Association of Killer Cell Immunoglobulin-like Receptor (KIR) Genes and their HLA Ligands with Susceptibility to Takayasu Arteritis in the Iranian Population

Takayasu arteritis (TA) is a chronic inflammatory disorder characterized by vascular damage and fibrosis in the intima that commonly occurs in the aorta. In many damaged sites in TA patients, natural killer (NK) cells have been shown to be hyperactivated and produce inflammatory cytokines and toxic components. Killer cell immunoglobulin-like receptors (KIRs) are found on NK cells and interact with human leukocyte antigen (HLA) class I ligands to activate or suppress NK cells. The present study assessed the possible role of KIR and their HLA ligand genes in susceptibility to TA in Iranian patients. This case-control study included 50 TA patients and 50 healthy subjects. DNA was extracted from whole peripheral blood samples, and polymerase chain reaction with sequence-specific primers (PCR-SSP) was performed to recognize the presence or absence of polymorphism in 17 KIR genes and 5 HLA class I ligands in each participant. Among the KIR and HLA genes, a significant decrease was detected in the frequency of 2DS4 (full allele) in TA patients (38%) compared with healthy controls (82%) (OR=0.13, 95% CI=0.05–0.34). However, none of the KIR and HLA genotypes or the interactions between these genes were associated with susceptibility to TA. The KIR2DS4 gene might be involved in the regulation of activation as well as the production of cytotoxic mediators of NK cells in patients with TA.

Accuracy of Machine Learning Models to Predict Mortality in COVID-19 Infection Using the Clinical and Laboratory Data at the Time of Admission

Aim

This study aimed to develop a predictive model to predict patients’ mortality with coronavirus disease 2019 (COVID-19) from the basic medical data on the first day of admission.

Methods

The medical data including the demographic, clinical, and laboratory features on the first day of admission of clinically diagnosed COVID-19 patients were documented. The outcome of patients was also recorded as discharge or death. Feature selection models were then implemented and different machine learning models were developed on top of the selected features to predict discharge or death. The trained models were then tested on the test dataset.

Results

A total of 520 patients were included in the training dataset. The feature selection demonstrated 22 features as the most powerful predictive features. Among different machine learning models, the naive Bayes demonstrated the best performance with an area under the curve of 0.85. The ensemble model of the naive Bayes and neural network combination had slightly better performance with an area under the curve of 0.86. The models had relatively the same performance on the test dataset.

Conclusion

Developing a predictive machine learning model based on the basic medical features on the first day of admission in COVID-19 infection is feasible with acceptable performance.

Feasibility of Radiomics to Differentiate Coronavirus Disease 2019 (COVID-19) from H1N1 Influenza Pneumonia on Chest Computed Tomography: A Proof of Concept

BACKGROUND

Chest computed tomography (CT) plays an essential role in diagnosing coronavirus disease 2019 (COVID-19). However, CT findings are often nonspecific among different viral pneumonia conditions. The differentiation between COVID-19 and influenza can be challenging when seasonal influenza concurs with the COVID-19 pandemic. This study was conducted to test the ability of radiomics-artificial intelligence (AI) to perform this task.

METHODS

In this retrospective study, chest CT images from 47 patients with COVID-19 (after February 2020) and 19 patients with H1N1 influenza (before September 2019) pneumonia were collected from three hospitals affiliated with Arak University of Medical Sciences, Arak, Iran. All pulmonary lesions were segmented on CT images. Multiple radiomics features were extracted from the lesions and used to develop support-vector machine (SVM), k-nearest neighbor (k-NN), decision tree, neural network, adaptive boosting (AdaBoost), and random forest.

RESULTS

The patients with COVID-19 and H1N1 influenza were not significantly different in age and sex (P=0.13 and 0.99, respectively). Nonetheless, the average time between initial symptoms/hospitalization and chest CT was shorter in the patients with COVID-19 (P=0.001 and 0.01, respectively). After the implementation of the inclusion and exclusion criteria, 453 pulmonary lesions were included in this study. On the harmonized features, random forest yielded the highest performance (area under the curve=0.97, sensitivity=89%, precision=90%, F1 score=89%, and classification accuracy=89%).

CONCLUSION

In our preliminary study, radiomics feature extraction, conjoined with AI, especially random forest and neural network, appeared to yield very promising results in the differentiation between COVID-19 and H1N1 influenza on chest CT.

Artificial Intelligence Empowers Radiologists to Differentiate Pneumonia Induced by COVID-19 versus Influenza Viruses

Background

Given the current pandemic, differentiation between pneumonia induced by COVID-19 or influenza viruses is of utmost clinical significance in the patients' management. For this purpose, this study was conducted to develop sensitive artificial intelligence (AI) models to assist radiologists to decisively differentiate pneumonia due to COVID-19 versus influenza viruses.

Methods

Cross sectional chest CT images (N=12744) from well-evaluated cases of pneumonias induced by COVID-19 or H1N1 Influenza viruses, and normal individuals were collected. We examined the computer tomographic (CT) chest images from 137 individuals. Various pre-trained convolutional neural network models, such as ResNet-50, InceptionV3, Wide ResNet, SqueezNet, VGG 16 and VGG 19 were fine-tuned on our datasets. The datasets were used for training (60%), validation (20%), and testing (20%) of the final models. Also, the predictive power and means of precision and recall were determined for each model.

Results

Fine-tuned ResNet-50 model differentiated the pneumonia due to COVID-19 or H1N1 influenza virus with accuracies of 96.7% and 92%, respectively This model outperformed all others, i.e., InceptionV3, Wide ResNet, SqueezNet, VGG 16 and VGG 19.

Conclusion

Fine-tuned and pre-trained image classifying models of AI enable radiologists to reliably differentiate the pneumonia induced by COVID-19 versus H1N1 influenza virus. For this purpose, ResNet-50 followed by InceptionV3 models proved more promising than other AI models. Also in the supplements, we share the source codes and our fine-tuned models for use by researchers and clinicians globally toward the critical task of image differentiation of patients infected with COVID-19 versus H1N1 Influenza viruses.

The comparison of visfatin levels of gingival crevicular fluid in systemic lupus erythematosus and chronic periodontitis patients with healthy subjects

BACKGROUND

Visfatin is an adipokine and has a crucial role in pro-inflammatory response. The aim of this study was investigating the visfatin levels of gingival crevicular fluid (GCF) in patients with systemic lupus erythematosus (SLE) and chronic periodontitis and healthy subjects.

MATERIALS AND METHODS

Sixty non-obese females were selected based on their clinical parameters into four groups: 15 healthy subjects (H-H), 15 systemically healthy individuals with chronic periodontitis (H-CP), 15 SLE patient with CP (SLE-CP), and 15 SLE patients without CP (SLE-H). GCF samples were collected to estimate the levels of visfatin using enzyme-linked immunosorbent assay (ELISA).

RESULTS

Investigating the amount of visfatin in the GCF showed that there is a significant difference between visfatin amount of GCF in SLE patients and chronic periodontitis (L-CP) in comparison with other groups (P < 0.001).

CONCLUSION

Visfatin levels have correlated positively with all the clinical periodontal parameters and its levels in (L-CP) group are highest in comparative with other groups. This finding suggests visfatin has a possible role in association between these two inflammatory conditions. Key Point • Visfatin in systemic lupus erythematosus.

Effect of Zafirlukast on improving lung function in patients with chronic obstructive pulmonary diseases

BACKGROUND

There is little evidence about the role of Zafirlukast (a highly selective LTD4 antagonist) in Chronic Obstructive Pulmonary Disease (COPD). The Zafirlukast can reduce the need for short-acting rescue β2 agonists, produce fewer exacerbations of asthma and increased quality of life as possible benefits treatment for asthma. The aim of our study was to evaluate the effects of Zafirlukast improvement of lung function in patients with COPD.

METHODS

Twenty five patients with moderate to severe COPD, in stable phase of the disease, participated in this interventional, quasi-experimental study. All patients were received 40mg oral Zafirlukast per day for 2 weeks. Pulmonary function Test was performed both at the baseline and at the end of the study. Data were analyzed with paired t-test using SPSS v.16.

RESULTS

The mean age of the patients was 67.29 (SD=5.56) years with the mean baseline for forced expiratory volume in first second (FEV1) equal to 41.79% (SD=14.96) of predicted value. After 2 weeks, the mean improvements in forced vital capacity (FVC), FEV1 and FEV1/FVC were 4.75% (SD=13.18), 3.71% (SD=9.19) and 9.33(SD=27.08), respectively. Zafirlukast produced a non-significant (p>0.05) bronchodilation, with maximum mean increase in FEV1 of 0.04 lit (3%) above baseline.

CONCLUSION

Results showed that Zafirlukast has no considerable bronchodilatory effect in COPD. Present study consisted of a very short treatment period and it is possible that the extension of this period could possibly have more effects. Additional larger studies are needed to verify the impact of leukoterien receptor antagonists on improving the lung function in COPD patients.