Search Results (292)

Infertility affects 10–30% of couples globally, with significant psychological and social impacts in sub-Saharan Africa, where fertility is closely tied to identity and social status. To explore how couples’ understanding of infertility causes influences their treatment-seeking behaviours and healthcare decision-making processes in Ghana, this cross-sectional qualitative study used in-depth interviews with 24 married participants (nine dyads and six individuals) experiencing current or past infertility in Greater Accra, Ghana, from August to October 2023. Data were analysed using thematic analysis with NVivo version 15. Couples demonstrated comprehensive knowledge of infertility causes spanning medical, spiritual, cultural, and lifestyle factors, although they lacked knowledge of clinical diagnostic criteria. Three main treatment pathways emerged: medical/orthodox, herbal, and spiritual interventions, pursued either sequentially or concurrently. Decision-making was influenced by internal factors (treatment effectiveness, financial constraints, and safety concerns) and external factors (family influence and peer testimonials). Four distinct navigation strategies were identified: informed notification, trial periods and evaluation, parallel relationship management, and strategic sequencing. Couples experiencing infertility are sophisticated healthcare consumers who skilfully navigate pluralistic healthcare systems through strategic decision-making. Rather than representing non-compliance, their multimodal approaches reflect rational responses to structural constraints and cultural values. Healthcare systems should recognise and accommodate these navigation strategies to improve therapeutic relationships and outcomes. Full article

Eutrophication is a major threat to freshwater ecosystems, leading to harmful algal blooms, biodiversity loss, and hypoxia. Excessive nutrient loading, primarily from nitrates and phosphates, is driven by fertilizer runoff, sewage discharge, and agricultural practices. Sediment microbial fuel cells (sMFCs) have emerged as a potential bioremediation strategy for nutrient removal while generating electricity. Although various studies have explored ways to enhance sMFC performance, limited research has examined the relationship between external resistance, electricity generation, and nutrient removal efficiency. This study demonstrated effective nutrient removal from overlying water, with 1200 Ω achieving the highest nitrate and phosphate removal efficiency at 59.0% and 32.2%, respectively. The impact of external resistances (510 Ω and 1200 Ω) on sMFC performance was evaluated, with the 1200 Ω configuration generating a maximum voltage of 466.7 mV and the 510 Ω configuration generating a maximum current of 0.56 mA. These findings show that external resistance plays a major role in both electrochemical performance and nutrient-removal efficiency. Higher external resistance consistently resulted in greater voltage output and improved removal of nitrate and phosphate. The findings also indicate that sMFCs can serve as a dual-purpose technology for nutrient removal and electricity generation. The power output may be sufficient to support small, eco-friendly biosensing devices in remote aquatic environments while mitigating eutrophication. Full article

To address the challenge of optimizing hydrological parameters for nitrogen pollution control in paddy polders, this study coupled the Stella eco-dynamics model with an external optimization algorithm and developed a nonlinear programming framework using the water surface ratio and inflow rate as decision variables and the maximum nitrogen removal rate as the objective function. The simulation and optimization conducted for the Hongze Lake polder area indicated that the model exhibited strong robustness, as verified through Monte Carlo uncertainty analysis, with coefficients of variation (CV) of nitrogen outlet concentrations all below 3%. Under the optimal regulation scheme, the maximum nitrogen removal rates (${\eta }_1$, ${\eta }_2$, and ${\eta }_4$) during the soaking, tillering, and grain-filling periods reached 98.86%, 98.74%, and 96.26%, respectively. The corresponding optimal inflow rates ($Q^{*}$) were aligned with the lower threshold limits of each growth period (1.20, 0.80, and 0.50 m³/s). The optimal channel water surface ratios ($A_1^{*}$) were 3.81%, 3.51%, and 3.34%, respectively, while the optimal pond water surface ratios ($A_2^{*}$) were 19.94%, 16.30%, and 17.54%, respectively. Owing to the agronomic conflict between “water retention without drainage” and concentrated fertilization during the heading period, the maximum nitrogen removal rate (${\eta }_3$) during this stage was only 37.34%. The optimal channel water surface ratio ($A_1^{*}$) was 2.37%, the pond water surface ratio ($A_2^{*}$) was 19.04%, and the outlet total nitrogen load increased to 8.39 mg/L. Morphological analysis demonstrated that nitrate nitrogen and organic nitrogen dominated the outlet water body. The “simulation–optimization” coupled framework established in this study can provides quantifiable decision-making tools and methodological support for the precise control and sustainable management of agricultural non-point source pollution in the floodplain area. Full article

Dissolved organic carbon (DOC) represents the most readily available and crucial carbon source for soil microorganisms, influencing their community structure, nutrient cycling, and metabolic functions. However, the interplay between functional genes and the organic components of DOC remains poorly understood. In this study, a 33-year fertilization experiment on black soil was carried out, setting up five fertilization treatments: unfertilized control (CK), nitrogen and potassium (NK), nitrogen, P and potassium (NPK), NPK plus straw (NPKS), and NPK plus manure (NPKM). The variation characteristics of soil DOC composition and carbon-degrading functional gene abundance under different fertilization treatments were systematically analyzed. The study found that applying chemical fertilizers combined with organic materials significantly increased soil organic carbon (SOC) and DOC contents in the thin-layer black soil of Gongzhuling. The soil DOC in this region is primarily derived from external inputs (Fresh plant-derived materials). Parallel factor analysis identified four fluorescent components: C1 as visible fulvic acid-like substances, C2 as humic acid-like substances, C3 as ultraviolet fulvic acid-like substances, and C4 as long-wavelength humic-like substances. Among these, NPK plus straw significantly enhanced the fluorescence intensity of the humic acid-like component (C2) and the total fluorescence intensity. The fluorescence intensity of the humic acid-like component increased by 36.0–208.9%, and the total fluorescence intensity increased by 23.8–270.9% compared to the CK. Moreover, the study found that the phylum composition of carbon-degrading microorganisms remained stable under different fertilization treatments. However, NPK plus straw significantly reduced the total abundance of carbon-degrading genes and influenced the composition and transformation of DOC by regulating the expression of key carbon-degrading genes ICL and abfA. These results offer new insights into the mechanisms by which fertilizer management affects the composition and stability of DOC in black soils via microbial functional gene pathways. Full article

Understanding how supplemental nitrogen (N) interacts with biological N₂ fixation (BNF) in modern soybean cultivars is essential for designing fertilization strategies that avoid unnecessary N inputs. We investigated N partitioning among soil, fertilizer and symbiotic sources in soybean grown in a greenhouse pot experiment on a tropical Oxisol. Plants were inoculated with Bradyrhizobium and subjected to four N managements: no external N, soil-applied ¹⁵N-urea (20 kg N ha⁻¹), foliar ¹⁵N-urea (2 kg N ha⁻¹, 0.7% w/v), and the combination of soil + foliar N. Using ¹⁵N isotope dilution, we quantified N derived from the atmosphere (NDFA), fertilizer (NDFF) and soil (NDFS) at organ and whole-plant scales, and related these fractions to nodulation, nitrogenase activity and yield. In the absence of external N, NDFA exceeded 97% in all organs, indicating a strong reliance on BNF and efficient internal N remobilization during grain filling, accompanied by higher leaf nitrate reductase activity. Soil and soil + foliar N markedly increased NDFF and NDFS while suppressing nodulation (particularly at V4) and reducing nitrogenase activity, yet they did not improve grain yield or vegetative biomass. Foliar N alone had only modest effects on N partitioning and did not enhance yield. Under these tropical soil conditions, symbiotic fixation and internal N remobilization were sufficient to meet grain N demand, highlighting the limited agronomic benefit and potential ecological cost of supplemental N during reproductive growth. Full article

Background/Objectives: Ergot alkaloids are mycotoxins produced mainly by fungi of the genus Claviceps, infecting a wide variety of plants, especially cereals. These toxins usually manifest as black, hardened sclerotia (ergots), though they may also be invisible when dispersed in grain. They pose a significant risk to animals and humans when present in contaminated cereals. They can cause ergotism, with vasoconstriction, ischemia, hallucinations, and in severe cases gangrene. This study was carried out in response to the European legislative actions which determine the permissible levels of ergot alkaloids in cereals. Historically, consumers manually removed visible sclerotia from grain, and farmers applied fertilizers or timed harvests to specific periods to mitigate contamination. However, these traditional methods have proven insufficient. We therefore explored advanced techniques for detecting and quantifying ergot-contaminated cereals, as well as methods for reducing ergot alkaloid concentrations. Methods: Searches were conducted in scientific databases including Google Scholar, PubMed, and Scopus to identify research articles, reviews, and experimental studies published mainly between 2012 and August 2025, including accepted or in-press manuscripts, with special attention to works from 2021 onward to capture the most recent advancements. Results/Conclusions: Ultra-high-performance liquid chromatography–tandem mass spectrometry (UHPLC-MS/MS) is the reference method for confirmatory, epimer-aware quantification of ergot alkaloids, and is already standardized. Recent QuEChERS-UHPLC-MS/MS workflows in cereal matrices, including oat-based products, routinely achieve limits of quantification of about 0.5–1.0 µg/kg with single-run analysis times of about 5–15 min. Rapid screening options complement, rather than replace, confirmatory mass spectrometry: magnetic bead-based immunoassays that use magnetic separation and a smartphone-linked potentiostat provide sub-hour turnaround and field portability for trained quality-assurance staff, although external validation and calibration traceable to LC-MS/MS remain prerequisites for routine use. In practice, operators are adopting tiered, orthogonal workflows (e.g., immunoassay or electronic-nose triage at intake followed by DNA-based checks on grain washings and LC–MS/MS confirmation, or hydrazinolysis “sum parameter” screening followed by targeted MS speciation). Such combinations reduce turnaround time while preserving analytical rigor. Biotechnology also offers potential solutions for reducing ergot alkaloid concentrations at the source. Finally, to enhance consumer safety, artificial intelligence and blockchain-based food traceability appear highly effective. These systems can connect all stakeholders from producers to consumers, allowing for real-time updates on food safety and rapid responses to contamination issues. This review primarily synthesizes advances in analytical detection of ergot alkaloids, while mitigation strategies and supply chain traceability are covered concisely as supporting context for decision making. Full article

Cactus pear (Opuntia ficus-indica (L.) Mill.) is increasingly recognized as a climate-resilient crop in arid and semi-arid regions, yet its performance is often constrained by poor soil fertility and limited external inputs. Arbuscular mycorrhizal fungi (AMF) are known to enhance phosphorus uptake, water relations, and stress tolerance in many species, but their contribution to cactus pear growth remains largely unexplored. One-year-old cladodes were grown in pots filled with sandy loam soil, either inoculated with a mixed AMF consortium or kept as non-inoculated controls. Plant growth was assessed after 6 and 12 months by measuring cladode number and surface area, shoot and root dry weight, and biomass allocation indices. Inoculated plants produced more cladodes, developed a larger canopy surface area, and accumulated greater root and shoot biomass than controls. These gains reflected an overall acceleration of growth, while biomass partitioning (root-to-shoot balance) remained stable. AMF inoculation substantially enhanced the vegetative growth of O. ficus-indica, pointing to its promise as a sustainable practice for improving cactus pear cultivation in nutrient-poor and water-limited soils. Full article

A very important issue in urban agglomerations is the proper management of green waste while reducing its negative impact on the environment. One potential solution is the utilization of green biomass—originating from the maintenance of parks, squares, and home gardens—for the production of compost and compost-based pellets as organic fertilizers. The aim of this study was to produce compost-based pellets intended for fertilization purposes from compost derived from green waste and conifer sawdust, and to analyze their mechanical and chemical properties. Ten variants of pellets with different compost-to-sawdust ratios were evaluated. Compost-based pellets exhibited the highest initial mechanical strength; however, their resistance to external loads decreased over time, whereas the best long-term stability was observed in pellets containing 50% sawdust. The seasoning process influenced the stabilization or improvement of the mechanical properties of certain mixtures. Chemical analyses showed that compost-based pellets contained the highest concentrations of nutrients (N, P, K), while increasing the proportion of sawdust reduced their fertilizing value. No exceedances of permissible heavy metal limits were detected. The results confirm the suitability of compost-based pellets made from green biomass as a sustainable alternative to mineral fertilizers, supporting the principles of the circular economy. Full article

Objectives: We aimed to develop and validate a per-cycle prediction model for in vitro fertilization (IVF) success using only preprocedural clinical variables available at the first consultation. Methods: We retrospectively analysed 1243 IVF/ICSI cycles (University of Szeged, 21 January 2022–12 December 2023). An Extreme Gradient Boosting (XGBoost version 1.7.7.1) classifier was trained on 14 baseline predictors (e.g., female age, AMH, BMI, FSH, LH, sperm concentration/motility, and infertility duration). A parsimonious 9-variable model was derived by feature importance. Model performance was assessed on the untouched test set and, as a final step, on an independent same-centre external validation cohort (n = 92) without re-fitting or recalibration. Results: The 9-variable model achieved an AUC of 0.876 on the internal test set, with an accuracy of 81.70% (95% CI 76.30–86.30%), sensitivity of 75.60%, specificity of 84.40%, PPV of 68.60%, and NPV of 88.50%. In external validation, the model maintained strong performance with an accuracy of 78.30%, confirming consistent discrimination on an independent same-centre cohort. Female age was the dominant high-impact feature, while AMH and BMI acted as “workhorse” predictors, and male factors added incremental value. Conclusions: IVF outcome can be predicted at the first visit using routinely collected preprocedural data. The model showed consistent discrimination internally and in external validation, supporting its potential utility for early, individualized counselling and treatment planning. Full article

Corn (Zea mays L.) yield productivity is driven by a multitude of factors, specifically genetics, environment, and management practices, along with their corresponding interactions. Despite continuous monitoring through proximal or remote sensors and advanced predictive models, understanding these complex interactions remains challenging. While predictive models are improving with regard to accurate predictions, they often fail to explain causal relationships, rendering them less interpretable than desired. Process-based or biophysical models such as the Agricultural Production Systems sIMulator (APSIM) incorporate these causalities, but the multitude of interactions are difficult to tease apart and are largely sensitive to external drivers, which often include stochastic variations. To address this limitation, we developed a novel methodology that reveals these hidden causal structures. We simulated corn production under varied conditions, including different planting dates, nitrogen fertilizer amounts, irrigation rules, soil and environmental conditions, and climate change scenarios. We then used the simulation results to rank features having the largest impact on corn yield through Random Forest modeling. The Random Forest model identified nitrogen uptake and annual transpiration as the most influential variables on corn yield, similar to the existing research. However, this analysis alone provided limited insight into how or why these features ranked highest and how the features interact with each other. Building on these results, we deployed a Causal Bayesian model, using a hybrid approach of score-based (hill climb) and constraint-based (injecting domain knowledge) models. The causal analysis provides a deeper understanding by revealing that genetics, environment, and management factors had causal impacts on nitrogen uptake and annual transpiration, which ultimately affected yield. Our methodology allows researchers and practitioners to unpack the “black box” of crop production systems, enabling more targeted and effective model development and management recommendations for optimizing corn production. Full article

The escalating demand for non-renewable resources is anticipated to intensify extractive activities, which are invariably associated with significant environmental externalities. The rehabilitation of mined landscapes, undertaken to mitigate ecological degradation and reinstate ecosystem functions and biodiversity, is frequently constrained by substantial financial requirements as well as intricate technical, logistical, and environmental challenges. As a consequence, a considerable proportion of extractive sites worldwide remain unreclaimed. There is a critical need for sustainable, cost-effective, and versatile restoration practices. This article presents a bibliographic review focusing on problems encountered in mine remediation and the role of technosols in addressing these issues. Mine restoration initiatives are confronted with a suite of interrelated challenges, including suboptimal soil physicochemical characteristics, hydrological instability, geomorphological hazards, and the exacerbating effects of extreme climatic events. Technosols, formulated from various waste materials, prove to be a versatile and cost-effective biotechnology that can significantly improve soil fertility, reduce erosion, enhance water retention, and restore biological activity. Their application, which can include mining waste and organic residues, substantially lowers costs estimated globally at EUR 829.711 billion for soil formation and contributes to a circular economy. Technosols represent a promising and efficient biotechnology for mine restoration. Their use facilitates the creation of stable, functional, and self-sustaining landscapes, enabling not only environmental recovery but also social and economic benefits through post-restoration land uses. Further research and knowledge transfer are vital for their broader and optimised implementation. Full article

Treatment-induced ovarian function loss is a significant concern for many young patients with breast cancer. Accurately predicting this risk is crucial for counselling young patients and informing their fertility-related decision-making. However, current risk prediction models for treatment-related ovarian function loss have limitations. To provide a broader representation of patient cohorts and improve feature selection, we combined retrospective data from six datasets within the FoRECAsT (Infertility after Cancer Predictor) databank, including 2679 pre-menopausal women diagnosed with breast cancer. This combined dataset presented notable missingness, prompting us to employ cross imputation using the k-nearest neighbours (KNN) machine learning (ML) algorithm. Employing Lasso regression, we developed an ML model to forecast the risk of treatment-related amenorrhea as a surrogate marker of ovarian function loss at 12 months after starting chemotherapy. Our model identified 20 variables significantly associated with risk of developing amenorrhea. Internal validation resulted in an area under the receiver operating characteristic curve (AUC) of 0.820 (95% CI: 0.817–0.823), while external validation with another dataset demonstrated an AUC of 0.743 (95% CI: 0.666–0.818). A cutoff of 0.20 was chosen to achieve higher sensitivity in validation, as false negatives—patients incorrectly classified as likely to regain menses—could miss timely opportunities for fertility preservation if desired. At this threshold, internal validation yielded sensitivity and precision rates of 91.3% and 61.7%, respectively, while external validation showed 92.9% and 60.0%. Leveraging ML methodologies, we not only devised a model for personalised risk prediction of amenorrhea, demonstrating substantial enhancements over existing models but also showcased a robust framework for maximally harnessing available data sources. Full article

The secondary sex ratio (SSR), defined as the ratio of male to female live births in a population, is a crucial indicator of reproductive and public health. External factors, such as lifestyle, natural disasters, environmental chemicals and infections, have been examined as potential trendsetters of the SSR. Several global challenges have emerged in recent years, such as climate change, wars, terrorist attacks and stressful political events. These aspects can potentially impact reproductive health outcomes, fertility rates, and the overall well-being of individuals. With respect to this, they may also affect the SSR. Through an in-depth examination of the existing literature, this manuscript elucidates the complex interconnections between global challenges and the SSR. Indeed, terrorist attacks and stressful political events have been linked to a decrease in the SSR. In contrast, high temperatures and warfare have shown a propensity to elevate the SSR in numerous scenarios. However, these associations require further validation through additional studies. The precise mechanisms through which these determinants exert their influence need to be elucidated. Understanding the unseen influences of global challenges on the SSR is crucial for understanding population trends and ensuring effective public health interventions. Full article

The European grayling is an ecologically and recreationally important salmonid fish species. However, its wild populations have declined in recent years across Europe due to habitat degradation, predation and overexploitation. Unfortunately, conservation measures such as stocking with hatchery-reared fish may threaten the genetic integrity of native populations. The use of triploid all-females, which display markedly reduced fertility, offers a potential solution to this problem. While protocols for inducing triploid and gynogenetic development of the species exist, an effective method for producing neo-males, essential for large-scale triploid female stock production, is still lacking. In the present study, the potential suitability of 11β-hydroxyandrostenedione (OHA) and 17α-methyltestosterone (MT) for masculinization of the European grayling was investigated, aiming to provide preliminary data to support the future development of a reliable biotechnique for neo-male production in this species. Pilot trials of hormonal masculinization were conducted by feeding 20-day post-hatch fry with diets supplemented with OHA (10 mg/kg—OHA_10ppm, 20 mg/kg—OHA_20ppm) or MT (3 mg/kg—MT_3ppm, 6 mg/kg—MT_6ppm) for ~80 days. In the OHA-treated groups, the proportion of externally male-like individuals ranged from 66.7% (OHA_10ppm) to 76.6% (OHA_20ppm). However, some of these specimens were found to be genetically female with ovaries (4.5% and 28.8%, respectively), which indicated a dissociation between external dimorphism and gonadal development. In turn, MT treatments resulted in strong disruption of the female gonads with the intersex individuals comprising 28.6% (MT_3ppm) and 57.1% (MT_6ppm), indicating that the applied hormonal treatment was insufficient for complete masculinization. The results indicate that androgen-mediated neo-male induction by OHA and MT is possible in the species but requires optimization of dose, timing and delivery, potentially combining embryonic immersion with prolonged dietary administration. Full article

As a staple cereal worldwide, winter wheat plays a pivotal role in food security. Leaf chlorophyll serves as a direct indicator of photosynthetic performance and nitrogen nutrition, making it critical for precision management and yield gains. Consequently, rapid, nondestructive, and high-accuracy remote-sensing retrievals are urgently needed to underpin field operations and precision fertilization. In this study, canopy hyperspectral reflectance together with destructive chlorophyll assays were systematically acquired from Yangling field trials conducted during 2018–2020. Three families of spectral indices were devised: classical empirical indices; two-dimensional optimal spectral indices (2D OSI) selected by correlation-matrix screening; and novel three-dimensional optimal spectral indices (3D OSI). The main contribution lies in devising novel 3D OSIs that combine three spectral bands and demonstrating how their fusion with classic two-band indices can improve chlorophyll quantification. Correlation analysis showed that most empirical vegetation indices were significantly associated with chlorophyll (p < 0.05), with the new double difference index (NDDI) giving the strongest relationship (R = 0.637). Within the optimal-index sets, the difference three-dimensional spectral index (DTSI; 680, 807, and 1822 nm) achieved a correlation coefficient of 0.703 (p < 0.05). Among all multi-input fusion schemes, fusing empirical indices with 3D OSI and training with RF delivered the best validation performance (R² = 0.816, RMSE = 0.307 mg g⁻¹, MRE = 11.472%), and external data further corroborated its feasibility. Altogether, integrating 3D spectral indices with classical vegetation indices and deploying RF enabled accurate, nondestructive estimation of winter wheat chlorophyll, offering a new hyperspectral pathway for monitoring crop physiological status and advancing precision agricultural management and fertilization, can guide in-season fertilization to optimize nitrogen use, thereby advancing precision agriculture. Full article