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BackgroundUnderstanding the characteristics of multiparametric MRI (mpMRI) in patients from different racial/ethnic backgrounds is important for reducing the observed gaps in clinical outcomes.PurposeTo investigate the diagnostic performance of mpMRI and quantitative MRI parameters of prostate cancer (PCa) in African American (AA) and matched White (W) men.Study typeRetrospective.SubjectsOne hundred twenty-nine patients (43 AA, 86 W) with histologically proven PCa who underwent mpMRI before radical prostatectomy.Field strength/sequence3.0 T, T2-weighted turbo spin echo imaging, a single-shot spin-echo EPI sequence diffusion-weighted imaging, and a gradient echo sequence dynamic contrast-enhanced MRI with an ultrafast 3D spoiled gradient-echo sequence.AssessmentThe diagnostic performance of mpMRI in AA and W men was assessed using detection rates (DRs) and positive predictive values (PPVs) in zones defined by the PI-RADS v2.1 prostate sector map. Quantitative MRI parameters, including Ktrans and ve of clinically significant (cs) PCa (Gleason score ≥ 7) tumors were compared between AA and W sub-cohorts after matching age, prostate-specific antigen (PSA), and prostate volume.Statistical testsWeighted Pearson's chi-square and Mann-Whitney U tests with a statistically significant level of 0.05 were used to examine differences in DR and PPV and to compare parameters between AA and matched W men, respectively.ResultsA total number of 264 PCa lesions were identified in the study cohort. The PPVs in the peripheral zone (PZ) and posterior prostate of mpMRI for csPCa lesions were significantly higher in AA men than in matched W men (87.8% vs. 68.1% in PZ, and 89.3% vs. 69.6% in posterior prostate). The Ktrans of index csPCa lesions in AA men was significantly higher than in W men (0.25 ± 0.12 vs. 0.20 ± 0.08 min-1; P

Multi-parametric MRI (mpMRI) is widely used for prostate cancer (PCa) diagnosis. Deep learning models show good performance in detecting PCa on mpMRI, but domain-specific PCa-related anatomical information is sometimes overlooked and not fully explored even by state-of-the-art deep learning models, causing potential suboptimal performances in PCa detection. Symmetric-related anatomical information is commonly used when distinguishing PCa lesions from other visually similar but benign prostate tissue. In addition, different combinations of mpMRI findings are used for evaluating the aggressiveness of PCa for abnormal findings allocated in different prostate zones. In this study, we investigate these domain-specific anatomical properties in PCa diagnosis and how we can adopt them into the deep learning framework to improve the models detection performance. We propose an anatomical-aware PCa detection Network (AtPCa-Net) for PCa detection on mpMRI. Experiments show that the AtPCa-Net can better utilize the anatomical-related information, and the proposed anatomical-aware designs help improve the overall model performance on both PCa detection and patient-level classification.

Objectives To investigate the clinical epidemiological characteristics and occurrence of post-traumatic stress symptoms (PTSS) in patients with traumatic fractures, we sought to analyze the factors that influence the prognosis of a length of hospital stay (LOS) and provide valuable insights to prevent PTSS in fracture patients and improve their prognosis. Methods Inpatients with traumatic fractures were recruited from a third-class comprehensive general hospital in southwest China between November 2019 and October 2020. Case data of traumatic fracture patients were collected, and a questionnaire that included general information and basic fracture details was completed. The post-traumatic stress disorder Self-rating Scale was used to assess PTSS among the fracture inpatients. Results A total of 204 inpatients who experienced traumatic fractures were included in this study. Falls accounted for the largest proportion of traumatic fractures. A Cox's regression analysis revealed that serious injury [Hazard Ratio (HR) = 2.44, 95% Confidence Interval (CI): 1.33–4.46], critical illness during hospitalization (HR = 1.70, 95% CI: 1.13–2.54), and undergoing two surgeries (HR = 1.87, 95% CI: 1.20–2.93) were risk factors for longer LOS. Among the fracture patients, 30.39% exhibited positive PTSD symptoms, and physical activity during the fracture [Odds Ratio (OR) = 0.63, 95% CI: 0.45–0.88] and increased pain (OR = 3.34, 95% CI: 1.82–6.11) were identified as influencing factors. Conclusions Given the high detection rate of PTSS following traumatic fractures, it is crucial for relevant departments to implement targeted measures to protect high risk individuals. Furthermore, strengthening the care provided to the patients' physical and mental health is urgently needed to reduce the incidence of PTSS.

High-resolution magnetic resonance imaging (MRI) sequences, such as 3D turbo or fast spin-echo (TSE/FSE) imaging, are clinically desirable but suffer from long scanning time-related blurring when reformatted into preferred orientations. Instead, multi-slice two-dimensional (2D) TSE imaging is commonly used because of its high in-plane resolution but is limited clinically by poor through-plane resolution due to elongated voxels and the inability to generate multi-planar reformations due to staircase artifacts. Therefore, multiple 2D TSE scans are acquired in various orthogonal imaging planes, increasing the overall MRI scan time. In this study, we propose a novel slice-profile transformation super-resolution (SPTSR) framework with deep generative learning for through-plane super-resolution (SR) of multi-slice 2D TSE imaging. The deep generative networks were trained by synthesized low-resolution training input via slice-profile downsampling (SP-DS), and the trained networks inferred on the slice profile convolved (SP-conv) testing input for 5.5x through-plane SR. The network output was further slice-profile deconvolved (SP-deconv) to achieve an isotropic super-resolution. Compared to SMORE SR method and the networks trained by conventional downsampling, our SPTSR framework demonstrated the best overall image quality from 50 testing cases, evaluated by two abdominal radiologists. The quantitative analysis cross-validated the expert reader study results. 3D simulation experiments confirmed the quantitative improvement of the proposed SPTSR and the effectiveness of the SP-deconv step, compared to 3D ground-truths. Ablation studies were conducted on the individual contributions of SP-DS and SP-conv, networks structure, training dataset size, and different slice profiles.

Prostate cancer is the second leading cause of cancer death among men in the United States. The diagnosis of prostate MRI often relies on accurate prostate zonal segmentation. However, state-of-the-art automatic segmentation methods often fail to produce well-contained volumetric segmentation of the prostate zones since certain slices of prostate MRI, such as base and apex slices, are harder to segment than other slices. This difficulty can be overcome by leveraging important multi-scale image-based information from adjacent slices, but current methods do not fully learn and exploit such cross-slice information. In this paper, we propose a novel cross-slice attention mechanism, which we use in a Transformer module to systematically learn cross-slice information at multiple scales. The module can be utilized in any existing deep-learning-based segmentation framework with skip connections. Experiments show that our cross-slice attention is able to capture cross-slice information significant for prostate zonal segmentation in order to improve the performance of current state-of-the-art methods. Cross-slice attention improves segmentation accuracy in the peripheral zones, such that segmentation results are consistent across all the prostate slices (apex, mid-gland, and base). The code for the proposed model is available at https://bit.ly/CAT-Net.

Genome-wide association studies have identified 19p13.3 locus associated with primary biliary cholangitis (PBC). Here we aim to identify causative variant(s) and initiate efforts to define the mechanism by which the 19p13.3 locus variant(s) contributes to the pathogenesis of PBC. A genome-wide meta-analysis of 1931 PBC subjects and 7852 controls in two Han Chinese cohorts confirms the strong association between 19p13.3 locus and PBC. By integrating functional annotations, luciferase reporter assay and allele-specific chromatin immunoprecipitation, we prioritize rs2238574, an AT-Rich Interaction Domain 3A (ARID3A) intronic variant, as a potential causal variant at 19p13.3 locus. The risk allele of rs2238574 shows higher binding affinity of transcription factors, leading to an increased enhancer activity in myeloid cells. Genome-editing demonstrates the regulatory effect of rs2238574 on ARID3A expression through allele-specific enhancer activity. Furthermore, knock-down of ARID3A inhibits myeloid differentiation and activation pathway, and overexpression of the gene has the opposite effect. Finally, we find ARID3A expression and rs2238574 genotypes linked to disease severity in PBC. Our work provides several lines of evidence that a non-coding variant regulates ARID3A expression, presenting a mechanistic basis for association of 19p13.3 locus with the susceptibility to PBC.

PurposeStack-of-radial MRI allows free-breathing abdominal scans, however, it requires relatively long acquisition time. Undersampling reduces scan time but can cause streaking artifacts and degrade image quality. This study developed deep learning networks with adversarial loss and evaluated the performance of reducing streaking artifacts and preserving perceptual image sharpness.MethodsA 3D generative adversarial network (GAN) was developed for reducing streaking artifacts in stack-of-radial abdominal scans. Training and validation datasets were self-gated to 5 respiratory states to reduce motion artifacts and to effectively augment the data. The network used a combination of three loss functions to constrain the anatomy and preserve image quality: adversarial loss, mean-squared-error loss and structural similarity index loss. The performance of the network was investigated for 3-5 times undersampled data from 2 institutions. The performance of the GAN for 5 times accelerated images was compared with a 3D U-Net and evaluated using quantitative NMSE, SSIM and region of interest (ROI) measurements as well as qualitative scores of radiologists.ResultsThe 3D GAN showed similar NMSE (0.0657 vs. 0.0559, p = 0.5217) and significantly higher SSIM (0.841 vs. 0.798, p

Abstract The development of semiconducting polymers with good processability in green solvents and competitive electrical performance is essential for realizing sustainable large‐scale manufacturing and commercialization of organic electronics. A major obstacle is the processability‐performance dichotomy that is dictated by the lack of ideal building blocks with balanced polarity, solubility, electronic structures, and molecular conformation. Herein, through the integration of donor, quinoid and acceptor units, an unprecedented building block, namely TQBT, is introduced for constructing a serial of conjugated polymers. The TQBT, distinct in non‐symmetric structure and high dipole moment, imparts enhanced solubility in anisole—a green solvent—to the polymer TQBT‐T. Furthermore, PTQBT‐T possess a highly rigid and planar backbone owing to the nearly coplanar geometry and quinoidal nature of TQBT, resulting in strong aggregation in solution and localized aggregates in film. Remarkably, PTQBT‐T films spuncast from anisole exhibit a hole mobility of 2.30 cm2 V‐1 s‐1, which is record high for green solvent‐processable semiconducting polymers via spin‐coating, together with commendable operational and storage stability. The hybrid building block emerges as a pioneering electroactive unit, shedding light on future design strategies in high‐performance semiconducting polymers compatible with green processing and marking a significant stride towards ecofriendly organic electronics.

Abstract The development of soft electronics and soft fiber devices has significantly advanced flexible and wearable technology. However, they still face the risk of damage when exposed to sharp objects in real‐life applications. Taking inspiration from nature, self‐healable materials that can restore their physical properties after external damage offer a solution to this problem. Nevertheless, large‐scale production of self‐healable fibers is currently constrained. To address this limitation, this study leverages the thermal drawing technique to create elastic and stretchable self‐healable thermoplastic polyurethane (STPU) fibers, enabling cost‐effective mass production of such functional fibers. Furthermore, despite substantial research into the mechanisms of self‐healable materials, quantifying their healing speed and time poses a persistent challenge. Thus, transmission spectra are employed as a monitoring tool to observe the real‐time self‐healing process, facilitating an in‐depth investigation into the healing kinetics and efficiency. The versatility of the fabricated self‐healable fiber extends to its ability to be doped with a wide range of functional materials, including dye molecules and magnetic microparticles, which enables modular assembly to develop distributed strain sensors and soft actuators. These achievements highlight the potential applications of self‐healable fibers that seamlessly integrate with daily lives and open up new possibilities in various industries.

OBJECTIVE: To identify which patient with prostate cancer (PCa) could safely avoid extended pelvic lymph node dissection (ePLND) by predicting lymph node invasion (LNI), via a radiomics-based machine learning approach. METHODS: An integrative radiomics model (IRM) was proposed to predict LNI, confirmed by the histopathologic examination, integrating radiomics features, extracted from prostatic index lesion regions on MRI images, and clinical features via SVM. The study cohort comprised 244 PCa patients with MRI and followed by radical prostatectomy (RP) and ePLND within 6 months between 2010 and 2019. The proposed IRM was trained in training/validation set and evaluated in an internal independent testing set. The models performance was measured by area under the curve (AUC), sensitivity, specificity, negative predictive value (NPV), and positive predictive value (PPV). AUCs were compared via Delong test with 95% confidence interval (CI), and the rest measurements were compared via chi-squared test or Fishers exact test. RESULTS: Overall, 17 (10.6%) and 14 (16.7%) patients with LNI were included in training/validation set and testing set, respectively. Shape and first-order radiomics features showed usefulness in building the IRM. The proposed IRM achieved an AUC of 0.915 (95% CI: 0.846-0.984) in the testing set, superior to pre-existing nomograms whose AUCs were from 0.698 to 0.724 (p < 0.05). CONCLUSION: The proposed IRM could be potentially feasible to predict the risk of having LNI for patients with PCa. With the improved predictability, it could be utilized to assess which patients with PCa could safely avoid ePLND, thus reduce the number of unnecessary ePLND. KEY POINTS: • The combination of MRI-based radiomics features with clinical information improved the prediction of lymph node invasion, compared with the model using only radiomics features or clinical features. • With improved prediction performance on predicting lymph node invasion, the number of extended pelvic lymph node dissection (ePLND) could be reduced by the proposed integrative radiomics model (IRM), compared with the existing nomograms.

A variable future climate would lead to changes in the biomass allocation pattern of plant communities, but its divergent changes under different environments have not been clearly explored, greatly limiting a better understanding of the responsess of grassland ecosystems to environmental changes. Herein, we conducted field samplings at 215 grassland sites in the Qinghai-Tibetan and Inner Mongolian Plateaus to analyze community biomass allocation patterns (specifically, the ratio of root to shoot, R/S) in various grasslands along environmental gradients. We found a gradual increase in R/S from humid to semi-arid and arid environments across both plateaus. In humid environment, soil factor had a significant effect on the R/S of alpine humid grassland, while climate factor had a significant effect on the R/S of humid grassland. In semi-arid environment, both climate and plant factors had significant effects on R/S in semi-arid grassland. In arid environments, climate and plant factors had significant effects on R/S in arid grassland. These findings emphasize the multifaceted role of environmental factors in shaping biomass allocations across diverse ecosystems, enriching our understanding of ecological dynamics. The current results are timely in advancing the fundamental understanding of the mechanisms of biomass allocation in grasslands and are useful for global research on grasslands in different environments.

Abstract Background The purpose of this study was to explore the relationship between quantitative epicardial adipose tissue (EAT) based on coronary computed tomography angiography (CCTA) and coronary slow flow (CSF). Methods A total of 85 patients with 0.05). Multivariate logistic regression analysis showed that EAT volume was independently related to CSF [odds ratio (OR) = 4.82, 95% confidence interval (CI): 3.06–7.27, P

In order to improve the processing quality of wheat, newly harvested wheat was treated with glow discharge cold plasma. The changes in the physicochemical properties of wheat flour and the rheological properties of wheat flour dough after the treatment were studied, and the molecular mass distribution and secondary structure of wheat flour proteins were furthermore analyzed. The results showed that the gluten index of wheat was significantly increased after cold plasma treatment with oxygen or argon as the gas source. Dough development time and stability time were improved, and the mixographic parameters midline integral at 8 min (MTxI) and midline width at 8 min (MTxW) were significantly increased (P < 0.05), while weakening slope (WS) was significantly decreased (P < 0.05). The content of macromolecular polymeric storage protein fraction F1 was increased, and the ratio between macromolecular polymeric storage protein fraction F1 and small-molecule polymeric storage protein fraction F2 was significantly increased (P < 0.05). The protein secondary structure was transformed from β-sheet and β-turn to more ordered intermolecular β-sheet. In conclusion, glow discharge cold plasma treatment changed the molecular mass distribution and secondary structure of wheat storage proteins, significantly enhanced the elasticity and mixing tolerance of dough, and improved the tensile resistance of dough, thereby enhancing the processing quality of wheat to some extent.

BackgroundMultiparametric MRI (mpMRI) is commonly recommended as a triage test prior to any prostate biopsy. However, there exists limited consensus on which patients with a negative prostate mpMRI could avoid prostate biopsy.PurposeTo identify which patient could safely avoid prostate biopsy when the prostate mpMRI is negative, via a radiomics-based machine learning approach.Study typeRetrospective.SubjectsThree hundred thirty patients with negative prostate 3T mpMRI between January 2016 and December 2018 were included.Field strength/sequenceA 3.0 T/T2-weighted turbo spin echo (TSE) imaging (T2 WI) and diffusion-weighted imaging (DWI).AssessmentThe integrative machine learning (iML) model was trained to predict negative prostate biopsy results, utilizing both radiomics and clinical features. The final study cohort comprised 330 consecutive patients with negative mpMRI (PI-RADS

YOLOv8, as an efficient object detection method, can swiftly and precisely identify objects within images. However, traditional algorithms encounter difficulties when detecting small objects in remote sensing images, such as missing information, background noise, and interactions among multiple objects in complex scenes, which may affect performance. To tackle these challenges, we propose an enhanced algorithm optimized for detecting small objects in remote sensing images, named HP-YOLOv8. Firstly, we design the C2f-D-Mixer (C2f-DM) module as a replacement for the original C2f module. This module integrates both local and global information, significantly improving the ability to detect features of small objects. Secondly, we introduce a feature fusion technique based on attention mechanisms, named Bi-Level Routing Attention in Gated Feature Pyramid Network (BGFPN). This technique utilizes an efficient feature aggregation network and reparameterization technology to optimize information interaction between different scale feature maps, and through the Bi-Level Routing Attention (BRA) mechanism, it effectively captures critical feature information of small objects. Finally, we propose the Shape Mean Perpendicular Distance Intersection over Union (SMPDIoU) loss function. The method comprehensively considers the shape and size of detection boxes, enhances the model’s focus on the attributes of detection boxes, and provides a more accurate bounding box regression loss calculation method. To demonstrate our approach’s efficacy, we conducted comprehensive experiments across the RSOD, NWPU VHR-10, and VisDrone2019 datasets. The experimental results show that the HP-YOLOv8 achieves 95.11%, 93.05%, and 53.49% in the mAP@0.5 metric, and 72.03%, 65.37%, and 38.91% in the more stringent mAP@0.5:0.95 metric, respectively.