Long-range multicore optical fiber displacement sensor.

In this Letter, a long-range optical fiber displacement sensor based on an extrinsic Fabry-Perot interferometer (EFPI) built with a strongly coupled multicore fiber (SCMCF) is proposed and demonstrated. To fabricate the device, 9.2 mm of SCMCF was spliced to a conventional single-mode fiber (SMF). The sensor reflection spectrum is affected by super-mode interference in the SCMCF and the interference produced by the EFPI. Displacement of the SMF-SCMCF tip with respect to a reflecting surface produces quantifiable changes in the amplitude and period of the interference pattern in the reflection spectrum. Since the multicore fiber is an efficient light collecting area, sufficient signal intensity can be obtained for displacements of several centimeters. By analyzing the interference pattern in the Fourier domain, it was possible to measure displacements up to 50 mm with a resolution of approximately 500 nm. To our knowledge, this is the first time that a multicore fiber has been used to build a displacement sensor. The dynamic measurement range is at least seven times larger than that achieved with an EFPI built with a conventional SMF. Moreover, the SMF-SCMCF tip is robust and easy to fabricate and replicate.

Cannabis use influence on peripheral brain-derived neurotrophic factor levels in antipsychotic-naïve first-episode psychosis.

The objective of this study is to determine whether cannabis influences BDNF levels in patients with psychosis (FEP) and healthy volunteers (HV) to help understand the role of BDNF in psychosis. We assessed the association between BDNF and cannabis in a cohort of FEP antipsychotic-naïve patients and HV, whilst controlling for other potential confounding factors. 70 FEP drug-naive patients and 57 HV were recruited. A sociodemographic variable collection, structured clinical interview, weight and height measurement, substance use determination, and blood collection to determine BDNF levels by ELISA analysis were done. In FEP patients, cannabis use was associated with BDNF levels (high cannabis use was associated with lower BDNF levels). Moreover, cannabis use was statistically significantly associated with age (high use of cannabis was associated with younger age). In HV, no relationship between cannabis use and BDNF levels was observed. Otherwise, cannabis use was significantly associated with tobacco use, so that high cannabis users were also high tobacco users. This study showed a different association between cannabis use and BDNF levels in FEP patients compared with HV, particularly, with high doses of cannabis. These findings may help understand the deleterious effects of cannabis in some vulnerable individuals, as well as discrepancies in the literature.

Methodology based on genetic heuristics for in-vivo characterizing the patient-specific biomechanical behavior of the breast tissues.

This paper presents a novel methodology to in-vivo estimate the elastic constants of a constitutive model proposed to characterize the mechanical behavior of the breast tissues. An iterative search algorithm based on genetic heuristics was constructed to in-vivo estimate these parameters using only medical images, thus avoiding invasive measurements of the mechanical response of the breast tissues. For the first time, a combination of overlap and distance coefficients were used for the evaluation of the similarity between a deformed MRI of the breast and a simulation of that deformation. The methodology was validated using breast software phantoms for virtual clinical trials, compressed to mimic MRI-guided biopsies. The biomechanical model chosen to characterize the breast tissues was an anisotropic neo-Hookean hyperelastic model. Results from this analysis showed that the algorithm is able to find the elastic constants of the constitutive equations of the proposed model with a mean relative error of about 10%. Furthermore, the overlap between the reference deformation and the simulated deformation was of around 95% showing the good performance of the proposed methodology. This methodology can be easily extended to characterize the real biomechanical behavior of the breast tissues, which means a great novelty in the field of the simulation of the breast behavior for applications such as surgical planing, surgical guidance or cancer diagnosis. This reveals the impact and relevance of the presented work.

Automatic supervision of gestures to guide novice surgeons during training.

BACKGROUND: Virtual surgery simulators enable surgeons to learn by themselves, shortening their learning curves. Virtual simulators offer an objective evaluation of the surgeon's skills at the end of each training session. The considered evaluation parameters are based on the analysis of the surgeon's gestures performed throughout the training session. Currently, this information is usually known by surgeons only at the end of the training session, but very limited during the training performance. In this paper, we present a novel method for automatic and interactive evaluation of the surgeon's skills that is able to supervise inexperienced surgeons during their training session with surgical simulators. METHODS: The method is based on the assumption that the sequence of gestures carried out by an expert surgeon in the simulator can be translated into a sequence (a character string) that should be reproduced by a novice surgeon during a training session. In this work, a string-matching algorithm has been modified to calculate the alignment and distance between the sequences of both expert and novice during the training performance. RESULTS: The results have shown that it is possible to distinguish between different skill levels at all times during the surgical training session. CONCLUSIONS: The main contribution of this paper is a method where the difference between an expert's sequence of gestures and a novice's ongoing sequence is used to guide inexperienced surgeons. This is possible by indicating to novices the gesture corrections to be applied during surgical training as continuous expert supervision would do.

Segmentation of the breast skin and its influence in the simulation of the breast compression during an X-ray mammography.

A novel method of skin segmentation is presented aimed to obtain as many pixels belonging to the real skin as possible. This method is validated by experts in radiology. In addition, a biomechanical model of the breast, which considers the skin segmented in this way, is constructed to study the influence of considering real skin in the simulation of the breast compression during an X-ray mammography. The reaction forces of the plates are obtained and compared with the reaction forces obtained using classical methods that model the skin as a 2D membranes that cover all the breast. The results of this work show that, in most of the cases, the method of skin segmentation is accurate and that real skin should be considered in the simulation of the breast compression during the X-ray mammographies.

A study about coefficients to estimate the error in biomechanical models used to virtually simulate the organ behaviors.

In this paper, a set of coefficients commonly used in Medical Image to estimate the committed error comparing two images is presented, which, combined together, allow to determine the similarity between volumes. Furthermore, an analysis of the behavior of these coefficients is performed to determine those coefficients that better discriminate the fit error, proving that these are Jaccard coefficient and a modification of Hausdorff coefficient. In addition, the combination of both coefficients is applied to compare two given biomechanical models of the lamb liver.

MRI skin segmentation for the virtual deformation of the breast under mammographic compression.

Breast Magnetic Resonance Image skin has similar intensity levels than dense tissue, and may produce segmentation errors if not managed correctly. In this work a novel skin segmentation method is presented and validated by experts, aimed to obtain as many pixels belonging to the real skin as possible. Segmented skin will be used to build a breast biomechanical model to register X-Ray Images with Magnetic Resonance Images in the future, using a virtually deformed Magnetic Resonance Image.

Pneumoperitoneum technique simulation in laparoscopic surgery on lamb liver samples and 3D reconstruction.

In this paper, a procedure to experimentally simulate the behavior of the liver when the pneumoperitoneum technique is applied in laparoscopic surgery is presented, as well as methodology to make the comparison of each sample before and after insufflating the gas. This comparison is carried out using the 3D reconstruction of the volume from the CT images when either pneumoperitoneum is applied and when it is not. This methodology has showed that there are perceptible changes of volume when the pneumoperitoneum is applied.

Prediction of osteoporotic hip fracture in postmenopausal women through patient-specific FE analyses and machine learning.

A great challenge in osteoporosis clinical assessment is identifying patients at higher risk of hip fracture. Bone Mineral Density (BMD) measured by Dual-Energy X-Ray Absorptiometry (DXA) is the current gold-standard, but its classification accuracy is limited to 65%. DXA-based Finite Element (FE) models have been developed to predict the mechanical failure of the bone. Yet, their contribution has been modest. In this study, supervised machine learning (ML) is applied in conjunction with clinical and computationally driven mechanical attributes. Through this multi-technique approach, we aimed to obtain a predictive model that outperforms BMD and other clinical data alone, as well as to identify the best-learned ML classifier within a group of suitable algorithms. A total number of 137 postmenopausal women (81.4 ± 6.95 years) were included in the study and separated into a fracture group (n = 89) and a control group (n = 48). A semi-automatic and patient-specific DXA-based FE model was used to generate mechanical attributes, describing the geometry, the impact force, bone structure and mechanical response of the bone after a sideways-fall. After preprocessing the whole dataset, 19 attributes were selected as predictors. Support Vector Machine (SVM) with radial basis function (RBF), Logistic Regression, Shallow Neural Networks and Random Forest were tested through a comprehensive validation procedure to compare their predictive performance. Clinical attributes were used alone in another experimental setup for the sake of comparison. SVM was confirmed to generate the best-learned algorithm for both experimental setups, including 19 attributes and only clinical attributes. The first, generated the best-learned model and outperformed BMD by 14pp. The results suggests that this approach could be easily integrated for effective prediction of hip fracture without interrupting the actual clinical workflow.

Deformable brain atlas validation of the location of subthalamic nucleus using T1-weighted MR images of patients operated on for Parkinson's.

Parkinson's disease is a degenerative disease of the central nervous system. One of the most effective treatments is deep brain stimulation. This technique requires the localization of an objective structure: the subthalamic nucleus. Unfortunately this structure is difficult to locate. In this work the creation of a deformable brain atlas that enables the identification of the subthalamic nucleus in T1-weighted magnetic resonance imaging (MRI) in an automatic, precise and fast way is presented. The system has been validated using data from 10 patients (20 nucleus) operated on for Parkinson's. Our system offers better results using a Wendland function with an error of 1.8853+/-0.9959mm.

A finite element-based machine learning approach for modeling the mechanical behavior of the breast tissues under compression in real-time.

This work presents a data-driven method to simulate, in real-time, the biomechanical behavior of the breast tissues in some image-guided interventions such as biopsies or radiotherapy dose delivery as well as to speed up multimodal registration algorithms. Ten real breasts were used for this work. Their deformation due to the displacement of two compression plates was simulated off-line using the finite element (FE) method. Three machine learning models were trained with the data from those simulations. Then, they were used to predict in real-time the deformation of the breast tissues during the compression. The models were a decision tree and two tree-based ensemble methods (extremely randomized trees and random forest). Two different experimental setups were designed to validate and study the performance of these models under different conditions. The mean 3D Euclidean distance between nodes predicted by the models and those extracted from the FE simulations was calculated to assess the performance of the models in the validation set. The experiments proved that extremely randomized trees performed better than the other two models. The mean error committed by the three models in the prediction of the nodal displacements was under 2 mm, a threshold usually set for clinical applications. The time needed for breast compression prediction is sufficiently short to allow its use in real-time (<0.2 s).

Real-time deformable models for surgery simulation: a survey.

Simulating the behaviour of elastic objects in real time is one of the current objectives of computer graphics. One of its fields of application lies in virtual reality, mainly in surgery simulation systems. In computer graphics, the models used for the construction of objects with deformable behaviour are known as deformable models. These have two conflicting characteristics: interactivity and motion realism. The different deformable models developed to date have promoted only one of these (usually interactivity) to the detriment of the other (biomechanical realism). In this paper, we present a classification of the different deformable models that have been developed. We present the advantages and disadvantages of each one. Finally, we make a comparison of deformable models and perform an evaluation of the state of the art and the future of deformable models.

Patient-specific simulation of the intrastromal ring segment implantation in corneas with keratoconus.

PURPOSE: The purpose of this study was the simulation of the implantation of intrastromal corneal-ring segments for patients with keratoconus. The aim of the study was the prediction of the corneal curvature recovery after this intervention. METHODS: Seven patients with keratoconus diagnosed and treated by implantation of intrastromal corneal-ring segments were enrolled in the study. The 3D geometry of the cornea of each patient was obtained from its specific topography and a hyperelastic model was assumed to characterize its mechanical behavior. To simulate the intervention, the intrastromal corneal-ring segments were modeled and placed at the same location at which they were placed in the surgery. The finite element method was then used to obtain a simulation of the deformation of the cornea after the ring segment insertion. Finally, the predicted curvature was compared with the real curvature after the intervention. RESULTS: The simulation of the ring segment insertion was validated comparing the curvature change with the data after the surgery. Results showed a flattening of the cornea which was in consonance with the real improvement of the corneal curvature. The mean difference obtained was of 0.74 mm using properties of healthy corneas. CONCLUSIONS: For the first time, a patient-specific model of the cornea has been used to predict the outcomes of the surgery after the intrastromal corneal-ring segments implantation in real patients.

A new methodology for the in vivo estimation of the elastic constants that characterize the patient-specific biomechanical behavior of the human cornea.

This work presents a methodology for the in vivo characterization of the complete biomechanical behavior of the human cornea of each patient. Specifically, the elastic constants of a hyperelastic, second-order Ogden model were estimated for 24 corneas corresponding to 12 patients. The finite element method was applied to simulate the deformation of human corneas due to non-contact tonometry, and an iterative search controlled by a genetic heuristic was used to estimate the elastic parameters that most closely approximates the simulated deformation to the real one. The results from a synthetic experiment showed that these parameters can be estimated with an error of about 5%. The results of 24 in vivo corneas showed an overlap of about 90% between simulation and real deformed cornea and a modified Hausdorff distance of 25 µm, which indicates the great accuracy of the proposed methodology.

Urinary excretion of sucrose and fructose as a predictor of sucrose intake in dietary intervention studies.

The urinary excretion of sucrose, glucose, and fructose was measured in 9 healthy subjects consuming a common Italian diet and after 3 days of a low sucrose diet, in which the intake of sucrose was restricted but the other main nutrients were unmodified. After the low sucrose diet, we observed a significant drop in the average urinary excretion of sucrose, glucose, and fructose determined at four different times (8:00 and 10:00 a.m.; 3:00 and 10:00 p.m.). The average urinary excretion of fructose in the four urine samples was significantly correlated with dietary sucrose intake. We also found a significant correlation between the average urinary excretion of sucrose and dietary sucrose intake. Urinary fructose can be used as a marker of sucrose intake in dietary intervention studies aimed at studying the effect of variation of carbohydrate intake on specific cancers.

Fluorescence-based detection of lacZ reporter gene expression in intact and viable bacteria including Mycobacterium species.

A variety of fluorescein di-beta-D-galactopyranoside (FDG)-based substrates were evaluated for measuring beta-galactosidase expression in bacteria. One substrate, 5-acetylamino-FDG (C2FDG), performed well in all bacteria tested, including the slow growing mycobacterium, Mycobacterium bovis BCG. The sensitivity of C2FDG in intact, viable BCG was similar to that of o-nitrophenyl-beta-D-galactopyranoside in cell lysates when used to measure lacZ reporter gene activity. C2FDG was approximately 70-fold more sensitive than green fluorescent protein (GFP) in BCG when assayed in a fluorescence plate reader, and comparable to GFP when measured by flow cytometry. These assays provide an important new alternative for the rapid measurement of reporter gene expression in viable bacteria.

Effect of a mixture of 15 commonly used pesticides on DNA levels of 8-hydroxy-2-deoxyguanosine and xenobiotic metabolizing enzymes in rat liver.

The level of 8-OH-2-deoxyguanosine in rat liver DNA was measured as an index of oxidative damage after treating rats for 10 days at a dose ranging from 0.75 to 10 mg/kg with a mixture of 15 pesticides (dithiocarbamate, benomyl, thiabendazole, diphenylamine, chlorthalonil, procimidone, methidathion, chlorpyrifos-ethyl, fenarimol, parathion-methyl, chlorpropham, parathion, vinclozolin, chlorfenvinphos, pirimiphos-ethyl) commonly found in foods of central Italy. At the doses of 0.75 and 1 mg/kg DNA levels of 8-OH-2-deoxyguanosine were significantly increased relative to controls, whereas at higher doses (2.5, 5, 10 mg/kg) the levels returned to control values. The administration of the pesticide mixture dose dependently reduced benzo(a)pyrene hydroxylase, N-demethylase activities, glutathione peroxidase, glutathione reductase, glutathione-S-transferase and thiol transferase activities in the liver. The results show that the pesticide mixture induced free radical DNA damage at low doses. However, at higher doses it produced a depression of cellular metabolism, inhibiting a further expression of oxidative damage.

Computer-aided periodontal disease diagnosis using computer vision.

Periodontal diseases are the major cause of tooth loss. The study of the evolution of these diseases is crucial to achieve adequate planning and treatment. Depth probing is essential to know the periodontal disease stage. In this paper we present a new system for Computer-Aided Periodontal Disease Diagnosis using computer vision. The system automates the depth probing and incorporates a colour camera fitted together with a plastic probe that automatically and exactly obtains the depth probing measure. The system has been tested by several periodontists and with 125 teeth of different patients. The differences between the values taken by the system and two periodontists have not been significant.

Effect of the slimming agent oleoyl-estrone in liposomes on the body weight of rats fed a cafeteria diet.

Weaned lean Zucker rats, 21-days old, were fed a cafeteria diet for 70 days. The cafeteria diet-obese rats were infused for 28 days (using miniosmotic pumps) with oleoyl-estrone in liposomes (Merlin-2) at a dose of 3.5 mmol/day.kg. Treatment resulted in loss of body weight: 11.6% (32 g), mainly due to fat: 20.0% (8.8 g), protein 5.2% (2.0 g) and water, preventing further increases in body weight and fat storage. Untreated rats increased their body weight: 7.6% (20 g), lipid: 10.5% (4.2 g) and protein: 13.2% (4.8 g). Plasma glucose, urea, triacylglycerols and cholesterol practically did not change with treatment. Merlin-2 decreased energy intake (to 83.7%) and energy output (to 87.7%, oxygen consumption). Decreases in nitrogen intake were partly compensated by higher digestive efficiency in treated rats. The size of the nitrogen gap was higher in treated rats than in controls. Essentially, protein balance was maintained and slimming was achieved with a minimal loss of body protein. Treated rats selected less carbohydrate, in particular sugars, in their diet than controls, but consumed practically the same protein and lipid. Treatment of cafeteria diet-fed rats with oleoylestrone in liposomes results in sustained loss of body weight--mainly lipid--for up to 28 days. Nitrogen balance is maintained overall. This is achieved through lower food intake--mainly of sugars--and less marked changes in energy output.

A new approach for the real-time simulation of tissue deformations in surgery simulation.

Simulation of the behaviour of elastic objects in real time is one of the present objectives of computer graphics. One of its fields of application lies in virtual reality, mainly in surgery simulation systems. Models used for the construction of objects with deformable behaviour in computer graphics are known as deformable models. These have two conflicting characteristics: interactivity and movement realism. The deformable models developed up till now have promoted one characteristic to the detriment of the other. In this paper, a new approach is proposed based on boundary element methods (BEM). This is characterised by a positive equilibrium between speed and realism and great robustness. These properties along with the experimental results described in this paper permit one to assert that establishing deformable models with BEM is a reliable method to model objects in virtual reality environments for surgery simulation. In addition to that, the required elasticity parameters could be obtained experimentally through the use of a pig's liver.