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Asiri A, Marwani H, Khan SB, Webster T. Int J Nanomedicine. 2015;10(1):89–96. The authors have advised the note section for Figure 1 on page 90 is incorrect. The correct Notes section is as follows: Notes: SEM images showing the distribution of CNF in the PLGA matrix at 50:50 wt% at (A) random orientation and (B) aligned orientation to mimic the natural anisotropy of cardiac tissue. (C) AFM line scan of the 50:50 CNF:PLGA aligned composite, demonstrating micrometer-scale alignment of the CNFs on the PLGA surface. AFM scans for the randomly oriented CNFs in PLGA matched that of the CNF patterned region. Scale bars =20 µm. Reproduced from Asiri A, Marwani H, Khan SB, Webster T. Greater cardiomyocyte density on aligned compared with random carbon nanofibers in polymer composites. Int J Nanomedicine. 2014;9(1):5533-5539.12 Read the original article

Sher Bahadar Khan,1,2 Khalid A Alamry,2 Nedaa A Alyahyawi,3 Abdullah M Asiri1,2 1Center of Excellence for Advanced Materials Research (CEAMR), King Abdulaziz University, Jeddah, Saudi Arabia; 2Chemistry Department, Faculty of Science, King Abdulaziz University, Jeddah, Saudi Arabia; 3Chemistry Department, Faculty of Science, University of Jeddah, Jeddah, Saudi Arabia Background: The intercalation of an antibiotic drug, cefadroxil (CD), into the inter-gallery of Zn, Al nitrate-layered double hydroxide (LDH) was accomplished using a co-precipitation method. This formed a nanostructured organic–inorganic hybrid material that can be exploited for the preparation of a controlled release formulation. Materials and methods: The drug–LDH nanohybrid was characterized by using field emission scanning electron microscopy (FE-SEM), energy dispersive X-ray spectroscopy (EDS), Fourier transform infrared spectroscopy (FTIR) thermogravimetric (TG) analysis, X-ray powder diffraction (XRD) and UV–visible (UV–vis) absorption spectroscopy, which confirmed the intercalation process. Release tests of nanohybrid in the presence or absence of NaCl or polyacrylamide (PAM) were performed in vitro in gastric (pH 1.2), lysosomal (pH 4.0), intestinal (pH 6.8) and blood (pH 7.4) simulated fluid using UV–vis spectroscopy. Results: At pH 1.2, LDH was dissolved and intercalated antibiotic released from ZnAl-CD in a molecular form, which led to a significant increase in the antibiotic’s solubility. Results showed that the release of drug from nanohybrid at pH 4.0, 6.8 and 7.4 was a sustained process. Conclusion: This material might reduce side effects by the release of the drug in a controlled manner. However, it was found that the presence of Cl or PAM species in the release media has a negative impact on the release behavior. The weathering mechanism is responsible for the release of CD from the nanocomposite at pH 1.2, while the mechanism of anion exchange may be responsible for the release behavior at pH 4.0, 6.8 and 7.4. A number of kinetic models were chosen to gain more insights into the mechanisms of drug release. At pH 1.2, the zero-order model most satisfactorily explained the release kinetics of CD, while the release data of CD at pH 4.0, 6.8 and 7.4 were governed by Bhaskar kinetics. Keywords: drug delivery system, ZnAl-CD LDH nanohybrid, hydrotalcite, antibiotic, drug release, kinetics

Abdullah M Asiri,1 Hadi M Marwani,1 Sher Bahadar Khan,1 Thomas J Webster1,2 1Center of Excellence for Advanced Materials Research, King Abdulaziz University, Jeddah, Saudi Arabia; 2Department of Chemical Engineering, Northeastern University, Boston, MA, USA Abstract: Previous studies have demonstrated greater cardiomyocyte density on carbon nanofibers (CNFs) aligned (compared to randomly oriented) in poly(lactic-co-glycolic acid) (PLGA) composites. Although such studies demonstrated a closer mimicking of anisotropic electrical and mechanical properties for such aligned (compared to randomly oriented) CNFs in PLGA composites, the objective of the present in vitro study was to elucidate a deeper mechanistic understanding of how cardiomyocyte densities recognize such materials to respond more favorably. Results showed lower wettability (greater hydrophobicity) of CNFs embedded in PLGA compared to pure PLGA, thus providing evidence of selectively lower wettability in aligned CNF regions. Furthermore, the results correlated these changes in hydrophobicity with increased adsorption of fibronectin, laminin, and vitronectin (all proteins known to increase cardiomyocyte adhesion and functions) on CNFs in PLGA compared to pure PLGA, thus providing evidence of selective initial protein adsorption cues on such CNF regions to promote cardiomyocyte adhesion and growth. Lastly, results of the present in vitro study further confirmed increased cardiomyocyte functions by demonstrating greater expression of important cardiomyocyte biomarkers (such as Troponin-T, Connexin-43, and α-sarcomeric actin) when CNFs were aligned compared to randomly oriented in PLGA. In summary, this study provided evidence that cardiomyocyte functions are improved on CNFs aligned in PLGA compared to randomly oriented in PLGA since CNFs are more hydrophobic than PLGA and attract the adsorption of key proteins (fibronectin, laminin, and vironectin) that are known to promote cardiomyocyte adhesion and expression of important cardiomyocyte functions. Thus, future studies should use this knowledge to further design improved CNF:PLGA composites for numerous cardiovascular applications. Keywords: cardiomyocytes, poly(lactic-co-glycolic acid), carbon nanofibers, aligned, nanotechnology, anisotropy, mechanism, vitronectin, fibronectin, laminin

Abdullah M Asiri,1 Hadi M Marwani,1 Sher Bahadar Khan,1 Thomas J Webster1,2 1Center of Excellence for Advanced Materials Research, King Abdulaziz University, Jeddah, Saudi Arabia; 2Department of Chemical Engineering, Northeastern University, Boston, MA, USA Abstract: Carbon nanofibers (CNFs) randomly embedded in poly(lactic-co-glycolic-acid) (PLGA) composites have recently been shown to promote cardiomyocyte growth when compared with conventional PLGA without CNFs. It was shown then that PLGA:CNF composites were conductive and that conductivity increased as greater amounts of CNFs were added to pure PLGA. Moreover, tensile tests showed that addition of CNFs increased the tensile strength of the PLGA composite to mimic that of natural heart tissue. Most importantly, throughout all cytocompatibility experiments, cardiomyocytes were viable and expressed important biomarkers that were greatest on 50:50 wt% CNF:PLGA composites. The increased selective adsorption of fibronectin and vitronectin (critical proteins that mediate cardiomyocyte function) onto such composites proved to be the mechanism of action. However, the natural myocardium is anisotropic in terms of mechanical and electrical properties, which was not emulated in these prior PLGA:CNF composites. Thus, the aim of this in vitro study was to create and characterize CNFs aligned in PLGA composites (at 50:50 wt%, including their mechanical and electrical properties and cardiomyocyte density), comparing such results with randomly oriented CNFs in PLGA. Specifically, CNFs were added to soluble biodegradable PLGA (50:50 PGA:PLA weight ratio) and aligned by applying a voltage and then allowing the polymer to cure. CNF surface micron patterns (20 µm wide) on PLGA were then fabricated through a mold method to further mimic myocardium anisotropy. The results demonstrated anisotropic mechanical and electrical properties and significantly improved cardiomyocyte density for up to 5 days on CNFs aligned in PLGA compared with being randomly oriented in PLGA. These results indicate that CNFs aligned in PLGA should be further explored for improving cardiomyocyte density, which is necessary in numerous cardiovascular applications. Keywords: cardiomyocytes, poly(lactic-co-glycolic acid), carbon nanofibers, aligned, nanotechnology, anisotropy

A novel Ag-alumina hybrid surface-enhanced Raman spectroscopy (SERS) platform has been designed for the spectroscopic detection of surface reactions in the steady state. Single crystalline and faceted silver (Ag) nanoparticles with strong light scattering were prepared in large quantity, which enables their reproducible self-assembly into large scale monolayers of Raman sensor arrays by the Langmuir-Blodgett technique. The close packed sensor film contains high density of sub-nm gaps between sharp edges of Ag nanoparticles, which created large local electromagnetic fields that serve as "hot spots" for SERS enhancement. The SERS substrate was then coated with a thin layer of alumina by atomic layer deposition to prevent charge transfer between Ag and the reaction system. The photocatalytic water splitting reaction on a monolayer of anatase TiO2 nanoplates decorated with Pt co-catalyst nanoparticles was employed as a model reaction system. Reaction intermediates of water photo-oxidation were observed at the TiO2/solution interface under UV irradiation. The surface-enhanced Raman vibrations corresponding to peroxo, hydroperoxo and hydroxo surface intermediate species were observed on the TiO2 surface, suggesting that the photo-oxidation of water on these anatase TiO2 nanosheets may be initiated by a nucleophilic attack mechanism. [Figure not available: see fulltext.] © 2014 Tsinghua University Press and Springer-Verlag Berlin Heidelberg.

Phytochemical investigation of the methanol extract of Vitex negundo afforded eight lignans; negundin A 1, negundin B 2, 6-hydroxy-4-(4-hydroxy-3-methoxy)-3-hydroxymethyl-7-methoxy- 3,4-dihydro-2-naphthaledehyde 3, vitrofolal E 4, (+) -lyoniresinol 5, (+)-lyoniresinol-3alpha-O-beta-d- glucoside 6, (+)-(-)-pinoresinol 7, and (+)- diasyringaresinol 8. The structures of these compounds were elucidated unambiguously by spectroscopic methods including 1D and 2D NMR analysis and also by comparing experimental data with literature data. The tyrosinase inhibitory potency of these compounds has been evaluated and attempts to justify their structure-activity relationships have been made in the present work. The compound 5 was found to be the most potent (IC(50)=3.21 microM) while other compounds demonstrated moderate to potent inhibitions. It was found that the substitution of functional group(s) at C-2 and C-3 positions and the presence of the -CH(2)OH group plays a vital role in the potency of the compounds. The compound 5 can act as a potential lead molecule to develop new drugs for the treatment of hyperpigmentation associated with the high production of melanocytes. [ABSTRACT FROM AUTHOR]

Potentially toxic dyes are introduced mainly to rivers through industrial effluents which have a high risk to human health and aquatic life. Activated carbon (AC) from the stem of Salvadora persica was synthesised to take off toxic industrial dyes from an aqueous solution. KOH was used as the activating agent throughout the preparation process for the AC. The morphology and composition of the prepared AC were studied by various analytical methods. From the overall results, it was found that the prepared AC is highly porous and thermal stability gained around 800 ℃. At room temperature, remediation of the dyes (cationic dye, methyl red and anionic dye, methylene blue) using the adsorption method was carried out to ascertain the impact of time and the quantity of AC on methylene blue (MB) and methyl red (MR) removal. During the initial 60 min, equilibrium was attained for the optimum dye concentration (200 mg/L). The data for adsorption on the AC obtained at equilibrium were examined by the Langmuir and Freundlich isotherm models. Both the isotherms accurately predicted the data, with regression values of 0.99 for MR and 0.90 for MB, respectively. The equilibrium adsorption data was also analysed by kinetic models. The adsorption data well fitted in 2nd order kinetic model. The results of MB and MR adsorption from solutions have demonstrated that the stem of Salvadora persica is one of the cheap and more eco-friendly options for remediation of toxic dyes from aqueous solutions., (© 2024. The Author(s), under exclusive licence to Springer Nature Switzerland AG.)

Medical imaging stands as a critical component in diagnosing various diseases, where traditional methods often rely on manual interpretation and conventional machine learning techniques. These approaches, while effective, come with inherent limitations such as subjectivity in interpretation and constraints in handling complex image features. This research paper proposes an integrated deep learning approach utilizing pre-trained models-VGG16, ResNet50, and InceptionV3-combined within a unified framework to improve diagnostic accuracy in medical imaging. The method focuses on lung cancer detection using images resized and converted to a uniform format to optimize performance and ensure consistency across datasets. Our proposed model leverages the strengths of each pre-trained network, achieving a high degree of feature extraction and robustness by freezing the early convolutional layers and fine-tuning the deeper layers. Additionally, techniques like SMOTE and Gaussian Blur are applied to address class imbalance, enhancing model training on underrepresented classes. The model's performance was validated on the IQ-OTH/NCCD lung cancer dataset, which was collected from the Iraq-Oncology Teaching Hospital/National Center for Cancer Diseases over a period of three months in fall 2019. The proposed model achieved an accuracy of 98.18%, with precision and recall rates notably high across all classes. This improvement highlights the potential of integrated deep learning systems in medical diagnostics, providing a more accurate, reliable, and efficient means of disease detection., (© 2024. The Author(s).)

In this study, hydrogel beads were fabricated using alginate (Algt) polymer containing dispersed nickel phthalocyanine (NTC) nanomaterial. The viscous solution of Algt and NTC was poured dropwise into a divalent Ca 2+ ions, resulting in the formation of hydrogel beads known as NTC@Algt-BDs. The surface of the NTC@Algt-BDs was further modified by coating them with different types of metal ions, yielding metal-coated M + /NTC@Algt-BDs. The adsorbed metal ions i.e., Cu +2 , Ag + , Ni +2 , Co +2 , and Fe +3 were subsequently reduced to zero-valent metal nanoparticles (M 0 ) by NaBH 4 . The prepared beads were characterized using scanning electron microscopy (SEM), energy-dispersive X-ray spectroscopy (EDS), X-ray diffraction (XRD) and X-ray photoelectron spectroscopy (XPS). Initially, M 0 /NTC@Algt-BDs were examined for the catalytic reduction of 4-nitrophenol (4-NP) to 4-aminophenol (4-AP). Among them, Cu 0 /NTC@Algt-BDs catalyst exhibited the highest reduction rate and therefore, investigated for reduction of different nitrophenols (NPs) and dyes, including 2-nitrophenol (2-NP), 2,6-dinitrophenol (2,6-DNP), methyl orange (MO), potassium ferrocyanide (PFC), congo red (CR), and acridine orange (ArO). The highest reduction rates of 2.019 and 1.394 min -1 were observed for MO and 2-NP, respectively. Furthermore, the fabricated catalysts were employed for the efficient production of H 2 gas by NaBH 4 methanolysis. Among which the Ag 0 /NTC@Algt-BDs catalyst showed excellent catalytic production of H 2 gas, exhibiting the lowest activation energy (Ea) of 25.169 kJ/mol at ambient temperature. Furthermore, the impact of NaBH 4 amount, and catalyst dosage on the reduction of 2-NP and H 2 gas production was conducted whereas the effect of temperature on methanolysis of NaBH 4 for evolution of H 2 gas was studied. The amount of H 2 gas was confirmed by GC-TCD system. Additionally, the recyclability of the catalyst was investigated, as it garnered significant research interest., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier B.V. All rights reserved.)

Deep learning has attained state-of-the-art results in general image segmentation problems; however, it requires a substantial number of annotated images to achieve the desired outcomes. In the medical field, the availability of annotated images is often limited. To address this challenge, few-shot learning techniques have been successfully adapted to rapidly generalize to new tasks with only a few samples, leveraging prior knowledge. In this paper, we employ a gradient-based method known as Model-Agnostic Meta-Learning (MAML) for medical image segmentation. MAML is a meta-learning algorithm that quickly adapts to new tasks by updating a model's parameters based on a limited set of training samples. Additionally, we use an enhanced 3D U-Net as the foundational network for our models. The enhanced 3D U-Net is a convolutional neural network specifically designed for medical image segmentation. We evaluate our approach on the TotalSegmentator dataset, considering a few annotated images for four tasks: liver, spleen, right kidney, and left kidney. The results demonstrate that our approach facilitates rapid adaptation to new tasks using only a few annotated images. In 10-shot settings, our approach achieved mean dice coefficients of 93.70%, 85.98%, 81.20%, and 89.58% for liver, spleen, right kidney, and left kidney segmentation, respectively. In five-shot sittings, the approach attained mean Dice coefficients of 90.27%, 83.89%, 77.53%, and 87.01% for liver, spleen, right kidney, and left kidney segmentation, respectively. Finally, we assess the effectiveness of our proposed approach on a dataset collected from a local hospital. Employing five-shot sittings, we achieve mean Dice coefficients of 90.62%, 79.86%, 79.87%, and 78.21% for liver, spleen, right kidney, and left kidney segmentation, respectively.

Antimicrobial resistance (AMR) has emerged as a significant and pressing public health concern, posing serious challenges to effectively preventing and treating persistent diseases. Despite various efforts made in recent years to address this problem, the global trends of AMR continue to escalate without any indication of decline. As AMR is well-known for antibiotics, developing new materials such as metal containing compounds with different mechanisms of action is crucial to effectively address this challenge. Copper, silver, and chitosan in various forms have demonstrated significant biological activities and hold promise for applications in medicine and biotechnology. Exploring the biological properties of these nanoparticles is essential for innovative therapeutic approaches in treating bacterial and fungal infections, cancer, and other diseases. To this end, the present study aimed to synthesize silver@copper oxide (Ag@CuO) nanoparticles and its chitosan nanocomposite (Chi-Ag@CuO) to investigate their antimicrobial efficacy. Various established spectroscopic and microscopic methods were employed for characterization purposes, encompassing scanning electron microscopy (SEM), energy-dispersive X-ray spectroscopy (EDX), X-ray diffraction (XRD), and Fourier transform infrared spectroscopy (FTIR). Subsequently, the antimicrobial activity of the nanoparticles was assessed through MIC (minimum inhibitory concentration), MBC (minimum bactericidal concentration), and well-disk diffusion assays against Pseudomonas aeruginosa , Acinetobacter baumannii Staphylococcus aureus , Staphylococcus epidermidis , and Candida albicans . The size of the CuO-NPs, Ag@CuO, and Chi-Ag@CuO NPs was found to be 70-120 nm with a spherical shape and an almost uniform distribution. The nanocomposites were found to possess a minimum inhibitory concentration (MIC) of 5 μg/mL and a minimum bactericidal concentration (MBC) of 250 μg/mL. Moreover, these nanocomposites generated varying clear inhibition zones, with diameters ranging from a minimum of 9 ± 0.5 mm to a maximum of 25 ± 0.5 mm. Consequently, it is evident that the amalgamation of copper-silver-chitosan nanoparticles has exhibited noteworthy antimicrobial properties in the controlled laboratory environment, surpassing the performance of other types of nanoparticles., (© 2024 Walter de Gruyter GmbH, Berlin/Boston.)

Brain tumor diagnosis using MRI scans poses significant challenges due to the complex nature of tumor appearances and variations. Traditional methods often require extensive manual intervention and are prone to human error, leading to misdiagnosis and delayed treatment. Current approaches primarily include manual examination by radiologists and conventional machine learning techniques. These methods rely heavily on feature extraction and classification algorithms, which may not capture the intricate patterns present in brain MRI images. Conventional techniques often suffer from limited accuracy and generalizability, mainly due to the high variability in tumor appearance and the subjective nature of manual interpretation. Additionally, traditional machine learning models may struggle with the high-dimensional data inherent in MRI images. To address these limitations, our research introduces a deep learning-based model utilizing convolutional neural networks (CNNs).Our model employs a sequential CNN architecture with multiple convolutional, max-pooling, and dropout layers, followed by dense layers for classification. The proposed model demonstrates a significant improvement in diagnostic accuracy, achieving an overall accuracy of 98% on the test dataset. The proposed model demonstrates a significant improvement in diagnostic accuracy, achieving an overall accuracy of 98% on the test dataset. The precision, recall, and F1-scores ranging from 97 to 98% with a roc-auc ranging from 99 to 100% for each tumor category further substantiate the model's effectiveness. Additionally, the utilization of Grad-CAM visualizations provides insights into the model's decision-making process, enhancing interpretability. This research addresses the pressing need for enhanced diagnostic accuracy in identifying brain tumors through MRI imaging, tackling challenges such as variability in tumor appearance and the need for rapid, reliable diagnostic tools., (© 2024. The Author(s).)

Brain tumor classification using MRI images is a crucial yet challenging task in medical imaging. Accurate diagnosis is vital for effective treatment planning but is often hindered by the complex nature of tumor morphology and variations in imaging. Traditional methodologies primarily rely on manual interpretation of MRI images, supplemented by conventional machine learning techniques. These approaches often lack the robustness and scalability needed for precise and automated tumor classification. The major limitations include a high degree of manual intervention, potential for human error, limited ability to handle large datasets, and lack of generalizability to diverse tumor types and imaging conditions.To address these challenges, we propose a federated learning-based deep learning model that leverages the power of Convolutional Neural Networks (CNN) for automated and accurate brain tumor classification. This innovative approach not only emphasizes the use of a modified VGG16 architecture optimized for brain MRI images but also highlights the significance of federated learning and transfer learning in the medical imaging domain. Federated learning enables decentralized model training across multiple clients without compromising data privacy, addressing the critical need for confidentiality in medical data handling. This model architecture benefits from the transfer learning technique by utilizing a pre-trained CNN, which significantly enhances its ability to classify brain tumors accurately by leveraging knowledge gained from vast and diverse datasets.Our model is trained on a diverse dataset combining figshare, SARTAJ, and Br35H datasets, employing a federated learning approach for decentralized, privacy-preserving model training. The adoption of transfer learning further bolsters the model's performance, making it adept at handling the intricate variations in MRI images associated with different types of brain tumors. The model demonstrates high precision (0.99 for glioma, 0.95 for meningioma, 1.00 for no tumor, and 0.98 for pituitary), recall, and F1-scores in classification, outperforming existing methods. The overall accuracy stands at 98%, showcasing the model's efficacy in classifying various tumor types accurately, thus highlighting the transformative potential of federated learning and transfer learning in enhancing brain tumor classification using MRI images., (© 2024. The Author(s).)

Categorizing Artificial Intelligence of Medical Things (AIoMT) devices within the realm of standard Internet of Things (IoT) and Internet of Medical Things (IoMT) devices, particularly at the server and computational layers, poses a formidable challenge. In this paper, we present a novel methodology for categorizing AIoMT devices through the application of decentralized processing, referred to as "Federated Learning" (FL). Our approach involves deploying a system on standard IoT devices and labeled IoMT devices for training purposes and attribute extraction. Through this process, we extract and map the interconnected attributes from a global federated cum aggression server. The aim of this terminology is to extract interdependent devices via federated learning, ensuring data privacy and adherence to operational policies. Consequently, a global training dataset repository is coordinated to establish a centralized indexing and synchronization knowledge repository. The categorization process employs generic labels for devices transmitting medical data through regular communication channels. We evaluate our proposed methodology across a variety of IoT, IoMT, and AIoMT devices, demonstrating effective classification and labeling. Our technique yields a reliable categorization index for facilitating efficient access and optimization of medical devices within global servers., (© 2024. The Author(s).)

Silver oxide doped iron oxide (Ag 2 O-Fe 2 O 3 ) nanocatalyst was prepared and coated on cotton cloth (CC) as well as wrapped in sodium alginate (Alg) hydrogel. Ag 2 O-Fe 2 O 3 coated CC (Ag 2 O-Fe 2 O 3 /CC) and Ag 2 O-Fe 2 O 3 wrapped Alg (Ag 2 O-Fe 2 O 3 /Alg) were utilized as catalysts in reduction reaction of 4-nitrophenol (4-NP), congo red (CR), methylene blue (MB) and potassium ferricyanide (K 3 [Fe(CN) 6 ]). Ag 2 O-Fe 2 O 3 /CC and Ag 2 O-Fe 2 O 3 /Alg were found to be effective and selective catalyst for the reaction of K 3 [Fe(CN) 6 ]. Further amount of catalyst, K 3 [Fe(CN) 6 ] quantity, amount of NaBH 4 , stability of catalyst and recyclability were optimized for the reaction of K 3 [Fe(CN) 6 ] reduction. Ag 2 O-Fe 2 O 3 /Alg and Ag 2 O-Fe 2 O 3 /CC were appeared to be the stable catalysts by maintaining high activity during recyclability tests showing highest reaction rate constants (k app ) of 0.3472 and 0.5629 min -1 , correspondingly. However, Ag 2 O-Fe 2 O 3 /CC can be easily recovered as compared to Ag 2 O-Fe 2 O 3 /Alg by simply removing from the reaction which is the main advantage of Ag 2 O-Fe 2 O 3 /CC. Moreover, Ag 2 O-Fe 2 O 3 /Alg and Ag 2 O-Fe 2 O 3 /CC were also examined in real samples and found useful for K 3 [Fe(CN) 6 ] reduction involving real samples. The Ag 2 O-Fe 2 O 3 /CC nanocatalyst is a cost and time saving material for economical reduction of K 3 [Fe(CN) 6 ] and environmental safety., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier Ltd. All rights reserved.)

Water contamination caused by toxic compounds has emerged as one of the most severe challenges worldwide. Biomass-based nanocomposites offer a sustainable and renewable alternative to conventional materials. In this study, a nanocomposite of mint and cellulose acetate (Mint-CA) was prepared and employed as a supportive material for Cu nanoparticles (CuNPs) and Ag nanoparticles (AgNPs). The selectivity of CuNPs@mint-CA and AgNPs@mint-CA was assessed by comparing their performance in the reduction reaction of various dyes solutions. AgNPs@mint-CA exhibited superior catalytic performance, with a removal of 95.2 % for methyl orange (MO) compared to 68 % with CuNPs@mint-CA. The absorption spectra of MO exhibited a distinct peak at 464 nm. The reduction reaction of MO by AgNPs@mint-CA followed pseudo-first-order-kinetic with a rate constant of k = 0.0063 min -1 (R 2  = 0.928). The highest removal of MO was achieved under the following conditions: a catalyst weight of 40 mg, an initial MO concentration of 0.07 mM, the addition of 0.5 mL of 0.1 M NaBH 4 , and a temperature of 25 °C. Furthermore, the AgNPs@mint-CA catalyst exhibited exceptional reducibility even after five use cycles, highlighting its potential for efficiently removing MO., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier B.V. All rights reserved.)

There is an increasing demand for efficient and precise plant disease detection methods that can quickly identify disease outbreaks. For this, researchers have developed various machine learning and image processing techniques. However, real-field images present challenges due to complex backgrounds, similarities between different disease symptoms, and the need to detect multiple diseases simultaneously. These obstacles hinder the development of a reliable classification model. The attention mechanisms emerge as a critical factor in enhancing the robustness of classification models by selectively focusing on relevant regions or features within infected regions in an image. This paper provides details about various types of attention mechanisms and explores the utilization of these techniques for the machine learning solutions created by researchers for image segmentation, feature extraction, object detection, and classification for efficient plant disease identification. Experiments are conducted on three models: MobileNetV2, EfficientNetV2, and ShuffleNetV2, to assess the effectiveness of attention modules. For this, Squeeze and Excitation layers, the Convolutional Block Attention Module, and transformer modules have been integrated into these models, and their performance has been evaluated using different metrics. The outcomes show that adding attention modules enhances the original models' functionality., Competing Interests: The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (© 2024 The Authors. Published by Elsevier Ltd.)

A shell and tube heat exchanger (STHE) for heat recovery applications was studied to discover the intricacies of its optimization. To optimize performance, a hybrid optimization methodology was developed by combining the Neural Fitting Tool (NFTool), Particle Swarm Optimization (PSO), and Grey Relational Analysis (GRE). STHE heat exchangers were analyzed systematically using the Taguchi method to analyze the critical elements related to a particular response. To clarify the complex relationship between the heat exchanger efficiency and operational parameters, grey relational grades (GRGs) are first computed. A forecast of the grey relation coefficients was then conducted using NFTool to provide more insight into the complex dynamics. An optimized parameter with a grey coefficient was created after applying PSO analysis, resulting in a higher grey coefficient and improved performance of the heat exchanger. A major and far-reaching application of this study was based on heat recovery. A detailed comparison was conducted between the estimated values and the experimental results as a result of the hybrid optimization algorithm. In the current study, the results demonstrate that the proposed counter-flow shell and tube strategy is effective for optimizing performance., Competing Interests: The authors have declared that no competing interests exist., (Copyright: © 2024 Kiran et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)

People who work in dangerous environments include farmers, sailors, travelers, and mining workers. Due to the fact that they must evaluate the changes taking place in their immediate surroundings, they must gather information and data from the real world. It becomes crucial to regularly monitor meteorological parameters such air quality, rainfall, water level, pH value, wind direction and speed, temperature, atmospheric pressure, humidity, soil moisture, light intensity, and turbidity in order to avoid risks or calamities. Enhancing environmental standards is largely influenced by IoT. It greatly advances sustainable living with its innovative and cutting-edge techniques for monitoring air quality and treating water. With the aid of various sensors, microcontroller (Arduino Uno), GSM, Wi-Fi, and HTTP protocols, the suggested system is a real-time smart monitoring system based on the Internet of Things. Also, the proposed system has HTTP-based webpage enabled by Wi-Fi to transfer the data to remote locations. This technology makes it feasible to track changes in the weather from any location at any distance. The proposed system is a sophisticated, efficient, accurate, cost-effective, and dependable weather station that will be valuable to anyone who wants to monitor environmental changes on a regular basis., Competing Interests: The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (© 2024 The Authors.)

Breast cancer, a prevalent cancer among women worldwide, necessitates precise and prompt detection for successful treatment. While conventional histopathological examination is the benchmark, it is a lengthy process and prone to variations among different observers. Employing machine learning to automate the diagnosis of breast cancer presents a viable option, striving to improve both precision and speed. Previous studies have primarily focused on applying various machine learning and deep learning models for the classification of breast cancer images. These methodologies leverage convolutional neural networks (CNNs) and other advanced algorithms to differentiate between benign and malignant tumors from histopathological images. Current models, despite their potential, encounter obstacles related to generalizability, computational performance, and managing datasets with imbalances. Additionally, a significant number of these models do not possess the requisite transparency and interpretability, which are vital for medical diagnostic purposes. To address these limitations, our study introduces an advanced machine learning model based on EfficientNetV2. This model incorporates state-of-the-art techniques in image processing and neural network architecture, aiming to improve accuracy, efficiency, and robustness in classification. We employed the EfficientNetV2 model, fine-tuned for the specific task of breast cancer image classification. Our model underwent rigorous training and validation using the BreakHis dataset, which includes diverse histopathological images. Advanced data preprocessing, augmentation techniques, and a cyclical learning rate strategy were implemented to enhance model performance. The introduced model exhibited remarkable efficacy, attaining an accuracy rate of 99.68%, balanced precision and recall as indicated by a significant F1 score, and a considerable Cohen's Kappa value. These indicators highlight the model's proficiency in correctly categorizing histopathological images, surpassing current techniques in reliability and effectiveness. The research emphasizes improved accessibility, catering to individuals with disabilities and the elderly. By enhancing visual representation and interpretability, the proposed approach aims to make strides in inclusive medical image interpretation, ensuring equitable access to diagnostic information., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2024 Al Moteri, Mahesh, Thakur, Vinoth Kumar, Khan and Alojail.)

The effective reduction of hazardous organic pollutants in wastewater is a pressing global concern, necessitating the development of advanced treatment technologies. Pollutants such as nitrophenols and dyes, which pose significant risks to both human and aquatic health, making their reduction particularly crucial. Despite the existence of various methods to eliminate these pollutants, they are not without limitations. The utilization of nanomaterials as catalysts for chemical reduction exhibits a promising alternative owing to their distinguished catalytic activity and substantial surface area. For catalytically reducing the pollutants NaBH 4 has been utilized as a useful source for it because it reduces the pollutants quiet efficiently and it also releases hydrogen gas as well which can be used as a source of energy. This paper provides a comprehensive review of recent research on different types of nanomaterials that function as catalysts to reduce organic pollutants and also generating hydrogen from NaBH 4 methanolysis while also evaluating the positive and negative aspects of nanocatalyst. Additionally, this paper examines the features effecting the process and the mechanism of catalysis. The comparison of different catalysts is based on size of catalyst, reaction time, rate of reaction, hydrogen generation rate, activation energy, and durability. The information obtained from this paper can be used to steer the development of new catalysts for reducing organic pollutants and generation hydrogen by NaBH 4 methanolysis., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. The authors declare no conflict of interest., (Copyright © 2024 Elsevier Ltd. All rights reserved.)

Until now, many efficient catalysts have been reported that are used for the reduction of nitroarenes. However, a catalyst reusability is a challenge that is often faced in practical environment. In this report, we designed a hydrogel composite (CMC-LDH), which act as support and making it possible to address this challenge. In this research work, zinc/aluminum based layered double hydroxides (Zn/Al LDH) have been assembled with carboxymethyl cellulose (CMC) to prepare CMC/LDH hydrogel beads. The CMC/LDH hydrogel beads were prepared by the ionotropic gelation method. For CMC/LDH/Au preparation, the already prepared CMC/LDH beads were kept in gold ion (Au 3+ ) solution, and their subsequent reduction with sodium borohydride (NaBH 4 ). For the characterization of the prepared samples different instrumental techniques, such as Fourier transform infrared spectroscopy (FTIR), X-ray diffraction (XRD), energy-dispersive X-ray spectroscopy, and scanning electron microscopy (SEM) were adopted. For the catalytic evaluation of CMC/LDH/Au, it was utilized as a catalyst in 4-NP and 4-NA reduction reactions. The continuity of the reaction was monitored by a UV-visible spectrophotometer. Rate constant (k app ) of 0.48474 min -1 and 0.7486 min -1 were obtained for 4-NP and 4-NA reduction, respectively. The hydrogel beads were recycled and reused for up to five successive cycles without significantly changing their catalytic efficiency., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024. Published by Elsevier B.V.)

This work reports silver nanoparticles (AgNPs) supported on biopolymer carboxymethyl cellulose beads (Ag-CMC) serves as an efficient catalyst in the reduction process of p-nitrophenol (p-NP) and methyl orange (MO). For Ag-CMC synthesis, first CMC beads were prepared by crosslinking the CMC solution in aluminium nitrate solution and then the CMC beads were introduced into AgNO 3 solution to adsorb Ag ions. Field emission scanning electron microscopy (FE-SEM) analysis suggests the uniform distribution of Ag nanoparticles on the CMC beads. The X-ray photoelectron spectroscopy (XPS) and X-ray diffraction (XRD) analysis revealed the metallic and fcc planes of AgNPs, respectively, in the Ag-CMC catalyst. The Ag-CMC catalyst exhibits remarkable reduction activity for the p-NP and MO dyes with the highest rate constant (k app ) of a chemical reaction is 0.519 and 0.697 min -1 , respectively. Comparative reduction studies of Ag-CMC with CMC, Fe-CMC and Co-CMC disclosed that Ag-CMC containing AgNPs is an important factore in reducing the organic pollutants like p-NP and MO dyes. During the recyclability tests, the Ag-CMC also maintained high reduction activity, which suggests that CMC protects the AgNPs from leaching during dye reduction reactions., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2023 Elsevier B.V. All rights reserved.)

A hydrogel membrane was prepared using activated carbon and sodium dodecyl sulphate modified montmorillonite clay incorporated into sodium alginate polymer. The activated carbon was prepared from a locally available susbine plant. The physiochemical characteristics of the synthesized hydrogel membrane were investigated using FTIR, SEM, EDX, and TGA techniques. The performance of the membrane was evaluated as an adsorbent by methyl red adsorption from water. The adsorption behavior of the hydrogel membrane was investigated under varying conditions of pH (2-10), membrane dose (0.0025-0.015 mg g -1 ), equilibrium adsorption time (30-360 minutes), solution temperature (25-45 °C) and dye concentration (100-500 mg L -1 ). The maximum adsorption capacity of the hydrogel membrane was 248.13 mg g -1 . The kinetics of methyl red adsorption on hydrogel membrane best followed the pseudo-second order (PSO). The equilibrium adsorption results suggested that it obeyed the Freundlich isotherm very closely ( R 2 = 0.994). The thermodynamics of methyl red adsorption on the hydrogel membrane revealed that the adsorption was spontaneous (Δ S ° = 16.15 kJ K -1 mol -1 ), favorable (Δ G ° = -3.51 kJ mol -1 ), and endothermic (Δ H ° = -1.48 kJ mol -1 ) in nature. These investigations suggested that the fabricated hydrogel membrane could be suitably used for methyl red adsorption from the solution., Competing Interests: The authors declare no conflict of interest., (This journal is © The Royal Society of Chemistry.)

Mechanical metamaterials with ultralight and ultrastrong mechanical properties are extensively employed in various industrial sectors, with three-periodic minimal surface (TPMS) structures gaining significant research attention due to their symmetry, equation-driven characteristics, and exceptional mechanical properties. Compared to traditional lattice structures, TPMS structures exhibit superior mechanical performance. The mechanical properties of TPMS structures depend on the base material, structural porosity (volume fraction), and wall thickness. Hard rigid lattice structures such as Gyroid, diamond, and primitive exhibit outstanding performance in terms of elastic modulus, energy absorption, heat dissipation, and heat transfer. Flexible TPMS lattice structures, on the other hand, offer higher elasticity and recoverable large deformations, drawing attention for use in applications such as seat cushions and helmet impact-absorbing layers. Conventional fabrication methods often fail to guarantee the quality of TPMS structure samples, and additive manufacturing technology provides a new avenue. Selective laser sintering (SLS) has successfully been used to process various materials. However, due to the layer-by-layer manufacturing process, it cannot eliminate the anisotropy caused by interlayer bonding, which impacts the mechanical properties of 3D-printed parts. This paper introduces a process data-driven optimization design approach for TPMS structure geometry by adjusting volume fraction gradients to overcome the elastic anisotropy of 3D-printed isotropic lattice structures. Experimental validation and analysis are conducted using TPMS structures fabricated using TPU material via SLS. Furthermore, the advantages of volume fraction gradient-designed TPMS structures in functions such as energy absorption and heat dissipation are explored.

According to the WHO (World Health Organization), lung cancer is the leading cause of cancer deaths globally. In the future, more than 2.2 million people will be diagnosed with lung cancer worldwide, making up 11.4% of every primary cause of cancer. Furthermore, lung cancer is expected to be the biggest driver of cancer-related mortality worldwide in 2020, with an estimated 1.8 million fatalities. Statistics on lung cancer rates are not uniform among geographic areas, demographic subgroups, or age groups. The chance of an effective treatment outcome and the likelihood of patient survival can be greatly improved with the early identification of lung cancer. Lung cancer identification in medical pictures like CT scans and MRIs is an area where deep learning (DL) algorithms have shown a lot of potential. This study uses the Hybridized Faster R-CNN (HFRCNN) to identify lung cancer at an early stage. Among the numerous uses for which faster R-CNN has been put to good use is identifying critical entities in medical imagery, such as MRIs and CT scans. Many research investigations in recent years have examined the use of various techniques to detect lung nodules (possible indicators of lung cancer) in scanned images, which may help in the early identification of lung cancer. One such model is HFRCNN, a two-stage, region-based entity detector. It begins by generating a collection of proposed regions, which are subsequently classified and refined with the aid of a convolutional neural network (CNN). A distinct dataset is used in the model's training process, producing valuable outcomes. More than a 97% detection accuracy was achieved with the suggested model, making it far more accurate than several previously announced methods.

One of the most prevalent cancers is oral squamous cell carcinoma, and preventing mortality from this disease primarily depends on early detection. Clinicians will greatly benefit from automated diagnostic techniques that analyze a patient's histopathology images to identify abnormal oral lesions. A deep learning framework was designed with an intermediate layer between feature extraction layers and classification layers for classifying the histopathological images into two categories, namely, normal and oral squamous cell carcinoma. The intermediate layer is constructed using the proposed swarm intelligence technique called the Modified Gorilla Troops Optimizer. While there are many optimization algorithms used in the literature for feature selection, weight updating, and optimal parameter identification in deep learning models, this work focuses on using optimization algorithms as an intermediate layer to convert extracted features into features that are better suited for classification. Three datasets comprising 2784 normal and 3632 oral squamous cell carcinoma subjects are considered in this work. Three popular CNN architectures, namely, InceptionV2, MobileNetV3, and EfficientNetB3, are investigated as feature extraction layers. Two fully connected Neural Network layers, batch normalization, and dropout are used as classification layers. With the best accuracy of 0.89 among the examined feature extraction models, MobileNetV3 exhibits good performance. This accuracy is increased to 0.95 when the suggested Modified Gorilla Troops Optimizer is used as an intermediary layer.

Prompt diagnostics and appropriate cancer therapy necessitate the use of gene expression databases. The integration of analytical methods can enhance detection precision by capturing intricate patterns and subtle connections in the data. This study proposes a diagnostic-integrated approach combining Empirical Bayes Harmonization (EBS), Jensen-Shannon Divergence (JSD), deep learning, and contour mathematics for cancer detection using gene expression data. EBS preprocesses the gene expression data, while JSD measures the distributional differences between cancerous and non-cancerous samples, providing invaluable insights into gene expression patterns. Deep learning (DL) models are employed for automatic deep feature extraction and to discern complex patterns from the data. Contour mathematics is applied to visualize decision boundaries and regions in the high-dimensional feature space. JSD imparts significant information to the deep learning model, directing it to concentrate on pertinent features associated with cancerous samples. Contour visualization elucidates the model's decision-making process, bolstering interpretability. The amalgamation of JSD, deep learning, and contour mathematics in gene expression dataset analysis diagnostics presents a promising pathway for precise cancer detection. This method taps into the prowess of deep learning for feature extraction while employing JSD to pinpoint distributional differences and contour mathematics for visual elucidation. The outcomes underscore its potential as a formidable instrument for cancer detection, furnishing crucial insights for timely diagnostics and tailor-made treatment strategies.

The mortality rates of patients contracting the Omicron and Delta variants of COVID-19 are very high, and COVID-19 is the worst variant of COVID. Hence, our objective is to detect COVID-19 Omicron and Delta variants from lung CT-scan images. We designed a unique ensemble model that combines the CNN architecture of a deep neural network-Capsule Network (CapsNet)-and pre-trained architectures, i.e., VGG-16, DenseNet-121, and Inception-v3, to produce a reliable and robust model for diagnosing Omicron and Delta variant data. Despite the solo model's remarkable accuracy, it can often be difficult to accept its results. The ensemble model, on the other hand, operates according to the scientific tenet of combining the majority votes of various models. The adoption of the transfer learning model in our work is to benefit from previously learned parameters and lower data-hunger architecture. Likewise, CapsNet performs consistently regardless of positional changes, size changes, and changes in the orientation of the input image. The proposed ensemble model produced an accuracy of 99.93%, an AUC of 0.999 and a precision of 99.9%. Finally, the framework is deployed in a local cloud web application so that the diagnosis of these particular variants can be accomplished remotely., Competing Interests: The authors declare no conflict of interest.

Objective: The objective of this study was to determine the diagnostic value of stress perfusion CMR for the detection of coronary artery disease., Methods: The was a retrospective cross sectional study in which 43 subjects were included from Cardiac MRI unit in the Hayatabad Medical Complex, Peshawar for study from 1 st April 2020 to 30 th November 2020. All the subjects who had been referred for stress perfusion CMR with suspected CAD were included in the study. Cardiac MRI both at rest and with adenosine stress perfusion was performed which was followed by invasive coronary angiography., Result: A total of 43 patients were enrolled for the detection or exclusion CAD who underwent stress perfusion CMRI and invasive coronary artery angiography. The study revealed strong and statistically significant association between positive stress perfusion CMR and positive coronary angiogram vs negative stress perfusion CMR and negative coronary angiogram (p= value 0.0001)., Conclusions: Stress perfusion CMRI can be considered as a first line, relatively safe, noninvasive test with significant accuracy to diagnose coronary artery disease in patients with suspected CAD without subjecting these patients to invasive coronary angiogram., (Copyright: © Pakistan Journal of Medical Sciences.)

Rotaviruses are rising as zoonotic viruses worldwide, causing the lethal dehydrating diarrhea in children, piglets, and other livestock of economic importance. A simple, swift, cost-effective, highly specific, and sensitive antigen-capture enzyme-linked immunosorbent assay (AC-ELISA) was developed for detection of porcine rotavirus-A (PoRVA) by employing rabbit (capture antibody) and murine polyclonal antibodies (detector antibody) produced against VP6 of PoRVA (RVA/Pig-tc/CHN/TM-a/2009/G9P23). Reactivity of the both polyclonal antibodies was confirmed by using an indirect ELISA, western-blot analysis and indirect fluorescence assay against rVP6 protein and PoRVA. The detection limit of AC-ELISA was found 50 ng/ml of PoRVA protein. The relative sensitivity and specificity of this in-house AC-ELISA were evaluated for detection of PoRVA from 295 porcine diarrhea samples, and results were compared with that of RT-PCR and TaqMan RT-qPCR. The relative sensitivity and specificity of AC-ELISA compared with those of TaqMan RT-qPCR were found as 94.4 and 99.2%, respectively, with the strong agreement (κ - 0.58 ) between these two techniques. Furthermore, AC-ELISA could not detect any cross-reactivity with porcine epidemic diarrhea virus, transmissible gastro-enteritis virus, pseudo rabies virus and porcine circovirus-2. This in-house AC-ELISA efficiently detected PoRVA from clinical samples, which suggests that this technique can be used for large-scale surveillance and timely detection of rotavirus infection in the porcine farms.

Silver oxide-doped tin oxide (SnAg 2 O 3 ) nanoparticles were synthesized and different spectroscopic techniques were used to structurally identify SnAg 2 O 3 nanoparticles. The reduction of 4-nitrophenol (4-NP), congo red (CR), methylene blue (MB), and methyl orange (MO) was studied using SnAg 2 O 3 as a catalyst. Only 1.0 min was required to reduce 95% MO; thus, SnAg 2 O 3 was found to be effective with a rate constant of 3.0412 min -1 . Being a powder, SnAg 2 O 3 is difficult to recover and recycle multiple times. For this reason, SnAg 2 O 3 was coated on adhesive tape (AT) to make it recyclable for large-scale usage. SnAg 2 O 3 @AT catalyst was assessed toward MO reduction under various conditions. The amount of SnAg 2 O 3 @AT, NaBH 4 , and MO was optimized for best possible reduction conditions. The catalyst had a positive effect since it speed up the reduction of MO by adding more SnAg 2 O 3 @AT and NaBH 4 as well as lowering the MO concentration. SnAg 2 O 3 @AT totally reduced MO (98%) in 3.0 min with a rate constant of 1.3669 min -1 . These findings confirmed that SnAg 2 O 3 @AT is an effective and useful catalyst for MO reduction that can even be utilized on a large scale for industrial purposes.

Kidney tumors represent a significant medical challenge, characterized by their often-asymptomatic nature and the need for early detection to facilitate timely and effective intervention. Although neural networks have shown great promise in disease prediction, their computational demands have limited their practicality in clinical settings. This study introduces a novel methodology, the UNet-PWP architecture, tailored explicitly for kidney tumor segmentation, designed to optimize resource utilization and overcome computational complexity constraints. A key novelty in our approach is the application of adaptive partitioning, which deconstructs the intricate UNet architecture into smaller submodels. This partitioning strategy reduces computational requirements and enhances the model's efficiency in processing kidney tumor images. Additionally, we augment the UNet's depth by incorporating pre-trained weights, therefore significantly boosting its capacity to handle intricate and detailed segmentation tasks. Furthermore, we employ weight-pruning techniques to eliminate redundant zero-weighted parameters, further streamlining the UNet-PWP model without compromising its performance. To rigorously assess the effectiveness of our proposed UNet-PWP model, we conducted a comparative evaluation alongside the DeepLab V3+ model, both trained on the "KiTs 19, 21, and 23" kidney tumor dataset. Our results are optimistic, with the UNet-PWP model achieving an exceptional accuracy rate of 97.01% on both the training and test datasets, surpassing the DeepLab V3+ model in performance. Furthermore, to ensure our model's results are easily understandable and explainable. We included a fusion of the attention and Grad-CAM XAI methods. This approach provides valuable insights into the decision-making process of our model and the regions of interest that affect its predictions. In the medical field, this interpretability aspect is crucial for healthcare professionals to trust and comprehend the model's reasoning.

Three-dimensional printing technology has fundamentally revolutionized the product development processes in several industries. Three-dimensional printing enables the creation of tailored prostheses and other medical equipment, anatomical models for surgical planning and training, and even innovative means of directly giving drugs to patients. Polymers and their composites have found broad usage in the healthcare business due to their many beneficial properties. As a result, the application of 3D printing technology in the medical area has transformed the design and manufacturing of medical devices and prosthetics. Polymers and their composites have become attractive materials in this industry because of their unique mechanical, thermal, electrical, and optical qualities. This review article presents a comprehensive analysis of the current state-of-the-art applications of polymer and its composites in the medical field using 3D printing technology. It covers the latest research developments in the design and manufacturing of patient-specific medical devices, prostheses, and anatomical models for surgical planning and training. The article also discusses the use of 3D printing technology for drug delivery systems (DDS) and tissue engineering. Various 3D printing techniques, such as stereolithography, fused deposition modeling (FDM), and selective laser sintering (SLS), are reviewed, along with their benefits and drawbacks. Legal and regulatory issues related to the use of 3D printing technology in the medical field are also addressed. The article concludes with an outlook on the future potential of polymer and its composites in 3D printing technology for the medical field. The research findings indicate that 3D printing technology has enormous potential to revolutionize the development and manufacture of medical devices, leading to improved patient outcomes and better healthcare services.

In the present study, a biosorbent was prepared through the radiation-induced graft polymerization (RIGP) technique by using a glycidyl methacrylate (GMA) monomer. Functionalized bamboo materials were used for grafting. The grafting percentage (G %) of GMA on bamboo fibers was assessed based on the optimization of the absorbed dose and concentration of the monomer. The chemical modification of the polymerized product into the sulfonated form of the grafted biopolymer was carried out by using sodium sulfite solution. The modification of the biopolymer at various stages was analyzed by Fourier transform infrared (FTIR) spectroscopy and X-ray diffraction (XRD) techniques. By performing scanning electron microscopy (SEM), the morphological changes of the prepared biopolymer were analyzed. The temperature stability of the synthesized material was assessed by the thermogravimetric analysis (TGA) technique. The prepared sulfonated biosorbent was used in the batch adsorption study for the uptake of copper. We examined a variety of variables, including pH, adsorbent dosage, and time. The adsorption kinetics were studied using pseudo-first-order (PFO) and pseudo-second-order (PSO) models. Adsorption isotherms and thermodynamic parameters were also applied to study the adsorption capacity of the biosorbent. The maximum copper adsorption capacity was found to be 198 mg g -1 from the Langmuir isotherm. Copper adsorption followed PSO kinetics ( R 2 = 0.999). This inexpensive and eco-friendly biosorbent removed 96% of copper ions from the solution., Competing Interests: The authors declare no competing financial interest., (© 2023 The Authors. Published by American Chemical Society.)

Heavy metals and dyes used in technological applications have a detrimental influence on human health and the environment. The most used methods for removing pollutants depend on high-cost materials. Therefore, this research was conducted on cost-effective alternatives derived from natural resources and food waste. Herein, we designed a composite hydrogel based on sodium alginate/coffee waste (Alg/coffee) as adsorbent for the removal of organic and inorganic pollutants from aquatic solutions. The selectivity study displayed that Alg/coffee is more effective in adsorbing Pb(II) and acridine orange dye (AO). Adsorption of Pb(II) and AO was studied at concentration range of 0-170 mgL -1 and 0-40 mgL -1 . Adsorption data of Pb(II) and AO reveals their fitting to Langmuir-isotherm and pseudo-second-order-kinetic models. The findings demonstrated that Alg/coffee hydrogel are more effective than coffee powder itself with an adsorption (%) approaching 98.44 % of Pb(II) and 80.53 % of AO. Real sample analysis reveals the efficiency of Alg/coffee hydrogel beads in Pb(II) adsorption. The adsorption cycle was examined four times providing high efficiency toward Pb(II) and AO. Desorption of Pb(II) and AO was easily performed using HCl eluent. Thus, Alg/coffee hydrogel beads could be promising adsorbent for the removal of organic and inorganic pollutants., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2023 Elsevier B.V. All rights reserved.)

Deep learning is playing a major role in identifying complicated structure, and it outperforms in term of training and classification tasks in comparison to traditional algorithms. In this work, a local cloud-based solution is developed for classification of Alzheimer's disease (AD) as MRI scans as input modality. The multi-classification is used for AD variety and is classified into four stages. In order to leverage the capabilities of the pre-trained GoogLeNet model, transfer learning is employed. The GoogLeNet model, which is pre-trained for image classification tasks, is fine-tuned for the specific purpose of multi-class AD classification. Through this process, a better accuracy of 98% is achieved. As a result, a local cloud web application for Alzheimer's prediction is developed using the proposed architectures of GoogLeNet. This application enables doctors to remotely check for the presence of AD in patients.

In this study, we use LSTM (Long-Short-Term-Memory) networks to evaluate Magnetic Resonance Imaging (MRI) data to overcome the shortcomings of conventional Alzheimer's disease (AD) detection techniques. Our method offers greater reliability and accuracy in predicting the possibility of AD, in contrast to cognitive testing and brain structure analyses. We used an MRI dataset that we downloaded from the Kaggle source to train our LSTM network. Utilizing the temporal memory characteristics of LSTMs, the network was created to efficiently capture and evaluate the sequential patterns inherent in MRI scans. Our model scored a remarkable AUC of 0.97 and an accuracy of 98.62%. During the training process, we used Stratified Shuffle-Split Cross Validation to make sure that our findings were reliable and generalizable. Our study adds significantly to the body of knowledge by demonstrating the potential of LSTM networks in the specific field of AD prediction and extending the variety of methods investigated for image classification in AD research. We have also designed a user-friendly Web-based application to help with the accessibility of our developed model, bridging the gap between research and actual deployment.