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Small cell lung cancer (SCLC) is a recalcitrant cancer of neuroendocrine (NE) origin. Changes in therapeutic approaches against SCLC have been lacking over the decades. Here, we use preclinical models to identify a new therapeutic vulnerability in SCLC consisting of the targetable Jumonji lysine demethylase (KDM) family. We show that Jumonji demethylase inhibitors block malignant growth and that etoposide-resistant SCLC cell lines are particularly sensitive to Jumonji inhibition. Mechanistically, small molecule-mediated inhibition of Jumonji KDMs activates endoplasmic reticulum (ER) stress genes, upregulates ER stress signaling, and triggers apoptotic cell death. Furthermore, Jumonji inhibitors decrease protein levels of SCLC NE markers INSM1 and Secretogranin-3 and of driver transcription factors ASCL1 and NEUROD1. Genetic knockdown of KDM4A, a Jumonji demethylase highly expressed in SCLC and a known regulator of ER stress genes, induces ER stress response genes, decreases INSM1, Secretogranin-3, and NEUROD1 and inhibits proliferation of SCLC in vitro and in vivo. Lastly, we demonstrate that two different small molecule Jumonji KDM inhibitors (pan-inhibitor JIB-04 and KDM4 inhibitor SD70) block the growth of SCLC tumor xenografts in vivo. Our study highlights the translational potential of Jumonji KDM inhibitors against SCLC, a clinically feasible approach in light of recently opened clinical trials evaluating this drug class, and establishes KDM4A as a relevant target across SCLC subtypes.

Background: Opicapone is a third-generation catechol-O-methyl-transferase inhibitor currently used for the treatment of motor fluctuations in Parkinson’s disease. Its benefit and safety have been established by clinical trials; however, data about its use in a real-life context, and particularly in an Italian population of patients with Parkinson’s disease, are missing. Objectives: We aimed to gather data about the real-life tolerability/safety of opicapone when used for the treatment of Parkinson’s disease-related motor fluctuations. Methods: We enrolled 152 consecutive patients with Parkinson’s disease and followed them for 2 years after opicapone introduction. We obtained baseline clinical and demographical information, including disease duration, stage, phenotype, as well as axial and non-motor symptoms. We collected the reasons for any treatment interruption and adverse events emerging after opicapone introduction. Results: Eighty-nine (58%) patients reported adverse events and 46 (30%) patients discontinued the treatment. Adverse events occurred less frequently in “earlier” patients accordingly to the disease course and l-Dopa treatment pathway; a motor fluctuation duration ≥12 months and Hoehn and Yahr scale score ≥2.5 were the main predictors of therapy withdrawal. Conclusions: This study confirms the good tolerability/safety profile of opicapone in a real-life setting and provides country-specific data for Italian patients with Parkinson’s disease.

In recent years, robotic arm exoskeletons have emerged as promising tools, finding widespread application in the rehabilitation of neurological disorders and as assistive devices for everyday activities, even alleviating the physical strain on labor-intensive tasks. Despite the growing prominence of exoskeletons in everyday life, a notable knowledge gap exists in the availability of open-source platforms for classroom-ready usage in educational settings. To address this deficiency, we introduce an open-source educational exoskeleton platform aimed at Science, Technology, Engineering, and Mathematics (STEM) education. This platform represents an enhancement of the commercial EduExo Pro by AUXIVO, tailored to serve as an educational resource for control engineering and biomedical engineering courses.

Heavy metals are chemical elements with unique properties that are toxic even in low concentrations and affect human health with different functions. Agricultural and industrial activities, improper disposal of household solid waste and residues related to industrial producers, discharge of household wastewater and agricultural fertilizers are the most important ways in which toxic heavy metals enter the environment, which harms human health and life. A narrative review of the literature was done from 2000 to 2022 based on searched databases included Google Scholar, PubMed, Springer, Web of Science, and Science Direct (Scopus). All relevant studies published 2000 until 2022 gathered. According to the databases, 820 articles were retrieved. 186 and 50 articles were found and selected based on records identified through database searching and additional records identified through other sources. In the next stage, 97 studies were screened after review and 64 full-text articles entered into the analysis process. Finally, 45 articles were selected in this study. Adverse effects of heavy metals on various conditions in the body depend on a number of factors, including dose, route of exposure and chemical species, as well as age, sex, genetics, nutritional status, and duration of exposure to the heavy metal. The existence of significant relationships between long-term and short-term exposure to toxic heavy metals and their adverse effects, including carcinogenicity, has been extensively studied and proven through numerous experiments. However, the mechanisms associated with this complication have not been properly identified, so in future research, there is a great need for comprehensive studies on the carcinogenicity of heavy metals.

Human gait activity recognition can be crucial to adapt the assistance provided by lower limb exoskeletons, as well as for biomechanical analysis. With this purpose, deep learning techniques can be applied to develop a classifier based on the acquisition of the lower limb kinematics. In this article, we present a 1-D convolutional neural network (CNN) to classify different activities from the hip, knee, and ankle flexion/extension angles, measured with wearable inertial sensors. The proposed CNN classifier achieves 99.56% accuracy with users not involved in the learning process. In addition, the gradient-weighted class activation map (Grad-CAM) and the t-distributed stochastic neighbor embedding (t-SNE) were used to understand the CNN model decision-making. Finally, how the accuracy of the CNN model is impacted by input reduction was analyzed to adapt the CNN model to multiple situations, and it can be concluded that the CNN maintains high accuracy with a single joint angle as input.

The study analyzes the microwave imaging of shoaling nearshore surface gravity waves during the process of steepening and breaking over two beaches for a wide range of environmental conditions. Data are sourced from two coherent X-band radars, operating under low-grazing angle conditions. Using automatic wave tracking on the radar images, the evolution of individual waves is followed over hundreds of meters. The extracted backscatter intensity and Doppler speed form distinct patterns that reveal a nonnegligible dependence on environmental conditions. Statistical representations of the backscattered signal are presented by conditional Doppler speed–intensity histograms. These are composed of ensembles extracted at the radar-facing fronts. This technique helps to focus on the steepening of the wave and minimizes the impact of extremely low-grazing angle imaging mechanisms that are still not well understood. The combination of wind speed and direction, as well as initial wave steepness, local depth, and degree of nonlinearity, contributes to the shapes and centroid positions of the histograms. The backscatter signature exhibited by breaking waves remained consistent and similar throughout all datasets. The results are consistent across radars. Before consolidating the findings, it is imperative to conduct a further investigation of radar imaging of nonlinear wave dynamics in shallow water to eliminate any possible influence of imaging mechanisms.

The objective of this study was to assess the SARS‐CoV‐2–specific humoral and T cell response after a two‐dose regimen of SARS‐CoV‐2 vaccine in patients with rheumatoid arthritis (RA). In this observational study, patients with RA who are ≥18 years of age and vaccinated for SARS‐CoV‐2 according to the Argentine National Health Ministry's vaccination strategy were included. Anti–SARS‐CoV‐2 immunoglobulin G (IgG) antibodies (ELISA‐COVIDAR test), neutralizing activity (cytotoxicity in VERO cells), and specific T cell response (IFN‐γ ELISpot Assay) were assessed after the first and second dose. A total of 120 patients with RA were included. Mostly, homologous regimens were used, including Gam‐COVID‐Vac (27.5%), ChAdOx1 (24.2%), and BBIBP‐CorV (22.5%). The most frequent combination was Gam‐COVID‐Vac/mRNA‐1273 (21.7%). After the second dose, 81.7% presented with anti–SARS‐CoV‐2 antibodies, 70.0% presented with neutralizing activity, and 65.3% presented with specific T cell response. The use of BBIBP‐CorV and treatment with abatacept (ABA) and rituximab (RTX) were associated with undetectable antibodies and no neutralizing activity after two doses. BBIBP‐CorV was also associated with the absence of T cell response. The total incidence of adverse events was 357.1 events per 1,000 doses, significantly lower with BBIBP‐CorV (166.7 events per 1,000 doses, P< 0.02). In this RA cohort vaccinated with homologous and heterologous regimens against COVID‐19, 2 out of 10 patients did not develop anti‐SARS‐CoV‐2 IgG, 70% presented with neutralizing activity, and 65% presented with specific T cell response. The use of BBIBP‐CorV was associated with deficient humoral and cellular response, whereas treatment with ABA and RTX resulted in an impaired anti‐SARS‐CoV‐2 IgG formation and neutralizing activity.

This article presents an advanced fault ride-through control strategy based on model predictive control (MPC) of high-power wind turbine (WT), which is able to provide voltage support for power grid under symmetrical and asymmetrical grid faults. An optimal cost function is first established to realize multi-objective control of wind power converter, considering current regulation, neutral-point voltage balance, and switching frequency control. The flexible positive and negative sequence control (FPNSC) method is applied to generate the current references complying with converter current and voltage limitations. Simulation and hardware-in-the-loop experimental verification are implemented to validate the effectiveness of the proposed fault-ride through operation strategy. The verification results show that the proposed strategy is able to effectively support voltage and implement fault ride-through (FRT) during grid faults with optimized neutral-point voltage and average switching frequency. The proposed MPC strategy can address different fault conditions with good adaptivity. It thus enhances the operation performance of high-power WT during faults events.

Many therapies are available to treat low-risk superficial basal cell carcinoma (lr-sBCC), which may complicate the shared decision-making (SDM) process.

Health personnel are often unavailable for supervised robot-aided neurorehabilitation in hospitals, and patients are usually challenged by transportation issues to get to the hospital. Thus, a discontinuity between therapy in the hospital and at home appears slowing down the upper extremity (UE) mobility recovery. The aim of this work was to develop a system, based on wearable devices and EMG armbands, able to assess the quality of the UE joint movements and intelligently guide the patients during home-based rehabilitation. This system fuses a classification model together with a dynamic time-warping (DTW) analysis. The classification model was trained with UE joint movements gathered from clinicians, obtaining more than 80% accuracy using only five joint angles. Then, the system was tested on two poststroke patients and a healthy subject. The results suggest that the proposed system can be: 1) a useful tool for clinicians to evaluate the rehabilitation therapy and 2) an intelligent system able to make decisions based on the quality of the activity executed at home.

Disadvantaged communities often find difficulty in meeting the eligibility criteria for federal funding opportunities. The Justice40 Initiative is the Biden administration's commitment to directing 40% of benefits from all Federal investments in clean energy and climate to disadvantaged communities. The U.S. Environmental Protection Agency has designated $20 million available for the Reducing Lead in Drinking Water Program (RLDWP). Here, our cohort of researchers share a policy brief for the underserved community of Watts in South-Central Los Angeles, which fears exclusion from the RLDWP funding from the Justice40 Initiative due to lack of support in proving eligibility. Our policy brief (1) demonstrates how this community meets the eligibility criteria for inclusion in funding from RLDWP and (2) demonstrates why this community and others like it, which suffer from structural disinvestment and marginalization, should be prioritized in the distribution of funding for this bill, and (3) proposes investment in community education as a policy alternative. Although Watts has access to a group of environmental scientists to support their concerns and collaborate on applying for grant applications, most under-resourced communities do not. We present this brief as an example of a policy intervention that could inform actions by the Justice40 Initiative for other disadvantaged communities to consider. [ABSTRACT FROM AUTHOR]

We aimed to identify diagnosis-specific/transdiagnostic/transoutcome multivariable candidate predictors (MCPs) of key outcomes in mental disorders. We conducted an umbrella review (protocol link), searching MEDLINE/Embase (19/07/2022), including systematic reviews of studies reporting on MCPs of response, remission, recovery, or relapse, in DSM/ICD-defined mental disorders. From published predictors, we filtered MCPs, validating MCP criteria. AMSTAR2/PROBAST measured quality/risk of bias of systematic reviews/individual studies. We included 117 systematic reviews, 403 studies, 299,888 individuals with mental disorders, testing 796 prediction models. Only 4.3%/1.2% of the systematic reviews/individual studies were at low risk of bias. The most frequently targeted outcome was remission (36.9%), the least frequent was recovery (2.5%). Studies mainly focused on depressive (39.4%), substance-use (17.9%), and schizophrenia-spectrum (11.9%) disorders. We identified numerous MCPs within disorders for response, remission and relapse, but none for recovery. Transdiagnostic MCPs of remission included lower disease-specific symptoms (disorders = 5), female sex/higher education (disorders = 3), and quality of life/functioning (disorders = 2). Transdiagnostic MCPs of relapse included higher disease-specific symptoms (disorders = 5), higher depressive symptoms (disorders = 3), and younger age/higher anxiety symptoms/global illness severity/ number of previous episodes/negative life events (disorders = 2). Finally, positive trans-outcome MCPs for depression included less negative life events/depressive symptoms (response, remission, less relapse), female sex (response, remission) and better functioning (response, less relapse); for schizophrenia, less positive symptoms/higher depressive symptoms (remission, less relapse); for substance use disorder, marital status/higher education (remission, less relapse). Male sex, younger age, more clinical symptoms and comorbid mental/physical symptoms/disorders were poor prognostic factors, while positive factors included social contacts and employment, absent negative life events, higher education, early access/intervention, lower disease-specific and comorbid mental and physical symptoms/conditions, across mental disorders. Current data limitations include high risk of bias of studies and extraction of single predictors from multivariable models. Identified MCPs can inform future development, validation or refinement of prediction models of key outcomes in mental disorders.

Adequate and desirable connections between suppliers and customers necessitate an appropriate flow of information. Therefore, a promising and proper data collaboration in the supply chain is of tremendous significance. Thus, the study’s main objective is to provide multiple objective programming models under uncertain conditions to assess the performance of suppliers. To meet that aim, a case study for the reliability assessment of the presented model is carried out. That section is associated with supply chain visibility (SCV). Likewise, the likelihood of unpredicted and undesirable incidents involving supply chain risk (SCR) is taken into consideration. The intimate relation between visibility and risk of the supply chain is deemed efficient for the performance of the supply chain. Incoherence in maximization and minimization of SCR and SCV and other factors, including costs, capacity, or demand, necessitates multiple objective programming models to assess suppliers’ performance to accomplish the before-mentioned aims. The study’s results indicate the high reliability of the proposed model. Besides, the numeral results reveal that decision-makers in selecting suppliers mainly decrease SCR and then attempt to enhance SCV. In conclusion, the provided model in the study can be a desirable model for analyzing and estimating supplier performance with SCR and SCV simultaneously.

Intermittent (on 4 days per week) antiretroviral therapy (ART) for patients with HIV-1 might be more convenient, better tolerated, and cheaper than continuous treatment. We aimed to establish the efficacy and safety of a 4-days-on and 3-days-off (intermittent) maintenance regimen versus a standard 7 day (continuous) maintenance regimen.

Crosstalk is a well-known problem in resistive sensor arrays (RSAs). The zero-potential method (ZPM) is a commonly used readout circuit that uses multiplexers and demultiplexers to reduce the main sources of crosstalk. However, the internal resistors of these switches cause a secondary crosstalk in the RSA that alters the RSA sensor values read. The solution to the effects of this secondary crosstalk is still a case of study. In previous literature, these resistances have been considered as known and, in most cases, equal for all switch channels. However, in a real situation, they are unknown and may vary between channels. In this work, a least-squares (LSQR) method is presented to obtain the true values of the switches from the read voltage signals. Calibration columns are added to the RSA for this purpose. To prove the performance of the method proposed, several simulations have been carried out with different values of RSA sensors, switch resistances, size of the RSA, and noise level. Results show that the proposed LSQR method allows obtaining simultaneously accurate values for both the RSA sensors and switch internal resistors. In this way, the problem of secondary crosstalk is neutralized. It also shows better performance when compared with existing approaches.

The increasing penetration of offshore wind power generation promotes the revolution of wind turbine toward high-power application. The development of high-power wind turbine undoubtedly poses new technical challenges. This article presents a comprehensive overview for high-power wind energy conversion system (WECS) from key technique aspects, including topologies, stability, reliability, and ancillary service capability, and further investigates the key challenges and potential solutions. The various topologies of power converter applied in high-power wind turbine are first reviewed and discussed. The different semiconductor technology, including recent wide bandgap devices, and their potential contributions for offshore high-power converters are discussed. Furthermore, the potential stability issues of high-power WECS are investigated and reviewed. Also, the stabilization control strategies are discussed. Further, the reliability issue and enhancement strategies of high-power WECS are discussed, including condition monitoring, active thermal control strategy, and fault-tolerant operation method. In addition, the ancillary service capability of high-power WECS, including frequency-active power control capability and voltage-reactive power control capability, is reviewed. Finally, the future trends and potential solutions are discussed in this article.

Training sensory discrimination of the skin has the potential to reduce chronic pain due to sensorimotor impairments and increase sensorimotor function. Currently, there is no such device that can systematically provide rich skin stimulation suitable for a training protocol for individuals with amputation or major sensory impairment. This study describes the development and validation of a non-invasive wearable device meant to repeatedly and safely deliver somatosensory stimulations. The development was guided by a structured design control process to ensure the verifiability and validity of the design outcomes. Two sub-systems were designed: 1) a tactile display for touch and vibration sensations, and 2) a set of bands for sliding, pressure, and strain sensations. The device was designed with a versatile structure that allows for its application on different body parts. We designed a device-paired interactive computer program to enable structured sensory training sessions. Validation was performed with 11 individuals with intact limbs whose upper arm tactile sensitivity was measured over 5 training sessions. Tactile discrimination and perception threshold were measured using the standard 2-point discrimination and Semmes-Weinstein monofilament tests, respectively. The results of the monofilament test showed a significant improvement (p = 0.011), but the improvement was not significant for the 2-point discrimination test(p = 0.141). These promising results confirm the potential of the proposed training to increase the sensory acuity in the upper arms of individuals with intact limbs. Further studies will be conducted to determine how to transfer the findings of this work to improve the pain and/or functional rehabilitation in individuals with sensorimotor impairments.

COVID-19 has spread worldwide since its appearance at the end of 2019. In Spain, 99-day long home confinement was set from March 15th 2020. Previous studies about events requiring situations of isolation suggested that mental health problems may occur among the general population and, more specifically, vulnerable groups such as individuals with serious mental illness (SMI). This study aims to assess the psychological effect of confinement in patients with mental illness admitted to an inpatient psychiatric unit.

This article presents the design, construction, and high-power test of two $X$ -band radio frequency (RF) accelerating structures built as part of a collaboration between CERN and the Paul Scherrer Institute (PSI) for the compact linear collider (CLIC) study. The structures are a modified “tuning-free” variant of an existing CERN design and were assembled using Swiss free electron laser (SwissFEL) production methods. The purpose of the study is two-fold. The first objective is to validate the RF properties and high-power performance of the tuning-free, vacuum brazed PSI technology. The second objective is to study the structures’ high-gradient behavior to provide insight into the breakdown and conditioning phenomena as they apply to high-field devices in general. Low-power RF measurements showed that the structure field profiles were close to the design values, and both structures were conditioned to accelerating gradients in excess of 100 MV/m in CERN’s high-gradient test facility. Measurements performed during the second structure test suggest that the breakdown rate (BDR) scales strongly with the accelerating gradient, with the best fit being a power law relation with an exponent of 31.14. In both cases, the test results indicate that stable, high-gradient operation is possible with tuning-free, vacuum brazed structures of this kind.

Immune checkpoint inhibitors (ICI) have transformed the therapeutic landscape in oncology. However, ICI can induce uncommon life-threatening autoimmune T-cell-mediated myotoxicities, including myocarditis and myositis. The thymus plays a critical role in T cell maturation. Here we demonstrate that thymic alterations are associated with increased incidence and severity of ICI myotoxicities. First, using the international pharmacovigilance database VigiBase, the Assistance Publique Hôpitaux de Paris–Sorbonne University data warehouse (Paris, France) and a meta-analysis of clinical trials, we show that ICI treatment of thymic epithelial tumors (TET, and particularly thymoma) was more frequently associated with ICI myotoxicities than other ICI-treated cancers. Second, in an international ICI myocarditis registry, we established that myocarditis occurred earlier after ICI initiation in patients with TET (including active or prior history of TET) compared to other cancers and was more severe in terms of life-threatening arrythmias and concurrent myositis, leading to respiratory muscle failure and death. Lastly, we show that presence of anti-acetylcholine-receptor antibodies (a biological proxy of thymic-associated autoimmunity) was more prevalent in patients with ICI myocarditis than in ICI-treated control patients. Altogether, our results highlight that thymic alterations are associated with incidence and seriousness of ICI myotoxicities. Clinico-radio-biological workup evaluating the thymus may help in predicting ICI myotoxicities.

Jim Scott was a larger-than-life figure who performed pioneering academic and commercial research, mainly on Raman spectroscopy, ferroelectrics, and multiferroics. His career developed at the same time as methodological advances were being made in thin-film deposition and in growth and patterning of complex nanoscale materials. Such advances gave him the opportunity to lead the charge in the integration of ferroelectric oxide films with semiconductors for microwave antennae (predominantly for mobile phones), and in the creation and integration of thin-film capacitor structures for ferroelectric random access memory (FeRAM) chips. He made particularly noteworthy contributions in overcoming the fatigue problem, critical to FeRAM technology. He also played a central role in pushing nanoscale ferroelectrics beyond thin films and into three-dimensional geometries and was an important figure in the birth, and in the renaissance, of magnetoelectric multiferroics. Like many great scientists, Jim was adept at spotting connections between seemingly unconnected topics, establishing new conceptual frameworks and discovering new phenomena that were often received sceptically at first and then embraced by the community.

The main objective of this study is to present a multi-objective and optimal hybrid PV/diesel generator/ battery Renewable Energy System (HRES) to provide this reliability in the Hotan county, placed in Taklamakan Desert. This study uses the ɛ-constraint method along with a developed version of the coyote optimization algorithm to achieve the best values of the component sized to decrease the loss of load probability, CO2emission value, and the annualized cost of the system. Sensitivity analysis also is performed to show each component’s impact on the system. The results demonstrate that the DG backup system improves the yearly cost of the system from 8347.2 $ to 9318.4 $, which shows about 10.42% increasing by increasing the fuel consumption. Here, the LLP increases from 0% to 9.19% and the CO2 emissions improve from 2531.2 kg/yr to 13257 kg/yr. Accordingly, the COE value is reduced from 0.39 $/kWh to 0.24 $/kWh over the PV penetration, reducing from 92.27% to 59.42%. This decreasing indicates that the system fuel cost has more impact than the cost of PV on the COE, which is due to the low cost required of conventional power production than the PV system. The results also indicate a noteworthy upshot on the battery storage unit size such that the size of ɛCO2has been enhanced from 27.4 kWh to 50 kWh in the range from 7000 kg/year to 25 kg/year. The results also are compared with the PSO-based optimal system and HOMER software results to show its excellence toward them.

Electric dipoles are common in insulators, but extremely rare in metals. This situation may be about to change, thanks to flexoelectricity.

Water chemistry and its ecological implications have been extensively investigated in temperate high‐mountain lakes because of their role as sentinels of global change. However, few studies have considered the drivers of water chemistry in tropical mountain lakes underlain by volcanic bedrock. A survey of 165 páramo lakes in the Cajas Massif of the Southern Ecuador Andes identified 4 independent chemical variation gradients, primarily characterized by divalent cations (hardness), organic carbon, silica, and iron levels. Hardness and silica factors showed contrasting relationships with parent rock type and age, vegetation, aquatic ecosystems in the watershed, and lake and watershed size. Geochemical considerations suggest that divalent cations (and related alkalinity, conductivity, and pH) mainly respond to the cumulative partial dissolution of primary aluminosilicates distributed throughout the subsurface of watersheds, and silica and monovalent cations are associated with the congruent dissolution of large amounts of secondary aluminosilicates localized in former hydrothermal or tectonic spots. Dissolved organic carbon was much higher than in temperate high‐mountain lakes, causing extra acidity in water. The smaller the lakes and their watersheds, the higher the likelihood of elevated organic carbon and metals and low hardness. The watershed wetland cover favored metal levels in the lakes but not organic carbon. Phosphorus, positively, and nitrate, negatively, weakly correlated with the metal gradient, indicating common influence by in‐lake processes. Overall, the study revealed that relatively small tropical lake districts on volcanic basins can show chemical variation equivalent to that in large mountain ranges with a combination of plutonic, metamorphic, and carbonate rock areas.

In this study, magnetic spinel copper ferrite (CuFe2O4) nanostructures were eco-friendly synthesized using Nasturtium officinaleextract. Physicochemical properties of these nanostructures were determined by transmission electron microscopy, field emission scanning electron microscopy (FE-SEM), X-ray diffraction (XRD), vibrating sample magnetometry, and energy dispersive X-ray mapping analysis. XRD patterns conform to the CuFe2O4formation. SEM results demonstrated ceramic spinel CuFe2O4nanostructures with spherical surface morphologies. The cytotoxicity effect of CuFe2O4nanostructures against rat pheochromocytoma (PC12) cells was evaluated based on MTT assay. The magnetic nanostructures had low toxicity at a concentration of 250 µg/mL. It appears that these nanostructures can be considered as suitable candidates for drug delivery and other biomedical applications, because of their low toxicity effects.