Since its discovery in 1882, the so-called Koch's bacillus (Mycobacterium tuberculosis, Mtb)
has never ceased to affect humanity. In 2020, tuberculosis (TB) became the second leading cause of
death in the world from a single pathological agent, Mycobacterium tuberculosis (Mtb), second only to
COVID-19. With the COVID-19 pandemic, there was an increase in the number of deaths from
tuberculosis due a lack of access to diagnosis and treatment, a fact that occurred for the first time in ten
years. Furthermore, the emergence of drug-resistant strains of TB makes urgent the search for less
toxic drugs and efforts to improve current treatment and bypass Mtb resistance mechanisms.
The shikimate pathway, which is present in bacteria, fungi and plants but absent in humans, has
been important for the development of new anti-TB therapeutic agents. The enzyme shikimate
kinase (SK) is a member of the Nucleoside Monophosphate Kinase (NMP) family, an important group
of enzymes that catalyze the reversible transfer of a phosphate from a nucleoside triphosphate to a
specific nucleoside diphosphate. This enzyme catalyzes the fifth step of the shikimate pathway, which
is shikimate phosphorylation (SKM), using ATP as a phosphate donor to form shikimate-3-phosphate
(S3P) and adenosine diphosphate (ADP). Based on the determination of the SKM binding site in a
crystallographic structure of SK complexed with ADP:SKM and the structure ATP:shikimate 3-
phosphotransferase, it was possible to have a better understanding of the intermolecular interactions
between the ligands and the enzyme.
In order to assist in the development of new drugs, computational tools can be used, as they
facilitate the detailed understanding of protein-ligand interactions. Therefore, in this work, we used
docking simulations to identify potential MtSK inhibitors from the library of molecules synthesized by
the Research Center for Molecular and Functional Biology (CPBMF), Brazil. Compounds that showed
the best binding energy predicted by docking simulations were subjected to in silico prediction of
toxicity and hepatotoxicity using pkCSM. Thus, the results obtained serve as a basis for further
efforts aimed at designing new anti-TB agents, as well as potential MtSK inhibitors.

Chagas disease, caused by the parasite Trypanosoma cruzi, represents a worldwide epidemiological, economic, and social problem. In the last decades, the trans-sialidase enzyme of Trypanosoma cruzi has been considered an attractive target for the development of new agents with potential trypanocidal activity. TcTS from Trypanosoma cruzi has received particular interest as a highly stereospecific trans-sialidase. Trypanosoma cruzi is incapable of synthesizing sialic acid (SA) de novo. Consequently, the expression of the trans-sialidase (TcTS) enzyme allows the cleavage of terminal SA residues present in glycoconjugates of host tissues. Sialidases catalyze trans-sialylation reactions via a classical ping-pong mechanism . The SA obtained from this process is afterwards transferred onto mucins on the parasite surface, creating a protective and adhesive coat against the immune system. Additionally, TcTS shedding into the bloodstream induces alterations in the sialylation pattern of host cells, generating immune dysfunction and hematological alterations. TcTS represents a potentially attractive drug target against T. cruzi since it is absent in mammalian hosts and because of its role in parasite survival. Molecular docking computationally predicts the conformation of a small molecule when binding to a receptor. Scoring functions are a vital piece of any molecular docking pipeline as they determine the fitness of sampled poses. Molecular docking results revealed that the new cleft may serve to accommodate the glycosyl acceptor. In this paper, we aim to study the anti-T. cruzi properties of two STLs isolated from Stevia species. In this sense, in vitro activities against different parasite forms and possible molecular mechanisms of parasite
inhibition are explored.

The process of post-combustion amine based CO₂ capture relies on large scale use of aqueous amine solutions. In such operations, it is associated with minor release of amine through the cleaned exhaust gas, as degraded solvent, as accidental spills, and amine transformation products in both liquid waste streams and atmosphere along with the treated flue gas. In this regard, it is necessary to study the concentration profiles of the by-products formed in aid of treating liquid waste streams. The present work includes chemistry and reaction mechanism studies of the reaction between CO₂ and ternary solvent systems consisting of triethanolamine (TEA) / 2-Amino-2-methyl-l-propanol (AMP), piperazine (PZ), and water. The chemical reactions of CO₂ with TEA, a tertiary amine is described by base-catalyzed hydration were carefully derived. The calculation was carried out using the Electrolyte Non-Random Two Liquid (NRTL) model in a rigorous rate-based non-equilibrium process simulation on Aspen Plus® 8.6. The results yield reasonable predictions on product concentration profiles and can be used as reference in future assessment of the by-products formed in CO₂ capture using the considered amine solvent system.

Depression, considered a mental disorder, is characterized by loss of interest/desire for everything, feelings of sadness and low self-esteem. And the most serious conditions can lead to suicide. This characteristic destroys hope and beauty in the lives of its bearers . According to Del Porto (1999), the feelings of sadness and joy are of fundamental importance for human beings, as they color the affective background of psychic life. Since, sadness composes a general response to situations of loss, defeat, disappointment, stress and other adversities . The World Health Organization, distinguishes depression by persistent sadness and lack of interest or pleasure in activities that were previously pleasurable. Even more so, it can negatively interfere with sleep and appetite, lack of concentration and increased fatigue. And it is the disease that most causes disability in the world. This mental disorder affects around 5% of adults worldwide. The causes of depression include complex interactions between social, psychological and biological factors. Life events such as adversity, loss, unemployment, social structure, whether in childhood or adulthood, contribute and can catalyze the development of depression . The depression brings with it a cost, which is usually very high. From monetary losses (losing a job) to life itself (suicide) . Depression affects all types of people – young and old, rich and poor, and women are more likely to experience depression than men . According to PAHO, PAHO – Pan American Health Organization, 1 out of 4 people in the Americas suffers from a mental illness . There are treatments for depression, from therapies to pharmacological. However, in low- and middle-income countries, treatment and support services for depression are often absent or underdeveloped. It is estimated that over 75% of people suffering from mental disorders in these countries do not receive treatment. Therefore, this work projects the study of molecular docking for safranal, a substance present in the medicinal plant Crocus sativus popularly known as saffron. In order to identify the activity relationship with selective serotonin, noradrenaline and/or dopamine reuptake blockers. Well, it is known that in depression there is a decrease in the levels of these neurotransmitters.

Tobacco smoking is a serious global epidemic disease that causes chemical, psychological, and behavioral dependence and is one of the greatest threats to public health, causing impoverishment and death. It is estimated to be responsible for the death of more than 8 million people per year, of which 1.2 million are passive smokers. In Brazil 443 people die a day because of smoking, 161,853 deaths annually causing a loss of R$125,148 billion in the health system and the economy. This disease is brought by the use of products derived from the plant Nicotiana tabacum of the solanaceae family, whose leaves are smoked mainly in the form of cigarettes. The nicotine majority alkaloid constituent about 98% of chemicals in tobacco, works as a neuroregulator which can disturb the central nervous system (CNS) resulting in the alteration of biochemical and physiological functions, In the liver about 80-90% of nicotine is transformed into cotinine, a stable metabolite with a relatively long half-life although they are found in smaller quantities in tobacco, cotinine and nornicotine are formed endogenously in the liver as metabolites of nicotine and the remainder is metabolised to trans-3′-hydroxycotinine (33–40%) and secondary metabolites, This being the main metabolite found in the urine of smokers, other secondary metabolites in nicotine are 5′- hydroxycotinine cotinine glucuronide, trans-3'-hydroxycotinine glucuronide, trans-3′-hydroxycotinine , etc . Furthermore, Cigarette smoke (CS) contains more than 7000 toxic chemicals and at least 69 of them may be carcinogenic (CSC), which contributes to the development of several types of carcinomas as heart disease, diabetes, cancer, emphysema, epigenetic problems, endocrine problems, and many other disorders, Among the chemical compounds present in cigarette smoke, nitrosamines are distinguished as carcinogens. Computational methods have been increasingly used to predict the molecular interactions and binding position of ligands with their target protein molecules, for the design of new inhibitors or/ and as an aid to the design of experimental and clinical trials. This work describes two papers that conducted as molecular docking studies with constituent chemicals from cigarette smoke, investigating the interference of nicotine metabolites on hormone against the three endocrine transport proteins and Jamal and Alharbi (2021) investigating the effect of carcinogenic nitrosamines on enzymes in the central nervous system (CNS), testing their hypothesis that nitrosamines can alter normal enzyme function and ultimately result in serious disease.

The gene HOXD13 is a member of the homeobox gene family. The homeobox genes encode a
highly conserved family of transcription factors involved in morphogenesis in all multicellular
organisms.HOXD13 is the first HOX gene known to be linked to human developmental disorders. Mutations
in HOXD13 are associated with limb deformities in both humans and mice, suggesting a critical
role in limb development.The methodology for the project is started by comparing our reference sequence which is of homo
sapiens and the name of our gene is HOXD13 with an excession id-3239 with all the other 14
sequences of different species. This query gene is present on chromosome number 2 and the locus
is NC_000002 and its length is equal to 8458bp.Observation-based study of the Evolution
of HOXD13 gene in 16 different species via Phylogenetic Analysis using MEGA-X andother Bioinformatics Tools.The methods we use is phylogenetics analysis (Mega X)Due to the mutagenesis occurring at various speciation events, the 16 different species have been divided into various groups and subgroups. Based on the amount of conserved sequence that they have inherited, the species are classified as closely linked and distantly linked. For the samereasons, outgroups are also produced. Thus, it can be seen that the mutations occurring in a singular
gene can produce such wide-ranging results in the structure of toes and fingers in various species.

Cancer is recognized as one of the most common fatal diseases of mankind, described as the uncontrolled growth and spread of abnormal cells, it has recently become one of the leading causes of death. Some agents used in the treatment of cancer (chemotherapy) cause numerous side effects due to their cytotoxic and mutagenic effects on healthy cells. This aroused interest on the part of the scientific community for the development of alternative drugs that do not have side effects, that are effective and selective. In recent years molecular hybridization has gained prominence, this technique consists of combining two or more bioactive pharmacophores to obtain a single molecule. Recently, using this approach, researchers have reported the synthesis of glycosides coupled to biologically active heterocyclics, showing an improvement in the pharmacological properties and bioavailability of the compounds, contributing to the water solubility and stability of organic molecules. In this study, molecular docking simulations carried out in two articles will be analyzed: “Design, synthesis, anticancer activity and molecular anchorage of new glycosides based on 1,2,3- triazole containing 1,3,4-thiadiazlil, indolyl and scaffolds of arylacetamide” and “New pyridines-N-βD-glycosides: synthesis, biological evaluation, and molecular docking investigations”, by the respective authors, Hussein H. Elganzory and Nuran Kahriman, for analysis of the software used in molecular editors and descriptors , database and ligand-receptor docking.

Cancer consists of disordered cell growth leading to changes in the genetic code. These conditions may be related to genetic factors or inadequate lifestyle conditions. Breast cancer is the second most common cancer in the world and the most common among women. According to the National Cancer Institute (INCA), at least one third of new cases of cancer that occur in the world annually could be prevented. The treatment of breast cancer can be arduous and painful both for the patient and the family members due to the symptoms and reactions of the same, so the treatment must be administered by a multidisciplinary team aiming at a comprehensive and humanized treatment for the patient. In general, the treatment modality is between surgery, radiotherapy and chemotherapy and hormone therapy. Faced with the occurrence and trauma left by existing conventional treatments, many studies have been carried out on the use of natural products in the treatment of diseases, including anticarcinogenic activities. These applications are often justified by the high toxicity of the drugs used. Natural chalcones can be found in vegetables, flowers and leaves, chemically, it is an α,β–unsaturated ketone. Data from 2016 from SciFinder record the existence of 92,000 chalcones from natural sources. The synthesis of chalcones has aroused interest in this area because it is considered a privileged structure due to its C6- C3-C6 skeleton, which in the chemistry of natural products arouses great pharmacological interest in the its versatility, structural variety and acceptance of substitutes, which makes the compounds obtained from its synthesis have diverse pharmacological properties, such as analgesic, anti-inflammatory, antibacterial, antituberculosis, antidiabetic, antioxidant, antiviral action, among others. Natural chalcones can be found in vegetables, flowers and leaves. Xanthohumol is a chalcone originally found in the hard resin (lupulin) of the female flower of Humulus Lupulus, known and used industrially as an agent responsible for the aroma, bitterness and natural preservative of beer. Xanthohumol has been extensively studied for its antiinflammatory, antioxidant and anticarcinogenic properties. It undergoes thermal isomerization to isoxanthohumol, as well as 8- and 6-prenylnaringenin. The in-silico study dealt with in this summary, bring the action of Xanthohumol in the activity of combating breast cancer by means of docking molecular.

An important signaling pathway along which the signal transduction is abnormal in psoriasis is the JAK/STAT signaling cascade. This study aimed to analyze the influence of cyclosporine A on the JAK/STAT signaling pathway in keratinocytes treated with lipopolysaccharide A compared with the untreated cells. Human, adult, low-Calcium, high-Temperature keratinocytes (HaCaT) were first incubated in 1 μg/mL of bacterial lipopolysaccharide A (LPS) for eight hours to induce an inflammatory condition, and then cyclosporine A was added to the culture at a concentration of 100 ng/mL for 2 (H_2), 8 (H_8), and 24 hours (H_24). Untreated cells constituted the control group. Changes in the expression of genes were determined using the HG-U 133_A2 microarray technique. 37 mRNAs connected with the JAK/STAT signaling pathway were selected from the Affymetrix database from among 22283 mRNAs present on the HGU-133A_2 microarray plate. The number of mRNAs differentiating it from the control culture depending on the time of cell exposure to the drug was as follows H_2 vs. C = 8 mRNAs, H_8 vs. C = 3 mRNAs, H_24 vs. C = 1 mRNA. On the other hand, only one mRNA, namely STAT3, differentiated the drug-treated culture from the control independent of the time of exposure. During therapy with cyclosporin A, it was confirmed the activation of the JAK/STAT cascade, and STAT3 might be a complementary molecular marker in monitoring the effectiveness of cyclospo therapy.

Prodigiosin (PDG) is a linear derivative of pyrrolyl dipyrromethene with a 4-methoxy,2-2-bi-pyrrole ring system. It is produced by some species of bacteria and eubacteria and is reputed for its anticancer activity against breast, colon and lung cancers via induced cellular stress. The study investigated the PDG binding interaction with several co-crystallized receptor tyrosine kinases (rTKs) to estimate the binding energies (E) and inhibition constants (Ki) of PDG. Prodigiosin was docked using AutoDock4.2 against 20 co-crystallized rTKs selected from the protein data bank, PDB. The E, Ki, RMSD, the number of H-bonds and the amino acids involved in the interactions of their best conformational poses were estimated and compared with those of doxorubicin, a potent cytotoxic agent. Comparatively, PDG interacted more efficiently with the collagen discoidin domain receptor subfamily 1 (DDR1) type II kinase protein (PDB: 4BKJ). A total of 16 amino acid residues were involved in hydrophobic (Val624, 2 Lys655, Glu672, Ile675, 2 Ile685, Met699, Thr701 and Asp784), hydrogen (2 Glu672, 3 Asp784) and π-stacking (Phe785) interactions with the DDR1 type II tyrosine kinase protein. A significant RMSD, E, Ki of 60.071 A, -10.04 Kcal/mol and 43.90 nM respectively for the binding of PDG to the rTK were obtained vis-a-viz native ligand, imatinib (78.961 A, -14.20 Kcal/mol and 39.11 ρM) and doxorubicin control (52.52 A, -8.65 Kcal/mol and 457.29 nM) respectively. The significantly higher inhibition of the DDR1 type II kinase protein by PDG compared with doxorubicin provides vital insights into understanding the molecular basis of the mechanism of anticancer activity and its clinical application in the treatment of breast, colon and lung cancers.

This study focuses on utilizing deep learning techniques for segmenting medical images, such as MRI and CT scans. The paper explores the limitations of traditional segmentation methods and highlights the potential of deep learning in overcoming these challenges. It provides an overview of Convolutional Neural Networks (CNNs) and their adaptation for medical image segmentation. Various architectures like U-Net, FCNs, and DeepLab are discussed, along with the importance of data augmentation and handling class imbalance. The paper also covers training processes, post-processing techniques, and evaluation metrics. It concludes by discussing current trends, challenges, and future directions in the field.

Traditional herbal medicine has been deeply rooted in the El Haouz region of the High Atlas Mountains in Morocco, where phytotherapy, using medicinal plants for disease prevention and treatment, has been widely practiced for many years. The local community has heavily relied on herbal remedies to alleviate various health conditions, including digestive issues, respiratory infections, joint discomfort, and skin disorders. The primary objective of our study is to meticulously document the specific medicinal plants employed by the region's inhabitants to address prevalent ailments.
To achieve this, an extensive search was conducted across various reputable databases, such as Google Scholar, Semantic Scholar, ResearchGate, Academia.edu, and PubMed. Relevant keywords, such as "High Atlas," "Phytotherapy," and "medicinal and aromatic plants," were employed to ensure comprehensive coverage. Our bibliographic investigation reveals abundant aromatic and medicinal plants in the El Haouz region.
The study findings illustrate that the local population in three areas of El Haouz (Imegdal, Amezmiz, and Asni) utilize 36 well-known remedies, categorized into 14 groups, to address a wide range of ailments. Notably, most of these plants exhibit multiple applications and are not limited to treating a single disease. Among the plant parts employed, leaves are the most commonly used (55%), followed by underground parts (40%, such as roots, tubers, bulbs, and rhizomes), flowers (18%), seeds (16%), and fruits (15%).
Additionally, we present detailed information on five specific aromatic and medicinal plants renowned for their effectiveness in treating various infections. These plants include Rubia tinctorum L., Ziziphus lotus (L.) Lam., Ridolfia segetum (L.) Moris (used for anemia), Thymus saturejoides Coss., and Rosmarinus officinalis L. (used for diabetes). The study delves into the specific utilisation methods for each of these plants.
The results of our inquiry provide substantial evidence of the local knowledge about plant species in the Al Haouz region, which have been traditionally employed for diverse ailments. Further exploration is warranted to investigate these documented plants' phytochemical, pharmacological, and toxicological aspects, with the potential to discover novel medications derived from them.

Background: Malaria is a disease caused by the Plasmodium parasite, which is transmitted through the bites of infected mosquitos. Only the Anopheles genus of mosquito can transmit malaria. The symptoms of this disease can include fever, vomiting, and headache. As millions of people are exposed to the threat of the Plasmodium parasite, it leads to millions of deaths annually. Therefore, there is a need to develop models for predicting compounds that can counteract this disease.
Objective: The primary objective of this research was to employ different techniques of machine learning on molecular descriptors obtained from Atomic Weight Vectors (AWV) and MD-LOVIs tool to predict the activity of potential antimalarial compounds.
Methods: Several machine learning techniques such as Ranger-ES-AWV (accuracy = 0.7714), Random Forest-ES-AWV (accuracy = 0.7718), SVMPoly-IB-AWV (accuracy = 0.787), C5.0-IB-AWV (accuracy = 0.7746), Ranger-IB-AWV (accuracy = 0.7854), GBM-IB-AWV (accuracy = 0.7882), and Treebag-IB-AWV (accuracy = 0.7798) were applied to predict the activity of antimalarial compounds.
Results: The results showed that the models obtained using machine learning techniques can be a powerful tool for predicting the activity of antimalarial compounds.
Conclusion: This study demonstrates the potential of machine learning techniques for predicting the activity of antimalarial compounds. These models can be used to identify new compounds with antimalarial properties and contribute to reducing the number of malaria-related deaths worldwide.

Computational methods have revolutionized the field of drug discovery, playing a vital role in the identification and development of potential therapeutic compounds. Among these methods, virtual screening has emerged as one of the most widely used approaches in the early stages of the drug discovery process. This approach utilizes computational techniques to sift through vast libraries of chemical compounds and predict their potential activity against a target of interest. One of the key tools employed in virtual screening is molecular docking, which allows researchers to simulate the binding interactions between small molecules (ligands) and target proteins (receptors). Scoring functions form a critical component of molecular docking, as they are responsible for evaluating and predicting the binding affinity between ligands and receptors. These scoring functions encompass a range of mathematical algorithms and empirical energy-based models that estimate the strength of the molecular interactions within a complex. By calculating scores based on predicted binding energies, scoring functions enable the ranking of compounds according to their potential to bind and interact with the target protein. This ranking process is crucial in identifying hit compounds that have the potential to be further developed into effective drugs. However, the accuracy of scoring functions is influenced by the inherent complexity of molecular recognition processes. Due to the computational limitations in accurately modeling all aspects of these processes, scoring functions rely on approximations to make predictions within a reasonable timeframe. These approximations introduce unavoidable inaccuracies, leading to a compromise between computational efficiency and predictive accuracy. Consequently, the performance of scoring functions is adversely affected, hindering their ability to effectively prioritize compounds and predict their actual binding affinities. To shed light on the foundations and limitations of current scoring functions, extensive studies and comparative analyses have been conducted. These investigations aim to evaluate the performance of different scoring functions in various scenarios, identify their strengths and weaknesses, and highlight strategies for overcoming the associated limitations. By comparing the results of these studies, researchers can gain insights into the relative performance of different scoring functions and make informed decisions about their implementation.

Furthermore, addressing the inaccuracies and limitations of scoring functions requires the development of innovative strategies and approaches. Researchers have proposed various strategies to improve the performance of scoring functions, such as incorporating more detailed and accurate representation of molecular interactions, refining the energy models used in scoring functions, and integrating machine learning and artificial intelligence techniques into the scoring process. These advancements have the potential to enhance the accuracy and reliability of scoring functions, empowering researchers to make better-informed decisions when selecting potential drug candidates.

When it comes to selecting a scoring scheme for structure-based virtual screening, several factors need to be considered. These include the nature of the target.

Due to their size, flexibility, biocompatibility, large surface area, and variable functionality nanoparticles have enormous industrial, agricultural, pharmaceutical and biotechnological applications. This has led to their widespread use in various fields. The advancement of knowledge in this field of research has altered our way of life from medicine to agriculture. One of the rungs of this revolution, which has somewhat reduced the harmful consequences, is nanotechnology. A helpful ingredient for plants, silicon (Si), is well-known for its preventive properties under adverse environmental conditions. Several studies have shown how biogenic silica helps plants recover from biotic and abiotic stressors. The majority of research have demonstrated the benefits of silicon-based nanoparticles (Si-NPs) for plant growth and development, particularly under stressful environments. In order to minimize the release of brine, heavy metals, and radioactive chemicals into water, remove metals, non-metals, and radioactive components, and purify water, silica has also been used in environmental remediation. Potentially toxic elements (PTEs) have become a huge threat to food security through their negative impact on agroecosystem. Si-NPs have the potentials to remove PTEs from agroecosystem and promote food security via the promotion of plant growth and development. In this review, we have outlined the various sources and ecotoxicological consequences of PTEs in agroecosystems. The potentials of Si-NPs in mitigating PTEs were extensively discussed and other applications of Si-NPs in agriculture to foster food security were also highlighted.

Cystitis is an inflammatory condition that primarily affects the bladder. It is often caused by a bacterial infection, with bacterial cystitis being the most common type among various urinary tract infections. This research aimed to extensively document ethnobotanical knowledge regarding the use of medicinal plants for treating cystitis due to their proven therapeutic properties. The study was conducted in the Rif region from March 1st, 2020, to April 15th, 2020. Semi-structured direct interviews were conducted with 657 participants to gather indigenous therapeutic wisdom. These surveys included information about the interviewees' demographics and ethnomedicinal practices. UR and MUV techniques were employed in data analysis. A total of 60 plant species, distributed among 51 genera and 31 families, were commonly used by our interviewees for cystitis therapy. Apiaceae had the highest representation of seven species, and Capparis spinosa L. was the most frequently recommended medicinal plant by the local population. Leaves were the most commonly utilized plant part (41.5%), and most herbal remedies were prepared through decoction (55%). This study constitutes the initial contribution to the ethnobotanical exploration of this region. It is recommended that the natural plant species identified in this research be further investigated to uncover their therapeutic effects and mechanisms of action. Primary attention should be given to conserving medicinal species, thoroughly documenting widespread medicinal knowledge, and biologically validating the listed species.

The enzyme acetylcholinesterase (AChE) is currently a therapeutic target for the treatment of neurodegenerative diseases. These diseases have highly variable causes but irreversible evolutions. Although the treatments are palliative, they help relieve symptoms and allow a better quality of life, so the search for new therapeutic alternatives is the focus of many scientists worldwide. In this study, we use a freely available dataset downloaded from the ChEMBL site composed of 1975 compounds of great structural diversity and with reported IC₅₀ enzyme inhibition against AChE. Using the MATLAB numerical computation system and the molecular descriptors implemented in the Dragon software, a QSAR-SVM classification model was developed; the obtained parameters are adequate for its adjustment (Q_TS = 88.63%), and the validation exercises verify that it is stable (Q_CV = 81.13%), with good predictive power (Q_PS = 81.15%) and is not the product of a casual correlation. In addition, its application domain was determined to guarantee the reliability of the predictions. Finally, the model was used to predict ACh inhibition by a group of quinazolinones and benzothiadiazine 1,1-dioxides obtained by chemical synthesis, resulting in 14 drug candidates with in silico activity comparable to acetylcholine.

Aminonitriles are substances frequently used to obtain other compounds. This circumstance raised the hypothesis that there was intrinsic to such structures relevant therapeutic potential to be scientifically explored. From this perspective, certain studies have identified that the performance of this group of molecules go beyond their participation only as an intermediary of new pharmacological formulations and demonstrate their own antibiotic evidence. In the face of studies carried out to support this research, it was observed that the potential of aminonitrils covers the fight against fungi, parasites, bacteria and even tumors. In this context, the present work aimed to carry out a screening of the antibacterial activity of substituted aminonitriles. Therefore, the disc-diffusion method was used to assess the antibacterial activity of seven new molecules of substituted aminonitrils (HAN-1 to HAN-7), at concentrations of 16, 32, 64, 128, 256, 512 and 1,024 µg /mL, against gram-positive bacteria Staphylococcus epidermidis ATCC 12228; Staphylococcus aureus ATCC 25923 and Enterococcus faecalis ATCC 29212, and gram-negative bacteria Pseudomonas aeruginosa ATCC 27853; Proteus mirabilis ATCC 25933 and Escherichia coli ATCC 25922. After incubation, it was observed that none of the tested molecules were able to form bacterial growth inhibition halo. Thus, it is necessary to continue investigating the antimicrobial potential of this group of compounds using more sensitive methodologies to confirm or rule out their bioactivity in these microorganisms.

There is today a fascination for blockchain technology and how it may improve healthcare systems but its concrete applications are still limited as many questions remain to be solved. Indeed, by definition, all users on a block can see the data shared which will necessarily hurt user’s – patients – privacy. As pointed out by OECD, ‘storing personal health data ‘on chain’ and thus, by definition, visible to other network participants, is a data privacy infringement. Rights under the EU General Data Protection Regulation, particularly the right to erasure, are incompatible with the immutability of blocks in a chain.’[1] Also, public authorities are in a ‘wait and see’ position: few regulations cover specifically the use of blockchain technology. Many states are still trying to understand blockchain technology and its benefits. As a consequence, actors involved in blockchain technology are facing legal and regulatory uncertainty. It is a necessity to adopt specific laws related to the implementation of blockchain technology in a broad manner and in healthcare especially. However, the European Union General Data Protection Regulation (GDPR) 2016 applies here as it deals with data protection and imposes a series of stringent obligations on Internet service providers (ISPs). It is interesting to note that OECD made recommendations regarding the use of blockchain technology in healthcare in order to meet key international standards. ‘Potential blockchain applications should be assessed within the framework provided by the Recommendation of the OECD Council on Health Data Governance and focus on four key aspects: fitness of the technology for the use to which it will be applied; alignment with laws and regulations; incremental adoption to allow time for evaluation; and a training and communications plan.’[2]

[1] See OECD, Blockchain Policy Series, Opportunities and Challenges of Blockchain Technologies in Health Care, p. 2, December 2020. Available online: https://www.oecd.org/finance/Opportunities-and-Challenges-of-Blockchain-Technologies-in-Health-Care.pdf.

[2] Id.

Virtual screening has been the basis for the design of new molecules with a wide variety of pharmacological activities. The great advantage of QSAR (Quantitative Structure-Activity Relationship) methods is that they are a low cost solution which allows the identification of molecules that are likely to present a specific activity.

Currently, the development of antibiotic resistance by microorganisms is one of the most important problems that have appeared in recent years in the treatment of infectious diseases. This increased resistance is associated with increased morbidity and mortality from infections, as well as an increase in healthcare costs.

QSAR methods appear as an increasing popular tool in the search of new treatment options against bacteria. In this paper, a tree-based classification method using Linear Discriminant Analysis (LDA) and discrete indices was used to create a QSAR model to predict antibacterial activity.

The model consists on a hierarchical decision tree in which, in a first step, a combination of discriminant functions capable of predicting antibacterial activity (FD1 and FD2) is applied to a database with 6375 commercial compounds, where 266 compounds were selected as candidates, from which 40.6% have this activity according to bibliography. The second step consists in the application of a discrete index, which is used to divide compounds into groups according to their values for said index in order to construct probability space.

The topological discrete indices R, PR1, PR2 and V4 have proven to have the ability to group active compounds effectively, considerably increasing the bibliographic success activity rate (up to 81.8%, 90.3%, 83.3% and 72.3%, respectively) which suggests a close relationship between them and the antibacterial activity of commercial compounds.

This methodology has proven to be a viable alternative to the traditional methods and its application provides interesting new drug candidates to be studied as repurposed antibacterial treatments.

This study examines the "Cultivate with the Family Workshop" project and its notable expansion into public schools in the Jaciara region, Mato Grosso, Brazil, with a special focus on São Francisco State School. This interdisciplinary initiative involved students from the Psychology and Agronomy programs at Eduvale College, emphasizing the importance of collaboration across different knowledge domains. Participants were provided with the opportunity to directly interact with the natural environment at the Farm School facilities, acquiring agricultural skills and benefiting from the psychological support provided by psychology students. The core of this project lies in promoting environmental awareness and encouraging the adoption of sustainable practices, which is especially relevant in a global context where environmental awareness plays an increasingly predominant role. The initiative aims to empower young individuals intellectually, equipping them with knowledge and a passion for environmental preservation, with a view to benefit future generations. From this perspective, the primary objective of this study is to conduct a comprehensive evaluation of the project, focusing on the benefits it offers to the community and the students, through participant testimonials and feedback from the assisted community. It is observed that the project, in addition to its positive impacts on the community, provides advantages to the university students involved, who gain substantial practical experience and play a significant role as agents of transformation. In the educational context, this project exemplifies how learning goes beyond the confines of conventional classrooms, offering an exceptional learning experience and preparing future generations for a more sustainable world. The agricultural skills acquired and a deeper understanding of the interconnection between humanity and the natural environment are invaluable assets that students will carry with them on their journeys. In summary, the "Cultivate with the Family Workshop" project is a paradigmatic initiative that has expanded its reach to public schools in the Jaciara region, Mato Grosso, Brazil, making a profound impact on the lives of the students involved. However, it is recommended to conduct a study that incorporates performance metrics for a more comprehensive appreciation of the project. This research highlights its significance as a successful intervention in promoting environmental awareness and sustainability, providing valuable lessons on how practical education can shape a more promising and ecologically conscious future.

The introduction of environmental education in the early grades of primary education is a response to the growing global concern for environmental preservation and sustainable development. The project arises from the need to sensitize future generations to these issues, aiming to shape more conscious and engaged citizens. The project establishes clear objectives, such as serving the local community, addressing ecological issues, and involving students from the early grades. The clarity of these objectives allows for a subsequent evaluation of their scope and effectiveness. The methodology employed in the project includes guided visits to the Farm School, organization into smaller groups, and the implementation of diverse practical activities, including storytelling, nature treasure hunts, and drawing workshops. Furthermore, the inclusion of Animal-Assisted Education (AAE) practices is an innovative and noteworthy approach. These activities provide a solid foundation for the project's implementation. Sensitization and environmental education are at the core of the project. The hands-on experience at the Farm School offers children a valuable learning opportunity. However, subsequent analysis should assess whether these activities have effectively raised children's awareness of the importance of environmental preservation and sustainable development. Project evaluation should encompass a comprehensive analysis of outcomes and impact. Results can be measured in terms of increased environmental awareness and understanding among visiting children. Additionally, the impact can be assessed by examining how the participation of Psychology scholars enriches their education. The collaboration between Eduvale College and Santa Rosa Municipal School is a central aspect of the project. The analysis should consider how this collaboration is maintained, evaluating its effectiveness and the mutual benefit of the involved parties. Systematic analysis of this project demonstrates its relevance in promoting sustainable environmental education from the early grades. However, it is crucial to conduct a rigorous evaluation to determine the project's effectiveness in raising awareness among visiting children and enriching the education of the scholars involved. Furthermore, interinstitutional collaboration is a critical factor for the project's success and should be continuously enhanced.

One of the largest lavender fields in Ukraine is located in the Vinnytsia region. The introduction of essential oil (EO) into the pharmaceutical industry must be subject to product quality control. Terpenes, such as linalool and linalyl acetate are markers of lavender EO quality. According to HPTLC analysis of EO sample revealed of 6 zones of varying intensity, with the zone of linalool and linalyl acetate being the most saturated have been established. Thus, the presence of marker compounds in EO can be used in further quantitative assessment of compounds and substantiation of the prospects of Ukrainian lavender raw materials for the pharmaceutical industry.

Due to the high interest that represents the study from the adverse reactions to the medications and the importance of foreseeing the same ones the employment of the methods of molecular modeling with such an becomes a novel fact. In this work was carried out the calculation of the spectral moments of the adjacency matrix between edges of the molecular graph with suppressed hydrogens, pondered in the main diagonal with different parameters that characterize as much to the connections as to the atoms in the molecules of 63 compound of analgesic action, using for it the methodology MODESLAB. 91 descriptors were calculated, which were used in a series of training divided in four groups, according to the type of adverse reaction but it frequents. With the objective of identifying the descriptors that better they discriminant against among those compound of each group and to define the group of functions of these descriptors able to distinguish with the biggest precision possible to the members of one or another group, an discriminant analysis was developed by means of the statistical software Statistical 8.5. three functions were generated that they constitute lineal combinations of 6 molecular descriptors, which code so much information steric as electronic of the molecules of each group. The obtained functions present a Lambda of very low minimum Wilks (0,07) and a high canonical correlation (0,82), that which demonstrates their power discriminant

The current raw water supply from Angat Dam is insufficient to address the demand in Metropolitan Manila, Philippines. One of the alternatives is the Laguna Lake, the largest freshwater lake in the country, and at the same time the world’s largest septic tank last 2007. Currently, there are drinking water treatment facilities around the lake and soon-to-be constructed for future purposes. Different water quality problems have been reported such as taste and odor (T&O). Geosmin and 2-Methylisoborneol are two of the most recognized T&O compounds from blue-green algae and can be treated through ozonation. However, presence of bromine is also expected since the lake is connected to seawater. This study used advanced chemical reaction kinetics using Polymath® software to verify the extent of treating these contaminants without spending for actual ozonation. Results showed that 3 mg/L of ozone can treat 55 ng/L of each of T&O compounds for 20 minutes while 0.45 mg/L of bromate was also produced. Simulation studies like these are recommended for institutions that are in-search for scientific solutions but quite hesitant to spend high capital costs at first. Simulations can also be used as guidance from conceptualization up to the actual operations of the facility.

Monkeypox is a zoonotic disease and endemic to western and central Africa, and was first discovered in 1958 and in 1970 when a 3-year-old child from the Equateur region of the Democratic Republic of Congo formed a clinical syndrome similar to smallpox in the region where the smallpox virus had already been interrupted. The cases increased up to 72 in early 2000s. DRC Ministry of Health gathered the data from 2010-2014 and came to the conclusion that the reported cases were exceeding 2000 cases annually. This virus has similar genetic makeup and pathophysiology to smallpox virus with the exclusion that this virus enters from the wildlife source by the means of small lesions on the skin or oral mucous membrane. There is still no proper treatment specifically against monkeypox infections however, monkeypox and smallpox share same genetic material so the antiviral drugs and vaccines developed to protect against smallpox can also be used to treat monkeypox infections. In this article we discussed the emergence, history, mode of infection and vaccines available for the virus. We also gave an insight on the pathophysiology of the virus.

Τhe application of new technologies in telemedicine shows increasing interest aiming at human health. Ιn this work an integrated heart rate and SpO2 monitoring system is presented. A portable device was created with the aim of sending the measurement data, in real time, to a central information system, where the doctor will be able to monitor the measurements related to the patient's health. Low cost components were used for construction, while the entire system is based on open-source software. During the evaluation, the results of the measurements are satisfactory corresponding to the measurements from the commercial instruments that were used.

El 35% de las compras en Amazon en 2019 fueron generadas por un sistema de recomendación de acuerdo con MacKenzie y ese mismo año de reportó una ganancia de $87.4 billones de dólares en su último cuarto.

La primer conferencia AMC Recsys fue llevada a cabo en 2007 donde presentó un problema por Netflix que buscaba mejorar su sistema de recomendación en un 10% y dando un premio de 1 millón de dólares. En 2009 concluyó el reto y se presentó la solución en la conferencia. A partir del siguiente año se creó RecSys Challenge donde una empresa presenta un reto de sistemas de recomendación donde se premia y presenta la mejor solución con el fin de generar nuevas maneras de implementar recomendaciones .

El trabajo toma los datos del RecSys Challenge 2022 que consiste las vistas y compras de ropa en una tienda en línea; se deben generar 100 recomendaciones de artículos por sesión. A partir del modelo ganador del reto se toma la base para generar las recomendaciones usando diferentes métodos de filtrado colaborativo y LightGBM.

Este estudio analiza la base de datos de la PROFECO, que incluye los precios semanales de productos supervisados por la institución desde 2015 hasta 20233. El foco de atención está en detectar aumentos injustificados de precios, especialmente en productos como huevo, tortilla, leche, frijol y carne de res2. El objetivo principal es explorar técnicas de Machine learning para modelar los precios de los productos básicos.

Metodología

• Fechas: se consideró la base desde 2015 a 2022, se tomó solamente la primera semana de cada mes.
• Se considero información de toda la República, para análisis donde se agrupan los datos se consideró por separado zonas rurales y urbanas1.
• Solamente se consideran los productos del Huevo, la Tortilla, Leche, Frijol, y Carne de Res.
• Se consideran las variables Presentación, Marca, Cadena Comercial, Giro, Estado, año, mes, y día.

El pronóstico de la inversión extranjera directa es importante para un país ya que ayuda a su crecimiento, funcionamiento, e impulsa su reputación entre los demás países. El objetivo de este proyecto es encontrar un modelo que pueda predecir el cambio en la inversión extranjera directa tomando como base la misma inversión en años anteriores, así como el cambio en el tipo de gobierno durante los mismos años.

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.

El tema de la detección de anomalías no es un tema nuevo, sin embargo su desarrollo en el área digital aun está lejos de terminar. El objetivo principal es poder identificar comportamientos atípicos en el comportamiento del sujeto analizado. La tarea de localizar datos anómalos puede ayudar en la seguridad contra ataques cibernéticos, detección de fraudes, forja de seguros o inclusive diagnósticos médicos. Según Market Analysis Report (2023), el 2022 se invirtió un monto de 4.3 mil millones de dólares para el desarrollo de herramientas que permitan localizar anomalías. En el presente trabajo se pretende analizar un año de transacciones de un E-commerce real de tamaño pequeño. Se pretende analizar y comparar diversos tipos de algoritmos de aprendizaje automático no supervisados con el objetivo de explorar los puntos fuertes y débiles de los métodos utilizados. De igual manera se discute como evaluar la precisión, ya que los datos no cuentan con una etiqueta que nos permita saber si son anómalos o no.

La igualación de prima en las aseguradoras es un problema muy importante debido a que existe un área específica destinada a dar solución. Se plantea sustituir las funciones mediante Inteligencia Artificial. El método actual es manual, llega la cotización de seguros y el tiempo de respuesta es de 1 - 2 horas para brindar un mejor precio. La solución propuesta es automatizar el método de asignación de precio para reducir los tiempos de respuesta en la compañía.

Objetivo: identificar, a través de Redes Neuronales Convolucionales (CNN) si una imagen es una cotización de una compañía de seguros.

En los últimos años se ha visto un incremento de casos de enfermedades de las vías respiratorias, ya sea por la calidad del aire de las zonas urbanas a consecuencia de la industrialización, los hábitos personales y el tabaquismo. El cáncer de pulmón tuvo una incidencia, en el 2020, de más de 2 millones de casos en el mundo y alrededor de 1.8 millones de muertes por esta causa. En México, se registraron 7 mil 588 casos nuevos y 7 mil 100 muertes por cáncer de pulmón (International Agency for Research on Cancer, 2020). La resección pulmonar es la extirpación quirúrgica de todo o parte del pulmón debido a un cáncer de pulmón u otra enfermedad pulmonar. Por tal motivo, es importante monitorear el éxito de estas cirugías y clasificar a los pacientes con mayor riesgo de fallo, con el fin de servir de apoyo al momento de elegir el tratamiento adecuado para cada paciente según sus características.

El mercado de valores/acciones ha estado presente en la economía mundial desde el establecimiento de la Ámsterdam Stock Exchange en 1602 en la ciudad de Ámsterdam (Chen, 2022). Desde ese momento y hasta nuestros días funge como un indicador de la economía. La idea de analizar el mercado de acciones ha estado presente desde el trabajo de Fama (1965), el cual concluyo que no era posible predecir el precio de las acciones en base a su desempeño previo. Sin embargo, en la actualidad, diversos estudios han demostrado lo contrario (Bhowmick, 2021). En el presente trabajo se modelaron 100 acciones del índice S&P 500 mediante el uso de modelos de series de tiempo. La finalidad del trabajo es demostrar que los modelos de series de tiempo pueden pronosticar de manera consistente el precio de las acciones. Adicionalmente, se comparó la precisión y los tiempos de ejecución de los modelos.

The central importance of materials in society and their relationship with various properties is highlighted. The growing relevance of artificial intelligence (AI), especially machine learning (ML) and deep learning algorithms, in mechanical engineering and materials science is emphasized. The ability of AI to predict features and create innovative materials is highlighted. Furthermore, the crucial steps for applying ML in materials innovation are described, from data collection and cleaning to algorithm selection and optimization, emphasizing the importance of understanding the nature of data and model validation. Finally, a comprehensive overview of the integration of AI and ML in materials research is provided, highlighting their fundamental role in the optimization and prediction of mechanical properties.

Cardiovascular diseases are one of the most serious problems of modern medicine. Creation of combinations of metabolitotropic cardioprotectors selectively affecting individual target links of ischemic cascade of myocardial damage with the involvement of information technologies is a new promising approach. The aim of the study: to determine experimentally the approaches to the directed metabolic pharmacocorrection of ischemic myocardium and to develop the principles of combined prescription of metabolitotropic cardioprotectors on the basis of the created information technology of computer prediction. We have developed the basic theoretical concepts of a new scientific complex methodology with the involvement of information technologies for the selection and creation of drug combinations of metabolitotropic agents with improved pharmacological and toxicological properties for metabolitotropic cardioprotection. The obtained results were used in the development of an expert system for in silico substantiation of rational combinations of metabolitotropic drugs. The expert system was developed in the form of web application. The obtained data became theoretical and experimental substantiation for the creation of combined drugs based on L-arginine, glycine, tryptophan, L-lysine. The application of new information technology in the targeted development of rational combinations of metabolitotropic drugs will increase the efficiency of complex therapy of cardiovascular diseases of ischemic genesis.

The majority of the compounds that are created using 2,3-dihydrobenzo[b][1,4]dioxine have pharmacological qualities and are employed as synthesis intermediates to create a variety of medications and medicinal substances that are used to treat different illnesses that affect the human body. Thus, the N-alkylation reaction between 2-(2-hydroxyethyl)isoindoline-1,3-dione and 2,3-dihydrobenzo[b][1,4]dioxine was examined in this study. It was investigated how different proton catalysts affected the N-alkylation process. The resulting compound's structure was examined by the application of physico-chemical study methods.

Power quality can be defined as the transmission of the generated energy to the end user without distortion in voltage and current values. In recent years, the use of nonlinear loads such as Flexible AC Transmission Systems (FACTS) devices, power electronic converters, arc generation devices, and Variable Frequency Drives (VFDs) has led to disturbances in power quality.

Ensuring the uninterrupted and smooth operation of systems, delivering power to consumers in a clean manner without disturbance in power quality, is crucial both from a technical and economic perspective. Therefore, rapid detection, classification, and the generation of solutions based on power quality disturbances are essential. This situation has led to a rapid increase in research and studies to solve the problem.

The classification of power quality disturbances essentially occurs in four stages. These stages are obtaining the signal, feature extraction, feature selection, and classification. The success of classification varies depending on the method used in each stage.

In this study, for feature extraction the Discrete Wavelet Transform (DWT) method and five different mother wavelets were used after the signals were obtained. These mother wavelets are Daubechies4(db4), Daubechies2(db2), Symlet4(Sym4), Bior3.3 and Coiflet3. In the feature selection stage, two different optimization algorithms were utilized. These are the Equilibrium Optimization Algorithm (EO) and the Swarm Optimization Algorithm (SSA) . Finally, the K Nearest Neighbour (KNN) algorithm was employed for classification .

In the first step of this study, nine power quality events, one of which is a pure sinusoidal signal, were obtained in the Matlab 2022a/Simulink software and 150 different signals were generated from each of them. In the second step, the feature extraction for 6 levels was conducted using mother wavelets and DWT, and a dataset was obtained from the resulting feature vectors. This dataset was both normalized and logarithmically transformed. In the third step, feature selection was performed using EO and SSA, and finally, classification was performed using the KNN, and accuracy rates were compared.

The impact of mother wavelet selection on classification success was examined. The obtained datasets were divided into five parts, each corresponding to a specific mother wavelet. For each mother wavelet dataset, feature selection was again performed using EO and SSA, and classification successes were compared. According to the results, the Daubechies4 mother wavelet and EO algorithm yielded the best result with a success rate of %95.56. The worst result is obtained with the Daubechies2 and SSA algorithm, at a rate of 85.16%.

La cáscara de nuez pecanera representa el 50 % del fruto [Carya illinoinensis (Wangenh) C. Koch], y son fuente de fitocompuestos polares como taninos, polifenoles y flavonoides con actividad antimicrobiana y antioxidante, entre otras. En este trabajo se obtuvo un extracto alcohólico de cáscara de nuez cosechada en Bustamante, Nuevo León, México. El extracto crudo presentó un rendimiento de 7.7% y en su purificación parcial por cromatografía líquida en columna se obtuvieron 6 fracciones con compuestos mayoritarios determinados por UHPLC, mismos que están siendo evaluadas en sus actividades biológicas, particularmente antioxidante y antimicrobiana.

La deserción o abandono estudiantil constituye una problemática actual, que afecta negativamente a los sistemas educativos alrededor del mundo, incidiendo en su efectividad, eficiencia y prestigio. Generando, además, consecuencias económicas y/o psicosociales negativas en los estudiantes y sus familias. En el ámbito de la Educación Superior, implica el retiro del estudiante de su carrera antes de alcanzar la titulación(Matos, 2021).

Según informes de la UNESCO y el Banco Mundial, en América Latina y el Caribe, menos de la mitad de todos los jóvenes que comenzaron los cursos de Educación Superior se gradúan (tasa de graduación del 46%), exceptuando a EUA con un 67% (Ferreyra et al., 2017).

La identificación de las posibles causas de la deserción se ha convertido en una tarea compleja para las universidades. Entre los factores causales podemos citar los académicos, psicosociales, familiares, económicos, factores psicológicos, así como los relacionados con las propias IES: infraestructura, vida estudiantil, entre otras. Ante esta complejidad, resulta vital la identificación temprana de aquellos estudiantes en riesgo de abandono, pues esto le permitirá a las IES adoptar diferentes medidas para mitigar el fracaso académico. Entre ellas se puede incluir, la asistencia individualizada de estudiantes, cursos de recuperación y sesiones de tutoría (Alvarado-Uribe et. al., 2022).

Actualmente la Educación Superior mexicana vive momentos de transformación hacia la excelencia en la calidad educativa. En este escenario, la UANL a través de su Plan de Desarrollo Institucional 2022-2030 convoca a incorporar diversas actividades y a fortalecer políticas institucionales que impacten en los índices de eficiencia terminal en escuelas y facultades.

La presente investigación está orientada a detectar los principales factores que desde el punto de vista académico inciden en la deserción estudiantil temprana en la FCFM de la UANL, con el empleo de técnicas de ML y utilizando registros de calificaciones de los estudiantes en el período comprendido de enero – junio de 2015 hasta agosto – diciembre de 2022.

Por lo antes expuesto, nuestro proyecto responde a las siguientes preguntas de investigación:

• ¿Las herramientas de ML permiten clasificar, con valores buenos o aceptables de exactitud (bajo errores de clasificación), a los estudiantes en riesgo de abandono en los tres primeros semestres siguiendo metodologías de la Ciencia de Datos?
• ¿Cuáles son los factores académicos que inciden en la deserción, tomando los resultados de los algoritmos de clasificación?