Dual-Network Collaborative Matrix Factorization for predicting small molecule-miRNA associations.

MicroRNAs (miRNAs) play crucial roles in multiple biological processes and human diseases and can be considered as therapeutic targets of small molecules (SMs). Because biological experiments used to verify SM-miRNA associations are time-consuming and expensive, it is urgent to propose new computational models to predict new SM-miRNA associations. Here, we proposed a novel method called Dual-network Collaborative Matrix Factorization (DCMF) for predicting the potential SM-miRNA associations. Firstly, we utilized the Weighted K Nearest Known Neighbors (WKNKN) method to preprocess SM-miRNA association matrix. Then, we constructed matrix factorization model to obtain two feature matrices containing latent features of SM and miRNA, respectively. Finally, the predicted SM-miRNA association score matrix was obtained by calculating the inner product of two feature matrices. The main innovations of this method were that the use of WKNKN method can preprocess the missing values of association matrix and the introduction of dual network can integrate more diverse similarity information into DCMF. For evaluating the validity of DCMF, we implemented four different cross validations (CVs) based on two distinct datasets and two different case studies. Finally, based on dataset 1 (dataset 2), DCMF achieved Area Under receiver operating characteristic Curves (AUC) of 0.9868 (0.8770), 0.9833 (0.8836), 0.8377 (0.7591) and 0.9836 ± 0.0030 (0.8632 ± 0.0042) in global Leave-One-Out Cross Validation (LOOCV), miRNA-fixed local LOOCV, SM-fixed local LOOCV and 5-fold CV, respectively. For case studies, plenty of predicted associations have been confirmed by published experimental literature. Therefore, DCMF is an effective tool to predict potential SM-miRNA associations.

Clinically Applicable Pan-Origin Cancer Detection for Lymph Nodes via Artificial Intelligence-Based Pathology.

INTRODUCTION: Lymph node metastasis is one of the most common ways of tumour metastasis. The presence or absence of lymph node involvement influences the cancer's stage, therapy, and prognosis. The integration of artificial intelligence systems in the histopathological diagnosis of lymph nodes after surgery is urgent. METHODS: Here, we propose a pan-origin lymph node cancer metastasis detection system. The system is trained by over 700 whole-slide images (WSIs) and is composed of two deep learning models to locate the lymph nodes and detect cancers. RESULTS: It achieved an area under the receiver operating characteristic curve (AUC) of 0.958, with a 95.2% sensitivity and 72.2% specificity, on 1,402 WSIs from 49 organs at the National Cancer Center, China. Moreover, we demonstrated that the system could perform robustly with 1,051 WSIs from 52 organs from another medical centre, with an AUC of 0.925. CONCLUSION: Our research represents a step forward in a pan-origin lymph node metastasis detection system, providing accurate pathological guidance by reducing the probability of missed diagnosis in routine clinical practice.

2D Porphyrin-Based Covalent-Organic Framework/PEG Composites: A Rational Strategy for Photocatalytic Hydrogen Evolution.

Two-dimensional porphyrin-based covalent-organic frameworks (2D-por-COFs) have gained significant attention as attractive platforms for efficient solar light conversion into hydrogen production. Herein, it is found that introducing transition metal zinc and polyethylene glycol (PEG) into 2D-por-COFs can effectively improve the photocatalytic hydrogen evolution performance. The photocatalytic hydrogen evolution rate of ZnPor-COF is 2.82 times higher than that of H2Por-COF. Moreover, ZnPor-COF@PEG has the highest photocatalytic hydrogen evolution efficiency, which is 1.31 and 3.7 times that of pristine ZnPor-COF and H2Por-COF, respectively. The filling of PEG makes the layered structure of COFs more stable. PEG reduces the distortion and deformation of the carbon skeleton after the experiment of photocatalytic hydrogen evolution. The layered stacking and crystallization of 2D-por-COFs are also enhanced. Meanwhile, the presence of PEG also accelerates the transfer of excited electrons and enhances the photocatalytic hydrogen evolution activity. This strategy will provide valuable insights into the design of 2D-por-COFs as efficient solid photocatalysts for solar-driven hydrogen production.

Highly selective detection of deoxyribonucleic acid in living cells using RecA-green fluorescent protein-single-stranded deoxyribonucleic acid filament fluorescence resonance energy transfer probe.

A fluorescence resonance energy transfer (FRET) method was developed for double-stranded deoxyribonucleic acid (dsDNA) detection in living cells using the RecA-GFP (green fluorescent protein) fusion protein filament. In brief, the thiol-modified single-stranded DNA (ssDNA) was attached to gold nanoparticles (AuNPs); on the contrary, the prepared RecA-GFP fusion protein interacted with ssDNA. Due to the FRET between AuNPs and RecA-GFP, fluorescence of RecA-GFP fusion protein was quenched. In the presence of homologous dsDNA, homologous recombination occurred to release RecA-GFP fusion protein. Thus, the fluorescence of RecA-GFP was recovered. The dsDNA concentration was detected using fluorescence intensity of RecA-GFP. Under optimal conditions, this method could detect dsDNA activity as low as 0.015 optical density (OD) Escherichia coli cells, with a wide linear range from 0.05 to 0.9 OD cells, and the regression equation was ΔF = 342.7c + 78.9, with a linear relationship coefficient of 0.9920. Therefore, it provided a promising approach for the selective detection of dsDNA in living cells for early clinical diagnosis of genetic diseases.

A dual amplified gold nanoparticle-based biosensor for ultrasensitive and selective detection of fibrin.

Ultrasensitive, selective, and non-invasive detection of fibrin in human serum is critical for disease diagnosis. So far, the development of high-performance and ultrasensitive biosensors maintains core challenges for biosensing. Herein, we designed a novel ribbon nanoprobe for ultrasensitive detection of fibrin. The probe contains gold nanoparticles (AuNPs) that can not only link with homing peptide Cys-Arg-Glu-Lys-Ala (CREKA) to recognize fibrin but also carry long DNA belts to form G-quadruplex-based DNAzyme, catalyzing the chemiluminescence of luminol-hydrogen peroxide (H2O2) reaction. Combined with the second amplification procedure of rolling circle amplification (RCA), the assay exhibits excellent sensitivity with a detection limit of 0.04 fmol L-1 fibrin based on the 3-sigma. Furthermore, the biosensor shows high specificity on fibrin in samples because the structure of antibody-fibrin-homing peptide was employed to double recognize fibrin. Altogether, the simple and inexpensive approach may present a great potential for reliable detection of biomarkers.

Tuning Piezoelectricity via Thermal Annealing at a Freestanding Ferroelectric Membrane.

Tuning the ferroelectric domain structure by a combination of elastic and electrostatic engineering provides an effective route for enhanced piezoelectricity. However, for epitaxial thin films, the clamping effect imposed by the substrate does not allow aftergrowth tuning and also limits the electromechanical response. In contrast, freestanding membranes, which are free of substrate constraints, enable the tuning of a subtle balance between elastic and electrostatic energies, giving new platforms for enhanced and tunable functionalities. Here, highly tunable piezoelectricity is demonstrated in freestanding PbTiO3 membranes, by varying the ferroelectric domain structures from c-dominated to c/a and a domains via aftergrowth thermal treatment. Significantly, the piezoelectric coefficient of the c/a domain structure is enhanced by a factor of 2.5 compared with typical c domain PbTiO3. This work presents a new strategy to manipulate the piezoelectricity in ferroelectric membranes, highlighting their great potential for nano actuators, transducers, sensors and other NEMS device applications.

RSL1D1 knockdown induces ferroptosis and mediates ferrous iron accumulation in senescent cells by inhibiting FTH1 mRNA stability.

Iron metabolism plays an important role in maintaining cellular multiple biological functions. Dysfunction of iron homeostasis-maintaining systems was observed in many diseases, including cancer. Ribosomal L1 domain-containing 1 (RSL1D1) is an RNA-binding protein involved in multiple cellular processes, including cellular senescence, proliferation and apoptosis. However, the regulatory mechanism of RSL1D1 underlying cellular senescence and its biological process in colorectal cancer (CRC) is not clearly understood. Here, we report that RSL1D1 expression is downregulated by ubiquitin-mediated proteolysis in senescence-like CRC cells. RSL1D1, as an anti-senescence factor, is frequently upregulated in CRC, and elevated RSL1D1 prevents CRC cells from senescence-like phenotype, and correlated with poor prognosis of CRC patients. Knockdown of RSL1D1 inhibited cell proliferation, and induced cell cycle arrest and apoptosis. Notably, RSL1D1 plays important roles in regulating iron metabolism of cancer cells. In RSL1D1-knockdown cells, FTH1 expression was significantly decreased, while transferrin receptor 1 expression was increased, leading to intracellular ferrous iron accumulation, which subsequently promoted ferroptosis, indicated by the increased malondialdehyde and decreased GPX4 levels. Mechanically, RSL1D1 directly bounds with 3' untranslated region of FTH1 and subsequently promoted the mRNA stability. Moreover, RSL1D1-mediated downregulation of FTH1 was also observed in H2O2-induced senescence-like cancer cells. Taken together, these findings support RSL1D1 plays an important role in regulating intracellular iron homeostasis in CRC, and suggest that RSL1D1 could be a potential therapeutic target for cancer treatment.

Cooperative Boron and Vanadium Doping of Nickel Phosphides for Hydrogen Evolution in Alkaline and Anion Exchange Membrane Water/Seawater Electrolyzers.

Developing low-cost and high-performance transition metal-based electrocatalysts is crucial for realizing sustainable hydrogen evolution reaction (HER) in alkaline media. Here, a cooperative boron and vanadium co-doped nickel phosphide electrode (B, V-Ni2 P) is developed to regulate the intrinsic electronic configuration of Ni2 P and promote HER processes. Experimental and theoretical results reveal that V dopants in B, V-Ni2 P greatly facilitate the dissociation of water, and the synergistic effect of B and V dopants promotes the subsequent desorption of the adsorbed hydrogen intermediates. Benefiting from the cooperativity of both dopants, the B, V-Ni2 P electrocatalyst requires a low overpotential of 148 mV to attain a current density of -100 mA cm-2  with excellent durability. The B, V-Ni2 P is applied as the cathode in both alkaline water electrolyzers (AWEs) and anion exchange membrane water electrolyzers (AEMWEs). Remarkably, the AEMWE delivers a stable performance to achieve 500 and 1000 mA cm-2  current densities at a cell voltage of 1.78 and 1.92 V, respectively. Furthermore, the developed AWEs and AEMWEs also demonstrate excellent performance for overall seawater electrolysis.

Assessment of automatic rib fracture detection on chest CT using a deep learning algorithm.

OBJECTIVES: To evaluate deep neural networks for automatic rib fracture detection on thoracic CT scans and to compare its performance with that of attending-level radiologists using a large amount of datasets from multiple medical institutions. METHODS: In this retrospective study, an internal dataset of 12,208 emergency room (ER) trauma patients and an external dataset of 1613 ER trauma patients taking chest CT scans were recruited. Two cascaded deep neural networks based on an extended U-Net architecture were developed to segment ribs and detect rib fractures respectively. Model performance was evaluated with a 95% confidence interval (CI) on both the internal and external dataset, and compared with attending-level radiologist readings using t test. RESULTS: On the internal dataset, the AUC of the model for detecting fractures at per-rib level was 0.970 (95% CI: 0.968, 0.972) with sensitivity of 93.3% (95% CI: 92.0%, 94.4%) at a specificity of 98.4% (95% CI: 98.3%, 98.5%). On the external dataset, the model obtained an AUC of 0.943 (95% CI: 0.941, 0.945) with sensitivity of 86.2% (95% CI: 85.0%, 87.3%) at a specificity of 98.8% (95% CI: 98.7%, 98.9%), compared to the sensitivity of 70.5% (95% CI: 69.3%, 71.8%) (p < .0001) and specificity of 98.8% (95% CI: 98.7%, 98.9%) (p = 0.175) by attending radiologists. CONCLUSIONS: The proposed DL model is a feasible approach to identify rib fractures on chest CT scans, at the very least, reaching a level on par with attending-level radiologists. KEY POINTS: • Deep learning-based algorithms automatically detected rib fractures with high sensitivity and reasonable specificity on chest CT scans. • The performance of deep learning-based algorithms reached comparable diagnostic measures with attending level radiologists for rib fracture detection on chest CT scans. • The deep learning models, similar to human readers, were susceptible to the inconspicuity and ambiguity of target lesions. More training data was required for subtle lesions to achieve comparable detection performance.

High accuracy epidermal growth factor receptor mutation prediction via histopathological deep learning.

BACKGROUND: The detection of epidermal growth factor receptor (EGFR) mutations in patients with non-small cell lung cancer is critical for tyrosine kinase inhibitor therapy. EGFR detection requires tissue samples, which are difficult to obtain in some patients, costing them the opportunity for further treatment. To realize EGFR mutation prediction without molecular detection, we aimed to build a high-accuracy deep learning model with only haematoxylin and eosin (H&E)-stained slides. METHODS: We collected 326 H&E-stained non-small cell lung cancer slides from Beijing Chest Hospital, China, and used 226 slides (88 with EGFR mutations) for model training. The remaining 100 images (50 with EGFR mutations) were used for testing. We trained a convolutional neural network based on ResNet-50 to classify EGFR mutation status on the slide level. RESULTS: The sensitivity and specificity of the model were 76% and 74%, respectively, with an area under the curve of 0.82. When applying the double-threshold approach, 33% of the patients could be predicted by the deep learning model as EGFR positive or negative with a sensitivity and specificity of 100.0% and 87.5%. The remaining 67% of the patients got an uncertain result and will be recommenced to perform further examination. By incorporating adenocarcinoma subtype information, we achieved 100% sensitivity in predicting EGFR mutations in 37.3% of adenocarcinoma patients. CONCLUSIONS: Our study demonstrates the potential of a deep learning-based EGFR mutation prediction model for rapid and cost-effective pre-screening. It could serve as a high-accuracy complement to current molecular detection methods and provide treatment opportunities for non-small cell lung cancer patients from whom limited samples are available.

Cotton swabs decorated with Ag@BPQD for the fluorescence determination of 3-aminosalicylic and 5-aminosalicylic acid.

Novel and portable cotton swab-based fluorometry was constructed for the first time for 3-aminosalicylic acid (3-ASA) and 5-aminosalicylic acid (5-ASA) detection. It was carried out by fluorescence enhancement on silver (Ag)-doped black phosphorus quantum dots (Ag@BPQD). Ag@BPQD were prepared from AgNO3 and bulk black phosphorus in N, N-dimethylformamide (DMF) solution by solvothermal decomposition after mechanical exfoliation. Ag@BPQD show blue fluorescence with a quantum yield (QY) of 2.43%. In the presence of Ag@BPQD, 3-ASA exhibited bright blue fluorescence (λex = 328 nm, λem = 448 nm). The fluorescence of 5-ASA was also enhanced significantly and exhibited bright green emission (λex = 328 nm, λem = 484 nm). The linear range of 3-ASA is 0-90 µM with a detection limit (LOD) of 0.10 µM, relative standard deviation (RSD) ≤ 2.04%, and a recovery range of 98.0-104.3%. The linear range of 5-ASA is 0-120 µM with a LOD of 0.12 µM, RSD ≤ 1.34%, and a recovery range of 98.0-101.3%. When 3-ASA and 5-ASA were mixed in different ratios, the fluorescence showed different colors. The possible mechanism of the interaction between 3-ASA (or 5-ASA) and Ag@BPQD may be ascribed to the generation of excited-state intramolecular proton transfer. To realize convenient detection of 3-ASA and 5-ASA, a Ag@BPQD portable sensing method using cotton swabs were built. The proposed approach provides the detection of 3-ASA and 5-ASA in environmental and biological samples with high efficiency, accuracy and portability.

Rapid Screening of Phenolic Compounds with Anti-Enteritis Activity from Camellia oleifera Oil Using a Smurf Drosophila Model and Molecular Docking Methods.

Screening and identifying the active compounds in foods are important for the development and utilization of functional foods. In this study, the anti-enteritis activity of ethanol extract from Camellia oleifera oil (PECS) was quickly evaluated using a Smurf Drosophila model and the metabolomics approach, combined with molecular docking techniques, were performed to rapidly screen and identify compounds with potential anti-enteritis activity in PECS. PECS showed good anti-enteritis activity and inhibited the activity of 5-lipoxygenase (LOX), cyclooxygenase 2 (COX-2) and inducible nitric oxide synthase (iNOS). In particular, wighteone and p-octopamine were newly identified in C. oleifera oil and were proven to have good anti-enteritis activity. The inhibitory activity of kaempferitrin (IC50 = 0.365 mmol L-1) was higher than that of wighteone (IC50 = 0.424 mmol L-1) and p-octopamine (IC50 = 0.402 mmol L-1). Of note, the IC50 value of salazosulfapyridine was 0.810 mmol L-1. Inhibition of LOX activity is likely one of the anti-enteritis mechanisms of PECS. These new findings lay the foundation for further investigations into the underlying mechanisms of anti-enteritis activity in C. oleifera oil.

Heterostructured V-Doped Ni2 P/Ni12 P5 Electrocatalysts for Hydrogen Evolution in Anion Exchange Membrane Water Electrolyzers.

Regulating the electronic structure and intrinsic activity of catalysts' active sites with optimal hydrogen intermediates adsorption is crucial to enhancing the hydrogen evolution reaction (HER) in alkaline media. Herein, a heterostructured V-doped Ni2 P/Ni12 P5 (V-Ni2 P/Ni12 P5 ) electrocatalyst is  fabricated through a hydrothermal treatment and controllable phosphidation process. In comparison with pure-phase V-Ni2 P, in/ex situ characterizations and theoretical calculations reveal a redistribution of electrons and active sites in V-Ni2 P/Ni12 P5 due to the V doping and heterointerfaces effect. The strong coupling between Ni2 P and Ni12 P5 at the interface leads to an increased electron density at interfacial Ni sites while depleting at P sites, with V-doping further promoting the electron accumulation at Ni sites. This is accompanied by the change of active sites from the anionic P sites to the interfacial Ni-V bridge sites in V-Ni2 P/Ni12 P5 . Benefiting from the interface electronic structure, increased number of active sites, and optimized H-adsorption energy, the V-Ni2 P/Ni12 P5 exhibits an overpotential of 62 mV to deliver 10 mA cm-2 and excellent long-term stability for HER. The V-Ni2 P/Ni12 P5 catalyst is applied for anion exchange membrane water electrolysis to deliver superior performance with a current density of 500 mA cm-2 at a cell voltage of 1.79 V and excellent durability.

Assessment of deep learning assistance for the pathological diagnosis of gastric cancer.

Previous studies on deep learning (DL) applications in pathology have focused on pathologist-versus-algorithm comparisons. However, DL will not replace the breadth and contextual knowledge of pathologists; rather, only through their combination may the benefits of DL be achieved. A fully crossed multireader multicase study was conducted to evaluate DL assistance with pathologists' diagnosis of gastric cancer. A total of 110 whole-slide images (WSI) (50 malignant and 60 benign) were interpreted by 16 board-certified pathologists with or without DL assistance, with a washout period between sessions. DL-assisted pathologists achieved a higher area under receiver operating characteristic curve (ROC-AUC) (0.911 vs. 0.863, P = 0.003) than unassisted in interpreting the 110 WSIs. Pathologists with DL assistance demonstrated higher sensitivity in detection of gastric cancer than without (90.63% vs. 82.75%, P = 0.010). No significant difference was observed in specificity with or without deep learning assistance (78.23% vs. 79.90%, P = 0.468). The average review time per WSI was shortened with DL assistance than without (22.68 vs. 26.37 second, P = 0.033). Our results demonstrated that DL assistance indeed improved pathologists' accuracy and efficiency in gastric cancer diagnosis and further boosted the acceptance of this new technique.

Polarization-independent photon up-conversion with a single lithium niobate waveguide.

We propose a polarization-independent up-conversion protocol for single-photon detection at telecom band with a single thin-film periodically poled lithium niobate waveguide. By choosing the proper waveguide parameters, the waveguide dispersion can compensate the crystal birefringence so that quasi-phase-matching conditions for transverse electric and transverse magnetic modes can be simultaneously fulfilled with single poling period. With this scheme, randomly-polarized single photons at 1550 nm can be up-converted with a normalized conversion efficiency of 163.8%/W cm2.

Selecting Candidates for Organ-Preserving Strategies After Neoadjuvant Chemoradiotherapy for Rectal Cancer: Development and Validation of a Model Integrating MRI Radiomics and Pathomics.

BACKGROUND: Histopathologic evaluation after surgery is the gold standard to evaluate treatment response to neoadjuvant chemoradiotherapy (nCRT) in locally advanced rectal cancer (LARC). However, it cannot be used to guide organ-preserving strategies due to poor timeliness. PURPOSE: To develop and validate a multiscale model incorporating radiomics and pathomics features for predicting pathological good response (pGR) of down-staging to stage ypT0-1N0 after nCRT. STUDY TYPE: Retrospective. POPULATION: A total of 153 patients (median age, 55 years; 109 men; 107 training group; 46 validation group) with clinicopathologically confirmed LARC. FIELD STRENGTH/SEQUENCE: A 3.0-T; fast spin echo T2 -weighted and single-shot EPI diffusion-weighted images. ASSESSMENT: The differences in clinicoradiological variables between pGR and non-pGR groups were assessed. Pretreatment and posttreatment radiomics signatures, and pathomics signature were constructed. A multiscale pGR prediction model was established. The predictive performance of the model was evaluated and compared to that of the clinicoradiological model. STATISTICAL TESTS: The χ2 test, Fisher's exact test, t-test, the minimum redundancy maximum relevance algorithm, the least absolute shrinkage and selection operator logistic regression algorithm, regression analysis, receiver operating characteristic curve (ROC) analysis, Delong method. P < 0.05 indicated a significant difference. RESULTS: Pretreatment radiomics signature (odds ratio [OR] = 2.53; 95% CI: 1.58-4.66), posttreatment radiomics signature (OR = 9.59; 95% CI: 3.04-41.46), and pathomics signature (OR = 3.14; 95% CI: 1.40-8.31) were independent factors for predicting pGR. The multiscale model presented good predictive performance with areas under the curve (AUC) of 0.93 (95% CI: 0.88-0.98) and 0.90 (95% CI: 0.78-1.00) in the training and validation groups, those were significantly higher than that of the clinicoradiological model with AUCs of 0.69 (95% CI: 0.55-0.82) and 0.68 (95% CI: 0.46-0.91) in both groups. DATA CONCLUSION: A model incorporating radiomics and pathomics features effectively predicted pGR after nCRT in patients with LARC. EVIDENCE LEVEL: 3 TECHNICAL EFFICACY: Stage 4.

Construction of a Turn-off-on Fluorescent System Based On Aggregation Induced Emission of Acetaldehyde Using Carbonized Polymer dots and Tb3.

It was the first time to report the aggregation induced emission (AIE) of acetaldehyde (AA) on the surface of carbonized polymer dots (CPDs) with the auxiliary of Tb3+. Based on the AIE of AA, a turn-off-on fluorescence method was established for AA detection using the porous CPDs-Tb3+ system. The one-pot hydrothermal method was used to obtain CPDs, using milk and polyethyleneimine (PEI) as precursors. In the presence of Tb3+, CPDs aggregated immediately and even forming precipitate, and the fluorescence intensity decreased obviously. AA can effectively embed on the surface of CPDs-Tb3+ due to the porous structure. AA displayed obviously blue fluorescence with excitation wavelength at 370 nm (emission peak at 460 nm), while there was no fluorescence peak when excited at 460 nm. In the CPDs-Tb3+ solution, AA exhibits obvious fluorescence enhancement effect (λex 460 nm, λem 545 nm). And then, AA can be determined by the turn-off-on system based on the linear relationship between fluorescence enhancement and the concentration of AA ranging from 0.04 mM to 42.48 mM. The limit of detection (LOD) was 0.02 mM. The turn-off-on system was successfully applied to determine AA in wine samples. The strategy may be exploited to monitor AA in more drinking or foodstuff samples.

Inner-filter Effect Induced Fluorescence Quenching of Carbon Dots for Cr(VI) Detection with High Sensitivity.

Carbon dots (CDs) were used to develop a sensitive sensing technique for detecting Cr(VI). CDs were made using a hydrothermal technique from citric acid and glutamic acid. These prepared CDs emitted blue fluorescence under excitation of 350 nm (λem = 420 nm), and the fluorescence quantum yield was 48.41%. Transmission electron microscope was used to examine the morphology of the CDs, which had an average size of 2.21 ± 0.39 nm. The elementary composition and bonding structure of the CDs were conducted by XPS and FT-IR spectrum. Cr(VI) quenched the fluorescence of CDs through a static quenching effect and an inner filter effect, allowing Cr(VI) to be detected quantitatively. This approach was used to detect Cr(VI) in two samples of water, with the findings demonstrating that it is reliable and accurate. The fluorescence intensity change was linearly related to the concentration of Cr(VI) in the range from 0.5 to 400 µM, with the detection limit being 0.10 µM. This approach has the virtues of wide detection range, low cost and fast response. The strategy has a great application prospect for detecting Cr(VI) in practical samples.

Simple and sensitive detection of deoxyribonucleic acid using a RecA-GFP fusion protein-DNA filament as probe.

A simple, rapid and highly sensitive method for detection of double-stranded DNA (dsDNA) was developed using a novel fluorescence probe composed of a RecA-GFP fusion protein that had specific recognition of ssDNA complexes (RecA-GFP-DNA filament). The RecA-GFP fusion protein not only had strong fluorescence, but could also occur by homologous recombination. In the presence of the target dsDNA, the complementary ssDNA of the RecA-GFP-DNA filaments invaded one end of the dsDNA chain. In addition, the other end of the ssDNA dissociated the RecA-GFP filaments. An assay of the probe showed a linear relationship with dsDNA concentration in the range 1-11 nM, with a correlation coefficient of 0.9923. The limit of detection for dsDNA was determined experimentally to be 0.3 nM (3δ). Compared with conventional methods, this method has the advantages of simple operation, high specificity, and high sensitivity.

Histopathological Diagnosis System for Gastritis Using Deep Learning Algorithm.

Objective To develope a deep learning algorithm for pathological classification of chronic gastritis and assess its performance using whole-slide images (WSIs). Methods We retrospectively collected 1,250 gastric biopsy specimens (1,128 gastritis, 122 normal mucosa) from PLA General Hospital. The deep learning algorithm based on DeepLab v3 (ResNet-50) architecture was trained and validated using 1,008 WSIs and 100 WSIs, respectively. The diagnostic performance of the algorithm was tested on an independent test set of 142 WSIs, with the pathologists' consensus diagnosis as the gold standard. Results The receiver operating characteristic (ROC) curves were generated for chronic superficial gastritis (CSuG), chronic active gastritis (CAcG), and chronic atrophic gastritis (CAtG) in the test set, respectively.The areas under the ROC curves (AUCs) of the algorithm for CSuG, CAcG, and CAtG were 0.882, 0.905 and 0.910, respectively. The sensitivity and specificity of the deep learning algorithm for the classification of CSuG, CAcG, and CAtG were 0.790 and 1.000 (accuracy 0.880), 0.985 and 0.829 (accuracy 0.901), 0.952 and 0.992 (accuracy 0.986), respectively. The overall predicted accuracy for three different types of gastritis was 0.867. By flagging the suspicious regions identified by the algorithm in WSI, a more transparent and interpretable diagnosis can be generated. Conclusion The deep learning algorithm achieved high accuracy for chronic gastritis classification using WSIs. By pre-highlighting the different gastritis regions, it might be used as an auxiliary diagnostic tool to improve the work efficiency of pathologists.