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Abstract The ovarian cortical reserve of follicles is vital for fertility. Some medical treatments are toxic to follicles, leading to premature ovarian insufficiency. Ovarian tissue cryopreservation is an established method to preserve fertility in adults and even applied in prepuberty despite unproven efficacy. Here, we analyze transcriptomes of 120 cortical follicles from children and adults for detailed comparison. We discover heterogeneity with two main types of follicles in both age groups: one with expected oocyte-granulosa profiles and another with predicted role in signaling. Transcriptional changes during growth to the secondary stage are similar overall in children and adults, but variations related to extracellular matrix, theca cells, and miRNA profiles are found. Notably, cyclophosphamide dose correlates with interferon signaling in child follicles. Additionally, morphology alone is insufficient for follicle categorization suggesting a need for additional markers. Marker genes for early follicle activation are determined. These findings will help refine follicular classification and fertility preservation techniques across critical ages.

Abstract Current therapeutics of endometriosis focus on hormonal disruption of endometriotic lesions (ectopic endometrium, EcE). Recent findings show higher glycolysis utilization in EcE, suggesting non-hormonal strategy for disease treatment that addresses cellular metabolism. Identifying metabolically altered cell types in EcE is important for targeted metabolic drug therapy without affecting eutopic endometrium (EuE). Here, using single-cell RNA-sequencing, we examine twelve metabolic pathways in paired samples of EuE and EcE from women with confirmed endometriosis. We detect nine major cell types in both EuE and EcE. Metabolic pathways are most differentially regulated in perivascular, stromal, and endothelial cells, with the highest changes in AMPK signaling, HIF-1 signaling, glutathione metabolism, oxidative phosphorylation, and glycolysis. We identify transcriptomic co-activation of glycolytic and oxidative metabolism in perivascular and stromal cells of EcE, indicating a critical role of metabolic reprogramming in maintaining endometriotic lesion growth. Perivascular cells, involved in endometrial stroma repair and angiogenesis, may be potential targets for non-hormonal treatment of endometriosis.

Abstract Background Endometriosis, defined as the presence of endometrial-like tissue outside of the uterus, is one of the most prevalent gynecological disorders. Although different theories have been proposed, its pathogenesis is not clear. Novel studies indicate that the gut microbiome may be involved in the etiology of endometriosis; nevertheless, the connection between microbes, their dysbiosis, and the development of endometriosis is understudied. This case–control study analyzed the gut microbiome in women with and without endometriosis to identify microbial targets involved in the disease. Methods A subsample of 1000 women from the Estonian Microbiome cohort, including 136 women with endometriosis and 864 control women, was analyzed. Microbial composition was determined by shotgun metagenomics and microbial functional pathways were annotated using the Kyoto Encyclopedia of Genes and Genomes (KEGG) database. Partitioning Around Medoids (PAM) algorithm was performed to cluster the microbial profile of the Estonian population. The alpha- and beta-diversity and differential abundance analyses were performed to assess the gut microbiome (species and KEGG orthologies (KO)) in both groups. Metagenomic reads were mapped to estrobolome-related enzymes’ sequences to study potential microbiome-estrogen metabolism axis alterations in endometriosis. Results Diversity analyses did not detect significant differences between women with and without endometriosis (alpha-diversity: all p-values > 0.05; beta-diversity: PERMANOVA, both R 2 0.05). No differential species or pathways were detected after multiple testing adjustment (all FDR p-values > 0.05). Sensitivity analysis excluding women at menopause (> 50 years) confirmed our results. Estrobolome-associated enzymes’ sequence reads were not significantly different between groups (all FDR p-values > 0.05). Conclusions Our findings do not provide enough evidence to support the existence of a gut microbiome-dependent mechanism directly implicated in the pathogenesis of endometriosis. To the best of our knowledge, this is the largest metagenome study on endometriosis conducted to date.

Background: Endometrial CD138+ plasma cells serve as a diagnostic biomarker for endometrial inflammation, and their elevated occurrence correlates positively with adverse pregnancy outcomes. Infertility-related conditions like polycystic ovary syndrome (PCOS) and recurrent implantation failure (RIF) are closely associated with systemic and local chronic inflammatory status, wherein endometrial CD138+ plasma cell accumulation could also contribute to endometrial pathology. Current methods for quantifying CD138+ cells typically involve laborious and time-consuming microscopic assessments of only a few random areas from a slide. These methods have limitations in accurately representing the entire slide and are susceptible to significant biases arising from intra- and interobserver variations. Implementing artificial intelligence (AI) for CD138+ cell identification could enhance the accuracy, reproducibility, and reliability of analysis. Methods: Here, an AI algorithm was developed to identify CD138+ plasma cells within endometrial tissue. The AI model comprised two layers of convolutional neural networks (CNNs). CNN1 was trained to segment epithelium and stroma across 28,363 mm2 (2.56 mm2 of epithelium and 24.87 mm2 of stroma), while CNN2 was trained to distinguish stromal cells based on CD138 staining, encompassing 7345 cells in the object layers (6942 CD138− cells and 403 CD138+ cells). The training and performance of the AI model were validated by three experienced pathologists. We collected 193 endometrial tissues from healthy controls (n = 73), women with PCOS (n = 91), and RIF patients (n = 29) and compared the CD138+ cell percentages based on cycle phases, ovulation status, and endometrial receptivity utilizing the AI model. Results: The AI algorithm consistently and reliably distinguished CD138− and CD138+ cells, with total error rates of 6.32% and 3.23%, respectively. During the training validation, there was a complete agreement between the decisions made by the pathologists and the AI algorithm, while the performance validation demonstrated excellent accuracy between the AI and human evaluation methods (intraclass correlation; 0.76, 95% confidence intervals; 0.36–0.93, p = 0.002) and a positive correlation (Spearman's rank correlation coefficient: 0.79, p

Background: The human endometrium undergoes a monthly cycle of tissue growth and degeneration. During the mid-secretory phase, the endometrium establishes an optimal niche for embryo implantation by regulating cellular composition (e.g., epithelial and stromal cells) and differentiation. Impaired endometrial development observed in conditions such as polycystic ovary syndrome (PCOS) and recurrent implantation failure (RIF) contributes to infertility. Surprisingly, despite the importance of the endometrial lining properly developing prior to pregnancy, precise measures of endometrial cellular composition in these two infertility-associated conditions are entirely lacking. Additionally, current methods for measuring the epithelial and stromal area have limitations, including intra- and inter-observer variability and efficiency. Methods: We utilized a deep-learning artificial intelligence (AI) model, created on a cloud-based platform and developed in our previous study. The AI model underwent training to segment both areas populated by epithelial and stromal endometrial cells. During the training step, a total of 28.36 mm2 areas were annotated, comprising 2.56 mm2 of epithelium and 24.87 mm2 of stroma. Two experienced pathologists validated the performance of the AI model. 73 endometrial samples from healthy control women were included in the sample set to establish cycle phase-dependent dynamics of the endometrial epithelial-to-stroma ratio from the proliferative (PE) to secretory (SE) phases. In addition, 91 samples from PCOS cases, accounting for the presence or absence of ovulation and representing all menstrual cycle phases, and 29 samples from RIF patients on day 5 after progesterone administration in the hormone replacement treatment cycle were also included and analyzed in terms of cellular composition. Results: Our AI model exhibited reliable and reproducible performance in delineating epithelial and stromal compartments, achieving an accuracy of 92.40% and 99.23%, respectively. Moreover, the performance of the AI model was comparable to the pathologists’ assessment, with F1 scores exceeding 82% for the epithelium and >96% for the stroma. Next, we compared the endometrial epithelial-to-stromal ratio during the menstrual cycle in women with PCOS and in relation to endometrial receptivity status in RIF patients. The ovulatory PCOS endometrium exhibited epithelial cell proportions similar to those of control and healthy women’s samples in every cycle phase, from the PE to the late SE, correlating with progesterone levels (control SE, r2 = 0.64, FDR < 0.001; PCOS SE, r2 = 0.52, FDR < 0.001). The mid-SE endometrium showed the highest epithelial percentage compared to both the early and late SE endometrium in both healthy women and PCOS patients. Anovulatory PCOS cases showed epithelial cellular fractions comparable to those of PCOS cases in the PE (Anovulatory, 14.54%; PCOS PE, 15.56%, p = 1.00). We did not observe significant differences in the epithelial-to-stroma ratio in the hormone-induced endometrium in RIF patients with different receptivity statuses. Conclusion: The AI model rapidly and accurately identifies endometrial histology features by calculating areas occupied by epithelial and stromal cells. The AI model demonstrates changes in epithelial cellular proportions according to the menstrual cycle phase and reveals no changes in epithelial cellular proportions based on PCOS and RIF conditions. In conclusion, the AI model can potentially improve endometrial histology assessment by accelerating the analysis of the cellular composition of the tissue and by ensuring maximal objectivity for research and clinical purposes.

Abstract Background Reproduction in women is at risk due to exposure to chemicals that can disrupt the endocrine system during different windows of sensitivity throughout life. Steroid hormone levels are fundamental for the normal development and function of the human reproductive system, including the ovary. This study aims to elucidate steroidogenesis at different life-stages in human ovaries. Methods We have developed a sensitive and specific LC-MS/MS method for 21 important steroid hormones and measured them at different life stages: in media from cultures of human fetal ovaries collected from elective terminations of normally progressing pregnancy and in media from adult ovaries from Caesarean section patients, and follicular fluid from women undergoing infertility treatment. Statistically significant differences in steroid hormone levels and their ratios were calculated with parametric tests. Principal component analysis (PCA) was applied to explore clustering of the ovarian-derived steroidogenic profiles. Results Comparison of the 21 steroid hormones revealed clear differences between the various ovarian-derived steroid profiles. Interestingly, we found biosynthesis of both canonical and “backdoor” pathway steroid hormones and corticosteroids in first and second trimester fetal and adult ovarian tissue cultures. 17α-estradiol, a less potent naturally occurring isomer of 17β-estradiol, was detected only in follicular fluid. PCA of the ovarian-derived profiles revealed clusters from: adult ovarian tissue cultures with relatively high levels of androgens; first trimester and second trimester fetal ovarian tissue cultures with relatively low estrogen levels; follicular fluid with the lowest androgens, but highest corticosteroid, progestogen and estradiol levels. Furthermore, ratios of specific steroid hormones showed higher estradiol/ testosterone and estrone/androstenedione (indicating higher CYP19A1 activity, p

The plasticizer di(2-ethylhexyl) phthalate (DEHP) is known to have endocrine-disrupting properties mediated by its many metabolites that form upon exposure in biological systems. In a previous study, we reported an inverse association between DEHP metabolites in the human ovarian follicular fluid (FF) and the responsiveness of the follicles to controlled ovarian stimulation during in vitro fertilization (IVF) treatments. Here, we explored this association further through molecular analysis of the ovarian FF samples.Ninety-six IVF patients from Swedish (N = 48) and Estonian (N = 48) infertility clinics were selected from the previous cohort (N = 333) based on the molar sum of DEHP metabolites in their FF samples to arrive at “high” (mean 7.7 ± SD 2.3 nM, N = 48) and “low” (0.8 ± 0.4 nM, N = 48) exposure groups. Extracellular miRNA levels and concentrations of 15 steroid hormones were measured across FF samples. In addition, FF somatic cells, available for the Estonian patients, were used for RNA sequencing.Differential expression (DE) and interactions between miRNA and mRNA networks revealed that the expression levels of genes in the cholesterol biosynthesis and steroidogenesis pathways were significantly decreased in the high compared to the low DEHP group. In addition, the DE miRNAs were predicted to target key enzymes within these pathways (FDR

Background: MicroRNAs (miRNAs) are key regulators of gene expression that have been implicated in gynecological and breast cancers. Understanding the cancer stage-wise expression patterns of miRNAs and their interactions with other RNA molecules in cancer is crucial to improve cancer diagnosis and treatment planning. Comprehensive web tools that integrate data on the transcriptome, circulating miRNAs, and their validated targets to derive beneficial conclusions in cancer research are lacking. Methods: Using the Shiny R package, we developed a web tool called ExplORRNet that integrates transcriptomic profiles from The Cancer Genome Atlas and miRNA expression data derived from various sources, including tissues, cell lines, exosomes, serum, and plasma, available in the Gene Expression Omnibus database. Differential expression analyses between normal and tumor tissue samples as well as different stages of cancer, accompanied by gene enrichment and survival analyses, can be performed using specialized R packages. Additionally, a miRNA-messenger RNA (mRNA)-long non-coding RNA (lncRNA) networks are constructed to identify regulatory modules. Results: Our tool identifies cancer stage-wise differentially regulated miRNAs, mRNAs, and lncRNAs in gynecological and breast cancers. Survival analysis identifies miRNAs associated with patient survival, and functional enrichment analysis provides insights into dysregulated miRNA-related biological processes and pathways. The miRNA–mRNA–lncRNA networks highlight interconnected regulatory molecular modules driving cancer progression. Case studies demonstrate the utility of the ExplORRNet for studying gynecological and breast cancers. Conclusion: ExplORRNet is an intuitive and user-friendly web tool that provides a deeper understanding of dysregulated miRNAs and their functional implications in gynecological and breast cancers. We hope our ExplORRNet tool has potential utility among the clinical and basic researchers and will be beneficial to the entire cancer genomics community to encourage and facilitate mining the rapidly growing public databases to progress the field of precision oncology. The ExplORRNet is available at https://mirna.cs.ut.ee.

Abstract The choice of targeted therapies for treatment of glioblastoma patients is currently limited, and most glioblastoma patients die from the disease recurrence. Thus, systematic studies in simplified model systems are required to pinpoint the choice of targets for further exploration in clinical settings. Here, we report screening of 5 compounds targeting epigenetic writers or erasers and 6 compounds targeting cell cycle-regulating protein kinases against 3 glioblastoma cell lines following incubation under normoxic or hypoxic conditions. The viability/proliferation assay indicated that PRMT5 inhibitor onametostat was endowed with high potency under both normoxic and hypoxic conditions in cell lines that are strongly MGMT-positive (T98-G), weakly MGMT-positive (U-251 MG), or MGMT-negative (U-87 MG). In U-251 MG and U-87 MG cells, onametostat also affected the spheroid formation at concentrations lower than the currently used chemotherapeutic drug lomustine. In T98-G cell line, treatment with onametostat led to dramatic changes in the transcriptome profile by inducing the cell cycle arrest, suppressing RNA splicing, and down-regulating several major glioblastoma cell survival pathways. Further validation by immunostaining in three cell lines confirmed that onametostat affects cell cycle and causes reduction in nucleolar protein levels. In this way, inhibition of epigenetic targets might represent a viable strategy for glioblastoma treatment even in the case of decreased chemo- and radiation sensitivity, although further studies in clinically more relevant models are required.

(1) Background: Endometriosis is a highly prevalent gynecological disease affecting 10% of women of reproductive age worldwide. miRNAs may play a role in endometriosis, though their exact function remains unclear. This study aimed to identify differentially expressed miRNAs in endometriosis and study their functions in the disease. (2) Methods: Endometrial tissue was collected from women with endometriosis (n = 15) and non-endometriosis controls (n = 17). Dysregulated miRNAs were identified through small RNA-sequencing, and their biological significance was explored by target gene prediction and pathway analysis. Selected miRNAs were examined in paired ectopic endometriomas and eutopic endometrium (n = 10) using qRT-PCR. Their roles in cell migration and proliferation were further examined in vitro using functional assays. To identify potential target genes, we performed mRNA sequencing on transfected cells and the endometrioma cohort. (3) Results: We identified 14 dysregulated miRNAs in the eutopic endometrium of women with endometriosis compared to endometrial tissue from women without endometriosis. Pathway analysis indicated enrichment in cell migration and proliferation-associated pathways. Further ex vivo studies of miR-193b-5p and miR-374b-5p showed that both miRNAs were upregulated in endometrioma. Overexpression of these two miRNAs in vitro inhibited cell migration, and mRNA sequencing revealed several migration-related genes that are targeted by these miRNAs. (4) Conclusions: Our study identified two key endometrial miRNAs that may be involved in the pathogenesis of endometriosis by regulating cell migration.

Abstract Background Peritonsillar abscess (PTA) is a severe deep neck space infection with an insufficiently characterized bacterial etiology. We aimed to reveal the bacteria associated with PTA applying next generation sequencing (NGS). Tonsil biopsies and pus samples of 91 PTA patients were analysed applying NGS method. Results Over 400 genera and 800 species belonging to 34 phyla were revealed. The most abundant species in both sample types were Streptococcus pyogenes, Fusobacterium necrophorum and Fusobacterium nucleatum. When present, S. pyogenes was normally a predominant species, although it was recovered as a minor population in some samples dominated by F. nucleatum and occasionally F. necrophorum. S. pyogenes and F. necrophorum were the predominant species (> 10% in a community) in 28 (31%) pus samples, while F. nucleatum in 21 (23%) and S. anginosus in 8 (9%) pus samples. We observed no substantial differences between the microbial findings in pus and tonsil biopsies. Conclusions The most probable causative agents of PTA according to our NGS-study include Streptococcus pyogenes, Fusobacterium necrophorum and Fusobacterium nucleatum. Some other streptococci (S. anginosus) and anaerobes (Prevotella, Porphyromonas) may contribute to the infection as well. Pus of the peritonsillar abscess is more representative specimen for microbiological examination than the tonsillar tissue. Our results are important in the context of optimizing the handling of the PTA patients.

Abstract Asherman’s Syndrome is characterized by intrauterine adhesions or scarring, which cause infertility, menstrual abnormalities, and recurrent pregnancy loss. The pathophysiology of this syndrome remains unknown, with treatment restricted to recurrent surgical removal of intrauterine scarring, which has limited success. Here, we decode the Asherman’s Syndrome endometrial cell niche by analyzing data from over 200,000 cells with single-cell RNA-sequencing in patients with this condition and through in vitro analyses of Asherman’s Syndrome patient-derived endometrial organoids. Our endometrial atlas highlights the loss of the endometrial epithelium, alterations to epithelial differentiation signaling pathways such as Wnt and Notch, and the appearance of characteristic epithelium expressing secretory leukocyte protease inhibitor during the window of implantation. We describe syndrome-associated alterations in cell-to-cell communication and gene expression profiles that support a dysfunctional pro-fibrotic, pro-inflammatory, and anti-angiogenic environment.

Abstract Expressional profiling of the endometrium enables the personalised timing of the window of implantation (WOI). This study presents and evaluates a novel analytical pipeline based on a TAC-seq (Targeted Allele Counting by sequencing) method for endometrial dating. The expressional profiles were clustered, and differential expression analysis was performed on the model development group, using 63 endometrial biopsies spanning over proliferative (PE, n = 18), early-secretory (ESE, n = 18), mid-secretory (MSE, n = 17) and late-secretory (LSE, n = 10) endometrial phases of the natural cycle. A quantitative predictor model was trained on the development group and validated on sequenced samples from healthy women, consisting of 52 paired samples taken from ESE and MSE phases and five LSE phase samples from 31 individuals. Finally, the developed test was applied to 44 MSE phase samples from a study group of patients diagnosed with recurrent implantation failure (RIF). In validation samples (n = 57), we detected displaced WOI in 1.8% of the samples from fertile women. In the RIF study group, we detected a significantly higher proportion of the samples with shifted WOI than in the validation set of samples from fertile women, 15.9% and 1.8% (p = 0.012), respectively. The developed model was evaluated with an average cross-validation accuracy of 98.8% and an accuracy of 98.2% in the validation group. The developed beREADY screening model enables sensitive and dynamic detection of selected transcriptome biomarkers, providing a quantitative and accurate prediction of endometrial receptivity status.

Abstract Endometriosis is a chronic inflammatory gynaecological disease characterized by the growth of endometrial tissue outside the uterine cavity. There are currently no definitive non-invasive diagnostic tools. Glycosylation is the most common posttranslational modification of proteins and altered glycosylation has been found in many diseases, including chronic inflammatory conditions and cancer. Sialylation and galactosylation on serum IgG have previously been found to be altered in endometriosis and serum sialylation changed after Zoladex (Goserelin Acetate) therapy. Using IgG and whole serum glycoproteins, we investigated N-glycosylation in two clinical cohorts of women with and without endometriosis. PNGase F-digested serum samples were fluorescently labelled and N-glycans were profiled by ultra-performance liquid chromatography. Clinical data was collected to link glycomic findings with metabolic and hormonal profiles. Total serum glycoprotein and IgG glycosylation differed in patients with endometriosis compared to control cases. The most significantly altered was glycan peak 3 from IgG, containing bisected biantennary glycans, which was decreased in the endometriosis cohorts (p = 0.0000005–0.018). In conclusion, this is the first pilot study to identify changes in N-glycans from whole serum glycoproteins associated with endometriosis. A larger validation study is now warranted and such studies should include the follow-up of surgically and pharmacologically treated patients.

This paper presents a technique that can potentially help to determine the receptivity stage of the endometrium from histology images by automatically measuring the stromal nuclear changes. The presented technique is composed of an image segmentation model and the statistical evolution of segmented areas in hematoxylin and eosin (HE)-stained histology images. Three different endometrium receptivity stages, namely pre-receptive, post-receptive, and receptive, were compared. An ensemble-based AI model was proposed for histology image segmentation, which is based on individual UNet++, UNet, and ResNet34-UNet segmentation models. The performance of the ensemble-based technique was assessed using the Dice score and intersection over unit (IoU) values. In comparison to alternative segmentation architectures that were applied singly, the current ensemble-based method obtained higher Dice score (0.95) and IoU (0.90) values. The statistical comparison highlighted a noticeable difference in the number of nuclei and the size of the stroma tissue. The proposed technique demonstrated the positive potential for practical implementation for automatic endometrial tissue analysis.

Miscarriage is a common, complex trait affecting ~15% of clinically confirmed pregnancies. Here we present the results of large-scale genetic association analyses with 69,054 cases from five different ancestries for sporadic miscarriage, 750 cases of European ancestry for multiple (≥3) consecutive miscarriage, and up to 359,469 female controls. We identify one genome-wide significant association (rs146350366, minor allele frequency (MAF) 1.2%, P = 3.2 × 10-8, odds ratio (OR) = 1.4) for sporadic miscarriage in our European ancestry meta-analysis and three genome-wide significant associations for multiple consecutive miscarriage (rs7859844, MAF = 6.4%, P = 1.3 × 10-8, OR = 1.7; rs143445068, MAF = 0.8%, P = 5.2 × 10-9, OR = 3.4; rs183453668, MAF = 0.5%, P = 2.8 × 10-8, OR = 3.8). We further investigate the genetic architecture of miscarriage with biobank-scale Mendelian randomization, heritability, and genetic correlation analyses. Our results show that miscarriage etiopathogenesis is partly driven by genetic variation potentially related to placental biology, and illustrate the utility of large-scale biobank data for understanding this pregnancy complication.

Abstract Background Prenatal alcohol exposure (PAE) affects embryonic development, causing a variable fetal alcohol spectrum disorder (FASD) phenotype with neuronal disorders and birth defects. We hypothesize that early alcohol-induced epigenetic changes disrupt the accurate developmental programming of embryo and consequently cause the complex phenotype of developmental disorders. To explore the etiology of FASD, we collected unique biological samples of 80 severely alcohol-exposed and 100 control newborns at birth. Methods We performed genome-wide DNA methylation (DNAm) and gene expression analyses of placentas by using microarrays (EPIC, Illumina) and mRNA sequencing, respectively. To test the manifestation of observed PAE-associated DNAm changes in embryonic tissues as well as potential biomarkers for PAE, we examined if the changes can be detected also in white blood cells or buccal epithelial cells of the same newborns by EpiTYPER. To explore the early effects of alcohol on extraembryonic placental tissue, we selected 27 newborns whose mothers had consumed alcohol up to gestational week 7 at maximum to the separate analyses. Furthermore, to explore the effects of early alcohol exposure on embryonic cells, human embryonic stem cells (hESCs) as well as hESCs during differentiation into endodermal, mesodermal, and ectodermal cells were exposed to alcohol in vitro. Results DPPA4, FOXP2, and TACR3 with significantly decreased DNAm were discovered—particularly the regulatory region of DPPA4 in the early alcohol-exposed placentas. When hESCs were exposed to alcohol in vitro, significantly altered regulation of DPPA2, a closely linked heterodimer of DPPA4, was observed. While the regulatory region of DPPA4 was unmethylated in both control and alcohol-exposed hESCs, alcohol-induced decreased DNAm similar to placenta was seen in in vitro differentiated mesodermal and ectodermal cells. Furthermore, common genes with alcohol-associated DNAm changes in placenta and hESCs were linked exclusively to the neurodevelopmental pathways in the enrichment analysis, which emphasizes the value of placental tissue when analyzing the effects of prenatal environment on human development. Conclusions Our study shows the effects of early alcohol exposure on human embryonic and extraembryonic cells, introduces candidate genes for alcohol-induced developmental disorders, and reveals potential biomarkers for prenatal alcohol exposure.

Childhood medulloblastoma is a malignant form of brain tumor that is widely classified into four subgroups based on molecular and genetic characteristics. Accurate classification of these subgroups is crucial for appropriate treatment, monitoring plans, and targeted therapies. However, misclassification between groups 3 and 4 is common. To address this issue, an AI-based R package called MBMethPred was developed based on DNA methylation and gene expression profiles of 763 medulloblastoma samples to classify subgroups using machine learning and neural network models. The developed prediction models achieved a classification accuracy of over 96% for subgroup classification by using 399 CpGs as prediction biomarkers. We also assessed the prognostic relevance of prediction biomarkers using survival analysis. Furthermore, we identified subgroup-specific drivers of medulloblastoma using functional enrichment analysis, Shapley values, and gene network analysis. In particular, the genes involved in the nervous system development process have the potential to separate medulloblastoma subgroups with 99% accuracy. Notably, our analysis identified 16 genes that were specifically significant for subgroup classification, including EP300, CXCR4, WNT4, ZIC4, MEIS1, SLC8A1, NFASC, ASCL2, KIF5C, SYNGAP1, SEMA4F, ROR1, DPYSL4, ARTN, RTN4RL1, and TLX2. Our findings contribute to enhanced survival outcomes for patients with medulloblastoma. Continued research and validation efforts are needed to further refine and expand the utility of our approach in other cancer types, advancing personalized medicine in pediatric oncology.

BackgroundEndometriosis is a debilitating and difficult-to-diagnose gynecological disease. Owing to limited information and awareness, women often rely on social media platforms as a support system to engage in discussions regarding their disease-related concerns. ObjectiveThis study aimed to apply computational techniques to social media posts to identify discussion topics about endometriosis and to identify themes that require more attention from health care professionals and researchers. We also aimed to explore whether, amid the challenging nature of the disease, there are themes within the endometriosis community that gather posts with positive sentiments. MethodsWe retrospectively extracted posts from the subreddits r/Endo and r/endometriosis from January 2011 to April 2022. We analyzed 45,693 Reddit posts using sentiment analysis and topic modeling–based methods in machine learning. ResultsSince 2011, the number of posts and comments has increased steadily. The posts were categorized into 11 categories, and the highest number of posts were related to either asking for information (Question); sharing the experiences (Rant/Vent); or diagnosing and treating endometriosis, especially surgery (Surgery related). Sentiment analysis revealed that 92.09% (42,077/45,693) of posts were associated with negative sentiments, only 2.3% (1053/45,693) expressed positive feelings, and there were no categories with more positive than negative posts. Topic modeling revealed 27 major topics, and the most popular topics were Surgery, Questions/Advice, Diagnosis, and Pain. The Survey/Research topic, which brought together most research-related posts, was the last in terms of posts. ConclusionsOur study shows that posts on social media platforms can provide insights into the concerns of women with endometriosis symptoms. The analysis of the posts confirmed that women with endometriosis have to face negative emotions and pain daily. The large number of posts related to asking questions shows that women do not receive sufficient information from physicians and need community support to cope with the disease. Health care professionals should pay more attention to the symptoms and diagnosis of endometriosis, discuss these topics with patients to reduce their dissatisfaction with doctors, and contribute more to the overall well-being of women with endometriosis. Researchers should also become more involved in social media and share new science-based knowledge regarding endometriosis.

IntroductionThe expression of genes in female reproductive organs is influenced by the cyclic changes in hormone levels during the menstrual cycle. While the molecular changes in the endometrium that facilitate embryo implantation have been extensively studied, there is limited knowledge about the impact of the menstrual cycle on cervical cells. Cervical cells can be easily and routinely collected using a cytobrush during gynecological examination, offering a standardized approach for diagnostic testing. In this study we investigated how the transcriptome of cervical cells changes during the menstrual cycle and assessed the utility of these cells to determine endometrial receptivity.MethodsEndocervical cells were collected with cytobrushes from 16 healthy women at different menstrual cycle phases in natural cycles and from four women undergoing hormonal replacement cycles. RNA sequencing was applied to gain insight into the transcriptome of cervical cells.ResultsTranscriptome analysis identified four differentially expressed genes (DEGs) between early- and mid-secretory samples, suggesting that the transcriptome of cervical cells does not change significantly during the opening of the implantation window. The most differences appeared during the transition to the late secretory phase (2136 DEGs) before the onset of menstruation. Cervical cells collected during hormonal replacement cycles showed 1899 DEGs enriched in immune system processes.ConclusionsThe results of our study suggested that cervical cells undergo moderate transcriptomic changes throughout the menstrual cycle; however, these changes do not reflect the gene expression pattern of endometrial tissue and offer little or no potential for endometrial receptivity diagnostics.