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Emergent resistance to antibiotics among Streptococcus pneumoniae isolates is a severe problem worldwide. Antibiotic resistance profiles for S pneumoniae isolates identified from pediatric patients in mainland China remains to be established. The clinical features, antimicrobial resistance, and multidrug resistance patterns of S pneumoniae were retrospectively analyzed at 10 children's hospitals in mainland China in 2016. Among the collected 6132 S pneumoniae isolates, pneumococcal diseases mainly occurred in children younger than 5 years old (85.1%). The resistance rate of S pneumoniae to clindamycin, erythromycin, tetracycline, and trimethoprim/sulfamethoxazole was 95.8%, 95.2%, 93.6%, and 66.7%, respectively. The resistance rates of S pneumoniae to penicillin were 86.9% and 1.4% in non-meningitis and meningitis isolates, while the proportions of ceftriaxone resistance were 8.2% and 18.1%, respectively. Pneumococcal conjugate vaccine was administered to only 4.1% of patients. Penicillin and ceftriaxone resistance, underling diseases, antibiotic resistant risk factors, and poor prognosis appeared more frequently in invasive pneumococcal diseases. The incidence of multidrug resistance (MDR) was 46.1% in patients with invasive pneumococcal disease which was more than in patients with non-invasive pneumococcal disease (18.3%). Patients with invasive pneumococcal disease usually have several MDR coexistence. S pneumoniae isolates showed high resistance to common antibiotics in mainland China. Penicillin and ceftriaxone resistance rate of invasive streptococcal pneumonia patients were significantly higher than that of non-invasive S pneumoniae patients. Alarmingly, 46.1% of invasive clinical isolates were multidrug resistant, so it is important to continued monitor the resistance of S pneumoniae when protein conjugate vaccine (PCV13) is coming in mainland China.

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Background Treatment options for Stenotrophomonas maltophilia (S. maltophilia) infections were limited. We assessed the efficacy of ceftazidime (CAZ), ceftazidime-avibactam (CAZ-AVI), aztreonam (ATM), and aztreonam-avibactam (ATM-AVI) against a selection of 76 S. maltophilia out of the 1179 strains isolated from the First Affiliated Hospital of Chongqing Medical University during 2011–2018. Methods We investigated the antimicrobial resistance profiles of the 1179 S. maltophilia clinical isolates from the first affiliated hospital of Chongqing Medical University during 2011–2018, a collection of 76 isolates were selected for further study of microbiological characterization. Minimum inhibitory concentrations (MICs) of CAZ, CAZ-AVI, ATM and ATM-AVI were determined via the broth microdilution method. We deemed that CAZ-AVI or ATM-AVI was more active in vitro than CAZ or ATM alone when CAZ-AVI or ATM-AVI led to a category change from “Resistant” or “Intermediate” with CAZ or ATM alone to “Susceptible” with CAZ-AVI or ATM-AVI, or if the MIC of CAZ-AVI or ATM-AVI was at least 4-fold lower than the MIC of CAZ or ATM alone. Results For the 76 clinical isolates included in the study, MICs of CAZ, ATM, CAZ-AVI and ATM-AVI ranged from 0.03–64, 1–1024, 0.016–64, and 0.06–64 μg/mL, respectively. In combined therapy, AVI was active at restoring the activity of 48.48% (16/33) and 89.71% (61/68) of S. maltophilia to CAZ and ATM, respectively. Furthermore, CAZ-AVI showed better results in terms of the proportion of susceptible isolates (77.63% vs. 56.58%, P < 0.001), and MIC50 (2 μg/mL vs. 8 μg/mL, P < 0.05) when compared to CAZ. According to our definition, CAZ-AVI was more active in vitro than CAZ alone for 81.58% (62/76) of the isolates. Similarly, ATM-AVI also showed better results in terms of the proportion of susceptible isolates (90.79% vs.10.53%, P < 0.001) and MIC50 (2 μg/mL vs. 64 μg/mL, P < 0.001) when compared to ATM. According to our definition, ATM-AVI was also more active in vitro than ATM alone for 94.74% (72/76) of the isolates. Conclusions AVI potentiated the activity of both CAZ and ATM against S. maltophilia clinical isolates in vitro. We demonstrated that CAZ-AVI and ATM-AVI are both useful therapeutic options to treat infections caused by S. maltophilia.

With the development of the Mobile Internet, more and more users publish multi-modal posts on social media platforms. Fake news detection has become an increasingly challenging task. Although there are many works using deep schemes to extract and combine textual and visual representation in the post, most existing methods do not sufficiently utilize the complementary multi-modal information containing semantic concepts and entities to complement and enhance each modality. Moreover, these methods do not model and incorporate the rich multi-level semantics of text information to improve fake news detection tasks. In this paper, we propose a novel end-to-end Multi-level Multi-modal Cross-attention Network (MMCN) which exploits the multi-level semantics of text and jointly integrates the relationships of duplicate and different modalities (textual and visual modality) of social multimedia posts in a unified framework. Firstly, pre-trained BERT and ResNet models are employed to generate high-quality representations for text words and image regions, respectively. A multi-modal cross-attention network is then designed to fuse the feature embeddings of the text words and image regions by simultaneously considering data relationships in duplicate and different modalities. Specially, due to different layers of the transformer architecture have different feature representations, we employ a multi-level encoding network to capture the rich multi-level semantics to enhance the presentations of posts. Extensive experiments on the two public datasets (WEIBO and PHEME) demonstrate that compared with the state-of-the-art models, the proposed MMCN has an advantageous performance.