Animo Repository - De La Salle University Research
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Structure-based virtual screening of Philippine natural products to discover novel inhibitors against mycobacterium leprae MurE ligase
No full textThe emergence of drug-resistant strain in the causative agent of leprosy, Mycobacterium leprae, propels the need to find a cure and new antibacterial targets. Cytoplasmic Mur ligase enzymes involved in peptidoglycan biosynthesis have recently been reassessed as attractive targets for antibiotics, as they are distinct to bacteria. This work aimed to characterize and model the M. leprae ATP-dependent MurE ligase protein structure and computationally identify potential antibacterial agents against this target. To do this, sequence alignment and residue conservation analysis were carried out, followed by homology modeling of MurE, docking studies, molecular dynamic simulations, and network analysis. The reliable homology model of the MurE protein was used to dock all the ADME screened ligands from the Philippine natural products for the identification of the top three docking hits, namely alpha-cadinene (C1), cis-b-guaiene (C2), and seychellene (C3). The native ligand (ATP) and products (ADP) were also docked to observe the behavior of the protein with respect to its original catalytic activity. Results showed that M. leprae MurE contains 3 conserved domains, in which the study focused on the central domain (D2) and the highly conserved residues and GKT motif for ATP binding. Molecular dynamics (MD) were then performed on the apo and the bound structures to analyze protein behavior upon ligand binding, and to confirm and compare the stability of the liganded complexes. MD analysis revealed structural domain-based movements of the protein upon ligand binding with a rigid binding pocket consisting of the identified binding residues and essential decomposition residues from interaction and decomposition analysis, respectively. Binding free energy analysis established the favorable binding affinity of the substrate and inhibitors to the protein, contrary to the unfavorable binding of the product. Lastly, network analysis identified key residues for each complex that signify crucial transmission points with a proposed role in both ligand binding and communication within the protein. Although, there are potential areas for future studies, such as the identified potential allosteric site of the C1 ligand, application of principal component analysis (PCA) on the MurE protein, and longer MD simulations and trajectory analysis for all the complexes for more deliberations and observations
EEG channel location estimation using CNN–based depression classifier
No full textDepression is a mental disorder that results to deteriorating effects in the lives of many people around the world. Traditional methods of diagnosing depression are based on interviews and questionaries. However, there were studies that have shown the possibilities of detecting depression biomarkers and perform classification. Electroencephalogram (EEG) analysis is one way of doing this. A series of signal processing techniques were used to streamline EEG data into a form which are recognizable by a machine learning algorithm. In this study, a convolutional neural network (CNN) –based depression classifier was used to classify depression using three EEG systems with 5, 16, and 128 channel locations. The aim is to estimate if there are differences in terms of classification performance. Results show that a system with locations for 5 and 16 EEG channels can achieve 98% accuracy like a 128-channel system. Hence, EEG systems with fewer number of electrodes can be utilized for depression classification applications
Pamana sa hapag-kultura: A qualitative study of bacoleños’ perceptions towards manokan country’s chicken inasal stalls modernization
No full textThe modernization of Manokan Country, a cultural landmark in Bacolod City renowned for its chicken inasal, sparked debates over cultural preservation and identity loss following the demolition of traditional stalls. The study employed a qualitative descriptive approach and semi-structured interviews to examine Bacoleños\u27 perceptions of modernization, its impact on cultural identity, and the efforts to uphold tradition amid change. Thematic analysis revealed that some owners welcomed modernization for its economic, tourism, and structural benefits. However, others feared the loss of cultural identity and expressed concerns about economic and emotional difficulties. Customers also voiced apprehensions about modernization\u27s potential impact, particularly on Manokan Country\u27s identity, rising prices, and the fear that the place they had long known and valued would no longer feel the same. Nostalgia, memories, and tradition were central to their concerns. Despite these worries, some customers acknowledged the need for modernization to improve safety and sanitation, though they hoped these changes would not completely alter the essence of Manokan Country. Both owners and customers exhibited varying levels of acceptance—some fully embraced modernization, some were open to it under certain conditions, while others remained deeply opposed. The findings underscored the tension between progress and preservation, highlighting that modernization challenged identity, memory, and heritage beyond physical changes. While the change was inevitable, both owners and customers hoped that Manokan Country\u27s cultural identity would remain intact for future generations
The economics of corruption in the Philippines: Distorted incentives, weakened institutions, and the cost to growth and freedom
No full textEvaluation of the antioxidant potential and HT-29 cytotoxicity of crude ethanolic peel extract from locally-grown pomelo (Citrus maxima)
No full textColorectal cancer is the third most common cancer worldwide. While treatments are available, they often have limitations and can be expensive, particularly for low-income Filipinos. This rationale encouraged the researchers to explore natural products as potential and sustainable treatment alternatives. Citrus maxima or pomelo, is native to Southeast Asian countries, including the Philippines. The peels of pomelo that are often discarded as waste are rich in bioactive compounds that can serve as therapeutic agents through their antioxidant, anti-inflammatory, and cytotoxic properties. This study investigated the antioxidant potential and cytotoxic activity of pomelo peel extract on the HT-29 colorectal adenocarcinoma cell line. The extract was prepared through maceration in 80% ethanol, followed by liquid-liquid extraction (LLE) with water and hexane, from which the polar partition was obtained. DPPH antioxidant assay, total flavonoid content (TFC), total phenolic content (TPC), and cytotoxicity assays were performed. The pomelo peel extract showed a moisture content of 75.39%, with the extraction process resulting in an overall extraction yield of 4.84% (w/w). Results showed a TFC of 17.29 ± 1.21 mg quercetin equivalent (QE)/g, TPC of 107.36 ± 0.73 mg gallic acid equivalent (GAE)/g, and an IC₅₀ of 5.958 mg/mL for the DPPH assay. In the cytotoxicity assay, the HT-29 cells exhibited a dose-dependent response to the pomelo peel extract, with an IC₅₀ value of 0.8752 mg/mL. Furthermore, varying concentrations of the pomelo peel extract combined with regulated cell death (RCD) inhibitors—Ferrostatin-1 (Fer-1, ferroptosis), Necrostatin-1 (Nec-1s, necroptosis), and ZVAD-FMK (apoptosis/pyroptosis)—showed no impact on the cell viability. This demonstrates the pomelo peel extract’s antioxidant and cytotoxic potential for colorectal cancer; however, the specific cell death pathway remains unclear. Therefore, additional replicates and time-dependent treatments with the extract and inhibitors are recommended to further elucidate the mechanisms involved and clarify the pathway dynamics
Stock market analysis using persistent homology
No full textMethods in machine learning have been used in recent decades to aid market participants in determining the future direction of stock markets, which is imperative for any investment decision to yield high financial returns and minimize risks. Several studies have integrated persistent homology into machine learning, and it has been shown that this approach improves accuracy in inferencing imaging datasets, recognizing patterns and predicting time series data. In computational topology, persistent homology is a tool that keeps track of data features that persist across different scales. Application of persistent homology obtains invariant topological features which may be used as input data for machine learning models. In this study, we choose indices from the Philippine Stock Exchange as our data for prediction: the Composite Index, the Service Index and the Industrial Sector Index. The stock returns, technical indicators and topological features obtained from the historical data are used in the machine learning models, artificial neural network and support vector machine. We compare performance of the models using the various inputs to show that the method using persistent homology is a strong option for investors on their stock market predictions and analysis