A panel of criteria for comprehensive assessment of severity of ultraviolet B radiation-induced non-melanoma skin cancers in SKH-1 mice

Bazin, M.; Purohit, Nupur K.; Merlin, Marine A.; Shah, Girish M.

doi:10.1016/j.jphotobiol.2020.111847

Cited by 7 publications

(9 citation statements)

References 31 publications

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“…The semi-axis of ellipsoidal bubbles is denoted as a = R b , b = λ R b , and c = β′λ R b . The approximate value of ellipsoid surface area SA can be calculated by using the Knud Thomsen formula (the error is 1.061%) normalS normalA = 4 · π · true( false( a · b false) 1.6075 + false( a · c false) 1.6075 + false( b · c false) 1.6075 3 true) 1 / 1.6075 where β′ is the deformation factor of ellipsoidal bubbles, assuming that ellipsoidal bubbles do not deform, β′ = 1. Therefore, the surface area can be calculated by the following equation normalS normalA = 4 · π · true( 2 false( λ R normalb 2 false) 1.6075 + false( λ 2 R normalb 2 false) 1.6075 3 true) 1 / 1.6075 …”

Section: Resultsmentioning

confidence: 99%

“…The semi-axis of ellipsoidal bubbles is denoted as a = R b , b = λ R b , and c = β′λ R b . The approximate value of ellipsoid surface area SA can be calculated by using the Knud Thomsen formula (the error is 1.061%) where β′ is the deformation factor of ellipsoidal bubbles, assuming that ellipsoidal bubbles do not deform, β′ = 1. Therefore, the surface area can be calculated by the following equation …”

Section: Resultsmentioning

confidence: 99%

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Prediction of Bubble Size Distribution above the Bubble-Breaking Plate

Wang

Zeng

et al. 2023

Ind. Eng. Chem. Res.

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The chord length distribution (CLD) of bubbles in the region above the bubble-breaking plate in the scrubbing−cooling chamber was studied experimentally using a conductivity probe. The predictive correlations of the shape parameters were established by fitting the CLD with a distribution function. The bubble shape was discussed, and the CLD was analytically converted to the size distribution of the direct contact of the probe. The results showed that the CLD has specific right-skewed characteristics, and the Rayleigh distribution has the best-fitting effect on the CLD. The correlation equation of the shape parameter k of the Rayleigh distribution concerning the Fr number, local gas holdup ε g , and dimensionless radius r/R was established. The predicted bubble size distributions (BSDs) based on ellipsoids are in good morphological agreement with the experimental size distributions. The relative error between the calculated and experimental values of the gas−liquid interface area was within ±20%, which verified the accuracy of the prediction of the BSD.

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Section: Resultsmentioning

confidence: 99%

“…The semi-axis of ellipsoidal bubbles is denoted as a = R b , b = λ R b , and c = β′λ R b . The approximate value of ellipsoid surface area SA can be calculated by using the Knud Thomsen formula (the error is 1.061%) where β′ is the deformation factor of ellipsoidal bubbles, assuming that ellipsoidal bubbles do not deform, β′ = 1. Therefore, the surface area can be calculated by the following equation …”

Section: Resultsmentioning

confidence: 99%

Prediction of Bubble Size Distribution above the Bubble-Breaking Plate

Wang

Zeng

et al. 2023

Ind. Eng. Chem. Res.

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“…The tissue was fixed in neutral buffered formalin, paraffin embedded, and H&E stained using standard protocols. Lesions were scored blindly by our histopathologist using published criteria 27,28 . Please see Supplementary Materials and Methods for further information.…”

Section: Methodsmentioning

confidence: 99%

“…Tumor burden of each mouse was assessed in two ways: 1) histologic evaluation of each harvested lesion (>2mm) and 2) weekly caliper measurements and subsequent calculation of a panel of parameters according to 28 : tumor volume, 2D tumor area, multiplicity (average number of tumors per mouse) and Knud Thomsen (KT) Three-Dimensional (3D) Surface (apportions optimal weightage to the three tumor dimensions) size. Table 2 shows a summary of all histologic observations (full data, Table S1) where fully and microinvasive tumors were combined as "invasive SCC" and preneoplastic and premalignant tumors were aggregated as "premalignant SCC".…”

Section: Uv-induced Tumor Severity and Burden Depends On Treatmentmentioning

confidence: 99%

Microbiome modulates immunotherapy response in cutaneous squamous cell carcinoma

Voigt

Walter

Prieto

et al. 2023

Preprint

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The gut microbiome is increasingly recognized to alter cancer risk, progression, and response to treatments such as immunotherapy, especially in cutaneous melanoma. However, whether the microbiome influences immune checkpoint inhibitor (ICI) immunotherapy response to non-melanoma skin cancer has not yet been defined. As squamous cell carcinomas (SCC) are in closest proximity to the skin microbiome, we hypothesized that the skin microbiome, which regulates cutaneous immunity, might affect SCC-associated anti-PD1 immunotherapy treatment response. We used ultraviolet radiation to induce SCC in SKH1 hairless mice. We then treated the mice with broad-band antibiotics to deplete the microbiome, followed by colonization by candidate skin and gut bacteria or persistent antibiotic treatment, all in parallel with ICI treatment. We longitudinally monitored skin and gut microbiome dynamics by 16S rRNA gene sequencing, and tumor burden by periodic tumor measurements and histologic assessment. Our study revealed that antibiotics-induced abrogation of the microbiome reduced tumor burden, suggesting a functional role of the microbiome in non-melanoma skin cancer therapy response.

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“…Bazin et al 9 inspected the impediments of the four models explicitly inevitability, grouping, locale, and volume of tumors for splendid radiation—began non‐melanoma skin malignancies, and recommended refinements in evaluating amazing quality. The producers propose the utilization of three additional standards, explicitly the Knud Thomsen tridimensional surface that courses ideal weightage to three tumor assessments, bit by bit capacity of new tumors, and tumor improvement rate to uncover commencement and headway of tumors before the plan and late season of non‐melanoma skin undermining advancements movement, autonomously.…”

Section: Related Workmentioning

confidence: 99%

An evolutionary algorithm based feature selection and fuzzy rule reduction technique for the prediction of skin cancer

Jha

Mehta

2021

Concurrency and Computation

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Summary In current years, the death rate from skin cancers (SCs) tends to develop pretty. Various research verified that SC rank third as a deadliest disease, after breast and lung cancer. It will become vital to diagnose this malignancy at an early stage. The objective of this research is to mix machine learning and soft computing techniques to gain higher accuracy within the prediction of SC. To play out the exploration work, we utilized two data sets, one from “Save Life Hospital,” India, and the other is the UCI repository skin cancer data set. In this article, three meta‐heuristic algorithms, the FS_GA, the FS_PSO, and the FS_ACO, were used to select the best features from the data set provided to it. The AFRG_algorithm generates a set of fuzzy rules automatically and the RR_algorithm reduces certain fuzzy rules from the fuzzy system. For the SCC_dataset, the end accuracy obtained was 97.67%, 98.45%, and 99.22%. For the UCI_dataset, the end accuracy obtained was 98.81%, 99.72%, and 99.67%. Experimental results on the used datasets show that the proposed method strikingly improves the forecast exactitude of skin malignancy.

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A panel of criteria for comprehensive assessment of severity of ultraviolet B radiation-induced non-melanoma skin cancers in SKH-1 mice

Cited by 7 publications

References 31 publications

Prediction of Bubble Size Distribution above the Bubble-Breaking Plate

Prediction of Bubble Size Distribution above the Bubble-Breaking Plate

Microbiome modulates immunotherapy response in cutaneous squamous cell carcinoma

An evolutionary algorithm based feature selection and fuzzy rule reduction technique for the prediction of skin cancer

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