The Detection and Treatment of Respiratory Illnesses

In the relentless pursuit of medical excellence, the emergence of COVID-19 has expedited the integration of personalised medicine into respiratory disease management, underscoring the critical role of diagnostics and therapeutics tailored to individual patient profiles.

Respiratory disease, after heart disease, and cancer, remain the third most common cause of death. To improve these dire statistics, distinguished experts in the global medical community shifted their focus to the groundbreaking advancements in the detection and treatment of respiratory illnesses, particularly through the lens of COVID-19 biomarkers. The synergy of biomarkers and advanced imaging techniques has opened new avenues for precision medicine, allowing us to visualise and quantify disease processes with unprecedented clarity. This holistic approach enhances our diagnostic accuracy, informs our therapeutic decisions, and ultimately leads to more effective and personalised care.

The evolution of modern medicine now relies heavily on our ability to combine these biomarkers with cutting-edge imaging, pushing the boundaries of what is possible in disease management and patient care. The journey through the pandemic has been arduous, but it has also catalysed innovations that will forever alter the landscape of healthcare, steering us towards a future where medicine is as unique as the patients we serve.

Recent key publications

1) Autoantibodies against chemokines post-SARS-CoV-2 infection correlate with disease course. Nat Immunol 2023; 24:604-611. Muri J, Cecchinato V, Cavalli A et al. Link to full article

In this comprehensive study, the authors reveal a significant discovery concerning the immune response following SARS-CoV-2 infection: the presence of antibodies targeting specific chemokines, which are associated with favourable COVID-19 outcomes and a diminished likelihood of developing long COVID a year after infection. This finding not only provides insight into the body's natural defence mechanisms but also highlights the potential for these antibodies as therapeutic agents. By comparing the nature of these antibodies in COVID-19 with those found in other diseases like HIV-1 and autoimmune disorders, this report opens new avenues for innovative treatments, offering hope for mitigating the prolonged effects of COVID-19 through immunological strategies.

2) A Biobanking System for Diagnostic Images: Architecture Development, COVID-19-Related Use Cases, and Performance Evaluation. JMIR Form Res 2023; 7:e42505. Esposito G, Allarà C, Randon M et al. Link to full article

The authors evaluate the Bio Check Up Srl (BCU) Imaging Biobank, a pioneering system designed to automate the management of diagnostic images and clinical data, leveraging the Extensible Neuroimaging Archive Toolkit. Through meticulous experimentation across various network setups, we scrutinise system performance during data uploads and user interactions, unveiling critical insights into resource consumption patterns. These findings not only confirm the biobank's capability to meet the demands of concurrent users but also offer valuable guidelines for optimising imaging biobank systems. This research is essential reading for professionals aiming to advance the intersection of healthcare, technology, and data management.

3) Immunoreactive trypsinogen in healthy newborns and infants with cystic fibrosis. Arch Dis Child Fetal Neonatal Ed 2023; 108:176-181. Fingerhut R, Rueegg CS, Imahorn O et al. Link to full article

This retrospective study leverages Switzerland's National Newborn Screening (NBS) database to elucidate the immunoreactive trypsinogen (IRT) levels in newborns, offering unprecedented insights into the early detection of cystic fibrosis (CF) and cystic fibrosis transmembrane conductance regulator related metabolic syndrome (CRMS)/CF screen positive, inconclusive diagnosis (CFSPID). By analysing IRT values from over 815,000 children and comparing these at two distinct time points, the research highlights the potential of a second IRT assessment in distinguishing between CF and CRMS/CFSPID cases. This article is a must-read for healthcare professionals and researchers dedicated to enhancing diagnostic accuracy and outcomes in newborn screening programmes.

4) Novel model integrating computed tomography-based image markers with genetic markers for discriminating radiation pneumonitis in patients with unresectable stage III non-small cell lung cancer receiving radiotherapy: a retrospective multi-center radiogenomics study. BMC Cancer 2024; 24:78. Li J, Li L, Tang S et al. Link to full article

This study pioneers the integration of CT-based radiomics and genomics to predict the risk of grade ≥ 2 radiation pneumonitis (RP) in patients with unresectable stage III non-small cell lung cancer (NSCLC) undergoing chemoradiotherapy. Through a meticulous retrospective multi-centre analysis of 100 patients, it establishes a novel predictive model that significantly outperforms traditional methods, incorporating histology, a radiomics-based Rad-score, and XRCC1 (rs25487) allele mutation as key predictors. The findings underscore the potential of combining clinical, radiomic, and genomic data to enhance early risk identification and tailor treatment strategies, offering a compelling read for experts aiming to advance precision medicine in oncology.

5) Auto-detection of the coronavirus disease by using deep convolutional neural networks and X-ray photographs. Sci Rep 2024; 14:534. Hussein AM, Sharifai AG, Alia OM et al. Link to full article

This study introduces a Custom Convolutional Neural Network (Custom-CNN) designed to update the detection of COVID-19 through chest X-rays, addressing the limitations of traditional testing methods such as high costs, delayed results, and sensitivity issues. With a remarkable classification accuracy of 98.19%, the Custom-CNN model leverages advanced deep learning techniques, including dropout and batch normalisation, to accurately differentiate between COVID-19, normal, and pneumonia cases. This study represents a significant step forward in the efficient and reliable diagnosis of COVID-19, offering a promising solution for healthcare systems worldwide. The findings highlight the potential of artificial intelligence to enhance diagnostic processes, making it essential reading for professionals in radiology, infectious disease, and healthcare technology.

6) PLUS-IS-LESS project: Procalcitonin and Lung UltraSonography-based antibiotherapy in patients with Lower rESpiratory tract infection in Swiss Emergency Departments: study protocol for a pragmatic stepped-wedge cluster-randomized trial. Trials 2024; 25:86. Bessat C, Bingisser R, Schwendinger M et al. Link to full article

The PLUS-IS-LESS study presents an original, multimodal diagnostic approach aimed at refining antibiotic prescription practices for lower respiratory tract infections (LRTIs) in emergency departments. By integrating clinical scores, lung ultrasound (LUS), and procalcitonin (PCT) levels, this pragmatic, stepped-wedge cluster-randomised trial conducted across 10 Swiss EDs seeks to enhance the precision of bacterial pneumonia diagnosis, thereby reducing unnecessary antibiotic use without compromising patient safety. The trial's design promises not only to offer insights into the efficacy of this synergistic diagnostic method but also to contribute significantly to the global effort against antibiotic resistance, making it an interesting read for healthcare professionals and researchers involved in infectious disease management and policy formulation.