Search Results (313)

Background: Parkinson’s disease (PD) often involves autonomic dysfunction, most notably impaired baroreflex sensitivity (BRS), which disrupts cardiovascular homeostasis and contributes to orthostatic hypotension (OH). Pharmacological and invasive treatments, including deep brain stimulation, have yielded inconsistent benefits and carry procedural risks, highlighting the need for safer, more accessible alternatives. In this systematic review, we evaluated non-invasive interventions—spanning somatosensory stimulation, exercise modalities, thermal therapies, and positional strategies—aimed at improving cardiovascular autonomic function in PD. Methods: We searched PubMed, Embase, MEDLINE (Ovid), Google Scholar, ScienceDirect, and Web of Science for studies published between January 2014 and December 2024. Eight original studies (n = 8) including 205 participants met the inclusion criteria for analyzing cardiac sympathovagal balance. Results: Five studies demonstrated significant post-intervention increases in BRS. Most reported favorable shifts in heart rate variability (HRV) and favorable changes in the low-frequency/high-frequency (LF/HF) ratio. Across modalities, systolic blood pressure (SBP) decreased by an average of 5%, and some interventions produced benefits that persisted up to 24 h. Conclusion: Although sample sizes were small and protocols heterogeneous, the collective findings support the potential of non-invasive neuromodulation to enhance BRS and overall cardiovascular regulation in PD. Future research should focus on standardized, higher-intensity or combined protocols with longer follow-up periods to establish durable, clinically meaningful improvements in autonomic function and quality of life for people living with PD. Full article

A systematic sensitivity analysis using three-dimensional discrete element models with discrete fracture networks (DEM-DFN) was conducted to evaluate underground excavation support in jointed rock masses at the CLAB2 site in Southeastern Sweden. The site features a joint network comprising six distinct joint sets, each with unique geometrical properties. The study examined 10 DFNs and 19 rock bolt patterns, both conventional and unconventional. It covered 200 scenarios, including 10 unsupported and 190 supported cases. Technical and economic criteria for stability were assessed for each support system. The results indicated that increasing rock bolt length enhances stability up to a certain point. However, multi-length rock bolt patterns with similar consumption can yield significantly different stability outcomes. Notably, the arrangement and properties of rock bolts are crucial for stability, particularly in blocks between bolting sections. These blocks remain interlocked in unsupported areas due to the induced pressure from supported sections. Although equal-length rock bolt patterns are commonly used, the analysis revealed that triple-length rock bolts (3, 6, and 9 m) provided the most effective support across all ten DFN scenarios. Full article

Common bean (Phaseolus vulgaris L.) is a critical protein-rich legume supporting food and nutritional security globally. However, Fusarium wilt, caused by Fusarium solani, remains a major constraint to production, with yield losses reaching up to 84%. While biocontrol strategies have been explored, most microbial agents are sourced from mesophilic environments and show limited effectiveness under abiotic stress. Here, we report the isolation and characterization of extremophilic Bacillus spp. from the hypersaline Lake Bogoria, Kenya, and their biocontrol potential against F. solani. From 30 isolates obtained via serial dilution, 9 exhibited antagonistic activity in vitro, with mycelial inhibition ranging from 1.07–1.93 cm 16S rRNA sequencing revealed taxonomic diversity within the Bacillus genus, including unique extremotolerant strains. Molecular screening identified genes associated with the biosynthesis of antifungal metabolites such as 2,4-diacetylphloroglucinol, pyrrolnitrin, and hydrogen cyanide. Enzyme assays confirmed substantial production of chitinase (1.33–3160 U/mL) and chitosanase (10.62–28.33 mm), supporting a cell wall-targeted antagonism mechanism. In planta assays with the lead isolate (B7) significantly reduced disease incidence (8–35%) and wilt severity (1–5 affected plants), while enhancing root colonization under pathogen pressure. These findings demonstrate that extremophile-derived Bacillus spp. possess robust antifungal traits and highlight their potential as climate-resilient biocontrol agents for sustainable bean production in arid and semi-arid agroecosystems. Full article

Background: Artificial intelligence (AI) and machine learning (ML) have been reshaping maternal, fetal, neonatal, and reproductive healthcare by enhancing risk prediction, diagnostic accuracy, and operational efficiency across the perinatal continuum. However, no comprehensive synthesis has yet been published. Objective: To conduct a scoping review of reviews of AI/ML applications spanning reproductive, prenatal, postpartum, neonatal, and early child-development care. Methods: We searched PubMed, Embase, the Cochrane Library, Web of Science, and Scopus through April 2025. Two reviewers independently screened records, extracted data, and assessed methodological quality using AMSTAR 2 for systematic reviews, ROBIS for bias assessment, SANRA for narrative reviews, and JBI guidance for scoping reviews. Results: Thirty-nine reviews met our inclusion criteria. In preconception and fertility treatment, convolutional neural network-based platforms can identify viable embryos and key sperm parameters with over 90 percent accuracy, and machine-learning models can personalize follicle-stimulating hormone regimens to boost mature oocyte yield while reducing overall medication use. Digital sexual-health chatbots have enhanced patient education, pre-exposure prophylaxis adherence, and safer sexual behaviors, although data-privacy safeguards and bias mitigation remain priorities. During pregnancy, advanced deep-learning models can segment fetal anatomy on ultrasound images with more than 90 percent overlap compared to expert annotations and can detect anomalies with sensitivity exceeding 93 percent. Predictive biometric tools can estimate gestational age within one week with accuracy and fetal weight within approximately 190 g. In the postpartum period, AI-driven decision-support systems and conversational agents can facilitate early screening for depression and can guide follow-up care. Wearable sensors enable remote monitoring of maternal blood pressure and heart rate to support timely clinical intervention. Within neonatal care, the Heart Rate Observation (HeRO) system has reduced mortality among very low-birth-weight infants by roughly 20 percent, and additional AI models can predict neonatal sepsis, retinopathy of prematurity, and necrotizing enterocolitis with area-under-the-curve values above 0.80. From an operational standpoint, automated ultrasound workflows deliver biometric measurements at about 14 milliseconds per frame, and dynamic scheduling in IVF laboratories lowers staff workload and per-cycle costs. Home-monitoring platforms for pregnant women are associated with 7–11 percent reductions in maternal mortality and preeclampsia incidence. Despite these advances, most evidence derives from retrospective, single-center studies with limited external validation. Low-resource settings, especially in Sub-Saharan Africa, remain under-represented, and few AI solutions are fully embedded in electronic health records. Conclusions: AI holds transformative promise for perinatal care but will require prospective multicenter validation, equity-centered design, robust governance, transparent fairness audits, and seamless electronic health record integration to translate these innovations into routine practice and improve maternal and neonatal outcomes. Full article

The increasing need for sustainable valorization of fossil-based and waste-derived materials has gained interest in converting complex organic matrices such as kerogen into valuable chemicals. This study explores a two-step oxidative strategy to decompose and valorize kerogen-rich oil shale, aiming to develop a locally based source of aliphatic dicarboxylic acids (DCAs). The method combines air oxidation with subsequent nitric acid treatment to enable selective breakdown of the organic structure under milder conditions. Air oxidation was conducted at 165–175 °C using 1% KOH as an alkaline promoter and 40 bar oxygen pressure (or alternatively 185 °C at 30 bar), targeting 30–40% carbon conversion. The resulting material was then subjected to nitric acid oxidation using an 8% HNO₃ solution. This approach yielded up to 23% DCAs, with pre-oxidation allowing a twofold reduction in acid dosage while maintaining efficiency. However, two-step oxidation was still accompanied by substantial degradation of the structure, resulting in elevated CO₂ formation, highlighting the need to balance conversion and carbon retention. The process offers a possible route for transforming solid fossil residues into useful chemical precursors and supports the advancement of regionally sourced, sustainable DCA production from unconventional raw materials. Full article

The report examines food availability and demand in the Anthropocene era, exploring the connections between global population growth and carrying capacity through an extended version of Cohen’s Condorcet concept. It recalls the super-Malthus and Verhulst-type scalings, matched with the recently introduced analytic relative growth rate. It focuses particularly on the ongoing Fifth Industrial Revolution (IR) and its interaction with the concept of a sustainable civilization. In this context, the significance of innovative food preservation technologies that can yield high-quality foods with health-promoting features, while simultaneously increasing food quantities and reducing adverse environmental impacts, is discussed. To achieve this, high-pressure preservation and processing (HPP) can play a dominant role. High-pressure ‘cold pasteurization’, related to room-temperature processing, has already achieved a global scale. Its superior features are notable and are fairly correlated with social expectations of a sustainable society and the technological tasks of the Fifth Industrial Revolution. The discussion is based on the authors’ experiences in HPP-related research and applications. The next breakthrough could be HPP-related sterilization. The innovative HPP path, supported by the colossal barocaloric effect, is presented. The mass implementation of pressure-related sterilization could lead to milestone societal, pro-health, environmental, and economic benefits. Full article

The integration of computational intelligence techniques into pharmaceutical wastewater treatment offers promising opportunities to improve process efficiency and minimize operational costs. This study compares the predictive capabilities of Response Surface Methodology (RSM) and Artificial Neural Network (ANN) models in forecasting the rejection efficiencies of caffeine and paracetamol using AFC 40 and AFC 80 nanofiltration (NF) membranes. Experiments were conducted under varying operating conditions, including transmembrane pressure, feed concentration, and flow rate. The predictive performance of both models was evaluated using statistical metrics such as the Coefficient of Determination (R²), Root Mean Square Error (RMSE), Marquardt’s Percentage Squared Error Deviation (MPSED), Hybrid fractional error function (HYBRID), and Average Absolute Deviation (AAD). Both models demonstrated strong predictive accuracy, with R² values of 0.9867 and 0.9832 for RSM and ANN, respectively, in AFC 40 membranes, and 0.9769 and 0.9922 in AFC 80 membranes. While both approaches closely matched the experimental results, the ANN model consistently yielded lower error values and higher R² values, indicating superior predictive performance. These findings support the application of ANNs as a robust modelling tool in optimizing NF membrane processes for pharmaceutical removal. Full article

The Qinghai–Tibetan Plateau (QTP), often referred to as the “Third Pole” of the world, harbors alpine grassland ecosystems that play an essential role as global carbon sinks, helping to mitigate the pace of climate change. Nonetheless, alterations in natural environmental conditions coupled with escalating human activities have disrupted the seasonal growth cycles of grasslands, thereby intensifying degradation processes. To date, the key drivers and lifecycle dynamics of Grassland Depletion across the QTP remain contentious, limiting our comprehension of its ecological repercussions and regulatory mechanisms. This study comprehensively investigates grassland degradation on the Qinghai–Tibetan Plateau, analyzing its drivers and changes in ecological suitability during the growing season. By integrating natural factors (e.g., precipitation and temperature) and anthropogenic influences (e.g., population density and grazing intensity), it examines observational data from over 160 monitoring stations collected between the 1980s and 2020. The findings reveal three distinct phases of grassland degradation: an acute degradation phase in 1990 (GDI, Grassland Degradation Index = 2.53), a partial recovery phase from 1996 to 2005 (GDI < 2.0) during which the proportion of degraded grassland decreased from 71.85% in 1990 to 51.22% in 2005, and a renewed intensification of degradation after 2006 (GDI > 2.0), with degraded grassland areas reaching 56.39% by 2020. Among the influencing variables, precipitation emerged as the most significant driver, interacting closely with anthropogenic factors such as grazing practices and population distribution. Specifically, the combined impacts of precipitation with population density, grazing pressure, and elevation were particularly notable, yielding interaction q-values of 0.796, 0.767, and 0.752, respectively. Our findings reveal that while grasslands exhibit superior carbon sink potential relative to forests, their productivity and ecological functionality are undergoing considerable declines due to the compounded effects of multiple interacting factors. Consequently, the spatial distribution of ecologically suitable zones has contracted significantly, with the remaining high-suitability regions concentrating in the “twin-star” zones of Baingoin and Zanda grasslands, areas recognized as focal points for future ecosystem preservation. Furthermore, the effects of climate change and intensifying anthropogenic activity have driven the reduction in highly suitable grassland areas, shrinking from 41,232 km² in 1990 to 24,485 km² by 2020, with projections indicating a further decrease to only 2844 km² by 2060. This study sheds light on the intricate mechanisms behind Grassland Depletion, providing essential guidance for conservation efforts and ecological restoration on the QTP. Moreover, it offers theoretical underpinnings to support China’s carbon neutrality and peak carbon emission goals. Full article

In deep, geologically complex environments characterized by high in situ stress and elevated formation temperatures, the mechanical behavior of rocks often transitions from brittle to ductile, differing significantly from that of shallow formations. Traditional rock failure criteria frequently fail to accurately assess the strength of rocks under such deep conditions. To address this, a novel failure criterion suitable for high-temperature and high-pressure conditions has been developed by modifying the Mohr–Coulomb criterion. This criterion incorporates a quadratic function of confining pressure to account for the attenuation rate of strength increase under high confining pressure and a linear function of temperature to reflect the linear degradation of strength at elevated temperatures. This criterion has been used to predict the strength of granite, shale, and carbonate rocks, yielding results that align well with the experimental data. The average coefficient of determination (R²) reached 97.1%, and the mean relative error (MRE) was 5.25%. Compared with the Hoek–Brown and Bieniawski criteria, the criterion proposed in this study more accurately captures the strength characteristics of rocks under high-temperature and high-pressure conditions, with a prediction accuracy improvement of 1.70–4.09%, showing the best performance in the case of carbonate rock. A sensitivity analysis of the criterion parameters n and B revealed notable differences in how various rock types respond to these parameters. Among the three rock types studied, granite exhibited the lowest sensitivity to both parameters, indicating the highest stability in the prediction results. Additionally, the predictive outcomes were generally more sensitive to changes in parameter B than in n. These findings contribute to a deeper understanding of rock mechanical behavior under extreme conditions and offer valuable theoretical support for drilling, completion, and stimulation operations in deep hydrocarbon reservoirs. Full article

Marine coral sand–clay mixtures (MCCM) are promising green fill materials in civil engineering projects, where their strength characteristics play a vital role in ensuring structural safety and stability. To investigate these properties, a series of triaxial shear tests were performed under diverse conditions, including variations in asperity spacing, asperity height, the number of reinforcement layers, confining pressure, and axial strain. This experimental campaign yielded a robust strength dataset for MCCM. Utilizing this dataset, several predictive models were developed, including a standard Support Vector Machine (SVM), an SVM optimized via Genetic Algorithm (GA-SVM), an SVM enhanced by Particle Swarm Optimization (PSO-SVM), and a hybrid model incorporating Logical Development Algorithm preprocessing a SVM model (LDA-SVM). Among these models, the LDA-SVM model exhibited the best performance, achieving a test RMSE of 1.67245 and a correlation coefficient (R) of 0.996, demonstrating superior prediction accuracy and strong generalization ability. Sensitivity analyses revealed that asperity spacing, asperity height, and confining pressure are the most influential factors affecting MCCM strength. Moreover, an explicit empirical equation was derived from the LDA-SVM model, allowing practitioners to estimate strength without relying on complex machine learning tools. The results of this study offer practical guidance for the optimized design and safety evaluation of MCCM in civil engineering applications. Full article

The pisco industry generates significant environmental waste, particularly grape pomace, which is a rich source of phenolic compounds. Emerging extraction technologies offer promising alternatives for recovering these bioactive components. This study evaluated enzyme-assisted extraction (EAE) and pressurized liquid extraction (PLE) techniques using response surface methodology to optimize phenolic compound yield and antioxidant capacity. Specifically, a D-optimal design was applied for EAE, and a Box–Behnken design was applied for PLE. The optimal extraction conditions for EAE were 0.75 U/mL of tannase, 40 U/mL of cellulase, 20 °C, and 15 min. For PLE, the optimal parameters were 54% ethanol, 113 °C, and three extraction cycles. These conditions yielded 38.49 mg GAE g⁻¹ dw and 50.03 mg GAE g⁻¹ dw of total polyphenols and antioxidant capacities of 342.47 μmol TE g⁻¹ dw and 371.00 μmol TE g⁻¹ dw, respectively. The extracts obtained under optimal conditions were further characterized through chromatographic techniques to determine their phenolic profiles. Seven phenolic compounds were identified: gallic acid, catechin, epicatechin, 4-hydroxybenzoic acid, quercetin-3-rutinoside hydrate, quercetin-3-O-rhamnoside, and kaempferol. PLE extracts exhibited the highest concentration of these compounds. These findings demonstrate that recovering antioxidant-rich phenolic compounds from pisco grape pomace using innovative extraction methods is a viable strategy for obtaining functional ingredients and supporting sustainable industrial practices. Full article

Background and Objectives: Idiopathic normal-pressure hydrocephalus (NPH) is a treatable, but diagnostically challenging condition in the elderly marked by gait disturbance, cognitive decline, and urinary incontinence. Ventriculoperitoneal (VP) shunting is effective, but the prognostic significance of symptom duration before surgery remains unclear. This systematic review evaluates symptom duration in NPH patients with postoperative outcomes. Methods: A systematic search of PubMed, Scopus, and Embase was conducted per PRISMA guidelines. Studies were included if they assessed clinical or radiological outcomes of VP shunting in adult NPH patients, reported symptom duration, and had a follow-up of at least one month. Clinical outcomes (MMSE, TUG, NPH score) were qualitatively analyzed due to study heterogeneity. Results: Twenty-four studies comprising 1169 patients were included (mean age: 72.45 years; mean symptom duration: 33.04 months). Most studies reported clinical improvement after VP shunting. However, few directly evaluated the effect of symptom duration, yielding inconsistent findings: some suggested better outcomes with shorter symptom duration, while others found no clear correlation. Larger studies often lacked conclusive data, and no randomized controlled trials were identified. Conclusions: VP shunting remains an effective intervention for NPH; however, evidence supporting the predictive value of preoperative symptom length is inconclusive. This review highlights the need for standardized diagnostic protocols and larger prospective studies to clarify this association and optimize surgical timing. Full article

In recent years, global agriculture has encountered several challenges exacerbated by the effects of changes in climate, such as extreme water shortages for irrigation and heat waves. Water-deficit stress adversely affects the morpho-physiology of numerous crops, including soybean (Glycine max L.), which is considered as promising crop in Bangladesh. Seaweed extract (SWE) has the potential to improve crop yield and alleviate the adverse effects of water-deficit stress. Remote and proximal sensing are also extensively utilized in estimating morpho-physiological traits owing to their cost-efficiency and non-destructive characteristics. The study was carried out to evaluate soybean morpho-physiological traits under the application of water extracts of Gracilaria tenuistipitata var. liui (red seaweed) with two varying irrigation water conditions (100% of total crop water requirement (TCWR) and 70% of TCWR). Principal component analysis (PCA) revealed that among the four treatments, the 70% irrigation + 5% (v/v) SWE and the 100% irrigation treatments overlapped, indicating that the application of SWE effectively mitigated water-deficit stress in soybeans. This result demonstrates that the foliar application of 5% SWE enabled soybeans to achieve morpho-physiological performance comparable to that of fully irrigated plants while reducing irrigation water use by 30%. Based on Pearson’s correlation matrix, a simple linear regression model was used to ascertain the relationship between unmanned aerial vehicle (UAV)-derived vegetation indices and the field-measured physiological characteristics of soybean. The Normalized Difference Red Edge (NDRE) strongly correlated with stomatal conductance (R² = 0.76), photosystem II efficiency (R² = 0.78), maximum fluorescence (R² = 0.64), and apparent transpiration rate (R² = 0.69). The Soil Adjusted Vegetation Index (SAVI) had the highest correlation with leaf relative water content (R² = 0.87), the Blue Normalized Difference Vegetation Index (bNDVI) with steady-state fluorescence (R² = 0.56) and vapor pressure deficit (R² = 0.74), and the Green Normalized Difference Vegetation Index (gNDVI) with chlorophyll content (R² = 0.73). Our results demonstrate how UAV and physiological data can be integrated to improve precision soybean farming and support sustainable soybean production under water-deficit stress. Full article

The accurate prediction of carbon dioxide (CO₂) adsorption in metal–organic frameworks (MOFs) is critical for accelerating the discovery of high-performance materials for post-combustion carbon capture. Experimental screening of MOFs is often costly and time-consuming, creating a strong incentive to develop reliable data-driven models. Despite extensive research, most studies rely on standalone models or generic ensemble strategies that fall short in handling the complex, nonlinear relationships inherent in adsorption data. In this study, a novel ensemble learning framework is developed by integrating five distinct regression algorithms: Random Forest, XGBoost, LightGBM, Support Vector Regression, and Multi-Layer Perceptron. These algorithms are combined into four custom ensemble strategies: equal-weighted voting, performance-weighted voting, stacking, and manual blending. A dataset comprising 1212 experimentally validated MOF entries with input descriptors including BET surface area, pore volume, pressure, temperature, and metal center is used to train and evaluate the models. The stacking ensemble yields the highest performance, with an R² of 0.9833, an RMSE of 1.0016, and an MAE of 0.6630 on the test set. Model reliability is further confirmed through residual diagnostics, prediction intervals, and permutation importance, revealing pressure and temperature to be the most influential features. Ablation analysis highlights the complementary role of all base models, particularly Random Forest and LightGBM, in boosting ensemble performance. This study demonstrates that custom ensemble learning strategies not only improve predictive accuracy but also enhance model interpretability, offering a scalable and cost-effective tool for guiding experimental MOF design. Full article

According to the literature, Schinus molle (SM) is an important source of bioactive phytochemicals, but the phytochemical content and composition of this species, which grows in high Andean geographic zones such as Tarma (Peru), is not known. In an effort to fill this gap, our work investigated the supercritical carbon dioxide extraction of SM leaves at three temperature levels (35, 45, and 55 °C) and three pressure levels (150, 250, and 350 bar). The results revealed the highest yield of extract at 150 bar, 45 °C, and 3.28 g CO₂/min. Under these conditions, the overall extraction curves (OEC) were modeled using the Spline, logistic, and Esquível models, allowing the generation of mass transfer parameters for SFE at the optimized conditions, resulting in a similar correlation with experimental data. Twenty-six compounds were identified in the SFE extract of SM leaves. The most abundant compound classes were sesquiterpenoids (57.17%), sesquiterpenes (24.50%), and triterpenoids (10.48%); of each class, the most abundant compounds were shyobunol (33.60%), bicyclogermacrene (12.68%), and lupeone (6.58%), respectively. The compounds detected possess bioactive properties that support further studies on the application of SFE extracts of SM as a functional ingredient in commercial products. Full article