Diffuse large B-cell lymphoma (DLBCL) is an aggressive form of non-Hodgkin lymphoma with a high recurrence rate. The molecular profiling of DLBCL tumors culminated in several immunohistochemistry algorithms for prognostic stratification. Among those, the Hans classifier is widely used for classifying DLBCL into germinal center B-cell-like (GCB) and non-germinal center/activated B-cell-like (non-GCB/ABC) subtypes. The Hans classifier primarily evaluates protein expression of tumor-associated markers, however the tumor microenvironment (TME) of DLBCL includes a myriad of immune and stromal cells, cytokines, and extracellular matrix components that contribute to tumor growth, immune evasion, and recurrence rate. Although the Hans classifier provides a practical method for subtype identification, incorporation of TME information may improve risk stratification and further refine patient groups. Here, we present an unbiased deep learning-based approach to extract meaningful features from TME of DLBCL tumors for the automated processing and analysis of multiplexed images of a DLBCL patient cohort. Our pipeline quantifies a range of features that describe tumor sample cell composition, morphology, and its spatial organization. We point to alterations in the proportions of several cell populations between GCB and ABC tumors including increased immune cell proportions of the ABC and its preferential interaction with the M2-macrophages. Our analysis offers an in-depth characterization of the DLBCL subtypes and is exemplary of how our pipeline can be used for detailed quantitative analysis of a tumor and its subtypes.
BACKGROUND:Late-life depression refers to a depression occurring in older adults, defined as individuals aged 65 years and older. It is common and often mismanaged due to complex clinical profiles, polypharmacy, and limited evidence-based guidance. Existing tools poorly address psychiatric nuances in older adults. This work aims to develop French recommendations based on experts' consensus on the use of antidepressants in unipolar late-life depression, to guide safer prescribing practices. METHODS:This tool was developed using a Delphi survey, based on a review of literature published between 2014 and 2024 and focused on "antidepressants" and "late-life depression" Experts from various fields: geriatric psychiatry, clinical pharmacy and general practice, rated items using a 9-point Likert scale. Items with a median score ≥ 7 and at least 80% agreement were validated and included in the final version of the tool. RESULTS:Twenty to 23 experts per round, different from the authors of the proposed items, participated in a four-round Delphi process. The resulting tool includes 57 validated items across 10 sections and a stepped-care algorithm for treating major depression in older adults. It addresses drug choice, dosing, monitoring, comorbidities, and treatment resistance, prioritizing safe first-line options like sertraline, citalopram, and escitalopram. CONCLUSION:A Delphi survey involving multidisciplinary experts led to a French consensus tool for prescribing antidepressants in unipolar late-life depression. It integrates clinical evidence and expert judgment to address treatment complexity, drug safety, and resistance. The tool offers practical, stepwise recommendations tailored to primary care, aiming to optimize antidepressant use, reduce iatrogenesis, and improve patient outcomes.
BACKGROUND:Opioid agonist treatment (OAT) is the mainstay for opioid use disorder (OUD). Long-acting injectable buprenorphine may address limitations of daily medications by reducing treatment burden and improving engagement. This study assessed retention and patient-reported outcomes with Buvidal® in France. METHODS:This multicenter, observational, retrospective study analyzed medical records of adults diagnosed with OUD who received ≥1 Buvidal® injection between July 2021 and August 2023. The primary endpoint was retention at 6 months. Key secondary endpoints included changes in opioid consumption, perceived improvement in OUD using the Patient Global Impression of Change (PGIC) scale, distancing from OUD, reduction in craving, and satisfaction with Buvidal® treatment. RESULTS:Among 101 participants (mean age 43.9 years; 72.3% male; 98.0% switched to Buvidal® from previous OAT), 74 (73.3%) were retained on Buvidal® at 6 months. Of those using non-prescribed or misused opioids at baseline, decreased consumption of non-prescribed or misused opioids during treatment with Buvidal® was reported by 80.0% (16/20) of retained participants and 66.7% (8/12) of non-retained participants. A significantly higher proportion of retained than non-retained participants reported improvement in OUD on PGIC (85.1% [63/74] vs 40.7% [11/27]; P < .001), increased distancing from OUD (90.5% [67/74] vs 70.4% [19/27]; P = .02), and reduced craving (91.9% [68/74] vs 66.7% [18/27]; P < .001). Satisfaction with Buvidal® was high overall (89.1% [90/101]), with 98.6% (73/74) of retained participants satisfied compared with 63.0% (17/27) of non-retained participants (P < .001). Among retained participants, 91.9% (68/74) expressed willingness to continue Buvidal® treatment beyond 6 months. CONCLUSIONS:Nearly three-quarters of participants initiating Buvidal® were retained in treatment at 6 months. Patient-reported outcomes indicated high satisfaction, perceived improvement in OUD, and reductions in opioid consumption and craving, even among individuals largely stabilized on OAT at baseline. These findings suggest that Buvidal® may support sustained engagement and meaningful improvements in patient experience under real-world conditions.
Systemic inflammation has been linked with major depressive episode (MDE) severity and treatment-resistant depression (TRD), but not for all patients. Brain mechanisms underlying these processes are still under investigation. Objectives: based on an integrative approach, we aimed at identifying clinical, inflammatory and perfusion markers predictive of depression outcome at 6 months. We conducted a longitudinal study including 60 patients diagnosed with MDE, focusing on anxiety and anhedonia as main clinical candidates, inflammation (C-Reactive Protein - CRP) and cerebral blood flow (CBF) using pseudo-continuous arterial spin labeling (pcASL) MRI. A bootstrapped elastic net regression analysis was conducted including clinical, CBF and inflammation as predictors with depressive severity at 6 months as the dependent variable. Our findings exhibited positive association of depression outcome with baseline depression intensity, duration of current episode, CRP, right accumbens, as well as left and right orbito-frontal CBF. Negative predictors were age, disease duration, right and left caudate nuclei, left amygdala, left mid frontal gyrus, and right ventromedial prefrontal cortex CBF. Neither anxiety nor anhedonia were significant predictors. Combining clinical, inflammation and brain imaging outperformed other models in diagnosing depression severity change over time, highlighting the interest of integrative approaches. These results suggested that systemic inflammation and cerebral perfusion abnormalities in key regions involved in emotion, reward processing and decision making, may serve as biomarkers for identifying patients at risk for persistence of depression.
In France, chronic insomnia affects 15.8
