Stabilization of Fluorescent [Ag-m](n+) Quantum Clusters in Multiphase Inorganic Glass-Ceramics for White LEDs

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Tunable white light emission from glass-ceramics containing Eu2+, Tb3+, Eu3+ co-doped SrLaF5 nanocrystals

International audienceThe Eu2 +/Tb3 +/Eu3 + co-doped fluorosilicate glass ceramics containing SrLaF5 nanocrystals were prepared. XRD results showed the rare earth ions were enriched into the precipitated nanophase. It deduced efficient energy transfers (ET) from Eu2 + to Tb3 + and Eu3 + and intense warm white luminescence of the glass ceramics. Comparing the glass, the luminescence quantum yield (QY) of the glass ceramics was enlarged by about 3 times. That demonstrated the potential WLED application of the present glass ceramics

White light generation of glass ceramics containing Ba2LaF7: Eu2+,Tb3+ and Sm3+ nanocrystals

International audienceThe Eu2+/Tb3+/Sm3+ co-doped oxyfluoride glass ceramics containing Ba2LaF7 nanocrystals are prepared in the reducing atmosphere. The X-ray diffraction results show that Eu2+, Tb3+ and Sm3+ ions are enriched into the precipitated Ba2LaF7 nanophase after the annealing process. It deduces efficient energy transfers from Eu2+ to Tb3+ and Sm3+ and intenses warm white luminescence of the glass ceramics. Comparing with the glass, the luminescence quantum yield of the glass ceramics is also enlarged by about 3 times. This demonstrates the potential white light-emitting diode application of the glass ceramics produced in this letter

Preparation and photoluminescence properties of fluorosilicate glass ceramics containing CeOF: Dy3+ nanocrystals

International audienceThe Ce3+ and Dy3+ co-doped oxyfluoride glasses and glass ceramics containing CeOF or CeF3 nanocrystals have been prepared in the reducing atmosphere. The crystallinity increased significantly with the Ce3+ concentration, while the crystal size of nanocrystals is mainly influenced by the annealing temperatures. The glasses and glass ceramics emitted white light, deriving from a combination of the Ce3+ blue and the Dy3+ yellow light. The emission intensity and CIE chromaticity coordinates of the Ce3+ and Dy3+ co-doped glasses can be tuned by adjusting the ratio of Ce3+/Dy3+ concentration or the annealing temperature

Preparation and luminescence properties of Ce3+/Dy3+-codoped fluorosilicate glass ceramics

International audienceThe Ce3+and Dy3+ co-doped fluorosilicate glass and glass ceramics containing SrF2 or CeF3 nanocrystals were prepared under reducing atmosphere. The precipitated nano-crystalline phase shifted from cubic SrF2 to hexagonal CeF3 gradually with the heat treatment temperature increasing from 620 to 680 °C. The glass and glass ceramics emitted white light, deriving from a combination of the Ce3+ blue and the Dy3+ yellow light. The CIE coordinates could be tuned by adjusting the ratio of Ce3+/Dy3+ concentration. The luminescence could be enhanced significantly by annealing the samples at the temperatures lower than 640 °C

Targeted elimination of mutant mitochondrial DNA in MELAS-iPSCs by mitoTALENs

Abstract Mitochondrial diseases are maternally inherited heterogeneous disorders that are primarily caused by mitochondrial DNA (mtDNA) mutations. Depending on the ratio of mutant to wild-type mtDNA, known as heteroplasmy, mitochondrial defects can result in a wide spectrum of clinical manifestations. Mitochondria-targeted endonucleases provide an alternative avenue for treating mitochondrial disorders via targeted destruction of the mutant mtDNA and induction of heteroplasmic shifting. Here, we generated mitochondrial disease patient-specific induced pluripotent stem cells (MiPSCs) that harbored a high proportion of m.3243A>G mtDNA mutations and caused mitochondrial encephalomyopathy and stroke-like episodes (MELAS). We engineered mitochondrial-targeted transcription activator-like effector nucleases (mitoTALENs) and successfully eliminated the m.3243A>G mutation in MiPSCs. Off-target mutagenesis was not detected in the targeted MiPSC clones. Utilizing a dual fluorescence iPSC reporter cell line expressing a 3243G mutant mtDNA sequence in the nuclear genome, mitoTALENs displayed a significantly limited ability to target the nuclear genome compared with nuclear-localized TALENs. Moreover, genetically rescued MiPSCs displayed normal mitochondrial respiration and energy production. Moreover, neuronal progenitor cells differentiated from the rescued MiPSCs also demonstrated normal metabolic profiles. Furthermore, we successfully achieved reduction in the human m.3243A>G mtDNA mutation in porcine oocytes via injection of mitoTALEN mRNA. Our study shows the great potential for using mitoTALENs for specific targeting of mutant mtDNA both in iPSCs and mammalian oocytes, which not only provides a new avenue for studying mitochondrial biology and disease but also suggests a potential therapeutic approach for the treatment of mitochondrial disease, as well as the prevention of germline transmission of mutant mtDNA

Deep Learning-Based Classification of Cancer Cell in Leptomeningeal Metastasis on Cytomorphologic Features of Cerebrospinal Fluid

Background: It is a critical challenge to diagnose leptomeningeal metastasis (LM), given its technical difficulty and the lack of typical symptoms. The existing gold standard of diagnosing LM is to use positive cerebrospinal fluid (CSF) cytology, which consumes significantly more time to classify cells under a microscope.Objective: This study aims to establish a deep learning model to classify cancer cells in CSF, thus facilitating doctors to achieve an accurate and fast diagnosis of LM in an early stage.Method: The cerebrospinal fluid laboratory of Xijing Hospital provides 53,255 cells from 90 LM patients in the research. We used two deep convolutional neural networks (CNN) models to classify cells in the CSF. A five-way cell classification model (CNN1) consists of lymphocytes, monocytes, neutrophils, erythrocytes, and cancer cells. A four-way cancer cell classification model (CNN2) consists of lung cancer cells, gastric cancer cells, breast cancer cells, and pancreatic cancer cells. Here, the CNN models were constructed by Resnet-inception-V2. We evaluated the performance of the proposed models on two external datasets and compared them with the results from 42 doctors of various levels of experience in the human-machine tests. Furthermore, we develop a computer-aided diagnosis (CAD) software to generate cytology diagnosis reports in the research rapidly.Results: With respect to the validation set, the mean average precision (mAP) of CNN1 is over 95% and that of CNN2 is close to 80%. Hence, the proposed deep learning model effectively classifies cells in CSF to facilitate the screening of cancer cells. In the human-machine tests, the accuracy of CNN1 is similar to the results from experts, with higher accuracy than doctors in other levels. Moreover, the overall accuracy of CNN2 is 10% higher than that of experts, with a time consumption of only one-third of that consumed by an expert. Using the CAD software saves 90% working time of cytologists.Conclusion: A deep learning method has been developed to assist the LM diagnosis with high accuracy and low time consumption effectively. Thanks to labeled data and step-by-step training, our proposed method can successfully classify cancer cells in the CSF to assist LM diagnosis early. In addition, this unique research can predict cancer’s primary source of LM, which relies on cytomorphologic features without immunohistochemistry. Our results show that deep learning can be widely used in medical images to classify cerebrospinal fluid cells. For complex cancer classification tasks, the accuracy of the proposed method is significantly higher than that of specialist doctors, and its performance is better than that of junior doctors and interns. The application of CNNs and CAD software may ultimately aid in expediting the diagnosis and overcoming the shortage of experienced cytologists, thereby facilitating earlier treatment and improving the prognosis of LM