Search Results (38)

Currently, there is a demand for effective flocculant systems that can be used without adverse impact on the environment and health. However, the challenge is to find the minimum dose to achieve the same efficacy as conventional flocculants. One technique involves using a mixture of natural and synthetic flocculants, the synergistic effects of which can enhance treatment efficiency. Thus, this work provides an approach using a low-cost chitosan (CH56)–polyaluminum chloride (PAC) mixture as a flocculant system for river water. Therefore, water quality was monitored in the Tamazula and Humaya rivers, which are sources of water for potabilization plants. According to the results of flocculation tests, the use of the mixture required a lower dosage (0.75 mg L⁻¹ of CH56 with 1 mg L⁻¹ of PAC; 0.75 mg L⁻¹ of CH56 with 2 mg L⁻¹ of PAC) than that used with individual flocculants (3 mg L⁻¹ of CH56; 5 mg L⁻¹ of PAC). Conveniently, the mixture produced larger and more compact flocs, favoring sedimentation kinetics and thus flocculation. Fractal dimension (F_D) and lacunarity (Λ) from microscopy images were used as indicators of the quality of the flocs formed. In general, CH56 and the mixture performed better than PAC, and the mixture allowed the best removal of the model microplastic (polystyrene). Flocculant mixtures reduced the concentration of copper ions by 58%, of tetracycline by 22%, of microplastics by 80%, and of bacteria by >90%. Hence, the authors believe that this work offers valuable information that could be used for potabilization plants aiming to reduce the dose of PAC and introduce chitosan into their coagulation–flocculation process. Full article

Venous perfusion plays a crucial role in vascular health, yet functional assessment of superficial veins remains limited. Near-infrared reflectance imaging (NIRI) devices, commonly used for vein visualization, may offer untapped potential in this context. We investigated whether texture analysis (TA) applied to NIRI-based vein finder images can detect dynamic changes in superficial venous structure during reactive hyperemia. Fourteen healthy adults underwent a suprasystolic occlusion protocol, with real-time images acquired from the hand dorsum. From defined regions of interest, we extracted classical texture parameters (e.g., contrast, correlation, entropy, energy, fractal dimension, and lacunarity) and vein width. While vein width significantly increased during occlusion (p < 0.001), most individual texture parameters remained stable. Notably, correlation increased during occlusion (p = 0.023), and lacunarity decreased during recovery (p = 0.024). We developed composite indices combining texture and morphological features. Entropy-to-width and correlation-to-width ratios decreased during occlusion (p < 0.001), while total entropic content rose (p < 0.001). A modest increase in the correlation-to-entropy ratio during recovery (p = 0.026) suggested delayed reorganization of venous texture. These findings indicate that TA of vein finder images captures functional vascular responses beyond morphology alone. Composite parameters enhance sensitivity and may support the development of non-invasive, low-cost tools for assessing venous function. Full article

Background and Objectives: Screening, primary prevention, and the early identification of high-risk individuals are crucial for minimising the burden of cardiovascular diseases (CVDs). In this study, we aimed to evaluate the association of retinal microvascular features with myocardial dysfunction and CVD risk factors in a group of patients with significant coronary artery disease (CAD) compared to patients with newly diagnosed isolated arterial hypertension and healthy controls. Materials and Methods: We performed a single-centre cross-sectional study on 214 individuals divided into three groups: a group of 99 cases diagnosed with significant CAD, a group of 61 cases with newly diagnosed isolated arterial hypertension, and a control group of 54 cases with no confirmed cardiovascular pathology. Colour optic disc-centred retinal photographs were taken in all cases, and the following parameters were quantified using MONA REVA 3.0.0 software (VITO Health, Mol, Belgium): central retinal arteriolar equivalent, central retinal venular equivalent, arteriovenous ratio, fractal dimension, tortuosity index, and lacunarity. Univariable and multivariable statistical analyses were performed to assess changes in retinal microvascular features in CVD. Results: Dyslipidaemia (p = 0.009), systolic blood pressure (p = 0.008), and LDL cholesterol (p = 0.003) were negatively associated while left ventricular (LV) strain (0.043) was positively associated with the CRAE. In the case of the CRVE, the coronary Agatston score (p = 0.016) proved a positive and HDL cholesterol (p = 0.018) a negative association. A lower fractal dimension was associated with the presence of diabetes mellitus (p = 0.006), dyslipidaemia (p = 0.011), and a history of acute myocardial infarction (p = 0.018), while a higher fractal dimension was associated with increased left ventricular ejection fraction (LVEF) (p = 0.006) and medical treatment (p = 0.005). Lacunarity was higher in patients of female gender (p = 0.005), with decreased HDL (p = 0.014) and LVEF (0.005), and with increased age (p < 0.001) and Agatston score (p = 0.001). The vessel tortuosity index increased with LV strain (p = 0.05), medical treatment (p = 0.043), and male gender (p = 0.006). Conclusions: Retinal microvascular features may serve as additional risk stratification tools in patients with CVD, particularly CAD, pending prospective validation. Full article

In many soil processes, including solute and gas dynamics, the architecture of intra-aggregate pores is a crucial component. Soil management practices and wetting-drying (W-D) cycles, the latter having a significant impact on pore aggregation, are two key factors that shape pore structure. This study examines the effects of W-D cycles on the architecture of intra-aggregate pores under three different soil management systems: no-tillage (NT), minimum tillage (MT), and conventional tillage (CT). The soil samples were subjected to 0 and 12 W-D cycles, and the resulting pore structures were scanned using X-ray micro-computed tomography, generating reconstructed 3D volumetric data. The data analyses were conducted in terms of multifractal spectra, normalized Shannon entropy, lacunarity, porosity, anisotropy, connectivity, and tortuosity. The multifractal parameters of capacity, correlation, and information dimensions showed mean values of approximately 2.77, 2.75, and 2.75 when considering the different management practices and W-D cycles; 3D lacunarity decreased mainly for the smallest boxes between 0 and 12 W-D cycles for CT and NT, with the opposite behavior for MT. The normalized 3D Shannon entropy showed differences of less than 2% before and after the W-D cycles for MT and NT, with differences of 5% for CT. The imaged porosity showed reductions of approximately 50% after 12 W-D cycles for CT and NT. Generally, the largest pores (>0.1 mm³) contributed the most to porosity for all management practices before and after W-D cycles. Anisotropy increased by 9% and 2% for MT and CT after the cycles and decreased by 23% for NT. Pore connectivity showed a downward trend after 12 W-D cycles for CT and NT. Regarding the pore shape, the greatest contribution to porosity and number of pores was due to triaxial-shaped pores for both 0 and 12 W-D cycles for all management practices. The results demonstrate that, within the resolution limits of the microtomography analysis, pore architecture remained resilient to changes, despite some observable trends in specific parameters. Full article

Objectives: We aimed to develop imaging biomarkers to predict radiation pneumonitis (RP) in non-small cell lung cancer (NSCLC) patients undergoing thoracic radiotherapy. We hypothesized that measuring morphometric complexity in the lung using simulation computed tomography may provide objective imaging biomarkers for lung parenchyma integrity, potentially forecasting the risk of RP. Materials and Methods: A retrospective study was performed on medical records of 175 patients diagnosed with NSCLC who had received thoracic radiotherapy. Three indices were utilized to measure the morphometric complexity of the lung parenchyma: box-counting fractal dimension, lacunarity, and minimum spanning tree (MST) fractal dimension. Patients were dichotomized into two groups at median values. Cox proportional hazard models were constructed to estimate the hazard ratios for grade ≥ 2 or grade ≥ 3 RP. Results and Conclusions: We found significant associations between lung parenchymal morphometric complexity and RP incidence. In univariate Cox-proportional hazard analysis, patients with a lower MST fractal dimension had a significantly higher hazard ratio of 2.296 (95% CI: 1.348–3.910) for grade ≥ 2 RP. When adjusted for age, sex, smoking status, category of the underlying lung disease, category of radiotherapy technique, clinical stage, histology, and DLCO, patients with a lower MST fractal dimension showed a significantly higher hazard ratio of 3.292 (95% CI: 1.722–6.294) for grade ≥ 2 RP and 7.952 (95% CI: 1.722 36.733) for grade ≥ 3 RP than those with a higher MST fractal dimension. Patients with lower lacunarity exhibited a significantly lower hazard ratio of 0.091 (95% CI: 0.015–0.573) for grade ≥ 3 RP in the adjusted model. We speculated that the lung tissue integrity is captured by morphometric complexity measures, particularly by the MST fractal dimension. We suggest the MST fractal dimension as an imaging biomarker for predicting the occurrence of symptomatic RP after thoracic radiotherapy. Full article

Fractal analysis is a mathematical approach employed to study and describe complex patterns and structures across various disciplines, including mathematics, physics, computer science, biology and finance. Introduced by mathematician Benoit Mandelbrot in the 1970s, fractals are intricate, self-similar patterns that repeat at different scales, exhibiting consistent structures upon magnification or reduction. This analysis involves generating fractals through iterative processes or recursive equations, resulting in highly detailed and complex formations. Fractal analysis enhances medical images by removing noise while preserving details and improving diagnostic quality in magnetic resonance and computed tomography scans. However, there is a lack of comprehensive studies on its application in ultrasound imaging, prompting this narrative review to investigate its use and methodology in this context. Selected papers on the use of fractal analysis in ultrasound imaging were analyzed. Out of 186 records screened, 60 duplicates were removed and 28 were discarded. The text content of 98 potentially eligible papers was checked, with 65 not meeting inclusion criteria. Finally, 33 studies were included in the review. Fractal analysis enhances ultrasound imaging by providing detailed tissue texture characterization, aiding in the diagnosis of conditions like breast and lung cancer, osteoporosis and hypertensive disorders in pregnancy. It quantifies biological structure complexity and improves diagnostic accuracy and reliability. This technique supports clinicians in making informed decisions by offering critical insights into various medical conditions. Full article

In this study, we propose an innovative concept that applies an AI-based approach using the random forest algorithm integrated with fractal and discrete wavelet transform features of nuclear chromatin. This strategy could be employed to identify subtle structural changes in cells that are in the early stages of programmed cell death. The code for the random forest model is developed using the Scikit-learn library in Python and includes hyperparameter tuning and cross-validation to optimize performance. The suggested input data for the model are chromatin fractal dimension, fractal lacunarity, and three wavelet coefficient energies obtained through high-pass and low-pass filtering. Additionally, the code contains several methods to assess the performance metrics of the model. This model holds potential as a starting point for designing simple yet advanced AI biosensors capable of detecting apoptotic cells that are not discernible through conventional microscopy techniques. Full article

The aim of this review paper is to survey the fractal morphology of scale-invariant patterns. We are particularly focusing on the scale and conformal invariance, as well as on the fractal non-uniformity (multifractality), inhomogeneity (lacunarity), and anisotropy (succolarity). We argue that these features can be properly quantified by the following six adimensional numbers: the fractal (e.g., similarity, box-counting, or Assouad) dimension, conformal dimension, degree of multifractal non-uniformity, coefficient of multifractal asymmetry, index of lacunarity, and index of fractal anisotropy. The difference between morphological properties of mathematical and real-world fractals is especially outlined in this review paper. Full article

Background: Idiopathic pulmonary fibrosis (IPF) has the potential to cause fatal pulmonary toxicity after radiotherapy and can increase the morbidity and mortality of non-small-cell lung cancer (NSCLC) patients. In this context, we aimed to develop imaging complexity biomarkers to predict the incidence of severe pulmonary toxicity in patients with NSCLC who have underlying IPF and are treated with radiotherapy. Methods: We retrospectively reviewed the medical records of 19 patients with NSCLC who had underlying IPF and were treated with radiotherapy at the Korea University Guro Hospital between March 2018 and December 2022. To quantify the morphometric complexity of the lung parenchyma, box-counting fractal dimensions and lacunarity analyses were performed on pre-radiotherapy simulation chest computed tomography scans. Results: Of the 19 patients, the incidence of grade 3 or higher radiation pneumonitis was observed in 8 (42.1%). After adjusting for age, sex, smoking status, histology, and diffusing capacity of the lung for carbon monoxide, eight patients with a lower fractal dimension showed a significantly higher hazard ratio of 7.755 (1.168–51.51) for grade 3 or higher pneumonitis than those with a higher fractal dimension. Patients with lower lacunarity exhibited significantly lower hazards in all models, both with and without adjustments. The lower-than-median lacunarity group also showed significantly lower incidence curves for all models built in this study. Conclusions: We devised a technique for quantifying morphometric complexity in NSCLC patients with IPF on radiotherapy and discovered lacunarity as a potential imaging biomarker for grade 3 or higher pneumonitis. Full article

Toxoplasma gondii is an obligate intracellular parasite existing in three infectious life stages—tachyzoites, bradyzoites, and sporozoites. Rupture of tissue cysts and re-conversion of bradyzoites to tachyzoites leads to reactivated toxoplasmosis (RT) in an immunocompromised host. The aim of this study was to apply ImageJ software for analysis of T. gondii brain cysts obtained from a newly established in vivo model of RT. Mice chronically infected with T. gondii (BGD1 and BGD26 strains) were treated with cyclophosphamide and hydrocortisone (experimental group—EG) or left untreated as infection controls (ICs). RT in mice was confirmed by qPCR (PCR+); mice remaining chronically infected were PCR−. A total of 90 images of cysts were analyzed for fractal dimension (FD), lacunarity (L), diameter (D), circularity (C), and packing density (PD). Circularity was significantly higher in PCR+ compared to IC mice (p < 0.05 for BGD1, p < 0.001 for the BGD26 strain). A significant negative correlation between D and PD was observed only in IC for the BGD1 strain (ρ = −0.384, p = 0.048), while fractal parameters were stable. Significantly higher D, C, and PD and lower lacunarity, L, were noticed in the BGD1 compared to the more aggressive BGD26 strain. In conclusion, these results demonstrate the complexity of structural alterations of T. gondii cysts in an immunocompromised host and emphasize the application potential of ImageJ in the experimental models of toxoplasmosis. Full article

This article portrays solid xerogel-type materials, based on chitosan, TEGylated phenothiazine, and TEG (tri-ethylene glycol), dotted with a large number of pores, that are effectively represented in their constitutive structure. They were assumed to be fractal geometrical entities and adjudged as such. The acoustic fractional propagation equation in a fractal porous media was successfully applied and solved with the help of Bessel functions. In addition, the fractal character was demonstrated by the produced fractal analysis, and it has been proven on the evaluated scanning electron microscopy (SEM) pictures of porous xerogel compounds. The fractal parameters (more precisely, the fractal dimension), the lacunarity, and the Hurst index were calculated with great accuracy. Full article

In this work, a computational scheme is proposed to identify the main combinations of handcrafted descriptors and deep-learned features capable of classifying histological images stained with hematoxylin and eosin. The handcrafted descriptors were those representatives of multiscale and multidimensional fractal techniques (fractal dimension, lacunarity and percolation) applied to quantify the histological images with the corresponding representations via explainable artificial intelligence (xAI) approaches. The deep-learned features were obtained from different convolutional neural networks (DenseNet-121, EfficientNet-b2, Inception-V3, ResNet-50 and VGG-19). The descriptors were investigated through different associations. The most relevant combinations, defined through a ranking algorithm, were analyzed via a heterogeneous ensemble of classifiers with the support vector machine, naive Bayes, random forest and K-nearest neighbors algorithms. The proposed scheme was applied to histological samples representative of breast cancer, colorectal cancer, oral dysplasia and liver tissue. The best results were accuracy rates of $94.83\%$ to $100\%$, with the identification of pattern ensembles for classifying multiple histological images. The computational scheme indicated solutions exploring a reduced number of features (a maximum of 25 descriptors) and with better performance values than those observed in the literature. The presented information in this study is useful to complement and improve the development of computer-aided diagnosis focused on histological images. Full article

Fractal analysis (FA) is a contemporary computational technique that can assist in identifying and assessing nuanced structural alterations in cells and tissues after exposure to certain toxic chemical agents. Its application in toxicology may be particularly valuable for quantifying structural changes in cell nuclei during conventional microscopy assessments. In recent years, the fractal dimension and lacunarity of cell nuclei, considered among the most significant FA features, have been suggested as potentially important indicators of cell damage and death. In this study, we demonstrate the feasibility of developing a random forest machine learning model that employs fractal indicators as input data to identify yeast cells treated with oxidopamine (6-hydroxydopamine, 6-OHDA), a powerful toxin commonly applied in neuroscience research. The model achieves notable classification accuracy and discriminatory power, with an area under the receiver operating characteristics curve of more than 0.8. Moreover, it surpasses alternative decision tree models, such as the gradient-boosting classifier, in differentiating treated cells from their intact counterparts. Despite the methodological challenges associated with fractal analysis and random forest training, this approach offers a promising avenue for the continued exploration of machine learning applications in cellular physiology, pathology, and toxicology. Full article

This paper introduces an AI model designed for the diagnosis and monitoring of the SARS-CoV-2 virus. The present artificial intelligence (AI) model founded on the machine learning concept was created for the identification/recognition, keeping under observation, and prediction of a patient’s clinical evaluation infected with the CoV-2 virus. The deep learning (DL)-initiated process (an AI subset) is punctually prepared to identify patterns and provide automated information to healthcare professionals. The AI algorithm is based on the fractal analysis of CT chest images, which is a practical guide to detecting the virus and establishing the degree of lung infection. CT pulmonary images, delivered by a free public source, were utilized for developing correct AI algorithms with the aim of COVID-19 virus observation/recognition, having access to coherent medical data, or not. The box-counting procedure was used with a predilection to determine the fractal parameters, the value of the fractal dimension, and the value of lacunarity. In the case of a confirmation, the analysed image is used as input data for a program responsible for measuring the degree of health impairment/damage using fractal analysis. The support of image scans with computer tomography assistance is solely the commencement part of a correctly established diagnostic. A profiled software framework has been used to perceive all the details collected. With the trained AI model, a maximum accuracy of 98.1% was obtained. This advanced procedure presents an important potential in the progress of an intricate medical solution to pulmonary disease evaluation. Full article

This study describes novel solid substances founded on chitosan and TEGylated phenothiazine that have a high ability for hydrargyrum recovery from watery liquid solutions. These compounds were taken into account, consisting of two distinct entity interactions inside of the classic fractal dynamics conjecture of an “interface”. They were assimilated through fractal-type mathematical objects and judged as such. The bi-stable behavior of two fractally connected objects was demonstrated both numerically and graphically. The fractal character was demonstrated by the fractal analysis made using SEM images of the xerogel compounds with interstitial fixed hydrargyrum. For the first time, SEM helped to verify such samples from two distinct bodies, with the multifractal parameter values being listed in continuation. The fractal dimension of the rectangular mask is D₁ = 1.604 ± 0.2798, the fractal dimension of the square mask is D₂ = 1.596 ± 0.0460, and the lacunarity is 0.0402. Full article