Prostate cancer disproportionately impacts African American men, who experience significantly higher mortality rates and earlier disease onset than other populations. Current diagnostic approaches, including prostate-specific antigen testing and biopsy, lack sufficient specificity and sensitivity, underscoring the need for accurate, molecular-level classification tools. This paper presents a machine learning framework for binary classification of genomic DNA sequences as cancerous or healthy. A dataset of 1684 FASTA-formatted sequences obtained from the National Library of Medicine - GenBank was analyzed, with 1662 sequences retained after quality control filtering. Feature engineering yielded 67 attributes, including GC content, Shannon entropy, sequence length, and trinucleotide k-mer frequencies. To address class imbalance, we applied the Synthetic Minority Over-sampling Technique to the training data. Seven classification algorithms were evaluated using stratified train-test splits, cross-validation, and hyperparameter optimization. Among the models, the optimized Random Forest classifier achieved superior performance, with a cross-validation accuracy of 97.2% (±0.006), a weighted F1-score of 0.95, a cancer-class recall of 0.96, and an ROC-AUC of 0.974. Feature importance analysis identified sequence length and Shannon entropy as the most discriminative predictors, followed by specific trinucleotide motifs (TTC, AAC, ACC, and GGG). These results demonstrate the potential of interpretable machine learning approaches for genomic sequence-based PCa classification, offering a promising pathway toward improved, equitable diagnostic tools for high-risk populations.
Cancer is a public health crisis affecting approximately 19.3 million globally annually. Lung cancer has the highest percentage of incidence and is responsible for 27% of cancer-related deaths. The lung cancer of prevalence in smokers is small-cell lung carcinoma. Lung cancer cells often exhibit altered gene expression patterns, compared to their normal counterparts. Yet this differential gene expression is not well understood. Herein, we investigated the differential expression of genes in small cell cancer cell lines compared to a healthy lung cell line that can lead to further insights into the proliferation, metastasis, and drug resistance of these lung cancer cells. Whole transcriptomic and small non-coding RNA sequencing was done by Next-generation sequencing (NGS) method, coupled with the Ingenuity Pathway Analysis (IPA) to decipher the core canonical pathways involved in these lung cancer cell-signaling mechanisms in disease pathophysiology. The findings of this investigation identified multiple genes and small non-coding tRNA/tRNA fragments that were differentially expressed, which thereby showed the possibility of potential diagnostic/prognostic and therapeutic targets. This important transcriptomic analysis can be a valuable tool for developing more effective treatment strategies for smokers lung cancer.
In the purpose to design novel antituberculosis (anti-TB) drugs agents against Mycobacterium tuberculosis (Mtb), we have built a molecular library around 42 Halimane Diterpenoids isolated from natural sources. Two Mtb enzymes drug targets (Mtb Mycothiol S-transferase and Mtb Homoserine transacetylase) have been adopted. The pharmacological potential was investigated through molecular docking, molecular dynamics simulation, density functional theory (gas phase and water) and ADMET analysis. Our results indicate that (2R,5R,6S)-1,2,3,4,5,6,7,8-octahydro-5-((E)-5-hydroxy-3-methylpent-3-enyl)-1,1,5,6-tetramethylnaphtha-lene-2-ol (compound 20) has displays higher docking score with each of the selected drug targets. In addition, this molecule exhibits a satisfactory drug potential activity and a good chemical reactivity. Its improved kinetic stability in the Mtb Mycothiol S-transferase enzyme reflects its suitability as a novel inhibitor of Mtb growth. This molecule has displayed a good absorption potential. Our results also show that its passive passage of the intestinal permeability barrier is more effective than that of first-line treatments (ethambutol, isoniazid). In the same way, this anti-TB druglikeness has shown to be able to cross the blood brain barrier.
The formations of [NAPA-A(H2O)n (n = 1, 2, 3, 4)] complexes have been studied employing DFT/wB97XD/cc-pVTZ computational level to understand the kinetics and thermodynamics for the hydration reactions of N-acetyl-phenylalaninylamide (NAPA). Thermodynamic parameters such as reaction energy (E), enthalpy (H), Gibb’s free energy (G), specific heat capacity (Cv), entropy (S), and change of these parameters (ΔEr, ΔHr, ΔGr, ΔCr, and ΔSr) were studied using the explicit solvent model. The predicted values of H, G, C, and S increase with the sequential addition of water in NAPA-A due to the increase in the total number of vibrational modes. On the other hand, the value of ΔEr, ΔHr, and ΔGr increases (more negative to less negative) gradually for n = 1, 2, 3, and 4 that indicates an increase of hydration in NAPA-A makes exothermic to endothermic reactions. The barrier heights for the transition states (TS) of [NAPA-A(H2O)n (n = 1, 2, 3, 4)] complexes are predicted to lie at 4.41, 4.05, 3.72 and 2.26 kcal/mol respectively below the reactants. According to the calculations, the formations of [NAPA-A(H2O)1] and [NAPA-A(H2O)2] complexes are barrierless reactions because both water molecules are strongly bonded via two hydrogen bonds in the backbone of NAPA-A. On the contrary, the reactions of [NAPA-A(H2O)3] and [NAPA-A(H2O)4] complexation are endothermic and the barrier heights are predicted to stay at 6.30 and 10.54 kcal/mol respectively above the reactants. The free energy of activation (Δ‡G0) for the reaction of [NAPA-A(H2O)1], [NAPA-A(H2O)2], [NAPA-A(H2O)3], and [NAPA-A(H2O)4] complexation are 4.43, 4.28, 3.83 and 5.11 kcal/mol respectively which are very low. As well as the rates of reactions are 3.490 × 109 s-1, 4.514 × 109 s-1, 9.688 × 109 s-1, and 1.108 × 109 s-1 respectively which are very fast and spontaneous.
Triple Negative Breast Cancer (TNBC) is a malignant form of cancer with very high mortality and morbidity. Epithelial to Mesenchymal Transition (EMT) is the most common pathophysiological change observed in cancer cells of epithelial origin that promotes metastasis, drug resistance and cancer stem cell formation. Since the information regarding differential gene expression in TNBC cells and cell signaling events leading to EMT is limited, this investigation was done by comparing transcriptomic data generated by RNA isolation and sequencing of a EMT model TNBC cell line in comparison to regular TNBC cells. RNA sequencing and Ingenuity Pathway Software Analysis (IPA) of the transcriptomic data revealed several upregulated and downregulated gene expressions along with novel core canonical pathways including Sirtuin signaling, Oxidative Phosphorylation and Mitochondrial dysfunction events involved in EMT changes of the TNBC cells.
With over 10 million points of genetic variation from person to person, every individual's genome is unique and provides a highly reliable form of identification.This is because the genetic code is specific to each individual and does not change over time.Genetic information has been used to identify individuals in a variety of contexts, such as criminal investigations, paternity tests, and medical research.In this study, each individual's genetic makeup has been formatted to create a secure, unique code that incorporates various elements, such as species, gender, and the genetic identification code itself.The combinations of markers required for this code have been derived from common single nucleotide polymorphisms (SNPs), points of variation found in the human genome.The final output is in the form of a 24 numerical code with each number having three possible combinations.The custom code can then be utilized to create various modes of identification on the decentralized blockchain network as well as personalized services and products that offer users a novel way to uniquely identify themselves in ways that were not possible before.
Micropeptin EI-964 is a cyclic peptide compound isolated from a marine cyanobacterium with potent inhibitory activity against serine proteases, particularly chymotrypsin and trypsin. It has shown promising activity against various cancer cell lines, making it a candidate for drug development. The unique structure and activity of Micropeptin EI-964 make it a promising lead compound for the development of novel serine protease inhibitors and anti-cancer drugs. Computational Chemistry and Molecular Modeling techniques can provide valuable insights into the chemical reactivity and pharmaceutical properties of Micropeptin EI-964, guiding the design and development of new compounds with enhanced bioactivity and improved drug-like properties.
Aim: This study aimed to investigate the effect of non-synonymous SNPs (nsSNPs) of the Glucagon-like peptide-1 Receptor (GLP-1R) gene in protein function and structure using different computational software. Introduction: The GLP1R gene provides the necessary instruction for the synthesis of the insulin hormones which is needed for glucose catabolism. Polymorphisms in this gene are associated with diabetes. The protein is an important drug target for the treatment of type-2 diabetes and stroke. Material and Methods: Different nsSNPs and protein-related sequences were obtained from NCBI and ExPASY database. Gene associations and interactions were predicted using GeneMANIA software. Deleterious and damaging effects of nsSNPs were analyzed using SIFT, Provean, and Polyphen-2. The association of the nsSNPs with the disease was predicted using SNPs & GO software. Protein stability was investigated using I-Mutant and MUpro software. The structural and functional impact of point mutations was predicted using Project Hope software. Project Hope analyzes the mutations according to their size, charge, hydrophobicity, and conservancy. Results: The GLP1R gene was found to have an association with 20 other different genes. Among the most important ones is the GCG (glucagon) gene which is also a trans membrane protein. Overall 7229 variants were seen, and the missense variants or nsSNPs (146) were selected for further analysis. The total number of nsSNPs obtained in this study was 146. After being subjected to SIFT software (27 Deleterious and 119 Tolerated) were predicted. Analysis with Provean showed that (20 deleterious and 7 neutral). Analysis using Polyphen-2 revealed 17 probably damaging, 2 possibly damaging and 1 benign nsSNPs. Using two additional software SNPs & GO and PHD-SNPs showed that 14 and 17 nsSNPs had a disease effect, respectively. Project Hope software predicts the effect of the 14 nsSNPs on the protein function due to differences in charge, size, hydrophobicity, and conservancy between the wild and mutant types. Conclusion: In this study, the 14 nsSNPs which were highly affected the protein function. This protein is providing the necessary instruction for the synthesis of the insulin hormones which is needed for glucose catabolism. Polymorphisms in this gene are associated with diabetes and also affect the treatment of diabetic patients due to the fact that the protein acts as an important drug target.
This paper presents a design method of H2 and H∞-feedback control loop for nonlinear smooth gene networks that are in control affine form. Formulaic solution methodology for solving the nonlinear partial differential equations, namely the Hamilton-Jacobi-Bellman and Hamilton-Jacobi-Isaacs equations through successive Galerkin’s approximation is implemented and the results are compared. Throughout the implementation, there were several caveats that need to be further resolved for practical applications in general cases. Such issues and the clarification of causes are mathematically established and reviewed.
Respiratory Syncytial Viral Infection is one of the most common viral infections in the respiratory airways and is more common in infants, younger children and some adults. Although ribavirin has been used in the management of RSV infection, many effective drugs are still under development. Also, structured based drug design using computational tools has shifted the paradigm of drug design and discovery. The fusion core of the Respiratory Syncytial Viral (RSV) structure is crucial for its fusion, entry and replication in the host cell. Herein, we investigated the interruption of the formation and/or the stability of the fusion core. 15 ligands were screened and 4 (Glimepiride, Glipizide, Canagloflozin, Glibenclamide) of them are potential drug candidate for experimental validation, preclinical trials, clinical trials and route of administration optimization. To the best of our knowledge, this is the first time of reporting lead molecules against RSV from oral hypoglycemic agents. It also suggests the ligands for consideration as prophylactics in infants and adults via the inhalation route.
This work was undertaken to see how Ru II complexes can be suitable for photodynamic therapy through theoretical prediction. For that, four Ru II complexes, α-RuCl2(Azpy)2, , and were used in unrestricted state by providing with no more energy than 2.68 eV. The unrestricted state allows the complex molecule to display each of its electrons in one orbital. All the calculations such as optimization, frequency and TD-DFT calculations were performed at WB97XD/Lanl2dz level. It resulted from this investigation that Ru II complexes are active for both mechanisms suitable for photodynamic therapy in presence or absence of 3O2. Moreover, this reaction was assumed to take place only with Guanine DNA base as demonstrated in literature. Therefore, Guanine is admitted as the base most reacting with ruthenium complexes for photodynamic therapy. This work confirms our prediction regarding metallic complexes that are assumed to be photosensitized in condition that an electron must be isolated to favor the excitation. Nevertheless, Ru II complexes are found suitable for superficial therapy while Ru III must be active for deep therapy.
