RapidET: a MEMS-based platform for label-free and rapid demarcation of tumors from normal breast biopsy tissues

Multioxide combinatorial libraries: fusing synthetic approaches and additive technologies for highly orthogonal electronic noses

This study evaluates the performance advancement of electronic noses, on-chip engineered multisensor systems, exploiting a combinatorial approach. We analyze a spectrum of metal oxide semiconductor materials produced by individual methods of liquid-phase synthesis and a combination of chemical deposition and sol-gel methods with hydrothermal treatment. These methods are demonstrated to enable obtaining a fairly wide range of nanomaterials that differ significantly in chemical composition, crystal structure, and morphological features. While synthesis routes foster diversity in material properties, microplotter printing ensures targeted precision in making on-chip arrays for evaluation of a combinatorial selectivity concept in the task of organic vapor, like alcohol homologs, acetone, and benzene, classification. The synthesized nanomaterials demonstrate a high chemiresistive response, with a limit of detection beyond ppm level. A specific combination of materials is demonstrated to be relevant when the number of sensors is low; however, such importance diminishes with an increase in the number of sensors. We show that on-chip material combinations could favor selectivity to a specific analyte, disregarding the others. Hence, modern synthesis methods and printing protocols supported by combinatorial analysis might pave the way for fabricating on-chip orthogonal multisensor systems.

Porous protein crystals: synthesis and applications

Large-pore protein crystals (LPCs) are an emerging class of biomaterials. The inherent diversity of proteins translates to a diversity of crystal lattice structures, many of which display large pores and solvent channels. These pores can, in turn, be functionalized via directed evolution and rational redesign based on the known crystal structures. LPCs possess extremely high solvent content, as well as extremely high surface area to volume ratios. Because of these characteristics, LPCs continue to be explored in diverse applications including catalysis, targeted therapeutic delivery, templating of nanostructures, structural biology. This Feature review article will describe several of the existing platforms in detail, with particular focus on LPC synthesis approaches and reported applications.

Acoustofluidic scanning fluorescence nanoscopy with a large field of view

Large-field nanoscale fluorescence imaging is invaluable for many applications, such as imaging subcellular structures, visualizing protein interactions, and high-resolution tissue imaging. Unfortunately, conventional fluorescence microscopy requires a trade-off between resolution and field of view due to the nature of the optics used to form the image. To overcome this barrier, we developed an acoustofluidic scanning fluorescence nanoscope that simultaneously achieves superior resolution, a large field of view, and strong fluorescent signals. The acoustofluidic scanning fluorescence nanoscope utilizes the superresolution capabilities of microspheres that are controlled by a programmable acoustofluidic device for rapid fluorescence enhancement and imaging. The acoustofluidic scanning fluorescence nanoscope resolves structures that cannot be resolved with conventional fluorescence microscopes with the same objective lens and enhances the fluorescent signal by a factor of ~5 without altering the field of view of the image. The improved resolution realized with enhanced fluorescent signals and the large field of view achieved via acoustofluidic scanning fluorescence nanoscopy provides a powerful tool for versatile nanoscale fluorescence imaging for researchers in the fields of medicine, biology, biophysics, and biomedical engineering.

An ultra-compact mechanical antenna based on PTFE highly charged electret for extremely low frequency communications

-Extremely-Low Frequencies (ELF, 30∼300Hz) transmitting antennas in wireless communications are often limited by antenna size and complex impedance matching networks. In this paper, we propose an ultra-small Artificial Electret Type Mechanical Antenna (AETMA), which is composed of a single charge electret and a driving structure, with high radiation efficiency and small size. In order to improve the electric dipole moment of the mechanical antenna, we employ a pin-plate corona polarization technique and a unidirectional stretching treatment to obtain a porous thin-film electret that can stably store a large amount of charge. Its surface charge density can reach 5.355 mC/m2 and we analyze its surface potential stability. To assess the radiation capability of AETMA, the radiation field models of three kinds of mechanical antennas are established and verified by simulation. Additionally, we simulate and compare the planar electret and curved electret configurations to determine the optimal form of AETMA. The radiation intensity of the planar electret is found to be superior under the same moment of inertia. Finally, a 1m-scale artificial electret antenna is designed based on the optimal model. Comparative analysis with existing rotary mechanical antenna schemes confirms the great potential of the proposed AETMA for portable, miniaturized and high-performance wireless communication devices.

Compartmentalized drug localization studies in extracellular vesicles for anticancer therapy

In the development of therapeutic extracellular vesicles (EVs), drug encapsulation efficiencies are significantly lower when compared with synthetic nanomedicines. This is due to the hierarchical structure of the EV membrane and the physicochemical properties of the candidate drug (molecular weight, hydrophilicity, lipophilicity, and so on). As a proof of concept, here we demonstrated the importance of drug compartmentalization in EVs as an additional parameter affecting the therapeutic potential of drug-loaded EVs. In human adipose mesenchymal stem cell (hADSC) derived EVs, we performed a comparative drug loading analysis using two formulations of the same chemotherapeutic molecule - free doxorubicin (DOX) and 1,2-distearoyl-sn-glycero-3-phosphoethanolamine (DSPE) lipid-conjugated doxorubicin (L-DOX) - to enhance the intracellular uptake and therapeutic efficacy. By nano surface energy transfer (NSET) and molecular simulation techniques, along with cryo-TEM analysis, we confirmed the differential compartmentalization of these two molecules in hADSC EVs. L-DOX was preferentially adsorbed onto the surface of the EV, due to its higher lipophilicity, whereas free DOX was mostly encapsulated within the EV core. Also, the L-DOX loaded EV (LDOX@EV) returned an almost three-fold higher DOX content as compared to the free DOX loaded EV (DOX@EV), for a given input mass of drug. Based on the cellular investigations, L-DOX@EV showed higher cell internalization than DOX@EV. Also, in comparison with free L-DOX, the magnitude of therapeutic potential enhancement displayed by the surface compartmentalized L-DOX@EV is highly promising and can be exploited to overcome the sensitivity of many potential drugs, which are impermeable in nature. Overall, this study illustrates the significance of drug compartmentalization in EVs and how this could affect intracellular delivery, loading efficiency, and therapeutic effect. This will further lay the foundation for the future systematic investigation of EV-based biotherapeutic delivery platforms for personalized medicine.

Label-free multimodal electro-thermo-mechanical (ETM) phenotyping as a novel biomarker to differentiate between normal, benign, and cancerous breast biopsy tissues

BACKGROUND

Technologies for quick and label-free diagnosis of malignancies from breast tissues have the potential to be a significant adjunct to routine diagnostics. The biophysical phenotypes of breast tissues, such as its electrical, thermal, and mechanical properties (ETM), have the potential to serve as novel markers to differentiate between normal, benign, and malignant tissue.

RESULTS

We report a system-of-biochips (SoB) integrated into a semi-automated mechatronic system that can characterize breast biopsy tissues using electro-thermo-mechanical sensing. The SoB, fabricated on silicon using microfabrication techniques, can measure the electrical impedance (Z), thermal conductivity (K), mechanical stiffness (k), and viscoelastic stress relaxation (%R) of the samples. The key sensing elements of the biochips include interdigitated electrodes, resistance temperature detectors, microheaters, and a micromachined diaphragm with piezoresistive bridges. Multi-modal ETM measurements performed on formalin-fixed tumour and adjacent normal breast biopsy samples from N = 14 subjects were able to differentiate between invasive ductal carcinoma (malignant), fibroadenoma (benign), and adjacent normal (healthy) tissues with a root mean square error of 0.2419 using a Gaussian process classifier. Carcinoma tissues were observed to have the highest mean impedance (110018.8 ± 20293.8 Ω) and stiffness (0.076 ± 0.009 kNm-1) and the lowest thermal conductivity (0.189 ± 0.019 Wm-1 K-1) amongst the three groups, while the fibroadenoma samples had the highest percentage relaxation in normalized load (47.8 ± 5.12%).

CONCLUSIONS

The work presents a novel strategy to characterize the multi-modal biophysical phenotype of breast biopsy tissues to aid in cancer diagnosis from small-sized tumour samples. The methodology envisions to supplement the existing technology gap in the analysis of breast tissue samples in the pathology laboratories to aid the diagnostic workflow.

Integrated "lab-on-a-chip" microfluidic systems for isolation, enrichment, and analysis of cancer biomarkers

The liquid biopsy has garnered considerable attention as a complementary clinical tool for the early detection, molecular characterization and monitoring of cancer over the past decade. In contrast to traditional solid biopsy techniques, liquid biopsy offers a less invasive and safer alternative for routine cancer screening. Recent advances in microfluidic technologies have enabled handling of liquid biopsy-derived biomarkers with high sensitivity, throughput, and convenience. The integration of these multi-functional microfluidic technologies into a 'lab-on-a-chip' offers a powerful solution for processing and analyzing samples on a single platform, thereby reducing the complexity, bio-analyte loss and cross-contamination associated with multiple handling and transfer steps in more conventional benchtop workflows. This review critically addresses recent developments in integrated microfluidic technologies for cancer detection, highlighting isolation, enrichment, and analysis strategies for three important sub-types of cancer biomarkers: circulating tumor cells, circulating tumor DNA and exosomes. We first discuss the unique characteristics and advantages of the various lab-on-a-chip technologies developed to operate on each biomarker subtype. This is then followed by a discussion on the challenges and opportunities in the field of integrated systems for cancer detection. Ultimately, integrated microfluidic platforms form the core of a new class of point-of-care diagnostic tools by virtue of their ease-of-operation, portability and high sensitivity. Widespread availability of such tools could potentially result in more frequent and convenient screening for early signs of cancer at clinical labs or primary care offices.

An aerosol deposition based MEMS piezoelectric accelerometer for low noise measurement

Potentially applied in low-noise applications such as structural health monitoring (SHM), a 1-axis piezoelectric MEMS accelerometer based on aerosol deposition is designed, fabricated, simulated, and measured in this study. It is a cantilever beam structure with a tip proof mass and PZT sensing layer. To figure out whether the design is suitable for SHM, working bandwidth and noise level are obtained via simulation. For the first time, we use aerosol deposition method to deposit thick PZT film during the fabrication process to achieve high sensitivity. In performance measurement, we obtain the charge sensitivity, natural frequency, working bandwidth and noise equivalent acceleration of 22.74 pC/g, 867.4 Hz, 10-200 Hz (within ±5% deviation) and 5.6 μ g / Hz (at 20 Hz). To demonstrate its feasibility for real applications, vibrations of a fan are measured by our designed sensor and a commercial piezoelectric accelerometer, and the results match well with each other. Moreover, shaker vibration measurement with ADXL1001 indicates that the fabricated sensor has a much lower noise level. In the end, we show that our designed accelerometer has good performance compared to piezoelectric MEMS accelerometers in relevant studies and great potential for low-noise applications compared to low-noise capacitive MEMS accelerometers.

Electrostatic potential difference between tumor and paratumor regulates cancer stem cell behavior and prognose tumor spread

Tumor spread is responsible for most deaths related to cancer. Increasing the accuracy of cancer prognosis is critical to reducing the high mortality rates in cancer patients. Here, we report that the electrostatic potential difference (EPD) between tumor and its paratumor tissue is a prognostic marker for tumor spread. This finding is concluded from the patient-specific EPD values and clinical observation. The electrostatic potential values were measured on tissue cryosections from 51 patients using Kelvin probe force microscopy (KPFM). A total of ~44% (15/34) patients of V_{tumor-paratumor} > 0 were featured with tumor spread, whereas only ~18% (2/11) patients of V_{tumor-paratumor} < 0 had tumor spread. Next, we found the increased enrichment of cancer stem cells in paratumors with lower electrostatic potentials using immunofluorescence imaging, which suggested the attribution of tumor spread to the galvanotaxis of cancer stem cells (CSCs) toward lower potential. The findings were finally validated in breast and lung spheroid models composed of differentiated cancer cells and cancer stem cells at the ratio of 1:1 and embedded in Matrigel dopped with negative-, neutral- and positive-charged polymers and CSCs prefer to spread out of spheroids to lower electrostatic potential sites. This work may inspire the development of diagnostic and prognostic strategies targeting at tissue EPDs and CSCs for tumor therapy.

Optimization of a silver-nanoprism conjugated with 3,3',5,5'-tetramethylbenzidine towards easy-to-make colorimetric analysis of acetaldehyde: a new platform towards rapid analysis of carcinogenic agents and environmental technology

Acetaldehyde acts as an important mediator in the metabolism of plants and animals; however, its abnormal level can cause problems in biological processes. Although acetaldehyde is found naturally in many organisms, exposure to high concentrations can have effects on the eyes, respiratory system, etc. Due to the importance of detecting acetaldehyde in environmental samples and biofluids, determination of its concentration is highly demanded. There are some reports showing exposure to high concentrations of acetaldehyde for a long time can increase the risk of cancer by reacting with DNA. In this work, we presented a novel colorimetric method for rapid and sensitive detection of acetaldehyde with high reproducibility using different AgNPs with various morphologies. The redox reaction between AgNPs, 3,3',5,5'-tetramethylbenzidine (TMB) solution, and analytes endows a color change in 15 minutes that is detectable by the naked eye. UV spectrophotometry was further used for quantitative analysis. An iron mold with a hexagonal pattern and liquid paraffin were also used to prepare the paper-based microfluidic substrate, as a low cost, accessible, and rapid detection tool. Different types of AgNPs showed different lower limits of quantification (LLOQ). The AgNPs-Cit and AgNPrs could identify acetaldehyde with linear range of 10^-7 to 10 M and an LLOQ of 10^-7 M. The AgNWs showed the best color change activity with a linear range 10^-5 to 10 M and the lowest diagnostic limit is 10^-5 M. Finally, analysis of human biofluids as real samples were successfully performed using this system.

A miniaturized transit-time ultrasonic flowmeter based on ScAlN piezoelectric micromachined ultrasonic transducers for small-diameter applications

Transit-time ultrasonic flowmeters (TTUFs) are among the most widely used devices for flow measurements. However, traditional TTUFs are usually based on a bulk piezoelectric transducer, which limits their application in small-diameter channels. In this paper, we developed a miniaturized TTUF based on scandium-doped aluminum nitride (ScAlN) piezoelectric micromachined ultrasonic transducers (PMUTs). The proposed TTUF contains two PMUT-based transceivers and a π-type channel. The PMUTs contain 13 × 13 square cells with dimensions of 2.8 × 2.8 mm². To compensate for the acoustic impedance mismatch with liquid, a layer of polyurethane is added to the surface of the PMUTs as a matching layer. The PMUT-based transceivers show good transmitting sensitivity (with 0.94 MPa/V surface pressure) and receiving sensitivity (1.79 mV/kPa) at a frequency of 1 MHz in water. Moreover, the dimensions of the π-type channel are optimized to achieve a measurement sensitivity of 82 ns/(m/s) and a signal-to-noise ratio (SNR) better than 15 dB. Finally, we integrate the fabricated PMUTs into the TDC-GP30 platform. The experimental results show that the developed TTUF provides a wide range of flow measurements from 2 to 300 L/h in a channel of 4 mm diameter, which is smaller than most reported channels. The accuracy and repeatability of the TTUF are within 0.2% and 1%, respectively. The proposed TTUF shows great application potential in industrial applications such as medical and chemical applications.

Cell deformability heterogeneity recognition by unsupervised machine learning from in-flow motion parameters

Cell deformability is a well-established marker of cell states for diagnostic purposes. However, the measurement of a wide range of different deformability levels is still challenging, especially in cancer, where a large heterogeneity of rheological/mechanical properties is present. Therefore, a simple, versatile and cost-effective recognition method for variable rheological/mechanical properties of cells is needed. Here, we introduce a new set of in-flow motion parameters capable of identifying heterogeneity among cell deformability, properly modified by the administration of drugs for cytoskeleton destabilization. Firstly, we measured cell deformability by identification of in-flow motions, rolling (R), tumbling (T), swinging (S) and tank-treading (TT), distinctively associated with cell rheological/mechanical properties. Secondly, from a pool of motion and structural cell parameters, an unsupervised machine learning approach based on principal component analysis (PCA) revealed dominant features: the local cell velocity (V_Cell/V_Avg), the equilibrium position (Y_Eq) and the orientation angle variation (Δφ). These motion parameters clearly defined cell clusters in terms of motion regimes corresponding to specific deformability. Such correlation is verified in a wide range of rheological/mechanical properties from the elastic cells moving like R until the almost viscous cells moving as TT. Thus, our approach shows how simple motion parameters allow cell deformability heterogeneity recognition, directly measuring rheological/mechanical properties.

Deep learning for non-parameterized MEMS structural design

The geometric designs of MEMS devices can profoundly impact their physical properties and eventual performances. However, it is challenging for researchers to rationally consider a large number of possible designs, as it would be very time- and resource-consuming to study all these cases using numerical simulation. In this paper, we report the use of deep learning techniques to accelerate the MEMS design cycle by quickly and accurately predicting the physical properties of numerous design candidates with vastly different geometric features. Design candidates are represented in a nonparameterized, topologically unconstrained form using pixelated black-and-white images. After sufficient training, a deep neural network can quickly calculate the physical properties of interest with good accuracy without using conventional numerical tools such as finite element analysis. As an example, we apply our deep learning approach in the prediction of the modal frequency and quality factor of disk-shaped microscale resonators. With reasonable training, our deep learning neural network becomes a high-speed, high-accuracy calculator: it can identify the flexural mode frequency and the quality factor 4.6 × 10³ times and 2.6 × 10⁴ times faster, respectively, than conventional numerical simulation packages, with good accuracies of 98.8 ± 1.6% and 96.8 ± 3.1%, respectively. When simultaneously predicting the frequency and the quality factor, up to ~96.0% of the total computation time can be saved during the design process. The proposed technique can rapidly screen over thousands of design candidates and promotes experience-free and data-driven MEMS structural designs.

Shape-based separation of drug-treated Escherichia coli using viscoelastic microfluidics

Here, we achieve shape-based separation of drug-treated Escherichia coli (E. coli) by viscoelastic microfluidics. Since shape is critical for modulating biological functions of E. coli, the ability to prepare homogeneous E. coli populations adopting uniform shape or sort bacterial sub-population based on their shape has significant implications for a broad range of biological, biomedical and environmental applications. A proportion of E. coli treated with 1 μg mL^-1 of the antibiotic mecillinam were found to exhibit changes in shape from rod to sphere, and the heterogeneous E. coli populations after drug treatment with various aspect ratios (ARs) ranging from 1.0 to 5.5 were used for experiment. We demonstrate that E. coli with a lower AR, i.e., spherical E. coli (AR ≤ 1.5), are directed toward the middle outlet, while rod-shaped E. coli with a higher AR (AR > 1.5) are driven to the side outlets. Further, we demonstrate that the separation performance of the viscoelastic microfluidic device is influenced by two main factors: sheath-to-sample flow rate ratio and the concentration of poly-ethylene-oxide (PEO). To the best of our knowledge, this is the first report on shape-based separation of a single species of cells smaller than 4 μm by microfluidics.