval [CI]: 0.89-0.98) in training cohort and 0.93 (95% CI: 0.87-0.99) in validation cohort, respectively. Three sets of CT values of MA in pre- and post-contrast phase, and their difference (D-value) (31±7.0, 51±12.6 and 20±9.3, respectively) were lower than those of NMA (37±5.6, 69±13.3 and 32±11.7, respectively). Comparing to the radiomics signature, using three sets of conventional CT values yielded relatively low diagnostic performance with AUC of 0.84 (95% CI: 0.78-0.88), 0.75 (95% CI: 0.69-0.81) and 0.78 (95% CI: 0.72-0.83), respectively. Conclusion This study demonstrated that CT radiomics features could be utilized as a noninvasive biomarker to identify MA patients from NMA of rectal cancer preoperatively, which is more accurate than using the conventional CT values.

Esophageal squamous cell carcinoma (ESCC) is a major cause of cancer-related deaths. We have previously connected a non-sulfated glycosaminoglycan, hyaluronic acid (HA), with a common hydrogen sulfide (H

BackgroundOvarian preservation treatment (OPT) was recommended in young women with early-stage endometrial cancer [superficial myometrial invasion (MI) and grades (G) 1/2-endometrioid adenocarcinoma (EEC)]. A radiomics nomogram was developed to assist radiologists in assessing the depth of MI and in selecting eligible patients for OPT.MethodsFrom February 2014 to May 2021, 209 G 1/2-EEC patients younger than 45 years (mean 39 ± 4.3 years) were included. Of them, 104 retrospective patients were enrolled in the primary group, and 105 prospective patients were enrolled in the validation group. The radiomics features were extracted based on multi-parametric magnetic resonance imaging, and the least absolute shrinkage and selection operator algorithm was applied to reduce the dimensionality of the data and select the radiomics features that correlated with the depth of MI in G 1/2-EEC patients. A radiomics nomogram for evaluating the depth of MI was developed by combing the selected radiomics features with the cancer antigen 125 and tumor size. Receiver operating characteristic (ROC) curves were used to evaluate the diagnostic performance of the radiomics nomogram and of radiologists without and with the aid of the radiomics nomogram. The net reclassification index (NRI) and total integrated discrimination index (IDI) based on the total included patients to assess the clinical benefit of radiologists with the radiomics nomogram were calculated.ResultsIn the primary group, for evaluating the depth of MI, the AUCs were 0.96 for the radiomics nomogram; 0.80 and 0.86 for radiologists 1 and 2 without the aid of the nomogram, respectively; and 0.98 and 0.98 for radiologists 1 and 2 with the aid of the nomogram, respectively. In the validation group, the AUCs were 0.88 for the radiomics nomogram; 0.82 and 0.83 for radiologists 1 and 2 without the aid of the nomogram, respectively; and 0.94 and 0.94 for radiologists 1 and 2 with the aid of the nomogram, respectively. The yielded NRI and IDI values were 0.29 and 0.43 for radiologist 1 and 0.23 and 0.37 for radiologist 2, respectively.ConclusionsThe radiomics nomogram outperformed radiologists and could help radiologists in assessing the depth of MI and selecting eligible OPTs in G 1/2-EEC patients.

OBJECTIVES: This study aims to evaluate diagnostic performance of radiomic analysis using computed tomography (CT) to identify lymphovascular invasion (LVI) in patients diagnosed with rectal cancer and assess diagnostic performance of different lesion segmentations. METHODS: The study is applied to 169 pre-treatment CT images and the clinical features of patients with rectal cancer. Radiomic features are extracted from two different volumes of interest (VOIs) namely, gross tumor volume and peri-tumor tissue volume. The maximum relevance and the minimum redundancy, and the least absolute shrinkage selection operator based logistic regression analyses are performed to select the optimal feature subset on the training cohort. Then, Rad and Rad-clinical combined models for LVI prediction are built and compared. Finally, the models are externally validated. RESULTS: Eighty-three patients had positive LVI on pathology, while 86 had negative LVI. An optimal multi-mode radiology nomogram for LVI estimation is established. The area under the receiver operating characteristic curves of the Rad and Rad-clinical combined model in the peri-tumor VOI group are significantly higher than those in the tumor VOI group (Rad: peri-tumor vs. tumor: 0.85 vs. 0.68; Rad-clinical: peri-tumor vs. tumor: 0.90 vs 0.82) in the validation cohort. Decision curve analysis shows that the peri-tumor-based Rad-clinical combined model has the best performance in identifying LVI than other models. CONCLUSIONS: CT radiomics model based on peri-tumor volumes improves prediction performance of LVI in rectal cancer compared with the model based on tumor volumes.

Background This study aims to investigate the value of radiomics parameters derived from contrast enhanced (CE) MRI in differentiation of hypovascular non-functional pancreatic neuroendocrine tumors (hypo-NF-pNETs) and solid pseudopapillary neoplasms of the pancreas (SPNs). Methods Fifty-seven SPN patients and twenty-two hypo-NF-pNET patients were enrolled. Radiomics features were extracted from T1WI, arterial, portal and delayed phase of MR images. The enrolled patients were divided into training cohort and validation cohort with the 7:3 ratio. We built four radiomics signatures for the four phases respectively and ROC analysis were used to select the best phase to discriminate SPNs from hypo-NF-pNETs. The chosen radiomics signature and clinical independent risk factors were integrated to construct a clinic-radiomics nomogram. Results SPNs occurred in younger age groups than hypo-NF-pNETs (P P = 0.0185). Age was a significant independent factor for the differentiation of SPNs and hypo-NF-pNETs revealed by logistic regression analysis. With AUC values above 0.900 in both training and validation cohort (0.978 [95% CI, 0.942–1.000] in the training set, 0.907 [95% CI, 0.765–1.000] in the validation set), the radiomics signature of the arterial phase was picked to build a clinic-radiomics nomogram. The nomogram, composed by age and radiomics signature of the arterial phase, showed sufficient performance for discriminating SPNs and hypo-NF-pNETs with AUC values of 0.965 (95% CI, 0.923–1.000) and 0.920 (95% CI, 0.796–1.000) in the training and validation cohorts, respectively. Delong Test did not demonstrate statistical significance between the AUC of the clinic-radiomics nomogram and radiomics signature of arterial phase. Conclusion CE-MRI-based radiomics approach demonstrated great potential in the differentiation of hypo-NF-pNETs and SPNs.

Background: Cervical lymph node (LN) metastasis of papillary thyroid carcinoma (PTC) is critical for treatment and prognosis. To examine the feasibility of MRI radiomics to preoperatively predict cervical LN metastasis in patients with PTC. Methods: Between January 2015 and March 2018, a total of 61 patients with pathologically confirmed PTC were analyzed retrospectively. The patients were divided into cervical LN metastasis group (n = 37) and no cervical LN metastasis (n = 24). T2WI and T2WI-fat-suppression (T2WI-FS) images were collected. A number of radiomic features were automatically extracted from the largest section of tumor. Three types of classifier (the random forests, the support vector machine classifier and the generalized linear model) based on T2WI and T2WI-FS images of cervical LN metastasis and no cervical LN metastasis were constructed and evaluated with a nested cross-validation scheme. Results: Radiomic features extracted from T2WI images were more discriminative than T2WI-FS images. The random forests model showed the best discriminate performance with the highest area under the curve (0.85, CI:0.76 -1), accuracy (0.87), sensitivity (0.83), specificity (1.00), positive predictive value (PPV = 1.00) and negative predictive value (NPV = 0.88). Conclusion: MRI radiomics analysis based on conventional T2WI and T2WI-FS can predict cervical LN metastasis in patients with PTC, and the radiomics is shown to be an assistant diagnosis tool for radiologists.

The objective of this study was to investigate the application of unenhanced computed tomography (CT) texture analysis in differentiating pancreatic adenosquamous carcinoma (PASC) from pancreatic ductal adenocarcinoma (PDAC).Preoperative CT images of 112 patients (31 with PASC, 81 with PDAC) were retrospectively reviewed. A total of 396 texture parameters were extracted from AnalysisKit software for further texture analysis. Texture features were selected for the differentiation of PASC and PDAC by the Mann-Whitney U test, univariate logistic regression analysis, and the minimum redundancy maximum relevance algorithm. Furthermore, receiver operating characteristic (ROC) curve analysis was performed to evaluate the diagnostic performance of the texture feature-based model by the random forest (RF) method. Finally, the robustness and reproducibility of the predictive model were assessed by the 10-times leave-group-out cross-validation (LGOCV) method.In the present study, 10 texture features to differentiate PASC from PDAC were eventually retained for RF model construction after feature selection. The predictive model had a good classification performance in differentiating PASC from PDAC, with the following characteristics: sensitivity, 95.7%; specificity, 92.5%; accuracy, 94.3%; positive predictive value (PPV), 94.3%; negative predictive value (NPV), 94.3%; and area under the ROC curve (AUC), 0.98. Moreover, the predictive model was proved to be robust and reproducible using the 10-times LGOCV algorithm (sensitivity, 90.0%; specificity, 71.3%; accuracy, 76.8%; PPV, 59.0%; NPV, 95.2%; and AUC, 0.80).The unenhanced CT texture analysis has great potential for differentiating PASC from PDAC.

By the traditional solid-state reaction method, Bi0.5(Na0.8K0.2)0.5Ti1−x(Fe0.5Nb0.5) x O3 (marked as BNKT-xFN; x = 0.00, 0.01, 0.02, 0.03 and 0.04) piezoelectric ceramics were fabricated, and the effects of FN substitution on the dielectric, ferroelectric, piezoelectric and electric field-induced strain performance were investigated. The relative dielectric permittivity and loss tangent reveal that the phase transition temperature between ferroelectric and ergodic relaxor phase is reduced from 90 °C to room temperature (RT), even below RT, with increasing FN content. Temperature dependence of polarization–electric field loops and strain-electric field curves exhibits that the ferroelectric order is disturbed gradually, and the ergodic relaxor phase forms with increasing FN content. A large unipolar strain of 0.42% and corresponding d33* (= Smax/Emax) of 642 pm/V for the sample with x = 0.04 are obtained under 6.5 kV/mm due to the phase transition from ergodic relaxor to ferroelectric. These results indicate that the (Fe0.5Nb0.5)4+ complex ion-modified BNKT-based ceramics would have great potentials for lead-free electromechanical actuator applications.

La3+ and Nb5+ co-doped 0.85Bi0.5Na0.5TiO3-0.11Bi0.5K0.5TiO3-0.04BaTiO3 (BNT-BKT-BT) samples were prepared through conventional solid oxide route, and the effects of doping ions on the phase structure, electrical properties, and electro-strain behavior were investigated. The XRD results showed that La3+ and Nb5+ co-dopant induced a phase transition from coexistence of ferroelectric tetragonal and rhombohedral phase to a pseudocubic phase. With increasing dopant content, the normal ferroelectric-to-ergodic relaxor (FE-to-ER) transition temperature (TF-R) decreased down from 120 °C to room temperature (RT), even below RT. A large unipolar strain of 0.34% (d33∗ = Smax/Emax = 680 pm/V) was achieved under 5 kV/mm and the largest electro-strain of 0.46% was achieved under an applied electric field of 7.5 kV/mm at RT for x = 0.0050. Temperature dependence of both polarization loops and bipolar strain curves from room temperature to 160 °C were also studied, and the results suggested that the origin of the large strain is attributed to a reversible nonpolar isotropic ergodic relaxor state to polar anisotropic ferroelectric phase transition.

0.8Bi0.5Na0.5Ti(1-x)NbxO3−0.2Sr0.85Bi0.1TiO3 (BNT-SBT-xNb, x = 0.00, 0.01, 0.02, and 0.03) piezoelectric ceramics were prepared by traditional solid state reaction and the influence of Nb substitution on the phase structure, ferroelectric, piezoelectric, and electric-field-induced strain properties in BNT-SBT ceramics were studied. XRD results exhibited that Nb5+ ions could fully diffuse into BNT-SBT structure to form a solid solution when x = 0.01. P-E loops and S-E curves suggested that the ferroelectric phase transformed to ergodic relaxor state (FE-to-ER) with the increasing the amount of Nb additive, indicating the ferroelectric long-ranged order was disturbed by the excess of Nb. With increasing Nb doping, phase transition temperature from normal ferroelectric to ergodic relaxor (short for TF-R) could be reduced from 120 °C to 40 °C. Furthermore, for sample with x = 0.01, the normalized strain d33* got a maximum value ~571 pm/V due to the phase transition from ergodic relaxor to ferroelectric (ER-to-FE) under electric field.

In magnetic topological insulators (TIs), the interplay between magnetic order and nontrivial topology can induce fascinating topological quantum phenomena, such as the quantum anomalous Hall effect, chiral Majorana fermions, and axion electrodynamics. Recently, a great deal of attention has been focused on the intrinsic magnetic TIs, where disorder effects can be eliminated to a large extent, which is expected to facilitate the emergence of topological quantum phenomena. Despite intensive efforts, experimental evidence of the topological surface states (SSs) remains elusive. Here, by combining first-principles calculations and angle-resolved photoemission spectroscopy (ARPES) experiments, we reveal that EuSn2As2 is an antiferromagnetic TI with the observation of Dirac SSs consistent with our prediction. We also observe nearly gapless Dirac SSs in antiferromagnetic TIs MnBi2nTe3n+1 (n=1 and 2), which are absent in previous ARPES results. These results provide clear evidence for nontrivial topology of these intrinsic magnetic TIs. Furthermore, we find that the topological SSs show no observable changes across the magnetic transition within the experimental resolution, indicating that the magnetic order has a quite small effect on the topological SSs, which can be attributed to weak hybridization between the localized magnetic moments, from either 4f or 3d orbitals, and the topological electronic states. This finding provides insights for further research that the correlations between magnetism and topological states need to be strengthened to induce larger gaps in the topological SSs, which will facilitate the realization of topological quantum phenomena at higher temperatures.

Purpose: To determine the potential of mammography (MG) and mammographic texture analysis in differentiation between Grade 1 (G1) and Grade 2/ Grade 3 (G2/G3) phyllodes tumors (PTs) of breast.Materials and methods: A total of 80 female patients with histologically proven PTs were included in this study. 45 subjects who underwent pretreatment MG from 2010 to 2017 were retrospectively analyzed, including 14 PTs G1 and 31 PTs G2/G3. Tumor size, shape, margin, density, homogeneity, presence of fat, or calcifications, a halo-sign as well as some indirect manifestations were evaluated. Texture analysis features were performed using commercial software. Receiver operating characteristic curve (ROC) was used to determine the sensitivity and specificity of prediction.Results: G2/G3 PTs showed a larger size (>4.0 cm) compared to PTs G1 (64.52 vs. 28.57%, p = 0.025). A strong lobulation or multinodular confluent was more common in G2/G3 PTs compared to PTs G1 (64.52 vs. 14.29%, p = 0.004). Significant differences were also observed in tumors' growth speed and clinical manifestations (p = 0.007, 0.022, respectively). Ten texture features showed significant differences between the two groups (p < 0.05), Correlation_AllDirection_offset7_SD and ClusterProminence_AllDirection_offset7_SD were independent risk factors. The area under the curve (AUC) of imaging-based diagnosis, texture analysis-based diagnosis and the combination of the two approaches were 0.805, 0.730, and 0.843 (90.3% sensitivity and 85.7% specificity).Conclusions: Texture analysis has great potential to improve the diagnostic efficacy of MG in differentiating PTs G1 from PTs G2/G3.