Estimation of Tidal Volume Using Load Cells on a Hospital Bed.

Although respiratory failure is one of the primary causes of admission to intensive care, the importance placed on measurement of respiratory parameters is commonly overshadowed compared to cardiac parameters. With the increased demand for unobtrusive yet quantifiable respiratory monitoring, many technologies have been proposed recently. However, there are challenges to be addressed for such technologies to enable widespread use. In this work, we explore the feasibility of using load cell sensors embedded on a hospital bed for monitoring respiratory rate (RR) and tidal volume (TV). We propose a globalized machine learning (ML)-based algorithm for estimating TV without the requirement of subject-specific calibration or training. In a study of 15 healthy subjects performing respiratory tasks in four different postures, the outputs from four load cell channels and the reference spirometer were recorded simultaneously. A signal processing pipeline was implemented to extract features that capture respiratory movement and the respiratory effects on the cardiac (i.e., ballistocardiogram, BCG) signals. The proposed RR estimation algorithm achieved a root mean square error (RMSE) of 0.6 breaths per minute (brpm) against the ground truth RR from the spirometer. The TV estimation results demonstrated that combining all three axes of the low-frequency force signals and the BCG heartbeat features best quantifies the respiratory effects of TV. The model resulted in a correlation and RMSE between the estimated and true TV values of 0.85 and 0.23 L, respectively, in the posture independent model without electrocardiogram (ECG) signals. This study suggests that load cell sensors already existing in certain hospital beds can be used for convenient and continuous respiratory monitoring in general care settings.

Accurate Ballistocardiogram Based Heart Rate Estimation Using an Array of Load Cells in a Hospital Bed.

The ballistocardiogram (BCG), a cardiac vibration signal, has been widely investigated for continuous monitoring of heart rate (HR). Among BCG sensing modalities, a hospital bed with multi-channel load-cells could provide robust HR estimation in hospital setups. In this work, we present a novel array processing technique to improve the existing HR estimation algorithm by optimizing the fusion of information from multiple channels. The array processing includes a Gaussian curve to weight the joint probability according to the reference value obtained from the previous inter-beat-interval (IBI) estimations. Additionally, the probability density functions were selected and combined according to their reliability measured by q-values. We demonstrate that this array processing significantly reduces the HR estimation error compared to state-of-the-art multi-channel heartbeat detection algorithms in the existing literature. In the best case, the average mean absolute error (MAE) of 1.76 bpm in the supine position was achieved compared to 2.68 bpm and 1.91 bpm for two state-of-the-art methods from the existing literature. Moreover, the lowest error was found in the supine posture (1.76 bpm) and the highest in the lateral posture (3.03 bpm), thus elucidating the postural effects on HR estimation. The IBI estimation capability was also evaluated, with a MAE of 16.66 ms and confidence interval (95%) of 38.98 ms. The results demonstrate that improved HR estimation can be obtained for a bed-based BCG system with the multi-channel data acquisition and processing approach described in this work.

Dicyanovinylnaphthalenes for neuroimaging of amyloids and relationships of electronic structures and geometries to binding affinities.

The positron-emission tomography (PET) probe 2-(1-[6-[(2-fluoroethyl)(methyl)amino]-2-naphthyl]ethylidene) (FDDNP) is used for the noninvasive brain imaging of amyloid-ß (Aß) and other amyloid aggregates present in Alzheimer's disease and other neurodegenerative diseases. A series of FDDNP analogs has been synthesized and characterized using spectroscopic and computational methods. The binding affinities of these molecules have been measured experimentally and explained through the use of a computational model. The analogs were created by systematically modifying the donor and the acceptor sides of FDDNP to learn the structural requirements for optimal binding to Aß aggregates. FDDNP and its analogs are neutral, environmentally sensitive, fluorescent molecules with high dipole moments, as evidenced by their spectroscopic properties and dipole moment calculations. The preferred solution-state conformation of these compounds is directly related to the binding affinities. The extreme cases were a nonplanar analog t-butyl-FDDNP, which shows low binding affinity for Aß aggregates (520 nM K(i)) in vitro and a nearly planar tricyclic analog cDDNP, which displayed the highest binding affinity (10 pM K(i)). Using a previously published X-ray crystallographic model of 1,1-dicyano-2-[6-(dimethylamino)naphthalen-2-yl]propene (DDNP) bound to an amyloidogenic Aß peptide model, we show that the binding affinity is inversely related to the distortion energy necessary to avoid steric clashes along the internal surface of the binding channel.

A review of imaging agent development.

This educational review highlights the processes, opportunities, and challenges encountered in the discovery and development of imaging agents, mainly positron emission tomography and single-photon emission computed tomography tracers. While the development of imaging agents parallels the drug development process, unique criteria are needed to identify opportunities for new agents. Imaging agent development has the flexibility to pursue functional or nonfunctional targets as long as they play a role in the specific disease or mechanism of interest and meet imageability requirements. However, their innovation is tempered by relatively small markets for diagnostic imaging agents, intellectual property challenges, radiolabeling constraints, and adequate target concentrations for imaging. At the same time, preclinical imaging is becoming a key translational tool for proof of mechanism and concept studies. Pharmaceutical and imaging industries face a common bottleneck in the form of the limited number of trials one company can possibly perform. However, microdosing and theranostics are evidence that partnerships between pharmaceutical and imaging companies can accelerate clinical translation of tracers and therapeutic interventions. This manuscript will comment on these aspects to provide an educational review of the discovery and development processes for imaging agents.

A computational positron emission tomography simulation model for imaging beta-amyloid in mice.

PURPOSE: We aimed to develop a computational simulation model for beta-amyloid (Abeta) positron emission tomography (PET) imaging. PROCEDURES: Model parameters were set to reproduce levels of Abeta within the PDAPP mouse. Pharmacokinetic curves of virtual tracers were computed and a PET detector simulator was configured for a commercially available preclinical PET-imaging system. RESULTS: We modeled the effects of Abeta therapy and tracer affinity on the ability to differentiate Abeta levels by PET. Varying affinity had a significant effect on the ability to quantitate Abeta. Further, PET tracers for Abeta monomers were more sensitive to the therapeutic reduction in Abeta levels than total brain amyloid. Following therapy, the decrease in total brain Abeta corresponded to the slow rate of change in total amyloid load as expected. CONCLUSIONS: We have developed a first proof-of-concept Abeta-PET simulation model that will be a useful tool in the interpretation of preclinical Abeta imaging data and tracer development.

2-Dialkylamino-6-acylmalononitrile substituted naphthalenes (DDNP analogs): novel diagnostic and therapeutic tools in Alzheimer's disease.

This article presents a comprehensive review of the in vitro and in vivo detection of neurofibrillary tangles (NFTs) and beta-amyloid senile plaques (SPs), neuropathological lesions found in the brains of the Alzheimer's disease (AD) patients, using FDDNP and its analogs. FDDNP and its analogs have excellent ability to bind to NFTs and SPs in vitro as shown by binding assays, confocal fluorescence microscopy with stained AD brain tissue and digital autoradiography with [18F]FDDNP. [18F]FDDNP-PET molecular imaging permits detection of these pathologies in living subjects. The discovery of a new binding site to Abeta(1-40) fibrils as a result of FDDNP binding also opens a therapeutic opportunity for early treatment of Alzheimer's disease. FDDNP shares a previously unrecognized common binding site on Abeta(1-40) fibrils and senile plaques with non-steroidal anti-inflammatory drugs (NSAIDs) (e.g., naproxen and ibuprofen). Naproxen, ibuprofen and even FDDNP significantly inhibit aggregation of the Abeta(1-40) peptide in the micromolar range. This new binding site on Abeta(1-40) fibrils also offers a molecular template for design of anti-aggregation drugs without the secondary effects of NSAIDs. Therefore it is anticipated that a new vision for prevention, early diagnosis and treatment of Alzheimer's disease would be rapidly developing.

Localization of neurofibrillary tangles and beta-amyloid plaques in the brains of living patients with Alzheimer disease.

The authors used 2-(1-(6-[(2-[18F]fluoroethyl)(methyl)amino]-2-naphthyl)ethylidene)malononitrile ([18F]FDDNP), a hydrophobic radiofluorinated derivative of 2-(1-[6-(dimethylamino)-2-naphthyl]ethylidene)malononitrile (DDNP), in conjunction with positron emission tomography to determine the localization and load of neurofibrillary tangles (NFTs) and beta-amyloid senile plaques (APs) in the brains of living Alzheimer disease (AD) patients. Previous work illustrated the in vitro binding characteristics of [18F]FDDNP to synthetic beta-amyloid(1-40) fibrils and to NFTs and APs in human AD brain specimens. In the present study, greater accumulation and slower clearance was observed in AP- and NFT-dense brain areas and correlated with lower memory performance scores. The relative residence time of the probe in brain regions affected by AD was significantly greater in patients with AD (n=9) than in control subjects (n=7; p=0.0007). This noninvasive technique for monitoring AP and NFT development is expected to facilitate diagnostic assessment of patients with AD and assist in response-monitoring during experimental treatments.

In vivo brain imaging of tangle burden in humans.

Cerebral neurofibrillary tangles (NFTs) accumulate in a predictable sequence decades before the clinical symptoms of Alzheimer's disease emerge, and the degree of tangle degeneration correlates with the severity of cognitive impairment. A valid in vivo marker of tangle burden, therefore, would be useful for presymptomatic and symptomatic disease detection and treatment monitoring. Recent advances using positron emission tomography (PET) indicate the feasibility of in vivo imaging that provides a combined signal of both neurofibrillary tangles and senile plaques. Such results are encouraging that a tangle-specific marker will be found; however, several methodological issues first need to be addressed, including scanner spatial resolution in the relatively small brain regions where tangles accumulate. NFT-specific imaging probes will need to be lipophilic in order to cross the blood-brain barrier and neuronal membranes and have a high binding affinity to NFTs with minimal nonspecific binding, which would result in a high signal-to-background ratio in PET images.