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Objective: To evaluate hemostatic efficacy, complications, and subsequent pregnancy outcomes in women with placenta previa who underwent combined vertical compression sutures in the lower uterine segment and intrauterine balloon tamponade (Hot-Dog method) to achieve hemostasis after cesarean section., Methods: We retrospectively reviewed data for 117 women with singleton pregnancy diagnosed with placenta previa who underwent cesarean section between 29 and 38 weeks' gestation. Treatments were as follows: (1) conventional-intravenous oxytocin administration after placental detachment and suturing of bleeding points at the detachment site as needed (conventional group) (n = 47). (2) Intrauterine balloon tamponade alone (balloon group) (n = 41). (3) Vertical compression sutures + intrauterine balloon tamponade (Hot-Dog group) (n = 29)., Results: The placenta accreta spectrum prevalence was significantly higher in the balloon and Hot-Dog groups versus the conventional group. The prevalence of anteriorly located placenta was significantly higher in the Hot-Dog versus balloon groups. Intraoperative and total blood loss were significantly higher in the Hot-Dog versus conventional groups. Postoperative blood loss was significantly lower in the Hot-Dog versus balloon groups. Fewer additional procedures for managing postoperative hemorrhage were required in the Hot-Dog versus conventional and balloon groups. The number of subsequent pregnancies in the conventional, balloon, and Hot-Dog groups was 11 (23.4%), 8 (19.5%), and 4 (13.8%), respectively; all resulted in live births at term without serious obstetric complications., Conclusion: The Hot-Dog method is a straightforward and safe hemostasis technique for placenta previa that preserves fertility and controls severe bleeding., (© 2024 The Author(s). International Journal of Gynecology & Obstetrics published by John Wiley & Sons Ltd on behalf of International Federation of Gynecology and Obstetrics.)

Background: Despite advancements in coronary computed tomography angiography (CTA), challenges in positive predictive value and specificity remain due to limited spatial resolution. The purpose of this experimental study was to investigate the effect of 2nd generation deep learning-based reconstruction (DLR) on the quantitative and qualitative image quality in coronary CTA., Methods: A vessel model with stepwise non-calcified plaque was scanned using 320-detector CT. Image reconstruction was performed using four techniques: hybrid iterative reconstruction (HIR), model-based iterative reconstruction (MBIR), DLR, and 2nd generation DLR. The luminal peak CT number, contrast-to-noise ratio (CNR), and edge rise slope (ERS) were quantitatively evaluated via profile curve analysis. Two observers qualitatively graded the graininess, lumen sharpness, and overall lumen visibility on the basis of the degree of confidence for the stenosis severity using a five-point scale., Results: The image noise with HIR, MBIR, DLR, and 2nd generation DLR was 23.0, 21.0, 16.9, and 9.5 HU, respectively. The corresponding CNR (25% stenosis) was 15.5, 15.9, 22.1, and 38.3, respectively. The corresponding ERS (25% stenosis) was 203.2, 198.6, 228.9, and 262.4 HU/mm, respectively. Among the four reconstruction methods, the 2nd generation DLR achieved the significantly highest CNR and ERS values. The score of 2nd generation DLR in all evaluation points (graininess, sharpness, and overall lumen visibility) was higher than those of the other methods (overall vessel visibility score, 2.6±0.5, 3.8±0.6, 3.7±0.5, and 4.6±0.5 with HIR, MBIR, DLR, and 2nd generation DLR, respectively)., Conclusions: 2nd generation DLR provided better CNR and ERS in coronary CTA than HIR, MBIR, and previous-generation DLR, leading to the highest subjective image quality in the assessment of vessel stenosis., Competing Interests: Conflicts of Interest: All authors have completed the ICMJE uniform disclosure form (available at https://qims.amegroups.com/article/view/10.21037/qims-23-1204/coif). D.U. reports that he has received research grants from Canon Medical Systems paid to Department of Diagnostic Radiology at Yokohama City University Graduate School of Medicine. T.T. reports that he is an employee of Canon Medical Systems Corporation. The other authors have no conflicts of interest to declare., (2024 Quantitative Imaging in Medicine and Surgery. All rights reserved.)

Accelerating the measurement for discrimination of samples, such as classification of cell phenotype, is crucial when faced with significant time and cost constraints. Spontaneous Raman microscopy offers label-free, rich chemical information but suffers from long acquisition time due to extremely small scattering cross-sections. One possible approach to accelerate the measurement is by measuring necessary parts with a suitable number of illumination points. However, how to design these points during measurement remains a challenge. To address this, we developed an imaging technique based on a reinforcement learning in machine learning (ML). This ML approach adaptively feeds back "optimal" illumination pattern during the measurement to detect the existence of specific characteristics of interest, allowing faster measurements while guaranteeing discrimination accuracy. Using a set of Raman images of human follicular thyroid and follicular thyroid carcinoma cells, we showed that our technique requires 3,333 to 31,683 times smaller number of illuminations for discriminating the phenotypes than raster scanning. To quantitatively evaluate the number of illuminations depending on the requisite discrimination accuracy, we prepared a set of polymer bead mixture samples to model anomalous and normal tissues. We then applied a home-built programmable-illumination microscope equipped with our algorithm, and confirmed that the system can discriminate the sample conditions with 104 to 4,350 times smaller number of illuminations compared to standard point illumination Raman microscopy. The proposed algorithm can be applied to other types of microscopy that can control measurement condition on the fly, offering an approach for the acceleration of accurate measurements in various applications including medical diagnosis., Competing Interests: Competing interests statement:The authors declare no competing interest.