3.1. XRD analysis
A comprehensive X-ray diffraction (XRD) analysis was conducted on all synthesized samples in powdered form to elucidate their crystallographic characteristics. Figure 1a meticulously depicts XRD patterns of desiccated grey powder (denoted as Ag NPs). Notably, patterns exhibit well-defined diffraction characteristic peaks of face-centered cubic (fcc) with space group (fm-3m) silver crystallites. These peaks appear at 2θ angles of 38.1042°, 44.2911°, 64.4495°, 77.4054°, and 81.5208°. Using Bragg's Law, these angles were meticulously converted to corresponding lattice spacings (d-spacings) of 2.35979 Å, 2.04345 Å, 1.44456 Å, 1.23193 Å, and 1.17982 Å; respectively. These peaks were unequivocally assigned to (111), (200), (220), (311), and (222) crystallographic planes of silver. These observations definitively corroborate the highly crystalline nature of synthesized Ag NPs and the structural integrity of fcc silver lattice. This conclusion aligns perfectly with the reference standard presented in the ICDD reference code: 01-080-4432 and FWHM obtained by Gaussian as proper statistical tool for peaks well-fitting (Suppl. Info. Fig. S1a). Notably, the absence of any discernible diffraction peaks corresponding to impurity phases, such as tetragonal silver oxide (AgO) or cubic silver(I) oxide (Ag₂O), further underscores the high purity of synthesized Ag NPs in powder form with no need for stabilized capping [53]. This is further corroborated by the lack of characteristic peaks for these oxides in (Suppl. Info., Fig. S1b).
Figure 1b presents XRD pattern of synthesized white powder ZnO, exhibiting a series of well-defined diffraction peaks at 2θ angles of 31.696°, 34.3872°, 36.2048°, 47.5137°, 56.5548°, 62.8323°, and 67.9552°. These peaks correspond to interplanar d-spacings of 2.82072 Å, 2.60587 Å, 2.47911 Å, 1.91209 Å, 1.62599 Å, 1.47779 Å, and 1.37832 Å; respectively. Through rigorous indexing procedures, peaks were unequivocally assigned to (100), (002), (101), (102), (110), (103), and (112) crystallographic planes of hexagonal ZnO phase. A meticulous analysis using full width at half maximum (FWHM) method (Suppl. Info., Fig. S2a) confirming presence of well-defined and relatively wide diffraction peaks, indicative of crystalline nature with fined sizes of ZnO crystallites. Furthermore, a comparative analysis with the reference patterns for Zn(OH)2 was conducted to ensure complete absence of any residual hydroxide phases. The observed diffraction pattern perfectly matches reference patterns for pure hexagonal ZnO phase documented in ICDD database under reference codes: 01-082-3143 and 01-073-8765. This comprehensive analysis conclusively confirms the successful synthesis of a pure phase hexagonal ZnO powder [14]. The average crystallite size of both Ag NPs and ZnO was calculated using well-known Scherer’s equation [54], as shown in Eq. 2. Using Eqs. 3 and 4, the dislocation density (δ) and micro-strain (ε) were obtained [55] and tabulated in Table 1 for all crystalline distinctive peaks .
$$D \left(crystallite size\right) in nm=\left[\frac{\left(\varvec{k}\right)\times \left(\varvec{\lambda }\right)}{\left({\varvec{\beta }}_{\varvec{D}}\right)\times \left(\varvec{C}\varvec{o}\varvec{s}\varvec{\theta }\right)}\right] Eq.$$
2
\(\text{δ = }\raisebox{1ex}{$\text{1}$}\!\left/ \!\raisebox{-1ex}{${\text{D}}^{\text{2}}$}\right.\) Eq. (3)
\(\text{ε = β cosθ4}\) Eq. (4)
In Table 1 shows data of the dislocation density (δ) and micro-strain (ε) within the materials. Notably, the average values of δ and ε were ascertained to be 0.0010 nm⁻² and 0.3762%, respectively, for the distinctive (111) and (002) planes of Ag NPs within 26.5 nm as average crystallite size. These remarkable findings are considered to be good indicative of exceptional stability and hardness exhibited by synthesized Ag0 nano powder [56]. Otherwise, ZnO QDs reveal an average crystallite size of 11.2 nm, bordering the quantum regime. This observation is complemented by a relatively high dislocation density (δ) of 0.0119 nm⁻² and microstrain (ε) of 0.8708%, suggesting a significant portion of particles exhibiting quantum size effect. These findings highlight the potential for manipulating the optoelectronic properties of ZnO NPs through precise control over crystallite size with associated lattice defects and imperfections [57].
Nitazoxanide (G6), as well-recognized organic active pharmaceutical ingredient (API) [58], was subjected to a comprehensive powder X-ray diffraction investigation to elucidate its crystallographic characteristics. The diffractogram (Fig. 1c) exhibits a multitude of well-defined diffraction peaks, indicative of a highly ordered crystalline structure [58]. These peaks correspond to specific Bragg angles (2θ) of 5.2477°, 12.4024°, 13.2053°, 15.8466°, 17.6935°, 21.1976°, 22.0920°, 23.1379°, 24.5654°, 24.9441°, 25.7312°, 26.6115°, and 26.8773°. Subsequent indexing yielded the corresponding d-spacings of 16.82648 Å, 7.13106 Å, 6.69926 Å, 5.58805 Å, 5.0087 Å, 4.18798 Å, 4.02041 Å, 3.84098 Å, 3.62093 Å, 3.56681 Å, 3.45946 Å, 3.34699 Å, and 3.31449 Å; respectively. Following probe-sonication, the Ag-Nitazoxanide composite (G3) was subjected to a thorough XRD analysis to investigate the presence and crystallographic properties of both components. The resulting diffractogram (Fig. 1d) reveals distinct characteristic peaks. The presence of silver nanoparticles (Ag-NPs) is confirmed by peaks at 2θ values of 38.0851°, 44.2581°, 64.4374°, 77.3793°, and 81.5146°, corresponding to d-spacings of 2.36092 Å, 2.04489 Å, 1.4448 Å, 1.23228 Å, and 1.17989 Å, respectively. These peaks can be indexed to the standard reference pattern for face-centered cubic (fcc) silver[59].The diffractogram (Fig. 1d) also exhibits peaks designated by asterisks (*), which can be attributed to the underlying Nitazoxanide substrate (NAZ) for all composites. These peaks appear at 2θ values of 5.2808°, 13.2106°, 15.8698°, 17.6981°, 21.2098°, and 23.1297°, corresponding to d-spacings of 16.72111 Å, 6.69655 Å, 5.57993 Å, 5.00741 Å, 4.18559 Å, and 3.84234 Å, respectively. These values are consistent with previously reported data for Nitazoxanide. This XRD analysis effectively demonstrates the successful incorporation of Ag NPs into Nitazoxanide matrix (G3). The presence of distinct diffraction peaks for both components signify their successful integration while maintaining their individual crystalline characteristics.
Figure 1e presents XRD pattern of ZnO/Nitazoxanide (G4) composite. The diffractogram reveals the presence of both crystalline components ZnO & Nitazoxanide. Peaks at 2θ values of 36.2°, 45.7°, and 56.5° can be attributed to ZnO QDs and are indexed to (101), (102), and (110) crystal planes of hexagonal wurtzite structure; respectively. Additionally, the diffractogram exhibits peaks at 2θ of 25.2578°, 15.8517°, and 21.1961°, which can be assigned to Nitazoxanide phase. These peaks correspond to d-spacings of 16.79436 Å, 5.58628 Å, and 4.18828 Å; respectively, and are in good agreement with previously reported values for Nitazoxanide. In Fig. 1f illuminates to the well-defined peaks for all of Ag NPs at 2θ of 31.9(100), 34.3(002), 56.4 (110) and ZnO QDs assigned 2θ at 38.03 (111), 44.22 (200), 64.39 (220), 77.3 (311) and Nitazoxanide (denoted asterisks) 5.22 (16.9 Å), 15.8 (5.60 Å) and 21.1(4.19 Å); respectively. The presence of distinct diffraction peaks for both Ag NPs, ZnO and Nitazoxanide signifies their successful integration within Ag/ZnO/Nitazoxanide composite (G6). In Fig. 1f, diffraction pattern reveals distinct, well-defined peaks corresponding to Ag-NPs and ZnO at precise 2θ values: 31.9 (100), 34.3 (002), 56.4 (110) for Ag NPs, and 38.03 (111), 44.22 (200), 64.39 (220), 77.3 (311) for ZnO; respectively. Additionally, Nitazoxanide exhibits characteristic peaks at 5.22 (16.9 Å), 15.8 (5.60 Å), and 21.1 (4.19 Å), as denoted by asterisks. This pattern underscores the successful incorporation and crystalline integrity of Ag-NPs, ZnO, and Nitazoxanide within the Ag/ZnO/Nitazoxanide composite (G6).
Figure 1. X-ray diffraction (XRD) patterns of investigated materials and showing d-spacing values for distinctive peaks observed in: (a) Ag NPs (G1), (b) ZnO QDs (G2), (c) nitazoxanide (G5), (d) Ag NPs/Nitazoxanide composite (G3), (e) ZnO QDs/Nitazoxanide composite, and (f) Ag NPs/ZnO QDs/Nitazoxanide composite (G3).
Table 1
Crystal structure parameters of Ag NPs and ZnO QDs.
Sample type | (hkl) | d-spacing (nm) | βD (rad) | D (Scherrer eq.) (nm) | δ (nm 2) | ε % |
Ag-NPs | (111) (002) | 0.235789395 0.204159994 | 0.00500944400.006359107 | 29.28536154 23.54414704 | 0.001166001 0.00180399 | 0.362314897 0.390211619 |
ZnO QDs | (100) )002) (101) (102) (110) (103) (112) | 0.281786461 0.26035866 0.247735634 0.191037727 0.1624583 0.147651858 0.137431408 | 0.01388985 0.00855001 0.01346539 0.01242866 0.01525382 0.01209879 0.03376356 | 10.3776702 16.9768429 10.8341038 12.1909537 10.3241049 13.433078 4.95874692 | 0.00928539 0.00346965 0.00851949 0.00672859 0.00938199 0.00554177 0.04066830 | 1.22189185 0.69012476 1.02898252 0.70517023 0.70811208 0.49462478 1.24717261 |
3.2. SEM, TEM and EDX investigation
Field Emission Scanning Electron Microscopy (FE-SEM) analysis (Figs. 2a-c) revealed well-dispersed silver nanoparticles (Ag NPs) with a variety of morphologies and orientations. The nanoparticles exhibited no discernible impurities or apparent agglomeration, suggesting good colloidal stability. The average particle size was approximately 67 nm, with quasi-spherical and elliptical shapes dominating the population alongside a range of other morphologies characteristic of the nanoscale. EDAX mapping (Fig. 2d) confirms the homogenous distribution of silver (orange map) throughout the sample. No significant evidence of impurities or contamination was observed. Additionally, EDAX analysis quantified the elemental composition, indicating a near 100% atomic percentage of Ag. This finding strongly supports the successful synthesis of a pure phase of Ag-NPs.
Figure 2 Multimodal characterization of Ag NPs, (a-c) FE-SEM images with various magnification and (d) EDX mapping analysis.
FE-SEM investigation of white ZnO NPs reveals presence of very small, finite-sized particles with variable degrees of grain agglomeration (Fig. 3). This aggregation likely arises due to the high surface energy of zincite NPs and signifies their low dimensionality, consistent with successful Ostwald ripening. The image depicts grain boundaries with a variety of shapes and sizes, indicative of quantum crystallites in proximity (Figs. 3a-b). Further investigation in Fig. 3c at 120,000X magnification suggests an average particle size distribution centered around 7.5 nm. This finding aligns with expected quantum size regime [60], for ZnO NPs (1–10 nm) and is further corroborated by HR-TEM analysis. EDX analysis confirms the stoichiometric composition of synthesized ZnO QDs. EDX spectrum (Fig. 3d) reveals well-distributed peaks corresponding to Zn and O elements, indicating their intimate association at the molecular level. Quantitative analysis yielded atomic percentages of 53.6% for O and 46.3% for Zn, closely matching the expected stoichiometry of ZnO (O: Zn = 0.8:0.8) according to the International Centre for Diffraction Data (ICDD) reference code: 01-082-3143 (Suppl. Info., PDF S1). HR-TEM analysis was employed to evaluate the size distribution of individual particles following a standard sonication protocol (5 minutes in water) and deposition onto a TEM grid. Figure 3e showcases highly dispersed, dark, condensed particles with an average diameter of approximately 7 nm, solidifying the quantum confinement of synthesized ZnO. Selected area electron diffraction (SAED) analysis confirms the crystalline nature of particles, with d-spacings of 2.740, 1.985, 1.408, and 1.006 nm, corresponding to the (100), (102), (200), and (114) crystal planes, respectively (Fig. 3). These findings are in excellent agreement too with ICDD: 01-082-3143.
Figure 3 Multimodal characterization of ZnO QDs, (a-c) FE-SEM images at various magnifications, (d) EDX mapping, and (e) HR-TEM at various magnifications alongside the corresponding SAED.
FE-SEM analysis (Fig. 4) reveals a significant increase in the apparent particle size of the commercially available nitazoxanide, compared to its expected pristine crystalline state. This suggests potential aggregation of nitazoxanide molecules during processing or storage. The observed morphology consists of agglomerated particles forming 3D structures, contrasting with the anticipated ordered crystalline blocks. This observation aligns with XRD analysis, which might depict the complete crystalline behavior, due to potential particle size effects. EDX elemental mapping confirms a homogeneous distribution of key elements (C, N, O, and S) associated with the organic moieties of nitazoxanide throughout visualized particles. This finding aligns well with the complementary bulk elemental analysis results, which corroborated the stoichiometric ratios of C, N, O, and S, as prescribed by the molecular formula (C12H9N3O5S) in contrast to carbon grid background and nondetectable hydrogen element by EDX. The absence of any distinctive impurity peaks in the elemental analysis further reinforces the high purity of the nitazoxanide employed.
Figure 4 Mapping analysis of Micro Nitazoxanide substrates elements sequenced from carbon, nitrogen, oxygen and sulfur alongside the corresponding elemental analysis.
A meticulous examination of Ag/nitazoxanide in Figs. 5a-c reveals presence of Ag NPs clustered around the surface and edges of nitazoxanide microcrystals. Meanwhile, Ag NPs appear to be attached and embedded as bulk grains, forming light-colored Ag particles contrasted against the grey nitazoxanide substrate. Notably, the composite exhibits a high degree of fragmentation and heterogeneity, with silver nanoparticles displaying a variety of shapes and sizes. Further investigation using elemental mapping that confirms presence of all major elements: carbon, nitrogen, oxygen, sulfur, and silver. While the distribution of other elements appears normal, silver distribution displays a distinct pattern. Ag NPs form dense clusters throughout the sample, with a lighter, more diffuse distribution also observed across the entire composite. However, this lighter distribution might not be fully captured in Fig. 5d, which depicts the elemental mapping sequence using EDX analysis. EDX analysis likely resulted in a series of mapping photographs showcasing the individual distributions of carbon, nitrogen, oxygen, sulfur, and silver.
Figures 6a-c depict the morphology of ZnO QDs loaded nitazoxanide micro clusters at different magnifications. The micrographs reveal a scattered and fragmented distribution of ZnO NPs around the sample. Larger nitazoxanide substrates appear loaded with tiny agglomerates of ZnO particles, while some scattered particles remain unloaded. The observed scattered and fragmented distribution of ZnO NPs around nitazoxanide micro-clusters suggests a weak interaction between two components. This observation contrasts with the tendency of ZnO QDs to self-agglomerate. Two potential elucidations for the observed feeble interaction can be delineated; Firstly, the elevated surface energy inherent to ZnO QDs emerges as a compelling factor. The diminutive dimensions coupled with an extensive surface area to volume ratio confer upon ZnO QDs a pronounced surface energy. Such heightened energy levels instigate spontaneous aggregation amongst QDs, driven by the imperative to minimize overall surface energy. This phenomenon of self-agglomeration may exert a formidable counterforce against the propensity of QDs to interface with the nitazoxanide substrate.
Secondly, the phenomenon of Ostwald ripening emerges as an alternative explanation. Ostwald ripening delineates a process wherein larger particles undergo growth at the expense of smaller counterparts. In the present context, extant aggregates of ZnO QDs might serve as nucleation sites, facilitating the accretion of smaller QDs while impeding their adherence to the nitazoxanide substrate. Thus, this phenomenon presents an additional mechanism hindering the desired interaction between QDs and substrate. The bonding might be attributed to Van der Waals forces or physical adsorption of ZnO onto the nitazoxanide surface. This possibility aligns with the relatively short sonication time and frequency used for the preparation process. Figure 6d presents an elemental mapping analysis of the composite material, The map displays the distribution of all major elements: carbon, nitrogen, oxygen, sulfur, and zinc. Each element exhibits a high degree of purity and a well-distributed presence throughout the sample. Notably, the zinc element is visualized by yellow dots, indicating a relatively low concentration compared to other elements. This observation reflects the addition of a comparatively small quantity of ZnO (10%) during the composite formation process.
Figure 5 Multimodal characterization of Ag/Nitazoxanide composite, (a-c) FE-SEM images at various magnifications, and (d) EDAX mapping for each element.
Figure 6 Multimodal characterization of Zinc ZnO QDs/ Nitazoxanide composite (a-c) FE-SEM images at various magnifications, and (d) EDAX mapping for each element.
3.4. Antimicrobial efficacy assays
Accordingly, we used various antibiotic discs to initially identify the tested pathogens' resistance to antibiotics as shown in (Table 2).
Table (2)
Antibiotics susceptibility test of tested human pathogens using Fluconazole (FLC-25 mcg). Chloramphenicol (C-30 µg), Erythromycin (E-15 µg), Aztreonam (ATM-30 µg), Ampicillin (AP-10 µg), Ciprofloxacin (CIP-5 mcg), Ketoconazole (KT 30 mcg), Oxacillin (OX-1 mcg), Tetracycline (T-30 µg), and Clotrimazole (CC-10mcg).
Human pathogens | Antibiotic sensitivity discs |
FLC − 25 mcg | C-30 µg | E-15 µg | ATM-30 µg | AP-10 µg | CIP-5 mcg | KT 30 mcg | OX-1 mcg | T-30 µg | CC-10mcg |
Gram-negative bacteria | Salmonella paratyphi | R | S | S | S | R | R | S | R | R | R |
Escherichia coli | S | R | R | R | R | R | R | S | S | R |
Klebsiella pneumoniae | S | R | R | R | S | S | S | S | S | S |
Pseudomonas aeruginosa | R | R | R | S | S | S | S | R | R | S |
Gram-positive bacteria | Staphylococcus epidermidis | S | S | S | R | R | S | R | S | S | S |
Staphylococcus aureus | R | R | S | R | S | R | S | S | S | R |
Bacillus cereus | S | R | S | R | R | S | R | R | R | S |
Bacillus subtilis | S | R | S | R | R | R | S | R | R | R |
yeast cells | Candida krusei | S | R | R | R | R | R | R | S | S | R |
Candida glabrata | S | R | R | R | R | R | R | S | S | S |
Candida albicans | S | S | S | R | R | S | R | S | S | S |
Candida parapsilosis | S | S | R | S | S | S | S | R | R | S |
Resistant ( R, inhibitory zones ≤ 5 mm) or sensitive (S, inhibitory zones > 5 mm).
Table 3 and Fig. 9i illustrate the most effective inhibitory zone found when G6 (Ag NPs/ZnO QDs/Nitazoxanide) was used against Gram-positive bacteria, Gram-negative bacteria, and then yeast cells. The biggest inhibition zones are detected against Bacillus subtilis (35.25 ± 5.21 mm), Staphylococcus aureus (29.15 ± 2.48 mm), Bacillus cereus (27.17 ± 0.65 mm), and Staphylococcus epidermidis (25.72 ± 4.56 mm). Furthermore, the moderately effective inhibitory zones are measured using G6-formula against both Pseudomonas aeruginosa (20.53 ± 3.65 mm) from Gram-negative bacteria and Candida glabrata (17.65 ± 3.96 mm) from yeast cells. To determine the more efficient formulations statistically, the mean values of calculated inhibitory zones are evaluated using ANOVA and Tukey Post Hoc tests, as seen in Figs. 2(ii and iii). Tukey's test means for each pairwise comparison (Fig. 9ii) is then shown in a boxplot to identify significant mean differences. Figure 9ii displays the mean values for G3 and G6- formulations, which are the lowest and highest values; respectively, in the interval plot among G3, G4, and G6 formulations. Within a boxplot, every dot with whiskers indicates the fluctuation in the interquartile range. Inhibitory effect distributions matching the tested formulations are displayed on a box-plot graph by Tukey-Kramer post-hoc analysis. Furthermore, the data acquired on the link between antimicrobial activity and tested formulations is statistically clustered, as demonstrated by box plot graph, with the highest efficacy observed at G6-formula. On 95% scale, Tukey simultaneous tests are used to calculate the adjusted confidence intervals. G6-formula intervals do not contain the zero line, according to our Tukey graph (Fig. 9iii). This means that, when compared to the control group and the other tested formulations, G6-formula differs in ways that are statistically significant. The final findings demonstrated that the tested G6-formula has antimicrobial qualities that were statistically significant.
Figure 8 Photographs plates depict antimicrobial activities that tested formulations labeled G1: Ag NPs, G2: ZnO QDs, G3: Ag NPs/Nitazoxanide, G4: Nitazoxanide/ZnO QDs, G5: Nitazoxanide, and G6: Ag NPs/ZnO QDs/Nitazoxanide, recorded against various multidrug-resistant human pathogens, such as A: Salmonella paratyphi, B: Escherichia coli, C: Klebsiella pneumoniae, D: Pseudomonas aeruginosa, E: Staphylococcus epidermidis, F: Staphylococcus aureus, G: Bacillus cereus, H: Bacillus subtilis, I: Candida krusei, J: Candida glabrata, K: Candida albicans, and L: Candida parapsilosis using agar-well diffusion bioassay.
Table (3): Antimicrobial efficacy results of tested formulations labeled G1: Ag NPs, G2: ZnO QDs, G3: Ag NPs/Nitazoxanide, G4: Nitazoxanide/ZnO QDs, G5: Nitazoxanide, and G6: Ag NPs/ZnO QDs/Nitazoxanide, measured against various multidrug-resistant human pathogens using an agar-well diffusion test.
Multidrug-resistant human pathogens | Inhibition zone diameters (mm ± SD) |
G1 | G2 | G3 | G4 | G5 | G6 |
Salmonella paratyphi | 0 | 0 | 4.31 ± 1.05bc | 7.32 ± 1.22b | 0 | 18.73 ± 2.36a |
Escherichia coli | 0 | 0 | 3.31 ± 0.58bc | 2.15 ± 0.33b | 0 | 19.78 ± 2.05a |
Klebsiella pneumoniae | 0 | 0 | 2.51 ± 1.24bc | 4.2 ± 0.28b | 0 | 16.18 ± 2.33a |
Pseudomonas aeruginosa | 0 | 0 | 3.52 ± 0.38bc | 5.21 ± 1.11b | 3.42 ± 0.26c | 20.53 ± 3.65a |
Staphylococcus epidermidis | 2.72 ± 0.25bc | 0 | 2.32 ± 0.39bc | 6.18 ± 2.23b | 0 | 25.72 ± 4.56a |
Staphylococcus aureus | 0 | 0 | 3.72 ± 1.24bc | 5.52 ± 0.25b | 0 | 29.15 ± 2.48a |
Bacillus cereus | 3.24 ± 0.54bc | 0 | 5.11 ± 2.24bc | 6.21 ± 1.24b | 0 | 27.17 ± 0.65a |
Bacillus subtilis | 0 | 3.12 ± 0.05c | 4.21 ± 1.33bc | 7.77 ± 0.59b | 0 | 35.25 ± 5.21a |
Candida krusei | 0 | 0 | 0 | 2.33 ± 0.33b | 0 | 14.63 ± 3.65a |
Candida glabrata | 0 | 0 | 2.13 ± 0.24bc | 4.23 ± 1.14b | 0 | 17.65 ± 3.96a |
Candida albicans | 0 | 0 | 0 | 0 | 0 | 12.32 ± 4.89a |
Candida parapsilosis | 0 | 0 | 1.25 ± 0.24bc | 2.33 ± 0.64b | 0 | 8.69 ± 0.59a |
The data is shown as the mean (millimeters) ± standard deviation (mm ± SD). The differences in the superscript letters are statistically significant at p ≤ 0.05. R-sq (83.01%), adj R-sq (81.73%), and pred R-sq (79.79%).
Figure 9 Agar-well diffusion analysis includes charts of calculated inhibition zones (i), A box-plot graph depicts the inhibitory value distributions corresponding to the tested formulations using Tukey-Kramer post-hoc analysis (ii), and simultaneous Tukey tests for mean difference (iii) for all of the tested formulations, including G1: Ag NPs, G2: ZnO QDs, G3: Ag NPs/Nitazoxanide, G4: Nitazoxanide/ZnO QDs, G5: Nitazoxanide, and G6: Ag NPs/ZnO QDs/Nitazoxanide, —recorded against various multidrug-resistant human pathogens, such as A: Salmonella paratyphi, B: Escherichia coli, C: Klebsiella pneumoniae, D: Pseudomonas aeruginosa, E: Staphylococcus epidermidis, F: Staphylococcus aureus, G: Bacillus cereus, H: Bacillus subtilis, I: Candida krusei, J: Candida glabrata, K: Candida albicans, and L: Candida parapsilosis
Figure 10 Reduction in biofilm generation of tested human pathogens, including A: Salmonella paratyphi, B: Escherichia coli, C: Klebsiella pneumoniae, D: Pseudomonas aeruginosa, E: Staphylococcus epidermidis, F: Staphylococcus aureus, G: Bacillus cereus, H: Bacillus subtilis, I: Candida krusei, J: Candida glabrata, K: Candida albicans, and L: Candida parapsilosis that were treated with all of the tested formulations; G1: AgNPs, G2: ZnO QDs, G3: Ag NPs/Nitazoxanide, G4: Nitazoxanide/ZnO QDs, G5: Nitazoxanide, and G6: Ag NPs/ZnO QDs/Nitazoxanide. Chart shows the percentage of biofilm reduction (i), simultaneous Tukey results for analyzing the overall group's difference (ii), and box-plot graph shows biofilm reduction value distributions corresponding to drug dosages via Tukey-Kramer post-hoc analysis (iii).
Table (3): Reduction in biofilm generation of tested human pathogens that were treated with all tested formulations; G1: Ag NPs, G2: ZnO QDs, G3: Ag NPs/Nitazoxanide, G4: Nitazoxanide/ZnO QDs, G5: Nitazoxanide, and G6: Ag NPs/ZnO QDs/Nitazoxanide using a biofilm inhibition assay via micro-dilution technique.
Multidrug-resistant human pathogens | Reduction in generated biofilm (%±SD) |
G1 | G2 | G3 | G4 | G5 | G6 |
Salmonella paratyphi | 17.81 ± 1.26cd | 20.23 ± 2.58c | 71.55 ± 4.56b | 77.59 ± 3.69ab | 7.85 ± 0.34d | 82.36 ± 4.56a |
Escherichia coli | 16.65 ± 5.58cd | 23.36 ± 1.19c | 73.62 ± 3.65b | 79.41 ± 0.57ab | 9.45 ± 0.54d | 89.45 ± 3.67a |
Klebsiella pneumoniae | 18.21 ± 5.26cd | 22.55 ± 5.98c | 75.36 ± 4.09b | 78.59 ± 3.24ab | 5.96 ± 0.32d | 87.68 ± 4.11a |
Pseudomonas aeruginosa | 14.78 ± 3.24cd | 20.93 ± 0.98c | 69.35 ± 0.96b | 75.01 ± .34ab | 7.79 ± 0.31d | 82.39 ± 2.16a |
Staphylococcus epidermidis | 20.56 ± 0.58cd | 31.25 ± 0.97c | 72.35 ± 3.98b | 81.58 ± 3.45ab | 13.56 ± 0.94d | 95.31 ± 3.65a |
Staphylococcus aureus | 22.35 ± 3.29cd | 29.91 ± 5.58c | 76.98 ± 2.58b | 80.32 ± 2.36ab | 14.32 ± 0.87d | 97.98 ± 4.65a |
Bacillus cereus | 23.54 ± 2.98cd | 35.48 ± 4.59c | 79.99 ± 2.09b | 85.74 ± 5.31ab | 11.57 ± 2.22d | 93.45 ± 2.11a |
Bacillus subtilis | 25.79 ± 2.33cd | 27.13 ± 0.98c | 80.55 ± 1.09b | 89.07 ± 0.97ab | 10.26 ± 1.54d | 98.54 ± 3.21a |
Candida krusei | 14.98 ± 1.58cd | 16.55 ± 1.29c | 45.25 ± 3.98b | 53.45 ± 0.54ab | 5.33 ± 0.56d | 70.29 ± 1.24a |
Candida glabrata | 10.76 ± 3.69cd | 14.04 ± 2.98c | 52.36 ± 2.89b | 57.68 ± 0.24ab | 9.02 ± 1.14d | 69.31 ± 5.65a |
Candida albicans | 11.26 ± 4.09cd | 18.83 ± 4.95c | 57.89 ± 0.98b | 60.31 ± 0.92ab | 3.44 ± 0.57d | 65.27 ± 1.54a |
Candida parapsilosis | 12.15 ± 3.69cd | 16.28 ± 0.57c | 59.35 ± 2.22b | 62.59 ± 0.98ab | 4.76 ± 0.14d | 68.18 ± 0.69a |
The data is shown as the mean (percentage) ± standard deviation (%±SD). Differences in the superscript letters are statistically significant at p ≤ 0.05. R-sq (92.11%), adj R-sq (91.51%), and pred R-sq (90.60%).
Additionally, the possibility that tested formulations can prevent the growth of used pathogens was examined using a biofilm inhibition test via the micro-dilution technique. G6-formula was found to have the greatest reduction in studied human pathogens' ability to build biofilms (Fig. 10i). As shown in Table 3, the highest percentages of anti-biofilm are seen against Bacillus subtilis (98.54 ± 3.21%), Staphylococcus aureus (97.98 ± 4.65%), Staphylococcus epidermidis (95.31 ± 3.65%), and Bacillus cereus (93.45 ± 2.11%). The mean value for G6 formulation, the highest value among all tested formulations, is shown in the interval plot (Fig. 10ii). The box plot graph (Fig. 9iii) further shows the statistical clustering of the data collected on the percentages of anti-biofilm for various tested formulations. Because there is no zero line in G6-formula intervals, the formula differs statistically significantly from the control group and other evaluated formulations. The studied G6-formula exhibits statistically significant anti-biofilm qualities, as demonstrated by the results.
Table (4)
shows the anti-biofilm results for all treated pathogens with varying G6 formula dosages by computing the log10CFU/ml.
Multidrug-resistant human pathogens | log10 CFU/mL ± SD |
G6-formula (Ag NPs/ZnO QDs/Nitazoxanide) |
0 µg/mL | 30 µg/mL | 60 µg/mL | 90 µg/mL | 120 µg/mL | 150 µg/mL | 180 µg/mL |
Salmonella paratyphi | 2.44 ± 0.71a | 2.42 ± 0.94ab | 2.21 ± 0.10ab | 2.14 ± 0.86b | 2.02 ± 0.56c | 1.05 ± 0.91d | 0.95 ± 0.01e |
Escherichia coli | 2.39 ± 0.46a | 2.36 ± 0.78ab | 2.34 ± 0.88ab | 2.31 ± 0.09b | 2.03 ± 0.89c | 1.25 ± 0.33d | 0.42 ± 0.02e |
Klebsiella pneumoniae | 2.53 ± 0.40a | 2.50 ± 0.33ab | 2.40 ± 0.61ab | 2.36 ± 0.29b | 1.71 ± 0.75c | 0.75 ± 0.03d | 0.72 ± 0.08e |
Pseudomonas aeruginosa | 2.60 ± 0.13a | 2.52 ± 0.85ab | 2.36 ± 0.85ab | 2.32 ± 0.19b | 1.98 ± 0.83c | 0.94 ± 0.11d | 0.62 ± 0.14e |
Staphylococcus epidermidis | 2.41 ± 0.93a | 2.21 ± 0.37ab | 2.15 ± 0.17ab | 2.14 ± 0.88b | 1.73 ± 0.59c | 0.74 ± 0.32d | 0.18 ± 0.07e |
Staphylococcus aureus | 2.47 ± 0.76a | 2.24 ± 0.38ab | 2.03 ± 0.68ab | 2.02 ± 0.97b | 1.61 ± 0.75c | 0.62 ± 0.21d | 0.13 ± 0.01e |
Bacillus cereus | 2.58 ± 0.56a | 2.58 ± 0.01ab | 2.18 ± 0.62ab | 2.07 ± 0.49b | 1.82 ± 0.78c | 0.82 ± 0.05d | 0.12 ± 0.04e |
Bacillus subtilis | 2.53 ± 0.88a | 2.46 ± 0.35ab | 2.44 ± 0.67ab | 2.27 ± 0.93b | 1.72 ± 0.71c | 0.74 ± 0.06d | 0.17 ± 0.09e |
Candida krusei | 2.58 ± 0.36a | 2.37 ± 0.61ab | 2.30 ± 0.62ab | 2.14 ± 0.76b | 1.62 ± 0.34c | 1.1 ± 0.65d | 0.48 ± 0.15e |
Candida glabrata | 2.63 ± 0.70a | 2.60 ± 0.36ab | 2.44 ± 0.67ab | 2.30 ± 0.94b | 1.80 ± 0.12c | 1.02 ± 0.33d | 0.82 ± 0.05e |
Candida albicans | 2.48 ± 0.46a | 2.47 ± 0.82ab | 2.40 ± 0.62ab | 2.34 ± 0.87b | 1.98 ± 0.38c | 0.94 ± 0.12d | 0.55 ± 0.12e |
Candida parapsilosis | 2.59 ± 0.95a | 2.54 ± 0.08ab | 2.50 ± 0.81ab | 2.48 ± 0.68b | 1.54 ± 0.09c | 0.97 ± 0.09d | 0.74 ± 0.38e |
SD = standard deviation. Differences in the superscript letters are statistically significant at p ≤ 0.05. R-sq (92.11%), adj R-sq (91.51%), and pred R-sq (90.60%).
Figure 11 (a) Predicted biofilm reduction (%) of all treated pathogens denoted as A: Salmonella paratyphi, B: Escherichia coli, C: Klebsiella pneumoniae, D: Pseudomonas aeruginosa, E: Staphylococcus epidermidis, F: Staphylococcus aureus, G: Bacillus cereus, H: Bacillus subtilis, I: Candida krusei, J: Candida glabrata, K: Candida albicans, and L: Candida parapsilosis with varying G6 formula dosages (Ag NPs/ZnO QDs/Nitazoxanide). Chart shows the percentage of biofilm reduction (i), simultaneous Tukey results for analyzing the overall group's difference, and box-plot graph shows biofilm reduction value distributions corresponding to drug dosages via Tukey-Kramer post-hoc analysis (ii). 11(b) Growth reduction (%) in cell viability is plotted along the incubation period between the treated multidrug-resistant human pathogens with G6-formula and their untreated (G6-formula-free) cells.
G6 formula's optimal concentration is determined using MIC method. The log10CFU/mL factor is used to determine the optimal dose of G6 formulation. In this assay, various G6 formulation dosages (0, 30, 60, 90, 120, 150, and 180 µg/mL) are employed against all tested pathogens, relying on microbial turbidity analysis. The positive controls contained uninoculated broth with G6 formulation doses. Additionally, the negative controls have microbial broth without G6 formula (0 µg/mL). The turbidity values of treated pathogens that received G6 formula dosages varied according to pathogen type, as indicated in Table 4. The estimated biofilm reduction (%) for each treated pathogen with varying G6 formula dosages are then displayed in Fig. 11a. As shown in Fig. 11ai, Bacillus cereus (96.13%) shows the highest percentages of biofilm reduction when treated with the 180 µg/mL dose of G6 formula, followed by Candida krusei (93.30%) and Escherichia coli (83.30%). The box plot graph in (Fig. 11aii) further shows the data collection of anti-biofilms (%) for all tested dosages of G6 formula in a statistically categorized pattern. Compared to other doses studied, the 180 µg/mL dose result of G6 formula shows a statistically significant difference. The results indicate that the highest anti-biofilm efficacy of G6 formula is statistically significant at 180 µg/mL. Then, in order to determine MIC values—the lowest dosage at which there is no microbial turbidity [61] (≥ 98%); G6 formulations are employed to treat each tested pathogens at various doses of 210, 240, 270, and 300 µg/mL. In our work, the MBC is also calculated as the lowest G6 formula concentration needed to reduce the initial microbial inoculum's viability by ≥ 99.9%. Therefore, it can be measured through MIC testing at a broth dilution and then sub-cultured onto nutrient agar plates without adding G6 formula. An MBC endpoint is reached at the lowest dose of G6 formula when 99.9% of the microbial population is destroyed based on the colony count assay [62]. The pathogen's species influences the measured ranges of MIC and MBC after a 24h incubation period at 37°C. For both yeast cells and Gram-negative bacteria, G6 formula's MIC and MBC varies from 270–300 µg/mL; respectively. On the other hand, MIC, and MBC of G6 formula for Gram-positive bacteria varies from 210–240 µg/mL.
To investigate time-kill kinetics of G6 formula further, the pathogens exhibit the most potent antimicrobial effect within each microbial group, were selected. Bacillus cereus, Candida krusei, and Escherichia coli are the strains used in time-kill assay to determine the proportion of viable cells after G6 treatment. Results of time-kill kinetics are collected in Table 5. Planktonic viable counts of every tested pathogen are successfully reduced by the tested G6 formulation at a concentration of 210 µg/mL. Compared to other examined pathogens, Bacillus cereus cells in G6 formulation dose lost significantly more planktonic viable counts across cultivation periods after 36h incubation period.
Table 5
In vitro time-kill kinetics of multidrug-resistant human pathogens treated with G6-formula, collectively with the comparable untreated cells.
Incubation period (h) | Multidrug-resistant human pathogens treated with G6-formula (210 µg/ml) |
Escherichia coli | Bacillus cereus | Candida krusei |
Cell viability (log10CFU/mL ± SD) | Biofilm reduction (%±SD) | Cell viability (log10CFU/mL± SD) | Biofilm reduction (%±SD) | Cell viability (log10CFU/mL ±SD) | Biofilm reduction (%±SD) |
Untreated | Treated | Untreated | Treated | Untreated | Treated |
0 | 2.94 ± 0.65 | 2.9395193 | 0.33 ± 0.46 | 2.50 ± 0.51 | 2.46 ± 0.23 | 0.16 ± 0.23 | 1.41 ± 0.51 | 1.42 ± 0.52 | 0.07 ± 0.51 |
6 | 7.17 ± 0.60 | 1.84 ± 0.56 | 74.28 ± 1.82 | 6.07 ± 0.91 | 1.84 ± 0.56 | 69.64 ± 2.89 | 4.98 ± 1.91 | 1.845098 | 63.01 ± 0.89 |
12 | 7.22 ± 0.78 | 1.60 ± 0.25 | 77.83 ± 2.50 | 6.36 ± 1.72 | 1.60 ± 0.25 | 74.81 ± 0.72 | 5.27 ± 1.72 | 1.60 ± 0.52 | 69.61 ± 3.03 |
18 | 7.23 ± 0.44 | 1.32 ± 0.22 | 81.71 ± 2.31 | 6.98 ± 2.67 | 1.32 ± 0.22 | 81.07 ± 1.53 | 5.89 ± 1.67 | 1.32 ± 0.25 | 77.57 ± 2.72 |
24 | 7.24 ± 0.33 | 1.04 ± 0.13 | 85.62 ± 2.15 | 7.34 ± 1.24 | 0.76 ± 0.34 | 89.60 ± 0.25 | 6.25 ± 1.24 | 0.76 ± 0.34 | 87.78 ± 2.98 |
30 | 2.30 ± 0.18 | 0.14 ± 0.61 | 93.64 ± 0.94 | 7.15 ± 1.36 | 0.08 ± 0.12 | 98.88 ± 1.18 | 6.06 ± 0.35 | 0.18 ± 0.24 | 97.02 ± 1.97 |
36 | 2.30 ± 0.12 | 0.11 ± 0.39 | 95.04 ± 0.81 | 7.08 ± 0.87 | 0.05 ± 0.09 | 99.29 ± 0.37 | 5.99 ± 1.28 | 0.15 ± 0.32 | 97.49 ± 2.58 |
42 | 2.04 ± 0.13 | 0.08 ± 0.02 | 96.08 ± 1.13 | 7.05 ± 2.21 | 0.04 ± 0.02 | 99.43 ± 0.26 | 5.96 ± 0.65 | 0.14 ± 0.05 | 97.65 ± 1.67 |
48 | 1.30 ± 0.10 | 0.05 ± 0.01 | 96.15 ± 1.68 | 7.01 ± 0.36 | 0.03 ± 0.1 | 99.57 ± 0.20 | 5.92 ± 1.23 | 0.13 ± 0.09 | 98.31 ± 0.81 |
As compared to comparable untreated cells, the percentage of cell viability in all treated pathogens decreased with the length of culturing period (Fig. 11b). Bacillus cereus (99.29 ± 0.37%), Candida krusei (97.49 ± 2.58%), and Escherichia coli (95.04 ± 0.81%) had the highest growth reduction percentages after the pathogens are cultured for 36h with G6 formula. After the incubation period of 48h, these reduction percentages considerably increase (Table 5). The period of G6 treatment needed to completely eradicate the pathogens' biofilm is also determined using time-kill kinetics test. After 96h, biofilms of treated Escherichia coli and Candida krusei show 0% CFU/mL; however, Bacillus cereus needed 72h to destroy the biofilms. In conclusion, G6 mix consisting of Ag NPs, ZnO QDs, and Nitazoxanide exhibits promising antimicrobial abilities associated with microbial infections caused by multidrug-resistant human pathogens. The intracellular metabolic processes are impacted by the generation of reactive oxygen species (ROS), which is encouraged by our tested formula. These modifications make it possible for recently produced ROS to interact with and disrupt DNA, proteins, carbohydrates, and lipids [63].