Search Results (135)

Pulmonary carcinoids (PCs) are rare tumors, with an incidence ranging from 0.2 to 2 cases per 100,000 population per year. They account for 1–2% of all invasive pulmonary malignancies and represent approximately one-fourth to one-third of all well-differentiated neuroendocrine tumors (NETs) in the body. PCs are generally classified as low- to intermediate-grade malignant tumors, further subdivided into typical carcinoid (TC) and atypical carcinoid (AC), respectively. These tumors exhibit neuroendocrine morphology and differentiation, originating from mature cells of the pulmonary diffuse neuroendocrine system. Traditionally, they are categorized as central or peripheral based on their location relative to the bronchial tree; however, they can arise anywhere within the lung parenchyma. Over 40% of cases may be detected incidentally on a standard chest X-ray, although contrast-enhanced computed tomography (CT) remains the diagnostic gold standard. Surgical resection is the treatment of choice for PCs, with the goal of complete tumor removal while preserving as much healthy lung tissue as possible. In contrast, advanced cases are typically not amenable to surgery, and medical management is focused on controlling hormone-related symptoms and limiting tumor progression. This review aims to provide an overview of the current diagnostic and therapeutic approaches to pulmonary carcinoids. Full article

Background/Objectives: Interstitial lung disease (ILD) is frequently complicated by pulmonary hypertension (PH), which is associated with reduced exercise capacity and poor prognosis. Early and accurate non-invasive detection of PH remains a clinical challenge. This study evaluated whether combining quantitative CT analysis of lung fibrosis with cardiac MRI-derived measures of right ventricular (RV) function improves the diagnostic accuracy of PH in patients with ILD. Methods: We retrospectively analyzed 72 ILD patients who underwent chest CT, cardiac MRI, and right heart catheterization (RHC). Lung fibrosis was quantified using a Gaussian Histogram Normalized Correlation (GHNC) software that computed the proportions of diseased lung, ground-glass opacity (GGO), honeycombing, reticulation, consolidation, and emphysema. MRI was used to assess RV end-systolic volume (RVESV), ejection fraction, and RV longitudinal strain. PH was defined as a mean pulmonary arterial pressure (mPAP) ≥ 20 mmHg and pulmonary vascular resistance ≥ 3 Wood units on RHC. Results: Compared to patients without PH, those with PH (n = 21) showed significantly reduced RV strain (−13.4 ± 5.1% vs. −16.4 ± 5.2%, p = 0.026) and elevated RVESV (74.2 ± 18.3 mL vs. 59.5 ± 14.2 mL, p = 0.003). CT-derived indices also differed significantly: diseased lung area (56.4 ± 17.2% vs. 38.4 ± 12.5%, p < 0.001), GGO (11.8 ± 3.6% vs. 8.65 ± 4.3%, p = 0.005), and honeycombing (17.7 ± 4.9% vs. 12.8 ± 6.4%, p = 0.0027) were all more prominent in the PH group. In receiver operating characteristic curve analysis, diseased lung area demonstrated an area under the curve of 0.778 for detecting PH. This increased to 0.847 with the addition of RVESV, and further to 0.854 when RV strain was included. Combined models showed significant improvement in risk reclassification: net reclassification improvement was 0.700 (p = 0.002) with RVESV and 0.684 (p = 0.004) with RV strain; corresponding IDI values were 0.0887 (p = 0.03) and 0.1222 (p = 0.01), respectively. Conclusions: Combining CT-based fibrosis quantification with cardiac MRI-derived RV functional assessment enhances the non-invasive diagnosis of PH in ILD patients. This integrated imaging approach significantly improves diagnostic precision and may facilitate earlier, more targeted interventions in the management of ILD-associated PH. Full article

Acute respiratory distress syndrome (ARDS) is an inflammatory pulmonary condition that remains at alarming rates of fatality, with neutrophils playing a vital role in its pathogenesis. Beyond their classical antimicrobial functions, neutrophils contribute to pulmonary injury via the release of reactive oxygen species, proteolytic enzymes, and neutrophil extracellular traps (NETs). To identify targets for treatment, it was found that epigenetic mechanisms, including histone modifications, hypomethylation, hypermethylation, and non-coding RNAs, regulate neutrophil phenotypic plasticity, survival, and inflammatory potential. It has been identified that neutrophils in ARDS patients exhibit abnormal methylation patterns and are associated with altered gene expression and prolonged neutrophil activation, thereby contributing to sustained inflammation. Histone citrullination, particularly via PAD4, facilitates NETosis, while histone acetylation status modulates chromatin accessibility and inflammatory gene expression. MicroRNAs have also been shown to regulate neutrophil activity, with miR-223 and miR-146a potentially being biomarkers and therapeutic targets. Neutrophil heterogeneity, as evidenced by distinct subsets such as low-density neutrophils (LDNs), varies across ARDS etiologies, including COVID-19. Single-cell RNA sequencing analyses, including the use of trajectory analysis, have revealed transcriptionally distinct neutrophil clusters with differential activation states. These studies support the use of epigenetic inhibitors, including PAD4, HDAC, and DNMT modulators, in therapeutic intervention. While the field has been enlightened with new findings, challenges in translational application remain an issue due to species differences, lack of stratification tools, and heterogeneity in ARDS presentation. This review describes how targeting neutrophil epigenetic regulators could help regulate hyperinflammation, making epigenetic modulation a promising area for precision therapeutics in ARDS. Full article

Pulmonary embolism (PE) is an under-recognised yet serious complication in patients with acute ischaemic stroke (AIS), contributing significantly to morbidity and mortality. The interplay of traditional risk factors—such as immobility, endothelial dysfunction, and hypercoagulability—with AIS-specific conditions, including atrial fibrillation, malignancy, and reperfusion therapies, complicates both diagnosis and management. Despite available prophylactic strategies, including low-molecular-weight heparin and intermittent pneumatic compression, their use remains limited by bleeding concerns and a lack of tailored guidelines. This review synthesises the current evidence on the incidence, risk factors, pathophysiology, diagnostic approaches, and preventive strategies for PE in AIS, identifying critical gaps in risk stratification and clinical decision-making. We propose a novel mechanistic framework—the Brain–Lung Thromboinflammatory Axis Hypothesis—which posits that stroke-induced systemic inflammation, neutrophil extracellular trap (NET) formation, and pulmonary endothelial activation may drive in situ pulmonary thrombosis independent of deep vein thrombosis. This conceptual model highlights new diagnostic and therapeutic targets and underscores the need for stroke-specific VTE risk calculators, biomarker-guided prophylaxis, and prospective trials to optimise prevention and outcomes in this vulnerable population. Full article

Background/Objectives: Albumin supplementation is widely used for hypoalbuminemia treatment in patients with critical illness, especially those with cirrhosis. However, studies have demonstrated that routine albumin administration is not always advantageous. We examined how albumin supplementation affects survival outcomes in patients with sepsis with hypoalbuminemia. Methods: This study was conducted by researchers in Taiwan using data from the TriNetX research platform, covering the period from 1 April 2014 to 30 April 2024. This platform aggregates real-world data from healthcare organizations worldwide. From this dataset, 1,147,433 patients who developed sepsis and hypoalbuminemia with albumin levels <3.5 g/dL were identified. The study population was stratified into two groups on the basis of whether they received albumin infusion or not. To compare outcomes, hazard ratios (HRs) and 95% confidence intervals (CIs) were calculated between propensity-score-matched patients who did and did not receive albumin supplementation. Subgroup analysis by albumin levels was conducted. Results: Albumin infusion was linked to increased risks of 30-day mortality (HR [95% CI] = 1.800 [1.774–1.827], p < 0.05), shock (HR [95% CI] = 1.436 [1.409–1.465], p < 0.05), septic shock (HR [95% CI] = 1.384 [1.355–1.415], p < 0.05), hypovolemic shock (HR [95% CI] = 1.496 [1.391–1.608], p < 0.05), cardiogenic shock (HR [95% CI] = 1.553 [1.473–1.637], p < 0.05), heart failure (HR [95% CI] = 1.098 [1.080–1.116], p < 0.05), and pulmonary edema (HR [95% CI] = 1.479 [1.438–1.520], p < 0.05). The subgroup analysis by albumin levels revealed a trend of increased mortality risk with albumin supplementation in patients with high baseline albumin levels. Conclusions: Patients with sepsis with hypoalbuminemia who received albumin supplementation exhibited high 30-day mortality rates and increased risks of shock, heart failure, and pulmonary edema compared with those who did not. These findings indicate that routine albumin administration may be linked with unfavorable outcomes in these patients. Full article

Neutrophil extracellular traps (NETs) are associated with the extracellular release of nuclear chromatin decorated with cytoplasmic proteins. Excessive release of NETs has been reported in chronic lung diseases, including chronic obstructive pulmonary disease (COPD). However, the role of NETs in the pathogenesis of COPD remains unclear. Peptidylarginine deaminase 4 (PAD4) contributes to NET formation. Therefore, in an elastase (ELS)-induced emphysema mouse model, we examined the role of PAD4 using Padi4 gene knockout (KO) mice. First, we confirmed that ELS induced NET formation in the parenchyma of the lungs. PAD4 deficiency suppressed ELS-induced NET expression and tended to ameliorate the lung tissue injury. The cellular profile of bronchoalveolar lavage fluid (BALF) did not differ between the two groups. Additionally, PAD4 deficiency ameliorated emphysema and apoptosis in lung cells. Finally, we examined the effects of PAD4 on comprehensive gene expression signatures using RNA sequencing. Enrichment analysis of the transcriptomic data revealed that the expression of several genes associated with COPD pathogenesis was altered in the KO mice. Overall, the results suggest that PAD4 deficiency improves NET formation and emphysema in the lungs; this pathway can be a potential therapeutic target for the treatment of COPD. Full article

Since the COVID-19 pandemic, interest in non-contact diagnostic technologies has grown, leading to increased research into remote biosignal monitoring. The respiratory rate, widely used in previous studies, offers limited insight into pulmonary volume. To redress this, we propose a thermal imaging-based framework for respiratory segmentation aimed at estimating non-invasive pulmonary function. The proposed method uses an optical flow magnitude-based thresholding technique to automatically extract exhalation frames and segment them into frame sequences. A TransUNet-based network, combining a Convolutional Neural Network (CNN) encoder–decoder architecture with a Transformer module in the bottleneck, is trained on these sequences. The model’s Accuracy, Precision, Recall, IoU, Dice, and F1-score were 0.9832, 0.9833, 0.9830, 0.9651, 0.9822, and 0.9831, respectively, which results demonstrate high segmentation performance. The method enables the respiratory volume to be estimated by detecting exhalation behavior, suggesting its potential as a non-contact tool to monitor pulmonary function and estimate lung volume. Furthermore, research on thermal imaging-based respiratory volume analysis remains limited. This study expands upon conventional respiratory rate-based approaches to provide a new direction for respiratory analysis using vision-based techniques. Full article

Introduction: Traumatic brain injury (TBI) involves a diverse group of head blunt and/or penetrating injuries and is a leading cause of death in the U.S., accounting for one-third of all injury-related deaths. A post-injury hyperglycemic state may commonly impact TBI prognosis and strongly correlate with injury severity. Diabetes mellitus (DM) may also be a source of concomitant hyperglycemia that can worsen prognosis, with previous literature suggesting that DM could be an independent predictor of poor outcome and mortality after TBI. Methods: Using the multi-center, national TriNetX database, we performed a propensity score-matched analysis of severe TBI patients with (DM) and without DM (NDM) from 2014 to 2024. We examined the risk of mortality and complications, including sepsis, cerebral infarction, and pulmonary embolism. We also performed a sub-group analysis comparing the risk of mortality and complications between patients with either insulin-dependent or insulin-independent forms of DM. Results: A total of 26,019 patients were included (4604 DM vs. 21,415 NDM). After propensity score matching, patients with DM had a significantly lower risk of mortality (RR: 0.815; 95% CI: 0.771–0.861; p < 0.05) and ventilator dependency (RR: 0.902; 95% CI: 0.844–0.963; p < 0.05) compared to NDM patients. However, patients with DM had a significantly higher risk of cerebral infarctions, seizures, pneumonia, and sepsis (p < 0.05). Sub-group analysis found no significant difference in mortality or complications between insulin-dependent and insulin-independent forms of DM. Conclusion: Our results suggest that hyperglycemia secondary to DM plays a complicated role in the outcomes after severe TBI. Unexpectedly, we identified both increased and decreased complications in patients with DM. These results reflect the current challenges in the literature surrounding pre-existing DM in patients’ outcomes, the impact of diabetic medications on patient outcomes, and the changing role of aggressive glucose management in critical care patients. Full article

Sickle cell disease (SCD) is a monogenic blood disorder characterized by abnormal hemoglobin S production, which polymerizes under hypoxia conditions to produce chronic red blood cell hemolysis, widespread organ damage, and vasculopathy. As a result of vaso-occlusion and ischemia-reperfusion injury, individuals with SCD have recurrent pain episodes, infection, pulmonary disease, and fall victim to early death. Oxidative stress due to chronic hemolysis and the release of hemoglobin and free heme is a key driver of the clinical manifestations of SCD. The net result is the generation of reactive oxygen species that consume nitric oxide and overwhelm the antioxidant system due to a reduction in enzymes such as superoxide dismutase and glutathione peroxidase. The primary mechanism for handling cellular oxidative stress is the activation of antioxidant proteins by the transcription factor NRF2, a promising target for treatment development, given the significant role of oxidative stress in the clinical severity of SCD. In this review, we discuss the role of oxidative stress in health and the clinical complications of SCD, and the potential of NRF2 as a treatment target, offering hope for developing effective therapies for SCD. This task requires our collective dedication and focus. Full article

Nowadays, Artificial Intelligence (AI) has revolutionized many fields and the medical field is no exception. Thanks to technological advancements and the emergence of Deep Learning (DL) techniques AI has brought new possibilities and significant improvements to medical practice. Despite the excellent results of DL models in terms of accuracy and performance, they remain black boxes as they do not provide meaningful insights into their internal functioning. This is where the field of Explainable AI (XAI) comes in, aiming to provide insights into the underlying workings of these black box models. In this present paper the visual explainability of deep models on chest radiography images are addressed. This research uses two datasets, the first on COVID-19, viral pneumonia, normality (healthy patients) and the second on pulmonary opacities. Initially the pretrained CNN models (VGG16, VGG19, ResNet50, MobileNetV2, Mixnet and EfficientNetB7) are used to classify chest radiography images. Then, the visual explainability methods (GradCAM, LIME, Vanilla Gradient, Gradient Integrated Gradient and SmoothGrad) are performed to understand and explain the decisions made by these models. The obtained results show that MobileNetV2 and VGG16 are the best models for the first and second datasets, respectively. As for the explainability methods, the results were subjected to doctors and were validated by calculating the mean opinion score. The doctors deemed GradCAM, LIME and Vanilla Gradient as the most effective methods, providing understandable and accurate explanations. Full article

According to the literature, cardiovascular diseases (CVDs)—including myocardial infarction, stroke, and venous thromboembolism (VTE)—are among the leading causes of mortality and morbidity worldwide. Evidence suggests that CVDs share common risk factors and pathophysiological mechanisms. Similar to the Mosaic Theory of Hypertension proposed by Irvine Page in 1949, the pathophysiology of VTE is multifactorial, involving multiple interacting processes. The concept of immunothrombosis, introduced by Engelmann and Massberg in 2009, describes the interplay between the immune system and thrombosis. Both thrombosis and hemostasis share core mechanisms, including platelet activation and fibrin formation. Additionally, immune mediators—such as monocytes, neutrophil extracellular traps (NETs), lymphocytes, selectins, and various molecular factors—play a critical role in thrombus formation. This review highlights inflammation as a key risk factor for pulmonary embolism (APE). Immunity is central to the complex interactions among the coagulation cascade, platelets, endothelium, reactive oxygen species (ROS), and genetic factors. Specifically, we examine the roles of the endothelium, immune cells, and microRNAs (miRNAs) in the pathophysiology of APE and explore potential therapeutic targets. This review aims to elucidate the roles of the endothelium, immune cells, and miRNAs in the pathophysiology of APE and explore potential future perspective. Full article

Lung cancer is by far the leading cause of cancer death among both men and women, making up almost 25% of all cancer deaths Each year, more people die of lung cancer than colon, breast, and prostate cancer combined. The early detection of lung cancer is critical for improving patient outcomes, and automation through advanced image analysis techniques can significantly assist radiologists. This paper presents the development and evaluation of a computer-aided diagnostic system for lung cancer screening, focusing on pulmonary nodule segmentation in low-dose CT images, by employing HighRes3DNet. HighRes3DNet is a specialized 3D convolutional neural network (CNN) architecture based on ResNet principles which uses residual connections to efficiently learn complex spatial features from 3D volumetric data. To address the challenges of processing large CT volumes, an efficient patch-based extraction pipeline was developed. This method dynamically extracts 3D patches during training with a probabilistic approach, prioritizing patches likely to contain nodules while maintaining diversity. Data augmentation techniques, including random flips, affine transformations, elastic deformations, and swaps, were applied in the 3D space to enhance the robustness of the training process and mitigate overfitting. Using a public low-dose CT dataset, this approach achieved a Dice coefficient of 82.65% on the testing set for 3D nodule segmentation, demonstrating precise and reliable predictions. The findings highlight the potential of this system to enhance efficiency and accuracy in lung cancer screening, providing a valuable tool to support radiologists in clinical decision-making. Full article

Neutrophil extracellular traps (NETs) are web-like structures composed of chromatin and proteins from neutrophil granules. Several studies highlight the heterogeneity of NETs, underscoring the challenges associated with their detection. In patients with COVID-19, high levels of NET fragments, called NET remnants, are detected in the circulation but also in alveoli and bronchioles. NET remnants are usually measured as complexes of DNA and myeloperoxidase (DNA−MPO). Taking advantage of proteomic data on NET composition, we developed new solid-phase assays to detect NET remnants, measuring complexes of DNA with alpha enolase (DNA−eno) or calprotectin (DNA−cal). The two assays were compared with the DNA−MPO test for the detection of in vitro-generated NET and serum NET remnants; all of them showed similar sensitivity in the detection of in vitro-generated NET. In an analysis of 40 patients with severe COVID-19 and 25 healthy subjects, the results of the three assays were highly correlated, and all detected significantly higher levels of NET remnants in patient sera. Moreover, the level of NET remnants correlated with impaired gas exchange and increased with the progressive decline of pulmonary function. The proposed assays thus represent a novel tool with which to evaluate NETosis; using antibodies to different NET constituents may allow their fingerprinting in different disorders. Full article

The accurate segmentation of lung regions in computed tomography (CT) scans is critical for the automated analysis of lung diseases such as chronic obstructive pulmonary disease (COPD) and COVID-19. This paper focuses on enhancing the accuracy of U-Net segmentation models through a robust preprocessing pipeline. The pipeline includes CT image normalization, binarization to extract lung regions, and morphological operations to remove artifacts. Additionally, the proposed method applies region-of-interest (ROI) filtering to isolate lung areas effectively. The dataset preprocessing significantly improves segmentation quality by providing clean and consistent input data for the U-Net model. Experimental results demonstrate that the Intersection over Union (IoU) and Dice coefficient exceeded 0.95 on training datasets. This work highlights the importance of preprocessing as a standalone step for optimizing deep learning-based medical image analysis. Full article

Background: Precise detection and characterization of pulmonary nodules on computed tomography (CT) is crucial for early diagnosis and management. Objectives: In this study, we propose the use of a deep learning-based algorithm to automatically detect pulmonary nodules in computed tomography (CT) scans. We evaluated the performance of the algorithm against the interpretation of radiologists to analyze the effectiveness of the algorithm. Materials and Methods: The study was conducted in collaboration with a tertiary cancer center. We used a collection of public (LUNA) and private (tertiary cancer center) datasets to train our deep learning models. The sensitivity, the number of false positives per scan, and the FROC curve along with the CPM score were used to assess the performance of the deep learning algorithm by comparing the deep learning algorithm and the radiology predictions. Results: We evaluated 491 scans consisting of 5669 pulmonary nodules annotated by a radiologist from our hospital; our algorithm showed a sensitivity of 90% and with only 0.3 false positives per scan with a CPM score of 0.85. Apart from the nodule-wise performance, we also assessed the algorithm for the detection of patients containing true nodules where it achieved a sensitivity of 0.95 and specificity of 1.0 over 491 scans in the test cohort. Conclusions: Our multi-institutional validated deep learning-based algorithm can aid radiologists in confirming the detection of pulmonary nodules through computed tomography (CT) scans and identifying further abnormalities and can be used as an assistive tool. This will be helpful in national lung screening programs guiding early diagnosis and appropriate management. Full article