Citations

Proper organization of the enteric nervous system (ENS) is critical for normal gastrointestinal (GI) physiology. Inflammatory bowel disease (IBD) disrupts key GI functions, including bowel motility. However, in many IBD patients, motility disorders persist even during remission, suggesting an irreversible ENS defect secondary to IBD. Here, we show that postinflammatory GI motility dysfunction arises from structural remodeling of the ENS, driven by a combination of neuronal loss and neurogenesis. During mucosal inflammation, enteric neurons upregulate CCL2 expression, facilitating the recruitment of monocytes into the myenteric plexus within the intestinal muscle. Monocyte-derived macrophages infiltrate the myenteric ganglia, contributing to excessive ENS remodeling and postinflammatory motility dysfunction. This neuroimmune axis is counterbalanced by a hypoxia-induced stress response in enteric neurons mediated by HIF1α. Enhancing the neuron-intrinsic hypoxia pathway limits ENS remodeling and preserves motility. In summary, this study presents a mechanistic model of postinflammatory GI motility dysfunction and proposes a therapeutic strategy to maintain ENS integrity and function during inflammation.

Regenerative medicine is transforming how we restore tissue function, leveraging advances in cell and molecular biology, biomaterials, and engineered microenvironments. While there have been notable advances and rapid progress over the past few decades, ongoing challenges persist in the technical development and effective translation of these advancements to clinical care. This perspective highlights clinically promising examples and critically assesses present challenges in translating tissue regenerative medicine therapies from the bench to the clinic. We further examine the evolving landscape of regenerative medicine by describing strategies to optimize the cellular microenvironment, the impact of patient demographics, and the use of artificial intelligence to shape the future of this field.

ABSTRACT Most brain organoids derived from human induced pluripotent stem cells (iPSCs) lack microglia and thus immune function. Microglia‐like cells (MGCs) can be differentiated from iPSCs, while the characteristics of isogenic MGC‐containing brain organoids in modeling neurodegeneration and cell‐cell communications have not been well investigated. In this study, iPSC‐derived MGCs are co‐cultured with isogenic forebrain cortical organoids (iFCo), which are stimulated with extracellular vesicles (EVs) of brain organoids differentiated from Alzheimer's disease (AD) patient‐derived iPSCs ( APOE ε4/ε4 and presenilin 1). The AD EV‐stimulated co‐culture organoids are treated with EVs from healthy MGCs or co‐culture. Differential responses of the co‐cultured organoids and the MGCs to AD EVs are demonstrated. The co‐cultured organoids mitigated pro‐inflammatory gene expressions. EVs from healthy MGCs or co‐culture reduced the expression of IL‐12β , iNOS , TREM2 , and CASS4 , which are associated with neural inflammation and degeneration, as well as showed regulation on genes involved in microglial activation and carbon metabolism. AD EV cargo analysis by proteomics and microRNA‐sequencing revealed APOE and APP proteins and microRNAs regulated pathways such as mitophagy. This study paves the way for understanding the role of microglia and brain organoids in modeling neural degeneration and the development of EV‐based cell‐free therapeutics for AD treatment.

Hepatic steatosis, characterized by the accumulation of triacylglycerol in hepatocytes, complicates liver transplantation by increasing the risk of ischemia-reperfusion injury (IRI), leading to suboptimal graft outcomes. This condition has become a major cause of liver graft discarding, exacerbating the ongoing organ shortage for transplantation. Recent advances in ex vivo normothermic perfusion machines (NMPs) have provided promising solutions for reconditioning and preserving steatotic liver. NMP supports cellular metabolism and mitigates IRI by maintaining the physiological conditions. In addition, pharmacological strategies to reduce hepatic steatosis have been explored to increase graft viability. In this review, we summarize current insights into hepatic lipid metabolism and the molecular mechanisms underlying steatosis, while discussing emerging pharmacological defatting approaches. These interventions have the potential to address the challenges posed by hepatic steatosis, improve graft quality, expand the availability of transplantable livers, and enhance clinical outcomes of transplant candidates. Finally, we highlight current limitations and outline directions for future research and clinical applications.

Abstract Background To determine if dose escalation improves outcomes for chondrosarcomas (CHS) of the base of the skull (BOS) and the cervical spine (CS). Methods A prospective, randomized, dose-escalation trial was designed for patients with CHS of the BOS and CS. Patients were randomly assigned to 70 Gy (RBE) or 76 Gy (RBE). The primary endpoint was local failure (LF). Secondary endpoints included overall survival (OS), cancer-specific survival (CSS), progression-free survival (PFS), and treatment-related toxicities. Results Between 1987 and 2007, 105 patients were randomized, median follow-up was 29.2 years (IQR, 26.7-32.4). Forty-six patients were assigned to 70 Gy (RBE) and 59 were assigned to 76 Gy (RBE). LF at 5-, 10-, and 20 years were 7%, 9%, and 11% for 70 Gy (RBE), and 10%, 15%, and 19% for 76 Gy (RBE) (P = .16). Like LF rates, there was no benefit in dose escalation for OS (P = .28) and CSS (P = .22). Progression-free survival at 5-, 10-, and 20 years was 93%, 89%, and 77% for the low-dose group and 83%, 71%, and 59% for the high-dose group (P = .069). Late RT injury ≥ grade 3 was reported in a total of 19 (18%) patients across both dose levels, with 11% in the low-dose group and 24% in the high-dose group (P = .13). Conclusions This is the first and largest trial of patients with grade I/II BOS and CS CHS evaluating dose escalation for tumor control. Proton-based RT is effective and safe for these tumors, but there is no apparent benefit in dose escalation. Clinical Trial Information NCT00592748.

Abstract Background Standardizing beam modeling in the treatment planning system is challenging due to machine‐specific configuration variations. Because of the proton therapy system (PTS) complexity and continuous development, there is no vendor's representative (“golden”) beam data like in LINACs at this stage. Purpose The primary goal of this study is to create a PTS‐specific repository of source model parameters and beam commissioning data for scanning proton therapy treatment planning systems and establish a set of reference values with associated bands of variations for this data. Methods We conducted a survey of 31 PTSs at 27 proton therapy centers to gather beam commissioning source model parameters used in their treatment planning system, ensuring a diverse selection of PTSs from major vendors: Hitachi, IBA, Varian, and Mevion. For each proton therapy center, we collected (i) beam meterset calibration in protons per MU, (ii) in‐air spot size at isocenter in terms of standard deviation in x‐ (crossline, σ x ) and y‐ (inline, σ y ) directions, (iii) in‐air angular spread at isocenter in x‐direction (θ x ) and y‐direction (θ y ) and (iv) distal falloff. For each PTS, we plotted each parameter as a function of nominal energy and performed polynomial regression analysis with optimal model selection based on Akaike Information Criterion. We calculated intraclass correlation coefficients (ICC) to quantify inter‐PTS variability and generated uncertainty bands representing standard deviations across PTSs for each parameter. Additionally, we computed percentage deviations of each individual PTS from the overall multi‐vendor mean to identify systematic vendor‐specific patterns and outliers across the 31 total PTSs analyzed. Results Multi‐vendor beam parameter analysis across 31 PTSs revealed systematic vendor‐specific patterns in inter‐PTS consistency and energy dependencies. Beam meterset calibration demonstrated excellent inter‐PTS consistency across most vendors (ICC: Varian ProBeam ≈ 0, IBA Proteus ONE = 0.006, Mevion S250i with HYPERSCAN = 0.015), with IBA Proteus PLUS systems showing moderate variation (ICC = 0.156 for the dedicated nozzle, ICC = 0.248, for the universal nozzle). Spot size and angular spread variability were highly vendor‐dependent, ranging from excellent consistency in IBA Proteus ONE to substantial variation in IBA Proteus PLUS with the universal nozzle (angular spread ICC = 0.566). Distal falloff maintained excellent consistency across all vendors (ICC < 0.023), indicating reliable energy spread characteristics. Uncertainty band analysis revealed characteristic energy‐dependent patterns, with most systems showing larger uncertainties at lower energies for spot size and angular spread, while IBA Proteus PLUS with the universal nozzle exhibited the largest uncertainty bands overall. Percentage deviation analysis demonstrated vendor‐specific clustering patterns, with some systems showing systematic positive or negative deviations from the multi‐vendor mean. Conclusions The reference data provided in this work will help to cross‐check the beam model during treatment planning system commissioning. Furthermore, this information is useful for Monte Carlo applications and research, where beam characteristics are required to set up phase space parameters, thereby avoiding the need to model the entire PTS. Finally, the results of this study will have indispensable educational value by providing valuable insights into the characteristics and capabilities of different PTSs.

PURPOSE Low-grade serous carcinoma (LGSOC) of the ovary, fallopian tube, or peritoneum is a hormonally driven, relatively chemoresistant malignancy with limited treatment options in the recurrent setting. Given frequent estrogen receptor (ER) expression and dysregulation of the cyclin-dependent kinases 4 and 6 (CDK4/6)–p16–Rb pathway, features shared with hormone receptor–positive breast cancer, dual endocrine, and CDK4/6 inhibition is a biologically rational strategy. This phase II trial evaluated ribociclib plus letrozole in recurrent LGSOC. METHODS This open-label, single-arm, multicenter phase II study enrolled women with measurable, recurrent LGSOC. Patients received ribociclib (600 mg orally, once daily, days 1-21 of a 28-day cycle) and letrozole (2.5 mg orally, once daily). The primary end point was investigator-assessed objective response rate (ORR) per RECIST 1.1. Secondary end points included clinical benefit rate (CBR), progression-free survival (PFS), overall survival (OS), and safety. RESULTS Of 74 patients screened, 51 were enrolled and 49 treated. The confirmed ORR was 30.6% (90% CI, 19.9 to 43.2), including one complete and 14 partial responses. Among responders, the median duration of response was 21.2 months. The CBR was 84% (90% CI, 72.5 to 91.6). The median PFS was 14.5 months (90% CI, 10.1 to 28.8), and the median OS was 44.5 months (90% CI, 31.8 to not reached). The most common grade ≥3 adverse event (AE) was neutropenia (47%), managed with dose modifications. Three grade 5 events (6%) occurred but were unrelated to treatment. Treatment discontinuation because of AEs occurred in 4%. No dose-limiting toxicities were observed. CONCLUSION Ribociclib plus letrozole met the primary end point, achieving meaningful response rates and durable disease control in recurrent LGSOC. The safety profile was consistent with prior CDK4/6 inhibitor studies. This combination represents a therapeutic option in this rare and genomically distinct subtype.

ABSTRACT A major challenge in hematopoietic progenitor cell apheresis is determining the whole blood (WB) volume to process to achieve a CD34+ cell collection goal. We developed a web‐based calculator app that uses the Collection Efficiency 2 (CE2) model to estimate personalized WB processing volumes needed to reach a prescribed CD34+ cell collection goal. Retrospective evaluation of the app using collection data from two academic medical centers showed that actual processed WB volumes were frequently higher than the app's recommendations. The frequency of potential under‐collection events is predicted to be low had the app‐calculated volumes been utilized. Our findings suggest that using the app's recommendations may lower total WB processing volumes, reducing over‐collection and time spent on the apheresis machine, while maintaining a low under‐collection risk profile.

Background: Accurate characterization of tumor stiffness and adhesion is crucial when surgically resecting meningiomas. Currently, such determinations are difficult to ascertain through standard preoperative imaging diagnostics and clinical experience. Herein, we explored magnetic resonance elastography (MRE) as a means of assessing these parameters and thereby improving neurosurgical resections. Methods: This prospective study of 193 patients with meningiomas involved three distinct patient subsets (cohort 1, 88 [2017–2021]; cohort 2, 55 [2022–2023]; cohort 3, 50 [2023–2024]. We analyzed preoperatively obtained MRE data on meningioma stiffness (cohorts 1 and 2) and adhesion (cohort 2) for accuracy, while matching subjects with or without MRE studies (cohort 3, 1:1 ratio) to define the role of preoperative MRE in improving operative outcomes. Results: Areas under the curve (AUCs) for MRE in gauging meningioma stiffness and adhesion were 0.87 and 0.89, respectively. Both parameters significantly surpassed empiric predictions of stiffness and adhesion rendered by surgeons and radiologists, based on routine magnetic resonance imaging (MRI). MRE-determined stiffness measures independently correlated with prolonged resection duration, whereas MRE-assessed adhesion showed independent associations with increased blood loss, longer duration of tumor resection, and higher risk of pial injury. Ultimately, results based on cohort 3 demonstrated that MRE has great potential in this setting to improve neurosurgical procedures by significantly reducing intraoperative bleeding, resection duration, and risks of pial injury. Conclusion: MRE is a promising, noninvasive tool for accurately assessing meningioma stiffness and adhesion, standing to improve the precision and safety of needed surgical intervention.

Abstract Background: For large language models (LLMs) to reach their potential as information technology tools that make medication use safer, clinically relevant benchmarks capable of automated grading and designed specifically to measure the performance of LLMs for medication tasks are required. The purpose of this study was to design a suite of benchmarking tests reflective of Comprehensive Medication Management (CMM; the standard of care for medication optimization) and quantify the baseline performance of the latest LLMs. Methods: We established six benchmarks representing critical stages of the CMM process: drug formulation matching, drug order (sig) generation, drug route matching, drug-drug interaction identification, renal dose identification, and drug-indication matching. For each benchmark, we curated a clinician-annotated dataset comprising 250 standardized input-output pairs including both inpatient and outpatient medications. We evaluated the clinical knowledge retrieval capabilities of three LLMs: GPT-4o-mini, MedGemma-27B, and LLaMA3-70B. We employed a zero-shot prompting strategy, excluding in-context examples, to assess the models’ internal clinical knowledge rather than their few-shot learning potential. To check reliability, each model was run three times using a temperature of 0.7 (a mid-range value of an LLM setting controlling text generation randomness). Performance was assessed using task-specific evaluation metrics including precision (positive predictive value), recall (sensitivity), F1-score, accuracy, and correctness consistency across trials. Results: Across six benchmarks, LLaMA3-70B demonstrated the highest performance in four tasks: drug-formulation matching (F1, 54.0% [95 CI: 50.1-58]), drug-order generation (accuracy, 88.0%), drug-route identification (F1, 74.3% [95 CI: 71-78]), and drug-indication identification (accuracy, 97.6% [95 CI: 95.6-99.2]). In the drug–drug interaction task, GPT-4o-mini achieved the highest overall accuracy (70.4% [95 CI: 64.8-75.7]). For renal dose–adjustment identification, GPT-4o-mini demonstrated the highest F1 score (83.3% [95 CI: 77.6-88]). Correctness-consistency scores ranged from 8.0% to 97.6% across benchmarks, with no model exhibiting uniformly superior consistency. Conclusions: Model performance varied substantially across medication-related tasks. LLaMA3-70B demonstrated promising baseline performance in tasks involving formulation, ordering, route, and indication. GPT-4o-mini showed potential advantages in drug–drug interaction detection and renal dose adjustment. These findings underscore the need for task-specific evaluation when deploying models for medication-focused clinical decision support.