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Explainable Artificial Intelligence (XAI) is a branch of AI that mainly focuses on developing systems that provide understandable and clear explanations for their decisions. In the context of cancer diagnoses on medical imaging, an XAI technology uses advanced image analysis methods like deep learning (DL) to make a diagnosis and analyze medical images, as well as provide a clear explanation for how it arrived at its diagnoses. This includes highlighting specific areas of the image that the system recognized as indicative of cancer while also providing data on the fundamental AI algorithm and decision-making process used. The objective of XAI is to provide patients and doctors with a better understanding of the system’s decision-making process and to increase transparency and trust in the diagnosis method. Therefore, this study develops an Adaptive Aquila Optimizer with Explainable Artificial Intelligence Enabled Cancer Diagnosis (AAOXAI-CD) technique on Medical Imaging. The proposed AAOXAI-CD technique intends to accomplish the effectual colorectal and osteosarcoma cancer classification process. To achieve this, the AAOXAI-CD technique initially employs the Faster SqueezeNet model for feature vector generation. As well, the hyperparameter tuning of the Faster SqueezeNet model takes place with the use of the AAO algorithm. For cancer classification, the majority weighted voting ensemble model with three DL classifiers, namely recurrent neural network (RNN), gated recurrent unit (GRU), and bidirectional long short-term memory (BiLSTM). Furthermore, the AAOXAI-CD technique combines the XAI approach LIME for better understanding and explainability of the black-box method for accurate cancer detection. The simulation evaluation of the AAOXAI-CD methodology can be tested on medical cancer imaging databases, and the outcomes ensured the auspicious outcome of the AAOXAI-CD methodology than other current approaches.

Excisional wounds are considered one of the most common physical injuries. This study aims to test the effect of a nanophytosomal formulation loaded with a dried hydroalcoholic extract of S. platensis on promoting excisional wound healing. The Spirulina platensis nanophytosomal formulation (SPNP) containing 100 mg PC and 50 mg CH exhibited optimum physicochemical characteristics regarding particle size (598.40 ± 9.68 nm), zeta potential (−19.8 ± 0.49 mV), entrapment efficiency (62.76 ± 1.75%), and Q6h (74.00 ± 1.90%). It was selected to prepare an HPMC gel (SPNP-gel). Through metabolomic profiling of the algal extract, thirteen compounds were identified. Molecular docking of the identified compounds on the active site of the HMGB-1 protein revealed that 12,13-DiHome had the highest docking score of −7.130 kcal/mol. SPNP-gel showed higher wound closure potential and enhanced histopathological alterations as compared to standard (MEBO® ointment) and S. platensis gel in wounded Sprague-Dawley rats. Collectively, NPS promoted the wound healing process by enhancing the autophagy process (LC3B/Beclin-1) and the NRF-2/HO-1antioxidant pathway and halting the inflammatory (TNF-, NF-κB, TlR-4 and VEGF), apoptotic processes (AIF, Caspase-3), and the downregulation of HGMB-1 protein expression. The present study’s findings suggest that the topical application of SPNP-gel possesses a potential therapeutic effect in excisional wound healing, chiefly by downregulating HGMB-1 protein expression.

A new abietane diterpene namely plectrabarbene (2), together with two known compounds: sugiol (1) and 11,14-dihydroxy-8,11,13-abietatrien-7-one (3) have been isolated from the aerial parts of Plectranthus barbatus Andr. (Labiatae). The structures of these compounds were determined by various spectral techniques (e.g., UV, IR, NMR, and FAB) and by comparison with the literature data. A molecular docking study of the isolated diterpenes (1&ndash, 3) was performed with AChE to gain an insight into their AChE inhibition mechanism. The results of docking experiments revealed that the all tested compounds showed binding affinity at the active site of AchE in comparison to donepezil.

The combination of liquid chromatography coupled to high resolution mass spectrometry (LC-HRESMS)-based dereplication and antiproliferative activity-guided fractionation was applied on the Red Sea-derived soft coral Sarcophyton sp. This approach facilitated the isolation of five new cembrane-type diterpenoids (1&ndash, 5), along with two known analogs (6 and 7), as well as the identification of 19 further, known compounds. The chemical structures of the new compounds were elucidated while using comprehensive spectroscopic analyses, including one-dimensional (1D) and two-dimensional (2D) NMR and HRMS. All of the isolated cembranoids (1&ndash, 7) showed moderate in vitro antiproliferative activity against a human breast cancer cell line (MCF-7), with IC50 ranging from 22.39&ndash, 27.12 &micro, g/mL. This class of compounds could thus serve as scaffold for the future design of anticancer leads.