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1

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Guidelines for the use and interpretation of assays for monitoring autophagy (4th edition) 1

Klionsky, Daniel J. ; Abdel-Aziz, Amal Kamal ; Abdelfatah, Sara ; [et al.]

Informa UK Limited ; 2021

In: Autophagy Vol. 17, No. 1 ( 2021-01-02), p. 1-382

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In: Autophagy, Informa UK Limited, Vol. 17, No. 1 ( 2021-01-02), p. 1-382

Type of Medium: Online Resource

ISSN: 1554-8627 , 1554-8635

URL: Article

DOI: 10.1080/15548627.2020.1797280

Language: English

Publisher: Informa UK Limited

Publication Date: 2021

detail.hit.zdb_id: 2262043-6

SSG: 12

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The surgical safety checklist and patient outcomes after surgery: a prospective observational cohort study, systematic review and meta-analysis

Abbott, T.E.F. ; Ahmad, T. ; Phull, M.K. ; [et al.]

Elsevier BV ; 2018

In: British Journal of Anaesthesia Vol. 120, No. 1 ( 2018-01), p. 146-155

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In: British Journal of Anaesthesia, Elsevier BV, Vol. 120, No. 1 ( 2018-01), p. 146-155

Type of Medium: Online Resource

ISSN: 0007-0912

URL: Article

DOI: 10.1016/j.bja.2017.08.002

Language: English

Publisher: Elsevier BV

Publication Date: 2018

detail.hit.zdb_id: 2011968-9

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3

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Prediction of postoperative infection in elderly using deep learning-based analysis: an observational cohort study

Li, Pinhao ; Wang, Yan ; Li, Hui ; [et al.]

Springer Science and Business Media LLC ; 2023

In: Aging Clinical and Experimental Research Vol. 35, No. 3 ( 2023-01-04), p. 639-647

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In: Aging Clinical and Experimental Research, Springer Science and Business Media LLC, Vol. 35, No. 3 ( 2023-01-04), p. 639-647

Abstract: Elderly patients are susceptible to postoperative infections with increased mortality. Analyzing with a deep learning model, the perioperative factors that could predict and/or contribute to postoperative infections may improve the outcome in elderly. This was an observational cohort study with 2014 elderly patients who had elective surgery from 28 hospitals in China from April to June 2014. We aimed to develop and validate deep learning-based predictive models for postoperative infections in the elderly. 1510 patients were randomly assigned to be training dataset for establishing deep learning-based models, and 504 patients were used to validate the effectiveness of these models. The conventional model predicted postoperative infections was 0.728 (95% CI 0.688–0.768) with the sensitivity of 66.2% (95% CI 58.2–73.6) and specificity of 66.8% (95% CI 64.6–68.9). The deep learning model including risk factors relevant to baseline clinical characteristics predicted postoperative infections was 0.641 (95% CI 0.545–0.737), and sensitivity and specificity were 34.2% (95% CI 19.6–51.4) and 88.8% (95% CI 85.6–91.6), respectively. Including risk factors relevant to baseline variables and surgery, the deep learning model predicted postoperative infections was 0.763 (95% CI 0.681–0.844) with the sensitivity of 63.2% (95% CI 46–78.2) and specificity of 80.5% (95% CI 76.6–84). Our feasibility study indicated that a deep learning model including risk factors for the prediction of postoperative infections can be achieved in elderly. Further study is needed to assess whether this model can be used to guide clinical practice to improve surgical outcomes in elderly.

Type of Medium: Online Resource

ISSN: 1720-8319

URL: Article

DOI: 10.1007/s40520-022-02325-3

Language: English

Publisher: Springer Science and Business Media LLC

Publication Date: 2023

detail.hit.zdb_id: 2119282-0

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4

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Severity detection of COVID-19 infection with machine learning of clinical records and CT images

Zhu, Fubao ; Zhu, Zelin ; Zhang, Yijun ; [et al.]

IOS Press ; 2022

In: Technology and Health Care Vol. 30, No. 6 ( 2022-11-12), p. 1299-1314

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In: Technology and Health Care, IOS Press, Vol. 30, No. 6 ( 2022-11-12), p. 1299-1314

Abstract: BACKGROUND: Coronavirus disease 2019 (COVID-19) is a deadly viral infection spreading rapidly around the world since its outbreak in 2019. In the worst case a patient’s organ may fail leading to death. Therefore, early diagnosis is crucial to provide patients with adequate and effective treatment. OBJECTIVE: This paper aims to build machine learning prediction models to automatically diagnose COVID-19 severity with clinical and computed tomography (CT) radiomics features. METHOD: P-V-Net was used to segment the lung parenchyma and then radiomics was used to extract CT radiomics features from the segmented lung parenchyma regions. Over-sampling, under-sampling, and a combination of over- and under-sampling methods were used to solve the data imbalance problem. RandomForest was used to screen out the optimal number of features. Eight different machine learning classification algorithms were used to analyze the data. RESULTS: The experimental results showed that the COVID-19 mild-severe prediction model trained with clinical and CT radiomics features had the best prediction results. The accuracy of the GBDT classifier was 0.931, the ROUAUC 0.942, and the AUCPRC 0.694, which indicated it was better than other classifiers. CONCLUSION: This study can help clinicians identify patients at risk of severe COVID-19 deterioration early on and provide some treatment for these patients as soon as possible. It can also assist physicians in prognostic efficacy assessment and decision making.

Type of Medium: Online Resource

ISSN: 0928-7329 , 1878-7401

URL: Article

DOI: 10.3233/THC-220321

Language: Unknown

Publisher: IOS Press

Publication Date: 2022

detail.hit.zdb_id: 2043772-9

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5

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Influence of bFGF on in vitro expansion and chondrogenic construction of articular cartilage-derived progenitor cells

Shen, Xu ; Zhu, Tianfeng ; Xue, Jixin ; [et al.]

AME Publishing Company ; 2022

In: Annals of Translational Medicine Vol. 10, No. 2 ( 2022-1), p. 36-36

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In: Annals of Translational Medicine, AME Publishing Company, Vol. 10, No. 2 ( 2022-1), p. 36-36

Type of Medium: Online Resource

ISSN: 2305-5839 , 2305-5847

URL: Journal

URL: Article

DOI: 10.21037/atm

DOI: 10.21037/atm-21-5604

Language: Unknown

Publisher: AME Publishing Company

Publication Date: 2022

detail.hit.zdb_id: 2893931-1

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6

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Prospective observational cohort study on grading the severity of postoperative complications in global surgery research

International Surgical Outcomes Study (ISOS) group ; Abbott, T E F ; Greaves, K E ; [et al.]

Oxford University Press (OUP) ; 2019

In: British Journal of Surgery Vol. 106, No. 2 ( 2019-01-08), p. e73-e80

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In: British Journal of Surgery, Oxford University Press (OUP), Vol. 106, No. 2 ( 2019-01-08), p. e73-e80

Abstract: The Clavien–Dindo classification is perhaps the most widely used approach for reporting postoperative complications in clinical trials. This system classifies complication severity by the treatment provided. However, it is unclear whether the Clavien–Dindo system can be used internationally in studies across differing healthcare systems in high- (HICs) and low- and middle-income countries (LMICs). Methods This was a secondary analysis of the International Surgical Outcomes Study (ISOS), a prospective observational cohort study of elective surgery in adults. Data collection occurred over a 7-day period. Severity of complications was graded using Clavien–Dindo and the simpler ISOS grading (mild, moderate or severe, based on guided investigator judgement). Severity grading was compared using the intraclass correlation coefficient (ICC). Data are presented as frequencies and ICC values (with 95 per cent c.i.). The analysis was stratified by income status of the country, comparing HICs with LMICs. Results A total of 44 814 patients were recruited from 474 hospitals in 27 countries (19 HICs and 8 LMICs). Some 7508 patients (16·8 per cent) experienced at least one postoperative complication, equivalent to 11 664 complications in total. Using the ISOS classification, 5504 of 11 664 complications (47·2 per cent) were graded as mild, 4244 (36·4 per cent) as moderate and 1916 (16·4 per cent) as severe. Using Clavien–Dindo, 6781 of 11 664 complications (58·1 per cent) were graded as I or II, 1740 (14·9 per cent) as III, 2408 (20·6 per cent) as IV and 735 (6·3 per cent) as V. Agreement between classification systems was poor overall (ICC 0·41, 95 per cent c.i. 0·20 to 0·55), and in LMICs (ICC 0·23, 0·05 to 0·38) and HICs (ICC 0·46, 0·25 to 0·59). Conclusion Caution is recommended when using a treatment approach to grade complications in global surgery studies, as this may introduce bias unintentionally.

Type of Medium: Online Resource

ISSN: 0007-1323 , 1365-2168

URL: Article

DOI: 10.1002/bjs.11025

Language: English

Publisher: Oxford University Press (OUP)

Publication Date: 2019

detail.hit.zdb_id: 2006309-X

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7

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Table_2.xlsx

Dong, Jiajun ; Wang, Shengnan ; Xie, Haoqun ; [et al.]

Frontiers Media SA ; 2023

In: Frontiers in Oncology Vol. 13 ( 2023-8-21)

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In: Frontiers in Oncology, Frontiers Media SA, Vol. 13 ( 2023-8-21)

Abstract: As a result of the COVID-19 pandemic, patients with glioblastoma (GBM) are considered a highly vulnerable population. Despite this, the extent of the causative relationship between GBM and COVID-19 infection is uncertain. Methods Genetic instruments for SARS-CoV-2 infection (38,984 cases and 1,644,784 control individuals), COVID-19 hospitalization (8,316 cases and 1,549,095 control individuals), and COVID-19 severity (4,792 cases and 1,054,664 control individuals) were obtained from a genome-wide association study (GWAS) from European populations. A total of 6,183 GBM cases and 18,169 controls from GWAS were enrolled in our study. Their associations were evaluated by applying Mendelian randomization (MR) including IVW meta-analysis, MR-Egger regression, and weighted-median analysis. To make the conclusions more robust and reliable, sensitivity analyses were performed. Results Our results showed that genetically predicted COVID−19 hospitalization increases the risk of GBM (OR = 1.202, 95% CI = 1.035–1.395, p = 0.016). In addition, no increased risk of SARS-CoV-2 infection, COVID-19 hospitalization and severity were observed in patients with any type of genetically predicted GBM. Conclusion Our MR study indicated for the first time that genetically predicted COVID−19 hospitalization was demonstrated as a risk factor for the development of GBM.

Type of Medium: Online Resource

ISSN: 2234-943X

URL: Article

DOI: 10.3389/fonc.2023.1185466

DOI: 10.3389/fonc.2023.1185466.s001

DOI: 10.3389/fonc.2023.1185466.s002

Language: Unknown

Publisher: Frontiers Media SA

Publication Date: 2023

detail.hit.zdb_id: 2649216-7

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8

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A Deep Learning-based Method to Extract Lumen and Media-Adventitia in Intravascular Ultrasound Images

Zhu, Fubao ; Gao, Zhengyuan ; Zhao, Chen ; [et al.]

SAGE Publications ; 2022

In: Ultrasonic Imaging Vol. 44, No. 5-6 ( 2022-11), p. 191-203

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In: Ultrasonic Imaging, SAGE Publications, Vol. 44, No. 5-6 ( 2022-11), p. 191-203

Abstract: Intravascular ultrasound (IVUS) imaging allows direct visualization of the coronary vessel wall and is suitable for assessing atherosclerosis and the degree of stenosis. Accurate segmentation and lumen and median-adventitia (MA) measurements from IVUS are essential for such a successful clinical evaluation. However, current automated segmentation by commercial software relies on manual corrections, which is time-consuming and user-dependent. We aim to develop a deep learning-based method using an encoder-decoder deep architecture to automatically and accurately extract both lumen and MA border. Inspired by the dual-path design of the state-of-the-art model IVUS-Net, our method named IVUS-U-Net++ achieved an extension of the U-Net++ model. More specifically, a feature pyramid network was added to the U-Net++ model, enabling the utilization of feature maps at different scales. Following the segmentation, the Pearson correlation and Bland-Altman analyses were performed to evaluate the correlations of 12 clinical parameters measured from our segmentation results and the ground truth. A dataset with 1746 IVUS images from 18 patients was used for training and testing. Our segmentation model at the patient level achieved a Jaccard measure (JM) of 0.9080 ± 0.0321 and a Hausdorff distance (HD) of 0.1484 ± 0.1584 mm for the lumen border; it achieved a JM of 0.9199 ± 0.0370 and an HD of 0.1781 ± 0.1906 mm for the MA border. The 12 clinical parameters measured from our segmentation results agreed well with those from the ground truth (all p-values are smaller than .01). Our proposed method shows great promise for its clinical use in IVUS segmentation.

Type of Medium: Online Resource

ISSN: 0161-7346 , 1096-0910

URL: Article

DOI: 10.1177/01617346221114137

Language: English

Publisher: SAGE Publications

Publication Date: 2022

detail.hit.zdb_id: 2012028-X

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9

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Deep learning-based diagnosis of disease activity in patients with Graves’ orbitopathy using orbital SPECT/CT

Yao, Ni ; Li, Longxi ; Gao, Zhengyuan ; [et al.]

Springer Science and Business Media LLC ; 2023

In: European Journal of Nuclear Medicine and Molecular Imaging Vol. 50, No. 12 ( 2023-10), p. 3666-3674

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In: European Journal of Nuclear Medicine and Molecular Imaging, Springer Science and Business Media LLC, Vol. 50, No. 12 ( 2023-10), p. 3666-3674

Abstract: Orbital [ 99m Tc]TcDTPA orbital single-photon emission computed tomography (SPECT)/CT is an important method for assessing inflammatory activity in patients with Graves’ orbitopathy (GO). However, interpreting the results requires substantial physician workload. We aim to propose an automated method called GO-Net to detect inflammatory activity in patients with GO. Materials and methods GO-Net had two stages: (1) a semantic V-Net segmentation network (SV-Net) that extracts extraocular muscles (EOMs) in orbital CT images and (2) a convolutional neural network (CNN) that uses SPECT/CT images and the segmentation results to classify inflammatory activity. A total of 956 eyes from 478 patients with GO (active: 475; inactive: 481) at Xiangya Hospital of Central South University were investigated. For the segmentation task, five-fold cross-validation with 194 eyes was used for training and internal validation. For the classification task, 80% of the eye data were used for training and internal fivefold cross-validation, and the remaining 20% of the eye data were used for testing. The EOM regions of interest (ROIs) were manually drawn by two readers and reviewed by an experienced physician as ground truth for segmentation GO activity was diagnosed according to clinical activity scores (CASs) and the SPECT/CT images. Furthermore, results are interpreted and visualized using gradient-weighted class activation mapping (Grad-CAM). Results The GO-Net model combining CT, SPECT, and EOM masks achieved a sensitivity of 84.63%, a specificity of 83.87%, and an area under the receiver operating curve (AUC) of 0.89 ( p 〈 0.01) on the test set for distinguishing active and inactive GO. Compared with the CT-only model, the GO-Net model showed superior diagnostic performance. Moreover, Grad-CAM demonstrated that the GO-Net model placed focus on the GO-active regions. For EOM segmentation, our segmentation model achieved a mean intersection over union (IOU) of 0.82. Conclusion The proposed Go-Net model accurately detected GO activity and has great potential in the diagnosis of GO.

Type of Medium: Online Resource

ISSN: 1619-7070 , 1619-7089

URL: Article

DOI: 10.1007/s00259-023-06312-2

Language: English

Publisher: Springer Science and Business Media LLC

Publication Date: 2023

detail.hit.zdb_id: 2098375-X

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10

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Spatiotemporal satellite data imputation using sparse functional data analysis

Zhu, Weicheng ; Zhu, Zhengyuan ; Dai, Xiongtao

Institute of Mathematical Statistics ; 2022

In: The Annals of Applied Statistics Vol. 16, No. 4 ( 2022-12-1)

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In: The Annals of Applied Statistics, Institute of Mathematical Statistics, Vol. 16, No. 4 ( 2022-12-1)

Type of Medium: Online Resource

ISSN: 1932-6157

URL: Article

DOI: 10.1214/21-AOAS1591

DOI: 10.1214/21-AOAS1591SUPPA

DOI: 10.1214/21-AOAS1591SUPPB

DOI: 10.1214/21-AOAS1591SUPPC

Language: Unknown

Publisher: Institute of Mathematical Statistics

Publication Date: 2022

detail.hit.zdb_id: 2376910-5

detail.hit.zdb_id: 2253657-7

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Kooperativer Bibliotheksverbund

Berlin Brandenburg