Faculty Bibliography

Protein folding dynamics emerge from subtle changes in intramolecular interactions, where hydration shapes free energy landscapes and influences conformational ensembles and supramolecular assembly. While these shape-shifting principles have been primarily explored in well-behaved systems with sharp transitions, their applicability to multidomain protein assemblies, particularly those containing partially folded or disordered regions, remains unclear. Here, we investigate how a single leucine-to-alanine mutation modulates the hierarchical interplay between folding, micelle formation, and macroscopic biomaterial behavior using two diblock protein block copolymers (BCPs): CE and C_L44AE. In solution, both BCPs exhibit spectral features consistent with their C/C_L44A monoblocks, with CE showing α-helical features and C_L44AE more disordered ones. Upon dehydration, however, both systems display increased structural order, with CE favoring β-sheets and C_L44AE adopting α-helical content. At the mesoscale, both BCPs assemble into amphiphilic micelles, but CE forms smaller, densely packed micelles, whereas C_L44AE generates larger micelles that template film networks of varying pore size depending on their packing density. These differences lead to distinct solvation behavior, with C_L44AE exhibiting greater uptake of predominantly mobile water, approximately twice that of CE, culminating in an actuation energy density of 2,043 kJ/m³, the highest reported for protein BCPs to date. Despite this increased uptake, C_L44AE shows reduced mechanical relaxation and a rougher DSC/TGA thermogram, indicative of a broader conformational ensemble compared to CE. Comparisons to monoblocks (C, C_L44A, E) and previously reported triblocks (CEC, C_L44AEC_L44A) further highlight the importance of asymmetric diblock architectures in promoting hydration-sensitive assemblies, laying the rational design groundwork for future high-performance, water-responsive protein biomaterials.

We propose Open Immune Oncology (OpenIO), a framework integrating generative AI and omics to advance precision oncology. By leveraging biological scaling laws and foundation models, we aim to transition immunotherapy from empirical screening to rational, AI-native engineering of therapeutic interventions.

Studies of brain morphology in mental illness often focus on a few neuroimaging phenotypes. Here we present a comprehensive morphological characterization in obsessive-compulsive disorder (OCD) in a large sample (2255 OCD, 2264 controls) using nine cortical and four subcortical phenotypes, including several not previously examined in OCD, among them a subcortical structural similarity network phenotype developed here. Spatially distinct regional alterations emerged across structural phenotypes: cortical curvature alterations in default mode and frontoparietal networks, increased structural similarity network node degree in sensorimotor regions, widespread volume reductions associated with medication use, and localized subcortical shape alterations. In brain-behavior predictive models, curvature phenotypes showed the strongest associations with clinical features. Cortical alterations, especially in structural similarity networks, were associated with specific gene expression patterns, implicating dysregulation of excitatory neurons. RNA-sequencing data from tissue collected during functional neurosurgery revealed that genes downregulated in the dorsolateral prefrontal cortex in OCD contributed to the gene expression patterns linked to cortical alterations. Previously reported differentially expressed genes from postmortem brain studies of OCD also contributed. These findings support the importance of a comprehensive approach to characterizing brain morphology and suggest that cortical curvature and structural similarity alterations reflect key pathophysiological processes in OCD.

Inadequate fruit and vegetable consumption is associated with increased risk of chronic disease. Yet, many individuals consume below the recommended intake according to the Dietary Guidelines for Americans (DGA). This study aimed to examine the association of adherence to the DGA (2020-2025) recommendations for fruit and vegetable (FV) intake of 1.5-2 cups of fruits and 2-3 cups of vegetables daily for adults among caretakers with a child(ren) living in households eligible for a Supplemental Nutrition Assistance Program (SNAP). We conducted a cross-sectional analysis of 85 caretakers with children in an urban neighborhood of low-income in the Bronx, New York (NY). Log-binomial regressions demonstrated that having more children (RR 1.36; 95% CI 1.15-1.59), younger children (RR 1.22; 95% CI 1.07-1.39), or children participating in a school lunch program (RR 1.47; 95% CI 1.16-1.85) was positively associated with caretakers' probability of adhering to the DGA recommendations for FV intake. Our study highlights the eating behaviors of families living with children ≤ 10 years of age, many of whom were participating in a school lunch program, and underscores the dietary benefits associated with these characteristics.

BACKGROUND:In expert centers, surgical resection rates of locally advanced pancreatic cancer (LAPC) following induction chemotherapy have increased beyond 20% with subsequent 25% five-year overall survival (OS). In the Netherlands, however, the historical low 8% LAPC resection rate compared with 23% in international expert centers reflects relative reluctance. Thereby, opportunities to achieve long-term survival in appropriately selected patients may be missed. This study evaluated whether nationwide implementation of international multidisciplinary best-practice for LAPC management is feasible while maintaining surgical safety benchmarks (in-hospital/30-day major morbidity <50% and mortality ≤5%). METHODS:A multidisciplinary protocol was designed in collaboration with four international experts and prospectively implemented nationwide within the Dutch Pancreatic Cancer Group (DPCG) (2022-2024). This observational cohort included consecutive patients diagnosed with LAPC, defined by DPCG criteria. Eligible patients had radiologically non-progressive disease after at least four months of multi-agent chemotherapy. All patients who underwent resection were included in this safety analysis. A predefined sub-group analysis included patients with National Comprehensive Cancer Network (NCCN) LAPC. Primary outcomes included in-hospital/30-day major morbidity (i.e., Clavien-Dindo grade ≥IIIa) and mortality. The expected number of resections was 53. RESULTS:Overall, 180 patients with LAPC underwent surgical exploration, of whom 155 (86%) underwent resection in 11 centers. Most (74%) resections were performed in the three LAPC surgical centers. Extended resections were performed in 77% of patients, including portomesenteric venous (60%), multivisceral (23%), and arterial (21%) resections. In-hospital/30-day major morbidity rate was 44% and mortality rate was 0.6%, both within pre-established safety benchmarks. Benchmarks were also reached for patients with NCCN LAPC (49% major morbidity, 2% mortality). CONCLUSION/CONCLUSIONS:Nationwide implementation of the international best-practice for LAPC was feasible with nearly three times more resections performed than expected, while morbidity and mortality remained well within predefined safety benchmarks.

While there have been great advancements in total knee arthroplasty (TKA) over the past 50 years, anterior knee pain (AKP) remains the most common concern among patients postoperatively. Despite exhausting evidence supporting no clinical difference in AKP with resurfaced vs. unresurfaced patellae in TKA, 87% of TKAs are resurfaced in the United States, compared with 2% in other countries. These large practice variations underscore a lack of consensus regarding the role of the patella and the approach to the anterior compartment of the knee in TKA. The aim of this review was to go beyond patellar resurfacing and describe the effects that surgical technique and implant design may have on AKP in current TKA.

BACKGROUND:Neurological disorders affect approximately 3 billion people globally, yet clinical trial success is often hindered by poorly selected outcome measures, impacting trial design, compliance, and interpretation. Over the past 25 years, Core Outcome Sets (COS) have emerged as standardized tools to enhance outcome selection, ensuring comparability across studies and reflecting the priorities of both researchers and patients. Despite the success of COS initiatives in other fields, their development in neurology remains limited, leaving many trialists without disease-specific guidance. OBJECTIVES/OBJECTIVE:This study aimed to conduct an AI-assisted thematic synthesis of outcome domains from existing COS, with the goal of identifying structural patterns common to these sets and generating descriptive reference framework to inform future COS development. METHODS:COS-Neuro was developed using AI-assisted thematic framework analysis, complemented by expert review. A modified five-step thematic analysis was conducted without pre-determined codes: 1. Dataset Gathering - Data was collected from the COMET database, and COS domains for neurological disorders were coded. 2. Prompt Design & Testing - Large language models (LLMs), including ChatGPT 3.5, Google Gemini 1.5 Flash and Meta Llama-2-70b, were trialled, and prompts refined based on their outputs. 3. Thematic Analysis - LLMs categorised domains into core areas. 4. Human Refinement - Experts reviewed LLM-generated core areas and selected those most appropriate for further interpretation. 5. Clinical Validation - Experts validated the domains, core areas, and concepts. This approach integrated AI with expert oversight to develop an AI-descriptive thematic map of existing neurological COS. RESULTS:Utilising LLMs, particularly ChatGPT, an AI-assisted conceptual framework synthesising existing neurological COS was developed based on the analysis of 112 published COS. Through adaptation of the OMERACT model, the final framework comprised four overarching concepts, 13 core areas, and 75 domains identified through expert consensus. CONCLUSION/CONCLUSIONS:COS-Neuro provides a preliminary AI-assisted descriptive synthesis of existing neurological COS, organised using the OMERACT Filter 2.1 as a structural reference. This hypothesis-generating framework may serve as a foundational resource for future COS research and trial design, particularly in areas where no disease-specific COS exists. However, it requires prospective validation through disease-specific, multi-stakeholder consensus processes before clinical application. COS-Neuro also demonstrates the feasibility of AI-assisted thematic synthesis in this context and warrants evaluation in other specialties.