Single agent rituximab in patients with follicular or mantle cell lymphoma: clinical and biological factors that are predictive of response and event-free survival as well as the effect of rituximab on the immune system: a study of the Swiss Group for Clinical Cancer Research (SAKK)

Background: Predictive factors of rituximab efficacy and its effect on the immune system are still not defined. Patients and methods: Three hundred and six patients with follicular or mantle cell lymphoma received four weekly doses of rituximab (induction) and no further treatment (arm A) or four more doses at 2-month intervals (arm B). Results: Response rate to induction was 44%. Independent predictive factors for response were disease bulk <5 cm, follicular histology, normal hemoglobin and low lymphocyte count. Factors associated with event-free survival (EFS) were having responded to induction, having received not more than one line of therapy, Ann Arbor stage I-III, high lymphocyte count, disease bulk <5 cm, Fc-gamma receptor genotype VV and receiving prolonged treatment. B cells were suppressed by treatment but recovered after a median of 12 months in arm A and 18 months in arm B. The median IgM level after 1 year was normal in arm A but was decreased to 73% of baseline in arm B. We observed 24 serious adverse events, equally distributed between arms. Ten patients receiving induction only and six patients receiving prolonged treatment developed a second tumor. Conclusions: We defined the characteristics predicting response and EFS to rituximab. Prolonged treatment results in longer EFS at the cost of a longer reduction in B cell and IgM levels, but without additional clinical toxicit

Protein C anticoagulant system—anti-inflammatory effects

Activated protein C (APC) plays active roles in preventing progression of a number of disease processes. These include thrombosis due to its direct anticoagulant activity which is likely augmented by its cytoprotective activity, thereby limiting exposure of procoagulant cellular membrane surfaces on cells. Beyond that, the pathway signals the cells to prevent apoptosis, to dampen inflammation, to increase endothelial barrier function, and to selectively downregulate some genes implicated in disease progression. Most of these functions are manifested to APC binding to endothelial protein C receptor (EPCR) allowing PAR1 activation, but activation of other PARS is also implicated in some cases. In addition to EPCR orchestrating these changes, CD11b is also capable of supporting APC signaling. Selective control of these pathways offers potential in new therapeutic approaches to disease

Multimodal Machine Learning Workflows for Prediction of Psychosis in Patients With Clinical High-Risk Syndromes and Recent-Onset Depression

Importance Diverse models have been developed to predict psychosis in patients with clinical high-risk (CHR) states. Whether prediction can be improved by efficiently combining clinical and biological models and by broadening the risk spectrum to young patients with depressive syndromes remains unclear. Objectives To evaluate whether psychosis transition can be predicted in patients with CHR or recent-onset depression (ROD) using multimodal machine learning that optimally integrates clinical and neurocognitive data, structural magnetic resonance imaging (sMRI), and polygenic risk scores (PRS) for schizophrenia; to assess models' geographic generalizability; to test and integrate clinicians' predictions; and to maximize clinical utility by building a sequential prognostic system. Design, Setting, and Participants This multisite, longitudinal prognostic study performed in 7 academic early recognition services in 5 European countries followed up patients with CHR syndromes or ROD and healthy volunteers. The referred sample of 167 patients with CHR syndromes and 167 with ROD was recruited from February 1, 2014, to May 31, 2017, of whom 26 (23 with CHR syndromes and 3 with ROD) developed psychosis. Patients with 18-month follow-up (n = 246) were used for model training and leave-one-site-out cross-validation. The remaining 88 patients with nontransition served as the validation of model specificity. Three hundred thirty-four healthy volunteers provided a normative sample for prognostic signature evaluation. Three independent Swiss projects contributed a further 45 cases with psychosis transition and 600 with nontransition for the external validation of clinical-neurocognitive, sMRI-based, and combined models. Data were analyzed from January 1, 2019, to March 31, 2020. Main Outcomes and Measures Accuracy and generalizability of prognostic systems. Results A total of 668 individuals (334 patients and 334 controls) were included in the analysis (mean [SD] age, 25.1 [5.8] years; 354 [53.0%] female and 314 [47.0%] male). Clinicians attained a balanced accuracy of 73.2% by effectively ruling out (specificity, 84.9%) but ineffectively ruling in (sensitivity, 61.5%) psychosis transition. In contrast, algorithms showed high sensitivity (76.0%-88.0%) but low specificity (53.5%-66.8%). A cybernetic risk calculator combining all algorithmic and human components predicted psychosis with a balanced accuracy of 85.5% (sensitivity, 84.6%; specificity, 86.4%). In comparison, an optimal prognostic workflow produced a balanced accuracy of 85.9% (sensitivity, 84.6%; specificity, 87.3%) at a much lower diagnostic burden by sequentially integrating clinical-neurocognitive, expert-based, PRS-based, and sMRI-based risk estimates as needed for the given patient. Findings were supported by good external validation results. Conclusions and RelevanceThese findings suggest that psychosis transition can be predicted in a broader risk spectrum by sequentially integrating algorithms' and clinicians' risk estimates. For clinical translation, the proposed workflow should undergo large-scale international validation.Question Can a transition to psychosis be predicted in patients with clinical high-risk states or recent-onset depression by optimally integrating clinical, neurocognitive, neuroimaging, and genetic information with clinicians' prognostic estimates? Findings In this prognostic study of 334 patients and 334 control individuals, machine learning models sequentially combining clinical and biological data with clinicians' estimates correctly predicted disease transitions in 85.9% of cases across geographically distinct patient populations. The clinicians' lack of prognostic sensitivity, as measured by a false-negative rate of 38.5%, was reduced to 15.4% by the sequential prognostic model. Meaning These findings suggest that an individualized prognostic workflow integrating artificial and human intelligence may facilitate the personalized prevention of psychosis in young patients with clinical high-risk syndromes or recent-onset depression.</p

An entertaining way to access web content

Abstract. This paper describes the 3D-Enter project’s innovative findings. The project is aimed at simplifying the access to web content and at defining an entertaining way to make use of Internet services. This software application implements an interpreter and a graphic engine, which allows the results of a web-based database search to be viewed as a “game-like ” 3D graphical representation. It offers a feeling of involvement directly with a world of objects. It simplifies the usability of computer systems, increasing users ’ immersion and information comprehension, thus increasing end-users’ acceptance