Lyme borreliosis and medical wandering: what do patients think about multidisciplinary management? A qualitative study in the context of scientific and social controversy

INTRODUCTION

To answer to patients' medical wandering, often due to "unexplained symptoms" of "unexplained diseases" and to misinformation, multidisciplinary care centers for suspected Lyme borreliosis (LB), such as the 5 Tick-Borne Diseases (TBDs) Reference Centers (TBD-RC), were created a few years ago in France, the Netherlands and Denmark. Our study consisted of a comprehensive analysis of the satisfaction of the patients managed at a TBD-RC for suspected LB in the context of scientific and social controversy.

METHODS

We included all adults who were admitted to one of the TBD-RC from 2017 to 2020. A telephone satisfaction survey was conducted 12 months after their first consultation. It consisted of 5 domains, including 2 free-text items: "What points did you enjoy?" and "What would you like us to change or to improve?". In the current study, the 2 free-items were analyzed with a qualitative method called reflexive thematic analysis within a semantic and latent approach.

RESULTS

The answer rate was 61.3% (349/569) and 97 distinctive codes from the 2-free-text items were identified and classified into five themes: (1) multidisciplinarity makes it possible to set up quality time dedicated to patients; (2) multidisciplinarity enables seamless carepaths despite the public hospital crisis compounded by the COVID-19 pandemic; (3) multidisciplinarity is defined as trust in the team's competences; (4) an ambivalent opinion and uncertainty are barriers to acceptance of the diagnosis, reflecting the strong influence of the controversy around LB; and (5) a lack of adapted communication about TBDs, their management, and ongoing research is present.

CONCLUSION

The multidisciplinary management for suspected LB seemed an answer to medical wandering for the majority of patients and helped avoid misinformation, enabling better patient-centered shared information and satisfaction, despite the context of controversy.

Cancer survival prediction by learning comprehensive deep feature representation for multiple types of genetic data

BACKGROUND

Cancer is one of the leading death causes around the world. Accurate prediction of its survival time is significant, which can help clinicians make appropriate therapeutic schemes. Cancer data can be characterized by varied molecular features, clinical behaviors and morphological appearances. However, the cancer heterogeneity problem usually makes patient samples with different risks (i.e., short and long survival time) inseparable, thereby causing unsatisfactory prediction results. Clinical studies have shown that genetic data tends to contain more molecular biomarkers associated with cancer, and hence integrating multi-type genetic data may be a feasible way to deal with cancer heterogeneity. Although multi-type gene data have been used in the existing work, how to learn more effective features for cancer survival prediction has not been well studied.

RESULTS

To this end, we propose a deep learning approach to reduce the negative impact of cancer heterogeneity and improve the cancer survival prediction effect. It represents each type of genetic data as the shared and specific features, which can capture the consensus and complementary information among all types of data. We collect mRNA expression, DNA methylation and microRNA expression data for four cancers to conduct experiments.

CONCLUSIONS

Experimental results demonstrate that our approach substantially outperforms established integrative methods and is effective for cancer survival prediction.

AVAILABILITY AND IMPLEMENTATION

https://github.com/githyr/ComprehensiveSurvival .

Effect of shared information and owner behavior on showing in dogs (Canis familiaris)

Dogs’ production of referential communicative signals, i.e., showing, has gained increasing scientific interest over the last years. In this paper, we investigate whether shared information about the present and the past affects success and form of dog–human interactions. Second, in the context of showing, owners have always been treated as passive receivers of the dog’s signals. Therefore, we examined whether the owner’s behavior can influence the success and form of their dog’s showing behavior. To address these questions, we employed a hidden-object task with knowledgeable dogs and naïve owners. Shared information about the present was varied via the spatial set-up, i.e., position of hiding places, within dog–owner pairs, with two conditions requiring either high or low precision in indicating the target location. Order of conditions varied between pairs, representing differences in shared knowledge about the past (communication history). Results do not support an effect of communication history on either success or showing effort. In contrast, the spatial set-up was found to affect success and choice of showing strategies. However, dogs did not adjust their showing effort according to different spatial set-ups. Our results suggest that the latter could be due to the owner’s influence. Owner behavior generally increased the effort of their dog’s showing behavior which was stronger in the set-up requiring low showing precision. Moreover, our results suggest that owners could influence their dog’s showing accuracy (and thereby success) which, however, tended to be obstructive.

The information theory of individuality

Despite the near universal assumption of individuality in biology, there is little agreement about what individuals are and few rigorous quantitative methods for their identification. Here, we propose that individuals are aggregates that preserve a measure of temporal integrity, i.e., "propagate" information from their past into their futures. We formalize this idea using information theory and graphical models. This mathematical formulation yields three principled and distinct forms of individuality-an organismal, a colonial, and a driven form-each of which varies in the degree of environmental dependence and inherited information. This approach can be thought of as a Gestalt approach to evolution where selection makes figure-ground (agent-environment) distinctions using suitable information-theoretic lenses. A benefit of the approach is that it expands the scope of allowable individuals to include adaptive aggregations in systems that are multi-scale, highly distributed, and do not necessarily have physical boundaries such as cell walls or clonal somatic tissue. Such individuals might be visible to selection but hard to detect by observers without suitable measurement principles. The information theory of individuality allows for the identification of individuals at all levels of organization from molecular to cultural and provides a basis for testing assumptions about the natural scales of a system and argues for the importance of uncertainty reduction through coarse-graining in adaptive systems.

Partial information decomposition as a unified approach to the specification of neural goal functions

In many neural systems anatomical motifs are present repeatedly, but despite their structural similarity they can serve very different tasks. A prime example for such a motif is the canonical microcircuit of six-layered neo-cortex, which is repeated across cortical areas, and is involved in a number of different tasks (e.g. sensory, cognitive, or motor tasks). This observation has spawned interest in finding a common underlying principle, a 'goal function', of information processing implemented in this structure. By definition such a goal function, if universal, cannot be cast in processing-domain specific language (e.g. 'edge filtering', 'working memory'). Thus, to formulate such a principle, we have to use a domain-independent framework. Information theory offers such a framework. However, while the classical framework of information theory focuses on the relation between one input and one output (Shannon's mutual information), we argue that neural information processing crucially depends on the combination of multiple inputs to create the output of a processor. To account for this, we use a very recent extension of Shannon Information theory, called partial information decomposition (PID). PID allows to quantify the information that several inputs provide individually (unique information), redundantly (shared information) or only jointly (synergistic information) about the output. First, we review the framework of PID. Then we apply it to reevaluate and analyze several earlier proposals of information theoretic neural goal functions (predictive coding, infomax and coherent infomax, efficient coding). We find that PID allows to compare these goal functions in a common framework, and also provides a versatile approach to design new goal functions from first principles. Building on this, we design and analyze a novel goal function, called 'coding with synergy', which builds on combining external input and prior knowledge in a synergistic manner. We suggest that this novel goal function may be highly useful in neural information processing.