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Abstract Wound infections resulting from pathogen infiltration pose a significant challenge in healthcare settings and everyday life. When the skin barrier is compromised due to injuries, surgeries, or chronic conditions, pathogens such as bacteria, fungi, and viruses can enter the body, leading to infections. These infections can range from mild to severe, causing discomfort, delayed healing, and, in some cases, life-threatening complications. Zinc oxide (ZnO) nanoparticles (NPs) have been widely recognized for their antimicrobial and wound healing properties, while cinnamic acid is known for its antioxidant and anti-inflammatory activities. Based on these properties, the combination of ZnO NPs with cinnamic acid (CA) was hypothesized to have enhanced efficacy in addressing wound infections and promoting healing. This study aimed to synthesize and evaluate the potential of ZnO-CN NPs as a multifunctional agent for wound treatment. ZnO-CN NPs were synthesized and characterized using key techniques to confirm their structure and composition. The antioxidant and anti-inflammatory potential of ZnO-CN NPs was evaluated through standard in vitro assays, demonstrating strong free radical scavenging and inhibition of protein denaturation. The antimicrobial activity of the nanoparticles was tested against common wound pathogens, revealing effective inhibition at a minimal concentration. A zebrafish wound healing model was employed to assess both the safety and therapeutic efficacy of the nanoparticles, showing no toxicity at tested concentrations and facilitating faster wound closure. Additionally, pro-inflammatory cytokine gene expression was analyzed to understand the role of ZnO-CN NPs in wound healing mechanisms. In conclusion, ZnO-CN NPs demonstrate potent antioxidant, anti-inflammatory, and antimicrobial properties, making them promising candidates for wound treatment. Given their multifunctional properties and non-toxicity at tested concentrations, ZnO-CN NPs hold significant potential as a therapeutic agent for clinical wound management, warranting further investigation in human models.

Introduction: Despite its importance in emergency medicine, the practice of triage in low- and middle-income countries remains rudimentary, due to dearth of human and material resources, among other reasons. Subjects and methods: In the Children Emergency Room of a teaching hospital, some nonclinical staff were intermittently deployed to coordinate patient traffic and provide brief first-contact triage on arrival. For this, they were internally trained in triage. The present study was aimed at assessing the value and accuracy of first-contact triage provided by nonclinical staf in a children emergency department of a tertiary hospital. Results: Data of 45 patients seen during the period were obtained from the records of the triage officers and included their age, gender, presenting complaints and the findings of the paediatric assessment triangle. There was a statistically significant relationship between the triage ratings of the nonCC –BY 4.0 clinical staff and the qSOFA / L- qSOFA scores obtained by the emergency room doctors, the time spent in CHER and the total duration of admission (R2= 0.36 - 0.44; p = 0.003), (R2= 0.23 - 0.28; p = 0.018) and (R2= 0.33 - 0.40; p = 0.007) respectively. Conclusion: This study shows that to a high degree, the nonclinical staff correctly categorized the severity of illness in the Paediatric Emergency Department of a tertiary level hospital, thus limiting undue tertiary delay in treatment and potential harm from such delays. Hence, the study exposes a potential source of human resources for paediatric emergenc triage in LMICs where there is a dearth of professional medical personnel.

Abstract Background Little leaf disease caused by phytoplasma infection is a significant threat to eggplant (also known as brinjal) cultivation in India. This study focused on the molecular characterisation of the phytoplasma strains and insect vectors responsible for its transmission and screening of brinjal germplasm for resistance to little leaf disease. Results Surveys conducted across districts in the Tamil Nadu state of India during 2021–2022 showed a higher incidence of phytoplasma during the Zaid (March to June), followed by Kharif (June to November) and Rabi (November to March) seasons with mean incidence ranging from 22 to 27%. As the name indicates, phytoplasma infection results in little leaf (reduction in leaf size), excessive growth of axillary shoots, virescence, phyllody, stunted growth, leaf chlorosis and witches’ broom symptoms. PCR amplification with phytoplasma-specific primers confirmed the presence of this pathogen in all symptomatic brinjal plants and in Hishimonus phycitis (leafhopper), providing valuable insights into the role of leafhoppers in disease transmission. BLAST search and phylogenetic analysis revealed the phytoplasma strain as “Candidatus Phytoplasma trifolii”. Insect population and disease dynamics are highly influenced by environmental factors such as temperature, relative humidity and rainfall. Further, the evaluation of 22 eggplant accessions revealed immune to highly susceptible responses where over 50% of the entries were highly susceptible. Finally, additive main effect and multiplicative interaction (AMMI) and won-where biplot analyses identified G18 as a best-performing accession for little leaf resistance due to its consistent responses across multiple environments. Conclusions This research contributes essential information on little leaf incidence, symptoms, transmission and resistance profiles of different brinjal genotypes, which together ensure effective and sustainable management of this important disease of eggplants.

Abstract Background Dental pathogens play a crucial role in oral health issues, including tooth decay, gum disease, and oral infections, and recent research suggests a link between these pathogens and oral cancer initiation and progression. Innovative therapeutic approaches are needed due to antibiotic resistance concerns and treatment limitations. Methods We synthesized and analyzed piperine-coated zinc oxide nanoparticles (ZnO-PIP NPs) using UV spectroscopy, SEM, XRD, FTIR, and EDAX. Antioxidant and antimicrobial effectiveness were evaluated through DPPH, ABTS, and MIC assays, while the anticancer properties were assessed on KB oral squamous carcinoma cells. Results ZnO-PIP NPs exhibited significant antioxidant activity and a MIC of 50 µg/mL against dental pathogens, indicating strong antimicrobial properties. Interaction analysis revealed high binding affinity with dental pathogens. ZnO-PIP NPs showed dose-dependent anticancer activity on KB cells, upregulating apoptotic genes BCL2, BAX, and P53. Conclusions This approach offers a multifaceted solution to combatting both oral infections and cancer, showcasing their potential for significant advancement in oral healthcare. It is essential to acknowledge potential limitations and challenges associated with the use of ZnO NPs in clinical applications. These may include concerns regarding nanoparticle toxicity, biocompatibility, and long-term safety. Further research and rigorous testing are warranted to address these issues and ensure the safe and effective translation of ZnO-PIP NPs into clinical practice.

Alternative teaching methodologies are required for grade-9 students diagnosed with difficulties learning chemistry. This study examines the progress made in learning atomic structure by 11 Spanish Students Diagnosed with Learning Difficulties (SDLD, experimental group), including those with poor academic performance, specific learning difficulties in reading, writing, and mathematics, attention deficit hyperactivity disorder, and borderline intellectual disability according to the Spanish curriculum. The study was conducted as part of the Program for Improving Learning and Performance (PMAR) and utilized the game "Atomizados", specifically designed for this purpose. A written test on atomic structure was used to evaluate learning with three learning levels established (naïve, transitional, and informed). The findings indicate that 63.63% of the SDLD (experimental) achieved an informed learning level (understanding the internal structure of the atom) as a result of utilizing a generative learning strategy. The Mann-Whitney U test demonstrated that this learning was statistically significantly greater than that achieved by other students diagnosed with learning difficulties who did not use the game (SDLD control group). No significant differences were found between the SDLD experimental group and two other groups of grade-9 students with no learning difficulties (NSDLD control groups). However, differences were detected between SDLD (control) and NSDLD (control) in favor of the latter groups. The average for these control groups was a transitional level (identifying some elements of the atomic structure) in the best cases. SDLD (experimental) exhibited a very positive perception of the game's influence on learning chemistry, highlighting its attractiveness, interest, and potential for generating classroom participation.

Abstract Due to growing network data dissemination in cloud, the elasticity, pay as you go options, globally accessible facilities, and security of networks have become increasingly important in today's world. Cloud service providers, including AWS, Azure, GCP, and others, facilitate worldwide expansion within minutes by offering decentralized communication network functions, hence providing security to cloud is still remains a challenging task. This paper aims to introduce and evaluate the Biz-SCOP model, a novel intrusion detection system developed for cloud security. The research addresses the pressing need for effective intrusion detection in cloud environments by combining hybrid optimization techniques and advanced deep learning methodologies. The study employs prominent intrusion datasets, including CSE-CIC-IDS 2018, CIC-IDS 2017, and a cloud intrusion dataset, to assess the proposed model's performance. The study's design involves implementing the Biz-SCOP model using Matlab 2019 software on a Windows 10 OS platform, utilizing 8 GB RAM and an Intel core i3 processor. The hybrid optimization approach, termed HyPSM, is employed for feature selection, enhancing the model's efficiency. Additionally, an intelligent deep learning model, C2AE, is introduced to discern friendly and hostile communication, contributing to accurate intrusion detection. Key findings indicate that the Biz-SCOP model outperforms existing intrusion detection systems, achieving notable accuracy (99.8%), precision (99.7%), F1-score (99.8%), and GEO (99.9%). The model excels in identifying various attack types, as demonstrated by robust ROC analysis. Interpretations and conclusions emphasize the significance of hybrid optimization and advanced deep learning techniques in enhancing intrusion detection system performance. The proposed model exhibits lower computational load, reduced false positives, ease of implementation, and improved accuracy, positioning it as a promising solution for cloud security.

Introduction: Recently, imidazooxazines have attracted more attention due to their therapeutic potential against tuberculosis (TB). The present study aimed to identify and develop potential inhibitors against Pks13-TE to combat the antimicrobial drug resistance of TB. Methods: Computer-aided drug design is a more highly valued technology than the traditional drug discovery approach. Herein, we computationally investigated a chemical dataset using QSAR models and virtually screened novel leads of imidazooxazines against the thioesterase domain, further subjecting them to molecular docking and dynamics simulation. Results: The present study identified two molecules, 1 and 3, promising leads with minimum energy conformations of −7.63 and −7.62 kcal, respectively, providing structural insight into Pks13 inhibition. The average values of MolSA, SASA, and PSA for molecules 1 and 3 were 382.41 Å, 77.65 Å, and 195.54 Å and 386.24 Å, 71.105 Å, and 184.46 Å, respectively. In conclusion, our research has demonstrated that imidazoxazines are promising leads to combat the resistance problem of TB. Among the two potent molecules 1 and 3, molecule 1 displayed favourable interactions in the active site with good stability, as confirmed by the RMSD, RMSF, RoG, H-bond, and SASA analyses. The Molecule 1 protein complex showed two strong hydrogen bonds, effectively maintained for 80–85 % of the simulation time, indicating its stability and potency. Conclusion: The identified two molecules and their conformations were highly stable; hence, these findings provide valuable insight into the evolution of new therapeutic agents to address the growing problem of TB and its resistance.