Endometriosis reduces ovarian response in controlled ovarian hyperstimulation independent of AMH, AFC, and women's age measured by follicular output rate (FORT) and number of oocytes retrieved.

PURPOSE: To determine the influence of endometriosis on the ovarian response during controlled ovarian hyperstimulation measured by number of oocytes retrieved and the follicular output rate (FORT). METHODS: A retrospective, single center study included 96 women, who underwent ICSI treatments for male factor infertility according to World Health Organisation between 2016 until 2018. A total of 96 patients were included in the study with 205 fresh ICSI cycles. The study group included 26 patients with endometriosis after surgical and medical treatment; the control group included 70 patients without endometriosis. The women with endometriosis underwent 47 and the control group 158 ICSI cycles. Women underwent fresh intracytoplasmatic sperm injection cycles after controlled ovarian hyperstimulation following a GnRH-antagonist protocol. The FORT was calculated as the ratio of pre-ovulatory follicle count × 100/small antral follicle count at baseline. RESULTS: A lower number of retrieved oocytes (5.89 vs. 7.25, p = 0.045), lower FORT (75.67 vs. 94.63, p = 0.046), lower number of metaphase II oocytes (4.87 vs. 6.04, p = 0.046), and lower fertilization rate after intracytoplasmatic sperm injection (40.61 vs. 57.76, p = 0.003) were found in women with endometriosis compared to women without endometriosis. The number of oocyctes retrieved was 0.71 lower in the group with endometriosis than in the group without (p = 0.026). The FORT was 24.55% lower in the group with endometriosis (p = 0.025). CONCLUSIONS: Endometriosis reduces the FORT and the number of metaphase-II oocytes after controlled ovarian hyperstimulation independly of women's age, antral follicle count and anti-Müllerian hormone.

Identification of a New and Effective Marker Combination for a Standardized and Automated Bin-Based Basophil Activation Test (BAT) Analysis.

(1) Background: The basophil activation test (BAT) is a functional whole blood-based ex vivo assay to quantify basophil activation after allergen exposure by flow cytometry. One of the most important prerequisites for the use of the BAT in the routine clinical diagnosis of allergies is a reliable, standardized and reproducible data analysis workflow. (2) Methods: We re-analyzed a public mass cytometry dataset from peanut (PN) allergic patients (n = 6) and healthy controls (n = 3) with our binning approach "pattern recognition of immune cells" (PRI). Our approach enabled a comprehensive analysis of the dataset, evaluating 30 markers to achieve optimal basophil identification and activation through multi-parametric analysis and visualization. (3) Results: We found FcεRIα/CD32 (FcγRII) as a new marker couple to identify basophils and kept CD63 as an activation marker to establish a modified BAT in combination with our PRI analysis approach. Based on this, we developed an algorithm for automated raw data processing, which enables direct data analysis and the intuitive visualization of the test results including controls and allergen stimulations. Furthermore, we discovered that the expression pattern of CD32 correlated with FcεRIα, anticorrelated with CD63 and was detectable in both the re-analyzed public dataset and our own flow cytometric results. (4) Conclusions: Our improved BAT, combined with our PRI procedure (bin-BAT), provides a reliable test with a fully reproducible analysis. The advanced bin-BAT enabled the development of an automated workflow with an intuitive visualization to discriminate allergic patients from non-allergic individuals.

PRI: Re-Analysis of a Public Mass Cytometry Dataset Reveals Patterns of Effective Tumor Treatments.

Recently, mass cytometry has enabled quantification of up to 50 parameters for millions of cells per sample. It remains a challenge to analyze such high-dimensional data to exploit the richness of the inherent information, even though many valuable new analysis tools have already been developed. We propose a novel algorithm "pattern recognition of immune cells (PRI)" to tackle these high-dimensional protein combinations in the data. PRI is a tool for the analysis and visualization of cytometry data based on a three or more-parametric binning approach, feature engineering of bin properties of multivariate cell data, and a pseudo-multiparametric visualization. Using a publicly available mass cytometry dataset, we proved that reproducible feature engineering and intuitive understanding of the generated bin plots are helpful hallmarks for re-analysis with PRI. In the CD4+T cell population analyzed, PRI revealed two bin-plot patterns (CD90/CD44/CD86 and CD90/CD44/CD27) and 20 bin plot features for threshold-independent classification of mice concerning ineffective and effective tumor treatment. In addition, PRI mapped cell subsets regarding co-expression of the proliferation marker Ki67 with two major transcription factors and further delineated a specific Th1 cell subset. All these results demonstrate the added insights that can be obtained using the non-cluster-based tool PRI for re-analyses of high-dimensional cytometric data.

Carbon flux through photosynthesis and central carbon metabolism show distinct patterns between algae, C3 and C4 plants.

Photosynthesis-related pathways are regarded as a promising avenue for crop improvement. Whilst empirical studies have shown that photosynthetic efficiency is higher in microalgae than in C3 or C4 crops, the underlying reasons remain unclear. Using a tailor-made microfluidics labelling system to supply 13CO2 at steady state, we investigated in vivo labelling kinetics in intermediates of the Calvin Benson cycle and sugar, starch, organic acid and amino acid synthesis pathways, and in protein and lipids, in Chlamydomonas reinhardtii, Chlorella sorokiniana and Chlorella ohadii, which is the fastest growing green alga on record. We estimated flux patterns in these algae and compared them with published and new data from C3 and C4 plants. Our analyses identify distinct flux patterns supporting faster growth in photosynthetic cells, with some of the algae exhibiting faster ribulose 1,5-bisphosphate regeneration and increased fluxes through the lower glycolysis and anaplerotic pathways towards the tricarboxylic acid cycle, amino acid synthesis and lipid synthesis than in higher plants.

Hypermethylated DAPK in serum DNA of women with uterine leiomyoma is a biomarker not restricted to cancer.

OBJECTIVE: Ovarian cancer is most frequently diagnosed at a late stage with a poor prognosis. No markers for early diagnosis have been established. Aberrantly methylated DNA appears as a promising molecular cancer marker. The aim of this study was to analyze the methylation status of the proapoptotic cancer related gene death-associated protein kinase (DAPK) in ovarian cancer patients, healthy controls and in patients suffering from a benign proliferative disease such as uterine leiomyoma. METHODS: Methylation-specific PCR (MSP) was used to detect DAPK methylation in primary tumor tissue and serum of both ovarian cancer (n=32) and uterine leiomyoma patients (n=17 primary tissue, n=30 serum). Serum samples from healthy women served as controls (n=20). MSP results were confirmed by restriction digest and sequencing analyses of cloned PCR products. RESULTS: DAPK methylation was detected in 50% and 35.3% of primary tissue and 56% and 23.8% of serum samples from ovarian cancer and leiomyoma patients, respectively. However, the association of methylation frequencies in tissue and serum was low (kappa=-0.053). Sequencing experiments revealed fully methylated MSP products in sera of both ovarian cancer and leiomyoma patients. In contrast sera from control patients showed only partially methylated DAPK sequences. CONCLUSION: DAPK hypermethylation was neither specific for the tissue of origin nor for cancer. The high prevalence of leiomyoma compromises the utility of this gene as a serum marker for early ovarian cancer detection. These data emphasize the necessity to co-analyze controls presenting with non-cancer proliferative disease in the quest for molecular cancer markers.