Racial differences in human platelet PAR4 reactivity reflect expression of PCTP and miR-376c.

Racial differences in the pathophysiology of atherothrombosis are poorly understood. We explored the function and transcriptome of platelets in healthy black (n = 70) and white (n = 84) subjects. Platelet aggregation and calcium mobilization induced by the PAR4 thrombin receptor were significantly greater in black subjects. Numerous differentially expressed RNAs were associated with both race and PAR4 reactivity, including PCTP (encoding phosphatidylcholine transfer protein), and platelets from black subjects expressed higher levels of PC-TP protein. PC-TP inhibition or depletion blocked PAR4- but not PAR1-mediated activation of platelets and megakaryocytic cell lines. miR-376c levels were differentially expressed by race and PAR4 reactivity and were inversely correlated with PCTP mRNA levels, PC-TP protein levels and PAR4 reactivity. miR-376c regulated the expression of PC-TP in human megakaryocytes. A disproportionately high number of microRNAs that were differentially expressed by race and PAR4 reactivity, including miR-376c, are encoded in the DLK1-DIO3 locus and were expressed at lower levels in platelets from black subjects. These results suggest that PC-TP contributes to the racial difference in PAR4-mediated platelet activation, indicate a genomic contribution to platelet function that differs by race and emphasize a need to consider the effects of race when developing anti-thrombotic drugs

Disease-induced and treatment-induced alterations in body composition in locally advanced head and neck squamous cell carcinoma

Background Chemoradiation or bioradiation treatment (CRT/BRT) of locally advanced head and neck squamous cell carcinoma (LAHNSCC) comes with high toxicity rates, often leading to temporary tube feeding (TF) dependency. Cachexia is a common problem in LAHNSCC. Yet changes in body composition and muscle weakness during CRT/BRT are underexplored. Strong evidence on the effect of TF on body composition during treatment is lacking. The aim of this cohort study was to assess (i) the relationship of fat-free mass index (FFMI) and handgrip strength (HGS) with CRT/BRT toxicity and outcome, (ii) body composition in patients treated with chemoradiation (cisplatin) vs. bioradiation (cetuximab), and (iii) the effect of the current TF regime on body composition and muscle strength. Methods Locally advanced head and neck squamous cell carcinoma patients treated with CRT/BRT between January 2013 and December 2016 were included (n = 137). Baseline measurements of body composition (bioelectrical impedance analysis) and HGS were performed. Toxicity grades (Common Terminology Criteria for Adverse Events) were scored. In a subset of 69 patients, weight loss, body composition, and HGS were additionally assessed during and after CRT/BRT. TF was initiated according to the Dutch guidelines for malnutrition. Results In this cohort (68% male, mean age 59 +/- 8 years), the incidence of baseline muscle wasting, defined as FFMI &lt;P-10, was 29%. Muscle wasting was present in 23 of 100 (23%) chemoradiation patients and 17 of 37 (46%) bioradiation patients (P = 0.009). Muscle-wasted patients required more unplanned hospitalizations during CRT (P = 0.035). In the chemoradiation subset, dose-limiting toxicity was significantly higher in wasted vs. non-wasted patients (57% vs. 25%, P = 0.004). Median follow-up was 32 months. Multivariate Cox regression analysis identified muscle wasting as independent unfavourable prognostic factor for overall survival [hazard ratio 2.1 (95% CI 1.1-4.1), P = 0.022] and cisplatin as favourable prognostic factor [hazard ratio 0.3 (95% CI 0.2-0.6), P = 0.001]. Weight and HGS significantly decreased during CRT/BRT, -3.7 +/- 3.5 kg (P &lt;0.001) and -3.1 +/- 6.0 kg (P &lt;0.001), respectively. Sixty-four per cent of the patients required TF 21 days (range 0-59) after CRT/BRT initiation. Total weight loss during CRT/BRT was significantly (P = 0.007) higher in the total oral diet group (5.5 +/- 3.7 kg) compared with the TF group (3.0 +/- 3.2 kg). Loss of FFM and HGS was similar in both groups. Conclusions In LAHNSCC patients undergoing CRT/BRT, FFMI &lt;P-10 is an unfavourable prognostic factor for overall survival, treatment toxicity, and tolerance. Patients experience significant weight and FFM loss during treatment. Current TF regime attenuates weight loss but does not overcome loss of muscle mass and function during therapy. Future interventions should consider nutritional intake and additional strategies specifically targeting metabolism, loss of muscle mass, and function.</p

Automated CT-derived skeletal muscle mass determination in lower hind limbs of mice using a 3D U-Net deep learning network

The loss of skeletal muscle mass is recognized as a complication of several chronic diseases and is associated with increased mortality and a decreased quality of life. Relevant and reliable animal models in which muscle wasting can be monitored noninvasively over time are instrumental to investigate and develop new therapies. In this work, we developed a fully automatic deep learning algorithm for segmentation of micro cone beam computed tomography images of the lower limb muscle complex in mice and subsequent muscle mass calculation. A deep learning algorithm was trained on manually segmented data from 32 mice. Muscle wet mass measurements were obtained from 47 mice and served as a data set for model validation and reverse model validation. The automatic algorithm performance was ~150 times faster than manual segmentation. Reverse validation of the algorithm showed high quantitative metrics (i.e., a Dice similarity coefficient of 0.93, a Hausdorff distance of 0.4 mm, and a center of mass displacement of 0.1 mm), substantiating the robustness and accuracy of the model. A high correlation (R(2) = 0.92) was obtained between the computed tomography-derived muscle mass measurements and the muscle wet masses. Longitudinal follow-up revealed time-dependent changes in muscle mass that separated control from lung tumor-bearing mice, which was confirmed as cachexia. In conclusion, this deep learning model for automated assessment of the lower limb muscle complex provides highly accurate noninvasive longitudinal evaluation of skeletal muscle mass. Furthermore, it facilitates the workflow and increases the amount of data derived from mouse studies while reducing the animal numbers.NEW & NOTEWORTHY This deep learning application enables highly accurate noninvasive longitudinal evaluation of skeletal muscle mass changes in mice with minimal requirement for operator involvement in the data analysis. It provides a unique opportunity to increase and analyze the amount of data derived from animal studies automatically while reducing animal numbers and analytical workload

Toolkit 'Verzuimregistratie en –analyse’

In het deelconvenant voor het Hoger Onderwijs en Wetenschappelijk Onderzoek (HOO) is vastgelegd dat de werkdruk en het ziekteverzuim in de sector teruggedrongen moet worden. Eén van de maatregelen om te komen tot een gericht beleid voor verzuimpreventie, verzuimbegeleiding en reïntegratie is het verbeteren en gelijkschakelen van de verzuimregistratie en –analyse binnen de instellingen. In dat kader is, in opdracht van het Arboservicepunt HOO, een ‘toolkit’ ontwikkeld. De toolkit bevat de volgende instrumenten: een visie op verzuim; input voor een verzuimregistratiesysteem; het procesmodel voor verzuimregistratie; van registratie naar managementinformatie; het interpreteren van verzuimgegevens

Deep Learning Based Automated Orthotopic Lung Tumor Segmentation in Whole-Body Mouse CT-Scans

SIMPLE SUMMARY: The development of more translatable orthotopic mouse models is essential in order to study lung cancer more realistically. However, a major challenge in these orthotopic mouse models is the monitoring of tumor take, tumor growth and the detection of therapeutic effects. Therefore, the aim of this study was to train and validate a deep learning algorithm for fully automatic lung tumor quantification in whole-body mouse µCBCT scans. This deep learning application enables highly accurate longitudinal evaluation of tumor volume changes in mice with minimal operator involvement in the data analysis. In addition to longitudinal quantification of tumor development, the algorithm can also be deployed to optimize the randomization and 3R animal welfare aspects of the experimental design in preclinical studies. ABSTRACT: Lung cancer is the leading cause of cancer related deaths worldwide. The development of orthotopic mouse models of lung cancer, which recapitulates the disease more realistically compared to the widely used subcutaneous tumor models, is expected to critically aid the development of novel therapies to battle lung cancer or related comorbidities such as cachexia. However, follow-up of tumor take, tumor growth and detection of therapeutic effects is difficult, time consuming and requires a vast number of animals in orthotopic models. Here, we describe a solution for the fully automatic segmentation and quantification of orthotopic lung tumor volume and mass in whole-body mouse computed tomography (CT) scans. The goal is to drastically enhance the efficiency of the research process by replacing time-consuming manual procedures with fast, automated ones. A deep learning algorithm was trained on 60 unique manually delineated lung tumors and evaluated by four-fold cross validation. Quantitative performance metrics demonstrated high accuracy and robustness of the deep learning algorithm for automated tumor volume analyses (mean dice similarity coefficient of 0.80), and superior processing time (69 times faster) compared to manual segmentation. Moreover, manual delineations of the tumor volume by three independent annotators was sensitive to bias in human interpretation while the algorithm was less vulnerable to bias. In addition, we showed that besides longitudinal quantification of tumor development, the deep learning algorithm can also be used in parallel with the previously published method for muscle mass quantification and to optimize the experimental design reducing the number of animals needed in preclinical studies. In conclusion, we implemented a method for fast and highly accurate tumor quantification with minimal operator involvement in data analysis. This deep learning algorithm provides a helpful tool for the noninvasive detection and analysis of tumor take, tumor growth and therapeutic effects in mouse orthotopic lung cancer models