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Viral pneumonia is frequently complicated by bacterial co- or superinfection (c/s) with adverse effects on patients’ outcomes. However, the incidence of c/s and its impact on the outcomes of patients might be dependent on the type of viral pneumonia. We performed a retrospective observational study in patients with confirmed COVID-19 pneumonia (CP) or influenza pneumonia (IP) from 01/2009 to 04/2022, investigating the incidence of c/s using a competing risk model and its impact on mortality in these patients in a tertiary referral center using multivariate logistic regressions. Co-infection was defined as pulmonary pathogenic bacteria confirmed in tracheal aspirate or bronchoalveolar lavage within 48 h after hospitalization. Superinfection was defined as pulmonary pathogenic bacteria detected in tracheal aspirate or bronchoalveolar lavage 48 h after hospitalization. We examined 114 patients with CP and 76 patients with IP. Pulmonary bacterial co-infection was detected in 15 (13.2%), and superinfection was detected in 50 (43.9%) of CP patients. A total of 5 (6.6%) co-infections (p = 0.2269) and 28 (36.8%) superinfections (p = 0.3687) were detected in IP patients. The overall incidence of c/s did not differ between CP and IP patients, and c/s was not an independent predictor for mortality in a study cohort with a high disease severity. We found a significantly higher probability of superinfection for patients with CP compared to patients with IP (p = 0.0017).

We aimed to automate Gram-stain analysis to speed up the detection of bacterial strains in patients suffering from infections. We performed comparative analyses of visual transformers (VT) using various configurations including model size (small vs. large), training epochs (1 vs. 100), and quantization schemes (tensor- or channel-wise) using float32 or int8 on publicly available (DIBaS, n = 660) and locally compiled (n = 8500) datasets. Six VT models (BEiT, DeiT, MobileViT, PoolFormer, Swin and ViT) were evaluated and compared to two convolutional neural networks (CNN), ResNet and ConvNeXT. The overall overview of performances including accuracy, inference time and model size was also visualized. Frames per second (FPS) of small models consistently surpassed their large counterparts by a factor of 1-2×. DeiT small was the fastest VT in int8 configuration (6.0 FPS). In conclusion, VTs consistently outperformed CNNs for Gram-stain classification in most settings even on smaller datasets.

Rifampin is one of the most important biofilm-active antibiotics in the treatment of periprosthetic joint infection (PJI), and antibiotic regimens not involving rifampin were shown to have higher failure rates. Therefore, an emerging rifampin resistance can have a devastating effect on the outcome of PJI. The aim of this study was to compare the incidence of rifampin resistance between two groups of patients with a PJI treated with antibiotic regimens involving either immediate or delayed additional rifampin administration and to evaluate the effect of this resistance on the outcome. In this retrospective analysis of routinely collected data, all patients who presented with an acute/chronic PJI between 2018 and 2020 were recorded in the context of a single-center comparative cohort study. Two groups were formed: Group 1 included 25 patients with a PJI presenting in 2018–2019. These patients received additional rifampin only after pathogen detection in the intraoperative specimens. Group 2 included 37 patients presenting in 2019–2020. These patients were treated directly postoperatively with an empiric antibiotic therapy including rifampin. In all, 62 patients (32 females) with a mean age of 68 years and 322 operations were included. We found a rifampin-resistant organism in 16% of cases. Rifampin resistance increased significantly from 12% in Group 1 to 19% in Group 2 (p < 0.05). The treatment failure rate was 16% in Group 1 and 16.2% in Group 2 (p = 0.83). The most commonly isolated rifampin-resistant pathogen was Staphylococcus epidermidis (86%) (p < 0.05). The present study shows a significant association between the immediate start of rifampin after surgical revision in the treatment of PJI and the emergence of rifampin resistance, however with no significant effect on outcome.

The uropathogenic Escherichia coli strain CFT073 causes kidney abscesses in mice Toll/interleukin-1 receptor domain-containing protein C (TcpC) dependently and the corresponding gene is present in around 40% of E. coli isolates of pyelonephritis patients. It impairs the Toll-like receptor (TLR) signaling chain and the NACHT leucin-rich repeat PYD protein 3 inflammasome (NLRP3) by binding to TLR4 and myeloid differentiation factor 88 as well as to NLRP3 and caspase-1, respectively. Overexpression of the tcpC gene stopped replication of CFT073. Overexpression of several tcpC-truncation constructs revealed a transmembrane region, while its TIR domain induced filamentous bacteria. Based on these observations, we hypothesized that tcpC expression is presumably tightly controlled. We tested two putative promoters designated P1 and P2 located at 5′ of the gene c2397 and 5′ of the tcpC gene (c2398), respectively, which may form an operon. High pH and increasing glucose concentrations stimulated a P2 reporter construct that was considerably stronger than a P1 reporter construct, while increasing FeSO4 concentrations suppressed their activity. Human urine activated P2, demonstrating that tcpC might be induced in the urinary tract of infected patients. We conclude that P2, consisting of a 240 bp region 5′ of the tcpC gene, represents the major regulator of tcpC expression.

The TIR-containing protein C (TcpC) of uropathogenic Escherichia coli strains is a powerful virulence factor by impairing the signaling cascade of Toll-like receptors (TLRs). Several other bacterial pathogens like Salmonella, Yersinia, Staphylococcus aureus but also non-pathogens express similar proteins. We discuss here the pathogenic potential of TcpC and its interaction with TLRs and TLR-adapter proteins on the molecular level and compare its activity with the activity of other bacterial TIR-containing proteins. Finally, we analyze and compare the structure of bacterial TIR-domains with the TIR-domains of TLRs and TLR-adapters.