Energy expenditure in chow-fed female non-human primates of various weights

<p>Abstract</p> <p>Background</p> <p>Until now no technology has been available to study energy metabolism in monkeys. The objective of this study was to determine daily energy expenditures (EE) and respiratory quotients (RQ) in female monkeys of various body weights and ages.</p> <p>Methods</p> <p>16 socially reared Bonnet Macaque female monkeys [5.5 ± 1.4 kg body weight, modified BMI (length measurement from head to base of the tail) = 28.8 ± 6.7 kg/crown-rump length, m²and 11.7 ± 4.6 years] were placed in the primate Enhanced Metabolic Testing Activity Chamber (Model 3000a, EMTAC Inc. Santa Barbara, CA) for 22-hour measurements of EE (kcal/kg) and RQ (VCO₂/VO₂). All were fed monkey chow (4.03 kcal/g) ad-libitum under a 12/12 hour light/dark cycle. Metabolic data were corrected for differences in body weight. Results were divided into day (8-hours), dark (12 hours) and morning (2-hours) periods. Data analysis was conducted utilizing SPSS (Version 13).</p> <p>Results</p> <p>Modified BMI negatively correlated with 22-hour energy expenditure in all monkeys (r = -0.80, p < 0.01). The large variability of daily energy intake (4.5 to 102.0 kcal/kg) necessitated division into two groups, non-eaters (< 13 kcal/kg) and eaters (> 23 kcal/kg). There were reductions (p < 0.05) in both 22-hour and dark period RQs in the "non-eaters" in comparison to those who were "eaters". Monkeys were also classified as "lean" (modified BMI < 25) or "obese" (modified BMI > 30). The obese group had lower EE (p < 0.05) during each time period and over the entire 22-hours (p < 0.05), in comparison to their lean counterparts.</p> <p>Conclusion</p> <p>The EMTAC proved to be a valuable tool for metabolic measurements in monkeys. The accuracy and sensitivity of the instrument allowed detection of subtle metabolic changes in relation to energy intake. Moreover, there is an association between a reduction of energy expenditure and a gain in body weight.</p

Role of alternatively spliced tissue factor in pancreatic cancer growth and angiogenesis

Increased tissue factor (TF) expression is observed in many types of cancer, associated with more aggressive disease, and in thrombosis. The mechanism by which TF promotes tumor growth remains unclear. Anticoagulation has been shown to result in a trend toward improved survival; no direct antitumor effect has been shown in cancer patients. Alternatively spliced tissue factor (asTF) was recently described, in which exon 5 is deleted. Because of a frame-shift in exon 6, the transmembrane and cytoplasmic domains are replaced with a unique COOH-terminal domain, making asTF soluble. Both alternatively spliced human tissue factor (asHTF) and full-length tissue factor (flTF) are expressed in human pancreatic cancer lines and in pancreatic cancer specimens. We studied the role of asHTF and flTF in a mouse model of pancreatic cancer. Although lacking procoagulant activity, asTF promotes primary growth of human pancreatic cancer cells in mice and augments tumor-associated angiogenesis. This body of work suggests a new paradigm for the role of TF in pancreatic cancer: that asHTF contributes to cancer growth, independent of procoagulant activity

Alternatively spliced human tissue factor promotes tumor growth and angiogenesis in a pancreatic cancer tumor model

Tissue Factor (TF) expression is observed in many types of cancer, associated with more aggressive disease, and thrombosis. Alternatively-spliced human tissue factor (asHTF) has recently been identified in which exon 5 is deleted. asHTF is soluble due to the substitution of the transmembrane and cytoplasmic domains of exon 6 with a unique COOH-terminal domain. We examine the expression and function of asHTF and full-length Tissue Factor ( FLTF) in six human pancreatic cancer cells. Further, we transfected asHTF, FLTF, and control expression vectors into a non-expressing, human pancreatic cancer line (MiaPaCa-2). We studied the procoagulant activity of asHTF and flTF and the effect on tumor growth in mice. asHTF is expressed in 5 of 6 human pancreatic cancer cell lines, but not in normal human fibroblasts, nor the MiaPaCa-2 line. flTF conferred procoagulant activity, but asHTF did not. Transfected cells were injected subcutaneously in athymic mice. Interestingly, compared with control transfection, flTF expression was associated with reduced tumor growth (mean 7 mg vs 85 mg), while asHTF-expression was associated with enhanced tumor growth (mean 389 mg vs. 85 mg). asHTF expression resulted in increased mitotic index and microvascular density. These data suggests that asHTF expression promotes tumor growth, and is associated with increased tumor cell proliferation and angiogenesis in vivo. Our results raise a new perspective on the understanding of the relationship between TF expression and cancer growth, by showing a dissociation of the procoagulant activity of flTF and the cancer-promoting activity of asHTF

Antemortem detection of Parkinson’s disease pathology in peripheral biopsies using artificial intelligence

Abstract The diagnosis of Parkinson\’s disease (PD) is challenging at all stages due to variable symptomatology, comorbidities, and mimicking conditions. Postmortem assessment remains the gold standard for a definitive diagnosis. While it is well recognized that PD manifests pathologically in the central nervous system with aggregation of α-synuclein as Lewy bodies and neurites, similar Lewy-type synucleinopathy (LTS) is additionally found in the peripheral nervous system that may be useful as an antemortem biomarker. We have previously found that detection of LTS in submandibular gland (SMG) biopsies is sensitive and specific for advanced PD; however, the sensitivity is suboptimal especially for early-stage disease. Further, visual microscopic assessment of biopsies by a neuropathologist to identify LTS is impractical for large-scale adoption. Here, we trained and validated a convolutional neural network (CNN) for detection of LTS on 283 digital whole slide images (WSI) from 95 unique SMG biopsies. A total of 8,450 LTS and 35,066 background objects were annotated following an inter-rater reliability study with Fleiss Kappa  = 0.72. We used transfer learning to train a CNN model to classify image patches (151 × 151 pixels at 20× magnification) with and without the presence of LTS objects. The trained CNN model showed the following performance on image patches: sensitivity: 0.99, specificity: 0.99, precision: 0.81, accuracy: 0.99, and F-1 score: 0.89. We further tested the trained network on 1230 naïve WSI from the same cohort of research subjects comprising 42 PD patients and 14 controls. Logistic regression models trained on features engineered from the CNN predictions on the WSI resulted in sensitivity: 0.71, specificity: 0.65, precision: 0.86, accuracy: 0.69, and F-1 score: 0.76 in predicting clinical PD status, and 0.64 accuracy in predicting PD stage, outperforming expert neuropathologist LTS density scoring in terms of sensitivity but not specificity. These findings demonstrate the practical utility of a CNN detector in screening for LTS, which can translate into a computational tool to facilitate the antemortem tissue-based diagnosis of PD in clinical settings

Antemortem detection of Parkinson’s disease pathology in peripheral biopsies using artificial intelligence

AbstractThe diagnosis of Parkinson’s disease (PD) is challenging at all stages due to variable symptomatology, comorbidities, and mimicking conditions. Postmortem assessment remains the gold standard for a definitive diagnosis. While it is well recognized that PD manifests pathologically in the central nervous system with aggregation of α-synuclein as Lewy bodies and neurites, similar Lewy-type synucleinopathy (LTS) is additionally found in the peripheral nervous system that may be useful as an antemortem biomarker. We have previously found that detection of LTS in submandibular gland (SMG) biopsies is sensitive and specific for advanced PD; however, the sensitivity is suboptimal especially for early-stage disease. Further, visual microscopic assessment of biopsies by a neuropathologist to identify LTS is impractical for large-scale adoption. Here, we trained and validated a convolutional neural network (CNN) for detection of LTS on 283 digital whole slide images (WSI) from 95 unique SMG biopsies. A total of 8,450 LTS and 35,066 background objects were annotated following an inter-rater reliability study with Fleiss Kappa = 0.72. We used transfer learning to train a CNN model to classify image patches (151 × 151 pixels at 20× magnification) with and without the presence of LTS objects. The trained CNN model showed the following performance on image patches: sensitivity: 0.99, specificity: 0.99, precision: 0.81, accuracy: 0.99, and F-1 score: 0.89. We further tested the trained network on 1230 naïve WSI from the same cohort of research subjects comprising 42 PD patients and 14 controls. Logistic regression models trained on features engineered from the CNN predictions on the WSI resulted in sensitivity: 0.71, specificity: 0.65, precision: 0.86, accuracy: 0.69, and F-1 score: 0.76 in predicting clinical PD status, and 0.64 accuracy in predicting PD stage, outperforming expert neuropathologist LTS density scoring in terms of sensitivity but not specificity. These findings demonstrate the practical utility of a CNN detector in screening for LTS, which can translate into a computational tool to facilitate the antemortem tissue-based diagnosis of PD in clinical settings.</jats:p

Additional file 1 of Antemortem detection of Parkinson’s disease pathology in peripheral biopsies using artificial intelligence

Additional file 1: Supplementary tables