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Fulltext Intelligent lung cancer MRI prediction analysis based on cluster prominence and posterior probabilities utilizing intelligent Bayesian methods on extracted gray-level co-occurrence (GLCM) features

Yang J, Yee PL, Khan AA, Karamti H, Eldin ET, Aldweesh A, et al.

Digit Health, 2023;9:20552076231172632.
PMID: 37256015 DOI: 10.1177/20552076231172632

Lung cancer is the second foremost cause of cancer due to which millions of deaths occur worldwide. Developing automated tools is still a challenging task to improve the prediction. This study is specifically conducted for detailed posterior probabilities analysis to unfold the network associations among the gray-level co-occurrence matrix (GLCM) features. We then ranked the features based on t-test. The Cluster Prominence is selected as target node. The association and arc analysis were determined based on mutual information. The occurrence and reliability of selected cluster states were computed. The Cluster Prominence at state ≤330.85 yielded ROC index of 100%, relative Gini index of 99.98%, and relative Gini index of 100%. The proposed method further unfolds the dynamics and to detailed analysis of computed features based on GLCM features for better understanding of the hidden dynamics for proper diagnosis and prognosis of lung cancer.
Fulltext Enhancing lung cancer detection through hybrid features and machine learning hyperparameters optimization techniques

Li L, Yang J, Por LY, Khan MS, Hamdaoui R, Hussain L, et al.

Heliyon, 2024 Feb 29;10(4):e26192.
PMID: 38404820 DOI: 10.1016/j.heliyon.2024.e26192

Machine learning offers significant potential for lung cancer detection, enabling early diagnosis and potentially improving patient outcomes. Feature extraction remains a crucial challenge in this domain. Combining the most relevant features can further enhance detection accuracy. This study employed a hybrid feature extraction approach, which integrates both Gray-level co-occurrence matrix (GLCM) with Haralick and autoencoder features with an autoencoder. These features were subsequently fed into supervised machine learning methods. Support Vector Machine (SVM) Radial Base Function (RBF) and SVM Gaussian achieved perfect performance measures, while SVM polynomial produced an accuracy of 99.89% when utilizing GLCM with an autoencoder, Haralick, and autoencoder features. SVM Gaussian achieved an accuracy of 99.56%, while SVM RBF achieved an accuracy of 99.35% when utilizing GLCM with Haralick features. These results demonstrate the potential of the proposed approach for developing improved diagnostic and prognostic lung cancer treatment planning and decision-making systems.
Raphanus sativus Seeds Oil Arrested in vivo Inflammation and Angiogenesis through Down-regulation of TNF-α

Asif M, Yousaf HM, Saleem M, Hussain L, Mahrukh, Zarzour RA, et al.

Curr Pharm Biotechnol, 2022;23(5):728-739.
PMID: 34225619 DOI: 10.2174/1389201022666210702120956

BACKGROUND: Raphanus sativus is traditionally used as an anti-inflammatory agent.
OBJECTIVES: The current study was designed to explore the in vivo anti-inflammatory and antiangiogenic properties of Raphanus sativus seeds oil.
METHODS: Cold press method was used for the extraction of oil (RsSO) and was characterised by using GC-MS techniques. Three in vitro antioxidant assays (DPPH, ABTS and FRAP) were performed to explore the antioxidant potential of RsSO. Disc diffusion methods were used to study in vitro antimicrobial properties. In vivo anti-inflammatory properties were studied in both acute and chronic inflammation models. In vivo chicken chorioallantoic membrane assay was performed to study antiangiogenic effects. Molecular mechanisms were identified using TNF-α ELISA kit and docking tools.
RESULTS: GC-MS analysis of RsSO revealed the presence of hexadecanoic and octadecanoic acid. Findings of DPPH, ABTS, and FRAP models indicated relatively moderate radical scavenging properties of RsSO. Oil showed antimicrobial activity against a variety of bacterial and fungal strains tested. Data of inflammation models showed significant (p < 0.05) anti-inflammatory effects of RsSO in both acute and chronic models. 500 mg/kg RsSO halted inflammation development significantly better (p < 0.05) as compared with lower doses. Histopathological evaluations of paws showed minimal infiltration of inflammatory cells in RsSO-treated animals. Findings of TNF-α ELSIA and docking studies showed that RsSO has the potential to down-regulate the expression of TNF-α, iNOS, ROS, and NF-κB respectively. Moreover, RsSO showed in vivo antiangiogenic effects.
CONCLUSION: Data of the current study highlight that Raphanus sativus seeds oil has anti-inflammatory, and antiangiogenic properties and can be used as an adjunct to standard NSAIDs therapy which may reduce the dose and related side effects.
Fulltext Therapeutic Potential of Standardized Extract of Melilotus indicus (L.) All. and Its Phytochemicals against Skin Cancer in Animal Model: In Vitro, In Vivo, and In Silico Studies

Bashir A, Asif M, Saadullah M, Saleem M, Khalid SH, Hussain L, et al.

ACS Omega, 2022 Jul 26;7(29):25772-25782.
PMID: 35910099 DOI: 10.1021/acsomega.2c03053

Melilotus indicus (L.) All. is known to have anti-inflammatory and anticancer properties. The present study explored the in vivo skin carcinogenesis attenuating potential of ethanolic extract of M. indicus (L.) All. (Miet) in a 7,12-dimethylbenz[a]anthracene (DMBA)-induced skin cancer model. The ethanolic extract of the plant was prepared by a maceration method. HPLC analysis indicated the presence of quercetin in abundance and also various other phytoconstituents. DPPH radical scavenging assay results showed moderate antioxidant potential (IC50 = 93.55 ± 5.59 μg/mL). A topical acute skin irritation study showed the nonirritant nature of Miet. Data for the skin carcinogenic model showed marked improvement in skin architecture in Miet and its primary phytochemicals (quercetin and coumarin) treated groups. Miet 50% showed comparable effects with 5-fluorouracil. Significant (p < 0.05) anticancerous effects were seen in coumarin-quercetin combination-treated animals than in single agent (coumarin and quercetin alone)-treated animals. Chorioallantoic membrane (CAM) assay results showed the antiangiogenic potential of Miet. Treatment with Miet significantly down-regulated the serum levels of CEA (carcinoembryonic antigen) and TNF-α (Tumor necrosis factor-α). Data for the docking study indicated the binding potential of quercetin and coumarin with TNF-α, EGFR, VEGF, and BCL2 proteins. Thus, it is concluded that Miet has skin cancer attenuating potential that is proposed to be due to the synergistic actions of its bioactive molecules. Further studies to explore the effects of Miet and its bioactive molecules as an adjuvant therapy with low dose anticancer drugs are warranted, which may lead to a new area of research.
Fulltext Redefining Cancer Research Priorities in Low- and Middle-Income Countries in the Post-COVID-19 Global Context: A Modified Delphi Consensus Process

Fox L, Santaolalla A, Handford J, Sullivan R, Torode J, Vanderpuye V, et al.

JCO Glob Oncol, 2023 Aug;9:e2300111.
PMID: 37561978 DOI: 10.1200/GO.23.00111

PURPOSE: The post-COVID-19 funding landscape for cancer research globally has become increasingly challenging, particularly in resource-challenged regions (RCRs) lacking strong research ecosystems. We aimed to produce a list of priority areas for cancer research in countries with limited resources, informed by researchers and patients.
METHODS: Cancer experts in lower-resource health care systems (as defined by the World Bank as low- and middle-income countries; N = 151) were contacted to participate in a modified consensus-seeking Delphi survey, comprising two rounds. In round 1, participants (n = 69) rated predetermined areas of potential research priority (ARPs) for importance and suggested missing ARPs. In round 2, the same participants (n = 49) rated an integrated list of predetermined and suggested ARPs from round 1, then undertook a forced choice priority ranking exercise. Composite voting scores (T-scores) were used to rank the ARPs. Importance ratings were summarized descriptively. Findings were discussed with international patient advocacy organization representatives.
RESULTS: The top ARP was research into strategies adapting guidelines or treatment strategies in line with available resources (particularly systemic therapy) (T = 83). Others included cancer registries (T = 62); prevention (T = 52); end-of-life care (T = 53); and value-based and affordable care (T = 51). The top COVID-19/cancer ARP was strategies to incorporate what has been learned during the pandemic that can be maintained posteriorly (T = 36). Others included treatment schedule interruption (T = 24); cost-effective reduction of COVID-19 morbidity/mortality (T = 19); and pandemic preparedness (T = 18).
CONCLUSION: Areas of strategic priority favored by cancer researchers in RCRs are related to adaptive treatment guidelines; sustainable implementation of cancer registries; prevention strategies; value-based and affordable cancer care; investments in research capacity building; epidemiologic work on local risk factors for cancer; and combatting inequities of prevention and care access.