A Priori Subcell Limiting Approach for the FR/CPR Method on Unstructured Meshes

A priori subcell limiting approach is developed for high-order flux reconstruction/correction procedure via reconstruction (FR/CPR) methods on two-dimensional unstructured quadrilateral meshes. Firstly, a modified indicator based on modal energy coefficients is proposed to detect troubled cells, where discontinuities exist. Then, troubled cells are decomposed into nonuniform subcells and each subcell has one solution point. A second-order finite difference shock-capturing scheme based on nonuniform nonlinear weighted (NNW) interpolation is constructed to perform the calculation on troubled cells while smooth cells are calculated by the CPR method. Numerical investigations show that the proposed subcell limiting strategy on unstructured quadrilateral meshes is robust in shock-capturing.     

The patellar ligament: A comprehensive review

The patellar ligament (PL) is an epiphyseal ligament and is part of the extensor complex of the knee. The ligament has gained attention due to its clinical relevance to autograft and tendinopathy. A variety of anatomical variations of the PL such as aplasia, numerical variations, and vascularity are being reported recently by clinicians and anatomists. The aim of this literature was to review the available literature to provide a consensus regarding anatomic variations of the PL, neurovasculature surrounding the PL, histology of the PL, and various aspects of PL measurements with relevance to the surgical considerations and sex and age-related differences. A narrative review of the patellar ligament was performed by conducting a detailed literature search and review of relevant articles. A total of 90 articles on the patellar ligament were included and were categorized into studies based on anatomical variations, neurovasculature, morphometrics, microanatomy, sex and age-related difference, and ACL reconstruction. The anatomical variations and morphometrics of the PL were found to correlate with the frequency of strain injuries, tendinopathy, and efficacy of the PL autograft in anterior cruciate ligament reconstruction. The sex differences in PL measurements and the effect of estrogen on collagen synthesis explained a higher incidence of patellar tendinopathy in women. An awareness of its variations enables careful selection of surgical incisions, thereby avoiding complications related to nerve injury. Accurate knowledge of the PL microanatomy assists in understanding the mechanism of ligament degeneration, rupture, autograft harvesting, and ligamentization results.     

Arterial spin labeling MR image denoising and reconstruction using unsupervised deep learning

Arterial spin labeling (ASL) imaging is a powerful magnetic resonance imaging technique that allows to quantitatively measure blood perfusion non-invasively, which has great potential for assessing tissue viability in various clinical settings. However, the clinical applications of ASL are currently limited by its low signal-to-noise ratio (SNR), limited spatial resolution, and long imaging time. In this work, we propose an unsupervised deep learning-based image denoising and reconstruction framework to improve the SNR and accelerate the imaging speed of high resolution ASL imaging. The unique feature of the proposed framework is that it does not require any prior training pairs but only the subject's own anatomical prior, such as T1-weighted images, as network input. The neural network was trained from scratch in the denoising or reconstruction process, with noisy images or sparely sampled k-space data as training labels. Performance of the proposed method was evaluated using in vivo experiment data obtained from 3 healthy subjects on a 3T MR scanner, using ASL images acquired with 44-min acquisition time as the ground truth. Both qualitative and quantitative analyses demonstrate the superior performance of the proposed txtc framework over the reference methods. In summary, our proposed unsupervised deep learning-based denoising and reconstruction framework can improve the image quality and accelerate the imaging speed of ASL imaging.     

关节镜双后内入路与切开手术治疗急性单纯后交叉韧带胫骨止点撕脱骨折早期疗效对比

目的:比较关节镜双后内入路与切开手术治疗急性单纯后交叉韧带胫骨止点撕脱骨折的疗效差异。方法:回顾性分析2016年6月至2020年6月经手术治疗的52例急性单纯性后交叉韧带胫骨止点撕脱骨折患者的临床资料,按手术方案不同分为两组,关节镜组27例患者行关节镜双后内入路手术治疗,其中男16例,女11例,年龄19~52(34.9±9.2)岁;切开复位组25例患者行膝关节后内侧切口手术治疗,其中男14例,女11例,年龄18~54(33.7±8.4)岁。观察并比较两组患者手术时间、切口长度、术中出血量、住院时间、住院费用、术后愈合情况、并发症以及术后12个月Lysholm、IKDC评分。结果:两组患者均顺利完成手术,无血管、神经损伤。52例均获得随访,时间6~24(15.0±1.7)个月。关节镜组手术时间、住院费用大于切开复位组(P<0.05);关节镜组术中出血量、切口长度、住院时间小于切开复位组(P<0.05);关节镜组和切开复位组术后12个月Lysholm评分分别为(95.9±1.7)分和(86.4±1.2)分,均较术前的(49.1±2.3)分和(48.9±1.1)分显著提高(P<0.05);关节镜组和切开复位组术后12个月IKDC总分分别为(96.9±1.5)分和(87.1±1.4)分,均较术前的(47.6±4.1)分和(48.1±3.9)分显著提高(P<0.05);关节镜组术后12个月膝关节Lysholm、IKDC评分均高于切开复位组(P<0.05)。结论:关节镜双后内入路治疗急性单纯后交叉韧带胫骨止点撕脱骨折,早期效果满意,疗效优于传统开放手术,具有创伤小、恢复快、操作简便等优点。     

核因子κB受体活化因子配体和肿瘤坏死因子α经炎性牙周膜干细胞外泌体促进破骨细胞分化

目的　慢性牙周炎表现的病理性骨吸收非常常见，牙周膜干细胞（PDLSCs）的外泌体（Exo）对骨吸收的作用和影响尚不明确，本研究分析了该Exo蛋白组分对破骨细胞分化的影响。方法　分别从正畸前拔牙患者和慢性牙周炎患者的牙周膜组织中提取PDLSCs，通过流式细胞术检测表面标记物；通过差速离心分别提取2种细胞的Exo，即Exo-WT和Exo-CP，并通过Western blot、透射电子显微镜（TEM）、蛋白浓度检测、纳米粒径追踪检测Exo特征；通过蛋白质谱检测2种Exo的蛋白组分；通过酶联免疫吸附（ELISA）验证了差异表达蛋白肿瘤坏死因子α（TNF-α）、核因子κB受体活化因子配体（RANKL）、白细胞介素（IL）-1α、转化生长因子β（TGF-β）、骨形态发生蛋白2（BMP-2）表达水平；将10、100、1 000 μg·mL^-1的Exo-CP或Exo-WT加入RAW264.7培养基中并于5 d时通过实时荧光定量聚合酶链反应（RT-qPCR）、Western blot、抗酒石酸酸性磷酸酶（TRAP）染色检测破骨分化相关指标表达情况；采用SPSS 24.0软件对实验数据进行统计学分析。结果　Exo-CP富集的差异表达蛋白主要与破骨细胞分化的TNF信号通路、核转录因子κB（NF-κB）信号通路相关；ELISA实验证实了Exo-CP中高表达TNF-α、RANKL、IL-1α，低表达TGF-β1、BMP-2（P<0.05）；Exo-CP作用于RAW264.7，显著提高了细胞的破骨分化相关基因及蛋白的表达水平，TRAP染色可见分化的破骨细胞，且呈现浓度依赖性，100、1 000 μg·mL^-1浓度Exo-CP对破骨细胞分化的促进作用显著高于10 μg·mL^-1浓度组（P<0.05）。结论　慢性牙周炎的病理性骨吸收可能由炎性PDLSCs所分泌的Exo通过促进破骨细胞分化引起，Exo中主要的作用蛋白可能为RANKL和TNF-α，本研究为慢性牙周炎骨吸收的发病机制提供了新视角。     

Isokinetic quadriceps symmetry helps in the decision to return to running after anterior cruciate ligament reconstruction

BackgroundAfter anterior cruciate ligament reconstruction (ACLR), the decision to allow a return to running is empirical, and the post-operative delay is the most-used criterion. The Quadriceps isokinetic-strength Limb Symmetry Index (Quadriceps LSI), with a cutoff of 60%, could be a useful criterion.ObjectiveTo determine the association between a Quadriceps LSI ≥ 60% and return to running after ACLR.MethodsOver a 10-year period, we retrospectively included 470 patients who underwent ACLR. Four months after ACLR, participants performed an isokinetic test; quadriceps concentric peak torque was used to calculate the Quadriceps LSI at 60?/s. With a Quadriceps LSI ≥ 60%, a return to running was suggested. At 6 months after ACLR, participants were clinically evaluated for a return to sport and post-operative middle-term complications. A multivariable predictive model was built to assess the efficiency diagnosis of this cutoff in order to consider cofounding factors. Quadriceps LSI cutoff ≥ 60% was assessed with sensitivity, specificity and the area under the receiver operating characteristic curve (AUC).ResultsAccording to our decision-making process with the 60% Quadriceps LSI cutoff at 60?/s, 285 patients were authorized to return to running at 4 months after ACLR and 185 were not, but 21% (n = 59) and 24% (n = 45), respectively, were not compliant with the recommendation. No iterative autograft rupture or meniscus pathology occurred at 6 months of follow-up. On multivariable logistic regression analysis, a return to running by using the 60% Quadriceps LSI cutoff was associated with undergoing the hamstring strand procedure (odds ratio 2.60, 95% confidence interval [CI] 1.75–3.84; P < 0.0001) and the absence of knee complications (1.18, 1.07–1.29; P = 0.001) at 4 months. The sensitivity and specificity of the 60% Quadriceps LSI cutoff were 83% and 70%, respectively. The AUC was 0.840 (95% CI 0.803–0.877).ConclusionsUsing the 60% cutoff of the isokinetic Quadriceps LSI at 4 months after ACLR could help in the decision to allow a return to running.     

DEep learning-based rapid Spiral Image REconstruction (DESIRE) for high-resolution spiral first-pass myocardial perfusion imaging

The objective of the current study was to develop and evaluate a DEep learning-based rapid Spiral Image REconstruction (DESIRE) technique for high-resolution spiral first-pass myocardial perfusion imaging with whole-heart coverage, to provide fast and accurate image reconstruction for both single-slice (SS) and simultaneous multislice (SMS) acquisitions. Three-dimensional U-Net–based image enhancement architectures were evaluated for high-resolution spiral perfusion imaging at 3 T. The SS and SMS MB = 2 networks were trained on SS perfusion images from 156 slices from 20 subjects. Structural similarity index (SSIM), peak signal-to-noise ratio (PSNR), and normalized root mean square error (NRMSE) were assessed, and prospective images were blindly graded by two experienced cardiologists (5: excellent; 1: poor). Excellent performance was demonstrated for the proposed technique. For SS, SSIM, PSNR, and NRMSE were 0.977 [0.972, 0.982], 42.113 [40.174, 43.493] dB, and 0.102 [0.080, 0.125], respectively, for the best network. For SMS MB = 2 retrospective data, SSIM, PSNR, and NRMSE were 0.961 [0.950, 0.969], 40.834 [39.619, 42.004] dB, and 0.107 [0.086, 0.133], respectively, for the best network. The image quality scores were 4.5 [4.1, 4.8], 4.5 [4.3, 4.6], 3.5 [3.3, 4], and 3.5 [3.3, 3.8] for SS DESIRE, SS L1-SPIRiT, MB = 2 DESIRE, and MB = 2 SMS-slice-L1-SPIRiT, respectively, showing no statistically significant difference (p = 1 and p = 1 for SS and SMS, respectively) between L1-SPIRiT and the proposed DESIRE technique. The network inference time was ~100 ms per dynamic perfusion series with DESIRE, while the reconstruction time of L1-SPIRiT with GPU acceleration was ~ 30 min. It was concluded that DESIRE enabled fast and high-quality image reconstruction for both SS and SMS MB = 2 whole-heart high-resolution spiral perfusion imaging.