Stepwise Sulfite Reduction on a Dinuclear Ruthenium Complex Leading to Hydrogen Sulfide.

Sulfite reduction by dissimilatory sulfite reductases is a key process in the global sulfur cycle. Sulfite reductases catalyze the 6e- reduction of SO32- to H2S using eight protons (SO32- + 8H+ + 6e- → H2S + 3H2O). However, detailed research into the reductive conversion of sulfite on transition-metal-based complexes remains unexplored. As part of our ongoing research into reproducing the function of reductases using dinuclear ruthenium complex {(TpRu)2(µ-Cl)(µ-pz)} (Tp = HB(pyrazolyl)3), we have targeted the function of sulfite reductase. The isolation of a key SO-bridged complex, followed by a sulfite-bridged complex, eventually resulted in a stepwise sulfite reduction. The reduction of a sulfite to a sulfur monoxide using 4H+ and 4e-, which was followed by conversion of the sulfur monoxide to a disulfide with concomitant consumption of 2H+ and 2e-, proceeded on the same platform. Finally, the production of H2S from the disulfide-bridged complex was achieved.

Machine learning for image-based detection of patients with obstructive sleep apnea: an exploratory study.

PURPOSE: In 2-dimensional lateral cephalometric radiographs, patients with severe obstructive sleep apnea (OSA) exhibit a more crowded oropharynx in comparison with non-OSA. We tested the hypothesis that machine learning, an application of artificial intelligence (AI), could be used to detect patients with severe OSA based on 2-dimensional images. METHODS: A deep convolutional neural network was developed (n = 1258; 90%) and tested (n = 131; 10%) using data from 1389 (100%) lateral cephalometric radiographs obtained from individuals diagnosed with severe OSA (n = 867; apnea hypopnea index > 30 events/h sleep) or non-OSA (n = 522; apnea hypopnea index < 5 events/h sleep) at a single center for sleep disorders. Three kinds of data sets were prepared by changing the area of interest using a single image: the original image without any modification (full image), an image containing a facial profile, upper airway, and craniofacial soft/hard tissues (main region), and an image containing part of the occipital region (head only). A radiologist also performed a conventional manual cephalometric analysis of the full image for comparison. RESULTS: The sensitivity/specificity was 0.87/0.82 for full image, 0.88/0.75 for main region, 0.71/0.63 for head only, and 0.54/0.80 for the manual analysis. The area under the receiver-operating characteristic curve was the highest for main region 0.92, for full image 0.89, for head only 0.70, and for manual cephalometric analysis 0.75. CONCLUSIONS: A deep convolutional neural network identified individuals with severe OSA with high accuracy. Future research on this concept using AI and images can be further encouraged when discussing triage of OSA.

Mechanochemical dry conversion of zinc oxide to zeolitic imidazolate framework.

Mechanochemical dry conversion that only uses zinc oxide and an imidazole ligand proved to be effective and reliable for fabrication of a zeolitic imidazolate framework with a polycrystalline grain boundary and a core-shell structure. The zinc oxide crystals are converted into a zeolitic imidazolate framework to a depth of approx. 10 nm below the surface.