Publications

The last decade has witnessed great progress in the field of adoptive cell therapies, with the authorization of Kymriah (tisagenlecleucel) in 2017 by the Food and Drug Administration (FDA) as a crucial stepstone. Since then, five more CAR-T therapies have been approved for the treatment of hematological malignancies. While this is a great step forward to treating several types of blood cancers, CAR-T cell therapies are still associated with severe side-effects such as Graft-versus-Host Disease (GvHD), cytokine release syndrome (CRS) and neurotoxicity. Because of this, there has been continued interest in Natural Killer cells which avoid these side-effects while offering the possibility to generate allogeneic cell therapies. Similar to T-cells, NK cells can be genetically modified to improve their therapeutic efficacy in a variety of ways. In contrast to T cells, viral transduction of NK cells remains inefficient and induces cytotoxic effects. Viral vectors also require a lengthy and expensive product development process and are accompanied by certain risks such as insertional mutagenesis. Therefore, non-viral transfection technologies are avidly being developed aimed at addressing these shortcomings of viral vectors. In this review we will present an overview of the potential of NK cells in cancer immunotherapies and the non-viral transfection technologies that have been explored to engineer them.Copyright © 2023 Elsevier Inc. All rights reserved.

JTD Keywords: adoptive cell therapy, cancer immunotherapy, immunotherapy, messenger-rna delivery, nanoparticle, nk cells, non -viral engineering, sonoporation, t-cell, transfection, ultrasound, Adoptive cell therapy, Cancer immunotherapy, Cell engineering, Natural-killer-cells, Nk cells, Non-viral engineering

Individual biohybrid microrobots have the potential to perform biomedical in vivo tasks such as remote-controlled drug and cell delivery and minimally invasive surgery. This work demonstrates the formation of biohybrid sperm-templated clusters under the influence of an external magnetic field and essential functionalities for wireless actuation and drug delivery. Ferromagnetic nanoparticles are electrostatically assembled around dead sperm cells, and the resulting nanoparticle-coated cells are magnetically assembled into three-dimensional biohybrid clusters. The aim of this clustering is threefold: First, to enable rolling locomotion on a nearby solid boundary using a rotating magnetic field; second, to allow for noninvasive localization; third, to load the cells inside the cluster with drugs for targeted therapy. A magneto-hydrodynamic model captures the rotational response of the clusters in a viscous fluid, and predicts an upper bound for their step-out frequency, which is independent of their volume or aspect ratio. Below the step-out frequency, the rolling velocity of the clusters increases nonlinearly with their perimeter and actuation frequency. During rolling locomotion, the clusters are localized using ultrasound images at a relatively large distance, which makes these biohybrid clusters promising for deep-tissue applications. Finally, we show that the estimated drug load scales with the number of cells in the cluster and can be retained for more than 10 h. The aggregation of microrobots enables them to collectively roll in a predictable way in response to an external rotating magnetic field, and enhances ultrasound detectability and drug loading capacity compared to the individual microrobots. The favorable features of biohybrid microrobot clusters place emphasis on the importance of the investigation and development of collective microrobots and their potential for in vivo applications.

JTD Keywords: drug delivery, magnetic actuation, microrobot aggregation, sperm, Driven, Drug delivery, Magnetic actuation, Magnetotactic bacteria, Microrobot aggregation, Microrobots, Motion, Movement, Propulsion, Sperm, Sphere, Ultrasound, Wall

© BME-PT. Ultrasound micromolding (USM) preparation of hybrid scaffolds based on polylactide (PLA) and hydroxyapatite (HAp) particles has been evaluated. PLA was stable under the applied ultrasound source since a minimum degradation was detected. Porous materials were achieved using polyethylene glycol (PEG) and NaCl salts to the initial PLA and the subsequent leaching of the micromolded specimens. To avoid cavitation and decomposition problems during micromolding, it was necessary to use HAp free of typical synthesis impurities like carbonate and nitrate compounds. Compact PLA/HAp pieces allowed a maximum HAp load of 60 wt%, while porous specimens could be obtained with a maximum load of 38 wt%. Physical characterization of new scaffolds was performed by X-ray diffraction, spectroscopic and calorimetric techniques, stress-strain tests and contact angle measurements. Results indicated that a degree of porosity of 35% and relatively good mechanical properties could be achieved (i.e., 580 MPa, 4%, and 15.6 MPa for the Young modulus, elongation at break, and tensile strength, respectively). Scaffolds showed the positive effect of HAp and porosity on cell proliferation; this latter was 40% higher than that detected for non-porous PLA specimens.

JTD Keywords: apatite, conformation, fabrication, hydroxyapatite, micropieces, polymers, porous scaffolds, proliferation, tissue, ultrasound micromolding, vibration, Composite scaffolds, Hydroxyapatite, Micropieces, Porous scaffolds, Processing technologies, Ultrasound micromolding

Cardiac motion estimation is an important diagnostic tool for detecting heart diseases and it has been explored with modalities such as MRI and conventional ultrasound (US) sequences. US cardiac motion estimation still presents challenges because of complex motion patterns and the presence of noise. In this work, we propose a novel approach to estimate cardiac motion using ultrafast ultrasound data. Our solution is based on a variational formulation characterized by the L 2-regularized class. Displacement is represented by a lattice of b-splines and we ensure robustness, in the sense of eliminating outliers, by applying a maximum likelihood type estimator. While this is an important part of our solution, the main object of this work is to combine low-rank data representation with topology preservation. Low-rank data representation (achieved by finding the k-dominant singular values of a Casorati matrix arranged from the data sequence) speeds up the global solution and achieves noise reduction. On the other hand, topology preservation (achieved by monitoring the Jacobian determinant) allows one to radically rule out distortions while carefully controlling the size of allowed expansions and contractions. Our variational approach is carried out on a realistic dataset as well as on a simulated one. We demonstrate how our proposed variational solution deals with complex deformations through careful numerical experiments. The low-rank constraint speeds up the convergence of the optimization problem while topology preservation ensures a more accurate displacement. Beyond cardiac motion estimation, our approach is promising for the analysis of other organs that exhibit motion.

JTD Keywords: Cardiac analysis, Low-rank representation, Motion estimation, Topology preservation, Ultrafast ultrasound

Abstract The use of the Hilbert–Huang transform in the analysis of biomedical signals has increased during the past few years, but its use for respiratory sound (RS) analysis is still limited. The technique includes two steps: empirical mode decomposition (EMD) and instantaneous frequency (IF) estimation. Although the mode mixing (MM) problem of EMD has been widely discussed, this technique continues to be used in many RS analysis algorithms. In this study, we analyzed the MM effect in RS signals recorded from 30 asthmatic patients, and studied the performance of ensemble EMD (EEMD) and noise-assisted multivariate EMD (NA-MEMD) as means for preventing this effect. We propose quantitative parameters for measuring the size, reduction of MM, and residual noise level of each method. These parameters showed that EEMD is a good solution for MM, thus outperforming NA-MEMD. After testing different IF estimators, we propose Kay׳s method to calculate an EEMD-Kay-based Hilbert spectrum that offers high energy concentrations and high time and high frequency resolutions. We also propose an algorithm for the automatic characterization of continuous adventitious sounds (CAS). The tests performed showed that the proposed EEMD-Kay-based Hilbert spectrum makes it possible to determine CAS more precisely than other conventional time-frequency techniques.

JTD Keywords: Hilbert–Huang transform, Ensemble empirical mode decomposition, Instantaneous frequency, Respiratory sounds, Continuous adventitious sounds

Assessment of asthma is a difficult procedure which is based on the correlation of multiple factors. A major component in the diagnosis of asthma is the assessment of BD response, which is performed by traditional spirometry. In this context, the analysis of respiratory sounds (RS) provides relevant and complementary information about the function of the respiratory system. In particular, continuous adventitious sounds (CAS), such as wheezes, contribute to assess the severity of patients with obstructive diseases. On the other hand, the intensity of normal RS is dependent on airflow level and, therefore, it changes depending on the level of obstruction. This study proposes a new approach to RS analysis for the assessment of asthmatic patients, by combining the quantification of CAS and the analysis of the changes in the normal sound intensity-airflow relationship. According to results obtained from three patients with different characteristics, the proposed technique seems more sensitive and promising for the assessment of asthma.

JTD Keywords: Asthma, Bronchodilator response, Continuous adventitious sound, Respiratory sound intensity, Wheezes

Sleep apnea-hypopnea syndrome (SAHS) is a serious sleep disorder, and snoring is one of its earliest and most consistent symptoms. We propose a new methodology for identifying two distinct types of snores: the so-called non-regular and regular snores. Respiratory sound signals from 34 subjects with different ranges of Apnea-Hypopnea Index (AHI = 3.7-109.9 h -1) were acquired. A total number of 74,439 snores were examined. The time interval between regular snores in short segments of the all night recordings was analyzed. Severe SAHS subjects show a shorter time interval between regular snores (p = 0.0036, AHI cp: 30 h -1) and less dispersion on the time interval features during all sleep. Conversely, lower intra-segment variability (p = 0.006, AHI cp: 30 h -1) is seen for less severe SAHS subjects. Features derived from the analysis of time interval between regular snores achieved classification accuracies of 88.2 % (with 90 % sensitivity, 75 % specificity) and 94.1 % (with 94.4 % sensitivity, 93.8 % specificity) for AHI cut-points of severity of 5 and 30 h -1, respectively. The features proved to be reliable predictors of the subjects' SAHS severity. Our proposed method, the analysis of time interval between snores, provides promising results and puts forward a valuable aid for the early screening of subjects suspected of having SAHS.

JTD Keywords: Sleep apnea, Snore sounds, Snore time interval

Objectives/Hypothesis: We used a new automatic snoring detection and analysis system to monitor snoring during full-night polysomnography to assess whether the acoustic characteristics of snores differ in relation to the apnea-hypopnea index (AHI) and to classify subjects according to their AHI Study Design: Individual Case-Control Study. Methods: Thirty-seven snorers (12 females and 25 males, ages 40-65 years; body mass index (BMI), 29.65 +/- 4.7 kg/m(2)) participated Subjects were divided into three groups: G1 (AHI <5), G2 (AHI >= 5, <15) and G3 (AHI >= 15) Snore and breathing sounds were : recorded with a tracheal microphone throughout 6 hours of nighttime polysomnography The snoring episodes identified were automatically and continuously analyzed with a previously trained 2-layer feed-forward neural network. Snore number, average intensity, and power spectral density parameters were computed for every subject and compared among AHI groups. Subjects were classified using different AHI thresholds by means of a logistic regression model. Results: There were significant differences in supine position between G1 and G3 in sound intensity, number of snores; standard deviation of the spectrum, power ratio in bands 0-500, 100-500, and 0-800 Hz, and the symmetry coefficient (P < .03); Patients were classified with thresholds AHI = 5 and AHI = 15 with a sensitivity (specificity) of 87% (71%) and 80% (90%), respectively. Conclusions: A new system for automatic monitoring and analysis of snores during the night is presented. Sound intensity and several snore frequency parameters allow differentiation of snorers according to obstructive sleep apnea syndrome severity (OSAS). Automatic snore intensity and frequency monitoring and analysis could be a promising tool for screening OSAS patients, significantly improving the managing of this pathology.

JTD Keywords: Breathing sounds, Signal interpretation, Sleep apnea syndromes, Snoring

The study of the mechanomyographic (MMG) signals of respiratory muscles is a promising technique in order to evaluate the respiratory muscles effort. A new approach for quantifying the relationship between respiratory MMG signals and respiratory effort is presented by analyzing the spatiotemporal patterns in the MMG signal using two non-linear methods: Renyi entropy and Lempel-Ziv (LZ) complexity analysis. Both methods are well suited to the analysis of non-stationary biomedical signals of short length. In this study, MMG signals of the diaphragm muscle acquired by means of a capacitive accelerometer applied on the costal wall were analyzed. The method was tested on an animal model (dogs), and the diaphragmatic MMG signal was recorded continuously while two non anesthetized mongrel dogs performed a spontaneous ventilation protocol with an incremental inspiratory load. The performance in discriminating high and low respiratory effort levels with these two methods was analyzed with the evaluation of the Pearson correlation coefficient between the MMG parameters and respiratory effort parameters extracted from the inspiratory pressure signal. The results obtained show an increase of the MMG signal Renyi entropy and LZ complexity values with the increase of the respiratory effort. Compared with other parameters analyzed in previous works, both Renyi entropy and LZ complexity indexes demonstrates better performance in all the signals analyzed. Our results suggest that these non-linear techniques are useful to detect and quantify changes in the respiratory effort by analyzing MMG respiratory signals.

JTD Keywords: Sound, Force