Supercritical-CO2 mediated preparation of porous carbon from Araucaria heterophylla biomass: A proficient nanomolar detection platform for phenolic water pollutant.

4-aminophenol (AP), an aromatic phenolic compound, is commonly found in commercial products that eventually enter and pollute environmental water sources. The precise detection and quantification of AP in environmental samples are critical for comprehensively assessing contamination levels, safeguarding public health, and formulating effective remediation strategies. In the shed of light, this work proposes an electrochemical sensing platform for detecting and quantifying AP using Araucaria heterophylla biomass-derived activated carbon (AH-AC) prepared via the SC-CO2 pathway. To evaluate the significance of SC-CO2-mediated chemical activation (SC-AHAC), a comparative study with conventional activation methods (C-AHAC) was also conducted. The physical characterizations such as structural, morphological, optical, and elemental analysis demonstrate the greater ID/IG value and enhanced surface functionalities of SC-AHAC than C-AHAC. The obtained lower empirical factor (R) value of 1.89 for SC-AHAC suggests increased disorder and a higher presence of single-layer amorphous carbon compared to C-AHAC (2.03). In the electrochemical analysis, the active surface area of the SC-AHAC modified electrode (0.069 cm2) is higher than that of the C-AHAC modified electrode (0.061 cm2), demonstrating the significance of SC-CO2 activation. Further, the quantitative analysis on SC-AHAC@SPCE resulted in a sensitivity of 3.225 µA µM-1 cm-2 with the detection limit and quantification limit of 2.13 and 7.11 nM L-1, respectively, in the linear range of 0.01-582.5 µM L-1 at the oxidation potential of 0.13V. This suggests that the prepared SC-AHAC could be a promising electrocatalyst for AP detection in the environmental and healthcare sectors.

Ultrasound-Mediated Lysozyme Microbubbles Targeting NOX4 Knockdown Alleviate Cisplatin-Exposed Cochlear Hair Cell Ototoxicity.

The nicotinamide adenine dinucleotide phosphate (NADPH) oxidase 4 (NOX4) protein plays an essential role in the cisplatin (CDDP)-induced generation of reactive oxygen species (ROS). In this study, we evaluated the suitability of ultrasound-mediated lysozyme microbubble (USMB) cavitation to enhance NOX4 siRNA transfection in vitro and ex vivo. Lysozyme-shelled microbubbles (LyzMBs) were constructed and designed for siNOX4 loading as siNOX4/LyzMBs. We investigated different siNOX4-based cell transfection approaches, including naked siNOX4, LyzMB-mixed siNOX4, and siNOX4-loaded LyzMBs, and compared their silencing effects in CDDP-treated HEI-OC1 cells and mouse organ of Corti explants. Transfection efficiencies were evaluated by quantifying the cellular uptake of cyanine 3 (Cy3) fluorescein-labeled siRNA. In vitro experiments showed that the high transfection efficacy (48.18%) of siNOX4 to HEI-OC1 cells mediated by US and siNOX4-loaded LyzMBs significantly inhibited CDDP-induced ROS generation to almost the basal level. The ex vivo CDDP-treated organ of Corti explants of mice showed an even more robust silencing effect of the NOX4 gene in the siNOX4/LyzMB groups treated with US sonication than without US sonication, with a marked abolition of CDDP-induced ROS generation and cytotoxicity. Loading of siNOX4 on LyzMBs can stabilize siNOX4 and prevent its degradation, thereby enhancing the transfection and silencing effects when combined with US sonication. This USMB-derived therapy modality for alleviating CDDP-induced ototoxicity may be suitable for future clinical applications.

Computer-Aided Diagnosis of Duchenne Muscular Dystrophy Based on Texture Pattern Recognition on Ultrasound Images Using Unsupervised Clustering Algorithms and Deep Learning.

OBJECTIVE: The feasibility of using deep learning in ultrasound imaging to predict the ambulatory status of patients with Duchenne muscular dystrophy (DMD) was previously explored for the first time. The present study further used clustering algorithms for the texture reconstruction of ultrasound images of DMD data sets and analyzed the difference in echo intensity between disease stages. METHODS: k-means (Kms) and fuzzy c-means (FCM) clustering algorithms were used to reconstruct the DMD data-set textures. Each image was reconstructed using seven texture-feature categories, six of which were used as the primary analysis items. The task of automatically identifying the ambulatory function and DMD severity was performed by establishing a machine-learning model. RESULTS: The experimental results indicated that the Gaussian Naïve Bayes and k-nearest neighbors classification models achieved an accuracy of 86.78% in ambulatory function classification. The decision-tree model achieved an identification accuracy of 83.80% in severity classification. A deep convolutional neural network model was established as the main structure of the deep-learning model while automatic auxiliary interpretation tasks of ambulatory function and severity were performed, and data augmentation was used to improve the recognition performance of the trained model. Both the visual geometry group (VGG)-16 and VGG-19 models achieved 98.53% accuracy in ambulatory-function classification. The VGG-19 model achieved 92.64% accuracy in severity classification. CONCLUSION: Regarding the overall results, the Kms and FCM clustering algorithms were used in this study to reconstruct the characteristic texture of the gastrocnemius muscle group in DMD, which was indeed helpful in quantitatively analyzing the deterioration of the gastrocnemius muscle group in patients with DMD at different stages. Subsequent combination of machine-learning and deep-learning technologies can automatically and accurately assist in identifying DMD symptoms and tracking DMD deterioration for long-term observation.

Supercritical CO2 mediated construction of aluminium waste recovered γ-Al2O3 impregnated Dracaena trifasciata biomass-derived carbon composite: A robust electrocatalyst for mutagenic pollutant detection.

Inspired by the waste-to-wealth concept, we have recovered the gamma phase aluminium oxide nanoparticles (γ-Al2O3 NPs) from waste aluminium (Al) foils and fabricated a composite with Dracaena trifasciata biomass-derived activated carbon matrix (DT-AC) using supercritical carbon-di-oxide (SC-CO2) pathway. The prepared samples are characterized altogether by various micro- and spectroscopic analyses. Based on the results, the recovered γ-Al2O3 NPs are well impregnated in the DT-AC surface by the action of the microbubble effect from the SC-CO2. The higher D-band and ID/IG value of 1.07 in the Al2O3/DT-AC nanocomposite indicate increased defects and the amorphous nature of the carbon materials. The effect of scan rate (ν) demonstrated greater linearity in ν1/2 vs peak current in the electrochemical detection study of the mutagenic pollutant 4-(methylamino) phenol hemi sulfate, showing a quasi-reversible electron transfer process undergoing diffusion-controlled kinetics. Furthermore, the limit of detection is determined to be 3.2 nM L-1 with an extensive linear range, spanning from 0.05 to 618.25 µM/L. The incredible sensitivity of 2.117 µA µM-1 cm-2, along with excellent selectivity, repeatability, and stability, is observed. Further, the respectable recovery percentage of 98.61 % in the environmental water sample is perceived. The observed outcomes suggest that the prepared Al2O3/DT-AC composite performs as an excellent electrocatalyst material, and the processing techniques used are thought to be sustainable in nature.