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Emissive supramolecular metallacages via coordination-driven self-assembly

Credit: @Science China Press
Metallacages prepared via coordination-driven self-assembly have received extensive attention because of their three-dimensional layout and cavity-cored nature. The construction of light-emitting materials employing metallacages as a platform has also gained significant interest due to their good modularity in photophysical properties, which bring emerging applications in fields as diverse as sensing, biomedicine, and catalysis.
However, the luminescence efficiency of conventional luminophores significantly decreases in the aggregate state because they encounter unfavorable aggregation-caused quenching (ACQ). Therefore, it was quite a challenge to fabricate light-emitting metallacages with high luminescence efficiency in various physical states.
In 2001, Tang s group discovered aggregation-induced emission (AIE) phenomenon that some nonluminous or weakly emissive materials in molecular state are highly emissive in aggregate state. The unde ....

Jun Zhao , United States , Salt Lake City , University Of Utah , Zhixuan Zhou , Guangfeng Li , Peterj Stang , Shanghai Jiao Tong University , National Natural Science Foundation Of China , Natural Science Foundation Of Shanghai , National Science , Beijing Based National Science Review , National Natural Science Foundation , Natural Science Foundation , ஜூன் ழோ , ஒன்றுபட்டது மாநிலங்களில் , உப்பு ஏரி நகரம் , பல்கலைக்கழகம் ஆஃப் உட்டா , ஷாங்காய் ஜியாவோ நாக்கு பல்கலைக்கழகம் , தேசிய இயற்கை அறிவியல் அடித்தளம் ஆஃப் சீனா , இயற்கை அறிவியல் அடித்தளம் ஆஃப் ஷாங்காய் , தேசிய அறிவியல் , பெய்ஜிங் அடிப்படையிலானது தேசிய அறிவியல் விமர்சனம் , தேசிய இயற்கை அறிவியல் அடித்தளம் , இயற்கை அறிவியல் அடித்தளம் ,

Artificial intelligence predicts gestational diabetes in Chinese women

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WASHINGTON Machine learning, a form of artificial intelligence, can predict which women are at high risk of developing gestational diabetes and lead to earlier intervention, according to a new study published in the Endocrine Society s
Journal of Clinical Endocrinology & Metabolism.
Gestational diabetes is a common complication during pregnancy that affects up to 15 percent of pregnant women. High blood sugar in the mother can be dangerous for the baby and lead to complications like stillbirth and premature delivery. Most women are diagnosed with gestational diabetes during the second trimester, but some women are at high risk and could benefit from earlier intervention. ....

Yu Wang , South Australia , Cheng Li , Shen Kang , United States , Ting Wu , Andrew Kawai , Yi Shi , Chen Jie Zhang , Lei Chen , Jian Zhong Sheng , Monash University , Endocrine Society , Zhejiang University In , Innovation Fund For Medical Sciences , Natural Science Foundation Of Shanghai , Shanghai Jiao Tong University School Of Medicine , National Natural Science Foundation Of China , Program Of Shanghai Academic Research Leader , Clinical Technology Innovation , Foundation Of Shanghai Municipal Commission Health , Development Program Of China , Shanghai Jiao Tong University , Shanghai Shenkang Hospital Development Center , International Peace Maternity , National Key Research ,

Artificial Intelligence Predicts Gestational Diabetes

Artificial Intelligence Predicts Gestational Diabetes
by Colleen Fleiss on
December 22, 2020 at 11:10 PM
Machine learning helped predict women at high risk of developing gestational diabetes, revealed a new study published in the Endocrine Society s Journal of Clinical Endocrinology & Metabolism. Machine learning is a form of artificial intelligence.
Gestational diabetes is a common complication during pregnancy that affects up to 15 percent of pregnant women. High blood sugar in the mother can be dangerous for the baby and lead to complications like stillbirth and premature delivery. Most women are diagnosed with gestational diabetes during the second trimester, but some women are at high risk and could benefit from earlier intervention. ....

Yu Wang , South Australia , Cheng Li , United States , Shen Kang , Ting Wu , Andrew Kawai , Lei Chen , Chen Jie Zhang , Retinopathydiabetesdiabetic Dietdiabetes , Yi Shi , Jian Zhong Sheng , Monash University , Zhejiang University In , Innovation Fund For Medical Sciences , Natural Science Foundation Of Shanghai , Shanghai Jiao Tong University School Of Medicine , National Natural Science Foundation Of China , Program Of Shanghai Academic Research Leader , Clinical Technology Innovation , Foundation Of Shanghai Municipal Commission Health , Development Program Of China , Shanghai Jiao Tong University , Shanghai Shenkang Hospital Development Center , International Peace Maternity , National Key Research ,