Thermally induced cationic polymerization of isobutyl vinyl ether in toluene in the presence of solvate ionic liquid

Radical polymerization of isobutyl vinyl ether (IBVE) was attempted with the aid of the interaction between the corresponding propagating radical and lithium cation (Li+). LiN(SO2CF3)2 (LiNTf2) and ester compounds, such as methyl methacrylate (MMA) and vinyl acetate (VAc), were added as a Li+ source and dissolving agent for LiNTf2, respectively. Homopolymers of cationically polymerizable IBVE were obtained despite the presence of radically polymerizable monomers such as MMA and VAc. Contrary to our expectation, the polymerization proceeded via not a radical mechanism but a cationic mechanism. However, this cationic polymerization was found to be unusual. In particular, the polymer yield increased with the polymerization temperature; successful polymerization was observed at 100 °C, whereas no polymerization occurred at lower temperatures such as at 0 °C. The behavior of the present system was therefore defined as “thermally induced cationic polymerization”. The mechanism of thermally induced cationic polymerization is still not clear, but it is assumed that the propagating cation is markedly stabilized through its interaction with the solvate ionic liquid formed between LiNTf2 and the Lewis base

A novel artificial intelligence-based endoscopic ultrasonography diagnostic system for diagnosing the invasion depth of early gastric cancer

The version of record of this article, first published in Journal of Gastroenterology, is available online at Publisher’s website: https://doi.org/10.1007/s00535-024-02102-1.Background: We developed an artificial intelligence (AI)-based endoscopic ultrasonography (EUS) system for diagnosing the invasion depth of early gastric cancer (EGC), and we evaluated the performance of this system. Methods: A total of 8280 EUS images from 559 EGC cases were collected from 11 institutions. Within this dataset, 3451 images (285 cases) from one institution were used as a development dataset. The AI model consisted of segmentation and classification steps, followed by the CycleGAN method to bridge differences in EUS images captured by different equipment. AI model performance was evaluated using an internal validation dataset collected from the same institution as the development dataset (1726 images, 135 cases). External validation was conducted using images collected from the other 10 institutions (3103 images, 139 cases). Results: The area under the curve (AUC) of the AI model in the internal validation dataset was 0.870 (95% CI: 0.796–0.944). Regarding diagnostic performance, the accuracy/sensitivity/specificity values of the AI model, experts (n = 6), and nonexperts (n = 8) were 82.2/63.4/90.4%, 81.9/66.3/88.7%, and 68.3/60.9/71.5%, respectively. The AUC of the AI model in the external validation dataset was 0.815 (95% CI: 0.743–0.886). The accuracy/sensitivity/specificity values of the AI model (74.1/73.1/75.0%) and the real-time diagnoses of experts (75.5/79.1/72.2%) in the external validation dataset were comparable. Conclusions: Our AI model demonstrated a diagnostic performance equivalent to that of experts

A machine learning model for predicting the lymph node metastasis of early gastric cancer not meeting the endoscopic curability criteria

The version of record of this article, first published in Gastric Cancer, is available online at Publisher’s website: https://doi.org/10.1007/s10120-024-01511-8.Background: We developed a machine learning (ML) model to predict the risk of lymph node metastasis (LNM) in patients with early gastric cancer (EGC) who did not meet the existing Japanese endoscopic curability criteria and compared its performance with that of the most common clinical risk scoring system, the eCura system. Methods: We used data from 4,042 consecutive patients with EGC from 21 institutions who underwent endoscopic submucosal dissection (ESD) and/or surgery between 2010 and 2021. All resected EGCs were histologically confirmed not to satisfy the current Japanese endoscopic curability criteria. Of all patients, 3,506 constituted the training cohort to develop the neural network-based ML model, and 536 constituted the validation cohort. The performance of our ML model, as measured by the area under the receiver operating characteristic curve (AUC), was compared with that of the eCura system in the validation cohort. Results: LNM rates were 14% (503/3,506) and 7% (39/536) in the training and validation cohorts, respectively. The ML model identified patients with LNM with an AUC of 0.83 (95% confidence interval, 0.76–0.89) in the validation cohort, while the eCura system identified patients with LNM with an AUC of 0.77 (95% confidence interval, 0.70–0.85) (P = 0.006, DeLong’s test). Conclusions: Our ML model performed better than the eCura system for predicting LNM risk in patients with EGC who did not meet the existing Japanese endoscopic curability criteria. Mini-abstract: We developed a neural network-based machine learning model that predicts the risk of lymph node metastasis in patients with early gastric cancer who did not meet the endoscopic curability criteria

Cationic homopolymerization of trans-anethole in the presence of solvate ionic liquid comprising LiN(SO2CF3)2 and Lewis bases

Cationic homopolymerization of a biomass-derived monomer, trans-4-methoxy-β-methylstyrene (trans-anethole: Ane), was achieved with a combination of bis(trifluoromethylsulfonyl)imide and solvate ionic liquid comprising lithium bis(trifluoromethylsulfonyl)imide and a Lewis base, such as ethyl acetate and diisopropyl ether (iPr2O). The number-average molecular weight (Mn) of the obtained poly(Ane) reached 15.6 × 103 by adding iPr2O in toluene at –10 °C. The solubility of poly(Ane) varied drastically with a change of solvent: the polymers obtained in CH2Cl2 were not completely soluble in common organic solvents such as toluene, chloroform, and tetrahydrofuran, except for 1,1,2,2,-tetrachloroethane (C2H2Cl4) at 140 °C, whereas the polymers obtained in toluene were soluble in these solvents. The 1H NMR spectrum measured in C2D2Cl4 at 140 °C revealed that the stereostructure of poly(Ane) depended significantly on the solvent and the temperature: a polymer with a more regulated stereostructure was obtained from polymerization in CH2Cl2 at –40 °C than those obtained by polymerization in toluene at –10 °C

Totally endoscopic pulmonary valve replacement

A 68-year-old man with a history of valve-sparing aortic root replacement and endoscopic aortic valve replacement was admitted to our hospital with dyspnea. Transthoracic echocardiography revealed severe pulmonary valve regurgitation. The patient had undergone cardiac surgery twice, through median sternotomy and right thoracotomy; therefore, we planned endoscopic pulmonary valve replacement via the left thoracic approach. The patient was placed in a modified right lateral decubitus position and underwent mild hypothermic cardiopulmonary bypass. An on-pump beating-heart technique was used during surgery. The 3D endoscopic system and trocars for surgical instruments were inserted through the left 3rd and 4th intercostal spaces. After incision of the pulmonary artery, the pulmonary cusps were resected. A 27-mm St Jude Medical Epic heart valve was implanted in the intra-annular position. Subsequently, the left atrial appendage was resected. The patient was discharged without complications. To our knowledge, this is the first case of totally endoscopic pulmonary valve replacement.</p