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Towards increasing the clinical applicability of machine learning biomarkers in psychiatry.

Due to a lack of objective biomarkers, psychiatric diagnoses still rely strongly on patient reporting and clinician judgement. The ensuing subjectivity negatively affects the definition and reliability of psychiatric diagnoses1,2. Recent research has suggested that a combination of advanced neuroimaging and machine learning may provide a solution to this predicament by establishing such objective biomarkers for psychiatric conditions, improving the diagnostic accuracy, prognosis and development of novel treatments3.These promises led to widespread interest in machine learning applications for mental health4, including a recent paper that reports a biological marker for one of the most difficult yet momentous questions in psychiatry—the assessment of suicidal behaviour5. Just et al. compared a group of 17 participants with suicidal ideation with 17 healthy controls, reporting high discrimination accuracy using task-based functional magnetic resonance imaging signatures of life- and death-related concepts3. The authors further reported high discrimination between nine ideators who had attempted suicide versus eight ideators who had not. While being a laudable effort into a difficult topic, this study unfortunately illustrates some common conceptual and technical issues in the field that limit translation into clinical practice and raise unrealistic hopes when the results are communicated to the general public.From a conceptual point of view, machine learning studies aimed at clinical applications need to carefully consider any decisions that might hamper the interpretation or generalizability of their results. Restrictiveness to an arbitrary setting may become detrimental for machine learning applications by providing overly optimistic results that are unlikely to generalize. As an example, Just et al. excluded more than half of the patients and healthy controls initially enrolled in the study from the main analysis due to missing desired functional magnetic resonance imaging effects (a rank accuracy of at least 0.6 based on all 30 concepts). This exclusion introduces a non-assessable bias to the interpretation of the results, in particular when considering that only six of the 30 concepts were selected for the final classification procedure. While Just et al. attempt to address this question by applying the trained classifier to the initially excluded 21 suicidal ideators, they explicitly omit the excluded 24 controls from this analysis, preventing any interpretation of the extent to which the classifier decision is dependent on this initial choice.From a technical point of view, machine learning-based predictions based on neuroimaging data in small samples are intrinsically highly variable, as stable accuracy estimates and high generalizability are only achieved with several hundreds of participants6,7. The study by Just et al. falls into this category of studies with a small sample size. To estimate the impact of uncertainty on the results by Just et al., we adapted a simulation approach with the code and data kindly provided by the authors, randomly permuting (800 times) the labels across the groups using their default settings and computing the accuracies. These results showed that the 95% confidence interval for classification accuracy obtained using this dataset is about 20%, leaving large uncertainty with respect to any potential findings.Special care is also required with respect to any subjective choices in feature and classifier settings or group selection. While ad-hoc selection of a specific setting is subjective, testing of different ones and outcome-based post-hoc justification of such leads to overfitting, thus limiting the generalizability of any classification. Such overfitting may occur when multiple models or parameter choices are tested with respect to their ability to predict the testing data and only those that perform best are reported. To illustrate this issue, we performed an additional analysis with the code and data kindly provided by Just et al. More specifically, in the code and the manuscript, we identified the following non-exhaustive number of prespecified settings: (1) removal of occipital cortex data; (2) subdivision of clusters larger than 11 mm; (3) selection of voxels with at least four contributing participants in each group; (4) selection of stable clusters containing at least five voxels; (5) selection of the 1,200 most stable features; and (6) manual copying and replacing of a cluster for one control participant. Importantly, according to the publication or code documentation, all of these parameters were chosen ad hoc and for none of these settings was a parameter search performed. We systematically evaluated the effect of each of these choices on the accuracy for differentiation between suicide ideators and controls in the original dataset provided by Just et al. As shown in Fig. 1, each of the six parameters represents an optimum choice for differentiation accuracy in this dataset, with any (even minor) change often resulting in substantially lower accuracy estimates. Similarly, data leakage may also contribute to optimistic results when information outside the training set is used to build a prediction model. More generally, whenever human interventions guide the development of machine learning models for the prediction of clinical conditions, a careful evaluation and reporting of any researcher’s degrees of freedom is essential to avoid data leakage and overfitting. Subsequent sharing of data processing and analysis pipelines, as well as collected data, is a further key step to increase reproducibility and facilitate replication of potential findings

oxLDL Downregulates the Dendritic Cell Homing Factors CCR7 and CCL21

Introduction. Dendritic cells (DCs) and oxLDL play an important role in the atherosclerotic process with DCs accumulating in the plaques during plaque progression. Our aim was to investigate the role of oxLDL in the modulation of the DC homing-receptor CCR7 and endothelial-ligand CCL21. Methods and Results. The expression of the DC homing-receptor CCR7 and its endothelial-ligand CCL21 was examined on atherosclerotic carotic plaques of 47 patients via qRT-PCR and immunofluorescence. In vitro, we studied the expression of CCR7 on DCs and CCL21 on human microvascular endothelial cells (HMECs) in response to oxLDL. CCL21- and CCR7-mRNA levels were significantly downregulated in atherosclerotic plaques versus non-atherosclerotic controls [90% for CCL21 and 81% for CCR7 (P < 0.01)]. In vitro, oxLDL reduced CCR7 mRNA levels on DCs by 30% and protein levels by 46%. Furthermore, mRNA expression of CCL21 was significantly reduced by 50% (P < 0.05) and protein expression by 24% in HMECs by oxLDL (P < 0.05). Conclusions. The accumulation of DCs in atherosclerotic plaques appears to be related to a downregulation of chemokines and their ligands, which are known to regulate DC migration. oxLDL induces an in vitro downregulation of CCR7 and CCL21, which may play a role in the reduction of DC migration from the plaques

Mitochondria-Targeted Antioxidants SkQ1 and MitoTEMPO Failed to Exert a Long-Term Beneficial Effect in Murine Polymicrobial Sepsis

Mitochondrial-derived reactive oxygen species have been deemed an important contributor in sepsis pathogenesis. We investigated whether two mitochondria-targeted antioxidants (mtAOX; SkQ1 and MitoTEMPO) improved long-term outcome, lessened inflammation, and improved organ homeostasis in polymicrobial murine sepsis. 3-month-old female CD-1 mice (n = 90) underwent cecal ligation and puncture (CLP) and received SkQ1 (5 nmol/kg), MitoTEMPO (50 nmol/kg), or vehicle 5 times post-CLP. Separately, 52 SkQ1-treated CLP mice were sacrificed at 24 h and 48 h for additional endpoints. Neither MitoTEMPO nor SkQ1 exerted any protracted survival benefit. Conversely, SkQ1 exacerbated 28-day mortality by 29%. CLP induced release of 10 circulating cytokines, increased urea, ALT, and LDH, and decreased glucose but irrespectively of treatment. Similar occurred for CLP-induced lymphopenia/neutrophilia and the NO blood release. At 48 h post-CLP, dying mice had approximately 100-fold more CFUs in the spleen than survivors, but this was not SkQ1 related. At 48 h, macrophage and granulocyte counts increased in the peritoneal lavage but irrespectively of SkQ1. Similarly, hepatic mitophagy was not altered by SkQ1 at 24 h. The absence of survival benefit of mtAOX may be due to the extended treatment and/or a relatively moderate-risk-of-death CLP cohort. Long-term effect of mtAOX in abdominal sepsis appears different to sepsis/inflammation models arising from other body compartments

Automatic evaluation of tumor budding in immunohistochemically stained colorectal carcinomas and correlation to clinical outcome

Background: Tumor budding, meaning a detachment of tumor cells at the invasion front of colorectal carcinoma (CRC) into single cells or clusters (&lt;=5 tumor cells), has been shown to correlate to an inferior clinical outcome by several independent studies. Therefore, it has been discussed as a complementary prognostic factor to the TNM staging system, and it is already included in national guidelines as an additional prognostic parameter. However, its application by manual evaluation in routine pathology is hampered due to the use of several slightly different assessment systems, a time-consuming manual counting process and a high inter-observer variability. Hence, we established and validated an automatic image processing approach to reliably quantify tumor budding in immunohistochemically (IHC) stained sections of CRC samples. Methods: This approach combines classical segmentation methods (like morphological operations) and machine learning techniques (k-means and hierarchical clustering, convolutional neural networks) to reliably detect tumor buds in colorectal carcinoma samples immunohistochemically stained for pan-cytokeratin. As a possible application, we tested it on whole-slide images as well as on tissue microarrays (TMA) from a clinically well-annotated CRC cohort. Results: Our automatic tumor budding evaluation tool detected the absolute number of tumor buds per image with a very good correlation to the manually segmented ground truth (R2 value of 0.86). Furthermore the automatic evaluation of whole-slide images from 20 CRC-patients, we found that neither the detected number of tumor buds at the invasion front nor the number in hotspots was associated with the nodal status. However, the number of spatial clusters of tumor buds (budding hotspots) significantly correlated to the nodal status (p-value = 0.003 for N0 vs. N1/N2). TMAs were not feasible for tumor budding evaluation, as the spatial relationship of tumor buds (especially hotspots) was not preserved. Conclusions: Automatic image processing is a feasible and valid assessment tool for tumor budding in CRC on whole-slide images. Interestingly, only the spatial clustering of the tumor buds in hotspots (and especially the number of hotspots) and not the absolute number of tumor buds showed a clinically relevant correlation with patient outcome in our data

Comparing the Toxicological Responses of Pulmonary Air–Liquid Interface Models upon Exposure to Differentially Treated Carbon Fibers

In recent years, the use of carbon fibers (CFs) in various sectors of industry has been increasing. Despite the similarity of CF degradation products to other toxicologically relevant materials such as asbestos fibers and carbon nanotubes, a detailed toxicological evaluation of this class of material has yet to be performed. In this work, we exposed advanced air–liquid interface cell culture models of the human lung to CF. To simulate different stresses applied to CF throughout their life cycle, they were either mechanically (mCF) or thermo-mechanically pre-treated (tmCF). Different aspects of inhalation toxicity as well as their possible time-dependency were monitored. mCFs were found to induce a moderate inflammatory response, whereas tmCF elicited stronger inflammatory as well as apoptotic effects. Furthermore, thermal treatment changed the surface properties of the CF resulting in a presumed adhesion of the cells to the fiber fragments and subsequent cell loss. Triple-cultures encompassing epithelial, macrophage, and fibroblast cells stood out with an exceptionally high inflammatory response. Only a weak genotoxic effect was detected in the form of DNA strand breaks in mono- and co-cultures, with triple-cultures presenting a possible secondary genotoxicity. This work establishes CF fragments as a potentially harmful material and emphasizes the necessity of further toxicological assessment of existing and upcoming advanced CF-containing materials