Population-level insights into temporal interference for focused deep brain neuromodulation.

The ability to stimulate deep brain regions in a focal manner brings new opportunities for treating brain disorders. Temporal interference (TI) stimulation has been suggested as a method to achieve focused stimulation in deep brain targets. Individual-level knowledge of the interferential currents has permitted personalizing TI montage via subject-specific digital human head models, facilitating the estimation of interferential electric currents in the brain. While this individual approach offers a high degree of personalization, the significant intra-and inter-individual variability among specific head models poses challenges when comparing electric-field doses. Furthermore, MRI acquisition to develop a personalized head model, followed by precise methods for placing the optimized electrode positions, is complex and not always available in various clinical settings. Instead, the registration of individual electric fields into brain templates has offered insights into population-level effects and enabled montage optimization using common scalp landmarks. However, population-level knowledge of the interferential currents remains scarce. This work aimed to investigate the effectiveness of targeting deep brain areas using TI in different populations. The results showed a trade-off between deep stimulation and unwanted cortical neuromodulation, which is target-dependent at the group level. A consistent modulated electric field appeared in the deep brain target when the same montage was applied in different populations. However, the performance in terms of focality and variability varied when the same montage was used among populations. Also, group-level TI exhibited greater focality than tACS, reducing unwanted neuromodulation volume in the cortical part by at least 1.5 times, albeit with higher variability. These results provide valuable population-level insights when considering TI montage selection.

Modeling and mobile home monitoring of behavioral and psychological symptoms of dementia (BPSD).

With the increasing global aging population, dementia care has rapidly become a major social problem. Current diagnosis of Behavior and Psychological Symptoms of Dementia (BPSD) relies on clinical interviews, and behavioral rating scales based on a period of behavior observation, but these methods are not suitable for identification of occurrence of BPSD in the daily living, which is necessary for providing appropriate interventions for dementia, though, has been studied by few research groups in the literature. To address these issues, in this study developed a BPSD monitoring system consisting of a Psycho-Cognitive (PsyCo) BPSD model, a Behavior-Physio-Environment (BePhyEn) BPSD model, and an implementation platform. The PsyCo BPSD model provides BPSD assessment support to caregivers and care providers, while the BePhyEn BPSD model provides instantaneous alerts for BPSD enabled by a 24-hour home monitoring platform for early intervention, and thereby alleviation of burden to patients and caregivers. Data for acquiring the models were generated through extensive literature review and regularity determined. A mobile robot was utilized as the implementation platform for improving sensitivity of sensors for home monitoring, and elderly individual following algorithms were investigated. Experiments in a virtual home environment showed that, a virtual BPSD elderly individual can be followed safely by the robot, and BPSD occurrence could be identified accurately, demonstrating the possibility of modeling and identification of BPSD in home environment.

Inhibition of ZDHHC16 promoted osteogenic differentiation and reduced ferroptosis of dental pulp stem cells by CREB.

BACKGROUND: The repair of bone defects caused by periodontal diseases is a difficult challenge in clinical treatment. Dental pulp stem cells (DPSCs) are widely studied for alveolar bone repair. The current investigation aimed to examine the specific mechanisms underlying the role of Zinc finger DHHC-type palmitoyl transferases 16 (ZDHHC16) in the process of osteogenic differentiation (OD) of DPSCs. METHODS: The lentiviral vectors ZDHHC16 or si-ZDHHC16 were introduced in the DPSCs and then the cells were induced by an odontogenic medium for 21 days. Subsequently, Quantitate Polymerase Chain Reaction (PCR), immunofluorescent staining, proliferation assay, ethynyl deoxyuridine (EdU) staining, and western blot analysis were used to investigate the specific details of ZDHHC16 contribution in OD of DPSCs. RESULTS: Our findings indicate that ZDHHC16 exhibited a suppressive effect on cellular proliferation and oxidative phosphorylation, while concurrently inducing ferroptosis in DPSCs. Moreover, the inhibition of ZDHHC16 promoted cell development and OD and reduced ferroptosis of DPSCs. The expression of p-CREB was suppressed by ZDHHC16, and immunoprecipitation (IP) analysis revealed that ZDHHC16 protein exhibited interconnection with cAMP-response element binding protein (CREB) of DPSCs. The CREB suppression reduced the impacts of ZDHHC16 on OD and ferroptosis of DPSCs. The activation of CREB also reduced the influences of si-ZDHHC16 on OD and ferroptosis of DPSCs. CONCLUSIONS: These findings provide evidences to support a negative association between ZDHHC16 and OD of DPSCs, which might be mediated by ferroptosis of DPSCs via CREB.

REC-NN: A reconstruction error compensation neural network for Magnetic Resonance Electrical Property Tomography (MREPT).

Electrical properties (EPs) are expected as biomarkers for early cancer detection. Magnetic resonance electrical properties tomography (MREPT) is a technique to non-invasively estimate the EPs of tissues from MRI measurements. While noise sensitivity and artifact problems of MREPT are being solved progressively through recent efforts, the loss of tissue contrast emerges as an obstacle to the clinical applications of MREPT. To solve the problem, we propose a reconstruction error compensation neural network scheme (REC-NN) for a typical analytic MREPT method, Stab-EPT. Two NN structures: one with only ResNet blocks, and the other hybridizing ResNet blocks with an encoder-decoder structure. Results of experiments with digital brain phantoms show that, compared with Stab-EPT, and conventional NN based reconstruction, REC-NN improves both reconstruction accuracy and tissue contrast. It is found that, the encoder-decoder structure could improve the compensation accuracy of EPs in homogeneous region but showed worse reconstruction than only ResNet structure for tumorous tissues unseen in the training samples. Future research is required to address overcompensation problems, optimization of NN structure and application to clinical data.

Corrigendum: Design of a SMA-based soft composite structure for wearable rehabilitation gloves.

[This corrects the article DOI: 10.3389/fnbot.2023.1047493.].

Electro-localization method using a muscle conductive phantom for needle position detection towards medical training.

Simulation in healthcare can help train, improve, and evaluate medical personnel's skills. In the case of needle insertion/manipulation inside the muscle during an nEMG examination, a training simulator Requires estimating the position of the needle to output the electrical muscle activity in real time according to the training plan. External cameras can be used to estimate the needle location; however, different error sources can make its implementation difficult and new medical sensing technologies are needed. This study introduces and demonstrates the feasibility of a conductive phantom that serves as the medium for needle insertion and senses the 3D needle position based on a technique named electro-localization for the first time. The proposed conductive phantom is designed so that different voltage distributions are generated in the phantom using electrodes placed on its borders. The needle is inserted in the phantom, and the recorded voltages are mapped to spatial coordinates using a finite element method (FEM)-based computational model of the conductive phantom to estimate the 3D needle tip position. Experimental and simulation results of phantom voltage distributions agreed. In 2D mapping (no depth consideration), the needle position error was 1.7 mm, which was marginally reduced if only the central area of the phantom was used (1.5 mm). In 3D mapping, the error was 4 mm. This study showed the feasibility of using a conductive muscle phantom as a new embedded sensor that estimates needle position for medical training of nEMG without relying on external sensors.

MagTetris: A simulator for fast magnetic field and force calculation for permanent magnet array designs.

In this paper, a simulator named "MagTetris" is proposed for fast magnetic field (B-field) and force calculation for permanent magnet arrays (PMAs) designs consisting of cuboid and arc-shaped magnets (approximated by cuboids) with arbitrary configurations. The proposed simulator can compute the B-field of a PMA on arbitrary observation planes and the magnetic force acting on any magnet/group of magnets. An accelerated calculation method for B-fields of PMAs is developed based on the current model of permanent magnet, which is further extended to magnetic force calculation. The proposed method and the associated codes were validated with numerical simulation and experimental results. The calculation speed of "MagTetris" is at least 500 times higher than that using finite-element method (FEM)-based software with uncompromised accuracy. Compared with a freeware in Python, Magpylib, "MagTetris" has a calculation acceleration of greater than 50% using the same language. "MagTetris" has a simple data structure, which can be easily migrated to other programming languages maintaining similar performances. This proposed simulator can accelerate a PMA design and/or allow designs with high flexibility considering the B-field and force simultaneously. It can facilitate and accelerate innovations of magnet designs to advance dedicated portable MRI in terms of compactness, weight, and performance.

Design of a SMA-based soft composite structure for wearable rehabilitation gloves.

The combination of smart soft composite structure based shape memory alloy (SMA) and exoskeleton technology has the advantages of light weight, energy saving, and great human-exoskeleton interaction. However, there are no relevant studies on the application of SMA-based soft composite structure (SSCS) in hand exoskeletons. The main difficulty is that directional mechanical properties of SSCS need to comply with fingers movement, and SSCS can deliver enough output torque and displacement to the relevant joints. This paper aims to study the application of SSCS for wearable rehabilitation gloves and explore its bionic driving mechanism. This paper proposes a soft wearable glove (Glove-SSCS) for hand rehabilitation actuated by the SSCS, based on finger force analysis under different drive modes. The Glove-SSCS can support five-finger flexion and extension, weighs only 120 g, and adopts modular design. Each drive module adopts a soft composite structure. And the structure integrates actuation, sensing and execution, including an active layer (SMA spring), a passive layer (manganese steel sheet), a sensing layer (bending sensor) and connection layers. To obtain a high-performance SMA actuators, the performance of SMA materials was tested in terms of temperature and voltage, temperature at the shortest length, pre-tensile length and load. And the human-exoskeleton coupling model of Glove-SSCS is established and analyzed from force and motion. The results show that the Glove-SSCS can realize bidirectional movements of fingers flexion and extension, with ranges of motion are 90-110° and 30-40°, and their cycles are 13-19 s and 11-13 s. During the use of Glove-SSCS, the temperature of gloves is from 25 to 67°C, and the surface temperature of hands is from 32 to 36°C. The temperature of Glove-SSCS can be kept at the lowest temperature of SMA operation without much impact on the human body.

Neural Network-Based Active Load-Sensing Scheme and Stiffness Adjustment for Pneumatic Soft Actuators for Minimally Invasive Surgery Support.

To provide a stable surgical view in Minimally Invasive Surgery (MIS), it is necessary for a flexible endoscope applied in MIS to have adjustable stiffness to resist different external loads from surrounding organs and tissues. Pneumatic soft actuators are expected to fulfill this role, since they could feed the endoscope with an internal access channel and adjust their stiffness via an antagonistic mechanism. For that purpose, it is essential to estimate the external load. In this study, we proposed a neural network (NN)-based active load-sensing scheme and stiffness adjustment for a soft actuator for MIS support with antagonistic chambers for three degrees of freedom (DoFs) of control. To deal with the influence of the nonlinearity of the soft actuating system and uncertainty of the interaction between the soft actuator and its environment, an environment exploration strategy was studied for improving the robustness of sensing. Moreover, a NN-based inverse dynamics model for controlling the stiffness of the soft actuator with different flexible endoscopes was proposed too. The results showed that the exploration strategy with different sequence lengths improved the estimation accuracy of external loads in different conditions. The proposed method for external load exploration and inverse dynamics model could be used for in-depth studies of stiffness control of soft actuators for MIS support.

Effects of chamber shapes on maneuverability and control property of endoscope-support soft actuators.

Introduction: Minimally Invasive Surgery (MIS) offers targeted surgical access with reduced invasiveness; however, the maneuverability challenges of traditional instruments in this domain underscore the need for innovative solutions. Soft actuators activated by fluids or gases present a promising strategy for augmenting endoscopic capabilities, thereby enhancing the surgical precision in MIS. This study aimed to explore the intricate dynamics of the interactions between soft actuators and endoscopes, with an emphasis on the pivotal role of cross-sectional chamber shapes. While previous studies have touched on the influence of chamber shapes on bending properties, we provide a comprehensive exploration. We explore how these shapes modulate friction forces, which in turn influence the interactions governing bending, response, and stiffness adjustability, all of which are essential for enhancing endoscope maneuverability in MIS contexts. Methods: A novel bilateral symmetrical air chamber design was adopted to investigate various chamber shapes. We employed finite element analysis (FEA) simulations followed by prototype testing to evaluate the interactions driven by these chamber shapes and to discern their impact on actuator properties. Recognizing the pivotal role of friction in these interactions, we conducted dedicated friction experiments. These experiments further deepened our understanding of the relationship between chamber shape and friction, and how this synergy influences the properties of the actuator. Results: Our findings showed that actuators with wider chambers generate larger friction forces, thereby enhancing the interaction and improving the bending, response, and stiffness adjustability. Additionally, the soft actuator significantly improved the maneuverability and bending radius of the endoscope, demonstrating enhanced navigation capabilities in complex environments. Discussion: The shape of a cross-sectional chamber plays a pivotal role in designing soft actuators for MIS applications. Our research emphasizes the importance of this design component, offering key insights for the development of endoscope-supporting soft actuators that can effectively handle intricate actuator-endoscope interactions, thereby enhancing surgical outcomes.

High-performance permanent magnet array design by a fast genetic algorithm (GA)-based optimization for low-field portable MRI.

Lightweight and compact permanent magnet arrays (PMAs) are suitable for portable dedicated magnetic resonance imaging (MRI). It is worth exploring different PMA design possibilities and optimization methods with an adequate balance between weight, size, and performance, in addition to Halbach arrays and C-shaped/H-shaped magnets which are widely used. In this paper, the design and optimization of a sparse high-performance inward-outward ring-pair PMA consisting of magnet cuboids is presented for portable imaging of the brain. The design is lightweight (151kg) and compact (inner bore diameter: 270mm, outer diameter: 616mm, length: 480mm, 5-Gauss range: 1840×1840×2340mm3). The optimization framework is based on the genetic algorithm with a consideration of both field properties and simulated image quality. The resulting PMA design has an average field strength of 101.5 mT and a field pattern with a built-in linear readout gradient. Subtracting the best fit to the linear gradient target resulted in a residual deviation from the target field of 0.76mT and an average linear regression coefficient of 0.85 to the linear gradient. The required radiofrequency bandwidth is 6.9% within a field of view (FoV) with a diameter of 200mm and a length of 125mm. It has a magnetic field generation efficiency of 0.67mT/kg, which is high among the sparse PMAs that were designed for an FoV with a diameter of 200mm. The field can be used to supply gradients in one direction working with gradient coils in the other two directions, or can be rotated to encode signals for imaging with axial slice selection. The encoding capability of the designed PMA was examined through the simulated reconstructed images. The force experienced by each magnet in the design was calculated, and the feasibility of a physical implementation was confirmed. The design can offer an increased field strength, and thus, an increased signal-to-noise ratio. It has a longitudinal field direction that allows the application of technologies developed for solenoidal magnets. This proposed design can be a promising alternative to supplying the main and gradient fields in combination for dedicated portable MRI. Lastly, the design is resulted from a fast genetic algorithm-based optimization in which fast magnetic field calculation was applied and high design flexibility was feasible. Within optimization iterations, image quality metrics were used for the encoding field of a magnet configuration to guide the design of the magnet array.

Physics Informed Neural Networks (PINN) for Low Snr Magnetic Resonance Electrical Properties Tomography (MREPT).

Electrical properties (EPs) of tissues facilitate early detection of cancerous tissues. Magnetic resonance electrical properties tomography (MREPT) is a technique to non-invasively probe the EPs of tissues from MRI measurements. Most MREPT methods rely on numerical differentiation (ND) to solve partial differential Equations (PDEs) to reconstruct the EPs. However, they are not practical for clinical data because ND is noise sensitive and the MRI measurements for MREPT are noisy in nature. Recently, Physics informed neural networks (PINNs) have been introduced to solve PDEs by substituting ND with automatic differentiation (AD). To the best of our knowledge, it has not been applied to MREPT due to the challenges in using PINN on MREPT as (i) a PINN requires part of ground-truth EPs as collocation points to optimize the network's AD, (ii) the noisy input data disrupts the optimization of PINNs despite the noise-filtering nature of NNs and additional denoising processes. In this work, we propose a PINN-MREPT model based on a canonical analytic MREPT model. A reference padding layer with known EPs was added to surround the region of interest for providing additive collocation points. Moreover, an optimizable diffusion coefficient was embedded in the analytic MREPT model used in the PINN-MREPT. The noise robustness of the proposed PINN-MREPT for single-sample reconstruction was tested by using numerical phantoms of human brain with extra tumor-like tissues at different noise levels. The results of numerical experiments show that PINN-MREPT outperforms two typical numerical MREPT methods in terms of reconstruction accuracy, sensitivity to the extra tissues, and the correlations of line profiles in the regions of interest. The advantage of the PINN-MREPT is shown by the results of an experiment on phantom measurement, too. Moreover, it is found that the diffusion term plays an important role to achieve a noise-robust PINN-MREPT. This is an important step moving forward to a clinical application of MREPT.

Physics-Coupled Neural Network Magnetic Resonance Electrical Property Tomography (MREPT) for Conductivity Reconstruction.

The electrical property (EP) of human tissues is a quantitative biomarker that facilitates early diagnosis of cancerous tissues. Magnetic resonance electrical properties tomography (MREPT) is an imaging modality that reconstructs EPs by the radio-frequency field in an MRI system. MREPT reconstructs EPs by solving analytic models numerically based on Maxwell's equations. Most MREPT methods suffer from artifacts caused by inaccuracy of the hypotheses behind the models, and/or numerical errors. These artifacts can be mitigated by adding coefficients to stabilize the models, however, the selection of such coefficient has been empirical, which limit its medical application. Alternatively, end-to-end Neural networks-based MREPT (NN-MREPT) learns to reconstruct the EPs from training samples, circumventing Maxwell's equations. However, due to its pattern-matching nature, it is difficult for NN-MREPT to produce accurate reconstructions for new samples. In this work, we proposed a physics-coupled NN for MREPT (PCNN-MREPT), in which an analytic model, cr-MREPT, works with diffusion and convection coefficients, learned by NNs from the difference between the reconstructed and ground-truth EPs to reduce artifacts. With two simulated datasets, three generalization experiments in which test samples deviate gradually from the training samples, and one noise-robustness experiment were conducted. The results show that the proposed PCNN-MREPT achieves higher accuracy than two representative analytic methods. Moreover, compared with an end-to-end NN-MREPT, the proposed method attained higher accuracy in two critical generalization tests. This is an important step to practical MREPT medical diagnoses.

Home-care robots - Attitudes and perceptions among older people, carers and care professionals in Ireland: A questionnaire study.

Many countries face major challenges to ensure that their health and social care systems are ready for the growing numbers of older people (OP). As a way of realising ageing in place, assistive technologies such as home-care robots are expected to play a greater role in the future. In Asia and Europe, robots are gradually being adopted as a public policy solution to the workforce shortage. Yet, there is still a strongly held belief that such technologies should not be part of human and personal care services such as OP's care. However, there has been little research into attitudes and perceptions of potential users regarding home-care robots which can provide companionship and support with activities of daily living. To explore these in more detail, a questionnaire study was carried out in Finland, Ireland and Japan. This study reports findings from the Irish cohort (114 older people [OP], 8 family carers and 56 Health and Social Care Professionals [HSCPs]). Seventy per cent of the total respondents (N = 178) reported being open to the use of home-care robots, and only one quarter had a negative image of robots. People with care responsibilities in their private capacity expressed more interest in, and readiness to use, home-care robots, while stressing the importance of 'privacy protection' and 'guaranteed access to human care'. Both OP and HSCPs identified observation and recording of OP's mental and physical condition as desirable functions of such robots, whereas practical functions such as fall prevention and mobility support were also deemed desirable by HSCPs. There is generally positive interest in home-care robots among Irish respondents. Findings strongly suggest that the interest is generated partly by great need among people who deliver care. Should such robots be developed, then careful consideration must be given to user-centred design, ethical aspects and national care policy.

Effect of changes in Skin Thickness on pain-relief Transcutaneous Electrical Nerve Stimulation (TENS).

Transcutaneous Electrical Nerve Stimulation (TENS) suppresses chronic pain by stimulating deep nerves near the fascia from electrodes on the skin's surface. TENS has different effects on patients of different ages due to the variation of the thickness of skin layers when one becomes older.In this paper, we aim to optimize the stimulation effectiveness of TENS for patients of different ages through investigation of TENS stimulations of three different skin types categorized by age, Young, Old, and Older. In this investigation, the skin layer (stratum corneum, epidermis layer, dermis layer) in each model was created, and the thickness was varied. The effect of sin wave stimulation at 1 Hz, 100 Hz, and 10 kHz on the nerve stimulation effect near the fascia was examined.It is found that besides the well-known effect of stratum corneum, the thickness of the dermis layer significantly affects the stimulating effect. In addition, by using a lumped circuit model, it is showed that the change in the current path causes a mitigation in the stimulation effect in the dermis layer.

Clinicopathological features and prognostic significance of C5aR in human solid tumors: a Meta-analysis.

BACKGROUND: C5aR has been extensively studied in recent years as an essential component of the complement system. However, the role of C5aR in tumors has not been sufficiently investigated and summarized. The aim of this meta-analysis was to investigate the prognostic value of C5aR in solid tumors as well as the correlation between C5aR and clinicopathological features. METHODS: Relevant study collection was performed in PubMed, Embase, Web of Science, BIOSIS Previews, Cochrane Library until July 10, 2021. Pooled hazard ratios (HRs), odds ratios (ORs), and 95% confidence intervals (CIs) were calculated. Sensitivity analyses were performed to assess the robustness of this study, while publication bias was tested by Begg's and Egger's tests. RESULTS: A total of 11 studies involving 1577 patients were included in the study. Our results suggest that the high-level C5aR expression in tumor tissue predicted unsatisfactory overall survival (OS) (HR = 1.92, 95% CI:1.47-2.50, P < 0.001) and recurrence-free survival (RFS) (HR = 2.19, 95% CI:1.47-3.27, P < 0.001). Besides, a higher level of C5aR expression was associated with larger tumor size (OR = 1.58, 95% CI: 1.18-2.10, P = 0.002) and the occurrence of metastases in lymph nodes (OR = 1.99, 95% CI: 1.46-2.72, P<0.001), whereas it was independent of tumor stage, vascular invasion and tumor differentiation. CONCLUSION: In conclusion, C5aR may be a potential biomarker for evaluating tumor prognosis and treatment.

Nasal metastases from neuroblastoma-a rare entity: Two case reports.

BACKGROUND: Neuroblastoma (NB) is one of the most common malignancies in children. Metastasis in NB is not uncommon. However, nasal metastases are rare. Here, we reported two pediatric cases of nasal metastases. CASE SUMMARY: Case 1 was a 3-year-old boy without a history of NB. Case 2 was a 10-year-old girl who had a history of NB for 6 years. Both of them presented with symptoms of nasal and sinus masses such as epistaxis or discharge from the nose. The radiologic imaging results revealed masses in the nasal cavity or nasopharynx in both cases and a mass in the right adrenal gland of case 1. The pathologic examination of biopsy samples of their nasal masses revealed "small round blue-cell tumor" along with abundant vascular fibrous septa. The tumor cells expressed synaptophysin, cluster of differentiation 56, chromogranin A, paired like homeobox protein 2B and a very high Ki67 index in both case but were negative for vimentin, desmin, leucocyte common antigen and cytokeratin. Myelocytomatosis viral related oncogene, neuroblastoma derived (MYCN) amplification was detected in both cases. Finally, the two cases were diagnosed as nasal metastases from NB based on the clinical and pathologic findings. The two patients affected by NB were > 18 mo old, the primary tumor location was adrenal gland, and they presented with multiple metastases. CONCLUSION: It is difficult to differentiate between metastatic NB in the nose and olfactory neuroblastoma in the absence of a history of NB. Paired like homeobox protein 2B can play an important role in the diagnosis and differential diagnosis of this disease.

Gate Mechanism and Parameter Analysis of Anodal-First Waveforms for Improving Selectivity of C-Fiber Nerves.

PURPOSE: Few investigations have been conducted on the selective stimulation of small-radius unmyelinated C nerves (C), which are critical to both the recovery of damaged nerves and pain suppression. The purpose of this study is to understand how an anodal pulse in an anodal-first stimulation could improve C-selectivity over myelinated nociceptive Aδ nerves (Aδ) and to further clarify the landscape of the solution space. MATERIALS AND METHODS: An adapted Hodgkin-Huxley (HH) model and the McIntyre-Richardson-Grill (MRG) model were used for modeling C and Aδ, respectively, to analyze the underlying ion dynamics and the influence of relevant stimulation waveforms, including monopolar, polarity-symmetric, and asymmetric pulses. RESULTS: The results showed that polarity asymmetric waveforms with preceding anodal stimulations benefit C-selectivity the most, underlain by the decrease in the potassium ion current of C. CONCLUSION: The optimal parameters for C-selectivity have been identified in the low-frequency band, remarkably benefiting the design of selective stimulation waveforms for the recovery of damaged nerves and pain management.

A comparison study of applying natural iron minerals and zero-valent metals as Fenton-like catalysts for the removal of imidacloprid.

Natural iron minerals and zero-valent metals have been widely tested as catalysts for the Fenton-like process, but the systematical comparison study about their catalytic performance was rarely conducted, and the risk of the secondary pollution of toxic heavy metals was still not uncertain. In this paper, a comparison study of applying pyrite, ilmenite, vanadium titano-magnetite (VTM), zero-valent iron (ZVI), and zero-valent copper (ZVC) as Fenton-like catalysts for the removal of imidacloprid was performed. The results showed that ZVI exhibited the highest activity among the recyclable solid catalysts with a removal rate of 96.8% at initial pH 3 using 10.78 mmol/L H2O2, due to iron corrosive dissolution. Vanadium titano-magnetite (VTM) exhibited the best activity at first use among tested minerals but with low reusability. Pyrite with stable morphology showed a medium but sustainable ability to degrade imidacloprid, achieving a removal rate of 10.5% in the fifth use. The reaction much favored the acidic condition of initial pH around 2 or 3. Meanwhile, there was a significant positive correlation between removal efficiency and dissolved Fe or Cu concentration. Pyrite was considered to be a promising catalyst in Fenton-like reaction. It was suggested that the system proceeded predominantly through a homogeneous route via dissolved Fe or Cu ions. Except ZVC and VTM, other tested catalysts showed the low possibility of causing secondary pollution of toxic metals in the application of Fenton-like process.

Erratum: LukS-PV Inhibits Hepatocellular Carcinoma Progression by Downregulating HDAC2 Expression.

[This corrects the article DOI: 10.1016/j.omto.2020.05.006.].