Exacerbated Activation of the NLRP3 Inflammasome in the Placentas from Women Who Developed Chronic Venous Disease during Pregnancy.

Chronic venous disease (CVD) comprises a spectrum of morphofunctional disorders affecting the venous system, affecting approximately 1 in 3 women during gestation. Emerging evidence highlights diverse maternofetal implications stemming from CVD, particularly impacting the placenta. While systemic inflammation has been associated with pregnancy-related CVD, preliminary findings suggest a potential link between this condition and exacerbated inflammation in the placental tissue. Inflammasomes are major orchestrators of immune responses and inflammation in different organs and systems. Notwithstanding the relevance of inflammasomes, specifically the NLRP3 (nucleotide-binding domain, leucine-rich-containing family, pyrin domain-containing-3)- which has been demonstrated in the placentas of women with different obstetric complications, the precise involvement of this component in the placentas of women with CVD remains to be explored. This study employs immunohistochemistry and real-time PCR (RT-qPCR) to examine the gene and protein expression of key components in both canonical and non-canonical pathways of the NLRP3 inflammasome (NLRP3, ASC-apoptosis-associated speck-like protein containing a C-terminal caspase recruitment domain-caspase 1, caspase 5, caspase 8, and interleukin 1ß) within the placental tissue of women affected by CVD. Our findings reveal a substantial upregulation of these components in CVD-affected placentas, indicating a potential pathophysiological role of the NLRP3 inflammasome in the development of this condition. Subsequent investigations should focus on assessing translational interventions addressing this dysregulation in affected patient populations.

Patients with Chronic Spinal Cord Injury and a Long Period of Evolution Exhibit an Altered Cytokine Production by CD4 and CD8 T Cell Populations.

Spinal cord injury (SCI) is a disabling neurological condition coursing with serious multisystem affections and morbidities. Changes in immune cell compartments have been consistently reported in previous works, representing a critical point of study for understanding the pathophysiology and progression of SCI from acute to chronic stages. Some relevant variations in circulating T cells have been noticed in patients with chronic SCI, although the number, distribution, and function of these populations remain to be fully elucidated. Likewise, the characterization of specific T cell subpopulations and their related cytokine production can aid in understanding the immunopathological role of T cells in SCI progression. In this sense, the objective of the present study was to analyze and quantify the total number of different cytokine-producers T cells in the serum of patients with chronic SCI (n = 105) in comparison to healthy controls (n = 38) by polychromatic flow cytometry. Having this goal, we studied CD4 and CD8 lymphocytes as well as naïve, effector, and effector/central memory subpopulations. SCI patients were classified according to the duration of the lesion in chronic SCI with a short period of evolution (SCI-SP) (comprised between 1 and 5 years since initial injury), early chronic phase (SCI-ECP) (between 5 and 15 years since initial injury) and late-chronic phase (SCI-LCP) (>15 years since initial injury). Our results show that patients with chronic SCI exhibited an altered immune profile of cytokine-producer T cells, including CD4/CD8 naïve, effector, and memory subpopulations in comparison to HC. In particular, IL-10 and IL-9 production seems to be importantly altered, especially in patients with SCI-LCP, whereas changes in IL-17, TNF-α, and IFN-γ T cell populations have also been reported in this and other chronic SCI groups. In conclusion, our study demonstrates an altered profile of cytokine-producer T cells in patients with chronic SCI, with marked changes throughout the course of the disease. In more detail, we have observed significant variations in cytokine production by circulating naive, effector, and effector/central memory CD4 and CD8 T cells. Future studies should be directed to explore the possible clinical consequences of these changes or develop additional translational approaches in these groups of patients.

Assessment of Tissue Expression of the Oxytocin-Vasopressin Pathway in the Placenta of Women with a First-Episode Psychosis during Pregnancy.

Psychosis refers to a mental health condition characterized by a loss of touch with reality, comprising delusions, hallucinations, disorganized thought, disorganized behavior, catatonia, and negative symptoms. A first-episode psychosis (FEP) is a rare condition that can trigger adverse outcomes both for the mother and newborn. Previously, we demonstrated the existence of histopathological changes in the placenta of pregnant women who suffer an FEP in pregnancy. Altered levels of oxytocin (OXT) and vasopressin (AVP) have been detected in patients who manifested an FEP, whereas abnormal placental expression of these hormones and their receptors (OXTR and AVPR1A) has been proven in different obstetric complications. However, the precise role and expression of these components in the placenta of women after an FEP have not been studied yet. Thus, the purpose of the present study was to analyze the gene and protein expression, using RT-qPCR and immunohistochemistry (IHC), of OXT, OXTR, AVP, and AVPR1a in the placental tissue of pregnant women after an FEP in comparison to pregnant women without any health complication (HC-PW). Our results showed increased gene and protein expression of OXT, AVP, OXTR, and AVPR1A in the placental tissue of pregnant women who suffer an FEP. Therefore, our study suggests that an FEP during pregnancy may be associated with an abnormal paracrine/endocrine activity of the placenta, which can negatively affect the maternofetal wellbeing. Nevertheless, additional research is required to validate our findings and ascertain any potential implications of the observed alterations.

The RANK-RANKL-OPG System: A Multifaceted Regulator of Homeostasis, Immunity, and Cancer.

The RANK-RANKL-OPG system is a complex signaling pathway that plays a critical role in bone metabolism, mammary epithelial cell development, immune function, and cancer. RANKL is a ligand that binds to RANK, a receptor expressed on osteoclasts, dendritic cells, T cells, and other cells. RANKL signaling promotes osteoclast differentiation and activation, which leads to bone resorption. OPG is a decoy receptor that binds to RANKL and inhibits its signaling. In cancer cells, RANKL expression is often increased, which can lead to increased bone resorption and the development of bone metastases. RANKL-neutralizing antibodies, such as denosumab, have been shown to be effective in the treatment of skeletal-related events, including osteoporosis or bone metastases, and cancer. This review will provide a comprehensive overview of the functions of the RANK-RANKL-OPG system in bone metabolism, mammary epithelial cells, immune function, and cancer, together with the potential therapeutic implications of the RANK-RANKL pathway for cancer management.

COVID-19 X-ray images classification based on enhanced fractional-order cuckoo search optimizer using heavy-tailed distributions.

Classification of COVID-19 X-ray images to determine the patient's health condition is a critical issue these days since X-ray images provide more information about the patient's lung status. To determine the COVID-19 case from other normal and abnormal cases, this work proposes an alternative method that extracted the informative features from X-ray images, leveraging on a new feature selection method to determine the relevant features. As such, an enhanced cuckoo search optimization algorithm (CS) is proposed using fractional-order calculus (FO) and four different heavy-tailed distributions in place of the Lévy flight to strengthen the algorithm performance during dealing with COVID-19 multi-class classification optimization task. The classification process includes three classes, called normal patients, COVID-19 infected patients, and pneumonia patients. The distributions used are Mittag-Leffler distribution, Cauchy distribution, Pareto distribution, and Weibull distribution. The proposed FO-CS variants have been validated with eighteen UCI data-sets as the first series of experiments. For the second series of experiments, two data-sets for COVID-19 X-ray images are considered. The proposed approach results have been compared with well-regarded optimization algorithms. The outcomes assess the superiority of the proposed approach for providing accurate results for UCI and COVID-19 data-sets with remarkable improvements in the convergence curves, especially with applying Weibull distribution instead of Lévy flight.

A Grouping Differential Evolution Algorithm Boosted by Attraction and Repulsion Strategies for Masi Entropy-Based Multi-Level Image Segmentation.

Masi entropy is a popular criterion employed for identifying appropriate threshold values in image thresholding. However, with an increasing number of thresholds, the efficiency of Masi entropy-based multi-level thresholding algorithms becomes problematic. To overcome this, we propose a novel differential evolution (DE) algorithm as an effective population-based metaheuristic for Masi entropy-based multi-level image thresholding. Our ME-GDEAR algorithm benefits from a grouping strategy to enhance the efficacy of the algorithm for which a clustering algorithm is used to partition the current population. Then, an updating strategy is introduced to include the obtained clusters in the current population. We further improve the algorithm using attraction (towards the best individual) and repulsion (from random individuals) strategies. Extensive experiments on a set of benchmark images convincingly show ME-GDEAR to give excellent image thresholding performance, outperforming other metaheuristics in 37 out of 48 cases based on cost function evaluation, 26 of 48 cases based on feature similarity index, and 20 of 32 cases based on Dice similarity. The obtained results demonstrate that population-based metaheuristics can be successfully applied to entropy-based image thresholding and that strengthening both exploitation and exploration strategies, as performed in ME-GDEAR, is crucial for designing such an algorithm.

Forecast Model Update Based on a Real-Time Data Processing Lambda Architecture for Estimating Partial Discharges in Hydrogenerator.

The prediction of partial discharges in hydrogenerators depends on data collected by sensors and prediction models based on artificial intelligence. However, forecasting models are trained with a set of historical data that is not automatically updated due to the high cost to collect sensors' data and insufficient real-time data analysis. This article proposes a method to update the forecasting model, aiming to improve its accuracy. The method is based on a distributed data platform with the lambda architecture, which combines real-time and batch processing techniques. The results show that the proposed system enables real-time updates to be made to the forecasting model, allowing partial discharge forecasts to be improved with each update with increasing accuracy.

Metaheuristics in the Optimization of Cryptographic Boolean Functions.

Generating Boolean Functions (BFs) with high nonlinearity is a complex task that is usually addresses through algebraic constructions. Metaheuristics have also been applied extensively to this task. However, metaheuristics have not been able to attain so good results as the algebraic techniques. This paper proposes a novel diversity-aware metaheuristic that is able to excel. This proposal includes the design of a novel cost function that combines several information from the Walsh Hadamard Transform (WHT) and a replacement strategy that promotes a gradual change from exploration to exploitation as well as the formation of clusters of solutions with the aim of allowing intensification steps at each iteration. The combination of a high entropy in the population and a lower entropy inside clusters allows a proper balance between exploration and exploitation. This is the first memetic algorithm that is able to generate 10-variable BFs of similar quality than algebraic methods. Experimental results and comparisons provide evidence of the high performance of the proposed optimization mechanism for the generation of high quality BFs.

Multi-Level Image Thresholding Based on Modified Spherical Search Optimizer and Fuzzy Entropy.

Multi-level thresholding is one of the effective segmentation methods that have been applied in many applications. Traditional methods face challenges in determining the suitable threshold values; therefore, metaheuristic (MH) methods have been adopted to solve these challenges. In general, MH methods had been proposed by simulating natural behaviors of swarm ecosystems, such as birds, animals, and others. The current study proposes an alternative multi-level thresholding method based on a new MH method, a modified spherical search optimizer (SSO). This was performed by using the operators of the sine cosine algorithm (SCA) to enhance the exploitation ability of the SSO. Moreover, Fuzzy entropy is applied as the main fitness function to evaluate the quality of each solution inside the population of the proposed SSOSCA since Fuzzy entropy has established its performance in literature. Several images from the well-known Berkeley dataset were used to test and evaluate the proposed method. The evaluation outcomes approved that SSOSCA showed better performance than several existing methods according to different image segmentation measures.

Bioceramic versus traditional biomaterials for endodontic sealers according to the ideal properties.

Odontology, as a scientific discipline, continuously collaborates with biomaterials engineering to enhance treatment characteristics and patients' satisfaction. Endodontics, a specialized field of dentistry, focuses on the study, diagnosis, prevention, and treatment of dental disorders affecting the dental pulp, root, and surrounding tissues. A critical aspect of endodontic treatment involves the careful selection of an appropriate endodontic sealer for clinical use, as it significantly influences treatment outcomes. Traditional sealers, such as zinc oxide-eugenol, fatty acid, salicylate, epoxy resin, silicone, and methacrylate resin systems, have been extensively used for decades. However, advancements in endodontics have given rise to bioceramic-based sealers, offering improved properties and addressing new challenges in endodontic therapy. In this review, a classification of these materials and their ideal properties are presented to provide evidence-based guidance to clinicians. Physicochemical properties, including sealing ability, stability over time and space, as well as biological properties such as biocompatibility and antibacterial characteristics, along with cost-effectiveness, are essential factors influencing clinicians' decisions based on individual patient evaluations.

Elevated tissue expression of RANKL and RANK is associated with poorer survival rates in pancreatic cancer patients.

Pancreatic cancer is a highly lethal malignancy with a growing incidence reported worldwide. Pancreatic ductal adenocarcinoma (PDAC) is the most common type of pancreatic cancer, which is often diagnosed at advanced stages, making its prognosis and medical management difficult. The identification of histopathological biomarkers has allowed a more precise stratification of pancreatic cancer patients, providing additional information about their prognosis and offering possible therapeutic targets to be explored. The prognostic value of the receptor activator of nuclear factor-kappa B (RANK) and its ligand (RANKL) has been evaluated in breast and prostate tumors, however, their usefulness has not been assessed in pancreatic cancer. In the present work, we analyzed the relationship between the protein expression of RANK and RANKL with the survival of 41 patients with pancreatic cancer followed for 60 months, by performing immunohistochemistry and Kaplan-Meier curves. Our results demonstrate a direct association of high expression levels of RANK and RANKL with poorer survival of pancreatic cancer patients in comparison to those with low/medium and null expression levels of both markers. Further studies should be conducted to explore the carcinogenic role of both components in this type of tumor, as well as additional promising translational uses.

PD-1/PD-L1 axis: implications in immune regulation, cancer progression, and translational applications.

The PD-1/PD-L1 axis is a complex signaling pathway that has an important role in the immune system cells. Programmed cell death protein 1 (PD-1) acts as an immune checkpoint on the T lymphocytes, B lymphocytes, natural killer (NK), macrophages, dendritic cells (DCs), monocytes, and myeloid cells. Its ligand, the programmed cell death 1 ligand (PD-L1), is expressed in the surface of the antigen-presenting cells (APCs). The binding of both promotes the downregulation of the T cell response to ensure the activation to prevent the onset of chronic immune inflammation. This axis in the tumor microenvironment (TME) performs a crucial role in the tumor progression and the escape of the tumor by neutralizing the immune system, the engagement of PD-L1 with PD-1 in the T cell causes dysfunctions, neutralization, and exhaustion, providing the tumor mass production. This review will provide a comprehensive overview of the functions of the PD-1/PD-L1 system in immune function, cancer, and the potential therapeutic implications of the PD-1/PD-L1 pathway for cancer management.

Connecting epigenetics and inflammation in vascular senescence: state of the art, biomarkers and senotherapeutics.

Vascular diseases pose major health challenges, and understanding their underlying molecular mechanisms is essential to advance therapeutic interventions. Cellular senescence, a hallmark of aging, is a cellular state characterized by cell-cycle arrest, a senescence-associated secretory phenotype macromolecular damage, and metabolic dysregulation. Vascular senescence has been demonstrated to play a key role in different vascular diseases, such as atherosclerosis, peripheral arterial disease, hypertension, stroke, diabetes, chronic venous disease, and venous ulcers. Even though cellular senescence was first described in 1961, significant gaps persist in comprehending the epigenetic mechanisms driving vascular senescence and its subsequent inflammatory response. Through a comprehensive analysis, we aim to elucidate these knowledge gaps by exploring the network of epigenetic alterations that contribute to vascular senescence. In addition, we describe the consequent inflammatory cascades triggered by these epigenetic modifications. Finally, we explore translational applications involving biomarkers of vascular senescence and the emerging field of senotherapy targeting this biological process.

Autophagy in Its (Proper) Context: Molecular Basis, Biological Relevance, Pharmacological Modulation, and Lifestyle Medicine.

Autophagy plays a critical role in maintaining cellular homeostasis and responding to various stress conditions by the degradation of intracellular components. In this narrative review, we provide a comprehensive overview of autophagy's cellular and molecular basis, biological significance, pharmacological modulation, and its relevance in lifestyle medicine. We delve into the intricate molecular mechanisms that govern autophagy, including macroautophagy, microautophagy and chaperone-mediated autophagy. Moreover, we highlight the biological significance of autophagy in aging, immunity, metabolism, apoptosis, tissue differentiation and systemic diseases, such as neurodegenerative or cardiovascular diseases and cancer. We also discuss the latest advancements in pharmacological modulation of autophagy and their potential implications in clinical settings. Finally, we explore the intimate connection between lifestyle factors and autophagy, emphasizing how nutrition, exercise, sleep patterns and environmental factors can significantly impact the autophagic process. The integration of lifestyle medicine into autophagy research opens new avenues for promoting health and longevity through personalized interventions.

Advances in 3D bioprinting to enhance translational applications in bone tissue engineering and regenerative medicine.

Bone defects are due to trauma, infections, tumors, or aging, including bone fractures, bone metastases, osteoporosis, or osteoarthritis. The global burden of these demands research into innovative strategies that overcome the limitations of conventional autografts. In this sense, the development of three-dimensional (3D) bioprinting has emerged as a promising approach in the field of tissue engineering and regenerative medicine (TERM) for the on-demand generation and transplantation of tissues and organs, including bone. It combines biological materials and living cells, which are precisely positioned layer by layer. Despite obtaining some promising results, 3D bioprinting of bone tissue still faces several challenges, such as generating an effective vascular network to increase tissue viability. In this review, we aim to collect the main knowledge on methods and techniques of 3D bioprinting. Then, we will review the main biomaterials, their composition, and the rationale for their application in 3D bioprinting for the TERM of bone.

Abnormal Histopathological Expression of Klotho, Ferroptosis, and Circadian Clock Regulators in Pancreatic Ductal Adenocarcinoma: Prognostic Implications and Correlation Analyses.

Pancreatic ductal adenocarcinoma (PDAC) is an extremely lethal tumor with increasing incidence, presenting numerous clinical challenges. The histopathological examination of novel, unexplored biomarkers offers a promising avenue for research, with significant translational potential for improving patient outcomes. In this study, we evaluated the prognostic significance of ferroptosis markers (TFRC, ALOX-5, ACSL-4, and GPX-4), circadian clock regulators (CLOCK, BMAL1, PER1, PER2), and KLOTHO in a retrospective cohort of 41 patients deceased by PDAC. Immunohistochemical techniques (IHC) and multiple statistical analyses (Kaplan-Meier curves, correlograms, and multinomial linear regression models) were performed. Our findings reveal that ferroptosis markers are directly associated with PDAC mortality, while circadian regulators and KLOTHO are inversely associated. Notably, TFRC emerged as the strongest risk marker associated with mortality (HR = 35.905), whereas CLOCK was identified as the most significant protective marker (HR = 0.01832). Correlation analyses indicate that ferroptosis markers are positively correlated with each other, as are circadian regulators, which also positively correlate with KLOTHO expression. In contrast, KLOTHO and circadian regulators exhibit inverse correlations with ferroptosis markers. Among the clinical variables examined, only the presence of chronic pathologies showed an association with the expression patterns of several proteins studied. These findings underscore the complexity of PDAC pathogenesis and highlight the need for further research into the specific molecular mechanisms driving disease progression.

Vascular Calcification: Molecular Networking, Pathological Implications and Translational Opportunities.

Calcification is a process of accumulation of calcium in tissues and deposition of calcium salts by the crystallization of PO43- and ionized calcium (Ca2+). It is a crucial process in the development of bones and teeth. However, pathological calcification can occur in almost any soft tissue of the organism. The better studied is vascular calcification, where calcium salts can accumulate in the intima or medial layer or in aortic valves, and it is associated with higher mortality and cardiovascular events, including myocardial infarction, stroke, aortic and peripheral artery disease (PAD), and diabetes or chronic kidney disease (CKD), among others. The process involves an intricate interplay of different cellular components, endothelial cells (ECs), vascular smooth muscle cells (VSMCs), fibroblasts, and pericytes, concurrent with the activation of several signaling pathways, calcium, Wnt, BMP/Smad, and Notch, and the regulation by different molecular mediators, growth factors (GFs), osteogenic factors and matrix vesicles (MVs). In the present review, we aim to explore the cellular players, molecular pathways, biomarkers, and clinical treatment strategies associated with vascular calcification to provide a current and comprehensive overview of the topic.

Improving the segmentation of digital images by using a modified Otsu's between-class variance.

Image segmentation is a critical stage in the analysis and pre-processing of images. It comprises dividing the pixels according to threshold values into several segments depending on their intensity levels. Selecting the best threshold values is the most challenging task in segmentation. Because of their simplicity, resilience, reduced convergence time, and accuracy, standard multi-level thresholding (MT) approaches are more effective than bi-level thresholding methods. With increasing thresholds, computer complexity grows exponentially. A considerable number of metaheuristics were used to optimize these problems. One of the best image segmentation methods is Otsu's between-class variance. It maximizes the between-class variance to determine image threshold values. In this manuscript, a new modified Otsu function is proposed that hybridizes the concept of Otsu's between class variance and Kapur's entropy. For Kapur's entropy, a threshold value of an image is selected by maximizing the entropy of the object and background pixels. The proposed modified Otsu technique combines the ability to find an optimal threshold that maximizes the overall entropy from Kapur's and the maximum variance value of the different classes from Otsu. The novelty of the proposal is the merging of two methodologies. Clearly, Otsu's variance could be improved since the entropy (Kapur) is a method used to verify the uncertainty of a set of information. This paper applies the proposed technique over a set of images with diverse histograms, which are taken from Berkeley Segmentation Data Set 500 (BSDS500). For the search capability of the segmentation methodology, the Arithmetic Optimization algorithm (AOA), the Hybrid Dragonfly algorithm, and Firefly Algorithm (HDAFA) are employed. The proposed approach is compared with the existing state-of-art objective function of Otsu and Kapur. Qualitative experimental outcomes demonstrate that modified Otsu is highly efficient in terms of performance metrics such as PSNR, mean, threshold values, number of iterations taken to converge, and image segmentation quality.

Liver Cancer Algorithm: A novel bio-inspired optimizer.

This paper introduces a new bio-inspired optimization algorithm named the Liver Cancer Algorithm (LCA), which mimics the liver tumor growth and takeover process. It uses an evolutionary search approach that simulates the behavior of liver tumors when taking over the liver organ. The tumor's ability to replicate and spread to other organs inspires the algorithm. LCA algorithm is developed using genetic operators and a Random Opposition-Based Learning (ROBL) strategy to efficiently balance local and global searches and explore the search space. The algorithm's efficiency is tested on the IEEE Congress of Evolutionary Computation in 2020 (CEC'2020) benchmark functions and compared to seven widely used metaheuristic algorithms, including Genetic Algorithm (GA), particle swarm optimization (PSO), Differential Evolution (DE), Adaptive Guided Differential Evolution Algorithm (AGDE), Improved Multi-Operator Differential Evolution (IMODE), Harris Hawks Optimization (HHO), Runge-Kutta Optimization Algorithm (RUN), weIghted meaN oF vectOrs (INFO), and Coronavirus Herd Immunity Optimizer (CHIO). The statistical results of the convergence curve, boxplot, parameter space, and qualitative metrics show that the LCA algorithm performs competitively compared to well-known algorithms. Moreover, the versatility of the LCA algorithm extends beyond mathematical benchmark problems. It was also successfully applied to tackle the feature selection problem and optimize the support vector machine for various biomedical data classifications, resulting in the creation of the LCA-SVM model. The LCA-SVM model was evaluated in a total of twelve datasets, among which the MonoAmine Oxidase (MAO) dataset stood out, showing the highest performance compared to the other datasets. In particular, the LCA-SVM model achieved an impressive accuracy of 98.704% on the MAO dataset. This outstanding result demonstrates the efficacy and potential of the LCA-SVM approach in handling complex datasets and producing highly accurate predictions. The experimental results indicate that the LCA algorithm surpasses other methods to solve mathematical benchmark problems and feature selection.

Enhanced feature selection technique using slime mould algorithm: a case study on chemical data.

Feature selection techniques are considered one of the most important preprocessing steps, which has the most significant influence on the performance of data analysis and decision making. These FS techniques aim to achieve several objectives (such as reducing classification error and minimizing the number of features) at the same time to increase the classification rate. FS based on Metaheuristic (MH) is considered one of the most promising techniques to improve the classification process. This paper presents a modified method of the Slime mould algorithm depending on the Marine Predators Algorithm (MPA) operators as a local search strategy, which leads to increasing the convergence rate of the developed method, named SMAMPA and avoiding the attraction to local optima. The efficiency of SMAMPA is evaluated using twenty datasets and compared its results with the state-of-the-art FS methods. In addition, the applicability of SMAMPA to work with real-world problems is evaluated by using it as a quantitative structure-activity relationship (QSAR) model. The obtained results show the high ability of the developed SMAMPA method to reduce the dimension of the tested datasets by increasing the prediction rate. In addition, it provides results better than other FS techniques in terms of performance metrics.