Acute Pneumonia Caused by Clinically Isolated Legionella pneumophila Sg 1, ST 62: Host Responses and Pathologies in Mice

Legionnaires’ disease is a severe form of lung infection caused by bacteria belonging to the genus Legionella. The disease severity depends on both host immunity and L. pneumophila virulence. The objective of this study was to describe the pathological spectrum of acute pneumonia caused by a virulent clinical isolate of L. pneumophila serogroup 1, sequence type 62. In A/JOlaHsd mice, we compared two infectious doses, namely, 104 and 106 CFU, and their impact on the mouse status, bacterial clearance, lung pathology, and blood count parameters was studied. Acute pneumonia resembling Legionnaires’ disease has been described in detail.

Prediction of Lung Infection during Palliative Chemotherapy of Lung Cancer Based on Artificial Neural Network

Lung infection seriously affects the effect of chemotherapy in patients with lung cancer and increases pain. The study is aimed at establishing the prediction model of infection in patients with lung cancer during chemotherapy by an artificial neural network (ANN). Based on the data of historical cases in our hospital, the variables were screened, and the prediction model was established. A logistic regression (LR) model was used to screen the data. The indexes with statistical significance were selected, and the LR model and back propagation neural network model were established. A total of 80 cases of advanced lung cancer patients with palliative chemotherapy were predicted, and the prediction performance of different model was evaluated by the receiver operating characteristic curve (ROC). It was found that age ≧ 60 years, length of stay ≧ 14 d, surgery history, combined chemotherapy, myelosuppression, diabetes, and hormone application were risk factors of infection in lung cancer patients during chemotherapy. The area under the ROC curve of the LR model for prediction lung infection was 0.729 ± 0.084 , which was less than that of the ANN model ( 0.897 ± 0.045 ). The results concluded that the neural network model is better than the LR model in predicting lung infection of lung cancer patients during chemotherapy.

Risk factor analysis and nomogram for predicting in-hospital mortality in ICU patients with sepsis and lung infection

Background Lung infection is a common cause of sepsis, and patients with sepsis and lung infection are more ill and have a higher mortality rate than sepsis patients without lung infection. We constructed a nomogram prediction model to accurately evaluate the prognosis of and provide treatment advice for patients with sepsis and lung infection. Methods Data were retrospectively extracted from the Medical Information Mart for Intensive Care (MIMIC-III) open-source clinical database. The definition of Sepsis 3.0 [10] was used, which includes patients with life-threatening organ dysfunction caused by an uncontrolled host response to infection, and SOFA score ≥ 2. The nomogram prediction model was constructed from the training set using logistic regression analysis, and was then internally validated and underwent sensitivity analysis. Results The risk factors of age, lactate, temperature, oxygenation index, BUN, lactate, Glasgow Coma Score (GCS), liver disease, cancer, organ transplantation, Troponin T(TnT), neutrophil-to-lymphocyte ratio (NLR), and CRRT, MV, and vasopressor use were included in the nomogram. We compared our nomogram with the Sequential Organ Failure Assessment (SOFA) score and Simplified Acute Physiology Score II (SAPSII), the nomogram had better discrimination ability, with areas under the receiver operating characteristic curve (AUROC) of 0.743 (95% C.I.: 0.713–0.773) and 0.746 (95% C.I.: 0.699–0.790) in the training and validation sets, respectively. The calibration plot indicated that the nomogram was adequate for predicting the in-hospital mortality risk in both sets. The decision-curve analysis (DCA) of the nomogram revealed that it provided net benefits for clinical use over using the SOFA score and SAPSII in both sets. Conclusion Our new nomogram is a convenient tool for accurate predictions of in-hospital mortality among ICU patients with sepsis and lung infection. Treatment strategies that improve the factors considered relevant in the model could increase in-hospital survival for these ICU patients.

An elite broadly neutralizing antibody protects SARS-CoV-2 Omicron variant challenge

The strikingly high transmissibility and antibody evasion of SARS-CoV-2 Omicron variant have posted great challenges on the efficacy of current vaccines and antibody immunotherapy. Here, we screened 34 BNT162b2-vaccinees and cloned a public broadly neutralizing antibody (bNAb) ZCB11 from an elite vaccinee. ZCB11 neutralized all authentic SARS-CoV-2 variants of concern (VOCs), including Omicron and OmicronR346K with potent IC50 concentrations of 36.8 and 11.7 ng/mL, respectively. Functional analysis demonstrated that ZCB11 targeted viral receptor-binding domain (RBD) and competed strongly with ZB8, a known RBD-specific class II NAb. Pseudovirus-based mapping of 57 naturally occurred single mutations or deletions revealed that only S371L resulted in 11-fold neutralization resistance, but this phenotype was not observed in the Omicron variant. Furthermore, prophylactic ZCB11 administration protected lung infection against both the circulating pandemic Delta and Omicron variants in golden Syrian hamsters. These results demonstrated that vaccine-induced ZCB11 is a promising bNAb for immunotherapy against pandemic SARS-CoV-2 VOCs.

Physiological predictors of cardiorespiratory fitness in children and adolescents with cystic fibrosis without ventilatory limitation

Objectives: [1] To investigate the cardiorespiratory fitness (CRF) levels in children and adolescents with cystic fibrosis (CF) with no ventilatory limitation (ventilatory reserve ⩾ 15%) during exercise, and [2] to assess which physiological factors are related to CRF. Methods: A cross-sectional study design was used in 8- to 18-year-old children and adolescents with CF. Cardiopulmonary exercise testing was used to determine peak oxygen uptake normalized to body weight as a measure of CRF. Patients were defined as having ‘low CRF’ when CRF was less than 82%predicted. Physiological predictors used in this study were body mass index z-score, P. Aeruginosa lung infection, impaired glucose tolerance (IGT) including CF-related diabetes, CF-related liver disease, sweat chloride concentration, and self-reported physical activity. Backward likelihood ratio (LR) logistic regression analysis was used. Results: Sixty children and adolescents (51.7% boys) with a median age of 15.3 years (25th–75th percentile: 12.9–17.0 years) and a mean percentage predicted forced expiratory volume in 1 second of 88.5% (±16.9) participated. Mean percentage predicted CRF (ppVO2peak/kg) was 81.4% (±12.4, range: 51%–105%). Thirty-three patients (55.0%) were classified as having ‘low CRF’. The final model that best predicted low CRF included IGT ( p = 0.085; Exp(B) = 6.770) and P. Aeruginosa lung infection (p = 0.095; Exp(B) = 3.945). This model was able to explain between 26.7% and 35.6% of variance. Conclusions: CRF is reduced in over half of children and adolescents with CF with normal ventilatory reserve. Glucose intolerance and P. Aeruginosa lung infection seem to be associated to low CRF in children and adolescents with CF.

SD-UNet: A Novel Segmentation Framework for CT Images of Lung Infections

Due to the outbreak of lung infections caused by the coronavirus disease (COVID-19), humans have to face an unprecedented and devastating global health crisis. Since chest computed tomography (CT) images of COVID-19 patients contain abundant pathological features closely related to this disease, rapid detection and diagnosis based on CT images is of great significance for the treatment of patients and blocking the spread of the disease. In particular, the segmentation of the COVID-19 CT lung-infected area can quantify and evaluate the severity of the disease. However, due to the blurred boundaries and low contrast between the infected and the non-infected areas in COVID-19 CT images, the manual segmentation of the COVID-19 lesion is laborious and places high demands on the operator. Quick and accurate segmentation of COVID-19 lesions from CT images based on deep learning has drawn increasing attention. To effectively improve the segmentation effect of COVID-19 lung infection, a modified UNet network that combines the squeeze-and-attention (SA) and dense atrous spatial pyramid pooling (Dense ASPP) modules) (SD-UNet) is proposed, fusing global context and multi-scale information. Specifically, the SA module is introduced to strengthen the attention of pixel grouping and fully exploit the global context information, allowing the network to better mine the differences and connections between pixels. The Dense ASPP module is utilized to capture multi-scale information of COVID-19 lesions. Moreover, to eliminate the interference of background noise outside the lungs and highlight the texture features of the lung lesion area, we extract in advance the lung area from the CT images in the pre-processing stage. Finally, we evaluate our method using the binary-class and multi-class COVID-19 lung infection segmentation datasets. The experimental results show that the metrics of Sensitivity, Dice Similarity Coefficient, Accuracy, Specificity, and Jaccard Similarity are 0.8988 (0.6169), 0.8696 (0.5936), 0.9906 (0.9821), 0.9932 (0.9907), and 0.7702 (0.4788), respectively, for the binary-class (multi-class) segmentation task in the proposed SD-UNet. The result of the COVID-19 lung infection area segmented by SD-UNet is closer to the ground truth compared to several existing models such as CE-Net, DeepLab v3+, UNet++, and other models, which further proves that a more accurate segmentation effect can be achieved by our method. It has the potential to assist doctors in making more accurate and rapid diagnosis and quantitative assessment of COVID-19.

A Mini Review on SARS-COVID-19-2 Omicron Variant (B.1.1.529)

The current world facing unpredictable problems with different variants of COVID-19; SARS-COV-19 is a significant lung infection caused by a coronavirus. Each type has one or more alterations to distinguish from each other. The viruses, including SARS-COV-19, continuously change the genetic code (mutations) during their genome replication. WHO labelled two variants in that we are experienced with delta (B.1.617.2) variant, now recently the omicron came (B.1.1.529) with highly mutatable strikes on it. So WHO predicted it is more dangerous than previous variants because of its mutatable capability. The mutatable strikes play an essential role in transmissibility. So there is a need to evaluate threatens raised with the new variant, so scientists are working on it. Till now, South Africa noticed major cases positive for the Omicron variant. Based on recent reports, the current paper summarized different properties of the omicron variant with others, including protein structure, diagnosis, spreadability, treatment, and potency of vaccines. Doi: 10.28991/SciMedJ-2021-0304-10 Full Text: PDF

The SARS-CoV-2 B.1.1.529 Omicron virus causes attenuated infection and disease in mice and hamsters

Despite the development and deployment of antibody and vaccine countermeasures, rapidly-spreading SARS-CoV-2 variants with mutations at key antigenic sites in the spike protein jeopardize their efficacy. The recent emergence of B.1.1.529, the Omicron variant1,2, which has more than 30 mutations in the spike protein, has raised concerns for escape from protection by vaccines and therapeutic antibodies. A key test for potential countermeasures against B.1.1.529 is their activity in pre-clinical rodent models of respiratory tract disease. Here, using the collaborative network of the SARS-CoV-2 Assessment of Viral Evolution (SAVE) program of the National Institute of Allergy and Infectious Diseases (NIAID), we evaluated the ability of multiple B.1.1.529 Omicron isolates to cause infection and disease in immunocompetent and human ACE2 (hACE2) expressing mice and hamsters. Despite modeling and binding data suggesting that B.1.1.529 spike can bind more avidly to murine ACE2, we observed attenuation of infection in 129, C57BL/6, and BALB/c mice as compared with previous SARS-CoV-2 variants, with limited weight loss and lower viral burden in the upper and lower respiratory tracts. Although K18-hACE2 transgenic mice sustained infection in the lungs, these animals did not lose weight. In wild-type and hACE2 transgenic hamsters, lung infection, clinical disease, and pathology with B.1.1.529 also were milder compared to historical isolates or other SARS-CoV-2 variants of concern. Overall, experiments from multiple independent laboratories of the SAVE/NIAID network with several different B.1.1.529 isolates demonstrate attenuated lung disease in rodents, which parallels preliminary human clinical data.

mRNA-1273 and Ad26.COV2.S vaccines protect against the B.1.621 variant of SARS-CoV-2

Since the emergence of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) in 2019, viral variants with greater transmissibility or immune evasion properties have arisen, which could jeopardize recently deployed vaccine and antibody-based countermeasures. Here, we evaluated in mice and hamsters the efficacy of preclinical non-GMP Moderna mRNA vaccine (mRNA-1273) and the Johnson & Johnson recombinant adenoviral-vectored vaccine (Ad26.COV2.S) against the B.1.621 (Mu) South American variant of SARS-CoV-2, which contains spike mutations T95I, Y144S, Y145N, R346K, E484K, N501Y, D614G, P681H, and D950N. Immunization of 129S2 and K18-human ACE2 transgenic mice with mRNA-1273 vaccine protected against weight loss, lung infection, and lung pathology after challenge with B.1.621 or WA1/2020 N501Y/D614G SARS-CoV-2 strain. Similarly, immunization of 129S2 mice and Syrian hamsters with a high dose of Ad26.COV2.S reduced lung infection after B.1.621 virus challenge. Thus, immunity induced by mRNA-1273 or Ad26.COV2.S vaccines can protect against the B.1.621 variant of SARS-CoV-2 in multiple animal models.