Hyperspectral imaging of the haemodynamic and metabolic states of the exposed cortex

A hyperspectral imaging (HSI) system to measure and quantify in vivo haemodynamic and metabolic signals from the exposed cerebral cortex of small animals was designed, developed and investigated in this thesis. Imaging brain tissue at multiple narrow wavelength bands in the visible and near-infrared (NIR) range allows one not only to monitor cerebral oxygenation and haemodynamics via mapping of haemoglobin concentration changes, but also to directly target the spatial quantification of cerebral metabolic activity via measurement of the redox states of mitochondrial cytochrome-c-oxidase (CCO). Having both these sets of information in vivo at high resolution on the exposed cortex can provide impactful insight on brain physiology and can help validate corresponding data acquired non-invasively using broadband near-infrared spectroscopy (bNIRS). Several designs and HSI configurations were assessed and compared, including different customised benchtop setups. In the end, a bespoke spectral-scanning HSI system called hNIR, using a supercontinuum laser coupled with a rotating Pellin-Broca prism and a scientific complementary metal-oxide semiconductor (sCMOS) camera, was built, characterised and validated on liquid optical phantoms. In addition, an in-house Monte Carlo (MC) framework for simulating HSI of the haemodynamic and metabolic states of the exposed cortex was also developed using an open-source MC code package (Mesh-based Monte Carlo) and integrated with hNIR, for aiding image reconstruction and enhance quantification, as well as to run computational investigations on the performances of HSI for brain haemodynamic and metabolic monitoring. hNIR was finally applied in vivo on the exposed cerebral cortex of three mice during different levels of hyperoxic and hypoxic stimulation, demonstrating its capability to retrieve high resolution and accurate maps of the relative changes in the concentrations of oxyhaemoglobin (HbO₂), deoxyhaemoglobin (HHb) and the oxidative state of CCO (oxCCO)

Design, Development and Investigations of a Novel X-ray Fluorescence and X-ray Luminescence Computed Tomography System for Theranostic Applications

Il presente lavoro di tesi riguarda il progetto, la costruzione, lo sviluppo e la sperimentazione di un sistema innovativo di imaging tramite tomografia computerizzata per fluorescenza a raggi X e luminescenza a raggi X (XFCT/XLCT). Tale sistema ha lo scopo di indagare su possibili applicazioni di tale tecnologia a scopi teragnostici, ovvero combinando diagnostica e trattamento terapeutico. Un esempio di tali applicazioni è la terapia fotodinamica a raggi X. I principali agenti teragnostici indagati con tale sistema si basano su nanotecnologia medica. L’utilizzo di raggi X per stimolare l’emissione di fluorescenza e luminescenza a raggi X permette un tipo di terapia selettiva e altamente localizzata, con la possibilità di usare tali radiazioni anche per monitorare il trattamento in tempo reale. Il sistema XFCT/XLCT dimostra eccellenti capacità di risoluzione spaziale (200 micron), sensitività (300 microgrammi/mL) e imaging multicolore e multimodale, altamente promettenti per sviluppi commerciali futuri e applicazioni precliniche. The current thesis work regards the design, the development, the construction and the investigations on a novel imaging system implementing X-ray Fluorescence and X-ray Luminescence Computed Tomography (XFCT/XLCT). Such system has the main purpose of investigating on potential applications of such technology for theranostics, i.e. the combination of diagnosis and therapeutic treatment. An example of such applications is X-ray Photodynamic Therapy. The primary theranostics agents tested with such system are based on medical nanotechnology. The use of x-rays for stimulating the emission of x-ray fluorescence and x-ray luminescence would permit a kind of selective and highly localized therapy, with the possibility to employ such radiation also for the monitoring of the treatment in real-time. The XFCT/XLCT system demonstrates excellent capabilities of spatial resolution (200 micron), sensitivity (300 micrograms/mL), and multimodal and multi-coloured imaging, which are highly promising for future commercial developments and preclinical applications

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

Identifying chromophore fingerprints of brain tumor tissue on hyperspectral imaging using principal component analysis

Hyperspectral imaging (HSI) is an optical technique that processes the electromagnetic spectrum at a multitude of monochromatic, adjacent frequency bands. The wide-bandwidth spectral signature of a target object's reflectance allows fingerprinting its physical, biochemical, and physiological properties. HSI has been applied for various applications, such as remote sensing and biological tissue analysis. Recently, HSI was also used to differentiate between healthy and pathological tissue under operative conditions in a surgery room on patients diagnosed with brain tumors. In this article, we perform a statistical analysis of the brain tumor patients' HSI scans from the HELICoiD dataset with the aim of identifying the correlation between reflectance spectra and absorption spectra of tissue chromophores. By using the principal component analysis (PCA), we determine the most relevant spectral features for intra- and inter-tissue class differentiation. Furthermore, we demonstrate that such spectral features are correlated with the spectra of cytochrome, i.e., the chromophore highly involved in (hyper) metabolic processes. Identifying such fingerprints of chromophores in reflectance spectra is a key step for automated molecular profiling and, eventually, expert-free biomarker discovery

271 AUTOMATED REAL TIME ECHOCARDIOGRAPHIC TOOL FOR EDGE TRACKING OF INFERIOR VENA CAVA AND NON-INVASIVE ESTIMATION OF RIGHT ATRIAL PRESSURE

The non-invasive estimation of right atrial pressure (RAP) would be a key advancement in several clinical scenarios, in which the knowledge of central venous filling pressure is vital for patients’ management. The echocardiographic estimation of RAP proposed by Guidelines, based on inferior vena cava (IVC) size and respirophasic collapsibility, is exposed to operator and patient dependent variability. We introduce an automated real time method to process ultrasound scans of IVC and to measure pulsatility indexes, which are then used, together with other non-invasive measurements, to estimate RAP. Specifically, our method is based on the cardiac collapsibility (cardiac caval index - CCI), tested in a monocentric retrospective cohort of patients undergoing echocardiography and right heart catheterization (RHC) within 24 hour in condition of clinical and therapeutic stability (170 patients, age 64±14, male 45%, with pulmonary arterial hypertension, heart failure, valvular heart disease, dyspnea or other pathologies). IVC size and CCI were integrated with other standard echocardiographic features using machine-learning approaches. Three RAP classes (low 10 mmHg) were generated and RHC values used as comparator. Our classifications showed a higher accuracy than Guidelines (71% and 61% for our machine-learning method and Guidelines, respectively), promoting the integration of IVC and echocardiographic features for an improved non-invasive estimation of RAP

Cerebrospinal Fluid Leak During Stapes Surgery: The Importance of Temporal Bone CT Reconstructions in Oblique Anatomically Oriented Planes.

Stapes gusher is a massive flow of perilymph and cerebrospinal fluid leak that fills the middle ear immediately after surgical opening of the labyrinth, such as during stapedectomy. Stapes gusher usually occurs as the result of a congenital malformation that causes an abnormal communication between the perilymphatic space and the subarachnoid space involving the internal auditory canal or the cochlear duct. To date, the potential risk of stapes gusher cannot be assessed preoperatively, as there are not pathognomonic signs suggestive of this complication. However, high-resolution computed tomography scan (HRCT) of the temporal bone can provide information that may help recognizing patients at risk. Recently, an anatomic evaluation of the inner ear with oblique reformation at HRCT has been described. This reformation offers a new and more detailed topographic vision of temporal bone structures compared to the classic axial and coronal planes and may help identifying anatomical alterations otherwise not visible. In this article, we present a case of stapes gusher and the role of preoperative HRCT with oblique reformation in its prevention

Glucose Metabolic Reprogramming of ER Breast Cancer in Acquired Resistance to the CDK4/6 Inhibitor Palbociclib

The majority of breast cancers express the estrogen receptor (ER) and are dependent on estrogen for their growth and survival. Endocrine therapy (ET) is the standard of care for these tumors. However, a superior outcome is achieved in a subset of ER positive (ER+)/human epidermal growth factor receptor 2 negative (HER2−) metastatic breast cancer patients when ET is administrated in combination with a cyclin-dependent kinases 4 and 6 (CDK4/6) inhibitor, such as palbociclib. Moreover, CDK4/6 inhibitors are currently being tested in ER+/HER2+ breast cancer and reported encouraging results. Despite the clinical advances of a combinatorial therapy using ET plus CDK4/6 inhibitors, potential limitations (i.e., resistance) could emerge and the metabolic adaptations underlying such resistance warrant further elucidation. Here we investigate the glucose-dependent catabolism in a series of isogenic ER+ breast cancer cell lines sensitive to palbociclib and in their derivatives with acquired resistance to the drug. Importantly, ER+/HER2− and ER+/HER2+ cell lines show a different degree of glucose dependency. While ER+/HER2− breast cancer cells are characterized by enhanced aerobic glycolysis at the time of palbociclib sensitivity, ER+/HER2+ cells enhance their glycolytic catabolism at resistance. This metabolic phenotype was shown to have prognostic value and was targeted with multiple approaches offering a series of potential scenarios that could be of clinical relevance

HyperProbe consortium: innovate tumour neurosurgery with innovative photonic solutions

Recent advancements in imaging technologies (MRI, PET, CT, among others) have significantly improved clinical localisation of lesions of the central nervous system (CNS) before surgery, making possible for neurosurgeons to plan and navigate away from functional brain locations when removing tumours, such as gliomas. However, neuronavigation in the surgical management of brain tumours remains a significant challenge, due to the inability to maintain accurate spatial information of pathological and healthy locations intraoperatively. To answer this challenge, the HyperProbe consortium have been put together, consisting of a team of engineers, physicists, data scientists and neurosurgeons, to develop an innovative, all-optical, intraoperative imaging system based on (i) hyperspectral imaging (HSI) for rapid, multiwavelength spectral acquisition, and (ii) artificial intelligence (AI) for image reconstruction, morpho-chemical characterisation and molecular fingerprint recognition. Our HyperProbe system will (1) map, monitor and quantify biomolecules of interest in cerebral physiology; (2) be handheld, cost-effective and user-friendly; (3) apply AI-based methods for the reconstruction of the hyperspectral images, the analysis of the spatio-spectral data and the development and quantification of novel biomarkers for identification of glioma and differentiation from functional brain tissue. HyperProbe will be validated and optimised with studies in optical phantoms, in vivo against gold standard modalities in neuronavigational imaging, and finally we will provide proof of principle of its performances during routine brain tumour surgery on patients. HyperProbe aims at providing functional and structural information on biomarkers of interest that is currently missing during neuro-oncological interventions

Cancer stem cells (CSCs) : metabolic strategies for their identification and eradication

Phenotypic and functional heterogeneity is one of the most relevant features of cancer cells within different tumor types and is responsible for treatment failure. Cancer stem cells (CSCs) are a population of cells with stem cell-like properties that are considered to be the root cause of tumor heterogeneity, because of their ability to generate the full rep- ertoire of cancer cell types. Moreover, CSCs have been invoked as the main drivers of metastatic dissemination and therapeutic resistance. As such, targeting CSCs may be a useful strategy to improve the effectiveness of classical anticancer therapies. Recently, metabolism has been considered as a relevant player in CSC biology, and indeed, onco- genic alterations trigger the metabolite-driven dissemination of CSCs. More interestingly, the action of metabolic pathways in CSC maintenance might not be merely a conse- quence of genomic alterations. Indeed, certain metabotypic phenotypes may play a causative role in maintaining the stem traits, acting as an orchestrator of stemness. Here, we review the current studies on the metabolic features of CSCs, focusing on the bio- chemical energy pathways involved in CSC maintenance and propagation. We provide a detailed overview of the plastic metabolic behavior of CSCs in response to microenvironment changes, genetic aberrations, and pharmacological stressors. In addition, we describe the potential of comprehensive metabolic approaches to identify and selectively eradicate CSCs, together with the possibility to ‘force’ CSCs within certain metabolic dependences, in order to effectively target such metabolic biochemical inflexibilities. Finally, we focus on targeting mitochondria to halt CSC dissemination and effectively eradicate cancer

Maternal outcomes and risk factors for COVID-19 severity among pregnant women.

Pregnant women may be at higher risk of severe complications associated with the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), which may lead to obstetrical complications. We performed a case control study comparing pregnant women with severe coronavirus disease 19 (cases) to pregnant women with a milder form (controls) enrolled in the COVI-Preg international registry cohort between March 24 and July 26, 2020. Risk factors for severity, obstetrical and immediate neonatal outcomes were assessed. A total of 926 pregnant women with a positive test for SARS-CoV-2 were included, among which 92 (9.9%) presented with severe COVID-19 disease. Risk factors for severe maternal outcomes were pulmonary comorbidities [aOR 4.3, 95% CI 1.9-9.5], hypertensive disorders [aOR 2.7, 95% CI 1.0-7.0] and diabetes [aOR2.2, 95% CI 1.1-4.5]. Pregnant women with severe maternal outcomes were at higher risk of caesarean section [70.7% (n = 53/75)], preterm delivery [62.7% (n = 32/51)] and newborns requiring admission to the neonatal intensive care unit [41.3% (n = 31/75)]. In this study, several risk factors for developing severe complications of SARS-CoV-2 infection among pregnant women were identified including pulmonary comorbidities, hypertensive disorders and diabetes. Obstetrical and neonatal outcomes appear to be influenced by the severity of maternal disease