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A metamaterial-based non-uniform dipole array antenna is presented for high gain 5G millimeter-wave applications with a wideband characteristic. Initially, a non-uniform dipole array is designed on a 0.202 mm thick Rogers RO4003C substrate, offering a wide operating bandwidth ranging from 23.1 GHz to 44.8 GHz. The dipole array antenna emits unidirectional end-fire radiation with a maximum gain of 8.1 dBi and an average gain of 6.7 dBi. Subsequently, to achieve high gain performance, a 5 × 7 metamaterial structure is designed in the direction of the antenna radiation. The implemented metamaterial structure is optimized for the operating frequency, enhancing the directivity of the antenna radiation and resulting in a gain increment of more than 3 dBi compared to the dipole array alone. The developed metamaterial-integrated dipole array antenna offers an operating bandwidth (S11 < −10 dB) of more than 21 GHz (63.92%), ranging from 23.1 GHz to 44.8 GHz, covering the most commonly used 5G millimeter-wave frequency bands (n257, n258, n259, n260, and n261). Furthermore, the presented antenna yields a stable high gain with a peak gain of 11.21 dBi and a good radiation efficiency of more than 64%. The proposed antenna is an excellent option for millimeter-wave 5G systems due to its overall properties, particularly its high gain and end-fire radiation characteristics, combined with a wide operating bandwidth. [ABSTRACT FROM AUTHOR]

Our research evaluates advanced artificial (AI) methodologies to enhance diagnostic accuracy in pulmonary radiography. Utilizing DenseNet121 and ResNet50, we analyzed 108,948 chest X-ray images from 32,717 patients and DenseNet121 achieved an area under the curve (AUC) of 94% in identifying the conditions of pneumothorax and oedema. The model's performance surpassed that of expert radiologists, though further improvements are necessary for diagnosing complex conditions such as emphysema, effusion, and hernia. Clinical validation integrating Latent Dirichlet Allocation (LDA) and Named Entity Recognition (NER) demonstrated the potential of natural language processing (NLP) in clinical workflows. The NER system achieved a precision of 92% and a recall of 88%. Sentiment analysis using DistilBERT provided a nuanced understanding of clinical notes, which is essential for refining diagnostic decisions. XGBoost and SHapley Additive exPlanations (SHAP) enhanced feature extraction and model interpretability. Local Interpretable Model-agnostic Explanations (LIME) and occlusion sensitivity analysis further enriched transparency, enabling healthcare providers to trust AI predictions. These AI techniques reduced processing times by 60% and annotation errors by 75%, setting a new benchmark for efficiency in thoracic diagnostics. The research explored the transformative potential of AI in medical imaging, advancing traditional diagnostics and accelerating medical evaluations in clinical settings. [ABSTRACT FROM AUTHOR]

Cardiovascular diseases (CVDs) remain a major global health challenge and a leading cause of mortality, highlighting the need for improved predictive models. We introduce an innovative agent-based dynamic simulation technique that enhances our AI models' capacity to predict CVD progression. This method simulates individual patient responses to various cardiovascular risk factors, improving prediction accuracy and detail. Also, by incorporating an ensemble learning model and interface of web application in the context of CVD prediction, we developed an AI dashboard-based model to enhance the accuracy of disease prediction and provide a user-friendly app. The performance of traditional algorithms was notable, with Ensemble learning and XGBoost achieving accuracies of 91% and 95%, respectively. A significant aspect of our research was the integration of these models into a streamlit-based interface, enhancing user accessibility and experience. The streamlit application achieved a predictive accuracy of 97%, demonstrating the efficacy of combining advanced AI techniques with user-centered web applications in medical prediction scenarios. This 97% confidence level was evaluated by Brier score and calibration curve. The design of the streamlit application facilitates seamless interaction between complex ML models and end-users, including clinicians and patients, supporting its use in real-time clinical settings. While the study offers new insights into AI-driven CVD prediction, we acknowledge limitations such as the dataset size. In our research, we have successfully validated our predictive proposed methodology against an external clinical setting, demonstrating its robustness and accuracy in a real-world fixture. The validation process confirmed the model's efficacy in the early detection of CVDs, reinforcing its potential for integration into clinical workflows to aid in proactive patient care and management. Future research directions include expanding the dataset, exploring additional algorithms, and conducting clinical trials to validate our findings. This research provides a valuable foundation for future studies, aiming to make significant strides against CVDs. [ABSTRACT FROM AUTHOR]

The main protease (Mpro) of severe acute respiratory syndrome coronavirus 2 (SARS‐CoV‐2) plays an important role in viral replication and transcription and received great attention as a vital target for drug/peptide development. Therapeutic agents such as small‐molecule drugs or peptides that interact with the Cys–His present in the catalytic site of Mpro are an efficient way to inhibit the protease. Although several emergency‐approved vaccines showed good efficacy and drastically dropped the infection rate, evolving variants are still infecting and killing millions of people globally. While a small‐molecule drug (Paxlovid) received emergency approval, small‐molecule drugs have low target specificity and higher toxicity. Besides small‐molecule drugs, peptide therapeutics are thus gaining increasing popularity as they are easy to synthesize and highly selective and have limited side effects. In this study, we investigated the therapeutic value of 67 peptides targeting Mpro using molecular docking. Subsequently, molecular dynamics (MD) simulations were implemented on eight protein–peptide complexes to obtain molecular‐level information on the interaction between these peptides and the Mpro active site, which revealed that temporin L, indolicidin, and lymphocytic choriomeningitis virus (LCMV) GP1 are the best candidates in terms of stability, interaction, and structural compactness. These peptides were synthesized using the solid‐phase peptide synthesis protocol, purified by reversed‐phase high‐performance liquid chromatography (RP‐HPLC), and authenticated by mass spectrometry (MS). The in vitro fluorometric Mpro activity assay was used to validate the computational results, where temporin L and indolicidin were observed to be very active against SARS‐CoV‐2 Mpro with IC50 values of 38.80 and 87.23 μM, respectively. A liquid chromatography–MS (LC–MS) assay was developed, and the IC50 value of temporin L was measured at 23.8 μM. The solution‐state nuclear magnetic resonance (NMR) structure of temporin L was determined in the absence of sodium dodecyl sulfate (SDS) micelles and was compared to previous temporin structures. This combined investigation provides critical insights and assists us to further develop peptide inhibitors of SARS‐CoV‐2 Mpro through structural guided investigation. [ABSTRACT FROM AUTHOR]

Despite numerous initiatives by both governmental and non-governmental organizations, primary level students' skills in English language are still below the expected level in Bangladesh (Hamid & Honan, 2012; Sultana, 2010). Our study examined reasons behind the limited participation of EFL (English as a Foreign Language) learners in primary level classrooms in the coastal areas of Bangladesh. To conduct the research, we followed an explanatory sequential mixed methods design (Creswell, 2014; Creswell & Creswell, 2018; Ivankova & Stick, 2007). We collected data from 37 male and 23 female students in grades four and five through questionnaire surveys and three focus group discussions (FGDs). We also collected data from five teachers through interviews and three class observations. We found that teachers had less motivation to create an interactive learning environment for the students due to heavy teaching loads and administrative assignments. Many of the students had low academic expectations and motivation, lived in poor socio-economic conditions that required them to work, and were impacted by frequent natural disasters that interrupted their regular classes. The results of our research provide insights for educationists and policymakers related to primary education in disaster-prone coastal areas as well as other rural parts of the country. [ABSTRACT FROM AUTHOR]

Synthetic vectors for therapeutic nucleic acid delivery are currently competing significantly with their viral counter parts due to their reduced immunogenicity, large payload capacity, and ease of manufacture under GMP‐compliant norms. The approval of Onpattro, a lipid‐based siRNA therapeutic, and the proven clinical success of two lipid‐based COVID‐19 vaccines from Pfizer‐BioNTech, and Moderna heralded the specific advantages of lipid‐based systems among all other synthetic nucleic acid carriers. Lipid‐based systems with diverse payloads—plasmid DNA (pDNA), antisense oligonucleotide (ASO), small interfering RNA (siRNA), microRNA (miRNA), small activating RNA (saRNA), and messenger RNA (mRNA)—are now becoming a mature technology, with growing impact in the clinic. Research over four decades identified the key factors determining the therapeutic success of these multi‐component systems. Here, we discuss the main nucleic acid‐based technologies, presenting their mechanism of action, delivery barriers facing them, the structural properties of the payload as well as the component lipids that regulate physicochemical properties, pharmacokinetics and biodistribution, efficacy, and toxicity of the resultant nanoparticles. We further detail on the formulation parameters, evolution of the manufacturing techniques that generate reproducible and scalable outputs, and key manufacturing aspects that enable control over physicochemical properties of the resultant particles. Preclinical applications of some of these formulations that were successfully translated from in vitro studies to animal models are subsequently discussed. Finally, clinical success and failure of these systems starting from 1993 to present are highlighted, in a holistic literature review focused on lipid‐based nucleic acid delivery systems. This article is categorized under:Therapeutic Approaches and Drug Discovery > Emerging TechnologiesTherapeutic Approaches and Drug Discovery > Nanomedicine for Oncologic DiseaseToxicology and Regulatory Issues in Nanomedicine > Toxicology of Nanomaterials [ABSTRACT FROM AUTHOR]