Arni University

Acute coronary syndrome (ACS) encompasses a spectrum of ischemic cardiac conditions associated with significant global morbidity and mortality, commonly driven by atherosclerotic plaque disruption and thrombosis, and remains a major clinical challenge despite advances in cardiovascular care. Persistent variability in early diagnosis, risk stratification, and the integration of emerging therapies into routine practice highlights existing gaps in translating evolving evidence into consistently improved clinical outcomes. This narrative clinical review aims to synthesise recent advances in diagnostic approaches and therapeutic strategies, focusing on biomarkers, imaging modalities, pharmacological interventions, and precision-based management. A structured narrative literature search was conducted using PubMed and Google Scholar for English-language human studies published between 2015 and 2025. Search terms included "acute coronary syndrome," "high-sensitivity troponin," "biomarkers," "coronary computed tomography angiography," "intravascular ultrasound," "optical coherence tomography," "antiplatelet therapy," "anticoagulation," "lipid-lowering therapy," and "precision medicine." Guidelines, randomised trials, systematic reviews, meta-analyses, and high-impact reviews were included, while duplicate, non-English, non-coronary, and clinically irrelevant studies were excluded. Study relevance and quality were narratively assessed using the Scale for the Assessment of Narrative Review Articles (SANRA) principles, with emphasis on justification of the manuscript's importance, clarity of review aims, appropriateness of the literature search, balanced evidence presentation, scientific reasoning, and clinical relevance of the synthesis. Findings indicate that high-sensitivity troponins, advanced imaging techniques, and contemporary antithrombotic and lipid-lowering therapies have enhanced diagnostic accuracy and improved clinical outcomes. Emerging anti-inflammatory therapies and digital health innovations further contribute to more individualised patient management. These developments support the integration of novel tools with established clinical pathways to optimise care delivery. Continued research and improved implementation strategies remain necessary to address disparities and refine management approaches. A multidimensional, patient-centred framework is essential for advancing outcomes and guiding future clinical practice in ACS.

Accurate crop yield prediction is critical for food security, agricultural policy-making, and supply chain management. This paper introduces CanadaYieldNet, a novel hybrid deep learning architecture that integrates a Vision Transformer (ViT) for spatial feature extraction from satellite imagery with a Temporal Fusion Transformer (TFT) for temporal sequence modeling of climate and vegetation data. A key innovation is the Adaptive Gated Fusion (AGF) mechanism that dynamically weights spatial and temporal feature contributions based on their predictive importance. Furthermore, a multi-quantile regression prediction head provides uncertainty-aware yield forecasts with interpretable prediction intervals. We conduct a comprehensive pilot study focusing on wheat yield prediction in Saskatchewan, Canada, leveraging multimodal data streams including Sentinel-2 surface reflectance, MODIS vegetation indices, ERA5-Land climate reanalysis, AAFC crop inventory, and Statistics Canada yield records. Experimental results demonstrate that CanadaYieldNet achieves state-of-the-art performance with an R² of 0.950, RMSE of 0.30 t/ha, and MAPE of 5.4%, significantly outperforming eight baseline models including Random Forest, XGBoost, LSTM, CNN-LSTM, Pure ViT, and Pure TFT. Ablation studies validate the contribution of each architectural component, while SHAP-based explainability analysis reveals that late-season NDVI and pre-season precipitation are the most influential predictors. This work represents the first Canada-focused hybrid ViT-TFT framework for crop yield forecasting and provides a scalable architecture extensible to all Canadian provinces and crop types.

Alzheimer's disease (AD) is a neurodegenerative disorder characterized by progressive cognitive and functional insufficiencies as well as communicative changes. The treatment of AD is mainly based on cholinesterase inhibitors and NMDA-receptor antagonists. In the present study, gold nanoparticles (NMEs) targeting amyloid-β (Aβ)-induced complex neurotoxicity have received considerable attention in the therapeutic and preventive treatments of AD. In this work, we designed a small-sized Pd hydride (PdH) NP as high-payload hydrogen carrier and Pd similar self-catalyst to realize the in situ sustained release of bioreductive hydrogen for the first time. We found that PdH NP could selectively scavenge highly cytotoxic OH in AD model cells and could ameliorate mitochondria dysfunction and promote cellular energy metabolism, inhibiting cellular apoptosis, and inhibiting Aβ-mediated peroxidase activity and Aβ-induced cytotoxicity. In addition, we found that intravenous injection of DBP-PLGA nanoparticulate significantly attenuated the Aβ accumulation, neuroinflammation, neuronal loss and cognitive dysfunction in the 5XFAD mice. These results suggest that DBP-PLGA-based drug delivery system could be a promising approach to obtain desirable drug-like properties by altering the biopharmaceutics and toxicological properties of the molecule.

The Plantago plant belongs to the family Plantaginaceae and order Lamiales, consisting of more than 200 plant species, perennial and annual, distributed all over the world. The Plantago species is used all over the world for food purposes and several therapeutic aspects. The gel-like properties of polysaccharides that are taken out of seeds and are not just used in remedial aspects, but also in the removal of toxic substances and drug delivery. This study aims to review the biochemical and pharmacological profiles of various Plantago species. A thorough literature review is performed via several online search engines like Google Scholar, Science Direct, Medline, PubMed, and Scopus, regarding the biochemical profile of several Plantago species and their respective pharmacological effects. Several important bioactive compounds such as flavonoids, phenolic compounds, glycosides, terpenes and iridoids have been identified from various Plantago species. Many plant components like stems, leaves and seeds are used therapeutically for the treatment of several diseases. Some of the major pharmacological activities of various Plantago species are antimicrobial, anthelmintic, antioxidative, wound healing, anticancer and hypolipidemic, following mechanistic pathways. From the extensive literature review, it has been found that Plantago species have been used traditionally in many regions of the world. Even in some regions of the world, several plants of the Plantago species have been used in the food industry. The biochemical profile of several Plantago plants have been signified from various studies. Their pharmacological aspects have also been explored with mechanistic pathways, signifying an impactful future prospect in treating several diseases clinically.

Accurate crop yield forecasting is fundamental to global food security, agricultural policy, and supply chain management. Over the past decade, deep learning approaches—particularly convolutional neural networks and recurrent architectures—have substantially advanced yield prediction accuracy beyond traditional statistical and machine learning methods. More recently, transformer architectures, originally developed for natural language processing, have emerged as a promising paradigm for agricultural forecasting due to their capacity to model long-range temporal dependencies and fuse heterogeneous data streams through self-attention mechanisms. This systematic review synthesises the peer-reviewed literature published between 2020 and 2025 on transformer-based crop yield forecasting, multi-source remote sensing data fusion, and explainable artificial intelligence in agricultural deep learning. Following a structured search across Scopus, Web of Science, IEEE Xplore, and Google Scholar, we identify and critically analyse studies spanning convolutional, recurrent, hybrid, and transformer architectures applied to yield prediction at field, county, and regional scales. Our synthesis reveals that while transformers such as the Multi-Modal Spatial-Temporal Vision Transformer (MMST-ViT), Informer, and Temporal Fusion Transformer have demonstrated competitive or superior performance compared to CNN-LSTM baselines, most studies remain confined to single crops in data-rich regions of the United States and China. Multi-crop forecasting frameworks, interpretable model designs, and uncertainty-aware architectures remain underexplored. A dedicated analysis of Canadian crop yield forecasting literature reveals that no published study has applied transformer architectures to multi-crop regional yield prediction across the Canadian Prairies, representing a significant and defensible research gap. We conclude with a future research agenda addressing multimodal transformer design, operational deployment, physics-informed learning, and the specific needs of Canadian Prairie agriculture