Women and Mid-Life Divorce Losses and Triumphs

The purpose or this study was to report the findings of a qualitative, exploratory study with women who divorced during the mid-life years of 35-55. Paul Bohannan\u27s six stations of divorce was used as a framework to explore the legal, economic, community, emotional, co-parental, and psychic losses expressed by women as a result of divorcing in mid-life and factors proving beneficial in coping with these losses. Data from ten open-ended, structured interviews were collected and analyzed. This data revealed themes of depression, anger and sadness, yet hope for further growth and a better life post-divorce recovery. Implications for social work practice include, the involvement by social workers in the legal and political arenas to make an impact on legislation affecting child support, being responsible in private practice to hear where the client is in her recovery and offer her support and resources appropriate to her present state with the caveat that the social worker must help her normalize her crisis and help her move at her own pace to healing

2D and 3D U-Nets for skull stripping in a large and heterogeneous set of head MRI using fastai

Skull stripping in brain imaging is the removal of the parts of images corresponding to non-brain tissue. Fast and accurate skull stripping is a crucial step for numerous medical brain imaging applications, e.g. registration, segmentation and feature extraction, as it eases subsequent image processing steps. In this work, we propose and compare two novel skull stripping methods based on 2D and 3D convolutional neural networks trained on a large, heterogeneous collection of 2777 clinical 3D T1-weighted MRI images from 1681 healthy subjects. We investigated the performance of the models by testing them on 927 images from 324 subjects set aside from our collection of data, in addition to images from an independent, large brain imaging study: the IXI dataset (n = 556). Our models achieved mean Dice scores higher than 0:978 and Jaccard indices higher than 0:957 on all tests sets, making predictions on new unseen brain MR images in approximately 1.4s for the 3D model and 12.4s for the 2D model. A preliminary exploration of the models’ robustness to variation in the input data showed favourable results when compared to a traditional, well-established skull stripping method. With further research aimed at increasing the models’ robustness, such accurate and fast skull stripping methods can potentially form a useful component of brain MRI analysis pipelines.publishedVersio

2D and 3D U-Nets for skull stripping in a large and heterogeneous set of head MRI using fastai

Skull stripping in brain imaging is the removal of the parts of images corresponding to non-brain tissue. Fast and accurate skull stripping is a crucial step for numerous medical brain imaging applications, e.g. registration, segmentation and feature extraction, as it eases subsequent image processing steps. In this work, we propose and compare two novel skull stripping methods based on 2D and 3D convolutional neural networks trained on a large, heterogeneous collection of 2777 clinical 3D T1-weighted MRI images from 1681 healthy subjects. We investigated the performance of the models by testing them on 927 images from 324 subjects set aside from our collection of data, in addition to images from an independent, large brain imaging study: the IXI dataset (n = 556). Our models achieved mean Dice scores higher than 0:978 and Jaccard indices higher than 0:957 on all tests sets, making predictions on new unseen brain MR images in approximately 1.4s for the 3D model and 12.4s for the 2D model. A preliminary exploration of the models’ robustness to variation in the input data showed favourable results when compared to a traditional, well-established skull stripping method. With further research aimed at increasing the models’ robustness, such accurate

Five dimensional relativity and two times

It is possible that null paths in 5D appear as the timelike paths of massive particles in 4D, where there is an oscillation in the fifth dimension around the hypersurface we call spacetime. A particle in 5D may be regarded as multiply imaged in 4D, and the 4D weak equivalence principle may be regarded as a symmetry of the 5D metric.Comment: 15 pages, in press in Phys. Lett.

Predicting conversion to Alzheimer’s disease in individuals with Mild Cognitive Impairment using clinically transferable features

Patients with Mild Cognitive Impairment (MCI) have an increased risk of Alzheimer’s disease (AD). Early identification of underlying neurodegenerative processes is essential to provide treatment before the disease is well established in the brain. Here we used longitudinal data from the ADNI database to investigate prediction of a trajectory towards AD in a group of patients defined as MCI at a baseline examination. One group remained stable over time (sMCI, n = 357) and one converted to AD (cAD, n = 321). By running two independent classification methods within a machine learning framework, with cognitive function, hippocampal volume and genetic APOE status as features, we obtained a cross-validation classification accuracy of about 70%. This level of accuracy was confirmed across different classification methods and validation procedures. Moreover, the sets of misclassified subjects had a large overlap between the two models. Impaired memory function was consistently found to be one of the core symptoms of MCI patients on a trajectory towards AD. The prediction above chance level shown in the present study should inspire further work to develop tools that can aid clinicians in making prognostic decisions.publishedVersio

Functional activity level reported by an informant is an early predictor of Alzheimer’s disease

Background Loss of autonomy in day-to-day functioning is one of the feared outcomes of Alzheimer’s disease (AD), and relatives may have been worried by subtle behavioral changes in ordinary life situations long before these changes are given medical attention. In the present study, we ask if such subtle changes should be given weight as an early predictor of a future AD diagnosis. Methods Longitudinal data from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) were used to define a group of adults with a mild cognitive impairment (MCI) diagnosis remaining stable across several visits (sMCI, n=360; 55-91 years at baseline), and a group of adults who over time converted from having an MCI diagnosis to an AD diagnosis (cAD, n=320; 55-88 years at baseline). Eleven features were used as input in a Random Forest (RF) binary classifier (sMCI vs. cAD) model. This model was tested on an unseen holdout part of the dataset, and further explored by three different permutation-driven importance estimates and a comprehensive post hoc machine learning exploration. Results The results consistently showed that measures of daily life functioning, verbal memory function, and a volume measure of hippocampus were the most important predictors of conversion from an MCI to an AD diagnosis. Results from the RF classification model showed a prediction accuracy of around 70% in the test set. Importantly, the post hoc analyses showed that even subtle changes in everyday functioning noticed by a close informant put MCI patients at increased risk for being on a path toward the major cognitive impairment of an AD diagnosis. Conclusion The results showed that even subtle changes in everyday functioning should be noticed when reported by relatives in a clinical evaluation of patients with MCI. Information of these changes should also be included in future longitudinal studies to investigate different pathways from normal cognitive aging to the cognitive decline characterizing different stages of AD and other neurodegenerative disorders.publishedVersio

Growing Prevalence and Incidence of Diabetes in Republic of Macedonia in the Past 5 Years Based on Data from the National System for Electronic Health Records

Introduction. Diabetes is a chronic metabolic disease characterized with a rapid progression of prevalence in last 3 decades, especially in countries with low and middle income. Next three decades this number of diabetes in the world is expected to be doubled. Early diagnosis and appropriate management of diabetes is the primary way to prevent and delay cardiovascular complications. Patients and methods. In this retrospective study, we used the data from National electronic system e-health which was performed in late 2014, wich gives us nearly precise data, and we made statistical analysis for diabetes in last 5 years (2015-2019). Results. In 2015 we have registered 103480 patients with DM, in 2016 108130 patients, in 2017 114408, in 2018 119999 and in 2019 124450 patients with DM. 95% of patients are with T2DM and 4, 1% with T1DM. According the data from State statistical office for population of Republic of Macedonia, the prevalence of T2DM for the years 2015-2019 is as follows: 5,66% in 2015, 6.13% In 2016, 6.55% I 2017, 7,06% in 2018 and 7,2% in 2019.   Conclusions. The number of registered  patients with diabetes in last 5 years has grown up for 20970 or 20%, in the last 5 years the number of patients with  type 2 diabetes has grown up for 18272 patients or 11%. The prevalence of T2DM has increased for 1.54%. Involvement of primary health care professionals has improved the early diagnosis of type 2 diabetes