Citations

Circulating glucagon concentrations differ between individuals with no diabetes (ND) and those with type 1 diabetes (T1D). We combined an isotope dilution technique using stable tracers [6,22-13C9,15N1]glucagon and [6,14,19,22-13C9,15N1]glucagon with splanchnic and leg catheterization in participants with ND (n = 8; age 23.1 ± 2.9 years, BMI 26.6 ± 3.5 kg/m2, HbA1c 5.0 ± 0.2% [31 ± 2 mmol/mol]) and T1D (n = 6; 29.0 ± 8.8 years, BMI 26.3 ± 5.0 kg/m2, HbA1c 7.9 ± 0.8% [63 ± 8 mmol/mol]) in the overnight fasted state. After baseline period, exogenous glucagon was infused at rates designed to achieve plasma glucagon concentrations spanning the physiological ranges, to determine the effects of rising glucagon concentrations on splanchnic and leg glucagon balance. At baseline, splanchnic glucagon extraction (SGE) was similar (30.7 ± 2.7 vs. 29.1 ± 2.9%) but leg glucagon extraction (LGE) was lower (27.0 ± 4.2 vs. 40.6 ± 3.1%) in participants with T1D versus those with ND. However, with increasing plasma glucagon concentrations, while SGE remained unchanged within and between groups, LGE fell in participants with ND (41 vs. 31 vs. 24%) but did not change in those with T1D. Despite a numerically lower net splanchnic glucagon production in participants with T1D than in those with ND, no changes were observed with increasing glucagon concentrations within the physiological range in both groups. This is the first human study applying novel glucagon isotopes that describes regional glucagon metabolism in participants with ND and T1D. Our observations provide translational relevance for dual hormone closed-loop systems and provide tools for probing the effects of GLP-1, dual, and triple receptor agonists on pancreatic α-cell functions. Article Highlights This study was conducted to assess splanchnic and leg glucagon metabolism in humans using stable glucagon isotopes. We wanted to evaluate whether splanchnic and leg glucagon metabolism differed between participants with no diabetes (ND) and those with type 1 diabetes (T1D) at glucagon concentrations spanning the physiological range. Whereas splanchnic glucagon extraction did not differ between participants with ND and those with T1D, leg glucagon extraction fell in those with ND but did not change in those with T1D as glucagon concentrations increased. Net splanchnic glucagon production did not change with exogenous glucagon infusion. Our study has implications for dual hormone closed-loop control in T1D where glucagon is infused for prevention of hypoglycemia and for investigating the effects of emerging GLP-1, glucose-dependent insulinotropic polypeptide, and glucagon receptor agonists on endogenous glucagon secretion and clearance.

Importance Nontraumatic subarachnoid hemorrhage (SAH) represents the third most common stroke type with unique etiologies, risk factors, diagnostics, and treatments. Nevertheless, epidemiological studies often cluster SAH with other stroke types leaving its distinct burden estimates obscure. Objective To estimate the worldwide burden of SAH. Design, Setting, and Participants Based on the repeated cross-sectional Global Burden of Disease (GBD) 2021 study, the global burden of SAH in 1990 to 2021 was estimated. Moreover, the SAH burden was compared with other diseases, and its associations with 14 individual risk factors were investigated with available data in the GBD 2021 study. The GBD study included the burden estimates of nontraumatic SAH among all ages in 204 countries and territories between 1990 and 2021. Exposures SAH and 14 modifiable risk factors. Main Outcomes and Measures Absolute numbers and age-standardized rates with 95% uncertainty intervals (UIs) of SAH incidence, prevalence, mortality, and disability-adjusted life-years (DALYs) as well as risk factor–specific population attributable fractions (PAFs). Results In 2021, the global age-standardized SAH incidence was 8.3 (95% UI, 7.3-9.5), prevalence was 92.2 (95% UI, 84.1-100.6), mortality was 4.2 (95% UI, 3.7-4.8), and DALY rate was 125.2 (95% UI, 110.5-142.6) per 100 000 people. The highest burden estimates were found in Latin America, the Caribbean, Oceania, and high-income Asia Pacific. Although the absolute number of SAH cases increased, especially in regions with a low sociodemographic index, all age-standardized burden rates decreased between 1990 and 2021: the incidence by 28.8% (95% UI, 25.7%-31.6%), prevalence by 16.1% (95% UI, 14.8%-17.7%), mortality by 56.1% (95% UI, 40.7%-64.3%), and DALY rate by 54.6% (95% UI, 42.8%-61.9%). Of 300 diseases, SAH ranked as the 36th most common cause of death and 59th most common cause of DALY in the world. Of all worldwide SAH-related DALYs, 71.6% (95% UI, 63.8%-78.6%) were associated with the 14 modeled risk factors of which high systolic blood pressure (population attributable fraction [PAF] = 51.6%; 95% UI, 38.0%-62.6%) and smoking (PAF = 14.4%; 95% UI, 12.4%-16.5%) had the highest attribution. Conclusions and Relevance Although the global age-standardized burden rates of SAH more than halved over the last 3 decades, SAH remained one of the most common cardiovascular and neurological causes of death and disabilities in the world, with increasing absolute case numbers. These findings suggest evidence for the potential health benefits of proactive public health planning and resource allocation toward the prevention of SAH.

Purpose Automating quality assurance (QA) for contours generated by automatic algorithms is critical in radiotherapy treatment planning. Manual QA is tedious, time-consuming, and prone to subjective experiences. Automatic segmentation reduces physician workload and improves consistency. However, an effective QA process for these automatic contours remains an unmet need in clinical practice. Materials and Methods The patient data used in this study was derived from the AAPM Thoracic Auto-Segmentation Challenge dataset, including left and right lungs, heart, esophagus, and spinal cord. Two groups of organ-at-risk (OAR) were generated. A ResNet-152 network was used as a feature extractor, and a one-class support vector machine (OC-SVM) was employed to classify contours as ‘high’ or ‘low’ quality. To evaluate the generalizability, we generated low-quality contours using translation and resizing techniques and assessed correlations between detection limits and metrics such as volume, Dice similarity coefficient (DSC), 95% Hausdorff distance (HD95), and mean surface distance (MSD). Results The proposed OC-SVM model outperformed binary classifiers n metrics such as balanced accuracy and area under the receiver operating characteristic curve (AUC) . It demonstrated superior performance in detecting various types of contour errors while maintaining high interpretability. Strong correlations were observed between detection limits and contour metrics. Conclusion Our proposed model integrates an attention mechanism with a one-class classification framework to automate QA for OAR delineations. This approach effectively detects diverse types of contour errors with high accuracy, significantly reducing the burden on physicians during radiotherapy planning.

Abstract Purpose: Recent advances have seen the development of targeted therapeutics against the receptor tyrosine kinase (RTK)/RAS/MAPK pathway, which, when aberrantly activated, drives the malignant transformation of many cancer indications. However, the efficacy of inhibitors targeting single molecules is dampened by pathway feedback activation and acquired drug resistance. We seek to evaluate the application of JAB-3312 (sitneprotafib), a potent inhibitor of SHP2, in RTK/RAS/MAPK pathway–targeted combination therapies. Furthermore, SHP2 plays a vital role in PD-1–mediated immunosuppression. The rational combination of JAB-3312 with PD-1 blocking therapies is also explored. Experimental Design: Biochemical and cellular assays were applied to evaluate the potency of JAB-3312 in SHP2 inhibition. Tumor cell lines and cell line– and patient-derived xenografts were used to test different combinations of JAB-3312 with KRASG12C, MEK, EGFR, and PD-1 inhibitors. Results: JAB-3312 produced potent in vitro inhibition of SHP2 activity and downstream ERK phosphorylation, with IC50 values of 1.44 nmol/L and 0.68 to 4.84 nmol/L, respectively. When used in combination, JAB-3312 significantly increased the antitumor activity of the KRASG12C inhibitor glecirasib in naïve and resistant models. The combination of JAB-3312 with MEK inhibitors significantly delayed RTK signaling reactivation and enhanced tumor growth inhibition in KRAS-mutated cancer models. The JAB-3312–osimertinib combination exhibited great efficacy in osimertinib-resistant non–small cell lung cancer models. Additionally, JAB-3312 enhanced the efficacy of PD-1 blockade therapies by promoting an antitumor microenvironment. Representative cases of patients who responded to the combination therapies from two ongoing clinical trials (NCT05288205 and NCT04720976) were reported. Conclusions: JAB-3312 in combination with RTK/RAS/MAPK or PD-1 blockade therapies is a promising strategy that warrants further clinical investigation.

Abstract Aims Artificial intelligence (AI) has the potential to transform cardiac electrophysiology (EP), particularly in arrhythmia detection, procedural optimization, and patient outcome prediction. However, a standardized approach to reporting and understanding AI-related research in EP is lacking. This scientific statement aims to develop and apply a checklist for AI-related research reporting in EP to enhance transparency, reproducibility, and understandability in the field. Methods and results An AI checklist specific to EP was developed with expert input from the writing group and voted on using a modified Delphi process, leading to the development of a 29-item checklist. The checklist was subsequently applied to assess reporting practices to identify areas where improvements could be made and provide an overview of the state of the art in AI-related EP research in three domains from May 2021 until May 2024: atrial fibrillation (AF) management, sudden cardiac death (SCD), and EP lab applications. The EHRA AI checklist was applied to 31 studies in AF management, 18 studies in SCD, and 6 studies in EP lab applications. Results differed between the different domains, but in no domain reporting of a specific item exceeded 55% of included papers. Key areas such as trial registration, participant details, data handling, and training performance were underreported (<20%). The checklist application highlighted areas where reporting practices could be improved to promote clearer, more comprehensive AI research in EP. Conclusion The EHRA AI checklist provides a structured framework for reporting AI research in EP. Its use can improve understanding but also enhance the reproducibility and transparency of AI studies, fostering more robust and reliable integration of AI into clinical EP practice.

428 Background: Prostate cancer management presents a significant healthcare burden, with the need to efficiently triage patients for treatment. Our objective is to leverage large language models to predict physician-recommended treatment plans from unstructured clinical notes. By accurately predicting treatment plans, we aim to risk stratify and triage patients effectively, thereby optimizing the allocation of physician resources. Methods: 448 unstructured initial urology consultation patient notes following first positive prostate cancer biopsy were identified. The recommended and final treatments received were manually annotated to establish ground truth labels (Table 1). The dataset was split 80:20 for training and testing, preprocessed to remove plan sections and formatted into question-answer (QA) format. A domain-specific large language model (LLM) inspired by GPT and a specialized tokenizer (PCa- LLM) for prostate cancer terminology were developed. QA models were built using the PCa-LLM and compared with those using GPT-2 as the backbone to predict recommended and final treatments. Results: For the physician-recommended treatment plans, our LLM (PCa-LLM) showed superior performance with higher AUROC scores for curative vs. non-curative treatments (0.78 vs. 0.65), chemo-hormonal vs. other non-curative treatments (0.89 vs. 0.65), and surveillance vs. all other treatments (0.72 vs. 0.70), while both models achieved the same high AUROC of 0.99 for chemo-hormonal vs. all other treatments. For final treatments, PCa-LLM demonstrated better AUROC for curative vs. non-curative treatments (0.77 vs. 0.74) and chemo-hormonal vs. other non-curative treatments (0.71 vs. 0.66), while GPT2 outperformed PCa-LLM for surveillance vs. all other treatments (0.78 vs. 0.70). Both models achieved an AUROC of 0.99 for chemo-hormonal vs. all other treatments. Conclusions: PCa-LLM accurately predicted most treatment categories better than GPT2, with higher AUROC scores, and can be utilized to triage prostate cancer patients using initial consultation notes. Task Physician -Recommended Treatment Plan Final Treatment Received Curative Prostatectomy/Radiation 228 230 Non-Curative Focal Therapy 22 15 Active Surveillance 40 45 Chemo-hormonal 30 30 Model Predictions (AUROC) GPT2 PCa-LLM GPT2 PCa-LLM Curative vs. non-curative 0.65 0.78 0.74 0.77 Chemohormonal vs. other non-curative 0.65 0.89 0.66 0.71 Chemohormonal vs. all other 0.99 0.99 0.99 0.99 Surveillance vs. other non-curative 0.64 0.60 0.67 0.59 Surveillance vs. all other 0.70 0.72 0.78 0.70

Abstract Objective Sex-specific differences impact vulnerability to alcohol effects, dependence (AD) and related phenotypes. Previous studies demonstrated that sex-related hormone and protein levels may contribute to those differences in sex-specific manner. However, previous studies were limited by sample size, the depths of phenotyping assessments and/or the number of hormones and proteins measured at the same time. Here, we investigated the association of the sex-related hormone (testosterone, estrogens, and progesterone) and protein (FSH, LH, SHBG, and albumin) levels with AD in males and females treated in community-based programs compared to age and sex-matched controls. We also investigated the associations of AD with the combined plasma sex-related hormone/protein “signatures” in the same groups of study subjects. Methods Sex-related hormone and protein levels were measured in plasma samples collected from 401 male and female subjects meeting DSM-IV-TR criteria for AD and 401 age and sex matched controls. Psychiatric Research Interview for Substance and Mental Disorder (PRISM) was used to capture DSM-IV diagnoses of substance use and other psychiatric disorders. All analyses were stratified by sex. Associations between each plasma sex-related hormone/protein level and AD was assessed with multivariable linear regression adjusted for age and BMI for males and age, BMI, and menopause status for females. Progesterone was defined as a continuous variable and a binary variable based on a lower laboratory detection limit of 0.1 ng/mL. We used principal component analysis to define hormone/protein signatures. Hormones and proteins with factor loadings =/> |0.4| were considered important drivers for the corresponding principal components (PCs). Resulting PCs were entered into a sex-stratified logistic regression model with adjustment of age and BMI to determine which PCs were associated with AD. Results Male AD subjects had higher estradiol and albumin levels (p <0.00625) as well as progesterone and LH levels (p <0.05) compared to controls. Progesterone was detected in a larger proportion of AD subjects than in the controls (62.6% vs. 47.7%; p = 0.017). In females, albumin levels were significantly higher (p <0.00385) while estrone and progesterone levels were lower (p <0.05) in AD subjects than the controls. Total testosterone (TT) levels were relatively higher in AD subjects but differences between groups were not significant (p >0.05). In males, the first PC (PC1) that accounted for the majority of data variance was significantly associated with AD (p=0.016) and was mostly driven by TT, estrone, estradiol and SHBG. PC3 (driven by albumin, progesterone and estradiol) and PC4 (driven by albumin and SHBG) were also associated with AD (p=0.007 and p<0.001, respectively). In females, PC1 (driven by estrone, estradiol, SHBG) was not significantly associated with AD. However, PC3 (driven by Albumin and progesterone), PC4 (driven by TT and Albumin) and PC5 (driven by progesterone and TT), were significantly associated with AD (p=0.009, p=0.001, p=0.004, respectively). Conclusion Our findings indicate that individual hormone/protein levels as well as hormone/protein combination signatures associated with AD are different in male and female alcoholics. Future studies should investigate physiological mechanisms reflected in hormone/protein signatures captured by PC analyses.

In this study, we propose a new mathematical model able to quantify C-peptide secretion and β-cell responsivity in response to a glucagon bolus in individuals without and with type 2 diabetes. We show that individuals with type 2 diabetes exhibit reduced β-cell responsivity to glucagon.