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  1. Fulltext Drought Vulnerability Assessment Using Geospatial Techniques in Southern Queensland, Australia
    Hoque M, Pradhan B, Ahmed N, Alamri A
    Sensors (Basel), 2021 Oct 18;21(20).
    PMID: 34696109 DOI: 10.3390/s21206896
    In Australia, droughts are recurring events that tremendously affect environmental, agricultural and socio-economic activities. Southern Queensland is one of the most drought-prone regions in Australia. Consequently, a comprehensive drought vulnerability mapping is essential to generate a drought vulnerability map that can help develop and implement drought mitigation strategies. The study aimed to prepare a comprehensive drought vulnerability map that combines drought categories using geospatial techniques and to assess the spatial extent of the vulnerability of droughts in southern Queensland. A total of 14 drought-influencing criteria were selected for three drought categories, specifically, meteorological, hydrological and agricultural. The specific criteria spatial layers were prepared and weighted using the fuzzy analytical hierarchy process. Individual categories of drought vulnerability maps were prepared from their specific indices. Finally, the overall drought vulnerability map was generated by combining the indices using spatial analysis. Results revealed that approximately 79.60% of the southern Queensland region is moderately to extremely vulnerable to drought. The findings of this study were validated successfully through the receiver operating characteristics curve (ROC) and the area under the curve (AUC) approach using previous historical drought records. Results can be helpful for decision makers to develop and apply proactive drought mitigation strategies.
  2. Fulltext Explainable AI in Scene Understanding for Autonomous Vehicles in Unstructured Traffic Environments on Indian Roads Using the Inception U-Net Model with Grad-CAM Visualization
    Kolekar S, Gite S, Pradhan B, Alamri A
    Sensors (Basel), 2022 Dec 10;22(24).
    PMID: 36560047 DOI: 10.3390/s22249677
    The intelligent transportation system, especially autonomous vehicles, has seen a lot of interest among researchers owing to the tremendous work in modern artificial intelligence (AI) techniques, especially deep neural learning. As a result of increased road accidents over the last few decades, significant industries are moving to design and develop autonomous vehicles. Understanding the surrounding environment is essential for understanding the behavior of nearby vehicles to enable the safe navigation of autonomous vehicles in crowded traffic environments. Several datasets are available for autonomous vehicles focusing only on structured driving environments. To develop an intelligent vehicle that drives in real-world traffic environments, which are unstructured by nature, there should be an availability of a dataset for an autonomous vehicle that focuses on unstructured traffic environments. Indian Driving Lite dataset (IDD-Lite), focused on an unstructured driving environment, was released as an online competition in NCPPRIPG 2019. This study proposed an explainable inception-based U-Net model with Grad-CAM visualization for semantic segmentation that combines an inception-based module as an encoder for automatic extraction of features and passes to a decoder for the reconstruction of the segmentation feature map. The black-box nature of deep neural networks failed to build trust within consumers. Grad-CAM is used to interpret the deep-learning-based inception U-Net model to increase consumer trust. The proposed inception U-net with Grad-CAM model achieves 0.622 intersection over union (IoU) on the Indian Driving Dataset (IDD-Lite), outperforming the state-of-the-art (SOTA) deep neural-network-based segmentation models.
  3. Fulltext Improved Otsu and Kapur approach for white blood cells segmentation based on LebTLBO optimization for the detection of Leukemia
    Deshpande NM, Gite S, Pradhan B, Kotecha K, Alamri A
    Math Biosci Eng, 2022 Jan;19(2):1970-2001.
    PMID: 35135238 DOI: 10.3934/mbe.2022093
    The diagnosis of leukemia involves the detection of the abnormal characteristics of blood cells by a trained pathologist. Currently, this is done manually by observing the morphological characteristics of white blood cells in the microscopic images. Though there are some equipment- based and chemical-based tests available, the use and adaptation of the automated computer vision-based system is still an issue. There are certain software frameworks available in the literature; however, they are still not being adopted commercially. So there is a need for an automated and software- based framework for the detection of leukemia. In software-based detection, segmentation is the first critical stage that outputs the region of interest for further accurate diagnosis. Therefore, this paper explores an efficient and hybrid segmentation that proposes a more efficient and effective system for leukemia diagnosis. A very popular publicly available database, the acute lymphoblastic leukemia image database (ALL-IDB), is used in this research. First, the images are pre-processed and segmentation is done using Multilevel thresholding with Otsu and Kapur methods. To further optimize the segmentation performance, the Learning enthusiasm-based teaching-learning-based optimization (LebTLBO) algorithm is employed. Different metrics are used for measuring the system performance. A comparative analysis of the proposed methodology is done with existing benchmarks methods. The proposed approach has proven to be better than earlier techniques with measuring parameters of PSNR and Similarity index. The result shows a significant improvement in the performance measures with optimizing threshold algorithms and the LebTLBO technique.
  4. Fulltext Detection of Iris Presentation Attacks Using Feature Fusion of Thepade's Sorted Block Truncation Coding with Gray-Level Co-Occurrence Matrix Features
    Khade S, Gite S, Thepade SD, Pradhan B, Alamri A
    Sensors (Basel), 2021 Nov 08;21(21).
    PMID: 34770715 DOI: 10.3390/s21217408
    Iris biometric detection provides contactless authentication, preventing the spread of COVID-19-like contagious diseases. However, these systems are prone to spoofing attacks attempted with the help of contact lenses, replayed video, and print attacks, making them vulnerable and unsafe. This paper proposes the iris liveness detection (ILD) method to mitigate spoofing attacks, taking global-level features of Thepade's sorted block truncation coding (TSBTC) and local-level features of the gray-level co-occurrence matrix (GLCM) of the iris image. Thepade's SBTC extracts global color texture content as features, and GLCM extracts local fine-texture details. The fusion of global and local content presentation may help distinguish between live and non-live iris samples. The fusion of Thepade's SBTC with GLCM features is considered in experimental validations of the proposed method. The features are used to train nine assorted machine learning classifiers, including naïve Bayes (NB), decision tree (J48), support vector machine (SVM), random forest (RF), multilayer perceptron (MLP), and ensembles (SVM + RF + NB, SVM + RF + RT, RF + SVM + MLP, J48 + RF + MLP) for ILD. Accuracy, precision, recall, and F-measure are used to evaluate the performance of the projected ILD variants. The experimentation was carried out on four standard benchmark datasets, and our proposed model showed improved results with the feature fusion approach. The proposed fusion approach gave 99.68% accuracy using the RF + J48 + MLP ensemble of classifiers, immediately followed by the RF algorithm, which gave 95.57%. The better capability of iris liveness detection will improve human-computer interaction and security in the cyber-physical space by improving person validation.
  5. Classification of ankle joint movements based on surface electromyography signals for rehabilitation robot applications
    Al-Quraishi MS, Ishak AJ, Ahmad SA, Hasan MK, Al-Qurishi M, Ghapanchizadeh H, et al.
    Med Biol Eng Comput, 2017 May;55(5):747-758.
    PMID: 27484411 DOI: 10.1007/s11517-016-1551-4
    Electromyography (EMG)-based control is the core of prostheses, orthoses, and other rehabilitation devices in recent research. Nonetheless, EMG is difficult to use as a control signal given the complex nature of the signal. To overcome this problem, the researchers employed a pattern recognition technique. EMG pattern recognition mainly involves four stages: signal detection, preprocessing feature extraction, dimensionality reduction, and classification. In particular, the success of any pattern recognition technique depends on the feature extraction stage. In this study, a modified time-domain features set and logarithmic transferred time-domain features (LTD) were evaluated and compared with other traditional time-domain features set (TTD). Three classifiers were employed to assess the two feature sets, namely linear discriminant analysis (LDA), k nearest neighborhood, and Naïve Bayes. Results indicated the superiority of the new time-domain feature set LTD, on conventional time-domain features TTD with the average classification accuracy of 97.23 %. In addition, the LDA classifier outperformed the other two classifiers considered in this study.
  6. Fulltext Deep Mining Generation of Lung Cancer Malignancy Models from Chest X-ray Images
    Horry M, Chakraborty S, Pradhan B, Paul M, Gomes D, Ul-Haq A, et al.
    Sensors (Basel), 2021 Oct 07;21(19).
    PMID: 34640976 DOI: 10.3390/s21196655
    Lung cancer is the leading cause of cancer death and morbidity worldwide. Many studies have shown machine learning models to be effective in detecting lung nodules from chest X-ray images. However, these techniques have yet to be embraced by the medical community due to several practical, ethical, and regulatory constraints stemming from the "black-box" nature of deep learning models. Additionally, most lung nodules visible on chest X-rays are benign; therefore, the narrow task of computer vision-based lung nodule detection cannot be equated to automated lung cancer detection. Addressing both concerns, this study introduces a novel hybrid deep learning and decision tree-based computer vision model, which presents lung cancer malignancy predictions as interpretable decision trees. The deep learning component of this process is trained using a large publicly available dataset on pathological biomarkers associated with lung cancer. These models are then used to inference biomarker scores for chest X-ray images from two independent data sets, for which malignancy metadata is available. Next, multi-variate predictive models were mined by fitting shallow decision trees to the malignancy stratified datasets and interrogating a range of metrics to determine the best model. The best decision tree model achieved sensitivity and specificity of 86.7% and 80.0%, respectively, with a positive predictive value of 92.9%. Decision trees mined using this method may be considered as a starting point for refinement into clinically useful multi-variate lung cancer malignancy models for implementation as a workflow augmentation tool to improve the efficiency of human radiologists.
  7. Fulltext Can Natural Products Targeting EMT Serve as the Future Anticancer Therapeutics?
    Anwar S, Malik JA, Ahmed S, Kameshwar VA, Alanazi J, Alamri A, et al.
    Molecules, 2022 Nov 08;27(22).
    PMID: 36431766 DOI: 10.3390/molecules27227668
    Cancer is the leading cause of death and has remained a big challenge for the scientific community. Because of the growing concerns, new therapeutic regimens are highly demanded to decrease the global burden. Despite advancements in chemotherapy, drug resistance is still a major hurdle to successful treatment. The primary challenge should be identifying and developing appropriate therapeutics for cancer patients to improve their survival. Multiple pathways are dysregulated in cancers, including disturbance in cellular metabolism, cell cycle, apoptosis, or epigenetic alterations. Over the last two decades, natural products have been a major research interest due to their therapeutic potential in various ailments. Natural compounds seem to be an alternative option for cancer management. Natural substances derived from plants and marine sources have been shown to have anti-cancer activity in preclinical settings. They might be proved as a sword to kill cancerous cells. The present review attempted to consolidate the available information on natural compounds derived from plants and marine sources and their anti-cancer potential underlying EMT mechanisms.
  8. Fulltext Extraction of Roads Using the Archimedes Tuning Process with the Quantum Dilated Convolutional Neural Network
    Khan MJ, Singh PP, Pradhan B, Alamri A, Lee CW
    Sensors (Basel), 2023 Oct 28;23(21).
    PMID: 37960482 DOI: 10.3390/s23218783
    Road network extraction is a significant challenge in remote sensing (RS). Automated techniques for interpreting RS imagery offer a cost-effective solution for obtaining road network data quickly, surpassing traditional visual interpretation methods. However, the diverse characteristics of road networks, such as varying lengths, widths, materials, and geometries across different regions, pose a formidable obstacle for road extraction from RS imagery. The issue of road extraction can be defined as a task that involves capturing contextual and complex elements while also preserving boundary information and producing high-resolution road segmentation maps for RS data. The objective of the proposed Archimedes tuning process quantum dilated convolutional neural network for road Extraction (ATP-QDCNNRE) technology is to tackle the aforementioned issues by enhancing the efficacy of image segmentation outcomes that exploit remote sensing imagery, coupled with Archimedes optimization algorithm methods (AOA). The findings of this study demonstrate the enhanced road-extraction capabilities achieved by the ATP-QDCNNRE method when used with remote sensing imagery. The ATP-QDCNNRE method employs DL and a hyperparameter tuning process to generate high-resolution road segmentation maps. The basis of this approach lies in the QDCNN model, which incorporates quantum computing (QC) concepts and dilated convolutions to enhance the network's ability to capture both local and global contextual information. Dilated convolutions also enhance the receptive field while maintaining spatial resolution, allowing fine road features to be extracted. ATP-based hyperparameter modifications improve QDCNNRE road extraction. To evaluate the effectiveness of the ATP-QDCNNRE system, benchmark databases are used to assess its simulation results. The experimental results show that ATP-QDCNNRE performed with an intersection over union (IoU) of 75.28%, mean intersection over union (MIoU) of 95.19%, F1 of 90.85%, precision of 87.54%, and recall of 94.41% in the Massachusetts road dataset. These findings demonstrate the superior efficiency of this technique compared to more recent methods.
  9. Fulltext A spatio-temporal agent-based approach for modeling the spread of zoonotic cutaneous leishmaniasis in northeast Iran
    Tabasi M, Alesheikh AA, Sofizadeh A, Saeidian B, Pradhan B, AlAmri A
    Parasit Vectors, 2020 Nov 11;13(1):572.
    PMID: 33176858 DOI: 10.1186/s13071-020-04447-x
    BACKGROUND: Zoonotic cutaneous leishmaniasis (ZCL) is a neglected tropical disease worldwide, especially the Middle East. Although previous works attempt to model the ZCL spread using various environmental factors, the interactions between vectors (Phlebotomus papatasi), reservoir hosts, humans, and the environment can affect its spread. Considering all of these aspects is not a trivial task.

    METHODS: An agent-based model (ABM) is a relatively new approach that provides a framework for analyzing the heterogeneity of the interactions, along with biological and environmental factors in such complex systems. The objective of this research is to design and develop an ABM that uses Geospatial Information System (GIS) capabilities, biological behaviors of vectors and reservoir hosts, and an improved Susceptible-Exposed-Infected-Recovered (SEIR) epidemic model to explore the spread of ZCL. Various scenarios were implemented to analyze the future ZCL spreads in different parts of Maraveh Tappeh County, in the northeast region of Golestan Province in northeastern Iran, with alternative socio-ecological conditions.

    RESULTS: The results confirmed that the spread of the disease arises principally in the desert, low altitude areas, and riverside population centers. The outcomes also showed that the restricting movement of humans reduces the severity of the transmission. Moreover, the spread of ZCL has a particular temporal pattern, since the most prevalent cases occurred in the fall. The evaluation test also showed the similarity between the results and the reported spatiotemporal trends.

    CONCLUSIONS: This study demonstrates the capability and efficiency of ABM to model and predict the spread of ZCL. The results of the presented approach can be considered as a guide for public health management and controlling the vector population .

  10. A comprehensive phytochemical, biological, toxicological and molecular docking evaluation of Suaeda fruticosa (L.) Forssk.: An edible halophyte medicinal plant
    Saleem H, Khurshid U, Sarfraz M, Tousif MI, Alamri A, Anwar S, et al.
    Food Chem Toxicol, 2021 Aug;154:112348.
    PMID: 34144099 DOI: 10.1016/j.fct.2021.112348
    Suaeda fruticosa is an edible medicinal halophyte known for its traditional uses. In this study, methanol and dichloromethane extracts of S. fruticosa were explored for phytochemical, biological and toxicological parameters. Total phenolic and flavonoid constituents were determined by using standard aluminum chloride and Folin-Ciocalteu methods, and UHPLC-MS analysis of methanol extract was performed for tentative identification of secondary metabolites. Different standard methods like DPPH, ABTS, FRAP, CUPRAC, total antioxidant capacity (TAC), and metal chelation assays were utilized to find out the antioxidant potential of extracts. Enzyme inhibition studies of extracts against acetylcholinesterase, butyrylcholinesterase, tyrosinase, α-amylase and, α-glucosidase enzymes were also studied. Likewise, the cytotoxicity was also assessed against MCF-7, MDA-MB-231, and DU-145 cell lines. The higher phenolic and flavonoids contents were observed in methanol extracts which can be correlated to its higher radical scavenging potential. Similarly, 11 different secondary metabolites were tentatively identified by UHPLC profiling. Both the extract showed significant inhibition against all the enzymes except for α-glucosidase. Moreover, docking studies were also performed against the tested enzymes. In the case of cytotoxicity, both the samples were found moderately toxic against the tested cell lines. This plant can be explored further for its potential therapeutic and edible uses.
  11. Fulltext Evaluation of the Phytochemical, Antioxidant, Enzyme Inhibition, and Wound Healing Potential of Calotropis gigantea (L.) Dryand: A Source of a Bioactive Medicinal Product
    Alafnan A, Sridharagatta S, Saleem H, Khurshid U, Alamri A, Ansari SY, et al.
    Front Pharmacol, 2021;12:701369.
    PMID: 34483902 DOI: 10.3389/fphar.2021.701369
    Traditionally, plants of the genus Calotropis have been used to cure various common diseases. The present research work explores the chemical and biological characterization of one of the most common species of this genus, i.e., Calotropis gigantea (L.) Dryand (syn. Calotropis gigantea (L.) Dryand.), having multiple folklore applications. The ethanolic extract of leaves of Calotropis gigantea (L.) Dryand was analyzed for the phytochemical composition by determining the total bioactive (total phenolic and total flavonoid) contents and UHPLC-MS secondary metabolites analysis. For phytopharmacological evaluation, in vitro antioxidant (including DPPH, ABTS, FRAP, CUPRAC, phosphomolybdenum, and metal chelation antioxidant assays) activities, enzyme inhibition potential (against AChE, BChE, α-amylase, and tyrosinase enzymes), and in vivo wound healing potential were determined. The tested extract has been shown to contain considerable flavonoid (46.75 mg RE/g extract) and phenolic (33.71 mg GAE/g extract) contents. The plant extract presented considerable antioxidant potential, being the most active for CUPRAC assays. Secondary metabolite UHPLC-MS characterization, in both the positive and negative ionization modes, indicated the tentative presence of 17 different phytocompounds, mostly derivatives of sesquiterpene, alkaloids, and flavonoids. Similarly, the tested extract exhibited considerable inhibitory effects on tyrosinase (81.72 mg KAE/g extract), whereas it showed weak inhibition ability against other tested enzymes. Moreover, in the case of in vivo wound healing assays, significant improvement in wound healing was observed in both the tested models at the doses of 0.5 percent w/w (p < 0.001) and 2.0 percent w/w (p < 0.01) on the 16th day. The outcomes of the present research work suggested that C. gigantea (L.) Dryand plant extract could be appraised as a potential origin of bioactive molecules having multifunctional medicinal uses.
  12. Investigation into the biological properties, secondary metabolites composition, and toxicity of aerial and root parts of Capparis spinosa L.: An important medicinal food plant
    Saleem H, Khurshid U, Sarfraz M, Ahmad I, Alamri A, Anwar S, et al.
    Food Chem Toxicol, 2021 Sep;155:112404.
    PMID: 34246708 DOI: 10.1016/j.fct.2021.112404
    Capparis spinose L. also known as Caper is of great significance as a traditional medicinal food plant. The present work was targeted on the determination of chemical composition, pharmacological properties, and in-vitro toxicity of methanol and dichloromethane (DCM) extracts of different parts of C. spinosa. Chemical composition was established by determining total bioactive contents and via UHPLC-MS secondary metabolites profiling. For determination of biological activities, antioxidant capacity was determined through DPPH, ABTS, CUPRAC, FRAP, phosphomolybdenum, and metal chelating assays while enzyme inhibition against cholinesterase, tyrosinase, α-amylase and α-glucosidase were also tested. All the extracts were also tested for toxicity against two breast cell lines. The methanolic extracts were found to contain highest total phenolic and flavonoids which is correlated with their significant radical scavenging, cholinesterase, tyrosinase and glucosidase inhibition potential. Whereas DCM extracts showed significant activity for reducing power, phosphomolybdenum, metal chelation, tyrosinase, and α-amylase inhibition activities. The secondary metabolites profiling of both methanolic extracts exposed the presence of 21 different secondary metabolites belonging to glucosinolate, alkaloid, flavonoid, phenol, triterpene, and alkaloid derivatives. The present results tend to validate folklore uses of C. spinose and indicate this plant to be used as a potent source of designing novel bioactive compounds.
  13. The emergence of an imported variant of dengue virus serotype 2 in the Jazan region, southwestern Saudi Arabia
    Dafalla O, Abdulhaq AA, Almutairi H, Noureldin E, Ghzwani J, Mashi O, et al.
    Trop Dis Travel Med Vaccines, 2023 Mar 15;9(1):5.
    PMID: 36922890 DOI: 10.1186/s40794-023-00188-8
    BACKGROUND: Dengue virus (DENV) infection is a global economic and public health concern, particularly in tropical and subtropical countries where it is endemic. Saudi Arabia has seen an increase in DENV infections, especially in the western and southwestern regions. This study aims to investigate the genetic variants of DENV-2 that were circulating during a serious outbreak in Jazan region in 2019.

    METHODS: A total of 482 serum samples collected during 2019 from Jazan region were tested with reverse transcription-polymerase chain reaction (RT-PCR) to detect and classify DENV; positive samples underwent sequencing and bioinformatics analyses.

    RESULTS: Out of 294 positive samples, type-specific RT-PCR identified 58.8% as DENV-2 but could not identify 41.2%. Based on sequencing and bioinformatics analyses, the samples tested PCR positive in the first round but PCR negative in the second round were found to be imported genetic variant of DENV-2. The identified DENV-2 imported variant showed similarities to DENV-2 sequences reported in Malaysia, Singapore, Korea and China. The results revealed the imported genetic variant of DENV-2 was circulating in Jazan region that was highly prevalent and it was likely a major factor in this outbreak.

    CONCLUSIONS: The emergence of imported DENV variants is a serious challenge for the dengue fever surveillance and control programmes in endemic areas. Therefore, further investigations and continuous surveillance of existing and new viral strains in the region are warranted.

  14. Deciphering the toxicological and computational assessment of Verbascum yemenese.: Integrating phytochemistry and bioinformatics tools
    Alghamdi A, A Awadh Ali N, Alafnan A, Zainal Abidin SA, Alamri A, Hussein W, et al.
    Food Chem Toxicol, 2024 Nov;193:115028.
    PMID: 39368542 DOI: 10.1016/j.fct.2024.115028
    This study explores the phytochemical composition and biological activities of Verbascum yemenense, a plant known for its medicinal properties. The plant extract revealed a rich presence of bioactive compounds that exhibited significant antioxidant properties against free radicals. The enzyme inhibition potential was particularly notable against cholinesterases (AChE: 2.56 mg GALAE/g; BChE: 1.98 mg GALAE/g), and tyrosinase (87.94 mg KAE/g), α-glucosidase suggesting potential therapeutic applications in neurodegenerative diseases, skin disorders and diabetes. Molecular docking studies and Molecular Dynamics simulations, providing insights into the interaction mechanisms of the identified compounds with the target proteins. Molecular docking studies revealed high binding affinities of the phytoconstituents, with compounds like VY4 and phyllanthusol-A (VY15) showing substantial docking scores against AChE (-9.840 kcal/mol) and BChE (-9.853 kcal/mol), respectively. For instance, the RMSD values during the MD simulations for compound VY17 in the AML complex showed a stable conformation, fluctuating within a range of 0.75 Å to 1.75 Å, indicating a strong and consistent interaction with the enzyme. MESP studies highlighted VY17's distinctive electrostatic features, notably a pronounced electronegative region, which might contribute to its binding efficiency. These findings suggest that V. yemenense is a promising candidate for developing novel therapeutic agents.
  15. Fulltext Baicalein prevents stress-induced anxiety behaviors in zebrafish model
    Selvaraj LK, Jeyabalan S, Wong LS, Sekar M, Logeshwari B, Umamaheswari S, et al.
    Front Pharmacol, 2022;13:990799.
    PMID: 36386131 DOI: 10.3389/fphar.2022.990799
    Baicalein is a flavonoid mainly obtained from plants with wide range of biological activities, including neuroprotection. An acute and unexpected chronic stress (UCS) protocol has recently been adapted to zebrafish, a popular vertebrate model in brain research. The present study was aimed to evaluate baicalein's anti-anxiety potential in a zebrafish model by induction, which included neuropharmacological evaluation to determine behavioural parameters in the novel tank diving test (NTDT) and light-dark preference test (LDPT). The toxicity was also assessed using the brine shrimp lethality assay, and the 50% lethal concentration (LC50) was determined. The animals were then stressed for 7 days before being treated with different doses of baicalein (1 and 2 mg/L) for another 7 days in UCS condition. Due to acute stress and UCS, the frequency of entries and time spent in the 1) top region and 2) light area of the novel tank reduced significantly, indicating the existence of elevated anxiety levels. The biological activity of baicalein was demonstrated by its high LC50 values (1,000 μg/ml). Additionally, baicalein administration increased the frequency of entries and duration spent in the light region, indicating a significant decrease in anxiety levels. Overall, the present results showed that baicalein has a therapeutic advantage in reversing the detrimental consequences of UCS and acute stress, making it is a promising lead molecule for new drug design, development, and therapy for stress.
  16. Discovery of oxindole-based FLT3 inhibitors as a promising therapeutic lead for acute myeloid leukemia carrying the oncogenic ITD mutation
    Bender O, Shoman ME, Ali TFS, Dogan R, Celik I, Mollica A, et al.
    Arch Pharm (Weinheim), 2023 Feb;356(2):e2200407.
    PMID: 36403191 DOI: 10.1002/ardp.202200407
    FMS-like tyrosine kinase 3 (FLT3) mutations occur in approximately 30% of acute myeloid leukemia (AML) patients. In the current study, the oxindole chemotype is employed as a structural motif for the design of new FLT3 inhibitors as potential hits for AML irradiation. Cell-based screening was performed with 18 oxindole derivatives and 5a-c inhibited 68%-73% and 83%-91% of internal tandem duplication (ITD)-mutated MV4-11 cell growth for 48- and 72-h treatments while only 0%-2% and 27%-39% in wild-type THP-1 cells. The most potent compound 5a inhibited MV4-11 cells with IC50 of 4.3 µM at 72 h while it was 8.7 µM in THP-1 cells, thus showing two-fold selective inhibition against the oncogenic ITD mutation. The ability of 5a to modulate cell death was examined. High-throughput protein profiling revealed low levels of the growth factors IGFBP-2 and -4 with the blockage of various apoptotic inhibitors such as Survivin. p21 with cellular stress mechanisms was characterized by increased expression of HSP proteins along with TNF-β. Mechanistically, compounds 5a and 5b inhibited FLT3 kinase with IC50 values of 2.49 and 1.45 µM, respectively. Theoretical docking studies supported the compounds' ability to bind to the FLT3 ATP binding site with the formation of highly stable complexes as evidenced by molecular dynamics simulations. The designed compounds also provide suitable drug candidates with no violation of drug likeability rules.
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