University of Tabriz

Background Inadequate treatment responses, chemotherapy resistance, significant heterogeneity, and lengthy treatment durations create an urgent need for new pancreatic cancer therapies. This study aims to investigate the effectiveness of gemcitabine-loaded nanoparticles enclosed in an organo-metallic framework under ketogenic conditions in inhibiting the growth of MIA-PaCa-2 cells. Methods Gemcitabine was encapsulated in Metal–organic frameworks (MOFs) and its morphology and size distribution were examined using transmission electron microscopy (TEM) and Dynamic light scattering (DLS) with further characterization including FTIR analysis. Various drug groups were established to evaluate their influences on cell cytotoxicity, apoptosis rate, cell cycle distribution, levels of superoxide dismutase (SOD), glutathione peroxidase (GPx), malondialdehyde (MDA), and cell migration. Results The gemcitabine-MOF was thoroughly analyzed to determine its size, morphology, and chemical composition, confirming its successful preparation. The treatment results showed an increase in the number of apoptotic cells following gemcitabine-MOF treatment, which was found to be associated with cell cycle arrest in the sub-G1 phase. Moreover, these treatments also resulted in reduced cell migration, decreased activity of antioxidant enzymes (SOD, GPx), and increased accumulation of MDA. Additionally, when exposed to ketogenic conditions (where beta-hydroxybutyrate is present in a glucose-limited medium), there was a further increase in cell cycle arrest, accompanied by a more pronounced decrease in SOD and GPx activity, as well as decreased migration. Conclusion The use of metal–organic framework to encapsulate gemcitabine yielded notable pro-apoptotic effects in MIA-PaCa-2 cells with which ketogenic conditions had a synergistic effect that can hold promise for improving therapeutic options. Graphical Abstract

Nowadays, structural health monitoring (SHM) is one of the most critical subjects in geotechnical engineering. All structures (such as buildings and bridges) have a limited life span. Phenomena such as corrosion, fatigue, and excessive loading cause damage to these structures. The main goal of SHM is the timely detection of structural damages during or after a dynamic excitation to prevent destructive damage due to failure. The study investigates the effectiveness of wavelet transform techniques (discrete and continuous approach) as a signal processing-based method to monitor the condition of Pile–soil-superstructure systems. The primary objective is to identify defects in these systems under earthquake excitations. In this way, the first step determined the seismic response of a single pile embedded in one layer of Nevada sand using Abaqus finite element software. In the second step, the ability of acceleration signal processing recorded in different parts of the pile section has been evaluated to identify pile defects. In the present study, damage is not simulated in the finite element model. The yielding of the pile section under the earthquake record was considered a defect. In this context, the main goal is to detect yielding in the pile cross section using acceleration signal processing during earthquake excitations.

Conferone is one of the most abundant sesquiterpene coumarins isolated from the Ferula and Heptaptera genera. In addition to three exported medicinal products, the genus Ferula is also used in traditional medicine to treat cough, asthma, toothache, stomach problems, and constipation. Conferone, as one of 31 sesquiterpene coumarins identified from the roots of Ferula species, has shown various medicinal activities such as anti-inflammatory, cytotoxic, P-glycoprotein (P-gp) inhibitor, anticancer, and anti-leishmanial. Despite recent studies on the anticancer activity of conferone, most of the related literature is usually scattered in different publications, which limits the possibility of further research on this secondary metabolite. This comprehensive review, collected conferone data from 1972 to August 2023 using major databases such as PubMed, Science Direct, Google Scholar, etc. Although there are not many published papers on this sesquiterpene coumarin. However, this review article aims to promote more research on conferone by summarizing the available literature on sources, extraction methods, isolation, identification, and determination of the structure to investigate the biological activities and the relationship of its structure with the reported activities. Therefore, in the present work, while reviewing the in vitro and in silico studies conducted on conferone, in the end, a critical evaluation will be presented to highlight the strengths and limitations along with solutions to solve them. Graphical abstract

This paper investigates the mean square exponential stability of stochastic neural networks relying on memristor with leakage delay with different types of activation functions. To this aim, we introduce a new suitable stochastic Lyapunov‐Krasovskii functional (SLKF) and employ Filippov solutions to derive stability criteria using It o^$\hat{o}$'s formula. We encounter with a nonlinear matrix inequality which should be converted to a linear matrix inequality (LMI) problem by using Schur complement lemma. The proposed problem is handled by using the CVX toolbox in MATLAB software. In the numerical examples section, we bring two examples related to two‐ and three‐dimentional memristor‐based neural networks whose coefficients satisfy in the Schur complement lemma. The figures show that the employed stochastic Lyapunov functions can capture the exponential stability conditions.

This study investigates the problem of finite-time contractive stability analysis and observer-based [Formula: see text] fault-tolerant control (FTC) for linear network-based control systems subject to network-induced time-varying delay. It is assumed that the faults occur in the both actuator and sensor components. For the sake of data transmission reduction, an aperiodic-sampling-based adaptive event-triggered scheme is used, in which the interval between two sampling instants varies within a certain known bound, and the event threshold is adjusted by using the adaptive rule. An unknown input observer (UIO) is used to estimate the system states and faults simultaneously. Then, using Lyapunov–Krasovskii stability theory, delay-dependent sufficient conditions for the observer-based FTC of the networked control system (NCS) are derived. These conditions are presented in the form of linear matrix inequalities (LMIs), ensuring that both the error system and the closed-loop NCS achieve finite-time contractive stability while simultaneously satisfying the [Formula: see text] performance index. Finally, a simulation example is provided to demonstrate the effectiveness of the proposed design approach.

Pathogenic mutations in the BRCA1 gene are among the most significant genetic risk factors for breast cancer. Identifying these mutations is crucial for advancing prevention and treatment strategies. A substantial portion of genetic variants in BRCA1 remains unclassified due to limited functional evidence. Mutations in the Interesting New Gene (RING) and BRCA1 C-terminal (BRCT) domains are particularly associated with increased cancer risk. This study aims to identify and analyze novel mutations in these domains. Seven newly identified mutations—Q12H, K20D, and E84G in the RING domain, and Y1666D, T1675S, T1681A, and L1764Q in the BRCT domain—were evaluated using molecular dynamics simulations. The simulations revealed significant changes in the protein stability, flexibility, compactness, hydrogen bonding, and solvent-accessible surface area, with BRCT domain mutations showing more pronounced effects. This study represents the first comprehensive computational analysis to assess the structural and functional impact of these novel mutations and predict stabilization possibilities. By elucidating the conformational dynamics of BRCA1 mutations, our findings provide a foundation for experimental validation and the development of targeted therapeutic interventions.

The present investigation seeks to customize the optical, magnetic, and structural characteristics of nickel oxide (NiO) nanopowders through chromium, iron, cobalt, copper, and zinc doping to enhance optoelectronic applications. In this regard, the preparation of pristine NiO and Ni0.95 × 0.05O (X = Cr, Fe, Co, Cu, and Zn) powders was successfully achieved through the co-precipitation method. The X-ray powder diffraction was employed to examine the prepared powders’ phase formation and crystal structure characteristics. The obtained results revealed the presence of a face-centered cubic structure in all samples. In addition, doping of Cr, Fe, Co, Cu, and Zn into the NiO system did not induce any other secondary phase. Moreover, the estimation of the crystalline size for the pristine and doped samples was carried out using the Debye-Scherrer formula, yielding values ranging from 16 to 28 nm which is deemed suitable for the study of doping effects. Moreover, the morphological characteristics of both the pristine and doped NiO powders were investigated using a field emission scanning electron microscope coupled with energy dispersive spectroscopy to confirm the presence of dopant elements and chemical composition. The morphological results revealed the growth of homogeneous nanocrystallites with fine particles. Furthermore, the samples underwent Fourier Transform Infrared Spectroscopy analysis to validate their purity, which revealed the presence of vibrational modes in the metal oxide bonds. An optical investigation was conducted on all samples utilizing a diffuse reflectance spectroscopy within the spectral range of 350–900 nm. Band gap values were estimated based on diffuse reflectance spectroscopy data through Tauc plot analysis, yielding a range from 2.77 to 3.46 eV. This analysis revealed a red shift in NiO with all dopants except for Zn doping. Additionally, numerical calculations utilizing the Kramers–Kronig relation were performed to assess the extinction coefficient (k) and refractive index (n) parameters from the reflectance data. The presence of room-temperature ferromagnetism was elucidated in all samples by the findings acquired through the application of the vibrating sample magnetometer technique. The parameters of coercivity exhibit an increase from 80.44 Oe for pristine NiO to 350.75 Oe for Zn-doped NiO, a phenomenon that is advantageous for applications in data storage. The introduction of iron into NiO nanoparticles has profoundly affected the magnetic properties, resulting in a transition of the material from a weak ferromagnetic state to a ferromagnetic state. The outcomes imply promising magnetic and optical applications for such earlier mentioned nanopowders. This observation suggests that the prepared NiO nanopowders have significant potential in both linear and nonlinear optical devices, optoelectronics, and data storage technologies.

Foliar spray of growth regulators and nutrients is a new approach to reduce the adverse impacts of salinity on plant performance. Thus, two experiments were undertaken as factorial with randomized complete block design in three replicates, to find out the mechanisms behind individual and simultaneous application of 1 mM salicylic acid (SA) and 3 mM Fe2O3 nanoparticles (Fe2O3-NPs) on ajowan plants under different salinity levels (non-saline and low, moderate and high NaCl salinities), paying particular attention to the effects on essential oil content and antioxidant capacity. Foliar application of SA, Fe2O3-NPs and particularly SA+Fe2O3-NPs decreased Na uptake and increased nutrients and SA contents, phenylalanine ammonia lyase and tyrosine ammonia lyase activities, and phenols under different levels of salinities, especially under moderate and high salinities. The seed essential oil content was reduced with progressing seed development. Rising salinity enhanced essential oil accumulation in seeds of treated and untreated ajowan plants at all developing stages. The highest essential oil and antioxidant activity was recorded for plants treated with SA+Fe2O3-NPs under all salinity levels. Therefore, foliar spray of SA+Fe2O3-NPs is suggested as the best treatment for improving production and antioxidant potential of ajowan essential oil.

Phthalic acid esters are widely used worldwide as plasticizers. The high consumption of phthalates in China makes it the world’s largest plasticizer market. The lack of phthalic acid ester’s chemical bonding with the polymer matrix facilitates their detachment from plastic products and subsequent release into the environment and causes serious threats to the health of living organisms. Thus, environmentally friendly and sustainable solutions for their removal are urgently needed. In this context, both natural and engineered bacterial and algal communities have played a crucial role in the degradation of various phthalic acid esters present in water and soil. When algae-bacteria co-culture is compared to a singular algae or bacteria system, this symbiotic system shows superior performance in the removal of dibutyl phthalates and diethyl phthalates from synthetic wastewater. This review provides an optimistic outlook for co-culture systems by in-depth examining single microorganisms, namely bacteria and algae, as well as algae-bacterial consortiums for phthalates degradation, which will draw attention to species co-existence for the removal of various pollutants from the environment. In addition, further development and research, particularly on the mechanisms, genes involved in the degradation of phthalic acid esters, and interactions between bacterial and algal species, will lead to the discovery of more adaptable species as well as the production of targeted species to address the environmental pollution crisis and provide a green, efficient, and sustainable approach to environmental protection. Discrepancies in knowledge and potential avenues for exploration will enhance the existing body of literature, enabling researchers to investigate this field more comprehensively. Graphical abstract

A dispersive solid-phase extraction approach was developed for extracting certain pesticides from strawberry samples. This method was combined with dispersive liquid–liquid microextraction to further enrich the samples. To achieve this, 5 mg of the synthesized nanocomposite was used for the extraction of the analytes from 5 mL of the sample solution under agitation by vortexing for 8 min. After centrifugation (at 5000 rpm for 3 min), the supernatant was removed. Then, the adsorbed analytes were eluted with 1.0 mL of ethanol using ultrasonication. The mixture was then centrifuged at 5000 rpm for 3 min, and the eluent was transferred into another microtube and mixed with 80 µL of chloroform. The mixture was aspirated into a syringe and then rapidly injected into 5-mL sodium chloride solution (4%, w/v). The resulting cloudy solution was centrifuged and 1 µL of the settled phase was used in the analysis step. The validity of the method was assessed after optimizing the experimental conditions. The obtained results showed that the limits of detection (LOD) and quantification (LOQ) for the analytes were in the ranges of 0.08–0.48 and 0.29–1.4 ng mL⁻¹, respectively. The calibration graphs were linear in the range of 1.4–3000 ng mL⁻¹ with a coefficient of determination (r²) ≥ 0.991. The relative standard deviations (RSDs) for replicate analyses were ≤ 5.2%. The enrichment factors (EFs) and extraction recoveries (ERs) for the studied analytes were in the ranges of 365–405 and 73–81%, respectively. The developed method was performed on several strawberry samples and the results showed that the samples were free of the studied pesticides.

Getting machine learning (ML) to perform accurate prediction needs a sufficient number of labeled samples. However, due to the either lack or small number of labeled samples in most domains, it is often beneficial to use domain adaptation (DA) and transfer learning (TL) to leverage a related auxiliary source domain to optimize the performance on target domain. In fact, the purpose of TL and DA is to use the labeled sample information (i.e., samples and the corresponding labels) for training the classifier to categorize the unlabeled samples. In this paper, we aim to propose a novel semi-supervised transfer learning method entitled “Latent Sparse subspace learning and visual domain classification via Balanced distribution alignment and Hilbert–Schmidt metric (LSBH)”. LSBH uses the latent sparse domain transfer learning for visual adaptation (LSDT) to adapt the samples with different distributions or feature spaces across domains and prevent the creation of local common subspace for source and target domains via the simultaneous learning of latent space and sparse reconstruction. LSBH proposes a novel robust classifier which maintains performance and accuracy even when faced with variations across the source and target domains. To this end, it utilizes the following two criteria in the optimization problem: maximum mean discrepancy and Hilbert–Schmidt independence criterion to reduce the marginal and conditional distribution disparities of domains and increase the dependency between samples and labels at the classification step. LSBH obtains the optimal coefficients for the classifier, which results in the minimum error in the loss function by solving the optimization problem. Thus, the error minimizing of the loss function is a part of the optimization problem. Also, to maintain the geometric structure of data in the classification step, the neighborhood graph of samples is used. The efficiency of the proposed method has been evaluated on different visual datasets and has been compared with new and prominent methods of domain adaptation and transfer learning. The results induce the superior performance of LSBH compared to the other state-of-the-art methods in label prediction.

This investigation presents extensive computational analyses of the compressible flow near ramp injector with double circular injectors at supersonic combustor of scramjet engine. Comparison of the fuel mixing and fuel jet penetration of hydrogen jet are done for two injector configurations at free stream Mach number of 2. The simulation of the supersonic flow near ramp injector is done via solving RANS equations with computational fluid dynamic technique. Effect of nozzle space on the fuel distribution and mixing mechanism has been investigated. Besides, interface of the free stream and jet behind the ramp are fully analyzed. Comparison of the circulation strength behind these two configurations indicates that increasing of the jet space led to higher circulation strength. However, the mixing efficiency of the model with low jet space is higher since the interaction of these two jet is key factor for diffusion and mixing inside the combustion chamber.

Dementia is a comprehensive term that refers to illnesses characterized by a decline in cognitive memory and other cognitive functions, affecting a person's overall ability to operate. The exact causes of dementia are unknown to this day. The heterogeneity of Alzheimer's indicates the contribution of genetic polymorphism to this disease. This disease is the most prevalent and damaging illness. Studies indicate that the global prevalence of Alzheimer's disease (AD) exceeds 26 million individuals. Investigation of variations in many genes indicates that these variations may be linked to the susceptibility to AD. Additional genetic factors could potentially influence AD. Analysis of several single-nucleotide polymorphisms in this context reveals a correlation between certain variants and AD. Regardless, Alzheimer's disease is always influenced by a particular APOE gene allele. The study's findings indicate that risk of Alzheimer's disease (AD) is linked to polymorphisms in the following genes: BDNF, presenilin-1 (PS-1), presenilin-2 (PS-2), LRP, APP, CTSD,5-6HT, TREM2, TNF-α, LPL, Clusterin (CLU), SORL1 (Sortilin-Related Receptor), PICALM, Complement Receptor 1 (CR1), and APOE genes.

Breast cancer (BC) commonly expresses estrogen receptors (ERs); hence, endocrine therapy targeting ERs is considered an effective treatment. Tamoxifen (TAM) resistance is an essential clinical complication leading to cancer progression and metastasis. This study investigated MicroRNAs (miRNAs) potentially implicated in drug resistance (miR-182-3p, miR-382-3p) or sensitivity (miR-93, miR- 142- 3p). This study aimed to provide new insights into serum microRNA expression profiles in BC. This case-control study included patients with luminal-A BC who received TAM for approximately one year. The case and control groups included 40 patients with or without local recurrence or metastasis. The expression levels of miR-182-3p, miR-382-3p, miR-93, and miR-142-3p in plasma samples were measured using real-time PCR with target-specific primers. The multivariate model of miR-93 (p = 0.0002), miR-182-3p (p = 0.0002), and miR-382-3p (p = 0.0028) demonstrated higher predictive power for TAM resistance. The only significant association was observed between miR-382-3p expression and lymphovascular invasion (LVI) (p = 0.0314). Moreover, lower expression levels of miR-93 and miR-382-3p were observed in the TAM-sensitive group compared to the TAM-resistant counterparts (p = 0.0002 and p = 0.0028, respectively). In contrast, the expression level of miR-182-3p was significantly higher in the TAM-sensitive group compared to the TAM-resistant group (p = 0.0002). receiver operating characteristic (ROC) curve analysis also indicated the expression of miR-182-3p (p < 0.001; area under curve (AUC): 0.753), miR-382-3p (p = 0.0028; AUC: 0.697), and miR-93 (p < 0.001; AUC: 0.762) as predictive markers for TAM resistance. Multivariate models based on miR-182-3p, miR-382-3p, and miR-93 can predict the response to hormone therapy. Measuring these miRNAs is also recommended for patients with luminal-subtype BC undergoing TAM therapy.

Following continuous progress to enhance the accuracy of prediction of the long-term behavior of tunnels via using more appropriate constitutive models, this paper adopts a non-linear stress–strain law in plastic zones rather than the general perfectly plastic behavior. For this aim, first of all, a theoretical solution is developed in which the viscoelastic-plastic rheological model (consisting of the Burgers model with either the generalized Hoek–Brown or Mohr–coulomb failure models connected in series) is assigned to the rock mass. In this model, when stresses do not satisfy these failure criteria, the rock mass behaves viscoelastic behavior with the Burgers rheological model. If stresses exceed the strength of rock mass, a plastic zone will appear. In this zone, assuming the non-linear stress–strain relationship, the values of mechanical parameters are updated based on the plastic shear strain. The result indicates the non-suitability of assigning the perfectly plastic behavior to the rock mass. Furthermore, it is found that the Non-linear post-peak behavior of the rock mass has a remarkable effect on the tunnel response. In the next step, the problem is simulated by FLAC3D software with the same assumptions made for the proposed analytical model. Then, the proposed approach is verified using the existing analytical methods. Also, in another effort, the obtained displacements in the analytical method are compared with measured displacements of a real case (Saint Martin La Porte access gallery). It is found that a good agreement exists between the results. The numerical simulation is extended via assigning the CVISC elastic-visco plastic rheological model (the Burgers model connected in series with a slider for prediction of failure) to the rock mass. The results demonstrate that it is possible to disregard the rheological behavior of the plastic zone without suffering a significant loss in accuracy.

Contrary to the implementation of multistep (or multivalue) methods based on Nordsieck technique, variable stepsize (VS) methods do not require updating the input quantities when the stepsize changes. In this paper, we study VS explicit general linear methods (GLMs) of the order p and stage order q=p. We derive explicit formulas for some coefficients of such methods as equivalent conditions for the order conditions. Moreover, we investigate the local discretization error of these methods and introduce a reliable local error estimator. A construction of a special class of the VS explicit GLMs is described and some examples of these methods up to order five are given. The efficiency of the proposed methods and the reliability of the introduced local error estimator are illustrated by providing the results of some numerical experiments.

Task-based planning can be conceptualized as the opportunity to work out task performance before the actual performance. It allows learners to process the content and language of their planned production at a deeper and more meaningful level. In the face of the wide range of research conducted on the effects of pre-task planning on L2 production, relatively little attention has been paid to the impacts of pressured within-task planning. The present study was, therefore, primarily aimed at investigating the effects of guided pressured within-task planning and unguided pressured within-task planning on the fluency of EFL learners’ written production. The participants of the study were 30 upper-intermediate EFL learners whose age ranged between 18 and 22. In both guided and unguided conditions, the participants were provided with two sample process-writings, the only difference was that in the guided condition, the participants were provided with the samples including underlined sequence markers, bolded passive verbs, and underlined simple present verbs plus a list of sequence markers to serve as guide during writing. The results obtained from independent-samples t-test revealed the fact that guided pressured planning condition resulted in greater fluency than unguided pressured planning condition. The findings of the study may have pedagogical implications for teachers to design sequences of instructional activities providing opportunities for the learners to benefit from different types of planning in task performance.