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Abstract Parkinson’s disease (PD) is a neurological condition characterized by the disruption of both motor and non-motor functions. Given the absence of a definitive diagnostic method, it is crucial to uncover its root causes. Consequently, individuals displaying symptoms of Parkinson’s disease can promptly receive treatment and comprehensive care. To address this, our study aims to develop an AI-powered system capable of detecting Parkinson’s disease and subsequently evaluating the primary factors influencing its development. We collected 12 distinct datasets from the well-known PPMI database, covering various medical assessments such as motor abilities, olfaction, cognition, sleep patterns, and depressive symptoms. Subsequently, we refined this raw data using advanced search techniques to tailor it to our model’s requirements. Moreover, we introduced a novel labeling approach known as the majority voting algorithm. Following data preparation, we conducted Single and Multi-Modality analyses, focusing on single-treatment approaches and integrating multiple treatments for a comprehensive therapeutic strategy. To analyze these both, we employed five distinct Machine Learning algorithms. Notably, the Support Vector Machine (linear) emerged as the top performer, reaching an accuracy of 100% in both single and multimodality analysis. Furthermore, we employed four tree-based models for feature selection, with the Gradient Boosted Decision Tree excels in identifying the most significant features. Finally, we employed an Artificial Neural Network utilizing these key features, achieving the highest accuracy of 91.41%.

Medical experts are utilizing neuroimaging and clinical assessments to enhance the early identification of Parkinson’s disease. The current research initiative offers ways to identify Parkinson’s disease using machine learning and transfer learning. To carry out this, we extracted 7500 MRI images from 2022 and 2023 and 12 clinical assessment records from 2010 to 2023 from the well-known Parkinson’s Progression Marker Initiative (PPMI) database. Then, we applied machine and transfer learning approaches using clinical assessment records and MRI images, respectively. To identify Parkinson’s Disease (PD) using samples from clinical assessments, four distinct resampling techniques were employed. Subsequently, three machine learning models were applied to train on these resample records, and the recall score was analyzed. A hybrid of SMOTE and ENN proved to be the most effective approach for handling all of the imbalanced data, according to the recall study. Later, four different feature selection methods were used to find the top 10 features using these new samples. Lastly, we trained and validated the model using nine machine-learning algorithms. We also used explainable AI techniques like LIME and SHAP to interpret clinical assessment records. The extra tree classifier outperformed the others in terms of accuracy, reaching 98.44% using the tree-based feature selection technique. In addition to examining clinical assessment samples, this study investigated Parkinson’s disease using neuroimaging data. In pursuit of this objective, four pre-trained architectures were employed to analyze MRI images through two distinct approaches. The first approach involved utilizing the convolutional layer while replacing the remaining two layers with a customized Artificial Neural Network (ANN). Subsequently, training and evaluation are performed using our MRI samples, followed by analyzing significant weights using a LIME interpretable explainer. The second approach employs an improvisational technique without directly replacing the last layer. Instead, we predicted the weights of our MRI samples using the knowledge of the pre-trained model and stored them. Finally, CNN architecture was utilized for Parkinson’s disease detection, achieving an optimal accuracy of 85.08% with the implementation of DenseNet169 and CNN.

The development of electrocatalysts with high catalytic performance and low costs for oxygen reduction reaction (ORR) is still challenging. Herein, an overall solution for ORR in alkaline media, from the catalyst synthesis to catalyst regeneration and to the development of a rapid, reliable and easy approach for electrode surface evaluation, is presented. We focused on the development and characterization of an efficient material for ORR in alkaline media, α-Fe2O3 N-doped graphene (Fe-N-Gr). The associative pathway for the four electron transfer ORR mechanism is sustained by the Raman spectra recorded from the electrode surface. We highlighted the possibility of catalyst regeneration by a simple electrochemical method. After two regeneration rounds and 1500 cycles in O2-saturated 1 M NaOH, the catalyst still retains 40.8 % catalytic activity. Finally, as a part of the overall solution, we demonstrated that a methodology based on Raman spectroscopic measurements and machine learning algorithms can be applied for graphene-based catalysts lifetime investigation.

Pope Francis focuses on essential topics for the Church and society in his pontificate. To describe the relationships between different social subjects, he often uses the metaphor of the polyhedron to highlight the importance of each individual and each social group for the equitable development of the entire society. Nowhere does it systematically describe this metaphor, but it uses it in different contexts: societal, ecclesial, ecumenical, and theological. The literature also lacks papers that systematically and critically question this metaphor, presenting possible consequences for the Church’s life, i.e., pastoral care, liturgical practices, morality, doctrine, etc. This paper aims to describe this metaphor of the Church as a polyhedron. The metaphor of the polyhedron that he uses in his documents will be described first. Then, the key features, advantages, and disadvantages of the metaphor of the Church as a polyhedron will be brought out. According to this metaphor, the Church can be understood through her main characteristics: as synodal Church, unity in diversity, missionary Church, and charity.

Abstract The common sand-dwelling Formica cinerea ants possess monomorphic workers, yet with considerable and easily identified size variation. Considering the importance of body size in polymorphic ants and other animals, we test whether size-dependent differences in behaviour occur in this species. We focus on the behaviour of large and small foragers in the context of rescue occurring between nestmates when one of them is entrapped and requires help. We show that workers of different sizes are characterized by a similar frequency of rescue activity and time delay to the first act of rescue. However, small workers rescue for longer than large workers. These results indicate that, although there is no size-related rescue specialization in F. cinerea foragers, small rescuers behave differently than large ones in terms of rescue persistence. Additionally, we show that small workers are more active when trapped. We suggest that variation in behavioural persistence of differently-sized workers may increase the efficiency of rescue actions. This study is the first to find a connection between body size and rescue behaviour in ants and the first to quantify and analyze the behaviour of individuals in need of rescue. These findings add substantially to our understanding of social insects and, more generally, highlight the need to study among-individual behavioural variation in social animals, including those in which body size is judged minute and irrelevant.

New Directory for Catechesis released in 2020 deals directly with problems arising from the environmental crisis. In this article, it will be explored how this document deals with problems that come from man’s relationship to the environment. The first part shows how the previous two Directories – “General Catechetical Directory” from 1971 and “General Directory for Catechesis” from 1997 – dealt with those issues, then the second part shows how the new one approaches them throughout the whole document and, especially, in numbers 381‒384. In the last part, several critical remarks will be made on the document. The main hypothesis is as follows: the new Directory strongly emphasizes that engagement related to environmental problems is at the heart of Christian identity. But, in some places of the text, this argument could be more emphasized and also adequately incorporated into other topics it deals with.

Oxymetholone [C21H32O3] and metribolone [C19H24O2] are synthetic anabolic-androgenic agents which are included in the steroid class. Their ability to form new solid forms and their possibility to be included in host-guest β-cyclodextrin complexes was explored. The recrystallization of the compounds in a wide variety of solvents was accomplished. Two oxymetholone polymorphs and one oxymetholone acetic acid solvate were obtained:, while metribolone is reported only in the starting form. Their crystal structures were elucidated using single-crystal X-ray diffraction and the energies of intermolecular interactions were analyzed. Moreover, oxymetholone also showed the ability to be complexed in a new form of oxymetholone-β-cyclodextrin complex. The materials were also investigated by powder X-ray diffraction, DSC/DTA/TGA analysis, and FT-IR spectroscopy.

GW501516, also known by the name of cardarine, is a synthetic peroxisome-proliferator-activated receptor delta (PPR-δ) agonist agent developed for applications in the treatment of metabolic disorders and cardiovascular diseases. A broad polymorph screening in various solvents and mixtures was completed in order to explore its capabilities to grow polymorphs. The crystal structures of four polymorphs were elucidated using single-crystal X-ray diffraction, while one structure was solved via a powder X-ray diffraction method. The solid state features (nature of intermolecular interactions) were investigated by computational methods. The polymorphs were further investigated by thermal DSC analysis and X-ray diffraction on powders. From a pharmaceutical perspective, the stability and solubility of the polymorphs were analyzed as well.

The synthesis of graphene-based materials for drug delivery represents an area of active research, and the use of graphene in drug delivery systems is promising due to its unique properties. Thus, in the present work, we discuss the potential of few-layer graphene in a hydrogel system for dopamine release. The hydrogels are frequently used for these systems for their special physico-chemical properties, which can ensure that the drug is effectively released in time. However, the release from such structures is mostly determined by diffusion alone, and to overcome this restriction, the hydrogel can be “improved” with nanoscale fillers like graphene. The release kinetics of the composite obtained were analyzed to better understand how the use of graphene, instead of the more common graphene oxide (GO) and reduced graphene oxide (rGO), affects the characteristics of the system. Thus, the systems developed in this study consist of three main components: biopolymer, graphene, and dopamine. The hydrogels with graphene were prepared by combining two different solutions, one with polyacrylic acid and agarose and one with graphene prepared by the exfoliation method with microwave irradiation. The drug delivery systems were developed by adding dopamine to the obtained hydrogels. After 24 h of release, the presence of dopamine was observed, demonstrating that the system developed can slow down the drug’s degradation because of the interactions with the graphene nanoplates and the polymer matrix.

Nandrolone (Estr-4-en-17β-ol-3-one) is a derivative of testosterone and a naturally occurring anabolic–androgenic agent which belongs to the steroid group. Crystal structures of four short, medium and long esterified forms of nandrolone, including propionate, phenylpropionate, cypionate and undecanoate were determined using single-crystal X-ray diffraction. Crystal packing, supramolecular features and intermolecular interactions were described based on a quantitative and qualitative Hirshfeld surfaces analysis accompanied by evaluation of crystal energies and intermolecular interactions computation. Also, the solubility of the esters was investigated from a pharmaceutical perspective.