Ultrasound wave assisted removal of Ceftriaxone sodium in aqueous media with novel nano composite g-C3N4/MWCNT/Bi2WO6 based on CCD-RSM model

Optimization of Protease Treatment Conditions for Chlorella pyrenoidosa Protein Extraction and Investigation of Its Potential as an Alternative Protein Source

This study aimed to determine enzymes that effectively extract Chlorella pyrenoidosa proteins and optimize the processing conditions using response surface methods. Furthermore, the potential of enzymatically hydrolyzed C. pyrenoidosa protein extract (CPE) as a substitute protein source was investigated. The enzymatic hydrolysis conditions for protein extraction were optimized using single-factor analysis and a response surface methodology-Box-Behnken design. The R2 value of the optimized model was 0.9270, indicating the reliability of the model, and the optimal conditions were as follows: a hydrolysis temperature of 45.56 °C, pH 9.1, and a hydrolysis time of 49.85 min. The amino acid composition of CPE was compared to that of C. pyrenoidosa powder (CP), which was found to have a higher content of essential amino acids (EAA). The electrophoretic profiles of CP and CPE confirmed that CPE has a low molecular weight. Furthermore, CPE showed higher antioxidant activity and phenol content than CP, with ABTS and DPPH radical scavenging abilities of 69.40 ± 1.61% and 19.27 ± 3.16%, respectively. CPE had high EAA content, antioxidant activity, and phenol content, indicating its potential as an alternative protein source. Overall, in this study, we developed an innovative, ecofriendly, and gentle enzymatic hydrolysis strategy for the extraction and refinement of Chlorella proteins.

A state-of-the-art analysis of pharmacological delivery and artificial intelligence techniques for inner ear disease treatment

Over the last several decades, both the academic and therapeutic fields have seen significant progress in the delivery of drugs to the inner ear due to recent delivery methods established for the systemic administration of drugs in inner ear treatment. Novel technologies such as nanoparticles and hydrogels are being investigated, in addition to the traditional treatment methods. Intracochlear devices, which utilize current developments in microsystems technology, are on the horizon of inner ear drug delivery methods and are designed to provide medicine directly into the inner ear. These devices are used for stem cell treatment, RNA interference, and the delivery of neurotrophic factors and steroids during cochlear implantation. An in-depth analysis of artificial neural networks (ANNs) in pharmaceutical research may be found in ANNs for Drug Delivery, Design, and Disposition. This prediction tool has a great deal of promise to assist researchers in more successfully designing, developing, and delivering successful medications because of its capacity to learn and self-correct in a very complicated environment. ANN achieved a high level of accuracy exceeding 0.90, along with a sensitivity of 95% and a specificity of 100%, in accurately distinguishing illness. Additionally, the ANN model provided nearly perfect measures of 0.99%. Nanoparticles exhibit potential as a viable therapeutic approach for bacterial infections that are challenging to manage, such as otitis media. The utilization of ANNs has the potential to enhance the effectiveness of nanoparticle therapy, particularly in the realm of automated identification of otitis media. Polymeric nanoparticles have demonstrated effectiveness in the treatment of prevalent bacterial infections in pediatric patients, suggesting significant potential for forthcoming therapeutic interventions. Finally, this study is based on a research of how inner ear diseases have been treated in the last ten years (2012-2022) using machine learning.

Application of nanotechnology in breast cancer screening under obstetrics and gynecology through the use of CNN and ANFIS

Breast cancer is the leading reason of death among women aged 35 to 54. Breast cancer diagnosis still presents significant challenges, and preventing the disease's most severe symptoms requires early detection. The role of nanotechnology in the tumor-treatment has recently attracted a lot of interest. In cancer therapies, nanotechnology plays a major role in the medication distribution process. Nanoparticles have the ability to target tumors. Nanoparticles are favorable and maybe preferable for usage in tumor detection and imaging due to their incredibly small size. Quantum dots, semiconductor crystals with increased labeling and imaging capabilities for cancer cells, are one of the particles that have received the most research attention. The design of the research is cross-sectional and descriptive. From April through September of 2020, data were gathered at the State Hospital. All pregnant women who came to the hospital throughout the first and second trimesters of the research's data collection were included in the study population. 100 pregnant women between the ages of 20 and 40 who had not yet had a mammogram comprised the research sample. 1100 digitized mammography images are included in the dataset, which was obtained from a hospital. Convolutional neural networks (CNN) were used to scan all images, and breast masses and mass comparisons were made using the malignant-benign categorization. The adaptive neuro-fuzzy inference system (ANFIS) then examined all of the data obtained by CNN in order to identify breast cancer early using inputs based on the nine different inputs. The precision of the mechanism used in this technique to determine the ideal radius value is significantly impacted by the radius value. Nine variables that define breast cancer indicators were utilized as inputs to the ANFIS classifier, which was then used to identify breast cancer. The parameters were given the necessary fuzzy functions, and the combined dataset was applied to train the method. Testing was initially performed by 30% of dataset that was later done with the real data obtained from the hospital. The accuracy of the results for 30% data was 84% (specificity =72.7%, sensitivity =86.7%) and the results for the real data was 89.8% (sensitivity =82.3%, specificity =75.9%), respectively.

Multifunctional ternary ZnMgFe LDH as an efficient adsorbent for ceftriaxone sodium and antimicrobial agent: sustainability of adsorption waste as a catalyst for methanol electro-oxidation

In order to achieve sustainable benefits for the adsorption of wastewater pollutants, spent adsorbents need to be recycled and/or valorized. This work studied a two-dimensional (2D) ZnMgFe layered double hydroxide (LDH) for ceftriaxone sodium (CTX) adsorption. This LDH showed a crystallite size of 9.8 nm, a BET surface area of 367.59 m2 g-1, and a micro-sphere-like morphology. The factors investigated in this study were the adsorbent dose, initial concentration, initial pH, and contact time. ZnMgFe LDH showed 99% removal of CTX with a maximum adsorption capacity of 241.75 mg g-1 at pH = 5. The Dubinin-Radushkevich model was found to be the most adequate isotherm model. The spent adsorbent (ZnMgFe LDH/CTX) was reused as an electro-oxidation catalyst for direct methanol fuel cells. ZnMgFe LDH/CTX showed almost a 10-fold increase in electrochemical activity for all scan rates compared to bare ZnMgFe LDH in 1 M KOH. As methanol concentration increases, the maximum current density generated by both the ZnMgFe LDH and ZnMgFe LDH/CTX samples increases. Moreover, the maximum current density for ZnMgFe LDH/CTX was 47 mA cm-2 at a methanol concentration of 3 M. Both samples possess reasonable stability over a 3600 S time window with no significant deterioration of electrochemical performance. Moreover, the antimicrobial studies showed that ZnMgFe LDH had a significant antifungal (especially Aspergillus, Mucor, and Penicillium species) and antibacterial (with greater action against Gram-positive than negative) impact on several severe infectious diseases, including Aspergillus. This study paves the way for the reuse and valorization of selected adsorbents toward circular economy requirements.

Nanocatalysts as fast and powerful medical intervention: Bridging cochlear implant therapies and advanced modelling using Hidden Markov Models (HMMs) for effective treatment of infections

As human population growth and waste from technologically advanced industries threaten to destabilise our delicate ecological equilibrium, the global spotlight intensifies on environmental contamination and climate-related changes. These challenges extend beyond our external environment and have significant effects on our internal ecosystems. The inner ear, which is responsible for balance and auditory perception, is a prime example. When these sensory mechanisms are impaired, disorders such as deafness can develop. Traditional treatment methods, including systemic antibiotics, are frequently ineffective due to inadequate inner ear penetration. Conventional techniques for administering substances to the inner ear fail to obtain adequate concentrations as well. In this context, cochlear implants laden with nanocatalysts emerge as a promising strategy for the targeted treatment of inner ear infections. Coated with biocompatible nanoparticles containing specific nanocatalysts, these implants can degrade or neutralise contaminants linked to inner ear infections. This method enables the controlled release of nanocatalysts directly at the infection site, thereby maximising therapeutic efficacy and minimising adverse effects. In vivo and in vitro studies have demonstrated that these implants are effective at eliminating infections, reducing inflammation, and fostering tissue regeneration in the ear. This study investigates the application of hidden Markov models (HMMs) to nanocatalyst-loaded cochlear implants. The HMM is trained on surgical phases in order to accurately identify the various phases associated with implant utilisation. This facilitates the precision placement of surgical instruments within the ear, with a location accuracy between 91% and 95% and a standard deviation between 1% and 5% for both sites. In conclusion, nanocatalysts serve as potent medicinal instruments, bridging cochlear implant therapies and advanced modelling utilising hidden Markov models for the effective treatment of inner ear infections. Cochlear implants loaded with nanocatalysts offer a promising method to combat inner ear infections and enhance patient outcomes by addressing the limitations of conventional treatments.

Green synthesis of Fe3O4 nanoparticles using Alliaceae waste (Allium sativum) for a sustainable landscape enhancement using support vector regression

The synthesis of metal nanoparticles using green chemistry methods has gained significant attention in the field of landscape enhancement. Researchers have paid close attention to the development of very effective green chemistry approaches for the production of metal nanoparticles (NPs). The primary goal is to create an environmentally sustainable technique for generating NPs. At the nanoscale, ferro- and ferrimagnetic minerals such as magnetite exhibit superparamagnetic properties (Fe3O4). Magnetic nanoparticles (NPs) have received increased interest in nanoscience and nanotechnology due to their physiochemical properties, small particle size (1-100 nm), and low toxicity. Biological resources such as bacteria, algae, fungus, and plants have been used to manufacture affordable, energy-efficient, non-toxic, and ecologically acceptable metallic NPs. Despite the growing demand for Fe3O4 nanoparticles in a variety of applications, typical chemical production processes can produce hazardous byproducts and trash, resulting in significant environmental implications. The purpose of this study is to look at the ability of Allium sativum, a member of the Alliaceae family recognized for its culinary and medicinal benefits, to synthesize Fe3O4 NPs. Extracts of Allium sativum seeds and cloves include reducing sugars like glucose, which may be used as decreasing factors in the production of Fe3O4 NPs to reduce the requirement for hazardous chemicals and increase sustainability. The analytic procedures were carried out utilizing machine learning as support vector regression (SVR). Furthermore, because Allium sativum is widely accessible and biocompatible, it is a safe and cost-effective material for the manufacture of Fe3O4 NPs. Using the regression indices metrics of root mean square error (RMSE) and coefficient of determination (R2), the X-ray diffraction (XRD) study revealed the lighter, smoother spherical forms of NPs in the presence of aqueous garlic extract and 70.223 nm in its absence. The antifungal activity of Fe3O4 NPs against Candida albicans was investigated using a disc diffusion technique but exhibited no impact at doses of 200, 400, and 600 ppm. This characterization of the nanoparticles helps in understanding their physical properties and provides insights into their potential applications in landscape enhancement.

Alginate@Fe3O4@Bentonite nanocomposite for formaldehyde removal from synthetic and real effluent: optimization by central composite design

In this study, Alginate@ Fe₃O₄/Bentonite nanocomposite was utilized to eliminate formaldehyde from wastewater. Structural features of bentonite, bentonite@Fe₃O₄, and Alginate@Fe₃O₄@Bentonite were determined using FT-IR, PXRD, Mapping, EDX, TEM, SEM, VSM, and BET analyses. The central composite design method was employed to find the optimal conditions for formaldehyde removal using Alg@Fe₃O₄@Bent nanocomposite. The maximum formaldehyde uptake efficiency (94.56%) was obtained at formaldehyde concentration of 10.69 ppm, the nanocomposite dose of 1.28 g/L, and pH of 9.96 after 16.53 min. Also, Alginate@Fe₃O₄@Bentonite composite was used to eliminate formaldehyde from Razi petrochemical wastewater and was able to eliminate 91.24% of formaldehyde, 70% of COD, and 68.9% of BOD₅. The isotherm and kinetic investigations demonstrated that the formaldehyde uptake process by the foresaid adsorbent follows the Langmuir isotherm and quasi-first-order kinetic models, respectively. Also, the maximum uptake capacity was obtained at 50.25 mg/g. Moreover, the formaldehyde uptake process by the aforementioned nanocomposite was exothermic and spontaneous. Furthermore, the formaldehyde adsorption efficiency decreased slightly after six reuse cycles (less than 10%), indicating that Alginate@Fe₃O₄@Bentonite nanocomposite has remarkable recyclability. Besides, the influence of interfering ions like nitrate, carbonate, chloride, phosphate, and sulfate was studied on the formaldehyde removal efficiency and the results displayed that all ions except nitrate ion have low interaction with formaldehyde (less than 3% reduction in removal efficiency).

Application of logistic regression, support vector machine and random forest on the effects of titanium dioxide nanoparticles using macroalgae in treatment of certain risk factors associated with kidney injuries

The use of titanium dioxide (TiO₂) nanoparticles in many biological and technical domains is on the rise. There hasn't been much research on the toxicity of titanium dioxide nanoparticles in biological systems, despite their ubiquitous usage. In the current investigation, samples were exposed to various dosages of TiO₂ nanoparticles for 4 days, 1 month, and 2 months following treatment. ICP-AES was used to dose TiO₂ into the tissues, and the results showed that the kidney had a significant TiO₂ buildup. On the other hand, apoptosis of renal tubular cells is one of the most frequent cellular processes contributing to kidney disease (KD). Nevertheless, the impact of macroalgal seaweed extract on KD remains undetermined. In this work, machine learning (ML) approaches have been applied to develop prediction algorithms for acute kidney injury (AKI) by use of titanium dioxide and macroalgae in hospitalized patients. Fifty patients with (AKI) and 50 patients (non-AKI group) have been admitted and considered. Regarding demographic data, and laboratory test data as input parameters, support vector machine (SVM), and random forest (RF) are utilized to build models of AKI prediction and compared to the predictive performance of logistic regression (LR). Due to its strong antioxidant and anti-inflammatory powers, the current research ruled out the potential of using G. oblongata red macro algae as a source for a variety of products for medicinal uses. Despite a high and fast processing of algorithms, logistic regression showed lower overfitting in comparison to SVM, and Random Forest. The dataset is subjected to algorithms, and the categorization of potential risk variables yields the best results. AKI samples showed significant organ defects than non-AKI ones. Multivariate LR indicated that lymphocyte, and myoglobin (MB) ≥ 1000 ng/ml were independent risk parameters for AKI samples. Also, GCS score (95% CI 1.4-8.3 P = 0.014) were the risk parameters for 60-day mortality in samples with AKI. Also, 90-day mortality in AKI patients was significantly high (P < 0.0001). In compared to the control group, there were no appreciable changes in the kidney/body weight ratio or body weight increases. Total thiol levels in kidney homogenate significantly decreased, and histopathological analysis confirmed these biochemical alterations. According to the results, oral TiO₂ NP treatment may cause kidney damage in experimental samples.

Application of nano remediation of mine polluted in acid mine drainage water using machine learning model

Acid mine drainage (AMD) is the term used to describe drainage from coal mines with high sulfur-bearing rocks. The oxidative weathering of metal sulfides leads to AMD. The acidic environment corrodes more harmful compounds in the soil, which is spread throughout the working area. One such significant metal is copper, which is extracted in massive quantities from ores rich in sulfide. A copper-extraction resin might be created by combining diatomaceous earth (DE) particles with polyethyleneimine (PEI), which is shown to have great selectivity and affinity for copper. In this effort, PEI-DE particles' copper absorption level was examined by using synthetic and actual acid mine drainage samples at varied pH values. The findings of the copper uptake particles have been examined through the Support Vector Machine (SVM) model. Using the n-fold 14 cross-validation approach, the quantities of parameters and C are estimated to be 0.001 and 0.01, respectively. The SVM analysis was correct, and the findings indicated that copper could bind to the material efficiently and preferentially at pH 4. Subsequent water elution studies at a pH value of 1 confirmed the pH-reliant interaction between dissolved Cu and PEI by demonstrating full release of the adsorbed Cu. In this research, the copper absorption of PEI-DE particles from synthetic and genuine AMD specimens was studied based on several pH conditions. The findings suggest that copper may attach to the material effectively and preferentially at pH 4. Studies of filtering water at pH1 later confirmed that all of the adsorbed Cu was released. This shows that the interaction between PEI and dissolved Cu depends on PH.

Waste Valorization of a Recycled ZnCoFe Mixed Metal Oxide/Ceftriaxone Waste Layered Nanoadsorbent for Further Dye Removal

Waste valorization of spent wastewater nanoadsorbents is a promising technique to support the circular economy strategies. The terrible rise of heavy metal pollution in the environment is considered a serious threat to the terrestrial and aquatic environment. This led to the necessity of developing cost-effective, operation-convenient, and recyclable adsorbents. ZnCoFe mixed metal oxide (MMO) was synthesized using co-precipitation. The sample was characterized using X-ray powder diffraction, Fourier transform infrared spectroscopy, and scanning electron microscopy. Factors affecting the adsorption process such as pH, the dose of adsorbent, and time were investigated. ZnCoFe MMO showed the maximum adsorption capacity of 118.45 mg/g for ceftriaxone sodium. The spent MMO was recycled as an adsorbent for malachite green (MG) removal. Interestingly, the spent adsorbent showed 94% removal percent for MG as compared to the fresh MMO (90%). The kinetic investigation of the adsorption process was performed and discussed. In addition, ZnCoFe MMO was tested as an antimicrobial agent. The proposed approach opens up a new avenue for recycling wastes after adsorption into value-added materials for utilization in adsorbent production with excellent performance as antimicrobial agents.

Enhanced artificial intelligence for electrochemical sensors in monitoring and removing of azo dyes and food colorant substances

It is necessary to determine whether synthetic dyes are present in food since their excessive use has detrimental effects on human health. For the simultaneous assessment of tartrazine and Patent Blue V, a novel electrochemical sensing platform was developed. As a result, two artificial azo colorants (Tartrazine and Patent Blue V) with toxic azo groups (-NN-) and other carcinogenic aromatic ring structures were examined. With a low limit of detection of 0.06 μM, a broad linear concentration range 0.09μM to 950μM, and a respectable recovery, scanning electron microscopy (SEM) was able to reveal the excellent sensing performance of the suggested electrode for patent blue V. The electrochemical performance of an electrode can be characterized using cyclic and differential pulse voltammetry, and electrochemical impedance spectroscopy. Moreover, the classification model was created by applying binary classification assessment using enhanced artificial intelligence comprises of support vector machine (SVM) and Genetic Algorithm (GA), respectively, a support vector machine and a genetic algorithm, which was then validated using the 50 dyes test set. The best binary logistic regression model has an accuracy of 83.2% and 81.1%, respectively, while the best SVM model has an accuracy of 90.3% for the training group of samples and 81.1% for the test group (RMSE = 0.644, R2 = 0.873, C = 205.41, and = 5.992). According to the findings, Cu-BTC MOF (copper (II)-benzene-1,3,5-tricarboxylate) has a crystal structure and is tightly packed with hierarchically porous nanomaterials, with each particle's edge measuring between 20 and 37 nm. The suggested electrochemical sensor's analytical performance is suitable for foods like jellies, condiments, soft drinks and candies.

A Review of Methods for Removal of Ceftriaxone from Wastewater

The presence of pharmaceuticals in surface water and wastewater poses a threat to public health and has significant effects on the ecosystem. Since most wastewater treatment plants are ineffective at removing molecules efficiently, some pharmaceuticals enter aquatic ecosystems, thus creating issues such as antibiotic resistance and toxicity. This review summarizes the methods used for the removal of ceftriaxone antibiotics from aquatic environments. Ceftriaxone is one of the most commonly prescribed antibiotics in many countries, including Tanzania. Ceftriaxone has been reported to be less or not degraded in traditional wastewater treatment of domestic sewage. This has piqued the interest of researchers in the monitoring and removal of ceftriaxone from wastewater. Its removal from aqueous systems has been studied using a variety of methods which include physical, biological, and chemical processes. As a result, information about ceftriaxone has been gathered from many sources with the searched themes being ceftriaxone in wastewater, ceftriaxone analysis, and ceftriaxone removal or degradation. The methods studied have been highlighted and the opportunities for future research have been described.

Sensitivity Analysis with the Monte Carlo Method and Prediction of Atenolol Removal Using Modified Multiwalled Carbon Nanotubes Based on the Response Surface Method: Isotherm and Kinetics Studies

Atenolol (ATN) is a β-blocker drug extensively used to treat arrhythmias and high blood pressure. Because the human body cannot metabolize it completely, this drug has been commonly found in many environmental matrices. In the present study, the response surface method (RSM) was used for adsorption prediction of ATN on modified multiwalled carbon nanotubes (M-MWCNTs) by NaOCl and ultrasonic. The sensitivity analysis was done by the Monte Carlo method. Sensitivity analysis was performed to determine the effective parameter by the Monte Carlo simulator. Statistical analysis of variance (ANOVA) was performed by using the nonlinear second-order model of RSM. The influential parameters including contact time (min), adsorbent dosage (g/L), pH, and the initial concentration (mg/L) of ATN were investigated, and optimal conditions were determined. Kinetic of ATN adsorption on M-MWCNTs was evaluated using pseudo-first, pseudo-second-order, and intraparticle diffusion models. Equilibrium isotherms for this system were analyzed by the ISOFIT software. As per our result, optimum conditions in the adsorption experiments were pH 7, 60 min of contact time, 0.5 mg/L ATN initial concentration, and 150 mg/L adsorbent dose. In terms of ATN removal efficiency, coefficients of R2 and adjusted R2 were 0.999 and 0.998, respectively. Sensitivity analysis also showed that contact time has the greatest effect on increasing the removal of ATN. Pseudo-first-order (R2 value of 0.99) was the best kinetic model for the adsorption of ATN, and for isotherm, BET (AICC value of 3.27) was the best model that fit the experimental data. According to the obtained results from sensitive analysis, time was the most important parameter, and after that, the adsorbent dose and pH affect positively on ATN removal efficiency. It can be concluded that the modified multiwalled carbon nanotubes can be applied as one of the best adsorbents to remove ATN from the aqueous solution.

Optimization and Evaluation of the Thermosensitive In Situ and Adhesive Gel for Rectal Delivery of Budesonide

Budesonide is a glucocorticoid for the treatment of ulcerative colitis (UC). The current study aims to develop a thermosensitive in situ and adhesive gel for rectal delivery of budesonide. HPMC K4M was selected as the adhesive agent based on the adhesive force and the effect on gel performance. The formulation of gel was optimized by using the central composite design-response surface methodology (CCD-RSM); a mathematical model was successfully developed to predict desired formulations as well as to analyze relationships between the amount of Pluronic F-127, Pluronic F-68, and HPMC K4M and the performances of gel. Based on CCD-RSM, a thermosensitive in situ and adhesive gel consisting of 0.002% budesonide, 0.74% HPMC, 4.87% F-68, and 19.0% F-127 was developed. Furthermore, the in vivo behavior of gel was evaluated in Sprague-Dawley rats. In comparison with budesonide solution, rectal administration of budesonide gel at 0.1 mg/kg in rats showed relative bioavailability of 230% with significant increase in rectum uptake.

Modelling and Optimizing Pyrene Removal from the Soil by Phytoremediation using Response Surface Methodology, Artificial Neural Networks, and Genetic Algorithm

This study aimed to model and optimize pyrene removal from the soil contaminated by sorghum bicolor plant using Response Surface Methodology (RSM) and Artificial Neural Network (ANN) with Genetic Algorithm (GA) approach. Here, the effects of indole acetic acid (IAA) and pseudomonas aeruginosa bacteria on increasing pyrene removal efficiency by phytoremediation process was studied. The experimental design was done using the Box-Behnken Design (BBD) technique. In the RSM model, the non-linear second-order model was in good agreement with the laboratory results. A two-layer Feed-Forward Back-Propagation Neural Network (FFBPNN) model was designed. Various training algorithms were evaluated and the Levenberg Marquardt (LM) algorithm was selected as the best one. Existence of eight neurons in the hidden layer leads to the highest R and lowest MSE and MAE. The results of the GA determined the optimum performance conditions. The results showed that using indole acetic acid and pseudomonas bacteria increased the efficiency of the sorghum plant in removing pyrene from the soil. The comparison obviously indicated that the prediction capability of the ANN model was much better than that of the RSM model.