Search Results (1,306)

Background/Objectives: Screening for late-onset and term preeclampsia (PE) is essential, as the early identification of women at high risk enables closer monitoring and reduces adverse outcomes. The existing algorithms combining maternal factors, biophysical and biochemical markers have not been validated outside the populations in which they were originally developed. This study aimed to evaluate the predictive performance of the Fetal Medicine Foundation (FMF) third-trimester algorithm in our population and develop a novel model to improve the predictions. Methods: An observational, analytical, prospective cohort follow-up study was conducted at the Health Department of Alicante, Dr. Balmis General University Hospital, including 1580 singleton pregnancies recruited between February 2022 and November 2023 during routine third-trimester ultrasounds. Maternal clinical characteristics, blood pressure, the uterine artery pulsatility index (UtA-PI), and the sFlt-1/PlGF ratio were recorded. The FMF third-trimester algorithm was retrospectively applied at the end of pregnancy using clinical, biophysical, and biochemical data from 30 + 0 to 37 + 6 weeks via the freely accessible online calculator. The data analysis was performed using SPSS v.28 and R v.4.3.1. Results: A total of 1580 women were included, with a prevalence of late-onset PE of 2.9%. The FMF model achieved an area under the curve (AUC) of 0.87 (95% CI: 0.81–0.92), while our own model showed a superior performance, with an AUC of 0.94 (95% CI: 0.92–0.97). Conclusions: The FMF third-trimester algorithm demonstrated a good predictive performance for late-onset PE. Our newly developed model achieves an even higher predictive accuracy and offers a simplified approach to excluding the UtA-PI, which facilitates its use in routine clinical practice. Full article

In Northeast Brazil, climatic factors and technology synergistically enhance melon productivity and fruit quality. However, the region requires further research on the efficient use of water resources, particularly in determining the crop coefficient (Kc), which comprises the evaporation coefficient (Ke) and the transpiration coefficient (Kcb). Air temperature affects crop growth and development, altering the spectral response and the Kcb. However, the direct influence of air temperature on Kcb and spectral response remains underemphasized. This study employed unmanned aerial vehicle (UAV) with RGB and Red-Green-NIR sensors imagery to extract biophysical parameters for improved water management in melon cultivation in semiarid northern Bahia. Field experiments were conducted during two distinct periods: warm (October–December 2019) and cool (June–August 2020). The ‘Gladial’ and ‘Cantaloupe’ cultivars exhibited higher Kcb values during the warm season (2.753–3.450 and 3.087–3.856, respectively) and lower during the cool season (0.815–0.993 and 1.118–1.317). NDVI-based estimates of Kcb showed strong correlations with field data (r > 0.80), confirming its predictive potential. The results demonstrate that UAV-derived NDVI enables reliable estimation of melon Kcb across seasons, supporting its application for evapotranspiration modeling and precision irrigation in the Brazilian semiarid context. Full article

Breast cancer continues to be the leading cancer diagnosis among women worldwide, affecting populations in both industrialized and developing regions. Given the rising number of diagnosed cases each year, there is an urgent need to explore novel compounds with potential anticancer properties. One group of such candidates includes cationic peptides, which have shown promise due to their unique membrane-targeting mechanisms that are difficult for cancer cells to resist. This study presents an initial biophysical assessment of NA-CATH:ATRA-1-ATRA-1, a synthetic peptide modeled after NA-CATH, originally sourced from the venom of the Chinese cobra (Naja atra). The peptide’s interactions with lipid bilayers mimicking cancerous and healthy cell membranes were examined using differential scanning calorimetry and Fourier-transform infrared spectroscopy. Findings revealed a pronounced affinity of NA-CATH:ATRA-1-ATRA-1 for eukaryotic membrane lipids, particularly phosphatidylserine, indicating that its mechanism likely involves electrostatic attraction to negatively charged lipids characteristic of cancer cell membranes. Such biophysical insights are vital for understanding how membrane-active peptides could be harnessed in future cancer therapies. Full article

This research introduces a hybrid framework that integrates stochastic modeling and machine learning for predicting postprandial glucose levels in individuals with Type 1 Diabetes (T1D). The primary aim is to enhance the accuracy of glucose predictions by merging a biophysical Glucose–Insulin–Meal (GIM) model with advanced machine learning techniques. This framework is tailored to utilize the Kaggle BRIST1D dataset, which comprises real-world data from continuous glucose monitoring (CGM), insulin administration, and meal intake records. The methodology employs the GIM model as a physiological prior to generate simulated glucose and insulin trajectories, which are then utilized as input features for the machine learning (ML) component. For this component, the study leverages the Light Gradient Boosting Machine (LightGBM) due to its efficiency and strong performance with tabular data, while Long Short-Term Memory (LSTM) networks are applied to capture temporal dependencies. Additionally, Bayesian regression is integrated to assess prediction uncertainty. A key advancement of this research is the transition from a deterministic GIM formulation to a stochastic differential equation (SDE) framework, which allows the model to represent the probabilistic range of physiological responses and improves uncertainty management when working with real-world data. The findings reveal that this hybrid methodology enhances both the precision and applicability of glucose predictions by integrating the physiological insights of Glucose Interaction Models (GIM) with the flexibility of data-driven machine learning techniques to accommodate real-world variability. This innovative framework facilitates the creation of robust, transparent, and personalized decision-support systems aimed at improving diabetes management. Full article

Background: There is an emerging need for new scalable behavioral assays, i.e., assays that are feasible to administer from the comfort of the person’s home, with ease and at higher frequency than clinical visits or visits to laboratory settings can afford us today. This need poses several challenges which we address in this work along with scalable solutions for behavioral data acquisition and analyses aimed at diversifying various populations under study here and to encourage citizen-driven participatory models of research and clinical practices. Methods: Our methods are centered on the biophysical fluctuations unique to the person and on the characterization of behavioral states using standardized biorhythmic time series data (from kinematic, electrocardiographic, voice, and video-based tools) in naturalistic settings, outside a laboratory environment. The methods are illustrated with three representative studies (58 participants, 8–70 years old, 34 males, 24 females). Data is presented across the nervous systems under a proposed functional taxonomy that permits data organization according to nervous systems’ maturation and decline levels. These methods can be applied to various research programs ranging from clinical trials at home, to remote pedagogical settings. They are aimed at creating new standardized biometric scales to screen and diagnose neurological disorders across the human lifespan. Results: Using this remote data collection system under our new unifying statistical platform for individualized behavioral analysis, we characterize the digital ranges of biophysical signals of neurotypical participants and report departure from normative ranges in neurodevelopmental and neurodegenerative disorders. Each study provides parameter spaces with self-emerging clusters whereby data points corresponding to a cluster are probability distribution parameters automatically classifying participants into different continuous Gamma probability distribution families. Non-parametric analysis reveals significant differences in distributions’ shape and scale (p < 0.01). Data reduction is realizable from full probability distribution families to a single parameter, the Gamma scale, amenable to represent each participant within each subclass, and each cluster of similar participants within each cohort. We report on data integration from stochastic analyses that serve to differentiate participants and propose new ways to highly scale our research, education, and clinical practices. Conclusions: This work highlights important methodological and analytical techniques for developing personalized and scalable biometrics across various populations outside a laboratory setting. Full article

Our understanding of the molecular mechanisms by which DNA meiotic recombination occurs has significantly increased in the past decades. A more representative molecular model has also undergone repeated revisions and upgrades with the continuous expansion of experimental data. Considering several apparent issues in the field, we intend to make necessary upgrades to previous models and reanalyze those data, exploring structural details and molecular mechanisms of DNA meiotic recombination. Eligible studies were identified from PubMed/Medline (up to June 2024). Key related publications and experimental data were retrieved from eligible studies, displaying five major issues. Meanwhile, the biophysical modeling method was used to establish an enlacement model. Then, the model was used to wholly reanalyze the collected data. An updated molecular model was supplemented. In the current model, a copy choice mechanism can initiate DNA meiotic recombination. The copy choice is based on a branched structure of DNA, which results from relative motion between homologous single strands. The reanalysis of previous experimental data based on this model can lead to new interpretations that can better address the discrepancies between previous experimental observations and theoretical models, including (1) the intertwinement model having embodied the particular characteristics of the SDSA model; (2) hDNA arising from JM resolution rather than being followed by a JM; (3) strand specificity of hDNA mismatch repair seeming to be an illusion and copy choice more likely to be the actual state; (4) parity in resolution patterns of a dHJ leading to parity of gene conversion; (5) the cooperation of multiple HJs readily generating a high correlation between gene conversion and crossover; and (6) transpositional recombination and site-specific recombination seeming to have a common pathway to meiotic recombination. The results indicate that both revisions and reanalysis are necessary. The novel interpretations would be critical to the understanding of the mechanisms of DNA recombination as well as its role in DNA repair. Additionally, the work could have implications for how the field views the importance of factors such as Spo11 or the mechanisms that drive meiotic pairing. Full article

Environmental degradation and soil desertification are among the most severe environmental issues of recent decades worldwide. Over time, these processes have led to increasingly extreme and highly dynamic climatic conditions. In Brazil, the Northeast Region is characterized by semi-arid and arid areas that exhibit high climatic variability and are extremely vulnerable to environmental changes and pressures from human activities. The application of geotechnologies and geographic information system (GIS) modeling is essential to mitigate the impacts and pressures on the various ecosystems of Northeastern Brazil (NEB), where the Caatinga biome is predominant and critically threatened by these factors. In this context, the objective was to map and assess the spatiotemporal patterns of land use and land cover (LULC), detecting significant trends of loss and gain, based on surface reflectance data and precipitation data over two decades (2000–2019). Remote sensing datasets were utilized, including Landsat satellite data (LULC data), MODIS sensor data (surface reflectance product) and TRMM data (precipitation data). The Google Earth Engine (GEE) software was used to process orbital images and determine surface albedo and acquisition of the LULC dataset. Satellite data were subjected to multivariate analysis, descriptive statistics, dispersion and variability assessments. The results indicated a significant loss trend over the time series (2000–2019) for forest areas (Z_MK = −5.872; Tau = −0.958; p < 0.01) with an annual loss of −3705.853 km² and a total loss of −74,117.06 km². Conversely, farming areas (agriculture and pasture) exhibited a significant gain trend (Z_MK = 5.807; Tau = 0.947; p < 0.01), with an annual gain of +3978.898 km² and a total gain of +79,577.96 km², indicating a substantial expansion of these areas over time. However, it is important to emphasize that deforestation of the region’s native vegetation contributes to reduced water production and availability. The trend analysis identified an increase in environmental degradation due to the rapid expansion of land use. LULC and albedo data confirmed the intensification of deforestation in the Northern, Northwestern, Southern and Southeastern regions of NEB. The Northwestern region was the most directly impacted by this increase due to anthropogenic pressures. Over two decades (2000–2019), forested areas in the NEB lost approximately 80.000 km². Principal component analysis (PCA) identified a significant cumulative variance of 87.15%. It is concluded, then, that the spatiotemporal relationship between biophysical conditions and regional climate helps us to understand and evaluate the impacts and environmental dynamics, especially of the vegetation cover of the NEB. Full article

Climate change is intensifying droughts, heatwaves, dust storms, and rainfall variability across Eastern Europe, undermining yields and soil stability. In Ukraine, decades of underinvestment and wartime damage have led to widespread degradation of field shelterbelts, while the adoption of agroforestry remains constrained by tenure ambiguity, fragmented responsibilities, and limited access to finance. This study develops a policy-and-technology framework to restore agroforestry at scale under severe fiscal and institutional constraints. We apply a three-stage approach: (i) a national baseline (post-1991 legislation, statistics) to diagnose the biophysical and legal drivers of shelterbelt decline, including wartime damage; (ii) a comparative synthesis of international support models (governance, incentives, finance); and (iii) an assessment of transferability of digital monitoring, reporting, and verification (MRV) tools to Ukraine. We find that eliminating tenure ambiguities, introducing targeted cost sharing, and enabling access to payments for ecosystem services and voluntary carbon markets can unlock financing at scale. A digital MRV stack—Earth observation, UAV/LiDAR, IoT sensors, and AI—can verify tree establishment and survival, quantify biomass and carbon increments, and document eligibility for performance-based incentives while lowering transaction costs relative to field-only surveys. The resulting sequenced policy package provides an actionable pathway for policymakers and donors to finance, monitor, and scale shelterbelt restoration in Ukraine and in similar resource-constrained settings. Full article

Proton therapy enables precise dose delivery to tumors while sparing healthy tissues, offering significant advantages over conventional radiotherapy. Accurate prediction of biological doses requires detailed knowledge of radiation interactions with biological targets, especially DNA, a key site of radiation-induced damage. While most biophysical models (LEM, mMKM, NanOx) rely on water as a surrogate, this simplification neglects the complexity of real biomolecules. In this work, we calculate the stopping power and range of protons in liquid water, dry DNA, and hydrated DNA using semi-empirical cross sections for ionization, electronic excitation, electron capture, and electron loss by protons and neutral hydrogen in the 10 keV–100 MeV energy range. Additionally, ionization cross sections for uracil are computed to explore potential differences between DNA and RNA damage. Our results show excellent agreement with experimental and ab initio data, highlighting significant deviations in stopping power and range between water and DNA. Notably, the stopping power of DNA exceeds that of water at most energies, reducing proton ranges in dry and hydrated DNA by up to 20% and 26%, respectively. These findings provide improved input for Monte Carlo simulations and biophysical models, enhancing RBE predictions and dose accuracy in hadrontherapy. Full article

The development of metallopharmaceuticals for diabetes treatment has garnered increasing attention due to its insulin-mimetic properties, particularly in vanadium complexes. In this study, we report the biophysical evaluation of a series of 3-hydroxy-4-pyridinone (3,4-HPO) vanadium complexes, designed to improve lipophilicity and biological cytocompatibility. Dynamic light scattering (DLS) was used to get insight on the size of the liposomes and Differential Scanning Calorimetry (DSC) was employed to investigate the interaction of these complexes with model biological membranes made from dimyristoylphosphatidylcholine (DMPC) unilamellar liposomes. The thermotropic phase behavior of the lipid bilayers was analyzed in the presence of vanadium complexes. The results reveal that the alkyl chain length of the 3,4-HPO ligands modulates membrane interaction of the respective vanadium compounds, with specific complexes inducing significant shifts in the lipid phase transition temperature (T_m), suggesting alterations in membrane fluidity and packing. These findings provide valuable insight into the membrane affinity of vanadium-based drug candidates and support their potential as next-generation antidiabetic agents. Full article

Fodder maize constitutes a key economic crop in Thailand, particularly in the northeastern region, where it significantly contributes to livestock feed production and local economic development. Nevertheless, the planning and management of cultivation areas remain a major challenge, especially in urban and peri-urban agricultural zones, due to the limited availability of spatial data and suitable analytical frameworks. These difficulties are exacerbated in urban settings, where the complexity of land use patterns and high spectral similarity among land cover types hinder accurate classification. The Google Earth Engine (GEE) platform provides an efficient and scalable solution for geospatial data processing, enabling rapid land use classification and spatiotemporal analysis. This study aims to enhance the classification accuracy of fodder maize cultivation areas in Mueang District, Nakhon Ratchasima Province, Thailand—an area characterized by a heterogeneous mix of urban development and agricultural land use. The research integrates GEE with four machine learning algorithms: Random Forest (RF), Support Vector Machine (SVM), Naïve Bayes (NB), and Classification and Regression Trees (CART). Eleven datasets were developed using Sentinel-2 imagery and a combination of biophysical variables, including elevation, slope, Normalized Difference Vegetation Index (NDVI), Normalized Difference Built-up Index (NDBI), and Modified Normalized Difference Water Index (MNDWI), to classify land use into six categories: fodder maize cultivation, urban and built-up areas, forest, water bodies, paddy fields, and other field crops. Among the 44 classification scenarios evaluated, the highest performance was achieved using Dataset 11—which integrated all spectral and biophysical variables—with the SVM classifier. This model attained an overall accuracy of 97.41% and a Kappa coefficient of 96.97%. Specifically, fodder maize was classified with 100% accuracy in both Producer’s and User’s metrics, as well as a Conditional Kappa of 100%. These findings demonstrate the effectiveness of integrating GEE with machine learning techniques for precise agricultural land classification. This approach also facilitates timely monitoring of land use changes and supports sustainable land management through informed planning, optimized resource allocation, and mitigation of land degradation in urban and peri-urban agricultural landscapes. Full article

Organoids, self-organizing, three-dimensional (3D) multicellular structures derived from tissues or stem cells, offer physiologically relevant models for studying human development and disease. Compared to conventional two-dimensional (2D) cell cultures and animal models, organoids more accurately recapitulate the architecture and function of human organs. Among the critical microenvironmental cues influencing organoid behavior, hypoxia and multilineage communication are particularly important for guiding cell fate, tissue organization, and pathological modeling. Hypoxia, primarily regulated by hypoxia-inducible factors (HIFs), modulates cellular proliferation, differentiation, metabolism, and gene expression, making it a key component in disease modeling. Similarly, multilineage communication, facilitated by intercellular interactions and extracellular matrix (ECM) remodeling, enhances organoid complexity and immunological relevance. This review explores the dynamic interplay between hypoxia and multilineage signaling in 3D organoid-based disease models, emphasizing recent advances in engineering hypoxic niches and co-culture systems to improve preclinical research fidelity. We also discuss their translational implications for drug screening, regenerative medicine, and precision therapies, while highlighting current challenges and future opportunities. By integrating biophysical, biochemical, and computational approaches, next-generation organoid models may be further optimized for translational research and therapeutic innovation. Full article

The mammalian epidermis forms a critical barrier against environmental insults and water loss. The formation of its outermost layer, the stratum corneum, involves a rapid terminal differentiation process that has traditionally been explained by the “bricks and mortar” model. Recent advances reveal a more dynamic mechanism governed by intracellular liquid–liquid phase separation (LLPS). This review proposes that the lifecycle of the granular layer is orchestrated by LLPS. Evidence is synthesized showing that keratohyalin granules (KGs) are biomolecular condensates formed by the phase separation of the intrinsically disordered protein filaggrin (FLG). The assembly, maturation, and pH-triggered dissolution of these condensates are essential for cytoplasmic remodeling and the programmed flattening of keratinocytes, a process known as corneoptosis. In parallel, an LLPS-based signaling pathway is described in which the kinase RIPK4 forms condensates that activate the Hippo pathway, promoting transcriptional reprogramming and differentiation. Together, these structural and signaling condensates drive skin barrier formation. This review further reinterprets atopic dermatitis, ichthyosis vulgaris, and Bartsocas-Papas syndrome as diseases of aberrant phase behavior, in which pathogenic mutations alter condensate formation or material properties. This integrative framework offers new insight into skin biology and suggests novel opportunities for therapeutic intervention through biophysics-informed biomaterial and regenerative design. Full article

Rhodopsin, the prototypical Class A G protein-coupled receptor (GPCR) and visual pigment of rod photoreceptors, has long served as a structural and mechanistic model for GPCR biology. Mutations in rhodopsin are the leading cause of autosomal dominant retinitis pigmentosa (adRP), making this receptor a critical therapeutic target. In this review, we summarize the chemical, structural, and biophysical features of small-molecule modulators of this receptor, spanning both classical retinoid analogs and emerging non-retinoid scaffolds. These ligands reveal recurrent binding modes within the orthosteric chromophore pocket as well as peripheral allosteric and bitopic sites, where they mediate folding, rescue trafficking, photocycle modulation, and mutant stabilization. We organize ligand performance into a three-tier framework linking binding affinity, cellular rescue potency, and stability gains. Chemotypes in tier 2, which show sub-micromolar to low-micromolar activity with broad mutant coverage, emerge as promising candidates for optimization into next-generation scaffolds. Across scaffolds, a recurring minimal pharmacophore is evident by a contiguous hydrophobic π-surface anchored in the β-ionone region, coupled with a strategically oriented polar handle that modulates the Lys296/Glu113 microenvironment, offering tractable design vectors for non-retinoid chemotypes. Beyond the chromophore binding pocket, we highlight opportunities to exploit extracellular loop epitopes, cytoplasmic microswitch clefts, dimer/membrane interfaces, and ion co-binding sites to engineer safer, state-biased control with fewer photochemical liabilities. By integrating rhodopsin photobiophysics with environment-aware, multi-state medicinal chemistry, and by addressing current translational challenges in drug delivery, this review outlines a rational framework for advancing rhodopsin-targeted therapeutics toward clinically credible interventions for RP and related retinal degenerations. Full article

Antimicrobial peptides (AMPs) are crucial components of innate immunity and promising leads for new anti-infective therapies, prized for their broad-spectrum activity and membrane-disruptive mechanisms. However, traditional models of antimicrobial action and resistance often focus on single-cell responses or genetically encoded resistance, overlooking the complex collective behaviors of bacteria at the population level. A growing body of evidence indicates that bacterial communities can profoundly influence AMP efficacy through emergent, community-level resistance mechanisms. In this review, we examine how population-level dynamics and interactions enable bacteria to withstand AMPs beyond what is predicted by cell-autonomous models. We first describe the mechanisms of peptide sequestration by bacterial debris, dead cells, outer membrane vesicles, and biofilm matrix polymers, which diminish the concentration of active peptide available to kill neighboring cells. We then analyze how population-level traits—including inoculum effects, phenotypic heterogeneity, and persister subpopulations—shape survival outcomes and promote regrowth after treatment. Cooperative processes such as protease secretion further enhance communal defenses by coordinating or amplifying protective responses. Beyond cataloging these mechanisms, we highlight recent advances in microfluidic tools, single-cell imaging, and biophysical modeling that reveal the spatial and temporal dynamics of AMP action in structured populations. Collectively, these insights show how bacterial communities absorb, neutralize, or delay AMP activity without genetic resistance, with important implications for therapeutic design and the evaluation of AMP efficacy. Full article