Comparison of Shear Bond Strengths of Ceramic Brackets Using Either Self-etching Primer or Conventional Method After Intracoronal Bleaching

Objective:To evaluate initial shear bond strengths (SBSs) of ceramic brackets using either a self-etching primer (SEP) or the conventional method (CM) after intracoronal bleaching with sodium perborate and distilled water.Materials and Method:Eighty human incisors were divided into 4 groups according to bleaching and bonding procedures: group 1, bleaching was not applied and brackets were bonded with SEP; group 2, bleaching was not applied and brackets were bonded with the CM; group 3, intracoronal bleaching with sodium perborate was applied for 3 weeks and brackets were bonded with SEP; group 4, intracoronal bleaching with sodium perborate was applied for 3 weeks and brackets were bonded with the CM. The SEP (Transbond Plus) was applied as recommended by the manufacturer. After SEP application, ceramic brackets were bonded with light cure adhesive (Transbond XT). For the CM, the teeth were etched with 37% phosphoric acid. After etching, a thin uniform coat of primer (Transbond XT Primer) was applied and ceramic brackets were bonded with light cure adhesive (Transbond XT). The SBSs were measured after water storage for 30 days, after 1000 cycles of thermocycling between 58C and 558C. Bond failure location was determined with the adhesive remnant index (ARI).Results:For the SEP method, there was no significant difference between the SBS values of the bleaching and nonbleaching groups. Furthermore, for the CM, the SBS value of the nonbleaching group was not significantly different from that of the bleaching group. The SBS values of the SEP method presented significant differences from the SBS values of the CM (p , 0.001). The SBS values of the SEP application decreased with and without bleaching. ARI scores did not show any significant difference between the groups (p = 0.174).Conclusion:Intracoronal bleaching with sodium perborate and distilled water did not affect the SBS values of ceramic brackets

Cytotoxicity and apoptotic effects of nickel oxide nanoparticles in cultured HeLa cells.

The aim of this study was to observe the cytotoxicity and apoptotic effects of nickel oxide nanoparticles on human cervix epithelioid carcinoma cell line (HeLa). Nickel oxide precursors were synthesized by an nickel sulphate-excess urea reaction in boiling aqueous solution. The synthesized NiO nanoparticles

Nutrition, atherosclerosis, arterial imaging, cardiovascular risk stratification, and manifestations in COVID-19 framework: a narrative review.

Background: Atherosclerosis is the primary cause of the cardiovascular disease (CVD). Several risk factors lead to atherosclerosis, and altered nutrition is one among those. Nutrition has been ignored quite often in the process of CVD risk assessment. Altered nutrition along with carotid ultrasound imaging-driven atherosclerotic plaque features can help in understanding and banishing the problems associated with the late diagnosis of CVD. Artificial intelligence (AI) is another promisingly adopted technology for CVD risk assessment and management. Therefore, we hypothesize that the risk of atherosclerotic CVD can be accurately monitored using carotid ultrasound imaging, predicted using AI-based algorithms, and reduced with the help of proper nutrition. Layout: The review presents a pathophysiological link between nutrition and atherosclerosis by gaining a deep insight into the processes involved at each stage of plaque development. After targeting the causes and finding out results by low-cost, user-friendly, ultrasound-based arterial imaging, it is important to (i) stratify the risks and (ii) monitor them by measuring plaque burden and computing risk score as part of the preventive framework. Artificial intelligence (AI)-based strategies are used to provide efficient CVD risk assessments. Finally, the review presents the role of AI for CVD risk assessment during COVID-19. Conclusions: By studying the mechanism of low-density lipoprotein formation, saturated and trans fat, and other dietary components that lead to plaque formation, we demonstrate the use of CVD risk assessment due to nutrition and atherosclerosis disease formation during normal and COVID times. Further, nutrition if included, as a part of the associated risk factors can benefit from atherosclerotic disease progression and its management using AI-based CVD risk assessment

Integration of cardiovascular risk assessment with COVID-19 using artificial intelligence

Artificial Intelligence (AI), in general, refers to the machines (or computers) that mimic "cognitive" functions that we associate with our mind, such as "learning" and "solving problem". New biomarkers derived from medical imaging are being discovered and are then fused with non-imaging biomarkers (such as office, laboratory, physiological, genetic, epidemiological, and clinical-based biomarkers) in a big data framework, to develop AI systems. These systems can support risk prediction and monitoring. This perspective narrative shows the powerful methods of AI for tracking cardiovascular risks. We conclude that AI could potentially become an integral part of the COVID-19 disease management system. Countries, large and small, should join hands with the WHO in building biobanks for scientists around the world to build AI-based platforms for tracking the cardiovascular risk assessment during COVID-19 times and long-term follow-up of the survivors

Cardiovascular/Stroke Risk Stratification in Diabetic Foot Infection Patients Using Deep Learning-Based Artificial Intelligence: An Investigative Study

A diabetic foot infection (DFI) is among the most serious, incurable, and costly to treat conditions. The presence of a DFI renders machine learning (ML) systems extremely nonlinear, posing difficulties in CVD/stroke risk stratification. In addition, there is a limited number of well-explained ML paradigms due to comorbidity, sample size limits, and weak scientific and clinical validation methodologies. Deep neural networks (DNN) are potent machines for learning that generalize nonlinear situations. The objective of this article is to propose a novel investigation of deep learning (DL) solutions for predicting CVD/stroke risk in DFI patients. The Preferred Reporting Items for Systematic reviews and Meta-Analyses (PRISMA) search strategy was used for the selection of 207 studies. We hypothesize that a DFI is responsible for increased morbidity and mortality due to the worsening of atherosclerotic disease and affecting coronary artery disease (CAD). Since surrogate biomarkers for CAD, such as carotid artery disease, can be used for monitoring CVD, we can thus use a DL-based model, namely, Long Short-Term Memory (LSTM) and Recurrent Neural Networks (RNN) for CVD/stroke risk prediction in DFI patients, which combines covariates such as office and laboratory-based biomarkers, carotid ultrasound image phenotype (CUSIP) lesions, along with the DFI severity. We confirmed the viability of CVD/stroke risk stratification in the DFI patients. Strong designs were found in the research of the DL architectures for CVD/stroke risk stratification. Finally, we analyzed the AI bias and proposed strategies for the early diagnosis of CVD/stroke in DFI patients. Since DFI patients have an aggressive atherosclerotic disease, leading to prominent CVD/stroke risk, we, therefore, conclude that the DL paradigm is very effective for predicting the risk of CVD/stroke in DFI patients

Design and baseline characteristics of the finerenone in reducing cardiovascular mortality and morbidity in diabetic kidney disease trial

Background: Among people with diabetes, those with kidney disease have exceptionally high rates of cardiovascular (CV) morbidity and mortality and progression of their underlying kidney disease. Finerenone is a novel, nonsteroidal, selective mineralocorticoid receptor antagonist that has shown to reduce albuminuria in type 2 diabetes (T2D) patients with chronic kidney disease (CKD) while revealing only a low risk of hyperkalemia. However, the effect of finerenone on CV and renal outcomes has not yet been investigated in long-term trials. Patients and Methods: The Finerenone in Reducing CV Mortality and Morbidity in Diabetic Kidney Disease (FIGARO-DKD) trial aims to assess the efficacy and safety of finerenone compared to placebo at reducing clinically important CV and renal outcomes in T2D patients with CKD. FIGARO-DKD is a randomized, double-blind, placebo-controlled, parallel-group, event-driven trial running in 47 countries with an expected duration of approximately 6 years. FIGARO-DKD randomized 7,437 patients with an estimated glomerular filtration rate >= 25 mL/min/1.73 m(2) and albuminuria (urinary albumin-to-creatinine ratio >= 30 to <= 5,000 mg/g). The study has at least 90% power to detect a 20% reduction in the risk of the primary outcome (overall two-sided significance level alpha = 0.05), the composite of time to first occurrence of CV death, nonfatal myocardial infarction, nonfatal stroke, or hospitalization for heart failure. Conclusions: FIGARO-DKD will determine whether an optimally treated cohort of T2D patients with CKD at high risk of CV and renal events will experience cardiorenal benefits with the addition of finerenone to their treatment regimen. Trial Registration: EudraCT number: 2015-000950-39; ClinicalTrials.gov identifier: NCT02545049

Water for all : Proceedings of the 7th international scientific and professional conference Water for all

The 7th International Scientific and Professional Conference Water for all is organized to honour the World Water Day by the Josip Juraj Strossmayer University of Osijek, European Hygienic Engineering & Design Group (EHEDG), Danube Parks, Croatian Food Agency, Croatian Water, Faculty of Food Technology Osijek, Faculty of Agriculture in Osijek, Faculty of Civil Engineering Osijek, Josip Juraj Strossmayer University of Osijek Department of Biology, Josip Juraj Strossmayer University of Osijek Department of Chemistry, Nature Park “Kopački rit”, Osijek- Baranja County, Public Health Institute of the Osijek- Baranja County and „Vodovod-Osijek“ -water supply company in Osijek. The topic of World Water Day 2017 was "Wastewater" emphasizing the importance and influence of wastewater treatments on global environment. The international scientific and professional conference Water for all is a gathering of scientists and experts in the field of water management, including chemists, biologists, civil and agriculture engineers, with a goal to remind people about the significance of fresh water and to promote an interdisciplinary approach and sustainability for fresh water resource management. The Conference has been held since 2011. About 300 scientists and engineers submitted 95 abstracts to the 7th International Scientific and Professional Conference Water for all, out of which 33 was presented orally and 62 as posters. 47 full papers were accepted by the Scientific Committee. 38 full papers became the part of the this Proceedings while 9 papers were accepted for publication in Croatian Journal of Food Science and Technology and Electronic Journal of the Faculty of Civil Engineering Osijek - e-GFOS