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Ren P, Zhang H, Chang L, Hong XD, Xing L. LncRNA NR2F1-AS1 promotes proliferation and metastasis of ESCC cells via regulating EMT. EUROPEAN REVIEW FOR MEDICAL AND PHARMACOLOGICAL SCIENCES 2021; 24:3686-3693. [PMID: 32329844 DOI: 10.26355/eurrev_202004_20831] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Subscribe] [Scholar Register] [Indexed: 11/12/2022]
Abstract
OBJECTIVE The aim of this study was to explore the expression of long non-coding ribonucleic acid (lncRNA) nuclear receptor subfamily 2 group F member 1-antisense RNA 1 (NR2F1-AS1) in esophageal squamous cell carcinoma (ESCC) tissues and cells and to investigate its effects on ESCC proliferation and metastasis. PATIENTS AND METHODS The expression level of NR2F1-AS1 in 51 pairs of ESCC tissues and corresponding adjacent tissues was detected via quantitative Reverse Transcription-Polymerase Chain Reaction (qRT-PCR). Meanwhile, NR2F1-AS1 expression in ESCC cells was measured via qRT-PCR as well. Subsequently, specific interference sequences of NR2F1-AS1 were designed, synthesized, and transiently transfected into ESCC cells. 48 h later, qRT-PCR assay was performed to detect the interference efficiency. The effects of small interfering (si)-NR2F1-AS1 on the proliferation of ESCC cells were determined through cell counting kit-8 (CCK-8) and colony formation assay. Wound healing and transwell assays were conducted to investigate the influences of si-NR2F1-AS1 on the migration and invasion of ESCC cells. Additionally, the changes in the expressions of epithelial-mesenchymal transition (EMT) molecular markers were detected by Western blotting. RESULTS QRT-PCR assay revealed that the expression level of NR2F1-AS1 was significantly up-regulated in 42 of 51 cases of ESCC tissues (42/51, 82.4%). Compared with esophageal mucosal epithelial HET-1A cells, NR2FA-AS1 was highly expressed in ESCC cells. CCK-8 and colony formation assay indicated that the proliferation of ESCC cells decreased remarkably after interference in NR2F1-AS1 expression. The results of wound healing and transwell assays showed that the migration and metastasis of cells were significantly lower in si-NR2F1-AS1 group than those in si-NC group. Western blotting demonstrated that the expressions of EMT molecular markers were changed after interfering with NR2F1-AS1 expression. CONCLUSIONS NR2F1-AS1 was highly expressed in ESCC tissues and cells. Furthermore, high expression of NR2F1-AS1 promoted the proliferation and metastasis of ESCC cells by modulating EMT.
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Yu J, Xing L, Cheng G, Chen L, Dong L, Fu X, Guo Y, Han Z, Jiang D, Li J, Lin Y, Liu A, Liu J, Liu J, Liu Y, Lv D, Ma C, Ren Y, Wang S, Wang Y, Xiao C, Yan S, Yang F, Yang W, Zang A, Zhang X, Zhang Y, Zhao R, Zhou J. P21.10 Real-World Treatment Patterns in Chinese Stage III NSCLC Patients - A Prospective, Non-Interventional Study (MOOREA trial). J Thorac Oncol 2021. [DOI: 10.1016/j.jtho.2021.01.590] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
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Xing L, Hou JB. [Application and evolution of hybrid OCT-IVUS intravascular imaging technique]. ZHONGHUA XIN XUE GUAN BING ZA ZHI 2021; 49:115-120. [PMID: 33611896 DOI: 10.3760/cma.j.cn112148-20201115-00910] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Subscribe] [Scholar Register] [Indexed: 11/05/2022]
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Pan YQ, Bahoussi AN, Guo F, Xing L. A single nucleotide distinguishes the SARS-CoV-2 in the Wuhan outbreak in December 2019 from that in Beijing-Xinfadi in June 2020, China. New Microbes New Infect 2021; 39:100835. [PMID: 33425367 PMCID: PMC7785950 DOI: 10.1016/j.nmni.2020.100835] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/01/2020] [Revised: 12/08/2020] [Accepted: 12/10/2020] [Indexed: 12/28/2022] Open
Abstract
Two major locally transmitted outbreaks of coronavirus disease 2019 occurred in China, one in Wuhan from December 2019 to April 2020, another in Beijing-Xinfadi in June 2020. Severe acute respiratory syndrome coronavirus 2 isolated from these two outbreaks can be distinguished by a conserved pyrimidine nucleotide located at nucleotide position 241 in the 5′-untranslated region of the virus genome.
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Xing L, Xia M, Jiao X, Fan L. Hsa_circ_0004831 serves as a blood-based prognostic biomarker for colorectal cancer and its potentially circRNA-miRNA-mRNA regulatory network construction. Cancer Cell Int 2020; 20:557. [PMID: 33292256 PMCID: PMC7678213 DOI: 10.1186/s12935-020-01651-8] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/06/2020] [Accepted: 11/09/2020] [Indexed: 01/16/2023] Open
Abstract
Background Colorectal cancer (CRC) is a common malignant tumor with unsatisfactory overall prognosis. CircRNAs could be promising prognostic biomarkers in cancers, and play important role in the process of tumorigenesis and progression. Here, we explored the role of hsa_circ_0004831 in blood extracellular vesicles and its prognostic value in CRC. Methods The circRNA and mRNA expression level matrix in extracellular vesicles of CRC and normal samples were obtained from the exoRBase database. The corresponding miRNA expression level matrix in extracellular vesicles was downloaded from the BBCancer database. Differentially expressed circRNAs, miRNAs and mRNAs were identified using the limma package of R software at the cut-off criteria of fold change (FC) > 2 and adj. p < 0.05. RT-qPCR assay was conducted to measure hsa_circ_0004831 expression level in CRC blood samples. A circRNA-miRNA-mRNA regulatory network of hsa_circ_0004831 was constructed based on competitive endogenous RNA mechanism and differentially expressed genes. The mRNAs co-expressed with hsa_circ_0004831 were screened at the cut-off criteria of pearson |r| > 0.3 and p < 0.05. Gene set enrichment analysis (GSEA) based on co-expressed mRNAs was used to explore the potential molecular function of hsa_circ_0004831. Results Differentially expressed circRNAs, miRNAs and mRNAs were identified and hsa_circ_0004831 had a FC value of 3.92 in CRC blood extracellular vesicles. The RT-qPCR assay showed that the hsa_circ_0004831 was up-regulated in CRC blood samples. The overall survival analysis found that high expression of hsa_circ_0004831 was linked with poorer prognosis. Finally, a circRNA-miRNA-mRNA regulatory network of hsa_circ_0004831 was constructed based on down-regulated miR-4326 and 12 up-regulated mRNAs. GSEA indicated that mRNAs co-expressed with hsa_circ_0004831 were involved in EMT, WNT and p53 signaling pathways. Conclusions The study confirmed the up-regulation of hsa_circ_0004831 in CRC, and it may act as a vital prognostic biomarker. The circRNA-miRNA-mRNA regulatory network of hsa_circ_0004831 could be used to uncover the tumorigenesis and progression of CRC.
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Wang F, Xing L, Bagshaw H, Buyyounouski M, Han B. Automated Needle Digitization in Ultrasound-based Prostate High Dose-rate Brachytherapy Using a Deep Learning Algorithm. Int J Radiat Oncol Biol Phys 2020. [DOI: 10.1016/j.ijrobp.2020.07.857] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022]
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Kiss M, Zhang H, Fix M, Manser P, Xing L. Z-Super Resolution CT-Image Generation With A Deep 3D Fully Convolutional Neural Network. Int J Radiat Oncol Biol Phys 2020. [DOI: 10.1016/j.ijrobp.2020.07.249] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
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Bibault J, Xing L. Predicting Survival in Prostate Cancer Patients with Interpretable Artificial Intelligence. Int J Radiat Oncol Biol Phys 2020. [DOI: 10.1016/j.ijrobp.2020.07.832] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/23/2022]
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Vasudevan V, Huang C, Simiele E, Yu L, Xing L, Schuler E. Combining Monte Carlo with Deep Learning: Predicting High-resolution, Low-noise Dose Distributions Using a Generative Adversarial Network for Fast and Precise Monte Carlo Simulations. Int J Radiat Oncol Biol Phys 2020. [DOI: 10.1016/j.ijrobp.2020.07.2157] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022]
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Wang Y, Dai J, Fang C, Zhang S, Wang J, Yin Y, Jiang S, Guo J, Lei F, Tu Y, Xing L, Hou J, Yu B. Predictors of plaque erosion in current smokers and non-current smokers presented with ST-segment elevation myocardial infarction: an optical coherence tomography study. Eur Heart J 2020. [DOI: 10.1093/ehjci/ehaa946.1780] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/13/2022] Open
Abstract
Abstract
Background
Plaque erosion with subsequent coronary thrombosis is considered as an important cause of ST-segment elevation myocardial infarction (STEMI). Smoking is a major risk factor for acute coronary thrombosis. However, the relationship between current smoking status and plaque erosion has not been systematically investigated.
Purpose
The present study aimed to investigate predictors of plaque erosion in current smokers and non-current smokers with STEMI by using optical coherence tomography (OCT).
Methods
Between January 2015 to December 2017, a total of 1313 STEMI patients underwent pre-intervention OCT of culprit lesion were enrolled and divided into two groups based on current smoking status: current smoking group (n=713) and non-current smoking group (n=600). Using established criteria, quantitative and qualitative underlying plaque characteristics were assessed by OCT. Clinical, angiographic and OCT characteristics of all enrolled patients were recorded. Univariable and multivariable logistic regression analyses were used to identify predictors of plaque erosion in two groups.
Results
Plaque erosion were found in 30.9% (220/713) culprit lesions in current smoking group and 20.8% (125/600) of those in non-current smoking group detected by OCT. In multivariate regression analysis, the predictors that strongly related to plaque erosion in the current smoking group were nearby bifurcation (OR: 4.84; 95% CI:2.38–9.87; p<0.001); the minimum fiber cap thickness (FCT, OR:1.05; 95% CI:1.03–1.08; p<0.001); thin-cap fibroatheroma (TCFA, OR: 0.22; 95% CI: 0.07–0.67; p=0.007) and lipid core length (OR: 0.91; 95% CI: 0.84–0.97; p=0.007). The predictors in the non-current smoking group were nearby bifurcation (OR: 4.84; 95% CI: 2.38–9.87; p=0.006); the minimal FCT (OR: 1.09; 95% CI: 1.06–1.13; p<0.001); multi-vessel disease (MVD, OR: 0.43; 95% CI: 0.19–0.97; p=0.042) and dyslipidemia (OR: 0.34; 95% CI: 0.14–0.84; p=0.020).
Conclusions
Predictors of plaque erosion causing STEMI onset are different between current smokers and non-current smoker, with nearby bifurcation and thicker minimal FCT both predicting plaque erosion in two groups of patients.
Funding Acknowledgement
Type of funding source: Public grant(s) – National budget only. Main funding source(s): National Key Research and Development Program of China, National Natural Science Foundation of China.
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Capaldi D, Binkley M, Ko R, Xing L, Maxim P, Diehn M, Loo B. Parametric Response Mapping as an Imaging Biomarker for Regional Ventilation in Stereotactic Ablative Radiotherapy. Int J Radiat Oncol Biol Phys 2020. [DOI: 10.1016/j.ijrobp.2020.07.2353] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/23/2022]
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Dong P, Xing L. Deep DoseNet: A Deep Neural Network based Dose Calculation Algorithm. Int J Radiat Oncol Biol Phys 2020. [DOI: 10.1016/j.ijrobp.2020.07.821] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/24/2022]
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Fang C, Dai J, Zhang S, Wang J, Wang Y, Li L, Xing L, Hou J, Yu B. Morphological characteristics of plaque erosion with noncritical coronary stenosis: an optical coherence tomography study. Eur Heart J 2020. [DOI: 10.1093/ehjci/ehaa946.1533] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/12/2022] Open
Abstract
Abstract
Background
Plaque erosion is a frequent and important mechanism of acute coronary thrombosis only secondary to plaque rupture. Recent studies suggested plaque erosion with noncritical stenosis could be treated conservatively that distinct from those with critical stenosis. However, characteristics of plaque erosions with different coronary stenosis remain unknown.
Purpose
The present study aimed to investigate morphological features of plaque erosions with different coronary stenosis using optical coherence tomography (OCT).
Methods
Consecutive ST-segment elevated myocardial infarction (STEMI) patients with OCT images of culprit lesion between August 2014 and December 2017 were enrolled and 348 cases presented with plaque erosion identified by OCT. Based on the severity of lumen area stenosis [calculated by (1-minimal lumen area/reference lumen area) * 100%], all culprit plaque erosions were divided into three groups: Group A (area stenosis<50%, n=50, 14.4%); Group B (50%≤area stenosis<75%, n=146, 42.0%); Group C (area stenosis≥75%, n=152, 43.7%). Clinical characteristics, lesion features detected by coronary angiography and OCT were compared among three groups.
Results
Of all 348 STEMI patients with plaque erosions, patients in Group A were youngest (p=0.008) and had the lowest frequency of hypertension (p=0.029) as compared with those in Group B and C. Angiographic analysis showed 72.0% of plaque erosions in Group A located in LAD, while 67.8% in Group B and 53.9% in Group C (p=0.039). OCT findings (Figure 1-A) showed the prevalence of fibrous plaque was significantly highest in Group A than those in Group B and C (82.0% vs. 54.8% vs. 34.9%, p<0.001), whereas lipid rich plaque was most frequent in Group C (16.0% vs. 43.8% vs. 62.5%, p<0.001). The prevalence of macrophage (p<0.001), microvessel (p=0.009) and cholesterol crystals (p<0.001) increased gradually from plaque erosion with lumen area stenosis <50% to 50–75% to ≥75%. Notably, compared with Group B and C, nearby bifurcation was most common in Group A (72.0% vs. 67.1% vs. 55.3%, p=0.036). Multivariable regression analyses (Figure 1-B) showed fibrous plaque and nearby bifurcation were independently associated with plaque erosion with noncritical stenosis (area stenosis<75%).
Conclusion
56.3% plaque erosion in STEMI patients presented with noncritical stenosis, having distinct morphological features from erosion with critical stenosis. Fibrous plaque and nearby bifurcation were independently associated with the presence of noncritically stenotic plaque erosion, remaining a desire to tailor treatment therapy to individual patients.
Figure 1
Funding Acknowledgement
Type of funding source: Foundation. Main funding source(s): National Key R&D Program of China
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Yang Y, Kovalchuk N, Gensheimer M, Beadle B, Bagshaw H, Buyyounouski M, Swift P, Chang D, Le Q, Xing L. Evaluation of a Knowledge-Guided Automated Treatment Planning Tool. Int J Radiat Oncol Biol Phys 2020. [DOI: 10.1016/j.ijrobp.2020.07.2365] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/23/2022]
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Tong X, Chen X, Qiu Q, Sun X, Xing L. Integrative Nomogram of CT-based Radiomics and Clinical Features for Predicting Oligometastases at Recurrent after Definitive Chemoradiotherapy for Locally Advanced Non-Small Cell Lung Cancer. Int J Radiat Oncol Biol Phys 2020. [DOI: 10.1016/j.ijrobp.2020.07.216] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/23/2022]
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Yan H, Schneider B, Graves E, Sun W, Xing L, MacDonald C, Liu W. Focused kV X-rays for Preclinical Studies of Radiation-based Neuromodulation. Int J Radiat Oncol Biol Phys 2020. [DOI: 10.1016/j.ijrobp.2020.07.2081] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/23/2022]
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Fan J, Xing L, Yang Y. Verification of the Machine Delivery Parameters of Treatment Plan via Deep Learning. Int J Radiat Oncol Biol Phys 2020. [DOI: 10.1016/j.ijrobp.2020.07.726] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
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Schueler E, Chuang C, Yang Y, Xing L, Zhao W. Mitigating the Uncertainty in Small Field Dosimetry for Stereotactic Body Radiation Therapy by Leveraging Machine Learning Strategies. Int J Radiat Oncol Biol Phys 2020. [DOI: 10.1016/j.ijrobp.2020.07.776] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/23/2022]
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Zhao W, Lv T, Chen Y, Xing L. Dual-energy CT Imaging Using a Single-energy CT Data via Deep Learning: A Contrast-enhanced CT Study. Int J Radiat Oncol Biol Phys 2020. [DOI: 10.1016/j.ijrobp.2020.07.2154] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/27/2022]
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Zhao W, Capaldi D, Chuang C, Xing L. Fiducial-Free Image-Guided Spinal Stereotactic Radiosurgery Enabled Via Deep Learning. Int J Radiat Oncol Biol Phys 2020. [DOI: 10.1016/j.ijrobp.2020.07.2348] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
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Chen Y, Xing L, Bagshaw H, Buyyounouski M, Han B. Deep Learning-Based Intraprostatic Lesion Segmentation Using Multi-Parametric MRI For Prostate Radiation Therapy. Int J Radiat Oncol Biol Phys 2020. [DOI: 10.1016/j.ijrobp.2020.07.2275] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/23/2022]
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Shah PT, Xing L. Puzzling increase and decrease in COVID-19 cases in Pakistan. New Microbes New Infect 2020; 38:100791. [PMID: 33101693 PMCID: PMC7568489 DOI: 10.1016/j.nmni.2020.100791] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/09/2020] [Revised: 10/11/2020] [Accepted: 10/13/2020] [Indexed: 12/15/2022] Open
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Bibault JE, Xing L. Intelligence artificielle interprétable pour la prédiction de la survie dans le cancer de prostate. Cancer Radiother 2020. [DOI: 10.1016/j.canrad.2020.08.014] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/23/2022]
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Li X, Xing L, Lai R, Yuan C, Humbert P. Literature mapping: association of microscopic skin microflora and biomarkers with macroscopic skin health. Clin Exp Dermatol 2020; 46:21-27. [PMID: 32786033 PMCID: PMC7754415 DOI: 10.1111/ced.14353] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/17/2019] [Revised: 03/09/2020] [Accepted: 05/27/2020] [Indexed: 10/27/2022]
Abstract
Associations between skin microbes or biomarkers and pathological conditions have been reported in the literature. However, there is a lack of clarity on the interaction between the coexistence of common skin microbes with skin physiology and subsequent development of clinical symptoms, and the role of biomarkers in mediating these changes before the development of skin disease. In this review, we aim to identify areas in which extensive research for the studied factors has already been conducted, and which research areas are under-represented. The SciFinder database was searched for articles containing key words including specific skin microbes, biomarkers, skin physiology and diseases from the beginning of the SciFinder data record to 26 April 2016, and we included an additional relevant recent publication from our group. Among the 8000 + articles selected, the frequency of keyword pairs between two roles [microscopic markers (microflora or biomarkers) and reactions (skin physiology or clinical symptoms, or skin disease)] was investigated. Associated research between the individual factors such as skin microflora or biomarkers (chosen based on our earlier publication) and specific biophysical parameters, symptoms or skin disease was identified. The present research heatmap emphasizes the significance of a structured review of research on concerned factor associations to identify early/subclinical clues that can be used to prevent progression to overt skin disease with the help of precise skin care or early intervention, as indicated by skin microflora, biomarkers and an interactive skin biophysics profile. The findings provide a novel approach to explore such associations and may guide future research directed towards predicting disease from early/subclinical symptoms.
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Bibault JE, Xing L, Giraud P, El Ayachy R, Giraud N, Decazes P, Burgun A, Giraud P. Radiomics: A primer for the radiation oncologist. Cancer Radiother 2020; 24:403-410. [PMID: 32265157 DOI: 10.1016/j.canrad.2020.01.011] [Citation(s) in RCA: 41] [Impact Index Per Article: 10.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/21/2020] [Accepted: 01/22/2020] [Indexed: 12/14/2022]
Abstract
PURPOSE Radiomics are a set of methods used to leverage medical imaging and extract quantitative features that can characterize a patient's phenotype. All modalities can be used with several different software packages. Specific informatics methods can then be used to create meaningful predictive models. In this review, we will explain the major steps of a radiomics analysis pipeline and then present the studies published in the context of radiation therapy. METHODS A literature review was performed on Medline using the search engine PubMed. The search strategy included the search terms "radiotherapy", "radiation oncology" and "radiomics". The search was conducted in July 2019 and reference lists of selected articles were hand searched for relevance to this review. RESULTS A typical radiomics workflow always includes five steps: imaging and segmenting, data curation and preparation, feature extraction, exploration and selection and finally modeling. In radiation oncology, radiomics studies have been published to explore different clinical outcome in lung (n=5), head and neck (n=5), esophageal (n=3), rectal (n=3), pancreatic (n=2) cancer and brain metastases (n=2). The quality of these retrospective studies is heterogeneous and their results have not been translated to the clinic. CONCLUSION Radiomics has a great potential to predict clinical outcome and better personalize treatment. But the field is still young and constantly evolving. Improvement in bias reduction techniques and multicenter studies will hopefully allow more robust and generalizable models.
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