Search Results (387)

Artificial intelligence (AI) has already played a significant role in the healthcare sector, particularly in image-based medical diagnosis. Deep learning models have produced satisfactory and useful results for accurate decision-making. Among the various types of medical images, magnetic resonance imaging (MRI) is frequently utilized in deep learning applications to analyze detailed structures and organs in the body, using advanced intelligent software. However, challenges related to performance and data privacy often arise when using medical data from patients and healthcare institutions. To address these issues, new approaches have emerged, such as federated learning. This technique ensures the secure exchange of sensitive patient and institutional data. It enables machine learning or deep learning algorithms to establish a client–server relationship, whereby specific parameters are securely shared between models while maintaining the integrity of the learning tasks being executed. Federated learning has been successfully applied in medical settings, including diagnostic applications involving medical images such as MRI data. This research introduces an analytical intelligence system based on an Internet of Medical Things (IoMT) framework that employs federated learning to provide a safe and effective diagnostic solution for brain tumor identification. By utilizing specific brain MRI datasets, the model enables multiple local capsule networks (CapsNet) to achieve improved classification results. The average accuracy rate of the CapsNet model exceeds 97%. The precision rate indicates that the CapsNet model performs well in accurately predicting true classes. Additionally, the recall findings suggest that this model is effective in detecting the target classes of meningiomas, pituitary tumors, and gliomas. The integration of these components into an analytical intelligence system that supports the work of healthcare personnel is the main contribution of this work. Evaluations have shown that this approach is effective for diagnosing brain tumors while ensuring data privacy and security. Moreover, it represents a valuable tool for enhancing the efficiency of the medical diagnostic process. Full article

Monitoring the structural health of bridges in road infrastructure is crucial for ensuring public safety and efficient maintenance. This paper presents a multi-level semi-automatic methodology for bridge monitoring, using Multi-Temporal Differential SAR Interferometry (MT-DInSAR) data. The proposed approach requires a dataset of satellite-derived MT-DInSAR measurements for the Area of Interest. The methodology involves creating a georeferenced database of bridges which allows the filtering of measurement points (generally named Persistent Scatterers—PSs) using spatial queries. Since existing datasets often provide only point geometries for bridge locations, additional data sources such as OpenStreetMaps-derived repositories have been utilized to obtain linear representations of bridges. These linear features are segmented into 20 m sections, which are then converted into polygonal geometries by applying a uniform buffer. Spatial joining between the bridge polygons and PS datasets allows the extraction of key statistics, such as mean displacement velocity, PS density and coherence levels. Based on predefined velocity thresholds, warning flags are triggered, indicating the need for further in-depth analysis. Finally, an upscaling step is performed to provide a practical tool for infrastructure managers, visually categorizing bridges based on the presence of flagged pixels. The proposed approach facilitates large-scale bridge monitoring, supporting the early detection of potential structural issues. Full article

Background: Mirror therapy (MT) creates a cerebral illusion of a normal movement in a paretic limb. Although mirror therapy has been studied as a suitable intervention for children with Unilateral Cerebral Palsy (UCP), a comprehensive understanding of its full range of benefits is still lacking. Thus, the aim of this systematic review and meta-analysis was to determine all motor and sensory effects of MT in children and adolescents with UCP. Methods: Clinical trials focused on the application of MT in the upper limb (UL) of children and adolescents with UCP were included. A search was performed in PubMed, Cochrane Library, Web of Science, and LILACS databases. Eleven studies were included in this systematic review. The PEDro scale and the MINORS scale were applied to evaluate the methodological quality of randomized and non-randomized controlled trials, respectively. The Risk of Bias tool was also employed to evaluate the potential bias. In addition, the TIDieR checklist was used to assess the quality of intervention reporting. A random-effects model was used for the meta-analysis. Results: The studies included children with UCP from three to eighteen years, classified in Manual Ability Classification System levels I–IV. Motor effects of MT were found in nine studies. Also, two studies reported sensory effects on registration, perception, and proprioception abilities. Qualitative and quantitative analysis showed that MT improved manual dexterity and tactile registration in children and adolescents with UCP. Conclusions: MT is a therapy capable of inducing motor and sensory improvements in the affected UL of children with UCP. Full article

To improve the post-earthquake resilience evaluation of concrete buildings with various construction types, this study presents a generalized recovery-based framework that ext-ends the FEMA P-58 methodology. The proposed method introduces a dynamic repair scheduling approach that incorporates two key construction-related parameters: the prefabrication ratio and the types of prefabricated components. These inputs govern the allocation of parallel or sequential repairs, enabling a more accurate estimation of recovery trajectories and downtime. Functional loss over time is modeled through component-level repair sequencing combined with mobilization delays. A case study involving three four-story prestressed concrete frame buildings (cast-in situ, partially prefabricated, and fully precast prestressed concrete (PCaPC) with mortise–tenon (MT) connections) demonstrated the framework’s applicability. The results show that higher prefabrication levels lead to significantly shorter median repair times, with up to a 97-day reduction observed for the fully prefabricated frame. Additionally, recovery differences emerge even between buildings with the same prefabrication ratio but different component configurations. Compared to conventional assessment methods, the proposed framework avoids the overestimation of mobilization and repair duration, offering a practical tool for the design and performance assessment of resilient precast and hybrid concrete building systems. Full article

An increasing number of scholars are raising concerns about the sustainability of digital health, calling for action to prevent its harmful effects on the environment. At this point, however, the comprehensive appraisal of emerging technology in the health sector remains theoretically challenging, and highly difficult to implement in practice and in ecological design. Indeed, background factors such as the rapid evolution of technology or effectiveness–efficiency tradeoffs complicate the task of distinguishing the benefits of digital health from its drawbacks, rendering early Health Technology Sustainability Assessment (HTSA) extremely complex. Within this context, the aim of this article is to draw attention to the pragmatism that should be adopted when anticipating the sustainability of technological innovation in the medical field, while simultaneously proposing an assessment framework grounded in a structural and conceptual dissection of the fundamental purpose of smart technologies and the Internet of Medical Things (IoMT). Building on this, we demonstrate how our framework can be strategically applied through a rapid back-of-the-envelope assessment of the economic and ecological balance when introducing IoMT prototypes for treating a specific condition, based on a preliminary simulation of a defined clinical outcome. In this manner, the article presents evidence that challenges two primary hypotheses, and also encourages reflection on the central role of information and its interpretation when addressing key barriers in the HTSA of digital health. Thereby, it contributes to advancing cost–benefit and cost-effectiveness evaluation tools that support eco design strategies and guide informed decision-making regarding the integration of sustainable IoMT systems into healthcare. Full article

A series of incidents involving damage to vehicle speed sensor cables occurred in an urban area in Japan. At the request of the police, DNA analysis was conducted to identify the animal species responsible. Swab samples collected from the damaged sections of the cables were subjected to PCR testing using mtDNA fragments. Sequencing analysis with universal primers (SCPH02500, SCPL02981) detected DNA from the Japanese marten (Martes melampus). A comprehensive examination that included morphological analysis of the cable damage and consideration of the ecological characteristics of the Japanese martens suggested that the damage was likely caused by this species. DNA analysis using mtDNA markers is a valuable tool for species identification in wildlife forensic veterinary investigations and serves as important scientific evidence in criminal cases involving animals. The findings from this case may contribute to future investigations in forensic veterinary science and ecological research and may also inform measures to prevent human–wildlife conflicts involving animals. Full article

Aquaculture is one of the fastest-growing sectors in food production. The widespread use of antibiotics in fish farming has been identified as a driver for the development of antibiotic resistance. One of the promising approaches to solving this problem is the use of probiotics. There are many promising aquaculture probiotics in the Bacillus genus, which produces non-ribosomal peptides (NRPs). NRPs are known as antimicrobial agents, although evidence is gradually accumulating that they may have other effects, especially at lower (subinhibitory) concentrations. The mechanisms of action of many NRPs remain unexplored, and molecular docking and molecular dynamics studies are invaluable tools for studying such mechanisms. The purpose of this study was to investigate the in silico inhibition of crucial bacterial targets by NRPs. Molecular docking analyses were conducted to assess the binding affinities of the NRPs of Bacillus for protein targets. Among the complexes evaluated, bacillibactin with glutamine synthetase, dihydrofolate reductase, and proaerolysin exhibited the lowest docking scores. Consequently, these complexes were selected for further investigation through molecular dynamics simulations. As a result, three additional potential mechanisms of action for bacillibactin were identified through in silico analyses, including the inhibition of glutamine synthetase, dihydrofolate reductase, and proaerolysin, which are critical bacterial enzymes and considered as the potential antibacterial targets. These findings were further supported by in vitro antagonism assays using bacillibactin-producing Bacillus velezensis strains MT55 and MT155, which demonstrated strong inhibitory activity against Pseudomonas aeruginosa and Aeromonas veronii. Full article

Osteoarthritis (OA) is the most prevalent degenerative joint disorder worldwide, causing significant declines in quality of life. The osteochondral junction (OCJ), a critical structural interface between deep cartilage and subchondral bone, plays an essential role in OA progression but is challenging to assess using conventional magnetic resonance imaging (MRI) due to its short T2 relaxation times. This study aimed to evaluate the utility of ultrashort echo time (UTE) MRI biomarkers, including macromolecular fraction (MMF), magnetization transfer ratio (MTR), and T2*, for in vivo quantification of OCJ changes in knee OA for the first time. Forty-five patients (mean age: 53.8 ± 17.0 years, 50% female) were imaged using 3D UTE-MRI sequences on a 3T clinical MRI scanner. Patients were stratified into two OA groups based on radiographic Kellgren–Lawrence (KL) scores: normal/subtle (KL = 0–1) (n = 21) and mild to moderate (KL = 2–3) (n = 24). Quantitative analysis revealed significantly lower MMF (15.8  ±  1.4% vs. 13.6 ± 1.2%, p < 0.001) and MTR (42.5 ± 2.5% vs. 38.2  ±  2.3%, p < 0.001) in the higher KL 2–3 group, alongside a higher trend in T2* values (19.7  ±  2.6 ms vs. 21.6  ±  3.8 ms, p = 0.06). Moreover, MMF and MTR were significantly negatively correlated with KL grades (r = −0.66 and −0.59; p < 0.001, respectively), while T2* showed a weaker positive correlation (r = 0.26, p = 0.08). Receiver operating characteristic (ROC) analysis demonstrated superior diagnostic accuracy for MMF (AUC = 0.88) and MTR (AUC = 0.86) compared to T2* (AUC = 0.64). These findings highlight UTE-MT techniques (i.e., MMF and MTR) as promising imaging tools for detecting OCJ degeneration in knee OA, with potential implications for earlier and more accurate diagnosis and disease monitoring. Full article

The utilization of magnetic fields in agricultural contexts has been demonstrated to exert a beneficial effect on various aspects of crop development, including germination, growth, and yield. The present study investigates the impact of magnetic biostimulation on seeds of purple maize (Zea mays L.), variety INIA 601, cultivated in Cajamarca, Peru, with a particular focus on their physical characteristics, yield, bioactive compounds, and antioxidant activity. The results demonstrated that seeds treated with pulsed (8 mT at 30 Hz for 30 min) and static (50 mT for 30 min) magnetic fields exhibited significantly longer cobs (16.89 and 16.53 cm, respectively) compared with the untreated control (15.79 cm). Furthermore, the application of these magnetic fields resulted in enhanced antioxidant activity in the bract, although the untreated samples exhibited higher values (110.56 µg/mL) compared with the pulsed (91.82 µg/mL) and static (89.61 µg/mL) treatments. The geographical origin of the samples had a significant effect on the physical development and the amount of total phenols, especially the antioxidant activity in the coronet and bract. Furthermore, a total of fourteen phenols were identified in various parts of the purple maize, with procyanidin B2 found in high concentrations in the bract and crown. Conversely, epicatechin, kaempferol, vanillin, and resveratrol were found in lower concentrations. These findings underscore the phenolic diversity of INIA 601 purple maize and its potential application in the food and pharmaceutical industries, suggesting that magnetic biostimulation could be an effective tool to improve the nutritional and antioxidant properties of crops. Full article

Despite growing international consensus on the urgency of climate action, global CO₂ emissions have continued to rise, exposing a critical implementation gap between environmental ambition and reality. This study explores the readiness and structural capacity of the world’s most CO₂-intensive countries to transition toward a green and hydrogen-based economy. We introduce and apply the Green Economy Index, a composite measure integrating 31 indicators across four core dimensions—political and regulatory efficiency, socio-economic status, infrastructure, and sustainable targets. Using data from 29 countries emitting over 200 Mt of CO₂ in 2022, the analysis combines principal component analysis, Random Forest modeling, and network-based correlation analysis to classify nations into frontrunners, transitional performers, and structural laggers. The results reveal significant disparities in green economy readiness, with high-income countries showing institutional maturity and infrastructural robustness, while middle-income nations remain constrained by fossil fuel dependencies and governance challenges. Importantly, we highlight the growing utility of machine learning and multivariate statistics in capturing complex sustainability interdependencies. The Green Economy Index framework offers a relevant tool to benchmark progress, diagnose barriers, and guide targeted interventions in global decarbonization efforts. Full article

Background/Objectives: In patients with acute ischemic stroke (AIS), the first pass effect (FPE) refers to the complete recanalization of an occluded vessel (TICI = 2C/3) with a single thrombectomy attempt. Achieving complete vessel recanalization is associated with better functional outcomes compared to lower reperfusion rates (TICI < 2B). There is no consensus on which thrombectomy technique provides the best recanalization results for AIS patients. Furthermore, there is a paucity of tools available to predict FPE prior to mechanical thrombectomy (MT). The objective of this study is to develop a radiomics model based on brain NCECT to predict which patients are more likely to achieve a FPE with thromboaspiration MT. Methods: The thrombi of 91 patients were semi-automatically segmented on NCECT. A total of 1167 radiomic features (RFs) were extracted for each patient. Some clinical data (age, gender, cardiovascular risk factors, smoking or alcohol abuse, clot density and clot laterality) were also collected. Results: A LASSO regression analysis identified nine RFs with nonzero coefficients. A logistic regression model for FPE prediction was developed with nine RFs and eight clinical variables. A total of six RFs were found to be statistically associated with FPE. The clinical variables did not demonstrate a statistically significant association with the likelihood of achieving FPE (p > 0.05). The prediction of which patients are likely to achieve FPE obtained an AUC, accuracy, sensitivity and specificity of 0.890, 0.813, 0.815 and 0.811, respectively (p < 0.05). Conclusions: Radiomics can help identify patients who are more likely to achieve FPE with thromboaspiration. Full article

The emergence of large language models (LLMs) has revolutionized the trajectory of NLP research. Transformers, combined with attention mechanisms, have increased computational power, and massive datasets have led to the emergence of pre-trained large language models (PLLMs), which offer promising possibilities for multilingual applications in low-resource settings. However, the scarcity of annotated resources and suitably pre-trained models continues to pose a significant hurdle for the low-resource abstractive text summarization of legal texts, particularly in Urdu. This study presents a transfer learning approach using pre-trained multilingual large models (the mBART and mT5, Small, Base, and Large) to generate abstractive summaries of Urdu legal texts. A curated dataset was developed with legal experts, who produced ground-truth summaries. The models were fine-tuned on this domain-specific corpus to adapt them for low-resource legal summarization. The experimental results demonstrated that the mT5-Large, fine-tuned on Urdu legal texts, outperforms all other evaluated models across standard summarization metrics, achieving ROUGE-1 scores of 0.7889, ROUGE-2 scores of 0.5961, and ROUGE-L scores of 0.7813. This indicates its strong capacity to generate fluent, coherent, and legally accurate summaries. The mT5-Base model closely follows with ROUGE-1 = 0.7774, while the mT5-Small shows moderate performance (ROUGE-1 = 0.6406), with reduced fidelity in capturing legal structure. The mBART50 model, despite being fine-tuned on the same legal corpus, performs lower (ROUGE-1 = 0.5914), revealing its relative limitations in this domain. Notably, models trained or fine-tuned on non-legal, out-of-domain data, such as the urT5 (ROUGE-1 = 0.3912), the mT5-XLSUM (ROUGE-1 = 0.0582), and the mBART50 (XLSUM) (ROUGE-1 = 0.0545), exhibit poor generalization to legal summaries, underscoring the necessity of domain adaptation when working in low-resource legal contexts. These findings highlight the effectiveness of fine-tuning multilingual LLMs for domain-specific tasks. The gains in legal summarization demonstrate the practical value of transfer learning in low-resource settings and the broader potential of AI-driven tools for legal document processing, information retrieval, and decision support. Full article

The development of automatic methods for early cognitive impairment (CI) detection has a crucial role to play in helping people obtain suitable treatment and care. Video-based analysis offers a promising, low-cost alternative to resource-intensive clinical assessments. This paper investigates visual features (eye blink rate (EBR), head turn rate (HTR), and head movement statistical features (HMSFs)) for distinguishing between neurodegenerative disorders (NDs), mild cognitive impairment (MCI), functional memory disorders (FMDs), and healthy controls (HCs). Following prior work, we improve the multiple thresholds (MTs) approach specifically for EBR calculation to enhance performance and robustness, while the HTR and HMSFs are extracted using methods from previous work. The EBR, HTR, and HMSFs are evaluated using an in-the-wild video dataset captured in challenging environments. This method leverages clinically validated cues and automatically extracts features to enable classification. Experiments show that the proposed approach achieves competitive performance in distinguishing between ND, MCI, FMD, and HCs on in-the-wild datasets, with results comparable to audiovisual-based methods conducted in a lab-controlled environment. The findings highlight the potential of visual-based approaches to complement existing diagnostic tools and provide an efficient home-based monitoring system. This work advances the field by addressing traditional limitations and offering a scalable, cost-effective solution for early detection. Full article

The Arabian leopard (Panthera pardus nimr), a critically endangered subspecies endemic to the Arabian Peninsula, faces severe threats from habitat loss, prey depletion, and inbreeding, with fewer than 200 individuals remaining. Genomic resources for this subspecies have been scarce, limiting insights into its evolutionary history and conservation needs. Here, we present the first complete mitochondrial DNA (mtDNA) sequence of P. pardus nimr, derived from a wild-born male sampled at the Oman Wildlife Breeding Centre in 2023. Using PacBio HiFi sequencing, we assembled a 16,781 bp mitogenome (GenBank: PQ283265) comprising 13 protein-coding genes, 22 tRNA genes, two rRNA genes, and a control region, with a GC content of 40.94%. Phylogenetic analysis, incorporating 17 Panthera mtDNA sequences, positions P. pardus nimr closest to African leopard populations from South Africa (Panthera pardus), while distinguishing it from Asian subspecies (P. pardus japonensis and P. pardus orientalis). This mitogenome reveals conserved vertebrate mitochondrial structure and provides a critical tool for studying Panthera genus evolution. Moreover, it enhances conservation genetics efforts for P. pardus nimr by enabling population structure analysis and informing breeding strategies to strengthen its survival. Full article

The postmortem interval (PMI) is defined as the time interval between physiological death and the examination of the corpse, playing a critical role in forensic investigations. Traditional PMI estimation methods are often influenced by subjective and environmental factors. Circular RNAs (circRNAs), known for their stability, abundance, and conservation in brain tissue, show promise as biomarkers for PMI estimation. However, research on circRNAs in this context remains limited. This study aimed to develop PMI estimation models using circRNAs across multiple temperatures. By employing semi-quantitative reverse transcription-PCR, circTulp4, circSlc8a1, and circStrn3 were identified as reliable biomarkers for mouse brain tissue. Mathematical models were constructed using the reference genes 28S rRNA, mt-co1, and circCDR1as. At 4 °C, most equations had p-values below 0.05, with the equation using circSlc8a1 as a marker exhibiting the highest goodness of fit. Validation results indicated that the equation using circTulp4 as the reference gene had the highest accuracy. When applying the combined aforementioned three circRNAs, the equation using circCDR1as as the reference gene showed better accuracy. At 25 °C, all equations had R² values greater than 0.86, but most cubic equations had p-values above 0.05. Validation results demonstrated that the circTulp4/mt-co1 equation had the highest accuracy. When applying combined circRNAs, the R² values improved, and long-term PMI estimation was more accurate than short-term PMI estimation. At 35 °C, the linear equations had significantly poorer goodness of fit compared to nonlinear equations, and nonlinear equations exhibited better accuracy than linear equations. When applying the combined aforementioned three circRNAs, the accuracy of the three reference genes was similar, and the accuracy of long-term PMI estimation was consistently higher than that of short-term estimation. For the three-dimensional models, all R² values exceeded 0.75 with p-values significantly below 0.0001. Validation results demonstrated higher accuracy at 25 °C and 35 °C, with superior performance for long-term PMI estimation. In summary, this study constructed PMI estimation models under multiple temperature conditions based on highly expressed circRNAs in mouse brain tissue, highlighting circTulp4, circSlc8a1, and circStrn3 as novel biomarkers. These findings offer a complementary tool for PMI estimation, particularly for long-term PMI estimation. Full article