Microfluidic Organ-Chips and Stem Cell Models in the Fight Against COVID-19.

SARS-CoV-2, the virus underlying COVID-19, has now been recognized to cause multiorgan disease with a systemic effect on the host. To effectively combat SARS-CoV-2 and the subsequent development of COVID-19, it is critical to detect, monitor, and model viral pathogenesis. In this review, we discuss recent advancements in microfluidics, organ-on-a-chip, and human stem cell-derived models to study SARS-CoV-2 infection in the physiological organ microenvironment, together with their limitations. Microfluidic-based detection methods have greatly enhanced the rapidity, accessibility, and sensitivity of viral detection from patient samples. Engineered organ-on-a-chip models that recapitulate in vivo physiology have been developed for many organ systems to study viral pathology. Human stem cell-derived models have been utilized not only to model viral tropism and pathogenesis in a physiologically relevant context but also to screen for effective therapeutic compounds. The combination of all these platforms, along with future advancements, may aid to identify potential targets and develop novel strategies to counteract COVID-19 pathogenesis.

A potential mechanism clue to the periodic storm from microglia activation and progressive neuron damage induced by paraquat exposure.

Paraquat (PQ), is characterized by neurotoxicity, which increases the potential risk of Parkinson's disease (PD) exposure in the long-term and low doses. Triggering microglia activation and neuroinflammation is deemed an early event resulting in PD. However, the underlying pathogenesis of PD by PQ is not clear yet. In this article, C57BL/6J mice treated with PQ could successfully act out Parkinson-like. In addition, we observed the fluorescence intensity enhancement of Iba-1 activated microglia with released pro-inflammatory, all ahead of both the damage of dopaminergic neurons in the substantia nigra and corpus striatum of the brain. Surprisingly, the injection of minocycline before PQ for many hours not only can effectively improve the neurobehavioral symptoms of mice but inhibit the activation of microglia and the release of pro-inflammatory substances, even controlling the gradual damage and loss of neurons. A further mechanism of minocycline hampered the expression levels of key signaling proteins PI3K, PDK1, p-AKT, and CD11b (the receptor of microglia membrane recognition), while a large number of inflammatory factors. Our results suggested that the CD11b/PI3K/NOX2 pathway may be a clue that microglia-mediated inflammatory responses and neuronal damage in a PQ-induced abnormal behavior Parkinson-like mouse.

Epidemiological and phylogenetic investigation of Babesia spp. in dogs of Hainan Province/Island, China.

Babesiosis is a significant tick-borne disease, which is globally prevalent. Many previous research studies have discussed the presence of Babesia gibsoni, Babesia vogeli, and Babesia canis in dogs in China. In the present study, we have used distinct molecular approaches to detect the presence of Babesia spp. in dogs of Hainan Province/Island, China. A total of 1106 dog blood samples were collected from the Island, of which 61 dog samples were found to be positive for Babesia vogeli. The highest infection rate was 56.7% (17/30) detected from Tunchang, followed by 25.0% (3/12) from Baisha and 10.4% (5/48) from Wenchang. There was only one positive case of Babesia gibsoni, and the infection rate was found to be 0.1% (1/1106). The sequencing results showed that the subjected sample sequences were identical and resembled the Babesia vogeli and Babesia gibsoni sequences available in the database. The results derived from this study will be helpful for planning effective strategies for the treatment, control, and prevention of babesiosis in dogs of Hainan Province/Island.

Atrazine induces phagocytotic dysfunction of microglia depends on nucleocytoplasmic translocation of acetylated HMGB1.

Atrazine (ATR) is a widely applied herbicide which was named an environmental endocrine disrupting chemical (EDC). Increasing evidence indicates ATR causes neurotoxic effects resulting in central nervous system (CNS) disease. As the primary immunocytes in the CNS, microglia cells carry out their phagocytosis to maintain the CNS microenvironment by preventing damage from healthy cells. However, the mechanism in which ATR affects the phagocytic function of microglia remains unclear. The present study was designed to investigate the effect of ATR on the phagocytosis of microglia. BV-2 cells and primary microglia selected as microglial models in which BV-2 cells were administrated by ATR at different concentrations (0, 4, 8, 16 µM) for 24 h. Results demonstrated ATR dose-dependently increased the expression of ionized calcium binding adapter molecule 1 (Iba-1), indicating that microglia were activated. Microglial phagocytotic activity induced by ATR fluctuated at the different time points, accompanied by fluctuations in membrane receptor MERTK and cytoplasmic lysosomal marker LAMP1 (two markers related to cell phagocytosis). In this period, the expression of iNOS gradually increased. A mechanistic study further demonstrated that the translocation of High Mobility Group Protein-B1 (HMGB1) from nucleus to cytoplasm in the BV-2 and primary microglial cells induced by ATR, and the process showed a positive correlation with phagocytosis activity of BV-2 cells induced by ATR (r = 0.8030, P = 0.05; α = 0.1). ATR was also shown to spur the acetylation of HMGB1 by breaking the balance between acetylase P300 and deacetylase SIRT1. Unexpectedly, the inhibition of acetylating HMGB1 by resveratrol (Res) was effectively retained by HMGB1 in the nucleus, reversed the SIRT1 and MERTK expression, and enhanced the phagocytosis activity in BV-2 cells. Our results suggested that ATR exposure influenced microglial phagocytosis by acetylating HMGB1 further translocated it in the nucleoplasm.

TDP-43 is a potential marker of dopaminergic neuronal damage caused by atrazine exposure.

Atrazine (ATR) is one of the herbicides widely used worldwide. Meanwhile, it is an environmental endocrine disruptor that can cross the blood-brain barrier and cause damage to the endocrine-nervous system, especially by affecting the normal secretion of dopamine (DA). Regrettably, effector markers and cascade response mechanisms in damaged dopaminergic neurons induced by ATR exposure remain elusive. In this paper, we focus on investigating aggregation and position change of transactive response DNA-binding protein-43 (TDP-43) after ATR exposure, and illustrating whether TDP-43 can serve as a potential marker of mitochondrial dysfunction which causes damage to dopaminergic neurons. In our study, we used rat adrenal pheochromocytoma cell line 12 (PC12) to establish an in vitro model of dopaminergic neurons. After PC12 was intervened by ATR, we found reduced DA cycling and DA levels, and that TDP-43 aggregated continuously in the cytoplasm and then translocated to mitochondria. Furthermore, the studies we have performed showed that the translocation can cause mitochondrial dysfunction through activating the unfolded mitochondrial protein response (UPRmt), ultimately causing damage to dopaminergic neuron. The research we have done suggests that TDP-43 can serve as a potential effector marker of dopaminergic neuron damaged caused by ATR exposure.

Taurine protects dopaminergic neurons in paraquat-induced Parkinson's disease mouse model through PI3K/Akt signaling pathways.

Taurine (Tau) is one of the most abundant amino acids in the brain and regulates physiological functions in the central nervous system, including anti-inflammatory effects. There is growing evidence that microglia-mediated neuro-inflammatory responses are an integral part of Parkinson's disease (PD) onset and progression. Among the many factors regulating the inflammatory response, phosphatidylinositol-3 kinase (PI3K) is susceptible to activation by a variety of cytokines and physicochemical factors, and subsequently recruits signaling proteins containing the pleckstrin homology structural domain to further regulate protein kinase B (AKT) expression involved in the regulation of the intracellular immune response and inflammatory response. Therefore, we established a PD mouse model using paraquat (PQ) intraperitoneal injection staining to explore the mechanism of Tau action on PI3K/AKT signaling pathway. Our study showed that PD mice with Tau intervention recovered motor and non-motor functions to some extent, and the number of dopaminergic (DAc) neurons in the substantia nigra and the level of dopamine (DA) secretion in the striatum were also significantly increased compared with the PQ-dyed group, and the protein content of PI3K and PDK-1 and the phosphorylation level of AKT were reduced in parallel with the reduction in the expression of microglia and related inflammatory factors. In conclusion, our results suggest that Tau may regulate microglia-mediated inflammatory responses through inhibition of the PI3K/AKT pathway in the midbrain of PD mice, thereby reducing DAc neurons damage.

Gut dysfunction may be the source of pathological aggregation of alpha-synuclein in the central nervous system through Paraquat exposure in mice.

BACKGROUND: One of the most common types of neurodegenerative diseases (NDDs) is Lewy body disease (LBD), which is characterized by excessive accumulation of α-synuclein (α-syn) in the neurons and affects around 6 million individuals globally. In recent years, due to the environmental factors that can affect the development of this condition, such as exposure to herbicides and pesticides, so it has become a younger disease. Currently, the vast majority of studies on the neurotoxic effects of paraquat (PQ) focus on the late mechanisms of neuronal-glial network regulation, and little is known about the early origins of this environmental factor leading to LBD. OBJECTIVE: To observe the effect of PQ exposure on intestinal function and to explore the key components of communicating the gut-brain axis by establishing a mouse model. METHODS AND RESULTS: In this study, C57BL/6J mice were treated by intraperitoneal injection of 15 mg/kg PQ to construct an LBD time-series model, and confirmed by neurobehavioral testing and pathological examination. After PQ exposure, on the one hand, we found that fecal particle counts and moisture content were abnormal. on the other hand, we found that the expression levels of colonic tight junction proteins decreased, the expression levels of inflammatory markers increased, and the diversity and abundance of gut microbiota altered. In addition, pathological aggregation of α-syn was consistent in the colon and midbrain, and the metabolism and utilization of short-chain fatty acids (SCFAs) were also markedly altered. This suggests that pathological α-syn and SCFAs form the gut may be key components of the communicating gut-brain axis. CONCLUSION: In this PQ-induced mouse model, gut microbiota disruption, intestinal epithelial barrier damage, and inflammatory responses may be the main causes of gut dysfunction, and pathological α-syn and SCFAs in the gut may be key components of the communicating gut-brain axis.

An approach based on a combination of toxicological experiments and in silico predictions to investigate the adverse outcome pathway (AOP) of paraquat neuro-immunotoxicity.

Paraquat (PQ) exposure is strongly associated with neurotoxicity. However, research on the neurotoxicity mechanisms of PQ varies in terms of endpoints of toxic assessment, resulting in a great challenge to understand the early neurotoxic effects of PQ. In this study, we developed an adverse outcome pathway (AOP) to investigate PQ-induced neuro-immunotoxicity from an immunological perspective, combining of traditional toxicology methods and computer simulations. In vivo, PQ can microstructurally lead to an early synaptic loss in the brain mice, which is a large degree regarded as a main reason for cognitive impairment to mice behavior. Both in vitro and in vivo demonstrated synapse loss is caused by excessive activation of the complement C1q/C3-CD11b pathway, which mediates microglial phagocytosis dysfunction. Additionally, the interaction between PQ and C1q was validated by molecular simulation docking. Our findings extend the AOP framework related to PQ neurotoxicity from a neuro-immunotoxic perspective, highlighting C1q activation as the initiating event for PQ-induced neuro-immunotoxicity. In addition, downstream complement cascades induce abnormal microglial phagocytosis, resulting in reduced synaptic density and subsequent non-motor dysfunction. These findings deepen our understanding of neurotoxicity and provide a theoretical basis for ecological risk assessment of PQ.

Non-Invasive Detection of Early-Stage Fatty Liver Disease via an On-Skin Impedance Sensor and Attention-Based Deep Learning.

Early-stage nonalcoholic fatty liver disease (NAFLD) is a silent condition, with most cases going undiagnosed, potentially progressing to liver cirrhosis and cancer. A non-invasive and cost-effective detection method for early-stage NAFLD detection is a public health priority but challenging. In this study, an adhesive, soft on-skin sensor with low electrode-skin contact impedance for early-stage NAFLD detection is fabricated. A method is developed to synthesize platinum nanoparticles and reduced graphene quantum dots onto the on-skin sensor to reduce electrode-skin contact impedance by increasing double-layer capacitance, thereby enhancing detection accuracy. Furthermore, an attention-based deep learning algorithm is introduced to differentiate impedance signals associated with early-stage NAFLD in high-fat-diet-fed low-density lipoprotein receptor knockout (Ldlr-/-) mice compared to healthy controls. The integration of an adhesive, soft on-skin sensor with low electrode-skin contact impedance and the attention-based deep learning algorithm significantly enhances the detection accuracy for early-stage NAFLD, achieving a rate above 97.5% with an area under the receiver operating characteristic curve (AUC) of 1.0. The findings present a non-invasive approach for early-stage NAFLD detection and display a strategy for improved early detection through on-skin electronics and deep learning.

Machine learning-directed electrical impedance tomography to predict metabolically vulnerable plaques.

The characterization of atherosclerotic plaques to predict their vulnerability to rupture remains a diagnostic challenge. Despite existing imaging modalities, none have proven their abilities to identify metabolically active oxidized low-density lipoprotein (oxLDL), a marker of plaque vulnerability. To this end, we developed a machine learning-directed electrochemical impedance spectroscopy (EIS) platform to analyze oxLDL-rich plaques, with immunohistology serving as the ground truth. We fabricated the EIS sensor by affixing a six-point microelectrode configuration onto a silicone balloon catheter and electroplating the surface with platinum black (PtB) to improve the charge transfer efficiency at the electrochemical interface. To demonstrate clinical translation, we deployed the EIS sensor to the coronary arteries of an explanted human heart from a patient undergoing heart transplant and interrogated the atherosclerotic lesions to reconstruct the 3D EIS profiles of oxLDL-rich atherosclerotic plaques in both right coronary and left descending coronary arteries. To establish effective generalization of our methods, we repeated the reconstruction and training process on the common carotid arteries of an unembalmed human cadaver specimen. Our findings indicated that our DenseNet model achieves the most reliable predictions for metabolically vulnerable plaque, yielding an accuracy of 92.59% after 100 epochs of training.

Hyperspectral Image Super-Resolution via Knowledge-Driven Deep Unrolling and Transformer Embedded Convolutional Recurrent Neural Network.

Hyperspectral (HS) imaging has been widely used in various real application problems. However, due to the hardware limitations, the obtained HS images usually have low spatial resolution, which could obviously degrade their performance. Through fusing a low spatial resolution HS image with a high spatial resolution auxiliary image (e.g., multispectral, RGB or panchromatic image), the so-called HS image fusion has underpinned much of recent progress in enhancing the spatial resolution of HS image. Nonetheless, a corresponding well registered auxiliary image cannot always be available in some real situations. To remedy this issue, we propose in this paper a newly single HS image super-resolution method based on a novel knowledge-driven deep unrolling technique. Precisely, we first propose a maximum a posterior based energy model with implicit priors, which can be solved by alternating optimization to determine an elementary iteration mechanism. We then unroll such iteration mechanism with an ingenious Transformer embedded convolutional recurrent neural network in which two structural designs are integrated. That is, the vision Transformer and 3D convolution learn the implicit spatial-spectral priors, and the recurrent hidden connections over iterations model the recurrence of the iterative reconstruction stages. Thus, an effective knowledge-driven, end-to-end and data-dependent HS image super-resolution framework can be successfully attained. Extensive experiments on three HS image datasets demonstrate the superiority of the proposed method over several state-of-the-art HS image super-resolution methods.

COL12A1 Acts as a Novel Prognosis Biomarker and Activates Cancer-Associated Fibroblasts in Pancreatic Cancer through Bioinformatics and Experimental Validation.

Pancreatic cancer remains one of the most challenging malignancies to date and is associated with poor survival. Cancer-associated fibroblasts (CAFs) are key stromal cells in the tumor microenvironment (TME) that play a crucial role in tumor progression in pancreatic cancer. Thus, uncovering the key genes involved in CAF progression and determining their prognostic value is critically important. Herein, we report our discoveries in this research area. Analysis of The Cancer Genome Atlas (TCGA) dataset and investigation of our clinical tissue samples indicated that COL12A1 expression was aberrantly highly expressed in pancreatic cancer. Survival and COX regression analyses revealed the significant clinical prognostic value of COL12A1 expression in pancreatic cancer. COL12A1 was mainly expressed in CAFs but not in tumor cells. This was verified with our PCR analysis in cancer cells and CAFs. The knocking down of COL12A1 decreased the proliferation and migration of CAFs and down-regulated the expression of CAF activation markers actin alpha 2 (ACTA2), fibroblast activation protein (FAP), and fibroblast-specific protein 1 (FSP1). Meanwhile, the interleukin 6 (IL6), CXC chemokine Ligand-5 (CXCL5), and CXC chemokine Ligand-10 (CXCL10) expressions were inhibited, and the cancer-promoting effect was reversed by COL12A1 knockdown. Therefore, we demonstrated the potential prognostic and target therapy value of COL12A1 expression in pancreatic cancer and elucidated the molecular mechanism underlying its role in CAFs. The findings of this study might provide new opportunities for TME-targeted therapies in pancreatic cancer.

3D Convolution neural network with multiscale spatial and temporal cues for motor imagery EEG classification.

Recently, deep learning-based methods have achieved meaningful results in the Motor imagery electroencephalogram (MI EEG) classification. However, because of the low signal-to-noise ratio and the various characteristics of brain activities among subjects, these methods lack a subject adaptive feature extraction mechanism. Another issue is that they neglect important spatial topological information and the global temporal variation trend of MI EEG signals. These issues limit the classification accuracy. Here, we propose an end-to-end 3D CNN to extract multiscale spatial and temporal dependent features for improving the accuracy performance of 4-class MI EEG classification. The proposed method adaptively assigns higher weights to motor-related spatial channels and temporal sampling cues than the motor-unrelated ones across all brain regions, which can prevent influences caused by biological and environmental artifacts. Experimental evaluation reveals that the proposed method achieved an average classification accuracy of 93.06% and 97.05% on two commonly used datasets, demonstrating excellent performance and robustness for different subjects compared to other state-of-the-art methods.In order to verify the real-time performance in actual applications, the proposed method is applied to control the robot based on MI EEG signals. The proposed approach effectively addresses the issues of existing methods, improves the classification accuracy and the performance of BCI system, and has great application prospects.

A self-assembled implantable microtubular pacemaker for wireless cardiac electrotherapy.

The current cardiac pacemakers are battery dependent, and the pacing leads are prone to introduce valve damage and infection, plus a complete pacemaker retrieval is needed for battery replacement. Despite the reported wireless bioelectronics to pace the epicardium, open-chest surgery (thoracotomy) is required to implant the device, and the procedure is invasive, requiring prolonged wound healing and health care burden. We hereby demonstrate a fully biocompatible wireless microelectronics with a self-assembled design that can be rolled into a lightweight microtubular pacemaker for intravascular implantation and pacing. The radio frequency was used to transfer energy to the microtubular pacemaker for electrical stimulation. We show that this pacemaker provides effective pacing to restore cardiac contraction from a nonbeating heart and have the capacity to perform overdrive pacing to augment blood circulation in an anesthetized pig model. Thus, this microtubular pacemaker paves the way for the minimally invasive implantation of leadless and battery-free microelectronics.

Effective Streaming Low-Tubal-Rank Tensor Approximation via Frequent Directions.

Low-tubal-rank tensor approximation has been proposed to analyze large-scale and multidimensional data. However, finding such an accurate approximation is challenging in the streaming setting, due to the limited computational resources. To alleviate this issue, this article extends a popular matrix sketching technique, namely, frequent directions (FDs), for constructing an efficient and accurate low-tubal-rank tensor approximation from streaming data based on the tensor singular value decomposition (t-SVD). Specifically, the new algorithm allows the tensor data to be observed slice by slice but only needs to maintain and incrementally update a much smaller sketch, which could capture the principal information of the original tensor. The rigorous theoretical analysis shows that the approximation error of the new algorithm can be arbitrarily small when the sketch size grows linearly. Extensive experimental results on both synthetic and real multidimensional data further reveal the superiority of the proposed algorithm compared with other sketching algorithms for getting low-tubal-rank approximation, in terms of both efficiency and accuracy.

Paraquat Inhibits Autophagy Via Intensifying the Interaction Between HMGB1 and α-Synuclein.

Paraquat, a widely used herbicide, is associated with an increased risk of Parkinson's disease (PD). PQ induces upregulation and accumulation of α-synuclein in neurons, which is one of the major pathological hallmarks of PD. Autophagy, as the major mechanism for the clearance of α-synuclein, is disrupted upon pesticide exposure as well as in PD patients. Meanwhile, HMGB1 is involved in autophagy dysfunction and particularly relevant to PD. However, whether PQ exposure affects HMGB1, α-synuclein, and autophagy function have rarely been reported. In this study, we found that PQ exposure impaired autophagy function via disturbing the complex formation of HMGB1 and Beclin1. Moreover, the expression of α-synuclein is modulated by HMGB1 and the interaction between HMGB1 and α-synuclein was intensified by PQ exposure. Taken together, our results revealed that HMGB1-mediated α-synuclein accumulation could competitively perturb the complex formation of HMGB1 and Beclin1, thereby inhibiting the autophagy function in SH-SY5Y cells.

SPLBoost: An Improved Robust Boosting Algorithm Based on Self-Paced Learning.

It is known that boosting can be interpreted as an optimization technique to minimize an underlying loss function. Specifically, the underlying loss being minimized by the traditional AdaBoost is the exponential loss, which proves to be very sensitive to random noise/outliers. Therefore, several boosting algorithms, e.g., LogitBoost and SavageBoost, have been proposed to improve the robustness of AdaBoost by replacing the exponential loss with some designed robust loss functions. In this article, we present a new way to robustify AdaBoost, that is, incorporating the robust learning idea of self-paced learning (SPL) into the boosting framework. Specifically, we design a new robust boosting algorithm based on the SPL regime, that is, SPLBoost, which can be easily implemented by slightly modifying off-the-shelf boosting packages. Extensive experiments and a theoretical characterization are also carried out to illustrate the merits of the proposed SPLBoost.

Paraquat-activated BV-2 microglia induces neuroinflammatory responses in the neuron model through NF-κB signaling pathway.

Paraquat (PQ), a non-selective contact herbicide, has been generally accepted as one of the environmental neurotoxicants. Despite the direct evidence that PQ could induce inflammation responses in microglia, little is known about the effects of the inflammatory microglia on neurons. Thus in the present study, mouse primary cortical neurons and PC12 cells, widely-used in vitro neuron models for neurotoxicity research were applied to investigate the neuroinflammatory effects of PQ-activated microglia on neurons. We observed that the secretion levels of TNF-α and IL-6 in PC12 cells were markedly increased upon treatment with the supernatants of inflammatory BV2 microglia, and NF-κB p65 protein expression was also elevated. Specific inhibition of NF-κB by PDTC dramatically attenuated the increase of TNF-α and IL-6 release. These results suggested that PQ-induced inflammatory microglia exerts secondary inflammatory effects on neurons through activation of NF-κB pathway.

Bimetallic nanoparticles decorated hollow nanoporous carbon framework as nanozyme biosensor for highly sensitive electrochemical sensing of uric acid.

An ultrasensitive electrochemical biosensor was developed to identify the low levels of uric acid (UA) in human serum. The gold/cobalt (Au/Co) bimetallic nanoparticles (NPs) decorated hollow nanoporous carbon framework (Au/Co@HNCF) was synthesized as a nanozyme by pyrolysis of the Au (III)-etching zeolitic imidazolate framework-67 (ZIF-67). The external Au NPs combined with internal Co NPs on the hollow carbon framework exhibited enhanced activity for UA oxidation, thereby generating superior signals. Accordingly, the Au/Co@HNCF biosensor presented ranking performances with a low detection limit of 0.023 µM (S/N = 3), an ultrahigh sensitivity of 48.4 µA µM-1 cm-2, and an extensive response in the linear region of 0.1-25 µM and the logarithmic region of 25-2500 µM. Owing to the ordered nanoporous framework and carbon interfacial features, the Au/Co@HNCF biosensor displayed adequate selectivity for UA sensing over a series of biomolecules. In addition, the Au/Co@HNCF biosensor was employed to quantify UA in human serum samples. The test results were basically consistent with those of a commercial apparatus, and thus demonstrated that the proposed Au/Co@HNCF biosensor was reliable for UA determination in clinical research.

Enhanced removal of aqueous acetaminophen by a laccase-catalyzed oxidative coupling reaction under a dual-pH optimization strategy.

Acetaminophen is one kind of pharmaceutical contaminant that has been detected in municipal water and is hard to digest. A laccase-catalyzed oxidative coupling reaction is a potential method of removing acetaminophen from water. In the present study, the kinetics of radical polymerization combined with precipitation was studied, and the dual-pH optimization strategy (the enzyme solution at pH7.4 being added to the substrate solution at pH4.2) was proposed to enhance the removal efficiency of acetaminophen. The reaction kinetics that consisted of the laccase-catalyzed oxidation, radical polymerization and precipitation were studied by UV in situ, LC-MS and DLS (dynamic light scattering) in situ. The results showed that the laccase-catalyzed oxidation is the rate-limiting step in the whole process. The higher rate of enzyme-catalyzed oxidation under a dual-pH optimization strategy led to much faster formation of the dimer, trimer and tetramer. Similarly, the formation of polymerized products that could precipitate naturally from water was faster. Under the dual-pH optimization strategy, the initial laccase activity was increased approximately 2.9-fold, and the activity remained higher for >250s, during which approximately 63.7% of the total acetaminophen was transformed into biologically inactive polymerized products, and part of these polymerized products precipitated from the water. Laccase belongs to the family of multi-copper oxidases, and the present study provides a universal method to improve the activity of multi-copper oxidases for the high-performance removal of phenol and its derivatives.