scholarly journals The genetic basis of susceptibility to leukemia induction in mice by 3-methylcholanthrene applied percutaneously.

1978 ◽  
Vol 147 (2) ◽  
pp. 459-469 ◽  
Author(s):  
M L Duran-Reynals ◽  
F Lilly ◽  
A Bosch ◽  
K J Blank
Keyword(s):  
Genetic Basis ◽  
Tumor Response ◽  
Malignant Tumors ◽  
Skin Tumors ◽  
Skin Tumor ◽  
Aryl Hydrocarbon Hydroxylase ◽  
Aryl Hydrocarbon ◽  
High Incidence ◽  
High Level ◽  
Skin Painting

Susceptibility to leukemia induction in mice by skin painting with 3-methylcholanthrene (MCA) is strain-specific, occurring only in strains relatively resistant to MCA-induced skin tumors. The Ah locus, which has a dominant allele (Ahb) for inducibility of the aryl hydrocarbon hydroxylase (AHH) enzyme system and a recessive allele (Ahd) for noninducibility, appears to be the major determinant of this trait. MCA-painted mice of strains and crosses carrying the Ahb allele usually show a high incidence of skin tumors (papillomas which may evolve into malignant tumors) and little or no leukemia, whereas in mice homozygous for the Ahd allele the treatment usually induces a high incidence of leukemia and few or no skin tumors. Among mice of a segregating backcross generation including both Ahb/Ahd heterozygotes and Ahd homozygotes, the occurrence of skin tumors was correlated directly with AHH inducibility and inversely with the leukemic response. Mice of Ahb strains with a high level of endogenous murine leukemia (MuLV) expression (C58, PL) show a much weaker skin tumor response than expected but no increase in leukemia incidence, and this observation tends to confirm the previous finding that MuLV infection of mice of low-MuLV strains results in reduced susceptibility to MCA tumorigenesis.

Aryl hydrocarbon hydroxylase induction levels in patients with malignant tumors associated with smoking

1984 ◽  
Vol 108 (3) ◽  
pp. 286-289 ◽  
Author(s):  
Rolf Korsgaard ◽  
Erik Trell ◽  
Bo G. Simonsson ◽  
G�ran Stiksa ◽  
Lars Janzon ◽  
...  
Keyword(s):  
Malignant Tumors ◽  
Aryl Hydrocarbon Hydroxylase ◽  
Aryl Hydrocarbon

scholarly journals THE ALGORITHM OF PROVIDING MEDICAL PREVENTIVE CARE OF PATIENTS OF RISK GROUP OF DEVELOPMENT OF MALIGNANT TUMORS OF SKIN

2019 ◽  
Vol 61 (5) ◽  
pp. 257-262
Author(s):  
M. A. Ufimtseva ◽  
Alexandra S. Shubina ◽  
N. L. Struin ◽  
V. V. Petkau ◽  
D. E. Emel’Yanov ◽  
...  
Keyword(s):  
Medical Care ◽  
Preventive Care ◽  
Malignant Tumors ◽  
Risk Group ◽  
Risk Groups ◽  
Skin Tumors ◽  
Skin Tumor ◽  
Visual Localization ◽  
Malignant Skin Tumors ◽  
Malignant Skin

The malignant skin tumors are neoplasms of visual localization and their diagnostic is extremely complicated because of diversity of their clinical forms. Among all skin malignant tumors, a melanoma holds a particular place being socially important problem because of high level of lethality related to significant metastatic potential of tumor and lower efficiency of therapy of later forms of disease. The article describes development and implementation of algorithm of rendering medical preventive care to patients of risk group of development of malignant skin tumor on the basis of investigation of actual epidemiological situation with morbidity of basalioma, melanoma, squamous cell carcinoma of skin in population of the Sverdlovsk region during 2000-2015 and detection of factors conditioning late diagnostic of malignant skin tumors. According to actual epidemiological data, an increasing of morbidity of both melanoma and other malignant skin tumors is observed in the Russian Federation on the whole and in the Sverdlovsk region. The article pays attention to that physicians of various specialties are to timely send patients of risk groups or with «suspicious» neoplasms to dermatovenerologist to specify character of neoplasm and to resolve issue concerning necessity of dispensary observation. The necessity is noted of tighter interaction of dermatoverologists and oncologists. Thereby, implementation of algorithm of rendering medical care to patients of risk groups of malignant skin tumors in the Sverdlovsk region promotes increasing of quality and accessibility of medical care to population, earlier detection of patients, decreasing of load of oncologic service by non-profile patients.

NEW CHARACTERIZATION METHODOLOGY FOR SKIN TUMORS CLASSIFICATION

2010 ◽  
Vol 10 (03) ◽  
pp. 467-477 ◽  
Author(s):  
M. MESSADI ◽  
A. BESSAID ◽  
A. TALEB-AHMED
Keyword(s):  
Neural Network ◽  
Benign Tumor ◽  
Malignant Tumors ◽  
Skin Tumors ◽  
Skin Tumor ◽  
Benign Lesions ◽  
Malignant Skin ◽  
Textural Parameters ◽  
Tumors Classification ◽  
Related Mortality

Our objective in this paper is to introduce the efficacies of texture in the interpretation of color skin images. Melanoma is the most malignant skin tumor, growing in melanocytes, the cells responsible for pigmentation. This type of cancer is nowadays increasing rapidly; its related mortality rate increases by more modest and inversely proportional to the thickness of the tumor. This rate can be decreased by an earlier detection and better prevention. Using the features of skin tumors, such as color, symmetry, and border regularity, an attempt is made to determinate if the skin tumor is a melanoma or a benign tumor. In this work, we are interested by adding to form parameters such as the asymmetry (A) and the shape irregularities of skin tumors (B), the textural parameters to estimate colors in dermatoscopic images. In this case, the images are analyzed using textural parameters computed in several directions. These parameters and the form parameters are added to obtain a better classification results. A statistical analysis is performed over these ratios to select the most highly discriminating textural parameters. The method has been tested successfully on 144 images and we found significant differences between the lesions (melanoma and benign). Finally, these parameters (form and parameters of texture selected) are only use to classify the benign and malignancy of the skin lesion. A multilayer neural network is employed to differentiate between malignant tumors and benign lesions.

Statistical analysis of distribution, dynamics and etiological structure of bovine diseases in Vologda region

2020 ◽  
pp. 3-4
Author(s):  
Oksana B. Badeeva ◽  
Keyword(s):  
Respiratory Diseases ◽  
Birth Rate ◽  
Statistical Data ◽  
Delayed Diagnosis ◽  
Specific Weight ◽  
Gastrointestinal Diseases ◽  
Vologda Region ◽  
High Incidence ◽  
Young Cattle ◽  
High Level

Statistical data of livestock for 30 years is reflected in the article. Author used the materials of the state veterinary reporting. A comparative analysis of the number, incidence and death rate of adult animals and young cattle for two five-year periods (2001-2005 and 2014-2018). the data of the analysis of veterinary statistical reports for 2018 on the specific weight of the large horned cattle and age dynamics of calves in farms of the Vologda region are shown. A significant decrease in livestock of the large horned cattle by 56.3% (from 1990 to 2018) is shown in the analysis of the data. Over the five years 2014-2018, there was a decrease in the number of the large horned cattle by 31.3%, the birth rate of calves - by 26.2%, and the incidence of calves - by 12.3% and the mortality rate decreased by 3.3%. Despite the decline in the number of livestock, in 2018 there is a high incidence of animal diseases (49.6%). The highest incidence rate was observed among calves under 10 days of age 43.3%, 31.7% - from 11 to 30 days, 15.8% - from one to three months, 6.5% - from three to six months and 2.7% - from 6 to 12 months. Of the total number of sick calves in 2018, 63.2% had gastrointestinal diseases, and death for this reason is 49.6% of the total number of victims. Respiratory diseases affect 21.8% of young animals, and death due to respiratory diseases is 18.2%. Analysis of statistical data showed that, despite the complex of veterinary and sanitary measures, the incidence and death of calves remain at a high level. This can be explained by delayed diagnosis and low therapeutic effectiveness in gastrointestinal and respiratory diseases of cattle.

MALIGNANT TUMORS ASSOCIATED WITH METABOLIC SYNDROME: THE EXPRESSION PROFILE OF THE TRANSCRIPTIONAL AND GROWTH FACTORS

2017 ◽  
Vol 63 (6) ◽  
pp. 817-823
Author(s):  
Natalya Yunusova ◽  
Irina Kondakova ◽  
Sergey Afanasev ◽  
Larisa Kolomiets ◽  
Alena Chernyshova
Keyword(s):  
Quality Of Life ◽  
Metabolic Syndrome ◽  
Growth Factors ◽  
Cell Motility ◽  
Expression Profile ◽  
Malignant Tumors ◽  
Molecular Targets ◽  
Factors Associated ◽  
High Incidence

The study of the pathogenetic features of malignant tumors associated with metabolic syndrome (MS) is relevant because of high incidence of these tumors. Investigations of the mechanisms of involvement of MS in the pathogenesis of cancer reasonably supplemented by the study of transcription and growth factors associated with energy imbalance of the cell and involved in proliferation, apoptosis, angiogenesis, cell motility and inflammation. More research is needed to identify the most promising molecular targets for therapy of malignant tumors associated with MS with a view to increasing the survival and quality of life of these patients.

Lung nodule classification using combination of CNN, second and higher order texture features

2021 ◽  
pp. 1-9
Author(s):  
Amrita Naik ◽  
Damodar Reddy Edla
Keyword(s):  
Malignant Tumors ◽  
Early Stage ◽  
Lung Nodule ◽  
Texture Features ◽  
Svm Classifier ◽  
Softmax Classifier ◽  
Spatial Features ◽  
High Level ◽  
Learning Architectures ◽  
Nodule Classification

Lung cancer is the most common cancer throughout the world and identification of malignant tumors at an early stage is needed for diagnosis and treatment of patient thus avoiding the progression to a later stage. In recent times, deep learning architectures such as CNN have shown promising results in effectively identifying malignant tumors in CT scans. In this paper, we combine the CNN features with texture features such as Haralick and Gray level run length matrix features to gather benefits of high level and spatial features extracted from the lung nodules to improve the accuracy of classification. These features are further classified using SVM classifier instead of softmax classifier in order to reduce the overfitting problem. Our model was validated on LUNA dataset and achieved an accuracy of 93.53%, sensitivity of 86.62%, the specificity of 96.55%, and positive predictive value of 94.02%.

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