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Impact of ovarian stimulation duration in GnRH antagonist protocols on the cumulative ongoing pregnancy rate in women with normal ovarian reserve: a cohort study
No full textAbstract Background The optimal duration of ovarian stimulation in GnRH antagonist protocols remains insufficiently explored, despite its potential impact on oocyte quality, embryo development, and endometrial receptivity. Identifying an optimal stimulation window may improve success rates in assisted reproductive technologies (ART) while minimizing unnecessary hormonal exposure. The aim of the study was to evaluate if the duration of ovarian stimulation in GnRH antagonist IVF/ICSI protocols influence cumulative ongoing pregnancy rates (COPR). Methods This retrospective single-centre study analyzed 1456 IVF/ICSI cycles conducted between 2019 and 2023 in women with normal ovarian reserve using an antagonist protocol. Stimulation duration was classified into three categories: ≤ 8 days, 9–13 days (reference), and ≥ 14 days corresponding to the ≤ 5th, 5th–95th, and ≥ 95th percentiles, respectively. Clinical, biological, and embryological outcomes were compared across groups using the Kruskal–Wallis test. Logistic regression was applied to identify independent predictors of cumulative ongoing pregnancy and pregnancy outcomes. Results A total of 1456 cycles were included in the study. At least one embryo was obtained in 95.2% of cycles, with 69.5% proceeding to fresh transfer. Cumulative ongoing pregnancy was assessed for 1339 cycles, among which 36.4% resulted in an ongoing pregnancy. Stimulation duration did not significantly influence COPR: ≤ 8 days (OR = 1.04, 95% CI 0.78–1.39), 9–13 days (reference), ≥ 14 days (OR = 0.84, 95% CI 0.64–1.11). Multivariate analysis identified younger age and higher AMH as independent predictors of COPR. Conclusions In women with normal ovarian reserve undergoing IVF/ICSI with a GnRH antagonist protocol, the duration of ovarian stimulation does not significantly impact cumulative pregnancy outcomes. These findings support a personalized approach to trigger timing based on ovarian response independent of stimulation length
Multimodal deep learning using preoperative CT and ultrasound for recurrence risk prediction in high-grade serous ovarian carcinoma
No full textAbstract Background High-grade serous ovarian carcinoma (HGSOC) is associated with a high risk of postoperative recurrence, and the timing of recurrence is closely related to patient survival outcomes and subsequent treatment strategies. However, current postoperative surveillance primarily relies on clinical indicators and routine imaging examinations, which offer limited predictive accuracy. Although deep learning–based image analysis has shown promise in capturing tumor heterogeneity and improving prognostic assessment, studies integrating preoperative contrast-enhanced computed tomography (CE-CT) and ultrasound imaging for recurrence risk prediction in HGSOC remain limited. Methods This single-center retrospective study enrolled 293 patients with pathologically confirmed HGSOC, who were randomly assigned to a training cohort and an internal validation cohort. Separate two-dimensional deep learning survival models were developed using preoperative CE-CT and ultrasound images. A Cox partial likelihood–based time-to-event loss function was applied to generate modality-specific deep learning scores (DL-scores). Independent clinical predictors were identified through univariate and multivariate Cox regression analyses and were integrated with CT-DL and US-DL scores to construct a multimodal Cox prognostic model, which was visualized using a nomogram. Model performance was assessed using the concordance index (C-index), time-dependent receiver operating characteristic (ROC) curves, Kaplan–Meier survival analysis, calibration curves, and decision curve analysis. Bootstrap resampling was performed to evaluate model robustness, and Gradient-weighted Class Activation Mapping (Grad-CAM) was used to visualize model attention. Results The multimodal integrated model demonstrated superior prognostic performance, achieving C-index values of 0.840 in the training cohort and 0.722 in the internal validation cohort. Time-dependent ROC analysis showed that the combined model achieved areas under the curve (AUCs) of 0.880, 0.890, and 0.892 for predicting 1-, 2-, and 3-year recurrence-free survival (RFS) in the training cohort, and 0.864, 0.781, and 0.772 in the validation cohort, respectively. Kaplan–Meier survival analysis revealed a significant separation between high- and low-risk groups (log-rank test, all P < 0.001). Calibration and decision curve analyses indicated good agreement between predicted and observed outcomes and a higher net clinical benefit. Bootstrap resampling further confirmed the robustness of the multimodal model. Grad-CAM visualizations suggested that the model primarily focused on tumor and peritumoral regions. Conclusions This study proposes a multimodal prognostic framework integrating deep learning features from CE-CT and ultrasound with clinical variables for preoperative prediction of postoperative recurrence risk in HGSOC. Using routinely acquired imaging data, the model shows consistent interpretability and performance, supporting its use as an exploratory decision-support and risk-stratification tool. Prospective, multicenter validation is required before clinical implementation
The well-being toll of revealed involuntary immobility: a quantitative study
No full textAbstract International migration is often driven by the desire to improve one’s well-being. For many, moving abroad represents a pathway to better economic opportunities, social conditions, or personal safety. Yet, not all individuals who aspire to migrate are able to do so. Involuntary immobility—a condition in which people wish to migrate but are constrained by external barriers such as legal restrictions, financial limitations, or lack of resources—may pose a significant challenge to subjective well-being. This study quantitatively examines the association between revealed involuntary immobility and subjective well-being using original survey data from over 12,000 young adults across 25 communities in Asia and Africa. We estimate this relationship using multiple regression models that control for other determinants of well-being and demographic characteristics. To address potential confounding between determinants of involuntary immobility and subjective well-being, we implement Propensity Score Matching. Our findings show that individuals who have experienced failed migration preparations or attempts report between 3% and 7% lower levels of subjective well-being compared to their peers. These results provide the first large-scale quantitative evidence on the toll of unfulfilled migration aspirations. We highlight the need to consider the indirect well-being consequences of migration-restrictive policies and call for policies that support individuals’ well-being in contexts of constrained mobility
Assemblable thermoelectric Lego blocks for reconfigurable, self-healing, and flexible power generators
No full textAbstract Thermoelectric devices offer a promising route for waste-heat recovery, yet conventional modules—consisting of multiple pairs of inorganic legs soldered to rigid metal electrodes—are intrinsically brittle and nearly impossible to repair or reconfigure once fabricated. Although recent incorporation of flexible or stretchable polymeric components has improved mechanical deformability, these integrated architectures cannot be modified for new functions or restored. In this study, we propose the concept of Lego-like thermoelectric leg blocks that enable on-demand repair and reconfiguration via modular assembly. Each block operates as an independent unit comprising PDMS-based, self-healing Ag-flake-embedded composite electrodes and 3D-printed BiSbTe and BiTeSe thermoelectric legs, yielding flexible, repairable, and modular devices. Assembled devices preserve performance under bending (radius ≈ 3.4 mm), stretching (40%), and even after cutting and reassembly. Moreover, repeated disassembly/reassembly into diverse geometries proceeds without measurable loss in power output. Our Lego-like blocks provide a versatile thermoelectric platform that combines flexibility, reparability, and reconfigurability
Investigation of the effect of graphene oxide loading on the morphology and thermal properties of poly(lactic acid)/ethylene vinyl acetate blends
No full textAbstract Poly(lactic acid) (PLA)/Ethylene vinyl acetate (EVA) blends are environmentally friendly and biocompatible materials whose usage aligns with numerous United Nations sustainability goals. However, immiscibility of the polymer interphases limits the applicability of the blends, necessitating reinforcements using suitable materials like graphene oxide (GO). This study investigated the effect of graphene oxide loading on the interfacial, morphological, and thermal properties of PLA/EVA blends, in contribution to the understanding of structure – property relationships of the blends. GO was synthesized via modified Hummer’s method and analysed using Fourier-transform infrared spectroscopy (FTIR). 70/30, 50/50, and 30/70 w/w PLA/EVA blends and their composites with 1, 3, and 5 wt% GO contents were prepared via melt mixing, and characterised through Melt Flow index (MFI), Surface energy evaluation system (SEES), Scanning electron microscopy (SEM), Differential scanning calorimetry (DSC), and Thermogravimetric (TGA). PLA and EVA had MFI values of 2.64 and 0.500 g/10 min, indicating complementary viscosities. SEES results suggested the possibility of GO settling on the interface of the two polymers, with a wetting coefficient of 0.523. SEM results illustrated the compatibility and phase homogenizing effect of GO on the PLA/EVA blends. DSC measurements portrayed partial miscibility and possible plasticisation effects of GO on the polymers. TGA analyses proved GO instrumental in improving the thermal stability of the polymers at higher temperatures. The 50/50 w/w PLA/EVA composition (blend and composites) was deemed ideal and superior in the analysed properties, compared to other compositions. Future work will focus on conducting mechanical and thermomechanical studies to develop an overall idea of the composites’ usability in applications like smart packaging, self-sensing and healing, and smart material design
Establishing a set of acceptable demographic questions for use in health research through public consultation
No full textAbstract Background The importance of inclusivity in health care and health research is increasingly recognised in the UK. However, there are currently no UK standards for collecting self-reported demographic data from research participants. To address this gap, we undertook a public involvement activity. We worked with patient and public involvement partners and members of the public to establish an acceptable set of demographic questions for adult participants, taken from national survey questions to ensure comparable data. Methods Our project team, which included two patient and public involvement partners, selected demographic questions that covered characteristics protected by the UKs Equality Act 2010 or groups identified as potentially underserved in research. These questions covered health, disability, and unpaid care; education and employment; sexual orientation and gender identity; and ethnicity, language, and religion. We conducted four discussion groups to review the proposed questions with diverse members of the public. We explored their views on questions, the explanatory text for the purpose of data collection, data storage (i.e. pseudonymised or anonymous), the length of the question set and any missing topics. Results Twenty-nine public contributors took part. Of these, at least ten were from a minority ethnic background and eleven had one or more disabilities or long-term health conditions. Five contributors were people of faith, three were members of the LGBTQIA+ community, and seven had experience of providing unpaid care. Of the 18 questions, three were removed and ten were modified. This resulted in a revised question set of 15 items. Conclusions The implementation of this question set will help to standardise data collection across studies, increasing comparability and researchers’ ability to evaluate inclusivity. The demographic question set is now available to non-commercial researchers across the UK as part of a pilot study to evaluate and improve its utility and performance
Cell-free DNA from cerebrospinal fluid cytology specimens as a novel liquid biopsy approach for pediatric patients with primary central nervous system tumors
No full textAbstract Background Assessing circulating cell-free DNA (cfDNA) in cerebrospinal fluid (CSF) has been proposed as a promising alternative to tissue biopsy. Advances in cfDNA sequencing have further underscored the potential of CSF liquid biopsies in the clinical setting. CSF is routinely collected for cytologic evaluation at diagnosis and at recurrence in both pediatric and adult central nervous system (CNS) tumors. Preliminary studies have shown that CSF cfDNA analysis may be more sensitive than CSF cytology for detecting the presence of tumor. Methods CSF specimens were prospectively collected from seven pediatric patients with primary CNS malignant tumors. When possible, CSF was collected fresh and from processed cytology specimens. Low-pass whole genome sequencing (LP-WGS) and next-generation sequencing (NGS) using a custom targeted sequencing panel were performed on the specimens to identify copy number alterations (CNAs), detect mutations, and estimate circulating tumor DNA (ctDNA) fractions. Results were compared with matched tumor tissue molecular profiles and corresponding imaging findings. Results Abnormalities in cfDNA were detected in four patients. Sequencing of CSF cytology supernatants demonstrated the presence of circulating tumor DNA with characteristic CNAs and mutations that matched what was seen the tumor tissue as well as the fresh CSF specimens. These studies also revealed tumor heterogeneity and genomic evolution over time. Conclusion This study demonstrates the feasibility of utilizing routinely discarded supernatants from CSF cytology specimens for LP-WGS and targeted NGS. Our approach optimizes the use of CSF that may be limited in pediatric patients as a source for liquid biopsy-based genomic studies. Future research will be necessary to optimize and validate the methodology to enable clinical implementation
Optimal types and doses of exercise for improving sleep quality in perinatal women: a systematic review and network meta-analysis based on randomized controlled trials
No full textAbstract Background The perinatal period spans from the onset of pregnancy to one year postpartum. Sleep quality is particularly critical during this period. An appropriate exercise regimen can significantly improve their sleep quality. Previous studies have demonstrated that exercise effectively enhances sleep quality; however, the optimal regimen remains unclear. To address this limitation, this study employs a network meta-analysis to systematically evaluate the effects of different types of exercise and dosage (including duration, frequency, and session length) on the sleep quality of perinatal women. This analysis provides evidence-based recommendations to improve sleep quality in perinatal women. Methods Using a combination of manual and computer-assisted search strategies, we searched PubMed, Web of Science, EBSCO, Cochrane Library, Scopus, Embase, ProQuest, CNKI, and Wanfang databases to identify randomized controlled trials on the effects of different exercise interventions on the sleep quality of perinatal women. The search was conducted up to August 5, 2025.The risk of bias was assessed using RevMan 5.4.1 software, and traditional meta-analysis and network meta-analysis were conducted using Stata 16.0 software to generate forest plots, network evidence plots, funnel plots, Surface Under the Cumulative Ranking Curve (SUCRA) plots, and GRADE evaluation plots. Results were reported using standardized mean difference (SMD) and 95% confidence intervals (CI). Results This study included 23 RCTs with 1,862 participants. Five different types of exercise were compared: Aerobic Exercise (AE), Aerobic Combined With Resistance Training (AE + RT), Yoga, Pilates, and Relaxation Exercises(RE). The highest-ranked intervention was RE [SMD = -2.71, 95% CI (-3.96, -1.45), SUCRA = 88.5]. Four intervention durations (≤ 4 weeks, 5–8 weeks, 9–12 weeks, 13–16 weeks), three frequencies (1–2 times per week, 3 times per week, 5 times per week), and three session durations (≤ 30 min, 30–60 min, > 60 min) were analyzed. Network meta-analysis revealed that RE was relatively effective [SMD = -2.71, 95% CI (-3.96, -1.45), SUCRA = 88.5]. For intervention duration, ≤ 4 weeks showed greater effectiveness [SMD = -3.13, 95% CI (-4.21, -2.05), SUCRA = 97.2]. Regarding frequency, 1–2 times per week was more effective [SMD = -2.54, 95% CI (-3.57, -2.05), SUCRA = 97.7]. For session duration, 30–60 min was the relatively effective [SMD = -2.54, 95% CI (-3.41, -1.67), SUCRA = 99.0]. Conclusion This network meta-analysis systematically evaluated different exercise types, durations, frequencies, and session lengths on the sleep quality of perinatal women, with a particular focus on the combination of exercise type and dosage. The findings revealed that RE lasting ≤ 4 weeks, performed 1–2 times per week, and 30–60 min per session, was the relatively effective. It is recommended that this regimen be prioritized in perinatal healthcare to optimize sleep and overall health. Future research should further explore the types, dosages, and combinations of exercise to provide more evidence for targeted interventions
Risk of miscarriage after benzodiazepine use during pregnancy: updated systematic review and meta-analysis
No full textAbstract Background Benzodiazepine use during pregnancy remains common despite guidelines discouraging it, except for short-term treatment of severe anxiety or agitation. Previous studies have suggested an increased risk of miscarriage after in utero exposure, raising concern at the European level. However, the most recent meta-analysis, published in 2020, included neither several large-scale observational studies nor an assessment by specific benzodiazepine agents. This study aimed to provide an updated synthesis of evidence on the association between early pregnancy exposure to benzodiazepines and miscarriages (excluding studies exclusively involving women with epilepsy), including agent-specific analyses. Methods A systematic review and meta-analysis were conducted according to Cochrane recommendations. Eligible studies evaluated the association between benzodiazepine use during pregnancy and miscarriage (pregnancy loss before 22 weeks of gestation); studies limited to women with epilepsy were excluded. Risk of bias was assessed using ROBINS-I. Pooled odds ratios (ORs) and 95% confidence intervals (CIs) were estimated using a random-effects model. Publication bias, heterogeneity, and sensitivity analyses by study design, control group, psychiatric indication and risk of bias were performed. Analyses by specific benzodiazepine agents, dose-response relationships, and E-values for unmeasured confounding were also conducted. Results Of 1,142 records screened, ten studies including over 8,000 exposed pregnancies were retained. Benzodiazepine exposure during early pregnancy was associated with a significantly increased risk of miscarriage (pooled OR: 1.68; 95% CI: 1.48–1.90; I² = 60%). After adjusting for publication bias, the association remained (adjusted OR: 1.58; 95% CI: 1.39–1.80). The E-value (2.74) suggested moderate robustness to unmeasured confounding. Sensitivity analyses confirmed the main findings. Pooled ORs for the seven most frequently used agents (e.g., lorazepam, clonazepam, alprazolam) ranged from 1.42 to 1.82, supporting a class effect. All three studies investigating the dose–response relationship found a dose–response trend. Conclusion This updated meta-analysis indicates that early pregnancy exposure to benzodiazepines is associated with an increased risk of miscarriage. The consistency across analyses and evidence of dose-response strengthen confidence in this association. Clinicians should carefully weigh risks and benefits, consider non-pharmacological alternatives, and ensure close monitoring when prescribing benzodiazepines to women of childbearing potential
Comparison of pregnancy outcomes and physical conditions of infants in patients with gestational diabetes mellitus treated with metformin and insulin: a meta-analysis study
No full textAbstract Background Gestational diabetes mellitus (GDM) is a common complication during pregnancy, which seriously affects the health of mothers and infants. Metformin and insulin are both commonly used therapeutic drugs, but the effects of the two on pregnancy outcomes and the physical condition of the infants remain undetermined. This study conducted a meta-analysis to compare the effects of two drugs in the treatment of gestational diabetes mellitus (GDM), providing a basis for clinical medication. Methods Our research systematically searched the PubMed, Embase, and Cochrane Library databases to include randomized controlled trials (RCTS) of metformin and insulin in the treatment of GDM. The search period was up to March 2025. Literature screening, data extraction and quality evaluation were independently completed by two researchers, and statistical analysis was performed using RevMan 5.4 software. Results Eventually, 8 RCTS were included, involving a total of 2,350 patients with GDM. Meta-analysis showed that the cesarean section rate in the metformin group was 26.3%, which was lower than 33.7% in the insulin group (RR = 0.78, 95%CI:0.75–0.81, P < 0.05). The incidence of gestational hypertension in the metformin group was 13.8% (26 out of 188 patients), which was lower than 18.6% (34 out of 183 patients) in the insulin group (RR = 0.74, 95% CI: 0.69–0.79, P < 0.05). The cesarean section rate was 30.9% (58/188) in the metformin group vs. 38.8% (71/183) in the insulin group (RR = 0.78, 95% CI:0.75–0.81, P < 0.05). Neonatal hypoglycemia occurred in 5.9% (11/188) of the metformin group vs. 9.8% (18/183) of the insulin group (RR = 0.60, 95% CI:0.57–0.63, P < 0.05). Macrosomia rates were 14.9% (28/188) vs. 19.7% (36/183) in the two groups, respectively (RR = 0.78, 95% CI:0.73–0.83, P < 0.05). Conclusion Compared with insulin, metformin in the treatment of gestational diabetes mellitus can reduce the rate of cesarean section, the incidence of gestational hypertension, the incidence of neonatal hypoglycemia and the incidence of macrosomia, and can be an effective treatment option for patients with GDM
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