Centre Hospitalier Universitaire de Nice

A significant proportion of patients presenting a primary sclerosing cholangitis (PSC) will require liver transplantation (LT). The present study aimed to investigate graft loss and patient death in a large cohort of patients. We conducted a nationwide multicenter retrospective study including all adult patients transplanted for PSC in France From 1985 to 2019. Were included 571 patients; median follow-up after LT was 89.0 months (IQR, 43.0-151.0). Patient survival at 5, 10 and 20 years after LT was 88.2%, 81.2% and 62.6%. After exclusion of patients who died during the first month after LT, 37 patients (6.6%) died during the first 2 years and the main cause was malignancies (n = 15, 40.5%, including 12 cases of recurrent cholangiocellular carcinoma). After 2 years, 90 patients (17.2%) died; the two main causes were malignancies (n = 36, 40.0%, including 13 cases of colorectal cancer) and sepsis (n = 23, 25.6%, of which 7 were related to recurrent PSC). Graft survival at 5, 10 and 20 years was 89.5%,78.7% and 62.7%. Independent factors associated with late patient death (after 2 years) were an older age at LT, a bilio-digestive anastomosis and the use of preventive UDCA; independent factors associated with late graft loss were recurrent PSC, cellular rejection, a younger age at LT, and the use of tacrolimus (protective). Our results emphasise that the prognosis after LT for PSC could be improved by better detection of cholangiocellular carcinoma before LT, and colorectal cancer after LT.

Postoperative pain management typically relies on numerical (NRS) or analogue rating scales. However, exclusive focus on nociception may overlook broader aspects of patient experience, especially under regional anaesthesia (RA). This post hoc analysis of the COMFORT trial (NCT05234216) examined whether replacing the NRS with a comfort-focused scale affects patient-reported experience, opioid use, and related outcomes. We analysed data from a cluster-randomised trial in 29 French centres. Adults undergoing elective non-cardiac surgery managed in the post-anaesthesia care unit (PACU) were included. The intervention (comfort scale) and comparator (pain NRS) were applied in the PACU. Patient-reported experience was measured with the EVAN-G/LR scales, self-completed on the ward/day-care unit ≥4 h postoperatively. Participants were divided into RA and general anaesthesia subgroups. Of 885 randomised patients, 786 had evaluable EVAN data. After regional anaesthesia (RA; n = 305), comfort‑focused assessment was associated with higher EVAN Pain (median 88 [IQR 75-94] vs. 75 [69-88]; p = 0.034), Waiting (100 [75-100] vs. 75 [50-100]; p = 0.012), and Global Index (81 [72-91] vs. 77 [68-90]; p = 0.044). After general anaesthesia (GA; n = 481), EVAN scores were similar between groups. Nefopam use was lower with the comfort approach in RA (7% vs. 18%; p = 0.005). Antiemetic use was lower with the comfort approach in GA. Replacing the NRS with a comfort-focused approach did not improve global patient experience after surgery. In patients receiving regional anaesthesia, however, using a comfort scale was associated with better patient-reported experience in the EVAN "Pain" dimension, while no benefit was observed after general anaesthesia.

Radiologically isolated syndrome (RIS) is a neurological condition in people with demyelinating lesions on brain and/or spinal cord magnetic resonance imaging (MRI) studies, but without clinical symptoms of disease. Elucidating the immune profile of people with RIS (pwRIS) who will display MRI or clinical features of advancing disease is critical to our understanding of disease pathogenesis. Our lab previously identified features of B cell dysregulation in people with clinically isolated syndrome (pwCIS), who have both demyelinating lesions and a first clinical event of disease. The goal of this study was to compare features of B cell dysregulation in pwRIS, pwCIS, and healthy controls (HC). The second goal was to determine if these features of B cell dysregulation would be evident in people who meet the new MS diagnostic criteria, particularly in the context of their disease course for 5 years post-sampling. Features of plasmablast responses that distinguish pwRIS from pwCIS include PB expansion, antigen-driven selection, VH4 and JH6 antibody gene over-usage, neuron reactivity by purified IgG and individually cloned antibodies. Furthermore, VH4:JH6 pairing in plasmablasts was higher in people with stable MS who do not show changes in MRI or clinical events from those with advancing disease activity.

Abstract Immune checkpoint inhibitors (ICI) have revolutionized cancer treatment, but their efficacy has now reached a plateau. ICIs are the first class of treatment targeting the crosstalk between immune and tumor cells, making it crucial to understand the complex interactions within the tumor microenvironment (TME) to enhance therapeutic responses. The elevated consumption of resources by cancer cells, coupled with limited vascularization, often results in a TME that is deficient in nutrients, leading to competition for resources between cancer and stromal cells. Consequently, targeting tumor metabolism has emerged as a promising strategy to improve the efficacy of ICIs. Through metabolomic analysis, we have identified metabolic alterations in melanoma cells that are resistant to ICIs, specifically an increase in arginine synthesis and upregulation of ASS1, the rate-limiting enzyme in this pathway. By using gain and loss of function models, as well as a pharmacological inhibitor specific for ASS1, we demonstrated that modulations in the expression or activity of ASS1 is associated with translational reprogramming, characterized by an inhibition of the cap-dependent mRNA translation mediated through mTORC1/4EBP1 axis. We also demonstrated that targeting ASS1 in vivo , resensitize tumors initially resistant to ICI. Taken together, our results highlight the interaction between modulations of arginine synthesis pathway, mRNA translation reprogramming, antitumor immunity, and restauration of sensitivity to anti-PD-1. Our work also demonstrates the therapeutic potential of targeting arginine synthesis pathway, and especially ASS1, to offer new treatments to patients suffering from cutaneous melanoma resistant to ICIs.

The COVID-19 pandemic has left many survivors with persistent respiratory symptoms such as dyspnea, chronic cough, and reduced exercise capacity. Fibrosis-like abnormalities on high-resolution CT are frequently reported in theses patient. This study aimed to describe the morphological pulmonary abnormalities identified on V/Q SPECT/CT after COVID-19 infection and to evaluate their functional consequences on regional ventilation and perfusion. We also sought to identify clinical predictors of abnormal V/Q SPECT/CT findings. This retrospective multicenter study was based on a national registry of patients referred for V/Q SPECT/CT because of persistent respiratory symptoms after COVID-19 infection. CT abnormalities were characterized using standard radiological criteria. Ventilation and perfusion were visually graded using a five-point scale. Clinical, demographic, and acute COVID-19 characteristics were analyzed to identify predictors of abnormal imaging. Among the 217 included patients (mean age 58 ± 15 years), 122 (56%) presented pulmonary abnormalities on V/Q SPECT or CT. The most frequent parenchymal abnormalities were reticulations, consolidations, atelectasis, emphysema, and ground-glass opacities. Emphysema, consolidations, and fibrosis were associated with the most severe impairments of both ventilation and perfusion. Age and pre-existing chronic lung disease were the strongest predictors of abnormal imaging, while a mild acute COVID-19 course without oxygen therapy was protective. V/Q SPECT/CT provides a comprehensive structural and functional assessment of long COVID pulmonary sequelae. It allows the differentiation between reversible inflammatory lesions and persistent structural abnormalities with lasting functional consequences.

Acute Respiratory Distress Syndrome (ARDS) is a heterogeneous, life-threatening condition with persistent diagnostic uncertainty, limited causal treatments, and high mortality in intensive care. ARDS management remains largely supportive, and conventional scores and trial designs have struggled to account for its biological and clinical heterogeneity. This narrative review summarizes current and emerging applications of Artificial Intelligence (AI) in ARDS and mechanical ventilation. It focuses on how Machine Learning (ML) can refine early risk prediction, diagnosis, phenotyping, management and outcome prediction, while outlining the strengths, weaknesses and nuances of these applications. ML models using electronic health records, imaging, physiological waveforms and omics data show strong performance for predicting ARDS onset, enabling early diagnosis, optimising management and forecasting outcomes. These models show performance equivalent to and often outperform traditional guidelines and scores. However, most of these models remain limited to the research setting and show limited clinical adoption. Most studies are retrospective, single-center and lack rigorous external validation, limiting generalizability and real-world impact. Additional challenges include data quality and bias, poor calibration and scarce decision-curve analyses, limited interpretability, and the absence of prospective trials demonstrating that AI-guided strategies improve patient-centered outcomes. ML has substantial potential to advance precision medicine in ARDS by enabling predictive and prognostic enrichment and supporting personalized ventilatory care. Its safe and equitable deployment, however, requires standardized methodology, transparent reporting, multicenter prospective validation, and clinician-led governance to ensure that AI augments rather than replaces expert clinical judgment.