Editorial: Precision Medicine in Chronic Inflammation

Current and future applications of light-sheet imaging for identifying molecular and developmental processes in autism spectrum disorders

Autism spectrum disorder (ASD) is identified by a set of neurodevelopmental divergences that typically affect the social communication domain. ASD is also characterized by heterogeneous cognitive impairments and is associated with cooccurring physical and medical conditions. As behaviors emerge as the brain matures, it is particularly essential to identify any gaps in neurodevelopmental trajectories during early perinatal life. Here, we introduce the potential of light-sheet imaging for studying developmental biology and cross-scale interactions among genetic, cellular, molecular and macroscale levels of circuitry and connectivity. We first report the core principles of light-sheet imaging and the recent progress in studying brain development in preclinical animal models and human organoids. We also present studies using light-sheet imaging to understand the development and function of other organs, such as the skin and gastrointestinal tract. We also provide information on the potential of light-sheet imaging in preclinical drug development. Finally, we speculate on the translational benefits of light-sheet imaging for studying individual brain-body interactions in advancing ASD research and creating personalized interventions.

Limited Utility of Keratic Precipitate Morphology as an Indicator of Underlying Diagnosis in Ocular Inflammation

OBJECTIVES

We aimed to establish the degree of consensus among clinicians on descriptors of KP morphology.

METHODS

A web-based exercise in which respondents associated KP descriptors, as identified through a scoping review of the published literature, to images from different disorders. Inter-observer agreement was assessed using the Krippendorff kappa alpha metric.

RESULTS

Of the 76 descriptive terms identified by the scoping review, the most used included "mutton-fat" (n = 93 articles, 36%), "fine/dust" (n = 76, 29%), "stellate" (n = 40, 15%), "large" (n = 33, 12%), and "medium" (n = 33, 12%). The survey of specialists (n = 26) identified inter-observer agreement for these descriptors to be poor ("stellate," kappa: 0.15, 95% confidence interval 0.13-0.17), limited ("medium": 0.27, 95% CI 0.25-0.29; "dust/fine": 0.36, 95% CI 0.34-0.37), or moderate ("mutton fat": 0.40, 95% CI 0.36-0.43; "large": 0.43, 95% CI 0.39-0.46).

CONCLUSION

The clinical utility of KP morphology as an indicator of disease classification is limited by low inter-observer agreement.

Stratification in systemic sclerosis according to autoantibody status versus skin involvement: a study of the prospective EUSTAR cohort

BACKGROUND

The current subclassification of systemic sclerosis into cutaneous subtypes does not fully capture the heterogeneity of the disease. We aimed to compare the performances of stratification into LeRoy's cutaneous subtypes versus stratification by autoantibody status in systemic sclerosis.

METHODS

For this cohort study, we assessed people with systemic sclerosis in the multicentre international European Scleroderma Trials and Research (EUSTAR) database. Individuals positive for systemic-sclerosis autoantibodies of two specificities were excluded, and remaining individuals were classified by cutaneous subtype, according to their systemic sclerosis-specific autoantibodies, or both. We assessed the performance of each model to predict overall survival, progression-free survival, disease progression, and different organ involvement. The three models were compared by use of the area under the curve (AUC) of the receiver operating characteristic and the net reclassification improvement (NRI). Missing data were imputed.

FINDINGS

We assessed the database on July 26, 2019. Of 16 939 patients assessed for eligibility, 10 711 patients were included: 1647 (15·4%) of 10 709 were male, 9062 (84·6%) were female, mean age was 54·4 (SD 13·8) years, and mean disease duration was 7·9 (SD 8·2) years. Information regarding cutaneous subtype was available for 10 176 participants and antibody data were available for 9643 participants. In the prognostic analysis, there was no difference in AUC for overall survival (0·82, 95% CI 0·81-0·84 for cutaneous only vs 0·84, 0·82-0·85 for antibody only vs 0·84, 0·83-0·86 for combined) or for progression-free survival (0·70, 0·69-0·71 vs 0·71, 0·70-0·72 vs 0·71, 0·70-0·72). However, at 4 years the NRI showed substantial improvement for the antibody-only model compared with the cutaneous-only model in prediction of overall survival (0·57, 0·46-0·71 for antibody only vs 0·29, 0·19-0·39 for cutaneous only) and disease progression (0·36, 0·29-0·46 vs 0·21, 0·14-0·28). The antibody-only model did better than the cutaneous-only model in predicting renal crisis (AUC 0·72, 0·70-0·74 for antibody only vs 0·66, 0·64-0·69 for cutaneous only) and lung fibrosis leading to restrictive lung function (AUC 0·76, 0·75-0·77 vs 0·71, 0·70-0·72). The combined model improved the prediction of digital ulcers and elevated systolic pulmonary artery pressure, but did poorly for cardiac involvement.

INTERPRETATION

The autoantibody-only model outperforms cutaneous-only subsetting for risk stratifying people with systemic sclerosis in the EUSTAR cohort. Physicians should be aware of these findings at the time of decision making for patient management.

FUNDING

World Scleroderma Foundation.

Tactile Vision, Epistemic Things and Data Visualization

Hans-Jörg Rheinberger constructed his historical epistemology of epistemic things by analyzing experimental practices in molecular biology during the 1970s and 80s. With genetic sequencing and multi-omics approaches, data has become a new resource in the life sciences, questioning the applicability of his concept of experimental system. By historicizing Rheinberger's epistemology, the paper focuses on its relatedness to Ludwik Fleck's notion of an aviso of resistance and points to a gradual shift in Rheinberger's emphasis, moving from an initial focus on writing and its differentiality to work on materials, preparations, and representations. By anchoring visualization in these material practices, Rheinberger also sheds new light on the changing conditions of experimentation in the life sciences due to big data, where visualization emphasizes patterns and correlations rather than substrates.

Zebrafish Is a Powerful Tool for Precision Medicine Approaches to Neurological Disorders

Personalized medicine is currently one of the most promising tools which give hope to patients with no suitable or no available treatment. Patient-specific approaches are particularly needed for common diseases with a broad phenotypic spectrum as well as for rare and yet-undiagnosed disorders. In both cases, there is a need to understand the underlying mechanisms and how to counteract them. Even though, during recent years, we have been observing the blossom of novel therapeutic techniques, there is still a gap to fill between bench and bedside in a patient-specific fashion. In particular, the complexity of genotype-to-phenotype correlations in the context of neurological disorders has dampened the development of successful disease-modifying therapeutics. Animal modeling of human diseases is instrumental in the development of therapies. Currently, zebrafish has emerged as a powerful and convenient model organism for modeling and investigating various neurological disorders. This model has been broadly described as a valuable tool for understanding developmental processes and disease mechanisms, behavioral studies, toxicity, and drug screening. The translatability of findings obtained from zebrafish studies and the broad prospect of human disease modeling paves the way for developing tailored therapeutic strategies. In this review, we will discuss the predictive power of zebrafish in the discovery of novel, precise therapeutic approaches in neurosciences. We will shed light on the advantages and abilities of this in vivo model to develop tailored medicinal strategies. We will also investigate the newest accomplishments and current challenges in the field and future perspectives.