Cancer disparities in low- and middle-income countries (LMICs) arise from multifaceted interactions between environmental exposures, infectious agents, and systemic inequities, such as limited access to care. The exposome, a framework encompassing the totality of non-genetic exposures throughout life, offers a powerful lens for understanding these disparities. In LMICs, populations are disproportionately affected by air and water pollution, occupational hazards, and oncogenic infections, including human papillomavirus (HPV), hepatitis B virus (HBV), Helicobacter pylori (H. pylori), human immunodeficiency virus (HIV), and neglected tropical diseases, such as schistosomiasis. These infectious agents contribute to increased cancer susceptibility and poor outcomes, particularly in immunocompromised individuals. Moreover, climate change, food insecurity, and barriers to healthcare access exacerbate these risks. This review adopts a population-level exposome approach to explore how environmental and infectious exposures intersect with genetic, epigenetic, and immune mechanisms to influence cancer incidence and progression in LMICs. We highlight the critical pathways linking chronic exposure and inflammation to tumor development and evaluate strategies such as HPV and HBV vaccination, antiretroviral therapy, and environmental regulation. Special attention is given to tools such as exposome-wide association studies (ExWASs), which offer promise for exposure surveillance, early detection, and public health policy. By integrating exposomic insights into national health systems, especially in regions such as sub-Saharan Africa (SSA) and South Asia, LMICs can advance equitable cancer prevention and control strategies. A holistic, exposome-informed strategy is essential for reducing global cancer disparities and improving outcomes in vulnerable populations.

An HPLC-DAD separation method for the simultaneous quantification of ten compounds from Moringa oleifera plant was developed. The method was validated with pure solvent and different matrices of M. oleifera products. This method was found to be linear in the concentration range of 1 to 10 mg L⁻¹ for all the compounds in the solvent and from 3 to 10 mg kg⁻¹ in the different matrices. The correlation coefficients ranged between 0.9900 and 0.9999. Intra-day and inter-day variability showed that the developed method is both repeatable and precise with percent relative standard deviation values less than 10% and 20%, respectively. Limits of detection ranged between 0.06 and 0.8 mg L⁻¹ for the solvent and 0.1–1.5 mg kg⁻¹ for the matrices, while the limit of quantification ranged between 0.2 and 2.8 mg L⁻¹ and 0.4–4.8 mg kg⁻¹, respectively. The validated method was applied successfully to thirty-two different M. oleifera products, whereby all ten compounds were detected in one of the samples. Principal component analysis was used to assess the correlation and variance between the products. Variations were observed in products from different regions and from different manufacturers.

Novel electrospun Mondia whitei/PVA blend nanofibres were fabricated for potential water treatment applications by blending a natural polymer extracted from Mondia whitei (MW) roots with poly (vinyl alcohol) (PVA). The fabricated nanofibres were shown to have a smooth and uniform morphology with an average diameter of 99 ± 0.025 nm. The FTIR, XPS, XRD, and TGA characterisation results indicated changes in functional groups, crystallinity, and thermal stability of the Mondia whitei/PVA blend nanofibres, as compared to the original material. This finding confirmed that the polymers interacted through hydrogen bonding of MW and hydroxyl groups of PVA. The performance of the fabricated nanofibres was investigated for the removal of acidic drugs from spiked water samples. Factors (concentration of acidic drugs, dosage of the nanofibers and contact time) which affect the removal efficiency of the nanofibres were optimised using ultrapure water. Using the nanofibres, 100% removal efficiency for acidic drugs (aspirin, ketoprofen, fenoprofen, diclofenac, and ibuprofen) was achieved. The removal efficiency of the influent wastewater was 76, 89, 97, 93 and 94% for aspirin, ketoprofen, fenoprofen, diclofenac and ibuprofen, respectively, while the removal efficiency of the effluent was 86, 96, 97, 97 and 95% for aspirin, ketoprofen, fenoprofen, diclofenac and ibuprofen, respectively.

Lippia multiflora Moldenke is widely used in Angola, on the African continent, and beyond for the treatment of many health conditions such as hypertension, enteritis, colds, gastrointestinal disturbances, stomachaches, jaundice, coughs, fevers, nausea, bronchial inflammation, conjunctivitis, malaria, and venereal diseases. However, there is limited literature about the active compounds linked with the reported biological activities. This study aims to assess the chemical profiles, antioxidant properties, and the cytotoxicity effects of the roots, stem bark, and leaves of L. multiflora. Chemical characterization of the crude extracts was assessed through quantification of total phenolic and flavonoid contents followed by Q exactive plus orbitrap™ ultra-high-performance liquid chromatography-mass spectrometer (UHPLC-MS) screening. The correlation between the extracts and the correlation between the compounds were studied using the multivariate analysis. Principal component analysis (PCA) loading scores and principal component analysis (PCA) biplots and correlation plots were used to connect specific compounds with observed biological activities. The antioxidant activities of the crude extracts were carried out in vitro using DPPH (2,2-diphenyl-1-picrylhydrazyl) free radical scavenging and reducing power assays, while the in vitro toxicology of the crude extracts was evaluated using the 3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyltetrazolium bromide (MTT) assay. A total of twenty constituents were characterized and identified using the UHPLC–Q/Orbitrap/MS. The methanol leaf extract showed the highest antioxidant activity in the DPPH free radical scavenging activity (IC₅₀ = 0.559 ± 0.269 μg/mL); however, the stem bark extract had the highest reducing power (IC_0.5 = 0.029 ± 0.026 μg/mL). High phenolic and flavonoid content was found in the dichloromethane leaf extract (32.100 ± 1.780 mg GAE/g) and stem bark extract (624.153 ± 29.442 mg QE/g), respectively. The results show the stem bark, methanol leaf, and dichloromethane leaf extracts were well-tolerated by the Vero cell line at concentrations up to 50 µg/mL. However, at 100 µg/mL onward, some toxicity was observed in the root, methanol leaf, and dichloromethane leaf extracts. The UHPLC–Q/Orbitrap/MS profiles showed the presence of terpenoids (n = 5), flavonoids (n = 5), phenols (n = 4), alkaloids (n = 3), coumarins (n = 1), fatty acids (n = 1), and organic acids (n = 1). According to several studies, these secondary metabolites have been reported and proven to be the most abundant for antioxidant potential. The identified flavonoids (catechin, quercitrin, and (−)-epigallocatechin) and phenolic compound (6-gingerol) can significantly contribute to the antioxidant properties of different plant parts of L. multiflora. The research findings obtained in this study provide a complete phytochemical profile of various parts of L. multiflora that are responsible for the antioxidant activity using UHPLC–Q/Orbitrap/MS analysis. Furthermore, the results obtained in this study contribute to the scientific information or data on the therapeutic properties of Lippia multiflora and provide a basis for further assessment of its potential as a natural remedy.

Colorectal cancer (CRC) is a type of cancer that affects the colon and rectum, with overexpression of epidermal growth factor receptor (EGFR) and human epidermal growth factor receptor 2 (HER2) observed in up to 85% of colorectal cancer cases. Although CRC treatment has progressed with the introduction of targeted drugs, current approaches have primarily focused on a single blockage of EGFR or HER2 to combat colon cancer. However, monotherapies that target either the EGFR or HER2 receptor frequently have low efficacy due to mutations in downstream effectors such as Kirsten Rat Sarcoma 2 Viral Oncogene Homologue (KRAS) and the activation of compensatory signaling pathways that support tumor survival and proliferation. Hence, the discovery and development of a therapy with the capability to combat CRC by simultaneously inhibiting both EGFR and HER2 remain avenues for further exploration. This study introduces a novel machine learning (ML)-based stacking ensemble framework for rapidly and accurately identifying dual EGFR and HER2 inhibitors using SMILES notation. A benchmark data set comprising active and inactive compounds against EGFR and HER2 was curated from the ChEMBL database. Based on this data set, 40 baseline models were developed and optimized using a comprehensive set of well-known molecular descriptors and ML algorithms (Figure 1). These models generated probabilistic features integrated via a logistic regression model as a final estimate to construct the final stacking ensemble model. The model was applied to bioactive compounds identified through LC-MS/MS profiling of Ceratonia siliqua extract as well as to a subset of natural products from the LOTUS database for virtual screening. The cytotoxic potential of Ceratonia siliqua was experimentally validated using the MTT assay against HCT116 colorectal cancer cells and noncancerous Vero cells, where the extract exhibited an IC₅₀ value of 13.32 ± 1.09 μg/mL against HCT116 cells, indicating its significant anticancer potential. Additionally, molecular docking and in silico ADMET studies were conducted on the top three compounds from both the LOTUS database and the predicted candidate from the LC-MS/MS data set identified by the stacking model, alongside four FDA-approved anticancer drugs for comparative analysis. Among these, LTS0131923 demonstrated the highest binding affinity against HER2 (PDB ID: 7MN5), with a binding energy of −11.2 kcal/mol and an inhibition constant of 0.00626 μM, outperforming Tucatinib, a standard CRC treatment. This study reveals the potential of ML-driven approaches to accelerate the discovery of dual-target inhibitors for CRC therapy and highlights Ceratonia siliqua L. as a promising source of bioactive compounds for cancer treatment.