Effects of LiO Doping on the Surface and Catalytic Properties of CoO–FeO Solids Precalcined at Different Temperatures

The effects of Li 2 O treatment on the solid–solid interactions and the surface and catalytic properties of the Co 3 O 4 –Fe 2 O 3 system have been studied using TG, DTA and XRD methods, nitrogen adsorption studies at −196°C and the catalytic oxidation of CO by O 2 at 150–350°C. The results obtained showed that Li 2 O doping followed by precalcination at 500–1000°C enhanced the formation of cobalt ferrite to an extent proportional to the amount of dopant added (0.52–6.0 mol% Li 2 O). The solid–solid interaction leading to the formation of CoFe 2 O 4 took place at temperatures ≥700°C in the presence of the Li 2 O dopant. Lithia doping modified the surface characteristics of the Co 3 O 4 –Fe 2 O 3 solids, both increasing and decreasing their BET surface areas depending on the amount of dopant added and the precalcination temperature employed for the treated solids. The activation energy of sintering (ΔE S ) of cobalt/ferric mixed oxides was determined for the pure and doped solids from the variation in their specific surface areas as a function of the precalcination temperature. Both an increase and a decrease in the value of ΔE S due to Li 2 O doping occurred depending on the amount of lithia added. The doping of Co 3 O 4 –FeO solids, followed by precalcination at 500°C, effected a significant increase (144%) in their catalytic activity towards CO oxidation by O 2 . Precalcination at 700–1000°C of the mixed oxide solids doped with Li 2 O (0.52 and 0.75 mol%) resulted in an increase in their catalytic activity which decreased upon increasing the amount of Li 2 O added above this limit. The activation energy of the catalyzed reaction was determined for the pure and variously doped solids studied

Internet gaming disorder and psychological well-being among university students in Egypt

Abstract Background Internet gaming disorder (IGD) is a serious rising problem affecting people of all ages. Many researchers reported that students’ addictive gaming behavior resulted in the loss of function and the development of psychological problems. In this study, we aimed mainly to measure the prevalence of internet gaming disorder among Mansoura University students and find its relationship with psychological well-being. Methods A cross-sectional observational study was carried out during the academic year (2021–2022) at the University of Mansoura. Students from four different faculties were included. Participants ages ranged from 18 to 25 years old. An online Google Form questionnaire gathering the tools (questionnaire of demographic and clinical data, Internet Gaming Disorder Short Form scale, Ryff’s scale of psychological well-being) was distributed among them. Results In this study, 870 students were included. The age range was 18–25 years. They were divided into three groups: 315 normal gamers (36%), 500 risky gamers (58%), and 55 disordered gamers (6%), with no significant gender difference in each group (p-value = 0.138). A negative correlation was found between IGD and psychological well-being (r = -0.303). Conclusions The prevalence of IGD was 6% among Mansoura University students. Participants in the theoretical faculties who started playing internet gaming at a younger age and spent more than 2 h per week playing and more than 3 h per week thinking about playing internet games were more likely to develop IGD. Whenever IGD scores increased, psychological well-being scores were found to decrease (r = -0.303)

Efficacy and toxicity of once versus twice daily regimens of amikacin in febrile neutropenic pediatric cancer patients

Purpose: To compare the pharmacokinetic profile, clinical efficacy and toxicity of once-daily dosing of amikacin compared to twice-daily dosing among pediatric cancer patients with fever and neutropenia. Methods: 134 pediatric patients with hematological malignancies were randomly assigned to receive 15 mg/kg/day amikacin intravenously, either once or twice-daily dosing. For pharmacokinetics, two blood samples were obtained from each patient, the first sample was taken after 1 h from the beginning of infusion and the second sample was taken after 3 h from the first sample. Treatment success was considered when the patient improved without a change in the assigned antibiotic regimen or mortality from infection. Nephrotoxicity was assessed by following the increase in serum creatinine level. Results: Pharmacokinetic data revealed superiority in once-daily dosing where maximum concentration Cmax, area under the concentration time curve in 24 h AUC24 and elimination half-life t1/2 were a significantly higher while minimum concentration Cmin, elimination rate constant Ke and clearance CL were significantly lower in once-daily dosing compared to the twice-daily dosing. Clinical response achieved in 76.8% in once-daily group compared to 69.2% in twice-daily group. Nephrotoxicity was recorded in two patients in once-daily group and six patients in the twice-daily group. After stratifying our patients according to age, a significant increase was observed in the volume of distribution V and CL in pediatrics with age ⩽five compared to >five years. Cmax and AUC24 were significantly lower in the age group of ⩽five years. Conclusions: Clinical efficacy and nephrotoxicity were slightly improved in once-daily dosing compared to twice-daily dosing

A CT-based radiomics model to detect prostate cancer lymph node metastases in PSMA radioguided surgery patients.

Purpose In recurrent prostate carcinoma, determination of the site of recurrence is crucial to guide personalized therapy. In contrast to prostate-specific membrane antigen (PSMA)-positron emission tomography (PET) imaging, computed tomography (CT) has only limited capacity to detect lymph node metastases (LNM). We sought to develop a CT-based radiomic model to predict LNM status using a PSMA radioguided surgery (RGS) cohort with histological confirmation of all suspected lymph nodes (LNs). Methods Eighty patients that received RGS for resection of PSMA PET/CT-positive LNMs were analyzed. Forty-seven patients (87 LNs) that received inhouse imaging were used as training cohort. Thirty-three patients (62 LNs) that received external imaging were used as testing cohort. As gold standard, histological confirmation was available for all LNs. After preprocessing, 156 radiomic features analyzing texture, shape, intensity, and local binary patterns (LBP) were extracted. The least absolute shrinkage and selection operator (radiomic models) and logistic regression (conventional parameters) were used for modeling. Results Texture and shape features were largely correlated to LN volume. A combined radiomic model achieved the best predictive performance with a testing-AUC of 0.95. LBP features showed the highest contribution to model performance. This model significantly outperformed all conventional CT parameters including LN short diameter (AUC 0.84), LN volume (AUC 0.80), and an expert rating (AUC 0.67). In lymph node-specific decision curve analysis, there was a clinical net benefit above LN short diameter. Conclusion The best radiomic model outperformed conventional measures for detection of LNM demonstrating an incremental value of radiomic features

CT-based radiomic features predict tumor grading and have prognostic value in patients with soft tissue sarcomas treated with neoadjuvant radiation therapy.

Purpose: In soft tissue sarcoma (STS) patients systemic progression and survival remain comparably low despite low local recurrence rates. In this work, we investigated whether quantitative imaging features (“radiomics”) of radiotherapy planning CT-scans carry a prognostic value for pre-therapeutic risk assessment. Methods: CT-scans, tumor grade, and clinical information were collected from three independent retrospective cohorts of 83 (TUM), 87 (UW) and 51 (McGill) STS patients, respectively. After manual segmentation and preprocessing, 1358 radiomic features were extracted. Feature reduction and machine learning modeling for the prediction of grading, overall survival (OS), distant (DPFS) and local (LPFS) progression free survival were performed followed by external validation. Results: Radiomic models were able to differentiate grade 3 from non-grade 3 STS (area under the receiver operator characteristic curve (AUC): 0.64). The Radiomic models were able to predict OS (C-index: 0.73), DPFS (C-index: 0.68) and LPFS (C-index: 0.77) in the validation cohort. A combined clinical-radiomics model showed the best prediction for OS (C-index: 0.76). The radiomic scores were significantly associated in univariate and multivariate cox regression and allowed for significant risk stratification for all three endpoints. Conclusion: This is the first report demonstrating a prognostic potential and tumor grading differentiation by CT-based radiomics

Tumor grading of soft tissue sarcomas using MRI-based radiomics.

Background: Treatment decisions for multimodal therapy in soft tissue sarcoma (STS) patients greatly depend on the differentiation between low-grade and high-grade tumors. We developed MRI-based radiomics grading models for the differentiation between low-grade (G1) and high-grade (G2/G3) STS.Methods: The study was registered at ClinicalTrials.gov (number NCT03798795). Contrast-enhanced T1-weighted fat saturated (T1FSGd), fat-saturated T2-weighted (T2FS) MRI sequences, and tumor grading following the French Federation of Cancer Centers Sarcoma Group obtained from pre-therapeutic biopsies were gathered from two independent retrospective patient cohorts. Volumes of interest were manually segmented. After preprocessing, 1394 radiomics features were extracted from each sequence. Features unstable in 21 independent multiple-segmentations were excluded. Least absolute shrinkage and selection operator models were developed using nested cross-validation on a training patient cohort (122 patients). The influence of ComBatHarmonization was assessed for correction of batch effects.Findings: Three radiomic models based on T2FS, T1FSGd and a combined model achieved predictive performances with an area under the receiver operator characteristic curve (AUC) of 0.78, 0.69, and 0.76 on the independent validation set (103 patients), respectively. The T2FS-based model showed the best reproducibility. The radiomics model involving T1FSGd-based features achieved significant patient stratification. Combining the T2FS radiomic model into a nomogram with clinical staging improved prognostic performance and the clinical net benefit above clinical staging alone.Interpretation: MRI-based radiomics tumor grading models effectively classify low-grade and high-grade soft tissue sarcomas. (C) 2019 The Authors. Published by Elsevier B.V