This paper shows that the percolation clusters from elementary cellular automata {30, 45, 60, 86, 99, 105, 129, 150, 153, 169, 182, 183, 184, 195 and 225 exhibit strong power law behavior, either under random initial conditions, a single occupied cell, or both. Most of the tail exponents are less than unity, implying diverging means and variances of cluster sizes. The analysis presented here is admittedly coarse in an effort to expedite its dissemination.

As technology and multimedia production have advanced, there has been a significant rise in attacks on digital media, resulting in duplicated, fraudulent, and altered data and the infringement of copyright laws. This paper presents a robust and secure digital image watermarking technique that has been implemented in the spatial domain and exploits the erratic and chaotic behaviour of the powerful elementary cellular automata rule-30. The crucial characteristics of the watermarking system, i.e., imperceptibility, capacity, and robustness, have been perfectly balanced by the suggested blind watermarking technique. In this approach, prior to embedding, the grayscale watermark image is downsized to its two Most Significant Bits (MSBs). Then, the 2-MSBs watermark is encrypted using an ECA rule-30 so as to level up the security attribute of the system. Then, the host image is scrambled using ECA rule-30 to distribute the watermark pixels throughout the host image and thus achieve the highest robustness against geometrical attacks. Finally, the encrypted watermark data is embedded into the scrambled host image using the ECA rule-30-based embedding key. The proposed method performs better in terms of imperceptibility, capacity, and robustness when compared to several systems with similar competencies. The simulation's findings demonstrate strong imperceptibility as evaluated by the Peak Signal-to-Noise Ratio (PSNR), which has an average value of 58.3735 dB and a high payload. The experimental outcomes, observed across a diverse range of standardized attack scenarios, unequivocally establish the ascendancy of the proposed algorithm over competing methodologies in the realm of image watermarking.

Quantum-dot cellular automata (QCA) is an emerging transistor-less field-coupled nanocomputing (FCN) approach to ultra-scale ‘nanochip’ integration. In QCA, to represent digital circuitry, electrostatic repulsion between electrons and the mechanism of electron tunnelling in quantum dots are used. QCA technology can surpass conventional complementary metal-oxide semiconductor (CMOS) technology in terms of clock speed, reduced occupied chip area, and energy efficiency. To develop QCA circuits, irreversible majority gates are typically used as the primary components. Recently, some studies have introduced reversible design techniques, using reversible majority gates as the main building block, to develop ultra-energy efficient QCA circuits. However, this approach resulted in time delays, an increase in the number of QCA cells used, and an increase in the chip area occupied. This work introduces a novel hybrid design strategy employing QCA irreversible, reversible, and partially reversible gates to establish an optimal balance between power consumption, delay time, and occupied area. The hybrid technique allows the designer to have more control over the circuit characteristics to meet different system needs. A combination of reversible, irreversible, and innovative partially reversible majority gates is used in the proposed hybrid design method. We have evaluated the hybrid design method by examining the half-adder circuit as a case study. We have developed four hybrid QCA half-adder circuits, each of which simultaneously incorporates various types of majority gates. The QCADesigner-E 2.2 simulation tool was used to simulate the performance and energy efficiency of the half-adders. This tool provides numerical results for the circuit input/output response and heat dissipation at the physical level, within a microscopic quantum mechanical model.

We explores the relational, dynamic elements of Complex Buildings, a type of architecture designed to incubate uses, located in urban areas with high housing density. The uses of Complex Buildings concern different elements, including the network of agents using or managing them, the environment, and the activities and functions that take place occasionally, temporarily or permanently. Data was gathered through ethnographic research lasting 6 months and a chronotopian approach was used to describe time and space. We analyzed and discussed the interaction of the elements of Complex Buildings through a cellular automaton model, a computational method that simulates the growth of complex systems. It was used here to generate patterns that suggest configurations of uses that can optimize management and therefore increase economic and social capital. The cellular automaton model was also used to develop an abstraction of the Centquatre, a public cultural center in Paris. This center is a good example of a Complex Building, being based on a public-private partnership and having an architectural configuration designed to host a wide range of art, social and productive activities. The building includes a large central space used as an urban public area open to different types of people. The importance of this case study lies in its capacity to produce economic value by combining different uses, and also by welcoming different people to the public space. Regarding the building as a living organism, the cellular automaton model reveals the determinant nature of the concepts of configuration, compatibility of uses and economic value generated by the presence of people. We argue that this approach makes it possible to show that the space-time design and public space dimensions are determinant factors in Complex Buildings.

Cellular automata (CA) models have emerged as a valuable tool in corrosion management. This manuscript provides an overview of the application of cellular automata models in corrosion research, highlighting their benefits and contributions to understanding the complex nature of corrosion processes. Cellular automata models offer a computational approach to simulate corrosion behavior at the microscale, capturing the intricate interactions between electrochemical reactions, material properties, and environmental factors generating a new vision of predictive maintenance. It discusses the key features of cellular automata, such as the grid-based representation of the material surface, the definition of state variables, and the rules governing cell-state transitions. The ability to model local interactions and emergent global behavior makes cellular automata particularly suitable for simulating corrosion processes. Finally, cellular automata models offer a powerful and versatile approach to studying corrosion processes, expanding models that can continue to enhance our understanding of corrosion and contribute to the development of effective corrosion prevention and control strategies.

Nowadays the practice of developing algorithms to maintain the confidentiality of data shows that there is a lack of some features, such as velocity, predictability, etc. Generating pseudorandom numbers is one such problem that lies in the basement of many algorithms, even in hardware microprograms. An unreliable generator can cause cyberattacks on it, despite the security in the upper layers. At the same time, the algorithm should be fast enough to provide uninterrupted circuit work for the entire system. The paper presents a new algorithm generating pseudorandom numbers on cellular automata, which is not only fast and easy-repeating, but unpredictable enough and can be used in cryptographic systems. Using the NIST statistical test suite for random and pseudorandom number generators (PRNG), it is shown that the presented algorithm is more than three times superior to the state-of-the-art methods and algorithms in terms of ? − ?????. A high level of the presented algorithm’s parallelization allows for implementation it effectively on calculators with parallel structure. CPU-based architecture, FPGA-based architecture, CUDA- based architecture of PRNG and different PRNG implementations are presented to confirm high performance of the proposed solution.

A correctly obtained Land-use/land-cover (LULC) prediction map is essential to under-standing and assessing future patterns. In the study, the LULC map of Urmia/Iran in 2030 was produced using two different prediction methods CA-Markov and Artificial Neural Network (ANN). In general, the study followed a methodology consisting of three steps. In the first steps, Landsat satellite images acquired in 2000, 2010 and 2020 were classified with maximum likelihood algorithm and LULC maps were prepared for each year. In the second stage, to investigate the LULC prediction methods' validation (CA-Markov and ANN) the LULC prediction map of 2020 was produced using the LULC map of 2000 and 2010; In this step, the predicted LULC map of 2020 and the actual LULC map of 2020 were evaluated by correctness, completeness and quality indexes. Finally, The LULC map for 2030 was prepared using all two algorithms and the change map was extracted. The results show that the area of soil and vegetation decreased, and built-up regions increased during the research period. The methods validation results show that the two algorithms are much closer to each other. Nevertheless, in general, ANN has the highest completeness (96.21%) and quality (93.8%) and CA-Markov the most correctness (96.47). This study shows that the CA-Markov algorithm is most successful in predicting the future that had larger areas and a higher percentage in the region (urban and vegetation cover) and the ANN algorithm in predicting phenomena that had smaller levels with fewer percentages (soil and rock).

Although, promotion of urbanization culture in recent decades has made inevitable development of cities in the world, however, the development can be guided in a direction that leave, to the extent possible, minimum socioeconomic and environmental impacts. For this, it is required to first forecast auto-spreading orientation of cities and suburbs in rural areas over time and then avoid shapeless growth of cities. This paper is an attempt to develop a dynamic hybrid model based on logistic regression (LR), Markov chain (MC), and cellular automata (CA) for prediction of future urban sprawl in fast-growing cities. The model was developed using 12 widely-used urban development criteria, whose significant coefficient was determined by logistic regression, and validated by relative operating characteristic (ROC) analysis. The validated model was run in Guilan, a tourist province in northern Iran with a very high rate of urban development. For this, changes in the area of urban land use were detected over the period of 1989 to 2013 and then, future sprawl of the province was forecasted by the years 2025 and 2037. The analysis results revealed that the area of urban land use was increased by more than 1.7 % from 36012.5 ha in 1989 to 59754.8 ha in 2013, and the area of Caspian Hyrcanian forestland was reduced by 31628 ha. The results also predicted an alarming increase in the rate of urban development in the province by the years 2025 and 2037, during which urban land use is predicted to develop 0.9 % and 1.38 %, respectively. The development pattern is expected to be uneven and scattered, without following any particular direction. The development will occur close to the existing or newly-formed urban basements as well as around major roads and commercial areas. This development, if not controlled, will lead to the loss of 13863 ha of Hyrcanian forests and if the trend continues, 21013 ha of Hyrcanian forests and 20208 ha of Barren/open lands are expected to be destroyed by the year 2037. In general, the proposed model is an efficient tool for the support of urban planning decisions and facilitates the process of sustainable development of cities by providing decision-makers with an overview on future development of cities where the growth rate is very fast.

Urban growth in various cities across the world, especially in developing countries, leads to land use change. Thus, predicting future urban growth in the most rapidly growing region of Nigeria becomes a significant endeavor. This study analyzes land use and land cover (LULC) change and predicts the future urban growth of the Lagos metropolitan region, using Cellular Automata (CA) model. To achieve this, the GlobeLand30 datasets from years 2000 and 2010 were used to obtain LULC maps, which were utilized for modeling and prediction. Change analysis and prediction for LULC scenario for 2030 were performed using LCM and CA_Markov chain modeling. The results show a substantial growth of artificial surfaces, which will cause further reductions in cultivated land, grassland, shrubland, wetland, and waterbodies. There was no appreciable impact of change for bare land, as its initial extent of cover later disappeared completely. Additionally, artificial surfaces/urban growth in Lagos expanded to the neighboring towns and localities in Ogun State during the study period, and it is expected that such growth will be higher in 2030. Lastly, the study findings will be beneficial to urban planners and land use managers in making key decisions regarding urban growth and improved land use management in Nigeria.

Energy efficiency considerations in terms of reduced power dissipation is a significant issue in the design of digital circuits for very large-scale integration (VLSI) systems. Quantum-dot cellular automata (QCA) is an emerging ultra-low power dissipation approach, distinct from the traditional complementary metal-oxide semiconductor (CMOS) technology, for building digital computing circuits. Developing fully reversible QCA circuits has the potential to significantly reduce energy dissipation. The multiplexer is a fundamental element in the construction of useful digital circuits. In this paper, a novel, multilayer, fully reversible QCA 8:1 multiplexer circuit, with ultralow energy dissipation, is introduced. The proposed multiplexer power dissipation is simulated using the QCADesigner-E version 2.2 tool, describing the microscopic physical mechanisms underlying the QCA operation. The results showed that the proposed reversible QCA 8:1 multiplexer consumes 89% less energy than the most energy-efficient 8:1 multiplexer circuit presented previously in the scientific literature.

Cellular automata are mathematical models that represent systems with complex behavior through simple interactions between their individual elements. These models can be used to study unconventional computational systems and complexity. One notable aspect of cellular automata is their ability to create structures known as gliders, which move in a regular pattern to represent the manipulation of information. This paper introduces the modification of mean-field theory applied to cellular automata, using random perturbations based on the system’s evolution rule. The original aspect of this approach is that the perturbation factor is tailored to the nature of the rule, altering the behavior of the mean-field polynomials. By combining the properties of both the original and perturbed polynomials, it is possible to detect when a cellular automaton is more likely to generate gliders without having to run evolutions of the system.This methodology is a useful approach to finding more examples of cellular automata that exhibit complex behavior. We start by examining elementary cellular automata, then move on to examples of automata that can generate gliders with more states. To illustrate the results of this methodology, we provide evolution examples of the detected automata.

In this study, our goal was to research land-use change by combining spatio–temporal land use/land cover monitoring (LULC (1989–2019) and urban growth modeling (1999–2039) in Benslimane, Morocco, to determine the effect of urban growth on different groups based on cellular automata (CA) and geospatial methods. A further goal was to test the reliability of the AC algorithm for urban expansion modeling. To do this, four years of satellite data were used at the same time as population density, downtown distance, slope, and ground road distance. The LULC satellite reported a rise of 3.8 km2 (318% variation) during 1989–2019. Spatial transformation analysis reveals a good classification similarity ranging from 89% to 91% with the main component analysis (PCA) technique. The statistical accuracy between the satellite scale and the replicated built region of 2019 gave 97.23 %t of the confusion matrix overall accuracy, and the region under the receiver operational characteristics (ROC) curve to 0.94, suggesting the model's high accuracy. Although the constructed area remains low relative to the total area of the municipality's territory, the LULC project shows that the urban area will extend to 5,044 km2 in 2019, principally in the western and southwestern sections. In 2019–2039, urban development is expected to lead to a transformation of the other class (loss of 1,364 km2), followed by vegetation cover (loss of 0.345 km2). In spatial modeling and statistical calculations, the GDAL and NumPy Python 3.8 libraries were successful.

Circulating tumor cells (CTCs) are cells released from the primary tumor and intravasate into the blood or lymphatic vessels, where they are transported to distant sites and act as seeds that initiate cancer metastases. Recent advances in CTC research have shown their relevance as prognostic markers for early and metastatic disease detection, predictive biomarkers for relapse, and response to medical intervention or therapy. The rapidly evolving landscape of CTC biology has opened new avenues for understanding cancer progression, metastasis, and treatment response. Additionally, translating these findings into clinical applications holds promise for improving cancer diagnostics, prognosis, and personalized therapeutic strategies. This review discusses the significance of CTCs in cancer research and their associated challenges. We explore recent developments in the detection and characterization of CTCs and their implications in cancer research and clinical practice.

Quantum-dot cellular automata (QCA) are a promising nanoscale computing technology that exploits the quantum mechanical tunneling of electrons between quantum dots in a cell and electrostatic interaction between dots, in neighboring cells. QCA can achieve higher speed, lower power, and smaller areas than conventional complementary metal–oxide–semiconductor (CMOS) technology. Developing QCA circuits in a logically and physically reversible manner can provide exceptional reductions in energy dissipation. The main challenge of reversible QCA design is to maintain reversibility down to the physical level. The arithmetic logic unit (ALU) is an essential component of the central processing unit (CPU) of a computer. It performs various arithmetic and logical operations on the data that the CPU processes. Current QCA ALU designs are either irreversible or logically reversible; however, they lack physical reversibility, a crucial requirement to increase energy efficiency. In this paper, we present a novel multilayer design for a logically and physically reversible QCA ALU based on majority gates as the key components, which can perform 16 different operations. We use QCADesigner-E software to simulate and evaluate energy dissipation. The proposed logically and physically reversible QCA ALU offers an improvement of 88.8% in energy efficiency. Compared to the next most efficient 16-operation QCA ALU, this ALU uses 51% fewer QCA cells and 47% less area.

In the last three decades the presence of phospholipids in the nucleus has been shown and thoroughly investigated. A great interest raised about nuclear inositol lipids mainly because of their role in signalling acting. Here we review the main issues of nuclear phospholipid localization and the role of nuclear inositol lipids and their related enzymes in cellular signaling both in physiological and pathological conditions.

High systolic blood pressure and increased blood pressure variability after the onset of ischemic stroke are associated with poor clinical outcomes. One of the key determinants of blood pressure is arteriolar size, determined by vascular smooth muscle tone and vasodilatory and vasoconstrictor substances that are released by the endothelium. The aim of this study is to outline alterations in vasomotor function in isolated peripheral arteries following ischemic stroke. The reactivity of thoracic aortic segments from male C57BL/6 mice to dilators and constrictors was quantified using wire myography. Acetylcholine induced endothelium-dependent vasodilation was impaired after ischemic stroke (LogIC50 Sham=-7.499, LogIC50 Stroke=-7.350, P=0.0132, n= 19, 31 respectively). Vasodilatory responses to SNP were identical in isolated aortas in sham and stroke groups. Phenylephrine induced vasoconstriction was impaired in aortas isolated from stroke animals in comparison to sham counterparts (Sham LogEC50= -6.652 vs Stroke LogEC50=-6.475, P< 0.001). Our study demonstrates that 24 hours post-ischemic stroke, peripheral vascular responses are impaired in remote arteries. Aortas from stroke animals exhibited reduced vasoconstrictor and endothelium-dependent vasodilator responses, while endothelium-independent vasodilatory responses were preserved. Since both vasodilatory and vasoconstrictor responses of peripheral arteries are impaired following ischemic stroke, our findings might explain increased blood pressure variability following ischemic stroke.

The process of oxygenic photosynthesis is primarily driven by two multiprotein complexes known as photosystem II (PSII) and photosystem I (PSI). PSII facilitates the light–induced reactions of water–splitting and plastoquinone reduction, while PSI functions as the light–driven plastocyanin-ferredoxin oxidoreductase. In contrast to the highly conserved structure of PSII among all oxygen–evolving photosynthetic organisms, the structures of PSI exhibit remarkable variations, especially for photosynthetic organisms that grow in special environments. In this review, we make a concise overview of the recent investigations of PSI from photosynthetic microorganisms including prokaryotic cyanobacteria and eukaryotic algae from the perspective of structural biology. All known PSI complexes contain a highly conserved heterodimeric core; however, their pigment compositions and peripheral light-harvesting proteins are substantially flexible. This structural plasticity of PSI reveals the dynamic adaptation to environmental changes for photosynthetic organisms.

Background : Newly generated cardiomyocytes (NGC) concur to recovery of human myocarditis. However, Methods: We retrospectively assessed NGC in 213 consecutive patients with endomyocardial biopsy denoting acute myocarditis, with normal coronaries and valves. Tissue samples were processed for histology (H&E), immunohistochemistry for the evaluation of inflammatory infiltrates, immunostaining for alpha-sarcomeric-actin, junctional connexin-43, Ki-67, phosphorylated STAT3 (p-STAT3) and Western Blot (WB) for HMGB1. Frozen samples were analyzed with polymerase chain reaction (PCR) for cardiotropic viruses. Controls included 20 normal surgical biopsies. Results: NGCs were defined as small myocytes (diameter < 10 µm) with nuclear positivity to Ki-67 and p-STAT3, positive immunostaining for cytoplasmic α-sarcomeric actin and connexin 43. Their number/mm2 in relation with age and pathway of integration were evaluated. NGCs crossed the membrane and grew integrated within the empty necrotic myocytes. NGC mean diameter was 6.6 ± 3.34 vs 22.5 ± 3.11 µm adult cells; their number, in comparable LVEF, was 86.3 ± 10.3/mm2 in patients between 10 and 40 years; 50.4 ±13.8/mm2 in those between 41 and 60; 15.1 ± 5.7/ mm2 in those between 61 and 80; control NGC mean diameter was 0.2± 0.2/mm2. PCR was positive for viral genomes in 16% of cases; NGCs were not statistically different in viral and non-viral myocarditis. WB analysis revealed a higher expression of HMGB1 in myocarditis compared to myocardial controls. Conclusions: NGCs are constantly recognizable in acute human myocarditis. Their number declines with age. Their integration within necrotic myocytes consents preservation of cardiac structure and function.

Several recent studies have indicated that miR-30a plays critical roles in various biological processes and diseases. However, the mechanism of miR-30a participation in the regulation of idiopathic pulmonary fibrosis (IPF) is ambiguous. Our previous study demonstrated that miR-30a may function as a novel therapeutic target for lung fibrosis by blocking mitochondrial fission, which is dependent on dynamin-related protein-1 (Drp-1). However, the regulatory mechanism between miR-30a and Drp-1 has yet to be investigated. In addition, whether miR-30a can act as a potential therapeutic has not been verified in vivo. In this study, the miR-30a expression in IPF patients was evaluated. Computational analysis and a dual luciferase reporter system assay were used to identify the target gene of miR-30a, and cell transfection was used to confirm this relationship. Ten-eleven translocation 1 (TET1) was validated as a direct target of miR-30a, and the transfection of miR-30a mimic/inhibitor significantly reduced/increased the expression of TET1 protein. Further experiment verified that the interference on TET1(siRNA) could inhibit the hydroxymethlation of the Drp-1 promoter. Finally, miR-30a agomir was designed and applied to identify and validate the therapeutic effect of miR-30a in vivo. Our study demonstrated that miR-30a could inhibit the TET1 expression by base pairing with complementary sites in the 3′ untranslated region to regulate the hydroxymethlation of the Drp-1 promoter. Furthermore, miR-30a could act as a potential therapeutic target for IPF.

Breast cancer poses a global health challenge, yet the influence of ethnicity on the tumor microenvironment (TME) remains understudied. In this investigation, we examined immune cell infiltration in 230 breast cancer samples, emphasizing diverse ethnic populations. Leveraging tissue microarrays (TMAs) and core samples, we applied multiplex immunofluorescence (mIF) to dissect immune cell subtypes across TME regions. Our analysis revealed distinct immune cell distribution patterns, particularly enriched in aggressive molecular subtypes triple-negative and HER2-positive tumors. We observed significant correlations between immune cell abundance and key clinicopathological parameters, including tumor size, lymph node involvement, and patient overall survival. Notably, immune cell location within different TME regions showed varying correlations with clinicopathologic parameters. Additionally, ethnicities exhibited diverse distributions of cells, with certain ethnicities showing higher abundance compared to others. In TMA samples, patients of Chinese and Caribbean origin displayed significantly lower numbers of B cells, TAMs, and FOXP3-positive cells. These findings highlight the intricate interplay between immune cells and breast cancer progression, with implications for personalized treatment strategies. Moving forward, integrating advanced imaging techniques, and exploring immune cell heterogeneity in diverse ethnic cohorts can uncover novel immune signatures and guide tailored immunotherapeutic interventions, ultimately improving breast cancer management.

Mesenchymal stem cells due to their multipotent ability are considered one of the best candidates to be used in regenerative medicine. The most used source of those stem cells long the last decades was the bone marrow, despite its limited cell resources and the painful and very invasive surgical treatment. Therefore, the scientific community investigated many alternative sources for the harvesting of these stem cell subpopulations. Adipose tissue attracted the biggest interest thanks to special features, such as great tissue abundance, easy availability, and in the end the large amount of positive mesenchymal stem cells. Once it was verified that these stem cell subpopulations possess the same bone marrow differentiation potential, it became evident that they would be the ideal candidate to be used in regenerative and tissue engineering applications. Even the regenerative use of these cells long become a sensitive point because not all the acquired phenotypes resulted in the best source for plastic procedures. As documented in the literature, the ideal fillers for regenerative is represented by stem cell population. Therefore, it became crucial to investigate any kind of biological mechanism on the differentiative ability of those mesenchymal stem cells, especially their regulative factors. One of the most studied and important regulators are the miRNAs. Indeed, miRNA play a crucial role in the differentiation process of adipose stem cell cultures isolated from patients or obtained by commercial cell lines toward osteocytes, chondrocytes, and adipocytes differentiated populations.

Liver fibrosis, a consequence of chronic liver damage or inflammation, is characterized by the excessive buildup of extracellular matrix components. This progressive condition significantly raises the risk of severe liver diseases like cirrhosis and hepatocellular carcinoma. The lack of approved therapeutics underscores the urgent need for novel anti-fibrotic drugs. Hepatic stellate cells (HSCs), key players in fibrogenesis, are promising targets for drug discovery. This study investigated the anti-fibrotic potential of Citrus hystrix DC. (KL) and its bioactive compound, β-citronellol (β-CIT), in a human HSC cell line (LX-2). Cells exposed to TGF-β1 to induce fibrogenesis were co-treated with crude KL extract and β-CIT. Gene expression was analyzed by Real-Time qRT-PCR to assess fibrosis-associated genes (ACTA2, COL1A1, TIMP1, SMAD2). The releasing of matrix metalloproteinase 9 (MMP-9) was measured by ELISA. Proteomic analysis and molecular docking identified potential signaling proteins and modeled protein-ligand interactions. Results showed that both crude KL extract and β-CIT suppressed HSC activation genes and MMP-9 levels. The MAPK signaling pathway emerged as a potential target of β-CIT. This study demonstrates the ability of KL extract and β-CIT to inhibit HSC activation during TGF-β1-induced fibrogenesis, suggesting a promising role of β-CIT in anti-hepatic fibrosis therapies.

Teachers’ time resources are becoming increasingly scarce and time poverty will have a direct impact on the long-term sustainability of their professional development. However, the current state of teachers’ time poverty and its potential harmful consequences are unclear. Therefore, this research conducts two cross-sectional studies to investigate the current situation and group differences in time poverty among Chinese teachers (Sample 1), as well as the influence of teachers’ time poverty on job burnout (Sample 2). The results indicate that: (1) there is a high level of time poverty among teachers; (2) there is no significant difference in gender, teaching years, or teaching section, and there is a significant difference in teaching responsibilities; (3) time poverty could positively and significantly predict job burnout, and job burnout could positively and significantly predict mental health; (4) mental health factors (including depression, anxiety and stress) play the mediating role in the relationship between teachers’ time poverty and job burnout. To create a sustainable development environment for teachers, it is suggested that teachers improve time management skills and establish a self-regulation mechanism; schools should clarify the boundaries of teachers’ work and respect their right to rest; and society should provide positive and adequate spiritual support.

Myocardial heterogeneity is an attribute of the normal heart. We have developed integrative models of cardiomyocytes from the subendocardial (ENDO) and subepicardial (EPI) ventricular regions that take into account experimental data on specific features of intracellular electromechanical coupling in the guinea pig heart. The models adequately simulate experimental data on the action potential and contraction of the ENDO and EPI cells. The modeling results predict that heterogeneity in the parameters of calcium handling and myofilament mechanics in isolated ENDO and EPI cardiomyocytes via cooperative mechanisms of mechano-calcium-electric feedback are essential to produce the differences in Ca²⁺ transients and contraction profiles and may further enhance transmural differences in the electrical properties between the cells. Simulation results predict that ENDO cells have greater sensitivity to changes in afterload than EPI cells. These data are important for understanding the behavior of cardiomyocytes in the intact heart.

Oligodendrocyte progenitor cells (OPCs) represent a subtype of glia giving rise to oligodendrocytes, the myelin forming cells in the central nervous system (CNS). While OPCs are highly proliferating during development, they become relatively quiescent during adulthood, when their fate is strictly influenced by the extracellular context. In traumatic injuries and chronic neurodegenerative conditions including those of autoimmune origin, oligodendrocytes undergo apoptosis, and demyelination starts. Adult OPCs become immediately activated, they migrate at the lesion site and proliferate to replenish the damaged area, but their efficiency is hampered by the presence of a glial scar, a barrier mainly formed by reactive astrocytes, microglia, and the deposition of inhibitory extracellular matrix components. If, on one hand, glial scar limits the lesion spreading, it also blocks tissue regeneration. Therapeutic strategies aimed at reducing astrocyte or microglia activation and shifting them toward a neuroprotective phenotype have been proposed, whereas the role of OPCs has been largely overlooked. In this review, we have considered glial scar from the perspective of OPCs, analysing their behaviour when lesions originate and exploring the potential therapies aimed at sustaining OPCs to efficiently differentiate and promote remyelination.

The correct selection of the value of p is a complex and iterative procedure that requires experience in the interpretation of the obtained interpolated maps. Inverse Distance Weighting is a method applied to the porosities of the K and L hydrocarbon reservoirs, discovered in the Neogene (Lower Pontian) subsurface sandstones in the northern Croatia (Pannonian Basin System). They represent small and large data samples. Also a standard statistical analysis of the data was made, followed by the qualitative-quantitative analysis of the maps, based on the selection of different values of the power distance exponent (p-value) for the K and L reservoirs maps. According to the qualitative analysis, for a small data set, p-value could be set on 1 or 2 giving the most optimal result, while for a large data set, a p value of 3 and 4 could be applied. By quantitative analysis, for the case of small data p=2 is recommended, resulting in a root mean square error value of 0.03458, a mean absolute error of 0.02013 and a median absolute deviation of 0.00546. Oppositely, a p-value of 3 and 4 is selected as appropriate for a large data set, with root mean square errors of 0.02435 and 0.02437, mean square errors of 0.01582 and 0.01509 and median absolute deviations 0.00896 and 0.00444. Eventually for a small data set, it is recommended to use a p-value of 2, and for a large data set, a p-value of 3 or 4.

Recently concluded, large-scale cancer genomics studies involving multiregion sequencing of primary tumors and paired metastases appear to indicate that many or most cancer patients have one or more “clonal" mutations in their tumors. Clonal mutations are those that are present in all of a patient’s cancer cells. Achilles Therapeutics is currently the only company specifically targeting clonal mutations. However, they are doing so with tumor-derived T cells. To address the potential limitations of immunotherapy, I have devised another approach for exploiting clonal mutations, which I call “Oncolytic Vector Efficient Replication Contingent on Omnipresent Mutation Engagement” (OVERCOME). The ideal version of OVERCOME would likely employ a bioengineered facultative intracellular bacterium. The bacterium would initially be attenuated, but (transiently) reverse its attenuation upon clonal mutation detection.

The unique structure of G4.0 PAMAM dendrimers allows the drug to be enclosed in internal spaces or immobilized on the surface. In the conducted research, the conditions for the formation of the active G4.0 PAMAM complex with doxorubicin (DOX) were optimized. The physicochemical properties of the system were monitored using DLS, CD, and fluorescence spectroscopy. The QCM-D method was chosen to determine the preferential conditions for the complex formation. The highest binding efficiency of the drug to the cationic dendrimer was observed under basic conditions when the DOX molecule was deprotonated. The decrease in the zeta potential of the complex confirms that DOX immobilizes through electrostatic interaction with the carrier's surface amine groups. The binding constants were determined from the fluorescence quenching of the DOX molecule in the presence of G4.0 PAMAM. The two-fold way of binding doxorubicin in the structure of dendrimers was visible in the ITC isotherm. Fluorescence spectra and release curves identified reversible binding of DOX to the nanocarrier. Among the selected cancer cells, the most promising anticancer activity of G4.0-DOX was observed in A375 malignant melanoma cells. Moreover, the preferred intracellular location of the complexes concerning the free drug was found, which is important from a therapeutic point of view.

Diabetes mellitus is one of the most common endocrinopathies, estimated to affect about 5.4% of the world's population around 2025. It is thought that nearly a third of diabetic patients have some type of skin condition. Frequently, the cutaneous effects of this heterogeneous syndrome are ac-curately detected late in the progressive form of the disease. The main objective of the present study was to identify the main cellular characteristics of the cutaneous microenvironment in exper- imentally induced diabetes mellitus. Skin fragments were harvested from adult white Wistar rats of both sexes, with the weight of 200 g, 12 weeks after streptozotocin diabetes was induced. In parallel with histopathological diagnosis by using hematoxylin and eosine staining, samples were processed by transmission electron microscopy technique and examined with a Philips CM100 microscope. In the apparently macroscopically unchanged tegument, photon microscopy revealed both progres ive thinning of the epidermis in the early stages of diabetes and a significant process of fibrosis and collagen hyalinization in the dermis. In addition, in the early stages, the electron microscopic study provided ultrastructural details characteristic of a senescent phenotype with reduced cell proliferation. The morphological changes in the skin may be the first signs of disruption of carbohydrate metabolism and in the case of established diabetes may reflect its progression and the efficacy of therapy.

The question of what generates conscious experience has mesmerized thinkers since the dawn of humanity, yet its origins remain a mystery. The topic of consciousness has gained traction in recent years, thanks to the development of large language models that now arguably pass the Turing test, an operational test for intelligence. However, intelligence and consciousness are not related in obvious ways, as anyone who suffers from a bad toothache can attest - pain generates intense feelings and absorbs all our conscious awareness yet nothing particularly intelligent is going on. Still, in the hard sciences, this topic is frequently met with skepticism because to date no protocol to measure the content or intensity of conscious experiences in an observer-independent manner has been agreed upon. Here we present a novel proposal: Conscious experience arises whenever a quantum mechanical superposition forms. Our proposal has several implications: First, it suggests that the structure of the superposition determines the qualia of the experience. Second, quantum entanglement naturally solves the binding problem, ensuring the unity of phenomenal experience. Finally, a moment of agency may coincide with the formation of a superposition state. We outline a research program to experimentally test our conjecture via a sequence of quantum biology experiments. Applying these ideas opens up the possibility of expanding human experience through brain-quantum computer interfaces.

In this paper, Hybrid Automata, which is a formal model for hybrid systems, has been introduced. A summary of its theory is presented and some of its special and important classes are listed and some properties that can be studied and checked for it are mentioned. Finally, the purposes of use, the most widely used areas, and the tools that provide H.A. Support are addressed.

Von Hippel-Lindau disease (VHL) is a multiple organ neoplastic syndrome with autosomal dominant transmission, complete penetrance, and variable expression caused by mutations in the VHL gene. Although VHL disease is hereditary in many cases, new mutations cause up to 20 % of the incidence. Mutations in VHL may cause the development of cysts and tumors in many organs, including brain and spinal cord hemangioblastoma, renal cell carcinoma (RCC), retinal heman-gioblastoma (RH), pheochromocytoma, epididymal and broad ligament cystadenomas, endo-lymphatic sac tumor, pancreatic neuroendocrine tumors, and renal and pancreatic cysts. VHL is a syndrome associated with functional inactivation of the Von Hippel–Lindau protein (pVHL). pVHL is a tumor suppressor mainly known for its role as a regulator of hypoxia-inducible factor (HIF) activity. The prevalence of VHL disease is between 1 in 39 000 and 1 in 91 000 individuals in different regional populations. Here, we review the etiology, epidemiology, pathophysiology, genetics, clinical manifestations, diagnosis, and current treatments, as well as the molecular aspects of this disease. We also discuss the animal models used to study this disease, VHL biomarkers, and current VHL clinical trials for VHL according to NIH.

This study investigated the relationship between fundamental movement skills (FMSs) and health-related fitness (HRF) among first and second graders in South Korean elementary schools. It aimed to provide foundational data for developing physical education programs tailored to the motor development stages and fitness levels of younger elementary school students. In October 2023, FMSs (jumping, running, hopping, static balance, dynamic balance, overhand throwing, and kicking) and HRF (muscular strength, cardiorespiratory endurance, and flexibility) were evaluated among 291 first and second graders. The collected data were analyzed through frequency and multiple regression analyses performed using the SPSS software. The results revealed that higher scores in jumping and hopping are associated with greater muscular strength, cardiorespiratory endurance, and flexibility. Running had no significant effect on HRF elements. Higher scores in static balance (single-leg stance) were associated with increased muscular strength, cardiorespiratory endurance, and flexibility, but dynamic balance (balance beam walking) did not have a significant effect. Higher scores in overhand throwing were associated with greater muscular strength and cardiorespiratory endurance, but kicking did not show a significant association. Overall, these findings emphasize the importance of prioritizing jumping and static balance in physical education for the well-rounded health development of first and second graders.

This article begins with exploring fundamental limits in the universe. It examines key principles, such as the uniformity of physical laws and Energy conservation, that lead to the principles of maximum speed, locality, and maximum density. These universal limits govern physical phenomena, culminating in very interesting results.They led to the exploration of Quantum black holes. Quantum black holes are relativistically revolving miniature charged black holes consisting of a single core at the highest possible density. They are the only structures small enough to keep all charges quantized (including gravitational charges). I then demonstrated that quarks and leptons comprise smaller entities - the Quantum Black Holes. Exploring these elementary particles reveals that they release their Energy outward, altering the curvature of nearby space-time and providing new insights. We can characterize movements and processes in local space-time with four sets of equations representing the four charge types and one set representing Dark Matter. By the end of the article, I prove that Quantum black holes explain the quantization of gravity, the nature of Luminous and dark Matter, the Mass, size, and (quantized) charges quantities of elementary particles and the nature of neutrino oscillations.

This study analyzes the correlation between the lifetime and the rest energy of the unstable particle states with a lifetime greater than the zeptosecond (10⁻²¹ s), using data available from the Particle Data Group. This set of states seems to be divided into three groups, in each of which the two quantities can be correlated through a remarkably accurate power law. Although this fact does not represent anything new compared to the predictions of the Standard Model, it nevertheless reveals an unexpected order structure in the set of particle decays, emerging from such predictions.

There are numerous approaches to model an influence of manufacturing parameters on the dissolution and disintegration of solid dosage forms. A modern approach for modelling and simulating complex and heterogeneous systems such as pharmaceutical tablets focuses on computationally-intensive finite-element or discreet element methods. Industrial challenges such as Process Analytical Technology (PAT) and Quality by Design are stimulating computer-based technologies to design, control and improve the quality of pharmaceutical compacts and their performance. In this study the disintegration and dissolution model based on the numerical solutions of the Noyes-Whitney equation and cellular automata supported disintegration model are proposed and evaluated. The results from in vitro release studies of the mefenamic acid formulations were compared to the calculated release patterns from the tablet structures obtained from microtomography experiments and the purely algorithmically created virtual tablets.

Many complex networks have a connectivity that might be only partially detected or that tends to grow over time, hence the prediction of non-observed links is a fundamental problem in network science. The aim of topological link prediction is to forecast these non-observed links by only exploiting features intrinsic to the network topology. It has a wide range of real applications, like suggesting friendships in social networks or predicting interactions in biological networks.The Cannistraci-Hebb theory is a recent achievement in network science that includes a theoretical framework to understand local-based link prediction on paths of length n. In this study we introduce two innovations: theory of modelling (science) and theory of realization (engineering). For the theory of modelling we first recall a definition of network automata as a general framework for modelling the growth of connectivity in complex networks. We then show that several deterministic models previously developed fall within this framework and we introduce novel network automata following the Cannistraci-Hebb rule. For the theory of realization, we present how to build adaptive network automata for link prediction, which incorporate multiple deterministic models of self-organization and automatically choose the rule that better explains the patterns of connectivity in the network under investigation. We compare Cannistraci-Hebb adaptive (CHA) network automaton against state-of-the-art link prediction methods such as structural perturbation method (SPM), stochastic block models (SBM) and artificial intelligence algorithms for graph embedding. CHA displays an overall higher link prediction performance across different evaluation frameworks on 1386 networks. Finally, we highlight that CHA offers the key advantage to explicitly explain the mechanistic rule of self-organization which leads to the link prediction performance, whereas SPM and graph embedding not. In comparison to CHA, SBM unfortunately shows irrelevant and unsatisfactory performance demonstrating that SBM modelling is not adequate for link prediction in real networks.

Immunosuppressed individuals, such as people living with HIV (PLWH), remain vulnerable to severe COVID-19. We analyzed the persistence of specific SARS-CoV-2 humoral and cellular immune responses in a retrospective, cross-sectional study in PLWH on antiretroviral therapy. Among 104 participants, 70.2% had anti-S IgG antibodies and 55.8% had significant neutralizing activity against the Omicron variant in a surrogate virus neutralization test. Only 38.5% were vaccinated (8.76 ± 4.1 months prior), all displaying anti-S IgG, 75% with neutralizing antibodies and anti-S IgA. 29.8% had no SARS-CoV-2 serologic markers and displayed significantly lower CD4 counts and HIV viral load. Severe immunosuppression (present in 12.5% of participants) was linked to lower levels of detectable anti-S IgG (0.0003), anti-S IgA (p &lt; 0.0001) and lack of neutralizing activity against Omicron variant (p &lt; 0.0001). T-cell responses were present in 86.7% of tested participants, even in those lacking serological markers. In PLWH without severe immunosuppression, neutralizing antibodies and T-cell responses persisted for up to 9 months post-infection or vaccination. Advanced immunosuppression led to diminished humoral immunity, but retained cellular immune.

It is agreed that before the creation of particles, space was absolutely devoid of matter and radiation. In this study, we assume that the absolute void comprises four dimensions; three spatial dimensions and a force equivalent representing the factor of change among the elementary dimensions. Our hypothesis is based on the expansion of the spatial dimensions, and the subsequent space instability. We demonstrated that when the outward force equivalent strength exceeds a critical limit, it collapses inward to restore equilibrium in the system. Then, the void inside the collapsed force equivalent acts as a void in a confined system, the energy of the system remains conserved at all stages. With the decrease of the spatial dimensions due to the collapse the energy density increases, at the final stage the energy in the confined system is concentrated forming a solid state of energy. In which, this solid state of energy is a particle become the source of the elementary particles. The created high-energy sources are controlled by the internal and external forces of the source and all entities in its external force field until equilibrium is reached. The article gives the summary of Big bang theory and its problems which are further discussed in inflation. This article will help in understanding that how elementary dimensions play a role in the formation of elementary particles. The quark gluon plasma, inflation, gravitational collapse, and gravitational lensing provide evidence supporting the elementary dimensions theory presented in this paper.

Plant transcriptomes are an extremely dynamic entities shaped spatially and temporally by many intracellular and environmental cues. In this review, we first summarize the complexity and diversity of plant genomes and transcriptomes as a start point for the multitude of transcriptomic responses. Numerous alterations within various tissue and organ-specific transcriptomes as well as the most relevant transcriptomic responses associated with plant acclimation to selected abiotic and biotic stress conditions, from the current studies employing high-throughput transcriptomic analysis are widely discussed. Understanding changes within plant transcriptomes, revealed by in silico functional analysis, allows for the characterization of stress-affected genes and stress acclimatory mechanisms, as well as allows to perform plant metabolic engineering. The latter allow cultivars to produce more secondary metabolites in the future, which are often desirable substances in the biomedical industry. Accordingly, in this review special attention was also paid to characterize the potential of transcriptomic analyses of medicinal species, particularly to search for new cultivars. Extensive characterization of transcriptomic responses in stress would also result in the development of new cultivars that display physiological and molecular mechanisms that allow them to cope with adverse environmental conditions more adequately.

The aim of this work is to broaden the theory of computation by making use of fuzzy sets and multisets, which are generalization of ordinary sets. We study fuzzy multiset finite automata (FMFA), which are characterized by the incorporation of vagueness when it comes to the choice of the next state and multiple occurrences of the same input symbol. The resulting automaton is a genaralization of both classical automata and fuzzy automata. We discuss the algebraic characteristics of FMFA sub-automata—separatedness, strongly connectedness, cyclic, directable, and triangular FMFA in terms of equivalence classes induced by an equivalence relation defined on state set of FMFA. Intrestingly, for a given FMFA, a new FMFA can be constructed that is a homomorphic image of the original FMFA. We define different posets structures that can be associated with a given FMFA and show that some of them are upper semilattices and discuss the inter-relationship of a given poset, a finite upper semilattice (FUSL), a FMFA and a posets/FUSL associated with a given FMFA. Finally, we introduce the concept of decomposition of a FMFA in two different ways and characterize the strongly connected, triangular and directable FMFA.

Integrated Model of Distributed Systems is used for modeling and verification. In formalism, the distributed system is modeled as a collection of server states and agent messages. The evolution of the system takes the form of actions that transform the global system configuration (states and messages) into a new configuration. Formalism is used in the Dedan verification environment for finding different kinds of deadlocks: communication deadlocks in the server view and resource deadlocks in the agent view. For other purposes, a conversion has been developed to equivalent models: to Petri nets for structural analysis and do Distributed Autonomous and Asynchronous Automata (DA3) for easy graphical modeling in terms of system components. In addition, it is possible to simulate a verified system on distributed components in DA3. The automata have two forms: Server-DA3 (S-DA3) for the server view and Agent-DA3 (A-DA3) for the agent view. DA3 formalism is compared to other concepts of distributed automata known from the literature.

Drug resistance in melanoma is a major hindrance in cancer therapy. Growth hormone (GH) plays a pivotal role in contributing to resistance to chemotherapy. Knocking down or blocking the GH receptor has been shown to sensitize the tumor cells to chemotherapy. Extensive studies have demonstrated that exosomes, a subset of extracellular vesicles, play an important role in drug resistance by transferring key factors to sensitize cancer cells to chemotherapy. In this study, we explore how GH modulates exosomal cargoes from melanoma cells and their role in drug resistance. We treated the melanoma cells with GH, doxorubicin, and GHR antagonist, pegvisomant, and analyzed the exosomes released. Additionally, we administered these exosomes to recipient cells. The GH-treated melanoma cells released exosomes with elevated levels of ABC transporters (ABCC1 and ABCB1), N-cadherin and MMP2, enhancing drug resistance and migration in recipient cells. GHR antagonism reduced these exosomal levels, restoring drug sensitivity and attenuating migration. Overall, our findings highlight a novel role of GH in modulating exosomal cargoes that drive chemoresistance and metastasis in melanoma. This understanding provides insights to the mechanisms of GH in melanoma chemoresistance and suggests GHR antagonism as a potential therapy to overcome chemoresistance in melanoma treatment.

Kisspeptin, a key neuropeptide derived from the KISS1 gene, is renowned for its critical role in regulating the hypothalamic-pituitary-gonadal axis and reproductive hormone secretion. Beyond its primary function in reproductive biology, emerging research has illuminated its influence in various cancers, mediating significant effects through its interaction with the G-protein coupled receptor, Kisspeptin receptor. This interaction has been implicated in modulating cellular pro-cesses such as proliferation and metastasis, making it a potential target for therapeutic interven-tion. Our study initially screened ten kisspeptin-10 analogs through cytotoxic effects of kisspep-tin-10 (KP10) and its analogs in several cancer types, including cervical, prostate, breast, and gastric cancers, with a particular focus on cervical cancer where the most profound effects were observed. Further exploration using kinase array assays revealed that these analogs specifically alter key kinases involved in cancer progression. Migration assays demonstrated a substantial decrease in cell motility, and Bioluminiscence Resonance Energy Transfer assays confirmed these analogs strong interactions with the kisspeptin receptor. Overall, our results indicate that these KP10 analogs not only hinder cervical cancer cell proliferation but also curtail migration through targeted modulation of kinase signaling, suggesting their potential as therapeutic ayfgents in managing cervical cancer progression. This comprehensive approach underscores the therapeutic promise of exploiting kisspeptin signaling in cancer treatment strategies.

It is shown that if there exists in nature a pair of heavy elementary particles each with a charge equal to that of the proton and with mass energy , then their direct interaction gives rise to a spontaneous fractionation of the two charges into three pairs of elementary particles according to two equiprobable channels, one hadronic and one leptonic. The leptons and quarks produced are in agreement with the first generation of the Standard Model of matter. Fractionation suggests the relative abundances of the elementary particles, introducing some questions about the possibility of the real existence of a currently unknown particle and its role in the formation of an apparent matter-antimatter asymmetry in the universe.

Economics has long sought to bridge the principles of microeconomics into the realm of macroeconomics. This paper presents a formal attempt to do so by using a maximum entropy based approach derived from statistical mechanics coupled with subjective game theory and elements from political philosophy. This approach is then applied to income distributions and to the Cobb-Douglas production function, to create a framework for future applications, and to illustrate where past work had made implicit assumptions regarding the system. The paper then explores the consequences of the approach, illustrating self-contradictions in the political philosophy of distributive justice, formally deriving an equation of state for the transactions on the Bitcoin network, and deriving from this the ideal gas law and polytropic process for an economy proving that expansionary monetary policy is not stimulative.

Our knowledge of electricity is based on two parallel lines of research: one focusing on ‘charge’, the other on ‘electron’. The distinction is blurred. The difficulty is that the fundamental nature of elementary charge is unknown. Here I present evidence that charge is a resting electron and electron is a moving charge – one physical entity two behavioural identities. That is, charge is nonrelativistic electron and electron is relativistic charge. Charges obey the laws of static electricity (electrostatics); electrons obey the laws of moving electricity (electrodynamics). Whether positive or negative, a charge is ejected from the atom at tremendous speed, losing the characteristics of ‘a charge’ and gaining those of ‘an electron’. I conclude that an atom, hence nature in general, contains an equal number of positive and negative electrons, recognised at rest as opposite charges. This solves the mystery of the electron-positron numerical asymmetry. It means that static positions and negatrons (charges) play equivalent roles as subatomic building blocks of matter, revealing a new atomic structure.

Darier disease (DD) is an autosomal dominant disorder due to mutation of ATP2A2 gene causing an isolated skin manifestation based on keratinocyte disconnection and apoptosis. Systemic manifestations of DD have not been demonstrated so far although a high incidence of neuropsychiatric syndromes suggest an involvement of central nervous system. A Novel ATP2A2 Gene Mutation c.118G>A, causing keratinocyte and cardiomyocyte disconnection in Darier disease, is reported. Their common pathologic pathway, still unreported, was documented by both skin and left ventricular endomyocardial biopsy because of cardiac dilatation and dysfunction appeared several decades after skin manifestations.Keratinocyte disconnection was paralleled by cardiomyocyte separation at lateral junction. Cardiomyocyte separation was associated to cell disarray, sarcoplasmic reticulum dilatation and increased myocyte apoptosis. Clinically, hyperkeratotic skin papules were associated to chest pain, severe muscle exhaustion and ventricular arrhythmias that improved following administration of aminophylline, a phosphodiesterase inhibitor enhancing SERCA2 protein phosphorylation.Specific mutations of ATP2A2 gene may cause cardiac involvement in patients with DD. Cardiac pathologic changes are similar to those documented in the skin including cardiomyocyte disconnection that promotes precordial pain and cardiac arrhythmias. Phosphodiesterase inhibitors, enhancing SERCA2 protein phosphorylation may substantially attenuate the symptoms.

Models of consciousness are usually developed within physical monist or dualistic frameworks, in which the structure and dynamics of the mind derive from the workings of the physical world (in particular, the brain). Little attention has been given to modeling within a mental monist framework, deriving the structure and dynamics of the mental world from primitive mental constituents only. Mental monism is gaining attention as a candidate solution to Chalmers’ Hard Problem, and it is therefore timely to examine possible formal models of consciousness within it. Here, we propose a minimal set of hypotheses that any credible model of consciousness (within mental monism) should respect. From those hypotheses, it is feasible to construct many formal models that permit universal computation in the mental world, through cellular automata. We need further hypotheses to define transition rules for particular models, and we propose a transition rule with the unusual property of deep copying in the time dimension. In conclusion, we hope to dispel the notion that mental monism requires a deus ex machina, by showing that a parsimonious set of assumptions can yield a naturalistic and computationally potent mental world.

Europium is one of the most reactive lanthanides and humans use it in many different applica-tions, but we still know little about its potential toxicity and cellular response to its exposure. Two strains of the eukaryotic microorganism model Tetrahymena thermophila adapted to high concentrations of two Eu(III) compounds (EuCl3 or Eu2O3) were obtained and compared to a control strain and cultures treated with both compounds. In this ciliate, EuCl3 is more toxic than Eu2O3. LC50 values show that this microorganism is more resistant to these Eu(III) compounds than other microorganisms. Oxidative stress originated mainly by Eu2O3 is minimized by overexpression of genes encoding important antioxidant enzymes. The overexpression of metallothionein genes under treatment with Eu(III) compounds supports the possibility that this lanthanide may interact with the -SH groups of the cysteine residues from metallothi-oneins and/or displace essential cations of these proteins during their homeostasis function. Both lipid metabolism (lipid droplets that fuse with europium-containing vacuoles) and au-tophagy are involved in the cellular response to europium stress. Bioaccumulation, together with a possible biomineralization to europium phosphate, seems to be the main mechanism of Eu(III) detoxification in these cells.

Background: Bacteria, among the earliest life forms on Earth, are integral to both human health and environmental ecosystems. They contribute to essential processes such as digestion, nutrient absorption, and immune defense, and play key roles in the biogeochemical cycles that sustain life. Objective: This paper explores the indispensable role of bacteria in human life and environmental health, emphasizing their contributions to digestion, nutrient absorption, immune protection, and ecosystem functioning. It also discusses advances in understanding the human microbiome and their implications for health and therapeutic interventions. Methods: The study reviews existing literature on the human microbiome, probiotics, and the role of bacteria in environmental nutrient cycling. It analyses experimental evidence and case studies, particularly focusing on the impact of fecal microbiota transplantation (FMT) as a therapeutic tool. Metagenomic analysis techniques are examined to highlight the diversity and functionality of gut bacteria. Results: Bacteria are found to be essential for breaking down food, providing vital nutrients, and protecting against pathogens. They play a critical role in maintaining a healthy immune system and are involved in the biogeochemical cycles necessary for ecosystem health. Metagenomic analysis reveals significant variations in bacterial populations associated with health and disease, emphasizing the importance of a balanced microbiota. FMT shows promise as an effective treatment for various diseases, including gastrointestinal, metabolic, and neurological disorders. Conclusions: Bacteria are crucial for human health and environmental stability. Maintaining a balanced microbiota is vital for overall well-being, and interventions such as probiotics and FMT can offer significant health benefits. Future research should focus on further understanding the complex interactions between bacteria and their hosts to develop targeted therapies for various health conditions.

Age is the primary risk factor for neurodegenerative diseases such as Alzheimer’s and Huntington’s disease. Alzheimer’s disease is the most common form of dementia and a leading cause of death in the elderly population of the United States. No effective treatments for these diseases currently exist. Identifying effective treatments for Alzheimer’s, Huntington’s, and other neurodegenerative diseases is a major current focus of national scientific resources, and there is a critical need for novel therapeutic strategies. Here we investigate the potential for targeting the kynurenine pathway metabolite 3-hydroxyanthranilic acid (3HAA) using Caenorhabditis elegans expressing amyloid-beta or a polyglutamine peptide in body wall muscle, modeling proteotoxicity in Alzheimer’s and Huntington’s disease-related, respectively. We show that knocking down the enzyme that degrades 3HAA, 3HAA dioxygenase (HAAO) delays age-associated paralysis in both models. This effect on paralysis was independent of protein aggregation in the polyglutamine model. We also show that the mechanism of protection against proteotoxicity from HAAO knockdown is mimicked by 3HAA supplementation, supporting elevated 3HAA as the mediating event liking HAAO knockdown to delayed paralysis. This work demonstrates potential for 3HAA as a targeted therapeutic in neurodegenerative disease, though the mechanism is yet to be explored.

Magnetogenetics emerges as a transformative approach for modulating cellular signaling pathways through the strategic application of magnetic fields and nanoparticles. This technique leverages the unique properties of magnetic nanoparticles (MNPs) to induce mechanical or thermal stimuli within cells, facilitating the activation of mechano- and thermosensitive proteins without the need for traditional ligand-receptor interactions. Unlike traditional modalities that often require invasive interventions and lack precision in targeting specific cellular functions, magnetogenetics offers a non-invasive alternative with the capacity for deep tissue penetration and the potential for targeting a broad spectrum of cellular processes. This review underscores magnetogenetics' broad applicability, from steering stem cell differentiation to manipulating neuronal activity and immune responses, highlighting its potential in regenerative medicine, neuroscience, and cancer therapy. Furthermore, the review explores the challenges and future directions of magnetogenetics, including the development of genetically programmed magnetic nanoparticles and the integration of magnetic field-sensitive cells for in vivo applications. Magnetogenetics stands at the forefront of cellular manipulation technologies, offering novel insights into cellular signaling and opening new avenues for therapeutic interventions.

Non-coding RNAs have been described as crucial regulators of gene expression and guards for cellular homeostasis. Some recent papers are focused on vault RNAs, one of the classes of non-coding RNA, and their role in cell proliferation, tumorigenesis, apoptosis, cancer response to therapy, and autophagy, which made them potential therapy targets in oncology. In the human genome, four vault RNA paralogues can be distinguished. They are associated with vault complexes, considered the largest ribonucleoprotein complex. The protein part of these complexes consists of a major vault protein (MVP) and two minor vault proteins (vPARP and TEP1). The name of the complex, as well as vault RNA, comes from the hollow barrel-shaped structure that reminds a vault. Their sequence and structure are highly evolutionary conserved and show many similarities in comparison with different species, but vault RNAs show various roles. Vaults were discovered in 1986, and their functions remained unclear for many years. Although not much is known about their contribution to cell metabolism, it has become clear that vault RNAs are involved in various processes and pathways associated with cancer progression and modulating cell functioning in normal and pathological stages. In this review, we discuss known functions of the human vault RNAs in the context of cellular metabolism, emphasizing processes related to cancer and cancer therapy efficacy.

This research paper aims to explicate the complex issue of the Riemann's Hypothesis and ultimately presents its elementary proof. The method implements one of the binomial coefficients, to demonstrate the maximal prime gaps bound. Maximal prime gaps bound constitutes a comprehensive improvement over the Bertrand's result, and becomes one of the key elements of the theory. Subsequently, implementing the theory of the primorial function and its error bounds, an improved version of the Gauss' offset logarithmic integral is developed. This integral serves as a Supremum bound of the prime counting function Pi(n). Due to its very high precision, it permits to verify the relationship between the prime counting function Pi(n) and the offset logarithmic integral of Carl Gauss. The collective mathematical theory, via the Niels F. Helge von Koch equation, enables to prove the RIemann's Hypothesis conclusively.

The spin magnetic moments and spin g factors (g_s = -2) of electron, muon and tau are explained based on the electric charges (EC) and lepton charges (LC) in terms of the three-dimensional quantized space model. The spin g factors of electron, muon and tau are g_s = -2 which is the sum of the EC g factor (g_EC = -1) and the LC g factor (g_LC = -1). The spin g factor (g_s = -2) of the electron is predicted by the Dirac’s equation. The orbit g factors of electron, muon and tau are g_L = g_EC = -1 from the EC g factor (g_EC = -1) without the contribution of the LC g factor (g_LC = -1). The spin g factors of the elementary fermions are calculated from the equation of g_s = g_EC + g_LC + g_CC where g_EC = EC/|EC|, g_LC = LC/|LC| and g_CC = CC/|CC|. For example, the spin g factors of the neutrinos and dark matters are g_s = -1. The spin g factors of the u and d quarks are g_s = 0 and g_s = -2, respectively. The g factor problem of neutrinos with the non-zero LC charges are solved by the LC Coulomb force of F_c(LC) ≈0. It is, for the first time, proposed that the binary motion (fluctuations) of the m_EC and m_LC masses for the electron, muon and tau leptons make the anomalous g factor. This binary motion could be originated from the virtual particle processes including the photons. Also, the weak force (beta) decay is closely related to the binary motion of the m_EC and m_LC for the electron, muon and tau leptons.

The holy grail of Artificial Intelligence (AI) has been to mimic human intelligence using computing machines. Autopoiesis which refers to a system with well-defined identity and is capable of re-producing and maintaining itself and cognition which is the ability to process information, apply knowledge, and change the circumstance are associated with resilience and intelligence. While classical computer science (CCS) with symbolic and sub-symbolic computing has given us tools to decipher the mysteries of physical, chemical and biological systems in nature and allowed us to model, analyze various observations and use information to optimize our interactions with each other and with our environment, it falls short in reproducing even the basic behaviors of living organisms. We present the foundational shortcomings of CCS and discuss the science of infor-mation processing structures (SIPS) that allows us to fill the gaps. SIPS allows us to model su-per-symbolic computations and infuse autopoietic and cognitive behaviors into digital machines. They use common knowledge representation from the information gained using both symbolic and sub-symbolic computations in the form of system-wide knowledge networks consisting of knowledge nodes and information sharing channels with other knowledge nodes. The knowledge nodes wired together fire together to exhibit autopoietic and cognitive behaviors.

The Internet of Things (IoT) signifies a revolutionary technological advancement, enhancing various applications through device interconnectivity while introducing significant challenges due to these devices' limited hardware and communication capabilities. To navigate these complexities, the Internet Engineering Task Force (IETF) has tailored the Routing Protocol for Low-Power and Lossy Networks (RPL) to meet the unique demands of IoT environments. However, RPL struggles with traffic congestion and load distribution issues, negatively impacting network performance and reliability. This paper presents a novel enhancement to RPL by integrating learning automata designed to optimize network traffic distribution. This enhanced protocol, the Learning Automata-based Load-Aware RPL (LALARPL), dynamically adjusts routing decisions based on real-time network conditions, achieving more effective load balancing and significantly reducing network congestion. Extensive simulations reveal that this approach outperforms existing methodologies, leading to notable improvements in packet delivery rates, end-to-end delay, and energy efficiency. The findings highlight the potential of our approach to enhance IoT network operations and extend the lifespan of network components. The effectiveness of learning automata in refining routing processes within RPL offers valuable insights that may drive future advancements in IoT networking, aiming for more robust, efficient, and sustainable network architectures.

Quantum cell automata (QCA) is a nanotechnology that uses a transistor-free structure to design digital circuits and overcome the limitations listed for CMOS circuits. The computing circuits that make up the bulk of computers can be designed and using this technology. Subtractors are a big part of computational circuits. This research deals with the design of a subtractor that due to the innovation in the design of this circuit and the novel arrangement of quantum cells of its kind has caused the number of cells used in the design of the circuit to reach its lowest value compared to previously designed circuits. The size of the circuit as well as the loss power are minimized compared to the circuits designed by the subtractors. Another trait clears of this circuit compared to many previous designs is its single-layer design, which reduces latency and noise in the output waveform. The circuit designed by the QCADesigner simulator is tested and simulated inadition the results are displayed in the simulated waveform. Also, the results of calculations for power dissipation in each QCA cell were evaluated by QCAPro software, which is a powerful tool for estimating power loss simulation. It has less implementation and level (single layer) than Previous designs.

Kynurenic acid (KYNA), a tryptophan metabolite, is believed to exert neuromodulatory and neuroprotective effects in the brain. The study aimed to examine the KYNA’s capacity to modify gene expression and the activity of cellular antioxidant enzymes in specific structures of the sheep brain. Anestrous sheep were infused intracerebroventricularly with two KYNA doses: lower (4×5 μg/60 μL/30 min, KYNA20) and higher (4×25 μg/60 μL/30 min, KYNA100) at 30min intervals. The abundance of superoxide dismutase (SOD2), catalase (CAT) and glutathione peroxidase (GPx1) mRNA, as well as enzyme activities (µM/µg of protein), were determined in the hypothalamic medial-basal (MBH) and preoptic (POA) areas, and in the hippocampal CA1 field. Both doses of KYNA caused a decrease (P&lt;0.01) in the expression of SOD2 and CAT mRNA in all structures examined compared to the control group (except for CAT in the POA at the KYNA100 dose). Furthermore, lower levels of SOD2 mRNA (P&lt;0.05) and CAT mRNA (P&lt;0.01) were found in the MBH and POA and in the POA and CA, respectively, in sheep administered with the KYNA20 dose. Different stimulatory effects on GPx1 mRNA expression were observed for both doses (P&lt;0.05-P&lt;0.01). KYNA exerted stimulatory, but dose-dependent effects on SOD2, CAT and GPx1 activities (P&lt;0.05-P&lt;0.01) in all brain tissues examined. The results indicate that KYNA may influence the level of oxidative stress in individual brain structures in sheep by modulating the expression of genes and activity of at least SOD2, CAT and GPx1. The present findings also expand the general knowledge about the potential neuroprotective properties of KYNA in the central nervous system.

Helicobacter pylori is a gastric oncopathogen that infects over half of the world’s human population. It is a Gram-negative, microaerophilic, helix-shaped bacterium that is equipped with flagella, which provide high motility. Colonization of the stomach is asymptomatic in up to 90% of people but is a recognized risk factor for developing various gastric disorders such as gastric ulcers, gastric cancer and gastritis. Invasion of the human stomach occurs via numerous virulence factors such as CagA and VacA. Similarly, outer membrane proteins (OMPs) play an important role in H. pylori pathogenicity as a means to adapt to the epithelial environment and thereby facilitate infection. While some OMPs are porins, others are adhesins. The epithelial cell receptors SabA, BabA, AlpA, OipA, HopQ and HopZ have been extensively researched to evaluate their epidemiology, structure, role and genes. Moreover, numerous studies have been performed to seek to understand the complex relationship between these factors and gastric diseases. Associations exist between different H. pylori virulence factors, the co-expression of which appears to boost pathogenicity of the bacterium. Improved knowledge of OMPs is a major step towards combatting this global disease. Here, we provide a current overview of different H. pylori OMPs and discuss their pathogenicity, epidemiology and correlation with various gastric diseases.

The time symmetry of T_c = CT is redefined based on the 4-D Euclidean space. The time inversion symmetry of T_c = CT changes the signs of the charge (q) and absolute time (ct) of the particle. The P symmetry changes the signs of the space momenta and handedness of the particle. The T_c symmetry changes the signs of the time momentum (P_t=E/c) and charges. The handedness are classified as the left-handedness and right-handedness on the 3-D space. The charges (|q| = cDt) and energies (E= cDtDV) of the particles are originated from the upward warping (q>0) and downward warping (q<0) of the particles along the time axis. The evolution of our matter universe with P_t > 0 since the big bang can be interpreted based on the CTP symmetry with the partner antimatter universe with p_t < 0. The photon space is called as the vacuum space. Then the E and M waves are the space fluctuations, and the gravitational G wave is the time fluctuations. And the E, M and G fields are newly explained. The zero E and M fields of the photon space indicates that the photon space and gamma ray are the 2EM waves. The rest mass energy of the particle is the 4-D space volume of the warped photon space. In the present work, the quantum wave function is considered as the 4-D vector with the space wave function and time axis wave function in the 4-D Euclidean space. The warped photon space corresponds to the squared wave function which is the probability density. It is concluded that, in the quantum mechanics, the imaginary number concept is introduced in the quantum wave functions instead of the time axis component of the 4-D wave function vector. The left-handed neutrino puzzle is explained from the handedness partner relation with the right-handed dark matters. Finally, the modified Lorentz transformations derived in the 4-D Euclidean space are compared with the Lorentz transformations of the special and general relativity theories in the 4-D Minkowski space.

The main purpose of the study was to study the effects of a specific rowing ergometer training program on the athletic performance of young adolescents (n=56; 11.73 ± 1.4 years old) com-pared to a workout based on general strength training. Eight-week training program was im-plemented, with four sessions per week and two-hours per session. Sample was divided in two groups: a control group (CG) that performed circuit training with exercises aimed at building general strength and an experimental group (EG) who focused on specifically training on a rowing ergometer (rowing machine). The data obtained in a rowing meter test over the compe-tition distance were analyzed to obtain the average power attained (W) at the beginning of the training, at the middle (4 weeks), at the end of the training (8 weeks) and one year after the ex-perimentation. The results show that, although both forms of training improve the average W obtained in both categories, the EG subjects (+29.94W) obtained better averages in all phases of the study compared to the CG (+5.88W). Furthermore, this increase was greater in male rowers (+ 34.06W) than in female rowers (+24.54W). These results reveal that a specific rower rowing ergometer training program has a significant effect than a general strength program and these effects can even be observed a year after the intervention.

La reflexión sobre la formación que necesitan los docentes panameños tiene poca tradición investigativa, aunque recientemente se han desarrollado programas de formación de docentes para enseñar matemáticas de acuerdo con las tendencias actuales en la enseñanza de esta materia. Este artículo explica uno de estos programas de formación de posgrado. Específicamente, se discuten los lineamientos para su diseño y algunos resultados de su segunda implementación. Entre los resultados más importantes para este artículo tenemos a) la creación de la plataforma así como el conjunto de tareas diseñadas, las cuales han sido utilizadas para la formación de posteriores promociones de docentes; b) Destaca el cambio en los criterios que orientan la práctica docente al final del programa, en relación con los criterios utilizados antes de la formación, destacando particularmente la relevancia que adquieren los aspectos emocionales y los relacionados con la interacción en este proceso.

This article addresses a challenge modeling novices face in model conceptualization: the recognition of behavior modes. Despite behavior modes being at the heart of system dynamics modeling, there is no unifying taxonomy for them, making them harder to learn for beginners. The article proposes criteria a taxonomy should satisfy, critically reviews previous taxonomies in the literature, and then introduces a new consistent taxonomy based on slope and curvature with mode names that refer only to visual cues and free of references to prior mathematical or domain-specific knowledge. Evidence from an exploratory experiment suggests that novices actually have difficulties classifying curves when using previous taxonomies. Links from these elementary modes to analogous terms in other taxonomies in diverse disciplines allow this taxonomy to be related to other ones. The article concludes by mentioning relevant limitations and future steps.

Extracellular vesicle (EV) release, which occurs in most eukaryotic cells, has recently been associated with peptidylarginine deiminase (PAD)-driven protein deimination. Evidence points to the involvement of deiminated cytoskeletal proteins and changes in histone deimination. Both PADs and EVs are associated with various pathologies including cancers, autoimmune and neurodegenerative diseases. The elevated PAD expression observed in cancers may contribute to increase in EV shedding observed from cancer cells, contributing to cancer progression. Similarly, elevated PAD expression observed in neurodegenerative diseases may cause increased EV shedding and spread of neurodegenerative EV cargo, contributing to disease progression and pathologies. Pharmacological inhibition of PAD-mediated deimination using pan-PAD inhibitor Cl-amidine, reduced cellular EV release in prostate cancer cells, rendering them significantly more susceptible to chemotherapeutic drugs. Studies on models of central nervous system damage have demonstrated critical functional roles for PADs and neuroprotective effects using PAD inhibitors in vivo, while human neurodegenerative iPSC in vitro models showed evidence of increased protein deimination. Besides using refined PAD inhibitors to selectively manipulate EV biogenesis for novel combination therapies in cancer treatment, we also speculate how EV biogenesis could be targeted via the newly identified PAD-pathway to ameliorate neurodegenerative disease progression.

Bone is a dynamic organ maintained by tightly regulated mechanisms. With old age, bone homeostasis which is maintained by an intricate balance between bone formation and bone resorption, undergoes deregulation. Oxidative stress-induced DNA damage, cellular apoptosis, and cellular senescence are all responsible for this tissue dysfunction and the imbalance in bone homeostasis. These cellular mechanisms have become a target for therapeutics to treat age-related osteoporosis. Pharmacological and genetic mouse models have shown the importance of senescent cell clearance in alleviating age-related osteoporosis. Senescent cells have an altered secretome, which may have an autocrine, paracrine, or endocrine function. The current review discusses the current and potential pathways which lead to a senescence profile in an aged skeleton. The review was written following an extensive literature survey of published studies, mostly excluding articles published on pre-print servers. The review discusses potential therapeutics targeting cellular senescence and the senescent secretome as underlying pathogenesis of an aging bone.

We suggest progress regarding the following six physics opportunities. List all elementary particles. Describe dark matter. Explain ratios of dark matter to ordinary matter. Explain eras in the history of the universe. Link properties of objects. Interrelate physics models. We use models based on Diophantine equations.

I demonstrate that the macrocosmic gravitational interaction between two masses and the microcosmic electrostatic interaction between two charges unify in simple concepts and mathematical laws when electric charge and ordinary mass are interpreted in reciprocal terms. No previous research has ever attempted to unify charge and mass. The difficulty has been lack of an intelligible definition of charge. A three-point paradigm shift solves the problem, giving convincing evidence – for the first time – that positron and negatron are the ultimate elementary particles. That is, matter is pure positive and negative grains of electricity. Paradigm shift #1: Electron is a moving charge; a charge is static electron – ‘one entity two identities’. This implies that ordinary matter contains equal numbers of positive and negative electrons – observed in motion as ‘electrons’ and at rest as ‘charges’. In motion, a positron-negatron pair obeys the laws of electrodynamics and annihilates; at rest, the pair obeys the laws of electrostatics and neutralizes. Paradigm shift #2: Electron mass and electrostatic field are either positive or negative. Opposite masses and fields, rather than indefinable ‘charge’, make opposite electrons physically different. Paradigm shift #3: Electric charge and ordinary mass interconvert. Positive charge (e⁺) and negative charge (e^-) neutralize to neutral charge (2e⁰), which is nature’s quantum of ordinary mass. Conversely, a quantum of ordinary mass splits to opposite charges e.g. in frictional electrification. The insights systematize the search, identification and classification of ‘elementary particles’, ending decades of confusion in the ‘elementary particle zoo’. They identify a new, stable subatomic particle – a third nucleon.

We study different aspects of an exactly solvable master equation (ME). Its simplicity allows one direct access to the details of its workings, highlighting significant features and peculiarities. The existence of phase transitions is unveiled.

The Steepest Descent (or Ascent) algorithm is one of the most widely used algorithms in Science, Technology, Engineering, and Mathematics (STEM). However, this powerful mathematical tool is neither taught nor even mentioned in K12 education. We study whether it is feasible for elementary school students to learn this algorithm, while also aligning with the standard school curriculum. We also look at whether it can be used to create enriching activities connected to children’s real-life experiences, thus enhancing the integration of STEM and fostering Computational Thinking. To address these questions, we conducted an empirical study in two phases. In the first phase, we tested the feasibility with teachers. In a face-to-face professional development work-shop with 457 mathematics teachers actively participating using an online platform, we found that after a 10-minute introduction they could successfully apply the algorithm and use it in a couple of models. They were also able to complete two complex and novel tasks: selecting models and adjusting the parameters of a model that uses the steepest descent algorithm. In a second phase, we tested the feasibility with 90 fourth graders from 3 low Socioeconomic Status (SES) schools. Using the same introduction and posing the same questions, we found that they were able to understand the algorithm and successfully complete the tasks on the online platform. Additionally, we found that close to 75% of the students completed the two complex modeling tasks and performed similarly to the teachers.

The Steepest Descent (or Ascent) algorithm is one of the most widely used algorithms in Science, Technology, Engineering, and Mathematics (STEM). However, this powerful mathematical tool is neither taught nor even mentioned in K12 education. We study whether it is feasible for elementary school students to learn this algorithm, while also aligning with the standard school curriculum. We also look at whether it can be used to create enriching activities connected to children’s real-life experiences, thus enhancing the integration of STEM and fostering Computational Thinking. To address these questions, we conducted an empirical study in two phases. In the first phase, we tested the feasibility with teachers. In a face-to-face professional development work-shop with 457 mathematics teachers actively participating using an online platform, we found that after a 10-minute introduction they could successfully apply the algorithm and use it in a couple of models. They were also able to complete two complex and novel tasks: selecting models and adjusting the parameters of a model that uses the steepest descent algorithm. In a second phase, we tested the feasibility with 90 fourth graders from 3 low Socioeconomic Status (SES) schools. Using the same introduction and posing the same questions, we found that they were able to understand the algorithm and successfully complete the tasks on the online platform. Additionally, we found that close to 75% of the students completed the two complex modeling tasks and performed similarly to the teachers.

Recent advancements in mass spectrometry have significantly enhanced our understanding of complex lipid profiles, opening new avenues for oncological diagnostics. This review highlights the importance of lipidomics in the comprehension of certain metabolic pathways and its potential for the detection and characterization of various cancers, in particular melanoma. Through detailed case studies, we demonstrate how lipidomic analysis has led to significant breakthroughs in the identification and understanding of cancer types and its potential for detecting unique biomarkers that are instrumental in its diagnosis. Additionally, the review addresses the technical challenges and future perspectives of these methodologies, including their potential expansion and refinement for clinical applications. The discussion underscores the critical role of lipidomic profiling in advancing cancer diagnostics, proposing a new paradigm in how we approach this devastating disease, with particular emphasis on its application in comparative oncology.

Immune responses to influenza (flu) antigens reflect memory to prior infections or vaccinations, which might influence immunity to new flu antigens. Memory of past antigens has been termed “original antigenic sin” or more recently “immune imprinting” and “seniority”. We have researched a comparison between the immune response to live flu infections and inactivated flu vaccina-tions. A brief history of antibody generation theories is presented culminating in new findings about the immune-network theory suggesting that a network of clones exists between an-ti-idiotypic antibodies and T cell receptors. Findings regarding the 2009 pandemic flu strain and immune responses to it are presented, including memory B cells and conserved regions within the hemagglutinin protein. The importance of CD4+ memory T cells and cytotoxic CD8+ T cells responding to both infections and vaccinations are discussed and compared. Innate immune cells like natural killer (NK) cells and macrophages are discussed regarding their roles in adaptive immune responses. Antigen presentation via macroautophagy processes are described. New vaccines in development are mentioned along with the results of some clinical trials. The manu-script concludes with how repeated vaccinations are impacting the immune system and a sketch of what might be behind the imprinting phenomenon, including future research directions.

One of the most striking findings in biogerontology in the 2010s was the demonstration that elimination of senescent cells delays many late-life diseases and extends lifespan in mice. This implied that accumulation of senescent cells promotes late-life diseases, particularly through action of senescent cell secretions (the senescence-associated secretory phenotype or SASP). But what exactly is a senescent cell? Subsequent to the initial characterization of cellular senescence it became clear that, prior to aging, this phenomenon is in fact adaptive. It supports tissue remodeling functions in a variety of contexts, including embryogenesis, parturition and acute inflammatory processes that restore normal tissue architecture and function, such as wound healing, tissue repair after infection, and amphibian limb regeneration. In these contexts such cells are normal and healthy, and not in any way senescent in the true sense of the word, as originally meant by Hayflick. Thus, it is misleading to refer to them as “senescent”. Similarly, the common assertion that senescent cells accumulate with age due to stress and DNA damage is no longer safe, particularly given their role in inflammation - a process that becomes persistent in later life. We therefore suggest that it would be useful to update some terminology, to bring it into line with contemporary understanding, and to avoid future confusion. To open a discussion of this issue, we propose replacing the term cellular senescence with remodeling activation, and SASP with RASP (remodeling-associated secretory phenotype).

Cognitive networks have evolved to cope with uncertain environments in order to make reliable decisions. Such decision making circuits need to respond to the external world in efficient and flexible ways, and one potentially general mechanism of achieving this is grounded in critical states. Mounting evidence has shown that brains operate close to such critical boundaries consistent with self-organized criticality (SOC). Is this also taking place in small-scale living systems, such as cells? Here we explore a recent model of engineered gene networks that have been shown to exploit the feedback between order and control parameters (as defined by expression levels of two coupled genes) to achieve a SOC state. We suggest that such SOC motif could be exploited to generate adaptive behavioral patterns and might help design fast responses in synthetic cellular and multicellular organisms.

Unification based upon the generalisation of proper time is proposed as a comprehensive framework to account for the fundamental structure of matter, in a manner contrasting with the more familiar approach based on extra dimensions of space. The elementary properties of matter to be incorporated include the Standard Model of particle physics together with a source for the dark sector and a coherent formalism for quantum gravity. We elaborate upon the manner in which all such material phenomena and empirical properties as distributed in an extended 4-dimensional spacetime can be encompassed within, and derived from, the continuous flow of time alone via a generalised arithmetic form for infinitesimal intervals of proper time. This approach will also be compared and contrasted with the basic structure of causal set theory as a means of demonstrating how it is possible to construct a full physical theory essentially from elements of time alone, as explicitly developed from the most elementary level. The conception of time as utilised and elucidated in this theory, with emphasis upon the causal continuum properties and as the basis for unification, will be clarified.

The meticulous study of finite automata has produced many important and useful results. Automata are simple yet efficient finite state machines that can be utilized in a plethora of situations. It comes, therefore, as no surprise that they have been used in classic game theory in order to model players and their actions. Game theory has recently been influenced by ideas from the field of quantum computation. As a result, quantum versions of classic games have already been introduced and studied. The PQ penny flip game is a famous quantum game introduced by Meyer in 1999. In this paper we investigate all possible finite games that can be played between the two players Q and Picard of the original PQ game. For this purpose we establish a rigorous connection between finite automata and the PQ game along with all its possible variations. Starting from the automaton that corresponds to the original game, we construct more elaborate automata for certain extensions of the game, before finally presenting a semiautomaton that captures the intrinsic behavior of all possible variants of the PQ game. What this means is that from the semiautomaton in question, by setting appropriate initial and accepting states, one can construct deterministic automata able to capture every possible finite game that can be played between the two players Q and Picard. Moreover, we introduce the new concepts of a winning automaton and complete automaton for either player.

We try to solve three decades-old physics challenges. List all elementary particles. Describe dark matter. Describe mechanisms that govern the rate of expansion of the universe. We propose new modeling. The modeling uses extensions to harmonic oscillator mathematics. The modeling points to all known elementary particles. The modeling suggests new particles. Based on those results, we do the following. We explain observed ratios of dark matter amounts to ordinary matter amounts. We suggest details about galaxy formation. We suggest details about inflation. We suggest aspects regarding changes in the rate of expansion of the universe. We interrelate the masses of some elementary particles. We interrelate the strengths of electromagnetism and gravity. Our work seems to offer new insight regarding applications of harmonic oscillator mathematics. Our work seems to offer new insight regarding three branches of physics. The branches are elementary particles, astrophysics, and cosmology.

We develop and apply new physics theory. The theory suggests specific unfound elementary particles. The theory suggests specific constituents of dark matter. We apply those results. We explain ratios of dark matter amounts to ordinary matter amounts. We suggest details about galaxy formation. We suggest details about inflation. We suggest aspects regarding changes in the rate of expansion of the universe. The theory points to relationships between masses of elementary particles. We show a relationship between the strength of electromagnetism and the strength of gravity. The mathematics basis for matching known and suggesting new elementary particles extends mathematics for harmonic oscillators.

Parkinson's disease (PD) is the second most common neurodegenerative disease worldwide. A growing body of evidence suggests that mitochondrial dysfunction and inflammation plays a crucial role as a pathogenetic mechanism in PD. The aim of the present study was to investigate the metabolic profile of PBMCs from PD patients and to search for a possible relationship be-tween cellular bioenergetics and YKL-40. Materials and methods: The study included 18 naïve PD and an age-matched control group (HC, n=7). Patients were diagnosed according to the MDS-PD, the UPDRS and the Hoen–Yahr scales. Mitochondrial activity was measured by a metabolic analyzer on isolated PBMCs from PD’s patients. Gene (qPCR) and protein (ELISA) expression levels of YKL40 were investigated. Results: New data are reported revealing changes in mito-chondrial activity and YKL-40 levels in PD patients. Bioenergetic parameters showed increased respiratory reserve capacity in the PD compared to HC. The protein levels of YKL-40 were three-fold higher in PD. We found a correlation between YKL-40 protein levels and basal respiration and between YKL-40 and ATP production. Conclusion: These observations suggest an interplay between YKL-40 and mitochondrial function in PD. We assume that YKL-40 gene and protein levels in combination with changes in mitochondrial function might serve as an additional tool to monitor the clinical course of PD.

Oxidative stress has been implicated in cellular senescence and aging, as well as in the onset and progression of many diverse genetic and acquired diseases and conditions. The search for biomarkers of oxidative stress has become relevant to many biomedical fields using many different methods and approaches. Fluorescence methodology is advantageous because of its simplicity and high sensitivity. Fluorescent probes are frequently used to investigate the role of reactive oxygen species in oxidative stress in experimental or clinical settings, often involving the use of flow cytometry and related single-cell based technologies. To assess the specific role of reactive oxygen species in oxidative stress studies by cytometric methodologies, it is essential to detect and characterize these species accurately. However, the detection and quantitation of individual intracellular ROS is still a challenge, but different and complementary strategies may focus on other endpoints of oxidative stress. In this review we present and discuss briefly the limitations and perspectives of such approaches. Based on our results, we also provide recommendations for proper design of cytometric studies of oxidative stress in order to prevent or minimize experimental errors..

Alzheimer’s and Parkinson's diseases (AD and PD) are amongst top of the prevalent neurodegenerative disease. One-third of PD patients are diagnosed with dementia, a pre-symptom of AD, but the underlying mechanism is elusive. Amyloid beta (Aβ) and α-synuclein are two of the most investigated proteins, whose pathological aggregation and spreading are crucial to the pathogenesis of AD and PD, respectively. Transcriptomic studies of the mammalian central nervous system shed light on gene expression profiles at molecular levels, regarding the complexity of neuronal morphologies and electrophysiological inputs/outputs. In the last decade, the booming of the single-cell RNA sequencing technique helped to understand gene expression patterns, alternative splicing, novel transcripts, and signal pathways in the nervous system at single-cell levels, providing insight for molecular taxonomy and mechanistic targets of the degenerative nervous system. Here, we re-visited the cell-cell transmission mechanisms of Aβ and α-synuclein in medi-ating disease propagation, and summarized recent single-cell transcriptome sequencing from different perspectives and discussed its understanding of neurodegenerative diseases.

In this review we summarize the recent development in understanding the role of PIP2 in cellular function and signaling. We first discuss the effect of PIP2 on actin binding proteins addressing the mechanism of the actin cytoskeletal dynamics such as polymerization or depolymerization of the filamentous network or the coupling to membrane to generate forces. Next, we outline the role of PIP2 in membrane dynamics. We summarized how the membrane organization depends upon PIP2 in the presence of ions or transmembrane proteins that are sensitive to membrane curvature. We discuss how clathrin coated pits interact with adaptor proteins during the endocytosis process, which is facilitated by PIP2. Finally, we discuss the role of PIP2 in cell signaling and diseases.

Background: Molecular genetic events are among the numerous factors affecting the clinical course of papillary thyroid carcinoma (PTC). Recent studies have demonstrated that aberrant expression of miRNA, as well as different thyroid-related genes, correlate with the aggressive clinical course of PTC and unfavorable treatment outcome, which opens up new avenues for using them in personalization of the treatment strategy for patients with PTC. In the present work, our goal was to assess the applicability of molecular markers in preoperative diagnosis of aggressive variants of papillary thyroid cancer. Methods: The molecular genetic profile (expression levels of 34 different markers and BRAF mutation) was studied for 108 cytology specimens collected by fine-needle aspiration biopsy in patients with PTC having different clinical manifestations. Results: Statistically significant differences with adjustment for multiple comparisons (p &lt; 0.0015) for clinically aggressive variants of PTC were obtained for four markers: miRNA-146b, miRNA-221, FN1, and CDKN2A. Weak statistical correlation (0.0015&lt; р&lt;0.05) was observed for miRNA-31, -375, -551b, -148b, -125b, mtDNA, CITED1, TPO, HMGA2, CLU, NIS, SERPINA1, TFF3, and TMPRSS4. Conclusions: The recurrence risk of papillary thyroid carcinoma can be preoperatively predicted using miRNA-221, the fibronectin and cyclin-dependent kinase inhibitor 2A genes.

Senescence is a stable cell cycle arrest that is either tumor suppressive or tumor promoting depending on context. Epigenetic changes such as histone methylation are known to affect both induction and suppression of senescence by altering expression of genes that regulate the cell cycle and the senescence-associated secretory phenotype. A conserved group of proteins containing a Jumonji C (JmjC) domain alter chromatin state, and therefore gene expression, by demethylating histones. Here, we will discuss what is currently known about JmjC demethylases in induction of senescence and how these enzymes suppress senescence to contribute to tumorigenesis.

In this paper we give approximations to the Mittag-Leffler functions in terms of elementary functions using different methods. This allowed us to establish a practical method we called integerization principle. This principle states that many fractional nonlinear oscillators may be solved by means of the solution to some integer-order Duffing oscillator equation. The accuracy of the obtained results is illsutrated in concrete examples. Formulas for estimating the errors in the approximations are also provided.

Parental refusal of childhood vaccines is a growing public health concern. Numerous reasons exist for this refusal, including religious, personal, and philosophical beliefs, and safety concerns. However, parental refusal of childhood vaccines is not simply an individualized problem for the family; this impacts herd immunity and affects the entire community. To improve vaccination rates among the pediatric population, understanding the thought process and decision-making behind parental opposition and refusal of vaccinations is essential. Using a survey developed to assess attitudes towards recommended childhood vaccines and the COVID-19 vaccine, this study examined the correlation between vaccine literacy and hesitancy among parents and legal guardians of elementary school-aged children in the Midwest. Responses were analyzed using Chi-squared tests on “R” software. Significant negative correlations were found between COVID-19 hesitancy and vaccine literacy, and resistance towards all vaccines and vaccine literacy. No significant negative correlations were found between hesitancy towards all vaccines and vaccine literacy, or hesitancy and literacy among different income and education brackets. Our results suggest that vaccine education may lessen vaccine hesitancy among parents and may be an essential factor in improving vaccination rates among the pediatric population.

Objective: To investigate fundamental movement skills (FMS) and physical activity differences among Hispanic and non-Hispanic White elementary school children. Materials and Methods: This study included 194 children aged between 4 and 11 in kindergarten, 2nd, and 5th grades. For evaluable participants, t-tests were used to test potential differences in fitness or movement skills. The characteristics of Hispanic and non-Hispanic White were compared using the p-value to detect statistical differences in anthropometric, FMS, and fitness characteristics. To identify any noticeable differences, the means and standard deviations of each group were compared. Results: There were significant group differences across the participants in age, height, Body Mass Index (BMI), BMI percentile, arm circumference, and waist circumference. In addition, there were significant differences between Hispanic and Non-Hispanic White in catch, run, dodge, punt, and two-hand strike (all p’s <0.05). Lastly, there were significant differences between Hispanic and Non-Hispanic White in pull-up, push-up, and sit-up (all p’s <0.05). Conclusion: Future studies should focus on decreasing obesity, increasing fitness, and FMS in elementary school children. Particularly, with Hispanic children whose FMS is negatively affected at such a young age, leading to eventual implications for their physical fitness and health in the future.

Cardiovascular disease (CVD), a group of disorders affecting the heart or blood vessels, are the primary cause of death worldwide, with an immense impact on patient quality of life and disa-bility. According to the World Health Organization, CVD takes an estimated 17.9 million lives each year, where more than four out of five CVD deaths are due to heart attacks and strokes. In the decades to come, increased prevalence of age-related CVD, such as atherosclerosis, coronary artery stenosis, myocardial infarction (MI), valvular heart disease, and heart failure (HF) will contribute to even greater health and economic burden as the global average life expectancy increases and consequently the world’s population continues to age. Considering this, it is important to focus our research efforts on understanding the fundamental mechanisms underlying CVD. In this review, we focus on cellular senescence and mitochondrial dysfunction, which have long been established to contribute to CVD. We also assess the recent advances in targeting mitochondrial dysfunction and senescence with a focus on therapies that influence both and therefore perhaps represent strategies with the most clinical potential, range, and utility.

Both the use of plastic and its decomposition products lead to the distribution of plastic all over the earth and finally to the uptake by all kind of living beings including humans. Still, it is widely unknown what risks harbor the widespread uptake of plastics for human health, espe-cially regarding contributing factors like size, shape and surface composition. We assessed the uptake of polymethylmethacrylate (PMMA)-nano- and microbeads for HEK293-, A549- and MRC5 cells. Via confocal microscopy, we localized multiple PMMA-beads inside the cytosol of cells. Uptake of PMMA-beads did not alter cell growth and cell division, implying no short-term toxicity towards human cells. Further, we used a cAMP response element binding protein (CREB)-mediated reporter assay to assess whether internalized PMMA-nanobeads alter cell-signaling pathways. In contrast to the in vitro transcription, where the addition of PMMA-nanobeads abolished the transcription, no changes regarding CREB-mediated cell sig-naling are given in HEK293-cells. Our data led to the assumption that PMMA-nano-and mi-crobeads are internalized via endocytosis and end up as lysosomes within the cells cytosol. Therefore, we concluded differences regarding the surface composition of the PMMA-nanobeads mainly affect its potential to alter cell signaling. These findings emphasize the key role the sur-face composition plays regarding microplastic and its risks for human health.

In several mammalian species including humans, complex stimulation patterns such as cognitive challenge and physical exercise lead to improvements in organ function, organism health and performance, as well as possibly longer lifespans. The hypothesis is presented here that activity-dependent transcriptional programs, induced by these environmental stimuli, temporarily and lightly de-differentiate somatic cells such as neurons and muscle cells into a state that resembles functionally younger cells to allow cellular remodeling and adaptation of the organism to environmental change. This cellular adaptation program targets several process classes that are heavily implicated in aging, such as mitochondrial metabolism, cell-cell communication, intracellular signaling and epigenetic information processing and leads to functional improvements in these areas. I reverse engineer these activity-dependent gene programs, identify critical molecular nexus points such as CREB, MEF2 and cFos and speculate as to how one might leverage them to prevent and attenuate human aging-related decline of body function, enhance human performance and restore more youthful levels of function and morphology. The findings presented here can serve as a basis for the study and development of effective longevity efforts as the underlying gene programs could be used as markers for treatment success and as targets for therapy development.

Over the last decades, cellular immunotherapy has revealed its curative potential. However, the inherent physiological characteristics of immune cells can limit the potency of this approach. Best defined in T cells, dysfunction associated with terminal differentiation, exhaustion, senescence, and activation-induced cell death undermine adoptive cell therapies. In this review, we concentrate on how the multiple mechanisms that articulate the various forms of immune dysfunction impact cellular therapies primarily involving conventional T cells, but also other lymphoid subtypes, in addition to the various strategies put in place to circumvent these effects. The repercussions of immune cell dysfunction across the full life cycle of cell therapy, from the source material, during manufacturing, and after adoptive transfer are discussed. Applicable to cellular products prepared from native and unmodified immune cells, as well as genetically engineered therapeutics, the understanding and potential modulation of dysfunctional features is key to the development of improved cellular immunotherapies.

This study presents the chemical synthesis, purification, and characterization of a novel non-natural synthetic amino acid. The compound was synthesized in solution, purified, and characterized using NMR spectroscopy, polarimetry, and melting point determination. Dynamic Light 22 Scattering (DLS) analysis demonstrated its ability to form aggregates with an average size of 391 nm, extending to low micrometric sizes. Furthermore, cellular biological assays revealed its ability to enhance fibroblast cell growth, highlighting its potential for tissue regenerative applications. Circular dichroism (CD) spectroscopy showed the ability of the synthetic amino acid to bind serum albumins (using bovine serum albumin (BSA) as a model), and CD deconvolution provided insights into the changes in secondary structures of BSA upon interaction with the amino acid ligand. Additionally, molecular docking using HDOCK software elucidated the most likely binding mode of the ligand inside the BSA structure. We also performed in silico oligomerization of the synthetic compound in order to obtain a model of aggregate to investigate computationally. More in detail, in the dimer formation achieved by molecular self-docking, two distinct poses were observed, corresponding to the lowest and comparable energies, with one pose exhibiting a quasi-coplanar arrangement characterized by a close alignment of two aromatic rings from the synthetic amino acids within the dimer, suggesting the presence of π-π stacking interactions. In contrast, the second pose displayed a non-coplanar configuration, with the aromatic rings oriented in a staggered arrangement, indicating distinct modes of interaction. Both poses were further utilized in the self-docking procedure. Notably, iterative molecular docking of amino acid structures resulted in the formation of higher-order aggregates, with a model of a 512-mer aggregate obtained through self-docking procedures. This model of aggregate presented a cavity capable of hosting therapeutic cargoes and biomolecules, rendering it a potential scaffold for cell adhesion and growth in tissue regenerative applications. Overall, our findings highlight the potential of this synthetic amino acid for tissue regenerative therapeutics and provide valuable insights into its molecular interactions and aggregation behavior.

Herpesvirus is a prevalent pathogen that primarily infects human epithelial cells and has the ability to reside in neurons. In the field of otolaryngology, herpesvirus infection primarily leads to hearing loss and vestibular neuritis, and is considered the primary hypothesis regarding the pathogenesis of vestibular neuritis. In this review, we provide a summary of the effects of herpes virus on cellular processes in both host cells and immune cells, with a focus on HSV-1 as illus-trative examples.

Tandem mass tag (TMT)-based proteomics facilitate multiplexing in mass spectrometry (MS)-based quantification and identification of proteins and their post-translational modifications. The use of TMT isobaric tags can enable multiplexing of up to 18 samples using commercially available kits. A single TMT experiment can quantify proteome, serine, threonine phosphorylation, and tyrosine phosphorylation. Of note, tyrosine phosphorylation is of low abundance, and identification/quantification can be improved using two complementary strategies. First, by employing SH2 superbinder which increases the number of identified sites. The SH2 Superbinder is more cost-effective than the commonly used phosphotyrosine antibodies. Second, by employing phosphotyrosine booster strategy, a pervanadate-treated channel to boost the signal of low-abundant phosphotyrosine. Noteworthy, pervanadate boost increases the likelihood of low abundant peptide to be selected for MS2, and facilitating the detection of > 6000 proteins, 10,000 unique pS/T and 1000 unique pY sites.

:Pulmonary fibrosis is a chronic and fatal lung disease that significantly impacts the aging population globally. To date, anti-fibrotic, immunosuppressive, and other adjunct therapy demonstrate a limited efficacy. Advancing our understanding of pathogenic mechanisms of lung fibrosis provides a future path for the cure. Cellular senescence has gained substantial interest in the past decades due to the increased incidence of fibroproliferative lung diseases in the older age group. Furthermore, the pathologic state of cellular senescence that includes maladaptive tissue repair, decreased regeneration, and chronic inflammation resembles key features of progressive lung fibrosis. This review describes regulatory pathways of cellular senescence and discusses the current knowledge on the senescence of critical cellular players of lung fibrosis, including epithelial cells (alveolar type 2 cells, basal cells, etc.), fibroblasts, and immune cells, their phenotypic changes, and the cellular and molecular mechanisms by which these cells contribute to the pathogenesis of pulmonary fibrosis. A few challenges in the field include establishing appropriate in vivo experimental models and identifying senescence targeted signaling molecules and specific therapy to target senescent cells, known collectively as "senolytic" or “senotherapeutic” agents.

Bacteria participate in a wide diversity of symbiotic associations with eukaryotic hosts that require precise interactions for bacterial recognition and persistence. Most commonly, host-associated bacteria interfere with host gene expression to modulate the immune response to the infection. However, many of these bacteria also interfere with host cellular differentiation pathways to create a hospitable niche, resulting in the formation of novel cell types, tissues, and organs. In both of these situations, bacterial symbionts must interact with eukaryotic regulatory pathways. Here, we detail what is known about how bacterial symbionts, from pathogens to mutualists, control host cellular differentiation across the central dogma, from epigenetic chromatin modifications, to transcription and mRNA processing, to translation and protein modifications. We identify four main trends from this survey. First, mechanisms for controlling host gene expression appear to evolve from symbionts co-opting cross-talk between host signalling pathways. Second, symbiont regulatory capacity is constrained by the processes that drive reductive genome evolution in host-associated bacteria. Third, the regulatory mechanisms symbionts exhibit correlate with the cost/benefit nature of the association. And, fourth symbiont mechanisms for interacting with host genetic regulatory elements are not bound by native bacterial capabilities. Using this knowledge, we explore how the ubiquitous intracellular Wolbachia symbiont of arthropods and nematodes may modulate host cellular differentiation to manipulate host reproduction. Our survey of the literature on how infection alters gene expression in Wolbachia and its hosts revealed that, despite their intermediate-sized genomes, different strains appear capable of a wide diversity of regulatory manipulations. Given this and Wolbachia’s diversity of phenotypes and eukaryotic-like proteins, we expect that many symbiont-induced host differentiation mechanisms will be discovered in this system.

T cells follow a triphasic distinct pathway of activation, proliferation and differentiation before becoming functionally and phenotypically ‘exhausted’ in settings of chronic infection, autoimmunity and in cancer. Exhausted T cells progressively lose canonical effector functions, exhibit altered transcriptional networks and epigenetic signatures and gain constitutive expression of a broad coinhibitory receptor suite. This review outlines recent advances in our understanding of exhausted T cell biology and examines cellular and molecular mechanisms by which a state of dysfunction or exhaustion is established, and mechanisms by which exhausted T cells may still contribute to pathogen or tumour control. Further, this review describes our understanding of exhausted T cell heterogeneity and outlines the mechanisms by which checkpoint blockade differentially engages exhausted T cell subsets to overcome exhaustion and recover T cell function.

Abstract: Chronic Obstructive Pulmonary Disease (COPD) is a continually advancing respiratory disorder, characterized by enduring airflow limitation along with long-lasting inflammation in the airways. An expanding pool of evidence is emphasizing how mitochondrial dysfunction directly impacts the development and progression of COPD. This comprehensive review delves deeper into the intricate cellular and molecular biology of mitochondria within the framework of COPD, spotlighting significant alterations in structure and function that accompany mitochondrial dysfunction. In this narrative, we delve into the observed changes in mitochondrial shape, behavior, and respiratory chain complex alterations seen in individuals afflicted with COPD. The discussion includes implications on cellular signaling pathways and how this affects the natural process of programmed cellular death (apoptosis) and cellular aging, particularly in reference to COPD. Additionally, we consolidate ongoing therapeutic strategies, honing in on the management of COPD through a focus on mitochondrial dysfunction. Prominent among these strategies is the potential role of antioxidants and mitochondrial biogenesis inducers, which are emerging as promising fields of intervention. Furthermore, we embark on an overview of the possible use of mitochondrial biomarkers as innovative tools in diagnosing COPD, evaluating disease progression, and monitoring the efficacy of COPD treatments. As we unravel the rather complex interplay between mitochondrial biology and COPD, this review highlights the critical importance of formulating treatments that target mitochondrial dysfunction. The ultimate goal of these strategic interventions is to slow down the rate of COPD progression and significantly improve patient outcomes. Lastly, though our understanding of the nexus between COPD and mitochondrial dysfunction has significantly enhanced, ample scope remains for further investigation. Improved insights into the mechanisms of mitochondrial dysfunction, discovered biomarkers, and targeted therapies could encompass a revolutionary step towards comprehensive disease management. The future of COPD treatment seems to lean towards a combination of strategies, prioritizing not only the management of symptoms but also focused on preserving and restoring lung function, improving physical capacity, and enhancing quality of life for COPD patients.