In vitro exploration of Acinetobacter strain (SG-5) for antioxidative potential and phytohormone biosynthesis in maize (Zea mays L.) cultivars differing in cadmium tolerance.

Cadmium (Cd) poses serious threats to plant growth and development, whereas the use of plant growth-promoting rhizobacteria (PGPR) has emerged a promising approach to diminish Cd retention in crops. A pot experiment was conducted to evaluate the effect of Cd tolerant strain Acinetobacter sp. SG-5 on growth, phytohormonal response, and Cd uptake of two maize cultivars (3062 and 31P41) under various Cd stress levels (0, 5, 12, 18, 26, and 30 µM CdCl2). The results revealed that CdCl2 treatment significantly suppressed the seed germination and growth together with higher Cd retention in maize cultivars in a dose-dependent and cultivar-specific manner with pronounced negative effect in 31P41. However, SG-5 strain exerted positive impact by up-regulating seed germination traits, plant biomass, photosynthetic pigments, enzymatic and non-enzymatic antioxidants, endogenous hormone level indole-3-acetic acid (IAA), abscisic acid (ABA), and sustained optimal nutrient's levels in both cultivars but predominantly in Cd-sensitive one (31P41). Further, Cd-resistant PGPR decreased the formation of reactive oxygen species in terms of malondialdehyde (MDA) and hydrogen peroxide (H2O2) verified through 3, 3'-diaminobenzidine (DAB) and nitroblue tetrazolium (NBT) analysis in conjunction with reduced Cd uptake and translocation in maize root and shoots in comparison to controls, advocating its sufficiency for bacterial-assisted Cd bioremediation. In conclusion, both SG-5 inoculated cultivars exhibited maximum Cd tolerance but substantial Cd tolerance was acquired by Cd susceptible cultivar-31P41 than Cd-tolerant one (3062). Current work recommended SG-5 strain as a promising candidate for plant growth promotion and bacterial-assisted phytomanagement of metal-polluted agricultural soils.

Comparative in-vivo bond failure rate of orthodontic brackets when bracket base is treated with micro-abrasive blasting vs. acid etching: eighteen month randomized control trial and scanning electron microscope study.

Background: The aim of this study was threefold. Firstly, it aimed to introduce and detail a novel method for chemically etching the bases of stainless-steel orthodontic brackets. Secondly, the study sought to investigate the structural alterations within the brackets' microstructure following chemical etching compared to those with sandblasted bases, using electron microscopy analysis. Lastly, the study aimed to evaluate and compare the long-term durability and survivability of orthodontic brackets with chemically etched bases versus those with sandblasted bases, both bonded using the conventional acid etch technique with Transbond XT adhesive, over an 18-month follow-up period. Methods: The study was a randomized clinical control trial with triple blinding and split-mouth study design and consisted of two groups. The brackets in the sandblasted group were prepared by sandblasting the intaglio surface of the base of the bracket with 50 µm SiO2 particles. Hydrofluoric acid was used to roughen the base in the acid-etched group. The bases of the brackets were viewed under an electron microscope to analyze the topographical changes. Results: A total of 5,803 brackets (3,006 acid-etch, 2,797 sandblasted) in 310 patients were bonded, in a split-mouth design by the same operator. The patients were followed for 18 months. The failure rate of 2.59% and 2.7% was noted in an acid-etched and sandblasted group, respectively. There was a close approximation of curves in the Kaplan-Meier plot, and the survival distribution of the two groups in the log-rank (Mantel-Cox) test was insignificant; x2 = 0.062 (P value = 0.804). Conclusion: Acid etching if the bases of the brackets can be used as an alternative to sandblasting furthermore acid etching can be performed on the chair side.

Cardiopulmonary exercise variables and their association with postoperative morbidity and mortality after major oesophagogastric cancer surgery-a multicentre observational study.

Background: Outcomes after oesophagogastric cancer surgery remain poor. Cardiopulmonary exercise testing (CPET) used for risk stratification before oesophagogastric cancer surgery is based on conflicting evidence. This study explores the relationship between CPET and postoperative outcomes, specifically for patients undergoing neoadjuvant treatment. Methods: Patients undergoing oesophagogastric cancer resection and CPET (pre- or post-neoadjuvant treatment, or both) were retrospectively enrolled into a multicentre pooled cohort study. Oxygen uptake at peak exercise (VO2 peak) was compared with 1-yr postoperative survival. Secondary analyses explored relationships between patient characteristics, tumour pathology characteristics, CPET variables (absolute, relative to weight, ideal body weight, and body surface area), and postoperative outcomes (morbidity, 1-yr and 3-yr survival) were assessed using logistic regression analyses. Results: Seven UK centres recruited 611 patients completing a 3-yr postoperative follow-up period. Oesophagectomy was undertaken in 475 patients (78%). Major complications occurred in 25%, with 18% 1-yr and 43% 3-yr mortality. No association between VO2 peak or other selected CPET variables and 1-yr survival was observed in the overall cohort. In the overall cohort, the anaerobic threshold relative to ideal body weight was associated with 3-yr survival (P=0.013). Tumour characteristics (ypT/ypN/tumour regression/lymphovascular invasion/resection margin; P<0.001) and Clavien-Dindo ≥3a (P<0.001) were associated with 1-yr and 3-yr survival. On subgroup analyses, pre-neoadjuvant treatment CPET; anaerobic threshold (absolute; P=0.024, relative to ideal body weight; P=0.001, body surface area; P=0.009) and VE/VCO2 at anaerobic threshold (P=0.026) were associated with 3-yr survival. No other CPET variables (pre- or post-neoadjuvant treatment) were associated with survival. Conclusions: VO2 peak was not associated with 1-yr survival after oesophagogastric cancer resection. Tumour characteristics and major complications were associated with survival; however, only some selected pre-neoadjuvant treatment CPET variables were associated with 3-yr survival. CPET in this cohort of patients demonstrates limited outcome predictive precision. Clinical trial registration: NCT03637647.

Hexagon MXene based heterojunction interface with high ion adsorption and electron transfer efficiency for soft-package potassium-ions aqueous supercapacitors.

Potassium-ion hybrid capacitors (PIHC) are considered as ideal large-scale rechargeable energy storage devices due to their low-cost, high-power density and environmental protection. However, a low energy density is the main factor restricting the practical application of PIHC. The interface is formed on the surface of electrode material of PIHC through strong correlation to construct heterojunction, which can significantly improve the performance of ion energy storage. However, how to reveal the influence of the interfacial state of the heterojunction on the adsorption and electron transmission of energy storage ions at the atomic level is still one of the key scientific problems in this field. In this work, metal ion intercalation and microwave-assisted in-situ etching are used to construct the Hexagon MXene Ti3C2 heterojunction with TiOHO strong correlation. At the interface of heterojunction, TiOHO highway for electron transmission is developed to improve the rate performance of PIHC. Through experimental and theoretical calculation, the optimum adsorption position and maximum adsorption amount of potassium-ion at the single interface of heterojunction are obtained, and the specific energy density of PIHC is increased. This lays a foundation for the practical application of high-performance soft-package PIHC.

Comparative analysis of various machine learning algorithms to predict strength properties of sustainable green concrete containing waste foundry sand.

The use of waste foundry sand (WFS) in concrete production has gained attention as an eco-friendly approach to waste reduction and enhancing cementitious materials. However, testing the impact of WFS in concrete through experiments is costly and time-consuming. Therefore, this study employs machine learning (ML) models, including support vector regression (SVR), decision tree (DT), and AdaBoost regressor (AR) ensemble model to predict concrete properties accurately. Moreover, SVR was employed in conjunction with three robust optimization algorithms: the firefly algorithm (FFA), particle swarm optimization (PSO), and grey wolf optimization (GWO), to construct hybrid models. Using 397 experimental data points for compressive strength (CS), 146 for elastic modulus (E), and 242 for split tensile strength (STS), the models were evaluated with statistical metrics and interpreted using the SHapley Additive exPlanation (SHAP) technique. The SVR-GWO hybrid model demonstrated exceptional accuracy in predicting waste foundry sand concrete (WFSC) strength characteristics. The SVR-GWO hybrid model exhibited correlation coefficient values (R) of 0.999 for CS and E, and 0.998 for STS. Age was found to be a significant factor influencing WFSC properties. The ensemble model (AR) also exhibited comparable prediction accuracy to the SVR-GWO model. In addition, SHAP analysis revealed an optimal content of input variables in the concrete mix. Overall, the hybrid and ensemble models showed exceptional prediction accuracy compared to individual models. The application of these sophisticated soft computing prediction techniques holds the potential to stimulate the widespread adoption of WFS in sustainable concrete production, thereby fostering waste reduction and bolstering the adoption of environmentally conscious construction practices.

Assessment of the impacts of artificial intelligence (AI) on intercultural communication among postgraduate students in a multicultural university environment.

Artificial intelligence (AI) broadly influences different aspects of human life, especially human communication. One of the main concerns of the broad use of AI in daily interactions among different people could be whether it helps them interact easily or complicates their interactions. To answer the mentioned question, this study assessed the impacts of AI on intercultural communication among postgraduate students in a multicultural university environment. A newly developed survey instrument was used to conduct this study. The participants of this study were 115 postgraduate students from nine different countries. The descriptive statistics, reliability analysis, and Bivariate correlation tests of the 29th version of IBM-SPSS software were used to analyze the quantitative data, and inductive coding and conceptual content analysis were used to code and analyze the qualitative data. Based on descriptive results, the vast majority (93%) of the participants already used and experienced AI in their daily lives, and the majority of them believed that AI and AI technologies connect different cultures, reduce language and cultural barriers, and help people of different cultures to interact and be connected. Based on the results from the correlation test, there were strong positive correlations between AI attitudes and AI benefits, and also between AI regulation and AI benefits.

M4X3 MXenes: Application in Energy Storage Devices.

MXene has garnered widespread recognition in the scientific community due to its remarkable properties, including excellent thermal stability, high conductivity, good hydrophilicity and dispersibility, easy processability, tunable surface properties, and admirable flexibility. MXenes have been categorized into different families based on the number of M and X layers in Mn+1Xn, such as M2X, M3X2, M4X3, and, recently, M5X4. Among these families, M2X and M3X2, particularly Ti3C2, have been greatly explored while limited studies have been given to M5X4 MXene synthesis. Meanwhile, studies on the M4X3 MXene family have developed recently, hence, demanding a compilation of evaluated studies. Herein, this review provides a systematic overview of the latest advancements in M4X3 MXenes, focusing on their properties and applications in energy storage devices. The objective of this review is to provide guidance to researchers on fostering M4X3 MXene-based nanomaterials, not only for energy storage devices but also for broader applications.

Evaluation of machine learning models for predicting TiO2 photocatalytic degradation of air contaminants.

The escalation of global urbanization and industrial expansion has resulted in an increase in the emission of harmful substances into the atmosphere. Evaluating the effectiveness of titanium dioxide (TiO2) in photocatalytic degradation through traditional methods is resource-intensive and complex due to the detailed photocatalyst structures and the wide range of contaminants. Therefore in this study, recent advancements in machine learning (ML) are used to offer data-driven approach using thirteen machine learning techniques namely XG Boost (XGB), decision tree (DT), lasso Regression (LR2), support vector regression (SVR), adaBoost (AB), voting Regressor (VR), CatBoost (CB), K-Nearest Neighbors (KNN), gradient boost (GB), random Forest (RF), artificial neural network (ANN), ridge regression (RR), linear regression (LR1) to address the problem of estimation of TiO2 photocatalytic degradation rate of air contaminants. The models are developed using literature data and different methodical tools are used to evaluate the developed ML models. XGB, DT and LR2 models have high R2 values of 0.93, 0.926 and 0.926 in training and 0.936, 0.924 and 0.924 in test phase. While ANN, RR and LR models have lowest R2 values of 0.70, 0.56 and 0.40 in training and 0.62, 0.63 and 0.31 in test phase respectively. XGB, DT and LR2 have low MAE and RMSE values of 0.450 min-1/cm2, 0.494 min-1/cm2 and 0.49 min-1/cm2 for RMSE and 0.263 min-1/cm2, 0.285 min-1/cm2 and 0.29 min-1/cm2 for MAE in test stage. XGB, DT, and LR2 have 93% percent errors within 20% error range in training phase. XGB has 92% and DT, and LR2 have 94% errors with 20% range in test phase. XGB, DT, LR2 models remained the highest performing models and XGB is the most robust and effective in predictions. Feature importances reveal the role of input parameters in prediction made by developed ML models. Dosage, humidity, UV light intensity remain important experimental factors. This study will impact positively in providing efficient models to estimate photocatalytic degradation rate of air contaminants using TiO2.

Telomere-to-telomere Genome Assembly of the Clubroot Pathogen Plasmodiophora Brassicae.

Plasmodiophora brassicae (Woronin, 1877), a biotrophic, obligate parasite, is the causal agent of clubroot disease in brassicas. The clubroot pathogen has been reported in more than 80 countries worldwide, causing economic losses of hundreds of millions every year. Despite its widespread impact, very little is known about the molecular strategies it employs to induce the characteristic clubs in the roots of susceptible hosts during infection, nor about the mechanisms it uses to overcome genetic resistance. Here, we provide the first telomere-to-telomere complete genome of P. brassicae. We generated ∼27 Gb of Illumina, Oxford Nanopore, and PacBio HiFi data from resting spores of strain Pb3A and produced a 25.3 Mb assembly comprising 20 chromosomes, with an N50 of 1.37 Mb. The BUSCO score, the highest reported for any member of the group Rhizaria (Eukaryota: 88.2%), highlights the limitations within the Eukaryota database for members of this lineage. Using available transcriptomic data and protein evidence, we annotated the Pb3A genome, identifying 10,521 protein-coding gene models. This high-quality, complete genome of P. brassicae will serve as a crucial resource for the plant pathology community to advance the much-needed understanding of the evolution of the clubroot pathogen.

A transfer learning enabled approach for ocular disease detection and classification.

Ocular diseases pose significant challenges in timely diagnosis and effective treatment. Deep learning has emerged as a promising technique in medical image analysis, offering potential solutions for accurately detecting and classifying ocular diseases. In this research, we propose Ocular Net, a novel deep learning model for detecting and classifying ocular diseases, including Cataracts, Diabetic, Uveitis, and Glaucoma, using a large dataset of ocular images. The study utilized an image dataset comprising 6200 images of both eyes of patients. Specifically, 70% of these images (4000 images) were allocated for model training, while the remaining 30% (2200 images) were designated for testing purposes. The dataset contains images of five categories that include four diseases, and one normal category. The proposed model uses transfer learning, average pooling layers, Clipped Relu, Leaky Relu and various other layers to accurately detect the ocular diseases from images. Our approach involves training a novel Ocular Net model on diverse ocular images and evaluating its accuracy and performance metrics for disease detection. We also employ data augmentation techniques to improve model performance and mitigate overfitting. The proposed model is tested on different training and testing ratios with varied parameters. Additionally, we compare the performance of the Ocular Net with previous methods based on various evaluation parameters, assessing its potential for enhancing the accuracy and efficiency of ocular disease diagnosis. The results demonstrate that Ocular Net achieves 98.89% accuracy and 0.12% loss value in detecting and classifying ocular diseases by outperforming existing methods.

Numerical and artificial intelligence based investigation on the development of design guidelines for pultruded GFRP RHS profiles subjected to web crippling.

This article presents a numerical and artificial intelligence (AI) based investigation on the web crippling performance of pultruded glass fiber reinforced polymers' (GFRP) rectangular hollow section (RHS) profiles subjected to interior-one-flange (IOF) loading conditions. To achieve the desired research objectives, a finite element based computational model was developed using one of the popular simulating software ABAQUS CAE. This model was then validated by utilizing the results reported in experimental investigation-based article of Chen and Wang. Once the finite element model was validated, an extensive parametric study was conducted to investigate the aforementioned phenomenon on the basis of which a comprehensive, universal, and coherent database was assembled. This database was then used to formulate the design guidelines for the web crippling design of pultruded GFRP RHS profiles by employing AI based gene expression programming (GEP). Based on the findings of numerical investigation, the web crippling capacity of abovementioned structural profiles subjected to IOF loading conditions was found to be directly related to that of section thickness and bearing length whereas inversely related to that of section width, section height, section's corner radii, and profile length. On the basis of the findings of AI based investigation, the modified design rules proposed by this research were found to be accurately predicting the web crippling capacity of aforesaid structural profiles. This research is a significant contribution to the literature on the development of design guidelines for pultruded GFRP RHS profiles subjected to web crippling, however, there is still a lot to be done in this regard before getting to the ultimate conclusions.

Effectiveness of H-files and Pro-Taper universal systems in removing Gutta-percha during endodontic retreatment: A comparative study.

Objective: This research was aimed at assessing the effectiveness of manual H-files versus a combination of a Pro-Taper universal rotary canal preparation system and retreatment system in removing gutta-percha (GP) during endodontic retreatment, by using a digital radiography technique. Methods: This ex vivo study used a non-probability consecutive sampling technique. The study sample comprised 60 extracted anterior permanent teeth, each with one root with a straight root canal (RC). After preparation, RCs were obturated with GP and sealer. Subsequently, teeth were stored for 2 weeks in a humid environment at 37 °C. Thirty teeth each were randomly assigned to the control (group I), and experimental (group II) groups. GP removal was performed with H-files {group I) or a combination of a Pro-Taper universal rotary canal preparation system and retreatment system (group 2). Digital radiographs were acquired with Carestream digital radiovisiography software (Kodak; version-VER.6.10.8.3-A), and the presence of residual GP was analyzed. AutoCAD (2006) software was used to demarcate the RC and residual root filling. The residual GP in both groups was compared with independent sample t-tests. Results: The remaining root filling did not significantly differ when GP was removed with conventional Hedstrom files versus a combination of Pro-Taper Universal preparation and retreatment file systems. The residual GP was confined to the apical third of the canals in both groups. Conclusions: Pro-Taper Universal preparation and retreatment file systems have similar effectiveness to manual H-files in GP removal in straight canals.

The Role of Metallurgical Features in the Microbially Influenced Corrosion of Carbon Steel: A Critical Review.

Microbially influenced corrosion (MIC) is a potentially critical degradation mechanism for a wide range of materials exposed to environments that contain relevant microorganisms. The likelihood and rate of MIC are affected by microbiological, chemical, and metallurgical factors; hence, the understanding of the mechanisms involved, verification of the presence of MIC, and the development of mitigation methods require a multidisciplinary approach. Much of the recent focus in MIC research has been on the microbiological and chemical aspects, with less attention given to metallurgical attributes. Here, we address this knowledge gap by providing a critical synthesis of the literature on the metallurgical aspects of MIC of carbon steel, a material frequently associated with MIC failures and widely used in construction and infrastructure globally. The article begins by introducing the process of MIC, then progresses to explore the complexities of various metallurgical factors relevant to MIC in carbon steel. These factors include chemical composition, grain size, grain boundaries, microstructural phases, inclusions, and welds, highlighting their potential influence on MIC processes. This review systematically presents key discoveries, trends, and the limitations of prior research, offering some novel insights into the impact of metallurgical factors on MIC, particularly for the benefit of those already familiar with other aspects of MIC. The article concludes with recommendations for documenting metallurgical data in MIC research. An appreciation of relevant metallurgical attributes is essential for a critical assessment of a material's vulnerability to MIC to advance research practices and to broaden the collective knowledge in this rapidly evolving area of study.

An Emerging Threat: A Systematic Review of Endocarditis Caused by Gemella Species.

Infective endocarditis caused by Gemella species is increasingly recognized as an emerging clinical entity. Gemella species are fastidious gram-positive cocci that are typically commensal organisms but can become opportunistic pathogens. This systematic review aimed to provide a comprehensive overview of endocarditis due to Gemella species by synthesizing existing evidence. A total of 52 case reports were identified through a rigorous search and selection process. The most prevalent causative species were G. morbillorum (46.3%) and G. haemolysans (25.9%), with a striking male predominance (79.6%). The clinical presentation was largely nonspecific, mirroring typical infective endocarditis. However, the indolent nature of the illness and fastidious growth requirements of Gemella species often led to diagnostic delays. Echocardiography, particularly transesophageal echocardiography, played a crucial role in the diagnosis, enabling the detection of valvular vegetation and the assessment of complications. Management posed significant challenges, including the need for broad-spectrum empirical antibiotic therapy and increasing antimicrobial resistance among Gemella isolates. Surgical intervention was frequently required for severe valvular dysfunction, persistent infection, or embolic complications. Despite advances in diagnosis and treatment, endocarditis due to Gemella species remains associated with significant morbidity and mortality, underscoring the importance of early recognition and multidisciplinary management. This review highlights the emerging clinical significance of Gemella species as causative agents of infective endocarditis and identifies areas for further research.

Genotypic insights to Panton-Valentine leukocidin positive methicillin-resistant Staphylococcus aureus isolated from cattle mastitis.

In this research communication we investigate the prevalence and antimicrobial susceptibility of S. aureus harboring virulent genes responsible for mastitis in cattle of Punjab, Pakistan. A total of 690 milk samples were collected from commercial dairy farms for analysis of the prevalence of subclinical and clinical mastitis and isolation of S. aureus. Virulence ability and methicillin resistance in S. aureus (MRSA) was determined by targeting the pvl (the gene for Panton-Valentine leukocidin) and mecA genes, respectively. A total of 175 S. aureus isolates exhibiting prevalence of pvl gene (6.28%) and mecA gene (22.28%) were determined. Antimicrobial susceptibility testing of pvl positive and negative MRSA against different classes of antibiotics revealed 100% resistance against ß-lactams while 100% sensitivity towards tylosin and linezolid.

Gazing Beyond the Horizon: A Systematic Review Unveiling the Theranostic Potential of Quantum Dots in Alzheimer's Disease.

Alzheimer's disease (AD), a neurodegenerative disorder characterized by cognitive decline, poses a significant healthcare challenge worldwide. The accumulation of amyloid-beta (Aß) plaques and hyperphosphorylated tau protein drives neuronal degeneration and neuroinflammation, perpetuating disease progression. Despite advancements in understanding the cellular and molecular mechanisms, treatment hurdles persist, emphasizing the need for innovative intervention strategies. Quantum dots (QDs) emerge as promising nanotechnological tools with unique photo-physical properties, offering advantages over conventional imaging modalities. This systematic review endeavors to elucidate the theranostic potential of QDs in AD by synthesizing preclinical and clinical evidence. A comprehensive search across electronic databases yielded 20 eligible studies investigating the diagnostic, therapeutic, or combined theranostic applications of various QDs in AD. The findings unveil the diverse roles of QDs, including inhibiting Aß and tau aggregation, modulating amyloidogenesis pathways, restoring membrane fluidity, and enabling simultaneous detection of AD biomarkers. The review highlights the potential of QDs in targeting multiple pathological hallmarks, delivering therapeutic payloads across the blood-brain barrier, and facilitating real-time imaging and high-throughput screening. While promising, challenges such as biocompatibility, surface modifications, and clinical translation warrant further investigation. This systematic review provides a comprehensive synthesis of the theranostic potential of QDs in AD, paving the way for translational research and clinical implementation.

Biodegradable MoNx@Mo-foil electrodes for human-friendly supercapacitors.

With the advancement in the field of biomedical research, there is a growing demand for biodegradable electronic devices. Biodegradable supercapacitors (SCs) have emerged as an ideal solution for mitigating the risks associated with secondary surgeries, reducing patient discomfort, and promoting environmental sustainability. In this study, MoNx@Mo-foil was prepared as an active material for biodegradable supercapacitors through high-temperature and nitridation processes. The composite electrode exhibited superior electrochemical performance in both aqueous and solid-state electrolytes. In the case of the solid-state electrolyte, the MoNx@Mo-foil composite electrode-based device demonstrated excellent cycling stability and electrochemical performance. Additionally, the composite electrode exhibited rapid and complete biodegradability in a 3% H2O2 solution. Through detailed experimental analysis and performance testing, we verified the potential application of the MoNx@Mo-foil composite electrode in biodegradable supercapacitors. This work provides a new choice of degradable material for developing biomedical electronic devices.

Molecular insights into expression and silencing of resistance determinants in Staphylococcus aureus.

OBJECTIVE: This study aimed to investigate the status of antimicrobial-resistant strains of Staphylococcus aureus in Pakistan, their association in terms of co-occurrence with the biofilm-forming genes, resistance profiling and associated discrepancies in diagnostic methods. METHODOLOGY: A total of 384 milk samples from bovine was collected by using convenient sampling technique and were initially screened for subclinical mastitis, further preceded by isolation and confirmation of S. aureus. The S. aureus isolates were subjected to evaluation of antimicrobial resistance by phenotypic identification using Kirby-Bauer disc diffusion method, while the genotypic estimation was done by polymerase chain reaction to declare isolates as methicillin, beta-lactam, vancomycin, tetracycline, and aminoglycoside resistant S. aureus (MRSA, BRSA, VRSA, TRSA, and ARSA), respectively. RESULTS: The current study revealed an overall prevalence of subclinical mastitis and S. aureus to be 59.11% and 46.69%, respectively. On a phenotypic basis, the prevalence of MRSA, BRSA, VRSA, TRSA, and ARSA was found to be 44.33%, 58.49%, 20.75%, 35.84%, and 30.18%, respectively. The results of PCR analysis showed that 46.80% of the tested isolates were declared as MRSA, 37.09% as BRSA, and 36.36% as VRSA, while the occurrence of TRSA and ARSA was observed in 26.31% and 18.75%, respectively. The current study also reported the existence of biofilm-producing genes (icaA and icaD) in 49.06% and 40.57% isolates, respectively. Lastly, this study also reported a high incidence of discrepancies for both genotypic and phenotypic identification methods of resistance evaluation, with the highest discrepancy ratio for the accA-aphD gene, followed by tetK, vanB, blaZ, and mecA genes. CONCLUSION: The study concluded that different antibiotic resistance strains of S. aureus are prevalent in study districts with high potential to transmit between human populations. The study also determined that there are multiple resistance determinants and mechanisms that are responsible for the silencing and expression of antibiotic resistance genes.

Preparation and application of hydroxypropyl methylcellulose blended with beeswax and essential oil edible coating to enhance the shelf life of sweet cherries.

The study involved preparing and applying edible nano-emulsion coatings containing hydroxypropyl methylcellulose (HPMC), beeswax (BW), and essential oils (thyme, cinnamon, clove, and peppermint) onto sweet cherries. The application was conducted at 4 °C, and the coated cherries were stored for 36 days. This research examines synthesized nano-emulsions physicochemical properties and antibacterial and antifungal activities (C1, C2, and C3). Additionally, it evaluates the quality parameters of control and coated sweet cherry samples. The features of the three edible coatings were assessed, and the findings from the zeta sizer, zeta potential, FTIR, and SEM analyses were deemed satisfactory. It was observed that the application of nano-emulsion coating C1 yielded positive results in maintaining quality attributes such as total suspended solids (TSS), total solids (TS), color, weight loss, respiration rate, firmness, total phenolic contents, and sensory evaluations. Nano-emulsion coating C1 demonstrated efficacy as an antibacterial and antifungal agent against foodborne pathogens E. coli and A. niger, respectively. The current research results are promising and applicable in food industries. The implications suggest that composite nano-emulsion, specifically nano-emulsion edible coatings, can be extensively and effectively used to preserve the quality and shelf life of fruits and vegetables. Furthermore, the environmental waste from conventional food packaging will be minimized using edible packaging applications.