Gray-Level Co-occurrence Matrix Analysis of Nuclear Textural Patterns in Laryngeal Squamous Cell Carcinoma: Focus on Artificial Intelligence Methods.

Gray-level co-occurrence matrix (GLCM) and discrete wavelet transform (DWT) analyses are two contemporary computational methods that can identify discrete changes in cell and tissue textural features. Previous research has indicated that these methods may be applicable in the pathology for identification and classification of various types of cancers. In this study, we present findings that squamous epithelial cells in laryngeal carcinoma, which appear morphologically intact during conventional pathohistological evaluation, have distinct nuclear GLCM and DWT features. The average values of nuclear GLCM indicators of these cells, such as angular second moment, inverse difference moment, and textural contrast, substantially differ when compared to those in noncancerous tissue. In this work, we also propose machine learning models based on random forests and support vector machine that can be successfully trained to separate the cells using GLCM and DWT quantifiers as input data. We show that, based on a limited cell sample, these models have relatively good classification accuracy and discriminatory power, which makes them suitable candidates for future development of AI-based sensors potentially applicable in laryngeal carcinoma diagnostic protocols.

Computational approaches for evaluating morphological changes in the corneal stroma associated with decellularization.

Decellularized corneas offer a promising and sustainable source of replacement grafts, mimicking native tissue and reducing the risk of immune rejection post-transplantation. Despite great success in achieving acellular scaffolds, little consensus exists regarding the quality of the decellularized extracellular matrix. Metrics used to evaluate extracellular matrix performance are study-specific, subjective, and semi-quantitative. Thus, this work focused on developing a computational method to examine the effectiveness of corneal decellularization. We combined conventional semi-quantitative histological assessments and automated scaffold evaluations based on textual image analyses to assess decellularization efficiency. Our study highlights that it is possible to develop contemporary machine learning (ML) models based on random forests and support vector machine algorithms, which can identify regions of interest in acellularized corneal stromal tissue with relatively high accuracy. These results provide a platform for developing machine learning biosensing systems for evaluating subtle morphological changes in decellularized scaffolds, which are crucial for assessing their functionality.

Gray level co-occurrence matrix and wavelet analyses reveal discrete changes in proximal tubule cell nuclei after mild acute kidney injury.

Acute kidney injury (AKI) relates to an abrupt reduction in renal function resulting from numerous conditions. Morbidity, mortality, and treatment costs related to AKI are relatively high. This condition is strongly associated with damage to proximal tubule cells (PTCs), generating distinct patterns of transcriptional and epigenetic alterations that result in structural changes in the nuclei of this epithelium. To this date, AKI-related nuclear chromatin redistribution in PTCs is poorly understood, and it is unclear whether changes in PTC chromatin patterns can be detected using conventional microscopy during mild AKI, which can progress to more debilitating forms of injury. In recent years, gray level co-occurrence matrix (GLCM) analysis and discrete wavelet transform (DWT) have emerged as potentially valuable methods for identifying discrete structural changes in nuclear chromatin architecture that are not visible during the conventional histopathological exam. Here we present findings indicating that GLCM and DWT methods can be successfully used in nephrology to detect subtle nuclear morphological alterations associated with mild tissue injury demonstrated in rodents by inducing a mild form of AKI through ischemia-reperfusion injury. Our results show that mild ischemic AKI is associated with the reduction of local textural homogeneity of PTC nuclei quantified by GLCM and the increase of nuclear structural heterogeneity indirectly assessed with DWT energy coefficients. This rodent model allowed us to show that mild ischemic AKI is associated with the significant reduction of textural homogeneity of PTC nuclei, indirectly assessed by GLCM indicators and DWT energy coefficients.

The Role of Machiavellianism and Interdependent Agency in Facebook Intrusion.

Facebook is one the most popular social networking sites in the world. The social aspect and perceiving oneself in the context of relationships seem to be crucial in the analysis of Facebook use. In recent years the number of Facebook users has been increasing, which is related to the problem of Facebook intrusion. Machiavellianism and selected interdependence agency indicators (conformity, relatedness, and autonomy) are associated with social functioning, which is an important aspect of Facebook intrusion. The aim of the study was to check how Machiavelianism and the interdependent agency indicators mentioned above affect Facebook intrusion and whether Facebook intrusion decreases satisfaction with life. The participants in the online study were 715 Polish Facebook users. We administered the Facebook Intrusion Scale, Conformity, Relatedness, and Autonomy Scales, and the Satisfaction With Life Scale. The results show that the selected interdependence agency indicators predict Facebook intrusion. Our results revealed that a high level of Machiavellianism was related to a high level of Facebook intrusion in women. They also show that conformity is positively and autonomy negatively related to Facebook intrusion and that Facebook decreases reduces satisfaction with life. The presented research thus contributes to knowledge about Facebook usage patterns.

Artificial neural networks in contemporary toxicology research.

Artificial neural networks (ANNs) have a huge potential in toxicology research. They may be used to predict toxicity of various chemical compounds or classify the compounds based on their toxic effects. Today, numerous ANN models have been developed, some of which may be used to detect and possibly explain complex chemico-biological interactions. Fully connected multilayer perceptrons may in some circumstances have high classification accuracy and discriminatory power in separating damaged from intact cells after exposure to a toxic substance. Regularized and not fully connected convolutional neural networks can detect and identify discrete changes in patterns of two-dimensional data associated with toxicity. Bayesian neural networks with weight marginalization sometimes may have better prediction performance when compared to traditional approaches. With the further development of artificial intelligence, it is expected that ANNs will in the future become important parts of various accurate and affordable biosensors for detection of various toxic substances and evaluation of their biochemical properties. In this concise review article, we discuss the recent research focused on the scientific value of ANNs in evaluation and prediction of toxicity of chemical compounds.

Trends of the Prevalence of Pre-gestational Diabetes in 2030 and 2050 in Belgrade Cohort.

The aim of this study was to analyze the trends in diabetes in pregnancy in Belgrade, Serbia for the period of the past decade and forecast the number of women with pre-gestational diabetes for the years 2030 and 2050. The study included the data on all pregnant women with diabetes from the registry of the deliveries in Belgrade, by the City Institute of Public Health of Belgrade, Serbia for the period between 2010 and 2020 and the published data on the deliveries on the territory of Belgrade. During the examined period the total number of live births in Belgrade was 196,987, and the prevalence of diabetes in pregnancy was 3.4%, with the total prevalence of pre-gestational diabetes of 0.7% and overall prevalence of GDM of 2.7%. The average age of women in our study was significantly lower in 2010 compared to 2020. The forecasted prevalence of pre-gestational diabetes among all pregnant women for 2030 is 2% and 4% for 2050 in our cohort. Our study showed that the prevalence of pre-gestational diabetes has increased both among all pregnant women and among women with diabetes in pregnancy in the past decade in Belgrade, Serbia and that it is expected to increase further in the next decades and to further double by 2050.

Analysis of Vascular Architecture and Parenchymal Damage Generated by Reduced Blood Perfusion in Decellularized Porcine Kidneys Using a Gray Level Co-occurrence Matrix.

There is no cure for kidney failure, but a bioartificial kidney may help address this global problem. Decellularization provides a promising platform to generate transplantable organs. However, maintaining a viable vasculature is a significant challenge to this technology. Even though angiography offers a valuable way to assess scaffold structure/function, subtle changes are overlooked by specialists. In recent years, various image analysis methods in radiology have been suggested to detect and identify subtle changes in tissue architecture. The aim of our research was to apply one of these methods based on a gray level co-occurrence matrix (Topalovic et al.) computational algorithm in the analysis of vascular architecture and parenchymal damage generated by hypoperfusion in decellularized porcine. Perfusion decellularization of the whole porcine kidneys was performed using previously established protocols. We analyzed and compared angiograms of kidneys subjected to pathophysiological arterial perfusion of whole blood. For regions of interest Santos et al. covering kidney medulla and the main elements of the vascular network, five major GLCM features were calculated: angular second moment as an indicator of textural uniformity, inverse difference moment as an indicator of textural homogeneity, GLCM contrast, GLCM correlation, and sum variance of the co-occurrence matrix. In addition to GLCM, we also performed discrete wavelet transform analysis of angiogram ROIs by calculating the respective wavelet coefficient energies using high and low-pass filtering. We report statistically significant changes in GLCM and wavelet features, including the reduction of the angular second moment and inverse difference moment, indicating a substantial rise in angiogram textural heterogeneity. Our findings suggest that the GLCM method can be successfully used as an addition to conventional fluoroscopic angiography analyses of micro/macrovascular integrity following in vitro blood perfusion to investigate scaffold integrity. This approach is the first step toward developing an automated network that can detect changes in the decellularized vasculature.

Artificial intelligence approaches to the biochemistry of oxidative stress: Current state of the art.

Artificial intelligence (AI) and machine learning models are today frequently used for classification and prediction of various biochemical processes and phenomena. In recent years, numerous research efforts have been focused on developing such models for assessment, categorization, and prediction of oxidative stress. Supervised machine learning can successfully automate the process of evaluation and quantification of oxidative damage in biological samples, as well as extract useful data from the abundance of experimental results. In this concise review, we cover the possible applications of neural networks, decision trees and regression analysis as three common strategies in machine learning. We also review recent works on the various weaknesses and limitations of artificial intelligence in biochemistry and related scientific areas. Finally, we discuss future innovative approaches on the ways how AI can contribute to the automation of oxidative stress measurement and diagnosis of diseases associated with oxidative damage.

Combined hereditary thrombophilias are responsible for poor placental vascularization development and low molecular weight heparins (LMWH) prevent adverse pregnancy outcomes in these patients.

BACKGROUND: Even though thrombophilias are associated with negative pregnancy outcomes (PO), there is not a consensus of when thrombophilias should be screened for, or how they affect placental vascularization during pregnancy. Therefore, the main aim of this study was to discover inherited thrombophilias (IHT) in the first trimester in women with otherwise no indications for thrombophilia screening, based on their vascularization parameters. LMWH treatment in improvement of placental vascularization and PO was also assessed. Finally, the classification of thrombophilias based on observed obstetric risks was proposed. METHODS: Women were included in study based on their poor gestational sac and later utero-placental juncture vascularization signal and screening for inherited thrombophilias. LMWH were then initiated and Resistance index of Uterine artery (RIAU) was followed alongside PO (preterm birth, preeclampsia, placental abruption, intrauterine growth reduction). Study group consisted of women with combined inherited thrombophilias. Control group consisted of patients with inherited thrombophilias who have received LMWH therapy since pregnancy beginning. FINDINGS: Out of 219 women, 93 had IHT, and 43 had combined IHT. All pregnancies both in both groups ended up with live births. Vaginal birth was more present in the control group (p < .001), and all women in study group delivered by CS. Premature birth was present in 8.4% of patients in control group, and in 32.55% of the patients in the study (p < .001). PE wasn't noted, and only 1 case of PA in control group. In the control group, 6.5% patients had IUGR, and 32.55% in the study group (p < .05). Based on RIAU and PO, thrombophilia categories were established: S (severe), MO (moderate), MI (mild) and L (low). Higher risk thrombophilias had higher RIAU later in the pregnancy, earlier pregnancy termination and Intrauterine Growth Reduction (IUGR). CONCLUSIONS: Thrombophilias should be considered and screened when poor vascularization is noted early in the pregnancy with Doppler sonography. Intervention with LMWH prevents adverse PO in these patients.

Gray-Level Co-occurrence Matrix Analysis for the Detection of Discrete, Ethanol-Induced, Structural Changes in Cell Nuclei: An Artificial Intelligence Approach.

Gray-level co-occurrence matrix (GLCM) analysis is a contemporary and innovative computational method for the assessment of textural patterns, applicable in almost any area of microscopy. The aim of our research was to perform the GLCM analysis of cell nuclei in Saccharomyces cerevisiae yeast cells after the induction of sublethal cell damage with ethyl alcohol, and to evaluate the performance of various machine learning (ML) models regarding their ability to separate damaged from intact cells. For each cell nucleus, five GLCM parameters were calculated: angular second moment, inverse difference moment, GLCM contrast, GLCM correlation, and textural variance. Based on the obtained GLCM data, we applied three ML approaches: neural network, random trees, and binomial logistic regression. Statistically significant differences in GLCM features were observed between treated and untreated cells. The multilayer perceptron neural network had the highest classification accuracy. The model also showed a relatively high level of sensitivity and specificity, as well as an excellent discriminatory power in the separation of treated from untreated cells. To the best of our knowledge, this is the first study to demonstrate that it is possible to create a relatively sensitive GLCM-based ML model for the detection of alcohol-induced damage in Saccharomyces cerevisiae cell nuclei.

Distribution of normal and pathological OGTTs among pregnant population and non-pregnant women with PCOS - the cross-sectional study.

ABSTRACT: Both pregnancy, as physiological, and polycystic ovary syndrome (PCOS), as a pathological condition, carry the risk for developing glucose metabolism abnormalities. In this retrospective cross-sectional study, we hypothesized that pregnancy as a physiological condition carries a higher likelihood for abnormal oral glucose tolerance test (OGTT) results than PCOS as a pathological condition.We have compared the prevalence and likelihood ratios for abnormal OGTT results between non-pregnant women with PCOS (Group A) and pregnant women at 24 to 28 weeks of gestation (Group B). Participants of both study groups underwent glucose tolerance testing with 75 g glucose OGTT. During the study period, 7411 women were tested, 3932 women encompassed Group A, and 3479 women comprised Group B.The numbers of yearly tested pregnant women and the corresponding proportion of tested women among all study participants have decreased during the study period, from 766 to 131 and 89.1% to 20.5%, respectively. Group A had a significantly lower prevalence (4.4%) of pathological OGTT results compared to Group B (8.1%). This has resulted in a 45.427 likelihood ratio (P < .001) for abnormal OGTT results in pregnant women compared to non-pregnant women with PCOS.We might conclude that pregnancy could have a more challenging influence on glucose metabolism and that carries higher risks for abnormal glucose metabolism than PCOS. The awareness of obstetricians regarding physiological changes during pregnancy that predisposes abnormal glucose metabolism is decreasing over time and the compliance concerning OGTT testing of pregnant women is decreasing too.

Association between Chromatin Structural Organization of Peripheral Blood Neutrophils and Self-Perceived Mental Stress: Gray-Level Co-occurrence Matrix Analysis.

Methods based on the evaluation of textural patterns in microscopy, such as the "gray-level co-occurrence matrix" (GLCM) analysis are modern and innovative computer and mathematical algorithms that can be used to quantify subtle structural changes in cells and their organelles. Potential application of GLCM method in the fields of psychophysiology and psychiatry to this date has not been systematically investigated. The main objective of our study was to test the existence and strength of the association between chromatin structural organization of peripheral blood neutrophils and levels of self-perceived mental stress. The research was done on a sample of 100 healthy student athletes, and the Depression, Anxiety, and Stress Scales (DASS-21) were used for the estimation of psychological distress. Chromatin textural homogeneity and uniformity were negatively correlated (p < 0.01) with mental distress and had relatively good discriminatory power in differentiating participants with normal and elevated stress levels. As an addition, we propose the creation of a machine learning model based on binomial logistic regression that uses these and other GLCM features to predict stress elevation. To the best of our knowledge, these results are one of the first to establish the link between neutrophil chromatin structural organization quantified by the GLCM method and indicators of normal psychological functioning.

Measurement invariance of the Phubbing Scale across 20 countries.

Mobile phone addiction is a robust phenomenon observed throughout the world. The social aspect of mobile phone use is crucial; therefore, phubbing is a part of the mobile phone addiction phenomenon. Phubbing is defined as ignoring an interlocutor by glancing at one's mobile phone during a face-to-face conversation. The main aim of this study was to investigate how the Phubbing Scale (containing 10 items) might vary across countries, and between genders. Data were collected in 20 countries: Belarus, Brazil, China, Croatia, Ecuador, India, Israel, Italy, Netherlands, Pakistan, Poland, Portugal, Serbia, Slovakia, Slovenia, Spain, Turkey, UK, Ukraine and USA. The mean age across the sample (N = 7696, 65.8% women, 34.2% men) was 25.32 years (SD = 9.50). The cross-cultural invariance of the scale was investigated using multigroup confirmatory factor analyses (MGCFA) as well as the invariance analyses. Additionally, data from each country were assessed individually via confirmatory factor analyses (CFAs). We obtained two factors, based on only eight of the items: (a) communication disturbances and (b) phone obsession. The 8 items Phubbing Scale.

Application of artificial intelligence for detection of chemico-biological interactions associated with oxidative stress and DNA damage.

In recent years, various AI-based methods have been developed in order to uncover chemico-biological interactions associated with DNA damage and oxidative stress. Various decision trees, bayesian networks, random forests, logistic regression models, support vector machines as well as deep learning tools, have great potential in the area of molecular biology and toxicology, and it is estimated that in the future, they will greatly contribute to our understanding of molecular and cellular mechanisms associated with DNA damage and repair. In this concise review, we discuss recent attempts to build machine learning tools for assessment of radiation - induced DNA damage as well as algorithms that can analyze the data from the most frequently used DNA damage assays in molecular biology. We also review recent works on the detection of antioxidant proteins with machine learning, and the use of AI-related methods for prediction and evaluation of noncoding DNA sequences. Finally, we discuss previously published research on the potential application of machine learning tools in aging research.

Effects of Iron Oxide Nanoparticles on Structural Organization of Hepatocyte Chromatin: Gray Level Co-occurrence Matrix Analysis.

Gray level co-occurrence matrix (GLCM) analysis is a contemporary and innovative computer-based algorithm that can be used for the quantification of subtle changes in a cellular structure. In this work, we use this method for the detection of discrete alterations in hepatocyte chromatin distribution after in vivo exposure to iron oxide nanoparticles (IONPs). The study was performed on 40 male, healthy C57BL/6 mice divided into four groups: three experimental groups that received different doses of IONPs and 1 control group. We describe the dose-dependent reduction of chromatin textural uniformity measured as GLCM angular second moment. Similar changes were detected for chromatin textural uniformity expressed as the value of inverse difference moment. To the best of our knowledge, this is the first study to investigate the impact of iron-based nanomaterials on hepatocyte GLCM parameters. Also, this is the first study to apply discrete wavelet transform analysis, as a supplementary method to GLCM, for the assessment of hepatocyte chromatin structure in these conditions. The results may present the useful basis for future research on the application of GLCM and DWT methods in pathology and other medical research areas.

Oxidopamine and oxidative stress: Recent advances in experimental physiology and pharmacology.

Oxidopamine (6-hydroxydopamine, 6-OHDA) is a toxin commonly used for the creation of experimental animal models of Parkinson's disease, attention-deficit hyperactivity disorder, and Lesch-Nyhan syndrome. Its exact mechanism of action is not completely understood, although there are many indications that it is related to the generation of reactive oxygen species (ROS), primarily in dopaminergic neurons. In certain experimental conditions, oxidopamine may also cause programmed cell death via various signaling pathways. Oxidopamine may also have a significant impact on chromatin structure and nuclear structural organization in some cells. Today, many researchers use oxidopamine-associated oxidative damage to evaluate different antioxidant-based pharmacologically active compounds as drug candidates for various neurological and non-neurological diseases. Additional research is needed to clarify the exact biochemical pathways associated with oxidopamine toxicity, related ROS generation and apoptosis. In this short review, we focus on the recent research in experimental physiology and pharmacology, related to the cellular and animal experimental models of oxidopamine - mediated toxicity.

Perinatal complications related to inherited thrombophilia: review of evidence in different regions of the world.

The term thrombophilia describes disorders associated with an increased predisposition of developing venous thromboembolism (VTE). It may be acquired, like in those with antiphospholipid syndrome or inherited. The aim of this review was to compare the complications and outcomes of pregnancies in women with inherited thrombophilia between different populations, including the population of our country where the results of the research are scarce. The review of literature included all papers indexed on PubMed and Medline in the last 20 years, with different study design, including other reviews of literature, systematic reviews with meta-analysis and several case-control studies and population-based cohort studies. We aimed to cover as many geographic regions as possible with the aim to show the differences in the different parts of the world and including our country. Our analysis showed that types of thrombophilia differ in different geographic regions. Also, the differences exist between one particular type of thrombophilia in different regions. Nevertheless, no matter what the differences are between prevalence, all authors investigated the association between inherited thrombophilia and poor pregnancy outcome and managed to find some kind of association. The case with our own country is similar. Although we lack in studies with this issue and the design of published studies is not powerful enough, we may conclude that in our samples, women with thrombophilia are in potential risk of several poor pregnancy outcomes. Further and better analyses are necessary to prove this hypothesis not only on the level of study sample but also on general population. Given the fact that thrombophilia certainly affects the pregnancy and its outcome, the urge to perform screening tests in every woman suspected to have this kind of disorder and with respect to differences that exist in different world regions is inevitable.

Country Indicators Moderating the Relationship Between Phubbing and Psychological Distress: A Study in 20 Countries.

Problematic mobile phone use can be related to negative mental states. Some studies indicate that behavioural dependency is related to variables associated with the country of origin. The aim of our study was to investigate if country indicators moderated the relationship between phubbing and psychological distress. Our sample consisted of 7,315 individuals from 20 countries, who completed the Phubbing Scale and the Kessler Psychological Distress Scale (K6). The analyses also included country indicators: the Gender Gap Index (GGI), the Human Development Index (HDI), the Social Progress Index (SPI), Hofstede's dimensions of culture, and the World Happiness Index (WHI). Our results showed that psychological distress was related to at least one dimension of phubbing (i.e., to communication disturbance or phone obsession) in all countries, which means this relationship is culturally universal. The results of the study demonstrate the importance of testing measurement invariance to determine what type of analysis and what type of conclusion are valid in a given study or comparison. Moreover, the increasing or decreasing correlation between phubbing and distress is related to some culture-level indices.

Gray-Level Co-Occurrence Matrix Analysis of Granule Neurons of the Hippocampal Dentate Gyrus Following Cortical Injury.

Traumatic brain injury (TBI) is a main cause of death and disabilities in young adults. Although learning and memory impairments are a major clinical manifestation of TBI, the consequences of TBI on the hippocampus are still not well understood. In particular, how lesions to the sensorimotor cortex damage the hippocampus, to which it is not directly connected, is still elusive. Here, we study the effects of sensorimotor cortex ablation (SCA) on the hippocampal dentate gyrus, by applying a highly sensitive gray-level co-occurrence matrix (GLCM) analysis. Using GLCM analysis of granule neurons, we discovered, in our TBI paradigm, subtle changes in granule cell (GC) morphology, including textual uniformity, contrast, and variance, which is not detected by conventional microscopy. We conclude that sensorimotor cortex trauma leads to specific changes in the hippocampus that advance our understanding of the cellular underpinnings of cognitive impairments in TBI. Moreover, we identified GLCM analysis as a highly sensitive method to detect subtle changes in the GC layers that is expected to significantly improve further studies investigating the impact of TBI on hippocampal neuropathology.