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Exploring the inhibitory performance of expired moxifloxacin and norfloxacin on copper corrosion in saline environment
Get PDFThe reuse of expired drugs has become a challenge to maintain environmental clean-liness and achieve economic benefit. In this report, two expired drugs, moxifloxacin and norfloxacin, were used as inhibitors for copper corrosion in 3.5 % NaCl solution at different temperatures using several experimental approaches including chemical, electrochemical and spectroscopic techniques. The interaction of these two molecules on the copper surface was also inspected using different adsorption models. Using a dose of 500 mg L-1 of these drugs at 298 K, maximum inhibition efficiencies (IE) of 88.7 and 85.2 % were estimated from the potentiodynamic polarization technique for Mox and Nor, respectively., confirming that they can be considered as promising and effective inhibitors. The IE values were enhanced with increasing drugs doses and reduced with rising temperature. The higher IE is due to the strong adsorption of these molecules on the copper surface and such adsorption is physical in nature and follows the Langmuir adsorption isotherm. This is due to their unique chemical structures as they contain a number of functional groups. Polarization experiments confirmed that the drugs were tuned to behave as mixed-type inhibitors with an anodic predominance. All thermos¬dynamic and kinetic parameters were calculated and discussed in details, and the inhibition mechanism is proposed. All experimental results obtained by different techniques were in agreement with each other
Hereditary hemorrhagic telangiectasis (Rendu-Osler-Weber syndrome) and atrial fibrillation – is it ever too late to do something?!
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Infective endocarditis as a clinical marker for the presence of occult colorectal disease
No full textWar and Collective Memory
No full textČlanak polazi od teoretskih istraživanja kompleksne relacije između kolektivnog i komunikativnog pamćenja. Iako se inače pretpostavlja da je komunikativno pamćenje striktno odvojeno od dominantnog diskursa, promjena kolektivnog pamćenja ne bi bila zamisliva bez uzajamnog utjecaja. Ratna tematika ima egzistencijalni značaj i često je povezana s traumatskim iskustvima, zbog čega je kritički, ponekad i subverzivni stav očit. Pet drama suvremenih dramatičara (Neva Lukić i Viktor Radonjić, Edi Matić, Nina Mitrović, Tonka Mršić i Ivan Vidić) u žarištu su istraživanja radi toga da se ustanove zajedničke crte i sličnosti u odnosu na prikaz rata. Ugroženost čovjeka u svakodnevici, strah od rata kao vrste nasilja, etička deformacija čovjeka kroz rat, sumnje u ratno junaštvo kao i u ideologiju crno-bijelih stereotipa te egzistencijalna ugroženost čovjeka ratom aspekti su zastupljeni u svim analiziranim dramama.The article is based on theoretical considerations of the complex relationships between collective and communicative memory. Even if it is generally assumed that collective memory is strictly separate from the dominant discourse, a change in collective memory would not be grateful without such a reciprocal influence. The theme of war has existential significance and is often associated with traumatic experiences, which is why a critical and subversive attitude is evident in literature and drama. Five dramas by contemporary playwrights (Neva Lukić i Viktor Radonjić, Edi Matić, Nina Mitrović, Tonka Mršić and Ivan Vidić) are at the center of the study, with the aim of identifying common traits and similarities in relation to war. The threat to people in everyday life, the fear of war as a form of violence, the ethical deformation of people through war, doubts about war heroism as well as the ideology of black and white stereotypes and the fundamental threat to human existence through war are aspects that are common and relevant in the analyzed dramas
The Association Between Quality of Life and Severity of Specific Symptoms of Major Depressive Disorder Amenable to Effective Nursing Interventions
No full textAim: Some of the symptoms of major depressive disorder and some aspects of quality of life (QoL) are amenable to effective nursing interventions. The specific aim of this research was to examine the association of QoL of patients with major depressive disorder with the severity of individual symptoms amenable to effective nurse intervention. Subjects and Methods: A unicentric, cross-sectional study was performed on a consecutive sample of 72 outpatients diagnosed with a major depressive disorder (ICD - 10: F32 and F33). Inclusion criteria were diagnosis of major depressive disorder, age 18-65 years, both sexes, outpatient treatment. The primary outcome was the subjective assessment of health-related QoL as measured by the visual-analogue scale of the EQ - 5D - 5L questionnaire. Results: We found a negative association between more severe pessimism as well as self-dislike of any intensity with QoL in patients with major depressive disorder. We have not confirmed the hypothesis regarding the association between sadness and QoL. Conclusion: Nurse interventions that decrease patient pessimism and self-dislike could contribute to QoL improvement in patients with major depressive disorder. This investigation could contribute to focused cognitive-behavioral interventions of psychiatric nurses in multidisciplinary care for outpatients with major depressive disorder, achieving better treatment outcomes and particularly improvements of QoL
3D-based Convolutional Neural Networks for Medical Image Segmentation: A Review
No full textMedical image segmentation is essential for disease screening and diagnosis, particularly through techniques like anatomical and lesion segmentation that can be used to isolate critical regions of interest. However, manual segmentation is labor-intensive, costly, and susceptible to subjective bias, underscoring the need for automation. Deep learning, particularly convolutional neural networks (CNNs), has significantly advanced segmentation accuracy and efficiency. With the introduction of 3D imaging, research has evolved from 2D CNNs to 3D CNNs, which leverage inter-slice information to improve segmentation precision. This paper aims to provide a literature review of studies published between 2018 and 2024 on platforms such as Google Scholar and ScienceDirect, where the identified relevant research are "3D segmentation" and "3D medical imaging". This study outlines the key stages of 3D CNN segmentation that include preprocessing, region-of-interest extraction, and post-processing. Furthermore, this study emphasizes the application of 3D CNN architectures to complex lung imaging scenarios, such as lung cancer and COVID-19. Although 3D CNNs outperform 2D CNNs in preserving spatial continuity across slices, they present notable limitations. Key challenges include heavy computational and high memory demands, as well as a dependency on large annotated datasets, which are often scarce in medical imaging. Additionally, effective multiscale feature learning remains a challenging issue, with current architectures struggling to generalize the features of interest across several usage variations. To further improve the segmentation performance, future research should prioritize developing adaptive algorithms and fostering interdisciplinary collaboration between computer scientists and medical professionals to design efficient and scalable models, designed specifically for clinical applications. This future research direction will enhance diagnostic accuracy and segmentation quality in 3D medical imaging
FusionNet- A Hybrid Deep Learning Approach for Accurate Drug-Target Binding Prediction
No full textIdentifying drug-target binding affinities (DTBA) is crucial in drug discovery, to understand how effectively drugs interact with their targets. However, traditional methods often struggle to accurately capture the complex relationships in biological data, leading to limitations in their predictive power. This paper introduces FusionNet, an advanced deep-learning model designed to improve DTBA prediction. FusionNet combines the strengths of Convolutional Neural Networks (CNNs), Long Short-Term Memory networks (LSTMs), and Transformers, to better understand both short-range and long-range interactions in biological sequences and employs the Layer-wise Adaptive Moments (LAMB) optimizer, which ensures the model is more efficient and stable, especially when working with large datasets. FusionNet achieved an MSE of 0.20 and an rm2 of 0.681 on the Davis dataset and an MSE of 0.18 and an rm2 of 0.71 on the KIBA dataset, significantly outperforming existing models like SimBoost, GANsDTA, DeepCDA, and DeepDTA, making it a powerful tool for drug discovery and bioinformatics. This work not only enhances the accuracy of DTBA prediction but also sets new performance standards by integrating advanced neural network architectures and optimizing their training process. FusionNet effectively addresses the limitations of previous approaches, offering a more reliable and efficient method for predicting drug-target interactions
Designing a resilient supply chain network with a decentralized strategy under uncertainty
No full textThis paper investigates the vulnerability of supply chains to unforeseen disruptions and proposes a novel decentralized approach to enhance resilience. Traditional centralized supply chains are prone to risks, and this study introduces a decentralized model where individual entities make decisions based on local information, improving overall performance. The primary aim is to optimize a three-level resilient supply chain using a multi-objective mathematical model to minimize operational costs and promote decentralization. To address this, the study utilizes three optimization algorithms namely NSGA-III, bat, and whale algorithms where NSGA-III proved most effective by providing the highest number of Pareto-optimal solutions. The bat algorithm showed weaker performance across various metrics. A detailed sensitivity analysis was also conducted, revealing that increasing cost parameters, such as construction, ordering, and transportation, enhances decentralization. For example, a 50% rise in construction costs led to a 40% improvement in decentralization. This research highlights the potential of decentralized models in optimizing supply chain resilience
Critical Function Placement based on Service Chains in Multi-administrative Federated Networks
No full textAlthough the Service Function Chains (SFCs) embedding problem is broadly investigated in the literature, few works address it in a sliced multi-administrative network federation. In this work, we provide several insights into the problem. First, we describe a new federated-level topology abstraction. Second, we introduce a novel optimization model and heuristic (for large scale), which solve SFC embedding. Third, we conduct experiments on various multi-domain topologies and compare the algorithms regarding resource allocation efficiency and runtime. We analyze the trade-off between slice deployment costs and link utilization. Finally, we emulate two security scenarios on Containernet, Docker, and Open vSwitch architecture