Classification of interstitial lung disease patterns with topological texture features

Topological texture features were compared in their ability to classify morphological patterns known as 'honeycombing' that are considered indicative for the presence of fibrotic interstitial lung diseases in high-resolution computed tomography (HRCT) images. For 14 patients with known occurrence of honey-combing, a stack of 70 axial, lung kernel reconstructed images were acquired from HRCT chest exams. A set of 241 regions of interest of both healthy and pathological (89) lung tissue were identified by an experienced radiologist. Texture features were extracted using six properties calculated from gray-level co-occurrence matrices (GLCM), Minkowski Dimensions (MDs), and three Minkowski Functionals (MFs, e.g. MF.euler). A k-nearest-neighbor (k-NN) classifier and a Multilayer Radial Basis Functions Network (RBFN) were optimized in a 10-fold cross-validation for each texture vector, and the classification accuracy was calculated on independent test sets as a quantitative measure of automated tissue characterization. A Wilcoxon signed-rank test was used to compare two accuracy distributions and the significance thresholds were adjusted for multiple comparisons by the Bonferroni correction. The best classification results were obtained by the MF features, which performed significantly better than all the standard GLCM and MD features (p < 0.005) for both classifiers. The highest accuracy was found for MF.euler (97.5%, 96.6%; for the k-NN and RBFN classifier, respectively). The best standard texture features were the GLCM features 'homogeneity' (91.8%, 87.2%) and 'absolute value' (90.2%, 88.5%). The results indicate that advanced topological texture features can provide superior classification performance in computer-assisted diagnosis of interstitial lung diseases when compared to standard texture analysis methods.Comment: 8 pages, 5 figures, Proceedings SPIE Medical Imaging 201

Infrared singularities in Landau gauge Yang-Mills theory

We present a more detailed picture of the infrared regime of Landau gauge Yang-Mills theory. This is done within a novel framework that allows one to take into account the influence of finite scales within an infrared power counting analysis. We find that there are two qualitatively different infrared fixed points of the full system of Dyson-Schwinger equations. The first extends the known scaling solution, where the ghost dynamics is dominant and gluon propagation is strongly suppressed. It features in addition to the strong divergences of gluonic vertex functions in the previously considered uniform scaling limit, when all external momenta tend to zero, also weaker kinematic divergences, when only some of the external momenta vanish. The second solution represents the recently proposed decoupling scenario where the gluons become massive and the ghosts remain bare. In this case we find that none of the vertex functions is enhanced, so that the infrared dynamics is entirely suppressed. Our analysis also provides a strict argument why the Landau gauge gluon dressing function cannot be infrared divergent.Comment: 29 pages, 25 figures; published versio

Ultrafast Infrared Nanoscopy with Sub-Cycle Temporal Resolution

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Are Tumor Marker Tests Applied Appropriately in Clinical Practice? A Healthcare Claims Data Analysis.

Tumor markers (TM) are crucial in the monitoring of cancer treatment. However, inappropriate requests for screening reasons have a high risk of false positive and negative findings, which can lead to patient anxiety and unnecessary follow-up examinations. We aimed to assess the appropriateness of TM testing in outpatient practice in Switzerland. We conducted a retrospective cohort study based on healthcare claims data. Patients who had received at least one out of seven TM tests (CEA, CA19-9, CA125, CA15-3, CA72-4, Calcitonin, or NSE) between 2018 and 2021 were analyzed. Appropriate determinations were defined as a request with a corresponding cancer-related diagnosis or intervention. Appropriateness of TM determination by patient characteristics and prescriber specialty was estimated by using multivariate analyses. A total of 51,395 TM determinations in 36,537 patients were included. An amount of 41.6% of all TM were determined appropriately. General practitioners most often determined TM (44.3%) and had the lowest number of appropriate requests (27.8%). A strong predictor for appropriate determinations were requests by medical oncologists. A remarkable proportion of TM testing was performed inappropriately, particularly in the primary care setting. Our results suggest that a considerable proportion of the population is at risk for various harms associated with misinterpretations of TM test results

Conformation-dependent ligand hot spots in the spliceosomal RNA helicase BRR2

The conversion of hits to leads in drug discovery involves the elaboration of chemical core structures to increase their potency. In fragment-based drug discovery, low-molecular-weight compounds are tested for protein binding and are subsequently modified, with the tacit assumption that the binding mode of the original hit will be conserved among the derivatives. However, deviations from binding mode conservation are rather frequently observed, but potential causes of these alterations remain incompletely understood. Here, two crystal forms of the spliceosomal RNA helicase BRR2 were employed as a test case to explore the consequences of conformational changes in the target protein on the binding behaviour of fragment derivatives. The initial fragment, sulfaguanidine, bound at the interface between the two helicase cassettes of BRR2 in one crystal form. Second-generation compounds devised by structure-guided docking were probed for their binding to BRR2 in a second crystal form, in which the original fragment-binding site was altered due to a conformational change. While some of the second-generation compounds retained binding to parts of the original site, others changed to different binding pockets of the protein. A structural bioinformatics analysis revealed that the fragment-binding sites correspond to predicted binding hot spots, which strongly depend on the protein conformation. This case study offers an example of extensive binding-mode changes during hit derivatization, which are likely to occur as a consequence of multiple binding hot spots, some of which are sensitive to the flexibility of the protein

Pathophysiology of septic encephalopathy - an unsolved puzzle

The exact cellular and molecular mechanisms of sepsis-induced encephalopathy remain elusive. The breakdown of the blood-brain barrier (BBB) is considered a focal point in the development of sepsis-induced brain damage. Contributing factors for the compromise of the BBB include cytokines and chemokines, activation of the complement cascade, phagocyte-derived toxic mediators, and bacterial products. To date, we are far from fully understanding the neuropathology that develops as a secondary remote organ injury as a consequence of sepsis. However, recent studies suggest that bacterial proteins may readily cross the functional BBB and trigger an inflammatory response in the subarachnoid space, in absence of a bacterial invasion. A better understanding of the pathophysiological events leading to septic encephalopathy appears crucial to advance the clinical care for this vulnerable patient population

Molecular mechanisms of inflammation and tissue injury after major trauma-is complement the "bad guy"?

Trauma represents the leading cause of death among young people in industrialized countries. Recent clinical and experimental studies have brought increasing evidence for activation of the innate immune system in contributing to the pathogenesis of trauma-induced sequelae and adverse outcome. As the "first line of defense", the complement system represents a potent effector arm of innate immunity, and has been implicated in mediating the early posttraumatic inflammatory response. Despite its generic beneficial functions, including pathogen elimination and immediate response to danger signals, complement activation may exert detrimental effects after trauma, in terms of mounting an "innocent bystander" attack on host tissue. Posttraumatic ischemia/reperfusion injuries represent the classic entity of complement-mediated tissue damage, adding to the "antigenic load" by exacerbation of local and systemic inflammation and release of toxic mediators. These pathophysiological sequelae have been shown to sustain the systemic inflammatory response syndrome after major trauma, and can ultimately contribute to remote organ injury and death. Numerous experimental models have been designed in recent years with the aim of mimicking the inflammatory reaction after trauma and to allow the testing of new pharmacological approaches, including the emergent concept of site-targeted complement inhibition. The present review provides an overview on the current understanding of the cellular and molecular mechanisms of complement activation after major trauma, with an emphasis of emerging therapeutic concepts which may provide the rationale for a "bench-to-bedside" approach in the design of future pharmacological strategies

Machine Learning for Outcome Prediction in First-Line Surgery of Prolactinomas.

Background First-line surgery for prolactinomas has gained increasing acceptance, but the indication still remains controversial. Thus, accurate prediction of unfavorable outcomes after upfront surgery in prolactinoma patients is critical for the triage of therapy and for interdisciplinary decision-making. Objective To evaluate whether contemporary machine learning (ML) methods can facilitate this crucial prediction task in a large cohort of prolactinoma patients with first-line surgery, we investigated the performance of various classes of supervised classification algorithms. The primary endpoint was ML-applied risk prediction of long-term dopamine agonist (DA) dependency. The secondary outcome was the prediction of the early and long-term control of hyperprolactinemia. Methods By jointly examining two independent performance metrics - the area under the receiver operating characteristic (AUROC) and the Matthews correlation coefficient (MCC) - in combination with a stacked super learner, we present a novel perspective on how to assess and compare the discrimination capacity of a set of binary classifiers. Results We demonstrate that for upfront surgery in prolactinoma patients there are not a one-algorithm-fits-all solution in outcome prediction: different algorithms perform best for different time points and different outcomes parameters. In addition, ML classifiers outperform logistic regression in both performance metrics in our cohort when predicting the primary outcome at long-term follow-up and secondary outcome at early follow-up, thus provide an added benefit in risk prediction modeling. In such a setting, the stacking framework of combining the predictions of individual base learners in a so-called super learner offers great potential: the super learner exhibits very good prediction skill for the primary outcome (AUROC: mean 0.9, 95% CI: 0.92 - 1.00; MCC: 0.85, 95% CI: 0.60 - 1.00). In contrast, predicting control of hyperprolactinemia is challenging, in particular in terms of early follow-up (AUROC: 0.69, 95% CI: 0.50 - 0.83) vs. long-term follow-up (AUROC: 0.80, 95% CI: 0.58 - 0.97). It is of clinical importance that baseline prolactin levels are by far the most important outcome predictor at early follow-up, whereas remissions at 30 days dominate the ML prediction skill for DA-dependency over the long-term. Conclusions This study highlights the performance benefits of combining a diverse set of classification algorithms to predict the outcome of first-line surgery in prolactinoma patients. We demonstrate the added benefit of considering two performance metrics jointly to assess the discrimination capacity of a diverse set of classifiers