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Journal articles on the topic 'CXMS'

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Author: Grafiati
Published: 10 December 2022
Last updated: 29 January 2023

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1

Cortés, Farid B., Karol Zapata, Benjamín A. Rojano, Francisco Carrasco-Marín, Jaime Gallego, M. Alejandra Hernández, and Camilo A. Franco. "Dual-Purpose Materials Based on Carbon Xerogel Microspheres (CXMs) for Delayed Release of Cannabidiol (CBD) and Subsequent Aflatoxin Removal." Molecules 24, no. 18 (September 19, 2019): 3398. http://dx.doi.org/10.3390/molecules24183398.

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The main objective of this study is to develop a novel dual-purpose material based on carbon xerogel microspheres (CXMs) that permits the delayed release of cannabidiol (CBD) and the removal of aflatoxin. The CXMs were prepared by the sol-gel method and functionalized with phosphoric acid (CXMP) and melamine (CXMN). The support and the modified materials were characterized by scanning electronic microscopy (SEM), N2 adsorption at −196 °C, X-ray photoelectron spectroscopy (XPS), and zeta potential. For the loading of the cannabidiol (CBD) in the porous samples, batch–mode adsorption experiments at 25 °C were performed, varying the concentration of CBD. The desorption kinetics was performed at two conditions for simulating the gastric (pH of 2.1) and intestinal (pH of 7.4) conditions at 37 °C based on in vitro CBD release. Posteriorly, the samples obtained after desorption were used to study aflatoxin removal, which was evaluated through adsorption experiments at pH = 7.4 and 37 °C. The adsorption isotherms of CBD showed a type I(b) behavior, with the adsorbed uptake being higher for the support than for the modified materials with P and N. Meanwhile, the desorption kinetics of CBD at gastric conditions indicated release values lower than 8%, and the remaining amount was desorbed at pH = 7.4 in three hours until reaching 100% based on the in vitro experiments. The results for aflatoxin showed total removal in less than 30 min for all the materials evaluated. This study opens a broader landscape in which to develop dual-purpose materials for the delayed release of CBD, improving its bioavailability and allowing aflatoxin removal in gastric conditions.
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2

Pechoušek, Jiří, Ernö Kuzmann, René Vondrášek, Anna Olina, Vlastimil Vrba, Lukáš Kouřil, Tomáš Ingr, Petr Král, and Miroslav Mashlan. "Successive Grinding and Polishing Effect on the Retained Austenite in the Surface of 42CrMo4 Steel." Metals 12, no. 1 (January 7, 2022): 119. http://dx.doi.org/10.3390/met12010119.

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Low-alloy 42CrMo4 steels were studied by 57Fe Mössbauer spectroscopy (MS), X-ray diffractometry (XRD), and Energy Dispersive X-ray Spectroscopy (EDS) measurements. The investigations were performed on metallographic samples, which were subjected to a series of successive grinding and polishing with a progressively finer grit. Conversion X-ray Mössbauer spectroscopy (CXMS) was used to determine the occurrence of austenite in steel samples. It is a unique method detecting the austenite content very sensitively. Six samples with different surface preparation were investigated, starting with 4.8% of austenite on an as-cut sample, and a large decrease in the retained austenite to 2.6% was observed after the first grinding of a hardened cut sample. Additionally, an unexpectedly large decrease in the austenite content to 2.3% was found due to the final polishing. A second time applied successive grinding and polishing of all samples resulted in identical austenite content determined by CXMS of approx. 5%, which proved the applicability of the CXMS method. Generally, the result calls attention to the importance of preparation of metallurgical samples by grinding and polishing where the results can vary significantly on the level of surface processing.
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3

Schaaf, P., V. Biehl, U. Gonser, M. Bamberger, M. Langohr, and F. Maisenhälder. "CXMS study of mild steel laser alloyed with CrB2." Hyperfine Interactions 57, no. 1-4 (July 1990): 2095–99. http://dx.doi.org/10.1007/bf02405769.

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4

Yang, Bing, Haifan Wu, Paul D. Schnier, Yansheng Liu, Jun Liu, Nanxi Wang, William F. DeGrado, and Lei Wang. "Proximity-enhanced SuFEx chemical cross-linker for specific and multitargeting cross-linking mass spectrometry." Proceedings of the National Academy of Sciences 115, no. 44 (October 15, 2018): 11162–67. http://dx.doi.org/10.1073/pnas.1813574115.

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Chemical cross-linking mass spectrometry (CXMS) is being increasingly used to study protein assemblies and complex protein interaction networks. Existing CXMS chemical cross-linkers target only Lys, Cys, Glu, and Asp residues, limiting the information measurable. Here we report a “plant-and-cast” cross-linking strategy that employs a heterobifunctional cross-linker that contains a highly reactive succinimide ester as well as a less reactive sulfonyl fluoride. The succinimide ester reacts rapidly with surface Lys residues “planting” the reagent at fixed locations on protein. The pendant aryl sulfonyl fluoride is then “cast” across a limited range of the protein surface, where it can react with multiple weakly nucleophilic amino acid sidechains in a proximity-enhanced sulfur-fluoride exchange (SuFEx) reaction. Using proteins of known structures, we demonstrated that the heterobifunctional agent formed cross-links between Lys residues and His, Ser, Thr, Tyr, and Lys sidechains. This geometric specificity contrasts with current bis-succinimide esters, which often generate nonspecific cross-links between lysines brought into proximity by rare thermal fluctuations. Thus, the current method can provide diverse and robust distance restraints to guide integrative modeling. This work provides a chemical cross-linker targeting unactivated Ser, Thr, His, and Tyr residues using sulfonyl fluorides. In addition, this methodology yielded a variety of cross-links when applied to the complex Escherichia coli cell lysate. Finally, in combination with genetically encoded chemical cross-linking, cross-linking using this reagent markedly increased the identification of weak and transient enzyme–substrate interactions in live cells. Proximity-dependent cross-linking will dramatically expand the scope and power of CXMS for defining the identities and structures of protein complexes.
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5

Morelos-López, E., A. Cabral-Prieto, N. Nava, F. García-Santibañez, and C. Nosetti. "CXMS and XRD analyses of heat treated A533B stainless steel." Hyperfine Interactions 226, no. 1-3 (January 24, 2014): 737–46. http://dx.doi.org/10.1007/s10751-013-1004-5.

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6

Davydov, Dmitri R., Bikash Dangi, Guihua Yue, Deepak S. Ahire, Bhagwat Prasad, and Victor G. Zgoda. "Exploring the Interactome of Cytochrome P450 2E1 in Human Liver Microsomes with Chemical Crosslinking Mass Spectrometry." Biomolecules 12, no. 2 (January 22, 2022): 185. http://dx.doi.org/10.3390/biom12020185.

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Aiming to elucidate the system-wide effects of the alcohol-induced increase in the content of cytochrome P450 2E1 (CYP2E1) on drug metabolism, we explored the array of its protein-protein interactions (interactome) in human liver microsomes (HLM) with chemical crosslinking mass spectrometry (CXMS). Our strategy employs membrane incorporation of purified CYP2E1 modified with photoreactive crosslinkers benzophenone-4-maleimide and 4-(N-succinimidylcarboxy)benzophenone. Exposure of bait-incorporated HLM samples to light was followed by isolating the His-tagged bait protein and its crosslinked aggregates on Ni-NTA agarose. Analyzing the individual bands of SDS-PAGE slabs of thereby isolated protein with the toolset of untargeted proteomics, we detected the crosslinked dimeric and trimeric complexes of CYP2E1 with other drug-metabolizing enzymes. Among the most extensively crosslinked partners of CYP2E1 are the cytochromes P450 2A6, 2C8, 3A4, 4A11, and 4F2, UDP-glucuronosyltransferases (UGTs) 1A and 2B, fatty aldehyde dehydrogenase (ALDH3A2), epoxide hydrolase 1 (EPHX1), disulfide oxidase 1α (ERO1L), and ribophorin II (RPN2). These results demonstrate the exploratory power of the proposed CXMS strategy and corroborate the concept of tight functional integration in the human drug-metabolizing ensemble through protein-protein interactions of the constituting enzymes.
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7

Ivanova, Tatiana, Miroslav Mashlan, Tomáš Ingr, Hana Doláková, Dmitry Sarychev, and Anna Sedláčková. "Mössbauer Spectroscopy for Additive Manufacturing by Selective Laser Melting." Metals 12, no. 4 (March 24, 2022): 551. http://dx.doi.org/10.3390/met12040551.

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Selective laser melting (SLM) is a technology of layer-by-layer additive manufacturing using a laser. This technology allows one to get complex-shaped, three-dimensional (3D) specimens directly from metal powder. In this technology, various metal powders are used, including different steels. Stainless steel 1.4404 (CL20ES) and maraging steel 1.2709 (CL50WS) have been investigated. The surface of samples manufactured from CL20ES and CL50WS powders by SLM (with and without combination sandblasting and annealing) was studied by conversion X-ray Mössbauer spectroscopy (CXMS) and conversion electron Mössbauer spectroscopy (CEMS). The surface morphology, elemental composition, and structure were examined by scanning electron microscopy (SEM), energy-dispersive X-ray spectroscopy (EDS), and X-ray powder diffraction (XRD). Samples with sandblasted (corundum powder) and non-sandblasted surfaces were annealed at 540 °C (CL50WS) or 550 °C (CL20ES) for 6 h in air. Oxidation processes on surfaces of samples manufactured from both initial powders were observed after post-process annealing by CEMS and CXMS, as well as confirmed by XRD. The transformation of the austenitic to ferritic phase was observed in a sandblasted and annealed CL20ES sample by CEMS and XRD.
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8

Linderhof, Fredericus, Miroslav Mashlan, Hana Doláková, Tomáš Ingr, and Tatiana Ivanova. "Surface Micromorphology and Structure of Stainless and Maraging Steel Obtained via Selective Laser Melting: A Mössbauer Spectroscopy Study." Metals 11, no. 7 (June 26, 2021): 1028. http://dx.doi.org/10.3390/met11071028.

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Selective laser melting (SLM) as an additive manufacturing method makes it possible to quickly produce complexly shaped three-dimensional (3D) metal specimens from a powder. This work describes how SLM affects the surface phase composition of a 3D printed specimen, as analyzed with conversion electron Mössbauer spectroscopy (CEMS), conversion X-ray Mössbauer spectroscopy (CXMS) and X-ray diffraction (XRD). Both stainless 1.4404 (CL20ES) steel and maraging 1.2709 (CL50WS) steel have been investigated. A transformation of the phase composition from the ferritic phase into an austenitic one was proven by comparing the initial CL50WS powder and the final specimen using CXMS. This transformation takes place during the SLM process. No transformation was identified in stainless steel. The differences identified via CEMS between the surface phase composition of the final non-annealed specimens and the surface of the final annealed specimens demonstrated the oxidation of the surface layer. The oxidation occurs during the annealing of the sample in surface layers less than 1 μm thick. The quality of the surface was examined using scanning electron microscopy (SEM), which presented imperfections on the face of the final specimen. Granules of the initial powder bonded to the surface of the specimen and both irregular and spherical pores were observed.
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9

Schaaf, P., Ph Bauer, and U. Gonser. "Mössbauer measurements backscattering technique (CXMS) of laser irradiated cold forming tool steel (X210CR12)." Hyperfine Interactions 46, no. 1-4 (March 1989): 541–48. http://dx.doi.org/10.1007/bf02398241.

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10

Rogalski, M. S., and I. Bibicu. "CEMS, CXMS, and transmission Mössbauer investigation of the RF isochronal annealing of Fe81B13.5Si3.5C2 glass." physica status solidi (b) 195, no. 2 (June 1, 1996): 531–36. http://dx.doi.org/10.1002/pssb.2221950221.

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11

Tomcho, Kayce A., Hannah E. Gering, Rathna J. Veeramachaneni, David J. Lapinsky, and Michael Cascio. "Targeted State Dependent Crosslinking Mass Spectrometry (CXMS) of the Human Alpha 1 Glycine Receptor (GLyR)." Biophysical Journal 116, no. 3 (February 2019): 392a. http://dx.doi.org/10.1016/j.bpj.2018.11.2120.

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12

Xiao, Kunhong, Yang Zhao, Minjung Choi, Hongda Liu, Adi Blanc, Jiang Qian, Thomas J. Cahill, et al. "Revealing the architecture of protein complexes by an orthogonal approach combining HDXMS, CXMS, and disulfide trapping." Nature Protocols 13, no. 6 (May 24, 2018): 1403–28. http://dx.doi.org/10.1038/nprot.2018.037.

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13

Tran, Bao Quoc, David R. Goodlett, and Young Ah Goo. "Advances in protein complex analysis by chemical cross-linking coupled with mass spectrometry (CXMS) and bioinformatics." Biochimica et Biophysica Acta (BBA) - Proteins and Proteomics 1864, no. 1 (January 2016): 123–29. http://dx.doi.org/10.1016/j.bbapap.2015.05.015.

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14

Abada, L., F. Aubertin, and U. Gonser. "CEMS, CXMS and X-ray investigation of the microstructure of the surface layer in plasma nitrocarburized steel." Hyperfine Interactions 94, no. 1 (December 1994): 2379–84. http://dx.doi.org/10.1007/bf02063792.

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15

Roseboom, Winfried, Madhvi Nazir, Nils Meiresonne, Tamimount Mohammadi, Jolanda Verheul, Hansuk Buncherd, Alexandre Bonvin, et al. "Mapping the Contact Sites of the Escherichia coli Division-Initiating Proteins FtsZ and ZapA by BAMG Cross-Linking and Site-Directed Mutagenesis." International Journal of Molecular Sciences 19, no. 10 (September 26, 2018): 2928. http://dx.doi.org/10.3390/ijms19102928.

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Cell division in bacteria is initiated by the polymerization of FtsZ at midcell in a ring-like structure called the Z-ring. ZapA and other proteins assist Z-ring formation and ZapA binds ZapB, which senses the presence of the nucleoids. The FtsZ–ZapA binding interface was analyzed by chemical cross-linking mass spectrometry (CXMS) under in vitro FtsZ-polymerizing conditions in the presence of GTP. Amino acids residue K42 from ZapA was cross-linked to amino acid residues K51 and K66 from FtsZ, close to the interphase between FtsZ molecules in protofilaments. Five different cross-links confirmed the tetrameric structure of ZapA. A number of FtsZ cross-links suggests that its C-terminal domain of 55 residues, thought to be largely disordered, has a limited freedom to move in space. Site-directed mutagenesis of ZapA reveals an interaction site in the globular head of the protein close to K42. Using the information on the cross-links and the mutants that lost the ability to interact with FtsZ, a model of the FtsZ protofilament–ZapA tetramer complex was obtained by information-driven docking with the HADDOCK2.2 webserver.
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16

Sedláčková, Anna, Tatiana Ivanova, Miroslav Mashlan, and Hana Doláková. "Phase Changes in the Surface Layer of Stainless Steel Annealed at a Temperature of 550 °C." Materials 15, no. 24 (December 12, 2022): 8871. http://dx.doi.org/10.3390/ma15248871.

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Stainless steels have the advantage of forming a protective surface layer to prevent corrosion. This layer results from phase and structural changes on the steel surface. Stainless steel samples (1.4404, 316L), whose alloying elements include Cr, Ni, Mo, and Mn, were subjected to the study of the surface layer. Prism-shaped samples (25 × 25 × 3) mm3 were made from CL20ES stainless steel powder, using selective laser melting. After sandblasting with corundum powder and annealing at 550 °C for different periods of time (2, 4, 8, 16, 32, 64, 128 h), samples were studied by conversion X-ray Mössbauer spectroscopy (CXMS), conversion electron Mössbauer spectroscopy (CEMS), scanning electron microscopy (SEM), energy-dispersive X-ray spectroscopy (EDS), and X-ray diffraction (XRD). The main topics of the research were surface morphology and elemental and phase composition. The annealing of stainless steel samples resulted in a new surface layer comprising leaf-shaped crystals made of chromium oxide. The crystals grew, and their number increased as annealing time was extended. The amount of chromium increased in the surface layer at the expense of iron and nickel, and the longer the annealing time was set, the more chromium was observed in the surface layer. Iron compounds (BCC iron, mixed Fe–Cr oxide) were found in the surface layer, in addition to chromium oxide. BCC iron appeared only after annealing for at least 4 h, which is the initial time of austenitic–ferritic transformation. Mixed Fe–Cr oxide was observed in all annealed samples. All phase changes were observed in the surface layer at approximately 0.6 µm depth.
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17

Tomcho, Kayce A., Hannah E. Gering, Amanda Pellegrino, David J. Lapinsky, and Michael Cascio. "Using a Network of Single Site Specific Mutations and Crosslinking Mass Spectrometry (CXMS) to Refine the Structure and Dynamics of the Human Alpha 1 Glycine Receptor (GLYR)." Biophysical Journal 118, no. 3 (February 2020): 581a. http://dx.doi.org/10.1016/j.bpj.2019.11.3154.

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18

Butler, Sara. "Tafasitamab-cxis (Monjuvi®)." Oncology Times 42, no. 22 (November 20, 2020): 22. http://dx.doi.org/10.1097/01.cot.0000723636.68056.b8.

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19

Subbaiah, K. V., and C. Sunil Sunny. "View-CXS." Annals of Nuclear Energy 31, no. 8 (May 2004): 923–31. http://dx.doi.org/10.1016/j.anucene.2003.11.007.

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20

Dasegowda, Giridhar, Mannudeep K. Kalra, Alain S. Abi-Ghanem, Chiara D. Arru, Monica Bernardo, Luca Saba, Doris Segota, et al. "Suboptimal Chest Radiography and Artificial Intelligence: The Problem and the Solution." Diagnostics 13, no. 3 (January 23, 2023): 412. http://dx.doi.org/10.3390/diagnostics13030412.

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Chest radiographs (CXR) are the most performed imaging tests and rank high among the radiographic exams with suboptimal quality and high rejection rates. Suboptimal CXRs can cause delays in patient care and pitfalls in radiographic interpretation, given their ubiquitous use in the diagnosis and management of acute and chronic ailments. Suboptimal CXRs can also compound and lead to high inter-radiologist variations in CXR interpretation. While advances in radiography with transitions to computerized and digital radiography have reduced the prevalence of suboptimal exams, the problem persists. Advances in machine learning and artificial intelligence (AI), particularly in the radiographic acquisition, triage, and interpretation of CXRs, could offer a plausible solution for suboptimal CXRs. We review the literature on suboptimal CXRs and the potential use of AI to help reduce the prevalence of suboptimal CXRs.
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21

Li, Long, Wenchao Yao, Sen Yan, Xianghui Dong, Zhenyi Lv, Qingxu Jing, Qiang Wang, et al. "Pan-Cancer Analysis of Prognostic and Immune Infiltrates for CXCs." Cancers 13, no. 16 (August 18, 2021): 4153. http://dx.doi.org/10.3390/cancers13164153.

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Background: CXCs are important genes that regulate inflammation and tumor metastasis. However, the expression level, prognosis value, and immune infiltration of CXCs in cancers are not clear. Methods: Multiple online datasets were used to analyze the expression, prognosis, and immune regulation of CXCs in this study. Network analysis of the Amadis database and GEO dataset was used to analyze the regulation of intestinal flora on the expression of CXCs. A mouse model was used to verify the fact that intestinal bacterial dysregulation can affect the expression of CXCs. Results: In the three cancers, multiple datasets verified the fact that the mRNA expression of this family was significantly different; the mRNA levels of CXCL3, 8, 9, 10, 14, and 17 were significantly correlated with the prognosis of three cancers. CXCs were correlated with six types of immuno-infiltrating cells in three cancers. Immunohistochemistry of clinical samples confirmed that the expression of CXCL8 and 10 was higher in three cancer tissues. Animal experiments have shown that intestinal flora dysregulation can affect CXCL8 and 10 expressions. Conclusion: Our results further elucidate the function of CXCs in cancers and provide new insights into the prognosis and immune infiltration of breast, colon, and pancreatic cancers, and they suggest that intestinal flora may influence disease progression through CXCs.
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22

Jimah, Bashiru Babatunde, Anthony Baffour Appiah, Benjamin Dabo Sarkodie, and Dorothea Anim. "Competency in Chest Radiography Interpretation by Junior Doctors and Final Year Medical Students at a Teaching Hospital." Radiology Research and Practice 2020 (November 6, 2020): 1–7. http://dx.doi.org/10.1155/2020/8861206.

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Background. Chest radiography (CXR) is a widely used imaging technique for assessing various chest conditions; however, little is known on the medical doctors’ and medical students’ level of skills to interpret the CXRs. This study assessed the residents, medical officers, house officers, and final year medical students’ competency in CXRs interpretation and how the patient’s clinical history influences the interpretation. Methods. We conducted a cross-sectional study in the Cape Coast Teaching Hospital in the Central Region of Ghana among 99 nonradiologists, comprising 10 doctors in residency programmes, 18 medical officers, 33 house officers, and 38 final year medical students. The data collection was done with a semistructured questionnaire in two phases. In phase 1, ten CXRs were presented without patient’s clinical history. Phase 2 involved the same ten CXRs presented in the same order alongside the patient’s clinical history. Participants were given 3 minutes to interpret each image. Median and interquartile ranges were used to describe continuous variables, while frequencies and percentages were used to describe categorical variables. Test of significant difference and association was conducted using a Wilcoxon rank-sum test/Kruskal–Wallis test and chi-square (X2) test, respectively. Results. The average score for interpreting CXRs was 7.0 (IQR = 5–8) and 4.0 (IQR = 3-4), when CXRs were, respectively, presented with and without clinical history. No significant difference was seen in average scores regarding the levels of formal training. Without clinical history, only 40.0% of residents, 22.2% of medical officers, 24.2% of house officers, and 13.2% of medical students correctly interpreted CXRs, while more than 75% each of all categories correctly interpreted CXRs when presented with clinical history. However, all participants had difficulties in identifying CXR with pneumothorax (27.3% vs. 30.3%), pneumomediastinum or left rib fracture (8.1% vs. 33.3%), and lung collapse (37.4% vs. 37.4%) in both situations, with and without patient clinical history. Conclusion. The patient’s clinical history was found to greatly influence doctors’ competence in interpreting CXRs. We found a gap in doctors’ and medical students’ ability to interpret CXRs; hence, the development of this skill should be improved at all levels of medical training.
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23

Delbaen, Freddy, Fabio Bellini, Valeria Bignozzi, and Johanna F. Ziegel. "Risk measures with the CxLS property." Finance and Stochastics 20, no. 2 (October 1, 2015): 433–53. http://dx.doi.org/10.1007/s00780-015-0279-6.

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24

Brooks, Wilson C., Konstantinos I. Votanopoulos, Gregory B. Russell, Perry Shen, and Edward A. Levine. "Evaluation of Chest Radiographs and Laboratory Testing during Melanoma Staging Procedures." American Surgeon 85, no. 5 (May 2019): 505–10. http://dx.doi.org/10.1177/000313481908500528.

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Chest radiographs (CXRs) and laboratory testing have historically been performed as a part of low-risk melanoma (clinical stage 1/2) workup. This study evaluates the utility of routine CXRs and laboratory testing during the staging of clinical stage 1 and 2 melanoma patients. This study was approved by the Institutional Review Board at Wake Forest University. A database of sentinel lymph node biopsies performed for clinical stage 1 or 2 melanoma was used to identify early-stage melanoma patients. The medical records of patients with melanoma were reviewed and pre-operative workup procedures were recorded. Four hundred sixty-three patients were reviewed. A total of 315 patients underwent a preoperative CXR, whereas 309 received some laboratory testing. After sentinel node biopsies, 168 patients had pathologic stage 1 disease, 103 stage 2, and 44 stage 3. None of the CXRs (0%) correctly identified metastatic melanoma. Suspicious locations on CXRs and laboratory testing did not lead to metastatic findings in any patient within a year. Metastatic melanoma was not found in any patient by screening with CXRs or laboratory testing during preoperative workup. We recommend not conducting CXRs or laboratory testing during workup for surgical melanoma patients because of charges and anxiety these tests can cause. CXRs, blood tests, and metabolic panels have historically been ordered for early melanoma patients, although debate remains on their efficacy. Surgical patient records were retrospectively reviewed for these tests and no benefit was found.
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Wang, Shangguang, Zibin Zheng, Zhengping Wu, Qibo Sun, Hua Zou, and Fangchun Yang. "Context-Aware Mobile Service Adaptation via a Co-Evolution eXtended Classifier System in Mobile Network Environments." Mobile Information Systems 10, no. 2 (2014): 197–215. http://dx.doi.org/10.1155/2014/890891.

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With the popularity of mobile services, an effective context-aware mobile service adaptation is becoming more and more important for operators. In this paper, we propose a Co-evolution eXtended Classifier System (CXCS) to perform context-aware mobile service adaptation. Our key idea is to learn user context, match adaptation rule, and provide the best suitable mobile services for users. Different from previous adaptation schemes, our proposed CXCS can produce a new user's initial classifier population to quicken its converging speed. Moreover, it can make the current user to predict which service should be selected, corresponding to an uncovered context. We compare CXCS based on a common mobile service adaptation scenario with other five adaptation schemes. The results show the adaptation accuracy of CXCS is higher than 70% on average, and outperforms other schemes.
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Tricarico, Davide, Marco Calandri, Matteo Barba, Clara Piatti, Carlotta Geninatti, Domenico Basile, Marco Gatti, Massimiliano Melis, and Andrea Veltri. "Convolutional Neural Network-Based Automatic Analysis of Chest Radiographs for the Detection of COVID-19 Pneumonia: A Prioritizing Tool in the Emergency Department, Phase I Study and Preliminary “Real Life” Results." Diagnostics 12, no. 3 (February 23, 2022): 570. http://dx.doi.org/10.3390/diagnostics12030570.

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The aim of our study is the development of an automatic tool for the prioritization of COVID-19 diagnostic workflow in the emergency department by analyzing chest X-rays (CXRs). The Convolutional Neural Network (CNN)-based method we propose has been tested retrospectively on a single-center set of 542 CXRs evaluated by experienced radiologists. The SARS-CoV-2 positive dataset (n = 234) consists of CXRs collected between March and April 2020, with the COVID-19 infection being confirmed by an RT-PCR test within 24 h. The SARS-CoV-2 negative dataset (n = 308) includes CXRs from 2019, therefore prior to the pandemic. For each image, the CNN computes COVID-19 risk indicators, identifying COVID-19 cases and prioritizing the urgent ones. After installing the software into the hospital RIS, a preliminary comparison between local daily COVID-19 cases and predicted risk indicators for 2918 CXRs in the same period was performed. Significant improvements were obtained for both prioritization and identification using the proposed method. Mean Average Precision (MAP) increased (p < 1.21 × 10−21 from 43.79% with random sorting to 71.75% with our method. CNN sensitivity was 78.23%, higher than radiologists’ 61.1%; specificity was 64.20%. In the real-life setting, this method had a correlation of 0.873. The proposed CNN-based system effectively prioritizes CXRs according to COVID-19 risk in an experimental setting; preliminary real-life results revealed high concordance with local pandemic incidence.
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Chiu, Hwa-Yen, Rita Huan-Ting Peng, Yi-Chian Lin, Ting-Wei Wang, Ya-Xuan Yang, Ying-Ying Chen, Mei-Han Wu, et al. "Artificial Intelligence for Early Detection of Chest Nodules in X-ray Images." Biomedicines 10, no. 11 (November 7, 2022): 2839. http://dx.doi.org/10.3390/biomedicines10112839.

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Early detection increases overall survival among patients with lung cancer. This study formulated a machine learning method that processes chest X-rays (CXRs) to detect lung cancer early. After we preprocessed our dataset using monochrome and brightness correction, we used different kinds of preprocessing methods to enhance image contrast and then used U-net to perform lung segmentation. We used 559 CXRs with a single lung nodule labeled by experts to train a You Only Look Once version 4 (YOLOv4) deep-learning architecture to detect lung nodules. In a testing dataset of 100 CXRs from patients at Taipei Veterans General Hospital and 154 CXRs from the Japanese Society of Radiological Technology dataset, the sensitivity of the AI model using a combination of different preprocessing methods performed the best at 79%, with 3.04 false positives per image. We then tested the AI by using 383 sets of CXRs obtained in the past 5 years prior to lung cancer diagnoses. The median time from detection to diagnosis for radiologists assisted with AI was 46 (3–523) days, longer than that for radiologists (8 (0–263) days). The AI model can assist radiologists in the early detection of lung nodules.
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Gregorio, M. Consuelo, Fritz J. Baumgartner, and Bassam O. Omari. "The Presenting Chest Roentgenogram in Acute Type A Aortic Dissection: A Multidisciplinary Study." American Surgeon 68, no. 1 (January 2002): 6–10. http://dx.doi.org/10.1177/000313480206800102.

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Acute type A aortic dissection requires early diagnosis and prompt surgical intervention. It is not entirely clear whether patients with this form of dissection have clear-cut chest roentgenogram (CXR) patterns or whether the CXR can guide the physician in directing further workup for acute aortic dissection. The purpose of this study is to evaluate the impact of the initial CXR in arousing suspicion for acute type A aortic dissection. Twelve physicians from four specialties (emergency medicine, radiology, cardiology, and cardiothoracic surgery) evaluated the presenting CXR of ten patients with acute type A aortic dissection and the CXRs of ten normal individuals in a blinded manner. They were asked whether the CXRs were normal or abnormal (part A) and what the findings were and then were asked whether the CXRs were suspicious for acute aortic dissection (part B) and what the findings were. In part A, of the normal CXRs 81 of 120 (68%) readings were recorded as normal. Of the dissection CXRs 112 of 120 (93%) readings were recorded as abnormal ( P < 0.001). In part B, the physicians were asked specifically about suspicion for aortic dissection. Of the normal CXRs 101 of 120 (84%) readings were listed as not suspicious for dissection (i.e., 16% of the normal CXRs were listed as supsicious for dissection). Of the dissection CXRs 88 of 120 (73%) readings were recorded as suspicious for dissection ( p < 0.001). The most frequent findings on a dissection CXR when physicians were specifically asked about dissection included widened mediastinum in 46 of 120 (38%) followed by not suspicious for dissection in 32 of 120 (27%). Among the physician specialties the only statistically significant finding was that the cardiology group was the most likely group to find an abnormality in a “normal” CXR. This data indicates that the presenting CXR is neither sensitive nor specific for acute type A dissection. In a patient with a suspicious history or physical examination, however, a CXR showing mediastinal widening or other aortic abnormalities should increase the suspicion for dissection and warrant further workup. Furthermore in a patient with a clinical suspicion a normal CXR reading should not delay echocardiography to rule out type A dissection.
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Pyrros, Ayis, Jorge Rodriguez Fernandez, Stephen M. Borstelmann, Adam Flanders, Daniel Wenzke, Eric Hart, Jeanne M. Horowitz, et al. "Validation of a deep learning, value-based care model to predict mortality and comorbidities from chest radiographs in COVID-19." PLOS Digital Health 1, no. 8 (August 1, 2022): e0000057. http://dx.doi.org/10.1371/journal.pdig.0000057.

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We validate a deep learning model predicting comorbidities from frontal chest radiographs (CXRs) in patients with coronavirus disease 2019 (COVID-19) and compare the model’s performance with hierarchical condition category (HCC) and mortality outcomes in COVID-19. The model was trained and tested on 14,121 ambulatory frontal CXRs from 2010 to 2019 at a single institution, modeling select comorbidities using the value-based Medicare Advantage HCC Risk Adjustment Model. Sex, age, HCC codes, and risk adjustment factor (RAF) score were used. The model was validated on frontal CXRs from 413 ambulatory patients with COVID-19 (internal cohort) and on initial frontal CXRs from 487 COVID-19 hospitalized patients (external cohort). The discriminatory ability of the model was assessed using receiver operating characteristic (ROC) curves compared to the HCC data from electronic health records, and predicted age and RAF score were compared using correlation coefficient and absolute mean error. The model predictions were used as covariables in logistic regression models to evaluate the prediction of mortality in the external cohort. Predicted comorbidities from frontal CXRs, including diabetes with chronic complications, obesity, congestive heart failure, arrhythmias, vascular disease, and chronic obstructive pulmonary disease, had a total area under ROC curve (AUC) of 0.85 (95% CI: 0.85–0.86). The ROC AUC of predicted mortality for the model was 0.84 (95% CI,0.79–0.88) for the combined cohorts. This model using only frontal CXRs predicted select comorbidities and RAF score in both internal ambulatory and external hospitalized COVID-19 cohorts and was discriminatory of mortality, supporting its potential use in clinical decision making.
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Le, Ngan, James Sorensen, Toan Bui, Arabinda Choudhary, Khoa Luu, and Hien Nguyen. "Enhance Portable Radiograph for Fast and High Accurate COVID-19 Monitoring." Diagnostics 11, no. 6 (June 12, 2021): 1080. http://dx.doi.org/10.3390/diagnostics11061080.

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This work aimed to assist physicians by improving their speed and diagnostic accuracy when interpreting portable CXRs as well as monitoring the treatment process to see whether a patient is improving or deteriorating with treatment. These objectives are in especially high demand in the setting of the ongoing COVID-19 pandemic. With the recent progress in the development of artificial intelligence (AI), we introduce new deep learning frameworks to align and enhance the quality of portable CXRs to be more consistent, and to more closely match higher quality conventional CXRs. These enhanced portable CXRs can then help the doctors provide faster and more accurate diagnosis and treatment planning. The contributions of this work are four-fold. Firstly, a new database collection of subject-pair radiographs is introduced. For each subject, we collected a pair of samples from both portable and conventional machines. Secondly, a new deep learning approach is presented to align the subject-pairs dataset to obtain a pixel-pairs dataset. Thirdly, a new PairFlow approach is presented, an end-to-end invertible transfer deep learning method, to enhance the degraded quality of portable CXRs. Finally, the performance of the proposed system is evaluated by UAMS doctors in terms of both image quality and topological properties. This work was undertaken in collaboration with the Department of Radiology at the University of Arkansas for Medical Sciences (UAMS) to enhance portable/mobile COVID-19 CXRs, to improve the speed and accuracy of portable CXR images and aid in urgent COVID-19 diagnosis, monitoring and treatment.
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Aboul-Enein, Mohamed Saad, William R. C. Knight, Daniel Foley, Luke McKnickle, Harrison Carter, and James A. Gossage. "Are routine post drain removal chest x-rays necessary after oesophagectomy?" International Surgery Journal 7, no. 10 (September 23, 2020): 3187. http://dx.doi.org/10.18203/2349-2902.isj20204111.

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Background: Routine chest X-rays (CXR) are often performed following the removal of chest drains placed during oesophagectomy. CXRs are costly and inconvenient for the patient, often being performed out of working hours. The aim of this study was to evaluate whether routine CXR is necessary following drain removal or if CXRs should only be performed when indicated by the clinical status of the patient.Methods: This was a retrospective study of oesophagectomies performed at a single high volume centre. Routine post chest drain removal CXRs were analyzed and compared to baseline post-operative CXRs. The clinical status of the patient before and after chest drain removal was recorded.Results: 188 patients were identified. 111/188 (59%) had a pleural effusion or pneumothorax on their baseline post-operative CXR. Abnormal findings on post drain removal CXR were common with 72/188 (38.3%) patients having a new or worse pleural effusion or pneumothorax. Only, 5.6% (11/188) of these patients actually developed clinical signs after chest drain removal. Of these, only 2.1% (4/188) required chest drain re-insertion. No patients underwent intervention without showing clinical deterioration. No re-intervention was prompted by CXR finding alone.Conclusions: Routine CXR following chest drain removal is unnecessary. It is safe to only perform CXRs on patients who develop clinical signs.
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Kaviani, Parisa, Mannudeep K. Kalra, Subba R. Digumarthy, Reya V. Gupta, Giridhar Dasegowda, Ammar Jagirdar, Salil Gupta, et al. "Frequency of Missed Findings on Chest Radiographs (CXRs) in an International, Multicenter Study: Application of AI to Reduce Missed Findings." Diagnostics 12, no. 10 (September 30, 2022): 2382. http://dx.doi.org/10.3390/diagnostics12102382.

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Background: Missed findings in chest X-ray interpretation are common and can have serious consequences. Methods: Our study included 2407 chest radiographs (CXRs) acquired at three Indian and five US sites. To identify CXRs reported as normal, we used a proprietary radiology report search engine based on natural language processing (mPower, Nuance). Two thoracic radiologists reviewed all CXRs and recorded the presence and clinical significance of abnormal findings on a 5-point scale (1—not important; 5—critical importance). All CXRs were processed with the AI model (Qure.ai) and outputs were recorded for the presence of findings. Data were analyzed to obtain area under the ROC curve (AUC). Results: Of 410 CXRs (410/2407, 18.9%) with unreported/missed findings, 312 (312/410, 76.1%) findings were clinically important: pulmonary nodules (n = 157), consolidation (60), linear opacities (37), mediastinal widening (21), hilar enlargement (17), pleural effusions (11), rib fractures (6) and pneumothoraces (3). AI detected 69 missed findings (69/131, 53%) with an AUC of up to 0.935. The AI model was generalizable across different sites, geographic locations, patient genders and age groups. Conclusion: A substantial number of important CXR findings are missed; the AI model can help to identify and reduce the frequency of important missed findings in a generalizable manner.
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Yuan, Kuo-Ching, Lung-Wen Tsai, Kevin S. Lai, Sing-Teck Teng, Yu-Sheng Lo, and Syu-Jyun Peng. "Using Transfer Learning Method to Develop an Artificial Intelligence Assisted Triaging for Endotracheal Tube Position on Chest X-ray." Diagnostics 11, no. 10 (October 6, 2021): 1844. http://dx.doi.org/10.3390/diagnostics11101844.

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Endotracheal tubes (ETTs) provide a vital connection between the ventilator and patient; however, improper placement can hinder ventilation efficiency or injure the patient. Chest X-ray (CXR) is the most common approach to confirming ETT placement; however, technicians require considerable expertise in the interpretation of CXRs, and formal reports are often delayed. In this study, we developed an artificial intelligence-based triage system to enable the automated assessment of ETT placement in CXRs. Three intensivists performed a review of 4293 CXRs obtained from 2568 ICU patients. The CXRs were labeled “CORRECT” or “INCORRECT” in accordance with ETT placement. A region of interest (ROI) was also cropped out, including the bilateral head of the clavicle, the carina, and the tip of the ETT. Transfer learning was used to train four pre-trained models (VGG16, INCEPTION_V3, RESNET, and DENSENET169) and two models developed in the current study (VGG16_Tensor Projection Layer and CNN_Tensor Projection Layer) with the aim of differentiating the placement of ETTs. Only VGG16 based on ROI images presented acceptable performance (AUROC = 92%, F1 score = 0.87). The results obtained in this study demonstrate the feasibility of using the transfer learning method in the development of AI models by which to assess the placement of ETTs in CXRs.
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Krupin, V. A., L. A. Klyuchnikov, K. V. Korobov, A. R. Nemets, M. R. Nurgaliev, A. V. Gorbunov, N. N. Naumenko, V. I. Troynov, S. N. Tugarinov, and F. V. Fomin. "MODERNIZED ACTIVE SPECTROSCOPIC DIAGNOSTICS (CXRS) OF THE Т-10 TOKAMAK." Problems of Atomic Science and Technology, Ser. Thermonuclear Fusion 37, no. 4 (2014): 60–70. http://dx.doi.org/10.21517/0202-3822-2014-37-4-60-70.

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Klyuchnikov, L. A., V. A. Krupin, K. V. Korobov, M. R. Nurgaliev, A. R. Nemets, A. Yu Dnestrovskij, N. N. Naumenko, S. N. Tugarinov, S. V. Serov, and D. S. Denschikov. "CAPABILITIES OF SPECTROSCOPIC DIAGNOSTICS CXRS IN T-10 TOKAMAK." Problems of Atomic Science and Technology, Ser. Thermonuclear Fusion 39, no. 1 (2016): 95–104. http://dx.doi.org/10.21517/0202-3822-2016-1-95-104.

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Klyuchnikov, L. A., V. A. Krupin, K. V. Korobov, M. R. Nurgaliev, A. R. Nemets, A. Yu Dnestrovskij, N. N. Naumenko, S. N. Tugarinov, S. V. Serov, and D. S. Denschikov. "CAPABILITIES OF SPECTROSCOPIC DIAGNOSTICS CXRS IN T-10 TOKAMAK." Problems of Atomic Science and Technology, Ser. Thermonuclear Fusion 39, no. 1 (2016): 95–104. http://dx.doi.org/10.21517/0202-3822-2016-39-1-95-104.

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Rajaraman, Sivaramakrishnan, Ghada Zamzmi, Les Folio, Philip Alderson, and Sameer Antani. "Chest X-ray Bone Suppression for Improving Classification of Tuberculosis-Consistent Findings." Diagnostics 11, no. 5 (May 7, 2021): 840. http://dx.doi.org/10.3390/diagnostics11050840.

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Chest X-rays (CXRs) are the most commonly performed diagnostic examination to detect cardiopulmonary abnormalities. However, the presence of bony structures such as ribs and clavicles can obscure subtle abnormalities, resulting in diagnostic errors. This study aims to build a deep learning (DL)-based bone suppression model that identifies and removes these occluding bony structures in frontal CXRs to assist in reducing errors in radiological interpretation, including DL workflows, related to detecting manifestations consistent with tuberculosis (TB). Several bone suppression models with various deep architectures are trained and optimized using the proposed combined loss function and their performances are evaluated in a cross-institutional test setting using several metrics such as mean absolute error (MAE), peak signal-to-noise ratio (PSNR), structural similarity index measure (SSIM), and multiscale structural similarity measure (MS–SSIM). The best-performing model (ResNet–BS) (PSNR = 34.0678; MS–SSIM = 0.9828) is used to suppress bones in the publicly available Shenzhen and Montgomery TB CXR collections. A VGG-16 model is pretrained on a large collection of publicly available CXRs. The CXR-pretrained model is then fine-tuned individually on the non-bone-suppressed and bone-suppressed CXRs of Shenzhen and Montgomery TB CXR collections to classify them as showing normal lungs or TB manifestations. The performances of these models are compared using several performance metrics such as accuracy, the area under the curve (AUC), sensitivity, specificity, precision, F-score, and Matthews correlation coefficient (MCC), analyzed for statistical significance, and their predictions are qualitatively interpreted through class-selective relevance maps (CRMs). It is observed that the models trained on bone-suppressed CXRs (Shenzhen: AUC = 0.9535 ± 0.0186; Montgomery: AUC = 0.9635 ± 0.0106) significantly outperformed (p < 0.05) the models trained on the non-bone-suppressed CXRs (Shenzhen: AUC = 0.8991 ± 0.0268; Montgomery: AUC = 0.8567 ± 0.0870).. Models trained on bone-suppressed CXRs improved detection of TB-consistent findings and resulted in compact clustering of the data points in the feature space signifying that bone suppression improved the model sensitivity toward TB classification.
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Kwack, Won-Gun. "Evaluation of the Daily Change in PaO2/FiO2 Ratio as a Predictor of Abnormal Chest X-rays in Intensive Care Unit Patients Post Mechanical Ventilation Weaning: A Retrospective Cohort Study." Medicina 58, no. 2 (February 17, 2022): 303. http://dx.doi.org/10.3390/medicina58020303.

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Background and Objectives: The routine daily chest X-ray (CXR) strategy is no longer recommended in intensive care unit (ICU) patients. However, it is difficult for intensivists to collectively accept the on-demand CXR strategy because of the ambiguous clinical criteria for conducting CXRs. This study evaluated the predictive value of the change in PaO2/FiO2 (PF ratio) for abnormal CXR findings in ICU patients after mechanical ventilation (MV). Materials and Methods: A retrospective cohort study was conducted between January 2016 and March 2021 on ICU patients with MV who had at least 48 h of MV, and stayed at least 72 h in the ICU post-MV. Routine daily CXRs and daily changes in the PF ratios were investigated during the three days post-MV. Results: The 186 patients included in the study had a median age of 77 years (interquartile range: 65–82), and 116 (62.4%) were men. One hundred and eight (58.1%) patients had abnormal CXR findings, defined as one or more abnormal CXRs among the daily CXRs during the three days post-extubation. The reintubation rate was higher in the abnormal CXR group (p = 0.01). Of the 558 CXRs (normal = 418, abnormal = 140) and PF ratios, the daily change in PF ratio had a significant predictive accuracy for abnormal CXR findings (AUROC = 0.741, p < 0.01). Conclusions: The change in PF ratio (the Youden index point: ≤−23) had a sensitivity of 65.7%, and a specificity of 79.9%. Based on these results, the daily change in the PF ratio could be utilized as a predictive indicator of abnormal CXRs in ICU patients after MV treatment.
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Chen and Cai. "Enhancement of Chest Radiograph in Emergency Intensive Care Unit by Means of Reverse Anisotropic Diffusion-Based Unsharp Masking Model." Diagnostics 9, no. 2 (April 24, 2019): 45. http://dx.doi.org/10.3390/diagnostics9020045.

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In intensive care units (ICUs), supporting devices play an important role, and the placement of these devices must be accurate, such as catheters and tubes. Taking portable chest radiograph (CXRs) for patients in ICU is a standard procedure. However, non-optimized exposure settings and misaligned body positions usually mean that portable CXRs are not in acceptable working condition. The purpose of this study was to enhance ICU CXRs to assist radiologists in the positioning of endotracheal, feeding, and nasogastric tubes in ICU patients. The unsharp masking model (USM) was a classical image enhancement technique. Because of the isotropic diffusion filter applied in this model, USM enhanced the edge information and noise simultaneously. In this paper, we proposed a reverse anisotropic diffusion (RAD)-based USM technique for enhancement of line structures in ICU CXRs. First, a RAD algorithm was applied to replace the Gaussian filter in the classical USM. The RAD algorithm only produced a smoothed image, in which edge information was smoothed while the noise was preserved. Then, the smoothed image was subtracted from the original image to produce the unsharp mask whereby only the edges were retained. Consequently, only edge information was enhanced in the final enhanced image by using the RAD-based USM model. The proposed method was tested for 87 ICU CXRs and the findings indicate that this approach can enhance image edges efficiently while suppressing noise.
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Klink, Richard R., Jason Q. Zhang, and Gerard A. Athaide. "Designing a Customer Experience Management Course." Journal of Marketing Education 42, no. 2 (December 19, 2018): 157–69. http://dx.doi.org/10.1177/0273475318818873.

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Customer experience is the latest battleground for business. Not only is customer experience management (CXM) one of the most promising approaches to marketing, but some observers also contend it is the future of marketing. While practitioners have embraced CXM for its considerable promise, marketing academicians have lagged in developing and disseminating CXM knowledge. Indeed, considerable evidence suggests that customer experiences are falling far short of company aspirations and customer expectations. To further CXM understanding, we conducted a three-credit undergraduate marketing course on CXM. Given the inherent experiential nature of CXM, we enhanced student learning by including a field immersion component. Specifically, on-campus class sessions were supplemented with a weeklong field immersion at Walt Disney World Resort. Empirical and qualitative evidence indicates that our approach fosters CXM understanding. We conclude by offering potential adaptations to our approach, including teaching CXM within existing coursework, curtailing the field immersion component, and making modifications for a graduate-level course.
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Hwang, Eui Jin, Ki Beom Kim, Jin Young Kim, Jae-Kwang Lim, Ju Gang Nam, Hyewon Choi, Hyungjin Kim, Soon Ho Yoon, Jin Mo Goo, and Chang Min Park. "COVID-19 pneumonia on chest X-rays: Performance of a deep learning-based computer-aided detection system." PLOS ONE 16, no. 6 (June 7, 2021): e0252440. http://dx.doi.org/10.1371/journal.pone.0252440.

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Chest X-rays (CXRs) can help triage for Coronavirus disease (COVID-19) patients in resource-constrained environments, and a computer-aided detection system (CAD) that can identify pneumonia on CXR may help the triage of patients in those environment where expert radiologists are not available. However, the performance of existing CAD for identifying COVID-19 and associated pneumonia on CXRs has been scarcely investigated. In this study, CXRs of patients with and without COVID-19 confirmed by reverse transcriptase polymerase chain reaction (RT-PCR) were retrospectively collected from four and one institution, respectively, and a commercialized, regulatory-approved CAD that can identify various abnormalities including pneumonia was used to analyze each CXR. Performance of the CAD was evaluated using area under the receiver operating characteristic curves (AUCs), with reference standards of the RT-PCR results and the presence of findings of pneumonia on chest CTs obtained within 24 hours from the CXR. For comparison, 5 thoracic radiologists and 5 non-radiologist physicians independently interpreted the CXRs. Afterward, they re-interpreted the CXRs with corresponding CAD results. The performance of CAD (AUCs, 0.714 and 0.790 against RT-PCR and chest CT, respectively hereinafter) were similar with those of thoracic radiologists (AUCs, 0.701 and 0.784), and higher than those of non-radiologist physicians (AUCs, 0.584 and 0.650). Non-radiologist physicians showed significantly improved performance when assisted with the CAD (AUCs, 0.584 to 0.664 and 0.650 to 0.738). In addition, inter-reader agreement among physicians was also improved in the CAD-assisted interpretation (Fleiss’ kappa coefficient, 0.209 to 0.322). In conclusion, radiologist-level performance of the CAD in identifying COVID-19 and associated pneumonia on CXR and enhanced performance of non-radiologist physicians with the CAD assistance suggest that the CAD can support physicians in interpreting CXRs and helping image-based triage of COVID-19 patients in resource-constrained environment.
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Andronikou, Savvas, Dirk Johannes Van der Merwe, Pierre Goussard, Robert P. Gie, and Nicolette Tomazos. "Usefulness of lateral radiographs for detecting tuberculous lymphadenopathy in children – confirmation using sagittal CT reconstruction with multiplanar cross-referencing." South African Journal of Radiology 16, no. 3 (September 10, 2012): 87–92. http://dx.doi.org/10.4102/sajr.v16i3.288.

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Background. Diagnosis of pulmonary tuberculosis (PTB) in children remains difficult. Lateral chest radiographs are frequently used to facilitate diagnosis, but interpretation is variable. In this study, lateral chest radiographs (CXRs) are evaluated against sagittal CT reconstructions for the detection of mediastinal lymphadenopathy. Aim. To correlate suspected lymphadenopathy on lateral CXR with sagittal CT reconstructions and determine which anatomical group of lymph nodes contributes to each lateral CXR location. Methods and materials. Thirty TB-positive children’s lateral CXRs were retrospectively reviewed for presence of mediastinal lymphadenopathy in 3 pre-determined locations in relation to the carina: retrocarinal, subcarinal and precarinal. Findings of the CT sagittal reconstructions were then correlated with the CXRs for the presence of lymphadenopathy in the same 3 pre-determined areas across the width of the mediastinum. Axial and coronal CT crossreferencing confirmed the position of the lymphadenopathy. Results. The most frequent locations for lymphadenopathy were the subcarinal (28) and right hilar (25). Sensitivity and specificity values of the CXRs were moderate, with the precarinal region having the best sensitivity and specificity for presence of lymphadenopathy. Contribution to each zonal group on lateral CXR were from multiple anatomical lymph node sites. Conclusion. The precarinal zone on CXR had the best specificity and sensitivity, and represented mainly subcarinal and right hilar lymph node groups. Attention should be paid to this area on lateral CXRs for detecting lymphadenopathy in children with suspected PTB.
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Mwaniki, Paul, Timothy Kamanu, Samuel Akech, and M. J. C. Eijkemans. "Using Machine Learning Methods Incorporating Individual Reader Annotations to Classify Paediatric Chest Radiographs in Epidemiological Studies." Wellcome Open Research 6 (August 25, 2022): 309. http://dx.doi.org/10.12688/wellcomeopenres.17164.2.

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Introduction: Epidemiological studies that involve interpretation of chest radiographs (CXRs) suffer from inter-reader and intra-reader variability. Inter-reader and intra-reader variability hinder comparison of results from different studies or centres, which negatively affects efforts to track the burden of chest diseases or evaluate the efficacy of interventions such as vaccines. This study explores machine learning models that could standardize interpretation of CXR across studies and the utility of incorporating individual reader annotations when training models using CXR data sets annotated by multiple readers. Methods: Convolutional neural networks were used to classify CXRs from seven low to middle-income countries into five categories according to the World Health Organization's standardized methodology for interpreting paediatric CXRs. We compared models trained to predict the final/aggregate classification with models trained to predict how each reader would classify an image and then aggregate predictions for all readers using unweighted mean. Results: Incorporating individual reader's annotations during model training improved classification accuracy by 3.4% (multi-class accuracy 61% vs 59%). Model accuracy was higher for children above 12 months of age (68% vs 58%). The accuracy of the models in different countries ranged between 45% and 71%. Conclusions: Machine learning models can annotate CXRs in epidemiological studies reducing inter-reader and intra-reader variability. In addition, incorporating individual reader annotations can improve the performance of machine learning models trained using CXRs annotated by multiple readers.
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Mwaniki, Paul, Timothy Kamanu, Samuel Akech, and M. J. C. Eijkemans. "Using Machine Learning Methods Incorporating Individual Reader Annotations to Classify Paediatric Chest Radiographs in Epidemiological Studies." Wellcome Open Research 6 (November 12, 2021): 309. http://dx.doi.org/10.12688/wellcomeopenres.17164.1.

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Introduction: Epidemiological studies that involve interpretation of chest radiographs (CXRs) suffer from inter-reader and intra-reader variability. Inter-reader and intra-reader variability hinder comparison of results from different studies or centres, which negatively affects efforts to track the burden of chest diseases or evaluate the efficacy of interventions such as vaccines. This study explores machine learning models that could standardize interpretation of CXR across studies and the utility of incorporating individual reader annotations when training models using CXR data sets annotated by multiple readers. Methods: Convolutional neural networks were used to classify CXRs from seven low to middle-income countries into five categories according to the World Health Organization's standardized methodology for interpreting paediatric CXRs. We compared models trained to predict the final/aggregate classification with models trained to predict how each reader would classify an image and then aggregate predictions for all readers using unweighted mean. Results: Incorporating individual reader's annotations during model training improved classification accuracy by 3.4% (multi-class accuracy 61% vs 59%). Model accuracy was higher for children above 12 months of age (68% vs 58%). The accuracy of the models in different countries ranged between 45% and 71%. Conclusions: Machine learning models can annotate CXRs in epidemiological studies reducing inter-reader and intra-reader variability. In addition, incorporating individual reader annotations can improve the performance of machine learning models trained using CXRs annotated by multiple readers.
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Liu, Chao-Yu, Po-Kuei Hsu, Ka-I. Leong, Chien-Kun Ting, and Mei-Yung Tsou. "Is tubeless uniportal video-assisted thoracic surgery for pulmonary wedge resection a safe procedure?" European Journal of Cardio-Thoracic Surgery 58, Supplement_1 (March 17, 2020): i70—i76. http://dx.doi.org/10.1093/ejcts/ezaa061.

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Abstract OBJECTIVES Tubeless uniportal video-assisted thoracic surgery (VATS), using a uniportal approach and non-intubated anaesthesia while avoiding postoperative chest drain insertion, for patients undergoing thoracoscopic surgery has been demonstrated to be feasible in selected cases. However, to date, the safety of the procedure has not been studied. METHODS We reviewed consecutive patients undergoing non-intubated uniportal VATS for pulmonary wedge resection at 2 medical centres between August 2016 and October 2019. The decision to avoid chest drain insertion was made in selected candidates. For those candidates in whom a tubeless procedure was performed, postoperative chest X-rays (CXRs) were taken on the day of the surgery [operation (OP) day], on postoperative day 1 and 1–2 weeks later. The factors associated with abnormal CXR findings were studied. RESULTS Among 135 attempts to avoid chest drain insertion, 13 (9.6%) patients ultimately required a postoperative chest drain. Among 122 patients in which a tubeless procedure was performed, 26 (21.3%) and 47 (38.5%) had abnormal CXR findings on OP day and postoperative day 1, respectively. Among them, 3 (2.5%) patients developed clinically significant abnormal CXRs and required intercostal drainage. Primary spontaneous pneumothorax was independently associated with a higher risk of postoperative abnormal CXRs. CONCLUSIONS Tubeless uniportal VATS for pulmonary wedge resection can be safely performed in selected patients. Most patients with postoperative abnormal CXRs presented subclinical symptoms that spontaneously resolved; only 2.5% of patients with postoperative abnormal CXRs required drainage.
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Coghlan, Ryan F., Robert C. Olney, Bruce A. Boston, Daniel T. Coleman, Brian Johnstone, and William A. Horton. "Norms for Clinical Use of CXM, a Real-Time Marker of Height Velocity." Journal of Clinical Endocrinology & Metabolism 106, no. 1 (October 9, 2020): e255-e264. http://dx.doi.org/10.1210/clinem/dgaa721.

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Abstract Context Height velocity (HV) is difficult to assess because growth is very slow. The current practice of calculating it from measurements taken at several-month intervals is insufficient for managing children with growth disorders. We identified a bone growth by-product (collagen X biomarker, CXM) in blood that in preliminary analysis in healthy children correlated strongly with conventionally determined HV and displayed a pattern resembling published norms for HV vs age. Objective The goal was to confirm our initial observations supporting the utility of CXM as an HV biomarker in a larger number of individuals and establish working reference ranges for future studies. Design, Settings, and Participants CXM was assessed in archived blood samples from 302 healthy children and 10 healthy adults yielding 961 CXM measurements. A total of 432 measurements were plotted by age, and sex-specific reference ranges were calculated. Serial values from 116 participants were plotted against observed HV. Matched plasma, serum, and dried blood spot readings were compared. Results A correlation of blood CXM with conventional HV was confirmed. Scatter plots of CXM vs age showed a similar pattern to current HV norms, and CXM levels demarcated the pubertal growth spurt both in girls and boys. CXM levels differed little in matched serum, plasma, and dried blood spot samples. Conclusions Blood CXM offers a potential means to estimate HV in real time. Our results establish sex-specific, working reference ranges for assessing skeletal growth, especially over time. CXM stability in stored samples makes it well suited for retrospective studies.
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47

Patrick, Ting, and Kasam Anish. "Applications of convolutional neural networks in chest X-ray analyses for the detection of COVID-19." Annals of Biomedical Science and Engineering 6, no. 1 (January 20, 2022): 001–7. http://dx.doi.org/10.29328/journal.abse.1001015.

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Throughout global efforts to defend against the spread of COVID-19 from late 2019 up until now, one of the most crucial factors that has helped combat the pandemic is the development of various screening methods to detect the presence of COVID-19 as conveniently and accurately as possible. One of such methods is the utilization of chest X-Rays (CXRs) to detect anomalies that are concurrent with a patient infected with COVID-19. While yielding results much faster than the traditional RT-PCR test, CXRs tend to be less accurate. Realizing this issue, in our research, we investigated the applications of computer vision in order to better detect COVID-19 from CXRs. Coupled with an extensive image database of CXRs of healthy patients, patients with non-COVID-19 induced pneumonia, and patients positive with COVID-19, convolutional neural networks (CNNs) prove to possess the ability to easily and accurately identify whether or not a patient is infected with COVID-19 in a matter of seconds. Borrowing and adjusting the architectures of three well-tested CNNs: VGG-16, ResNet50, and MobileNetV2, we performed transfer learning and trained three of our own models, then compared and contrasted their differing precisions, accuracies, and efficiencies in correctly labeling patients with and without COVID-19. In the end, all of our models were able to accurately categorize at least 94% of the CXRs, with some performing better than the others; these differences in performance were largely due to the contrasting architectures each of our models borrowed from the three respective CNNs.
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48

Sahu, Amit K., Anandmoyee Dhar, and Bharat Aggarwal. "Radiographic features of COVID-19 infection at presentation and significance of chest X-ray: Early experience from a super-specialty hospital in India." Indian Journal of Radiology and Imaging 31, S 01 (January 2021): S128—S133. http://dx.doi.org/10.4103/ijri.ijri_368_20.

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Abstract Background: Due to the relative early lockdown in India, relative greater availability of reverse transcription polymerase chain reaction (RT-PCR) testing, and mandate to admit all positive corona virus disease 2019 (COVID-19) patients, the protocol in our hospital is to perform a baseline chest X-ray (CXR) at the time of admission and for follow up. There are currently limited publications demonstrating the radiographic findings and the role of CXR of COVID-19 patients at presentation. Aims: Evaluatethe radiographic findings on CXR in COVID-19 patients at presentation. Recommend a guideline for its judicious use. Settings and Design: Retroprospective study performed on RT-PCR confirmed COVID-19 patients admitted in our hospital between March 31,2020 to May 25, 2020. The study included symptomatic and asymptomatic patients. CXR was performed for218 patients. Materials and Methods: Portable bedside CXR was performed. The CXRs were evaluated by three radiologists to record the findings and grade the disease. All variables were expressed as mean, ranges, counts, and percentages. Results: 157 patients (72%) were symptomatic and 61 (28%) were asymptomatic. 104 CXRs (48%) were abnormal (97 in symptomatic (62%) and fourin asymptomatic (6%)). 74 patients (47%) in the symptomatic group had known comorbidities and of these, 62 (84%) had abnormal CXR. 97 CXRs (93%) had bilateral findings and 87 CXRs (84%) had peripherally predominant abnormalities. The lower zone was the most common area of involvement (73%). Ground glass opacity (GGO) was the most common finding (94%–98 CXRs). Mild disease was seen in 56 (54%). Conclusion: CXR can be used to assess symptomatic COVID-19 patients at presentation and to grade the severity of disease. It may be avoided in asymptomatic patients.
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49

Rao, Vishal, M. S. Priyanka, A. Lakshmi, A. G. J. Faheema, Alex Thomas, Karan Medappa, Anand Subhash, et al. "Predicting COVID-19 pneumonia severity on chest X-ray with convolutional neural network: A retrospective study." Indian Journal of Medical Sciences 72 (December 31, 2020): 132–40. http://dx.doi.org/10.25259/ijms_349_2020.

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Objectives: Radiological lung changes in COVID-19 infections present a noteworthy avenue to develop chest X-ray (CXR) -based testing models to support existing rapid detection techniques. The purpose of this study is to evaluate the accuracy of artificial intelligence (AI) -based screening model employing deep convolutional neural network for lung involvement. Material and Methods: An AI-based screening model was developed with state-of-the-art neural networks using Indian data sets from COVID-19 positive patients by authors of CAIR, DRDO, in collaboration with the other authors. Our dataset was comprised of 1324 COVID-19, 1108 Normal, and 1344 Pneumonia CXR images. Transfer learning was carried out on Indian dataset using popular deep neural networks, which includes DenseNet, ResNet50, and ResNet18 network architectures to classify CXRs into three categories. The model was retrospectively used to test CXRs from reverse transcriptase-polymerase chain reaction (RT-PCR) proven COVID-19 patients to test positive predictive value and accuracy. Results: A total of 460 RT-PCR positive hospitalized patients CXRs in various stages of disease involvement were retrospectively analyzed. There were 248 males (53.92%) and 212 females (46.08%) in the cohort, with a mean age of 50.1 years (range 12–89 years). The commonly observed alterations included lung consolidations, ground-glass opacities, and reticular–nodular opacities. Bilateral involvement was more common compared to unilateral involvement. Of the 460 CXRs analyzed, the model reported 445 CXRs as COVID -19 with an accuracy of 96.73%. Conclusion: Our model, based on a two-level classification decision fusion and output information computation, makes it a robust, accurate and reproducible tool. Based on the initial promising results, our application can be used for mass screening.
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50

Kemp, Oliver J., Daniel J. Watson, Carla L. Swanson-Low, James A. Cameron, and Johannes Von Vopelius-Feldt. "Comparison of chest X-ray interpretation by Emergency Department clinicians and radiologists in suspected COVID-19 infection: a retrospective cohort study." BJR|Open 2, no. 1 (November 2020): 20200020. http://dx.doi.org/10.1259/bjro.20200020.

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Objectives: We describe the inter-rater agreement between Emergency Department (ED) clinicians and reporting radiologists in the interpretation of chest X-rays (CXRs) in patients presenting to ED with suspected COVID-19. Methods: We undertook a retrospective cohort study of patients with suspected COVID-19. We compared ED clinicians’ and radiologists’ interpretation of the CXRs according to British Society of Thoracic Imaging (BSTI) guidelines, using the area under the receiver operator curve (ROC area). Results: CXRs of 152 cases with suspected COVID-19 infection were included. Sensitivity and specificity for ‘classic’ COVID-19 CXR findings reported by ED clinician was 84 and 83%, respectively, with a ROC area of 0.84 (95%CI 0.77 to 0.90). Accuracy improved with ED clinicians’ experience, with ROC areas of 0.73 (95%CI 0.45 to 1.00), 0.81 (95%CI 0.73 to 0.89), 1.00 (95%CI 1.00 to 1.00) and 0.90 (95%CI 0.70 to 1.00) for foundation year doctors, senior house officers, higher speciality trainees and ED consultants, respectively (p < 0.001). Conclusions: ED clinicians demonstrated moderate inter-rater agreement with reporting radiologists according to the BSTI COVID-19 classifications. The improvement in accuracy with ED clinician experience suggests training of junior ED clinicians in the interpretation of COVID-19 related CXRs might be beneficial. Large-scale survey studies might be useful in the further evaluation of this topic. Advances in knowledge: This is the first study to examine inter-rater agreement between ED clinicians and radiologists in regards to COVID-19 CXR interpretation. Further service configurations such as 24-hr hot reporting of CXRs can be guided by these data, as well as an ongoing, nationwide follow-up study.
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