Search Results (508)

Background: The Nerve Growth Factor (NGF) is essential for neuronal survival and function and represents a key therapeutic target for pain and inflammation-related disorders, as well as for neurodegenerative diseases. Small-molecule antagonists of human NGF (hNGF) offer advantages over monoclonal antibodies, including oral availability and reduced immunogenicity. However, their development is often hindered by solubility challenges, necessitating the use of solvents like dimethyl sulfoxide (DMSO). This study investigates whether DMSO directly interacts with hNGF and affects its receptor-binding properties. Methods: Integrative/hybrid computational and experimental biophysical approaches were used to assess DMSO-NGF interaction by combining machine-learning tools and Nuclear Magnetic Resonance (NMR), Fourier Transform Infrared (FT-IR) spectroscopy, Differential Scanning Fluorimetry (DSF) and Grating-Coupled Interferometry (GCI). These techniques evaluated binding affinity, conformational stability, and receptor-binding dynamics. Results: Our findings demonstrate that DMSO binds hNGF with low affinity in a specific yet non-disruptive manner. Importantly, DMSO does not induce significant conformational changes in hNGF nor affect its interactions with its receptors. Conclusions: These results highlight the importance of considering solvent–protein interactions in drug discovery, as these low-affinity yet specific interactions can affect experimental outcomes and potentially alter the small molecules binding to the target proteins. By characterizing DMSO-NGF interactions, this study provides valuable insights for the development of NGF-targeting small molecules, supporting their potential as effective alternatives to monoclonal antibodies for treating pain, inflammation, and neurodegenerative diseases. Full article

Cell membrane capacitance (C_m) is a potential biomarker that reflects the structural and functional integrity of cell membranes. It is essential for physiological processes such as signal transduction, ion transport, and cellular homeostasis. In clinical practice, C_m can be determined using bioimpedance spectroscopy (BIS), a non-invasive technique for analysing the intrinsic electrical properties of biological tissues across a range of frequencies. C_m may be relevant in various clinical fields, where high capacitance is associated with healthy and intact membranes, while low capacitance indicates cellular damage or disease. Despite its promise as a prognostic indicator, several knowledge gaps limit the broader clinical application of C_m. These include variability in measurement techniques (e.g., electrode placement, frequency selection), the lack of standardised measurement protocols, uncertainty on how C_m is related to pathology, and the relatively low amount of C_m research. By addressing these gaps, C_m may become a valuable tool for examining cellular health, early disease detection, and evaluating treatment efficacy in clinical practice. This review explores the fundamental principles of C_m measured with the BIS technique, its mathematical basis and relationship to the biophysical Cole model, and its potential clinical applications. It identifies current gaps in our knowledge and outlines future research directions to enhance the understanding and use of C_m. For example, C_m has shown promise in identifying membrane degradation in sepsis, predicting malnutrition in anorexia nervosa, and as a prognostic factor in cancer. Full article

Canopy chlorophyll content (CCC) is a key indicator for assessing the carbon sequestration capacity and material cycling efficiency of ecosystems, and its accurate retrieval holds significant importance for analyzing ecosystem functioning. Although numerous destructive and remote sensing methods have been developed to estimate CCC, the accurate estimation of CCC remains a significant challenge in mountainous regions with complex terrain and heterogeneous vegetation types. Through the synergistic analysis of ground hyperspectral and Sentinel-2 data, this study employed Pearson correlation analysis and spectral resampling techniques to identify Sentinel-2 blue band B1 (443 nm) and red band B4 (665 nm) as chlorophyll-sensitive bands through spectral matching with the hyperspectral reflectance of typical grassland vegetation. Based on this, we developed a new four-band vegetation index (VI), the Dual Red-edge and Coastal Aerosol Vegetation Index (DRECAVI), for estimating the CCC of heterogeneous grasslands in the middle section of the Tianshan Mountains. DRECAVI incorporates red-edge anti-saturation modules (bands B4 and B7) and aerosol correction modules (bands B1 and B8). In order to test the performance of the new index, we compared it with eight commonly used indices and a hybrid model, the Sentinel-2 Biophysical Processor (S2BP). The results indicated the following: (1) DRECAVI demonstrated the highest accuracy in CCC retrieval for mountainous vegetation (R² = 0.74, RMSE = 16.79, MAE = 12.50) compared to other VIs and hybrid methods, effectively mitigating saturation effects in high biomass areas and capturing a weak bimodal distribution pattern of CCC in the montane meadow. (2) The blue band B1 enhances atmospheric correction robustness by suppressing aerosol scattering, and the red-edge band B7 overcomes the sensitivity limitations of conventional red-edge indices (such as NDVI705, CIred-edge, and NDRE), demonstrating the potential application of the synergy mechanism between the blue band and the red-edge band. (3) Although the S2BP achieved high accuracy (R² = 0.73, RMSE = 19.83, MAE = 14.71) without saturation effects and detected a bimodal distribution of CCC in the montane meadow of the study area, its algorithmic complexity hindered large-scale operational applications. In contrast, DRECAVI maintained similar precision while reducing algorithmic complexity, making it more suitable for regional-scale grassland dynamic monitoring. This study confirms that the synergistic use of multi-source data effectively overcomes the limitations of the spectral–spatial resolution of a single data source, providing a novel methodology for the precision monitoring of mountain ecosystems. Full article

Background/Objective: Small-for-gestational-age (SGA) status constitutes a significant risk factor for adverse neonatal outcomes and predisposes individuals to long-term health complications. Detecting pregnancies at risk early in gestation could significantly improve perinatal outcomes. Recent evidence suggests that ophthalmic artery Doppler assessment in the first trimester may contribute to the prediction of impaired placentation reflected in increased risk for preeclampsia. This study aimed to investigate the association between first-trimester ophthalmic artery Doppler parameters and the subsequent birth of small-for-gestational-age (SGA) neonates. Methods: In this prospective observational analysis, 4054 pregnant women underwent ophthalmic artery Doppler evaluation at 11–13 weeks gestation. Maternal demographics, biophysical and biochemical markers, and ophthalmic artery Doppler measurements of pulsatility index (PI) and peak systolic velocity (PSV) ratio were obtained. Outcomes were classified based on birthweight into the ≤3rd percentile and >3rd percentile and ≤10th percentile and >10th percentile groups. To determine the predictive value of Doppler indices, statistical methods included comparative analyses and the receiver operating characteristic (ROC) curves. Results: The analysis indicated that increased PSV ratio at 11–13 weeks gestation correlated with an increased risk of SGA. The PI was not found to be a significant discriminator between pregnancies complicated by SGA and non-SGA pregnancies. Conclusions: First-trimester ophthalmic artery Doppler assessment offers promise as a non-invasive technique for the early identification of pregnancies at risk for SGA neonates. Further validation through large, multicenter studies is needed to confirm its utility and to standardize its use in clinical protocols. Full article

Acetone carboxylase (AC) from Xanthobacter autotrophicus is a 360 KDa α₂β₂γ₂ heterohexamer that catalyzes the ATP-dependent formation of phosphorylated acetone and bicarbonate intermediates that react at Mn(II) metal active sites to form acetoacetate. Structural models of X. autotrophicus AC (XaAC) with and without nucleotides reveal that the binding and phosphorylation of the two substrates occurs ~40 Å from the Mn(II) active sites where acetoacetate is formed. Based on the crystal structures, a significant conformational change was proposed to open and close a tunnel that facilitates the passage of reaction intermediates between the sites for nucleotide binding and phosphorylation of substrates and Mn(II) sites of acetoacetate formation. We have employed electron paramagnetic resonance (EPR), kinetic assays, and hydrogen/deuterium exchange mass spectrometry (HDX-MS) of poised ligand-bound states and site-specific amino acid variants to complete an in-depth analysis of Mn(II) coordination and allosteric communication throughout the catalytic cycle. In contrast with the established paradigms for carboxylation, our analyses of XaAC suggested a carboxylate shift that couples both local and long-range structural transitions. Shifts in the coordination mode of a single carboxylic acid residue (αE89) mediate both catalysis proximal to a Mn(II) center and communication with an ATP active site in a separate subunit of a 180 kDa α₂β₂γ₂ complex at a distance of 40 Å. This work demonstrates the power of combining structural models from X-ray crystallography with solution-phase spectroscopy and biophysical techniques to elucidate functional aspects of a multi-subunit enzyme. Full article

Introduction: Intermittent claudication, an early symptom of peripheral artery disease, can be treated by cilostazol to alleviate symptoms and improve walking distance. Our previous investigation focused on cilostazol-induced alterations in the thermodynamic properties of plasma, utilizing differential scanning calorimetry (DSC) as a potential monitoring tool. The current proof-of-concept study aimed to enhance the interpretation of DSC data through deconvolution techniques, specifically examining protein transitions within the plasma proteome during cilostazol therapy. Results: Notable differences in thermal unfolding profiles were found between cilostazol-treated patients and healthy controls. The fibrinogen-associated transition exhibited a downward shift in denaturation temperature and decreased enthalpy by the third month. The albumin-related transition shifted to higher temperatures, accompanied by lower enthalpy. Transitions associated with globulins showed changes in thermal stability, while the transferrin-related peak demonstrated increased structural rigidity in treated patients compared to controls. Discussion: These observations suggest that cilostazol induces systemic changes in the thermodynamic behavior of plasma proteins. DSC, when combined with deconvolution methods, presents a promising approach for detecting subtle, therapy-related alterations in plasma protein stability. Materials and methods: Ten patients (median age: 58.6 years) received 100 milligrams of cilostazol twice daily. Blood samples were collected at the baseline and after 2 weeks, 1 month, 2 months, and 3 months of therapy. Walking distances were also assessed. The DSC curves were retrieved from the thermal analysis investigated by deconvolution mathematical methods. Conclusions: Although the exact functional consequences remain unclear, the observed biophysical changes may reflect broader molecular adaptations involving protein–protein interactions, post-translational modifications, or acute phase response elements. Full article

Cholesterol plays an essential role in biological membranes and is crucial for maintaining their stability and functionality. In addition to biological membranes, cholesterol is also used in various synthetic lipid-based structures such as liposomes, proteoliposomes, and nanodiscs. Cholesterol regulates membrane properties by influencing the density of lipids, phase separation into liquid-ordered (Lo) and liquid-disordered (Ld) areas, and stability of protein–membrane interactions. For planar bilayers, cholesterol thickens the membrane, decreases permeability, and brings lipids into well-ordered domains, thereby increasing membrane rigidity by condensing lipid packing, while maintaining lateral lipid mobility in disordered regions to preserve overall membrane fluidity. It modulates membrane curvature in curved bilayers and vesicles, and stabilizes low-curvature regions, which are important for structural integrity. In liposomes, cholesterol facilitates drug encapsulation and release by controlling bilayer flexibility and stability. In nanodiscs, cholesterol enhances structural integrity and protein compatibility, which enables the investigation of protein–lipid interactions under physiological conditions. In proteoliposomes, cholesterol regulates the conformational stability of embedded proteins that have implications for protein–lipid interaction. Developments in molecular dynamics (MD) techniques, from coarse-grained to all-atom simulations, have shown how cholesterol modulates lipid tail ordering, membrane curvature, and flip-flop behavior in response to concentration. Such simulations provide insights into the mechanisms underlying membrane-associated diseases, aiding in the design of efficient drug delivery systems. In this review, we combine results from MD simulations to provide a synoptic explanation of cholesterol’s complex function in regulating membrane behavior. This synthesis combines fundamental biophysical information with practical membrane engineering, underscoring cholesterol’s important role in membrane structure, dynamics, and performance, and paving the way for rational design of stable and functional lipid-based systems to be used in medicine. In this review, we gather evidence from MD simulations to provide an overview of cholesterol’s complex function regulating membrane behavior. This synthesis connects the fundamental biophysical science with practical membrane engineering, which highlights cholesterol’s important role in membrane structure, dynamics, and function and helps us rationally design stable and functional lipid-based systems for therapeutic purposes. Full article

This study investigates urban warming in Rajshahi City, Bangladesh, by examining changes in land surface temperature (LST) from 1990 to 2023 and exploring its relationship with key biophysical factors. LST was derived from Landsat thermal imagery, and both spatial and temporal variations were analyzed using Geographic Information Systems (GIS). Key biophysical indices, including Normalized Difference Vegetation Index (NDVI), Normalized Difference Built-up Index (NDBI), Normalized Difference Water Index (NDWI), Normalized Difference Moisture Index (NDMI), and Normalized Difference Bareness Soil Index (NDBSI), were calculated using corresponding Landsat satellite sensors, and they evaluated the impact of LULC types (vegetation, water, soil, and built-up areas) on thermal variations. LULC was derived following the Support Vector Machine classification technique. The Urban Thermal Field Variance Index (UTFVI) was employed to assess surface urban heat island (SUHI) effects, warming conditions, ecological stress, and thermal comfort zones. Spatial trend and hotspot analyses of LST change were performed using spatial trend analysis and the Getis-Ord Gi* statistic, respectively. Linear regression analysis examined the relationship between LST and biophysical indices. Results show that winter mean LST increased by 2.66 °C during the 33-year period, with maximum LST rising by 4.29 °C. The most significant warming occurred in central-northern, central-western, and south-eastern zones. The rise in LST and the growing intensity of SUHI effects are largely due to urban growth, especially where green spaces and water bodies have been replaced by impervious surfaces. Hotspot analysis identified clusters of high-temperature zones, while UTFVI analysis confirmed a marked expansion of strong heat island conditions, especially in central urban areas. Linear regression results showed notable links between LST and key biophysical variables, where higher LST values were commonly linked to greater built-up density and declines in vegetation cover and surface water. Overall, the results highlight the need for better urban planning approaches such as increasing green cover, using permeable materials, and adopting strategies that can adapt to climate impacts. This study presents a framework for analyzing urban climate dynamics that can be adapted to other rapidly growing cities, aiding efforts to promote sustainable development and build urban resilience. Full article

Natural and human-made disasters, community violence, climate change, and political instability engender mental health problems worldwide. Childhood traumas, now recognized as commonplace and global in nature, augment the urgent need for mental health interventions that are accessible and scalable. The World Health Organization has called for innovative strategies that extend beyond traditional cognitive approaches. Biologically based methods are gaining recognition for their significant role in affect regulation and wellness promotion. This paper explores the potential for interventions focusing on interoceptive awareness, or noticing sensations arising from the body, to address mental health challenges, especially relevant for populations affected by trauma. The Community Resiliency Model (CRM)^®, a low-intensity, body-based intervention that cultivates interoceptive awareness, is described and compared to other well-being interventions. Available research studies, program evaluations and anecdotal reports are presented in addition to CRM’s biological and theoretical underpinnings. The neurobiology of trauma, interoception research, and the concept of neural synchrony are briefly introduced, further explaining the likely mechanism of action and an underlying rationale for the reported improvements in well-being and resilience among individuals and communities who learn CRM body awareness techniques. Given increasing global demand and limited access to conventional mental health services, CRM and the six core skills that are taught in this model offer a promising, transferable, self-care strategy. Community dissemination has the potential to expand access in underserved populations. This review concludes by suggesting future research directions, such as the exploration of biophysical outcomes, intra- and interpersonal synchrony, and evaluation of interoceptive training for emotional regulation and populations affected by trauma or violence. Full article

Background and Objectives: PAX2 serves as a critical transcription factor integral to the process of embryogenesis. Variations in the PAX2 gene could result in the aberrant development of numerous organs. Despite the identification of numerous mutations within the PAX2 gene, the correlation between specific genotypes has yet to be fully clarified. The objective of this study was to examine the clinical phenotypes and genotypes associated with PAX2 mutation-induced disorders in pediatric patients of Chinese descent. The aim of our study was to forecast the pathogenic potential of these genetic mutations and to ascertain possible correlations between genotypic variations and the clinical manifestations of disorders linked to PAX2 mutations. Materials and Methods: We recruited 14 pediatric subjects with PAX2 mutations, meticulously examining the clinical characteristics and genetic alterations present in these individuals. Computational techniques were utilized to evaluate the pathogenicity, stability, and biophysical characteristics. A range of computational tools were employed for this assessment, including PredictSNP, MAGPIE, iStable, Align GVGD, ConSurf, and SNP effect. Results: The age at onset ranged from prenatal to 12 years. Five patients progressed to end-stage renal disease. Proteinuria and bilateral renal hypoplasia were observed in 92% of cases. Ocular and auditory abnormalities were also noted. We identified eleven different PAX2 mutations, including five novel variants not previously reported in the literature. We predicted that all mutations, with the exception of p.F27-L33 del and N188S, exhibited high pathogenicity scores. In particular, R117P and R140W are strongly associated with disease pathogenicity and are likely to cause more significant damage than other gene mutants. Conclusions: This study expands the mutational and phenotypic spectrum of PAX2-related disorders in the pediatric population. The identification of five novel variants enhances our understanding of the genetic basis of these conditions. Despite recurrent mutations, marked phenotypic heterogeneity persists, underscoring the need for further research. Full article

Carbonat—open-water mangroves have high organic carbon (OC) content, apparently due to sediments’ biophysical characteristics. However, the role of key regulators such as salinity and hydroperiod, which modulate the forest structure and, therefore, carbon dynamics, has been little explored. This study evaluates the influence of salinity on the accumulation of aerial and underground OC (production of litter and roots), in open—water karstic forests. To this end, an experimental design was implemented on San Andrés Island, where an edaphic salinity gradient exists due to the water regime. Three physiographic types of mangroves, characterized by different saline regimes, were selected for the study. Two inland forests were selected, both of which exhibited a mesohaline regime (9.63 ± 6.26 and 11.54 ± 7.46 PSU), while a third site corresponded to a euhaline fringe forest (37.47 ± 5.76 PSU). The final location was characterized by a hyperhaline regime basin forest (62.36 ± 10.54 PSU). The fundamental hypothesis posited an inverse relationship between salinity and litter production, and a direct relationship between salinity and root production. To assess root production, the growth core implantation technique (108 soil cores) was employed, with live roots selected based on diameter (<2, 2–5, and 5–20 mm). The mean (±SD) OC content in dry litter ($\mathrm{M}\mathrm{g} \mathrm{C} {\mathrm{h}\mathrm{a}}^{-1}{\mathrm{y}}^{-1}$) was 8.96 ± 0.28; 5.57 ± 0.15; 6.31 ± 0.27; and 4.54 ± 0.8; while The production of dry roots was 0.41 ± 0.08; 1.19 ± 0.46; 1.30 ± 0.5; and 0.24 ± 0.20, for the mesohaline forests, the euhaline forest, and the hyperhaline forest, respectively. The proposed hypotheses were confirmed when considering only the extreme salinity ranges. Upon incorporating all salinity ranges from the four forests into the analysis, it was observed that litter production exhibited a tendency to decrease with increasing salinity, while root production demonstrated a tendency to increase. However, this trend did not attain statistical significance, thereby suggesting that, in addition to salinity, other factors may also regulate production processes. These findings serve to affirm the high productivity of carbonate environments and the contribution of autochthonous production. Full article

Nitroxides are stable organic free radicals with a wide range of applications. They have found applications in chemistry, biochemistry, biophysics, molecular biology, and biomedicine as EPR/NMR imaging techniques. As spin labels and probes, they are used in electron paramagnetic resonance (EPR) spectroscopy in the study of proteins, lipids, nucleic acids, and enzymes, as well as for measuring oxygen concentration in cells and cellular organelles, as well as tissues and intracellular pH. Their unique redox properties have allowed them to be used as exogenous antioxidants. In this review, we have discussed the chemical properties of nitroxides and their antioxidant properties. Furthermore, we have considered their use as radioprotectors and protective agents in ischemia/reperfusion in vivo and in vitro. We also presented other applications of nitroxides in protecting cells and tissues from oxidative stress and in protein studies and discussed their use in EPR/MRI. Full article

Estimating confidence intervals in small or noisy datasets is a recurring challenge in biomolecular research, particularly when data contain outliers or exhibit high variability. This study introduces a robust statistical method that combines a hybrid bootstrap procedure with Steiner’s most frequent value (MFV) approach to estimate confidence intervals without removing outliers or altering the original dataset. The MFV technique identifies the most representative value while minimizing information loss, making it well suited for datasets with limited sample sizes or non-Gaussian distributions. To demonstrate the method’s robustness, we intentionally selected a dataset from outside the biomolecular domain: a fast-neutron activation cross-section of the ¹⁰⁹Ag(n, 2n)^108mAg reaction from nuclear physics. This dataset presents large uncertainties, inconsistencies, and known evaluation difficulties. Confidence intervals for the cross-section were determined using a method called the MFV–hybrid parametric bootstrapping (MFV-HPB) framework. In this approach, the original data points were repeatedly resampled, and new values were simulated based on their uncertainties before the MFV was calculated. Despite the dataset’s complexity, the method yielded a stable MFV estimate of 709 mb with a 68.27% confidence interval of [691, 744] mb, illustrating the method’s ability to provide interpretable results in challenging scenarios. Although the example is from nuclear science, the same statistical issues commonly arise in biomolecular fields, such as enzymatic kinetics, molecular assays, and diagnostic biomarker studies. The MFV-HPB framework provides a reliable and generalizable approach for extracting central estimates and confidence intervals in situations where data are difficult to collect, replicate, or interpret. Its resilience to outliers, independence from distributional assumptions, and compatibility with small-sample scenarios make it particularly valuable in molecular medicine, bioengineering, and biophysics. Full article

Cloud detection in satellite imagery plays a pivotal role in achieving high-accuracy retrieval of biophysical parameters and subsequent remote sensing applications. Although numerous methods have been developed and operationally deployed, their accuracy over challenging surfaces—such as snow-covered mountains, saline–alkali lands in deserts or Gobi regions, and snow-covered surfaces—remains limited. Additionally, the efficiency of collecting training samples for prevalent deep learning-based methods heavily relies on large-scale pixel-level annotations, which are both time-consuming and labor-intensive. To address these challenges, we propose a Texture-Enhanced Network that integrates an object-oriented dynamic threshold pseudo-labeling method and a texture-feature-enhanced attention module to enhance both the efficiency of deep learning methods and detection accuracy over challenging surfaces. First, an object-oriented dynamic threshold pseudo-labeling approach is developed by leveraging object-oriented principles and adaptive thresholding techniques, enabling the efficient collection of large-scale labeled samples for challenging surfaces. Second, to exploit the spatial continuity of clouds, cross-channel correlations, and their distinctive texture features, a texture-feature-enhanced attention module is designed to improve feature discrimination for challenging positive and negative samples. Extensive experiments on a Chinese GaoFen satellite imagery dataset demonstrate that the proposed method achieves state-of-the-art performance. Full article

Time-resolved fluorescence techniques, such as fluorescence lifetime imaging microscopy (FLIM), fluorescence correlation spectroscopy (FCS), and time-resolved fluorescence spectroscopy, are ideally suited for investigating molecular dynamics and interactions in biological and chemical systems. However, the analysis and interpretation of these datasets require advanced computational tools capable of handling diverse models and datasets. This paper presents a comprehensive software solution designed for model generation and analysis of time-resolved fluorescence data with a strong focus on fluorescence for quantitative structural analysis and biophysics. The software supports the integration of multiple fluorescence techniques and provides users with robust tools for performing complex model analysis across diverse experimental data. By enabling global analysis, model generation, data visualization, and sampling over model parameters, the software enhances the interpretability of intricate fluorescence phenomena. By providing flexible modeling capabilities, this solution offers a versatile platform for researchers to extract meaningful insights from time-resolved fluorescence data, aiding in the understanding of dynamic biomolecular processes. Full article