Deep Plasma Proteomics with Data-Independent Acquisition: Clinical Study Protocol Optimization with a COVID-19 Cohort.

Plasma proteomics is a precious tool in human disease research but requires extensive sample preparation in order to perform in-depth analysis and biomarker discovery using traditional data-dependent acquisition (DDA). Here, we highlight the efficacy of combining moderate plasma prefractionation and data-independent acquisition (DIA) to significantly improve proteome coverage and depth while remaining cost-efficient. Using human plasma collected from a 20-patient COVID-19 cohort, our method utilizes commonly available solutions for depletion, sample preparation, and fractionation, followed by 3 liquid chromatography-mass spectrometry/MS (LC-MS/MS) injections for a 360 min total DIA run time. We detect 1321 proteins on average per patient and 2031 unique proteins across the cohort. Differential analysis further demonstrates the applicability of this method for plasma proteomic research and clinical biomarker identification, identifying hundreds of differentially abundant proteins at biological concentrations as low as 47 ng/L in human plasma. Data are available via ProteomeXchange with the identifier PXD047901. In summary, this study introduces a streamlined, cost-effective approach to deep plasma proteome analysis, expanding its utility beyond classical research environments and enabling larger-scale multiomics investigations in clinical settings. Our comparative analysis revealed that fractionation, whether the samples were pooled or separate postfractionation, significantly improved the number of proteins quantified. This underscores the value of fractionation in enhancing the depth of plasma proteome analysis, thereby offering a more comprehensive landscape for biomarker discovery in diseases such as COVID-19.

[Contribution of free software to medical imaging]. / Apport du logiciel libre en imagerie médicale.

Medical research uses increasingly massive, complex and interdependent data, the analysis of which requires the use of specialized algorithms. In order to independently reproduce and validate the results of a scientific study, it is no longer sufficient to share the text of the article as an open-access document, together with the raw research data according to the open-data approach. It is now also needed to share the algorithms used to analyze the data with other research teams. Free and open-source software precisely responds to this need to disseminate technical knowledge at a large scale. In this paper, we present several examples of free software used in medicine, with a particular focus on medical imaging.

La recherche médicale recourt à des données de plus en plus massives, complexes et interdépendantes dont l'analyse nécessite l'usage d'algorithmes spécialisés. Afin de reproduire et valider les résultats d'une étude scientifique de manière indépendante, il n'est, dès lors, plus suffisant de partager le texte de l'article en «open-access¼ complété avec les données brutes en «open-data¼. Il convient désormais d'également partager les algorithmes qui ont servi à l'analyse des données avec d'autres équipes de chercheurs. Le logiciel libre et «open-source¼ répond précisément à ce besoin de diffuser les connaissances techniques à grande échelle. Dans cet article, nous présentons plusieurs exemples de logiciels libres utilisés en médecine, avec une attention particulière portée à l'imagerie médicale.

[The use of medical imaging as a therapeutic patient education tool in radiotherapy : a feasibility study]. / L'utilisation de l'imagerie médicale en tant qu'outil d'éducation thérapeutique du patient en radiothérapie : étude de faisabilité.

Because of its prevalence and high mortality rate, cancer is a major public health challenge. Radiotherapy is an important treatment option, and makes extensive use of medical imaging. Until now, this type of tool has been reserved to professionals, but it is now opening up to wider use, including by patients themselves for educational purposes. However, this type of usage has been little explored so far. An experimental feasibility study was carried out in the radiotherapy department of the University Hospital of Liège on adult patients with cancer or pulmonary metastases, assigned to two randomized groups. In addition to the usual information given by the radiotherapist, the patients of the experimental group benefited from an intervention consisting in the 3D visualization of their own medical images via the free and open-source computer software «Stone of Orthanc¼. The study results show a low refuse rate (8.2 %) for the 15 patients recruited. Although non-significant, the experimental group showed a median gain in global perception of knowledge, a decrease in anxiety scores and emotional distress. A significant reduction (p = 0.043) was observed for the depression score. The positive results of the feasibility study encourage further work and reinforce the positioning of medical imaging as a tool for therapeutic patient education.

De par sa fréquence et son taux de mortalité élevé, le cancer représente un problème de santé publique majeur. Parmi les traitements possibles, la radiothérapie tient une place importante et fait appel massivement à l'imagerie médicale. Jusqu'ici réservé aux professionnels, ce type d'outil s'ouvre à un usage plus large, y compris par le patient lui-même dans une perspective éducative. Mais cette utilisation est restée peu explorée jusqu'à présent. Une étude expérimentale de faisabilité a ainsi été menée au sein du service de Radiothérapie du CHU de Liège sur des patients adultes avec cancer ou métastases pulmonaires, répartis en deux groupes randomisés. En plus des informations habituellement données par le radiothérapeute, le groupe expérimental a bénéficié d'une intervention consistant en la visualisation en 3D de ses propres images médicales via le logiciel libre et open-source «Stone of Orthanc¼. Les résultats de l'étude indiquent un taux de refus faible (8,2 %) pour les 15 patients recrutés. Bien que non significatif, le groupe expérimental a montré, par rapport au groupe contrôle, un gain médian dans la perception globale de connaissances ainsi qu'une diminution des scores liés à l'anxiété et à la détresse émotionnelle. Une réduction significative (p = 0,043) est observée pour le score de dépression. Les résultats positifs de l'étude de faisabilité encouragent la poursuite des travaux et renforcent le positionnement de l'usage de l'imagerie médicale en tant qu'outil d'éducation thérapeutique du patient.

Integrated and Interoperable Platform for Detecting Masses on Mammograms.

The screening and diagnosis of breast cancer is a major public health issue. Although deep learning models are proving highly effective in breast imaging, these models are not yet readily accessible to a wide audience. In order to promote the widespread dissemination of such models, this article introduces a free and open-source, integrated platform for the automated detection of masses on mammograms. A state-of-the-art RetinaNet model is trained on this task and the results of the inference are encoded using the DICOM-SR interoperable format. These contributions present a significant step towards overcoming the accessibility gap in deep learning for breast imaging.