Label-free single-particle imaging approach for ultra-rapid detection of pathogenic bacteria in clinical samples.

Rapid detection of pathogenic bacteria within a few minutes is the key to control infectious disease. However, rapid detection of pathogenic bacteria in clinical samples is quite a challenging task due to the complex matrix, as well as the low abundance of bacteria in real samples. Herein, we employ a label-free single-particle imaging approach to address this challenge. By tracking the scattering intensity variation of single particles in free solution, the morphological heterogeneity can be well identified with particle size smaller than the diffraction limit, facilitating the morphological identification of single bacteria from a complex matrix in a label-free manner. Furthermore, the manipulation of convection in free solution enables the rapid screening of low-abundance bacteria in a small field of view, which significantly improves the sensitivity of single-particle detection. As a proof of concept demonstration, we are able to differentiate the group B streptococci (GBS)-positive samples within 10 min from vaginal swabs without using any biological reagents. This is the most rapid and low-cost method to the best of our knowledge. We believe that such a single-particle imaging approach will find wider applications in clinical diagnosis and disease control due to its high sensitivity, rapidity, simplicity, and low cost.

DNA-Programmed Four-Bit Quaternary Fluorescence Encoding (FLUCO) Enables 51-Colored Bioimaging Analysis.

Color encoding plays a crucial role in painting, digital photography, and spectral analysis. Achieving accurate, target-responsive color encoding at the molecular level has the potential to revolutionize scientific research and technological innovation, but significant challenges persist. Here, we propose a multibit DNA self-assembly system based on computer-aided design (CAD) technology, enabling accurate, target-responsive, amplified color encoding at the molecular level, termed fluorescence encoding (FLUCO). As a model, we establish a quaternary FLUCO system using four-bit DNA self-assembly, which can accurately encode 51 colors, presenting immense potential in applications such as spatial proteomic imaging and multitarget analysis. Notably, FLUCO enables the simultaneous imaging of multiple targets exceeding the limitations of channels using conventional imaging equipment, and marks the integration of computer science for molecular encoding and decoding. Overall, our work paves the way for target-responsive, controllable molecular encoding, facilitating spatial omics analysis, exfoliated cell analysis, and high-throughput liquid biopsy.

Spectral CT assists differentiation of osteoblastic bone metastasis from bone island in newly diagnosed cancer patients.

OBJECTIVES: To investigate measurements derived from plain and enhanced spectral CT in differentiating osteoblastic bone metastasis (OBM) from bone island (BI). MATERIALS AND METHODS: From January to November 2020, 73 newly diagnosed cancer patients with 201 bone lesions (OBM = 92, BI = 109) having received spectral CT were retrospectively enrolled. Measurements including CT values of 40-140 keV, slope of the spectral curve, effective atomic number (Zeff), water (calcium) density, calcium (water) density, and Iodine (calcium) density were derived from manually segmented lesions on plain and enhanced spectral CT, and then analyzed using Student t-test and Pearson's correlation. Multivariate analysis was performed to build models (plain spectral model, enhanced spectral CT model, and combined model) for the discrimination of OBM and BI with performance evaluated using receiver operator characteristics curve and DeLong test. RESULTS: All features were significantly different between the BI group and OBM group (all p < 0.05), highly correlated with the corresponding features between plain and enhanced spectral CT both in OBM (r: 0.392-0.763) and BI (r: 0.430-0.544). As for the model performance, the combined model achieved the best performance (AUC = 0.925, 95% CI: 0.879 to 0.957), which significantly outperformed the plain spectral CT model (AUC = 0.815, 95% CI: 0.754 to 0.866, p < 0.001) and enhanced spectral CT model (AUC = 0.901, 95% CI: 0.852 to 0.939, p = 0.024) in differentiating OBM and BI. CONCLUSION: In addition to plain spectral CT measurements, enhanced spectral CT measurements would further significantly benefit the differential diagnosis. CLINICAL RELEVANCE STATEMENT: Measurements derived either from plain or enhanced spectral CT could provide additional valuable information to improve the differential diagnosis between OBM and BI in newly diagnosed cancer patients. KEY POINTS: • We intend to investigate plain and enhanced spectral CT measurements in differentiating OBM from BI. • Both plain and enhanced spectral CT help in discriminating OBM and BI in newly diagnosed cancer patients. • Enhanced spectral CT measurements further improve plain spectral CT measurements-based differential diagnosis.

Tumor-associated antigen-specific cell imaging based on upconversion luminescence and nucleic acid rolling circle amplification.

Tumor-associated antigen (TAA)-based diagnosis has gained prominence for early tumor screening, treatment monitoring, prognostic assessment, and minimal residual disease detection. However, limitations such as low sensitivity and difficulty in extracting non-specific binding membrane proteins still exist in traditional detection methods. Upconversion luminescence (UCL) exhibits unique physical and chemical properties under wavelength near-infrared light excitation. Rolling circle amplification (RCA) is an efficient DNA amplification technique with amplification factors as high as 105. Therefore, the above two excellent techniques can be employed for highly accurate imaging analysis of tumor cells. Herein, we developed a novel nanoplatform for TAA-specific cell imaging based on UCL and RCA technology. An aptamer-primer complex selectively binds to Mucin 1 (MUC1), one of TAA on cell surface, to trigger RCA reaction, generating a large number of repetitive sequences. These sequences provide lots of binding sites for complementary signal probes, producing UCL from lanthanide-doped upconversion nanoparticles (UCNPs) after releasing quencher group. The experimental results demonstrate the specific attachment of upconversion nanomaterials to cancer cells which express a high level of MUC1, indicating the potential of UCNPs and RCA in tumor imaging.

Predicting machine's performance record using the stacked long short-term memory (LSTM) neural networks.

PURPOSE: The record of daily quality control (QC) items shows machine performance patterns and potentially provides warning messages for preventive actions. This study developed a neural network model that could predict the record and trend of data variations quantitively. METHODS AND MATERIALS: The record of 24 QC items for a radiotherapy machine was investigated in our institute. The QC records were collected daily for 3 years. The stacked long short-term memory (LSTM) model was used to develop the neural network model. A total of 867 records were collected to predict the record for the next 5 days. To compare the stacked LSTM, the autoregressive integrated moving average model (ARIMA) was developed on the same data set. The accuracy of the model was quantified by the mean absolute error (MAE), root-mean-square error (RMSE), and coefficient of determination (R2 ). To validate the robustness of the model, the record of four QC items was collected for another radiotherapy machine, which was input into the stacked LSTM model without changing any hyperparameters and ARIMA model. RESULTS: The mean MAE, RMSE, and R 2 ${\rm{\;}}{R^2}$ with 24 QC items were 0.013, 0.020, and 0.853 in LSTM, while 0.021, 0.030, and 0.618 in ARIMA, respectively. The results showed that the stacked LSTM outperforms the ARIMA. Moreover, the mean MAE, RMSE, and R 2 ${\rm{\;}}{R^2}$ with four QC items were 0.102, 0.151, and 0.770 in LSTM, while 0.162, 0.375, and 0.550 in ARIMA, respectively. CONCLUSIONS: In this study, the stacked LSTM model can accurately predict the record and trend of QC items. Moreover, the stacked LSTM model is robust when applied to another radiotherapy machine. Predicting future performance record will foresee possible machine failure, allowing early machine maintenance and reducing unscheduled machine downtime.

Exosomal microRNAs in hepatocellular carcinoma.

Hepatocellular carcinoma is one of the most common malignant tumors worldwide and the fourth leading cause of cancer-related deaths. The prognosis of hepatocellular carcinoma patients is extremely poor due to the occult onset and high metastasis of hepatocellular carcinoma. Therefore, biomarkers with high specificity and sensitivity are of great importance in early screening, diagnosis prognosis, and treatment of hepatocellular carcinoma patients. Exosomes are tiny vesicles secreted by various types of cells, which can serve as mediators of intercellular communication to regulate the tumor microenvironment, and play a key role in the occurrence, development, prognosis, monitor and treatment of hepatocellular carcinoma. As microRNA deliverer, exosomes are involved in multiple life activities by regulating target genes of recipient cells such as proliferation, invasion, metastasis and apoptosis of cancer cells. In this review, we summarized the composition, active mechanism and function of exosomal microRNAs in hepatocellular carcinoma, and elaborated on their potential application value of early diagnosis and treatment in hepatocellular carcinoma.

A Paper Based Milli-Cantilever Sensor for Detecting Hydrocarbon Gases via Smartphone Camera.

Hydrocarbon gases, especially toxic ones like benzene and xylene, pose threats to human health and the environment. But existing detection techniques, like bulky GC-MS or portable PID, cannot fulfill people's requirement of affordable and reliable hydrocarbons monitoring for the purpose of personal exposure assessment. Here, a simple, low cost, and light hydrocarbon gases sensor using a smartphone camera as a readout was developed based on the paper based milli-cantilever bending induced by polymer swelling. Its sensing cantilever was composed of three layers: functional layer of polyethylene film, adhesive layer of double-side tape, and a substrate of weighing paper. And the dimensions of the milli-fabricated sensing cantilever are 8 mm long, 0.5 mm wide, and 50 µm thick. The sensor response was the displacement of milli-cantilever free end. As proof of concept, its performance to typical hydrocarbons of xylene, hexane, and BTEX was carefully examined. For all of them, the sensor showed good performance of linear response to hydrocarbon concentrations, wide detection range, low detection, and fast response. Taking xylene for example, the sensor showed wide detection range of 15-140 ppm, low detection limit of 15 ppm, and fast response of 30 s. The sensor cross-sensitivity to other hydrocarbons was consistent with polymer swelling theory that the more carbons the hydrocarbon has, the higher the sensor sensitivity. Taking advantage of the rough materials chosen and simple fabrication procedure, the developed sensors also had high stability with time, low cost, and good uniformity. The developed sensor is affordable both physically and financially, has good performance, could meet hydrocarbons monitoring requirements for occupational safety or air pollution in petroleum industry, and would benefit people's health.

Differentiating novel coronavirus pneumonia from general pneumonia based on machine learning.

BACKGROUND: Chest CT screening as supplementary means is crucial in diagnosing novel coronavirus pneumonia (COVID-19) with high sensitivity and popularity. Machine learning was adept in discovering intricate structures from CT images and achieved expert-level performance in medical image analysis. METHODS: An integrated machine learning framework on chest CT images for differentiating COVID-19 from general pneumonia (GP) was developed and validated. Seventy-three confirmed COVID-19 cases were consecutively enrolled together with 27 confirmed general pneumonia patients from Ruian People's Hospital, from January 2020 to March 2020. To accurately classify COVID-19, region of interest (ROI) delineation was implemented based on ground-glass opacities (GGOs) before feature extraction. Then, 34 statistical texture features of COVID-19 and GP ROI images were extracted, including 13 gray-level co-occurrence matrix (GLCM) features, 15 gray-level-gradient co-occurrence matrix (GLGCM) features and 6 histogram features. High-dimensional features impact the classification performance. Thus, ReliefF algorithm was leveraged to select features. The relevance of each feature was the average weights calculated by ReliefF in n times. Features with relevance larger than the empirically set threshold T were selected. After feature selection, the optimal feature set along with 4 other selected feature combinations for comparison were applied to the ensemble of bagged tree (EBT) and four other machine learning classifiers including support vector machine (SVM), logistic regression (LR), decision tree (DT), and K-nearest neighbor with Minkowski distance equal weight (KNN) using tenfold cross-validation. RESULTS AND CONCLUSIONS: The classification accuracy (ACC), sensitivity (SEN), specificity (SPE) of our proposed method yield 94.16%, 88.62% and 100.00%, respectively. The area under the receiver operating characteristic curve (AUC) was 0.99. The experimental results indicate that the EBT algorithm with statistical textural features based on GGOs for differentiating COVID-19 from general pneumonia achieved high transferability, efficiency, specificity, sensitivity, and impressive accuracy, which is beneficial for inexperienced doctors to more accurately diagnose COVID-19 and essential for controlling the spread of the disease.

CT findings of patients infected with SARS-CoV-2.

BACKGROUND: We aimed to describe the chest CT findings in sixty-seven patients infected by Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). METHODS: We retrospectively reviewed 67 patients hospitalized in Ruian People's Hospital. All the patients received the positive diagnosis of SARS-CoV-2 infection. The CT and clinical data were collected between January 23rd, 2020 and February 10th, 2020. The CT images were analyzed by the senior radiologists. RESULTS: There are 54 patients with positive CT findings and 13 patients with negative CT findings. The typical CT findings in hospitalized patients with SARS-CoV-2 infection were ground glass opacities (42/54), lesions located in the peripheral area (50/54), multiple lesions (46/54), and lesions located in the lower lobes (42/54). There were less typical CT findings, including air bronchogram (18/54), pleural thickening or pleural effusion (14/54), consolidation (12/54), lesions in the upper lobes (12/54), interlobular septal thickening (11/54), reversed halo sign (9/54), single lesion (8/54), air cavities (4/54), bronchial wall thickening (3/54), and intrathoracic lymph node enlargement (2/54). CONCLUSIONS: CT features can play an important role in the early diagnosis and follow-up of COVID-19 patients.

Dosimetric comparison of distal esophageal carcinoma plans for patients treated with small-spot intensity-modulated proton versus volumetric-modulated arc therapies.

BACKGROUND: Esophageal carcinoma is the eighth most common cancer in the world. Volumetric-modulated arc therapy (VMAT) is widely used to treat distal esophageal carcinoma due to high conformality to the target and good sparing of organs at risk (OAR). It is not clear if small-spot intensity-modulated proton therapy (IMPT) demonstrates a dosimetric advantage over VMAT. In this study, we compared dosimetric performance of VMAT and small-spot IMPT for distal esophageal carcinoma in terms of plan quality, plan robustness, and interplay effects. METHODS: 35 distal esophageal carcinoma patients were retrospectively reviewed; 19 patients received small-spot IMPT and the remaining 16 of them received VMAT. Both plans were generated by delivering prescription doses to clinical target volumes (CTVs) on phase-averaged 4D-CT's. The dose-volume-histogram (DVH) band method was used to quantify plan robustness. Software was developed to evaluate interplay effects with randomized starting phases for each field per fraction. DVH indices were compared using Wilcoxon rank-sum test. For fair comparison, all the treatment plans were normalized to have the same CTVhigh D95% in the nominal scenario relative to the prescription dose. RESULTS: In the nominal scenario, small-spot IMPT delivered statistically significantly lower liver Dmean and V30Gy[RBE] , lung Dmean , heart Dmean compared with VMAT. CTVhigh dose homogeneity and protection of other OARs were comparable between the two treatments. In terms of plan robustness, the IMPT and VMAT plans were comparable for kidney V18Gy[RBE] , liver V30Gy[RBE] , stomach V45Gy[RBE] , lung Dmean , V5Gy[RBE] , and V20Gy[RBE] , cord Dmax and D 0.03 c m 3 , liver Dmean , heart V20Gy[RBE] , and V30Gy[RBE] , but IMPT was significantly worse for CTVhigh D95% , D 2 c m 3 , and D5% -D95% , CTVlow D95% , heart Dmean , and V40Gy[RBE] , requiring careful and experienced adjustments during the planning process and robustness considerations. The small-spot IMPT plans still met the standard clinical requirements after interplay effects were considered. CONCLUSIONS: Small-spot IMPT decreases doses to heart, liver, and total lung compared to VMAT as well as achieves clinically acceptable plan robustness. Our study supports the use of small-spot IMPT for the treatment of distal esophageal carcinoma.

Chemical Sensing in Real Time with Plants Using a Webcam.

It has been established that plants can smell and respond to chemicals in order to adapt to and survive in a changing chemical environment. Here we show that a plant responds to chemicals in air, and the response can be detected rapidly to allow tracking of air pollution in real time. We demonstrate this capability by detecting subtle color and shape changes in the leaves of mosses upon exposure to sulfur dioxide in air with a simple webcam and an imaging-processing algorithm. The leaves of mosses consist of a monolayer of cells, providing a large surface-to-volume ratio for highly sensitive chemical sensing. The plant sensor responds linearly to sulfur dioxide within a wide concentration range (0-180 ppm), and it can tolerate humidity variation (15-85% relative humidity) and chemical interference and regenerate itself. We envision that plants can help alert chemical exposure danger as a part of our living environment using low-cost CMOS imagers, and their chemical-sensing capabilities may be further improved with genetic engineering.

Small-spot intensity-modulated proton therapy and volumetric-modulated arc therapies for patients with locally advanced non-small-cell lung cancer: A dosimetric comparative study.

PURPOSE: To compare dosimetric performance of volumetric-modulated arc therapy (VMAT) and small-spot intensity-modulated proton therapy for stage III non-small-cell lung cancer (NSCLC). METHODS AND MATERIALS: A total of 24 NSCLC patients were retrospectively reviewed; 12 patients received intensity-modulated proton therapy (IMPT) and the remaining 12 received VMAT. Both plans were generated by delivering prescription doses to clinical target volumes (CTV) on averaged 4D-CTs. The dose-volume-histograms (DVH) band method was used to quantify plan robustness. Software was developed to evaluate interplay effects with randomized starting phases of each field per fraction. DVH indices were compared using Wilcoxon rank sum test. RESULTS: Compared with VMAT, IMPT delivered significantly lower cord Dmax , heart Dmean , and lung V5 Gy[ RBE ] with comparable CTV dose homogeneity, and protection of other OARs. In terms of plan robustness, the IMPT plans were statistically better than VMAT plans in heart Dmean , but were statistically worse in CTV dose coverage, cord Dmax , lung Dmean , and V5 Gy[ RBE ] . Other DVH indices were comparable. The IMPT plans still met the standard clinical requirements with interplay effects considered. CONCLUSIONS: Small-spot IMPT improves cord, heart, and lung sparing compared to VMAT and achieves clinically acceptable plan robustness at least for the patients included in this study with motion amplitude less than 11 mm. Our study supports the usage of IMPT to treat some lung cancer patients.

Thyroid nodule recognition in computed tomography using first order statistics.

BACKGROUND: Computed tomography (CT) is one of the popular tools for early detection of thyroid nodule. The pixel intensity of thyroid in CT image is very important information to distinguish nodule from normal thyroid tissue. The pixel intensity in normal thyroid tissues is homogeneous and smooth. In the benign or malignant nodules, the pixel intensity is heterogeneous. Several studies have shown that the first order features in ultrasound image can be used as imaging biomarkers in nodule recognition. METHODS: In this paper, we investigate the feasibility of utilizing the first order texture features to identify nodule from normal thyroid tissue in CT image. A total of 284 thyroid CT images from 113 patients were collected in this study. We used 150 healthy controlled thyroid CT images from 55 patients and 134 nodule images (50 malignant and 84 benign nodules) from 58 patients who have undergone thyroid surgery. The final diagnosis was confirmed by histopathological examinations. In the presented method, first, regions of interest (ROIs) from axial non-enhancement CT images were delineated manually by a radiologist. Second, average, median, and wiener filter were applied to reduce photon noise before feature extraction. The first-order texture features, including entropy, uniformity, average intensity, standard deviation, kurtosis and skewness were calculated from each ROI. Third, support vector machine analysis was applied for classification. Several statistical values were calculated to evaluate the performance of the presented method, which includes accuracy, sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and area of under receiver operating characteristic curve (AUC). RESULTS: The entropy, uniformity, mean intensity, standard deviation, skewness (P < 0.05), except kurtosis (P = 0.104) of thyroid tissue with nodules have a significant difference from those of normal thyroid tissue. The optimal classification was obtained from the presented method. The accuracy, sensitivity, specificity, positive predictive value (PPV) and negative predictive value (NPV) are 0.880, 0.821, 0.933, 0.917, 0.854, and 0.953 respectively. CONCLUSION: First order texture features can be used as imaging biomarkers, and the presented system can be used to assist radiologists to recognize the nodules in CT image.

Digitizing Gold Nanoparticle-Based Colorimetric Assay by Imaging and Counting Single Nanoparticles.

Gold colloid changes its color when the internanoparticle distance changes. On the basis of analyte-induced aggregation or disaggregation behavior of gold nanoparticles (AuNPs), versatile colorimetric assays have been developed for measuring various kinds of analytes including proteins, DNA, small molecules, and ions. Traditional read-out signals, which are usually measured by a spectrometer or naked eyes, are based on the averaged extinction properties of a bulk solution containing billions of nanoparticles. Averaged extinction property of a large amount of nanoparticles diminished the contribution from rare events when the analyte concentration was low, thus resulting in limited detection sensitivity. Instead of measuring the averaged optical property from bulk colloid, in the present work, we proposed a digital counterpart of the colorimetric assay by imaging and counting individual AuNPs. This method quantified the analyte concentration with the number percentage of large-sized AuNPs aggregates, which were digitally counted with surface plasmon resonance microscopy (SPRM), a plasmonic imaging technique recently developed by us and other groups. SPRM was able to identify rare AuNPs aggregates despite their small population and greatly improved the detection sensitivity as demonstrated by two model systems based on analyte-induced aggregation and disaggregation, respectively. Furthermore, besides plasmonic AuNPs, SPRM is also suitable for imaging and counting nonplasmonic nanomaterials such as silica and metal oxide with poor extinction properties. It is thus anticipated that the present digitized assay holds a great potential for expanding the colorimetric assay to broad categories of nonplasmonic nanoparticles.

Simple and Sensitive Colorimetric Assay for Pb2+ Based on Glutathione Protected Ag Nanoparticles by Salt Amplification.

A simple and sensitive colorimetric assay for Pb2+ detection has been reported using glutathione protected silver nanoparticles (AgNPs) by salt amplification. The naked AgNPs aggregate under the influence of salt. Glutathione (GSH) can bind to AgNPs via Ag-S bond, helping AgNPs to against salt-induced aggregation. However, GSH binding to AgNPs can be compromised by the interaction between Pb2+ and GSH. As a result, Pb2+-mediated aggregation of AgNPs under the influence of salt is reflected by the UV-Visible spectrum, and the qualitative and quantitative detection for Pb2+ is accomplished, with the detection range 0.5-4 µM and a detection limit of 0.5 µM. At the same time, Pb2+ in real water sample is detected. Furthermore, the high selectivity and low cost of the assay means it is promising for enviromental applications.

Assessment of different head tilt angles in volumetric modulated arc therapy for hippocampus-avoidance whole-brain radiotherapy.

Purpose: In the field of radiation therapy for brain metastases, whole-brain hippocampus-avoidance treatment is commonly employed. this study aims to examine the impact of different head tilt angles on the dose distribution in the whole-brain target area and organs at risk. It also aims to determine the head tilt angle to achieve optimal radiation therapy outcomes. Methods: CT images were collected from 8 brain metastases patients at 5 different groups of head tilt angle. The treatment plans were designed using the volumetric modulated arc therapy (VMAT) technique. The 5 groups of tilt angle were as follows: [0°,10°), [10°,20°), [20°,30°), [30°,40°), and [40°,45°]. The analysis involved assessing parameters such as the uniformity index, conformity index, average dose delivered to the target, dose coverage of the target, hot spots within the target area, maximum dose, and average dose received by organs at risk. Additionally, the study evaluated the correlation between hippocampal dose and other factors, and established linear regression models. Results: Significant differences in dosimetric results were observed between the [40°,45°] and [0°,10°) head tilt angles. The [40°,45°] angle showed significant differences compared to the [0°,10°) angle in the average dose in the target area (31.49 ± 0.29 Gy vs. 31.99 ± 0.29 Gy, p=0.016), dose uniformity (1.20 ± 0.03 vs. 1.24 ± 0.03, p=0.016), hotspots in the target area (33.64 ± 0.35 Gy vs. 34.42 ± 0.49 Gy, p=0.016), maximum hippocampal dose (10.73 ± 0.36 Gy vs. 11.66 ± 0.59 Gy, p=0.008), maximum dose in the lens (2.82 ± 1.10 Gy vs. 4.99 ± 0.16 Gy, p=0.016), and average dose in the lens (1.93 ± 0.29 Gy vs. 4.22 ± 0.26 Gy, p=0.008). There is a moderate correlation between the maximum dose in the hippocampi and the PTV length (r=0.49, p=0.001). Likewise, the mean dose in the hippocampi is significantly correlated with the hippocampi length (r=0.34, p=0.04). Conclusion: The VMAT plan with a head tilt angle of [40°,45°] met all dose constraints and demonstrated improved uniformity of the target area while reducing the dose to organs at risk. Furthermore, the linear regression models suggest that increasing the head tilt angle within the current range of [0°,45°] is likely to lead to a decrease in the average hippocampal dose.

Dynamic Addressing Molecular Robot (DAMR): An Effective and Efficient Trial-and-Error Approach for the Analysis of Single Nucleotide Polymorphisms.

Accurate and efficient molecular recognition plays a crucial role in the fields of molecular detection and diagnostics. Conventional trial-and-error-based molecular recognition approaches have always been challenged in distinguishing minimal differences between targets and non-targets, such as single nucleotide polymorphisms (SNPs) of oligonucleotides. To address these challenges, here, a novel concept of dynamic addressing analysis is proposed. In this concept, by dissecting the regions of the target and creating a corresponding recognizer, it is possible to eliminate the inaccuracy and inefficiency of recognition. To achieve this concept, a Dynamic Addressing Molecular Robot (DAMR), a DNA-based dynamic addressing device is developed which is capable of dynamically locating targets. DAMR is designed to first bind to the conserved region of the target while addressing the specific region dynamically until accurate recognition is achieved. DAMR has provided an approach for analyzing low-resolution targets and has been used for analyzing SNP of miR-196a2 in both cell and serum samples, which has opened new avenues for effective and efficient molecular recognition.

Microfluidics for diagnosis and treatment of cardiovascular disease.

Cardiovascular disease (CVD), a type of circulatory system disease related to the lesions of the cardiovascular system, has become one of the main diseases that endanger human health. Currently, the clinical diagnosis of most CVDs relies on a combination of imaging technology and blood biochemical test. However, the existing technologies for diagnosis of CVDs still have limitations in terms of specificity, detection range, and cost. In order to break through the current bottleneck, microfluidic with the advantages of low cost, simple instruments and easy integration, has been developed to play an important role in the early prevention, diagnosis and treatment of CVDs. Here, we have reviewed the recent various applications of microfluidic in the clinical diagnosis and treatment of CVDs, including microfluidic devices for detecting CVD markers, the cardiovascular models based on microfluidic, and the microfluidic used for CVDs drug screening and delivery. In addition, we have briefly looked forward to the prospects and challenges of microfluidics in diagnosis and treatment of CVDs.

Evaluation of the Yield of DNA Double-Strand Breaks for Carbon Ions Using Monte Carlo Simulation and DNA Fragment Distribution.

PURPOSE: The aim of this work was to provide a method to evaluate the yield of DNA double-strand breaks (DSBs) for carbon ions, overcoming the bias in existing methods due to the nonrandom distribution of DSBs. METHODS AND MATERIALS: A previously established biophysical program based on the radiation track structure and a multilevel chromosome model was used to simulate DNA damage induced by x-rays and carbon ions. The fraction of activity retained (FAR) as a function of absorbed dose or particle fluence was obtained by counting the fraction of DNA fragments larger than 6 Mbp. Simulated FAR curves for the 250 kV x-rays and carbon ions at various energies were compared with measurements using constant-field gel electrophoresis. The doses or fluences at the FAR of 0.7 based on linear interpolation were used to estimate the simulation error for the production of DSBs. RESULTS: The relative difference of doses at the FAR of 0.7 between simulation and experiment was -8.5% for the 250 kV x-rays. The relative differences of fluences at the FAR of 0.7 between simulations and experiments were -17.5%, -42.2%, -18.2%, -3.1%, 10.8%, and -14.5% for the 34, 65, 130, 217, 2232, and 3132 MeV carbon ions, respectively. In comparison, the measurement uncertainty was about 20%. Carbon ions produced remarkably more DSBs and DSB clusters per unit dose than x-rays. The yield of DSBs for carbon ions, ranging from 10 to 16 Gbp-1Gy-1, increased with linear energy transfer (LET) but plateaued in the high-LET end. The yield of DSB clusters first increased and then decreased with LET. This pattern was similar to the relative biological effectiveness for cell survival for heavy ions. CONCLUSIONS: The estimated yields of DSBs for carbon ions increased from 10 Gbp-1Gy-1 in the low-LET end to 16 Gbp-1Gy-1 in the high-LET end with 20% uncertainty.

Lantern-shaped flexible RNA origami for Smad4 mRNA delivery and growth suppression of colorectal cancer.

mRNA delivery has shown high application value in the treatment of various diseases, but its effective delivery is still a major challenge at present. Herein, we propose a lantern-shaped flexible RNA origami for mRNA delivery. The origami is composed of a target mRNA scaffold and only two customized RGD-modified circular RNA staples, which can compress the mRNA into nanoscale and facilitate its endocytosis by cells. In parallel, the flexible structure of the lantern-shaped origami allows large regions of the mRNA to be exposed and translated, exhibiting a good balance between endocytosis and translation efficiency. The application of lantern-shaped flexible RNA origami in the context of the tumor suppressor gene, Smad4 in colorectal cancer models demonstrates promising potential for accurate manipulation of protein levels in in vitro and in vivo settings. This flexible origami strategy provides a competitive delivery method for mRNA-based therapies.