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Many amines with high toxicity always cause a serious threat to the ecological environment and human health; thus, their detection is important. Herein, a dual-mode colorimetric and ratiometric fluorescent sensor based on cellulose for detecting amines has been constructed by a new strategy. This sensor is made of a "negative response" indicator (Lum-MDI-CA) and a "positive response" indicator (perylene tetracarboxylic acid, PTCA). Lum-MDI-CA was obtained by attaching luminol onto cellulose chains, which emitted blue fluorescence and was quenched upon contact with amines. A possible mechanism of fluorescence quenching phenomenon is proposed by the intramolecular charge transfer (ICT) of Lum-MDI-CA. Subsequently, by simply mixing Lum-MDI-CA with PTCA, a dual-mode fluorescence sensor was designed for visual detection and classification of amines. When adding ammonia (NH3), morpholine (MOR), benzylamine (BNZ), diethylamine (DEA), and triethylamine (TEA), respectively, the dual-mode sensor showed visible different color changes under both UV light and daylight. In addition, owing to the excellent processibility and formability of cellulose acetate backbone, the prepared sensor can be easily processed into different material forms, including inks, coatings, films, and fibers, which still exhibit excellent fluorescence emission. Such sensors based on cellulose fluorescent materials are of great value in anti-counterfeiting and information encryption.

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Acute myocardial infarction (AMI) is a serious medical condition generally known as heart attack, which is caused by the decreased or completely blocked blood flow to a part of the heart muscle. It is a significant cause of both mortality and morbidity throughout the world. Cardiac troponin-I (cTnI) is an important biomarker at different stages of AMI and is one of the most specific and widely used cardiac skeletal muscle proteins. Delays in medical treatment and inaccurate diagnosis might be the main cause of death of AMI patients. To overcome the death rate of AMI patients, early diagnosis of this disease is crucial. In the current study, surface-enhanced Raman spectroscopy (SERS) is employed for the characterization and diagnosis of this disease using blood serum samples from 49 clinically confirmed acute myocardial infarction (AMI) patients and 17 healthy persons. Silver nanoparticles (AgNPs) are used as the SERS substrate for the recognition of characteristic SERS spectral features, differentiating between healthy and AMI-positive samples. The acute myocardial infarction-positive blood serum samples reveal remarkable differences in spectral intensities at 534, 697, 744, 835, 927, 941, 988, 1221, 1303, 1403, 1481, 1541, 1588 and 1694 cm-1. For the differentiation and quantitative analysis of the SERS spectra, multivariate chemometric tools (including principal component analysis (PCA) and partial least squares regression (PLSR)) are employed. A PLSR model established on the basis of differentiating the SERS spectral features is found to be helpful in the prediction of the levels of cardiac troponin-I (cTnI) in the blood serum samples with the root mean square error of calibration (RMSEC) value of 2.98 ng mL-1 and root mean square errors of prediction (RMSEP) value of 3.98 ng mL-1 for S7.

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Xylanases are essential hydrolytic enzymes which break down the plant cell wall polysaccharide, xylan composed of D-xylose monomers. Surface-enhanced Raman Spectroscopy (SERS) was utilized for the characterization of interaction of xylanases with xylan at varying concentrations. The study focuses on the application of SERS for the characterization of enzymatic activity of xylanases causing hydrolysis of Xylan substrate with increase in its concentration which is substrate for this enzyme in the range of 0.2% to 1.0%. SERS differentiating features are identified which can be associated with xylanases treated with different concentrations of xylan. SERS measurements were performed using silver nanoparticles as SERS substrate to amplify Raman signal intensity for the characterization of xylan treated with xylanases. Principal Component Analysis (PCA) and Partial Least Square Discriminant Analysis (PLS-DA) were applied to analyze the spectral data to analyze differentiation between the SERS spectra of different samples. Mean SERS spectra revealed significant differences in spectral features particularly related to carbohydrate skeletal mode and O-C-O and C-C-C ring deformations. PCA scatter plot effectively differentiates data sets, demonstrating SERS ability to distinguish treated xylanases samples and the PC-loadings plot highlights the variables responsible for differentiation. PLS-DA was employed as a quantitative classification model for treated xylanase enzymes with increasing concentrations of xylan. The values of sensitivity, specificity, and accuracy were found to be 0.98%, 0.99%, and 100% respectively. Moreover, the AUC value was found to be 0.9947 which signifies the excellent performance of PLS-DA model. SERS combined with multivariate techniques, effectively characterized and differentiated xylanase samples as a result of interaction with different concentrations of the Xylan substrate. The identified SERS features can help to characterize xylanases treated with various concentrations of xylan with promising applications in the bio-processing and biotechnology industries.

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OBJECTIVE: To develop a Raman spectroscopy-based analytical model for quantification of solid dosage forms of active pharmaceutical ingredient (API) of Atenolol.Significance: For the quantitative analysis of pharmaceutical drugs, Raman Spectroscopy is a reliable and fast detection method. As part of this study, Raman Spectroscopy is explored for the quantitative analysis of different concentrations of Atenolol. METHODS: Various solid-dosage forms of Atenolol were prepared by mixing API with excipients to form different solid-dosage formulations of Atenolol. Multivariate data analysis techniques, such as Principal Component Analysis (PCA) and Partial least square regression (PLSR) were used for the qualitative and quantitative analysis, respectively. RESULTS: As the concentration of the drug increased in formulation, the peak intensities of the distinctive Raman spectral characteristics associated with the API (Atenolol) gradually increased. Raman spectral data sets were classified using PCA due to their distinctive spectral characteristics. Additionally, a prediction model was built using PLSR analysis to assess the quantitative relationship between various API (Atenolol) concentrations and spectral features. With a goodness of fit value of 0.99, the root mean square errors of calibration (RMSEC) and prediction (RMSEP) were determined to be 1.0036 and 2.83 mg, respectively. The API content in the blind/unknown Atenolol formulation was determined as well using the PLSR model. CONCLUSIONS: Based on these results, Raman spectroscopy may be used to quickly and accurately analyze pharmaceutical samples and for their quantitative determination.

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Urinary tract infection caused by Klebsiella, Proteus and Streptococcus is a urease dependent process, hence treatment of these infections by antibacterial compounds lies in inhibition of their virulence factors. The crude methanolic extracts derived from sumac fruit, pomegranate peel and Indian almond leaves were separated into anthocyanin and non-anthocyanin fractions using solid phase cartridges. The inhibitory effect of these fractions was determined on the growth of urease producing species and jack bean urease activity. Known compounds in the fractions were also docked with ureases of different biological origins viz. K. pneumoniae (PDB ID: 8HCN), K. aerogenes (PDB ID: 2KAU), Helicobacter pylori (PDB ID:8HC1)and Canavalia ensiformis (jack bean) (PDB ID: 3LA4) to determine their binding affinities and interaction with the enzyme. All the fractions showed significant inhibition growth for P. mirabilis, S. epidermidis and K.pneumoniae. Among the samples, sumac showed greatest inhibition against all (MIC 6-25 mg.mL-1) while among the fractions, anthocyanin was found to be most active (MIC 6-12 mg/mL). Likewise, all fractions inhibited urease with lowest ICs50 shown by sumac fractions (21-116 µg.mL-1). Out of 39 compounds docked, 27 showed interaction with movable flaps and/or active site of ureases which explains their mode of inhibition.

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Fossil fuels are considered vital natural energy resources on the Earth, and sulfur is a natural component present in them. The combustion of fossil fuels releases a large amount of sulfur in the form of SO x in the atmosphere. SO x is the major cause of environmental problems, mainly air pollution. The demand for fuels with ultra-low sulfur is growing rapidly. In this aspect, microorganisms are proven extremely effective in removing sulfur through a process known as biodesulfurization. A major part of sulfur in fossil fuels (coal and oil) is present in thiophenic structures such as dibenzothiophene (DBT) and substituted DBTs. In this study, the identification and characterization of DBT desulfurizing bacteria (Chryseobacterium sp. IS, Gordonia sp. 4N, Mycolicibacterium sp. J2, and Rhodococcus sp. J16) based on their specific biochemical constituents were conducted using surface-enhanced Raman spectroscopy (SERS). By differentiating DBT desulfurizing bacteria, researchers can gain insights into their unique characteristics, thus leading to improved biodesulfurization strategies. SERS was used to differentiate all these species based on their biochemical differences and different SERS vibrational bands, thus emerging as a potential technique. Moreover, multivariate data analysis techniques such as principal component analysis (PCA) and partial least squares discriminant analysis (PLS-DA) were employed to differentiate these DBT desulfurizing bacteria on the basis of their characteristic SERS spectral signals. For all these isolates, the accuracy, sensitivity, and specificity are above 90%, and an AUC (area under the curve) value of close to 1 was achieved for all PLS-DA models.

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The process of dissolving cellulose is a pivotal step in transforming it into functional, value-added materials, necessitating a thorough comprehension of the underlying mechanisms to refine its advanced processing. This article reviews cellulose dissolution using various solvent systems, along with an in-depth exploration of the associated dissolution mechanisms. The efficacy of different solvents, including aqueous solvents, organic solvents, ionic liquids, hybrid ionic liquid/cosolvent systems, and deep eutectic solvents, in dissolving cellulose is scrutinized, and their limitations and advantages are highlighted. In addition, this review methodically outlines the mechanisms at play within these various solvent systems and the factors influencing cellulose solubility. Conclusions drawn highlight the integral roles of the degree of polymerization, crystallinity, particle size, the type and sizes of cations and anions, alkyl chain length, ionic liquid/cosolvent ratio, viscosity, solvent acidity, basicity, and hydrophobic interactions in the dissolution process. This comprehensive review aims to provide valuable insights for researchers investigating biopolymer dissolution in a broader context, thereby paving the way for broader applications and innovations of these solvent systems.

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In this study, Gordonia sp. HS126-4N was employed for dibenzothiophene (DBT) biodesulfurization, tracked over 9 days using SERS. During the initial lag phase, no significant spectral changes were observed, but after 48 h, elevated metabolic activity was evident. At 72 h, maximal bacterial population correlated with peak spectrum variance, followed by stable spectral patterns. Despite 2-hydroxybiphenyl (2-HBP) induced enzyme suppression, DBT biodesulfurization persisted. PCA and PLS-DA analysis of the SERS spectra revealed distinctive features linked to both bacteria and DBT, showcasing successful desulfurization and bacterial growth stimulation. PLS-DA achieved a specificity of 95.5 %, sensitivity of 94.3 %, and AUC of 74 %, indicating excellent classification of bacteria exposed to DBT. SERS effectively tracked DBT biodesulfurization and bacterial metabolic changes, offering insights into biodesulfurization mechanisms and bacterial development phases. This study highlights SERS' utility in biodesulfurization research, including its use in promising advancements in the field.

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Introduction: Rheumatoid arthritis (RA) is a chronic autoimmune disease characterized by inflammation and synovial joint destruction. Aims: The current study investigated the possible beneficial effect of zinc oxide nanoparticles doped curcumin (ZnONPs-DC) on the recovery of RA and antioxidant status of experimental rabbits. Methods: RA was induced in experimental rabbits by injecting complete Freund's adjuvant and collagen type-II emulsion (100 µL/kg body weight) in the base of their tail. Arthritic rabbits were orally treated with ZnONPs, curcumin, and ZnONPs-DC(250 µL/kg bodyweight). Serumsamples fromthe control and study groupswere collected before and afterRAinduction and after treatment. The sera were subjected to analysis of biological markers of RA and antioxidant status. Results: The complete Freund's adjuvant and collagen type II treatment resulted in positive rheumatoid factor and C-reactive protein elevated oxidative stress and decreased antioxidant potential. Each treatment showed the absence of rheumatoid factor and C-reactive protein decreased oxidative stress and improved antioxidant potential compared to the control. However, ZnONPs-DC treatment showed a comparatively higher decline in serum malondialdehyde MDA content and an elevation in the antioxidant activity of RA animals. Conclusions: In conclusion, using zinc oxide nanoparticles-doped curcumin may be an effective anti-arthritic and anti-inflammatory drug in controlling RA.

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Bacterial resistance towards antibiotics is a significant challenge for public health, and surface-enhanced Raman spectroscopy (SERS) has great potential to be a promising technique to provide detailed information about the effect of antibiotics against biofilms. SERS is employed to check the antibacterial potential of a lab synthesized drug ([bis(1,3-dipentyl-1H-imidazol-2(3H)-ylidene)silver(i)] bromide) against Bacillus subtilis and to analyze various SERS spectral features of unexposed and exposed Bacillus strains by observing biochemical changes in DNA, protein, lipid and carbohydrate contents induced by the lab synthesized imidazole derivative. Further, PCA and PLS-DA are employed to differentiate the SERS features. PCA was employed to differentiate the biochemical contents of unexposed and exposed Bacillus strains in the form of clusters of their representative SERS spectra and is also helpful in the pairwise comparison of two spectral data sets. PLS-DA provides authentic information to discriminate different unexposed and exposed Bacillus strains with 91% specificity, 93% sensitivity and 97% accuracy. SERS can be employed to characterize the complex and heterogeneous system of biofilms and to check the changes in spectral features of Bacillus strains by exposure to the lab synthesized imidazole derivative.

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Pretreatment is the key step to convert lignocelluloses to sustainable biofuels, biochemicals or biomaterials. In this study, a green pretreatment method based on choline chloride-lactic acid deep eutectic solvent (ChCl-LA) and niobium-based single-atom catalyst (Nb/CN) was developed for the fractionation of corn straw and further enzymatic hydrolysis of cellulose. With this strategy, significant lignin removal of 96.5 % could be achieved when corn straw was pretreated by ChCl-LA (1:2) DES over Nb/CN under 120 °C for 6 h. Enzymatic hydrolysis of the cellulose-enriched fraction (CEF) presented high glucose yield of 92.7 % and xylose yield of 67.5 %. In-depth investigations verified that the high yields of fractions and monosaccharides was attributed to the preliminary fractionation by DES and the deep fractionation by Nb/CN. Significantly, compared to other reported soluble catalysts, the synthesized single-atom catalyst displayed excellent reusability by simple filtration and enzymatic hydrolysis. The recyclability experiments showed that the combination of ChCl-LA DES and Nb/CN could be repeated at least three times for corn straw fractionation, moreover, the combination displayed remarkable feedstock adaptability.

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In this study, surface-enhanced Raman spectroscopy (SERS) technique, along with principal component analysis (PCA) and partial least-squares discriminant analysis (PLS-DA), is used as a simple, quick, and cost-effective analysis method for identifying biochemical changes occurring due to induced mutations in the Aspergillus niger fungus strain. The goal of this study is to identify the biochemical changes in the mutated fungal cells (cell mass) as compared to the control/nonmutated cells. Furthermore, multivariate data analysis tools, including PCA and PLS-DA, are used to further confirm the differentiating SERS spectral features among fungal samples. The mutations are caused in A. niger by the clustered regularly interspaced palindromic repeat CRISPR-Cas9 genomic editing method to improve their biotechnological potential for the production of cellulase enzyme. SERS was employed to detect the changes in the cells of mutated A. niger fungal strains, including one mutant producing low levels of an enzyme and another mutant producing high levels of the enzyme as a result of mutation as compared with an unmutated fungal strain as a control sample. The distinctive features of SERS corresponding to nucleic acids and proteins appear at 546, 622, 655, 738, 802, 835, 959, 1025, 1157, 1245, 1331, 1398, and 1469 cm-1. Furthermore, PLS-DA is used to confirm the 89% accuracy, 87.7% precision, 87% sensitivity, and 88.9% specificity of this method, and the value of the area under the curve (AUROC) is 0.67. It has been shown that surface-enhanced Raman spectroscopy is an effective method for identifying and differentiating biochemical changes in genome-modified fungal samples.

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The ability of surface-enhanced Raman spectroscopy (SERS) to generate spectroscopic fingerprints has made it an emerging tool for biomedical applications. The objective of this study is to confirm the potential use of Raman spectroscopy for early disease diagnosis based on blood serum. In this study, a total of sixty blood serum samples, consisting of forty from diseased patients and twenty (controls) from healthy individuals, was used. Because disease biomarkers, found in the lower molecular weight fraction, are suppressed by higher molecular weight proteins, 50 kDa Amicon ultrafiltration centrifugation devices were used to produce two fractions from whole blood serum consisting of a filtrate, which is a low molecular weight fraction, and a residue, which is a high molecular weight fraction. These fractions were then analyzed, and their SERS spectral data were compared with those of healthy fractions. The SERS technique was utilized on blood serum, filtrate and residue of patients with tuberculosis to identify characteristic SERS spectral features associated with the development of disease, which can be used to differentiate them from healthy samples using silver nanoparticles as a SERS substrate. For further analysis, the effective chemometric technique of principal component analysis (PCA) was used to qualitatively differentiate all the analyzed samples based on their SERS spectral features. Partial least squares discriminant analysis (PLS-DA) accurately classified the filtrate portions of healthy and tuberculosis samples with 97% accuracy, 97% specificity, 98% sensitivity, and an area under the receiver operating characteristic (AUROC) curve of 0.74.

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Large amount of sulphur is released by the combustion of fossil fuels in the form of SoX which affects human health and leads to acid rain. To overcome this issue, it is essential to eliminate sulphur moieties from heterocyclic organo-sulphur compounds like Dibenzothiophene (DBT) present in the petrol. In this study Surface enhanced Raman scattering (SERS) spectroscopy is used to analyze the desulfurizing activity of Tsukamurella paurometabola bacterial strain. The most prominent SERS peaks observed at 791, 837, 944 and 1032 cm-1, associated to C-S stretching, are solely observed in dibenzothiophene and its metabolite-I (DBTS) but absent in 2-Hydroxybiphenyl (metabolite-II) and extraction sample of supernatant as a result of biodesulfurization. Moreover, the SERS peaks observed at 974 (characteristic peak of benzene ring) and 1015 cm-1 is associated to C-C ring breathing while 1642 and 1655 cm-1 assigned to CC bonds of aromatic ring. These peaks are only observed in 2-Hydroxybiphenyl (metabolite-II) and extraction sample of supernatant as a result of biodesulfurization. Notably, these peaks are absent in the Dibenzothiophene and its metabolite-I which indicate that aromatic ring is carrying sulfur in this fraction. Moreover, multivariate data analytical tools like principal component analysis (PCA) and PCA-loadings are applied to further differentiate between dibenzothiophene and its metabolites that are Dibenzothiophene sulphone (metabolite-I) and 2-Hydroxybiphenyl (metabolite-II).

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Identification of adulterants in commercial samples of methyl eugenol is necessary because it is a botanical insecticide, a tephritid male attractant lure that is used to attract and kill invasive pests such as oriental fruit flies and melon flies on crops. In this study, Raman spectroscopy was used to qualitatively and quantitatively assess commercial methyl eugenol along with adulterants. For this purpose, commercial methyl eugenol was adulterated with different concentrations of xylene. The Raman spectral features of methyl eugenol and xylene in liquid formulations were examined, and Raman peaks were identified as associated with the methyl eugenol and adulterant. Principal component analysis (PCA) and partial least-squares regression analysis (PLSR) have been used to qualitatively and quantitatively analyze the Raman spectral features. PCA was applied to differentiate Raman spectral data for various concentrations of methyl eugenol and xylene. Additionally, PLSR has been used to develop a predictive model to observe a quantitative relationship between various concentrations of adulterated methyl eugenol and their Raman spectral data sets. The root-mean-square errors of calibration and prediction were calculated using this model, and the results were found to be 1.90 and 3.86, respectively. The goodness of fit of the PLSR model is found to be 0.99. The proposed approach showed excellent potential for the rapid, quantitative detection of adulterants in methyl eugenol, and it may be applied to the analysis of a range of pesticide products.

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Uric acid (UA) is a significant indicator of human health because it is linked to several diseases, including renal failure, kidney stones, arthritis, and gout. Uric acid buildup in the joints is the source of chronic and painful diseases. When UA is present in large quantities, it causes tissue injury in the joints that are afflicted. In this research, silver oxide-doped activated carbon nanoparticles were synthesized and then functionalized with an ionic liquid. The synthesized nanomaterial assembly was employed as a colorimetric sensing platform for uric acid. Activated carbon offers a large internal surface area that acts as a good carrier for catalytic reactions. A salt-melting approach was used to synthesize the silver oxide-doped activated carbon nanocomposite. The synthesis was confirmed through various techniques, such as UV-vis spectrophotometer, FTIR, XRD, SEM, and EDX. The colorimetric change from blue-green to colorless was observed with the naked eye and confirmed by UV-vis spectroscopy. To obtain the best colorimetric change, several parameters, such as pH, capped NP loading, TMB concentration, hydrogen peroxide concentration, and time, were optimized. The optimized experimental conditions for the proposed sensor were pH 4 with 35 µL of NPs, a 40 mM TMB concentration, and a 4 minutes incubation time. The sensor linear range is 0.001-0.36 µM, with an R2 value of 0.999. The suggested sensor limits of detection and quantification are 0.207 and 0.69 nM, respectively. Potential interferers, such as ethanol, methanol, urea, Ca2+, K+, and dopamine, did not affect the detection of uric acid.

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Methicillin-resistant Staphylococcus aureus (MRSA) is gram positive bacteria and leading cause of a wide variety of diseases. It is a common cause of hospitalized and community-acquired infections. Development of increasing antibiotic-resistance by methicillin-resistant S. aureus (MRSA) strains demand to develop alternate novel therapies. Bacteriophages are now widely used as antibacterial therapies against antibiotic-resistant gram-positive pathogens. So, there is an urgent need to find fast detection techniques to point out phage susceptible and resistant strains of methicillin-resistant S. aureus (MRSA) bacteria. Samples of two separate strains of bacteria, S. aureus, in form of pellets and supernatant, were used for this purpose. Strain-I was resistant to phage, while the other (strain-II) was sensitive. Surface Enhanced Raman Spectroscopy (SERS) has detected significant biochemical changes in these bacterial strains of pellets and supernatants in the form of SERS spectral features. The protein portion of these two types of strains of methicillin-resistant S. aureus (MRSA) in their relevant pellets and supernatants is major distinguishing biomolecule as shown by their representative SERS spectral features. In addition, multivariate data analysis techniques such as principal component analysis (PCA) and a partial least squares-discriminant analysis (PLS-DA) were found to be helpful in identifying and characterizing various strains of S. aureus which are sensitive and resistant to bacteriophage with 100% specificity, 100% accuracy, and 99.8% sensitivity in case of SERS spectral data sets of bacterial cell pellets. Moreover, in case of supernatant samples, the results of PLS-DA model including 95.5% specificity, 96% sensitivity, and 96.5% accuracy are obtained.

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The production of crops depending on many factors including water, nutrient, soil types, climate and crops types, water stress and drought is in one of the important factors affecting crop productivity. The experiment was conducted in pots to evaluate the effect of biofertilizers (Bacillus simplex) with deficit irrigations on the early development and growth of maize crop under greenhouse condition. Pre sowing seed was inoculated with strain of bacteria (B+/B-) and different irrigation levels (no stress: 100% (I1) and deficit irrigation: 75 (I2), 50 (I3), 25 (I4) % of required water amount to reach pot capacity) was performed. Data was collected on different morphological characteristics and root characteristic of maize crop. Highest plant height (125 cm), stem diameter (18.02 mm), leaf area (350 cm- 2), plant weight (180.42 g in fresh, 73.58 g in dry), root length (92.83 cm) root ((91.70 g in fresh, (28.66 g in dry) weight were recorded in pots applied with 100% irrigation followed by 75%. Bacillus treated plants showed significant increase in leaf area (214.20 cm- 2), plant fresh weight (91.65 g) and dry weight (42.05 g), root length (79.20 cm), root fresh (53.52 g) and dry weight (16.70 g) compared with control (without bacteria). Likewise highest relative water content of leaf was observed with I3 followed by I2 and I1 respectively. Highest water use efficiency was recorded as 0.67 g pot- 1 mm- 1 in I1 with B + treatment. Likewise, Bacillus inoculated pots resulted in increased water use efficiency (0.44 g pot- 1 mm- 1) compared with no application (0.36 g pot- 1 mm- 1). It can be endorsed from the outcome that Bacillus inoculation increased plant biomass, root biomass of maize and water use efficiency during early growth stage of maize despite of water stress and can be used under limited water condition for crop combating during moderate to lower stress conditions.

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Escherichia coli biofilms are a major cause of gastrointestinal tract diseases, such as esophageal, stomach and intestinal diseases. Nowadays, these are the most commonly occurring diseases caused by consuming contaminated food. In this study, we evaluated the efficacy of probiotics in controlling multidrug-resistant E. coli and reducing its ability to form biofilms. Our results substantiate the effective use of probiotics as antimicrobial alternatives and to eradicate biofilms formed by multidrug-resistant E. coli. In this research, surface enhanced Raman spectroscopy (SERS) was utilized to identify and evaluate Escherichia coli biofilms and their response to the varying concentrations of the organometallic compound bis(1,3-dihexylimidazole-2-yl) silver(i) hexafluorophosphate (v). Given the escalating challenge of antibiotic resistance in bacteria that form biofilms, understanding the impact of potential antibiotic agents is crucial for the healthcare sector. The combination of SERS with principal component analysis (PCA) and partial least squares discriminant analysis (PLS-DA) enabled the detection and characterization of the biofilm, providing insights into the biochemical changes induced by the antibiotic candidate. The identified SERS spectral features served as indicators for elucidating the mode of action of the potential drug on the biofilm. Through PCA and PLS-DA, metabolic variations allowing the differentiation and classification of unexposed biofilms and biofilms exposed to different concentrations of the synthesized antibiotic were successfully identified, with 95% specificity, 96% sensitivity, and a 0.75 area under the curve (AUC). This research underscores the efficiency of surface enhanced Raman spectroscopy in differentiating the impact of potential antibiotic agents on E. coli biofilms.

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In the current study, surface-enhanced Raman scattering (SERS) was performed to evaluate the antibacterial activity of lab-synthesized drug (1-isopentyl-3-pentyl-1H-imidazole-3-ium bromide salt) and commercial drug tinidazole againstBacillus subtilis. The changes in SERS spectral features were studied for unexposed bacillus and exposed one with various dosages of drug synthesized in the lab (1-isopentyl-3-pentyl-1H-imidazole-3-ium bromide salt), and SERS bands were assigned associated with the drug-induced biochemical alterations in bacteria. Multivariate data analysis tools including principal component analysis (PCA) and partial least-squares discriminant analysis (PLS-DA) have been utilized to analyze the antibacterial activity of the imidazole derivative (lab drug). PCA was employed in differentiating all the SERS spectral data sets associated with the various doses of the lab-synthesized drug. There is clear discrimination among the spectral data sets of a bacterial strain treated with different concentrations of the drug, which are analyzed by PLS-DA with 86% area under the curve in receiver operating curve (ROC), 99% sensitivity, 100% accuracy, and 98% specificity. Various dominant spectral features are observed with a gradual increase in the different concentrations of the applied drug including 715, 850, 1002, 1132, 1237, 1396, 1416, and 1453 cm-1, which indicate the possible biochemical changes caused in bacteria during the antibacterial activity of the lab-synthesized drug. Overall, the findings show that imidazole and imidazolium compounds generated from tinidazole with various alkyl lengths in the amide substitution can be effective antibacterial agents with low cytotoxicity in humans, and these results indicate the efficiency of SERS in pharmaceuticals and biomedical applications.