MISD-IR: material-image subspace decomposition-based iterative reconstruction with spectrum estimation for dual-energy computed tomography.

Background: Dual-energy computed tomography (DECT) is a promising technique, which can provide unique capability for material quantification. The iterative reconstruction of material maps requires spectral information and its accuracy is affected by spectral mismatch. Simultaneously estimating the spectra and reconstructing material maps avoids extra workload on spectrum estimation and the negative impact of spectral mismatch. However, existing methods are not satisfactory in image detail preservation, edge retention, and convergence rate. The purpose of this paper was to mine the similarity between the reconstructed images and the material images to improve the imaging quality, and to design an effective iteration strategy to improve the convergence efficiency. Methods: The material-image subspace decomposition-based iterative reconstruction (MISD-IR) with spectrum estimation was proposed for DECT. MISD-IR is an optimized model combining spectral estimation and material reconstruction with fast convergence speed and promising noise suppression capability. We proposed to reconstruct the material maps based on extended simultaneous algebraic reconstruction techniques and estimation of the spectrum with model spectral. To stabilize the iteration and alleviate the influence of errors, we introduced a weighted proximal operator based on the block coordinate descending algorithm (WP-BCD). Furthermore, the reconstructed computed tomography (CT) images were introduced to suppress the noise based on subspace decomposition, which relies on non-local regularization to prevent noise accumulation. Results: In numerical experiments, the results of MISD-IR were closer to the ground truth compared with other methods. In real scanning data experiments, the results of MISD-IR showed sharper edges and details. Compared with other one-step iterative methods in the experiment, the running time of MISD-IR was reduced by 75%. Conclusions: The proposed MISD-IR can achieve accurate material decomposition (MD) without known energy spectrum in advance, and has good retention of image edges and details. Compared with other one-step iterative methods, it has high convergence efficiency.

Staging liver fibrosis with various diffusion-weighted magnetic resonance imaging models.

BACKGROUND: Diffusion-weighted imaging (DWI) has been developed to stage liver fibrosis. However, its diagnostic performance is inconsistent among studies. Therefore, it is worth studying the diagnostic value of various diffusion models for liver fibrosis in one cohort. AIM: To evaluate the clinical potential of six diffusion-weighted models in liver fibrosis staging and compare their diagnostic performances. METHODS: This prospective study enrolled 59 patients suspected of liver disease and scheduled for liver biopsy and 17 healthy participants. All participants underwent multi-b value DWI. The main DWI-derived parameters included Mono-apparent diffusion coefficient (ADC) from mono-exponential DWI, intravoxel incoherent motion model-derived true diffusion coefficient (IVIM-D), diffusion kurtosis imaging-derived apparent diffusivity (DKI-MD), stretched exponential model-derived distributed diffusion coefficient (SEM-DDC), fractional order calculus (FROC) model-derived diffusion coefficient (FROC-D) and FROC model-derived microstructural quantity (FROC-µ), and continuous-time random-walk (CTRW) model-derived anomalous diffusion coefficient (CTRW-D) and CTRW model-derived temporal diffusion heterogeneity index (CTRW-α). The correlations between DWI-derived parameters and fibrosis stages and the parameters' diagnostic efficacy in detecting significant fibrosis (SF) were assessed and compared. RESULTS: CTRW-D (r = -0.356), CTRW-α (r = -0.297), DKI-MD (r = -0.297), FROC-D (r = -0.350), FROC-µ (r = -0.321), IVIM-D (r = -0.251), Mono-ADC (r = -0.362), and SEM-DDC (r = -0.263) were significantly correlated with fibrosis stages. The areas under the ROC curves (AUCs) of the combined index of the six models for distinguishing SF (0.697-0.747) were higher than each of the parameters alone (0.524-0.719). The DWI models' ability to detect SF was similar. The combined index of CTRW model parameters had the highest AUC (0.747). CONCLUSION: The DWI models were similarly valuable in distinguishing SF in patients with liver disease. The combined index of CTRW parameters had the highest AUC.

Structural changes in corticospinal tract profiling via multishell diffusion models and their relation to overall survival in glioblastoma.

PURPOSE: Advanced MR fiber tracking imaging reflects fiber bundle invasion by glioblastoma, particularly of the corticospinal tract (CST), which is more susceptible as the largest downstream fiber tracts. We aimed to investigate whether CST features can predict the overall survival of glioblastoma. METHODS: In this prospective secondary analysis, 40 participants (mean age, 58 years; 16 male) pathologically diagnosed with glioblastoma were enrolled. Diffusion spectrum MRI was used for CST reconstruction. Fifty morphological and diffusion indicators (DTI, DKI, NODDI, MAP and Q-space) were used to characterize the CST. Optimal parameters capturing fiber bundle damage were obtained through various grouping methods. Eventually, the correlation with overall survival was determined by the hazard ratios (HRs) from various Cox proportional hazard model combinations. RESULTS: Only intracellular volume fraction (ICVF) and non-Gaussianity (NG) values on the affected tumor level were significant in all four groups or stratified comparisons (all P < .05). During the median follow-up 698 days, only the ICVF on the affected tumor level was independently associated with overall survival, even after adjusting for all classic prognostic factors (HR [95 % CI]: 0.611 [0.403, 0.927], P = .021). Moreover, stratification by the ICVF on the affected tumor level successfully predicted risk (P < .01) and improved the C-index of the multivariate model (from 0.695 to 0.736). CONCLUSIONS: This study demonstrates a relationship between NODDI-derived CST features, ICVF on the affected tumor level, and overall survival in glioblastoma. Independent of classical prognostic factors for glioblastoma, a lower ICVF on the affected tumor level might predict a lower overall survival.

Crown-ether threaded covalent organic polyrotaxane framework (COPF) towards synergistic crown/Zn2+/photothermal/photodynamic antibacterial and infected wound healing therapy.

Controllable preparation of materials with new structure has always been the top priority of polymer materials science research. Here, the supramolecular binding strategy is adopted to develop covalent organic frameworks (COFs) with novel structures and functions. Based on this, a two-dimensional crown-ether ring threaded covalent organic framework (COF), denoted as Crown-COPF with intrinsic photothermal (PTT) and photodynamic (PDT) therapeutic capacity, was facilely developed using crown-ether threaded rotaxane and porphyrin as building blocks. Crown-COPF with discrete mechanically interlocked blocks in the open pore could be used as a molecular machine, in which crown-ether served as the wheel sliding along the axle under the laser stimulation. As a result, Crown-COPF combining with the bactericidal power of crown ether displayed a significant photothermal and photodynamic antibacterial activity towards both the Gram-negative (Escherichia coli) and Gram-positive (Staphylococcus aureus), far exceeding the traditional Crown-free COF. Noteworthily, the bactericidal performance could be further enhanced via impregnation of Zn2+ ions (Crown-COPF-Zn) flexible coordinated with the multiple coordination sites (crown-ether, bipyridine, and porphyrin), which not only endow the positive charge with the skeleton, enhancing its ability to bind to the bacterial membrane, but also introduce the bactericidal ability of zinc ions. Notably, in vivo experiments on mice with back infections indicates Crown-COPF-Zn with self-adaptive multinuclear zinc center, could effectively promote the repairing of wounds. This study paves a new avenue for the effectively preparation of porous polymers with brand new structure, which provides opportunities for COF and mechanically interlocked polymers (MIPs) research and applications.

Staging liver fibrosis by a continuous-time random-walk diffusion model.

PURPOSE: Noninvasive assessment of liver fibrosis holds significant clinical importance. We aimed to evaluate the clinical potential of using a continuous-time random-walk diffusion model (CTRW) for staging liver fibrosis. METHODS: This prospective study included 52 patients suspected of liver disease and scheduled for liver biopsy. All patients underwent multi-b value diffusion-weighted imaging (DWI) using a 1.5 T MR scanner to derive the anomalous diffusion coefficient (D) and temporal (α) and spatial (ß) diffusion heterogeneity indexes sourced from the CTRW. The mono-exponential DWI-derived apparent diffusion coefficient (ADC), transient elastography-derived liver stiffness measurement (LSM), aspartate aminotransferase-to-platelet ratio index (APRI), and fibrosis-4 (FIB-4) index were calculated. We assessed and compared the correlations of these parameters with fibrosis stages and their efficacy in staging liver fibrosis. RESULTS: Significant correlations with fibrosis stages were found for APRI (r = 0.336), FIB-4 (r = 0.351), LSM (r = 0.523), D (r = -0.458), and ADC (r = -0.473). Significant differences were observed between APRI, LSM, D, and ADC of different fibrosis stages. The diagnostic performance of an index that combined D, α, ß, ADC, and LSM was superior to that of ADC or LSM alone for fibrosis stage F ≥ 2 and better than the index that combined D, α, ß for fibrosis stage F ≥ 4. CONCLUSIONS: Accurate liver fibrosis staging was achieved with a model that combined CTRW-derived parameters (D, α, and ß), ADC, and LSM. The model could serve as a reliable tool for noninvasive fibrosis evaluation.

Heterogeneity matching and IDH prediction in adult-type diffuse gliomas: a DKI-based habitat analysis.

Objective: To explain adult-type diffuse gliomas heterogeneity through diffusion kurtosis imaging-based habitat characteristics and develop and validate a comprehensive model for predicting isocitrate dehydrogenase (IDH) status. Materials and methods: In this prospective secondary analysis, 103 participants (mean age, 52 years; range, 21-77; 54 [52%] male) pathologically diagnosed with adult-type diffuse gliomas were enrolled between June 2018 and February 2022. The Otsu method was used to generate habitat maps with mean diffusivity (MD) and mean kurtosis (MK) for a total of 4 subhabitats containing 16 habitat features. Habitat heatmaps were created based on the Pearson correlation coefficient. The Habitat imAging aNd clinicraD INtegrated prEdiction SyStem (HANDINESS) was created by combining clinical features, conventional MRI morphological features, and habitat image features. ROC, calibration curve, and decision curve analyses were used to select the optimal model after 32 pipelines for model training and validation. Results: In the restricted diffusion and high-density subhabitat, MK was highly correlated with MD (R2 = 0.999), volume (0.608) and percentage of volume (0.663), and this region had the highest MK value (P<.001). The unrestricted diffusion and low-density subhabitat had the highest MD value (P<.001). When MK was less than the Otsu threshold, there was still a difference between restricted diffusion and low-density and unrestricted diffusion and low-density subhabitats (P<.01). The HANDINESS enabled more accurate prediction of the IDH status in the training (AUC=0.951 [0.902-0.987]) and internal validation cohorts (0.938 [0.881-0.949]). AUC values for single-modality models and independent factors ranged from 0.593 to 0.916. Calibration and decision curve analyses showed that the HANDINESS demonstrated a high level of clinical applicability and predictive consistency. Conclusion: Diffusion kurtosis imaging-based habitat analysis provides additional important information on microscopic tumor spatial heterogeneity. The HANDINESS has higher diagnostic performance and robustness than single-modality models.

Application of multi-echo Dixon and MRS in quantifying hepatic fat content and staging liver fibrosis.

This study associated the liver proton density fat fraction (PDFF), measured by multi-echo Dixon (ME-Dixon) and breath-hold single-voxel high-speed T2-corrected multi-echo 1H magnetic resonance spectroscopy (HISTO) at 1.5 T, with serum biomarkers and liver fibrosis stages. This prospective study enrolled 75 patients suspected of liver fibrosis and scheduled for liver biopsy and 23 healthy participants with normal liver function. The participant underwent ME-Dixon and HISTO scanning. The agreement of PDFF measured by ME-Dixon (PDFF-D) and HISTO (PDFF-H) were compared. Correlations between PDFF and serum fat biomarkers (total cholesterol, triglyceride, and high- and low-density lipoproteins) and the liver fibrosis stages were assessed. PDFF were compared among the liver fibrosis stages (F0-F4) based on clinical liver biopsies. The Bland-Altman plot showed agreement between PDFF-D and PDFF-H(LoA, - 4.44 to 6.75), which have high consistency (ICC 0.752, P < 0.001). The correlations with the blood serum markers were mild to moderate (PDFF-H: r = 0.261-0.410, P < 0.01; PDFF-D: r = 0.265-0.367, P < 0.01). PDFF-D, PDFF-H, and steatosis were distributed similarly among the liver fibrosis stages. PDFF-H showed a slight negative correlation with the liver fibrosis stages (r = - 0.220, P = 0.04). Both ME-Dixon and HISTO sequences measured liver fat content noninvasively. Liver fat content was not directly associated with liver fibrosis stages.

Model incorporating multiple diffusion MRI features: development and validation of a radiomics-based model to predict adult-type diffuse gliomas grade.

OBJECTIVES: To develop and validate a radiomics-based model (ADGGIP) for predicting adult-type diffuse gliomas (ADG) grade by combining multiple diffusion modalities and clinical and imaging morphologic features. METHODS: In this prospective study, we recruited 103 participants diagnosed with ADG and collected their preoperative conventional MRI and multiple diffusion imaging (diffusion tensor imaging, diffusion kurtosis imaging, neurite orientation dispersion and density imaging, and mean apparent propagator diffusion-MRI) data in our hospital, as well as clinical information. Radiomic features of the diffusion images and clinical information and morphological data from the radiological reports were extracted, and multiple pipelines were used to construct the optimal model. Model validation was performed through a time-independent validation cohort. ROC curves were used to evaluate model performance. The clinical benefit was determined by decision curve analysis. RESULTS: From June 2018 to May 2021, 72 participants were recruited for the training cohort. Between June 2021 and February 2022, 31 participants were enrolled in the prospective validation cohort. In the training cohort (AUC 0.958), internal validation cohort (0.942), and prospective validation cohort (0.880), ADGGIP had good accuracy in predicting ADG grade. ADGGIP was also significantly better than the single-modality prediction model (AUC 0.860) and clinical imaging morphology model (0.841) (all p < .01) in the prospective validation cohort. When the threshold probability was greater than 5%, ADGGIP provided the greatest net benefit. CONCLUSION: ADGGIP, which is based on advanced diffusion modalities, can predict the grade of ADG with high accuracy and robustness and can help improve clinical decision-making. CLINICAL RELEVANCE STATEMENT: Integrated multi-modal predictive modeling is beneficial for early detection and treatment planning of adult-type diffuse gliomas, as well as for investigating the genuine clinical significance of biomarkers. KEY POINTS: • Integrated model exhibits the highest performance and stability. • When the threshold is greater than 5%, the integrated model has the greatest net benefit. • The advanced diffusion models do not demonstrate better performance than the simple technology.

Development of Deep Learning with RDA U-Net Network for Bladder Cancer Segmentation.

In today's high-order health examination, imaging examination accounts for a large proportion. Computed tomography (CT), which can detect the whole body, uses X-rays to penetrate the human body to obtain images. Its presentation is a high-resolution black-and-white image composed of gray scales. It is expected to assist doctors in making judgments through deep learning based on the image recognition technology of artificial intelligence. It used CT images to identify the bladder and lesions and then segmented them in the images. The images can achieve high accuracy without using a developer. In this study, the U-Net neural network, commonly used in the medical field, was used to extend the encoder position in combination with the ResBlock in ResNet and the Dense Block in DenseNet, so that the training could maintain the training parameters while reducing the overall identification operation time. The decoder could be used in combination with Attention Gates to suppress the irrelevant areas of the image while paying attention to significant features. Combined with the above algorithm, we proposed a Residual-Dense Attention (RDA) U-Net model, which was used to identify organs and lesions from CT images of abdominal scans. The accuracy (ACC) of using this model for the bladder and its lesions was 96% and 93%, respectively. The values of Intersection over Union (IoU) were 0.9505 and 0.8024, respectively. Average Hausdorff distance (AVGDIST) was as low as 0.02 and 0.12, respectively, and the overall training time was reduced by up to 44% compared with other convolution neural networks.

Long non-coding RNA MYU promotes ovarian cancer cell proliferation by sponging miR-6827-5p and upregulating HMGA1.

Background: Long non-coding RNAs (lncRNAs) have been confirmed to play vital roles in tumorigenesis. LncRNA MYU has recently been reported as an oncogene in several kinds of tumors. However, MYU's expression status and potential involvement in ovarian cancer (OC) remain unclear. In this study, we explored the underlying role of MYU in OC. Methods and results: The expression of MYU was upregulated in OC tissues, and MYU's overexpression was significantly correlated with the FIGO stage and lymphatic metastasis. Knockdown of MYU inhibited cell proliferation in SKOV3 and A2780 cells. Mechanistically, MYU directly interacted with miR-6827-5p in OC cells; HMGA1 is a downstream target gene of miR-6827-5p. Furthermore, MYU knockdown increased the expression of miR-6827-5p and decreased the expression of HMGA1. Restoration of HMGA1 expression reversed the influence on cell proliferation caused by MYU knockdown. Conclusion: MYU functions as a ceRNA that positively regulates HMGA1 expression by sponging miR-6827-5p in OC cells, which may provide a potential target and biomarker for the diagnosis or prognosis of OC.

Image-spectral decomposition extended-learning assisted by sparsity for multi-energy computed tomography reconstruction.

Background: Multi-energy computed tomography (CT) provides multiple channel-wise reconstructed images, and they can be used for material identification and k-edge imaging. Nonetheless, the projection datasets are frequently corrupted by various noises (e.g., electronic, Poisson) in the acquisition process, resulting in lower signal-noise-ratio (SNR) measurements. Multi-energy CT images have local sparsity, nonlocal self-similarity in spatial dimension, and correlation in spectral dimension. Methods: In this paper, we propose an image-spectral decomposition extended-learning assisted by sparsity (IDEAS) method to fully exploit these intrinsic priors for multi-energy CT image reconstruction. Particularly, a nonlocal low-rank Tucker decomposition (TD) is employed to utilize the correlation and nonlocal self-similarity priors. Moreover, considering the advantages of multi-task tensor dictionary learning (TDL) in sparse representation, an adaptive spatial dictionary and an adaptive spectral dictionary are trained during the iterative reconstruction process. Furthermore, a weighted total variation (TV) regularization term is employed to encourage local sparsity. Results: Numerical simulation, physical phantom, and preclinical mouse experiments are performed to validate the proposed IDEAS algorithm. Specifically, in the simulation experiments, the proposed IDEAS reconstructed high-quality images that are very close to the references. For example, the root mean square error (RMSE) of IDEAS image in energy bin 1 is as low as 0.0672, while the RMSE of other methods are higher than 0.0843. Besides, the structural similarity (SSIM) of IDEAS reconstructed image in energy bin 1 is greater than 0.98. For material decomposition, the RMSE of IDEAS bone component is as low as 0.0152, and other methods are higher than 0.0199. In addition, the computational cost of IDEAS is as low as 98.8 s for one iteration, and the competing tensor decomposition method is higher than 327 s. Conclusions: To further improve the quality of the reconstructed multi-energy CT images, multiple prior regularizations are introduced to the multi-energy CT reconstructed model, leading to an IDEAS method. Both qualitative and quantitative evaluation of our results confirm the outstanding performance of the proposed algorithm compared to the state-of-the-arts.

Evaluation of diffuse glioma grade and proliferation activity by different diffusion-weighted-imaging models including diffusion kurtosis imaging (DKI) and mean apparent propagator (MAP) MRI.

PURPOSE: To evaluate two advanced diffusion models, diffusion kurtosis imaging and the newly proposed mean apparent propagation factor-magnetic resonance imaging, in the grading of gliomas and the assessing of their proliferative activity. METHODS: Fifty-nine patients with clinically diagnosed and pathologically proven gliomas were enrolled in this retrospective study. All patients underwent DKI and MAP-MRI scans. Manually outline the ROI of the tumour parenchyma. After delineation, the imaging parameters were extracted using only the data from within the ROI including mean diffusion kurtosis (MK), return-to-origin probability (RTOP), Q-space inverse variance (QIV) and non-Gaussian index (NG), and the differences in each parameter in the classification of glioma were compared. Receiver operating characteristic (ROC) curve analysis was used to evaluate the diagnostic performance of these parameters. RESULTS: MK, NG, RTOP and QIV were significantly different amongst the different grades of glioma. MK, NG and RTOP had excellent diagnostic value in differentiating high-grade from low-grade glioma, with largest areas under the curve (AUCs; 0.929, 0.933 and 0.819, respectively; P < 0.01). MK and NG had the largest AUCs (0.912 and 0.904) when differentiating grade II tumours from III tumours (P < 0.01) and large AUCs (0.791 and 0.786) when differentiating grade III from grade IV tumours. Correlation analysis of tumour proliferation activity showed that MK, NG and QIV were strongly correlated with the Ki-67 LI (P < 0.001). CONCLUSION: MK, RTOP and NG can effectively represent the microstructure of these altered tumours. Multimodal diffusion-weighted imaging is valuable for the preoperative evaluation of glioma grade and tumour proliferative activity.

Applying MAP-MRI to Identify the WHO Grade and Main Genetic Features of Adult-type Diffuse Gliomas: A Comparison of Three Diffusion-weighted MRI Models.

RATIONALE AND OBJECTIVES: Currently, there is no noninvasive method to effectively judge the genotype of diffuse gliomas. We explored the association between mean apparent propagator-MRI (MAP-MRI) and WHO grade 2/3, IDH 1/2 mutations, and chromosome 1p/19q combined deletion genotypes in adult-type diffuse gliomas and compared it with the diagnostic efficiency of diffusion tensor imaging (DTI) and diffusional kurtosis imaging (DKI). MATERIALS AND METHODS: We prospectively recruited 67 participantshistopathologically diagnosed with adult-type diffuse gliomas. Routine MRI, DKI, and DSI were performed before surgery. The extreme and average partial diffusion indexes of solid tumors were measured. A comprehensive assessment of statistically significant diffusion parameters was performed after Bonferroni correction, including ROC curves, correct classification percentage (CCP), integrated discrimination improvement (IDI), net reclassification improvement (NRI), and k-fold cross validation. RESULTS: For differentiating WHO grade 2/3, q-space inverse variance (QIV), mean kurtosis (MK), non-Gaussianity (NG), and return to the origin probability (RTOP) were different (p' < .05), with the mean QIV exhibiting the best diagnostic efficacy and stability (AUC = 0.973, CCP = 0.906). We observed significant differences in mean diffusivity (MD), mean square displacement, QIV, MK, and RTOP between the IDH wild-type and IDH mutant groups (p' < .001) (AUC, 0.806-0.978) and MAP-MRI showed a higher IDI than DTI and DKI (0.094-0.435, NRI > 0, respectively). For the chromosome 1p/19q combined deletion, the minimum QIV was different between the overall (p' < .05) and no significant differences  in MD and MK was observed. CONCLUSION: MAP-MRI effectively predicts the WHO grade 2/3, IDH 1/2 mutations, and chromosome 1p/19q combined deletion in adult-type diffuse gliomas, and it may perform better than DTI and DKT.

Effects of explosive strength training on lower limbs in taekwondo athletes / Efeitos do treinamento com força explosiva sobre os membros inferiores em atletas de taekwondo / Efectos del entrenamiento con fuerza explosiva en miembros inferiores en atletas de taekwondo

ABSTRACT Introduction: Taekwondo is a sport of highly competitive intensity, and the gain in physical fitness of its practitioners is a work that involves analysis of combat techniques, sports biomechanics, and theories of physical training. However, studies of current methods, such as explosive strength training, are still superficial. Objective: Study the effect of different explosive strength training protocols on the lower limbs of taekwondo athletes. Methods: 24 taekwondo athletes were randomly selected as volunteers and divided into experimental and control groups. These were subdivided to perform traditional and different strength training combinations. The experiment lasted eight weeks, with the first four weeks dedicated to strength training adaptation. Data on body shape, maximal strength, explosive strength, and fitness were collected for statistical comparison. Results: Through repeated measures analysis of variance to test lower limb explosive strength on exercise level, it can be seen that there is a significant difference between the test data before and after the approach (P<0.05), this difference was not observed in the control group (P>0.05). The mean value of all indexes in the experimental group was higher than in the control group. Conclusion: Explosive strength training and its different combinations on lower limbs can significantly improve the physical fitness of taekwondo athletes. The different combinations of strength training presented can be used as an effective resource in the lower limbs athletes' explosive strength gain. Level of evidence II; Therapeutic studies - investigation of treatment outcomes.

RESUMO Introdução: O Taekwondo Competitivo é um esporte de alta intensidade competitiva e o ganho na aptidão física de seus praticantes é um trabalho que envolve análise das técnicas de combate, biomecânica esportiva e teorias de treinamento físico. Porém, o estudo de métodos recentes, como o treinamento de força explosiva, ainda é superficial. Objetivo: Estudar o efeito de distintos protocolos de treinamento de força explosiva sobre os membros inferiores em atletas de Taekwondo. Métodos: 24 atletas de taekwondo foram aleatoriamente selecionados como voluntários e divididos em grupos experimental e de controle. Estes foram subdivididos para a execução do treinamento de força tradicional combinado com diferentes combinações de treinamento de força. O experimento durou 8 semanas, sendo as 4 primeiras dedicadas à adaptação do treino de força. Dados sobre forma corporal, força máxima, força explosiva e aptidão física foram coletados para comparação estatística dos resultados. Resultados: Através da análise de medidas repetidas de variância para testar a força explosiva dos membros inferiores segundo o nível de exercício, pode-se ver que existe uma diferença significativa entre os dados do teste antes e depois da abordagem (P<0,05), essa diferença não foi observada no grupo controle (P>0,05). O valor médio de todos os índices do grupo experimental foi maior que no controle. Conclusão: O treinamento de força explosiva e suas diferentes combinações sobre os membros inferiores pode melhorar significativamente a aptidão física dos atletas praticantes de taekwondo. As diferentes combinações de treinamento de força apresentadas podem ser utilizadas como um recurso eficaz no ganho sobre a força explosiva dos membros inferiores desses atletas. Nível de evidência II; Estudos terapêuticos - investigação dos resultados do tratamento.

Resumen Introducción: El Taekwondo Competitivo es un deporte de alta intensidad competitiva y la progresión en aptitud física de sus practicantes es un trabajo que implica el análisis de las técnicas de combate, la biomecánica deportiva y las teorías del entrenamiento físico. Sin embargo, los estudios sobre métodos recientes, como el entrenamiento de fuerza explosiva, siguen siendo superficiales. Objetivo: Estudiar el efecto de diferentes protocolos de entrenamiento de fuerza explosiva sobre los miembros inferiores en atletas de Taekwondo. Métodos: Se seleccionaron aleatoriamente 24 atletas de taekwondo como voluntarios y se dividieron en grupos experimental y de control. Éstos se subdividieron para realizar un entrenamiento de fuerza tradicional compuesto con diferentes combinaciones de entrenamiento de fuerza. El experimento duró 8 semanas, las 4 primeras dedicadas a la adaptación al entrenamiento de fuerza. Se recogieron datos sobre la forma corporal, la fuerza máxima, la fuerza explosiva y la forma física para la comparación estadística de los resultados. Resultados: Mediante el análisis de varianza de medidas repetidas para probar la fuerza explosiva de las extremidades inferiores según el nivel de ejercicio, se observa que hay una diferencia significativa entre los datos de la prueba antes y después del abordaje (P<0,05), esta diferencia no se observó en el grupo de control (P>0,05). El valor medio de todos los índices en el grupo experimental fue superior al del grupo de control. Conclusión: El entrenamiento de la fuerza explosiva y sus diferentes combinaciones en las extremidades inferiores pueden mejorar significativamente la forma física de los atletas de taekwondo. Las diferentes combinaciones de entrenamiento de fuerza presentadas pueden utilizarse como un recurso eficaz en la obtención de fuerza explosiva de los miembros inferiores de estos atletas. Nivel de evidencia II; Estudios terapéuticos - investigación de los resultados del tratamiento.

LncRNA HOTAIR regulates cell invasion and migration in endometriosis through miR-519b-3p/PRRG4 pathway.

Endometriosis is a common benign disease in gynecology and has malignant biological behaviors, such as hyperplasia, invasion, metastasis, and recurrence. However, the pathogenesis of endometriosis remains unclear. The present study aimed to investigate whether LncRNA HOTAIR regulates cell invasion and migration in endometriosis by regulating the miR-519b-3p/PRRG4 pathway. The qRT-PCR results showed that the average relative expression of LncRNA HOTAIR was much higher in ectopic endometrial tissues than in eutopic endometrial tissues. Scratch and transwell assays showed that the cell migration and invasion ability of LncRNA HOTAIR overexpression group was significantly higher than those in the control group. Conversely, the LncRNA HOTAIR knockdown group showed the opposite results. Bioinformatics analysis suggested that the downstream target genes of LncRNA HOTAIR were miR-519b-3p and Prrg4. Knockdown of LncRNA HOTAIR can reduce the up-regulation of Prrg4 by miR-519b-3p and then inhibit the invasion and migration ability of endometrial stromal cells. In Conclusion, LncRNA HOTAIR can regulate the ability of invasion and migration of endometrial stromal cells, and its mechanism is proved by regulating the miR-519b-3p/PRRG4 pathway.

Diagnostic performance of mono-exponential DWI versus diffusion kurtosis imaging in breast lesions: A meta-analysis.

BACKGROUND: This meta-analysis aimed to explore the diagnostic value of diffusion kurtosis imaging (DKI) compared to mono-exponential diffusion weighted imaging (DWI) in the diagnosis of breast cancer. METHODS: A systematic electronic literature search (up to September 2020) was conducted for published English-language studies comparing the diagnostic values of DKI and DWI for the detection of breast cancer. The data of mean kurtosis (MK), mean diffusivity (MD), and apparent diffusion coefficient (ADC) were extracted to construct 2 × 2 contingency tables. The pooled sensitivities, specificities, and areas under the receiver operating characteristic curve (AUCs) were compared between DKI and DWI in the diagnosis of breast cancer. RESULTS: Eight studies were finally included, with a total of 771 patients in the same population. Pooled sensitivities were 82.0% [95% confidence interval (95% CI), 78.2-85.3%] for ADC, 87.3% (95% CI, 83.9-90.1%) for MK, and 83.9% (95% CI, 80.2-87.1%) for MD. Pooled specificities were 81.1% (95% CI, 76.7-84.9%) for ADC, 85.1% (95% CI, 81.1-88.5%) for MK, and 83.2% (95% CI, 79.0-86.8%) for MD. According to the summary receiver operator characteristic curve analyses, the AUCwas 0.901 for ADC, 0.930 for MK, and 0.918 for MD (ADC vs MK, P = .353; ADC vs MD, P = .611). No notable publication bias was found, while significant heterogeneity was observed. CONCLUSIONS: Although DKI is feasible for identifying breast cancer, MD and MK offer similar diagnostic performance to ADC values. Thus, we recommend that DKI should not be included in the routine evaluation of breast lesions now.

Struma Ovarii Associated with Ascites and Elevated CA125: Two Case Reports and Review of the Literature.

Struma ovarii is a rare variety of specialized monodermal mature ovarian teratoma, it is composed predominantly of thyroid tissue. Ascites is present in one third of patients. The combination of struma ovarii, marked ascites and elevated CA125 is a rare condition, which may mimic ovarian cancer. We described two cases presenting with pelvic mass, ascites and elevated serum CA125 levels, frozen section and final pathology turned out to be struma ovarii. Ascites disappeared and the level of CA125 returned to normal level after operation. One of the cases was associated with pleural effusion, leading to a condition called pseudo-Meigs' syndrome. Then we reviewed the related literatures to explore the possible mechanism of ascites and pleural effusion, the reason of CA125 elevation and imaging manifestations of struma ovarii. In conclusion, struma ovarii should be considered in the differential diagnosis preoperatively, when presented with pelvic mass, ascites and an elevated CA125 level.

Quantitative RECIST derived from multiparametric MRI in evaluating response of esophageal squamous cell carcinoma to neoadjuvant therapy.

OBJECTIVE: To develop a quantitative Response Evaluation Criteria in Solid Tumors (qRECIST) for evaluating response to neoadjuvant therapy (nT) in ESCCs relying on multiparametric (mp) MRI. METHODS: Patients with cT2-T4a/N0-N3/M0 ESCC undergoing pre-nT and post-nT esophageal mpMRI before radical resection were prospectively included. Images were reviewed by two experienced radiologists. qRECIST was redefined using four methods including conventional criterion (cRECIST) and three model-dependent RECIST relying on quantitative MRI measurements at pre-nT, post-nT, and delta pre-post nT, respectively. Pathological tumor regression grades (TRGs) were used as a reference standard. The rates of agreement between four qRECIST methods and TRGs were determined with a Cronbach's alpha test, area under the curve (AUC), and a diagnostic odds ratio meta-analysis. RESULTS: Ninety-one patients were enrolled. All four methods revealed high inter-reader agreements between the two radiologists, with a Kappa coefficient of 0.96, 0.87, 0.88, and 0.97 for cRECIST, pre-nT RECIST, post-nT RECIST, and delta RECIST, respectively. Among them, delta RECIST achieved the highest overall agreement rate (67.0% [61/91]) with TRGs, followed by post-nT RECIST (63.8% [58/91]), cRECIST (61.5% [56/91]), and pre-nT RECIST (36.3% [33/91]). Especially, delta RECIST achieved the highest accuracy (97.8% [89/91]) in distinguishing responders from non-responders, with 97.3% (34/35) for responders and 98.2% (55/56) for non-responders. Post-nT RECIST achieved the highest accuracy (93.4% [85/91]) in distinguishing complete responders from non-pCRs, with 77.8% (11/18) for pCRs and 94.5% (69/73) for non-pCRs. CONCLUSION: The qRECIST with mpMRI can assess treatment-induced changes and may be used for early prediction of response to nT in ESCC patients. KEY POINTS: • Quantitative mpMRI can reliably assess tumor response, and delta RECIST model had the best performance in evaluating response to nT in ESCCs, with an AUC of 0.98, 0.95, 0.80, and 0.82 for predicting TRG0, TRG1, TRG2, and TRG3, respectively. • In distinguishing responders from non-responders, the rate of agreement between delta RECIST and pathology was 97.3% (34/35) for responders and 98.2% (55/56) for non-responders, resulting in an overall agreement rate of 97.8% (89/91). • In distinguishing pCRs from non-pCR, the rate of agreement between MRI and pathology was 77.8% (11/18) for pCRs and 94.5% (69/73) for non- pCRs, resulting in an overall agreement rate of 91.2% (83/91).