Intravoxel incoherent motion and enhanced T2*-weighted angiography for preoperative prediction of microvascular invasion in hepatocellular carcinoma.

Objective: To investigate the value of the combined application of intravoxel incoherent motion (IVIM) and enhanced T2*-weighted angiography (ESWAN) for preoperative prediction of microvascular invasion (MVI) in hepatocellular carcinoma (HCC). Materials and methods: 76 patients with pathologically confirmed HCC were retrospectively enrolled and divided into the MVI-positive group (n=26) and MVI-negative group (n=50). Conventional MRI, IVIM, and ESWAN sequences were performed. Three region of interests (ROIs) were placed on the maximum axial slice of the lesion on D, D*, and f maps derived from IVIM sequence, and R2* map derived from ESWAN sequence, and intratumoral susceptibility signal (ITSS) from the phase map derived from ESWAN sequence was also automatically measured. Receiver operating characteristic (ROC) curves were drawn to evaluate the ability for predicting MVI. Univariate and multivariate logistic regression were used to screen independent risk predictors in clinical and imaging information. The Delong's test was used to compare the differences between the area under curves (AUCs). Results: The D and D* values of MVI-negative group were significantly higher than those of MVI-positive group (P=0.038, and P=0.023), which in MVI-negative group were 0.892×10-3 (0.760×10-3, 1.303×10-3) mm2/s and 0.055 (0.025, 0.100) mm2/s, and in MVI-positive group were 0.591×10-3 (0.372×10-3, 0.824×10-3) mm2/s and 0.028 (0.006, 0.050)mm2/s, respectively. The R2* and ITSS values of MVI-negative group were significantly lower than those of MVI-positive group (P=0.034, and P=0.005), which in MVI-negative group were 29.290 (23.117, 35.228) Hz and 0.146 (0.086, 0.236), and in MVI-positive group were 43.696 (34.914, 58.083) Hz and 0.199 (0.155, 0.245), respectively. After univariate and multivariate analyses, only AFP (odds ratio, 0.183; 95% CI, 0.041-0.823; P = 0.027) was the independent risk factor for predicting the status of MVI. The AUCs of AFP, D, D*, R2*, and ITSS for prediction of MVI were 0.652, 0.739, 0.707, 0.798, and 0.657, respectively. The AUCs of IVIM (D+D*), ESWAN (R2*+ITSS), and combination (D+D*+R2*+ITSS) for predicting MVI were 0.772, 0.800, and, 0.855, respectively. When IVIM combined with ESWAN, the performance was improved with a sensitivity of 73.1% and a specificity of 92.0% (cut-off value: 0.502) and the AUC was significantly higher than AFP (P=0.001), D (P=0.038), D* (P=0.023), R2* (P=0.034), and ITSS (P=0.005). Conclusion: The IVIM and ESWAN parameters showed good efficacy in prediction of MVI in patients with HCC. The combination of IVIM and ESWAN may be useful for noninvasive prediction of MVI before clinical operation.

Elucidating the reproductive toxicity mechanisms in female zebrafish: A transcriptomic study of lifetime tris(2-chloroethyl) phosphate exposure.

Tris(2-chloroethyl) phosphate (TCEP), emerging as a predominant substitute for brominated flame retardants (BFRs), is now increasingly recognized as a prevalent contaminant in aquatic ecosystems. The extent of its reproductive toxicity in aquatic species, particularly in zebrafish (Danio rerio), remains insufficiently characterized. This study subjected zebrafish embryos to various concentrations of TCEP (0, 0.8, 4, 20, and 100 µg/L) over a period of 120 days, extending through sexual maturation, to assess its impact on female reproductive health. Notable reductions in body weight (0.59- and 0.76-fold) and length (0.71- and 0.77-fold) were observed at concentrations of 20 and 100 µg/L, with a concomitant decrease by 0.21- to 0.61-fold in the gonadal somatic index across all treatment groups. The reproductive output, as evidenced by egg production and hatchability, was adversely affected. Histopathological analysis suggested that TCEP exposure impedes ovarian development. Endocrine alterations were also evident, with testosterone and 11-ketotestosterone levels significantly diminished by 0.38- and 0.08-fold at the highest concentration tested, while 17ß-estradiol was elevated by 0.09- to 0.14-fold in all exposed groups. Transcriptomic profiling illuminated numerous differentially expressed genes (DEGs) integral to reproductive processes, including hormone regulation, neuroactive ligand-receptor interactions, oocyte meiosis, and progesterone-mediated maturation pathways. Collectively, these findings indicate that lifelong exposure to TCEP disrupts ovarian development and maturation in female zebrafish, alters gene expression within the hypothalamic-pituitary-gonadal axis, and perturbs sex hormone synthesis, culminating in pronounced reproductive toxicity.

A rho-type GTPase activating protein affects the growth and development of Cordyceps cicadae.

Cordyceps cicadae is recognized for its medicinal properties, attributed to bioactive constituents like polysaccharides and adenosine, which have been shown to improve kidney and liver functions and possess anti-tumor properties. Rho GTPase activating proteins (Rho GAPs) serve as inhibitory regulators of Rho GTPases in eukaryotic cells by accelerating the GTP hydrolysis of Rho GTPases, leading to their inactivation. In this study, we explored the function of the CcRga8 gene in C. cicadae, which encodes a Rho-type GTPase activating protein. Our study found that the knockout of CcRga8 resulted in a decrease in polysaccharide levels and an increase in adenosine concentration. Furthermore, the mutants exhibited altered spore yield and morphology, fruiting body development, decreased infectivity, reduced resistance to hyperosmotic stress, oxidative conditions, and cell wall inhibitors. These findings suggest that CcRga8 plays a crucial role in the development, stress response, and bioactive compound production of C. cicadae.

Black rice diet alleviates colorectal cancer development through modulating tryptophan metabolism and activating AHR pathway.

Consumption of dietary fiber and anthocyanin has been linked to a lower incidence of colorectal cancer (CRC). This study scrutinizes the potential antitumorigenic attributes of a black rice diet (BRD), abundantly rich in dietary fiber and anthocyanin. Our results demonstrate notable antitumorigenic effects in mice on BRD, indicated by a reduction in both the size and number of intestinal tumors and a consequent extension in life span, compared to control diet-fed counterparts. Furthermore, fecal transplants from BRD-fed mice to germ-free mice led to a decrease in colonic cell proliferation, coupled with maintained integrity of the intestinal barrier. The BRD was associated with significant shifts in gut microbiota composition, specifically an augmentation in probiotic strains Bacteroides uniformis and Lactobacillus. Noteworthy changes in gut metabolites were also documented, including the upregulation of indole-3-lactic acid and indole. These metabolites have been identified to stimulate the intestinal aryl hydrocarbon receptor pathway, inhibiting CRC cell proliferation and colorectal tumorigenesis. In summary, these findings propose that a BRD may modulate the progression of intestinal tumors by fostering protective gut microbiota and metabolite profiles. The study accentuates the potential health advantages of whole-grain foods, emphasizing the potential utility of black rice in promoting health.

Protocol for CHAMPION study: a prospective study of maximal-cytoreductive therapies for patients with de novo metastatic hormone-sensitive prostate cancer who achieve oligopersistent metastases during systemic treatment with apalutamide plus androgen deprivation therapy.

BACKGROUND: The proposed trial is to examine the feasibility of prostate-specific membrane antigen (PSMA) positron emission tomography/computed tomography (PET/CT)-guided cytoreduction plus apalutamide and androgen deprivation therapy (ADT) for newly diagnosed metastatic hormone-sensitive prostate cancer (mHSPC) at oligometastatic state. METHODS: CHAMPION (NCT05717582) is an open-label, single-arm, phase II trial, planning to enroll newly diagnosed mHSPC cases with oligometastases (≤ 10 distant metastatic sites in conventional imaging). Patients will receive 6 cycles of apalutamide plus ADT. Patients with oligometastatic disease at PSMA PET/CT after 3 treatment cycles will receive cytoreductive radical prostatectomy. PSMA PET/CT-guided metastasis-directed external radiation therapy will be determined by the investigators. Apalutamide plus ADT will be continued for 2 weeks postoperatively. The primary endpoint is the proportion of patients with undetectable prostate-specific antigen (PSA), no disease progression, and no symptom deterioration after 6 cycles of apalutamide plus ADT. Secondary endpoints include the percentage of patients with PSA ≤ 0.2 ng/mL and oligometastases by the end of 3 treatment cycles, PSA response rate, and safety. Fleming's two-stage group sequential design will be adopted in the study, where the null hypothesis is that the rate of patients with an undetectable PSA is ≤ 40% after 6 cycles of treatment, while the alternate hypothesis is an undetectable PSA of > 60%; with one-sided α = 0.05, power = 0.80, and an assumed dropout rate of 10%, the required number of patients for an effective analysis is 47. Enrolment in the study commenced in May 2023. DISCUSSION: The multi-modal therapy based on treatment response may improve the prognosis of newly diagnosed mHSPC patients with oligometastases. TRIAL REGISTRATION: The study is registered with Clinical Trials.Gov (NCT05717582). Registered on 8th February 2023.

Roles of CcDFR and CcOMT9 in the cyanidin biosynthesis and development of Cordyceps cicadae.

Introduction: Cordyceps cicadae is a traditional Chinese medicinal fungus known for its rich production of bioactive substances, particularly cyanidin, an anthocyanin commonly found in plants with notable anti-inflammatory, anti-tumor, antiviral, and antibacterial properties. This study revealed two key genes, CcDFR and CcOMT9, affecting cyanidin biosynthesis in C. cicadae. Methods: The roles of these genes in cyanidin production, growth, and development were elucidated through the gene knockout method, phenotypic analysis, transcriptomics, and metabolomics. Results: CcDFR deletion led to reduced cyanidin-3-O-glucoside (C3G), suppressed expression of cyanidin biosynthesis genes, impaired synnemata formation, decreased polysaccharide and adenosine content, and diminished chitinase activity. Meanwhile, the ΔCcOMT9 mutant exhibited an increase in C3G production, promoted expression of cyanidin biosynthesis genes and rising bioactive compounds, suppressed RNA methylation, and led to phenylalanine accumulation with no effect on fruiting body formation. Discussion: We revealed a distinct anthocyanin biosynthesis pathway in C. cicadae and identified two genes with opposite functions, laying the foundation for future genetic modification of cyanidin-producing strains using modern biological techniques. This will shorten the production period of this valuable compound, facilitating the industrial-scale production of cyanidin.

IL-17A deficiency inhibits lung cancer-induced osteoclastogenesis by promoting apoptosis of osteoclast precursor cells.

Osteoclasts are crucial in the events leading to bone metastasis of lung cancer. Interleukin-17A (IL-17A) affects osteogenesis by regulating the survival of osteoclast precursors (OCPs) and is enriched in lung cancer cells. However, how factors derived from tumor cells that metastasize to bone affect osteoclastogenesis remains poorly understood. We examined whether IL-17A derived from lung cancer cells affects osteoclast differentiation by regulating OCP apoptosis. IL-17A expression was inhibited in A549 non-small cell lung cancer cells using RNA interference. Compared with conditioned medium (CM) from A549 cells (A549-CM), CM from IL-17A-deficient A549 cells (A549-si-CM) suppressed osteoclastogenesis. The mRNA expression of osteoclast-specific genes was downregulated following A549-si-CM treatment. Furthermore, A549-si-CM promoted osteoclast precursor apoptosis at an early stage of osteoclastogenesis, which was related to the promotion of caspase-3 expression by A549-si-CM during osteoclast differentiation. In vivo experiments also showed that inhibition of IL-17A expression in A549 cells reduced osteoclast activation and bone tissue destruction. Collectively, our results indicate that IL-17A deficiency inhibits lung cancer-induced osteoclast differentiation by promoting apoptosis of osteoclast precursors in the early stage of osteoclast formation and that IL-17A is a potential therapeutic target for cancer-associated bone resorption in patients with lung cancer.

Two-stage multi-task deep learning framework for simultaneous pelvic bone segmentation and landmark detection from CT images.

PURPOSE: Pelvic bone segmentation and landmark definition from computed tomography (CT) images are prerequisite steps for the preoperative planning of total hip arthroplasty. In clinical applications, the diseased pelvic anatomy usually degrades the accuracies of bone segmentation and landmark detection, leading to improper surgery planning and potential operative complications. METHODS: This work proposes a two-stage multi-task algorithm to improve the accuracy of pelvic bone segmentation and landmark detection, especially for the diseased cases. The two-stage framework uses a coarse-to-fine strategy which first conducts global-scale bone segmentation and landmark detection and then focuses on the important local region to further refine the accuracy. For the global stage, a dual-task network is designed to share the common features between the segmentation and detection tasks, so that the two tasks mutually reinforce each other's performance. For the local-scale segmentation, an edge-enhanced dual-task network is designed for simultaneous bone segmentation and edge detection, leading to the more accurate delineation of the acetabulum boundary. RESULTS: This method was evaluated via threefold cross-validation based on 81 CT images (including 31 diseased and 50 healthy cases). The first stage achieved DSC scores of 0.94, 0.97, and 0.97 for the sacrum, left and right hips, respectively, and an average distance error of 3.24 mm for the bone landmarks. The second stage further improved the DSC of the acetabulum by 5.42%, and this accuracy outperforms the state-of-the-arts (SOTA) methods by 0.63%. Our method also accurately segmented the diseased acetabulum boundaries. The entire workflow took ~ 10 s, which was only half of the U-Net run time. CONCLUSION: Using the multi-task networks and the coarse-to-fine strategy, this method achieved more accurate bone segmentation and landmark detection than the SOTA method, especially for diseased hip images. Our work contributes to accurate and rapid design of acetabular cup prostheses.

Annotation-efficient training of medical image segmentation network based on scribble guidance in difficult areas.

PURPOSE: The training of deep medical image segmentation networks usually requires a large amount of human-annotated data. To alleviate the burden of human labor, many semi- or non-supervised methods have been developed. However, due to the complexity of clinical scenario, insufficient training labels still causes inaccurate segmentation in some difficult local areas such as heterogeneous tumors and fuzzy boundaries. METHODS: We propose an annotation-efficient training approach, which only requires scribble guidance in the difficult areas. A segmentation network is initially trained with a small amount of fully annotated data and then used to produce pseudo labels for more training data. Human supervisors draw scribbles in the areas of incorrect pseudo labels (i.e., difficult areas), and the scribbles are converted into pseudo label maps using a probability-modulated geodesic transform. To reduce the influence of the potential errors in the pseudo labels, a confidence map of the pseudo labels is generated by jointly considering the pixel-to-scribble geodesic distance and the network output probability. The pseudo labels and confidence maps are iteratively optimized with the update of the network, and the network training is promoted by the pseudo labels and the confidence maps in turn. RESULTS: Cross-validation based on two data sets (brain tumor MRI and liver tumor CT) showed that our method significantly reduces the annotation time while maintains the segmentation accuracy of difficult areas (e.g., tumors). Using 90 scribble-annotated training images (annotated time: ~ 9 h), our method achieved the same performance as using 45 fully annotated images (annotation time: > 100 h) but required much shorter annotation time. CONCLUSION: Compared to the conventional full annotation approaches, the proposed method significantly saves the annotation efforts by focusing the human supervisions on the most difficult regions. It provides an annotation-efficient way for training medical image segmentation networks in complex clinical scenario.

Deep learning-based workflow for hip joint morphometric parameter measurement from CT images.

Objective.Precise hip joint morphometry measurement from CT images is crucial for successful preoperative arthroplasty planning and biomechanical simulations. Although deep learning approaches have been applied to clinical bone surgery planning, there is still a lack of relevant research on quantifying hip joint morphometric parameters from CT images.Approach.This paper proposes a deep learning workflow for CT-based hip morphometry measurement. For the first step, a coarse-to-fine deep learning model is designed for accurate reconstruction of the hip geometry (3D bone models and key landmark points). Based on the geometric models, a robust measurement method is developed to calculate a full set of morphometric parameters, including the acetabular anteversion and inclination, the femoral neck shaft angle and the inclination, etc. Our methods were validated on two datasets with different imaging protocol parameters and further compared with the conventional 2D x-ray-based measurement method.Main results. The proposed method yields high bone segmentation accuracies (Dice coefficients of 98.18% and 97.85%, respectively) and low landmark prediction errors (1.55 mm and 1.65 mm) on both datasets. The automated measurements agree well with the radiologists' manual measurements (Pearson correlation coefficients between 0.47 and 0.99 and intraclass correlation coefficients between 0.46 and 0.98). This method provides more accurate measurements than the conventional 2D x-ray-based measurement method, reducing the error of acetabular cup size from over 2 mm to less than 1 mm. Moreover, our morphometry measurement method is robust against the error of the previous bone segmentation step. As we tested different deep learning methods for the prerequisite bone segmentation, our method produced consistent final measurement results, with only a 0.37 mm maximum inter-method difference in the cup size.Significance. This study proposes a deep learning approach with improved robustness and accuracy for pelvis arthroplasty planning.

Noncoplanar Volumetric Modulated Arc Therapy for Hepatocellular Carcinoma Based on a Cage-Like Radiotherapy System: A Simulation Study.

BACKGROUND: The incorporation of noncoplanar beam arrangements has been proposed in liver radiotherapy modalities, which can reduce the dose in normal tissues compared to coplanar techniques. Noncoplanar radiotherapy techniques for hepatocellular carcinoma treatment based on the Linac design have a limited effective arc angle to avoid collisions. PURPOSE: To propose a novel noncoplanar volumetric modulated arc therapy technique based on a cage-like radiotherapy system and investigate its performance in hepatocellular carcinoma patients. METHODS: The computed tomography was deflected 90° to meet the structure of a cage-like radiotherapy system and design the noncoplanar volumetric modulated arc therapy technique based on a cage-like radiotherapy system plan in the Pinnacle3 planning system. An noncoplanar volumetric modulated arc therapy technique based on a cage-like radiotherapy system plan was customized for each of 10 included hepatocellular carcinoma patients, with 6 dual arcs ranging from -30° to 30°. Six couch angles were set with an interval of 36° and distributed along with the longest diameter of planning target volume. The dosimetric parameters of noncoplanar volumetric modulated arc therapy technique based on a cage-like radiotherapy system plan were compared with the noncoplanar volumetric modulated arc therapy and volumetric modulated arc therapy plan. RESULTS: The 3 radiotherapy techniques regarding planning target volume were statistically different for D98%, D2%, conformity index, and homogeneity index with χ2 = 9.692, 14.600, 8.600, and 12.600, and P = .008, .001, .014, and .002, respectively. Further multiple comparisons revealed that noncoplanar volumetric modulated arc therapy technique based on a cage-like radiotherapy system significantly reduced the mean dose (P = .005) and V5 (P = .005) of the normal liver, the mean dose (P = .005) of the stomach, and V30 (P = .028) of the lung compared to noncoplanar volumetric modulated arc therapy. Noncoplanar volumetric modulated arc therapy technique based on a cage-like radiotherapy system significantly reduced the mean dose (P = .005) and V5 (P = .005) of the normal liver, the mean dose (P = .017) of the spinal cord, V50 (P = .043) of the duodenum, the maximum dose (P = .007) of the esophagus, and V30 (P = .047) of the whole lung compared to volumetric modulated arc therapy. The results indicate that noncoplanar volumetric modulated arc therapy technique based on a cage-like radiotherapy system protects the normal liver, stomach, and lung better than noncoplanar volumetric modulated arc therapy and protects the normal liver, spinal cord, duodenum, esophagus, and lung better than volumetric modulated arc therapy. CONCLUSIONS: The noncoplanar volumetric modulated arc therapy technique based on a cage-like radiotherapy system technique with the arrangement of noncoplanar arcs provided optimal dosimetric gains compared with noncoplanar volumetric modulated arc therapy and volumetric modulated arc therapy, except for the heart. Noncoplanar volumetric modulated arc therapy technique based on a cage-like radiotherapy system should be considered in more clinically challenging cases.

[Hepatocellular carcinoma segmentation and pathological differentiation degree prediction method based on multi-task learning].

Hepatocellular carcinoma (HCC) is the most common liver malignancy, where HCC segmentation and prediction of the degree of pathological differentiation are two important tasks in surgical treatment and prognosis evaluation. Existing methods usually solve these two problems independently without considering the correlation of the two tasks. In this paper, we propose a multi-task learning model that aims to accomplish the segmentation task and classification task simultaneously. The model consists of a segmentation subnet and a classification subnet. A multi-scale feature fusion method is proposed in the classification subnet to improve the classification accuracy, and a boundary-aware attention is designed in the segmentation subnet to solve the problem of tumor over-segmentation. A dynamic weighted average multi-task loss is used to make the model achieve optimal performance in both tasks simultaneously. The experimental results of this method on 295 HCC patients are superior to other multi-task learning methods, with a Dice similarity coefficient (Dice) of (83.9 ± 0.88)% on the segmentation task, while the average recall is (86.08 ± 0.83)% and an F1 score is (80.05 ± 1.7)% on the classification task. The results show that the multi-task learning method proposed in this paper can perform the classification task and segmentation task well at the same time, which can provide theoretical reference for clinical diagnosis and treatment of HCC patients.

IL-17A regulates autophagy and promotes osteoclast differentiation through the ERK/mTOR/Beclin1 pathway.

Bone is a frequent target of tumor metastasis, with high incidence rate and poor prognosis. Osteoclasts play a key role in the process of tumor bone metastasis. Interleukin-17A (IL-17A) is an inflammatory cytokine, highly expressed in a variety of tumor cells, that can alter the autophagic activity of other cells, thereby causing corresponding lesions. Previous studies have shown that low concentration IL-17A can promote osteoclastogenesis. The aim of this study was to clarify the mechanism of low concentration IL-17A promoting osteoclastogenesis by regulating autophagic activity. The results of our study showed that IL-17A could promote the differentiation of osteoclast precursors (OCPs) into osteoclasts in the presence of RANKL, and increase the mRNA levels of osteoclast-specific genes. Moreover, IL-17A increased the expression of Beclin1 by inhibiting the phosphorylation of ERK and mTOR, leading to enhanced autophagy of OCPs, accompanied by decreased OCP apoptosis. Furthermore, knockdown of Beclin1 and suppression of autophagy by 3-methyladenine (3-MA) significantly attenuated the enhanced osteoclastogenesis induced by IL-17A. In summary, these results indicate that low concentration IL-17A enhances the autophagic activity of OCPs through the ERK/mTOR/Beclin1 pathway during osteoclastogenesis, and further promotes osteoclast differentiation, suggesting that IL-17A may serve as a potential therapeutic target for cancer-related bone resorption in cancer patients.

Efficient contour-based annotation by iterative deep learning for organ segmentation from volumetric medical images.

PURPOSE: Training deep neural networks usually require a large number of human-annotated data. For organ segmentation from volumetric medical images, human annotation is tedious and inefficient. To save human labour and to accelerate the training process, the strategy of annotation by iterative deep learning recently becomes popular in the research community. However, due to the lack of domain knowledge or efficient human-interaction tools, the current AID methods still suffer from long training time and high annotation burden. METHODS: We develop a contour-based annotation by iterative deep learning (AID) algorithm which uses boundary representation instead of voxel labels to incorporate high-level organ shape knowledge. We propose a contour segmentation network with a multi-scale feature extraction backbone to improve the boundary detection accuracy. We also developed a contour-based human-intervention method to facilitate easy adjustments of organ boundaries. By combining the contour-based segmentation network and the contour-adjustment intervention method, our algorithm achieves fast few-shot learning and efficient human proofreading. RESULTS: For validation, two human operators independently annotated four abdominal organs in computed tomography (CT) images using our method and two compared methods, i.e. a traditional contour-interpolation method and a state-of-the-art (SOTA) convolutional network (CNN) method based on voxel label representation. Compared to these methods, our approach considerably saved annotation time and reduced inter-rater variabilities. Our contour detection network also outperforms the SOTA nnU-Net in producing anatomically plausible organ shape with only a small training set. CONCLUSION: Taking advantage of the boundary shape prior and the contour representation, our method is more efficient, more accurate and less prone to inter-operator variability than the SOTA AID methods for organ segmentation from volumetric medical images. The good shape learning ability and flexible boundary adjustment function make it suitable for fast annotation of organ structures with regular shape.

Waterproofing performance of polypropylene - concrete wall of underground silo under combined compressive stress and water pressure.

Safe, economical and high-quality storage of huge amount of grain for a longer duration under COVID-19 is a challenge and underground storage is a good alternative due to stable temperature, less cooling consumption and better pest control effect. However, the underground silo has very high requirement of waterproof and the performance of underground silo under combined compression and water pressure was rarely studied. In this study, a new composite structure, polypropylene - concrete wall (PPCW) for underground silo was proposed. Total three PPCWs with different size were manufactured to test the waterproofing features under joint effect of compression and hydropower of water. The strains, lateral displacement and cracking conditions of PPCWs were investigated. According to the experimental results, the PP board and concrete presented very good performance of interaction working under compression. The maximum water pressure of the specimens with stud spacing of 250mm increased by about 15.7% compared with that of the specimens with stud spacing of 350mm. The welding and strength of PP board has the greatest influence on the ultimate performance of PPCW. Based on the empirical coefficient method of concrete flat-slab and tested results, a new modified method was proposed to predict the bending moment at mid-span of PPCW by using an adjustment coefficient, R m . Considering this experimental case only, the adopting a R m = 0.64 could control the relative errors between test and analysis under 15.6%.

Influence of Voice Interactive Educational Robot Combined with Artificial Intelligence for the Development of Adolescents.

In the context of multicultural information, to explore and analyze the use effect of voice interactive educational robot in the classroom of adolescent students, and the physical and mental impact of movie characters on adolescent students, and to lay the foundation for studying the positive development of adolescents, under the guidance of positive psychology theory, the relationship between positive psychology and adolescent mental health is analyzed, the application of adolescent educational robot is discussed, and the relationship between adolescent educational robot combined with movies characters and positive development of adolescent is analyzed. The questionnaire is used to collect data, including the questionnaire on the influence of movie characters on the positive development of adolescents, the questionnaire of pre- and postpopularization of artificial intelligence in primary and secondary schools, and the satisfaction questionnaire of voice interactive educational robot. The reliability and validity of the questionnaire are analyzed. The results show that students who watch more than 20 movies are 20% in grade one and that is only 10% in grade three. 79% of the students think that the movie characters have an impact on themselves. The distribution of the number of the grade two students exposed to the movie is relatively uniform, 40% of them watch 10-20 movies, and 82% of them think that the movie characters have an impact on themselves; the Cronbach coefficient of classroom satisfaction questionnaire is 0.929 > 0.9, the average value of the correlation of total correction items of the corresponding item is 0.612 > 0.5, the Kaiser-Meyer-Olkin measurement value is 0.812 > 0.6, and the Sig value is 0.000 < 0.05, indicating that the reliability and validity of the questionnaire are very high and 92.3% of the students are very satisfied with the classroom. This shows that voice interactive educational robot combined with movie characters can promote the positive development of adolescents.

Clinical evaluation of a novel atlas-based PET/CT brain image segmentation and quantification method for epilepsy.

Background: Positron emission tomography (PET)/computed tomography (CT) with [18F]fluorodeoxyglucose {[18F]FDG} has been shown to be an effective imaging method for the lateralization and localization of epilepsy. However, the efficacy of PET/CT image processing and analysis needs to be improved for clinical application. Our previous research proposed a novel atlas-based image method for PET brain image segmentation and quantification; in this study, we evaluated its effectiveness in clinical patients. Methods: For image segmentation, a head anatomy template was registered to the subject image by integrating dual-modality image registration and landmark-constraint. The localizations of abnormalities were examined by quantitative comparison using the collected database. The PET/CT images of 20 reference patients and 11 patients with epilepsy were used to compare results between the proposed manual method and statistical parameter mapping (SPM). A dice coefficient analysis was performed on the six central brain regions to assess the segmentation effectiveness, and the diagnostic results of the epileptic regions were examined using pathological results as a reference. Results: The dice results of the proposed method were generally higher than those of SPM, with the averaged dice values for the proposed method and SPM being 0.78 and 0.55, respectively, in the reference group (P<0.001), and 0.73 and 0.48, respectively, in the epileptic group (P<0.001). Our proposed method detected all the pathologically reported epileptic defects; however, using the visual assessment method, epileptic defects were missed in three patients. Both the proposed and visual assessment methods incorrectly identified non-epileptic areas as epileptic areas. Conclusions: The results provide strong evidence of the feasibility of using our proposed method for accurate brain region segmentation in the diagnosis of epilepsy. Our atlas-based approach has promise for clinical application in the image processing and diagnosis of patients with epilepsy.

Combination of Androgen Deprivation Therapy with Radical Local Therapy Versus Androgen Deprivation Therapy Alone for Newly Diagnosed Oligometastatic Prostate Cancer: A Phase II Randomized Controlled Trial.

BACKGROUND: Previous studies suggested that men with metastatic prostate cancer might benefit from local treatment of the primary tumor. OBJECTIVE: To determine whether radical local therapy (RLT) improves survival for men with oligometastatic prostate cancer (OMPCa). DESIGN, SETTING, AND PARTICIPANTS: This open-label randomized controlled trial included patients with newly diagnosed OMPCa defined as five or fewer bone or extrapelvic lymph node metastases and no visceral metastases. INTERVENTION: Patients were randomly allocated to androgen deprivation therapy (ADT) or ADT and RLT. Men allocated RLT received either cytoreductive radical prostatectomy (RP) or prostate radiation therapy (RT) with a radical dose schedule. OUTCOME MEASUREMENTS AND STATISTICAL ANALYSIS: The primary outcome was radiographic progression-free survival (rPFS). Secondary outcomes were overall survival (OS) and prostate-specific antigen (PSA) progression-free survival. RESULTS AND LIMITATIONS: Between September 2015 and March 2019, 200 patients were randomized, with 100 men allocated to each group. The median age was 68 yr and the median PSA at diagnosis was 99 ng/ml. In the study group, 96 patients underwent RLT (85 RP and 11 RT). In the control group, 17 patients eventually received RLT (15 RP and two RT). All patients were included for an intention-to-treat analysis. After a median follow-up of -48 mo, the median rPFS was not reached in the study group and was 40 mo in the control group (hazard ratio [HR] 0.43, 95% confidence interval [CI] 0.27-0.70; p = 0.001). The 3-yr OS rate was 88% for the study group and 70% for the control group (HR 0.44, 95% CI 0.24-0.81; p = 0.008). CONCLUSIONS: Men with newly diagnosed OMPCa who received ADT plus RLT (mainly prostatectomy) had significantly higher rates of rPFS and OS than those who received ADT alone. PATIENT SUMMARY: This study investigated the effect of radical local therapy (RLT) of the primary tumor on survival in patents with oligometastatic prostate cancer. In our group, RLT improved radiographic progression-free and overall survival.

The novel transcriptomic signature of angiogenesis predicts clinical outcome, tumor microenvironment and treatment response for prostate adenocarcinoma.

Angiogenesis plays the critical roles in promoting tumor progression, aggressiveness, and metastasis. Although few studies have revealed some angiogenesis-related genes (ARGs) could serve as prognosis-related biomarkers for the prostate cancer (PCa), the integrated role of ARGs has not been systematically studied. The RNA-sequencing data and clinical information of prostate adenocarcinoma (PRAD) were downloaded from The Cancer Genome Atlas (TCGA) as discovery dataset. Twenty-three ARGs in total were identified to be correlated with prognosis of PRAD by the univariate Cox regression analysis, and a 19-ARG signature was further developed with significant correlation with the disease-free survival (DFS) of PRAD by the least absolute shrinkage and selection operator (LASSO) Cox regression with tenfold cross-validation. The signature stratified PRAD patients into high- and low-ARGs signature score groups, and those with high ARGs signature score were associated with significantly poorer outcomes (median DFS: 62.71 months vs unreached, p < 0.0001). The predicting ability of ARGs signature was subsequently validated in two independent cohorts of GSE40272 & PRAD_MSKCC. Notably, the 19-ARG signature outperformed the typical clinical features or each involved ARG in predicting the DFS of PRAD. Furthermore, a prognostic nomogram was constructed with three independent prognostic factors, including the ARGs signature, T stage and Gleason score. The predicted results from the nomogram (C-index = 0.799, 95%CI = 0.744-0.854) matched well with the observed outcomes, which was verified by the calibration curves. The values of area under receiver operating characteristic curve (AUC) for DFS at 1-, 3-, 5-year for the nomogram were 0.82, 0.83, and 0.83, respectively, indicating the performance of nomogram model is of reasonably high accuracy and robustness. Moreover, functional enrichment analysis demonstrated the potential targets of E2F targets, G2M checkpoint pathways, and cell cycle pathways to suppress the PRAD progression. Of note, the high-risk PRAD patients were more sensitive to immune therapies, but Treg might hinder benefits from immunotherapies. Additionally, this established tool also could predict response to neoadjuvant androgen deprivation therapy (ADT) and some chemotherapy drugs, such as cisplatin, paclitaxel, and docetaxel, etc. The novel ARGs signature, with prognostic significance, can further promote the application of targeted therapies in different stratifications of PCa patients.