Search Results (198)

Objectives: Insomnia is a highly prevalent sleep disorder involving complex neurochem-ical dysregulation; however, the spatial distribution of neurotransmitters and small-molecule metabolites across distinct brain microregions in the insomniac state re-mains poorly characterized. This study aimed to map region-specific metabolic perturba-tions in situ in a p-chlorophenylalanine (PCPA)-induced insomnia rat model using opti-mized matrix-assisted laser desorption/ionization mass spectrometry imaging (MAL-DI-MSI) integrated with targeted metabolomics validation. Methods: On-tissue chemical derivatization MALDI-MSI was optimized using α-cyano-4-hydroxycinnamic acid (CHCA) as the matrix on a Bruker tims TOF flex mass spectrometer. The rats received PCPA (400 mg/kg, intraperitoneal) for three days to establish the insomnia model. Metabolite identifi-cation was conducted using MetaboScape® software (2020b) and the Human Metabolome Database. Ultra-performance liquid chromatography–tandem mass spectrometry was em-ployed to quantify nine key neurotransmitters and metabolites across six brain microre-gions. Key synthetic enzyme expression was evaluated by immunofluorescence and West-ern blotting analyses. Results: TMP-TFB-derived brain slices clearly showed the distribu-tion of neurotransmitters in brain microregions. MALDI-MSI demonstrated that the spatial distribution and abundance of eight neurotransmitters were disturbed in brains of PCPA-induced insomniac rats. A total of 346 metabolites were characterized across six brain microregions (cerebellum, cortex, hippocampus, brainstem, hypothalamus, and stri-atum), with principal coordinate analysis revealing clear metabolic separation between control and insomniac rats. PCPA treatment markedly disrupted tryptophan and tyrosine metabolism, evidenced by decreased 5-HT, 5-HTP, and 5-HIAA, alongside region-specific alterations in DA, NE, HVA, and Ach. Additionally, GABA levels decreased in the hippo-campus, striatum, and hypothalamus, whereas glutamate increased throughout the brain. Targeted metabolomics validated the MSI findings, and Bland–Altman analysis confirmed good consistency between the two analytical platforms. PCPA further disturbed the meta-bolic enzymes MAOA, DDC, and TPH2 within the Trp-5-HTP-5-HT-5-HIAA metabolic pathway in the brainstem and TYH and DBA within the tyrosine-DA-NE metabolic pathway in the striatum. Conclusions: This study demonstrates that region-specific altera-tions in tryptophan and tyrosine metabolism pathways provide mechanistic insights into insomnia pathogenesis and potential therapeutic targets. Full article

Background: Parkinson’s disease (PD) is characterized by a complex interplay of dopaminergic degeneration, glial activation, and lipid metabolic dysregulation. However, accurately describing how natural product interventions remodel these pathologies across distinct brain regions and cellular microenvironments remains a critical challenge. Methods: We established an integrated multi-omics framework to decode the neuroprotective mechanisms of solanesol (Sol) in an MPTP-induced PD mouse model. We combined single-cell eQTL-based Mendelian randomization (scMR), transcriptomic localization, and virtual knockout analyses to prioritize cell-type-specific regulatory nodes across neuronal, glial, and vascular populations, avoiding the limitations of traditional bulk targeting. In vivo behavioral assays were conducted, alongside orthogonal validation via airflow-assisted desorption electrospray ionization mass spectrometry imaging (AFADESI-MSI) and gene–metabolite co-enrichment analysis, to map regional metabolic networks and structural spatial reprogramming. Results: Computational prioritization highlighted cell-type-specific regulatory nodes including PRKCB, PRKCE, PDGFRB, and FABP3/5. In vivo, Sol attenuated motor and cognitive deficits and largely restored the highly compartmentalized spatial distributions of striatal dopamine, L-DOPA, and acetylcholine. Crucially, AFADESI-MSI and co-enrichment analysis revealed that Sol specifically reversed MPTP-induced spatial disruptions by rescuing key neuromodulatory metabolites—including cervonoyl ethanolamide, phosphatidylcholine species, taurine, and NADHX—which were tightly coupled to sphingolipid signaling, fatty-acid transport, mitochondrial translation, and cell-adhesion pathways. Conclusions: Sol ameliorates PD-like pathology not through a singular target, but by choreographing a spatially and cellularly compartmentalized restoration of lipid–inflammatory homeostasis. Furthermore, our integrated single-cell and spatial metabolomic blueprint sets a new methodological paradigm for elucidating the precise execution programs of natural neurotherapeutics. Full article

Spatial multi-omics analyzes biomolecules such as the proteome, metabolome, and lipidome within their native spatial context in tissues or cells. Mass spectrometry imaging (MSI) has emerged as a powerful technique for mapping the region-specific molecular distribution in regions of interest (ROIs). Laser capture microdissection coupled with mass spectrometry (LCM-MS) is another well-established workflow, enabling the accurate characterization of biomolecules in ROIs. To advance the current analytical application, we expanded a matrix-assisted laser desorption/ionization (MALDI)-MSI-guided LCM-MS workflow for integrated multi-omics analysis and applied it to mouse brain tissue as a proof-of-principle validation. MALDI-MSI annotated 387 putative metabolites and lipids, revealing distinct molecular distributions between the cortex and hippocampus. Both regions were subsequently isolated as ROIs using LCM and analyzed by LC-MS/MS metabolomics, lipidomics, and proteomics to achieve accurate biomolecular profiling. LC-MS/MS metabolomics and lipidomics annotated 249 compounds, several of which exhibited distinct abundance patterns between the two regions. LC-MS/MS proteomics matched to over 3500 protein groups across the two regions. Biological network analysis revealed strong associations between molecular pathways and known region-specific phenotypes. Overall, this MALDI-MSI-guided LCM-MS workflow enables comprehensive spatial multi-omics profiling and quantitative biomolecular analysis, providing valuable insights into complex biological systems and spatial molecular organization. Full article

Background/Objectives: Cytological analysis of pleural and peritoneal effusions is minimally invasive but may show limited sensitivity for malignancy detection because tumor cells can be scarce or morphologically difficult to identify. Proteomics-based mass spectrometry imaging (MSI) enables direct molecular profiling of cytological specimens and may improve the distinction between malignant and benign samples. This study investigated whether MALDI-based MSI profiling of formalin-fixed paraffin-embedded (FFPE) cytology cell blocks could discriminate high-grade serous ovarian carcinoma (HGSOC) from benign effusions and identify discriminatory peptide signatures. Methods: Forty-one FFPE cytological specimens derived from pleural and peritoneal effusions were analyzed, including 18 malignant samples and 23 benign control specimens with reactive or inflammatory cytological backgrounds. MALDI-MSI analyses were performed using proteomic profiling. In a preliminary comparative experiment, two section thicknesses (3 µm and 5 µm) were evaluated to optimize ion peak intensity yield. Classification analyses using linear discriminant analysis (LDA) and support vector machine (SVM) models were performed to identify discriminatory peptide signatures. Results: Comparative analysis of average mass spectra identified multiple differentially expressed ions with significant discriminatory performance (AUROC ≥ 0.7 or ≤0.3; Wilcoxon/Kruskal–Wallis, p < 0.001). Classification analyses achieved accuracy ranged from 91% to 94% for the discrimination of malignant and benign samples. Differential proteomic profiling identified Complement C3, Perilipin-3, arachidonate 5-lipoxygenase, Leukotriene A-4 hydrolase, fibrinogen beta and gamma chains, and serotransferrin as discriminatory proteins associated with immune modulation, lipid metabolism, cytoskeletal and extracellular matrix organization, and metabolic regulation. Notably, Complement C3 was found to be overexpressed in malignant tumor cells, supporting its potential role as a marker of tumor presence and progression. Conclusions: Proteomics-based mass spectrometry imaging enabled reliable discrimination of HGSOC from benign cytological specimens and revealed cancer-associated proteins linked to relevant biological processes. These findings support MALDI-MSI as a complementary molecular approach for the classification of challenging cytological specimens in routine diagnostic pathology. Full article

2-Ethylhexyl-4-methoxycinnamate (EHMC) is among the most widely adopted organic UVB filters in commercial sunscreens. Nevertheless, its practical application potential is limited by unfavorable formulation compatibility and safety risks stemming from systemic exposure after topical administration. In this study, an oil-continuous structured gel matrix consisting of EHMC, disteardimonium hectorite (DDH) and dextrin palmitate (DP) was constructed to enhance UVB photoprotection and modulate the plasma exposure profile of EHMC following topical application. Comprehensive characterizations including rheology, XRD, Raman spectroscopy, FTIR spectroscopy, TGA and SEM collectively revealed that the combined incorporation of DDH and DP facilitates matrix structural rearrangement, enables EHMC to bind within the structured network, and promotes the formation of more intact continuous surface films. In vitro SPF assays demonstrated that the finished topical formulation SC-4 delivered superior UVB blocking efficacy compared with the EHMC-only control SC-1; furthermore, SC-4 exhibited improved short-term physical stability under the preset thermal and centrifugal acceleration test conditions. Follow-up skin safety assessments, mass spectrometry imaging (MSI) and pharmacokinetic assays verified that SC-4 elicited no remarkable acute skin irritation across all experimental conditions. Relative to SC-1, the reference formulation with EHMC as the sole UV filter, SC-4 displayed weaker EHMC-related distribution signals in skin tissues, accompanied by lower early plasma EHMC concentrations and a slightly lower AUC_0–48h trend. Collectively, these findings indicate that DDH/DP co-assembly serves as a viable matrix-structuring strategy to modulate EHMC-related skin distribution and early plasma exposure. Further research into UVA blocking performance, photostability, skin retention and transdermal permeation profiles, as well as long-term storage stability, is required to advance the development of broad-spectrum sunscreen formulations built on this novel matrix platform. Full article

Psilocybin, a psychedelic drug with reported anxiolytic and antidepressant potential, is rapidly metabolized to its active metabolite psilocin. However, a lack of adequate toxicity studies and tissue distribution studies currently restricts its development and application. This study combined behavioral assays in zebrafish with desorption electrospray ionization mass spectrometry imaging (DESI-MSI) to systematically evaluate the acute neurotoxicity of psilocybin and characterize the in vivo spatial distribution of its active metabolite, psilocin. The novel tank test was used to evaluate zebrafish following a 4 h exposure to psilocybin at three different doses (20, 40, and 80 μM; n = 6 per group). Statistical analysis of the data was performed using ANOVA. Behavioral analyses revealed that exposure to psilocybin induced pronounced neurobehavioral alterations, including hyperactivity and disrupted swimming patterns, as evidenced by significant increases in the number of zone transitions and shuttle frequency. We established a DESI-MSI-based method for quantitative mapping and visualization of psilocin in zebrafish tissues. Methodological validation indicated that a linear relationship between ion intensity, spotted amount (R² = 0.9947), and reproducibility (RSD < 15%) is suitable for quantitative analysis of psilocin in zebrafish tissues. Spatial distribution maps showed that following continuous exposure for 4 h, psilocin was widely distributed across multiple tissues, such as the eye, brain, heart, liver, and kidney, with marked accumulation in the brain and the periportal regions of the liver. Relative psilocin signal intensity revealed a dose-dependent increase in tissue drug levels. The dose-dependent increase in both behavioral hyperactivity and brain psilocin levels points to a consistent relationship, in line with a central site of action. Collectively, these findings demonstrate that DESI-MSI provides a visual and efficient strategy for studying drug distribution in biological tissues from exposed animals. The neurobehavioral toxicity phenotypes and distinct tissue distribution patterns of psilocin uncovered in this study offer critical insights into the biological effects and potential risks of this psychoactive substance. Full article

The advancements made in the mass spectrometry imaging (MSI) field have allowed for the generation of very large-scale data sets. These data are often interrogated by machine learning (ML), although storing and handling data sets of this size can be difficult. To aid impacted researchers, we seek to evaluate feature reduction strategies that will minimize the amount of data stored while still maintaining the ability to correctly classify the data. Two different feature selection strategies are tested on six different data sets, leveraging XGBoost as the machine learning algorithm. The study provides evidence that selecting features based on the greatest average abundance across all samples is best suited to scale down the feature set at a more modest trimming level, while selecting features based on statistical analysis via a Student’s t-test is better suited for a more aggressive trimming level. These trends were present regardless of training set size or cross-validation strategy. The results from this work provide insight into when these feature filtering steps can be used effectively and when another data reduction strategy, including not restricting the data set, should be considered. Full article

Pterocephalus hookeri (C.B.Clarke) Höeck is a classic traditional Tibetan medicinal herb with multiple pharmacological activities. The inconsistent usage of its medicinal parts (whole herb, aboveground part (AP), and underground part (UP)) in commercial circulation severely restricts its clinical safety and quality stability. Currently, most existing chemical investigations focus on the whole herb, whereas the intraspecific chemical discrepancies between AP and UP remain poorly clarified. Herein, an integrated analytical strategy combining ultra-high-performance liquid chromatography–quadrupole time-of-flight mass spectrometry (UPLC-Q-TOF/MS)-based untargeted metabolomics, feature-based molecular networking (FBMN), and paper-based analytical device desorption electrospray ionization mass spectrometry imaging (PAD-DESI-MSI) was established to characterize differential metabolites and their spatial distribution in P. hookeri. A total of 101 compounds were annotated, and 12 vital differential metabolites were further screened with variable importance in projection (VIP) values > 1. The visualized distribution differences of these biomarkers were validated via heatmap and PAD-DESI-MSI analysis. Obvious differences in chemical accumulation characteristics were confirmed between AP and UP, which can guide reasonable clinical medication and rational dosage regulation referring to metabolite abundance. Moreover, optimized data filtering thresholds effectively eliminated metabolomic false positives, and FBMN exhibited excellent capacity for differential biomarker screening. This study provides a solid chemical basis for the quality evaluation and rational medicinal application of P. hookeri. Full article

Background: Diabetic nephropathy (DN) is characterized by complex and region-specific metabolic dysregulation that is not captured by conventional biomarkers. However, the spatiotemporal organization of metabolic alterations across renal compartments in type 1 diabetes remains poorly understood. Methods: In this study, spatial metabolomics based on air flow-assisted desorption electrospray ionization mass spectrometry imaging (AFADESI-MSI) was applied to investigate metabolic alterations in kidney tissues from alloxan-induced diabetic rats at 4 and 8 weeks post-induction. Complementary LC–MS/MS metabolite profiling and label-free proteomic analysis were performed to support pathway interpretation. Results: Spatial metabolomics revealed pronounced region- and time-dependent metabolic reprogramming in diabetic kidneys. Early-stage (DN-4w) changes were characterized by elevated glucose and activation of glucose-associated pathways, including the polyol pathway, accompanied by accumulation of acylcarnitines and lipid intermediates, indicating metabolic substrate overload. At later stages (DN-8w), glucose and related metabolites declined, reflecting impaired metabolic capacity and mitochondrial dysfunction. Broad remodeling of lipid metabolism, including glycerophospholipids, fatty acids, and hexosylceramide, was observed, along with dysregulation of amino acid metabolism and redox-related pathways. These alterations exhibited clear regional heterogeneity across renal cortex and medulla, highlighting compartment-specific metabolic vulnerability. Conclusions: This study provides a comprehensive spatial characterization of metabolic perturbations during DN progression, revealing coordinated alterations in glucose utilization, lipid metabolism, and mitochondrial function. The findings demonstrate the value of spatial metabolomics in uncovering region-specific metabolic mechanisms and provide new insights into the pathogenesis of diabetic nephropathy. Full article

Lilium lancifolium Thunb. is an important economic crop widely cultivated and traded across Asia and has significant pharmacological activity. Despite decades of research on their chemical composition, the spatial distribution patterns of characteristic secondary metabolites within the bulbs remain poorly understood. In this study, we used matrix-assisted laser desorption/ionization mass spectrometry imaging (MALDI-MSI) technology to characterize and spatially visualize multiple metabolites within the bulb for the first time. Additionally, ultra-high-performance liquid chromatography-Orbitrap Exploris mass spectrometry (UHPLC-OE-MS) was used to obtain comprehensive metabolite information from the bulbs. Using spatial metabolomics, we successfully identified nine steroidal saponins, three phenolic acid glycerides, and six other metabolites. Subsequently, we analyzed the spatial distribution of steroidal saponins and phenolic acid glycerides, which are key bioactive components. The analysis revealed that most of the steroidal saponins and phenolic acid glycerides, such as deacylbrownioside and regaloside A, exhibited a similar distribution pattern, mainly being enriched in the outer regions (A2, B2) and basal regions (B1, B2) on an individual scale. Further metabolomic and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses indicated that 11 substances detected in the bulbs, including diosgenin, phenylalanine, and acetyl-CoA, were jointly associated with 39 metabolic pathways, including “phenylpropanoid biosynthesis” and “terpenoid backbone biosynthesis”. Based on the above findings, we propose biosynthetic pathways and accumulation patterns of steroidal saponins and phenolic acid glycerides in bulbs. This study provides a basis for precise resource utilization of L. lancifolium bulbs and a methodology to elucidate the biosynthesis of plant metabolites. Full article

Background: Mass spectrometry imaging (MSI) enables in situ mapping of metabolite distributions within tissues, and spatial clustering is a key step for delineating metabolically distinct regions. Nevertheless, spatial clustering methods have not been systematically benchmarked for spatial metabolomics data. Methods: Here, we evaluated the effects of ion filtering and clustering method selection on clustering performance and established a dual-metric framework that jointly assesses the spatial continuity of cluster labels and inter-cluster metabolic heterogeneity. We benchmarked 30 clustering algorithms across 12 heterogeneous MSI datasets spanning three major ion sources, four mass analyzers, and multiple spatial resolutions, covering approaches from non-spatial methods to advanced spatially aware models. Results: Noise filtering markedly improved the spatial continuity of results generated by non-spatial methods (mean improvement, approximately 28%) but provided limited benefit for spatially aware methods. Across the 12 datasets, a median of only 11 methods satisfied both evaluation criteria simultaneously, whereas SSC and DRSC met the dual-metric thresholds in at least nine datasets. In the mbrain2_pos50 dataset, the top-ranked method based on the composite dual-metric score achieved 22% higher concordance between cluster assignments and cell-type annotations than the lowest-ranked method. Conclusions: Together, the proposed evaluation framework and the online platform SMcluster provide a standardized resource for benchmarking and selecting MSI clustering methods. Our results highlight the critical roles of preprocessing and method selection in determining spatial clustering performance and offer practical guidance for spatial metabolomics studies. Full article

The identification of dyes in ancient textiles is crucial for provenance research and scientific conservation. However, the extremely significant value of these cultural relics necessitates the use of non-destructive analytical techniques. To establish a non-destructive, in-situ, accurate, and rapid method for identifying natural dyes in ancient silk fabric samples, we employed desorption electrospray ionization high-resolution mass-spectrometry imaging (DESI-MSI). By optimizing key instrumental parameters—including sample pretreatment method, DESI spray solvent composition, and DESI heated transfer line (HTL) temperature—we determined the optimal mass-spectrometry imaging conditions. The optimal conditions for achieving the highest mass-spectrometry ion peak signal intensity and the best imaging quality were as follows: employing sample pretreatment using double-sided adhesive tape; a spray solvent composed of methanol (100%, v/v) with 0.1% formic acid and 0.1 μg/mL of leucine enkephalin; and an HTL temperature of 400 °C. The characteristic compound in the G42 silk fabric sample was successfully separated. Based on the characteristic mass-to-charge ratio of the major component, the compound was preliminarily identified as berberine. This result was further verified by tandem mass-spectrometry imaging and tandem mass spectra and finally confirmed by comparison with the mass spectrum of a reference standard. Consequently, the source of the dye in the sample was determined to be amur cork tree. The experiments confirmed the applicability and accuracy of the DESI-MSI method for the non-destructive analysis of precious textiles. This work underscores the urgent need to use such non-destructive techniques to provide technical support for the identification of high-value, inaccessible, or fragile silk artifacts and guide the historical tracing and preservation of these cultural relics. Full article

Although sorafenib (SOR) is effective for advanced hepatocellular carcinoma (HCC), significant metabolic heterogeneity limits its therapeutic effect. In this study, we employed high-resolution matrix-assisted laser desorption/ionization mass spectrometry imaging (MALDI MSI) to profile the spatial lipidomic alterations in 3D HepG2 spheroids following SOR treatment. Interestingly, sphingophospholipid and glycerophospholipid metabolism played crucial roles. In an orthotopic HCC mouse model, immunohistochemical and immunofluorescence staining confirmed that SOR induced immunological and inflammatory changes. Moreover, transcriptomic and Q-PCR analyses showed increased expression of Stat1, Zbp1, Parp14, Irf1, and Tifa along with decreased Eif4e2 in the SOR treatment group compared to the tumor control group. Bio-layer interferometry and molecular docking data also indicated that ZBP1 possessed favorable binding affinities with SOR. Overall, our findings demonstrated that SOR dramatically disrupted sphingolipid metabolism in tumor cell spheroids and, in an orthotopic model, activated the NOD-like receptor signaling pathway, accompanied by altered secretion of inflammatory factors and macrophage polarization. These results suggest that SOR exerts dual effects on tumor cell lipid metabolism and the tumor immune microenvironment. These findings provide a conceptual basis for future exploration of lipid-modulating therapeutic strategies in HCC. Full article

Background/Objectives: Colorectal cancer (CRC) is a leading cause of cancer-related mortality, with colorectal liver metastasis (CRLM) being the major determinant of poor prognosis. Tumor metabolic reprogramming and spatial heterogeneity complicate biomarker discovery and clinical management. This study aimed to characterize the spatial metabolomic landscape of CRC and identify progression-associated metabolic alterations and potential metabolic signatures for liver metastasis. Methods: A total of 23 tissue samples were collected from patients with CRC, with and without liver metastasis. Air flow-assisted desorption electrospray ionization mass spectrometry imaging (AFADESI-MSI) was used to map the spatial metabolite distributions. Region-of-interest analysis guided by histopathology enabled comparative metabolomic profiling across different tissue types. Multivariate statistical analysis, pathway enrichment, and receiver operating characteristic (ROC) curve analyses were performed to identify key metabolic alterations and evaluate potential biomarker performance. Results: Distinct spatial metabolomic profiles were observed across normal mucosa, primary tumors, liver metastases, and normal liver tissues. In primary colorectal tumors, amino acid, purine, and choline metabolism were significantly upregulated, whereas liver metastases were characterized by elevated levels of triglycerides, diglycerides, cholesteryl esters, and acylcarnitines, indicating enhanced lipid synthesis, incomplete fatty acid oxidation, and/or mitochondrial dysfunction. Progression-associated analyses across tissue types revealed consistently increasing trends in glycerides and acylcarnitines, along with heterogeneous alterations in amino acids and phospholipids. Furthermore, 122 differential metabolites were identified between metastatic and non-metastatic CRC, and a four-lipid panel demonstrated strong discriminatory performance. Conclusions: This study provides a spatially resolved characterization of metabolic reprogramming during CRC progression and liver metastasis, highlighting lipid and amino acid metabolism as key features and revealing the metabolic signatures of CRLM. Full article

Angiogenesis is a spatially regulated hallmark of colorectal cancer (CRC) progression, yet current analytical frameworks fail to resolve how nutraceutical bioactive compounds interact with angiogenic signalling within the heterogeneous tumour microenvironment. This review advances a central hypothesis: that the spatial localisation of palm oil mill effluent (POME)-derived bioactive compounds within CRC tumour tissues is predictive of their functional anti-angiogenic activity. POME—the largest waste stream of palm oil processing—contains a chemically diverse array of bioactives, including tocotrienols, phenolics, carotenoids, and fatty acids, with reported antioxidant, anti-inflammatory, and anti-angiogenic properties. However, the existing evidence is predominantly derived from bulk in vitro analyses, limiting mechanistic conclusions about compound behaviour within spatially organised tumour architectures. To address this gap, we propose an integrated framework positioning mass spectrometry imaging (MSI)—across matrix-assisted laser desorption/ionisation (MALDI), desorption electrospray ionisation (DESI), and secondary ion mass spectrometry (SIMS) platforms—as the analytical bridge between compound localisation and angiogenic function. By enabling the label-free, spatially resolved co-localisation of POME-derived compounds with key angiogenic mediators, including VEGF, HIF-1α, and NF-κB, within intact CRC tissues, MSI provides a mechanistic platform that transcends the limitations of conventional molecular analyses. A four-component translational roadmap is outlined, encompassing POME bioactive profiling, spatial compound mapping, angiogenic co-localisation analysis, and functional validation. Critically, the existing evidence on oil palm-derived bioactives is appraised with respect to study quality, mechanistic depth, and translational limitations, identifying the most analytically tractable candidate compounds for spatial investigation. Collectively, this framework positions POME valorisation within a precision nutraceutical oncology paradigm, offering a spatially informed strategy for anti-angiogenic intervention in CRC while simultaneously addressing the environmental burden of palm oil processing waste. Full article