scholarly journals Multivariate Analysis Applied to Polymer Imaging Data Obtained by Near-Field Infrared Microscopy

2017 ◽  
Vol 15 (0) ◽  
pp. 19-24 ◽  
Author(s):  
Takayuki Yamagishi ◽  
Kenta Honobe ◽  
Satoka Aoyagi ◽  
Mayumi Okawa ◽  
Tomoko Kawashima
Keyword(s):  
Multivariate Analysis ◽  
Near Field ◽  
Imaging Data ◽  
Infrared Microscopy

scholarly journals Relative Frequency and Risk Factors of COVID-19 Related Headache in a Sample of Egyptian Population: A Hospital-Based Study

Pain Medicine ◽  
2021 ◽  
Author(s):  
Mona Hussein ◽  
Wael Fathy ◽  
Ragaey A Eid ◽  
Hoda M Abdel-Hamid ◽  
Ahmed Yehia ◽  
...  
Keyword(s):  
Multivariate Analysis ◽  
Platelet Count ◽  
Relative Frequency ◽  
Clinical Laboratory ◽  
Primary Headache ◽  
Female Gender ◽  
Imaging Data ◽  
Laboratory Findings ◽  
Neutrophil Lymphocyte Ratio ◽  
The Impact

Abstract Objectives Headache is considered one of the most frequent neurological manifestations of coronavirus disease 2019 (COVID-19). This work aimed to identify the relative frequency of COVID-19-related headache and to clarify the impact of clinical, laboratory findings of COVID-19 infection on headache occurrence and its response to analgesics. Design Cross-sectional study. Setting Recovered COVID-19 patients. Subjects In total, 782 patients with a confirmed diagnosis of COVID-19 infection. Methods Clinical, laboratory, and imaging data were obtained from the hospital medical records. Regarding patients who developed COVID-19 related headache, a trained neurologist performed an analysis of headache and its response to analgesics. Results The relative frequency of COVID-19 related headache among our sample was 55.1% with 95% confidence interval (CI) (.516–.586) for the estimated population prevalence. Female gender, malignancy, primary headache, fever, dehydration, lower levels of hemoglobin and platelets and higher levels of neutrophil/lymphocyte ratio (NLR) and CRP were significantly associated with COVID-19 related headache. Multivariate analysis revealed that female gender, fever, dehydration, primary headache, high NLR, and decreased platelet count were independent predictors of headache occurrence. By evaluating headache response to analgesics, old age, diabetes, hypertension, primary headache, severe COVID-19, steroid intake, higher CRP and ferritin and lower hemoglobin levels were associated with poor response to analgesics. Multivariate analysis revealed that primary headache, steroids intake, moderate and severe COVID-19 were independent predictors of non-response to analgesics. Discussion Headache occurs in 55.1% of patients with COVID-19. Female gender, fever, dehydration, primary headache, high NLR, and decreased platelet count are considered independent predictors of COVID-19 related headache.

Scanning near-field infrared microscopy based on tapered silver–halide probes

2004 ◽  
Vol 84 (4) ◽  
pp. 637-639 ◽  
Author(s):  
P. Ephrat ◽  
K. Roodenko ◽  
L. Nagli ◽  
A. Katzir
Keyword(s):  
Near Field ◽  
Silver Halide ◽  
Infrared Microscopy

scholarly journals Toward Mid-Infrared, Subdiffraction, Spectral-Mapping of Human Cells and Tissue: SNIM (Scanning Near-Field Infrared Microscopy) Tip Fabrication

2016 ◽  
Vol 34 (4) ◽  
pp. 1212-1219 ◽  
Author(s):  
Giorgos S. Athanasiou ◽  
Johanna Ernst ◽  
David Furniss ◽  
Trevor M. Benson ◽  
Jasbinder Chauhan ◽  
...  
Keyword(s):  
Near Field ◽  
Human Cells ◽  
Infrared Microscopy ◽  
Spectral Mapping ◽  
Mid Infrared

scholarly journals Evaluation of EELS spectrum imaging data by spectral components and factors from multivariate analysis

Microscopy ◽  
2017 ◽  
Vol 67 (suppl_1) ◽  
pp. i133-i141 ◽  
Author(s):  
Siyuan Zhang ◽  
Christina Scheu
Keyword(s):  
Multivariate Analysis ◽  
Imaging Data ◽  
Sn Doping ◽  
Si Substrates ◽  
Spectral Components ◽  
Quantitative Analyses ◽  
Spectrum Imaging ◽  
Doping Profiles ◽  
Electron Energy Loss ◽  
Background Models

Abstract Multivariate analysis is a powerful tool to process spectrum imaging datasets of electron energy loss spectroscopy. Most spatial variance of the datasets can be explained by a limited numbers of components. We explore such dimension reduction to facilitate quantitative analyses of spectrum imaging data, supervising the spectral components instead of spectra at individual pixels. In this study, we use non-negative matrix factorization to decompose datasets from Fe2O3 thin films with different Sn doping profiles on SnO2 and Si substrates. Case studies are presented to analyse spectral features including background models, signal integrals, peak positions and widths. Matlab codes are written to guide microscopists to perform these data analyses.

Apertureless Scanning Near-Field Infrared Microscopy of a Rough Polymeric Surface

Langmuir ◽  
2001 ◽  
Vol 17 (9) ◽  
pp. 2774-2781 ◽  
Author(s):  
Boris B. Akhremitchev ◽  
Steven Pollack ◽  
Gilbert C. Walker
Keyword(s):  
Near Field ◽  
Infrared Microscopy ◽  
Polymeric Surface
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