Clinical evaluation of transvaginal myomectomy surgery: a retrospective study of 138 cases

Objectives: The aim of this study was to evaluate the safety, feasibility, and effectiveness of transvaginal myomectomy surgery. Material and methods: We conducted a retrospective study in Shengjing Hospital of China Medical University. In all, 138 patients underwent transvaginal myomectomy from March 2009 to March 2019. The perioperative clinical data, such as position and size of myomas, operative duration, blood loss, intraoperative and postoperative complications, and hospitalization time were retrospectively analyzed. Results: All transvaginal myomectomies were performed without conversion to laparotomy. The mean vaginal operating time was 56.0 (± 17.2) minutes. The mean operative estimated blood loss was 89.2 (± 36.8) mL. No significant intraoperative complications occurred. The median time of intestinal function recovery after operation was 1 day (range 1–4 days). The median time of hospital stay was 4 days (range 3–10 days); 12 (8.7%) patients experienced postoperative morbidity. Conclusions: Transvaginal myomectomy is a minimally invasive surgery that can be performed without leaving a scar on the body surface. It can be performed safely and effectively by a skilled surgeon in cases with a specific surgical indication for this approach

Transvaginal salpingo-oophorectomy with gasless laparoscopy — an optional pure natural orifice transluminal endoscopic surgery

Objectives: To establish the appropriate technique for salpingo-oophorectomy via transvaginal natural orifice transluminalendoscopic surgery (NOTES), under gasless laparoscopy.Material and methods: Ten patients with clinical indication underwent gasless laparoscopic transvaginal salpingo-oophorectomywith concurrent vaginal hysterectomy. An abdominal-wall lifting device was used after removal of the uterus,and the adnexa was removed trans-vaginally by gasless laparoscopy. The perioperative clinical data, such as operativeduration, volume of blood loss, morbidity, intraoperative and postoperative complications, and length of hospital stay,were retrospectively analyzed.Results: All procedures were successfully done, without any intraoperative or major postoperative complications, and noadditional transabdominal ports were required. The salpingo-oophorectomy part of the procedure was completed in approximately11–40 minutes, with minimal blood loss. All of the patients were discharged, scar-free, 2–4 days after surgery.Conclusions: Transvaginal NOTES with gasless laparoscopy is a feasible and safe surgical technique in cases involving difficultvaginal salpingo-oophorectomy, which avoids conversion to an abdominal route

Genetic variation in eight Chinese cattle breeds based on the analysis of microsatellite markers

Genetic variability and genetic relationships were investigated among eight Chinese cattle breeds using 12 microsatellite markers. Three hundred and fifty-two alleles were detected and the average number of alleles per locus ranged from 8.33 ± 1.67 in the Jiaxian breed to 21.33 ± 5.60 in the Qinchuan breed with a mean value of 13.91. The total number of alleles per microsatellite ranged from 21 (INRA005, HEL1) to 40 (HEL13), with a mean of 29.33 per locus. The fixation indices at the 12 loci in the eight breeds were very low with a mean of 0.006. A principal components analysis and the construction of a neighborjoining tree showed that these eight Chinese cattle breeds cluster into three groups i.e. the Yanbian andChineseHolstein, theNanyang and Jiaxian, and the four remaining breeds.This clustering agrees with the origin and geographical distributions of these Chinese breeds

HS-Pose: Hybrid Scope Feature Extraction for Category-level Object Pose Estimation

In this paper, we focus on the problem of category-level object pose estimation, which is challenging due to the large intra-category shape variation. 3D graph convolution (3D-GC) based methods have been widely used to extract local geometric features, but they have limitations for complex shaped objects and are sensitive to noise. Moreover, the scale and translation invariant properties of 3D-GC restrict the perception of an object's size and translation information. In this paper, we propose a simple network structure, the HS-layer, which extends 3D-GC to extract hybrid scope latent features from point cloud data for category-level object pose estimation tasks. The proposed HS-layer: 1) is able to perceive local-global geometric structure and global information, 2) is robust to noise, and 3) can encode size and translation information. Our experiments show that the simple replacement of the 3D-GC layer with the proposed HS-layer on the baseline method (GPV-Pose) achieves a significant improvement, with the performance increased by 14.5% on 5d2cm metric and 10.3% on IoU75. Our method outperforms the state-of-the-art methods by a large margin (8.3% on 5d2cm, 6.9% on IoU75) on the REAL275 dataset and runs in real-time (50 FPS).Comment: Accepted by the 2023 IEEE/CVF Computer Vision and Pattern Recognition Conference (CVPR

On the Evolution of Knowledge Graphs: A Survey and Perspective

Knowledge graphs (KGs) are structured representations of diversified knowledge. They are widely used in various intelligent applications. In this article, we provide a comprehensive survey on the evolution of various types of knowledge graphs (i.e., static KGs, dynamic KGs, temporal KGs, and event KGs) and techniques for knowledge extraction and reasoning. Furthermore, we introduce the practical applications of different types of KGs, including a case study in financial analysis. Finally, we propose our perspective on the future directions of knowledge engineering, including the potential of combining the power of knowledge graphs and large language models (LLMs), and the evolution of knowledge extraction, reasoning, and representation

[1,5]-Hydride Shift-Cyclization versus C(sp2)-H Functionalization in the Knoevenagel-Cyclization Domino Reactions of 1,4- and 1,5-Benzoxazepines

Domino cyclization reactions of N-aryl-1,4- and 1,5-benzoxazepine derivatives involving [1,5]-hydride shift or C(sp2)-H functionalization were investigated. Neuroprotective and acetylcholinesterase activities of the products were studied. Domino Knoevenagel-[1,5]-hydride shift-cyclization reaction of N-aryl-1,4-benzoxazepine derivatives with 1,3-dicarbonyl reagents having active methylene group afforded the 1,2,8,9-tetrahydro-7bH-quinolino [1,2-d][1,4]benzoxazepine scaffold with different substitution pattern. The C(sp3)-H activation step of the tertiary amine moiety occurred with complete regioselectivity and the 6-endo cyclization took place in a complete diastereoselective manner. In two cases, the enantiomers of the chiral condensed new 1,4-benzoxazepine systems were separated by chiral HPLC, HPLC-ECD spectra were recorded, and absolute configurations were determined by time-dependent density functional theory- electronic circular dichroism (TDDFT-ECD) calculations. In contrast, the analogue reaction of the regioisomeric N-aryl-1,5-benzoxazepine derivative did not follow the above mechanism but instead the Knoevenagel intermediate reacted in an SEAr reaction [C(sp2)-H functionalization] resulting in a condensed acridane derivative. The AChE inhibitory assays of the new derivatives revealed that the acridane derivative had a 6.98 uM IC50 value

Using brain structural neuroimaging measures to predict psychosis onset for individuals at clinical high-risk

Machine learning approaches using structural magnetic resonance imaging (sMRI) can be informative for disease classification, although their ability to predict psychosis is largely unknown. We created a model with individuals at CHR who developed psychosis later (CHR-PS+) from healthy controls (HCs) that can differentiate each other. We also evaluated whether we could distinguish CHR-PS+ individuals from those who did not develop psychosis later (CHR-PS-) and those with uncertain follow-up status (CHR-UNK). T1-weighted structural brain MRI scans from 1165 individuals at CHR (CHR-PS+, n = 144; CHR-PS-, n = 793; and CHR-UNK, n = 228), and 1029 HCs, were obtained from 21 sites. We used ComBat to harmonize measures of subcortical volume, cortical thickness and surface area data and corrected for non-linear effects of age and sex using a general additive model. CHR-PS+ (n = 120) and HC (n = 799) data from 20 sites served as a training dataset, which we used to build a classifier. The remaining samples were used external validation datasets to evaluate classifier performance (test, independent confirmatory, and independent group [CHR-PS- and CHR-UNK] datasets). The accuracy of the classifier on the training and independent confirmatory datasets was 85% and 73% respectively. Regional cortical surface area measures-including those from the right superior frontal, right superior temporal, and bilateral insular cortices strongly contributed to classifying CHR-PS+ from HC. CHR-PS- and CHR-UNK individuals were more likely to be classified as HC compared to CHR-PS+ (classification rate to HC: CHR-PS+, 30%; CHR-PS-, 73%; CHR-UNK, 80%). We used multisite sMRI to train a classifier to predict psychosis onset in CHR individuals, and it showed promise predicting CHR-PS+ in an independent sample. The results suggest that when considering adolescent brain development, baseline MRI scans for CHR individuals may be helpful to identify their prognosis. Future prospective studies are required about whether the classifier could be actually helpful in the clinical settings.</p

Sampling for network motif detection and estimation of Q-matrix and learning trajectories in DINA model

Monte Carlo methods provide tools to conduct statistical inference on models that are difficult or impossible to compute analytically and are widely used in many areas of statistical applications, such as bioinformatics and psychometrics. This thesis develops several sampling algorithms to address open issues in network analysis and educational assessments. The first problem we investigate is network motif detection. Network motifs are substructures that appear significantly more often in the given network than in other random networks. Motif detection is crucial for discovering new characteristics in biological, developmental, and social networks. We propose a novel sequential importance sampling strategy to estimate subgraph frequencies and detect network motifs. The method is developed by sampling subgraphs sequentially node by node using a carefully chosen proposal distribution. The method generates subgraphs from a distribution close to uniform and performs better than competing methods. We apply the method to four real-world networks and demonstrate outstanding performance in practical examples. The other two issues are related to educational measurement in psychometrics. Cognitive diagnosis models (CDMs) are partially ordered latent class models to classify students into skill mastery profiles. In educational assessment, these models help researchers analyze students' mastery of skills and learning process based on their responses to test items. The deterministic inputs, noisy "AND" gate model (DINA) is a popular psychometric model for cognitive diagnosis. We investigate the estimation of Q-matrix in DINA model. Q matrix is a binary matrix which maps the test item to its corresponding required attributes. We propose a Bayesian framework for estimating the DINA Q matrix. The proposed algorithms ensure that the estimated Q matrices always satisfy the identifiability constraints. We present Monte Carlo simulations to support the accuracy of parameter recovery and apply our algorithms to Tatsuoka's fraction-subtraction dataset. The last project is related to the recovery of learning process. The increasing presence of electronic and online learning resources presents challenges and opportunities for psychometric techniques that can assist in the measurement of abilities and even hasten their mastery. CDMs can assist in carefully navigating through the training and assessment of these skills in e-learning applications. We propose a class of CDMs for modeling changes in attributes, which we refer to as learning trajectories. We focus on the development of Bayesian procedures for estimating parameters of a first-order hidden Markov model and apply the developed model to a spatial rotation experimental intervention