Depression in Chinese Patients with Cleft Lip and/or Palate: A Preliminary Study

(1) Objectives: To investigate the difference in prevalence of depression between patients with CL/P (cleft lip and/or palate) and analyze the possible demographic factors that affect the prevalence of depression in Chinese patients with CL/P. (2) Methods: Patients with CL (cleft lip only), CP (cleft palate), and CLP (cleft lip and palate) were included in the study group. Non-CL/P individuals were included in the control group. The Patient Health Questionnaire (PHQ-9) was used to screen the depression of Chinese patients with CL/P. The different proportions of different depression groups between the CL/P group and the control groups were tested by the Fisher-Freeman-Halton test and Bonferroni correction. The scores between the study groups and the control group were analyzed by one-way ANOVA. In the study groups, demographic and clinical data of the patients, including diagnosis (CL, CP, CLP), gender, age, the only child or not, and region were collected to analyze whether they were the possible factors affecting depression through one-way independent-samples t-test. Pearson correlation analysis was used to analyze the correlation between monthly family income and depression. (3) Results: 111 and 80 valid questionnaires were collected from the study and control groups, respectively. The mean PHQ-9 score of the study group (5.459 ± 6.082) was relatively higher than the control group (4.362 ± 3.384), and the difference in proportions of depression groups was statistically significant between the CL/P group and the control group (p = 0.01), especially in the mild depression (p < 0.05) and moderately severe depression groups (p < 0.05). Statistically significant differences in PHQ-9 scores were observed between the individuals of different genders (p = 0.036) and ages (p = 0.007) in patients with CL/P, the individuals who were the only child or not in patients with CL (p = 0.007), and the individuals of different ages in patients with CP (p = 0.016). (4) Conclusions: The prevalence of depression in Chinese patients with CL/P was different compared with those without CL/P, while gender, age, the only child or not, and region played significant roles in affecting depression psychology.

Safety of intraocular anti-VEGF antibody treatment under in vitro HTLV-1 infection

Introduction

HTLV-1 (human T-cell lymphotropic virus type 1) is a retrovirus that infects approximately 20 million people worldwide. Many diseases are caused by this virus, including HTLV-1-associated myelopathy, adult T-cell leukemia, and HTLV-1 uveitis. Intraocular anti-vascular endothelial growth factor (VEGF) antibody injection has been widely used in ophthalmology, and it is reportedly effective against age-related macular degeneration, complications of diabetic retinopathy, and retinal vein occlusions. HTLV-1 mimics VEGF₁₆₅, the predominant isoform of VEGF, to recruit neuropilin-1 and heparan sulfate proteoglycans. VEGF₁₆₅ is also a selective competitor of HTLV-1 entry. Here, we investigated the effects of an anti-VEGF antibody on ocular status under conditions of HTLV-1 infection in vitro.

Methods

We used MT2 and TL-Om1 cells as HTLV-1-infected cells and Jurkat cells as controls. Primary human retinal pigment epithelial cells (HRPEpiCs) and ARPE19 HRPEpiCs were used as ocular cells; MT2/TL-Om1/Jurkat cells and HRPEpiCs/ARPE19 cells were co-cultured to simulate the intraocular environment of HTLV-1-infected patients. Aflibercept was administered as an anti-VEGF antibody. To avoid possible T-cell adhesion, we lethally irradiated MT2/TL-Om1/Jurkat cells prior to the experiments.

Results

Anti-VEGF antibody treatment had no effect on activated NF-κB production, inflammatory cytokines, chemokines, HTLV-1 proviral load (PVL), or cell counts in the retinal pigment epithelium (RPE) under MT2 co-culture conditions. Under TL-Om1 co-culture conditions, anti-VEGF antibody treatment did not affect the production of activated NF-κB, chemokines, PVL, or cell counts, but production of the inflammatory cytokine IL-6 was increased. In addition, anti-VEGF treatment did not affect PVL in HTLV-1-infected T cells.

Conclusion

This preliminary in vitro assessment indicates that intraocular anti-VEGF antibody treatment for HTLV-1 infection does not exacerbate HTLV-1-related inflammation and thus may be safe for use.

Cytomegalovirus Anterior Uveitis: Clinical Manifestations, Diagnosis, Treatment, and Immunological Mechanisms

Little is known regarding anterior uveitis (AU), the most common ocular disease associated with cytomegalovirus (CMV) infection in immunocompetent populations. CMV AU is highly prevalent in Asia, with a higher incidence in men. Clinically, it manifests mainly as anterior chamber inflammation and elevated intraocular pressure (IOP). Acute CMV AU may resemble Posner-Schlossman syndrome with its recurrent hypertensive iritis, while chronic CMV AU may resemble Fuchs uveitis because of its elevated IOP. Without prompt treatment, it may progress to glaucoma; therefore, early diagnosis is critical to prognosis. Knowledge regarding clinical features and aqueous humor analyses can facilitate accurate diagnoses; so, we compared and summarized these aspects. Early antiviral treatment reduces the risk of a glaucoma surgery requirement, and therapeutic effects vary based on drug delivery. Both oral valganciclovir and topical ganciclovir can produce positive clinical outcomes, and higher concentration and frequency are beneficial in chronic CMV retinitis. An extended antiviral course could prevent relapses, but should be limited to 6 months to prevent drug resistance and side effects. In this review, we have systematically summarized the pathogenesis, clinical features, diagnostic and therapeutic aspects, and immunological mechanisms of CMV AU with the goal of providing a theoretical foundation for early clinical diagnosis and treatment.

Assessment of Silicone Oil Emulsification: A Comparison of Currently Applied Methods

Purpose

To compare ultrasound biomicroscopy (UBM), Coulter counter, and B-scan ultrasonography in the evaluation of silicone oil (SO) emulsification.

Methods

Patients who underwent primary pars plana vitrectomy with SO tamponade for rhegmatogenous retinal detachment and SO removal were included. UBM images were acquired before the SO removal, and B-scan images were taken after removal. The number of droplets in the first and last 2 mL of washout fluid was analyzed using a Coulter counter. The correlations between these measurements were analyzed.

Results

Thirty-four eyes received both UBM and Coulter counter analysis for the first 2 mL of washout fluid, and 34 underwent B-scan and Coulter counter analysis of the last 2 mL washout fluid. The mean UBM grading was 26.41 ± 9.71 (range: 1-36); the mean SO index obtained with B-scan was 5.25 ± 5.00% (range: 0.10-16.49%), and the mean number of SO droplets was 1.26 ± 2.45 × 10⁷/mL and 3.34 ± 4.22 × 10⁶/mL in the first and last 2 mL of washout fluid, respectively. There were significant correlations between UBM grading and SO droplets in the first 2 mL and between B-scan grading and SO droplets in the last 2 mL (all P < 0.05).

Conclusions

UBM, Coulter counter, and B-scan ultrasonography could all be used in the evaluation of SO emulsification, and their findings were comparable.

Novel Hina alleles created by genome editing increase grain hardness and reduce grain width in barley

The hordoindolina genes (Hina and Hinb) are believed to play critical roles in barley (Hordeum vulgare L.) grain texture. In this study, we created novel alleles of the Hina gene using CRISPR/Cas9 (Clustered regularly inter spaced short palindromic repeat-associated protein, CRISPR-Cas) genome editing. Mutagenesis of single bases in these novel alleles led to loss of Hina protein function in edited lines. The grain hardness index of hina mutants was 95.5 on average, while that of the wild type was only 53.7, indicating successful conversion of soft barley into hard barley. Observation of cross-sectional grain structure using scanning electron microscopy revealed different adhesion levels between starch granules and protein matrix. Starch granules were loose and separated from the protein matrix in the wild type, but deeply trapped and tightly integrated with the protein matrix in hina02 mutants. In addition, the grain width and thousand-grain weight of the hina02 mutant were significantly lower than those of the wild type.

Immune mechanism of low bone mineral density caused by ankylosing spondylitis based on bioinformatics and machine learning

Background and Objective: This study aims to find the key immune genes and mechanisms of low bone mineral density (LBMD) in ankylosing spondylitis (AS) patients. Methods: AS and LBMD datasets were downloaded from the GEO database, and differential expression gene analysis was performed to obtain DEGs. Immune-related genes (IRGs) were obtained from ImmPort. Overlapping DEGs and IRGs got I-DEGs. Pearson coefficients were used to calculate DEGs and IRGs correlations in the AS and LBMD datasets. Louvain community discovery was used to cluster the co-expression network to get gene modules. The module most related to the immune module was defined as the key module. Metascape was used for enrichment analysis of key modules. Further, I-DEGs with the same trend in AS and LBMD were considered key I-DEGs. Multiple machine learning methods were used to construct diagnostic models based on key I-DEGs. IID database was used to find the context of I-DEGs, especially in the skeletal system. Gene-biological process and gene-pathway networks were constructed based on key I-DEGs. In addition, immune infiltration was analyzed on the AS dataset using the CIBERSORT algorithm. Results: A total of 19 genes were identified I-DEGs, of which IFNAR1, PIK3CG, PTGER2, TNF, and CCL3 were considered the key I-DEGs. These key I-DEGs had a good relationship with the hub genes of key modules. Multiple machine learning showed that key I-DEGs, as a signature, had an excellent diagnostic performance in both AS and LBMD, and the SVM model had the highest AUC value. Key I-DEGs were closely linked through bridge genes, especially in the skeletal system. Pathway analysis showed that PIK3CG, IFNAR1, CCL3, and TNF participated in NETs formation through pathways such as the MAPK signaling pathway. Immune infiltration analysis showed neutrophils had the most significant differences between case and control groups and a good correlation with key I-DEG. Conclusion: The key I-DEGs, TNF, CCL3, PIK3CG, PTGER2, and IFNAR1, can be utilized as biomarkers to determine the risk of LBMD in AS patients. They may affect neutrophil infiltration and NETs formation to influence the bone remodeling process in AS.

[Risk factors and chemoprophylaxis of ulcerative colitis-colorectal cancer]

Ulcerative colitis-colorectal cancer (UC-CRC) is one of the most serious complications in patients with ulcerative colitis (UC), with worse prognosis and higher mortality than sporadic colorectal cancer (CRC). Since most UC-CRC developed through the "inflammation-dysplasia-carcinoma" approach, early detection of dysplasia through identification of high-risk groups reasonable monitoring and active prevention are extremely important. However, there is no consensus on the risk factors of UC carcinogenesis and the drugs that can be used for chemoprevention currently. This article combined with relevant literature at home and abroad, reviewed the current risk factors and chemopreventive drugs for UC carcinogenesis, in order to provide reference for early prevention, early detection and early diagnosis of UC-CRC.

Ferroptosis in AS progression: role of miRNA

We review the relationship between miRNAs associated with ferroptosis and the evolution of AS. Even though, more evidence is asked to determine the role of miRNAs associated with ferroptosis in the AS, this review will help us understand the role of miRNAs in ferroptosis and AS and may provide new insights for probing new biomarkers for the diagnosis and treatment of AS for the time to come. This is a narrative essay. Using PubMed as the main source, a literature search strategy was randomly implemented to index Scopus articles. No specific terminology is used. Studies have shown that ferroptosis plays a crucial role in the development of AS, and a large amount of ferroptosis in cells can lead to the progression of AS. MicroRNAs (MiRNAs) have been proved to be taken part in the biological course of ferroptosis and thus the process of AS is affected. The exact regulatory mechanism behind this appearance remains unclear. In order to clarify this, a growing number of studies have concentrated the regulatory role of miRNAs in the process of generation and development of ferroptosis, as well as the function of ferroptosis in the progression of AS. MiRNAs play a significant role in the process of ferroptosis and are incredibly significant in the occurrence, development, clinical diagnosis, treatment and prognosis evaluation of AS.

Enhanced Sample Self-Revised Network for Cross-Dataset Facial Expression Recognition

Recently, cross-dataset facial expression recognition (FER) has obtained wide attention from researchers. Thanks to the emergence of large-scale facial expression datasets, cross-dataset FER has made great progress. Nevertheless, facial images in large-scale datasets with low quality, subjective annotation, severe occlusion, and rare subject identity can lead to the existence of outlier samples in facial expression datasets. These outlier samples are usually far from the clustering center of the dataset in the feature space, thus resulting in considerable differences in feature distribution, which severely restricts the performance of most cross-dataset facial expression recognition methods. To eliminate the influence of outlier samples on cross-dataset FER, we propose the enhanced sample self-revised network (ESSRN) with a novel outlier-handling mechanism, whose aim is first to seek these outlier samples and then suppress them in dealing with cross-dataset FER. To evaluate the proposed ESSRN, we conduct extensive cross-dataset experiments across RAF-DB, JAFFE, CK+, and FER2013 datasets. Experimental results demonstrate that the proposed outlier-handling mechanism can reduce the negative impact of outlier samples on cross-dataset FER effectively and our ESSRN outperforms classic deep unsupervised domain adaptation (UDA) methods and the recent state-of-the-art cross-dataset FER results.

Silicone oil residual after vitrectomy for rhegmatogenous retinal detachment

OBJECTIVE

To explore the presence of residual emulsified silicone oil (SO) droplets in patients with rhegmatogenous retinal detachment (RRD) and their possible risk factors.

METHODS

Patients who underwent primary pars plana vitrectomy with SO injection for RRD and SO removal at the same eye centre were included. Approximately 10 weeks after SO removal, B-scan ultrasonography was performed, and using ImageJ, the silicone oil index (SOI) was measured, and its possible correlations with other clinical factors were explored.

RESULTS

A total of 101 eyes were included. Residual SO particles were found in all the patients (100%), and the mean SOI was 4.04% ± 5.16% (range 0.06%-19.88%). Multiple linear regression revealed that, among all the clinical factors, axial length (AL) and ocular hypertension (intraocular pressure (IOP) > 21 mmHg or the use of antiglaucoma medications) before SO removal were positively and significantly associated with the SOI (all P < 0.05). Patients with ocular hypertension after SO removal had a higher SOI, a longer SO duration, a higher IOP before SO removal and a longer AL than those without (all P < 0.05).

CONCLUSIONS

Patients with a larger AL and higher IOP before SO removal were more prone to have more residual SO droplets, which might in turn lead to an elevated IOP. In these eyes, thorough irrigation or repeated fluid-air exchange might be necessary.

Progressive distribution adapted neural networks for cross-corpus speech emotion recognition

In this paper, we investigate a challenging but interesting task in the research of speech emotion recognition (SER), i.e., cross-corpus SER. Unlike the conventional SER, the training (source) and testing (target) samples in cross-corpus SER come from different speech corpora, which results in a feature distribution mismatch between them. Hence, the performance of most existing SER methods may sharply decrease. To cope with this problem, we propose a simple yet effective deep transfer learning method called progressive distribution adapted neural networks (PDAN). PDAN employs convolutional neural networks (CNN) as the backbone and the speech spectrum as the inputs to achieve an end-to-end learning framework. More importantly, its basic idea for solving cross-corpus SER is very straightforward, i.e., enhancing the backbone's corpus invariant feature learning ability by incorporating a progressive distribution adapted regularization term into the original loss function to guide the network training. To evaluate the proposed PDAN, extensive cross-corpus SER experiments on speech emotion corpora including EmoDB, eNTERFACE, and CASIA are conducted. Experimental results showed that the proposed PDAN outperforms most well-performing deep and subspace transfer learning methods in dealing with the cross-corpus SER tasks.

Micro-Expression Recognition Using Uncertainty-Aware Magnification-Robust Networks

A micro-expression (ME) is a kind of involuntary facial expressions, which commonly occurs with subtle intensity. The accurately recognition ME, a. k. a. micro-expression recognition (MER), has a number of potential applications, e.g., interrogation and clinical diagnosis. Therefore, the subject has received a high level of attention among researchers in affective computing and pattern recognition communities. In this paper, we proposed a straightforward and effective deep learning method called uncertainty-aware magnification-robust networks (UAMRN) for MER, which attempts to address two key issues in MER including the low intensity of ME and imbalance of ME samples. Specifically, to better distinguish subtle ME movements, we reconstructed a new sequence by magnifying the ME intensity. Furthermore, a sparse self-attention (SSA) block was implemented which rectifies the standard self-attention with locality sensitive hashing (LSH), resulting in the suppression of artefacts generated during magnification. On the other hand, for the class imbalance problem, we guided the network optimization based on the confidence about the estimation, through which the samples from rare classes were allotted greater uncertainty and thus trained more carefully. We conducted the experiments on three public ME databases, i.e., CASME II, SAMM and SMIC-HS, the results of which demonstrate improvement compared to recent state-of-the-art MER methods.

Adapting Multiple Distributions for Bridging Emotions from Different Speech Corpora

In this paper, we focus on a challenging, but interesting, task in speech emotion recognition (SER), i.e., cross-corpus SER. Unlike conventional SER, a feature distribution mismatch may exist between the labeled source (training) and target (testing) speech samples in cross-corpus SER because they come from different speech emotion corpora, which degrades the performance of most well-performing SER methods. To address this issue, we propose a novel transfer subspace learning method called multiple distribution-adapted regression (MDAR) to bridge the gap between speech samples from different corpora. Specifically, MDAR aims to learn a projection matrix to build the relationship between the source speech features and emotion labels. A novel regularization term called multiple distribution adaption (MDA), consisting of a marginal and two conditional distribution-adapted operations, is designed to collaboratively enable such a discriminative projection matrix to be applicable to the target speech samples, regardless of speech corpus variance. Consequently, by resorting to the learned projection matrix, we are able to predict the emotion labels of target speech samples when only the source label information is given. To evaluate the proposed MDAR method, extensive cross-corpus SER tasks based on three different speech emotion corpora, i.e., EmoDB, eNTERFACE, and CASIA, were designed. Experimental results showed that the proposed MDAR outperformed most recent state-of-the-art transfer subspace learning methods and even performed better than several well-performing deep transfer learning methods in dealing with cross-corpus SER tasks.

An engineered prime editor with enhanced editing efficiency in plants

Prime editing is a versatile genome-editing technology, but it suffers from low editing efficiency. In the present study, we introduce optimized prime editors with substantially improved editing efficiency. We engineered the Moloney-murine leukemia virus reverse transcriptase by removing its ribonuclease H domain and incorporated a viral nucleocapsid protein with nucleic acid chaperone activity. Each modification independently improved prime editing efficiency by ~1.8-3.4-fold in plant cells. When combined in our engineered plant prime editor (ePPE), the two modifications synergistically enhanced the efficiency of base substitutions, deletions and insertions at various endogenous sites by on average 5.8-fold compared with the original PPE in cell culture. No significant increase in byproducts or off-target editing was observed. We used the ePPE to generate rice plants tolerant to sulfonylurea and imidazolinone herbicides, observing an editing frequency of 11.3% compared with 2.1% using PPE. We also combined ePPE with the previously reported dual-prime editing guide (peg) RNAs and engineered pegRNAs to further increase efficiency.

Activation of the GABA-alpha receptor by berberine rescues retinal ganglion cells to attenuate experimental diabetic retinopathy

Purpose

The aim of this study was to investigate the role and mechanism of berberine (BBR) in the protection of injured retinal ganglion cells (RGCs) in diabetic retinopathy (DR).

Methods

Experimental diabetic retinopathy rat model was successfully induced by a single intraperitoneal injection of streptozotocin (STZ, 60 mg/kg) in male SD rats with sufficient food and water for 8 weeks. Animals were randomly divided into four groups: (1) non-diabetic, (2) diabetic, (3) diabetic + BBR + PBS, and (4) diabetic + BBR + SR95531. BBR (100 mg/kg) was given daily by gavage to rats in the group (3) and group (4) for 8 weeks, and weekly intravitreal injections were conducted to rats in the group (3) with 5 μL of 1×PBS and rats in the group (4) with 5 μL of GABA-alpha receptor antagonist SR95531 to investigate the underlying mechanisms. The survival and apoptosis of RGCs were observed by fluorescence gold labeling technology and TUNEL staining. Visual function was evaluated by visual electrophysiological examination. Western blotting and immunofluorescence staining were used to analyze the expression of GABA-alpha receptors in RGCs.

Results

In an animal model, BBR can increase the survival of RGCs, reduce RGCs apoptosis, and significantly improve the visual function. The reduction of GABA, PKC-α, and Bcl-2 protein expression caused by DR can be considerably increased by BBR. SR95531 inhibits BBR's protective effect on RGC and visual function, as well as its upregulation of PKC-α and Bcl-2.

Conclusion

BBR is a promising preventive or adjuvant treatment for DR complications, and its key protective effect may involve the regulation of RGC apoptosis through the GABA-alpha receptor/protein kinase C-alpha (GABAAR/PKC-α) pathway.

Progressively Discriminative Transfer Network for Cross-Corpus Speech Emotion Recognition

Cross-corpus speech emotion recognition (SER) is a challenging task, and its difficulty lies in the mismatch between the feature distributions of the training (source domain) and testing (target domain) data, leading to the performance degradation when the model deals with new domain data. Previous works explore utilizing domain adaptation (DA) to eliminate the domain shift between the source and target domains and have achieved the promising performance in SER. However, these methods mainly treat cross-corpus tasks simply as the DA problem, directly aligning the distributions across domains in a common feature space. In this case, excessively narrowing the domain distance will impair the emotion discrimination of speech features since it is difficult to maintain the completeness of the emotion space only by an emotion classifier. To overcome this issue, we propose a progressively discriminative transfer network (PDTN) for cross-corpus SER in this paper, which can enhance the emotion discrimination ability of speech features while eliminating the mismatch between the source and target corpora. In detail, we design two special losses in the feature layers of PDTN, i.e., emotion discriminant loss Ld and distribution alignment loss La. By incorporating prior knowledge of speech emotion into feature learning (i.e., high and low valence speech emotion features have their respective cluster centers), we integrate a valence-aware center loss Lv and an emotion-aware center loss Lc as the Ld to guarantee the discriminative learning of speech emotions except an emotion classifier. Furthermore, a multi-layer distribution alignment loss La is adopted to more precisely eliminate the discrepancy of feature distributions between the source and target domains. Finally, through the optimization of PDTN by combining three losses, i.e., cross-entropy loss Le, Ld, and La, we can gradually eliminate the domain mismatch between the source and target corpora while maintaining the emotion discrimination of speech features. Extensive experimental results of six cross-corpus tasks on three datasets, i.e., Emo-DB, eNTERFACE, and CASIA, reveal that our proposed PDTN outperforms the state-of-the-art methods.

Clinical features of patients with septic shock-triggered Takotsubo syndrome: a single-center 7 case series

BACKGROUND

Myocardial dysfunction is common in septic shock and has long been recognized. Takotsubo syndrome is an acute and usually reversible myocardial injury without evidence of an obstructive coronary artery disease, yet little is known about this syndrome in septic shock patients.

CASE PRESENTATION

Among 84 septic shock patients admitted to the ICU over a period of 8 months, 7 patients (8.3%) were diagnosed with Takotsubo syndrome. The percentage of men was 71%, and the mean age was 58 (19-87) years. Sudden hemodynamic deterioration and/or dyspnea were the presenting symptoms in 6 patients. T-wave inversion was the major ECG anomaly in 5 patients. The mean left ventricular ejection fraction was 31.8% (20.0-53.0). Mild elevation of cardiac troponin disproportionate to the extent of regional wall motion abnormalities was present in all patients. Cardiac complications occurred in 6 patients. The mean time to recover the cardiac function was 6.5 (3-11) days. In-hospital death was observed in 2 patients.

CONCLUSIONS

Takotsubo syndrome is not uncommon in septic shock patients and may be the cause of some patients with sepsis-induced myocardial dysfunction. New-onset hemodynamic and respiratory worsening could arouse the suspicion of Takotsubo syndrome and prompt the screening for this syndrome using echocardiography in this clinical context.

Activation of peroxymonosulfate by α-MnO2 for Orange Ⅰ removal in water

Developing high-efficiency catalysts for peroxymonosulfate (PMS)-based advanced oxidation processes is important for eliminating pollutants in water. Herein, α-MnO₂ with major exposed {110} and {100} facets prepared via a hydrothermal method were used as catalysts to activate PMS for the degradation of Orange Ⅰ (OⅠ). α-MnO₂-100, with more abundant surface hydroxyl groups and greater reductive ability, performed remarkably better than α-MnO₂-110 for degrading OⅠ. OⅠ removal of 86.20% was obtained in the α-MnO₂-100/PMS system. The apparent rate constant of OⅠ removal over α-MnO₂-100 was 2.11 times higher than that of α-MnO₂-110. The effects of PMS concentration, catalyst dosage, OⅠ concentration, initial pH, anions and humic acid (HA) on OⅠ degradation in the α-MnO₂-100/PMS system were systematically investigated. Quenching experiments and electron paramagnetic resonance (EPR) demonstrated that SO₄^•-, •OH, O₂^•- and ¹O₂ were the reactive oxygen species (ROS) in the α-MnO₂-100/PMS system. Moreover, the possible degradation pathway of OⅠ in the α-MnO₂-100/PMS system was proposed. This work provides an ideal metal oxide catalyst for sewage remediation.