Optical Image Analysis Research Articles

Development of electron optical capabilities for manufacturing of large components by electron beam welding

Electron beam welding (EBW) is a fast process that shows promise to be used for high-productivity welding of thick-section components in the nuclear industry. Nuclear AMRC has been working for many years on developing the use of EBW for small modular reactor (SMR) applications, such as joining thick-section pressure vessel components. The major strength of EB is the single-pass process that can radically reduce fabrication times. This is also one of the major issues with EB. The stress on the welding operator is high as any error generally means failure. In chamber EB operation, such as the Pro-beam K2000 system at Nuclear AMRC, once the chamber is closed, the only interaction between the operator and the work piece is through a camera or the electron beam. Tracking features of the material or the joint line is important to gather as much information on the situation as possible. Nuclear AMRC has used the functions of the Pro-beam multimode to gain reliability during welding. The seam tracker relies on electron optical image analysis to assess the deviation of the beam from the joint line, but it is the duty of the operator to assess whether the correction should be applied. The team has demonstrated that based on the results of the seam tracker, more work is necessary before welding. It was also demonstrated that the seam tracker can be further used to align the beam/gun to the joint and assess variations in a gap in the joint or a variation of working distance for further process reliability.

Integration of satellite SAR and optical acquisitions for the characterization of the Lake Sarez landslides in Tajikistan

AbstractTajikistan is an area characterized by several lakes created after rockfalls and moraine deposits collapsed due to its geographic position on the collisional area of the Euro-Asian and Indian tectonic plates. In 1911, a violent earthquake generated the Usoi dam, which led to the creation of Lake Sarez. The purpose of the article is to provide an overview of the ground deformation of the two landslides that affect and threaten the area of Lake Sarez exploiting the combination of two different techniques, the interferometric synthetic aperture radar (InSAR) method with the SqueeSAR approach and optical image analysis through the use of COSI-Corr software. The combination of these two techniques turned out to be useful and provide more information because InSAR method balances lack the optical analysis and vice versa giving the possibility to obtain a complete overview of the displacement of the area. Moreover, InSAR data have been used to reconstruct the geometry and depth of the sliding surface of a potential landslide that could affect the lake, generating a wave that could overtop the dam and affect the villages downstream.

RETRACTED ARTICLE: Optical imaging medical diagnosis and analysis of drug treatment for endometriosis and the impact of serum VEGF and MMP-9

RETRACTED ARTICLE: Optical imaging medical diagnosis and analysis of drug treatment for endometriosis and the impact of serum VEGF and MMP-9

A Novel Method of Boreal Zone Reforestation/Afforestation Estimation Using PALSAR-1,2 and Landsat-5,8 Data

Nowadays, global remote sensing studies of tropical forest parameters are relevant for assessing carbon sequestration, whereas boreal forests receive little attention. This is due to the current idea that forests with greater aboveground biomass absorb more carbon. However, new research indicates that rapidly growing young forests take up more carbon than mature ones. Therefore, it is necessary to develop universal methods of remote reforestation/afforestation monitoring. The existing reforestation methods rely on the separate analysis of multispectral optical images and radar data. Here, we propose a method for analyzing the joint dynamics of NDVI (or the Normalized Burn Ratio, NBR) and the radar vegetation index (RVI) on a 2D plot for a test reforestation site. NDVI and NBR time series were derived from Landsat-5,8 data, and the RVI was derived from ALOS-1,2 and PALSAR-1,2 for 2007–2020 using the resources of Google Earth Engine. The quantitative parameters to evaluate the degree of reforestation and changes in the species composition of young trees have been suggested. The suggested method enables a more thorough evaluation of reforestation by measuring the coupled dynamics of the projective cover of young trees and aboveground biomass.

The effect of selected process parameters on shape fidelity in extrusion-based bioprinting of natural and synthetic hydrogels

Recently application of 3D bioprinting to bioengineering has gained interest for the availability of advanced biomaterials with promising properties for regenerative medicine and drug delivery. In particular, extrusion-based bioprinting has the possibility to use a wide range of hydrogels of natural and synthetic origin. One of the most critical weaknesses of bioprinting is the poor post-printing shape fidelity of 3D scaffold possibly due to the porous-viscoelastic nature and swelling behavior of extruded polymers through the printing head. In this work, a quantitative index, termed shape fidelity index, is proposed to evaluate the discrepancy between ideal and real printed scaffolds. Scaffolds were printed with natural polysaccharides, such as alginate and cellulose (Xplore®), and synthetic polymers, such as polyethylene glycol (START®). Four process parameters on 2 levels were selected for the study according to a DOE analysis: inlet pressure, printing speed, temperature and type of biomaterial. 16 experiments were performed with an extrusion-based bioprinter to produce 3D porous scaffolds, the shape and surface areas of which were characterized with optical microscopy and image analysis. Shape fidelity indices were characterized to guide the optimization of the bioprinting process. The proposed approach permits also to determine the process parameter influencing the most the printing outcome and to rank the parameter importance for optimizing the shape fidelity of the bioprinted manufacts with soft biomaterials.

CylinGCN: Cylindrical structures segmentation in 3D biomedical optical imaging by a contour-based graph convolutional network

Cylindrical organs, e.g., blood vessels, airways, and intestines, are ubiquitous structures in biomedical optical imaging analysis. Image segmentation of these structures serves as a vital step in tissue physiology analysis. Traditional model-driven segmentation methods seek to fit the structure by constructing a corresponding topological geometry based on domain knowledge. Classification-based deep learning methods neglect the geometric features of the cylindrical structure and therefore cannot ensure the continuity of the segmentation surface. In this paper, by treating the cylindrical structures as a 3D graph, we introduce a novel contour-based graph neural network for 3D cylindrical structure segmentation in biomedical optical imaging. Our proposed method, which we named CylinGCN, adopts a novel learnable framework that extracts semantic features and complex topological relationships in the 3D volumetric data to achieve continuous and effective 3D segmentation. Our CylinGCN consists of a multiscale 3D semantic feature extractor for extracting inter-frame multiscale semantic features, and a residual graph convolutional network (GCN) contour generator that combines the semantic features and cylindrical topological priors to generate segmentation contours. We tested the CylinGCN framework on two types of optical tomographic imaging data, small animal whole body photoacoustic tomography (PAT) and endoscopic airway optical coherence tomography (OCT), and the results show that CylinGCN achieves state-of-the-art performance. Code will be released at https://github.com/lzc-smu/CylinGCN.git.

Pressure-based process monitoring of direct-ink write material extrusion additive manufacturing

As additive manufacturing (AM) has become a reliable method for creating complex and unique hardware rapidly, the quality assurance of printed parts remains a priority. In situ process monitoring offers an approach for performing quality control while simultaneously minimizing post-production inspection. For extrusion printing processes, direct linkages between extrusion pressure fluctuations and print defects can be established by integrating pressure sensors onto the print head. In this work, the sensitivity of process monitoring is tested using engineered spherical defects. Pressure and force sensors located near an ink reservoir and just before the nozzle are shown to assist in identification of air bubbles, changes in height between the print head and build surface, clogs, and particle aggregates with a detection threshold of 60–70% of the nozzle diameter. Visual evidence of printed bead distortion is quantified using optical image analysis and correlated to pressure measurements. This methodology provides an ability to monitor the quality of AM parts produced by extrusion printing methods and can be accomplished using commonly available pressure-sensing equipment.

Development and validation of a rapid multicompartment body composition model using 3-dimensional optical imaging and bioelectrical impedance analysis

Background & AimsThe multicompartment approach to body composition modeling provides a more precise quantification of body compartments in healthy and clinical populations. We sought to develop and validate a simplified and accessible multicompartment body composition model using 3-dimensional optical (3DO) imaging and bioelectrical impedance analysis (BIA). MethodsSamples of adults and collegiate-aged student-athletes were recruited for model calibration. For the criterion multicompartment model (Wang-5C), participants received measures of scale weight, body volume (BV) via air displacement, total body water (TBW) via deuterium dilution, and bone mineral content (BMC) via dual energy x-ray absorptiometry. The candidate model (3DO-5C) used stepwise linear regression to derive surrogate measures of BV using 3DO, TBW using BIA, and BMC using demographics. Test-retest precision of the candidate model was assessed via root mean square error (RMSE). The 3DO-5C model was compared to criterion via mean difference, concordance correlation coefficient (CCC), and Bland-Altman analysis. This model was then validated using a separate dataset of 20 adults. Results67 (31 female) participants were used to build the 3DO-5C model. Fat-free mass (FFM) estimates from Wang-5C (60.1 ± 13.4 kg) and 3DO-5C (60.3 ± 13.4 kg) showed no significant mean difference (-0.2 ± 2.0 kg; 95% limits of agreement [LOA] -4.3 to +3.8) and the CCC was 0.99 with a similar effect in fat mass that reflected the difference in FFM measures. In the validation dataset, the 3DO-5C model showed no significant mean difference (0.0 ± 2.5 kg; 95% LOA -3.6 to +3.7) for FFM with almost perfect equivalence (CCC = 0.99) compared to the criterion Wang-5C. Test-retest precision (RMSE = 0.73 kg FFM) supports the use of this model for more frequent testing in order to monitor body composition change over time. ConclusionsBody composition estimates provided by the 3DO-5C model are precise and accurate to criterion methods when correcting for field calibrations. The 3DO-5C approach offers a rapid, cost-effective, and accessible method of body composition assessment that can be used broadly to guide nutrition and exercise recommendations in athletic settings and clinical practice.

Multicrystalline Informatics Applied to Multicrystalline Silicon for Unraveling the Microscopic Root Cause of Dislocation Generation.

A comprehensive analysis of optical and photoluminescence images obtained from practical multicrystalline silicon wafers is conducted, utilizing various machine learning models for dislocation cluster region extraction, grain segmentation, and crystal orientation prediction. As a result, a realistic 3D model that includes the generation point of dislocation clusters is built. Finite element stress analysis on the 3D model coupled with crystal growth simulation reveals inhomogeneous and complex stress distribution and that dislocation clusters are frequently formed along the slip plane with the highest shear stress among twelve equivalents, concentrated along bending grain boundaries (GBs). Multiscale analysis of the extracted GBs near the generation point of dislocation clusters combined with ab initio calculations has shown that the dislocation generation due to the concentration of shear stress is caused by the nanofacet formation associated with GB bending. This mechanism cannot be captured by the Haasen-Alexander-Sumino model. Thus, this research method reveals the existence of a dislocation generation mechanism unique to the multicrystalline structure. Multicrystalline informatics linking experimental, theoretical, computational, and data science on multicrystalline materials at multiple scales is expected to contribute to the advancement of materials science by unraveling complex phenomena in various multicrystalline materials.

AutoSourceID-FeatureExtractor

Aims. In astronomy, machine learning has been successful in various tasks such as source localisation, classification, anomaly detection, and segmentation. However, feature regression remains an area with room for improvement. We aim to design a network that can accurately estimate sources’ features and their uncertainties from single-band image cutouts, given the approximated locations of the sources provided by the previously developed code AutoSourceID-Light (ASID-L) or other external catalogues. This work serves as a proof of concept, showing the potential of machine learning in estimating astronomical features when trained on meticulously crafted synthetic images and subsequently applied to real astronomical data. Methods. The algorithm presented here, AutoSourceID-FeatureExtractor (ASID-FE), uses single-band cutouts of 32x32 pixels around the localised sources to estimate flux, sub-pixel centre coordinates, and their uncertainties. ASID-FE employs a two-step mean variance estimation (TS-MVE) approach to first estimate the features and then their uncertainties without the need for additional information, for example the point spread function (PSF). For this proof of concept, we generated a synthetic dataset comprising only point sources directly derived from real images, ensuring a controlled yet authentic testing environment. Results. We show that ASID-FE, trained on synthetic images derived from the MeerLICHT telescope, can predict more accurate features with respect to similar codes such as SourceExtractor and that the two-step method can estimate well-calibrated uncertainties that are better behaved compared to similar methods that use deep ensembles of simple MVE networks. Finally, we evaluate the model on real images from the MeerLICHT telescope and the Zwicky Transient Facility (ZTF) to test its transfer learning abilities.

Deep learning and correlative microscopy for quantification of grain orientation in sintered FeNdB-type permanent magnets by domain pattern analysis

Based on a data-driven approach, a computer-assisted workflow for the quantitative analysis of optical Kerr microscopy images of sintered FeNdB-type permanent magnets was developed. By analyzing the domain patterns visible in the Kerr image with data-driven approaches such as traditional machine learning and advanced deep learning, we can quantify grain orientation and size with a better trade-off between accuracy and higher throughput than electron backscatter diffraction (EBSD). The key distinction between traditional machine learning and advanced deep learning lies in feature extraction. Traditional methods require manual, user-dependent feature extraction from input data, while advanced deep learning achieves this automatically. The predictions from the trained models were compared to the measurements from EBSD for performance evaluation. The proposed data-driven model is trained on the dataset created from the correlative microscopy technique, which requires the images of grains extracted from the Kerr microscopy and corresponding EBSD grain orientation data (Euler angles). The fine-tuned deep learning model shows better generalization ability than the traditional machine learning models trained on the manually extracted features and resulted in a mean absolute error of less than 5° for grain orientation of the anisotropic magnet samples when evaluated against the measured EBSD values. The developed approach has reduced the measurement effort for grain orientation by 5 times and have sufficient accuracy when compared to the EBSD.Further, the application of the proposed approach to determine the quality of the alignment or texture in anisotropic sintered magnets and its relationship with the magnetic remanence based on reliable statistical grain orientation data has been discussed. This approach could emerge as a tool for rapidly analyzing large-scale samples to discover and quantify heterogeneities in grain size and grain orientation.

Naturally Occurring Atherosclerosis Progression and In-stent Restenosis: Exploring Histomorphologic Associations Using Optical Coherence Tomography.

The mechanism of in-stent restenosis (ISR) remains elusive, and in-stent neoatherosclerosis (ISNA) may hold significant pathophysiologic implications. Nevertheless, the correlation between ISNA and the progression of untreated coronary segments affected by native atherosclerosis remains incompletely investigated. This study enrolled 225 patients diagnosed with coronary heart disease and multivessel disease. These patients underwent successful percutaneous coronary intervention and intraoperative placement of the drug-eluting stent, followed by optical coherence tomography assessment of the culprit stent. The mechanism of ISR was examined through qualitative and quantitative analysis of optical coherence tomography imaging. A significantly higher proportion of patients in the ISR with nontarget lesion progression (N-TLP) group exhibited lipid plaque formation compared with the ISR without N-TLP group (69.0% vs. 39.8%, P < 0.001). The incidence of thin-cap fibroatheroma (33.3% vs. 11.4%, P = 0.001) and ISNA (60.7% vs. 38.6%, P < 0.001) was markedly elevated in the ISR with N-TLP group compared with the ISR without N-TLP group. Regarding manifestations, heterogeneous hyperplasia was predominantly observed in the ISR with N-TLP group (76.2% vs. 38.6%, P < 0.001), whereas homogeneous hyperplasia was primarily presented in the ISR without N-TLP group (61.4% vs. 23.8%, P < 0.001). Patients displaying notable progression of naturally occurring atherosclerosis manifest histomorphologic features of ISR, primarily characterized by heterogeneous intimal hyperplasia and a higher prevalence of ISNA. In contrast, patients without substantial progression of naturally occurring atherosclerosis exhibit histomorphologic features of ISR primarily characterized by homogeneous intimal hyperplasia.

Deep-learning based analysis of intracoronary optical coherence tomography images: detection and characterization of lipid plaques

Abstract Background Intracoronary optical coherence tomography (OCT) enables in vivo detection and characterization of atherosclerotic disease. However, manual assessment of OCT images is time-consuming and subject to intra- and interobserver variability. Therefore, automated and trustworthy methods for plaque assessment are warranted. Purpose To develop and validate an artificial intelligence based algorithm for detection and characterization of lipid plaques on OCT images. Methods From the prospective, observational PECTUS-obs study, we studied a representative subset of OCT pullbacks performed in fractional flow reserve-negative non-culprit lesions in patients presenting with myocardial infarction. Manual segmentation of cross-sectional frames (e.g. lumen, tunica intima including the fibrous cap, tunica media and lipid pools) was performed by trained experts. Pullbacks were randomly divided into a training and test set. For automated segmentation and analysis, a two-dimensional no-new U-shaped neural network (nnUNet) was constructed with 5-fold cross validation. To test the diagnostic performance of the nnUnet for detection of lipid plaques, sensitivity, specificity and Cohen’s kappa were calculated. As for plaque characterization, the lipid arc and minimal fibrous cap thickness were measured manually in the test set on each frame containing a lipid plaque. Values of lipid arc and minimal fibrous cap thickness obtained following automated assessment were compared to manual analysis using the intraclass correlation coefficient (ICC) for absolute agreement. Results In total, 1215 frames were used for training and 162 frames for testing of the nnUNet. Lipid plaques were present in 77 frames (47.5%) in the test set. Substantial agreement (κ=0.79) for detection of a lipid plaque was achieved with a sensitivity and specificity of 96.1% and 83.5%, respectively. As for plaque characterization, we found good reliability for lipid arc assessment (ICC 0.88, mean difference 6±32º) and moderate reliability for assessment of the minimal fibrous cap thickness (ICC 0.54, mean difference 23±140µm). Conclusion We developed a deep learning algorithm for automated analysis of intracoronary OCT images, that was able to accurately detect lipid plaques. Moreover, the algorithm showed promising results in terms of plaque characterization. With further refinements, the developed algorithm has the potential to reduce the time required for OCT interpretation, ultimately enabling more efficient, real-time use of this imaging modality in clinical practice.

Nanoscale strain gauges on flexible polymer substrates

Biological cell force is important for proper cell and tissue function and can be an indicator of disease. Therefore, measuring cell force has potential in disease diagnosis and treatment. However, biological cell force measurement approaches are limited and typically slow due to the analysis of optical images before and after cell application or other methods that have low throughput. This work seeks to overcome this bottleneck by the use of nanoscale strain gauges which can measure cell forces as an electrical signal in real time, as well as being able to be scaled to measure tens of thousands of cells, simultaneously. This paper presents the design, COMSOL simulation, fabrication, as well as electrical and mechanical testing of gold nanometer scale strain gauges embedded in soft polydimethylsiloxane (PDMS) using a sacrificial aluminum layer method. A process flow using an aluminum sacrificial layer is presented, which successfully fabricated gold strain gauges with 100 nm dimensions in soft PDMS polymer and have been used to measure strain applied to the PDMS surface. Compressive strains ranging from 0.4% to 1.7% in the PDMS surface, corresponding to forces of 718 nN to 2.0 μN have been detected with resistance changes of 1%–8%. To the best of our knowledge, these are the smallest metal strain gauges to be made on soft polymers and is a promising new approach for biological cell force measurement.

Mineralized Microgels via Electrohydrodynamic Atomization: Optimization and In Vitro Model for Dentin-Pulp Complex.

There is growing interest in the use of micro-sized hydrogels, including bioactive signals, as efficient platforms for tissue regeneration because they are able to mimic cell niche structure and selected functionalities. Herein, it is proposed to optimize bioactive composite microgels via electrohydrodynamic atomization (EHDA) to regenerate the dentin-pulp complex. The addition of disodium phosphate (Na2HPO4) salts as mineral precursors triggered an in situ reaction with divalent ions in solution, thus promoting the encapsulation of different amounts of apatite-like phases. Morphological analysis via image analysis of optical images confirmed a narrow distribution of perfectly rounded particles, with an average diameter ranging from 223 ± 18 μm to 502 ± 64 μm as a function of mineral content and process parameters used. FTIR, TEM, and EDAX analyses confirmed the formation of calcium phosphates with a characteristic Ca/P ratio close to 1.67 and a needle-like crystal shape. In vitro studies-using dental pulp stem cells (DPSCs) in crown sections of natural teeth slices-showed an increase in cell viability until 14 days, recording a decay of proliferation at 21 days, independent on the mineral amount, suggesting that differentiation is started, as confirmed by the increase of ALP activity at 14 days. In this view, mineralized microgels could be successfully used to support in vitro osteogenesis, working as an interesting model to study dental tissue regeneration.

Systematic Study of Wettability Alteration of Glass Surfaces by Dichlorooctamethyltetrasiloxane Silanization-A Guide for Contact Angle Modification.

To investigate the effects of wettability on multiphase flow in porous media, glass bead packs or micromodels are commonly used. Their wettability can be altered by the surface treatment method-silanization. Although silanization is widely used for glass wettability modification, comparable systematic approaches over a large range of geometries, treatment conditions, and measurement systems are scarce. In this work, dichlorooctamethyltetrasiloxane (Surfasil) treatment was systematically investigated, resulting in a guide for achieving a wide range of contact angles. Initially, the influence of the Surfasil solvent, treatment time, and Surfasil-to-solvent ratio was investigated on glass plates using the sessile drop method. By varying these variables, it was possible to achieve a wide range of comparable, repeatable, and stable contact angles, from approximately 20-95° for air-water systems. Due to the linear increase of contact angle with larger Surfasil exposure, either due to the time or concentration, contact angle tuning is possible until the critical point. Beyond the critical point of exposure, a system-specific plateau value is reached, independent of the approach. After establishing a clear relationship between the parameters and contact angles, the same treatment parameters were applied to single beads, micromodels, and beadpacks with heptane as the chosen solvent. Optical image analysis was used for the microchips, and micro CT data analysis was used for the bead packs. The treatment appeared to be transferable to all geometries, resulting in similar wetting conditions within the limitations of the measurements. It is concluded that a glass plate can be used as an analogue for obtaining the contact angle alteration trends for more complex porous media with similar compositions. Data analysis methods and surface roughness could have an effect on the obtained contact angle spread.

Problemlösung bei komplizierten kornealen Verhältnissen: Sklerallinsen und deren Anpassung mit neuen modernen Messmethoden

Purpose. The case report describes the follow up fitting with scleral contact lenses for visual rehabilitation in advanced keratoconus and the influence of the scleral fitting characteristics on the daily wearing time as well as the wearing comfort of the contact lenses. Material and Methods. Scleral contact lenses were fitted using Scheimpflug images and analysis of optical coherence tomography cross-sectional images. Based on this data an optimization of the contact lens design could be performed. Results. In the present case, a significant improvement in visual acuity, daily wearing time and wearing comfort was achieved due to scleral lens re-fitting with individual scleral lens sagittal height calculation and custom fitting according to corneo-scleral profile measurement data. Conclusion. Scleral lenses can be a good alternative to other contact lens treatments to achieve visual rehabilitation in complex corneal situations. Analysis of the corneo-scleral profile by Scheimpflug technique and accurate evaluation of the sagittal height are of great benefit, in addition to the assessment of the fitting and landing zone of scleral lenses by anterior segment OCT. This results in significant time savings for the fitting and leads to a more physiologically compatible contact lens treatment result. Keywords keratoconus, scleral lenses, optical coherence tomography (OCT), corneo-scleral profile (CSP)

Recent Advances and Applications of Ambient Mass Spectrometry Imaging in Cancer Research: An Overview.

Cancer metabolic variability has a significant impact on both diagnosis and treatment outcomes. The discovery of novel biological indicators and metabolic dysregulation, can significantly rely on comprehension of the modified metabolism in cancer, is a research focus. Tissue histology is a critical feature in the diagnostic testing of many ailments, such as cancer. To assess the surgical margin of the tumour on patients, frozen section histology is a tedious, laborious, and typically arbitrary method. Concurrent monitoring of ion images in tissues facilitated by the latest advancements in mass spectrometry imaging (MSI) is far more efficient than optical tissue image analysis utilized in conventional histopathology examination. This article focuses on the "desorption electrospray ionization (DESI)-MSI" technique's most recent advancements and uses in cancer research. DESI-MSI can provide wealthy information based on the variances in metabolites and lipids in normal and cancerous tissues by acquiring ion images of the lipid and metabolite variances on biopsy samples. As opposed to a systematic review, this article offers a synopsis of the most widely employed cutting-edge DESI-MSI techniques in cancer research.

Determination of spinal tracer dispersion after intrathecal injection in a deformable CNS model.

Background: Traditionally, there is a widely held belief that drug dispersion after intrathecal (IT) delivery is confined locally near the injection site. We posit that high-volume infusions can overcome this perceived limitation of IT administration. Methods: To test our hypothesis, subject-specific deformable phantom models of the human central nervous system were manufactured so that tracer infusion could be realistically replicated in vitro over the entire physiological range of pulsating cerebrospinal fluid (CSF) amplitudes and frequencies. The distribution of IT injected tracers was studied systematically with high-speed optical methods to determine its dependence on injection parameters (infusion volume, flow rate, and catheter configurations) and natural CSF oscillations in a deformable model of the central nervous system (CNS). Results: Optical imaging analysis of high-volume infusion experiments showed that tracers spread quickly throughout the spinal subarachnoid space, reaching the cervical region in less than 10min. The experimentally observed biodispersion is much slower than suggested by the Taylor-Aris dispersion theory. Our experiments indicate that micro-mixing patterns induced by oscillatory CSF flow around microanatomical features such as nerve roots significantly accelerate solute transport. Strong micro-mixing effects due to anatomical features in the spinal subarachnoid space were found to be active in intrathecal drug administration but were not considered in prior dispersion theories. Their omission explains why prior models developed in the engineering community are poor predictors for IT delivery. Conclusion: Our experiments support the feasibility of targeting large sections of the neuroaxis or brain utilizing high-volume IT injection protocols. The experimental tracer dispersion profiles acquired with an anatomically accurate, deformable, and closed in vitro human CNS analog informed a new predictive model of tracer dispersion as a function of physiological CSF pulsations and adjustable infusion parameters. The ability to predict spatiotemporal dispersion patterns is an essential prerequisite for exploring new indications of IT drug delivery that targets specific regions in the CNS or the brain.

Psychostimulants prescribed to children for ADHD following distal radius fractures significantly reduce bone density as a function of duration.

Methylphenidate and mixed amphetamine salts (MAS) are psychostimulant medications widely prescribed for various psychiatric disorders. Although these medications are known to adversely impact bone mineral content and density, as well as biomechanical integrity during skeletal development in rats, their effect on bone density in children remains largely unknown. The primary aim of this work was to investigate the effects of methylphenidate and MAS on bone density following distal radius fractures in pediatric populations, and secondarily assess any impact on healing. The retrospective case-control study was designed to assess fracture healing in patients treated with stimulant drugs and matched controls. For the primary outcome, X-rays ( n = 188) were evaluated using an optical density image analysis technique to compare bone density throughout the bone healing process. Results showed that methylphenidate and MAS significantly reduced bone healing by approximately 20% following distal radius fractures in these children. The data also suggested that duration of psychostimulant use played a role in bone healing; the longer the treatment (1-5 years), the lower the bone density was observed (by approximately 52%) as compared to controls (no medication). However, subjects taking these drugs for longer than 5 years did not show a significant difference. Our results suggested that children taking psychostimulants for up to 5 years had slower bone healing following distal radius fractures. Orthopedic surgeons planning elective surgeries should be cognizant of this as a potential issue in recovery after any elective bone procedures and preoperatively optimize bone health as well as counsel patients and their families.