Prognostication of Ovarian Function after Ovarian Torsion Using Intraoperative Indocyanine Green Angiography.

STUDY OBJECTIVE: To quantitatively evaluate the blood flow in ovaries (ischemic ovaries) that underwent torsion using indocyanine green angiography (ICGA) and assess the use of ICGA as an indicator for functional preservation of the ovaries. DESIGN: In vivo animal study. SETTING: The University of Yamanashi Animal Experimentation Center. SUBJECTS: Eighteen female Wistar albino rats. INTERVENTIONS: As an alternative to ovarian torsion, we occluded an ovary in each rat for 24 hours, after which we performed ICGA before and after releasing ischemia and extracted the following 8 parameters: Fmax (maximum F value before releasing ischemia); Tmax (time taken from the onset of an increase in F to reaching Fmax); T½max (time taken from the onset of an increase in F to reaching half of Fmax); slope (Fmax/Tmax); time ratio (T½max/Tmax); F'max (maximum F value after releasing ischemia); reperfusion rate (F'max/Fmax); and reperfusion gap (F'max - Fmax). Four weeks later, we counted the total number of primordial and primary follicles and classified the rats into functional and nonfunctional groups. MEASUREMENTS AND MAIN RESULTS: On the basis of the total number of primordial and primary follicles, 13 rats had "functional" ovaries on the clamped side, and 5 rats had "nonfunctional" ovaries. The area under the curve values for each parameter were as follows: Fmax, 0.908; Tmax, 0.569; T½max, 0.546; time ratio, 0.746; slope, 0.877; F'max, 0.723; reperfusion rate, 0.938; and reperfusion gap, 0.862. CONCLUSION: ICGA can be used to quantitatively evaluate ovaries that have been subjected to ischemia, and the magnitude of fluorescence intensity can be an excellent predictor of ovarian necrosis. Quantifying the degree of reperfusion immediately after the release of ischemia can be an equally excellent predictor of necrosis.

Automated Volumetry of Medial Temporal Lobe Subregions in Mild Cognitive Impairment and Alzheimer Disease.

PURPOSE: Hippocampal subfield volumetry should be more useful than whole hippocampal (WH) volumetry for diagnosing Alzheimer disease (AD). This study sought to confirm this. METHODS: We investigated cognitively normal (CN) participants and patients with mild cognitive impairment (MCI) or AD using high-resolution T2-weighted and 3-dimensional T1-weighted magnetic resonance imaging. Using medial temporal subregion volumetry, we investigated discriminative power for MCI and AD versus CN. PATIENTS: We recruited 30 CN participants, 30 amnestic MCI patients, and 49 AD patients between April 2015 and October 2016. RESULTS: For AD, discriminative power of the combined volumes of the subiculum, entorhinal cortex, and cornu ammonis 1 was highest [area under the curve (AUC)=0.915; 85.7% sensitivity, 86.7% specificity, 86.1% accuracy], and was significantly higher than that of the WH volume (AUC=0.887; 90.0% sensitivity, 75.5% specificity, 84.5% accuracy) (P=0.019). For MCI, discriminative power of the subiculum volume was highest (AUC=0.747; 80.0% sensitivity, 73.3% specificity, 76.7% accuracy), but was only slightly higher than that of the WH volume (AUC=0.730; 56.7% sensitivity, 90.0% specificity, 73.3% accuracy). CONCLUSIONS: Using the combined volumes of the subiculum, entorhinal cortex, and cornu ammonis 1 may enable greater diagnostic accuracy compared with the WH volume or any single subfield in AD patients.

The factor structure of "expectant mothers of concern" as perceived by midwives.

AIM: To clarify the factor structure of "expectant mothers of concern" for whom midwives anticipate difficulties in future childrearing. METHODS: The participants were 2633 midwives working at hospitals and clinics in Japan. Data were collected using a 108-item, five-point Likert scale questionnaire about the behaviors, appearance, and family relationships of the expectant mothers of concern to the nurses. Items with a mean equal to or greater than 4.0 were selected as items considered relevant to expectant mothers of concern by midwives. Exploratory factor analysis, confirmatory factor analysis, and further secondary factor analysis were conducted. RESULTS: The factor structure of the expectant mothers of concern as perceived by midwives comprised seven factors, including 23 items: "Suspected of being a victim of intimate partner violence," "Uneasy feeling about the expectant mother's marital relationship," "Perception that the expectant mother is conflicted about her pregnancy," "Uneasy feeling about the expectant mother's actions/behaviors concerning her medical checkups," "Engages in physically risky actions and behaviors," "Does not appear to be able to build relationships with children," and "Makes remarks that indicate possible bonding disorder" (goodness-of-fit index = 0.910, adjusted goodness-of-fit index = 0.879, comparative fit index = 0.939, and root mean square error of approximation = 0.070). Further, a secondary factor, "Expectant mothers who must urgently be connected to support," was extracted. CONCLUSIONS: The elucidation of the factor structure of the expectant mothers of concern could help midwives identify expectant mothers who may face difficulties in future childrearing.

Random forest approach for determining risk prediction and predictive factors of type 2 diabetes: large-scale health check-up data in Japan.

INTRODUCTION: Early intervention in type 2 diabetes can prevent exacerbation of insulin resistance. More effective interventions can be implemented by early and precise prediction of the change in glycated haemoglobin A1c (HbA1c). Artificial intelligence (AI), which has been introduced into various medical fields, may be useful in predicting changes in HbA1c. However, the inability to explain the predictive factors has been a problem in the use of deep learning, the leading AI technology. Therefore, we applied a highly interpretable AI method, random forest (RF), to large-scale health check-up data and examined whether there was an advantage over a conventional prediction model. RESEARCH DESIGN AND METHODS: This study included a cumulative total of 42 908 subjects not receiving treatment for diabetes with an HbA1c <6.5%. The objective variable was the change in HbA1c in the next year. Each prediction model was created with 51 health-check items and part of their change values from the previous year. We used two analytical methods to compare the predictive powers: RF as a new model and multivariate logistic regression (MLR) as a conventional model. We also created models excluding the change values to determine whether it positively affected the predictions. In addition, variable importance was calculated in the RF analysis, and standard regression coefficients were calculated in the MLR analysis to identify the predictors. RESULTS: The RF model showed a higher predictive power for the change in HbA1c than MLR in all models. The RF model including change values showed the highest predictive power. In the RF prediction model, HbA1c, fasting blood glucose, body weight, alkaline phosphatase and platelet count were factors with high predictive power. CONCLUSIONS: Correct use of the RF method may enable highly accurate risk prediction for the change in HbA1c and may allow the identification of new diabetes risk predictors.