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  1. Article ; Online: A new scheduling method based on sequential time windows developed to distribute first-aid medicine for emergency logistics following an earthquake.

    Jiaqi Fang / Hanping Hou / Changxiang Lu / Haiyun Pang / Qingshan Deng / Yong Ye / Lingle Pan

    PLoS ONE, Vol 16, Iss 2, p e

    2021  Volume 0247566

    Abstract: After an earthquake, affected areas have insufficient medicinal supplies, thereby necessitating substantial distribution of first-aid medicine from other supply centers. To make a proper distribution schedule, we considered the timing of supply and ... ...

    Abstract After an earthquake, affected areas have insufficient medicinal supplies, thereby necessitating substantial distribution of first-aid medicine from other supply centers. To make a proper distribution schedule, we considered the timing of supply and demand. In the present study, a "sequential time window" is used to describe the time to generate of supply and demand and the time of supply delivery. Then, considering the sequential time window, we proposed two multiobjective scheduling models with the consideration of demand uncertainty; two multiobjective stochastic programming models were also proposed to solve the scheduling models. Moreover, this paper describes a simulation that was performed based on a first-aid medicine distribution problem during a Wenchuan earthquake response. The simulation results show that the methodologies proposed in this paper provide effective schedules for the distribution of first-aid medicine. The developed distribution schedule enables some supplies in the former time windows to be used in latter time windows. This schedule increases the utility of limited stocks and avoids the risk that all the supplies are used in the short-term, leaving no supplies for long-term use.
    Keywords Medicine ; R ; Science ; Q
    Subject code 690
    Language English
    Publishing date 2021-01-01T00:00:00Z
    Publisher Public Library of Science (PLoS)
    Document type Article ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  2. Article ; Online: Radiomics Is Effective for Distinguishing Coronavirus Disease 2019 Pneumonia From Influenza Virus Pneumonia

    Liaoyi Lin / Jinjin Liu / Qingshan Deng / Na Li / Jingye Pan / Houzhang Sun / Shichao Quan

    Frontiers in Public Health, Vol

    2021  Volume 9

    Abstract: Objectives: To develop and validate a radiomics model for distinguishing coronavirus disease 2019 (COVID-19) pneumonia from influenza virus pneumonia.Materials and Methods: A radiomics model was developed on the basis of 56 patients with COVID-19 ... ...

    Abstract Objectives: To develop and validate a radiomics model for distinguishing coronavirus disease 2019 (COVID-19) pneumonia from influenza virus pneumonia.Materials and Methods: A radiomics model was developed on the basis of 56 patients with COVID-19 pneumonia and 90 patients with influenza virus pneumonia in this retrospective study. Radiomics features were extracted from CT images. The radiomics features were reduced by the Max-Relevance and Min-Redundancy algorithm and the least absolute shrinkage and selection operator method. The radiomics model was built using the multivariate backward stepwise logistic regression. A nomogram of the radiomics model was established, and the decision curve showed the clinical usefulness of the radiomics nomogram.Results: The radiomics features, consisting of nine selected features, were significantly different between COVID-19 pneumonia and influenza virus pneumonia in both training and validation data sets. The receiver operator characteristic curve of the radiomics model showed good discrimination in the training sample [area under the receiver operating characteristic curve (AUC), 0.909; 95% confidence interval (CI), 0.859–0.958] and in the validation sample (AUC, 0.911; 95% CI, 0.753–1.000). The nomogram was established and had good calibration. Decision curve analysis showed that the radiomics nomogram was clinically useful.Conclusions: The radiomics model has good performance for distinguishing COVID-19 pneumonia from influenza virus pneumonia and may aid in the diagnosis of COVID-19 pneumonia.
    Keywords COVID-19 ; influenza ; nomogram ; radiomics ; computed tomography ; Public aspects of medicine ; RA1-1270
    Subject code 600
    Language English
    Publishing date 2021-06-01T00:00:00Z
    Publisher Frontiers Media S.A.
    Document type Article ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  3. Article ; Online: Image Quality Control in Lumbar Spine Radiography Using Enhanced U-Net Neural Networks

    Xiao Chen / Qingshan Deng / Qiang Wang / Xinmiao Liu / Lei Chen / Jinjin Liu / Shuangquan Li / Meihao Wang / Guoquan Cao

    Frontiers in Public Health, Vol

    2022  Volume 10

    Abstract: PurposeTo standardize the radiography imaging procedure, an image quality control framework using the deep learning technique was developed to segment and evaluate lumbar spine x-ray images according to a defined quality control standard.Materials and ... ...

    Abstract PurposeTo standardize the radiography imaging procedure, an image quality control framework using the deep learning technique was developed to segment and evaluate lumbar spine x-ray images according to a defined quality control standard.Materials and MethodsA dataset comprising anteroposterior, lateral, and oblique position lumbar spine x-ray images from 1,389 patients was analyzed in this study. The training set consisted of digital radiography images of 1,070 patients (800, 798, and 623 images of the anteroposterior, lateral, and oblique position, respectively) and the validation set included 319 patients (200, 205, and 156 images of the anteroposterior, lateral, and oblique position, respectively). The quality control standard for lumbar spine x-ray radiography in this study was defined using textbook guidelines of as a reference. An enhanced encoder-decoder fully convolutional network with U-net as the backbone was implemented to segment the anatomical structures in the x-ray images. The segmentations were used to build an automatic assessment method to detect unqualified images. The dice similarity coefficient was used to evaluate segmentation performance.ResultsThe dice similarity coefficient of the anteroposterior position images ranged from 0.82 to 0.96 (mean 0.91 ± 0.06); the dice similarity coefficient of the lateral position images ranged from 0.71 to 0.95 (mean 0.87 ± 0.10); the dice similarity coefficient of the oblique position images ranged from 0.66 to 0.93 (mean 0.80 ± 0.14). The accuracy, sensitivity, and specificity of the assessment method on the validation set were 0.971–0.990 (mean 0.98 ± 0.10), 0.714–0.933 (mean 0.86 ± 0.13), and 0.995–1.000 (mean 0.99 ± 0.12) for the three positions, respectively.ConclusionThis deep learning-based algorithm achieves accurate segmentation of lumbar spine x-ray images. It provides a reliable and efficient method to identify the shape of the lumbar spine while automatically determining the radiographic image quality.
    Keywords deep learning ; quality control ; U-net ; medical imaging ; radiography ; image segmentation ; Public aspects of medicine ; RA1-1270
    Subject code 006
    Language English
    Publishing date 2022-04-01T00:00:00Z
    Publisher Frontiers Media S.A.
    Document type Article ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  4. Article ; Online: Proteomic Profiling for Identification of Novel Biomarkers Differentially Expressed in Human Ovaries from Polycystic Ovary Syndrome Patients.

    Li Li / Jiangyu Zhang / Qingshan Deng / Jieming Li / Zhengfen Li / Yao Xiao / Shuiwang Hu / Tiantian Li / Qiuxiao Tan / Xiaofang Li / Bingshu Luo / Hui Mo

    PLoS ONE, Vol 11, Iss 11, p e

    2016  Volume 0164538

    Abstract: To identify differential protein expression pattern associated with polycystic ovary syndrome (PCOS).Twenty women were recruited for the study, ten with PCOS as a test group and ten without PCOS as a control group. Differential in-gel electrophoresis ( ... ...

    Abstract To identify differential protein expression pattern associated with polycystic ovary syndrome (PCOS).Twenty women were recruited for the study, ten with PCOS as a test group and ten without PCOS as a control group. Differential in-gel electrophoresis (DIGE) analysis and mass spectroscopy were employed to identify proteins that were differentially expressed between the PCOS and normal ovaries. The differentially expressed proteins were further validated by western blot (WB) and immunohistochemistry (IHC).DIGE analysis revealed eighteen differentially expressed proteins in the PCOS ovaries of which thirteen were upregulated, and five downregulated. WB and IHC confirmed the differential expression of membrane-associated progesterone receptor component 1 (PGRMC1), retinol-binding protein 1 (RBP1), heat shock protein 90B1, calmodulin 1, annexin A6, and tropomyosin 2. Also, WB analysis revealed significantly (P<0.05) higher expression of PGRMC1 and RBP1 in PCOS ovaries as compared to the normal ovaries. The differential expression of the proteins was also validated by IHC.The present study identified novel differentially expressed proteins in the ovarian tissues of women with PCOS that can serve as potential biomarkers for the diagnosis and development of novel therapeutics for the treatment of PCOS using molecular interventions.
    Keywords Medicine ; R ; Science ; Q
    Subject code 616
    Language English
    Publishing date 2016-01-01T00:00:00Z
    Publisher Public Library of Science (PLoS)
    Document type Article ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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    Inter-library loan at ZB MED

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