Особенности конфликтогенных зон у больных невротическими расстройствами женщин

Представлены данные о различии конфликтогенных зон у женщин и мужчин, страдающих невротическими расстройствами. Показано, что выявленные особенности необходимо учитывать в диагностике и психотерапии невротических расстройств.The authors report the data about the differences in conflectogenic zones among women and men with neurotic disorders. It was shown that the revealed peculiarities should be taken into consideration in diagnosis and psychotherapy of neurotic disorders

CD83 antibody inhibits human B cell responses to antigen as well as dendritic cell-mediated CD4 T cell responses

Anti-CD83 Ab capable of Ab-dependent cellular cytotoxicity can deplete activated CD83 human dendritic cells, thereby inhibiting CD4 T cell-mediated acute graft-versus-host disease. As CD83 is also expressed on the surface of activated B lymphocytes, we hypothesized that anti-CD83 would also inhibit B cell responses to stimulation. We found that anti-CD83 inhibited total IgM and IgG production in vitro by allostimulated human PBMC. Also, Ag-specific Ab responses to immunization of SCID mice xenografted with human PBMC were inhibited by anti-CD83 treatment. This inhibition occurred without depletion of all human B cells because anti-CD83 lysed activated CD83 B cells by Ab-dependent cellular cytotoxicity and spared resting (CD83) B cells. In cultured human PBMC, anti-CD83 inhibited tetanus toxoid-stimulated B cell proliferation and concomitant dendritic cell-mediated CD4 T cell proliferation and expression of IFN-γ and IL-17A, with minimal losses of B cells (80% of B cells but had no effect on CD4 T cell proliferation and cytokine expression. By virtue of the ability of anti-CD83 to selectively deplete activated, but not resting, B cells and dendritic cells, with the latter reducing CD4 T cell responses, anti-CD83 may be clinically useful in autoimmunity and transplantation. Advantages might include inhibited expansion of autoantigen- or alloantigen-specific B cells and CD4 T cells, thus preventing further production of pathogenic Abs and inflammatory cytokines while preserving protective memory and regulatory cells

A Molecular Host Response Assay to Discriminate Between Sepsis and Infection-Negative Systemic Inflammation in Critically Ill Patients: Discovery and Validation in Independent Cohorts

Background: Systemic inflammation is a whole body reaction having an infection-positive (i.e., sepsis) or infection-negative origin. It is important to distinguish between these two etiologies early and accurately because this has significant therapeutic implications for critically ill patients. We hypothesized that a molecular classifier based on peripheral blood RNAs could be discovered that would (1) determine which patients with systemic inflammation had sepsis, (2) be robust across independent patient cohorts, (3) be insensitive to disease severity, and (4) provide diagnostic utility. The goal of this study was to identify and validate such a molecular classifier. Methods and Findings: We conducted an observational, non-interventional study of adult patients recruited from tertiary intensive care units (ICUs). Biomarker discovery utilized an Australian cohort (n = 105) consisting of 74 cases (sepsis patients) and 31 controls (post-surgical patients with infection-negative systemic inflammation) recruited at five tertiary care settings in Brisbane, Australia, from June 3, 2008, to December 22, 2011. A four-gene classifier combining CEACAM4, LAMP1, PLA2G7, and PLAC8 RNA biomarkers was identified. This classifier, designated SeptiCyte Lab, was validated using reverse transcription quantitative PCR and receiver operating characteristic (ROC) curve analysis in five cohorts (n = 345) from the Netherlands. Patients for validation were selected from the Molecular Diagnosis and Risk Stratification of Sepsis study (ClinicalTrials.gov, NCT01905033), which recruited ICU patients from the Academic Medical Center in Amsterdam and the University Medical Center Utrecht. Patients recruited from November 30, 2012, to August 5, 2013, were eligible for inclusion in the present study. Validation cohort 1 (n = 59) consisted entirely of unambiguous cases and controls; SeptiCyte Lab gave an area under curve (AUC) of 0.95 (95% CI 0.91–1.00) in this cohort. ROC curve analysis of an independent, more heterogeneous group of patients (validation cohorts 2–5; 249 patients after excluding 37 patients with an infection likelihood of “possible”) gave an AUC of 0.89 (95% CI 0.85–0.93). Disease severity, as measured by Sequential Organ Failure Assessment (SOFA) score or Acute Physiology and Chronic Health Evaluation (APACHE) IV score, was not a significant confounding variable. The diagnostic utility of SeptiCyte Lab was evaluated by comparison to various clinical and laboratory parameters available to a clinician within 24 h of ICU admission. SeptiCyte Lab was significantly better at differentiating cases from controls than all tested parameters, both singly and in various logistic combinations, and more than halved the diagnostic error rate compared to procalcitonin in all tested cohorts and cohort combinations. Limitations of this study relate to (1) cohort compositions that do not perfectly reflect the composition of the intended use population, (2) potential biases that could be introduced as a result of the current lack of a gold standard for diagnosing sepsis, and (3) lack of a complete, unbiased comparison to C-reactive protein. Conclusions: SeptiCyte Lab is a rapid molecular assay that may be clinically useful in managing ICU patients with systemic inflammation. Further study in population-based cohorts is needed to validate this assay for clinical use

A Molecular Host Response Assay to Discriminate Between Sepsis and Infection-Negative Systemic Inflammation in Critically Ill Patients: Discovery and Validation in Independent Cohorts

Systemic inflammation is a whole body reaction having an infection-positive (i.e., sepsis) or infection-negative origin. It is important to distinguish between these two etiologies early and accurately because this has significant therapeutic implications for critically ill patients. We hypothesized that a molecular classifier based on peripheral blood RNAs could be discovered that would (1) determine which patients with systemic inflammation had sepsis, (2) be robust across independent patient cohorts, (3) be insensitive to disease severity, and (4) provide diagnostic utility. The goal of this study was to identify and validate such a molecular classifier. We conducted an observational, non-interventional study of adult patients recruited from tertiary intensive care units (ICUs). Biomarker discovery utilized an Australian cohort (n = 105) consisting of 74 cases (sepsis patients) and 31 controls (post-surgical patients with infection-negative systemic inflammation) recruited at five tertiary care settings in Brisbane, Australia, from June 3, 2008, to December 22, 2011. A four-gene classifier combining CEACAM4, LAMP1, PLA2G7, and PLAC8 RNA biomarkers was identified. This classifier, designated SeptiCyte Lab, was validated using reverse transcription quantitative PCR and receiver operating characteristic (ROC) curve analysis in five cohorts (n = 345) from the Netherlands. Patients for validation were selected from the Molecular Diagnosis and Risk Stratification of Sepsis study (ClinicalTrials.gov, NCT01905033), which recruited ICU patients from the Academic Medical Center in Amsterdam and the University Medical Center Utrecht. Patients recruited from November 30, 2012, to August 5, 2013, were eligible for inclusion in the present study. Validation cohort 1 (n = 59) consisted entirely of unambiguous cases and controls; SeptiCyte Lab gave an area under curve (AUC) of 0.95 (95% CI 0.91-1.00) in this cohort. ROC curve analysis of an independent, more heterogeneous group of patients (validation cohorts 2-5; 249 patients after excluding 37 patients with an infection likelihood of "possible") gave an AUC of 0.89 (95% CI 0.85-0.93). Disease severity, as measured by Sequential Organ Failure Assessment (SOFA) score or Acute Physiology and Chronic Health Evaluation (APACHE) IV score, was not a significant confounding variable. The diagnostic utility of SeptiCyte Lab was evaluated by comparison to various clinical and laboratory parameters available to a clinician within 24 h of ICU admission. SeptiCyte Lab was significantly better at differentiating cases from controls than all tested parameters, both singly and in various logistic combinations, and more than halved the diagnostic error rate compared to procalcitonin in all tested cohorts and cohort combinations. Limitations of this study relate to (1) cohort compositions that do not perfectly reflect the composition of the intended use population, (2) potential biases that could be introduced as a result of the current lack of a gold standard for diagnosing sepsis, and (3) lack of a complete, unbiased comparison to C-reactive protein. SeptiCyte Lab is a rapid molecular assay that may be clinically useful in managing ICU patients with systemic inflammation. Further study in population-based cohorts is needed to validate this assay for clinical us

Physician agreement on the diagnosis of sepsis in the intensive care unit: estimation of concordance and analysis of underlying factors in a multicenter cohort

Abstract Background Differentiating sepsis from the systemic inflammatory response syndrome (SIRS) in critical care patients is challenging, especially before serious organ damage is evident, and with variable clinical presentations of patients and variable training and experience of attending physicians. Our objective was to describe and quantify physician agreement in diagnosing SIRS or sepsis in critical care patients as a function of available clinical information, infection site, and hospital setting. Methods We conducted a post hoc analysis of previously collected data from a prospective, observational trial (N = 249 subjects) in intensive care units at seven US hospitals, in which physicians at different stages of patient care were asked to make diagnostic calls of either SIRS, sepsis, or indeterminate, based on varying amounts of available clinical information (clinicaltrials.gov identifier: NCT02127502). The overall percent agreement and the free-marginal, inter-observer agreement statistic kappa (κ free) were used to quantify agreement between evaluators (attending physicians, site investigators, external expert panelists). Logistic regression and machine learning techniques were used to search for significant variables that could explain heterogeneity within the indeterminate and SIRS patient subgroups. Results Free-marginal kappa decreased between the initial impression of the attending physician and (1) the initial impression of the site investigator (κ free 0.68), (2) the consensus discharge diagnosis of the site investigators (κ free 0.62), and (3) the consensus diagnosis of the external expert panel (κ free 0.58). In contrast, agreement was greatest between the consensus discharge impression of site investigators and the consensus diagnosis of the external expert panel (κ free 0.79). When stratified by infection site, κ free for agreement between initial and later diagnoses had a mean value + 0.24 (range − 0.29 to + 0.39) for respiratory infections, compared to + 0.70 (range + 0.42 to + 0.88) for abdominal + urinary + other infections. Bioinformatics analysis failed to clearly resolve the indeterminate diagnoses and also failed to explain why 60% of SIRS patients were treated with antibiotics. Conclusions Considerable uncertainty surrounds the differential clinical diagnosis of sepsis vs. SIRS, especially before organ damage has become highly evident, and for patients presenting with respiratory clinical signs. Our findings underscore the need to provide physicians with accurate, timely diagnostic information in evaluating possible sepsis

SeptiScores, likelihood ratios, and disease probabilities.

<p>SeptiScores, likelihood ratios, and disease probabilities.</p

Performance of PCT, clinical parameters, and SeptiCyte Lab for the discrimination of sepsis versus infection-negative systemic inflammation.

<p>This comparative analysis used the largest set of patients (<i>n</i> = 157) for which values were available for all comparisons. Patients having an infection likelihood of possible were excluded. Performance is reported as AUC. The error bars indicate the 95% CI for the AUC, obtained from a 50 × 2 cross-validation with training and test samples selected in a 1:1 ratio. The significance levels (<i>p</i>-value by <i>t-</i>test) for pairwise comparisons to SeptiCyte Lab are indicated in the figure. Details of the analysis are given in <a href="http://www.plosmedicine.org/article/info:doi/10.1371/journal.pmed.1001916#pmed.1001916.s007" target="_blank">S7 Data</a>. NS, not significant; Top 3, top three clinical parameters; Top 5, top five clinical parameters.</p

Characteristics of the discovery cohort.

<p>Characteristics of the discovery cohort.</p