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Mangifera indica peels are a rich source of diverse flavonoids and xanthonoids; however, generally these are discarded. Computational studies revealed that mangiferin significantly interacts with amino acid residues of transcriptional regulators 1IK3, 3TOP, and 4f5S. The methanolic extract of Langra variety of mangoes contained the least phenol concentrations (22.6 ± 0.32 mg/gGAE [gallic acid equivalent]) compared to the chloroform (214.8 ± 0.12 mg/gGAE) and ethyl acetate fractions (195.6 ± 0.14 mg/gGAE). Similarly, the methanolic extract of Sindhri variety contained lower phenol concentrations (42.3 ± 0.13 mg/gRUE [relative utilization efficiency]) compared with the chloroform (85.6 ± 0.15 mg/gGAE) and ethyl acetate (76.1 ± 0.32 mg/gGAE) fractions. Langra extract exhibited significant α‐glucosidase inhibition (IC50 0.06 mg/mL), whereas the ethyl acetate fraction was highly active (IC50 0.12 mg/mL) in Sindhri variety. Mangiferin exhibited significant inhibition (IC50 0.026 mg/mL). A moderate inhibition of 15‐LOX was observed in all samples, whereas mangiferin was least active. In advanced glycation end product inhibition assay, the chloroform fraction of Langra variety exhibited significant inhibition in nonoxidative (IC50 64.4 μg/mL) and oxidative modes (IC50 54.7 μg/mL). It was concluded that both Langra and Sindhri peel extracts and fractions possess significant antidiabetic activities. The results suggest the potential use of peel waste in the management and complications of diabetes. [ABSTRACT FROM AUTHOR]

The significance of this study lies in its exploration of bioactive plant extracts as a promising avenue for combating oral bacterial pathogens, offering a novel strategy for biofilm eradication that could potentially revolutionize oral health treatments. Oral bacterial infections are common in diabetic patients; however, due to the development of resistance, treatment options are limited. Considering the excellent antimicrobial properties of phenolic compounds, we investigated them against isolated oral pathogens using in silico and in vitro models. We performed antibiogram studies and minimum inhibitory concentration (MIC), antibiofilm, and antiquorum sensing activities covering phenolic compounds. Bacterial strains were isolated from female diabetic patients and identified by using 16S rRNA sequencing as Pseudomonas aeruginosa, Bacillus chungangensis, Bacillus paramycoides, and Paenibacillus dendritiformis. Antibiogram studies confirmed that all strains were resistant to most tested antibiotics except imipenem and ciprofloxacin. Molecular docking analysis revealed the significant interaction of rutin, quercetin, gallic acid, and catechin with transcription regulator genes 1RO5, 4B2O, and 5OE3. All tested molecules followed drug-likeness rules except rutin. The MIC values of the tested compounds varied from 0.0625 to 0.5 mg/mL against clinical isolates. Significant antibiofilm activity was recorded in the case of catechin (73.5% ± 1.6% inhibition against B. paramycoides), cinnamic acid (80.9% ± 1.1% inhibition against P. aeruginosa), and vanillic acid and quercetin (65.5% ± 1.7% and 87.4% ± 1.4% inhibition, respectively, against B. chungangensis) at 0.25-0.125 mg/mL. None of the phenolic compounds presented antiquorum sensing activity. It was, therefore, concluded that polyphenolic compounds may have the potential to be used against oral bacterial biofilms, and further detailed mechanistic investigations should be performed. [ABSTRACT FROM AUTHOR]

Oral bacterial biofilms are the main reason for the progression of resistance to antimicrobial agents that may lead to severe conditions, including periodontitis and gingivitis. Essential oil-based nanocomposites can be a promising treatment option. We investigated cardamom, cinnamon, and clove essential oils for their potential in the treatment of oral bacterial infections using in vitro and computational tools. A detailed analysis of the drug-likeness and physicochemical properties of all constituents was performed. Molecular docking studies revealed that the binding free energy of a Carbopol 940 and eugenol complex was -2.0 kcal/mol, of a Carbopol 940-anisaldehyde complex was -1.9 kcal/mol, and a Carbapol 940-eugenol-anisaldehyde complex was -3.4 kcal/mol. Molecular docking was performed against transcriptional regulator genes 2XCT, 1JIJ, 2Q0P, 4M81, and 3QPI. Eugenol cinnamaldehyde and cineol presented strong interaction with targets. The essential oils were analyzed against Staphylococcus aureus and Staphylococcus epidermidis isolated from the oral cavity of diabetic patients. The cinnamon and clove essential oil combination presented significant minimum inhibitory concentrations (MICs) (0.0625/0.0312 mg/mL) against S. epidermidis and S. aureus (0.0156/0.0078 mg/mL). In the anti-quorum sensing activity, the cinnamon and clove oil combination presented moderate inhibition (8 mm) against Chromobacterium voilaceum with substantial violacein inhibition (58% ± 1.2%). Likewise, a significant biofilm inhibition was recorded in the case of S. aureus (82.1% ± 0.21%) and S. epidermidis (84.2% ± 1.3%) in combination. It was concluded that a clove and cinnamon essential oil-based formulation could be employed to prepare a stable nanocomposite, and Carbapol 940 could be used as a compatible biopolymer. [ABSTRACT FROM AUTHOR]

Detecting pipeline leaks is an essential factor in maintaining the integrity of fluid transport systems. This paper introduces an advanced deep learning framework that uses continuous wavelet transform (CWT) images for precise detection of such leaks. Transforming acoustic signals from pipelines under various conditions into CWT scalograms, followed by signal processing by non-local means and adaptive histogram equalization, results in new enhanced leak-induced scalograms (ELIS) that capture detailed energy fluctuations across time-frequency scales. The fundamental approach takes advantage of a deep belief network (DBN) fine-tuned with a genetic algorithm (GA) and unified with a least squares support vector machine (LSSVM) to improve feature extraction and classification accuracy. The DBN-GA framework precisely extracts informative features, while the LSSVM classifier precisely distinguishes between leaky and non-leak conditions. By concentrating solely on the advanced capabilities of ELIS processed through an optimized DBN-GA-LSSVM model, this research achieves high detection accuracy and reliability, making a significant contribution to pipeline monitoring and maintenance. This innovative approach to capturing complex signal patterns can be applied to real-time leak detection and critical infrastructure safety in several industrial applications. [ABSTRACT FROM AUTHOR]

By making use of the concept of basic (or q-) calculus, a subclass of q-starlike functions with reference to symmetric points, which is associated with the q-exponential function, is introduced in the open unit disc. Further, we derived upper bounds for the third-order Hankel determinant for the defined class. For the validity of our results, relevant connections with those in earlier works are also pointed out. [ABSTRACT FROM AUTHOR]

This paper presents a novel framework for classifying ongoing conditions in centrifugal pumps based on signal processing and deep learning techniques. First, vibration signals are acquired from the centrifugal pump. The acquired vibration signals are heavily affected by macrostructural vibration noise. To overcome the influence of noise, pre-processing techniques are employed on the vibration signal, and a fault-specific frequency band is chosen. The Stockwell transform (S-transform) is then applied to this band, yielding S-transform scalograms that depict energy fluctuations across different frequencies and time scales, represented by color intensity variations. Nevertheless, the accuracy of these scalograms can be compromised by the presence of interference noise. To address this concern, an additional step involving the Sobel filter is applied to the S-transform scalograms, resulting in the generation of novel SobelEdge scalograms. These SobelEdge scalograms aim to enhance the clarity and discriminative features of fault-related information while minimizing the impact of interference noise. The novel scalograms heighten energy variation in the S-transform scalograms by detecting the edges where color intensities change. These new scalograms are then provided to a convolutional neural network (CNN) for the fault classification of centrifugal pumps. The centrifugal pump fault classification capability of the proposed method outperformed state-of-the-art reference methods. [ABSTRACT FROM AUTHOR]