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Objective: This study aimed to explore the research trends of percutaneous endoscopic lumbar discectomy in treating lumbar disc herniation using bibliometrics over the past ten years., Methods: Relevant publications on the clinical application of percutaneous endoscopic lumbar discectomy in lumbar disc herniation were searched in the Web of Science Core Collection. Subsequently, the characteristics of all these articles were collected. Visualizing data of annual publications, journals, cited journals, authors, cited authors, countries, institutions, keywords, and cited references was performed by using CiteSpace (6.1.R6)., Results: A total of 642 publications were extracted between 2013 and 2022. The number of publications peaked in the year 2020. The most prolific journal was World Neurosurgery (81), and Spine (597) as the cited journal was the most popular one. China (393) was the most prolific country, followed by South Korea (100). The institution with the most productivity was Tongji University (35). Yue Zhou (20) was the most prolific author, and Sebastian Ruetten (310) was the most cited author. The keyword "interlaminar" was top of research developments with the highest citation burst (8.69). "Lumbar disc herniation", "surgical technique", and "complication" were popular keywords. The surgical procedures and complications of percutaneous endoscopic lumbar discectomy have been the hot topics of recent research., Conclusion: This study summarized the current situation and development trends of percutaneous endoscopic lumbar discectomy clinical research in the form of visualization, and these findings may help researchers explore new directions in the future., Competing Interests: The authors declare that there is no conflict of interest regarding the publication of this paper., (© 2023 Zhang et al.)

Image super-resolution (SR) significantly improves the quality of low-resolution images, and is widely used for image reconstruction in various fields. Although the existing SR methods have achieved distinguished results in objective metrics, most methods focus on real-world images and employ large and complex network structures, which are inefficient for medical diagnosis scenarios. To address the aforementioned issues, the distinction between pathology images and real-world images was investigated, and an SR Network with a wider and deeper attention module called Channel Attention Retention is proposed to obtain SR images with enhanced high-frequency features. This network captures contextual information within and across blocks via residual skips and balances the performance and efficiency by controlling the number of blocks. Meanwhile, a new linear loss was introduced to optimize the network. To evaluate the work and compare multiple SR works, a benchmark dataset bcSR was created, which forces a model training on wider and more critical regions. The results show that the proposed model outperforms state-of-the-art methods in both performance and efficiency, and the newly created dataset significantly improves the reconstruction quality of all compared models. Moreover, image classification experiments demonstrate that the suggested network improves the performance of downstream tasks in medical diagnosis scenarios. The proposed network and dataset provide effective priors for the SR task of pathology images, which significantly improves the diagnosis of relevant medical staff. The source code and the dataset are available on https://github.com/MoyangSensei/CARN-Pytorch., Competing Interests: The authors declare there are no competing interests., (©2023 Jia et al.)

Introduction: As populations age, osteoporosis has become a hot topic of global public concern. The beneficial effects of traditional Chinese exercises on the musculoskeletal system have been demonstrated. However, previous research findings on osteoporosis are inconsistent, and it is unclear which type of exercise and its frequency and duration have the best effect on osteoporosis. This study aims to investigate the most appropriate exercise modality for people with osteoporosis through systematic evaluation and network meta-analysis to guide clinical practice., Methods and Analysis: The Cochrane Library, Web of Science, MEDLINE, Embase, China Biomedical Literature, China Knowledge Network, China Science and Technology Journal and Wanfang databases will be searched until January 2022. The language of the articles should be English or Chinese. All clinical randomised controlled trials on the effect of traditional Chinese exercises on osteoporosis will be included. We will use RevMan, Stata and GeMTC software to complete our network meta-analysis. We will perform risk of bias assessment, subgroup analysis and sensitivity analysis to correct the results. Finally, we will use the Grading of Recommendations Assessment, Development and Evaluation guideline development tool and Confidence in Network Meta-Analysis (CINeMA, a new method for assessing CINeMA results) approach to evaluate the reliability of our final results., Ethics and Dissemination: All data for this study will be obtained from published studies, so no ethical review will be needed. We will publish the results of the study in a peer-reviewed journal., Prospero Registration Number: CRD42022323622., Competing Interests: Competing interests: None declared., (© Author(s) (or their employer(s)) 2022. Re-use permitted under CC BY-NC. No commercial re-use. See rights and permissions. Published by BMJ.)

Classical forward and reverse mouse genetics require germline mutations and, thus, are unwieldy to study sleep functions of essential genes or redundant pathways. It is also time-consuming to conduct EEG/EMG-based mouse sleep screening because of labor-intensive surgeries and genetic crosses. Here, we describe a highly accurate SleepV (video) system and adeno-associated virus (AAV)-based adult brain chimeric (ABC)-expression/KO platform for somatic genetics analysis of sleep in adult male or female mice. A pilot ABC screen identifies CREB and CRTC1, of which constitutive or inducible expression significantly reduces quantity and/or quality of non-rapid eye movement sleep. Whereas ABC-KO of exon 13 of Sik3 by AAV-Cre injection in Sik3-E13 flox/flox adult mice phenocopies Sleepy (Sik3 Slp /+ ) mice, ABC-CRISPR of Slp/Sik3 reverses hypersomnia of Sleepy mice, indicating a direct role of SLP/SIK3 kinase in sleep regulation. Multiplex ABC-CRISPR of both orexin/hypocretin receptors causes narcolepsy episodes, enabling one-step analysis of redundant genes in adult mice. Therefore, this somatic genetics approach should facilitate high-throughput analysis of sleep regulatory genes, especially for essential or redundant genes, in adult mice by skipping mouse development and minimizing genetic crosses. SIGNIFICANCE STATEMENT The molecular mechanisms of mammalian sleep regulation remain unclear. Classical germline mouse genetics are unwieldy to study sleep functions of essential genes or redundant pathways. The EEG/EMG-based mouse sleep screening is time-consuming because of labor-intensive surgeries and lengthy genetic crosses. To overcome these "bottlenecks," we developed a highly accurate video-based sleep analysis system and adeno-associated virus-mediated ABC-expression/KO platform for somatic genetics analysis of sleep in adult mice. These methodologies facilitate rapid identification of sleep regulatory genes, but also efficient mechanistic studies of the molecular pathways of sleep regulation in mice., (Copyright © 2022 Wang, Li et al.)

Backgrounds: Traditional Chinese medicine and Western medicine combination (TCM-WMC) increased the complexity of compounds ingested., Objective: To develop a method for screening hepatotoxic compounds in TCM-WMC based on chemical structures using artificial intelligence (AI) methods., Methods: Drug-induced liver injury (DILI) data was collected from the public databases and published literatures. The total dataset formed by DILI data was randomly divided into training set and test set at a ratio of 3:1 approximately. Machine learning models of SGD (Stochastic Gradient Descent), kNN (k-Nearest Neighbor), SVM (Support Vector Machine), NB (Naive Bayes), DT (Decision Tree), RF (Random Forest), ANN (Artificial Neural Network), AdaBoost, LR (Logistic Regression) and one deep learning model (deep belief network, DBN) were adopted to construct models for screening hepatotoxic compounds., Result: Dataset of 2035 hepatotoxic compounds was collected in this research, in which 1505 compounds were as training set and 530 compounds were as test set. Results showed that RF obtained 0.838 of classification accuracy (CA), 0.827 of F1-score, 0.832 of Precision, 0.838 of Recall, 0.814 of area under the curve (AUC) on the training set and 0.767 of CA, 0.731 of F1, 0.739 of Precision, 0.767 of Recall, 0.739 of AUC on the test set, which was better than other eight machine learning methods. The DBN obtained 82.2% accuracy on the test set, which was higher than any other machine learning models on the test set., Conclusion: The DILI AI models were expected to effectively screen hepatotoxic compounds in TCM-WMC., (© 2022. The Author(s).)

Diabetic retinopathy, the most serious ocular complication of diabetes, imposes a serious economic burden on society. Automatic and objective assessment of vessel changes can effectively manage diabetic retinopathy and prevent blindness. Optical coherence tomography angiography (OCTA) metrics have been confirmed to be used to assess vessel changes. The accuracy and reliability of OCTA metrics are restricted by vessel segmentation methods. In this study, a multi-branch retinal vessel segmentation method is proposed, which is comparable to the segmentation results obtained from the manual segmentation, effectively extracting vessels in low contrast areas and improving the integrity of the extracted vessels. OCTA metrics based on the proposed segmentation method were validated to be reliable for further analysis of the relationship between OCTA metrics and diabetes and the severity of diabetic retinopathy. Changes in vessel morphology are influenced by systemic risk factors. However, there is a lack of analysis of the relationship between OCTA metrics and systemic risk factors. We conducted a cross-sectional study that included 362 eyes of 221 diabetic patients and 1,151 eyes of 587 healthy people. Eight systemic risk factors were confirmed to be closely related to diabetes. After controlling these systemic risk factors, significant OCTA metrics (such as vessel complexity index, vessel diameter index, and mean thickness of retinal nerve fiber layer centered in the macular) were found to be related to diabetic retinopathy and severe diabetic retinopathy. This study provides evidence to support the potential value of OCTA metrics as biomarkers of diabetic retinopathy., Competing Interests: Author BingX was employed by company Jiangsu Simcere Diagnostics Co., Ltd., and company Nanjing Simcere Medical Laboratory Science Co., Ltd. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2021 Xu, Chen, Zhang, Shen, Lan, Chen, Yan and Xu.)

Background: Osteoporosis (OP) is a systemic bone disease characterized by decreased bone mass, destruction of the bone tissue microstructure, increased bone brittleness and an increased risk of fracture. OP has a high incidence rate and long disease course and is associated with serious complications. Yigu decoction (YGD) is a compound prescription in traditional Chinese medicine that is used to treat OP. However, its mechanism in OP is not clear. This study used a tandem mass tag (TMT)quantitative proteomics method to explore the potential bone-protective mechanism of YGD in an osteoporotic rat model., Materials and Methods: A rat model of OP was established by ovariectomy. Eighteen 12-week-old specific-pathogen-free female Wistar rats weighing 220 ± 10 g were selected. The eighteen rats were randomly divided into 3 groups (n = 6 in each group): the normal, model and YGD groups. The right femurs from each group were subjected to quantitative biological analysis. TMT quantitative proteomics was used to analyze the proteins extracted from the bone tissue of rats in the model and YGD groups, and the differentially expressed proteins after intervention with YGD were identified as biologically relevant proteins of interest. Functional annotation correlation analysis was also performed to explore the biological function and mechanism of YGD., Result: Compared with the model group, the YGD group showed significant upregulation of 26 proteins (FC > 1.2, P < 0.05) and significant downregulation of 39 proteins (FC < 0.833, P < 0.05). Four important targets involved in OP and 5 important signaling pathways involved in bone metabolism were identified., Conclusions: YGD can significantly increase the bone mineral density (BMD) of osteoporotic rats and may play a therapeutic role by regulating target proteins involved in multiple signaling pathways. Therefore, these results improve the understanding of the OP mechanism and provide an experimental basis for the clinical application of YGD in OP treatment., (© 2021. The Author(s).)

The outbreak and rapid spread of coronavirus disease 2019 (COVID-19) has had a huge impact on the lives and safety of people around the world. Chest CT is considered an effective tool for the diagnosis and follow-up of COVID-19. For faster examination, automatic COVID-19 diagnostic techniques using deep learning on CT images have received increasing attention. However, the number and category of existing datasets for COVID-19 diagnosis that can be used for training are limited, and the number of initial COVID-19 samples is much smaller than the normal's, which leads to the problem of class imbalance. It makes the classification algorithms difficult to learn the discriminative boundaries since the data of some classes are rich while others are scarce. Therefore, training robust deep neural networks with imbalanced data is a fundamental challenging but important task in the diagnosis of COVID-19. In this paper, we create a challenging clinical dataset (named COVID19-Diag) with category diversity and propose a novel imbalanced data classification method using deep supervised learning with a self-adaptive auxiliary loss (DSN-SAAL) for COVID-19 diagnosis. The loss function considers both the effects of data overlap between CT slices and possible noisy labels in clinical datasets on a multi-scale, deep supervised network framework by integrating the effective number of samples and a weighting regularization item. The learning process jointly and automatically optimizes all parameters over the deep supervised network, making our model generally applicable to a wide range of datasets. Extensive experiments are conducted on COVID19-Diag and three public COVID-19 diagnosis datasets. The results show that our DSN-SAAL outperforms the state-of-the-art methods and is effective for the diagnosis of COVID-19 in varying degrees of data imbalance., Competing Interests: The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (© 2021 Elsevier B.V. All rights reserved.)

Detecting signet ring cells on histopathologic images is a critical computer-aided diagnostic task that is highly relevant to cancer grading and patients' survival rates. However, the cells are densely distributed and exhibit diverse and complex visual patterns in the image, together with the commonly observed incomplete annotation issue, posing a significant barrier to accurate detection. In this article, we propose to mitigate the detection difficulty from a model reinforcement point of view. Specifically, we devise a Classification Reinforcement Detection Network (CRDet). It is featured by adding a dedicated Classification Reinforcement Branch (CRB) on top of the architecture of Cascade RCNN. The proposed CRB consists of a context pooling module to perform a more robust feature representation by fully making use of context information, and a feature enhancement classifier to generate a superior feature by leveraging the deconvolution and attention mechanism. With the enhanced feature, the small-sized cell can be better characterized and CRDet enjoys a more accurate signet ring cell identification. We validate our proposal on a large-scale real clinical signet ring cell data set. It is shown that CRDet outperforms several popular convolutional neural network-based object detection models on this particular task.

Background: Iatrogenic radial nerve injures are a common complication during the placement of external fixator pins at the lateral aspect of the humeral shaft. This study uses a three-dimensional measurement technique to locate a safe entry point for humeral pins when externally fixating the elbow. Methods: We fixed a guide wire to the radial nerve by a suture string, and used computed tomography (CT) to scan the upper limbs of cadaver specimens. Then, we measured the deviation angles of the radial nerve on the CT scans, and the distance from the radial nerve to the "elbow rotation center" (ERC). Result: The average distance from the radial nerve to the ERC was 87.3 ± 8.5 mm (range: 68-100 mm), 58.3 ± 11.3 mm (range: 32.12-82.84 mm), 106.3 ± 5.8 mm (range: 86.93-115.08 mm), and 113.9 ± 4.8 mm (range: 97.93-120.22 mm) at radial nerve deviation angles of 0°, -30°, 30°, and 45°, respectively. The average radial nerve deviation angle was -37.7° ± 7.7° and 123.9° ± 19.9° at 50 and 150 mm, respectively. Relative to 0°, the distance between the radial nerve and the ERC at radial nerve deviation angles of -30°, 30°, and 45° showed a significant difference ( t  = 18.20, p  < 0.05; Z  = 6.07, p  < 0.001; Z  = 6.40, p  < 0.001, respectively). Conclusions: Pins inserted into the proximal humerus should be about 150 mm from the ERC with a radial nerve deviation angle of 30° anteriorly, and 50 mm from the ERC with a deviation angle of 30°-45° posteriorly.

Background: This study aims to evaluate the association between serum alkaline phosphatase (ALP), calcium (Ca) and phosphorus (P), C-reactive protein (CRP) and D-dimer (D-D), and hemoglobin (Hb) in postoperative and preoperative osteoporotic hip fracture elderly patients., Methods: A total of 32 operation patients with osteoporotic hip fracture over the age of 65 years old were admitted to the orthopedic unit and prospectively evaluated. All patients were treated according to specific protocols, according to the type of fracture. Fasting blood samples were taken, and serum ALP, Ca and P measurements were respectively performed in six periods: at the time of admission, post-operation, and at postoperative one week, two weeks, one month and three months. Hb, CRP and D-D were also analyzed, and the fracture healing was recorded., Results: Finally, 32 cases were selected for the present study. The analysis results revealed that the level of serum Ca and ALP slowly increased at two weeks after surgery, and slightly dropped back at three months after the operation. Furthermore, D-D and CRP had a significant effect at pre- and post-operation, and exhibited an obvious downward trend after postoperative one week. The fracture healing and recovery of activities were associated with the Hb levels. The serum levels of ALP, which were adjusted by Ca and P, were associated with Hb and CRP, but not with D-D., Conclusions: Interestingly, there was an association between CRP and D-D. These findings suggest that early control of inflammation and loss of Ca could play a positive role for the healing of osteoporotic hip fractures.

Lumbar decompressive surgery is the gold standard treatment for lumbar spinal stenosis. Minimally invasive surgical techniques have been introduced with the aim of reducing the morbidity associated with open surgery. The purpose of the present study was to systematically search the literature and perform a meta-analysis of studies comparing the outcomes between biportal endoscopic technique and microscopic technique for lumbar canal stenosis decompression. A comprehensive search of the PubMed, Google Scholar, Web of Science, Embase and the Cochrane Library databases was performed to identify relevant articles up to 15th of December 2019. Eligible studies were retrieved, data were extracted by two authors independently and risks of bias were assessed. A total of six studies involving 438 patients were selected for review. The results of the pooled analysis indicated similar operative times [mean difference (MD), -3.41; 95% CI, -10.78-3.96; P<0.36], similar complications (MD, 0.70; 95% CI, 0.33-1.46; P=0.34), similar visual analogue scale scores for back and leg pain at the time of the final follow-up and similar Oswestry disability indexes (MD, -0.28; 95% CI, -1.25-0.69; P=0.58) for the two procedures. In conclusion, biportal endoscopic technique is a viable alternative to microscopic technique for lumbar canal stenosis decompression with similar operative time, clinical outcomes and complications., (Copyright: © Chen et al.)

Distant supervised relation extraction is an important task in the field of natural language processing. There are two main shortcomings for most state-of-the-art methods. One is that they take all sentences of an entity pair as input, which would result in a large computational cost. But in fact, few of most relevant sentences are enough to recognize the relation of an entity pair. To tackle these problems, we propose a novel hierarchical selective attention network for relation extraction under distant supervision. Our model first selects most relevant sentences by taking coarse sentence-level attention on all sentences of an entity pair and then employs word-level attention to construct sentence representations and fine sentence-level attention to aggregate these sentence representations. Experimental results on a widely used dataset demonstrate that our method performs significantly better than most of existing methods., (Copyright © 2018 Elsevier Ltd. All rights reserved.)

How to read Uyghur text from biomedical graphic images is a challenge problem due to the complex layout and cursive writing of Uyghur. In this paper, we propose a system that extracts text from Uyghur biomedical images, and matches the text in a specific lexicon for semantic analysis. The proposed system possesses following distinctive properties: first, it is an integrated system which firstly detects and crops the Uyghur text lines using a single fully convolutional neural network, and then keywords in the lexicon are matched by a well-designed matching network. Second, to train the matching network effectively an online sampling method is applied, which generates synthetic data continually. Finally, we propose a GPU acceleration scheme for matching network to match a complete Uyghur text line directly rather than a single window. Experimental results on benchmark dataset show our method achieves a good performance of F-measure 74.5%. Besides, our system keeps high efficiency with 0.5s running time for each image due to the GPU acceleration scheme.

Molasses alcohol wastewater (MAW) is difficult to be bio-treated and converted into biogas. In this study, MAW mixed with pig manure (PM) in different ratios was co-digested. Biogas production, chemical oxygen demand (COD) removal and the structure of microbial communities were monitored in the process. Our results showed that under the optimal COD ratio of PM:MAW (1.0:1.5), CODremoval and biogas yield were the highest. And in fermentation tanks with different PM to MAW ratios, the structure and composition of bacterial communities varied in the early and late stage. Furthermore, the type of main bacterial operational taxonomic units (OTUs) have no differences, yet the relative abundance of OTUs varied. The current research showed that there was a good potential to the use of PM as a co-digested material to anaerobic treatment of MAW and provided references for further improving bio-treatment of MAW., (Copyright © 2014 Elsevier Ltd. All rights reserved.)