Predicted concentrations of antineoplastic drugs in the aquatic environment: The case of Ría de Vigo (NW, Spain).

The European Medicines Agency (EMA) mandates Environmental Risk Assessments (ERAs) since 2006 to determine potential risks of new marketed medicines. Drugs with a Predicted Environmental Concentration (PEC) in inland surface waters exceeding 0.01 µg L-1 require further environmental risk assessment. PEC may be refined based on prevalence data and/or based on the treatment regimen. In this study, based on EMA regulations, refined PEC of 108 antineoplastic drugs in coastal waters were determined based on the consumption in a coastal health area during 2021, identifying six drugs with potential environmental risk in surface waters (hydroxyurea, capecitabine, abiraterone, ibrutinib, imatinib and 5-fluorouracil) and two in marine ecosystem (hydroxyurea and capecitabine). Comparison of these refined PECs with data from marketing laboratories revealed significant disparities, suggesting the need for regular updates, especially with changes in drug indications or financing. Notably, the identified drugs are not yet on the main reference lists of emerging contaminants.

The metabolic phenotype of the patient influences the reduction in carotid intima-media thickness achieved following metabolic surgery.

OBJECTIVES: To determine whether metabolic phenotype is associated with the change in carotid intima-media thickness (CIMT) in patients undergoing bariatric /metabolic surgery (BMS). METHODS: We performed a case-control study of BMS candidates who had metabolically unhealthy obesity (MUO) or metabolically healthy obesity (MHO). We measured the change in CIMT during the 9 months following BMS. The plasma tumor necrosis factor-α, interleukin-1ß, adiponectin, leptin, nitric oxide (NO), vascular endothelial growth factor A (VEGF-A), and malondialdehyde concentrations were determined, adipocyte area was measured histologically, and adipose tissue area was estimated using computed tomography. RESULTS: Fifty-six patients (mean age 44.5 years, mean body mass index 44.9 kg/m2, 53% women, and 53% had MUO) were studied. Nine months following BMS, the MUO phenotype was not associated with a significant reduction in CIMT, and that of the MHO group was larger. In addition, fewer participants achieved a 10% reduction in CIMT in the MUO group. A CIMT reduction was associated with lower VEGF-A and NO in the MUO group, while that in the MHO group was associated with a higher NO concentration. CONCLUSION: The metabolic phenotype of patients may influence their change in CIMT following BMS, probably through circulating vasodilatory and pro-inflammatory molecules.

Comparison of CPU and GPU bayesian estimates of fibre orientations from diffusion MRI.

BACKGROUND: The correct estimation of fibre orientations is a crucial step for reconstructing human brain tracts. Bayesian Estimation of Diffusion Parameters Obtained using Sampling Techniques (bedpostx) is able to estimate several fibre orientations and their diffusion parameters per voxel using Markov Chain Monte Carlo (MCMC) in a whole brain diffusion MRI data, and it is capable of running on GPUs, achieving speed-up of over 100 times compared to CPUs. However, few studies have looked at whether the results from the CPU and GPU algorithms differ. In this study, we compared CPU and GPU bedpostx outputs by running multiple trials of both algorithms on the same whole brain diffusion data and compared each distribution of output using Kolmogorov-Smirnov tests. RESULTS: We show that distributions of fibre fraction parameters and principal diffusion direction angles from bedpostx and bedpostx_gpu display few statistically significant differences in shape and are localized sparsely throughout the whole brain. Average output differences are small in magnitude compared to underlying uncertainty. CONCLUSIONS: Despite small amount of differences in output between CPU and GPU bedpostx algorithms, results are comparable given the difference in operation order and library usage between CPU and GPU bedpostx.

Identification of adipose tissue-related predictors of the reduction in cardiovascular risk induced by metabolic surgery.

OBJECTIVES: We aimed to determine whether parameters associated with adipose tissue (adipocyte density and the circulating concentrations of markers of adipose tissue pathology) predict cardiovascular risk (CVR) modification after metabolic surgery (MS). METHODS: We performed a case-control study of patients with morbid obesity who were candidates for MS. CVR was defined using flow-mediated dilation (FMD) and carotid intima media thickness (CIMT), which were measured during the 9 months following MS. Subgroups of CVR reduction were defined using the following cut-offs: CIMT 10% and/or a two-fold increase in FMD. RESULTS: We studied 40 patients with morbid obesity (mean age 44.5 years, 75% women, mean body mass index 46.4 kg/m2) and high prevalences of the metabolically unhealthy obesity phenotype, hypertension, and diabetes mellitus. A significant reduction in CVR was associated with lower vascular endothelial growth factor-A concentration (6.20 vs. 1.59 pg/mL, respectively), low adipocyte density in visceral adipose tissue (100 vs. 80 cells/field), low infiltration with CD68+ cells (18 vs. 8 cells/field) and higher concentrations of lipid peroxidation markers and malondialdehyde (313.7 vs. 405.7 ng/mL). CONCLUSION: The characteristics of adipose tissue and the circulating concentrations of markers of adipose pathology might represent useful predictors of the reduction in CVR following MS.Clinical trial registration number: NCT0356198 (https://clinicaltrials.gov).

Enlarged adipocytes from subcutaneous vs. visceral adipose tissue differentially contribute to metabolic dysfunction and atherogenic risk of patients with obesity.

Morphological characteristics and source of adipose tissue as well as adipokines may increase cardiometabolic risk. This study aimed to explore whether adipose tissue characteristics may impact metabolic and atherogenic risks. Subcutaneous Adipose Tissue (SAT), Visceral Adipose Tissue (VAT) and peripheral blood were obtained from obese patients submitted to bariatric surgery. Adipose tissue (morphometry), plasma adiponectin, TNF-α, resistin (multiplexing) and biochemical chemistry were analyzed; as well as endothelial dysfunction (Flow Mediated Dilation, FMD) and atherogenesis (Carotid Intima Media Thickness, CIMT). Subgroups divided by adipocyte size and source were compared; as well as correlation and multivariate analysis. Sixty patients 36.6% males, aged 44 years-old, BMI 46.7 kg/m2 were included. SAT's adipocytes showed a lower range of size expandability than VAT's adipocytes. Independent from their source, larger adipocytes were associated with higher glucose, lower adiponectin and higher CIMT. Particularly, larger adipocytes from SAT were associated with higher blood pressure, lower insulin and HDL-cholesterol; and showed positive correlation with glucose, HbA1c, systolic/diastolic values, and negatively correlated with insulin and adiponectin. VAT's larger adipocytes particularly associated with lower resistin and lower FMD values. Gender and Diabetes Mellitus significantly impacted the relation of adipocyte size/source with the metabolic and atherogenic risk. Multivariable analysis suggested hypertension-resistin-HbA1c interactions associated with SAT's larger adipocytes; whereas potential insulin-adiponectin associations were observed for VAT's larger adipocytes. Adipocyte morphology and source are differentially related with cardiometabolic and atherogenic risk in population with obesity, which are potentially affected by gender and Diabetes Mellitus.

Use of multi-flip angle measurements to account for transmit inhomogeneity and non-Gaussian diffusion in DW-SSFP.

Diffusion-weighted steady-state free precession (DW-SSFP) is an SNR-efficient diffusion imaging method. The improved SNR and resolution available at ultra-high field has motivated its use at 7T. However, these data tend to have severe B1 inhomogeneity, leading not only to spatially varying SNR, but also to spatially varying diffusivity estimates, confounding comparisons both between and within datasets. This study proposes the acquisition of DW-SSFP data at two-flip angles in combination with explicit modelling of non-Gaussian diffusion to address B1 inhomogeneity at 7T. Data were acquired from five fixed whole human post-mortem brains with a pair of flip angles that jointly optimize the diffusion contrast-to-noise (CNR) across the brain. We compared one- and two-flip angle DW-SSFP data using a tensor model that incorporates the full DW-SSFP Buxton signal, in addition to tractography performed over the cingulum bundle and pre-frontal cortex using a ball & sticks model. The two-flip angle DW-SSFP data produced angular uncertainty and tractography estimates close to the CNR optimal regions in the single-flip angle datasets. The two-flip angle tensor estimates were subsequently fitted using a modified DW-SSFP signal model that incorporates a gamma distribution of diffusivities. This allowed us to generate tensor maps at a single effective b-value yielding more consistent SNR across tissue, in addition to eliminating the B1 dependence on diffusion coefficients and orientation maps. Our proposed approach will allow the use of DW-SSFP at 7T to derive diffusivity estimates that have greater interpretability, both within a single dataset and between experiments.

Improved fibre dispersion estimation using b-tensor encoding.

Measuring fibre dispersion in white matter with diffusion magnetic resonance imaging (MRI) is limited by an inherent degeneracy between fibre dispersion and microscopic diffusion anisotropy (i.e., the diffusion anisotropy expected for a single fibre orientation). This means that estimates of fibre dispersion rely on strong assumptions, such as constant microscopic anisotropy throughout the white matter or specific biophysical models. Here we present a simple approach for resolving this degeneracy using measurements that combine linear (conventional) and spherical tensor diffusion encoding. To test the accuracy of the fibre dispersion when our microstructural model is only an approximation of the true tissue structure, we simulate multi-compartment data and fit this with a single-compartment model. For such overly simplistic tissue assumptions, we show that the bias in fibre dispersion is greatly reduced (~5x) for single-shell linear and spherical tensor encoding data compared with single-shell or multi-shell conventional data. In in-vivo data we find a consistent estimate of fibre dispersion as we reduce the b-value from 3 to 1.5 ms/µm2, increase the repetition time, increase the echo time, or increase the diffusion time. We conclude that the addition of spherical tensor encoded data to conventional linear tensor encoding data greatly reduces the sensitivity of the estimated fibre dispersion to the model assumptions of the tissue microstructure.

Neutrophil-to-lymphocyte ratio and its relation with pro-inflammatory mediators, visceral adiposity and carotid intima-media thickness in population with obesity.

BACKGROUND: Atherosclerosis represents a cardiovascular risk. Chronic inflammation is a key factor for atherogenic progression. Neutrophil-to-lymphocyte ratio (NLR) has been proposed as a novel biomarker for cardiovascular risks. We aimed to explore whether NLR was related to surrogate pro-atherogenic promoters driving atherogenic progression, as measured by carotid intima-media thickness (CIMT). STUDY DESIGN: Thirty-one patients with obesity candidates for bariatric surgery were recruited from Centro Médico Nacional "20 de Noviembre", ISSSTE, Mexico City. The results are part of the "CROP" study (NCT03561987). NLR was calculated from routine complete blood count, and its relation with plasma pro-inflammatory mediators (hsCRP, TNF-α and IL-1ß), adipokines (adiponectin and leptin), adiposity markers (visceral adipose tissue [VAT] determined from CT scan image and VAT individual adipocyte area at histological sample) and CIMT were determined. RESULTS: Neutrophil-to-lymphocyte ratio correlated with hsCRP (Spearman's r = 0.70 [95% CI 0.46 to 0.85], P < 0.01), TNF-α (r = 0.69 [0.44 to 0.84], P < 0.0001) and adiponectin (r = -0.69 [-0.84 to -0.45], P < 0.03), as well as with VAT individual adipocyte area (r = 0.64 [0.37 to 0.81], P < 0.0001) and with VAT area (r = 0.43; [0.07 to 0.68], P < 0.01). Leptin and adiponectin showed further independent association with higher NLR (multivariate regression analysis OR 7.9 [95% CI 1.1 to 56.2] P = 0.03 and 0.1 [0.01 to 1.0] P = 0.05, respectively). Moreover, NLR distribution significantly varied between subgroups divided according to progressive CIMT (P = 0.05); whereas adiponectin and VAT adipocyte area associated with CIMT > 0.9 mm (univariate analysis OR 0.1 [0.01 to 1.0] P = 0.05 and 13.1 [1.4 to 126.3] P = 0.03, respectively). CONCLUSION: Neutrophil-to-lymphocyte ratio was related to pro-inflammatory, adiposity biomarkers and progressive subclinical atherogenesis.

Using GPUs to accelerate computational diffusion MRI: From microstructure estimation to tractography and connectomes.

The great potential of computational diffusion MRI (dMRI) relies on indirect inference of tissue microstructure and brain connections, since modelling and tractography frameworks map diffusion measurements to neuroanatomical features. This mapping however can be computationally highly expensive, particularly given the trend of increasing dataset sizes and the complexity in biophysical modelling. Limitations on computing resources can restrict data exploration and methodology development. A step forward is to take advantage of the computational power offered by recent parallel computing architectures, especially Graphics Processing Units (GPUs). GPUs are massive parallel processors that offer trillions of floating point operations per second, and have made possible the solution of computationally-intensive scientific problems that were intractable before. However, they are not inherently suited for all problems. Here, we present two different frameworks for accelerating dMRI computations using GPUs that cover the most typical dMRI applications: a framework for performing biophysical modelling and microstructure estimation, and a second framework for performing tractography and long-range connectivity estimation. The former provides a front-end and automatically generates a GPU executable file from a user-specified biophysical model, allowing accelerated non-linear model fitting in both deterministic and stochastic ways (Bayesian inference). The latter performs probabilistic tractography, can generate whole-brain connectomes and supports new functionality for imposing anatomical constraints, such as inherent consideration of surface meshes (GIFTI files) along with volumetric images. We validate the frameworks against well-established CPU-based implementations and we show that despite the very different challenges for parallelising these problems, a single GPU achieves better performance than 200 CPU cores thanks to our parallel designs.