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This study conducts an in-depth analysis of clustering small molecules using spectral geometry and deep learning techniques. We applied a spectral geometric approach to convert molecular structures into triangulated meshes and used the Laplace–Beltrami operator to derive significant geometric features. By examining the eigenvectors of these operators, we captured the intrinsic geometric properties of the molecules, aiding their classification and clustering. The research utilized four deep learning methods: Deep Belief Network, Convolutional Autoencoder, Variational Autoencoder, and Adversarial Autoencoder, each paired with k-means clustering at different cluster sizes. Clustering quality was evaluated using the Calinski–Harabasz and Davies–Bouldin indices, Silhouette Score, and standard deviation. Nonparametric tests were used to assess the impact of topological descriptors on clustering outcomes. Our results show that the DBN + k-means combination is the most effective, particularly at lower cluster counts, demonstrating significant sensitivity to structural variations. This study highlights the potential of integrating spectral geometry with deep learning for precise and efficient molecular clustering. [ABSTRACT FROM AUTHOR]

Background: Metformin (dimethylbiguanide) is part of the biguanide class of antidiabetic drugs. It is a first-line drug in the case of type 2 diabetes. After oral administration is quickly absorbed. Its most common side effects are diarrhea, anorexia, nausea, vomiting. The main mechanism of action of Metformin is through the activation of AMP (adenosine monophosphate)-activated protein kinase, a central pathway in metabolic regulation, cell protection and cell survival. As we age, high blood pressure can occur along with diabetes and rise the risk of renal failure and cardiovascular complications. Objective: This paper aims to evaluate the interactions of Metformin with antihypertensive medication and other types of interactions such as with vitamin B12 and how does vitamin B12 affects the cardiovascular system. Material and methods: This systematic review of literature was shaped trough the usage of databases such as: MedScape, PubMed, UpToDate and Google Scholar. All the literature included in the study was published in English. Results : Metformin interacts with antihypertensive medication as it follows: Verapamil decreases the effect of Metformin by possibly inhibiting hepatic uptake of Metformin via OCT1 or other transporters; Thiazides and loop diuretics may reduce the blood glucose-lowering effect of Metformin by inhibiting the function of the beta-islet pancreas; Angiotensin-converting enzyme inhibitors increase the toxicity of Metformin by an unknown mechanism; Amlodipine decreases the effect of Metformin by pharmacodynamic antagonism; Betablockers and modulators of sympathetic activity cause a decrease in blood glucose by masking the adrenergic symptoms that signal hypoglycemia. Vitamin B12 has two effects on the cardiovascular system: by preventing the conversion of homocysteine to methionine and through the endocrine system. Homocysteine affects the endothelial function and intrinsic thrombolysis. The endocrine system is sensitive to B12 deficiency. The vitamin is needed for the structure of cell membranes, so that the pituitary gland can measure how much of each hormone is in the blood and adjust as needed. A lack of B12 can cause high levels of neurotransmitters, and excitatory hormones, leading to high blood pressure. Metformin may induce hypovitaminosis B12 through different possible mechanisms, such as altered intestinal motility and interaction with the endocytic receptor cubilin. Conclusions: Type 2 diabetes is frequently associated with high blood pressure, especially in the elderly patient. Interactions of Metformin with antihypertensive therapy must be analyzed to maximize the therapeutic effects and minimize the risks. The aim is of increasing the quality of life and life expectancy of patients with complex pathologies. [ABSTRACT FROM AUTHOR]

Along with the disappearance of the communist regime in Romania, the interest in the integration of people with disabilities (PD) has been growing, both from the side of authorities and civil society. This has led to an evolution and modification of legislation on the protection of PD. An increasing number of Non-Governmental Organization (NGOs) and other agencies have also been created, their involvement being particularly important and necessary for the success of the inclusion of PD. However, the employment rate of PD in Romania is much lower than in other EU countries. Romania's entry into the EU has led to the alignment of the legislation to the European one, but the mentality and attitude of society has remained strongly influenced by the mentality implemented during the communist period, which leads to the discrimination of PD, even after 30 years. This article shows practices and attitudes that influence the inclusion on the labor market of PD, able to work. A careful analysis indicates the necessity of substantial and profound changes in Romania, starting with the terminology used in legislation and continuing with access to education, employer mentality, collective mentality, inclusion strategies, employment system and practices, etc. [ABSTRACT FROM AUTHOR]

One of the fields in which human-machine interaction is more and more relevant is medicine, especially because, in this area, a specialized digital system has better chances of detecting illnesses, injuries or other significant aspects, than human perception. This paper proposes an experimental solution, designed to help guide electrocardiography (ECG) sensor placement on a patient. At the core of the solution is a Microsoft Kinect camera, providing both color and depth information that is used for generating a cyber representation of the patient's skeleton. Electrode placement locations are calculated and then overlaid on an RGB image, so the user has real-time feedback for correctly placing the sensors. The purpose of this solution is to contribute to the process of assistive ECG measurements, enabling home treated patients, students and doctors to experiment guided measurements procedures without the need of specialized medical assistance or second opinion confirmation. [ABSTRACT FROM AUTHOR]

Background. The past decade has shown an increase in the relevance of inflammation and its mediators in vascular biology. It is more likely that the inflammation remains one part of the complex pathophysiology linking hypertension to vascular disease. Plasma levels of circulating inflammatory molecules, such as the C-reactive protein (CRP), have been shown to be predictive of future cardiovascular disease (CVD), and drugs which modify their levels can reduce the risk of myocardial infarction and stroke. Methods. For the comparison of the efficiency of the angiotensin II converting enzyme inhibitors (ACEi) and the angiotensin II receptor antagonists (ARAs) on the plasma C-reactive protein (hs-CRP) we administered Enalaprilum or Candesartan for a period of 12 months to 64 patients with arterial hypertension degrees 1, 2 and 3. This was a comparative clinical study, open, randomized, non-interventional on parallel groups of subjects. Results. In the group treated with Candesartan (35 patients) we observed a significant decrease in the plasma level of hs-CRP from 2.29±2.39 to 1.02±0.88 mg/dl (p<0.001) and in the group treated with Enalaprilum (29 patients) the plasma hs-CRP level decreased from 3.04±2.12 to 1.70±1.87 mg/dl (p<0.001). Between the two groups we have not observed a statistically significant difference in lowering the plasma levels of hs-CRP. There were no significant correlations between hs-CRP and systolic blood pressure, diastolic blood pressure and lipoprotein levels. There was a significant correlation between initial hs-CRP and waist size (r = 0.32). Conclusions. The present results show that 12 months of Candesartan or Enalaprilum therapy improved plasmatic levels of hs-CRP. In conclusion, the angiotensin II converting enzyme inhibitors and the angiotensin II receptor antagonists have the ability to positively influence cardiovascular outcomes of essential arterial hypertension. [ABSTRACT FROM AUTHOR]

Angiotensin II (ATII) causes th rough the central nervous mechanism certain cardiovascular disorders. The participation of a central nervous component in the genesis of cardiac arrhythmia caused by Angiotensin is stated. The present paper describes the action exerted by ATII on the cardiac rhythm, after intracerebroventricular administration (icv) in rats anesthetized with ethylic urethane. The disorders of the cardiac rhythm induced by the ATII may be prevented or attenuated by blocking the AT1 receptors with and adequate antagonist, administered through icv. [ABSTRACT FROM AUTHOR]

This study investigates the clustering patterns of human β-secretase 1 (BACE-1) inhibitors using complex network methodologies based on various distance functions, including Euclidean, Tanimoto, Hamming, and Levenshtein distances. Molecular descriptor vectors such as molecular mass, Merck Molecular Force Field (MMFF) energy, Crippen partition coefficient (ClogP), Crippen molar refractivity (MR), eccentricity, Kappa indices, Synthetic Accessibility Score, Topological Polar Surface Area (TPSA), and 2D/3D autocorrelation entropies are employed to capture the diverse properties of these inhibitors. The Euclidean distance network demonstrates the most reliable clustering results, with strong agreement metrics and minimal information loss, indicating its robustness in capturing essential structural and physicochemical properties. Tanimoto and Hamming distance networks yield valuable clustering outcomes, albeit with moderate performance, while the Levenshtein distance network shows significant discrepancies. The analysis of eigenvector centrality across different networks identifies key inhibitors acting as hubs, which are likely critical in biochemical pathways. Community detection results highlight distinct clustering patterns, with well-defined communities providing insights into the functional and structural groupings of BACE-1 inhibitors. The study also conducts non-parametric tests, revealing significant differences in molecular descriptors, validating the clustering methodology. Despite its limitations, including reliance on specific descriptors and computational complexity, this study offers a comprehensive framework for understanding molecular interactions and guiding therapeutic interventions. Future research could integrate additional descriptors, advanced machine learning techniques, and dynamic network analysis to enhance clustering accuracy and applicability.