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Inflammation plays a crucial role in the pathogenesis of major depressive disorder (MDD) and bipolar disorder (BD). This study aimed to examine whether the dysregulation of complement components contributes to brain structural defects in patients with mood disorders.
A total of 52 BD patients, 35 MDD patients, and 53 controls were recruited. The human complement immunology assay was used to measure the levels of complement factors. Whole brain-based analysis was performed to investigate differences in gray matter volume (GMV) and cortical thickness (CT) among the BD, MDD, and control groups, and relationships were explored between neuroanatomical differences and levels of complement components.
GMV in the medial orbital frontal cortex (mOFC) and middle cingulum was lower in both patient groups than in controls, while the CT of the left precentral gyrus and left superior frontal gyrus were affected differently in the two disorders. Concentrations of C1q, C4, factor B, factor H, and properdin were higher in both patient groups than in controls, while concentrations of C3, C4 and factor H were significantly higher in BD than in MDD. Concentrations of C1q, factor H, and properdin showed a significant negative correlation with GMV in the mOFC at the voxel-wise level.
BD and MDD are associated with shared and different alterations in levels of complement factors and structural impairment in the brain. Structural defects in mOFC may be associated with elevated levels of certain complement factors, providing insight into the shared neuro-inflammatory pathogenesis of mood disorders.
To integrate the uneven terrain adaptivity of legged robots and the fast capacity of wheeled robots on even terrains, a four wheel-legged robot is addressed and the cooperative control strategy of wheels and legs based on attitude balance is investigated. Firstly, the kinematics of wheel-legged robot is analyzed, which contains the legged and wheeled motion modal. Secondly, the cooperative control strategy of wheel-legged robot based on attitude balance is proposed. The attitude is calculated by using the quaternion method and complementary filtering, and the attitude stability control of the wheel-legged robot is studied. The trajectory planning of leg motion including walk and trot gait is implemented, and the differential control of wheeled motion is deduced. And then, the cooperative motion control of wheels and legs is achieved by keeping the attitude balance of robot body. Finally, a small prototype is set up to validate the feasibility and effectiveness of proposed method. The experimental results show that the established wheel-legged robot can do walk, trot, and wheel-leg compound motion to overcome many complex terrains and environments.
Due to the lack of research between the inner layers in the structure of colonic mucous and the metabolism of fatty acid in the constipation model, we aim to determine the changes in the mucous phenotype of the colonic glycocalyx and the microbial community structure following treatment with Rhubarb extract in our research. The constipation and treatment models are generated using adult male C57BL/6N mice. We perform light microscopy and transmission electron microscopy (TEM) to detect a Muc2-rich inner mucus layer attached to mice colon under different conditions. In addition, 16S rDNA sequencing is performed to examine the intestinal flora. According to TEM images, we demonstrate that Rhubarb can promote mucin secretion and find direct evidence of dendritic structure-linked mucus structures with its assembly into a lamellar network in a pore size distribution in the isolated colon section. Moreover, the diversity of intestinal flora has noticeable changes in constipated mice. The present study characterizes a dendritic structure and persistent cross-links have significant changes accompanied by the alteration of intestinal flora in feces in models of constipation and pretreatment with Rhubarb extract.
We investigated the drug resistance of Mycobacterium tuberculosis isolates from patients with tuberculosis (TB) and HIV, and those diagnosed with only TB in Sichuan, China. TB isolates were obtained from January 2018 to December 2020 and subjected to drug susceptibility testing (DST) to 11 anti-TB drugs and to GeneXpert MTB/RIF testing. The overall proportion of drug-resistant TB (DR-TB) isolates was 32.1% (n = 10 946). HIV testing was not universally available for outpatient TB cases, only 29.5% (3227/10 946) cases had HIV testing results. The observed proportion of multidrug-resistant TB (MDR-TB) isolates was almost double than that of the national level, with approximately 1.5% and 0.1% of the isolates being extensively drug resistant and universally drug resistant, respectively. The proportions of resistant isolates were generally higher in 2018 and 2019 than in 2020. Furthermore, the sensitivities of GeneXpert during 2018–2020 demonstrated a downward trend (80.9, 95% confidence intervals (CI) 76.8–85.0; 80.2, 95% CI 76.4–84.1 and 75.4, 95% CI 70.7–80.2, respectively). Approximately 69.0% (7557/10 946) of the TB cases with DST results were subjected to GeneXpert detection. Overall, the DR-TB status and the use of GeneXpert in Sichuan have improved, but DR-TB challenges remain. HIV testing for all TB cases is recommended.
Due to less light scattering and a better signal-to-noise ratio in deep imaging, two-photon fluorescence microscopy (TPFM) has been widely used in biomedical photonics since its advent. However, optical aberrations degrade the performance of TPFM in terms of the signal intensity and the imaging depth and therefore restrict its application. Here, we introduce adaptive optics based on the genetic algorithm to detect the distorted wavefront of the excitation laser beam and then perform aberration correction to optimize the performance of TPFM. By using a spatial light modulator as the wavefront controller, the correction phase is obtained through a signal feedback loop and a process of natural selection. The experimental results show that the signal intensity and imaging depth of TPFM are improved after aberration correction. Finally, the method was applied to two-photon fluorescence lifetime imaging, which helps to improve the signal-to-noise ratio and the accuracy of lifetime analysis. Furthermore, the method can also be implemented in other experiments, such as three-photon microscopy, light-sheet microscopy, and super-resolution microscopy.
Neuroimaging- and machine-learning-based brain-age prediction of schizophrenia is well established. However, the diagnostic significance and the effect of early medication on first-episode schizophrenia remains unclear.
To explore whether predicted brain age can be used as a biomarker for schizophrenia diagnosis, and the relationship between clinical characteristics and brain-predicted age difference (PAD), and the effects of early medication on predicted brain age.
The predicted model was built on 523 diffusion tensor imaging magnetic resonance imaging scans from healthy controls. First, the brain-PAD of 60 patients with first-episode schizophrenia, 60 healthy controls and 21 follow-up patients from the principal data-set and 40 pairs of individuals in the replication data-set were calculated. Next, the brain-PAD between groups were compared and the correlations between brain-PAD and clinical measurements were analysed.
The patients showed a significant increase in brain-PAD compared with healthy controls. After early medication, the brain-PAD of patients decreased significantly compared with baseline (P < 0.001). The fractional anisotropy value of 31/33 white matter tract features, which related to the brain-PAD scores, had significantly statistical differences before and after measurements (P < 0.05, false discovery rate corrected). Correlation analysis showed that the age gap was negatively associated with the positive score on the Positive and Negative Syndrome Scale in the principal data-set (r = −0.326, P = 0.014).
The brain age of patients with first-episode schizophrenia may be older than their chronological age. Early medication holds promise for improving the patient's brain ageing. Neuroimaging-based brain-age prediction can provide novel insights into the understanding of schizophrenia.
Based on hubs of neural circuits associated with addiction and their degree centrality (DC), this study aimed to construct the addiction-related brain networks for patients diagnosed with heroin dependence undertaking stable methadone maintenance treatment (MMT) and further prospectively identify the ones at high risk for relapse with cluster analysis.
Sixty-two male MMT patients and 30 matched healthy controls (HC) underwent brain resting-state functional MRI data acquisition. The patients received 26-month follow-up for the monthly illegal-drug-use information. Ten addiction-related hubs were chosen to construct a user-defined network for the patients. Then the networks were discriminated with K-means-clustering-algorithm into different groups and followed by comparative analysis to the groups and HC. Regression analysis was used to investigate the brain regions significantly contributed to relapse.
Sixty MMT patients were classified into two groups according to their brain-network patterns calculated by the best clustering-number-K. The two groups had no difference in the demographic, psychological indicators and clinical information except relapse rate and total heroin consumption. The group with high-relapse had a wider range of DC changes in the cortical−striatal−thalamic circuit relative to HC and a reduced DC in the mesocorticolimbic circuit relative to the low-relapse group. DC activity in NAc, vACC, hippocampus and amygdala were closely related with relapse.
MMT patients can be identified and classified into two subgroups with significantly different relapse rates by defining distinct brain-network patterns even if we are blind to their relapse outcomes in advance. This may provide a new strategy to optimize MMT.
The present study evaluated whether fat mass assessment using the triceps skinfold (TSF) thickness provides additional prognostic value to the Global Leadership Initiative on Malnutrition (GLIM) framework in patients with lung cancer (LC). We performed an observational cohort study including 2672 LC patients in China. Comprehensive demographic, disease and nutritional characteristics were collected. Malnutrition was retrospectively defined using the GLIM criteria, and optimal stratification was used to determine the best thresholds for the TSF. The associations of malnutrition and TSF categories with survival were estimated independently and jointly by calculating multivariable-adjusted hazard ratios (HR). Malnutrition was identified in 808 (30·2 %) patients, and the best TSF thresholds were 9·5 mm in men and 12 mm in women. Accordingly, 496 (18·6 %) patients were identified as having a low TSF. Patients with concurrent malnutrition and a low TSF had a 54 % (HR = 1·54, 95 % CI = 1·25, 1·88) greater death hazard compared with well-nourished individuals, which was also greater compared with malnourished patients with a normal TSF (HR = 1·23, 95 % CI = 1·06, 1·43) or malnourished patients without TSF assessment (HR = 1·31, 95 % CI = 1·14, 1·50). These associations were concentrated among those patients with adequate muscle mass (as indicated by the calf circumference). Additional fat mass assessment using the TSF enhances the prognostic value of the GLIM criteria. Using the population-derived thresholds for the TSF may provide significant prognostic value when used in combination with the GLIM criteria to guide strategies to optimise the long-term outcomes in patients with LC.
To establish optimal gestational weight gain (GWG) in Chinese pregnant women by Chinese-specific BMI categories and compare the new recommendations with the Institute of Medicine (IOM) 2009 guidelines.
Multicentre, prospective cohort study. Unconditional logistic regression analysis was used to evaluate the OR, 95 % CI and the predicted probabilities of adverse pregnancy outcomes. The optimal GWG range was defined as the range that did not exceed a 1 % increase from the lowest predicted probability in each pre-pregnancy BMI group.
From nine cities in mainland China.
A total of 3731 women with singleton pregnancy were recruited from April 2013 to December 2014.
The optimal GWG (ranges) by Chinese-specific BMI was 15·0 (12·8–17·1), 14·2 (12·1–16·4) and 12·6 (10·4–14·9) kg for underweight, normal weight and overweight pregnant women, respectively. Inappropriate GWG was associated with several adverse pregnancy outcomes. Compared with women gaining weight within our proposed recommendations, women with excessive GWG had higher risk for macrosomia, large for gestational age and caesarean section, whereas those with inadequate GWG had higher risk for low birth weight, small for gestational age and preterm delivery. The comparison between our proposed recommendations and IOM 2009 guidelines showed that our recommendations were comparable with the IOM 2009 guidelines and could well predict the risk of several adverse pregnancy outcomes.
Inappropriate GWG was associated with higher risk of several adverse pregnancy outcomes. Optimal GWG recommendations proposed in the present study could be applied to Chinese pregnant women.
The TanDEM-X DEM is a valuable data source for estimating glacier mass balance. However, the accuracy of TanDEM-X elevation over glaciers can be affected by microwave penetration and phase decorrelation. To investigate the bias of TanDEM-X DEMs of glaciers on the Tibetan Plateau, these DEMs were subtracted from SPOT-6 DEMs obtained around the same time at two study sites. The average bias over the studied glacier areas in West Kunlun (175.0 km2) was 2.106 ± 0.012 m in April 2014, and it was 1.523 ± 0.011 m in Geladandong (228.8 km2) in October 2013. By combining backscatter coefficients and interferometric coherence maps, we found surface decorrelation and baseline decorrelation can cause obvious bias in addition to microwave penetration. If the optical/laser data and winter TanDEM-X data were used as new and historic elevation sources for mass-balance measurements over an arbitrary observation period of 10 years, the glacier mass loss rates in West Kunlun and Geladandong would be potentially underestimated by 0.218 ± 0.016 and 0.158 ± 0.011 m w.e. a−1, respectively. The impact is therefore significant, and users should carefully treat the bias of TanDEM-X DEMs when retrieving a geodetic glacier mass balance.
This study aimed to evaluate to what extent the different interval times between trophectoderm (TE) biopsy and vitrification influence the clinical outcomes in preimplantation genetic testing (PGT) cycles. Patients who underwent frozen embryo transfer (FET) after PGT between 2015 and 2019 were recruited. In total, 297 cycles with single day 5 euploid blastocyst transfer were included. These cycles were divided into three groups according to the interval times: <1 h group, 1–2 h group, and ≥2 h group. Blastocyst survival, clinical pregnancy, miscarriage, and ongoing pregnancy rates were compared. The results showed that, in PGT-SR cycles, survival rate in the ≥2 h group (96.72%) was significantly lower than in the <1 h group (100%, P = 0.047). The clinical pregnancy rate in the ≥2 h group was 55.93%, significantly lower than in the <1 h group (74.26%, P = 0.017). The ongoing pregnancy rates in the 1–2 h group and the ≥2 h group were 48.28% and 47.46%, respectively, significantly lower than that in the <1 h group (67.33%, P < 0.05). The miscarriage rate in the 1–2 h group was 18.42%, significantly higher than that in the <1 h group (5.33%, P = 0.027). In PGT-A cycles, the clinical pregnancy and ongoing pregnancy rates in the <1 h group were 67.44% and 53.49%, respectively, higher than that in the 1–2 h group (52.94%, 47.06%, P > 0.05) and the ≥2 h group (52.63%, 36.84%, P > 0.05). In conclusion, vitrification of blastocysts beyond 1 h after biopsy significantly influences embryo survival and clinical outcomes and is therefore not recommended.
The severe acute respiratory syndrome-coronavirus-2 (SARS-CoV-2) is highly contagious, and the coronavirus disease 2019 (COVID-19) pandemic caused by it has forced many countries to adopt ‘lockdown’ measures to prevent the spread of the epidemic through social isolation of citizens. Some countries proposed universal mask wearing as a protection measure of public health to strengthen national prevention efforts and to limit the wider spread of the epidemic. In order to reveal the epidemic prevention efficacy of masks, this paper systematically evaluates the experimental studies of various masks and filter materials, summarises the general characteristics of the filtration efficiency of isolation masks with particle size, and reveals the actual efficacy of masks by combining the volume distribution characteristics of human exhaled droplets with different particle sizes and the SARS-CoV-2 virus load of nasopharynx and throat swabs from patients. The existing measured data show that the filtration efficiency of all kinds of masks for large particles and extra-large droplets is close to 100%. From the perspective of filtering the total number of pathogens discharged in the environment and protecting vulnerable individuals from breathing live viruses, the mask has a higher protective effect. If considering the weighted average filtration efficiency with different particle sizes, the filtration efficiencies of the N95 mask and the ordinary mask are 99.4% and 98.5%, respectively. The mask can avoid releasing active viruses to the environment from the source of infection, thus maximising the protection of vulnerable individuals by reducing the probability of inhaling a virus. Therefore, if the whole society strictly implements the policy of publicly wearing masks, the risk of large-scale spread of the epidemic can be greatly reduced. Compared with the overall cost of social isolation, limited personal freedoms and forced suspension of economic activities, the inconvenience for citizens caused by wearing masks is perfectly acceptable.
Drug-induced liver injury (DILI) is a common adverse drug reaction leading to the interruption of tuberculosis (TB) therapy. We aimed to identify whether the hepatitis B virus (HBV) infection would increase the risk of DILI during first-line TB treatment. A meta-analysis of cohort studies searched in PubMed, Web of Science and China National Knowledge Infrastructure was conducted. Effect sizes were reported as risk ratios (RRs) and 95% confidence intervals (CIs) and calculated by R software. Sixteen studies with 3960 TB patients were eligible for analysis. The risk of DILI appeared to be higher in TB patients co-infected with HBV (RR 2.66; 95% CI 2.13–3.32) than those without HBV infection. Moreover, patients with positive hepatitis B e antigen (HBeAg) were more likely to develop DILI (RR 3.42; 95% CI 1.95–5.98) compared to those with negative HBeAg (RR 2.30; 95% CI 1.66–3.18). Co-infection with HBV was not associated with a higher rate of anti-TB DILI in latent TB patients (RR 4.48; 95% CI 0.80–24.99). The effect of HBV infection on aggravating anti-TB DILI was independent of study participants, whether they were newly diagnosed with TB or not. Besides, TB and HBV co-infection patients had a longer duration of recovery from DILI compared to non-co-infected patients (SMD 2.26; 95% CI 1.87–2.66). To conclude, the results demonstrate that HBV infection would increase the risk of DILI during TB therapy, especially in patients with positive HBeAg, and close liver function monitoring is needed for TB and HBV co-infection patients.
Generating designs via machine learning has been an on-going challenge in computer-aided design. Recently, deep learning methods have been applied to randomly generate images in fashion, furniture and product design. However, such deep generative methods usually require a large number of training images and human aspects are not taken into account in the design process. In this work, we seek a way to involve human cognitive factors through brain activity indicated by electroencephalographic measurements (EEG) in the generative process. We propose a neuroscience-inspired design with a machine learning method where EEG is used to capture preferred design features. Such signals are used as a condition in generative adversarial networks (GAN). First, we employ a recurrent neural network Long Short-Term Memory as an encoder to extract EEG features from raw EEG signals; this data are recorded from subjects viewing several categories of images from ImageNet. Second, we train a GAN model conditioned on the encoded EEG features to generate design images. Third, we use the model to generate design images from a subject’s EEG measured brain activity. To verify our proposed generative design method, we present a case study, in which the subjects imagine the products they prefer, and the corresponding EEG signals are recorded and reconstructed by our model for evaluation. The results indicate that a generated product image with preference EEG signals gains more preference than those generated without EEG signals. Overall, we propose a neuroscience-inspired artificial intelligence design method for generating a design taking into account human preference. The method could help improve communication between designers and clients where clients might not be able to express design requests clearly.
In a high-level radioactive waste repository, bentonite may react with the alkaline solution produced by cement degradation. In this study, bentonite was mixed with alkaline solution in a closed system and reacted for 3–24 months. Furthermore, swelling tests were conducted on the alkaline-dissolved bentonite immersed in distilled water. The swelling deformation decreased significantly with increases in the concentration of NaOH solution and reaction time, and this was mainly due to montmorillonite dissolution. The fractal e–p relationship (e is the void ratio and p is the vertical pressure) with two calculation coefficients (the swelling coefficient and the fractal dimension) was employed to determine the swelling of alkaline-dissolved bentonite. The fractal dimension increased slightly with increasing reaction time or concentration of NaOH solution, as the dissolution traces caused by the alkaline solution favoured an increase in the irregularity and fractality of the bentonite surface. The swelling coefficient decreased linearly with decreasing montmorillonite content. In addition, the swelling coefficient and the fractal dimension were related exponentially to the reaction time in alkaline solution. A relationship between the swelling of alkaline-dissolved samples and the reaction time was proposed, which might be used to assess the swelling properties of bentonite barriers that would be affected by long-term dissolution of the alkaline solution in a closed repository.
The microbiota–gut–brain axis, especially the microbial tryptophan (Trp) biosynthesis and metabolism pathway (MiTBamp), may play a critical role in the pathogenesis of major depressive disorder (MDD). However, studies on the MiTBamp in MDD are lacking. The aim of the present study was to analyze the gut microbiota composition and the MiTBamp in MDD patients.
We performed shotgun metagenomic sequencing of stool samples from 26 MDD patients and 29 healthy controls (HCs). In addition to the microbiota community and the MiTBamp analyses, we also built a classification based on the Random Forests (RF) and Boruta algorithm to identify the gut microbiota as biomarkers for MDD.
The Bacteroidetes abundance was strongly reduced whereas that of Actinobacteria was significantly increased in the MDD patients compared with the abundance in the HCs. Most noteworthy, the MDD patients had increased levels of Bifidobacterium, which is commonly used as a probiotic. Four Kyoto Encyclopedia of Genes and Genomes (KEGG) orthologies (KOs) (K01817, K11358, K01626, K01667) abundances in the MiTBamp were significantly lower in the MDD group. Furthermore, we found a negative correlation between the K01626 abundance and the HAMD scores in the MDD group. Finally, RF classification at the genus level can achieve an area under the receiver operating characteristic curve of 0.890.
The present findings enabled a better understanding of the changes in gut microbiota and the related Trp pathway in MDD. Alterations of the gut microbiota may have the potential as biomarkers for distinguishing MDD patients form HCs.
The dipeptide dl-methionyl-dl-methionine (Met-Met) has extremely low water solubility and better absorption than other methionine sources (such as dl-methionine and l-methionine) available in the market. Therefore, six diets (D1, D2, D3, D4, D5 and D6) containing 0, 0·07, 0·15, 0·21, 0·28 and 0·38 % Met-Met were formulated to investigate the effects of Met-Met in juvenile Nile tilapia, Oreochromis niloticus (17 g initial body weight). The results indicated that percentage weight gain and specific growth rate of fish fed with D2 and D3 diets were higher than those fed with D1, D4–D6 diets. The levels of total essential amino acid in whole body of fish fed with D3 and D4 diets were significantly higher than those fed the D1 diet. Superoxide dismutase activity and malondialdehyde content have no significant difference in fish fed the diet with or without Met-Met supplementation. Majority of reads derived from the fish intestine belonged to members of Fusobacteria, followed by Bacteroidetes and Proteobacteria. Diversity of intestinal microbiota and total antioxidant capacity in fish fed with D3 diet was significantly higher than that of other groups. Based on the growth results, the authors conclude that the optimal level of Met is 0·61 % Met with the addition of 0·15 % Met-Met for grower-phase O. niloticus.
High inductive helical support provides a solution to controlling the alignment error of inner electrodes in magnetically insulated transmission lines (MITLs). Three-dimensional particle-in-cell simulations were performed to examine the current loss mechanism and the effects of structural parameters on electron flow in an MITL with a helical inductor. An empirical expression related to the ratio of electron current loss to anode current and the ratio of anode current to self-limited current was obtained. Electron current loss caused by helical inductor with different structures was displayed. The results indicate that the current loss in an MITL, near an inductive helical support, comprises both the inductor current and the electron current loss. The non-uniform structure and current of a helical inductor cause an abrupt change in the magnetic field near the helical support, which leads to anomalous behavior and current loss of electron flow. In addition, current loss in the inductive helical-supported MITL is negligible when the inductance of the support is sufficiently high. This work facilitates the estimation of electron current loss caused by the inductive helical support in MITLs.
Echinococcus granulosus sensu stricto (s.s.), Echinococcus multilocularis and Echinococcus canadensis are the common causes of human echinococcosis in China. An accurate species identification tool for human echinococcosis is needed as the treatments and prognosis are different among species. The present work demonstrates a method for the simultaneous detection of these three Echinococcus species based on multiplex polymerase chain reaction (mPCR). Specific primers of this mPCR were designed based on the mitochondrial genes and determined by extensive tests. The method can successfully detect either separated or mixed target species, and generate expected amplicons of distinct size for each species. Sensitivity of the method was tested by serially diluted DNA, showing a detection threshold as less as 0.32 pg for both E. granulosus s.s. and E. canadensis, and 1.6 pg for E. multilocularis. Specificity assessed against 18 other parasites was found to be 100% except weakly cross-react with E. shiquicus. The assay was additionally applied to 69 echinococcosis patients and 38 healthy persons, confirming the high reliability of the method. Thus, the mPCR described here has high application potential for clinical identification purposes, and can further provide a useful tool for evaluation of serology and imaging method.
Paediatric Mycoplasma pneumoniae pneumonia (MPP) is a major cause of community-acquired pneumonia in China. Data on epidemiology of paediatric MPP from China are little known. This study retrospectively collected data from June 2006 to June 2016 in Beijing Children's Hospital, Capital Medical University of North China and aims to explore the epidemiological features of paediatric MPP and severe MPP (SMPP) in North China during the past 10 years. A total of 27 498 paediatric patients with pneumonia were enrolled. Among them, 37.5% of paediatric patients had MPP. In this area, an epidemic took place every 2–3 years at the peak, and the positive rate of MPP increased during these peak years over time. The peak age of MPP was between the ages of 6 and 10 years, accounting for 75.2%, significantly more compared with other age groups (χ2 = 1384.1, P < 0.0001). The epidemics peaked in September, October and November (χ2 = 904.9, P < 0.0001). Additionally, 13.0% of MPP paediatric patients were SMPP, but over time, the rate of SMPP increased, reaching 42.6% in 2016. The mean age of paediatric patients with SMPP (6.7 ± 3.0 years old) was younger than that of patients with non-SMPP (7.4 ± 3.2 years old) (t = 3.60, P = 0.0001). The prevalence of MPP and SMPP is common in China, especially in children from 6 to 10 years old. Paediatric patients with SMPP tend to be younger than those with non-SMPP. MPP outbreaks occur every 2–3 years in North China. September, October and November are the peak months, unlike in South China. Understanding the epidemiological characteristics of paediatric MPP can contribute to timely treatment and diagnosis, and may improve the prognosis of children with SMPP.