Introduction: Chronic Otitis Media (COM) is a leading cause of preventable hearing loss, particularly in developing nations. It is characterised by tympanic membrane perforation and recurrent ear discharge resulting from long-standing middle ear infections. The disease is classified into inactive (dry) and active (wet) types based on the absence or presence of mucopurulent discharge. Aim: To compare the success rate of graft uptake and hearing improvement in tympanoplasty performed in patients with inactive (dry) and active (wet) mucosal COM. Materials and Methods: This prospective interventional study was conducted at FH Medical College and Hospital, Agra, Uttar Pradesh, India over a period of 1.5 years. A total of 110 patients diagnosed with mucosal COM were divided into two groups: inactive and active. Preoperative and postoperative assessments, including Pure Tone Audiometry (PTA), were performed to evaluate hearing improvement, and clinical examination was used to assess graft uptake. The follow-up period was three months post-surgery. The two groups were compared for graft uptake and hearing improvement using an Independent samples t-test. Results: The graft uptake rate was 87.5% in the inactive group and 84.78% in the active group. Hearing improvement, measured by reduction in PTA thresholds, was slightly better in the inactive group (12.19 dB improvement) compared to the active group (11.09 dB improvement). However, the difference was not statistically significant. Conclusion: Tympanoplasty is an effective surgical intervention for mucosal COM, with high success rates in both inactive and active cases. Although inactive ears demonstrated marginally better graft uptake and hearing improvement, the presence of active discharge at the time of surgery did not significantly affect the overall surgical outcome.

Obtaining clean-catch urine (CCU) samples from non-continent infants is a common clinical challenge due to low urine volume and irregular urination. Non-invasive stimulation techniques, such as the bladder stimulation technique (BST) and the Quick-Wee method, have been proposed to improve success rates and reduce contamination. However, the supporting evidence remains inconsistent, and no multicentre randomised trials have directly compared the effectiveness and safety of Quick-Wee, BST and standard CCU. The study will enrol 342 infants aged 1-12 months requiring urinalysis, recruited from paediatric wards across three tertiary hospitals in China. Eligible participants will be randomly assigned in a 1:1:1 ratio to one of three intervention groups: BST, Quick-Wee or standard CCU. The primary endpoint is the success rate of urine collection within 5 min of intervention. Secondary endpoints include time to urination, 5 min urination rate, infant discomfort scores, parental and clinician satisfaction, and urine contamination rates. Safety will be evaluated by monitoring the incidence of adverse events. This study was approved by the Biomedical Ethics Review Committee of West China Hospital, Sichuan University (No. 114/2025). Written informed consent will be obtained from all participants' parents prior to enrolment. Study findings will be published in peer-reviewed journals and presented at relevant conferences. Individual participant data will be kept strictly confidential and securely stored in compliance with data protection regulations. ChiCTR2500098691.

Zeolites are essential catalysts for organic transformations owing to their confined nanoporous environments. However, experimental mechanistic studies are costly, and traditional simulations lack scalability, relying on manual structural manipulation. Here we introduce pore transition-state finder (PoTS), an automated pipeline for locating transition states (TS) in zeolites. PoTS identifies gas-phase TSs via density functional theory, docks them near active sites in zeolite pores and uses their reaction modes to seed condensed-phase TS searches with the dimer method. This automation reduces user intervention, improves success rates and bypasses the need for long path-following calculations. We apply PoTS to a density functional theory-level dataset of zeolite-confined TSs, finding good experimental agreement in both cases. Last, we propose a path to address the limitations we observe regarding unsuccessful TS searches and insufficient theory in other reactions, such as alkene cracking.

Abstract Introducing insect biocontrol agents to manage invasive plants is an effective and sustainable management option. However, biocontrol agents sometimes fail to establish or are ineffective in the new range. Heather beetle Lochmaea suturalis, (Thomson, 1866) (Coleoptera: Chrysomelidae) sourced from the United Kingdom (UK), was introduced into New Zealand’s North Island Central Plateau (CP) to control invasive Calluna vulgaris (L.) Hull (heather). The beetle was difficult to establish and the few populations that did, achieved poor population growth rates and expansion in contrast to the rapid rates associated with its conspecifics in the UK. We hypothesize that low soil nutrients such as nitrogen and phosphorous (N and P) on the CP may be linked to changes in C. vulgaris’ primary nutritional and secondary defensive metabolites, leading to poor beetle performance. Testing this hypothesis involved application of N and P to soil in C. vulgaris field plots, measurement of foliar N and Carbon (C), plus using non-targeted metabolomics, exploring differences in the primary and secondary metabolites between treatments. Raising soil nutrients amplified several primary metabolites including N-based amino acids and concomitantly reduced the majority of phenylpropanoids, a secondary metabolite group containing many defensive compounds. This work seeks to better understand, how abiotic conditions in a new environment, influence invasive plant metabolism, potentially altering the efficiency of foliage assimilation which may impair biocontrol agent establishment and effectiveness. Such understanding may be applied to mitigate potential foliar deficiencies at release sites and contribute to improving success rates of biocontrol as a management strategy for invasive plants.

DNA-encoded libraries (DELs) facilitate high-throughput screening of trillions of molecules against protein targets through split-pool synthesis and DNA tagging. Despite their potential, only a few DEL-derived compounds have advanced to clinical trials or reached the market. A better understanding of the defining characteristics of target proteins, particularly those with binding pockets suitable for DEL screening, is critical to improving success rates. However, existing approaches remain limited in assessing pocket flexibility and functional similarity. Here, we present ErePOC, a pocket representation model based on contrastive learning with ESM-2 embeddings to address these challenges. ErePOC captures both structural and functional features of binding pockets, enabling identification of shared characteristics among DEL targets. By integrating analyses of low-dimensional physicochemical properties and high-dimensional ErePOC embeddings, we provide a comprehensive view of DEL target space. With 98% precision in downstream classification tasks, ErePOC demonstrates high performance in pocket representation, which is then applied to predict human proteins suitable for DEL screening, with enrichment uncovered across 18 functional categories. This work establishes a framework for enhancing DEL-based drug discovery through more effective target selection and pocket similarity analysis.

In underwater reconnaissance and patrol, AUV has to sense and judge traversability in cluttered areas that include reefs, cliffs, and seabed infrastructure. A narrow sonar field of view, occlusion, and current-driven disturbances leave the vehicle with local, time-varying information, so decisions are made with incomplete and uncertain observations. A path-planning framework is built around two coupled components: spatiotemporal graph neural network prediction and conditional normalizing flow (CNF)-based probabilistic environment reconstruction. Forward-looking sonar and inertial navigation system (INS) measurements are fused online to form a local environment graph with temporal encoding. Cross-temporal message passing captures how occupancy and maneuver patterns evolve, which supports path prediction under dynamic reachability and collision-avoidance constraints. For regions that remain unobserved, CNF performs conditional generation from the available local observations, producing probabilistic completion and an explicit uncertainty output. Conformal calibration then maps model confidence to credible intervals with controlled miscoverage, giving a consistent probabilistic interface for risk budgeting. To keep pace with ocean currents and moving targets, edge weights and graph connectivity are updated online as new observations arrive. Compared with Informed Random Tree star (RRT*), D* Lite, Soft Actor-Critic (SAC), and Graph Neural Network-Probabilistic Roadmap (GNN-PRM), the proposed method achieves a near 100% success rate at 20% occlusion and maintains about an 80% success rate even under 70% occlusion. In dynamic obstacle scenarios, it yields about a 4% collision rate at low speeds and keeps the collision rate below 20% when obstacle speed increases to 3 m/s. Ablation studies further demonstrate that temporal modeling improves success rate by about 7.1%, CNF-based probabilistic completion boosts success rate by about 13.2% and reduces collisions by about 17%, while conformal calibration reduces coverage error by about 6.6%, confirming robust planning under heavy occlusion and time-varying uncertainty.

Frequent drilling fluid lost circulation in the Kuqa foreland area of the Tarim Oilfield severely constrains drilling efficiency and safety. The complex formation structures and diverse lost circulation types in this region are compounded by a lack of systematic classification in existing studies and weak correlation between mechanism analysis and field plugging measures, leading to a deficiency in quantitative decision-making for lost circulation prevention and control. Based on lithology analysis, loss zone pressure differential calculation, well log interpretation, and core observations, this study establishes an integrated “formation–lithology–pressure” diagnostic and classification method for lost circulation. A systematic classification framework comprising five types of lost circulation channels and mechanisms was developed. Based on this, the dominant lost circulation types and characteristics of three typical vertical formations in the Kuqa foreland were clarified: ① The supra-salt sandy conglomerate formations (e.g., Q1x, N2k) are dominated by permeability loss, where the loss rate (V) and bottomhole pressure differential (ΔP) exhibit a strong positive correlation (V ∝ ΔP). On-site application of graded bridging plugging formulations achieved a first-attempt success rate of ≥90%. ② The salt–gypsum formations (E1-2km) are primarily characterized by induced fracture loss, with a weak correlation between V and ΔP and dynamic fracture opening/closing behavior. Conventional rigid plugging materials showed limited effectiveness, resulting in a first-attempt success rate of <50%. ③ The K1bs formation is dominated by vertically developed natural fracture loss, where V and ΔP also demonstrate a strong positive correlation. In a specific Keshen block, a power-law relationship between the fracture aperture (W) and loss rate was established (W = 0.26·V0.62, R2 = 0.98), providing a basis for predicting fracture aperture and optimizing plugging formulations, with a plugging success rate of ≥80%. The classification system and quantitative criteria developed in this study effectively link lost circulation mechanisms, dynamic characteristics, and engineering countermeasures, offering theoretical support and a decision-making framework for optimizing lost circulation prevention and control measures and improving success rates in the Kuqa foreland area.

Abstract We present new vector paleomagnetic data from 13 radiometrically dated lava‐flows in southern La Palma (Canary Islands) spanning from 1 to 56 ka, which covers most of the Late Pleistocene to prehistoric Holocene volcanic record in the island. Using a paleointensity multimethod approach including Thellier‐type and Shaw‐type techniques, and combining detailed rock magnetic and mineralogical analyses, we assess the reliability and possible biases in paleointensity estimations in volcanic rocks affected by low‐temperature oxidation and coarse ferromagnetic grains. Results indicate a strong viscous component linked to maghemitization, which compromises paleointensity reliability and accuracy. Low temperature demagnetization pretreatments significantly mitigated the viscosity contribution, improving success rates by highlighting the original thermoremanent magnetization (TRM) and revealing possible overestimations in standard Thellier‐type treated samples affected by maghemitization. The full vector results, compared with several paleosecular variation curves, exhibited both low and high field intensity periods, including a relative paleointensity minimum at ∼27 ka (VADM ∼26 ZAm 2 ) and the record of the Levant intensity high (VADM ∼108 ZAm 2 ). This study contributes with valuable constraints for improving geomagnetic models, especially for low‐latitude regions, and underscores the importance of integrating magnetic mineralogy with paleointensity protocols to mitigate bias in geomagnetic reconstructions.

Abstract Autonomous drone navigation with deep reinforcement learning (DRL) is hindered by the difficulty of specifying reward functions for vision-based, continuous control in complex environments. We address this by using inverse reinforcement learning (IRL) to recover a task-aligned reward directly from expert demonstrations; specifically, we employ adversarial IRL (AIRL) to learn the reward. In evaluation, the learned-reward policy improves success rate, smoothness and trajectories consistency compared with a carefully tuned human-designed reward and baselines reward function. These results indicate that learning the reward from demonstrations provides a precise and transferable objective for autonomous flight, achieving better performance and better guidance under verification of our evaluation protocol without manual reward engineering. To the best of our knowledge, this is the first work to successfully apply an AIRL framework for visual drone navigation.

Background The successful insertion of dental implants is highly proportional to the condition and quantity of the alveolar bone at the edentulous area. This pilot study aimed at evaluating clinically & radiographically the efficacy of preservation of alveolar ridge by making use of platelet- rich fibrin (PRF) & β – tricalcium phosphate {TCP)+rh- bone morphogenic protein (BMP)-2 followed by delayed implant placement. Materials & Methods Post-extraction sockets were augmented using a combination of autologous PRF and alloplastic bone graft (β-TCP) mixed with growth factor rh-BMP 2. At three months, the clinical and radiographic study was carried out, and implants were placed using two staged protocols. Outcome clinical parameters involving mean modified plaque index (MPI), modified bleeding index (MBI), clinical implant mobility score (CIMS), full mouth plaque index (FMPI), full mouth papillary bleeding index (FMPBI), probing measurements such as probing pocket depth (PPD), gingival recession (GR), and width of keratinized gingiva (WKG) around implants were evaluated. Results A total of 12 sockets in 9 patients were treated using the ridge preservation technique. At three months after extraction & ridge preservation, baseline evaluation revealed minimal buccolingual dimensional changes (7.0 mm ± 1.21 mm). Following implant placement, radiographic evaluation at 3 months showed minimum vertical & horizontal loss in bone (mesial: 0.33 ± 0.33, distal: 0.25 ± 0.33). Use of rhBMP-2+ β -TCP+rh-BMP-2 reduces the dimensional changes owing to 100% survival of the implants. Conclusion Atraumatic extraction employing ridge preservation using PRF & β -TCP+rh-BMP-2 followed by delayed implant placement minimizes dimensional changes and helps in the improved success rate of the implant BJMS, Vol. 25 No. 01 January’26 Page : 334-342

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). 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: ≤ 8days, 9-13days (reference), and ≥ 14days 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. 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: ≤ 8days (OR = 1.04, 95% CI 0.78-1.39), 9-13days (reference), ≥ 14days (OR = 0.84, 95% CI 0.64-1.11). Multivariate analysis identified younger age and higher AMH as independent predictors of COPR. 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.

Ultrasound-guided peripheral intravenous catheter (USG-PIVC) insertion improves success rates in patients with difficult intravenous access (DIVA) and reduces complications. However, widespread implementation remains limited by training demands and workflow integration challenges. This quality improvement initiative aimed to assess the implementation of a nurse-led USG-PIVC pilot program in a tertiary emergency department using a service design approach. A three-cohort nurse training program was conducted between June 2023 and February 2025 at the McGill University Health Centre. Training included didactic instruction, simulation, and supervised insertions. We conducted descriptive quantitative analysis of insertion outcomes and qualitative analysis of nurse interviews. Program adaptations were made between cohorts in response to participant feedback. A total of nine nurses participated across three cohorts and attempted 101 insertions. Independent insertion success rates were 67%, 63%, and 77% in Cohorts 1, 2, and 3 respectively. While no nurses achieved certification in the first cohort, 100% did so in subsequent cohorts. Common challenges included difficulty visualizing the needle tip and limited practice opportunities during routine shifts. Modifications using a service design approach, such as smaller training groups, protected practice time in a calmer setting, and individualized feedback with formal evaluations were associated with improved performance and skill retention. Using the Tool+Team+Routine lens, we found that effective implementation required not only technical training (tool), but also team structure and dedicated learning environments (team and routine). These insights may inform broader implementation of nurse-led procedural innovations in acute care.

Introduction: Infertility affects a significant proportion of couples worldwide and may be associated with psychological difficulties. Assisted reproductive technologies (ART), including in vitro fertilization (IVF), provide a solution but are associated with emotional challenges such as anxiety, depression, and stress, which may impact treatment outcomes. Respondents and methods: The study involved 200 women undergoing IVF at the Clinic for Gynecology and Obstetrics. Psychological distress was assessed using the CORE-OM questionnaire before treatment. One year later, respondents were contacted to determine IVF outcomes. Statistical analyses explored the association between psychological factors and treatment success. Results: Higher levels of depressive symptoms were found to have negative implications for IVF outcomes. Regression analysis confirmed that depression was the only significant negative predictor of treatment success. Self-directed risk behaviors showed a trend toward significance, while anxiety and overall risk behaviors, although correlated with IVF outcomes, did not emerge as significant predictors in the regression model. Conclusion: The results showed that higher levels of depressive symptoms were associated with less favorable IVF outcomes, while the association with anxiety was not confirmed in the regression analysis. Integrating psychological support into infertility treatment may contribute to improved success rates and enhanced well-being of patients. Future research should include biological factors and longitudinal designs to provide a more comprehensive understanding of the issue.

The success of embryo implantation and pregnancy depends on a complex interaction between the trophoblast and the endometrial environment, where endometrial receptivity plays a crucial role in this process. Assisted reproductive technologies (ARTs) are essential in overcoming biological barriers and enabling implantation in women with fertility issues. However, one of the main challenges in ART is ensuring that the endometrium is receptive at the time of implantation. Therefore, identifying endometrial receptivity biomarkers is essential to optimize ART treatments, improving success rates. A comprehensive literature review was conducted by searching peer-reviewed articles published in PubMed, Scopus, and Web of Science databases. The search included studies focusing on molecular and cellular mechanisms underlying endometrial receptivity in both natural and stimulated cycles. Various experimental methods, including proteomic and microRNA studies, have identified key biomarkers involved in endometrial receptivity, such as adhesion molecules, growth factors, and others. However, ovarian stimulation in fertility treatments can alter endometrial receptivity, making approaches like frozen embryo transfer necessary. Despite advancements, many questions persist regarding the endometrial receptivity and implantation mechanisms in both natural and stimulated cycles. This article reviews the main molecules involved in endometrial receptivity in natural and stimulated cycles, highlighting their potential role as biomarkers for embryo implantation.

Background: The development of pharmacological interventions for perioperative complications, particularly spinal anesthesia-induced hypotension during cesarean section, faces substantial challenges including prolonged discovery timelines, high attrition rates, and limited translation of preclinical findings to clinical efficacy. Traditional drug development approaches require extensive time and financial investment, often spanning 10-15 years from initial target identification to regulatory approval. Ondansetron, a selective serotonin 5-HT3 receptor antagonist, exemplifies successful drug repurposing for preventing hypotension in obstetric anesthesia, yet its identification for this indication emerged through clinical observation rather than systematic screening approaches. Aim: This article examines innovative strategies in pharmaceutical development that can accelerate therapeutic discovery and validation, with particular emphasis on high-throughput screening methodologies, computational target identification, and translational research frameworks applicable to perioperative medicine. Discussion: Modern drug discovery increasingly leverages high-throughput screening platforms, structure-based drug design, and pharmacogenomic approaches to identify lead compounds with optimal therapeutic profiles. These strategies enable simultaneous evaluation of thousands of molecular entities against validated biological targets, dramatically reducing initial screening phases. Target identification and validation through genomic and proteomic technologies, combined with advanced preclinical modeling systems, facilitate more accurate prediction of clinical efficacy and safety profiles. Impact: Implementation of these accelerated development strategies has demonstrated substantial reductions in discovery timelines and improved success rates in clinical translation, particularly for repurposed medications and precision medicine applications. Perspective: Future advancement requires integration of artificial intelligence-driven prediction models, patient-specific precision medicine approaches, and adaptive clinical trial designs to further optimize therapeutic development for perioperative and obstetric applications.

The development of artificial intelligence (AI) tools and technology has made AI-driven drug discovery a more prominent field. We are firmly in the AI era, with hybrid designs that eventually comprise deep learning (DL) and conventional machine learning (ML). Although traditional models can predict ADMET (Absorption, Distribution, Metabolism, Excretion, and Toxicity) properties, they remain relatively unsuccessful, and improving the accuracy of predictions remains challenging. Recently, several researchers have developed a hybrid learning model that successfully addresses these problems and improves prediction accuracy. The systematic tendencies facing AI-powered transformation from conventional DL and ML to hybrid learning AI models are examined in this review. Compared with traditional ML and DL, hybrid AI models have increased efficiency by reducing drug development time and costs, and improved success rates. In this context, the ongoing development of new ADMET software based on hybrid AI and multimodeling techniques can enhance the accuracy of pharmacokinetic-pharmacodynamic predictions, improve ADMET endpoint predictions, and expedite the drug discovery of new chemical entities. Moreover, this review covers the future of AI in pharmaceutical sciences and ADMET predictions, including AI-driven prediction models that range from basic ML/DL to newly developed hybrid models, evaluation parameters, and their applications in ADMET property prediction. SIGNIFICANCE STATEMENT: The article covers the compilation of ongoing research in the development of ADMET (Absorption, Distribution, Metabolism, Excretion, and Toxicity) software based on hybrid artificial intelligence and multimodeling techniques, which may increase the accuracy of pharmacokinetic-pharmacodynamic predictions, improve ADMET endpoint predictions, and accelerate drug discovery.

Introduction Establishing intravenous access is a common clinical procedure, yet peripheral venous catheter placement can be challenging in certain patient populations. Using ultrasound improves success rates but remains operator-dependent and requires training. Simulation-based training enhances procedural skills without risk to patients but requires a standardized measure of the acquired skills. This study presents and validates a new assessment tool for simulation-based training in ultrasound-guided peripheral venous catheterization, enabling safe transition from simulation to clinical implementation. Methods This exploratory, rater-blinded study recruited participants from two Danish university hospitals. Participants were allocated to one of two groups according to experience level. Each participant performed three ultrasound-guided peripheral venous catheter placements on a phantom. All attempts were video recorded and rated by two blinded raters using the new assessment tool. Validity evidence was examined using Messick's framework, focusing on content validity, response process, internal structure, relationships to other variables, and consequences of testing. Results Fifty participants were enrolled, of whom 46 were included in the final analysis. Internal consistency was high, with an intraclass correlation coefficient (ICC) of 0.85. The tool effectively discriminated between novices and experienced participants (p < 0.001). A pass/fail score of 18 points was established, with a false-negative rate of 4.5% and a false-positive rate of 12.5%. Conclusion We found the new assessment tool valid and reliable, effectively distinguishing novices from experienced participants and setting a passing cutoff score. This supports its use in simulation-based training to standardize competence evaluation, serving as a gateway to subsequent assessment in clinical practice.

Many patients with heart failure (HF) progress to an advanced stage, characterized by persistent symptoms despite maximal therapy. Heart transplantation (HT) remains the most effective treatment option for improving the survival of patients with advanced HF. This is true even with advancements in medical therapy for HF and the development of mechanical circulatory support systems. Over the past few decades, HT has undergone significant evolution, leading to greatly improved success rates. However, HT recipients face the risk of several potential complications that can negatively impact their outcomes. In this article, we aim to provide a practical framework for clinicians involved in heart transplant medicine. Additionally, we offer an update on a recent and relatively unknown complication of HT: eosinophilic myocarditis (EM).

From clinic to lab: Sperm parameters for equine in vitro embryo production.

Pessary treatment is a popular option for women with pelvic organ prolapse; however, success rates are limited, mostly due to dislodgement of the pessary. In literature, several support mechanisms are proposed such as support by the levator ani muscle and the uterus which acts as a lever to keep the pessary in place. To improve success rates of pessary therapy, the support mechanism should be further investigated. The aim of this study is to identify ring-shaped pessary support mechanisms by comparing upright magnetic resonance imaging data of successful and unsuccessful pessary users. Upright magnetic resonance scans were performed after obtaining informed consent in patients with and without pessary in 30 successful and 29 unsuccessful cases. The study was approved by the medical ethics committee. We analyzed the position of the pessary with respect to the levator ani muscle, the pubic symphysis, and the uterus. For comparison of the levator ani muscle position with respect to the pessary, principal component analysis was used. Statistical analyses were performed using SPSS version 29.0.1.0 (SPSS, Chicago, IL). Differences between the groups were analyzed using a 2-sided t-test or Mann-Whitney U test where appropriate for the continuous data. For the categorical data, a chi-square test was performed. The pessary is significantly lower in the pelvis in the unsuccessful group (median 1.9 cm below the Pelvic Inclination Correction System (PICS) line [range, -0.9; 4.0]) than in the successful group (median 0.6 cm below the PICS line [range, -1.8; 2.6]). The angles of the pessary with respect to the PICS line, and the distances of bladder and uterus to the PICS line, showed no significant difference between groups. The first principal component of the levator ani muscle shows a significantly lower position of the pessary in the levator ani muscle for the unsuccessful group (P<.001) and accounted for 64.8% of shape variation. With regards to the uterus position, in the successful group, the cervix is in the center of the pessary, with the proximal edge of the pessary positioned in the posterior fornix. While in the unsuccessful group, the cervix is located near the proximal edge of the pessary. Additionally, the unsuccessful group had 7 women with a hysterectomy, compared to none in the successful group, although this was not part of the inclusion/exclusion criteria. These findings support the hypothesis that the pessary is kept in place by the levator ani muscle and the uterus lever mechanism. Knowledge on these support mechanisms can guide development of new pessary shapes or even personalized pessaries for unsuccessful users.