Maximizing performance and efficiency in 3D printing of polylactic acid biomaterials: Unveiling of microstructural morphology, and implications of process parameters and modeling of the mechanical strength, surface roughness, print time, and print energy for fused filament fabricated (FFF) bioparts

Medical stents, artificial teeth, and grafts are just some of the many applications for additive manufacturing techniques like bio-degradable polylactic acid 3D printing. However, there are drawbacks associated with fused filament fabrication-fabricated objects, including poor surface quality, insufficient mechanical strength, and a lengthy construction time for even a relatively small object. Thus, this study aims to identify the finest polylactic acid 3D printing parameters to maximize print quality while minimizing energy use, print time, flexural and tensile strengths, average surface roughness, and print time, respectively. Specifically, the infill density, printing speed, and layer thickness are all variables that were selected. A full-central-composite design generated 20 samples to test the prediction models' experimental procedures. Validation trial tests were used to show that the experimental findings agreed with the predictions, and analysis of variance was used to verify the importance of the performance characteristics (ANOVA). At layer thickness = 0.26 mm, infill density = 84 %, and print speed = 68.87 mm/s, the following optimized values were measured for PLA: flexural strength = 70.1 MPa, tensile strength = 39.2 MPa, minimum surface roughness = 7.8 μm, print time = 47 min, and print energy = 0.18 kwh. Firms and clinicians may benefit from utilizing the developed, model to better predict the required surface characteristic for various aspects afore trials.

Replacement of failed items in a two commodity retrial queueing-inventory system with multi-component demand and vacation interruption

This study investigates a crucial aspect of inventory management, which is the process of replacing failed items. In dynamic commercial environments, it is essential to efficiently and strategically replace failed items to maintain operational efficiency and ensure profitability. We consider a two-commodity retrial queueing-inventory system with vacation interruption. Upon purchasing the first commodity, the second commodity is provided as a complimentary item. In contrast, no item is given as a complimentary for the purchase of the second item. Only the first commodity is stored in a dedicated pooled storage for replacement when it fails. The ( s , Q ) policy governs replenishing the first commodity while the second is replenished through instantaneous ordering. The model considers the multi-component demand rate for customer arrivals. Server vacations are initiated during customer absence in waiting hall or when the first commodity is unavailable. We formulate a level-dependent quasi-birth-and-death process, and its steady-state probability vector is computed using Neuts and Rao's truncation method. The stability condition for the system is derived, and various system performance measures, including expected total cost, number of replaceable items, and customers in the waiting hall and orbit, are established. The comparative analysis between the system with replacement is done with the regular model without replacement, which revealed the efficiency of replacement. The analysis of multi-component demand towards homogeneous arrival highlights the impact of multi-component demand on boosting customer arrival. Also, parametric sensitivity analysis has been conducted numerically over total cost, mean number of failed items for replacement, and mean number of customers in the waiting hall and orbit.

Modeling of two-stage anaerobic onsite wastewater sanitation system to predict effluent soluble chemical oxygen demand through machine learning

The present research aims to predict effluent soluble chemical oxygen demand (SCOD) in anaerobic digestion (AD) process using machine-learning based approach. Anaerobic digestion is a highly sensitive process and depends upon several environmental and operational factors, such as temperature, flow, and load. Therefore, predicting output characteristics using modeling is important not only for process monitoring and control, but also to reduce the operating cost of the treatment plant. It is difficult to predict COD in a real time mode, so it is better to use Complex Mathematical Modeling (CMM) for simulating AD process and forecasting output parameters. Therefore, different Machine Learning algorithms, such as Linear Regression, Decision Tree, Random Forest and Artificial Neural Networks, have been used for predicting effluent SCOD using data acquired from in situ anaerobic wastewater treatment system. The result of the predicted data using different algorithms were compared with experimental data of anaerobic system. It was observed that the Artificial Neural Networks is the most effective simulation technique that correlated with the experimental data with the mean absolute percentage error of 10.63 and R2 score of 0.96. This research proposes an efficient and reliable integrated modeling method for early prediction of the water quality in wastewater treatment.

Bentonite SDBS-loaded composite for methylene blue removal from wastewater: An experimental and theoretical investigation

This study addresses the urgent need for practical solutions to industrial water contamination. Utilizing Algerian Bentonite as an adsorbent due to its regional prevalence, we focused on the efficiency of the Bentonite/Sodium dodecylbenzene sulfonate (SDBS) matrix in Methylene Blue (MB) removal. The zero-charge point and IR spectroscopy characterized the adsorbent. Acidic pH facilitated SDBS adsorption on Bentonite, achieving equilibrium in 30 min with a pseudo-second-order model. The UPAC and Freundlich model indicated a qmax of 25.97 mg/g. SDBS adsorption was exothermic at elevated temperatures. The loaded Bentonite exhibited excellent MB adsorption (pH 3-9) with PSOM kinetics. Maximum adsorption capacity using IUPAC and GILES-recommended isotherms was qmax = 23.54 mg/g. The loaded Bentonite's specific surface area was 70.01 m2/g, and the Sips model correlated well with experimental data (R2 = 0.98). This study highlights adsorption, mainly Bentonite/SDBS matrices, as a promising approach for remediating polluted areas by efficiently capturing and removing surfactants and dyes, contributing valuable insights to address industrial water contamination challenges.

Harnessing Sulforaphane Potential as a Chemosensitizing Agent: A Comprehensive Review

Recent advances in oncological research have highlighted the potential of naturally derived compounds in cancer prevention and treatment. Notably, sulforaphane (SFN), an isothiocyanate derived from cruciferous vegetables including broccoli and cabbage, has exhibited potent chemosensitizing capabilities across diverse cancer types of bone, brain, breast, lung, skin, etc. Chemosensitization refers to the enhancement of cancer cell sensitivity to chemotherapy agents, counteracting the chemoresistance often developed by tumor cells. Mechanistically, SFN orchestrates this sensitization by modulating an array of cellular signaling pathways (e.g., Akt/mTOR, NF-κB, Wnt/β-catenin), and regulating the expression and activity of pivotal genes, proteins, and enzymes (e.g., p53, p21, survivin, Bcl-2, caspases). When combined with conventional chemotherapeutic agents, SFN synergistically inhibits cancer cell proliferation, invasion, migration, and metastasis while potentiating drug-induced apoptosis. This positions SFN as a potential adjunct in cancer therapy to augment the efficacy of standard treatments. Ongoing preclinical and clinical investigations aim to further delineate the therapeutic potential of SFN in oncology. This review illuminates the multifaceted role of this phytochemical, emphasizing its potential to enhance the therapeutic efficacy of anti-cancer agents, suggesting its prospective contributions to cancer chemosensitization and management.

Peristaltic transport of Sutterby nanofluid flow in an inclined tapered channel with an artificial neural network model and biomedical engineering application

Modern energy systems are finding new applications for magnetohydrodynamic rheological bio-inspired pumping systems. The incorporation of the electrically conductive qualities of flowing liquids into the biological geometries, rheological behavior, and propulsion processes of these systems was a significant effort. Additional enhancements to transport properties are possible with the use of nanofluids. Due to their several applications in physiology and industry, including urine dynamics, chyme migration in the gastrointestinal system, and the hemodynamics of tiny blood arteries. Peristaltic processes also move spermatozoa in the human reproductive system and embryos in the uterus. The present research examines heat transport in a two-dimensional deformable channel containing magnetic viscoelastic nanofluids by considering all of these factors concurrently, which is vulnerable to peristaltic waves and hall current under ion slip and other situations. Nanofluid rheology makes use of the Sutterby fluid model, while nanoscale effects are modeled using the Buongiorno model. The current study introduces an innovative numerical computing solver utilizing a Multilayer Perceptron feed-forward back-propagation artificial neural network (ANN) with the Levenberg-Marquardt algorithm. Data were collected for testing, certifying, and training the ANN model. In order to make the dimensional PDEs dimensionless, the non-similar variables are employed and calculated by the Homotopy perturbation technique. The effects of developing parameters such as Sutterby fluid parameter, Froude number, thermophoresis, ion-slip parameter, Brownian motion, radiation, Eckert number, and Hall parameter on velocity, temperature, and concentration are demonstrated. The machine learning model chooses data, builds and trains a network, and subsequently assesses its performance using the mean square error metric. Current results declare that the improving Reynolds number tends to increase the pressure rise. Improving the Hall parameter is shown to result in a decrease in velocity. When raising a fluid's parameter, the temperature profile rises.

Increased Peripheral Venous Catheter Bloodstream Infections during COVID-19 Pandemic, Switzerland

Studies suggest that central venous catheter bloodstream infections (BSIs) increased during the COVID-19 pandemic. We investigated catheter-related BSIs in Switzerland and found peripheral venous catheter (PVC) BSI incidence increased during 2021-2022 compared with 2020. These findings should raise awareness of PVC-associated BSIs and prompt inclusion of PVC BSIs in surveillance systems.

Design and validation of a pediatric gait assistance exoskeleton system with fast non-singular terminal sliding mode controller

Existing exoskeletons for pediatric gait assistance have limitations in anthropometric design, structure weight, cost, user safety features, and adaptability to diverse users. Additionally, creating precise models for pediatric rehabilitation is difficult because the rapid anthropometric changes in children result in unknown model parameters. Furthermore, external disruptions, like unpredictable movements and involuntary muscle contractions, add complexity to the control schemes that need to be managed. To overcome these limitations, this study aims to develop an affordable stand-aided lower-limb exoskeleton specifically for pediatric subjects (8-12 years, 25-40 kg, 128-132 cm) in passive-assist mode. The authors modified a previously developed model (LLESv1) for improved rigidity, reduced mass, simplified motor arrangement, variable waist size, and enhanced mobility. A computer-aided design of the new exoskeleton system (LLESv2) is presented. The developed prototype of the exoskeleton appended with a pediatric subject (age: 12 years old, body mass: 40 kg, body height: 132 cm) is presented with real-time hardware architecture. Thereafter, an improved fast non-singular terminal sliding mode (IFNSTSM) control scheme is proposed, incorporating a double exponential reaching law for expedited error convergence and enhanced stability. The Lyapunov stability warrants the control system's performance despite uncertainties and disturbances. In contrast to fast non-singular terminal sliding mode (FNSTSM) control and time-scaling sliding mode (TSSM) control, experimental validation demonstrates the effectiveness of IFNSTSM control by a respective average of 5.39% and 42.1% in tracking desired joint trajectories with minimal and rapid finite time converging errors. Moreover, the exoskeleton with the proposed IFNSTSM control requires significantly lesser control efforts than the exoskeleton using contrast FNSTSM control. The Bland-Altman analysis indicates that although there is a minimal mean difference in variables when employing FNSTSM and IFNSTSM controllers, the latter exhibits significant performance variations as the mean of variables changes. This research contributes to affordable and effective pediatric gait assistance, improving rehabilitation outcomes and enhancing mobility support.

Brownian motion of soft particles near a fluctuating lipid bilayer

The dynamics of a soft particle suspended in a viscous fluid can be changed by the presence of an elastic boundary. Understanding the mechanisms and dynamics of soft-soft surface interactions can provide valuable insights into many important research fields, including biomedical engineering, soft robotics development, and materials science. This work investigates the anomalous transport properties of a soft nanoparticle near a visco-elastic interface, where the particle consists of a polymer assembly in the form of a micelle and the interface is represented by a lipid bilayer membrane. Mesoscopic simulations using a dissipative particle dynamics model are performed to examine the impact of micelle's proximity to the membrane on its Brownian motion. Two different sizes are considered, which correspond to ≈10-20nm in physical units. The wavelengths typically seen by the largest micelle fall within the range of wavenumbers where the Helfrich model captures fairly well the bilayer mechanical properties. Several independent simulations allowed us to compute the micelle trajectories during an observation time smaller than the diffusive time scale (whose order of magnitude is similar to the membrane relaxation time of the largest wavelengths), this time scale being hardly accessible by experiments. From the probability density function of the micelle normal position with respect to the membrane, it is observed that the position remains close to the starting position during ≈0.05τd (where τd corresponds to the diffusion time), which allowed us to compare the negative excess of mean-square displacement (MSD) to existing theories. In that time range, the MSD exhibits different behaviors along parallel and perpendicular directions. When the micelle is sufficiently close to the bilayer (its initial distance from the bilayer equals approximately twice its gyration radius), the micelle motion becomes quickly subdiffusive in the normal direction. Moreover, the temporal evolution of the micelle MSD excess in the perpendicular direction follows that of a nanoparticle near an elastic membrane. However, in the parallel direction, the MSD excess is rather similar to that of a nanoparticle near a liquid interface.

Cattaneo-Christov heat flow model at mixed impulse stagnation point past a Riga plate: Levenberg-Marquardt backpropagation method

Applications of artificial intelligence (AI) via soft computing procedures have attracted the attention of researchers due to their effective modeling, simulation procedures, and detailed analysis. In this article, the designing of intelligence computing through a neural network that is backpropagated with the Levenberg-Marquardt method (NN-BLMM) to study the Cattaneo-Christov heat flow model at the mixed impulse stagnation point (CCHFM-MISP) past a Riga plate is investigated. The original model CCHFM-MISP in terms of PDEs is converted into non-linear ODEs through suitable similarity variables. A data set is generated for all scenarios of CCHFM-MISP through Lobatto IIIA numerical solver by varying Hartman number, velocity ratio parameter, inverse Darcy number, mixed impulse variable, non-dimensional constraint, Eckert number, heat generation variable, Prandtl number, thermal relaxation variable. To find the physical impacts of parameters of interest associated with the presented fluidic system CCHFM-MISP, the approximate solution of NN-BLMM is carried out by performing training (80 %), testing (10 %), and validation (10 %), and then the results are equated with the reference data to ensure the perfection of the proposed model. Through MSE, state transition, error histogram, and regression analysis, the outcomes of NN-BLMM are presented and analyzed. The graphical illustration and numerical outcomes confirm the authentication and effectiveness of the solver. Moreover, mean square errors for validation, training and testing data points along with performance measures lie around 10-10 and the solution plots generated through deterministic (Lobatto IIIA) approach and stochastic numerical solver are matching up to 10-6, which surely validate the solver NN-BLMM. The outcomes of M and B on velocity present the similar impacts. The velocity of material particles decreases under D a while, it increases through velocity ratio and magnetic parameters.

Biodegradable Packaging Based on Poly(vinyl Alcohol) and Carboxymethyl Cellulose Films Incorporated with Ascorbic Acid for Food Packaging Applications

Petroleum-based plastics are used as packaging materials because of their low cost and high availability; however, continuous use of these nondegradable materials especially in the food industry has led to environmental pollution. The present study aimed to synthesize antibacterial and biodegradable films based on natural biopolymers carboxymethyl cellulose (CMC), poly(vinyl alcohol) (PVA), and ascorbic acid (AA) cross-linked in the presence of glutaraldehyde (GA). The films were synthesized in two different concentrations, 60PVA:40CMC:AA and 70PVA:30CMC:AA with a fixed amount of AA. Films with smooth texture and overall uniform thickness were obtained. Fourier transform infrared spectroscopy (FTIR) confirmed the cross-linking between the aldehyde group of GA and hydroxyl of PVA through detection of acetal and ether bridges. The synthesized films were thermally stable in the temperature range of 180-300 °C; however, 70PVA:30CMC:AA showed higher weight loss in this range as compared to the 60PVA:40CMC:AA film. Soil burial test demonstrated that the 60PVA:40CMC:AA film was more degradable (71% at day 15) as compared to the 70PVA:30CMC:AA film (65% at day 15). The films exhibited excellent antimicrobial activity against Gram-positive staphylococcus aureus(inhibition zone of 21 mm) and Gram-negative Escherichia coli (inhibition zone of 15 mm). In comparison, the 60PVA:40CMC:AA film showed better results in terms of high mechanical strength, uniform morphology, higher soil burial degradation, and lower water vapor transmission rate. Therefore, the prepared film could be used as a promising candidate in the food packaging industry.

Network of Extracellular Traps in the Pathogenesis of Sterile Chronic Inflammatory Diseases: Role of Oxidative Stress and Potential Clinical Applications

Significance: Extracellular traps (ETs) represent structured frameworks that comprised DNA embellished with histones and granular proteins extruded by immune cells in response to various stimuli. Immune cells contribute to adverse effects of chronic inflammation via ET generation, promoting the release of nuclear chromatin, reactive oxygen species (ROS), and bioactive proteins into the extracellular matrix. Recent Advances: The occurrence of ET formation has been documented across diverse immune cell types. The excessive production of ROS during the activation of these cells has the potential to initiate substantial DNA damage, culminating in chromosome decondensation. The inflammatory microenvironment fosters ROS and ET generation, impacting tissue microenvironment remodeling. Recent studies reveal ET involvement in sustaining persistent inflammation, promoting angiogenesis, and initiating thrombotic processes. Critical Issues: This review elucidates ET participation in chronic inflammatory disease etiology, detailing ROS-dependent and ROS-independent ET formation mechanisms and their contextual manifestations. It discusses diverse immune cell-derived ETs in the inflammatory milieu and their responses to therapies. Furthermore, the review emphasizes the significance of ETs as potential biomarkers and envisions prophylactic strategies against ET-associated chronic inflammation. Future Directions: Subsequent investigations are warranted to uncover the intricate mechanisms governing the resolution of inflammation through ETs in normal physiological processes. Moreover, a comprehensive understanding of the aberrant pathways driving ET formation in persistent inflammation is imperative. Prospective research endeavors should focus on executing expansive clinical studies to discern the involvement of ETs in both the diagnostic and prognostic facets of inflammatory diseases, thereby shedding light on their prospective utility as biomarkers.

Entropy analysis on EMHD 3D micropolar tri-hybrid nanofluid flow of solar radiative slendering sheet by a machine learning algorithm

The purpose of this paper is to analyze the heat transfer behavior of the electromagnetic 3D micropolar tri-hybrid nanofluid flow of a solar radiative slendering sheet with non-Fourier heat flux model. The conversion of solar radiation into thermal energy is an area of significant interest as the demand for renewable heat and power continues to grow. Due to their enhanced ability to promote heat transmission, nanofluids can significantly contribute to enhancing the efficiency of solar-thermal systems. The combination of silicon oil-based silicon (Si), magnesium oxide (MgO), and titanium (Ti) nanofluids has attracted attention for their ability to improve the performance of solar-thermal systems. The present study discloses a new approach for intelligent numerical computing solving, which utilizes an MLP feed-forward back-propagation ANN and the Levenberg-Marquard algorithm. The collection of data was conducted for the purpose of testing, certifying, and training the ANN model. The Bvp4c solver in MATLAB is utilized to solve the nonlinear equations governing the momentum, temperature, skin-friction coefficient, and Nusselt number. The characteristics of numerous dimensionless parameters such as porosity parameter [Formula: see text], vortex viscosity parameter [Formula: see text], electric field parameter [Formula: see text], thermal relaxation time [Formula: see text], heat source/sink parameter, [Formula: see text] thermal radiation parameter [Formula: see text], temperature ratio parameter [Formula: see text],nanoparticle volume fraction [Formula: see text] on Si + MgO + Ti/silicon oil micropolar tri-hybrid nanofluida are analyzed. The ANN model engages in a process of data selection, network construction, training, and evaluation of its effectiveness through the utilization of mean square error. Tables and graphs are used to show how essential parameters affect fluid transport properties. The velocity profile is decreased by higher values of the porosity parameter, whereas the temperature profile is increased. The temperature profile is inversely proportional to higher values of the electric field parameter. The micro-rotation profiles reduced by expanding values vortex viscosity parameter. It has been determined that entropy generation and Bejan number intensifications for enlarged nanoparticle volume fraction.

Synthesis and characterization of mesoporous bioactive glass nanoparticles loaded with peganum harmala for bone tissue engineering

Globally, there is an increase in a number of bone disorders including osteoarthritis (OA), osteomyelitis, bone cancer, and etc., which has led to a demand for bone tissue regeneration. In order to take use of the osteogenic potential of natural herbs, mesoporous bioactive glass nanoparticles (MBGNs) have the ability to deliver therapeutically active chemicals locally. MBGNs influence bioactivity and osteointegration of materials making them suitable for bone tissue engineering (BTE). In the present study, we developed Peganum Harmala (P. harmala) loaded MBGNs (PH-MBGNs) synthesized via modified Stöber process. The MBGNs were analyzed in terms of surface morphology, chemical make-up, amorphous nature, chemical interaction, pore size, and surface area before and after loading with P. harmala. A burst release of drug from PH-MBGNs was observed within 8 h immersion in phosphate buffer saline (PBS). PH-MBGNs effectively prevented Staphylococcus aureus (S. aureus) and Escherichia coli (E. coli) from spreading. Furthermore, PH-MBGNs developed a hydroxyapatite (HA) layer in the presence of simulated body fluid (SBF) after 21 days, which confirmed the in-vitro bioactivity of MBGNs. In conclusion, PH-MBGNs synthesized in this work are potential candidate for scaffolding or a constituent in the coatings for BTE applications.

Effect and mechanism of nano-materials on plant resistance to cadmium toxicity: A review

Cadmium (Cd), one of the most toxic heavy metals, has been extensively studied by environmental scientists because of its detrimental effects on plants, animals, and humans. Increased industrial activity has led to environmental contamination with Cd. Cadmium can enter the food chain and pose a potential human health risk. Therefore, reducing the accumulation of Cd in plant species and enhancing their detoxification abilities are crucial for remediating heavy metal pollution in contaminated areas. One innovative technique is nano-phytoremediation, which employs nanomaterials ranging from 1 to 100 nm in size to mitigate the accumulation and detrimental effects of Cd on plants. Although extensive research has been conducted on using nanomaterials to mitigate Cd toxicity in plants, it is important to note that the mechanism of action varies depending on factors such as plant species, level of Cd concentration, and type of nanomaterials employed. This review aimed to consolidate and organize existing data, providing a comprehensive overview of the effects and mechanisms of nanomaterials in enhancing plant resistance to Cd. In particular, its deep excavation the mechanisms of detoxification heavy metals of nanomaterials by plants, including regulating Cd uptake and distribution, enhancing antioxidant capacity, regulating gene expression, and regulating physiological metabolism. In addition, this study provides insights into future research directions in this field.

Transfer learning for accurate fetal organ classification from ultrasound images: a potential tool for maternal healthcare providers

Ultrasound imaging is commonly used to aid in fetal development. It has the advantage of being real-time, low-cost, non-invasive, and easy to use. However, fetal organ detection is a challenging task for obstetricians, it depends on several factors, such as the position of the fetus, the habitus of the mother, and the imaging technique. In addition, image interpretation must be performed by a trained healthcare professional who can take into account all relevant clinical factors. Artificial intelligence is playing an increasingly important role in medical imaging and can help solve many of the challenges associated with fetal organ classification. In this paper, we propose a deep-learning model for automating fetal organ classification from ultrasound images. We trained and tested the model on a dataset of fetal ultrasound images, including two datasets from different regions, and recorded them with different machines to ensure the effective detection of fetal organs. We performed a training process on a labeled dataset with annotations for fetal organs such as the brain, abdomen, femur, and thorax, as well as the maternal cervical part. The model was trained to detect these organs from fetal ultrasound images using a deep convolutional neural network architecture. Following the training process, the model, DenseNet169, was assessed on a separate test dataset. The results were promising, with an accuracy of 99.84%, which is an impressive result. The F1 score was 99.84% and the AUC was 98.95%. Our study showed that the proposed model outperformed traditional methods that relied on the manual interpretation of ultrasound images by experienced clinicians. In addition, it also outperformed other deep learning-based methods that used different network architectures and training strategies. This study may contribute to the development of more accessible and effective maternal health services around the world and improve the health status of mothers and their newborns worldwide.

A study on the effect of bioactive glass and hydroxyapatite-loaded Xanthan dialdehyde-based composite coatings for potential orthopedic applications

The most important challenge faced in designing orthopedic devices is to control the leaching of ions from the substrate material, and to prevent biofilm formation. Accordingly, the surgical grade stainless steel (316L SS) was electrophoretically deposited with functional composition of biopolymers and bioceramics. The composite coating consisted of: Bioglass (BG), hydroxyapatite (HA), and lawsone, that were loaded into a polymeric matrix of Xanthan Dialdehyde/Chondroitin Sulfate (XDA/CS). The parameters and final composition for electrophoretic deposition were optimized through trial-and-error approach. The composite coating exhibited significant adhesion strength of "4B" (ASTM D3359) with the substrate, suitable wettability of contact angle 48°, and an optimum average surface roughness of 0.32 µm. Thus, promoting proliferation and attachment of bone-forming cells, transcription factors, and proteins. Fourier transformed infrared spectroscopic analysis revealed a strong polymeric network formation between XDA and CS. scanning electron microscopy and energy dispersive X-ray spectroscopy analysis displayed a homogenous surface with invariable dispersion of HA and BG particles. The adhesion, hydrant behavior, and topography of said coatings was optimal to design orthopedic implant devices. The said coatings exhibited a clear inhibition zone of 21.65 mm and 21.04 mm with no bacterial growth against Staphylococcus aureus (S. Aureus) and Escherichia coli (E. Coli) respectively, confirming the antibacterial potential. Furthermore, the crystals related to calcium (Ca) and HA were seen after 28 days of submersion in simulated body fluid. The corrosion current density, of the above-mentioned coating was minimal as compared to the bare 316L SS substrate. The results infer that XDA/CS/BG/HA/lawsone based composite coating can be a candidate to design coatings for orthopedic implant devices.

EEG-based epileptic seizure detection using binary dragonfly algorithm and deep neural network

Electroencephalogram (EEG) is one of the most common methods used for seizure detection as it records the electrical activity of the brain. Symmetry and asymmetry of EEG signals can be used as indicators of epileptic seizures. Normally, EEG signals are symmetrical in nature, with similar patterns on both sides of the brain. However, during a seizure, there may be a sudden increase in the electrical activity in one hemisphere of the brain, causing asymmetry in the EEG signal. In patients with epilepsy, interictal EEG may show asymmetric spikes or sharp waves, indicating the presence of epileptic activity. Therefore, the detection of symmetry/asymmetry in EEG signals can be used as a useful tool in the diagnosis and management of epilepsy. However, it should be noted that EEG findings should always be interpreted in conjunction with the patient's clinical history and other diagnostic tests. In this paper, we propose an EEG-based improved automatic seizure detection system using a Deep neural network (DNN) and Binary dragonfly algorithm (BDFA). The DNN model learns the characteristics of the EEG signals through nine different statistical and Hjorth parameters extracted from various levels of decomposed signals obtained by using the Stationary Wavelet Transform. Next, the extracted features were reduced using the BDFA which helps to train DNN faster and improve its performance. The results show that the extracted features help to differentiate the normal, interictal, and ictal signals effectively with 100% accuracy, sensitivity, specificity, and F1 score with a 13% selected feature subset when compared to the existing approaches.

Crosstalk between Non-Coding RNAs and Wnt/β-Catenin Signaling in Head and Neck Cancer: Identification of Novel Biomarkers and Therapeutic Agents

Head and neck cancers (HNC) encompass a broad spectrum of neoplastic disorders characterized by significant morbidity and mortality. While contemporary therapeutic interventions offer promise, challenges persist due to tumor recurrence and metastasis. Central to HNC pathogenesis is the aberration in numerous signaling cascades. Prominently, the Wnt signaling pathway has been critically implicated in the etiology of HNC, as supported by a plethora of research. Equally important, variations in the expression of non-coding RNAs (ncRNAs) have been identified to modulate key cancer phenotypes such as cellular proliferation, epithelial-mesenchymal transition, metastatic potential, recurrence, and treatment resistance. This review aims to provide an exhaustive insight into the multifaceted influence of ncRNAs on HNC, with specific emphasis on their interactions with the Wnt/β-catenin (WBC) signaling axis. We further delineate the effect of ncRNAs in either exacerbating or attenuating HNC progression via interference with WBC signaling. An overview of the mechanisms underlying the interplay between ncRNAs and WBC signaling is also presented. In addition, we described the potential of various ncRNAs in enhancing the efficacy of chemotherapeutic and radiotherapeutic modalities. In summary, this assessment posits the potential of ncRNAs as therapeutic agents targeting the WBC signaling pathway in HNC management.

Ten golden rules for optimal antibiotic use in hospital settings: the WARNING call to action

Antibiotics are recognized widely for their benefits when used appropriately. However, they are often used inappropriately despite the importance of responsible use within good clinical practice. Effective antibiotic treatment is an essential component of universal healthcare, and it is a global responsibility to ensure appropriate use. Currently, pharmaceutical companies have little incentive to develop new antibiotics due to scientific, regulatory, and financial barriers, further emphasizing the importance of appropriate antibiotic use. To address this issue, the Global Alliance for Infections in Surgery established an international multidisciplinary task force of 295 experts from 115 countries with different backgrounds. The task force developed a position statement called WARNING (Worldwide Antimicrobial Resistance National/International Network Group) aimed at raising awareness of antimicrobial resistance and improving antibiotic prescribing practices worldwide. The statement outlined is 10 axioms, or "golden rules," for the appropriate use of antibiotics that all healthcare workers should consistently adhere in clinical practice.

Development of a mobile-based intelligent module for identification and tracking of household appliances

Every year manufacturers of household appliances improve their devices, trying to make everyday life easier for users. New smart devices have many useful features, but not all users can easily cope with the complexity of the devices. One of the main tasks of household appliance manufacturers is to ensure the convenience of using appliances, taking into account the increasing complexity. Therefore, any manufacturer supplies equipment with a short but useful instruction manual. Practice shows that no printed user manual can compare with a demonstration of the device operation by a professional consultant. Instructions for home appliances using augmented reality technology will allow users to get the necessary detailed information about the device in a short period of time. As part of this work, the task of developing an artificial intelligence-based module is being solved. This module consists of developed classification, matching, and tracking submodules that can provide simple and fast visual instructions to users of household appliances in real time. The identification of household appliances is performed with more than 0.9 accuracy, and the tracking inside an unidentified object using the camera of a mobile device is processed with the success score of about 0.68 and frames per second (FPS) about 7. Mobile applications based on the proposed intelligent modules for Android and iOS were developed.

Improving diagnosis accuracy with an intelligent image retrieval system for lung pathologies detection: a features extractor approach

Detecting lung pathologies is critical for precise medical diagnosis. In the realm of diagnostic methods, various approaches, including imaging tests, physical examinations, and laboratory tests, contribute to this process. Of particular note, imaging techniques like X-rays, CT scans, and MRI scans play a pivotal role in identifying lung pathologies with their non-invasive insights. Deep learning, a subset of artificial intelligence, holds significant promise in revolutionizing the detection and diagnosis of lung pathologies. By leveraging expansive datasets, deep learning algorithms autonomously discern intricate patterns and features within medical images, such as chest X-rays and CT scans. These algorithms exhibit an exceptional capacity to recognize subtle markers indicative of lung diseases. Yet, while their potential is evident, inherent limitations persist. The demand for abundant labeled data during training and the susceptibility to data biases challenge their accuracy. To address these formidable challenges, this research introduces a tailored computer-assisted system designed for the automatic retrieval of annotated medical images that share similar content. At its core lies an intelligent deep learning-based features extractor, adept at simplifying the retrieval of analogous images from an extensive chest radiograph database. The crux of our innovation rests upon the fusion of YOLOv5 and EfficientNet within the features extractor module. This strategic fusion synergizes YOLOv5's rapid and efficient object detection capabilities with EfficientNet's proficiency in combating noisy predictions. The result is a distinctive amalgamation that redefines the efficiency and accuracy of features extraction. Through rigorous experimentation conducted on an extensive and diverse dataset, our proposed solution decisively surpasses conventional methodologies. The model's achievement of a mean average precision of 0.488 with a threshold of 0.9 stands as a testament to its effectiveness, overshadowing the results of YOLOv5 + ResNet and EfficientDet, which achieved 0.234 and 0.257 respectively. Furthermore, our model demonstrates a marked precision improvement, attaining a value of 0.864 across all pathologies-a noteworthy leap of approximately 0.352 compared to YOLOv5 + ResNet and EfficientDet. This research presents a significant stride toward enhancing radiologists' workflow efficiency, offering a refined and proficient tool for retrieving analogous annotated medical images.

Exosomal noncoding RNA-mediated spatiotemporal regulation of lipid metabolism: Implications in immune evasion and chronic inflammation

The hallmark of chronic inflammatory diseases is immune evasion. Successful immune evasion involves numerous mechanisms to suppress both adaptive and innate immune responses. Either direct contact between cells or paracrine signaling triggers these responses. Exosomes are critical drivers of these interactions and exhibit both immunogenic and immune evasion properties during the development and progression of various chronic inflammatory diseases. Exosomes carry diverse molecular cargo, including lipids, proteins, and RNAs that are crucial for immunomodulation. Moreover, recent studies have revealed that exosomes and their cargo-loaded molecules are extensively involved in lipid remodeling and metabolism during immune surveillance and disease. Many studies have also shown the involvement of lipids in controlling immune cell activities and their crucial upstream functions in regulating inflammasome activation, suggesting that any perturbation in lipid metabolism results in abnormal immune responses. Strikingly, the expanded immunometabolic reprogramming capacities of exosomes and their contents provided insights into the novel mechanisms behind the prophylaxis of inflammatory diseases. By summarizing the tremendous therapeutic potential of exosomes, this review emphasizes the role of exosome-derived noncoding RNAs in regulating immune responses through the modulation of lipid metabolism and their promising therapeutic applications.

Use of biogenic silver nanoparticles on the cathode to improve bioelectricity production in microbial fuel cells

To date, research on microbial fuel cells (MFCs) has. focused on the production of cost-effective, high-performance electrodes and catalysts. The present study focuses on the synthesis of silver nanoparticles (AgNPs) by Pseudomonas sp. and evaluates their role as an oxygen reduction reaction (ORR) catalyst in an MFC. Biogenic AgNPs were synthesized from Pseudomonas aeruginosa via facile hydrothermal synthesis. The physiochemical characterization of the biogenic AgNPs was conducted via scanning electron microscopy (SEM), X-ray diffraction (XRD), and UV-visible spectrum analysis. SEM micrographs showed a spherical cluster of AgNPs of 20-100 nm in size. The oxygen reduction reaction (ORR) ability of the biogenic AgNPs was studied using cyclic voltammetry (CV). The oxygen reduction peaks were observed at 0.43 V, 0.42 V, 0.410 V, and 0.39 V. Different concentrations of biogenic AgNPs (0.25-1.0 mg/cm2) were used as ORR catalysts at the cathode in the MFC. A steady increase in the power production was observed with increasing concentrations of biogenic AgNPs. Biogenic AgNPs loaded with 1.0 mg/cm2 exhibited the highest power density (PDmax) of 4.70 W/m3, which was approximately 26.30% higher than the PDmax of the sample loaded with 0.25 mg/cm2. The highest COD removal and Coulombic efficiency (CE) were also observed in biogenic AgNPs loaded with 1.0 mg/cm2 (83.8% and 11.7%, respectively). However, the opposite trend was observed in the internal resistance of the MFC. The lowest internal resistance was observed in a 1.0 mg/cm2 loading (87 Ω), which is attributed to the high oxygen reduction kinetics at the surface of the cathode by the biogenic AgNPs. The results of this study conclude that biogenic AgNPs are a cost-effective, high-performance ORR catalyst in MFCs.

Enhanced brain tumor classification using graph convolutional neural network architecture

The Brain Tumor presents a highly critical situation concerning the brain, characterized by the uncontrolled growth of an abnormal cell cluster. Early brain tumor detection is essential for accurate diagnosis and effective treatment planning. In this paper, a novel Convolutional Neural Network (CNN) based Graph Neural Network (GNN) model is proposed using the publicly available Brain Tumor dataset from Kaggle to predict whether a person has brain tumor or not and if yes then which type (Meningioma, Pituitary or Glioma). The objective of this research and the proposed models is to provide a solution to the non-consideration of non-Euclidean distances in image data and the inability of conventional models to learn on pixel similarity based upon the pixel proximity. To solve this problem, we have proposed a Graph based Convolutional Neural Network (GCNN) model and it is found that the proposed model solves the problem of considering non-Euclidean distances in images. We aimed at improving brain tumor detection and classification using a novel technique which combines GNN and a 26 layered CNN that takes in a Graph input pre-convolved using Graph Convolution operation. The objective of Graph Convolution is to modify the node features (data linked to each node) by combining information from nearby nodes. A standard pre-computed Adjacency matrix is used, and the input graphs were updated as the averaged sum of local neighbor nodes, which carry the regional information about the tumor. These modified graphs are given as the input matrices to a standard 26 layered CNN with Batch Normalization and Dropout layers intact. Five different networks namely Net-0, Net-1, Net-2, Net-3 and Net-4 are proposed, and it is found that Net-2 outperformed the other networks namely Net-0, Net-1, Net-3 and Net-4. The highest accuracy achieved was 95.01% by Net-2. With its current effectiveness, the model we propose represents a critical alternative for the statistical detection of brain tumors in patients who are suspected of having one.