The data presented here derives from the Sustainable Working Conditions in Academia survey (STAIRCASE) on researcher mental health. The survey addresses the ongoing mental health crisis in academia by providing comprehensive, multilevel data on researcher well-being and its structural determinants. We employed a cross-sectional between-participant study design to collect data from 4,296 researchers between September 15, 2023, and August 26, 2024. The sample, which primarily includes researchers based in European countries, has a mean age of 38.1 years (SD = 10.7) and consists of 63.7% female participants. Participants provided data on key mental health outcomes – including depression, anxiety, stress, burnout, and well-being – alongside detailed assessments of working conditions, leadership behavior, and organizational characteristics. The dataset facilitates a holistic, multilevel investigation of academic mental health beyond individual risk factors by including the individual, leadership, institutional, and national context. By enabling analyses across hierarchical levels this dataset provides the necessary evidence to identify systemic drivers of mental (ill) health and inform the development of effective, system-wide strategies for meaningful change in academic work environments.

We employ Kuramoto model to assess the presence of synchronization in individuals who fulfill a cooperation task. Our input data is a couple of signals obtained from functional Near-Infrared Spectroscopy Data Acquisition and Pre-processing technology that is used to capture the brain activity of an individual by measuring the oxyhemoglobin (HbO) level. We consider 1 min signal for individuals in three distinct states: (i) rest; (ii) before a disturb happens; (iii) after the disturbance. We estimate global and local order parameters synchronization with the purpose to compare the conditions of reaching a synchronous state in the networks corresponding to different states for distinct individuals and hemispheres of the prefrontal cortices of same individual. Experimental results confirmed once more that coherent state is reached not for same conditions in both individuals and hemispheres of the prefrontal cortices. Furthermore, condition changes even for different events. The computation of the effective frequencies for each degree class indicates clearly the network difference in rest, before and after disturb. Finally, we investigate the dynamic connectivity matrix and consider the similarity between distinct prefrontal cortices over time.

Objectives: Inherited retinal dystrophies are characterized by high genetic and phenotypic heterogeneity, and their clinical progression may alternate between latent and active phases. Identifying the onset of the active phase may support earlier intervention for inflammatory retinal degeneration. Plasma proteomics has shown potential for characterizing predictive biomarkers in retinal diseases, but its application remains experimental. This study aimed to develop a methodological simulation exercise to evaluate the performance of machine learning (ML) models in distinguishing active and latent phases using an artificially generated dataset.Methods: An artificial dataset of 500 samples was created, and plasma proteomic profiles were generated for each sample using arbitrary values. Sample classification was based on a pathway activation score. Four ML models were tested: support vector machine, random forest, logistic regression, and extreme gradient boosting. Each model was trained across a range of hyperparameters.Results: Logistic regression achieved the best performance, with an accuracy of 0.73, precision of 0.73, and F1-score of 0.73.Conclusions: This simulation study showed that synthetic proteomic datasets can be used to evaluate ML approaches for distinguishing active and latent phases of retinal dystrophies when real data are scarce. Synthetic data can support the creation of targeted datasets for proteins associated with retinal dystrophies, helping to address the limited availability of suitable open-source data.

Purpose: Despite advancements in genetic testing, a significant number of patients with retinal dystrophy receive negative test results due to single-allele mutations in genes with autosomal recessive inheritance patterns. This study aimed to address this diagnostic challenge by proposing a preliminary mathematical model based on mutational burden analysis. This approach can identify unique digenic or oligogenic combinations, potentially prioritizing them for further investigation. Methods: We retrospectively analyzed clinical and genetic data from 527 subjects comprising 453 patients with retinal dystrophies (cases) and 74 controls and created semi-synthetic datasets. Protein– protein interaction networks were analyzed, and the significance of mutational burden in specific molecular pathways was investigated. Results: Our model identified two metabolic pathways that are highly correlated to each other, namely “retinoid cycle disease events” and “canonical retinoid cycle in rods (twilight vision).” These pathways were significantly impacted by the burden of variants we found in the case group. Moreover, the gene combinations (ABCA4, RDH12) and (ABCA4, RBP3) were identified exclusively in cases of retinal dystrophies. Conclusion: The proposed preliminary model provides a systematic approach to calculate the mutational burden in patients with retinal dystrophies and previous negative genetic test results. Moreover, the bioinformatic analysis facilitated the identification of recurrent combinations of involved genes, suggesting possible new digenic or oligogenic inheritance patterns.