New Microbes and New Infections最新文献

The mRNA vaccines have proven to be very effective in preventing severe disease and death from SARS-CoV-2 in the general population. However, in patients with chronic kidney disease (CKD) in dialysis or with kidney transplants (KT) the vaccine responses vary, with severe breakthrough infections as a consequence. In this intervention study we investigated the magnitude and quality of the responses to mRNA vaccination administered prior to kidney replacement therapy (KRT). Twenty patients with CKD G4/5 and nine healthy controls were followed for 12 months after receiving two doses of BNT162b2 four weeks apart and a booster dose after 3–6 months. Induction of anti-Spike and anti-RBD IgG in plasma followed the same kinetics in CKD patients and controls, with a trend towards higher titers in controls. In accordance, there was no differences in the establishment of Spike-specific memory B-cells between groups. In contrast, the CKD patients showed lower levels of anti-Spike IgG in saliva and Spike-specific CD8⁺ T-cells in blood, possibly influencing the capacity of viral clearance which can contribute to an elevated risk of severe breakthrough infections. In conclusion, we found that CKD patients, despite having a reduced mucosal and cytotoxic immunity to BNT162b2, demonstrated a serological response in plasma similar to healthy controls. This suggests that immunization prior to RRT is efficient and motivated. (EudraCT-nr 2021-000988-68).

Background

Large vision models (LVM) pretrained by large datasets have demonstrated their enormous capacity to understand visual patterns and capture semantic information from images. We proposed a novel method of knowledge domain adaptation with pretrained LVM for a low-cost artificial intelligence (AI) model to quantify the severity of SARS-CoV-2 pneumonia based on frontal chest X-ray (CXR) images.

Methods

Our method used the pretrained LVMs as the primary feature extractor and self-supervised contrastive learning for domain adaptation. An encoder with a 2048-dimensional feature vector output was first trained by self-supervised learning for knowledge domain adaptation. Then a multi-layer perceptron (MLP) was trained for the final severity prediction. A dataset with 2599 CXR images was used for model training and evaluation.

Results

The model based on the pretrained vision transformer (ViT) and self-supervised learning achieved the best performance in cross validation, with mean squared error (MSE) of 23.83 (95 % CI 22.67–25.00) and mean absolute error (MAE) of 3.64 (95 % CI 3.54–3.73). Its prediction correlation has the $R^2$ of 0.81 (95 % CI 0.79–0.82) and Spearman ρ of 0.80 (95 % CI 0.77–0.81), which are comparable to the current state-of-the-art (SOTA) methods trained by much larger CXR datasets.

Conclusion

The proposed new method has achieved the SOTA performance to quantify the severity of SARS-CoV-2 pneumonia at a significantly lower cost. The method can be extended to other infectious disease detection or quantification to expedite the application of AI in medical research.

Background

Blood culture-negative endocarditis (BCNE) is a diagnostic challenge, therefore our objective was to pinpoint high-risk cohorts for BCNE.

Methods

The study included adult patients with definite endocarditis. Data were collected via the Infectious Diseases International Research Initiative (ID-IRI). The study analysing one of the largest case series ever reported was conducted across 41 centers in 13 countries. We analysed the database to determine the predictors of BCNE using univariate and logistic regression analyses.

Results

Blood cultures were negative in 101 (11.65 %) of 867 patients. We disclosed that as patients age, the likelihood of a negative blood culture significantly decreases (OR 0.975, 95 % CI 0.963–0.987, p < 0.001). Additionally, factors such as rheumatic heart disease (OR 2.036, 95 % CI 0.970–4.276, p = 0.049), aortic stenosis (OR 3.066, 95 % CI 1.564–6.010, p = 0.001), mitral regurgitation (OR 1.693, 95 % CI 1.012–2.833, p = 0.045), and prosthetic valves (OR 2.539, 95 % CI 1.599–4.031, p < 0.001) are associated with higher likelihoods of negative blood cultures. Our model can predict whether a patient falls into the culture-negative or culture-positive groups with a threshold of 0.104 (AUC±SE = 0.707 ± 0.027). The final model demonstrates a sensitivity of 70.3 % and a specificity of 57.0 %.

Conclusion

Caution should be exercised when diagnosing endocarditis in patients with concurrent cardiac disorders, particularly in younger cases.