Degradation of Reactive Yellow 18 Using Ionizing Radiation Based Advanced Oxidation Processes: Cytotoxicity, Mutagenicity and By-Product Distribution

The degradation of Reactive Yellow 18 (RY-18), induced by gamma radiation in aqueous medium, was carried out as a function of gamma radiation dose (5–20 kGy) and concentration of hydrogen peroxide, the initial dye concentration and pH of the solution were optimized for the maximum degradation efficiency. Gamma radiations alone and in combination with H2O2 were used to degrade the RY-18. A degradation rate of 99% was achieved using an absorbed dose of 20 kGy, 0.6 mL H2O2 in acidic pH. Variations in the functional groups of untreated and treated RY-18 were determined by FTIR analysis. The LCMS technique was used to determine the intermediates formed during the degradation process. The cytotoxicity and mutagenicity of RY-18 were studied by hemolytic and Ames tests, respectively. There were significant reductions in cytotoxicity and mutagenicity in response to gamma radiation treatment. Cytotoxicity was reduced from 15.1% to 7.6% after treatment with a 20 kGy absorbed dose of gamma radiations with 0.6 mL H2O2. Mutagenicity was reduced by 81.3% and 82.3% against the bacterial strains TA98 and TA100 after treatment with a 20 kGy absorbed dose with 0.6 mL H2O2. The advanced oxidation process efficiency was evaluated using the byproduct formations, which were low-molecular-weight organic acid units, which through further oxidation were converted into carbon dioxide and water end products. Based on RY-18 degradation, cytotoxicity and mutagenicity reduction, the gamma radiation in combination with H2O2 has potential for the removal of dye from the effluents

Degradation of Reactive Yellow 18 Using Ionizing Radiation Based Advanced Oxidation Processes: Cytotoxicity, Mutagenicity and By-Product Distribution

The degradation of Reactive Yellow 18 (RY-18), induced by gamma radiation in aqueous medium, was carried out as a function of gamma radiation dose (5–20 kGy) and concentration of hydrogen peroxide, the initial dye concentration and pH of the solution were optimized for the maximum degradation efficiency. Gamma radiations alone and in combination with H2O2 were used to degrade the RY-18. A degradation rate of 99% was achieved using an absorbed dose of 20 kGy, 0.6 mL H2O2 in acidic pH. Variations in the functional groups of untreated and treated RY-18 were determined by FTIR analysis. The LCMS technique was used to determine the intermediates formed during the degradation process. The cytotoxicity and mutagenicity of RY-18 were studied by hemolytic and Ames tests, respectively. There were significant reductions in cytotoxicity and mutagenicity in response to gamma radiation treatment. Cytotoxicity was reduced from 15.1% to 7.6% after treatment with a 20 kGy absorbed dose of gamma radiations with 0.6 mL H2O2. Mutagenicity was reduced by 81.3% and 82.3% against the bacterial strains TA98 and TA100 after treatment with a 20 kGy absorbed dose with 0.6 mL H2O2. The advanced oxidation process efficiency was evaluated using the byproduct formations, which were low-molecular-weight organic acid units, which through further oxidation were converted into carbon dioxide and water end products. Based on RY-18 degradation, cytotoxicity and mutagenicity reduction, the gamma radiation in combination with H2O2 has potential for the removal of dye from the effluents

Assessment of the Validity of the 2HELPS2B Score for Inpatient Seizure Risk Prediction

Importance: Seizure risk stratification is needed to boost inpatient seizure detection and to improve continuous electroencephalogram (cEEG) cost-effectiveness. 2HELPS2B can address this need but requires validation. Objective: To use an independent cohort to validate the 2HELPS2B score and develop a practical guide for its use. Design, Setting, and Participants: This multicenter retrospective medical record review analyzed clinical and EEG data from patients 18 years or older with a clinical indication for cEEG and an EEG duration of 12 hours or longer who were receiving consecutive cEEG at 6 centers from January 2012 to January 2019. 2HELPS2B was evaluated with the validation cohort using the mean calibration error (CAL), a measure of the difference between prediction and actual results. A Kaplan-Meier survival analysis was used to determine the duration of EEG monitoring to achieve a seizure risk of less than 5% based on the 2HELPS2B score calculated on first- hour (screening) EEG. Participants undergoing elective epilepsy monitoring and those who had experienced cardiac arrest were excluded. No participants who met the inclusion criteria were excluded. Main Outcomes and Measures: The main outcome was a CAL error of less than 5% in the validation cohort. Results: The study included 2111 participants (median age, 51 years; 1113 men [52.7%]; median EEG duration, 48 hours) and the primary outcome was met with a validation cohort CAL error of 4.0% compared with a CAL of 2.7% in the foundational cohort (P =.13). For the 2HELPS2B score calculated on only the first hour of EEG in those without seizures during that hour, the CAL error remained at less than 5.0% at 4.2% and allowed for stratifying patients into low- (2HELPS2B = 0; 25%) groups. Each of the categories had an associated minimum recommended duration of EEG monitoring to achieve at least a less than 5% risk of seizures, a 2HELPS2B score of 0 at 1-hour screening EEG, a 2HELPS2B score of 1 at 12 hours, and a 2HELPS2B score of 2 or greater at 24 hours. Conclusions and Relevance: In this study, 2HELPS2B was validated as a clinical tool to aid in seizure detection, clinical communication, and cEEG use in hospitalized patients. In patients without prior clinical seizures, a screening 1-hour EEG that showed no epileptiform findings was an adequate screen. In patients with any highly epileptiform EEG patterns during the first hour of EEG (ie, a 2HELPS2B score of ≥2), at least 24 hours of recording is recommended.SCOPUS: cp.jinfo:eu-repo/semantics/publishe

Quantitative epileptiform burden and electroencephalography background features predict post-traumatic epilepsy

BACKGROUND: Post-traumatic epilepsy (PTE) is a severe complication of traumatic brain injury (TBI). Electroencephalography aids early post-traumatic seizure diagnosis, but its optimal utility for PTE prediction remains unknown. We aim to evaluate the contribution of quantitative electroencephalograms to predict first-year PTE (PTE(1)). METHODS: We performed a multicentre, retrospective case-control study of patients with TBI. 63 PTE(1) patients were matched with 63 non-PTE(1) patients by admission Glasgow Coma Scale score, age and sex. We evaluated the association of quantitative electroencephalography features with PTE(1) using logistic regressions and examined their predictive value relative to TBI mechanism and CT abnormalities. RESULTS: In the matched cohort (n=126), greater epileptiform burden, suppression burden and beta variability were associated with 4.6 times higher PTE(1) risk based on multivariable logistic regression analysis (area under the receiver operating characteristic curve, AUC (95% CI) 0.69 (0.60 to 0.78)). Among 116 (92%) patients with available CT reports, adding quantitative electroencephalography features to a combined mechanism and CT model improved performance (AUC (95% CI), 0.71 (0.61 to 0.80) vs 0.61 (0.51 to 0.72)). CONCLUSIONS: Epileptiform and spectral characteristics enhance covariates identified on TBI admission and CT abnormalities in PTE(1) prediction. Future trials should incorporate quantitative electroencephalography features to validate this enhancement of PTE risk stratification models

Quantitative epileptiform burden and electroencephalography background features predict post-traumatic epilepsy

Background Post-traumatic epilepsy (PTE) is a severe complication of traumatic brain injury (TBI). Electroencephalography aids early post-traumatic seizure diagnosis, but its optimal utility for PTE prediction remains unknown. We aim to evaluate the contribution of quantitative electroencephalograms to predict first-year PTE (PTE1). Methods We performed a multicentre, retrospective case-control study of patients with TBI. 63 PTE1 patients were matched with 63 non-PTE1 patients by admission Glasgow Coma Scale score, age and sex. We evaluated the association of quantitative electroencephalography features with PTE1 using logistic regressions and examined their predictive value relative to TBI mechanism and CT abnormalities. Results In the matched cohort (n=126), greater epileptiform burden, suppression burden and beta variability were associated with 4.6 times higher PTE1 risk based on multivariable logistic regression analysis (area under the receiver operating characteristic curve, AUC (95% CI) 0.69 (0.60 to 0.78)). Among 116 (92%) patients with available CT reports, adding quantitative electroencephalography features to a combined mechanism and CT model improved performance (AUC (95% CI), 0.71 (0.61 to 0.80) vs 0.61 (0.51 to 0.72)). Conclusions Epileptiform and spectral characteristics enhance covariates identified on TBI admission and CT abnormalities in PTE1 prediction. Future trials should incorporate quantitative electroencephalography features to validate this enhancement of PTE risk stratification models