Then, cells can be imaged directly on the magnetic sifter array and harvested by moving out the field and washing. Improvements include higher efficiency in capture with better throughput, rapid imaging of captured cells, and harvesting of viable cells, avoiding the loss of cells in preparatory steps, compared with other methods.Importantly, a portable device (CellCollector?, GILUPI NanoMedizin, Potsdam, Germany) based on EpCAM expression has been developed in the last years. This medical system showed high specificity and sensitivity for isolation of CTCs in vivo from circulating peripheral blood of breast cancer or non-small cell lung cancer (NSCLC) patients. The system is inserted through a standard venous cannula into the cubital vein for 30 min.

After the enrichment step, CTCs are identified by EpCAM and/or cytokeratin expression [31]. This device is considered a promising tool for monitoring the course of the cancer disease and the efficacy of anticancer treatment in vivo.2.2. Physical PropertiesCTCs could be also separated from blood cells according to their size. Isolation based on cell size has two main advantages: a higher capture efficiency and independence of antigen expression. When epithelia-mesenchymal transition (EMT) takes place as the step previous to metastasis, some epithelial markers are lost [65]. We have recently described the acquisition of a plasticity and stemness phenotype in CTCs from endometrial cancer patients, probab
The widespread use of information and communication technologies in today’s medical and health services, usually described by the term eHealth, is drastically changing the face of healthcare delivery.

More recently, the rapid advances in mobile and wireless communications offering almost ubiquitous and continuous connectivity through intelligent mobile devices, such as smartphones, tablets, personal digital assistants (PDAs), etc., have added a new component to the eHealth paradigm, known as mHealth [1]. A broad range of eHealth/mHealth scenarios with benefits for both patients and healthcare providers are envisioned, including remote patient monitoring, active management of chronic diseases, such as diabetes, support for independent aging to the elderly and the tracking of personal fitness activities to improve health and well-being [2].

A number of small and autonomous medical sensor devices, either wearable or implantable, are usually employed at the patient’s Anacetrapib side. Each sensor is typically highly specialized to perform a specific task, such as collecting patient’s vital signs (e.g., body temperature, brain activity, heart rate, etc.), measuring external parameters (e.g., ambient temperature, motion patterns, patient location, etc.) or even performing specific actions (e.g., the administration of a specific dosage of insulin). The sensors are usually connected to a sink central device that acts as a coordinator (i.e.

One approach in the field of crime scene documentation used the optical digitizer ��Konica-Minolta Vivid 910�� in different sensor configurations to generate a multi-resolution map [8]. This triangulation-based range sensor works within a range between 0.6 m and 2.5 m with an accuracy up to a tenth of a millimeter [13] and can be equipped with different optics (wide, middle, tele) to acquire point-clouds in different resolution classes. Using these optics, a simulated crime scene was imaged from multiple viewpoints, and various point clouds with different resolutions were recorded. These point-clouds were aligned using the semiautomatic procedure ��ImAlign�� from the commercial PolyWorks software.Nevertheless, most of the approaches required the interaction of an operator for the alignment of single 3D point-clouds or the fusion of 3D range and 2D image data.

Locating and aligning 3D-models to a scene containing multiple different objects is a well-known problem in computer vision. So-called 3D keypoint detectors [14,15] are used to generate and describe a set of distinct points of the model and all points of the scene. Thus, homologous keypoints of the model and the scene can be used to calculate the 3D-transformation matrix between both datasets.A further application where automated object recognition is required is the well-known ��bin-picking problem�� in robotics. The goal of the bin-picking approach is the automated interaction of a robot with its direct environment. Therefore, objects that should be picked up by a robot have to be identified in a 2D or 3D image.

The recognition step is commonly realized by using different feature descriptors, like, e.g., scale-invariant feature transform (SIFT) [16] or fast directional chamfer matching (FDCM) [17] in the 2D case and, e.g., the RANSAM (random sample matching) algorithm [18] in the 3D case.Using established commercial sensors for data acquisition is quite expensive and requires Batimastat intensive operator training. With the rise of different low-cost 3D sensors, an economic and simple imaging alternative is available. These sensors usually work within the same accuracy range as the more expensive ones, but with the advantage of much lower investment costs (Table 1) [19]. Furthermore, the technology of low-cost systems, which are based on consumer products, is commonly very user-friendly; thus, they are usable without intensive training.Table 1.Overview of the main sensor properties for the David and the Kinect sensor.In this study, we present an automated alignment approach for 3D point-clouds. This approach combines the advantages of multiple sensors regarding measuring volume and resolution by generating a multi-resolution map.

Point to point communication exploits an ID code that can be assigned to every autonomous sensor with the aim of univocally individuating the device. This principle is implemented in RFID technology in particular. Nowadays several RFID communication standards exist, with different working ranges and data rates, which are applied to different applications.In this paper some autonomous sensors working without batteries are presented and discussed. A classification of autonomous sensors into ��passive autonomous sensors�� and ��self powered autonomous sensors�� is introduced. ��Passive autonomous sensors�� are defined those that are just passive elements, interrogated wirelessly by a readout unit. ��Self-powered autonomous sensors�� are those that have a power-harvesting module or are supplied power by an electromagnetic field.

In the next section the general architectures of passive and self-powered autonomous sensors are described and discussed.2.?Architectures of Autonomous SensorsA general architecture of a measurement system based on a passive autonomous sensor is shown in Figure 1. The passive autonomous sensor is the sensing element in the harsh or remote area, while the readout unit is placed in the safety zone. The two elements are connected by a wireless communication exploiting an electric-magnetic, optic or acoustic link. Between the sensing element and the readout unit there is usually a barrier whose characteristics (mainly material and geometry) influence the system’s performance. The sensing element is a passive device that does not require any power supply.

The quantity under measurement is usually seen as reflected impedance by the front-end electronics contained into the readout unit.Figure 1.Block diagram of a passive autonomous sensor.Some sensing devices can be classified as passive autonomous sensors: examples are quoted in [13, 25-32]. In [25] a NiFe sensor is associated to a remote magnetic transducer and provides a contactless temperature measurement with a readout distance of about 4 mm. In [26], LED based chemical sensors use passive elements constituted by chemical sensing materials placed in the harsh environment. These elements are remotely interrogated through transmittance and reflectance absorptiometric measurements. In [27] a magnetostrictive cantilever coupled with a bio-recognition element is remotely actuated and sensed using magnetic signals.

Most passive autonomous Dacomitinib sensors use a telemetric communication constituted by two inductors, one connected to the sensitive element (in the following referred as ��readout inductor��), and the other to the measuring circuit [13, 28, 30]. In [28] a system for environmental wireless monitoring consists of a LC sensor and two loop antennas (transmitter and receiver).