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Sunburn
Sunburn
Sunburn is the inflammatory response of the skin to ultraviolet (UV) radiation exposure.
It is characterized by reddening, pain, swelling, and often blistering of the affected skin.
Sunburn can range from mild irritation to severe tissue damage, depending on the duration and intensity of UV exposure.
Factors such as skin type, time of day, and geographic location can influence an individual's susceptibility to sunburn.
Proper sun protection, including the use of sunscreen, protective clothing, and avoidance of peak UV hours, can help prevent sunburn and reduce the risk of long-term skin damage.
Effectvie management of sunburn may involve pain relief, hydration, and in severe cases, medical treatment.
Understanding the mechanisms and risk factors associated with sunburn is crucial for maintaining skin health and reducing the burden of UV-related skin conditions.
It is characterized by reddening, pain, swelling, and often blistering of the affected skin.
Sunburn can range from mild irritation to severe tissue damage, depending on the duration and intensity of UV exposure.
Factors such as skin type, time of day, and geographic location can influence an individual's susceptibility to sunburn.
Proper sun protection, including the use of sunscreen, protective clothing, and avoidance of peak UV hours, can help prevent sunburn and reduce the risk of long-term skin damage.
Effectvie management of sunburn may involve pain relief, hydration, and in severe cases, medical treatment.
Understanding the mechanisms and risk factors associated with sunburn is crucial for maintaining skin health and reducing the burden of UV-related skin conditions.
Most cited protocols related to «Sunburn»
Cancer of Skin
Child
Eye Color
Gender
Hair Color
Health Risk Assessment
Parent
Population at Risk
Skin Pigmentation
Sunburn
Arm, Upper
Cheek
Erythema
Face
Forearm
Light
Medical Devices
Melanins
Pressure
Skin
Skin Pigmentation
Spectrophotometry
Sunburn
Cancer of Skin
Child
Ethnicity
Parent
Sunburn
To assess beliefs about actual and mythical cancer causes, participants were presented with the closed risk factor questions of the CAM [32] and the CAM—MYthical Causes Scale (CAM-MYCS) [33] . These measures have both been validated using UK populations.
The closed risk factor questions of the CAM ask about 11 known cancer risk factors (active smoking; passive smoking; any alcohol consumption; low fruit and vegetable consumption; any red/processed meat consumption; being overweight; sunburnt more than once as a child; being aged 70 years or older; having a relative with cancer; having an infection with HPV; low physical activity). The CAM-MYCS measure asks about 12 factors commonly believed to cause cancer for which there is no scientific evidence (drinking from plastic bottles; eating food containing artificial sweeteners; eating genetically modified food; eating food containing additives; using microwave ovens; using aerosol containers; using mobile phones; using cleaning products; living near power lines; feeling stressed; physical trauma; exposure to electromagnetic frequencies, i.e. non-ionizing radiation of low and high frequencies such as WiFi and Radio/TV frequencies). For both the CAM and CAM-MYCS, participants are asked ‘How much do you agree that each of these can increase a person's chance of developing cancer?’ with response options on a five-point Likert scale (strongly disagree, disagree, unsure, agree, strongly agree).
For the purposes of this analysis, responses were coded as follows. CAM and CAM-MYCS items were dichotomised into ‘correct’ (strongly agree/agree for CAM; strongly disagree/disagree for CAM-MYCS) and ‘incorrect’ (unsure/disagree/strongly disagree for CAM; unsure/agree/strongly agree for CAM-MYCS) responses. This resulted in a total score of 0–11 and 0–12 for the CAM and CAM-MYCS, respectively. The total score was converted to a ‘percentage correct’ (0–100) score, using the percent of maximum possible method to ensure comparability of both scales. The dichotomised ‘correct’ CAM and CAM-MYCS responses were also added together, resulting in a 0–23 CAM total score, converted into ‘percentage correct’ (0–100) score as before. Because data were approximately normally distributed, no further conversions were required.
The closed risk factor questions of the CAM ask about 11 known cancer risk factors (active smoking; passive smoking; any alcohol consumption; low fruit and vegetable consumption; any red/processed meat consumption; being overweight; sunburnt more than once as a child; being aged 70 years or older; having a relative with cancer; having an infection with HPV; low physical activity). The CAM-MYCS measure asks about 12 factors commonly believed to cause cancer for which there is no scientific evidence (drinking from plastic bottles; eating food containing artificial sweeteners; eating genetically modified food; eating food containing additives; using microwave ovens; using aerosol containers; using mobile phones; using cleaning products; living near power lines; feeling stressed; physical trauma; exposure to electromagnetic frequencies, i.e. non-ionizing radiation of low and high frequencies such as WiFi and Radio/TV frequencies). For both the CAM and CAM-MYCS, participants are asked ‘How much do you agree that each of these can increase a person's chance of developing cancer?’ with response options on a five-point Likert scale (strongly disagree, disagree, unsure, agree, strongly agree).
For the purposes of this analysis, responses were coded as follows. CAM and CAM-MYCS items were dichotomised into ‘correct’ (strongly agree/agree for CAM; strongly disagree/disagree for CAM-MYCS) and ‘incorrect’ (unsure/disagree/strongly disagree for CAM; unsure/agree/strongly agree for CAM-MYCS) responses. This resulted in a total score of 0–11 and 0–12 for the CAM and CAM-MYCS, respectively. The total score was converted to a ‘percentage correct’ (0–100) score, using the percent of maximum possible method to ensure comparability of both scales. The dichotomised ‘correct’ CAM and CAM-MYCS responses were also added together, resulting in a 0–23 CAM total score, converted into ‘percentage correct’ (0–100) score as before. Because data were approximately normally distributed, no further conversions were required.
Artificial Sweeteners
Child
Electromagnetics
Factor XII
Food
Food, Genetically Modified
Food Additives
Fruit
Malignant Neoplasms
Microwaves
MYC protein, human
Papillomavirus Infections, Human
Physical Examination
Population Group
Radiation, Nonionizing
Red Meat
Sunburn
Vegetables
Wounds and Injuries
All animal procedures have been approved by the University of Chicago Institutional Animal Care and Use Committee. Hairless SKH-1 mice were obtained from Charles River. Mice were exposed to UVB (100 mJ/cm2, dose selected to avoid visible sunburn) dorsally or sham-irradiated, three times a week for up to 25 weeks, to monitor tumor formation and growth. One hour prior to each UVB irradiation, mice were treated with vehicle (acetone), AICAR (1 μmol) or metformin (2 μmol). In UVB-irradiated tumor-bearing mice, metformin was given either topically (2 μmol) or by gavage (300 mg/kg). Mouse skin samples were fixed in formalin for histological analysis or immunohistochemical analysis for Ki67-positive cells (Immunohistochemistry Core facility), or snap-frozen for immunoblotting analysis. Mice were housed five animals per cage, and there was no evidenceof dorsal wounds caused by fighting or sunburn.
Acetone
AICA ribonucleotide
Animals
Cells
Formalin
Freezing
Immunoblotting
Immunohistochemistry
Institutional Animal Care and Use Committees
Metformin
Mice, Hairless
Mus
Neoplasms
Rivers
Skin
Sunburn
Tube Feeding
Wounds
Most recents protocols related to «Sunburn»
Example 14
Variables tested include: concentration of HA, concentration of zinc oxide, concentration of titanium dioxide, addition of vitamin C, and serum preparation method.
A serum of the present disclosure can be made with from about 0.25% to about 10% sodium hyaluronate (increasing % results in more viscous serum). 0.5% to about 10% silk solutions can be used to prepare a serum of the present disclosure. A serum of the present disclosure can be clear and have a yellow tinted color. A serum of the present disclosure can have a pH=6. A serum of the present disclosure can have a lubricious texture that is rubbed in easily without residue.
Concentration of HA:
Hyaluronic acid (Sodium Hyaluronate) was tested as an ingredient in the UV silk serum due to its hygroscopic properties and widely accepted use in cosmetic products to promote hydration of skin. 1%, 2.5% and 5% HA solutions were tested. With increasing HA %, the serum became more viscous and gel like. 1% HA was not feasible for the UV serum due to the fact that the UV additives (zinc oxide, titanium dioxide) are not water soluble and need to be dispersed. 1% HA was not viscous enough for dispersion and the UV additives precipitated out. 2.5% gave the best consistency based on preferred feel, texture and viscosity and was able to disperse the UV additives. 5% was a very thick, viscous serum.
Concentration of Mineral Filters: Zinc Oxide and Titanium Dioxide:
Zinc oxide and titanium dioxide were explored as UV additives that are considered safe. These additives mechanically protect from UV radiation by forming a physical reflective barrier on the skin. Both are not soluble in water and must be dispersed for the current aqueous solution. Zinc oxide concentration varied from 2.5%, 3.75%, 5%, 5.625%, 10%, 12% and 15%. Titanium dioxide concentrations varied from 1.25%, 1.875%, 3%, 5% and 10%. Increasing the concentration of UV additives resulted in minor increases of white residue and how well dispersed the additives were, however if mixed well enough the effects were negligible. Zinc oxide and titanium dioxide were mixed together into serums in order to achieve broad spectrum protection. Zinc oxide is a broad spectrum UV additive capable of protecting against long and short UV A and UV B rays. However titanium dioxide is better at UV B protection and often added with zinc oxides for best broad spectrum protection. Combinations included 3.75%/1.25% ZnO/TiO2, 5.625%/1.875% ZnO/TiO2, 12%/3% ZnO/TiO2, 15%/5% ZnO/TiO2. The 3.75%/1.25% ZnO/TiO2 resulted in spf 5 and the 5.625%/1.875% ZnO/TiO2 produced spf 8.
Vitamin C:
Sodium ascorbyl phosphate was used as a vitamin C source. Formulations were created with the vitamin C concentration equal to that in the silk gel (0.67%). Formulations were also created with 20% sodium ascorbyl phosphate which is soluble in water.
Serum Preparation:
The vitamin C (sodium ascorbyl phosphate) must first be dissolved in water. Sodium hyaluronate is then added to the water, mixed vigorously and left to fully dissolve. The result is a viscous liquid (depending on HA %). The viscosity of the HA solution allows even dispersion of the zinc oxide and titanium dioxide and therefore HA must be mixed before addition of UV additives. The zinc oxide and titanium dioxide are then added to the solution and mixed vigorously with the use of an electric blender. Silk solution is then added and mixed to complete the serum formulation.
Chemical Filters:
A UV serum of the present disclosure can include one, or a combination of two or more, of these active chemical filter ingredients: oxybenzone, avobenzone, octisalate, octocrylene, homosalate and octinoxate. A UV serum of the present disclosure can also include a combination of zinc oxide with chemical filters.
In an embodiment, a UV serum of the present disclosure can be applied approximately 15 minutes before sun exposure to all skin exposed to sun, and can be reapplied at least every 2 hours. In an embodiment, a UV serum of the present disclosure includes water, zinc oxide, sodium hyaluronate, titanium dioxide, silk, and vitamin C or a vitamin C derivative such as sodium ascorbyl phosphate. In an embodiment, a UV serum of the present disclosure protects skin and seals in moisture with the power of silk protein. In an embodiment, a UV serum of the present disclosure improves skin tone, promotes collagen production and diminishes the appearance of wrinkles and fine lines with the antioxidant abilities of vitamin C. In an embodiment, a UV serum of the present disclosure delivers moisture for immediate and long-term hydration throughout the day with concentrated hyaluronic acid. In an embodiment, a UV serum of the present disclosure helps prevent sunburn with the combined action of zinc oxide and titanium dioxide. In an embodiment, a UV serum of the present disclosure is designed to protect, hydrate, and diminish fine lines while shielding skin from harsh UVA and UVB rays. In an embodiment, the silk protein in a UV serum of the present disclosure stabilizes and protects skin while sealing in moisture, without the use of harsh chemical preservatives or synthetic additives. In an embodiment, the vitamin C/derivative in a UV serum of the present disclosure acts as a powerful antioxidant that supports skin rejuvenation. In an embodiment, the sodium hyaluronate in a UV serum of the present disclosure nourishes the skin and delivers moisture for long-lasting hydration. In an embodiment, the zinc oxide and titanium dioxide in a UV serum of the present disclosure shields skin from harmful UVA and UVB rays. The silk protein stabilization matrix in a UV serum of the present disclosure protects the active ingredients from the air, to deliver their full benefits without the use of harsh chemicals or preservatives. The silk matrix also traps moisture within the skin furthering the hydrating effect of the sodium hyaluronate.
Acids
Antioxidants
Ascorbic Acid
avobenzone
Collagen
Electricity
Feelings
Figs
Furuncles
homosalate
Hyaluronic acid
Minerals
octinoxate
octisalate
octocrylene
oxybenzone
Pharmaceutical Preservatives
Proteins
Radiation
Rejuvenation
SERPINA3 protein, human
Serum
Serum Proteins
Silk
Skin
Skin Pigmentation
sodium ascorbyl phosphate
Sodium Hyaluronate
Strains
Sunburn
titanium dioxide
Viscosity
Vitamin A
Vitamins
west indian lemongrass oil
Zinc Oxide
The study included volunteers who were recruited through healthcare workers, acquaintances, students and friends. The inclusion criteria were: age between 18 and 80 years and Fitzpatrick skin types I–III. The Fitzpatrick classification system categorizes skin based on the tan in six types:
I: those who have pale skin and freckles, always sunburn and do not tan at all;
II: those who have light-colored skin, sunburn easily and tan with difficulty;
III: those who have olive/golden honey skin, sunburn mildly, have immediate pigment darkening and tan uniformly;
IV: those who have moderate brown skin, burn minimally and always tan well;
V: those who have dark brown skin, rarely burn and tan easily;
VI: those who have deeply pigmented dark brown to darkest brown skin that never burns.
The exclusion criteria were: scars, tattoos or other skin marks on the volar side of the forearm of the dominant hand; active malignant disease; diabetes mellitus; cardiovascular disease; hepatic and renal disease; chronic and acute skin disease; skin xerosis; smoking; and excessive alcohol consumption. The medical records of all potential subjects were checked prior to their inclusion in the study. There were 281 subjects screened for inclusion; however, 44 subjects did not meet the aforementioned criteria. Hence, the final number of included subjects was 237.
I: those who have pale skin and freckles, always sunburn and do not tan at all;
II: those who have light-colored skin, sunburn easily and tan with difficulty;
III: those who have olive/golden honey skin, sunburn mildly, have immediate pigment darkening and tan uniformly;
IV: those who have moderate brown skin, burn minimally and always tan well;
V: those who have dark brown skin, rarely burn and tan easily;
VI: those who have deeply pigmented dark brown to darkest brown skin that never burns.
The exclusion criteria were: scars, tattoos or other skin marks on the volar side of the forearm of the dominant hand; active malignant disease; diabetes mellitus; cardiovascular disease; hepatic and renal disease; chronic and acute skin disease; skin xerosis; smoking; and excessive alcohol consumption. The medical records of all potential subjects were checked prior to their inclusion in the study. There were 281 subjects screened for inclusion; however, 44 subjects did not meet the aforementioned criteria. Hence, the final number of included subjects was 237.
Acquaintances
Acute Disease
Burns
Cardiovascular Diseases
Cicatrix
Darkness
Diabetes Mellitus
Forearm
Friend
Health Personnel
Honey
Kidney Diseases
Light
Olivary Nucleus
Pigmentation
Skin
Skin Diseases
Student
Sunburn
Voluntary Workers
This cross-sectional study was carried out between 21 September 2020, and 21 October 2020, in Kahramanmaras in the Mediterranean region of Turkey. This region has an average UV index of 4.75 throughout the year and an average sunshine duration of 8.7–10 h between June and September. There were 18,603 teachers employed at Kahramanmaras [27 ]. Accordingly, the sample was found to be 641, with a 5% margin of error and a 99% confidence interval. Six hundred and forty-seven teachers participated in this study. Schools were selected by a simple random method. Teachers in 30 schools in Kahramanmaras took part in this study. Based on the literature review on skin cancer, the researchers created a 40-question, three-part questionnaire. The questionnaire was applied to 25 people and rearranged according to the feedback. The questionnaire was then emailed to the teachers via Google Forms. Surveys were implemented with Google Forms as it would be difficult to conduct in-person surveys for geographical reasons. Before this study, teachers were informed via e-mail and telephone applications. A consent form and questionnaire, prepared via Google Forms, were sent to those who wanted to take part in this study. Teachers who gave consent and completed the questionnaire were included in this study. Teachers who gave incomplete answers to the skin cancer knowledge questions were excluded from this study.
The first part of the questionnaire included questions about the sociodemographic (age, gender, marital status, and if they had children) and occupational characteristics of the teachers, and the second part of the questions gathered information on the teachers’ skin characteristics (skin types, plenty of nevi, and birthmarks on the skin), sun-protection methods on sunny days, number of sunburns, and family history (SC). “Plenty nevi” was defined as the presence of more than 50 nevi. Skin types were shown according to the participant’s responses with the Fitzpatrick classification, which Thomas B. Fitzpatrick developed to predict the response of different skin types to ultraviolet (UV) light [28 (link)].
In the third part of the questionnaire, questions revealed teachers’ general knowledge about SC, its risks, symptoms, and treatment. The reliability of knowledge questions on SC was assessed using Cronbach’s internal consistency coefficient. The Cronbach alpha value was 0.799.
The first part of the questionnaire included questions about the sociodemographic (age, gender, marital status, and if they had children) and occupational characteristics of the teachers, and the second part of the questions gathered information on the teachers’ skin characteristics (skin types, plenty of nevi, and birthmarks on the skin), sun-protection methods on sunny days, number of sunburns, and family history (SC). “Plenty nevi” was defined as the presence of more than 50 nevi. Skin types were shown according to the participant’s responses with the Fitzpatrick classification, which Thomas B. Fitzpatrick developed to predict the response of different skin types to ultraviolet (UV) light [28 (link)].
In the third part of the questionnaire, questions revealed teachers’ general knowledge about SC, its risks, symptoms, and treatment. The reliability of knowledge questions on SC was assessed using Cronbach’s internal consistency coefficient. The Cronbach alpha value was 0.799.
Cancer of Skin
Child
Gender
Nevus
School Teachers
Skin
Sunburn
Sunlight
Ultraviolet Rays
We described the differences between skin type groups with regards to personal characteristics, knowledge, attitudes and behaviors (Supplementary Table S1 ). However, as the balance of the risks and benefits of sun exposure differs according to skin type, we restricted further analyses to people who reported having fair or medium skin who are at markedly higher risk for skin cancer. Analyses of those with olive/dark skin will be presented elsewhere. A priori we considered state/territory of residence, age, sex, skin color and educational attainment to be covariates in multivariable analysis, so participants with missing data for any of these variables were excluded from the analysis.
We described participant characteristics and knowledge, attitudes and behavior variables using numbers and simple percentages. To estimate associations between: (1) personal characteristics and knowledge/attitudes; (2) personal characteristics and behavior; (3) knowledge/attitudes and behavior; and (4) knowledge/attitudes and being sunburnt, we calculated prevalence ratios (PRs) and 95% confidence intervals (95% CI) using log-binomial regression. In cases where the log-binomial model did not converge or was unable to estimate the covariance matrix, we used Poisson regression.
We used directed acyclic graphs to help identify additional potential confounders of the associations of interest, including propensity to sunburn, occupational status and history of skin cancer treatment. In multivariable analysis, we first applied a change in estimate approach (>10%) to decide whether to include or exclude a covariate in the final model. In addition, we repeated analyses separately for men and women to examine whether the associations of interest differed by sex. All analyses were performed using R version 4.1.2.
We described participant characteristics and knowledge, attitudes and behavior variables using numbers and simple percentages. To estimate associations between: (1) personal characteristics and knowledge/attitudes; (2) personal characteristics and behavior; (3) knowledge/attitudes and behavior; and (4) knowledge/attitudes and being sunburnt, we calculated prevalence ratios (PRs) and 95% confidence intervals (95% CI) using log-binomial regression. In cases where the log-binomial model did not converge or was unable to estimate the covariance matrix, we used Poisson regression.
We used directed acyclic graphs to help identify additional potential confounders of the associations of interest, including propensity to sunburn, occupational status and history of skin cancer treatment. In multivariable analysis, we first applied a change in estimate approach (>10%) to decide whether to include or exclude a covariate in the final model. In addition, we repeated analyses separately for men and women to examine whether the associations of interest differed by sex. All analyses were performed using R version 4.1.2.
Cancer of Skin
Olea europaea
Skin
Skin Pigmentation
Sunburn
Woman
We used data from the National Health Interview Survey (NHIS), an annual, cross-sectional survey conducted by the US Census Bureau. Sample weights provided by the NHIS are used to yield estimates representative of the non-military, non-institutionalized US population. The NHIS is conducted using face-to-face interviews in respondents’ homes, though follow-ups may be conducted via telephone. Of note, due to the COVID-19 pandemic, the NHIS shifted from in-person interviewing to all-telephone interviewing starting in late March 2020 and continuing through June 2020. Data regarding sun-protective behaviors and sunburn are collected by the NHIS in 5-year interval. We collected data from 2010, 2015, and 2020, the most recent databases which contain our data of interest. This study was exempt from New York University Institutional Review Board.
COVID 19
Ethics Committees, Research
Face
Military Personnel
Sunburn
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More about "Sunburn"
Sunburn is the dermal inflammation caused by overexposure to ultraviolet (UV) radiation, often from the sun.
This skin condition is characterized by reddening, pain, swelling, and sometimes blistering.
The severity can range from mild irritation to severe tissue damage, depending on the duration and intensity of UV exposure.
Factors like skin type, time of day, and geographic location can influence an individual's susceptibility to sunburn.
Effective prevention involves sun protection measures like sunscreen, protective clothing, and avoiding peak UV hours.
Managing sunburn may require pain relief, hydration, and in severe cases, medical treatment.
Understanding sunburn's mechanisms and risk factors is crucial for maintaining skin health and reducing UV-related skin issues.
Explore the latest research on sunburn using advanced AI tools like PubCompare.ai, which can help identify optimal protocols from literature, preprints, and patents to enhance reproducibility and accuracy.
Streamline your sunlburn studies with intellgent AI-powered insights from SAS 9.4, Mexameter MX18, Multiprobe Adapter MPA-5, SKH-1 hairless mice, and SAS Online Doc 9.1.3.
This skin condition is characterized by reddening, pain, swelling, and sometimes blistering.
The severity can range from mild irritation to severe tissue damage, depending on the duration and intensity of UV exposure.
Factors like skin type, time of day, and geographic location can influence an individual's susceptibility to sunburn.
Effective prevention involves sun protection measures like sunscreen, protective clothing, and avoiding peak UV hours.
Managing sunburn may require pain relief, hydration, and in severe cases, medical treatment.
Understanding sunburn's mechanisms and risk factors is crucial for maintaining skin health and reducing UV-related skin issues.
Explore the latest research on sunburn using advanced AI tools like PubCompare.ai, which can help identify optimal protocols from literature, preprints, and patents to enhance reproducibility and accuracy.
Streamline your sunlburn studies with intellgent AI-powered insights from SAS 9.4, Mexameter MX18, Multiprobe Adapter MPA-5, SKH-1 hairless mice, and SAS Online Doc 9.1.3.