Mechanical Response of Mouse Cervices Lacking Decorin and Biglycan during Pregnancy: Data

These files contain mechanical data from a study evaluating the mechanical role of small leucine rich proteoglycans in the murine cervix during pregnancy. The experimental_data folder contains a .csv file corresponding to each tested sample. Each file includes the experimental data of each tensile test. The names of the file indicate the sample information and follow the format: gestation_genotype_sample#. Gestation: NP = nonpregnant, d18 = gestation day 18. Genotype: WT = wildtype, DcnKO = Dcn^{-/-}Bgn^{+/+}, BgnKO = Dcn^{+/+}Bgn^{-/-} DB = Dcn^{-/-}Bgn^{+/-}, BD = Dcn^{+/-}Bgn^{-/-}, and DKO = Dcn^{-/-}Bgn^{-/-} sample#: 1-5 (except for DKO) For example, NP_WT_1_raw_data corresponds to nonpregnant wildtype sample #1. In these files, the columns correspond to: Time [s], Force [N], cervical opening [mm], standard deviation of cervical opening [mm], width [mm], standard deviation of width [mm], length [mm], standard deviation of length [mm], height [mm], and standard deviation of height [mm]. The time and force data are from the universal testing machine and the geometries are determined by segmented camera images

Prevalence of breast and ovarian cancer subtypes in Hispanic populations from Puerto Rico

Abstract Background Previous epidemiological studies aimed at describing characteristics of breast (BC) and ovarian cancer (OC) patients tend to examine Hispanic populations using a mix of individuals that come from ethnically different Hispanic backgrounds. Since most USA cancer statistics do not include cancer data from Puerto Rico (PR), there is a lack of historical and descriptive data analysis for Hispanic women in the island that suffer from these diseases. Therefore, the aim of our study is to provide a comprehensive clinicopathological characterization of BC and OC cases in PR. Methods Our study consisted of a longitudinal retrospective review of archived pathology reports at Southern Pathology Services (SPS), which mostly serves southwestern PR, from years 2000–2015. After filtering SPS records with pre-established criteria, tumor samples from 3451 BC and 170 OC cases were used for descriptive statistics and analysis using R program. Results In our cohort, the mean age of diagnosis for BC was 60.5 years and 60.3 years for OC. Available data for subtype characterization from BC cases, exhibited an expected subtype distribution that remained stable over time (Luminal A = 68.8%, Luminal B = 9.7%, HER-2 = 6.1% and Triple negative = 15.4%). Additionally, tumor grades distribution varied within different BC subtypes in which the majority of Luminal A tumors were G2 and most Triple negative tumors were G3. For OC cases, available subtype and tumor grade information identified serous histology in 64.71% of all cases and G3 as being the most prevalent tumor grade. Pathology reports revealed that 39.42% of all OC cases were described as late stage, while 50.5% as early stage (by pathological staging). Conclusion Our data suggests that OC and BC subtypes distribution in Hispanic populations from PR are in-line with national averages. In a significant number of BC cases, subtype could not be determined due to study limitations, health insurance coverage, or other reasons described here and may constitute a health disparity. Altogether, and despite these gaps, this study represents one of the most complete reviews of BC and OC in PR and provides an opportunity to further study this population separate from other US Hispanic populations

Genetics and genomics of endometriosis

Endometriosis is an estrogen-dependent, progesterone-resistant, inflammatory disease with symptoms that include pelvic pain, infertility, and compromised quality of life in millions of women worldwide. Approximately 50% of the risk of developing endometriosis is due to genetic factors with the remaining 50% due to environmental (i.e., exposome) causes. Treatments are hormonal, surgical, or both, with limited efficacy in the long term. Diagnosis of pelvic endometriosis is through visual identification and confirmatory histopathology of lesions. Recent innovations in genomics, genetics and epigenetics, molecular and cell biology, imaging, and a worldwide effort to standardize patient phenotyping and biospecimen collection have contributed to understanding mechanisms underlying the pathogenesis and pathophysiology of endometriosis. Furthermore, integration of “big data” obtained through these technologies holds great promise for novel targeted therapies, noninvasive diagnostics, and prognostic indicators. This chapter reviews current advances in genomics, genetics, and epigenetics of endometriosis that are providing translational approaches for preventing, diagnosing, and effectively treating this enigmatic disease