Tissue-specific reprogramming leads to angiogenic neutrophil specialization and tumor vascularization in colorectal cancer.

Neutrophil (PMN) tissue accumulation is an established feature of ulcerative colitis (UC) lesions and colorectal cancer (CRC). To assess the PMN phenotypic and functional diversification during the transition from inflammatory ulceration to CRC we analyzed the transcriptomic landscape of blood and tissue PMNs. Transcriptional programs effectively separated PMNs based on their proximity to peripheral blood, inflamed colon, and tumors. In silico pathway overrepresentation analysis, protein-network mapping, gene signature identification, and gene-ontology scoring revealed unique enrichment of angiogenic and vasculature development pathways in tumor-associated neutrophils (TANs). Functional studies utilizing ex vivo cultures, colitis-induced murine CRC, and patient-derived xenograft models demonstrated a critical role for TANs in promoting tumor vascularization. Spp1 (OPN) and Mmp14 (MT1-MMP) were identified by unbiased -omics and mechanistic studies to be highly induced in TANs, acting to critically regulate endothelial cell chemotaxis and branching. TCGA data set and clinical specimens confirmed enrichment of SPP1 and MMP14 in high-grade CRC but not in patients with UC. Pharmacological inhibition of TAN trafficking or MMP14 activity effectively reduced tumor vascular density, leading to CRC regression. Our findings demonstrate a niche-directed PMN functional specialization and identify TAN contributions to tumor vascularization, delineating what we believe to be a new therapeutic framework for CRC treatment focused on TAN angiogenic properties.

Evaluation of 18 CT signs in diagnosing cecal volvulus: a multi-reader retrospective study.

PURPOSE: To assess the diagnostic performance and reliability of 18 CT signs to diagnose cecal volvulus, a surgical emergency, versus a group of non-volvulus mimickers. MATERIALS AND METHODS: Four radiologists retrospectively and independently assessed 18 CT signs in 191 patients with cecal volvulus (n = 63) or a non-volvulus control group ((n = 128), including cecal bascule (n = 19), mobile cecum (n = 95), and colonic pseudo-obstruction (n = 14)) at a single institution from 2013 to 2021. Fleiss' kappa coefficient was used to assess inter-reader agreement. For diagnostic performance metrics, we assessed sensitivity, specificity, and positive and negative predictive values. For predictive performance, all 18 signs were included in bivariate and stepwise lasso multivariate logistic regression models to diagnose cecal volvulus. Performance was assessed by ROC curves. RESULTS: 191 patients (mean age: 63 years +/- 15.5 [SD]; 135 women) were included in the study. Nine of the 18 CT signs of cecal volvulus demonstrated good or better (> 0.6) inter-reader agreement. Individual CT signs with sensitivity, specificity, positive and negative predictive values all above 70% for diagnosing cecal volvulus were transition point, bird beak, and X-marks-the-spot. A lasso regression model determined four CT features: transition point, bird beak, coffee bean, and whirl had excellent prediction (AUC = .979) for cecal volvulus if all present. CONCLUSION: CT signs for cecal volvulus that have high sensitivity and specificity include: transition point, bird beak, and X-marks-the-spot and were reliable in distinguishing non-volvulus mimickers. If the following four features were present: transition point, bird beak, coffee bean, and whirl, there was excellent prediction (AUC = .979) for cecal volvulus.

The Brain Tumor Segmentation (BraTS-METS) Challenge 2023: Brain Metastasis Segmentation on Pre-treatment MRI.

Clinical monitoring of metastatic disease to the brain can be a laborious and timeconsuming process, especially in cases involving multiple metastases when the assessment is performed manually. The Response Assessment in Neuro-Oncology Brain Metastases (RANO-BM) guideline, which utilizes the unidimensional longest diameter, is commonly used in clinical and research settings to evaluate response to therapy in patients with brain metastases. However, accurate volumetric assessment of the lesion and surrounding peri-lesional edema holds significant importance in clinical decision-making and can greatly enhance outcome prediction. The unique challenge in performing segmentations of brain metastases lies in their common occurrence as small lesions. Detection and segmentation of lesions that are smaller than 10 mm in size has not demonstrated high accuracy in prior publications. The brain metastases challenge sets itself apart from previously conducted MICCAI challenges on glioma segmentation due to the significant variability in lesion size. Unlike gliomas, which tend to be larger on presentation scans, brain metastases exhibit a wide range of sizes and tend to include small lesions. We hope that the BraTS-METS dataset and challenge will advance the field of automated brain metastasis detection and segmentation.

A Framework for Harmonization of Radiomics Data for Multicenter Studies and Clinical Trials.

PURPOSE: Variability in computed tomography images intrinsic to individual scanners limits the application of radiomics in clinical and research settings. The development of reproducible and generalizable radiomics-based models to assess lesions requires harmonization of data. The purpose of this study was to develop, test, and analyze the efficacy of a radiomics data harmonization model. MATERIALS AND METHODS: Radiomic features from biopsy-proven untreated hepatic metastasis (N = 380) acquired from 167 unique patients with pancreatic, colon, and breast cancers were analyzed. Radiomic features from volume-match 551 samples of normal liver tissue and 188 hepatic cysts were included as references. A novel linear mixed effect model was used to identify effects associated with lesion size, tissue type, and scanner model. Six separate machine learning models were then used to test the effectiveness of radiomic feature harmonization using multivariate analysis. RESULTS: Proposed model identifies and removes scanner-associated effects while preserving cancer-specific functional dependence of radiomic features on the tumor size. Data harmonization improves the performance of classification models by reducing the scanner-associated variability. For example, the multiclass logistic regression model, LogitBoost, demonstrated the improvement in sensitivity in the range from 15% to 40% for each type of liver metastasis, whereas the overall model accuracy and the kappa coefficient increased by 5% and 8% accordingly. CONCLUSION: The model removed scanner-associated effects while preserving cancer-specific functional dependence of radiomic features.

Role of hepatic metastatic lesion size on inter-reader reproducibility of CT-based radiomics features.

OBJECTIVES: To evaluate the effect of hepatic metastatic lesion size on inter-reader reproducibility of CT-based 2D radiomics imaging features. METHODS: Computerized tomography (CT) scans of 59 liver metastases from 34 patients with colorectal cancer were evaluated. Image segmentation was performed manually by three readers blinded to each other's results. For each radiomics feature, we created two datasets by sorting measurements according to size, i.e., (i) from the smallest to the largest lesion and (ii) from the largest to the smallest lesion. The Lin concordance correlation coefficient (CCC) was employed to analyze the reproducibility of radiomics features. In particular, the CCC was computed as a function of a number of elements in the dataset, by gradually adding lesions from each sorted dataset. To evaluate the effect of lesion size, we analyzed the difference between these two functions thus assessing the contribution of small and large lesions into the reproducibility of radiomics features. RESULTS: Inter-reader reproducibility of CT-based 2D radiomics features assessed using Lin's CCC demonstrates tumor-size dependence. For example, the Lin CCC for GLCM contrast equals 0.88 (95% C.I. 0.84 to 0.92, p < 0.003) and could change by an additional + / - 0.06 depending on the presence of large or small lesions. CONCLUSIONS: Groups of "large" and "small" lesions show different inter-reader reproducibility. The inter-reader reproducibility from the mixed group consisting of "large" and "small" lesions depends on the lesion-size distribution and can vary widely. This finding could partially explain variability in reproducibility of radiomics features in the literature. KEY POINTS: • Groups of "large" and "small" lesions show different inter-reader reproducibility. • The inter-reader reproducibility from the mixed group consisting of "large" and "small" lesions depends on the lesion-size distribution.

Radiomics of hepatocellular carcinoma: promising roles in patient selection, prediction, and assessment of treatment response.

Radiomics refers to the process of conversion of conventional medical images into quantifiable data ("features") which can be further mined to reveal complex patterns and relationships between the voxels in the image. These high throughput features can potentially reflect the histology of biologic tissues at macroscopic and microscopic levels. Several studies have investigated radiomics of hepatocellular carcinoma (HCC) before and after treatment. HCC is a heterogeneous disease with diverse phenotypical and genotypical landscape. Due to this inherent heterogeneity, HCC lesions can manifest variable aggressiveness with different response to treatment options, including the newer targeted therapies. Hence, radiomics can be used as a potential tool to enable patient selection for therapies and to predict response to treatments and outcome. Additionally, radiomics may serve as a tool for earlier and more efficient assessment of response to treatment. Radiomics, radiogenomics, and radio-immunoprofiling and their potential roles in management of patients with HCC will be discussed and critically reviewed in this article.

Association Between the Size and 3D CT-Based Radiomic Features of Breast Cancer Hepatic Metastasis.

PURPOSE: To evaluate the effect of the anatomic size on 3D radiomic imaging features of the breast cancer hepatic metastases. MATERIALS AND METHODS: CT scans of 81 liver metastases from 54 patients with breast cancer were evaluated. Ten most common 3D radiomic features from the histogram and gray level co-occurrence matrix (GLCM) categories were calculated for the hepatic metastases (HM) and compared to normal liver (NL). The effect of size was evaluated by using linear mixed-effects regression models. The effect of size on different radiomic features was analyzed for both liver lesions and background liver. RESULTS: Three-dimensional radiomic features from GLCM demonstrate an important size dependence. The texture-feature size dependence was found to be different among feature categories and between the HM and NL, thus demonstrating a discriminatory power for the tissue type. Significant difference in the slope was found for GLCM homogeneity (NL slope = 0.004, slope difference 95% confidence interval [CI] 0.06-0.1, p <0.001), contrast (NL slope = 45, slope difference 95% CI 205-305, p <0.001), correlation (NL slope = 0.04, slope difference 95% CI 0.11-0.21, p <0.001), and dissimilarity (NL slope = 0.7, slope difference 95% CI 3.6-5.4, p <0.001). The GLCM energy (NL slope = 0.002, slope difference 95% CI -0.0005 to -0.0003, p <0.007), and entropy (NL slope = 1.49, slope difference 95% CI 0.07-0.52, p <0.009) exhibited size-dependence for both NL and HM, although demonstrating a difference in the slope between themselves. CONCLUSION: Radiomic features of breast cancer hepatic metastasis exhibited significant correlation with tumor size. This finding demonstrates the complex behavior of imaging features and the need to include feature-specific properties into radiomic models.

Application of Iterative Metal Artifact Reduction Algorithm to CT Urography for Patients With Hip Prostheses.

OBJECTIVE. The purpose of this study is to retrospectively assess the impact of iterative metal artifact reduction (IMAR) with iterative reconstruction (IR) on the image quality and diagnostic performance of CT urography in the evaluation of patients with hip prostheses, compared with IR alone. MATERIALS AND METHODS. CT urography examinations that were reconstructed using IR with and without IMAR were analyzed for 57 patients (29 women and 28 men; mean age, 74 years [range, 22-94 years]) with hip prostheses (40 unilateral and 17 bilateral). For quantitative analysis, image noise within the bladder was measured. Two radiologists (radiologist 1 [RAD1] and radiologist 2 [RAD2]) qualitatively evaluated the images using both a 5-point scale to assess the degree of visualization of artifacts and a 6-point scale to determine diagnostic confidence in visualization of the bladder, ureters, prostate or uterus, pelvic calcifications, and genitourinary abnormalities involving the bladder, distal ureters, prostate, uterus, and ovaries. RESULTS. The combination of IMAR and an IR technique provided improvement in quantitative and qualitative measurements (p < 0.05). Forty-three genitourinary abnormalities were detected in 29 patients. Quantitative and qualitative comparisons of scans obtained with and without the use of IMAR, respectively, revealed image noise of 99.6 versus 173.3 HU and the following radiologist scores: for improvement of artifacts, 3.2 versus 1.6 (for RAD1) and 3.1 versus 1.6 (for RAD2); for visualization of the bladder, 3.6 versus 1.5 (RAD1) and 3.8 versus 1.6 (RAD2); visualization of the ureters, 3.8 versus 1.6 (RAD1) and 3.9 versus 1.7 (RAD2); visualization of the uterus, 4.3 versus 2.8 (RAD1) and 4.3 versus 2.6 (RAD2); visualization of the prostate, 4.5 versus 2.3 (RAD1) and 4.5 versus 2.2 (RAD2); diagnostic confidence for calcifications, 4.7 versus 3.5 (RAD1) and 4.7 versus 3.3 (RAD2); and diagnostic confidence for genitourinary abnormalities, 5.0 versus 3.2 (RAD1) and 4.8 versus 2.9 (RAD2), respectively. CONCLUSION. The addition of IMAR to IR led to statistically significant improvement in the retrospective diagnostic performance and image quality of CT urography for patients with hip prostheses, compared with IR alone.

Differentiation of Papillary Renal Cell Carcinoma Subtypes on MRI: Qualitative and Texture Analysis.

OBJECTIVE: The objective of this study was to determine whether quantitative texture analysis of MR images would improve the ability to distinguish papillary renal cell carcinoma (RCC) subtypes, compared with analysis of qualitative MRI features alone. MATERIALS AND METHODS: A total of 47 pathologically proven papillary RCC tumors were retrospectively evaluated, with 31 (66%) classified as type 1 tumors and 16 (34%) classified as type 2 tumors. MR images were reviewed by two readers to determine tumor size, signal intensity, heterogeneity, enhancement pattern, margins, perilesional stranding, vein thrombosis, and metastasis. Quantitative texture analysis of gray-scale images was performed. A logistic regression was derived from qualitative and quantitative features. Model performance was compared with and without texture features. RESULTS: The significant qualitative MR features noted were necrosis, enhancement appearance, perilesional stranding, and metastasis. A multivariable model based on qualitative features did not identify any factor as an independent predictor of a type 2 tumor. The logistic regression model for predicting papillary RCCs on the basis of qualitative and quantitative analysis identified probability of the 2D volumetric interpolated breath-hold examination (VIBE) sequence (AUC value, 0.87; 95% CI, 0.77-0.98) as an independent predictor of a type 2 tumor. No difference in the model AUC value was noted when texture features were included in the analysis; however, the model had increased sensitivity and an improved predictive value without loss of specificity. CONCLUSION: The addition of texture analysis to analysis of conventional qualitative MRI features increased the probability of predicting a type 2 papillary RCC tumor, which may be clinically important.

Molecular design for growth of supramolecular membranes with hierarchical structure.

Membranes with hierarchical structure exist in biological systems, and bio-inspired building blocks have been used to grow synthetic analogues in the laboratory through self-assembly. The formation of these synthetic membranes is initiated at the interface of two aqueous solutions, one containing cationic peptide amphiphiles (PA) and the other containing the anionic biopolymer hyaluronic acid (HA). The membrane growth process starts within milliseconds of interface formation and continues over much longer timescales to generate robust membranes with supramolecular PA-HA nanofibers oriented orthogonal to the interface. Computer simulation indicates that formation of these hierarchically structured membranes requires strong interactions between molecular components at early time points in order to generate a diffusion barrier between both solutions. Experimental studies using structurally designed PAs confirm simulation results by showing that only PAs with high ζ potential are able to yield hierarchically structured membranes. Furthermore, the chemical structure of such PAs must incorporate residues that form ß-sheets, which facilitates self-assembly of long nanofibers. In contrast, PAs that form low aspect ratio nanostructures interact weakly with HA and yield membranes that exhibit non-fibrous fingering protrusions. Furthermore, experimental results show that increasing HA molecular weight decreases the growth rate of orthogonal nanofibers. This result is supported by simulation results suggesting that the thickness of the interfacial contact layer generated immediately after initiation of self-assembly increases with polymer molecular weight.

Performance of tumor growth kinetics as an imaging biomarker for response assessment in colorectal liver metastases: correlation with FDG PET.

PURPOSE: To correlate RECIST, volumetric criteria, and tumor growth kinetics at multidetector-computed tomography with tumor metabolic activity at FDG PET in colorectal liver metastases (CRCLM) treated with bevacizumab-based chemotherapy. METHODS: Thirty-two CRCLM in 20 patients treated with bevacizumab-based chemotherapy were evaluated. Pre- and post-treatment CT scans were used to calculate reciprocal of doubling time (RDT), percentage change in the lesion's longest transaxial diameter (RECIST 1.1), and percentage change in the tumor volume. The accuracy of these parameters in predicting response based on standard uptake value analysis at FDG PET was assessed. Data were analyzed using Spearman's correlation, student's t, Mann-Whitney, Wilcoxon signed-rank, and Fisher's exact tests. RESULTS: According to FDG PET, 24/32 (75%) lesions were categorized as responders and 8/32 (25%) lesions as nonresponders. Based on RDT, 26/32 (81.25%) lesions were classified as responders and 6/32 (18.75%) lesions as nonresponders. Response classification according to RDT and FDG PET was concordant in 30/32 (93.75%) lesions, whereas RECIST 1.1 and volumetric criteria were concordant with FDG PET for 20/32 (62.5%) and 21/32 (65.63%) lesions, respectively. A strong association was found between RDT and response based on FDG PET (odds ratio = 127.4; 95% CI 5.54-2997; P < 0.0001). CONCLUSIONS: Tumor growth kinetics may be an effective imaging biomarker for response evaluation in CRCLM.

Tumor growth kinetics versus RECIST to assess response to locoregional therapy in breast cancer liver metastases.

RATIONALE AND OBJECTIVES: The aim of our study was to evaluate changes in growth kinetics of breast cancer liver metastasis in response to locoregional therapy and compare them to Response Evaluation Criteria in Solid Tumors (RECIST). MATERIALS AND METHODS: This Health Insurance Portability and Accountability Act-compliant retrospective study was Institutional Review Board approved. Thirty-four chemorefractory breast cancer liver metastases from 21 patients treated with yttrium-90 ((90)Y) were evaluated. Pre- and posttreatment computed tomography (CT) scans were used to calculate tumor growth kinetics. The growth parameter analyzed was reciprocal of doubling time (RDT). RDT range for stable disease (SD) was defined by the measurement error rate. A negative RDT below the SD range defined response and was categorized as either partial response (PR) or complete response, whereas a positive RDT value above the SD range indicated progressive disease (PD). Comparison was made to tumor response classification according to percentage change in the lesion's maximal diameter per RECIST. Lin's concordance correlation coefficient, Bland-Altman plot, Wilcoxon signed rank test, and Student t test were used for analysis. Significance was set at 0.05. RESULTS: RDT range for SD ranged from -0.46 to +2.17. Six lesions with PR based on RECIST showed PR based on their volumetric growth rate (mean RDT of -17.3 ± 2.6). Similarly, one lesion with PD according to RECIST was categorized as PD based on its growth kinetics (RDT of 10.2). However, 14 (51.85%) lesions classified as SD by RECIST had PR according to growth kinetics (mean RDT of -7.8), six (22.22%) lesions were categorized as SD (mean RDT of 0.8), whereas seven (25.93%) lesions showed PD (mean RDT of 4.5). Growth kinetic parameters were significantly different for lesions with PR when compared to lesions with PD (P < .0001). CONCLUSIONS: In patients with breast cancer liver metastases undergoing locoregional therapy, RECIST categorization may not be an accurate reflection of treatment response.

Electrostatic control of structure in self-assembled membranes.

Self-assembling peptide amphiphiles (PAs) can form hierarchically ordered membranes when brought in contact with aqueous polyelectrolytes of the opposite charge by rapidly creating a diffusion barrier composed of filamentous nanostructures parallel to the plane of the incipient membrane. Following this event, osmotic forces and charge complexation template nanofiber growth perpendicular to the plane of the membrane in a dynamic self-assembly process. In this work, we show that this hierarchical structure requires massive interfacial aggregation of PA molecules, suggesting the importance of rapid diffusion barrier formation. Strong PA aggregation is induced here through the use of heparin-binding PAs with heparin and also with polyelectrolytes of varying charge density. Small angle X-ray scattering shows that in the case of weak PA-polyelectrolyte interaction, membranes formed display a cubic phase ordering on the nanoscale that likely results from clusters of PA nanostructures surrounded by polyelectrolyte chains.

Electric Field Controlled Self-Assembly of Hierarchically Ordered Membranes.

Self-assembly in the presence of external forces is an adaptive, directed organization of molecular components under nonequilibrium conditions. While forces may be generated as a result of spontaneous interactions among components of a system, intervention with external forces can significantly alter the final outcome of self-assembly. Superimposing these intrinsic and extrinsic forces provides greater degrees of freedom to control the structure and function of self-assembling materials. In this work we investigate the role of electric fields during the dynamic self-assembly of a negatively charged polyelectrolyte and a positively charged peptide amphiphile in water leading to the formation of an ordered membrane. In the absence of electric fields, contact between the two solutions of oppositely charged molecules triggers the growth of closed membranes with vertically oriented fibrils that encapsulate the polyelectrolyte solution. This process of self-assembly is intrinsically driven by excess osmotic pressure of counterions, and the electric field is found to modify the kinetics of membrane formation, and also its morphology and properties. Depending on the strength and orientation of the field we observe a significant increase or decrease of up to nearly 100% in membrane thickness, as well as the controlled rotation of nanofiber growth direction by 90 degrees, resulting in a significant increase in mechanical stiffness. These results suggest the possibility of using electric fields to control structure in self-assembly processes involving diffusion of oppositely charged molecules.

The role of nanoscale architecture in supramolecular templating of biomimetic hydroxyapatite mineralization.

Understanding and mimicking the hierarchical structure of mineralized tissue is a challenge in the field of biomineralization and is important for the development of scaffolds to guide bone regeneration. Bone is a remarkable tissue with an organic matrix comprised of aligned collagen bundles embedded with nanometer-sized inorganic hydroxyapatite (HAP) crystals that exhibit orientation on the macroscale. Hybrid organic-inorganic structures mimic the composition of mineralized tissue for functional bone scaffolds, but the relationship between morphology of the organic matrix and orientation of mineral is poorly understood. Herein the mineralization of supramolecular peptide amphiphile templates, that are designed to vary in nanoscale morphology by altering the amino acid sequence, is reported. It is found that 1D cylindrical nanostructures direct the growth of oriented HAP crystals, while flatter nanostructures fail to guide the orientation found in biological systems. The geometric constraints associated with the morphology of the nanostructures may effectively control HAP nucleation and growth. Additionally, the mineralization of macroscopically aligned bundles of the nanoscale assemblies to create hierarchically ordered scaffolds is explored. Again, it is found that only aligned gel templates of cylindrical nanostructures lead to hierarchical control over hydroxyapatite orientation across multiple length scales as found in bone.

Spontaneous and x-ray-triggered crystallization at long range in self-assembling filament networks.

We report here crystallization at long range in networks of like-charge supramolecular peptide filaments mediated by repulsive forces. The crystallization is spontaneous beyond a given concentration of the molecules that form the filaments but can be triggered by x-rays at lower concentrations. The crystalline domains formed by x-ray irradiation, with interfilament separations of up to 320 angstroms, can be stable for hours after the beam is turned off, and ions that screen charges on the filaments suppress ordering. We hypothesize that the stability of crystalline domains emerges from a balance of repulsive tensions linked to native or x-ray-induced charges and the mechanical compressive entrapment of filaments within a network. Similar phenomena may occur naturally in the cytoskeleton of cells and, if induced externally in biological or artificial systems, lead to possible biomedical and lithographic functions.

Low-radii transitions in co-assembled cationic-anionic cylindrical aggregates.

We investigate the formation of charged patterns on the surface of cylindrical micelles from co-assembled cationic and anionic amphiphiles. The competition between the net incompatibility chi (which arises from the different chemical nature of oppositely charged molecules) and electrostatic interactions (which prevent macroscopic segregation) results in the formation of surface domains. We employ Monte Carlo simulations to study the domains at thermal equilibrium. Our results extend previous work by studying the effect of the Bjerrum length l(B) at different values of the cylinder's radius R and chi and analyze how it affects the transition between helical, ring, and isotropic patterns. A critical surface in the space (l(B), R, chi) separating these three phases is found, and we show how it corresponds to a first-order phase transition. This confirms that the Bjerrum length l(B) is a significant parameter in the control of the helical-ring transition; the ring pattern is strongly associated with short-range forces, whereas the helical pattern develops from dominant long-range electrostatic interactions.

Ion condensation on charged patterned surfaces.

We study ion condensation on a patterned surface with stripes of alternating charge. The competition between adsorbed ion-ion and adsorbed ion-surface interactions leads to the formation of different strongly correlated structures of condensed ions in the low-temperature limit (LTL). We consider two types of arrangements which have lowest energy in the LTL: (1) ions adsorbed onto the stripe center lines and (2) arrays of dipoles at the interfaces between charged domains. We determine the preferred arrangement as a function of surface charge density, the chemical potential of the ions in the surrounding medium, and the geometric parameters of the system. We determine the conditions for the appearance of more complex ionic patterns by considering simple perturbations of the stripe-centered and dipolar array structures.

Self-assembly of large and small molecules into hierarchically ordered sacs and membranes.

We report here the self-assembly of macroscopic sacs and membranes at the interface between two aqueous solutions, one containing a megadalton polymer and the other, small self-assembling molecules bearing opposite charge. The resulting structures have a highly ordered architecture in which nanofiber bundles align and reorient by nearly 90 degrees as the membrane grows. The formation of a diffusion barrier upon contact between the two liquids prevents their chaotic mixing. We hypothesize that growth of the membrane is then driven by a dynamic synergy between osmotic pressure of ions and static self-assembly. These robust, self-sealing macroscopic structures offer opportunities in many areas, including the formation of privileged environments for cells, immune barriers, new biological assays, and self-assembly of ordered thick membranes for diverse applications.

Molecular simulation study of peptide amphiphile self-assembly.

We study the self-assembly of peptide amphiphile (PA) molecules, which is governed by hydrophobic interactions between alkyl tails and a network of hydrogen bonds between peptide blocks. We demonstrate that the interplay between these two interactions results in the formation of assemblies of different morphology, in particular, single beta-sheets connected laterally by hydrogen bonds, stacks of parallel beta-sheets, spherical micelles, micelles with beta-sheets in the corona, and long cylindrical fibers. We characterize the size distribution of the aggregates as a function of the molecular interactions. Our results suggest that the formation of nanofibers of peptide amphiphiles obeys an open association model, which resembles living polymerization.