Nanosheet-Assembled Zirconium-Porphyrin Frameworks Enabling Surface-Confined, Initiator-Free Photosynthesis of Ultrahigh Molecular Weight Polymers.

Metal-organic frameworks with well-organized low-dimensional architectures provide significant thermodynamic and/or kinetic benefits for diverse applications. We present here the controlled synthesis of a novel class of hierarchical zirconium-porphyrin frameworks (ZrPHPs) with nanosheet-assembled hexagonal prism morphology. The crystal growth behaviors and structural evolution of ZrPHPs in an additive-modulated solvothermal synthesis are examined, showing an "assembly-hydrolysis-reassembly" mechanism towards the formation of 2D nanosheets with ordered arrangement. Because of the highly-accessible active sites harvesting broadband photons, ZrPHPs serve as adaptable photocatalysts to regulate macromolecular synthesis under full-range visible light and natural sunlight. An initiator-free, oxygen-tolerant photopolymerization system is established, following a distinctive mechanism involving direct photo-induced electron transfer to dormant species and hole-mediated reversible deactivation. Specifically, ZrPHPs provide a surface-confined effect towards the propagating chains which inhibits their recombination termination, enabling the highly-efficient synthesis of ultrahigh molecular weight polymers (Mn >1,500,000) with relatively low dispersity (D≈1.5).

Deep learning model improves radiologists' performance in detection and classification of breast lesions.

OBJECTIVE: Computer-aided diagnosis using deep learning algorithms has been initially applied in the field of mammography, but there is no large-scale clinical application. METHODS: This study proposed to develop and verify an artificial intelligence model based on mammography. Firstly, mammograms retrospectively collected from six centers were randomized to a training dataset and a validation dataset for establishing the model. Secondly, the model was tested by comparing 12 radiologists' performance with and without it. Finally, prospectively enrolled women with mammograms from six centers were diagnosed by radiologists with the model. The detection and diagnostic capabilities were evaluated using the free-response receiver operating characteristic (FROC) curve and ROC curve. RESULTS: The sensitivity of model for detecting lesions after matching was 0.908 for false positive rate of 0.25 in unilateral images. The area under ROC curve (AUC) to distinguish the benign lesions from malignant lesions was 0.855 [95% confidence interval (95% CI): 0.830, 0.880]. The performance of 12 radiologists with the model was higher than that of radiologists alone (AUC: 0.852 vs. 0.805, P=0.005). The mean reading time of with the model was shorter than that of reading alone (80.18 s vs. 62.28 s, P=0.032). In prospective application, the sensitivity of detection reached 0.887 at false positive rate of 0.25; the AUC of radiologists with the model was 0.983 (95% CI: 0.978, 0.988), with sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) of 94.36%, 98.07%, 87.76%, and 99.09%, respectively. CONCLUSIONS: The artificial intelligence model exhibits high accuracy for detecting and diagnosing breast lesions, improves diagnostic accuracy and saves time.

Diagnostic performance of MR for hepatocellular carcinoma based on LI-RADS v2018, compared with v2017.

BACKGROUND: The Liver Imaging Reporting and Data System (LI-RADS) is widely adopted for noninvasive diagnosis of hepatocellular carcinoma (HCC). It's updated to version 2018 recently, with some major changes compared with v2017. However, the diagnostic performance of LI-RADS v2018 and its difference with v2017 are yet to be validated. PURPOSE: To compare the diagnostic performances of LI-RADS on MR for diagnosing HCC between v2017 and v2018. STUDY TYPE: Retrospective. SUBJECTS: In all, 181 patients with 217 hepatic observations (146 HCCs, 16 non-HCC malignancies and 55 benign lesions) with liver MRI and pathological or follow-up imaging diagnoses. FIELD STRENGTH/SEQUENCE: 1.5 T or 3 T MRI. Dual-echo T1 WI, T2 WI, diffusion-weighted imaging, and a liver acquisition with volume acceleration. Assessment Images were independently interpreted by three radiologists, and then in consensus for observations with different LR categories, according to LI-RADS v2017 and v2018, separately. STATISTICAL TESTS: Sensitivity, specificity, accuracy, positive predictive value (PPV), negative predictive value (NPV), positive likelihood ratio (+LR), and Youden index. RESULTS: When adopting LR-5 as a predictor of HCC, the sensitivity (80.8% vs. 71.2%), NPV (69.6% vs. 60.7%), and accuracy (83.9% vs. 77.9%) were all increased for LI-RADS v2018 compared with v2017, with a greater Youden index (0.709 vs. 0.627). However, the diagnostic performances of MRI for diagnosing HCC were not changed while adopting LR-4/5 as a predictor. The threshold growths of 76% (19/25) observations in v2017 were revised to subthreshold growth in v2018, and 16 LR-4 observations in v2017 were changed to LR-5 based on v2018. DATA CONCLUSION: The diagnostic performance of LI-RADS v2018 for diagnosing HCC is superior to v2017, with a greater sensitivity, NPV, and accuracy. The revisions in v2018 mainly affect the categorization when adopting LR-5 as a predictor of HCC. LEVEL OF EVIDENCE: 4 Technical Efficacy Stage: 2 J. Magn. Reson. Imaging 2019;50:746-755.

Single Crystal Perovskite/Graphene Self-Driven Photodetector with Fast Response Speed.

Recently, the combination of two-dimensional (2D) materials and perovskites has gained increasing attention in optoelectronic applications owing to their excellent optical and electrical characteristics. Here, we report a self-driven photodetector consisting of a monolayer graphene sheet and a centimeter-sized CH3NH3PbBr3 single crystal, which was prepared using an optimized wet transfer method. The photodetector exhibits a short response time of 2/30 µs by virtue of its high-quality interface, which greatly enhances electron-hole pair separation in the heterostructure under illumination. In addition, a responsivity of ~0.9 mA/W and a detectivity over 1010 Jones are attained at zero bias. This work inspires new methods for preparing large-scale high-quality perovskite/2D material heterostructures, and provides a new direction for the future enhancement of perovskite optoelectronics.

Assisting scalable diagnosis automatically via CT images in the combat against COVID-19.

The pandemic of Coronavirus Disease 2019 (COVID-19) is causing enormous loss of life globally. Prompt case identification is critical. The reference method is the real-time reverse transcription PCR (RT-PCR) assay, whose limitations may curb its prompt large-scale application. COVID-19 manifests with chest computed tomography (CT) abnormalities, some even before the onset of symptoms. We tested the hypothesis that the application of deep learning (DL) to 3D CT images could help identify COVID-19 infections. Using data from 920 COVID-19 and 1,073 non-COVID-19 pneumonia patients, we developed a modified DenseNet-264 model, COVIDNet, to classify CT images to either class. When tested on an independent set of 233 COVID-19 and 289 non-COVID-19 pneumonia patients, COVIDNet achieved an accuracy rate of 94.3% and an area under the curve of 0.98. As of March 23, 2020, the COVIDNet system had been used 11,966 times with a sensitivity of 91.12% and a specificity of 88.50% in six hospitals with PCR confirmation. Application of DL to CT images may improve both efficiency and capacity of case detection and long-term surveillance.

Pluripotency of mesenchymal stem cells derived from adult marrow.

We report here that cells co-purifying with mesenchymal stem cells--termed here multipotent adult progenitor cells or MAPCs--differentiate, at the single cell level, not only into mesenchymal cells, but also cells with visceral mesoderm, neuroectoderm and endoderm characteristics in vitro. When injected into an early blastocyst, single MAPCs contribute to most, if not all, somatic cell types. On transplantation into a non-irradiated host, MAPCs engraft and differentiate to the haematopoietic lineage, in addition to the epithelium of liver, lung and gut. Engraftment in the haematopoietic system as well as the gastrointestinal tract is increased when MAPCs are transplanted in a minimally irradiated host. As MAPCs proliferate extensively without obvious senescence or loss of differentiation potential, they may be an ideal cell source for therapy of inherited or degenerative diseases.