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For the mechanism of decarboxylation reactions, all textbooks state that electrons move from the πC=O bond. However, the most electron-donating bond orbital in the carbonyl group is expected to be the lone pair(s) on the oxygen. Thus, orbital theory with delocalization from a lone pair might be more appropriate than delocalization from the πC=O orbital. We confirmed this idea by theoretical calculations. In the transition state of 2-substituted β-keto acids, a boat conformation is expected to result in the exclusive generation of (E)-enols. Normally, the decarboxylation reaction occurs in polar solvents, so the resulting enols should be transformed into the corresponding ketones by tautomerization. The use of heteroatoms to obtain enolate or enol ethers without tautomerization would offer a diastereoselective enol(ate) synthesis with regioselectivity because the C=C double bond would always be introduced between two carbonyl groups. After screening heteroatoms by theoretical calculations, we found that boron is suitable for this purpose. We confirmed our idea by theoretical calculations, offering a new boradecarboxylation reaction for the diastereo- and regioselective production of enolates.

Purpose Dry eye disease affects hundreds of millions of people worldwide and is one of the most common causes for visits to eye care practitioners. The fluorescein tear breakup time test is currently widely used to diagnose dry eye disease, but it is an invasive and subjective method, thus resulting in variability in diagnostic results. This study aimed to develop an objective method to detect tear breakup using the convolutional neural networks on the tear film images taken by the non-invasive device KOWA DR-1α. Methods The image classification models for detecting characteristics of tear film images were constructed using transfer learning of the pre-trained ResNet50 model. The models were trained using a total of 9,089 image patches extracted from video data of 350 eyes of 178 subjects taken by the KOWA DR-1α. The trained models were evaluated based on the classification results for each class and overall accuracy of the test data in the six-fold cross validation. The performance of the tear breakup detection method using the models was evaluated by calculating the area under curve (AUC) of receiver operating characteristic, sensitivity, and specificity using the detection results of 13,471 frame images with breakup presence/absence labels. Results The performance of the trained models was 92.3%, 83.4%, and 95.2% for accuracy, sensitivity, and specificity, respectively in classifying the test data into the tear breakup or non-breakup group. Our method using the trained models achieved an AUC of 0.898, a sensitivity of 84.3%, and a specificity of 83.3% in detecting tear breakup for a frame image. Conclusions We were able to develop a method to detect tear breakup on images taken by the KOWA DR-1α. This method could be applied to the clinical use of non-invasive and objective tear breakup time test.