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.

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.

Piperine-coated zinc oxide nanoparticles target biofilms and induce oral cancer apoptosis via BCl-2/BAX/P53 pathway.

BACKGROUND: Dental pathogens play a crucial role in oral health issues, including tooth decay, gum disease, and oral infections, and recent research suggests a link between these pathogens and oral cancer initiation and progression. Innovative therapeutic approaches are needed due to antibiotic resistance concerns and treatment limitations. METHODS: We synthesized and analyzed piperine-coated zinc oxide nanoparticles (ZnO-PIP NPs) using UV spectroscopy, SEM, XRD, FTIR, and EDAX. Antioxidant and antimicrobial effectiveness were evaluated through DPPH, ABTS, and MIC assays, while the anticancer properties were assessed on KB oral squamous carcinoma cells. RESULTS: ZnO-PIP NPs exhibited significant antioxidant activity and a MIC of 50 µg/mL against dental pathogens, indicating strong antimicrobial properties. Interaction analysis revealed high binding affinity with dental pathogens. ZnO-PIP NPs showed dose-dependent anticancer activity on KB cells, upregulating apoptotic genes BCL2, BAX, and P53. CONCLUSIONS: This approach offers a multifaceted solution to combatting both oral infections and cancer, showcasing their potential for significant advancement in oral healthcare. It is essential to acknowledge potential limitations and challenges associated with the use of ZnO NPs in clinical applications. These may include concerns regarding nanoparticle toxicity, biocompatibility, and long-term safety. Further research and rigorous testing are warranted to address these issues and ensure the safe and effective translation of ZnO-PIP NPs into clinical practice.

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.

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.

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.

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.

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.

Selenium Nanoparticles as Neuroprotective Agents: Insights into Molecular Mechanisms for Parkinson's Disease Treatment.

Oxidative stress and the accumulation of misfolded proteins in the brain are the main causes of Parkinson's disease (PD). Several nanoparticles have been used as therapeutics for PD. Despite their therapeutic potential, these nanoparticles induce multiple stresses upon entry. Selenium (Se), an essential nutrient in the human body, helps in DNA formation, stress control, and cell protection from damage and infections. It can also regulate thyroid hormone metabolism, reduce brain damage, boost immunity, and promote reproductive health. Selenium nanoparticles (Se-NPs), a bioactive substance, have been employed as treatments in several disciplines, particularly as antioxidants. Se-NP, whether functionalized or not, can protect mitochondria by enhancing levels of reactive oxygen species (ROS) scavenging enzymes in the brain. They can also promote dopamine synthesis. By inhibiting the aggregation of tau, α-synuclein, and/or Aß, they can reduce the cellular toxicities. The ability of the blood-brain barrier to absorb Se-NPs which maintain a healthy microenvironment is essential for brain homeostasis. This review focuses on stress-induced neurodegeneration and its critical control using Se-NP. Due to its ability to inhibit cellular stress and the pathophysiologies of PD, Se-NP is a promising neuroprotector with its anti-inflammatory, non-toxic, and antimicrobial properties.

Predictive modelling and optimization of WEDM parameter for Mg-Li alloy using ANN integrated CRITIC-WASPAS approach.

This work intended to improve the precision and machining efficiency of Magnesium alloy (Mg-Li-Sr) using Wire electrical discharge machining (WEDM). Mg-Li-Sr alloy is prepared through inert gas assisted stir casting route. Taguchi approach is used for experimental design for WEDM parameter such as pulse OFF time, pulse ON time, wire feed rate, servo voltage and current. L27 orthogonal array is considered to understand the influence of control parameter such as Kerf Width (KW), Roughness of the surface (Ra), Material Removal Rate (MRR). Integration of the CRITIC (Criteria Importance Through Intercriteria Correlation) -WASPAS (Weighted Aggregated Sum Product Assessment) multi-objective optimization method with Artificial Neural Network (ANN) modelling with different network structure for prediction and optimization is a novel approach that significantly improves prediction accuracy and machining outcomes. The developed ANN model with better R2 value of 99.9 % has better ability for prediction while correlated with formulated conventional regression equation. The error percentages identified through confirmation tests for regression and ANN models are Ra - 8.5 % and 3.4 %, MRR - 5.9 % and 2.8 %, KW - 6.7 % and 2.2 % respectively. Optimal output response attained by CRITIC-WASPAS approach yields surface roughness of 4.62 µm, material removal rate of 0.073 g/min and kerf width of 0.388 µm.

Development of Free Fatty Acid (FFA) monitoring device for evaluation of oil samples used for biodiesel production.

Diversion of oil sources for biodiesel production has been gaining importance to meet the environmental concerns and energy demand. The free fatty acid (FFA) content of the feedstock is a significant factor in biodiesel production. The FFA values determine the complexity of the biodiesel production. Until date, an experimental procedure has been used to determine the FFA concentration of an oil source; this method is dependent on titration, which is a laborious process involving significant volumes of chemicals. Hence, in the present study, an attempt was made to develop a device for the identification of FFA of the oils. Waste cooking oil samples subjected to wide range of cooking conditions like cooking time, temperature, type of food are collected from different food outlets. Subsequently, the composition of oil samples and the variation in their quality were analysed using gas chromatography flame ionization detector (GC - FID). Biodiesel is prepared from the oil samples through transesterification and the impact of FFA and their respective methyl esters in the quality and properties have been investigated. The properties of biodiesel were determined as per ASTM standards. The study was further extended to correlate the properties of biodiesel with the composition of the oil from which it was derived. The analysis evidently proved the dependence of biodiesel properties on the FFA percentage and the composition of the oil. The results have been further substantiated with the performance and emission characteristics of internal combustion engine fuelled with the prepared biodiesel samples.

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.

Dynamic clustering based risk aware congestion control technique for vehicular network.

In order to improve road safety and offer extra services to cars and their users, vehicular ad hoc networks, or VANETs, are essential parts of intelligent transportation systems (ITS). The primary aim of this research is to suggest and assess a new approach for mitigating network congestion in VANET technology through the implementation of dynamic grouping of vehicles for safety (DGVS). To do this, virtual regions are created around cars using the DBSCAN and K Mean method. This allows vehicles to communicate directly with others within the same DGVS, eliminating the need to broadcast messages across the entire network.The ultimate goal is to drastically decrease traffic while preserving vital data transfer for VANET traffic control and safety. The suggested technique determines the optimal transmission rate in accordance with the existing channel circumstances, yielding a balanced performance concerning both packet delivery and channel congestion. With this novel method, cars may only talk to other vehicles that are in the same DGVS, thus there's no need to broadcast messages to the whole network. With the use of this technique, the efficacy of DGVS in reducing VANET congestion and enhancing network performance will be thoroughly assessed. The study's conclusions demonstrate how well the suggested dynamic grouping of vehicles for safety (DGVS) technique works to reduce VANET congestion. It was shown through simulation-based studies that, in comparison to current approaches, DGVS dramatically decrease network congestion, resulting in notable gains in network performance and decreased communication delay. Additionally, the study found a number of possible uses for DGVS in the transportation industry, such as emergency response, traffic management, and accident prevention.

Hierarchically, Low Band Gap Nanohybrid InVO4-CdS Heterojunction for Visible Light-Driven Toxic Organic Dye Degradations.

The synthesis of InVO4-CdS heterojunction photocatalysts has been achieved by a novel two-step approach, including a microwave-assisted technique, followed by a moderate hydrothermal method, marking the first successful instance of such a synthesis. X-ray diffraction, field-emission scanning electron microscopy, elemental color mapping, high-resolution transmission electron microscopy, UV-vis diffuse reflectance spectroscopy, Raman analysis, photoluminescence, X-ray photoelectron spectroscopy, and Brunauer-Emmett-Teller were employed to investigate the crystal structures, surface morphologies and particle sizes, chemical compositions, and optical characteristics of the as-synthesized materials. The research results indicated that the heterojunction InVO4-CdS, as synthesized, consisted of InVO4 microrods with an average size of around 15 nm and cadmium sulfide (CdS) microflowers with a diameter of 1.5 µm. Furthermore, all of the heterojunctions had favorable photoabsorption properties throughout the visible-light spectrum. The photocatalytic efficiency of the samples obtained was thoroughly assessed by the degradation of acid violet 7 (AV 7) under visible light irradiation with a wavelength greater than 420 nm. The photocatalytic efficiency for the decomposition of AV 7 was greatly enhanced in the InVO4-CdS (IVCS) heterojunctions when compared to prepared bare InVO4 and CdS. Additionally, it was observed that the composite material consisting of IVCS 3 wt % InVO4 combined with CdS exhibited the most significant enhancement in catalytic effectiveness for the photodegradation of AV 7 dye. Specifically, the catalytic performance of this composite material was found to be around 69.4 and 76.2 times greater than that of pure InVO4 and CdS, respectively. Furthermore, the experimental procedure including active species trapping provided evidence that h+ and •O2- radicals were the primary active species involved in the photocatalytic reaction process. Additionally, a potential explanation for the improved photocatalytic activity of the InVO4-CdS heterojunction was presented, taking into account the determination of band positions.

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.

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.

Performance assessment of hybrid machine learning approaches for breast cancer and recurrence prediction.

Breast cancer is a major health concern for women everywhere and a major killer of women. Malignant tumors may be distinguished from benign ones, allowing for early diagnosis of this disease. Therefore, doctors need an accurate method of diagnosing tumors as either malignant or benign. Even if therapy begins immediately after diagnosis, some cancer cells may persist in the body, increasing the risk of a recurrence. Metastasis and recurrence are the leading causes of death from breast cancer. Therefore, detecting a return of breast cancer early has become a pressing medical issue. Evaluating and contrasting various Machine Learning (ML) techniques for breast cancer and recurrence prediction is crucial to choosing the best successful method. Inaccurate forecasts are common when using datasets with a large number of attributes. This study addresses the need for effective feature selection and optimization methods by introducing Recursive Feature Elimination (RFE) and Grey Wolf Optimizer (GWO), in response to the limitations observed in existing approaches. In this research, the performance evaluation of methods is enhanced by employing the RFE and GWO, considering the Wisconsin Diagnostic Breast Cancer (WDBC) and Wisconsin Prognostic Breast Cancer (WPBC) datasets taken from the UCI-ML repository. Various preprocessing techniques are applied to raw data, including imputation, scaling, and others. In the second step, relevant feature correlations are used with RFE to narrow down candidate discriminative features. The GWO chooses the best possible combination of attributes for the most accurate result in the next step. We use seven ML classifiers in both datasets to make a binary decision. On the WDBC and WPBC datasets, several experiments have shown accuracies of 98.25% and 93.27%, precisions of 98.13% and 95.56%, sensitivities of 99.06% and 96.63%, specificities of 96.92% and 73.33%, F1-scores of 98.59% and 96.09% and AUCs of 0.982 and 0.936, respectively. The hybrid approach's superior feature selection improved the accuracy of breast cancer performance indicators and recurrence classification.

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.

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.