Wireless system could track tumors, dispense medicine

Dina Katabi and MIT CSAIL colleagues have developed ReMix, which uses lo power wireless signals to pinponit the location of implants in the body.  The tiny implants could be used as tracking devices on shifting tumors to monitor  movements, and in the future to deliver drugs to specific regions.

The technology showed centimeter-level accuracy in animal tests.

Markers in the body reflect the signal transmitted by the wireless device outside the body, therefore a battery or external power source are not required.


Join ApplySci at the 9th Wearable Tech + Digital Health + Neurotech Boston conference on September 24, 2018 at the MIT Media Lab.  Speakers include:  Rudy Tanzi – Mary Lou Jepsen – George ChurchRoz PicardNathan IntratorKeith JohnsonJuan EnriquezJohn MattisonRoozbeh GhaffariPoppy Crum – Phillip Alvelda Marom Bikson – Ed Simcox – Sean Lane

AI – optimized glioblastoma chemotherapy

Pratik Shah, Gregory Yauney,  and MIT Media Lab researchers have developed an AI  model that could make glioblastoma chemotherapy regimens less toxic but still effective. It analyzes current regimens and iteratively adjusts doses to optimize treatment with the lowest possible potency and frequency toreduce tumor sizes.

In simulated trials of 50 patients, the machine-learning model designed treatment cycles that reduced the potency to a quarter or half of the doses It often skipped administration, which were then scheduled twice a year instead of monthly.

Reinforced learning was used to teach the model to favor certain behavior that lead to a desired outcome.  A combination of  temozolomide and procarbazine, lomustine, and vincristine, administered over weeks or months, were studied.

As the model explored the regimen, at each planned dosing interval it decided on actions. It either initiated or withheld a dose. If it administered, it then decided if the entire dose, or a portion, was necessary. It pinged another clinical model with each action to see if the the mean tumor diameter shrunk.

When full doses were given, the model was penalized, so it instead chose fewer, smaller doses. According to Shah, harmful actions were reduced to get to the desired outcome.

The J Crain Venter Institute’s Nicholas Schork said that the model offers a major improvement over the conventional “eye-balling” method of administering doses, observing how patients respond, and adjusting accordingly.


Join ApplySci at the 9th Wearable Tech + Digital Health + Neurotech Boston conference on September 24, 2018 at the MIT Media Lab.  Speakers include:  Rudy Tanzi – Mary Lou Jepsen – George ChurchRoz PicardNathan IntratorKeith JohnsonJuan EnriquezJohn MattisonRoozbeh GhaffariPoppy Crum – Phillip Alvelda Marom Bikson – Ed Simcox – Sean Lane

Hydrogen peroxide sensor to determine effective chemotherapy

MIT’s Hadley Sikes has developed a sensor that determines whether cancer cells respond to a particular type of chemotherapy by detecting hydrogen peroxide inside human cells.

The technology could help identify new cancer drugs that boost levels of hydrogen peroxide, which induces programmed cell death. The sensors could also be adapted to screen individual patients’ tumors to predict whether such drugs would be effective against them.


Join ApplySci at the 9th Wearable Tech + Digital Health + Neurotech Boston conference on September 24, 2018 at the MIT Media Lab.  Speakers include:  Rudy Tanzi – Mary Lou Jepsen – George ChurchRoz PicardNathan IntratorKeith JohnsonJuan EnriquezJohn MattisonRoozbeh GhaffariPoppy Crum – Phillip Alvelda Marom Bikson – Ed Simcox – Sean Lane

Urine test for cancer biomarkers

Minoru Sakairi and Hitachi scientists have developed a urine test for early cancer detection.

5,000 types of metabolites can be analyzed for cancer biomarkers in urine.  The team began a study three years ago, resulting in the identification of 30 metabolites that can be used to discriminate between healthy people and cancer patients.  Further validation studies will begin in September at Nagoya University.

According to Sakairi:  “For the comprehensive analysis of urine metabolites, we used a liquid chromatograph/mass spectrometer (LC/MS). Taking measurements with an LC/MS, and focusing on differences in the water-and fat-solubility of metabolites so as to optimize measurement conditions, we were able to detect over 1,300 metabolites in the urine samples. Using 30 biomarkers from among these, a look at their measured values for 15 cases each of breast cancer patients, colorectal cancer patients, and healthy subjects showed that we had made a breakthrough in being able to discriminate the difference between cancer and not cancer.”


Join ApplySci at the 9th Wearable Tech + Digital Health + Neurotech Boston conference – September 25, 2018 a the MIT media Lab

Remote photodynamic therapy targets inner-organ tumors

NUS researchers Zhang Yong and John Ho have developed a tumor-targeting method that remotely conveys light  for  photodynamic treatment.

The tiny, wireless, implanted device delivers doses of light over a long period  in a programmable and repeatable manner.

PDT is usually used on surface diseases because of  low infiltration of light through organic tissue. This remote approach to light conveyance allows PDT to be used on the inner organs with fine control.  The team believes that it could successfully treat brain and liver malignancies in the future, and allow therapies that could be tailored during the course of treatment.


Join ApplySci at Wearable Tech + Digital Health + Neurotech Silicon Valley on February 26-27, 2018 at Stanford University. Speakers include:  Vinod Khosla – Justin Sanchez – Brian Otis – Bryan Johnson – Zhenan Bao – Nathan Intrator – Carla Pugh – Jamshid Ghajar – Mark Kendall – Robert Greenberg – Darin Okuda – Jason Heikenfeld – Bob Knight – Phillip Alvelda – Paul Nuyujukian –  Peter Fischer – Tony Chahine – Shahin Farshchi – Ambar Bhattacharyya – Adam D’Augelli – Juan-Pablo Mas – Shreyas Shah– Walter Greenleaf – Jacobo Penide – David Sarno – Peter Fischer

Registration rates increase on February 2nd

 

AI detects bowel cancer in less than 1 second in small study

Yuichi Mori and Showa University colleagues haved used AI to identify bowel cancer by analyzing colonoscopy derived polyps in less than a second.

The  system compares a magnified view of a colorectal polyp with 30,000 endocytoscopic images. The researchers claimed  86% accuracy, based on a study of 300 polyps.

While further testing the technology, Mori said that the team will focus on creating a system that can automatically detect polyps.

Click to view Endoscopy Thieme video


Join ApplySci at Wearable Tech + Digital Health + Neurotech Silicon Valley on February 26-27, 2018 at Stanford University. Speakers include:  Vinod Khosla – Justin Sanchez – Brian Otis – Bryan Johnson – Zhenan Bao – Nathan Intrator – Carla Pugh – Jamshid Ghajar – Mark Kendall – Robert Greenberg – Darin Okuda – Jason Heikenfeld – Bob Knight – Phillip Alvelda

Machine learning improves breast cancer detection

MIT’s Regina Barzilay has used AI to improve breast cancer detection and diagnosis. Machine learning tools predict if a high-risk lesion identified on needle biopsy after a mammogram will upgrade to cancer at surgery, potentially eliminating unnecessary procedures.

In current practice, when a mammogram detects a suspicious lesion, a needle biopsy is performed to determine if it is cancer. Approximately 70 percent of the lesions are benign, 20 percent are malignant, and 10 percent are high-risk.

Using a method known as a “random-forest classifier,” the AI model resulted in 30 per cent fewer  surgeries, compared to the strategy of always doing surgery, while diagnosing more cancerous lesions (97 per cent vs 79 per cent) than the strategy of only doing surgery on traditional “high-risk lesions.”

Trained on information about 600 high-risk lesions, the technology looks for data patterns that include demographics, family history, past biopsies, and pathology reports.

MGH radiologists will begin incorporating the method into their clinical practice over the next year.


Join ApplySci at Wearable Tech + Digital Health + Neurotech Silicon Valley on February 26-27, 2018 at Stanford University, featuring:  Vinod KhoslaJustin SanchezBrian OtisBryan JohnsonZhenan BaoNathan IntratorCarla PughJamshid Ghajar – Mark Kendall – Robert Greenberg Darin Okuda Jason Heikenfeld

CRISPR platform targets RNA and DNA to detect cancer, Zika

Broad and Wyss scientists have used an RNA-targeting CRISPR enzyme to detect  the presence of as little as a single target molecule. SHERLOCK (Specific High Sensitivity Enzymatic Reporter UnLOCKing) could one day be used to respond to viral and bacterial outbreaks, monitor antibiotic resistance, and detect cancer.

Demonstrated applications included:

  • Detecting the presence of Zika virus in patient blood or urine samples within hours;
  • Distinguishing between the genetic sequences of African and American strains of Zika virus;
  • Discriminating specific types of bacteria, such as E. coli;
  • Detecting antibiotic resistance genes;
  • Identifying cancerous mutations in simulated cell-free DNA fragments; and
  • Rapidly reading human genetic information, such as risk of heart disease, from a saliva sample.

The tool can be paper-based, not requiring refrigeration, and suited for fast deployment at field hospitals or rural clinics.


Join ApplySci at Wearable Tech + Digital Health + NeuroTech Boston – Featuring: Joi Ito, Ed Boyden, Roz Picard, George Church, Tom Insel, John Rogers, Jamshid Ghajar, Phillip Alvelda and Nathan Intrator – September 19, 2017 at the MIT Media Lab

Nanorobots optimize cancer drug delivery, preserve surrounding tissues

Sylvain Martel and Polytechnique Montréal and McGill University colleagues have developed nanorobotic agents that can specifically target active cancerous cells of tumors.  Optimal targeting  could help preserve surrounding organs and healthy tissues, and allow reduced dosage.

The nanorobotic agents can autonomously detect oxygen-depleted tumour areas, and deliver the drug to them. These “hypoxic” zones are often resistant to therapies.

According to professor Martel:  “This innovative use of nanotransporters will have an impact not only on creating more advanced engineering concepts and original intervention methods, but it also throws the door wide open to the synthesis of new vehicles for therapeutic, imaging and diagnostic agents. Chemotherapy, which is so toxic for the entire human body, could make use of these natural nanorobots to move drugs directly to the targeted area, eliminating the harmful side effects while also boosting its therapeutic effectiveness.”


Wearable Tech + Digital Health + NeuroTech Silicon Valley – February 7-8, 2017 @ Stanford University