estradiol (Vagifem)

Publisher Correction: Genomic landscape of the human vaginal microbiome is linked to host genetics and population of origin.

Jie Zhuye Z, Liang Weiting W, Ding Qiuxia Q, Liu Xiaomin X et al.

Functional Outcomes After Vaginal Natural Orifice Transluminal Endoscopic Surgery Hysterectomy: A Systematic Review of Sexual Function and Pelvic Floor Integrity.

Wignall Charlotte M CM, McDonnell Jane E JE, Nadim Batool B

Vaginal hysterectomy is guideline-preferred for benign disease, yet uptake is declining. Vaginal Natural Orifice Transluminal Endoscopic Surgery (vNOTES) offers enhanced visualisation while optimising recovery; however, impact on functional outcomes remains unclear. To evaluate sexual function and pelvic floor outcomes following vNOTES hysterectomy compared with minimally invasive alternatives. A systematic review was conducted per PRISMA 2020 and Cochrane guidelines (PROSPERO CRD420251247524). MEDLINE, Embase, Cochrane and Scopus were searched to February 2026. Comparative studies (≥ 25 vNOTES patients) reporting functional outcomes (Female Sexual Function Index [FSFI], Pelvic Floor Distress Inventory [PFDI], International Consultation on Incontinence Questionnaire [ICIQ], Pelvic Organ Prolapse Quantification [POP-Q]) were included. Non-comparative studies and those without validated measures were excluded. Risk of bias was assessed using RoB2 and Newcastle-Ottawa Scale. Results were synthesised narratively (SWiM). Five studies (n = 526; one randomised trial, four cohort studies) were included. Sexual function was equivalent between vNOTES and laparoscopic approaches at 3-6 months. One study with variable follow-up extending beyond 6 months reported higher FSFI scores (p = 0.003), whereas others showed no difference (p > 0.05). Dyspareunia rates were comparable (2 studies). Pelvic floor outcomes (1 study) were limited, suggesting improved anterior compartment support (POP-Q Aa; p < 0.003), with similar apical and posterior support. Overall certainty of evidence was low. vNOTES hysterectomy demonstrates comparable medium-term sexual and pelvic floor outcomes to laparoscopic approaches, with no signal of harm. Small samples, observational design and heterogeneity limited evidence, restricting definitive conclusions. Standardised long-term studies are required to define functional safety and guide counselling.

Development and validation of a postpartum hemorrhage risk prediction model for advanced maternal age women undergoing vaginal delivery: a comparison of multiple machine learning algorithms.

Zhou Yun Y, Yao Xiao X, Zhou Jue J, Duan Yaoxiang Y et al.

Postpartum hemorrhage (PPH) is the leading cause of maternal mortality in women of advanced maternal age (AMA). Accurate antenatal prediction remains challenging. This study aimed to develop and compare the performance of multiple machine learning (ML) algorithms for predicting PPH risk following vaginal delivery in AMA parturients. A retrospective cohort study was conducted using data from 5,369 women who delivered vaginally at Shanghai First Maternity and Infant Hospital. LASSO regression was used to identify key predictors. Seven machine learning algorithms-logistic regression (LR), decision tree (DT), random forest (RF), XGBoost, LightGBM, support vector machine (SVM), and artificial neural network (ANN)-were employed to build prediction models. Model performance was evaluated on an independent validation set using the area under the receiver operating characteristic curve (AUC), sensitivity, specificity, and F1 score. The optimal model was selected based on a composite criterion prioritizing the highest sensitivity and best F1 score, while requiring a competitive AUC. Eight predictors were selected for model construction. Among the seven models, the SVM model achieved the highest sensitivity (0.482) and the highest F1 score (0.504), and was therefore selected as the clinically preferred model, while maintaining a competitive AUC of 0.782 (95% CI: 0.758-0.806). SVM achieved a favorable balance between sensitivity and specificity, indicating better clinical utility for early screening and risk stratification of postpartum hemorrhage. Among seven machine learning models, the SVM model was selected as the clinically preferred predictor due to its highest sensitivity and F1-score. Its good discriminative ability and balanced performance make it suitable for clinical application as a screening tool. The integration of SHAP analysis can further enhance model interpretability and facilitate individualized risk assessment.

Metabolic Reprogramming of Cancer Stem Cells: Targeting Lipid Flux and Mitochondrial Plasticity to Overcome Therapeutic Resistance.

Uti Daniel Ejim DE, Alum Esther Ugo EU, Egbung Josephine E JE, Mohammedsaleh Zuhair M ZM et al.

Cancer stem cells (CSCs) are increasingly recognized as metabolically plastic subpopulations within malignant tissues that drive tumor initiation, metastatic dissemination, and relapse after therapy. Although traditional models of cancer metabolism have emphasized aerobic glycolysis, CSCs rarely exhibit a single, clearly defined bioenergetic phenotype. Rather, they dynamically remodel glucose utilization, oxidative phosphorylation, redox regulation, de novo fatty acid synthesis, lipid uptake, lipid sequestration, and fatty acid oxidation in response to hypoxic conditions, nutrient restriction, stromal interactions, and therapeutic perturbations. This review focuses on two interconnected aspects of metabolic flexibility: lipid flux and mitochondrial plasticity. We examine how de novo lipogenesis, CD36-mediated fatty acid uptake, fatty acid-binding protein trafficking, cholesterol biosynthesis, and lipid-droplet turnover contribute to stemness, membrane remodeling, metastatic potential, and resistance to cytotoxic agents. We also examine how mitochondrial dynamics, including fusion, fission, mitophagy, and biogenesis, together with reactive oxygen species buffering and shifts in oxidative phosphorylation, facilitate CSC survival during chemotherapy, radiotherapy, targeted therapy, and immune-mediated cytotoxicity. Particular emphasis is placed on the integration of fatty acid oxidation to respiratory metabolism, on the epigenetic consequences associated with the acetyl-CoA availability, and the metabolic crosstalk linking CSCs to adipocytes, fibroblasts, mesenchymal cells, and immune cell populations in the tumor microenvironment. Finally, we evaluate therapeutic strategies involving inhibitors of fatty acid synthase (FASN), acetyl-CoA carboxylase (ACC), stearoyl-CoA desaturase-1 (SCD1), carnitine palmitoyltransferase-1 (CPT1), and OXPHOS. We also discuss combination therapies, nanotechnology-based drug delivery, and emerging artificial intelligence (AI)-guided approaches. Taken together, current evidence identifies the lipid-mitochondrial axis as a critical systems-level driver of therapeutic resistance and a promising target for improving long-term cancer control.