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Radiation Effects

Radiation Effects: A concise overview of the impacts and applications of radiation, including its effects on living organisms, materials, and research.
Covers topics such as ionizing radiation, non-ionizing radiation, radiation exposure, and strategies for optimizing radiation-based research and development.
Designed to provide a clear, informative summary for researchers and professionals in related fields. *Includes details on how PubCompare.ai can enhance radiation effects research through AI-driven protocol comparisons to improve reproducibility and outcomes.

Most cited protocols related to «Radiation Effects»

Analyzing Radiation Damage in Cryo-EM Maps

To identify the degradation of map signal with radiation damage, we used EMRinger with a single model across multiple dose-fractionated maps. Individual reconstructions were calculated based on each of the 24 frames of data collected using the alignments generated from the full reconstruction in Frealign ^{21 (link)}. Five-frame averages were generated by performing voxel-by-voxel averaging between each of the five frames using the CCP4 ‘mapmask’ tool. For each five-frame averaged dose-fractionated map, the EMRinger Score is calculated for the full model. We additionally calculated EMRinger scores for subsets of the model comprised only of the aromatic, positively charged, or negatively charged residues, respectively, to compare the differential radiation damage effects for different amino acid classes.
In addition to calculating EMRinger scores, Radiation damage can lead to a negative scattering contribution near the true (rotameric) position in subsequent maps. Because the rotameric peak of the original map can therefore be lowered below the baseline, EMRinger will then identify a new peak at a different local maximum in the damaged map. This new local maximum is more likely to occur at non-rotameric angles because the original rotameric angle is now suppressed by negative scattering contributions in the damaged map. The results of the EMRinger analysis on dose-fractionated data suggest that reconstructions based on different doses may be required to maximize the resolvability of different sets of side chains, just as different degrees of sharpening are commonly used now during model building.
Residue-specific sampling is performed by the emringer_residue.py script.

Barad B.A., Echols N., Wang R.Y., Cheng Y., DiMaio F., Adams P.D, & Fraser J.S. (2015). EMRinger: Side-chain-directed model and map validation for 3D Electron Cryomicroscopy. Nature methods, 12(10), 943-946.

Publication 2015

Amino Acids Microtubule-Associated Proteins Radiation Radiation Effects Reading Frames Reconstructive Surgical Procedures

Cancer Risk Modeling with Radiation and Smoking

Regression models to describe cancer risks included a description of the rates for unexposed (zero dose) nonsmokers (baseline rate) with additional terms for radiation and smoking effects. We described the joint effects of radiation and smoking in various ways, including additive and multiplicative ERR models and additive excess rate models (EAR). Ignoring smoking, the ERR model was:

B K G_{A L L} (1 + E R R_{r a d}),

where

B K G_{A L L}

represents baseline rates for those not exposed to radiation (i.e., unexposed), and

E R R_{r a d}

was the excess relative risk for radiation exposure.
The multiplicative ERR model for the joint effect of radiation and smoking was:

B K G_{N S} (1 + E R R_{s m k}) (1 + E R R_{r a d}),

in which

B K G_{N S}

was the baseline rate for unexposed nonsmokers,

E R R_{s m k}

was the excess relative risk for smoking, and

E R R_{r a d}

was the excess relative risk for radiation. In this model,

E R R_{r a d}

described the radiation-associated proportional increase in rates relative to unexposed people with the same smoking history. If smoking was not an effect modifier (that is,

E R R_{r a d}

did not depend on smoking history), this increase was independent of smoking history.
The additive ERR model of the joint effect of radiation and smoking was:

B K G_{N S} (1 + E R R_{s m k} + E R R_{r a d}) .

In this model,

E R R_{r a d}

describes the radiation-associated proportional increase in rates relative to the risk for unexposed, nonsmokers.
An additive excess rate (or EAR) model for the joint effect of radiation and smoking on cancer rates was:

B K G_{N S} + E A R_{s m k} + E A R_{r a d},

where

E A R_{s m k}

and

E A R_{r a d}

described the smoking and radiation effects in terms of rate differences.
What follows are details of the model forms used for the baseline, ERR and EAR terms considered in these analyses.

Bösl M.R., Takaku K., Oshima M., Nishimura S, & Taketo M.M. (1997). Early embryonic lethality caused by targeted disruption of the mouse selenocysteine tRNA gene (Trsp). Proceedings of the National Academy of Sciences of the United States of America, 94(11), 5531-5534.

Publication 2017

Joints Malignant Neoplasms Non-Smokers Radiation Radiation Effects Radiation Exposure

Gamma Radiation Attenuation in Polymers

The linear attenuation coefficient (μ) is a key factor for evaluating the effect of gamma radiation of appropriate energy with the studied material and can be deduced from Beer-Lambert Law [14 (link)] as follows in Equation (1):

μ = \frac{1}{x} \ln (\frac{I_{0}}{I}) = \frac{1}{x} \ln (\frac{A r e a_{w i t h o u t}}{A r e a_{w i t h}})

The initial and transmitted intensities are I₀ and I respectively, across a target material of thickness x. I₀ and I were determined by evaluating the area under the photopeak in without the polymer absorber Area_without and with the polymer sample Area_with respectively.
The ability of the considered polymers to be checked as radiation protecting materials by calculating the mass attenuation coefficient (μ/ρ) by dividing the calculated linear attenuation coefficient (μ) of a particular polymer by its density (ρ). Theoretically, (μ/ρ) can be evaluated using Equation (2) [15 (link)]:

\frac{μ}{ρ} = \sum_{i} w_{i} {(\frac{μ}{ρ})}_{i}

where w_i and (μ/ρ)_i was the weight fraction and the mass attenuation coefficient of the ith constituent element in the polymer material, respectively.
The half, tenth value layers or HVL and TVL are two important parameters in designing a suitable radiation shielding material. These parameters are defined as the attenuator thicknesses needed to decrease the γ-ray intensity to 50% and 10% of its initial value and estimated using Equations (3) and (4), respectively [16 (link)]:

H V L = \frac{\ln 2}{μ}

T V L = \frac{\ln 10}{μ}

Due to the interaction of gamma rays with the polymer sample, the mean-free path (MFP) is known as the medium distance traveled by a photon between two successive reactions is defined as and described in Equation (5) [17 (link),18 (link)]:

M F P = \frac{1}{μ}

The MFP is also, practically, the attenuator distance which decreases the initial photon intensity of 36.8% when passing across the polymer absorber. The (Z_eff) is another useful radiation interaction factor used to discuss the attenuating properties of the mixtures or compounds in terms of pure elements and depends on the incoming photon energy. Z_eff values for the studied polymers can be obtained using Equation (6) [18 (link)]:

Z_{e f f} = \frac{\sum_{i} f_{i} A_{i} {(\frac{μ}{ρ})}_{i}}{\sum_{j} \frac{A_{j}}{Z_{j}} {(\frac{μ}{ρ})}_{j}}

where f_i, Z_i and A_i, refer to the molar fraction, atomic number and atomic weight of the ith constituent element in the selected polymer, respectively.
The effective electron density (N_eff), measured in electrons/g, defined as the number of electrons per unit mass of the polymer material and is derived using the calculated Z_eff according to Equation (7) [19 ]:

N_{e f f} = \frac{N_{A} Z_{e f f}}{〈 A 〉}

where

〈 A 〉 = \sum_{i} f_{i} A_{i}

represents the mean atomic mass of the polymer, and N_A is Avogadro’s number.
When choosing a shielding material, the exposure buildup factor (EBF) must be considered to edit the absorption calculations resulting from buildup of secondary photons resulting from Compton scattering [20 (link)]. To determine the EBF for the selected polymers, the Geometric-Progression fitting method (GP) was employed, and the computations were determined according to the three following steps [7 (link)]:
The (Z_eq), which is an energy-dependent parameter describing the properties of the investigated polymers in terms of their equivalent elements, was first calculated using the next formula [21 (link),22 (link)]:

Z_{e q} = \frac{Z_{1} (\log R_{2} - \log R) + Z_{2} (\log R - \log R_{1})}{\log R_{2} - \log R_{1}}

where R₁ and R₂ are the (μ_Comp/μ_total) ratios corresponding to the elements with atomic numbers Z₁ and Z₂, respectively, and R is the (μ_Comp/μ_total) ratio for the selected polymer at a specific energy, which lies between the ratios R₁ and R₂.
The computed Z_eq values of the investigated polymers were then used to interpolate the GP fitting EBFs (b, c, a, X_K, d) in the range of energy 0.015–15 MeV using the interpolation formula [22 (link)] (9):

C = \frac{C_{1} (\log Z_{2} - \log Z_{e q}) + C_{2} (\log Z_{e q} - \log Z_{1})}{\log Z_{2} - \log Z_{1}}

where C₁ and C₂ are GP fitting parameters, taken from the ANSI/ANS-6.4.3 standard database [23 (link)], corresponding to Z₁ and Z₂ between which Z_eq of the selected polymer lies. As an example, the GP fitting parameters and the Z_eq for PMP (C₆H₁₂) in the energy range 0.015–15 MeV are listed in Table 1.
Finally, the EBF for the selected polymers were then estimated with the help of the obtained GP fitting parameters, using the following relations [24 (link),25 (link)]:

B (E, x) = 1 + \frac{b - 1}{K - 1} (K^{x} - 1), K \neq 1

and

B (E, x) = 1 + (b - 1) x, K = 1

where

K (E, x) = c x^{a} + d \frac{\tanh (x / X_{K} - 2) - \tanh (- 2)}{1 - \tanh (- 2)} for x \leq 40 mfp

where E is incident γ-ray energy and x is the penetration depth in terms of mfp.

Alabsy M.T., Alzahrani J.S., Sayyed M.I., Abbas M.I., Tishkevich D.I., El-Khatib A.M, & Elsafi M. (2021). Gamma-Ray Attenuation and Exposure Buildup Factor of Novel Polymers in Shielding Using Geant4 Simulation. Materials, 14(17), 5051.

Publication 2021

A-factor (Streptomyces) Beer Disease Progression Electrons factor A Gamma Rays Molar Polymers Radiation Effects

Radiation Dose-Response Modeling for LSS Cohort

The previous analyses (4 (link)) focused on age-at-exposure dependent excess absolute rate (EAR) models in which the radiation dose effect could vary with time since exposure within age-at-exposure groups. As we examined the current data, it became apparent that simpler models similar to those used for solid cancers (9 (link)), in which the excess risk varies smoothly with age-at-exposure and time can often describe the data at least as well as the models used in the previous analyses. Therefore, we considered both EAR and excess relative risk (ERR) models in which the excess risk varies with age at exposure and either attained age or time since exposure. In an EAR model, the disease rate can be written as:

λ_{0} (c, s, a, b) + ρ (d) ε_{a} (c, s, t, e) .

While in the ERR model it is
The term λ₀(c, s, a, b) is a parametric model for the baseline (zero dose) rates that depends on attained age (a), gender (s), and factors such as birth cohort (b) and city (c). In the primary dose response model, ρ(d)ε(c, s, t, e), ρ(d) describes the shape of the dose response and ε(c, s, t, e) describes effect modification associated with the effect of dose d, i.e. how the level of the radiation-related excess risk varies with city (c), gender (s), age at exposure (e), and time(t), where time can be functions of either time since exposure or attained age. For this report (as in most analyses of the LSS data) effect modification was described using log-linear functions of the variables of interest. In descriptions of these models, unless explicitly noted, they are based on log attained age and log time since exposure. In general, age at exposure and time since exposure were centered or scaled so that the dose effect parameters correspond to the risk for a person who was 30 years old at the time of the bombs for incidence 25 (attained age 55) or 40 (attained age 70) years after exposure. For some outcomes, we considered extensions of the effect modification model, including gender-dependent age-at-exposure and time effects, interactions between age at exposure and time, or categorical age at exposure and time effects.
The dose response functions considered in this report included:
β₁ and β₂ are the linear and quadratic dose response parameters respectively. In a linear-quadratic model, the curvature of the dose response is defined as the ratio of the quadratic and linear dose effects, i.e. β₂/β₁. In the single-knot linear-spline/threshold models, c is a dose join point and when θ₁ equals 0 this is a threshold model. In the non-parametric dose response model the dose response varies by dose category without any smoothing (ρ(d)= θ_dcat). Although the bone marrow dose estimates used in all of these analyses were adjusted to allow for the effects of dose uncertainty (24 (link)), dose response models also included a multiplicative dichotomous factor for those with shielded kerma estimates in excess of 4 Gy to allow for dose uncertainties not captured by the standard adjustment methods or high dose effects such as cell killing.
Analyses were limited to first primary malignancies diagnosed during the follow-up period among cohort members with DS02 dose estimates. Cases that were diagnosed outside of Hiroshima or Nagasaki prefectures were excluded. Maximum likelihood estimates of the parameters in these models were computed using the data in the person-year (PYR) table described above. P-values and confidence intervals for model parameters were based on the profile likelihood function. Uncertainty in the various risk estimates were summarized using 95% confidence intervals. The models were fit using the Epicure risk regression software (25 ). Akaike information criteria (AIC) (26 ) values were used to aid in the comparison of non-nested models. The models used are described and estimates of some of the key parameters are given in the Results section. However, details of the parameterizations used and the parameter estimates for our preferred models are presented in the supplementary material (Table S2).

Hsu W.L., Preston D.L., Soda M., Sugiyama H., Funamoto S., Kodama K., Kimura A., Kamada N., Dohy H., Tomonaga M., Iwanaga M., Miyazaki Y., Cullings H.M., Suyama A., Ozasa K., Shore R.E, & Mabuchi K. (2013). The incidence of leukemia, lymphoma, and multiple myeloma among atomic bomb survivors: 1950 – 2001. Radiation research, 179(3), 10.1667/RR2892.1.

Publication 2013

A-factor (Streptomyces) Bone Marrow Cells factor A Malignant Neoplasms Radiation Radiation Effects

Psychological Distress among Fukushima Disaster Survivors

The outcome variable was non-specific psychological distress as measured by the K6 scale.¹³ (link) This scale, which ranges from zero to 24, asks respondents whether they have experienced six mental health symptoms during the past 30 days. Each question is rated on a five-point Likert scale, with higher scores signifying higher psychological distress. The Japanese version of the K6 score has been validated.¹⁴ (link) We defined psychological distress as a K6 score ≥ 13.¹³ (link)
We measured participants’ beliefs about the potential health effects of radiation exposure¹⁵ based on their responses to the following questions: (i) What do you think is the likelihood of having immediate health damage (e.g. dying within one month) as a result of your current level of radiation exposure? (ii) What do you think is the likelihood of damage to your health (e.g. cancer onset) in later life as a result of your current level of radiation exposure? (iii) What do you think is the likelihood that the health of your future (i.e. as yet unborn) children and grandchildren will be affected as a result of your current level of radiation exposure? These items were translated into Japanese, then back to English, and modified after discussion with the authors of the questionnaire. Participants were asked to respond to each question using a four-point Likert scale as follows: very unlikely (1), unlikely (2), likely (3) or very likely (4).
We also collected data on individual characteristics including age, gender, educational attainment (elementary school or junior high school, high school, vocational college or junior college, university or graduate school) and history of mental illness. Age was categorized as follows: 15–49 years (reproductive age),¹⁶ 50–64 years and older than 64 years.
Information on disaster-related stressors was collected from the questionnaire, including: living place (in or out of Fukushima prefecture); living arrangement at the time of the survey (evacuation shelter, temporary housing, rental housing/apartment, relative’s house, own house or other); employment (full-time, part-time or unemployed); loss of employment (yes/no); decrease in income (yes/no); damage to house (no damage, partial damage, partial collapse, partial but extensive collapse or total collapse) and death of someone close (yes/no). To examine the effect of multiple disaster stressors, we created a new variable (the number of stressors) equal to the sum of disaster-related stressors in the highest category. The variable was reclassified into quartiles for inclusion in regression models.

Suzuki Y., Yabe H., Yasumura S., Ohira T., Niwa S.I., Ohtsuru A., Mashiko H., Maeda M, & Abe M. (2015). Psychological distress and the perception of radiation risks: the Fukushima health management survey. Bulletin of the World Health Organization, 93(9), 598-605.

Publication 2015

Child Disasters Gender Healthy Volunteers Japanese Malignant Neoplasms Mental Disorders Mental Health Psychological Distress Radiation Effects Radiation Exposure Reproduction Shock

Most recents protocols related to «Radiation Effects»

Synergistic Effects of TG02 and Radiation on Glioblastoma

Not available on PMC !

Example 10

Radiation is an effective treatment for glioblastoma. But tumor resistance and recurrence develops in all patients.

A panel of GBM PDCLs, see Example 9, were chosen for evaluation of the combination of TG02 and radiation therapy for the treatment of glioblastoma (FIG. 31). Cells were treated first with TG02 at increasing concentrations. Within 30 minutes, cells were treated with increasing doses of radiation and cell proliferation was measured 72 hours post-treatment. TG02 alone had anti-proliferative activity in these cell lines. The addition of TG02 augmented the effects radiation in a synergistic manner. The combination of TG02 and radiation exceeds the Bliss predicted model (greater than a 10% change from the Bliss predicted model), demonstrating synergy between TG02 and radiation in multiple PDCLs.

US11865116B2. Treatment of cancer with TG02 (2024-01-09). Cothera Bioscience, Inc. [KY]. Inventors: Thomas M. Estok [US], Eckard Weber [US], Tracy Lee Lawhon [US].

Patent 2024

5-(2'-naphthyl)-7-4-chlorophenyl-(2,3,6,8-tetrahydro)pyrrolo-(3,4-e)(1,4)-diazepine-6-thioxo-8-(1H,7H)one Cell Proliferation Cells Glioblastoma Malignant Neoplasms Neoplasms Patients Radiation Radiation Effects Radiotherapy Recurrence TG02

Sterilization of Boswellia carteri Resin

The oleo-gum-resin of B. carteri Birdw. was purchased from a local market in Shiraz (Iran). The samples were authenticated and deposited in the herbarium of the Phytopharmaceutical Department of the School of Pharmacy, Shiraz University of Medical Sciences (Shiraz, Iran) under voucher number PM400-5. This medicinal herb was chosen for research based on its market availability, safety, affordable price, and potential for a novel formulation that has not yet been reported. The sample was subjected to quality control tests. According to Food and Drug Administration regulations, any topical medication used to treat burns, wounds, and chronic ulcers must be sterile to avoid introducing exogenous microorganisms. ^{17 (link)}The oleo-gum-resin of B. carteri was frozen (-20 °C) for two hours before being powdered in an electric grinder and packaged in double-wrapped plastic bags for gamma sterilization.
Based on the initial microbial count, radiation dosimetry was conducted. To prevent any interference with radiation-induced paramagnetic effects, packages were stored at room temperature in darkness for at least 72 hours after radiation (irradiator: 60 Co). We used gas chromatography-mass spectrometry to analyze the essential oils before and after irradiation and then compared the chromatograms to determine the effect of sterilization on the active components. ¹⁸For aseptic preparation, the ingredients of the traditional medicine-based formulation (B. carteri oleo-gum-resin 40% in an oily duck fat base) were sterilized along with all devices and instruments using approved techniques and methods. ¹⁹

Badr P., Afsharypuor S., Tohidinik H.R., Mohammadi A.A, & Daneshamouz S. (2023). Burn Wound Healing Effect of a Sterilized Traditional Formulation of Boswellia carteri vs. Silver Sulfadiazine Cream 1% in Patients Presenting Second-degree Burn Wounds: A Randomized, Double-blind Clinical Trial. Iranian Journal of Medical Sciences, 48(2), 137-145.

Publication 2023

Asepsis Burns Darkness Ducks Electricity Electromagnetic Radiation Freezing Gamma Rays Gas Chromatography-Mass Spectrometry Medical Devices Medicinal Herbs Oils Oils, Volatile Pharmaceutical Preparations Radiation Effects Radiometry Radiotherapy Resins, Plant Safety Sterility, Reproductive Ulcer Wounds

Evaluating AMOC response to climate forcing

We use CMIP6 and CMIP5 experiments to evaluates the AMOC and its indices as well as their response to climate forcing (Table S1). CMIP6 employs the latest generation of climate models. We use the model output of 8 CMIP6 models with a total of 70 ensemble members and 8 CMIP5 models with a total of 31 ensemble members for all-forcing historical simulations. We also use 7 CMIP6 models (with one member for each) for PI control simulations. We found the spread among members within one model is comparable to that among different models. Therefore, the MMEM in the present study is calculated across all the available model members rather than across models’ ensemble means, although the results are similar.
We further investigate the attribution of AMOC and its fingerprints using simulations from the Detection and Attribution Model Intercomparison Project (DAMIP; Table S2). As a part of CMIP6, DAMIP isolates the individual effect of anthropogenic GHGs (Hist-GHG), anthropogenic aerosols (Hist-aer), natural forcing (e.g., solar activity and volcanic eruptions; Hist-nat) and stratospheric ozone (e.g., ozone depletion; Hist-stratO3). We define the anthropogenic aerosol forcing as the total absorbed short-wave radiation (ASR) in the Northern Hemisphere (ASR_NH) derived from the simulations forced by anthropogenic aerosols. Compared with CMIP5, more models with indirect aerosol forcing (e.g., aerosol-cloud microphysical effect) are included in CMIP6. The inclusion of indirect aerosol forcing reinforces the cooling effect of aerosols, leading to an increasing radiative cooling of ~2 W/m² between the 1880s and the 1980s (Fig. 1f). After the 1980s, the cooling effect of anthropogenic aerosols decreases by ~0.5 W/m². Our estimate of aerosol forcing agrees well with the gridded aerosol community datasets (CEDS)^{30 (link),61 (link)}. The larger magnitude (by ~0.3 W/m²) in our estimate compared with CEDS may be attributed partly to the overestimation of aerosol forcing in CMIP6 models and partly to the climate feedbacks in models, notably the sea ice-albedo feedback (i.e., more reflection of incoming short-wave radiation by increasing sea ice in response to aerosol’s cooling effect). The GHGs concentration, on the other hand, increases continually since the industrial revolution, notably a rapid increase in atmospheric CO₂ from 290 to more than 400 ppm (Fig. 1f), corresponding to an increase in radiative warming effect of 3.5 W/m² (ref. ^{61 (link)}). We also use abrupt-4xCO2 experiments (Table S1) to study the response time scales of AMOC indices to abrupt CO₂ quadrupling.

Zhu C., Liu Z., Zhang S, & Wu L. (2023). Likely accelerated weakening of Atlantic overturning circulation emerges in optimal salinity fingerprint. Nature Communications, 14, 1245.

Publication 2023

1-(methacryloyloxymethyl)ethyl hydrogen maleate Anthropogenic Effects Climate Electromagnetic Radiation Ozone Depletion Radiation Radiation Effects Reflex Sea Ice Cover Short Waves Solar Activity Stratospheric Ozone Volcanic Eruptions

Atomic Bomb Survivor Radiation Dose

We used the dose of exposure to radiation from neutrons and gamma rays estimated by using the Atomic Bomb Survivor 1993 Dose (ABS93D). ABS93D was described in detail in a previous report (28 (link)). Briefly, radiation dose calculated by ABS93D is based on individual exposure status such as distance from the hypocenter, shielding and age at time of bombing. Hoshi et al. (28 (link)) showed that the dose evaluation of ABS93D was close to that of the Dosimetry system 1986 (DS86) by Radiation Effects Research Foundation. We used the weighted radiation dose of the colon, which is often chosen as the whole-body irradiation exemplary organ, by calculating the sum of the gamma ray dose and 10 times the neutron dose considering the biological effectiveness of neutrons.

Kishimoto S., Oda N., Maruhashi T., Tanigawa S., Mizobuchi A., Yusoff F.M., Fujita A., Uchiki T., Kajikawa M., Yoshimura K., Yamaji T., Harada T., Hashimoto Y., Nakano Y., Hirota S., Yoshinaga S., Goto C., Nakashima A, & Higashi Y. (2023). Effect of exposure to radiation caused by an atomic bomb on endothelial function in atomic bomb survivors. Frontiers in Cardiovascular Medicine, 10, 1122794.

Publication 2023

Atomic Bomb Survivors Biopharmaceuticals Colon Gamma Rays Radiation Radiation Effects Radiometry Whole-Body Irradiation

Gamma-Irradiated Elastomer-Algal Composites

The studied polymeric material, ethylene–propylene copolymer (EPR), is an elastomer produced by ARPECHIM (Pitesti, Romania) as TERPIT C. The pristine rubber has an ethylene/propylene ratio of 3:1. Algal extract from kelp (Ascophyllum nodosum) harvested between May and November in Canada was available from the market by Z-Company (Eindhoven, the Netherlands).
The polymer samples were prepared by the dissolution of elastomer in chloroform, whose evaporation at room temperature leaves unchanged the polymer film. After the filtration of this primary solution, aliquots of 10 mL were transferred into three other separate glass flasks, where appropriate amounts of algal powder were put in for the preparation of three other second set solutions containing 0.5, 1, and 2 phr of additive. These last solutions were the sample sources from which 100 mL of liquid was poured into previously weighted aluminum round pans. After gentle drying on the table at room temperature, thin films were obtained, whose weights are placed around 3 mg.
The γ-exposure of probes was accomplished in air at room temperature in a specialized machine, Ob Servo Sanguis type irradiation equipment (Budapest, Hungary) provided with ⁶⁰Co source at four total doses of 0, 25, 50, and 100 kGy by permanent rotation of the processing can. Gamma irradiation of polymer samples was carried out at a dose rate of 0.5 kGy h⁻¹, which is a convenient value for the attendance of oxidative degradation. Occasionally, for the study of radiation effects on algal extract, two doses of 12.5 kGy and 75 kGy were also applied. Both control and modified samples were investigated immediately after the end of each irradiation, avoiding any structural modification due to the decay of short-life radicals.
Chemiluminescence (CL) measurements are considered the most appropriate analytical procedure through which the induced effects of γ-radiolysis, a convenient accelerated procedure, may be pertinently controlled. The CL determinations were carried out with LUMIPOL 3 (Institute of Polymers, Slovak Academy of Sciences, Bratislava, Slovakia), when the evaluation of degradation could be performed with a low temperature error (±0.5 °C). This extremely sensitive method for the investigation [73 ] of structural modifications occurring in the studied polymer matrices is based on photon emission through the de-excitation of carbonyl compounds when they are formed by the reactions of free radicals with molecular oxygen [74 (link)]. For the isothermal CL measurements, three values of temperatures (160 °C, 170 °C, and 180 °C) were selected, as they were appropriate investigation conditions for their convenient oxidation rates. For nonisothermal CL assay a suitable heating rate, 10 °C min⁻¹, was preferred. A similar measurement parameter (10 °C min⁻¹) was applied when the stability evaluation was conducted by heating specimens immersed in water and physiological serum at 80 °C for 5 h and 10 h. The confidence of CL values is very high because the average differences between the analogous values for emission intensities are ±50 Hz. This allows for a very low measurement error of less than 10⁻²%.

, & Zaharescu T. (2023). Stabilization Activity of Kelp Extract in Ethylene–Propylene Rubber as Safe Packaging Material. Polymers, 15(4), 977.

Publication 2023

Aluminum Ascophyllum Blood Chemiluminescence Chemiluminescent Assays Chloroform Cold Temperature Elastomers Ethylenes Filtration Free Radicals Gamma Rays Kelp Oxygen physiology Polymers Powder propylene Radiation Effects Radiotherapy Rubber Serum

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Clinac 4 100 by Agilent Technologies

Sourced in United States

The Clinac 4/100 is a linear accelerator designed for medical applications. It generates high-energy X-rays or electrons for the treatment of cancer patients. The device is used to deliver precise and targeted radiation therapy.

Cell counting kit 8 cck 8 by Dojindo Laboratories

Sourced in Japan, United States, China, Germany

The Cell Counting Kit-8 (CCK-8) is a colorimetric assay used to measure the number of viable cells in cell proliferation and cytotoxicity assays. It utilizes a water-soluble tetrazolium salt that is reduced by cellular dehydrogenases, resulting in the formation of a colored formazan dye. The amount of formazan dye is directly proportional to the number of living cells in the culture, which can be quantified by measuring the absorbance of the solution.

What are the different types of radiation effects and how do they impact research?

Radiation effects can be broadly categorized into ionizing and non-ionizing radiation. Ionizing radiation, such as x-rays and gamma rays, can cause direct damage to DNA and cells, while non-ionizing radiation, like UV light and microwaves, primarily induces thermal effects. Understanding the unique characteristics and impacts of these different radiation types is crucial for designing effective research protocols and optimizing radiation-based applications. Proper selection and comparison of radiation protocols is essential for ensuring reproducibility and accuracy in your research.

How can PubCompare.ai help optimize radiation effects research?

PubCompare.ai is an innovative tool that can enhance your radiation effects research by leveraging AI to streamline protocol comparisons. The platform allows you to efficiently screen the literature and pinpoint critical insights that can help you identify the most effective protocols for your specific research goals. By comparing protocols from published literature, pre-prints, and patents, PubCompare.ai can highlight key differences in protocol effectiveness, enabling you to choose the best option to improve reproducibility and accuracy in your radiation effects studies. This can save you time and improve the overall quality of your research outcomes.

What are some common challenges in working with radiation effects that PubCompare.ai can help address?

One of the main challenges in radiation effects research is ensuring protocol reproducibility and consistency across different studies. Variations in factors like radiation dosage, exposure time, and experimental conditions can significantly impact research outcomes. PubCompare.ai's AI-driven protocol analysis can help you identify the most effective and consistent protocols from the literature, allowing you to optimize your experimental design and minimize variability. Additionally, the platform can assist in locating the latest advancements and best practices in radiation effects research, keeping you up-to-date and informed on the most effective techniques.

How can PubCompare.ai help in specific applications of radiation effects research?

Radiation effects research has a wide range of applications, from studying the impacts on living organisms to material science and medical imaging. PubCompare.ai can be particularly useful in areas like radiobiology, where you need to understand the effects of radiation on cellular and tissue-level processes. The platform can also assist in radiation-based medical treatments, such as cancer therapy, by helping you identify the most effective protocols for delivering targeted radiation doses. Morever, in materials science research, PubCompare.ai can aid in selecting the optimal radiation protocols for testing the durability and performance of materials under various radiation conditions.

How does PubCompare.ai's AI-driven approach differ from traditional manual protocol comparisons?

Compared to manual protocol comparisons, PubCompare.ai's AI-driven approach offers several key advantages. The platform's machine learning algorithms can rapidly scre* the literature, pre-prints, and patents to identify the most relevant and effective radiation protocols, saving you significant time and effort. Additionally, the AI-powered analysis can detect subtle differences in protocol parameters that may be overlooked in manual reviews, helping you make more informed decisions about the most suitable protocols for your research. This AI-driven approach enables you to optimize your radiation effects research, improving reproducibility, accuracy, and overall efficiency.

More about "Radiation Effects"

Radiation effects encompass the diverse impacts of ionizing and non-ionizing radiation on living organisms, materials, and research applications.
This includes the study of radiation exposure, dosimetry, and strategies for optimizing radiation-based experiments and procedures.
Key subtopics include cell survival, DNA damage, radiobiology, radiochemistry, and radiation shielding.
Relevant techniques and instruments include PRISM 6 spectroscopy, SAS 9.4 statistical analysis, Avance III NMR spectrometry, Stata 15 data analysis, DMSO solvents, Neo sCMOS cameras, FACSCalibur flow cytometry, RS-2000 biological irradiators, and Clinac 4/100 linear accelerators.
The Cell Counting Kit-8 (CCK-8) is a common assay for measuring radiation-induced cytotoxity.
Advances in AI-driven protocol comparison, as exemplified by PubCompare.ai, can enhance reproducibility and outcomes in radiation effects research by identifying the most effective experimental approaches from the literature.
Whether you're a researcher, clinician, or industry professional, understanding the nuances of radiation effects is crucial for optimizing your work in this important field.