A Theoretically Guaranteed Deep Optimization Framework for Robust Compressive Sensing MRI

Magnetic Resonance Imaging (MRI) is one of the most dynamic and safe imaging techniques available for clinical applications. However, the rather slow speed of MRI acquisitions limits the patient throughput and potential indi cations. Compressive Sensing (CS) has proven to be an efficient technique for accelerating MRI acquisition. The most widely used CS-MRI model, founded on the premise of reconstructing an image from an incompletely filled k-space, leads to an ill-posed inverse problem. In the past years, lots of efforts have been made to efficiently optimize the CS-MRI model. Inspired by deep learning techniques, some preliminary works have tried to incorporate deep architectures into CS-MRI process. Unfortunately, the convergence issues (due to the experience-based networks) and the robustness (i.e., lack real-world noise modeling) of these deeply trained optimization methods are still missing. In this work, we develop a new paradigm to integrate designed numerical solvers and the data-driven architectures for CS-MRI. By introducing an optimal condition checking mechanism, we can successfully prove the convergence of our established deep CS-MRI optimization scheme. Furthermore, we explicitly formulate the Rician noise distributions within our framework and obtain an extended CS-MRI network to handle the real-world nosies in the MRI process. Extensive experimental results verify that the proposed paradigm outperforms the existing state-of-the-art techniques both in reconstruction accuracy and efficiency as well as robustness to noises in real scene

Clinical Assistant Diagnosis for Electronic Medical Record Based on Convolutional Neural Network

Automatically extracting useful information from electronic medical records along with conducting disease diagnoses is a promising task for both clinical decision support(CDS) and neural language processing(NLP). Most of the existing systems are based on artificially constructed knowledge bases, and then auxiliary diagnosis is done by rule matching. In this study, we present a clinical intelligent decision approach based on Convolutional Neural Networks(CNN), which can automatically extract high-level semantic information of electronic medical records and then perform automatic diagnosis without artificial construction of rules or knowledge bases. We use collected 18,590 copies of the real-world clinical electronic medical records to train and test the proposed model. Experimental results show that the proposed model can achieve 98.67\% accuracy and 96.02\% recall, which strongly supports that using convolutional neural network to automatically learn high-level semantic features of electronic medical records and then conduct assist diagnosis is feasible and effective.Comment: 9 pages, 4 figures, Accepted by Scientific Report

Detecting Slow Wave Sleep Using a Single EEG Signal Channel

Background: In addition to the cost and complexity of processing multiple signal channels, manual sleep staging is also tedious, time consuming, and error-prone. The aim of this paper is to propose an automatic slow wave sleep (SWS) detection method that uses only one channel of the electroencephalography (EEG) signal. New Method: The proposed approach distinguishes itself from previous automatic sleep staging methods by using three specially designed feature groups. The first feature group characterizes the waveform pattern of the EEG signal. The remaining two feature groups are developed to resolve the difficulties caused by interpersonal EEG signal differences. Results and comparison with existing methods: The proposed approach was tested with 1,003 subjects, and the SWS detection results show kappa coefficient at 0.66, an accuracy level of 0.973, a sensitivity score of 0.644 and a positive predictive value of 0.709. By excluding sleep apnea patients and persons whose age is older than 55, the SWS detection results improved to kappa coefficient, 0.76; accuracy, 0.963; sensitivity, 0.758; and positive predictive value, 0.812. Conclusions: With newly developed signal features, this study proposed and tested a single-channel EEG-based SWS detection method. The effectiveness of the proposed approach was demonstrated by applying it to detect the SWS of 1003 subjects. Our test results show that a low SWS ratio and sleep apnea can degrade the performance of SWS detection. The results also show that a large and accurately staged sleep dataset is of great importance when developing automatic sleep staging methods

Get Out of the Valley: Power-Efficient Address Mapping for GPUs

GPU memory systems adopt a multi-dimensional hardware structure to provide the bandwidth necessary to support 100s to 1000s of concurrent threads. On the software side, GPU-compute workloads also use multi-dimensional structures to organize the threads. We observe that these structures can combine unfavorably and create significant resource imbalance in the memory subsystem causing low performance and poor power-efficiency. The key issue is that it is highly application-dependent which memory address bits exhibit high variability. To solve this problem, we first provide an entropy analysis approach tailored for the highly concurrent memory request behavior in GPU-compute workloads. Our window-based entropy metric captures the information content of each address bit of the memory requests that are likely to co-exist in the memory system at runtime. Using this metric, we find that GPU-compute workloads exhibit entropy valleys distributed throughout the lower order address bits. This indicates that efficient GPU-address mapping schemes need to harvest entropy from broad address-bit ranges and concentrate the entropy into the bits used for channel and bank selection in the memory subsystem. This insight leads us to propose the Page Address Entropy (PAE) mapping scheme which concentrates the entropy of the row, channel and bank bits of the input address into the bank and channel bits of the output address. PAE maps straightforwardly to hardware and can be implemented with a tree of XOR-gates. PAE improves performance by 1.31 x and power-efficiency by 1.25 x compared to state-of-the-art permutation-based address mapping

Investigating the influence of an adjustable zoned air mattress on sleep: a multinight polysomnography study

IntroductionA comfortable mattress should improve sleep quality. In this study, we sought to investigate the specific sleep parameters that could be affected by a mattress and explore any potential differences between the effects felt by each sex.MethodsA total of 20 healthy young adults (10 females and 20 males; 22.10 ± 1.25 years) participated in the experiments. A smart adjustable zoned air mattress was designed to maintain comfortable support, and an ordinary mattress was used for comparison. The participants individually spent four nights on these two mattresses in four orders for polysomnography (PSG) scoring. Sleep architecture, electroencephalogram (EEG) spectrum, and heart rate variability (HRV), which reflect the central and autonomic nervous activities, were used to compare the difference between the two mattresses.ResultsAn individual difference exited in sleep performance. The modes of influence of the mattresses were different between the sexes. The adjustable air mattress and the increase in experimental nights improved female participants' sleep efficiency, while male participants exhibited a smaller response to different mattresses. With an increasing number of experiment nights, both sexes showed increased REM and decreased N2 proportions; the N3 sleep proportion decreased in the male participants, and the heart rate decreased in both sexes. The performance of the EEG spectrum supports the above results. In addition, the adjustable air mattress weakened automatic nerve activity during N3 sleep in most participants. The female participants appeared to be more sensitive to mattresses. Experiment night was associated with psychological factors. There were differences in the results for this influence between the sexes.ConclusionThis study may shed some light on the differences between the ideal sleep environment of each sex

Non-coding RNAs and gastrointestinal cancers prognosis: an umbrella review of systematic reviews and meta-analyses of observational studies

AimProvide an overview and a systematic evaluation of the evidence quality on the association between non-coding RNAs (ncRNAs) and prognosis value for gastrointestinal cancers (GICs).MethodsWe searched the literature from three electronic databases: Pubmed, Embase, and Web of science, then carefully screened and extracted the primary information and results from the included articles. We use A measurable systematic review and meta-analysis evaluation tool (AMSTAR2) to evaluate the quality of methodology and then use the Grading of Recommendations Assessment 2, Development and Evaluation guideline (GRADE) make sure the reliability of the meta-analysis.ResultsOverall, 182 meta-analyses from 58 studies were included in this study. Most of these studies are of low or very low quality. Using the scoring tool, we found that only two meta-analyses were rated as high reliability, and 17 meta-analyses were rated as medium reliability.ConclusionsAlthough ncRNA has good prognostic value in some studies, only a tiny amount of evidence is highly credible at present. More research is needed in the future.PROSPERO registration numberCRD42022382296

Mesenchymal Progenitor Cells and Their Orthopedic Applications: Forging a Path towards Clinical Trials

Mesenchymal progenitor cells (MPCs) are nonhematopoietic multipotent cells capable of differentiating into mesenchymal and nonmesenchymal lineages. While they can be isolated from various tissues, MPCs isolated from the bone marrow are best characterized. These cells represent a subset of bone marrow stromal cells (BMSCs) which, in addition to their differentiation potential, are critical in supporting proliferation and differentiation of hematopoietic cells. They are of clinical interest because they can be easily isolated from bone marrow aspirates and expanded in vitro with minimal donor site morbidity. The BMSCs are also capable of altering disease pathophysiology by secreting modulating factors in a paracrine manner. Thus, engineering such cells to maximize therapeutic potential has been the focus of cell/gene therapy to date. Here, we discuss the path towards the development of clinical trials utilizing BMSCs for orthopaedic applications. Specifically, we will review the use of BMSCs in repairing critical-sized defects, fracture nonunions, cartilage and tendon injuries, as well as in metabolic bone diseases and osteonecrosis. A review of www.ClinicalTrials.gov of the United States National Institute of Health was performed, and ongoing clinical trials will be discussed in addition to the sentinel preclinical studies that paved the way for human investigations

Defective Osteogenic Differentiation in the Development of Osteosarcoma

Osteosarcoma (OS) is associated with poor prognosis due to its high incidence of metastasis and chemoresistance. It often arises in areas of rapid bone growth in long bones during the adolescent growth spurt. Although certain genetic conditions and alterations increase the risk of developing OS, the molecular pathogenesis is poorly understood. Recently, defects in differentiation have been linked to cancers, as they are associated with high cell proliferation. Treatments overcoming these defects enable terminal differentiation and subsequent tumor inhibition. OS development may be associated with defects in osteogenic differentiation. While early regulators of osteogenesis are unable to bypass these defects, late osteogenic regulators, including Runx2 and Osterix, are able to overcome some of the defects and inhibit tumor propagation through promoting osteogenic differentiation. Further understanding of the relationship between defects in osteogenic differentiation and tumor development holds tremendous potential in treating OS