A prediction model for secondary invasive fungal infection among severe SARS-CoV-2 positive patients in ICU.

Background: The global COVID-19 pandemic has resulted in over seven million deaths, and IFI can further complicate the clinical course of COVID-19. Coinfection of COVID-19 and IFI (secondary IFI) pose significant threats not only to healthcare systems but also to patient lives. After the control measures for COVID-19 were lifted in China, we observed a substantial number of ICU patients developing COVID-19-associated IFI. This creates an urgent need for predictive assessment of COVID-19 patients in the ICU environment for early detection of suspected fungal infection cases. Methods: This study is a single-center, retrospective research endeavor. We conducted a case-control study on severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) positive patients. The cases consisted of patients who developed any secondary IFI during their ICU stay at Jilin University China-Japan Union Hospital in Changchun, Jilin Province, China, from December 1st, 2022, to August 31st, 2023. The control group consisted of SARS-CoV-2 positive patients without secondary IFI. Descriptive and comparative analyses were performed, and a logistic regression prediction model for secondary IFI in COVID-19 patients was established. Additionally, we observed an increased incidence of COVID-19-associated pulmonary aspergillosis (CAPA) during this pandemic. Therefore, we conducted a univariate subgroup analysis on top of IFI, using non-CAPA patients as the control subgroup. Results: From multivariate analysis, the prediction model identified 6 factors that are significantly associated with IFI, including the use of broad-spectrum antibiotics for more than 2 weeks (aOR=4.14, 95% CI 2.03-8.67), fever (aOR=2.3, 95%CI 1.16-4.55), elevated log IL-6 levels (aOR=1.22, 95% CI 1.04-1.43) and prone position ventilation (aOR=2.38, 95%CI 1.15-4.97) as independent risk factors for COVID-19 secondary IFI. High BMI (BMI ≥ 28 kg/m2) (aOR=0.85, 95% CI 0.75-0.94) and the use of COVID-19 immunoglobulin (aOR=0.45, 95% CI 0.2-0.97) were identified as independent protective factors against COVID-19 secondary IFI. The Receiver Operating Curve (ROC) area under the curve (AUC) of this model was 0.81, indicating good classification. Conclusion: We recommend paying special attention for the occurrence of secondary IFI in COVID-19 patients with low BMI (BMI < 28 kg/m2), elevated log IL-6 levels and fever. Additionally, during the treatment of COVID-19 patients, we emphasize the importance of minimizing the duration of broad-spectrum antibiotic use and highlight the potential of immunoglobulin application in reducing the incidence of IFI.

Chemical-protein interaction extraction via Gaussian probability distribution and external biomedical knowledge.

MOTIVATION: The biomedical literature contains a wealth of chemical-protein interactions (CPIs). Automatically extracting CPIs described in biomedical literature is essential for drug discovery, precision medicine, as well as basic biomedical research. Most existing methods focus only on the sentence sequence to identify these CPIs. However, the local structure of sentences and external biomedical knowledge also contain valuable information. Effective use of such information may improve the performance of CPI extraction. RESULTS: In this article, we propose a novel neural network-based approach to improve CPI extraction. Specifically, the approach first employs BERT to generate high-quality contextual representations of the title sequence, instance sequence and knowledge sequence. Then, the Gaussian probability distribution is introduced to capture the local structure of the instance. Meanwhile, the attention mechanism is applied to fuse the title information and biomedical knowledge, respectively. Finally, the related representations are concatenated and fed into the softmax function to extract CPIs. We evaluate our proposed model on the CHEMPROT corpus. Our proposed model is superior in performance as compared with other state-of-the-art models. The experimental results show that the Gaussian probability distribution and external knowledge are complementary to each other. Integrating them can effectively improve the CPI extraction performance. Furthermore, the Gaussian probability distribution can effectively improve the extraction performance of sentences with overlapping relations in biomedical relation extraction tasks. AVAILABILITY AND IMPLEMENTATION: Data and code are available at https://github.com/CongSun-dlut/CPI_extraction. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.