Effects of isoflurane anesthesia on physiological parameters in murine subcutaneous tumor allografts measured via diffuse reflectance spectroscopy

Greening, Gage J.; Miller, Kathryn P.; Spainhour, Caroline R.; Cato, M.; Muldoon, Timothy J.

doi:10.1364/boe.9.002871

Cited by 15 publications

(12 citation statements)

References 56 publications

(63 reference statements)

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“…During radiation treatment, mice were kept under anesthesia using a mixture of isoflurane (1.5% v/v) and oxygen while the entire animal body was covered under lead blocks except the tumor. Greening et al have shown this combination and dose to closely mimic no anesthesia conditions [ 22 ]. Mice were monitored daily, and tumors were excised if (1) tumor volume reached 1500 mm 3 (2) tumor necrosis was observed (3) other health related issues occurred.…”

Section: Methodsmentioning

confidence: 99%

Spectroscopic investigation of radiation-induced reoxygenation in radiation-resistant tumors

et al. 2021

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Fractionated radiation therapy is believed to reoxygenate and subsequently radiosensitize surviving hypoxic cancer cells. Measuring tumor reoxygenation between radiation fractions could conceivably provide an early biomarker of treatment response. However, the relationship between tumor reoxygenation and local control is not well understood. We used noninvasive optical fiber-based diffuse reflectance spectroscopy to monitor radiation-induced changes in hemoglobin oxygen saturation (sO 2 ) in tumor xenografts grown from two head and neck squamous cell carcinoma cell lines – UM-SCC-22B and UM-SCC-47. Tumors were treated with 4 doses of 2 Gy over 2 consecutive weeks and diffuse reflectance spectra were acquired every day during the 2-week period. There was a statistically significant increase in sO 2 in the treatment-responsive UM-SCC-22B tumors immediately following radiation. This reoxygenation trend was due to an increase in oxygenated hemoglobin (HbO 2 ) and disappeared over the next 48 h as sO 2 returned to preradiation baseline values. Conversely, sO 2 in the relatively radiation-resistant UM-SCC-47 tumors increased after every dose of radiation and was driven by a significant decrease in deoxygenated hemoglobin (dHb). Immunohistochemical analysis revealed significantly elevated expression of hypoxia-inducible factor (HIF-1) in the UM-SCC-47 tumors prior to radiation and up to 48 h postradiation compared with the UM-SCC-22B tumors. Our observation of a decrease in dHb, a corresponding increase in sO 2 , as well as greater HIF-1α expression only in UM-SCC-47 tumors strongly suggests that the reoxygenation within these tumors is due to a decrease in oxygen consumption in the cancer cells, which could potentially play a role in promoting radiation resistance.

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Section: Methodsmentioning

confidence: 99%

Spectroscopic investigation of radiation-induced reoxygenation in radiation-resistant tumors

et al. 2021

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“…30 Bounds for C were set to be the minimum and maximum values for volume fraction. 31 Initial conditions did not affect outcomes as long as they were between the lower and upper bounds. Initial and boundary conditions were constant for all validation phantoms and SDSs.…”

Section: Validation Of Lookup-table Inverse Modelmentioning

confidence: 98%

Sampling depth of a diffuse reflectance spectroscopy probe for in-vivo physiological quantification of murine subcutaneous tumor allografts

et al. 2018

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Diffuse reflectance spectroscopy (DRS) is a probe-based spectral biopsy technique used in cancer studies to quantify tissue reduced scattering (μs') and absorption (μa) coefficients and vary in source-detector separation (SDS) to fine-tune sampling depth. In subcutaneous murine tumor allografts or xenografts, a key design requirement is ensuring that the source light interrogates past the skin layer into the tumor without significantly sacrificing signal-to-noise ratio (target of ≥15 dB). To resolve this requirement, a DRS probe was designed with four SDSs (0.75, 2.00, 3.00, and 4.00 mm) to interrogate increasing tissue volumes between 450 and 900 nm. The goal was to quantify percent errors in extracting μa and μs', and to quantify sampling depth into subcutaneous Balb/c-CT26 colon tumor allografts. Using an optical phantom-based experimental method, lookup-tables were constructed relating μa,μs', diffuse reflectance, and sampling depth. Percent errors were <10 % and 5% for extracting μa and μs', respectively, for all SDSs. Sampling depth reached up to 1.6 mm at the first Q-band of hemoglobin at 542 nm, the key spectral region for quantifying tissue oxyhemoglobin concentration. This work shows that the DRS probe can accurately extract optical properties and the resultant physiological parameters such as total hemoglobin concentration and tissue oxygen saturation, from sufficient depth within subcutaneous Balb/c-CT26 colon tumor allografts. Methods described here can be generalized for other murine tumor models. Future work will explore the feasibility of the DRS in quantifying volumetric tumor perfusion in response to anticancer therapies.

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“…Tumor response [1][2][3][4], antibody expression [5,6], and cell metabolism [7,8] are examples of the different situations where researchers have used longitudinal designs to study some physiological response. Because the frequency of the measurements in a longitudinal study is dependent on the biological phenomena of interest and the experimental design of the study, the frequency of such measurements can range from minute intervals to study a short-term response such as anesthesia effects in animals [9], to weekly measurements would be necessary to enroll more subjects. At the same time, if the excluded observations contain insightful information that is not used, their elimination from the analysis may limit the demonstration of significant differences between groups.…”

Section: Introductionmentioning

confidence: 99%

“…Tumor response 1–4 , antibody expression 5,6 , and cell metabolism 7,8 are examples of different situations where researchers have used longitudinal designs to study some physiological response. Because the frequency of the measurements in a longitudinal study is dependent on the biological phenomena of interest and the experimental design of the study, the frequency of such measurements can range from minute intervals to study a short-term response such as anesthesia effects in animals 9 , to weekly measurements to analyze a mid-term response like the evolution of dermatitis symptoms in breast cancer patients 10 , to monthly measurements to study a long-term response such as mouth opening following radiotherapy (RT) in neck cancer patients 11 .…”

Section: Introductionmentioning

confidence: 99%

Generalized additive models to analyze non-linear trends in biomedical longitudinal data using R: Beyond repeated measures ANOVA and Linear Mixed Models

Mundo

Tipton

Muldoon

2021

Preprint

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In biomedical research, the outcome of longitudinal studies has been traditionally analyzed using the repeated measures analysis of variance (rm-ANOVA) or more recently, linear mixed models (LMEMs). Although LMEMs are less restrictive than rm-ANOVA in terms of correlation and missing observations, both methodologies share an assumption of linearity in the measured response, which results in biased estimates and unreliable inference when they are used to analyze data where the trends are non-linear, which is a common occurrence in biomedical research. In contrast, generalized additive models (GAMs) relax the linearity assumption, and allow the data to determine the fit of the model while permitting missing observations and different correlation structures. Therefore, GAMs present an excellent choice to analyze non-linear longitudinal data in the context of biomedical research. This paper summarizes the limitations of rm-ANOVA and LMEMs and uses simulated data to visually show how both methods produce biased estimates when used on non-linear data. We also present the basic theory of GAMs, and using trends of oxygen saturation in tumors reported in the biomedical literature, we simulate example longitudinal data (2 treatment groups, 10 subjects per group, 6 repeated measures for each group) to demonstrate how these models can be computationally implemented. We show that GAMs are able to produce estimates that are consistent with the trends of biomedical non-linear data even in the case when missing observations exist (with 40% of the observations missing), allowing reliable inference from the data. To make this work reproducible, the code and data used in this paper are available at: https://github.com/aimundo/GAMs-biomedical-research.

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Effects of isoflurane anesthesia on physiological parameters in murine subcutaneous tumor allografts measured via diffuse reflectance spectroscopy

Cited by 15 publications

References 56 publications

Spectroscopic investigation of radiation-induced reoxygenation in radiation-resistant tumors

Spectroscopic investigation of radiation-induced reoxygenation in radiation-resistant tumors

Sampling depth of a diffuse reflectance spectroscopy probe for in-vivo physiological quantification of murine subcutaneous tumor allografts

Generalized additive models to analyze non-linear trends in biomedical longitudinal data using R: Beyond repeated measures ANOVA and Linear Mixed Models

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