Microengineering 3D Collagen Matrices with Tumor-Mimetic Gradients in Fiber Alignment.

Collagen fibers in the 3D tumor microenvironment (TME) exhibit complex alignment landscapes that are critical in directing cell migration through a process called contact guidance. Previous in vitro work studying this phenomenon has focused on quantifying cell responses in uniformly aligned environments. However, the TME also features short-range gradients in fiber alignment that result from cell-induced traction forces. Although the influence of graded biophysical taxis cues is well established, cell responses to physiological alignment gradients remain largely unexplored. In this work, fiber alignment gradients in biopsy samples are characterized and recreated using a new microfluidic biofabrication technique to achieve tunable sub-millimeter to millimeter scale gradients. This study represents the first successful engineering of continuous alignment gradients in soft, natural biomaterials. Migration experiments on graded alignment show that HUVECs exhibit increased directionality, persistence, and speed compared to uniform and unaligned fiber architectures. Similarly, patterned MDA-MB-231 aggregates exhibit biased migration toward increasing fiber alignment, suggesting a role for alignment gradients as a taxis cue. This user-friendly approach, requiring no specialized equipment, is anticipated to offer new insights into the biophysical cues that cells interpret as they traverse the extracellular matrix, with broad applicability in healthy and diseased tissue environments.

Comparing Simulation Training of Bronchoscopy-Guided Percutaneous Dilatational Tracheostomy Using Conventional Versus 3D Printed Simulators (TRAC-Sim Study).

Background: Individual implementation rate of bronchoscopy-guided percutaneous dilatational tracheostomy (PDT) varies among intensivists. Simulation training (ST) can increase the safety of medical procedures by reducing stress levels of the performing team. The aim of this study was to evaluate the benefit of ST in PDT regarding procedural time, quality of performance, and percepted feelings of safety of the proceduralist and to compare conventional simulators (CSIM) with simulators generated from 3D printers (3DSIM). Methods: We conducted a prospective, single-center, randomized, blinded cross-over study comparing the benefit of CSIM versus 3DSIM for ST of PDT. Participants underwent a standardized theoretical training and were randomized to ST with CSIM (group A) or 3DSIM (group B). After ST, participants' performance was assessed by two blinded examiners on a porcine trachea regarding time required for successful completion of PDT and correct performance (assessed by a performance score). Percepted feelings of safety were assessed before and after ST. This was followed by a second training and second assessment of the same aspects with crossed groups. Results: 44 participants were included: 24 initially trained with CSIM (group A) and 20 with 3DSIM (group B). Correctness of the PDT performance increased significantly in group B (p < .01) and not significantly in group A (p = .14). Mean procedural time required for performing a PDT after their second ST compared to the first assessment (p < .01) was lower with no difference between group A and group B and irrespective of the participants' previous experience regarding PDT, age, and sex. Moreover, percepted feelings of safety increased after the first ST in both groups (p < .001). Conclusions: ST can improve procedural skills, procedural time, and percepted feelings of safety of the proceduralist in simulated PDT.

A model for oversight of rare disease studies: The 25-year experience of the cystic fibrosis foundation data safety monitoring board.

This manuscript addresses the development and operating procedures of the Cystic Fibrosis Foundation Data Safety Monitoring Board (CFF-DSMB) and its role in the development and approval of new therapies through complex clinical trials with an emphasis on ensuring patient safety and study integrity. The authors describe the processes that have been developed over the last 25 years including the development of educational curricula for DSMB members and patient representation on DSMBs. The experience and success of the CFF-DSMB can serve as a model for providing high quality oversight of clinical trials for other groups who are dedicated to developing treatments for rare and complex diseases.

Comparative Analysis of Non-Rigid Registration Techniques for Liver Surface Registration.

Non-rigid surface-based soft tissue registration is crucial for surgical navigation systems, but its adoption still faces several challenges due to the large number of degrees of freedom and the continuously varying and complex surface structures present in the intra-operative data. By employing non-rigid registration, surgeons can integrate the pre-operative images into the intra-operative guidance environment, providing real-time visualization of the patient's complex pre- and intra-operative anatomy in a common coordinate system to improve navigation accuracy. However, many of the existing registration methods, including those for liver applications, are inaccessible to the broader community. To address this limitation, we present a comparative analysis of several open-source, non-rigid surface-based liver registration algorithms, with the overall goal of contrasting their strength and weaknesses and identifying an optimal solution. We compared the robustness of three optimization-based and one data-driven nonrigid registration algorithms in response to a reduced visibility ratio (reduced partial views of the surface) and to an increasing deformation level (mean displacement), reported as the root mean square error (RMSE) between the pre-and intra-operative liver surface meshed following registration. Our results indicate that the Gaussian Mixture Model - Finite Element Model (GMM-FEM) method consistently yields a lower post-registration error than the other three tested methods in the presence of both reduced visibility ratio and increased intra-operative surface displacement, therefore offering a potentially promising solution for pre- to intra-operative nonrigid liver surface registration.

The Diagnostic Utility of Repeat Fine-needle Aspirations of Benign Thyroid Nodules.

Objective This study aims to analyze the diagnostic utility of multiple repeat FNA on thyroid nodules with initially benign diagnosis. Methods In a 5-year period, 1658 thyroid nodules with initially benign FNAs were retrospectively reviewed and followed for subsequent resection and repeat biopsy. Results Out of 2150 thyroid nodules, 1658 (77.1%) were diagnosed as benign on FNAs. The average age was 57.4 years (range 11-93 years), and most were females (83.8%). Repeat FNA was performed on 183 benign nodules, of which 141 (8.5%) were sampled a second time and 42 (2.5%) had 2 or more repeat samplings. For the benign nodules without repeat FNAs, 124 had benign resection. Of cases with one-time repeat FNA, most (n=101) remained benign on repeat FNAs, 13 of which were benign on resection. Eleven had atypical repeat FNAs, 5 were resected, 4 of which were benign and one was atypical follicular neoplasm with HRAS and TERT promoter mutations. Of cases with multiple repeat FNA, most (n=35) were still benign on repeat FNAs, one had benign resection. Two had atypical repeat biopsies, one was PTC on resection with CCD6::RET fusion. The positive predictive value significantly decreased from 41.1% on single FNA to 8.3% on one-time repeat (p<0.001) and 16.7% on multiple repeat (p=0.002). The total cost for workup of previously benign nodules was $285,454. Conclusions Repeat FNA biopsies did not provide an additional diagnostic value in the evaluation of benign thyroid nodules, and often led to unwarranted follow-up procedures and significantly increased health care cost.

Association between CFTR modulators and changes in iron deficiency markers in cystic fibrosis.

BACKGROUND: Iron deficiency (ID) is a common extrapulmonary manifestation in cystic fibrosis (CF). CF transmembrane conductance regulator (CFTR) modulator therapies, particularly highly-effective modulator therapy (HEMT), have drastically improved health status in a majority of people with CF. We hypothesize that CFTR modulator use is associated with improved markers of ID. METHODS: In a multicenter retrospective cohort study across 4 United States CF centers 2012-2022, the association between modulator therapies and ID laboratory outcomes was estimated using multivariable linear mixed effects models overall and by key subgroups. Summary statistics describe the prevalence and trends of ID, defined a priori as transferrin saturation (TSAT) <20 % or serum iron <60 µg/dL (<10.7 µmol/L). RESULTS: A total of 568 patients with 2571 person-years of follow-up were included in analyses. Compared to off modulator therapy, HEMT was associated with +8.4 % TSAT (95 % confidence interval [CI], +6.3-10.6 %; p < 0.0001) and +34.4 µg/dL serum iron (95 % CI, +26.7-42.1 µg/dL; p < 0.0001) overall; +5.4 % TSAT (95 % CI, +2.8-8.0 %; p = 0.0001) and +22.1 µg/dL serum iron (95 % CI, +13.5-30.8 µg/dL; p < 0.0001) in females; and +11.4 % TSAT (95 % CI, +7.9-14.8 %; p < 0.0001) and +46.0 µg/dL serum iron (95 % CI, +33.3-58.8 µg/dL; p < 0.0001) in males. Ferritin was not different in those taking modulator therapy relative to off modulator therapy. Hemoglobin was overall higher with use of modulator therapy. The prevalence of ID was high throughout the study period (32.8 % in those treated with HEMT). CONCLUSIONS: ID remains a prevalent comorbidity in CF, despite availability of HEMT. Modulator use, particularly of HEMT, is associated with improved markers for ID (TSAT, serum iron) and anemia (hemoglobin).

Investigating Transformer Encoding Techniques to Improve Data-Driven Volume-to-Surface Liver Registration for Image-Guided Navigation.

Due to limited direct organ visualization, minimally invasive interventions rely extensively on medical imaging and image guidance to ensure accurate surgical instrument navigation and target tissue manipulation. In the context of laparoscopic liver interventions, intra-operative video imaging only provides a limited field-of-view of the liver surface, with no information of any internal liver lesions identified during diagnosis using pre-procedural imaging. Hence, to enhance intra-procedural visualization and navigation, the registration of pre-procedural, diagnostic images and anatomical models featuring target tissues to be accessed or manipulated during surgery entails a sufficient accurate registration of the pre-procedural data into the intra-operative setting. Prior work has demonstrated the feasibility of neural network-based solutions for nonrigid volume-to-surface liver registration. However, view occlusion, lack of meaningful feature landmarks, and liver deformation between the pre- and intra-operative settings all contribute to the difficulty of this registration task. In this work, we leverage some of the state-of-the-art deep learning frameworks to implement and test various network architecture modifications toward improving the accuracy and robustness of volume-to-surface liver registration. Specifically, we focus on the adaptation of a transformer-based segmentation network for the task of better predicting the optimal displacement field for nonrigid registration. Our results suggest that one particular transformer-based network architecture-UTNet-led to significant improvements over baseline performance, yielding a mean displacement error on the order of 4 mm across a variety of datasets.