Archive for month: August, 2020

Pancreatic cancer is the third leading cause of cancer deaths in the United States, in part because it is very difficult for chemotherapy drugs to reach the pancreas, which is located deep within the abdomen.

To help overcome that obstacle, researchers from MIT and Massachusetts General Hospital have now developed a small, implantable device that delivers chemotherapy drugs directly to pancreatic tumours. In a study of mice, they found that this approach was up to 12 times more effective than giving chemotherapy drugs by intravenous injection, which is how most pancreatic cancer patients are treated.

A method for data analysis used in medical diagnostics has been tested for the first time on resting state functional magnetic resonance imaging (fMRI) data. The method, which relies on ‘fuzziness’, proved to be as robust as the well-known and regularly used sample entropy (SampEn) method but with the advantage of offering greater detail than sample entropy.
Do not be misled by the word ‘fuzzy’: Fuzzy Approximate Entropy (fApEn) is a method that offers better sensitivity for understanding the complexity of noisy images produced by functional magnetic resonance imaging (fMRI). fMRI is a medical imaging technique which, when applied to the brain allows non-invasive observation of neural activity associated with specific human behaviour . However, just ‘looking’ at these images is not enough to understand what is going on, and different methods exist that analyse, filter and reconstruct the signals to enable scientists to understand the brain’s complex activity.
fApEn has been used to analyse electrocardiograms, electroencephalograms and
electromyograms, but this is the first time it is used with fMRI because 3D fMRI computation is complex. ‘Until now scientists have preferred to use a reliable method, Sample Entropy (sampEn), which, however, suffers several limitations’, explains Moses Sokunbi, research scientist at the International School for Advanced Studies (SISSA) in Trieste and first author of the study.
‘In this paper we demonstrated not only that fApEn can indeed be used but that compared with sampEn analysis on the same recordings, it gave superior results which were not detected by SampEn ‘.
‘The advantage of fApEn is that it’s a non-linear method’, Sokunbi points out. ‘All too often, in fact, data from the brain are analysed using linear methods, but the brain is a complex system that produces signals that are non-linear and dynamic in nature and analysing with these linear methods results in loss of information ‘.
The non-linear fApEn method was used to test a hypothesis regarding brain activity. ‘ We tested the fMRI data of 86 healthy individuals with age ranging between 19 and 85 years’, explains Sokunbi. ‘The complexity of brain activity is thought to decrease over the years: a young adult brain is more complex than an older adult brain. This hypothesis is supported by several observations so we decided to test it by scanning the brains of individuals of varying age with functional magnetic resonance imaging and analysing the data both with fApEn and SampEn’.
fApEn showed better signal detection in comparison to SampEn. With sampEn there was a tendency in the direction predicted by the hypothesis, but this was not significant. In contrast, fApEn analysis on the same data provided a clear and significant tendency in the expected direction’.

SISSA http://tinyurl.com/zua4jyh

Assuming that we could visualize pathological processes such as cancer at a very early stage and additionally distinguish the various different cell types, this would represent a giant step for personalized medicine. Xenon magnetic resonance imaging has the potential to fulfill this promise – if suitable contrast media are found that react sensitively enough to the ‘exposure’. Researchers at the Leibniz-Institut fur Molekulare Pharmakologie in Berlin have now found that a class of pumpkin-shaped molecules called cucurbiturils together with the inert gas xenon, enables particularly good image contrast – namely around 100 times better than has been possible up to now. This finding points the way to the tailoring of new contrast agents to different cell types and has the potential to enable molecular diagnostics even without tissue samples in the future.
Personalized medicine instead of one treatment for all – especially in cancer medicine, this approach has led to a paradigm shift. Molecular diagnostics is the key that will give patients access to tailor-made therapy. However, if tumours are located in poorly accessible areas of the body or several tumour foci are already present, this often fails due to a lack of sufficient sensitivity of the diagnostic imaging. But such sensitivity is needed to determine the different cell types, which differ considerably even within a tumour. Although even the smallest of tumour foci and other pathological changes can be detected using the PET-CT, a differentiation according to cell type is usually not possible.
Scientists from the FMP are therefore focusing on xenon magnetic resonance imaging: The further development of standard magnetic resonance imaging makes use of the ‘illuminating power’ of the inert gas xenon, which can provide a 10,000-fold enhanced signal in the MRI. To do this, it must be temporarily captured by so-called ‘cage molecules’ in the diseased tissue. This has been more or less successful with the molecules used to date, but the experimental approach is still far from a medical application.
The research group led by Dr. Leif Schroder at the Leibniz-Institut fur Molekulare Pharmakologie (FMP) has now discovered a molecule class for this purpose that eclipses all of the molecules used to date. Cucurbituril exchanges around 100 times more xenon per unit of time than its fellow molecules, which leads to a much better image contrast. ‘It very quickly became clear that cucurbituril might be suitable as a contrast medium,’ reports Leif Schroder. ‘However, it was surprising that areas marked with it were imaged with a much better contrast than previously.’ The explanation is to be found in the speed. Upon exposure, so to speak, cucurbituril generates contrast more rapidly than all molecules used to date, as it only binds the xenon very briefly and thus transmits the radio waves to detect the inert gas to very many xenon atoms within a fraction of a second. In this way, the inert gas is passed through the molecule much more efficiently.
In the study the world’s first MRI images with cucurbituril have been achieved. With the aid of a powerful laser and a vaporized alkali metal, the researchers initially greatly strengthened the magnetic properties of normal xenon. The hyperpolarized gas was then introduced into a test solution with the cage molecules. A subsequent MRI image showed the distribution of the xenon in the object. In a second image, the curcurbituril together with radio waves destroyed the magnetization of the xenon, leading to dark spots on the images.
‘Comparison of the two images demonstrates that only the xenon in the cages has the right resonance frequency to produce a dark area,’ explains Schroder. ‘This blackening is possible to a much better degree with cucurbituril than with previous cage molecules, for it works like a very light-sensitive photographic paper. The contrast is around 100 times stronger.’
Initial tests were performed with cell material in which cucurbituril is also able to detect a certain enzyme that commonly occurs in cancer cells. On the basis of the enzyme reaction, it is possible to conclude the malignancy of the cells. What is special about this is that relatively little cell material is then sufficient to image the tumour cells in the MRI. The researchers believe that it may be possible to detect even very small tumour foci using this new method in the future. However, there is still a long way to go. To begin with, animal studies must be conducted to determine whether it is possible to transfer the test results obtained to date to the living organism. If so, they can be used to develop highly sensitive contrast media that are able to mark further enzymes and thus a range of different cell types.

Leibniz-Institut fur Molekulare Pharmakologiehttp://tinyurl.com/h2bn6go

Epilepsy affects more than 65 million people worldwide. One-third of these patients have seizures that are not controlled by medications. In addition, one-third have brain lesions, the hallmark of the disease, which cannot be located by conventional imaging methods. Researchers at the Perelman School of Medicine at the University of Pennsylvania have piloted a new method using advanced non-invasive neuroimaging to recognize the neurotransmitter glutamate, thought to be the culprit in the most common form of medication-resistant epilepsy.
Glutamate is an amino acid which transmits signals from neuron to neuron, telling them when to fire. Glutamate normally docks with the neuron, gives it the signal to fire and is swiftly cleared. In patients with epilepsy, stroke and possibly ALS, the glutamate is not cleared, leaving the neuron overwhelmed with messages and in a toxic state of prolonged excitation.
In localization-related epilepsy, the most common form of medication-resistant epilepsy, seizures are generated in a focused section of the brain; in 65 percent of patients, this occurs in the temporal lobe. Removal of the seizure-generating region of the temporal lobe, guided by preoperative MRI, can offer a cure. However, a third of these patients have no identified abnormality on conventional imaging studies and, therefore, more limited surgical options.
‘Identification of the brain region generating seizures in location-related epilepsy is associated with significantly increased chance of seizure freedom after surgery,’ said the new study’s lead author, Kathryn Davis, MD, MSTR, an assistant professor of Neurology at Penn. ‘The aim of the study was to investigate whether a novel imaging method, developed at Penn, could use glutamate to localize and identify the epileptic lesions and map epileptic networks in these most challenging patients.’
‘We theorized that if we could develop a technique which allows us to track the path of and make non-invasive measurements of glutamate in the brain, we would be able to better identify the brain lesions and epileptic foci that current methods miss,’ said senior author Ravinder Reddy, PhD, a professor of Radiology and director of Penn’s Center for Magnetic Resonance and Optical Imaging.
Reddy’s lab developed the glutamate chemical exchange saturation transfer (GluCEST) imaging method, a very high resolution magnetic resonance imaging contrast method not available before now, to measure how much glutamate was in different regions of the brain including the hippocampi, two structures within the left and right temporal lobes responsible for short- and long-term memory and spatial navigation and the most frequent seizure onset region in adult epilepsy patients.
The study tested four patients with medication-resistant epilepsy and 11 controls. In all four patients, concentrations of glutamate were found to be higher in one of the hippocampi, and confirmatory methods (electroencephalography and magnetic resonance spectra) verified independently that the hippocampus with the elevated glutamate was located in the same hemisphere as the epileptic focus/lesion. Consistent lateralization to one side was not seen in the control group.

Penn Medicinehttp://tinyurl.com/jrbr5se

Researchers at MIT and Boston Children’s Hospital have developed a system that can take MRI scans of a patient’s heart and, in a matter of hours, convert them into a tangible, physical model that surgeons can use to plan surgery.
The models could provide a more intuitive way for surgeons to assess and prepare for the anatomical idiosyncrasies of individual patients. ‘Our collaborators are convinced that this will make a difference,’ says Polina Golland, a professor of electrical engineering and computer science at MIT, who led the project. ‘The phrase I heard is that surgeons see with their hands,’ that the perception is in the touch.’
This fall, seven cardiac surgeons at Boston Children’s Hospital will participate in a study intended to evaluate the models’ usefulness.
Danielle Pace, an MIT graduate student in electrical engineering and computer science, is first author on the paper and spearheaded the development of the software that analyses the MRI scans. Mehdi Moghari, a physicist at Boston Children’s Hospital, developed new procedures that increase the precision of MRI scans tenfold, and Andrew Powell, a cardiologist at the hospital, leads the project’s clinical work.
MRI data consist of a series of cross sections of a three-dimensional object. Like a black-and-white photograph, each cross section has regions of dark and light, and the boundaries between those regions may indicate the edges of anatomical structures. Then again, they may not.
Determining the boundaries between distinct objects in an image is one of the central problems in computer vision, known as ‘image segmentation.’ But general-purpose image-segmentation algorithms aren’t reliable enough to produce the very precise models that surgical planning requires.
Typically, the way to make an image-segmentation algorithm more precise is to augment it with a generic model of the object to be segmented. Human hearts, for instance, have chambers and blood vessels that are usually in roughly the same places relative to each other. That anatomical consistency could give a segmentation algorithm a way to weed out improbable conclusions about object boundaries.
The problem with that approach is that many of the cardiac patients at Boston Children’s Hospital require surgery precisely because the anatomy of their hearts is irregular. Inferences from a generic model could obscure the very features that matter most to the surgeon.
In the past, researchers have produced printable models of the heart by manually indicating boundaries in MRI scans. But with the 200 or so cross sections in one of Moghari’s high-precision scans, that process can take eight to 10 hours.
‘They want to bring the kids in for scanning and spend probably a day or two doing planning of how exactly they’re going to operate,’ Golland says. ‘If it takes another day just to process the images, it becomes unwieldy.’
Pace and Golland’s solution was to ask a human expert to identify boundaries in a few of the cross sections and allow algorithms to take over from there. Their strongest results came when they asked the expert to segment only a small patch -one-ninth of the total area – of each cross section.
In that case, segmenting just 14 patches and letting the algorithm infer the rest yielded 90 percent agreement with expert segmentation of the entire collection of 200 cross sections. Human segmentation of just three patches yielded 80 percent agreement.
‘I think that if somebody told me that I could segment the whole heart from eight slices out of 200, I would not have believed them,’ Golland says. ‘It was a surprise to us.’
Together, human segmentation of sample patches and the algorithmic generation of a digital, 3-D heart model takes about an hour. The 3-D-printing process takes a couple of hours more.
Currently, the algorithm examines patches of unsegmented cross sections and looks for similar features in the nearest segmented cross sections. But Golland believes that its performance might be improved if it also examined patches that ran obliquely across several cross sections. This and other variations on the algorithm are the subject of ongoing research.

MIThttp://tinyurl.com/pz4su4s