Citations

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.

The adult chronic pancreatitis (CP) working group, formed at the inception of the Consortium for the Study of Chronic Pancreatitis, Diabetes, and Pancreatic Cancer (CPDPC), has developed a robust framework and infrastructure to conduct clinical, translational, and mechanistic studies of CP. At its core is PRO spective Evaluation of C hronic Pancreatitis for E pid E miologic and Translational Stu D ies (PROCEED), the first longitudinal cohort study of CP in US adults, launched in 2017. PROCEED has developed a well-annotated clinical data set of over 2000 deeply-phenotyped participants, a biorepository and an imaging repository. Investigators have published promising data on blood-based and imaging-based biomarkers of CP diagnosis and pain, which are awaiting validation. Clinical observations include classification of patients into mechanism-based pain phenotypes using responses to PROMIS questionnaires, prevalence and predictors of opioid use, prevalence of psychological comorbidity, and osteopathy in CP patients. Pilot clinical trials have evaluated the effect of oral indomethacin on pancreatic fluid prostaglandin E2 levels and Internet-based cognitive behavioral therapy (CBT) for chronic pain. Results of pilot trials have led to an ongoing definitive randomized clinical trial for CBT. The rich environment and resources have facilitated mentorship and academic development of many early-stage investigators. Investigators outside of the consortium have opportunities for collaboration. In the next 5 years, the working group plans to further strengthen the research platform, complete primary and key secondary analyses of PROCEED and ancillary studies, continue efforts to develop biomarkers for diagnosis and prognosis of CP, and complete ongoing clinical trials to address unanswered questions in the field.

Summary Background Postoperative nausea and vomiting (PONV) is a common consequence of anaesthesia, affecting up to 30% of postoperative patients. Female sex is one of the strongest risk factors for PONV, yet no dedicated analysis has examined how this association varies across surgical settings and timepoints. This systematic review and meta-analysis aimed to quantify sex differences in PONV incidence across different surgical contexts. Methods A systematic search was conducted using PRISMA guidelines across Medline and Embase from inception to September 1, 2025. Eligible studies were observational cohort studies (n≥500) of adult patients that conducted multivariate regression analyses including sex as a variable. Two reviewers independently screened, extracted data, and assessed risk of bias using ROBINS-E. A random-effects meta-analysis was performed. Subgroup analyses and multiple sensitivity analyses were completed. Results From 4620 identified studies, 23 met the inclusion criteria, including 462,828 patients across various surgical settings and specialties (52% female). The pooled incidence of PONV was 21% (95% CI[16-27%]), with high heterogeneity (I 2 =99.9%). Meta-analysis confirmed females had a higher risk of developing PONV compared to males (pooled OR=2.40, 95% CI[2.06-2.79], I 2 =93.1%, p<0.0001). Sensitivity analyses confirmed robustness of pooled estimates. Subgroup analyses demonstrated consistently elevated risk of PONV for females at all three timepoints (PACU, 24-hour, 48-hour post-operative) and across studies including and excluding female-only surgeries. Studies were generally at high risk of bias. Discussion Female sex is a strong risk factor for PONV across surgical settings. Further research into precise subgroups, underlying mechanisms of sex-differences, and the use of prophylaxis may help improve this inequity.

Abstract Background Radiation-induced heart disease (RIHD) may develop months to years following radiation therapy for cancers near the heart such as lung or breast cancer. Several mouse models of RIHD exist to study the pathogenesis of disease, but to our knowledge no studies have examined whether sex differences exist in RIHD in mice. Methods Female and male BALB/cJ mice received one whole heart dose of 22 Gray (Gy) radiation via a C5 collimator vs. sham controls that received 0 Gy, anesthesia and were placed in the X-ray machine for the same length of time. Mechanisms of cardiotoxicity were examined at 24 h (innate cardiotoxicity), day 10 (acute cardiotoxicity) and day 35 (chronic cardiotoxicity) post irradiation. Results Females and males developed dsDNA breaks from radiation at day 10 but females had more damage than males ( p < 0.001). Vessel area and cardiac hypertrophy were increased by radiation at day 10 and 35 but were higher in males than females at day 35 ( p < 0.001, p = 0.001, respectively). Bulk RNA sequencing of the heart at day 10 revealed that females and males developed similar gene pathway responses to radiation by upregulating cellular repair pathways and downregulated mitochondrial function. However, females upregulated the master regulator of mitochondrial function, Pgc1α ( p = 0.0001), suggesting improved mitochondrial repair in females. By day 35, males developed heart failure with reduced ejection fraction (HFrEF), inflammatory dilated cardiomyopathy (iDCM) and fibrosis while females developed heart failure with preserved ejection fraction (HFpEF) without inflammation or fibrosis. Conclusions We observed sex differences in nearly every cardiac parameter that we examined following radiation exposure. BALB/c males exposed to 22 Gy radiation developed HFrEF and iDCM by day 35 while females developed HFpEF. Further research is needed to better understand mechanisms driving sex differences in RIHD using translational animal models.

Abstract Background In children with non-metastatic atypical teratoid/rhabdoid tumor (ATRT), proton therapy is increasingly utilized worldwide due to the radiosensitivity of afflicted young patients. General questions persist, however, surrounding dose and volume of radiotherapy. Methods Between 2009-2024, 39 children from a single institution with non-metastatic intracranial ATRT received focal proton therapy at a median age of 2.4 years (range 0.7-19). Twenty-two patients had supratentorial tumors and 27 had gross/near total resection prior to radiation. Most common chemotherapy regimens were ACNS0333 and SJYC07 (n = 10 each) and Dana-Farber (n = 6). Four patients received consolidative high-dose chemotherapy after radiation. The median radiation dose was 50.4 Gy (range, 50.4-54). The cumulative incidence method assessed freedom from selected endpoints.The Fine-Gray method assessed the level of statistical significance between levels of selected prognostic factors. Results With a median followup of 5.2 years (range 0.5-16.2), the 5-yr overall survival, progression-free survival, and local control was 63%, 64%, and 97%, respectively. Thirteen patients developed isolated leptomeningeal failure as first site of recurrence. Only subtotal resection was significantly associated with local recurrence (100% vs 92%, p &lt; 0.05). Patient age, tumor site, extent of resection, radiation dose, and chemotherapy regimen were otherwise not significantly associated with disease control or survival. Four patients experienced symptomatic radiation necrosis, including 1 death: Three of 4 cases received 54 Gy and consolidative intrathecal methotrexate (n = 2) or high dose ACNS0333 intravenous chemotherapy (n = 1, fatal) after radiation. The other patient received 50.4 Gy and necrosis was immediately preceded by ventriculoperitoneal shunt failure. Six patients developed hearing loss requiring aids. One patient developed radiation-induced meningioma a decade following treatment. Conclusion This represents the largest experience reporting 5-year outcomes following focal radiation in children with non-metastatic intracranial ATRT. When applied with a variety of modern chemotherapy regimens in real-world settings, focal proton therapy at a dose of 50.4 Gy may offer the best therapeutic ratio but does not eliminate serious toxicity. If avoidance of craniospinal radiation remains an ongoing priority, further improvement in survival will depend upon systemic therapy.

Abstract Large Language Models (LLMs) demonstrate strong capabilities in healthcare applications. However, recruiting human evaluators to assess usability and effectiveness becomes challenging in the large-scale LLM-generated outcomes. Existing metrics are often outdated for evaluating LLM-based healthcare technologies. This paper proposes a Research through Evaluation (RtE) approach that can refine metrics and improve assessments of LLM-generated outputs. We deploy RtE in a retrospective comparative study of prostate cancer patient inquiries, comparing responses from human clinical care teams with those generated by a GPT-4-based in-basket bot. Through three rounds of co-evaluations with clinical professionals and an LLM-as-graders study with four LLMs, the RtE method shows that starting with small, iterative evaluations helps human graders adjust metrics to suit their daily practices better. Additionally, in the LLM-as-graders study, LLMs exhibit similar behavior, preferring the in-basket bot's responses over the clinical care team. Our methodology and findings demonstrate that the RtE method can enhance the transparency and robustness of evaluation metrics for LLM-generated content, with the potential to generalize to evaluations in other clinical practices.

Abstract Background Glioblastoma (GBM) remains a devastating cancer with limited treatment options, largely due to its heterogeneity. While supramaximal resection has recently provided survival benefits, therapeutic profiling of different tumor compartments, particularly its infiltrative edge remains largely unexplored. Methods Here, we leveraged magnetic resonance imaging (MRI)-guided multi-sampling, collecting 2 cores and 2 margins per case, to query GBM heterogeneity. Whole-exome and RNA-seq with drug testing in two patient-derived 3D models were used to reveal similarities and differences in genomic and transcriptomic makeups, cellular compositions, and drug responses across cores and margins. Bioinformatics interrogations further identified response biomarkers. Results Mutation analysis showed that oncogenes exhibited a higher degree of spatial heterogeneity than tumor suppressor genes, regardless of MRI status. While the mesenchymal transcriptional subtype with extracellular matrix remodeling, stress response, and immune programs were preferentially enriched in enhancing cores, proneural tumors with neurological processes favored non-enhancing margins. Using a 15-drug GBM-targeted panel, ERK (ulixertinib) and PI3K pathway (paxalisib, CC-115) inhibitors showed preferential efficacy in enhancing cores and non-enhancing margins, respectively. The anti-apoptosis, pan-Bcl2 agent navitoclax and the epigenetic drug trotabresib represented the most effective, tumor-wide monotherapies. Importantly, drug combinations generally outperformed single agents across all regions. Conclusions This work demonstrates the regional heterogeneity of therapeutic vulnerabilities in GBM ex vivo, showing various drugs with tumor-wide or MRI-enhancement informed activity. These findings offer preclinical bases of numerous monotherapies and drug combinations for future clinical trial design.

Abstract Aims Heart failure (HF), a major global health challenge, affects millions worldwide and poses substantial healthcare and economic burdens. It is estimated that a large proportion of those with early systolic dysfunction remain asymptomatic at a stage when guideline-directed medical therapies have been shown to prevent disease progression. To develop an artificial intelligence (AI) model capable of predicting abnormal left ventricular ejection fraction (EF) directly from static, non-gated, non-contrast chest computed tomography (CT) scans as a form of opportunistic screening, Methods and results Using a multi-institutional dataset of 34 058 paired non- contrast CT images and echocardiogram reports from two academic centres, we trained our model of classification for predicting left-ventricle ejection fraction (LVEF) categories: abnormal EF (EF &lt; 50%) vs. normal on 25 948 studies. We validated the model on 8110 paired chest CT and echocardiogram results from a separate institution. The model achieved an area under the receiver operating characteristic (AUROC) curve of 0.786 on the hold-out test set and 0.762 on external validation to detect an abnormal EF (&lt;50%). Beyond strong predictive performance, the AI model surpassed expert radiologists in both accuracy and efficiency and provided interpretable visualizations highlighting imaging features linked to reduced LVEF. Conclusion In this study, we developed and validated an AI model capable of predicting abnormal LVEF directly from static, non-gated, non-contrast chest CT scans, a novel application for an imaging modality typically used for unrelated indications as a form of opportunistic screening. This technology holds significant promise for early detection of systolic HF, reducing the diagnostic gap, and improving outcomes in asymptomatic HF patients.