An Engineered Nanocomplex with Photodynamic and Photothermal Synergistic Properties for Cancer Treatment.

Photodynamic therapy (PDT) and photothermal therapy (PTT) are promising therapeutic methods for cancer treatment; however, as single modality therapies, either PDT or PTT is still limited in its success rate. A dual application of both PDT and PTT, in a combined protocol, has gained immense interest. In this study, gold nanoparticles (AuNPs) were conjugated with a PDT agent, meso-tetrahydroxyphenylchlorin (mTHPC) photosensitizer, designed as nanotherapeutic agents that can activate a dual photodynamic/photothermal therapy in SH-SY5Y human neuroblastoma cells. The AuNP-mTHPC complex is biocompatible, soluble, and photostable. PDT efficiency is high because of immediate reactive oxygen species (ROS) production upon mTHPC activation by the 650-nm laser, which decreased mitochondrial membrane potential (∆ψm). Likewise, the AuNP-mTHPC complex is used as a photoabsorbing (PTA) agent for PTT, due to efficient plasmon absorption and excellent photothermal conversion characteristics of AuNPs under laser irradiation at 532 nm. Under the laser irradiation of a PDT/PTT combination, a twofold phototoxicity outcome follows, compared to PDT-only or PTT-only treatment. This indicates that PDT and PTT have synergistic effects together as a combined therapeutic method. Our study aimed at applying the AuNP-mTHPC approach as a potential treatment of cancer in the biomedical field.

Eradication of multidrug-resistant pseudomonas biofilm with pulsed electric fields.

Biofilm formation is a significant problem, accounting for over eighty percent of microbial infections in the body. Biofilm eradication is problematic due to increased resistance to antibiotics and antimicrobials as compared to planktonic cells. The purpose of this study was to investigate the effect of Pulsed Electric Fields (PEF) on biofilm-infected mesh. Prolene mesh was infected with bioluminescent Pseudomonas aeruginosa and treated with PEF using a concentric electrode system to derive, in a single experiment, the critical electric field strength needed to kill bacteria. The effect of the electric field strength and the number of pulses (with a fixed pulse length duration and frequency) on bacterial eradication was investigated. For all experiments, biofilm formation and disruption were confirmed with bioluminescent imaging and Scanning Electron Microscopy (SEM). Computation and statistical methods were used to analyze treatment efficiency and to compare it to existing theoretical models. In all experiments 1500 V are applied through a central electrode, with pulse duration of 50 µs, and pulse delivery frequency of 2 Hz. We found that the critical electric field strength (Ecr) needed to eradicate 100-80% of bacteria in the treated area was 121 ± 14 V/mm when 300 pulses were applied, and 235 ± 6.1 V/mm when 150 pulses were applied. The area at which 100-80% of bacteria were eradicated was 50.5 ± 9.9 mm(2) for 300 pulses, and 13.4 ± 0.65 mm(2) for 150 pulses. 80% threshold eradication was not achieved with 100 pulses. The results indicate that increased efficacy of treatment is due to increased number of pulses delivered. In addition, we that showed the bacterial death rate as a function of the electrical field follows the statistical Weibull model for 150 and 300 pulses. We hypothesize that in the clinical setting, combining systemic antibacterial therapy with PEF will yield a synergistic effect leading to improved eradication of mesh infections.

A Real-Time Kinect Signature-Based Patient Home Monitoring System.

Assessment of body kinematics during performance of daily life activities at home plays a significant role in medical condition monitoring of elderly people and patients with neurological disorders. The affordable and non-wearable Microsoft Kinect ("Kinect") system has been recently used to estimate human subject kinematic features. However, the Kinect suffers from a limited range and angular coverage, distortion in skeleton joints' estimations, and erroneous multiplexing of different subjects' estimations to one. This study addresses these limitations by incorporating a set of features that create a unique "Kinect Signature". The Kinect Signature enables identification of different subjects in the scene, automatically assign the kinematics feature estimations only to the subject of interest, and provide information about the quality of the Kinect-based estimations. The methods were verified by a set of experiments, which utilize real-time scenarios commonly used to assess motor functions in elderly subjects and in subjects with neurological disorders. The experiment results indicate that the skeleton based Kinect Signature features can be used to identify different subjects in high accuracy. We demonstrate how these capabilities can be used to assign the Kinect estimations to the Subject of Interest, and exclude low quality tracking features. The results of this work can help in establishing reliable kinematic features, which can assist in future to obtain objective scores for medical analysis of patient condition at home while not restricted to perform daily life activities.

Calling louder and longer: how bats use biosonar under severe acoustic interference from other bats.

Active-sensing systems such as echolocation provide animals with distinct advantages in dark environments. For social animals, however, like many bat species, active sensing can present problems as well: when many individuals emit bio-sonar calls simultaneously, detecting and recognizing the faint echoes generated by one's own calls amid the general cacophony of the group becomes challenging. This problem is often termed 'jamming' and bats have been hypothesized to solve it by shifting the spectral content of their calls to decrease the overlap with the jamming signals. We tested bats' response in situations of extreme interference, mimicking a high density of bats. We played-back bat echolocation calls from multiple speakers, to jam flying Pipistrellus kuhlii bats, simulating a naturally occurring situation of many bats flying in proximity. We examined behavioural and echolocation parameters during search phase and target approach. Under severe interference, bats emitted calls of higher intensity and longer duration, and called more often. Slight spectral shifts were observed but they did not decrease the spectral overlap with jamming signals. We also found that pre-existing inter-individual spectral differences could allow self-call recognition. Results suggest that the bats' response aimed to increase the signal-to-noise ratio and not to avoid spectral overlap.

Human body parts tracking and kinematic features assessment based on RSSI and inertial sensor measurements.

Acquisition of patient kinematics in different environments plays an important role in the detection of risk situations such as fall detection in elderly patients, in rehabilitation of patients with injuries, and in the design of treatment plans for patients with neurological diseases. Received Signal Strength Indicator (RSSI) measurements in a Body Area Network (BAN), capture the signal power on a radio link. The main aim of this paper is to demonstrate the potential of utilizing RSSI measurements in assessment of human kinematic features, and to give methods to determine these features. RSSI measurements can be used for tracking different body parts' displacements on scales of a few centimeters, for classifying motion and gait patterns instead of inertial sensors, and to serve as an additional reference to other sensors, in particular inertial sensors. Criteria and analytical methods for body part tracking, kinematic motion feature extraction, and a Kalman filter model for aggregation of RSSI and inertial sensor were derived. The methods were verified by a set of experiments performed in an indoor environment. In the future, the use of RSSI measurements can help in continuous assessment of various kinematic features of patients during their daily life activities and enhance medical diagnosis accuracy with lower costs.

A predictive model for personalization of nanotechnology-based phototherapy in cancer treatment.

A major challenge in radiation oncology is the prediction and optimization of clinical responses in a personalized manner. Recently, nanotechnology-based cancer treatments are being combined with photodynamic therapy (PDT) and photothermal therapy (PTT). Predictive models based on machine learning techniques can be used to optimize the clinical setup configuration, including such parameters as laser radiation intensity, treatment duration, and nanoparticle features. In this article we demonstrate a methodology that can be used to identify the optimal treatment parameters for PDT and PTT by collecting data from in vitro cytotoxicity assay of PDT/PTT-induced cell death using a single nanocomplex. We construct three machine learning prediction models, employing regression, interpolation, and low- degree analytical function fitting, to predict the laser radiation intensity and duration settings that maximize the treatment efficiency. To examine the accuracy of these prediction models, we construct a dedicated dataset for PDT, PTT, and a combined treatment; this dataset is based on cell death measurements after light radiation treatment and is divided into training and test sets. The preliminary results show that the performance of all three models is sufficient, with death rate errors of 0.09, 0.15, and 0.12 for the regression, interpolation, and analytical function fitting approaches, respectively. Nevertheless, due to its simple form, the analytical function method has an advantage in clinical application and can be used for further analysis of the sensitivity of performance to the treatment parameters. Overall, the results of this study form a baseline for a future personalized prediction model based on machine learning in the domain of combined nanotechnology- and phototherapy-based cancer treatment.

Turning the Finger into a Writing Tool.

A novel writing platform composed of a wearable sensor on the fingernail and classification algorithms is described. Findings from using this platform to translate fingertip writing into shapes, letters, and numbers on a range of surfaces are reported. The new wearable platform leverages an architecture with miniaturized electronic circuitry to precisely measure a set of forces in the longitudinal and transverse directions using multiple strain gauges. We find that the directional pressure patterns are translated from the fingertip to the fingernail. Deformation of fingernails in the longitudinal and transverse directions are detected by the fingernail sensor which sends the data wirelessly to a portable electronic system. Fingernail pressure patterns are categorized through signal processing to recognize a range of shapes, numbers, and letters, enabling fingertip writing recognition. Use of the writing platform following a short training session, shows human fingertip writing on multiple surfaces were automatically transcribed to a computer.

Wearable Nail Deformation Sensing for Behavioral and Biomechanical Monitoring and Human-Computer Interaction.

The dynamics of the human fingertip enable haptic sensing and the ability to manipulate objects in the environment. Here we describe a wearable strain sensor, associated electronics, and software to detect and interpret the kinematics of deformation in human fingernails. Differential forces exerted by fingertip pulp, rugged connections to the musculoskeletal system and physical contact with the free edge of the nail plate itself cause fingernail deformation. We quantify nail warpage on the order of microns in the longitudinal and lateral axes with a set of strain gauges attached to the nail. The wearable device transmits raw deformation data to an off-finger device for interpretation. Simple motions, gestures, finger-writing, grip strength, and activation time, as well as more complex idioms consisting of multiple grips, are identified and quantified. We demonstrate the use of this technology as a human-computer interface, clinical feature generator, and means to characterize workplace tasks.

Noncontact tremor characterization using low-power wideband radar technology.

Continuous monitoring and analysis of tremor is important for the diagnosis and establishment of treatments in many neurological disorders. This paper describes noncontact assessment of tremor characteristics obtained by an experimental new ultrawideband (UWB) system. The system is based on transmission of a wideband electromagnetic signal with extremely low power, and analysis of the received signal, which is composed of many propagation paths reflected from the patient and its surroundings. A description of the physical principles behind the technology, a criterion, and efficient algorithms to assess tremor characteristics from the bulk UWB measurements are given. A feasibility test for the technology was conducted using a UWB system prototype, an arm model that mimics tremor, and a reference video system. The set of UWB system frequencies and amplitudes estimations were highly correlated with the video system estimations with an average error in the scale of 0.1 Hz and 1 mm for the frequency and amplitude estimations, respectively. The new UWB-based system does not require attaching active markers or inertial sensors to the body, can give displacement information and kinematic features from multiple body parts, is not limited by the range captured by the optical lens, has high indoor volume coverage as it can penetrate through walls, and does not require calibration to obtain amplitude estimations.