Single-cell RNA sequencing (scRNA-seq) technology has significantly advanced our understanding of the diversity of cells and how this diversity is implicated in diseases. Yet, translating these findings across various scRNA-seq datasets poses challenges due to technical variability and dataset-specific biases. To overcome this, we present a novel approach that employs both an LLM-based framework and explainable machine learning to facilitate generalization across single-cell datasets and identify gene signatures to capture disease-driven transcriptional changes. Our approach uses scBERT, which harnesses shared transcriptomic features among cell types to establish consistent cell-type annotations across multiple scRNA-seq datasets. Additionally, we employ a symbolic regression algorithm to pinpoint highly relevant yet minimally redundant models and features for inferring a cell type’s disease state based on its transcriptomic profile. We ascertain the versatility of these cell-specific gene signatures across datasets, showcasing their resilience as molecular markers to pinpoint and characterize disease-associated cell types. Validation is carried out using four publicly available scRNA-seq datasets from both healthy individuals and those suffering from ulcerative colitis (UC). This demonstrates our approach’s efficacy in bridging disparities specific to different datasets, fostering comparative analyses. Notably, the simplicity and symbolic nature of the retrieved gene signatures facilitate their interpretability, allowing us to elucidate underlying molecular disease mechanisms using these models.

The time required to reach a correct diagnosis is one of the most important problems for rare disease (RD) patients. Diagnostic delay can be intolerably long, to the point that it is usually described as a “diagnostic odyssey” and, sometimes, a diagnosis might never occur. The International Rare Disease Research Consortium proposed, as ultimate goal for 2017-2027, to enable all people with a suspected RD to be diagnosed within one year if the disorder is known, and to enter a globally coordinated diagnostic and research pipeline for the unsolved cases. In-depth analysis of the genotype through next generation sequencing, together with a standardized in-depth phenotype description and sophisticated high-throughput approaches, have been applied as diagnostic tools to increase the chance of a timely and accurate diagnosis. This approach is very fruitful as, according to the Orphanet database, from 2010 to March 2017 more than 600 new RDs have been described and about 3600 genes linked to RDs have been identified. However, combination of -omics and phenotype data and international sharing of this information raise ethical concerns. Values to be assessed include not only patient autonomy but also family implications, beneficence, non-maleficence, justice, solidarity and reciprocity, which must be respected and promoted and, at the same time, balanced among each other. In this work we suggest that, to maximise patients involvement in the search for a diagnosis and identification of new causative genes, undiagnosed patients should have the possibility to: 1) actively participate in the description of their phenotype; 2) choose the level of visibility of their profile in matchmaking databases; 3) express their preferences regarding return of new findings, in particular which level of Variant of Unknown Significance (VUS) significance should be considered relevant to them. The quality of the relation between individual patients and physicians, and between the patient community and the scientific community is critically important for making the best use of available data and combining efforts in the search for matches with similar cases worldwide that will help to solve unsolved cases. The contribution of patients to collecting and coding data comprehensively is critical for efficient use of data downstream of data collection.

Background: Over the past decades, efforts have been made to improve the nutritional well-being in people with cystic fibrosis (pwCF). Due to the correlation observed between nutritional indices and lung function, prevailing recommendations consistently advocate for BMI percentile goals at or above the 50th percentile in pwCF. Recent global trends show a notable increase in overweight and obese statuses among pwCF. This study aims to explore the nutritional status of Italian pwCF. Methods: Data from the Italian CF Patient’s Registry were analysed to assess the proportion of individuals categorized as underweight, target weight, overweight, and obese from 2010 to 2021. Patient-level comparison data from 2021 were also examined to identify potential determinants of overweight and obesity. Results: Analysis spanning 2010 to 2021 reveals a decrease of approximately 40% in underweight status among adults, while the proportion of malnourished patients 90, and lower prevalence of Pseudomonas aeruginosa colonization. Conclusions: Our study confirms the evolving nutritional status landscape among Italian adult pwCF, with a significant shift towards overweight and obesity over the past decade. These trends highlight the need for proactive measures within CF standards of care to adapt and address the changing needs of patients.

The present exploratory study tested the hypothesis that computerized cognitive training (CCT) in home telemonitoring may beneficially affect eyes-closed resting-state electroencephalographic (rsEEG) rhythms in Parkinson's disease patients with cognitive deficits (PDCD). A Eurasian database provided clinical-demographic-rsEEG datasets in 40 PDCD patients, 29 PD patients without cognitive deficits (PDNCD), 40 Alzheimer’s disease patients with cognitive deficits (ADCD), and 40 cognitively normal older adults (Healthy). Sixteen of the 40 PDCD patients completed a cross-over unsupervised CCT program of simple, serious video games (versus a sham program) consisting of 14 daily sessions of approximately 20 minutes each in the patients' homes. Compared to the Healthy, PDNCD, and ADCD groups, the PDCD group was characterized by greater diffuse rsEEG delta (about 2-4 Hz) and theta (about 4-7 Hz) source activity. The PDCD patients who underwent the CCT program showed an improvement in video game performance and a reduction in these delta-theta source activities after this program compared to the control condition. In conclusion, these results suggest that the 2-week CCT program in home telemonitoring may mitigate the abnormal “slowing” of rsEEG rhythms in PDCD patients, possibly enhancing the regulation of brain arousal and quiet vigilance.

The present study was developed based on the data of The PDWAVES Consortium (www.pdwaves.eu) and the PharmaCog project. The Partners and institutional affiliations are reported on the cover page of this manuscript. In this study, the clinical, neuropsychological, and magnetic resonance imaging data collection and analysis in patients with ADMCI and healthy control participants were partially supported by the funds of “Ricerca Corrente 2022-2023” (Italian Ministry of Health) to the IRCCS Synlab SDN of Naples (Italy), IRCCS Ospedale San Martino of Genoa (Italy), Oasi Research Institute-IRCCS, Troina (Italy), IRCCS Fatebenefratelli of Brescia (Italy), and IRCCS San Raffaele Pisana of Rome (Italy). Prof. Claudio Del Percio’s and Dr. Roberta Biundo’s work for this study was supported by funds of the “PRIN-2022” project entitled "AmyEEG” (Italian Ministry of University and Research, Prot. 2022FJAXY8). At the same time, Prof. Claudio Babiloni’s work was supported by funds of the “Horizon Marie S. Curie Doctoral Network ” project entitled "CombiDiag” (European Committee, Proposal: 101071485).