Outcome of vitrified-warmed blastocyst transfer performed on days 5-7 after urine LH detection.

RESEARCH QUESTION: What is the clinical importance of vitrified-warmed blastocyst transfer timing if performed on days 5, 6 and 7 after detecting the LH surge using urine tests? DESIGN: Between 2013 and 2019, 2080 vitrified-warmed blastocyst transfers in a true natural cycle were performed and later analysed at the Department of Reproductive Medicine, University Medical Centre Maribor, Slovenia. Urine LH tests were performed twice daily to monitor the onset of the LH surge. Vitrified-warmed blastocyst transfer (frozen embryo transfer [FET]) was performed on day 5 (group 1), 6 (group 2) or 7 (group 3) after the LH surge in 18%, 77% and 4% of cycles, respectively. The patient and cycle characteristics among the groups were compared using the Cochran-Mantel-Haenszel test and respective generalized linear mixed models. Propensity score matching was used to adjust for potential differences among the groups. RESULTS: There were no statistically significant differences between groups 1, 2 and 3 in the cycle and patient characteristics, clinical pregnancy rate (38% versus 39% versus 31%), implantation rate (34% versus 36% versus 31%), miscarriage rate (7% versus 9% versus 7%) and delivery rate (31% versus 31% versus 24%). The day of FET after the LH surge detected using a urine test was not significantly associated with live births. CONCLUSIONS: The results of the current study suggested that the vitrified-warmed blastocyst transfer could be scheduled on day 5, 6 or 7 after a positive LH urine test without having a significant impact on the clinical outcome.

Can methods of artificial intelligence aid in optimizing patient selection in patients undergoing intrauterine inseminations?

PURPOSE: AI and its machine learning algorithms have proven useful in several fields of medicine, including medically assisted reproduction. The purpose of the study was to construct several predictive models based on clinical data and select the best models to predict IUI procedure outcomes. METHODS: Clinical data (patient baseline characteristics, sperm quality, hormonal status, and cycle data) from 1029 IUI procedures performed in 413 couples stimulated by clomiphene citrate, letrozole, or gonadotropins were used to build several models to predict clinical pregnancy. The models included ANN, random forest, PLS, SVM, and linear models using the caret package in R. The models were evaluated using ROC analysis by means of random CV on test data. RESULTS: Out of the best performing models, the random forest model achieved an AUC of 0.66, a sensitivity of 0.432, and a specificity of 0.756. This performance was followed by the PLS model, which achieved a sensitivity of 0.459 and specificity of 0.734. The other models achieved significantly lower AUCs. When adjusting the predictive cutoff value, confusion matrices show that clinical pregnancy is twice as likely in the case of positive prediction. CONCLUSION: Among the compared methods, the random forest and PLS models demonstrated superior performance in predicting the clinical outcome of IUI. With additional research and clinical validation, AI methods may be successfully used in improving patient selection and consequently lead to better clinical results.

Gynaecological cancers and their cell lines.

Cell lines are widely used for various research purposes including cancer and drug research. Recently, there have been studies that pointed to discrepancies in the literature and usage of cell lines. That is why we have prepared a comprehensive overview of the most common gynaecological cancer cell lines, their literature, a list of currently available cell lines, and new findings compared with the original studies. A literature review was conducted via MEDLINE, PubMed and ScienceDirect for reviews in the last 5 years to identify research and other studies related to gynaecological cancer cell lines. We present an overview of the current literature with reference to the original studies and pointed to certain inconsistencies in the literature. The adherence to culturing rulesets and the international guidelines helps in minimizing replication failure between institutions. Evidence from the latest research suggests that despite certain drawbacks, variations of cancer cell lines can also be useful in regard to a more diverse genomic landscape.

Identification of novel diagnostic biomarkers in endometrial cancer using targeted metabolomic profiling.

PURPOSE: Endometrial cancer (EC) is the most common gynecological malignancy with high disease burden especially in advanced stages of the disease. Our study investigated the metabolomic profile of EC patient's serum with the aim of identifying novel diagnostic biomarkers that could be used especially in early disease detection. MATERIAL AND METHODS: Using targeted metabolomic serum profiling based on HPLC-TQ/MS, women with EC (n â€‹= â€‹15) and controls (n â€‹= â€‹21) were examined for 232 endogenous metabolites. RESULTS: Top performing biomarkers included ceramides, acylcarnitines and 1-methyl adenosine. Top 4 biomarkers combined achieved 94% sensitivity with 75% specificity with AUC 92.5% (CI 90.5-94.5%). Individual markers also provided significant predictive values: C16-ceramide achieved sensitivity 73%, specificity 81%, AUC 0.83, C22-ceramide sensitivity 67%, specificity 81%, AUC 0.77, hydroxyhexadecenoylcarnitine sensitivity 60%, specificity 96%, AUC 0.76 and 1-methyladenosine sensitivity 67%, specificity 81%, AUC 0.75. The individual markers, however, did not reach the high sensitivity and specificity of the 4-biomarker combination. CONCLUSIONS: Using mass spectrometry targeted metabolomic profiling, ceramides, acylcarnitines and 1-methyladenosine were identified as potential diagnostic biomarkers for EC. Additionally, these identified metabolites may provide additional insight into cancer cell metabolism.

Diagnosis and management of rudimentary horn pregnancy rupture, misinterpreted as bicornuate uterus in the 14th week of pregnancy.

We present a 26-year-old primigravida with rudimentary horn pregnancy rupture at 14 weeks of pregnancy. Uterine anomaly was first diagnosed at the time of nuchal translucency scan and was presumed to be a bicornuate uterus with normal intrauterine pregnancy in the right horn. One day later, she was admitted to our department with abdominal pain, shortly leading to massive hemoperitoneum and hypovolemic shock. Uterine rupture was confirmed ultrasonically, followed by immediate laparotomy. Ruptured rudimentary horn with already expulsed pregnancy was encountered during surgery. Despite significant advances in ultrasonography, diagnosis of prerupture stage remains controversial. However, high mortality of the condition should ensure low threshold for surgical exploration.

Identification of Novel Diagnostic Biomarkers in Breast Cancer Using Targeted Metabolomic Profiling.

INTRODUCTION: Breast cancer (BC) is the most common cancer in women, with a high disease burden, especially in the advanced disease stages. Our study investigated the metabolomic profile of breast cancer patients' serum with the aim of identifying novel diagnostic biomarkers that could be used, especially for early disease detection. MATERIALS AND METHODS: Using targeted metabolomic serum profiling based on high-performance liquid chromatography mass spectrometry, women with BC (n = 39) and a control group (n = 21) were examined for 232 endogenous metabolites. RESULTS: The top performing biomarkers included acylcarnitines (ACs) and 9,12-linoleic acid. A combined panel of the top 4 biomarkers achieved 83% sensitivity and 81% specificity, with an area under the curve (AUC) of 0.839 (95% confidence interval, 0.811-0.867). Individual markers also provided significant predictive values: AC 12:0, sensitivity of 72%, specificity of 67%, and AUC of 0.71; AC 14:2, sensitivity of 74%, specificity of 71%, and AUC of 0.73; AC 14:0: sensitivity of 67%, specificity of 81%, and AUC of 0.73; and 9,12-linoleic acid, sensitivity of 69%, specificity of 67%, and AUC of 0.71. The individual markers, however, did not reach the high sensitivity and specificity of the 4-biomarker combination. CONCLUSION: Using mass spectrometry-targeted metabolomic profiling, ACs and 9,12-linoleic acid were identified as potential diagnostic biomarkers for breast cancer. Additionally, these identified metabolites could provide additional insight into cancer cell metabolism.

Endometrial cancer and its cell lines.

Endometrial cancer is one of the most common gynaecological malignancies worldwide. One type of research in this field is the growing of cell lines (CLs) and cultures, which can be used to explore the biological mechanisms of cancer. The purpose of this review is to offer an overview of the current literature and highlight the importance of correct CL studies. We carried out a literature analysis of more than 60 articles from the Pubmed, Medline databases that were almost exclusively published in indexed journals in the last 10 years as well as the primary originating scientific studies of specific CLs. We then summarized the newest findings and recommendations. Cell lines are becoming widely used as in vitro tumour models. Recent work has shown inconsistencies in nomenclature and culturing of CLs. Their genomic evolution leads to a high degree of variation across CL strains therefore it is of the utmost importance to recognize the variability within laboratory cancer models. Laboratories must adapt, incorporate additional characterisation techniques and view this situation as an opportunity to improve the reproducibility of pre-clinical cancer research. The authors offer a comprehensive literature review about endometrial cancer CLs, a review of the current literature and advice on culturing CLs.

Data on metabolomic profiling of ovarian cancer patients' serum for potential diagnostic biomarkers.

The data presented here are related to the research paper entitled "Metabolomic profiling suggests long chain ceramides and sphingomyelins as a possible diagnostic biomarker of epithelial ovarian cancer." (Kozar et al., 2018) [1]. Metabolomic profiling was performed on 15 patients with ovarian cancer, 21 healthy controls and 21 patients with benign gynecological conditions. HPLC-TQ/MS was performed on all samples. PLS-DA was used for the first line classification of epithelial ovarian cancer and healthy control group based on metabolomic profiles. Random forest algorithm was used for building a prediction model based over most significant markers. Univariate analysis was performed on individual markers to determine their distinctive roles. Furthermore, markers were also evaluated for their biological significance in cancer progression.

Metabolomic profiling suggests long chain ceramides and sphingomyelins as a possible diagnostic biomarker of epithelial ovarian cancer.

INTRODUCTION: Epithelial ovarian cancer (EOC) is a disease with a poor survival rate mostly due to its discovery in late stages. The aim of this study was to investigate the metabolomic profile of ovarian cancer with the intention of identifying and describing novel biomarkers with diagnostic potential. MATERIAL AND METHODS: Targeted serum metabolomic profiling was performed on 15 patients with ovarian cancer, 21 healthy controls and 21 patients with benign ovarian conditions, using HPLC-TQ/MS. RESULTS: Panel of 49 top performing biomarkers shows separation between EOC and controls with 87% specificity and 87% sensitivity with AUC of 93% (CI 90%-95%). Using only 5 top biomarkers, specificity of 80% and sensitivity of 83% was achieved on extended sample set. Most significant biomarkers belong to sphingolipid classes, especially long chain ceramides and sphingomyelins. Different concentrations of various fatty acid chain lengths ceramides and sphingomyelins are also implying their respective roles in cell proliferation and growth inhibition. CONCLUSION: Long chain ceramides and sphingomyelins may serve as a novel biomarker for epithelial ovarian cancer detection and may also offer insight into the role of sphingolipid metabolism in cell proliferation.

The value of ultrasound-guided surgery for breast cancer.

OBJECTIVE: An increasing number of breast-conserving surgeries (BCS) has lead clinicians to the dilemma how to provide patients with the best pathological, short-term and long-term outcomes, while at the same time improving the cosmetic outcome and the patients' quality of life. A proposed solution is the use of intraoperative ultrasound (IOUS) for lesion resection in palpable as well as non-palpable breast cancer. METHODS: This review identifies and compares evidence on palpation-guided/wire-guided vs. US-guided localization as well as stand-alone observational IOUS studies published between June 2001 and July 2017, indexed in Medline. A cornerstone of this review is the discussion on technology advancement as well as alternative IOUS approaches and their feasibility in treatment of patients with calcifications and multifocal lesions currently not treated with IOUS localization. RESULTS: In comparison to other available methods, IOUS provided in most studies better rates of clear margins, lower rates of re-excisions as well as better cosmetic outcomes. Currently, there is a lack of available multicenter data on method comparisons as well as several limitations to the use of IOUS. CONCLUSIONS: With a comparable follow-up rate of loco-regional recurrences and a higher reported long-term quality of life, IOUS should be a highly regarded method of localization in the planning of BCS.