Background Complicated intra-abdominal infections (cIAIs) result in significant morbidity, mortality, and cost. Carbapenem-resistant sepsis has increased dramatically in the last decade, resulting in infections that are difficult to treat and associated with high mortality rates. To prevent further antibacterial resistance, it is necessary to use carbapenem selectively. The objective of this study was to compare the effectiveness and safety of carbapenems vs alternative β-lactam monotherapy or combination therapy for the treatment of cIAIs. Methods The PubMed, Embase, Medline (via Ovid SP), and Cochrane library databases were systematically searched. We included randomized controlled trials (RCTs) comparing carbapenems vs alternative β-lactam monotherapy or combination therapy for the treatment of cIAIs. Results Twenty-two studies involving 7720 participants were included in the analysis. There were no differences in clinical treatment success (odds ratio [OR], 0.86; 95% confidence interval [CI], 0.71–1.05; I2 = 35%), microbiological treatment success (OR, 0.88; 95% CI, 0.71–1.09; I2 = 25%), adverse events (OR, 0.98; 95% CI, 0.87–1.09; I2 = 17%), or mortality (OR, 0.96; 95% CI, 0.68–1.35; I2 = 7%). Patients treated with imipenem were more likely to experience clinical or microbiological failure than those treated with alternative β-lactam monotherapy or combination therapy. Conclusions No differences in clinical outcomes were observed between carbapenems and noncarbapenem β-lactams in cIAIs. Patients treated with imipenem were more likely to experience clinical or microbiological failure than those treated with alternative β-lactam monotherapy or combination therapy.

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With the rapid development of autonomous driving, real-time object detection on 360° images becomes more and more important. In this paper, we propose a panoramic virtual dataset for training object detectors on 360° images. The most important feature of our dataset includes (1) an auto-generated city scene is created for rendering 360° dataset. (2) annotation work for this dataset is automatic. In addition, we propose a modified YOLOv3 model called Pano-YOLO for real-time panoramic object detection. Compared with YOLOv3, mAP of Pano-YOLO drops 0.39%. While speed is 32.47% faster. Experiments are performed to show that models trained on our virtual dataset can be applied in real world. And Pano-YOLO is capable of real-time object detection task on highresolution 360° panoramic images and videos.