Obstructive sleep apnea (OSA) is a common sleep disorder leading to increased risk of developing cardiovascular diseases (CVDs) by supporting a low-grade chronic inflammation as one of the pathological mechanisms. The continuous positive airway pressure (CPAP) device is used as an effective treatment for moderate and severe OSA. Neutrophil–lymphocyte ratio (NLR), platelet–lymphocyte ratio (PLR), white blood cell–mean platelet volume ratio (WMR), C-reactive protein–albumin ratio (CAR) and fibrinogen–albumin ratio (FAR) are new potential inflammatory biomarkers that are widely available and were shown to be possibly favorable screening or follow-up tools for moderate- or severe-grade OSA, as well as indirect indicators for cardiovascular risk. Our study evaluated the impact of CPAP therapy in patients with severe OSA and acceptable therapy adherence on NLR, PLR, WMR, FAR and CAR. Of 57 patients who were initially enrolled and had no exclusion criteria, 37 had a satisfactory CPAP adherence (usage of ≥4 h per night) after a minimum of 6 months of therapy. There was a statistically significant difference in NLR (2.122 ± 0.745 before therapy vs. 1.888 ± 0.735 after therapy) and FAR (86.445 ± 18.763 before therapy vs. 77.321 ± 19.133 after therapy) suggesting a positive effect of the CPAP therapy on chronic inflammatory states, thereby possibly reducing cardiovascular risk.

BACKGROUND AND PURPOSE: In subjects with agenesis of the corpus callosum, a variety of structural brain alterations is already present during prenatal life. Quantification of these alterations in fetuses with associated brain or body malformations (corpus callosum agenesis and other related anomalies) and so-called isolated cases may help to optimize the challenging prognostic prenatal assessment of fetuses with corpus callosum agenesis. This fetal MR imaging study aimed to identify differences in the size of the prenatal hippocampus between subjects with isolated corpus callosum agenesis, corpus callosum agenesis and other related anomalies, and healthy controls. MATERIALS AND METHODS: Eighty-five in utero fetal brain MR imaging scans, (20–35 gestational weeks) were postprocessed using a high-resolution algorithm. On the basis of multiplanar T2-TSE sequences, 3D isovoxel datasets were generated, and both hippocampi and the intracranial volume were segmented. RESULTS: Hippocampal volumes increased linearly with gestational weeks in all 3 groups. One-way ANOVA demonstrated differences in hippocampal volumes between control and pathologic groups (isolated corpus callosum agenesis: left, P = .02; right, P = .04; corpus callosum agenesis and other related anomalies: P < .001). Differences among the pathologic groups were also present for both sides. Intracranial volume and right and left hippocampal volume ratios were different between corpus callosum agenesis cases and controls (P < .001). When we corrected for intracranial volume, no differences were found between corpus callosum agenesis and other associated anomalies and isolated corpus callosum agenesis (left, P = .77; right, P = .84). Hippocampal size differences were more pronounced at a later gestational age. CONCLUSIONS: Callosal agenesis apparently interferes with the normal process of hippocampal formation and growth, resulting in underdevelopment, which could account for certain learning and memory deficits in individuals with agenesis of the corpus callosum in later life.

The purpose of the study was to investigate the interrelation of the signal intensities and thicknesses of the transient developmental zones in the cingulate and neocortical telencephalic wall, using T2-weighted 3 T-magnetic resonance imaging (MRI) and histological scans from the same brain hemisphere. The study encompassed 24 postmortem fetal brains (15–35 postconceptional weeks, PCW). The measurements were performed using Fiji and NDP.view2. We found that T2w MR signal-intensity curves show a specific regional and developmental stage profile already at 15 PCW. The MRI-histological correlation reveals that the subventricular-intermediate zone (SVZ-IZ) contributes the most to the regional differences in the MRI-profile and zone thicknesses, growing by a factor of 2.01 in the cingulate, and 1.78 in the neocortical wall. The interrelations of zone or wall thicknesses, obtained by both methods, disclose a different rate and extent of shrinkage per region (highest in neocortical subplate and SVZ-IZ) and stage (highest in the early second half of fetal development), distorting the zones’ proportion in histological sections. This intrasubject, slice-matched, 3 T correlative MRI-histological study provides important information about regional development of the cortical wall, critical for the design of MRI criteria for prenatal brain monitoring and early detection of cortical or other brain pathologies in human fetuses.

This study aimed to explore the potential of multi-phase dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) radiomics for classifying breast cancer surrogate subtypes. This retrospective study analyzed 360 breast cancers from 319 patients who underwent pretreatment DCE-MRI between January 2015 and January 2019. The cohort consisted of 33 triple-negative, 26 human epidermal growth factor receptor 2 (HER2)-positive, 109 luminal A-like, 144 luminal B-like HER2-negative, and 48 luminal B-like HER2-positive lesions. A total of 1781 radiomic features were extracted from manually segmented breast cancers in each DCE-MRI sequence. The model was internally validated and selected using ten times repeated five-fold cross-validation on the primary cohort, with further evaluation using a validation cohort. The most successful models were logistic regression models applied to the third post-contrast subtraction images. These models exhibited the highest area under the curve (AUC) for discriminating between luminal A like vs. others (AUC: 0.78), luminal B-like HER2 negative vs. others (AUC: 0.57), luminal B-like HER2 positive vs. others (AUC: 0.60), HER2 positive vs. others (AUC: 0.81), and triple negative vs. others (AUC: 0.83). In conclusion, the radiomic features extracted from multi-phase DCE-MRI are promising for discriminating between breast cancer subtypes. The best-performing models relied on tissue changes observed during the mid-stage of the imaging process.