Citations

ABSTRACT Objectives To investigate pharmacological treatment patterns in individuals with bipolar disorder (BD) with and without comorbid substance use disorder (SUD) and anxiety disorder (AX), we leveraged the Global Bipolar Cohort to analyze cross‐regional practices across North America, Europe, and the Pacific. Methods Fourteen cohorts contributed aggregate data on pharmacotherapy, demographics, diagnostic subtypes, and comorbidities. Proportional meta‐analyses using generalized linear mixed models were conducted to examine prescription trends and identify clinical differences. Results The sample ( N = 11,521) was 60% female and 84% Caucasian. Participants were categorized into four mutually exclusive groups based on comorbidity status: those with comorbid AX only, comorbid SUD only, both AX and SUD, or neither. The AX+SUD subgroup showed higher rates of attention‐deficit/hyperactivity disorder (ADHD), post‐traumatic stress disorder (PTSD), rapid cycling, obesity, and unemployment, reflecting a more severe clinical profile. Regional variations were notable: North American cohorts reported higher prevalence of AX and SUD than European and Pacific cohorts. Antidepressants use for AX were more common in Europe and the Pacific, while North American prescribing patterns were more variable. Benzodiazepine use was high among individuals with SUD across all regions. Lithium and first‐generation antipsychotic prescriptions varied, with higher rates observed in Europe. Conclusions Findings underscore the heterogeneity of BD and the influence of comorbid AX and SUD on illness burden and treatment. Regional prescribing variations underscore the need for context‐specific guidelines. Gaps in data on medication‐assisted treatment for SUD point to areas for future research. These insights can support more individualized and effective care for complex BD presentations.

Background: Despite guideline support for plain radiography (XR) in low-risk pediatric blunt trauma, CT remains the predominant imaging modality for cervical spine evaluation, increasing radiation exposure. We evaluated contemporary XR utilization and its diagnostic performance in children meeting consensus criteria for XR-first imaging. Study Design: We performed a prospective, multicenter observational study of 19,995 children enrolled in the Western Pediatric Cervical Spine Study across 72 U.S. trauma centers (2022–2025). Children appropriate for XR screening (Glasgow Coma Scale score >13, asymptomatic, non-obtunded) were identified using consensus criteria. XR utilization, sensitivity, and negative predictive value (NPV) were assessed, and multivariable logistic regression identified predictors of XR use. Results: XR was performed in 39% of patients overall and varied significantly by trauma center type (pediatric 48% vs adult 14% vs mixed 15%; p<0.001), whereas CT was used in 60%. Among XR-appropriate children (n=12,650), an estimated 7,705 CT examinations could potentially have been replaced with XR. In the overall cohort, XR sensitivity for any cervical spine injury was 30.3% (95% CI 26.2–34.6%) with an NPV of 95.2% (95% CI 94.7–95.7%). In XR-appropriate children, only two clinically significant cervical spine injuries were missed, yielding a sensitivity of 75.0% (95% CI 34.9–96.8%) and an NPV of 99.9% (95% CI 99.9%–100.0%). Conclusions: Plain radiography is substantially underutilized in children meeting consensus criteria for XR-first imaging. In appropriately selected patients, XR demonstrated an NPV of 99.9% for clinically significant cervical spine injury, supporting broader implementation of XR-first imaging strategies to reduce CT utilization and pediatric radiation exposure.

Background/Objectives: To develop and evaluate a deep learning-based quality control system for Lumbar Spinal Digital Radiographs (LSDR), designed to automate and improve their evaluation and reduce reliance on manual reviews. Methods: This retrospective study utilized a deep learning workflow comprising image segmentation, feature extraction, and a classification model. The dataset, including anteroposterior (AP) and lateral (LAT) X-ray images, was expanded through data augmentation techniques. Four U-Net-based models were assessed: standard U-Net, Swin-UNet, Attention U-Net, and Attention U-Net with the weight map, with the latter selected for its superior performance. Extracted features, such as brightness, contrast, and anatomical positioning, were used in an XGBoost classifier, which was evaluated using mean intersection over union (mIoU), accuracy, sensitivity, specificity, and AUC. Results: The Attention U-Net with weighted attention outperformed the other models, achieving high mIoU scores in both AP and LAT views. The XGBoost classifier achieved the best performance in classifying images as “qualified” or “unqualified,” with an AUC of approximately 0.9, high accuracy, and balanced sensitivity and specificity. This approach effectively addressed class imbalances and improved model accuracy compared to traditional machine learning models such as MLP and SVM. Conclusions: The developed automated quality control system demonstrated potential for enhancing image quality, enhancing diagnostic reliability, and optimizing clinical workflow efficiency.

ABSTRACT Introduction Chronic gastroduodenal disorders remain challenging to manage, and new diagnostic approaches are needed to better delineate underlying causes and guide therapeutic decisions. Body Surface Gastric Mapping (BSGM) technologies combine high‐resolution gastric myoelectrical activity measurements with symptom and psychological profiling to provide mechanistic insights into gastric motor and sensory dysfunction. An International Working Group convened to derive the first consensus classification of BSGM phenotypes (the “Auckland Classification”). Methods A Technical Group conducted a systematic literature and clinical database review to identify objective test biomarkers and candidate disease mechanisms. Evidence was synthesized across 50 studies (primarily in gastroparesis, chronic nausea and vomiting, and functional dyspepsia), and BSGM phenotypes were mapped to existing treatment guidelines. Subsequently, iterative review and development of consensus was performed by a Consensus Group composed of international clinical experts familiar with BSGM. Eleven statements underlying the classification were then derived and circulated as a final survey to establish agreement. Results Six BSGM phenotypes were endorsed: three defined by myoelectrical abnormalities (Dysrhythmic, High Frequency, and Low Meal Response) and three by characteristic symptom associations (Sensorimotor, Continuous, and Delayed Onset Symptoms). Published studies plausibly linked these phenotypes to mechanisms including interstitial cell of Cajal depletion, vagal impairment, hypomotility, visceral hypersensitivity, gut‐brain dysregulation, and small bowel dysfunction. Phenotypes were also mapped to existing mechanism‐based treatment guidelines. Ten out of the eleven statements had > 80% agreement. Conclusions The Auckland Classification, derived by international consensus, presents a structured framework for BSGM‐defined patient phenotypes. Evidence and mechanism‐based treatment options are suggested for each phenotype to provide a foundation for research, further validation, and a pathway for integrating BSGM into clinical care.

Abstract Background/Purpose Diagnosing wrist ligament injuries is challenging; early detection and treatment are important to prevent osteoarthritis progression. Interosseous proximity maps, a proxy measure for joint space, can be generated from volumetric imaging data and may provide important information about wrist health. Artificial intelligence (AI) could enhance accuracy of noninvasive diagnosis based on imaging-derived metrics. This work demonstrates feasibility of AI training using synthetic proximity map data generated from finite element models (FEMs). Methods Personalized wrist FEMs for two asymptomatic participants were created from four-dimensional computed tomography-derived anatomic and kinematic data. Monte Carlo sampling varied 22 ligament material properties and simulated 7,500 unique injury scenarios generating 9,000,000 labeled red, green, and blue (RGB) images of interosseous proximity vector fields from FEM-derived motions. Images were associated with 17 descriptive metrics, including gross wrist angles and bone surface pairs, and used to develop mixed-input convolutional neural networks (CNNs). Model performance was evaluated for identifying specific ligament injuries. Results Average area under receiver operating characteristic curve (AUROC) for CNNs was 0.757 across all injury types and kinematics. In a subset with clinically-relevant functional angles, the average AUROC was 0.824. Best-performing individual ligament AUROCs ranged from 0.807 to 0.999. Sensitivities and specificities exceeded 0.99 for some ligament injury simulations under specific wrist angles and bone surface pairs. Conclusion This study demonstrates the feasibility of using synthetic data from FEMs to train AI models for classifying wrist ligament injuries. Proximity-based RGB images may be a relevant biomarker of ligamentous injury.

e16159 Background: Pivotal trials established atezolizumab-bevacizumab and durvalumab-tremelimumab as first-line therapies for unresectable hepatocellular carcinoma (HCC). Atezolizumab plus bevacizumab demonstrated superior survival outcomes compared to sorafenib for unresectable HCC in the IMbrave150 trial in 2020, while durvalumab plus tremelimumab showed superior survival in the HIMALAYA trial in 2022. However, data on head-to-head comparison of these two combinations as first-line systemic therapy for the patient with decompensated HCC are limited. This study aims to compare survival outcomes between these two treatment regimens in decompensated HCC patients to address a critical gap in the current literature, as decompensated patients have largely been excluded from clinical trials. Methods: This retrospective study utilized electronic health records from 146 U.S. healthcare organizations within the TriNetX Network. Adults aged ≥21 years diagnosed with decompensated HCC receiving atezolizumab/bevacizumab and durvalumab/tremelimumab from 10/21/2022 to 8/20/2024 were included. The comparison group received atezolizumab/bevacizumab, and the control group received durvalumab/tremelimumab. The primary outcome was all-cause mortality. A 1:1 propensity score matching was conducted to balance baseline characteristics (age, gender, race/ethnicity, lab results including liver function tests, platelet counts, INR, gamma glutamyl transferase, alpha-fetoprotein, and underlying liver diseases). Kaplan–Meier survival analysis and Cox regression was used to estimate the mortality risk. Subgroup analysis was stratified by HCC etiology. Results: A total of 302 and 664 patients received durvalumab/tremelimumab, atezolizumab/bevacizumab, respectively. After propensity matching, 253 patients per group were included. Median OS was 283 days with durvalumab/tremelimumab and 391 days with atezolizumab/bevacizumab. The two treatment regimens showed no significant difference in survival (hazard ratio [HR]: 0.875, 95% confidence intervals [CI] 0.696–1.101, p=0.254). Subgroup analyses demonstrated no significant differences in survival between the two groups of patients in MASLD (HR: 0.698, 95% CI: 0.468–1.040), alcohol associated liver disease (HR: 0.821, 95% CI: 0.603–1.118), and chronic viral hepatitis (HR: 0.852, 95% CI: 0.596–1.219). Conclusions: There was no statistically significant difference in survival outcomes between atezolizumab plus bevacizumab and durvalumab plus tremelimumab in patients with decompensated advanced HCC. These results support current real-world practice in which both regimens may be considered viable first-line treatment options in this high-risk population with liver decompensation.

Abstract Background The Global Bipolar Cohort (GBC) was established to identify existing bipolar disorder (BD) cohorts worldwide and foster collaborations focused on descriptive and analytic outcomes relevant to BD. A distributed analytic framework has been implemented to engage multiple sites without the need for central data pooling. This report describes the GBC endeavor and global functional impairment patterns. Cross-cohort comparisons of functional correlates are limited by heterogeneous measures and data-sharing constraints. Large, culturally diverse comparisons are needed to distinguish broadly reproducible correlates from cohort-specific effects. Participating sites completed a 28-item descriptive survey covering diagnostic methods, cognition, genetics, treatment, functioning, and follow-up strategies. We implemented a harmonized local logistic regression model of dichotomized functional outcome and shared summary statistics only. Results We identified 69 cohorts across five continents. Thirty-seven cohorts contributed functional outcome analyses from 17,130 participants. Outcome measures included clinician-rated disability scales and social indicators such as employment and marital status. The proportion classified with poor functioning ranged from 16% to 77% (mean 50%). In 32 of 37 cohorts, the overall regression model significantly explained variance in functioning. Current depressive symptoms were the most robust and reproducible correlate of poor functional outcome: they were assessed in 29 cohorts, significant in 22 (75.8%), ranked among the top three correlates in 22, and were the top-ranked correlates in 19. Associations between depressive burden and poor functioning were observed across clinician-rated disability scales and work or social indicators, and across geographically diverse cohorts. Comorbid substance use disorder and medication-related variables were associated with poorer functioning in subsets of cohorts, whereas sex, ancestry, bipolar subtype, psychosis history, and premorbid IQ showed weak or inconsistent associations. Cognitive measures, available in a minority of regression models, showed modest and non-uniform effects. Conclusions Across heterogeneous international cohorts, current depressive symptom burden emerged as the most consistent correlate of poor functioning in bipolar disorder. These findings replicate earlier multisite work at a larger scale, show that protocol-based distributed analyses can identify reproducible clinical signals without sharing individual-level data, and support prioritizing detection and treatment of depressive symptoms when aiming to improve real-world functioning. Future work should expand longitudinal harmonization and representation of under-studied populations.

Study Design. Subanalysis of a prospective, multicenter, observational study at 72 US adult, pediatric, and mixed trauma centers. Objective. We investigated trauma-related risk factors and patient-level factors associated with multiregional spinal column injury, defined as cervical spine injury (CSI) requiring treatment and thoracolumbar injury (TLI). Summary of Background Data. Traumatic spinal injury causes morbidity and mortality in pediatric patients. Injuries to multiple spinal column regions require early identification and thoughtful management. Methods. All children <18 years undergoing cervical spine evaluation after blunt multisystem trauma were prospectively identified and enrolled. Clinically significant CSI was defined by cervical spine surgery or halo placement. TLI incidence and management was recorded. The primary outcome was multiregional spinal column injury (CSI+TLI). Results. Of 19,651 enrolled patients, 1484 children (7.5%) experienced a TLI, 159 (0.81%) had CSI requiring treatment, and 30 (0.15%) had CSI+TLI. Multivariate analysis revealed that the trauma-related factors mechanism of injury ( P <0.001), rib fractures (odds ratio (OR) 2.8 [2.3-3.4]), hemothorax/pneumothorax (OR 2.0 [1.7-2.4]), bowel injury (OR 4.0 [2.9-5.4]), pelvic fracture (OR 1.5 [1.2-1.9]), and clinically significant CSI (OR 1.9 [1.2-2.9]) were independently associated with TLI. Of the 159 children with clinically significant CSI, 30 also experienced TLI (19%). Multivariate analysis identified prehospitalization intubation (OR 3.1 [1.1-8.4]) and rib fractures (OR 3.9 [1.2-12.8]) as independent associations with CSI+TLI. Conclusions. Of children with CSI requiring treatment, 19% experienced TLI. MOI and various trauma-related injuries were independently associated with TLI, whereas prehospitalization intubation and rib fractures were independent risk factors for CSI+TLI. These factors should trigger screening for multiregional spinal column injury in pediatric trauma patients. Furthermore, if there is known TLI, the cervical spine should be evaluated, and if there is significant CSI, then dedicated imaging of the thoracic and lumbar spine is indicated. Level of Evidence. III

ABSTRACT Myelodysplastic syndrome (MDS) and chronic myelomonocytic leukemia (CMML) are clonal myeloid neoplasms shaped by genetic lesions and immune dysregulation, both of which contribute to disease progression and poor outcomes. Existing prognostic systems, such as IPSS‐R and IPSS‐M, do not incorporate immune alterations. We assessed the biological and clinical relevance of an immune dysfunction signature (IDS) across multi‐center MDS and CMML cohorts. IDS scores, derived from bulk transcriptomic data, were significantly associated with inferior leukemia‐free and overall survival. In multivariable analyses, IDS retained independent prognostic value alongside IPSS‐R, IPSS‐M, and bi‐allelic TP53 inactivation. Incorporation of IDS into existing models improved prognostic discrimination and time‐dependent predictive accuracy. Longitudinal analyses revealed that rising IDS scores paralleled disease progression and acute transformation, whereas declining scores were observed in remission. Biologically, IDS‐high cases demonstrated reduced cytotoxic T‐cell activity, expansion of regulatory T cells, enrichment of primitive progenitor signals, and increased expression of checkpoint pathway genes. These findings were validated across multiple independent MDS cohorts and consistently reproduced in CMML, where IDS also stratified risk and tracked disease evolution. Finally, integration with drug response signatures from the BeatAMLv2 cohort suggested potential therapeutic vulnerability of IDS‐high cases to multikinase and NF‐κB pathway inhibitors. These results establish IDS as a robust and dynamic biomarker in MDS and CMML, with applications in refined risk stratification, longitudinal disease monitoring, and guiding personalized therapeutic strategies targeting immune dysfunction. Trial Registration: #201709072RINC, #202109078RINB, #202207050RINB

Abstract BACKGROUND: Tumor-specific T cells are often characterized by reduced TCR signaling due to central and peripheral tolerance pathways that limit their responsiveness. These signals are further weakened by insufficient co-stimulation and active co-inhibitory pathways, collectively establishing a high threshold for activation. The integration of antigen recognition with co-stimulatory and inhibitory cues ultimately determines whether a T cell becomes activated or remains tolerant. Early TCR signaling must therefore be precisely regulated to prevent autoreactivity while still supporting protective immunity. Although phosphorylation and ubiquitination are well-established regulators of proximal TCR signaling, additional post-translational mechanisms remain less defined. SIRT2, a cytosolic NAD+-dependent deacetylase with emerging roles in immune regulation, has not been examined in the context of TCR signaling. METHODS: We evaluated proximal TCR signaling events in wild-type and SIRT2-deficient T cells using flow cytometry, immunoblotting, calcium flux assays, and RNA-sequencing. SIRT2-associated pathways were defined by mapping its interactome and acetylated substrates through mass spectrometry and immunoprecipitation. We screened LCK post-translational modifications by mass spectrometry and assessed SIRT2 enzymatic activity using an HPLC-based deacetylase assay. Conformational effects of LCK modification were examined using fluorescence-polarization binding assays and AlphaFold structural modeling. SIRT2 was deleted in human tumor infiltrating lymphocytes (TILs) via CRISPR/Cas9, and the impact of SIRT2 targeting was tested in lung cancer patient-derived xenograft models reconstituted with autologous TILs. RESULTS: SIRT2 deficiency amplified proximal TCR signaling, leading to elevated phosphorylation of early signaling mediators and increased calcium flux in both naïve and anergic T cells. Loss of SIRT2 also altered thymic selection dynamics and expanded TCR repertoire diversity. Mechanistically, SIRT2 interacted with and deacetylated LCK, the initiating kinase of proximal TCR signaling. Mass spectrometry identified lysine K228 in the LCK linker region as a SIRT2-regulated deacetylation site that governs LCK conformation and kinase activity. Functionally, SIRT2 inhibition in exhausted mouse and human TILs restored TCR signaling capacity and improved anti-tumor responses. CONCLUSION: Here we identify SIRT2-regulated deacetylation of LCK as a previously unrecognized mechanism that sets the strength and threshold of proximal TCR signaling. Accordingly, SIRT2 targeting reverses the exhausted phenotype of tumor-reactive T cells. Citation Format: Imene Hamaidi, Pingyan Cheng, Soo Young Jun, Min-Hsuan Wang, Min Zhang, Odesha Taylor, Luis Lopez-bailon, Ismail Can, Bin Fang, Anders Berglund, Bradford Perez, Ben Creelan, Andriy Marusyk, Virginia Shapiro, Haitao Ji, Jose R. Conejo-Garcia, Sungjune Kim. Sirt2 dictates TCR activation thresholds through post-translational control of LCK conformational state [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2026; Part 1 (Regular Abstracts); 2026 Apr 17-22; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2026;86(7 Suppl):Abstract nr 4250.