Current Models Research Articles

Persistence of Basal Ganglia Oscillatory Activity During Tremor Attenuation by Movement in Parkinson's Disease Patients.

One of the characteristics of parkinsonian tremor is that its amplitude decreases with movement. Current models suggest an interaction between basal ganglia (BG) and cerebello-thalamo-cortical circuits in parkinsonian tremor pathophysiology. We aimed to correlate central oscillation in the BG with electromyographic activity during re-emergent tremor in order to detect changes in BG oscillatory activity when tremor is attenuated by movement. We performed a prospective, observational study on consecutive parkinsonian patients who underwent deep brain stimulation surgery and presented re-emergent tremor. Coherence analysis between subthalamic nucleus/globus pallidus internus (STN/GPi) tremorous activity measured by microrecording (MER) and electromyogram (EMG) from flexor and extensor wrist muscles during rest, posture, and re-emergent tremor pause was performed during surgery. The statistical significance level of the MER-EMG coherence was determined using surrogate data analysis, and the directionality of information transfer between BG and muscle was performed using entropy transfer analysis. We analyzed 148 MERs with tremor-like activity from 6 patients which were evaluated against the simultaneous EMGs, resulting in 296 correlations. Of these, 26 presented a significant level of coherence at tremor frequency, throughout rest and posture, with a complete EMG stop in between. During the pause, all recordings showed sustained MER peaks at tremor frequency (±1.5 Hz). Information flows preferentially from BG to muscle during rest and posture, with a loss of directionality during the pause. Our results suggest that oscillatory activity in STN/GPi functionally linked to tremor sustains firing frequency during re-emergent tremor pause, thus suggesting no direct role of the BG circuit on tremor attenuation due to voluntary movements. © 2024 International Parkinson and Movement Disorder Society.

Adipose Tissue in Breast Cancer Microphysiological Models to Capture Human Diversity in Preclinical Models.

Female breast cancer accounts for 15.2% of all new cancer cases in the United States, with a continuing increase in incidence despite efforts to discover new targeted therapies. With an approximate failure rate of 85% for therapies in the early phases of clinical trials, there is a need for more translatable, new preclinical in vitro models that include cellular heterogeneity, extracellular matrix, and human-derived biomaterials. Specifically, adipose tissue and its resident cell populations have been identified as necessary attributes for current preclinical models. Adipose-derived stromal/stem cells (ASCs) and mature adipocytes are a normal part of the breast tissue composition and not only contribute to normal breast physiology but also play a significant role in breast cancer pathophysiology. Given the recognized pro-tumorigenic role of adipocytes in tumor progression, there remains a need to enhance the complexity of current models and account for the contribution of the components that exist within the adipose stromal environment to breast tumorigenesis. This review article captures the current landscape of preclinical breast cancer models with a focus on breast cancer microphysiological system (MPS) models and their counterpart patient-derived xenograft (PDX) models to capture patient diversity as they relate to adipose tissue.

Diurnal fuel moisture content variations of live and dead Calluna vegetation in a temperate peatland

The increasing frequency and severity of UK wildfires, attributed in part to the effects of climate change, highlights the critical role of fuel moisture content (FMC) of live and dead vegetation in shaping wildfire behaviour. However, current models used to assess wildfire danger do not perform well in shrub-type fuels such as Calluna vulgaris, requiring in part an improved understanding of fuel moisture dynamics on diurnal and seasonal scales. To this end, 554 samples of upper live Calluna canopy, live Calluna stems, upper dead Calluna canopy, dead Calluna stems, moss, litter and organic layer (top 5 cm of organic material above mineral soil) were sampled hourly between 10:00 and 18:00 on seven days from March-August. Using a novel statistical method for investigating diurnal patterns, we found distinctive diurnal and seasonal trends in FMC for all fuel layers. Notably, significant diurnal patterns were evident in dead Calluna across nearly all sampled months, while diurnal trends in live Calluna canopy were pronounced in March, June, and August, coinciding with the peak occurrence of UK wildfires. In addition, the moisture content of moss and litter was found to fluctuate above and below their relative ignition thresholds throughout the day on some sampling days. These findings underscore the impact of diurnal FMC variations on wildfire danger during early spring and late summer in Calluna dominated peatlands and the need to consider such fluctuations in management and fire suppression strategies.

Diabetes Promotes Myocardial Fibrosis via AMPK/EZH2/PPAR-γ Signaling Pathway.

Diabetes-induced cardiac fibrosis is one of the main mechanisms of diabetic cardiomyopathy. As a common histone methyltransferase, enhancer of zeste homolog 2 (EZH2) has been implicated in fibrosis progression in multiple organs. However, the mechanism of EZH2 in diabetic myocardial fibrosis has not been clarified. In the current study, rat and mouse diabetic model were established, the left ventricular function of rat and mouse were evaluated by echocardiography and the fibrosis of rat ventricle was evaluated by Masson staining. Primary rat ventricular fibroblasts were cultured and stimulated with high glucose (HG) in vitro. The expression of histone H3 lysine 27 (H3K27) trimethylation, EZH2, and myocardial fibrosis proteins were assayed. In STZ-induced diabetic ventricular tissues and HG-induced primary ventricular fibroblasts in vitro, H3K27 trimethylation was increased and the phosphorylation of EZH2 was reduced. Inhibition of EZH2 with GSK126 suppressed the activation, differentiation, and migration of cardiac fibroblasts as well as the overexpression of the fibrotic proteins induced by HG. Mechanical study demonstrated that HG reduced phosphorylation of EZH2 on Thr311 by inactivating AMP-activated protein kinase (AMPK), which transcriptionally inhibited peroxisome proliferator-activated receptor γ (PPAR-γ) expression to promote the fibroblasts activation and differentiation. Our data revealed an AMPK/EZH2/PPAR-γ signal pathway is involved in HG-induced cardiac fibrosis.

Validation of Population Pharmacokinetic Models for Clozapine Dosage Prediction.

Clozapine is unique in its capacity to ameliorate severe schizophrenia but at high risk of toxicity. A relationship between blood concentration and clinical response and evidence for concentration-response relationships to some adverse effects justify therapeutic drug monitoring of clozapine. However, the relationship between drug dose and blood concentration is quite variable. This variability is, in part, due to inductive and inhibitory interactions varying the activity of cytochrome P450 1A2 (CYP1A2), the principal pathway for clozapine elimination. Several population pharmacokinetic models have been presented to facilitate dose selection and to identify poor adherence in individual patients. These models have faced little testing for validity in independent populations or even for persisting validity in the source population. Therefore, we collected a large population of clozapine-treated patients (127 patients, 1048 timed plasma concentrations) in whom dosing and covariate information could be obtained with high certainty. A population pharmacokinetic model was constructed with data collected in the first 6 weeks from study enrolment (448 plasma concentrations), to estimate covariate influences and to allow alignment with previously published models. The model was tested for its performance in predicting the concentrations observed at later time intervals up to 5 years. The predictive performances of 6 published clozapine population models were then assessed in the entire population. The population pharmacokinetic model based on the first 6 weeks identified significant influences of sex, smoking, and cotreatment with fluvoxamine on clozapine clearance. The model built from the first 6 weeks had acceptable predictive performance in the same patient population up to the first 26 weeks using individual parameters, with a median predictive error (PE) of -0.1% to -15.9% and median absolute PE of 22.9%-27.1%. Predictive performance fell progressively with time after 26 weeks. Bayesian addition of plasma concentration observations within each prediction period improved individual predictions. Three additional observations extended acceptable predictive performance into the second 6 months of therapy. When the published models were tested with the entire data set, median PE ranged from -8% to +35% with a median absolute PE of >39% in all models. Thus, none of the tested models was successful in external validation. Bayesian addition of single patient observations improved individual predictions from all models but still without achieving acceptable performances. We conclude that the relationship between covariates and blood clozapine concentrations differs between populations and that relationships are not stable over time within a population. Current population models for clozapine are not capturing influential covariates.

Patterns of subregional cerebellar atrophy across epilepsy syndromes: An ENIGMA-Epilepsy study.

The intricate neuroanatomical structure of the cerebellum is of longstanding interest in epilepsy, but has been poorly characterized within the current corticocentric models of this disease. We quantified cross-sectional regional cerebellar lobule volumes using structural magnetic resonance imaging in 1602 adults with epilepsy and 1022 healthy controls across 22 sites from the global ENIGMA-Epilepsy working group. A state-of-the-art deep learning-based approach was employed that parcellates the cerebellum into 28 neuroanatomical subregions. Linear mixed models compared total and regional cerebellar volume in (1) all epilepsies, (2) temporal lobe epilepsy with hippocampal sclerosis (TLE-HS), (3) nonlesional temporal lobe epilepsy, (4) genetic generalized epilepsy, and (5) extratemporal focal epilepsy (ETLE). Relationships were examined for cerebellar volume versus age at seizure onset, duration of epilepsy, phenytoin treatment, and cerebral cortical thickness. Across all epilepsies, reduced total cerebellar volume was observed (d = .42). Maximum volume loss was observed in the corpus medullare (dmax = .49) and posterior lobe gray matter regions, including bilateral lobules VIIB (dmax = .47), crus I/II (dmax = .39), VIIIA (dmax = .45), and VIIIB (dmax = .40). Earlier age at seizure onset ( = .05) and longer epilepsy duration ( = .06) correlated with reduced volume in these regions. Findings were most pronounced in TLE-HS and ETLE, with distinct neuroanatomical profiles observed in the posterior lobe. Phenytoin treatment was associated with reduced posterior lobe volume. Cerebellum volume correlated with cerebral cortical thinning more strongly in the epilepsy cohort than in controls. We provide robust evidence of deep cerebellar and posterior lobe subregional gray matter volume loss in patients with chronic epilepsy. Volume loss was maximal for posterior subregions implicated in nonmotor functions, relative to motor regions of both the anterior and posterior lobe. Associations between cerebral and cerebellar changes, and variability of neuroanatomical profiles across epilepsy syndromes argue for more precise incorporation of cerebellar subregional damage into neurobiological models of epilepsy.

Development of a new damping ratio prediction model for recycled aggregate concrete: Incorporating modified admixtures and carbonation effects

Abstract Recycled aggregate concrete (RAC) has been widely used in practical engineering construction. However, the ability of buildings to resist wind-induced vibration and earthquake effects plays an important role in building safety. It is urgent to ensure that recycled concrete still has good anti-vibration ability within the allowable strength range. By conducting damping tests on recycled concrete specimens, the results show that the damping performance of RAC is better improved compared with natural aggregate concrete. Moreover, the influence of internal factors of recycled aggregates and external environmental conditions on damping performance can be determined, and corresponding damping ratio prediction models can be constructed. However, the current prediction models still have limitations in theory and practice. The existing damping ratio prediction models have a large span of independent variables and do not consider the gradual carbonation effect in the actual environment over time. To overcome these limitations, a new damping ratio prediction model is proposed. Based on the replacement rate of recycled aggregates (RAs) and the amplitude of excitation force, the influence of modified admixtures and carbonation on damping performance is considered, and the corresponding model prediction formula is constructed. In addition, the influence mechanism is further demonstrated and explained from the macroscopic aspect of specimen profile and the microscopic aspect of electron microscopy tests. It is found that, considering both strength and cost factors, recycled concrete still has good damping performance when the replacement rate of recycled aggregates (RAs) is 40%.

A PINN-based modelling approach for hydromechanical behaviour of unsaturated expansive soils

Hydromechanical behaviour of unsaturated expansive soils is complex, and current constitutive models failed to accurately reproduce it. Different from conventional modelling, this study proposes a novel physics-informed neural networks (PINN)-based model utilising long short-term memory as the baseline algorithm and incorporating a physical constraint (water retention) to modify the loss function. Firstly, a series of laboratory tests on Zaoyang expansive clay, including wetting and drying cycle tests and triaxial tests, was compiled into a dataset and subsequently fed into the PINN-based model. Subsequently, a specific equation representing the soil water retention curve (SWRC) for expansive clay was derived by accounting for the influence of the void ratio, which was integrated into the PINN-based model as a physical law. The ultimate predictions from the PINN-based model are compared with experimental data of unsaturated expansive clay with excellent agreement. This study demonstrates the capability of the proposed PINN in modelling the hydromechanical response of unsaturated soils and provides an innovative approach to establish constitutive models in the unsaturated soil mechanics field.

Linkage mapping of root shape traits in two carrot populations.

This study investigated the genetic basis of carrot root shape traits using composite interval mapping in two biparental populations (n = 119 and n = 128). The roots of carrot F2:3 progenies were grown over 2 years and analyzed using a digital imaging pipeline to extract root phenotypes that compose market class. Broad-sense heritability on an entry-mean basis ranged from 0.46 to 0.80 for root traits. Reproducible quantitative trait loci (QTL) were identified on chromosomes 2 and 6 on both populations. Colocalization of QTLs for phenotypically correlated root traits was also observed and coincided with previously identified QTLs in published association and linkage mapping studies. Individual QTLs explained between 14 and 27% of total phenotypic variance across traits, while four QTLs for length-to-width ratio collectively accounted for up to 73% of variation. Predicted genes associated with the OFP-TRM (OVATE Family Proteins-TONNEAU1 Recruiting Motif) and IQD (IQ67 domain) pathway were identified within QTL support intervals. This observation raises the possibility of extending the current regulon model of fruit shape to include carrot storage roots. Nevertheless, the precise molecular mechanisms through which this pathway operates in roots characterized by secondary growth originating from cambium layers remain unknown.

The dissolution of planetesimals in electrostatic fields

ABSTRACT Planetesimals or smaller bodies in protoplanetary discs are often considered to form as pebble piles in current planet formation models. They are supposed to be large but loose, weakly bound clusters of more robust dust aggregates. This makes them easy prey for destructive processes. In microgravity experiments, we apply strong electric fields on clusters of slightly conductive dust aggregates. We find that this generates enough tensile stress on the fragile clusters to sequentially rip off the aggregates from the cluster. These experiments imply that electric fields in protoplanetary discs can dissolve pebble pile planetesimals. This process might induce a bias for the local planetesimal reservoir in regions with strong fields. Planetesimals prevail with certain kinds of compositions where they are either good isolators or compacted bodies. The less lucky ones generate pebble clouds that might be observable as signposts of electrostatic activity in protoplanetary discs.

Enhancer selectivity in space and time: from enhancer-promoter interactions to promoter activation.

The primary regulators of metazoan gene expression are enhancers, originally functionally defined as DNA sequences that can activate transcription at promoters in an orientation-independent and distance-independent manner. Despite being crucial for gene regulation in animals, what mechanisms underlie enhancer selectivity for promoters, and more fundamentally, how enhancers interact with promoters and activate transcription, remain poorly understood. In this Review, we first discuss current models of enhancer-promoter interactions in space and time and how enhancers affect transcription activation. Next, we discuss different mechanisms that mediate enhancer selectivity, including repression, biochemical compatibility and regulation of 3D genome structure. Through 3D polymer simulations, we illustrate how the ability of 3D genome folding mechanisms to mediate enhancer selectivity strongly varies for different enhancer-promoter interaction mechanisms. Finally, we discuss how recent technical advances may provide new insights into mechanisms of enhancer-promoter interactions and how technical biases in methods such as Hi-C and Micro-C and imaging techniques may affect their interpretation.

DECIPHERING THE DEEP: MACHINE LEARNING APPROACHES TO UNDERSTANDING OCEANIC ECOSYSTEMS

This paper presents a detailed exploration of the transformative role of Machine Learning (ML) in oceanographic research, encapsulating the paradigm shift towards more efficient and comprehensive analysis of marine ecosystems. It delves into the multifaceted applications of ML, ranging from predictive modeling of ocean currents to in-depth biodiversity analysis and deciphering the complexities of deep-sea ecosystems through advanced computer vision techniques. The discussion extends to the challenges and opportunities that intertwine with the integration of AI and ML in oceanography, emphasizing the need for robust data collection, interdisciplinary collaboration, and ethical considerations. Through a series of case studies and thematic discussions, this paper underscores the profound potential of ML to revolutionize our understanding and preservation of oceanic ecosystems, setting a new frontier for future research and conservation strategies in the realm of oceanography.

Oncopig bladder cancer cells recapitulate human bladder cancer treatment responses in vitro.

Bladder cancer is a common neoplasia of the urinary tract that holds the highest cost of lifelong treatment per patient, highlighting the need for a continuous search for new therapies for the disease. Current bladder cancer models are either imperfect in their ability to translate results to clinical practice (mouse models), or rare and not inducible (canine models). Swine models are an attractive alternative to model the disease due to their similarities with humans on several levels. The Oncopig Cancer Model has been shown to develop tumors that closely resemble human tumors. However, urothelial carcinoma has not yet been studied in this platform. We aimed to develop novel Oncopig bladder cancer cell line (BCCL) and investigate whether these urothelial swine cells mimic human bladder cancer cell line (5637 and T24) treatment-responses to cisplatin, doxorubicin, and gemcitabine in vitro. Results demonstrated consistent treatment responses between Oncopig and human cells in most concentrations tested (p>0.05). Overall, Oncopig cells were more predictive of T24 than 5637 cell therapeutic responses. Microarray analysis also demonstrated similar alterations in expression of apoptotic (GADD45B and TP53INP1) and cytoskeleton-related genes (ZMYM6 and RND1) following gemcitabine exposure between 5637 (human) and Oncopig BCCL cells, indicating apoptosis may be triggered through similar signaling pathways. Molecular docking results indicated that swine and humans had similar Dg values between the chemotherapeutics and their target proteins. Taken together, these results suggest the Oncopig could be an attractive animal to model urothelial carcinoma due to similarities in in vitro therapeutic responses compared to human cells.

Primary study of the relative and compound biological effectiveness model for boron neutron capture therapy based on nanodosimetry.

The current radiobiological model employed for boron neutron capture therapy (BNCT) treatment planning, which relies on microdosimetry, fails to provide an accurate representation the biological effects of BNCT. The precision in calculating the relative biological effectiveness (RBE) and compound biological effectiveness (CBE) plays a pivotal role in determining the therapeutic efficacy of BNCT. Therefore, this study focuses on how to improve the accuracy of the biological effects of BNCT. The purpose of this study is to propose new radiation biology models based on nanodosimetry to accurately assess RBE and CBE for BNCT. Nanodosimetry, rooted in ionization cluster size distributions (ICSD), introduces a novel approach to characterize radiation quality by effectively delineating RBE through the ion track structure at the nanoscale. In the context of prior research, this study presents a computational model for the nanoscale assessment of RBE and CBE. We establish a simplified model of DNA chromatin fiber using the Monte Carlo code TOPAS-nBio to evaluate the applicability of ICSD to BNCT and compute nanodosimetric parameters. Our investigation reveals that both homogeneous and heterogeneous nanodosimetric parameters, as well as the corresponding biological model coefficients α and β, along with RBE values, exhibit variations in response to varying intracellular 10B concentrations. Notably, the nanodosimetric parameter effectively captures the fluctuations in model coefficients α and RBE. Our model facilitates a nanoscale analysis of BNCT, enabling predictions of nanodosimetric quantities for secondary ions as well as RBE, CBE, and other essential biological metrics related to the distribution of boron. This contribution significantly enhances the precision of RBE calculations and holds substantial promise for future applications in treatment planning.

The GM-JMNS-CPHD Filtering Algorithm for Nonlinear Systems Based on a Generalized Covariance Intersection.

Some fusion criteria in multisensor and multitarget motion tracking cannot be directly applied to nonlinear motion models, as the fusion accuracy applied in nonlinear systems is relatively low. In response to the above issue, this study proposes a distributed Gaussian mixture cardinality jumping Markov-cardinalized probability hypothesis density (GM-JMNS-CPHD) filter based on a generalized inverse covariance intersection. The state estimation of the JMNS-CPHD filter combines the state evaluation of traditional CPHD filters with the state estimation of jump Markov systems, estimating the target state of multiple motion models without knowing the current motion models. The performances of the generalized covariance intersection (GCI)GCI-GM-JMNS-CPHD and generalized inverse covariance intersection (GICI)GICI-GM-JMNS-CPHD methods are evaluated via simulation results. The simulation results show that, compared with algorithms such as Sensor1, Sensor2, GCI-GM-CPHD, and GICI-GM-CPHD, this algorithm has smaller optimal subpattern assignment (OSPA) errors and a higher fusion accuracy.

An improved gray wolf optimization to solve the multi-objective tugboat scheduling problem.

With the continuous prosperity of maritime transportation on a global scale and the resulting escalation in port trade volume, tugboats assume a pivotal role as essential auxiliary tools influencing the ingress and egress of vessels into and out of ports. As a result, the optimization of port tug scheduling becomes of paramount importance, as it contributes to the heightened efficiency of ship movements, cost savings in port operations, and the promotion of sustainable development within the realm of maritime transportation. However, a majority of current tugboat scheduling models tend to focus solely on the maximum operational time. Alternatively, the formulated objective functions often deviate from real-world scenarios. Furthermore, prevailing scheduling methods exhibit shortcomings, including inadequate solution accuracy and incompatibility with integer programming. Consequently, this paper introduces a novel multi-objective tugboat scheduling model to align more effectively with practical considerations. We propose a novel optimization algorithm, the Improved Grey Wolf Optimization (IGWO), for solving the tugboat scheduling model. The algorithm enhances convergence performance by optimizing convergence parameters and individual updates, making it particularly suited for solving integer programming problems. The experimental session designs several scale instances according to the reality of the port, carries out simulation experiments comparing several groups of intelligent algorithms, verifies the effectiveness of IGWO, and verifies it in the comprehensive port area of Huanghua Port to get the optimal scheduling scheme of this port area, and finally gives management suggestions to reduce the cost of tugboat operation through sensitivity analysis.

Non-Mutational Key Features in the Biology of Thymomas.

Thymomas (THs) are a unique group of heterogeneous tumors of the thymic epithelium. In particular, the subtypes B2 and B3 tend to be aggressive and metastatic. Radical tumor resection remains the only curative option for localized tumors, while more advanced THs require multimodal treatment. Deep sequencing analyses have failed to identify known oncogenic driver mutations in TH, with the notable exception of the GTF2I mutation, which occurs predominantly in type A and AB THs. However, there are multiple alternative non-mutational mechanisms (e.g., perturbed thymic developmental programs, metabolism, non-coding RNA networks) that control cellular behavior and tumorigenesis through the deregulation of critical molecular pathways. Here, we attempted to show how the results of studies investigating such alternative mechanisms could be integrated into a current model of TH biology. This model could be used to focus ongoing research and therapeutic strategies.

Data-Driven Approaches for Vibroacoustic Localization of Leaks in Water Distribution Networks

This study aims to propose Micro-electromechanical System (MEMS) accelerometers for leak localization in the water distribution network and assess the performance of machine learning models in accurately estimating leak locations. Intensive field experimentation was conducted to collect data for model development. Machine learning algorithms were employed to develop leak localization models, specifically artificial neural network (ANN) and support vector machine (SVM). Seventeen time-domain and frequency-domain features were extracted, and feature selection was performed using the backward elimination method. The results indicate that the ANN and SVM models are suitable classifiers for localizing leak distance. Both models achieved leak location predictions with over 80% accuracy, and the mean absolute errors were measured at 0.858 and 0.95 for the ANN and SVM models, respectively. The validation results demonstrated that the models maintained accuracies close to 80% when the distance between sensors and the leak was less than 15 m. However, the performance of the model deteriorates when leaks occur at distances greater than 15 m. This study demonstrates the applicability of MEMS accelerometers for leak localization in water distribution networks. The findings highlight the promising potential of employing MEMS accelerometers-based ANN and SVM models for accurate leak localization in urban networks, even under real-world, uncontrolled conditions. However, the current model exhibits limited performance in long-distance leak localization, requiring further research to address and resolve this issue.

Feed Error Prediction and Compensation of CNC Machine Tools Based on Whale Particle Swarm Backpropagation Neural Network

Current modeling methods of machine tool feed error are challenging to meet the demand of high-precision machining when facing complex machining conditions. To enhance the model’s predictive accuracy and the effectiveness of actual compensation, the Whale Particle Swarm Optimization (WPSO) algorithm is proposed to optimize the Backpropagation Neural Network (BPNN). Subsequently, the optimized network incorporates screw elongation and feed position as inputs to establish a feed-error prediction model. Ultimately, the established model was compared with other models and applied to real-time compensation experiments. The research results show that the proposed prediction model outperforms the BPNN model, the particle swarm-optimized BPNN model, and the whale-optimized BPNN model in various indicators. The accuracy of the prediction model was 93.12%, and the errors ranged from −3.80 μm to 4.57 μm with an average error of −0.30 μm. Under different operating conditions, the maximum backward and forward errors are reduced by 33.21% and 87.21%, and the average backward and forward errors are reduced by 57.15% and 84.37%, respectively. The error range is reduced by 67.41%. Beyond elevating prediction accuracy and compensation efficacy, the proposed model offers robust theoretical guidance for practical production.

Establishing chronic models of age-related macular degeneration via long-term iron ion overload.

Age-related macular degeneration (AMD) is characterized by the degenerative senescence in the retinal pigment epithelium (RPE) and photoreceptors, which is accompanied by the accumulation of iron ions in the aging retina. However, current models of acute oxidative stress are still insufficient to simulate the gradual progression of AMD. To address this, we established chronic injury models by exposing the aRPE-19 cells, 661W cells, and mouse retina to iron ion overload over time. Investigations at the levels of cell biology and molecular biology were performed. It was demonstrated that long-term treatment of excessive iron ions induced senescence-like morphological changes, decreased cell proliferation, and impaired mitochondrial function, contributing to apoptosis. Activation of the mitogen-activated protein kinase (MAPK) pathway and the downstream molecules were confirmed both in the aRPE-19 and 661W cells. Furthermore, iron ion overload resulted in dry AMD-like lesions and decreased visual function in the mouse retina. These findings suggest that chronic exposure to overloading iron ions plays a significant role in the pathogenesis of retinopathy and provide a potential model for future studies on AMD.NEW & NOTEWORTHY To explore the possibility of constructing reliable research carriers on age-related macular degeneration (AMD), iron ion overload was applied to establish models in vitro and in vivo. Subsequent investigations into cellular physiology and molecular biology confirmed the presence of senescence in these models. Through this study, we hope to provide a better option of feasible methods for future researches into AMD.