Diagnostic Performance of a Deep Learning Model Deployed at a National COVID-19 Screening Facility for Detection of Pneumonia on Frontal Chest Radiographs.

(1) Background: Chest radiographs are the mainstay of initial radiological investigation in this COVID-19 pandemic. A reliable and readily deployable artificial intelligence (AI) algorithm that detects pneumonia in COVID-19 suspects can be useful for screening or triage in a hospital setting. This study has a few objectives: first, to develop a model that accurately detects pneumonia in COVID-19 suspects; second, to assess its performance in a real-world clinical setting; and third, by integrating the model with the daily clinical workflow, to measure its impact on report turn-around time. (2) Methods: The model was developed from the NIH Chest-14 open-source dataset and fine-tuned using an internal dataset comprising more than 4000 CXRs acquired in our institution. Input from two senior radiologists provided the reference standard. The model was integrated into daily clinical workflow, prioritising abnormal CXRs for expedited reporting. Area under the receiver operating characteristic curve (AUC), F1 score, sensitivity, and specificity were calculated to characterise diagnostic performance. The average time taken by radiologists in reporting the CXRs was compared against the mean baseline time taken prior to implementation of the AI model. (3) Results: 9431 unique CXRs were included in the datasets, of which 1232 were ground truth-labelled positive for pneumonia. On the "live" dataset, the model achieved an AUC of 0.95 (95% confidence interval (CI): 0.92, 0.96) corresponding to a specificity of 97% (95% CI: 0.97, 0.98) and sensitivity of 79% (95% CI: 0.72, 0.84). No statistically significant degradation of diagnostic performance was encountered during clinical deployment, and report turn-around time was reduced by 22%. (4) Conclusion: In real-world clinical deployment, our model expedites reporting of pneumonia in COVID-19 suspects while preserving diagnostic performance without significant model drift.

Paeoniflorin inhibits high glucose-induced macrophage activation through TLR2-dependent signal pathways.

ETHNOPHARMACOLOGICAL RELEVANCE: Paeoniflorin(PF), extracted from the root peeled of Paeonia lactiflora Pall(Family: Ranunculaceae), has therapeutic potential in many animal models of inflammatory diseases. AIM OF THE STUDY: Although the anti-inflammatory efficacy of PF has been well illustrated in several animal models, whether it could attenuate diabetic nephropathy and detailed mechanisms are still obscure. Till now, accumulating evidence has proposed the pivotal role of toll-like receptors (TLRs) in renal inflammation in diabetic patients. In this setting, the current study aimed to investigate the effects and underlying mechanism of PF on high glucose-induced activation of toll like-receptor 2 (TLR2) signaling in macrophages. MATERIALS AND METHODS: Bone marrow-derived macrophages (BMDM) were isolated from male Tlr2tm1kir (TLR2-/-) mice and wild-type littermates (C57BL/6JWT). The level of TLR2 and activation of downstream signaling were evaluated in response to 30mmol/L high glucose (HG)-containing medium. Macrophages behaviors, which include cell viability, migration and inflammatory cytokines production, were also determined. RESULTS: PF suppressed HG-induced production of TLR2, activation of downstream signaling and synthesis of inducible nitric oxide synthase (iNOS). PF could further inhibit MyD88-dependent pathway in HG-induced models in which TLR2 was knocked out. Moreover, deletion of TLR2 inhibited the HG-induced activation of MyD88-dependent pathway, but not TIR domain containing adapter inducing interferon-ß (Trif) signal pathway in BMDMs. As HG stimulation polarizes macrophages into M1 phenotype, treatment of PF or knockout of TLR2 significantly reduces M1 markers on the membrane of macrophages. Additionally, levels of inflammatory cytokines and iNOS were remarkably reduced in response to PF or TLR2 deficiency. CONCLUSION: Collectively, these data demonstrated that HG activated macrophages primarily through TLR2-dependent mechanisms which aggravated the severity of renal inflammation and eventually contributed to DN. Additionally, PF might be applied as a potential therapeutic agent in the battle against progressive DN.