A series of benzoquinoline-employing heterocycles was synthesized by treating 3-chlorobenzo[f]quinoline-2-carbaldehyde with N-phenyl-3-methylpyrazolone, 4-aminoacetophenone, 1,2-diaminoethane, and 2-cyanoethanohydrazide. Also, pyridine, chromene, α,β-unsaturated nitrile, thiosemicarbazone, and 1,2-bis-aryl hydrazine derivatives were prepared from the cyanoethanohydrazone obtained. The DFT calculations and experiment outcomes were consistent. In vitro screening of their antiproliferative efficacy was examined against HCT116 and MCF7 cancer cell lines. The pyrazolone 2 and cyanoethanohydrazone 5 derivatives exhibited the most potency, which was demonstrated by their molecular docking towards the CDK-5 enzyme. The binding energies of compounds 2 and 5 were - 6.6320 kcal/mol (with RMSD of 0.9477 Å) and - 6.5696 kcal/mol (with RMSD of 1.4889 Å), respectively, which were near to that of co-crystallized ligand (EFP). This implies a notably strong binding affinity towards the CDK-5 enzyme. Thus, pyrazolone derivative 2 would be considered a promising candidate for further optimization to develop new chemotherapeutic agents. In addition, the ADME (absorption, distribution, metabolism, and excretion) analyses displayed its desirable drug-likeness and oral bioavailability properties.

This article reviews the current knowledge and recent advancements in computational modeling of the cell cycle. It offers a comparative analysis of various modeling paradigms, highlighting their unique strengths, limitations, and applications. Specifically, the article compares deterministic and stochastic models, single-cell versus population models, and mechanistic versus abstract models. This detailed analysis helps determine the most suitable modeling framework for various research needs. Additionally, the discussion extends to the utilization of these computational models to illuminate cell cycle dynamics, with a particular focus on cell cycle viability, crosstalk with signaling pathways, tumor microenvironment, DNA replication, and repair mechanisms, underscoring their critical roles in tumor progression and the optimization of cancer therapies. By applying these models to crucial aspects of cancer therapy planning for better outcomes, including drug efficacy quantification, drug discovery, drug resistance analysis, and dose optimization, the review highlights the significant potential of computational insights in enhancing the precision and effectiveness of cancer treatments. This emphasis on the intricate relationship between computational modeling and therapeutic strategy development underscores the pivotal role of advanced modeling techniques in navigating the complexities of cell cycle dynamics and their implications for cancer therapy.

BACKGROUND: High consumption of processed meat and unprocessed red meat is associated with increased risk of multiple chronic diseases, although there is substantial uncertainty regarding the relationship for unprocessed red meat. We developed a microsimulation model to estimate how reductions in processed meat and unprocessed red meat consumption could affect rates of type 2 diabetes, cardiovascular disease, colorectal cancer, and mortality in the US adult population.
METHODS: We used data from two versions of the US National Health and Nutrition Examination Survey, one conducted during 2015-16 and one conducted during 2017-18, to create a simulated US population. The starting cohort was restricted to respondents aged 18 years or older who were not pregnant and had 2 days of dietary-recall data. First, we used previously developed risk models to estimate the baseline disease risk of an individual. For type 2 diabetes we used a logistic-regression model and for cardiovascular disease and colorectal cancer we used Cox proportional-hazard models. We then multiplied baseline risk by relative risk associated with individual processed meat and unprocessed red meat consumption. Prevented occurrences of type 2 diabetes, cardiovascular disease, colorectal cancer, and mortality were computed by taking the difference between the incidence in the baseline and intervention scenarios. All stages were repeated for ten iterations to correspond to a 10-year time span. Scenarios were reductions of 5%, 10%, 30%, 50%, 75%, and 100% in grams consumed of processed meat, unprocessed red meat, or both. Each scenario was repeated 50 times for uncertainty analysis.
RESULTS: The total number of individual respondents included in the simulated population was 8665, representing 242 021 876 US adults. 4493 (51·9%) of 8665 individuals were female and 4172 (48·1%) were male; mean age was 49·54 years (SD 18·38). At baseline, weighted mean daily consumption of processed meat was 29·1 g, with a 30% reduction being 8·7 g per day, and of unprocessed red meat was 46·7 g, with a 30% reduction being 14·0 g per day. We estimated that a 30% reduction in processed meat intake alone could lead to 352 900 (95% uncertainty interval 345 500-359 900) fewer occurrences of type 2 diabetes, 92 500 (85 600-99 900) fewer occurrences of cardiovascular disease, 53 300 (51 400-55 000) fewer occurrences of colorectal cancer, and 16 700 (15 300-17 700) fewer all-cause deaths during the 10-year period. A 30% reduction in unprocessed red meat intake alone could lead to 732 600 (725 700-740 400) fewer occurrences of type 2 diabetes, 291 500 (283 900-298 800) fewer occurrences of cardiovascular disease, 32 200 (31 500-32 700) fewer occurrences of colorectal cancer, and 46 100 (45 300-47 200) fewer all-cause deaths during the 10-year period. A 30% reduction in both processed meat and unprocessed red meat intake could lead to 1 073 400 (1 060 100-1 084 700) fewer occurrences of type 2 diabetes, 382 400 (372 100-391 000) fewer occurrences of cardiovascular disease, 84 400 (82 100-86 200) fewer occurrences of colorectal cancer, and 62 200 (60 600-64 400) fewer all-cause deaths during the 10-year period.
CONCLUSIONS: Reductions in processed meat consumption could reduce the burden of some chronic diseases in the USA. However, more research is needed to increase certainty in the estimated effects of reducing unprocessed red meat consumption.
BACKGROUND: The Wellcome Trust.

Delays in nerve transmission are an important topic in the field of neuroscience. Spike signals fired or received by the dendrites of a neuron travel from the axon to a presynaptic cell. The spike signal then triggers a chemical reaction at the synapse, wherein a presynaptic cell transfers neurotransmitters to the postsynaptic cell, regenerates electrical signals via a chemical reaction through ion channels, and transmits them to neighboring neurons. In the context of describing the complex physiological reaction process as a stochastic process, this study aimed to show that the distribution of the maximum time interval of spike signals follows extreme-order statistics. By considering the statistical variance in the time constant of the leaky Integrate-and-Fire model, a deterministic time evolution model for spike signals, we enabled randomness in the time interval of the spike signals. When the time constant follows an exponential distribution function, the time interval of the spike signal also follows an exponential distribution. In this case, our theory and simulations confirmed that the histogram of the maximum time interval follows the Gumbel distribution, one of the three forms of extreme-value statistics. We further confirmed that the histogram of the maximum time interval followed a Fréchet distribution when the time interval of the spike signal followed a Pareto distribution. These findings confirm that nerve transmission delay can be described using extreme value statistics and can therefore be used as a new indicator of transmission delay.

Long QT Syndrome type 8 (LQT8) is a cardiac arrhythmic disorder associated with Timothy Syndrome, stemming from mutations in the CACNA1C gene, particularly the G406R mutation. While prior studies hint at CACNA1C mutations\' role in ventricular arrhythmia genesis, the mechanisms, especially in G406R presence, are not fully understood. This computational study explores how the G406R mutation, causing increased transmural dispersion of repolarization, induces and sustains reentrant ventricular arrhythmias. Using three-dimensional numerical simulations on an idealized left-ventricular model, integrating the Bidomain equations with the ten Tusscher-Panfilov ionic model, we observe that G406R mutation with 11% and 50% heterozygosis significantly increases transmural dispersion of repolarization. During S1-S4 stimulation protocols, these gradients facilitate conduction blocks, triggering reentrant ventricular tachycardia. Sustained reentry pathways occur only with G406R mutation at 50% heterozygosis, while neglecting transmural heterogeneities of action potential duration prevents stable reentry, regardless of G406R mutation presence.

Antennas play a crucial role in designing an efficient communication system. However, reducing the maximum sidelobe level (SLL) of the beam pattern is a crucial challenge in antenna arrays. Pattern synthesis in smart antennas is a major area of research because of its widespread application across various radar and communication systems. This paper presents an effective technique to minimize the SLL and thus improve the radiation pattern of the linear antenna array (LAA) using the chaotic inertia-weighted Wild Horse optimization (IERWHO) algorithm. The wild horse optimizer (WHO) is a new metaheuristic algorithm based on the social behavior of wild horses. The IERWHO algorithm is an improved Wild Horse optimization (WHO) algorithm that combines the concepts of chaotic sequence factor, nonlinear factor, and inertia weights factor. In this paper, the method is applied for the first time in antenna array synthesis by optimizing parameters such as inter-element spacing and excitation to minimize the SLL while keeping other constraints within the boundary limits, while ensuring that the performance is not affected. For performance evaluation, the simulation tests include 12 benchmark test functions and 12 test functions to verify the effectiveness of the improvement strategies. According to the encouraging research results in this paper, the IERWHO algorithm proposed has a place in the field of optimization.

To achieve the accuracy and anti-interference of the motion control of the soft robot more effectively, the motion control strategy of the pneumatic soft bionic robot based on the improved Central Pattern Generator (CPG) is proposed. According to the structure and motion characteristics of the robot, a two-layer neural network topology model for the robot is constructed by coupling 22 Hopfield neuron nonlinear oscillators. Then, based on the Adaptive Neuro-Fuzzy Inference System (ANFIS), the membership functions are offline learned and trained to construct the CPG-ANFIS-PID motion control strategy for the robot. Through simulation research on the impact of CPG-ANFIS-PID input parameters on the swimming performance of the robot, it is verified that the control strategy can quickly respond to input parameter changes between different swimming modes, and stably output smooth and continuous dynamic position signals, which has certain advantages. Then, the motion performance of the robot prototype is analyzed experimentally and compared with the simulation results. The results show that the CPG-ANFIS-PID motion control strategy can output coupled waveform signals stably, and control the executing mechanisms of the pneumatic soft bionic robot to achieve biological rhythms motion propulsion waveforms, confirming that the control strategy has accuracy and anti-interference characteristics, and enable the robot have certain maneuverability, flexibility, and environmental adaptability. The significance of this work lies in establishing a CPG-ANFIS-PID control strategy applicable to pneumatic soft bionic robot and proposing a rhythmic motion control method applicable to pneumatic soft bionic robot.

Artificial intelligence has revolutionized the field of protein structure prediction. However, with more powerful and complex software being developed, it is accessibility and ease of use rather than capability that is quickly becoming a limiting factor to end users. LazyAF is a Google Colaboratory-based pipeline which integrates the existing ColabFold BATCH software to streamline the process of medium-scale protein-protein interaction prediction. LazyAF was used to predict the interactome of the 76 proteins encoded on the broad-host-range multi-drug resistance plasmid RK2, demonstrating the ease and accessibility the pipeline provides.

BACKGROUND: The SLC29A3 gene, which encodes a nucleoside transporter protein, is primarily located in intracellular membranes. The mutations in this gene can give rise to various clinical manifestations, including H syndrome, dysosteosclerosis, Faisalabad histiocytosis, and pigmented hypertrichosis with insulin-dependent diabetes. The aim of this study is to present two Iranian patients with H syndrome and to describe a novel start-loss mutation in SLC29A3 gene.
METHODS: In this study, we employed whole-exome sequencing (WES) as a method to identify genetic variations that contribute to the development of H syndrome in a 16-year-old girl and her 8-year-old brother. These siblings were part of an Iranian family with consanguineous parents. To confirmed the pathogenicity of the identified variant, we utilized in-silico tools and cross-referenced various databases to confirm its novelty. Additionally, we conducted a co-segregation study and verified the presence of the variant in the parents of the affected patients through Sanger sequencing.
RESULTS: In our study, we identified a novel start-loss mutation (c.2T > A, p.Met1Lys) in the SLC29A3 gene, which was found in both of two patients. Co-segregation analysis using Sanger sequencing confirmed that this variant was inherited from the parents. To evaluate the potential pathogenicity and novelty of this mutation, we consulted various databases. Additionally, we employed bioinformatics tools to predict the three-dimensional structure of the mutant SLC29A3 protein. These analyses were conducted with the aim of providing valuable insights into the functional implications of the identified mutation on the structure and function of the SLC29A3 protein.
CONCLUSIONS: Our study contributes to the expanding body of evidence supporting the association between mutations in the SLC29A3 gene and H syndrome. The molecular analysis of diseases related to SLC29A3 is crucial in understanding the range of variability and raising awareness of H syndrome, with the ultimate goal of facilitating early diagnosis and appropriate treatment. The discovery of this novel biallelic variant in the probands further underscores the significance of utilizing genetic testing approaches, such as WES, as dependable diagnostic tools for individuals with this particular condition.

BACKGROUND: Many cancer survivors experience cancer-related cognitive impairment (CRCI), often with significant negative consequences across various life domains. Emerging evidence suggests that allowing additional time to process information before acting may be a useful strategy for those with CRCI to mitigate some of its impacts. The Wisconsin Card Sorting Task (WCST), a measure of general cognition, has shown that for some cancer survivors, longer task completion time facilitates similar task performance outcomes to control populations concerning perseveration errors; a key performance metric of the WCST. However, assessing if this strategy may be useful, as well as determining for whom it may be useful, with regard to strengths and weaknesses among select cognitive domains, is challenging due to factors such as the problem of task impurity. Accordingly, this study provides an initial computational and experimental assessment of whether additional time to process information before acting is a useful strategy for those with CRCI.
METHODS: We simulated individual cognitive differences observed in humans by varying contributions of executive functioning components (updating, shifting, inhibition) to yield 48 distinct computational models of the WCST. Our main manipulation was then to provide these models with more or less time (at three levels of 20, 40 and 60 cycles) before models executed an action to sort a given card. We compared the number of perseveration errors on the WCST produced by the computational models. Additionally, we determined models that simulated the performance of cancer survivors on the WCST by comparing the number of perseveration errors produced by the models to human data.
RESULTS: Additional processing time resulted in the models producing significantly fewer perseveration errors, supporting our hypothesis. In addition, 8 unique models simulated the performance of cancer survivors on the WCST. Additional time appeared to have a positive influence on performance primarily by mitigating the impacts of severe inhibition impairments. For more severe global executive function impairments, a substantial amount of additional time was required to mitigate the impacts of the impairments. For the most severe impairments, additional time was unable to adequately mitigate the impact on performance.
CONCLUSIONS: Additional processing time may be a useful strategy to rectify perseveration errors among cancer survivors with CRCI. Our findings have implications for the development of practical strategies, such as workload and deadline management in occupational settings, which may mitigate the negative effects of CRCI.