Transcriptomic analysis provides an insight into the function of CmGH9B3, a key gene of ß-1, 4-glucanase, during the graft union healing of oriental melon scion grafted onto squash rootstock.

The melon (Cucumis melo L.) is a globally cherished and economically significant crop. The grafting technique has been widely used in the vegetative propagation of melon to promote environmental tolerance and disease resistance. However, mechanisms governing graft healing and potential incompatibilities in melons following the grafting process remain unknown. To uncover the molecular mechanism of healing of grafted melon seedlings, melon wild type (Control) and TRV-CmGH9B3 lines were obtained and grafted onto the squash rootstocks (C. moschata). Anatomical differences indicated that the healing process of the TRV-CmGH9B3 plants was slower than that of the control. A total of 335 significantly differentially expressed genes (DEGs) were detected between two transcriptomes. Most of these DEGs were down-regulated in TRV-CmGH9B3 grafted seedlings. GO and KEGG analysis showed that many metabolic, physiological, and hormonal responses were involved in graft healing, including metabolic processes, plant hormone signaling, plant MAPK pathway, and sucrose starch pathway. During the healing process of TRV-CmGH9B3 grafted seedlings, gene synthesis related to hormone signal transduction (auxin, cytokinin, gibberellin, brassinolide) was delayed. At the same time, it was found that most of the DEGs related to the sucrose pathway were down-regulated in TRV-CmGH9B3 grafted seedlings. The results showed that sugar was also involved in the healing process of melon grafted onto squash. These results deepened our understanding of the molecular mechanism of GH9B3, a key gene of ß-1, 4-glucanase. It also provided a reference for elucidating the gene mechanism and function analysis of CmGH9B3 in the process of graft union healing.

Significance of Estrogen/Progesterone Receptor Expression in Metaplastic Breast Carcinoma.

Introduction: Metaplastic breast carcinoma (MBC) is a rare subgroup of breast neoplasms associated with adverse outcomes because of its aggressive nature. Typically, MBCs show triple-negative hormone receptor (HR) status. Determining the HR status of breast cancer is an integral part because it is an important prognostic factor and helps in the treatment course of the disease. This study aimed to determine the HR status of MBC, its significance, and its association with various clinicopathological parameters. Methods: This was a retrospective study conducted at the Department of Histopathology, Liaquat National Hospital. A total of 140 biopsy-proven cases of MBC were enrolled in the study. Clinical and pathological data were retrieved from the institutes' archives. Immunohistochemical studies were conducted to determine the estrogen receptor (ER) and progesterone receptor (PR) status. Results: The mean age of MBC in our population was found to be 52.18 ± 12.19 years. The HR positivity rate in our population was found to be 32.9%. A significant association was found between HR status and tumor laterality, tumor size, tumor grade, tumor stage, and recurrence. ER/PR-negative MBCs were most probably associated with higher grade and higher tumor stage and were larger in size (6.62 ± 3.43 cm) than ER/PR-positive MBCs (4.20 ± 1.88 cm). Moreover, ER/PR-positive MBCs showed a higher recurrence rate than ER/PR-negative MBCs (43.5% vs. 25.5%, respectively). No statistically significant relationship was found between HR status and patient age, histological subtype, or survival rate. Conclusion: MBC is a rare breast neoplasm. MBC was found to be triple negative in most cases, but a significant percentage were HR (ER/PR) positive. Moreover, we found an association between HR status and various clinicopathological features, indicating that HR status is a significant predictor of MBC prognosis.

Estimation of population median using bivariate auxiliary information in simple random sampling.

To estimate the unknown population median, several researchers have developed efficient estimators but these estimators are unable to provide efficient results in the existence of outliers. Keeping this point in view, the present work suggests enhanced class of robust estimators to estimate population median under simple random sampling in case of outliers/extreme observations. The suggested estimators are a mixture of bivariate auxiliary information and robust measures with the linear combination of deciles mean, tri-mean and Hodges Lehmann estimator. Mathematical properties associated with the improved class of robust estimators are evaluated in terms of bias and mean squared error. Moreover, the potentiality of our suggested estimators as compared to already available estimators is checked by considering two real-life data sets with outlier(s). In addition, a simulation study is also added in this regard. From theoretical and numerical findings, it is observed that our newly suggested estimators outperforms as compared to its competitors.

Tragacanth gum-based copper oxide nanoparticles: Comprehensive characterization, antibiofilm, antimicrobial and photocatalytic potentials.

Hereunder, we pioneered the synthesis of Copper Oxide nanoparticles (CuO NPs) utilizing Tragacanth gum (TG). The NPs were characterized using advanced techniques and assessed for different pharmaceutical and environmental perspectives. The successful formation of a colloidal NPs solution was confirmed by the appearance of a distinct black color and a distinct peak at 260 nm in UV-Visible spectrophotometry. The FTIR analysis unveiled a spectrum of functional groups responsible for the reduction and stabilization of CuO NPs. Dynamic Light Scattering (DLS) and Transmission Electron Microscopy (TEM) revealed size of NPs as 36.24 nm and 28 ±â€¯04 nm respectively. Energy Dispersive X-ray (EDX) Analysis indicated weight percentages of 70.38 % for Cu and 18.88 % for O, with corresponding atomic percentages. The X-ray Diffraction (XRD) analysis revealed the orthorhombic crystal structure of the prepared CuO NPs. Antimicrobial assessments through disc-diffusion assays demonstrated significant zones of inhibition (ZOI) against gram-positive bacterial strains (Bacillus Halodurans and Micrococcus leutus) and a gram-negative bacterial strain (E. coli). Against the fungal strain Aspergillus niger, a ZOI of 18.5 ±â€¯0.31 mm was observed. The NPs exhibited remarkable antioxidant potential determined through 2,2-Diphenyl-1-picrylhydrazyl (DPPH) and H2O2 scavenging assays. At a concentration of 3 mg/mL, the NPs demonstrated biofilm inhibition rates of 96 %, 90 %, 89.60 %, and 72.10 % against Micrococcus luteus, Bacillus halodurans, MRSA and E.coli respectively. Furthermore, the CuO NPs showed a high photocatalytic potential towards the degradation of safranin dye under sunlight irradiation. In conclusion, the findings underline the promising multifunctional properties of TG-based CuO NPs for different practical applications.

Utilization of microalgae for agricultural runoff remediation and sustainable biofuel production through an integrated biorefinery approach.

Generally wastewater such agricultural runoff is considered a nuisance; however, it could be harnessed as a potential source of nutrients like nitrates and phosphates in integrated biorefinery context. In the current study, microalgae Chlorella sp. S5 was used for bioremediation of agricultural runoff and the leftover algal biomass was used as a potential source for production of biofuels in an integrated biorefinery context. The microalgae Chlorella sp. S5 was cultivated on Blue Green (BG 11) medium and a comprehensive optimization of different parameters including phosphates, nitrates, and pH was carried out to acquire maximum algal biomass enriched with high lipids content. Dry biomass was quantified using the solvent extraction technique, while the identification of nitrates and phosphates in agricultural runoff was carried out using commercial kits. The algal extracted lipids (oils) were employed in enzymatic trans-esterification for biodiesel production using whole-cell biomass of Bacillus subtilis Q4 MZ841642. The resultant fatty acid methyl esters (FAMEs) were analyzed using Fourier transform infrared (FTIR) spectroscopy and gas chromatography coupled with mass spectrometry (GC-MS). Subsequently, both the intact algal biomass and its lipid-depleted algal biomass were used for biogas production within a batch anaerobic digestion setup. Interestingly, Chlorella sp. S5 demonstrated a substantial reduction of 95% in nitrate and 91% in phosphate from agricultural runoff. The biodiesel derived from algal biomass exhibited a noteworthy total FAME content of 98.2%, meeting the quality standards set by American Society for Testing and Materials (ASTM) and European union (EU) standards. Furthermore, the biomethane yields obtained from whole biomass and lipid-depleted biomass were 330.34 NmL/g VSadded and 364.34 NmL/g VSadded, respectively. In conclusion, the findings underscore the potent utility of Chlorella sp. S5 as a multi-faceted resource, proficiently employed in a sequential cascade for treating agricultural runoff, producing biodiesel, and generating biogas within the integrated biorefinery concept.

Enhancing brain tumor diagnosis: an optimized CNN hyperparameter model for improved accuracy and reliability.

Hyperparameter tuning plays a pivotal role in the accuracy and reliability of convolutional neural network (CNN) models used in brain tumor diagnosis. These hyperparameters exert control over various aspects of the neural network, encompassing feature extraction, spatial resolution, non-linear mapping, convergence speed, and model complexity. We propose a meticulously refined CNN hyperparameter model designed to optimize critical parameters, including filter number and size, stride padding, pooling techniques, activation functions, learning rate, batch size, and the number of layers. Our approach leverages two publicly available brain tumor MRI datasets for research purposes. The first dataset comprises a total of 7,023 human brain images, categorized into four classes: glioma, meningioma, no tumor, and pituitary. The second dataset contains 253 images classified as "yes" and "no." Our approach delivers exceptional results, demonstrating an average 94.25% precision, recall, and F1-score with 96% accuracy for dataset 1, while an average 87.5% precision, recall, and F1-score, with accuracy of 88% for dataset 2. To affirm the robustness of our findings, we perform a comprehensive comparison with existing techniques, revealing that our method consistently outperforms these approaches. By systematically fine-tuning these critical hyperparameters, our model not only enhances its performance but also bolsters its generalization capabilities. This optimized CNN model provides medical experts with a more precise and efficient tool for supporting their decision-making processes in brain tumor diagnosis.

Nonparametric mixed exponentially weighted moving average-moving average control chart.

This research designed a distribution-free mixed exponentially weighted moving average-moving average (EWMA-MA) control chart based on signed-rank statistic to effectively identify changes in the process location. The EWMA-MA charting statistic assigns more weight to information obtained from the recent w samples and exponentially decreasing weights to information accumulated from all other past samples. The run-length profile of the proposed chart is obtained by employing Monte Carlo simulation techniques. The effectiveness of the proposed chart is evaluated under symmetrical distributions using a variety of individual and overall performance measures. The analysis of the run-length profile indicates that the proposed chart performs better than the existing control charts discussed in the literature. Additionally, an application from a gas turbine is provided to demonstrate how the proposed chart can be used in practice.

Approach towards sustainable leather: Characterization and effective industrial application of proteases from Bacillus sps. for ecofriendly dehairing of leather hide.

Tanneries are one of the most polluted industries known for production of massive amount of solid and liquid wastes without proper management and disposal. In this project we demonstrated the ecofriendly single step dehairing of leather hides with minimum pollution load. In this study, Bacillus species (Bacillus paralicheniformis strain BL.HK, Bacillus cereus strain BS.P) capable of producing proteases was successfully isolated by employing the new optimized selective media named M9-PEA as confirmed by 16sRNA genes sequencing. Sequence of 1493 bp long 16S rRNA genes of Bacillus paralicheniformis strain BL.HK and Bacillus cereus strain BS. P was submitted to GenBank under the accession number OP612692.1, OP612721.1 respectively The Bacillus paralicheniformis strain BL.HK, Bacillus cereus strain BS.P produced extracellur proteases of 28 and 37 KDa as resolved by SDS-PAGE respectively. The enzymes showed temperature optima at 50 °C and 55 °C and pH optima at 8.5, 9.5 respectively. The Proteases of Bacillus paralicheniformis strain BL.HK, Bacillus cereus strain BS.P were employed for dehairing of animal hides. The process resulted in significant removal of interfibriller substances without damage to collagen layer after one hour treatment, which was confirmed by histology, scanning electron microscopy. The quantification of various skin constituents (collagen, uronic acid, hexosamines, and GAGs) and pollution load parameters revealed that enzymatic treatment are more reliable. The results of skin application trials at industrial level with complete elimination of chemicals remark the biotechnological potential of these proteases for ecofriendly dehairing of animal hides without affecting the quality of the leathers produced.

Effect of biochar, zeolite and bentonite on physiological and biochemical parameters and lead and zinc uptake by maize (Zea mays L.) plants grown in contaminated soil.

Heavy metals (HMs) are common contaminants with major concern of severe environmental and health problems. This study evaluated the effects of organo-mineral amendments (mesquite biochar (MB), zeolite (ZL) and bentonite (BN) alone and in combination) applied at different rates to promote the maize (Zea mays L.) growth by providing essential nutrient and improving the soil physio-chemical properties under zinc (Zn) and lead (Pb) contamination. Result revealed that the incorporation of organo-mineral amendments had significantly alleviated Pb and Zn contamination by maize plants and improved the physiological and biochemical attributes of plants. Combined application of organo-mineral amendments including BMA-1, BMA-2 and BMA-3 performed excellently in terms of reducing Pb and Zn concentrations in both leaves (19-60%, 43-75%, respectively) and roots (24-59%, 42-68%, respectively) of maize. The amendments decreased the extractable, reducible, oxidisable and residual fractions of metals in soil and significantly reduced the soil DTPA-extractable Pb and Zn. BMA-1 substantially improved antioxidant enzyme activities in metal-stressed plants. This study indicated that combined use of organo-mineral amendments can effectively reduce the bioavailability and mobility of Pb and Zn in co-contaminated soils. Combined application of organo-mineral amendments could be viable remediation technology for immobilization and metal uptake by plants in polluted soils.

Advancing brain tumor detection: harnessing the Swin Transformer's power for accurate classification and performance analysis.

The accurate detection of brain tumors through medical imaging is paramount for precise diagnoses and effective treatment strategies. In this study, we introduce an innovative and robust methodology that capitalizes on the transformative potential of the Swin Transformer architecture for meticulous brain tumor image classification. Our approach handles the classification of brain tumors across four distinct categories: glioma, meningioma, non-tumor, and pituitary, leveraging a dataset comprising 2,870 images. Employing the Swin Transformer architecture, our method intricately integrates a multifaceted pipeline encompassing sophisticated preprocessing, intricate feature extraction mechanisms, and a highly nuanced classification framework. Utilizing 21 matrices for performance evaluation across all four classes, these matrices provide a detailed insight into the model's behavior throughout the learning process, furthermore showcasing a graphical representation of confusion matrix, training and validation loss and accuracy. The standout performance parameter, accuracy, stands at an impressive 97%. This achievement outperforms established models like CNN, DCNN, ViT, and their variants in brain tumor classification. Our methodology's robustness and exceptional accuracy showcase its potential as a pioneering model in this domain, promising substantial advancements in accurate tumor identification and classification, thereby contributing significantly to the landscape of medical image analysis.

Bridging drug discovery through hierarchical subtractive genomics against asd, trpG, and secY of pneumonia causing MDR Staphylococcus aureus.

Staphylococcus aureus (S. aureus) is an opportunistic gram-positive, non-motile, and non-sporulating bacteria that induces pneumonia, a provocative lung infection affecting mainly the terminal bronchioles and the small air sacs known as alveoli. Recently, it has developed antibiotic resistance to the available consortium as per the WHO reports; thereby, novel remedial targets and resilient medications to forestall and cure this illness are desperately needed. Here, using pan-genomics, a total of 1,387 core proteins were identified. Subtractive proteome analyses further identified 12 proteins that are vital for bacteria. One membrane protein (secY) and two cytoplasmic proteins (asd and trpG) were chosen as possible therapeutic targets concerning minimum % host identity, essentiality, and other cutoff values, such as high resistance in the MDR S. aureus. The UniProt AA sequences of the selected targets were modelled and docked against 3 drug-like chemical libraries. The top-ranked compounds i.e., ZINC82049692, ZINC85492658 and 3a of Isosteviol derivative for Aspartate-semialdehyde dehydrogenase (asd); ZINC38222743, ZINC70455378, and 5 m Isosteviol derivative for Anthranilate synthase component II (trpG); and finally, ZINC72292296, ZINC85632684, and 7 m Isosteviol derivative for Protein translocase subunit secY (secY), were further subjected to molecular dynamics studies for thermodynamic stability and energy calculation. Our study proposes new therapeutic targets in S. aureus, some of which have previously been reported in other pathogenic microorganisms. Owing to further experimental validation, we anticipate that the adapted methodology and the predicted results in this work could make major contributions towards novel drug discovery and their targets in S. aureus caused pneumonia.

In Vitro and Biological Evaluation of Oral Fast-Disintegrating Films Containing Ranitidine HCl and Syloid® 244FP-Based Ternary Solid Dispersion of Flurbiprofen.

Flurbiprofen (FBP), a nonsteroidal anti-inflammatory drug (NSAID), is commonly used to treat the pain of rheumatoid arthritis, but in prolonged use it causes gastric irritation and ulcer. To avoid these adverse events of NSAIDs, the simultaneous administration of H2 receptor antagonists such as ranitidine hydrochloride (RHCl) is obligatory. Here, we developed composite oral fast-disintegrating films (ODFs) containing FBP along with RHCl to provide a gastroprotective effect as well as to enhance the solubility and bioavailability of FBP. The ternary solid dispersion (TSD) of FBP was fabricated with Syloid® 244FP and poloxamer® 188 using the solvent evaporation technique. The synthesized FBP-TSD (coded as TSD) was loaded alone (S1) and in combination with plain RHCl (S2) in the composite ODFs based on hydroxypropyl methyl cellulose E5 (HPMC E5). The synthesized composite ODFs were evaluated by in vitro (thickness, folding endurance, tensile strength, disintegration, SEM, FTIR, XRD and release study) and in vivo (analgesic, anti-inflammatory activity, pro-inflammatory cytokines and gastroprotective assay) studies. The in vitro characterization revealed that TSD preserved its integrity and was effectively loaded in S1 and S2 with optimal compatibility. The films were durable and flexible with a disintegration time ≈15 s. The release profile at pH 6.8 showed that the solid dispersion of FBP improved the drug solubility and release when compared with pure FBP. After in vitro studies, it was observed that the analgesic and anti-inflammatory activity of S2 was higher than that of pure FBP and other synthesized formulations (TSD and S1). Similarly, the level of cytokines (TNF-α and IL-6) was also markedly reduced by S2. Furthermore, a gastroprotective assay confirmed that S2 has a higher safety profile in comparison to pure FBP and other synthesized formulations (TSD and S1). Thus, composite ODF (S2) can effectively enhance the FBP solubility and its therapeutic efficacy, along with its gastroprotective effect.

Enhanced Regularized Ensemble Encoderdecoder Network for Accurate Brain Tumor Segmentation.

BACKGROUND: Segmenting tumors in MRI scans is a difficult and time-consuming task for radiologists. This is because tumors come in different shapes, sizes, and textures, making them hard to identify visually. OBJECTIVE: This study proposes a new method called the enhanced regularized ensemble encoder-decoder network (EREEDN) for more accurate brain tumor segmentation. METHODS: The EREEDN model first preprocesses the MRI data by normalizing the intensity levels. It then uses a series of autoencoder networks to segment the tumor. These autoencoder networks are trained using back-propagation and gradient descent. To prevent overfitting, the EREEDN model also uses L2 regularization and dropout mechanisms. RESULTS: The EREEDN model was evaluated on the BraTS 2020 dataset. It achieved high performance on various metrics, including accuracy, sensitivity, specificity, and dice coefficient score. The EREEDN model outperformed other methods on the BraTS 2020 dataset. CONCLUSION: The EREEDN model is a promising new method for brain tumor segmentation. It is more accurate and efficient than previous methods. Future studies will focus on improving the performance of the EREEDN model on complex tumors.

Isolation and characterization of PGPR obtained from different arsenic-contaminated soil samples and their effect on photosynthetic characters of maize grown under arsenic stress.

Contamination of the environment due to speedup of anthropogenic activities has become a serious threat to modern humanity. Among the contaminants, the new emerging concern is the heavy metal (HM) contamination in the environment. Because the persistence and harmfulness of heavy metals affect the ecosystem and the health of plants, animals, and humans, they are the most toxic substances in the environment. Among them, Arsenic (As) emerged as major environmental constraint leading to enormous negative effects on the plant, animal, and human health. Even in minute quantity, As is known to cause various critical diseases in humans and toxicity in plants. Research was performed to observe the capability of plant growth-promoting strains of bacteria in enhancing Zea mays (L.) growth in arsenic polluted soil. Total 30 bacterial strains were isolated from the polluted soils, screened for plant growth promotion potential and arsenic tolerance. Eighteen isolates showed resistance to different levels of sodium arsenate (ranging from 0 to 50 mM) in agar plate using LB media. Of 18 isolates, 83.3% produced IAA, methyl red, and hydrogen cyanide; 55.5% exhibited catalase activity; 61.1% showed siderophore production; 88.8% showed phosphate solubilization; and 44.4% showed oxidase, Voges proskauer activity, and KOH solubility. The most efficient isolates SR3, SD5, and MD3 with significant arsenic tolerance and plant growth-promoting (PGP) activity were examined via sequencing of amplified 16S rRNA gene. Isolates of bacteria, i.e., SR3, SD5, and MD3, showing multiple PGP-traits were identified as Bacillus pumilus (NCBI accession number: OR459628), Paenibacillus faecalis (NCBI accession number: OR461560), and Pseudochrobactrum asaccharolyticum (NCBI accession number: OR458922), respectively. Maize seeds treated with these PGPR strains were grown in pots contaminated with 50 ppm and 100 ppm sodium arsenate. Compared to untreated arsenic stressed plants, bacterial inoculation P. asaccharolyticum (MD3) resulted 20.54%, 18.55%, 33.45%, 45.08%, and 48.55% improvement of photosynthetic pigments (carotenoid content, chlorophyll content, stomatal conductance (gs), substomatal CO2, and photosynthetic rate), respectively. Principal component analysis explained that first two components were more than 96% of the variability for each tested parameter. The results indicate that in comparison to other isolates, P. asaccharolyticum isolate can be used as efficient agent for improving maize growth under arsenic polluted soil.

Nanofiltration PVP Membrane with Gradient Cross-Linking for Efficient Polycyclic Aromatic Hydrocarbons Removal.

Polycyclic aromatic hydrocarbons (PAHs) are persistent organic pollutants that may contaminate various water sources and pose serious dangers to human health and the environment. Due to their capacity for size-based separation, nanofiltration membranes have become efficient instruments for PAH removal. However, issues such as membrane fouling and ineffective rejection still exist. To improve PAH rejection while reducing fouling problems, this work created a new gradient cross-linking poly(vinylpyrrolidone) (PVP) nanofiltration membrane. The gradient cross-linking technique enhanced the rejection performance and antifouling characteristics of the membrane. The results demonstrated that the highest membrane flow was achieved at a 0.15% SDS-PVP membrane. There is a trade-off between membrane flux and salt rejection since salt rejection increases with SDS owing to the growth of big pores. The membrane flux was reduced for the 0.25% SDS-PVP membrane owing to poor SDS dispersion. The prepared membrane showed enhanced removal efficiencies for the removal of the PAH compounds. The PVP membrane has the potential to be used in several water treatment applications, improving water quality, and preserving the environment.

C5L2 CRISPR KO enhances dental pulp stem cell-mediated dentinogenesis via TrkB under TNFα-induced inflammation.

Background and Objectives: Dental caries is one of the most common human pathological conditions resulting from the invasion of bacteria into the dentin. Current treatment options are limited. In many cases, endodontic therapy leads to permanent pulp tissue loss. Dentin-pulp complex regeneration involves dental pulp stem cells (DPSCs) that differentiate into odontoblast-like cells under an inflammatory context. However, limited information is available on how DPSC differentiation processes are affected under inflammatory environments. We identified the crucial role of complement C5a and its receptor C5aR in the inflammation-induced odontoblastic DPSC differentiation. Methodology: Here, we further investigated the role of a second and controversial C5a receptor, C5L2, in this process and explored the underlying mechanism. Human DPSCs were examined during 7-, 10-, and 14-day odontogenic differentiation treated with TNFα, C5L2 CRISPR, and tyrosine receptor kinase B (TrkB) antagonist [cyclotraxin-B (CTX-B)]. Results: Our data demonstrate that C5L2 CRISPR knockout (KO) enhances mineralization in TNFα-stimulated differentiating DPSCs. We further confirmed that C5L2 CRISPR KO significantly enhances dentin sialophosphoprotein (DSPP) and dentin matrix protein-1 (DMP-1) expression after 14-day odontoblastic DPSC differentiation, and treatment with CTX-B abolished the TNFα/C5L2 CRISPR KO-induced DSPP and DMP-1 increase, suggesting TrkB's critical role in this process. Conclusion and Key applications: Our data suggest a regulatory role of C5L2 and TrkB in the TNFα-induced odontogenic DPSC differentiation. This study may provide a useful tool to understand the mechanisms of the role of inflammation in dentinogenesis that is required for successful DPSC engineering strategies.

Koetjapic acid: unveiling its potential as a saviour in the realm of biological and medicinal properties, with a focus on anticancer mechanism of action.

Scientists have been compelled to search for alternative treatments due to the increasing prevalence of chemoresistance as well as the agonising and distressing side effects of both chemotherapy and radiation. Plant extracts have been exploited to treat various medical conditions for ages. Considering this fact, the main focus of various recent studies that are being conducted to find new and potent anticancer drugs involves the identification and utilisation of potential therapeutic chemicals present in plant extracts. Koetjapic acid (KJA), which belongs to the family of triterpenes, is primarily isolated from Sandoricum koetjape. Ongoing investigations into its therapeutic applications have revealed its tendency to impede the growth and proliferation of cancer cells. Koetjapic acid activates the intrinsic apoptotic pathway and promotes the death of cancer cells. Moreover, it inhibits angiogenesis and the dissemination of tumour (metastasis) by targeting the VEGF signalling cascade. Therefore, this study aims to elucidate the underlying mechanism of anticancer activity of koetjapic acid, providing significant insight into the compound's potential as an anticancer agent.