Search Results (224)

As people with cystic fibrosis (PwCF) live longer, kidney disease is emerging as a significant comorbidity that is increasingly linked to cardiovascular complications and progression to end-stage kidney disease. In our recent review, we proposed the unifying term CF-related kidney disease (CFKD) to encompass the spectrum of kidney dysfunction observed in this population. Early detection of kidney injury is critical for improving long-term outcomes, yet remains challenging due to the limited sensitivity of conventional laboratory tests, particularly in individuals with altered muscle mass and unique CF pathophysiology. Emerging approaches, including novel blood and urinary biomarkers, urinary extracellular vesicles, and genetic risk profiling, offer promising avenues for identifying subclinical kidney damage. When integrated with machine learning algorithms, these tools may enable the development of personalized risk stratification models and targeted therapeutic strategies. This precision medicine approach has the potential to transform kidney disease management in PwCF, shifting care from reactive treatment of late-stage disease to proactive monitoring and early intervention. Full article

Alzheimer’s disease (AD) is increasingly recognized as a multifactorial disorder driven by a combination of disruptions in proteostasis and organelle communication. The 2020 Lancet commission reported that approximately 10 million people worldwide were affected by AD in the mid-20th century. AD is the most prevalent cause of dementia. By early 2030, the global cost of dementia is projected to rise by USD 2 trillion per year, with up to 85% of that cost attributed to daily patient care. Several factors have been implicated in the progression of neurodegeneration, including increased oxidative stress, the accumulation of misfolded proteins, the formation of amyloid plaques and aggregates, the unfolded protein response (UPR), and mitochondrial–endoplasmic reticulum (ER) calcium homeostasis. However, the exact triggers that initiate these pathological processes remain unclear, in part because clinical symptoms often emerge gradually and subtly, complicating early diagnosis. Among the early hallmarks of neurodegeneration, elevated levels of reactive oxygen species (ROS) and the buildup of misfolded proteins are believed to play pivotal roles in disrupting proteostasis, leading to cognitive deficits and neuronal cell death. The accumulation of amyloid-β (Aβ) plaques and tau neurofibrillary tangles is a characteristic feature of AD. These features contribute to chronic neuroinflammation, which is marked by the release of pro-inflammatory cytokines and chemokines that exacerbate oxidative stress. Given these interconnected mechanisms, targeting stress-related signaling pathways, such as oxidative stress (ROS) generated in the mitochondria and ER, ER stress, UPR, and cytosolic chaperones, represents a promising strategy for therapeutic intervention. This review focuses on the relationship between stress chaperone responses and organelle function, particularly the interaction between mitochondria and the ER, in the development of new therapies for AD and related neurodegenerative disorders. Full article

The transition to clean energy is not inherently positive or negative, and its impacts depend on the social context, power relations, and mechanisms to include marginalized voices. India, with its ambitious climate targets and commitment to the UN SDG Agenda, is a key country for ensuring an inclusive and sustainable transition. This paper aims to understand whether India’s commitment to the SDG Agenda’s overarching principle of ‘leaving no one behind’ is reflected in the design of its domestic solar policies. It analyzes how energy justice concerns are addressed in state-level solar policies. To that end, a pragmatic framework was developed to identify key justice dimensions and indicators, linked to the SDG targets, that are essential for an inclusive transition. This research conducted a qualitative interpretive content analysis of 29 solar energy policies, using the three identified framework dimensions—income growth, enhancing inclusion, and equal opportunities. We found that the themes around energy access, employment, and skill development are reflected in policies, while those around the inclusion of the poor, women, and community remain limited. The findings indicate that the policies have focused on low-impact justice concerns, lacking structural transformation. To address these gaps, the study proposes targeted subsidies, community ownership, and gender-inclusive mechanisms. The framework offers a pragmatic tool for the evaluation of clean energy policies in the Global South, and the empirical results provide insights for the synergistic implementation of the climate and sustainable development agenda. Full article

We propose a secure and scalable file-encryption scheme for cloud systems by integrating Post-Quantum Cryptography (PQC), Quantum Key Distribution (QKD), and Advanced Encryption Standard (AES) within a distributed architecture. While prior studies have primarily focused on secure key exchange or authentication protocols (e.g., layered PQC-QKD key distribution), our scheme extends beyond key management by implementing a distributed encryption architecture that protects large-scale files through integrated PQC, QKD, and AES. To support high-throughput encryption, our proposed scheme partitions the target file into fixed-size subsets and distributes them across slave nodes, each performing parallel AES encryption using a locally reconstructed key from a PQC ciphertext. Each slave node receives a PQC ciphertext that encapsulates the AES key, along with a PQC secret key masked using QKD based on the BB84 protocol, both of which are centrally generated and managed by the master node for secure coordination. In addition, an encryption and transmission pipeline is designed to overlap I/O, encryption, and communication, thereby reducing idle time and improving resource utilization. The master node performs centralized decryption by collecting encrypted subsets, recovering the AES key, and executing decryption in parallel. Our evaluation using a real-world medical dataset shows that the proposed scheme achieves up to 2.37× speedup in end-to-end runtime and up to 8.11× speedup in encryption time over AES (Original). In addition to performance gains, our proposed scheme maintains low communication cost, stable CPU utilization across distributed nodes, and negligible overhead from quantum key management. Full article

Municipal solid waste (MSW) is rising globally, and improper management harms the environment and public health. As a result, there is heightened interest in finding effective solutions, and identifying research trends helps determine the best management and valorization pathways. However, the existing reviews often focus narrowly on specific technologies or regional case studies, lacking a comprehensive analysis of global research trends. This study addresses this significant gap by conducting a large-scale trend analysis based on 15,646 relevant articles screened from 25,068 Scopus-indexed publications from 1904 to 2023 using title, abstract, and keyword analysis. Literature-based comparative assessments were conducted to critically evaluate the pathways through TEE (techno-economic and environmental), SWOT (strengths, weaknesses, opportunities, and threats), and PESTEL (political, economic, social, technological, environmental, and legal) frameworks. Since 1990, article publication has increased by about 10% annually, consistently concentrating on thermochemical conversion and, more recently, on sustainability and circular economy perspectives. Seven distinct pathways for MSW management were identified, with recycling and material recovery, followed by thermochemical conversion for high-calorific waste and biochemical conversion for high-organic waste, showing the most promise. The findings aim to help researchers understand MSW research trends and assist planners in identifying effective management and valorization strategies. Full article

The $\mathsf{\mu }$PPET [mu($\mathsf{\mu }$)on Probe with J-PET] project aims to investigate the “Muon Puzzle” seen in cosmic ray air showers. This puzzle arises from the observation of a significantly larger number of muons on Earth’s surface than that predicted by the current theoretical models. The investigated hypothesis is based on recently observed asymmetries in the parameters for the strong interaction cross-section and trajectory of an outgoing particle due to projectile–target polarization. The measurements require detailed information about muons at the ground level, including their track and charge distributions. To achieve this, the two PET scanners developed at the Jagiellonian University in Krakow (Poland), the J-PET detectors, will be employed, taking advantage of their well-known resolution and convenient location for detecting muons that reach long depths in the atmosphere. One station will be used as a muon tracker, while the second will reconstruct the core of the air shower. In parallel, the existing hadronic interaction models will be modified and fine-tuned based on the experimental results. In this work, we present the conceptualization and preliminary designs of $\mathsf{\mu }$PPET. Full article

The increasing global population and intensifying resource limitations present a formidable challenge for sustainable crop production, especially in developing regions. This review explores the pivotal role of microbial ecosystem services in alleviating environmental stresses that impede agricultural productivity. Soil microbiota, particularly plant growth-promoting microbes (PGPMs), are integral to soil health and fertility and plant resilience against both abiotic (drought, salinity, temperature extremes, heavy metals) and biotic (pathogen) stresses. These microorganisms employ a variety of direct and indirect mechanisms, including the modulation of phytohormones, nutrient solubilization, the production of stress-alleviating enzymes, and the synthesis of antimicrobial compounds, to enhance plant growth and mitigate adverse environmental impacts. Advances in microbial biotechnology have expanded the toolkit for harnessing beneficial microbes, enabling the development of microbial inoculants and consortia tailored for specific stress conditions. This review highlights the multifaceted contributions of soil microbes, such as improving nutrient uptake, promoting root development, facilitating pollutant degradation, and supporting carbon sequestration, all of which underpin ecosystem resilience and sustainable agricultural practices. Furthermore, the synergistic interactions between plant roots and rhizospheric microbes are emphasized as key drivers of soil structure enhancement and long-term productivity. By synthesizing current research on the mechanisms of microbe-mediated stress tolerance, this review underscores the potential of microbial interventions to bridge the gap between food security and environmental conservation. The integration of microbial solutions into agroecosystems offers a promising, eco-friendly strategy to revitalize soils, boost crop yields, and ensure agricultural sustainability in the face of mounting environmental challenges. Full article

Climate-induced migration has emerged as a major concern in India and Bangladesh, due to their geographical vulnerability and socioeconomic conditions. Coastal areas, such as the Sundarbans and the Ganges–Brahmaputra Delta, face relentless threats due to rising sea levels, cyclones, and floods. These factors force millions to relocate, resulting in rural–urban transitions and cross-border movements that worsen urban challenges and socioeconomic vulnerabilities. For this, a systematic literature review of the Scopus database was undertaken using Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. A detailed review analysis of 65 papers was carried out. The study highlighted key climatic and non-climatic drivers of migration, including natural disasters, resource depletion, poverty, and poor governance. Despite existing adaptation strategies, such as early warning systems, micro-insurance, and climate-resilient practices, gaps remain in addressing long-term resilience and legal recognition for climate migrants. The research emphasizes the need for a holistic, multi-stakeholder approach, integrating adaptive infrastructure, sustainable livelihoods, and international cooperation. Recommendations include bridging research gaps, increasing community participation, and implementing global frameworks, like the Fund for Responding to Loss and Damage. Addressing climate migration through fair, inclusive measures is essential for building resilience and ensuring long-term development in the region. Full article

Background: Metritis, a common postpartum uterine infection in bovines, poses substantial challenges in livestock management, including compromised fertility and economic losses. Poor uterine drug penetration and systemic side effects, necessitating innovative localised delivery systems and limiting current systemic antibiotic treatments. Aim: This study aimed to develop and evaluate the potential effect of the ofloxacin-loaded hydrogel as a localised drug delivery system to treat metritis in bovine. The focus was on achieving sustained drug release, enhanced antibacterial efficacy and reduced inflammation in the endometrium. Materials and Methods: The CS/PVA hydrogel was synthesised using a freeze–thaw method and further optimised for drug encapsulation efficiency (96.7 ± 2.1%), stability and biocompatibility. Physicochemical characterisation included swelling behaviour, mechanical properties and rheological analysis. In vitro drug release profiles in the simulated uterine fluid were assessed over 72 h and antibacterial activity was tested against common uterine pathogens such as Escherichia coli and S. aureus. In vivo studies were conducted on bovines diagnosed with endometritis to evaluate clinical recovery. Results: The SEM image of the ofloxacin-loaded CS/PVA hydrogel resulted in a smooth and porous structure demonstrating larger pore size than the blank. The rheological study suggested higher stability and elastic behaviour. Antibacterial assays on E. coli and S. aureus revealed significant inhibition zones, respectively, indicating potent efficacy. In vivo, evaluated on treated bovine, reduced bacterial loads were exhibited (2.86 × 10^5A CFU/mL → 6.37 × 10^2B CFU/mL), clinical improvement was marked and uterine inflammation was resolved. Conclusions: Ofloxacin-loaded hydrogels represent a promising localised treatment for bovine metritis, offering sustained antibacterial action and improved clinical outcomes. This approach addresses the limitations of systemic antibiotic therapies and provides a practical solution for enhanced veterinary care. Further studies are recommended to validate these findings in more extensive field trials and explore commercialisation potential. Full article

Microorganisms play an important role in regulating various biological processes in our bodies. In women, abnormal changes in the reproductive tract microbiome are associated with various gynecological diseases and infertility. Recent studies suggest that patients with recurrent implantation failure (RIF) have a reduced genus Lactobacillus population, a predominant bacterial species in the vagina and uterus that protects the reproductive tract from pathogenic bacterial growth via the production of various metabolites (e.g., lactic acid, bacteriocin, and H₂O₂). Moreover, a higher percentage of pathogenic bacteria genera, including Atopobium, Gardnerella, Prevotella, Pseudomonas, and Streptococcus, was found in the uterus of RIF patients. This review aimed to examine the role of pathogenic bacteria in RIF, determine the factors altering the endometrial microbiome, and assess the impact of the microbiome on embryo implantation in RIF. Several factors can influence microbial balance, including the impact of extrinsic elements such as semen and antibiotics, which can lead to dysbiosis in the female reproductive tract and affect implantation. Additionally, probiotics such as Lacticaseibacillus rhamnosus were reported to have clinical potential in RIF patients. Future studies are needed to develop targeted probiotic therapies to restore microbial balance and enhance fertility outcomes. Research should also focus on understanding the mechanisms by which microorganisms generate metabolites to suppress pathogenic bacteria for embryo implantation. Identifying these interactions may contribute to innovative microbiome-based interventions for reproductive health. Full article

Hsp70, a 70 kDa molecular chaperone, plays a crucial role in maintaining protein homeostasis. It interacts with the DnaJ family of co-chaperones to modulate the functions of client proteins involved in various cellular processes, including transmembrane transport, extracellular vesicle trafficking, complex formation, and proteasomal degradation. Its presence in multiple cellular organelles enables it to mediate stress responses, apoptosis, and inflammation, highlighting its significance in disease progression. Initially recognized for its essential roles in protein folding, disaggregation, and degradation, later studies have demonstrated its involvement in several human diseases. Notably, Hsp70 is upregulated in multiple cancers, where it promotes tumor proliferation and serves as a tumor immunogen. Additionally, epichaperome networks stabilize protein–protein interactions in large and long-lived assemblies, contributing to both cancer progression and neurodegeneration. However, extracellular Hsp70 (eHsp70) in the tumor microenvironment can activate immune cells, such as natural killer (NK) cells, suggesting its potential in immunotherapeutic interventions, including CAR T-cell therapy. Given its multifaceted roles in cellular physiology and pathology, Hsp70 holds immense potential as both a biomarker and a therapeutic target across multiple human diseases. This review highlights the structural and functional importance of Hsp70, explores its role in disease pathogenesis, and discusses its potential in diagnostic and therapeutic applications. Full article

In the oil and gas IIoT environment, fire detection systems heavily depend on fire sensor data, which can be prone to inaccuracies due to faulty or unreliable sensors. These sensor issues, such as noise, missing values, outliers, sensor drift, and faulty readings, can lead to delayed or missed fire predictions, posing significant safety and operational risks in the oil and gas industrial IoT environment. This paper presents an approach for handling faulty sensors in edge servers within an IIoT environment to enhance the reliability and accuracy of fire prediction through multi-sensor fusion preprocessing, machine learning (ML)-driven probabilistic model adjustment, and uncertainty handling. First, a real-time anomaly detection and statistical assessment mechanism is employed to preprocess sensor data, filtering out faulty readings and normalizing data from multiple sensor types using dynamic thresholding, which adapts to sensor behavior in real-time. The proposed approach also deploys machine learning algorithms to dynamically adjust probabilistic models based on real-time sensor reliability, thereby improving prediction accuracy even in the presence of unreliable sensor data. A belief mass assignment mechanism is introduced, giving more weight to reliable sensors to ensure they have a stronger influence on fire detection. Simultaneously, a dynamic belief update strategy continuously adjusts sensor trust levels, reducing the impact of faulty readings over time. Additionally, uncertainty measurements using Hellinger and Deng entropy, along with Dempster–Shafer Theory, enable the integration of conflicting sensor inputs and enhance decision-making in fire detection. This approach improves decision-making by managing sensor discrepancies and provides a reliable solution for real-time fire predictions, even in the presence of faulty sensor readings, thereby mitigating the fire risks in IIoT environments. Full article

Coal accounts for over half of India’s energy needs currently. However, it has resulted in significant environmental impacts such as altering land cover and land surface temperatures. This study quantifies the land surface temperature (LST) of Dhanbad City (India)—home to India’s largest coal reserves. It uses the Landsat 8 image data to evaluate urban and rural temperature variations across different land use–land cover (LULC) classes. Using a Geographically Weighted Regression Model (GWR), we examined the spatial heterogeneity of the LST using key environmental indices, such as the Normalized Difference Vegetation Index (NDVI), Modified Normalized Difference Water Index (MNDWI), Normalized Difference Moisture Index (NDMI), and Normalized Difference Barren Index (NDBI). The seasonal LST variations revealed significant urban–rural area temperature disparities, with rural regions exhibiting stronger correlations with the key indices above. The GWR model accounted for 78.31% of the spatial variability in LST, with unexplained heterogeneity in urban areas linked to anomalies identified in the coal mining area fire map. These findings underscore the necessity of targeted mitigation strategies to reduce high LST values in coal fire-affected regions, with localized spatial measures in mining areas. Full article

The increasing complexity and scale of modern software-defined networking demands advanced solutions to address security challenges, particularly distributed denial-of-service (DDoS) attacks in multi-controller environments. Traditional single-controller implementations are struggling to effectively counter sophisticated cyber threats, necessitating a faster and scalable solution. This study introduces a novel approach for attack detection and mitigation with optimized multi-controller software-defined networking (SDN) using machine learning (ML). The study focuses on the design, implementation, and assessment of the optimal placement of multi-controllers using K-means++ and OPTICS in real topologies and an intrusion detection system (IDS) using the XGBoost classification algorithm to detect and mitigate attacks efficiently with accuracy, precision, and recall of 98.5%, 97.0%, and 97.0%, respectively. Additionally, the IDS decouples from the controllers, preserves controller resources, and allows for efficient near-real-time attack detection and mitigation. The proposed solution outperforms well by autonomously identifying anomalous behaviors in networks through successfully combining the controller placement problem (CPP) and DDoS security. Full article

Quantum Machine Learning (QML) opens up exciting possibilities for tackling problems that are incredibly complex and consume a lot of time. The drive to make QML a reality has sparked significant progress in material science, inspiring a growing number of research publications in the field. In this study, we extracted articles from the Scopus database to understand the contribution of material science in the advancement of QML. This scientometric analysis accumulated 1926 extracted publications published over 11 years spanning from 2014 to 2024. A total of 55 countries contributed to this domain of QML, among which the top 10 countries contributed 65.7% out of the total number of publications; the USA is on top, with 19.47% of the publications globally. A total of 57 authors contributed to this research area from 55 different countries. From 2014 to 2024, publications had an average citation impact of 32.12 citations per paper; the year 2015 received 16.7% of the total citations, which is the highest in the 11 years, and the year 2014 had the highest number of citations per paper, which is 61.4% of the total. The study also identifies the most significant document in the year 2017, with the source title Journal of Physics Condensed Matter, having a citation count of 2649 and a normalized citation impact index (NCII) of 91.34. Full article