Deep Learning-Based Multiparametric MRI Model for Preoperative T-Stage in Rectal Cancer.

BACKGROUND: Conventional MRI staging can be challenging in the preoperative assessment of rectal cancer. Deep learning methods based on MRI have shown promise in cancer diagnosis and prognostication. However, the value of deep learning in rectal cancer T-staging is unclear. PURPOSE: To develop a deep learning model based on preoperative multiparametric MRI for evaluation of rectal cancer and to investigate its potential to improve T-staging accuracy. STUDY TYPE: Retrospective. POPULATION: After cross-validation, 260 patients (123 with T-stage T1-2 and 134 with T-stage T3-4) with histopathologically confirmed rectal cancer were randomly divided to the training (N = 208) and test sets (N = 52). FIELD STRENGTH/SEQUENCE: 3.0 T/Dynamic contrast enhanced (DCE), T2-weighted imaging (T2W), and diffusion-weighted imaging (DWI). ASSESSMENT: The deep learning (DL) model of multiparametric (DCE, T2W, and DWI) convolutional neural network were constructed for evaluating preoperative diagnosis. The pathological findings served as the reference standard for T-stage. For comparison, the single parameter DL-model, a logistic regression model composed of clinical features and subjective assessment of radiologists were used. STATISTICAL TESTS: The receiver operating characteristic curve (ROC) was used to evaluate the models, the Fleiss' kappa for the intercorrelation coefficients, and DeLong test for compare the diagnostic performance of ROCs. P-values less than 0.05 were considered statistically significant. RESULTS: The Area Under Curve (AUC) of the multiparametric DL-model was 0.854, which was significantly higher than the radiologist's assessment (AUC = 0.678), clinical model (AUC = 0.747), and the single parameter DL-models including T2W-model (AUC = 0.735), DWI-model (AUC = 0.759), and DCE-model (AUC = 0.789). DATA CONCLUSION: In the evaluation of rectal cancer patients, the proposed multiparametric DL-model outperformed the radiologist's assessment, the clinical model as well as the single parameter models. The multiparametric DL-model has the potential to assist clinicians by providing more reliable and precise preoperative T staging diagnosis. EVIDENCE LEVEL: 3 TECHNICAL EFFICACY: Stage 2.

Preoperative Differentiation of HER2-Zero and HER2-Low from HER2-Positive Invasive Ductal Breast Cancers Using BI-RADS MRI Features and Machine Learning Modeling.

BACKGROUND: Accurate determination of human epidermal growth factor receptor 2 (HER2) is important for choosing optimal HER2 targeting treatment strategies. HER2-low is currently considered HER2-negative, but patients may be eligible to receive new anti-HER2 drug conjugates. PURPOSE: To use breast MRI BI-RADS features for classifying three HER2 levels, first to distinguish HER2-zero from HER2-low/positive (Task-1), and then to distinguish HER2-low from HER2-positive (Task-2). STUDY TYPE: Retrospective. POPULATION: 621 invasive ductal cancer, 245 HER2-zero, 191 HER2-low, and 185 HER2-positive. For Task-1, 488 cases for training and 133 for testing. For Task-2, 294 cases for training and 82 for testing. FIELD STRENGTH/SEQUENCE: 3.0 T; 3D T1-weighted DCE, short time inversion recovery T2, and single-shot EPI DWI. ASSESSMENT: Pathological information and BI-RADS features were compared. Random Forest was used to select MRI features, and then four machine learning (ML) algorithms: decision tree (DT), support vector machine (SVM), k-nearest neighbors (k-NN), and artificial neural nets (ANN), were applied to build models. STATISTICAL TESTS: Chi-square test, one-way analysis of variance, and Kruskal-Wallis test were performed. The P values <0.05 were considered statistically significant. For ML models, the generated probability was used to construct the ROC curves. RESULTS: Peritumoral edema, the presence of multiple lesions and non-mass enhancement (NME) showed significant differences. For distinguishing HER2-zero from non-zero (low + positive), multiple lesions, edema, margin, and tumor size were selected, and the k-NN model achieved the highest AUC of 0.86 in the training set and 0.79 in the testing set. For differentiating HER2-low from HER2-positive, multiple lesions, edema, and margin were selected, and the DT model achieved the highest AUC of 0.79 in the training set and 0.69 in the testing set. DATA CONCLUSION: BI-RADS features read by radiologists from preoperative MRI can be analyzed using more sophisticated feature selection and ML algorithms to build models for the classification of HER2 status and identify HER2-low. TECHNICAL EFFICACY: Stage 2.

Ribosomal protein L22-like1 promotes prostate cancer progression by activating PI3K/Akt/mTOR signalling pathway.

Prostate cancer (PCa) is one of the most common malignancies in men. Ribosomal protein L22-like1 (RPL22L1), a component of the ribosomal 60 S subunit, is associated with cancer progression, but the role and potential mechanism of RPL22L1 in PCa remain unclear. The aim of this study was to investigate the role of RPL22L1 in PCa progression and the mechanisms involved. Bioinformatics and immunohistochemistry analysis showed that the expression of RPL22L1 was significantly higher in PCa tissues than in normal prostate tissues. The cell function analysis revealed that RPL22L1 significantly promoted the proliferation, migration and invasion of PCa cells. The data of xenograft tumour assay suggested that the low expression of RPL22L1 inhibited the growth and invasion of PCa cells in vivo. Mechanistically, the results of Western blot proved that RPL22L1 activated PI3K/Akt/mTOR pathway in PCa cells. Additionally, LY294002, an inhibitor of PI3K/Akt pathway, was used to block this pathway. The results showed that LY294002 remarkably abrogated the oncogenic effect of RPL22L1 on PCa cell proliferation and invasion. Taken together, our study demonstrated that RPL22L1 is a key gene in PCa progression and promotes PCa cell proliferation and invasion via PI3K/Akt/mTOR pathway, thus potentially providing a new target for PCa therapy.

Development and evaluation of machine learning models and nomogram for the prediction of severe acute pancreatitis.

BACKGROUND AND AIM: Severe acute pancreatitis (SAP) in patients progresses rapidly and can cause multiple organ failures associated with high mortality. We aimed to train a machine learning (ML) model and establish a nomogram that could identify SAP, early in the course of acute pancreatitis (AP). METHODS: In this retrospective study, 631 patients with AP were enrolled in the training cohort. For predicting SAP early, five supervised ML models were employed, such as random forest (RF), K-nearest neighbors (KNN), and naive Bayes (NB), which were evaluated by accuracy (ACC) and the areas under the receiver operating characteristic curve (AUC). The nomogram was established, and the predictive ability was assessed by the calibration curve and AUC. They were externally validated by an independent cohort of 109 patients with AP. RESULTS: In the training cohort, the AUC of RF, KNN, and NB models were 0.969, 0.954, and 0.951, respectively, while the AUC of the Bedside Index for Severity in Acute Pancreatitis (BISAP), Ranson and Glasgow scores were only 0.796, 0.847, and 0.837, respectively. In the validation cohort, the RF model also showed the highest AUC, which was 0.961. The AUC for the nomogram was 0.888 and 0.955 in the training and validation cohort, respectively. CONCLUSIONS: Our findings suggested that the RF model exhibited the best predictive performance, and the nomogram provided a visual scoring model for clinical practice. Our models may serve as practical tools for facilitating personalized treatment options and improving clinical outcomes through pre-treatment stratification of patients with AP.

Moderate-Severe White Matter Lesion Predicts Delayed Intraventricular Hemorrhage in Intracerebral Hemorrhage.

BACKGROUND: Most existing studies have focused on the correlation between white matter lesion (WML) and baseline intraventricular hemorrhage (IVH) in patients with intracerebral hemorrhage (ICH), whereas few studies have investigated the relationship between WML severity and delayed IVH after admission. This study aimed to investigate the correlation between WML severity and delayed IVH and to verify the association between WML and baseline IVH. METHODS: A total of 480 patients with spontaneous ICH from February 2018 to October 2020 were selected. WML was scored using the Van Swieten Scale, with scores of 0-2 representing nonslight WML and scores of 3-4 representing moderate-severe WML. We determined the presence of IVH on baseline (< 6 h) and follow-up computed tomography (< 72 h) images. Univariate analysis and multiple logistic regression were used to analyze the influencing factors of baseline and delayed IVH. RESULTS: Among 480 patients with ICH, 172 (35.8%) had baseline IVH, and there was a higher proportion of moderate-severe WML in patients with baseline IVH (20.3%) than in those without baseline IVH (12.7%) (P = 0.025). Among 308 patients without baseline IVH, delayed IVH was found in 40 patients (12.9%), whose proportion of moderate-severe WML (25.0%) was higher than that in patients without delayed IVH (10.8%) (P = 0.012). Multiple logistic regression results showed that moderate-severe WML was independently correlated with baseline IVH (P = 0.006, odds ratio = 2.266, 95% confidence interval = 1.270-4.042) and delayed IVH (P = 0.002, odds ratio = 7.009, 95% confidence interval = 12.086-23.552). CONCLUSIONS: Moderate-severe WML was an independent risk factor for delayed IVH as well as baseline IVH.

Machine learning-based radiomics for multiple primary prostate cancer biological characteristics prediction with 18F-PSMA-1007 PET: comparison among different volume segmentation thresholds.

BACKGROUND: PET-based radiomics features could predict the biological characteristics of primary prostate cancer (PCa). However, the optimal thresholds to predict the biological characteristics of PCa are unknown. This study aimed to compare the predictive power of 18F-PSMA-1007 PET radiomics features at different thresholds for predicting multiple biological characteristics. METHODS: One hundred and seventy-three PCa patients with complete preoperative 18F-PSMA-1007 PET examination and clinical data before surgery were collected. The prostate lesions' volumes of interest were semi-automatically sketched with thresholds of 30%, 40%, 50%, and 60% maximum standardized uptake value (SUVmax). The radiomics features were respectively extracted. The prediction models of Gleason score (GS), extracapsular extension (ECE), and vascular invasion (VI) were established using the support vector machine. The performance of models from different thresholding regions was assessed using receiver operating characteristic curve and confusion matrix-derived indexes. RESULTS: For predicting GS, the 50% SUVmax model showed the best predictive performance in training (AUC, 0.82 [95%CI 0.74-0.88]) and testing cohorts (AUC, 0.80 [95%CI 0.66-0.90]). For predicting ECE, the 40% SUVmax model exhibit the best predictive performance (AUC, 0.77 [95%CI 0.68-0.84] and 0.77 [95%CI 0.63-0.88]). As for VI, the 50% SUVmax model had the best predictive performance (AUC, 0.74 [95%CI 0.65-0.82] and 0.74 [95%CI 0.56-0.82]). CONCLUSION: The 18F-1007-PSMA PET-based radiomics features at 40-50% SUVmax showed the best predictive performance for multiple PCa biological characteristics evaluation. Compared to the single PSA model, radiomics features may provide additional benefits in predicting the biological characteristics of PCa.

MTANS: Multi-Scale Mean Teacher Combined Adversarial Network with Shape-Aware Embedding for Semi-Supervised Brain Lesion Segmentation.

The annotation of brain lesion images is a key step in clinical diagnosis and treatment of a wide spectrum of brain diseases. In recent years, segmentation methods based on deep learning have gained unprecedented popularity, leveraging a large amount of data with high-quality voxel-level annotations. However, due to the limited time clinicians can provide for the cumbersome task of manual image segmentation, semi-supervised medical image segmentation methods present an alternative solution as they require only a few labeled samples for training. In this paper, we propose a novel semi-supervised segmentation framework that combines improved mean teacher and adversarial network. Specifically, our framework consists of (i) a student model and a teacher model for segmenting the target and generating the signed distance maps of object surfaces, and (ii) a discriminator network for extracting hierarchical features and distinguishing the signed distance maps of labeled and unlabeled data. Besides, based on two different adversarial learning processes, a multi-scale feature consistency loss derived from the student and teacher models is proposed, and a shape-aware embedding scheme is integrated into our framework. We evaluated the proposed method on the public brain lesion datasets from ISBI 2015, ISLES 2015, and BRATS 2018 for the multiple sclerosis lesion, ischemic stroke lesion, and brain tumor segmentation respectively. Experiments demonstrate that our method can effectively leverage unlabeled data while outperforming the supervised baseline and other state-of-the-art semi-supervised methods trained with the same labeled data. The proposed framework is suitable for joint training of limited labeled data and additional unlabeled data, which is expected to reduce the effort of obtaining annotated images.

RFDCR: Automated brain lesion segmentation using cascaded random forests with dense conditional random fields.

Segmentation of brain lesions from magnetic resonance images (MRI) is an important step for disease diagnosis, surgical planning, radiotherapy and chemotherapy. However, due to noise, motion, and partial volume effects, automated segmentation of lesions from MRI is still a challenging task. In this paper, we propose a two-stage supervised learning framework for automatic brain lesion segmentation. Specifically, in the first stage, intensity-based statistical features, template-based asymmetric features, and GMM-based tissue probability maps are used to train the initial random forest classifier. Next, the dense conditional random field optimizes the probability maps from the initial random forest classifier and derives the whole tumor regions referred as the region of interest (ROI). In the second stage, the optimized probability maps are further intergraded with features from the intensity-based statistical features and template-based asymmetric features to train subsequent random forest, focusing on classifying voxels within the ROI. The output probability maps will be also optimized by the dense conditional random fields, and further used to iteratively train a cascade of random forests. Through hierarchical learning of the cascaded random forests and dense conditional random fields, the multimodal local and global appearance information is integrated with the contextual information, and the output probability maps are improved layer by layer to finally obtain optimal segmentation results. We evaluated the proposed method on the publicly available brain tumor datasets BRATS 2015 & BRATS 2018, as well as the ischemic stroke dataset ISLES 2015. The results have shown that our framework achieves competitive performance compared to the state-of-the-art brain lesion segmentation methods. In addition, contralateral difference and skewness were identified as the important features in the brain tumor and ischemic stroke segmentation tasks, which conforms to the knowledge and experience of medical experts, further reflecting the reliability and interpretability of our framework.

Diagnosis of Benign and Malignant Breast Lesions on DCE-MRI by Using Radiomics and Deep Learning With Consideration of Peritumor Tissue.

BACKGROUND: Computer-aided methods have been widely applied to diagnose lesions detected on breast MRI, but fully-automatic diagnosis using deep learning is rarely reported. PURPOSE: To evaluate the diagnostic accuracy of mass lesions using region of interest (ROI)-based, radiomics and deep-learning methods, by taking peritumor tissues into consideration. STUDY TYPE: Retrospective. POPULATION: In all, 133 patients with histologically confirmed 91 malignant and 62 benign mass lesions for training (74 patients with 48 malignant and 26 benign lesions for testing). FIELD STRENGTH/SEQUENCE: 3T, using the volume imaging for breast assessment (VIBRANT) dynamic contrast-enhanced (DCE) sequence. ASSESSMENT: 3D tumor segmentation was done automatically by using fuzzy-C-means algorithm with connected-component labeling. A total of 99 texture and histogram parameters were calculated for each case, and 15 were selected using random forest to build a radiomics model. Deep learning was implemented using ResNet50, evaluated with 10-fold crossvalidation. The tumor alone, smallest bounding box, and 1.2, 1.5, 2.0 times enlarged boxes were used as inputs. STATISTICAL TESTS: The malignancy probability was calculated using each model, and the threshold of 0.5 was used to make a diagnosis. RESULTS: In the training dataset, the diagnostic accuracy was 76% using three ROI-based parameters, 84% using the radiomics model, and 86% using ROI + radiomics model. In deep learning using the per-slice basis, the area under the receiver operating characteristic (ROC) was comparable for tumor alone, smallest and 1.2 times box (AUC = 0.97-0.99), which were significantly higher than 1.5 and 2.0 times box (AUC = 0.86 and 0.71, respectively). For per-lesion diagnosis, the highest accuracy of 91% was achieved when using the smallest bounding box, and that decreased to 84% for tumor alone and 1.2 times box, and further to 73% for 1.5 times box and 69% for 2.0 times box. In the independent testing dataset, the per-lesion diagnostic accuracy was also the highest when using the smallest bounding box, 89%. DATA CONCLUSION: Deep learning using ResNet50 achieved a high diagnostic accuracy. Using the smallest bounding box containing proximal peritumor tissue as input had higher accuracy compared to using tumor alone or larger boxes. LEVEL OF EVIDENCE: 3 Technical Efficacy: Stage 2.

ZNF185-derived peptide induces fertility suppression in mice.

Zinc finger protein 185 (ZNF185) belongs to the ZNF family and is involved in male reproduction. However, it is unclear whether ZNF185 may be a target candidate for contraceptive vaccines. In this study, antigenic peptides derived from ZNF185 were prepared, and their immune contraceptive effects were investigated using mice. Results from enzyme-linked immunosorbent assay (ELISAs) showed that peptide immunization induced an antibody titre increase that reached a peak in week 12. Peptide-3 and peptide-4 were then chosen for subsequent experiments. The results of the fertility assays showed that peptide immunization inhibited the mating and fertility rates of the mice, whereas there were no obvious changes in the number of pups per litter. Subsequently, epididymal sperm was analysed. The results demonstrated that the sperm count and sperm motility were significantly decreased in the peptide group, while the amount of abnormal sperm was significantly increased in the peptide-3 group. The male reproductive organs were also evaluated. There were no obvious differences in testis or epididymal weights, in the diameters of the seminiferous tubules, or in the thicknesses of the seminiferous epithelium between the peptide group and the phosphate buffer saline (PBS) group. In addition, histological analysis indicated that there were no obvious pathologic changes in testis and epididymal histology in the peptide group; however, the number of spermatozoa present in the epididymal lumen of the peptide group was significantly decreased when compared with the PBS group. Our study demonstrates for the first time that peptides derived from ZNF185 may induce fertility suppression in mice without damaging reproductive organs. These peptides have the potential to be used as a male contraceptive vaccine.

Growth retardation induced by avian leukosis virus subgroup J associated with down-regulated Wnt/ß-catenin pathway.

Avian leukosis virus subgroup J (ALV-J), an oncogenic retrovirus, induces growth retardation and neoplasia in chickens, leading to enormous economic losses in poultry industry. Increasing evidences showed several signal pathways involved in ALV-J infection. However, what signaling pathway involved in growth retardation is largely unknown. To explore the possible signaling pathway, we tested the cell proliferation and associated miRNAs in ALV-J infected CEF cells by CCK-8 and Hiseq, respectively. The results showed that cell proliferation was significantly inhibited by ALV-J and three associated miRNAs were identified to target Wnt/ß-catenin pathway. To verify the Wnt/ß-catenin pathway involved in cell growth retardation, we analyzed the key molecules of Wnt pathway in ALV-J infected CEF cells. Our data demonstrated that protein expression of ß-catenin was decreased significantly post ALV-J infection compared with the normal (P < 0.05). The impact of this down-regulation caused low expression of known target genes (Axin2, CyclinD1, Tcf4 and Lef1). Further, to obtain in vivo evidence, we set up an ALV-J infection model. Post 7 weeks infection, ALV-J infected chickens showed significant growth retardation. Subsequent tests showed that the expression of ß-catenin, Tcf1, Tcf4, Lef1, Axin2 and CyclinD1 were down-regulated in muscles of growth retardation chickens. Taken together, all data demonstrated that chicken growth retardation caused by ALV-J associated with down-regulated Wnt/ß-catenin signaling pathway.

Avian leukosis virus subgroup J induces its receptor--chNHE1 up-regulation.

BACKGROUND: Avian leukosis virus subgroup J (ALV-J) is an oncogenic retrovirus which causes immunosuppression and neoplasia in meat-type and egg-type chickens. ALV-J infects host cells via specific interaction between the viral Env and the cell surface receptor -chicken sodium hydrogen exchanger type 1 (chNHE1). NHE1 involved in altering the cellular pH and playing a critical role in tumorigenesis. However, little is known about the other relationship between ALV-J and chNHE1. METHODS AND RESULTS: In ALV-J infected DF-1 cells, the mRNA level of chNHE1 was up-regulated with time-dependent manner tested by real time PCR, and accordingly, intracellular pH was increased tested by spectrofluorometer. In vivo, the mRNA level of chNHE1 was determined by real time PCR in ALV-J infected experimental chickens and field cases. The result showed that the mRNA level of chNHE1 was up-regulated after virus shedding, especially in continuous viremic shedders (CS group). However, no significant difference was found between non-shedding group (NS group) and control group. In field cases, mRNA level of chNHE1 was positively correlated with increasing ALV-J load in tumor bearing and immune tolerance chickens. Furthermore, immunohistochemistry results showed that the protein expression of chNHE1 was up-regulated in different organs of both experimental chickens and tumor bearing chickens compared with the control. CONCLUSION: Taken together, we conclude that ALV-J induces chNHE1 up-regulation in viremia and neoplasia chickens.

LPA signaling is required for dopaminergic neuron development and is reduced through low expression of the LPA1 receptor in a 6-OHDA lesion model of Parkinson's disease.

Lysophosphatidic acid (LPA) is a bioactive phospholipid that activates at least five known G-protein-coupled receptors (GPCRs): LPA1-LPA5. The nervous system is a major locus for LPA1 expression. LPA has been shown to regulate neuronal proliferation, migration, and differentiation during central nervous system development as well as neuronal survival. Furthermore, deficient LPA signaling has been implicated in several neurological disorders including neuropathic pain and schizophrenia. Parkinson's disease (PD) is a neurodegenerative movement disorder that results from the loss of dopaminergic (DA) neurons in the substantia nigra pars compacta (SNc). The specific molecular pathways that lead to DA neuron degeneration, however, are poorly understood. The influence of LPA in the differentiation of mesenchymal stem cells (MSCs) into DA neurons in vitro and LPA1 expression in a 6-hydroxydopamine (6-OHDA) lesion model of PD in vivo were examined in the present study. LPA induced neuronal differentiation in 80.2 % of the MSC population. These MSCs developed characteristic neuronal morphology and expressed the neuronal marker, neuron-specific enolase (NSE), while expression of the glial marker, glial fibrillary acidic protein (GFAP), was absent. Moreover, 27.6 % of differentiated MSCs were positive for tyrosine hydroxylase (TH), a marker for DA neurons. In the 6-OHDA PD rat model, LPA1 expression in the substantia nigra was significantly reduced compared to control. These results suggest LPA signaling via activation of LPA1 may be necessary for DA neuron development and survival. Furthermore, reduced LPA/LPA1 signaling may be involved in DA neuron degeneration thus contributing to the pathogenesis of PD.

miR-125b-1-3p inhibits trophoblast cell invasion by targeting sphingosine-1-phosphate receptor 1 in preeclampsia.

Preeclampsia (PE) is the leading cause of maternal and perinatal mortality and morbidity. Understanding the molecular mechanisms underlying placentation facilitates the development of better intervention of this disease. MicroRNAs are strongly implicated in the pathogenesis of this syndrome. In current study, we found that miR-125b-1-3p was elevated in placentas derived from preeclampsia patients. Transfection of miR-125b-1-3p mimics significantly inhibited the invasiveness of human trophoblast cells, whereas miR-125b-1-3p inhibitor enhanced trophoblast cell invasion. Luciferase assays identified that S1PR1 was a novel direct target of miR-125b-1-3p in the placenta. Overexpression of S1PR1 could reverse the inhibitory effect of miR-125b-1-3p on the invasion of trophoblast cells. These findings suggested that abnormal expression of miR-125b-1-3p might contribute to the pathogenesis of preeclampsia.

[Progress of research on intracranial pressure monitoring].

At present, the monitoring methods fwor intracranial pressure adopted in clinical practice are almost all invasive. The invasive monitoring methods for intracranial pressure were accurate, but they were harmful to the patient's body. Therefore, non-invasive methods for intracranial pressure monitoring must be developed. Since 1980, many non-invasive methods have been sprung out in succession, but they can not be used clinically. In this paper, research contents and progress of present non-invasive intracranial pressure monitoring are summarized. Advantages and disadvantages of various ways are analyzed. And finally, perspectives of development for intracranial pressure monitoring are presented.

Motion-artifact-augmented pseudo-label network for semi-supervised brain tumor segmentation.

MRI image segmentation is widely used in clinical practice as a prerequisite and a key for diagnosing brain tumors. The quest for an accurate automated segmentation method for brain tumor images, aiming to ease clinical doctors' workload, has gained significant attention as a research focal point. Despite the success of fully supervised methods in brain tumor segmentation, challenges remain. Due to the high cost involved in annotating medical images, the dataset available for training fully supervised methods is very limited. Additionally, medical images are prone to noise and motion artifacts, negatively impacting quality. In this work, we propose MAPSS, a motion-artifact-augmented pseudo-label network for semi-supervised segmentation. Our method combines motion artifact data augmentation with the pseudo-label semi-supervised training framework. We conduct several experiments under different semi-supervised settings on a publicly available dataset BraTS2020 for brain tumor segmentation. The experimental results show that MAPSS achieves accurate brain tumor segmentation with only a small amount of labeled data and maintains robustness in motion-artifact-influenced images. We also assess the generalization performance of MAPSS using the Left Atrium dataset. Our algorithm is of great significance for assisting doctors in formulating treatment plans and improving treatment quality.

Mechanism of balloon burst during transcatheter aortic valve replacement pre-dilatation: Image observation and validation by finite element analysis.

BACKGROUND: Balloon burst during transcatheter aortic valve replacement (TAVR) is serious complication. This study pioneers a novel approach by combining image observation and computer simulation validation to unravel the mechanism of balloon burst in a patient with bicuspid aortic valve (BAV) stenosis. METHOD: A new computational model for balloon pre-dilatation was developed by incorporating the element failure criteria according to the Law of Laplace. The effects of calcification and aortic tissue material parameters, friction coefficients, balloon types and aortic anatomy classification were performed to validate and compare the expansion behavior and rupture mode of actual balloon. RESULTS: Balloon burst was dissected into three distinct stages based on observable morphological changes. The mechanism leading to the complete transverse burst of the non-compliant balloon initiated at the folding edges, where contacted with heavily calcified masses at the right coronary sinus, resulting in high maximum principal stress. Local sharp spiked calcifications facilitated rapid crack propagation. The elastic moduli of calcification significantly influenced balloon expansion behavior and crack morphology. The simulation case of the calcific elastic modulus was set at 12.6 MPa could closely mirror clinical appearance of expansion behavior and crack pattern. Furthermore, the case of semi-compliant balloons introduced an alternative rupture mechanism as pinhole rupture, driven by local sharp spiked calcifications. CONCLUSIONS: The computational model of virtual balloons could effectively simulate balloon dilation behavior and burst mode during TAVR pre-dilation. Further research with a larger cohort is needed to investigate the balloon morphology during pre-dilation by using this method to guide prosthesis sizing for potential favorable outcomes.

Differential evolution-assisted salp swarm algorithm with chaotic structure for real-world problems.

There is a new nature-inspired algorithm called salp swarm algorithm (SSA), due to its simple framework, it has been widely used in many fields. But when handling some complicated optimization problems, especially the multimodal and high-dimensional optimization problems, SSA will probably have difficulties in convergence performance or dropping into the local optimum. To mitigate these problems, this paper presents a chaotic SSA with differential evolution (CDESSA). In the proposed framework, chaotic initialization and differential evolution are introduced to enrich the convergence speed and accuracy of SSA. Chaotic initialization is utilized to produce a better initial population aim at locating a better global optimal. At the same time, differential evolution is used to build up the search capability of each agent and improve the sense of balance of global search and intensification of SSA. These mechanisms collaborate to boost SSA in accelerating convergence activity. Finally, a series of experiments are carried out to test the performance of CDESSA. Firstly, IEEE CEC2014 competition fuctions are adopted to evaluate the ability of CDESSA in working out the real-parameter optimization problems. The proposed CDESSA is adopted to deal with feature selection (FS) problems, then five constrained engineering optimization problems are also adopted to evaluate the property of CDESSA in dealing with real engineering scenarios. Experimental results reveal that the proposed CDESSA method performs significantly better than the original SSA and other compared methods.

Microcystin­leucine arginine promotes colorectal cancer cell proliferation by activating the PI3K/Akt/Wnt/ß­catenin pathway.

Microcystin­leucine arginine (MC­LR) is an environmental toxin produced by cyanobacteria and is considered to be a potent carcinogen. However, to the best of our knowledge, the effect of MC­LR on colorectal cancer (CRC) cell proliferation has never been studied. The aim of the present study was to investigate the effect of MC­LR on CRC cell proliferation and the underlying mechanisms. Firstly, a Cell Counting Kit­8 (CCK­8) assay was conducted to determine cell viability at different concentrations, and 50 nM MC­LR was chosen for further study. Subsequently, a longer CCK­8 assay and a cell colony formation assay showed that MC­LR promoted SW620 and HT29 cell proliferation. Furthermore, western blotting analysis showed that MC­LR significantly upregulated protein expression of PI3K, p­Akt (Ser473), p­GSK3ß (Ser9), ß­catenin, c­myc and cyclin D1, suggesting that MC­LR activated the PI3K/Akt and Wnt/ß­catenin pathways in SW620 and HT29 cells. Finally, the pathway inhibitors LY294002 and ICG001 were used to validate the role of the PI3K/Akt and Wnt/ß­catenin pathways in MC­LR­accelerated cell proliferation. The results revealed that MC­LR activated Wnt/ß­catenin through the PI3K/Akt pathway to promote cell proliferation. Taken together, these data showed that MC­LR promoted CRC cell proliferation by activating the PI3K/Akt/Wnt/ß­catenin pathway. The present study provided a novel insight into the toxicological mechanism of MC­LR.

Cell division cycle 42 effector protein 4 inhibits prostate cancer progression by suppressing ERK signaling pathway.

Prostate cancer (PCa) is the most common malignancy among men worldwide. The cell division cycle 42 effector protein 4 (CDC42EP4) functions downstream of CDC42, yet its role and molecular mechanisms in PCa remain unexplored. This study aimed to elucidate the role of CDC42EP4 in the progression of PCa and its underlying mechanisms. Bioinformatical analysis indicated that CDC42EP4 expression was significantly lower in PCa tissue compared to normal prostate tissue. Cellular phenotyping analysis suggested that CDC42EP4 markedly inhibited the proliferation, migration, and invasion of PCa cells. Xenograft tumor assays further demonstrated that CDC42EP4 suppressed the growth of PCa cells in vivo. Mechanistically, the study established that CDC42EP4 inhibited the ERK pathway in PCa cells. Additionally, the ERK pathway inhibitor PD0325901 was employed, revealing that PD0325901 significantly nullified the effects of CDC42EP4 on PCa cell proliferation, migration, and invasion. Collectively, our findings demonstrate that CDC42EP4 acts as a critical tumor suppressor gene, inhibiting PCa cell proliferation, migration, and invasion through the ERK pathway, thereby presenting potential targets for PCa therapy.