Association between LRH-1 single nucleotide polymorphisms and unexplained recurrent spontaneous abortion in Chinese Han couples.

To explore the genetic relationships between LRH-1 (rs2816948), CYP19 (rs727479 and rs700518), and P450scc (rs4077582) as a potential mechanism behind unexplained recurrent spontaneous abortions in a Chinese Han population. A case-control study was used and featured two groups: Patients with unexplained recurrent miscarriage (n = 82, abortion group) and those who voluntary surrendered of a normal early pregnancy (n = 97, control group). Abortion villi samples were obtained from all patients. Genomic DNA was later extracted and sequenced, after which statistical analyses performed to assess the relationship between single nucleotide polymorphisms and unexplained recurrent spontaneous abortions. There were significant differences in the genotypic and allelic distribution (p < .05) for CYP19 (rs727479) between the abortion and the control groups. There were no significant differences in the genotypic or allelic distributions (p > .05) for either the LRH-1 (rs2816948) or CYP19 (rs700518). There were also significant genotypic differences (p < .05) for P450scc (rs4077582), but no significant differences for its allelic distribution (p > .05). There was a significant correlation between the occurrence of unexplained recurrent spontaneous abortion and CYP19 (rs727479) single nucleotide polymorphisms.

Deep learning-aided 3D proxy-bridged region-growing framework for multi-organ segmentation.

Accurate multi-organ segmentation in 3D CT images is imperative for enhancing computer-aided diagnosis and radiotherapy planning. However, current deep learning-based methods for 3D multi-organ segmentation face challenges such as the need for labor-intensive manual pixel-level annotations and high hardware resource demands, especially regarding GPU resources. To address these issues, we propose a 3D proxy-bridged region-growing framework specifically designed for the segmentation of the liver and spleen. Specifically, a key slice is selected from each 3D volume according to the corresponding intensity histogram. Subsequently, a deep learning model is employed to pinpoint the semantic central patch on this key slice, to calculate the growing seed. To counteract the impact of noise, segmentation of the liver and spleen is conducted on superpixel images created through proxy-bridging strategy. The segmentation process is then extended to adjacent slices by applying the same methodology iteratively, culminating in the comprehensive segmentation results. Experimental results demonstrate that the proposed framework accomplishes segmentation of the liver and spleen with an average Dice Similarity Coefficient of approximately 0.93 and a Jaccard Similarity Coefficient of around 0.88. These outcomes substantiate the framework's capability to achieve performance on par with that of deep learning methods, albeit requiring less guidance information and lower GPU resources.

A Colorectal Coordinate-Driven Method for Colorectum and Colorectal Cancer Segmentation in Conventional CT Scans.

Automated colorectal cancer (CRC) segmentation in medical imaging is the key to achieving automation of CRC detection, staging, and treatment response monitoring. Compared with magnetic resonance imaging (MRI) and computed tomography colonography (CTC), conventional computed tomography (CT) has enormous potential because of its broad implementation, superiority for the hollow viscera (colon), and convenience without needing bowel preparation. However, the segmentation of CRC in conventional CT is more challenging due to the difficulties presenting with the unprepared bowel, such as distinguishing the colorectum from other structures with similar appearance and distinguishing the CRC from the contents of the colorectum. To tackle these challenges, we introduce DeepCRC-SL, the first automated segmentation algorithm for CRC and colorectum in conventional contrast-enhanced CT scans. We propose a topology-aware deep learning-based approach, which builds a novel 1-D colorectal coordinate system and encodes each voxel of the colorectum with a relative position along the coordinate system. We then induce an auxiliary regression task to predict the colorectal coordinate value of each voxel, aiming to integrate global topology into the segmentation network and thus improve the colorectum's continuity. Self-attention layers are utilized to capture global contexts for the coordinate regression task and enhance the ability to differentiate CRC and colorectum tissues. Moreover, a coordinate-driven self-learning (SL) strategy is introduced to leverage a large amount of unlabeled data to improve segmentation performance. We validate the proposed approach on a dataset including 227 labeled and 585 unlabeled CRC cases by fivefold cross-validation. Experimental results demonstrate that our method outperforms some recent related segmentation methods and achieves the segmentation accuracy in DSC for CRC of 0.669 and colorectum of 0.892, reaching to the performance (at 0.639 and 0.890, respectively) of a medical resident with two years of specialized CRC imaging fellowship.

Deep learning for colorectal cancer detection in contrast-enhanced CT without bowel preparation: a retrospective, multicentre study.

BACKGROUND: Contrast-enhanced CT scans provide a means to detect unsuspected colorectal cancer. However, colorectal cancers in contrast-enhanced CT without bowel preparation may elude detection by radiologists. We aimed to develop a deep learning (DL) model for accurate detection of colorectal cancer, and evaluate whether it could improve the detection performance of radiologists. METHODS: We developed a DL model using a manually annotated dataset (1196 cancer vs 1034 normal). The DL model was tested using an internal test set (98 vs 115), two external test sets (202 vs 265 in 1, and 252 vs 481 in 2), and a real-world test set (53 vs 1524). We compared the detection performance of the DL model with radiologists, and evaluated its capacity to enhance radiologists' detection performance. FINDINGS: In the four test sets, the DL model had the area under the receiver operating characteristic curves (AUCs) ranging between 0.957 and 0.994. In both the internal test set and external test set 1, the DL model yielded higher accuracy than that of radiologists (97.2% vs 86.0%, p < 0.0001; 94.9% vs 85.3%, p < 0.0001), and significantly improved the accuracy of radiologists (93.4% vs 86.0%, p < 0.0001; 93.6% vs 85.3%, p < 0.0001). In the real-world test set, the DL model delivered sensitivity comparable to that of radiologists who had been informed about clinical indications for most cancer cases (94.3% vs 96.2%, p > 0.99), and it detected 2 cases that had been missed by radiologists. INTERPRETATION: The developed DL model can accurately detect colorectal cancer and improve radiologists' detection performance, showing its potential as an effective computer-aided detection tool. FUNDING: This study was supported by National Science Fund for Distinguished Young Scholars of China (No. 81925023); Regional Innovation and Development Joint Fund of National Natural Science Foundation of China (No. U22A20345); National Natural Science Foundation of China (No. 82072090 and No. 82371954); Guangdong Provincial Key Laboratory of Artificial Intelligence in Medical Image Analysis and Application (No. 2022B1212010011); High-level Hospital Construction Project (No. DFJHBF202105).

A combination of TEXTCNN model and Bayesian classifier for microblog sentiment analysis.

More and more individuals are paying attention to the research on the emotional information found in micro-blog comments. TEXTCNN is growing rapidly in the short text space. However, because the training model of TEXTCNN model itself is not very extensible and interpretable, it is difficult to quantify and evaluate the relative importance of features and themselves. At the same time, word embedding can't solve the problem of polysemy at one time. This research suggests a microblog sentiment analysis method based on TEXTCNN and Bayes that addresses this flaw. First, the word embedding vector is obtained by word2vec tool, and based on the word vector, the ELMo word vector integrating contextual features and different semantic features is generated by ELMo model. Second, the local features of ELMo word vector are extracted from multiple angles by using the convolution layer and pooling layer of TEXTCNN model. Finally, the training task of emotion data classification is completed by combining Bayes classifier. On the Stanford Sentiment Classification Corpus data set SST (Stanford Sentiment Classification Corpus Data bank), the experimental findings demonstrate that the model in this paper is compared with TEXTCNN, LSTM, and LSTM-TEXTCNN models. The Accuracy, Precision, Recall, and F1-score of the experimental results of this research have all greatly increased. Their values are respectively 0.9813, 0.9821, 0.9804 and 0.9812, which are superior to other comparison models and can be effectively used for emotional accurate analysis and identification of events in microblog emotion analysis.

Full-Spectrum-Knowledge-Aware Tensor Model for Energy-Resolved CT Iterative Reconstruction.

Energy-resolved computed tomography (ErCT) with a photon counting detector concurrently produces multiple CT images corresponding to different photon energy ranges. It has the potential to generate energy-dependent images with improved contrast-to-noise ratio and sufficient material-specific information. Since the number of detected photons in one energy bin in ErCT is smaller than that in conventional energy-integrating CT (EiCT), ErCT images are inherently more noisy than EiCT images, which leads to increased noise and bias in the subsequent material estimation. In this work, we first deeply analyze the intrinsic tensor properties of two-dimensional (2D) ErCT images acquired in different energy bins and then present a F ull- S pectrum-knowledge-aware Tensor analysis and processing (FSTensor) method for ErCT reconstruction to suppress noise-induced artifacts to obtain high-quality ErCT images and high-accuracy material images. The presented method is based on three considerations: (1) 2D ErCT images obtained in different energy bins can be treated as a 3-order tensor with three modes, i.e., width, height and energy bin, and a rich global correlation exists among the three modes, which can be characterized by tensor decomposition. (2) There is a locally piecewise smooth property in the 3-order ErCT images, and it can be captured by a tensor total variation regularization. (3) The images from the full spectrum are much better than the ErCT images with respect to noise variance and structural details and serve as external information to improve the reconstruction performance. We then develop an alternating direction method of multipliers algorithm to numerically solve the presented FSTensor method. We further utilize a genetic algorithm to tackle the parameter selection in ErCT reconstruction, instead of manually determining parameters. Simulation, preclinical and synthesized clinical ErCT results demonstrate that the presented FSTensor method leads to significant improvements over the filtered back-projection, robust principal component analysis, tensor-based dictionary learning and low-rank tensor decomposition with spatial-temporal total variation methods.

Multi-energy computed tomography reconstruction using a nonlocal spectral similarity model.

Multi-energy computed tomography (MECT) is able to acquire simultaneous multi-energy measurements from one scan. In addition, it allows material differentiation and quantification effectively. However, due to the limited energy bin width, the number of photons detected in an energy-specific channel is smaller than that in traditional CT, which results in image quality degradation. To address this issue, in this work, we develop a statistical iterative reconstruction algorithm to acquire high-quality MECT images and high-accuracy material-specific images. Specifically, this algorithm fully incorporates redundant self-similarities within nonlocal regions in the MECT image at one bin and rich spectral similarities among MECT images at all bins. For simplicity, the presented algorithm is referred to as 'MECT-NSS'. Moreover, an efficient optimization algorithm is developed to solve the MECT-NSS objective function. Then, a comprehensive evaluation of parameter selection for the MECT-NSS algorithm is conducted. In the experiment, the datasets include images from three phantoms and one patient to validate and evaluate the MECT-NSS reconstruction performance. The qualitative and quantitative results demonstrate that the presented MECT-NSS can successfully yield better MECT image quality and more accurate material estimation than the competing algorithms.

Attribute-based encryption scheme with multi-keyword search and supporting attribute revocation in cloud storage.

We propose an attribute-based encryption scheme with multi-keyword search and supporting attribute revocation in cloud storage environment, in which binary attributes and AND-gate access policy are used. Our proposal enjoys several advantages. Firstly, multi-keyword search is available, and only when a data user's attribute set satisfies access policy in keyword index, and keyword token generated by data user matches index successfully, then data user can obtain ciphertext containing keywords. In this way, more accurate keyword search is achievable. Secondly, the search privacy of data user is protected owing to cloud servers cannot obtain any knowledge of keywords which data user is interested in. Meanwhile, the ciphertext is able to be decrypted when data user's attribute set satisfies access policy specified in the ciphertext, which can both improve security of encryption and achieve secure fine-grained access control. Thirdly, the proposed scheme supports attribute revocation, in our scheme when a data user's attribute is revoked, the version number of attribute, non-revoked data users' secret keys and related ciphertexts will be updated, such that data user whose attribute is revoked does not decrypt updated ciphertext anymore. In addition, based on the assumption of decisional linear (DL) and decisional Diffie-Hellman (DDH), our scheme is proved to be secure against selectively chosen-keyword attacks and selectively chosen-plaintext attacks respectively, and it also ensures token privacy security.