An Integrated Radiologic-Pathologic Understanding of COVID-19 Pneumonia

Assessment of progression of pulmonary fibrosis based on metabonomics and analysis of intestinal microbiota

The main purpose of this study was to explore the changes of biomarkers in different developmental stages of bleomycin-induced pulmonary fibrosis (PF) in rats via comprehensive pathophysiology, UPLC-QTOF/MS metabonomic technology, and 16S rRNA gene sequencing of intestinal microbiota. The rats were randomly divided into normal control and 1-, 2- and 4-week model group. The rat model of PF was established by one-time intratracheal instillation of bleomycin. The levels of inflammatory and fibrosis-related factors such as hydroxyproline (HYP), type III procollagen (COL-III), type IV collagen (COL-IV), hyaluronidase (HA), laminin (LN), interleukin (IL)-1β, IL-6, malondialdehyde (MDA) increased and superoxide dismutase (SOD) decreased as the PF cycle progressed. In the 1-, 2- and 4-week model group, 2, 19 and 18 potential metabolic biomarkers and 3, 16 and 12 potential microbial biomarkers were detected, respectively, which were significantly correlated. Glycerophospholipid metabolism pathway was observed to be an important pathway affecting PF at 1, 2 and 4 weeks; arginine and proline metabolism pathways significantly affected PF at 2 weeks. Linoleic acid metabolism pathway exhibited clear metabolic abnormalities at 2 and 4 weeks of PF, and alpha-linolenic acid metabolism pathway significantly affected PF at 4 weeks.

Prediction of short-term progression of COVID-19 pneumonia based on chest CT artificial intelligence: during the Omicron epidemic

BACKGROUND AND PURPOSE

The persistent progression of pneumonia is a critical determinant of adverse outcomes in patients afflicted with COVID-19. This study aimed to predict personalized COVID-19 pneumonia progression between the duration of two weeks and 1 month after admission by integrating radiological and clinical features.

METHODS

A retrospective analysis, approved by the Institutional Review Board, encompassed patients diagnosed with COVID-19 pneumonia between December 2022 and February 2023. The cohort was divided into training and validation groups in a 7:3 ratio. A trained multi-task U-Net network was deployed to segment COVID-19 pneumonia and lung regions in CT images, from which quantitative features were extracted. The eXtreme Gradient Boosting (XGBoost) algorithm was employed to construct a radiological model. A clinical model was constructed by LASSO method and stepwise regression analysis, followed by the subsequent construction of the combined model. Model performance was assessed using ROC and decision curve analysis (DCA), while Shapley's Additive interpretation (SHAP) illustrated the importance of CT features.

RESULTS

A total of 214 patients were recruited in our study. Four clinical characteristics and four CT features were identified as pivotal components for constructing the clinical and radiological models. The final four clinical characteristics were incorporated as well as the RS_radiological model to construct the combined prediction model. SHAP analysis revealed that CT score difference exerted the most significant influence on the predictive performance of the radiological model. The training group's radiological, clinical, and combined models exhibited AUC values of 0.89, 0.72, and 0.92, respectively. Correspondingly, in the validation group, these values were observed to be 0.75, 0.72, and 0.81. The DCA curve showed that the combined model exhibited greater clinical utility than the clinical or radiological models.

CONCLUSION

Our novel combined model, fusing quantitative CT features with clinical characteristics, demonstrated effective prediction of COVID-19 pneumonia progression from 2 weeks to 1 month after admission. This comprehensive model can potentially serve as a valuable tool for clinicians to develop personalized treatment strategies and improve patient outcomes.

The Short and Long of COVID-19: A Review of Acute and Chronic Radiologic Pulmonary Manifestations of SARS-2-CoV and Their Clinical Significance

Coronavirus disease 2019 (COVID-19) pneumonia has had catastrophic effects worldwide. Radiology, in particular computed tomography (CT) imaging, has proven to be valuable in the diagnosis, prognostication, and longitudinal assessment of those diagnosed with COVID-19 pneumonia. This article will review acute and chronic pulmonary radiologic manifestations of COVID-19 pneumonia with an emphasis on CT and also highlighting histopathology, relevant clinical details, and some notable challenges when interpreting the literature.

A nomogram to predict severe COVID-19 patients with increased pulmonary lesions in early days

Objectives

This study aimed to predict severe coronavirus disease 2019 (COVID-19) progression in patients with increased pneumonia lesions in the early days. A simplified nomogram was developed utilizing artificial intelligence (AI)-based quantified computed tomography (CT).

Methods

From 17 December 2019 to 20 February 2020, a total of 246 patients were confirmed COVID-19 infected in Jingzhou Central Hospital, Hubei Province, China. Of these patients, 93 were mildly ill and had follow-up examinations in 7 days, and 61 of them had enlarged lesions on CT scans. We collected the neutrophil-to-lymphocyte ratio (NLR) and three quantitative CT features from two examinations within 7 days. The three quantitative CT features of pneumonia lesions, including ground-glass opacity volume (GV), semi-consolidation volume (SV), and consolidation volume (CV), were automatically calculated using AI. Additionally, the variation volumes of the lesions were also computed. Finally, a nomogram was developed using a multivariable logistic regression model. To simplify the model, we classified all the lesion volumes based on quartiles and curve fitting results.

Results

Among the 93 patients, 61 patients showed enlarged lesions on CT within 7 days, of whom 19 (31.1%) developed any severe illness. The multivariable logistic regression model included age, NLR on the second time, an increase in lesion volume, and changes in SV and CV in 7 days. The personalized prediction nomogram demonstrated strong discrimination in the sample, with an area under curve (AUC) and the receiver operating characteristic curve (ROC) of 0.961 and a 95% confidence interval (CI) of 0.917-1.000. Decision curve analysis illustrated that a nomogram based on quantitative AI was clinically useful.

Conclusion

The integration of CT quantitative changes, NLR, and age in this model exhibits promising performance in predicting the progression to severe illness in COVID-19 patients with early-stage pneumonia lesions. This comprehensive approach holds the potential to assist clinical decision-making.

Recent advances and new insights on the construction of photocatalytic systems for environmental disinfection

Photocatalysis, as a sustainable and environmentally friendly green technology, has garnered widespread recognition and application across various fields. Especially its potential in environmental disinfection has been highly valued by researchers. This study commences with foundational research on photocatalytic disinfection technology and provides a comprehensive overview of its current developmental status. It elucidates the complexity of the interface reaction mechanism between photocatalysts and microorganisms, providing valuable insights from the perspectives of materials and microorganisms. This study reviews the latest design and modification strategies (Build heterojunction, defect engineering, and heteroatom doping) for photocatalysts in environmental disinfection. Moreover, this study investigates the research focuses and links in constructing photocatalytic disinfection systems, including photochemical reactors, light sources, and material immobilization technologies. It studies the complex challenges and influencing factors generated by different environmental media during the disinfection process. Simultaneously, a comprehensive review extensively covers the research status of photocatalytic disinfection concerning bacteria, fungi, and viruses. It reveals the observable efficiency differences caused by the microstructure of microorganisms during photocatalytic reactions. Based on these influencing factors, the economy and effectiveness of photocatalytic disinfection systems are analyzed and discussed. Finally, this study summarizes the current application status of photocatalytic disinfection products. The challenges faced by the synthesis and application of future photocatalysts are proposed, and the future development in this field is discussed. The potential for research and innovation has been further emphasized, with the core on improving efficiency, reducing costs, and strengthening the practical application of photocatalysis in environmental disinfection.

Chronic Lung Injury after COVID-19 Pneumonia: Clinical, Radiologic, and Histopathologic Perspectives

With the COVID-19 pandemic having lasted more than 3 years, concerns are growing about prolonged symptoms and respiratory complications in COVID-19 survivors, collectively termed post-COVID-19 condition (PCC). Up to 50% of patients have residual symptoms and physiologic impairment, particularly dyspnea and reduced diffusion capacity. Studies have also shown that 24%-54% of patients hospitalized during the 1st year of the pandemic exhibit radiologic abnormalities, such as ground-glass opacity, reticular opacity, bronchial dilatation, and air trapping, when imaged more than 1 year after infection. In patients with persistent respiratory symptoms but normal results at chest CT, dual-energy contrast-enhanced CT, xenon 129 MRI, and low-field-strength MRI were reported to show abnormal ventilation and/or perfusion, suggesting that some lung injury may not be detectable with standard CT. Histologic patterns in post-COVID-19 lung disease include fibrosis, organizing pneumonia, and vascular abnormality, indicating that different pathologic mechanisms may contribute to PCC. Therefore, a comprehensive imaging approach is necessary to evaluate and diagnose patients with persistent post-COVID-19 symptoms. This review will focus on the long-term findings of clinical and radiologic abnormalities and describe histopathologic perspectives. It also addresses advanced imaging techniques and deep learning approaches that can be applied to COVID-19 survivors. This field remains an active area of research, and further follow-up studies are warranted for a better understanding of the chronic stage of the disease and developing a multidisciplinary approach for patient management.

Quantitative Analysis for Lung Disease on Thin-Section CT

Thin-section computed tomography (CT) is widely employed not only for assessing morphology but also for evaluating respiratory function. Three-dimensional images obtained from thin-section CT provide precise measurements of lung, airway, and vessel volumes. These volumetric indices are correlated with traditional pulmonary function tests (PFT). CT also generates lung histograms. The volume ratio of areas with low and high attenuation correlates with PFT results. These quantitative image analyses have been utilized to investigate the early stages and disease progression of diffuse lung diseases, leading to the development of novel concepts such as pre-chronic obstructive pulmonary disease (pre-COPD) and interstitial lung abnormalities. Quantitative analysis proved particularly valuable during the COVID-19 pandemic when clinical evaluations were limited. In this review, we introduce CT analysis methods and explore their clinical applications in the context of various lung diseases. We also highlight technological advances, including images with matrices of 1024 × 1024 and slice thicknesses of 0.25 mm, which enhance the accuracy of these analyses.

Cytomegalovirus Pneumonia in Inflammatory Bowel Disease: Literature Review and Clinical Recommendations

Aim

The objective was to elucidate the correlation between CMVP and immunosuppressive therapy in IBD patients, we hope this review could expand on the significance of CMV as an opportunistic pathogen and the potential impact on morbidity and mortality in IBD patients.

Methods

Records and clinical trajectories linked to CMVP in IBD patients were extracted from the PubMed database, irrespective of language barriers. The reference lists incorporated in these studies were manually inspected. Conclusions were generated using straightforward descriptive analysis.

Results

In total, 18 IBD patients, including Crohn's disease (CD, 67%) and Ulcerative Colitis (UC, 33%), affected by CMVP were identified from 17 published articles. A minority of these patients (17%) exhibited active disease, whereas the majority (83%) presented with quiescent disease. Fever (100%) and dyspnea (44%) emerged as the most prevalent clinical symptoms. All the patients had undergone immunosuppressive therapy. A significant proportion, up to 89%, had received thiopurine treatment prior to the CMVP diagnosis. Interestingly, none of the patients were subjected to biological therapy. Half of the patients manifested with Hemophagocytic Lymphohistiocytosis (HLH). Almost all patients (94%) were administered antiviral treatment and a substantial 83% experienced full recovery. Immunosuppressive agents were either tapered or discontinued altogether. A subset of patients, 17%, suffered fatal outcomes.

Conclusion

Our findings underscore the need for heightened suspicion of CMVP in IBD patients who exhibit symptoms such as fever and dyspnea. During the COVID-19 pandemic, CMVP should be considered a potential differential diagnosis. It was observed that CMVP primarily transpires during CD remission. Azathioprine emerged as the predominant immunosuppressant linked to CMV reactivation. The prompt application of effective antiviral therapy can substantially enhance patient outcomes. CMV vaccine might serve as a viable prevention strategy.

Dominant CT Patterns and Immune Responses during the Early Infection Phases of Different SARS-CoV-2 Variants

Ground-glass opacity (GGO) and organizing pneumonia (OP) are dominant pulmonary CT lesions associated with COVID-19. However, the role of different immune responses in these CT patterns remains unclear, particularly following the emergence of the Omicron variant. In this prospective observational study, we recruited patients hospitalized with COVID-19, before and after the emergence of Omicron variants. Semi-quantitative CT scores and dominant CT patterns were retrospectively determined for all patients within five days of symptom onset. Serum levels of IFN-α, IL-6, CXCL10, and VEGF were assessed using ELISA. Serum-neutralizing activity was measured using a pseudovirus assay. We enrolled 48 patients with Omicron variants and 137 with precedent variants. While the frequency of GGO patterns was similar between the two groups, the OP pattern was significantly more frequent in patients with precedent variants. In patients with precedent variants, IFN-α and CXCL10 levels were strongly correlated with GGO, whereas neutralizing activity and VEGF were correlated with OP. The correlation between IFN-α levels and CT scores was lower in patients with Omicron than in those with precedent variants. Compared to preceding variants, infection with the Omicron variant is characterized by a less frequent OP pattern and a weaker correlation between serum IFN-α and CT scores.