Ovarian cancer (OC) is the most common cause of cancer-related mortality in gynecology. It is crucial to develop novel therapeutic strategies to achieve effective long-term disease stabilization for this malignant tumor. In this study, we evaluated prognostic genes associated with RNA modification (RM) andProgrammed cell death (PCD) in OC and explored the underlying mechanisms of these genes through enrichment analysis and drug sensitivity analysis. Additionally, based on single-cell RNA sequencing data of OC, we further investigated the association between these genes and the efficacy of immunotherapy for OC. The OC-related datasets, TCGA-OC, GSE63885, and E-MTAB-8107, were obtained from the public databases. Firstly, the Weighted gene co-expression network analysis (WGCNA) was performed to identify the module genes related to RM, which was intersected with DEGs and PCDRGs. Subsequently, the optimal combination was selected from the 101 machine learning algorithms in TCGA-OC and verified in GSE63885. Key genes were subsequently identified from this optimal combination, and their risk coefficients were calculated to construct a risk model using the risk score formula.Then, genes with significant overall survival differences based on Kaplan-Meier (KM) curve were selected as prognostic genes. Finally, a single cell-RNA sequencing dataset, E-MTAB-8107, was used to explore the expression levels in different cell types and identify key cells. The cell-cell communication and pseudotime analysis were used to study the heterogeneity of key cells. Additionally, the clinic samples were collected to verify the expression of prognostic genes by real-time qPCR and Immunohistochemistry. In TCGA-OC, a sum of 122 candidate genes were screened, and then 12 feature genes related to prognosis were screened in the univariate Cox analysis. Afterwards, survival SVM (C-index = 0.607) was determined as the optimal combination generating a risk model. Specially, the high-risk subgroups had worse prognosis. Then, HERC1, TPCN2, EPHA2, CASP2, and BLOC1S1 were identified as prognostic genes. In E-MTAB-8107, a total of 12 different cell types were annotated. The fibroblasts had strong interaction with monocytes. In real-time qPCR and IHC assay, the expression level of CASP2 was significantly up-regulated in the OC group. Overall, HERC1, TPCN2, EPHA2, CASP2, and BLOC1S1 were mined as potential therapeutic targets, and fibroblasts were determined as key cells in OC, which provides new perspectives for the treatment of OC.
Optimizing oocyte maturation in high ovarian responders undergoing in vitro fertilization is crucial for reproductive outcomes and minimizing ovarian hyperstimulation syndrome (OHSS). It remains uncertain whether a gonadotropin-releasing hormone agonist (GnRH-a) combined with low-dose hCG (dual trigger) provides advantages over GnRH-a. This meta-analysis evaluated the efficacy and safety of dual trigger versus GnRH-a trigger in high ovarian responders. A systematic search of PubMed, Embase, Web of Science, the Cochrane Library, and China National Knowledge Infrastructure was conducted to identify randomized controlled trials (RCTs) and cohort studies comparing dual trigger with GnRH-a trigger alone in high responders. The primary outcomes were metaphase II (MII) oocyte rate and moderate or severe OHSS. Secondary outcomes included the number of retrieved oocytes, embryos, and high-quality embryos; normal fertilization rate; clinical pregnancy rate (CPR); miscarriage rate; and live birth rate. The study analyzed binary and continuous variables using relative risk (RR) and weighted mean difference (WMD), with 95
Prolonged menstrual cycles may indicate potential cardiometabolic conditions; however, there are limited data on how these associations vary among women without polycystic ovary syndrome (PCOS) undergoing in vitro fertilization (IVF). In this study, we aimed to investigate the association between menstrual cycle length, pregnancy complications, and adverse birth outcomes among women without PCOS undergoing frozen embryo transfer (FET). Women who delivered singleton live births and had complete pre-pregnancy menstrual cycle data were eligible for inclusion in this study, and were grouped according to the menstrual cycle length (≤ 27, 28–31, 32–35, and ≥ 36 days). Multivariable logistic regression models adjusted for age, body mass index, parity, infertility factors, IVF-FET parameters, and embryo characteristics were used to estimate the adjusted odds ratios (aORs) for gestational diabetes mellitus (GDM) and gestational hypertension. Women with prolonged cycles (≥ 36 days) had a higher risk of GDM (aOR 1.54; 95
Ovarian cancer remains highly lethal despite advances in surgery, chemotherapy, and maintenance therapy, largely because many patients are diagnosed at an advanced stage and recurrent tumors frequently acquire resistance to platinum and taxane-based chemotherapy. Classical resistance mechanisms, including enhanced DNA repair, reduced drug accumulation, apoptosis evasion, and cell plasticity, are increasingly linked to metabolic adaptation. Under therapeutic pressure, ovarian cancer cells adjust mitochondrial respiration, glycolysis, lipid utilization, and antioxidant defenses to maintain DNA repair capacity, membrane integrity, and stress tolerance. This review discusses the contribution of energy metabolism, lipid remodeling, ferroptosis, and cuproptosis to ovarian cancer chemoresistance. Rather than representing a single fixed phenotype, resistant tumors may show distinct metabolic and cell-death states, such as glycolysis-dominant, mitochondria-dependent, lipid-adapted, ferroptosis-resistant, or cuproptosis-sensitive states, which change during chemotherapy, recurrence, and microenvironmental selection. Defining these features may help guide biologically informed treatment strategies for ovarian cancer, moving therapeutic design away from broadly applied combinations toward more rational and individualized approaches.
Ovarian endometrioma (OEM) remains a challenging manifestation of endometriosis, particularly in women with infertility, recurrent disease, or concern about ovarian reserve. Ultrasound-guided sclerotherapy has emerged as a minimally invasive treatment option in selected patients, but its appropriate use depends heavily on imaging-based decision-making. This review examines the role of ultrasound across the care pathway of sclerotherapy for OEM, with emphasis on diagnosis, risk stratification, patient selection, procedural planning, treatment guidance, and post-treatment surveillance. English-language literature up to May 2026 was searched in PubMed, Embase, Web of Science, and Scopus using terms related to OEM, endometriosis, transvaginal ultrasound, ultrasound-guided aspiration, sclerotherapy, ethanol sclerotherapy, recurrence, ovarian reserve, infertility, and assisted reproduction. Current evidence indicates that transvaginal ultrasound is central not only to confirming the diagnosis of OEM, but also to excluding mimickers and malignant red flags before intervention. In women considered for sclerotherapy, ultrasound provides key information on lesion morphology, disease extent, ovarian mobility, pelvic adhesions, accessibility, and puncture safety. Ultrasound-guided sclerotherapy appears to be a feasible minimally invasive option in selected women, particularly when preservation of ovarian reserve is prioritized. However, the available literature remains heterogeneous with regard to patient selection, procedural protocols, sclerosant exposure, definitions of response and recurrence, and follow-up intervals. Ultrasound may therefore play an important role not only in procedural guidance, but also in decision-making throughout the care pathway of OEM sclerotherapy. A standardized ultrasound-based framework may improve candidate selection, procedural consistency, and post-treatment surveillance, and may help better define the role of sclerotherapy in clinical practice.
In vitro maturation (IVM) offers a promising alternative for patients ineligible for conventional ovarian stimulation. However, its success rates remain suboptimal, largely due to a limited understanding of the underlying metabolic mechanisms. In this study, we performed metabolomic profiling of spent IVM medium from patients with polycystic ovary syndrome (PCOS). A machine learning approach based on Orthogonal Projections to Latent Structures Discriminant Analysis (OPLS-DA) and Support Vector Machine (SVM) was employed to characterize metabolic signatures linked to oocyte maturation. Among 248 detected metabolites, 15 overlapping candidate markers were consistently identified by both OPLS-DA and SVM to distinguish between mature and immature samples. Notably, the immature group exhibited notable upregulation of steroidogenesis, accumulation of TCA cycle intermediates, and dysregulation of the serine-glycine-one-carbon metabolism pathway. Collectively, our findings reveal distinct metabolic features of the IVM medium associated with oocyte maturation, providing candidate biomarkers for monitoring and personalizing IVM protocols in PCOS patients.
Ovulation is a highly coordinated physiological process initiated by the luteinizing hormone (LH) surge and accompanied by extensive transcriptional, biochemical, and structural remodeling of the preovulatory follicle. For decades, ovulation has been recognized as sharing features with localized inflammatory reactions, including leukocyte influx and accumulation, cytokine production, extracellular matrix (ECM) remodeling, and prostaglandin (PG) synthesis. Early human studies using follicular fluid analysis and granulosa cell culture demonstrated that multiple immune-associated mediators are present in the periovulatory follicle and can modulate steroidogenesis, PG production, and follicular remodeling, but these approaches were limited in their ability to define cellular sources. Recent advances in transcriptomic profiling, particularly single-cell RNA sequencing (scRNA-seq), have substantially refined this view by identifying heterogeneous leukocyte populations in human follicular aspirates, including distinct macrophage subsets, dendritic cells, natural killer cells, T and B lymphocytes, and neutrophils, each characterized by specific transcriptional profiles. These leukocytes express cytokines, chemokines, growth factors, angiogenic mediators, PG biosynthetic enzymes, and ECM-regulatory factors that overlap with mediators previously detected in periovulatory follicles or follicular fluid, thereby providing cellular context for earlier biochemical observations. This review integrates descriptive biochemical and functional studies with single-cell-based human data into a temporally resolved, cell-source-attributed model of immune-endocrine crosstalk during ovulation, clarifying which leukocyte populations produce which mediator, and when, and discuss how leukocyte-associated signals may contribute to ECM remodeling, cumulus expansion, granulosa cell differentiation, and luteinization. Although most mechanistic insights in humans derive from in vitro granulosa or granulosa-lutein cell models and association-based analyses of follicular fluid composition, the integration of descriptive biochemical studies with cell-specific transcriptomic profiling supports the concept that leukocytes are active participants in the human ovulatory process. Future research integrating time-resolved sampling, spatial transcriptomic mapping, and functional interrogation will be essential to define how immune-derived signals regulate in vivo human ovulatory process and their impacts on clinical reproductive outcomes.
Ovarian aging has emerged as a major challenge to female reproductive health worldwide, primarily characterized by a decline in oocyte quality and/or quantity, diminished granulosa cell function, and impairment of the surrounding microenvironment. Within this microenvironment, glucose metabolism participates in the synergistic regulation of ovarian homeostasis through specific metabolic networks and metabolite-driven signaling. Metabolic disruption can impair intercellular communication mechanisms, induce oxidative stress and mitochondrial dysfunction, and ultimately drive ovarian aging. Ovarian granulosa cells surround the oocyte and facilitate bidirectional communication via gap junctions or molecular signaling. These cells continuously supply energy substrates to support oocyte growth and maturation, while the oocyte, in turn, promotes granulosa cell proliferation and differentiation. Impairment of glycolysis in granulosa cells leads to energy deficits and triggers apoptosis. Concurrently, metabolic reprogramming in the oocyte links glucose metabolic homeostasis with mechanisms governing developmental competence. Glucose-derived metabolites drive various post-translational modifications (PTMs), including glycosylation, acetylation, phosphorylation, and succinylation, that contribute to ovarian function and intersect with pathological processes such as oxidative stress, chronic inflammation, and autophagy. Therefore, therapeutic strategies targeting glucose metabolism, such as modulating metabolic pathways, metabolite signaling, and associated PTMs, offer promising avenues for treating ovarian aging. However, the glucose metabolic network within the ovarian microenvironment is highly complex, and targeting a single node is often insufficient to restore metabolic homeostasis. A comprehensive approach that integrates multiple strategies to simultaneously intervene at different regulatory levels is needed. This review summarizes recent advances in understanding the ovarian glucose metabolic network, metabolite-driven post-translational modifications, and the interplay among various pathological mechanisms. We further explore potential regulators that could restore glucose metabolic balance and propose that future efforts should focus on developing precision strategies, guided by artificial intelligence and multi-omics data to modulate the metabolic reprogramming of both the microenvironment and functional cells, ultimately translating these insights into clinical interventions for ovarian aging.
Spontaneous ovarian hyperstimulation syndrome (sOHSS) is an exceptionally rare condition, particularly when associated with profound primary hypothyroidism (Type III sOHSS). Unlike the common iatrogenic form, it occurs without exogenous ovulation induction. We report a 26-year-old female with a history of total thyroidectomy for papillary carcinoma who presented to the emergency department with severe abdominal pain, diarrhea and tense ascites. Laboratory evaluation revealed a critical elevation of thyroid-stimulating hormone (TSH) and normal follicle-stimulating hormone (FSH) levels. Imaging confirmed bilateral multicystic ovarian enlargement and massive ascites. The clinical presentation was consistent with OHSS triggered by “specificity spillover,” where extreme TSH levels cross-activate FSH receptors. Prompt recognition of severe hypothyroidism as a cause of sOHSS is vital to avoid unnecessary surgical interventions. Management centered on levothyroxine replacement and supportive care resulted in the complete resolution of symptoms and radiologic findings, with a satisfactory clinical evolution.
Accurate and individualized prediction of cumulative live birth rates (CLBR) remains an unmet need, as existing models largely overlook individual risk preferences and the translational gap between predictive outputs and actionable clinical decisions. We aimed to develop and validate a machine learning algorithm integrating causal inference and threshold optimization for individualized CLBR prediction and clinical decision support in conventional IVF patients. This retrospective study enrolled 1,058 couples undergoing 1,419 complete IVF cycles (including associated frozen-thawed embryo transfers) from Yantai, China. The cohort was divided into a development cohort (2019–2022) and a temporal validation cohort (2023). Variables were selected from patient characteristics, clinical parameters, and embryo data. Eight machine learning algorithms were employed to construct CLBR prediction models. Multiple counterfactual explanation and causal inference methods, including serial mediation analysis, diverse counterfactual explanations (DiCE), double machine learning (DML), and T-learner, were employed to analyze causal effects of modifiable variables and determine threshold ranges. The final eight-predictor gradient boosting machine model (CLBR-GBM) achieved optimal overall performance and was robustly validated in the temporal validation cohort (AUROC 0.851, F1 score 0.801, Brier score 0.155, ABscore 0.848). Interactive decision curve analysis (iDCA) demonstrated favorable clinical utility, with a cohort-specific optimal threshold of 0.429 yielding a net benefit (NB) of 0.331. The model maintained robust NB (0.312–0.379) across a clinically applicable threshold band of 0.3–0.5. Synergistic adjustment involving decreased BMI (average treatment effect [ATE], -0.010) and increased embryo number (ATE, 0.068) represented the primary pattern shifting predicted probabilities across the 0.429 threshold toward live birth. Among these, BMI intervention demonstrated the most robust causal efficacy in the young high-responder obese subgroup (conditional average treatment effect [CATE], -0.105). The model was deployed as a publicly accessible platform (https://fertility-yts.shinyapps.io/CLBR_GBM) for individualized CLBR prediction, serving both patients and clinicians. Causally augmented CLBR-GBM identifies temporally validated optimal decision threshold bands with balanced discrimination and robust transportability for cumulative live birth strategies. Not applicable (retrospective non-interventional study).
The association between body mass index (BMI) and assisted reproductive technology (ART) outcomes remains inconsistent, potentially due to confounding by metabolic comorbidities and underlying infertility etiologies. This study introduced metabolic health stratification to reproductive medicine to examine whether the impact of BMI on ART outcomes differs by metabolic status, independent of ovarian reserve impairment or reproductive endocrinopathies. This retrospective cohort study included 3,770 women with tubal factor infertility and normal ovarian reserve who underwent their first IVF/ICSI cycle between January 2016 and March 2026. The cohort was divided into metabolically healthy (n = 1,849) and metabolically unhealthy (n = 1,921) subgroups based on blood pressure, glucose metabolism, and lipid profiles. Outcomes included oocyte yield (with basal FSH examined as a potential intermediate variable), embryological parameters (MII oocyte rate, 2PN rate, day 3 good-quality embryo rate, good-quality blastocyst formation rate), and clinical outcomes (clinical pregnancy, miscarriage, live birth). Indirect pathway analysis and multivariable logistic regression with power and effect size analyses were performed. BMI was positively associated with oocyte yield, and the indirect pathway through FSH accounted for 4.7–6.3
Diminished ovarian reserve (DOR) represents a critical clinical challenge in assisted reproductive technology (ART), and the gonadotropin-releasing hormone (GnRH) antagonist protocol has become a commonly used regimen for DOR treatment. Although estradiol (E₂) is well established as a pivotal regulator of embryo implantation, the prognostic impact of E₂ decrease from the human chorionic gonadotropin (hCG) trigger day to the embryo transfer day on ART outcomes remains unclear in DOR patients undergoing a GnRH-antagonist protocol. This retrospective study included 570 eligible in vitro fertilization/intracytoplasmic sperm injection (IVF/ICSI) cycles of DOR patients classified as POSEIDON Groups 3 and 4, who received the GnRH-antagonist protocol at Renmin Hospital of Wuhan University between July 2019 and September 2024. The optimal cut-off value for E₂ decrease was determined using receiver operating characteristic (ROC) curve analysis. Cycles were stratified into two groups based on the cut-off: Group A (E₂ decrease < 51
The 2023 International Federation of Gynecology and Obstetrics (FIGO) staging system introduced Stage IA3 to categorize a subset of synchronous endometrial and ovarian carcinomas (SEOC) with favorable outcomes. This study aims to evaluate the clinical and biological rationale of the FIGO 2023 Stage IA3 criteria by analyzing paired molecular profiles and assessing the prognostic impact of ovarian versus endometrial clinicopathological features. A retrospective cohort of 48 SEOC patients was evaluated and stratified into three cohorts: Group A (Stage IA3, n = 12), Group B (Advanced Concordant, n = 26), and Group C (Independent Discordant, n = 10). Survival outcomes were assessed alongside clinical interventions. Given the retrospective design, clonal relationships were assessed in a sub-cohort of 17 patients for whom complete paired immunohistochemistry (IHC) data for mismatch repair (MMR) proteins and p53 were available for both tumor sites. Univariate analysis showed that ovarian factors, including histological grade and lymph node metastasis, were significantly associated with overall survival (OS, p < 0.01). In contrast, endometrial parameters, such as myometrial invasion (MI) depth and FIGO stage, did not show statistical significance (all p > 0.5). Group A had a 5-year OS of 90.9
Ovarian hyperstimulation syndrome (OHSS) is a potentially life-threatening complication of assisted reproduction, primarily mediated by increased vascular endothelial growth factor (VEGF) activity. The purpose of this study was to evaluate the efficacy of faricimab, a novel bispecific monoclonal antibody, in preventing OHSS in an experimental rat model, and to compare its effects with those of established preventive agents, cabergoline and gonadotropin-releasing hormone (GnRH) antagonists. Thirty-six prepubertal female Wistar albino rats were randomized into six groups: Control, OHSS, OHSS+GnRH antagonist, OHSS + low-dose faricimab, OHSS + high-dose faricimab, and OHSS+cabergoline. OHSS was induced using gonadotropins followed by hCG. Treatments were administered over two days. Ovarian weight, follicle counts, VEGF immunostaining in ovarian tissue, and serum VEGF levels were assessed. A significant difference was found in secondary follicle (p < 0.01), Graafian follicle (p = 0.02), atretic follicle (p = 0.02), and corpus luteum (p = 0.01) counts in low-dose faricimab compared with the OHSS group, with all of these counts significantly lower in the low-dose faricimab group. VEGF staining intensity in ovarian tissue was significantly lower in the OHSS + low-dose faricimab, OHSS+high-dose faricimab, and OHSS+cabergoline groups than the OHSS group (p < 0.01, p < 0.01, and p = 0.04, respectively). Serum VEGF levels were significantly lower in the OHSS + low-dose faricimab, OHSS+high-dose faricimab, and OHSS+cabergoline groups compared with the OHSS group (p < 0.01, p < 0.01, and p = 0.01, respectively). Low-dose faricimab, a novel bispecific monoclonal anti-VEGF antibody, seems to be effective in reducing the risk of OHSS with decreasing VEGF expression in ovarian tissue and serum VEGF levels.
The systemic immune-inflammation index (SII) has emerged as a composite marker integrating platelet, neutrophil, and lymphocyte counts, reflecting both proinflammatory and immunoregulatory pathways. While increasing clinical evidence links the SII to ovarian reserve and assisted reproductive technology (ART) outcomes, a comprehensive synthesis of its clinical utility and underlying mechanisms is lacking. This narrative review summarizes the evidence linking the SII to anti-Müllerian hormone (AMH) levels, ovarian reserve, polyendocrine metabolic ovarian syndrome (PMOS), and ART outcomes, while proposing a unified “inflammation–oxidative stress–fibrosis” model for ovarian damage. Large cohort studies have demonstrated a robust negative correlation between the SII and the AMH/antral follicle count, independent of age and body mass index. In PMOS patients, the SII is associated with hyperandrogenism, insulin resistance, and dyslipidemia, and predicts embryo arrest and a reduced available embryo rate with clear threshold effects (SII > 733, log SII > 6.72). Mechanistically, an elevated SII reflects a dual imbalance: increased neutrophil‑platelet activation drives follicular inflammation and neutrophil extracellular trap formation (NETosis), while relative lymphopenia weakens immune tolerance at the maternal–fetal interface. This imbalance triggers oxidative stress and TGF‑β1‑mediated fibrosis, forming a vicious cycle of ovarian dysfunction and follicular atresia. Lifestyle and pharmacological interventions targeting this pathway, such as weight loss, metformin, and melatonin, show promise for improving fertility outcomes. The SII is a low‑cost, accessible marker with strong potential for risk stratification in fertility management. Prospective trials are needed to validate SII‑guided interventions.
A randomized, open-label, phase 2 trial assessed fuzuloparib (a PARP inhibitor) plus apatinib (a vascular endothelial growth factor receptor [VEGFR] inhibitor) versus fuzuloparib alone in patients with recurrent ovarian cancer (ROC). Patients with ROC who had failed ≥ 2 lines of platinum-based chemotherapy were enrolled. Patients with primary platinum resistance were also eligible. Patients were randomized 1:1 to receive fuzuloparib plus apatinib or fuzuloparib. The primary endpoint was objective response rate (ORR). As of Jan 11, 2024, 76 eligible patients were enrolled (39 with fuzuloparib plus apatinib and 37 with fuzuloparib). The ORR was 38.5
Ovarian aging, characterized by declines in both oocyte quantity and quality, represents a major challenge to female reproductive health. However, the age-associated transcriptional alterations underlying human oocyte aging remain incompletely understood. Differential expression analysis between young and aged oocytes identified 63 age-associated differentially expressed genes, including increased expression of ribosome-associated genes, oxidative-phosphorylation-related genes such as COX5B and UQCRC2, the ubiquitin-associated gene UBA52, and mtDNA-encoded OXPHOS transcripts including MT-ND4 and MT-ND4L, together with reduced expression of the antioxidant-response gene GSTA1 and the mitochondrial respiratory-chain gene NDUFA9. Functional enrichment analyses revealed biological processes related mainly to translation, protein targeting, ribosomal functions, and oxidative phosphorylation. Further subcluster analysis identified an aged-enriched oocyte population with broader mitochondrial-related transcriptional alterations and enrichment of DNA-damage-response pathways. Subsequent PPI analysis of the intersecting genes identified two exploratory functional modules related to mitochondrial respiratory-chain processes and ribosome/ubiquitin-associated proteostasis. These findings highlight age-associated transcriptional signatures in human oocytes involving mitochondrial metabolism and proteostasis-associated processes, and reveal molecular heterogeneity among aged oocyte populations. Together, these results provide insights into the molecular changes associated with human oocyte aging and establish a transcriptomic framework for future studies of reproductive aging.
Ovarian cancer remains a major challenge among gynecologic malignancies because it often develops with vague or nonspecific symptoms and frequently disseminates within the peritoneal cavity before detection. Currently, no screening strategy has been proven to effectively reduce mortality in the general population, so many patients are diagnosed at an advanced stage. Therefore, improving diagnostic accuracy and treatment precision is important for better clinical outcomes. Fluorescent probe–based imaging has gained increasing attention in ovarian cancer diagnosis, surgical navigation, and treatment monitoring. Owing to their molecular targeting ability, these probes can help visualize small peritoneal implants, micrometastases, and tumor margins that may be difficult to identify under conventional white-light observation. This may improve lesion detection and support more complete cytoreductive surgery. In addition, some probe systems are being investigated for drug-delivery tracking and theranostic applications, combining imaging with therapeutic functions. In recent years, probes targeting ovarian cancer–associated biomarkers such as folate receptor(FR), epidermal growth factor receptor(EGFR), γ-glutamyl transpeptidase(γ-GGT), gonadotropin-releasing hormone receptor(GnRHR), and β-galactosidase(β-Gal) activity have been developed. Some of these platforms incorporate activatable fluorescence, photothermal effects, or drug-loading capacity, providing potential strategies for image-guided surgery and targeted therapy. However, several limitations remain. Conventional fluorescent dyes may have limited tissue penetration, photobleaching, background autofluorescence, and dependence on specialized imaging equipment. Off-target accumulation or biomarker expression in normal tissues can also reduce tumor-to-background contrast. Future studies should integrate nanotechnology, chemical probe design, and genetic engineering to improve penetration depth, signal stability, and tumor specificity. Before clinical application, these probes require rigorous validation in standardized trials to assess their safety, reproducibility, imaging performance, and therapeutic value.
The clinical interpretation of relative serum estradiol response during controlled ovarian stimulation remains uncertain. We assessed its association with the cycle-level probability of obtaining at least one eligible blastocyst and, secondarily, with pregnancy outcomes after IVF/ICSI. This retrospective cohort study included 9146 women, each contributing her first recorded ovarian-stimulation IVF/ICSI cycle between January 2020 and December 2022. The E2 response ratio was calculated as (trigger-day E2 minus basal E2)/basal E2 and categorized into quartiles. The primary outcome was cycle-level blastocyst success, defined as at least one eligible blastocyst. The primary association was estimated using robust Poisson regression with prespecified pretreatment covariates; logistic regression was retained as a sensitivity analysis. Clinical pregnancy and live birth were analysed among 4191 fresh embryo-transfer cycles. Among 9146 first ovarian-stimulation cycles, 5060 (55.3
