Detection of intrinsically disordered peptides by biological nanopore.

Intrinsically disordered protein regions (IDPRs) are pivotal in the regulation of transcription and the facilitation of signal transduction. Because of their multiple conformational states of structure, characterizing the highly flexible structures of IDPRs becomes challenging. Herein, we employed the wild-type (WT) aerolysin nanopore as a real-time biosensor for the identification and monitoring of long peptides containing IDPRs. This sensor successfully identified three intrinsically disordered peptides, with the lengths up to 43 amino acids, by leveraging the unique signatures of blockade current and translocation duration time. The analysis of the binding constant revealed that interactions between the nanopore and peptide are critical for peptide translocation, suggesting that mechanisms beyond mere volume exclusion are at play. Furthermore, by examining the detailed current traces of blockade events, we were able to compare the conformational stabilities of various IDPRs. Our approach can detect the conformational changes of IDPR in a confined nanopore space. These insights broaden the understanding of peptide structural changes under the confined space. This nanopore biosensor showed the potential to study the conformations change of IDPRs, IDPRs transmembrane interactions, and protein drug discovery.

Multiparameter Mechanical Phenotyping for Accurate Cell Identification Using High-Throughput Microfluidic Deformability Cytometry.

Mechanical phenotyping has been widely employed for single-cell analysis over recent years. However, most previous works on characterizing the cellular mechanical properties measured only a single parameter from one image. In this paper, the quasi-real-time multiparameter analysis of cell mechanical properties was realized using high-throughput adjustable deformability cytometry. We first extracted 12 deformability parameters from the cell contours. Then, the machine learning for cell identification was performed to preliminarily verify the rationality of multiparameter mechanical phenotyping. The experiments on characterizing cells after cytoskeletal modification verified that multiple parameters extracted from the cell contours contributed to an identification accuracy of over 80%. Through continuous frame analysis of the cell deformation process, we found that temporal variation and an average level of parameters were correlated with cell type. To achieve quasi-real-time and high-precision multiplex-type cell detection, we constructed a back propagation (BP) neural network model to complete the fast identification of four cell lines. The multiparameter detection method based on time series achieved cell detection with an accuracy of over 90%. To solve the challenges of cell rarity and data lacking for clinical samples, based on the developed BP neural network model, the transfer learning method was used for the identification of three different clinical samples, and finally, a high identification accuracy of approximately 95% was achieved.

High-throughput enrichment of portal venous circulating tumor cells for highly sensitive diagnosis of CA19-9-negative pancreatic cancer patients using inertial microfluidics.

The carbohydrate antigen 19-9 (CA19-9) is commonly used as a representative biomarker for pancreatic cancer (PC); however, it lacks sensitivity and specificity for early-stage PC diagnosis. Furthermore, some patients with PC are negative for CA19-9 (<37 U/mL), which introduces additional limitations to their accurate diagnosis and treatment. Hence, improved methods to accurately detect PC stages in CA19-9-negative patients are warranted. In this study, tumor-proximal liquid biopsy and inertial microfluidics were coupled to enable high-throughput enrichment of portal venous circulating tumor cells (CTCs) and support the effective diagnosis of patients with early-stage PC. The proposed inertial microfluidic system was shown to provide size-based enrichment of CTCs using inertial focusing and Dean flow effects in slanted spiral channels. Notably, portal venous blood samples were found to have twice the yield of CTCs (21.4 cells per 5 mL) compared with peripheral blood (10.9 CTCs per 5 mL). A combination of peripheral and portal CTC data along with CA19-9 results showed to greatly improve the average accuracy of CA19-9-negative PC patients from 47.1% with regular CA19-9 tests up to 87.1%. Hence, portal venous CTC-based microfluidic biopsy can be used with high sensitivity and specificity for the diagnosis of early-stage PC, particularly in CA19-9-negative patients.

Elasto-inertial particle focusing in sinusoidal microfluidic channels.

Dean flow existing in sinusoidal channels could enhance the throughput and efficiency for elasto-inertial particle focusing. However, the fundamental mechanisms of elasto-inertial focusing in sinusoidal channels are still unclear. This work employs four microfluidic devices with symmetric and asymmetric sinusoidal channels to explore the elasto-inertial focusing mechanisms over a wide range of flow rates. The effects of rheological property, flow rate, sinusoidal channel curvature, particle size, and asymmetric geometry on particle focusing performance are investigated. It is intriguing to find that the Dean flow makes a substantial contribution to the particle elasto-inertial focusing. The results illustrate that a better particle focusing performance and a faster focusing process are obtained in the sinusoidal channel with a small curvature radius due to stronger Dean flow. In addition, the particle focusing performance is also related to particle diameter and rheological properties, the larger particles show a better focusing performance than smaller particles, and the smaller flow rate is required for particles to achieve stable focusing at the outlet in the higher concentration of polyvinylpyrrolidone solutions. Our work offers an increased knowledge of the mechanisms of elasto-inertial focusing in sinusoidal channels. Ultimately, these results provide supportive guidelines into the design and development of sinusoidal elasto-inertial microfluidic devices for high-performance focusing.

High-throughput and simultaneous inertial separation of tumor cells and clusters from malignant effusions using spiral-contraction-expansion channels.

Tumor cell clusters are regarded as critical factors in cancer pathophysiology, and increasing evidence of their higher treatment resistance and metastasis compared to single tumor cells has been obtained. However, existing cell separation methods that are designed for single tumor cells cannot be used to simultaneously purify tumor cell clusters. To address this problem, we demonstrated a microfluidic approach for the high-throughput, continuous-flow ternary separation of single tumor cells, tumor cell clusters, and WBCs from clinical pleural or abdominal effusions by coupling slanted spiral channels and periodic contraction-expansion arrays. We first systematically explored the influence of particle size and flow rate on particle focusing. The separation performance indicated that 94.0% of WBCs were removed and more than 97% of MDA-MB-231 tumor cells were recovered at a high flow rate of 3500 µL/min. Moreover, more than 90% of tumor cell clusters were effectively preserved after separation. Finally, we successfully applied our device for the ternary separation of single tumor cells, tumor cell clusters, and WBCs from different malignant effusions collected from patients with metastatic cancer. Thus, our spiral-contraction-expansion device has potential as a sample pretreatment tool for the cytological diagnosis of malignant effusions.

A "turn off-on" fluorescent sensor for detection of Cr(â ¥) based on upconversion nanoparticles and nanoporphyrin.

Hexavalent chromium (Cr(Ⅵ)) is a significant environmental pollutant because of its toxic and carcinogenic properties and wide use in various industries. Hence, there is an urgent need to develop accurate and selective approaches to detect the concentration of Cr(Ⅵ) in agricultural and aquaculture products to help humans avoid potential hazards of indirectly taking in Cr(Ⅵ). In this work, we report a "turn off-on" fluorescent sensor based on citric acid coated, 808 nm-excited core-shell upconversion nanoparticles (CA-UCNPs) and self-assembled copper porphyrin nanoparticles (nano CuTPyP) for sensitive and specific detection of Cr(Ⅵ). Nano copper 5, 10, 15, 20-tetra(4-pyridyl)-21H-23H- porphine obtained by acid-base neutralization micelle-confined self-assembly method function as an effective quencher due to its excellent optical property and water solubility. Through electrostatic interactions, positively charged nano CuTPyP are attracted to the surface of negatively charged CA-UCNPs, which can almost completely quench the fluorescence emission. In the presence of Cr(Ⅵ), nano CuTPyP can discriminatively interact with Cr(Ⅵ) and form nano CuTPyP/Cr(Ⅵ) complex, which separates nano CuTPyP from CA-UCNPs and restores the fluorescence. The sensing system exhibits a good linear response to Cr(Ⅵ) concentration in the range from 0.5 to 400 µM with a detection limit of 0.36 µM. The sensing method also displays high selectivity against other common ions including trivalent chromium and is applied to the analysis of Cr(Ⅵ) in actual rice and fish samples with satisfactory results.

Strengthen oriented poly (L-lactic acid) monofilaments via mechanical training.

Polymer materials have found extensive applications in the clinical and medical domains due to their exceptional biocompatibility and biodegradability. Compared to metallic counterparts, polymers, particularly Poly (L-lactic acid) (PLLA), are more suitable for fabricating biodegradable stents. As a viscoelastic material, PLLA monofilaments exhibit a creep phenomenon under sustained tensile stress. This study explores the use of creep to enhance the mechanical attributes of PLLA monofilaments. By subjecting the highly oriented monofilaments to controlled, constant force stretching, we achieved notable improvements in their mechanical characteristics. The results, as confirmed by tensile testing and dynamic mechanical analysis, revealed a remarkable 67 % increase in total elongation and over a 20 % rise in storage modulus post-mechanical training. Further microscopic analyses, including Atomic Force Microscopy (AFM) and Scanning Electron Microscopy (SEM), revealed enhanced spacing and cavity formation. These mechanical advancements are attributed to the unraveling and a more orderly arrangement of molecular chains in the amorphous regions. This investigation offers a promising approach for augmenting the mechanical properties of PLLA monofilaments, potentially benefiting their application in biomedical engineering.

Modified Theoretical Model Predicts Radial Support Capacity of Polymer Braided Stents.

BACKGROUND AND OBJECTIVE: Self-expanding polymer braided stents are expected to replace metallic stents in the treatment of Peripheral Arterial Disease, which seriously endangers human health. To restore the patency of blocked peripheral arteries with different properties and functions, the radial supporting capacity of the stent should be considered corresponding to the vessel. A theoretical model can be established as an effective method to study the radial supporting capacity of the stent which can shorten the stent design cycle and realize the customization of the stent according to lesion site. However, the classical model developed by Jedwab and Clerc of radial force is only limited to metallic braided stents, and the predictions for polymer braided stents are deviated. METHODS: In this paper, based on the limitation of the J&C model for polymer braided stents, a modified radial force model for polymer braided stents was proposed, which considered the friction between monofilaments and the torsion of the monofilaments. And the modified model was verified by radial force tests of polymer braided stents with different structures and monofilaments. RESULTS: Compared with the J&C model, the proposed modified model has better predictability for the radial force of polymer braided stents that prepared with different braided structure and polymer monofilaments. The root mean squared error of modified model is 0.041±0.026, while that of the J&C model is 0.246±0.111. CONCLUSIONS: For polymer braided stents, the friction between the polymer monofilaments and the torsion of the monofilaments during the radial compression cannot be ignored. The radial force prediction accuracy of the modified model considering these factors was significantly improved. This work provides a research basis on the theoretical model of polymer braided stents, and improves the feasibility of rapid personalized customization of polymer braided stents.

Particle focusing mechanisms in λ-DNA solution flowing in a straight microchannel.

Most biological fluids (such as blood, saliva, and lymph) in nature have certain viscoelasticity and are beginning to be used as the carrying fluids for viscoelastic microfluidics. However, the particle-focusing mechanisms in these new biological viscoelastic fluids are still unclear. In this work, the particle-focusing mechanisms in λ-DNA solutions were systematically explored. We first explored the particle focusing dynamics in a square cross-section under varied flow rates to uncover the effects of flow rate on particle focusing. Three focusing stages, from the classic five-position viscoelastic focusing to single-stream focusing and finally to multiplex-stream focusing, were clearly demonstrated. In addition, the particle focusing process along the channel length was demonstrated, and a first-fast-and-then-slow focusing process was clearly observed. Then, the effects of λ-DNA concentrations on particle focusing were explored and compared using the solutions with 0-25 ppm λ-DNA. Finally, we discussed the inferences of blockage ratio on particle focusing by changing the particle diameter and cross-sectional dimensions. Our work may provide a deeper understanding on the particle focusing mechanisms in biological viscoelastic fluids and lays a foundation for the subsequent particle counting and analysis and the development of low-cost portable flow cytometers.

ZIF-67 Anchored on MoS2/rGO Heterostructure for Non-Enzymatic and Visible-Light-Sensitive Photoelectrochemical Biosensing.

Graphene and graphene-like two-dimensional layered nanomaterials-based photoelectrochemical (PEC) biosensors have recently grown rapidly in popularity thanks to their advantages of high sensitivity and low background signal, which have attracted tremendous attention in ultrahigh sensitive small molecule detection. This work proposes a non-enzymatic and visible-light-sensitive PEC biosensing platform based on ZIF-67@MoS2/rGO composite which is synthesized through a facile and one-step microwave-assisted hydrothermal method. The combination of MoS2 and rGO could construct van der Waals heterostructures, which not only act as visible-light-active nanomaterials, but facilitate charge carriers transfer between the photoelectrode and glassy carbon electrode (GCE). ZIF-67 anchored on MoS2/rGO heterostructures provides large specific surface areas and a high proportion of catalytic sites, which cooperate with MoS2 nanosheets, realizing rapid and efficient enzyme-free electrocatalytic oxidation of glucose. The ZIF-67@MoS2/rGO-modified GCE can realize the rapid and sensitive detection of glucose at low detection voltage, which exhibits a high sensitivity of 12.62 µAmM-1cm-2. Finally, the ZIF-67@MoS2/rGO PEC biosensor is developed by integrating the ZIF-67@MoS2/rGO with a screen-printed electrode (SPE), which exhibits a high sensitivity of 3.479 µAmM-1cm-2 and a low detection limit of 1.39 µM. The biosensor's selectivity, stability, and repeatability are systematically investigated, and its practicability is evaluated by detecting clinical serum samples.

High-Throughput Sorting and Single-Cell Mechanotyping by Hydrodynamic Sorting-Mechanotyping Cytometry.

The existence of many background blood cells hinders the accurate identification of circulating tumor cells (CTCs) in the blood of cancer patients. To unlock this limitation, a hydrodynamic sorting-mechanotyping cytometry (HSMC) integrated with a sorting-concentration chip and a detection chip is proposed for simultaneously achieving the high-throughput cell sorting and the multi-parameter mechanotyping of the sorted tumor cells. The HSMC adopts the spiral inertial microfluidics for label-free sorting of cells in a high-throughput manner, allowing the efficient enrichment of tumor cells from the large background blood cells. Then, the sorted cells are concentrated by the concentration unit and finally passed through the detection unit for hydrodynamic deformation. The HSMC has a high throughput for sorting and detection and can successfully reveal the differences in the cellular mechanical properties. After characterizing and optimizing the single chips, the identification of white blood cells (WBCs) and three types of tumor cells (A549, MCF-7, and MDA-MB-231 cells) is successfully achieved. The identification accuracies for WBCs and different tumor cells are all larger than 94%, while the highest identification accuracy is up to 99.2%. This study envisions that the HSMC will offer an avenue for the analysis of single cell intrinsic mechanics in clinical medicine.

Inertial Multi-Force Deformability Cytometry for High-Throughput, High-Accuracy, and High-Applicability Tumor Cell Mechanotyping.

Previous on-chip technologies for characterizing the cellular mechanical properties often suffer from a low throughput and limited sensitivity. Herein, an inertial multi-force deformability cytometry (IMFDC) is developed for high-throughput, high-accuracy, and high-applicability tumor cell mechanotyping. Three different deformations, including shear deformations and stretch deformations under different forces, are integrated with the IMFDC. The 3D inertial focusing of cells enables the cells to deform by an identical fluid flow, and 10 parameters, such as cell area, perimeter, deformability, roundness, and rectangle deformability, are obtained in three deformations. The IMFDC is able to evaluate the deformability of different cells that are sensitive to different forces on a single chip, demonstrating the high applicability of the IMFDC in analyzing different cell lines. In identifying cell types, the three deformations exhibit different mechanical responses to cells with different sizes and deformability. A discrimination accuracy of ≈93% for both MDA-MB-231 and MCF-10A cells and a throughput of ≈500 cells s-1 can be achieved using the multiple-parameters-based machine learning model. Finally, the mechanical properties of metastatic tumor cells in pleural and peritoneal effusions are characterized, enabling the practical application of the IMFDC in clinical cancer diagnosis.

Viscoelastic-Sorting Integrated Deformability Cytometer for High-Throughput Sorting and High-Precision Mechanical Phenotyping of Tumor Cells.

The counts and phenotypes of circulating tumor cells (CTCs) in whole blood are useful for disease monitoring and prognostic assessment of cancer. However, phenotyping CTCs in the blood is difficult due to the presence of a large number of background blood cells, especially some blood cells with features similar to those of tumor cells. Herein, we presented a viscoelastic-sorting integrated deformability cytometer (VSDC) for high-throughput label-free sorting and high-precision mechanical phenotyping of tumor cells. A sorting chip for removing large background blood cells and a detection chip for detecting multiple cellular mechanical properties were integrated into our VSDC. Our VSDC has a sorting efficiency and a purity of over 95% and over 81% for tumor cells, respectively. Furthermore, multiple mechanical parameters were used to distinguish tumor cells from white blood cells using machine learning. An accuracy of over 97% for identifying tumor cells was successfully achieved with the highest identification accuracy of 99.4% for MCF-7 cells. It is envisioned that our VSDC will open up new avenues for high-throughput and label-free single-cell analysis in various biomedical applications.

High-throughput adjustable deformability cytometry utilizing elasto-inertial focusing and virtual fluidic channel.

Cell mechanical properties provide a label-free marker for indicating cell states and disease processes. Although microfluidic deformability cytometry has demonstrated great potential and successes in mechanical phenotyping in recent years, its universal applicability for characterizing multiple sizes of cells using a single device has not been realized. Herein, we propose high-throughput adjustable deformability cytometry integrated with three-dimensional (3D) elasto-inertial focusing and a virtual fluidic channel. By properly adjusting the flow ratio of the sample and sheath, the virtual fluidic channel in a wide solid channel can generate a strong shear force in the normal direction of the flow velocity and simultaneously squeeze cells from both sides to induce significant cell deformation. The combination of elasto-inertial focusing and a virtual fluidic channel provides a great hydrodynamic symmetrical force for inducing significant and homogeneous cell deformation. In addition, our deformability cytometry system not only achieves rapid and precise cell deformation, but also allows the adjustable detection of multiple sizes of cells at a high throughput of up to 3000 cells per second. The mini-bilateral segmentation network (mini-BiSeNet) was developed to identify cells and extract features quickly. The classification of different cell populations (A549, MCF-7, MDA-MB-231, and WBCs) was carried out based on the cell size and deformation. By applying deep learning to cell classification, a high accuracy reaching approximately 90% was achieved. We also revealed the potential of our deformability cytometry for characterizing pleural effusions. The flexibility of our deformability cytometry holds promise for the mechanical phenotyping and detection of various biological samples.

Triboelectric-induced ion mobility for artificial intelligence-enhanced mid-infrared gas spectroscopy.

Isopropyl alcohol molecules, as a biomarker for anti-virus diagnosis, play a significant role in the area of environmental safety and healthcare relating volatile organic compounds. However, conventional gas molecule detection exhibits dramatic drawbacks, like the strict working conditions of ion mobility methodology and weak light-matter interaction of mid-infrared spectroscopy, yielding limited response of targeted molecules. We propose a synergistic methodology of artificial intelligence-enhanced ion mobility and mid-infrared spectroscopy, leveraging the complementary features from the sensing signal in different dimensions to reach superior accuracy for isopropyl alcohol identification. We pull in "cold" plasma discharge from triboelectric generator which improves the mid-infrared spectroscopic response of isopropyl alcohol with good regression prediction. Moreover, this synergistic methodology achieves ~99.08% accuracy for a precise gas concentration prediction, even with interferences of different carbon-based gases. The synergistic methodology of artificial intelligence-enhanced system creates mechanism of accurate gas sensing for mixture and regression prediction in healthcare.

Regulating mechanical performance of poly (l-lactide acid) stent by the combined effects of heat and aqueous media.

Annealing process has been applied to the development of thermoforming polymer braided stent and treating its basic constitute monofilaments, especially for Poly (l-lactide acid) (PLLA) condensed by lactic acid monomer made from the plant starch. In this work, high performance monofilaments were produced by melting spun and solid-state drawing methods. Inspired by the effects of water plasticization on semi-crystal polymer, PLLA monofilaments were annealed with and without constraint in vacuum and aqueous media. Then, the co-effects of water infestation and heat on the micro-structure and mechanical properties of these filaments were characterized. Furtherly, mechanical performance of PLLA braided stents shaped by different annealing methods was also compared. Results showed that annealing in aqueous media generated more obvious structure change of PLLA filaments. Interestingly, the combined effects of aqueous phase and thermal effectively increased the crystallinity, and decreased the molecular weight and orientation of PLLA filaments. Therefore, higher modulus, smaller strength, and elongation at the break for filaments could be obtained, which could furtherly realize better radial compression resistance of the braided stent. This annealing strategy could provide new perspectives between anneal and material properties of PLLA monofilaments, and provide more suitable manufacturing technics for polymer braided stent.

Boosting the performance of an iontophoretic biosensing system with a graphene aerogel and Prussian blue for highly sensitive and noninvasive glucose monitoring.

Diabetes and impaired glucose regulation (IGR) threaten the lives and health of numerous patients. Interstitial fluid (ISF) glucose, displaying an excellent correlation with blood glucose, is highly desired to address the limitations of invasive and minimally invasive glucose detection. Herein, we present a screen-printed iontophoretic biosensing system to extract ISF noninvasively and perform in situ instant glucose detection. A three-dimensional graphene aerogel combined with Prussian blue (GA@PB) was introduced as an electron mediator, providing suitable support for glucose oxidase (GOx) immobilization, highly boosting the detection sensitivity. Additionally, a self-made diffuse cell and an ex vivo model were developed to demonstrate the efficacy of ISF extraction based on reverse iontophoresis technology. Highly sensitive and accurate detection of ISF glucose could be achieved with an LOD of 0.26 mM over a 0-15 mM range. Finally, tests on healthy volunteers were conducted to further validate the feasibility of this as-proposed system. Combined with its well flexible and biocompatible features, it holds considerable prospects in the development of wireless wearable biosensors for continuous blood glucose monitoring.

Magnetic nanoparticle loaded biodegradable vascular stents for magnetic resonance imaging and long-term visualization.

Benefiting from their good biosafety and bioabsorbability, polymeric biodegradable stents (BDSs) have promising application prospects in the treatment of cardiovascular diseases. However, due to the low density of the polymer itself, it is difficult to visualize with medical imaging techniques such as CT and MRI, which leads to difficulties in accurate BDS localization and subsequent non-invasive evaluation. Therefore, modification of BDSs to adapt to monitoring techniques for clinical use without affecting their biocompatibility and mechanical properties is a promising strategy to support the clinical translation of BDSs. In this study, Fe3O4 superparamagnetic iron oxide nanoparticles (SPIONs) were synthesized to modify the BDS by ultrasonic spraying. Due to the T2-weighted MR imaging enhancement capability of SPIONs, the fabricated SPION-BDS can be entirely visualized and long-term monitored under MR imaging. Further, a stent degradation assessment method based on the analysis of image gray value changes was established. In conclusion, the constructed SPION-BDS provides a possible solution for precise localization of BDSs after implantation, and furthermore, opens up opportunities for long-term non-invasive monitoring of in vivo BDS degradation and multimodal imaging assessment of vascular endothelial remodeling.

Rapid estimation approach for glycosylated serum protein of human serum based on the combination of deep learning and TD-NMR technology.

Rapid and precise estimation of glycosylated serum protein (GSP) of human serum is of great importance for the treatment and diagnosis of diabetes mellitus. In this study, we propose a novel method for estimation of GSP level based on the combination of deep learning and time domain nuclear magnetic resonance (TD-NMR) transverse relaxation signal of human serum. Specifically, a principal component analysis (PCA)-enhanced one-dimensional convolutional neural network (1D-CNN) is proposed to analyze the TD-NMR transverse relaxation signal of human serum. The proposed algorithm is proved by accurate estimation of GSP level for the collected serum samples. Furthermore, the proposed algorithm is compared with 1D-CNN without PCA, long short-term memory network (LSTM) and some conventional machine learning algorithms. The results indicate that PCA-enhanced 1D-CNN (PC-1D-CNN) has the minimum error. This study proves that proposed method is feasible and superior to estimate GSP level of human serum using TD-NMR transverse relaxation signals.

Next-generation liquid biopsy instruments: Challenges and opportunities.

Conventional cancer diagnosis needs to excise diseased tissue from the patient's body for biopsy, causing severe injury to patients. Liquid biopsy (LB), with the superior advantage of minimal invasiveness, has shown its ability to cancer diagnosis in real-time and has been developing promising diagnostic instruments. However, until today, the developed instrument still cannot be an alternative to tissue biopsy in the majority of research and clinical settings. In this paper, we first summarize the challenges and limitations suffered by the existing LB instrument. Then, the opportunities and future progression of the next-generation instrument are discussed in detail. In all, we hope that the future LB instrument can be eventually integrated into the clinical workflow and serve as a validated and reliable tool for cancer diagnosis.