Two in one: Overlapping CT findings of COVID-19 and underlying lung diseases

Durhan, Gamze; Düzgün, Selin Ardalı; Baytar, Yusuf; Akpınar, Meltem Gülsün; Demirkazık, Figen Başaran; Arıyürek, Orhan Macit

doi:10.1016/j.clinimag.2022.11.005

Cited by 5 publications

(2 citation statements)

References 75 publications

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“…In recent years, PLDEs have been thought to communicate with animal cells across many species, and their role in immune regulation cannot be ignored. A compromised immune system and inflammation in the lungs are hallmarks of coronavirus disease 2019 (COVID-19) infection [ 68 ]. In a study involving mice exposed to severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), researchers found that exosomal Nsp12 and Nsp13 activated nuclear factor kappa B (NF-κB) in lung macrophages, leading to the expression of inflammatory cytokines such as TNF-α, interleukin (IL)-6 and IL-1β (as shown in Figure 1 A).…”

Section: Therapeutic Targeting Of Pldes In Inflammatory Diseasesmentioning

confidence: 99%

Advances in the Therapeutic Applications of Plant-Derived Exosomes in the Treatment of Inflammatory Diseases

Wei

Zhang

et al. 2023

Biomedicines

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Plant-derived exosomes (PLDEs) are small extracellular vesicles that encapsulate proteins, nucleic acids and lipids, and they are usually involved in intercellular communication and molecular transport in plants. PLDEs are widely used in the therapy of diseases due to their abundance and easy availability. The diverse roles of PLDEs, which include transportation of drugs, acting as biomarkers for diagnosis of diseases and their roles in different therapies, suggest that there is a need to fully understand all the mechanisms involved in order to provide the optimum conditions for their therapeutic use. This review summarizes the biogenesis, components and functions of PLDEs and focuses on their use as therapeutic agents in the treatment of inflammatory diseases. It also explores new ideas for novel approaches in which PLDEs could potentially help patients with inflammatory diseases in the future.

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Section: Therapeutic Targeting Of Pldes In Inflammatory Diseasesmentioning

confidence: 99%

Advances in the Therapeutic Applications of Plant-Derived Exosomes in the Treatment of Inflammatory Diseases

Wei

Zhang

et al. 2023

Biomedicines

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“…Extensive pneumonia can be a serious infectious disease, as it impairs the exchange of respiratory gases and alters minute ventilation. Consequently, respiratory-related acid-base imbalances are expected complications in COVID-19 patients[ 4 ]. Additionally, acute tubular injury is a common complication of the disease.…”

Section: Introductionmentioning

confidence: 99%

Physico-chemical characterization of acid base disorders in patients with COVID-19: A cohort study

Souza,

Caldas,

Lopes

et al. 2024

World J Nephrol

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BACKGROUND Acid-base imbalance has been poorly described in patients with coronavirus disease 2019 (COVID-19). Study by the quantitative acid-base approach may be able to account for minor changes in ion distribution that may have been overlooked using traditional acid-base analysis techniques. In a cohort of critically ill COVID-19 patients, we looked for an association between metabolic acidosis surrogates and worse clinical outcomes, such as mortality, renal dialysis, and length of hospital stay. AIM To describe the acid-base disorders of critically ill COVID-19 patients using Stewart’s approach, associating its variables with poor outcomes. METHODS This study pertained to a retrospective cohort comprised of adult patients who experienced an intensive care unit stay exceeding 4 days and who were diagnosed with severe acute respiratory syndrome coronavirus 2 infection through a positive polymerase chain reaction analysis of a nasal swab and typical pulmonary involvement observed in chest computed tomography scan. Laboratory and clinical data were obtained from electronic records. Categorical variables were compared using Fisher’s exact test. Continuous data were presented as median and interquartile range. The Mann-Whitney U test was used for comparisons. RESULTS In total, 211 patients were analyzed. The mortality rate was 13.7%. Overall, 149 patients (70.6%) presented with alkalosis, 28 patients (13.3%) had acidosis, and the remaining 34 patients (16.2%) had a normal arterial pondus hydrogenii. Of those presenting with acidosis, most had a low apparent strong ion difference (SID) (20 patients, 9.5%). Within the group with alkalosis, 128 patients (61.0%) had respiratory origin. The non-survivors were older, had more comorbidities, and had higher Charlson’s and simplified acute physiology score 3. We did not find severe acid-base imbalance in this population. The analyzed Stewart’s variables (effective SID, apparent SID, and strong ion gap and the effect of albumin, lactate, phosphorus, and chloride) were not different between the groups. CONCLUSION Alkalemia is prevalent in COVID-19 patients. Although we did not find an association between acid-base variables and mortality, the use of Stewart’s methodology may provide insights into this severe disease.

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Computer‐aided diagnosis of cystic lung diseases using CT scans and deep learning

Zhu,

Xing,

Yang

et al. 2024

Medical Physics

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BackgroundAuxiliary diagnosis of different types of cystic lung diseases (CLDs) is important in the clinic and is instrumental in facilitating early and specific treatments. Current clinical methods heavily depend on accumulated experience, restricting their applicability in regions with less developed medical resources. Thus, how to realize the computer‐aided diagnosis of CLDs is of great clinical value.PurposeThis work proposed a deep learning‐based method for automatically segmenting the lung parenchyma in computed tomography (CT) slice images and accurately diagnosing the CLDs using CT scans.MethodsA two‐stage deep learning method was proposed for the automatic classification of normal cases and five different CLDs using CT scans. Lung parenchyma segmentation is the foundation of CT image analysis and auxiliary diagnosis. To meet the requirements of different sizes of the lung parenchyma, an adaptive region‐growing and improved U‐Net model was employed for mask acquisition and automatic segmentation. The former was achieved by a self‐designed adaptive seed point selection method based on similarity measurement, and the latter introduced multiscale input and multichannel output into the original U‐Net model and effectively achieved the lightweight design by adjusting the structure and parameters. After that, the middle 30 consecutive CT slice images of each sample were segmented to obtain lung parenchyma, which was employed for training and testing the proposed multichannel parallel input recursive MLP‐Mixer network (MPIRMNet) model, achieving the computer‐aided diagnosis of CLDs.ResultsA total of 4718 and 16 290 CT slice images collected from 543 patients were employed to validate the proposed segmentation and classification methods, respectively. Experimental results showed that the improved U‐Net model can accurately segment the lung parenchyma in CT slice images, with the Dice, precision, volumetric overlap error, and relative volume difference of 0.96 ± 0.01, 0.93 ± 0.04, 0.05 ± 0.02, and 0.05 ± 0.03, respectively. Meanwhile, the proposed MPIRMNet model achieved appreciable classification effect for normal cases and different CLDs, with the accuracy, sensitivity, specificity, and F1 score of 0.8823 ± 0.0324, 0.8897 ± 0.0325, 0.9746 ± 0.0078, and 0.8831 ± 0.0334, respectively. Compared with classical machine learning and convolutional neural networks‐based methods for this task, the proposed classification method had a preferable performance, with a significant improvement of accuracy of 10.74%.ConclusionsThe work introduced a two‐stage deep learning method, which can achieve the segmentation of lung parenchyma and the classification of CLDs. Compared to previous diagnostic tasks targeting single CLD, this work can achieve various CLDs’ diagnosis in the early stage, thereby achieving targeted treatment and increasing the potential and value of clinical applications.

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Two in one: Overlapping CT findings of COVID-19 and underlying lung diseases

Cited by 5 publications

References 75 publications

Advances in the Therapeutic Applications of Plant-Derived Exosomes in the Treatment of Inflammatory Diseases

Advances in the Therapeutic Applications of Plant-Derived Exosomes in the Treatment of Inflammatory Diseases

Physico-chemical characterization of acid base disorders in patients with COVID-19: A cohort study

Computer‐aided diagnosis of cystic lung diseases using CT scans and deep learning

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