Algorithm of automatic identification of diabetic retinopathy foci based on ultra-widefield scanning laser ophthalmoscopy.

AIM: To propose an algorithm for automatic detection of diabetic retinopathy (DR) lesions based on ultra-widefield scanning laser ophthalmoscopy (SLO). METHODS: The algorithm utilized the FasterRCNN (Faster Regions with CNN features)+ResNet50 (Residua Network 50)+FPN (Feature Pyramid Networks) method for detecting hemorrhagic spots, cotton wool spots, exudates, and microaneurysms in DR ultra-widefield SLO. Subimage segmentation combined with a deeper residual network FasterRCNN+ResNet50 was employed for feature extraction to enhance intelligent learning rate. Feature fusion was carried out by the feature pyramid network FPN, which significantly improved lesion detection rates in SLO fundus images. RESULTS: By analyzing 1076 ultra-widefield SLO images provided by our hospital, with a resolution of 2600×2048 dpi, the accuracy rates for hemorrhagic spots, cotton wool spots, exudates, and microaneurysms were found to be 87.23%, 83.57%, 86.75%, and 54.94%, respectively. CONCLUSION: The proposed algorithm demonstrates intelligent detection of DR lesions in ultra-widefield SLO, providing significant advantages over traditional fundus color imaging intelligent diagnosis algorithms.

Bezafibrate alleviates diabetes-induced spermatogenesis dysfunction by inhibiting inflammation and oxidative stress.

The metabolic disorders caused by diabetes can lead to various complications, including male spermatogenesis dysfunction. Exploring effective therapeutics that attenuate diabetes mellitus (DM)-induced male subfertility is of great importance. Pharmaceuticals targeting PPARα activation such as bezafibrate have been regarded as an important strategy for patients with diabetes. In this study, we use streptozocin (STZ) injection to establish a type 1 DM mice model and use bezafibrate to treat DM mice and evaluate the effects of bezafibrate on the spermatogenic function of the DM male mice. Bezafibrate treatment exhibited protective effects on DM-induced spermatogenesis deficiency, as reflected by increased testis weight, improved histological morphology of testis, elevated sperm parameters, increased serum testosterone concentration as well as increased mRNA levels of steroidogenesis enzymes. Meanwhile, testicular cell apoptosis, inflammation accumulation and oxidative stress status were also shown to be alleviated by bezafibrate compared with the DM group. In vivo and in vitro studies, PPARα specific inhibitor and PPARα knockout mice were further used to investigate the role of PPARα in the protective effects of bezafibrate on DM-induced spermatogenesis dysfunction. Our results indicated that the protection of bezafibrate on DM-induced spermatogenesis deficiency was abrogated by PPARα inhibition or deletion. Our study suggested that bezafibrate administration could ameliorate DM-induced spermatogenesis dysfunction and may represent a novel practical strategy for male infertility.

penA profile of Neisseria gonorrhoeae in Guangdong, China: Novel penA alleles are related to decreased susceptibility to ceftriaxone or cefixime.

BACKGROUND: Resistance to extended-spectrum cephalosporins (ESCs) has become a public health concern with the spread of Neisseria gonorrhoeae and increasing antimicrobial resistance. Mutation of penA, encoding penicillin-binding protein 2, represents a mechanism of ESC resistance. This study sought to assess penA alleles and mutations associated with decreased susceptibility (DS) to ESCs in N. gonorrhoeae. MATERIALS AND METHODS: In 2021, 347 gonococci were collected in Guangdong, China. Minimum inhibitory concentations (MICs) of ceftriaxone and cefixime were determined, and whole-genome sequencing and phylogenetic analysis were performed. Multi-locus sequence typing (MLST) and conventional resistance determinants such as penA, mtrR, PonA and PorB were analysed. penA was genotyped and sequence-aligned using PubMLST. RESULTS: Genome-wide phylogenetic analysis revealed that the prevalence of DS to ESCs was highest in Clade 11.1 (100.0%), Clade 2 (66.7%) and Clade 0 (55.7%), and the leading cause was strains with penA-60.001 or new penA alleles in clades. The penA phylogenetic tree is divided into two branches: non-mosaic penA and mosaic penA. The latter contained penA-60.001, penA-10 and penA-34. penA profile analysis indicated that A311V and T483S are closely related to DS to ESCs in mosaic penA. The new alleles NEIS1753_2840 and NEIS1753_2837 are closely related to penA-60.001, with DS to ceftriaxone and cefixime of 100%. NEIS1753_2660, a derivative of penA-10 (A486V), has increased DS to ceftriaxone. NEIS1753_2846, a derivative of penA-34.007 (G546S), has increased DS to cefixime. CONCLUSION: This study identified critical penA alleles related to elevated MICs, and trends of gonococcus-evolved mutated penA associated with DS to ESCs in Guangdong.

Morphological and functional characteristics of the meibomian gland in pediatric patients with epiblepharon.

BACKGROUND: To observe morphologic and functional changes in meibomian glands in pediatric patients with and without lower eyelid epiblepharon. METHODS: In this prospective observation study, 55 eyes of 55 patients( 24 males, 31 females; mean age ± SD,9.82 ± 2.59 years; range 6-14 years) and 60 eyes of 60 controls ( 32 males, 28 females; mean age ± SD,10.57 ± 2.75 years; range 6-14 years) were included. The following tests were performed: eyelid margin abnormality by slit-lamp examination, measurement of noninvasive keratographic break-up time (NIKBUT), grading of absence of meibomian gland (meibography score) assessed with noncontact meibography, morphologic changes of meibomian glands (thinning, dilatation and distortion), tear production by the Schirmer 1 test, and grading of meibum quality and meibomian gland expressibility. RESULTS: The morphologic changes in meibomian glands were more common in the epiblepharon group (56.36%) than in the control group (28.33%) (p = 0.002). The meibum quality was worse in the epiblepharon group than in the control group (p = 0.009), and the NIKBUT was significantly shorter in the epiblepharon group than in the control group (p = 0.012). There was no significant difference in the Schirmer 1 test, meibomian gland expressibility, eyelid margin abnormality score or total meibography score between the two groups. Morphologic changes in the meibomian glands in the upper eyelids (38.18%) were more common than those in the lower eyelids (20%) (p = 0.036) in the epiblepharon group, and the meibography score was higher in the upper eyelids than in the lower eyelids (p = 0.001). CONCLUSION: There are morphological and functional changes in meibomian glands in pediatric patients with lower eyelid epiblepharon. Although the inverted eyelashes were located in the lower eyelid, morphological changes in the meibomian glands were more common in the upper eyelid.

Epileptic focus localization using transfer learning on multi-modal EEG.

The standard treatments for epilepsy are drug therapy and surgical resection. However, around 1/3 of patients with intractable epilepsy are drug-resistant, requiring surgical resection of the epileptic focus. To address the issue of drug-resistant epileptic focus localization, we have proposed a transfer learning method on multi-modal EEG (iEEG and sEEG). A 10-fold cross-validation approach was applied to validate the performance of the pre-trained model on the Bern-Barcelona and Bonn datasets, achieving accuracy rates of 94.50 and 97.50%, respectively. The experimental results have demonstrated that the pre-trained model outperforms the competitive state-of-the-art baselines in terms of accuracy, sensitivity, and negative predictive value. Furthermore, we fine-tuned our pre-trained model using the epilepsy dataset from Chongqing Medical University and tested it using the leave-one-out cross-validation method, obtaining an impressive average accuracy of 90.15%. This method shows significant feature differences between epileptic and non-epileptic channels. By extracting data features using neural networks, accurate classification of epileptic and non-epileptic channels can be achieved. Therefore, the superior performance of the model has demonstrated that the proposed method is highly effective for localizing epileptic focus and can aid physicians in clinical localization diagnosis.

An isothermal CRISPR-based diagnostic assay for Neisseria gonorrhoeae and Chlamydia trachomatis detection.

IMPORTANCE: A method for Neisseria gonorrhoeae (NG)/Chlamydia trachomatis (CT) detection is developed using multiplex-recombinase polymerase amplification and Cas12a/Cas13a. This method can detect NG and CT simultaneously with high sensitivity and specificity. This method has great potential to be further developed into larger-scale screening and point-of-care testing (POCT).

[Non-local attention and multi-task learning based lung segmentation in chest X-ray].

Precise segmentation of lung field is a crucial step in chest radiographic computer-aided diagnosis system. With the development of deep learning, fully convolutional network based models for lung field segmentation have achieved great effect but are poor at accurate identification of the boundary and preserving lung field consistency. To solve this problem, this paper proposed a lung segmentation algorithm based on non-local attention and multi-task learning. Firstly, an encoder-decoder convolutional network based on residual connection was used to extract multi-scale context and predict the boundary of lung. Secondly, a non-local attention mechanism to capture the long-range dependencies between pixels in the boundary regions and global context was proposed to enrich feature of inconsistent region. Thirdly, a multi-task learning to predict lung field based on the enriched feature was conducted. Finally, experiments to evaluate this algorithm were performed on JSRT and Montgomery dataset. The maximum improvement of Dice coefficient and accuracy were 1.99% and 2.27%, respectively, comparing with other representative algorithms. Results show that by enhancing the attention of boundary, this algorithm can improve the accuracy and reduce false segmentation.

Feature separation and adversarial training for the patient-independent detection of epileptic seizures.

An epileptic seizure is the external manifestation of abnormal neuronal discharges, which seriously affecting physical health. The pathogenesis of epilepsy is complex, and the types of epileptic seizures are diverse, resulting in significant variation in epileptic seizure data between subjects. If we feed epilepsy data from multiple patients directly into the model for training, it will lead to underfitting of the model. To overcome this problem, we propose a robust epileptic seizure detection model that effectively learns from multiple patients while eliminating the negative impact of the data distribution shift between patients. The model adopts a multi-level temporal-spectral feature extraction network to achieve feature extraction, a feature separation network to separate features into category-related and patient-related components, and an invariant feature extraction network to extract essential feature information related to categories. The proposed model is evaluated on the TUH dataset using leave-one-out cross-validation and achieves an average accuracy of 85.7%. The experimental results show that the proposed model is superior to the related literature and provides a valuable reference for the clinical application of epilepsy detection.

Dissemination of Neisseria gonorrhoeae with decreased susceptibility to extended-spectrum cephalosporins in Southern China, 2021: a genome-wide surveillance from 20 cities.

BACKGROUND: Antimicrobial resistance (AMR) of untreatable gonococcal infection is an emerging threat, especially in Guangdong, a prosperous province in Southern China. METHODS: N.gonorrhoeae was isolated from 20 cities in Guangdong and determined antimicrobial susceptibility. Through whole-genome sequencing (WGS), multilocus sequence typing (MLST), N.gonorrhoeae multiantigen sequence typing (NG-MAST), and N.gonorrhoeae sequence typing for antimicrobial resistance (NG-STAR) were obtained based on the PubMLST database ( https://pubmlst.org/ ). Phylogenetic analysis was used for dissemination and tracking analysis. RESULTS: Antimicrobial susceptibility was performed on 347 isolates, and 50 isolates were identified as decreased susceptibility (DS) to cephalosporins. Of which 16.0% (8/50) were ceftriaxone DS, 38.0% (19/50) were cefixime DS, and 46.0% (23/50) were both ceftriaxone and cefixime DS. In all, the dual-resistant rate of the cephalosporin-DS isolates was 96.0% for penicillin and 98.0% for tetracycline-resistant, and 10.0% (5/50) were resistant to azithromycin. All cephalosporin-DS isolates were resistant to ciprofloxacin but sensitive to spectinomycin. The predominant MLSTs were ST7363 (16%, 8/50), ST1903 (14%, 7/50), ST1901 (12%, 6/50), and ST7365 (10%, 5/50). Besides some isolates that failed genotyping (NA), NG-STAR ST1143 (n = 6) and NG-MAST ST17748 (n = 4) were the most prevalent. Twelve isolates with mosaic penA-60.001 allele retained the most elevated cephalosporin MIC (Minimum Inhibitory Concentration). Phylogenetic analysis revealed that epidemic penA-60.001 clones, either domestic or foreign, had spread to nine cities in Guangdong, and 9/12 clones were from the Pearl River Delta region. CONCLUSIONS: N. gonorrhoeae with cephalosporins-DS was extensively disseminated in Guangdong, Southern China, requiring strict surveillance.

Patient-specific approach using data fusion and adversarial training for epileptic seizure prediction.

Epilepsy is the second common neurological disorder after headache, accurate and reliable prediction of seizures is of great clinical value. Most epileptic seizure prediction methods consider only the EEG signal or extract and classify the features of EEG and ECG signals separately, the improvement of prediction performance from multimodal data is not fully considered. In addition, epilepsy data are time-varying, with differences between each episode in a patient, making it difficult for traditional curve-fitting models to achieve high accuracy and reliability. In order to improve the accuracy and reliability of the prediction system, we propose a novel personalized approach based on data fusion and domain adversarial training to predict epileptic seizures using leave-one-out cross-validation, which achieves an average accuracy, sensitivity and specificity of 99.70, 99.76, and 99.61%, respectively, with an average error alarm rate (FAR) of 0.001. Finally, the advantage of this approach is demonstrated by comparison with recent relevant literature. This method will be incorporated into clinical practice to provide personalized reference information for epileptic seizure prediction.

Emergence and Genomic Characterization of Neisseria gonorrhoeae Isolates with High Levels of Ceftriaxone and Azithromycin Resistance in Guangdong, China, from 2016 to 2019.

Currently, antibiotic resistance (especially ceftriaxone and azithromycin dual resistance) in Neisseria gonorrhoeae is the main obstacle affecting the efficacy of treatment. As analysis of drug sensitivity, molecular features, and dissemination of dual-resistant strains is important for gonococcal prevention and control, MIC, genotyping, and genome analysis were conducted to reveal the molecular characteristics and phylogeny of N. gonorrhoeae isolates. During 2016 to 2019, 5 out of 4,113 strains were defined as dual-resistant clones, with ceftriaxone MICs of 0.25 to ≥1 mg/L and azithromycin MICs of 2 to ≥2,048 mg/L. In particular, two strains with a ceftriaxone MIC above 0.5 mg/L were characterized as penA-60.001 FC428-related clones, and two isolates with a high-level azithromycin MIC above 1,024 mg/L featuring a 23S rRNA mutation were identified. Furthermore, phylogenetic analysis confirmed that the dual-resistant strains were closer to the evolutionary origin of F89 in France, global FC428-related clones, and high-level dual-resistant clones in Australia and the United Kingdom. Dual-resistant strains, including FC428-related clones and high-level azithromycin-resistant clones, have circulated in Guangdong, China. The ability of laboratories to perform real-time drug susceptibility and genetic analyses should be strengthened to monitor the spread of threatening strains. IMPORTANCE Here, we report five sporadic dual-resistant isolates, including FC428-related ceftriaxone-resistant clones with MICs of ≥0.5 mg/L and high-level azithromycin resistance with MICs of ≥1,024 mg/L. This study highlights that dual-resistant clones with the same evolutionary origin as FC428, A2735, and F89 have circulated in Guangdong, China, which suggests that the capacity for antibiotic resistance testing and genome analysis should be strengthened in daily epidemiological surveillance.

Application of allogeneic sclera combined with tarso-conjunctival flap in total excision of divided eyelid nevus.

OBJECTIVE: To explore the clinical effect of allogeneic sclera transplantation combined with tarso-conjunctival flap in total excision of divided eyelid nevus. METHODS: Eleven patients (three male and eight female patients) who experienced divided nevus of the eyelids between January 2014 and April 2020 were recruited to this retrospective study. All lesions were thick, darkly pigmented, presented with a wart-like appearance, and invaded the eyelid margin and tarsal conjunctiva. The surgical method involved a full-thickness lesion excision; then, the posterior defect was reconstructed by sliding the residual tarso-conjunctival flap forward and allogeneic sclera transplantation, and the anterior defect was reconstructed with sliding flaps, rotating flaps, and free skin grafts. RESULTS: Neither malignant transformations nor recurrences were observed after a follow-up of more than one year. The eyelid shape was normal, the rim of the eyelid was smooth, there was no dissolution or rejection of the allogeneic sclera, and the eyelid had good mobility. All the flaps used were viable, soft, and thin. The most frequent complication was the loss of eyelashes in the reconstructed area. CONCLUSION: For divided nevus of the eyelids invaded the eyelid margin and tarsal conjunctiva, total excision is a better decision, regardless of tumor recurrence or aesthetic considerations. The posterior defect reconstruction through sliding residual tarso-conjunctival flaps combined with allogeneic sclera transplantation is simple and effective.

Spectroscopic profiling-based geographic herb identification by neural network with random weights.

Daodi medicinal material plays an important role in traditional Chinese medicine (TCM). This study researches and validates the NNRW (neural network with random weights) model on spectroscopic profiling data for geographical origin identification. NNRW is a special neural network model that does not require an iterative training process. It has been proved effective in various resource-limited data-driven applications. However, whether NNRW works for spectroscopic profiling data remains to be explored. In this study, the Raman and UV (ultraviolet) profiling data of 160 radix astragali samples from four geographic regions are trained and evaluated by four classification models, i.e., NNRW, MLP (multi-layer perceptron), SVM (support vector machine), and DTC (decision tree classifier). Their validation accuracies are 96.3%, 98.0%, 98.4%, and 92.8% respectively. The training/fitting times are 0.372 ms (milli-seconds), 57.9 ms, 2.033 ms, and 3.351 ms, respectively. This study shows that NNRW has a significant training time cut while keeping a high prediction accuracy, and it is a promising solution to resource-limited edge computing applications.

[Epilepsy detection and analysis method for specific patient based on data augmentation and deep learning].

In recent years, epileptic seizure detection based on electroencephalogram (EEG) has attracted the widespread attention of the academic. However, it is difficult to collect data from epileptic seizure, and it is easy to cause over fitting phenomenon under the condition of few training data. In order to solve this problem, this paper took the CHB-MIT epilepsy EEG dataset from Boston Children's Hospital as the research object, and applied wavelet transform for data augmentation by setting different wavelet transform scale factors. In addition, by combining deep learning, ensemble learning, transfer learning and other methods, an epilepsy detection method with high accuracy for specific epilepsy patients was proposed under the condition of insufficient learning samples. In test, the wavelet transform scale factors 2, 4 and 8 were set for experimental comparison and verification. When the wavelet scale factor was 8, the average accuracy, average sensitivity and average specificity was 95.47%, 93.89% and 96.48%, respectively. Through comparative experiments with recent relevant literatures, the advantages of the proposed method were verified. Our results might provide reference for the clinical application of epilepsy detection.

Learning to predict in-hospital mortality risk in the intensive care unit with attention-based temporal convolution network.

BACKGROUND: Dynamic prediction of patient mortality risk in the ICU with time series data is limited due to high dimensionality, uncertainty in sampling intervals, and other issues. A new deep learning method, temporal convolution network (TCN), makes it possible to deal with complex clinical time series data in ICU. We aimed to develop and validate it to predict mortality risk using time series data from MIMIC III dataset. METHODS: A total of 21,139 records of ICU stays were analysed and 17 physiological variables from the MIMIC III dataset were used to predict mortality risk. Then we compared the model performance of the attention-based TCN with that of traditional artificial intelligence (AI) methods. RESULTS: The area under receiver operating characteristic (AUCROC) and area under precision-recall curve (AUC-PR) of attention-based TCN for predicting the mortality risk 48 h after ICU admission were 0.837 (0.824 -0.850) and 0.454, respectively. The sensitivity and specificity of attention-based TCN were 67.1% and 82.6%, respectively, compared to the traditional AI method, which had a low sensitivity (< 50%). CONCLUSIONS: The attention-based TCN model achieved better performance in the prediction of mortality risk with time series data than traditional AI methods and conventional score-based models. The attention-based TCN mortality risk model has the potential for helping decision-making for critical patients. TRIAL REGISTRATION: Data used for the prediction of mortality risk were extracted from the freely accessible MIMIC III dataset. The project was approved by the Institutional Review Boards of Beth Israel Deaconess Medical Center (Boston, MA) and the Massachusetts Institute of Technology (Cambridge, MA). Requirement for individual patient consent was waived because the project did not impact clinical care and all protected health information was deidentified. The data were accessed via a data use agreement between PhysioNet, a National Institutes of Health-supported data repository (https://www.physionet.org/), and one of us (Yu-wen Chen, Certification Number: 28341490). All methods were carried out in accordance with the institutional guidelines and regulations.

Markedly Increasing Antibiotic Resistance and Dual Treatment of Neisseria gonorrhoeae Isolates in Guangdong, China, from 2013 to 2020.

The emergence of multidrug resistance in Neisseria gonorrhoeae is concerning, especially the cooccurrence of azithromycin resistance and decreased susceptibility to extended-spectrum cephalosporin. This study aimed to confirm the antibiotic resistance trends and provide a solution for N. gonorrhoeae treatment in Guangdong, China. A total of 5,808 strains were collected for assessment of antibiotic MICs. High resistance to penicillin (53.80 to 82%), tetracycline (88.30 to 100%), ciprofloxacin (96 to 99.8%), cefixime (6.81 to 46%), and azithromycin (8.60 to 20.03%) was observed. Remarkably, spectinomycin and ceftriaxone seemed to be the effective choices, with resistance rates of 0 to 7.63% and 2.00 to 16.18%, respectively. Moreover, the rates of azithromycin resistance combined with decreased susceptibility to ceftriaxone and cefixime reached 9.28% and 8.64%, respectively. Furthermore, genotyping identified NG-STAR-ST501, NG-MAST-ST2268, and MLST-ST7363 as the sequence types among representative multidrug-resistant isolates. Evolutionary analysis showed that FC428-related clones have spread to Guangdong, China, which might be a cause of the rapid increase in extended-spectrum cephalosporin resistance currently. Among these strains, the prevalence of N. gonorrhoeae was extremely high, and single-dose ceftriaxone treatment might be a challenge in the future. To partially relieve the treatment pressure, a susceptibility test for susceptibility to azithromycin plus extended-spectrum cephalosporin dual therapy was performed. The results showed that all the representative isolates could be effectively killed with the coadministration of less than 1 mg/liter azithromycin and 0.125 mg/liter extended-spectrum cephalosporin, with a synergistic effect according to a fractional inhibitory concentration (FIC) of <0.5. In conclusion, dual therapy might be a powerful measure to treat refractory N. gonorrhoeae in the context of increasing antibiotic resistance in Guangdong, China.

Query Optimization for Distributed Spatio-Temporal Sensing Data Processing.

The unprecedented development of Internet of Things (IoT) technology produces humongous amounts of spatio-temporal sensing data with various geometry types. However, processing such datasets is often challenging due to high-dimensional sensor data geometry characteristics, complex anomalistic spatial regions, unique query patterns, and so on. Timely and efficient spatio-temporal querying significantly improves the accuracy and intelligence of processing sensing data. Most existing query algorithms show their lack of supporting spatio-temporal queries and irregular spatial areas. In this paper, we propose two spatio-temporal query optimization algorithms based on SpatialHadoop to improve the efficiency of query spatio-temporal sensing data: (1) spatio-temporal polygon range query (STPRQ), which aims to find all records from a polygonal location in a time interval; (2) spatio-temporal k nearest neighbors query (STkNNQ), which directly searches the query point's k closest neighbors. To optimize the STkNNQ algorithm, we further propose an adaptive iterative range optimization algorithm (AIRO), which can optimize the iterative range of the algorithm according to the query time range and avoid querying irrelevant data partitions. Finally, extensive experiments based on trajectory datasets demonstrate that our proposed query algorithms can significantly improve query performance over baseline algorithms and shorten response time by 81% and 35.6%, respectively.

Interpretable instance disease prediction based on causal feature selection and effect analysis.

BACKGROUND: In the big wave of artificial intelligence sweeping the world, machine learning has made great achievements in healthcare in the past few years, however, these methods are only based on correlation, not causation. The particularities of the healthcare determines that the research method must comply with the causality norm, otherwise the wrong intervention measures may bring the patients a lifetime of misfortune. METHODS: We propose a two-stage prediction method (instance feature selection prediction and causal effect analysis) for instance disease prediction. Feature selection is based on the counterfactual and uses the reinforcement learning framework to design an interpretable qualitative instance feature selection prediction. The model is composed of three neural networks (counterfactual prediction network, fact prediction network and counterfactual feature selection network), and the actor-critical method is used to train the network. Then we take the counterfactual prediction network as a structured causal model and improve the neural network attribution algorithm based on gradient integration to quantitatively calculate the causal effect of selection features on the output results. RESULTS: The results of our experiments on synthetic data, open source data and real medical data show that our proposed method can provide qualitative and quantitative causal explanations for the model while giving prediction results. CONCLUSIONS: The experimental results demonstrate that causality can further explore more essential relationships between variables and the prediction method based on causal feature selection and effect analysis can build a more reliable disease prediction model.

Dissemination and genome analysis of high-level ceftriaxone-resistant penA 60.001 Neisseria gonorrhoeae strains from the Guangdong Gonococcal antibiotics susceptibility Programme (GD-GASP), 2016-2019.

Background: After Neisseria gonorrhoeae FC428 was first found in Japan, ceftriaxone-resistant strains disseminated globally, and the gonococcal resistance rate increased remarkably. Epidemiological investigations are greatly significant for the analysis of antimicrobial resistance (AMR) trends, molecular features and evolution. Objectives: To clarify the AMR trend from 2016-2019 and reveal the molecular characteristics and evolution of ceftriaxone-resistant penA 60.001 isolates. Methods: The minimum inhibitory concentrations (MICs) of antibiotics against 4113 isolates were detected by the agar dilution method. N. gonorrhoeae multiantigen sequence typing (NG-MAST), multilocus sequence typing (MLST) and N.gonorrhoeae sequence typing for antimicrobial resistance (NG-STAR) were used to identify the sequence types. Genome analysis was conducted to analyze resistance genes, virulence factors, and evolutionary sources. Results: Isolates with decreased ceftriaxone susceptibility have increased from 2.05% (2016) to 16.18% (2019). Six ceftriaxone-resistant isolates possessing penA 60.001 appeared in Guangdong Province, and were resistant to ceftriaxone, penicillin, tetracycline, ciprofloxacin and cefixime, but susceptible to azithromycin and spectinomycin. Single-nucleotide polymorphisms (SNPs) in the porB gene were the major cause of different NG-MAST types. ST1903 was the main NG-STAR genotype and only strain-ZH545 was ST7365, with molecular features consistent with the MICs. Furthermore, different MLSTs suggested diverse evolutionary sources. Genome analysis revealed a set of virulence factors along with the resistance genes "penA" and "blaTEM-1B". Half of penA 60.001 strains were fully mixed with global FC428-related strains. Conclusions: Global FC428-related clones have disseminated across Guangdong, possibly causing decreased ceftriaxone susceptibility. Enhanced gonococcal surveillance will help elucidate the trajectory of transmission and curb further dissemination.

Downregulation of EZH2 in Trophoblasts Induces Decidual M1 Macrophage Polarization: a Potential Cause of Recurrent Spontaneous Abortion.

Macrophages are known to be pivotal for ensuring the establishment of the immune tolerance microenvironment at the maternal-fetal interface. In particular, trophoblasts stay in close contact with decidual macrophages (DMs), which have been reported to play an active role in the modulation of the polarization of DMs. Thus, any dysfunction of trophoblasts might be associated with certain pregnancy-related complications, such as recurrent spontaneous abortion (RSA). Enhancer of zeste homolog 2 (EZH2) is an important epigenetic regulatory gene that has been previously shown to be related to immune regulation. The present study assessed the expression of EZH2 in villi tissue obtained from healthy controls and RSA patients. Trophoblasts conditioned medium was collected to incubate macrophages differentiated from the THP-1 cell line. The expression and function of EZH2 in trophoblasts were knocked down either by the use of siRNA or GSK126 as an inhibitor. Our results show a significant decrease in the expression of EZH2 in villi tissue from RSA patients as compared to healthy controls. Further, the inhibition of expression or function of EZH2 in trophoblasts promoted M1 macrophage polarization, which might be involved in the pathogenesis of RSA. Moreover, the suppression of EZH2 was found to affect the secretion of immune and inflammatory cytokines in trophoblasts. Altogether, these results indicated the importance of EZH2 in the regulation of immune functions of trophoblasts and thus highlighted its potential to be explored as a therapeutic target to prevent and treat pregnancy loss.