Lung CT Research Articles

Radiomic Features Based Automatic Classification of CT Lung Findings for COVID-19 Patients.


The lung CT images of COVID-19 patients can be typically characterized by three differentfindings-
Ground Glass Opacity (GGO), consolidation and pleural effusion. GGOs have been shown to precede consolidations and has different heterogeneous appearance. Conventional severity scoring only uses total area of lung involvement ignoring appearance of the effected regions. This study proposes a baseline to select heterogeneity/radiomic features that can distinguish these three pathological lung findings.
Methods:
Four approaches were implemented to select features from a pool of 44 features. First one is a manual feature selection method. The rest are automatic feature selection methods based on Genetic Algorithm (GA) coupled with 1) K-Nearest-Neighbor (GA-KNN), 2) binary-decision-tree (GA-BDT) and 3) Artificial-Neural-
Network (GA-ANN). For the purpose of validation, an ANN was trained using the selected features and tested on a completely independent data set.
Results:
Manual selection of nine radiomic features was found to provide the most accurate results with the highest sensitivity, specificity and accuracy (85.7% overall accuracy and 0.90 area under receiver operating characteristic curve) followed by GA-BDT, GA-KNN and GA-ANN (accuracy 78%, 77.5% and 76.8%).
Conclusion:
Manually selected nine radiomic features can be used in accurate severity scoring allowing the clinician to plan for more effective personalized treatment. They can also be useful for monitoring the progression of COVID-19 and response to therapy for clinical trials.&#xD.

Pulmonary CT Registration Network Based on Deformable Cross Attention.

Current Transformer structure utilizes the self-attention mechanism to model global contextual relevance within image, which makes an impact on medical image registration. However, the use of Transformer in handling large deformation lung CT registration is relatively straightforwardly. These models only focus on single image feature representation neglecting to employ attention mechanism to capture the across image correspondence. This hinders further improvement in registration performance. To address the above limitations, we propose a novel registration method in a cascaded manner, Cascaded Swin Deformable Cross Attention Transformer based U-shape structure (SD-CATU), to address the challenge of large deformations in lung CT registration. In SD-CATU, we introduce a Cross Attention-based Transformer (CAT) block that incorporates the Shifted Regions Multihead Cross-attention (SR-MCA) mechanism to flexibly exchange feature information and thus reduce the computational complexity. Besides, a consistency constraint in the loss function is used to ensure the preservation of topology and inverse consistency of the transformations. Experiments with public lung datasets demonstrate that the Cascaded SD-CATU outperforms current state-of-the-art registration methods (Dice Similarity Coefficient of 93.19% and Target registration error of 0.98mm). The results further highlight the potential for obtaining excellent registration accuracy while assuring desirable smoothness and consistency in the deformed images.

Atomized inhalation of indocyanine green in thoracoscopic surgery for intralobar pulmonary sequestration: a multicenter study

BackgroundInvestigate the safety and efficacy of preoperative atomization inhalation of indocyanine green (ICG) solution in precise lesion resection of pediatric thoracoscopic intralobar pulmonary sequestration.MethodsA multicenter 1:1 matched case-control study was adopted, to compare the safety and efficacy of the ICG group (preoperative atomization inhalation of 0.5 mg/kg ICG solution) with traditional group (no preoperative atomization inhalation of ICG solution). The baseline, intraoperative, and postoperative recovery conditions of the two groups were observed. Outpatient follow-up visits were conducted 3 to 6 months after surgery, including lung CT scans and pulmonary ventilation function tests.Results134 patients were included in the study. The ICG group included 67 patients, and the traditional surgery group included 67 patients matched at a ratio of 1:1 according to age and lesion location. There were no reports of deaths or adverse reactions. The postoperative chest drainage tube indwelling time [(53.19 ± 8.15) hours vs. (73.25 ± 15.51) hours, P < 0.001] and postoperative hospital stay [(4.81 ± 1.84) days vs. (6.72 ± 1.31) days, P < 0.001] were shorter in the ICG group than in the traditional group. More importantly, the postoperative pulmonary function in the ICG group was better than that in the traditional group. No residual lesions were found in the postoperative CT examination of both groups.ConclusionsThe innovative application of atomization inhalation of ICG provides the possibility for precise localization and lesion resection of pediatric thoracoscopic intralobar pulmonary sequestration. This maximizes the preservation of normal lung parenchyma, better improves postoperative pulmonary function, and shortens postoperative recovery time.

“Lung Cancer Prediction Using Machine Learning: A Comprehensive Study”

Lung cancer remains one of the leading causes of cancer-related deaths worldwide, largely due to the fact that it is often diagnosed at a late stage. This research aims to develop an automated system for early detection of lung cancer using machine learning techniques. By utilizing the Lungs CT Scan Dataset from Kaggle, we implement advanced image processing methods and convolutional neural networks (CNN) to accurately identify and classify lung nodules as either benign or malignant. Our methodology includes important data preprocessing steps, such as normalization and augmentation, to enhance the performance of the model.” “The results of our study show a significant improvement in detection accuracy compared to traditional diagnostic methods, with our model achieving a high accuracy rate. Additionally, our system has the potential to reduce diagnostic errors, increase early detection rates, and offer a cost-effective screening solution. By integrating this automated tool into clinical workflows, we aim to provide radiologists with reliable AI-generated insights, ultimately improving patient outcomes and easing the burden on healthcare systems.” This study highlights the transformative potential of machine learning in medical diagnostics and emphasizes the importance of continued research to further optimize these technologies for clinical use..

Decreases in lung volume and lung height among ankylosing spondylitis patients with pulmonary function impairment: a 3D CT investigation

BackgroundRespiratory compromise among ankylosing spondylitis (AS) patients is characterized by restrictive pulmonary function, leading to the need for a meticulous assessment of lung volume. Compared with conventional pulmonary function tests (PFTs), CT-based lung volume measurements have superior accuracy and are crucial for understanding functional limitations in AS. This study investigated the correlation between CT lung volume and PFT parameters in AS patients, with a focus on changes in CT parameters and lung volume in patients with compromised pulmonary function.MethodsA total of 79 AS patients were included, and their full-length radiographs, thoracic CT scans, and PFT data were analysed. Specialized software was used to estimate the total and lobar lung volumes from the CT scans. The relationship between lung volume and PFT results was examined, and a multiple linear regression model was constructed to determine the influence of radiographic and CT parameters on total lung volume (TLV). Patients were classified into normal or impaired pulmonary function groups based on PFT outcomes, thus facilitating comparative analyses of radiographic and CT parameters and lung volumes between these groups.ResultsAmong the 79 AS patients, 19 had normal function, 4 had mixed dysfunction, and 56 experienced restrictive dysfunction. PFT parameters, including FVC, FEV1, TLC, FEV1%, and TLC%, showed varying correlations with TLV and individual lobe volume. Patients with compromised pulmonary function exhibited more pronounced spinal kyphosis and experienced a decline in TLV. Multiple regression analysis revealed that lung height and horizontal and vertical lung diameters independently influenced TLV. Notably, a decrease in lung height was observed in patients with impaired pulmonary function, whereas the horizontal and vertical diameters of the lungs remained stable.ConclusionsIn AS patients, TLV was found to be correlated with pulmonary function, particularly parameter such as FVC, FEV1, and TLC. A significant reduction in TLV was observed in those with impaired pulmonary function, with the primary contributing factor being a decrease in lung height.

Multi-level digital-twin models of pulmonary mechanics: correlation analysis of 3D CT lung volume and 2D Chest motion

Creating multi-level digital-twin models for mechanical ventilation requires a detailed estimation of regional lung volume. An accurate generic map between 2D chest surface motion and 3D regional lung volume could provide improved regionalisation and clinically acceptable estimates localising lung damage. This work investigates the relationship between CT lung volumes and the forced vital capacity (FVC) a surrogate of tidal volume proven linked to 2D chest motion. In particular, a convolutional neural network (CNN) with U-Net architecture is employed to build a lung segmentation model using a benchmark CT scan dataset. An automated thresholding method is proposed for image morphology analysis to improve model performance. Finally, the trained model is applied to an independent CT dataset with FVC measurements for correlation analysis of CT lung volume projection to lung recruitment capacity. Model training results show a clear improvement of lung segmentation performance with the proposed automated thresholding method compared to a typically suggested fixed value selection, achieving accuracy greater than 95% for both training and independent validation sets. The correlation analysis for 160 patients shows a good correlation ofRsquared value of 0.73 between the proposed 2D volume projection and the FVC value, which indicates a larger and denser projection of lung volume relative to a greater FVC value and lung recruitable capacity. The overall results thus validate the potential of using non-contact, non-invasive 2D measures to enable regionalising lung mechanics models to equivalent 3D models with a generic map based on the good correlation. The clinical impact of improved lung mechanics digital twins due to regionalising the lung mechanics and volume to specific lung regions could be very high in managing mechanical ventilation and diagnosing or locating lung injury or dysfunction based on regular monitoring instead of intermittent and invasive lung imaging modalities.

Correlations between blood vessel distribution, lung function and structural change in idiopathic pulmonary fibrosis.

Correlations between the image analysis of CT scan, lung function and quality of life in patients with idiopathic pulmonary fibrosis (IPF) remain unclear. This study aimed to investigate the impacts of pulmonary blood-vessel distribution and the extent of fibrosis on the lung function and quality of life of patients with IPF. Patients were enrolled in an IPF registry and had completed pulmonary function tests, chest HRCT, St. George Respiratory Questionnaire (SGRQ) and echocardiography. Pulmonary blood-vessel distribution, specific image-derived airway volume (siVaw) and fibrosis extent (siVfib) were quantitatively calculated by functional respiratory imaging on HRCT. The study subjects were categorized into DLco <40% pred. (n = 40) and DLco ≥40% pred. (n = 19) groups. Patients with DLco <40% pred. had significantly higher scores of SGRQ, composite physiologic index (CPI), exercise oxygen desaturation (∆SpO2), siVaw, lower FVC% pred. and 6-minute walking distance% pred. The proportion of small blood vessels in the upper lobes (BV5PR-UL) was significantly correlated with CPI, DLco % Pred., FVC% pred., SGRQ and ∆SpO2. Only BV5PR-UL had a significant impact on all indices but not BV5PR in the lower lobes (BV5PR-LL). siVfib was significantly negatively correlated with BV5PR-UL, DLco% pred. and FVC% pred., as well as positively correlated with CPI, ∆SpO2 and siVaw. BV5PR-UL and siVfib had significant correlations with lung function and may become important indicators to assess the severity of IPF and the impact on quality of life.

A Case Report of Isotretinoin: Acute Eosinophilic Pneumonia, A Rare Adverse Reaction

Abstract This is a case of acute eosinophilic pneumonia (AEP) in a 21-year-old woman with acne vulgaris who was treated with isotretinoin. During treatment, her blood tests showed normal results until the third month, when there was a slight increase in eosinophil count. In the fifth month of treatment, the patient presented to the pulmonology outpatient clinic with complaints of shortness of breath. Lung CT imaging revealed infiltration in both lungs characterized by ground glass opacities. AEP was diagnosed based on blood and imaging results. Isotretinoin therapy was terminated, and oral steroids were initiated, resulting in the improvement of the patient’s symptoms. The patient had no additional risk factors except for smoking, but she had not started or resumed smoking after quitting. This case report highlights the possibility of hypersensitivity pneumonitis following isotretinoin administration.

B-Lines in the Assessment of Interstitial Lung Disease Associated with Scleroderma: The Role of Handheld Devices.

Background: Timely detection and aggressive management of interstitial lung disease (ILD) in systemic sclerosis (SSc) are essential to improving outcomes and reducing risks of irreversible lung injury. Objective: to explore the usefulness of an ultraportable ultrasound device for the management of SSc-related ILD and to compare it with clinical and instrumental data. Methods: A total of 19 consecutive SSc patients underwent a comprehensive pulmonary evaluation: clinical, pulmonary function tests (PFTs) (spirometry, DLCO), lung CT (1.5 mm slice thickness reconstruction; HRCT), and lung ultrasound (LUS). A total score was calculated based on the number of color-coded B-lines recorded for each lung sliding. B-lines were analyzed against dyspnea, cough, Velcro, CT imaging (Warrick's score), and PFTs. Global and subgroup analysis were performed (diffuse versus limited cutaneous SSc, Warrick's < 7 versus >7). Results: Symptomatic lung involvement with varying degrees of dyspnea was reported in about 74% of cases (functional NYHA > 2 in more than half), chronic dry cough in one-third, Velcro rales in 42%. A total of 84.24% were classified as SSc with ILD on CT imaging. Statistically significant mild-to-moderate correlations between B-lines and clinical manifestations were demonstrated, as well as PFTs and Warrick's scores (more B-lines, lower pulmonary function, but higher extent and severity on CT) (p < 0.05); there were differences between SSc patients without and with ILD in terms of the number and distribution of B-lines (p < 0.05), as well as different B-lines patterns and numbers in diffuse versus limited SSc (p < 0.05). Conclusions: Ultraportable handheld LUS is a promising method suitable for the management (screening, early detection, and evaluation) of SSc patients.

Development of a nomogram-based model incorporating radiomic features from follow-up longitudinal lung CT images to distinguish invasive adenocarcinoma from benign lesions: a retrospective study

PurposeTo develop and validate a radiomic model for differentiating pulmonary invasive adenocarcinomas from benign lesions based on follow-up longitudinal CT images.MethodsThis is a retrospective study including 336 patients (161 with invasive adenocarcinomas and 175 with benign lesions) who underwent baseline (T0) and follow-up (T1) CT scans from January 2016 to June 2022. The patients were randomized in a 7:3 ratio into training and test sets. Radiomic features were extracted from lesion volumes of interest on longitudinal CT images at T0 and T1. Differences in radiomic features between T1 and T0 were defined as delta-radiomic features. Logistic regression was used to build models based on clinicoradiological (CR), T0, T1, and delta radiomic features and compute signatures. Finally, a nomogram based on the CR, T0, T1 and delta signatures was constructed. Model performance was evaluated for calibration, discrimination, and clinical utility.ResultsThe T1 radiomic model was superior to the other independent models. In the training set, it had an area under the curve (AUC) of 0.858), superior to the CR model (AUC 0.694), the T0 radiomic model (AUC 0.825), and the delta radiomic model (AUC 0.734). In the test set, it had an AUC of 0.817, again outperforming the CR model (AUC 0.578), the T0 radiomic model (AUC 0.789), and the delta radiomic model (AUC 0.647). The nomogram incorporating the CR, T0, T1 and delta signatures showed the best predictive performance in both the training (AUC: 0.906) and test sets (AUC: 0.856), and it exhibited excellent fit with calibration curves. Decision curve analysis provided additional validation of the clinical utility of the nomogram.ConclusionA nomogram utilizing radiomic features extracted from longitudinal CT images can enhance the discriminative capability between pulmonary invasive adenocarcinomas and benign lesions.

Anomaly detection scheme for lung CT images using vector quantized variational auto-encoder with support vector data description.

This study aims to develop an anomaly-detection scheme for lesions in CT images. Our database consists of lung CT images obtained from 1500 examinees. It includes 1200 normal and 300 abnormal cases. In this study, SVDD (Support Vector Data Description) mapping the normal latent variables into a hypersphere as small as possible on the latent space is introduced to VQ-VAE (Vector Quantized-Variational Auto-Encoder). VQ-VAE with SVDD is constructed from two encoders, two decoders, and an embedding space. The first encoder compresses the input image into the latent-variable map, whereas the second encoder maps the normal latent variables into a hypersphere as small as possible. The first decoder then up-samples the mapped latent variables into a latent-variable map with the original size. The second decoder finally reconstructs the input image from the latent-variable map replaced by the embedding representations. The data of each examinee is classified as abnormal or normal based on the anomaly score defined as the combination of the difference between the input image and the reconstructed image and the distance between the latent variables and the center of the hypersphere. The area under the ROC curve for VQ-VAE with SVDD was 0.76, showing an improvement when compared with the conventional VAE (0.63, p < .001). VQ-VAE with SVDD developed in this study can yield higher anomaly-detection accuracy than the conventional VAE. The proposed method is expected to be useful for identifying examinees with lesions and reducing interpretation time in CT screening.

Lethal mediastinal emphysema caused by inhalation of paraquat: a case report

This paper reports on a 22-year-old male presenting with persistent chest pain accompanied by mediastinal emphysema. We firstly considered mediastinal emphysema induced by community acquired pneumonia. Pathogen detection was performed but no positive results were found. Based on the results of a subsequent lung CT scan, paraquat poisoning was suspected. Although there was no trace of paraquat in the blood, the nebulizer masks used by patient at home was found to be positive for paraquat. The diagnosis was ultimately established as paraquat poisoning via inhalation with mediastinal emphysema. This case report explores the clinical manifestations, diagnostic challenges, and treatment complexities of inhaled paraquat poisoning, emphasizing the importance of recognizing this rare poisoning route and its atypical symptoms.

Streamlining management in thoracic trauma: radiomics- and AI-based assessment of patient risks.

In blunt chest trauma, patient management is challenging because clinical guidelines miss tools for risk assessment. No clinical scale reliably measures the severity of cases and the chance of complications. The objective of the study was to optimize the management of patients with blunt chest trauma by creating models prognosticating the transfer to the intensive care unit and in-hospital length of stay (LOS). The study cohort consisted of 212 cases. We retrieved information on the cases from the hospital's trauma registry. After segmenting the lungs with Lung CT Analyzer, we performed volumetric feature extraction with data-characterization algorithms in PyRadiomics. To predict whether the patient will require intensive care, we used the three groups of findings: ambulance, admission, and radiomics data. When trained on the ambulance data, the models exhibited a borderline performance. The metrics improved after we retrained the models on a combination of ambulance, laboratory, radiologic, and physical examination data (81.5% vs. 94.4% Sn). Radiomics data were the top-accurate predictors (96.3% Sn). Age, vital signs, anthropometrics, and first aid time were the best-performing features collected by the ambulance service. Laboratory findings, AIS scores for the lower extremity, abdomen, head, and thorax constituted the top-rank predictors received on admission to the hospital. The original first-order kurtosis had the highest predictive value among radiomics data. Top-informative radiomics features were derived from the right hemithorax because the right lung is larger. We constructed regression models that can adequately reflect the in-hospital LOS. When trained on different groups of data, the machine-learning regression models showed similar performance (MAE/ROV 8%). Anatomic scores for the body parts other than thorax and laboratory markers of hemorrhage had the highest predictive value. Hence, the number of injured body parts correlated with the case severity. The study findings can be used to optimize the management of patients with a chest blunt injury as a specific case of monotrauma. The models we built may help physicians to stratify patients by risk of worsening and overcome the limitations of existing tools for risk assessment. High-quality AI models trained on radiomics data demonstrate superior performance.

Beneficial effect of sequential treatment with high-dose steroids and short-course oral glucocorticoids in patients with severe influenza virus-associated pneumonia

Accumulating evidence supports that glucocorticoid treatment for viral pneumonia (VPA) can shorten the disease course and improve survival. However, currently, the use of glucocorticoids in treating VPA remains controversial. Moreover, a unified standard for the dosage and duration of glucocorticoid therapy has not been presented in published articles. A retrospective analysis was conducted in patients who were hospitalized for severe influenza virus-associated pneumonia, and they received sequential treatment with high-dose glucocorticoids and short-course oral glucocorticoids. Patients were followed up for 3 months. A total of 11 patients were included in the study (average age 56 years). There was no gender difference, but age and underlying diseases could be risk factors for severe influenza virus-associated pneumonia. The types of viruses causing pneumonia included influenza A/B. The main clinical symptoms of patients were fever, cough, sputum production, and dyspnea. Chest computed tomography showed multiple ground-glass shadows in the lobes, and the presence of bacterial and fungal infections was accompanied by consolidation shadows. After glucocorticoid therapy, the symptoms improved. None of the patients underwent tracheal intubation, and all survived. After a 3-month follow-up, lung CT absorption in all patients had reached more than 80%, and lung imaging absorption in 20% patients was complete. No serious complications occurred in any of the patients. Sequential treatment with high-dose steroids and short-course oral glucocorticoids may be helpful for reducing the tracheal intubation rate and mortality rate in patients with severe influenza virus-associated pneumonia. Additionally, short-course oral glucocorticoids may reduce pulmonary fibrosis in patients with severe influenza virus-associated pneumonia without any serious complications.

Software using artificial intelligence for nodule and cancer detection in CT lung cancer screening: systematic review of test accuracy studies

ObjectivesTo examine the accuracy and impact of artificial intelligence (AI) software assistance in lung cancer screening using CT.MethodsA systematic review of CE-marked, AI-based software for automated detection and analysis of...

Radiomultiomics: quantitative CT clusters of severe asthma associated with multi-omics.

Lung quantitative computed tomographic (qCT) severe asthma clusters have been reported, but their replication and underlying disease mechanisms are unknown. We identified and replicated qCT clusters of severe asthma in two independent asthma cohorts and determined their association with molecular pathways. We used consensus clustering on qCT measurements of airway and lung CT scans, performed in 105 severe asthmatic adults from the U-BIOPRED cohort. The same qCT measurements were used to replicate qCT clusters in a subsample of the ATLANTIS asthma cohort (n=97). We performed integrated enrichment analysis using blood, sputum, bronchial biopsies, bronchial brushings and nasal brushings transcriptomics and blood and sputum proteomics to characterize radiomultiomic-associated clusters (RACs). qCT clusters and clinical features in U-BIOPRED were replicated in the matched ATLANTIS cohort. In the U-BIOPRED cohort, RAC1 (n=30) was predominantly female with elevated BMI, mild airflow limitation, normal qCT parameters and upregulation of the complement pathway. RAC2 (n=34) subjects had a lower degree of airflow limitation, airway wall thickness and dilatation, with upregulation of proliferative pathways, including neurotrophic receptor tyrosine kinase 2/tyrosine kinase receptor B (NTRK2/TRKB), and down-regulation of semaphorin pathways. RAC3 (n=41) showed increased lung attenuation area and air trapping, severe airflow limitation, hyperinflation, and upregulation of cytokine signaling and signaling by interleukin pathways, and matrix metallopeptidase 1, 2 and 9. U-BIOPRED severe asthma qCT clusters were replicated in a matched independent asthmatic cohort and associated with specific molecular pathways. Radiomultiomics might represent anovel strategy to identify new molecular pathways in asthma pathobiology.

Prediction of benign and malignant ground glass pulmonary nodules based on multi-feature fusion of attention mechanism.

The purpose of this study was to develop and validate a new feature fusion algorithm to improve the classification performance of benign and malignant ground-glass nodules (GGNs) based on deep learning. We retrospectively collected 385 cases of GGNs confirmed by surgical pathology from three hospitals. We utilized 239 GGNs from Hospital 1 as the training and internal validation set, and 115 and 31 GGNs from Hospital 2 and Hospital 3, respectively, as external test sets 1 and 2. Among these GGNs, 172 were benign and 203 were malignant. First, we evaluated clinical and morphological features of GGNs at baseline chest CT and simultaneously extracted whole-lung radiomics features. Then, deep convolutional neural networks (CNNs) and backpropagation neural networks (BPNNs) were applied to extract deep features from whole-lung CT images, clinical, morphological features, and whole-lung radiomics features separately. Finally, we integrated these four types of deep features using an attention mechanism. Multiple metrics were employed to evaluate the predictive performance of the model. The deep learning model integrating clinical, morphological, radiomics and whole lung CT image features with attention mechanism (CMRI-AM) achieved the best performance, with area under the curve (AUC) values of 0.941 (95% CI: 0.898-0.972), 0.861 (95% CI: 0.823-0.882), and 0.906 (95% CI: 0.878-0.932) on the internal validation set, external test set 1, and external test set 2, respectively. The AUC differences between the CMRI-AM model and other feature combination models were statistically significant in all three groups (all p<0.05). Our experimental results demonstrated that (1) applying attention mechanism to fuse whole-lung CT images, radiomics features, clinical, and morphological features is feasible, (2) clinical, morphological, and radiomics features provide supplementary information for the classification of benign and malignant GGNs based on CT images, and (3) utilizing baseline whole-lung CT features to predict the benign and malignant of GGNs is an effective method. Therefore, optimizing the fusion of baseline whole-lung CT features can effectively improve the classification performance of GGNs.

Investigate the association between genetic polymorphisms of ACE and ACE-2 with some biomarkers in Iraqi patients with COVID-19.

BackgroundThe angiotensin-converting enzyme 2 (ACE2) receptor plays a critical role in mediating SARS-CoV-2 infection. Understanding the genetic factors influencing COVID-19 severity is crucial for developing effective treatment strategies. This study aimed to investigate the association between genetic polymorphism of the ACE gene, specifically the insertion/deletion (I/D) polymorphism in the promoter region, and clinical parameters in COVID-19 patients. MethodsA case-control study was conducted with 225 participants from an Iraqi population. Blood samples were collected for hematological analysis and ACE genotyping. The association between ACE genotypes (DD, ID, II), demographic factors, and clinical parameters, including D-dimer, ferritin, lactate dehydrogenase (LDH), C-reactive protein (CRP), white blood cell (WBC) count, lymphocyte count, packed cell volume (PCV), red blood cell (RBC) count, hemoglobin (HB), and platelet count, was assessed. ResultsCOVID-19 patients exhibited significant differences compared to controls regarding D-dimer, ferritin, LDH, CRP, WBC, lymphocytes, PCV, and RBC (P < 0.05), while no significant differences were found for HB and platelet counts. The DD genotype was predominant (43.11 %), followed by ID (39.56 %) and II (17.33 %). Notably, a significant association was observed between D-dimer levels and ACE genotype (P < 0.0001), with higher levels observed in individuals with the DD genotype. Additionally, a significant correlation was found between ACE genotype and sex (P = 0.0163). No significant association was observed between genotype and ICU admission or lung CT severity. ConclusionOur findings suggest a potential link between the ACE D/D genotype and elevated D-dimer levels in COVID-19 patients, indicating a potential role for ACE gene polymorphism in influencing disease severity. Further research is warranted to elucidate the underlying mechanisms and explore the potential for personalized treatment approaches based on ACE genotype.

Comparison of Hounsfield Unit variability between two same model photon counting CT detector systems. A phantom study applied to lung CT

Accurate quantification of lung density, in Hounsfield Units (HU), is of high importance to monitor progression of diseases such as emphysema using chest CT imaging. Reproducibility of HU quantification on independent photon counting detector CT (PCD-CT) systems with a focus on lung imaging have not yet been evaluated. We thus aimed to evaluate HU reproducibility on 2 independent PCD-CT systems using a repeatable phantom setup with identical acquisition and image reconstruction settings. A COPDGene phantom comprising densities of air, water and lung was scanned on 2 independent PCCT systems using 3 different radiation exposures, 2 medium-sharpness reconstruction kernels (Br40 and Qr36), with and without iterative reconstruction (levels 0 vs 3). Our results demonstrate that acquisitions performed with full dose (3.2 mGy), half dose (1.6 mGy), and one-eighth dose (0.4 mGy) had minimal influence on HU accuracy (<6 HU) when using Br40 and Qr36 kernels. The level of iterative reconstruction also has a minimal impact (<6 HU) with the same kernels. Between the 2 PCD-CT systems evaluated, reproducible HU quantification was achieved for changes to CTDIvol, iterative reconstruction level and reconstruction kernel.

Ventilator-associated pneumonia caused by a new-found opportunistic fungal pathogen—Myceliophthora heterothallica: a case report

BackgroundMyceliophthora heterothallica belonging to Myceliophthora is considered as an environmental fungus and has not been reported to be pathogenic or colonizing in recent literature. The present case firstly reports a ventilation-associated pneumonia caused by Myceliophthora heterothallica among the aged adult.Case presentationA 67-years-old Asian female patient suffering from a sudden disturbance of consciousness for 3 h was admitted to our hospital. Cardiac arrest occurred during emergency transport, and sinus rhythm was restored after cardiopulmonary resuscitation. Invasive mechanical ventilation was given to this patient for respiratory failure. After mechanical ventilation, the lung CT images showed multiple cuneiform nodules arranging subpleural accompanying with ground-glass opacity. On the 5th day of mechanical ventilation, Myceliophthora heterothallica was cultured from endotracheal aspirates. Two methods, namely automatic microbial identification system and internal transcribed spacer sequencing were employed to identify this fungus. The present case firstly uncovered the colonization ability and pathogenicity of Myceliophthora heterothallica in the respiratory tract. After 28d of treatment with piperacillin-tazobactam, this patient weaned from the ventilator and recovered from consciousness with lung infection disappearance.ConclusionsThis is the first case report of ventilation-associated pneumonia in the aged patient caused by Myceliophthora heterothallica. This current case is worth for the clinical diagnosis and treatment of Myceliophthora heterothallica infection, and also enriches new pathogenic species found of thermothelomyces species.