The sclerotic component of metabolic syndrome: Fibroblast activities may be the central common denominator driving organ function loss and death.

Fibrosis is a common feature of more than 50 different diseases and the cause of more than 35% of deaths worldwide, of which liver, kidney, skin, heart and, recently, lungs are receiving the most attention. Tissue changes, resulting in loss of organ function, are both a cause and consequence of disease and outcome. Fibrosis is caused by an excess deposition of extracellular matrix proteins, which over time results in impaired organ function and organ failure, and the pathways leading to increased fibroblast activation are many. This narrative review investigated the common denominator of fibrosis, fibroblasts, and the activation of fibroblasts, in response to excess energy consumption in liver, kidney, heart, skin and lung fibrosis. Fibroblasts are the main drivers of organ function loss in lung, liver, skin, heart and kidney disease. Fibroblast activation in response to excess energy consumption results in the overproduction of a range of collagens, of which types I, III and VI seem to be the essential drivers of disease progression. Fibroblast activation may be quantified in serum, enabling profiling and selection of patients. Activation of fibroblasts results in the overproduction of collagens, which deteriorates organ function. Patient profiling of fibroblast activities in serum, quantified as collagen production, may identify an organ death trajectory, better enabling identification of the right treatment for use in different metabolic interventions. As metabolically activated patients have highly elevated risk of kidney, liver and heart failure, it is essential to identify which organ to treat first and monitor organ status to correct treatment regimes. In direct alignment with this, it is essential to identify the right patients with the right organ deterioration trajectory for enrolment in clinical studies.

Biomarker-guided drug development provides value for patients, payers and drug developers: lessons learned from 25 years in the biomarker industry.

INTRODUCTION: There is an urgent, persistent, need for better biomarkers in clinical drug development. More informative biomarkers can increase the likelihood of drug advancement or approval, and implementing biomarkers increases the success rate in drug development. Biomarkers may guide decisions and allow resources to be directed to the projects most likely to succeed. However, biomarkers that are validated to high standards are needed, reflecting biological and pathological processes accurately. Such biomarkers are needed to develop treatments faster, and to improve and guide clinical trial design by selecting and de-selecting patients. METHODS: In this review based on the authors' previous published experience and interaction with pharmaceutical- and biomarker stakeholders, we highlight the use and value of biomarkers in clinical development according to the BEST guidelines. We highlight the value of 3 types of biomarkers that may provide optimal value to stakeholders: diagnostic, prognostic and pharmacodynamic biomarkers. RESULTS: A more appropriate clinical trial design, increasing the ratio between benefits and side effects, may come from a more tailored biomarker-approach identifying suitable molecular endotypes of patients to treat. DISCUSSION: Biomarkers may guide drug developers in selecting the optimal projects to progress, when designing clinical studies and development paths. Biomarkers may aid in the diagnosis and prognostic assessment of patients and assist in matching the molecular endotype to the selected treatment, which improves the success rate of clinical development progression. The aim of this paper is to provide a comprehensive ideation framework for how to utilize biomarkers in clinical development, with a focus on utility for patients, payers and drug developers.

Research alignment in the U.S. national park system: Impact of transformative science policy on the supply and demand for scientific knowledge for protected area management.

The US National Park System encompasses diverse environmental and tourism management regimes, together governed by the 1916 Organic Act and its dual mandate of conservation and provision of public enjoyment. However, with the introduction of transformative science policy in the 2000's, the mission scope has since expanded to promote overarching science-based objectives. Yet despite this paradigm shift instituting "science for parks, parks for science", there is scant research exploring the impact of the National Park Science Policy on the provision of knowledge. We address this gap by developing a spatiotemporal framework for evaluating research alignment, here operationalized via quantifiable measures of supply and demand for scientific knowledge. Specifically, we apply a machine learning algorithm (Latent Dirichlet analysis) to a comprehensive park-specific text corpus (combining official needs statements -i.e. demand- and scientific research metadata -i.e. supply-) to define a joint topic space, which thereby facilitates quantifying the direction and degree of alignment at multiple levels. Results indicate an overall robust degree of research alignment, with misaligned topics tending to be over-researched (as opposed to over-demanded), which may be favorable to many parks, but is inefficient from the park system perspective. Results further indicate that the transformative science policy exacerbated the misalignment in mandated research domains. In light of these results, we argue for improved decision support mechanisms to achieve more timely alignment of research efforts towards distinctive park needs, thereby fostering convergent knowledge co-production and leveraging the full value of National Parks as living laboratories.

Matrix metalloproteinase-12 cleaved fragment of titin as a predictor of functional capacity in patients with heart failure and preserved ejection fraction.

OBJECTIVES: Serum levels of matrix metalloproteinase-12 cleaved fragment of titin (TIM), a novel circulatory biomarker specific for cardiac titin degradation, has emerged as a potential biomarker in cardiovascular diseases. In this work, we aimed to evaluate the association between TIM and maximal functional capacity assessed by the percentage of predicted peak exercise oxygen uptake (pp-peakVO2) in patients with heart failure and preserved ejection fraction (HFpEF). Design. In this post-hoc study, we included 46 stable symptomatic (New York Heart Association II-III) HFpEF patients enrolled in the TRAINING-HF study (NCT02638961). pp-peak-VO2 was calculated from baseline values. Baseline circulating levels of TIM were measured by competitive ELISA in serum from the TRAINING-HF patients. The independent association between TIM and pp-peakVO2 was evaluated by multivariate linear regression analysis. Results. The mean age of the sample was 73.8 ± 8.7 years, 56.5% were females, and 76.1% were on NYHA II. The medians of pp-peakVO2 and TIM were 60.9% (50.4-69.3), and 130.1 ng/mL (98.1-159.5), respectively. The median of NT-proBNP was 912 pg/mL (302-1826). pp-peakVO2 was significant and inversely correlated with TIM (r= -41, p = .005). In multivariate analysis, after adjusting for NYHA class, hypertension, body mass index, and glomerular filtration rate, higher TIM was significantly associated with lower pp-peak VO2 (p = .029). Conclusions. In this sample of stable and symptomatic HFpEF patients, higher serum levels of TIM identified patients with worse functional status.

Exploration of Multiparameter Hematoma 3D Image Analysis for Predicting Outcome After Intracerebral Hemorrhage.

BACKGROUND: Rapid diagnosis and proper management of intracerebral hemorrhage (ICH) play a crucial role in the outcome. Prediction of the outcome with a high degree of accuracy based on admission data including imaging information can potentially influence clinical decision-making practice. METHODS: We conducted a retrospective multicenter study of consecutive ICH patients admitted between 2012-2017. Medical history, admission data, and initial head computed tomography (CT) scan were collected. CT scans were semiautomatically segmented for hematoma volume, hematoma density histograms, and sphericity index (SI). Discharge unfavorable outcomes were defined as death or severe disability (modified Rankin Scores 4-6). We compared (1) hematoma volume alone; (2) multiparameter imaging data including hematoma volume, location, density heterogeneity, SI, and midline shift; and (3) multiparameter imaging data with clinical information available on admission for ICH outcome prediction. Multivariate analysis and predictive modeling were used to determine the significance of hematoma characteristics on the outcome. RESULTS: We included 430 subjects in this analysis. Models using automated hematoma segmentation showed incremental predictive accuracies for in-hospital mortality using hematoma volume only: area under the curve (AUC): 0.85 [0.76-0.93], multiparameter imaging data (hematoma volume, location, CT density, SI, and midline shift): AUC: 0.91 [0.86-0.97], and multiparameter imaging data plus clinical information on admission (Glasgow Coma Scale (GCS) score and age): AUC: 0.94 [0.89-0.99]. Similarly, severe disability predictive accuracy varied from AUC: 0.84 [0.76-0.93] for volume-only model to AUC: 0.88 [0.80-0.95] for imaging data models and AUC: 0.92 [0.86-0.98] for imaging plus clinical predictors. CONCLUSIONS: Multiparameter models combining imaging and admission clinical data show high accuracy for predicting discharge unfavorable outcome after ICH.

The Magnitude of Blood Pressure Reduction Predicts Poor In-Hospital Outcome in Acute Intracerebral Hemorrhage.

BACKGROUND: Early systolic blood pressure (SBP) reduction is believed to improve outcome after spontaneous intracerebral hemorrhage (ICH), but there has been a limited assessment of SBP trajectories in individual patients. We aimed to determine the prognostic significance of SBP trajectories in ICH. METHODS: We collected routine data on spontaneous ICH patients from two healthcare systems over 10 years. Unsupervised functional principal components analysis (FPCA) was used to characterize SBP trajectories over first 24 h and their relationship to the primary outcome of unfavorable shift on modified Rankin scale (mRS) at hospital discharge, categorized as an ordinal trichotomous variable (mRS 0-2, 3-4, and 5-6 defined as good, poor, and severe, respectively). Ordinal logistic regression models adjusted for baseline SBP and ICH volume were used to determine the prognostic significance of SBP trajectories. RESULTS: The 757 patients included in the study were 65 ± 23 years old, 56% were men, with a median (IQR) Glasgow come scale of 14 (8). FPCA revealed that mean SBP over 24 h and SBP reduction within the first 6 h accounted for 76.8% of the variation in SBP trajectories. An increase in SBP reduction (per 10 mmHg) was significantly associated with unfavorable outcomes defined as mRS > 2 (adjusted-OR = 1.134; 95% CI 1.044-1.233, P = 0.003). Compared with SBP reduction < 20 mmHg, worse outcomes were observed for SBP reduction = 40-60 mmHg (adjusted-OR = 1.940, 95% CI 1.129-3.353, P = 0.017) and > 60 mmHg, (adjusted-OR = 1.965, 95% CI 1.011, 3.846, P = 0.047). Furthermore, the association of SBP reduction and outcome varied according to initial hematoma volume. Smaller SBP reduction was associated with good outcome (mRS 0-2) in small (< 7.42 mL) and medium-size (≥ 7.42 and < 30.47 mL) hematomas. Furthermore, while the likelihood of good outcome was low in those with large hematomas (≥ 30.47 mL), smaller SBP reduction was associated with decreasing probability of severe outcome (mRS 5-6). CONCLUSION: Our analyses suggest that in the first 6 h SBP reduction is significantly associated with the in-hospital outcome that varies with initial hematoma volume, and early SBP reduction > 40 mmHg may be harmful in ICH patients. For early SBP reduction to have an effective therapeutic effect, both target levels and optimum SBP reduction goals vis-à-vis hematoma volume should be considered.

Blood Pressure Variability: A New Predicting Factor for Clinical Outcomes of Intracerebral Hemorrhage.

Spontaneous primary intracerebral hemorrhage (ICH) is a stroke subtype associated with the highest mortality rate. High blood pressure (BP) is the most common cause of non-lobar ICH. Recent clinical trials have been inconclusive regarding the efficacy of aggressive BP lowering to improve ICH outcome. The association between high BP and ICH prognosis is rather complex and parameters other than absolute BP levels may be involved. In this regard, there is accruing evidence that BP variability (BPV) plays a major role in ICH outcome. Different BPV indices have been used to predict hematoma growth, neurological deterioration, and functional recovery. This review highlights the available evidence about the relationship between BPV and clinical outcomes among patients. We identified standard deviation (SD), residual SD, coefficient of variation, mean absolute change, average real variability, successive variation, spectral analysis using Fourier analysis, and functional successive variation (FSV) as indices to assess BPV. Most studies have demonstrated the association of BPV with ICH outcome, suggesting a need to monitor and control BP fluctuations in the routine clinical care of ICH patients. When large inter-subject variability exists, FSV is a viable alternative quantification of BPV as its computation is less sensitive to differences in the patient-specific observation schedules for BP than that of traditional indices.

Blood Pressure Variability Predicts Poor In-Hospital Outcome in Spontaneous Intracerebral Hemorrhage.

Background and Purpose- There is increasing evidence that higher systolic blood pressure variability (SBPV) may be associated with poor outcome in patients with intracerebral hemorrhage (ICH). We explored the association between SBPV and in-hospital ICH outcome. Methods- We collected 10-years of consecutive data of spontaneous ICH patients at 2 healthcare systems. Demographics, medical history, laboratory tests, computed tomography scan data, in-hospital treatments, and neurological and functional assessments were recorded. Blood pressure recordings were extracted up to 24 hours postadmission. SBPV was measured using SD, coefficient of variation, successive variation (SV), range and 1 novel index termed functional SV. The effects of SBPV on the functional outcome at discharge were evaluated by multivariate logistic and ordinal regression analyses for dichotomous and trichotomous modified Rankin Scale categorizations, respectively. In secondary analyses, associations between SBPV, history of hypertension, and hematoma expansion were explored. Results- The analysis included 762 subjects. All 5 SBPV indices were significantly associated with the probability of unfavorable outcome (modified Rankin Scale score, 4-6) in logistic models. In ordinal models, SD, coefficient of variation, range, and functional SV were found to have a significant effect on the probabilities of poor (modified Rankin Scale score, 3-4) and severe/death (modified Rankin Scale score, 5-6) outcomes. Normotensive patients had significantly lower mean SBPV compared with the untreated-hypertension cohort for all SBPV indices and compared with treated-hypertension patients for 3 out of 5 SBPV indices. Lower mean SBPV of treated-hypertension subjects compared with untreated-hypertension subjects was only detected in the SV and functional SV indices (P=0.045). None of the SBPV indices were significantly associated with the probability of hematoma expansion. Conclusions- Higher SBPV in the first 24 hours of admission was associated with unfavorable in-hospital outcome among ICH patients. Further prospective studies are warranted to understand any cause-effect relationship and whether controlling for SBPV may improve the ICH outcome.

Quantifying the impact of weak, strong, and super ties in scientific careers.

Scientists are frequently faced with the important decision to start or terminate a creative partnership. This process can be influenced by strategic motivations, as early career researchers are pursuers, whereas senior researchers are typically attractors, of new collaborative opportunities. Focusing on the longitudinal aspects of scientific collaboration, we analyzed 473 collaboration profiles using an egocentric perspective that accounts for researcher-specific characteristics and provides insight into a range of topics, from career achievement and sustainability to team dynamics and efficiency. From more than 166,000 collaboration records, we quantify the frequency distributions of collaboration duration and tie strength, showing that collaboration networks are dominated by weak ties characterized by high turnover rates. We use analytic extreme value thresholds to identify a new class of indispensable super ties, the strongest of which commonly exhibit >50% publication overlap with the central scientist. The prevalence of super ties suggests that they arise from career strategies based upon cost, risk, and reward sharing and complementary skill matching. We then use a combination of descriptive and panel regression methods to compare the subset of publications coauthored with a super tie to the subset without one, controlling for pertinent features such as career age, prestige, team size, and prior group experience. We find that super ties contribute to above-average productivity and a 17% citation increase per publication, thus identifying these partnerships--the analog of life partners--as a major factor in science career development.

Reputation and impact in academic careers.

Reputation is an important social construct in science, which enables informed quality assessments of both publications and careers of scientists in the absence of complete systemic information. However, the relation between reputation and career growth of an individual remains poorly understood, despite recent proliferation of quantitative research evaluation methods. Here, we develop an original framework for measuring how a publication's citation rate Δc depends on the reputation of its central author i, in addition to its net citation count c. To estimate the strength of the reputation effect, we perform a longitudinal analysis on the careers of 450 highly cited scientists, using the total citations Ci of each scientist as his/her reputation measure. We find a citation crossover c×, which distinguishes the strength of the reputation effect. For publications with c < c×, the author's reputation is found to dominate the annual citation rate. Hence, a new publication may gain a significant early advantage corresponding to roughly a 66% increase in the citation rate for each tenfold increase in Ci. However, the reputation effect becomes negligible for highly cited publications meaning that, for c ≥ c×, the citation rate measures scientific impact more transparently. In addition, we have developed a stochastic reputation model, which is found to reproduce numerous statistical observations for real careers, thus providing insight into the microscopic mechanisms underlying cumulative advantage in science.

Self-organization of meaning and the reflexive communication of information.

Following a suggestion from Warren Weaver, we extend the Shannon model of communication piecemeal into a complex systems model in which communication is differentiated both vertically and horizontally. This model enables us to bridge the divide between Niklas Luhmann's theory of the self-organization of meaning in communications and empirical research using information theory. First, we distinguish between communication relations and correlations among patterns of relations. The correlations span a vector space in which relations are positioned and can be provided with meaning. Second, positions provide reflexive perspectives. Whereas the different meanings are integrated locally, each instantiation opens global perspectives - 'horizons of meaning' - along eigenvectors of the communication matrix. These next-order codifications of meaning can be expected to generate redundancies when interacting in instantiations. Increases in redundancy indicate new options and can be measured as local reduction of prevailing uncertainty (in bits). The systemic generation of new options can be considered as a hallmark of the knowledge-based economy.

Suivant une suggestion de Warren Weaver, nous étendons le modèle de communication au coup par coup de Shannon à un modèle de systèmes complexes où la communication est différenciée à la fois verticalement et horizontalement. Ce modèle nous permet de combler le fossé entre la théorie de l'auto-organisation du sens dans les communications de Luhmann et la recherche empirique qui utilise la théorie de l'information. Tout d'abord, nous établissons une distinction entre relations de communication et corrélations entre distributions de relations. Les corrélations couvrent un espace vectoriel où les relations sont positionnées et où l'on peut leur attribuer un sens. Deuxièmement, les positions offrent des perspectives réflexives. Alors que les différents sens sont intégrés localement, chaque instanciation ouvre des perspectives globales ­ des horizons de sens ­ le long des vecteurs propres de la matrice de communication. On peut s'attendre à ce que ces codifications de sens voisines puissent générer des redondances mutuelles lorsqu'elles interagissent en instanciations. L'augmentation des redondances indique de nouvelles options et peut être mesurée comme une réduction locale de la prévalence de l'incertitude (en bits). La génération systémique de nouvelles options peut être considérée comme la marque de fabrique de l'économie de la connaissance.

Persistence and uncertainty in the academic career.

Understanding how institutional changes within academia may affect the overall potential of science requires a better quantitative representation of how careers evolve over time. Because knowledge spillovers, cumulative advantage, competition, and collaboration are distinctive features of the academic profession, both the employment relationship and the procedures for assigning recognition and allocating funding should be designed to account for these factors. We study the annual production n(i)(t) of a given scientist i by analyzing longitudinal career data for 200 leading scientists and 100 assistant professors from the physics community. Our empirical analysis of individual productivity dynamics shows that (i) there are increasing returns for the top individuals within the competitive cohort, and that (ii) the distribution of production growth is a leptokurtic "tent-shaped" distribution that is remarkably symmetric. Our methodology is general, and we speculate that similar features appear in other disciplines where academic publication is essential and collaboration is a key feature. We introduce a model of proportional growth which reproduces these two observations, and additionally accounts for the significantly right-skewed distributions of career longevity and achievement in science. Using this theoretical model, we show that short-term contracts can amplify the effects of competition and uncertainty making careers more vulnerable to early termination, not necessarily due to lack of individual talent and persistence, but because of random negative production shocks. We show that fluctuations in scientific production are quantitatively related to a scientist's collaboration radius and team efficiency.

Quantitative and empirical demonstration of the Matthew effect in a study of career longevity.

The Matthew effect refers to the adage written some two-thousand years ago in the Gospel of St. Matthew: "For to all those who have, more will be given." Even two millennia later, this idiom is used by sociologists to qualitatively describe the dynamics of individual progress and the interplay between status and reward. Quantitative studies of professional careers are traditionally limited by the difficulty in measuring progress and the lack of data on individual careers. However, in some professions, there are well-defined metrics that quantify career longevity, success, and prowess, which together contribute to the overall success rating for an individual employee. Here we demonstrate testable evidence of the age-old Matthew "rich get richer" effect, wherein the longevity and past success of an individual lead to a cumulative advantage in further developing his or her career. We develop an exactly solvable stochastic career progress model that quantitatively incorporates the Matthew effect and validate our model predictions for several competitive professions. We test our model on the careers of 400,000 scientists using data from six high-impact journals and further confirm our findings by testing the model on the careers of more than 20,000 athletes in four sports leagues. Our model highlights the importance of early career development, showing that many careers are stunted by the relative disadvantage associated with inexperience.

A quantitative perspective on ethics in large team science.

The gradual crowding out of singleton and small team science by large team endeavors is challenging key features of research culture. It is therefore important for the future of scientific practice to reflect upon the individual scientist's ethical responsibilities within teams. To facilitate this reflection we show labor force trends in the US revealing a skewed growth in academic ranks and increased levels of competition for promotion within the system; we analyze teaming trends across disciplines and national borders demonstrating why it is becoming difficult to distribute credit and to avoid conflicts of interest; and we use more than a century of Nobel prize data to show how science is outgrowing its old institutions of singleton awards. Of particular concern within the large team environment is the weakening of the mentor-mentee relation, which undermines the cultivation of virtue ethics across scientific generations. These trends and emerging organizational complexities call for a universal set of behavioral norms that transcend team heterogeneity and hierarchy. To this end, our expository analysis provides a survey of ethical issues in team settings to inform science ethics education and science policy.

Effects of empagliflozin on collagen biomarkers in patients with heart failure: Findings from the EMPEROR trials.

AIMS: Extracellular matrix remodelling is one of the key pathways involved in heart failure (HF) progression. Sodium-glucose cotransporter 2 inhibitors (SGLT2i) may have a role in attenuating myocardial fibrosis. The impact of SGLT2i on blood markers of collagen turnover in humans is not fully elucidated. This study aimed to investigate the effect of empagliflozin on serum markers of collagen turnover in patients enrolled in the EMPEROR-Preserved and EMPEROR-Reduced trials. METHODS AND RESULTS: Overall, 1084 patients (545 in empagliflozin and 539 in placebo) were included in the analysis. Procollagen type I carboxy-terminal propeptide (PICP), a fragment of N-terminal type III collagen (PRO-C3), procollagen type I amino-terminal peptide (PINP), a fragment of C-terminal type VIa3 collagen (PRO-C6), a fragment of type I collagen (C1M), and a fragment of type III collagen (C3M) were measured in serum at baseline, 12 and 52 weeks. A mixed model repeated measurements model was used to evaluate the effect of empagliflozin versus placebo on the analysed biomarkers. Higher baseline PICP, PRO-C6 and PINP levels were associated with older age, a more severe HF presentation, higher levels of natriuretic peptides and high-sensitivity troponin T, and the presence of comorbid conditions such as chronic kidney disease and atrial fibrillation. Higher PICP levels were associated with the occurrence of the study primary endpoint (a composite of HF hospitalization or cardiovascular death), and PRO-C6 and PINP were associated with the occurrence of sustained worsening of kidney function. On the other hand, PRO-C3, C1M, and C3M were not associated with worse HF severity or study outcomes. Compared to placebo, empagliflozin reduced PICP at week 12 by 5% and at week 52 by 8% (week 12: geometric mean ratio = 0.95, 95% confidence interval [CI] 0.91-0.99, p = 0.012; week 52: geometric mean ratio = 0.92, 95% CI 0.88-0.97, p = 0.003). Additionally, empagliflozin reduced PRO-C3 at week 52 by 7% (week 12: geometric mean ratio = 0.98, 95% CI 0.95-1.02, p = 0.42; week 52: geometric mean ratio = 0.93, 95% CI 0.89-0.98, p = 0.003), without impact on other collagen markers. CONCLUSION: Our observations are consistent with experimental observations that empagliflozin down-regulates profibrotic signalling. The importance of such an effect for the clinical benefits of SGLT2i in HF remains to be elucidated.

The fibroblast hormone endotrophin is a biomarker of mortality in chronic diseases.

Fibrosis, driven by fibroblast activities, is an important contributor to morbidity and mortality in most chronic diseases. Endotrophin, a signaling molecule derived from processing of type VI collagen by highly activated fibroblasts, is involved in fibrotic tissue remodeling. Circulating levels of endotrophin have been associated with an increased risk of mortality in multiple chronic diseases. We conducted a systematic literature review collecting evidence from original papers published between 2012 and January 2023 that reported associations between circulating endotrophin (PRO-C6) and mortality. Cohorts with data available to the study authors were included in an Individual Patient Data (IPD) meta-analysis that evaluated the association of PRO-C6 with mortality (PROSPERO registration number: CRD42023340215) after adjustment for age, sex and BMI, where available. In the IPD meta-analysis including sixteen cohorts of patients with different non-communicable chronic diseases (NCCDs) (N=15,205) the estimated summary hazard ratio for 3-years all-cause mortality was 2.10 (95% CI 1.75-2.52) for a 2-fold increase in PRO-C6, with some heterogeneity observed between the studies (I2=70%). This meta-analysis is the first study documenting that fibroblast activities, as quantified by circulating endotrophin, are independently associated with mortality across a broad range of NCCDs. This indicates that, irrespective of disease, interstitial tissue remodeling, and consequently fibroblast activities, has a central role in adverse clinical outcomes, and should be considered with urgency from drug developers as a target to treat.

Bankruptcy risk model and empirical tests.

We analyze the size dependence and temporal stability of firm bankruptcy risk in the US economy by applying Zipf scaling techniques. We focus on a single risk factor--the debt-to-asset ratio R--in order to study the stability of the Zipf distribution of R over time. We find that the Zipf exponent increases during market crashes, implying that firms go bankrupt with larger values of R. Based on the Zipf analysis, we employ Bayes's theorem and relate the conditional probability that a bankrupt firm has a ratio R with the conditional probability of bankruptcy for a firm with a given R value. For 2,737 bankrupt firms, we demonstrate size dependence in assets change during the bankruptcy proceedings. Prepetition firm assets and petition firm assets follow Zipf distributions but with different exponents, meaning that firms with smaller assets adjust their assets more than firms with larger assets during the bankruptcy process. We compare bankrupt firms with nonbankrupt firms by analyzing the assets and liabilities of two large subsets of the US economy: 2,545 Nasdaq members and 1,680 New York Stock Exchange (NYSE) members. We find that both assets and liabilities follow a Pareto distribution. The finding is not a trivial consequence of the Zipf scaling relationship of firm size quantified by employees--although the market capitalization of Nasdaq stocks follows a Pareto distribution, the same distribution does not describe NYSE stocks. We propose a coupled Simon model that simultaneously evolves both assets and debt with the possibility of bankruptcy, and we also consider the possibility of firm mergers.

Endotrophin, a fibroblast matrikine, may be a driver of fibroblast activation in fibro-inflammatory diseases.

Extracellular matrix proteins harbor signaling domains that once released from the parent molecule can trigger cellular responses. One of these molecules is endotrophin, a type VI collagen derived fragment, whose circulatory levels have been associated to an increased risk of adverse outcome in heart failure with preserved ejection fraction (HFpEF). Here we show that the stimulation of human cardiac fibroblasts by endotrophin upregulates the synthesis of type I collagen, the main interstitial collagen that accumulates in the myocardium during fibrogenesis. These data provide a possible mechanistic explanation for the relation between circulating endotrophin levels and risk of outcome in HFpEF.

Biomarkers of collagen turnover and wound healing in chronic thromboembolic pulmonary hypertension patients before and after pulmonary endarterectomy.

BACKGROUND: In chronic thromboembolic pulmonary hypertension (CTEPH), fibrotic remodeling of tissue and thrombi contributes to disease progression. Removal of the thromboembolic mass by pulmonary endarterectomy (PEA) improves hemodynamics and right ventricular function, but the roles of different collagens before as well as after PEA are not well understood. METHODS: In this study, hemodynamics and 15 different biomarkers of collagen turnover and wound healing were evaluated in 40 CTEPH patients at diagnosis (baseline) and 6 and 18 months after PEA. Baseline biomarker levels were compared with a historical cohort of 40 healthy subjects. RESULTS: Biomarkers of collagen turnover and wound healing were increased in CTEPH patients compared with healthy controls, including a 35-fold increase in the PRO-C4 marker of type IV collagen formation and a 55-fold increase in the C3M marker of type III collagen degradation. PEA reduced pulmonary pressures to almost normal levels 6 months after the procedure, with no further improvement at 18 months. There were no changes in any of the measured biomarkers after PEA. CONCLUSIONS: Biomarkers of collagen formation and degradation are increased in CTEPH suggesting a high collagen turnover. While PEA effectively reduces pulmonary pressures, collagen turnover is not significantly modified by surgical PEA.

Endotrophin, a Key Marker and Driver for Fibroinflammatory Disease.

Our overview covers several key areas related to recent results obtained for collagen type VI and endotrophin (ETP): i) An introduction to the history of ETP, including how it was identified, how it is released and its function and potential receptors. ii) An introduction to the collagen family, with a focus on what differentiates collagen type VI from an evolutionary standpoint. iii) An overview of collagen type VI, the six individual chains (COL6A1, A2, A3, A4, A5 and A6), their differences and similarities, as well as their expression profiles and function. iv) A detailed analysis of COL6A3, including the cleaved product endotrophin, and what separates it from the other five collagen 6 molecules, including its suggested function based on insights gained from knockout and gain of function mouse models. v) An introduction to the history of ETP, including how it was identified, how it is released and its function and potential receptors. vi) The pathology of ETP. What leads to its presence and release and what are the consequences thereof? vii) Functional implications of circulating ETP. Here we review the data with the functional roles of ETP in mind. viii) We propose that ETP is a mediator for fibrotic (or fibro-inflammatory? ) disorders. Based on what we know about ETP, we have to consider it as a target for the treatment of fibrotic (or fibro-inflammatory) disorders. What segment(s) of the patient population would most dramatically respond to an ETP-targeted intervention? How can we find the population that would profit most from an intervention? We aim to present a broad overview over the ETP field at large, providing an assessment of where the future research efforts need to be placed to tap into the vast potential of ETP, both as a marker and as a target in different diseases.