Search

A series of nitroxanthone derivatives (1–6) were synthesized and evaluated for their potential efficacy against estrogen-receptor positive (MCF-7) and triple-negative breast cancer cell lines (MDA-MB-231). Cell viability assays identified compound 1 at 10 µM as the most promising candidate due to its potent growth inhibitory activity (22.05 ± 2.40 %) against the MCF-7 cell line. The half-maximal inhibitory concentration (IC50) of compound 1 was 7.00 ± 0.00 µM for MCF-7 cells, compared to 250.00 ± 70.71 µM for HaCaT and 800.00 ± 0.00 µM for RAW 264.7 cells, yielding selectivity indices (SI) of 35.71 and 114.29, respectively. Additionally, compound 1 exhibited mortality concentrations of 1736.58 µM and 3660.35 µM for zebrafish and brine shrimp embryos, with SI values of 522.91 and 248.08, respectively. Molecular docking analysis showed that compound 1 binds more efficiently to the target enzyme aromatase compared to other derivatives, likely due to its optimal number of nitro groups, orientations, and polarizabilities. Crystal structure analysis revealed that compound 1 crystallizes in the monoclinic system with the C2/c space group. In summary, compound 1 demonstrates selective toxicity towards tumor cells (MCF-7) while being non-toxic to normal cell lines (HaCaT and RAW 264.7) and in vivo studies with brine shrimp and zebrafish. These findings suggest that compound 1 holds promise as a lead compound to target breast cancer cells.

The wavy flow of generalized fluid namely, Cross fluid is mathematically formulated on a gravitationally affected vertical wavy surface with significant physical effects which is the main theme of this work. This particular dynamics of chemically reactive materials is further explored with an irreversible process. A significant heat transfer is measured through thermal resistance during the consideration of entropy optimization. Since entropy generation is associated with the mechanical system energy loss is more helpful in monitoring the entropy production in different engineering and industrial processes. Specifically, such irreversibility process appearance causing a noticeable low efficiency in the relevant practical sectors. For better performance in the minimization of friction on the wavy surface, flow is dissipated in terms of heat. However, the measure of loss in terms of work is introduced in the form of thermal radiation, viscous dissipation, magnetic field, and first-order chemical reaction. Additionally, the expected pressure drooping on the wavy surface is another goal of the entire study. The non-linear mathematical equations are determined by following the maximum viscosity approach. Relevant and important results are portrayed to show the various features of the newly developed work. The significant findings are approximated via one of the collocation methods while using MATLAB software. The Richardson number and buoyancy parameter enhanced the flow speed of the materials. The heat transfer rate is decreased by both Eckert number and wavy amplitude and the chemical reaction factor enhanced the mass transfer rate. The heat energy of the materials is escalated with the help of electromagnetic wave radiation and temperature ratio factor. Strengthening the chemical reaction factor declined the mass flow during the typical motion of the liquid. In the last, a comparison is provided to show the accuracy of the numerical approach.

This review paper discusses the latest advances in transfiguring Azadirachta indica (neem) biomass into an adsorbent and how it can be used to clean the environment. As an economical and environmentally favorable adsorbent material, neem biomass has become increasingly popular due to its bioactive properties and abundance. The review breaks down modification techniques into chemical and physical processes. Important physical changes covered include preactivation, carbonization, and using fluidized bed technologies and rotary kilns. It has been shown that these methods greatly improve the surface properties of neem biomass, which makes it better at absorbing things and holding more. The changes that were made also have an effect on the adsorption kinetics and isotherms, which helps us understand the adsorption rates and equilibrium behaviors of the changed adsorbents better. Neem biomass adsorbents are illustrated through their versatility and efficacy in the removal of organic pollutants, dyes, and heavy metals from effluent. This is illustrated through specific applications. Additionally, computational studies and artificial intelligence are looked into to see if they can help us understand how adsorption works at the molecular level, improve the efficiency of modification processes, and guess how adsorption will behave. The review also talks about the research gaps and suggests areas for future research. The review emphasizes the crucial importance of integrating experimental and computational methods to enhance the performance of the modified neem biomass and increase its environmental cleaning potential.

Bismuth based MOFs have appealed much curiosity in different catalytic processes due to their remarkable properties, which include their porous structure, less toxicity, abundance and high specific surface area. With their distributed active sites and constrained reaction regions, Bi based MOFs have a bright future as catalysts for extremely focused CO2 reduction by electrocatalysis reactions (ECO2RR). Formic acid (HCOOH), one of the byproducts of these processes, is notable because of its high economic worth. An extensive summary of Bi-MOFs and their derivatives used in ECO2RR and the photocatalytic reduction of CO2 into useful compounds is given in this review. Bi-MOFs synthesis methods for both electro and photocatalyst applications are discussed, along with an analysis of their unique benefits. Interestingly, a variety of Bi-MOFs and related offshoots are highlighted, including bimetallic catalysts and Bi-based MOF-derived nanocomposites. Bi-MOFs catalysts’ catalytic efficacy is demonstrated to be closely related to the MOF structure blocks-metal ions and organic linkers as well as particular circumstances controlling their derivatization. As a result, Bi-MOFs catalysts have a wide range of functions and provide the possibility of controlling the catalytic performance. This review describes the current obstacles in this area and makes recommendations for future research paths to advance the use of Bi-MOFs as electro- and photocatalysts.

This study applied a subtractive genomics approach to identify a potential drug target in the Porphyromonas gingivalis strain (ATCC BAA-308/W83). The aim was to characterize the whole proteome and hypothetical proteins (HPs) through structural, functional, and pathway predictions. The proteome was systematically reduced to identify essential proteins (EPs), non-homologous proteins (NHPs), and non-paralogous proteins (NPPs) while excluding those that were similar to the human proteome. Out of 1,836 proteins, the cluster database at high identity with tolerance algorithm identified 36 sequences as paralogous, having 80% identity. The resulting 1,827 proteins were compared to the human proteome using BLASTp (e-value 10−3), resulting in 1,427 NHPs. These were then aligned with the DEG database using BLASTp (e-value of 10−5), identifying 396 NHPs essential for pathogen survival. CELLO predicted the sub-cellular localization, and KEGG Automated Annotation Server identified potential metabolic pathways using a BLASTp similarity search of NHPs and EPs against the infrequently updated KEGG database. A total of 79 HPs essential for P. gingivalis were selected, and their molecular weights were determined. HPs were screened for metabolic pathway prediction, and the 3D structures of the proposed HPs were determined using homology modeling, and validation was performed. Only one HP (putative arginine deiminase) was qualified and found to be involved in the arginine and proline metabolic pathway.

The present work aimed to highlight an efficient, readily accessible, and cost-effective adsorbent derived from Dalbergia sissoo (DS) leaf powder for removing the environmentally hazardous dye “alizarin red S” (ARS) from hydrous medium. A variant of the adsorbent is activated via sulfuric acid and composited with magnetic iron oxide nanoparticles (DSMNC). Both adsorbents are thoroughly characterized using techniques such as Fourier transform infrared spectroscopy, point of zero charge, energy-dispersive X-ray spectroscopy, and scanning electron microscopy, which show that they have a porous structure rich in active sites. Different adsorption conditions are optimized with the maximum removal efficiency of 76.63% for DS and 97.89% for DSMNC. The study was highlighted via the application of various adsorption isotherms, including Freundlich, Langmuir, Temkin, and Dubinin–Radushkevich, to adsorption data. Pseudo-first-order, pseudo-second-order, and intra-particle diffusion models were utilized to investigate the kinetics and mechanism of adsorption. The Freundlich model and pseudo-second-order kinetics exhibited the best fit, suggesting a combination of physical interactions, as confirmed by the D–R and Temkin models. The dominant adsorbate–adsorbent interactive interactions responsible for ARS removal were hydrogen bonding, dispersion forces, and noncovalent aromatic ring adsorbent pi-interactions. Thermodynamic parameters extracted from adsorption data indicated that the removal of the mutagenic dye “ARS” was exothermic and spontaneous on both DS and DSMNC, with DSMNC exhibiting higher removal efficiency.

Abstract Various sets of thiazole, thiophene, and 2-pyridone ring structures containing a dimethylaniline component were synthesized. Substituted thiazoles 2–3 and thiophenes 5–7 were produced by reacting thiocarbamoyl compound 4 with α-halogenated reagents in different basic conditions. Also, a series of 2-pyridone derivatives 9a–f substituted with dimethylaniline was synthesized through Michael addition of malononitrile to α,β-unsaturated nitrile derivatives 8a–f. The synthesized products were structurally proven by spectroscopic methods such as IR, 1H NMR, 13C NMR, and MS data. Furthermore, the anti-cancer efficacy of the compounds was assessed using the MTT assay on two cell lines: hepatocellular carcinoma (HepG-2) and breast cancer (MDA-MB-231). The results showed the highest growth inhibition for derivatives 2, 6, 7, and 9c, which were further examined for their IC50 values. The IC50 for compound 2 showed equipotent activity (IC50 = 1.2 µM) against the HepG-2 cell line compared to Doxorubicin (IC50 = 1.1 µM). Compounds 2, 6, 7 and 9c showed very good ADME assessments for further drug administration. Moreover, the PASS theoretical prediction for the compounds showed high antimitotic and antineoplastic activities for compounds 2, 6, 7, and 9c, as well as potent inhibition activity for the insulysin enzyme (IDE). Molecular docking stimulations were performed on CDK1/CyclinB1/CKS2 (PDB ID: 4y72) and BPTI (PDB ID: 2ra3). When docked into (PDB ID: 4y72), all of the tested compounds showed considerable inhibition, and the 2-pyridone derivative 9d had the maximum binding affinity (− 8.1223 kcal/mol). While thiophene derivative 6 offered the maximum binding affinity (− 7.5094 kcal/mol) when docked into (PDB ID: 2ra3). Graphical Abstract

Models suggest that biomass burning causes thousands of premature deaths annually in Southeast Asia due to excessive exposure to particulate matter (PM) in smoke. However, measurements of surface air quality are sparse across the region, and consequently estimates for the public health impacts of seasonal biomass burning, are not well constrained. We use the nested GEOS-Chem model of chemistry and transport (horizontal resolution of 0.25°×0.3125°) to simulate atmospheric composition over Southeast Asia during the peak burning months of March and September in the moderate burning year of 2014. Model simulations with GEOS-Chem indicate that regional surface levels of PM2.5 (fine particulate matter with a diameter ≤2.5 µm) greatly exceed World Health Organization guidelines during the burning seasons, resulting in up to 10 000 premature deaths in a single month. However, the model substantially underestimates the regional aerosol burden compared to satellite observations of aerosol optical depth (AOD) (20 %–52 %) and ground-based observations of PM (up to 54 %), especially during the early burning season in March. We investigate potential uncertainties limiting the model representation of biomass burning aerosols and develop sensitivity simulations that improve model–measurement agreement in March (to within 31 %) and increase the estimated number of PM2.5-related premature deaths that month by almost half. Our modifications have a much smaller impact on the same metrics for September, but we find that this is due to canceling errors in the model. Compared to PM2.5 simulated directly with GEOS-Chem, PM2.5 derived from satellite AOD is less sensitive to model uncertainties and may provide a more accurate foundation for public health calculations in the short term, but continued investigation of uncertainties is still needed so that model analysis can be applied to support mitigation efforts. Further reduction of uncertainties can be achieved with the deployment of more aerosol measurements across Southeast Asia.

Abstract The oxidation of tetra‐n‐butylammonium acetate, propionate, and pivalate was studied in rigorously anhydrous acetonitrile by conventional linear sweep and convolution voltammetry (LSV and ConV, respectively). The results suggest oxidation occurs via a concerted dissociative electron transfer pathway (RCO2−→R⋅+CO2+e−). The addition of water lowers the intrinsic barrier, signaling a possible change in mechanism to stepwise dissociative electron transfer. In rigorously dry acetonitrile, the oxidation potentials of CH3CO2− (0.60±0.09), CH3CH2CO2−, (0.47±0.05) and (CH3)3CCO2− (0.40±0.04 V vs. Ag/AgNO3 (CH3CN, 0.1 M)) are reported. These values parallel the stabilities of the resulting free radicals, consistent with a possible concerted pathway, although differential solvation of the carboxylate anions cannot be completely excluded as a contributor to this trend.

In this investigation, CuO NPs, Cu-MnNCs, and Cu-Co NCs were synthesized by the sol gel method in the presence of the stabilizing agent polyvinyl alcohol (PVA). These nanoparticles were characterized by Fourier transform infrared spectroscopy (FTIR), scanning electron microscopy (SEM), and X-ray diffraction (XRD) techniques. The chemical structure and existence of bonding of PVA integration with nanoparticles were verified by the FTIR analysis. The SEM investigations revealed that the average particle sizes of CuO NPs, Cu-Mn NCs, and Cu-Co NCs were 64.5, 87.5, and 69.0 nm, respectively. Additionally, the XRD analysis supported their nano sizing.The antioxidant and enzyme inhibition activities were assessed against 2, 2-diphenyl-1-picryl-hydrazyl (DPPH) with IC50 values of 78.9, 67.8, and 60.8 g/ml, respectively. The antioxidant activities showed that they inhibited the effects of oxidative metabolites.The IC50 value is a quantitative measure that reveals the presence of certain inhibitory chemicals required to block the biological process in vitro .The biological component could be an enzyme, microorganism, or cell receptor. The enzyme inhibition activities of CuO NPs, Cu-Mn NCs, and Cu-Co NCs against urease were found to be 18.5, 23.7, and 34.5 uM, respectively. These characteristic properties suggested that these nanocomposites have biomedical applications. Moreover, they can be efficiently employed for therapeutic purposes.

Vapour generalized liquids hydrodynamics plays a pivotal role in the absorption columns or design of distillation using structured packing since they indomitable the fluid dynamics restrictions of the columns and control rates of mass balance which is the main purpose of this particular work. Particularly, a first attempt is made regarding this work to explore an investigation for time-dependent thin film flow of Cross nano-liquid over a moving surface. Additionally, some physical aspects during this illustration in terms of heat absorption/generation, thermal radiation, magnetic field, non-constant thermal conductivity, and activation energy with chemical reaction are studied. Some of the suitable similarity variables are introduced to convert the leading problem in the form of ordinary differential equations and then the transformed problem is approximated by one of the built-in collocation methods by using MATLAB. Very interesting results in terms of monotonic reduction in the film thickness for various uplifting values of unsteadiness and magnetic parameters and vice versa for the Wessenberg number are found. Moreover, temperature and concentration are concluded as enhancement functions of variable thermal conductivity and activation energy, respectively. The last comparative work is presented to validate the entire approach and where the method shows an excellent correlation with the previously published results.

Medical health records hold personal medical information and should only be accessed by authorized medical personnel or concerned patients. The importance of medical health records privacy is increasing as these records are shared in cloud environments. In this paper, we propose an enhanced system for securing patient data (Medical Health Records) embedded in multiple medical images in fragments for secure transmission and public sharing on the cloud or other environments. To protect the patient’s privacy, Medical Records are first encrypted, and then the ciphertext is broken into several fragments based on the number of multimodal medical images of a patient. A key generator randomly selects medical images from the multimodal image data to embed the encrypted patient health record segment using a modified least significant bit embedding process. The proposed technique enables an extra layer of security as even if files fall into the wrong hands and a fragment of the file is decrypted, it will not present any understandable information until all fragments from other medical images are extracted and combined in the correct order. The experiments are performed using multimodal 3255 MRI scans of 21 patients. The robustness of the proposed method was measured using different metrics such as PSNR, MSE, and SSIM. The results show that the proposed system is robust and that image quality is also maintained. To further study the stego image quality, a deep learning-based classification was applied to the images, and the results show that the diagnosis using stego medical images and performance remains unaffected even after embedding the encrypted data.

This study focuses on the synthesis of new nanostructured absorbent materials and removal of hazardous Rosaniline Hydrochloride (fuchsine) dye from polluted water. For this, Mg doped FeTiO nanocomposite (FTMO-NCs) was synthesized by co-precipitation method and thoroughly characterized to study its surface properties. The TEM image shows the FTMO-NCs are consists of crystalline particles sized in few nanometers of scale. The SAED pattern suggests the nanoscale polycrystalline nature of the FTMO-NCs. The homogeneously distributed MgO on the FeTiO-NCs further validates the spectra. Furthermore, the successful mixture of MgO with the FeTiO surface and the formation of FTMO-NCs are indicated by the well-defined elemental mapping of Fe, Ti, O, and Mg. Dye adsorption capacity, stability, and regeneration capability of the synthesized FTMO-NCs was evaluated by a series of experiments and varying parameters, such as, agitation time, nanocomposite mass, solution pH, dye concentration, etc. The pseudo-second-order kinetic model was found to be applicable based on the kinetic parameters calculated with a high correlation coefficient of 0.999. The findings indicate that the Langmuir isotherm is a better fit for the experimental data for the fuchsine adsorption on the FTMO-NPs in comparison to the other four models and shows the maximum adsorption capacity (Qmax) of 555.5 mg/g. Moreover, a double layer model was applied to study the statistical prediction of the favorability of the adsorption process of the dye on the nanocomposite. The Nm value for fuchsine adsorption was 550 mg/g, 255 mg/g, and 218 mg/g, respectively at 298 K, 308 K, and 318 K. The use of Mg-doped FeTiO nanocomposites (FTMO-NCs) for removing Rosaniline Hydrochloride (fuchsine) dye is a novel approach that brings together the unique properties of both materials. The incorporation of magnesium (Mg) into the FeTiO nanocomposite introduces new synergies and enhances the adsorption capabilities of the material. This innovation is particularly significant because Mg doping can influence the surface chemistry and electronic structure of the nanocomposite, leading to improved dye removal efficiency.

This work aims to study the influence of shock/ gradual annealing temperature on the properties of ZnO thin film with different film thickness. ZnO thin films have been prepared on glass substrates using a sol-gel method deposing by dip-coating technique. Zinc acetate dihydrate, monoethanolamine, and 2-methoxyethanol were used as starting materials, stabilizer, and solvent respectively. The X-ray diffraction patterns show that all films have the wurtzite structure and the (002) diffraction peak dominated in all of them. The ZnO films with a higher thickness and annealed under thermal shock, show higher peaks intensity and lower grain size compared to the films annealed by gradually increasing temperature. Scanning electron microscopy images confirmed the results of DRX. The best electrical and optical properties have been obtained for the samples with higher thicknesses and annealed by heat shock. ZnO thin films show a resistance value of 1,296 Ω.cm and a band gap value of 3.245 eV with high transmittance (>90%).

Abstract Synthetic strategy for the synthesis of thiophene 2-carboxamide derivatives substituted with hydroxyl, methyl and amino groups at position-3 was proposed. The strategy includes the cyclization of the precursor ethyl 2-arylazo-3-mercapto-3-(phenylamino)acrylate derivatives, 2-acetyl-2-arylazo-thioacetanilide derivatives and N-aryl-2-cyano-3-mercapto-3-(phenylamino)acrylamide derivatives with N-(4-acetylphenyl)-2-chloroacetamide in alcoholic sodium ethoxide. IR, 1H NMR, and mass spectroscopic analyses were used to characterize the synthesized derivatives. In addition, molecular, electronic properties of the synthesized products were studied by the density functional theory (DFT) where they exhibited close HOMO–LUMO energy gap (ΔEH-L) in which the amino derivatives 7a-c have the highest while the methyl derivatives 5a-c were the lowest. Using the ABTS method, the antioxidant properties of the produced compounds were evaluated, where amino thiophene-2-carboxamide 7a exhibit significant inhibition activity 62.0% compared to ascorbic acid The antibacterial activity against two pathogenic Gram-positive bacteria (Staphylococcus aureus and Bacillus subtilis) and two of pathogenic Gram-negative bacteria (Escherichia coli and Pseudomonas aeruginosa) revealed that 7b records the highest activity index compared to ampicillin 83.3, 82.6, 64.0, 86.9%, respectively. Furthermore, the thiophene-2-carboxamide derivatives were docked with five different proteins with the use molecular docking tools and the results explained interactions between amino acid residue of enzyme and compounds. Compounds 3b and 3c showed the highest binding score with 2AS1 protein. Graphical Abstract

Abstract A new series of pyridine, thiazole, and pyrazole analogues were synthesized. The pyridone analogues 4a-e were synthesized by treating N-aryl-2-cyano-3-(4-(diphenylamino)phenyl)acrylamides 3a-e with malononitrile. Many 4-arylidene-thiazolidin-5-one analogues 6a-d were obtained by Knoevenagel reactions of 4-(diphenylamino)benzaldehyde (1) with their corresponding thiazolidin-5-one derivatives 5a-d. The structural elucidation of the products was proven by the collections of spectroscopic methods such as IR, 1H NMR, 13C NMR, and MS data. Their anti-cancer activity was examined against two cell lines, MDA-MB-231 (mammary carcinomas) and A-549 (lung cancer). Compared with cisplatin as a reference standard drug, 6-amino-4-(4-(diphenylamino)phenyl)-2-oxo-1-(p-tolyl)-1,2-dihydropyridine-3,5-dicarbonitrile (4b) and 6-amino-4-(4-(diphenylamino)phenyl)-1-(4-nitrophenyl)-2-oxo-1,2-dihydropyridine-3,5-dicarbonitrile (4e) exhibited better efficiency against the A-549 cell line, with IC50 = 0.00803 and 0.0095 μM, respectively. Also, these compounds 4b and 4e showed the most potency among the examined compounds against MDA-MB-231 with IC50 = 0.0103 and 0.0147 μM, respectively. The newly synthesized compounds were docked inside the active sites of the selected proteins and were found to demonstrate proper binding. 2-Cyano-2-(4,4-(diphenylamino)benzylidene)-5-oxo-3-phenylthiazolidin-2-ylidene)-N-(p-tolyl)acetamide (6c) offered the highest binding affinity (− 8.1868 kcal/mol) when docked into (PDB ID:2ITO), in addition to 2-cyano-N-(4-(diethylamino)phenyl)-2-(4-(4-(diphenylamino)benzylidene)-5-oxo-3-phenylthiazolidin-2-ylidene)acetamide (6a) gave the highest energy score (− 9.3507 kcal/mol) with (PDB ID:2A4L).

Antibiotic residues in wastewater are considered lethal to the crops and aquatic life. One of the promising way to treat this kind of wastewater is the use of constructed wetlands coupled with microbial fuel cell (CW-MFC). In this treatment, bacterial action and redox operation occurs at anaerobic anode and aerobic cathode respectively. Four different configurations of CW-MFCs was applied for comparison. This study focusses on the investigation of the removal of Co-trimoxazole (CMX), other co-existing pollutants and furthermore, the generation of green electricity with low hydraulic retention time (HRT) was studied. Results revealed that the effluent from configuration 1 (gravel based) had lower (CMX) concentration. This removal performance may be associated to greater electrode absorption capacity for (CMX). The removal efficiency was 92.58% at HRT of 3d and effluent concentration was 4 mg/L. On the other hand, CW-MFC1 exhibited the BOD removal (60.60%) regardless of the increasing effluent antibiotic concentration. Pure strain of Geobactor sulfereducens, adjusted with anaerobic sludge increased the bio-film growth. Maximum power density of 480.2 mW/m−3 observed for CW-MFC1. Electricity generation characteristic were also found to be effected with HRT.

Abstract A heterogenized alternative to the homogeneous precapture of CO2 with amines and subsequent hydrogenation to MeOH was developed using aminated silica and a Ru‐MACHOTM catalyst. Commercial mesoporous silica was modified with three different amino‐silane monomers and used as support for the Ru catalyst. These composites were studied by TEM and solid‐state NMR spectroscopy before and after the catalytic reaction. These catalytic reactions were conducted at 155 °C at a H2 and CO2 pressures of 75 and 2 bar, respectively, with the heterogeneous system (gas‐solid) being probed with gas‐phase infrared spectroscopy used to quantify the resulting products. High turnover number (TON) values were observed for the samples aminated with secondary amines.

Abstract Antibiotic resistance is a global problem. This is the reason why scientists search for alternative treatments. In this regard, seven novel silver chromite nanocomposites were synthesized and assayed to evaluate their antimicrobial, antiviral, and cytotoxic activity. Five bacterial species were used in this study: three Gram-positive (Bacillus subtilis, Micrococcus luteus, and Staphylococcus aureus) and two Gram-negative (Escherichia coli and Salmonella enterica). Three fungal species were also tested: Candida albicans, Aspergillus niger, and A. flavus. The MIC of the tested compounds was determined using the bifold serial dilution method. The tested compounds showed good antibacterial activity. Maximum antibacterial activity was attained in the case of 15 N [Cobalt Ferrite (0.3 CoFe2O4) + Silver chromite (0.7 Ag0.5Cr2.5O4)] against M. luteus. Concerning antifungal activity, C. albicans was the most susceptible fungal species. The maximum inhibition was recorded also in case of 15 N [Cobalt Ferrite (0.3 CoFe2O4) + Silver chromite (0.7 Ag0.5Cr2.5O4)]. The most promising antimicrobial compound 15 N [Cobalt Ferrite (0.3 CoFe2O4) + Silver chromite (0.7 Ag0.5Cr2.5O4)] was assayed for its antiviral and cytotoxic activity. The tested compound showed weak antiviral activity. The cytotoxic activity against Mammalian cells from African Green Monkey Kidney (Vero) cells was detected. The inhibitory effect against Hepatocellular carcinoma cells was detected using a MTT assay. The antimicrobial effect of the tested compounds depends on the tested microbial species. The tested compounds could be attractive and alternative antibacterial compounds that open a new path in chemotherapy.

The use of photovoltaic (PV) panels in interior spaces is expected to increase due to the proliferation of low-power sensor devices in the IoT domain. PV models are critical for estimating the I–V curves that define their performance at various light intensities. These models and the extraction of their parameters have been extensively studied under outdoor conditions, but their indoor illumination performance is less studied. With respect to the latter, several studies have used the parameter-scaling technique. However, the model’s accuracy degrades when the light level decreases. In this study, we propose a simple PV modeling technique that can be applied at various illuminance levels by only using characteristic points (short-circuit current, open-circuit voltage, and maximum-power voltage points) at a reference illumination level. The model uses the characteristic point translation technique to translate the reference characteristic points to other operating conditions. Then, parameter extraction technique is used to extract the model’s parameters. The proposed model’s accuracy is verified using two commercial PV panels and different indoor lighting technologies. The results indicate that the proposed model outperforms the other examined works in terms of accuracy, with an average improvement of 15.75%.

Abstract Background Prunella vulgaris is medicinally important plant containing high-valued chemical metabolites like Prunellin which belong to family Lamiaceae and it is also known as self-heal. In this research, calli culture were exposed to differential ratios of gold (Au) and silver (Ag) nanoparticles (1:1, 1:2, 1:3, 2:1 and 3:1) along with naphthalene acetic acid (2.0 mg NAA) to investigate its antimicrobial potential. A well diffusion method was used for antimicrobial properties. Results Here, two concentrations (1 and 2 mg/6 µl) of all treated calli cultures and wild plants were used against Escherichia coli, Pseudomonas aeruginosa, Salmonella typhi, Bacillus atrophaeus, Bacillus subtilis, Agrobacterium tumefaciens, Erwinia caratovora and Candida albicans. Dimethyl sulfoxide (DMSO) and antibiotics were used as negative and positive controls. Here, the calli exposed to gold (Au) nanoparticles (NPs) and 2.0 mg naphthalene acetic acid (NAA) displayed the highest activity (25.7 mm) against Salmonella typhi than other extracts, which was considered the most susceptible species, while Agrobacterium tumefaciens and Candida albicans was the most resistance species. A possible mechanism of calli induced nanoparticles was also investigated for cytoplasmic leakage. Conclusion From the above data it is concluded that Prunella vulgaris is medicinally important plant for the development of anti-microbial drugs using nanotechnology and applicable in various pharmaceutical research.

In this study, varying % Bi-doped on 1% Ce-doped ZnO (1CZ) nanoparticles (X% B-1CZ) were synthesized via a facile, simple, low-cost, sol–gel process. Various characterization techniques were employed to characterize the synthesized compound, while the dielectric properties i.e. dielectric constant, dielectric loss and AC conductivity against frequency were studied with the help of a precision impedance analyzer. It was observed that by increasing bismuth content in the nanoparticles, the dielectric constant also increased in the range (1.47 × 106 – 4.02 × 106) at 20 Hz, and vice versa for dielectric loss decreased from 1.05 × 106 to 0.39 × 106. The role of prepared compounds as photocatalysts was also investigated against methylene blue under ultraviolet irradiation. The degradation efficiency, as well as the dielectric properties of 7% Bi-doped on 1% Ce-doped ZnO (7%B-1CZ), were found to be the best. Overall, it was found that the synthesized compounds proved promising candidates with enhanced photocatalytic & dielectric properties and hence could safely be employed for environmental remediation purposes and energy storage devices.

This study describes the synthesis, characterization, and biological activities of halogenated xanthone, 3-O substituted xanthone, and prenylated xanthone derivatives. The molecular structures of these compounds were identified using 1H NMR, 13C NMR, 2D NMR, FTIR and DIMS. All synthesized compounds were evaluated against two cancer cell lines: breast adenocarcinoma MCF-7 and MDA-MB-231 by application of the MTT assay. With this approach, xanthones 9 and 13 were identified as putative aromatase activating agents through the inhibition of interactions with MCF-7 and MDA-MB-231. The selected compounds, 9 and 13, were further examined for toxicity against the African Green Monkey Kidney cell (VERO) and Non-tumorigenic Lung Epithelial cell lines (BEAS-2B). In addition, in-silico docking studies were conducted to estimate the binding poses and residues implicated in probable MCF-7 and MDA-MB-231 interactions.

The CO2 and N2 adsorption on small-pore zeolite |Na12-xKx|-A was hypothesized to be affected by the ion exchange sequence used for the zeolite preparation. Zeolites were prepared by ion exchange of a commercially available zeolite |Na12|-A (4A) and a zeolite |K12|-A (3A) composition prepared from zeolite 4A. The CO2 and N2 adsorption properties were studied experimentally, and the binary CO2-over-N2 selectivity was estimated from single-component adsorption data using the apparent Henry's law coefficients. It was observed that the level of CO2 adsorption was reduced by increasing the K content for both series of zeolite NaKA. Zeolite |Na12-xKx|-A-from-4A had the highest CO2 adsorption capacity (at 1 atm and 273 K) for a given K content. At low K content, zeolite |Na12-xKx|-A-from-3A had the highest CO2-over-N2 selectivity. At an intermediate K content, the zeolites prepared from 4A had the highest selectivity. These differences show that non-equilibrium processes during the ion exchange are important for the CO2 and N2 adsorption properties of the derived zeolites. As of now, we refrain from speculating whether they relate to the detailed positioning of K+ and Na+ cations in the local structure of the zeolite or to mass-transport-related concentration gradients of the cations in the structure. Irrespectively, it was observed that the ion-exchange sequence affects the CO2 and N2 adsorption properties of the zeolites, which could be of general importance when it comes to the tuning of the properties of cation-rich zeolites.

There is an increased trend of drug delivery via skin due to its convenience and affordability. To accomplish this task in a better way and more successfully, nanotechnology has opened the door to transport drugs for the treatment of acute diseases in unique ways. For partially solvable drugs especially, designing new drug transportation systems is always challenging. However, it has been fixed by utilizing nanomaterials as effective carriers for drugs. These carriers allow the controlled and continuous drug release at the specific spotted site and have been successfully manipulated for the creation of innovative drug-delivery systems. These innovative systems are applied to overcome the challenge of reduced water solubility that may improve the drug accessibility, sustained release, and high metabolic stability. The prime emphasis of this review is particularly on the universal routes of drug administration through the skin, limitations of conventional drug delivery systems (DDS), and types and properties of nanomaterials (NMs) used. Recent advancements in NMs such as carbon-based NMs, inorganic-/metal-based NMs, polymeric NMs, and hybrid NMs for drug delivery and their mode of action have been summarized. This review further discusses existing constraints and difficulties that impede the integration of nanomaterials from research to practice, as well as recommendations for more efficient use of nanomaterials in a number of diseases.

The use of pesticides is unavoidable in agricultural practices. This class of chemicals is highly toxic for the environment as well as for humans. The present work was carried out to assess the presence of some pesticides (diafenthiuron, lufenuron, azoxystrobin, difenoconazole, and chlorothalonil) residues in five of the very commonly used vegetables (eggplant, capsicum, apple gourd, cauliflower, and sponge gourd). Matrix solid phase dispersion (MSPD) technique was used to extract the pesticides and subsequently their quantification was performed through high performance liquid chromatography (HPLC) coupled to ultraviolet-visible (UV-Vis) detector. The elution was accomplished at wavelength of 254 nm by injecting 20 µL of standards or samples into chromatographic system. The mobile phase consisted of acetonitrile and water (80:20 v/v), where the flow rate was adjusted at 1.0 ml/min. The linearity was good (R2 ≥ 0.994) over a concentration range from 20 to 100 μg/ml for the investigated pesticides. The low detection limits showed a quite appreciable potential of the method to detect (1.12–1.61 μg/L) and quantify (3.73–5.36 μg/ml) the pesticides under study. The accuracy was demonstrated in terms of percent recovery which ranged between 88.5% and 116.9% for all the pesticides under investigation. These results justify the suitability of the technique for the intended purpose. The concentration of difenoconazole in apple gourd (20.97 mg/kg), cauliflower (10.28 mg/kg), and sponge gourd (40.32 mg/kg) whereas diafenthiuron in cauliflower (0.66 mg/kg) exceeded the maximum residue level (MRLs) as defined by Food and Agriculture Organization of the United Nations and the World Health Organization (FAO/WHO). Target hazard quotient (THQ) values of difenoconazole and diafenthiuron (except for adults) were more than one which indicates the significant effect on human health on consumption of apple gourd, cauliflower, and sponge gourd.

The field of scene recognition, which lies at the crossroads of computer vision and artificial intelligence, has experienced notable progress because of scholarly pursuits. This article introduces a novel methodology for scene recognition by combining convolutional neural networks (CNNs) with feature selection techniques based on mutual information (MI). The main goal of our study is to address the limitations inherent in conventional unimodal methods, with the aim of improving the precision and dependability of scene classification. The focus of our research is around the formulation of a comprehensive approach for scene detection, utilizing multimodal deep learning methodologies implemented on a solitary input image. Our work distinguishes itself by the innovative amalgamation of CNN- and MI-based feature selection. This integration provides distinct advantages and enhanced capabilities when compared to prevailing methodologies. In order to assess the effectiveness of our methodology, we performed tests on two openly accessible datasets, namely, the scene categorization dataset and the AID dataset. The results of these studies exhibited notable levels of precision, with accuracies of 100% and 98.83% achieved for the corresponding datasets. These findings surpass the performance of other established techniques. The primary objective of our end-to-end approach is to reduce complexity and resource requirements, hence creating a robust framework for the task of scene categorization. This work significantly advances the practical application of computer vision in various real-world scenarios, leading to a large improvement in the accuracy of scene recognition and interpretation.

The rapid development in underground infrastructure is encouraging faster and more modern ways, such as TBM tunneling, to meet the needs of the world. However, tunneling activities generate complex and heterogeneous data, which makes it difficult to visualize the performance of a project. Advancements in information technology, such as digital twins and machine learning, provide platforms for digital demonstration, visualization, and system performance monitoring of such data. Therefore, this study proposes a digital twin-driven framework for TBM performance prediction through machine learning, visualization, and monitoring. This novel approach integrates machine learning and real-time performance data to predict, visualize, and monitor the status of the tunnel construction progress. A digital twin virtual model of TBM was constructed based on TBM design parameters, the input parameter, boring energy, RPM, torque, thrust force, speed, gripper pressure, total revolution, and Q-value provided to SVR and ANN models to predict the TBM AR and PR, and TBM daily progress was visualized continuously. The predictive performance indices R2 (0.97) and RMSE (0.011) were estimated for AR prediction, showing the accuracy of the proposed model. To demonstrate the proposed framework, this study shows the its effectiveness. By implementing this framework, stakeholders can minimize the risk associated with the cost and schedule of a tunneling project by simultaneously visualizing and monitoring the performance of TBMs through digital twin and machine learning algorithms.

Current research is moving towards iron and ammonia elimination from groundwater. Here, we are using a poly acrylic–poly acrylamide hydrogel that is grafted with 3-chloroaniline. This copolymer was synthesized by addition polymerization technique. The effects of agitation time, dosage and adsorbent temperature on the removal process sensitivity were investigated. The copolymer was described experientially and theoretically. Isothermal kinetic adsorption models are discussed. This hydrogel could be regenerated efficiently (98.3% removal of iron and 100% removal of ammonia). The density functional theory (DFT) method, using B3LYP/6-311G(d,p), and the LANL2DZ level of the theory were managed to investigate the stationary states of the grafted copolymer and the complexation energy of the hydrogel with the studied cations. DFT has been used to investigate the Natural Bond Orbital (NBO) properties to locate the most negative centers on the hydrogel. The calculated complexation energy showed hydrogel selectivity with regard to the studied cations.

L-methioninase degrades methionine, which is essential in methionine-dependent cancer cells, resulting in specific cell death. Normal cells can synthesize their own methionine amino acids even in the absence of exogenous methionine. This selective targeting of cancer cells makes L-methioninase a promising therapeutic candidate for cancer. In this study, L-methioninase was partially purified from Brevibacterium linens BL2. The specific activity of the enzyme was found as 3.055 units/mg. IC50 values (24 h) of the enzyme were 5.792 units/mL for U87MG cell line and 5.215 units/mL for T98G cell line. When L-methioninase and etoposide were used in combination, synergistic cytotoxic and cell migration inhibition effects on U87MG and T98G cells alongside decreased cytotoxic activity on the Mouse Embryonic Fibroblast and HaCaT cells compared to etoposide alone were observed. Additionally, colony numbers of U87MG cells were significantly reduced by L-methioninase and etoposide administration after 21 days of incubation. Furthermore, L-methioninase suppressed the expression levels of survivin and c-Myc while increasing the expression level of Caspase-3 in both glioblastoma cell lines. These effects were enhanced when etoposide was used in combination with etoposide. This investigation reveals that the L-methioninase enzyme not only exhibited cytotoxic effects on U87MG and T98G cells but also enhanced the anti-proliferative effects of etoposide when used in combination while also demonstrating fewer adverse effects on normal cells.

White Rot Fungi (WRF) are a class of microorganisms widely understood for their ability to breakdown an extensive range of pollutants generally found in industrial wastewater. This specific literature review centers on the abilities of the White Rot Fungi to treat industrial wastewater, analysis of the biodegradation mechanism, future applications, and scaling up for practical implementation. WRF usually carry out the degradation process with ligninolytic enzyme by targeting complex industrial pollutants, such as aromatic hydrocarbons, dyes, pharmaceuticals, and products of personal care. The unique enzymatic system of WRF converts the complex and harmful industrial pollutants into harmless end and byproducts, thus minimizing the impact on the environment and ecosystem. This review paper also discusses the potential applications of WRF, such as bioremediation, biosorption, and co-culturing with bacteria to enhance the pollutants removal efficiency of already functional wastewater treatment plants. However, there are still challenges in scaling up WRF-based treatment facilities due to factors such as the optimization of conditions and processes of already functional conventional wastewater treatment plants, cost effectiveness, and design modifications of treatment facilities. Conclusively, WRF fungi can play an important role in degrading complex organic and inorganic pollutants specifically, which are not usually treated by conventional wastewater treatment plants.

Modern distributed systems that operate concurrently generate interleaved logs. Identifiers (ID) are always associated with active instances or entities in order to track them in logs. Consequently, log messages with similar IDs can be categorized to aid in the localization and detection of anomalies. Current methods for achieving this are insufficient for overcoming the following obstacles: (1) Log processing is performed in a separate component apart from log mining. (2) In modern software systems, log format evolution is ongoing. It is hard to detect latent technical issues using simple monitoring techniques in a non-intrusive manner. Within the scope of this paper, we present a reliable and consistent method for the detection and localization of anomalies in interleaved unstructured logs in order to address the aforementioned drawbacks. This research examines Log Sequential Anomalies (LSA) for potential performance issues. In this study, IDs are used to group log messages, and ID relation graphs are constructed between distributed components. In addition to that, we offer a data-driven online log parser that does not require any parameters. By utilizing a novel log parser, the bundled log messages undergo a transformation process involving both semantic and temporal embedding. In order to identify instance–granularity anomalies, this study makes use of a heuristic searching technique and an attention-based Bi-LSTM model. The effectiveness, efficiency, and robustness of the paper are supported by the research that was performed on real-world datasets as well as on synthetic datasets. The neural network improves the F1 score by five percent, which is greater than other cutting-edge models.

Cassava production generates significant amounts of by-products such as leaves and tuber peel. Instead of considering them as waste, valorization aims to find sustainable ways to utilize them. However, the presence of cyanide and insoluble fibers poses a major obstacle to their conversion into valuable products. Therefore, the objective of this study is to investigate the changes in cyanide concentration and quality of cassava leaves after mechanical pressing and in tuber peel after treatment with an enzyme solution. Frozen leaves were screw-pressed into their fractions: juice, and press cake. The results show that the cyanide level in the press cake was reduced to 73.56% and was concentrated by 97.48% in the juice compared to the frozen leaves. However, the crude protein values of the frozen leaves, juice, and press cake did not differ significantly (p > 0.05), and were 27.09%, 25.47%, and 23.82%, respectively. In addition, the results for the peel revealed that pretreatment with Viscozyme® L, which assists in the mechanical peeling of cassava tubers, also contributed to a reduction in cyanide and insoluble fiber in the peel. Cyanide content was lowered by 53.89–58.94% in enzyme-treated peel from all three runs (ETP1-3) when compared to fresh peel (FP), while the reduction was only 8.63% in the control peel (CP) treated with hot water without enzyme solution. The insoluble fibers in cassava peel, such as neutral detergent fiber (NDF), acid detergent fiber (ADF), acid detergent lignin (ADL), and crude fiber (CF), were also degraded more effectively after treatment with an enzyme solution than with hot water.