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This study aims to develop high-performing Machine Learning and Deep Learning models in predicting hospital length of stay (LOS) while enhancing interpretability. We compare performance and interpretability of models trained only on structured tabular data with models trained only on unstructured clinical text data, and on mixed data. The structured data was used to train fourteen classical Machine Learning models including advanced ensemble trees, neural networks and k-nearest neighbors. The unstructured data was used to fine-tune a pre-trained Bio Clinical BERT Transformer Deep Learning model. The structured and unstructured data were then merged into a tabular dataset after vectorization of the clinical text and a dimensional reduction through Latent Dirichlet Allocation. The study used the free and publicly available Medical Information Mart for Intensive Care (MIMIC) III database, on the open AutoML Library AutoGluon. Performance is evaluated with respect to two types of random classifiers, used as baselines. The best model from structured data demonstrates high performance (ROC AUC = 0.944, PRC AUC = 0.655) with limited interpretability, where the most important predictors of prolonged LOS are the level of blood urea nitrogen and of platelets. The Transformer model displays a good but lower performance (ROC AUC = 0.842, PRC AUC = 0.375) with a richer array of interpretability by providing more specific in-hospital factors including procedures, conditions, and medical history. The best model trained on mixed data satisfies both a high level of performance (ROC AUC = 0.963, PRC AUC = 0.746) and a much larger scope in interpretability including pathologies of the intestine, the colon, and the blood; infectious diseases, respiratory problems, procedures involving sedation and intubation, and vascular surgery. Our results outperform most of the state-of-the-art models in LOS prediction both in terms of performance and of interpretability. Data fusion between structured and unstructured text data may significantly improve performance and interpretability.

Pathogens have evolved smart strategies to invade hosts and hijack their immune responses. One such strategy is the targeting of the host RhoGTPases by toxins or virulence factors to hijack the cytoskeleton dynamic and immune processes. In response to this microbial attack, the host has evolved an elegant strategy to monitor the function of virulence factors and toxins by sensing the abnormal activity of RhoGTPases. This innate immune strategy of sensing bacterial effector targeting RhoGTPase appears to be a bona fide example of effector-triggered immunity (ETI). Here, we review recently discovered mechanisms by which the host can sense the activity of these toxins through NOD and NOD-like receptors (NLRs).

Abstract Patients with schizophrenia (SCZ) represent a vulnerable population who have been understudied in COVID-19 research. We aimed to establish whether health outcomes and care differed between patients with SCZ and patients without a diagnosis of severe mental illness. We conducted a population-based cohort study of all patients with identified COVID-19 and respiratory symptoms who were hospitalized in France between February and June 2020. Cases were patients who had a diagnosis of SCZ. Controls were patients who did not have a diagnosis of severe mental illness. The outcomes were in-hospital mortality and intensive care unit (ICU) admission. A total of 50 750 patients were included, of whom 823 were SCZ patients (1.6%). The SCZ patients had an increased in-hospital mortality (25.6% vs 21.7%; adjusted OR 1.30 [95% CI, 1.08–1.56], P = .0093) and a decreased ICU admission rate (23.7% vs 28.4%; adjusted OR, 0.75 [95% CI, 0.62–0.91], P = .0062) compared with controls. Significant interactions between SCZ and age for mortality and ICU admission were observed (P = .0006 and P < .0001). SCZ patients between 65 and 80 years had a significantly higher risk of death than controls of the same age (+7.89%). SCZ patients younger than 55 years had more ICU admissions (+13.93%) and SCZ patients between 65 and 80 years and older than 80 years had less ICU admissions than controls of the same age (−15.44% and −5.93%, respectively). Our findings report the existence of disparities in health and health care between SCZ patients and patients without a diagnosis of severe mental illness. These disparities differed according to the age and clinical profile of SCZ patients, suggesting the importance of personalized COVID-19 clinical management and health care strategies before, during, and after hospitalization for reducing health disparities in this vulnerable population.

Background The Brief Coping Orientation to Problems Experienced (Brief COPE) inventory is the most usual measure to identify the nature of coping strategies implemented by individuals and explore 14 coping strategies. The availability of a structure with fewer factors rather than the initial 14-factor structure may be of interest for both healthcare professionals and researchers. We report the validation process of a 4-factor structure of the French version of the Brief COPE in a French sample of individuals facing a singular life event, such as cancer, including patients and their caregivers. Methods The cross-sectional study included cancer patients and their caregivers. Self-administered data were collected including: socio-demographic (age, gender, marital status, employment status, and education level), coping strategies using the French version of the Brief COPE, quality of life (QoL) using the French version of the short form health survey questionnaire (SF36). Construct validity, internal consistency, reliability, and external validity were tested. Results The sample included 398 individuals. The principal component factor analysis identified a 4-factor structure. The dimensions were labeled according to their constitutive items: social support (8 items), problem solving (4), avoidance (10), and positive thinking (6). The 4-factor structure was supported by different theoretical models of coping and showed satisfactory psychometric properties. Conclusion The 4-factor structure of the French version of the Brief COPE, validated in a sample of individuals facing a singular stressful event, including cancer patients and their caregivers, makes the instrument easier to use both in clinical practice and clinical research.

Abstract Comorbid major depressive disorder (MDD) in schizophrenia (SZ; SZ-MDD) has been identified as a major prognostic factor. However, the prevalence and associated factors of SZ-MDD have never been explored in a meta-analysis. All studies assessing the prevalence of SZ-MDD in stabilized outpatients with a standardized scale or with structured interviews were included. The Medline, Web of Science, PsycINFO, and Google Scholar databases were searched. Using random effects models, we calculated the pooled estimate of the prevalence of SZ-MDD. We used meta-regression and subgroup analyses to evaluate the potential moderators of the prevalence estimates, and we used the leave-one-out method for sensitivity analyses. Of the 5633 potentially eligible studies identified, 18 studies (n = 6140 SZ stabilized outpatients) were retrieved in the systematic review and included in the meta-analysis. The pooled estimate of the prevalence of SZ-MDD was 32.6% (95% CI: 27.9–37.6); there was high heterogeneity (I2 = 92.6%), and Egger’s test did not reveal publication bias (P = .122). The following factors were found to be sources of heterogeneity: publication in or after 2015, the inclusion of patients from larger studies, the assessment tools, the inclusion of patients with substance use disorder or somatic chronic diseases, age, education level, the lifetime number of hospitalizations, and antidepressant use. Two-thirds of the extracted variables could not be explored due to an insufficient amount of published data. The prevalence of MDD is high among SZ individuals. Healthcare providers and public health officials should have an increased awareness of the burden of SZ-MDD.

Psoriatic arthritis (PsA) is a chronic inflammatory disease that frequently develops in patients with psoriasis (PsO) but can also occur spontaneously. As a result, PsA diagnosis and treatment is commonly delayed, or even missed outright due to the manifold of clinical presentations that patients often experience. This inevitably results in progressive articular damage to axial and peripheral joints and entheses. As such, patients with PsA frequently experience reduced expectancy and quality of life due to disability. More recently, research has aimed to improve PsA diagnosis and prognosis by identifying novel disease biomarkers. Here, we conducted a systematic review of the published literature on candidate biomarkers for PsA diagnosis and prognosis in MEDLINE(Pubmed), EMBase and the Cochrane library with the goal to identify clinically applicable PsA biomarkers. Meta-analyses were performed when a diagnostic bone and cartilage turnover biomarker was reported in 2 or moredifferent cohorts of PsA and control. We identified 1444 publications and 124 studies met eligibility criteria. We highlighted bone and cartilage turnover biomarkers, genetic markers, and autoantibodies used for diagnostic purposes of PsA, as well as acute phase reactant markers and bone and cartilage turnover biomarkers for activity or prognostic severity purposes. Serum cartilage oligometrix metalloproteinase levels were significantly increased in the PsA sera compared to Healthy Control (HC) with a standardized mean difference (SMD) of 2.305 (95%CI 0.795-3.816, p=0.003) and compared to osteoarthritis (OA) with a SMD of 0.783 (95%CI 0.015-1.551, p=0.046). The pooled serum MMP-3 levels were significantly higher in PsA patients than in PsO patients with a SMD of 0.419 (95%CI 0.119-0.719; p=0.006), but no significant difference was highlighted when PsA were compared to HC. While we did not identify any new genetic biomarkers that would be useful in the diagnosis of PsA, recent data with autoantibodies appear to be promising in diagnosis, but no replication studies have been published. In summary, no specific diagnostic biomarkers for PsA were identified and further studies are needed to assess the performance of potential biomarkers that can distinguish PsA from OA and other chronic inflammatory diseases.

Irritable bowel syndrome (IBS) has been associated with high prevalence of psychological disorders. However, it remains unclear whether IBS and each of its subtypes (predominant diarrhea IBS-D, constipation IBS-C, mixed IBS-M) are associated with higher anxiety and depressive symptoms levels. This study aimed to determine the associations of IBS and each of its subtypes with anxiety and/or depression. We conducted a systematic review and meta-analysis using five electronic databases (PubMed, PsychINFO, BIOSIS, Science Direct, and Cochrane CENTRAL). We selected case–control studies comparing anxiety and depression levels of patients with IBS to healthy controls, using standardized rating scales. Outcomes were measured as random pooled standardized mean differences (SMD). Ten studies were included in our analysis (885 patients and 1,384 healthy controls). Patients with IBS had significant higher anxiety and depression levels than controls (respectively, SMD = 0.76, 95 % CI 0.47; 0.69, p  < 0.01, I 2 = 81.7 % and SMD = 0.80, 95 % CI 0.42; 1.19, p  < 0.01, I 2 = 90.7 %). This significant difference was confirmed for patients with IBS-C and -D subtypes for anxiety, and only in IBS-D patients for depression. However, other IBS subtypes had a statistical trend to be associated with both anxiety and depressive symptomatology, which suggests a lack of power due to the small number of studies included. Patients with IBS had significantly higher levels of anxiety and depression than healthy controls. Anxiety and depression symptomatology should be systematically checked and treated in IBS patients, as psychological factors are important moderators of symptom severity, symptom persistence, decisions to seek treatment, and response to treatment.

In the last decade, a major dogma in the field of immunology has been called into question by the identification of a cell autonomous innate immune memory. This innate immune memory (also named trained immunity) was found to be mostly carried by innate immune cells and to be characterized by an exacerbated inflammatory response with a heightened expression of proinflammatory cytokines, including TNF-α, IL-6 and IL-1β. Unlike the vast majority of cytokines, IL-1β is produced as a proform (pro-IL-1β) and requires a proteolytic cleavage to exert its biological action. This cleavage takes place mainly within complex molecular platforms named inflammasomes. These platforms are assembled upon both the infectious or sterile activation of NOD-like receptors (NLRs), thereby allowing for the recruitment and activation of caspases and the subsequent maturation of pro-IL-1β into IL-1β. The NLRP3 inflammasome has recently been implicated both in western diet-induced trained immunity, and in the detection of microbial virulence factors (effector-triggered immunity (ETI)). Here, we will attempt to link these two immune processes and provide arguments to hypothesize the existence of trained immunity triggered by microbial virulence factors (effector-triggered trained immunity (ETTI)).

Introduction: A rapid literature review (RLR) is an alternative to systematic literature review (SLR) that can speed up the analysis of newly published data. The objective was to identify and summarize available information regarding different approaches to defining RLR and the methodology applied to the conduct of such reviews. Methods: The Medline and EMBASE databases, as well as the grey literature, were searched using the set of keywords and their combination related to the targeted and rapid review, as well as design, approach, and methodology. Of the 3,898 records retrieved, 12 articles were included. Results: Specific definition of RLRs has only been developed in 2021. In terms of methodology, the RLR should be completed within shorter timeframes using simplified procedures in comparison to SLRs, while maintaining a similar level of transparency and minimizing bias. Inherent components of the RLR process should be a clear research question, search protocol, simplified process of study selection, data extraction, and quality assurance. Conclusions: There is a lack of consensus on the formal definition of the RLR and the best approaches to perform it. The evidence-based supporting methods are evolving, and more work is needed to define the most robust approaches.

CSF-1 and IL-34 share the CSF-1 receptor and no differences have been reported in the signaling pathways triggered by both ligands in human monocytes. IL-34 promotes the differentiation and survival of monocytes, macrophages and osteoclasts, as CSF-1 does. However, IL-34 binds other receptors, suggesting that differences exist in the effect of both cytokines. In the present study, we compared the differentiation and polarization abilities of human primary monocytes in response to CSF-1 or IL-34. CSF-1R engagement by one or the other ligands leads to AKT and caspase activation and autophagy induction through expression and activation of AMPK and ULK1. As no differences were detected on monocyte differentiation, we investigated the effect of CSF-1 and IL-34 on macrophage polarization into the M1 or M2 phenotype. We highlighted a striking increase in IL-10 and CCL17 secretion in M1 and M2 macrophages derived from IL-34 stimulated monocytes, respectively, compared to CSF-1 stimulated monocytes. Variations in the secretome induced by CSF-1 or IL-34 may account for their different ability to polarize naïve T cells into Th1 cells. In conclusion, our findings indicate that CSF-1 and IL-34 exhibit the same ability to induce human monocyte differentiation but may have a different ability to polarize macrophages.