Citations

Diffuse large B-cell lymphoma (DLBCL), the most common type of lymphoma, arises from various pathogenic mechanisms including gene translocations and fusions. Detection of gene rearrangements using fluorescence in situ hybridization (FISH) is a standard practice for DLBCL diagnosis and guides treatment decisions. High-throughput chromosome conformation capture (Hi-C) DNA sequencing is a next-generation sequencing-based technology to identify genome-wide rearrangements using a single assay. In this study, Hi-C sequencing was performed using FFPE tissues from 159 patients with DLBCL, and identified 746 cancer genes at or proximal to the rearrangement breakpoints with a total of 1903 occurrences. Focusing on clinically important rearrangements, Hi-C detected 102 rearrangements of MYC, BCL2, and/or BCL6 in 92 patients and revealed the fusion partners, including 25 rearrangements missed by FISH. Causes of FISH negative results included FISH-cryptic breakpoints, complex or faint FISH signals, low percentage of FISH positivity, and issues related to tissue fixation. Moreover, in 20 patients (12.6%), Hi-C sequencing detected 22 rearrangements characteristic of other lymphoma types, including CCND1 rearrangements that could lead to reclassification as mantle cell lymphoma. Survival analysis for genome-wide rearrangements using machine learning models identified MYC, PD-L1, CCND1, BCL2, NTRK1/PRCC, RRAS, and FANCE rearrangements with significant prognostic effects in the DLBCL cohort. In conclusion, Hi-C sequencing detects gene rearrangements crucial for diagnosis in an unbiased and molecular manner and showed high sensitivity and specificity in our study. These advantages of Hi-C sequencing offer help to improve the workflow of clinical pathology laboratories, diagnostic precision, and treatment of large B-cell lymphoma.

ABSTRACT Objective This study assessed the impact of sex and age on weight loss outcomes in patients receiving tirzepatide in real‐world clinical practice. Methods We conducted a retrospective cohort study of adults with overweight or obesity who initiated tirzepatide at Mayo Clinic between June 2022 and May 2024 and completed ≥ 12 months of continuous therapy. Primary outcome was total body weight loss percentage (TBWL%) at 15 months, stratified by sex and age groups (≤ 45, 46–59, ≥ 60 years). Results Among 1039 patients (57% women; mean age 56 ± 11 years), women achieved significantly greater TBWL% than men after 15 months of treatment (15.1% vs. 10.7%, p < 0.001). Patients aged ≤ 45 years had greater weight loss than those ≥ 60 years (15.1% vs. 12.3%, p = 0.024). In multivariable analyses, greater weight loss was independently associated with female sex, absence of type 2 diabetes, no prior obesity medication use, no concomitant weight gain‐promoting medications, and higher tirzepatide dosage. Age was not an independent predictor. Conclusions Sex, but not age, predicted weight loss with tirzepatide among individuals using the medication continuously for ≥ 12 months. These findings underscore the importance of incorporating sex‐specific considerations into personalized obesity treatment strategies.

Introduction To assess and compare the accuracy, readability, and overall performance of large language models (LLMs) in answering questions about functional hypothalamic amenorrhea (FHA) for patients and healthcare professionals. Methods A total of 11 patient-level and 15 clinician-level FHA-related questions were entered separately into four LLMs: ChatGPT 3.5 (free version), ChatGPT 4.0 (updated, paid subscription), Gemini, and OpenEvidence. OpenEvidence was used only for clinician-based questions. Responses were evaluated by three expert reviewers blinded to the LLM used who rated them as accurate and complete, accurate but incomplete, or inaccurate. A fourth reviewer resolved discordant scores. Readability for patient-level questions was assessed using the Flesch Reading Ease Score (FRES) and word count. Lower FRES scores indicate more difficult reading. Accuracy and completeness were compared using odds ratios (95% CI) with ChatGPT 3.5 as the reference model, and differences in readability were analyzed using Friedman’s test. Results LLM performance varied across question types. For patient-level questions, ChatGPT 4.0 achieved the highest accuracy (9 of 11; 82%), followed by ChatGPT 3.5 and Gemini (each 8 of 11; 73%), with no statistically significant differences. Among clinician-level questions, OpenEvidence demonstrated perfect accuracy (15 of 15; 100%), compared with 93% for and 80% for ChatGPT 4.0 and Gemini. Completeness followed similar patterns, with OpenEvidence providing the most complete clinician responses (93%) and ChatGPT 4.0 the most complete patient-level responses (89%). Readability differed significantly among models ( p = 0.012), with Gemini producing the most readable patient-level content (median FRES 43.5 [IQR 36.8–53.4]) compared with ChatGPT 3.5 (30.6 [16.8–48.4]) and ChatGPT 4.0 (28.8 [22.1–37.6]). Word counts did not differ significantly ( p = 0.39). Discussion LLMs demonstrated good overall performance in answering FHA-related questions but often provided incorrect or incomplete information. Fine tuning field-specific data, engineered prompts, and obtaining human-in-the-loop feedback may help improve the accuracy of these models.

Abstract Background Mono-allelic Dehydrodolichyl Diphosphate Synthase ( DHDDS) variants are associated with juvenile Parkinsonism, developmental delay and seizures. Symptoms are progressive, and various mechanisms, such as defective glycosylation, lysosomal dysfunction and cholesterol accumulation have been hypothesized to underlie disease symptoms. There is no treatment for DHDDS-related disease. Methods Patient-derived cortical forebrain organoids were created to elucidate disease mechanisms and evaluate potential treatments. In these neuronal models, glycosylation, lipidomics, proteomics, cholesterol/ganglioside accumulation, mitochondrial function and electrophysiological activity were assessed. Finally, we investigated the effects of nicotinamide mononucleotide (NMN), identified through a yeast-based drug screen, in neuronal cell models and in six patients in an off-label, N-of-1, observational series. Results DHDDS-patient derived organoids showed visual signs of degeneration after four months of culturing. This was accompanied by significant cholesterol accumulation in astrocytes, decreased mitochondrial respiration and loss of deep-layer neurons. In addition, we identified glycosylation abnormalities, showing for the first time that glycosylation in human tissue is affected by monoallelic DHDDS variants. Proteomic analysis revealed altered protein expression of proteins involved in lipid metabolism, cytoskeletal organization and neuronal development. We found that oral Nicotinamide Mononucleotide supplementation led to significant improvement in mitochondrial respiration and electrophysiological parameters in organoids, concurring with clinical improvements in all of the treated patients, particularly regarding their ataxia and tremor. Conclusion Our findings reveal a progressive phenotype in DHDDS-patient-derived brain organoids, with mitochondrial dysfunction and astrocyte-specific metabolic alterations contributing to disease pathology. Notably, NMN treatment led to clinical improvements in patients with heterozygous DHDDS variants, highlighting its potential as a therapeutic strategy.

Background: The current guidelines recommend that women at elevated risk for breast cancer (BC), defined as lifetime BC risk ≥ 20%, undergo annual screening with breast magnetic resonance imaging (MRI) and mammogram. However, limited evidence exists in the literature to guide the optimal timing of the MRI relative to the mammogram. Our study evaluated women undergoing high-risk BC screenings to determine the impact of timing for supplemental MRI on BC detection. Methods: Patients who completed high-risk screening MRI at a single institution from January 2019 through June 2022 were included. Baseline characteristics and clinical outcomes were collected through retrospective chart review. MRI exams were divided into two groups based on timing of MRI: concurrent (<90 days from mammogram) and staggered (≥90 days from mammogram). Results: Of 1115 patients initially identified, 770 met inclusion criteria, with a total of 3707 screening exams performed (2073 mammograms and 1634 MRIs). The final analysis included 1355 MRI exams for 712 patients, where a prior mammogram and MRI were available. Of the MRIs included, 784 (57.9%) were concurrent and 571 (42.1%) were staggered. Additional imaging was performed for 12.5% (98/784) of concurrent MRIs and 9.6% (55/571) of staggered MRIs. Biopsy rates were 9.8% (77/784) for concurrent and 7.0% (40/571) for staggered MRIs. In this high-risk population, there were surprisingly low BC detection rates, with a BC incremental detection rate of 0.5% in both the concurrent and staggered groups (p = 1.0), with the median size of detected invasive BCs equaling 5 mm in the concurrent group and 4 mm in the staggered group (p = 0.72). Conclusions: When comparing concurrent and staggered MRI regimens, there were no significant differences in incremental cancer detection rate, tumor size, rates of additional imaging, or negative biopsies. Further investigation with prospective analysis is needed to validate these findings.

The spinal cord plays a central role in the pathophysiology and clinical manifestations of multiple sclerosis (MS), yet remains under-studied compared with the brain. This review summarizes key insights from the 2025 North American Imaging in MS Spinal Cord Imaging Workshop, highlighting recent advances, ongoing challenges, and future opportunities in MS spinal cord imaging. We review pathological studies and outline the clinical relevance of spinal cord lesions and atrophy for diagnosis, prognosis, and disease monitoring, highlighting emerging biomarkers of progression independent of relapse activity. Correlations between magnetic resonance imaging, histopathology, and clinical outcomes support the validation and translational potential of advanced spinal cord imaging techniques. Finally, we discuss spinal cord–specific processing pipelines and reproducibility challenges. Collectively, these insights underscore the need to integrate advanced and quantitative spinal cord imaging into clinical trials, research studies, and—when feasible—clinical care, to fully capture the extent of MS pathology, and ultimately improve patient outcomes.

Introduction The Centers for Medicare & Medicaid skilled nursing facility (SNF) 3-day waiver permits patients attributed to an Accountable Care Organization (ACO) to be admitted to an SNF or transitional care unit from an emergency department, outpatient setting, inpatient stay (<3 days), or observational status without Medicare’s requisite 3-day inpatient stay. Methods Our institution’s ACO implemented the waiver program in 2023. We report our experience with implementing the waiver and describe benefits and barriers, as well as interventions developed, outcomes achieved, and opportunities presented for patients and health care facilities. Results Through a streamlined process, patients can more readily access essential care, and health care facilities benefit from enhanced efficiency. Furthermore, the ACO can more effectively manage health care utilization and cost. Although the program offers benefits to patients, their families, and health care facilities, implementing and maintaining the program has challenges, with barriers including underdeveloped workflows, gaps in staff education and communication, difficulties in patient identification, and challenges with specific facility requirements. Conclusions The SNF 3-day rule waiver has been valuable in enhancing patient care and optimizing health care delivery within our ACO. Ongoing efforts to refine processes and address challenges will further improve the program’s effectiveness and sustainability.

Abstract Background Accurate and efficient dose calculation is essential for online adaptive planning in proton therapy. Deep learning (DL) has shown promising dose prediction results for pencil beam scanning proton therapy (PBSPT) in recent years, but existing DL‐based dose prediction methods still suffer from limited generalizability and an inability to effectively handle outlier clinical cases. This may lead to inaccurate dose delivery to targets or excessive irradiation to organs at risk (OARs), thereby compromising the safety and efficacy of online adaptive proton therapy. Purpose To design a physics‐aware and generalizable AI‐based PBSPT dose prediction method that incorporates underlying physics to enhance generalizability, particularly in handling outlier clinical cases. Methods This study analyzed PBSPT plans of 103 prostate (93 for training and 10 for testing) and 78 lung cancer patients (68 for training and 10 for testing) from our institution, with each case comprising CT images and structure sets. Using the doses generated by our Monte Carlo‐based dose engine as the reference standard, we compared three methods: the region of interest (ROI)‐based method, the beam mask and sliding window method, and the proposed noisy probing dose method, which rapidly generates a low‐statistics dose via uniformly weighted spots on an expanded spot‐placement target volume without optimization. To evaluate the generalizability of these methods to rare treatment planning scenarios, 12 cases with uncommon beam angles or prescription doses were used to assess their performance, which was evaluated using dose‐volume histogram (DVH) indices, 3D Gamma passing rates (3%/2 mm/10%), and Dice coefficients for dose agreement, while prediction times were measured to gauge model efficiency. Results The proposed noisy probing dose method consistently outperformed the ROI‐based and beam mask baselines across all evaluated metrics, with more accurate dose agreement and superior generalizability. For DVH indices, the noisy probing dose method achieved the smallest deviation in clinical target volume (CTV) dose coverage: in prostate cancer, CTV D98 deviation was reduced by 45% (from 0.53 ± 0.22 Gy [RBE] for ROI‐based) and 29% (from 0.41 ± 0.28 Gy [RBE] for beam mask) to 0.29 ± 0.06 Gy [RBE]; in lung cancer, similar improvements were observed, with CTV D98 deviations reduced to 0.34 ± 0.12 Gy [RBE]. The 3D Gamma passing rates improved to 99.65% ± 1.15% for prostate targets and 97.04% ± 1.17% for lung targets. The dice coefficients of the 90% iso‐dose lines were also the highest with the noisy probing dose method (prostate: 0.983 ± 0.005; lung: 0.967 ± 0.01). For the 12 outlier cases, the noisy probing dose method maintained superior generalizability, yielding higher 3D Gamma passing rates (prostate targets: 96.79% ± 0.83%, OARs: 94.29% ± 1.01%; lung targets: 93.38% ± 1.34%, OARs: 93.95% ± 1.32%), demonstrating robust generalizability to rare clinical scenarios. The dose predictions for all testing cases were completed within 0.3 seconds. Conclusions A novel noisy probing dose method was proposed for PBSPT dose prediction in prostate and lung cancer patients. By embedding more proton‐specific physics, this method demonstrated an improvement in the generalizability of dose prediction.