Towards a Transportable Causal Network Model Based on Observational Healthcare Data

Over the last decades, many prognostic models based on artificial intelligence techniques have been used to provide detailed predictions in healthcare. Unfortunately, the real-world observational data used to train and validate these models are almost always affected by biases that can strongly impact the outcomes validity: two examples are values missing not-at-random and selection bias. Addressing them is a key element in achieving transportability and in studying the causal relationships that are critical in clinical decision making, going beyond simpler statistical approaches based on probabilistic association. In this context, we propose a novel approach that combines selection diagrams, missingness graphs, causal discovery and prior knowledge into a single graphical model to estimate the cardiovascular risk of adolescent and young females who survived breast cancer. We learn this model from data comprising two different cohorts of patients. The resulting causal network model is validated by expert clinicians in terms of risk assessment, accuracy and explainability, and provides a prognostic model that outperforms competing machine learning methods.</p

Towards a Transportable Causal Network Model Based on Observational Healthcare Data

Over the last decades, many prognostic models based on artificial intelligence techniques have been used to provide detailed predictions in healthcare. Unfortunately, the real-world observational data used to train and validate these models are almost always affected by biases that can strongly impact the outcomes validity: two examples are values missing not-at-random and selection bias. Addressing them is a key element in achieving transportability and in studying the causal relationships that are critical in clinical decision making, going beyond simpler statistical approaches based on probabilistic association. In this context, we propose a novel approach that combines selection diagrams, missingness graphs, causal discovery and prior knowledge into a single graphical model to estimate the cardiovascular risk of adolescent and young females who survived breast cancer. We learn this model from data comprising two different cohorts of patients. The resulting causal network model is validated by expert clinicians in terms of risk assessment, accuracy and explainability, and provides a prognostic model that outperforms competing machine learning methods

Bioranden richtig lagern und Lagerfäule vermeiden

Randen sind eine interessante Lagerkultur, etwa für die Direktvermarktung oder den Gastrobereich. Als optimale Bedingungen für die Lagerung von Randen wird zu einer Kühlung auf 4 bis 5 Grad mit einer Luftfeuchtigkeit von 95 bis 98 Prozent geraten. Je nach Infrastruktur können diese Bedingungen nicht immer gewährleistet werden, es kann zu grösseren Ausfällen durch Lagerfäule kommen

Lagerfäulen bei Bio-Randen

In den vergangenen Jahren ist es in der Schweizer Bio-Randenproduktion immer wieder zu Problemen mit Lagerfäulen gekommen. Zur Bekämpfung der Lagerfäulen dürfen bei geernteten Produkten keine Pflanzenschutzmittel eingesetzt werden. Deshalb kann auch bei Randen guter Erntequalität eine lange Haltbarkeit nicht garantiert werden. Werden sie bis zum Frühjahr gelagert, kommt es oft zu grossen Verlusten, weshalb dann auf Importware zurückgegriffen werden muss

Risk Assessment of Lymph Node Metastases in Endometrial Cancer Patients:A Causal Approach

Assessing the pre-operative risk of lymph node metastases in endometrial cancer patients is a complex and challenging task. In principle, machine learning and deep learning models are flexible and expressive enough to capture the dynamics of clinical risk assessment. However, in this setting we are limited to observational data with quality issues, missing values, small sample size and high dimensionality: we cannot reliably learn such models from limited observational data with these sources of bias. Instead, we choose to learn a causal Bayesian network to mitigate the issues above and to leverage the prior knowledge on endometrial cancer available from clinicians and physicians. We introduce a causal discovery algorithm for causal Bayesian networks based on bootstrap resampling, as opposed to the single imputation used in related works. Moreover, we include a context variable to evaluate whether selection bias results in learning spurious associations. Finally, we discuss the strengths and limitations of our findings in light of the presence of missing data that may be missing-not-at-random, which is common in real-world clinical settings.</p

Causal Discovery with Missing Data in a Multicentric Clinical Study

Causal inference for testing clinical hypotheses from observational data presents many difficulties because the underlying data-generating model and the associated causal graph are not usually available. Furthermore, observational data may contain missing values, which impact the recovery of the causal graph by causal discovery algorithms: a crucial issue often ignored in clinical studies. In this work, we use data from a multi-centric study on endometrial cancer to analyze the impact of different missingness mechanisms on the recovered causal graph. This is achieved by extending state-of-the-art causal discovery algorithms to exploit expert knowledge without sacrificing theoretical soundness. We validate the recovered graph with expert physicians, showing that our approach finds clinically-relevant solutions. Finally, we discuss the goodness of fit of our graph and its consistency from a clinical decision-making perspective using graphical separation to validate causal pathways.</p

Causal Discovery with Missing Data in a Multicentric Clinical Study

Causal inference for testing clinical hypotheses from observational data presents many difficulties because the underlying data-generating model and the associated causal graph are not usually available. Furthermore, observational data may contain missing values, which impact the recovery of the causal graph by causal discovery algorithms: a crucial issue often ignored in clinical studies. In this work, we use data from a multi-centric study on endometrial cancer to analyze the impact of different missingness mechanisms on the recovered causal graph. This is achieved by extending state-of-the-art causal discovery algorithms to exploit expert knowledge without sacrificing theoretical soundness. We validate the recovered graph with expert physicians, showing that our approach finds clinically-relevant solutions. Finally, we discuss the goodness of fit of our graph and its consistency from a clinical decision-making perspective using graphical separation to validate causal pathways

Reduce the post-harvest losses in organic beetroot production

The market for organic agriculture is rapidly growing. In Switzerland, the production of organic Beetroot is particularly renowned. However, their storage until spring has become increasingly difficult in recent years, and losses due to post-harvest rots can lead to over 50% by March. The causes for the various storage rots in beetroot are currently unclear, and therefore there are few measures to prevent them in organic production. Pathogen infections causing storage rots in beetroot can occur via the seed, in the field, or post-harvest. Understanding the process of infection is, therefore, critical to find preventive solutions. Here, we present the results of a two-year project that aim to reduce post harvest losses and elucidate the causes of storage rots in organic beetroot production. Analysis of stored beetroot revealed Fusarium and Phoma as predominant pathogens, while Botrytis, Rhizoctonia, and Pythium as additional causative agents of storage rots. Field trials in cooperation with four producers of organic beetroot were performed, where the production from sowing to storage was monitored. Different measures, such as steam sterilization of the seed, the use of biocontrol products in the field and before storage, or processing and cooling methods after harvest, as well as cultivar differences were investigated. The various measures were found to affect seed health, seedling emergence, leaf health, and the quality of beetroot after storage

CLINICAL RESULTS OF COLLAGENASE TREATMENT FOR DUPUYTREN’S DISEASE: A CASE SERIES STUDY WITH 2-YEARS FOLLOW-UP

ABSTRACT Objectives: This study aims to report our experience with Clostridium Histolyticum collagenase (CCH) to support the importance of its clinical use and assess its clinical efficacy, complications, and recurrences. Methods: This prospective observational study of 66 patients with a 2-year follow-up. Patients with an extension lag major of 20° at the metacarpophalangeal joint (MPJ) and/or proximal interphalangeal joint (PIPJ) were included. We collected data on demographic and anamnestic details, MPJ and PIPJ contracture degrees, DASH score, complications, and recurrences. Results: The mean pre-injection contracture was 34° for MPJ and 31° for PIPJ. At the 2-year follow-up, the mean contracture for the MPJ and PIPJ were respectively 3° and 14.5°. The mean DASH score decreased from 21.8 before injection to 10,4 after 2 years. The disease recurrence occurred in 34.8% of the patients, all with PIPJ contracture. The main complication was skin breakage (25.7%). Conclusion: The CCH injections remain a consistent option in treating DD; withdrawal from the European market deprives surgeons and patients of low invasiveness and safe tool for treating DD. Level of evidence IV, Therapeutic study investigating treatment results, Case series

Secretion of Novel SEL1L Endogenous Variants Is Promoted by ER Stress/UPR via Endosomes and Shed Vesicles in Human Cancer Cells

We describe here two novel endogenous variants of the human endoplasmic reticulum (ER) cargo receptor SEL1LA, designated p38 and p28. Biochemical and RNA interference studies in tumorigenic and non-tumorigenic cells indicate that p38 and p28 are N-terminal, ER-anchorless and more stable relative to the canonical transmembrane SEL1LA. P38 is expressed and constitutively secreted, with increase after ER stress, in the KMS11 myeloma line and in the breast cancer lines MCF7 and SKBr3, but not in the non-tumorigenic breast epithelial MCF10A line. P28 is detected only in the poorly differentiated SKBr3 cell line, where it is secreted after ER stress. Consistently with the presence of p38 and p28 in culture media, morphological studies of SKBr3 and KMS11 cells detect N-terminal SEL1L immunolabeling in secretory/degradative compartments and extracellularly-released membrane vesicles. Our findings suggest that the two new SEL1L variants are engaged in endosomal trafficking and secretion via vesicles, which could contribute to relieve ER stress in tumorigenic cells. P38 and p28 could therefore be relevant as diagnostic markers and/or therapeutic targets in cancer