Lung Area Research Articles

Respiratory Support Effects over Time on Regional Lung Ventilation Assessed by Electrical Impedance Tomography in Premature Infants.

Background and objectives: Respiratory distress syndrome (RDS) frequently necessitates respiratory support. While non-invasive methods are typically the preferred approach, mechanical ventilation becomes necessary for patients with insufficient response. Our study aimed to compare two common respiratory support modes, volume-targeted mechanical ventilation and non-invasive ventilation continuous positive airway pressure (CPAP) and high-flow nasal cannula (HFNC), using electrical impedance tomography. Materials and Methods: Infants with very low birth weight and gestational ages of less than 32 weeks were eligible for inclusion in the study. All enrolled infants were beyond the transitional period (>72 h of age). The infants were divided into two groups: infants receiving invasive respiratory support through an endotracheal tube and infants receiving non-invasive respiratory support. We used electrical impedance tomography to assess end-expiratory lung impedance (EELZ), DeltaZ, heterogeneity, and regional ventilation distribution. Patients were evaluated at 0, 30, and 60 min after assuming the supine position to examine potential time-related effects. Results: Our study initially enrolled 97 infants, and the final analysis included a cohort of 72 infants. Ventilated infants exhibited significantly larger EELZ compared to their non-invasive counterparts (p = 0.026). DeltaZ was also greater in the invasive respiratory support group (p < 0.001). Heterogeneity was higher in the non-invasive group and did not change significantly over time. The non-invasive group demonstrated significantly greater ventilation in the dependent lung areas compared to intubated patients (p = 0.005). Regional distribution in the left lung was lower than in the right lung in both groups; however, this difference was significantly more pronounced in intubated patients (p < 0.001). Conclusions: Our study revealed that volume-targeted mechanical ventilation results in higher EELZ and DeltaZ compared to spontaneously breathing infants receiving non-invasive respiratory support. However, lung heterogeneity was lower during mechanical ventilation. Our study also reaffirmed that spontaneous breathing promotes greater involvement of the dependent lung compared to mechanical ventilation.

Quantitative Characterization of Respiratory Patterns on Dynamic Higher Temporal Resolution MRI to Stratify Postacute Covid-19 Patients by Cardiopulmonary Symptom Burden.

Postacute Covid-19 patients commonly present with respiratory symptoms; however, a noninvasive imaging method for quantitative characterization of respiratory patterns is lacking. To evaluate if quantitative characterization of respiratory pattern on free-breathing higher temporal resolution MRI stratifies patients by cardiopulmonary symptom burden. Prospective analysis of retrospectively acquired data. A total of 37 postacute Covid-19 patients (25 male; median [interquartile range (IQR)] age: 58 [42-64] years; median [IQR] days from acute infection: 335 [186-449]). 0.55 T/two-dimensional coronal true fast imaging with steady-state free precession (trueFISP) at higher temporal resolution. Patients were stratified into three groups based on presence of no (N = 11), 1 (N = 14), or ≥2 (N = 14) cardiopulmonary symptoms, assessed using a standardized symptom inventory within 1 month of MRI. An automated lung postprocessing workflow segmented each lung in each trueFISP image (temporal resolution 0.2 seconds) and respiratory curves were generated. Quantitative parameters were derived including tidal lung area, rates of inspiration and expiration, lung area coefficient of variability (CV), and respiratory incoherence (departure from sinusoidal pattern) were. Pulmonary function tests were recorded if within 1 month of MRI. Qualitative assessment of respiratory pattern and lung opacity was performed by three independent readers with 6, 9, and 23 years of experience. Analysis of variance to assess differences in demographic, clinical, and quantitative MRI parameters among groups; univariable analysis and multinomial logistic regression modeling to determine features predictive of patient symptom status; Akaike information criterion to compare the quality of regression models; Cohen and Fleiss kappa (κ) to quantify inter-reader reliability. Two-sided 5% significance level was used. Tidal area and lung area CV were significantly higher in patients with two or more symptoms than in those with one or no symptoms (area: 15.4 cm2 vs. 12.9 cm2 vs. 12.8 cm2 ; CV: 0.072, 0.067, and 0.058). Respiratory incoherence was significantly higher in patients with two or more symptoms than in those with one or no symptoms (0.05 vs. 0.043 vs. 0.033). There were no significant differences in patient age (P = 0.19), sex (P = 0.88), lung opacity severity (P = 0.48), or pulmonary function tests (P = 0.35-0.97) among groups. Qualitative reader assessment did not distinguish between groups and showed slight inter-reader agreement (κ = 0.05-0.11). Quantitative respiratory pattern measures derived from dynamic higher-temporal resolution MRI have potential to stratify patients by symptom burden in a postacute Covid-19 cohort. 3 TECHNICAL EFFICACY: Stage 3.

Intelligent Diagnosis of Lung Cancer Using Deep Learning

Introduction: Lung cancer, a highly prevalent disease worldwide, poses a significant risk to individuals. Nodules, which manifest as minuscule masses in the lungs, serve as crucial indicators of the early stages of the disease, with the possibility of being either benign or malignant in nature. Prompt diagnosis of this ailment plays a pivotal role in saving patients' lives, thus rendering the utilization of computed aided diagnosis methods exceedingly valuable within this domain.Material and Methods: The methodology employed for presentation purposes is deeply rooted in the principles of deep learning, a field that epitomizes the amalgamation of artificial intelligence and neuroscience. Delving into the specifics, the initial phase of this process entails the preprocessing of data, wherein the lung area is meticulously isolated from computed tomography (CT) scan images. Subsequently, in the second stage, the identification of nodules is facilitated through the employment of the mask region convolutional neural network (RCNN) technique, which effectively entails the delineation of masks and bounding boxes. The third and final step involves the classification of the identified nodules, achieved through the utilization of a singular convolutional neural network, ultimately segregating the nodules into three distinct categories: benign, malignant, and ambiguous. In order to evaluate the efficacy of the proposed method, the LIDC-IDRI dataset was employed as a means of testing, thereby furnishing tangible evidence to substantiate the claim that the presented method is on par with its counterparts within the realm of detecting and classifying lung nodules.Results: It is worth noting that the proposed method has yielded a remarkable accuracy rate of 95% in the phase of nodule detection, further bolstering its credibility and reliability. Furthermore, the accuracy rate achieved during the step of nodule classification stands at an impressive 97.3%, thereby cementing the efficacy of the proposed method in a comprehensive manner.Conclusion: The purpose of this work to provide an intelligent system for reducing the amount of the workload of the physicians in this field. After examining and studying some data set, the LIDC-IDRI data set is presented, for this work because of being suitable for two works of detecting nodule place and nodule classification, lots of data, and because of being reliable and used in previous reliable works that can provide the ability to compare the results.

Comparison of the Discrimination Performance of AI Scoring and the Brixia Score in Predicting COVID-19 Severity on Chest X-Ray Imaging: Diagnostic Accuracy Study.

The artificial intelligence (AI) analysis of chest x-rays can increase the precision of binary COVID-19 diagnosis. However, it is unknown if AI-based chest x-rays can predict who will develop severe COVID-19, especially in low- and middle-income countries. The study aims to compare the performance of human radiologist Brixia scores versus 2 AI scoring systems in predicting the severity of COVID-19 pneumonia. We performed a cross-sectional study of 300 patients suspected with and with confirmed COVID-19 infection in Jakarta, Indonesia. A total of 2 AI scores were generated using CAD4COVID x-ray software. The AI probability score had slightly lower discrimination (area under the curve [AUC] 0.787, 95% CI 0.722-0.852). The AI score for the affected lung area (AUC 0.857, 95% CI 0.809-0.905) was almost as good as the human Brixia score (AUC 0.863, 95% CI 0.818-0.908). The AI score for the affected lung area and the human radiologist Brixia score had similar and good discrimination performance in predicting COVID-19 severity. Our study demonstrated that using AI-based diagnostic tools is possible, even in low-resource settings. However, before it is widely adopted in daily practice, more studies with a larger scale and that are prospective in nature are needed to confirm our findings.

Respiratory function after 30+ years following sulfur mustard exposure in survivors in Sweden.

Sulfur mustard (SM) exposure causes acute and chronic respiratory diseases. The extent of small airway dysfunction (SAD) in individuals exposed to SM is unclear. This study evaluated and compared SAD in SM-exposed and SM-unexposed participants using noninvasive lung function tests assessing small airway function. This retrospective cohort study involved SM-exposed (n = 15, mean age: 53 ± 8 years) and SM-unexposed (n = 15, mean age: 53 ± 7 years) Kurdish-Swedish individuals in Sweden. Small airway resistance and reactance were assessed using impulse oscillometry (IOS). Nitrogen (N2) multiple breath washout (MBW) was employed to assess lung ventilation heterogeneity. The gas-exchanging capacity of the lungs was assessed using the diffusing capacity of the lungs for the carbon monoxide (DLCO) test. Lung function outcomes were reported as absolute values and z-scores. Group comparisons were performed using the Mann-Whitney U test. No statistically significant differences in age, height, or body mass index were observed between the two groups. IOS showed significantly increased small airway resistance, while N2MBW exhibited significantly increased global and acinar ventilation heterogeneity in SM-exposed individuals compared to that in unexposed individuals. SAD was identified in 14 of 15 SM-exposed individuals, defined as at least one abnormal IOS difference between resistance at 5 and 20 Hz (R5-R20) and/or area of reactance (AX) or N2MBW lung's acinar zone (Sacin), and DLCO adjusted to the alveolar volume (DLCO/VA) outcome. Of these 14 individuals, only 5 demonstrated concordant findings across the IOS and N2MBW tests. Exposure to SM was positively associated with long-term impairment of respiratory tract function in the small airways in the majority of the previously SM-exposed individuals in the present study. Furthermore, both IOS and N2MBW should be employed to detect SAD in SM-exposed survivors as they provide complementary information. Identifying and characterizing the remaining pathology of the small airways in survivors of SM exposure is a first step toward improved treatment and follow-up.

Sublobar resection or lobectomy and postoperative respiratory complications in emphysematous lungs.

Pulmonary resection in patients with severe emphysema may impact postoperative respiratory complications. Low-attenuation areas evaluated using three-dimensional computed tomography to assess emphysematous changes are strongly associated with postoperative respiratory complications. Herein, we investigated the relationship between low-attenuation area, the surgical procedure and resected lung volume, which has not been explored in previous studies. We retrospectively evaluated patients with primary or metastatic lung cancer who underwent surgical resection. The low-attenuation area percentage (low-attenuation area/total lung area × 100) and resected lung volume were calculated using three-dimensional computed tomography software, and the relationship with postoperative respiratory complications was analysed. Postoperative respiratory complications occurred in 66 patients (17%) in the total cohort (n = 383). We set the median value of 1.1% as the cut-off value for low-attenuation area percentage to predict postoperative respiratory complications, which occurred in 24% and 10% of patients with low-attenuation area >1.1% and <1.1%, respectively (P < 0.001). Postoperative respiratory complications occurred in approximately one-third of the patients with low-attenuation area >1.1%, whose resected lung volume was ≥15.8% or ≥5 resected subsegments. Multivariable analysis revealed that sublobar resection was associated with a significantly lower risk of postoperative respiratory complications in patients with low-attenuation area >1.1% (odds ratio 0.4, 95% confidence interval 0.183-0.875). Emphysema is a risk factor for postoperative respiratory complications, and lobectomy is an independent predictive risk factor. Preserving more lung parenchyma may yield better short-term prognoses in patients with emphysematous lungs.

In vivo study to assess fat embolism resulting from the Reamer-Irrigator-Aspirator 2 system compared to a novel aspirator-based concept for intramedullary bone graft harvesting

IntroductionFat embolism (FE) following intramedullary (IM) reaming can cause severe pulmonary complications and sudden death. Recently, a new harvesting concept was introduced in which a novel aspirator is used first for bone marrow (BM) aspiration and then for subsequent aspiration of morselized endosteal bone during sequential reaming (A + R + A). In contrast to the established Reamer-Irrigator-Aspirator (RIA) 2 system, the new A + R + A concept allows for the evacuation of fatty BM prior to reaming. In this study, we hypothesized that the risk of FE, associated coagulopathic reactions and pulmonary FE would be comparable between the RIA 2 system and the A + R + A concept.Materials and methodsIntramedullary bone graft was harvested from intact femora of 16 Merino sheep (age: 1–2 years) with either the RIA 2 system (n = 8) or the A + R + A concept (n = 8). Fat intravasation was monitored with the Gurd test, coagulopathic response with D-dimer blood level concentration and pulmonary FE with histological evaluation of the lungs.ResultsThe total number and average size of intravasated fat particles was similar between groups (p = 0.13 and p = 0.98, respectively). D-dimer concentration did not significantly increase within 4 h after completion of surgery (RIA 2: p = 0.82; A + R + A: p = 0.23), with an interaction effect similar between groups (p = 0.65). The average lung area covered with fat globules was similar between groups (p = 0.17).ConclusionsThe use of the RIA 2 system and the novel A + R + A harvesting concept which consists of BM evacuation followed by sequential IM reaming and aspiration of endosteal bone, resulted in only minor fat intravasation, coagulopathic reactions and pulmonary FE, with no significant differences between the groups. Our results, therefore, suggest that both the RIA 2 system and the new A + R + A concept are comparable technologies in terms of FE-related complications.

Stacked neural nets for increased accuracy on classification on lung cancer

Lung cancer is regarded as one of the most lethal diseases endangering human survival. It is difficult to detect lung cancer in its early stages, because of the ambiguity in the lung regions in the medical images. Healthcare business is automating itself with the use of image recognition and data analytics, much as the computing sector has completely automated. This article proposes a novel architecture, the Stacked Neural Network (SNN), for the detection and classification of lung cancer using CT scan data. The goal of the proposed technique is to investigate the accuracy levels of different Neural Networks (NN) and determine the early stage of lung cancer. The most effective technique for processing medical images, classifying and detecting lung nodules, extracting features, and predicting the stage of lung cancer is deep learning. First, lung areas are extracted using image processing techniques. SNN is used for the segmentation process. Various neural network techniques are utilised for the classification process once the features are retrieved from the segmented pictures. The suggested methods' performances are assessed using F1-Measure, accuracy, precision, and recall metrics. 96% classification accuracy is shown in the testing findings, which is comparatively greater than other methods currently in use. Proposed algorithm is clearly supported for real-world clinical practice.

Portable Dynamic Chest Radiography: Literature Review and Potential Bedside Applications.

Dynamic digital radiography (DDR) is a high-resolution radiographic imaging technique using pulsed X-ray emission to acquire a multiframe cine-loop of the target anatomical area. The first DDR technology was orthostatic chest acquisitions, but new portable equipment that can be positioned at the patient's bedside was recently released, significantly expanding its potential applications, particularly in chest examination. It provides anatomical and functional information on the motion of different anatomical structures, such as the lungs, pleura, rib cage, and trachea. Native images can be further analyzed with dedicated post-processing software to extract quantitative parameters, including diaphragm motility, automatically projected lung area and area changing rate, a colorimetric map of the signal value change related to respiration and motility, and lung perfusion. The dynamic diagnostic information along with the significant advantages of this technique in terms of portability, versatility, and cost-effectiveness represents a potential game changer for radiological diagnosis and monitoring at the patient's bedside. DDR has several applications in daily clinical practice, and in this narrative review, we will focus on chest imaging, which is the main application explored to date in the literature. However, studies are still needed to understand deeply the clinical impact of this method.

Investigation of the correlation between the change in the projected lung area and forced vital capacity using biphasic chest dynamic digital radiography: a cross-sectional study.

Chest dynamic digital radiography (DDR) is used as a supplementary tool for the routine pulmonary function test (PFT); however, its potential as a novel standard PFT method has yet to be explored. Therefore, the present study aimed to investigate the correlation between the change in the projected lung area (ΔPLA) and forced vital capacity (FVC) using chest DDR, and to establish a DDR-FVC estimation model and a predictive value model for the ΔPLA. In total, 139 participants who underwent chest DDR and the PFT in the same period at The First Affiliated Hospital of Guangzhou Medical University from April 2022 to February 2023 were prospectively included in the study. The patients' age, gender, height, and weight measurements were recorded. Additionally, the ΔPLA was measured, and the IWS workstation software was used for automated outlining and calculation. Subsequently, a correlation analysis and regression analysis models were employed to examine the relationship between the ΔPLA, FVC, and individual physiological characteristics. Additionally, an independent sample t-test was used to determine whether there were any significant differences between the normal and abnormal FVC groups. The 139 participants were grouped according to the results of the ratio of measured/predicted FVC values (FVC%pred); those with an FVC%pred ≥80%, were allocated to the normal FVC group, and those with an FVC%pred <80% were allocated to the abnormal FVC group. The correlation coefficient was >0.8 in the full sample; the ΔPLA showed a significant linear correlation with the measured FVC value [r=0.81, 95% confidence interval (CI): 0.75-0.86, P<0.001]. There was a significant difference in the ΔPLA between the normal and abnormal FVC groups. With the ΔPLA, age, gender, height, and weight as predictor variables, the following DDR-FVC estimation model was established: DDR-FVC estimation model = -0.997 + 1.35×10-4 × ΔPLA + 0.017 × height - 0.014 × age + 0.249 × gender (1 for male and 0 for female) [adjusted R2 (adj. R2)=0.731, F=94.615, P<0.001]. The following formula was used to determine the predictive value of the ΔPLA: Predictive value of ΔPLA = -12,504.287 + 173.185 × height + 62.971 × weight - 84.933 × age (adj. R2=0.393, F=20.453, P<0.001). There was a linear correlation between the ΔPLA measured by biphasic chest DDR and the FVC. A model for estimating the FVC was established based on the ΔPLA, which allows the FVC to be assessed by the ΔPLA measured by biphasic chest DDR. A predictive value model for the ΔPLA was also established to provide ΔPLA reference values for assessment and comparison.

Derivation and validation of novel integrated inpatient mortality prediction score for COVID-19 (IMPACT) using clinical, laboratory, and AI—processed radiological parameter upon admission: a multicentre study

Limited studies explore the use of AI for COVID-19 prognostication. This study investigates the relationship between AI-aided radiographic parameters, clinical and laboratory data, and mortality in hospitalized COVID-19 patients. We conducted a multicentre retrospective study. The derivation and validation cohort comprised of 512 and 137 confirmed COVID-19 patients, respectively. Variable selection for constructing an in-hospital mortality scoring model was performed using the least absolute shrinkage and selection operator, followed by logistic regression. The accuracy of the scoring model was assessed using the area under the receiver operating characteristic curve. The final model included eight variables: anosmia (OR: 0.280; 95%CI 0.095–0.826), dyspnoea (OR: 1.684; 95%CI 1.049–2.705), loss of consciousness (OR: 4.593; 95%CI 1.702–12.396), mean arterial pressure (OR: 0.928; 95%CI 0.900–0.957), peripheral oxygen saturation (OR: 0.981; 95%CI 0.967–0.996), neutrophil % (OR: 1.034; 95%CI 1.013–1.055), serum urea (OR: 1.018; 95%CI 1.010–1.026), affected lung area score (OR: 1.026; 95%CI 1.014–1.038). The Integrated Inpatient Mortality Prediction Score for COVID-19 (IMPACT) demonstrated a predictive value of 0.815 (95% CI 0.774–0.856) in the derivation cohort. Internal validation resulted in an AUROC of 0.770 (95% CI 0.661–0.879). Our study provides valuable evidence of the real-world application of AI in clinical settings. However, it is imperative to conduct prospective validation of our findings, preferably utilizing a control group and extending the application to broader populations.

Comparison of lung cancer occurring in fibrotic versus non-fibrotic lung on chest CT

PurposeEvaluate the behavior of lung nodules occurring in areas of pulmonary fibrosis and compare them to pulmonary nodules occurring in the non-fibrotic lung parenchyma.MethodsThis retrospective review of chest CT scans and electronic medical records received expedited IRB approval and a waiver of informed consent. 4500 consecutive patients with a chest CT scan report containing the word fibrosis or a specific type of fibrosis were identified using the system M*Model Catalyst (Maplewood, Minnesota, U.S.). The largest nodule was measured in the longest dimension and re-evaluated, in the same way, on the follow-up exam if multiple time points were available. The nodule doubling time was calculated. If the patient developed cancer, the histologic diagnosis was documented.ResultsSix hundred and nine patients were found to have at least one pulmonary nodule on either the first or the second CT scan. 274 of the largest pulmonary nodules were in the fibrotic tissue and 335 were in the non-fibrotic lung parenchyma. Pathology proven cancer was more common in nodules occurring in areas of pulmonary fibrosis compared to nodules occurring in areas of non-fibrotic lung (34% vs 15%, p < 0.01). Adenocarcinoma was the most common cell type in both groups but more frequent in cancers occurring in non-fibrotic tissue. In the non-fibrotic lung, 1 of 126 (0.8%) of nodules measuring 1 to 6 mm were cancer. In contrast, 5 of 49 (10.2%) of nodules in fibrosis measuring 1 to 6 mm represented biopsy-proven cancer (p < 0.01). The doubling time for squamous cell cancer was shorter in the fibrotic lung compared to non-fibrotic lung, however, the difference was not statistically significant (p = 0.24). 15 incident lung nodules on second CT obtained ≤ 18 months after first CT scan was found in fibrotic lung and eight (53%) were diagnosed as cancer.ConclusionsNodules occurring in fibrotic lung tissue are more likely to be cancer than nodules in the nonfibrotic lung. Incident pulmonary nodules in pulmonary fibrosis have a high likelihood of being cancer.

Artificial intelligence-based analysis of the spatial distribution of abnormal computed tomography patterns in SARS-CoV-2 pneumonia: association with disease severity

BackgroundThe substantial heterogeneity of clinical presentations in patients with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) pneumonia still requires robust chest computed tomography analysis to identify high-risk patients. While extension of ground-glass opacity and consolidation from peripheral to central lung fields on chest computed tomography (CT) might be associated with severely ill conditions, quantification of the central-peripheral distribution of ground glass opacity and consolidation in assessments of SARS-CoV-2 pneumonia remains unestablished. This study aimed to examine whether the central-peripheral distributions of ground glass opacity and consolidation were associated with severe outcomes in patients with SARS-CoV-2 pneumonia independent of the whole-lung extents of these abnormal shadows.MethodsThis multicenter retrospective cohort included hospitalized patients with SARS-CoV-2 pneumonia between January 2020 and August 2021. An artificial intelligence-based image analysis technology was used to segment abnormal shadows, including ground glass opacity and consolidation. The area ratio of ground glass opacity and consolidation to the whole lung (GGO%, CON%) and the ratio of ground glass opacity and consolidation areas in the central lungs to those in the peripheral lungs (GGO(C/P)) and (CON(C/P)) were automatically calculated. Severe outcome was defined as in-hospital death or requirement for endotracheal intubation.ResultsOf 512 enrolled patients, the severe outcome was observed in 77 patients. GGO% and CON% were higher in patients with severe outcomes than in those without. Multivariable logistic models showed that GGO(C/P), but not CON(C/P), was associated with the severe outcome independent of age, sex, comorbidities, GGO%, and CON%.ConclusionIn addition to GGO% and CON% in the whole lung, the higher the ratio of ground glass opacity in the central regions to that in the peripheral regions was, the more severe the outcomes in patients with SARS-CoV-2 pneumonia were. The proposed method might be useful to reproducibly quantify the extension of ground glass opacity from peripheral to central lungs and to estimate prognosis.

Changes in lung mechanics and ventilation-perfusion match: comparison of pulmonary air- and thromboembolism in rats

BackgroundPulmonary air embolism (AE) and thromboembolism lead to severe ventilation-perfusion defects. The spatial distribution of pulmonary perfusion dysfunctions differs substantially in the two pulmonary embolism pathologies, and the effects on respiratory mechanics, gas exchange, and ventilation-perfusion match have not been compared within a study. Therefore, we compared changes in indices reflecting airway and respiratory tissue mechanics, gas exchange, and capnography when pulmonary embolism was induced by venous injection of air as a model of gas embolism or by clamping the main pulmonary artery to mimic severe thromboembolism.MethodsAnesthetized and mechanically ventilated rats (n = 9) were measured under baseline conditions after inducing pulmonary AE by injecting 0.1 mL air into the femoral vein and after occluding the left pulmonary artery (LPAO). Changes in mechanical parameters were assessed by forced oscillations to measure airway resistance, lung tissue damping, and elastance. The arterial partial pressures of oxygen (PaO2) and carbon dioxide (PaCO2) were determined by blood gas analyses. Gas exchange indices were also assessed by measuring end-tidal CO2 concentration (ETCO2), shape factors, and dead space parameters by volumetric capnography.ResultsIn the presence of a uniform decrease in ETCO2 in the two embolism models, marked elevations in the bronchial tone and compromised lung tissue mechanics were noted after LPAO, whereas AE did not affect lung mechanics. Conversely, only AE deteriorated PaO2, and PaCO2, while LPAO did not affect these outcomes. Neither AE nor LPAO caused changes in the anatomical or physiological dead space, while both embolism models resulted in elevated alveolar dead space indices incorporating intrapulmonary shunting.ConclusionsOur findings indicate that severe focal hypocapnia following LPAO triggers bronchoconstriction redirecting airflow to well-perfused lung areas, thereby maintaining normal oxygenation, and the CO2 elimination ability of the lungs. However, hypocapnia in diffuse pulmonary perfusion after AE may not reach the threshold level to induce lung mechanical changes; thus, the compensatory mechanisms to match ventilation to perfusion are activated less effectively.

Primary pulmonary Ewing sarcoma: a rare case report

Ewing sarcoma (ES) is a malignant soft tissue tumor that consists of undifferentiated neuroectodermal cells. The anatomical sites of ES are commonly the pelvis and long bones. Metastasis is the cause of the most prevalent pulmonary ES. The primary lung origin of ES is extremely uncommon. Here, we report a rare case of primary pulmonary ES diagnosed from cytology and biopsy material. A chest X-ray revealed a lesion with a 9 × 7.5 cm diameter in the pericardiac area of the right lung. Clinical and radiological examinations (computed tomography and positron emission tomography) demonstrated that the lesion was a primary lesion. No distant metastasis was detected. Bronchoscopy-guided fine-needle aspiration and cytological analysis of the lesion revealed uniformly shaped small round cell morphology. Immunohistochemistry performed on the cell block produced positive results for CD99 and FLI-1. These immunohistochemical findings support the ES diagnosis.

Оценка экспрессии эндотелиальных и некоторых маркеров воспаления в экспериментальной модели острого респираторного дистресс-синдрома

Introduction. In the pathogenesis of acute respiratory distress syndrome (ARDS), signaling molecules such as cytokines play an important role in initiating, enhancing, and consolidating the inflammatory response. We aimed to identify the most indicative immunohistochemical markers for ARDS and the areas of studying them and to establish the optimal age of animals (i.e., rats) for ARDS induction. Materials and methods. We conducted a histological and immunohistochemical study of rats’ lung sections in two intact groups and two groups with a lipopolysaccharide-induced model of ARDS in three areas: bronchial epithelium, lung consolidation, and unchanged parenchyma. The rats were also divided into two age groups. We compared a microscopic appearance, the number of goblet cells, and immunohistochemical expression of IL-1β, IL-6, TNF-α, Ki-67, and CD31. Results. Both groups saw a significant increase in the number of goblet cells. At 8–10 weeks of age, the rats developed increased basal levels of IL-1β and proliferative activity in the lungs; the area of the capillary network decreased. In the experimental groups, the animals aged 4–6 weeks showed an increase in the level of IL-1β in all lung areas; however, in the animals aged 8–10 weeks, we observed this growth only in the area of unchanged parenchyma. The relative density of the capillary lung network decreased in the ARDS group aged 4–6 weeks. Conclusion. We would recommend IL-1β as an inflammatory cytokine and CD31 as endothelial marker, which reflects changes in vascular density, as immunohistochemical markers of ARDS severity on day 4 of its development. Marker changes are more stable in the 4–6-week-old animals. Keywords: IL-1b, TNF-α, Ki-67, CD31, respiratory distress syndrome, lipopolysaccharide, lung

Effect of Dexketoprofen on the Disposition Kinetics of Moxifloxacin in Plasma and Lung in Male and Female Rats.

The simultaneous use of NSAIDs and antibiotics is recommended for bacterial diseases in human and veterinary medicine. Moxifloxacin (MFX) and dexketoprofen (DEX) can be used simultaneously in bacterial infections. However, there are no studies on how the simultaneous use of DEX affects the pharmacokinetics of MFX in rats. The aim of this study was to determine the effect of DEX on plasma and lung pharmacokinetics of MFX in male and female rats. A total of 132 rats were randomly divided into 2 groups: MFX (n=66, 33 males/33 females) and MFX+DEX (n=66, 33 females/33 males). MFX at a dose of 20 mg/kg and DEX at a dose of 25 mg/kg were administered intraperitoneally. Plasma and lung concentrations of MFX were determined using the highperformance liquid chromatography-UV and pharmacokinetic parameters were evaluated by noncompartmental analysis. Simultaneous administration of DEX increased the plasma and lung area under the curve from 0 to 8 h (AUC0-8) and peak concentration (Cmax) of MFX in rats, while it significantly decreased the total body clearance (CL/F). When female and male rats were compared, significant differences were detected in AUC0-8, Cmax, CL/F and volume of distribution. The AUC0-8lung/AUC0-8plasma ratios of MFX were calculated as 1.68 and 1.65 in female rats and 5.15 and 4.90 in male rats after single and combined use, respectively. MFX was highly transferred to the lung tissue and this passage was remarkably higher in male rats. However, DEX administration increased the plasma concentration of MFX in both male and female rats but did not change its passage to the lung. However, there is a need for a more detailed investigation of the difference in the pharmacokinetics of MFX in male and female rats.

Explainable optimization of deep learning model for COVID-19 detection using chest images

The COVID-19 pandemic has now become endemic, yet it remains essential to identify and diagnose the virus. The X-ray images on chest evaluations have utilized numerous deep-learning techniques and optimization algorithms. The Visual Geometry Group (VGG) is a widely recognized deep-learning architecture for COVID-19 detection. This architecture incorporates various optimization algorithms, such as Adadelta, Adam, Adamax, Stochastic Gradient Descent (SGD), Adagrad, Adam, and Root Mean Square Propagation (RMSprop), to ascertain the most favorable learning parameters. Currently, researchers evaluate the performance of the deep learning and optimization algorithm to detect COVID-19 by computing the statistical parameters, i.e., the accuracy, precision, recall, specificity, and F-1 score. However, the way in which deep learning and optimization algorithms work to predict the outcome still needs a comprehensive explanation. This study aims to explain how deep learning and optimization predict COVID-19 or normal chest using visual and quantitative analysis of a Gradient Class Activation Mapping (Grad-CAM) and statistical evaluation. The methodology involved data collection of chest images from a public data set, preprocessing, training-testing, and result analysis on statistical parameters and Grad-CAM heatmap. The evaluation of statistical parameters yielded favorable outcomes from all the optimization techniques, with an average accuracy of up to 99 %. It was then followed by visually observing the Grad-CAM activation heatmap, which indicated crucial regions in the images that influenced the model's prediction outcomes. Grad-CAM allows the visualization and quantification of the activation map in each optimization. It reveals distinct activation maps for the lung area in both COVID-19 and normal pictures. The visual observation was also confirmed by quantitative analysis of Grad-CAM using root mean square error (RMSE) of correlation between each optimization algorithm. The researcher compares two heatmaps from different optimization methods by computing the RMSE of each heatmap pixel value. The RMSE confirmed that the heatmap supports the explanation of the optimization performance. In conclusion, this research explained how deep learning and optimization algorithms predict COVID-19 from the chest images.

Balloon Dilatation for Bronchoscope Delivery in a Swine Model: A Novel Technique for Ultra-Peripheral Lung Field Access and Accurate Biopsy.

In transbronchial biopsy of peripheral pulmonary lesions, the bronchoscope can reach only a limited depth due to the progressive narrowing of bronchi, which may reduce the diagnostic rate. This study examined the balloon dilatation for bronchoscope delivery (BDBD) technique, employing a novel balloon device to enhance bronchoscopy into the peripheral lung areas. Anaesthetised swine served as our primary model. Using computed tomography (CT) scans, we positioned virtual targets characterised by a positive bronchus sign and a diameter of 20 mm beneath the pleura. The bronchoscope was navigated along the pathways determined from the CT images. We performed balloon dilatation when bronchial narrowing obstructed progress to assess whether balloon dilatation would enable the bronchoscope to enter further into the periphery. We established 21 virtual targets on the CT scans. An average of 12.1 branches were identified along the pathways on the CT scans; however, bronchoscopy without BDBD only allowed access to an average of 6.7 branches. Based on 72 balloon dilatations with 3.0-mm or 4.0-mm ultra-thin bronchoscopes, there was an average increased access of 3.43 and 5.14 branches per route, respectively, with no significant BDBD complications. The bronchoscope was able to reach the planned location along all pathways, and the mean final bronchoscopic endpoints were at an average distance of 14.7 mm from the pleura. Post-procedure CT confirmed biopsy accuracy. The BDBD technique can enhance access of a flexible bronchoscope into the peripheral lung fields, which could potentially allow more accurate transbronchial interventions for peripheral targets.

Assessment of Treatment Response in Patients With Severe Asthma Using Visual and Quantitative Analysis of Chest CT.

To evaluate the role of visual and quantitative chest CT parameters in assessing treatment response in patients with severe asthma. Korean participants enrolled in a prospective multicenter study, named the Precision Medicine Intervention in Severe Asthma study, from May 2020 to August 2021, underwent baseline and follow-up chest CT scans (inspiration/expiration) 10-12 months apart, before and after biologic treatment. Two radiologists scored bronchiectasis severity and mucus plugging extent. Quantitative parameters were obtained from each CT scan as follows: normal lung area (normal), air trapping without emphysema (AT without emph), air trapping with emphysema (AT with emph), and airway (total branch count, Pi10). Clinical parameters, including pulmonary function tests (forced expiratory volume in 1 s [FEV1] and FEV1/forced vital capacity [FVC]), sputum and blood eosinophil count, were assessed at initial and follow-up stages. Changes in CT parameters were correlated with changes in clinical parameters using Pearson or Spearman correlation. Thirty-four participants (female:male, 20:14; median age, 50.5 years) diagnosed with severe asthma from three centers were included. Changes in the bronchiectasis and mucus plugging extent scores were negatively correlated with changes in FEV1 and FEV1/FVC (ρ = from -0.544 to -0.368, all P < 0.05). Changes in quantitative CT parameters were correlated with changes in FEV1 (normal, r = 0.373 [P = 0.030], AT without emph, r = -0.351 [P = 0.042]), FEV1/FVC (normal, r = 0.390 [P = 0.022], AT without emph, r = -0.370 [P = 0.031]). Changes in total branch count were positively correlated with changes in FEV1 (r = 0.349 [P = 0.043]). There was no correlation between changes in Pi10 and the clinical parameters (P > 0.05). Visual and quantitative CT parameters of normal, AT without emph, and total branch count may be effective for evaluating treatment response in patients with severe asthma.