Physician marriage survey reveals sex and specialty differences in marital satisfaction factors.

Physician marriage is a valuable indicator of how vocational factors (e.g. work hours, stressors) impact satisfaction in relationships and physician wellness overall. Previous studies suggest that gender and specialty influence marriage satisfaction for physicians, though these often come from limited, local, cohorts. A cross-sectional survey was designed and distributed to publicly available email addresses representing academic and private practice physician organizations across the United States, receiving 321 responses (253 complete). Responses included data on demographics, medical specialty, age at marriage, stage of training at marriage, number of children, and factors leading to marital satisfaction/distress. A multivariable ordinal logistic regression was conducted to find associations between survey variables and marriage satisfaction. Survey results indicated that 86.5% of physicians have been married (average age at first marriage was 27.8 years old), and the rate of first marriages ending is at least 14.7%. Men had significantly more children than women. Physicians married at least once averaged 1.98 children. "Other" specialty physicians had significantly more children on average than psychiatrists. Marrying before medical school predicted practicing in private practice settings. Job stress, work hours, children, and sex were most frequently sources of marital distress, while strong communication, finances, and children were most frequently sources of marital stability. Sex differences were also found in distressing and stabilizing marital factors: Female physicians were more likely to cite their spouse's work hours and job stress as sources of marital distress. Finally, surgery specialty and Judaism were associated with higher marriage satisfaction, whereas possession of an M.D. degree was associated with lower marriage satisfaction. This study elucidated new perspectives on physician marriage and families based on specialty, practice setting, and stage of training at marriage. Future studies may focus on factors mediating specialty and sex's impact on having children and marriage satisfaction. To our knowledge, this study is the first physician marriage survey which integrates multiple factors in the analysis of physician marriages.

Twelve Weeks of Intermittent Caloric Restriction Diet Mitigates Neuroinflammation in Midlife Individuals with Multiple Sclerosis: A Pilot Study with Implications for Prevention of Alzheimer's Disease.

BACKGROUND: Multiple sclerosis (MS) is a prototype neuroinflammatory disorder with increasingly recognized role for neurodegeneration. Most first-line treatments cannot prevent the progression of neurodegeneration and the resultant disability. Interventions can improve symptoms of MS and might provide insights into the underlying pathology. OBJECTIVE: To investigate the effect of intermittent caloric restriction on neuroimaging markers of MS. METHODS: We randomized ten participants with relapsing remitting MS to either a 12-week intermittent calorie restriction (iCR) diet (n = 5) or control (n = 5). Cortical thickness and volumes were measured through FreeSurfer, cortical perfusion was measured by arterial spin labeling and neuroinflammation through diffusion basis spectrum imaging. RESULTS: After 12 weeks of iCR, brain volume increased in the left superior and inferior parietal gyri (p: 0.050 and 0.049, respectively) and the banks of the superior temporal sulcus (p: 0.01). Similarly in the iCR group, cortical thickness improved in the bilateral medial orbitofrontal gyri (p: 0.04 and 0.05 in right and left, respectively), the left superior temporal gyrus (p: 0.03), and the frontal pole (p: 0.008) among others. Cerebral perfusion decreased in the bilateral fusiform gyri (p: 0.047 and 0.02 in right and left, respectively) and increased in the bilateral deep anterior white matter (p: 0.03 and 0.013 in right and left, respectively). Neuroinflammation, demonstrated through hindered and restricted water fractions (HF and RF), decreased in the left optic tract (HF p: 0.02), and the right extreme capsule (RF p: 0.007 and HF p: 0.003). CONCLUSION: These pilot data suggest therapeutic effects of iCR in improving cortical volume and thickness and mitigating neuroinflammation in midlife adults with MS.

Diffusion histology imaging differentiates distinct pediatric brain tumor histology.

High-grade pediatric brain tumors exhibit the highest cancer mortality rates in children. While conventional MRI has been widely adopted for examining pediatric high-grade brain tumors clinically, accurate neuroimaging detection and differentiation of tumor histopathology for improved diagnosis, surgical planning, and treatment evaluation, remains an unmet need in their clinical management. We employed a novel Diffusion Histology Imaging (DHI) approach employing diffusion basis spectrum imaging (DBSI) derived metrics as the input classifiers for deep neural network analysis. DHI aims to detect, differentiate, and quantify heterogeneous areas in pediatric high-grade brain tumors, which include normal white matter (WM), densely cellular tumor, less densely cellular tumor, infiltrating edge, necrosis, and hemorrhage. Distinct diffusion metric combination would thus indicate the unique distributions of each distinct tumor histology features. DHI, by incorporating DBSI metrics and the deep neural network algorithm, classified pediatric tumor histology with an overall accuracy of 85.8%. Receiver operating analysis (ROC) analysis suggested DHI's great capability in distinguishing individual tumor histology with AUC values (95% CI) of 0.984 (0.982-0.986), 0.960 (0.956-0.963), 0.991 (0.990-0.993), 0.950 (0.944-0.956), 0.977 (0.973-0.981) and 0.976 (0.972-0.979) for normal WM, densely cellular tumor, less densely cellular tumor, infiltrating edge, necrosis and hemorrhage, respectively. Our results suggest that DBSI-DNN, or DHI, accurately characterized and classified multiple tumor histologic features in pediatric high-grade brain tumors. If these results could be further validated in patients, the novel DHI might emerge as a favorable alternative to the current neuroimaging techniques to better guide biopsy and resection as well as monitor therapeutic response in patients with high-grade brain tumors.

Diffusion Histology Imaging Combining Diffusion Basis Spectrum Imaging (DBSI) and Machine Learning Improves Detection and Classification of Glioblastoma Pathology.

PURPOSE: Glioblastoma (GBM) is one of the deadliest cancers with no cure. While conventional MRI has been widely adopted to examine GBM clinically, accurate neuroimaging assessment of tumor histopathology for improved diagnosis, surgical planning, and treatment evaluation remains an unmet need in the clinical management of GBMs. EXPERIMENTAL DESIGN: We employ a novel diffusion histology imaging (DHI) approach, combining diffusion basis spectrum imaging (DBSI) and machine learning, to detect, differentiate, and quantify areas of high cellularity, tumor necrosis, and tumor infiltration in GBM. RESULTS: Gadolinium-enhanced T1-weighted or hyperintense fluid-attenuated inversion recovery failed to reflect the morphologic complexity underlying tumor in patients with GBM. Contrary to the conventional wisdom that apparent diffusion coefficient (ADC) negatively correlates with increased tumor cellularity, we demonstrate disagreement between ADC and histologically confirmed tumor cellularity in GBM specimens, whereas DBSI-derived restricted isotropic diffusion fraction positively correlated with tumor cellularity in the same specimens. By incorporating DBSI metrics as classifiers for a supervised machine learning algorithm, we accurately predicted high tumor cellularity, tumor necrosis, and tumor infiltration with 87.5%, 89.0%, and 93.4% accuracy, respectively. CONCLUSIONS: Our results suggest that DHI could serve as a favorable alternative to current neuroimaging techniques in guiding biopsy or surgery as well as monitoring therapeutic response in the treatment of GBM.

Deep learning with diffusion basis spectrum imaging for classification of multiple sclerosis lesions.

OBJECTIVE: Multiple sclerosis (MS) lesions are heterogeneous with regard to inflammation, demyelination, axonal injury, and neuronal loss. We previously developed a diffusion basis spectrum imaging (DBSI) technique to better address MS lesion heterogeneity. We hypothesized that the profiles of multiple DBSI metrics can identify lesion-defining patterns. Here we test this hypothesis by combining a deep learning algorithm using deep neural network (DNN) with DBSI and other imaging methods. METHODS: Thirty-eight MS patients were scanned with diffusion-weighted imaging, magnetization transfer imaging, and standard conventional MRI sequences (cMRI). A total of 499 regions of interest were identified on standard MRI and labeled as persistent black holes (PBH), persistent gray holes (PGH), acute black holes (ABH), acute gray holes (AGH), nonblack or gray holes (NBH), and normal appearing white matter (NAWM). DBSI, diffusion tensor imaging (DTI), and magnetization transfer ratio (MTR) were applied to the 43,261 imaging voxels extracted from these ROIs. The optimized DNN with 10 fully connected hidden layers was trained using the imaging metrics of the lesion subtypes and NAWM. RESULTS: Concordance, sensitivity, specificity, and accuracy were determined for the different imaging methods. DBSI-DNN derived lesion classification achieved 93.4% overall concordance with predetermined lesion types, compared with 80.2% for DTI-DNN model, 78.3% for MTR-DNN model, and 74.2% for cMRI-DNN model. DBSI-DNN also produced the highest specificity, sensitivity, and accuracy. CONCLUSIONS: DBSI-DNN improves the classification of different MS lesion subtypes, which could aid clinical decision making. The efficacy and efficiency of DBSI-DNN shows great promise for clinical applications in automatic MS lesion detection and classification.