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levonorgestrel-releasing intrauterine system (Liletta / LNG20 / LNG 20)

✓ Approved

AbbVie, Inc. · PGR · Steroids

What is levonorgestrel-releasing intrauterine system?

levonorgestrel-releasing intrauterine system is a steroids developed by AbbVie, Inc.. It is approved for therapeutic indications via others or surgical implantation.

Drug Profile

Brand NamesLiletta, LNG20, LNG 20
CompanyAbbVie, Inc.
Drug ClassSteroids, Small Molecule
Molecular TargetPGR
RouteOthers, Surgical Implantation
StatusApproved
Sources: ClinicalTrials.gov, AbbVie, Inc.

Mechanism of Action

Molecular Targets

levonorgestrel-releasing intrauterine system acts on 1 molecular target:

PGRprogesterone receptor (NR3C3, PR)
Want deeper analysis?Noah AI can explain complex mechanisms and compare to similar drugs.

Therapeutic Indications

levonorgestrel-releasing intrauterine system is developed for 1 unique indication across 1 therapeutic area.

Therapeutic AreaConditionPhase
Reproductive system and breast disordersHeavy menstrual bleeding✓ Approved

Related Research Articles

PubMedCureus2026-09-20

Recurrent Deep (Aggressive) Angiomyxoma of the Pelvis: Serial MRI Documentation of Sustained Complete Radiologic Response During Eight Years of Gonadotropin-Releasing Hormone (GnRH) Agonist Therapy.

Fevereiro Beatriz B, Fonseca Ricardo R, Cunha Teresa Margarida TM

Deep (aggressive) angiomyxoma is a rare mesenchymal tumor that primarily affects premenopausal women and is frequently misdiagnosed because of its deep pelvic location, indolent growth, and nonspecific clinical presentation. It is typically multicompartmental and demonstrates a characteristic laminated ("swirled") appearance on T2-weighted magnetic resonance imaging (MRI), reflecting alternating myxoid and fibrous stromal components. Local recurrence is common after surgical resection, making long-term imaging surveillance essential. Tumor expression of estrogen and progesterone receptors provides a biological rationale for hormonal therapy. We report a case of recurrent pelvic deep (aggressive) angiomyxoma in a 45-year-old woman who achieved complete and sustained radiologic remission documented by serial MRI throughout 8 years of continuous gonadotropin-releasing hormone (GnRH) agonist therapy. This uncommon long-term outcome highlights both the potential for durable disease control with hormonal therapy in deep (aggressive) angiomyxoma and the value of MRI for longitudinal assessment of treatment response.

PMID 42763626
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PubMedJournal of multidisciplinary healthcare2026-09-20

User Needs for an Artificial Intelligence-Empowered Basic Activities of Daily Living Training System After Stroke: A Qualitative Study.

Wang Qing Q, Xiong Jing J, Lin Suzhu S, Lin Xiujiao X et al.

To explore patients' and primary caregivers' willingness to use an artificial intelligence (AI)-empowered basic activities of daily living (BADL) training system and their needs, concerns, and expectations regarding the system. A descriptive qualitative study was conducted in the Department of Neurology at the Second Affiliated Hospital of Fujian Medical University, Fujian, China. Patients with poststroke hemiplegia and primary caregivers were recruited using purposive sampling. Semistructured, face-to-face interviews were conducted with 11 patients with poststroke hemiplegia and 13 primary caregivers to capture both patient and caregiver perspectives. Data were collected on participant-reported BADL difficulties, willingness to use the AI-empowered BADL training system, perceived needs and concerns, and expectations for system design. Data were analyzed using inductive thematic analysis. Four themes were identified: 1) BADL limitations created shared difficulties for patients and caregivers; 2) acceptance of the AI-empowered BADL training system depended on usefulness and functional fit; 3) participants expected low-barrier, understandable, and sustainable training support; and 4) the system was viewed as a supplement to professional rehabilitation and family caregiving. An AI-empowered BADL training system may be acceptable to patients with poststroke hemiplegia and their primary caregivers if it is safe, reliable, easy to use, and appropriate to patients' functional status. These findings may guide the future development of task-oriented AI-empowered BADL training systems that support patient-caregiver collaboration, safety assurance, sustained engagement, and human-AI collaboration in rehabilitation.

PMID 42763734
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PubMedEuropean radiology2026-09-20

Classification of histology and molecular subtypes of brain gliomas and glioneuronal and neuronal tumors using a deep learning approach.

Wu Minghao M, Sun Yuchen Y, Bao Dan D, Li Junjie J et al.

To develop a deep learning classification system for integrated histology and molecular subtyping of gliomas and glioneuronal/neuronal tumors (GNTs). Preoperative multi-parametric MRI data from 1,844 patients with gliomas or GNTs, encompassing T1WI, post-contrast T1WI (T1(+C)), T2Flair, T2WI, and DWI, were analyzed. Subjects were classified into six categories: (1) oligodendroglioma, IDH-mutant, and 1p/19q-codeleted; (2) astrocytoma, IDH-mutant; (3) glioblastoma, IDH-wildtype; (4) pilocytic astrocytoma (PA); (5) pleomorphic xanthoastrocytoma (PXA); and (6) GNTs. A three-level classification system (MRI-G/GNTs) using MobileNetV2 was trained and validated in internal and external datasets. Sequential forward feature selection (SFFS) was utilized to optimize multi-modal network combinations. Furthermore, the MRI-G/GNTs outputs were integrated into structured virtual pathology reports. The average Dice for the brain tumor segmentation model was 0.92. Using SFFS, we identified optimal MRI sequence combinations per classification step. The external tests AUCs reached 0.92 for distinguishing adult-type diffuse gliomas from circumscribed astrocytic gliomas/GNTs (T1(+C), Flair, and T2WI combination), 0.92 for distinguishing IDH status for adult-type diffuse glioma (T1(+C)/Flair/ADC), 0.83 for distinguishing 1p/19q status for IDH-mutant diffuse glioma (ADC/Flair), 0.84 for circumscribed astrocytic gliomas vs GNTs (ADC/T1(+C)), 0.95 for PA vs PXA (ADC/T1(+C)/T2WI). Based on the automated prediction pipeline, the integrated classification performance of the MRI-G/GNTs system achieved AUCs of 0.92, 0.81, 0.87, 0.82, 0.94, and 0.69, with accuracy of 0.88, 0.88, 0.89, 0.81, 0.91, and 0.95 across the above six tumor categories. The MRI-G/GNTs system demonstrates strong capability in distinguishing both histological and molecular subtypes of brain gliomas and GNTs. Question How about the deep learning model based on MRI for integrated histology and molecular classification of brain GNTs, and can it be improved? Findings The MRI-G/GNTs system achieved AUCs of 0.69-0.92 with accuracy of 0.81-0.95 for distinguishing six tumor categories of GNTs. Clinical relevance The three-level deep learning classification system (MRI-G/GNTs) based on optimized multimodal MRI integration firstly achieves comprehensive histology and molecular subtype classification of brain GNTs.

PMID 42763331
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PubMedOdontology2026-09-20

Predicting the final color of layered resin composites from background-dependent optical measurements: a translucency-informed model.

Buldur Mehmet M, Doğu Kaya Bengü B, Sezer Berkant B

The final color of layered resin composite restorations is influenced by complex optical interactions between composite layers and surrounding backgrounds. Therefore, this study investigated whether background-dependent optical measurements and translucency parameters could be used to predict final color coordinates and assessed model performance in an independent composite system. During model development, five composite shades (D3, D2, D1, E2, and E1) were used in both the 2-mm base and 1-mm top positions, generating all 25 possible ordered bilayer combinations. Background-dependent L*, a*, and b* coordinates and translucency parameters obtained from independent 1-mm and 2-mm layers were used to develop multivariable linear regression models for predicting the color coordinates of 3-mm layered specimens. Model performance was assessed using adjusted R², mean absolute error, root mean square error, and leave-one-out cross-validation. Independent-system evaluation was performed using 16 layered combinations from one additional composite system, and predictive accuracy was evaluated using CIEDE2000 color differences (ΔE00). The regression models demonstrated adjusted R² values of 0.799, 0.843, and 0.964 for the L*, a*, and b* coordinates, respectively. During the independent-system evaluation, the mean ΔE00 value was 0.939 ± 0.333; 37.5% of the combinations were at or below the perceptibility threshold (ΔE00 ≤ 0.8), while all combinations (100%) were within the clinical acceptability threshold (ΔE00 ≤ 1.8). Background-dependent optical measurements and translucency parameters enabled accurate and clinically acceptable estimation of the final color coordinates of layered resin composites. The low prediction errors observed in one additional composite system provide preliminary evidence of predictive performance beyond the development material under standardized experimental conditions.

PMID 42763372
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PubMedArthroscopy : the journal of arthroscopic & related surgery : official publication of the Arthroscopy Association of North America and the International Arthroscopy Association2026-09-20

An Artificial Intelligence-Based Preoperative Planning System for High Tibial Osteotomy Significantly Enhances Planning Speed With Comparable Accuracy to Surgeons.

Li Songlin S, Li Zhe Z, Zhang Hongkai H, Chen Yuqian Y et al.

To develop an artificial intelligence-based system capable of measuring preoperative alignment parameters, calculating correction angles, and visualizing outcomes for open wedge high tibial osteotomy (OWHTO) and to validate the accuracy and efficiency of the system in clinical cases. Between January and December 2023, standing hip-to-ankle radiographs from patients with varus knee osteoarthritis were retrospectively collected as the development cohort. The OWHTO auxiliary planning system (OAPS) was developed using a UNet convolutional neural network to detect 28 anatomical landmarks. The system was validated against manual measurements by attending surgeons and further evaluated through an external validation cohort across 2 institutions to compare accuracy and efficiency among junior residents using manual, semiautomatic, and OAPS-assisted methods. The study included 346 knees for development and an external validation cohort of 110 knees. On the test set, the OAPS achieved a mean radial error of 2.703 pixels and a successful detection rate at the 6-pixel threshold of 0.951. For hip-knee-ankle and correction angles, the mean absolute errors were 0.47° (95% CI: 0.39°-0.56°) and 0.43° (95% CI: 0.36°-0.51°), respectively. No significant differences were found between OAPS and attending surgeons across all alignment parameters (P > .05). OAPS processed each image in 9.27 seconds, which was significantly faster than the 4.30 minutes required for manual planning on the test set (P < .001). External validation confirmed that OAPS-assisted resident measurements matched attending-level accuracy and were significantly faster than manual and semiautomatic methods. The artificial intelligence-based OAPS enables accurate, efficient preoperative planning for OWHTO, achieving measurement precision comparable to experienced surgeons while significantly reducing planning time. By automating anatomical measurement and planning, the OAPS reduces interobserver variability and enhances preoperative plan efficiency within OWHTO workflows, streamlining the preparation phase for surgical teams.

PMID 42763426
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PubMedJournal of healthcare leadership2026-09-20

Public Health Challenges in Somalia: Strengthening Health Systems for Resilient and Equitable Healthcare Delivery.

Mohamoud Jamal Hassan JH, Adam Mohamed Hussein MH, Sheikh Mohamed Ahmed Mahad AM

Somalia continues to face multidimensional public health challenges driven by prolonged conflict, political instability, fragmented governance, climate-related disasters, displacement, poverty, and a fragile health system. The country carries a dual burden of communicable and non-communicable diseases (NCDs), alongside persistently high maternal and child mortality and recurrent nutrition and WASH emergencies. These pressures are intensified by workforce shortages, limited laboratory and surveillance capacity, unequal access to care, and heavy dependence on externally financed programmes. In this perspective, health-system resilience refers to Somalia's capacity to anticipate, absorb, adapt to, and recover from shocks while maintaining equitable delivery of essential services. We argue that resilience cannot be achieved through disease-specific or short-term humanitarian programmes alone; it requires stronger public stewardship, coordinated federal-state governance, sustainable financing, integrated primary health care, a retained and equitably distributed workforce, interoperable surveillance, and climate-resilient WASH and referral systems. The central recommendation is therefore a transition from fragmented, crisis-driven service delivery toward an accountable, primary-health-care-centred system that links communities, public and private providers, laboratories, referral facilities, and emergency response mechanisms.

PMID 42763727
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