Individual Cells Research Articles

Integrated Crashworthiness Analysis of Electric Vehicle Battery Shells and Chassis: A Finite Element Study with Abaqus

This paper aims to comprehend and predict the mechanical behavior of the genuine Tesla Model-S chassis and the battery module during a crash in Abaqus/Explicit. The parametric method was incorporated with varying impact velocities from 27.7, 55.5, and 100m/s and battery shell thickness from 1mm to 3mm. The asymmetry model was considered for both the chassis and battery pack. Aluminum 6061 and ASI430 SS are assigned to the chassis profile and the battery shells (cells). A Johnson-Cook (JC) plastic and damage failure model has been implemented to simulate realistic crash behavior. Displacement, energy, and force results were captured during the simulation. A battery shell thickness of 3mm showed higher resistance than a 1mm thick shell at 55.5 m/s. The numerical findings reveal the dynamic response in displacement and compression under various loading conditions for both individual profiles. Additionally, the study presents a detailed inspection of each cell module, graphically demonstrating how the individual cells respond to initial positions, crashes, and external deformation (shear) caused by collision energy. The finite element model is validated against previous experimental and numerical studies, successfully simulating crashworthiness. The present study provides significant insights that have the potential to improve the safety and efficiency of battery-operated vehicles through the design and optimization of their structures.

Glucose and HODEs regulate Aspergillus ochraceus quorum sensing through the GprC-AcyA pathway

Aspergillus ochraceus is the traditional ochratoxin A (OTA)-producing fungus with density-dependent behaviors, which is known as quorum sensing (QS) that is mediated by signaling molecules. Individual cells trend to adapt environmental changes in a “whole” flora through communications, allowing fungus to occupy an important ecological niche. Signals perception, transmission, and feedback are all rely on a signal network that constituted by membrane receptors and intracellular effectors. However, the interference of density information in signal transduction, which regulates most life activities of Aspergillus, have yet to be elucidated. Here we show that the G protein-coupled receptor (GPCR) to cAMP pathway is responsible for transmitting density information, and regulates the key point in life cycle of A. ochraceus. Firstly, the quorum sensing phenomenon of A. ochraceus is confirmed, and identified the density threshold is 103 spores/mL, which represents the low density that produces the most OTA in a series quorum density. Moreover, the GprC that classified as sugar sensor, and intracellular adenylate cyclase (AcyA)-cAMP-PKA pathway that in response to ligands glucose and HODEs are verified. Furthermore, GprC and AcyA regulate the primary metabolism as well as secondary metabolism, and further affects the growth of A. ochraceus during the entire life cycle. These studies highlight a crucial G protein signaling pathway for cell communication that is mediated by carbohydrate and oxylipins, and clarified a comprehensive effect of fungal development, which include the direct gene regulation and indirect substrate or energy supply. Our work revealed more signal molecules that mediated density information and connected effects on important adaptive behaviors of Aspergillus ochraceus, hoping to achieve comprehensive prevention and control of mycotoxin pollution from interrupting cell communication.

Heterogeneous distribution of mitochondria and succinate dehydrogenase activity in human airway smooth muscle cells

AbstractSuccinate dehydrogenase (SDH) is a key mitochondrial enzyme involved in the tricarboxylic acid cycle, where it facilitates the oxidation of succinate to fumarate, and is coupled to the reduction of ubiquinone in the electron transport chain as Complex II. Previously, we developed a confocal‐based quantitative histochemical technique to determine the maximum velocity of the SDH reaction (SDHmax) in single cells and observed that SDHmax corresponds with mitochondrial volume density. In addition, mitochondrial volume and motility varied within different compartments of human airway smooth muscle (hASM) cells. Therefore, we hypothesize that the SDH activity varies relative to the intracellular mitochondrial volume within hASM cells. Using 3D confocal imaging of labeled mitochondria and a concentric shell method for analysis, we quantified mitochondrial volume density, mitochondrial complexity index, and SDHmax relative to the distance from the nuclear membrane. The mitochondria within individual hASM cells were more filamentous in the immediate perinuclear region and were more fragmented in the distal parts of the cell. Within each shell, SDHmax also corresponded to mitochondrial volume density, where both peaked in the perinuclear region and decreased in more distal parts of the cell. Additionally, when normalized to mitochondrial volume, SDHmax was lower in the perinuclear region when compared to the distal parts of the cell. In summary, our results demonstrate that SDHmax measures differences in SDH activity within different cellular compartments. Importantly, our data indicate that mitochondria within individual cells are morphologically heterogeneous, and their distribution varies substantially within different cellular compartments, with distinct functional properties.

Physical Forces in Regeneration of Cells and Tissues.

The ability to regenerate after the loss of a part is a hallmark of living systems and occurs at both the tissue and organ scales, but also within individual cells. Regeneration entails many processes that are physical and mechanical in nature, including the closure of wounds, the repositioning of material from one place to another, and the restoration of symmetry following perturbations. However, we currently know far more about the genetics and molecular signaling pathways involved in regeneration, and there is a need to investigate the role of physical forces in the process. Here, we will provide an overview of how physical forces may play a role in wound healing and regeneration, in which we compare and contrast regenerative processes at the tissue and cell scales.

Reconfigurable Metamaterial Antenna based an Electromagnetic Ground Plane Defects for Modern Wireless Communication Devices

In this paper, a design of a microstrip antenna based on metamaterial (MTM) and electromagnetic band gap (EBG) arrays. The patch is structured from 5×3 MTM array to enhance the antenna bandwidth gain product. The individual unit cell is structured as a split ring (SRR) with a T-resonator. The ground plane is defected with an EBG to suppress the surface waves diffraction from the substrate edges. The antenna is printed on a Roger substrate with permittivity of 10.2 and 1 mm thickness. It is found that the proposed antenna provides a frequency resonance around 2.45 GHz and 3.5 GHz with another band between 4.6 GHz to 5.6 GHz which are very suitable for Wi-Fi and 5G networks. Nevertheless, the antenna gain is found to vary from 3.5 dBi to less than 6 dBi. The antenna size is reduced enough to λ/5 of the guided wavelength to fit an area of 12 mm×20 mm. The proposed antenna performance is controlled with two PIN diodes for reconfiguration process. The antenna frequency resonance bands are found to be well controlled by stopping the current motion at the particular band. The antenna is fabricated and tested experimentally. Finally, the simulated results are compared to those obtained from measurements to provide an excellent agreement to each other with error of less than 3%.

Critical review of single-cell mechanotyping approaches for biomedical applications.

Accurate mechanical measurements of cells has the potential to improve diagnostics, therapeutics and advance understanding of disease mechanisms, where high-resolution mechanical information can be measured by deforming individual cells. Here we evaluate recently developed techniques for measuring cell-scale stiffness properties; while many such techniques have been developed, much of the work examining single-cell stiffness is impacted by difficulties in standardization and comparability, giving rise to large variations in reported mechanical moduli. We highlight the role of underlying mechanical theories driving this variability, and note opportunities to develop novel mechanotyping devices and theoretical models that facilitate convenient and accurate mechanical characterisation. Moreover, many high-throughput approaches are confounded by factors including cell size, surface friction, natural population heterogeneity and convolution of elastic and viscous contributions to cell deformability. We nevertheless identify key approaches based on deformability cytometry as a promising direction for further development, where both high-throughput and accurate single-cell resolutions can be realized.

ScVSC: Deep variational subspace clustering for single-cell transcriptome data.

Single-cell RNA sequencing (scRNA-seq) is a potent advancement for analyzing gene expression at the individual cell level, allowing for the identification of cellular heterogeneity and subpopulations. However, it suffers from technical limitations that result in sparse and heterogeneous data. Here, we propose scVSC, an unsupervised clustering algorithm built on deep representation neural networks. The method incorporates the variational inference into the subspace model, which imposes regularization constraints on the latent space and further prevents overfitting. In a series of experiments across multiple datasets, scVSC outperforms existing state-of-the-art unsupervised and semi-supervised clustering tools regarding clustering accuracy and running efficiency. Moreover, the study indicates that scVSC could visually reveal the state of trajectory differentiation, accurately identify differentially expressed genes, and further discover biologically critical pathways.

The prognostic genes model of breast cancer drug resistance based on single-cell sequencing analysis and transcriptome analysis

Breast cancer (BC) represents a multifaceted malignancy, with escalating incidence and mortality rates annually. Chemotherapy stands as an indispensable approach for treating breast cancer, yet drug resistance poses a formidable challenge. Through transcriptome data analysis, we have identified two sets of genes exhibiting differential expression in this context. Furthermore, we have confirmed the overlap between these genes and those associated with exosomes, which were subsequently validated in cell lines. The investigation screened the identified genes to determine prognostic markers for BC and utilized them to formulate a prognostic model. The disparities in prognosis and immunity between the high- and low-risk groups were validated using the test dataset. We have discerned different BC subtypes based on the expression levels of prognostic genes in BC samples. Variations in prognosis, immunity, and drug sensitivity among distinct subtypes were examined. Leveraging data from single-cell sequencing and prognostic gene expression, the AUCell algorithm was employed to score individual cell clusters and analyze the pathways implicated in high-scoring groups. Prognostic genes (CCT4, CXCL13, MTDH, PSMD2, and RAB27A) were subsewoquently validated using RT-qPCR. Consequently, we have established a model for predicting prognosis in breast cancer that hinges on drug resistance and ERGs. Furthermore, we have evaluated the prognostic value of this model. The genes identified as prognostic markers can now serve as a reference for precise treatment of this condition.

Cell size and deformation measurement using constrictions integrated into a microfluidic device

Abstract In this study, we developed a microfluidic device to evaluate cell size and deformability. As a specific method, a physical pushing load was applied to the cells using a channel having a constriction with a height of 1 μm. In addition, by orienting the constriction in a vertical direction, it was possible to measure the cell area easily using a microscope under load. The system constructed in this study can evaluate the contact area between the cells and the glass surface before and after applying a load under a microscope. The only input parameter was the syringe flow rate, and it was possible to evaluate multiple cells in a cell suspension simultaneously. Also, since the flow rate is 50 μm/min or less, there is no need for a high-speed camera. This time, we evaluated cell types with different characteristics: NIH/3T3 and smooth muscle cells (SMC). To evaluate deformability, we focused on the circularity of the cells during load application. Due to the influence of the flow within the channel, cells with high deformability assumed an almost elliptical shape and flowed through the constriction. Using the device developed in this study, we confirmed that SMCs, which are muscle cells, have large variations in cell size and hardness among individual cells. Finally, we discussed these results and possible future applications.

Excitable dynamics driven by mechanical feedback in biological tissues

Pulsatory activity patterns, driven by mechanochemical feedback, are prevalent in many biological systems. However, the role of cellular mechanics and geometry in the propagation of pulsatory signals remains poorly understood. Here we present a theoretical framework to elucidate the mechanical origin and regulation of pulsatile activity patterns within excitable multicellular tissues. We show that a simple mechanical feedback at the level of individual cells – activation of contractility upon stretch and subsequent inactivation upon turnover of active elements – is sufficient to explain the emergence of quiescent states, long-range wave propagation, and traveling activity pulse at the tissue-level. We find that the transition between a propagating pulse and a wave is driven by the competition between timescales associated with cellular mechanical response and geometrical disorder in the tissue. This sheds light on the fundamental role of cell packing geometry on tissue excitability and spatial propagation of activity patterns.

A Capillary-Force-Driven, Single-Cell Transfer Method for Studying Rare Cells

The characterization of individual cells within heterogeneous populations (e.g., rare tumor cells in healthy blood cells) has a great impact on biomedical research. To investigate the properties of these specific cells, such as genetic biomarkers and/or phenotypic characteristics, methods are often developed for isolating rare cells among a large number of background cells before studying their genetic makeup and others. Prior to using real-world samples, these methods are often evaluated and validated by spiking cells of interest (e.g., tumor cells) into a sample matrix (e.g., healthy blood) as model samples. However, spiking tumor cells at extremely low concentrations is challenging in a standard laboratory setting. People often circumvent the problem by diluting a solution of high-concentration cells, but the concentration becomes inaccurate after series dilution due to the fact that a cell suspension solution can be inhomogeneous, especially when the cell concentration is very low. We report on an alternative method for low-cost, accurate, and reproducible low-concentration cell spiking without the use of external pumping systems. By inducing a capillary force from sudden pressure drops, a small portion of the cellular membrane was aspirated into the reservoir tip, allowing for non-destructive single-cell transfer. We investigated the surface membrane tensions induced by cellular aspiration and studied a range of tip/tumor cell diameter combinations, ensuring that our method does not affect cell viability. In addition, we performed single-cell capture and transfer control experiments using human acute lymphoblastic leukemia cells (CCRF-CEM) to develop calibrated data for the general production of low-concentration samples. Finally, we performed affinity-based tumor cell isolation using this method to generate accurate concentrations ranging from 1 to 15 cells/mL.

Programmable multistable kirigami chain: Decoupling energy barrier and snapping force/displacement in a unified topology

Arraying bistable cells in a chain results in a multistable mechanical structure that is capable of sequential deformation paths and structural reconfigurability. However, to customize the multistability, for example, a prescribed energy barrier with variable snapping force/displacement, is constrained by the difficulty of decoupling energy from kinematic characteristics since they are always governed by the same geometric parameters. This paper proposes a bistable kirigami cut topology to realize decoupling. Cut parameters are classified into independent groups, corresponding to objective energy barrier or force/displacement, which are consequently independent also. On this basis, a machine learning (ML) and genetic algorithm (GA) based approach is presented to guide the inverse design of the nonlinear responses. Optimal individual kirigami cells that meet decoupled multi-objectives such as energy barrier, snapping force and stable displacement are obtained to construct kirigami chains. The conceivable deformation sequences, configurations and energy/force-displacement curves are validated by both the simulation and experimental results. The proposed mechanism decoupled strategy and inverse design framework open up new horizons for programming energy landscape and provide a general recipe for tailoring multistability in metamaterials.

Trigger waves in an oscillator loop and their applications in voltage‐controlled oscillation with high tuning gain

SummaryTrigger waves, propagating in a loop of a one‐dimensional lattice of tunnel‐diode (TD) oscillators, can be effectively applied to voltage‐controlled oscillation with high tuning gain. The lattice under investigation can generate both phase and trigger waves by varying the bias voltage to the TD. We demonstrate a voltage‐controlled oscillator (VCO) achieving high tuning gain through the utilization of rotating trigger waves. As the loop size decreases, the trigger wave becomes bound to itself or to adjacent waves. At this point, the excitation motion of the individual lattice cell is constrained by the global interaction caused by the trigger wave(s), leading to coherent behavior. Consequently, the bifurcation curve of the trigger wave seamlessly connects to that of the phase wave with changes in the TD bias voltage. Owing to the trigger wave's property of significantly increasing velocity with the TD bias voltage, a VCO with very high tuning gain can be developed, leveraging the seamless dependence of the oscillation frequency on the TD bias voltage.

IrGSEA: the integration of single-cell rank-based gene set enrichment analysis.

irGSEA is an R package designed to assess the outcomes of various gene set scoring methods when applied to single-cell RNA sequencing data. This package incorporates six distinct scoring methods that rely on the expression ranks of genes, emphasizing relative expression levels over absolute values. The implemented methods include AUCell, UCell, singscore, ssGSEA, JASMINE and Viper. Previous studies have demonstrated the robustness of these methods to variations in dataset size and composition, generating enrichment scores based solely on the relative gene expression of individual cells. By employing the robust rank aggregation algorithm, irGSEA amalgamates results from all six methods to ascertain the statistical significance of target gene sets across diverse scoring methods. The package prioritizes user-friendliness, allowing direct input of expression matrices or seamless interaction with Seurat objects. Furthermore, it facilitates a comprehensive visualization of results. The irGSEA package and its accompanying documentation are accessible on GitHub (https://github.com/chuiqin/irGSEA).

Single‐cell RNA sequencing in pediatric research: Focusing on differentiation trajectories and immune microenvironment of neuroblastoma

AbstractRecently, single‐cell RNA sequencing (scRNA‐seq) has emerged as a novel and high‐resolution technique for identifying cell types, states, and subpopulations. This technique enables researchers to uncover cellular heterogeneity and detect rare cell populations that might be indistinguishable in bulk RNA‐seq data. The primary aim of scRNA‐seq analysis is to investigate cellular heterogeneity and distinguish distinct cell types or states. scRNA‐seq provides a detailed understanding of intercellular differences and diversity by obtaining gene expression data for each individual cell. Moreover, clustering methods in scRNA‐seq can be used to group cells bring into subpopulations based on their gene expression patterns, thereby uncovering similarities and differences that assist in identifying and defining cell types. Newly discovered cell types can be validated and named by labeling known cell marker genes. Additionally, scRNA‐seq helps in identifying genes specifically expressed at different developmental stages, in various tissue types, or under various disease states. Recently, there has been a growing trend in using single‐cell transcriptome sequencing technology for neuroblastoma (NB) research. Through conducting a comprehensive review of relevant articles published thus far, our understanding of NB has been significantly enriched from three critical perspectives: differentiation trajectory, tumor heterogeneity, and immune microenvironment. Firstly, in exploring the differentiation trajectory of NB, we have summarized the tumor's origin and subsequent directions of differentiation. By elucidating a complete tumor differentiation pathway, we can enhance our understanding of the mechanisms underlying spontaneous tumor regression. Secondly, we have summarized the heterogeneity of tumors, which encompasses different states, cell morphologies, and characteristic genes of NB identified through single‐cell sequencing technology. This consolidation of knowledge enhances our understanding of the heterogeneity of NB. Lastly, we have employed single‐cell sequencing technology to analyze the immune microenvironment, focusing on the cellular components within the tumor's surrounding environment and the diverse states of immune cells. This valuable information contributes to the advancement of NB diagnosis, treatment, and prognosis. In conclusion, the application of single‐cell sequencing technology in NB research has significantly advanced our understanding of the disease and carries great significance.

Drug screening on digital microfluidics for cancer precision medicine

Drug screening based on in-vitro primary tumor cell culture has demonstrated potential in personalized cancer diagnosis. However, the limited number of tumor cells, especially from patients with early stage cancer, has hindered the widespread application of this technique. Hence, we developed a digital microfluidic system for drug screening using primary tumor cells and established a working protocol for precision medicine. Smart control logic was developed to increase the throughput of the system and decrease its footprint to parallelly screen three drugs on a 4 × 4 cm2 chip in a device measuring 23 × 16 × 3.5 cm3. We validated this method in an MDA-MB-231 breast cancer xenograft mouse model and liver cancer specimens from patients, demonstrating tumor suppression in mice/patients treated with drugs that were screened to be effective on individual primary tumor cells. Mice treated with drugs screened on-chip as ineffective exhibited similar results to those in the control groups. The effective drug identified through on-chip screening demonstrated consistency with the absence of mutations in their related genes determined via exome sequencing of individual tumors, further validating this protocol. Therefore, this technique and system may promote advances in precision medicine for cancer treatment and, eventually, for any disease.

Label-free active single-cell encapsulation enabled by microvalve-based on-demand droplet generation and real-time image processing

Droplet microfluidics-based single-cell encapsulation is a critical technology that enables large-scale parallel single-cell analysis by capturing and processing thousands of individual cells. As the efficiency of passive single-cell encapsulation is limited by Poisson distribution, active single-cell encapsulation has been developed to theoretically ensure that each droplet contains one cell. However, existing active single-cell encapsulation technologies still face issues related to fluorescence labeling and low throughput. Here, we present an active single-cell encapsulation technique by using microvalve-based drop-on-demand technology and real-time image processing to encapsulate single cells with high throughput in a label-free manner. Our experiments demonstrated that the single-cell encapsulation system can encapsulate individual polystyrene beads with 96.3% efficiency and HeLa cells with 94.9% efficiency. The flow speed of cells in this system can reach 150 mm/s, resulting in a corresponding theoretical encapsulation throughput of 150 Hz. This technology has significant potential in various biomedical applications, including single-cell omics, secretion detection, and drug screening.

A comparative study on the dose–effect of low-dose radiation based on microdosimetric analysis and single-cell sequencing technology

The biological mechanisms triggered by low-dose exposure still need to be explored in depth. In this study, the potential mechanisms of low-dose radiation when irradiating the BEAS-2B cell lines with a Cs-137 gamma-ray source were investigated through simulations and experiments. Monolayer cell population models were constructed for simulating and analyzing distributions of nucleus-specific energy within cell populations combined with the Monte Carlo method and microdosimetric analysis. Furthermore, the 10 × Genomics single-cell sequencing technology was employed to capture the heterogeneity of individual cell responses to low-dose radiation in the same irradiated sample. The numerical uncertainties can be found both in the specific energy distribution in microdosimetry and in differential gene expressions in radiation cytogenetics. Subsequently, the distribution of nucleus-specific energy was compared with the distribution of differential gene expressions to guide the selection of differential genes bioinformatics analysis. Dose inhomogeneity is pronounced at low doses, where an increase in dose corresponds to a decrease in the dispersion of cellular-specific energy distribution. Multiple screening of differential genes by microdosimetric features and statistical analysis indicate a number of potential pathways induced by low-dose exposure. It also provides a novel perspective on the selection of sensitive biomarkers that respond to low-dose radiation.

ProBac-seq, a bacterial single-cell RNA sequencing methodology using droplet microfluidics and large oligonucleotide probe sets.

Methods that measure the transcriptomic state of thousands of individual cells have transformed our understanding of cellular heterogeneity in eukaryotic cells since their introduction in the past decade. While simple and accessible protocols and commercial products are now available for the processing of mammalian cells, these existing technologies are incompatible with use in bacterial samples for several fundamental reasons including the absence of polyadenylation on bacterial messenger RNA, the instability of bacterial transcripts and the incompatibility of bacterial cell morphology with existing methodologies. Recently, we developed ProBac sequencing (ProBac-seq), a method that overcomes these technical difficulties and provides high-quality single-cell gene expression data from thousands of bacterial cells by using messenger RNA-specific probes. Here we provide details for designing large oligonucleotide probe sets for an organism of choice, amplifying probe sets to produce sufficient quantities for repeated experiments, adding unique molecular indexes and poly-A tails to produce finalized probes, in situ probe hybridization and single-cell encapsulation and library preparation. This protocol, from the probe amplification to the library preparation, requires ~7 d to complete. ProBac-seq offers several advantages over other methods by capturing only the desired target sequences and avoiding nondesired transcripts, such as highly abundant ribosomal RNA, thus enriching for signal that better informs on cellular state. The use of multiple probes per gene can detect meaningful single-cell signals from cells expressing transcripts to a lesser degree or those grown in minimal media and other environmentally relevant conditions in which cells are less active. ProBac-seq is also compatible with other organisms that can be profiled by in situ hybridization techniques.

CBX1 is involved in hepatocellular carcinoma progression and resistance to sorafenib and lenvatinib via IGF-1R/AKT/SNAIL signaling pathway.

Chromobox Homolog 1 (CBX1) plays a crucial role in the pathogenesis of numerous diseases, including the evolution and advancement of diverse cancers. The role of CBX1 in pan-cancer and its mechanism in hepatocellular carcinoma (HCC), however, remains to be further investigated. Bioinformatics approaches were harnessed to scrutinize CBX1's expression profile, its association with tumor staging, and its potential impact on patient outcomes across various cancers. Single-cell RNA sequencing data facilitated the investigation of CBX1 expression patterns at the individual cell level. The CBX1 expression levels in HCC and adjacent non-tumor tissues were quantified through Real-Time Polymerase Chain Reaction (RT-PCR), Western Blotting (WB), and Immunohistochemical analyses. A tissue microarray was employed to explore the relationship between CBX1 levels, patient prognosis, and clinicopathological characteristics in HCC. Various in vitro assays-including CCK-8, colony formation, Transwell invasion, and scratch tests-were conducted to assess the proliferative and motility properties of HCC cells upon modulation of CBX1 expression. Moreover, the functional impact of CBX1 on HCC was further discerned through xenograft studies in nude mice. CBX1 was found to be upregulated in most cancer forms, with heightened expression correlating with adverse patient prognoses. Within the context of HCC, elevated levels of CBX1 were consistently indicative of poorer clinical outcomes. Suppression of CBX1 through knockdown methodologies markedly diminished HCC cell proliferation, invasive capabilities, migratory activity, Epithelial-mesenchymal transition (EMT) processes, and resistance to Tyrosine kinase inhibitors (TKIs). Contrastingly, CBX1 augmentation facilitated the opposite effects. Subsequent investigative efforts revealed CBX1 to be a promoter of EMT and a contributor to increased TKI resistance within HCC cells, mediated via the IGF-1R/AKT/SNAIL signaling axis. The oncogenic activities of CBX1 proved to be attenuable either by AKT pathway inhibition or by targeted silencing of IGF-1R. The broad overexpression of CBX1 in pan-cancer and specifically in HCC positions it as a putative oncogenic entity. It is implicated in forwarding HCC progression and exacerbating TKI resistance through its interaction with the IGF-1R/AKT/SNAIL signaling cascade.