Search Results (35)

Against the backdrop of global climate change, enhancing farmers’ adaptive capacity to reduce crop production risks has emerged as a critical concern for governments and researchers worldwide. Drawing on social capital theory, this study develops a four-dimensional measurement framework comprising social networks, social trust, social norms, and social participation, utilizing survey data from 1772 households in the Yellow River Basin. We employ factor analysis to construct comprehensive social capital scores and apply ordered Probit models to examine how social capital influences farmers’ climate adaptation behaviors, with particular attention to the moderating roles of agricultural extension interaction and digital literacy. Key findings include: (1) Adoption patterns: Climate adaptation behavior adoption remains low (60%), with technical adaptation measures showing particularly poor uptake (13%); (2) Direct effects: Social capital significantly promotes adaptation behaviors, with social trust (p < 0.01), networks (p < 0.01), and participation (p < 0.05) demonstrating positive effects, while social norms show no significant impact; (3) Heterogeneous effects: Impact mechanisms differ by crop type, with grain producers relying more heavily on social networks (+, p < 0.01) and cash crop producers depending more on social trust (+, p < 0.01); (4) Moderating mechanisms: Agricultural extension interaction exhibits scale-dependent effects, negatively moderating the relationship for large-scale farmers (p < 0.05) while showing no significant effects for smaller operations; digital literacy consistently demonstrates negative moderation, whereby higher literacy levels weaken social capital’s promotional effects (p < 0.01). Policy recommendations: Effective climate adaptation strategies should integrate strengthened rural social organization development, differentiated agricultural extension systems tailored to farm characteristics, and enhanced rural digital infrastructure investment. Full article

Over the last decade, scientists have shifted their focus to the development of smart carriers for the delivery of chemotherapeutics in order to overcome the problems associated with traditional chemotherapy, such as poor aqueous solubility and bioavailability, low selectivity and targeting specificity, off-target drug side effects, and damage to surrounding healthy tissues. Nanofiber-based drug delivery systems have recently emerged as a promising drug delivery system in cancer therapy owing to their unique structural and functional properties, including tunable interconnected porosity, a high surface-to-volume ratio associated with high entrapment efficiency and drug loading capacity, and high mass transport properties, which allow for controlled and targeted drug delivery. In addition, they are biocompatible, biodegradable, and capable of surface functionalization, allowing for target-specific delivery and drug release. One of the most common fiber production methods is electrospinning, even though the relatively two-dimensional (2D) tightly packed fiber structures and low production rates have limited its performance. Forcespinning is an alternative spinning technology that generates high-throughput, continuous polymeric nanofibers with 3D structures. Unlike electrospinning, forcespinning generates fibers by centrifugal forces rather than electrostatic forces, resulting in significantly higher fiber production. The functionalization of nanocarriers on nanofibers can result in smart nanofibers with anticancer capabilities that can be activated by external stimuli, such as light. This review addresses current trends and potential applications of light-responsive and dual-stimuli-responsive electro- and forcespun smart nanofibers in cancer therapy, with a particular emphasis on functionalizing nanofiber surfaces and developing nano-in-nanofiber emerging delivery systems for dual-controlled drug release and high-precision tumor targeting. In addition, the progress and prospective diagnostic and therapeutic applications of light-responsive and dual-stimuli-responsive smart nanofibers are discussed in the context of combination cancer therapy. Full article

The complex structure, chemical composition, and biomechanical properties of craniofacial cartilaginous structures make them challenging to reconstruct. Autologous grafts have limited tissue availability and can cause significant donor-site morbidity, homologous grafts often require immunosuppression, and alloplastic grafts may have high rates of infection or displacement. Furthermore, all these grafting techniques require a high level of surgical skill to ensure that the reconstruction matches the original structure. Current research indicates that additive manufacturing shows promise in overcoming these limitations. Autologous stem cells have been developed into cartilage when exposed to the appropriate growth factors and culture conditions, such as mechanical stress and oxygen deprivation. Additive manufacturing allows for increased precision when engineering scaffolds for stem cell cultures. Fine control over the porosity and structure of a material ensures adequate cell adhesion and fit between the graft and the defect. Several recent tissue engineering studies have focused on the trachea, nose, and ear, as these structures are often damaged by congenital conditions, trauma, and malignancy. This article reviews the limitations of current reconstructive techniques and the new developments in additive manufacturing for tracheal, nasal, and auricular cartilages. Full article

The importance of detecting and preventing ovarian cancer is of utmost significance for women’s overall health and wellness. Referred to as the “silent killer,” ovarian cancer exhibits inconspicuous symptoms during its initial phases, posing a challenge for timely identification. Identification of ovarian cancer during its advanced stages significantly diminishes the likelihood of effective treatment and survival. Regular screenings, such as pelvic exams, ultrasound, and blood tests for specific biomarkers, are essential tools for detecting the disease in its early, more treatable stages. This research makes use of the Soochow University ovarian cancer dataset, containing 50 features for the accurate detection of ovarian cancer. The proposed predictive model makes use of a stacked ensemble model, merging the strengths of bagging and boosting classifiers, and aims to enhance predictive accuracy and reliability. This combination harnesses the benefits of variance reduction and improved generalization, contributing to superior ovarian cancer prediction outcomes. The proposed model gives 96.87% accuracy, which is currently the highest model result obtained on this dataset so far using all features. Moreover, the outcomes are elucidated utilizing the explainable artificial intelligence method referred to as SHAPly. The excellence of the suggested model is demonstrated through a comparison of its performance with that of other cutting-edge models. Full article

Breast cancer stands out as the most frequently identified malignancy, ranking as the fifth leading cause of global cancer-related deaths. The American College of Radiology (ACR) introduced the Breast Imaging Reporting and Data System (BI-RADS) as a standard terminology facilitating communication between radiologists and clinicians; however, an update is now imperative to encompass the latest imaging modalities developed subsequent to the 5th edition of BI-RADS. Within this review article, we provide a concise history of BI-RADS, delve into advanced mammography techniques, ultrasonography (US), magnetic resonance imaging (MRI), PET/CT images, and microwave breast imaging, and subsequently furnish comprehensive, updated insights into Molecular Breast Imaging (MBI), diagnostic imaging biomarkers, and the assessment of treatment responses. This endeavor aims to enhance radiologists’ proficiency in catering to the personalized needs of breast cancer patients. Lastly, we explore the augmented benefits of artificial intelligence (AI), machine learning (ML), and deep learning (DL) applications in segmenting, detecting, and diagnosing breast cancer, as well as the early prediction of the response of tumors to neoadjuvant chemotherapy (NAC). By assimilating state-of-the-art computer algorithms capable of deciphering intricate imaging data and aiding radiologists in rendering precise and effective diagnoses, AI has profoundly revolutionized the landscape of breast cancer radiology. Its vast potential holds the promise of bolstering radiologists’ capabilities and ameliorating patient outcomes in the realm of breast cancer management. Full article

Oxidative stress plays a significant role in the development of cancer. Inhibiting the protein-protein interaction (PPI) between Keap1 and Nrf2 offers a promising strategy to activate the Nrf2 antioxidant pathway, which is normally suppressed by the binding of Keap1 to Nrf2. This study aimed to identify natural compounds capable of targeting the kelch domain of KEAP1 using structure-based drug design methods. A pharmacophore model was constructed based on the KEAP1-inhibitor complex, leading to the selection of 6178 compounds that matched the model. Subsequently, docking and MM/GBSA analyses were conducted, resulting in the identification of 10 compounds with superior binding energies compared to the reference compound. From these, three compounds (ZINC000002123788, ZINC000002111341, and ZINC000002125904) were chosen for further investigation. Ligand–residue interaction analysis revealed specific interactions between these compounds and key residues, indicating their stability within the binding site. ADMET analysis confirmed that the selected compounds possessed desirable drug-like properties. Furthermore, molecular dynamics simulations were performed, demonstrating the stability of the ligand–protein complexes over a 100 ns duration. These findings underscore the potential of the selected natural compounds as agents targeting KEAP1 and provide valuable insights for future experimental studies. Full article

Alzheimer’s disease (AD) is a devastating illness with limited therapeutic interventions. The aim of this study is to investigate the pathophysiological mechanisms underlying AD and explore the potential neuroprotective effects of cocoa, either alone or in combination with other nutraceuticals, in an animal model of aluminum-induced AD. Rats were divided into nine groups: control, aluminum chloride (AlCl₃) alone, AlCl₃ with cocoa alone, AlCl₃ with vinpocetine (VIN), AlCl₃ with epigallocatechin-3-gallate (EGCG), AlCl₃ with coenzyme Q10 (CoQ10), AlCl₃ with wheatgrass (WG), AlCl₃ with vitamin (Vit) B complex, and AlCl₃ with a combination of Vit C, Vit E, and selenium (Se). The animals were treated for five weeks, and we assessed behavioral, histopathological, and biochemical changes, focusing on oxidative stress, inflammation, Wnt/GSK-3β/β-catenin signaling, ER stress, autophagy, and apoptosis. AlCl₃ administration induced oxidative stress, as evidenced by elevated levels of malondialdehyde (MDA) and downregulation of cellular antioxidants (Nrf2, HO-1, SOD, and TAC). AlCl3 also upregulated inflammatory biomarkers (TNF-α and IL-1β) and GSK-3β, leading to increased tau phosphorylation, decreased brain-derived neurotrophic factor (BDNF) expression, and downregulation of the Wnt/β-catenin pathway. Furthermore, AlCl₃ intensified C/EBP, p-PERK, GRP-78, and CHOP, indicating sustained ER stress, and decreased Beclin-1 and anti-apoptotic B-cell lymphoma 2 (Bcl-2) expressions. These alterations contributed to the observed behavioral and histological changes in the AlCl₃-induced AD model. Administration of cocoa, either alone or in combination with other nutraceuticals, particularly VIN or EGCG, demonstrated remarkable amelioration of all assessed parameters. The combination of cocoa with nutraceuticals attenuated the AD-mediated deterioration by modulating interrelated pathophysiological pathways, including inflammation, antioxidant responses, GSK-3β-Wnt/β-catenin signaling, ER stress, and apoptosis. These findings provide insights into the intricate pathogenesis of AD and highlight the neuroprotective effects of nutraceuticals through multiple signaling pathways. Full article

One of the popular ways to minimise the impact of emissions produced by engines is by enabling alternative fuels. Out of the many trending options for alternative fuels, biogas provides some unique advantages, such as being considered to be environmentally friendly, obeying the laws of renewable energy and generating the smallest carbon footprints. The two major drawbacks of traditional diesel engines are their high rate of NOx and significant amount of soot. The best candidates for overcoming these issues are HCCI engines; HCCI engines can provide better control over NOx generation and overall thermal efficiency can be improved to a greater level. These types of engines are compatible with both SI and CI. Now, to understand and analyse the behaviour of HCCI, the present work was focused on a modified single-cylinder CI engine. It was made to operate in HCCI mode by enabling the combination of biogas, along with diethyl ether (DEE), as a fuel mixture. To achieve better combustion, biogas was combined with air, while DEE acted as an ignition source, which can be introduced at three different locations. In total, the experiment was performed sixty times so as to achieve the best injection position. To obtain this information, other parameters, such as biogas flow rate, torque, methane fraction and DEE injection position, were also incorporated. The main results were consolidated by warping the output parameters such as brake thermal efficiency, equivalence ratio, air–fuel ratio, and brake-specific fuel consumption. Emission such as CO, HC, NOx, and smoke were taken into account. The results indicate that port injection provides higher thermal efficiency than manifold injections, while lower emissions were observed in manifold injections. Full article

Schistosomiasis is a neglected tropical disease with a significant socioeconomic impact. It is caused by several species of blood trematodes from the genus Schistosoma, with S. mansoni being the most prevalent. Praziquantel (PZQ) is the only drug available for treatment, but it is vulnerable to drug resistance and ineffective in the juvenile stage. Therefore, identifying new treatments is crucial. SmHDAC8 is a promising therapeutic target, and a new allosteric site was discovered, providing the opportunity for the identification of a new class of inhibitors. In this study, molecular docking was used to screen 13,257 phytochemicals from 80 Saudi medicinal plants for inhibitory activity on the SmHDAC8 allosteric site. Nine compounds with better docking scores than the reference were identified, and four of them (LTS0233470, LTS0020703, LTS0033093, and LTS0028823) exhibited promising results in ADMET analysis and molecular dynamics simulation. These compounds should be further explored experimentally as potential allosteric inhibitors of SmHDAC8. Full article

Investigating the fiscal decentralization’s effect on the carbon intensity of agricultural production may assist the United States in reaching its carbon peak and becoming carbon neutral. This paper delves into the investigation of the spatiotemporal patterns and internal relationships between fiscal decentralization, agricultural carbon intensity, and environmental regulation. The goal was achieved by using the spatial Durbin model using panel data for 49 states of the United States from 2000 to 2019. The study has found that environmental regulations play a significant role in reducing regional carbon emissions in agriculture and contribute positively to carbon emissions control. However, fiscal decentralization, which grants local governments more financial autonomy, has a positive but insignificant impact on carbon emissions, indicating that the prioritization of economic development and carbon control over environmental protection is favored by local governments. In examining the impact of environmental regulations on carbon emissions, the study reveals that fiscal decentralization does not play a substantial role in moderating this relationship. To promote low-carbon agriculture projects and ensure coordinated economic and environmental development, the study recommends optimizing the fiscal decentralization system, formulating different policies for different regions, and regulating the competencies of local governments through an effective examination system. The study concludes that it is crucial to obtain data at the city or county level to accurately understand the relationship between agricultural carbon intensity, environmental regulation, and fiscal decentralization. As a result, the central government must focus on perfecting the fiscal decentralization system, developing a differentiated agricultural carbon emission control system, controlling competition among local governments, and perfecting a political performance assessment system. Full article

Breast cancer (BC) is one of the main types of cancer that endangers women’s lives. The characteristics of triple-negative breast cancer (TNBC) include a high rate of recurrence and the capacity for metastasis; therefore, new therapies are urgently needed to combat TNBC. Dual targeting HDAC6 and Hsp90 has shown good synergistic effects in treating metastatic TNBC. The goal of this study was to find potential HDAC6 and Hsp90 dual inhibitors. Therefore, several in silico approaches have been used. An e-pharmacophore model generation based on the HDAC6-ligand complex and subsequently a pharmacophore-based virtual screening on 270,450 natural compounds from the ZINC were performed, which resulted in 12,663 compounds that corresponded to the obtained pharmacophoric hypothesis. These compounds were docked into HDAC6 and Hsp90. This resulted in the identification of three compounds with good docking scores and favorable free binding energy against the two targets. The top three compounds, namely ZINC000096116556, ZINC000020761262, and ZINC000217668954, were further subjected to ADME prediction and molecular dynamic simulations, which showed promising results in terms of pharmacokinetic properties and stability. As a result, these three compounds can be considered potential HDAC6 and Hsp90 dual inhibitors and are recommended for experimental evaluation. Full article

Turkey is increasingly concerned about the effects of climate change, weather unpredictability, and severe events on agricultural production, food loss, and livelihoods. Turkey has long struggled against climate variability and catastrophic climatic events to prevent further declines in agricultural output. This study assessed the risk of climate change in Turkey from the perspective of loss in food grains and food security domain considering exposure to extreme climate events using the data from 1991 to 2019. This paper makes a theoretical contribution to the literature by identifying the relationship between food waste and food import, food prices and economic growth. It also makes an empirical contribution by administering and econometrically analyzing the impact of the loss of food grains on the aforementioned independent variables. Policy implications for the current national agriculture policy were provided using the vector auto-regression (VAR) model and derivative analysis. Food grain loss negatively correlates with food security since it increases reliance on food imports from outside. Moreover, the losses in food supplies contributes greatly to price increases. The GDP growth rate, however, was shown to be a feeble instigator. Climate change threatens food security, and the country’s progress toward sustainable development objectives is hampered in general, particularly concerning no poverty and zero hunger goals. In conclusion, climate change and its associated factors harm Turkey’s food security and economy. Full article

Achieving high production with limited resources is a major challenge faced by poultry farmers in countries with developing economies, such as Pakistan. Optimization of the technical efficiency (TE) of poultry business operations is a promising strategy. A representative sample of 210 poultry farms in the province of Punjab in Pakistan was analyzed for TE. The studied sample comprised 105 layer chicken farms (battery cage system, egg production) and 105 broiler chicken farms (environmental control shed system, meat production). A Cobb–Douglas stochastic frontier production analysis approach with the inefficiency effect model was used to simultaneously estimate TE levels and identify factors that influence efficiency. The results indicated that flock size, labor, feed, and water consumption are positively related to egg production, whereas vaccination was found to be insignificant. For broiler businesses, flock size, feed, and water consumption were positively related to the output, whereas labor and vaccination were found to be insignificant. The results of the TE inefficiency effect model revealed that farmer age, education, experience, access to credit, and access to extension services all had a significant and positive influence on the technical efficiency of both layer and broiler farmers. The estimated mean TE level of layer and broiler poultry farmers was 89% and 92%, respectively, evaluated against the benchmark of the identified frontier of efficient production with prevailing systems. The study concludes that it is possible to increase egg production by 11% and meat production by 8% by making more efficient use of the available resources and technology. To improve poultry farmers’ efficiency, policy interventions should focus more on the pronounced effects of variables such as education, farmer experience, credit access, and extension services. Full article

Methylene blue (MB), an organic thiazine dye, has numerous industrial and medical applications. However, MB is a wastewater contaminant that is harmful to humans and aquatic life. Hence, its removal from water bodies is essential. In this work, five novel deep eutectic solvents (DESs) were synthesized using different precursors, screened, and studied for the extraction of methylene blue (MB) from aqueous solution using liquid–liquid extraction. The first, TOP-SA, was synthesized using trioctylphosphine (TOP) as a hydrogen bond acceptor (HBA) and 2-hydroxy benzoic acid as a hydrogen bond donor (HBD). Among these, TOP-SA had the highest MB removal efficiency. The effects of pH, contact time, initial MB concentration, volumetric ratio, temperature, and ionic strength were studied and optimized. A 99.3% removal was achieved in 5 min for a 200 mg dm⁻³ MB solution mixed in a 1:10 ratio with TOP-SA at 25.0 °C. The structural properties of TOP-SA and its interactions with MB were investigated using FTIR. TOP-SA’s toxicity was investigated using human cells in vitro. TOP-SA was found to be comparatively less toxic and is a more efficient MB remover than other literature reported ionic liquids (ILs). Full article

Intrusion detection systems are widely utilized in the cyber security field, to prevent and mitigate threats. Intrusion detection systems (IDS) help to keep threats and vulnerabilities out of computer networks. To develop effective intrusion detection systems, a range of machine learning methods are available. Machine learning ensemble methods have a well-proven track record when it comes to learning. Using ensemble methods of machine learning, this paper proposes an innovative intrusion detection system. To improve classification accuracy and eliminate false positives, features from the CICIDS-2017 dataset were chosen. This paper proposes an intrusion detection system using machine learning algorithms such as decision trees, random forests, and SVM (IDS). After training these models, an ensemble technique voting classifier was added and achieved an accuracy of 96.25%. Furthermore, the proposed model also incorporates the XAI algorithm LIME for better explainability and understanding of the black-box approach to reliable intrusion detection. Our experimental results confirmed that XAI LIME is more explanation-friendly and more responsive. Full article