The application of Perturbation Theory in machine learning (PTML) models was investigated to address various problems in nanotoxicology and nanomedicine. The article by Halder et al. (2020) proposes an in-silico model based on PTML to evaluate the genotoxicity of metal oxide nanoparticles, achieving high precision and predictive capacity, thus revolutionizing the safety evaluation of nanomaterials. Munteanu et al. (2021) applied PTML to predict the effectiveness of drug delivery systems in the treatment of glioblastoma, obtaining accurate results and suggesting the applicability of this approach in nanomedicine. Finally, the study by Santana et al. (2020) used PTML in the design of drug delivery systems, highlighting its efficacy and specificity, with the PTML-RF model showing higher sensitivity and accuracy. These findings support the widespread utility of Perturbation Theory, and PTML in particular, as an advanced tool in the prediction and design of nanomaterials and drug delivery systems, with potential significant implications for the safety and efficacy of these technologies (Halder et al., 2020; Munteanu et al., 2021; Santana et al., 2020).

The discussion between the three scientific articles reveals the innovative use of entropy in different scientific contexts and applications. In the article by González-Durruthy et al. (2017), Shannon entropy is used to transform the Raman spectra of carbon nanotubes, developing nano-PT-QSPR regression models that successfully predict the effect of nanotubes on mitochondrial respiration. This in silico approach provides a deep understanding of how nanomaterials affect biological systems. On the other hand, Prado-Prado et al. (2011) use the entropy of drug and protein graphs to develop an mt-QSAR model, focused on predicting the FDA drug-target network. This research combines theoretical and experimental studies, demonstrating the usefulness of entropy in predicting drug-target interactions and in understanding the structure of drug target proteins. The article by Roy et al. (2022) highlights the use of entropy in the context of high-entropy alloy-based catalysts for the selective reduction of CO2 to methanol. Machine-assisted machine learning is used to explore the diversity of metal combinations in high-entropy alloys, demonstrating the ability to predict adsorption energy and identify promising catalysts for methanol synthesis. In comparison, all three studies employ entropy creatively to model and predict phenomena in biological systems, drug-target networks, and high-entropy catalysts. Although each article focuses on a different field, they share the common strength of using entropy as a valuable tool to quantify diversity, complexity, and information in diverse systems, demonstrating its versatility and applicability in scientific research. Furthermore, they highlight the importance of machine learning and in silico approaches to advance the understanding and application of entropy in various scientific fields.

In this work, a liquid crystal system as a potential bone-repairing booster has been induced from a polyacrylic acid (PAAc) gel loaded with synthetic hydroxyapatite nanoparticles (nHA) under physiological conditions of pH and temperature that resemble the early stages of bone formation. Dynamic processes at the microscale in biological systems require a material capable of behaving following a specific order and fluency. Besides, they must fulfill conditions such as biocompatibility. The occurrence of mesophases at different environments of pH and temperature has been shown by an optical microscope with crossed polaroids and retardation plate. The biocompatibility has been demonstrated by hemolysis analysis and coagulation assays. The results have shown a biocompatible system with excellent adaptation properties under physiological in vitro conditions.

La globalización ha ampliado las cadenas de suministro a nivel mundial, aumentando tanto el consumo como la complejidad de la distribuciónde productos y servicios. La eficiencia en este sistema complejo es crítica, requiriendo herramientas avanzadas para optimizar el uso derecursos y minimizar las pérdidas. Entre estas, los sistemas de predicción de demanda son fundamentales para evitar el exceso de inventario ylas oportunidades de venta perdidas, ayudando a reducir costos significativamente. En el sector minorista, la precisión de estas predicciones enel nivel más detallado es vital debido a su impacto económico directo. Sin embargo, el análisis de pronóstico de demanda afronta el desafío deerrores inherentes al no considerar las correlaciones entre los diferentes niveles jerárquicos, lo que hace esencial la implementación de técnicasde reconciliación de pronósticos

La predicción del sentido del voto ha sido un problema que se ha abordado desde el nacimiento de la democracia misma. Este es un problema multifacético, debido al constante y cambiante carácter de la sociedad y la infinidad de diferencias que construyen la individualidad de las personas. La importancia de la elección de un presidente es una que impacta múltiples aspectos de la vida política, económica y social de un país. Es por esto que la previsión del resultado electoral sirve como principio importante de planificación en muchos sentidos. Uno de estos sentidos sería el económico ya que la diferencia en corrientes entre candidatos puede poner el riesgo la operación de industrias enteras que pueden afectar el rumbo económico.

La metodología a utilizar es se construye en tres partes. La primera parte es sobre la definición de de los datos a utilizar, siendo la limpieza de la base de datos y la selección de variables lo más relevante, la segunda parte consiste en el uso de la metodología de Extreme Gradient Boosting (XGBoost) o refuerzo de gradientes extremo para el entrenamiento del modelo en base a los datos de 2016 y por último la tercera parte consiste en la generación de resultados al utilizar el modelo de datos entrenado con los datos de 2016 para predecir el voto en la elección presidencial de 2020 en Estados Unidos de América.

In this study, an in silico research about the anticancer properties of Passiflora incarnata alkaloids was realized. As a result, these alkaloids were found to be more effective on Hs 683 Oligodendroglioma cells belonging to brain tissue with the higher Pa and the lower Pi values.

Introduction

Cancer is a disease in which some body cells proliferate out of control and invade other bodily regions [1]. It is a major public health concern in the United States and around the world, despite the amazing advancements made in its treatment. A projected 1,958,310 new instances of cancer will be diagnosed in the US alone in 2023, and 609,820 individuals will pass away from the illness. Research on the role of several biological components like immunity, metabolism, genetics, and epigenetics in mediating these disparities is ongoing. Men are more likely than women to get bladder, colon, and brain cancer, among other cancer types [2].

It has been established that a few plant bioactive components have anti-cancer properties. An estimated 50–60% of cancer patients in the US use complementary and alternative medicine - a treatment approach that uses substances derived from various plant parts or nutrients - either exclusively or in conjunction with conventional treatment plans like chemotherapy and/or radiation therapy [3]. These include, to mention a few, lycopene from tomato, diallyl sulfide from garlic, genistein from soybean, resveratrol from grapes, sulforaphane from broccoli, isothiocyanates from cruciferous vegetables, curcumin from tumeric, genistein from soybean, apigenin from parsley, and gingerol from gingers [4].

Wild passion flower, also known as passion vine, maypop, or Passiflora incarnata L. (Passifloraceae) is a climber herb that has tasty golden berries that resemble corona-shaped blooms, brightly colored, showy flowers, and auxiliary tendrils with herbaceous or woody branches. The Latin word "Passio" which was originally discovered by Spanish explorers in 1529 and was described as a metaphor for "Christ's Passion", is the source of the expression "Passiflora." Ayurveda, Siddha, and Unani are among the traditional medical systems that have documented its therapeutic usage.

Materials and Methods

β-carboline alkaloids, including harmine, harmaline, harmol, harmalol, and harmane are present in P. incarnata. In this study, the Way2Drug Platform's CLC-Pred service were used to in silico investigate the compounds' interactions with tumor and non-tumor cell lines as well as their potential for organ-specific carcinogenesis [5]. At the end, the higher results for each alkaloid are compared in each paragraph.

Results and Discussion

The cell line on which harmine was most effective was Hs 683 Oligodendroglioma cells belonging to brain tissue with Pa value of 0.911. In this feature, having a Pi value as small as 0.002 indicates the reliability of the result obtained within this study. It is followed by M19-MEL Melanoma cells in the skin and NCI-H295R Adrenal cortex carcinoma cells in the adrenal cortex with Pa values of 0.472 and 0.43, respectively, which are approximately 2 times lower than Hs 683 Oligodendroglioma cells.

The cell line most affected by harmaline also was Hs 683 Oligodendroglioma cells of brain tissue with Pa value of 0.841. In this feature, having a Pi value as small as 0.003 indicates the reliability of the result obtained within this study. It is followed by M19-MEL Melanoma cells in the skin and PC6 Small cell lung carcinoma cells in the lung with Pa values of 0.424 and 0.399, respectively, which are approximately 2 times lower than Hs 683 Oligodendroglioma cells.

The most affected cell line on harmol was Hs 683 Oligodendroglioma cells belonging to brain tissue with Pa value of 0.848. In this feature, having a Pi value as small as 0.003 indicates the reliability of the result obtained within this study. The next results are followed by PC-3 Prostate carcinoma in prostate and NCI-H295R Adrenal cortex carcinoma cells in adrenal cortex with 29% and 39% less (Pi 0.604 and 0.521, respectively) than other alkaloids. A-375 Malignant melanoma (Pa 0.462) and M19-MEL Melanoma cells (Pa 0.45) in the skin, DU-145 Prostate carcinoma in the prostate (Pa 0.439), RKO Colon carcinoma in the colon (Pa 0.413), HOP-18 Non-small cell lung carcinoma cells in the lung (Pa 0.406) were affected by about 2 times less than Hs 683 Oligodendroglioma cells.

The most affected cell line by harmalol was brain tissue Hs 683 Oligodendroglioma cells with Pa value of 0.894. In this feature, having a Pi value as small as 0.002 indicates the reliability of the result obtained within this study. Then it is followed by HOP-18 Non-small cell lung carcinoma and PC6 Small cell lung carcinoma cells in the lung with Pa values of 0.468 and 0.449, approximately 2 times less than Hs 683 Oligodendroglioma cells.

And finally, the most affected cell line by harmane was Hs 683 Oligodendroglioma cells belonging to brain tissue with Pa value of 0.918. In this feature, having a Pi value as small as 0.002 indicates the reliability of the result obtained within this study. It is followed by M19-MEL Melanoma cells in the skin and NCI-H295R Adrenal cortex carcinoma cells in the adrenal cortex with Pa values of 0.466 and 0.446, respectively, which are approximately 2 times lower than Hs 683 Oligodendroglioma cells.

As a final result, we can note that the highest anticancer effect of all alkaloids analyzed was on Hs 683 Oligodendroglioma cells belonging to brain tissue. When comparing these alkaloids, the order of Pa values is as follows:

Harmane > Harmine > Harmalol > Harmol > Harmaline with Pa 0.918 > 0.911 > 0.894 > 0.848 > 0.841

That is, the effects of harman and harmine on Hs 683 Oligodendroglioma cells are close to each other and more than other alkaloids. Compared to other alkaloids analyzed, harmol was the alkaloid with the wide spectrum of action expressed by higher Pa values in the cell lines. In general, M19-MEL Melanoma, NCI-H295R Adrenal cortex carcinoma, PC6 Small cell lung carcinoma, HOP-18 Non-small cell lung carcinoma cells were the common points in the effect spectrum of these alkaloids. Cell lines with higher Pa values of harmol compared to other alkaloids included PC-3 Prostate carcinoma, A-375 Malignant melanoma, DU-145 Prostate carcinoma, RKO Colon carcinoma indicated that harmol has both a wider and more diverse spectrum of anticancer effects.

Conclusions

Overall, these in silico calculations are very important in terms of both the breadth of results obtained and the cost-effectiveness. The acquainted results can be useful for obtaining new anticancer drugs by continuing with in vitro and clinical trials.

References

1. National Cancer Institute. (2021). What Is Cancer? Retrieved January 11, 2021, from https://www.cancer.gov/about-cancer/understanding/what-is-cancer

2. American Association for Cancer Research (AACR). AACR Cancer Progress Report. Cancer in 2023. https://cancerprogressreport.aacr.org/progress/cpr23-contents/cpr23-cancer-in-2023/

3. Gutheil, W. G., Reed, G., Ray, A., Anant, S., & Dhar, A. (2012). Crocetin: an agent derived from saffron for prevention and therapy for cancer. Current pharmaceutical biotechnology, 13(1), 173–179. https://doi.org/10.2174/138920112798868566

4. Filimonov D.A., Lagunin A.A., Gloriozova T.A., Rudik A.V., Druzhilovskii D.S., Pogodin P.V., Poroikov V.V. (2014). Prediction of the biological activity spectra of organic compounds using the PASS online web resource. Chemistry of Heterocyclic Compounds, 50(3), 444-457.

5. Nasibova T., Huseynova N.S., Zeynalova G.R., Gafarova D.S., Ismayilova S.Y. (2023). In silico prediction of cancer-related properties of Passiflora incarnata alkaloids. In V.V. Poroikov & R.G. Efremov (Eds.), Proceedings book of the XXIX Symposium “Bioinformatics and Computer-aided Drug Discovery”: Institute of Biomedical Chemistry 2023 (pp. 130). https://doi.org/10.18097/BCADD2023

For the first time, the mathematical and theoretical foundations of a complex approach to the creation of a computer program for the virtual screening of NO scavengers in a number of newly synthesized compounds were developed. Approaches to the implementation of the software complex are described.
In our work, for the first time, the antioxidant activity of 532 xanthine derivatives was evaluated in vitro for NO inhibition. For the first time, with the help of semi-empirical quantum chemical methods, the main descriptors of the frontier molecular orbitals of xanthine derivatives have been substantiated
by their influence on the ability of these compounds to bind NO. This research aims to assess the in vitro antioxidant properties of 532 xanthine derivatives with regard to NO inhibition. The dependence of antioxidant activity on the quantum chemical parameters of xanthine derivatives was analyzed using machine learning algorithms using the following models: Linear Regression, Support Vector Machine Regression, Random Forest Regression, Gradient Boosting Regression, K-Nearest Neighbor Regression. As a result of our analysis, we tested several models for solving regression problems. The best models without optimization turned out to be the "Support Vector Machine Regression" and "K-Nearest Neighbors Regression" models. When optimizing the studied models, the Gradient Boosting Regression model showed the best generalizing ability with an error within 16%. This model can be used for the prediction of antioxidant activity based on quantum chemical parameters. The model's quality can be further improved by increasing the training and test samples, as well as expanding the features to deepen the model and improve the generalization ability. A program of virtual screening of substances with the properties of NO scavengers has been developed and created. In the process of testing the new synthesized xanthine derivatives, a computer program made it possible to predict the most pronounced properties of the NO scavenger in 8- enzylaminotheophilinyl-7-acetic acid hydrazide (C-3). In vitro experiments confirmed the prediction of the properties of the NO scavenger in C-3 (267.3%). Addition of C-3 (10 -5 M) to the incubation mixture leads to a decrease in nitrotyrosine by 45% and oxidized glutathione by 53.2% concomitantly with an increase in the concentration of reduced glutathione by 43.8% and increase in the activity of GSH-dependent enzymes - GPR by 337% and GR by 195% (p < 0.05). It should be noted that the antioxidant effect of C-3 is accompanied by an increase in concentration of HSP ₇₀ by 34.7%. By regulating the level of NO and its cytotoxic forms, C- 3 is able to reduce the suppression of GSH, which determines the concentration of HSP ₇₀ . In terms of potency, C-3 is significantly superior to Mexidol (10 ^-5 M). The obtained results in vitro confirm the results of the NO scavenger’s C-3 compound obtained as a result of the virtual screening.

Periodontics, as a specialized field in dentistry, plays a pivotal role in the maintenance of oral health by focusing on the prevention, diagnosis, and treatment of periodontal diseases. In recent years, the integration of Artificial Intelligence (AI) has emerged as a transformative force, promising advancements in diagnostics, treatment planning, and patient management within the realm of periodontics. This review aims to explore and evaluate the current state of AI applications in Periodontics, examining its potential impact on clinical practice, research, and education.

The review begins by elucidating the fundamental concepts of AI and its various subfields, such as machine learning and deep learning, that contribute to the development of intelligent systems. Subsequently, an in-depth analysis is conducted to highlight the diverse applications of AI in Periodontics, ranging from image analysis for radiographic interpretation to predictive modeling for treatment outcomes. The discussion also addresses the challenges and limitations inherent in the current AI implementations, including issues related to data privacy, interpretability, and ethical considerations.

Furthermore, the review investigates the integration of AI-driven technologies into periodontal research, emphasizing the role of big data analytics and computational modeling in enhancing our understanding of disease mechanisms and treatment responses. It explores how AI can contribute to the personalization of treatment plans, allowing for more tailored and efficient interventions based on individual patient profiles.

The critical assessment also sheds light on the educational aspects of AI in Periodontics, discussing the potential role of AI in training programs, simulation exercises, and virtual patient scenarios. The review concludes by outlining future prospects and recommendations for the responsible and effective incorporation of AI in periodontal practice, emphasizing the need for interdisciplinary collaboration, ongoing research, and ethical considerations to harness the full potential of AI while ensuring patient safety and well-being. Overall, this review serves as a comprehensive guide for dental professionals, educators, and researchers seeking to navigate the evolving landscape of AI in the field of Periodontics.

En México y otras naciones manufactureras, la industria automotriz es considerada un pilar estratégico económico por los beneficios que trae consigo como: generación de empleos a gran escala, las recaudaciones fiscales de las operaciones comerciales, capacitación del personal, desarrollo de proveedores locales y modernización tecnológica. La industria automotriz representó al 3.6% del PIB en México en el año 2022 (Asociación Mexicana de la Industria Automotriz, A.C., 2023).

El objetivo de este análisis es pronosticar las ventas de automóviles ligeros nuevos para anticipar algún impacto para las partes involucradas.

Problema Central: A pesar de la creciente relevancia del aprendizaje federado, la integración de prácticas de MLOps en estos entornos para fortalecer la seguridad y privacidad de los datos no ha sido suficientemente explorada. Este vacío en la investigación destaca la necesidad de un estudio detallado y comparativo que aborde específicamente esta intersección.

Objetivos: Investigar cómo las metodologías de MLOps pueden ser aplicadas efectivamente en entornos de aprendizaje federado para mejorar la protección de datos. Así como proporcionar un análisis comparativo de diferentes métodos de MLOps y evaluar su impacto en la privacidad y seguridad de los datos en el aprendizaje federado.