Measurement of body segment parameters using dual energy X-ray absorptiometry and three-dimensional geometry: An application in gait analysis

Body segment parameters (BSP) are essential input for the computations in kinetics of motion applied in the field of biomechanics. These data are usually obtained from population-specific predictive equations which present limitations in being representative of the population under study. More recently, medical imaging techniques have been adopted but are limited to two-dimensional (2-D) measurements or required extensive tomographic images for three-dimensional (3-D) reconstruction. We proposed an in vivo method to measure 3-D BSP using X-ray imaging and 3-D exterior geometry. Criterion values of the BSP were determined using magnetic resonance imaging (MRI) which has previously been validated. Errors for all BSP values were less than 2% when values derived from our method were compared to the criterion values. We found no significant difference between our method and four selected BSP models in both stance and swing phase. Significant phase effects were observed for our method and other BSP models between stance and swing phase. Significant differences (p<0.05) between root mean square error (RMSE) ranged from 0.0177 to 0.0234 and 0.0234 to 0.097 Nm kg^?1 for the knee and hip joints, respectively. However, these BSP variations brought about effects on moment output that were less than 0.09 Nm kg^?1. Our findings suggest joint kinetic computations during normal gait are relatively insensitive to BSP variations. However, the influence of BSP cannot be undermined in movements that generate higher acceleration at the limbs. Considering the accuracy of our method, it could be used as a novel in vivo method to obtain direct 3-D BSP measurements.     

A novel methodology for generating 3D finite element models of the hip from 2D radiographs

Finite element (FE) modelling has been proposed as a tool for estimating fracture risk and patient-specific FE models are commonly based on computed tomography (CT). Here, we present a novel method to automatically create personalised 3D models from standard 2D hip radiographs. A set of geometrical parameters of the femur were determined from seven a–p hip radiographs and compared to the 3D femoral shape obtained from CT as training material; the error in reconstructing the 3D model from the 2D radiographs was assessed. Using the geometry parameters as the input, the 3D shape of another 21 femora was built and meshed, separating a cortical and trabecular compartment. The material properties were derived from the homogeneity index assessed by texture analysis of the radiographs, with focus on the principal tensile and compressive trabecular systems. The ability of these FE models to predict failure load as determined by experimental biomechanical testing was evaluated and compared to the predictive ability of DXA. The average reconstruction error of the 3D models was 1.77 mm (±1.17 mm), with the error being smallest in the femoral head and neck, and greatest in the trochanter. The correlation of the FE predicted failure load with the experimental failure load was r²=64% for the reconstruction FE model, which was significantly better (p<0.05) than that for DXA (r²=24%). This novel method for automatically constructing a patient-specific 3D finite element model from standard 2D radiographs shows encouraging results in estimating patient-specific failure loads.     

Reconstruction and analysis of a deciduous sapling using digital photographs or terrestrial-LiDAR technology

Background and Aims

To meet the increasing need for rapid and non-destructive extraction of canopy traits, two methods were used and compared with regard to their accuracy in estimatating 2-D and 3-D parameters of a hybrid poplar sapling.

Methods

The first method consisted of the analysis of high definition photographs in Tree Analyser (TA) software (PIAF-INRA/Kasetsart University). TA allowed the extraction of individual traits using a space carving approach. The second method utilized 3-D point clouds acquired from terrestrial light detection and ranging (T-LiDAR) scans. T-LiDAR scans were performed on trees without leaves to reconstruct the lignified structure of the sapling. From this skeleton, foliage was added using simple modelling rules extrapolated from field measurements. Validation of the estimated dimension and the accuracy of reconstruction was then achieved by comparison with an empirical data set.

Key Results

TA was found to be slightly less precise than T-LiDAR for estimating tree height, canopy height and mean canopy diameter, but for 2-D traits both methods were, however, fully satisfactory. TA tended to over-estimate total leaf area (error up to 50 %), but better estimates were obtained by reducing the size of the voxels used for calculations. In contrast, T-LiDAR estimated total leaf area with an error of <6 %. Finally, both methods led to an over-estimation of canopy volume. With respect to this trait, T-LiDAR (14·5 % deviation) greatly surpassed the accuracy of TA (up to 50 % deviation), even if the voxels used were reduced in size.

Conclusions

Taking into account their magnitude of data acquisition and analysis and their accuracy in trait estimations, both methods showed contrasting potential future uses. Specifically, T-LiDAR is a particularly promising tool for investigating the development of large perennial plants, by itself or in association with plant modelling.     

The reliability and criterion validity of 2D video assessment of single leg squat and hop landing

The objective was to assess the intra-tester, within and between day reliability of measurement of hip adduction (HADD) and frontal plane projection angles (FPPA) during single leg squat (SLS) and single leg landing (SLL) using 2D video and the validity of these measurements against those found during 3D motion capture. 15 healthy subjects had their SLS and SLL assessed using 3D motion capture and video analysis. Inter-tester reliability for both SLS and SLL when measuring FPPA and HADD show excellent correlations (ICC_2,1 0.97–0.99). Within and between day assessment of SLS and SLL showed good to excellent correlations for both variables (ICC_3,1 0.72–91). 2D FPPA measures were found to have good correlation with knee abduction angle in 3-D (r = 0.79, p = 0.008) during SLS, and also to knee abduction moment (r = 0.65, p = 0.009). 2D HADD showed very good correlation with 3D HADD during SLS (r = 0.81, p = 0.001), and a good correlation during SLL (r = 0.62, p = 0.013). All other associations were weak (r < 0.4). This study suggests that 2D video kinematics have a reasonable association to what is being measured with 3D motion capture.     

Cognitive and psychomotor responses to high-altitude exposure in sea level and high-altitude residents of Ecuador

Background

High-altitude inhabitants have cardiovascular and respiratory adaptations that are advantageous for high-altitude living, but they may have impaired cognitive function. This study evaluated the influence of altitude of residence on cognitive and psychomotor function upon acute exposure to very high altitude.

Findings

Ecuadorians (31 residing at 0–1,500 m [LOW], 78 from 1,501–3,000 m [MOD], and 23 living >3,000 m [HIGH]) were tested upon their arrival to a hut at 4,860 m on Mount Chimborazo. Cognitive/psychomotor measurements included a go-no-go test (responding to a non-visual stimulus), a verbal fluency test (verbalizing a series of words specific to a particular category), and a hand movement test (rapidly repeating a series of hand positions). Mean differences between the three altitude groups on these cognitive/psychomotor tests were evaluated with one-way ANOVA. There were no significant differences (p = 0.168) between LOW, MOD, and HIGH for the verbal fluency test. However, the go-no-go test was significantly lower (p < 0.001) in the HIGH group (8.8 ± 1.40 correct responses) than the LOW (9.8 ± 0.61) or MOD (9.8 ± 0.55) groups, and both MOD (97.9 ± 31.2) and HIGH (83.5 ± 26.7) groups completed fewer correct hand movements than the LOW (136.6 ± 37.9) subjects (p < 0.001).

Conclusions

Based on this field study, high-altitude residents appear to have some impaired cognitive function suggesting the possibility of maladaptation to long-term exposure to hypobaric hypoxia.     

Calibration of Terra/MODIS gross primary production over an irrigated cropland on the North China Plain and an alpine meadow on the Tibetan Plateau

This paper evaluated the MODerate resolution Imaging Spectroradiometer (MODIS) gross primary production (GPP) product (MOD17) by using estimated GPP from eddy‐covariance flux measurements over an irrigated winter wheat and maize double‐cropping field on the North China Plain in 2003–2004, and an alpine meadow on the Tibetan Plateau in 2002–2003. The mean annual GPP from MOD17 accounted for 1/2–2/3 of the surface estimated mean annual GPP for the alpine meadow, but only about 1/5–1/3 for the cropland. This underestimation was partly attributed to low estimates of leaf area index by a MODIS product (MOD15) because it is used to calculate absorbed photosynthetically active radiation in the MOD17 algorithm. The main reason is that the parameter maximum light use efficiency (ε_max) in the MOD17 algorithm was underestimated for the two biomes, especially for the cropland. Contrasted to the default, ε_max was optimized using surface measurements. The optimized ε_max for winter wheat, maize and meadow was 1.18, 1.81 and 0.73 g C/MJ, respectively. By using the surface measurements and optimized ε_max , the MOD17 algorithm significantly improved the accuracy of GPP estimates. The optimum MOD17 algorithm explained about 82%, 68%, and 79% of GPP variance for winter wheat, maize, and meadow, respectively. These results suggest that it is necessary to adjust the MOD17 parameters for the estimation of cropland and meadow GPP, particularly over cropland.     

The Repeatability Assessment of Three-Dimensional Capsule-Intraocular Lens Complex Measurements by Means of High-Speed Swept-Source Optical Coherence Tomography

Purpose

To rebuild the three-dimensional (3-D) model of the anterior segment by high-speed swept-source optical coherence tomography (SSOCT) and evaluate the repeatability of measurement for the parameters of capsule-intraocular lens (C-IOL) complex.

Methods

Twenty-two pseudophakic eyes from 22 patients were enrolled. Three continuous SSOCT measurements were performed in all eyes and the tomograms obtained were used for 3-D reconstruction. The output data were used to evaluate the measurement repeatability. The parameters included postoperative aqueous depth (PAD), the area and diameter of the anterior capsule opening (Area and D), IOL tilt (IOL-T), horizontal, vertical, and space decentration of the IOL, anterior capsule opening, and IOL-anterior capsule opening.

Results

PAD, IOL-T, Area, D, and all decentration measurements showed high repeatability. Repeated measure analysis showed there was no statistically significant difference among the three continuous measurements (all P > .05). Pearson correlation analysis showed high correlation between each pair of them (all r >0.90, P<0.001). ICCs were all more than 0.9 for all parameters. The 95% LoAs of all parameters were narrow for comparison of three measurements, which showed high repeatability for three measurements.

Conclusion

SSOCT is available to be a new method for the 3-D measurement of C-IOL complex after cataract surgery. This method presented high repeatability in measuring the parameters of the C-IOL complex.     

Which evaluation criteria of the short-term efficacy can better reflect the long-term outcomes for patients with nasopharyngeal carcinoma?

PurposeTo compare the consistency of one-dimensional Response Evaluation Criteria in Solid Tumors (1D-RECIST), two-dimensional WHO criteria (2D-WHO), and three-dimensional (3D) measurement for therapeutic response assessment of nasopharyngeal carcinoma (NPC).Materials and methodsRetrospective data of 288 newly diagnosed NPC patients were reviewed. Tumor size was assessed on magnetic resonance imaging (MRI) according to the 1D-RECIST, 2D-WHO, and 3D measurement criteria. Agreement between tumor responses was assessed using unweighted k statistics. The receiver operating characteristic (ROC) curve was used to determine the optimal cut-off point of the PTV. The Kaplan–Meier method and Cox regression were used for the survival analysis.ResultsThe optimal cut-off point of the PTV for progression-free survival (PFS) was 29.6%. Agreement with PTV measurement was better for 1D measurement than for 2D and 3D measurements (kappa values of 0.646, 0.537, and 0.577 for 1D, 2D, and 3D measurements, respectively; P < 0.05). The area under the curve of the 1D measurement (AUC=0.596) was similar to that of the PTV measurement (AUC=0.621). Compared with 2D and 3D measurements, 1D measurement is superior for predicting prognosis in NPC (C-index of 0.672, 0.663, and 0.646 were for 1D, 2D, and 3D measurements, respectively; P < 0.005). Survival analysis showed that patients with non-responders had worse prognosis (P < 0.05).ConclusionsThe 1D measurement more closely agreed with the PTV measurement than the 2D and 3D measurements for predicting therapeutic responses in NPC. Therefore, we recommend using the less time-consuming 1D-RECIST criteria in routine clinical practice.     

Adaptive Iterative Dose Reduction Using Three Dimensional Processing (AIDR3D) Improves Chest CT Image Quality and Reduces Radiation Exposure

Objective

To assess the advantages of Adaptive Iterative Dose Reduction using Three Dimensional Processing (AIDR3D) for image quality improvement and dose reduction for chest computed tomography (CT).

Methods

Institutional Review Boards approved this study and informed consent was obtained. Eighty-eight subjects underwent chest CT at five institutions using identical scanners and protocols. During a single visit, each subject was scanned using different tube currents: 240, 120, and 60 mA. Scan data were converted to images using AIDR3D and a conventional reconstruction mode (without AIDR3D). Using a 5-point scale from 1 (non-diagnostic) to 5 (excellent), three blinded observers independently evaluated image quality for three lung zones, four patterns of lung disease (nodule/mass, emphysema, bronchiolitis, and diffuse lung disease), and three mediastinal measurements (small structure visibility, streak artifacts, and shoulder artifacts). Differences in these scores were assessed by Scheffe''s test.

Results

At each tube current, scans using AIDR3D had higher scores than those without AIDR3D, which were significant for lung zones (p<0.0001) and all mediastinal measurements (p<0.01). For lung diseases, significant improvements with AIDR3D were frequently observed at 120 and 60 mA. Scans with AIDR3D at 120 mA had significantly higher scores than those without AIDR3D at 240 mA for lung zones and mediastinal streak artifacts (p<0.0001), and slightly higher or equal scores for all other measurements. Scans with AIDR3D at 60 mA were also judged superior or equivalent to those without AIDR3D at 120 mA.

Conclusion

For chest CT, AIDR3D provides better image quality and can reduce radiation exposure by 50%.     

Conformational properties of l-fucose and the tetrasaccharide building block of the sulfated l-fucan from Lytechinus variegatus

Although the 3D structure of carbohydrates is known to contribute to their biological roles, conformational studies of sugars are challenging because their chains are flexible in solution and consequently the number of 3D structural restraints is limited. Here, we investigate the conformational properties of the tetrasaccharide building block of the Lytechinus variegatus sulfated fucan composed of the following structure [l-Fucp4(SO₃⁻)-α(1-3)-l-Fucp2,4(SO₃⁻)-α(1-3)-l-Fucp2(SO₃⁻)-α(1-3)-l-Fucp2(SO₃⁻)] and the composing monosaccharide unit Fucp, primarily by nuclear magnetic resonance (NMR) experiments performed at very low temperatures and using H₂O as the solvent for the sugars rather than using the conventional deuterium oxide. By slowing down the fast chemical exchange rates and forcing the protonation of labile sites, we increased the number of through-space ¹H–¹H distances that could be measured by NMR spectroscopy. Following this strategy, additional conformational details of the tetrasaccharide and l-Fucp in solution were obtained. Computational molecular dynamics was performed to complement and validate the NMR-based measurements. A model of the NMR-restrained 3D structure is offered for the tetrasaccharide.     

3D representation and analysis of enthesis morphology

This comparison of methods for assessing the development of muscle insertion sites, or entheses, suggests that three‐dimensional (3D) quantification of enthesis morphology can produce a picture of habitual muscle use patterns in a past population that is similar to one produced by ordinal scores for describing enthesis morphology. Upper limb skeletal elements (humeri, radii, and ulnae) from a sample of 24 middle‐aged adult males from the Pottery Mound site in New Mexico were analyzed for both fibrous and fibrocartilaginous enthesis development with three different methods: ordinal scores, two‐dimensional (2D) area measurements, and 3D surface areas. The methods were compared using tests for asymmetry and correlations among variables in each quantitative data set. 2D representations of enthesis area did not agree as closely as ordinal scores and 3D surface areas did regarding which entheses were significantly asymmetrical. There was significant correlation between 3D and 2D data, but correlation coefficients were not consistently high. Intraobserver error was also assessed for the 3D method. Cronbach's alpha values fell between 0.68 and 0.73, and error rates for all entheses fell between 10% and 15%. Marginally acceptable intraobserver error and the analytic versatility of 3D images encourage further investigation of using 3D scanning technology for quantifying enthesis development. Am J Phys Anthropol 152:417–424, 2013. © 2013 Wiley Periodicals, Inc.     

On the statistical assessment of classifiers using DNA microarray data

Background

In this paper we present a method for the statistical assessment of cancer predictors which make use of gene expression profiles. The methodology is applied to a new data set of microarray gene expression data collected in Casa Sollievo della Sofferenza Hospital, Foggia – Italy. The data set is made up of normal (22) and tumor (25) specimens extracted from 25 patients affected by colon cancer. We propose to give answers to some questions which are relevant for the automatic diagnosis of cancer such as: Is the size of the available data set sufficient to build accurate classifiers? What is the statistical significance of the associated error rates? In what ways can accuracy be considered dependant on the adopted classification scheme? How many genes are correlated with the pathology and how many are sufficient for an accurate colon cancer classification? The method we propose answers these questions whilst avoiding the potential pitfalls hidden in the analysis and interpretation of microarray data.

Results

We estimate the generalization error, evaluated through the Leave-K-Out Cross Validation error, for three different classification schemes by varying the number of training examples and the number of the genes used. The statistical significance of the error rate is measured by using a permutation test. We provide a statistical analysis in terms of the frequencies of the genes involved in the classification. Using the whole set of genes, we found that the Weighted Voting Algorithm (WVA) classifier learns the distinction between normal and tumor specimens with 25 training examples, providing e = 21% (p = 0.045) as an error rate. This remains constant even when the number of examples increases. Moreover, Regularized Least Squares (RLS) and Support Vector Machines (SVM) classifiers can learn with only 15 training examples, with an error rate of e = 19% (p = 0.035) and e = 18% (p = 0.037) respectively. Moreover, the error rate decreases as the training set size increases, reaching its best performances with 35 training examples. In this case, RLS and SVM have error rates of e = 14% (p = 0.027) and e = 11% (p = 0.019). Concerning the number of genes, we found about 6000 genes (p < 0.05) correlated with the pathology, resulting from the signal-to-noise statistic. Moreover the performances of RLS and SVM classifiers do not change when 74% of genes is used. They progressively reduce up to e = 16% (p < 0.05) when only 2 genes are employed. The biological relevance of a set of genes determined by our statistical analysis and the major roles they play in colorectal tumorigenesis is discussed.

Conclusions

The method proposed provides statistically significant answers to precise questions relevant for the diagnosis and prognosis of cancer. We found that, with as few as 15 examples, it is possible to train statistically significant classifiers for colon cancer diagnosis. As for the definition of the number of genes sufficient for a reliable classification of colon cancer, our results suggest that it depends on the accuracy required.     

Screening GPS telemetry data for locations having unacceptable error

Technological improvements in battery life and physical dimensions of Global Positioning System (GPS) telemetry have increased the number of locations one can collect, but due to relatively unimproved GPS accuracy this also increases the number of locations with unacceptable measurement error. We propose and show an example of a new method for screening data for locations with unacceptable error. We also propose a new screening metric: estimated elevation error (EEE). EEE identifies unacceptable xy-coordinate error in some cases better than do methods that use horizontal dilution of precision (HDOP) or fix dimension (2-D or 3-D). Our screening method combines test data and a model-averaging information-theoretic framework that uses a priori candidate models of telemetry measurement error. We demonstrate this method using experimental data collected on banded mongooses (Mungos mungo). One can adapt this screening method to any GPS data.     

Phase Error Correction in Time-Averaged 3D Phase Contrast Magnetic Resonance Imaging of the Cerebral Vasculature

Purpose

Volume flow rate (VFR) measurements based on phase contrast (PC)-magnetic resonance (MR) imaging datasets have spatially varying bias due to eddy current induced phase errors. The purpose of this study was to assess the impact of phase errors in time averaged PC-MR imaging of the cerebral vasculature and explore the effects of three common correction schemes (local bias correction (LBC), local polynomial correction (LPC), and whole brain polynomial correction (WBPC)).

Methods

Measurements of the eddy current induced phase error from a static phantom were first obtained. In thirty healthy human subjects, the methods were then assessed in background tissue to determine if local phase offsets could be removed. Finally, the techniques were used to correct VFR measurements in cerebral vessels and compared statistically.

Results

In the phantom, phase error was measured to be <2.1 ml/s per pixel and the bias was reduced with the correction schemes. In background tissue, the bias was significantly reduced, by 65.6% (LBC), 58.4% (LPC) and 47.7% (WBPC) (p < 0.001 across all schemes). Correction did not lead to significantly different VFR measurements in the vessels (p = 0.997). In the vessel measurements, the three correction schemes led to flow measurement differences of -0.04 ± 0.05 ml/s, 0.09 ± 0.16 ml/s, and -0.02 ± 0.06 ml/s. Although there was an improvement in background measurements with correction, there was no statistical difference between the three correction schemes (p = 0.242 in background and p = 0.738 in vessels).

Conclusions

While eddy current induced phase errors can vary between hardware and sequence configurations, our results showed that the impact is small in a typical brain PC-MR protocol and does not have a significant effect on VFR measurements in cerebral vessels.     

Differences in motor unit behavior during isometric contractions in patients with diabetic peripheral neuropathy at various disease severities

The aim of this study was to determine whether HD-sEMG is sensitive to detecting changes in motor unit behavior amongst healthy adults and type 2 diabetes mellitus (T2DM) patients presenting diabetic peripheral neuropathy (DPN) at different levels. Healthy control subjects (CON, n = 8) and T2DM patients presenting no DPN symptoms (ABS, n = 8), moderate DPN (MOD, n = 18), and severe DPN (SEV, n = 12) performed isometric ankle dorsiflexion at 30 % maximum voluntary contraction while high-density surface EMG (HD-sEMG) was recorded from the tibialis anterior muscle. HD-sEMG signals were decomposed, providing estimates of discharge rate, motor unit conduction velocity (MUCV), and motor unit territory area (MUTA). As a result, the ABS group presented reduced MUCV compared to CON. The groups with diabetes presented significantly larger MUTA compared to the CON group (p < 0.01), and the SEV group presented a significantly lower discharge rate compared to CON and ABS (p < 0.01). In addition, the SEV group presented significantly higher CoV_force compared to CON and MOD. These results support the use of HD-SEMG as a method to detect peripheral and central changes related to DPN.     

Three new D:A Friedo-oleanane triterpenes from the stem bark of Anacolosa poilanei and their cytotoxic activities

Three new D:A friedo-oleanane triterpenes, 3α-p-coumaroyl-D:A-friedo-oleanan-27-oic acid (1), 3α-(3,4-dihydroxycinnamoyl)-D:A-friedo-oleanan-27-oic acid (2), and 3α-(3,4-dihydroxycinnamoyl)-D:A-friedo-oleanan-27,15α-lactone (3) along with three known compounds, trichadenic acid A (4), trichadonic acid (5), and amentoflavone (6), were isolated from the stem barks of Anacolosa poilanei Gagnep. Their structures were established by spectral analysis, such as mass spectrometry, 1D-NMR, and 2D-NMR. Compound 1 exhibited cytotoxicity against LU-1, HepG2, MCF7, and KB cell lines. Compounds 2 and 3 were more active against KB and HepG2 compared to the LU-1 and MCF7 cells.     

Cardiac Mechanics and Ventricular Twist by Three-Dimensional Strain Analysis in Relation to B-Type Natriuretic Peptide as a Clinical Prognosticator for Heart Failure Patients

BackgroundThree dimensional (3D) echocardiography-derived measurements of myocardial deformation and twist have recently advanced as novel clinical tools. However, with the exception of left ventricular ejection fraction and mass quantifications in hypertension and heart failure populations, the prognostic value of such imaging techniques remains largely unexplored.MethodsWe studied 200 subjects (mean age: 60.2±16 years, 54% female, female n = 107) with known hypertension (n = 51), diastolic heart failure (n = 61), or systolic heart failure (n = 30), recruited from heart failure outpatient clinics. Fifty-eight healthy volunteers were used as a control group. All participants underwent 3D-based myocardial deformation and twist analysis (Artida, Toshiba Medical Systems, Tokyo, Japan). We further investigated associations between these measures and brain natriuretic peptide levels and clinical outcomes.ResultsThe global 3D strain measurements of the healthy, hypertension, diastolic heart failure, and systolic heart failure groups were 28.03%, 24.43%, 19.70%, and 11.95%, respectively (all p<0.001). Global twist measurements were estimated to be 9.49°, 9.77°, 8.32°, and 4.56°, respectively. We observed significant differences regarding 3D-derived longitudinal, radial, and global 3D strains between the different disease categories (p<0.05), even when age, gender, BMI and heart rate were matched. In addition, 3D-derived longitudinal, circumferential, and 3D strains were all highly correlated with brain natriuretic peptide levels (p<0.001). At a mean 567.7 days follow-up (25^th–75^th IQR: 197–909 days), poorer 3D-derived longitudinal, radial, and global 3D strain measurements remained independently associated with a higher risk of cardiovascular related death or hospitalization due to heart failure, after adjusting for age, gender, and left ventricular ejection fraction (all p<0.05).Conclusions3D-based strain analysis may be a feasible and useful diagnostic tool for discriminating the extent of myocardial dysfunction. Furthermore, it is able to provide a prognostic value beyond traditional echocardiographic parameters in terms of ejection fraction.     

Cricetid rodents: Is molar root morphology an indicator of diet?

The relationship between tooth roots and diet is largely unexplored, although a logical relationship between harder diets and increased root surface area is suggested. Existing studies of primates, carnivorans and phyllostomid bats have indicated a relationship between diet hardness, bite force and tooth root surface area. The goal of this study was to determine whether root surface area can act as a potential surrogate for bite force and diet in cricetid rodents. Using microcomputed tomography (microCT), tooth root morphology from six species of rodents, two grass eaters (Calomys callosus and Reithrodon auritus), two seed eaters (Phyllotis darwini and Ochrotomys nuttalli) and two insect eaters (Akodon azarae and Oxymycterus hispidus) were compared. Similar to other studies, these rodents did exhibit differences in tooth root surface area based on diet classification, but food hardness did not seem to be a factor. Grass-eating species showed significantly larger roots relative to the other diet groups (p = 0.001). Bite force was estimated using skull measurements. Seed eaters were found to have a larger bite force, followed by grass and insect eaters, though the trend did not reach statistical significance (p = 0.058). No strong relationship was found between estimated bite force and tooth root surface area. In this study, the mechanics of grass eating seem to have a stronger effect on tooth root surface area than bite force. microCT allows the nondestructive quantification of previously difficult-to-access tooth morphology; this method shows the potential for tooth roots to provide valuable dietary, behavioral and ecological information in rodents.     

Association of Urinary Cadmium with Mortality in Patients at a Coronary Care Unit

Background

Determine the effect of the day 1 urinary excretion of cadmium (D1-UE-Cd) on mortality of patients admitted to a coronary care unit (CCU).

Methods

A total of 323 patients were enrolled in this 6-month study. Urine and blood samples were taken within 24 h after CCU admission. Demographic data, clinical diagnoses, and hospital mortality were recorded. The scores of established systems for prediction of mortality in critically ill patients were calculated.

Results

Compared with survivors (n = 289), non-survivors (n = 34) had higher levels of D1-UE-Cd. Stepwise multiple linear regression analysis indicated that D1-UE-Cd was positively associated with pulse rate and level of aspartate aminotransferase, but negatively associated with serum albumin level. Multivariate Cox analysis, with adjustment for other significant variables and measurements from mortality scoring systems, indicated that respiratory rate and D1-UE-Cd were independent and significant predictors of mortality. For each 1 μg/day increase of D1-UE-Cd, the hazard ratio for CCU mortality was 3.160 (95% confidence interval: 1.944–5.136, p < 0.001). The chi-square value of Hosmer-Lemeshow goodness-of-fit test for D1-UE-Cd was 10.869 (p = 0.213). The area under the receiver operating characteristic curve for D1-UE-Cd was 0.87 (95% confidence interval: 0.81–0.93).

Conclusions

The D1-UE-Cd, an objective variable with no inter-observer variability, accurately predicted hospital mortality of CCU patients and outperformed other established scoring systems. Further studies are needed to determine the physiological mechanism of the effect of cadmium on mortality in CCU patients.