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In response to the lockdowns and travel bans during the coronavirus disease 2019 pandemic, Peter C. Butler at the University of California, Los Angeles (UCLA), started a virtual islet biology seminar series. After the authors of this article joined him as co-organizers, this initiative became the Islet Research Seminar Series (IRSS). Like islets of Langerhans adapt to their changing environment, the islet biology community quickly embraced this new format. The IRSS evolved into a lasting scientific forum that convenes weekly and is attended by islet biologists from the U.S., Canada, Europe, and Israel. The series covers a range of topics in islet biology, with presentations from scientists representing all career stages. It has proven particularly valuable for trainees and early-stage investigators in exposing them to a variety of topics in islet biology without travel required and facilitating more spontaneous interactions with senior scientists than at in-person meetings. While the online format is not meant to replace live scientific conferences, we believe that the IRSS plays a unique role in keeping the islet biology community connected and abreast of the most recent scientific discoveries in our field. The success of this platform stands as a testament to the scientific community to adapt and thrive through challenges. This article is dedicated to Peter C. Butler, UCLA, who initiated the IRSS.

Abstract Introduction: Hormone receptor positive (HR+), HER2- breast cancer (BC) is the most common subtype of BC. Despite advances in chemotherapy and endocrine therapy optimization, local and systemic recurrences frequently occur, especially for patients with node-positive disease. Recent data suggest a subset of patients with this tumor phenotype may benefit from the addition of anti-Programmed Death-1(aPD1) to neoadjuvant chemotherapy. However, T-cell infiltration (TCI), a biomarker of response to aPD1 and chemotherapy, is typically low in this BC subtype. Preclinical models show that combining preoperative radiation therapy (preop RT) with immune checkpoint inhibitors can enhance TCI and anti-tumor immunity. We conducted the first randomized trial to test whether no-, low- or high-dose RT combined with aPD1 increases TCI and improves responses in non-irradiated, metastatic lymph nodes in HR+/HER2- BC (NCT04443348). Materials & Methods: Eligible patients had ER and/or PR≥10%, HER2 negative invasive BC with clipped and biopsy-proven positive axillary node(s), cT1c-T4c, cN1-3, and at least one of the following criteria: grade 3 or grade 1-2 with cN2-N3, Ki67 >20%, or high genomic assay score. Between 2022-2024, 51 patients from 10 centers were randomized to no (0Gy; control arm), low (9Gy) or high (24Gy) doses of preop RT to the primary tumor with concurrent pembrolizumab followed by a 2-week tumor biopsy. Stratification factors were T1c vs. T2-4 and N1 vs. N2-3. Patients subsequently received pembrolizumab with 12 weeks of paclitaxel, followed by 4 cycles of adriamycin-cyclophosphamide (q2wk or q3wk) with pembrolizumab (200 mg q3 wks or 400 mg q6 wks). Surgery and adjuvant therapy followed standard-of-care. Dual co-primary endpoints were 2wk tumor TCI and nodal pathologic complete response (ypN0) at time of definitive surgery. TCI was assessed by multiplex immunofluorescence (pan-cytokeratin, CD3, CD8), using a rank-based Immunoscore (range 0-1), compared to baseline reference biopsies via two-tailed Mann-Whitney test. ypN0 was evaluated non-comparatively. Secondary endpoints included pCR and Residual Cancer Burden (RCB) 0/1. Results: Among 51 patients enrolled, 49 were evaluable for TCI and 48 for the ypN0 endpoint. Median age was 49.5 years (range 23-78). The cohort had a high-risk clinical profile: 92% had cT2-4, 50% N2-3, and 62% grade 3 tumors. Median ER and PR positivity were 90% (20-100) and 68% (1-98), respectively. Multifocal or multicentric tumors were present in 32%. TCI was assessed at 2 weeks post-treatment. The proportion of tumors with TCI in the upper quartile increased with RT dose escalation: 31% (0Gy), 40% (9Gy), and 53% (24Gy). Median TCI scores at 2 weeks were 0.60 (0Gy), 0.56 (9Gy), and 0.82 (24Gy), compared to a median baseline value of 0.50 from untreated reference samples. A statistically significant increase in TCI was observed only in the 24Gy cohort relative to the untreated group (p=0.027). 70.8% underwent mastectomy, with 39.6% receiving immediate reconstruction. 8% experienced G2/3 wound complications, balanced across arms. Median number of lymph nodes removed was 6.5 (1-42). The overall ypN0 rate was 29%, and 24%(0Gy), 29%(9Gy) and 33%(24Gy) per arm. pCR/RCB 0-1 rates were 18%/27% in the entire cohort and 6%/18%(0Gy), 29%/29%(9Gy), and 19%/33%(24Gy), respectively. Conclusion: The addition of 24Gy preop RT to aPD1 significantly increased tumor TCI in HR+/HER2- early-stage BC. Non-irradiated nodal response rates were promising despite over one-third of the study population with grade 1/2 tumors and high disease burden, laying the foundation for a future randomized trial comparing the efficacy of this novel approach against standard of care for node-positive HR+/HER2- BC. Citation Format: G. Gupta, A. Y. Ho, F. Gharibpoor, Y. Abdou, J. D. Anampa, R. C. Blitzblau, B. C. Calhoun, D. L. Casey, S. F. Dent, L. A. Carey, J. R. Bellon, L. E. Warren, J. Lu, T. J. Hieken, A. G. Taghian, A. Bardia, T. Traina, G. Plitas, K. Gallagher, K. Daugherty, A. Thompson, I. Krop, A. Wolff, E. Mittendorf, J. Fox, H. Garber, E. Hwang, A. J. Khan, J. P. Loene, R. Mutter, S. Patil, C. Santa-Maria, B. V. Vincent, J. L. Wright, L. Spring.Primary Results from the HR+/HER2- Cohort of TBCRC-053 (P-RAD): A Randomized Trial of No, Low, or High Dose Preoperative RADiation with Pembrolizumab and Chemotherapy in Node-Positive, HER2-Negative Breast Cancer [abstract]. In: Proceedings of the San Antonio Breast Cancer Symposium 2025; 2025 Dec 9-12; San Antonio, TX. Philadelphia (PA): AACR; Clin Cancer Res 2026;32(4 Suppl):Abstract nr GS2-05.

Abstract Background Hidradenitis suppurativa (HS) is a chronic, relapsing, debilitating skin disease characterized by deep‐seated nodules, abscesses and tunnels. Objectives To investigate the efficacy of bimekizumab using International Hidradenitis Suppurativa Severity Score System (IHS4) outcomes in patients with moderate to severe HS in the pooled BE HEARD I and II Phase 3 studies. Methods Patients received (initial/maintenance) bimekizumab 320 mg every 2 weeks (Q2W)/Q2W, bimekizumab Q2W/every 4 weeks (Q4W), bimekizumab Q4W/Q4W or placebo/bimekizumab Q2W. Change from baseline (CfB) in IHS4, IHS4 severity stages and the dichotomous IHS4‐55/75/90/100 (an improvement of at least 55%/75%/90%/100% in IHS4 from baseline) are reported to Week 48. Lesion count data by lesion type are reported to Week48. Results In total, 1014 patients were enrolled across BE HEARD I and II. At baseline, mean (standard deviation [SD]) IHS4 for bimekizumab‐treated patients ranged from 33.4 (25.4) for bimekizumab Q2W/Q2W to 36.0 (34.0) for bimekizumab Q2W/Q4W versus 30.6 (21.8) for placebo. At Week16, patients receiving bimekizumab demonstrated greater reductions in mean (SD) IHS4 from baseline, ranging from −17.2 (24.8) for bimekizumab Q4W/Q4W to −17.8 (21.1) for bimekizumab Q2W/Q2W versus −6.1 (16.3) for placebo. Proportions of patients with severe HS at baseline ranged from 83.7% (bimekizumab Q2W/Q2W) to 88.4% (bimekizumab Q2W/Q4W) and decreased by ~50% to Week48 across all treatment arms, ranging from 28.6% (placebo/bimekizumab Q2W) to 34.3% (bimekizumab Q2W/Q2W). Proportions of patients with IHS4 = 0 at Week48 ranged from 23.7% (bimekizumab Q2W/Q4W) to 26.7% (placebo/bimekizumab Q2W). At Week16, IHS4‐55, IHS4‐75, IHS4‐90 and IHS4‐100 thresholds were achieved by greater proportions of patients treated with bimekizumab versus placebo, while comparable improvements were seen across treatment arms from Week16 to Week48. At Week16, lesion counts were lower for patients treated with bimekizumab versus placebo, decreasing to comparable numbers across all groups at Week48. Conclusions Bimekizumab‐treated patients showed improvements across IHS4 outcome measures.

Background/Objectives: Precocious puberty (PP), defined as the onset of secondary sexual characteristics before 8 years in girls and 9 years in boys, is associated with psychosocial distress, compromised adult height, and long-term metabolic risk. Early identification remains challenging, as current diagnostic approaches are largely reactive and rely on invasive or resource-intensive testing. This narrative review examines how artificial intelligence (AI) can support earlier risk prediction and detection of PP through integration of clinical, hormonal, imaging, lifestyle, and environmental data. Methods: A narrative literature review was conducted using PubMed, Scopus, Embase, Web of Science, and Google Scholar to identify relevant studies published between 2005 and 2025. Eligible studies included original research and high-quality reviews that examined AI-based approaches, such as machine learning and deep learning, in pediatric endocrinology, particularly for the prediction or diagnosis of central or peripheral precocious puberty. Studies incorporating clinical, hormonal, radiological, lifestyle, environmental, or multi-omics data relevant to AI modeling were included. Results: AI models, including XGBoost, random forest, convolutional neural networks, and regression-based approaches, have demonstrated potential utility in predicting central precocious puberty using hormonal, imaging, and growth data. Reported applications include automated bone age assessment, lifestyle and dietary risk stratification, and exploratory use of wearable-derived behavioral data. However, progress is limited by small pediatric datasets, population bias, limited interpretability, and unresolved ethical challenges related to privacy, consent, and equity. Conclusions: Artificial intelligence represents a promising decision-support approach for earlier, non-invasive, and individualized risk assessment in precocious puberty. Future progress will depend on the integration of longitudinal, multimodal data, the development of ethical models, and interdisciplinary collaboration among pediatric endocrinologists, data scientists, and public health stakeholders.

ABSTRACT Background Patients with gastric dysmotility exhibit abnormal transit and reduced contractility, for which quantification is limited with current methodologies. The Atmo Capsule is a wireless motility capsule that measures regional gastrointestinal transit. We tested its technical capabilities to evaluate gastric contractility by detecting changes in capsule acceleration during transit. Methods Data from 91 healthy controls and 182 patients with suspected dysmotility who ingested the Atmo Capsule following a standardized protocol were included. Gastric emptying times (GET) ≥ 5 h were considered delayed. Contraction frequencies (cycles per minute [cpm]) and amplitudes (as arbitrary units [au]) were identified following Fast Fourier Transformation of accelerometer data using 5‐min windows and were assessed in 30‐min windows post‐ingestion (early gastric phase) and before gastric emptying (late gastric phase). Data were compared via ANOVA, using Bonferroni corrections, between healthy controls, patients with normal gastric emptying, and patients with delayed gastric emptying. Key Results Gastric emptying was delayed in 53/182 (29%) patients. Three cpm contraction frequencies were identified across both gastric phases in controls and patients. Contraction amplitudes did not differ between groups in the early gastric phase. In the late phase, mean amplitudes were lower in patients with delayed gastric emptying (6.4 au) compared with subjects with normal gastric emptying (controls, 9.7 au, p < 0.0001; patients, 8.6 au, p < 0.0001). Conclusions & Inference The Atmo Capsule enables precise quantification of gastric contraction profiles, permitting differentiation of normal from pathological motor patterns. Findings of this technical assessment support its potential for evaluating gastric physiology and detecting pathophysiological patterns of gastric dysmotility.

Abstract INTRODUCTION The Centiloid scale is the standard for amyloid (Aβ) PET quantification in research and clinical settings. However, variability between tracers and scanners remains a challenge. METHODS This study introduces DeepSUVR, a deep learning method to correct Centiloid quantification, by penalizing implausible longitudinal trajectories during training. The model was trained using data from 2,129 participants (7,149 Aβ positron emission tomography [PET] scans) in the Australian Imaging, Biomarkers and Lifestyle Study of ageing (AIBL)/Alzheimer's Disease Neuroimaging Initiative (ADNI) and validated using 15,807 Aβ PET scans from 10,543 participants across 10 external datasets. RESULTS DeepSUVR increased correlation between tracers, and reduced variability in the Aß‐negatives. It showed significantly stronger association with cognition, visual reads, neuropathology, and increased longitudinal consistency between studies. DeepSUVR also increased the effect size for detecting small treatment related slowing of amyloid accumulation in the A4 study. DISCUSSION DeepSUVR substantially advances Aβ PET quantification, outperforming all standard approaches, which is particularly important for clinical decision making and to detect subtle or early changes in Aβ. Highlights Novel artificial intelligence (AI)‐method that penalizes biologically implausible longitudinal trajectories, enabling the model to learn standardized uptake value ratios (SUVR) correction factors without requiring longitudinal data at inference time. Improves Centiloid consistency across tracers and studies, significantly enhancing cross‐sectional and longitudinal amyloid positron emission tomography (PET) quantification. DeepSUVR‐derived Centiloids show stronger associations with cognition, visual reads, and neuropathology. Longitudinal variability is reduced three‐ to five‐fold, enabling more reliable tracking of amyloid accumulation and better detection of treatment effects. Novel reference and target masks derived from DeepSUVR replicate most of the model's performance, offering a practical alternative for integration into existing pipelines.

Abstract Background Cardiovascular disease (CVD) is the leading cause of death globally for both men and women, yet women remain historically underrepresented in cardiovascular clinical trials (CV trials), despite facing a disproportionately high burden of morbidity and mortality in many forms of CVD. Purpose We aim to determine the representation of women across a broad range of CV trials. Methods The participation of women in CV trials registered on ClinicalTrials.gov from 2017 to 2023 was systematically determined through the extraction of publicly available information. Data was extracted to identify the country of study, disease type, trial size, clinical intervention and age of the participants. We calculated the proportion of women and the ratio of number of females to male participants (F:M ratio) for each trial. The female participation to prevalence ratio (PPR) was estimated for each trial based on the relative prevalence of the disease by sex in the specified region. Results We identified 1,079 registered CV trials, which included 1,396,104 participants, of whom 571,641 (41.0%) were women. The median F:M ratio was significantly lower for studies on arrhythmia (0.5), coronary heart disease (CHD) (0.39), acute coronary syndrome (ACS) (0.32) and heart failure (HF) (0.51), but was higher for obesity (1.44) and pulmonary hypertension (PH) (2.86) CV trials. F:M ratio was higher for trials on lifestyle intervention (1.51) than for drug trials. PPRs were low for clinical trials on CHD (0.66), ACS (0.79), and stroke (0.74). There was a trend to increasing PPR from 2017 to 2023, p=0.056. Conclusion Representation of women in CV trials varies by disease state, region, intervention, and sponsor type. Better understanding of reasons for these differences may enable more tailored interventions to improve representation of women globally and across therapeutic areas.Figure 1 Figure 2

Limited information on the business practices of liver transplant (LT) centers worldwide has been published. Characterizing this data could help identify best practices as well as opportunities for improvement. As such, the International Liver Transplant Society (ILTS) Business Practice Committee conducted a global online survey of LT centers, which was sent to the ILTS membership. Questions focused on 5 main domains: transplant practice and volumes, workforce, finances, quality assessment and performance improvement, and overall program function. Data was compared across program geographic regions and transplant volume. A total of 89 discrete centers were represented, of which 76.4% were academic/university-affiliated, and about one-third each were from Europe (36.0%) and North America (31.5%). The top 3 problems programs reported were finances/funding (60.7%), adequate program support/guidance (48.3%), and transplant volumes (43.8%). In all, 59.6% of respondents felt their salary was undercompensated, consistent across geographic regions. In addition, 69.7% felt their center was not adequately funded to meet program goals, with programs in Europe (N=28/32, 87.5%) and Oceania (N=2/3, 66.7%) most impacted. Transplant surgeon retainment was noted as more difficult for lower volume programs (<50 liver transplants/year, N=13/31). Nearly half (42.7%) of all programs felt under-resourced to provide high-quality care, and the majority (80.9%) felt there was room for growth and improvement under their current model. While international concerns varied widely among LT centers, inadequate salary and center funding, low transplant and referral volumes, and staff retainment were persistent themes. Focusing on adopting region-specific best practices and developing transplant systems of care that focus on these elements is critical to provide optimal care to LT patients worldwide.