Evaluation of hyperspectral imaging measurements of changes in hemoglobin oxygenation and oxidation of cytochrome-c-oxidase using a liquid blood phantom

Optical imaging is a non-invasive technique that is able to monitor hemodynamic and metabolic responses during neurosurgery. However, a robust quantification is complicated to perform. To overcome this issue, phantoms that mimic biological tissues are required for the development of imaging systems in order to reach a true standardization. In this work, we explore the possibility to use a combined liquid blood phantom with cytochrome contained yeast to evaluate the reliability of hyperspectral imaging to measure oxygenation and metabolic changes. This phantom can be used to verify the reliability of intraoperative optical setups before moving on to clinical application

A digital instrument simulator to optimize the development of a hyperspectral imaging system for neurosurgery

In recent years, hyperspectral imaging (HSI) has demonstrated its capacity to non-invasively differentiate tumors from healthy tissues and identify cancerous regions during neurosurgery. Indeed, the spectral information contained in the HS images allows to identify more chromophores, refining the information provided by the imaging system, and allowing to identify the unique signature of each tissue types more accurately. Our HyperProbe project aims at developing a novel HSI system optimized for neurosurgery. As part of this project, we are developing a digital instrument simulator (DIS), based on Monte-Carlo (MC) simulations of the light propagation in tissues, in order to optimize both the hardware and data processing pipeline of our novel instrument. This framework allows us (1) to test the effect on the accuracy of the measurement of several hardware parameters, like the numerical aperture or sensitivity of the detector; (2) to be used as numerical phantoms to test various data processing algorithms; and (3) to generate generic data to develop and train machine learning (ML) algorithms. To do so, our framework is based on a 2-step method. Firstly, MC simulations are run to produce an ideal dataset of the photon transport in tissue. Then, the raw output parameters of the simulations, such as the exit positions and directions of the photons, are processed to take into account the physical parameters of an instrument in order to produce realistic images and test various scenarios. We present here the initial development of this DIS

Living donor hand-assisted laparoscopic nephrectomy in a healthy individual with situs inversus totalis:no need to turn down the donor

A 70-year-old healthy male individual offered to undergo a living donor hand-assisted laparoscopic nephrectomy to enable kidney transplantation for a close relative. As required for all living transplant donor candidates, extensive screening was performed to exclude potential contraindications for donation. Tests revealed a situs inversus totalis, meaning a complete transposition of the thoracic and abdominal organs in the sagittal plane. As other contraindications for living kidney donation were absent, the feasibility of this procedure was determined multidisciplinary. A successful donation procedure was performed without surgical complications for the donor and good short-term transplant outcomes. In line with current developments that have resulted in more liberal criteria for potential living kidney donors, major anatomical deviations should not automatically be a contraindication. With multidisciplinary efforts and thorough surgical preparation at a high-volume transplant centre, this procedure is feasible and safe

Structural instability and fibrillar aggregation of non-expanded human ataxin-3 revealed under high pressure and temperature.

Protein misfolding and formation of structured aggregates are considered to be the earliest events in the development of neurodegenerative diseases, but the mechanism of these biological phenomena remains to be elucidated. Here, we report a study of heat- and pressure-induced unfolding of human Q26 and murine Q6 ataxin-3 using spectroscopic methods. UV absorbance and fluorescence revealed that heat and pressure induced a structural transition of both proteins to a molten globule conformation. The unfolding pathway was partly irreversible and led to a protein conformation where tryptophans were more exposed to water. Furthermore, the use of fluorescent probes (8-anilino-1-naphthalenesulfonate and thioflavin T) allowed the identification of different intermediates during the process of pressure-induced unfolding. At high temperature and pressure, human Q26, but not murine Q6, underwent concentration-dependent aggregation. Fourier transform infrared and circular dichroism spectroscopy revealed that these aggregates are characterized by an increased beta-sheet content. As revealed by electron microscopy, heat- and pressure-induced aggregates were different; high temperature treatment led to fibrillar microaggregates (8-10-nm length), whereas high pressure induced oligomeric structures of globular shape (100 nm in diameter), which sometimes aligned to higher order suprastructures. Several intermediate structures were detected in this process. Two factors appear to govern ataxin unfolding and aggregation, the length of the polyglutamine tract and its protein context

Classification of brain injury severity using a hybrid broadband NIRS and DCS instrument with a machine learning approach

Optical biomarkers of neonatal hypoxic ischemic (HI) brain injury can offer the advantage of continuous, cot-side assessment of the degree of injury; research thus far has focused on examining different optical measured brain physiological signals and feature combinations to achieve this. To maximize the breadth of physiological characteristics being taken into consideration, a multimodal optical platform has been developed, allowing unique physiological insights into brain injury. In this paper we present an assessment of severity of injury using a state-of-the-art hybrid broadband Near Infrared Spectrometer (bNIRS) and Diffusion Correlation Spectrometer (DCS) instrument called FLORENCE with a machine learning pipeline. We demonstrate in the preclinical neonatal model (the newborn piglet) that our approach can identify different HI insult severity (controls, mild, severe). We show that a machine learning pipeline based on k-means clustering can be used to differentiate between the controls and the HI piglets with an accuracy of 78%, the mild severity insult piglets from the severe insult piglets with an accuracy of 90% and can also differentiate the 3 piglet groups with an accuracy of 80%. So, this analytics pipeline demonstrates how optical data from multiple instruments can be processed towards markers of brain health

Cymbopleura pyrenaica sp. nov. (Bacillariophyceae) et d'autres espèces du même genre rarement recensées dans quelques lacs des Pyrénées françaises

Lors d’une étude des diatomées des lacs français des Pyrénées, une nouvelle espèce de Cymbopleura, C. pyrenaica sp. nov., a été mise en évidence. La morphologie détaillée de ce taxon a été examinée à l’aide du microscope photonique (MP) et du microscope électronique à balayage (MEB). La nouvelle espèce est très proche de Cymbella laevis Naegeli mais l’absence de champs apicaux de pores la place dans le genre Cymbopleura. Le caractère ultra-structural le plus frappant est la variabilité morphologique des aréoles. Cymbopleura pyrenaica sp. nov. est présente dans plusieurs lacs mais avec une très faible occurrence; elle tolère un large éventail de la conductivité. Une courte revue des autres espèces de Cymbopleura recensées dans les lacs prospectés est présentée

Learnable real-time inference of molecular composition from diffuse spectroscopy of brain tissue

SIGNIFICANCE: Diffuse optical modalities such as broadband near-infrared spectroscopy (bNIRS) and hyperspectral imaging (HSI) represent a promising alternative for low-cost, non-invasive, and fast monitoring of living tissue. Particularly, the possibility of extracting the molecular composition of the tissue from the optical spectra deems the spectroscopy techniques as a unique diagnostic tool. AIMS: No established method exists to streamline the inference of the biochemical composition from the optical spectrum for real-time applications such as surgical monitoring. We analyze a machine learning technique for inference of changes in the molecular composition of brain tissue. APPROACH: We propose modifications to the existing learnable methodology based on the Beer–Lambert law. We evaluate the method’s applicability to linear and nonlinear formulations of this physical law. The approach is tested on data obtained from the bNIRS- and HSI-based monitoring of brain tissue. RESULTS: The results demonstrate that the proposed method enables real-time molecular composition inference while maintaining the accuracy of traditional methods. Preliminary findings show that Beer–Lambert law-based spectral unmixing allows contrasting brain anatomy semantics such as the vessel tree and tumor area. CONCLUSION: We present a data-driven technique for inferring molecular composition change from diffuse spectroscopy of brain tissue, potentially enabling intra-operative monitoring

TREND towards more energy-efficient optical networks

International audienceWith one third of the world population online in 2013 and an international Internet bandwidth multiplied by more than eight since 2006, the ICT sector is a non-negligible contributor of worldwide greenhouse gases emissions and power consumption. Indeed, power consumption of telecommunication networks has become a major concern for all the actors of the domain, and efforts are made to reduce their impact on the overall figure of ICTs, and to support its foreseen growth in a sustainable way. In this context, the contributors of the European Network of Excellence TREND have developed innovative solutions to improve the energy efficiency of networks. This paper gives an overview of the solutions related to optical networks