Identification of adequate neurally adjusted ventilatory assist (NAVA) during systematic increases in the NAVA level

Neurally adjusted ventilatory assist (NAVA) delivers airway pressure (P(aw)) in proportion to the electrical activity of the diaphragm (EAdi) using an adjustable proportionality constant (NAVA level, cm·H(2)O/μV). During systematic increases in the NAVA level, feedback-controlled down-regulation of the EAdi results in a characteristic two-phased response in P(aw) and tidal volume (Vt). The transition from the 1st to the 2nd response phase allows identification of adequate unloading of the respiratory muscles with NAVA (NAVA(AL)). We aimed to develop and validate a mathematical algorithm to identify NAVA(AL). P(aw), Vt, and EAdi were recorded while systematically increasing the NAVA level in 19 adult patients. In a multistep approach, inspiratory P(aw) peaks were first identified by dividing the EAdi into inspiratory portions using Gaussian mixture modeling. Two polynomials were then fitted onto the curves of both P(aw) peaks and Vt. The beginning of the P(aw) and Vt plateaus, and thus NAVA(AL), was identified at the minimum of squared polynomial derivative and polynomial fitting errors. A graphical user interface was developed in the Matlab computing environment. Median NAVA(AL) visually estimated by 18 independent physicians was 2.7 (range 0.4 to 5.8) cm·H(2)O/μV and identified by our model was 2.6 (range 0.6 to 5.0) cm·H(2)O/μV. NAVA(AL) identified by our model was below the range of visually estimated NAVA(AL) in two instances and was above in one instance. We conclude that our model identifies NAVA(AL) in most instances with acceptable accuracy for application in clinical routine and research.

Glucagon-like peptide-1 versus somatostatin receptor targeting reveals 2 distinct forms of malignant insulinomas

Glucagon-like peptide-1 (GLP-1) receptor imaging is superior to somatostatin receptor subtype 2 (sst(2)) imaging in localizing benign insulinomas. Here, the role of GLP-1 and sst(2) receptor imaging in the management of malignant insulinoma patients was investigated.

SMARTDIAB: a communication and information technology approach for the intelligent monitoring, management and follow-up of type 1 diabetes patients

SMARTDIAB is a platform designed to support the monitoring, management, and treatment of patients with type 1 diabetes mellitus (T1DM), by combining state-of-the-art approaches in the fields of database (DB) technologies, communications, simulation algorithms, and data mining. SMARTDIAB consists mainly of two units: 1) the patient unit (PU); and 2) the patient management unit (PMU), which communicate with each other for data exchange. The PMU can be accessed by the PU through the internet using devices, such as PCs/laptops with direct internet access or mobile phones via a Wi-Fi/General Packet Radio Service access network. The PU consists of an insulin pump for subcutaneous insulin infusion to the patient and a continuous glucose measurement system. The aforementioned devices running a user-friendly application gather patient's related information and transmit it to the PMU. The PMU consists of a diabetes data management system (DDMS), a decision support system (DSS) that provides risk assessment for long-term diabetes complications, and an insulin infusion advisory system (IIAS), which reside on a Web server. The DDMS can be accessed from both medical personnel and patients, with appropriate security access rights and front-end interfaces. The DDMS, apart from being used for data storage/retrieval, provides also advanced tools for the intelligent processing of the patient's data, supporting the physician in decision making, regarding the patient's treatment. The IIAS is used to close the loop between the insulin pump and the continuous glucose monitoring system, by providing the pump with the appropriate insulin infusion rate in order to keep the patient's glucose levels within predefined limits. The pilot version of the SMARTDIAB has already been implemented, while the platform's evaluation in clinical environment is being in progress.

Multi-Scale Modeling for Image Analysis of Brain Tumor Studies

Image-based modeling of tumor growth combines methods from cancer simulation and medical imaging. In this context, we present a novel approach to adapt a healthy brain atlas to MR images of tumor patients. In order to establish correspondence between a healthy atlas and a pathologic patient image, tumor growth modeling in combination with registration algorithms is employed. In a first step, the tumor is grown in the atlas based on a new multi-scale, multi-physics model including growth simulation from the cellular level up to the biomechanical level, accounting for cell proliferation and tissue deformations. Large-scale deformations are handled with an Eulerian approach for finite element computations, which can operate directly on the image voxel mesh. Subsequently, dense correspondence between the modified atlas and patient image is established using nonrigid registration. The method offers opportunities in atlasbased segmentation of tumor-bearing brain images as well as for improved patient-specific simulation and prognosis of tumor progression.

Bombesin antagonist-based radioligands for translational nuclear imaging of gastrin-releasing peptide receptor-positive tumors

Bombesin receptors are overexpressed on a variety of human tumors. In particular, the gastrin-releasing peptide receptor (GRPr) has been identified on prostate and breast cancers and on gastrointestinal stromal tumors. The current study aims at developing clinically translatable bombesin antagonist-based radioligands for SPECT and PET of GRPr-positive tumors.

Evaluation of 177Lu-DOTA-sst2 antagonist versus 177Lu-DOTA-sst2 agonist binding in human cancers in vitro

Somatostatin receptor targeting of neuroendocrine tumors using radiolabeled somatostatin agonists is today an established method to image and treat cancer patients. However, in a study using an animal tumor model, somatostatin receptor antagonists were shown to label sst(2)- and sst(3)-expressing tumors in vivo better than agonists, with comparable affinity even though they are not internalized into the tumor cell. In the present study, we evaluated the in vitro binding of the antagonist (177)Lu-DOTA-pNO(2)-Phe-c (DCys-Tyr-DTrp-Lys-Thr-Cys) DTyrNH(2) ((177)Lu-DOTA-BASS) or the (177)Lu-DOTATATE agonist to sst(2)-expressing human tumor samples.

PET of somatostatin receptor-positive tumors using 64Cu- and 68Ga-somatostatin antagonists: the chelate makes the difference

Somatostatin-based radiolabeled peptides have been successfully introduced into the clinic for targeted imaging and radionuclide therapy of somatostatin receptor (sst)-positive tumors, especially of subtype 2 (sst2). The clinically used peptides are exclusively agonists. Recently, we showed that radiolabeled antagonists may be preferable to agonists because they showed better pharmacokinetics, including higher tumor uptake. Factors determining the performance of radioantagonists have only scarcely been studied. Here, we report on the development and evaluation of four (64)Cu or (68)Ga radioantagonists for PET of sst2-positive tumors.

68Ga-DOTATOC versus 68Ga-DOTATATE PET/CT in functional imaging of neuroendocrine tumors

Radiolabeled somatostatin analogs represent valuable tools for both in vivo diagnosis and therapy of neuroendocrine tumors (NETs) because of the frequent tumoral overexpression of somatostatin receptors (sst). The 2 compounds most often used in functional imaging with PET are (68)Ga-DOTATATE and (68)Ga-DOTATOC. Both ligands share a quite similar sst binding profile. However, the in vitro affinity of (68)Ga-DOTATATE in binding the sst subtype 2 (sst2) is approximately 10-fold higher than that of (68)Ga-DOTATOC. This difference may affect their efficiency in the detection of NET lesions because it is the sst2 that is predominantly overexpressed in NET. We thus compared the diagnostic value of PET/CT with both radiolabeled somatostatin analogs ((68)Ga-DOTATATE and (68)Ga-DOTATOC) in the same NET patients.

Response and long-term control of bone metastases after peptide receptor radionuclide therapy with (177)Lu-octreotate

Peptide receptor radionuclide therapy (PRRT) is an efficient treatment for gastroenteropancreatic neuroendocrine tumors (GEP NETs), with outstanding overall response rates and survival. However, little is known about the particular efficacy regarding bone metastasis (BM).