Federal University, Lokoja

The high mortality and morbidity rates associated with lung cancer pose serious health burden globally. This worrisome circumstance is aggravated by challenges of non-specific targeting, drug resistance, among others. This has necessitated a deliberate search for newer alternatives. In this study, a validated QSAR model (R2train = 0.901, R2adj = 0.870, Q2CV = 0.852, R2Test = 0.813) was used to design potent pyridineamide-based inhibitors of EGFR through structural optimization of the most active member of the dataset selected as template molecule (Tm). The new ligands were found to bind more spontaneously to the receptor with average Gibb’s free energy change (∆G) that ranges from − 9.7 to − 10.3 kcal/mol. When compared with ∆G value of − 7.1 kcal/mol calculated for Erlotinib (Erlo), an approved anti-lung cancer drug used herein as positive control, the design ligands form more stable complexes. Additionally, the designed ligands display sound pharmacokinetics and toxicity profiles. Thus, they could be potential sources of novel drug candidates against lung cancer. Hence, they are recommended for further in vitro and in vivo investigations.

Spiders are one of the least studied groups in sandy beach ecosystems, despite being an important component of these habitats. As a result, their potential as model species for ecological studies and indicator of human impacts on beaches remains largely underexplored. This study investigated the influence of local and landscape factors on burrow abundance of the wolf spider Allocosa brasiliensis across 30 sandy beach sectors in southeastern Brazil. We tested the Cumulative Harshness Hypothesis (CHH), which predicts that human disturbances amplify the impact of natural beach harshness. We surveyed 10 beach sectors from each morphodynamic type, performing standardized counts of wolf spider burrows in the supralittoral zone near coastal vegetation margin. Simultaneously, we collected sediment samples from the retention zone and supralittoral for granulometric analysis, measured the beach slope, captured potential prey, and georeferenced the coastline to obtain satellite-based data on urbanization levels, vegetation cover, proximity to rivers, and erosion rates. The wolf spider was more abundant on dissipative beaches compared to intermediate and reflective morphodynamic types. Multiple regression analyses revealed that higher burrow abundance was associated with smaller sediment grain size in the supralittoral zone, besides with relatively stable coastal displacement, lower urbanization levels, greater vegetation cover, and also higher abundance of potential prey. The CHH was not supported, as the species responded similarly to urbanization across all morphodynamic types according to generalized linear models. Therefore, our results suggest that the wolf spider can be used as an indicator species of human disturbances across different beach morphodynamic types.

Background: Phytochemicals, bioactive compounds derived from medicinal plants, have long contributed to drug discovery by providing diverse chemical scaffolds and broad biological activities. Despite strong preclinical evidence across inflammatory, metabolic, infectious, and neoplastic diseases, the clinical translation of many phytochemicals has been inconsistent, largely due to pharmacokinetic limitations, safety concerns, and regulatory challenges. Objective: This review critically examines the translational landscape of phytochemicals by adopting a mechanism-centered framework that links molecular actions to quantitative ADMET constraints, formulation strategies, and human clinical outcomes, with the aim of identifying factors that govern successful or failed clinical advancement. Methods: A comprehensive literature search was conducted across major scientific databases, including PubMed, Scopus, and Web of Science, covering studies published between 2000 and 2025. Original research articles, clinical trials, and recent reviews were screened using predefined inclusion criteria. Evidence was synthesized by grouping phytochemicals according to shared molecular mechanisms and mapping these to pharmacokinetic profiles, safety data, and stages of clinical development. Results: Phytochemicals exert pleiotropic effects through modulation of oxidative stress, inflammatory signaling pathways such as NF-kappa B and MAPK, and apoptosis-related processes. However, compounds such as curcumin and resveratrol demonstrate limited clinical efficacy despite robust mechanistic activity, primarily due to poor oral bioavailability and rapid metabolism. In contrast, formulation optimization and regulatory alignment have enabled the successful clinical development of select botanical drugs. Conclusion: This review highlights that mechanistic potency alone is insufficient for clinical translation. Integrating early ADMET evaluation, rational formulation design, and regulatory planning is essential to advance phytochemicals from experimental promise to clinically viable therapeutics.

Sentiment analysis is a cornerstone of social media–based public opinion monitoring, yet the optimal approach for pandemic-related discourse remains contested. This study conducts a comparative evaluation of two dominant paradigms: lexicon-based sentiment analysis and transformer-based deep learning models. The proliferation of user-generated content on social media platforms during global crises has catalyzed the use of sentiment analysis as a tool for understanding public perceptions and behaviours. However, the choice of sentiment analysis tools significantly influences the accuracy, interpretability, and applicability of results. This study conducts a comparative evaluation of three widely used sentiment analysis tools—VADER (Valence Aware Dictionary for Sentiment Reasoning), TextBlob, Orange Data Mining’s integrated sentiment classifier and transformer-based deep learning (BERT) —in analysing COVID-19–related Twitter data. Using a dataset of approximately 57,000 tweets collected during multiple pandemic phases, the tools’ performance in terms of classification accuracy, precision, recall, F1-score, computational efficiency, and alignment are assessed with human-labelled ground truth data. The findings reveal that while VADER outperforms in capturing nuanced sentiment in short, informal texts, TextBlob demonstrates strengths in polarity scoring but suffers from over-generalization, and Orange’s classifier provides strong baseline performance with enhanced usability for non-programmers. The study offers practical guidelines for selecting sentiment analysis tools in pandemic-related social media research, balancing accuracy with accessibility for multidisciplinary research teams.

Time-lapse electrical resistivity tomography (TL-ERT) has evolved as a powerful investigation tool that can be used in sequence with common point-based procedures to model seasonal moisture content (SMC) dynamics within the subsurface. This serves as a means of monitoring abnormal accumulation of water beneath structures in engineering and mine sites. These applications are crucial to mitigating the potential risk of geotechnical instabilities. TL-ERT data sets from Delta State, southern Nigeria, were employed in monitoring seasonal moisture content (SMC) dynamics at an engineering site. Twelve (12) 2D ERT profiles acquired in a 100 by 80 m2 grid during two time seasons were used to perform 2D and 3D ERT simultaneous inversion for monitoring of SMC using a TL-ERT inversion code. The entire 2D ERT data acquired in the x and y directions at the engineering site were merged to create 3D ERT data sets in the rainy and dry seasons. The 2D ERT data characterized the subsurface to a depth of 17.1 m, with high and low water content (with a resistivity range of 200–356 Ωm) observed in both seasons, attributed to the presence of soil layers with differences in their water-retention capacity. Borehole data obtained from the engineering site shows that the subsurface geology consists of clayey sand, fine sand, coarse sand, and lateritic sand. The fine/clayey sand layers with fine particles that retain more water can initiate geotechnical instabilities such as landslides, sinkholes, or mass movement, making the overlying materials susceptible to downslope movement and failure. The 3D ERT slices of inverted resistivity in the x and y directions imaged the subsurface to a depth of 19.8 m and revealed high and low water content in both seasons. The 3D ERT dynamic slice shows the precise anomalous water accretion zones, and the anomaly detected was at a depth of 6.6 m within the subsurface. These results serve as a proxy to track anomalous accretion of water underneath structures and intrusion or seepage into structures. These processes often create a low electrical resistivity (ER) anomaly that can be used to predict degradation of buildings, road structures, and railway embankments. The percentage differences in electrical resistivity (ER) between the monitoring periods showed little or no significant change in the ER of the sandy layer. Based on these findings, TL-ERT (a non-invasive geophysical technique), along with in-situ geotechnical data, can be applied for long-term geotechnical stability monitoring and mining site performance to mitigate the environmental impacts of structural instabilities of buildings, road structures, and railway embankments.