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Currently there is no accurate objective measure for monitoring pain during the state of drug-induced unconsciousness (such as during surgical anesthesia). Moreover, the absence of an objective measure for detecting pain hampers the physician's ability to provide an optimal dose of analgesics. We have developed a novel method for detecting pain by quantifying skin blood flow dynamics using a miniaturized dynamic light scattering (mDLS) sensor placed on the skin. Healthy awake volunteers were studied with mDLS sensors placed on both index fingers while being subjected to a series of cutaneous painful stimuli (electric shock and heat), randomly applied in a range between the subjects’ pain threshold and tolerance. Power spectrum analysis of the recorded signal was performed with a focus on two frequency bands, representing relative blood flow of non-pulsatile vessels and larger pulsatile arterioles. Relative blood flow of pulsatile vessels decreased while flow of non-pulsatile vessels increased in response to painful stimulation, with a high correlation between the responses obtained on the right and left index fingers. The changes in hemodynamics that occur during painful stimulation suggest a redistribution of blood flow between pulsatile and non-pulsatile vessels, probably related to central activation of the sympathetic system combined with local dynamic autoregulatory responses. Thus, optical parameters of skin blood flow can detect nociceptive stimuli and consequently can serve as objective biomarkers of pain.

Self-measurement of blood pressure (BP) is important for monitoring treatment of hypertension, but current instruments are cumbersome and at times also impractical, especially for the older population. Current optical solutions, such as PPG-based technologies that were developed for improving convenience, provide derived measurements that are often inaccurate, particularly for diastolic values. Alternatively, by using dynamic light scattering (DLS) we are able to measure the direct hemodynamic response. We propose a simple physical model that explains the relation between arterial pressure values and the hemodynamic response which is measured from the finger root following changes in externally applied pressure. Based on this model we have developed a small-scaled, optical, mobile device that measures BP at the finger using dynamic light scattering. The apparatus is positioned at the base of the index finger and contains a ring with an inflatable cuff with two miniaturized dynamic light scattering (mDLS) sensors situated distal to the cuff. The cuff is inflated to above systolic pressure, and changes in blood flow (hemodynamics) are measured during cuff deflation. BP measurement is carried out using specially designed algorithms based on hemodynamic indexes and waveform analysis which capture systolic and diastolic points in real-time. Using this apparatus, we measured BP from 69 patients visiting a hypertension outpatient clinic, and a control group of 15 healthy subjects. BP readings were compared with measurements recorded at the arm location with an Omron device used in the clinic. The mean absolute error (MAE) for systolic and diastolic blood pressure was 7.8 and 9 mmHg, respectively at all ranges of BP measured. In conclusion, using Elfi-Tech's innovative technology, it is possible to measure BP accurately at the finger location using a compact, convenient mDLS-based device with high accuracy.

Recent advances have enabled powerful methods to sort tumors into prognosis and treatment groups. We are still missing, however, a general theoretical framework to understand the vast diversity of tumor gene expression and mutations. Here we present a framework based on multi-task evolution theory, using the fact that tumors need to perform multiple tasks that contribute to their fitness. We find that trade-offs between tasks constrain tumor gene-expression to a continuum bounded by a polyhedron whose vertices are gene-expression profiles, each specializing in one task. We find five universal cancer tasks across tissue-types: cell-division, biomass and energy, lipogenesis, immune-interaction and invasion and tissue-remodeling. Tumors that specialize in a task are sensitive to drugs that interfere with this task. Driver, but not passenger, mutations tune gene-expression towards specialization in specific tasks. This approach can integrate additional types of molecular data into a framework of tumor diversity grounded in evolutionary theory.

Recent advances have enabled powerful methods to sort tumors into prognosis and treatment groups. We are still missing, however, a general theoretical framework to understand the vast diversity of tumor gene expression and mutations. Here we present a framework based on multi-task evolution theory, using the fact that tumors need to perform multiple tasks that contribute to their fitness. We find that trade-offs between tasks constrain tumor gene-expression to a continuum bounded by a polyhedron whose vertices are gene-expression profiles, each specializing in one task. We find five universal cancer tasks across tissue-types: cell-division, biomass and energy, lipogenesis, immune-interaction and invasion and tissue-remodeling. Tumors that specialize in a task are sensitive to drugs that interfere with this task. Driver, but not passenger, mutations tune gene-expression towards specialization in specific tasks. This approach can integrate additional types of molecular data into a framework of tumor diversity grounded in evolutionary theory.

Funder: Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung (Swiss National Science Foundation); doi: https://doi.org/10.13039/501100001711, Funder: Abisch-Frenkel-Stiftung (Abisch-Frenkel Foundation); doi: https://doi.org/10.13039/501100007362, Recent advances have enabled powerful methods to sort tumors into prognosis and treatment groups. We are still missing, however, a general theoretical framework to understand the vast diversity of tumor gene expression and mutations. Here we present a framework based on multi-task evolution theory, using the fact that tumors need to perform multiple tasks that contribute to their fitness. We find that trade-offs between tasks constrain tumor gene-expression to a continuum bounded by a polyhedron whose vertices are gene-expression profiles, each specializing in one task. We find five universal cancer tasks across tissue-types: cell-division, biomass and energy, lipogenesis, immune-interaction and invasion and tissue-remodeling. Tumors that specialize in a task are sensitive to drugs that interfere with this task. Driver, but not passenger, mutations tune gene-expression towards specialization in specific tasks. This approach can integrate additional types of molecular data into a framework of tumor diversity grounded in evolutionary theory.