A surgical tool includes opposing jaws, handles and at least one pressure sensor. Another aspect of a surgical tool includes opposing jaws with each having an organ-contacting surface area of at least 50 mm.sup.2. A further aspect of a surgical tool includes an electronic controller connected to at least one pressure sensor and automatically adapted to calculate or determine an organ-hardness from a sensor when jaws are moved to an organ-compressing position. In yet another aspect of a surgical tool, a pressure sensor is mounted to a pancreas-compressing surface and a displacement transducer or sensor is mounted to and/or located within a handle coupled to the surface, and an electronic controller is mounted to and/or located within the handle for calculating a hardness of a pancreas and/or other organ.

The subject matter of the present disclosure generally relates to techniques for neuromodulation that include applying energy (e.g., ultrasound energy) into the tissue to cause a change in a glucose transporter pathway molecule and/or an incretin pathway molecule. In one embodiment, the neuromodulation is performed as a treatment of a metabolic disorder.

Embodiments of the present invention relate to non-invasive methods and compositions for collecting detecting, measuring, and identifying target molecules. In some embodiments, methods and compositions relate to target molecules in gastrointestinal lavage fluid or feces.

The present invention provides method for monitoring physiological status of an organ in a subject by monitoring morphological changes over time in transplanted tissue on an eye of the subject.

Disclosed are an imaging method using fluoroquinolone antibiotics and an imaging device for the same, in which biological tissue is stained with Moxifloxacin as one of fluoroquinolone antibiotics, and the stained biological tissue is subjected to fluorescent image-capture through single-photon excitation with either near-ultraviolet or visible wavelength light instead of either a middle-ultraviolet light source or a femtosecond near-infrared laser device, thereby obtaining morphological information of cells in the biological tissue at a high speed without damage. To this end, an imaging method of using fluoroquinolone antibiotics includes: staining cells of the biological tissue with fluoroquinolone antibiotics; illuminating the excitation light from a light source to the biological tissue stained with the fluoroquinolone antibiotics; and capturing an image of the biological tissue through the fluoroquinolone antibiotics based fluorescence caused by the excitation light illuminated to the biological tissue, wherein the excitation light from the light source includes either near-violet or short visible wavelength light for single photon excitation of the fluoroquinolone antibiotics.

The present invention provides a system for sensing physiological characteristics. The system includes a stimulating light emitting unit, a potential measuring unit, an analog-to-digital converting unit, a characteristic parameter group filtering unit, a characteristic parameter group storing unit, a comparing and calculating unit, an analysis unit, a display unit and a power supply unit. The present invention utilizes external signal light beams to stimulate the skin to fetch data of potential changes, and further observes the status of various organs or systems in the living creature by numerical means. The characteristic parameter group storing unit with the analysis unit and the display unit can remove the error probability caused by man-made operation in the prior arts, suitable for scientific research applications.

In some aspects, the present disclosure provides gadolinium based sensors which may be used to image zinc ions in vivo. In some embodiments, the compounds show appropriate reactivity with zinc ions while maintaining high relaxivity to achieve improved background relative to other sensors.

A medical image analyzing system and a medical image analyzing method are provided and include inputting at least one patient image into a first model of a first neural network module to obtain a result having determined positions and ranges of an organ and a tumor of the patient image; inputting the result into a plurality of second models of a second neural network module, respectively, to obtain a plurality of prediction values corresponding to each of the plurality of second models and a model number predicting having cancer in the plurality of prediction values; and outputting a determined result based on the model number predicting having cancer and a number threshold value. Further, processes between the first model and the second models can be automated, thereby improving identification rate of pancreatic cancer.

Methods for imaging beta cells in pancreatic tissue using radioisotopes of manganese, which may be referred to as radiomanganese, are described. Example radioisotopes of manganese include Mn-52g, Mn-52m, and Mn-51. As one example, radiomanganese can be used to image pancreatic beta cells, in which radiomanganese shows a preferential uptake. This provides for applications such as quantifying beta cell mass (e.g., functional beta cell mass), assessing transplant viability, and monitoring the efficacy of drug treatments. A pharmacological agent can be administered to modulate the uptake of divalent metals by the pancreatic beta cells, which can be correlated to a modulated uptake of radiomanganese to estimate pancreatic beta cell mass, function, or both.

A medical image analyzing system and a medical image analyzing method are provided and include inputting at least one patient image into a first model of a neural network module to obtain a result having determined positions and ranges of an organ and a tumor of the patient image; inputting the result into a second model of a first analysis module and a third model of a second analysis module, respectively, to obtain at least one first prediction value and at least one second prediction value corresponding to the patient image; and outputting a determined result based on the first prediction value and the second prediction value. Further, processes between the first model, the second model and the third model can be automated, thereby improving identification rate of pancreatic cancer.