Deep Learning for the Accurate Prediction of Triggered Drug Delivery.

The need to mitigate the adverse effects of chemotherapy has driven the exploration of innovative drug delivery approaches. One emerging trend in cancer treatment is the utilization of Drug Delivery Systems (DDSs), facilitated by nanotechnology. Nanoparticles, ranging from 1 nm to 1000 nm, act as carriers for chemotherapeutic agents, enabling precise drug delivery. The triggered release of these agents is vital for advancing this novel drug delivery system. Our research investigated this multifaceted delivery capability using liposomes and metal organic frameworks as nanocarriers and utilizing all three targeting techniques: passive, active, and triggered. Liposomes are modified using targeting ligands to render them more targeted toward certain cancers. Moieties are conjugated to the surfaces of these nanocarriers to allow for their binding to receptors overexpressed on cancer cells, thus increasing the accumulation of the agent at the tumor site. A novel class of nanocarriers, namely metal organic frameworks, has emerged, showing promise in cancer treatment. Triggering techniques (both intrinsic and extrinsic) can be used to release therapeutic agents from nanoparticles, thus enhancing the efficacy of drug delivery. In this study, we develop a predictive model combining experimental measurements with deep learning techniques. The model accurately predicts drug release from liposomes and MOFs under various conditions, including low- and high-frequency ultrasound (extrinsic triggering), microwave exposure (extrinsic triggering), ultraviolet light exposure (extrinsic triggering), and different pH levels (intrinsic triggering). The deep learning-based predictions significantly outperform linear predictions, proving the utility of advanced computational methods in drug delivery. Our findings demonstrate the potential of these nanocarriers and highlight the efficacy of deep learning models in predicting drug release behavior, paving the way for enhanced cancer treatment strategies.

Synthesis and performance analysis of zeolitic imidazolate frameworks for CO2 sensing applications.

In this paper, we investigate the potential use of Zeolitic Imidazolate Frameworks (ZIF-8) as a sensing material for CO2 detection. Three synthesis techniques are considered for the preparation of ZIF-8, namely room temperature, microwave-assisted, and ball milling. The latter is a green and facile alternative for synthesis with its solvent-free, room-temperature operation. In addition, ball milling produces ZIF-8 samples with superior CO2 adsorption and detection characteristics, as concluded from fluorescence measurements. Characterization tests including X-ray diffraction (XRD), Fourier transform infrared (FTIR), Thermogravimetric analysis (TGA), Field emission scanning electron microscopy (FE-SEM) and Energy-dispersive X-ray spectroscopy (EDS) are conducted to inspect the structural morphology, the thermal stability, and elements content of the ZIF-8 samples obtained from the different aforementioned synthesis techniques. The characterization tests revealed the appearance of a new phase of ZIF-8 which is ZIF-L when deploying the ball milling technique with different structure, morphology, response to CO2 exposure and thermal stability when compared to its counterparts. Fluorescence measurements are carried out to evaluate the limit of detection (LOD), selectivity, and recyclability of the different ZIF-8 samples. The LOD of the ZIF-8 sample synthesized based on ball milling synthesis technique is 815.2 ppm, while LODs of the samples obtained from microwave and room temperature-based synthesis techniques are 1780.6 ppm and 723.8 ppm, respectively. This indicates that the room temperature and ball milling produced MOFs have comparable LODs. However, the room temperature procedure requires the use of a harmful solvent. The range of LOD demonstrates the suitable use of ZIF-8 for indoor air quality monitoring and other industrial applications.

Detection Methods for Multi-Modal Inertial Gas Sensors.

We investigate the rich potential of the multi-modal motions of electrostatically actuated asymmetric arch microbeams to design higher sensitivity and signal-to-noise ratio (SNR) inertial gas sensors. The sensors are made of fixed-fixed microbeams with an actuation electrode extending over one-half of the beam span in order to maximize the actuation of asymmetry. A nonlinear dynamic reduced-order model of the sensor is first developed and validated. It is then deployed to investigate the design of sensors that exploit the spatially complex and dynamically rich motions that arise due to veering and modal hybridization between the first symmetric and the first anti-symmetric modes of the beam. Specifically, we compare among the performance of four sensors implemented on a common platform using four detection mechanisms: classical frequency shift, conventional bifurcation, modal ratio, and differential capacitance. We find that frequency shift and conventional bifurcation sensors have comparable sensitivities. On the other hand, modal interactions within the veering range and modal hybridization beyond it offer opportunities for enhancing the sensitivity and SNR of bifurcation-based sensors. One method to achieve that is to use the modal ratio between the capacitances attributed to the symmetric and asymmetric modes as a detector, which increases the detection signal by three orders of magnitude compared to a conventional bifurcation sensor. We also present a novel sensing mechanism that exploits a rigid arm extending transversely from the arch beam mid-point and placed at equal distances between two side electrodes. It uses the asymmetry of the arch beam motions to induce rotary motions and realize a differential sensor. It is found to increase the detection signal by two orders of magnitude compared to a conventional bifurcation sensor.

Technical feasibility of a pneumatically driven vehicle.

In this work, the technical feasibility of an all-air vehicle is investigated. A test rig has been built for this purpose in order to assess the proposed system experimentally. The operating pressures selected are deliberately low to mitigate the heat generated/dissipated during charging and discharging of the air cylinders driving an air motor, respectively. The experimental setup consists of three cylinders charged up to 5 bar and operated via solenoid valves to control the discharge of the cylinders via a programmable logic controller. The operating modes vary according to the expected load demand on the vehicle during startup and also during cruise. The three cylinders are discharged in tandem if the demand calls for high power density, then they are operated sequentially to augment the operational range of the vehicle. A simple sprocket-chain mechanism is used for its simplicity in this proof-of-concept stage to better understand the parameters pertinent to vehicle operation, which will later be replaced by a continuously variable transmission (CVT) gear. The results show a great potential for such mode of transport, especially for vast locales, such as a hospital, golf course or a university campus, with top velocities estimated to be around 14 km/h velocities and driven sprocket powers of 0.7 hp. Other combinations of drive gear ratio and cylinder discharge sequences result in a wide range of output power and maximum speed possibilities.

Real-time tumor ablation simulation based on the dynamic mode decomposition method.

PURPOSE: The dynamic mode decomposition (DMD) method is used to provide a reliable forecasting of tumor ablation treatment simulation in real time, which is quite needed in medical practice. To achieve this, an extended Pennes bioheat model must be employed, taking into account both the water evaporation phenomenon and the tissue damage during tumor ablation. METHODS: A meshless point collocation solver is used for the numerical solution of the governing equations. The results obtained are used by the DMD method for forecasting the numerical solution faster than the meshless solver. The procedure is first validated against analytical and numerical predictions for simple problems. The DMD method is then applied to three-dimensional simulations that involve modeling of tumor ablation and account for metabolic heat generation, blood perfusion, and heat ablation using realistic values for the various parameters. RESULTS: The present method offers very fast numerical solution to bioheat transfer, which is of clinical significance in medical practice. It also sidesteps the mathematical treatment of boundaries between tumor and healthy tissue, which is usually a tedious procedure with some inevitable degree of approximation. The DMD method provides excellent predictions of the temperature profile in tumors and in the healthy parts of the tissue, for linear and nonlinear thermal properties of the tissue. CONCLUSIONS: The low computational cost renders the use of DMD suitable for in situ real time tumor ablation simulations without sacrificing accuracy. In such a way, the tumor ablation treatment planning is feasible using just a personal computer thanks to the simplicity of the numerical procedure used. The geometrical data can be provided directly by medical image modalities used in everyday practice.