Prophylactic effects of apigenin against hyperglycemia-associated amnesia via activation of the Nrf2/ARE pathway in zebrafish.

The escalating focus on ageing-associated disease has generated substantial interest in the phenomenon of cognitive impairment linked to diabetes. Hyperglycemia exacerbates oxidative stress, contributes to ß-amyloid accumulation, disrupts mitochondrial function, and impairs cognitive function. Existing therapies have certain limitations, and apigenin (AG), a natural plant flavonoid, has piqued interest due to its antioxidant, anti-inflammatory, and anti-hyperglycemic properties. So, we anticipate that AG might be a preventive medicine for hyperglycemia-associated amnesia. To test our hypothesis, naïve zebrafish were trained to acquire memory and pretreated with AG. Streptozotocin (STZ) was administered to mimic hyperglycemia-induced memory dysfunction. Spatial memory was assessed by T-maze and object recognition through visual stimuli. Acetylcholinesterase (AChE) activity, antioxidant enzyme status, and neuroinflammatory genes were measured, and histopathology was performed in the brain to elucidate the neuroprotective mechanism. AG exhibits a prophylactic effect and improves spatial learning and discriminative memory of STZ-induced amnesia in zebrafish under hyperglycemic conditions. AG also reduces blood glucose levels, brain oxidative stress, and AChE activity, enhancing cholinergic neurotransmission. AG prevented neuronal damage by regulating brain antioxidant response elements (ARE), collectively contributing to neuroprotective properties. AG demonstrates a promising effect in alleviating memory dysfunction and mitigating pathological changes via activation of the Nrf2/ARE mechanism. These findings underscore the therapeutic potential of AG in addressing memory dysfunction and neurodegenerative changes associated with hyperglycemia.

EEGDepressionNet: A Novel Self Attention-Based Gated DenseNet With Hybrid Heuristic Adopted Mental Depression Detection Model Using EEG Signals.

World Health Organization (WHO) has identified depression as a significant contributor to global disability, creating a complex thread in both public and private health. Electroencephalogram (EEG) can accurately reveal the working condition of the human brain, and it is considered an effective tool for analyzing depression. However, manual depression detection using EEG signals is time-consuming and tedious. To address this, fully automatic depression identification models have been designed using EEG signals to assist clinicians. In this study, we propose a novel automated deep learning-based depression detection system using EEG signals. The required EEG signals are gathered from publicly available databases, and three sets of features are extracted from the original EEG signal. Firstly, spectrogram images are generated from the original EEG signal, and 3-dimensional Convolutional Neural Networks (3D-CNN) are employed to extract deep features. Secondly, 1D-CNN is utilized to extract deep features from the collected EEG signal. Thirdly, spectral features are extracted from the collected EEG signal. Following feature extraction, optimal weights are fused with the three sets of features. The selection of optimal features is carried out using the developed Chaotic Owl Invasive Weed Search Optimization (COIWSO) algorithm. Subsequently, the fused features undergo analysis using the Self-Attention-based Gated Densenet (SA-GDensenet) for depression detection. The parameters within the detection network are optimized with the assistance of the same COIWSO. Finally, implementation results are analyzed in comparison to existing detection models. The experimentation findings of the developed model show 96% of accuracy. Throughout the empirical result, the findings of the developed model show better performance than traditional approaches.

Role of artificial intelligence (Google bard) in morphological, histopathological, and radiological image identifications: Objective Structured Practical Examination (OSPE) type-based performance.

OBJECTIVES: To evaluate the role of artificial intelligence (Google Bard) in figures, scans, and image identifications and interpretations in medical education and healthcare sciences through an Objective Structured Practical Examination (OSPE) type of performance. METHODS: The OSPE type of question bank was created with a pool of medical sciences figures, scans, and images. For assessment, 60 figures, scans and images were selected and entered into the given area of the Google Bard to evaluate the knowledge level. RESULTS: The marks obtained by Google Bard in brain structures, morphological and radiological images 7/10 (70%); bone structures, radiological images 9/10 (90%); liver structure and morphological, pathological images 4/10 (40%); kidneys structure and morphological images 2/7 (28.57%); neuro-radiological images 4/7 (57.14%); and endocrine glands including the thyroid, pancreas, breast morphological and radiological images 8/16 (50%). The overall total marks obtained by Google Bard in various OSPE figures, scans, and image identification questions were 34/60 (56.7%). CONCLUSION: Google Bard scored satisfactorily in morphological, histopathological, and radiological image identifications and their interpretations. Google Bard may assist medical students, faculty in medical education and physicians in healthcare settings.

Identification of bioactive compounds of Zanthoxylum armatum as potential inhibitor of pyruvate kinase M2 (PKM2): Computational and virtual screening approaches.

PKM2 (Pyruvate kinase M2) is the isoform of pyruvate kinase which is known to catalyse the last step of glycolysis that is responsible for energy production. This specific isoform is known to be highly expressed in certain cancerous conditions. Considering the role of this protein in various cancer conditions, we used PKM2 as a target protein to identify the potential compounds against this target. In this study, we have examined 96 compounds of Zanthoxylum armatum using an array of computational and in silico tools. The compounds were assessed for toxicity then their anticancer potential was predicted. The virtual screening was done with molecular docking followed by a detailed examination using molecular dynamics simulation. The majority of the compounds showed a higher probability of being antineoplastic. Based on toxicity, predicted anticancer potential, binding affinity, and binding site, three compounds (nevadensin, asarinin, and kaempferol) were selected as hit compounds. The binding energy of these compounds with PKM2 ranged from -7.7 to -8.3 kcal/mol and all hit compounds interact at the active site of the protein. The selected hit compounds formed a stable complex with PKM2 when simulated under physiological conditions. The dynamic analysis showed that these compounds remained attached to the active site till the completion of molecular simulation. MM-PBSA analysis showed that nevadensin exhibited a higher affinity towards PKM2 compared to asarinin and kaempferol. These compounds need to be assessed properties in vivo and in vitro to validate their efficacy.

Electrical transport properties of [(1 - x)succinonitrile:xpoly(ethylene oxide)]-LiCF3SO3-Co[tris-(2,2'-bipyridine)]3(TFSI)2-Co[tris-(2,2'-bipyridine)]3(TFSI)3 solid redox mediators.

A solid redox mediator (solid electrolyte) with an electrical conductivity (σ25°C) greater than 10-4 S cm-1 is an essential requirement for a dye-sensitized solar cell in the harsh weather of Gulf countries. This paper reports the electrical properties of solid redox mediators prepared using highly dissociable ionic salts: Co[tris-(2,2'-bipyridine)]3(TFSI)2, Co[tris-(2,2'-bipyridine)]3(TFSI)3, and LiCF3SO3 as a source of Co2+, Co3+, and Li+ ions, respectively, in a solid matrix: [(1 - x)succinonitrile:xpoly(ethylene oxide)], where x = 0, 0.5, and 1 in weight fraction. In the presence of large size of cations (Co2+ and Co3+) and large-sized and weakly-coordinated anions (TFSI- and CF3SO3-), only the succinonitrile-poly(ethylene oxide) blend (x = 0.5) resulted in highly conductive amorphous regions with σ25°C of 4.7 × 10-4 S cm-1 for EO/Li+ = 108.4 and 3.1 × 10-4 S cm-1 for EO/Li+ = 216.8. These values are slightly lower than 1.5 × 10-3 S cm-1 for x = 0 and higher than 6.3 × 10-7 S cm-1 for x = 1. Only blend-based electrolytes exhibited a downward curve in the log σ-T-1 plot, a low value of pseudo-activation energy (0.06 eV), a high degree of transparency, and high thermal stability, making it useful for device applications.

Identification and characterization of major bioactive compounds from Andrographis paniculata (Burm. f.) extracts showed multi-biomedical applications.

The Andrographis paniculata recognized as most valuable medicinal plant in folk medicine. Hence, this research was designed to evaluate antibacterial potential of petroleum ether (PE) and methanol (ME) extracts of A. paniculata against skin infection causing bacterial pathogens such as Staphylococcus aureus, Streptococcus pyogenes, Klebsiella pneumoniae, Enterobacter aerogenes, Proteus vulgaris, and Propionibacterium acnes. Also assessed the antidiabetic (α-glucosidase and α-amylase inhibition assay), antioxidant, and photoprotective potential of PE and ME extract analyses. The major bioactive compounds were identified and characterized through UV, FTIR, 1H-NMR and 13C-NMR spectra analyses. The ME extract contain more number of phytochemicals (alkaloids, flavonoids, saponins, terpenoids, glycoside, protein, and phytosterol) than PE extract. The antibacterial activity result also revealed that the ME (as dose dependent) extract showed better activity at 250 mg mL-1 as in the following order: P. acnes (6-29 mm) > K. pneumoniae (3-28 mm) > S. aureus (3-27 mm) > P. vulgaris (3-26 mm) > S. pyogenes (2-25 mm) > E. aerogenes (1-23 mm). PE: E. aerogenes (3-20 mm) > P. vulgaris (2-19 mm) > P. acnes (3-18 mm) > K. pneumoniae (3-17 mm) > S. aureus (2-16 mm) > S. pyogenes (0-11 mm). The MIC value of ME extract was found as 100-150 mg mL-1 and it was better than PE extract. Similarly, the ME also possesses dose based α-glucosidase inhibition activity as up to 85% at 250 mg mL-1 concentration. The fluorescence spectra analysis method also stated that the ME extract possess photoprotective bioactive agent. The ME fractions (F01 and F02) obtained from TLC and column chromatogram were identified as 3-O-ß-d-glucosyl-14- deoxyandrographiside and 14-deoxyandrographolide respectively through UV, FTIR, 1H-NMR and 13C-NMR spectra analyses. Such compounds may be responsible for significant antibacterial activity against pathogenic bacteria causing skin infections, excellent antidiabetic activity, as well as photoprotective potential.

Artificial intelligence-driven malware detection framework for internet of things environment.

The Internet of Things (IoT) environment demands a malware detection (MD) framework for protecting sensitive data from unauthorized access. The study intends to develop an image-based MD framework. The authors apply image conversion and enhancement techniques to convert malware binaries into RGB images. You only look once (Yolo V7) is employed for extracting the key features from the malware images. Harris Hawks optimization is used to optimize the DenseNet161 model to classify images into malware and benign. IoT malware and Virusshare datasets are utilized to evaluate the proposed framework's performance. The outcome reveals that the proposed framework outperforms the current MD framework. The framework generates the outcome at an accuracy and F1-score of 98.65 and 98.5 and 97.3 and 96.63 for IoT malware and Virusshare datasets, respectively. In addition, it achieves an area under the receiver operating characteristics and the precision-recall curve of 0.98 and 0.85 and 0.97 and 0.84 for IoT malware and Virusshare datasets, accordingly. The study's outcome reveals that the proposed framework can be deployed in the IoT environment to protect the resources.

Phenolic Acids-Mediated Regulation of Molecular Targets in Ovarian Cancer: Current Understanding and Future Perspectives.

Cancer is a global health concern with a dynamic rise in occurrence and one of the leading causes of mortality worldwide. Among different types of cancer, ovarian cancer (OC) is the seventh most diagnosed malignant tumor, while among the gynecological malignancies, it ranks third after cervical and uterine cancer and sadly bears the highest mortality and worst prognosis. First-line treatments have included a variety of cytotoxic and synthetic chemotherapeutic medicines, but they have not been particularly effective in extending OC patients' lives and are associated with side effects, recurrence risk, and drug resistance. Hence, a shift from synthetic to phytochemical-based agents is gaining popularity, and researchers are looking into alternative, cost-effective, and safer chemotherapeutic strategies. Lately, studies on the effectiveness of phenolic acids in ovarian cancer have sparked the scientific community's interest because of their high bioavailability, safety profile, lesser side effects, and cost-effectiveness. Yet this is a road less explored and critically analyzed and lacks the credibility of the novel findings. Phenolic acids are a significant class of phytochemicals usually considered in the nonflavonoid category. The current review focused on the anticancer potential of phenolic acids with a special emphasis on chemoprevention and treatment of OC. We tried to summarize results from experimental, epidemiological, and clinical studies unraveling the benefits of various phenolic acids (hydroxybenzoic acid and hydroxycinnamic acid) in chemoprevention and as anticancer agents of clinical significance.

Activating the delivery of a model drug to lipid membrane by encapsulation of cyclodextrin: Combined experimental and molecular docking studies.

Drug delivery science is always an important topic as it studies the delivery of therapeutic payloads to the desired target cells without affecting the healthy tissues/cells, thus minimizing drug-induced toxicity. Aiming towards the targeted drug delivery, the present project deals with the delivery of a polarity-sensitive solvatochromic model drug, namely, salt of 8-anilinonaphthalene-1-sulphonic acid (ANSA) to the model bio-membrane (which mimic several aspects of the real cell membrane), more precisely at the lipid-water interface of L-α-Dipalmitoylphosphatidylcholine (DPPC) phospholipid. The drug delivery process has been activated through the binding of dye with cyclodextrin, acting as a drug transporter. Detailed steady-state and time-resolved spectroscopic studies including molecular docking analysis imply the targeted drug delivery of dye, ANSA, towards the lipid-water interface region of lipid bilayers through encapsulation within the cyclodextrin void. Stronger binding interaction of the dye with the lipid bilayers relative to ß-cyclodextrin (ß-CD) is the foremost reason for the targeted delivery. The present biophysical interaction studies of drug-lipid interaction, thus, may provide a cordial approach for drug formulation and drug delivery.

Adsorptive Elimination of Heavy Metals from Aqueous Solution Using Magnetic Chitosan/Cellulose-Fe(III) Composite as a Bio-Sorbent.

Magnetic chitosan/cellulose nanofiber-Fe(III) [M-Ch/CNF-Fe(III)] composites were isolated for the elimination of Cr(VI), Cu(II), and Pb(II) from aqueous solution. Various analytical methods, such as field emission scanning electron microscopy (FE-SEM), transmission electron microscopy (TEM), Fourier-transform infrared spectroscopy (FT-IR), X-ray diffraction analysis (XRD), and thermogravimetric analysis (TGA) were employed to determine the morphological, physicochemical, and thermal properties of the isolated M-Ch/CNF-Fe(III) composites. It was found that the M-Ch/CNF-Fe(III) composites were porous materials, and they have the potential to be implemented as an adsorbent for heavy metals removal. The adsorption efficiency of M-Ch/CNF-Fe(III) composites was determined for Cr(VI), Cu(II), and Pb(II) elimination with changing pH (pH 1.0-8.0), adsorbent doses (0.05-1.0 g), time (15-90 min), and temperature (28-80 °C). In addition, isothermal and kinetics studies were conducted to assess the adsorption behavior and mass transfer phenomena of M-Ch/CNF-Fe(III) composites as an adsorbent for Cr(VI), Cu(II) and Pb(II) elimination from aqueous solution. The outcomes of the present study reveal that the M-Ch/CNF-Fe(III) composites could be utilized as an adsorbent for the Cr(VI), Cu(II), and Pb(II) elimination from industrial effluents.

Antiproliferative Mechanisms of a Polyphenolic Combination of Kaempferol and Fisetin in Triple-Negative Breast Cancer Cells.

Herein, we investigate the combinatorial therapeutic effects of naturally occurring flavonoids kaempferol (K) and fisetin (F) on triple-negative breast cancer (TNBC: MDA-MB-231 cell line). Dose-dependent MTT assay results show that K and F exhibited cytotoxicity in MDA-MB-231 cells at 62 and 75 µM (IC50), respectively, after 24 h. However, combined K + F led to 40% and more than 50% TNBC cell death observed at 10 and 20 µM, respectively, which revealed the synergistic association of both. The combination of K and F was determined to be more effective in inhibiting cell viability than either of the agents alone. The morphological changes associated with significant apoptotic cell death were observed under a fluorescent microscope, strongly supporting the synergistic association between K and F. We also proposed that combining the effects of both polyphenols, as opposed to their individual effects, would increase their in vitro efficacy. Furthermore, we assessed the cell death pathway by the combinational treatment via reactive oxygen species-induced DNA damage and the mitochondrially mediated apoptotic pathway. This study reveals the prominent synergistic role of phytochemicals, which helps in elevating the therapeutic efficacy of dietary nutrients and that anticancer effects may be a result of nutrients that act in concert.

Quantum Computational, Spectroscopic (FT-IR, FT-Raman, NMR, and UV-Vis) Hirshfeld Surface and Molecular Docking-Dynamics Studies on 5-Hydroxymethyluracil (Monomer and Trimer).

For many decades, uracil has been an antineoplastic agent used in combination with tegafur to treat various human cancers, including breast, prostate, and liver cancer. Therefore, it is necessary to explore the molecular features of uracil and its derivatives. Herein, the molecule's 5-hydroxymethyluracil has been thoroughly characterized by NMR, UV-Vis, and FT-IR spectroscopy by means of experimental and theoretical analysis. Density functional theory (DFT) using the B3LYP method at 6-311++G(d,p) was computed to achieve the optimized geometric parameters of the molecule in the ground state. For further investigation and computation of the NLO, NBO, NHO analysis, and FMO, the improved geometrical parameters were utilized. The potential energy distribution was used to allocate the vibrational frequencies using the VEDA 4 program. The NBO study determined the relationship between the donor and acceptor. The molecule's charge distribution and reactive regions were highlighted using the MEP and Fukui functions. Maps of the hole and electron density distribution in the excited state were generated using the TD-DFT method and PCM solvent model in order to reveal electronic characteristics. The energies and diagrams for the lowest unoccupied molecular orbital (LUMO) and the highest occupied molecular orbital (HOMO) were also provided. The HOMO-LUMO band gap estimated the charge transport within the molecule. When examining the intermolecular interactions in 5-HMU, Hirshfeld surface analysis was used, and fingerprint plots were also produced. The molecular docking investigation involved docking 5-HMU with six different protein receptors. Molecular dynamic simulation has given a better idea of the binding of the ligand with protein.

Numerical Assessment of Dipole Interaction with the Single-Phase Nanofluid Flow in an Enclosure: A Pseudo-Transient Approach.

Nanofluids substantially enhance the physical and thermal characteristics of the base or conducting fluids specifically when interacting with the magnetic field. Several engineering processes like geothermal energy extraction, metal casting, nuclear reactor coolers, nuclear fusion, magnetohydrodynamics flow meters, petrochemicals, and pumps incorporate magnetic field interaction with the nanofluids. On the other hand, an enhancement in heat transfer due to nanofluids is essentially required in various thermal systems. The goal of this study is to figure out that how much a magnetic field affects nanofluid flow in an enclosure because of a dipole. The nanofluid is characterized using a single-phase model, and the governing partial differential equations are computed numerically. A Pseudo time based numerical algorithm is developed to numerically solve the problem. It can be deduced that the Reynolds number and the magnetic parameter have a low effect on the Nusselt number and skin friction. The Nusselt number rises near the dipole location because of an increase in the magnetic parameter Mn and the Reynolds number Re. The imposed magnetic field alters the region of high temperature nearby the dipole, while newly generated vortices rotate in alternate directions. Furthermore, nanoparticle volume fraction causes a slight change in the skin friction while it marginally reduces the Nusselt number.