Development and external validation of a prediction model for the transition from mild to moderate or severe form of COVID-19

H2 inhalation therapy in patients with moderate COVID-19 (H2COVID): a prospective ascending-dose phase I clinical trial

The coronavirus disease 2019 (COVID-19) pandemic, caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), has triggered a serious global health crisis, resulting in millions of reported deaths since its initial identification in China in November 2019. The global disparities in immunization access emphasize the urgent need for ongoing research into therapeutic interventions. This study focuses on the potential use of molecular dihydrogen (H2) inhalation as an adjunctive treatment for COVID-19. H2 therapy shows promise in inhibiting intracellular signaling pathways associated with inflammation, particularly when administered early in conjunction with nasal oxygen therapy. This phase I study, characterized by an open-label, prospective, monocentric, and single ascending-dose design, seeks to assess the safety and tolerability of the procedure in individuals with confirmed SARS-CoV-2 infection. Employing a 3 + 3 design, the study includes three exposure durations (target durations): 1 day (D1), 3 days (D2), and 6 days (D3). We concluded that the maximum tolerated duration is at least 3 days. Every patient showed clinical improvement and excellent tolerance to H2 therapy. To the best of our knowledge, this phase I clinical trial is the first to establish the safety of inhaling a mixture of H2 (3.6%) and N2 (96.4%) in hospitalized COVID-19 patients. The original device and method employed ensure the absence of explosion risk. The encouraging outcomes observed in the 12 patients included in the study justify further exploration through larger, controlled clinical trials.

CLINICAL TRIALS

This study is registered with ClinicalTrials.gov as NCT04633980.

[The contribution of artificial intelligence (AI) subsequent to the processing of thoracic imaging]

The contribution of artificial intelligence (AI) to medical imaging is currently the object of widespread experimentation. The development of deep learning (DL) methods, particularly convolution neural networks (CNNs), has led to performance gains often superior to those achieved by conventional methods such as machine learning. Radiomics is an approach aimed at extracting quantitative data not accessible to the human eye from images expressing a disease. The data subsequently feed machine learning models and produce diagnostic or prognostic probabilities. As for the multiple applications of AI methods in thoracic imaging, they are undergoing evaluation. Chest radiography is a practically ideal field for the development of DL algorithms able to automatically interpret X-rays. Current algorithms can detect up to 14 different abnormalities present either in isolation or in combination. Chest CT is another area offering numerous AI applications. Various algorithms have been specifically formed and validated for the detection and characterization of pulmonary nodules and pulmonary embolism, as well as segmentation and quantitative analysis of the extent of diffuse lung diseases (emphysema, infectious pneumonias, interstitial lung disease). In addition, the analysis of medical images can be associated with clinical, biological, and functional data (multi-omics analysis), the objective being to construct predictive approaches regarding disease prognosis and response to treatment.