Search Results (3,777)

Soil pore architecture and hydraulic functioning strongly regulate water flow and retention. However, despite the growing application of X-ray computed tomography (X-ray CT) in soil science, its application in characterizing the pore system and hydraulic functioning of native forest soils converted to sugarcane production systems in northeast Brazil is still poorly known. This study therefore quantified the soil structure, pore system, and hydraulic functioning of a native forest (NF) and an adjacent sugarcane field receiving vinasse and managed without intercropping (sole sugarcane (SG)) and with Brachiaria ruziziensis intercropping (SG + Bra intercrop) in northeastern Brazil, using conventional soil physical measurements and X-ray CT, in three soil layers (0–10, 10–20, and 20–40 cm). Soil physical and hydraulic properties, as well as soil water retention, were quantified. The native forest soil exhibited a uniformly sandy texture across all depths, whereas sugarcane systems ranged from loam to sandy textures in surface layers due to long-term management. Soil organic matter and total nitrogen in the 0–10 cm layer were approximately 75 and 65% higher, respectively, in sole Sole SG and SG + Bra intercrop than in NF. Soil bulk density increased with depth under sugarcane, reaching values about 10%–13% higher than NF in the 20–40 cm layer. Saturated hydraulic conductivity in the surface layer was higher in the NF, approximately five to nine times greater than in sole SG and SG + Bra intercrop, respectively. Conventional water retention analysis showed that sole SG and SG + Bra intercrop had greater total porosity (0.49–0.55 m³ m⁻³), microporosity (0.26–0.36 m³ m⁻³), field capacity (0.19–0.33 m³ m⁻³), and plant available water (0.09–0.15 m³ m⁻³) in the upper 20 cm compared with the NF (≤0.10 m³ m⁻³ available water). In contrast, X-ray CT revealed higher macroporosity (0.20–0.23 mm³ mm⁻³) and pore connectivity in the NF across all depths, with predominantly complex, inclined to near-horizontal pores and low anisotropy. Intercropping sugarcane with Brachiaria did not significantly alter (p > 0.05) bulk density, hydraulic conductivity, or CT-derived pore connectivity relative to sole sugarcane. The degree of anisotropy and fractal dimension derived from X-ray CT were significantly correlated (p < 0.05) with conventionally measured hydraulic properties. The X-ray computed tomography proved effective in linking pore-scale architecture to soil hydraulic functioning, providing insights beyond conventional measurements. The short-term inclusion of Brachiaria as a cover crop at 10 kg seed ha⁻¹ did not result in significant improvements in soil pore structure, indicating that longer-term adoption and/or higher planting densities may be required to induce measurable changes in pore system architecture and soil hydraulic functioning. Full article

Background/Objectives: Inclusion complexes among drugs and cyclodextrin-modified polymers are a topic of recent interest in pharmaceutical research and industry as they might expand the solubility, bioavailability, and stability of the guest molecules. Polyurethanes derived from cyclodextrins show some biomedical applications. In this study, two cross-linked polyurethane networks based on hydroxypropyl-β-cyclodextrin (HPβCD) and polyethylene glycols (PEG 2000 or PEG 6000) were synthesized with NCO/OH molar ratio 4.3 and 6.3 by the typical two-step polymerization method. Methods: Inclusion complexes of clotrimazole (CLOT) with two HPβCD-modified polyurethane networks and their corresponding physical mixtures were prepared using kneading methods and physical mixing in a 1:6 weight ratio of CLOT:HPβCD. Results: Obtained prepolymers, previously end-capped with isocyanate groups forming urethane links with HPβCD, which were confirmed by FTIR analysis. TGA results indicate a slight increase in thermal stability of the prepared complexes. The characteristic endothermic peak of the CLOT at around 145.90 °C did not appear in the DSC curve of the drug-loaded inclusion complexes. The XRD patterns of physical mixtures showed specific peaks corresponding to pure clotrimazole. SEM micrographs confirmed an elliptical/spherical- and plate-shaped particles without phase segregation, indirectly confirming that CLOT is not separately present due to inclusion into HPβCD and entrapment into polyurethane networks. Novel complexes PUR2/HPβCD-CLOT-IC and PUR3/HPβCD-CLOT-IC were applied as drug carriers, and diffusion-controlled kinetics of CLOT release were best described using Higuchi model. Conclusions: The obtained in vitro results showed surprisingly slow/prolonged clotrimazole release from modified polyurethane networks due to the significant influence of NCO/OH molar ratio and the chosen polyol soft segments chain length with potential in vivo applications. Full article

Background: Artificial intelligence (AI) shows promising results in lymphoma detection, prediction, and classification. However, translating these findings into practice requires a rigorous assessment of potential biases, clinical utility, and further validation of research models. Objective: The goal of this study was to summarize existing studies on artificial intelligence models for the histopathological detection of lymphoma. Design: This study adhered to the PRISMA Extension for Scoping Reviews (PRISMA-ScR) guidelines. A systematic search was conducted across three major databases (Scopus, PubMed, Web of Science) for English-language articles and reviews published between 2016 and 2025. Seven precise search queries were applied to identify relevant publications, accounting for variations in study modality, algorithmic architectures, and disease-specific terminology. Results: The search identified 612 records, of which 36 articles met the inclusion criteria. These studies presented 36 AI models, comprising 30 diagnostic and six prognostic applications, with Convolutional Neural Networks (CNNs) being the predominant architecture. Regarding data sources, 83% (30/36) of datasets utilized Hematoxylin and Eosin (H&E)-stained images, while the remainder relied on diverse modalities, including IHC-stained slides, bone marrow smears, and other tissue preparations. Studies predominantly utilized retrospective, private cohorts with sample sizes typically ranging from 50 to 400 patients; only a minority leveraged open-access repositories (e.g., Kaggle, TCGA). The primary application was slide-level multi-class classification, distinguishing between specific lymphoma subtypes and non-neoplastic controls. Beyond diagnosis, a subset of studies explored advanced prognostic tasks, such as predicting chemotherapy response and disease progression (e.g., in CLL), as well as automated biomarker quantification (c-MYC, BCL2, PD-L1). Reported diagnostic performance was generally high, with accuracy ranging from 60% to 100% (clustering around 90%) and AUC values spanning 0.70 to 0.99 (predominantly > 0.90). Conclusions: While AI models demonstrate high diagnostic accuracy, their translation into practice is limited by unstandardized protocols, morphological complexity, and the “black box” nature of algorithms. Critical issues regarding data provenance, image noise, and lack of representativeness raise risks of systematic bias, hence the need for rigorous validation in diverse clinical environments. Full article

Accurate and early prediction of crop yield at the sub-field scale is essential for precision-agriculture and food-system planning. This study evaluates a phenology-based machine learning framework for winter wheat yield prediction using Sentinel-2 satellite imagery, climate reanalysis data, and field-level yield data. Phenological metrics derived from the normalised difference vegetation index (NDVI), the normalised difference water index (NDWI), and the normalised difference red-edge index (NDRE) were combined with accumulated seasonal rainfall and seasonal potential evapotranspiration, and multiple modelling strategies were assessed using a leave-one-field-out cross-validation (LOFO CV) scheme to ensure spatial generalisation. Among the evaluated models, the Random Forest (RF) algorithm achieved the highest overall performance, explaining up to 73% of the yield variability with a root mean square error (RMSE) of 0.88 t ha⁻¹ at optimal prediction timing (day of year 160–175). Integrating phenological and climatic covariates consistently improved prediction accuracy compared to models based only on phenological variables, while the inclusion of soil properties provided limited additional benefit at the examined spatial scale. Phenological metrics based on red-edge data, particularly the maximum NDRE, were the most influential predictors, highlighting the added value of red-edge spectral information beyond traditional red–near-infrared indices. Uncertainty analysis revealed spatially heterogeneous prediction uncertainty, particularly near field boundaries and in areas of complex spatial patterns. Overall, the proposed framework enables robust, early, and interpretable yield prediction at the sub-field scale, supporting uncertainty-aware decision-making in precision agriculture and offering a scalable foundation for regional crop monitoring. Full article

This study evaluated the effect of water vapor generated by fresh-cut mango (Mangifera indica) on the release of β-carotene from β-cyclodextrin complexes (β-C:β-CD) under stored Modified Atmosphere Packaging (MAP) and to demonstrate β-carotene stabilization and passive–active packaging behavior under MAP conditions. Containers with fresh-cut mangoes, with and without MAP (4% O₂, 6% CO₂, 90% N₂), were prepared for monitoring over 6 days at 4 °C. β-C:β-CD complexes were incorporated into the lids of containers. The physicochemical, relative humidity, antioxidant, erythroprotective, microbiological, and biofunctional qualities of freshly cut mangoes during storage were analyzed. Active metabolic respiration of plant tissue led to a progressive decrease in O₂ and an increase in CO₂ in sealed containers, a phenomenon intensified by cutting, high humidity, and the system’s limited gas permeability. Application of MAP effectively modulated this microenvironment, reducing respiration rate, water loss, acidification, and the degradation of bioactive compounds. Compared to treatments without MAP, mangoes stored under modified atmosphere showed greater color stability, a slower rate of change in pH and titratable acidity, less loss of antioxidant activity and phenolic compounds, and significant preservation of erythroprotective capacity. Furthermore, MAP maintained microbial counts within the limits established by current regulations until the sixth day of storage. The encapsulation of β-C in β-CD effectively protected its bioactivity from oxidation, especially under MAP, although its release into the food matrix was limited, suggesting a predominantly passive behavior of the active packaging system. Overall, the results demonstrate that the combination of MAP constitutes a promising strategy for extending the shelf life and biofunctional stability of fresh-cut mangoes and β-C into the complex. Full article

Background: Oesophageal cancer is a diagnosis carrying significant morbidity and mortality. Gold standard treatment is resection; however, this requires a complex operation. Despite progression to minimally invasive approaches, post-operative pain is a significant issue. Methadone is emerging as an additive intraoperative analgesic across specialities, with a single intra-operative dose seen to improve post-operative pain and reduce post-operative opioid use. This is promising for oesophagectomy patients, where pain is a significant issue; however, it remains poorly characterised. Aim: This paper aimed to assess the literature surrounding intra-operative methadone (IOM) in oesophagectomy, then broadly consider related evidence to consider how it may be applicable to patients undergoing oesophagectomy for oesophageal cancer. Methods: The search assessed existing evidence for efficacy and safety of IOM for patients undergoing oesophagectomy for oesophageal cancer. Of 1856 studies, only one fit inclusion criteria. Following this, the search was broadened to assess IOM use in related surgical contexts, deriving applicability to oesophagectomy. Results: There is very limited evidence for IOM use in oesophagectomy. Several papers explore its use in other intraabdominal and intrathoracic procedures. This evidence may be leveraged for oesophagectomy patients. There remain several safety concerns, most notably respiratory and cardiac risks. Further, several knowledge gaps remain. Conclusions: Overall, IOM represents a promising analgesic option. Unfortunately, current evidence is limited, predominantly derived from non-generalisable studies. This paper provides an up-to-date review of evidence, highlighting clear gaps. It is clear oesophagectomy patients are a vulnerable group who would benefit from improved pain and post-operative quality of life. As such, further focused research should be done to evaluate the role of IOM in oesophagectomy for oesophageal cancer. Full article

Healthcare visualization has become a crucial approach for interpreting complex medical data, supporting informed clinical decision-making, and enhancing public health management. However, existing reviews tend to focus on specific technologies or application scenarios, offering limited insight into the field’s overall knowledge structure, developmental trajectory, and interdisciplinary integration. To address this gap, this study systematically reviews 1121 publications from 1994 to 2025 indexed in the Web of Science Core Collection. By combining bibliometric analysis with qualitative assessment, it maps the field’s evolution and underlying research paradigms. The findings reveal a clear shift from early innovation in technical tools toward the realization of clinical value, giving rise to an integrated research system that connects technology, data, clinical practice, and public health. Recent research has progressed beyond initial explorations of medical imaging, standalone devices, and isolated techniques, moving instead toward core domains such as immersive medical visualization, medical data visualization and analytics, health information systems and decision support, AI-assisted epidemic prediction and diagnosis, and integrated IoT-based healthcare frameworks. Looking ahead, an assessment of future trends suggests that, among other directions, the deep integration of explainable artificial intelligence (XAI) with visualization analysis, the development of IoT-driven real-time interactive systems, and the extension of visualization-enabled services from clinical applications toward inclusive population-level health coverage represent core driving forces for the future development of this field. These insights offer strategic guidance for future research, inform the design principles of next-generation visualization systems, and provide new models of interdisciplinary collaboration. The results also offer evidence-based support for health resource planning, technological innovation, and policy formulation. Full article

Metal additive manufacturing (AM) facilitates the production of geometrically complex components, yet its broader industrial use remains limited by the risk of defect formation and uncertainties in their detection, originating from the highly dynamic and high-temperature process environment. To make additive manufacturing more reliable and establish high-quality parts, it is important to understand how these defects form and how their characteristics appear during the process. This review explains the main causes of common defects, such as cracking, porosity, lack of fusion, and inclusions in metal AM processes, including Powder Bed Fusion and Directed Energy Deposition. It also connects main defect formation mechanisms to the optical, thermal, acoustic, and spectroscopic signals that can be measured during the process. Moreover, it is described how commonly used in situ monitoring systems work and how their signals correspond to melt pool dynamics, vapor plume, particle movement, and the solidification process for each kind of defect. An overview is provided of how data from these systems are analyzed, including the extraction of features from images, the evaluation of temperature fields, and the use of time and frequency domain techniques for various signals. By linking the physics of defect formation to measurable process signals, the interpretation of sensor data is enabled, and potential strategies for monitoring specific problems are outlined. Finally, recent developments are examined, including the integration of multiple sensors, advanced feature-representation approaches, and real-time data interpretation coupled with adaptive control. Together, these directions represent promising advances towards more intelligent and reliable monitoring systems for the future of metal AM. Full article

Background/Objectives: Hospitals prioritize effective resource allocation and patient satisfaction as key performance indicators. Improving the performance of the ultrasonography department remains a major challenge for hospital management due to the inherently unplanned and stochastic nature of its operations. Arrival patterns vary throughout the day, and examination durations differ depending on patients’ clinical pathways and examination types. This study focuses on the ultrasonography department of a private healthcare facility located in one of the most densely populated regions of Istanbul. The primary objective of this study was to improve departmental performance in terms of average waiting time, total time spent in the system, and resource utilization. Methods: To address the variability in patient arrivals and service times across different ultrasonography procedures, a simulation-based optimization approach was employed. Current system performance was evaluated, and multiple alternative operational scenarios were developed and simulated. In addition, the potential impact of Internet of Things applications on the performance of the ultrasonography department was investigated by incorporating alternative system configurations into the simulation model. Results: The simulation results enabled a comparative evaluation of alternative scenarios based on key performance indicators. The findings demonstrate that optimized system configurations can significantly reduce patient waiting times and total system time while improving resource utilization. The inclusion of Internet of Things applications further contributed to performance improvements in the selected scenarios. Conclusions: The proposed simulation-based approach provides a systematic decision-support framework for evaluating alternative operational scenarios in ultrasonography departments. By optimizing resource allocation and leveraging Internet of Things applications, hospital managers can improve operational efficiency and patient satisfaction. The results highlight the value of data-driven decision-making in managing complex and stochastic healthcare systems. Full article

The forensic reliability of biological fluids in sexual assault investigations depends on substrate type, environmental exposure, time since deposition, and analytical methodology. This systematic review evaluates the forensic reliability of major biological fluids, semen, blood, saliva, and vaginal secretions by comparing detectability and persistence on porous and non-porous substrates, assessing environmental and temporal effects on DNA integrity, and examining the performance of identification methods. A systematic search of PubMed, Scopus, and Web of Science (2001–2025) was conducted following PRISMA 2020 guidelines. Eligible studies investigated fluid persistence, degradation, or identification reliability under controlled or casework-relevant conditions. A weighted scoring framework categorised relative reliability. Twenty-seven studies met inclusion criteria. Semen and blood demonstrated higher reliability across substrates, particularly when collected within recommended timeframes. Porous substrates reduced surface detectability but occasionally preserved DNA from rapid degradation. Elevated temperature, humidity, and prolonged intervals consistently reduced DNA quality and detection success. Molecular approaches, including mRNA profiling, showed enhanced specificity in degraded or mixed samples, though methodological variability limited direct comparability across studies. The forensic reliability of biological fluids is context-dependent, shaped by complex interactions between substrate characteristics, environmental exposure, and analytical technique. Semen and blood remain robust DNA sources, while emerging technologies offer improved specificity in challenging scenarios. Standardised evaluation frameworks and timely evidence collection remain essential to enhance evidential value and minimise misinterpretation in sexual assault investigations. Full article

The digital divide between large enterprises and SMEs (Small and Medium-sized Enterprises) within industrial clusters poses a significant challenge to achieving collective digital transformation, exacerbated by the quasi-public goods, attributes of digital inclusion ecosystems, and the prevalence of free-riding behavior. This paper investigates whether platform enterprises, as core actors occupying structural holes in cluster networks, can foster the co-construction of a digitally inclusive ecosystem. We developed a complex network public goods game model, incorporating performance feedback into a modified Fermi learning to capture firms’ adaptive decision-making based on historical and social aspirations. The model simulates strategic interactions on both small-world and scale-free networks, characteristic of industrial clusters. Numerical simulations reveal that: (1) The core driver of co-construction is the investment return coefficient; (2) Performance feedback amplifies individual rationality, accelerating the formation or collapse of cooperation depending on the investment return coefficient; (3) Platform empowerment—specifically, selectively connecting and incentivizing cooperative firms—effectively promotes ecosystem co-construction, with this strategy proving most impactful when investment returns are moderate. Furthermore, while this selective empowerment strategy benefits the cluster overall, its effect on the platform’s own revenue is network-dependent, showing a more pronounced decline in small-world structures. This study provides a novel analytical framework for understanding strategic interactions in digital inclusion and offers practical insights for policymakers and platform leaders in orchestrating collaborative digital transformation. Full article

Background: Enhanced recovery pathways and modern fixation systems have shortened admission after lumbar spine surgery, yet the interplay between implant choice, comorbidity, and early morbidity remains incompletely defined. Methods: We undertook a retrospective, single-center cohort study of lumbar procedures performed at SANADOR Clinical Hospital (Bucharest, Romania) between 1 January 2023 and 31 May 2024. Eighty-six adult patients (64 women, 22 men; mean age 64.9 ± 10.8 years) met the inclusion criteria. Outcomes included length of stay (LOS), early postoperative neurological change (Frankel/American Spinal Injury Association (ASIA) Impairment Scale), and unplanned reoperation within 90 days. Analyses were performed in Python 3.11 (pandas, SciPy, statsmodels) and verified in IBM SPSS 28.0; α = 0.05. Results: Spondylolisthesis was the predominant diagnosis (60.5%), followed by lumbar stenosis (17.4%). Instrumentation was used in 75 cases (87.2%). Median LOS was 3 days (mean 3.8 ± 2.1), and most patients were discharged by postoperative day 4. LOS did not differ by interbody cage status (Mann–Whitney p = 0.459; median 3 vs. 3 days). Early postoperative neurological change occurred in 34.9% but improved or resolved in all cases by discharge; no permanent motor deficits were observed. Unplanned reoperation within 90 days occurred in 17.6%. In multivariable logistic regression for prolonged hospitalization (LOS > 4 days), early postoperative neurological change was associated with increased odds of prolonged LOS (OR 4.45, 95% CI 1.29–15.43; p = 0.018), whereas age showed only a borderline association (OR 1.06 per year, 95% CI 1.00–1.14; p = 0.065). Conclusions: In this single-center retrospective cohort, postoperative hospitalization was generally short. Prolonged LOS was more closely associated with early postoperative neurological change than with baseline comorbidity or interbody cage use. These findings should be interpreted as short-term, context-specific observations from a complex, predominantly instrumented referral cohort. Full article

Objectives: Medication adherence in elderly patients is shaped by cognitive, behavioral, systemic, and socioeconomic factors. This review aimed to identify determinants and effective strategies to improve adherence in older adults. Methods: A systematic search of PubMed, Scopus, and ScienceDirect (2015–2025) followed PRISMA 2020 guidelines. From 5116 records, 53 studies met inclusion criteria. Randomized controlled trials were meta-analyzed using standardized mean differences under a random-effects model. Risk of bias in the 10 pooled trials was assessed using the Cochrane RoB 2 tool, and certainty of evidence was evaluated using the GRADE framework. Results: Adherence ranged from 25.3% in institutionalized patients to 97.6% in pharmacist-led schizophrenia programs. Cognitive impairment and frailty reduced adherence (54.2%), while caregiver involvement improved rates, especially in dementia and schizophrenia (77.4–97.6%). Socioeconomic barriers, including medication cost, contributed to nonadherence but were mitigated by subsidies. Digital tools enhanced adherence in chronic disease, and machine learning models accurately predicted nonadherence (AUC up to 0.935). Effective interventions—caregiver support, digital platforms, and single-pill regimens—increased adherence by 25–59% and reduced cardiovascular events. The meta-analysis demonstrated a significant pooled effect (Standardized Mean Difference, SMD = 0.71, 95% CI: 0.11–1.54), although heterogeneity was high (I² = 99%). The RoB 2 assessment of the 10 pooled trials identified 2 at low risk, 4 with some concerns, and 4 at high risk of bias; the GRADE certainty of evidence was rated Very Low. Conclusions: Multiple factors, including frailty, cognitive deficits, socioeconomic barriers, regimen complexity, and the level of caregiver support, appear to be consistently associated with medication adherence in older adults. Strategies such as caregiver engagement, digital health tools, regimen simplification, and mental health support may contribute to improved adherence, although effect sizes vary considerably across study contexts. Given the substantial heterogeneity, Very Low certainty of evidence (GRADE), and variable study quality, findings should be interpreted with caution. System-level reforms, financial assistance programs, and culturally tailored approaches may further support adherence, while the successful implementation of digital health solutions will require addressing literacy, accessibility, and integration challenges. Full article

Warfarin is one of the most used oral anticoagulants, even after the arrival of non-vitamin K oral anticoagulants. Warfarin has been implicated in approximately one-third of emergency hospitalizations for adverse drug events among older adults in national U.S. data. Warfarin dose has been shown to vary between patients with up to 10 times the standard dose. This variability is due to multiple factors such as age, gender, diet, body size, co-medications, and the genetic background of the patient, where the genetic background accounts for 50% of warfarin dose variability among Europeans. Sadly, these findings do not apply to Caribbean Hispanic populations such as Puerto Ricans due to them having an admixed genetic profile. In the field of pharmacogenomics (PGx), the utility of machine learning (ML) has been used to predict individual drug responses by analyzing complex genetic and clinical data, which helps personalize medicine by tailoring treatments to a patient’s genetic makeup. Inclusion of ethno-specific variants has demonstrated improvement on the application of ML to a specific population. This study compares eight ML methods to predict warfarin sensitivity in Puerto Rican Caribbean Hispanics. This study is a secondary analysis of genetic and clinical data from 217 Puerto Rican patients treated with warfarin for thromboembolic disorders. After quality control filtering and exclusion of participant records with incomplete genetic and clinical data, 146 participants are retained for analysis. Data are divided into 65% and 35% to be used as training and test sets. Model performance is determined by comparing the precision and accuracy metrics, computed through the corresponding confusion matrixes. A gradient boosting classifier (GDB) achieves the highest overall accuracy (0.7500) and weighted precision of (0.7642); however, sensitivity for detecting warfarin-sensitive patients remains low. Feature importance analysis suggests that rs202201137 could contribute to model predictions, although overall detection of warfarin-sensitive individuals remains limited. Full article

The pedological component of geodiversity represents a fundamental—yet often overlooked—aspect of the abiotic environment with profound implications for ecosystem functioning and the provision of essential ecosystem services. It is shaped by the complex interplay of lithology, hydrological regimes, relief and its ruggedness, climate, human activity, and time; soil systems mediate crucial ecological processes across spatial and temporal scales. Understanding these interdependencies is critical for sustainable natural resource management and biodiversity conservation. Even more so, soils and the processes related to them become vital when measuring, evaluating, and protecting geodiversity since they can promote groundwater recharge, nutrient cycling, organic matter decomposition, carbon storage, biomass, and food production and habitat provision. Soils provide opportunities for recreation and geotourism, and can contribute to landscape aesthetics. Hence, they are a direct link between abiotic and biotic nature. Given increasing threats from erosion, degradation, pollution, and other changes, this review synthesizes and reviews current research on the pedological component of geodiversity and its connections to hydrological, relief, and other processes. From this perspective, it highlights the need for integrative strategies that safeguard soil functionality and ensure the long-term provision of ecosystem services. By performing that, it provides directions for further discussion and inclusion of soils and their diversity within geodiversity evaluations. Full article