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Purpose The purpose of this study was to determine perspectives of surgeons regarding simultaneous surgery in patients undergoing posterior spine instrumentation and fusion (PSIF) for adolescent idiopathic scoliosis (AIS). Methods A survey was administered to orthopaedic trainees and faculty regarding simultaneous surgery for primary PSIF for AIS. A five-point Likert scale (1: ‘Strongly Disagree’ to 5: ‘Strongly Agree’) was used to assess agreement with statements about simultaneous surgery. We divided simultaneous surgery into concurrent, when critical portions of operations occur at the same time, and overlapping, when noncritical portions occur at the same time. Results The 72 respondents (78.3% of 92 surveyed) disagreed with concurrent surgery for ‘one of my patients’ (response mean 1.76 (sd 1.03)) but were more accepting of overlapping surgery (mean 3.94 (sd 0.99); p < 0.0001). The rating difference between concurrent and overlapping surgery was smaller for paediatric and spine surgeons (-1.25) than for residents or those who did not identify a subspecialty (-2.17; p = 0.0246) or other subspecialty surgeons (-2.57; p = 0.0026). Respondents were more likely to agree with explicit informed consent for concurrent surgery compared with overlapping (mean 4.32 (sd 0.91) versus 3.44 (sd 1.14); p < 0.001). Conclusion Orthopaedic surgeons disagreed with concurrent but were more accepting of overlapping surgery and anaesthesia for PSIF for AIS. Respondents were in greater agreement that patients should be explicitly informed of concurrence than of overlap. The surgical community's evidence and position regarding simultaneous surgery, in particular overlapping, must be more effectively presented to the public in order to bridge the gap in perspectives. Level of Evidence IV

Background The aim of the study was to define the clinical significance of circulating tumor cells (CTCs)/circulating tumor endothelial cells (CTECs) and their subtypes in small cell lung cancer (SCLC) patients. Methods CTCs/CTECs and their subtypes were determined using SE‐iFISH technology in 33 SCLC patients before initial treatment (B1), after two cycles of chemotherapy (B2), at the completion of chemotherapy (B3), and disease progression (B4). The correlations with clinical characteristics, progression‐free survival (PFS), and overall survival (OS) were analyzed. Results CTCs and CTECs were detected in 96.6% and 65.5% of patients, respectively. Patients had higher levels of CTCs compared with CTECs in circulation (p, It has been proved in many studies that CTCs can be used as a prognostic biomarker in SCLC patients. Here, we report that CTC‐WBC clusters may also be a valuable predictor for SCLC prognosis. In this study, patients with CTC‐WBC clusters before chemotherapy (17.3 vs. 22.6 months, p = 0.041) and after two cycles of treatment (19.9 vs. 25.2 months, p = 0.018) tended to have a significantly shorter overall survival duration.

Nowadays radar systems have been facing with the disordered electromagnetic spectrum access and utilization in shared spectrum environments with radio communication systems. Numerous waveform recognition methods have been studied with feature engineering and conventional machine learning (ML) for intelligent radar systems, but they are critically challenged by practical problems of scalability and reliability. Deep learning (DL) with the ability to automatically learn the representational features is leveraged to handle the aforementioned obstacles effectively. In this work, we proposed a high-accurate waveform recognition method for intelligent radar systems by developing a novel residual-attention multiscale-accumulation convolutional network (RamNet). By deliberately incorporating the residual connection and attention connection in selective-feature improvement blocks, RamNet can enrich high-impact features without vanishing gradient. Moreover, a structural multiscale-accumulation connection is deployed to improve feature utilization by gathering the high-relevant features at multiple signal resolutions. Experimental results from exhaustive simulation demonstrate that RamNet recognizes waveform robustly under rigorous channel impairments and presents superior performance compared to traditional ML and state-of-the-art DL models.

The accuracy of preoperative patient-reported weight was never evaluated in patients undergoing lower extremity procedures. The purpose of this study was to: (1) compare the disparity between patient-reported and measured weights in patients undergoing lower extremity total joint arthroplasty (LE-TJA) and arthroscopy; and (2) investigate the association between patient-specific factors (patient age, BMI, zip code, and psychiatric comorbidities) and the accuracy of patient-reported weight. Preoperative self-reported weights were retrospectively compared to measured weights in 400 LE-TJA and 85 control arthroscopy patients. The difference between reported and measured weights was calculated. Additionally, the percent of accurate reporting within 0.5, 1, and 5 kg ranges of the measured weight was calculated. Outcomes were compared between surgical modalities as well as between patient-specific factors. There was low disparity (p = 0.838) between patient-reported and measured weights among LE-TJA (mean difference 0.18 ± 3.63 kg; p = 0.446) and that of arthroscopy (0.27 ± 4.08 kg; p = 0.129) patients. Additionally, LE-TJA patients were equally likely to report weights accurately within 0.5 kg of the measured weight (74% vs. 71.76%; p = 0.908). LE-TJA and arthroscopy patients had similar reporting accuracy within 1 and 5 kg of the measured weights (p > 0.05). Preoperative patient-reported weights demonstrated acceptable accuracy in both LE-TJA and lower extremity arthroscopic orthopaedic patient populations making it a potentially reliable parameter of preoperative assessment.

Objective: With the scenario of limited labeled dataset, this paper introduces a deep learning-based approach that leverages Diabetic Retinopathy (DR) severity recognition performance using fundus images combined with wide-field swept-source optical coherence tomography angiography (SS-OCTA). Methods: The proposed architecture comprises a backbone convolutional network associated with a Twofold Feature Augmentation mechanism, namely TFA-Net. The former includes multiple convolution blocks extracting representational features at various scales. The latter is constructed in a two-stage manner, i.e., the utilization of weight-sharing convolution kernels and the deployment of a Reverse Cross-Attention (RCA) stream. Results: The proposed model achieves a Quadratic Weighted Kappa rate of 90.2% on the small-sized internal KHUMC dataset. The robustness of the RCA stream is also evaluated by the single-modal Messidor dataset, of which the obtained mean Accuracy (94.8%) and Area Under Receiver Operating Characteristic (99.4%) outperform those of the state-of-the-arts significantly. Conclusion: Utilizing a network strongly regularized at feature space to learn the amalgamation of different modalities is of proven effectiveness. Thanks to the widespread availability of multi-modal retinal imaging for each diabetes patient nowadays, such approach can reduce the heavy reliance on large quantity of labeled visual data. Significance: Our TFA-Net is able to coordinate hybrid information of fundus photos and wide-field SS-OCTA for exhaustively exploiting DR-oriented biomarkers. Moreover, the embedded feature-wise augmentation scheme can enrich generalization ability efficiently despite learning from small-scale labeled data.

Background and purpose — In Malawi, both skeletal traction (ST) and intramedullary nailing (IMN) are used in the treatment of femoral shaft fractures, ST being the mainstay treatment. Previous studies have found that IMN has improved outcomes and is less expensive than ST. However, no cost-effectiveness analyses have yet compared IMN and ST in Malawi. We report the results of a cost-utility analysis (CUA) comparing treatment using either IMN or ST. Patients and methods — This was an economic evaluation study, where a CUA was done using a decision-tree model from the government healthcare payer and societal perspectives with an 1-year time horizon. We obtained EQ-5D-3L utility scores and probabilities from a prospective observational study assessing quality of life and function in 187 adult patients with femoral shaft fractures treated with either IMN or ST. The patients were followed up at 6 weeks, and 3, 6, and 12 months post-injury. Quality adjusted life years (QALYs) were calculated from utility scores using the area under the curve method. Direct treatment costs were obtained from a prospective micro costing study. Indirect costs included patient lost productivity, patient transportation, meals, and childcare costs associated with hospital stay and follow-up visits. Multiple sensitivity analyses assessed model uncertainty. Results — Total treatment costs were higher for ST ($1,349) compared with IMN ($1,122). QALYs were lower for ST than IMN, 0.71 (95% confidence interval [CI] 0.66–0.76) and 0.77 (CI 0.71–0.82) respectively. Based on lower cost and higher utility, IMN was the dominant strategy. IMN remained dominant in 94% of simulations. IMN would be less cost-effective than ST at a total procedure cost exceeding $880 from the payer’s perspective, or $1,035 from the societal perspective. Interpretation — IMN was cost saving and more effective than ST in the treatment of adult femoral shaft fractures in Malawi, and may be an efficient use of limited healthcare resources. publishedVersion

Asprosin, coiled-coil domain-containing 80(CCDC80) and angiopoietin-like 4(ANGPTL4) are newly discovered adipocytokine that affects glucose tolerance, insulin resistance and cardiovascular diseases. The goal of this study was to investigate if a relationship exists among asprosin, CCDC80 and ANGPTL4 and inflammatory bowel disease (IBD). Fifty subjects with newly diagnosed IBD and fifty healthy individuals were enrolled. Patients were treated with standard therapies for 3 months. Plasma asprosin, CCDC80 and ANGPTL4 levels were measured with enzyme-linked immunosorbent assay. High resolution ultrasound was used to measure brachial artery diameter at rest, after reactive hyperemia (flow-mediated dilation, FMD) and after sublingual glyceryltrinitrate. Compare with healthy individuals, plasma CCDC80, erythrocyte sedi¬mentation rate (ESR), C-reactive protein (CRP) levels and homeostasis model assessment of insulin resistance (HOMA-IR) were significantly higher (p < 0.05, respectively), whereas plasma asprosin, ANGPTL4 levels and FMD were significantly lower in both UC and CD patients (p < 0.05). Plasma CCDC80 levels were significantly higher in patients with CD (p < 0.05), while plasma asprosin and ANGPTL4 levels were lower (pP < 0.05) as compared with those in patients with UC. Standard therapies increased plasma asprosin, ANGPTL4 levels and FMD in both UC and CD (p < 0.05), UC and CD patientswhile decreased plasma CCDC80, ESR, CRP levels and HOMA-IR (p < 0.05). The changes in HOMA-IR and FMD were correlated with the changes in plasma asprosin, CCDC80 and ANGPTL4 levels over the study period (p < 0.05). Plasma asprosin, CCDC80 and ANGPTL4 levels may be applied as a significant marker for early stage of insulin resistance and atherosclerosis in IBD, especially of CD.

Nowadays, enhancing the living standard with smart healthcare via the Internet of Things is one of the most critical goals of smart cities, in which artificial intelligence plays as the core technology. Many smart services, deployed according to wearable sensor-based physical activity recognition, have been able to early detect unhealthy daily behaviors and further medical risks. Numerous approaches have studied shallow handcrafted features coupled with traditional machine learning (ML) techniques, which find it difficult to model real-world activities. In this work, by revealing deep features from deep convolutional neural networks (DCNNs) in fusion with conventional handcrafted features, we learn an intermediate fusion framework of human activity recognition (HAR). According to transforming the raw signal value to pixel intensity value, segmentation data acquired from a multisensor system are encoded to an activity image for deep model learning. Formulated by several novel residual triple convolutional blocks, the proposed DCNN allows extracting multiscale spatiotemporal signal-level and sensor-level correlations simultaneously from the activity image. In the fusion model, the hybrid feature merged from the handcrafted and deep features is learned by a multiclass support vector machine (SVM) classifier. Based on several experiments of performance evaluation, our fusion approach for activity recognition has achieved the accuracy over 96.0% on three public benchmark data sets, including Daily and Sport Activities, Daily Life Activities, and RealWorld. Furthermore, the method outperforms several state-of-the-art HAR approaches and demonstrates the superiority of the proposed intermediate fusion model in multisensor systems.

BackgroundFemur fracture patients require significant in-hospital care. The burden incurred by caregivers of such patients amplifies the direct costs of these injuries and remains unquantified. Aim Here we aim to establish the in-hospital economic burden faced by informal caregivers of femur fracture patients. Methods 70 unique caregivers for 46 femoral shaft fracture patients were interviewed. Incurred economic burden was determined by the Human Capital Approach, using standardized income data to quantify productivity loss (in $USD). Linear regression assessed the relationship between caregiver burden and patient time-in-hospital.ResultsThe average economic burden incurred was $149, 9% of a caregiver’s annual income and positively correlated with patient time in hospital (p

Objectives The relationship between the 18FDG PET-CT maximum standard uptake value (SUVmax) and the type of lung adenocarcinoma is still not established. The aim of this study was to investigate the relationship between SUVmax value and histological grade and pathological subtype of lung adenocarcinoma, and to determine the optimum SUVmax cutoffs for distinguishing different histological grades. Materials and Methods The data of 618 lung adenocarcinoma patients were retrospectively analyzed. The relationship between SUVmax measured on preoperative 18FDG-PET-CT and the histological grade and pathological subtype was examined. The Kruskal-Wallis test was used to compare differences among groups, and the Bonferroni-Dunn test for pairwise comparison among groups. ROC analysis was applied to determine the optimal cut-off values for distinguishing different groups. In addition, the cut-off value was verified in an independent cohort of 85 consecutive lung adenocarcinoma cases. Results The SUVmax was significantly different between the low, intermediate, and high-grade groups(p Conclusion SUVmax can be used to predict pathological subtype and histological grade of lung adenocarcinoma. Thus, 18FDG PET-CT can serve as a noninvasive tool for precise diagnosis and help in the preoperative formulation of patient-specific treatment strategies.

As perception-related applications are of great importance in industrial production and daily life nowadays, solutions for understanding given images semantically receive numerous attention from the literature. To this end, significant accomplishments have been reached for such pixel-wise segmentation problem thanks to novel manipulations of integrating global context into local details in convolutional neural networks. However, this strategy in the existing work did not exhaustively exploit middle-level features, which carry reasonable balance between fine-grained and semantic information. Therefore, this paper introduces a Cross-Attentional Bracket-shaped Convolutional Network (CAB-Net) to leverage their contribution to the tournament of constructing pixel-wise labeled map. In concrete, fine-to-coarse feature maps of interest from the backbone network are densely combined by an efficient fusion of channel-wisely and spatially attentional schemes in crossing manner, namely Cross-Attentional Fusion, to embed semantically rich features into finer patterns. Continuously, these newly decoded outputs repeat the same procedure round-by-round until shaping a final feature map having finest resolution for complete scene understanding. Consequently, the proposed CAB-Net achieves competitive mean Intersection of Union performance on PASCAL VOC 2012 (83.6% without MS-COCO pretraining), CamVid (76.4%) and Cityscapes (78.3%) datasets.

Immunotherapy has become the mainstay for lung cancer treatment, providing sustained therapeutic responses and improved prognosis compared with those obtained with surgery, chemotherapy, radiotherapy, and targeted therapy. It has the potential for anti-tumor treatment and killing tumor cells by activating human immunity and has moved the targets of anti-cancer therapy from malignant tumor cells to immune cell subsets. Two kinds of immune checkpoints, cytotoxic T-lymphocyte-associated antigen 4 (CTLA-4) and programmed death-1 (PD-1)/programmed death ligand 1 (PD-L1), are the main targets of current immunotherapy in lung cancer. Despite the successful outcomes achieved by immune checkpoint inhibitors, a small portion of lung cancer patients remain unresponsive to checkpoint immunotherapy or may ultimately become resistant to these agents as a result of the complex immune modulatory network in the tumor microenvironment. Therefore, it is imperative to exploit novel immunotherapy targets to further expand the proportion of patients benefiting from immunotherapy. This review summarizes the molecular features, biological function, and clinical significance of several novel checkpoints that have important roles in lung cancer immune responses beyond the CTLA-4 and PD-1/PD-L1 axes, including the markers of co-inhibitory and co-stimulatory T lymphocyte pathways and inhibitory markers of macrophages and natural killer cells.

Introduction Although matching lumbar lordosis (LL) with pelvic incidence (PI) is an important surgical goal for adult spinal deformity (ASD), there is concern that overcorrection may lead to proximal junctional kyphosis (PJK). We introduce the upper instrumented vertebra–femoral angle (UIVFA) as a measure of appropriate postoperative position in the setting of lower thoracic to pelvis surgical correction for patients with sagittal imbalance. We hypothesize that a more posterior UIV position in relation to the center of the femoral head is associated with an increased risk of PJK given compensatory hyperkyphosis above the UIV. Methods In this retrospective cohort study, adult patients undergoing lower thoracic (T9–T12) to pelvis correction of ASD with a minimum of 2-year follow-up were included. UIVFA was measured as the angle subtended by a line from the UIV centroid to the femoral head center to the vertical axis. Patients who developed PJK and those who did not were compared with preoperative and postoperative UIVFA as well as change between postoperative and preoperative UIVFA (deltaUIVFA). Results Of 119 patients included with an average 3.6-year follow-up, 51 (42.9%) had PJK and 24 (20.2%) had PJF. Patients with PJK had significantly higher postoperative UIVFA (12.6 ± 4.8° vs. 9.4 ± 6.6°, p = 0.04), deltaUIVFA (6.1 ± 7.6° vs. 2.1 ± 5.6°, p p = 0.04), postoperative lumbar lordosis (47.7 ± 13.9° vs. 42.4 ± 13.1, p = 0.04) and postoperative thoracic kyphosis (44.9 ± 13.2 vs. 31.6 ± 18.8) than patients without PJK. With multivariate logistic regression, postoperative UIVFA and deltaUIVFA were found to be independent risk factors for PJK (p p Conclusion The more posterior the UIV is from the femoral head center after lower thoracic to pelvis surgical correction for ASD, the more patients are at risk for PJK. The greater the magnitude of posterior translation of the UIV from the femoral head center from preop to postop, the greater the likelihood for PJF.

For person identification in non-interaction biometric systems, gait recognition has been recently encouraged in literature and industrial applications instead of face recognition. Although numerous advanced methods that learn object appearance by conventional machine learning models have been discussed in the last decade, most of them are strongly sensitive to scene background motion. In this research, we address the drawbacks of existing works by comprehensively studying gait information from 3D human skeleton data with a deep learning-based identifier. To capture the statistic gait information in the spatial dimension, we first extract the geometric gait features of joint distance and orientation. The dynamic gait information is then obtained by calculating the temporal description features with the mean and standard deviation of geometric features. Accordingly, the fully gait information of an individual is finally learned via a compact Deep Convolutional Neural Network which is explicitly designed with multiple stacks of asymmetric convolutional filters to fully gain the spatial correlation of in-frame body joints and the temporal relation of frame-wise posture at multiple scales. Based on the experimental results evaluated on four benchmark 3D gait datasets commonly used for person identification, including UPCV Gait, UPCV Gait K2, KS20 VisLab Multi-View Kinect Skeleton, and SDUGait, the proposed method presents the superior performance over that of several state-of-the-art approaches while maintaining a low computational capacity.

Recently, numerous methods have been introduced for three-dimensional (3-D) action recognition using handcrafted feature descriptors coupled traditional classifiers. However, they cannot learn high-level features of a whole skeleton sequence exhaustively. In this paper, a novel encoding technique—namely, pose feature to image (PoF2I), is introduced to transform the pose features of joint–joint distance and orientation to color pixels. By concatenating the features of all skeleton frames in a sequence, a color image is generated to depict spatial joint correlations and temporal pose dynamics of an action appearance. The strategy of end-to-end fine-tuning a pretrained deep convolutional neural network, which completely capture multiple high-level features at multiscale action representation, is implemented for learning recognition models. We further propose an efficient data augmentation mechanism for informative enrichment and overfitting prevention. The experimental results on six challenging 3-D action recognition datasets demonstrate that the proposed method outperforms state-of-the-art approaches.

Purpose To evaluate the ability of patients to return to work following anteromedialization (AMZ) tibial tubercle osteotomy (TTO) due to isolated patellofemoral osteoarthritis or pain. Methods Consecutive patients undergoing AMZ TTO were reviewed retrospectively at a minimum of 1 year postoperatively. Patients completed a subjective work questionnaire, a visual analog scale for pain, as well as a Kujala questionnaire and satisfaction questionnaire. Results Fifty-seven patients (61 knees; average age: 32.7 ± 9.6 years) were contacted at an average follow-up of 4.86 ± 2.84 years postoperatively. The preoperative Kujala score improved from 55.7 ± 17.8 to 84.6 ± 15.8 at final follow-up ( P < 0.001). Thirty-seven patients (64.9%) were employed within 3 years prior to surgery and 34 patients (91.9%) were able to return to work by 2.8 ± 2.6 months postoperatively. However, only 27 patients (73.0%) of patients were able to return to the same level of occupational intensity. Patients who held sedentary, light-, medium-, or high-intensity occupations were able to return to work at a rate of 100.0%, 93.8%, 77.8%, and 100.0% by 2.2 months, 3.0 months, 3.1 months, and 4.0 months, postoperatively. No patients underwent revision TTO or conversion to arthroplasty by the time of final follow-up. Conclusion In patients with focal patellofemoral osteoarthritis or pain, AMZ TTO provides a high rate of return to work (91.9%) by 2.8 ± 2.6 months postoperatively. Patients with higher intensity occupations may take longer to return to work than those with less physically demanding occupations. Level of Evidence III.

This letter proposes a cost-efficient convolutional neural network (CNN) for robust automatic modulation classification (AMC) deployed for cognitive radio services of modern communication systems. The network architecture is designed with several specific convolutional blocks to concurrently learn the spatiotemporal signal correlations via different asymmetric convolution kernels. Additionally, these blocks are associated with skip connections to preserve more initially residual information at multi-scale feature maps and prevent the vanishing gradient problem. In the experiments, MCNet reaches the overall 24-modulation classification rate of 93.59% at 20 dB SNR on the well-known DeepSig dataset.

Recently, skeleton-based human action recognition has received more interest from industrial and research communities for many practical applications thanks to the popularity of depth sensors. A large number of conventional approaches, which have exploited handcrafted features with traditional classifiers, cannot learn high-level spatiotemporal features to precisely recognize complex human actions. In this paper, we introduce a novel encoding technique, namely Pose-Transition Feature to Image (PoT2I), to transform skeleton information to image-based representation for deep convolutional neural networks (CNNs). The spatial joint correlations and temporal pose dynamics of an action are exhaustively depicted by an encoded color image. For learning action models, we fine-tune end-to-end a pre-trained network to thoroughly capture multiple high-level features at multi-scale action representation. The proposed method is benchmarked on several challenging 3D action recognition datasets (e.g., UTKinect-Action3D, SBU-Kinect Interaction, and NTU RGB+D) with different parameter configurations for performance analysis. Outstanding experimental results with the highest accuracy of 90.33% on the most challenging NTU RGB+D dataset demonstrate that our action recognition method with PoT2I outperforms state-of-the-art approaches.

Background Family with sequence similarity 83 member H antisense RNA 1 (FAM83H-AS1) is a novel long non-coding RNA. Increasing studies have reported that FAM83H-AS1 is abnormally expressed in a variety of tumors and is associated with poor outcome. However, the clinical prognostic significance of lncRNA FAM83H-AS1 in tumors is not completely known. Methods In this meta-analysis, literature was collected up until February 5, 2020 through multifarious retrieval strategies by searching through electronic databases of PubMed, Cochrane Library, EMBASE, Medline, Web of Science, CNKI, Weipu, and Wanfang. A total of 14 studies that met the inclusion criteria with relevant clinical data and prognostic information were included in the meta-analysis. Results The combined results revealed that high expression of FAM83H-AS1 was associated with poor overall survival (OS) (HR = 1.63, 95% CI 1.24–2.14, P = 0.0004) in a variety of cancers. Additionally, upregulated FAM83H-AS1 expression was significantly correlated with tumor TNM stage (III/IV vs. I/II, OR = 2.40, 95% CI 1.36–4.23, P = 0.003) and lymph node metastasis (positive vs. negative, OR = 1.70, 95% CI 1.14–2.52, P = 0.008) in patients with cancer. Conclusions Our results of this meta-analysis indicated that elevated FAM83H-AS1 expression could predict poor prognosis in patients with cancer and suggested that FAM83H-AS1 might serve as a novel biomarker for cancer.

Purpose To develop a prediction model based on 18F-fludeoxyglucose (18F-FDG) positron emission tomography/computed tomography (PET/CT) for solid pulmonary nodules (SPNs) with high malignant probability. Patients and Methods We retrospectively reviewed the records of CT-undetermined SPNs, which were further evaluated by PET/CT between January 2008 and December 2015. A total of 312 cases were included as a training set and 159 as a validation set. Logistic regression was applied to determine independent predictors, and a mathematical model was deduced. The area under the receiver operating characteristic curve (AUC) was compared to other models. Model fitness was assessed based on the American College of Chest Physicians guidelines. Results There were 215 (68.9%) and 127 (79.9%) malignant lesions in the training and validation sets, respectively. Eight independent predictors were identified: age [odds ratio (OR) = 1.030], male gender (OR = 0.268), smoking history (OR = 2.719), lesion diameter (OR = 1.067), spiculation (OR = 2.530), lobulation (OR = 2.614), cavity (OR = 2.847), and standardized maximum uptake value of SPNs (OR = 1.229). Our AUCs (training set, 0.858; validation set, 0.809) was better than those of previous models (Mayo: 0.685, P = .0061; Peking University People’s Hospital: 0.646, P = .0180; Herder: 0.708, P = .0203; Zhejiang University: 0.757, P = .0699). The C index of the nomogram was 0.858. Our model reduced the diagnosis of indeterminate nodules (26.4% vs. 79.2%, 53.5%, 39.6%, and 34.0%, respectively) while improved sensitivity (81.3% vs. 16.4%, 49.2%, 62.5%, and 68.0%, respectively) and accuracy (65.4% vs. 16.4%, 39.6%, 52.8%, and 58.5%, respectively). Conclusion Our model could permit accurate diagnoses and may be recommended to identify malignant SPNs with high malignant probability, as our data pertain to a very high-prevalence cohort only.

Sunitinib resistance is a major challenge in systemic therapy for renal cell carcinoma (RCC). The role of circular RNAs (circRNAs) in regulating sunitinib resistance of RCC is largely unknown. We established sunitinib-resistant RCC cell lines in vivo. Through RNA-sequencing, we identified circSNX6, whose expression is upregulated in sunitinib-resistant cells compared with their parental cells. High circSNX6 expression was correlated with sunitinib resistance and worse oncologic outcomes in a cohort of 81 RCC patients. In vitro and in vivo experiments confirmed that circSNX6 could promote sunitinib resistance in RCC. circSNX6 acts as a molecular "sponge" to relieve the suppressive effect of microRNA (miR)-1184 on its target gene, glycerophosphocholine phosphodiesterase 1 (GPCPD1), which increases intracellular lysophosphatidic acid (LPA) levels and, ultimately, promotes sunitinib resistance in RCC cells. Our findings demonstrated that the circSNX6/miR-1184/GPCPD1 axis had a critical role in regulation of intracellular LPA levels and sunitinib resistance in RCC; they also provide a novel prognostic indicator and promising therapeutic targets.

Study Design: Retrospective cohort study. Objective: Overcorrection in adult spinal deformity (ASD) surgery may lead to proximal junctional kyphosis (PJK) because of posterior spinal displacement. The aim of this paper is to determine if the L1 position relative to the gravity line (GL) is associated with PJK. Methods: ASD patients fused from the lower thoracic spine to sacrum by 4 spine surgeons at our hospital were retrospectively studied. Lumbar-only and upper thoracic spine fusions were excluded. Spinopelvic parameters, the L1 plumb line (L1PL), L1 distance to the GL (L1-GL), and Roussouly type were measured. Results: One hundred fourteen patients met inclusion criteria (63 patients with PJK, 51 without). Mean age and follow up was 65.51 and 3.39 years, respectively. There was no difference between the PJK and the non-PJK groups in baseline demographics, pre-operative and immediate post-operative pelvic incidence-lumbar lordosis mismatch, sagittal vertical axis, or coronal Cobb. The immediate postoperative L1-GL was -7.24 cm in PJK and -3.45 cm in non-PJK ( P < 0.001), L1PL was 1.71 cm in PJK and 3.07 cm in non-PJK ( P = 0.004), and PT (23.76° vs 18.90°, P = 0.026) and TK (40.56° vs 31.39°, P < 0.001) were larger in PJK than in non-PJK. After univariate and multivariate analyses, immediate postoperative TK and immediate postoperative L1-GL were independent risk factors for PJK without collinearity. Conclusions: A dorsally displaced L1 relative to the GL was associated with an increased risk of PJK after ASD surgery. The postoperative L1-GL distance may be a factor to consider during ASD surgery.

Background: The mortality and recurrence of patients with cancer is of high prevalence. SET-binding factor 2 (SBF2) antisense RNA1 (lncRNA-SBF2-AS1) is a promising long non-coding RNA. There is increasing evidence that SBF2-AS1 is abnormally expressed in various tumors and is associated with cancer prognosis. However, the identification of the effect of lncRNA SBF2-AS1 in tumors remains necessary. Materials and Methods: Up to November 2, 2020, electronic databases, including PubMed, Cochrane Library, EMBASE, Medline, and Web of Science, were searched. The results were evaluated by pooled odds ratios (ORs) and hazard ratios (HRs) with 95% confidence intervals (CIs). Results: A total of 11 literatures on cancer patients were included for the present meta-analysis. The combined results revealed that high expression of SBF2-AS1 was significantly associated with unfavorable overall survival (OS) (HR = 1.48, 95% CI: 1.34-1.62, P < 0.00001) in a variety of cancers. In additional, the increase in SBF2-AS1 expression was also correlated with tumor size ((larger vs. smaller) OR = 2.34, 95% CI: 1.47-3.70, P = 0.0003), advanced TNM stage ((III/IV vs. I/II) OR = 2.78, 95% CI: 1.75-4.41, P < 0.0001), lymph node metastasis ((Positive vs. Negative) OR = 3.06, 95% CI: 1.93-4.86, P < 0.00001), and histological grade ((poorly vs. well/moderately) OR = 2.58, 95% CI: 1.47-4.52, P = 0.001) in patients with cancer. Furthermore, The Cancer Genome Atlas (TCGA) dataset valuated that SBF2-AS1 was upregulated in a variety of tumors, and predicted the worse prognosis. Conclusions: Our results of this meta-analysis demonstrate that high SBF2-AS1 expression may become a potential target for predicting the prognosis of human cancers.

BACKGROUND: Return to work after shoulder arthroplasty for glenohumeral osteoarthritis (OA) is an important consideration for an aging workforce. QUESTIONS/PURPOSES: The aim of this study was to compare the shoulder function, pain levels, and rate of return to work in patients treated with anatomic total shoulder arthroplasty (aTSA) versus humeral hemiarthroplasty (HHA). METHODS: A retrospective review of consecutive HHA patients was performed of our institution’s shoulder arthroplasty registry. Inclusion criteria were pre-operative diagnosis of end-stage OA and more than 2 years’ follow-up. HHA patients were statistically matched to aTSA patients and then screened for pre-operative work status; 26 HHA and 23 aTSA patients worked before surgery. There was no difference in average age (HHA, 62.4 years; aTSA, 61.7 years) or follow-up (HHA, 67.5 months; aTSA, 66.9 months). RESULTS: Average American Shoulder and Elbow Surgeons (ASES) scores (HHA, 37.6 to 70.3; aTSA, 35.6 to 80.1) and visual analogue scale (VAS) for pain scores (HHA, 6.1 to 2.3; aTSA, 6.5 to 0.6) improved in both groups. However, HHA patients had worse final VAS scores, and aTSA patients were more satisfied (100% vs 77%); 61.5% of HHA patients returned to work post-operatively versus 87.0% of aTSA patients. There was no difference in time to return to work (HHA, 1.9 ± 2.3 months; aTSA, 1.3 ± 1.0 months). CONCLUSION: Patients with shoulder OA undergoing aTSA have higher rates of return to work, function, and satisfaction than those undergoing HHA. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (10.1007/s11420-019-09692-0) contains supplementary material, which is available to authorized users.

Summary Background Identification of high-risk localised renal cell carcinoma is key for the selection of patients for adjuvant treatment who are at truly higher risk of reccurrence. We developed a classifier based on single-nucleotide polymorphisms (SNPs) to improve the predictive accuracy for renal cell carcinoma recurrence and investigated whether intratumour heterogeneity affected the precision of the classifier. Methods In this retrospective analysis and multicentre validation study, we used paraffin-embedded specimens from the training set of 227 patients from Sun Yat-sen University (Guangzhou, Guangdong, China) with localised clear cell renal cell carcinoma to examine 44 potential recurrence-associated SNPs, which were identified by exploratory bioinformatics analyses of a genome-wide association study from The Cancer Genome Atlas (TCGA) Kidney Renal Clear Cell Carcinoma (KIRC) dataset (n=114, 906 600 SNPs). We developed a six-SNP-based classifier by use of LASSO Cox regression, based on the association between SNP status and patients' recurrence-free survival. Intratumour heterogeneity was investigated from two other regions within the same tumours in the training set. The six-SNP-based classifier was validated in the internal testing set (n=226), the independent validation set (Chinese multicentre study; 428 patients treated between Jan 1, 2004 and Dec 31, 2012, at three hospitals in China), and TCGA set (441 retrospectively identified patients who underwent resection between 1998 and 2010 for localised clear cell renal cell carcinoma in the USA). The main outcome was recurrence-free survival; the secondary outcome was overall survival. Findings Although intratumour heterogeneity was found in 48 (23%) of 206 cases in the internal testing set with complete SNP information, the predictive accuracy of the six-SNP-based classifier was similar in the three different regions of the training set (areas under the curve [AUC] at 5 years: 0·749 [95% CI 0·660–0·826] in region 1, 0·734 [0·651–0·814] in region 2, and 0·736 [0·649–0·824] in region 3). The six-SNP-based classifier precisely predicted recurrence-free survival of patients in three validation sets (hazard ratio [HR] 5·32 [95% CI 2·81–10·07] in the internal testing set, 5·39 [3·38–8·59] in the independent validation set, and 4·62 [2·48–8·61] in the TCGA set; all p Interpretation Our six-SNP-based classifier could be a practical and reliable predictor that can complement the existing staging system for prediction of localised renal cell carcinoma recurrence after surgery, which might enable physicians to make more informed treatment decisions about adjuvant therapy. Intratumour heterogeneity does not seem to hamper the accuracy of the six-SNP-based classifier as a reliable predictor of recurrence. The classifier has the potential to guide treatment decisions for patients at differing risks of recurrence. Funding National Key Research and Development Program of China, National Natural Science Foundation of China, Guangdong Provincial Science and Technology Foundation of China, and Guangzhou Science and Technology Foundation of China.

BACKGROUND There are few validated instruments that serve as a proxy for fracture-healing after lower-extremity trauma in low-resource settings. The squat-and-smile test (S&S) has been under development by SIGN (Surgical Implant Generation Network) Fracture Care International to monitor outcomes of lower-extremity long-bone fractures after intramedullary nailing in resource-limited settings. The goals of this study were to develop and identify domains of the S&S test. METHODS The S&S domains were developed through an iterative process, and consensus was achieved regarding 3: squat depth, support needed to squat, and facial expression. Adult patients with an OTA/AO type-32 femoral shaft fracture were included in this retrospective study and had the S&S administered at 6 weeks and 3, 6, and 12 months postoperatively. Two authors independently assessed photographs of the patients performing the S&S. S&S domains were correlated with the EuroQol 5-Dimensions (EQ-5D) index score, and comparisons were made between S&S domains and reoperation status. Interrater and test-retest reliability was assessed using the kappa statistic. Sensitivity and specificity analyses were performed. RESULTS Six hundred and nine S&S images were evaluated for 231 patients. Each domain improved over time and correlated positively with EQ-5D scores (p < 0.05). Squat depth and support needed to squat correlated with the need for a reoperation (p ≤ 0.01), and both had high specificity (0.95 and 0.97, respectively) for ruling out the need for a reoperation at 1 year. All 3 domains had high test-retest reliability (κ = 0.95, 0.92, and 0.96). Squat depth and need for support also had strong interrater reliability (κ = 0.75 and 0.78). CONCLUSIONS The S&S is a potential tool for monitoring clinical and functional outcome of femoral shaft fractures in low-resource settings. Our data support the binary assessment of squat depth and need for support, but not facial expression, as a proxy for fracture-healing. Future prospective studies in external populations are warranted to evaluate the validity, reliability, and responsiveness of the S&S. CLINICAL RELEVANCE The S&S provides a valuable proxy for femoral shaft fracture assessment for middle to low-income countries because it is locally relevant (based on squatting), it is easy to administer, and assessment can be performed remotely via mobile telephone or text messaging.

Background: The Short Musculoskeletal Function Assessment (SMFA) tool measures function and quality of life in patients with musculoskeletal conditions. Objective: This study aimed to translate and adapt culturally the SMFA into Chichewa, and assess its clinimetric properties. Methods: The translated Chichewa version was administered to 53 patients with musculoskeletal disorders. To assess repeatability, an additional 20 patients answered the questionnaire twice over a time interval of two weeks. Internal consistency, floor and ceiling effects, and repeatability were tested; construct validity was assessed with the World Health Organization Quality of Life Assessment tool (WHOQOL-BREF). Results: There was good internal consistency for both Dysfunction and Bothersome indices (Cronbach’s alpha 0.90) and good construct validity between both indices with the WHOQOL-BREF. Pearson’s correlation coefficient and intraclass correlation coefficient (ICC) for repeatability for the Dysfunction Index were 0.941 and 0.922 (95% CI: 0.772, 0.971) respectively, and 0.877 and 0.851 (95% CI: 0.629, 0.941) for the Bothersome Index respectively. Conclusion: The translated Chichewa SMFA is a valid tool for populations that speak the Chichewa language. publishedVersion

Titanium dioxide has received great attention as a photocatalyst. However, due to the relatively large bandgap energy of TiO2 and fast recombination of photogenerated electron–hole pairs, various a...

High-mobility group box 1 (HMGB1) is newly discovered protein, which play a crucial role in the pathogenesis of systemic inflammation. Recent studies showed that HMGB1 is one of the important pathophysiological mechanisms in the occurrence and development of atherosclerosis. The purpose of the present study was to investigate the relationship between serum HMGB1 levels and endothelial function in patients with polycystic ovary syndrome (PCOS). Eighty newly diagnosed patients with PCOS and eighty normal women of similar age were selected. Metformin treatment (1,500 mg/day) was initiated in all patients for a period of consecutive 3 months. Serum HMGB1 levels were measured by ELISA. High resolution ultrasound was used to measure the brachial artery diameter at rest, after reactive hyperemia (flow-mediated arterial dilation, FMD) and after sublingual glyceryltrinitrate. Serum HMGB1 levels in PCOS were 24.87±14.93 ng/ml, which were significantly higher than that in controls (8.82±3.55 ng/ml, p

This review aims to (1) assess the breadth of pediatric orthopaedic research in low- and middle-income countries (LMICs) and (2) determine the impact of academic collaboration (an LMIC and a non-LMIC investigator) in published LMIC research. Pediatric orthopaedic clinical studies conducted in LMICs from 2004 to 2014 were extracted from Embase, Cochrane, and Pubmed databases. Of 22,714 searched studies, 129 met inclusion criteria. 85% generated low-quality evidence (level IV or lower). 21% were collaborative, and these were more likely than non-collaborative papers to generate level III evidence or higher (25% vs 13%, p = 0.141). Pediatric orthopaedic research produced by LMICs rarely achieves level I–III evidence, but collaborative studies are associated with higher levels of evidence. Level of evidence: N/A.

INTRODUCTION Traumatic lower extremity injuries requiring multidisciplinary treatment pose a challenge in low- and middle-income countries, where access to specialists may be limited. The surgical management and reconstructive training (SMART) course teaches orthopaedic surgeons muscle and fasciocutaneous flap procedures to address this scarcity. The purpose of this study is to assess the effectiveness of the SMART course in improving competency and comfort in performing common lower extremity flap procedures among participants. METHODS Sixty-four orthopaedic surgeons from different regions of Nepal and Bhutan participated in the Nepal SMART course in 2016 and 2017. A competency test-consisting of questions from US in-training plastic and orthopaedic surgery examinations-was administered to attendees before and after the course. Thirty-two participants from 2016 were asked to rate their comfort level in performing flap procedures both pre- and postcourse. RESULTS Overall competency test scores, as well as scores in the plastic surgery section, increased significantly after the course (P < 0.01). The precourse competency test scores were higher in 2016 compared with 2017 (P = 0.02). There was a higher increase in overall competency test scores after the course in 2016 compared with 2017 (P = 0.03). The procedure comfort levels reported by attendees increased for all flaps (P < 0.01). CONCLUSIONS These results demonstrate the ability of the SMART course to improve the competency and comfort levels of orthopaedic surgeons in performing common lower extremity flap procedures. Despite the differences in years in practice and previous experience in performing flaps, the course yielded significantly better results in 2017 compared with 2016, showing that the implementation of the course has been improving.

The novel coronavirus 2019 (COVID-19) becomes recently a global pandemic as declared by the World Health Organization (WHO) in March 2020. COVID-19 rapidly spread and attacked people in more than 200 countries worldwide. The use of artificial intelligence (AI) techniques has become urgent to prevent the exacerbation of the astounding spread of this pernicious disease. This paper presents a novel rapid deep learning computer-aided Diagnosis (CAD) framework for simultaneously detecting and diagnosing the COVID-19 against different respiratory diseases such as pneumonia, atelectasis, cardiomegaly, infiltration, mass, pneumothorax, nodule, and effusion. To develop a useful patient triage system for detecting COVID-19 in early-stage, rapidly extract the visual diagnosis knowledge from the input chest X-ray (CXR) images is extremely required. The proposed CAD framework shows its capability to automatically detect and diagnose COVID-19 with a detection accuracy of 96.31% and a classification accuracy of 97.40%. Meanwhile, the real-time prediction speed of 0.0093 seconds is achieved for a single testing CXR image. To achieve a high accuracy of the knowledge extraction from the entire CXR images with a high prediction speed would represent a key for developing a comprehensive and useful smart patient triage system in hospitals and healthcare systems.

After neoadjuvant chemoradiotherapy (NCRT) in locally advanced esophageal squamous cell cancer (ESCC), roughly 40% of the patients may achieve pathologic complete response (pCR). Those patients may benefit from organ-saving strategy if the probability of pCR could be correctly identified before esophagectomy. A reliable approach to predict pathological response allows future studies to investigate individualized treatment plans.MethodAll eligible patients treated in our center from June 2012 to June 2019 were retrospectively collected. Radiomics features extracted from pre-/post-NCRT CT images were selected by univariate logistic and LASSO regression. A radiomics signature (RS) developed with selected features was combined with clinical variables to construct RS+clinical model with multivariate logistic regression, which was internally validated by bootstrapping. Performance and clinical usefulness of RS+clinical model were assessed by receiver operating characteristic (ROC) curves and decision curve analysis, respectively.ResultsAmong the 121 eligible patients, 51 achieved pCR (42.1%) after NCRT. Eighteen radiomics features were selected and incorporated into RS. The RS+clinical model has improved prediction performance for pCR compared with the clinical model (corrected area under the ROC curve, 0.84 vs. 0.70). At the 60% probability threshold cutoff (i.e., the patient would opt for observation if his probability of pCR was >60%), net 13% surgeries could be avoided by RS+clinical model, equivalent to implementing organ-saving strategy in 31.37% of the 51 true-pCR cases.ConclusionThe model built with CT radiomics features and clinical variables shows the potential of predicting pCR after NCRT; it provides significant clinical benefit in identifying qualified patients to receive individualized organ-saving treatment plans.

Study Design: Retrospective cohort. Objective: Reoperation to lumbar spinal fusion creates significant burden on patient quality of life and healthcare costs. We assessed rates, etiologies, and risk factors for reoperation following elective 1 to 2 level lumbar fusion. Methods: Patients undergoing elective 1 to 2 level lumbar fusion were identified using the Health Care Utilization Project (HCUP) state inpatient databases from Florida and California. Patients were tracked for 5 years for any subsequent lumbar fusion. Cox proportional hazard analyses for reoperation were assessed using the following covariates: fusion approach type, age, race, Charlson comormidity index, gender, and length of stay. Distribution of etiologies for reoperation was then assessed. Results: 71, 456 patients receiving elective 1 to 2 level lumbar fusion were included. A 5-year reoperation rate of 13.53% and mortality rate of 2.22% was seen. Combined anterior-posterior approaches (HR = 0.904, p < 0.05) and TLIF (HR = 0.867, p < 0.001) were associated with reduced risk of reoperation compared to stand-alone anterior approaches and non-TLIF posterior approaches. Age, gender, and number of comorbidities were not associated with risk of reoperation. From 1 to 5 years, degenerative disease rose from 43.50% to 50.31% of reoperations; mechanical failure decreased from 37.65% to 29.77%. Conclusions: TLIF and combined anterior-posterior approaches for 1 to 2 level lumbar fusion are associated with the lowest rate of reoperation. Number of comorbidities and age are not predictive of reoperation. Primary etiologies leading to reoperation were degenerative disease and mechanical failure. Mortality rate is not increased from baseline following 1 to 2 level lumbar fusion.

Deep learning (DL) nowadays activates the advancement of multiple research fields, from signal processing to computer vision and communication. Compared with traditional machine learning (ML) algorithms, some DL architectures such as convolutional neural network (CNN) allow learning feature automatically without expert knowledge in a specific domain. Inspired by the notable achievement of DL for general image classification, this work studies an efficient CNN-based approach for automatic modulation classification which is recognized as the fundamental task in several modern communication systems. Besides the architecture organized by several blocks of parallel asymmetric convolutional layers, the proposed network is specialized by two mechanisms: skip-connection for the prevention of vanishing gradient and reusable-feature depth-concatenation for an optimal feature utilization via accumulating the deep feature at multi-scale representation. According performance evaluation, our method gains the 24-modulation classification rate of 85.10% at +10 dB SNR on the most challenging and large-scale DeepSig: RadioML (version 2018.01A) dataset.

Background: Exploring predictive biomarkers to individualize patients with small cell lung cancer (SCLC) remains challenging in clinical practice, and current imaging techniques cannot fully meet the clinical needs due to their inherent limitations. Methods: A total of 127 longitudinal biological DNA samples from 35 SCLC patients treated with first-line standard therapy were analyzed by capture-based targeted (average coverage for tissue and circulating tumor DNA (ctDNA) samples were 841× and 2670.5×, respectively) genome next-generation sequencing, which was designed to cover coding sequencing or hot exons of 1,021 genes frequently mutated in solid tumors. PyClone was used to infer the molecular tumor burden index (mTBI). Pre-treatment tumor tissues [T1] and serial plasma samples were collected (pre-treatment [B1], after two [B2], six [B3] cycles of chemotherapy and at progression [B4]). Findings: In total, 38 SCLC patients were enrolled with 35 patients included in the analysis set. The overall concordance rate between tissue and plasma sequencing in 30 patients with matched T1 and B1 samples was 66.5%. The median progression-free survival (PFS) was 9.8 months (95% confidence interval [CI]: 6.3-13.3). Patients with persisted RB1 and/or TP53 mutations at B2 demonstrated inferior PFS compared with others (5.2 months [95%CI: 3.8-6.6] vs. 13.1 months [95%CI: 9.8-16.4], p=0.06). The median PFS for patients with persisted RB1 mutations at B2 was significantly worse than the other two groups (4.2 months [95%CI: 2.8-5.6] vs. not reached, p

The Erdős-Rényi (ER) random graph G(n, p) analytically characterizes the behaviors in complex networks. However, attempts to fit real-world observations need more sophisticated structures (e.g., multilayer networks), rules (e.g., Achlioptas processes), and projections onto geometric, social, or geographic spaces. The p-adic number system offers a natural representation of hierarchical organization of complex networks. The p-adic random graph interprets n as the cardinality of a set of p-adic numbers. Constructing a vast space of hierarchical structures is equivalent for combining number sequences. Although the giant component is vital in dynamic evolution of networks, the structure of multiple big components is also essential. Fitting the sizes of the few largest components to empirical data was rarely demonstrated. The p-adic ultrametric enables the ER model to simulate multiple big components from the observations of genetic interaction networks, social networks, and epidemics. Community structures lead to multimodal distributions of the big component sizes in networks, which have important implications in intervention of spreading processes., Scientific Reports, 11 (1), ISSN:2045-2322

BACKGROUND CONTEXT Although matching lumbar lordosis (LL) with pelvic incidence (PI) is an important surgical goal for adult spinal deformity (ASD), there is concern that overcorrection may lead to proximal junctional kyphosis (PJK). We introduce the upper instrumented vertebra – femoral angle (UIVFA) as a measure of appropriate postoperative position in the setting of lower thoracic to pelvis surgical correction for patients with sagittal imbalance. We hypothesize that a more posterior UIV position in relation to the center of the femoral head is associated with an increased risk of PJK given compensatory hyperkyphosis above the UIV. PURPOSE The purpose of this study is to evaluate if increased UIVFA is associated with PJK in operative management of ASD. STUDY DESIGN/SETTING Retrospective cohort study. PATIENT SAMPLE Adult patients undergoing lower thoracic (T9-T12) to pelvis correction of ASD with a minimum of two-year follow-up were included. OUTCOME MEASURES The primary outcome is PJK and proximal junctional failure (PJF). Methods In this retrospective cohort study, adult patients undergoing lower thoracic (T9-T12) to pelvis correction of ASD with a minimum of two-year follow-up were included. UIVFA was measured as the angle subtended by a line from the UIV centroid to the femoral head center to the vertical axis. UIV posterior to the femoral head center was positive (+) and UIV anterior the femoral head center was negative (-). A >10° kyphotic angle between the UIV and UIV+2 or a >10° increase from preop to postop were used to determine PJK. Patients with PJK that required revision surgery were defined as proximal junctional failure (PJF). Patients who developed PJK and those who did not were compared with preoperative and postoperative UIVFA, change between postoperative and preoperative UIVFA (deltaUIVFA), sagittal vertical axis (SVA), pelvic tilt (PT), sacral slope (SS), pelvic incidence (PI), lumbar lordosis (LL), pelvic incidence minus lumbar lordosis (PI-LL), thoracic kyphosis at T5-12 (TK) and the central sacral vertical line (CSVL). Results Of the 119 patients included in the study with an average 3.6-year follow-up, 51 (42.9%) had PJK and 24 (20.2%) had PJF. The average preoperative UIVFA was 7.0±0.7°, the average postoperative UIVFA 10.8±0.6°, and the average deltaUIVFA was 3.8±0.6°. Patients with PJK had significantly higher postoperative UIVFA (12.6±4.8° vs 9.4±6.6°, p=0.04), deltaUIVFA (6.1±7.6° vs 2.1±5.6°, p Conclusions The more posterior the UIV is from the femoral head center after lower thoracic to pelvis surgical correction for ASD, the more patients are at risk for PJK. The greater the magnitude of posterior translation of the UIV from the femoral head center from preop to postop, the greater the likelihood for PJF. Further investigation into the consequences of sagittal overcorrection in the setting of ASD is warranted. FDA DEVICE/DRUG STATUS This abstract does not discuss or include any applicable devices or drugs.

Modulation classification, recognized as the intermediate step between signal detection and demodulation, is widely deployed in several modern wireless communication systems. Although many approaches have been studied in the last decades for identifying the modulation format of an incoming signal, they often reveal the obstacle of learning radio characteristics for most traditional machine learning algorithms. To overcome this drawback, we propose an accurate modulation classification method by exploiting deep learning for being compatible with constellation diagram. Particularly, a convolutional neural network is developed for proficiently learning the most relevant radio characteristics of gray-scale constellation image. The deep network is specified by multiple processing blocks, where several grouped and asymmetric convolutional layers in each block are organized by a flow-in-flow structure for feature enrichment. These blocks are connected via skip-connection to prevent the vanishing gradient problem while effectively preserving the information identify throughout the network. Regarding several intensive simulations on the constellation image dataset of eight digital modulations, the proposed deep network achieves the remarkable classification accuracy of approximately 87% at 0 dB signal-to-noise ratio (SNR) under a multipath Rayleigh fading channel and further outperforms some state-of-the-art deep models of constellation-based modulation classification., Comment: Accepted for presentation at IEEE GLOBECOM 2020

Pseudo-random sequence is widely used in the field of information security. This design uses Verilog HDL to complete the construction of Logistic chaotic pseudo-random sequence generator with modular programming ideas. The test results show that the correlation performance and test statistics of the output sequence can meet the requirements of chaotic pseudo-sequence. The effect of applying it to image encryption also verifies that the sequence has good randomness.

In this decade, although numerous conventional methods have been introduced for three-dimensional (3D) skeleton-based human action recognition, they have posed a primary limitation of learning a vulnerable recognition model from low-level handcrafted features. This paper proposes an effective deep convolutional neural network (CNN) with a dual-stream architecture to simultaneously learn the geometric-based static pose and dynamic motion features for high-performance action recognition. Each stream consists of several advanced blocks of regular and grouped convolutional layers, wherein various kernel sizes are configured to enrich representational features. Remarkably, the blocks in each stream are associated via skip-connection scheme to overcome the vanishing gradient problem, meanwhile, the blocks of two stream are jointly connected via a customized layer to partly share high-relevant knowledge gained during the model training process. In the experiments, the action recognition method is intensively evaluated on the NTU RGB+D dataset and its upgraded version with up to 120 action classes, where the proposed CNN achieves a competitive performance in terms of accuracy and complexity compared to several other deep models.

Coronavirus disease 2019 (COVID-19) is a novel harmful respiratory disease that has rapidly spread worldwide. At the end of 2019, COVID-19 emerged as a previously unknown respiratory disease in Wuhan, Hubei Province, China. The world health organization (WHO) declared the coronavirus outbreak a pandemic in the second week of March 2020. Simultaneous deep learning detection and classification of COVID-19 based on the full resolution of digital X-ray images is the key to efficiently assisting patients by enabling physicians to reach a fast and accurate diagnosis decision. In this paper, a simultaneous deep learning computer-aided diagnosis (CAD) system based on the YOLO predictor is proposed that can detect and diagnose COVID-19, differentiating it from eight other respiratory diseases: atelectasis, infiltration, pneumothorax, masses, effusion, pneumonia, cardiomegaly, and nodules. The proposed CAD system was assessed via five-fold tests for the multi-class prediction problem using two different databases of chest X-ray images: COVID-19 and ChestX-ray8. The proposed CAD system was trained with an annotated training set of 50,490 chest X-ray images. The regions on the entire X-ray images with lesions suspected of being due to COVID-19 were simultaneously detected and classified end-to-end via the proposed CAD predictor, achieving overall detection and classification accuracies of 96.31% and 97.40%, respectively. Most test images from patients with confirmed COVID-19 and other respiratory diseases were correctly predicted, achieving average intersection over union (IoU) greater than 90%. Applying deep learning regularizers of data balancing and augmentation improved the COVID-19 diagnostic performance by 6.64% and 12.17% in terms of the overall accuracy and the F1-score, respectively. It is feasible to achieve a diagnosis based on individual chest X-ray images with the proposed CAD system within 0.0093 s. Thus, the CAD system presented in this paper can make a prediction at the rate of 108 frames/s (FPS), which is close to real-time. The proposed deep learning CAD system can reliably differentiate COVID-19 from other respiratory diseases. The proposed deep learning model seems to be a reliable tool that can be used to practically assist health care systems, patients, and physicians.

Modulation classification, an intermediate step between signal detection and demodulation, is commonly deployed in many modern wireless communication systems. Although many approaches have been introduced in the last decades for identifying the modulation format of the incoming signal, they have the obstacle of mining radio characteristics for most traditional machine learning algorithms. To effectively handle this limitation, we propose an accurate modulation classification method by exploiting deep learning for being compatible with constellation diagram. A convolutional neural network (CNN), namely CRNet, is developed to proficiently learn the most relevant radio characteristics from transformed gray-scale constellation image by cross-residual connection, a novel structure for associating the intrinsic information between two processing flows specified by regular and grouped convolutional layers. Based on the experimental evaluation, CRNet achieves the classification rate of approximately 90% at +10 dB signal-to-noise ratio (SNR) under a multipath Rayleigh fading channel and further performs more accurately than some existing deep models for constellation-based modulation classification.

Background Open tibia fractures are a major source of disability in low- and middle-income countries (LMICs) due to the high incidence of complications, particularly infection and chronic osteomyelitis. One proposed adjunctive measure to reduce infection is prophylactic local antibiotic delivery, which can achieve much higher concentrations at the surgical site than can safely be achieved with systemic administration. Animal studies and retrospective clinical studies support the use of gentamicin for this purpose, but no high-quality clinical trials have been conducted to date in high- or low-income settings. Methods We describe a protocol for a pilot study conducted in Dar es Salaam, Tanzania, to assess the feasibility of a single-center masked randomized controlled trial to compare the efficacy of locally applied gentamicin to placebo for the prevention of fracture-related infection in open tibial shaft fractures. Discussion The results of this study will inform the design and feasibility of a definitive trial to address the use of local gentamicin in open tibial fractures. If proven effective, local gentamicin would be a low-cost strategy to reduce complications and disability from open tibial fractures that could impact care in both high- and low-income countries. Trial registration ClinicalTrials.gov, Registration # NCT03559400; Registered June 18, 2018.

As a new emerging candidate for solid-state phosphors, copper nanoclusters (CuNCs) have gained tremendous interest in the field of white light-emitting devices (WLEDs). However, their further applications are impeded by the low photoluminescence quantum yield (PLQY) and poor emission color tunability of CuNCs. This work demonstrates the synthesis of cyan and orange emitting CuNCs, and their combination as color conversion phosphors in WLEDs. The cyan and orange emitting CuNCs were prepared employing 2-mercapto-1-methylimidazole (MMI) and N-acetyl-l-cysteine (NAC), respectively, as stabilizing-cum-reducing agents. The dispersions of MMI-CuNCs and NAC-CuNCs are weakly emissive. However, after processing into powders, they both possess ultrahigh PLQYs (45.2% for MMI-CuNCs, and 64.6% for NAC-CuNCs) owing to the effect of aggregation-induced emission (AIE). All-CuNC based WLEDs are then designed and developed using powdered MMI-CuNC and NAC-CuNC samples on commercially available 365 nm GaN LED chips. They display acceptable white light characteristics with a Commission Internationale de l'Eclairage coordinate value and color rendering index of (0.26, 0.30) and 83, respectively. We believe that these cost-effective and eco-friendly CuNCs with interesting AIE properties will vigorously promote the development of high-quality WLEDs for commercial applications.

Background To investigate the value of using contrast-enhanced transrectal ultrasound (CETRUS) to reduce unnecessary collection of biopsies during prostate cancer diagnosis and its utility in predicting biochemical recurrence in patients with localized prostate cancer. Methods This was a prospective study of suspected prostate cancer patients who were evaluated with CETRUS followed by a prostate biopsy. Prostate blood flow via CETRUS was graded using a 5-point scale. The relationship between CETRUS score and biopsy outcome was then analyzed for all patients; univariate and multi-variate analyses were used to determine the probable prognostic factors for biochemical recurrence in patients with localized prostate cancer that underwent a radical prostatectomy. Results A total of 347 patients were enrolled in the study. Prostate cancer was found in 164 patients. A significant positive correlation (r = 0.69, p 3; p = 0.015). Multivariate Cox regression analysis indicated that CETRUS score was an independent predictor of biochemical recurrence (HR: 7.02; 95% CI: 2.00–24.69; p = 0.002). Conclusions CETRUS scores may be a useful tool for reducing the collection unnecessary biopsy samples during prostate cancer diagnosis and are predictive of biochemical recurrence in patients with localized prostate cancer following a radical prostatectomy.

Diabetic Retinopathy (DR), the complication leading to vision loss, is generally graded according to the amalgamation of various structural factors in fundus photography such as number of microaneurysms, hemorrhages, vascular abnormalities, etc. To this end, Convolution Neural Network (CNN) with impressively representational power has been exhaustively utilized to address this problem. However, while existing multi-stream networks are costly, the conventional CNNs do not consider multiple levels of semantic context, which suffers from the loss of spatial correlations between the aforementioned DR-related signs. Therefore, this paper proposes a Densely Reversed Attention based CNN (DRAN) to leverage the learnable integration of channel-wise attention at multi-level features in a pretrained network for unambiguously involving spatial representations of important DR-oriented factors. Consequently, the proposed approach gains a quadratic weighted kappa of 85.6% on Kaggle DR detection dataset, which is competitive with the state-of-the-arts.

Being able to precisely control the carrier polarity and conductivity plays a vital role while developing future two-dimensional (2D) transition metal dichalcogenides (TMDs)-based devices. Achieving such controllability in TMD material, however, remains challenging as a result of the strong Fermi-level pinning with contact metals [1] . MoTe 2 , one of the group-VI TMDs, has high mobility, a moderate bandgap, and the lower energy difference between polymorphic semiconducting 2H and metallic 1T’ phases, allowing versatile electrical properties. It’s known that controlling the number of chalcogen defects in TMD considerably alters its electrical characteristics [2] , [3] . In this study, we report the results of engineering Te defects in MoTe 2 by plasma treatment where (1) 2H phase is stable at a Te/Mo ratio between 1.88 and 2.13, and (2) MoTe 2 transistors can be converted from p- to n-type conduction by the defect-induced conduction band edge (CBE) lowering.