Bacterial cell wall polymers (peptidoglycan-polysaccharide) cause reactivation of arthritis.

Intraperitoneal (i.p.) injection of peptidoglycan-polysaccharide derived from group A streptococci (PG-APS) causes chronic arthritis with spontaneous remissions and exacerbations. We hypothesized that, following i.p. injection, PG-APS released from hepatic stores mediated spontaneous recurrences of arthritis. We tested whether transplanted livers with large amounts of PG-APS were able to reactivate quiescent arthritis. Saline-loaded (group 1) or PG-APS-loaded (group 2) livers were transplanted into rats which had been injected intra-articularly 10 days earlier with PG-APS in one joint and saline in the other. A comparison was made with the arthritis that occurred in rats injected i.p. with PG-APS which did not receive transplants (group 3). Arthritis was monitored by serial measurement of joint diameters. Transplantation of saline-loaded livers (group 1) caused no reactivation of arthritis. However, transplantation of PG-APS-loaded livers (group 2) reactivated arthritis (P < 0.0001). Injection of PG-APS i.p. (group 3) induced the most-severe arthritis. PG-APS levels in plasma decreased with time, and PG-APS accumulated in the spleen in groups 2 and 3. Plasma and hepatic levels of PG-APS in rats injected i.p. with PG-APS were greater than levels in rats transplanted with PG-APS-loaded livers, which in turn were greater than levels in rats with saline-loaded livers. Plasma tumor necrosis factor did not correlate with recurrence of arthritis. Transplantation with PG-APS-loaded livers induced reactivation of arthritis in preinjured joints. The extent of arthritis was proportional to hepatic PG-APS content. Reactivation of arthritis may be mediated by slow release of liver-sequestered PG-APS or cytokines (not tumor necrosis factor) released by the liver

A longitudinal study of household water, sanitation, and hygiene characteristics and environmental enteropathy markers in children less than 24 months in Iquitos, Peru

Funding Information: Financial support: The MAL-ED is carried out as a collaborative project supported by the Bill & Melinda Gates Foundation, the Foundation for the National Institutes of Health, and the National Institutes of Health, Fogarty International Center. While conducting this work, Natalie Exum was supported by The NSF IGERT Grant 1069213, The Osprey Foundation of Maryland Grant 1602030014, the Johns Hopkins Water Institute, Johns Hopkins Fisher Center Discovery Program Grant 010 KOS2015, The Kazuyoshi Kawata fund in Sanitary Engineering and Science, and the Dr. C. W. Kruse Memorial Fund Scholarship. Publisher Copyright: © 2018 by The American Society of Tropical Medicine and Hygiene.Peer reviewedPublisher PD

Natural language processing for automated quantification of bone metastases reported in free-text bone scintigraphy reports

Background The widespread use of electronic patient-generated health data has led to unprecedented opportunities for automated extraction of clinical features from free-text medical notes. However, processing this rich resource of data for clinical and research purposes, depends on labor-intensive and potentially error-prone manual review. The aim of this study was to develop a natural language processing (NLP) algorithm for binary classification (single metastasis versus two or more metastases) in bone scintigraphy reports of patients undergoing surgery for bone metastases. Material and methods Bone scintigraphy reports of patients undergoing surgery for bone metastases were labeled each by three independent reviewers using a binary classification (single metastasis versus two or more metastases) to establish a ground truth. A stratified 80:20 split was used to develop and test an extreme-gradient boosting supervised machine learning NLP algorithm. Results A total of 704 free-text bone scintigraphy reports from 704 patients were included in this study and 617 (88%) had multiple bone metastases. In the independent test set (n = 141) not used for model development, the NLP algorithm achieved an 0.97 AUC-ROC (95% confidence interval [CI], 0.92-0.99) for classification of multiple bone metastases and an 0.99 AUC-PRC (95% CI, 0.99-0.99). At a threshold of 0.90, NLP algorithm correctly identified multiple bone metastases in 117 of the 124 who had multiple bone metastases in the testing cohort (sensitivity 0.94) and yielded 3 false positives (specificity 0.82). At the same threshold, the NLP algorithm had a positive predictive value of 0.97 and F1-score of 0.96. Conclusions NLP has the potential to automate clinical data extraction from free text radiology notes in orthopedics, thereby optimizing the speed, accuracy, and consistency of clinical chart review. Pending external validation, the NLP algorithm developed in this study may be implemented as a means to aid researchers in tackling large amounts of data

Effects of a nanoscopic filler on the structure and dynamics of a simulated polymer melt and the relationship to ultra-thin films

We perform molecular dynamics simulations of an idealized polymer melt surrounding a nanoscopic filler particle to probe the effects of a filler on the local melt structure and dynamics. We show that the glass transition temperature $T_g$ of the melt can be shifted to either higher or lower temperatures by appropriately tuning the interactions between polymer and filler. A gradual change of the polymer dynamics approaching the filler surface causes the change in the glass transition. We also find that while the bulk structure of the polymers changes little, the polymers close to the surface tend to be elongated and flattened, independent of the type of interaction we study. Consequently, the dynamics appear strongly influenced by the interactions, while the melt structure is only altered by the geometric constraints imposed by the presence of the filler. Our findings show a strong similarity to those obtained for ultra-thin polymer films (thickness $\lesssim 100$ nm) suggesting that both ultra-thin films and filled-polymer systems might be understood in the same context

Physiologically-based pharmacokinetic modeling of quinidine to establish a CYP3A4, P-gp, and CYP2D6 drug-drug-gene interaction network

The antiarrhythmic agent quinidine is a potent inhibitor of cytochrome P450 (CYP) 2D6 and P-glycoprotein (P-gp) and is therefore recommended for use in clinical drug-drug interaction (DDI) studies. However, as quinidine is also a substrate of CYP3A4 and P-gp, it is susceptible to DDIs involving these proteins. Physiologically-based pharmacokinetic (PBPK) modeling can help to mechanistically assess the absorption, distribution, metabolism, and excretion processes of a drug and has proven its usefulness in predicting even complex interaction scenarios. The objectives of the presented work were to develop a PBPK model of quinidine and to integrate the model into a comprehensive drug-drug(-gene) interaction (DD(G)I) network with a diverse set of CYP3A4 and P-gp perpetrators as well as CYP2D6 and P-gp victims. The quinidine parent-metabolite model including 3-hydroxyquinidine was developed using pharmacokinetic profiles from clinical studies after intravenous and oral administration covering a broad dosing range (0.1-600 mg). The model covers efflux transport via P-gp and metabolic transformation to either 3-hydroxyquinidine or unspecified metabolites via CYP3A4. The 3-hydroxyquinidine model includes further metabolism by CYP3A4 as well as an unspecific hepatic clearance. Model performance was assessed graphically and quantitatively with greater than 90% of predicted pharmacokinetic parameters within two-fold of corresponding observed values. The model was successfully used to simulate various DD(G)I scenarios with greater than 90% of predicted DD(G)I pharmacokinetic parameter ratios within two-fold prediction success limits. The presented network will be provided to the research community and can be extended to include further perpetrators, victims, and targets, to support investigations of DD(G)Is.Horizon 2020 (H2020)Personalised Therapeutic

A molecularly characterized preclinical platform of subcutaneous renal cell carcinoma (RCC) patient-derived xenograft models to evaluate novel treatment strategies

Renal cell carcinoma (RCC) is a kidney cancer with an onset mainly during the sixth or seventh decade of the patient’s life. Patients with advanced, metastasized RCC have a poor prognosis. The majority of patients develop treatment resistance towards Standard of Care (SoC) drugs within months. Tyrosine kinase inhibitors (TKIs) are the backbone of first-line therapy and have been partnered with an immune checkpoint inhibitor (ICI) recently. Despite the most recent progress, the development of novel therapies targeting acquired TKI resistance mechanisms in advanced and metastatic RCC remains a high medical need. Preclinical models with high translational relevance can significantly support the development of novel personalized therapies. It has been demonstrated that patient-derived xenograft (PDX) models represent an essential tool for the preclinical evaluation of novel targeted therapies and their combinations. In the present project, we established and molecularly characterized a comprehensive panel of subcutaneous RCC PDX models with well-conserved molecular and pathological features over multiple passages. Drug screening towards four SoC drugs targeting the vascular endothelial growth factor (VEGF) and PI3K/mTOR pathway revealed individual and heterogeneous response profiles in those models, very similar to observations in patients. As unique features, our cohort includes PDX models from metastatic disease and multi-tumor regions from one patient, allowing extended studies on intra-tumor heterogeneity (ITH). The PDX models are further used as basis for developing corresponding in vitro cell culture models enabling advanced high-throughput drug screening in a personalized context. PDX models were subjected to next-generation sequencing (NGS). Characterization of cancer-relevant features including driver mutations or cellular processes was performed using mutational and gene expression data in order to identify potential biomarker or treatment targets in RCC. In summary, we report a newly established and molecularly characterized panel of RCC PDX models with high relevance for translational preclinical research

Improving estimation of the prognosis of childhood psychopathology; combination of DSM-III-R/DISC diagnoses and CBCL scores [IF: 2.7]

Objective: To compare the predictive validity of the clinical-diagnostic and the empirical-quantitative approach to assessment of childhood psychopathology, and to investigate the usefulness of combining both approaches. Method: A referred sample (N=96), aged 6 to 12 years at initial assessment, was followed up across - on average - a period of 3.2 years. It was assessed to what extent DISC/DSM-III-R diagnoses - representing the clinical-diagnostic approach, and CBCL scores - representing the empirical-quantitative approach, predicted the following signs of poor outcome: outpatient/inpatient treatment, or parents' wish for professional help for the child at follow-up, disciplinary problems in school, and police/judicial contacts. Results: Both diagnostic systems added significantly to the prediction of poor outcome, and neither of the two systems was superior. Use of both systems simultaneously provided the most accurate estimation of the prognosis, reflected by the occurrence of future poor outcome. Even diagnostic concepts that are generally regarded as relatively similar, such as ADHD (DSM) and attention problems (CBCL), or conduct disorder (DSM) and delinquent behavior (CBCL), appeared to differ in their ability to predict poor outcome. Conclusions: The present study supports the use of the empirical-quantitative approach and the clinical-diagnostic approach simultaneously, both in research and in clinical settings, to obtain a comprehensive view of the prognosis of psychopathology in children. © Association for Child Psychology and Psychiatry, 2004

Age at first birth in women is genetically associated with increased risk of schizophrenia

Prof. Paunio on PGC:n jäsenPrevious studies have shown an increased risk for mental health problems in children born to both younger and older parents compared to children of average-aged parents. We previously used a novel design to reveal a latent mechanism of genetic association between schizophrenia and age at first birth in women (AFB). Here, we use independent data from the UK Biobank (N = 38,892) to replicate the finding of an association between predicted genetic risk of schizophrenia and AFB in women, and to estimate the genetic correlation between schizophrenia and AFB in women stratified into younger and older groups. We find evidence for an association between predicted genetic risk of schizophrenia and AFB in women (P-value = 1.12E-05), and we show genetic heterogeneity between younger and older AFB groups (P-value = 3.45E-03). The genetic correlation between schizophrenia and AFB in the younger AFB group is -0.16 (SE = 0.04) while that between schizophrenia and AFB in the older AFB group is 0.14 (SE = 0.08). Our results suggest that early, and perhaps also late, age at first birth in women is associated with increased genetic risk for schizophrenia in the UK Biobank sample. These findings contribute new insights into factors contributing to the complex bio-social risk architecture underpinning the association between parental age and offspring mental health.Peer reviewe