Sub-Nanogram Resolution Measurement of Inertial Mass and Density Using Magnetic-Field-Guided Bubble Microthruster.

Artificial micro/nanomotors using active particles hold vast potential in applications such as drug delivery and microfabrication. However, upgrading them to micro/nanorobots capable of performing precise tasks with sophisticated functions remains challenging. Bubble microthruster (BMT) is introduced, a variation of the bubble-driven microrobot, which focuses the energy from a collapsing microbubble to create an inertial impact on nearby target microparticles. Utilizing ultra-high-speed imaging, the microparticle mass and density is determined with sub-nanogram resolution based on the relaxation time characterizing the microparticle's transient response. Master curves of the BMT method are shown to be dependent on the viscosity of the solution. The BMT, controlled by a gamepad with magnetic-field guidance, precisely manipulates target microparticles, including bioparticles. Validation involves measuring the polystyrene microparticle mass and hollow glass microsphere density, and assessing the mouse embryo mass densities. The BMT technique presents a promising chip-free, real-time, highly maneuverable strategy that integrates bubble microrobot-based manipulation with precise bioparticle mass and density detection, which can facilitate microscale bioparticle characterizations such as embryo growth monitoring.

Novel model integrating computed tomography-based image markers with genetic markers for discriminating radiation pneumonitis in patients with unresectable stage III non-small cell lung cancer receiving radiotherapy: a retrospective multi-center radiogenomics study.

BACKGROUND: Chemoradiotherapy is a critical treatment for patients with locally advanced and unresectable non-small cell lung cancer (NSCLC), and it is essential to identify high-risk patients as early as possible owing to the high incidence of radiation pneumonitis (RP). Increasing attention is being paid to the effects of endogenous factors for RP. This study aimed to investigate the value of computed tomography (CT)-based radiomics combined with genomics in analyzing the risk of grade ≥ 2 RP in unresectable stage III NSCLC. METHODS: In this retrospective multi-center observational study, 100 patients with unresectable stage III NSCLC who were treated with chemoradiotherapy were analyzed. Radiomics features of the entire lung were extracted from pre-radiotherapy CT images. The least absolute shrinkage and selection operator algorithm was used for optimal feature selection to calculate the Rad-score for predicting grade ≥ 2 RP. Genomic DNA was extracted from formalin-fixed paraffin-embedded pretreatment biopsy tissues. Univariate and multivariate logistic regression analyses were performed to identify predictors of RP for model development. The area under the receiver operating characteristic curve was used to evaluate the predictive capacity of the model. Statistical comparisons of the area under the curve values between different models were performed using the DeLong test. Calibration and decision curves were used to demonstrate discriminatory and clinical benefit ratios, respectively. RESULTS: The Rad-score was constructed from nine radiomic features to predict grade ≥ 2 RP. Multivariate analysis demonstrated that histology, Rad-score, and XRCC1 (rs25487) allele mutation were independent high-risk factors correlated with RP. The area under the curve of the integrated model combining clinical factors, radiomics, and genomics was significantly higher than that of any single model (0.827 versus 0.594, 0.738, or 0.641). Calibration and decision curve analyses confirmed the satisfactory clinical feasibility and utility of the nomogram. CONCLUSION: Histology, Rad-score, and XRCC1 (rs25487) allele mutation could predict grade ≥ 2 RP in patients with locally advanced unresectable NSCLC after chemoradiotherapy, and the integrated model combining clinical factors, radiomics, and genomics demonstrated the best predictive efficacy.

Model integrating CT-based radiomics and genomics for survival prediction in esophageal cancer patients receiving definitive chemoradiotherapy.

BACKGROUND: Definitive chemoradiotherapy (dCRT) is a standard treatment option for locally advanced stage inoperable esophageal squamous cell carcinoma (ESCC). Evaluating clinical outcome prior to dCRT remains challenging. This study aimed to investigate the predictive power of computed tomography (CT)-based radiomics combined with genomics for the treatment efficacy of dCRT in ESCC patients. METHODS: This retrospective study included 118 ESCC patients who received dCRT. These patients were randomly divided into training (n = 82) and validation (n = 36) groups. Radiomic features were derived from the region of the primary tumor on CT images. Least absolute shrinkage and selection operator (LASSO) regression was conducted to select optimal radiomic features, and Rad-score was calculated to predict progression-free survival (PFS) in training group. Genomic DNA was extracted from formalin-fixed and paraffin-embedded pre-treatment biopsy tissue. Univariate and multivariate Cox analyses were undertaken to identify predictors of survival for model development. The area under the receiver operating characteristic curve (AUC) and C-index were used to evaluate the predictive performance and discriminatory ability of the prediction models, respectively. RESULTS: The Rad-score was constructed from six radiomic features to predict PFS. Multivariate analysis demonstrated that the Rad-score and homologous recombination repair (HRR) pathway alterations were independent prognostic factors correlating with PFS. The C-index for the integrated model combining radiomics and genomics was better than that of the radiomics or genomics models in the training group (0.616 vs. 0.587 or 0.557) and the validation group (0.649 vs. 0.625 or 0.586). CONCLUSION: The Rad-score and HRR pathway alterations could predict PFS after dCRT for patients with ESCC, with the combined radiomics and genomics model demonstrating the best predictive efficacy.

Assessing an aqueous flow cell designed for in situ crystal growth under X-ray nanotomography and effects of radiolysis products.

Nucleation and growth of minerals has broad implications in the geological, environmental and materials sciences. Recent developments in fast X-ray nanotomography have enabled imaging of crystal growth in solutions in situ with a resolution of tens of nanometres, far surpassing optical microscopy. Here, a low-cost, custom-designed aqueous flow cell dedicated to the study of heterogeneous nucleation and growth of minerals in aqueous environments is shown. To gauge the effects of radiation damage from the imaging process on growth reactions, radiation-induced morphological changes of barite crystals (hundreds of nanometres to ∼1 µm) that were pre-deposited on the wall of the flow cell were investigated. Under flowing solution, minor to major crystal dissolution was observed when the tomography scan frequency was increased from every 30 min to every 5 min (with a 1 min scan duration). The production of reactive radicals from X-ray induced water radiolysis and decrease of pH close to the surface of barite are likely responsible for the observed dissolution. The flow cell shown here can possibly be adopted to study a wide range of other chemical reactions in solutions beyond crystal nucleation and growth where the combination of fast flow and fast scan can be used to mitigate the radiation effects.

CT-based radiomics signatures can predict the tumor response of non-small cell lung cancer patients treated with first-line chemotherapy and targeted therapy.

OBJECTIVES: The goal of this study was to evaluate the effectiveness of radiomics signatures on pre-treatment computed tomography (CT) images of lungs to predict the tumor responses of non-small cell lung cancer (NSCLC) patients treated with first-line chemotherapy, targeted therapy, or a combination of both. MATERIALS AND METHODS: This retrospective study included 322 NSCLC patients who were treated with first-line chemotherapy, targeted therapy, or a combination of both. Of these patients, 224 were randomly assigned to a cohort to help develop the radiomics signature. A total of 1946 radiomics features were obtained from each patient's CT scan. The top-ranked features were selected by the Minimum Redundancy Maximum Relevance (MRMR) feature-ranking method and used to build a lightweight radiomics signature with the Random Forest (RF) classifier. The independent predictive (IP) features (AUC > 0.6, p value < 0.05) were further identified from the top-ranked features and used to build a refined radiomics signature by the RF classifier. Its prediction performance was tested on the validation cohort, which consisted of the remaining 98 patients. RESULTS: The initial lightweight radiomics signature constructed from 15 top-ranked features had an AUC of 0.721 (95% CI, 0.619-0.823). After six IP features were further identified and a refined radiomics signature was built, it had an AUC of 0.746 (95% CI, 0.646-0.846). CONCLUSIONS: Radiomics signatures based on pre-treatment CT scans can accurately predict tumor response in NSCLC patients after first-line chemotherapy or targeted therapy treatments. Radiomics features could be used as promising prognostic imaging biomarkers in the future. KEY POINTS: The radiomics signature extracted from baseline CT images in patients with NSCLC can predict response to first-line chemotherapy, targeted therapy, or both treatments with an AUC = 0.746 (95% CI, 0.646-0.846). The radiomics signature could be used as a new biomarker for quantitative analysis in radiology, which might provide value in decision-making and to define personalized treatments for cancer patients.

Micro-continuum approach for mineral precipitation.

Rates and extents of mineral precipitation in porous media are difficult to predict, in part because laboratory experiments are problematic. It is similarly challenging to implement numerical methods that model this process due to the need to dynamically evolve the interface of solid material. We developed a multiphase solver that implements a micro-continuum simulation approach based on the Darcy-Brinkman-Stokes equation to study mineral precipitation. We used the volume-of-fluid technique in sharp interface implementation to capture the propagation of the solid mineral surface. Additionally, we utilize an adaptive mesh refinement method to improve the resolution of near interface simulation domain dynamically. The developed solver was validated against both analytical solution and Arbitrary Lagrangian-Eulerian approach to ensure its accuracy on simulating the propagation of the solid interface. The precipitation of barite (BaSO4) was chosen as a model system to test the solver using variety of simulation parameters: different geometrical constraints, flow conditions, reaction rate and ion diffusion. The growth of a single barite crystal was simulated to demonstrate the solver's capability to capture the crystal face specific directional growth.

Machine Learning for Histologic Subtype Classification of Non-Small Cell Lung Cancer: A Retrospective Multicenter Radiomics Study.

BACKGROUND: Histologic phenotype identification of Non-Small Cell Lung Cancer (NSCLC) is essential for treatment planning and prognostic prediction. The prediction model based on radiomics analysis has the potential to quantify tumor phenotypic characteristics non-invasively. However, most existing studies focus on relatively small datasets, which limits the performance and potential clinical applicability of their constructed models. METHODS: To fully explore the impact of different datasets on radiomics studies related to the classification of histological subtypes of NSCLC, we retrospectively collected three datasets from multi-centers and then performed extensive analysis. Each of the three datasets was used as the training dataset separately to build a model and was validated on the remaining two datasets. A model was then developed by merging all the datasets into a large dataset, which was randomly split into a training dataset and a testing dataset. For each model, a total of 788 radiomic features were extracted from the segmented tumor volumes. Then three widely used features selection methods, including minimum Redundancy Maximum Relevance Feature Selection (mRMR), Sequential Forward Selection (SFS), and Least Absolute Shrinkage and Selection Operator (LASSO) were used to select the most important features. Finally, three classification methods, including Logistics Regression (LR), Support Vector Machines (SVM), and Random Forest (RF) were independently evaluated on the selected features to investigate the prediction ability of the radiomics models. RESULTS: When using a single dataset for modeling, the results on the testing set were poor, with AUC values ranging from 0.54 to 0.64. When the merged dataset was used for modeling, the average AUC value in the testing set was 0.78, showing relatively good predictive performance. CONCLUSIONS: Models based on radiomics analysis have the potential to classify NSCLC subtypes, but their generalization capabilities should be carefully considered.

Application of computed tomography-based radiomics signature analysis in the prediction of the response of small cell lung cancer patients to first-line chemotherapy.

The aim of the present study was to investigate the utility of a computed tomography (CT)-based radiomics signature for the early prediction of the tumor response of small cell lung cancer (SCLC) patients to chemotherapy. A dataset including 92 patients from a clinical trial was retrospectively assembled. All of the patients received the standard first-line regimen of etoposide and cisplatin. According to the Response Evaluation Criteria in Solid Tumors 1.1, the patients were divided into two groups: Response and no response groups. A total of 21 radiomics features were extracted from CT images prior to and after two cycles of chemotherapy and a radiomics signature was constructed via a binary logistic regression model. The area under the receiver operating characteristics curve (AUC) was determined to evaluate the performance of the radiomics signature to predict the response to chemotherapy. The clinicopathological factors associated with chemotherapy in patients with SCLC were also evaluated, and a predictive model was established using a binary logistic regression analysis. The 21 radiological features were used to establish a radiomics signature that was significantly associated with the efficacy of SCLC chemotherapy (P<0.05). The performance of the radiomics signature to predict the chemotherapy efficacy (AUC=0.797) was better than that of the model using clinicopathological parameters (AUC=0.670). Therefore, the present study demonstrated that radiomics features may be promising prognostic imaging biomarkers to predict the response of SCLC patients to chemotherapy and may thus be utilized to guide appropriate treatment planning.

Dosimetric evaluation of four whole brain radiation therapy approaches with hippocampus and inner ear avoidance and simultaneous integrated boost for limited brain metastases.

AIMS: To perform a dosimetric evaluation of four different simultaneous integrated boost whole brain radiotherapy modalities with hippocampus and inner ear avoidance in the treatment of limited brain metastases. METHODS: Computed tomography/magnetic resonance imaging data of 10 patients with limited (1-5) brain metastases were used to replan step-and-shoot intensity-modulated radiotherapy (sIMRT), dynamic intensity-modulated radiation therapy (dIMRT), volumetric-modulated arc therapy (VMAT), and helical tomotherapy (Tomo). The prescribed doses of 40-50 Gy in 10 fractions and 30 Gy in 10 fractions were simultaneously delivered to the metastatic lesions and the whole-brain volume, respectively. The hippocampal dose met the RTOG 0933 criteria for hippocampal avoidance (Dmax ≤17 Gy, D100% ≤10 Gy). The inner ear dose was restrained to Dmean ≤15 Gy. Target coverage (TC), homogeneity index (HI), conformity index (CI), maximum dose (Dmax), minimum dose (Dmin) and dose to organs at risk (OARs) were compared. RESULTS: All plans met the indicated dose restrictions. The mean percentage of planning target volume of metastases (PTVmets) coverage ranged from 97.1 to 99.4%. For planning target volume of brain (PTVbrain), Tomo provided the lowest average D2% (37.5 ± 2.8 Gy), the highest average D98% (25.2 ± 2.0 Gy), and the best TC (92.6% ± 2.1%) and CI (0.79 ± 0.06). The two fixed gantry IMRT modalities (step and shot, dynamic) provided similar PTVbrain dose homogeneity (both 0.76). Significant differences across the four approaches were observed for the maximum and minimum doses to the hippocampus and the maximum doses to the eyes, lens and optic nerves. CONCLUSION: All four radiotherapy modalities produced acceptable treatment plans with good avoidance of the hippocampus and inner ear. Tomo obtained satisfactory PTVbrain coverage and the best homogeneity index. TRIAL REGISTRATION: Clinicaltrials.gov, NCT03414944 . Registered 29 January 2018.

Manipulation of Single Cells Using a Ferromagnetic Nanorod Cluster Actuated by Weak AC Magnetic Fields.

A unique noncontact single cell manipulation technique based on the actuation of magnetic nanorods (MNRs) or clusters (MCs) by nonuniform alternating magnetic fields (nuAMFs) is demonstrated. Compared to the actuation of MNRs/MCs by conventional magnetophoresis, the motion of MNRs/MCs actuated by nuAMFs can be tuned by additional parameters including the shape of MNRs/MCs and the frequency of the applied magnetic fields. The manipulation of a single cell by an actuated MNR/MC are divided into five stages, i.e., approaching, pushing, carrying, dragging, and releasing. The interactions between the MNR/MC and the cell in these stages are investigated in detail both experimentally and numerically. Other applications of cell manipulation, such as concentrating cells at target locations and accumulating MNRs/MCs onto a single cell, are also demonstrated. The single cell manipulation system is simple, low-cost, and low-power consumption, and helps advance the state-of-the-art of single-particle manipulation.

A physical catalyst for the electrolysis of nitrogen to ammonia.

Ammonia synthesis consumes 3 to 5% of the world's natural gas, making it a significant contributor to greenhouse gas emissions. Strategies for synthesizing ammonia that are not dependent on the energy-intensive and methane-based Haber-Bosch process are critically important for reducing global energy consumption and minimizing climate change. Motivated by a need to investigate novel nitrogen fixation mechanisms, we herein describe a highly textured physical catalyst, composed of N-doped carbon nanospikes, that electrochemically reduces dissolved N2 gas to ammonia in an aqueous electrolyte under ambient conditions. The Faradaic efficiency (FE) achieves 11.56 ± 0.85% at -1.19 V versus the reversible hydrogen electrode, and the maximum production rate is 97.18 ± 7.13 µg hour-1 cm-2. The catalyst contains no noble or rare metals but rather has a surface composed of sharp spikes, which concentrates the electric field at the tips, thereby promoting the electroreduction of dissolved N2 molecules near the electrode. The choice of electrolyte is also critically important because the reaction rate is dependent on the counterion type, suggesting a role in enhancing the electric field at the sharp spikes and increasing N2 concentration within the Stern layer. The energy efficiency of the reaction is estimated to be 5.25% at the current FE of 11.56%.

Structure and Dynamics of Polymeric Canopies in Nanoscale Ionic Materials: An Electrical Double Layer Perspective.

Nanoscale ionic materials (NIMs) are an emerging class of materials consisting of charged nanoparticles and polymeric canopies attaching to them dynamically by electrostatic interactions. Using molecular simulations, we examine the structure and dynamics of the polymeric canopies in model NIMs in which the canopy thickness is much smaller than the nanoparticle diameter. Without added electrolyte ions, the charged terminal groups of polymers adsorb strongly on charged walls, thereby electrostatically "grafting" polymers to the wall. These polymers are highly stretched. They rarely desorb from the wall, but maintain modest in-plane mobility. When electrolyte ion pairs are introduced, the counterions adsorb on the wall, causing some electrostatically "grafted" polymers to desorb. The desorbed polymers, however, are less than the adsorbed counter-ions, which leads to an overscreening of wall charges. The desorbed polymers' charged terminal groups do not distribute uniformly across the canopy but are depleted in some regions; they adopt conformation similar to those in bulk and exchange with the "grafted" polymers rapidly, hence dilating the canopy and accelerating its dynamics. We understand these results by taking the canopy as an electrical double layer, and highlight the importance of the interplay of electrostatic and entropic effects in determining its structure and dynamics.

Flow of quasi-two dimensional water in graphene channels.

When liquids confined in slit channels approach a monolayer, they become two-dimensional (2D) fluids. Using molecular dynamics simulations, we study the flow of quasi-2D water confined in slit channels featuring pristine graphene walls and graphene walls with hydroxyl groups. We focus on to what extent the flow of quasi-2D water can be described using classical hydrodynamics and what are the effective transport properties of the water and the channel. First, the in-plane shearing of quasi-2D water confined between pristine graphene can be described using the classical hydrodynamic equation, and the viscosity of the water is ∼50% higher than that of the bulk water in the channel studied here. Second, the flow of quasi-2D water around a single hydroxyl group is perturbed at a position of tens of cluster radius from its center, as expected for low Reynolds number flows. Even though water is not pinned at the edge of the hydroxyl group, the hydroxyl group screens the flow greatly, with a single, isolated hydroxyl group rendering drag similar to ∼90 nm2 pristine graphene walls. Finally, the flow of quasi-2D water through graphene channels featuring randomly distributed hydroxyl groups resembles the fluid flow through porous media. The effective friction factor of the channel increases linearly with the hydroxyl groups' area density up to 0.5 nm-2 but increases nonlinearly at higher densities. The effective friction factor of the channel can be fitted to a modified Carman equation at least up to a hydroxyl area density of 2.0 nm-2. These findings help understand the liquid transport in 2D material-based nanochannels for applications including desalination.

Manipulation of magnetic nanorod clusters in liquid by non-uniform alternating magnetic fields.

It is discovered that a non-uniform alternating magnetic field can induce a translational motion of an anisotropic magnetic particle or cluster near a surface. Unlike a permanent magnet pulling a magnetic particle, the particle moves away from the magnetic source with a periodic fluctuation in its trajectory that varies with a frequency that is twice that of the field frequency. The moving speed can be tuned by varying the magnetic field strength and gradient, its alternating frequency, and the particle size. A hydrodynamic model is developed that can qualitatively explain all of the phenomena observed. Such a simple particle manipulation method has a great potential in applications such as cell biology and microfluidics.

Thermodynamics and Kinetics of Gas Storage in Porous Liquids.

The recent synthesis of organic molecular liquids with permanent porosity opens up exciting new avenues for gas capture, storage, and separation. Using molecular simulations, we study the thermodynamics and kinetics for the storage of CH4, CO2, and N2 molecules in porous liquids consisting of crown-ether-substituted cage molecules in a 15-crown-5 solvent. It is found that the intrinsic gas storage capacity per cage molecule follows the order CH4 > CO2 > N2, which does not correlate simply with the size of gas molecules. Different gas molecules are stored inside the cage differently; e.g., CO2 molecules prefer the cage's core whereas CH4 molecules favor both the core and the branch regions. All gas molecules considered can enter the cage essentially without energy barriers and leave the cage on a nanosecond time scale by overcoming a modest energy penalty. The molecular mechanisms of these observations are clarified.

Self-Diffusiophoresis of Janus Catalytic Micromotors in Confined Geometries.

The self-diffusiophoresis of Janus catalytic micromotors (JCMs) in confined environment is studied using direct numerical simulations. The simulations revealed that, on average, the translocation of a JCM through a short pore is moderately slowed down by the confinement. This slowdown is far weaker compared to the transport of particles through similar pores driven by forces induced by external means or passive diffusiophoresis. Pairing of two JCMs facilitates the translocation of the one JCM entering the pore first but slows down the second JCM. Depending on its initial orientation, a JCM near the entrance of a pore can exhibit different rotational motion, which determines whether it can enter the pore. Once a JCM enters a narrow pore, it can execute a self-alignment process after which it becomes fully aligned with the pore axis and moves to the center line of the pore. Analysis of these results showed that, in addition to hydrodynamic effect, the translation and rotation of JCM is also affected by the "chemical effects", i.e., the modification of the chemical species concentration around a JCM by confining walls and neighboring JCMs. These chemical effects are unique to the self-diffusiophoresis of JCMs and should be considered in design and operations of JCMs in confined environment.