Automatic pulmonary artery-vein separation in CT images using a twin-pipe network and topology reconstruction.

Background: With the wide application of CT scanning, the separation of pulmonary arteries and veins (A/V) based on CT images plays an important role for assisting surgeons in preoperative planning of lung cancer surgery. However, distinguishing between arteries and veins in chest CT images remains challenging due to the complex structure and the presence of their similarities. Methods: We proposed a novel method for automatically separating pulmonary arteries and veins based on vessel topology information and a twin-pipe deep learning network. First, vessel tree topology is constructed by combining scale-space particles and multi-stencils fast marching (MSFM) methods to ensure the continuity and authenticity of the topology. Second, a twin-pipe network is designed to learn the multiscale differences between arteries and veins and the characteristics of the small arteries that closely accompany bronchi. Finally, we designed a topology optimizer that considers interbranch and intrabranch topological relationships to optimize the results of arteries and veins classification. Results: The proposed approach is validated on the public dataset CARVE14 and our private dataset. Compared with ground truth, the proposed method achieves an average accuracy of 90.1% on the CARVE14 dataset, and 96.2% on our local dataset. Conclusions: The method can effectively separate pulmonary arteries and veins and has good generalization for chest CT images from different devices, as well as enhanced and noncontrast CT image sequences from the same device.

A Yeast Cell Wall Derived Hybrid Hydrogel with Photothermal and Immune Combined Modality Therapy for Enhanced Anti-Melanoma Efficacy.

Introduction: The effect of traditional treatment for melanoma is quite limited, especially for its recurrence. As the major components of yeast cell wall, chitin and ß-glucan exhibit good immune activation effect and are promising candidates for adjuvant. Therefore, melanoma cell membrane (CM) and indocyanine green (ICG) was loaded in a chitin and ß-glucan hybrid hydrogel to achieve an enhanced anti-melanoma therapy. Methods: The novel hybrid hydrogel was prepared, and its physicochemical properties were examined. Its effect towards melanoma prevention and treatment was evaluated via a melanoma-bearing mice model. Results: The CM-ICG-hybrid hydrogel was successfully prepared with excellent injectability, self-healing, drug loading, rheological, in vitro and in vivo photothermal stability, and retention properties. It also exhibited good cellular and in vivo safety profiles. In the primary melanoma mice model, it quickly ablated the in-situ melanoma, effectively inhibited the tumor growth, increased the survival rate of melanoma-bearing mice, and increased the level of IFN-γ and TNF-α. In the distal secondary melanoma model, it efficiently prevented the reoccurrence of melanoma and activated the memory T cells. In both models, a synergistic effect of photothermal therapy and immune therapy was found. The hydrogel effectively recruited CD3+ CD4+ T cells and CD3+ CD8+ T cells, inhibited the proliferation of melanoma cells, and induced the apoptosis of melanoma cells. Conclusion: The hybrid hydrogel was successfully prepared, and it showed excellent efficacy towards melanoma prevention and treatment due to its efficient tumor ablation and immune activation capability.

Mesoporous Hollow Manganese Doped Ceria Nanoparticle for Effectively Prevention of Hepatic Ischemia Reperfusion Injury.

Introduction: Hepatic ischemia-reperfusion injury (HIRI) is the main reason for liver dysfunction or failure after liver resection and liver transplantation. As excess accumulation of reactive oxygen species (ROS) is the leading factor, ceria nanoparticle, a cyclic reversible antioxidant, is an excellent candidate for HIRI. Methods: Manganese doped mesoporous hollow ceria nanoparticles (MnOx-CeO2 NPs) were prepared, and the physicochemical characteristics, such as particle size, morphology, microstructure, etc. were elucidated. The in vivo safety and liver targeting effect were examined after i.v. injection. The anti-HIRI was determined by a mouse HIRI model. Results: MnOx-CeO2 NPs with 0.40% Mn doped exhibited the strongest ROS-scavenging capability, which may due to the increased specific surface area and surface oxygen concentration. The nanoparticles accumulated in the liver after i.v. injection and exhibited good biocompatibility. In the HIRI mice model, MnOx-CeO2 NPs significantly reduced the serum ALT and AST level, decreased the MDA level and increased the SOD level in the liver, prevent pathological damages in the liver. Conclusion: MnOx-CeO2 NPs were successfully prepared and it could significantly inhibit the HIRI after i.v. injection.

Radiomics approach with deep learning for predicting T4 obstructive colorectal cancer using CT image.

OBJECTIVES: Patients with T4 obstructive colorectal cancer (OCC) have a high mortality rate. Therefore, an accurate distinction between T4 and T1-T3 (NT4) in OCC is an important part of preoperative evaluation, especially in the emergency setting. This paper introduces three models of radiomics, deep learning, and deep learning-based radiomics to identify T4 OCC. METHODS: We established a dataset of computed tomography (CT) images of 164 patients with pathologically confirmed OCC, from which 2537 slides were extracted. First, since T4 tumors penetrate the bowel wall and involve adjacent organs, we explored whether the peritumoral region contributes to the assessment of T4 OCC. Furthermore, we visualized the radiomics and deep learning features using the t-distributed stochastic neighbor embedding technique (t-SNE). Finally, we built a merged model by fusing radiomic features with deep learning features. In this experiment, the performance of each model was evaluated by the area under the receiver operating characteristic curve (AUC). RESULTS: In the test cohort, the AUC values predicted by the radiomics model in the dilated region of interest (dROI) was 0.770. And the AUC value of the deep learning model with the patches extended 20-pixel reached 0.936. Combining the characteristics of radiomics and deep learning, our method achieved an AUC value of 0.947 in the T4 and non-T4 (NT4) classification, and increased the AUC value to 0.950 after the addition of clinical features. CONCLUSION: The prediction results of our merged model of deep learning radiomics outperformed the deep learning model and significantly outperformed the radiomics model. The experimental results demonstrate that combining the peritumoral region improves the prediction performance of the radiomics model and the deep learning model.

Learning multi-view and centerline topology connectivity information for pulmonary artery-vein separation.

BACKGROUND: Automatic pulmonary artery-vein separation has considerable importance in the diagnosis and treatment of lung diseases. However, insufficient connectivity and spatial inconsistency have always been the problems of artery-vein separation. METHODS: A novel automatic method for artery-vein separation in CT images is presented in this work. Specifically, a multi-scale information aggregated network (MSIA-Net) including multi-scale fusion blocks and deep supervision, is proposed to learn the features of artery-vein and aggregate additional semantic information, respectively. The proposed method integrates nine MSIA-Net models for artery-vein separation, vessel segmentation, and centerline separation tasks along with axial, coronal, and sagittal multi-view slices. First, the preliminary artery-vein separation results are obtained by the proposed multi-view fusion strategy (MVFS). Then, centerline correction algorithm (CCA) is used to correct the preliminary results of artery-vein separation by the centerline separation results. Finally, the vessel segmentation results are utilized to reconstruct the artery-vein morphology. In addition, weighted cross-entropy and dice loss are employed to solve the class imbalance problem. RESULTS: We constructed 50 manually labeled contrast-enhanced computed CT scans for five-fold cross-validation, and experimental results demonstrated that our method achieves superior segmentation performance of 97.7%, 85.1%, and 84.9% on ACC, Pre, and DSC, respectively. Additionally, a series of ablation studies demonstrate the effectiveness of the proposed components. CONCLUSION: The proposed method can effectively solve the problem of insufficient vascular connectivity and correct the spatial inconsistency of artery-vein.

Renal Safety of Sacubitril/Valsartan: A Meta-Analysis of Randomized Controlled Trials.

ABSTRACT: As a first-line therapy, sacubitril/valsartan (S/V) plays a significant role in the treatment of heart failure. However, its effect on renal function is still uncertain. We searched PubMed, EMBASE, the Cochrane Library, and Clinical Trials for randomized controlled trials to evaluate the effect of S/V on renal function in patients. The results are reported as the mean difference, relative ratio, and 95% confidence intervals. A total of 13 randomized controlled trials were included (19,367 patients). Among them, 11 studies focused on patients with heart failure, 1 on patients with acute myocardial infarction, and 1 on patients with chronic kidney disease. We found that fewer worsening renal function events, elevated creatine level events, and severe hyperkalemia events (blood potassium >6.0 mmol/L) occurred in the S/V group than those in the renin-angiotensin-aldosterone system inhibitor (RASi) group. The estimated glomerular filtration rate decreased in both the S/V group and the RASi group, but the change was more obvious in the RASi group. There was no significant difference in hyperkalemia events (blood potassium >5.5 mmol/L) between the 2 groups. Subgroup analysis showed that with the extension of follow-up time (>6 months), worsening renal function events occurred less frequently in the S/V group than in the RASi group. Existing evidence has shown that S/V is superior to RASi in general renal safety. Perhaps with the prolongation of treatment time, the advantages of S/V are more obvious.

LncRNA MEG3 inhibits the proliferation and migration abilities of colorectal cancer cells by competitively suppressing MiR-31 and reducing the binding of MiR-31 to target gene SFRP1.

To explore the potential mechanism of long-chain non-coding ribonucleic acid (lncRNA) maternal expression gene 3 (MEG3) in colorectal cancer (CRC). The relationship between MEG3 and miR-31 was detected by dual-luciferase assay. Quantitative polymerase chain reaction was utilized to determine the expression of MEG3 in CRC cell lines. Cell Counting Kit-8 assay was performed to detect cell proliferation. Transwell, cell scratch wound assay, and monoclonal proliferation assay were used to detect the proliferation, migration, and invasion of cells. In addition, cell motility was evaluated by detecting the expression of cellular pseudopodia protein α-actinin via immunofluorescence assay, and cell proliferation and motility were judged by determining the expressions of Ki-67, MMP2 and MMP9 via Western blotting. The effect of MEG3 and miR-31 on the development of colorectal cancer was verified by nude mouse tumor-bearing assay and HE staining. Transient transfection with MEG3 overexpression plasmid revealed that MEG3 inhibited the proliferation and motility of cells. The results of dual-luciferase assay showed that MEG3 could specifically inhibit the expression of miR-31, which inhibits the development of colorectal cancer. Transwell, cell scratch wound assay, and monoclonal proliferation experiment showed that miR-31 enhanced cell proliferation, migration and invasion. MEG3 overexpression plasmid was capable of reversing the proliferation and motility of CRC cells enhanced by miR-31. MEG3 can inhibit the proliferation and motility of CRC cells by competitively suppressing the binding of miR-31 to the target gene SFRP1, thus playing an inhibitory role in the pathogenesis of CRC.

A Lower False Positive Pulmonary Nodule Detection Approach for Early Lung Cancer Screening.

Pulmonary nodule detection with low-dose computed tomography (LDCT) is indispensable in early lung cancer screening. Although existing methods have achieved excellent detection sensitivity, nodule detection still faces challenges such as nodule size variation and uneven distribution, as well as excessive nodule-like false positive candidates in the detection results. We propose a novel two-stage nodule detection (TSND) method. In the first stage, a multi-scale feature detection network (MSFD-Net) is designed to generate nodule candidates. This includes a proposed feature extraction network to learn the multi-scale feature representation of candidates. In the second stage, a candidate scoring network (CS-Net) is built to estimate the score of candidate patches to realize false positive reduction (FPR). Finally, we develop an end-to-end nodule computer-aided detection (CAD) system based on the proposed TSND for LDCT scans. Experimental results on the LUNA16 dataset show that our proposed TSND obtained an excellent average sensitivity of 90.59% at seven predefined false positives (FPs) points: 0.125, 0.25, 0.5, 1, 2, 4, and 8 FPs per scan on the FROC curve introduced in LUNA16. Moreover, comparative experiments indicate that our CS-Net can effectively suppress false positives and improve the detection performance of TSND.

A hybrid hydrogel composed of chitin and ß-glucan for the effective management of wound healing and scarring.

Non-functional scars commonly form after cutaneous injuries. At present, most clinical treatments for scar eradication typically have long treatment courses, low curative effects, and are expensive. In this research, three hydrogels, namely chitin hydrogel, ß-glucan hydrogel, and hybrid hydrogel composed of chitin and ß-glucan, were successfully prepared, and they exhibited good shear thinning property and bioadhesiveness. In a full-thickness skin defect mouse model, the three hydrogels were found to effectively promote wound closure and inhibit scar formation. Through the immunohistochemistry staining and immunofluorescence staining of the wound tissues, the hydrogels could significantly promote the formation of collagen III, the regeneration of hair follicles, and the expression of keratin K14 and K15. They could also regulate the production of VEGF and immune factors such as IL-10 and IL-12, and inhibit the expression of the fibroblast regulatory factor En-1 in the wound site, which correlated well with their improved wound healing promoting effect and anti-scarring effect. Among all the hydrogels, the hybrid hydrogel was the best to promote wound healing and inhibit scarring. This study, for the first time, proved the excellent therapeutic effect of chitin and ß-glucan hydrogels in the management of wound healing and skin regeneration without scar formation, which will lay a solid foundation for the development of skin regeneration medication and future clinical transformation.

Cucurbitacin E reduces the cognitive dysfunction induced by sevoflurane in rats by regulating NF-κB pathway.

BACKGROUND: Perioperative neurocognitive disorders (PND) occur frequently and refer to alterations in cognitive function after surgery, especially in elderly patients. PND is characterized as abnormalities of learning, memory, language, and emotions. Cucurbitacin E has been reported to possess various pharmacological properties, including anticancer, antiviral, and anti-inflammatory effects. In this study, we investigated whether cucurbitacin E could alleviate sevoflurane-induced cognitive dysfunction in rats. METHODS: Sprague-Dawley male rats (~6 weeks old) were randomly assigned to three groups: the control group, the Sevoflurane group, and the Sevoflurane + Cucurbitacin E group. Subsequently, the cognitive dysfunction of the rats was evaluated through the morris water maze test. Hematoxylin and eosin (HE) staining was used to measure the pathological change in brain tissues. Enzyme-linked immunosorbent assay (ELISA) kits were used for determinations of S-100 calcium binding protein B (S-100ß) and neuron-specific enolase (NSE) and cytokine. Cell apoptosis was analyzed by TdT-Mediated Nick-End Labeling (TUNEL) staining. Protein levels were confirmed by Western blotting. RESULTS: Cucurbitacin E relieved brain injury in rats induced by sevoflurane. Cucurbitacin E alleviated sevoflurane-induced S-100ß and NSE levels. Additionally, the Morris water maze task revealed that cucurbitacin E attenuated cognition impairment in sevoflurane-induced rats. Sevoflurane increased levels of IL-6, TNF-α and IL-1ß levels, and decreased the level of IL-10. However, cucurbitacin E exhibited opposite effects on these cytokines, which were induced by sevoflurane. Furthermore, cucurbitacin E inhibited sevoflurane-induced neuron apoptosis and NF-κB pathway in rats. CONCLUSION: These findings indicate that cucurbitacin E can improve sevoflurane-induced cognitive dysfunction in rats by regulating NF-κB pathway, which provided a new strategy for PND treatment.

L-Se-methylselenocysteine loaded mucoadhesive thermogel for effective treatment of Vulvar candidiasis.

Vulvar candidiasis (VVC) is a vaginitis caused by vaginal mucosa infection of Candida, which greatly impairs women's health. Although there are more and more thiazoles on the market, new classes of antifungal drugs are still missing, it is still challenging to treat azole-resistant candidal vaginitis. We found that L-Se-methylselenocysteine (L-SeMC) could effectively inhibit the growth of Candida albicans, reduce the density and length of the mycelia. To extend the retention time of L-SeMC in the vaginal tract and enhance its therapeutic effect for VVC, a mucoadhesive thermogel (NAC-HA thermogel) was successfully synthesized and prepared. The gelation window was around 29-56 °C for L-SeMC loaded mucoadhesive thermogel (L-SeMC@NAC-HA thermogel), which exhibited a sustained release profile in the in vitro release study and an extended retention time in the vaginal tract. Besides, L-SeMC@NAC-HA thermogel exhibited a good safety profile in the in vivo safety study. The in vivo anti-VVC effect was examined in a rat VVC model and L-SeMC@NAC-HA thermogel significantly reduced the number of Candida albicans in the vaginal secreta, mitigated the vaginal damage and reduced the secretion of proinflammatory factors (TNF-α, IL-1α and IL-ß). Therefore, it is a promising therapy for the clinical treatment of VVC in the near future.

Investigation on the interaction between myricetin and dihydromyricetin with trypsin, α-chymotrypsin, lysozyme by spectroscopy and molecular docking methods.

The interaction between myricetin and dihydromyricetin with trypsin, α-chymotrypsin and lysozyme was investigated using multispectral and molecular docking methods. The results of fluorescence quenching revealed that myricetin and dihydromyricetin could quench the intrinsic fluorescence of three different proteinases through a static quenching procedure. The binding constant and number of binding sites at different temperatures were measured. The thermodynamic parameters obtained at different temperatures showed van der Waals interactions and hydrogen bonds played the main roles in the interaction of myricetin with trypsin and lysozyme, hydrophobic force was dominant both in myricetin with α-chymotrypsin interaction and dihydromyricetin with trypsin and lysozyme interaction, as for the electrostatic forces, it was mainly the driving force in dihydromyricetin binding to α-chymotrypsin. There was non-radiative energy transfer between three proteinases and myricetin or dihydromyricetin with high probability. The microenvironment of trypsin, α-chymotrypsin and lysozyme is changed. The docking studies revealed that myricetin and dihydromyricetin entered the hydrophobic cavity of three proteinases and formed hydrogen bonds. The binding affinity of myricetin or dihydromyricetin is different with the trypsin, α-chymotrypsin and lysozyme due to the different molecular structure.

A two-stage network with prior knowledge guidance for medullary thyroid carcinoma recognition in ultrasound images.

PURPOSE: Accurate recognition of medullary thyroid carcinoma (MTC) is of great importance in medical diagnosis, as MTC is rare but second-most malignant thyroid cancers with a high case-fatality ratio.1 But there is a lower recognition rate on distinguishing MTC from other thyroid nodules in ultrasound images, even by experienced experts. This paper introduces the computer-aided method to tackle the challenge of recognizing MTC from ultrasound images, including limited MTC samples, and ambiguities among MTC, benign nodules, and papillary thyroid carcinoma (PTC). METHODS: The recognition of MTC based on large MTC samples of ultrasound images has never been explored, as only one existing work presented a relevant dataset with a limited 21 MTC samples. This study proposes a novel method for primarily differentiating MTC samples from benign nodules and PTC that is the most common thyroid cancer. Our method is a two-stage schema with two important components including a cascaded coarse-to-fine segmentation network and a knowledge-based classification network. The cascaded coarse-to-fine segmentation network incorporates two U-Net++ networks for improving the segmentation results of thyroid nodules. Meanwhile, our knowledge-based classification network extracts and fuses semantic features of solid tissues and calcification for better recognizing the segmented nodules from the ultrasound images. In our experiments, dice similarity coefficient (DSC), intersection over union (IoU), precision, recall, and Hausdorff distance (HD) are adopted for evaluating the segmentation results of thyroid nodules, and accuracy, precision, recall, and F1-score are used for classification evaluation. RESULTS: We present a well-annotated dataset including samples of 248 MTC, 240 benign nodules, and 239 PTC. For thyroid nodule segmentation, our designed cascaded segmentation network attains values of 0.776 DSC, 0.689 IoU, 0.778 precision, and 0.821 recall, respectively. By incorporating prior knowledge, our method achieves a mean accuracy of 82.1% in classifying thyroid nodules of MTC, PTC, and benign ones. Especially, our method gains the higher performance in recognizing MTC with an accuracy of 86.8%, compared to nearly 70% diagnosis accuracy of experienced doctors. The experimental results on our Fujian Provincial Hospital dataset further validate the efficiency of our proposed method. CONCLUSIONS: Our proposed two-stage method incorporates pipelines of thyroid nodules segmentation and classification of MTC, individually. Quantitative and qualitative results indicate that our proposed model achieves accurate segmentation of thyroid nodules. The results also validate that our learning-based framework facilitates the recognition of MTC, which gains better classification accuracy than experienced doctors.

Coarse-to-fine airway segmentation using multi information fusion network and CNN-based region growing.

BACKGROUND AND OBJECTIVES: Automatic airway segmentation from chest computed tomography (CT) scans plays an important role in pulmonary disease diagnosis and computer-assisted therapy. However, low contrast at peripheral branches and complex tree-like structures remain as two mainly challenges for airway segmentation. Recent research has illustrated that deep learning methods perform well in segmentation tasks. Motivated by these works, a coarse-to-fine segmentation framework is proposed to obtain a complete airway tree. METHODS: Our framework segments the overall airway and small branches via the multi-information fusion convolution neural network (Mif-CNN) and the CNN-based region growing, respectively. In Mif-CNN, atrous spatial pyramid pooling (ASPP) is integrated into a u-shaped network, and it can expend the receptive field and capture multi-scale information. Meanwhile, boundary and location information are incorporated into semantic information. These information are fused to help Mif-CNN utilize additional context knowledge and useful features. To improve the performance of the segmentation result, the CNN-based region growing method is designed to focus on obtaining small branches. A voxel classification network (VCN), which can entirely capture the rich information around each voxel, is applied to classify the voxels into airway and non-airway. In addition, a shape reconstruction method is used to refine the airway tree. RESULTS: We evaluate our method on a private dataset and a public dataset from EXACT09. Compared with the segmentation results from other methods, our method demonstrated promising accuracy in complete airway tree segmentation. In the private dataset, the Dice similarity coefficient (DSC), Intersection over Union (IoU), false positive rate (FPR), and sensitivity are 93.5%, 87.8%, 0.015%, and 90.8%, respectively. In the public dataset, the DSC, IoU, FPR, and sensitivity are 95.8%, 91.9%, 0.053% and 96.6%, respectively. CONCLUSION: The proposed Mif-CNN and CNN-based region growing method segment the airway tree accurately and efficiently in CT scans. Experimental results also demonstrate that the framework is ready for application in computer-aided diagnosis systems for lung disease and other related works.

Prognosis of liver transplantation: Does postoperative ileus matter?

BACKGROUND: Nowadays, liver transplantation has become a main therapy for end-stage liver disease. However, studies show that there are high mortality and severe complications after liver transplantation. Although gastrointestinal dysfunction is a common and major complication after liver transplantation, there was rarely relative research. This study aims to elucidate the factors about ileus after liver transplantation and patients' survival. METHODS: We collected and analyzed the data (n = 318, 2016-2019) from the First Affiliated Hospital of Xi'an Jiaotong University. After excluding cases, a total of 293 patients were included for this study. The subjects were divided into a non-ileus group and an ileus group. We reviewed 38 variables (including preoperative, operative and postoperative relative factors). Additionally, other complications after liver transplantation and survival data were compared between two groups. RESULTS: Of the 293 patients, 23.2% (n = 68) experienced postoperative ileus. Ileus patients were not different with non-ileus patients in preoperative, operative and postoperative factors. HBV-positive patients with ileus had a lower MELD score (P = 0.025), and lower postoperative total bilirubin was correlated with ileus (P = 0.049). Besides, Child-Pugh score of HCC patients with ileus was low (P = 0.029). The complications after liver transplantation were not different between two groups. Compared with the patients without ileus, the patients with ileus had a higher mortality rate. CONCLUSION: According to our research, ileus-patients had a lower 1-year survival rates. The preoperative MELD score and postoperative total bilirubin of HBV-positive patients with ileus were lower, and Child-Pugh score of HCC patients with ileus was also lower.

Interpretative computer-aided lung cancer diagnosis: From radiology analysis to malignancy evaluation.

BACKGROUND AND OBJECTIVE: Computer-aided diagnosis (CAD) systems promote accurate diagnosis and reduce the burden of radiologists. A CAD system for lung cancer diagnosis includes nodule candidate detection and nodule malignancy evaluation. Recently, deep learning-based pulmonary nodule detection has reached satisfactory performance ready for clinical application. However, deep learning-based nodule malignancy evaluation depends on heuristic inference from low-dose computed tomography (LDCT) volume to malignant probability, and lacks clinical cognition. METHODS: In this paper, we propose a joint radiology analysis and malignancy evaluation network called R2MNet to evaluate pulmonary nodule malignancy via the analysis of radiological characteristics. Radiological features are extracted as channel descriptor to highlight specific regions of the input volume that are critical for nodule malignancy evaluation. In addition, for model explanations, we propose channel-dependent activation mapping (CDAM) to visualize features and shed light on the decision process of deep neural networks (DNNs). RESULTS: Experimental results on the lung image database consortium image collection (LIDC-IDRI) dataset demonstrate that the proposed method achieved an area under curve (AUC) of 96.27% and 97.52% on nodule radiology analysis and nodule malignancy evaluation, respectively. In addition, explanations of CDAM features proved that the shape and density of nodule regions are two critical factors that influence a nodule to be inferred as malignant. This process conforms to the diagnosis cognition of experienced radiologists. CONCLUSION: The network inference process conforms to the diagnostic procedure of radiologists and increases the confidence of evaluation results by incorporating radiology analysis with nodule malignancy evaluation. Besides, model interpretation with CDAM features shed light on the focus regions of DNNs during the estimation of nodule malignancy probabilities.

Quantitative Determination of p-Cymene, Thymol, Neryl Acetate, and ß-Caryophyllene in Different Growth Periods and Parts of Eupatorium fortunei Turcz. by GC-MS/MS.

Eupatorium fortunei Turcz. is a widely used Chinese herbal medicine in China. In this study, a gas chromatography-triple quadrupole mass spectrometry (GC-MS/MS) method was developed and validated to simultaneously determine the contents of p-cymene, thymol, neryl acetate, and ß-caryophyllene in roots, stems, and leaves of Eupatorium fortunei Turcz. harvested at different growth periods. All four constituents could be detected in leaves, three could be detected in stems except ß-caryophyllene, and only thymol could be detected in roots. The order of the total contents of four constituents in different parts was leaves > stems > roots. It indicated that the leaves could be the proper medicinal parts of Eupatorium fortunei Turcz. The content of four constituents in leaves varied a lot among different growth periods and showed an M-shaped change trend with the growth of Eupatorium fortunei Turcz. The four constituents accumulated to the highest values in early July followed by mid-September. Accordingly, the best harvest time of Eupatorium fortunei Turcz. is early July and mid-September.

Pre-anesthetic clinic internship: new teaching method of pre-anesthesia evaluation for undergraduates.

BACKGROUND: This study aimed to observe the effect of internship in a pre-anesthetic clinic on the teaching quality of pre-anesthesia evaluation for undergraduates. METHODS: A total of 120 undergraduates from July 2017 to July 2018 in the anesthesia department of our hospital were randomly divided into two groups: pre-anesthetic clinic internship teaching group (n = 60) and traditional teaching group (n = 60). The knowledge in the pre-anesthesia evaluation teaching chapters was evaluated between the two groups of undergraduates. RESULTS: There were no significant differences in the demographic information between the two groups. The scores in the case analysis and theoretical knowledge test in the pre-anesthetic clinic internship teaching group were significantly higher than those in the traditional teaching group. In addition, the students' satisfaction with the curriculum design was significantly higher in the pre-anesthetic clinic internship teaching group than in the traditional teaching group. CONCLUSION: Pre-anesthetic clinic internships can improve the quality of pre-anesthesia assessment teaching for undergraduates.

Uptake and Distribution Characteristic and Health Risk Assessment of Heavy Metal(loid)s in Platycodon Grandiflorum (Jacq.) A.DC. with Growth from a Medicinal Herb Garden of Xi'an, China.

The different parts of Platycodon grandiflorum were collected from a medicinal herb garden to determine five heavy metal(loid)s (Pb, Cd, As, Hg, and Cu) contents at different growth stages. The data showed that the plant accumulated varying amounts of metal(loid)s in the order Cu > Hg > Pb > As > Cd. Five heavy metal(loid) concentrations decreased in the early growth stage and then increased in the flowering season. The contents of heavy metal(loid)s except Hg in the stem were relatively lower than other tissues. The flower of Platycodon grandiflorum can highly accumulate heavy metal(loid)s, especially for Cu in the flowering period. Pb, Cd, and Cu contents in stem generally increased with growth time, while Cd and Cu in root decreased during growth time. The average daily intake doses of five heavy metal(loid)s in the root of Platycodon grandiflorum were all below the safety guideline and the target hazard quotient was less than 1.

Lidocaine inhibits glioma cell proliferation, migration and invasion by modulating the circEZH2/miR-181b-5p pathway.

BACKGROUND: Lidocaine is well known as a local anesthetic that has been reported to play an antitumor role in numerous cancers, including glioma. Circular RNAs (circRNAs) play multiple biological roles in cancers. The aim of this study was to determine the effects of lidocaine in glioma in vitro and in vivo and explore functional mechanisms. METHODS: The effects of lidocaine on glioma progression were investigated by cell proliferation, migration and invasion using 3-(4, 5-dimethylthiazol-2-yl)-2, 5-diphenyltetrazolium bromide (MTT) assay, colony formation assay and transwell assay. The expression of CD133 and glial fibrillary acidic protein (GFAP) was quantified by western blot to assess cell differentiation. The expression of circEZH2 and miR-181b-5p was detected by a quantitative real-time PCR (qRT-PCR). The target relationship between circEZH2 and miR-181b-5p was verified by dual-luciferase reporter assay and RNA immunoprecipitation (RIP) assay. The effect of lidocaine on tumor growth in vivo was investigated by establishing Xenograft models. RESULTS: Lidocaine inhibited proliferation, migration, invasion and induced differentiation of glioma cells in vitro. Lidocaine suppressed the expression of circEZH2, and circEZH2 was highly expressed in glioma tissues and cells. CircEZH2 overexpression partly inhibited the function of lidocaine. CircEZH2 was a sponge of miR-181b-5p, and miR-181b-5p was downregulated in glioma tissues and cells. Besides, miR-181b-5p restoration reversed the effects of circEZH2 overexpression to repress the malignant behaviors of glioma cells. In addition, lidocaine mediated the circEZH2/miR-181b-5p axis to inhibit tumor growth in vivo. CONCLUSION: Lidocaine suppressed glioma progression by modulating the circEZH2/miR-181b-5p pathway.