Search

Aims: The present study investigated receptor activator of nuclear factor kappa-Β ligand (RANKL), osteoprotegerin (OPG), and Runt-related transcription factor 2 (RUNX2) gene expressions in giant cell tumour of bone (GCTB) patients in relationship with tumour recurrence. We also aimed to investigate the influence of CpG methylation on the transcriptional levels of RANKL and OPG. Methods: A total of 32 GCTB tissue samples were analyzed, and the expression of RANKL, OPG, and RUNX2 was evaluated by quantitative polymerase chain reaction (qPCR). The methylation status of RANKL and OPG was also evaluated by quantitative methylation-specific polymerase chain reaction (qMSP). Results: We found that RANKL and RUNX2 gene expression was upregulated more in recurrent than in non-recurrent GCTB tissues, while OPG gene expression was downregulated more in recurrent than in non-recurrent GCTB tissues. Additionally, we proved that changes in DNA methylation contribute to upregulating the expression of RANKL and downregulating the expression of OPG, which are critical for bone homeostasis and GCTB development. Conclusion: Our results suggest that the overexpression of RANKL/RUNX2 and the lower expression of OPG are associated with recurrence in GCTB patients. Cite this article: Bone Joint Res 2024;13(2):84–91.

The distinction between 'captive' and 'choice' user categories has long been a prevalent framework in transportation literature and planning circles. While 'captive' users rely on a sole mode, 'choice' users intentionally choose a particular mode based on perceived benefits. However, this binary classification is increasingly critiqued for its failure to capture the complexities of non-transit users. This study delves into a nuanced perspective on car dependency, focusing on the degree of attachment to cars, and aims to categorize car-captive individuals beyond traditional definitions. Examining the feasibility and implications of such categorization, this research adopts a tailored approach within the context of New Cairo, Egypt. An online geo-referenced survey was employed to measure car dependency potential clustering. The survey encompassed demographic information, travel behaviours, car dependency motives, and experimental travel mode change scenarios. Participants were deliberately selected from a comparable built environment to mitigate external factors' influence. Data analysis involved descriptive and inferential statistics, including Chi-Square tests, with IBM SPSS Statistics 22 used for two-step clustering. Iterative efforts aimed to establish a cohesive model, with emphasis on specific variables which exhibited the highest values. In conclusion, this study proposed two other clusters, ‘car reluctant’ and ‘car inclined’ which differ in their level of attachment to car. By exploring potential categorizations beyond the conventional binary, this research contributes to urban planning and transportation design, fostering a more sustainable and user-centric mobility system. Also, it offers insights into the choice dynamics and the rigid concept of "car captivity." Received: 12 November 2023 Accepted: 30 December 2023 Published: 26 February 2024

Abstract Background This study evaluates the efficacy and safety of micropulse transscleral cyclophotocoagulation (MP-CPC) in paediatric patients. Methods This retrospective case-series recruited 44 eyes for glaucoma patients who were younger than 17 years and were treated with MP-CPC and followed for at least one year. Pre- and post-intervention intraocular pressure (IOP) was compared out to at least one year postoperatively. Success rates at 6 months and 1 year postoperatively were assessed. P

Abstract Biomaterials play a vital role in targeting therapeutics. Over the years, several biomaterials have gained wide attention in the treatment and diagnosis of diseases. Scientists are trying to make more personalized treatments for different diseases, as well as discovering novel single agents that can be used for prognosis, medication administration, and keeping track of how a treatment works. Theranostics based on nano-biomaterials have higher sensitivity and specificity for disease management than conventional techniques. This review provides a concise overview of various biomaterials, including carbon-based materials like fullerenes, graphene, carbon nanotubes (CNTs), and carbon nanofibers, and their involvement in theranostics of different diseases. In addition, the involvement of imaging techniques for theranostics applications was overviewed. Theranostics is an emerging strategy that has great potential for enhancing the accuracy and efficacy of medicinal interventions. Despite the presence of obstacles such as disease heterogeneity, toxicity, reproducibility, uniformity, upscaling production, and regulatory hurdles, the field of medical research and development has great promise due to its ability to provide patients with personalised care, facilitate early identification, and enable focused treatment.

New Radio Unlicensed (NR-U) is the key representative access technology beyond 5G implementation to alleviate the spectrum crunch. NR-U shares a 5 GHz unlicensed band with WiFi, which has contention challenges for the coexisting systems due to physical and link layer protocols disparity. Being a scheduled access system, NR-U transmissions can only start at strict periodic time slots, which requires introducing a synchronization gap period in the listen-before-talk (LBT) approach. In this paper, we address these issues and analyze the impact of various gap-based NR-U approaches to the fair and efficient coexistence of the two networks. The dependency of successful spectrum access of the two systems on the gap period is also investigated. We also present a machine learning data-driven approach to unlicensed channel selection for spectrum sharing by NR-U. The results based on actual data collected from real-life WiFi deployment scenarios indicate significant improvement in coexistence performance and spectrum utilization of the unlicensed band with the proposed approach. It is shown through simulation results that the gap period before the backoff procedure provides better coexistence performance compared to the gap-based approach, where the synchronization gap is introduced after the LBT backoff. Further, the results indicate that if the gap interval exceeds a certain threshold value for each coexistence scenario, the WiFi network starts dominating the unlicensed channel, completely blocking the NR-U transmissions.

The recent rise of Artificial Intelligence (AI) has already revolutionized human lives and is improving the quality of human life in many ways. The field of AI, Natural Language Processing (NLP), helps to understand, comprehend and even generate new content. NLP is used for various content analysis tasks such as sentiment analysis, fake news detection, etc. However, human personality traits detection is a new research domain. Analyzing users generated content has a significant role in identifying and understanding users’ views and behaviors. Users’ traits detection can be helpful in analysis of consumers’ personalization, finding top candidates for recruitment, career counselling, etc. In this research study, our aim is to predict personality of extroversion behaviors using machine and deep learning approaches. Extroversion means whether a person is an introvert or extrovert as this trait is relevant to certain jobs like team management, social ties etc. For empirical analysis, we investigate MBTI dataset with various feature engineering techniques including textual features like Term Frequency-Inverse Document Frequency (TF-IDF), Parts of Speech (PoS) tagging, as well as deep word embeddings ok word2vec, GloVe. The state-of-the-art shallow machine learning, ensemble modelling and deep learning models are applied. The main novelty is the exploration of latest sentence embeddings which captures semantic information of content in a better manner. Thus, the comprehensive results analysis reveals sentence embeddings as features to Bi-LSTM achieves highest accuracy of 92.52% and outperforms the existing studies in the relevant literature.

Bronchiectasis is a chronic respiratory disorder characterized by the dilation and damage of bronchial walls due to recurrent and prolonged episodes of inflammation and infection. The diagnosis of bronchiectasis requires objective measurement of abnormal bronchial dilation. It is confirmed radiologically using a chest tomography (C.T.). scan where the pathognomonic feature is increased broncho-arterial ratio (BAR) (>0.8 in children) and, often accompanied by other features such as bronchial wall thickening (W.T.). Developing image processing-based methods facilitates quicker interpretation of the scans and enable detailed evaluations according to the lobes and segments. However, challenges, such as inclined and oblique orientations of structures, and partial volume effect, can complicate accurate measurements in the upper and middle lobes using the same algorithms. Therefore, the accurate detection and measurement of airway and artery regions for BAR and wall thickness in each lobe require specialized image processing/machine learning methods and approaches. Here, we adopt a step-by-step approach and propose methods for three steps: 1. Separating right lower lobe (RLL) region from full-length C.T. scans using tracheal bifurcation (Carina) point as a central marker; 2. Updated technique to locate inner diameter of airways and outer diameter of arteries for BAR measurement; and 3. Measuring airway wall thickness (W.T.) by identifying the outer and inner diameter of airway boundaries (perimeter). Our analysis of 13 high resolution C.T. scans (HRCT) with varying thicknesses (0.67mm, 1mm, 2mm) demonstrates that the frame containing the tracheal bifurcation can be detected accurately in most cases, with a deviation of ±2 frames in some cases. Similarly, a Windows app is developed for measuring inner airway diameter, artery diameter, BAR, and wall thickness, allowing users to draw bounding around visible discrete B.A. pairs in the RLL region. Measurements of 10 B.A. pairs revealed accurate results comparable to those of a human reader, with deviations of ±0.10-0.15mm observed across all measurements. Additional studies and validation are necessary to consolidate inter- and intra-rater variability and enhance the proposed methods.

Dental health plays a pivotal role in determining the general health and quality of life of a person. Dental caries is a prevalent health problem in the world; therefore, prompt and effective dental caries treatment is necessary for individuals who require pain relief. Conventional methods of diagnosing dental diseases, like visual examination and radiographic testing, depend on qualified medical professionals and can be incredibly labor-intensive and imprecise in their diagnosis. To overcome these challenges, this study designed a deep learning (DL) model for the diagnosis of several dental conditions such as tooth decay, non-periodontal, and periodontal disease. A novel model named periodontal disease classification network (PDCNET) based on convolutional neural network (CNN) has been developed for the identification of periodontal disease using dental radiographs. Additionally, the proposed PDCNET model has been evaluated on two publicly available benchmark datasets of dental caries. To handle the imbalanced classes of the dental caries dataset, this study used the SMOTE TOMEK method to generate new synthetic samples for the minority categories to ensure the periodontal disease dataset is balanced. The proposed PDCNET model acquired a 99.79% AUC, 98.39% recall, 98.39% accuracy, 98.39% precision, and an F1-score of 98.31%. Furthermore, the performance of the proposed PDCNET model is contrasted using the six baseline pre-trained classifiers such as EfficientNet-B0 (M1), DenseNet-201 (M2), Vgg-16 (M3), Vgg-19 (M4), Inception-V3 (M5), and MobileNet (M6) in terms of many parameters. The levels of accuracy attained by M1, M2, M3, M4, M5, and M6 are 85.94%, 94.37%, 91.96%, 93.57%, 89.95%, and 94.77%, respectively. The findings showed that the PDCNET model has superior outcomes as compared to baseline models and provides significant assistance to the dentist in the diagnosis of dental disease.

Natural languages are written and spoken languages, and NLP (Natural Language Processing) is the ability of a computer program to recognize both written and spoken languages. Word Sense Disambiguation (WSD) is identified as a challenging area of research in Artificial Intelligence (AI), and Machine Translation (MT). WSD is the procedure for selecting the exact meaning of a word that has more than one meaning. This is an essential application for all-natural language processing applications. There are various knowledge-based, supervised, and unsupervised approaches to WSD process. The Naïve Bayes classifier as an example of approach supervised and unsupervised approaches is the most important method. In this paper, we emphasize on the use of the Naïve Bayes approach for text classification in WSD techniques. Bayes’ hypothesis is a probabilistic model and a reliable approach for text classification. Bayes’ hypothesis acknowledges that the occurrence of some other features is not dependent on the presence of a particular element in a class. This calculation can be used to solve multi-class prediction problems. This classifier performs better compared to the other methods of different approaches. This paper gives an itemized investigation of Naïve Bayes algorithms, which depicts its ideas, covering up Naïve Bayes,’ text characterization, traditional innocent Bayes,’ and machine learning. We have used the collocation method of feature extraction for the WSD of English sentences. Using this model we have disambiguate ambiguous English words by predicting part-of-speech inclusive of “noun,” “verb,” “adverb,” and “adjective.” This disambiguation module is an enhancement in machine translation. The system reported the performance measure of seventy-eight (78%) percent of the scale on F1-measure.

Otitis Media (OM), predominantly affecting children, is a significant global health issue, with an estimated 360 million pediatric cases yearly worldwide. OM causes mild and moderate conductive hearing loss which can be disabling for young children, particularly during the first three years of life when brain growth is rapid, resulting in poor speech and language development, poor communication skills, and increased vulnerability on entering school. OM therefore contributes to the global burden of all-cause hearing loss. This systematic review seeks to provide a comprehensive evaluation of pre-trained Artificial Intelligence (AI) models, including both classical Machine Learning (ML) and Deep Learning (DL), in the context of OM. This review proposes six research questions, and it summarizes the body of research across multiple domains, including the diversity and quantity of source material for training and testing models, including otoscopy images, videos, and tympanometry, and the methods used to assess quality and effectiveness in real-time settings. In addition, the review aims to provide insight into the impact and potential of AI in improving OM diagnosis and cast light on the existing challenges, such as model interpretability, limited medical expert involvement, and the need for knowledge discovery and unanswered questions, including the evolving landscape of OM diagnosis within this domain. The findings of this systematic review emphasize the importance of developing more interpretable AI models that incorporate both still images of the tympanic membrane and video recordings (with multiple frames) to maximize sensitivity and specificity of the model. In addition, collaboration with consumers and medical professionals in multiple specialties (general practice, pediatricians, audiologists and ear, nose, throat (ENT) surgeons) is needed to ensure applicability and confidence of these diagnostic digital support systems in real-world healthcare settings.

With flexible learning, students are actively engaged in their own education and are held to high standards of performance. Online academic courses make it easier for students to receive personalized education because they provide students with more flexibility to concentrate on what is most important to them and give them greater control over their own education. This study’s objective was to investigate whether there is a correlation between how well students succeed in online classes and the extent to which they make use of the schedule and the geographical and resource flexibility offered by such programmes. This article uses a developing approach for predicting and classifying the flexibility in online learning of students who are at risk of failing due to academic and demographic variables. The K-nearest neighbours (KNN) method, the random forest (RF) method, and the logistic regression method were used to categorise the students participating in flexible online learning. The information for the dataset came from Kaggle, and it was gathered for use in testing machine learning. The dataset had a total of 1,875 instances representing 11 different features. Also, accuracy, precision, sensitivity and f-score metrics were applied to evaluate the system. The results show that the RF algorithm has a high accuracy percentage of 85%. The empirical findings demonstrate that students formed distinct patterns of learning time, location and access to knowledge. This suggests that flexibility was used to a significant degree. Patterns in learning time and the availability of learning materials were shown to have a substantial relationship with the accomplishments of the students. Understanding flexibility use habits may help adapt lessons and boost collaboration among similar students.

Background: Subvastus approach and medial parapatellar approach are two major approaches for total knee replacement (TKR). There is no global consensus on the superiority of either approach in terms of functional outcomes. Objective: The present study aimed to evaluate the functional outcome of TKR through subvastus approach and medial parapatellar approach by using patient-reported scores at 3-, 6-, and 12-month post-operative follow-ups. Methods: This prospective cohort follow-up study included patients with knee osteoarthritis who underwent elective primary TKR either through the subvastus or medial parapatellar approaches at King Abdullah Medical City, Makkah city, Kingdom of Saudi Arabia, from January 2019 to December 2022. Scores from the self-reported Oxford Knee Score (OKS) and the Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC) were compared in the two groups of patients at 3-, 6-, and 12-month post-operative follow-ups. Results: A total of 98 patients were included, of which 37 underwent TKR through the subvastus approach and 61 through the medial parapatellar approach. There was an overall significant change over time in both WOMAC and OKS scores (P < 0.001). Patients who underwent the subvastus approach had significantly higher mean of WOMAC and OKS than patients with the medial parapatellar approach at the 3- and 6-month follow-ups (P < 0.05), but not at the 12-month follow-up. Conclusions: For TKR, the medial parapatellar approach results in better functional outcomes at the 3- and 6- month follow-up periods compared with the subvastus approach.

The Saudi Initiative for Asthma 2024 (SINA-2024) is the sixth version of asthma guidelines for the diagnosis and management of asthma for adults and children that was developed by the SINA group, a subsidiary of the Saudi Thoracic Society. The main objective of the SINA is to have guidelines that are up-to-date, simple to understand, and easy to use by healthcare workers dealing with asthma patients. To facilitate achieving the goals of asthma management, the SINA Panel approach is mainly based on the assessment of symptom control and risk for both adults and children. The approach to asthma management is aligned for age groups: adults, adolescents, children aged 5–12 years, and children aged

Introduction: Type 2 diabetes (T2D) candidate genes, protein tyrosine phosphatase receptor type D (PTPRD), and serine racemase (SRR) were suggested by a genome-wide association study (GWAS) in the Chinese population. Association studies have been replicated among East Asian populations. The association of PTPRD and SRR genetic variants with T2D in Southeast Asian populations still needs to be studied. This study aimed to investigate the association of PTPRD and SSR genetic variants with T2D in Malaysian Indian subjects. Methods: The single nucleotide polymorphisms (SNPs) of PTPRD (rs649891 and rs17584499) and SRR (rs4523957, rs391300, and rs8081273) were genotyped in 397 T2D and 285 normal Malaysian Indian subjects. Results: The homozygous dominant genotype of rs17584499 is frequent in diabetic patients (56.5%) compared to normal subjects (47.3%). In contrast, the homozygous recessive genotype of rs8081273 is more frequent among normal subjects (12.5%) than diabetic patients (5.6%). The dominant genetic model showed that PTPRD rs17584499 (CC) is a risk factor for T2D (OR = 1.42, P = 0.029), whereas the recessive genetic model showed that SRS SNP rs8081273 was protective for T2D (OR = 0.42, P = 0.003). Conclusion: This study confirmed the association of PTPRD rs17584499 genetic variations with T2D in Malaysian Indians. While the SRR rs8081273 (TT) genotype showed protection against T2D, more investigation in different populations is required to confirm this protection.

A novel automated multi-classification approach is proposed for the anticipation of lung abnormalities using chest X-ray and CT images. The study leverages a publicly accessible dataset with an insufficient and unbalanced number of images, addressing this issue by employing the data augmentation approach DCGAN to balance the dataset. Various preprocessing procedures are applied to improve features and reduce noise in lung pictures. As the base for the model, the vision trans-former and convolution-based compact convolutional transformer (CCT) model is utilized. To determine the best model configuration, an ablation study is performed on the original CCT model using a CT scan dataset with image dimensions of $32\times32$ . Following that, this model is trained on the X-ray dataset to evaluate performance on an entirely other modality. The performances are compared to six pre-trained models with $32\times 32$ images. While traditional models achieved modest performance, with test accuracies ranging from 43% to 77% and 49% to 73% requiring lengthy training times, the suggested model performed exceptionally well, obtaining test accuracies of 99.77% and 95.37% for CT and X-ray, respectively with a short training duration of 10–12 and 40–42 seconds/epoch. Robustness is demonstrated through the progressive reduction of the number of training images, with findings indicating that the model maintains good performance even on a reduced dataset. An explainable AI technique Grad-CAM is used to explain the model’s judgment. Grad-CAM-based color visualization is shown to explain model assessments and help health specialists make quick, confident decisions. This study used image preprocessing and deep learning techniques to detect lung anomalies, and it addressed the challenges of training time and computational complexity.

Large Language Models (LLMs) recently demonstrated extraordinary capability in various natural language processing (NLP) tasks including language translation, text generation, question answering, etc. Moreover, LLMs are new and essential part of computerized language processing, having the ability to understand complex verbal patterns and generate coherent and appropriate replies in a given context. Though this success of LLMs has prompted a substantial increase in research contributions, rapid growth has made it difficult to understand the overall impact of these improvements. Since a plethora of research on LLMs have been appeared within a short time, it is quite impossible to track all of these and get an overview of the current state of research in this area. Consequently, the research community would benefit from a short but thorough review of the recent changes in this area. This article thoroughly overviews LLMs, including their history, architectures, transformers, resources, training methods, applications, impacts, challenges, etc. This paper begins by discussing the fundamental concepts of LLMs with its traditional pipeline of the LLMs training phase. Then the paper provides an overview of the existing works, the history of LLMs, their evolution over time, the architecture of transformers in LLMs, the different resources of LLMs, and the different training methods that have been used to train them. The paper also demonstrates the datasets utilized in the studies. After that, the paper discusses the wide range of applications of LLMs, including biomedical and healthcare, education, social, business, and agriculture. The study also illustrates how LLMs create an impact on society and shape the future of AI and how they can be used to solve real-world problems. Finally, the paper also explores open issues and challenges to deploy LLMs in real-world scenario. Our review paper aims to help practitioners, researchers, and experts thoroughly understand the evolution of LLMs, pre-trained architectures, applications, challenges, and future goals.

Background: Although breakfast skipping among university students is a significant concern, its prevalence and the contributing factors among university students have received little attention in the literature. This study aims to determine the prevalence of skipping breakfast among Jordanian university students and examine the associated factors and variations in rates of skipping breakfast by day of the week. Materials and Methods: A cross-sectional study was conducted among undergraduate students between March and May 2022 through a self-questionnaire. A convenience sample of 891 students was chosen at four Jordanian public universities. The data were analyzed using descriptive and inferential statistics. Results: The prevalence of skipping breakfast among university students was 66%. The reasons for skipping breakfast were having no time due to oversleeping and having no feeling of hunger (59% for both), followed by having no energy to prepare the breakfast and making no difference (49% and 48%), and not being able to afford to eat or buy breakfast (19%). There is a strong correlation between eating fast food and skipping breakfast. With whom the student eats breakfast is significantly associated with breakfast skipping, revealing that the highest percentages of skipping occur with friends. About 63% of students skipped breakfast through university days compared with 37% on the weekend, while 37% of them had breakfast through university days compared with 67% on the weekend. Conclusions: A high percentage of university students in Jordan skip breakfast. More attention should be paid to correlating factors and developing interventions to help students adhere to the breakfast.

Background and Purpose Outcomes following mechanical thrombectomy (MT) are strongly correlated with successful recanalization, traditionally defined as modified Thrombolysis in Cerebral Infarction (mTICI) ≥2b. This retrospective cohort study aimed to compare the outcomes of patients with low Alberta Stroke Program Early Computed Tomography Score (ASPECTS; 2–5) who achieved mTICI 2b versus those who achieved mTICI 2c/3 after MT. Methods This study utilized data from the Stroke Thrombectomy and Aneurysm Registry (STAR), which combined databases from 32 thrombectomy-capable stroke centers between 2013 and 2023. The study included only patients with low ASPECTS who achieved mTICI 2b, 2c, or 3 after MT for internal carotid artery or middle cerebral artery (M1) stroke. Results Of the 10,229 patients who underwent MT, 234 met the inclusion criteria. Of those, 98 (41.9%) achieved mTICI 2b, and 136 (58.1%) achieved mTICI 2c/3. There were no significant differences in baseline characteristics between the two groups. The 90-day favorable outcome (modified Rankin Scale score: 0–3) was significantly better in the mTICI 2c/3 group than in the mTICI 2b group (adjusted odds ratio 2.35; 95% confidence interval [CI] 1.18–4.81; P=0.02). Binomial logistic regression revealed that achieving mTICI 2c/3 was significantly associated with higher odds of a favorable 90-day outcome (odds ratio 2.14; 95% CI 1.07–4.41; P=0.04). Conclusion In patients with low ASPECTS, achieving an mTICI 2c/3 score after MT is associated with a more favorable 90-day outcome. These findings suggest that mTICI 2c/3 is a better target for MT than mTICI 2b in patients with low ASPECTS.

Background: Despite advancements in critical care and coronary revascularization, cardiogenic shock (CS) outcomes remain poor. Implementing a shock team and use of veno-arterial extracorporeal membrane oxygenation (VA-ECMO) have been associated with improved CS outcomes, but its feasibility in remote and rural areas remains unknown. Methods: This retrospective study included patients with CS who required mechanical circulatory support (MCS) at Health Sciences North, Sudbury, Ontario. The analysis aimed to accomplish 2 objectives: first, to review the outcomes associated with use of Impella (Abiomed, Danvers, MA) and, second, to assess the feasibility of establishing a shock team to facilitate the local implementation of VA-ECMO. The primary endpoint was in-hospital mortality. Results: The outcomes of 15 patients with CS who received Impella between 2015 and 2021 were reviewed. Their average age was 65 years (standard deviation [SD]: 13), and 8 patients (53%) were female. CS was ischemic in 12 patients (80%). Transfemoral Impella CP (cardiac power) was the most frequently used (93%). Thirteen patients (87%) died during the index hospital stay post-Impella because of progressive circulatory failure. The shock team was established following consultations with several Canadian MCS centres, leading to the development of a protocol to guide use of MCS. There have been 4 cases in which percutaneous VA-ECMO using Cardiohelp (Getinge/Maquet, Wayne, NJ) has been used; 3 (75%) survived beyond the index hospitalization. Conclusions: This analysis demonstrated the feasibility of implementing a shock team in remote Northern Ontario, enabling the use of VA-ECMO with success in a centre with a sizeable rural catchment area. This initiative helps address the gap in cardiac care outcomes between rural and urban areas in Ontario. Résumé: Introduction: En dépit des avancées des soins aux patients en phase critique et de la revascularisation coronarienne, les résultats du choc cardiogénique (CC) semblent mauvais. La mise en place d’une équipe de choc et l’utilisation de l’oxygénation extracorporelle (ECMO, de l’anglais extracorporeal membrane oxygenation) par voie veino-artérielle (VA) (VA-ECMO) ont été associées à de meilleurs résultats du CC, mais on ignore sa faisabilité dans les régions éloignées et rurales. Méthodes: La présente étude rétrospective portait sur des patients en CC qui ont eu besoin d’une assistance circulatoire mécanique (ACM) à Horizon Santé-Nord, à Sudbury, en Ontario. L’analyse visait 2 objectifs : le premier objectif était de passer en revue les résultats associés à l’utilisation de Impella (Abiomed, Danvers, MA) et, le deuxième était d’évaluer la faisabilité de la mise en place d’une équipe de choc pour faciliter la mise en œuvre locale de la VA-ECMO. Le principal critère d’évaluation était la mortalité intrahospitalière. Résultats: Nous avons passé en revue les résultats cliniques de 15 patients ayant subi un CC qui avaient reçu une Impella entre 2015 et 2021. L’âge moyen était de 65 ans (écart type [ET] : 13), et 8 patients (53 %) étaient des femmes. Le CC était d’origine ischémique chez 12 patients (80 %). L’Impella CP (cardiac power, soit la pompe cardiaque) par voie transfémorale était la plus fréquemment utilisée (93 %). Treize patients (87 %) sont morts durant le séjour de référence à l’hôpital après l’utilisation de l’Impella en raison d’insuffisance circulatoire progressive. La mise en place de l’équipe de choc à la suite des consultations dans plusieurs centres canadiens d’ACM a mené à l’élaboration d’un protocole d’utilisation de l’ACM. Il y a eu 4 cas chez lesquels la VA-ECMO par voie percutanée à l’aide de Cardiohelp (Getinge/Maquet, Wayne, New Jersey, É.-U.) a été utilisée ; 3 (75 %) ont survécu après l’hospitalisation de référence. Conclusions: Cette analyse a démontré la faisabilité de la mise en place d’une équipe de choc dans les régions éloignées du nord de l’Ontario, qui a permis d’utiliser efficacement la VA-ECMO dans un centre d’une circonscription hospitalière rurale non négligeable. Cette initiative aide à remédier à l’écart des résultats en soins cardiaques entre les régions rurales et urbaines de l’Ontario.

The influences of rare earth (RE) samarium (Sm) added contents to ZRE1 (Mg-Zn-Zr) magnesium (Mg) cast alloy over strength properties to be investigated. Sm at 0.5, 1.0, 1.5, and 2.0 wt.% were added separately as an alloying element to ZRE1 alloy. Optical Microscope (OM), X-ray powder diffraction (XRD), and scanning electron microscope Energy-dispersive X-ray (SEM/EDS) were used to investigate the microstructure of alloy, while mechanical properties investigated include Ultimate Tensile Strength (UTS)and Micro-Hardness (MH) tests. The result revealed that as the Sm level reached 1.5 wt.%, the grain size decreased by 20.9 %. Additionally, UTS and Yield Strength (YS) showed improvements of 8.5 % and 7.9 %, respectively, with the addition of 1.5 wt.% of Sm. In addition, elongation and hardness have been improved by 32.3 % and 10.9 % respectively at 1.5 wt.% Sm addition. Mg-Zn-Ce-Sm was formed as a new phase upon the addition of Sm and was detected via XRD analysis. The addition of Sm to the ZRE1 alloy had significant effects on the refinement of the material's microstructure, leading to an increase in its mechanical and physical properties. Therefore, the new ZRE1-RE magnesium alloy was developed.

Bronchiectasis, one of the most neglected chronic lung conditions, has a high individual disease burden and economic cost and causes poor quality of life in children/adolescents and adults. Advances in image quality and a dramatic reduction in acquisition times, multiple high-resolution chest tomography (HRCT) acquisitions, and reconstructions of the lung have resulted in accurate categorization and determination of the extent of lung parenchyma and airway abnormality. For bronchiectasis, the diagnosis is confirmed using the key feature of abnormally increased broncho-arterial (B.A.) ratio (BAR), with or without other abnormalities, e.g., bronchial wall thickening, lack of bronchial tapering, and mucus plugging. Most of these features require shape analysis of the airway and artery regions to perform various assessments that can have inter-rater variability and are time-consuming. This challenge is amplified in pediatric patients due to age-related anatomical variations. The anatomical differences and variations in airway structures between Infants, Early Childhood, and middle Childhood can impact how the images can be processed and analyzed. To address this, we proposed two novel image-processing methods to detect and measure the B.A. pairs. The first method uses an optimized connected component labelling (CCL) algorithm to construct bounding boxes around the objects (airway, artery) and extract the regions of interest (ROIs) for potential B.A. pairs. The second method allows us to calculate 4 or 6 diameters for each object in the ROIs and use their mean value as the final diameter, demonstrating agreement with manual readings. Evaluating against a diverse set of HRCT scans from various categories validates the significance and practical utility of our proposed methods in detecting and measuring the disjointed B.A. pairs to assess increased BAR.

Purpose This study investigated the effect of recycled Olive Pruning Residues (OLPR) and Spent Coffee Grounds (SCG) on substrate temperature (temp) and duration (dur) of Pleurotus ostreatus growing stages. This study also sheds light on the correlation between selected parameters, yield, and substrate composition.Method The experiment consisted of 2 sole substrates wheat straw (WS; control) and SCG, and 6 mixtures of WS, OLPR, and SCG (v/v): WS:SCG 3:1, WS:SCG 1:3, WS:OLPR:SCG 1:1:1, WS:OLPR:SCG 3:1:1, WS:OLPR:SCG 1:3:1, and WS:OLPR:SCG 1:1:3.Results Increased SCG proportions caused the substrate temperature to decrease, whereas, OPLR proportions caused it to rise. All stages showed earliness in productive substrates. The highest organic matter loss (OML) was reported by WS (76.7%) (76.7%). WS:SCG 3:1 OML depicted a correlation (0.8 ≤r≥ 0.9) with dur Stages 2 and 3. OML of WS:SCG 1:3 denoted a correlation (0.8 ≤r≥ 0.9) with dur Stages 1, 2, and 3. OML of WS:OLPR:SCG 3:1:1 and WS:OLPR:SCG 1:1:1 correlated (R2 ≥ 0.7) with dur Stages 3 and 4, respectively. WS:SCG 1:3 temp had a strong influence on economic yield (EY) at all stages. At Stage 2, combined substrate temp positively affected biological efficiency (BE) and biological yield (BY). Spawn run initiation (SRI), pinhead formation (PNF), and harvest of the first flush (HF1) strongly correlated with major minerals and fatty acids in SCG substrates. Increased Mg and Fe contents affected negatively the complete mycelial colonization (100% MC) in combined substrates.Conclusion SCG reduces OML and causes early production.

Algeria's tourism sector plays a critical role in its economy, especially as the country faces an economic crisis due to its reliance on fossil fuels. However, despite its potential, the tourism industry has remained limited to coastal and desert tourism in the north and south, respectively. Therefore, diversifying the tourism product by exploring inland regions is necessary to restore regional balance and create geographical homogeneity. Djelfa, a city located in the central highlands of the Atlas Mountains, offers unique natural resources and cultural heritage, making it an ideal destination for ecotourism. This study evaluates the potential of Djelfa for tourism planning purposes, using scientific documentation, field survey techniques, and analytical methods like SWOT analysis. In addition, a survey was conducted amongst tourists to collect their opinions on the city's attraction characteristics. The data was processed using principal component analysis (PCA) in SPSS software. The study emphasizes the importance of tourism planning in the area and proposes suitable tourism projects for ecotourism and investment in the Senelba forest site. These projects will positively influence the environment, local population, and lead to real development at the local and national levels while preserving the area's cultural and natural heritage.

Abstract Background MicroRNAs (miRNAs) can control several biological processes. Thus, the existence of these molecules plays a significant role in regulating human iron metabolism or homeostasis. Purpose The study aimed to determine the role of circulating microRNAs and hepcidin in controlling iron homeostasis and evaluating possible anemia among school children. Methods The study was based on a biochemical and cross-sectional survey study that included three hundred fifty school children aged 12–18 years old. RT–PCR and immunoassay analysis were accomplished to estimate iron concentration, Hgb, serum ferritin (SF), soluble transferrin receptor (sTfR), total body iron stores (TIBs), total oxidative stress (TOS), total antioxidant capacity (TAC), α-1-acid glycoprotein (AGP), high sensitive C-reactive protein (hs-CRP), and miRNAs; miR-146a, miR-129b, and miR-122 in 350 school adolescents. Results Iron disorders were cross-sectionally predicted in 28.54% of the study population; they were classified into 14.26% with ID, 5.7% with IDA, and 8.6% with iron overload. The overall proportion of iron depletion was significantly higher in girls (20.0%) than in boys (8.6%). MicroRNAs; miR-146a, miR-125b, and miR-122 were significantly upregulated with lower hepcidin expression in adolescence with ID and IDA compared to iron-overloaded subjects, whereas downregulation of these miRNAs was linked with higher hepcidin. Also, a significant correlation was recorded between miRNAs, hepcidin levels, AGP, hs-CRP, TAC, and other iron-related indicators. Conclusion Molecular microRNAs such as miR-146a, miR-125b, and miR-122 were shown to provide an additional means of controlling or regulating cellular iron uptake or metabolism either via the oxidative stress pathway or regulation of hepcidin expression via activating genes encoding Hfe and Hjv activators, which promote iron regulation. Thus, circulating miRNAs as molecular markers and serum hepcidin could provide an additional means of controlling or regulating cellular iron and be associated as valuable markers in diagnosing and treating cases with different iron deficiencies.

Numerical modeling of gas flows in rarefied regimes is crucial in understanding fluid behavior in microscale applications. Rarefied regimes are characterized by a decrease in molecular collisions, and they lead to unusual phenomena such as gas phase separation, which is not acknowledged in hydrodynamic equations. In this work, numerical investigation of miscible gaseous mixtures in the rarefied regime is performed using a modified lattice Boltzmann model. Slip boundary conditions are adapted to arbitrary geometries. A ray-tracing algorithm-based wall function is implemented to model the non-equilibrium effects in the transition flow regime. The molecular free flow defined by the Knudsen diffusion coefficient is integrated through an effective and asymmetrical binary diffusion coefficient. The numerical model is validated with mass flow measurements through microchannels of different cross-section shapes from the near-continuum to the transition regimes, and gas phase separation is studied within a staggered arrangement of spheres. The influence of porosity and mixture composition on the gas separation effect are analyzed. Numerical results highlight the increase in the degree of gas phase separation with the rarefaction rate and the molecular mass ratio. The various simulations also indicate that geometrical features in porous media have a greater impact on gaseous mixtures’ effective permeability at highly rarefied regimes. Finally, a permeability enhancement factor based on the lightest species of the gaseous mixture is derived.

Rock climbing has propelled from niche sport to mainstream free-time activity and Olympic sport. Moreover, climbing can be studied as an example of a high-stakes perception-action task. However, understanding what constitutes an expert climber is not simple or straightforward. As a dynamic and high-risk activity, climbing requires a precise interplay between cognition, perception, and precise action execution. While prior research has predominantly focused on the movement aspect of climbing (i.e., skeletal posture and individual limb movements), recent studies have also examined the climber’s visual attention and its links to their performance. To associate the climber’s attention with their actions, however, has traditionally required frame-by-frame manual coding of the recorded eye-tracking videos. To overcome this challenge and automatically contextualize the analysis of eye movements in indoor climbing, we present deep learning-driven (YOLOv5) hold detection that facilitates automatic grasp recognition. To demonstrate the framework, we examined the expert climber’s eye movements and egocentric perspective acquired from eye-tracking glasses (SMI and Tobii Glasses 2). Using the framework, we observed that the expert climber’s grasping duration was positively correlated with total fixation duration (r = 0.807) and fixation count (r = 0.864); however, it was negatively correlated with the fixation rate (r = −0.402) and saccade rate (r = −0.344). The findings indicate the moments of cognitive processing and visual search that occurred during decision making and route prospecting. Our work contributes to research on eye–body performance and coordination in high-stakes contexts, and informs the sport science and expands the applications, e.g., in training optimization, injury prevention, and coaching.

Background: Obesity is considered a health issue associated with increased morbidity and a risk factor for multiple conditions, such as type 2 diabetes, cardiovascular diseases and infections. It may affect the pharmacokinetics and pharmacodynamics of many drugs, including antimicrobials like ciprofloxacin. Regrettably, data on ciprofloxacin’s efficacy in obese patients remain scarce. This study aims to evaluate the impact of obesity on the efficacy of ciprofloxacin in treating Gram-negative bacterial infections. Methods: A retrospective multicenter cohort study was conducted in two tertiary hospitals in Saudi Arabia. Adult patients (≥18 years) treated with ciprofloxacin for confirmed Gram-negative infection between January 2017 and April 2023 were included. Patients were excluded if they received ciprofloxacin empirically, had inadequate source control within 72 h, or had missing weight and height information at ciprofloxacin initiation. The primary outcome was clinical cure, defined as the resolution of the clinical infection manifestations without additional therapeutic management by the end of treatment. Other secondary and safety outcomes were also assessed. Results: A total of 99 patients were included, divided into obese (n = 42) and non-obese (n = 57) groups. The obese group had a significantly lower median age (50 years) compared to the non-obese group (64 years) (p = 0.002). The obese group had fewer male patients (38.10% vs. 68.42%; p = 0.004), higher body weight (90 (81–97) vs. 63 (55–70) days; p < 0.001), and lower height (158 (155–165) vs. 165 (158–172) days; p = 0.008) compared to non-obese. Urinary tract infection was the most common type, with Escherichia coli being the most common isolate. The median hospital length of stay was shorter in the obese group than in the non-obese group (1 vs. 3 days, p = 0.007). There were no significant differences in clinical cure rates between obese (85.71%) and non-obese (85.96%) patients (p = 1). No significant differences were observed in terms of in-hospital mortality, 30-day mortality, or 60-day infection recurrence rates between the two groups. Microbiological eradication was not achieved in the obese group, whereas a 10.53% eradication rate was observed in the non-obese group (p = 0.037). However, the majority of the patients had indeterminate eradication. The incidence of adverse drug reactions (ADRs) was lower in the obese group (4.76%) compared to the non-obese group (17.54%, p = 0.066). Conclusions: Treatment with ciprofloxacin in obese patients has similar efficacy and safety outcomes compared to non-obese patients with infections due to Gram-negative pathogens.

Suppose R is a local ring with invariants p,n,r,m,k and mr=4, that is R of order p4. Then, R=R0+uR0+vR0+wR0 has maximal ideal J=uR0+vR0+wR0 of order p(m−1)r and a residue field F of order pr, where R0=GR(pn,r) is the coefficient subring of R. In this article, the goal is to improve the understanding of linear codes over small-order non-chain rings. In particular, we produce the MacWilliams formulas and generator matrices for linear codes of length N over R. In order to accomplish that, we first list all such rings up to isomorphism for different values of p,n,r,m,k. Furthermore, we fully describe the lattice of ideals in R and their orders. Next, for linear codes C over R, we compute the generator matrices and MacWilliams identities, as shown by numerical examples. Given that non-chain rings are not principal ideals rings, it is crucial to acknowledge the difficulties that arise while studying linear codes over them.

The increase in antibiotic-resistant bacteria presents a significant risk to worldwide public health, emphasizing the necessity of novel approaches to address infections. Quorum sensing, an essential method of communication among bacteria, controls activities like the formation of biofilms, the production of virulence factors, and the synthesis of secondary metabolites according to the number of individuals in the population. Quorum quenching, which interferes with these processes, emerges as a vital approach to diminish bacterial virulence and prevent biofilm formation. Nanocarriers, characterized by their small size, high surface-area-to-volume ratio, and modifiable surface chemistry, offer a versatile platform for the disruption of bacterial communication by targeting various stages within the quorum sensing pathway. These features allow nanocarriers to infiltrate biofilms, disrupt cell membranes, and inhibit bacterial proliferation, presenting a promising alternative to traditional antibiotics. Integrating nanocarrier-based systems into combination therapies provides a multi-pronged approach to infection control, enhancing both the efficacy and specificity of treatment regimens. Nonetheless, challenges related to the stability, safety, and clinical effectiveness of nanomaterial-based antimicrobial treatments remain. Continued research and development are essential to overcoming these obstacles and fully harnessing the potential of nano-antimicrobial therapies. This review emphasizes the importance of quorum sensing in bacterial behavior and highlights the transformative potential of nanotechnology in advancing antimicrobial treatments, offering innovative solutions to combat antibiotic-resistant pathogens.

Let u,v, and w be indeterminates over Fpm and let R=Fpm+uFpm+vFpm+wFpm, where p is a prime. Then, R is a ring of order p4m, and R≅Fpm[u,v,w]I with maximal ideal J=uFpm+vFpm+wFpm of order p3m and a residue field Fpm of order pm, where I is an appropriate ideal. In this article, the goal is to improve the understanding of linear codes over local non-chain rings. In particular, we investigate the symmetrized weight enumerators and generator matrices of linear codes of length N over R. In order to accomplish that, we first list all such rings up to the isomorphism for different values of the index of nilpotency l of J, 2≤l≤4. Furthermore, we fully describe the lattice of ideals of R and their orders. Next, for linear codes C over R, we compute the generator matrices and symmetrized weight enumerators, as shown by numerical examples.

Marine microorganisms are renowned for being a rich source of new secondary metabolites that are significant to humans. The fungi strain KHW-7 was isolated from the seawater collected from the Gulf of Khambhat, India, and identified as Curvularia verruculosa KHW-7. On a next-generation sequencing platform, C. verruculosa KHW-7’s whole-genome sequencing (WGS) and gene annotation were carried out using several bioinformatic methods. The 31.59 MB genome size, 52.3% GC, and 158 bp mean read length were discovered using WGS. This genome also contained 9,745 protein-coding genes, including 852 secreted proteins and 2048 transmembrane proteins. The antiSMASH algorithm used to analyze genomes found 25 secondary metabolite biosynthetic gene clusters (BGCs) that are abundant in terpene, non-ribosomal peptide synthetase (NRPS), and polyketides type 1 (T1PKS). To our knowledge, this is the first whole-genome sequence report of C. verruculosa. The WGS analysis of C. verruculosa KHW-7 indicated that this marine-derived fungus could be an efficient generator of bioactive secondary metabolites and an important industrial enzyme, both of which demand further investigation and development.

Objectives: The objectives of this study were to explore the rate and types of inappropriate referrals to the wellness services and to assess the impact of multi-level interventions on the rate of inappropriate referrals at Rawdat Al-Khail health center in Qatar. Methods: This study employed a retrospective analysis of all referrals data to Rawdat Al-Khail wellness services extracted from the Electronic Health Records (EHRs) between July 2022 and August 2023. The monthly rates and types of inappropriate referrals were calculated. In this study, pre-post analyses were performed to evaluate the impact of two sets of interventions on reducing inappropriate referral rates. The first set involved the development and distribution of e-referral pathways training manual in September 2022. The second set, implemented in April 2023, included close monitoring the rate and types of referrals, and the initiation of feedback communication between wellness services supervisors and referring physicians for advice and corrective actions. Results: A total of 966 referrals were received during the study period, with 1:5 male-to-female ratio. Of all referrals, 34.9 % were classified as inappropriate, exhibiting considerable variations among different referring health centers. The most common reason for inappropriate referrals was due to the lack of “exercise wellness gym assessment form” (23.8 %). While interventions aimed at enhancing the referral process, they did not result in a significant overall reduction in inappropriate referral rates. However, there was a noteworthy reduction in the inappropriate referrals caused by the lack of “exercise wellness gym assessment form” observed from March to August 2023 (41 %–18 %). Conclusion: This study sheds light on the complexities of wellness services referrals, revealing a high rate of inappropriate referrals that require closer scrutiny. Despite interventions not significantly reducing the rate of these referrals, it emphasizes the need for ongoing improvement strategies. Structured, periodic interventions at higher levels are recommended to enhance referral appropriateness.

Objective Early diagnosis of breast cancer can lead to effective treatment, possibly increase long-term survival rates, and improve quality of life. The objective of this study is to present an automated analysis and classification system for breast cancer using clinical markers such as tumor shape, orientation, margin, and surrounding tissue. The novelty and uniqueness of the study lie in the approach of considering medical features based on the diagnosis of radiologists. Methods Using clinical markers, a graph is generated where each feature is represented by a node, and the connection between them is represented by an edge which is derived through Pearson's correlation method. A graph convolutional network (GCN) model is proposed to classify breast tumors into benign and malignant, using the graph data. Several statistical tests are performed to assess the importance of the proposed features. The performance of the proposed GCN model is improved by experimenting with different layer configurations and hyper-parameter settings. Results Results show that the proposed model has a 98.73% test accuracy. The performance of the model is compared with a graph attention network, a one-dimensional convolutional neural network, and five transfer learning models, ten machine learning models, and three ensemble learning models. The performance of the model was further assessed with three supplementary breast cancer ultrasound image datasets, where the accuracies are 91.03%, 94.37%, and 89.62% for Dataset A, Dataset B, and Dataset C (combining Dataset A and Dataset B) respectively. Overfitting issues are assessed through k-fold cross-validation. Conclusion Several variants are utilized to present a more rigorous and fair evaluation of our work, especially the importance of extracting clinically relevant features. Moreover, a GCN model using graph data can be a promising solution for an automated feature-based breast image classification system.

Aim: This study aims to compare the effectiveness of single-skill and multiple skills patient care simulation scenarios in enhancing nursing competency and to evaluate nurses' perceptions of these different simulation scenarios. Methods: A mixed-method design was used. In the quantitative part, 130 newly hired nurses with less than one year of experience participated in this study. For the qualitative part, a subset of 26 of these nurses was selected for in-depth interviews until data saturation was achieved. The study was conducted in the simulation-based learning (SBL) lab in a specialized Jordan cancer center. Data collection took place between June 2022 and March 2023. Results: A significant difference was found in the IV skill between single and multiple skills scenarios; the mean score for the single-skill group was 44.42 (SD = 3.85), the mean score for the multiple-skill group was 45.63 (SD = 2.26) (P = 0.014). Moreover, a significant difference was found between the pre-and post-test scores for single-skill and multiple-skill groups. The mean score for the medication skill scenario pre-intervention single group was (M = 23.90, SD = 5.33); however, the score was increased post-intervention (M = 45.38, SD = 3.33), (P =

N-Arylpyrazoles are important building blocks in many biologically active compounds, natural products, pharmaceuticals, and industries. It is an important moiety of various drugs and exhibits extensive biological activities viz., antibiotic, anticancer, antifungal, anti-inflammatory, anti-coagulant, analgesic, antipyretic, anti-depressant, insecticidal, and hyperglycemic activities. Various protocols have been designed for the synthesis of N-arylpyrazoles facilitated by a wide variety of metal-based catalysts at high or room temperature, metal free reactions, various named reactions, multicomponent reactions, and those occurring under different electromagnetic radiations. Various ligands are found to promote Cu-catalyzed N-arylation of pyrazole under mild conditions in short time. This review describes the synthesis and biological applications of N-arylpyrazole derivatives reported during 2019–2023.

The rivers are severely contaminated by human activities due to its massive outflow and industrial effluents. Three economically significant fish species Sperata seenghala, Wallago attu, and Cyprinus carpio from the river water were selected for the assessment of heavy metals (HMs). The edible part of the collected fish from the river sampling sites were analyzed to determine the metal concentrations and potential health risks to humans. The Atomic Absorption Spectroscopy (AAS) technique was used to measure HMs including lead (Pb), chromium (Cr), and copper (Cu) for both water and fish samples. Order of HMs in the fish was found as Pb > Cd > Cr. Levels of HMs in all species were found higher than the European Commission (EC) recommended acceptable levels. The metal pollution index (MPI) indicates that W. attu and S. seenghala, two highly consumed fish species, have accumulated HMs in higher concentrations. Wallago attu has the highest bioaccumulation factor (BAF) for both Pb and Cd permissible levels. For adult and children, the Hazard Index (HI) and Targeted Hazard Quotient (THQ) values were less than 1, which indicated that there would be no non-carcinogenic health consequences. Carcinogenic risk (CR) exposure to Pb, Cd, and Cr for all consumers was found to be higher than the permitted limit (10−6-10−4), while children were found more vulnerable than adults in terms of CR values. The study findings identified several sources of contaminants in the area, including agricultural waste, lubricant usage, and electroplating material production dumping near into the river water. Based on the results of the analysis, metal concentrations exceeded the thresholds for carcinogenic health risks, indicating that fish from the area are generally unsafe to consume. Therefore, immediate action must be taken to reduce or prevent the discharge of contaminants into the river. Additionally, regular monitoring of the river's water quality should be conducted to control the contamination of water.

Natural product synthesis is an important area of chemical study having various prospects for medicinal, industrial, and agricultural industry. Grignard reaction was developed as a flexible and necessary tool for the production of complex organic compounds in synthetic procedures. Additionally, the synthetic flexibility of the Grignard reaction facilitates its usage in the construction of more complicated ring frameworks and stereoselective transformations. It has been substantially applied in natural product synthesis, demonstrating its effectiveness in the generation of crucial C-C, C-X bonds and the development of intricate molecular structures. Use of Grignard reaction has been significantly studied towards the synthesis of biologically active natural products such as terpenoids, polyketides, alkaloids and amino acids. This review outlines the numerous instances where Grignard reagents have been exploited in the synthesis of notable natural products since past two years.

In this paper new theorems a bout fixed point are given for applications in 2- metric space. The common fixed points of two operators exhibit a shared periodicity in their occurrences. The theorem demonstrates that, certain conditions, there exists a specific pattern or sequence of operators where the fixed points exhibit a common periodicity.

Abstract Objectives There is a lack of data regarding the hygiene practices and sources of care among individuals in Yemen who wear removable complete and partial dentures. The purpose of this study was to explore the behaviors, hygiene habits, and sources of care information among patients who utilize complete and partial dentures in Yemen. Materials and Methods A descriptive cross‐sectional research design was utilized, and a sample of 217 consecutive participants who wore removable complete and/or partial dentures were enlisted. A questionnaire was employed to collect data on demographic information, educational attainment, denture habits, denture cleaning practices, and encountered difficulties. Statistical analysis was conducted using SPSS software, and significance was determined using chi‐square tests, with a significance level of .05. Results The data analysis revealed that a majority of the participants were male (72.4%) and had an average age of 65.14 years. Fifty‐three percent of the participants wore partial dentures, while 34.6% wore complete dentures. Only 6.5% of the participants wore both complete and partial dentures, and 6% wore overdentures. The majority of participants (88.47%) had dentures made of acrylic material. Additionally, 43% of participants wore dentures while sleeping, and 61.3% stored their dentures in dry places without a water‐filled container when not in use. The most commonly reported cleaning methods were water only (24.4%), followed by water and soap (19.4%). Furthermore, a large portion of the participants (59.4%) received denture care information from dentists. However, the majority (59%) did not visit a dentist for regular denture maintenance or any denture‐related issues. The data analysis did not reveal any significant association between age or education level and denture cleaning methods. Conclusion This cross‐sectional survey provides insight into the hygiene knowledge and practices of removable denture wearers in Yemen. The findings underscore the necessity for enhanced oral hygiene education and awareness within this population. The study offers valuable insights for oral health professionals to design targeted interventions and educational initiatives aimed at promoting proper denture care and maintenance. These efforts have the potential to enhance the oral health and overall well‐being of removable denture wearers.

BackgroundDespite the established effectiveness of the BNT162b2 Vaccine, the novel technology demands careful safety monitoring. While global studies have explored its safety, local data remains limited and exhibits some variability. This study investigated short-term side effects among BNT162b2 vaccinated individuals in Qatar.MethodsA retrospective analysis was conducted using data extracted from the electronic health records of individuals aged 18 or older across 8 primary health centers who received either the first or second dose of the BNT162b2 vaccine during the period from December 23, 2020, to April 24, 2021. The proportions of individuals experiencing short-term side effects after each dose were calculated. Logistic regression and log binomial regression analyses were used to explore associations with the side effects.ResultsAmong 7,764 participants, 5,489 received the first dose and 2,275 the second, with similar demographics between the groups. After the first dose, 5.5% reported at least one local side effect, compared to 3.9% after the second, with a 1.4 times higher incidence after the first dose (RR 1.4, 95% CI 1.14–1.75) compared to the second. Systemic side effects after the second dose were 2.6 times more common than after the first (RR 2.6, 95% CI 2.15–3.14). Gender, nationality, history of prior COVID-19 infection, and obesity were significantly associated with side effects after the first dose, while age, gender, and nationality, were significant factors after the second dose.ConclusionThe rates of side effects following the BNT162b2 vaccine in Qatar were relatively low, with age, gender, nationality, previous infection, and obesity identified as significant predictors. These results emphasize the need for tailored vaccination strategies and contributes valuable insights for evidence-based decision-making in ongoing and future vaccination campaigns.

4-Acetylpyridine 1 and malononitrile 2 were allowed to react in a 3MCRs with dimedone 3a or cyclohexa-1,3-dione 3b under reflux to afford 4-methyl-4-(pyridin-4-yl)-5,6,7,8-tetrahydro-4H-chromene derivatives 4a,b respectively. The mechanism of the reaction has been studied and the structures elucidated by analytical, spectral as well as X-ray crystallographic data. Heterocyclic compounds find widespread application in pharmaceutical and agrochemical products. Docking analyses were performed on the synthesized compounds to assess their binding modes with various amino acids of the target protein tubulin (PDB Code - 1SA0). The results indicated promising binding scores for compounds 4a and 4b, suggesting a strong affinity for the tubulin binding site. Finally, ADMET for the synthesized compounds 4a, 4b, 5, 8a and 8b were carried out. The drug likeness and pharmacokinetic properties of the prepared compounds were also evaluated. Notably, all of the novel compounds adhered to Lipinski's rule (Ro5) without any violations.

Theophylline, a nitrogen-containing heterocycle, serves as a promising focal point for medicinal researchers aiming to create derivatives with diverse pharmacological applications. In this work, we present an improved synthetic method for a range of theophylline-1,2,4-triazole-S-linked N-phenyl acetamides (4a‒g) utilizing ultrasound-assisted synthetic approach. The objective was to assess the effectiveness of synthesized theophylline-1,2,4-triazoles (4a‒g) as inhibitors of HCV serine protease and as antibacterial agents against B. subtilis QB-928 and E. coli AB-274. Theophylline-1,2,4-triazoles were obtained in good to excellent yields (69%–95%) in a shorter time than conventional approach. 4-Chlorophenyl moiety containing theophylline-1,2,4-triazole 4c displayed significantly higher inhibitory activity against HCV serine protease enzyme (IC50 = 0.015 ± 0.25 mg) in comparison to ribavirin (IC50 = 0.165 ± 0.053 mg), but showed excellent binding affinity (−7.55 kcal/mol) with the active site of serine protease, better than compound 4c (−6.90 kcal/mol) as well as indole-based control compound 5 (−7.42 kcal/mol). In terms of percentage inhibition of serine protease, 2-chlorophenyl compound 4b showed the maximum percentage inhibition (86%), more than that of the 3,4-dichlorophenyl compound 4c (76%) and ribavirin (81%). 3,4-Dimethylphenyl-based theophylline-1,2,4-triazole 4g showed the lowest minimum inhibitory concentration (MIC = 0.28 ± 0.50 μg/mL) against the B. subtilis bacterial strain as compared to the standard drug penicillin (MIC = 1 ± 1.50 μg/mL). The other 4-methylphenyl theophylline-1,2,4-triazole 4e (MIC = 0.20 ± 0.08 μg/mL) displayed the most potent antibacterial potential against E. coli in comparison to the standard drug penicillin (MIC = 2.4 ± 1.00 μg/mL). Molecular docking studies further helped in an extensive understanding of all of the interactions between compounds and the enzyme active site, and DFT studies were also employed to gain insights into the molecular structure of the synthesized compounds. The results indicated that theophylline-linked triazole derivatives 4b and 4c showed promise as leading contenders in the fight against the HCV virus. Moreover, compounds 4e and 4g demonstrated potential as effective chemotherapeutic agents against E. coli and B. subtilis, respectively. To substantiate these findings, additional in vivo studies and clinical trials are imperative, laying the groundwork for their integration into future drug design and development.

With the rapid growth of information and communication technologies, governments worldwide are embracing digital transformation to enhance service delivery and governance practices. In the rapidly evolving landscape of information technology (IT), secure data management stands as a cornerstone for organizations aiming to safeguard sensitive information. Robust data modeling techniques are pivotal in structuring and organizing data, ensuring its integrity, and facilitating efficient retrieval and analysis. As the world increasingly emphasizes sustainability, integrating eco-friendly practices into data management processes becomes imperative. This study focuses on the specific context of Pakistan and investigates the potential of cloud computing in advancing e-governance capabilities. Cloud computing offers scalability, cost efficiency, and enhanced data security, making it an ideal technology for digital transformation. Through an extensive literature review, analysis of case studies, and interviews with stakeholders, this research explores the current state of e-governance in Pakistan, identifies the challenges faced, and proposes a framework for leveraging cloud computing to overcome these challenges. The findings reveal that cloud computing can significantly enhance the accessibility, scalability, and cost-effectiveness of e-governance services, thereby improving citizen engagement and satisfaction. This study provides valuable insights for policymakers, government agencies, and researchers interested in the digital transformation of e-governance in Pakistan and offers a roadmap for leveraging cloud computing technologies in similar contexts. The findings contribute to the growing body of knowledge on e-governance and cloud computing, supporting the advancement of digital governance practices globally. This research identifies monitoring parameters necessary to establish a sustainable e-governance system incorporating big data and cloud computing. The proposed framework, Monitoring and Assessment System using Cloud (MASC), is validated through secondary data analysis and successfully fulfills the research objectives. By leveraging big data and cloud computing, governments can revolutionize their digital governance practices, driving transformative changes and enhancing efficiency and effectiveness in public administration.

Some plant species may exhibit new microenvironments which lead to significant changes in the cover and diversity of the coexisting species. In this investigation, we evaluated the effects of Plantago lagopus L. on the cover and diversity of the associated plant species in the urban vegetation. A total of 70 plots were conducted in sites with- and without this species in urban gardens. Cover of the associated species and different diversity indices including species richness, Shannon-Wiener, evenness, and Simpson indices were measured. The allelopathic potential of P. lagopus was verified using its rhizosphere and non-rhizosphere soils on two target species existing within the same environment. Some soil criteria and seed sizes of the associated species were also determined. Most of the coexisting weeds were reduced in terms of their cover in plots with Plantago. The reduction of plant diversity depended on its cover. Besides, the aboveground biomass was reduced in sites comprising Plantago. The degree of inhibition was not related to the seed size of the species found. This species reduced the incident solar radiation and the local temperature over the soil surface. The locations exhibiting such species contained lower contents of available potassium and zinc. Rhizosphere soil of P. lagopus substantially inhibited germination and growth of Amaranthus viridis, but it didn't do so for Medicago lupulina. Reduction in cover, diversity, and biomass of the urban weeds associated with P. lagopus may be related to the reduction of received solar radiation, soil temperature, and nutrient availability. The allelopathic potential of P. lagopus may have a partial role in this reduction. These results suggest that P. lagopus may create a microenvironment of new conditions not favorable for most of the coexisting species.

Objective: The prevalence of obesity has increased over the past three decades. Proopiomelanocortin (POMC) neurons in the hypothalamic arcuate nucleus (ARC) play a vital role in induction of satiety. Chronic consumption of high-fat diet is known to reduce hypothalamic neuronal sensitivity to hormones like leptin, thus contributing to the development and persistence of obesity. The functional and morphological effects of a high-calorie diet on POMC neurons and how these effects contribute to the development and maintenance of the obese phenotype are not fully understood. For this purpose, POMC-Cre transgenic mice model was exposed to high-fat diet (HFD) and at the end of a 3- and 6-month period, electrophysiological and morphological changes, and the role of POMC neurons in homeostatic nutrition and their response to leptin were thoroughly investigated. Methods: Effects of HFD on POMC-satiety neurons in transgenic mice models exposed to chronic high-fat diet were investigated using electrophysiological (patch-clamp), chemogenetic and Cre recombinase advanced technological methods. Leptin, glucose and lipid profiles were determined and analyzed. Results: In mice exposed to a high-fat diet for 6 months, no significant changes in POMC dendritic spine number or projection density from POMC neurons to the paraventricular hypothalamus (PVN), lateral hypothalamus (LH), and bed nucleus stria terminalis (BNST) were observed. It was revealed that leptin hormone did not change the electrophysiological activities of POMC neurons in mice fed with HFD for 6 months. In addition, chemogenetic stimulation of POMC neurons increased HFD consumption. In the 3-month HFD-fed group, POMC activation induced an orexigenic response in mice, whereas switching to a standard diet was found to abolish orexigenic behavior in POMC mice. Conclusions: Chronic high fat consumption disrupts the regulation of POMC neuron activation by leptin. Altered POMC neuron activation abolished the neuron's characteristic behavioral anorexigenic response. Change in nutritional content contributes to the reorganization of developing maladaptations.

Summary:. Beckwith–Wiedemann syndrome (BWS) is a complex congenital overgrowth disorder necessitating a multidisciplinary approach for effective management. A 5-year-old Saudi girl with BWS received comprehensive care involving various specialists, including a plastic surgeon who performed a keyhole technique tongue reduction to address macroglossia. The intervention resulted in significant improvements in speech and quality of life, with no postoperative complications. Intensive speech therapy further enhanced speech development. This case report emphasizes the importance of a multidisciplinary approach and the critical role of the plastic surgeon in managing BWS patients with macroglossia to achieve optimal outcomes.

Copper-based metal-organic frameworks (BDC-Cu MOFs) were synthesized via a casting approach using 1,4-benzene dicarboxylic (BDC) as organic ligand and their properties characterized. The obtained materials were then utilized to immobilize the α-amylase enzyme. The chemical composition and functional components of the synthesized support (BDC-Cu MOFs) were investigated with Fourier transform infrared spectroscopy (FTIR), the surface morphology was determined with scanning electron microscopy (SEM), and the elemental composition was established with energy dispersive X-ray (EDX) analyses. X-ray diffraction (XRD) was employed to analyze the crystallinity of the synthesized DBC-Cu MOFs. The zeta potentials of DBC-Cu MOFs and DBC-Cu MOFs@α-amylase were determined. The immobilized α-amylase demonstrated improved catalytic activity and reusability compared to the free form. Covalent attachment of the α-amylase to BDC-Cu provided an immobilization yield (IY%) of 81% and an activity yield (AY%) of 89%. The immobilized α-amylase showed high catalytic activity and 81% retention even after ten cycles. Storage at 4 °C for eight weeks resulted in a 78% activity retention rate for DBC-Cu MOFs@α-amylase and 49% retention for the free α-amylase. The optimum activity occurred at 60 °C for the immobilized form, whereas the free form showed optimal activity at 50 °C. The free and immobilized α-amylase demonstrated peak catalytic activities at pH 6.0. The maximum reaction velocities (Vmax) values were 0.61 U/mg of protein for free α-amylase and 0.37 U/mg of protein for BDC-Cu MOFs@α-amylase, while the Michaelis‒Menten affinity constants (Km) value was lower for the immobilized form (5.46 mM) than for the free form (11.67 mM). Treatments of maize flour and finger millet samples with free and immobilized α-amylase resulted in increased total phenolic contents. The enhanced antioxidant activities of the treated samples were demonstrated with decreased IC50 values in ABTS and DPPH assays. Overall, immobilization of α-amylase on BDC-Cu MOFs provided improved stability and catalytic activity and enhanced the antioxidant potentials of maize flour and finger millet.