Background Terminal duct lobular units (TDLUs) are the predominant source of breast cancers. Lesser degrees of age-related TDLU involution have been associated with increased breast cancer risk, but factors that influence involution are largely unknown. We assessed whether circulating hormones, implicated in breast cancer risk, are associated with levels of TDLU involution using data from the Susan G. Komen Tissue Bank (KTB) at the Indiana University Simon Cancer Center (2009-2011). Methods We evaluated three highly reproducible measures of TDLU involution, using normal breast tissue samples from the KTB (n=390): TDLU counts, median TDLU span, and median acini counts per TDLU. Relative risks (RRs, for continuous measures), odds ratios (ORs, for categorical measures), 95% confidence intervals (CIs), and p-trends were calculated to assess the association between tertiles of estradiol, testosterone, sex hormone-binding globulin (SHBG), progesterone, and prolactin with TDLU measures. All models were stratified by menopausal status and adjusted for confounders. Results Among premenopausal women, higher prolactin levels were associated with higher TDLU counts (RRT3vsT1:1.18, 95% CI: 1.07-1.31; p-trend=0.0005), but higher progesterone was associated with lower TDLU counts (RRT3vsT1: 0.80, 95% CI: 0.72-0.89; p-trend<0.0001). Among postmenopausal women, higher levels of estradiol (RRT3vsT1:1.61, 95% CI: 1.32-1.97; p-trend<0.0001) and testosterone (RRT3vsT1: 1.32, 95% CI: 1.09-1.59; p-trend=0.0043) were associated with higher TDLU counts. Conclusions These data suggest that select hormones may influence breast cancer risk potentially through delaying TDLU involution. Impact Increased understanding of the relationship between circulating markers and TDLU involution may offer new insights into breast carcinogenesis.

Within the complex branching system of the breast, terminal duct lobular units (TDLUs) are the anatomical location where most cancer originates. With aging, TDLUs undergo physiological involution, reflected in a loss of structural components (acini) and a reduction in total number. Data suggest that women undergoing benign breast biopsies that do not show age appropriate involution are at increased risk of developing breast cancer. To date, TDLU assessments have generally been made by qualitative visual assessment, rather than by objective quantitative analysis. This paper introduces a technique to automatically estimate a set of quantitative measurements and use those variables to more objectively describe and classify TDLUs. To validate the accuracy of our system, we compared the computer-based morphological properties of 51 TDLUs in breast tissues donated for research by volunteers in the Susan G. Komen Tissue Bank and compared results to those of a pathologist, demonstrating 70% agreement. Secondly, in order to show that our method is applicable to a wider range of datasets, we analyzed 52 TDLUs from biopsies performed for clinical indications in the National Cancer Institute’s Breast Radiology Evaluation and Study of Tissues (BREAST) Stamp Project and obtained 82% correlation with visual assessment. Lastly, we demonstrate the ability to uncover novel measures when researching the structural properties of the acini by applying machine learning and clustering techniques. Through our study we found that while the number of acini per TDLU increases exponentially with the TDLU diameter, the average elongation and roundness remain constant.

Medication errors are one of the safety problems most frequently seen in hospital organizations. It is estimated that 12.2% of all hospitalized patients are involved in some form of adverse drug event (ADE) [1]. A significant amount of ADEs result from handing the incorrect drug to a patient or prescribing the wrong medication.This paper introduces a simple yet robust classification technique that can be used to automatically identify prescriptions drugs within images. The system uses a modified shape distribution technique to examine the shape, color, and imprint of a pill and create an invariant descriptor that can be used to recognize the same drug under different viewing conditions. The proposed technique has been successfully evaluated with 568 of the most prescribed drugs in the United States and has shown a 91.13% accuracy in automatically identifying the correct medication.

In this work, the drawings collected from healthy individuals and people with PD were classified by RFC. The proposed method employed pencil drawings digitized from ordinary sheet of paper, making it very simple to be applied in the context of scarce financial resources. Despite the small number of images in the available data set (51 per class), the obtained results were satisfactory and accurate by discriminating drawings of healthy people from those with PD. HOG parameters were tested in default values (10x10 pixels per cell, 2x2 cells per block and 9 bins in the histogram with 0-180°o rientation) focus on good performance showed by Dalal and Triggs [4] and the HOG result was passed to the classifier. This is the first reported study considering the application of HOG estimates in combination with the RFC applied to the automatic classification of data obtained from people with PD. In the future, it will be necessary to obtain more image drawings and different shapes to increase the database and test more parameters. Parkinson's disease (PD) is a neurological disorder that is progressive and causes losses of dopaminergic neurons from the substantia nigra, a region in the human brain. The decrease of dopamine in this area implies the worsening of motor symptoms such as tremors, bradykinesia, rigidity, gait impairment, and non-motor symptoms such as depression, loss of cognitive functions, sleep problems and nerve pain [1]. PD affects 1% of the world's population aged 60 years and over, and despite scientific advancement, the disease remains incurable. The diagnosis of PD is complex, with a seasoned specialist being necessary to make it [1, 2]. Tremors are a common symptom in PD and it can be classified into many types: resting tremor, postural tremor, kinetic, essential, cerebellar, and others. Each type manifests in different situations and frequency ranges [3]. This work proposes to classify images of handwritten drawings collected from healthy individuals and people with PD. The identification and discrimination of motor symptoms in PD is a fundamental step in the diagnosis and follow-up of the disorder.

<p>Supplementary Table 5 -Associations between sex steroid hormone levels and TDLU counts, among premenopausal women,stratified by menstrual cycle phase, in the KTB.</p>