Search Results (75)

Time-lapse microscopy of mesenchymal stem cell (MSC) cultures allows for the quantitative observation of their self-renewal, proliferation, and differentiation. However, the rigorous comparison of two conditions, baseline (A) versus perturbation (B) (the addition of molecular factors, environmental shifts, genetic modification, etc.), remains difficult because morphology, division timing, and migratory behavior are highly heterogeneous at the single-cell scale. MSCs can be used as an in vitro model to study cell morphology and kinetics in order to assess the effect of, for example, gene therapy and prime editing in the near future. By combining static, frame-wise morphology with dynamic descriptors, we can obtain weight profiles that highlight which morphological and behavioral dimensions drive divergence. In this study, we present A/B Cells Policy: a modular, open-source Python package implementing a robust cell tracking pipeline. It integrates a YOLO-based architecture as a two-stage assignment framework with fallback and recovery passes, re-identification of lost tracks, and lineage reconstruction. The framework links descriptive statistics to a transferable system, opening up avenues for regenerative medicine, pharmacology, and early translational pipelines. It does this by providing an interpretable, measurement-based bridge between in vitro imaging and in silico intervention strategy planning. Full article

Cathepsin L (CatL) is a critical protease involved in cleaving the spike protein of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), facilitating viral entry into host cells. Inhibition of CatL is essential for preventing SARS-CoV-2 cell entry, making it a potential therapeutic target for drug development. Six QSAR models were established to predict the inhibitory activity (expressed as IC₅₀ values) of candidate compounds against CatL. These models were developed using statistical method heuristic methods (HMs), the evolutionary algorithm gene expression programming (GEP), and the ensemble method random forest (RF), along with the kernel-based machine learning algorithm support vector regression (SVR) configured with various kernels: radial basis function (RBF), linear-RBF hybrid (LMIX2-SVR), and linear-RBF-polynomial hybrid (LMIX3-SVR). The particle swarm optimization algorithm was applied to optimize multi-parameter SVM models, ensuring low complexity and fast convergence. The properties of novel CatL inhibitors were explored through molecular docking analysis. The LMIX3-SVR model exhibited the best performance, with an $R^2$ of 0.9676 and 0.9632 for the training set and test set and RMSE values of 0.0834 and 0.0322. Five-fold cross-validation $R_{5-fold}^2$ = 0.9043 and leave-one-out cross-validation $R_{loo}^2$ = 0.9525 demonstrated the strong prediction ability and robustness of the model, which fully proved the correctness of the five selected descriptors. Based on these results, the IC₅₀ values of 578 newly designed compounds were predicted using the HM model, and the top five candidate compounds with the best physicochemical properties were further verified by Property Explorer Applet (PEA). The LMIX3-SVR model significantly advances QSAR modeling for drug discovery, providing a robust tool for designing and screening new drug molecules. This study contributes to the identification of novel CatL inhibitors, which aids in the development of effective therapeutics for SARS-CoV-2. Full article

The southern region of Kazakhstan represents the northernmost boundary of the natural habitat of five wild almond species, among which Amygdalus communis L. is of particular interest due to a range of favorable traits for use in breeding programs and cultivation in the region. The current distribution range of common almond growth was clarified using GPS to determine precise coordinates, and a schematic map was developed. Monitoring revealed a significant reduction in population size. In the surveyed areas, 54 trees were selected and described. Seed material was collected from 34 genotypes and characterized according to a descriptor. Genotypes A3, A8, and A15 were identified as having favorable trait combinations. To restore populations and preserve the gene pool of Amygdalus communis L., a method of clonal micropropagation was employed. The composition of the nutrient medium was optimized for establishment, multiplication, and rhizogenesis. It was determined that Murashige and Skoog (MS) medium without phytohormones is effective for in vitro establishment (70% regeneration rate). For multiplication, MS medium with 0.5 mg/L BAP (6-benzylaminopurine) was used (with a multiplication rate of 3.5 per explant). For rhizogenesis, MS medium with 0.5 mg/L BAP, 0.02 mg/L gibberellic acid (GA), and 0.1 mg/L IBA (indole-3-butyric acid) was used. A total of 340 clonal Amygdalus communis L. plants with closed root systems were grown for field collection. The research results can be applied for the restoration, propagation, and conservation of populations both in vitro and in situ, as well as for the inclusion of selected high-performing genotypes in breeding programs. Full article

The ongoing genetic erosion of natural Prunus armeniaca populations in their native habitats underscores the urgent need for targeted conservation and restoration strategies. This study provides the first comprehensive characterization of P. armeniaca populations in the Almaty region of Kazakhstan, integrating morphological descriptors (46 parameters), molecular markers, geobotanical, and remote sensing analyses. Geobotanical and remote sensing analyses enhanced understanding of accession distribution, geological features, and ecosystem health across sites, while also revealing their vulnerability to various biotic and abiotic threats. Of 111 morphologically classified accessions, 54 were analyzed with 13 simple sequence repeat (SSR) markers and four DNA barcoding regions. Our findings demonstrate the necessity of integrated morphological and molecular analyses to differentiate closely related accessions. Genetic analysis identified 11 distinct populations with high heterozygosity and substantial genetic variability. Eight populations exhibited 100% polymorphism, indicating their potential as sources of adaptive genetic diversity. Cluster analysis grouped populations into three geographic clusters, suggesting limited gene flow across Gorges (features of a mountainous landscape) and greater connectivity within them. These findings underscore the need for site-specific conservation strategies, especially for genetically distinct, isolated populations with unique allelic profiles. This study provides a valuable foundation for prioritizing conservation targets, confirming genetic redundancies, and preserving genetic uniqueness to enhance the efficiency and effectiveness of the future conservation and use of P. armeniaca genetic resources in the region. Full article

Background/Objectives: The resistance mutations EGFR^{L858R/T790M/C797S} in epidermal growth factor receptor (EGFR) are key factors in the reduced efficacy of Osimertinib. Predicting the inhibitory effects of Osimertinib derivatives against these mutations is crucial for the development of more effective inhibitors. This study aims to predict the inhibitory effects of Osimertinib derivatives against EGFR^{L858R/T790M/C797S} mutations. Methods: Six models were established using heuristic method (HM), random forest (RF), gene expression programming (GEP), gradient boosting decision tree (GBDT), polynomial kernel function support vector machine (SVM), and mixed kernel function SVM (MIX-SVM). The descriptors for these models were selected by the heuristic method or XGBoost. Comprehensive learning particle swarm optimizer was adopted to optimize hyperparameters. Additionally, the internal and external validation were performed by leave-one-out cross-validation ($Q_{LOO}^2$), 5-fold cross validation ($Q_{5-fold}^2$) and concordance correlation coefficient (CCC), $Q_{F1}^2$, and $Q_{F2}^2$. The properties of novel EGFR inhibitors were explored through molecular docking analysis. Results: The model established by MIX-SVM whose kernel function is a convex combination of three regular kernel functions is best: $R^2$ and RMSE for training set and test set are 0.9445, 0.1659 and 0.9490, 0.1814, respectively; $Q_{LOO}^2$, $Q_{5-fold}^2$, CCC, $Q_{F1}^2$, and $Q_{F2}^2$ are 0.9107, 0.8621, 0.9835, 0.9689, and 0.9680. Based on these results, the IC₅₀ values of 162 newly designed compounds were predicted using the HM model, and the top four candidates with the most favorable physicochemical properties were subsequently validated through PEA. Conclusions: The MIX-SVM method will provide useful guidance for the design and screening of novel EGFR^{L858R/T790M/C797S} inhibitors. Full article

Anticancer drug design has been reformed by the creation of heterocyclic hybrids. The introduction of a quinoline scaffold affects the activity, toxicity, and bioavailability of new compounds. The aim of this study was to synthesize and evaluate the biological activity of hybrids of 1,4-naphthoquinone with the 8-hydroxyquinoline moiety. The structure of the new compounds was characterized using spectroscopic methods, such as HR-MS, NMR, and IR. The analysis was supplemented by calculated NMR and IR spectra. The physicochemical properties and bioavailability of the compounds were examined using in silico methods. An analysis of reactivity descriptors showed that the compounds are good electron acceptors and exhibit high reactivity. Bioavailability properties confirm that hybrids could be good oral administration drugs. The biological potential of hybrids was examined by designation of the enzymatic conversion rate of the NQO1 protein and in vitro against cancer cell lines with overexpression of the gene encoding the NQO1 protein. The possibility of interaction between the tested ligand and the NQO1 protein was examined by molecular docking methods. Full article

Spinocerebellar ataxia type 2 (SCA2) is an autosomal dominant neurodegenerative disorder marked by cerebellar dysfunction, ataxic gait, and progressive motor impairments. SCA2 is caused by the pathologic expansion of CAG repeats in the ataxin-2 (ATXN2) gene, leading to a toxic gain-of-function mutation of the ataxin-2 protein. Currently, SCA2 therapeutic efforts are expanding beyond symptomatic relief to include disease-modifying approaches such as antisense oligonucleotides (ASOs), high-throughput screening (HTS) for small molecule inhibitors, and gene therapy aimed at reducing ATXN2 expression. In the present study, data mining and machine learning techniques were employed to analyze HTS data and identify robust molecular properties of potential inhibitors of ATXN2. Three HTS datasets were selected for analysis: ATXN2 gene expression, CMV promoter expression, and biochemical control (luciferase) gene expression. Compounds displaying significant ATXN2 inhibition with minimal impact on control assays were deciphered based on effectiveness (E) values (n = 1321). Molecular descriptors associated with these compounds were calculated using MarvinSketch (n = 82). The molecular descriptor data (MD model) was analyzed separately from the experimentally determined screening data (S model) as well as together (MD-S model). Compounds were clustered based on structural similarity independently for the three models using the SimpleKMeans algorithm into the optimal number of clusters (n = 26). For each model, the maximum response assay values were analyzed, and E values and total rank values were applied. The S clusters were further subclustered, and the molecular properties of compounds in the top candidate subcluster were compared to those from the bottom candidate subcluster. Six compounds with high ATXN2 inhibiting potential and 16 molecular descriptors were identified as significantly unique to those compounds (p < 0.05). These results are consistent with a quantitative HTS study that identified and validated similar small-molecule compounds, like cardiac glycosides, that reduce endogenous ATXN2 in a dose-dependent manner. Overall, these findings demonstrate that the integration of HTS analysis with data mining and machine learning is a promising approach for discovering chemical properties of candidate drugs for SCA2. Full article

Bunch compactness (BC) is a complex, multi-trait characteristic that has been studied mostly in the context of wine grapes, with table grapes being scarcely considered. As these groups have marked phenotypic and genetic differences, including BC, the study of this trait is reported here using a genetically diverse collection of 116 Vitis vinifera L. cultivars and lines enriched for table grapes over two seasons. For this, 3D scanning-based morphological data were combined with ground measurements of 14 BC-related traits, observing high correlations among both approaches (R² > 0.90–0.97). The multivariate analysis suggests that the attributes ‘berries per bunch’, ‘berry weight and width’, and ‘bunch weight and length’ could be considered as the main descriptors for BC, optimizing evaluation times. Then, GWASs based on a set of 70,335 SNPs revealed that GBS analysis in this same population enabled the detection of several SNPs associated with different sub-traits, with a locus for ‘berries per bunch’ in chromosome (chr) 18 being the most prominent. Enrichment analysis of significant and frequent SNPs found simultaneously in several traits and seasons revealed the over-representation of discrete functions such as alpha-linolenic acid metabolism and glycan degradation. In summary, the utility of 3D automated phenotyping was validated for table grape backgrounds, and new SNPs and candidate genes associated with the BC trait were detected. The latter could eventually become a selection tool for grapevine breeding programs. Full article

The rising prevalence of Alzheimer’s disease (AD), particularly among older adults, has driven increased research into its underlying mechanisms and risk factors. Aging, genetic susceptibility, and cardiovascular health are recognized contributors to AD, but how the age of onset affects disease progression remains underexplored. This study investigates the role of early- versus late-onset Alzheimer’s disease (EOAD and LOAD, respectively) in shaping the trajectory of cognitive decline. Leveraging data from the Religious Orders Study and Memory and Aging Project (ROSMAP), two cohorts were established: individuals with early-onset AD and those with late-onset AD. Comprehensive analyses, including differential gene expression profiling, pathway enrichment, and gene co-expression network construction, were conducted to identify distinct molecular signatures associated with each cohort. Network modularity learning algorithms were used to discern the inner structure of co-expression networks and their related functional features. Computed network descriptors provided deeper insights into the influence of age at onset on the biological progression of AD. Full article

Diagnostic practices for schizophrenia are unreliable due to the lack of a stable biomarker. However, machine learning holds promise in aiding in the diagnosis of schizophrenia and other neurological disorders. Dysregulated miRNAs were extracted from public sources. Datasets of miRNAs selected from the literature and random miRNAs with designated gene targets along with related pathways were assigned as descriptors of machine-learning models. These data were preprocessed and classified using WEKA and TensorFlow, and several classifiers were tested to train the model. The Sequential neural network developed by authors performed the best of the classifiers tested, achieving an accuracy of 94.32%. Naïve Bayes was the next best model, with an accuracy of 72.23%. MLP achieved an accuracy of 65.91%, followed by Hoeffding tree with an accuracy of 64.77%, Random tree with an accuracy of 63.64%, Random forest, which achieved an accuracy of 61.36%, and lastly ADABoostM1, which achieved an accuracy of 53.41%. The Sequential neural network and Naïve Bayes classifier were tested to validate the model as they achieved the highest accuracy. Naïve Bayes achieved a validation accuracy of 72.22%, whereas the sequential neural network achieved an accuracy of 88.88%. Our results demonstrate the practicality of machine learning in psychiatric diagnosis. Dysregulated miRNA combined with machine learning can serve as a diagnostic aid to physicians for schizophrenia and potentially other neurological disorders as well. Full article

During human history, many pandemic events have threatened and taken many human lives over the years. The deadliest outbreaks were caused by bacteria such as Yersinia pestis. Nowadays, antimicrobial resistance (AMR) in bacteria is a huge problem for the public worldwide, threatening and taking many lives each year. The present work aimed to gather current evidence published in scientific literature that addresses AMR risks. A literature review was conducted using the following descriptors: antimicrobial resistance, AMR, bacteria, and Boolean operators. The results showed that antimicrobial-resistant genes and antibiotic-resistant bacteria in organisms cause critical infectious diseases and are responsible for the infections caused by antibiotic-resistant bacteria (ARB). This review emphasizes the importance of this topic. It sheds light on the risk of reemerging infections and their relationship with AMR. In addition, it discusses the mechanisms and actions of antibiotics and the mechanisms behind the development of resistance by bacteria, focusing on demonstrating the importance of the search for new drugs, for which research involving peptides is fundamental. Full article

Multidrug resistance (MDR) presents a significant challenge in modern pharmacotherapy, greatly diminishing the effectiveness of chemotherapeutic agents. A primary mechanism contributing to MDR is the overexpression of P-glycoprotein (P-gp), also known as MDR1, encoded by the ABCB1 gene, which hampers the success of cancer treatments. Plants from the Plectranthus genus (Lamiaceae) have been traditionally acknowledged for their diverse therapeutic applications. The principal diterpene from Plectranthus grandidentatus Gürke, 7α-acetoxy-6β-hydroxyroyleanone (Roy), has demonstrated anticancer properties against various cancer cell lines. Previously synthesized ester derivatives of Roy have shown improved binding affinity to P-gp. This study employs previously acquired in vitro data on the P-gp activity of Roy derivatives to develop a ligand-based pharmacophore model, highlighting critical features necessary for P-gp modulation. Utilizing these data, we predict the potential of five novel ester derivatives of Roy to modulate P-gp in vitro against resistant NCI-H460 cells. In silico structure–activity relationship (SAR) studies were conducted on 17 previously synthesized royleanone derivatives. A binary classification model was constructed, distinguishing inactive from active compounds, generating 11,016 molecular interaction field (MIF) descriptors from structures optimized at the DFT level. After variable reduction and selection, 12 descriptors were chosen, resulting in a model with two latent variables (LV), using only 34.14% of the encoded information for calibration (LV1: 26.82%; LV2: 7.32%). The activity prediction of new derivatives suggested that four of them have a high likelihood of activity, which will be validated in future in vitro biological assays. Full article

Background/Objectives: In 2020, ovarian cancer ranked fourth in global incidence among gynecological cancers and remains the deadliest cancer affecting women’s health. Survival rates are significantly higher when the disease is detected at early stages; however, the lack of effective early detection methods underscores the importance of identifying risk factors in order to implement preventive strategies. The objective of this work is to provide an overview of the risk factors of ovarian cancer in South America, emphasizing those linked to social determinants, genetic components, and comorbidities. Methods: A literature search was performed using PubMed and Google Scholar. MeSH descriptors and keywords, such as “BRCA1 genes,” “BRCA2 genes”, “Latin America”, and “ovarian neoplasms” were used, along with terms related to socioeconomic and health factors. Inclusion criteria focused on original studies published in the last five years involving South American women. Results: Studies were identified from Argentina, Brazil, Chile, Colombia, Ecuador, and Peru. These studies addressed genetic factors, health status at diagnosis, and sociodemographic factors, revealing important data gaps, particularly on contraception and hormone replacement therapy. The prevalence of BRCA1 and BRCA2 mutations in South America is estimated to be 15–20% among women with inherited risk factors. Social, demographic and economic factors vary by country, although commonalities include a higher prevalence among women over 50 years of age, those with limited education, and those who face barriers to accessing health care. Conclusions: Although the literature does not conclusively establish a direct link between obesity and/or diabetes and the development of ovarian cancer, the indirect association highlights the need for further clinical studies. A general research gap related to risk factors of ovarian cancer could be observed in the South American region. Full article

The severity of adenovirus infection or the success of adenovirus-vectorized gene therapy largely depends on the efficiency of viral entry into cells. Various drugs can alter viral entry. This study evaluated the effects of dexamethasone, paracetamol, diclofenac, ibuprofen, and ketorolac on adenovirus entry into cells in vitro and in vivo. SiHa cell cultures pretreated with dexamethasone, paracetamol, diclofenac, ibuprofen, ketorolac, or no drug were exposed to the Ad-BGal vector. The percentage of cells showing vector entry was quantified microscopically. In vivo, BALB-C mice pretreated for 7 days with the drugs or no drug were exposed to the Ad-BGal vector intravenously (IV) or via oral (VO). Organs showing vector entry were identified by X-Gal staining and eosin counterstaining. Hepatic areas with adenovirus entry were quantified in µm². Dexamethasone, paracetamol, and ibuprofen increased adenovirus entry both in vitro and in vivo. Diclofenac increased entry only in vitro. Ketorolac did not affect adenoviral entry. The liver exhibited the most significant changes, with dexamethasone, paracetamol, and ibuprofen increasing adenovirus entry the most. Oral administration of the vector showed that dexamethasone increased its entry into the pharynx. Some physicochemical properties of the drugs (MW (g/mol), LogP, MR [cm³/mol], tPSA, CMR, LogS, and ClogP) were analyzed, and their possible implications on cell membrane properties that could potentially influence adenovirus entry through mechanisms independent of cellular receptors were discussed. Anti-inflammatory drugs could alter adenoviral infections and adenovirus vector-based gene therapies, necessitating further research. Full article

Polychlorinated dibenzo-p-dioxins and polychlorinated dibenzo-p-furans (PCDD/Fs) are a group of organic chemicals containing three-ring structures that can be substituted with one to eight chlorine atoms, leading to 75 dioxin and 135 furan congeners. As endocrine-disrupting chemicals (EDCs), they can alter physiological processes causing a number of disorders. In this study, quantitative structure–toxicity relationship (QSTR) studies were used to determine the correlations between the PCDD/Fs’ molecular structures and various toxicity endpoints. Strong QSTR models, with the coefficients of determination (r²) values greater than 0.95 and ANOVA p-values less than 0.0001 were established between molecular descriptors and the endpoints of bioconcentration, fathead minnow LC50, and Daphnia magna LC50. The ability of PCDD/Fs to bind to several nuclear receptors was investigated via molecular docking studies. The results show comparable, and in some instances better, binding affinities of PCDD/Fs toward the receptors relative to their natural agonistic and antagonistic ligands, signifying possible interference with the receptors’ natural biological activities. These studies were accompanied by the molecular dynamics simulations of the top-binding PCDD/Fs to show changes in the receptor–ligand complexes during binding and provide insights into these compounds’ ability to interfere with transcription and thereby modify gene expression. This introspection of PCDD/Fs at the molecular level provides a deeper understanding of these compounds’ toxicity and opens avenues for future studies. Full article