Diagnostic Efficacy of a Novel Rotating Brush for Endoscopic Sampling of Malignant Biliary Strictures: A Multicenter Prospective Study.

INTRODUCTION: Although cytologic examination of biliary stricture brushings obtained by endoscopic retrograde cholangiopancreatography is commonly used for diagnosing malignant biliary strictures (MBSs), it has low sensitivity. Several new brushes have capabilities that are still being debated. We have developed a novel brush working from conventional back-and-forth movement to rotation in situ (RIS) that may be more efficient for MBS sampling. We aimed to compare the MBS detection sensitivity of our RIS brush with that of the conventional brush. METHODS: In this multicenter prospective study, we enrolled patients who underwent endoscopic retrograde cholangiopancreatography for suspected MBSs involving biliary stricture brushings obtained using our RIS brush. The historical control group consisted of the 30-brushing arm of our previous randomized trial (patient inclusion, 2018-2020) that used the study design in the same centers and with the same endoscopists as were used in this study. The primary outcome was to compare the sensitivity and specificity of detecting MBSs by cytologic evaluation of biliary stricture brushings between the 2 groups. RESULTS: We enrolled 155 patients in the intent-to-treat analysis. Using the same number of brushing cycles, the RIS brush showed a higher sensitivity than the conventional brush (0.73 vs 0.56, P = 0.003). In per-protocol population, the sensitivity was also higher in the RIS brush group than in the conventional brush group (0.75 vs 0.57, P = 0.002). Multivariate analysis revealed that the RIS brush was the only predictive factor for MBS detection. No significant differences were observed in procedure-related complications between the 2 groups. DISCUSSION: The RIS brush was a promising tool for effective and safe MBS sampling and diagnosis. Further randomized studies are warranted to confirm our results (Chictr.org.cn, identifier: ChiCTR2100047270).

Deep Learning Prediction of Ovarian Malignancy at US Compared with O-RADS and Expert Assessment.

Background Deep learning (DL) algorithms could improve the classification of ovarian tumors assessed with multimodal US. Purpose To develop DL algorithms for the automated classification of benign versus malignant ovarian tumors assessed with US and to compare algorithm performance to Ovarian-Adnexal Reporting and Data System (O-RADS) and subjective expert assessment for malignancy. Materials and Methods This retrospective study included consecutive women with ovarian tumors undergoing gray scale and color Doppler US from January 2019 to November 2019. Histopathologic analysis was the reference standard. The data set was divided into training (70%), validation (10%), and test (20%) sets. Algorithms modified from residual network (ResNet) with two fusion strategies (feature fusion [hereafter, DLfeature] or decision fusion [hereafter, DLdecision]) were developed. DL prediction of malignancy was compared with O-RADS risk categorization and expert assessment by area under the receiver operating characteristic curve (AUC) analysis in the test set. Results A total of 422 women (mean age, 46.4 years ± 14.8 [SD]) with 304 benign and 118 malignant tumors were included; there were 337 women in the training and validation data set and 85 women in the test data set. DLfeature had an AUC of 0.93 (95% CI: 0.85, 0.97) for classifying malignant from benign ovarian tumors, comparable with O-RADS (AUC, 0.92; 95% CI: 0.85, 0.97; P = .88) and expert assessment (AUC, 0.97; 95% CI: 0.91, 0.99; P = .07), and similar to DLdecision (AUC, 0.90; 95% CI: 0.82, 0.96; P = .29). DLdecision, DLfeature, O-RADS, and expert assessment achieved sensitivities of 92%, 92%, 92%, and 96%, respectively, and specificities of 80%, 85%, 89%, and 87%, respectively, for malignancy. Conclusion Deep learning algorithms developed by using multimodal US images may distinguish malignant from benign ovarian tumors with diagnostic performance comparable to expert subjective and Ovarian-Adnexal Reporting and Data System assessment. © RSNA, 2022 Online supplemental material is available for this article.

Deep learning-based pelvic levator hiatus segmentation from ultrasound images.

Purpose: To automatically segment and measure the levator hiatus with a deep learning approach and evaluate the performance between algorithms, sonographers, and different devices. Methods: Three deep learning models (UNet-ResNet34, HR-Net, and SegNet) were trained with 360 images and validated with 42 images. The trained models were tested with two test sets. The first set included 138 images to evaluate the performance between the algorithms and sonographers. An independent dataset including 679 images assessed the performances of algorithms between different ultrasound devices. Four metrics were used for evaluation: DSC, HDD, the relative error of segmentation area, and the absolute error of segmentation area. Results: The UNet model outperformed HR-Net and SegNet. It could achieve a mean DSC of 0.964 for the first test set and 0.952 for the independent test set. UNet was creditable compared with three senior sonographers with a noninferiority test in the first test set and equivalent in the two test sets collected by different devices. On average, it took two seconds to process one case with a GPU and 2.4 s with a CPU. Conclusions: The deep learning approach has good performance for levator hiatus segmentation and good generalization ability on independent test sets. This automatic levator hiatus segmentation approach could help shorten the clinical examination time and improve consistency.

An efficient strategy based on liquid-liquid extraction and pH-zone-refining counter-current chromatography for selective enrichment, separation, and purification of alkaloids and organic Acids from natural products.

This study presents an efficient strategy based on liquid-liquid extraction and pH-zone-refining counter-current chromatography for selective enrichment, separation, and purification of alkaloids and organic acids from natural products. First, an acid or base modified two-phase solvent system with maximum or minimum partition coefficient was developed for the liquid-liquid extraction of the crude extract. As a result, alkaloids or organic acids could be selectively enriched in the upper or lower phase. Then pH-zone-refining counter-current chromatography was employed to separate and purify the selectively enriched alkaloids or organic acids efficiently. The selective enrichment and separation of five bufadienolide from toad venom of Bufo marinus was used as an example to show the advantage of this strategy. As a result, 759 mg of selectively enriched bufadienolide was obtained from 2 g of crude extract and the total content of five targets was increased from 14.64 to 83%. A total of 31 mg of marinobufagin-3-adipoyl-l-arginine, 42 mg of telocinobufagin-3-pimeloyl-l-arginine, 51 mg of telocinobufagin-3-suberoyl-l-arginine, 132 mg of marinobufagin-3-suberoyl-l-arginine, and 57 mg of bufalin-3-suberoyl-l-arginine were all simultaneously separated from 500 mg of selectively enriched sample, with the purity of 92.4, 97.5, 90.3, 92.1, and 92.8%, respectively.

The Chinese Mental Resilience Scale and its psychometric properties.

This study aimed to develop a Chinese Mental Resilience Scale. A total of 2500 healthy participants, in two representative samples of the Chinese population, were administered the scale. Exploratory factor analysis, confirmatory factor analysis, and correlation analysis were used to obtain the relevant coefficients and verify the reliability and validity of the scale. Five factors were extracted: willpower, family support, optimism and self-confidence, problem solving, and interpersonal interaction, plus a lying subscale, which together accounted for 54 percent of the total variance. The Chinese Mental Resilience Scale demonstrated good psychometric properties. It can be used to evaluate the mental resilience level of general Chinese population.