Effect of Global Ventilation to Perfusion Ratio, for Normal Lungs, on Desflurane and Sevoflurane Elimination Kinetics

Background Kinetics of the uptake of inhaled anesthetics have been well studied, but the kinetics of elimination might be of more practical importance. The objective of the authors’ study was to assess the effect of the overall ventilation/perfusion ratio ( .VA/.Q ), for normal lungs, on elimination kinetics of desflurane and sevoflurane. Methods The authors developed a mathematical model of inhaled anesthetic elimination that explicitly relates the terminal washout time constant to the global lung .VA/.Q ratio. Assumptions and results of the model were tested with experimental data from a recent study, where desflurane and sevoflurane elimination were observed for three different .VA/.Q conditions: normal, low, and high. Results The mathematical model predicts that the global .VA/.Q ratio, for normal lungs, modifies the time constant for tissue anesthetic washout throughout the entire elimination. For all three .VA/.Q conditions, the ratio of arterial to mixed venous anesthetic partial pressure Part/Pmv reached a constant value after 5 min of elimination, as predicted by the retention equation. The time constant corrected for incomplete lung clearance was a better predictor of late-stage kinetics than the intrinsic tissue time constant. Conclusions In addition to the well-known role of the lungs in the early phases of inhaled anesthetic washout, the lungs play a long-overlooked role in modulating the kinetics of tissue washout during the later stages of inhaled anesthetic elimination. The .VA/.Q ratio influences the kinetics of desflurane and sevoflurane elimination throughout the entire elimination, with more pronounced slowing of tissue washout at lower .VA/.Q ratios. Editor’s Perspective What We Already Know about This Topic What This Article Tells Us That Is New

Arterial and Mixed Venous Kinetics of Desflurane and Sevoflurane, Administered Simultaneously, at Three Different Global Ventilation to Perfusion Ratios in Piglets with Normal Lungs

Background Previous studies have established the role of various tissue compartments in the kinetics of inhaled anesthetic uptake and elimination. The role of normal lungs in inhaled anesthetic kinetics is less understood. In juvenile pigs with normal lungs, the authors measured desflurane and sevoflurane washin and washout kinetics at three different ratios of alveolar minute ventilation to cardiac output value. The main hypothesis was that the ventilation/perfusion ratio ( .VA/.Q ) of normal lungs influences the kinetics of inhaled anesthetics. Methods Seven healthy pigs were anesthetized with intravenous anesthetics and mechanically ventilated. Each animal was studied under three different .VA/.Q conditions: normal, low, and high. For each .VA/.Q condition, desflurane and sevoflurane were administered at a constant, subanesthetic inspired partial pressure (0.15 volume% for sevoflurane and 0.5 volume% for desflurane) for 45 min. Pulmonary arterial and systemic arterial blood samples were collected at eight time points during uptake, and then at these same times during elimination, for measurement of desflurane and sevoflurane partial pressures. The authors also assessed the effect of .VA/.Q on paired differences in arterial and mixed venous partial pressures. Results For desflurane washin, the scaled arterial partial pressure differences between 5 and 0 min were 0.70 ± 0.10, 0.93 ± 0.08, and 0.82 ± 0.07 for the low, normal, and high .VA/.Q conditions (means, 95% CI). Equivalent measurements for sevoflurane were 0.55 ± 0.06, 0.77 ± 0.04, and 0.75 ± 0.08. For desflurane washout, the scaled arterial partial pressure differences between 0 and 5 min were 0.76 ± 0.04, 0.88 ± 0.02, and 0.92 ± 0.01 for the low, normal, and high .VA/.Q conditions. Equivalent measurements for sevoflurane were 0.79 ± 0.05, 0.85 ± 0.03, and 0.90 ± 0.03. Conclusions Kinetics of inhaled anesthetic washin and washout are substantially altered by changes in the global .VA/.Q ratio for normal lungs. Editor’s Perspective What We Already Know about This Topic What This Article Tells Us That Is New

COVID-19 diagnosis from chest x-rays: developing a simple, fast, and accurate neural network

Purpose Chest x-rays are a fast and inexpensive test that may potentially diagnose COVID-19, the disease caused by the novel coronavirus. However, chest imaging is not a first-line test for COVID-19 due to low diagnostic accuracy and confounding with other viral pneumonias. Recent research using deep learning may help overcome this issue as convolutional neural networks (CNNs) have demonstrated high accuracy of COVID-19 diagnosis at an early stage. Methods We used the COVID-19 Radiography database [36], which contains x-ray images of COVID-19, other viral pneumonia, and normal lungs. We developed a CNN in which we added a dense layer on top of a pre-trained baseline CNN (EfficientNetB0), and we trained, validated, and tested the model on 15,153 X-ray images. We used data augmentation to avoid overfitting and address class imbalance; we used fine-tuning to improve the model’s performance. From the external test dataset, we calculated the model’s accuracy, sensitivity, specificity, positive predictive value, negative predictive value, and F1-score. Results Our model differentiated COVID-19 from normal lungs with 95% accuracy, 90% sensitivity, and 97% specificity; it differentiated COVID-19 from other viral pneumonia and normal lungs with 93% accuracy, 94% sensitivity, and 95% specificity. Conclusions Our parsimonious CNN shows that it is possible to differentiate COVID-19 from other viral pneumonia and normal lungs on x-ray images with high accuracy. Our method may assist clinicians with making more accurate diagnostic decisions and support chest X-rays as a valuable screening tool for the early, rapid diagnosis of COVID-19.

CO-ResNet: Optimized ResNet model for COVID-19 diagnosis from X-ray images

This paper focuses on the application of deep learning (DL) based model in the analysis of novel coronavirus disease (COVID-19) from X-ray images. The novelty of this work is in the development of a new DL algorithm termed as optimized residual network (CO-ResNet) for COVID-19. The proposed CO-ResNet is developed by applying hyperparameter tuning to the conventional ResNet 101. CO-ResNet is applied to a novel dataset of 5,935 X-ray images retrieved from two publicly available datasets. By utilizing resizing, augmentation and normalization and testing different epochs our CO-ResNet was optimized for detecting COVID-19 versus pneumonia with normal healthy lung controls. Different evaluation metrics such as the classification accuracy, F1 score, recall, precision, area under the receiver operating characteristics curve (AUC) are used. Our proposed CO-ResNet obtains consistently best performance in the multi-level data classification problem, including health lung, pneumonia affected lung and COVID-19 affected lung samples. In the experimental evaluation, the detection rate accuracy in discerning COVID-19 is 98.74%, and for healthy normal lungs, pneumonia affected lungs are 92.08% and 91.32% respectively for our CO-ResNet with ResNet101 backbone. Further, our model obtained accuracy values of 83.68% and 82% for healthy normal lungs and pneumonia affected lungs with ResNet152 backbone. Experimental results indicate the potential usage of our new DL driven model for classification of COVID-19 and pneumonia.

Gene Expression Levels of the Prolyl Hydroxylase Domain Proteins PHD1 and PHD2 but Not PHD3 Are Decreased in Primary Tumours and Correlate with Poor Prognosis of Patients with Surgically Resected Non-Small-Cell Lung Cancer

Background: Hypoxia correlates with poor prognosis in several cancer types, including lung cancer. Prolyl hydroxylase domain proteins (PHDs) play a role in cell oxygen sensing, negatively regulating the hypoxia-inducible factor (HIF) pathway. Our study aim was to evaluate PHD1, PHD2 and PHD3 mRNA expression levels in primary tumours and normal lungs of non-small-cell lung cancer (NSCLC) patients and to correlate it with selected regulators of HIF signalling, with clinicopathological characteristics and overall survival (OS). Methods: Tumour tissue samples were obtained from 60 patients with surgically resected NSCLC who were treated with radical surgery. In 22 out of 60 cases, matching morphologically normal lung tissue was obtained. PHD1, PHD2 and PHD3 mRNA expressions were measured using RT-qPCR. Results: The PHD1 and PHD2 mRNA levels in primary tumours were significantly decreased compared to those in normal lungs (both p < 0.0001). PHD1 and PHD2 expression in tumours was positively correlated (rs = 0.82; p < 0.0001) and correlated well with HIF pathway downstream genes HIF1A, PKM2 and PDK1. Decreased PHD1 and PHD2 were associated with larger tumour size, higher tumour stage (PHD1 only) and squamous cell carcinoma. Patients with low PHD1 and patients with low PHD2 expression had shorter OS than patients with high PHD1 (p = 0.02) and PHD2 expression (p = 0.01). PHD1 showed borderline independent prognostic values in multivariate analysis (p = 0.06). In contrast, we found no associations between PHD3 expression and any of the observed parameters. Conclusions: Our results show that reduced expression of PHD1 and PHD2 is associated with the development and progression of NSCLC. PHD1 could be further assessed as a prognostic marker in NSCLC.

Mesenchymal–Epithelial Transition in Fibroblasts of Human Normal Lungs and Interstitial Lung Diseases

. In passages above ten and growing very actively, we observed that some human lung fibroblasts cultured under standard conditions were transformed into a lineage of epithelial-like cells (ELC). To systematically evaluate the possible mesenchymal–epithelial transition (MET) occurrence, fibroblasts were obtained from normal lungs and also from lungs affected by idiopathic interstitial diseases. When an unusual epithelial-like phenotypic change was observed, cultured cells were characterized by confocal immunofluorescence microscopy, immunoblotting, immunocytochemistry, cytofluorometry, gelatin zymography, RT-qPCR, and hybridization in a whole-transcript human microarray. Additionally, microvesicles fraction (MVs) from ELC and fibroblasts were used to induce MET, while the microRNAs (miRNAs) contained in the MVs were identified. Pattern-gene expression of the original fibroblasts and the derived ELC revealed profound changes, upregulating characteristic epithelial-cell genes and downregulating mesenchymal genes, with a marked increase of E-cadherin, cytokeratin, and ZO-1, and the loss of expression of α-SMA, collagen type I, and Thy-1 cell surface antigen (CD90). Fibroblasts, exposed to culture media or MVs from the ELC, acquired ELC phenotype. The miRNAs in MVs shown six expressed exclusively in fibroblasts, and three only in ELC; moreover, twelve miRNAs were differentially expressed between fibroblasts and ELC, all of them but one was overexpressed in fibroblasts. These findings suggest that the MET-like process can occur in human lung fibroblasts, either from normal or diseased lungs. However, the biological implication is unclear.

Role of Omentin in Obesity Paradox in Lung Cancer

Lung cancer remains the second-most-common cancer worldwide and is associated with the highest number of cancer-related mortality. While tobacco smoking is the most important risk factor for lung cancer, many other lifestyles and occupational factors significantly contribute. Obesity is a growing global health concern and contributes to ~30% cancer-related mortality, but unlike other lifestyle diseases, lung cancer is negatively associated with obesity. We meta-analyzed multiple case-control studies confirming increased survival and better outcomes in overweight and obese lung cancer patients. Tumor heterogeneity analysis showed significant enrichment of adipocytes and preadipocytes in normal lungs compared to lung cancers. Interestingly, one of the understudied adipokine, omentin, was significantly and consistently lower in lung neoplasms compared to normal lungs. Omentin has been examined in relation to osteoarthritis, inflammatory bowel disease, cardiovascular diseases, diabetes, chronic liver disease, psoriasis and some other cancers. Aberrant expression of omentin has been reported in solid tumors; however, little is known about its role in lung cancer. We found omentin to be consistently downregulated in lung cancers, and it exhibited a negative correlation with important transcription factors FOXA1, EN1, FOXC1 and ELK4. We, therefore, suggest that omentin may serve as a prognostic factor in lung cancer and explain the “obesity paradox” in lung cancer.

Angular Coherence Observation in Fingerprints and Lungs for Fingerprint Classification and COVID-19 Differentiation

Feature extraction and lung detection are critical phases for COVID-19 detection. Hence, the features by which normal lungs and abnormal lungs can be differentiated are significantly important. In this paper, the x-ray images are enhanced and the corresponding angles, coming from ribs, are extracted as the major features. According to the behavior of the angles, the image is bisected in order to evaluate each lung individually. The new definition of normal lungs is proposed so as to discriminate normal lungs from COVID-19 lungs. Considering the definition, the right and the left lungs are cropped from the main image. Subsequently, the Histogram of Oriented Gradient (HOG) features are extracted from the cropped images. Two neural networks with the same topology are trained by the features. First, one of the neural networks is trained by cropped images. Second, another neural network is trained by HOG features obtained from the cropped images. The simulation is performed by MATLAB and the database is comprised of 522 images and 96% accuracy is obtained. Furthermore, a novel method by which fingerprints are classified in eight categories is proposed in this paper. In fact, because of inevitable rotation, brought about during data acquisition procedure in fingerprints, the feature extraction procedure might be afflicted with the rotation. Hence, a new approach is suggested so that the rotation is rectified prior to the feature extraction process. From the enhanced images of fingerprints, the angles of ridges are calculated. According to the extracted angles, new points, called Origin Points, are mentioned as the origins around which decisive blocks are cropped. For each block, a Fourier series model is calculated so as to form a training data for the classifier. The classifier chosen is a Generalized Regression Neural Network (GRNN). FVC2004 is utilized for both training and test phases and 98.2% accuracy is achieved.

The caliber of segmental and subsegmental vessels in COVID-19 pneumonia is enlarged: a distinctive feature in comparison with other forms of inflammatory and thromboembolic disease

Objective: to compare COVID-19 patients’ vessel caliber with that of normal lungs and lungs interested by other inflammatory and thromboembolic processes. Methods: between March and April 2020, 42 patients affected by COVID-19 pneumonia [COV-P] underwent a CT scan of the lung at Verona University Hospital for clinical indications. Lung images were compared to 4 different groups of patients (normal lung [NL], distal thromboembolism [DTE], bacterial and fungal pneumonia [Bact-P, Fung-P]) by a 4-year-experienced radiologist. Results: COV-P patients’ segmental and subsegmental vessels, as evaluated as the ratio with the corresponding bronchial branch (V/B ratio) were larger with respect to NL, DTE in the apparently healthy parenchyma, a result confirmed in the zones of opacification with respect to Bact-P and Fung-P. Conclusions: This is the first study to comparatively showing that segmental and subsegmental COVID-19 patients’ vessel caliber is significantly enlarged. This is a distinctive feature of COVID-19 pneumonia suggesting distinct pathophysiology as compared to other inflammatory and thromboembolic diseases and alerting radiologists to consider it when evaluating CT scan of suspected patients.