Immunomodulatory zymosan/ι-carrageenan/ agarose hydrogel for targeting M2 to M1 macrophages (antitumoral).

Several studies have been performed on the immunomodulatory effects of yeast ß-(1,3) glucan, but there is no proper evaluation of the thermal and immunomodulating properties of zymosan (ZM). Thermogravimetry analysis indicated a 54% weight loss of ZM at 270 °C. Circular dichroism showed absorption peaks in the region of 250 to 400 nm, suggesting a helical coil ß-sheet configuration. XRD showed a broad peak at 2θ of 20.38°, indicating the crystalline nature, and the size was found to be 23 nm. ZM is biocompatible and showed no toxicity against L929 and RAW 264.7 cell lines (cell viability > 90%). Immunomodulatory studies with PCR showed upregulation of M1 genes in human differentiated THP-1 macrophage cell lines, which were responsible for antitumor properties. The uptake of ZM particles inside the differentiated THP-1 macrophages and Raw 264.7 cells was confirmed (Video clip). ZM particle uptake via Dectin-1 was identified by competitive receptor blocking. Seaweed derived carrageenan/ZM/agarose hydrogel was successfully prepared (@5 : 5 wt%) and was seen to support the growth of L929 cells (1 × 105 cells per mL) and have a higher swelling (≈250-280%). This study indicates that ZM-based hydrogel could be a potential drug carrier (Rifampicin and Levofloxacin) for targeting tumour-associated macrophages (M2).

Weighted Bayesian Belief Network for diabetics: a predictive model.

Diabetes is an enduring metabolic condition identified by heightened blood sugar levels stemming from insufficient production of insulin or ineffective utilization of insulin within the body. India is commonly labeled as the "diabetes capital of the world" owing to the widespread prevalence of this condition. To the best of the authors' last knowledge updated on September 2021, approximately 77 million adults in India were reported to be affected by diabetes, reported by the International Diabetes Federation. Owing to the concealed early symptoms, numerous diabetic patients go undiagnosed, leading to delayed treatment. While Computational Intelligence approaches have been utilized to improve the prediction rate, a significant portion of these methods lacks interpretability, primarily due to their inherent black box nature. Rule extraction is frequently utilized to elucidate the opaque nature inherent in machine learning algorithms. Moreover, to resolve the black box nature, a method for extracting strong rules based on Weighted Bayesian Association Rule Mining is used so that the extracted rules to diagnose any disease such as diabetes can be very transparent and easily analyzed by the clinical experts, enhancing the interpretability. The WBBN model is constructed utilizing the UCI machine learning repository, demonstrating a performance accuracy of 95.8%.

BT-CNN: a balanced binary tree architecture for classification of brain tumour using MRI imaging.

Deep learning is a very important technique in clinical diagnosis and therapy in the present world. Convolutional Neural Network (CNN) is a recent development in deep learning that is used in computer vision. Our medical investigation focuses on the identification of brain tumour. To improve the brain tumour classification performance a Balanced binary Tree CNN (BT-CNN) which is framed in a binary tree-like structure is proposed. It has a two distinct modules-the convolution and the depthwise separable convolution group. The usage of convolution group achieves lower time and higher memory, while the opposite is true for the depthwise separable convolution group. This balanced binarty tree inspired CNN balances both the groups to achieve maximum performance in terms of time and space. The proposed model along with state-of-the-art models like CNN-KNN and models proposed by Musallam et al., Saikat et al., and Amin et al. are experimented on public datasets. Before we feed the data into model the images are pre-processed using CLAHE, denoising, cropping, and scaling. The pre-processed dataset is partitioned into training and testing datasets as per 5 fold cross validation. The proposed model is trained and compared its perforarmance with state-of-the-art models like CNN-KNN and models proposed by Musallam et al., Saikat et al., and Amin et al. The proposed model reported average training accuracy of 99.61% compared to other models. The proposed model achieved 96.06% test accuracy where as other models achieved 68.86%, 85.8%, 86.88%, and 90.41% respectively. Further, the proposed model obtained lowest standard deviation on training and test accuracies across all folds, making it invariable to dataset.

Polystyrene nanoplastics synergistically exacerbate diclofenac toxicity in embryonic development and the health of adult zebrafish.

In this study, we investigated the possible ecotoxicological effect of co-exposure to polystyrene nanoplastics (PS-NPs) and diclofenac (DCF) in zebrafish (Danio rerio). After six days of exposure, we noticed that the co-exposure to PS-NP (100 µg/L) and DCF (at 50 and 500 µg/L) decreased the hatching rate and increased the mortality rate compared to the control group. Furthermore, we noted that larvae exposed to combined pollutants showed a higher frequency of morphological abnormalities and increased oxidative stress, apoptosis, and lipid peroxidation. In adults, superoxide dismutase and catalase activities were also impaired in the intestine, and the co-exposure groups showed more histopathological alterations. Furthermore, the TNF-α, COX-2, and IL-1ß expressions were significantly upregulated in the adult zebrafish co-exposed to pollutants. Based on these findings, the co-exposure to PS-NPs and DCF has shown an adverse effect on the intestinal region, supporting the notion that PS-NPs synergistically exacerbate DCF toxicity in zebrafish.

Multifunctional curcumin mediated zinc oxide nanoparticle enhancing biofilm inhibition and targeting apoptotic specific pathway in oral squamous carcinoma cells.

BACKGROUND: Oral health remains a significant global concern with the prevalence of oral pathogens and the increasing incidence of oral cancer posing formidable challenges. Additionally, the emergence of antibiotic-resistant strains has complicated treatment strategies, emphasizing the urgent need for alternative therapeutic approaches. Recent research has explored the application of plant compounds mediated with nanotechnology in oral health, focusing on the antimicrobial and anticancer properties. METHODS: In this study, curcumin (Cu)-mediated zinc oxide nanoparticles (ZnO NPs) were synthesized and characterized using SEM, EDAX, UV spectroscopy, FTIR, and XRD to validate their composition and structural features. The antioxidant and antimicrobial activity of ZnO-CU NPs was investigated through DPPH, ABTS, and zone of inhibition assays. Apoptotic assays and gene expression analysis were performed in KB oral squamous carcinoma cells to identify their anticancer activity. RESULTS: ZnO-CU NPs showcased formidable antioxidant prowess in both DPPH and ABTS assays, signifying their potential as robust scavengers of free radicals. The determined minimal inhibitory concentration of 40 µg/mL against dental pathogens underscored the compelling antimicrobial attributes of ZnO-CU NPs. Furthermore, the interaction analysis revealed the superior binding affinity and intricate amino acid interactions of ZnO-CU NPs with receptors on dental pathogens. Moreover, in the realm of anticancer activity, ZnO-CU NPs exhibited a dose-dependent response against Human Oral Epidermal Carcinoma KB cells at concentrations of 10 µg/mL, 20 µg/mL, 40 µg/mL, and 80 µg/mL. Unraveling the intricate mechanism of apoptotic activity, ZnO-CU NPs orchestrated the upregulation of pivotal genes, including BCL2, BAX, and P53, within the KB cells. CONCLUSIONS: This multifaceted approach, addressing both antimicrobial and anticancer activity, positions ZnO-CU NPs as a compelling avenue for advancing oral health, offering a comprehensive strategy for tackling both oral infections and cancer.

Insitu synthesis of Al- MgAl2O4 composites and parametric optimization of tribological characteristics.

In this research, multiobjective optimization of tribological characteristics of Al-4Mg/in-situ MgAl2O4 composites fabricated via ultrasonic cavitation treatment assisted stir casting technique was carried out. Al-4Mg alloy dispersed with 0.5, 1 and 2 wt% in-situ MgAl2O4 was prepared and the microstructural and mechanical characterisation of the same has been carried out. Reinforcement addition, load and sliding velocity at 3 different levels was considered to attain the responses wear rate and friction coefficient. To identify optimised process condition for the developed composites to attain reduced friction coefficient and wear rate condition, grey analysis is tried out. Experimental results analysed via Grey relation and analysis of variance (ANOVA) proved wt.% of MgAl2O4 particles as significant parameter trailed by load and speed. Based on grey relational grade, minimal wear loss at lowest frictional coefficient can be attained for the composite dispersed with 2 wt% of in-situ MgAl2O4 at 20 N load and 2 m/s sliding velocity. Mechanisms behind the wear loss was analysed from worn out surface micrographs.

Optimizing laser cladding powder injection parameters to shape bioactive glass nano-coated zirconium oxide for biomedical application.

Human body is highly sensitive and repairing often incurs pain and expenses. Strength of the materials degraded by poor joint (either weld or link). New material technology is proposed many biomaterials for repairing bone and tissue and also many bio-implantation applications. Especially bioactive material like bioactive glass is used for biomedical applications for replacement and repairing organs in human body. This research work focuses on suggesting material of S53P4 bioactive glass Nano-coated Zirconium dioxide for manufacturing artificial knee implant for fixing in human body. The substrate of Zirconium dioxide is Nano-coated with S53P4 bioactive glass by means of laser cladding process. The laser cladding process parameters were optimized by Taguchi method to enhance mechanical properties like compressive strength, wear resistance and microhardness of Zirconium dioxide implant material. The key parameters like Laser Power (1 kW, 2 kW, 3 kW and 4 kW), beam diameter (2 mm, 3 mm, 4 mm and 5 mm), powder feed rate (10 g/min, 15 g/min, 20 g/min and 25 g/min) and scanning speed (3 mm/s, 4 mm/s, 5 mm/s and 6 mm/s) were considered. The optimal parameters result the higher compressive strength and microhardness are obtained as 373 MPa and 898.37 HV0.2 and minimum wear volume is attained as 0.148 mm3 in the Nano-coated implant material.

Spectral energy balancing system with massive MIMO based hybrid beam forming for wireless 6G communication using dual deep learning model.

This work aims to provide an effective hybrid beam forming method with Dual-Deep-Network to overcome overhead for mm-wave massive MIMO systems. In this paper, a Dual-Deep-Network technique is described for the extraction of statistical structures from a hybrid beam forming model based on mmWave logics, as well as training logic for the network map functions. The proposed approach of DDN is trained with proper data sequences used for communication and the training phase is conducted with the norms of numerous channel variants. With the nature of diverse channel states, a Dual-Deep-Network is required to manipulate the level of presence and abilities even after training as well. The performance level improvements are practically summarized in both the transmission and reception entities with the help of the proposed hybrid network architecture and the associated Dual Deep Network algorithm. Specifically, the BER versus SNR and spectral efficiency versus SNR are evaluated as well as the resulting accuracy levels are cross validated with numerous classical communication techniques. This paper shows the processing difficulties of the proposed approach and typically cross-validates with other beam forming logics. The computational cost and performance estimations are improved, and the metrics are clearly visualized on this paper based on improved beamforming procedures as well as the proposed approach of DDN based Multi-Resolution Code Book performance metrics are estimated clearly with proper mathematical model investigations. With 7Kbits/s/Hz and 1e-1, respectively, the key metrics of spectral efficiency and BER are enhanced.

A comprehensive review of critical analysis of biodegradable waste PCM for thermal energy storage systems using machine learning and deep learning to predict dynamic behavior.

This article explores the use of phase change materials (PCMs) derived from waste, in energy storage systems. It emphasizes the potential of these PCMs in addressing concerns related to fossil fuel usage and environmental impact. This article also highlights the aspects of these PCMs including reduced reliance on renewable resources minimized greenhouse gas emissions and waste reduction. The study also discusses approaches such as integrating nanotechnology to enhance thermal conductivity and utilizing machine learning and deep learning techniques for predicting dynamic behavior. The article provides an overall view of research on biodegradable waste-based PCMs and how they can play a promising role in achieving energy-efficient and sustainable thermal storage systems. However, specific conclusions drawn from the presented results are not explicitly outlined, leaving room, for investigation and exploration in this evolving field. Artificial neural network (ANN) predictive models for thermal energy storage devices perform differently. With a 4% adjusted mean absolute error, the Gaussian radial basis function kernel Support Vector Regression (SVR) model captured heat-related charging and discharging issues. The ANN model predicted finned tube heat and heat flux better than the numerical model. SVM models outperformed ANN and ANFIS in some datasets. Material property predictions favored gradient boosting, but Linear Regression and SVR models performed better, emphasizing application- and dataset-specific model selection. These predictive models provide insights into the complex thermal performance of building structures, aiding in the design and operation of energy-efficient systems. Biodegradable waste-based PCMs' sustainability includes carbon footprint, waste reduction, biodegradability, and circular economy alignment. Nanotechnology, machine learning, and deep learning improve thermal conductivity and prediction. Circular economy principles include waste reduction and carbon footprint reduction. Specific results-based conclusions are not stated. Presenting a comprehensive overview of current research highlights biodegradable waste-based PCMs' potential for energy-efficient and sustainable thermal storage systems.

A mathematical model for investigation of dry band location on 22 kV bushing with and without RTV coating: Experimental study.

As different pollutants are deposited on the high voltage bushings, a dry band forms, which causes a flashover. The bushing's contaminated layer will weaken its insulation and have an impact on its electrical characteristics. The performance of bushings in dry band conditions of various lengths was investigated in this proposed piece of work, and a dynamic arc model is presented for the arc process in polluted bushings. It shows satisfactory performance in modelling the arc variables for various dry band positions. The developed dynamic open model for contaminated bushings with and without RTV coating predicted the flashover voltage and dry band positions. Any type of contamination, such as sea salt, road salt, and industrial pollutants prevalent in several sites, can be studied using the established model. Ultimately, it was discovered that there was good agreement between the model's results and the outcomes of the experiments. •Mathematical modeling of 22 kV bushing is conceded out for diverse polluted dry band location at lead-in, lead-out and middle region of bushing surface.•Dynamic arc modeling involved in bushing flashover process for different dry band location is done and flashover voltage is predicted•Experimental work is carried out to find FOV for the bushing with different dry location and compared with predicted FOV.

Advances in functional lipid nanoparticles: from drug delivery platforms to clinical applications.

Since Doxil's first clinical approval in 1995, lipid nanoparticles have garnered great interest and shown exceptional therapeutic efficacy. It is clear from the licensure of two RNA treatments and the mRNA-COVID-19 vaccination that lipid nanoparticles have immense potential for delivering nucleic acids. The review begins with a list of lipid nanoparticle types, such as liposomes and solid lipid nanoparticles. Then it moves on to the earliest lipid nanoparticle forms, outlining how lipid is used in a variety of industries and how it is used as a versatile nanocarrier platform. Lipid nanoparticles must then be functionally modified. Various approaches have been proposed for the synthesis of lipid nanoparticles, such as High-Pressure Homogenization (HPH), microemulsion methods, solvent-based emulsification techniques, solvent injection, phase reversal, and membrane contractors. High-pressure homogenization is the most commonly used method. All of the methods listed above follow four basic steps, as depicted in the flowchart below. Out of these four steps, the process of dispersing lipids in an aqueous medium to produce liposomes is the most unpredictable step. A short outline of the characterization of lipid nanoparticles follows discussions of applications for the trapping and transporting of various small molecules. It highlights the use of rapamycin-coated lipid nanoparticles in glioblastoma and how lipid nanoparticles function as a conjugator in the delivery of anticancer-targeting nucleic acids. High biocompatibility, ease of production, scalability, non-toxicity, and tailored distribution are just a meager of the enticing allowances of using lipid nanoparticles as drug delivery vehicles. Due to the present constraints in drug delivery, more research is required to utterly realize the potential of lipid nanoparticles for possible clinical and therapeutic purposes.

The impact of 180° return bend inclination on pressure drop characteristics and phase distribution during oil-water flow.

The present work aims to capture the influence of the inclination of the return bend on flow patterns and pressure drop during oil-water flow. The experiments were carried out for different inclinations (0°, 15°, 30°, and 45°) of return bend for various superficial velocity combinations of oil (kerosene) and water ranging from 0.07 to 0.66 m/s. The experiments showed that pressure drop increases with the increase in inclination. However, the pressure drop at a fixed inclination (say 15°) decreases with the increase in the superficial velocity of the water. Distinct flow patterns observed in the return bend were droplet flow, film inversion, slug flow, plug flow and large slug flow. Droplet flow dominates at the lower range of kerosene (i.e., Usk = 0.07-0.2 m/s) and higher range of water superficial velocity (i.e., Usw = 0.40-0.66 m/s) at all the inclinations considered in this study. Additionally, comparisons between the experimental and numerical simulation results were made. The numerical solution utilized the Euler-Euler approach, considering the different phases as interpenetrating continua. The Volume of Fluid (VOF) model was used within this approach, monitoring the volume fraction of each phase over the domain while calculating one set of momentum equations for each phase. To capture the turbulent effects accurately, the k-ε turbulence model was incorporated. It happened to be found that the numerical findings showed remarkable agreement with the experimental data.

Marine-derived κ-carrageenan-coated zinc oxide nanoparticles for targeted drug delivery and apoptosis induction in oral cancer.

BACKGROUND: Kappaphycus alvarezii, a marine red algae species, has gained significant attention in recent years due to its versatile bioactive compounds. Among these, κ-carrageenan (CR), a sulfated polysaccharide, exhibits remarkable antimicrobial properties. This study emphasizes the synergism attained by functionalizing zinc oxide nanoparticles (ZnO NPs) with CR, thereby enhancing its antimicrobial efficacy and target specificity against dental pathogens. METHODS: In this study, we synthesized ZnO-CR NPs and characterized them using SEM, FTIR, and XRD techniques to authenticate their composition and structural attributes. Moreover, our investigation revealed that ZnO-CR NPs possess better free radical scavenging capabilities, as evidenced by their effective activity in the DPPH and ABTS assay. RESULTS: The antimicrobial properties of ZnO-CR NPs were systematically assessed using a zone of inhibition assay against dental pathogens of S. aureus, S. mutans, E. faecalis, and C. albicans, demonstrating their substantial inhibitory effects at a minimal concentration of 50 µg/mL. We elucidated the interaction between CR and the receptors of dental pathogens to further understand their mechanism of action. The ZnO-CR NPs demonstrated a dose-dependent anticancer effect at concentrations of 5 µg/mL, 25 µg/mL, 50 µg/mL, and 100 µg/mL on KB cells, a type of Human Oral Epidermal Carcinoma. The mechanism by which ZnO-CA NPs induced apoptosis in KB cells was determined by observing an increase in the expression of the BCL-2, BAX, and P53 genes. CONCLUSION: Our findings unveil the promising potential of ZnO-CR NPs as a candidate with significant utility in dental applications. The demonstrated biocompatibility, potent antioxidant and antiapoptotic activity, along with impressive antimicrobial efficacy position these NPs as a valuable resource in the ongoing fight against dental pathogens and oral cancer.

Optimizing the selection of natural fibre reinforcement and polymer matrix for plastic composite using LS-SVM technique.

The manufacturing sector is paying close attention to plastic matrix composites (PMCs) reinforced with natural fibres for improving their products. Due to the fact that PMC reinforced with naturally occurring fibres is more affordable and has superior mechanical qualities. Based on the application material requirements, An important step in the production of PMC is choosing the right natural fibres for reinforcing and determining how much of each. This investigation aimed that Artificial Intelligence (AI) or soft computing based approaches are used to determine the right amount of natural fibres in PMCs to make the manufacturing process simpler. However, techniques in the literature are not concentrated on finding suitable material. Hence in this investigation, a local search with support vector machine (LS-SVM) optimization technique is proposed for the optimal selection of appropriate proportions of suitable fibres. Modelling of the Proposed LS-SVM Optimization was demonstrated. In this proposed technique around four kinds of polymers/plastics and 14 natural fibres are considered, which are optimized in various proportions. The optimization performance is evaluated based on the tensile strength, flexural yield strength and flexural yield modulus. The proposed LS-SVM Optimization was evacuated by developing solutions for medical applications (Case 1), Transportation applications (Case 2) and other notable applications (Case 3) in terms of tensile and flexural properties of the material. The maximum flexure stress in case 1, case 2, and case 3 is observed as 53 MPa, 45 MPa and 26 MPa respectively. Similarly, the maximum flexure stress in case 1, case 2, and case 3 is observed as 53 MPa, 45 MPa and 26 MPa respectively. Hence the proposed method recommended for choosing optimal decision on the choice of fiber and their quantity in the composite matrix.

Preventing Risks of Infections and Medication Errors in IV therapy (PRIME): a patient safety initiative.

BACKGROUND: Two major avoidable reasons for adverse events in hospital are medication errors and intravenous therapy-induced infections or complications. Training for clinical staff and compliance to patient safety principles could address these. METHODS: Joint Commission International (JCI) consultants created a standardised, 6-month training programme for clinical staff in hospitals. Twenty-one tertiary care hospitals from across south-east Asia took part. JCI trained the clinical consultants, who trained hospital safety champions, who trained nursing staff. Compliance and knowledge were assessed, and monthly audits were conducted. RESULTS: There was an overall increase of 29% in compliance with parameters around medication preparation and vascular access device management. CONCLUSION: The programme improved safe practice around preparing medications management and managing vascular access devices. The approach could be employed as a continuous quality improvement initiative for the prevention of medication errors and infusion-associated complications.

A Comparative Study on Predication of Appropriate Mechanical Ventilation Mode through Machine Learning Approach.

Ventilation mode is one of the most crucial ventilator settings, selected and set by knowledgeable critical care therapists in a critical care unit. The application of a particular ventilation mode must be patient-specific and patient-interactive. The main aim of this study is to provide a detailed outline regarding ventilation mode settings and determine the best machine learning method to create a deployable model for the appropriate selection of ventilation mode on a per breath basis. Per-breath patient data is utilized, preprocessed and finally a data frame is created consisting of five feature columns (inspiratory and expiratory tidal volume, minimum pressure, positive end-expiratory pressure, and previous positive end-expiratory pressure) and one output column (output column consisted of modes to be predicted). The data frame has been split into training and testing datasets with a test size of 30%. Six machine learning algorithms were trained and compared for performance, based on the accuracy, F1 score, sensitivity, and precision. The output shows that the Random-Forest Algorithm was the most precise and accurate in predicting all ventilation modes correctly, out of the all the machine learning algorithms trained. Thus, the Random-Forest machine learning technique can be utilized for predicting optimal ventilation mode setting, if it is properly trained with the help of the most relevant data. Aside from ventilation mode, control parameter settings, alarm settings and other settings may also be adjusted for the mechanical ventilation process utilizing appropriate machine learning, particularly deep learning approaches.

Patient Experience With Vascular Access Management Informs Satisfaction With Overall Hospitalization Experience.

Peripheral intravenous catheter (PIVC) insertion is a common invasive procedure performed during hospitalization. The present study reports results from a survey of 544 patients who have had PIVC insertion during their hospital stay in Singapore and the Philippines during the period between November 2018 and February 2019. The survey assessed the importance of 5 domains of patient-centered care on patient satisfaction with their hospitalization experience, including pain management, infection prevention, health care provider competence with vascular access, physical comfort, and effectiveness of communication during vascular access management. Health care provider competence, infection prevention, and pain management ranked as the most important determinants of patient satisfaction. Patients were more likely to lose their trust in health care providers and express anxiety if they experienced multiple needle insertion attempts or PIVC-related complications, whereas patients who were satisfied with their PIVC insertion were more likely to express satisfaction with their overall hospital stay. Improving vascular access management with a focus on enhancing vascular access skills, infection prevention, and pain management may improve patient satisfaction.

Design and simulation of AI-based low-cost mechanical ventilator: An approach.

In this situation of COVID 19, many people are being exposed to coronavirus, resulting in difficulty in breathing and a drop in oxygen percentage of blood. A mechanical ventilator is playing a vital role in tackling this situation but the ventilation process is neither readily available nor affordable. The idea behind this work is to propose a simplified design of a mechanical ventilator to reduce the cost and automate the Mechanical ventilation process. The simplified design, it's working, and required components are elaborated in this paper. The simulation of the proposed design is made in MATLAB/Simulink platform which is also discussed below. Taking into account the work done in the area of cost reduction of the mechanical ventilation process, the mechanical ventilator with a simplified design comprising of compressed air and oxygen source is being considered. The parameters considered for mechanical ventilation are positive end-expiratory pressure (PEEP), pressure wave, respiratory rate (RR), tidal volume, etc. These parameters of oxygen and air mixture are to be controlled with the help of electronic devices which are pressure regulator, solenoid valve, flow sensor, proportional valve, microprocessor, etc depending upon the condition of patient and type of disease. Simulation results are promising and precise which allows the study on ventilator model without jeopardizing the life of human subjects as in clinical approach and hides the complexity of computational models from the user. Furthermore, advancements in this model are done by the machine learning approach.