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We present the results of ALMA-ACA 7 m array observations in 12CO(J = 2–1), 13CO(J = 2–1), and C18O(J = 2–1) line emission toward the molecular-gas disk in the Local Group spiral galaxy M33 at an angular resolution of 7.″31 × 6.″50 (30 × 26 pc). We combined the ACA 7 m array 12CO(J = 2–1) data with the IRAM 30 m data to compensate for emission from diffuse molecular-gas components. The ACA+IRAM combined 12CO(J = 2–1) map clearly depicts the cloud-scale molecular-gas structure over the M33 disk. Based on the ACA+IRAM 12CO(J = 2–1) cube data, we cataloged 848 molecular clouds with a mass range from 103–106 M ⊙. We found that high-mass clouds (≥105 M ⊙) tend to associate with the 8 μm bright sources in the spiral arm region, while low-mass clouds (<105 M ⊙) tend to be apart from such 8 μm bright sources and to exist in the inter-arm region. We compared the cataloged clouds with GMCs observed by the IRAM 30 m telescope at 49 pc resolution (IRAM GMC), and found that a small IRAM GMC is likely to be identified as a single molecular cloud even in ACA+IRAM CO data, while a large IRAM GMC can be resolved into multiple ACA+IRAM clouds. The velocity dispersion of a large IRAM GMC is mainly dominated by the line-of-sight velocity difference between small clouds inside the GMC rather than the internal cloud velocity broadening.

We present an approach to quantify drug–target engagement using in vivo fluorescence endomicroscopy, validated with in vitro measurements. Doxorubicin binding to chromatin changes the fluorescence lifetime of histone-GFP fusions that we measure in vivo at single-cell resolution using a confocal laparo/endomicroscope. We measure both intra- and inter-tumor heterogeneity in doxorubicin chromatin engagement in a model of peritoneal metastasis of ovarian cancer, revealing striking variation in the efficacy of doxorubicin–chromatin binding depending on intra-peritoneal or intravenous delivery. Further, we observe significant variations in doxorubicin–chromatin binding between different metastases in the same mouse and between different regions of the same metastasis. The quantitative nature of fluorescence lifetime imaging enables direct comparison of drug–target engagement for different drug delivery routes and between in vitro and in vivo experiments. This uncovers different rates of cell killing for the same level of doxorubicin binding in vitro and in vivo. The engagement of DNA-binding drugs to their target is difficult to study, particularly in vivo. Here the authors develop an in vivo fluorescence lifetime imaging confocal laparo/endomicroscope to show intra- and inter-tumor heterogeneity in doxorubicin binding to peritoneal metastases, which depends on the route of administration.

We developed and validated an artificial intelligence (AI) algorithm for the automated grading of periventricular hyperintensity (PVH) and deep subcortical white matter hyperintensity (DWMH) using magnetic resonance imaging. Overall, 246 patients were evaluated, with 137 and 109 allocated to the training and testing groups, respectively. AI-predicted grading according to the Fazekas scale was compared with expert assessments using accuracy, F1-score, and mean absolute error. Inter-rater agreement was evaluated using Fleiss’ kappa to assess consistency among human raters and Cohen’s kappa to measure agreement between the AI and individual human raters. The AI demonstrated superior multi-class accuracy in PVH classification compared with the human expert, achieving an accuracy of 0.798 versus 0.743. In DWMH classification, the AI outperformed the expert specifically in distinguishing Fazekas 0/1/2 from the 3 classification, achieving an accuracy of 0.954 compared with the expert’s 0.927. Inter-rater agreement analysis showed that for PVH and DWMH, the AI achieved “good agreement” with human raters. For PVH, the AI’s agreement exceeded the human inter-rater agreement. The developed AI also exhibited lower variability in volume ratio distribution within the same grade compared with human raters. The developed AI algorithm effectively distinguished between PVH and DWMH, achieving accuracy comparable to human performance.

Resuscitative endovascular balloon occlusion of the aorta (REBOA) is an endovascular procedure for hemorrhage control. In REBOA, the balloon must be placed in the precise place, but it may be performed without X-ray fluoroscopy. This study aimed to estimate the REBOA zones from the body surface using deep learning for safe balloon placement. A total of 198 abdominal computed tomography (CT) datasets containing the regions of the REBOA zones were collected from open data libraries. Then, depth images of the body surface generated from the CT datasets and the images corresponding to the zones were labeled for deep learning training and validation. DeepLabV3+, a deep learning semantic segmentation model, was employed to estimate the zones. We used 176 depth images as training data and 22 images as validation data. A nine-fold cross-validation was performed to generalize the performance of the network. The median Dice coefficients for Zones 1-3 were 0.94 (inter-quarter range: 0.90–0.96), 0.77 (0.60–0.86), and 0.83 (0.74–0.89), respectively. The median displacements of the zone boundaries were 11.34 mm (5.90–19.45), 11.40 mm (4.88–20.23), and 14.17 mm (6.89–23.70) for the boundary between Zones 1 and 2, between Zones 2 and 3, and between Zone 3 and out of zone, respectively. This study examined the feasibility of REBOA zone estimation from the body surface only using deep learning-based segmentation without aortography.

The nature of molecular clouds and their statistical behavior in subsolar metallicity environments are not fully explored yet. We analyzed data from an unbiased CO (J = 2–1) survey at the spatial resolution of ∼2 pc in the northern region of the Small Magellanic Cloud with the Atacama Compact Array to characterize the CO cloud properties. A cloud-decomposition analysis identified 426 spatially/velocity-independent CO clouds and their substructures. Based on the cross-matching with known infrared catalogs by Spitzer and Herschel, more than 90% CO clouds show spatial correlations with point sources. We investigated the basic properties of the CO clouds and found that the radius–velocity linewidth (R–σ v ) relation follows the Milky Way-like power-law exponent, but the intercept is ∼1.5 times lower than that in the Milky Way. The mass functions (dN/dM) of the CO luminosity and virial mass are characterized by an exponent of ∼1.7, which is consistent with previously reported values in the Large Magellanic Cloud and in the Milky Way.

Although it is known that red blood cell (RBC) parameters and platelet count depend on ethnicity and sex, their reference intervals in healthy Asian populations are limited. The aim of this study was to establish reference intervals for RBC parameters and platelet count for healthy adults in Japan. A total of 750 healthy adults (447 women and 303 men; median age 40 years (18–67 years) at seven Japanese centers who participated in regular medical checkups entered this study. Their RBC parameters and platelet count were measured using automated hematocytometers. The reference intervals of the RBC parameters and platelet count according to sex in healthy adults were determined. There was an age-specific decrease in RBC counts and an age-specific increase in mean corpuscular volume in men. This study emphasizes the need to consider sex and age in the clinical use of reference intervals of RBC parameters.

We aimed to develop a new artificial intelligence software that can automatically extract and measure the volume of white matter hyperintensities (WMHs) in head magnetic resonance imaging (MRI) using only thick-slice fluid-attenuated inversion recovery (FLAIR) sequences from multiple centers. We enrolled 1092 participants in Japan, comprising the thick-slice Private Dataset. Based on 207 randomly selected participants, neuroradiologists annotated WMHs using predefined guidelines. The annotated images of participants were divided into training (n = 138) and test (n = 69) datasets. The WMH segmentation model comprised a U-Net ensemble and was trained using the Private Dataset. Two other models were trained for validation using either both thin- and thick-slice MRI datasets or the thin-slice dataset alone. The voxel-wise Dice similarity coefficient (DSC) was used as the evaluation metric. The model trained using only thick-slice MRI showed a DSC of 0.820 for the test dataset, which is comparable to the accuracy of human readers. The model trained with the additional thin-slice dataset showed only a slightly improved DSC of 0.822. This automatic WMH segmentation model comprising a U-Net ensemble trained on a thick-slice FLAIR MRI dataset is a promising new method. Despite some limitations, this model may be applicable in clinical practice.

The nucleus of oocytes (germinal vesicle) is unusually large and its nuclear envelope (NE) is densely packed with nuclear pore complexes (NPCs) that are stockpiled for embryonic development. We showed that breakdown of this specialized NE is mediated by an Arp2/3-nucleated F-actin ‘shell’ in starfish oocytes, in contrast to microtubule-driven tearing in mammalian fibroblasts. Here, we address the mechanism of F-actin-driven NE rupture by correlated live-cell, super-resolution and electron microscopy. We show that actin is nucleated within the lamina, sprouting filopodia-like spikes towards the nuclear membranes. These F-actin spikes protrude pore-free nuclear membranes, whereas the adjoining stretches of membrane accumulate NPCs that are associated with the still-intact lamina. Packed NPCs sort into a distinct membrane network, while breaks appear in ER-like, pore-free regions. We reveal a new function for actin-mediated membrane shaping in nuclear rupture that is likely to have implications in other contexts, such as nuclear rupture observed in cancer cells.

Background Non-occlusive mesenteric ischemia (NOMI) has a high mortality rate, but the analyses of preoperative prognostic factors for improving survival in patients suspected of having NOMI are scarce. We aimed to analyze the prognostic factors of preoperative examinations for NOMI. Methods The clinical data of 224 patients with NOMI were retrospectively collected for a multicenter survey. Clinicophysiological factors were compared between the survivors and non-survivors ( N  = 107/117) and between the operative and non-operative cases ( N  = 180/44) by univariate analysis using chi-square test and multivariate analysis using Cox proportional hazard models. In the operative cases, the prognostic operative factors were also analyzed. Results The overall mortality rate for NOMI was 52.2%. There were 129 male and 95 female patients. The mean age was 71.23 (14–94) years. Univariate analysis showed that cardiovascular complication, shock, abdominal pain, average blood pressure, systemic inflammatory response syndrome, aspartic aminotransferase, alanine transaminase, creatine phosphokinase, lactate dehydrogenase, base excess, prothrombin time-international normalized ratio, D-dimer, and fibrinogen degradation products were independent prognostic factors. Multivariate analysis showed that average blood pressure and base excess were independent prognostic factors. Among patients undergoing surgery, those with bowel resection had better prognosis than those without bowel resection, but those with long bowel resection had worse prognosis than those with short resection. Additional postoperative treatment was not effective compared with operation alone ( P  = 0.011). Conclusions Prognostic factors of preoperative examinations for NOMI were average blood pressure and base excess. Patients with long bowel resection should be carefully monitored owing to their poor prognosis.