Application of clinical and CT imaging features in the evaluation of disease progression in patients with COVID-19

Efficacy and safety of mesenchymal stem cells therapy in COVID-19 patients: a systematic review and meta-analysis of randomized controlled trials

BACKGROUND

The coronavirus disease 2019 (COVID-19) has become a serious public health issue. In COVID-19 patients, the elevated levels of inflammatory cytokines lead to the manifestation of COVID-19 symptoms, such as lung tissue edema, lung diffusion dysfunction, acute respiratory distress syndrome (ARDS), secondary infection, and ultimately mortality. Mesenchymal stem cells (MSCs) exhibit anti-inflammatory and immunomodulatory properties, thus providing a potential treatment option for COVID-19. The number of clinical trials of MSCs for COVID-19 has been rising. However, the treatment protocols and therapeutic effects of MSCs for COVID-19 patients are inconsistent. This meta-analysis was performed to systematically determine the safety and efficacy of MSC infusion in COVID-19 patients.

METHODS

We conducted a comprehensive literature search from PubMed/Medline, Web of Science, EMBASE, and Cochrane Library up to 22 November 2023 to screen for eligible randomized controlled trials. Inclusion and exclusion criteria for searched literature were formulated according to the PICOS principle, followed by the use of literature quality assessment tools to assess the risk of bias. Finally, outcome measurements including therapeutic efficacy, clinical symptoms, and adverse events of each study were extracted for statistical analysis.

RESULTS

A total of 14 randomized controlled trials were collected. The results of enrolled studies demonstrated that patients with COVID-19 pneumonia who received MSC inoculation showed a decreased mortality compared with counterparts who received conventional treatment (RR: 0.76; 95% CI [0.60, 0.96]; p = 0.02). Reciprocally, MSC inoculation improved the clinical symptoms in patients (RR: 1.28; 95% CI [1.06, 1.55]; p = 0.009). In terms of immune biomarkers, MSC treatment inhibited inflammation responses in COVID-19 patients, as was indicated by the decreased levels of CRP and IL-6. Importantly, our results showed that no significant differences in the incidence of adverse reactions or serious adverse events were monitored in patients after MSC inoculation.

CONCLUSION

This meta-analysis demonstrated that MSC inoculation is effective and safe in the treatment of patients with COVID-19 pneumonia. Without increasing the incidence of adverse events or serious adverse events, MSC treatment decreased patient mortality and inflammatory levels and improved the clinical symptoms in COVID-19 patients. However, large-cohort randomized controlled trials with expanded numbers of patients are required to further confirm our results.

A Critical Assessment of Generative Models for Synthetic Data Augmentation on Limited Pneumonia X-ray Data

In medical imaging, deep learning models serve as invaluable tools for expediting diagnoses and aiding specialized medical professionals in making clinical decisions. However, effectively training deep learning models typically necessitates substantial quantities of high-quality data, a resource often lacking in numerous medical imaging scenarios. One way to overcome this deficiency is to artificially generate such images. Therefore, in this comparative study we train five generative models to artificially increase the amount of available data in such a scenario. This synthetic data approach is evaluated on a a downstream classification task, predicting four causes for pneumonia as well as healthy cases on 1082 chest X-ray images. Quantitative and medical assessments show that a Generative Adversarial Network (GAN)-based approach significantly outperforms more recent diffusion-based approaches on this limited dataset with better image quality and pathological plausibility. We show that better image quality surprisingly does not translate to improved classification performance by evaluating five different classification models and varying the amount of additional training data. Class-specific metrics like precision, recall, and F1-score show a substantial improvement by using synthetic images, emphasizing the data rebalancing effect of less frequent classes. However, overall performance does not improve for most models and configurations, except for a DreamBooth approach which shows a +0.52 improvement in overall accuracy. The large variance of performance impact in this study suggests a careful consideration of utilizing generative models for limited data scenarios, especially with an unexpected negative correlation between image quality and downstream classification improvement.