Search Results (332)

The 2021 WHO classification of brain tumours revolutionised the oncological field by emphasising the role of molecular, genetic and pathogenetic advances in classifying brain tumours. In this context, incidental gliomas have been increasingly identified due to the widespread performance of standard and advanced MRI sequences and represent a diagnostic and therapeutic challenge. The impactful decision to perform a surgical procedure deeply relies on the non-invasive identification of features or parameters that may correlate with brain tumour genetic profile and grading. Therefore, it is paramount to reach an early and proper diagnosis through neuroradiological techniques, such as MRI. Standard MRI sequences are the cornerstone of diagnosis, while consolidated and emerging roles have been awarded to advanced sequences such as Diffusion-Weighted Imaging/Apparent Diffusion Coefficient (DWI/ADC), Perfusion-Weighted Imaging (PWI), Magnetic Resonance Spectroscopy (MRS), Diffusion Tensor Imaging (DTI) and functional MRI (fMRI). The current novelty relies on the application of AI in brain neuro-oncology, mainly based on radiomics and radiogenomics models, which enhance standard and advanced MRI sequences in predicting glioma genetic status by identifying the mutation of multiple key biomarkers deeply impacting patients’ diagnosis, prognosis and treatment, such as IDH, EGFR, TERT, MGMT promoter, p53, H3-K27M, ATRX, Ki67 and 1p19. AI-driven models demonstrated high accuracy in glioma detection, grading, prognostication, and pre-surgical planning and appear to be a promising frontier in the neuroradiological field. On the other hand, standardisation challenges in image acquisition, segmentation and feature extraction variability, data scarcity and single-omics analysis, model reproducibility and generalizability, the black box nature and interpretability concerns, as well as ethical and privacy challenges remain key issues to address. Future directions, rooted in enhanced standardisation and multi-institutional validation, advancements in multi-omics integration, and explainable AI and federated learning, may effectively overcome these challenges and promote efficient AI-based models in glioma management. The aims of our multidisciplinary review are to: (1) extensively present the role of standard and advanced MRI sequences in the differential diagnosis of iLGGs as compared to HGGs (High-Grade Gliomas); (2) give an overview of the current and main applications of AI tools in the differential diagnosis of iLGGs as compared to HGGs (High-Grade Gliomas); (3) show the role of MRI, radiomics and radiogenomics in unravelling glioma genetic profiles. Standard and advanced MRI, radiomics and radiogenomics are key to unveiling the grading and genetic profile of gliomas and supporting the pre-operative planning, with significant impact on patients’ differential diagnosis, prognosis prediction and treatment strategies. Today, neuroradiologists are called to efficiently use AI tools for the in vivo, non-invasive, and comprehensive assessment of gliomas in the path towards patients’ personalised medicine. Full article

To improve the accuracy of three-dimensional (3D) reconstruction under microscopic conditions for laser-induced breakdown spectroscopy (LIBS), this study developed a novel visual platform by integrating an industrial CCD camera with a microscope. A customized microscale calibration target was designed to calibrate intrinsic and extrinsic camera parameters accurately. Based on the pinhole imaging model, disparity maps were obtained via pixel matching to reconstruct high-precision 3D ablation morphology. A mathematical model was established to analyze how key imaging parameters—baseline distance, focal length, and depth of field—affect reconstruction accuracy in micro-imaging environments. Focusing on trace element detection in WC-Co alloy samples, the reconstructed ablation craters enabled the precise calculation of ablation volumes and revealed their correlations with laser parameters (energy, wavelength, pulse duration) and the physical-chemical properties of the samples. Multivariate regression analysis was employed to investigate how ablation morphology and plasma evolution jointly influence LIBS quantification. A nonlinear calibration model was proposed, significantly suppressing matrix effects, achieving R² = 0.987, and reducing RMSE to 0.1. This approach enhances micro-scale LIBS accuracy and provides a methodological reference for high-precision spectral analysis in environmental and materials applications. Full article

Objectives: To synthesize evidence on structural and functional neuroplasticity in patients after anterior cruciate ligament reconstruction (ACLR) and its clinical implications. Methods: Adhering to the PRISMA guidelines for systematic reviews and meta-analyses, a literature search was conducted using PubMed, Embase, Web of Science, Scopus, and Cochrane CENTRAL (2018–2025) using specific keyword combinations, screening the results based on predetermined inclusion and exclusion criteria. Results: Among the 27 included studies were the following: (1) sensory cortex reorganization with compensatory visual dependence (5 EEG/fMRI studies); (2) reduced motor cortex efficiency evidenced by elevated AMT (TMS, 8 studies) and decreased γ-CMC (EEG, 3 studies); (3) progressive corticospinal tract degeneration (increased radial diffusivity correlating with postoperative duration); (4) enhanced sensory-visual integration correlated with functional recovery. Conclusions: This review provides a novel synthesis of evidence from transcranial magnetic stimulation (TMS), electroencephalography (EEG), functional near-infrared spectroscopy (fNIRS), diffusion tensor imaging (DTI), and functional magnetic resonance imaging (fMRI) studies. It delineates characteristic patterns of post-ACLR structural and functional neural reorganization. Targeting visual–cognitive integration and corticospinal facilitation may optimize rehabilitation. Full article

Mid-infrared passive spectroscopic imaging is a novel non-invasive and remote sensing method based on Planck’s law. It enables the acquisition of component-specific information from the human body by measuring naturally emitted thermal radiation in the mid-infrared region. Unlike active methods that require an external light source, our passive approach harnesses the body’s own emission, thereby enabling safe, long-term monitoring. In this study, we successfully demonstrated the simultaneous, non-invasive measurements of blood glucose and lactate levels of the human body using this method. The measurements, conducted over approximately 80 min, provided emittance data derived from mid-infrared passive spectroscopy that showed a temporal correlation with values obtained using conventional blood collection sensors. Furthermore, to evaluate localized metabolic changes, we performed k-means clustering analysis of the spectral data obtained from the upper arm. This enabled visualization of time-dependent lactate responses with spatial resolution. These results demonstrate the feasibility of multi-component monitoring without physical contact or biological sampling. The proposed technique holds promise for translation to medical diagnostics, continuous health monitoring, and sports medicine, in addition to facilitating the development of next-generation healthcare technologies. Full article

Background: Potent androgen receptor pathway inhibitors induce small cell neuroendocrine prostate cancer (SCNC), a highly aggressive subtype of metastatic androgen deprivation-resistant prostate cancer (ARPC) with limited treatment options and poor survival rates. Patients with metastases in the liver have a poor prognosis relative to those with bone metastases alone. The mechanisms that underlie the different behavior of ARPC in bone vs. liver may involve factors intrinsic to the tumor cell, tumor microenvironment, and/or systemic factors, and identifying these factors is critical to improved diagnosis and treatment of SCNC. Metabolic reprogramming is a fundamental strategy of tumor cells to colonize and proliferate in microenvironments distinct from the primary site. Understanding the metabolic plasticity of cancer cells may reveal novel approaches to imaging and treating metastases more effectively. Methods: Using magnetic resonance (MR) imaging and spectroscopy, we interrogated the physiological and metabolic characteristics of SCNC patient-derived xenografts (PDXs) propagated in the bone and liver, and used correlative biochemical, immunohistochemical, and transcriptomic measures to understand the biological underpinnings of the observed imaging metrics. Results: We found that the influence of the microenvironment on physiologic measures using MRI was variable among PDXs. However, the MR measure of glycolytic capacity in the liver using hyperpolarized ¹³C pyruvic acid recapitulated the enzyme activity (lactate dehydrogenase), cofactor (nicotinamide adenine dinucleotide), and stable isotope measures of fractional enrichment of lactate. While in the bone, the congruence of the glycolytic components was lost and potentially weighted by the interaction of cancer cells with osteoclasts/osteoblasts. Conclusion: While there was little impact of microenvironmental factors on metabolism, the physiological measures (cellularity and perfusion) are highly variable and necessitate the use of combined hyperpolarized ¹³C MRI and multiparametric (anatomic, diffusion-, and perfusion- weighted) ¹H MRI to better characterize pre-treatment tumor characteristics, which will be crucial to evaluate treatment response. Full article

As demand for plant-based beverages grows, analytical tools are needed to classify and understand their structural and compositional diversity. This study applied a multi-analytical approach to characterize 41 commercial almond-, oat-, rice- and soy-based beverages, evaluating attenuated total reflectance Fourier transform infrared (ATR-FTIR) spectroscopy, protein secondary structure proportions, colorimetry, and microscopic image texture analysis. A total of 26 variables, derived from ATR-FTIR and protein secondary structure assessment, were employed in multivariate models, using partial least squares discriminant analysis (PLS-DA) and orthogonal PLS-DA (OPLS-DA) to evaluate classification performance. The results indicated clear group separation, with soy and rice beverages forming distinct clusters while almond and oat samples showing partial overlap. Variable importance in projection (VIP) scores revealed that β-turn and α-helix protein structures, along with carbohydrate-associated spectral bands, were the key features for beverages’ classification. Textural features derived from microscopy images correlated with sugar and carbohydrate content and color parameters were also employed to describe beverages’ differences related to sugar content and visual appearance in terms of homogeneity. These findings demonstrate that combining ATR-FTIR spectral data with protein secondary structure data enables the effective classification of plant-based beverages, while microscopic image textural and color parameters offer additional extended product characterization. Full article

Background/Objectives: In septic shock, microcirculatory dysfunction contributes to organ failure and mortality. While sidestream dark-field (SDF) imaging is the reference method for assessing microvascular perfusion, its complexity limits routine use. This study evaluates near-infrared spectroscopy (NIRS) with vascular occlusion testing (VOT) as a potential bedside tool for monitoring microcirculatory changes following fluid resuscitation. Methods: Sixty-three fluid-responsive patients with sepsis were randomized to receive either 20% albumin or crystalloid. NIRS-VOT and sublingual SDF measurements were obtained at baseline and 60 min post-resuscitation. The reoxygenation slope (ReOx) derived from NIRS was calculated and compared with clinical severity scores and SDF-derived microcirculatory parameters. Results: ReOx significantly increased from baseline to 60 min in the albumin group (p = 0.025), but not in the crystalloid group. However, between-group differences at 60 min were not statistically significant. ReOx at 60 min was inversely correlated with APACHE II score (ρ = −0.325) and lactate (ρ = −0.277) and showed a weak inverse trend with norepinephrine dose. AUROC for ICU survival based on ReOx was 0.616. NIRS ReOx showed weak correlations with SDF parameters, including the number of crossings (p = 0.03) and the consensus proportion of perfused vessels (CPPV; p = 0.004). Conclusions: NIRS-VOT detected microcirculatory trends after albumin administration but showed limited agreement with SDF imaging. These findings suggest that NIRS and SDF assess different physiological domains. Further studies are warranted to define the clinical utility of NIRS as a microcirculation monitoring tool (Clinicaltrials.gov: NCT05357339). Full article

This work explored an electrochemical approach for synthesizing lanthanum-modified nickel oxide (NiO_x:La) as a hole transport layer (HTL) in inverted perovskite solar cells (IPSCs). By varying the La³⁺ concentration, the chemical, charge transport, structural, and morphological properties of the NiO_x:La film and the HTL/PVK interface were evaluated to enhance photovoltaic performance. X-ray photoelectron spectroscopy (XPS) confirmed La³⁺ incorporation, a higher Ni³⁺/Ni³⁺ ratio, and a valence band shift, improving p-type conductivity. Electrochemical impedance spectroscopy and Mott–Schottky analyses indicated that NiO_x:La 0.5% exhibited the lowest resistance and the highest carrier density, correlating with higher recombination resistance. The NiO_x:La 0.5% based cell achieved a PCE of 20.08%. XRD and SEM confirmed no significant changes in PVK structure, while photoluminescence extinction demonstrated improved charge extraction. After 50 days, this cell retained 80% of its initial PCE, whereas a pristine NiO_x device retained 75%. Hyperspectral imaging revealed lower optical absorption loss and better homogeneity. These results highlight NiO_x:La as a promising HTL for efficient and stable IPSCs. Full article

Copper antimony sulfide (CuSbS₂) is an affordable and eco-friendly solar absorber with an optimal bandgap and high absorption coefficient, and it stands out as a promising candidate for thin-film solar cells. This study investigates the effects of indium tin oxide (ITO), fluorine-doped tin oxide (FTO), and glass substrates on the microstructural, morphological, and optical properties of CuSbS₂ (CAS) layers synthesized via spray pyrolysis. X-ray Diffraction (XRD) and Raman spectroscopy analyses revealed that CAS phases formed on ITO and FTO substrates exhibited a phase composition without additional copper phases. However, the CAS layer on glass contained a copper sulfide (CuS) phase, which can be detrimental for solar cell applications. Furthermore, the influences of the substrate morphology and contact angle on the growth mechanisms of CAS layers was examined, highlighting the relationship between the substrate micromorphology and the resultant film characteristics. Advanced image processing techniques applied to Atomic Force Microscopy (AFM) images of the substrate surfaces facilitated a comprehensive comparison with the surface characteristics of the CAS films grown on those substrates. Field Emission Scanning Electron Microscopy (FESEM) indicated that CAS layers on ITO possessed larger grains than FTO, whereas those on FTO exhibited lower roughness with a more uniform grain distribution. Notably, the optical properties of the CAS layers correlated strongly with their microstructural and morphological characteristics. This work highlights the critical influence of substrate choice on the growth and characteristics of CAS layers through a comparative analysis. Full article

In this study, praseodymium-doped cobalt ferrite nanoparticles (CoFe_2−yPr_yO₄, y = 0–0.2) were synthesized via sol-gel auto-combustion and systematically characterized to assess their structural, morphological, magnetic, and biological properties. X-ray diffraction (XRD) confirmed single-phase cubic cobalt ferrite formation for samples with y ≤ 0.05 and the emergence of a secondary orthorhombic PrFeO₃ phase at higher dopant concentrations. FTIR spectroscopy identified characteristic metal–oxygen vibrations and revealed a progressive shift of absorption bands with increasing praseodymium (Pr) content. Vibrating sample magnetometry (VSM) demonstrated a gradual decline in saturation (Ms) and remanent (Mr) magnetization with Pr doping, an effect further intensified by cyclodextrin surface coating. TEM analyses revealed a particle size increase correlated with dopant level, while SEM images displayed a porous morphology typical of combustion-synthesized ferrites. In vitro cell viability assays showed minimal toxicity in normal human keratinocytes (HaCaT), while significant antiproliferative activity was observed against human cancer cell lines A375 (melanoma), MCF-7 (breast adenocarcinoma), and HT-29 (colorectal adenocarcinoma), particularly in Pr 6-CD and Pr 7-CD samples. These findings suggest that Pr substitution and cyclodextrin coating can effectively modulate the physicochemical and anticancer properties of cobalt ferrite nanoparticles, making them promising candidates for future biomedical applications. Full article

Objectives: This study aimed to evaluate the impact of the different hydration states of magnesium stearate (Mg.st) anhydrate (AH), monohydrate (MH), and dihydrate (DH) on the aerodynamic performance and stability of carrier-based dry powder inhalation (DPI) formulations using arformoterol and budesonide as model drugs. Methods: DPI formulations were prepared using Inhalac 251 lactose and Mg.st in various hydrated forms. The physicochemical properties of Mg.st were characterized using powder X-ray diffraction, differential scanning calorimetry, Fourier-transform infrared spectroscopy, Karl Fischer titration, dynamic vapor absorption, and Raman imaging. The aerodynamic performance was assessed employing a next-generation impactor under initial and accelerated conditions (40 °C, 75% relative humidity). Results: Mg.st-MH exhibited the highest crystallinity and the most stable moisture sorption profile, and showed the smallest particle size within the formulation as observed in the Raman images. Formulations containing Mg.st-MH demonstrated significantly higher fine particle fractions for both arformoterol (51.02 ± 5.16%) and budesonide (61.98 ± 4.09%) compared to formulations with Mg.st-AH or -DH forms. Mg.st-MH also exhibited improved performance retention under accelerated conditions, correlating with its physicochemical stability. Conclusions: The monohydrate form of magnesium stearate was the most effective force control agent, which reduced interparticulate interactions, thereby enhancing the inhalation efficiency and formulation stability. Thus, selecting an appropriate hydration form of Mg.st can improve DPI performance. Full article

Recent work has identified biomphalysin (BM) protein from the snail Biomphalaria glabrata as a cytolytic toxin against the Schistosoma mansoni parasite. Ex vivo interactome studies further evidenced BM’s ability to bind bacterial outer membrane proteins, but its specific antibacterial mechanisms and selectivity remain unclear. Accordingly, this study aims to elucidate the interaction between BM and two model bacteria with distinct cell surface architectures: Escherichia coli (Gram−) and Micrococcus luteus (Gram+). Employing a multiscale approach, we used in vivo single-molecule force spectroscopy (SMFS) to probe molecular interactions at the single cell level. Combined with cell aggregation assays, immunoblotting and Atomic Force Microscopy (AFM) imaging, SMFS results evidenced a selective interaction of BM from snail plasma with M. luteus but not E. coli. Exposure of M. luteus to BM compromised cell surface integrity and induced cell aggregation. These effects correlated with a patch-like distribution of BM on M. luteus reminiscent of pore-forming toxins, as revealed by the anti-BM antibody-functionalized AFM tip. Overall, this work highlights the utility of SMFS in dissecting host–pathogen molecular dialogs. It reveals BM’s selective action against M. luteus, potentially via surface clustering, and it shows spatially heterogeneous responses to the toxin within and between individual cells. 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

With the surge in digital farming, real-time quality management of fresh produce has become essential. For apples (Malus domestica Borkh.), consumer demand extends beyond sweetness, texture, and appearance to internal quality factors such as moisture content. Existing non-destructive methods, however, involve costly equipment, complex calibration, and sensitivity to environmental conditions. This study hypothesizes that thermal diffusivity indices derived from surface heating and cooling patterns can accurately predict apple moisture content non-destructively. A total of 823 apples from seven varieties were analyzed using a thermal imaging sensor in a 120-s process comprising 40 s of heating and 80 s of cooling. Key thermal diffusivity indices—minimum, maximum, mean, and max–min values—were extracted and correlated with actual moisture content measured via the drying method. Multiple linear regression and leave-one-out cross-validation confirmed that mean temperature-based models provided the most stable predictions ($R_{CV}^2$ ≥ 0.90 for some varieties). Frame optimization and artificial neural networks further improved prediction accuracy for varieties exhibiting higher variability. The proposed method is cost-effective, requires minimal calibration, and is less affected by surface reflectance, outperforming conventional optical methods (e.g., NIR spectroscopy, hyperspectral imaging), especially regarding robustness against surface reflectance variability and calibration complexity. This offers a practical solution for monitoring apple freshness and quality during sorting and distribution processes, with expanded research on sugar content and acidity expected to accelerate commercialization. Full article