Sunday, May 30, 2010

Microbubbles and photoacoustic probes energize cancer researchers

The Canary Foundation’s eighth year of activities was marked with a symposium held at Stanford University May 25-27, 2010. The Canary Foundation focuses on the early detection of cancer, specifically lung, ovarian, pancreatic, and prostate cancer, in a three-step process of blood tests, imaging, and targeted treatment.

Imaging advances: microbubbles and photoacoustic probes
Imaging is an area that continues to make advances. One exciting development is the integration of multiple technologies, for example superimposing molecular information onto traditional CT scans. Contemporary scans may show that certain genes are over-expressed in the heart, for example, but obscure the specific nodule (tumor) location. Using integrins to bind to cancerous areas may allow their specific location to be detected (4 mm nodules now, and perhaps 2-3 mm nodules as scanning technologies continue to improve).

Other examples of integrated imaging technologies include microbubbles, which are gassy and can be detected with an ultrasound probe as they are triggered to vibrate. Similarly, photoacoustic probes use light to perturb cancerous tissue, and then sound detection tools transmit the vibrations. Smart probes are being explored to detect a variety of metaloproteases on the surface of cancer cells, breaking apart and entering cancer cells where they can be detected with an ultrasound probe.

Systems biology approaches to cancer
Similar to aging research, some of the most promising progress points in cancer research are due to a more systemic understanding of disease, and the increasing ability to use tools like gene expression analysis to trace processes across time. One example is being able to identify and model not just one, but whole collections of genes that may be expressed differentially in cancers, seeing that whole pathways are disrupted, and the downstream implications of this.

Cancer causality
Also as in aging research, the 'chicken or the egg' problem arises as multiple things that go wrong are identified, but which happens first, and causality, is still unknown. For example, in ovarian cancer, where there are often mutations in the p53 gene, and gene rearrangements and CNV (copy number variation; different numbers of copies of certain genes), but which occurs first and what causes both is unknown.

Predictive disease modeling
There continues to be a need for models that predict clinical outcome, and serve as accurate representations of disease. DNA and gene expression, integrated with traits and other phenotypic data in global coherent datasets could allow the ability to build probabilistic causal models of disease. It also may be appropriate to shift to physics/accelerator-type models to manage the scale of data now being generated and reviewed in biomedicine.

Monday, May 24, 2010

Human-data interface

There is starting to be more data than ever available to individuals, including personal data, every detail of interactions, activities, behaviors, and health habits tracked. Some contend that humans are not ready to interact with massive data and that a common response is to ignore it. However, humans quickly adapt to most situations and this is the likely course with big data interaction.

Data is likely to only become more pervasive and intimate, and extraordinarily useful to those who can harness it.

There is a structural factor with data collection that could be masking the value of the information – timeframes and scale. Data is collected on daily or hourly timeframes, but may be most useful 1) being dormant, a reserve reference with which to compare when anomalies arise, and 2) longitudinally, reflecting attributes over long time scales; seeing a change in sleep patterns over decades for example. Another example is that while it might seem useless to record one's temperature every day, knowing the average temperatures and heart rates of individuals in a community and any deltas (changes) could be quite useful in predicting the spread and magnitude of pandemics such as H1N1.

Sunday, May 16, 2010

Unified health data climate

The future of health management and biosecurity is having always-on access to the health data climate of individuals, families, communities, and countries. A whole new era of health awareness and self-management could be possible. Ideally, health data streams would be automatically captured and parsed into a comprehensive tableau of status monitoring and action-taking.

Key health data streams (Figure 1):

  1. Genome - whole human genome sequence, abnormal tissue sequences (cancer, etc.)
  2. Phenotype - current status of a wide range of biophysical markers including blood-based organ-secreted proteins prognosticating disease, cholesterol levels, blood pressure, and emotional state
  3. Diseasome - catalog of cumulative immune system exposures and predicted response to toxins
  4. Microbiome - microflora bacteria profile (gut, genital, skin, oral, etc.)
  5. Environmentome - external environment measures including air and water quality, pollen/allergens count

Figure 1: Key health data streams.

Sunday, May 09, 2010

The big data graph era

With the start of the big data era and the ability to collect, store and render meaningful numerous data points, the cultural outlook of the world is shifting too. Graphs, graphs, graphs. Individuals and communities have a social graph, taste graph, preference graph, affinity graph, attention graph, intention graph, values graph, emotion graph, health graph and more.

Graphing theory is being applied to many new contexts such as social networks, media consumption, nanotechnology fabrication, gaming, and genomic analysis and could be one of the many data analysis techniques applied to any large dataset. VLDS – very large datasets – and moving back into the cloud mean that sophisticated data analysis and artificial intelligence techniques could be an expected feature of websites just like social networking commentary and gaming elements have become today.

Sunday, May 02, 2010

The preference economy

There is now the new era of a multicurrency society. Numerous non-monetary currencies are coveted, amassed and exchanged including reputation, social graph, time, ideas, intention, attention, affinity, preference, health, and resource access.

The internet is already doing a good job of serving as a clearing exchange and means of valuation for the currencies of reputation, social graph, intention, and attention.

The next generation of economy 3.0 startups is building even more dimensionality into the multicurrency society.

Blippy broadcasts purchasing activity and serves as a leading indicator for public company quarterly sales; a real-time economy feed.
Hunch goes a step further with the grand vision of mapping and predicting the affinity of all people for all objects.
For example, what is any individual’s preference for Nike, TikTok, Slaughterhouse-Five, Ulan Bator, existentialism, or any other noun, brand, product, item, object or idea. Social feed “likes” are already being mined for preference, affinity, and revenue.

Value, preference, and affinity could become an expected attribute of any product, brand, website, and experience just like social networking is and gaming principles are starting to be. These seemingly unobtrusive currencies could stream nicely into exchange via automatic markets.