Researchers analyze molecular characteristics of cerebrospinal fluid from LMM patients

Patients with advanced melanoma who develop metastases in the leptomeninges, the fluid filled membranes surrounding the brain and spinal cord, have an extremely dismal prognosis. Most patients only survive for 8 to 10 weeks after diagnosis. One reason for this poor prognosis is that very little information is known about the molecular development of leptomeningeal melanoma metastases (LMM), making it difficult to develop effective therapies. Researchers in Moffitt Cancer Center's Donald A. Adam Melanoma and Skin Cancer Center of Excellence and the Department of Neuro-Oncology sought to change this by performing an extensive analysis of the molecular characteristics of the cerebrospinal fluid of patients with LMM. Their findings were published in Clinical Cancer Research, a journal of the American Association for Cancer Research.

Cancer development and progression are highly regulated by intricate interactions between cancer cells and the surrounding environment. Melanoma cells that invade and metastasize into the leptomeninges interact with the surrounding cerebrospinal fluid. Moffitt researchers wanted to improve their understanding of the development of LMM by analyzing the protein and RNA composition of cerebrospinal fluid from patients with LMM. They compared the molecular profiles of 8 control patients without LMM to 8 patients with LMM, including one LMM patient who had an extraordinary response to treatment and was still alive more than 35 months after diagnosis.

They discovered that the cerebrospinal fluid from LMM patients was enriched for proteins involved in innate immunity, proteases and the IGF-signaling pathway. The most commonly altered protein was TGF-β1. Interestingly, the one patient who had an extraordinary response to treatment displayed high levels of these proteins at baseline, but expression levels decreased as the patient responded to treatment. However, the protein expression patterns in the remaining LMM patients who had poor responses to treatment were high at baseline and remained high throughout treatment and disease progression.

The researcher team, led by Keiran Smalley, Ph.D., director of the Donald A. Adam Melanoma and Skin Cancer Center of Excellence and Peter Forsyth, M.D., Chair of the Department of Neuro-Oncology, hypothesized that the cerebrospinal fluid of LMM patients could impact melanoma cells by modulating their molecular profile. They confirmed this hypothesis by incubating cerebrospinal fluid from the LMM patients with melanoma cells and discovered that the fluid was able to induce activation of proteins and signaling pathways involved in malignant progression, including the PI3K/AKT pathway, integrins, B cell signaling, mitotic cell cycle progression, TNFR, TGF-β and oxidative stress.

Their findings demonstrate that the cerebrospinal fluid from LMM patients who did not respond to treatment promoted survival of melanoma cells, while the cerebrospinal fluid from the extraordinary responder did not promote survival. These observations suggest that molecules exist within the cerebrospinal fluid that can stimulate melanoma cell survival and prevent cell death. The researchers reported that one of these survival molecules is TGF-β. The patient who responded well to treatment had very low to undetectable levels of cerebrospinal fluid TGF-β, while those patients who did not respond to treatment had much higher levels of TGF-β.

The researchers hope that their data will provide important knowledge about LMM and offer insights into potential therapeutic targets.

It is likely that the environment of LMM is a key regulator of both disease progression and therapeutic response. Improved knowledge about the microenvironment of LMM may allow novel therapeutic strategies to be developed that can delay disease progression."

Keiran Smalley, Ph.D., director of the Donald A. Adam Melanoma and Skin Cancer Center of Excellence.

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Two strains of one bacterium combine to cause deadly flesh-eating infection

A new study used genetic analysis to reveal how two different strains of a single species of flesh-eating bacteria worked in concert to become more dangerous than either one strain alone. The work suggests that other difficult-to-treat infections may be polymicrobial and treating only one organism in a polymicrobial infection could be the cause of many secondary infections and chronic infections that resist treatment. The results of the study were published in the Journal Proceedings of the National ,Academy of Sciences, 2019.

In recent years, scientists have found that serious infections that progress rapidly and resist treatment are often caused by multiple microbes interacting with one another. Very little is known about these so-called polymicrobial infections, but traditional diagnostic methods often misidentify them as monomicrobial, or single-microbe, infections.

"This research provides clear evidence that a very severe infection considered to be caused by a single species of a naturally occurring bacterium actually had two strains," said Rita Colwell, a Distinguished University Professor in the University of Maryland Institute for Advanced Computer Studies and a co-author of the study.

"One of the strains produces a toxin that breaks down muscle tissue and allows the other strain to migrate into the blood system and infect the organs." The original infection -cultured from a patient who developed the severe flesh-eating disease known as necrotising fasciitis- was diagnosed as a monomicrobial disease. Traditional diagnostics could only determine that the infection was caused by a single species of bacteria called Aeromonas hydrophila.

But the disease baffled clinicians when it rapidly turned deadly, requiring a quadruple amputation to save the patient's life. Through genetic analysis of the culture, Colwell and her team discovered important differences among the individual bacterial cultures that could not be detected through standard diagnostic methods.

The ability to identify the agents involved in polymicrobial infections, whether they are different species or variant strains of a single species, can significantly improve treatment outcomes for infected patients.

"When we treat with a given antibiotic, we're clearing an organism out of the body," Colwell said. "But if there's another organism that's participating in the infection and that's also pathogenic, then any antibiotic treatment that doesn't also target that organism may just be clearing ground for it to grow like crazy."

Treating only one organism in a polymicrobial infection could be the cause of many secondary infections and chronic infections that resist treatment. According to Colwell, a mixture of antibiotics or therapeutic drugs may be necessary to treat polymicrobial infections. Routinely using the genomic approach that was developed in this study to analyze infections could result in more effective targeted treatments for diseases caused by polymicrobial infection.

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Advanced MRI brain scan can predict stroke-related dementia

Advancement of technology has made it easier for practitioner to diagnose diseases which were earlier tough to predict and at times went undetected.

A study has found that an advanced MRI brain scan analysis in patients with stroke-related, small vessel disease, helped predict problems with thinking, memory and even dementia.

When a stroke or other disease damages tiny blood vessels in the brain, the condition is known as small vessel disease. This condition is the most common cause of thinking problems ( planning,  organising information and processing speed) and can even lead to dementia. Although early treatment could help patients at risk, no effective test is available to identify them.

This study evaluated the accuracy of a new MRI analysis technique using diffusion tensor imaging (DTI) , in predicting thinking problems and dementia related to small vessel disease.

A single scan measured the brain in fine detail to reveal damaged areas. By comparing these images to a healthy person's, researchers were able to classify the brain into areas of healthy versus damaged tissue.

Results showed that participants with the most brain damage were much more likely to develop thinking problems. The analysis also helped predict 3/4ths of the dementia cases that occurred during the study.

We've developed a useful tool for monitoring patients at risk of developing dementia and could target those who need early treatment, said senior author of the study.


The study included 99 patients with small vessel disease caused by ischemic stroke, a type of stroke that blocks the blood vessels deep within the brain. Slightly more than 1/3rd were female, average age 68, and most were Caucasian.

Patients received the MRI scans annually for 3 years and thinking tests annually for 5 years. 18 participants developed dementia during the study, with an average time to onset of approximately 3 years and 4 months.

This advanced MRI analysis offers a highly accurate and sensitive marker of small vessel diseases severity in a single measure that can be used to detect who will and will not go on to develop dementia in a 5 year period , noted the author.

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New AI detects heart failure with 100% accuracy

With the help pf Artificial Intelligence (AI), researchers have developed a neural network developed that can accurately identify congestive heart failure with 100% accuracy through analysis of just one raw electrocardiogram (ECG) heartbeat.

Congestive heart failure (CHF) is a chronic progressive condition that affects the pumping power of the heart muscles. Associated with high prevalence, significant mortality rates and sustained healthcare costs, clinical practitioners and health systems urgently require efficient detection processes.

The researchers have worked to tackle these important concerns by using Convolutional Neural Networks (CNN)- hierarchial neural networks highly effective in recognising patterns and structures in data.

We trained and tested the CNN model on large publicly available ECG datasets featuring subjects with CHF as well as healthy, non-arrhythmic hearts. Our model delivered 100% accuracy by checking just one heartbeat we are able to detect whether or not a person has heart failure, said the researcher.

The Prof. said, our model is also one of the 1st known to be able to identify the ECG's morphological features specifically associated to the severity of the condition.

The research drastically improves existing CHF detection methods typically focused on heart rate variability that, whilst effective, are time-consuming and prone to errors. Conversely, their new model uses a combination of advanced signal processing and machine learning tools on raw ECG signals, delivering 100% accuracy.

With approximately 26 million people worldwide affected by a form of heart failure, our research presents a major advancement on the current methodology, said the researcher.


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With the help of Artificial Intelligence(AI), researchers ha ..

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New test can eliminate need for surgery to diagnose ovarian cancer

Researchers have developed a novel blood test called biomarker test that offers the possibility of more precise diagnostics of ovarian cancer without the need for surgery. Biomarker test is based on analysis of 11 proteins and is performed on a blood sample where ultrasound indicates abnormalities to identify women without cancer.

As majority of women who undergo surgery for suspected ovarian cancer do not have cancer, this research could lead to a reduction in unnecessary surgery and in earlier detection and treatment for affected women. Ovarian cancer is often discovered at a late stage and has a high mortality rate. Out of 10 patients, only 3-4 survive 5 years after treatment, and there has been no test specific enough to justify screening, reported the study published in the Journal of Communications Biology.

Women with accidental findings of an ovarian cyst or with symptoms instead undergo an ultrasound and if abnormalities are seen, surgery is the only way to make sure all cancers are detected. This means that many women are operated on without having cancer, resulting in unnecessary surgery and increased risks for women.

We need to develop more accurate pre-surgery diagnostics. To detect one cancer, we operate on up to five women – yet this is currently the best option when abnormalities are detected by ultrasound and cancer is suspected. There is a great need for a simple blood test that could identify women who do not need surgery,” said one of the researchers.

For cases in which physicians chose to operate, the cancer rate could increase from one in five to one in three. This would greatly reduce unnecessary surgery and the risk of complications related to surgery. The biomarker profile can also detect borderline cases and early stages of the disease.

“Our results are promising enough to consider screening for early discovery of ovarian cancer. In Sweden, we have long experience of screening for cervical cancer. I see great prospects of developing a strategy for screening for ovarian cancer as well, which could save lives and minimise the need for surgery to rule out cancer,” said another researcher of the study.

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Artificial Intelligence can help treat brain tumours

Researchers have developed an artificial intelligence-based (AI) method for analysis of brain tumours, paving the way for individualised treatment of tumours.

According to the study, AI machine learning methods, carefully trained on standard magnetic resonance imaging (MRI), are more reliable and precise than established radiological methods in the treatment of gliomas.

Glioma, a type of tumour that occurs in the brain and spinal cord, is common and most malignant of brain tumours in adults.

“With this study, we were able to demonstrate the potential of artificial neural networks in radiological diagnostics,” said a researcher.
 
For the study, the team included 500 brain tumour patients. Using a reference database with MRI scans of patients, the algorithms automatically recognised and localised brain tumours using artificial neural networks.

The algorithms were also enabled to volumetrically measure the individual areas (contrast medium-absorbing tumour portion, peritumoral edema).

“We want to advance the technology for automated high-throughput analysis of medical image data and transfer it not only to brain tumours but also to other diseases like brain metastases or multiple sclerosis,” said a researcher.

Glioma tumours often cannot be completely removed by surgery. Chemotherapy or radiotherapy are only effective to a limited extent because tumours are highly resistant. Therefore, new and precisely validated treatment approaches are urgently needed, the team noted.

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IBM Develops Fingernail Sensor That Uses AI to Track Patient Health, Disease Progressions

IBM researchers have developed a first-of-a-kind "fingernail sensor" prototype that uses artificial intelligence and machine learning to monitor and analyse human health as well as disease progression.

The wearable, wireless device continuously measures how a person's fingernail bends and moves, which is a key indicator of grip strength.

Although skin-based sensors can help capture things like motion, the health of muscles and nerve cells, and can also reflect the intensity of a person's emotional state, these can often cause problems, including infection with older patients.
 
But the new system uses signals from the fingernail bends such as the tactile sensing of pressure, temperature, surface textures.
"Our fingernails deform - bend and move - in stereotypic ways when we use them for gripping, grasping, and even flexing and extending our fingers. This deformation is usually on the order of single digit microns and not visible to the naked eye," said a researcher.

 The new device, consists of strain gauges attached to the fingernail and a small computer that samples strain values, collects accelerometer data and communicates with a smartwatch.
The watch also runs machine learning models to rate bradykinesia, tremor, and dyskinesia which are all symptoms of Parkinson's disease.

"By pushing computation to the end of our fingers, we've found a new use for our nails by detecting and characterising their subtle movements," he said.

"With the sensor, we can derive health state insights and enable a new type of user interface. This work has also served as the inspiration for a new device modelled on the structure of the fingertip that could one day help quadriplegics communicate," he noted.

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Scientists develop 90% accurate Alzheimer's blood test

Scientists have developed a blood test for Alzheimer's disease which can tell with 90 per cent accuracy if a patient is at the risk of developing the brain disorder.

The blood test, currently only available for research- related purposes, measures a specific peptide to inform if a patient has the very earliest stages of Alzheimer's.

The test developed and validated by scientists could speed up the pace of Alzheimer's disease drug trials.

"This new test has the potential to eventually disrupt the expensive and invasive scanning and spinal fluid technologies," said a Professor.

"In the first instance, however, it will be an invaluable tool in increasing the speed of screening potential patients for new drug trials," said a Prof. who co-led the research.

Progress in developing new therapeutic strategies for Alzheimer's disease has been disappointingly slow, researchers said.

None of the three drugs currently on the market treat the underlying disease. New drugs are urgently required, and the only way to do that is to speed up the whole process, they said.

"That requires trials with rigorous and economical patient selection, to avoid recruiting patients who may not even have Alzheimer's disease. Due to the long time-spans involved, pharmaceutical companies require accurate predictions of who is most at risk," said the Prof.

One of the essential hallmarks of Alzheimer's disease is the buildup of an abnormal peptide in the brain, called beta- amyloid.

The process starts silently about 30 years before any outward signs of dementia.

Current tests for beta-amyloid are expensive, invasive and include brain scans with radioactive tracers, or analysing spinal fluid taken via a lumbar puncture.

Diagnosis is usually made without these tools, rather by assessing a patient's range of symptoms.

The new test was the result of an extensive international collaboration.


The key technology, known as IP-MS, was developed by researchers recently.

Blood samples from patients in a large study for Geriatrics and Gerontology were analysed to identify the relevant peptides.

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